Forestry, Faculty of
DSpace
UBCV
Catomeris, Catriona
Master of Science - MSc
University of British Columbia
Soils support vast microbial biodiversity, which control nutrient cycling and can form symbioses with plants; however, overabundant herbivores directly and indirectly affect soils. Introduced Sitka black-tailed deer (Odocoileus hemionus sitchensis) have dramatically altered plant communities on Haida Gwaii, BC, but their effects on soils are unknown. As such, I compared soil physicochemical properties and microbial communities on deer-free and deer-inhabited islands within the archipelago. I additionally assessed the ability of deer repellents and arbuscular mycorrhizal fungal inoculum (AMF) to reduce western redcedar (Thuja plicata) browse damage and increase growth. Distinct plant and bryophyte communities on deer-free and deer-inhabited islands were influenced by Gaultheria shallon, Polystichum munitum, Rhytidiadelphus triquetrus, Hylocomium splendens, Kindbergia oregana, and Scapania bolanderi. Phospholipid fatty acid profiling revealed soils on deer-inhabited islands contained lower fungal and bacterial biomass (NS) than deer-free islands. Gram-negative bacteria were more Â¹³C-depleted, likely due to the higher bryophyte cover or soil moisture, on deer-inhabited islands. Plant Root Simulator® probe-measured supply rates of NHââº, Fe³âº, and AÂ¹³âº were higher (NS), while POâ-, Kâº, and Mn²âº were lower (NS), on deer-inhabited islands. Soil pH was lower, while penetration resistance was higher on deer-inhabited islands. In the greenhouse, differences in chemical properties rather than AMF inoculum were likely responsible for the greater redcedar biomass when grown in soil from: a deer-inhabited island versus a deer-free island; and, a redcedar cutblock versus a cedar-free cutblock. Seedling growth did not differ between seedlings inoculated with a commercial AMF inoculum (RootGrowâ¢) and non-inoculated seedlings in the field. A commercial deer repellent (Plantskydd®) reduced browse damage on redcedar seedlings and increased the proportion of time a seedling remained unbrowsed. Commercial inoculum did not promote seedling growth, but browse frequency was lowest on redcedar that received both deer repellent and inoculum. Browse frequency of redcedar boughs was almost a third lower in experimental plots treated with wolf urine than those treated with water. Introduced deer dramatically altered plant and soil communities, which may slow decomposition and nutrient cycling. Deer did not reduce AMF inoculum potential, but browse frequency was high, suggesting fear-inducing deer repellents are a better strategy for redcedar protection.
THE EFFECTS OF INTRODUCED SITKA BLACK-TAILED DEER (ODOCOILEUS HEMIONUS SITCHENSIS) ON PLANT AND SOIL MICROBIAL COMMUNITIES ON HAIDA GWAII AND SILVICULTURAL TOOLS TO IMPROVE WESTERN REDCEDAR SURVIVAL by Catriona Catomeris Hons. B.Sc., University of Western Ontario, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE In THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2018 © Catriona Catomeris, 2018 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: The effects of introduced Sitka black-tailed deer (Odocoileus hemionus sitchensis) on plant and soil microbial communities on Haida Gwaii and silvicultural tools to improve western redcedar survival. submitted by Catriona Catomeris in partial fulfillment of the requirements for the degree of Master of Science in Forestry. Examining Committee: Dr. Sue Grayston Supervisor Dr. Cindy Prescott Supervisory Committee Member Dr. Rob Guy Supervisory Committee Member Dr. Juli Carrillo Non-departmental Examiner iii Abstract Soils support vast microbial biodiversity, which control nutrient cycling and can form symbioses with plants; however, overabundant herbivores directly and indirectly affect soils. Introduced Sitka black-tailed deer (Odocoileus hemionus sitchensis) have dramatically altered plant communities on Haida Gwaii, BC, but their effects on soils are unknown. As such, I compared soil physicochemical properties and microbial communities on deer-free and deer-inhabited islands within the archipelago. I additionally assessed the ability of deer repellents and arbuscular mycorrhizal fungal inoculum (AMF) to reduce western redcedar (Thuja plicata) browse damage and increase growth. Distinct plant and bryophyte communities on deer-free and deer-inhabited islands were influenced by Gaultheria shallon, Polystichum munitum, Rhytidiadelphus triquetrus, Hylocomium splendens, Kindbergia oregana, and Scapania bolanderi. Phospholipid fatty acid profiling revealed soils on deer-inhabited islands contained lower fungal and bacterial biomass (NS) than deer-free islands. Gram-negative bacteria were more 13C-depleted, likely due to the higher bryophyte cover or soil moisture, on deer-inhabited islands. Plant Root Simulator® probe-measured supply rates of NH4+, Fe3+, and Al3+ were higher (NS), while PO4-, K+, and Mn2+ were lower (NS), on deer-inhabited islands. Soil pH was lower, while penetration resistance was higher on deer-inhabited islands. In the greenhouse, differences in chemical properties rather than AMF inoculum were likely responsible for the greater redcedar biomass when grown in soil from: a deer-inhabited island versus a deer-free island; and, a redcedar cutblock versus a cedar-free cutblock. Seedling growth did not differ between seedlings inoculated with a commercial AMF inoculum (RootGrowâ¢) and non-inoculated seedlings in the field. A commercial deer repellent (Plantskydd®) reduced browse iv damage on redcedar seedlings and increased the proportion of time a seedling remained unbrowsed. Commercial inoculum did not promote seedling growth, but browse frequency was lowest on redcedar that received both deer repellent and inoculum. Browse frequency of redcedar boughs was almost a third lower in experimental plots treated with wolf urine than those treated with water. Introduced deer dramatically altered plant and soil communities, which may slow decomposition and nutrient cycling. Deer did not reduce AMF inoculum potential, but browse frequency was high, suggesting fear-inducing deer repellents are a better strategy for redcedar protection. v Lay summary Soil microorganisms control nutrient cycling and can form beneficial relationships with plants. I compared plant and soil communities on deer-free and deer-inhabited islands on Haida Gwaii, BC. Plant cover and soil fungal and bacterial biomass were lower, while moss/liverwort cover was higher on deer-inhabited islands. In the greenhouse, redcedar biomass was higher when grown in soil from a deer-inhabited island versus soil from a deer-free island; higher Al3+ and NH4+ in deer-inhabited island soil, rather than beneficial fungi, were likely responsible for growth variation. A commercial deer repellent reduced deer browsing on redcedar seedlings. Deer browse frequency was lowest on seedlings that received both deer repellent and a commercial fungal inoculum. Wolf urine reduced deer browse frequency on redcedar branches by roughly 30% compared to controls. Introduced deer alter the plant and soil community, which may slow decomposition and nutrient cycling, but repellents can promote redcedar survival. vi Preface The island comparison experimental design used in Chapters 2 and 3 was developed by Dr. Sue Grayston, Dylan Mendenhall, and I. 2016 field data were collected by Dr. Sue Grayston, Dylan Mendenhall, and I. Supplemental field data collected in 2017 were additionally aided by Morgane Maillard, Maria Continentino, and Yonadav Anbar. I performed all laboratory work in Chapter 3 with the exception of soil moisture and bulk density. I extracted PLFAs according to the Belowground Ecosystem Group protocol. I measured soil phosphorus and prepared soil samples for % C and N analysis. Morgane Maillard and I measured soil pH. Dylan Mendenhall collected soil moisture and bulk density data. A portion of the data in Chapter 3 was presented at the Society for Ecological Restoration Western Canada Conference 2018. I designed and implemented the greenhouse bioassays in Chapters 4 and 5. Dr. Sue Grayston and Janice Stadley collected the soil used in the greenhouse bioassay in Chapter 5. Maria Continentino and Yonadav Anbar aided in seedling harvest, growth measurements, and root staining. Under my training and supervision, Maria Continentino and Yonadav Anbar determined the AMF % colonization of roots. I designed the field experiments in Chapters 5. Dr. Jean-Louis Martin suggested the addition of the wolf urine experiment. Dr. Sue Grayston, Mike Moody, Yonadav Anbar, and Bill Crocker aided me in the set-up of the AMF field experiment. Mike Moody and Yonadav Anbar aided me in the set up the wolf urine field experiment. Janice Stadley and I monitored browse damage on redcedar boughs. Ben Levesque and Janice Stadley monitored seedling growth and browse damage in the inoculum-repellent field experiment. A portion of the data presented in Chapter vii 5 was discussed in the following publication: Catomeris, C. & Grayston, S.J. 2018. Can we help western redcedar survive in the presence of deer? Branchlines. 28: 16. I performed the data analyses and interpretation in all chapters. viii Table of contents Abstract ..................................................................................................................................... iii Lay summary ............................................................................................................................... v Preface....................................................................................................................................... vi Table of contents ..................................................................................................................... viii List of tables ............................................................................................................................. xiii List of figures ............................................................................................................................ xv List of abbreviations and symbols .............................................................................................xxi Acknowledgements.................................................................................................................. xxii 1. Introduction ............................................................................................................................ 1 1.1. Aboveground impacts of herbivores ................................................................................. 3 1.1.1. Changes to plant community composition, diversity, and cover ................................ 3 1.1.2. Changes to leaf and litter quality and quantity........................................................... 6 1.2. Belowground impacts of herbivores ................................................................................. 8 1.2.1. Direct effects of herbivores ....................................................................................... 9 1.2.1.1. Urine and fecal inputs ......................................................................................... 9 1.2.1.2. Soil compaction ................................................................................................ 10 1.2.2. Indirect effects of herbivores ................................................................................... 11 1.2.3. Changes to litter quality and quantity ...................................................................... 11 1.2.4. Soil microbial response to aboveground herbivores ................................................ 13 ix 1.3. Importance of soil microbes in forests ............................................................................ 16 1.3.1. Nutrient cycling and storage .................................................................................... 16 1.3.2. Plant-microbe symbioses ......................................................................................... 16 1.3.3. Soil biodiversity ....................................................................................................... 17 1.4. Soil microbial community methods of analysis ............................................................... 18 1.4.1. Phospholipid fatty acids ........................................................................................... 18 1.4.2. Stable isotopes ........................................................................................................ 19 1.4.3. Arbuscular mycorrhizal fungal colonization ............................................................. 20 1.4.4. Molecular methods ................................................................................................. 20 1.5. Implications of deer for forestry: redcedar limitations and management potentials ....... 21 1.5.1. Implications of deer for forestry .............................................................................. 21 1.5.2. Deer impair redcedar regeneration and growth ....................................................... 22 1.5.3. Tools to improve western redcedar regeneration .................................................... 23 1.6. Research objectives and hypotheses .............................................................................. 25 1.6.1. Research questions .................................................................................................. 25 1.6.2. Hypotheses .............................................................................................................. 26 2. Understory community composition and diversity differ between deer-free and deer-inhabited islands ....................................................................................................................... 28 2.1. Introduction ................................................................................................................... 28 2.2. Methods ......................................................................................................................... 31 2.2.1. Study site ................................................................................................................. 31 2.2.2. Experimental design ................................................................................................ 33 x 2.2.3. Vegetation and bryophyte surveys .......................................................................... 34 2.2.4. Statistical analyses ................................................................................................... 35 2.3. Results............................................................................................................................ 36 2.3.1. Plant community composition and diversity ............................................................ 36 2.3.2. Bryophyte community composition and diversity .................................................... 39 2.4. Discussion ...................................................................................................................... 41 3. Soil properties differ between deer-free and deer-inhabited islands ..................................... 44 3.1. Introduction ................................................................................................................... 44 3.2. Methods ......................................................................................................................... 48 3.2.1. Soil sampling............................................................................................................ 48 3.2.2. Soil physical and chemical analyses ......................................................................... 49 3.2.3. Microbial community structure and biomass ........................................................... 50 3.2.4. Microbial d13CPDB values ........................................................................................... 52 3.2.5. Statistical analyses ................................................................................................... 52 3.3. Results............................................................................................................................ 54 3.3.1. Soil physical and chemical properties ...................................................................... 54 3.3.2. Microbial community structure and biomass ........................................................... 59 3.3.3. Microbial d13CPDB values ........................................................................................... 64 3.4. Discussion ...................................................................................................................... 65 4. Arbuscular mycorrhizal fungal inoculum potential on deer-free versus deer-inhabited islands 4 4.1. Introduction ................................................................................................................... 72 xi 4.2. Methods ......................................................................................................................... 74 4.2.1. Site description and soil sampling ............................................................................ 74 4.2.2. Experimental design ................................................................................................ 75 4.2.3. Seedling growth and AMF colonization .................................................................... 75 4.2.4. Soil chemical data .................................................................................................... 76 4.2.5. Statistical analyses ................................................................................................... 76 4.3. Results............................................................................................................................ 77 4.4. Discussion ...................................................................................................................... 87 5. Potential silvicultural tools to improve western redcedar regeneration in the presence of deer .......................................................................................................................................... 90 5.1. Introduction ................................................................................................................... 90 5.2. Methods ......................................................................................................................... 93 5.2.1. Study site and soil sampling ..................................................................................... 93 5.2.2. Greenhouse bioassay ............................................................................................... 94 5.2.3. Greenhouse bioassay soil chemical data .................................................................. 95 5.2.4. AMF inoculum and deer repellent field experiment ................................................. 96 5.2.5. Wolf urine experiment............................................................................................. 98 5.2.6. Statistical analyses ................................................................................................... 99 5.3. Results............................................................................................................................ 99 5.4. Discussion .................................................................................................................... 114 6. Conclusions and future directions ....................................................................................... 119 xii 6.1. Main findings................................................................................................................ 119 6.2. Contributions to the field ............................................................................................. 122 6.3. Limitations and future directions .................................................................................. 122 References .............................................................................................................................. 125 xiii List of tables Table 1.1: Litter chemical traits correlated with species palatability and/or decomposition rates ................................................................................................................................................. 13 Table 2.1: Island size, location, years of deer colonization, and the number of sampling plots .. 32 Table 2.2: Understory vegetation percent cover classes ........................................................... 35 Table 2.3: Plant diversity and cover on deer-free (n = 16) and deer-inhabited (n = 27) islands. Values are means ± SE .............................................................................................................. 39 Table 2.4: Bryophyte diversity and cover on deer-free (n = 12) and deer-inhabited islands (n = 20). Values are means ± SE ....................................................................................................... 41 Table 3.1: PLFA biomarkers and their associated taxonomic groups .......................................... 51 Table 3.2: Soil moisture, bulk density, and pH on deer-free (n = 16) and deer-inhabited islands (n = 27). Values are means ± SE ................................................................................................ 54 Table 3.3: Soil % C, N, and total P on deer-free (n = 16) and deer-inhabited islands (n = 25). Values are means ± SE .............................................................................................................. 56 Table 4.1: Redcedar tree and seedling data from sampling locations on deer-free (n = 6) and deer-inhabited (n = 6). Values are means ± SE .......................................................................... 77 Table 4.2: Soil properties on the deer-free (n = 6) and deer-inhabited (n = 5). Values are means ± SE .. ........................................................................................................................................ 78 Table 5.1: Seedling browse damage score ................................................................................ 98 xiv Table 5.2: Soil chemical properties in the cedar (n = 3) and cedar-free (n = 3) stands. Values are means ± SE ............................................................................................................................. 100 xv List of figures Figure 2.1: Map of Laskeek Bay and Gwaii Haanas in Haida Gwaii, BC. Red markers indicate Reef, Lyell, and Ramsay Islands and the Windy Bay region of Lyell Island. White circles are used to indicate the remaining islands in the study. Google Earth 2017a .......................................... 33 Figure 2.2: Sampling plots in Laskeek Bay and Gwaii Haanas, Haida Gwaii, BC. Red symbols indicate a plot. Google Earth 2017b ......................................................................................... 34 Figure 2.3: PCoA of Hellinger-transformed understory vegetation community composition on deer-free (n = 16) and deer-inhabited islands (n = 27). Model formula: capscale(veg.hel ~ 1, dist = "bray") ................................................................................................................................... 37 Figure 2.4: CAP of Hellinger-transformed understory vegetation community composition on deer-free (n = 16) and deer-inhabited islands (n = 27). Model formula: capscale(veg.hel ~ Treatment + Island + Geology + C:N, data = env.z, distance = "bray") ........................................ 38 Figure 2.5: PCoA of Hellinger-transformed bryophyte community composition on deer-free (n = 12) and deer-inhabited islands (n = 20). Model formula: capscale(formula = moss.hel ~ 1, dist = "bray") ...................................................................................................................................... 40 Figure 2.6: CAP of Hellinger-transformed bryophyte community composition on deer-free (n = 12) and deer-inhabited islands (n = 20). Model formula: capscale(moss.hel ~ Treatment + Island + Bulk Density + Soil Moisture + pH + Organic Depth + C:N, data = env.z, distance = "bray") ..... 40 Figure 3.1: Soil penetration resistance on deer-free (n = 9) and deer-inhabited islands (n = 27). Values are means ± SE .............................................................................................................. 55 xvi Figure 3.2: Forest floor depth on deer-free (n = 16) and deer-inhabited islands (n = 27). Values are means ± SE ......................................................................................................................... 55 Figure 3.3: Partial-RDA of available soil nutrients on deer-free (n = 9) and deer-inhabited islands (n = 26). Model formula: rda(prs.isl ~ Treatment + Island + C + Plant Richness + Soil Moisture + Condition(Removed Early), data=env.z) .................................................................................... 57 Figure 3.4: Available soil N and P on deer-free (n = 9) and deer-inhabited (n = 26) islands. Values are means ± SE. *Mean burial time = 354 ± 2 days .................................................................... 58 Figure 3.5: Other influential available soil nutrients on deer-free (n = 9) and deer-inhabited (n = 26) islands. Values are means ± SE. *Mean burial time = 354 ± 2 days ..................................... 58 Figure 3.6: PCoA of known and unknown PLFAs (µg PLFA/g soil) on deer-free (n = 16) and deer-inhabited islands (n = 21). Model formula: capscale(PLFA ~ 1, dist='bray') ................................ 60 Figure 3.7: PCoA of known PLFAs (mol %) on deer-free (n = 16) and deer-inhabited islands (n = 21). Model formula: capscale(PLFAcl ~ 1, dist='bray') ................................................................ 60 Figure 3.8: Partial-CAP of known PLFAs (mol %) on deer-free (n = 16) and deer-inhabited islands (n = 21). Model formula: capscale(PLFAcl ~ Treatment + Island + Plant Diversity + Soil P + Condition(Dilution), data=env.z, distance = 'bray') .................................................................... 61 Figure 3.9: Fungal biomass on deer-free (n = 16) and deer-inhabited islands (n = 21). Values are means ± SE ............................................................................................................................... 62 Figure 3.10: Bacterial biomass, separated by bacterial groups, on deer-free (n = 16) and deer-inhabited islands (n = 21). Values are means ± SE ..................................................................... 63 xvii Figure 3.11: Fungal:bacterial ratio on deer-free (n = 16) and deer-inhabited islands (n = 21). Values are means ± SE .............................................................................................................. 63 Figure 3.12: d13CPDB of PLFAs on deer-free (n = 16) and deer-inhabited islands (n = 21). Labels: All = biomarker and unknown PLFAs; Bacteria = bacterial biomarkers; GramPositive = Gram-positive bacterial biomarkers; GramNegative = Gram-negative bacterial biomarkers (no deer n = 16, deer n = 20); Fungi = fungal biomarkers .............................................................................. 64 Figure 4.1: Field available soil N and P on the deer-free (n = 6) and deer-inhabited (n = 6) island. Values are means ± SE. *Data from PRS probes. Burial time: Deer = 355 ± 0 days; No Deer = 363 ± 1 day ...................................................................................................................................... 79 Figure 4.2: Other available soil nutrients on the deer-free (n = 6) and deer-inhabited (n = 6) island. Values are means ± SE. *Data from PRS probes. Burial time: Deer = 355 ± 0 days; No Deer = 363 ± 1 day .................................................................................................................... 80 Figure 4.3: Proportion of redcedar seeds germinated in autoclaved (deer n = 10, no deer n = 10), untreated (deer n = 10, no deer n = 10), and inoculated (deer n = 10, no deer n = 10) soil. Values are means ± SE .............................................................................................................. 81 Figure 4.4: Redcedar aboveground biomass in autoclaved (deer n = 4, no deer n = 7), untreated (deer n = 7, no deer n = 7), and inoculated (deer n = 9, no deer n = 4) soil. Values are means ± SE ................................................................................................................................................. 82 Figure 4.5: Redcedar height in autoclaved (deer n = 4, no deer n = 7), untreated (deer n = 7, no deer n = 7), and inoculated (deer n = 9, no deer n = 4) soil. Values are means ± SE .................. 83 xviii Figure 4.6: Redcedar root mass in autoclaved (deer n = 4, no deer n = 7), untreated (deer n = 7, no deer n = 7), and inoculated (deer n = 9, no deer n = 4) soil. Values are means ± SE. ............ 84 Figure 4.7: Redcedar root length in autoclaved (deer n = 4, no deer n = 7), untreated (deer n = 7, no deer n = 7), and inoculated (deer n = 9, no deer n = 4) soil. Values are means ± SE ............. 85 Figure 4.8: Redcedar root collar diameter in autoclaved (deer n = 4, no deer n = 7), untreated (deer n = 7, no deer n = 7), and inoculated (deer n = 9, no deer n = 4) soil ............................... 85 Figure 4.9: AMF % colonization of redcedar roots in autoclaved (deer n = 4, no deer n = 7), untreated (deer n = 6, no deer n = 7), and inoculated (deer n = 8, no deer n = 4) soil. Values are means ± SE ................................................................................................................................ 86 Figure 5.1: Approximate location of field sites in Aero, Moresby Island, Haida Gwaii, BC. Google Earth 2017c .............................................................................................................................. 94 Figure 5.2: Commercial AMF inoculum and deer repellent field experiment block design ......... 97 Figure 5.3: Proportion of redcedar seeds that germinated in autoclaved, untreated, and inoculated soil. Values are means ± SE. N = 5 for each treatment and each stand ................... 100 Figure 5.4: Redcedar aboveground biomass in autoclaved (cedar n = 1, cedar-free n = 1), untreated (cedar n = 4, cedar-free n = 5), and inoculated (cedar n = 5, cedar-free n = 5) soil. Values are means ± SE ............................................................................................................ 102 Figure 5.5: Redcedar height in autoclaved (cedar n = 1, cedar-free n = 1), untreated (cedar n = 4, cedar-free n = 5), and inoculated (cedar n = 5, cedar-free n = 5) soil. Values are means ± SE .. 102 xix Figure 5.6: Redcedar root biomass in autoclaved (cedar n = 1, cedar-free n = 1), untreated (cedar n = 4, cedar-free n = 5), and inoculated (cedar n = 5, cedar-free n = 5) soil. Values are means Â± SEâ¦.. ......................................................................................................................... 103 Figure 5.7: Redcedar root length in autoclaved (cedar n = 1, cedar-free n = 1), untreated (cedar n = 4, cedar-free n = 5), and inoculated (cedar n = 5, cedar-free n = 5) soil. Values are means ± SE ............................................................................................................................................ 104 Figure 5.8: Redcedar RCD in autoclaved (cedar n = 1, cedar-free n = 1), untreated (cedar n = 4, cedar-free n = 5), and inoculated (cedar n = 5, cedar-free n = 5) soil. Values are means ± SE .. 104 Figure 5.9: AMF % colonization of redcedar roots in autoclaved (cedar n = 1, cedar-free n = 1), untreated (cedar n = 4, cedar-free n = 5), and inoculated (cedar n = 5, cedar-free n = 5) soil. Values are means ± SE ............................................................................................................ 105 Figure 5.10: Mean redcedar seedling bi-weekly height increase in two recently harvested stands in Aero, Moresby Island. Control n = 9. AMF n = 13. Repellent n = 22. Both n = 22. âBothâ refers to the treatment that received both AMF inoculum and deer repellent .................................. 106 Figure 5.11: Mean redcedar bi-weekly scale length increase in two recently harvested stands in Aero, Moresby Island. Control n = 9. AMF n = 13. Repellent n = 22. Both n = 22. âBothâ refers to the treatment that received both AMF inoculum and deer repellent ...................................... 107 Figure 5.12: Mean redcedar bi-weekly scale width increase in two recently harvested stands in Aero, Moresby Island. Control n = 9. AMF n = 13. Repellent n = 22. Both n = 22. âBothâ refers to the treatment that received both AMF inoculum and deer repellent ...................................... 108 xx Figure 5.13: Mean redcedar bi-weekly RCD increase in two recently harvested stands in Aero, Moresby Island. Control n = 9. AMF n = 13. Repellent n = 22. Both n = 22. âBothâ refers to the treatment that received both AMF inoculum and deer repellent ............................................ 109 Figure 5.14: Redcedar seedling browse damage 16 weeks following planting and treatment with mycorrhizal inoculum (AMF), deer repellent (Repellent), both treatments (Both) or untreated (Control) in two recently harvested stands in Aero, Moresby Island. N = 24 for each treatmentâ¦. ............................................................................................................................................... 111 Figure 5.15: Proportion of time redcedar seedlings avoided browse damage over 16 weeks of monitoring following planting and treatment with mycorrhizal inoculum (AMF), deer repellent (Repellent), both treatments (Both) or untreated (Control)in two recently harvested stands in Aero, Moresby Island. N = 24 for each treatment .................................................................... 112 Figure 5.16: Proportion of redcedar seedlings browsed 16 weeks following planting and treatment with mycorrhizal inoculum (AMF), deer repellent (Repellent), both treatments (Both) or untreated (Control) at each block (n = 8) in two recently harvested stands in Aero, Moresby Island ...................................................................................................................................... 113 Figure 5.17: Proportion of redcedar boughs browsed over 6 days in control plots (n = 4) and wolf urine plots (n = 4) in two recently harvested stands in Aero, Moresby Island. Values are means ± SE ............................................................................................................................. 114 xxi List of abbreviations and symbols d Del (D = delta) â° Per mil or mille AMF Arbuscular mycorrhizal fungi ANOVA Analysis of variance C Carbon CAP Canonical analysis of principal coordinates DNA Deoxyribose nucleic acid GC-MS Gas Chromatograph-Mass Spectrometer GC-IRMS Gas Chromatograph-Isotope Ratio Mass Spectrometer N Nitrogen NS Not significant P Phosphorus PCoA Principal coordinates analysis PCR Polymerase chain reaction PDB PeeDee Belemnite PLFA Phospholipid fatty acid PRS® Plant Root Simulator® qPCR Quantitative polymerase chain reaction RCD Root collar diameter TRFLP Terminal restriction fragment length polymorphism VPDB Vienna PeeDee Belemnite xxii Acknowledgements I would first like to thank my supervisor, Dr. Sue Grayston, for sharing her expertise and for providing me the opportunity to perform this research. I would also like to thank my committee members, Drs. Cindy Prescott and Rob Guy, for their insightful feedback. Thank you also to Drs. Jean-Louis Martin and Simon Chollet for their guidance in the field. This research was funded by NSERC, Parks Canada, the Research Group on Invasive Species (RGIS), and the Faculty of Forestry at the University of British Columbia. I would like to acknowledge RGIS and Gwaii Haanas National Park Reserve and Haida Heritage Site for the use of their facilities in the field. I would also like to thank Taan Forest â particularly Jeff Mosher, Bill Crocker, Ben Levesque, and Janice Stadley â for the collaboration. I would like to thank the Haida Nation for allowing us to conduct this research. Thanks to current and former Belowground Ecosystem Group members for their friendship and advice. A particular thanks to Morgane Maillard and Dr. Tim Philpott for statistical advice. Thanks to the field crew: Dr. Sue Grayston, Dylan Mendenhall, Morgane Maillard, Maria Continentino, and Yonadav Anbar. I would like to acknowledge Drs. Faride Unda and Shawn Mansfield for assistance with freeze-drying. Thanks to Katie Neufeld for guidance on PLFA analysis. Thanks to Mina Momayyezi, Alice Chang, and Gord Stewart for operating the Elemental Analyzer, GC-IRMS, and GC-MS, respectively. I would especially like to thank Lesley Dampier, Dr. Les Lavkulich, and Dr. Sandra Brown in the Faculty of Land and Food Systems for the use of their equipment and facilities. xxiii Lastly, I would like to thank my family, fiancé, friends, and future in-laws for their endless support and encouragement. 1 1. Introduction Haida Gwaii is a remote archipelago located off the coast of northern British Columbia. Due to its isolation, Haida Gwaii contains less biodiversity than the mainland, but supports many endemic subspecies (Reimchen & Byun 2005). Most of the archipelago falls within the Coastal Western Hemlock Biogeoclimatic Zone (Meidinger & Pojar 1991). The main tree species are western hemlock (Tsuga heterophylla), Sitka spruce (Picea sitchensis), and western redcedar (Thuja plicata). The southern 15% of the archipelago is part of a protected area (Golumbia 2007), Gwaii Haanas National Park Reserve and Haida Heritage Site, which will be referred to as Gwaii Haanas for the rest of this thesis. Sitka black-tailed deer (Odocoileus hemionus sitchensis) were first introduced to the north end of Haida Gwaii in 1878; the deer spread south across the archipelago as more individuals were introduced over the next 47 years (Golumbia et al. 2008). As they have no natural predator, the introduced deer have reached very high population densities. Based on density estimates ranging from approximately 13-30 deer/km2 (Martin & Baltzinger 2002), there are a predicted 110,500 to 255,500 deer inhabiting the archipelago (Golumbia et al. 2008). The lowest density estimate of 13 deer/km2 was gathered on the largest islands, Graham and Moresby, as these are the islands with permanent human habitation and greatest hunting pressure. Deer densities are presumed to be higher on medium-sized offshore islands with counts (Daufresne & Martin 1997) and culls (Gaston et al. 2008) yielding estimates of 30+ deer/km2. The deer have invaded all of the islands in the archipelago except for a few of the smaller, isolated islands (Golumbia et al. 2008). 2 Humans have significantly altered deer populations worldwide through species introductions and reintroductions, predator reductions, and hunting restrictions (Côté et al. 2004; Ward 2005). This mismanagement can result in deer overabundance, which affects ecosystems in a multitude of ways. As such, the effects of mammalian herbivores on ecosystems have been the focus of countless studies (e.g. Horsley & Marquis 1983; Pastor et al. 1988; Pastor et al. 1993; Wardle et al. 2001; Harrison & Bardgett 2004; Stockton et al. 2005; Baltzinger et al. 2009; Pellerin et al. 2010). Commonly used methods to elucidate the effects of herbivores include the comparison of communities within and outside of herbivore exclosures (Risenhoover & Maass 1987; Kielland & Bryant 1998; Stroh et al. 2008) and gradients in deer densities (Sitters et al. 2017) â including the comparison of deer-inhabited and deer-absent islands (Gaston et al. 2006; Martin et al. 2011). In forests, herbivore overabundance reduces vegetation cover (Wardle et al. 2001; Stockton et al. 2005; Baltzinger et al. 2009) and changes plant community composition (Horsley & Marquis 1983; Wardle et al. 2001; Pellerin et al. 2010). However, by comparison, there is a poor understanding of the belowground effects of herbivores. It is important to understand how herbivores affect the belowground community as soils regulate ecosystem processes, such as nutrient cycling and plant productivity (Barrios 2007). The primary mechanisms by which deer and other herbivores affect the soil community are: 1) changes to belowground C allocation by individual plants in response to herbivory; 2) modified litter quality and quantity through browsing and species replacement; and, 3) labile nitrogen inputs through urine and feces (Bardgett & Wardle 2003). Aboveground herbivores can positively (Sitters et al. 2017) or negatively (Harrison & Bardgett 2004) alter nutrient 3 availability, but herbivory usually reduces mycorrhizal fungal symbioses (Gehring & Whitham 1994) â both of which affect aboveground productivity. In order to make informed wildlife management decisions, it is essential to understand how herbivores affect the entire ecosystem â above- and belowground. 1.1. Aboveground impacts of herbivores Mammalian herbivores â both their conservation and their overabundance â have been a subject of conservation concern since the genesis of ecology (Côté et al. 2004). Overabundant herbivores often have dramatic effects on plant communities. Herbivores can affect plants at the individual, community, and population scale. The aboveground response to herbivores can include plant physiological responses and changes to diversity, cover, and species composition. The aboveground effects of ungulates, including studies on Haida Gwaii and internationally, will be discussed in the following subsections. There is an in-depth review of the studies specific to Laskeek Bay, Haida Gwaii in Section 2.1. 1.1.1. Changes to plant community composition, diversity, and cover Preferential browsing by herbivores can shift the vegetation community from palatable to unpalatable species. Indeed, herbivores shifted vegetation community composition, whereby palatable species such as large-leaved plants were abundant inside exclosures, while small-leaved shrubs and some sedges and ferns were more abundant outside of exclosures (Wardle et al. 2001). Similarly, palatable and/or browse-sensitive species (e.g. Anemone nemorosa, Rubus spp., Acer spp., Carex spp., and Festuca spp.) were more abundant in deer exclosures, while unpalatable Lamium spp. and browse-tolerant grasses were more abundant outside of exclosures in a temperate mixed forest in north-eastern France (Pellerin et al. 2010). 4 Preferential browsing of Rubus allegheniensis by deer in a Pennsylvania broadleaf forest promoted the growth of the weedy species, Dennstaedia punctilobula (Horsley & Marquis 1983). Deer can additionally reduce tree seedling establishment indirectly as R. allegheniensis controls the growth of D. punctilobula, which is a species that interferes with seedling establishment (Horsley & Marquis 1983). Indeed, herbivores can dramatically reduce plant cover or diversity of palatable species. Vegetation cover â including trees, herbs, and shrubs â below browse height was significantly reduced on Haida Gwaii outside of deer exclosures, relative to inside, within four years of deer exclusion (Baltzinger et al. 2009). Herbivores reduced plant species diversity and richness in the browse layer, but not in the ground layer outside of exclosures in New Zealand (Wardle et al. 2001). Additionally, moose browsing reduced shrub height by up to 50% in three of four dominant Salix spp., and substantially reduced twig density of all four species, outside of exclosures in a taiga community (Kielland & Bryant 1998). Deer altered the architecture of Holodiscus discolor with a reduced diameter to height ratio, and more dead stems, in a forest on deer-inhabited versus deer-free islands in the George Basin, Pacific Northwest (Martin et al. 2011). However, herbivores typically increase the abundance or diversity of browse-tolerant or unpalatable plants. A negative relationship between the amount of bare forest floor and herbivore densities was evident twenty years after the cessation of the experimental manipulation of herbivore densities in former clearcuts in Pennsylvania (Nuttle et al. 2014). The lower percentage of bare ground on sites with previously higher herbivore densities was attributed to the avoidance of ferns by deer, which allowed them to dominate while other 5 species decreased in abundance (Nuttle et al. 2014). Indeed, preferential browsing can benefit unpalatable species through reduced competition. Bryophyte diversity and cover were significantly higher on deer-inhabited than deer-free islands on Haida Gwaii as bryophytes were rarely browsed, while higher plants were frequently browsed (Chollet et al. 2013a). Similarly, the higher woody plant diversity in grazed plots than moose exclosures on Isle Royale was attributed to reduced competition, especially for light, after herbivores reduced the height of palatable species (Risenhoover & Maass 1987). The severity of herbivore effects on plant communities can increase with time or herbivore densities. Introduced deer virtually eliminated the understory plant community from the forest interior on islands with the longest browse history (50+ years) on Haida Gwaii, although the reductions in understory plant cover were less severe on islands with intermediate browse history (<20 years; Stockton et al. 2005). Similarly, woody seedling and sapling cover decreased as past herbivore densities increased in former experimental field sites in Pennsylvania (Nuttle et al. 2014). However, the effects of herbivores are also dependent on spatial patterns. For instance, aboveground production was on average 48% higher within temporary, grazed exclosures than within permanent, ungrazed exclosures in grasslands and a wet sedge meadow in Yellowstone National Park (Frank & McNaughton 1993). This was attributed to the migratory feeding pattern of the elk, which enabled plant regrowth after browsing and/or the relocation of labile nutrients between communities (Frank & McNaughton 1993). While herbivores can dramatically alter plant communities, herbivore exclosures may act as a source population for plant species reestablishment in the surrounding area (Baltzinger et al. 2009). Indeed, Baltzinger et al. (2009) observed that understory species, particularly herbs, 6 frequently grew outside of exclosures after appearing inside exclosures in an old growth forest on Haida Gwaii. Similarly, Pellerin et al. (2010), found that the significantly higher woody and herbaceous species diversity observed in deer exclosures declined after one year due to the appearance of new, particularly herbaceous, species exclusively in grazed plots. Therefore, herbivore exclosures should be considered for management. However, it is important to note that exclosures do not necessarily represent ânaturalâ conditions. Rather, exclosures demonstrate the response of the community to the exclusion of herbivores and, herbivores would have historically been present in many of the ecosystems studied. 1.1.2. Changes to leaf and litter quality and quantity Herbivore-mediated changes to the aboveground community can cascade belowground through the alteration of litter quantity and quality. The consumption of aboveground biomass by herbivores may reduce the quantity of plant litter entering the soil, but the consumed plant material eventually enters the soil in the form of feces. Herbivores can additionally alter litter quality as plants often possess physical and/or chemical traits to aid in defense from browsing (Côté et al. 2004). For instance, western redcedar (Thuja plicata) saplings that evolved with deer on mainland BC contained higher levels of monoterpenes than those that evolved without deer on Haida Gwaii, making Haida Gwaii redcedar the preferred forage option (Vourcâh et al. 2001). The suppression of plant defenses in the absence of herbivores suggests their high energetic costs (Côté et al. 2004). As such, deer can alter litter quality through the production of anti-herbivore defense compounds. Chapman et al. (2006) propose that herbivory will result in improved litter quality for evergreen species and reduced litter quality for deciduous species. The recalcitrance of 7 induced secondary defense compounds produced by deciduous trees may reduce litter quality, while browsing may impede nutrient resorption in conifers, resulting in greater litter N (Chapman et al. 2006). Alternatively, herbivores can drive changes in litter quality through browse-mediated shifts in plant community composition. While Kielland et al. (1997) observed faster Salix alexensis litter decomposition and nutrient mineralization rates outside of moose exclosures, they suggest that the opposite will be in true in the long-term due to a herbivore-driven transition to a conifer-dominated forest. This is supported by the fact that plant palatability â based on insect herbivore feeding â was positively correlated with decomposition rates in 43 plant species (Grime et al. 1996). Field N mineralization was reduced in sites with the highest browsing intensity relative to exclosures due to decreased litter quantity at both sites and, decreased litter quality at the most heavily browsed site; the reduction in litter quality was attributed to the preferential browsing of deciduous trees and the avoidance of Picea spp. by moose in Michigan (Pastor et al. 1993). In contrast, the relative abundance of deciduous and evergreen shrubs decreased in heavily-grazed versus lightly-grazed communities in a Norwegian tundra, while the relative abundance of graminoids increased by roughly 40-80% (Sitters et al. 2017). The replacement of the browse-resistant shrubs by the browse-tolerant graminoids, which was associated with a decrease in litter C:N, was likely a result of the high browsing pressure and non-selective trampling by reindeer (Sitters et al. 2017). The effects of these aboveground changes to litter quantity and quality belowground are discussed in greater detail in Section 1.2.3. 8 1.2. Belowground impacts of herbivores The belowground effects of herbivores are not as well understood as the aboveground effects. It is essential to determine the belowground effects of herbivores due to the importance of soil microbes in ecosystem functioning (see Section 1.3). Herbivores are generally thought to: 1) benefit nutrient availability in nutrient-rich plant communities in which positive feedbacks exist between herbivores, the soil, and browse-tolerant species or 2) reduce nutrient availability in nutrient-poor plant communities in which browsing favours unpalatable, nutrient-poor species (Bardgett & Wardle 2003). Although, herbivores affect soils through several additional mechanisms, such as trampling, which must also be considered for a more comprehensive understanding of herbivore-soil interactions (Schrama et al. 2013b; Sitters & Olde Venterink 2015). The unpredictability of the belowground response to aboveground herbivores may be explained by the conflicting mechanisms by which herbivores affect the soil. For instance, idiosyncratic responses to herbivores at the plot level resulted in no net difference in soil microbial biomass or microbial biomass C:N between herbivore exclosures and browsed control plots in New Zealand (Wardle et al. 2001). Herbivores have direct and indirect effects on soil, which can have stimulatory or inhibitory effects. Direct effects of herbivores include fecal and urine inputs (Day & Detling 1990) and trampling (Gass & Binkley 2011); indirect effects include browse-mediated changes to plants at the individual level, such as changes to belowground C allocation (Bassman & Dickman 1985; Hamilton & Frank 2001; Hamilton et al. 2008), or changes to plant community composition (Sitters et al. 2017; see Section 1.1.3). 9 1.2.1. Direct effects of herbivores 1.2.1.1. Urine and fecal inputs Herbivore urine and feces return labile nutrients to the soil (Powell et al. 1999), but nutrient inputs will be heterogeneous. After a year of moose feces decomposition, ammonium and nitrate were higher in soil directly beneath the feces than nearby soil in a riparian willow community; however, the difference was not significant in a dry conifer site, indicating the importance of site characteristics such as soil moisture (Guernsey et al. 2015). The available N in herbivore urine and feces can promote the growth of grasses. Indeed, root and shoot N content of grasses, Poa pratensis and Schizachyrium scoparium, growing on urine patches were significantly higher than on urine-free patches in a grassland (Day & Detling 1990). Urine patches accounted for a disproportionately high amount of the total biomass in the study site due to the more than three-fold increase in P. pratensis aboveground biomass relative to urine-free patches (Day & Detling 1990). However, the effect of feces and urine on soil nutrients is likely highly localized (Bardgett & Wardle 2003; Harrison & Bardgett 2004). Indeed, spatial distributions of herbivores can affect nutrient mineralization and inputs. Localized nutrient inputs may have contributed to the heterogeneous net N mineralization rates observed outside herbivore exclosures in a grassland while other factors contributed to the landscape-scale heterogeneity (Frank & Groffman 1998). Herbivore exclosures in Rocky Mountain National Park revealed that elk feces and urine added 2.22 g N/m2 to a short willow community, but the elk consumed 4.23 g N/m2 in the form of vegetation (Singer & Schoenecker 2003). The net N loss could be a result of N movement out of the community by herbivores as there was no net N loss from the upland grass community (Singer & Schoenecker 2003). 10 Therefore, urine and feces provide nutrient inputs to the soil, but herbivores can result in net nutrient losses through the removal of vegetation. 1.2.1.2. Soil compaction Herbivores can additionally alter the physical environment through trampling, which compacts soil. Soil bulk density was a quarter higher in grazed plots than 16-year old deer exclosures in riparian willow communities in Rocky Mountain National Park (Gass & Binkley 2011). Similarly, soil bulk density was significantly higher in grazed plots than in 37-year-old cattle exclosures or hand-mown plots in a salt marsh in The Netherlands (Schrama et al. 2013a). The higher bulk density can increase soil moisture and reduce porosity, but the response is influenced by soil texture. Soil moisture was significantly higher, while air-filled porosity was lower in grazed plots than exclosures in a clay soil; in contrast, air-filled porosity was not significantly lower in grazed plots with sandy soil (Schrama et al. 2013a). Schrama et al. (2013b) proposed that compaction, caused by herbivore trampling, will have a net negative effect on N mineralization in wet or dry soils due to slowed decomposition from anoxia or moisture-limitation, respectively. They additionally suggested that compaction is less important in coarse-textured soils and soils with intermediate moisture as they are less prone to anoxic or moisture-limited conditions (Schrama et al. 2013b). Indeed, faunal activity and N mineralization rates were lower in grazed plots than exclosures with clay soil, but not in sandy soil (Schrama et al. 2013a). Therefore, herbivores directly alter soil physical properties, which may affect soil organisms and nutrient mineralization rates. 11 1.2.2. Indirect effects of herbivores Herbivory can decrease belowground C allocation in some plants, such as woody species, while increasing belowground C allocation in grasses. For instance, 14C-labelling revealed that leaf clipping of Populus x euramericana tended to decrease C allocation to roots in comparison to non-defoliated controls in a greenhouse experiment â a trend that was evident 3 and 5 weeks after defoliation (Bassman & Dickman 1985). The removal of leaves, by hand, from Acer saccharum in an experimental forest resulted in small, but significant decreases in the amount of sucrose, homoserine, and threonine in root exudates, while fructose and lysine increased relative to the non-defoliated tree (Smith 1971). However, only the change in lysine was significant in both pairs of trees studied (Smith 1971). In contrast, leaf clipping rapidly increased root exudation in the browse tolerant grass, Poa pratensis L., in a greenhouse (Hamilton & Frank 2001) and field experiment (Hamilton et al. 2008). In fact, the increased root exudation in response to defoliation, which promotes microbial activity and, subsequently, available N (Hamilton & Frank 2001; Hamilton et al. 2008), may explain the large-scale increase in N availability observed in a grazed grassland (Frank & Groffman 1998). A review found that clipping and insect herbivory stimulated belowground C flow and exudation in several grassland species (Bardgett et al. 1998). 1.2.3. Changes to litter quality and quantity It is apparent that herbivores can dramatically alter the plant community, which can have consequences for litter quality and quantity. Herbivores can alter litter quality through browse-induced alterations to litter chemistry and browse-induced shifts in community composition. Browsing is expected to either concentrate nutrients in root and foliar tissue, thereby 12 increasing litter quality or, decrease litter quality through the production of anti-herbivore defense compounds (Wardle & Bardgett 2004). Salix alaxensis litter had a lower C:N in grazed plots and it decomposed more quickly than S. alaxensis litter within exclosures (Kielland et al. 1997). In contrast, polyphenol concentrations in Populus deltoides saplings increased in response to a spider mite invasion in the greenhouse, which resulted in litter that decomposed roughly half as quickly as litter from un-browsed saplings (Findlay et al. 1996). Leaf litter decomposition rates did not differ for 17 tropical plant species damaged by insect herbivores, versus undamaged leaves, because the traits correlated with decomposition (i.e. Cu:Mn and condensed tannins) were not affected by herbivore damage (Cárdenas et al. 2015). Therefore, decomposition will slow if herbivory induces select defense compounds that inhibit decomposer organisms, but will hasten if herbivores improve litter quality. Preferential browsing by deer can shift plant community composition towards less palatable species, thereby reducing litter quality. There was a significant relationship between the effect of deer on the understory density of a plant species and the decomposition rate of its litter: the greater extent to which deer decreased the density of a given species, the higher its decomposition rate, and vice versa (Wardle et al. 2002). Browse-induced changes to plant community composition are likely to affect C mineralization more than N or P, as only litter C compounds were significantly related to both decomposition rates and palatability (Wardle et al. 2002). Recalcitrant C compounds in litter may be better predictors of both speciesâ palatability and decomposition rates than other chemical traits (Table 1.1). Herbivores can additionally alter litterfall through the consumption of aboveground biomass and the excretion of nutrients in waste. While there was no difference in the amount 13 of herbaceous litterfall between exclosures and grazed plots in Rocky Mountain National Park, shrub litterfall was reduced by 50%-82% outside of exclosures (Singer & Schoenker 2003). Moose altered the ratio of litter types, but not the amount of litter in browsed plots versus exclosures in Alaska (Kielland & Bryant 1998). Moose feces contributed roughly half as much N to grazed plots as did leaf litter, indicating that fecal inputs can offset the consumption of plant biomass in this low litterfall community (Kielland & Bryant 1998). However, inorganic N concentrations in the soil differed over short distances between dung piles and nearby soil, indicating the heterogeneity of herbivore-transformed nutrient inputs (Guernsey et al. 2015). Herbivores can additionally contribute to N losses through the consumption of vegetation, nutrient volatilization, and herbivore movement/migration (Singer & Schoenecker 2003). Table 1.1: Litter chemical traits correlated with species palatability and/or decomposition rates Litter trait Mn:Cu Condensed tannins Lignin N:P Phenols Fibre Decomposition + - - - - - Palatability 0 0, - - 0 - - Data compiled from Cárdenas et al. 2015, Findlay et al. 1996, Wardle et al. 2002. + indicates a positive correlation, - indicates a negative correlation, and 0 indicates NS or no correlation. 1.2.4. Soil microbial response to aboveground herbivores Herbivore-induced changes to nutrient inputs have consequences for the belowground community. For instance, terminal restriction fragment length polymorphism (TRFLP) revealed that browsed plots in a deciduous forest in Pennsylvania had significantly more even fungal communities than deer exclosures after 4.5 years, although there was no difference in fungal diversity (Burke et al. 2011). Ectomycorrhizal colonization of roots was up to 42% higher in herbivore exclosures than control plots in an early successional Alaskan taiga likely due to a 14 reduction in root exudate C or changes to N inputs (Rossow et al. 1997). In a review of herbivore effects on mycorrhizal fungi, Gehring & Whitham (1994) similarly found that mycorrhizal fungal, particularly arbuscular mycorrhizal fungal (AMF), symbioses with plants typically decrease in response to herbivores, presumably due to the increased demand for photosynthate carbon within the shoot. Turnover rates differ substantially for AMF hyphae and ectomycorrhizal fungal rhizomorphs â the former has a lifespan of days, while the latter persists for close to a year (Churchland & Grayston 2014). If fungi are particularly sensitive to herbivores, as was shown for mycorrhizal fungi, a shift towards a bacterial-dominated community would likely affect the rate of decomposition and nutrient turnover. Although, root exudates are also important for bacteria, particularly Gram-negative bacteria (Paterson et al. 2007). Bacteria are primarily involved in the rapid decomposition of labile resources and fungi are the main decomposers of recalcitrant compounds, though their roles are not mutually exclusive (de Boer et al. 2005). Mycorrhizal fungi typically have greater importance under nutrient-poor conditions, so a reduction in mycorrhizal symbioses (Gehring & Whitham 1994; Rossow et al. 1997), in combination with browse-mediated reductions in litter quality and quantity (Pastor et al. 1993), suggests that deer may exacerbate nutrient limitations of plants growing in nutrient-poor ecosystems. Herbivores can have stimulatory effects on soil microbes and nutrients in browse tolerant grasslands, but suppressive effects in nutrient-limited communities â as suggested by Bardgett & Wardle (2003). The stimulatory effect of defoliation in a Yellowstone National Park grassland was revealed by 13CO2-labelling of Poa pratensis in clipped and unclipped plots. Soil and roots were 13C-enriched in clipped plots, suggesting plants increased belowground C allocation, which 15 resulted in higher microbial C and N and, N mineralization in clipped plots (Hamilton et al. 2008). In contrast, herbivores can reduce nutrient availability if they supress carbon inputs. Indeed, in a short willow community in Rocky Mountain National Park, Gass & Binkley (2011) observed a reduction in soil C and N in grazed plots relative to exclosures. Similarly, the soil at the site with the most severe moose grazing, versus an exclosure, in a boreal forest had significantly lower: C and N concentrations, field N mineralization rate, potentially mineralizable N, cation exchange capacity, and microbial respiration (Pastor et al. 1988). The differences in soil properties were attributed to the lower litter quantity and the higher proportion of conifer needles in the grazed plot (Pastor et al. 1988). In a regenerating woodland in the Scottish Highlands, higher microbial biomass C:N, measured by fumigation-extraction, and lower N mineralization were found in grazed plots relative to exclosures. Both dissolved organic C and available N were lower in grazed plots, but the reduction in available N was almost twice that of dissolved organic C (Harrison & Bardgett 2004). However, more research is required to determine the influence of other soil properties on the response of the soil microbial community to herbivores. For instance, significant differences in microbial biomass and activity were observed between exclosures and grazed plots in New Zealand, but the idiosyncrasy between plots resulted in no overall effect of herbivores (Wardle et al. 2001). Similarly, Pastor et al. (1988) observed significantly higher microbial biomass inside one of three herbivore exclosures. Curiously, the higher biomass was in the one exclosure that had a similar volume of litter, but a higher proportion of conifer needles as the grazed paired plot (Pastor et al. 1988). Many studies have used paired exclosure and control plots to assess the belowground effects of herbivores; however, soils exhibit high spatial variability even over 16 short distances. Additionally, the use of exclosures means that there could be a legacy effect of the herbivores. Therefore, the comparison of sites inhabited by deer and sites with no history of deer may provide better insight into the effects of herbivores on belowground organisms. 1.3. Importance of soil microbes in forests 1.3.1. Nutrient cycling and storage Soils are the largest global terrestrial carbon sink. In more than 60% of the world, soil organic carbon (SOC) accounts for more than 75% of the total terrestrial carbon stock (Scharlemann et al. 2014). In fact, SOC makes up almost 90% of the total terrestrial carbon stock in moist, cool temperate areas (Scharlemann et al. 2014), such as coastal British Columbia. Therefore, soil management is of the utmost concern for climate change. Soil microbes are particularly important as they exhibit important controls over ecosystem processes, including plant productivity and diversity, nutrient acquisition and losses, and decomposition (Barrios 2007; van der Heijden et al. 2008). The first major mechanism by which microbes influence plant productivity is through nutrient cycling. One of the ways soil microbes control nutrient cycling is through the production of enzymes, which are involved in the mineralization of nutrients like N and P (Burke et al. 2011). Soil microbes are also responsible for nutrient immobilization; however, the consumption of microbes by soil microfauna aids in nutrient mineralization (Beare et al. 1992). 1.3.2. Plant-microbe symbioses Microbes also influence plant productivity directly through root symbioses, which can be formed by both bacteria and fungi. Among the best-known examples of plant-microbe symbioses are nitrogen-fixing bacteria and mycorrhizal fungi. N-fixing bacteria, which form 17 nodules in plant roots, are able to convert atmospheric N2 into plant available N (Malloch et al. 1980). Mycorrhizal fungi form symbioses with plants in which the plants benefit from enhanced nutrient uptake (Fellbaum et al. 2012) and protection from pathogens (Liu et al. 2007) while the fungus benefits from accessibility to plant-fixed carbon compounds. It is commonly accepted that mycorrhizal fungi form symbioses with the vast majority of all plant species and families on land (Wang & Qui 2006). Although arbuscular mycorrhizal fungi form symbioses with the greatest proportion of plant species, ectomycorrhizal fungi are more commonly the symbionts of temperate tree species (Parniske 2008). Symbioses between plants and mycorrhizal fungi can be obligate or facultative (Malloch et al. 1980). Mycorrhizal fungi can alter competitive interactions between plants (Callaway et al. 2004), although the symbioses tend to be most beneficial in nutrient-poor conditions (Parniske 2008). Indeed, plants can allocate less C to mycorrhizal fungi in improved nutrient conditions (Johnson et al. 2003). However, mycorrhizal fungi are exceptionally important in forest ecosystems as many trees and most conifers are obligately mycorrhizal (Wang & Qui 2006). A tree girdling experiment in a Pinus sylvetris L. forest in Sweden revealed that ectomycorrhizal hyphae made up a third or more of soil microbial biomass and were responsible for almost half of the dissolved organic C in a forest ecosystem (Högberg & Högberg 2002). This emphasizes the importance of fungi in forests, both in volume and in activity. 1.3.3. Soil biodiversity Soils support a plethora of biota in addition to mycorrhizal fungi. A single gram of organic forest soil can contain up to 1 billion bacteria and, fungal hyphae can total more than half a kilometer (Richter & Markewitz 1995). Soil organisms also contribute enormously to global 18 biodiversity: there are estimated to be 1 million species of bacteria and 1.5 million species of fungi, but only 300,000 species of vascular plants (Barrios 2007). The vast majority of soil microbes are not yet described (Torsvik & Ovreås 2002), but the high diversity of microbes provides functional redundancy between species (Setälä et al. 2005). Therefore, analysis of microbial functional groups may provide greater insight into soil processes than does species diversity. 1.4. Soil microbial community methods of analysis 1.4.1. Phospholipid fatty acids Phospholipid fatty acids (PLFAs) are one method to study microbial functional diversity. PLFAs, which are in the cell membranes of organisms, characterize only the living microbial community (Marschner 2007). PLFAs can be extracted and analyzed to identify fungal and bacterial functional groups, including Gram-negative, Gram-positive, and actinobacteria (Frostegård et al. 1993a,b; Zelles 1997; Zogg et al. 1997; Zelles 1999). PLFA analysis does not provide the high phylogenetic resolution of molecular methods; however, PLFAs revealed differences between treatments in 20% more studies than molecular methods, suggesting that PLFA analysis is more sensitive to changes in the microbial community (Ramsey et al. 2006). It is also considered a relatively time- and cost-efficient analysis (Frostegård et al. 2011). PLFA analysis can be combined with carbon isotopic analysis for a greater understanding of carbon flows within the microbial community (see Section 1.4.2; Ruess & Chamberlain 2010). 19 1.4.2. Stable Isotopes Many elements have isotopes, which have approximately the same chemical properties, but different atomic masses. This results in a phenomenon known as the âisotopic effectâ, whereby an isotope of a given element â typically the lighter one for biochemical processes â is preferred for a specific process (Werth & Kuzyakov 2010). As isotopic signatures differ between compounds (Schweizer et al. 1999), the ratio of stable isotopes can be used to make inferences about the flow of a given element in an ecosystem. Naturally, carbon is an element of particular interest. Carbon has two stable isotopes, 12C and 13C, and two radioactive isotopes, 11C and 14C. Isotopic ratios are expressed in parts per thousand using the delta notation calculated as: d C"# = 1000 Ã )*+ / )(./0123)*5 6 )*+ / )(.7/89/:9)*5)*+ / )(.7/89/:9)*5 . The d13C is commonly reported relative to the standard PDB, which refers to a sample of fossilized belemnite in the Cretaceous geological formation in PeeDee, South Carolina. Trees and other C3 plants discriminate against 13C more than C4 plants (Werth & Kuzyakov 2010), making C3 communities such as forests especially 13C-depleted. The d13C can be used to study trophic interactions in belowground communities (Ruess & Chamberlain 2010) as isotopic signatures differ between substrates. For instance, the d13C of bulk leaf biomass of C3 plants is typically similar to that of litter, while older carbon sources, such as coarse woody debris, are enriched by comparison (Bowling et al. 2008). The d13C of soil microbial biomass measured by chloroform- or extraction-fumigation extraction was 1.6â° enriched relative to total soil C of C3 soils (Dijkstra et al. 2006). Therefore, d13C can provide insight into the food sources of soil microbes. 20 1.4.3. Arbuscular mycorrhizal fungal colonization Bardgett et al. (2014) emphasize the importance of roots for many ecosystem processes and, consequently, the need to elucidate the roles of specific root traits, such as biotic interactions. Biotic root traits, specifically mycorrhizal fungi, can be studied by clearing and staining roots to determine AMF colonization. An ink-vinegar solution provides an inexpensive, safe, and reliable method for staining roots prior to measuring AMF colonization (Vierheilig et al. 1998). The grid-line intersect method enables the rapid assessment of the extent of AMF colonization in a large root sample (Giovannetti & Mosse 1980). This may be preferable over the detailed analysis of a small root sample as AMF colonization spreads from a given infection point in roots, resulting in a potential proximity effect (Giovannetti & Mosse 1980). Although the grid-line intersect method was the most popular AMF colonization assessment between 1980 and 1990, its use of a relatively low magnification (40X) makes it difficult to differentiate between fungal structures and may result in differences between observers (McGonigle et al. 1990). As such, it is better used as a relative assessment of colonization (McGonigle et al. 1990). 1.4.4. Molecular methods The advent of molecular methods enabled the rapid advancement of microbial community ecology, thereby outcompeting older methods (Lindahl et al. 2013). PCR-based molecular methods provide researchers with an objective way to study non-culturable soil microbes, but the differing sensitivity of microbes to cell lysis can bias results (Kirk et al. 2004). DNA sequencing produces large data outputs and provides high taxonomic resolution (Lindahl et al. 2013), which is useful for diversity studies. Despite the many advantages of molecular methods, it is difficult to achieve species-level taxonomic resolution for AMF using PCR-based methods 21 because of high intraspecific variation and a lack of reliable reference data (Stockinger et al. 2010). 1.5. Implications of deer for forestry: redcedar limitations and management potentials 1.5.1. Implications of deer for forestry It is suggested that deer cause $750 million in losses to the U.S. timber industry annually (Conover 1997). Deer herbivory can reduce seedling regeneration, stunt growth, and modify species composition (Conover et al. 1995). Browsing reduced the survival and growth of Euptelea polyandra, Idesia polycarpa, Morus australis, and Zanthoxylum ailanthoides, relative to protected seedlings, in a temperate coniferous forest in eastern Japan (Tamura & Nakajima 2017). However, seedling protection from browsing only increased seedling growth and survival under a canopy gap, suggesting that competition for light was also an important factor (Tamura & Nakajima 2017). Preferential browsing of palatable species can alter seedling height class frequencies. Pellerin et al. (2010) observed an increase in abundance of Abies alba seedlings, but a decrease in Quercus spp. saplings in the presence of herbivores. Similarly, roughly three times more unprotected than protected seedlings were in the smallest height class, while over 70% of protected seedlings were in the medium height class after 8 years of protection from herbivores (Stroh et al. 2008). It is apparent that the effects of herbivores on young trees can be problematic for the forestry industry, especially since many tree species are expected to take more than a decade for seedlings to escape deer browse height (Rooney et al. 2002; Kern et al. 2012). 22 1.5.2. Deer impair redcedar regeneration and growth Western redcedar (Thuja plicata) is predicted to take decades to escape deer browse due to its high palatability and slow growth rate (Stroh et al. 2008). This is problematic as redcedar produces timber worth 1.5 to 4 times more than other conifers (Antos et al. 2016). Redcedar also holds great cultural importance, including its use for canoes and baskets, for many Pacific Northwest First Nations (French 1965; Turner & Bell 1971). However, the negative effects of herbivores on redcedar has been well-documented. Redcedar regeneration is threatened in the presence of herbivores due to preferential browsing. In an 8-year study in the Oregon Coast Range, more than half of redcedar seedlings were browsed; frequent browsing resulted in seedlings that were almost 1 m shorter than protected seedlings (Maas-Hebner et al. 2005). Brandeis et al. (2002) observed a lower proportion of browsed redcedar seedlings but found that seedlings were almost half of their initial volume after three years of browsing. Similarly, simulated herbivory on western redcedar seedlings resulted in significantly lower diameter and volumetric growth than unclipped seedlings (Cockle & Ettl 2010). In addition to decreasing seedling size, deer have been shown to reduce the abundance and survival of redcedar seedlings and increase the frequency of seedlings with multiple stems (Stroh et al. 2008). Redcedar is a main, albeit subdominant, species in British Columbiaâs coastal old-growth forests; however, it has been virtually eliminated from young second-growth stands (LePage & Banner 2014). The low seedling establishment and high economic value of redcedar means additional management strategies are necessary for it to remain or increase in prevalence (Antos et al. 2016). 23 1.5.3. Tools to improve redcedar regeneration There is obviously a need for redcedar seedling protection when replanting or underplanting previously harvested stands. However, plastic mesh tubing significantly reduced redcedar seedling growth after 4 years and had little effect on browse frequency or seedling mortality (Brandeis et al. 2002). Solid protective plastic tubes, or âtree sheltersâ, can affect seedling growth through the creation of a microclimate. The higher survival of seedlings in tree shelters on a saline site was attributed to the increased soil moisture associated with the shelters (Sun et al. 1994). Tree shelters increased Quercus rubra seedling size relative to unprotected seedlings, but only increased survival by 11% (Lantagne 1995). Annual seedling growth was only significantly higher than unprotected seedlings when seedlings were shorter than their shelters; 60% of seedlings collapsed when removed from the tallest shelters (Mayhead & Boothman 1997). The varying success of tree shelters between studies is likely explained by differences in shelter material, shelter height, and tree species. The limited success of tree shelters coupled with the high costs of installation and maintenance (Lantagne 1995) warrants the exploration of alternative seedling protection. Redcedar browse damage was reduced in areas accessible to hunters, suggesting that fear may be a successful mechanism to deter browsing (Martin & Baltzinger 2002). Some fear-inducing deer repellents were shown to reduce browse damage on redcedar in Washington State, although the effectiveness of several repellents declined over time (Wagner & Nolte 2001). Additionally, wolf urine promoted vigilance in Sitka black-tailed deer on Haida Gwaii, resulting in significantly less time spent at bait stations treated with wolf urine, more time sniffing, and a lower proportion of bait consumed than at bait stations treated with water or other predator 24 odours (Chamaillé-Jammes et al. 2013). Therefore, the efficacy of deer repellents should be explored further. There is another potential way to improve seedling regeneration, which to date, has not been assessed for its efficacy as a silvicultural tool for redcedar: arbuscular mycorrhizal fungal (AMF) inoculation. AMF require a host to survive (Fellbaum et al. 2012); similarly, western redcedar require AMF to survive (Curran & Dunsworth 1988). The symbiosis with AMF makes redcedar unique as most other temperate tree species have ectomycorrhizal associates (Newman & Reddell 1978). The effects of inoculum potential on seedling growth have been demonstrated. On Vancouver Island, soil from a forest dominated by ectomycorrhizal-tree species (Tsuga heterophylla and Abies amabilis) had lower AMF inoculum potential than soil from a stand containing redcedar, which resulted in reduced growth and survival of redcedar seedlings in a greenhouse experiment (Weber et al. 2005). This suggests that AMF may not be viable in the soil in the absence of its host. Alternatively, it has been suggested that competitive relationships exist between AMF and ectomycorrhizal fungi â at least within the same root system (Chilvers et al. 1987; Lodge & Wentworth 1990). However, native soil transplants can serve as a fungal inoculum source. Soil from an area of high inoculum potential was used to inoculate soil from an area of low inoculum potential in Argentina (Nuñez et al. 2009). Pinus spp. and Pseudotsuga menziesii seedlings grown in inoculated soil in the greenhouse were significantly larger and, seedling establishment in the field was significantly higher in inoculated soil (Nuñez et al. 2009). Therefore, native soil has potential for use as a cost-effective tool to improve the AMF inoculum potential of the soil. 25 It is necessary to assess whether deer reduce AMF inoculum potential through the preferential browsing of redcedar and, whether lower AMF inoculum potential of the soil contributes to the poor regeneration of redcedar in the presence of deer. Although redcedar is present as a canopy species in many mature coastal forests (LePage & Banner 2014), its regeneration is still a concern due to herbivory (Stroh et al. 2008). Therefore, AMF inoculum and deer repellent should be assessed for their potential to promote redcedar growth and survival in the presence of deer. 1.6. Research objectives and hypotheses The main objective of this research was to determine how introduced deer on Haida Gwaii affect belowground plant and bryophyte community abundance, belowground microbial community structure, and nutrient availability. Therefore, the goals of this research were to 1) assess changes in the vegetation community in response to deer, 2) quantify the belowground effects of deer, and 3) assess management options to improve western redcedar regeneration in the presence of introduced deer. 1.6.1. Research questions The questions addressed by this research were: 1. How does understory plant and bryophyte community composition differ between deer-free islands and deer-inhabited islands? 2. How do soil physical and chemical properties differ between deer-free islands and deer-inhabited islands? 3. How does soil microbial community structure and the d13CPDB natural isotopic abundance differ between deer-free and deer-inhabited islands? 26 4. How do arbuscular mycorrhizal fungal (AMF) inoculum potential and redcedar growth differ between: i. Deer-free islands and deer-inhabited islands ii. Ectomycorrhizal- and redcedar second growth stands iii. Non-inoculated soil vs. soil inoculated with commercial AMF inoculum. 5. Do Plantskydd® herbivore repellent and wolf urine reduce herbivory on western redcedar? In order to address these questions, I studied vegetation and soil in replicate plots on deer-free and deer-inhabited islands with the same parent material in Laskeek Bay and in Gwaii Haanas, Haida Gwaii. I surveyed the plant community to test for differences in community composition. I collected soil samples for chemical (i.e. available nutrients, pH, and total C, N, and P), physical (i.e. bulk density and soil moisture), and biological analyses. I used Plant Root Simulator® probes to determine plant available nutrients in each plot over a 1-year period. I analyzed phospholipid fatty acids to compare the structure of the soil microbial community. I used a greenhouse experiment and a soil bioassay to assess the AMF inoculum potential of the soil. Lastly, I performed a field experiment to assess the use of commercial AMF inoculum and deer repellent as silvicultural tools to improve redcedar growth and survival. 1.6.2. Hypotheses For the above research questions, I developed the following hypotheses: 1. There will be a shift in the understory vegetation community, from higher plants on deer-free islands, to bryophytes on deer-inhabited islands. Plant species diversity will be 27 lower on deer-inhabited islands than deer-free islands, while bryophyte diversity will be higher on deer-inhabited islands. 2. Soil nutrients will be lower, while bulk density and soil penetration resistance will be higher on deer-inhabited islands than deer-free islands. Soil will be less acidic on deer-inhabited islands than deer-free islands. 3. Bacterial and fungal biomass will be lower on deer-inhabited islands than deer-free islands. The microbial d13CPDB values will be enriched on deer-inhabited islands relative to deer-free islands. 4. The AMF inoculum potential of the soil and redcedar seedling growth will be lower: i. On deer-inhabited than deer-free islands. ii. On a second-growth harvested site previously dominated by ectomycorrhizal-associated conifers than on a site previously containing a redcedar component. iii. In non-inoculated soil than soil inoculated with commercial AMF inoculum. 5. Western redcedar seedlings treated with Plantskydd® herbivore repellent or wolf urine will have less browse damage than untreated western redcedar seedlings. 28 2. Understory community composition and diversity differ between deer-free and deer-inhabited islands 2.1. Introduction The effects of introduced deer on the vegetation of Haida Gwaii have been studied using exclosures (Stroh et al. 2008; Baltzinger et al. 2009) and island comparisons (Stockton et al. 2005; Gaston et al. 2006; Chollet et al. 2013a). However, hunting pressure and fear can alter deer behaviour and the extent of browse damage (Martin & Baltzinger 2002; Le Saout et al. 2014), so exclosure studies may be subject to human influence. As such, this section will focus on the island comparisons on Haida Gwaii, which have primarily been conducted in Laskeek Bay. Several studies have demonstrated the dramatic changes caused by introduced deer to the understory community on Haida Gwaii (Stockton et al. 2005; Gaston et al. 2008; Chollet et al. 2013a). In Laskeek Bay and Ramsay Island, dendrochronology revealed that red huckleberry (Vaccinium parvifolium) plants were significantly older and produced fewer new stems on deer-inhabited than deer-free islands, suggesting that deer impede the regeneration of red huckleberry (Vila et al. 2004b). Another comparison between deer-free and deer-inhabited islands in Laskeek Bay revealed that herbaceous vegetation and shrub cover below browse height decreased with increasing length of browse history (Stockton et al. 2005). In fact, understory plant cover in the forest interior and in shoreline plots was 70-80% lower on deer-free islands than on islands colonized by deer for >50 years (Martin et al. 2010). On deer-inhabited islands, plant species density and cover were significantly greater on refuges that offer protection from browsing than on the forest floor (Chollet et al. 2013b). A 29 temporary reduction in the number of deer on Reef and SGang Gwaay Islands resulted in a 25-50% increase in vegetation cover in the 5-15 cm height class in the forest interior by the fifth year of the cull (Gaston et al. 2008). These results suggest that introduced deer reduce plant cover on Haida Gwaii, but reduced browsing pressure may allow for recovery. However, plant species differ in their palatability and sensitivity to deer. Monitoring of Sitka black-tailed deer browsing behaviour in Laskeek Bay revealed that the deer spent almost 50% of foraging time on tree species, >40% of foraging time on grasses, and 10% of the time browsing shrubs and forbs, while none of their time was spent browsing bryophytes (Chollet et al. 2013a). As such, higher plant species such as Rubus parviflorum, Maianthemum dilatatum, and others have been virtually eliminated from the forest interior on islands inhabited by deer for <20 and >50 years (Martin et al. 2010). In contrast, Tsuga heterophylla abundance below browse height peaked on islands inhabited by deer for <20 years, suggesting it benefits from intermediate browsing (Martin et al. 2010). Therefore, deer reduce the abundance of palatable and/or browse-sensitive plants on Haida Gwaii, which enables unpalatable and/or browse tolerant species to persist â or thrive, in the case of bryophytes. Indeed, the almost complete avoidance of bryophytes as forage by deer, along with the reduced competition for light, has resulted in >70% increase in bryophyte cover on deer-inhabited than deer-free islands (Chollet et al. 2013a). Prior to the 1997 cull on Reef Island, regenerating Picea sitchensis and T. heterophylla were the dominant plant species in the forest interior understory (Gaston et al. 2008). However, the cover of several shrub species, such as Gaultheria shallon and V. parvifolium, increased more than three-fold after the cull (Gaston et al. 2008). Indeed, V. parvifolium and G. shallon had the highest frequency of browse damage on 30 other deer-inhabited islands in Laskeek Bay with 74% and 84% of studied individuals exhibiting browse damage, respectively (Chollet et al. 2013a). The effects of deer on plant diversity and richness are evident, but perhaps not as drastic as the changes to plant cover. Plant species diversity (Martin et al. 2010) and richness (Stockton et al. 2005) â particularly in shoreline communities â were higher on deer-free than deer-inhabited islands. In contrast, bryophyte diversity was significantly higher on deer-inhabited than deer-free islands (Chollet et al. 2013a). The aforementioned changes to plant and bryophyte diversity and richness were observed at the plot-scale, rather than the island-scale. Eight additional plant species were recorded on Reef Island after the deer cull, although sampling effort likely played a role in these discoveries and no additional species were observed on SGang Gwaay, which was also culled (Gaston et al. 2008). In Laskeek Bay, larger, less-isolated islands support a greater number of species than small, isolated islands; however, among the small-islands (<25 ha), there was a negative species-area relationship due to the presence of deer (Gaston et al. 2006). This suggests that the effect of deer on species richness may be under-estimated if there is a large difference in size â as is usually the case â between deer-free and deer-inhabited islands. While the comparison of remote deer-free and deer-inhabited islands eliminates the potential human influence that exists when using herbivore exclosures, it introduces other confounding variables. For instance, islands can differ in size, exposure to natural disturbance, and parent material â all of which can influence plants and/or soils. Some of the study sites used for this thesis have been used in the previous island comparisons conducted on Haida Gwaii, including: Low (Stockton et al. 2005; Gaston et al. 2006, 2008; Chollet et al. 2013a,b), 31 South Skedans (Stockton et al. 2005; Gaston et al. 2006), Ramsay (Vila et al. 2004b), and Reef (Gaston et al. 2006, 2008). However, none of these studies accounted for differences in parent material between the islands. Parent material affects soil properties and, consequently, plant growth. Indeed, parent material can influence foliar nutrient content and tree height (Ferwerda et al. 2006). 2.2. Methods 2.2.1. Study site The invasion by deer of all but a few of the islands on Haida Gwaii, BC provided the unique opportunity to conduct a natural experiment using an island comparison. The islands included in this study were located in Laskeek Bay (W 131.55°, N 52.9°) and Gwaii Haanas (W 131.42°, N 52.62°) in the Wet Hypermaritime Coastal Western Hemlock biogeoclimatic subzone of the Skidegate Plateau (Golumbia 2007). The islands are covered by primary forest. Although a large portion of Lyell Island is second growth (Davis et al. 2004), plots were restricted to the Windy Bay region, which still supports primary forest. The islands were chosen in part based on their geology. The Laskeek Bay islands are part of the Yakoun group geological formation consisting of agglomerate, flow breccia, sandstone, conglomerate, and minor shale (Haggart 2004). In contrast, the geology of the Gwaii Haanas islands consists of intercalated mafic to felsic lava flows and pyroclatic rocks with areas of epiclastic rocks (Haggart 2004). Windy Bay, the region of Lyell Island in which the plots were located, consists of a different parent material â primarily alluvium, Pleistocene till, and marine drift (Haggart 2004). The deer-inhabited islands sampled were Ramsay, Lyell, Reef, and South Skedans (Figure 2.1). The herbivore histories differed between some of the islands: deer colonized Reef and 32 Ramsay Islands over 60 years ago, while South Skedans has had deer for less than half that time (Vila et al. 2004a,b). The deer on Reef Island were culled from 1997-2003, which greatly reduced, but did not eradicate the population (Gaston et al. 2008). Based on the findings of Vila et al. (2004a,b), and Lyell Islandâs size and location, it can be inferred that deer invaded Lyell at least 60 years ago. The three deer-free islands included in the study were: Tar, Agglomerate, and Low (Figure 2.1). Tar is a group of islands â the largest of which was sampled for this study. Low and Tar Islands are considered to be among the largest of the deer-free islands (Vila et al. 2004b) and Agglomerate is larger still. Although, all of the deer-free islands are smaller than the deer-inhabited islands other than South Skedans (Table 2.1). Table 2.1: Island size, location, years of deer colonization, and the number of sampling plots Island Island Group Deer Colonization (years) Island Size (ha) Number of Plots Agglomerate Gwaii Haanas 0 22.9 7 Low Laskeek Bay 0 9.6 3 Lyell Gwaii Haanas 60+ >17,300 6 Ramsay Gwaii Haanas 60+ 1,622.8 14 Reef Laskeek Bay 60+ 249 6 South Skedans Laskeek Bay <30 5.6 1 Tar Gwaii Haanas 0 6 6 Island size from Davis et al. (2004), Vila et al. (2004a,b), and Major et al. (2011). Browse history from Vila et al. (2004a,b), except for Agglomerate and Lyell Island for which it was inferred based on size and location. 33 Figure 2.1: Map of Laskeek Bay and Gwaii Haanas in Haida Gwaii, BC. Red markers indicate Reef, Lyell, and Ramsay Islands and the Windy Bay region of Lyell Island. White circles are used to indicate the remaining islands in the study. Google Earth 2017a. 2.2.2. Experimental design Data was collected in July and August 2016. Between one and fourteen 20 m x 20 m plots were randomly placed on each island (Table 2.1; Figure 2.2). Plots within 100 m from the shoreline were classified as edge plots, while plots farther than 100 m from the shoreline were classified as forest interior. Plots were photographed and site conditions were recorded. Untitled Map Write a description for your map. Legend 20 kmNâ¤Data SIO, NOAA, U.S. Navy, NGA, GEBCOData SIO, NOAA, U.S. Navy, NGA, GEBCOImage © 2017 DigitalGlobeImage © 2017 DigitalGlobei i l lData LDEO-Columbia, NSF, NOAAData LDEO-Columbia, NSF, NOAAl iImage © 2017 Province of British ColumbiaImage © 2017 Province of British Columbiai i i l iLaskeek Bay Gwaii Haanas Tar Island Agglomerate Island Low Island South Skedans Island 34 Figure 2.2: Sampling plots in Laskeek Bay and Gwaii Haanas, Haida Gwaii, BC. Red symbols indicate a plot. Google Earth 2017b. 2.2.3. Vegetation and bryophyte surveys All understory species within each 20 m x 20 m plot were recorded and assigned a cover class using a modified scale (Table 2.2) of the Braun-Blanket method (Braun-Blanket 1932). Bryophyte total percent cover was estimated in each plot. On the Gwaii Haanas islands, bryophyte species presence was recorded in twenty randomly placed 20 cm x 20 cm subplots. To determine bryophyte species cover, the frequency (i.e. count/20 subplots) was multiplied by the midpoint of the total moss cover for each species. A value of 0.01% was assigned to species that were present in the plot, but not in the subplots. Species richness, evenness, and Shannonâs diversity were calculated. Untitled Map Write a description for your map. Legend 20 kmNâ¤Data SIO, NOAA, U.S. Navy, NGA, GEBCOData SIO, NOAA, U.S. Navy, NGA, GEBCOImage Landsat / CopernicusImage Landsat / CopernicusiData LDEO-Columbia, NSF, NOAAData LDEO-Columbia, NSF, NOAAl i 35 Table 2.2: Understory vegetation percent cover classes Cover Class Percent Cover Range Midpoint A <0.25% 0.125% B 0.25-0.5% 0.375% C 0.5-1% 0.75% D 1-5% 3% E 5-15% 10% F 15-25% 20% G 25-50% 37.5% H 50-75% 62.5% I 75-95% 85% J 95-100% 97.5% 2.2.4. Statistical analyses Statistical analyses and figures were completed in RStudio Version 1.0.136 (RStudio Team 2016). Data was transformed using the Hellinger transformation for ordinations. Numerical explanatory variables were transformed to their z-scores (i.e. mean = 0, variance = 1) for use in the constrained ordination. Principal coordinates analysis (PCoA), an unconstrained ordination, was used for preliminary assessment of vegetation and bryophyte communities. A canonical analysis of principal coordinates (CAP) was performed for hypothesis testing using the capscale function in the vegan package and ggplot2 was used for plotting. The Bray-Curtis dissimilarity matrix was used for PCoA and partial-CAP. Similarity percentages (SIMPER) using the simper function were used to identify the species responsible for the differences between islands. The anova function was used to conduct a type I F-test (permutations = 999) to test the significance of models and terms. 36 Shannon diversity was calculated using the diversity function in the vegan package. Species richness was determined using specnumber and species evenness was calculated as: diversity/log(specnumber). Shapiro Wilkâs test (shapiro.wilk) was used to assess the normality of data. Normal data was assessed for equal variances using the var.test function. Studentâs t-test or an analysis of variance (ANOVA) was performed on parametric data. Mann Whitney U or Kruskal-Wallis tests were performed on non-parametric data. In the case that neither a t-test nor Mann Whitney U test were suitable, a one-way ANOVA with a permutation test (perm = 999) was used. 2.3. Results 2.3.1. Plant community composition and diversity Similar trends in the understory plant community were revealed by PCoA and CAP: the vegetation community was more similar between islands of the same treatment (Figures 2.3 & 2.4). In both ordinations, deer-inhabited and deer-free islands separated along the first axis (Figure 2.4; CAP1 F = 26.86, p = 0.001). Of the islands with intermediate browse history â Reef and South Skedans â some of the plots separated intermediately between the deer-free and deer-inhabited islands. The deer-free islands exhibited greater dispersion, due to a separation between islands, along the second axis than the deer-inhabited islands (Figure 2.4; CAP2 F = 5.91, p = 0.006). The understory community was defined using the model capscale(formula = veg.hel ~ Treatment + Island + Geology + C:N, data = env.z, distance = "bray") (F = 5.80, p = 0.001). A Type I ANOVA of the ordination model revealed that differences in understory vegetation community were explained by deer presence/absence (F = 27.43, p = 0.001), island (F = 2.87, p = 0.001), geology (F = 2.30, p = 0.013), and soil C:N (F = 2.28, p = 0.075). SIMPER 37 revealed that G. shallon and Polystichum munitum were the most influential species accounting for 66% and 90% of the Bray-Curtis dissimilarity between plots, respectively. Figure 2.3: PCoA of Hellinger-transformed understory vegetation community composition on deer-free (n = 16) and deer-inhabited islands (n = 27). Model formula: capscale(veg.hel ~ 1, dist = "bray"). â1012â1.0 â0.5 0.0 0.5 1.0PCoA1PCoA2TreatmentDeerNoDeerIslandAgglomerateLowLyellRamsayReefSkedansTarPCoA on Hellingerâtransformed veg Midpoints 38 Figure 2.4: CAP of Hellinger-transformed understory vegetation community composition on deer-free (n = 16) and deer-inhabited islands (n = 27). Model formula: capscale(veg.hel ~ Treatment + Island + Geology + C:N, data = env.z, distance = "bray"). Understory plant diversity was three times higher on deer-inhabited than deer-free islands (Table 2.3; t = 7.99, p = 7.07e-10). However, the difference in species richness was marginal (t = 1.79, p = 0.082) with about two additional species in plots on deer-inhabited islands than deer-free islands (Table 2.3). Species evenness was more than twice as high on deer-inhabited islands as it was on deer-free islands (F = 48.07, p (perm) = 0.001). In contrast, plant cover was almost 70% lower on deer-inhabited than deer-free islands (F = 97.78, p (perm) = 0.001; Table 2.3). 0.00.5â0.4 0.0 0.4CAP1CAP2TreatmentDeerNoDeerIslandAgglomerateLowLyellRamsayReefSkedansTarCAP on Hellingerâtransformed Understory veg Midpoints 39 Table 2.3: Plant diversity and cover on deer-free (n = 16) and deer-inhabited (n = 27) islands. Values are means ± SE. 2.3.2. Bryophyte community composition and diversity PCoA and CAP both revealed a clustering of the bryophyte community within deer-free and deer-inhabited islands (Figures 2.5 & 2.6). There is a clear separation between bryophytes on deer-inhabited and deer-free islands along the first axis (Figure 2.6; CAP1 F = 21.54, p = 0.001). Ramsay Island exhibited the greatest dispersion along the second axis, although there is also a separation between Tar and Agglomerate Islands (Figure 2.6; CAP2 F = 6.18, p = 0.002). The bryophyte community was defined using the model capscale(formula = moss.hel ~ Treatment + Island + Bulk Density + Soil Moisture + pH + Organic Depth + C:N, data = env.z, distance = "bray") (F = 6.45, p = 0.001). A Type I ANOVA of the ordination model revealed that differences in bryophyte community composition were explained by deer presence/absence (F = 27.02, p = 0.001), island (F = 5.11, p = 0.001), soil moisture (F = 4.09, p = 0.007). Several other variables increased the variation explained in the bryophyte community, but did not have a significant effect on the model: soil pH (NS; F = 1.89, p = 0.11), organic soil depth (F = 2.14, p = 0.081), and soil C:N (F = 2.23, p = 0.082). SIMPER revealed that Rhytidiadelphus triquetrus, Hylocomium splendens, Kindbergia oregana, Scapania bolanderi accounted for 26%, 47%, 63% and 71% of the Bray-Curtis dissimilarity between plots, respectively. Treatment Richness Evenness Shannon Diversity % Cover Deer 9 ± 0.6 0.79 ± 0.05 1.67 ± 0.12 4 ± 1.5 No deer 7 ± 0.6 0.28 ± 0.04 0.53 ± 0.08 73 ± 8.7 40 Figure 2.5: PCoA of Hellinger-transformed bryophyte community composition on deer-free (n = 12) and deer-inhabited islands (n = 20). Model formula: capscale(formula = moss.hel ~ 1, dist = "bray"). Figure 2.6: CAP of Hellinger-transformed bryophyte community composition on deer-free (n = 01â0.5 0.0 0.5PCoA1PCoA2TreatmentDeerNoDeerIslandAgglomerateLyellRamsayTarPCoA on Hellingerâtransformed moss Midpointsâ0.250.000.250.500.75â0.6 â0.3 0.0 0.3 0.6CAP1CAP2TreatmentDeerNoDeerIslandAgglomerateLyellRamsayTarCAP on Hellingerâtransformed moss Midpoints 41 12) and deer-inhabited islands (n = 20). Model formula: capscale(moss.hel ~ Treatment + Island + Bulk Density + Soil Moisture + pH + Organic Depth + C:N, data = env.z, distance = "bray"). Bryophyte diversity was non-significantly higher on deer-inhabited than deer-free islands (Table 2.4; t = 1.64, p = 0.11). In contrast, species evenness was non-significantly higher on deer-free islands (t = -1.57, p = 0.13). Species richness was significantly higher on deer-inhabited islands (t = 4.49, p = 9.69e-5) with about two more species in plots than on deer-free islands (Table 2.4). Bryophyte cover was more than 60% higher on deer-inhabited than deer-free islands (F = 52.23, p (perm) = 0.001; Table 2.4). Table 2.4: Bryophyte diversity and cover on deer-free (n = 12) and deer-inhabited islands (n = 20). Values are means ± SE. *Only percent cover includes data from Laskeek Bay islands, so deer n = 27 and no deer n = 15. 2.4. Discussion The vegetation and bryophyte communities were distinct between deer-free and deer-inhabited islands. Deer-inhabited islands supported less plant cover and more even plant communities, but greater bryophyte cover and species richness than deer-free islands. Differences between islands, including geology, also contributed to differences between understory communities. The reduced plant cover in the presence of herbivores, such as deer, has been demonstrated by many others on Haida Gwaii (Stockton et al. 2005; Gaston et al. 2008; Baltzinger et al. 2009; Chollet et al. 2013b) and elsewhere (Singer & Schoenecker 2003; Martin Treatment Richness Evenness Shannon Diversity % Cover* Deer 10 ± 0.3 0.78 ± 0.017 1.79 ± 0.047 78 ± 6.2 No deer 8 ± 0.4 0.83 ± 0.024 1.67 ± 0.056 12 ± 4.7 42 et al. 2011). In contrast to the higher plant species diversity observed on deer-inhabited islands in this study, previous studies have reported lower plant species diversity or richness in the presence of deer (Wardle et al. 2001; Stockton et al. 2005; Gaston et al. 2008). However, the increased diversity in our study was driven by species evenness, rather than species richness, likely due to the fact that most species on deer-inhabited islands were present in low abundances, while larger differences in abundances existed between species on deer-free islands. A study of the species-area relationship in Laskeek Bay demonstrated that of the plant species recorded on 10 islands, only half of the species were recorded on the five small islands (<25 ha), while 95% of the species were found on the five large islands (>25 ha) (Gaston et al. 2006). Among the small islands, species richness was more than 20% higher on deer-free islands than on islands inhabited by deer for >50 years (Gaston et al. 2006). Therefore, the slightly higher plant species richness on deer-inhabited islands than deer-free islands in our study was likely a factor of island size, rather than deer presence. Indeed, deer simplified the plant community on deer-inhabited islands in comparison to deer-free islands (Martin et al. 2010). In contrast, the bryophyte community has benefited from deer presence. Chollet et al. (2013a) also observed higher bryophyte cover and species diversity on deer-inhabited islands than deer-free islands on Haida Gwaii. Therefore, deer browsing shifted the understory community from higher plants to bryophytes. Nuttle et al. (2014) observed a reduction in seedling and forb cover, but an increase in fern cover, with increasing deer densities â a browsing legacy that was evident 20 years after experimentally manipulated deer densities 43 were returned to ambient densities in an early successional forest. Deer have similarly been found to decrease the abundance of palatable and browse sensitive species outside of exclosures (Wardle et al. 2001; Pellerin et al. 2010). Therefore, intensive browsing can result in the dominance of vegetation that is avoided by herbivores. The shift in understory from nutrient-rich higher plant species to unpalatable vegetation such as bryophytes, which contain a polyphenol that impedes digestion (Prins 1982), will likely have cascading effects on ecosystem and soil processes. The installation of deer exclosures (Stroh et al. 2008) and the reduction of deer densities (Gaston et al. 2008) have demonstrated the potential for recovery â albeit slow â in the forest understory, but active management may be necessary to remove a lasting herbivore legacy (Nuttle et al. 2014). The understory plant community on islands with a disrupted (Reef) or shorter (South Skedans) browse history were less distinct than deer-free or deer-inhabited islands in the current study, which could indicate a potential for recovery. Additionally, refuges â such as rocky outcrops (Chollet et al. 2013b), cliffs and gullies (Gaston et al. 2006) and coarse woody debris (Collings 2012) â provide protection from deer and can support growth of some plant species on deer-inhabited islands. In fact, Chollet et al. (2013b) suggest that refuges can also be used as reference sites indicative of deer-free conditions, which can be useful for setting conservation goals. 44 3. Soil properties differ between deer-free and deer-inhabited islands 3.1. Introduction The idiosyncratic effects of aboveground herbivores on soil microbes and processes are a result of the multiple and conflicting mechanisms by which deer can affect the soil (Bardgett & Wardle 2003). The belowground effects of herbivores are dependent on several factors, including: soil properties (Schrama et al. 2013b), plant palatability and browse tolerance (Wardle et al. 2002), and browse intensity (Pastor et al. 1988) or duration (Allombert et al. 2005). Decomposition and mineralization rates are influenced by herbivore-driven changes to the physical environment (Schrama et al. 2013b). Herbivores typically decrease the abundance of palatable plants, while increasing the abundance of unpalatable species (Pellerin et al. 2010; Chollet et al. 2013a), which can reduce litter quality (Wardle et al. 2002). The magnitude of the belowground response to herbivores is also dependent on browsing intensity (Pastor et al. 1988). Herbivores can indirectly alter soil properties by reducing plant cover and increasing light availability. Moose browsing created a more open canopy, resulting in higher light intensity and soil temperature, but lower soil moisture and humidity outside of exclosures in an Alaskan taiga (Kielland & Bryant 1998). These physical changes were associated with faster litter decomposition (Kielland et al. 1997) and higher soil pH (Kielland & Bryant 1998) in grazed plots. Similarly, unfavourable changes to microclimate were presumed to be responsible for the consistently lower soil meso- and macrofaunal abundance outside of deer exclosures in New Zealand (Wardle et al. 2001). The direction and magnitude of the response to herbivores is dependent on site-specific conditions, such as soil texture and moisture. Soil N was lower in 45 grazed plots than herbivore exclosures across sites which differed in soil texture, but available N and N mineralization were highly variable (Gass & Binkley 2011). The variability could have resulted from the higher soil bulk density, but lower soil moisture in grazed plots (Gass & Binkley 2011), as N mineralization is predicted to slow if soil compaction exacerbates anoxic conditions (Schrama et al. 2013b). Herbivores tend to depress nutrient availability in communities in which palatable species are negatively affected by herbivores. Unpalatable plant species â those that deer promoted or did not supress â produced litter that decomposed more slowly than palatable species in New Zealand (Wardle et al. 2002). Both palatability and decomposition were negatively related to foliar concentrations of phenolics and fibre (Wardle et al. 2002). Therefore, reduced litter quality was likely responsible for the lower soluble C in humus in grazed plots than in deer exclosures, while other soil chemical properties exhibited idiosyncratic responses (Wardle et al. 2001). Similarly, in a disturbed boreal forest in Newfoundland, the lower litterfall in grazed plots reduced the quantity of litter C, N, and P in comparison to moose exclosures; however, litter nutrient ratios were the same between grazed plots and exclosures as plant diversity did not differ (Ellis & Leroux 2017). Herbivores tend to negatively affect soil microbial function and structure in communities in which resource quality decreases. Microbial respiration was consistently higher in exclosures than grazed plots in the boreal forest, but the difference was only significant at the site with the highest browsing intensity, which differed in litter quality and quantity (Pastor et al. 1988). There were no differences in fungal diversity or richness between grazed plots and exclosures in a temperate deciduous forest, but species evenness â a diversity measure that utilizes both 46 richness and abundance â was significantly lower (i.e. more heterogeneous) for the ectomycorrhizal and saprotrophic fungal community in exclosures than grazed plots (Burke et al. 2011). The lower fungal evenness, or greater species heterogeneity, inside exclosures may be attributable to higher litter quantity/quality or root exudation â neither of which were measured in the study. While the difference in microbial biomass between exclosures and grazed plots was inconsistent, microbial biomass in humus inside exclosures was correlated with N concentration (Wardle et al. 2001). Just as herbivore-induced reductions in resource quality can supress soil microbes, herbivores will stimulate soil microbial growth and nutrient availability if resource quality or quantity is increased. For instance, 13CO2-labelling revealed that root exudation was higher from clipped Poa pratensis than from unclipped individuals and, soil microbial biomass was significantly higher in clipped plots than unclipped plots (Hamilton et al. 2008). High reindeer browsing pressure promoted the dominance of nutrient-rich graminoids, over shrubs, in nutrient poor arctic tundra communities, resulting in lower litter C:N and higher soil extractable N (Sitters et al. 2017). Indeed, graminoids frequently exhibit positive responses to herbivores â likely due to available N in urine and feces, which stimulates their growth (Day & Detling 1990). Carbon isotopes can be used in combination with phospholipid fatty acid analysis to study C flow in soil organisms (Ruess & Chamberlain 2010). For instance, saprotrophic fungi are more 13C-enriched than mycorrhizal fungi, which reflects the difference in substrate utilization (Bowling et al. 2008). Similarly, the d13CPDB differed between a C4 grass, Brachiaria humidicola, and a C3 shrub, Desmoium ovalifolium (Schweizer et al. 1999). Lignin in the leaves, litter, and 47 roots of both species was 13C-depleted relative to bulk plant material, although the d13CPDB of the bulk material was similar after 0 and 119 days of decomposition (Schweizer et al. 1999). The d13CPDB isotopic signature of the soil will resemble that of the plant community (Werth & Kuzyakov 2010). The range of d13CVPDB for C3 plants is -20â° to -37â°, although 13C-depletion at the lower end of the range is uncommon (Kohn 2010). Microbial biomass measured by chloroform fumigation extraction or incubation has an average enrichment of 1.2â° relative to soil organic matter (Werth & Kuzyakov 2010). However, the extent of 13C-enrichment or depletion in fatty acids extracted from fungal and bacterial cultures differed between substrates, species, and PLFAs (Abraham et al. 1998). Despite the thorough research on the aboveground effects of deer on Haida Gwaii, the belowground community has received little attention. Although, a study on invertebrates in Laskeek Bay revealed that deer supressed some litter fauna and promoted others. Specifically, gastropod species density was significantly lower on islands inhabited by deer for >50 years than on deer-free islands in Laskeek Bay, while the abundance and species density of weevils increased with increasing length of island browse history (Allombert et al. 2005). The negative response of gastropods to deer likely resulted from the reduced understory cover observed previously (e.g. Stockton et al. 2005), while weevils were dependent on conifer density (Allombert et al. 2005). Soils play essential and diverse roles in ecosystems (van der Heijden et al. 2008), so it is important to understand how else deer affect the soil on Haida Gwaii. Researchers have argued the need for an integrated approach to study herbivore-soil interactions that considers plant feedbacks and soil physicochemical properties (Schrama et al. 2013b; Sitters & Olde Venterink 2015). As such, I collected extensive data on the above- and 48 belowground communities on deer-free and deer-inhabited islands on Haida Gwaii to elucidate the effects of deer. Specifically, I analysed the following soil physicochemical properties: bulk density, soil moisture, pH, total P and soil % C and N. Plant Root Simulator® (PRS®) probes were used to measure soil available nutrients. Microbial biomass, community structure, and d13CPDB were also analyzed using phospholipid fatty acids. 3.2. Methods 3.2.1. Soil sampling Soil samples were collected from 1-14 plots on each of the deer-free and deer-inhabited islands described in Section 2.2.1. A soil probe, 2.5 cm in diameter and 30 cm in depth, was used to collect the F layer of the forest floor for microbial and chemical analyses. Approximately 100 soil cores were composited to create one 1-2 L sample per plot. Bulk density was measured by sampling a separate soil core (4.1 cm in diameter and 5.4 cm long) from the F Horizon, where possible. The excavation method was used when the core method was not suitable. Soil was stored in coolers on ice until transport to the lab (within 4 weeks of sampling) at which point soil was sieved to 4.75 mm to remove large roots and homogenize samples. Sieved soil was stored at -20 °C in September 2016. Soil subsamples were freeze-dried in October 2016 for microbial community analysis. Additional soil was freeze-dried in November 2017 and ground using a mortar and pestle for soil C, N, and P analyses. The South Skedans soil sample was misplaced after fieldwork, so only soil moisture, bulk density, and available nutrient data were available. 49 3.2.2. Soil physical and chemical analyses Soil cores were dried at 105 °C for 72 h to determine soil moisture and bulk density. Soil penetration resistance was measured (50 replicates/plot) using a pocket penetrometer in the summer of 2017 on all islands except for Agglomerate, as this island was not revisited. Also in the summer of 2017, the depth of the forest floor was measured at five locations in each plot and averaged â except for Agglomerate Island for which one measurement was taken per plot in 2016. Soil pH was measured in a 1:10 air-dried soil to 0.01 M CaCl2 solution. Four pairs of anion and cation Plant Root Simulator® (PRS®) probes were buried in the organic soil just below the litter layer in each plot except for Agglomerate Island. PRS® probes were collected after a 1-year incubation in the summer of 2017, cleaned with distilled water, and sent to Western Ag (Western Ag Innovations Inc., SK, Canada) for analysis. Ions were eluted from the exchange resin with 0.5 M HCl and measured using automated flow injection analysis (NO2- and NO3-, NH4+) or inductively coupled mass spectrometry (all other ions; Western Ag 2017). Soil % C and N of freeze-dried soil were measured on an Elementar Vario EL Elemental Analyzer. Total P was digested using the sodium hypobromite/sodium hydroxide alkaline oxidation method (OâHalloran & Cade-Menum 2008) with the following modifications: samples were heated using an aluminum block digester (Cihacek & Lizotte 1990); and, extracts were filtered (Kuo 1996), rather than centrifuged, through Whatman No. 1 filter paper. Phosphorus was measured colourimetrically using the ascorbic acid method (OâHalloran & Cade-Menum 2008) on a Milton Roy Spectronic® 20D Spectrophotometer. 50 3.2.3. Microbial community structure and biomass Phospholipid fatty acids (PLFAs) were extracted from 0.75 g of freeze-dried soil using the revised Bligh and Dyer (1959) method described in Frostegård et al. (1991), which was based on the modifications by White et al. (1979). The extraction used a 1:2:0.8 (v/v/v) ratio of chloroform, methanol and citrate buffer. Lipids were eluted through Solid Phase Extraction cartridges and methyl nonadecanoate (19:0) was added to the remaining PLFAs. Samples were then methylated, dried under N2 gas, stored at -20 °C, and diluted with 200 µL hexane for analysis on a Gas Chromatograph-Mass Spectrometer (GC-MS) and a Gas Chromatograph-Isotope Ratio Mass Spectrometer (GC-IRMS; see section 3.2.5). Soil microbial community structure was determined through the separation of PLFA methyl esters on an Agilent GC-MS at the Life Sciences Centre, University of British Columbia. Injections of 2 µL were made in splitless mode, using helium as the carrier gas. The column used was a 30 m x 250 µm Agilent HP5ms with a 0.25 µm film thickness. The following temperature program was used: 50 °C initial temperature, increased to 150 °C at 15 °C/min, increased to 190 °C at 4 °C/min, increased to 250 °C for 5 min at 1 °C/min, and finally increased to 270 °C for 10 min at 5 °C/min. PLFAs were identified using the retention times of the bacterial acid methyl ester standard (BAME; Sigma-Aldrich, Canada) and the ChemStation Enhanced Data Analysis software library. Microbial biomass was calculated using chromatograph peak areas relative to the internal standard 19:0. PLFA nomenclature denotes the number of C atoms in the fatty acid, the number of double bonds after a colon, and the position of the double bond from the methyl end proceeding an w; cy indicates a cyclopropyl ring (Ruess & Chamberlain 2010). Fungal and bacterial signature fatty 51 acids were selected as biomarkers (Table 3.1). The fatty acid 18:2Ï6,9 was used as a fungal biomarker (Zelles 1999). Although 18:1Ï9 is another commonly used fungal biomarker, it can be unreliable as it is also present in bacteria (Frostegård et al. 2011) and was found in non-inoculated autoclaved sand (Sakamoto et al. 2004). The following bacterial biomarkers were used: i15:0, a15:0, 15:0, i16:0, 16:1Ï7c, i17:0, a17:0, 10Me17:0, 10Me18:0, and cy17:0 (Frostegård et al. 1993a,b; Zogg et al. 1997; Zelles 1999). Anteiso and iso branching fatty acids, indicated by an a or i preceding the number of carbon atoms in the fatty acid, are common to gram-positive bacteria (Zelles 1997), including: i15:0, a15:0, i16:0 i17:0, and a17:0. The following fatty acids were used as Gram-negative bacterial biomarkers: 16:1Ï7c and cy17:0. Although cy19:0 is a suitable indicator of Gram-negative bacteria, it was not included as a signature fatty acid as it was not present in the majority of samples. The fatty acids 10Me16:0 and 10Me18:0 represented actinobacteria (Frostegård et al. 1993a). Table 3.1: PLFA biomarkers and their associated taxonomic groups Biomarker Predominant taxonomic group Reference i15:0a,b Gram-positive bacteria Frostegård et al. 1993b a15:0a,b Gram-positive bacteria Zelles 1999 15:0a Bacteria Frostegård et al. 1993b i16:0a,b Gram-positive bacteria Frostegård et al. 1993b 16:1Ï7ca Gram-negative bacteria Zogg et al. 1997 10Me16:0a Actinobacteria Frostegård et al. 1993a; Zelles 1997 i17:0a,b Gram-positive bacteria Frostegård et al. 1993b a17:0a Gram-positive bacteria Zelles 1999 10Me18:0a Actinobacteria Frostegård et al. 1993a; Zelles 1997 cy17:0a,b Gram-negative bacteria Zelles 1999 18:2Ï6,9a,b Fungi Zelles 1999 52 a Biomarker used for microbial community structure analyses b Biomarker used for 13C analyses 3.2.4. Microbial d13CPDB values Natural 13C isotopic abundance of PLFAs was assessed on an Agilent 6890 Gas Chromatograph-Isotopic Mass Spectrometer at the Belowground Ecosystem Group Stable Isotope Facility, University of British Columbia. Icosanoic acid methyl esters from the University of Indiana, C20:0 #2 and C20:0 #X, were used as reference materials to two-point calibrate microbial d13CPDB values. The fatty acid ester C20:0 #2 has a mean d13CPDB value of -30.68 +/- 0.02 â°. The enriched fatty acid ester, C20:0 #X, has a mean d13CPDB value of -6.91 +/- 0.04 â°. 3.2.5. Statistical Analyses In order to use soil chemical and physical properties as explanatory variables in the ordinations (in Chapters 2 and 3), the few missing data points were completed using averages. The mean of Ramsay plots was used to complete missing soil data for Ramsay plot 4. Since there were no additional plots on South Skedans, the mean of all deer-free and deer-inhabited plots was used for soil pH, C, and N for use as explanatory variables in vegetation and soil nutrient ordinations. While South Skedans is inhabited by deer, it has been colonized for approximately half the time of the other deer-inhabited islands (Table 2.1, Section 2.2.1.). Previous studies in Laskeek Bay have demonstrated the intermediate effects of deer on the understory on islands with a shorter browse history (Section 2.1), so I felt it was justified to use the mean from deer-free and deer-inhabited islands as the value for South Skedans. Numerical explanatory variables were transformed to their z-scores (Ramette 2007) using the scale function for use in ordinations. 53 Several of the available nutrients were measured below method detection limits (mdl), including: NO3, Cu, B, Pb, and Cd. Pb and Cd were excluded from statistical analyses due to the high number of zero values. Available nutrients were log(x+ 1) transformed, due to the high variability between nutrients, and analyzed using partial redundancy analysis (partial-RDA) with the rda function in RStudio Version 1.0.136 (RStudio Team 2016). While the remaining graphs for available nutrients were produced in Microsoft Excel Version 16.11.1 (Microsoft 2017). Soil physical and chemical data were transformed, if necessary, and compared using the t-test or Mann Whitney U test. ANOVA with a permutation test was used if both the t-test and Mann Whitney U test were unsuitable. Bonferroni correction was applied where necessary. Microbial analyses were performed on fatty acids from C14:0 to C20:0; fatty acids that were present in sample blanks were removed from the analysis of all samples (Quideau et al. 2016). Fatty acids present in less than 5% of samples were excluded from analyses (McIntosh et al. 2013). Fatty acids that were highly 13C-depleted (d13CPDB â¤ -43.0â°) or highly enriched (d13CPDB â¥ -10.0â°) were additionally excluded from analysis. Ramsay 02, Ramsay 07, Ramsay 14, and Lyell 05 were excluded from analyses as they contained â¤ 55% of the biomarker PLFAs. PLFA data were Hellinger transformed for multivariate analyses. PCoA was used to display known and unknown PLFAs. However, PLFA amounts must be adjusted using their molecular weights (Quideau et al. 2016), so a partial-CAP was only used to analyze known PLFAs using the vegan and ggplot2 packages in RStudio. The anova function was used to conduct a type I F-test (permutations = 999) to test the significance of models and terms. PLFA d13CPDB values were compared using F-tests for equal variances and two sample t-tests. If necessary, data were transformed to meet test assumptions and a Bonferroni correction was applied. 54 3.3. Results 3.3.1. Soil physical and chemical properties Soil physical properties and pH Proportionally, soil moisture was more than 60% higher on deer-inhabited islands relative to deer-free islands (W = 127, p = 0.025; Table 3.2). Soil pH was significantly lower on deer-inhabited than deer free-islands (t = -4.54, p = 0.0002). Bulk density was almost 20% higher on deer-inhabited islands, but the difference was not significant (F = 2.41, p (perm) = 0.12). In contrast, soil penetration resistance was more than three times higher on deer-inhabited than deer-free islands (t = 28.49, p = 2.2e-16; Figure 3.1). The depth of the forest floor on deer-inhabited islands was less than half that of deer-free islands (t = -8.36, p = 1.24e-9; Figure 3.2). Table 3.2: Soil moisture, bulk density, and pH on deer-free (n = 16) and deer-inhabited islands (n = 27). Values are means ± SE. *Deer n = 25 due to missing South Skedans and Ramsay samples Treatment Soil Moisture (%) Bulk Density (g/cm3) pH* Deer 202 ± 23 0.099 ± 0.008 3.48 ± 0.038 No deer 126 ± 11 0.12 ± 0.001 3.87 ± 0.078 55 Figure 3.1: Soil penetration resistance on deer-free (n = 9) and deer-inhabited islands (n = 27). Values are means ± SE. Figure 3.2: Forest floor depth on deer-free (n = 16) and deer-inhabited islands (n = 27). Values are means ± SE. Penetration Resistance (kg/cm2 ) 56 Soil Chemical Properties Soil % C, N, and total P were similar between treatments (Table 3.3). There were no differences between soil C (F = 0.73, p = 0.47), N (t = 0.81, p = 0.42), or C:N (t = -0.30, p = 0.77). Soil P did not differ between deer-inhabited and deer-free islands (t = -1.50, p = 0.14). Table 3.3: Soil % C, N, and total P on deer-free (n = 16) and deer-inhabited islands (n = 25). Values are means ± SE. Partial-redundancy analysis revealed distinct groupings of available nutrients between deer-free and deer-inhabited islands (Figure 3.3). The PRS® probe composite samples for two plots on Ramsay consisted of 3 cation probes, rather than 4 cation probes. There was a separation between deer-inhabited and deer-free islands along the first axis (Figure 3.3; RDA1 F = 12.54, p = 0.001). South Skedans and Reef clustered close to the deer-free islands, while the deer-inhabited islands, Lyell and Ramsay, exhibited greater variation along the first axis. There was also a separation between plots and islands along the second axis (Figure 3.3; RDA2 F = 5.31, p = 0.028). Available nutrients were defined using the model rda(prs.isl ~ Treatment + Island + C + Plant Richness + Soil Moisture + Condition(Removed Early), data=env.z) (F = 2.84, p = 0.001, adjusted R2 = 0.30). As three cation probes on Ramsay were removed early by a human or animal, a partial-RDA was used to remove the effect of early removal from the model using the âconditionâ term. A Type I ANOVA of the ordination model revealed that differences in available nutrients were explained by deer presence/absence (F = 6.71, p = 0.001), island (F = 2.05, p = Treatment C (%) N (%) C:N Total P (ppm) Deer 47.0 ± 0.4 1.1 ± 0.03 42.2 ± 1 133 ± 4.5 No deer 48.0 ± 0.9 1.2 ± 0.02 41.6 ± 1 143 ± 5.9 57 0.016), total C (F = 2.91, p = 0.029), soil moisture (F = 2.68, p = 0.035), and marginally explained by plant richness (F = 2.22, p = 0.059). The most influential nutrients on the model, in addition to NH4+, were assessed for differences in means between deer-free and deer-inhabited islands. While there were no significant differences, the following trends were evident: NO3- was similar between treatments; NH4+, Fe3+, and Al3+ were higher on deer-inhabited islands; and, PO4-, K+, and Mn2+ were higher on deer-free islands (Figures 3.4 & 3.5). Figure 3.3: Partial-RDA of available soil nutrients on deer-free (n = 9) and deer-inhabited islands (n = 26). Model formula: rda(prs.isl ~ Treatment + Island + C + Plant Richness + Soil Moisture + Condition(Removed Early), data=env.z). 58 Figure 3.4: Available soil N and P on deer-free (n = 9) and deer-inhabited (n = 26) islands. Values are means ± SE. *Mean burial time = 354 ± 2 days. Figure 3.5: Other influential available soil nutrients on deer-free (n = 9) and deer-inhabited (n = 26) islands. Values are means ± SE. *Mean burial time = 354 ± 2 days. 00.511.522.533.544.5NO3-N NH4-N PO4Supply Rate (µg/10 cm2 /year*)Deer No Deer01020304050607080Fe Al K MnSupply Rate (µg/10 cm2 /year*)Deer No Deer 59 3.3.2. Microbial community structure and biomass Microbial community structure PCoA of known and unknown fatty acids revealed clustering, primarily by island (Figure 3.6). However, when unknown PLFAs were removed and known fatty acids were converted to mol %, there was a separation between deer-free and deer-inhabited islands (Figures 3.7 & 3.8). There was a separation between islands along the first axis (Figure 3.8; CAP1 F = 12.14, p = 0.001). Deer-inhabited and deer-free islands separated along the second axis, while Reef Island was intermediary between the two groups (Figure 3.8; CAP2 F = 5.04, p = 0.019). Microbial community structure was defined using the model capscale(PLFAcl ~ Treatment + Island + Plant Diversity + Soil P + Condition(Diluted), data=env.z, distance = 'bray') (F = 4.19, p = 0.001, adjusted R2 = 0.35). A partial-CAP was used to remove the effect of sample dilutions â due to hexane evaporation â from the model using the âconditionâ term. A Type I ANOVA of the ordination model revealed that differences in microbial community structure were explained by deer presence/absence (F = 3.91, p = 0.003) and island (F = 5.25, p = 0.001). The following variables increased the variation explained in the microbial community, but had only a marginal effect on the model: plant diversity (F = 2.27, p = 0.07) and soil P (F = 2.14, p = 0.068). Batch was also found to have a significant effect on the model (F = 5.36, p = 0.003); however, this was not included in the model, since constrained ordination only displays the variance that is explained by the variables in the model (Ramette 2007). 60 Figure 3.6: PCoA of known and unknown PLFAs (µg PLFA/g soil) on deer-free (n = 16) and deer-inhabited islands (n = 21). Model formula: capscale(PLFA ~ 1, dist='bray'). 61 Figure 3.7: PCoA of known PLFAs (mol %) on deer-free (n = 16) and deer-inhabited islands (n = 21). Model formula: capscale(PLFAcl ~ 1, dist='bray'). Figure 3.8: Partial-CAP of known PLFAs (mol %) on deer-free (n = 16) and deer-inhabited islands (n = 21). Model formula: capscale(PLFAcl ~ Treatment + Island + Plant Diversity + Soil P + Condition(Dilution), data=env.z, distance = 'bray'). Fungal and Bacterial Biomass There were no significant differences in fungal (t = 1.50, p = 0.14) or bacterial (t = -1.57, p = 0.12) biomass between deer-free and deer-inhabited islands, although both fungal and bacterial biomass were about 20% lower on deer-inhabited (Figures 3.9 & 3.10). There was no difference in fungal:bacterial ratio between deer-free and deer-inhabited islands (F = 0.034, p (perm) = 0.83; Figures 3.11). Bacterial biomass was consistently lower on deer-inhabited islands, with Gram-positive bacteria exhibiting the largest absolute difference between 62 treatments (Figure 3.10). However, there were no significant differences for actinobacteria (t = -1.06, adjusted p = 1), general (t = -0.92, adjusted p = 1), Gram-negative (t = -1.36, adjusted p = 0.74), or Gram-positive (t = -1.90, adjusted p = 0.26) bacteria between deer-free and deer-inhabited islands. Figure 3.9: Fungal biomass on deer-free (n = 16) and deer-inhabited islands (n = 21). Values are means ± SE. 63 Figure 3.10: Bacterial biomass, separated by bacterial groups, on deer-free (n = 16) and deer-inhabited islands (n = 21). Values are means ± SE. 64 Figure 3.11: Fungal:bacterial ratio on deer-free (n = 16) and deer-inhabited islands (n = 21). Values are means ± SE. 3.3.3. Microbial d13CPDB values Fungi were more d13CPDB depleted than bacteria and all PLFAs (Figure 3.12), but the d13CPDB were similar between deer-free and deer-inhabited islands for all (t = 1.34, p = 0.19), bacterial (t = -1.01, p = 0.32), and fungal (t = 0.28, p = 0.78) PLFAs. Among the bacteria, Gram-negative were the most depleted in d13CPDB (Figure 3.12). Gram-negative bacteria were significantly more d13CPDB depleted (F = 5.94, adjusted p = 0.044) on deer-inhabited islands. There was no difference in d13CPDB values between deer-free and deer-inhabited islands for Gram-positive bacteria (t = 0.62, adjusted p = 1). Figure 3.12: d13CPDB of PLFAs on deer-free (n = 16) and deer-inhabited islands (n = 21). Labels: All = biomarker and unknown PLFAs; Bacteria = bacterial biomarkers; GramPositive = Gram-d13C PDB 65 positive bacterial biomarkers; GramNegative = Gram-negative bacterial biomarkers (no deer n = 16, deer n = 20); Fungi = fungal biomarkers. 3.4. Discussion The soils on deer-inhabited islands were wetter, more acidic, and more resistant to penetration than the soils on deer-free islands. Available soil nutrients differed between deer-inhabited and deer-free islands, while % C, N, and P were similar. Microbial community structure also differed between deer-free and deer-inhabited islands; and, Gram-negative bacteria were more d13CPDB depleted on deer-inhabited islands. Microbial community structure and available nutrients on islands with the intermediate browse history (i.e. Reef and South Skedans) tended to cluster between deer-inhabited and deer free islands; this pattern was also observed in the plant community composition (Chapter 2). The relationship between the strength of the aboveground plant response to length of browse history has been demonstrated previously (Stockton et al. 2005). Trampling is one of the important mechanisms by which herbivores affect soils (Schrama et al. 2013b). Soil penetration resistance was significantly higher on deer-inhabited than deer-free islands, suggesting that trampling by deer increases soil compaction. Indeed, bulk density was higher in grazed areas than ungrazed or mowed areas in a salt marsh in The Netherlands, which was associated with greater soil moisture (Schrama et al. 2013a). I observed higher soil bulk density (NS) and soil moisture on deer-inhabited islands than deer-free islands. However, some of the plots overlapped with riparian habitats on the large, deer-inhabited islands, but not on the smaller deer-free islands, so the difference in soil moisture may be a factor of island size 66 rather than deer presence. Gaston et al. (2006) also noted that wetlands were minimal on several islands, which were much smaller than Ramsay or Lyell, in Laskeek Bay due to their size. In contrast with the higher soil acidity found on deer-inhabited islands in this study, previous studies have observed higher soil pH in grazed plots than herbivore exclosures (Kielland & Bryant 1998; Harrison & Bardgett 2004). Soil moisture also tended to be lower in grazed plots while available K and Mg were significantly higher (Kielland & Bryant 1998), which suggests that base cation leaching was lower in grazed plots. Alternatively, the plant community may contribute to differences in pH. The soil was less acidic in grazed plots than moose exclosures in disturbed forests in Newfoundland, but there were site-specific differences in vegetation (Ellis & Leroux 2017). As deer presence removed the understory from our study sites (Chapter 2), conifer needles and bryophytes make up a greater proportion of the litter on deer-inhabited islands. Therefore, the higher soil moisture and/or the reduction of understory plants on deer-inhabited islands may have contributed to the greater soil acidity on deer-inhabited islands. Higher soil acidity increases the availability of metals, while reducing the availability of other important nutrients, such as K and P (Bojórquez-Quintal et al. 2017). Therefore, the lower soil pH on deer-inhabited islands may explain the lower (NS) available K and P, but higher Fe and Al. Similarly, the concentration of base cations, including Na, K, and Mg, and cation exchange capacity were significantly lower in the most heavily browsed site than in the moose exclosure in a boreal forest (Pastor et al. 1993). However, it should be noted in the current study that the soil was very acidic on both deer-free and deer-inhabited islands. Alternatively, the shallower organic horizon, higher soil bulk density (NS), and greater soil moisture on deer- 67 inhabited islands may have promoted the movement of Al and Fe from the mineral soil to the organic horizon, resulting in higher available Al and Fe and lower PO4-. Indeed, Al and Fe concentrations in humus were significantly higher in water discharge areas than recharge areas in the boreal forest, suggesting that groundwater redistributed these P-fixing elements upwards from the mineral soil into humus (Geisler et al. 2002). The forest floor was roughly half as deep on deer-inhabited islands as on deer-free islands in this study, likely due to the almost 70% lower plant cover on deer-inhabited islands (Chapter 2). Similarly, litterfall was significantly lower at two of three grazed plots than moose exclosures (Pastor et al. 1993). The authors also noted a visual reduction in the thickness of the organic soil horizon at the site with the highest grazing intensity (Pastor et al. 1988). Interestingly, there was no difference in % soil C, N, or P between deer-inhabited and deer-free islands in our study, which may suggest that deer reduce litter quantity but not quality. This is in agreement with the findings that preferred tree/shrub height and litterfall were substantially lower in grazed plots than moose exclosures, but there were no differences in diversity, richness, litter quality, or soil C, N and P (Ellis & Leroux 2017). In contrast, heavy reindeer browsing increased the relative abundance of nutrient-rich graminoids, which was associated with lower plant, litter and soil C:N and, greater soil extractable N relative to lightly grazed sites in nutrient-rich tundra communities in Norway (Sitters et al. 2017). There was a non-significant increase in NH4+ on deer-inhabited islands relative to deer-free islands in our study, while NO3- was similar between treatments. Deer promoted the dominance of unpalatable bryophytes at our study sites (Chapter 2) and soil C:N 68 was similar on deer-free and deer-inhabited islands, so it seems unlikely that the shift in the aboveground community was responsible for the difference in NH4+. Rather, labile inputs from urine and feces may be responsible. Ammonium and nitrate were significantly higher in soil beneath moose feces after 1-year of decomposition than nearby non-fecal soil in a riparian willow community, although the difference was not significant at a dry conifer site (Guernsey et al. 2015). In a woodland with low deer densities (<5 deer/km2), NH4+ and NO3- in grazed plots were roughly half as much as inside exclosures (Harrison & Bardgett 2004). However, deer fecal and urine N inputs are likely more important in our study as deer densities are estimated to be 30+ deer/km2 on offshore islands on Haida Gwaii (Daufresne & Martin 1997; Gaston et al. 2008). The reduced plant cover (Chapter 2) and root abundance (personal obs.) on deer-inhabited islands in our study likely contributed to the non-significant reduction in microbial biomass. Migratory ungulates resulted in a dramatic, but non-significant, reduction in shrub biomass and bacterial gene abundance â as revealed by quantitative PCR â relative to exclosures in a sagebrush steppe in Wyoming (Peschel et al. 2015). Similarly, microbial biomass was significantly lower at one of three grazed sites in a boreal forest in Michigan, despite the increased proportion of conifer needles in the exclosure (Pastor et al. 1988). In contrast, fungal biomass increased by a third and bacterial biomass did not change after the simulated defoliation of dwarf bamboo in a forest in Japan (Niwa et al. 2008). Indeed, short-term increases in root exudation in response to simulated herbivory can increase microbial biomass (Hamilton et al. 2008). 69 Alternatively, the contrasting effects of herbivores on microbial biomass may be explained by the herbivore-soil interactions, such as trampling, that are overlooked in a defoliation study. In a study by Stark et al. (2003), as reindeer affected soil moisture differently in oligotrophic and mesotrophic forests, the authors attributed the significantly lower soil respiration and microbial metabolic activity in grazed plots to labile C limitation. However, bulk density was consistently higher in grazed plots, which also could have contributed to the lower microbial activity (Stark et al. 2003). Therefore, the observed differences in soil physicochemical properties between deer-free and deer-inhabited islands may have contributed to the difference in microbial biomass. However, the shift in plant community structure was likely an important influence on microbial biomass and community structure in our study as plant diversity significantly contributed to the ordination model. Similarly, the bacterial taxonomic and functional community differed between ungulate winter forage area and exclosures in a sagebrush steppe, while the difference was non-significant for the fungal functional community and marginal for the taxonomic community (Peschel et al. 2015). These differences were likely mediated by the lower shrub biomass in grazed plots as lignocellulolytic genes contributed to the difference in microbial functional community composition (Peschel et al. 2015). In contrast, there were no distinct differences in microbial functional community between grazed plots and deer exclosures in a riparian willow community in Rocky Mountain National Park (Gass & Binkley 2011). The lack of response of the microbial community to grazing in that study could be attributed to site-specific conditions, such as soil type (organic vs. mineral), texture, moisture, or the conflicting mechanisms by which herbivores can affect soil. 70 The differences in d13CPDB between microbial groups indicate differences in substrate usage. Fungal PLFAs were more 13C-depleted than bacteria, which is likely due to the dependence of mycorrhizal fungi on root exudates. Indeed, root exudates (Gougherty et al. 2018) are less enriched than older carbon sources, such as coarse woody debris (Bowling et al. 2008), relative to bulk leaf biomass. Differences in isotopic signatures between Gram-negative and Gram-positive bacteria have been reported previously (Kramer & Gleixner 2008; Churchland 2013). The isotopic difference is likely a result of the greater utilization of rhizodeposits by Gram-negative bacteria than Gram-positive bacteria, as revealed by 13C-labelled exudates from a simulated root system (Paterson et al. 2007). The lower d13CPDB of Gram-negative bacteria on deer-inhabited islands suggests the utilization of a substrate that is more 13C-depleted relative to deer-free islands. It is unlikely that nutrient transformation by deer is responsible for the observed difference in d13CPDB as moose fecal pellets were highly enriched in comparison to leaf litter (Kielland & Bryant 1988). The d13CPDB of C3 plants are typically lowest under the forest canopy and in ecosystems with high mean annual precipitation, but wet microhabitats can also result in 13C-depletion (Kohn 2010). Therefore, the substantially higher soil moisture on deer-inhabited islands may have contributed to the low d13CPDB of Gram-negative bacteria. Alternatively, the 13C-depletion of Gram-negative bacteria on deer-inhabited islands may be a result of the increased dominance of bryophytes. Although the range of d13CPDB values for bryophytes is similar to that of C3 vascular plants, several species in humid regions of Europe had d13CPDB values of -30â° to -37.5â° (Rundel et al. 1979). 71 The d13CPDB of fungal biomarker, 18:2w6,9, ranged from -36.6â° to -38.1â° in soil from a C3 grass crop in Germany (Kramer & Gleixner 2008). While fungi were not as 13C-depleted in our study, Gram-negative bacteria â particularly on deer-inhabited islands â were similarly depleted. The reduction of roots on deer-inhabited islands (personal obs.) likely means that there is a reduction in root exudates, which may have contributed to the 13C-depletion of Gram-negative bacteria. There were dramatic differences between many soil physical properties on deer-free and deer-inhabited islands, while differences in soil chemical and biological properties were often non-significant â possibly due to limited differences in resource quality. Therefore, future research should focus on the effects of herbivores on resource quality and microbial activity. Indeed, litter traits can provide insight into soil nutrients (Sitters et al. 2017) and decomposition (Wardle et al. 2002). Microbial activity and substrate usage can be further explained using 13CO2-labelling and by measuring the change in d13CPDB between substrates and microbes (Paterson et al. 2007). Additionally, it is evident that browse intensity (Pastor et al. 1993; Sitters et al. 2017) and browse history (Allombert et al. 2005) influence the ecosystem response to herbivores. As such, it is important to consider historic herbivore populations both for future research and for wildlife management. Gradients in herbivores densities, such as this island comparison, may provide better conservation targets than herbivore exclosures, which do not provide insight into ambient herbivore densities prior to deer exclusion. 72 4. Arbuscular mycorrhizal fungal inoculum potential on deer-free vs. deer-inhabited islands 4.1. Introduction Arbuscular mycorrhizal fungi (AMF) aid in nutrient and water acquisition for plants and reduce soil erosion and nutrient leaching, along with a wealth of other roles (Asmelash et al. 2016). AMF are obligate biotrophs whose nutrient transfer to the host plant is stimulated by the receipt of C from the plant (Fellbaum et al. 2012). However, plants can reduce the belowground allocation of C in response to defoliation (Bassman & Dickman 1985). As such, AMF colonization is often lower in browsed plants than un-browsed plants (review by Gehring & Whitham 1994). Indeed, AMF spore density was significantly lower in rooting zone soils in one of two cattle grazed plots than in herbivore exclosures in Ethiopia, despite the fact that cut and carry grass harvest took place in the exclosures (Birhane et al. 2010). Therefore, AMF abundance will be lower in soil from which its host plants are reduced or absent. Only one of forty redcedar seedlings grown in soil taken from an ectomycorrhizal-dominated conifer stand (Tsuga heterophylla and Abies amabilis) on Vancouver Island, BC was colonized by AMF, while the proportion of seedlings colonized in soil from a conifer stand containing AMF-associated species (Thuja plicata and Tsuga heterophylla) increased with light intensity in the greenhouse (Weber et al. 2005). Mortality was also higher, while the relative growth rate was lower, for seedlings grown in the ectomycorrhizal stand soil than the AMF stand soil (Weber et al. 2005). Similarly, Nuñez et al. (2009) demonstrated that the low ectomycorrhizal potential of soils in native forests in Argentina has prevented the spread of several introduced Pinaceae species. Indeed, many trees are obligately mycorrhizal (Wang & Qui 2006) and Pinaceae are primarily ectomycorrhizal (Newman & Reddell 1978). 73 In contrast, western redcedar forms an obligate symbiosis with AMF (Curran & Dunsworth 1988). Redcedar is also a preferred forage of deer (Martin & Baltzinger 2002), particularly on Haida Gwaii where overabundant deer have dramatically reduced available forage in the understory (Stockton et al. 2005; this thesis Chapter 2). Indeed, the preferential browsing of redcedar on Haida Gwaii by introduced Sitka black-tailed deer greatly reduced redcedar cover below browse height (1.5 m) and reduced redcedar survival relative to exclosures (Stroh et al. 2008). However, it is not known whether the poor regeneration of redcedar in the presence of introduced deer has reduced the AMF inoculum potential of the soils on Haida Gwaii. Asmelash et al. (2016) argue for the use of AMF inoculum as a restoration tool, particularly due to the status of AMF as obligate biotrophs and their low dispersal ability. The use of whole soil transplants from a prairie to a former agricultural field promoted the growth of several late successional grassland species and reduced the growth of early successional species in Indiana (Middleton & Bever 2012). Alternatives to whole soil inoculum include the use of AMF isolates (Kumar et al. 2017) and commercial inoculum (Rowe et al. 2007). Inoculation with a single AMF species was more beneficial to seedling growth than inoculation with two AMF species in two (i.e. Leucaena leucocephala and Parkia roxburghii) of four tree species studied in a net house â a shaded growing chamber â in India (Kumar et al. 2017). However, a meta-analysis by Hoeksema et al. (2010) revealed that the biotic complexity of the inoculum source and the background soil affected plant growth in laboratory experiments, but not field experiments, with: 1) whole soil inoculum being more beneficial than single-species inoculation and, 2) the presence of non-mycorrhizal microbes in the background soil (i.e. the main growth medium) having a positive effect on plant response to inoculation. 74 Consequently, this raises two important questions: 1) do deer, by preventing redcedar regeneration (Stroh et al. 2008), reduce the AMF inoculum potential of soils on Haida Gwaii and, 2) if deer reduce the AMF inoculum potential of the soil, can whole soil transplants from deer-free areas be used to increase the inoculum potential of the soil? I used a greenhouse bioassay in which I measured redcedar growth and AMF colonization in soils from deer-free and deer-inhabited islands on Haida Gwaii. 4.2. Methods 4.2.1. Site description and soil sampling Soil for the AMF greenhouse bioassay experiment was collected from Tar (deer-free) and Ramsay (deer inhabited) Islands. Both islands are dominated by western hemlock (Tsuga heterophylla) followed by Sitka spruce (Picea sitchensis). Western redcedar (T. plicata) is a sub-dominant canopy species on both islands. The same Tar Island plots used in Chapters 2 and 3 were sampled for this experiment, while only Ramsay edge plots were sampled. Soil F horizon was collected using a shovel from 3-5 random locations in each Tar Island plot (n = 6) and in each Ramsay Island plot (n = 6) to assess the AMF inoculum potential of the soil. Diameter at breast height (DBH) was measured on all T. plicata Â³1.3 m in height in plots. Root collar diameter (RCD) was measured on T. plicata seedlings between 15 cm and 1.3 m tall in each plot. Soil was stored in coolers on ice until transport to the lab at which point soil was homogenized within each island and used for the greenhouse experiment. Soil samples were collected in August 2016. Field tree and seedling data were collected in the summers of 2016 and 2017. 75 4.2.2. Experimental design Redcedar seedlings were grown from seed in one of the following treatments, with 10 replicates each for both islands: autoclaved soil, untreated soil, or inoculated soil. Control soil was autoclaved for 1 hour at 121 °C (Rowe et al. 2007); however, sterilization was unsuccessful and fungal growth appeared shortly after sowing. Consequently, control soil was autoclaved two additional times for 1 hour at 121 °C and the autoclaved treatment pots were re-sown. Untreated soil was either from a deer-free island (Tar) or deer-inhabited island (Ramsay). The inoculated treatment consisted of untreated soil with soil from the opposite island as the inoculum source. Haida Gwaii class B Thuja plicata seeds from the BC Tree Seed Centre (lot # 45052) were surface-sterilized in 30% hydrogen peroxide for 10 minutes, rinsed with autoclaved distilled water (Smith et al. 1998), and three seeds were sown in each pot. If more than one seed germinated, additional seedlings were removed so only one seedling grew in each pot. Pots (~350 mL) were filled more than three quarters with the respective soil, seeds were sown, and the pots were filled to the top. For the inoculated treatment, 10 mL of the inoculum soil (i.e. slightly more than the 1/40 inoculum to total volume ratio used by Nuñez et al. 2009) was placed directly over the sown seeds before filling the pot to the brim with the main soil. Seedlings were watered ad libitum. 4.2.3. Seedling growth and AMF colonization Seedlings were harvested approximately 6 months (i.e. 179 days) after seeds were sown. Seedling height, shoot biomass, root biomass, root length, and stem diameter were measured. Aboveground biomass was dried at 70 °C for 72 h and dry mass was measured. However, only 76 aboveground fresh mass was analyzed as fresh roots were required for staining, so root dry mass could not be measured. Roots were washed in autoclaved distilled water, non-woody roots were cut into ~1 cm segments, and homogenized. Up to 0.5 g of roots â or less for small root systems â were stored at room temperature in distilled water for 72 h until being cleared and stained. Roots were cleared by heating at 70 °C in 10% KOH (wt/v) for 15 minutes. Roots were stained by heating at 70 °C in a 5% (v/v) solution of Scheaffer black ink and 5% acetic acid (i.e. household white vinegar) for 3 minutes (Vierheilig et al. 1998). Roots were de-stained in water acidified with vinegar for at least 20 minutes and stored in fresh tap water until analysis (Vierheilig et al. 1998). The percent AMF colonization of stained roots was estimated using the gridline intersection method (Giovannetti & Mosse 1980). The percent colonization was determined on the first 100 root-gridline intersections under a dissecting scope at 40X magnification; the sample was mixed and the percent colonization was measured a second time. 4.2.4. Soil chemical data Soil chemical analyses were not performed directly on greenhouse soil. Rather, the soil chemical data presented in Chapter 3 was used in the analysis, but data was restricted to the 6 plots on Ramsay and Tar Island that were sampled for the greenhouse experiment. 4.2.5. Statistical analyses A two-way ANOVA or Kruskal-Wallis test was used to test for differences in redcedar size or AMF colonization. Tukeyâs or Dunnâs test was used post hoc using the function TukeyHSD in the stats package or dunnTest in the FSA package in RStudio. Data were transformed to meet assumptions, if necessary. Soil physical and chemical data were compared using the t-test or 77 Mann Whitney U test. ANOVA with a permutation test was used if both the t-test and Mann Whitney U test were unsuitable. 4.3. Results Redcedar count and soil chemical properties of sampling plots There were marginally fewer redcedar trees (t = -1.92, p = 0.084) on the deer-inhabited island than the deer-free island, but DBH was not significantly different between islands (t = 1.48, p = 0.18; Table 4.1). Redcedar regeneration was similarly low on both islands (F = 1.62, p = 0.34), although none of the plots on the deer-inhabited island had any redcedar seedlings (Table 4.1). Table 4.1: Redcedar tree and seedling data from sampling locations on deer-free (n = 6) and deer-inhabited (n = 6). Values are means ± SE. There were no differences in soil %C (F = 1.29, p (perm) = 0.27) or % N (t = 0.99, p = 0.35) between the deer-free island and deer-inhabited island (Table 4.2). Total P was about 10% higher on the deer-free island (NS; t = -0.82, p = 0.43), whilst the soil was significantly more acidic on the deer-inhabited island (t = -4.70, p = 0.0011). All field available nutrients were tested for differences between the deer-free and deer-inhabited island, but the only nutrient that was significantly different between the islands was available K (F = 8.37, adjusted p (perm) = 0.039). The nutrients for which there was a large difference between the deer-free and deer-inhabited islands are shown in Figures 4.1 & 4.2. Island Number of trees DBH (cm) Number of seedlings Deer 2 ± 1 50.6 ± 7.7 0 ± 0 No deer 5 ± 2 34.8 ± 7.2 1 ± 0.7 78 The following available nutrients were higher (NS) in plots on the deer-inhabited island: NO3- (F = 2.20, adjusted p (perm) = 0.99), NH4+ (F = 11.62, adjusted p (perm) = 0.12), and Fe3+ (F = 4.58, adjusted p (perm) = 0.27). Al3+ was not significantly higher on the deer-inhabited island (F = 1.11, adjusted p (perm) = 1) due to the high variation between plots. The very large error bar for available Al on the deer-inhabited island was a result of a plot with an order-of-magnitude higher supply rate than the other plots. The following available nutrients were lower in plots on the deer-inhabited island: PO4+ (F = 7.29, adjusted p (perm) = 0.23) and K+ (F = 8.37, adjusted p (perm) = 0.039). Table 4.2: Soil properties on the deer-free (n = 6) and deer-inhabited (n = 5). Values are means ± SE. Island C (%) N (%) Total P (ppm) pH Deer 48.8 ± 0.5 1.3 ± 0.04 133 ± 10.1 3.44 ± 0.06 No deer 48.0 ± 0.6 1.2 ± 0.02 147 ± 12.5 4.12 ± 0.12 79 Figure 4.1: Field available soil N and P on the deer-free (n = 6) and deer-inhabited (n = 6) island. Values are means ± SE. *Data from PRS probes. Burial time: Deer = 355 ± 0 days; No Deer = 363 ± 1 day. 0.00.51.01.52.02.53.03.54.0NO3-N NH4-N PO4Supply Rate (µg/10 cm2 /year*)Deer No Deer 80 Figure 4.2: Other available soil nutrients on the deer-free (n = 6) and deer-inhabited (n = 6) island. Values are means ± SE. *Data from PRS probes. Burial time: Deer = 355 ± 0 days; No Deer = 363 ± 1 day. Greenhouse bioassay A higher proportion of redcedar seeds germinated in untreated soil than autoclaved soil (Z = -3.09, adjusted p = 0.006), but there were no differences between the other inoculum treatments (Z = -1.55, adjusted p = 0.37) or islands (KW c2 = 0.17, p = 0.68; Figure 4.3). Redcedar aboveground biomass did not differ between inoculum treatments (F = 1.04, p = 0.37). Aboveground biomass was significantly greater in seedlings grown in soil from the deer-inhabited island than the deer-free island regardless of inoculum treatment (F = 31.55, p = 3.30e-6; Figure 4.4). Redcedar seedlings were taller when grown in soil from the deer-inhabited island than the deer-free island (F = 37.21, p = 2.35e-6). Dunnâs test revealed no significant 0102030405060Fe Al K MnSupply Rate (µg/10cm2 /year*)Deer No Deer 81 differences in seedling height between inoculum treatments (Figure 4.5), but there were significant interactions between island and inoculum treatments (e.g. No Deer Autoclaved : Deer Untreated, adjusted p = 0.0002). While there were no differences in root biomass between inoculum treatments (F = 2.47, p = 0.10), root biomass was more than two-fold higher on seedlings grown in soil from the deer-inhabited islands than deer-free island soil (F = 32.73, p = 2.44e-6; Figure 4.6). There were no differences in root length between inoculum treatments (F = 1.89, p = 0.17). Roots were significantly longer when grown in deer-inhabited island soil overall (F = 8.35, p = 0.0069), but the differences between islands were non-significant when compared within each inoculum treatment (adjusted p > 0.05; Figure 4.7). There were no differences in RCD between inoculum treatments (KW c2 = 0.89, p = 0.64; Figure 4.8), but RCD was significantly higher in seedlings grown in soil from the deer-inhabited island (KW c2 = 15.64, p = 7.66e-5; Figure 4.8). 82 Figure 4.3: Proportion of redcedar seeds germinated in autoclaved (deer n = 10, no deer n = 10), untreated (deer n = 10, no deer n = 10), and inoculated (deer n = 10, no deer n = 10) soil. Values are means ± SE. Figure 4.4: Redcedar aboveground biomass in autoclaved (deer n = 4, no deer n = 7), untreated (deer n = 7, no deer n = 7), and inoculated (deer n = 9, no deer n = 4) soil. Values are means ± SE. Asterisks indicate significant differences between islands at the a = 0.05 level. Different letters indicate significant differences between inoculum treatments at the a = 0.05 level. A lack of letters or asterisks indicates no significant differences. * * * 83 Figure 4.5: Redcedar height in autoclaved (deer n = 4, no deer n = 7), untreated (deer n = 7, no deer n = 7), and inoculated (deer n = 9, no deer n = 4) soil. Values are means ± SE. Asterisks indicate significant differences between islands at the a = 0.05 level. Different letters indicate significant differences between inoculum treatments at the a = 0.05 level. A lack of letters or asterisks indicates no significant differences. * * * 84 Figure 4.6: Redcedar root mass in autoclaved (deer n = 4, no deer n = 7), untreated (deer n = 7, no deer n = 7), and inoculated (deer n = 9, no deer n = 4) soil. Values are means ± SE. Asterisks indicate significant differences between islands at the a = 0.05 level. Different letters indicate significant differences between inoculum treatments at the a = 0.05 level. A lack of letters or asterisks indicates no significant differences. * * * 85 Figure 4.7: Redcedar root length in autoclaved (deer n = 4, no deer n = 7), untreated (deer n = 7, no deer n = 7), and inoculated (deer n = 9, no deer n = 4) soil. Values are means ± SE. Significant differences discussed in text. Figure 4.8: Redcedar root collar diameter in autoclaved (deer n = 4, no deer n = 7), untreated 86 (deer n = 7, no deer n = 7), and inoculated (deer n = 9, no deer n = 4) soil. Values are means ± SE. Significant differences discussed in text. There were significant differences in the % colonization of roots by AMF between inoculum treatments (F = 23.72, p = 6.64e-7), but not between islands (F = 0.075, p = 0.79). The % colonization of roots was more than twice as high in untreated (adjusted p = 7.0e-7) and inoculated soil (adjusted p = 4.9e-5) compared to autoclaved soil, but there was no difference between untreated and inoculated soil (adjusted p = 0.33; Figure 4.9). Figure 4.9: AMF % colonization of redcedar roots in autoclaved (deer n = 4, no deer n = 7), untreated (deer n = 6, no deer n = 7), and inoculated (deer n = 8, no deer n = 4) soil. Values are means ± SE. Asterisks indicate significant differences between islands at the a = 0.05 level. Different letters â lower case for deer, capital for no deer â indicate significant differences A B B a b ab 87 between inoculum treatments at the a = 0.05 level. A lack of letters or asterisks indicates no significant differences. 4.4. Discussion Contrary to expectations, natural redcedar regeneration was very low on both the deer-free and the deer-inhabited islands. While the abundance of mature redcedar trees was non-significantly higher on the deer-free island, the mean DBH was higher (NS) on the deer-inhabited island. The poor regeneration of redcedar in the presence of introduced deer is in agreement with previous studies on Haida Gwaii (Martin & Baltzinger 2002). Redcedar regeneration was also very low on the deer-inhabited island, which is likely a result of increased competition on the deer-free island due to the high understory cover (Chapter 2). While deer exclusion on Haida Gwaii promoted redcedar abundance, there was little growth between 4 and 8 years of exclusion and there was a negative correlation between redcedar and Gaultheria shallon cover (Stroh et al. 2008). A greenhouse experiment demonstrated that redcedar were significantly smaller when grown in the same pot as G. shallon than seedlings that were grown independently (Mallik & Prescott 2001). Indeed, G. shallon cover in the current study was more than 70% higher on the deer-free than deer-inhabited island. The proportion of redcedar seeds that germinated and the % colonization of the roots by AMF were significantly lower on redcedar seedlings grown in autoclaved soils, but there were no differences in germination or % AMF colonization in roots of seedlings grown in untreated or inoculated soils. Seedling height and root length did not differ between inoculum treatments, but were consistently higher on seedlings grown in soil from the deer-inhabited island than from the deer-free island. 88 The greater AMF colonization of redcedar seedlings grown in non-sterilized soils compared to autoclaved soils concurs with previous studies demonstrating the successful use of whole soil as an AMF inoculum source (Nuñez et al. 2009; Middleton & Bever 2012). However, % AMF root colonization was not higher between the inoculation treatments in the current study. This suggests that the AMF inoculum potential of the soil was similar between the deer-free and deer-inhabited islands, presumably due to the similar abundance of redcedar in the understory and canopy between the islands. Consequently, the higher biomass of redcedar grown in soil from the deer-inhabited island is presumably due to differences in soil chemical properties. As Al availability is influenced by pH and Al affects plant nutrient uptake, Ryan et al. (1986) assessed the growth and tissue chemistry of Pacific Northwest conifer seedlings grown in various pH and Al treatments in nutrient culture tanks. Redcedar growth biomass was higher when grown in less acidic conditions (pH = 4.8) than acidic conditions (pH = 3.0), but redcedar height was significantly higher under acidic conditions with the addition of Al (Ryan et al. 1986). Several foliar and root nutrient concentrations differed between the Al and Al-absent treatments, including P, which was significantly lower in foliage, but higher in roots, in the Al treatment (Ryan et al. 1986). Similarly, in our study, whilst soil pH was significantly lower on the deer-inhabited island, the soil was acidic from both islands and Al3+ was higher in the deer-inhabited island soil. Therefore, the higher Al3+ on deer-inhabited islands may have alleviated the negative effects of the more acidic soil on redcedar growth. The very high field soil Al3+ in one plot on the deer-inhabited island likely originated from the mineral horizon as the plot had a comparatively shallow forest floor (depth = 7.4 cm). 89 Alternatively, the AMF inoculum potential of the soil may be higher on the deer-free island, but AMF may be relatively more important in the more P-limited soil from the deer-inhabited island. In a greenhouse bioassay, AMF colonization of Zea mays roots was more than twice as high in urban soil as in deciduous forest soil from South Carolina, while the opposite was observed in Acer rubrum grown in the same urban and deciduous forest soil in the field (Wiseman & Wells 2005). The higher AMF inoculum potential of the urban landscape soil, but higher colonization of Acer rubrum in the forest soil in the field, may reflect differences in host preference or soil nutrients as extractable P was lower (NS) in the acidic forest soil (Wiseman & Wells 2005). Similarly, in our study, available PO4- was lower, but NH4+ was higher on the deer-inhabited island than the deer-free island. A meta-analysis by Hoeksema et al. (2010) showed that AMF inoculation was more beneficial to plants with higher foliar N:P as N-limitation can be inhibitive to photosynthesis and, hence, affect the C supply to microbes. The findings from this study emphasize the role of nutrient dynamics in AMF-plant symbioses. More importantly, these findings emphasize the need for active management to promote redcedar regeneration in the presence of herbivores. Gaston et al. (2008) noted a greater abundance of redcedar seedlings and saplings one year after a series of culls that dramatically reduced deer densities on Reef and SGÌ±ang Gwaay Islands on Haida Gwaii. Therefore, the management of the deer population has the potential to promote redcedar regeneration, but some browsing pressure may be necessary to reduce competition from other plants. 90 5. Potential silvicultural tools to improve western redcedar regeneration in the presence of deer 5.1. Introduction In mid- to northern coastal British Columbia, western redcedar (Thuja plicata) is all but absent from young second-growth forest stands (LePage & Banner 2014). Redcedar management is a topic of particular concern due to its poor establishment, but high economic importance (Antos et al. 2016). Redcedar is an essential economic species that accounts for a substantial amount of the timber harvested from Haida Gwaii (Joint Technical Working Group 2012). In addition to its ecological and economic value, the cultural importance of western redcedar on Haida Gwaii is even evident in the paleoecological record. Cupressaceae pollen, which is indicative of redcedar, in lake sediments on SGÌ±ang Gwaay decreased within a few hundred years of the formation of two nearby Haida villages (Lacourse 2010). Indeed, redcedar was used by the Haida for homes, canoes, totem poles and weaving, and continues to be used for many art forms. However, deer overabundance is a threat to the persistence of redcedar on the islands as deer have a strong preference for redcedar over western hemlock and Sitka spruce (Martin & Baltzinger 2002). Redcedar seedlings on Haida Gwaii are especially vulnerable to herbivory due to their high palatability relative to mainland redcedar and, the low levels of monoterpenes and diterpenes in seedlings (Vourcâh et al. 2001). Consequently, redcedar seedling mortality was three times higher in grazed plots than deer exclosures over 8 years on Graham Island, Haida Gwaii (Stroh et al. 2008). Protective tree cones are often used to protect redcedar from 91 herbivores, but they are costly and often have limited success at increasing seedling survival (Lantagne 1995; Brandeis et al. 2002). Therefore, the use of fear as a means to reduce herbivory on redcedar should be explored as an alternative mitigation technique. Indeed, deer spent significantly less time at bait stations treated with wolf urine and consumed a smaller proportion of the bait in comparison to bait stations treated with water, cologne, or black bear urine on Haida Gwaii (Chamaillé-Jammes et al. 2013). Similarly, redcedar saplings were more abundant and browse damage was lower in hunter-accessible sites than in remote sites on Haida Gwaii (Martin & Baltzinger 2002). However, Martin & Baltzinger (2002) estimated that only about 10% of deer in the hunter-accessible areas in the study are killed, suggesting that fear may play a role in the reduced herbivory on redcedar in these areas. Similarly, commercial deer repellents that induce fear (via olfactory cues) were most successful at reducing herbivory on redcedar in Washington State (Wagner & Nolte 2001). As natural predators of deer are not present on Haida Gwaii, this avenue of protection from herbivores requires further attention. Protecting redcedar from herbivores is especially important as redcedar growth and survival can be higher on sites that naturally support redcedar. For instance, the densities of redcedar seedlings and saplings were more than twice as high in naturally-regenerating stands on Haida Gwaii that consisted of >60% redcedar prior to harvest than stands that were composed of 0-40% redcedar prior to harvest (Martin & Baltzinger 2002). While this presumably reflects differences in the seedbank between stands, it may also indicate differences in AMF inoculum potential as redcedar is an obligate AMF host (Curran & Dunsworth 1988). Indeed, Weber et al. (2005) demonstrated that the low AMF inoculum potential of soil from an ectomycorrhizal 92 stand contributed to the low survival of redcedar on northern Vancouver Island. Therefore, it is important to assess the AMF inoculum potential of soil and potential tools to increase AMF inoculum in soil. The use of inoculum containing diverse, native AMF is expected to be advantageous over the use of inoculum containing non-native, or few, AMF species (Asmelash et al. 2016). Two commercial inocula â containing a total of 8 AMF species and several ectomycorrhizal species â did not increase AMF colonization or plant biomass of grasses or forbs in experimental plots in Washington State (Cook et al. 2011). Far fewer montane plant species inoculated with either of two commercial inocula were colonized by AMF than those inoculated with field soil in a greenhouse bioassay (Rowe et al. 2007). Of the plants colonized by AMF, those inoculated with the commercial inoculum containing multiple AMF species exhibited a positive growth response in 5 of 7 plant species, while the commercial inoculum containing only Glomus intraradices negatively affected the growth of all seven plant species (Rowe et al. 2007). In contrast, inoculation of a field soil-mix with only one- or two- species of AMF isolates significantly increased Aegle marmelos and Parkia roxburghii seedling height and biomass relative to seedlings grown in non-inoculated soil in a net house (Kumar et al. 2017). In fact, the mycorrhizal dependency â a measure of biomass produced by inoculated seedlings relative to non-inoculated seedlings â of Leucaena leucocephala and P. roxburghii was significantly lower in seedlings inoculated with both AMF species than seedlings with single-species inoculation (Kumar et al. 2017). While inoculation with single species of AMF isolates did not significantly increase the size of Commiphora leptophloeos in a greenhouse experiment, inoculated plants contained higher concentrations of phenols and tannins and lower concentrations of soluble 93 carbohydrates (Lima et al. 2017). This suggests that AMF inoculation, through the promotion of herbivore defense compounds, has the potential to reduce herbivory on plants. Therefore, the relatively low AMF diversity of many commercial inocula may not limit their efficacy in promoting redcedar growth and survival in the presence of deer. We used a greenhouse bioassay to assess: 1) the AMF inoculum potential of the soil in a second-growth cutblock that previously supported some redcedar trees and one that has not supported redcedar for more than 60 years and, 2) whether whole soil transplants increased the growth or AMF colonization of redcedar. In the field, in the two cutblocks, redcedar seedling growth and browse damage were monitored after treatment with a commercial inoculum and/or a commercial deer repellent in order to assess their use as silvilcultural tools to improve redcedar survival and growth on Haida Gwaii. Lastly, I assessed the potential for wolf urine to reduce browse damage on redcedar boughs. 5.2. Methods 5.2.1. Study site and soil sampling In September 2016, organic soil was randomly collected from two cutblocks that were harvested earlier that year in Aero (W 132°, N 53.06°), Moresby Island, Haida Gwaii (Figure 3). One cutblock, AER004, previously supported a second growth stand (~60 years old) of western hemlock (Tsuga heterophylla), Sitka spruce (Picea sitchensis), and red alder (Alnus rubra). This cutblock is described as the cedar-free stand. The other cutblock, AER005, previously supported a second growth stand (~60 years old) of western hemlock, Sitka spruce, and western redcedar (T. plicata). This cutblock is described as the cedar stand. The soil in both stands contained 94 organic and exposed mineral soil due to forest harvesting machinery. The main vegetation in the cutblocks were graminoids and seedlings, although the cedar-free stand also had thistles. Figure 5.1: Approximate location of field sites in Aero, Moresby Island, Haida Gwaii, BC. Google Earth 2017c. 5.2.2. Greenhouse bioassay Redcedar seedlings were grown in one of the following treatments with 5 replicates each for both stands: autoclaved soil, untreated soil, or inoculated soil. Control soil was autoclaved for 1 hour at 121° C (Rowe et al. 2007) three times. Untreated soil was either from the cedar-free stand or the cedar stand. The inoculated treatment consisted of untreated soil with soil from the opposite stand as the inoculum source. Pots (~350 mL) were filled more than three quarters with the respective soil, surface-sterilized seeds were sown (see below), and the pots Untitled Map Write a description for your map. Legend 100 kmNâ¤Image Landsat / CopernicusImage Landsat / CopernicusiImage IBCAOImage IBCAOData LDEO-Columbia, NSF, NOAAData LDEO-Columbia, NSF, NOAAl iData SIO, NOAA, U.S. Navy, NGA, GEBCOData SIO, NOAA, U.S. Navy, NGA, GEBCO 95 were filled to the top. For the inoculated treatment, 10 mL of the inoculum soil (i.e. slightly more than the 1/40 inoculum to total volume ratio used by Nuñez et al. 2009) was placed directly over the sown seeds before filling the pot to the brim with the main soil. Haida Gwaii class B Thuja plicata seeds from the BC Tree Seed Centre (lot # 45052) were surface-sterilized in 30% hydrogen peroxide for 10 minutes, rinsed with autoclaved distilled water (Smith et al. 1998), and two seeds were sown in each pot. If more than one seed germinated, the additional seedling was removed so only one seedling grew in each pot. Seedlings were harvested approximately 5.5 months (i.e. 167 days) after seeds were sown. Seedling height, shoot biomass, root biomass, root length, and stem diameter were measured. Aboveground biomass was dried at 70 °C for 72 h and dry mass was measured. However, only aboveground fresh mass was analyzed as fresh roots were required for staining, so root dry mass could not be measured. Roots were stained and cleared (Vierheilig et al. 1998) and the AMF percent colonization of the roots was determined (Giovannetti & Mosse 1980) as described in Section 4.2.3. 5.2.3. Greenhouse bioassay soil chemical data Untreated soil was retained after seedlings were harvested, air-dried, and stored at room temperature. Three soil subsamples were freeze-dried in November 2017 for each stand. Freeze-dried soil was ground using a mortar and pestle for soil C, N, and P analyses. Soil % C and N were measured on an Elementar Vario EL Elemental Analyzer. Total P was digested using sodium hypobromite/sodium hydroxide alkaline oxidation (OâHalloran & Cade-Menum 2008) with the modifications described in Chapter 3. Phophorus was measured colorimetrically using 96 the ascorbic acid method (OâHalloran & Cade-Menum 2008). The pH of air-dried soil was measured in a 1:10 soil to 0.01 M CaCl2 solution. 5.2.4. AMF inoculum and deer repellent field experiment Additional redcedar seedlings were greenhouse-grown under the same conditions as above for a field experiment. One to two surface-sterilized seeds were germinated and grown in soil from either the cedar-free stand or the redcedar stand. Seedling trays were 6 cm tall; each well was ~4 cm x 4 cm at the top and ~2 cm x 2 cm at the bottom. Seedlings were transplanted to larger wells (~4 cm x 6 cm at the top and ~2 cm x 3 cm at the bottom) 153 days after they were first sown. Seedlings were transported to Haida Gwaii, BC in June 2017 and allowed to grow in the trays outdoors, protected from deer, until field planting in August 2017. Four experimental blocks were set up in both the cedar-free and redcedar stands at Aero, Moresby Island, with the following treatments: control, deer repellent, AMF inoculum (AMF), and both inoculum and repellent (âbothâ). The control treatment received neither AMF inoculum nor deer repellent. The deer repellent treatment seedlings were each sprayed twice with Plantskydd® (Tree World Plant Care Products Inc.) according to application directions; however, Plantskydd® was re-applied to seedlings 24 h after planting due to the heavy rain during both applications. The AMF inoculum treatment received 10 mL of RootGrowâ¢ (PlantWorks Limited, England) mycorrhizal inoculum, which is a clay substrate containing species of both AMF and ectomycorrhizal fungi. The mycorrhizal inoculum was placed in the base of the small holes which were dug at each location to plant the seedlings. The treatment plots were located 3 m from the centre of the block in each of the cardinal directions (Figure 5.2). Each treatment plot contained three T. plicata seedlings approximately 2 97 m away from each other. One seedling was lost in the field during experimental set-up, so one block had only two replicates. In this block, the mean browse data of the two seedlings was used in place of the missing seedling in order to keep a balanced design for statistical analyses. Seedling browse damage and survival were monitored once a week for 16 weeks and 24 h after initial treatment. A damage score was assigned to browsed seedlings and the midpoint % was used for analysis (Table 5.1). Seedlings were frequently uprooted after one incidence of herbivory, so only one browse score was assigned to each seedling over the course of the study. Seedling height, root collar diameter (RCD), and scale length and width were measured every other week for 16 weeks (August 22-December 12, 2017). Scale length was measured as the length from the stem to the end of the branch. Scale length and width were measured on one branch per seedling; the same branch was measured on each seedling over the course of the study. Once a seedling was browsed, it was excluded from growth analyses. Figure 5.2: Commercial AMF inoculum and deer repellent field experiment block design Control AMF inoculum Deer repellent Repellent and inoculum (âBothâ) Redcedar seedling 98 Table 5.1: Seedling browse damage score Browse Score Browse Damage Estimate (% biomass removed) 0 0 1 1 - 30 2 31 - 59 3 60 - 90 4 91 - 100 5.2.5. Wolf urine experiment Boughs ~0.5 m in length were collected from mature redcedar trees in Sandspit and Skidegate, Haida Gwaii in August 2017. Boughs were randomly assigned to a wolf urine or control treatment. Urine and control plots consisted of fifteen approximately evenly spaced boughs inserted into the soil to stand upright in 5 m x 5 m plots, randomly located within the same cutblocks at Aero used for the AMF and deer repellent seedling experiment. All boughs in a given plot received the same treatment. A spray bottle was used to spray (3 times) wolf urine or water to the base of each bough and, a bottle containing either wolf urine or water was buried at the centre of the plot for wolf urine and control treatments, respectively. Each urine plot was located at least 100 m from its paired control plot. Two pairs of treatment plots were set up in each of the two Aero cutblocks described in section 5.2.1. The number of browsed boughs was recorded 6 days after experimental set up. 99 5.2.6. Statistical Analyses Due to inconsistencies in the field seedling growth measurements from experimental set up (week 0) to the measurements taken on week 2, the first set of measurements (week 0) were excluded from analysis. This is justified as redcedar seedlings on Haida Gwaii grow very slowly (Stroh et al. 2008). Bi-weekly seedling growth was calculated and averaged for the duration of the study. Mean growth of each seedling was used for statistical testing. Shapiro Wilkâs test was used to assess the normality of data. Normal data was assessed for equal variances using the var.test function. Parametric data was analyzed using Studentâs t-test or an ANOVA. Tukeyâs honest significant difference test was used post hoc where necessary. Non-parametric data was analyzed using the Mann Whitney U test or Kruskal-Wallis test. Dunnâs test was used post hoc where necessary. The wolf urine data was compared using a paired t-test. The treatment which received deer repellent and AMF inoculum was denoted as âbothâ in figures in Section 5.3. 5.3. Results Soil Chemical Properties There were no differences in total soil % C (t = 1.90, p = 0.13) or % N (t = 0.62) between the stands (Table 5.2). Total P was not significantly different between stands due to the high variability (t = -0.10, p = 0.92). The soil was significantly more acidic in the cedar stand than the cedar-free stand (t = -8.99, p = 0.0008). 100 Table 5.2: Soil chemical properties in the cedar (n = 3) and cedar-free (n = 3) stands. Values are means ± SE. Greenhouse bioassay A significantly higher proportion of redcedar seeds germinated in untreated (Z = -2.93, adjusted p = 0.01) and inoculated (Z = -4.03, adjusted p = 0.0002) soil than autoclaved soil (Z = -3.09, adjusted p = 0.006; Figure 5.3). There were no differences in germination between untreated and inoculated soil treatments (Z = 1.11, adjusted p = 0.81) or stands (KW c2 = 0.0005, p = 0.98). Figure 5.3: Proportion of redcedar seeds that germinated in autoclaved, untreated, and inoculated soil. Values are means ± SE. N = 5 for each treatment and each stand. Stand Total C (%) Total N (%) Total P (ppm) pH Cedar 44.7 ± 0.8 1.4 ± 0.03 162 ± 18 3.33 ± 0.09 Cedar-free 43.2 ± 0.3 1.3 ± 0.02 142 ± 23 4.27 ± 0.06 101 Redcedar aboveground biomass did not differ between inoculum treatments (F = 0.45, p = 0.64), but aboveground biomass was significantly greater in seedlings grown in soil from the cedar-free stand than the cedar stand (F = 15.92, p = 0.00095; Figure 5.4). Seedling height was greater when grown in soil from the cedar-free stand (F = 7.92, p = 0.01), but there were no differences between inoculum treatments (F = 0.48 p = 0.63; Figure 5.5). Root biomass was significantly different on seedlings grown in soil from the different stands (F = 12.42, p = 0.0031) and marginally different between treatments (F = 3.55, p = 0.055; Figure 5.6). Root biomass was significantly higher on seedlings grown in soil from the cedar-free stand than the cedar stand (adjusted p = 0.0031). Root biomass was marginally lower in seedlings grown in the untreated (adjusted p = 0.061) and inoculated (adjusted p = 0.051) soils than in the autoclaved soil. Root length did not differ between treatments (F = 0.33, p = 0.72) or stands (F = 0.11, p = 0.75; Figure 5.7). RCD did not differ between treatments (KW c2 = 0.54, p = 0.76), but it was significantly higher on seedlings grown in soil from the cedar-free stand (KW c2 = 5.55, p = 0.018; Figure 5.8). 102 Figure 5.4: Redcedar aboveground biomass in autoclaved (cedar n = 1, cedar-free n = 1), untreated (cedar n = 4, cedar-free n = 5), and inoculated (cedar n = 5, cedar-free n = 5) soil. Values are means ± SE. 103 Figure 5.5: Redcedar height in autoclaved (cedar n = 1, cedar-free n = 1), untreated (cedar n = 4, cedar-free n = 5), and inoculated (cedar n = 5, cedar-free n = 5) soil. Values are means ± SE. Figure 5.6: Redcedar root biomass in autoclaved (cedar n = 1, cedar-free n = 1), untreated (cedar n = 4, cedar-free n = 5), and inoculated (cedar n = 5, cedar-free n = 5) soil. Values are means ± SE. 104 Figure 5.7: Redcedar root length in autoclaved (cedar n = 1, cedar-free n = 1), untreated (cedar n = 4, cedar-free n = 5), and inoculated (cedar n = 5, cedar-free n = 5) soil. Values are means ± SE. 105 Figure 5.8: Redcedar RCD in autoclaved (cedar n = 1, cedar-free n = 1), untreated (cedar n = 4, cedar-free n = 5), and inoculated (cedar n = 5, cedar-free n = 5) soil. Values are means ± SE. There were significant differences in the % colonization of roots by AMF between inoculum treatments (F = 12.43, p = 0.0005); stand also had a marginal effect (F = 3.23, p = 0.09). The % colonization of roots was higher in redcedar grown in untreated soil than in inoculated (adjusted p = 0.016) or autoclaved soil (adjusted p = 0.0006). AMF colonization of seedlings in the inoculated treatment was more than twice as high as in the autoclaved treatment (adjusted p = 0.029). AMF colonization of the roots was marginally higher in seedlings grown in soil from the cedar-free stand than the cedar stand (adjusted p = 0.091; Figure 5.9). Figure 5.9: AMF % colonization of redcedar roots in autoclaved (cedar n = 1, cedar-free n = 1), untreated (cedar n = 4, cedar-free n = 5), and inoculated (cedar n = 5, cedar-free n = 5) soil. Values are means ± SE. 106 Field Experiment: Commercial AMF Inoculum and Deer Repellent There were no differences in the mean redcedar seedling height (KW c2 = 1.32, p = 0.72), scale length (KW c2 = 0.41, p = 0.94), scale width (KW c2 = 2.50, p = 0.48), or RCD (KW c2 = 0.94, p = 0.82) between treatments (Figures 5.10-5.13). Any negative outliers in the boxplots were a result of the limited precision in growth measurements. Figure 5.10: Mean bi-weekly redcedar seedling height increase in two recently harvested stands in Aero, Moresby Island. Control n = 9. AMF n = 13. Repellent n = 22. Both n = 22. âBothâ refers to the treatment that received both AMF inoculum and deer repellent. â0.40.00.40.8Cedar CedarFreeStandMeanTreatment Control AMF Repellent BothMean Growth (cm) 107 Figure 5.11: Mean bi-weekly redcedar scale length increase in two recently harvested stands in Aero, Moresby Island. Control n = 9. AMF n = 13. Repellent n = 22. Both n = 22. âBothâ refers to the treatment that received both AMF inoculum and deer repellent. â0.10.00.10.20.30.4Cedar CedarFreeStandMeanTreatment Control AMF Repellent BothMean Growth (cm) 108 Figure 5.12: Mean bi-weekly redcedar scale width increase in two recently harvested stands in Aero, Moresby Island. Control n = 9. AMF n = 13. Repellent n = 22. Both n = 22. âBothâ refers to the treatment that received both AMF inoculum and deer repellent. â0.2â0.10.00.1Cedar CedarFreeStandMeanTreatment Control AMF Repellent BothMean Growth (cm) 109 Figure 5.13: Mean bi-weekly redcedar RCD increase in two recently harvested stands in Aero, Moresby Island. Control n = 9. AMF n = 13. Repellent n = 22. Both n = 22. âBothâ refers to the treatment that received both AMF inoculum and deer repellent. Seedling Browse Damage Redcedar seedling browse damage was significantly higher in the cedar-free stand than the cedar stand (KW c2 = 5.54, p = 0.019; Figure 5.14), but there were no differences between stands in the proportion of time without browse damage (KW c2 = 2.54, p = 0.11; Figure 5.15) or the proportion of seedlings browsed (KW c2 = 2.03, p = 0.15; Figure 5.16). There were significant differences between treatments in the extent of browse damage (KW c2 = 21.50, p = 8.29e-5), the proportion of time without browse damage (KW c2 = 26.74, p = 6.69e-6), and the proportion of seedlings browsed (KW c2 = 13.65, p = 0.0034). Browse damage was significantly more severe on the AMF treatment than the repellent (Z = 3.11, adjusted p = 0.011) or AMF-â0.015â0.010â0.0050.0000.0050.0100.015Cedar CedarFreeStandMeanTreatment Control AMF Repellent BothMean Growth (cm) 110 repellent (âbothâ) treatments (Z = 3.65, adjusted p = 0.0016). Browse damage was significantly more severe on seedlings in the control treatment than the repellent (Z = 2.86, adjusted p = 0.026) or âbothâ treatments (Z = -3.39, adjusted p = 0.0042). Browse damage was not significantly different between the control and AMF treatments (Z = 0.26, adjusted p = 1), but browse damage in the AMF treatment was higher than the control in the cedar-free stand and lower than the control in the cedar stand (Figure 5.14). The proportion of time a seedling lasted without browse damage was significantly higher for: repellent (Z = -3.75, adjusted p = 0.0011) and âbothâ (Z = 4.21, adjusted p = 0.0002) than the control; and, repellent (Z = -3.00, adjusted p = 0.016) and âbothâ (Z = -3.46, adjusted p = 0.0033) than the AMF treatment (Figure 5.15). The proportion of seedlings browsed was significantly lower for the âbothâ treatment than the control (Z = -2.91, adjusted p = 0.022) and AMF treatments (Z = 2.76, adjusted p = 0.035; Figure 5.14). The proportion of seedlings browsed was only marginally lower for the repellent treatment than the control treatment (Z = 2.42, adjusted p = 0.093) and was not significantly lower than the AMF treatment (Z = 2.27, adjusted p = 0.14; Figure 5.16). There were no significant differences in browse damage, time, or frequency between AMF and control treatments or, between the repellent and âbothâ treatments. Almost one-third fewer redcedar boughs were browsed in the wolf urine plots than the control plots (t = -4.61, p = 0.019; Figure 5.17). 111 Figure 5.14: Redcedar seedling browse damage 16 weeks following planting and treatment with mycorrhizal inoculum (AMF), deer repellent (Repellent), both treatments (Both) or untreated (Control) in two recently harvested stands in Aero, Moresby Island. N = 24 for each treatment. 0255075100Cedar CedarFreeStandBrowsePercentTreatment Control AMF Repellent Both% Biomass Removed 112 Figure 5.15: Proportion of time redcedar seedlings avoided browse damage over 16 weeks of monitoring following planting and treatment with mycorrhizal inoculum (AMF), deer repellent (Repellent), both treatments (Both) or untreated (Control)in two recently harvested stands in Aero, Moresby Island. N = 24 for each treatment. 0.000.250.500.751.00Cedar CedarFreeStandPropTimeTreatment Control AMF Repellent BothProportion of Time 113 Figure 5.16: Proportion of redcedar seedlings browsed 16 weeks following planting and treatment with mycorrhizal inoculum (AMF), deer repellent (Repellent), both treatments (Both) or untreated (Control) at each block (n = 8) in two recently harvested stands in Aero, Moresby Island. 0.000.250.500.751.00Cedar Stand Cedarâfree StandStandPropBrowsedTreatment Control AMF Repellent BothBrowse Frequency 114 Figure 5.17: Proportion of redcedar boughs browsed over 6 days in control plots (n = 4) and wolf urine plots (n = 4) in two recently harvested stands in Aero, Moresby Island. Values are means ± SE. 5.4. Discussion In the greenhouse bioassay, redcedar germination and AMF colonization were lowest in the autoclaved soil. None of the redcedar seedling growth measurements differed significantly between inoculum treatments, but seedlings were significantly larger when grown in soil from the cedar-free stand. Redcedar growth did not differ between treatments in the field experiment. Browse damage and the proportion of seedlings browsed were lower, and the amount of time before a seedling was browsed was longer, in the deer repellent treatments. The proportion of browsed redcedar boughs was lower in the wolf urine treatment than the control treatment. 115 Contrary to expectations, the AMF inoculum potential of the soil was not higher in the cedar stand; however, this is likely explained by the fact that redcedar made up <1% of the volume/ha of live trees prior to harvest in the cedar stand. Redcedar densities were lowest in naturally-regenerating stands that contained <10% redcedar prior to harvest on Haida Gwaii (Martin & Baltzinger 2002). If the findings of Martin & Baltzinger (2002) reflect the AMF inoculum potential of the soil, then in addition to the presumably lower seedbank, the AMF inoculum potential of the soil from the cedar stand in the current study was likely also low. Although the higher AMF colonization in seedlings grown in untreated soil than the autoclaved soil in the greenhouse in our study suggests that there was AMF inoculum in both stands. Therefore, soil chemical properties were likely responsible for the differences in redcedar growth in soils taken from the cedar and cedar-free stands. Redcedar seedlings grown in nutrient culture tanks with a pH of 4.8 had significantly higher shoot and root biomass than seedlings grown in nutrient culture tanks a pH of 3.0, but there was no difference in root length (Ryan et al. 1986). Similarly, redcedar seedlings grown in the more acidic soil from the cedar stand in our study produced less biomass, while root length did not differ. Redcedar has a higher foliar Ca content than other common conifers in the Pacific Northwest (Prescott 2010). While Ryan et al. (1986) did not observe differences in foliar Ca, Ca content in redcedar roots in their study was almost twice as high in seedlings grown in the less acidic treatment. Therefore, differences in pH, Ca, and other soil nutrients are likely responsible for the differences in redcedar growth and AMF colonization in our study. Differences in soil chemical properties may also contribute to the slightly higher AMF colonization in the redcedar seedlings grown in cedar-free soil as nutrient limitation affects 116 mycorrhizal dependency (Hoeksema et al. 2010). AMF colonization of redcedar was lower in the seedlings grown in inoculated compared to untreated soil, which could indicate a competitive interaction between microbes. Indeed, mycorrhizal inoculation was more beneficial to plant growth when only a single species of AMF rather than two AMF species was used to inoculate seedlings in two of four tree species studied in India (Kumar et al. 2017). The benefit of the single species may have been a result of taxonomic differences between the AMF or functional compatibility with the host (Kumar et al. 2017). Similarly, ectomycorrhizal fungi have been shown to outcompete AMF in the colonization of Eucalyptus dumosa roots whereby the % colonization by ectomycorrhizal fungi increased over time while AMF % colonization decreased (Chilvers et al. 1987). The marginally higher root biomass of redcedar seedlings grown in the autoclaved soil may be explained by the lower colonization by AMF. Although AMF-plant interactions are typically thought of as symbiotic, AMF inoculation can decrease plant biomass relative to a control (Hoeksema et al. 2010). Alternatively, the process of autoclaving may have altered soil properties in a way that promoted the growth of the few seeds that germinated. A greenhouse experiment revealed that soil soluble organic C, DTPA-extractable Mn, and Triticum aestivum root biomass, but not shoot biomass or soil macronutrients, were higher in autoclaved soil versus unautoclaved soil (Mahmoud et al. 2014). Rowe et al. (2007) found that one of two commercial inocula did not promote plant growth in several montane species and both inocula decreased the proportion of plants colonized in experimental plots in Washington State. Therefore, the lack of differences in redcedar growth between treatments may demonstrate the inefficacy of commercial AMF inoculum to promote redcedar growth. More likely, however, is that seedlings were entering dormancy during the 117 monitoring period as redcedar photosynthesis and growth decline with decreasing fall temperatures (Grossnickle & Russel 2006). Indeed, seedling growth over the course of the study was negligible, so a longer monitoring period over the summer may be required to detect the effect of the commercial inoculum on growth. The slow growth of redcedar on Haida Gwaii has been demonstrated previously (Stroh et al. 2008). Wagner & Nolte (2001) demonstrated the efficacy of the fear-inducing deer repellent, Plantskydd®, at reducing herbivory on redcedar and other conifers in Washington State. Our results indicate that although the deer do not have a natural predator on Haida Gwaii, the active agent in Plantskydd® â animal protein â effectively reduced the extent of browse damage and increased the amount of time a seedling remained un-browsed. The proportion of seedlings browsed was lower in both treatments that received the deer repellent, but the difference was only significant in the treatment that received both the repellent and the inoculum. This may indicate that the AMF inoculum aided in the reduction of herbivory since AMF can increase the production of secondary metabolites (Lima et al. 2017). However, browse damage in the AMF-only treatment was idiosyncratic between the cedar-free and cedar stand. Deer consumed a smaller proportion of redcedar boughs in wolf urine plots up to six days after the predator odour had been applied. Chamaillé-Jammes et al. (2013) similarly observed that bait consumption was lower at stations treated with wolf urine within 24 h of application. These results suggest herbivory is lower in proximity to wolf urine â presumably through increased avoidance and/or vigilance behaviour in deer in response to perceived predation risk. In Australia, rats spent more effort foraging in areas without predator body odour than those with predator body odour, but this vigilance behaviour was not evident after the predator 118 odour had aged 24 h (Bytheway et al. 2013). Although fewer boughs were browsed in the wolf urine treatment, the browse frequency was still high (i.e. 65%), which could be a result of the aging predator odour. Soil chemical properties play an important role in redcedar growth (Ryan et al. 1986) and so should be considered when identifying redcedar planting locations. Neither native soil nor commercial inoculum increased the AMF colonization or growth of redcedar seedlings in the field. Although, the commercial inoculum and deer repellent treatments reduced browse damage and increased the length of time before browsing occurred, suggesting that the mitigation of herbivory is a relatively more important indirect effect of mycorrhizal inoculation and is a better strategy for conservation or silviculture at our study site. Indeed, fear is an effective mechanism by which to reduce herbivory (Wagner & Nolte 2001; Martin & Baltzinger 2002; Chamaillé-Jammes et al. 2013); however, the efficacy of predator olfactory cues declines over time (Bytheway et al. 2013). Future research should aim to increase the longevity of the active compound in wolf urine for use in commercial repellents in order to make it an economically efficient repellent. Antos et al. (2016) argue that another avenue of study to be explored is whether underplanting â the process of planting seedlings under overstory trees â reduces browsing on redcedar due to the tendency of deer to browse in open areas. 119 6. Conclusions and future directions 6.1. Main findings In this thesis, I studied the effects of introduced deer on plant and soil microbial communities on Haida Gwaii. I also assessed the efficacy of AMF inoculum and deer repellents as restoration or silvicultural tools to improve redcedar survival and growth on harvested sites. In order to assess the effects of introduced deer, I compared vegetation cover and diversity and soil physical, chemical properties and microbiological communities on deer-free and deer-inhabited islands. I used PLFAs to determine microbial community structure and PLFA d13C values to assess potential C sources used for microbial growth. I additionally used greenhouse bioassays to assess the effects of whole soil transplants from deer-free and deer-inhabited islands, and from harvested sites that contained or lacked redcedar in the forest stand, on redcedar seedling growth and AMF colonization. I used field experiments to determine the potential of commercial AMF inoculum, a commercial deer repellent, and wolf urine to enhance redcedar growth and prevent browse damage. As predicted, deer had a positive effect on the bryophyte community, but a negative effect on the understory plant community on Haida Gwaii. Bryophyte species richness and cover were significantly higher on deer-inhabited islands, while understory plant cover was lower. The higher plant species evenness on deer-inhabited islands suggests that deer homogenize the understory plant community. The higher plant diversity on deer-inhabited islands was likely a result of the higher species evenness and island size. Indeed, Gaston et al. (2006) observed lower species richness on small deer-inhabited islands than deer-free islands, but richness was highest on the large islands despite the presence of deer. Ordinations revealed distinct 120 clustering of the plant and bryophyte communities within the deer-free islands and the deer-inhabited islands. The intermediate separation of plots on South Skedans and Reef suggest that the vegetation community under shorter (Skedans) or disrupted (Reef) browse history is transitional between deer-free islands and islands with a long browse history. The differences in the understory plant community likely contributed to some of the differences in the soil physical and chemical properties between deer-free and deer-inhabited islands. There were no differences in soil % C, % N, or P in the F horizon, but the forest floor was reduced by ~50% on deer-inhabited islands relative to deer-free islands. Soil moisture, bulk density, and soil penetration resistance were higher, while soil pH was lower, on deer-inhabited islands; however, differences between islands (e.g. size) likely contributed to the differences in soil moisture and bulk density. The 13C-enrichment of Gram-positive bacteria, relative to fungi and Gram-negative bacteria, likely reflects the lower utilization of root exudates by Gram-positive bacteria (Paterson et al. 2007). Gram-negative bacteria were more 13C-depleted on deer-inhabited islands, which may be a result of the higher bryophyte cover or greater soil moisture on these islands versus deer-free islands. Soil microbial community structure, indicated by PLFAs, clustered within treatment (i.e. deer presence) and islands. Fungal and bacterial biomass were non-significantly lower on deer-inhabited islands, but there was no difference in the fungal:bacterial ratio. Similarly, available nutrient profiles, quantified using PRS® probes, were distinct between deer-free and deer-inhabited islands, but the differences in supply rate were non-significant when analyzed individually. Reef Island separated intermediately between deer-free and deer-inhabited islands for both microbial structure and 121 available nutrient ordinations, which again could indicate a transitional gradient in browse history. The greenhouse bioassays revealed that whole soil inoculum did not increase AMF colonization or growth of redcedars. Contrary to expectations, redcedar growth was higher in the cedar-free cutblock and the deer-inhabited island than the cedar cutblock or deer-free island, respectively. However, AMF colonization of the roots was not significantly higher in the cedar-free cutblock or the deer-inhabited island, suggesting that soil chemical properties were responsible for the differences in redcedar growth between cutblocks or islands. The field experiment to assess the potential of commercial AMF inoculum and a commercial deer repellent to enhance redcedar growth and prevent browse damage did not reveal differences in redcedar growth between any of the treatments. These findings may indicate that commercial AMF inoculum did not promote plant growth over the natural AMF in the soil, as has been demonstrated previously (Cook et al. 2011). Alternatively, any benefits to growth may not have been detectable due to the slow growth of redcedar (Stroh et al. 2008) and the relatively short monitoring period (4 months). Interestingly, browse damage was lowest on seedlings that were treated with both AMF inoculum and deer repellent. As AMF inoculation can increase the production of secondary metabolites (Lima et al. 2017), commercial inoculum may indirectly benefit redcedar by reducing palatability to deer. The commercial deer repellent reduced browse damage on redcedar and wolf urine reduced browse frequency on redcedar boughs. 122 6.2. Contributions to the field This study is unique in that it compared plant and soil communities on islands invaded by deer to those with no history of herbivores. This experimental design enabled us to study how herbivore presence alters the forest community and, provides a reference site for conservation targets in cases where deer are removed from islands. By collecting extensive data on both the plant and soil community, this study has helped identify and link the effects of introduced deer on these communities in a coastal temperate rainforest. In particular, the analysis of the natural d13C signature of PLFAs provides insight into the effects of introduced deer on substrate usage by fungi and bacteria. This study also revealed that the AMF inoculum potential of the soil has not been reduced in the presence of introduced, overabundant deer, likely due to the presence of mature redcedar on these islands. This research assessed the efficacy of various techniques to promote the growth and survival of an important economic and cultural species: western redcedar. I found that neither whole soil nor commercial inoculum increased the growth of redcedar, but that the commercial inoculum may work synergistically with the commercial deer-repellent to reduce browse damage. Additionally, this study revealed that deer respond to olfactory cues â namely a commercial deer repellent and wolf urine â even after roughly a century without exposure to natural predators. 6.3. Limitations and future directions As mentioned previously, the comparison of multiple islands introduces confounding variables. In particular, some of the deer-inhabited islands differed in browse history. The inclusion of islands with differing browse histories produced interesting results (i.e. a possible 123 gradient in response); however, the inclusion of replicate islands for each of the intermediate browse histories would have strengthened this conclusion. The use of PRS® probes enabled the analysis of multiple available nutrients, but the long burial period may have masked the extent of the differences between treatments. Additionally, PRS® probes could not be buried on Agglomerate Island (deer-free) for logistical reasons; however, this data would have increased the statistical power and likely would have aided in data interpretation. The analysis of PLFAs is advantageous because PLFAs give insight into microbial community structure and biomass, but the additional use of qPCR could have revealed changes in functional diversity (Peschel et al. 2015). Alternatively, the higher taxonomic resolution of molecular methods could reveal if herbivores affect particular species or genera of microbes differently. This would be particularly beneficial to further study AMF-redcedar symbioses as previous studies have shown that single-species AMF isolates can benefit plants (Kumar et al. 2017; Lima et al. 2017). As such, future research should focus on identifying the AMF species with which redcedar forms a symbiosis, assessing their ubiquity, and monitoring redcedar growth after inoculation with isolates. Whole soil was used in the greenhouse bioassay to determine its success for practical applications (i.e. for restoration or silviculture); however, soil chemistry contributed to differences in redcedar growth and AMF colonization. Therefore, the use of a sterile, standardized medium could help elucidate differences in the AMF inoculum potential of native soil. The AMF and deer repellent experiment took place in the fall during a period of potential seedling dormancy, so additional research on the use of commercial inoculum as a silvicultural 124 tool is required. Additionally, the use of commercial AMF inoculum â in combination with deer repellent â as a means to deter browsing of palatable species should be explored further. Future research should additionally focus on the long-term monitoring of the vegetation and soil after the removal or reduction of overabundant herbivore populations. 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Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
The effects of introduced Sitka black-tailed deer (Odocoileus hemionus sitchensis) on plant and soil microbial communities on Haida Gwaii and silvicultural tools to improve western redcedar survival
Text