Open Collections

UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Evaluating interior spruce genetic resource management practices through GIS-based tracking of seed deployment… Ding, Chen 2009

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Notice for Google Chrome users:
If you are having trouble viewing or searching the PDF with Google Chrome, please download it here instead.

Item Metadata

Download

Media
24-ubc_2009_spring_ding_chen.pdf [ 5.23MB ]
Metadata
JSON: 24-1.0067090.json
JSON-LD: 24-1.0067090-ld.json
RDF/XML (Pretty): 24-1.0067090-rdf.xml
RDF/JSON: 24-1.0067090-rdf.json
Turtle: 24-1.0067090-turtle.txt
N-Triples: 24-1.0067090-rdf-ntriples.txt
Original Record: 24-1.0067090-source.json
Full Text
24-1.0067090-fulltext.txt
Citation
24-1.0067090.ris

Full Text

     EVALUATING INTERIOR SPRUCE GENETIC RESOURCE MANAGEMENT PRACTICES THROUGH GIS-BASED TRACKING OF SEED DEPLOYMENT OVER TIME IN BRITISH COLUMBIA    by  CHEN DING   B.Sc., Beijing Forestry University, 2006      A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF   MASTER OF SCIENCE   in   THE FACULTY OF GRADUATE STUDIES  (Forestry)         THE UNIVERSITY OF BRITISH COLUMBIA  (Vancouver)  April 2009       © CHEN DING, 2009 ii ABSTRACT To improve current understanding of how genetically improved stocks are spatially and temporally deployed, tree breeders, gene resource managers, and forestry research community can gain insights from an analysis of currently available geospatial data.  This analysis can also help and provide the raw material for better understanding of the relationship between gene resource management (GRM) and issues related to climate change and other risks such as the current mountain pine beetle epidemic.  GIS links the latest information management concepts and methods to assist GRM achieve higher genetic gain, resilience and conservation goals. According to the Chief Forester's standards for seed use and the target of the Forest Genetics Council of BC, 75% of seed use will be from selected seed sources. This research developed a GIS based method to monitor and assess the spatial temporal variability of seed deployment in one seed planning zone of interior spruce and employed map representations to visualize the spatial cluster dynamics of reforestation plantations in BC.  The investigation of deployment areas and stem number of seed stock inform our knowledge of forest recovery in the context of gene resources management.  Class A and B+ seed use increased dramatically after 1995 in the Prince George Seed Production Zone (SPZ).  The A class ratio in PG total seed deployment is 48.1% from 1995 to 2004 with seed orchard 214 being the leading seed source for SX reforestation in PG at the SPU level.  The Sub-boreal spruce zone is the main natural habitat area of SX, where intensive forest management activity undergoes as the plantation hotspots. Observed changes in genetic class indicate the intensive reforestation with selected seed sources.  The applicability of GIS modeling methods is available at different SPZ levels and species scopes.  The system construction of an updated GRM criteria and decision making support is noteworthy for BC foresters to more wisely harvest, recover, and manage the forests in a more sustainable manner. iii TABLE OF CONTENTS ABSTRACT   ....................................................................................................................................... ii TABLE OF CONTENTS   .................................................................................................................. iii LIST OF FIGURES   ......................................................................................................................... vii LIST OF ABBREVIATIONS   .............................................................................................................. x ACKNOWLEDGEMENTS   ................................................................................................................ xi DEDICATION   .................................................................................................................................. xii 1 Introduction   ........................................................................................................................... 1 1.1 Genetic resources management   .................................................................................... 1 1.1.1 Genetic diversity—the foundation of genetic resources management   ...................... 1 1.1.2 GRM in BC   ................................................................................................................. 2 1.1.3 Brief history of GRM in BC   ......................................................................................... 4 1.2 Brief review of GIS applications in GRM and related fields   ......................................... 10 1.3 Climate change and GRM   ............................................................................................ 13 1.4 Objectives and overview of the thesis   .......................................................................... 15 2 Technical scope   .................................................................................................................. 16 2.1 Species studied   ............................................................................................................ 16 2.2 Geographic scale   ......................................................................................................... 17 2.2.1 Silviculture opening level   ......................................................................................... 18 2.2.2 Seed planning zone   ................................................................................................. 20 2.2.3 BEC zone   ................................................................................................................. 23 2.3 Reporting period   ........................................................................................................... 25 2.4 Genetic sources   ........................................................................................................... 25 3 Methods and data analysis   ................................................................................................ 26 3.1 Data sources   ................................................................................................................ 26 3.1.1 Spatial data sources   ................................................................................................ 26 3.1.2 Non-spatial data sources   ......................................................................................... 28 3.2 Data preparation  ........................................................................................................... 30 3.2.1 Error checking and data cleaning   ............................................................................ 30 3.2.2 Spatial data preparation   ........................................................................................... 31 3.3 Spatial modeling and analysis   ...................................................................................... 32 3.3.1 Clip   ........................................................................................................................... 33 3.3.2 Feature to points   ...................................................................................................... 34 3.3.3 Identity   ..................................................................................................................... 36 3.3.4 Table join and query   ................................................................................................ 36 3.3.5 Frequency   ................................................................................................................ 38 3.3.6 Spatial analysis and kernel density   .......................................................................... 38 3.4 Model description   ......................................................................................................... 39 3.4.1 Introduction   .............................................................................................................. 39 3.4.2 ArcGIS ModelBuilder and model components   ......................................................... 39 3.4.3 SPZ model   ............................................................................................................... 41 3.4.4 SPU model   ............................................................................................................... 42 3.4.5 BEC zone model   ...................................................................................................... 44 4 Results   ................................................................................................................................. 46 4.1 Introduction   ................................................................................................................... 46 4.2 Background tabular data report   .................................................................................... 47 4.2.1 Silviculture openings   ................................................................................................ 47 4.2.2 Adjacent SPZs   ......................................................................................................... 55 4.2.3 Natural regeneration openings   ................................................................................ 58 4.3 Multiple species   ............................................................................................................ 60 4.3.1 Spatial openings   ...................................................................................................... 60 4.3.2 Non-spatial openings   ............................................................................................... 63 4.3.3 Spatial and non-spatial openings   ............................................................................. 66 4.4 PG Seed Planning Zone   .............................................................................................. 70 4.4.1 Silviculture openings   ................................................................................................ 70 iv 4.4.2 Natural regeneration   ................................................................................................ 77 4.5 BEC Zone   ..................................................................................................................... 80 4.5.1 Silviculture openings   ................................................................................................ 80 4.5.2 Natural regeneration   ................................................................................................ 88 4.6 SPU   .............................................................................................................................. 90 4.6.1 Silviculture openings   ................................................................................................ 90 4.6.2 Natural regeneration   ................................................................................................ 94 4.7 Map representations for openings  ................................................................................ 97 4.7.1 Silviculture openings   ................................................................................................ 97 4.7.2 Distribution of genetic classes   ............................................................................... 102 4.7.3 Natural regeneration   .............................................................................................. 106 5 Discussion   ......................................................................................................................... 109 5.1 Incremental genetic resources improvement   ............................................................. 109 5.1.1 Natural regeneration openings and silviculture openings overview   ...................... 109 5.1.2 Prince George zone overview   ................................................................................ 111 5.2 Seed deployment by species   ..................................................................................... 113 5.3 Spatio-temporal data overview and visualization   ....................................................... 114 5.4 For further studies   ...................................................................................................... 115 6 Conclusion   ......................................................................................................................... 117 References   ................................................................................................................................. 119 Appendix 1   ................................................................................................................................. 126 Appendix 2 Model graphics and geodatabase   ..................................................................... 127 v LIST OF TABLES Table 1 Forest Tree Seed Planning Zones in BC (1974) (adapted from www.for.gov.bc.ca/HTI/spar/chronologySZ/Seed_Zones_1974.jpg, May 31, 2008)   ...................... 6 Table 2 SPZ_A Sx area (adapted from http://www.for.gov.bc.ca/hti/spar/help/SPR107.htm, Oct. 10th 2008)   ...................................................................................................................................... 21 Table 3 Spatial data sources and geometry   ................................................................................. 26 Table 4 Metadata of Spatial data sources. The projected coordinate system is NAD_1983_Albers. Geographic coordinate system name is GCS_North_American_1983.   ........................................ 27 Table 5 Non-spatial data sources summary.   ................................................................................ 29 Table 6 The report of major variables and processes for the SPZ model.   ................................... 42 Table 7 Model report of major variables and process for SPU model (1. Identity).   ...................... 43 Table 8 Model report of major variables and process for SPU model (2. Frequency).   ................. 44 Table 9 Model report of major variables and process for Step3   ................................................... 45 Table 10 Number of trees (stems) by genetic source for interior spruce seed deployed (planted) across reporting period (1970-1987), British Columbia (‘Blank’ records mean no data available for that year).   ....................................................................................................................................... 48 Table 11 Number of trees of interior spruce by genetic classes seed deployed (planted) across reporting period (1988-2004), British Columbia.   ........................................................................... 50 Table 12 Stems percentage for genetic sources of interior spruce seed deployed (planted) from 1988 to 2004, British Columbia.   .................................................................................................... 52 Table 13 Area for genetic sources of interior spruce seed deployed (planted) across reporting period (1988-2004), British Columbia.   ........................................................................................... 54 Table 14 Area percentage for genetic sources of interior spruce seed deployed (planted) across reporting period (1988-2004), British Columbia.   ........................................................................... 55 Table 15 Total number of trees for interior spruce by genetic sources overtime in SPZ_A_Sx BVP, PG and PGN (1988-2004).   ............................................................................................................ 56 Table 16 Total area treated (planted) with interior spruce by genetic sources and year in SPZ_A_Sx BVP, PG and PGN (1988-2004).   ................................................................................ 57 Table 17 Area of natural regeneration and plantation of interior spruce, BC (1970-2004).   .......... 58 Table 18 Total area (ha) treated (planted) with all species reported in PG (spatial openings) from 1995 to 2004.  Other species include Black spruce (Sb), Western red cedar (Cw), Sieberian larch (Ls), Black cotton wood (Act), Paper birch (Ep), Tamarack (LT) and Yellow pine(Py).   ................ 61 Table 19 Percentage of Area Treated (Planted) with All Species Reported in PG (Spatial Openings) from 1995 to 2004, other species include Black spruce (SB), Western red cedar (Cw), Sieberian larch (Ls), Black cotton wood (Act), Paper birch (Ep), Tamarack (LT) and Yellow pine(Py).   ........................................................................................................................................ 62 Table 20 Total Area (ha) treated (planted) with all species reported in PG (Non-spatial Openings) from 1995 to 2004.  Other species include Sitka spruce(Ss), Western larch (LW), Amabilis fir (Ba), Yellow cedar (Yc), Poplar (Ac), Siberian larch(Ls), Black cotton wood (Act), Paper birch (Ep), Lodgepole pine coastal (Plc).   ........................................................................................................ 64 Table 21 Percentile of area treated (planted) with all species reported in PG (Non-spatial Openings) from 1995 to 2004.  Other species include Sitka spruce(Ss), Western larch (LW), Amabilis fir (Ba), Yellow cedar (Yc), Poplar (Ac), Siberian larch(Ls), Black cotton wood (Act), Paper birch (Ep), Lodgepole pine coastal (Plc).   ............................................................................ 65 Table 22 Total area treated (planted) with all species reported in non-spatial and spatial openings in PG from1995 to 2004.   ............................................................................................................... 66 Table 23 Percentage (opening types) of Total Area Treated (Planted) with All Species Reported in Non-spatial and Spatial Openings in PG from1995 to 2004.   ..................................................... 67 Table 24 Total area and stem treated with Sx and Pli reported in PG (All Openings) by genetic classes from 1995 to 2004.   ........................................................................................................... 69 Table 25 Total area treated (planted) with Sx in SPZ_A_Sx PG by genetic class and year (1995- 2004).   ............................................................................................................................................. 71 Table 26 Percentage of total area treated (planted) with interior spruce in Sx PG SPZ-A by and genetic class and year (1995-2004).   ............................................................................................. 72 vi Table 27 Total area treated (planted) with interior spruce, stems and density in SPZ_A_Sx PG by year (1995-2004).   .......................................................................................................................... 73 Table 28 Total area treated (planted) with orchard 214 seeds in SPZ_A_Sx PG by year (1995- 2004)   .............................................................................................................................................. 74 Table 29 Total area treated (planted), stem and density with orchard 214 seeds from different Seedlots in SPZ_A_Sx PG (1995-2004)   ....................................................................................... 75 Table 30 Area percentage of seedlot 60269, 60119, 60118 deployed versus orchard 214 seed deployment area in SPZ_A_Sx PG (1995-2004).   ......................................................................... 76 Table 31 Comparison of natural regeneration area and planted area in SPZ_A_Sx PG (1995- 2004)   .............................................................................................................................................. 77 Table 32 Total area regenerated with interior spruce in SPZ_Sx_A PG by management units (1995-2004)   ................................................................................................................................... 78 Table 33 Total area regenerated (natural) with interior spruce in Sx PG SPZ-A in Prince George TSA by year (1995-2004)   .............................................................................................................. 79 Table 34 Total area treated (planted) with interior spruce in Sx PG SPZ-A by BEC zone (1995- 2004).   ............................................................................................................................................. 80 Table 35 Total area treated (planted) with interior spruce in Sx PG SPZ-A by BEC zone and year (1995-2004).   .................................................................................................................................. 82 Table 36 Total area treated (planted) with interior spruce in Sx PG SPZ-A by BEC zone and genetic sources (1995-2004).   ........................................................................................................ 83 Table 37 Total area treated (planted) with interior spruce ‘A’ class seeds in SPZ_A_Sx PG by BEC zone and Year (1995-2004)   .................................................................................................. 84 Table 38 Total Area Treated (Planted) with Interior Spruce (SO 214 Seeds) in Sx PG SPZ-A by BEC zone (1995-2004).   ................................................................................................................. 85 Table 39 Total Area Treated (Planted) and Density with orchard 214 Seeds from Different Seedlots in SBS BEC zone in SPZ_A_Sx PG (1995-2004).   ......................................................... 86 Table 40 Total area treated (planted) with interior spruce (orchard 214 seeds) in Sx PG SPZ-A within ESSF, ICH and SBS by Year.   ............................................................................................. 87 Table 41 Total Area Regenerated (Natural) with Interior Spruce in Sx PG SPZ-A by BEC zone and by Year (1995-2004).   .............................................................................................................. 88 Table 42 Total Area regenerated (natural) with interior spruce in Sx PG SPZ-A by BEC zone and by Management Unit (1995-2004).   ................................................................................................ 89 Table 43 Total Area Treated (Planted) with Interior Spruce in Sx PG SPZ-A by SPU and by Year (1995-2004)   ................................................................................................................................... 91 Table 44 Total area treated (planted) with interior spruce in Sx PG SPZ-A by SPU and by genetic sources (1995-2004)   ..................................................................................................................... 92 Table 45 Total area treated (planted) with interior spruce ‘A’ class seeds in Sx PG SPZ-A by SPU and year (1995-2004).   ................................................................................................................... 93 Table 46 Total area treated (planted) with interior spruce (SO 214 Seeds) in Sx PG SPZ-A by SPU and by year (1996-2004).   ...................................................................................................... 94 Table 47 Total area regenerated (natural) with interior spruce in Sx PG SPZ-A by SPU and by Year (1995-2004).  .......................................................................................................................... 95 Table 48 Total area regenerated (natural) with interior spruce in Sx PG SPZ-A by SPU and by Management Unit (1995-2004)   ...................................................................................................... 96 vii LIST OF FIGURES Figure 1 Douglas-fir seed zone map for Canada (adapted from http://www.for.gov.bc.ca/HTI/spar/chronologySZ/Fd_zones.jpg, May 31, 2008)   ............................ 5 Figure 2 Natural stand Seed Planning Zones (adapted from http://www.for.gov.bc.ca/HTI/spar/chronologySZ/Natural_Stand_SPZ_Map.jpg, May 31, 2008)   ... 7 Figure 3 SeedMap in BC (adapted from http://webmaps.gov.bc.ca/imf5/imf.jsp?site=mofr_seedmap, Oct 10th 2008)   ................................. 8 Figure 4 Illustration of interior spruce distribution in North America. Green area shows the Picea glauca and Picea engelmannii distribution. (Adapted from www.efloras.org, on March 21st, 2008)   ....................................................................................................................................................... 17 Figure 5 Examples of spatial openings in PG SPZ_A   .................................................................. 19 Figure 6 The distribution of SPUs of Sx in interior BC.  ................................................................. 20 Figure 7 Interior spruce seed planning units in PG Seed Planning Zone. ‘High’ is PG high, and ‘low’ is PG low.   ............................................................................................................................... 22 Figure 8 PG SPZ in the BEC zones of BC.   .................................................................................. 24 Figure 9 Occurrence frequency of openings with the same opening ID (a snap shot as an example).   ....................................................................................................................................... 31 Figure 10 Illustration of clip process.   ............................................................................................ 33 Figure 11 Illustration of Feature to Point.   ..................................................................................... 35 Figure 12 Illustration of Identity.   .................................................................................................... 36 Figure 13 Illustration of table join process.   ................................................................................... 37 Figure 14 Flow chart of analysis steps.   ........................................................................................ 41 Figure 15 Total number of trees of interior spruce by genetic class overtime (1970-1987).   ........ 48 Figure 16 Total number of trees of interior spruce by genetic classes overtime (1988-2004).   .... 50 Figure 17 Stem percentage of genetic classes of interior spruce from 1988 2004, British Columbia.   ....................................................................................................................................... 52 Figure 18 Area treated (planted) with interior spruce by genetic class from 1988 to 2004, British Columbia.   ....................................................................................................................................... 54 Figure 19 Number of trees of interior spruce by genetic class overtime in SPZ_A_Sx BVP, PG and PGN (1988-2004).   .................................................................................................................. 56 Figure 20 Total area treated (planted) with interior spruce by genetic class and year in SPZ_A_Sx BVP, PG and PGN (1970-2004).   ................................................................................ 57 Figure 21 Total area of natural regeneration and plantation with interior spruce, BC (1970-2004).   ....................................................................................................................................................... 59 Figure 22 Total area treated (planted) with interior spruce, interior Douglas-fir, Lodgepole pine and Sub-alpine fir reported in PG (spatial openings) from 1995 to 2004.   ..................................... 61 Figure 23 Total area treated (planted) with all species reported in PG (spatial openings) from 1995 to 2004.   ................................................................................................................................. 62 Figure 24 Total Area Treated (Planted) with Interior Spruce, Douglas-fir, Lodgepole pine Reported in PG (Non-spatial Openings) from1995 to 2004.  Other species include Sitka spruce(SS), Western larch (LW), Amabilis fir (Ba), Yellow cedar (Yc), Poplar (Ac), Siberian larch(Ls), Black cotton wood (Act), Paper birch (Ep), Lodgepole pine coastal (Plc).   .................... 64 Figure 25 Total Area Treated (Planted) with All Species Reported in PG (Non-spatial Openings) from 1995 to 2004.  Other species include Sitka spruce(SS), Western larch (LW), Amabilis fir (Ba), Yellow cedar (Yc), Poplar (Ac), Siberian larch(Ls), Black cotton wood (Act), Paper birch (Ep), Lodgepole pine coastal (Plc).   ........................................................................................................ 65 Figure 26 Comparison of total area treated (planted) with all species reported in spatial and non- spatial openings in PG from1995 to 2004.   .................................................................................... 67 Figure 27 Comparison of total area treated (planted) with all species reported in PG from1995 to 2004.   .............................................................................................................................................. 68 Figure 28 Total area treated with Sx reported in PG (All Openings) by genetic classes from 1995 to 2004.   .......................................................................................................................................... 69 Figure 29 Total area treated with Pli reported in PG (All Openings) by genetic classes from 1995 to 2004.   .......................................................................................................................................... 69 Figure 30 Total area treated (planted) with interior spruce and stems in SPZ_A_Sx by gene glass and year (1995-2004).   ................................................................................................................... 71 viii Figure 31 Percentage of total area treated (planted) with interior spruce in Sx PG SPZ-A by and genetic class and year (1995-2004).   ............................................................................................. 72 Figure 32 Planting density of interior spruce improved seeds deployed overtime in SPZ_A_Sx PG (1995-2004).   ............................................................................................................................ 73 Figure 33 Total area treated (planted) with orchard 214 seeds in SPZ_A_Sx PG by year (1995- 2004)   .............................................................................................................................................. 74 Figure 34 Area weight of three seedlots within 214 orchard seed deployment changes over time (1995-2004).   .................................................................................................................................. 76 Figure 35 Total area naturally regenerated and treated with interior spruce in Sx PG SPZ-A by year (1995-2004)   ........................................................................................................................... 77 Figure 36 Total Area Regenerated (Natural) with Interior Spruce in Sx PG SPZ-A in Prince George TSA by Year (1995-2004)  ................................................................................................. 79 Figure 37 Total area treated (planted) with interior spruce in Sx PG SPZ-A by BEC zone (1995- 2004).   ............................................................................................................................................. 81 Figure 38 Total area treated (Planted) with Interior Spruce in SPZ_A_Sx PG by BEC zone and Year (1995-2004).  .......................................................................................................................... 82 Figure 39 Total area treated (planted) with interior spruce in Sx PG SPZ-A by BEC zone and genetic classes (1995-2004).   ........................................................................................................ 83 Figure 40 Total area treated (planted) with interior spruce ‘A’ class in SPZ_A_Sx PG by BEC zone and Year (1995-2004).   .......................................................................................................... 84 Figure 41 Total area treated (planted) with Interior spruce (SO 214 Seeds) in Sx PG SPZ-A by BEC zone (1995-2004).   ................................................................................................................. 85 Figure 42 Total area treated (planted) and density with orchard 214 Seeds from Different seedlots in SBS BEC zone in SPZ_A_Sx PG (1995-2004).   ......................................................... 86 Figure 43 Total Area Treated (Planted) with Interior Spruce (SO 214 Seeds) in Sx PG SPZ-A within ESSF, ICH and SBS by Year.   ............................................................................................. 87 Figure 44 Total Area Regenerated (Natural) percentage with Interior Spruce in Sx PG SPZ-A by BEC zone and by Year (1995-2004).   ............................................................................................ 88 Figure 45 Total area regenerated (natural) with interior spruce in Sx PG SPZ-A by BEC zone and by Management Unit (1995-2004).   ................................................................................................ 89 Figure 46 Total area treated (planted) with interior spruce in Sx PG SPZ-A by SPU and by year (1995-2004)   ................................................................................................................................... 91 Figure 47 Total area treated (planted) with interior spruce in Sx PG SPZ-A by SPU and by genetic class (1995-2004)   ............................................................................................................. 92 Figure 48 Total area treated (planted) with interior spruce ‘A’ class seeds in Sx PG SPZ-A by SPU and year (1995-2004).   ........................................................................................................... 93 Figure 49 Total area treated (planted) with interior spruce (SO 214 Seeds) in Sx PG SPZ-A by SPU and by year (1995-2004).   ...................................................................................................... 94 Figure 50 Total area regenerated (natural) with interior spruce in Sx PG SPZ-A by SPU and by year (1995-2004).   .......................................................................................................................... 95 Figure 51 Total area regenerated (natural) with interior spruce in Sx PG SPZ-A by SPU and by Management Unit (1995-2004)   ...................................................................................................... 96 Figure 52 Temporal changes of opening distributions of interior spruce planted over time (1995- 2004).   ............................................................................................................................................. 98 Figure 53 Distribution of interior spruce planted over time and space (1995 -2004) (overlayed with the opening occurrence surface).   ........................................................................................ 100 Figure 54 Hotspot of plantation intensity in silviculture openings (1995-2004).   ......................... 101 Figure 55 Locations of interior spruce planted over time (1995-2004).   ...................................... 103 Figure 56 Temporal changes of opening distributions with different genetic classes seeds deployed overlayed with the opening occurrence probability surface (1995-2004).  Red area has denser distribution of ‘A’ class seed deployment.  Green area shows few occurrence of plantation. ‘B+’ and ‘B’ class are in the middle part of the spectrum, as dark orange and yellow.   ............... 104 Figure 57 Locations of interior spruce with SO 214 seeds over time (1996-2004).   ................... 105 Figure 58 Locations of interior spruce naturally regenerated over time (1995-2004)   ................ 107 Figure 59 Distributions of openings with interior spruce naturally regenerated over time and space (1995-2004) (overlayed with the opening occurrence surface).   ....................................... 108 ix Figure 60 Non-spatial opening ratio in PG zone from 1995 to 2004   .......................................... 116  Appendix Figures Figure A0.1 Geodatabase of SPZ model   ................................................................................... 127 Figure A0.2 Model flow chart of Step 1-SPZ model.   .................................................................. 127 Figure A0.3 Geodatabase of Step 2.   .......................................................................................... 128 Figure A2.4 Model flow chart of Step 2-1   ................................................................................... 128 Figure A2.5 Model flow chart of Step 2-2.   .................................................................................. 129 Figure A2.6 Geodatabase of Step 3.   .......................................................................................... 130 Figure A2.7 Model flow chart of Step 3.   ..................................................................................... 131 Figure A2.8 Geodatabase of Kernel density modeling   ............................................................... 132 Figure A2.9 Model flow chart of Kernel density modeling.   ......................................................... 133  x LIST OF ABBREVIATIONS ‘A’ class Seed Seed orchard seed ‘B’ class Seed Wild seed ‘B+’ class Seed Wild seed with superior provenance ‘N’ class Seed Seed class data not available BC  The province of British Columbia BEC Zone Biogeoclimatic Ecosystem Classification Zone EUFORGEN European Forest Genetic Resources Programme FTAS  Forest Tenure Administration System GIS  Geographical Information System GRM Gene resources management or Genetic resources management LRDW Land Resource Data Warehouse NR Natural regeneration OP_ID Opening identification number RESULTS Reporting Silviculure Updates Tracking System SPAR Seed Planning and Registry System SPU  Seed Planning Unit SPZ  Seed planning zone SPZ_Sx_A Seed planning zone_interior spruce_A class seed SPZ_Sx_A PG  Prince George zone in Seed planning zone_interior spruce_A class seed Sx Interior spruce VSOC Vernon seed orchard  xi ACKNOWLEDGEMENTS I sincerely express my thanks to those who have encouraged, guided and supported me throughout my life and studies.  To Dr. Michael Meitner and Dr. Yousry El-Kassaby, thanks for your continued efforts to establish a social and studying relationship that is energized by curiosity and all things abstract.  Thanks to Ms. Leslie McAuley, your emails, visits to UBC and your care and effort through my research and writing process.  Thanks for your will to serve as my advisors for guiding me in different disciplines. Thank you to Dr. Deng Wenbi of the Beijing Forestry University, for mentoring me and for planning the seed of the inevitable pursuit toward knowledge and learning in GIS.  I would not be where I am today without your encouragement.  Thank you to Dr. Stephen Sheppard, Dr. Nicholas Coops and Dr. Val Lemay for your kind advisory in my course work and encouragement for the explorations in sciences. Thank you to the former secretary Lori Nelson, Jessica Amorim, Lucinda Ridgway and Tracey Teasdale, the Forest Resource Management staff Marissa Relova, Heather Akai, Harry Verwoerd and Jerry Maedel for making the details in my life a lot smoother.  Thank you to Dr. Ryan Gandy for your pizza in Mike’s house.  Thank you to Brent Chamberlain and Andrea Chamberlain for your kindness, friendships, hugs, hours of revising my papers and countless dinners.  Thank you to Patricia Masupyi for your shining smiles and warm company.  Thank you Julian.  Thank you to Daniel Berheide and Dr. Catherine Berheide, for your wonderful conversations and rich knowledge.  Thank you to Candice (Juan) Chen, for your critics, encourages and hours of sharing your knowledge and life experiences with me.  Thank you to Yin, Ben Lai and Dr. Spring (Xiaochun) Qin for the cheerful days in 2239 and 3232 lab.  Thanks for Jason (Guangyu) Wang’s coffees and holidays’ get-togethers.  Thanks for Yazhen GONG and Jinhong YUAN.  Thanks for Wayne Feng, Qin Wei, Jerry (Shijun) You and Yu (Vivian) Li. xii DEDICATION    This thesis is whole-heartedly dedicated to my parents. Thanks for your dedications in raising and educating me. Thank you for doing all you could to make my life full of opportunity.   I would also like to dedicate this thesis to my friend and former mentor and dear friend Dr. Sihui LIU (Linda), whose inspiration continues to stir moments of gratitude in my heart.  1 1 Introduction The forests in British Columbia are in the midst of dramatic changes as a result of the mountain pine beetle epidemic and the emergence of climate change.  Developing effective methods to adapt to this change requires a greater understanding of how genetically improved seed stock has been distributed throughout the province.  In BC, forest tree genetic diversity of our forests ensures the sustainability of long-term forest management for both socio-economic values and ecological benefits.  Genetic Resource Management (GRM) is an approach to ensure the maintenance of genetic diversity for forestry practices and potential benefits for British Columbians.  This thesis presents a GIS-based method of tracking genetic improvements over time and space for trees within British Columbia’s forests.  This pilot project focuses on a historical analysis of plantings within interior spruce silviculture openings and natural regeneration from 1995 to 2004, including a spatial assessment at the seed orchard level.  The long term goal of this research is to create a decision support system aimed at the development of a seed deployment strategy that helps to manage genetic diversity of BC’s forests, while minimizing risk due to future uncertainties. 1.1 Genetic resources management 1.1.1 Genetic diversity—the foundation of genetic resources management Forest tree genetic diversity includes gene variation within and among populations, which is the fundamental component of biological diversity (Austin et al., 2008; Province of British Columbia, 2006).  Genetic diversity provides the gene pool for natural selection and tree improvement programs (White et al., 2006).  Forest timber production, tree breeding and ecosystem protection rely on the good foundation of genetic diversity (Frankel, 1974; Ledig, 1986; Lambeth and McCullough 1997, White et al., 2006). Due to the disturbances caused by human activities and environmental changes (Frankel, 1974), the resulting loss of gene diversity may increase potential vulnerability (Ledig, 1986, Timberline Forest Inventory Consultants Ltd., 2006; Tree Improvement Branch, 2006).  It also may cause a constraint for tree selection and breeding for commercial use as tree species are perennial, while 2 commercial plantation stocks have to be genetically improved to face greater vulnerability in nature for tree’s longevity (Ledig, 1986).  GRM encompasses gene conservation, tree improvement, forest seed planning and seed use to ensure the sustainability of forest ecosystems over long time scales (Timberline Forest Inventory Consultants Ltd., 2006; Tree Improvement Branch, 2006).  In BC, GRM is a dynamic and cooperative program which allows people to manage forestry in a more sustainable way with balanced economic, environmental and social benefits (Timberline Forest Inventory Consultants Ltd., 2006; Tree Improvement Branch, 2006). 1.1.2 GRM in BC GRM is set up within the sustainable forest management framework (SFM).  SFM is defined as “management to maintain and enhance the long-term health of forest ecosystems while providing ecological, economic, social and cultural opportunities for the benefit of present and future generations” (Canadian Model Forest Network, 2008).  GRM maintains and fosters a solid genetic foundation for providing continued evolution, improvement and adaptation of public forests in BC in order to meet the economic, social and ecological needs of British Columbians (Tree Improvement Branch, 2007).  SFM sets the context for GRM strategies and practices, while GRM supports BC to achieve its objectives of SFM. The strategies and practices of GRM consist of three key elements; namely, gain, conservation and resilience (Tree Improvement Branch, 2007).  Gain refers to genetic worth rating, which is the measure of seedlot gene quality.  For example, seed orchard seed (‘A’ class), and natural stand with superior provenance (‘B+’) are considered as selected seed sources with higher gain. Conservation includes in situ and ex situ approaches, which maintains the gene pool of natural tree species.  Resilience, a relatively new concept, refers to a broad spectrum of management practices and outcomes, including minimizing the risks of plantation failure and deforestation, by deploying proper and improved seed stocks (Tree Improvement Branch, 2007).  To achieve the goals of SFM, the GRM system involves strategic analysis and planning, operational management and services which includes seed planning, seed registration, seed storage, seed selection and use, seed transfer, management of information services and communications, 3 monitoring and training systems (Timberline Forest Inventory Consultants Ltd., 2006; Tree Improvement Branch, 2007). The spatial scales for GRM include Seed Planning Zones (SPZ) and seed planning units (SPU). SPZs are geographic areas where trees are similarly adapted to the local environmental conditions (Watts and Tolland, 2005).  SPUs are geographic units within each SPZ that form the lower operation spatial units for seed production (Timberline Forest Inventory Consultants Ltd., 2006; Tree Improvement Branch, 2006).  SPUs are categorized by their SPZs and elevation bands, which represent genetic distinction over low and high elevation gradients. SPZs for interior spruce (Sx) represent relative homogeneity within their boundaries in terms of the responses of this species to long-term environment changes.  When delineating Seed Planning Zones, there is an assumption that native tree populations within one particular area such as Prince George (PG), have adapted to the climate and ecological conditions through long- term evolution (Morgenstern, 1996). This is tested in provenance trails for different species’ seed transfer units (Watts and Tolland, 2005).  Though species adapt to a range of climatic and environmental conditions, once their seeds are moved too far away from their original environment, there may be maladaptation risks. However, sometimes seed transfer is associated with better performance. There are still transition zones, which form the overlapping adjacent zonal areas between some major SPZs.  For example, BVP is the transition zone of the BV (Bulkley Valley) and PG (Prince George) zones, and so is the PGN zone (Prince George and Nelson zone).  Seeds are allowed to be transferred from SPZs to their transition zones.  Within SPZs, seed transfer is based on provincial guidelines aimed at minimizing the production loss (Timberline Forest Inventory Consultants Ltd., 2006; Tree Improvement Branch, 2006).  Seed transfer is the moving distance of seeds and seedlings from their parent origins to the plantation area (Morgenstern, 1996), which has legal limitations in longitude, latitude and elevation.  According to the seed transfer guidelines in BC, the SPZ boundary is fixed while the transfer is floating (Ying & Yanchuk, 2006). 4 SPZ and SPU zonal systems construct the geographical framework of seed registration, seed transfer and seed planning.  According to the Chief Forester’s Standards for Seed Use’ (Province of British Columbia Ed., 2007) each seedlot or vegetative lot information is stored in the Seed Planning and Registry system (SPAR) and is attached with identification number upon registration approval (Tree Improvement Branch, 2006).  This information, assigned to each seedlot or vegetative lot, includes attributes such as genetic class, genetic gain, area of uses and stem quantity (Timberline Forest Inventory Consultants Ltd., 2006; Tree Improvement Branch, 2006).  This information is linked to the silviculture opening level. Silviculture openings are stored on the Reporting Silviculture Updates and Landstatus Tracking System (RESULTS) (Timberline Forest Inventory Consultants Ltd., 2006; Tree Improvement Branch, 2006; Province of British Columbia, 2008). Meanwhile, these seed sources must meet the standards of testing prior to and after registration, as well as the standard of genetic quality.  These sets of standards include seed storage methods, processing, handling and record keeping.  In selection and deployment, the seeds used in public forest reforestation meet the minimum standards of genetic quality such as the number of parents in seed orchard and the genetic worth (Timberline Forest Inventory Consultants Ltd., 2006; Tree Improvement Branch, 2006).  The standards also make sure that these seeds and vegetative materials can represent their seed sources to reduce the risk of genetic maladaptation.  Genetic diversity of plantations is monitored under the FREP (Forest Resource Evaluation Program) (Province of British Columbia, 2007a), while seed use practice and standards are evaluated at the stand, operational, and landscape levels.  Species diversity and forest health are also under monitoring.  Thus, the GRM system provides a framework to increase genetic gain, resilience and resources conservation, which supports forest stewardship and seed planning, timber supply analysis and operational reforestation at multiple geographical scales. 1.1.3 Brief history of GRM in BC The history of GRM in BC begins in the 1940s.  In 1946, initial Seed Planning Zones were delineated only for Vancouver Island and the south coast of the mainland, but not for the interior. 5 In 1962 the Seed Planning Zone and seed collection zone maps were made for Douglas-fir with seven seed zones in the coast and interior BC (Figure 1).  This was the first SPZ for a specific species.  In May 1974, Forest Tree Seed Zones were created by the BC forest service.  Sixty- seven numerical seed zones within eight regions were established for seed collections (Table 1). This progress helped foresters to collect seeds for each species.  Figure 1 Douglas-fir seed zone map for Canada (adapted from http://www.for.gov.bc.ca/HTI/spar/chronologySZ/Fd_zones.jpg, May 31, 2008) 6 Table 1 Forest Tree Seed Planning Zones in BC (1974) (adapted from www.for.gov.bc.ca/HTI/spar/chronologySZ/Seed_Zones_1974.jpg, May 31, 2008) In 1986, after an extensive review lead by the Silviculture Branch, Ministry of Forests, the interior Seed Planning Zones were revised as seven southern zones and fourteen central-northern zones. Maps of the BC interior Seed Planning Zones were available in hard copy format at a scale of 1:600,000 (Figure 2).  The zones are delineated based on the ecological boundaries, topography characteristics and Forest District boundaries.  In the same year, the Coastal Douglas-fir Seed Zones and Seed Transfer Rules were released as the milestone of transfer guidelines in BC.  At that time, however, PG zone was not formally delineated yet.  There are neither improved seed SPZs nor seed orchards operated at that time. In 1987, the Seed Planning Zones and Transfer Guidelines for Interior Spruce and Lodgepole Pine were established by the Silviculture Branch in order to assist foresters in selecting proper seedlots.  In July 1989, the Interior Seed Transfer Guidelines for Cone Collection Planning and Seedlot Selection was released based on progeny and provenance trials records.  The plantation structure of Sx genetic classes changed significantly after 1987.  The next historical node was 1995.  The Forest Practices Code Seed and Vegetative Material Guidebook were created and it referenced the Seed Planning Zone maps for the BC Interior consolidated in 1986 and for the Coast in 1989.  It was suggested that all forestry practitioners need to use detailed SPZ maps for operating areas, because it set up the requirement for collecting, using and transferring seed and vegetative material in GRM.  Meanwhile, the legal framework for reforestation was documented in the Forest Practices Code of BC Act and the Silviculture Practices Regulation.  The creation of new digitized seed planning zones for genetic class A seed including SPZ_A_SX PG established in 1996 with a review process, which is the study area of the thesis. Orchard 214 was designed to provide superior seedlings for SPZ_A_SX PG zone plantation. Region Number of zones West Coast (1000) 16 Southern Dry(2000) 6 Interior Wet (3000) 11 Kootenay Dry (4000) 3 Central Dry (5000) 8 Sub-boreal (6000) 10 Boreal (7000) 8 NW Plateau (8000) 5 7  Figure 2 Natural stand Seed Planning Zones (adapted from http://www.for.gov.bc.ca/HTI/spar/chronologySZ/Natural_Stand_SPZ_Map.jpg, May 31, 2008) In 2001, GRM (Orchard) Seed Planning Zone maps (pdfs based on 1:500,000 BEC maps) were made available.  And in 2005, the GIS files, Chief Standards for Seed Use SPZ Spatial Data file folder was published on the Tree Improvement Branch (TIB) website.  The natural stand SPZ data sets were available, even earlier, through the TIB link for clientele use in 2004.  For the transfer guidelines, there have been nine major updates for the Seed and Vegetative Material Guidebook since 1995.  For the ‘Chief Forester’s Standards for Seed Use’, there are continued periods of preparation and refinement before they come into operational effect.  These standards were generated by referencing Seed and Vegetative Material Guidebook, which includes new limits and suggestions for seed collection, seed transfer and seed deployment.  In 2006, amendments to the Chief Foresters’ Standards were made via appendices.  At present, there is 8 web-based SeedMap of BC for tree improvement information requisition and communication, which is a milestone in the history of GRM in BC (Figure 3).  Figure 3 SeedMap in BC (adapted from http://webmaps.gov.bc.ca/imf5/imf.jsp?site=mofr_seedmap, Oct 10th 2008)  There are two key organizations involved in GRM in BC the Tree Improvement Branch (TIB) and the Forest Genetics Council (FGC).  The TIB is responsible for management information services and management operations, while the FGC focuses more on GRM strategic planning and collaborative projects (Province of British Columbia, 2007c). The TIB coordinates and is responsible for the Genetic Resources Decision Support (GRDS), subprogram, which develops projects and decision support tools for the long-term stewardship and sustainable resource management of the tree genetic resources for the future of BC.  Its functions include: • Providing the planning and analytical support at the strategic level for resources management. • Building the GRDS framework for land use and forest stewardship plans and regeneration plans. • Managing and maintaining GRM datasets and registries. 9 • Providing information access for tree improvement products. • Developing monitoring systems with various criteria and indicators for GRM. • Integrating genetic gain in timber supply analysis by using GIS. In the strategic planning phase and operational phase, the TIB contributes significantly to GRM in several ways: • Creating standard and methods for spatial planning of GRM. • Developing seed plans for addressing relevant forest management issues including seed demands and seed shortage, which is important for forest industries, seed users, seed producers, and other clients. • Providing access to SeedMap and tutorial for clients. • Providing web based Seed Planning and Registry Application (SPAR) services and training programs. The TIB not only conserves forest gene resources, and improves adaptive potentials and tolerance of forest trees and genetic diversity in BC, but also provides genetic information services, seed transfer guides, consultancies and GRM monitoring. Forest Genetics Council of British Columbia (FGC) is a guiding and coordinating forum appointed by B.C.’s chief forester for tree improvement plans and activities.  It is responsible for the strategic planning, management objective setting and business plan fulfillment in GRM in BC (Woods, 2006).  There are several groups of stakeholders in FGC: the Ministry of Forests and Range (MoFR), TIB, seed producers, seed users, universities, and the Canadian Forest Service and forestry industries representatives. The Ministry of Forests and Range is responsible for the stewardship of the forest tree genetic resources in BC. Its responsibility is stated in the Forest Act, Forest and Range Practices Act and the Chief Forester’s Standards for Seed Use as well as associated regulations procedures (Province of British Columbia, 2007c).  The goals of FGC may change from year to year based on their strategic plans.  For example, in FGC Strategic Plan (2004-2008), the following goals were set: 10 • Increase the average volume gain of select seed deployed for plantation to 20% by 2020; • Increase the improved seed for plantation using to 75% on provincial scale by 2013; • Continuing gene conservation research and developing the project of cataloguing of in situ forest genetic resources; • Coordinate stakeholder activities and secure resources to meeting business plan priorities; • Monitoring progresses in GRM. • Constructing the business plans for GRM with broad communication within different members in GRM. FGC creates tighter relationships among the multi-stakeholder forest management participants involved in the planning and GRM activities.  The FGC program manager is in charge of the Forest Investment Account Tree Improvement Program and plays a leading role in the strategic advisory process for GRM society. For other parts of the world, genetic resources management is conducted slightly differently compared to GRM in BC.  For example in Finland, where forests cover about 80% of the total land area, gene diversity is the most important component of forest genetic resources management (Rusanen et al., 2004). 1.2 Brief review of GIS applications in GRM and related fields GIS which is the acronym of Geographical Information Systems or Geographical Information Science, is a tool to use and manage spatial data.  It integrates geographically referenced data along with non-spatial data and includes operations as spatial analysis.  The primary goal of using a GIS is to support decision-making processes, such as planning and managing land use, resources, transportation, and forest Genetic Resource Management seed planning.  GIS has been applied in natural resources management for various uses such as cataloguing genetic 11 resources in BC, gap analysis in US, and other similar research in ecology and forest management. Geostatistical methods and genecology research help to improve seed transfer for GRM. Hamann et al.  (2000) use GIS and spatial statistics in developing the seed transfer guidelines and seed zones according to the interrelationship within genotype and environment conditions. Although they only conducted this research on one species (Alnus rubra), it is a good precedent for applying GIS tools in other GRM related studies.  Hamann et al.  (2004) used GIS based methods to catalogue the in situ protection level of genetic diversity of eleven commercial conifer tree species in British Columbia.  In situ protection can ensure the sustainability of the population size and its adaptability to radical environmental changes.  They investigated whether these tree populations were well represented in these protection areas.  They used maps of Seed Planning Units (SPU) and forest inventory data to evaluate how well the species are represented in the protected area (Hamann et al., 2004).  They also used Seed Planning Units (SPU) data, BEC zone data, and forest inventory data (timber supply area) to evaluate the gaps of in situ protection for GRM.  They provided methodology of conservation assessment at landscape level rather than stand level.  Their species scope focuses on commercial conifer species while there are 50 tree species in BC.  Their method sets up guidance for GRM field reconnaissance and conservation, while the detailed boundaries of SPU and BEC they used need to be updated. Besides genetic resources research, GIS is also applied in ecological conservation, such as GAP analysis, which has been well developed in US since 1987.  It focuses on the method, which identifies which species are not adequately represented in the conserved areas.  The national scale program offers a regular resource for producing gap information for different species (Jennings, 2000), which is called the National Gap Analysis Program (GAP).  The definition of “conservation gaps” was first developed by Burley (1988), indicating “the elements of biodiversity not sufficiently represented in conserved areas”.  The fundamental assumption for gap analysis related to the best period for protecting the endangered species, which occurs before the population has decreased to the point of endangerment (Jennings, 2000).  To achieve this, GIS 12 analysis plays a key role, allowing for computation of large spatial data sets and integrating spatial data for habitats, hotspots and reserves studied (Jennings, 2000). Lipow et al.  (2003) carried out a gap analysis of conserved genetic resources in the forests of US in the coastal regions of Oregon and Washington.  They evaluated whether large populations of each species in reserves were protected and how the areas with smaller tree populations are conserved.  They employed the coverage data of protected areas, aerial photography data, field reconnaissance, and potential vegetation data in the research forests, Landsat Thematic Mapper digital data and Oregon GAP landcover data.  They developed and assessed the species distribution spatially.  They applied gap analysis at seed and breeding zone and ecoregion levels. The effectiveness of gap analysis is helpful for gene resources monitoring such as conifers at the landscape scale.  However, the quality consistency of spatial data and population size calculation may bring potential errors in the final results (Lipow, et al., 2004) There are other applications in forestry and related fields as well.  Baker et al.  (1997) applied GIS to model tree population parameters in forest-tundra ecotone (FTE).  The tool they used is the GRASS geographical information system.  The parameters they investigated include seedling density in patch forest and krummholz openings, as well as annual krummholz height growth. They addressed that the population parameters extrapolated spatially may provide a useful guide to the future change spatial distribution.  Powell et al.  (2005) applied GIS modeling to construct the habitat distribution for endangered species in Australia.  Bateman and Lovett (1998) used GIS and large area data bases to predict the Sitka spruce yield class in Wales.  Ji and Leberg (2002) applied GIS to assess the regional conservation status of genetic diversity in south Appalachians. High quality GIS source data is critical for analysis.  The Tree Improvement Branch offers good public data sources of seed deployment with various attributes related to silvicultural and genetics information.  However, there are often errors in spatial and aspatial data processing which will affect the final results.  There are several sources of error, data collection errors, errors introduced through the combination of different data types, errors of result interpretation.  It is necessary to understand these error generating processes and reduce their occurrence.  Scales 13 and boundaries are also important in analysis because different data sources may not be compatible with each other.  Therefore, it is critical to realize these problems in any GIS applications.  Finally, it is critical to embed the method well into the research context, which may affect the results of analysis significantly. 1.3 Climate change and GRM Climate change is a long-term weather change that has significant impacts on our society and natural environment.  According to EUFORGEN climate change report (2007), in some locations such as Europe, the frequency of storms, and the mean annual temperatures compared to its historical range, the drought impacts, and pest epidemics will turn out to be very challenging to forestry sector and other social communities.  With the emergence of increasing uncertainties and extreme weather events caused by climate change, it is likely that biodiversity and natural populations of vulnerable tree species are facing risks in adaptation.  In order to conserve the sustainability of forestry development, improving the adaptability potential and the resilience of forest trees is essential for solving climatic crisis puzzles. Changes in climate can affect forest ecosystems in unpredictable ways.  However, the mechanism of these disturbances and process of the impacts are not well explained.  Wang et al. (2006) investigated the growth response functions developed for lodgepole pine populations with observations from comprehensive provenance trials.  Their climate model for genecology can give reliable guidance for seed transfer, seed production and planning.  With the scale-free climate model “ClimateBC” and other models related, Wang’s group can simulate the differences of the species ranges of distribution between the present and future, which indicates the potential impact and consequences in BC forests.  Though there are limitations of the research, such as small-scale and other lack of information from biological ground truthing, it may still lead to a better understanding of uncertainties between climate and the forest changes on both long-term and short- term time scales.  Thus this research may help us to minimize the maladaptation caused by such environmental change (Rehfeldt et al., 1999). 14 In Canada, the strategies for facing maladaptation risks of forest tree species caused by climate change are still necessary.  As biodiversity stress is not well represented in the Kyoto discussion (Noss, 2001), the potential loss of species may be higher than expected, if a resilience strategy is not applied.  The present forest ecosystem is the result of the long-term process of natural and artificial selection (Koskela et al., 2007).  However, unstable, uncertain and rapid changes in the natural environment may give rise to massive maladaptation in some locations, especially where the biodiversity is already under greater stress.  From the fossil records of vegetation species, rapid change of environment, may lead to mass extinction of species (Graham, 1999), and the development of new ecosystems.  Therefore the diversification of natural species gives more potential of forests’ ability for returning to the equilibrium state of the existing species.  Within one species, for example, different varieties and different populations may not respond similarly or an environmental condition change such as drought.  Some populations may suffer from maladaptation, while others may be more adaptive.  Therefore, the geographical range of species may suffer from boundary shifts during the process of climate change (Koskela et al., 2007).  Also, it is reasonable to emphasize the importance of some well adapted populations in order to optimize them for future use.  Thus, diversification and a flexible management method is like the wise course in the sake of rapid climate change (Koskela et al., 2007). GRM in this context refers to the adjustment and management of genetic resources in the response to climate change effects in ecological, genetic, economic and societal benefits (Koskela et al., 2007).  It is necessary to update seed transfer and seed deployment guidelines under the emergence of possible seed planning boundaries changes (Ying, 2006).  Latest seed deployment and climatic data are valuable for both short-term and long-term analysis.  Secondly, the studies of the relationships between climate and biomes are more important, which helps to revise the climatic model for forecasting tree habitat shifts.  Thirdly, the long term monitoring of forest health and productivity is also necessary.  Geographical information will play an important role in monitoring of forest tree populations in the natural environment from seed planning stage to the tree performance stage.  Finally, the modeling for climate change research needs high resolution data on both spatial and temporal scales. 15 1.4 Objectives and overview of the thesis This thesis aims to provide a GIS-based approach to track the temporal and spatial changes of seed deployment in BC.  It is an exploratory investigation to improve the understanding of spatiotemporal patterns of silviculture activities and forest regeneration.  GIS is employed to examine the trends and impacts of historical seed deployment with both tabular and map representations.  More detailed objectives of the thesis are as follows: • Provide an overview of the historical development of GRM activities in BC, GIS applications in forest genetic resources management and evaluation based on the technical scope set in the GRM context with other policy and operational background information; • Create GIS methodology for investigating spatiotemporal trends in GRM data that is portable to other species and SPZs; • Conduct an exploratory analysis of interior spruce (Sx) trends in genetic composition (seed selection), geographical distribution and plantation intensity, frequency, extent and discuss the possible causes of these trends. 16 2 Technical scope The technical scope of this research is limited in the following ways.  The interior spruce data are derived from the Reporting Silviculture Updates and Landstatus Tracking System (RESULTS). The geographical range is within Prince George (PG) SPZ for interior spruce.  The scales include silviculture openings, SPUs, SPZ and BEC zone level.  The time frame is from 1970 to 2004 for background report, and from 1995 to 2004 for PG zonal reporting.  The genetic source is categorized into natural regeneration and planted (‘A’ class, ‘B’ class and ‘B+’). 2.1 Species studied Interior spruce (Sx) has been identified as the species of primary interest because of its economic importance, which is one of the three top commercial tree species in the interior of British Columbia. It is also significant in the genecology program of tree species with well-established research, tree breeding and tree improvement programs. Within the reporting period (1995-2004), Sx has better data integrity and quality. The first interior spruce seed orchard crops for commercial use were produced in the early to mid 1990s, for example, Orchard 209 of Kalamalka, in 1990, Orchard 206 of Skimikin in 1993, Vernon Seed Orchard Company (VSOC), orchard 214 after 1994 and orchard 211 in 2002. Seed orchard 214 is an important seed source for reforestation in PG area for interior spruce. Interior spruce is an operational name for the white-Engelmann spruce complex. The two species ranges overlap and they lack reproductive barriers, thus they hybridize. White-Engelmann spruce occupies the same ranges and their offspring are pure or hybrid performing well on their sites. White spruce (Picea glauca) is distributed in the northern part of North America, which covers much of interior British Columbia and Alberta. Engelmann spruce (Picea engelmannii) is also native to North America. In montane areas the species can distribute from 1,000 m to 3,000 m area. Figure 4 shows the geographical range of these two species, in which the green colour illustrates the region of existing populations. They cover a large percentage of area of the northern part of North America as well as some subalpine areas in Middle West in the United States. 17  Figure 4 Illustration of interior spruce distribution in North America. Green area shows the Picea glauca and Picea engelmannii distribution. (Adapted from www.efloras.org, on March 21st, 2008)  2.2 Geographic scale The study area comprises the area within the Prince George (PG) breeding population, comprising the Prince George Seed Planning Zone (SPZ), and the Prince George Nelson (PGN) and the Bulkley Valley Prince George (BVP) transition “overlapping” zones. Associated Biogeoclimatic Ecosystem Classification Zones (BEC zone) and seed planning units (SPU) are also included as part of the study area. However, for the initial exploratory study, the geographic scale is focused on the PG zone only. The forest management operating area is identified through Silviculture Openings (by opening ID), excluding those openings contained within Tree Farm Licenses and Woodlots. In this study forest management includes the area bounded by both natural regeneration and planting reforestation treatments. Most of the results show the capacity of the method for mining data and reporting, so using PG is better for this deeper 18 analysis because it is a smaller and less complex dataset. The modeling method is applicable to other zones. 2.2.1 Silviculture opening level Openings are former cut blocks or open areas resulting from treatments such as spacing, which are also potential reforestation areas.  Openings may also be referred to as reforestation blocks. In RESULTS, the disturbance or silviculture activities in the openings are recorded. These data are collected via the Electronic Submission Framework (ESF), which keeps the opening information updated for clients of Ministry of Forests and Range in BC. Openings are the smallest unit for seed deployment used in the research. The silviculture activity and natural regeneration are all observed at the scale of openings and recorded by openings and years. The opening data are stored in the LRDW, where spatial information is integrated for different Ministries for collaborative projects. Meanwhile, seed deployment information is recorded in RESULTS. Each opening has an opening ID, which ranges from “-63150000” to “1097331”. There are two types of openings in the datasets, spatial openings and non-spatial openings. Data from the LRDW are spatial openings, which have geometry information for each record. The area of these opening varies, from less than one hectare to more than 700 ha. Non-spatial openings also have silviculture activity or forest cover recorded without having polygon data associated. Only spatial data was analyzed in this study. The results focus on the reported plantation area rather than the opening area as the plantation area may be different than the opening area. The map below shows the example of spatial openings in PG SPZ_A. The digits for each opening are its opening ID. 19  Figure 5 Examples of spatial openings in PG SPZ_A Seed Planning Units (SPUs) are different elevation bands within SPZs.  Each commercial tree species has its SPU within its Seed Planning Zone (SPZ).  The SPUs are different elevation bands within SPZs.  In PG zone, there are SPU PG high which is from 1,200 m to1,550 m as well as SPU PG low which is from 600 m to 1,200 m.  For PG high and PG low, different seeds are used.    Forest Genetics Council (FGC) Species Committees develop species plans for SPUs with the highest expected return of potential economic benefits.  Usually, seed sources of higher genetic gain are preferred for each SPU, to ensure better productivity.  The figure below shows all the SPUs in interior BC for Sx. 20  Figure 6 The distribution of SPUs of Sx in interior BC. 2.2.2 Seed planning zone Seed planning zones play a key role in GRM.  SPZs are the geographic units that indicate genetic similarity.  They are also used to describe the unique breeding populations for the tree breeding programs.  In interior BC, there are SPZs for five major species for ‘A’ class (seed orchard program), which are composed of interior spruce (Sx), interior Douglas-fir (Fdi), interior lodgepole pine (Pli), western larch (Lw) and western white pine (Pw).  There are overlapping SPZs for most interior orchard species except western white pine.  For Sx, there are thirteen SPZs in BC. According to the table below, the total area of PG SPZ_A is 9,417,692 ha.  PGN is 3,598,540 ha and BVP is 1,933,980 ha.  The total area of SPZ_A Sx is 103,086,016 ha.  In PG zone, PG low (600-1,200m) and PG high (1,200-1,550m), seed source reflects geographical variations in forest 21 recovery.  Other SPUs are out of the 600-1,550 m range, with fewer plantations, which are called unclassified SPUs.  And in Figure 7, the territory of PG within BC is illustrated, with PG low and PG high highlighted in grey and dark colours. Table 2 SPZ_A Sx area (adapted from http://www.for.gov.bc.ca/hti/spar/help/SPR107.htm, Oct. 10th 2008) SPZ_A Sx code SPZ_A_Sx name Area (ha) ZND Zone not defined 26,047,336 M Maritime 20,123,411 PR Peace River 12,247,639 PG Prince George 9,417,692 TO Thompson Okanagan 8,316,446 SM Submaritime 8,093,140 NE Nelson 5,399,909 PGN Prince George / Nelson 3,598,540 EK East Kootenay 3,064,883 BV Bulkley Valley 2,939,702 BVP Bulkley Valley / Prince George 1,933,980 TON Thompson Okanagan / Nelson 988,520 NEK Nelson / East Kootenay 914,818 Total area (ha)  103,086,016 22  Figure 7 Interior spruce seed planning units in PG Seed Planning Zone. ‘High’ is PG high, and ‘low’ is PG low. 23 2.2.3 BEC zone BEC zone system was created to develop a ‘permanent’, land-based, ecological classification, which organizes the knowledge of the natural environment as a basis for managing natural resources (Watts and Tolland, 2004).  It has three levels combined together: vegetation classification, zonal classification, and site classification.  Climatic, geographical, biological and ecological factors are dominant environmental conditions of tree species distribution.  BEC zones classify the PG zone into ten different sub zones.  The Sub-Boreal Spruce (SBS)and Engelmann spruce –Subalpine Spruce (ESSF) zones overlap with SPU low and SPU high, which are the dominant BEC zones covering PG (Figure 8).  These BEC zones represent the ecological and environmental boundary of interior spruce natural populations.  It is a solid land basis for gene resources management.  By using the BEC zone system, it helps to reference the management regions of different species in the breeding program.  More detailed information of each BEC zone is listed in Appendix 2. 24  Figure 8 PG SPZ in the BEC zones of BC.  25 2.3 Reporting period The reporting (and analysis) period comprises a timeframe of approximately 10 years (1995 – 2004).  This time period was selected based on best available information, the availability and integrity of attribute data and spatial data.  The background report from 1970 to 2005 shows that the integrity of data records after 1990s improves significantly, as there are much fewer blank records in the attribute tables.  This time period is also indicative of a recent increase in higher gain Sx PG seed use, resulting from seed produced from the VSOC seed orchard. 2.4 Genetic sources The study reports on four different genetic sources of seed, deployed through natural regeneration and planting (seedlots).  It does not include an analysis of deployment originating from vegetative material (vegetative lots).  The genetic origin of seedlots includes seed sources from both natural stand (wild stand collections) and orchard (tree improvement program collections).  There are four genetic classes studied here, ‘A’ class (from seed orchard seeds), ‘B+’ (from superior natural stand seeds), ‘B’ class (from wild seeds or the seeds from natural stands), and NR (Natural Regeneration).  26 3 Methods and data analysis The analysis was conducted using ArcGIS 9.3.  The data input can be changed for different SPZ, SPU or other species as this method was developed to be deployed beyond its current technical scope.  However, because of the limitation of time, the method is only verified within PG at present.  Further research is necessary to improve and refine the model to verify its ability to be portable to other species or regions.  Data preparation includes extensive data and error checking to ensure project integrity and quality assurance.  Spatial modeling is the major part of methods, which uses GIS tools to extract useful information for seed deployment and GRM operations. 3.1 Data sources 3.1.1  Spatial data sources The spatial data includes BEC zone data, SPZ and SPU geometries, and opening geometries. BEC zone coverage is from the Ministry of Forests and Range (www.for.gov.bc.ca). LRDW’s openings, SPZs and SPUs are also important as spatial data sources. The details of these data are listed as below: Table 3 Spatial data sources and geometry Spatial data items Data sources and versions Data geometry Biogeoclimatic ecosystem classification (BEC) zones Version six MoF (10 July 2006) BEC V6 LRDW openings LRDW (April 2007) open_north_polygon open_south_polygon LRDW Seed Planning Zones LRDW (April 2007) spz_sx_polygon LRDW seed planning units LRDW (April 2007) spu_polygon The LRDW data are converted from coverage files. BEC zones define the hierarchical and ecological boundaries. Openings are the logging and silviculture areas, where spacing and reforestation can be applied. SPZ is Seed Planning Zone polygon, which is the spatial range for seed production and deployment. SPU is the subzone polygon of elevation within SPZs. More details about these spatial data are summarized in Table 4.  These are based on the metadata of these feature classes, which are imported into the working geodatabase. 27 Table 4 Metadata of Spatial data sources. The projected coordinate system is NAD_1983_Albers. Geographic coordinate system name is GCS_North_American_1983. Dataset Format Main Attribute  (The data type is shown in the parentheses.) spu_polygon Data format: Personal GeoDatabase Feature Class  File or table name: spu_polygon feature count: 182697 Shape (Geometry) OBJECTID(OID) AREA (Float) PERIMETER (Float) SPU_Sx (Character) SPU_ID (Integer) Shape_Length (Double) Shape_Area (Double) spz_sx_polygon Data format: Personal GeoDatabase Feature Class  File or table name: spz_sx  count: 1213 Shape (Geometry) OBJECTID(OID) AREA (Float) PERIMETER (Float) SPZA_Sx_CODE (Character) SPZ_Sx_ID (Integer) Shape_Length (Double) Shape_Area (Double) open_north_polygon  Entity type Feature Class Entity type count: 83519 FID (OID) Shape (Geometry) AREA (Float) PERIMETER (Float) OPENING_ID_NORTH (Binary) open_north_dave Entity type Feature Class Number of records: 60620 Shape (Geometry) OPENING_ID_NORTH (Binary) OBJECTID(OID) PERIMETER (Float) PERIMETER (Float) AREA (Float) OPEN_NORTH_ID (Integer) Shape_Length (Double) Shape_Area (Double) open_south_polygon  Entity type type: Feature Class Entity type count: 133857 FID (OID) Shape (Geometry) AREA (Float) PERIMETER (Float) OPENING_ID_SOUTH (Binary) Biogeoclimatic ecosystem classification (BEC) Version 6 zones  Entity type type: Feature Class Entity type count: 12194 Shape (Geometry) FCODE (String) ZONE (String) SUBZONE (String) VARIANT (String) PHASE (String) BECLABEL (String) OBJECTID(Number) 28 3.1.2  Non-spatial data sources The non-spatial data is the tabular data of opening attributes.  The silviculture and forest regeneration data are all recorded separately in this format.  This data is restricted on the technical scopes of the project using common query tools.  The data is the record of silviculture materials for the reforestations of openings.  Natural regeneration data maintain the species, opening ID, time of plantation, and regeneration area.  The silviculture opening data is called planted, which maintains opening ID, species, year of plantation and area, tree numbers.  The table below summarizes the data sources in detail.  29 Table 5 Non-spatial data sources summary. Non-spatial data Dataset Field Other information Natural_dave-coster M Unit Year YR_ROLLUP ROLLUP_REGION_CODE SILV_TREE_SPECIES_CODE SPP_ROLLUP ZONE SPZ-A-Fdi SPZ-A-Pli SPZ-A-Sx SPZ-B Area Spatial? Data sheets: 'natural_dave- coster$'; Source: LRDW (April 2007); Query: SQL is to choose the Sx openings within PG SPZ_A_Sx from 1995 to 2004; Original data set of forest regeneration with all species and SPZs Planted_dan-turner ID Year ROLLUP_REGION_CODE OPENING_ID SEEDLOT_NUMBER Orchard # SILV_TREE_SPECIES_CODE spatial_bgc_zone results_bgc_zone SPZ-A-fd SPZ-A-Pl SPZ-A-Sx SPZ-B Gen_class genetic_worth_code genetic_worth_rtng SumOfarea_ha SumOfNUMBER_PLANTED Data sheets: 'planted_dan- turner$’; Source: LRDW (April 2007); Query: SQL is to choose the Sx openings within PG SPZ_A_Sx from 1995 to 2004; Treated (planted) opening data with all species and SPZs. Planted_spatial and non- spatial _Leslie As above Filtered data set of treated (planted) based on the technical scopes, with spatial and non-spatial openings labelled separately. Natural _spatial and non- spatial _Leslie As above Filtered data set of forest regeneration based on the technical scopes, with spatial and non-spatial openings labelled separately.   30 3.2 Data preparation 3.2.1 Error checking and data cleaning A project geodatabase was developed comprising attribute data and spatial data sets for the area of study.  The geodatabase is used to store, organize and manage the data inputs and outputs. There are three types of geodatabases in ArcGIS: Personal Geodatabase, File Geodatabase and ArcSDE Geodatabase.  The project selected the second one, for the data size limit of Personal Geodatabase. Data cleaning ensures the quality and integrity of the input data, which is important for both spatial data and tabular data.  For example, duplicates of openings with the same OP_ID are not allowed in the spatial data, because OP_ID is regarded as the Primary key of the database. Specifically, there are three types of data errors that needed to be dealt with: opening duplicates, null records, and zero records. Duplicate records for spatial openings are converted to multipart openings, which uses OP_ID (opening ID) as the primary key. For example, opening “-739000000” in planted openings. In the attribute dataset, opening duplicate refer to repeating silvicultural activities within the same opening area, which is maintained for further analysis.  The Null data and “0” data are treated differently. Null records are kept. Null records of genetic class report the plantation activity without genetic cla ss records assigned.  Zero records means there is no plantation activities in the opening, which is suitable to be eliminated in the analysis. After dissolving the opening polygons, there are the multi-part openings created.  Multipart openings are smaller openings (polygon features) not adjacent to each other, which have the same opening ID (OP_ID) and are treated as one item in the attribute data. In Figure 9_a, all the openings shown are multipart openings, the darker colour the higher the frequency of small openings sharing the same OP_ID.  In the snapshot, it shows 3402 black openings share the same opening ID.  In Figure 9_b, openings with the same OP_ID are assigned with the same colour, in the same location as Figure 9.  These Multipart openings are able to keep their opening 31 ID as their primary keys after being dissolved. It is also clear that these black ‘smaller openings’ share OP_ID as zero which may distribute far away from each other. Opening ID ‘0’ is not allowed in the attribute data for further analysis, as there are no silviculture activities reported within it. Thus ‘0’ openings to be eliminated will not affect further steps.  a b Figure 9 Occurrence frequency of openings with the same opening ID (a snap shot as an example). 3.2.2 Spatial data preparation The spatial data preparation is a major part of the process of this work.  These processes extract the input spatial data and attribute data as well as limiting the study area for GIS modelling analysis.  It consists of three steps: dissolve, merge and eliminate.  Each step is based on one ArcGIS tool in the Toolbox and involves changes in geometry as well as attributes for the purpose of reducing data uncertainties and for producing better quality reports. Dissolve is used to aggregate opening polygons based on their opening ID (OP_ID), which can be used as the primary key of the opening layer.  Then it can be joined to the non-spatial data 32 based on the primary key, OP_ID. By using Dissolve, it is possible to remove the boundaries among the multi-part openings.  After this step there are not any OP_ID replicates among all the openings.  So the OP_ID can be regarded as the primary key in the latter steps. In ESRI documentation, the output feature classes with multi parts are also regarded as Multipart features, which is created by Dissolve. It is a single feature that comprises of discrete parts (e.g. ‘smaller openings’), which is assigned as one record in the attribute table.  By generating Multipart features, the shape area and shape length are recalculated in Dissolve.  In the meantime, a new object ID is assigned to each opening.  For example, before Dissolve, there are 24,606 openings in the opening layer imported in PG and 24,551 openings after. The openings in PG come from two distinct opening data sets in the source data, North and South.  Merge is a tool to combine two input layers, specifically the north opening layer and the south opening layer, as the PG area is located on the border of the north layer and the south layer. By using Merge, the openings in PG area can be grouped together in one layer.  The opening attributes are kept in the output as well.  However, the opening ID of North and South are stored separately in the output. In order to keep the opening ID, it is necessary to create a new field called “OP_ID” to store both of them.  The Eliminate tool is then used to generate polygons within the study boundary.  The openings with “0” area are eliminated as well as the openings with “0” OP-ID as they are redundant openings for analysis. 3.3 Spatial modeling and analysis Data analysis is conducted in ArcGIS’s model builder.  There are five major steps involved in the spatial modelling, clip, feature to points, identity, table join and frequency.  The object of spatial modelling is to combine the spatial and non-spatial data to summarize the silviculture and natural regeneration results for spatial openings.  This is the essential part of the methodology, which allows foresters, tree breeders and forest managers to keep updating the seed deployment situation in a specific SPZ.  The limitation for the analysis is based on the technical scope, which narrows down the spatial and temporal scope for this exploratory research. 33 3.3.1 Clip In this step, the polygons for the study area are extracted.  After clipping, the dataset will be smaller in volume, which makes it faster for processing.   Clip is to cut out the opening polygons using SPZ-PG polygon into another polygon layer, which only has openings within the PG area. The figure below shows the illustration of clip.  Figure 10 Illustration of clip process. In clip, the attribute table of opening layer is maintained for the input dataset, while the amount of openings decreases.  Because there are transition openings, which lie on the boundary of adjacent SPZ, e.g., PG and PGN, PG and BVP, it is possible to cut those openings into smaller polygons.  In this case, it is not very accurate to use the smaller opening to represent the original 34 input openings, as the areas are different after clipping.  It is possible to use point layer instead of polygon layer to do clip to solve this problem, as centroids can be used to represent the opening locations and are not susceptible to area reductions.  Clip is also used to generate BEC zone layer in PG. 3.3.2 Feature to points This step creates the point feature class for openings in PG.  There many reasons to convert polygons into points. First, point features take less space and energy to process in ArcGIS, which can make the analysis faster.  Secondly, polygons have more complexity than point in geometry, which may overestimate the deployment in later reporting.  For example, in Clip, it is common to create about 700 broken transition polygons, which lie on the boundary of PG SPZ and these openings will maintain the OP_ID, while they are not in the actual opening size for planting records.  Thirdly, the points feature class is better suited for the spatial statistics analysis, such as generating generating probability surfaces based on the actual silviculture and natural regeneration data.  A point representation has additional advantages in mapping the openings for visualization in later steps.  Feature to Points creates a point opening based on the input polygons opening.  The attributes of input features are present in the output points.  The points are all the centroids of the input polygons.  35   Figure 11 Illustration of Feature to Point. The algorithm calculates the centroid of polygon to represent the input polygons.  For multi-part openings, the centroid is the average of all polygons.  For other regular polygons, the centre is the gravity centre of the opening polygon.  For the inside option, it means that points are inside polygons. There is an issue in the sequence of clip and convert.  One is to convert BC openings into points and clip the PG points afterwards.  The other is clip PG openings first and then convert PG opening polygons into points.  The second one is applied in the research.  This results in an all inclusive approach to opening within the region. However, when adapting this methodology to a multi-region analysis the former method is preferred to eliminate dual counting For example, the first method produces 24,193 openings in PG, while the second method produces 24, 606 openings. The first one processes fast, and has less errors output. For the second method there will be more potential risks of boundary errors such as sliver polygons. However, the first one loses openings because centroids of openings outside of the PG boundary are not included. As the later analysis only requires the attribute data of PG openings or the point feature of opening polygons, it does not affect the later steps and can therefore be adjusted on a case by case basis. 36 3.3.3 Identity Identity is a tool in the Overlay toolbox, which overlap the input layers into a new layer maintaining the tabular information for both.  The point openings are combined with the SPU layer in PG area or the BEC zone layer.  Point layer is the input layer; other layers are used as identity layer.  This tool generates new output openings points by intersecting these two kinds of inputs.  Figure 12 Illustration of Identity. It calculates the spatial intersection of the opening points in PG and SPU or BEC zone polygons with an output as points, which have SPU and BEC zone information attached as attributes.  It keeps the attribute of identity polygons, such as the SPU codes or BEC zone labels intact.  It is a process to spatially attach the SPU and BEC zone information to the opening without OP_ID, both in geometry and in attribute table. 3.3.4 Table join and query This step joins the tabular data to the spatial data, which appends the fields of openings to the Planted and Natural regeneration data. In ArcGIS, there is a tool called “Add join” for model builder, which needs the inputs in the form of table view or feature layer. Here, the input field to link tables is opening_ID in the Planted table. The associated field in the opening layer is OP_ID, which are the same items. This field is also called foreign key, which is able to connect different attribute tables. This connection is based on a one-to-many relationship. Even though the records 37 in both tables may not match all the time they will all be kept in the output. The figure below shows the input and output of this tool as an example.  Figure 13 Illustration of table join process. The column with italic fonts is the foreign key.  The Null records in the output table mean that there is no such an opening matched with Planted opening of the same opening ID.  Therefore, the matched rows are called spatial opening type as they exist on the opening layer and the rows attached with the silviculture information such as area and planting year.  As well, openings in tabular data are divided into two types the spatial and non-spatial. For later steps, the non-spatial records are deselected from the table, which only allows spatial openings to by the Frequency tool.  The reasons why only the spatial openings are analyzed are as following: first, the majority of openings are of spatial type, 96.51%; second, the grand total of spatial and non-spatial openings are both reported in multiple species section of result section; 38 thirdly, only the spatial opening can be represented on the map, so the analysis is more related to the map representation. 3.3.5 Frequency Frequency is used to calculate summary statistics of the table after join.  It creates a list of frequency field (e.g., “Planted year”, or “Gene_class”), while summarizing the specified field (e.g., “SUMOFAREA_”) for each item.  Each item shares different record combinations of the frequency field.  Therefore the input table has the frequency fields and the summarizing field as well.  The item includes these combinations as following: SPZ and year, Genetic class and year, SL, SO and year, SO etc.  The specified summarizing item is area or stem number.  These frequency output tables present the seed deployment results, for example, the SPZ frequency output shows the treated area or stem number for each SPZ in the input table; the Genetic class and year frequency shows different seed sources deployed in the input region.  The report is predominately based on the frequency output. All the outputs are exported as DBF IV format, which are accessible to EXCEL. 3.3.6 Spatial analysis and kernel density This part of the project is an exploratory analysis with raster based spatial analysis tools to generate probability surfaces for the spatial openings.  All the surfaces are generated by interpolation points based on the data input, which predicts the value of the cells from limited sample input data spatially. Different algorithms may have different outputs and limitations of inputs. The tool used for opening occurrence visualization is kernel density. The assumption of this analysis is based on the spatial correlation between different openings. The similarity between the predicted and known cells is related to the neighborhood distance positively. Kernel density tool creates a surface of predictive value per unit area from the point opening features with kernel function.  As stated in the ESRI help manual, the function of kernel density is to calculate a magnitude per unit area from point or polyline features using a kernel function to fit a smoothly tapered surface to each point or polyline.  Therefore, the closer the predicted cell is to 39 the sample cell, the more similarity of density they may have.  As the predicted cell becomes further away from the known point feature, the predicting value is decreasing while the distance is increasing.  According to this, the kernel density allows the map to show surface of opening occurrence within PG. The point feature of opening is the input feature.  In order to create a population field, which is the quantity to apply to the whole raster, an integer field called “occur” is created.  All the spatial openings selected will be assigned as one in the field.  Then change Extent in General Settings of environment settings as the combined PG, BVP and PGN group areas because the default raster output does not cover the whole PG, it is better to choose bigger extent for mapping. 3.4 Model description 3.4.1 Introduction By using ArcGIS Model Builder, we can prepare and analyze seed deployment data with clear work flow and accurate documentation.  The task of the model is to analyze the seed deployment data to determine the impacts of GRM operations on the forest landscape.  The model consists of these main components: • preparation of source data, which is discussed in chapter 3.1 and 3.2; • SPZ level analysis, which is Step 1; • SPU level analysis, named as Step 2; • BEC zone level analysis—Step 3; • The spatial statistics module to enhance the visualization based on density analysis. 3.4.2 ArcGIS ModelBuilder and model components This analysis is constructed with ArcGIS ModelBuilder, which is the spatial analysis module for geodata processing. With ModelBuilder, it is easy to organize, edit and configure geoprocessing tools from the Toolbox. It is also efficient to add input data and to control output data. It also helps to manipulate the display properties for output layers. With ModelBuilder, it is possible to embed 40 scripts into models to create new processing tools for more complex analysis, such as Python script (Chamberlain and Meitner, 2008). There are several major tools involved in the modeling, which are introduced in the last section, such as clip, feature to points and identity. Each model includes processing tools, input and output data in the geodatabase. The model component of each step has its Modelbuilder module, data files and geodatabase. Step 1, Step 2 and Step 3 are all built in ModelBuilder, but the data preparation is finished in ArcMap without detailed model diagrams. The input data include spatial data and non spatial data, which are produced in data preparation section. The workflow chart of each step is similar in concept, which is built upon geodatabase and ModelBuilder, overlaying spatial layer, such as SPU, BEC zone, and producing frequency analysis tables. In the flowchart below, the geodatabase provides a work space for each analysis step. ModelBuilder is the major part of model construction, which organize and process the data input. SPZ data, SPU and BEC zone data are the three main spatial layers overlayed in three steps. SPZ data defines the PG boundary and study area, which is also the spatial analysis scale of Step 1. The SPU layer provides the spatial scale for Step 2, which is the inner subsection of SPZ PG. Step 3 uses the BEC zone layer to generate overlay with the SPU and SPZ, which enables the analysis at the BEC zone scale. The frequency summary includes plantation and natural regeneration tracking by year, genetic class tracking overtime for each space scale, and other specific items such as A class seed deployment, 214 seed orchard seed tracking, seedlot deployment and natural regeneration over some management units. Future work is needed to improve the integrity and automatic ability of the model in other SPZs for other species. Each step will be introduced in later sections. The model flow charts and geodatabases are shown in Appendix 2. 41  Figure 14 Flow chart of analysis steps. 3.4.3 SPZ model This model produces the data report on the SPZ scale.  The analysis of BVP, PGN and PG, the three adjacent Seed Planning Zones (BPP), follows the same process, but is not included in ModelBuilder, which is an exploratory process on the zonal level before this step and uses ArcInfo tools to integrate the zonal opening polygon feature class and zonal silviculture report data to produce frequency analysis tables.  For this model the frequency analysis treats all openings within PG area as one spatial unit and does not divide them into finer groups.  The output DBF IV files are the sources for the tabular report and charts in the results chapter.  The table below summarizes the major variables and processes in this step (Table 6).  42 Table 6 The report of major variables and processes for the SPZ model. Items in model Process and variables Brief explanation Geodatabase Step_1test.gdb Work space; Input data PG_DissolveID (polygon feature class) Dissolved opening polygon feature class in PG without OP_ID overlapped; Planted-PGclean  (DBF IV file ) Silviculture data prepared for Step 1; NAT_OPSPT_BPPNov13 Natural regeneration data of spatial openings prepared for Step1; Process Add join  Join spatial and non-spatial data; Frequency Frequency summary; Output data PlanteDPGGCYr.dbf  Summary table of plantation in PG openings by seed genetic class and year of plantation; PlanteDPGSOYr.dbf   Summary table of plantation in PG openings by seed genetic class and year of plantation; PlanteDPGSlotYr.dbf  Summary table of plantation in PG openings by seed lot and year of plantation; PlanteDPGMGUnitYr.dbf Summary table of plantation in PG openings by management unit and year of plantation; NAT_PG_Freq_SPZYr.dbf  Summary table of natural regeneration in PG openings by year; NAT_PG_Freq_SPZYrMGTU.dbf Summary table of natural regeneration in PG openings by year and by management units 3.4.4 SPU model SPU model produces the data report of Seed planning units (SPU) level analysis.  There are two sub-sections in this step.  The first one is the Identity step, which produces overlayed opening layers with SPU attributes.  The conversion of the opening feature class maintains the shape area information by adding a new column to record.  The frequency analysis is similar to the SPZ model.  However, the frequency results are summarized at the SPU level, which is on a larger 43 geographic scale than SPZ model.  The two tables below list the content of SPU model in more detail in following tables. Table 7 Model report of major variables and processes for the SPU model (1. Identity). Items in model Process and variables Brief explanation Geodatabase Step_2test.gdb Work space; Input data OP_PG_DissolveID (polygon feature class) Dissolved opening polygon feature class in PG without OP_ID overlapped; PG_SPU_Disso (polygon feature class) Dissolved SPU polygons based on the SPU name field; Process Add Field Add a new field to record the shape area of openings; Feature To Point Convert opening polygons into points; Identity Overlay SPU data and opening points; Output data PointPG_Ident_NKEEP feature class Point layer of openings in PG, after identity with SPU layer, NKEEP means no spatial relationship will be recorded for the point and polygon, such as Right _polygon or Left_polygon.  44 Table 8 Model report of major variables and processes for the SPU model (2. Frequency). Items in model Process and variables Brief explanation Geodatabase Step_2test.gdb Work space; Input data Planted_PG_clean1.dbf NAT_OPSPT_BPPNov13.dbf Attribute data (non-spatial) prepared; PointPG_Ident_NKEEP (polygon feature class) The output of Identity step; SPU feature class Dissolved based on the SPU name field; Main Tools used  Add join Join spatial and non- spatial data; Frequency Frequency summary; Make table view / feature layer Make table view/layer for join; Output data Planted_PG_Freq_SPUYEAR  Frequency summary by openings’ SPU name and year of seed deployment; PlaPG_Freq_SPUSO.dbf  Frequency summary; by openings’ SPU name and seed orchard number associated; Planted_PG_Freq_SPUSLot.dbf Frequency summary by openings’ SPU name and seedlot identification number associated; Planted_PG_Freq_SPUGC.dbf  Frequency summary by openings’ SPU name and genetic class associated; NAT_PG_Freq_SPUYEAR.dbf  Frequency summary of natural regeneration by openings’ SPU name and reporting year; NAT_PG_Freq_SPUMGTUYEAR.dbf  Frequency summary of natural regeneration by openings’ SPU name and reporting year; NAT_PG_Freq_SPUMGT.dbf Frequency summary of natural regeneration by openings’ SPU name and Management units. 3.4.5 BEC zone model The BEC zone model reports at the SPZ, SPU and BEC zone level.  The analysis of BEC zone and SPU is integrated in this step, which is produced by their spatial intersection.  The model style is similar to the former ones but divides PG openings into finer groups based on the BEC zone boundaries.  Table 9 summarizes the major variables and processes. 45 Table 9 Model report of major variables and process for Step3 Items in model Process and variables Brief explanation Geodatabase Step_3test.gdb Work space Input data Planted_PG_clean1.dbf NAT_OPSPT_BPPNov13.dbf Attribute data (non- spatial) prepared PointPG_Ident_NKEEPEp (polygon feature class) SPU feature class Dissolved based on the SPU name field BEC_PG polygon feature class Extraction of BEC zone data in PG Main Tools used  Add join Add spatial data to planted table and natural regeneration attribute table based on OP_ID Frequency Frequency summary Make table view / feature layer Make table view/layer for join Output data BEC_PGSPU_Identity polygon feature class Overlay of SPU and BEC data in PG openings Planted_PG_Freq_BECYR.dbf  Frequency summary by openings’ BEC zone name and year of seed deployment; Planted_PG_Freq_BECGC.dbf  Frequency summary by openings’ BEC zone name and genetic class associated; Planted_PG_Freq_BECSOSlotYr.dbf Frequency summary by openings’ BEC zone name, their seed orchard ID number and their seedlot identification number associated; NAT_FreqBECYr.dbf Frequency summary of natural regeneration by openings’ BEC zone name and reporting year; NAT_FreqBECMGT.dbf Frequency summary of natural regeneration by openings’ BEC zone name and Management units.  46 4 Results 4.1 Introduction This section of the thesis shows the details of the results.  The focus is the seed deployment change over time, including plantation and natural regeneration areas developed through the reported period.  There are four sections of data report, • Background report, which includes aspatial (tabular) data report for Sx in all interior SPZs from 1970 to 2004 and multispecies report in PG SPZ_A_Sx; • SPZ level report, which summarizes the genetic classes (planted) within PG zone, seed orchards seeds deployment over time in PG, and different forest cover types distributions (planted versus regeneration) over time and space; multiple species report within PG zone is also provided; • BEC zone level report, which depicts the genetic classes of planted forest area, ‘A’ class seed deployment by orchard and seedlots of 214 seed orchard, and the natural regeneration situation based on the technical scope; • SPU report within PG zone, which shows the seed deployment and natural regeneration in PG from 1995 to 2004, for different genetic classes, ‘A’ class seeds and orchard 214 seeds. The map representations illustrate the genetic resources distribution and variation spatially and temporally based on the technical scope and is an important visualization tool for data mining and analysis.  These provide a detailed comparison of silviculture stands planted and forest cover regenerated within the study area and period.  It also provides different levels of genetic quality comparison of the stands established in PG SPZ_A_Sx, which offers evidence for investigating genetic adaption though the range (area and location) and the frequency of seed use for different genetic resources of interior spruce. 47 4.2 Background tabular data report This section provides an overview of Sx openings with silviculture activity from 1970 to 2004 in interior BC based on the tabular data (non-spatial plus spatial attribute data).  There are four genetic classes reported for silviculture practices, orchard seeds (‘A’ class), natural stands or wild seeds (B class), natural stand with superior provenances (‘B+’), and data not available for genetic sources class (N).  For natural regeneration, there are total areas of forest cover reported for Sx in interior BC based on the natural regeneration tabular data.  The provincial background report is based on non-spatial data only.  This allows the dataset to maintain all opening records if they are identified with a natural regeneration history or silviculture activity.  Each report is based on the output of each model. 4.2.1 Silviculture openings The following tables and charts report the overall genetic sources trends at provincial scale, which present in the overall values and genetic class percentages by year.  Table 10 depicts the total stem planted with different genetic classes from 1970 to 1987 which shows total tree number increase greatly before 1988. And Figure 15 shows the trends of wild seeds (B), natural stand of superior provenances (B+), data not available (N) and aggregate changes overtime.  There are no orchard seeds (A class) reported before 1988, and as such they are not included in following tables and figures.  Before 1988, the ‘B’ class ratio increases mildly before the policy change in 1988.  Meanwhile, N class is the major seed sources for plantation before 1988.  48 Table 10 Number of trees (stems) by genetic source for interior spruce seed deployed (planted) across reporting period (1970-1987), British Columbia (‘Blank’ records mean no data available for that year). Year Genetic sources (stems) Data not available  (N) Total stems B ‘B+’ 1970 217,200  3,889,000 4,106,200 1971 71,000  4,799,058 4,870,058 1972 82,200  1,868,000 1,950,200 1973 594,825  4,217,000 4,811,825 1974 828,800  9,713,890 10,542,690 1975 1,324,300  15,503,900 16,828,200 1976 2,133,550  17,257,800 19,391,350 1977 939,663  19,014,000 19,953,663 1978 1,331,000  10,181,000 11,512,000 1979 1,290,600  14,921,350 16,211,950 1980 2,706,400  22,633,700 25,340,100 1981 4,545,497  21,740,350 26,285,847 1982 3,149,160  20,914,050 24,063,210 1983 5,531,975  27,600,380 33,132,355 1984 8,257,055  33,422,300 41,679,355 1985 6,908,988  27,056,980 33,965,968 1986 9,263,950 4,200 34,136,450 43,404,600 1987 7,931,460 14,700 45,187,921 53,134,081 Total stems 57,107,623 18,900 334,057,129 391,183,652  Figure 15 Total number of trees of interior spruce by genetic class overtime (1970-1987). Table 11 depicts the number of tree by genetic class after 1987 by year and Figure 16 shows the temporal comparison of genetic classes based on the stem amount.  ‘A’ class stems increased by 49 500 times in these openings in percentile from 1988 to 2004.  ‘B’ class and N genetic class are less broadly deployed after 1995, thus the genetic gain increases significantly after 1995.  50 Table 11 Number of trees of interior spruce by genetic classes seed deployed (planted) across reporting period (1988-2004), British Columbia. Year Genetic sources (stems) Data not available  (N) Total stems A B ‘B+’ 1988 66,100 12,268,871 3,955 36,531,229 48,870,155 1989 711,160 10,538,283 19,300 37,107,373 48,376,116 1990 1,519,259 21,933,666 22,000 36,008,553 59,483,478 1991 1,860,059 37,104,002 408,157 26,942,343 66,314,561 1992 3,017,968 28,737,694 176,774 19,751,424 51,683,860 1993 3,964,019 28,068,180 164,255 32,235,192 64,431,646 1994 8,382,827 82,893,210 433,992 6,972,992 98,683,021 1995 9,874,393 83,211,566 1,074,919 3,581,454 97,742,332 1996 20,355,538 69,539,990 529,118 1,372,009 91,796,655 1997 25,760,834 54,541,254 345,155 1,186,617 81,833,860 1998 35,878,702 35,434,660 251,762 2,397,034 73,962,158 1999 39,992,956 25,307,173 206,736 1,575,466 67,082,331 2000 42,403,796 21,442,599 63,469 1,781,854 65,691,718 2001 48,938,729 21,778,759 99,316 147,350 70,964,154 2002 42,988,776 20,481,288 228,205 8,448 63,706,717 2003 44,565,606 18,674,912 38,590 2,760 63,281,868 2004 42,132,645 18,639,738 41,821  60,814,204 Total Stems 372,413,367 590,595,845 4,107,524 207,602,098 1,174,718,834  Figure 16 Total number of trees of interior spruce by genetic classes overtime (1988-2004).  51 Table 12 depicts the stem percentile of four different genetic sources after 1987 by year and Figure 17 shows the temporal trends and genetic classes contrast based on the stem percentile. After 1987, approximately 31.7% of the total openings tree number across the interior has been planted with ‘A’ class seeds.  52  Table 12 Stems percentage for genetic sources of interior spruce seed deployed (planted) from 1988 to 2004, British Columbia. Year Genetic sources (stems percentage) Data not available  (N)% A% B% ‘B+’ % 1988 0.14 25.11 0.01 74.75 1989 1.47 21.78 0.04 76.71 1990 2.55 36.87 0.04 60.54 1991 2.80 55.95 0.62 40.63 1992 5.84 55.60 0.34 38.22 1993 6.15 43.56 0.25 50.03 1994 8.49 84.00 0.44 7.07 1995 10.10 85.13 1.10 3.66 1996 22.17 75.75 0.58 1.49 1997 31.48 66.65 0.42 1.45 1998 48.51 47.91 0.34 3.24 1999 59.62 37.73 0.31 2.35 2000 64.55 32.64 0.10 2.71 2001 68.96 30.69 0.14 0.21 2002 67.48 32.15 0.36 0.01 2003 70.42 29.51 0.06 0.00 2004 69.28 30.65 0.07 0.00 Total percentage 31.70 50.28 0.35 17.67  Figure 17 Stem percentage of genetic classes of interior spruce from 1988 2004, British Columbia.  53 Table 13 depicts the area sum of different genetic classes after 1987 per year and Figure 18 shows the temporal trends of genetic classes based on the area sum.  ‘A’ class seed deployment has risen dramatically from 56.47 ha in 1988 to 32,182.83 ha in 2004.  The ‘B’ class seed deployment area rose to 64,154.82 ha in 1995 and then decreased to 13,476 ha.  Before 1988, no ‘A’ class seed were deployed, but after 1988, ‘A’ class stands increased steadily.  ‘B’ class drops to about 43% of ‘A’ class in 2004.  ‘N’ class drops from 95 % in 1970 to nearly 0 in 2004.  54  Table 13 Area for genetic sources of interior spruce seed deployed (planted) across reporting period (1988-2004), British Columbia. Year Genetic sources (areas/ha) Data not available (N) Total area A B ‘B+’ 1988 56 11,910 3 28,533 40,502 1989 653 9,203 16 28,610 38,482 1990 1,214 16,476 12 28,094 45,796 1991 1,502 22,819 321 19,648 44,290 1992 2,550 23,672 142 14,921 41,286 1993 3,525 21,337 191 25,346 50,399 1994 6,537 63,603 359 5,522 76,021 1995 8,063 64,155 860 2,792 75,869 1996 15,788 52,251 418 1,103 69,561 1997 19,593 39,202 350 905 60,050 1998 27,394 26,211 185 1,841 55,631 1999 28,224 18,921 148 1,134 48,427 2000 30,958 15,665 76 1,339 48,037 2001 35,404 15,947 88 85 51,524 2002 31,687 14,876 182 10 46,755 2003 32,468 13,476 31 3 45,978 2004 32,183 13,884 28  46,095 Total area 277,798 443,608 3,408 159,887 884,702  Figure 18 Area treated (planted) with interior spruce by genetic class from 1988 to 2004, British Columbia. 0 10 20 30 40 50 60 70 80 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a/ ha 千 Year Total Area Treated (Planted) with Interior Spruce by Genetic Class from 1988 to 2004, British Columbia A B B+ N 55 Table 14 depicts the area percentile of different genetic classes after 1987 per year.  In 2004, the ‘A’ class seed use reaches 69% in BPP area, which is close to the 75% provincial target (Woods, 2006). Table 14 Area percentage for genetic sources of interior spruce seed deployed (planted) across reporting period (1988-2004), British Columbia. Year Genetic sources (areas/ha) Data not available  (N)% A% B% ‘B+’ % 1988 0.14 29.41 0.01 70.45 1989 1.70 23.92 0.04 74.35 1990 2.65 35.98 0.03 61.35 1991 3.39 51.52 0.72 44.36 1992 6.18 57.34 0.34 36.14 1993 6.99 42.34 0.38 50.29 1994 8.60 83.66 0.47 7.26 1995 10.63 84.56 1.13 3.68 1996 22.70 75.12 0.60 1.59 1997 32.63 65.28 0.58 1.51 1998 49.24 47.12 0.33 3.31 1999 58.28 39.07 0.30 2.34 2000 64.45 32.61 0.16 2.79 2001 68.71 30.95 0.17 0.17 2002 67.77 31.82 0.39 0.02 2003 70.62 29.31 0.07 0.01 2004 69.82 30.12 0.06 0.00 Total % 31.40 50.15 0.39 18.07 4.2.2 Adjacent SPZs In SPZ_A_Sx, there are two nearby SPZs, PGN and BVP which are necessary to investigate near PG.  It is allowable that stems and seeds from PG are deployed in these transition zones. The adjacency zones are also important for studying PG seed deployment.  The following tables and charts show the trends of seed deployment in PG, PGN and BVP (BPP) as a whole.  The report period is from 1988 to 2004.  And there are only Sx included in the report.  The stem and area deployed before 1988 are much less than they are after 1988. Table 15 depicts the stem sum of different genetic classes from 1988 to 2004.  Figure 19 shows the temporal trends of genetic classes based on stem sum in BPP area.  Table 16 depicts the area sum of different genetic classes after 1987.  56 Table 15 Total number of trees for interior spruce by genetic sources overtime in SPZ_A_Sx BVP, PG and PGN (1988-2004). Year Genetic sources (stems) Data not available  (N) Total stems A B ‘B+’ 1988  107,755  1,113,525 1,221,280 1989 693,000 3,424,319  9,302,281 13,419,600 1990 411,500 10,657,451 115,200 21,277,614 32,461,765 1991 20,100 9,517,437 345,593 17,127,202 27,010,332 1992 69,760 10,544,887 155,230 22,063,467 32,833,344 1993 57,700 12,835,242 27,802 24,121,372 37,042,116 1994 1,703,701 42,528,421 25,760 394,676 44,652,558 1995 1,230,434 49,448,972 68,840 625,701 51,373,947 1996 5,560,116 44,701,428 149,275 124,532 50,535,351 1997 5,245,861 38,796,261 1,219,455 363,283 45,624,860 1998 7,549,288 27,827,526 1,625,960 948,504 37,951,278 1999 8,088,050 27,321,762 1,253,094 543,347 37,206,253 2000 9,772,711 24,646,730 374,993 848,862 35,643,296 2001 9,436,288 20,808,863 655,048 81,580 30,981,779 2002 10,388,389 13,678,607 905,443 15,343 24,987,782 2003 12,140,962 17,188,304 1,575,890  30,905,156 2004 14,902,472 14,776,279 4,209,963  33,888,714 Total stems 87,270,332 368,810,244 12,707,546 98,951,289 567,739,411  Figure 19 Number of trees of interior spruce by genetic class overtime in SPZ_A_Sx BVP, PG and PGN (1988-2004).  57 Table 16 Total area treated (planted) with interior spruce by genetic sources and year in SPZ_A_Sx BVP, PG and PGN (1988-2004). Year Genetic sources (area/ha) Data not available  (N) Total area A B ‘B+’ 1988  96  958 1,054 1989 639 2,992  7,448 11,079 1990 339 8,362 67 15,770 24,538 1991 20 7,410 257 13,121 20,808 1992 46 8,630 112 16,353 25,141 1993 50 9,803 18 17,970 27,841 1994 1,226 31,230 21 264 32,741 1995 1,038 43,862 49 484 45,433 1996 4,071 33,520 98 134 37,822 1997 3,906 28,513 1,103 285 33,807 1998 5,958 20,020 1,166 619 27,763 1999 5,668 18,777 974 378 25,796 2000 6,971 17,975 337 548 25,831 2001 6,671 15,766 519 58 23,014 2002 7,271 9,727 783 36 17,817 2003 8,643 13,186 1,324  23,153 2004 11,391 11,332 3,281  26,005 Total area  63,907 281,203 10,108 74,424 429,642  Figure 20 Total area treated (planted) with interior spruce by genetic class and year in SPZ_A_Sx BVP, PG and PGN (1970-2004). Figure 20 depicts the temporal trends of genetic classes based on area sum.  Seed deployment is dominated by ‘B’ class and N class, which represents approximately 84% of total plantation area.  For ‘A’ class, PG is the leading seed production area among these three zones.  Especially 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Ar ea (h a) Year Total Area Treated (Planted) with Interior Spruce by Genetic Class and Year in SPZ_A_SX BVP, PG and PGN (1988-2004) A B B+ N Total 58 after 1995, 90.6 % of ‘A’ class seeds in BPP are deployed in PG zone other than BVP and PGN. ‘B’ class seed source has a peak of deployment in 1995. 4.2.3 Natural regeneration openings Table 17 depicts the area sum and percentile of natural regeneration with contrast to the planted area sum and percentage from 1970 to 2004.  And Figure 21 depicts the temporal trends of genetic classes based on sum area. Table 17 Area of natural regeneration and plantation of interior spruce, BC (1970-2004). Year NR area (ha) NR% Planted area (ha) Planted% Total area (ha) 1970 43 1.33 3,187 98.67 3,230 1971 1,018 20.61 3,921 79.39 4,939 1972 252 14.37 1,502 85.63 1,754 1973 1,461 26.73 4,004 73.27 5,465 1974 2,557 22.65 8,734 77.35 11,291 1975 1,633 10.59 13,792 89.41 15,425 1976 1,737 9.86 15,880 90.14 17,617 1977 1,826 10.38 15,759 89.62 17,585 1978 1,047 9.56 9,905 90.44 10,952 1979 1,845 12.44 12,988 87.56 14,833 1980 2,473 10.87 20,278 89.13 22,751 1981 1,538 7.08 20,176 92.92 21,714 1982 1,392 6.59 19,745 93.41 21,137 1983 385 1.51 25,063 98.49 25,448 1984 1,900 5.46 32,897 94.54 34,797 1985 1,608 5.42 28,052 94.58 29,660 1986 959 2.87 32,431 97.13 33,390 1987 1,163 2.65 42,717 97.35 43,880 1988 1,137 2.73 40,502 97.27 41,639 1989 2,094 5.16 38,482 94.84 40,576 1990 1,972 4.13 45,796 95.87 47,768 1991 2,880 6.11 44,290 93.89 47,170 1992 1,280 3.01 41,286 96.99 42,566 1993 1,008 1.96 50,399 98.04 51,407 1994 316 0.41 76,021 99.59 76,337 1995 4,658 5.78 75,869 94.22 80,527 1996 3,496 4.79 69,561 95.21 73,057 1997 5,883 8.92 60,050 91.08 65,933 1998 5,689 9.28 55,631 90.72 61,320 1999 3,219 6.23 48,427 93.77 51,646 2000 1,936 3.87 48,037 96.13 49,973 2001 2,026 3.78 51,524 96.22 53,550 2002 2,313 4.71 46,755 95.29 49,068 2003 3,610 7.28 45,978 92.72 49,588 2004 2,359 4.87 46,095 95.13 48,454 Total area (ha) 70,713 5.58 1,195,733 94.42 1,266,446 59  Figure 21 Total area of natural regeneration and plantation with interior spruce, BC (1970- 2004). Natural regeneration (NR) is less than plantation in terms of the total growing area.  NR is present in only 2.5% of openings in the results (Table 17 and Figure 21), the planted and natural regenerated openings with Sx deployed are compared with each other.  NR is only about 5.6% of the area of plantation in BC and the ratio of plantation to NR remains above 90% after the 1970s. 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 A re a/ ha Total Area of Natural Regeneration and Treated with Interior Spruce, BC, 1970-2004 Planted area NR area 60 4.3 Multiple species There are more than ten species planted in PG zone according to the planted tabular data.  This part covers the study period from 1995 to 2004.  Spatial and non-spatial openings are both reported, which are labeled specifically in each table and figure.  The genetic classes are summarized as a whole.  There are 21,576 records identified with silviculture activity in the planted dataset, within which 16,986 openings are spatial opening records and 4,590 are non- spatial opening records. 4.3.1 Spatial openings Table 18 below shows the total planted area with all species deployed in PG zone based on the spatial data.  For spatial openings, the total amount of all species planted from 1995 to 2002 decreases from 31,552 ha to 14,881 ha, while it steps up to 22,790 ha in 2004.  Figure 22 illustrates the total area planted with interior spruce, Douglas fir, Lodgepole pine and Sub-alpine fir in the reporting period.  The total area planted for interior spruce, Douglas fir, lodgepole pine and sub-alpine fir in the reporting period. 61 Table 18 Total area (ha) treated (planted) with all species reported in PG (spatial openings) from 1995 to 2004.  Other species include Black spruce (Sb), Western red cedar (Cw), Sieberian larch (Ls), Black cotton wood (Act), Paper birch (Ep), Tamarack (LT) and Yellow pine(Py). Year Sx  Pli Fdi Bl Other species Total area (ha) 1995 17,227 13,730 523 58 14 31,552 1996 17,044 15,351 1,060 78  33,533 1997 13,621 12,393 785 260 13 27,072 1998 11,234 10,505 697 220  22,656 1999 9,957 10,449 653 161 2 21,222 2000 8,669 10,885 904 74   20,532 2001 8,660 9,733 869 37 2 19,301 2002 7,291 7,289 260 7 34 14,881 2003 8,200 10,566 697 82 113 19,658 2004 10,802 11,229 644 34 81 22,790 Total area (ha) 112,705 112,130 7,092 1,011 259 233,197  Figure 22 Total area treated (planted) with interior spruce, interior Douglas-fir, Lodgepole pine and Sub-alpine fir reported in PG (spatial openings) from 1995 to 2004. 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a/ ha Year Total Area Treated (Planted) with Interior Spruce, Douglas-fir, Lodgepole pine and Sub-alpine fir Reported in PG (Spatial Openings) from1995 to 2004 SX PLI FDI BL 62 Table 19 shows the ratio of area planted with all species spatially in reported period.  In the table, the column is the area ratio calculated by dividing total area of planted against the species total from 1995 to 2004.  Although more openings are recorded after 1995, there are only four species, Sx, Fdi, Pli and Bl that are planted each year in PG, which represent more than 99% accumulatively of the reported total area, while for other species the proportion is much lower. Table 19 Percentage of Area Treated (Planted) with All Species Reported in PG (Spatial Openings) from 1995 to 2004, other species include Black spruce (SB), Western red cedar (Cw), Sieberian larch (Ls), Black cotton wood (Act), Paper birch (Ep), Tamarack (LT) and Yellow pine(Py). Species Total (%) Sx 48.33 Pli 48.08 Fdi 3.04 Bl 0.43 Other species 0.11 Total 100 Figure 23 shows the intensification of silviculture operation among eleven species based on their area percentile as above. Although PG SPZ is overlapped by Sx and Pli Seed Planning Zones, Sx is slightly more frequent planted than Pli in area ratio.  Figure 23 Total area treated (planted) with all species reported in PG (spatial openings) from 1995 to 2004.    63 4.3.2 Non-spatial openings In Table 20 the silviculture area of all species within non-spatial openings are depicted from 1995 to 2004.  In Figure 24, the area differences among three species Sx, Fdi and Pli in non-spatial openings are illustrated across all study periods.  The total area of these three decreases dramatically after 1995, while for other species they represent less than 1 % of the total.  There are observable differences between Pli and other species.  64  Table 20 Total Area (ha) treated (planted) with all species reported in PG (Non-spatial Openings) from 1995 to 2004.  Other species include Sitka spruce(Ss), Western larch (LW), Amabilis fir (Ba), Yellow cedar (Yc), Poplar (Ac), Siberian larch(Ls), Black cotton wood (Act), Paper birch (Ep), Lodgepole pine coastal (Plc). Year Sx Fdi Pli Cw Bl Other species Total area (ha) 1995 1,360 802 18,242 2  38 20,444 1996 785 349 6,044  9 28 7,215 1997 519 607 8,044 76 17 55 9,318 1998 261 202 5,725 35 16 45 6,284 1999 167 309 4,701 13  1 5,191 2000 297 312 5,538  62  6,209 2001 225 178 3,897  45  4,345 2002 68 142 2,984 6  2 3,202 2003 96 289 3,405 12 46  3,848 2004 301 271 3,497 18  2 4,089 Total area (ha) 4,079 3,461 62,077 162 195 171 70,145  Figure 24 Total Area Treated (Planted) with Interior Spruce, Douglas-fir, Lodgepole pine Reported in PG (Non-spatial Openings) from1995 to 2004.  Other species include Sitka spruce(SS), Western larch (LW), Amabilis fir (Ba), Yellow cedar (Yc), Poplar (Ac), Siberian larch(Ls), Black cotton wood (Act), Paper birch (Ep), Lodgepole pine coastal (Plc). 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a/ ha Year Total Area Treated (Planted) with Interior Spruce, Douglas-fir, Lodgepole pine Reported in PG (Non-spatial Openings) from1995 to 2004 SX FDI PLI 65 Table 21 shows a clear trend of decreasing of area planted for non spatial openings by year in PG.  Figure 25 depicts the silviculture area percentage for different species within non-spatial openings in PG. Table 21 Percentile of area treated (planted) with all species reported in PG (Non-spatial Openings) from 1995 to 2004.  Other species include Sitka spruce(Ss), Western larch (LW), Amabilis fir (Ba), Yellow cedar (Yc), Poplar (Ac), Siberian larch(Ls), Black cotton wood (Act), Paper birch (Ep), Lodgepole pine coastal (Plc). Year Sx Fdi Pli Cw Bl Other species 1995 6.65 3.92 89.21 0.01  0.2 1996 10.88 4.84 83.77  0.12 0.39 1997 5.57 6.51 86.33 0.82 0.18 0.59 1998 4.15 3.21 91.1 0.56 0.25 0.72 1999 3.22 5.95 90.56 0.25   0.02 2000 4.78 5.02 89.19   1 2001 5.18 4.1 89.69   1.04 2002 2.12 4.43 93.19 0.19   0.06 2003 2.49 7.51 88.49 0.31 1.2 2004 7.36 6.63 85.52 0.44   0.05 Total 5.81 4.93 88.49 0.23 0.28 0.27  Figure 25 Total Area Treated (Planted) with All Species Reported in PG (Non-spatial Openings) from 1995 to 2004.  Other species include Sitka spruce(SS), Western larch (LW), Amabilis fir (Ba), Yellow cedar (Yc), Poplar (Ac), Siberian larch(Ls), Black cotton wood (Act), Paper birch (Ep), Lodgepole pine coastal (Plc).  66 4.3.3 Spatial and non-spatial openings Table 22 shows the contrast between spatial openings and non-spatial openings based on their treated area.  Table 23 shows the area percentage (planted) of different species within each opening type.  The right column shows the percentile of different species reported within spatial and non-spatial openings in PG.  In non-spatial openings, Pli is the dominant species. Table 22 Total area treated (planted) with all species reported in non-spatial and spatial openings in PG from1995 to 2004. Species Opening types Total Non-spatial Spatial Pli 62,077 112,130 174,207 Sx 4,079 112,705 116,784 Fdi 3,461 7,092 10,553 Cw 162 224 386 Bl 195  195 Ba 60  60 Lw 32  32 Ss 27  27 Yc 21  21 Act 6 14 20 Ep 18 1 19 Ls 2 9 11 Sb  8 8 Ac 4  4 Py  2 2 Plc 1  1 Lt  1 1 Total 70,145 233,197 303,342  67 Table 23 Percentage (opening types) of Total Area Treated (Planted) with All Species Reported in Non-spatial and Spatial Openings in PG from1995 to 2004. Species Opening types Total Non-spatial Spatial Pli 35.63 64.37 57.43 Sx 3.49 96.51 38.50 Fdi 32.80 67.20 3.48 Cw 41.97 58.03 0.13 Bl 100.00  0.06 Ba 100.00  0.02 LW 100.00  0.01 Ss 100.00  0.01 Yc 100.00  0.01 Act 30.00 70.00 0.01 Ep 94.74 5.26 0.01 Ls 18.18 81.82 Sb  100.00 Ac 100.00 Py  100.00 Plc 100.00 Lt  100.00 Total 23.13 76.87 100.00 Figure 26 depicts the contrast between spatial and non-spatial opening area treated for all species in PG.  Spatial openings are dominant among all species in PG, which is 77% against 33% of the non-spatial in area.  Figure 26 Comparison of total area treated (planted) with all species reported in spatial and non-spatial openings in PG from1995 to 2004.  Non-spatial 23% Spatial 77% Comparison of Total Area Treated with All Species Reported in Spatial and Non-spatial Openings in PG from1995 to 2004 Non-spatial Spatial 68 Figure 27 depicts the area treated percentile of all species reported within spatial and non-spatial openings in PG.  Figure 27 Comparison of total area treated (planted) with all species reported in PG from1995 to 2004. Table 24 depicts the genetic class composition of Sx and Pli reported within spatial and non- spatial openings of in PG.  The deployment levels of ‘A’ class seed of the Sx and Pli plantations differ from each.  In Sx ‘A’ class is 55,970 ha from 1995 to 2004 in PG, while for Pli there is only 7,732 ha.  For ‘B’ class, there are 59,144 ha in Sx, while there are 154,482 ha in Pli.  For ‘B+’ there are much more Pli planted than Sx in PG, while the ‘N’ category is similar in both Sx and Pli. Figure 28 shows the composition of genetic class in Sx openings and Figure 29 shows the Pli case.  ‘A’ class has taken less than 50% of area accumulatively, while ‘B’ class is more than half of total Sx.  However, ‘A’ class is less than Pli in PG (4.4% in area), while Pli ‘B’ class takes about 88% after 1995 in PG.  For ‘B+’, there is more deployment in Pli than in Sx.  Pli 58% Sx 39% Fdi 3% Comparison of Total Area Treated with All Species Reported in PG from1995 to 2004 PLI SX FDI CW BL BA LW SS YC ACT EP LS SB AC AC PY PLC LT 69 Table 24 Total area and stem treated with Sx and Pli reported in PG (All Openings) by genetic classes from 1995 to 2004. Genetic class Sx Pli Area (ha) Stem Area (ha) Stem ‘A’ Total 55,970 75,841,593 7,732 10,975,372 ‘B’ Total 59,144 79,251,231 154,482 198,957,237 ‘B+’ Total 211 296,500 11,993 14,956,035 ‘N’ Total 1,459 2,047,213 1,498 1,954,745 Total 116,784 157,436,537 174,207 224,888,644  Figure 28 Total area treated with Sx reported in PG (All Openings) by genetic classes from 1995 to 2004.  Figure 29 Total area treated with Pli reported in PG (All Openings) by genetic classes from 1995 to 2004. A Total 47.93% B Total 50.64% B+ Total 0.18% N Total 1.25% Total Area Treated with SX Reported in PG (All Openings) by Gene Class  from 1995 to 2004 A Total B Total B+ Total N Total A Total 4.40% B Total 87.92% B+ Total 6.83% N Total 0.85% Total Area Treated with PLI Reported in PG (All Openings) by Gene Classes  from 1995 to 2004 A Total B Total B+ Total N Total 70 4.4 PG Seed Planning Zone This part is based on the technical scope defined, which identifies the spatial openings with Sx in PG from 1995 to 2004.  Four genetic classes are calculated and the differences between natural regeneration and planted types are compared. 4.4.1 Silviculture openings Table 25 and Figure 30 depicts seed deployment areas of four genetic classes in PG within the study period Table 25 and Figure 30 show that ‘A’ class deployment has increased since 1995, while ‘B’ class seed has been deployed less often than ‘A’ class after 1999.  The ‘A’ class proportion has risen from 2.93% in 1995 to 95.65% in 2004, while ‘B’ class has a reverse trend.  71 Table 25 Total area treated (planted) with Sx in SPZ_A_Sx PG by genetic class and year (1995-2004). Year Genetic classes (area/ha) Data not available (N) Total area (ha) A B ‘B+’ 1995 507  16,482  1  293  17,283 1996 2,889  14,144  4  7  17,044 1997 3,120  10,251  2  248  13,621 1998 5,378  5,569  15  272  11,234 1999 5,361  4,360   236  9,957 2000 6,295  2,143   231  8,669 2001 6,478  2,170   12  8,660 2002 6,444  842   5  7,291 2003 7,436  764    8,200 2004 10,332  470    10,802 Total area (ha) 54,240  57,195  22  1,304  112,761  Figure 30 Total area treated (planted) with interior spruce and stems in SPZ_A_Sx by gene glass and year (1995-2004). 0 5 10 15 20 25 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 St em m ill io ns A re a/ ha Year Total Area Treated (Planted) with Interior Spruce and stems in SX PG SPZ-A by Gene Class and Year(1995-2004) A B B+ N Total Area Total Stem 72 Table 26 depicts the area percentiles of four genetic classes across study periods.  In Figure 31, the temporal trends of area percentile are showed with bars chart.  ‘B+’ and ‘N’ class drops down quickly which are less than 2% across 1995 to 2004. Table 26 Percentage of total area treated (planted) with interior spruce in Sx PG SPZ-A by and genetic class and year (1995-2004). Year Genetic classes (area %) Data not available (N) A B ‘B+’ 1995 2.93 95.37 0.01 1.70 1996 16.95 82.99 0.02 0.04 1997 22.91 75.26 0.01 1.82 1998 47.87 49.57 0.13 2.42 1999 53.84 43.79  2.37 2000 72.62 24.72  2.66 2001 74.80 25.06  0.14 2002 88.38 11.55  0.07 2003 90.68 9.32 2004 95.65 4.35 Total 48.10 50.72 0.02 1.16  Figure 31 Percentage of total area treated (planted) with interior spruce in Sx PG SPZ-A by and genetic class and year (1995-2004). Table 27 depicts the density of all Sx planted from 1995 to 2004 with stems and area amount listed.  And Figure 32 illustrates the trends of density changes in PG for all planted genetic classes.  The density of plantation within this period does not change in regular which is about 1,400 stems/ha as an average. 0% 25% 50% 75% 100% 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a % Year Percentage of Total Area Treated (Planted) with Interior Spruce in SX PG SPZ-A by Gene Class and Year(1995-2004) N B+ B A 73 Table 27 Total area treated (planted) with interior spruce, stems and density in SPZ_A_Sx PG by year (1995-2004). Year Total Stem Total Area(ha) Density (stem/ha) 1995 22,715,150 17,283 1,314 1996 23,097,269 17,044 1,355 1997 18,558,722 13,621 1,363 1998 14,780,541 11,234 1,316 1999 13,933,772 9,957 1,399 2000 11,939,107 8,669 1,377 2001 11,736,323 8,660 1,355 2002 10,121,247 7,291 1,388 2003 11,194,751 8,200 1,365 2004 14,095,384 10,802 1,305 Total 152,172,266 112,761 1,350  Figure 32 Planting density of interior spruce improved seeds deployed overtime in SPZ_A_Sx PG (1995-2004). Table 28 depicts orchard 214 seeds deployment over time in PG.  Figure 33 depicts the trends of area deployed with 214 seeds as well as their percentile against total area planted in PG.  The total seed amount of orchard 214 orchards rises steadily from 1,270 ha to 9,796 ha, which is from 7.45% to 90.88% of the total planted area in PG from 1996 to 2004.  Orchard 214 seed represent a large percentage of the genetic improvement in PG openings after 2003, 91% in 2004. 1,300 1,320 1,340 1,360 1,380 1,400 1,420 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 St em /h a Year Planting Density of Interior Spruce Improved Seeds Deployed Overtime in SPZ_A_SX PG (1995-2004) Density 74 Table 28 Total area treated (planted) with orchard 214 seeds in SPZ_A_Sx PG by year (1995-2004) Year 214 Area Total Area(ha) Area Percentage 1996 1,270  17,044  7.45 1997 1,458  13,621  10.70 1998 3,729  11,234  33.19 1999 4,305  9,957  43.24 2000 4,297  8,669  49.57 2001 4,545  8,660  52.48 2002 4,637  7,291  63.60 2003 6,764  8,200  82.49 2004 9,796  10,802  90.69 Total 40,801  112,761  36.18  Figure 33 Total area treated (planted) with orchard 214 seeds in SPZ_A_Sx PG by year (1995-2004)  0.00 20.00 40.00 60.00 80.00 100.00 0 5000 10000 15000 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a% A re a/ ha Year Total Area Treated (Planted) with Orchard 214 Seeds in SPZ_A_SX PG by Year (1995-2004) 214 Area Area Percentage 75 Table 29 depicts the total area, density planted with orchard 214 seeds from different seedlots in PG, among which seedlot 60269 and 60118 have the largest plantation area.  Seedlot 61035 has the highest density of plantation of all, while the 61147 has the lowest value of 1,221 stem per ha. Table 29 Total area treated (planted), stem and density with orchard 214 seeds from different Seedlots in SPZ_A_Sx PG (1995-2004) Seedlot# Stem Area (ha) Area % of 214 Density (stem/ha) 60269 9,893,198 7,041  17.26  1,405 60118 9,444,176 6,873  16.85  1,374 60119 7,476,498 5,405  13.25  1,383 61148 5,136,904 3,850  9.44  1,334 61037 4,816,574 3,463  8.49  1,391 61038 3,934,780 2,964  7.26  1,328 61044 3,619,337 2,846  6.98  1,272 60117 2,273,744 1,685  4.13  1,349 60098 2,085,642 1,479  3.62  1,410 61036 1,903,361 1,372  3.36  1,387 60097 1,567,597 1,140  2.79  1,375 61147 1,309,019 1,072  2.63  1,221 60115 905,798 669  1.64  1,354 61035 808,836 533  1.31  1,518 60116 440,765 333  0.82  1,324 61189 51,840 43  0.11  1,206 60272 47,005 33  0.08  1,424 Total 55,715,074  40,801   1,366  76 Table 30 and Figure 34 show the area ratio change of three major seedlots in orchard 214 seed orchard over time.  It is calculated by dividing the 214 seed deployment area per year. The present trend indicates that these seedlots were used with 2 to 5 years, within which they have a peak year of seedling production or seed use. Table 30 Area percentage of seedlot 60269, 60119, 60118 deployed versus orchard 214 seed deployment area in SPZ_A_Sx PG (1995-2004). Year 60269 ratio 60119 ratio 60118 ratio 1997  3.54 6.24 1998  41.77 68.46 1999  49.13 37.19 2000 3.96 15.91 59.72 2001 80.09 19.85 2002 59.37 17.32 1.34 2003 3.93 9.60 2004 2.16 2.20  Figure 34 Area weight of three seedlots within 214 orchard seed deployment changes over time (1995-2004).  0 20 40 60 80 100 1997 1998 1999 2000 2001 2002 2003 2004 A re a %  o f 2 14  s ee d de pl oy m en t Year Area percentage of seed lot 60269, 60119, 60118 deployed versus orchard 214 seed deployment area in SPZ_A_SX PG (1995-2004) 60269 ratio 60119 ratio 60118 ratio 77 4.4.2 Natural regeneration Table 31 depicts the comparison of planted and natural regenerated area in PG.  NR is less frequent compared with plantation.  The area drops from 1,298 ha to 312 ha in 2001, and finally it decreases to 136 ha in 2004.  And Figure 35 shows the trends and comparison of total area regenerated versus planted in PG from 1995 to 2004, which illustrates the trends of the natural generated versus planted in percentile. The ratio of NR in total forest recovery area decreases from about 7% to 1.2%. Table 31 Comparison of natural regeneration area and planted area in SPZ_A_Sx PG (1995- 2004) Year NR area (ha) Planted area (ha) NR area % Planted area % Total 1995 1,298 17,283 6.98 93.02 18,581 1996 976 17,044 5.42 94.58 18,020 1997 984 13,621 6.74 93.26 14,605 1998 782 11,234 6.51 93.49 12,016 1999 966 9,957 8.84 91.16 10,923 2000 330 8,669 3.66 96.34 8,999 2001 312 8,660 3.48 96.52 8,972 2002 771 7,291 9.56 90.44 8,062 2003 350 8,200 4.09 95.91 8,550 2004 136 10,802 1.24 98.76 10,938 Total 6,904 112,761 5.77 94.23 119,665  Figure 35 Total area naturally regenerated and treated with interior spruce in Sx PG SPZ-A by year (1995-2004) Table 32 depicts the total area regenerated with Sx within different management units in PG. Within the PG natural regenerated area, there are nine management (MGT) units such as timber 0 5000 10000 15000 20000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a/ ha Year Total Area Naturally Regenerated and Treated with Interior Spruce in SX PG SPZ-A by Year (1995-2004) Planted area NR area 78 supply areas.  Their NR intensity indicates the harvest and recovery dynamics.  NR among different management units differs in deployment area.  In the Prince George Timber Supply Area, NR openings are most frequently reported, while its recovery area does not change regularly. Table 32 Total area regenerated with interior spruce in SPZ_Sx_A PG by management units (1995-2004) Management area Area (ha) Area % Prince George TSA 3,232 46.75 Quesnel TSA 2,470 35.73 Williams Lake TSA 993 14.36 Woodlot 120 1.74 TFL 53 59 0.86 MacKenzie TSA 32 0.46 Robson Valley TSA 4 0.06 Fort Nelson TSA 2 0.03 Dawson Creek TSA 1 0.01 Total 6,913 Table 33 depicts the total area regenerated with Sx within Prince George TSA unit by year in PG. And Figure 36 illustrates the temporal trends of area planted within PG TSA from 1995 to 2004, which is not stable by year. 79 Table 33 Total area regenerated (natural) with interior spruce in Sx PG SPZ-A in Prince George TSA by year (1995-2004) Year Area (ha) 1995 396 1996 191 1997 758 1998 303 1999 884 2000 172 2001 5 2002 455 2003 66 2004 2 Total 3,232  Figure 36 Total Area Regenerated (Natural) with Interior Spruce in Sx PG SPZ-A in Prince George TSA by Year (1995-2004) 0 200 400 600 800 1,000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a/ ha Year Total Area Regenerated (Natural) with Interior Spruce in SX PG SPZ-A in Prince George TSA by Year (1995-2004) Area 80 4.5 BEC Zone 4.5.1 Silviculture openings The table below depicts the area planted in Sx PG SPZ-A by BEC zone from 1995 to 2004. Figure 37 depicts total area planted in Sx PG SPZ-A by BEC zone in PG.  There are five BEC zones in PG; SBS, ESSF, SBPS, ICH and MS, among which SBS and ESSF are dominant in plantation areas.  SBS and ESSF may represent the Sx geographic range in PG.  SBS has an area of 82,347 ha with a density of 1,353 stems per ha.  For ESSF, there are 23,921 ha plantation area with a density of 1,366 stems per ha.  MS has the lowest level in PG, while its density is also low at 944 stems per ha. Table 34 Total area treated (planted) with interior spruce in Sx PG SPZ-A by BEC zone (1995-2004). BEC zone Stem Area (ha) Density (stem/ha) SBS 111,403,201 82,347 1,353 ESSF 32,677,673 23,921 1,366 SBPS 3,717,148 3,240 1,147 ICH 4,092,471 2,988 1,370 MS 184,073 195 944 Total 152,074,566 112,691 1,349  900 1,000 1,100 1,200 1,300 1,400 0 20,000 40,000 60,000 80,000 100,000 SBS ESSF SBPS ICH MS D en si ty  (s te m /h a) A re a/ ha BEC Zone Total Area Treated (Planted) with Interior Spruce in SX PG SPZ-A by BEC zone (1995-2004) Area Density 81 Figure 37 Total area treated (planted) with interior spruce in Sx PG SPZ-A by BEC zone (1995-2004). Table 35 depicts the area planted in five BEC zones by year in PG.  And Figure 38 depicts the trends of area planted within BEC zones by year in PG.  More plantations are located in SBS than other zones (73%), which decreases after 1995.  This trend is similar to the over time overall plantation in PG.  82 Table 35 Total area treated (planted) with interior spruce in Sx PG SPZ-A by BEC zone and year (1995-2004). Year BEC zones (area/ha) Total area (ha) ESSF ICH MS SBPS SBS 1995 4,170 292 68 275 12,408 17,213 1996 3,215 927 15 336 12,551 17,044 1997 2,521 438 79 441 10,142 13,621 1998 2,532 271  412 8,019 11,234 1999 2,332 252 7 351 7,015 9,957 2000 1,558 221  312 6,578 8,669 2001 1,985 181 12 241 6,241 8,660 2002 1,728 282 5 184 5,092 7,291 2003 2,000 50 2 185 5,963 8,200 2004 1,880 74 7 503 8,338 10,802 Total area (ha) 23,921 2,988 195 3,240 82,347 112,691  Figure 38 Total area treated (Planted) with Interior Spruce in SPZ_A_Sx PG by BEC zone and Year (1995-2004). Table 36 depicts the area planted in different BEC zones by genetic classes in PG.  Figure 39 illustrates the differences of area planted with four genetic classes by BEC zones.  For ‘A’ class seed, they are not deployed evenly in PG either, which are mainly planted in SBS and ESSF. Other zones do not have the major population of ‘A’ class.  More ‘A’ class are planted in SBS than other genetic classes, where class A and B are its major seed sources. Total Area Treated (Planted) with Interior Spruce in SX PG SPZ-A by BECzone (1995-2004) 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 BECzone A re a/ ha ESSF ICH MS SBPS SBS Total 83 Table 36 Total area treated (planted) with interior spruce in Sx PG SPZ-A by BEC zone and genetic sources (1995-2004). BEC zone Genetic sources (area/ha) Data not available (N) Total area (ha) A B ‘B+’ SBS 43,460 37,953 22 912 82,347 ESSF 7,937 15,656  328 23,921 SBPS 1,500 1,689  51 3,240 ICH 1,317 1,658  13 2,988 MS 26 169   195 Data not available  70   70 Total area (ha) 54,240 57,195 22 1,304 112,761  Figure 39 Total area treated (planted) with interior spruce in Sx PG SPZ-A by BEC zone and genetic classes (1995-2004). Table 37 depicts total area planted with ‘A’ class seed by BEC zones each year in PG.  And Figure 40 depicts the temporal trends of ‘A’ class seed deployment area within different BEC zones in PG.  SBS zone has higher genetic class seeds deployed, while ESSF has 7,937 ha of ‘A’ class with more ‘B’ class, 15,656 ha.  Other BEC zones are minor in PG in terms of their plantation.  84  Table 37 Total area treated (planted) with interior spruce ‘A’ class seeds in SPZ_A_Sx PG by BEC zone and Year (1995-2004) Year BEC zones (area/ha) Total area (ha) ESSF  ICH  MS  SBPS  SBS 1995 118 30  4 355 507 1996 307 106  21 2,455 2,889 1997 431 86  61 2,542 3,120 1998 576 211  1 4,590 5,378 1999 726 143  70 4,422 5,361 2000 742 183  285 5,085 6,295 2001 1,065 152 12 218 5,031 6,478 2002 1,174 282 5 174 4,809 6,444 2003 1,287 50 2 167 5,930 7,436 2004 1,511 74 7 499 8,241 10,332 Total area (ha) 7,937 1,317 26 1,500 43,460 54,240  Figure 40 Total area treated (planted) with interior spruce ‘A’ class in SPZ_A_Sx PG by BEC zone and Year (1995-2004). 0 2,000 4,000 6,000 8,000 10,000 12,000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a/ ha Year Total Area Treated (Planted) with Interior Spruce A Class Seeds in Sx PG SPZ-A by BECzone (1995-2004) ESSF Area ICH Area MS Area SBPS Area SBS Area Total 85 Table 38 and Figure 41 depict the area planted with orchard 214 seeds by BEC zones each year in PG. Table 38 Total Area Treated (Planted) with Interior Spruce (SO 214 Seeds) in Sx PG SPZ-A by BEC zone (1995-2004). Year BEC zones (area/ha) Total area (ha) ESSF ICH MS SBPS SBS 1996 66 106  21 1,077 1,270 1997 343 76  10 1,029 1,458 1998 484 211   3,034 3,729 1999 633 143  1 3,528 4,305 2000 566 124  82 3,525 4,297 2001 808 50 3 17 3,667 4,545 2002 802 99  121 3,615 4,637 2003 1,096 43 2 153 5,470 6,764 2004 1,480 60 7 429 7,820 9,796 Total area (ha) 6,278 912 12 834 32,765 40,801  Figure 41 Total area treated (planted) with Interior spruce (SO 214 Seeds) in Sx PG SPZ-A by BEC zone (1995-2004). 0 2,000 4,000 6,000 8,000 10,000 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a/ ha Year Total Area Treated (Planted) with Interior Spruce (SO 214 Seeds) in SX PG SPZ-A by BECzone (1995-2004) ESSF ICH MS SBPS SBS Total 86 Table 39 depicts differences of area planted with orchard 214 seedlots in SBS zone in PG. Figure 43 shows their trends with bars and a density line.  For orchard’s 214 seedlots, 60269, 60118, 60119 are the major seedlots that support the SBS zone plantation. Table 39 Total Area Treated (Planted) and Density with orchard 214 Seeds from Different Seedlots in SBS BEC zone in SPZ_A_Sx PG (1995-2004). Seedlot Stem Area (ha) Density (stem/ha) 60269 8,754,699 6,196 1,413 60118 8,488,930 6,169 1,376 61148 4,925,994 3,698 1,332 60119 4,651,314 3,320 1,401 61037 3,898,827 2,856 1,365 61038 3,318,033 2,431 1,365 60117 2,050,169 1,491 1,375 61044 1,835,096 1,479 1,241 61036 1,620,181 1,176 1,378 60097 1,481,835 1,084 1,367 60098 1,463,837 1,002 1,461 61147 1,133,219 894 1,268 60115 816,578 609 1,341 60116 260,445 190 1,371 61035 180,042 127 1,418 60272 43,065 29 1,485 61189 16,560 14 1,183 Total 44,938,824 32,765 1,372  Figure 42 Total area treated (planted) and density with orchard 214 Seeds from Different seedlots in SBS BEC zone in SPZ_A_Sx PG (1995-2004). Table 40 depicts total area planted with 214 seeds in SBS ESSF and ICH zones in PG with the area percentile calculated by dividing 214 seed area against ‘A’ class area in each zone.  Figure 1,000 1,100 1,200 1,300 1,400 1,500 1,600 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 D en si ty  (s te m /h a) Ar ea /h a Seedlot# Total Area Treated (Planted) and Density with Orchard 214 Seeds from Different Seedlots in SBS Bec zone in SPZ_A_SX PG (1995-2004) Area Density 87 44 shows the trends among these zones with 214 seeds area ratio changing by year, where the bubble size shows the 214 area percentile of these BEC zones in the width.  ESSF Area (A) means ‘A’ class seed deployment area in ESSF zone.  Orchard 214 seed are used more intensively for SBS, ESSF and ICH zones as the bubble size showing that the area ratio of orchard 214 seed out of ‘A’ class is different among these zones by year.  In the SBS, 75.5% of it ‘A’ class seed deployed are from orchard 214.  In ESSF, more than 79% of ‘A’ class seeds are from orchard 214 as well. Table 40 Total area treated (planted) with interior spruce (orchard 214 seeds) in Sx PG SPZ-A within ESSF, ICH and SBS by Year. Year 214  in ESSF ESSF  Area (A) ESSF% 214 in ICH ICH Area (A) ICH% 214 in SBS SBS Area (A) SBS% 1996 66 307 21.50 106 106 100.00 1,077 2,455 43.87 1997 343 431 79.58 76 86 88.37 1,029 2,542 40.48 1998 484 576 84.03 211 211 100.00 3034 4590 6610 1999 633 726 87.19 143 143 100.00 3,528 4,422 79.78 2000 566 742 76.28 124 183 67.76 3,525 5,085 69.32 2001 808 1,065 75.87 50 152 32.89 3,667 5,031 72.89 2002 802 1,174 68.31 99 282 35.11 3,615 4,809 75.17 2003 1,096 1,287 85.16 43 50 86.00 5,470 5,930 92.24 2004 1,480 1,511 97.95 60 74 81.08 7,820 8,241 94.89 Total 6,278 7,819 80.29 912 1,287 70.86 32,765 43,105 76.01  Figure 43 Total Area Treated (Planted) with Interior Spruce (SO 214 Seeds) in Sx PG SPZ-A within ESSF, ICH and SBS by Year. 10 100 1,000 10,000 100,000 1994 1996 1998 2000 2002 2004 2006 A re a/ ha Year Total Area Treated (Planted) with Interior Spruce (SO 214 Seeds) in SX PG SPZ-A within ESSF, ICH and SBS BECzone by Year (1995-2004) ICH ESSF SBS 88 4.5.2 Natural regeneration Table 41 depicts total area regenerated in PG by BEC zones in PG, within which nine ha area is labeled as BVP in 1999.  NR in PG takes only 6% of the total plantation area.  Figure 44 shows the temporal trends of total area regenerated ratio by BEC zones and by year.  The NR in BEC zones in PG changes differently by the magnitude of area where SBPS and SBS are the two dominant BEC zones, 93% of total NR reported in PG. Table 41 Total Area Regenerated (Natural) with Interior Spruce in Sx PG SPZ-A by BEC zone and by Year (1995-2004). Year BEC zone (area/ha) Total area (ha) ESSF ICH MS SBPS SBS 1995 2 27 78 534 657 1,298 1996   85 577 314 976 1997    222 762 984 1998  1 107 265 409 782 1999 5   72 898 975 2000    88 242 330 2001 1   62 250 312 2002 119   6 645 771 2003  34  86 229 350 2004 2   1 133 136 Total area (ha) 129 63 269 1,913 4,540 6,913  Figure 44 Total Area Regenerated (Natural) percentage with Interior Spruce in Sx PG SPZ- A by BEC zone and by Year (1995-2004).  0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a % Year Total Area Regenerated (Natural) Percentage with Interior Spruce in SX PG SPZ-A by BEC zone and by Year (1995-2004) SBS SBPS MS ICH ESSF 89 Table 42 and Figure 45 depict total area regenerated naturally in PG by Management unit (except TFL) and BEC zones in PG.  The Prince George TSA and SBS intersection represents half the NR cover, which suggests these zones, could be an intensively managed area in PG.  ESSF plantation mainly distributes in Prince George TSA.  SBS plantation is in multiple management units. Table 42 Total Area regenerated (natural) with interior spruce in Sx PG SPZ-A by BEC zone and by Management Unit (1995-2004). Management unit BEC zone (area/ha) Total area (ha) SBS ESSF SBPS ICH MS Prince George TSA 3,076 120  27  3,223 Quesnel TSA 669  1,532  269 2,470 Williams Lake TSA 575  382 36  993 Woodlot 120     120 TFL 53 58 2    59 MacKenzie TSA 32     32 Robson Valley TSA  4    4 Fort Nelson TSA  2    2 Dawson Creek TSA  1    1 Total area (ha) 4,531 129 1,913 63 269 6,904  Figure 45 Total area regenerated (natural) with interior spruce in Sx PG SPZ-A by BEC zone and by Management Unit (1995-2004).  90 4.6 SPU 4.6.1 Silviculture openings There are four SPU classes in the study, PG high (High), PG low (Low), Unclassified high (Unclassified-high) and Unclassified low (Unclassified-low), and PG high and low are the main focuses.  In the Table below (Table 43), the total area planted in each SPU in PG is listed.  It shows that in PG low there is a larger plantation area than PG high.  And Figure 46 depicts the temporal trends of area planted with Sx by SPU in PG.  91 Table 43 Total Area Treated (Planted) with Interior Spruce in Sx PG SPZ-A by SPU and by Year (1995-2004) Year Low High Unclassified-high Unclassified-low Total area (ha) 1995 14,465 2,666 82  17,213 1996 14,627 2,338 34 45 17,044 1997 11,644 1,956 21  13,621 1998 9,385 1,843 6  11,234 1999 8,702 1,252 3  9,957 2000 7,794 805 70  8,669 2001 7,422 1,238   8,660 2002 6,157 1,128 6  7,291 2003 6,901 1,299   8,200 2004 9,665 1,131  6 10,802 Total area (ha) 96,762 15,656 222 51 112,691  Figure 46 Total area treated (planted) with interior spruce in Sx PG SPZ-A by SPU and by year (1995-2004)  0 5,000 10,000 15,000 20,000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a/ ha Year Total Area Treated (Planted) with Interior Spruce in SX PG SPZ-A by SPU and by Year (1995-2004) Low High Unclassified-high Unclassified-low Total 92 In the Table below (Table 44), the total areas planted in each SPU in PG by genetic class are listed.  Figure 47 depicts the differences of area planted with Sx by SPU and by genetic class in PG (May include six ha in 2005).  There are 96,762 ha planted in PG Low and 15,656 ha in PG High.  In PG high, more ratio of ‘B’ class is deployed than PG low.  In PG, all the ‘B+’ is located in PG low. Table 44 Total area treated (planted) with interior spruce in Sx PG SPZ-A by SPU and by genetic sources (1995-2004) SPU Genetic sources (area/ha) Data not available N (ha) Total area (ha) A B ‘B+’ Low 48,523  47,205  22  1,012  96,762 High 5,627  9,737   292  15,656 Unclassified-high 84  138    222 Data not available  70    70 Unclassified-low 6  45    51 Total area (ha) 54,240  57,195  22  1,304  112,761  Figure 47 Total area treated (planted) with interior spruce in Sx PG SPZ-A by SPU and by genetic class (1995-2004)  1 10 100 1,000 10,000 100,000 Low High Unclassified-high Data not available Unclassified-low A re a/ ha SPU Total Area Treated (Planted) with Interior Spruce in SX PG SPZ-A by SPU and by Genetic Class (1995-2004) A B B+ N 93 In Table 45, the total areas planted with ‘A’ class seeds in each SPU in PG are listed.  For ‘A’ class, PG Low has its half plantation as A, and half as B class.  And Figure 48 depicts the temporal trends of area planted with ‘A’ class seeds by SPU in PG.  The total area share of ‘A’ class increases steadily in PG high and PG low, which is about 48% of total planted area in PG during the reported period. Table 45 Total area treated (planted) with interior spruce ‘A’ class seeds in Sx PG SPZ-A by SPU and year (1995-2004). Year Low Area High Area Unclassified-low Area Unclassified-high Area Total area (ha) 1995 446 61   507 1996 2,598 291   2,889 1997 2,650 456  14 3,120 1998 4,835 543   5,378 1999 4,963 398   5,361 2000 5,755 470  70 6,295 2001 5,596 882   6,478 2002 5,640 804   6,444 2003 6,613 823   7,436 2004 9,427 899 6  10,332 Total area (ha) 48,523 5,627 6 84 54,240  Figure 48 Total area treated (planted) with interior spruce ‘A’ class seeds in Sx PG SPZ-A by SPU and year (1995-2004). 0 2,000 4,000 6,000 8,000 10,000 12,000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a/ ha Year Total Area Treated (Planted) with Interior Spruce A Class Seeds in SX PG SPZ-A by SPU and Year (1996-2004) Low Area High Area Total 94 In Table 45, the total area planted with 214 orchard seeds in each SPU in PG is listed by year. There are 48,523 ha of ‘A’ class planted in PG low and 5,627 ha in PG high, within which 68% are from the 214 seed orchard in PG low.  And Figure 48 depicts the temporal trends of area planted with 214 class seeds by SPU in PG. Table 46 Total area treated (planted) with interior spruce (SO 214 Seeds) in Sx PG SPZ-A by SPU and by year (1996-2004). Year Low High Unclassified-low Unclassified-high Total area (ha) 1996 1,225 45   1,270 1997 1,081 363  14 1,458 1998 3,283 446   3,729 1999 3,998 307   4,305 2000 4,008 219  70 4,297 2001 3,980 565   4,545 2002 4,053 584   4,637 2003 6,142 622   6,764 2004 9,006 784 6  9,796 Total area (ha) 36,776 3,935 6 84 40,801  Figure 49 Total area treated (planted) with interior spruce (SO 214 Seeds) in Sx PG SPZ-A by SPU and by year (1995-2004). 4.6.2  Natural regeneration Table 47 and Figure 50 depict the total area of natural regeneration in each SPU in PG.  NR area is mainly shared by PG high and PG low among SPUs.  In PG high and PG low, NR fluctuates over time with a decreasing trend. 95 Table 47 Total area regenerated (natural) with interior spruce in Sx PG SPZ-A by SPU and by Year (1995-2004). Year Low High Total area (ha) 1995 797 500 1,298 1996 678 297 976 1997 945 39 984 1998 499 283 782 1999 959 16 975 2000 330  330 2001 263 50 312 2002 651 120 771 2003 346 3 350 2004 136  136 Total area (ha) 5,604 1,309 6,913  Figure 50 Total area regenerated (natural) with interior spruce in Sx PG SPZ-A by SPU and by year (1995-2004). Table 48 depicts the total area of natural regeneration in each SPU by management unit in PG. NR in PG low SPU is often located in Prince George TSA.  And Figure 51 depicts the differences of area regenerated by management unit in each SPU in PG.  0 200 400 600 800 1,000 1,200 1,400 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 A re a/ ha Year Total Area Regenerated (Natural) with Interior Spruce in SX PG SPZ-A by SPU and by Year (1995-2004) Low High 96 Table 48 Total area regenerated (natural) with interior spruce in Sx PG SPZ-A by SPU and by Management Unit (1995-2004) Management Unit Low High Total area (ha) Prince George TSA 3,099 133 3,232 Quesnel TSA 1,445 1,025 2,470 Williams Lake TSA 848 145 993 Woodlot 120  120 TFL 53 59  59 MacKenzie TSA 32  32 Robson Valley TSA  4 4 Fort Nelson TSA  2 2 Dawson Creek TSA 1  1 Total area (ha) 5,604 1,309 6,913  Figure 51 Total area regenerated (natural) with interior spruce in Sx PG SPZ-A by SPU and by Management Unit (1995-2004)  0 500 1,000 1,500 2,000 2,500 3,000 3,500 A re a/ ha Management Unit Total Area Regenerated (Natural) with Interior Spruce in SX PG SPZ-A by SPU and by Management Unit (1995-2004) Low High 97 4.7 Map representations for openings Map representations illustrate the spatial distribution and patterns of openings based on their related attributes, such as plantation magnitudes, genetic classes and plantation frequencies. The openings are showed with different symbology to provide visualization representations of different genetic classes in PG.  Kernel density tool is used to interpolate the probability surface of opening occurrence as well.  There are silviculture openings, which are planted, and natural regeneration openings reported as following. 4.7.1 Silviculture openings Figure 52 illustrates the temporal changes of opening distribution with all genetic classes planted in PG from 1995 to 2004.  The bubbles size indicates the stem sum amount for each opening by using the Quantities Symbology in ArcMap.  In 1995 and 1996, openings with more stems planted cluster near the west of the mountain range.  More plantations occur along the mountain chain. However, in the south of PG zone, plantation is not very frequent, where only small amount of openings with less stems are planted, there are three hotspots in PG accumulatively from 1995 to 2004.  One is in middle north, near Prince George itself, one is in the south and the third one varies its shape and location merging with the south area in 2004. 98  1995 1996 1997  1998 1999 2000  2001 2002 2003     2004 Figure 52 Temporal changes of opening distributions of interior spruce planted over time (1995-2004). Planted Stem 0 - 20000 20001 - 40000 40001 - 200000 SPZ_PG 99 Figure 53 depicts the occurrence of spatial openings with silviculture activity from 1995 to 2004. It is generated with kernel density tool, whose output is of raster dataset type.  The red area indicates more openings occur here, which are more clustered to each other.  The green area has fewer openings planted relatively.  Figure 54 shows the intensity hotspot of plantation in PG openings from 1995 to 2004. 100  1995 1996 1997  1998 1999 2000  2001 2002 2003     2004  Figure 53 Distribution of interior spruce planted over time and space (1995 -2004) (overlayed with the opening occurrence surface).  Plantation    No plantation 101  Figure 54 Hotspot of plantation intensity in silviculture openings (1995-2004).  102 4.7.2 Distribution of genetic classes Figure 58 depicts the genetic class distribution among planted openings in PG from 1995 to 2004, according to the composition of genetic classes of Sx seeds deployed.  There are more ‘A’ class openings clustering in the south and the northwest of PG city.  There is higher ratio of ‘B’ class in the northern part of PG, and ‘B’ class is intensively deployed in similar locations to ‘A’ class except in the south eastern area.  N class distribute in the east, mixing with ‘A’ class and B class. ‘B+’ is not frequent on the map.  It represents 39% of total plantation in PG (Figure 27).  Figure 56 depicts the plantation of different genetic classes clustering as hotspots over time. 103  Figure 55 Locations of interior spruce planted over time (1995-2004).  104  1995 1996 1997  1998 1999 2000  2001 2002 2003     2004  Figure 56 Temporal changes of opening distributions with different genetic classes seeds deployed overlayed with the opening occurrence probability surface (1995-2004).  Red area has denser distribution of ‘A’ class seed deployment.  Green area shows few occurrence of plantation.  ‘B+’ and ‘B’ class are in the middle part of the spectrum, as dark orange and yellow. A   B+  B     No plantation 105  Figure 57 depicts openings deployed with orchard 214 seeds from 1996 to 2004 in PG.  Figure 57 Locations of interior spruce with SO 214 seeds over time (1996-2004).  106 4.7.3 Natural regeneration The figure below depicts natural regeneration openings within PG.  And Figure 59 shows the spatio-temporal change of NR opening distribution in PG from 1995 to 2004.  There are similar NR hotspots as planted openings in the south of PG.  There are also similar patterns as orchard 214 seed in the south and north of Prince George city (Figure 57).  The distribution of NR openings is similar to the pattern of southern plantation openings, while they are smaller in amount than plantation openings.  107   Figure 58 Locations of interior spruce naturally regenerated over time (1995-2004).  108  1995 1996 1997  1998 1999 2000  2001 2002 2003     2004  Figure 59 Distributions of openings with interior spruce naturally regenerated over time and space (1995-2004) (overlayed with the opening occurrence surface).  More Regeneration    No regeneration reported 109 5 Discussion Based on the results above, there are two major findings suggested: First, the temporal trends show that natural regeneration and ‘B’ class use in propagation is replaced by ‘A’ class seed use over time.  Consistent with the previous studies of genetic diversity for BC (Timberline Forest Inventory Consultants Ltd., 2007), the temporal changes and geographic-distribution patterns of genetic resources deployment within PG zone show that selected seed use targets are achieved in different spatial and temporal scales, which leads to the present genetic diversity status from both the silvicultural and natural regeneration perspectives.  Also plantation spatial distribution patterns are not random in the study area, which reveals the spatial clustering of openings with different seed sources. Secondly, GIS modeling allows us to describe the detailed spatio-temporal changes of the seed deployment process.  It provides a summary report of seed deployment with different seed classes over time and space, as well as produces mapping outputs for seed deployment visualization at the landscape-level.  The GIS models developed have an effective capacity to generate knowledge by summarizing and mapping GRM data in multiple zones for different species as set out in the second thesis objective.  This exploratory research may lay the ground for further analysis related to climate change and site level environmental conditions constrains for reforestation, as well as population genetics researches. 5.1 Incremental genetic resources improvement 5.1.1 Natural regeneration openings and silviculture openings overview The comparison of silviculture plantation versus natural regeneration depicts the dynamics of improved seed sources deployed at different scales, especially for ‘A’ class and ‘B’ class seeds. Natural regeneration is treated as a minor forest recovery factor in the research which is different from artificial regeneration as more selected seeds deployed in silviculture openings (Province of British Columbia, 2007; Tree Improvement Branch, 2007).  The increasing trend of NR is less significant than artificial regeneration across silviculture openings (Table 17 and Figure 21). According to Timberline Forest Inventory Consultants Ltd. (2007), the NR across the whole 110 reporting period is 533,795 ha, which is about eight times larger than the result in PG alone. Therefore, a high proportion of the NR openings are not discussed here, due to the limitation of data scope.  Also NR would occur in planted openings and would likely remain unreported; therefore the estimates of NR would likely be under-represented in this analysis. The NR distribution varies at different spatial scales.  In PG zone, NR area, which mainly distributes in SBS zone (Figure 45), is smaller than plantation (Table 31 and Figure 35).  The overall trend of NR among BEC zones decreases overtime and is indicative of its minor role of forest recovery versus plantation (Table 41).  NR of Sx forest cover follows harvesting trends. For an instance, the Prince George TSA and the Quesnel TSA have the most plantations in PG (Table 32).  The recovery of Sx forest cover follows the ecological and climatic gradient similar to its natural population.  The SBS area overlaps the major Sx timber supply region (Figure 45), which has more NR occurrence.  Though management units are not a major reporting unit here, these results suggest that NR records show a random pattern over time (Table 33 and Figure 36). Because NR is mainly derived from the field survey, while plantation is better documented with seedling numbers and area, NR doesn’t play a significant part in terms of its magnitude. For all silvicultural openings in the database 23.8% of opening area reported from 1970 to 2004 contains improved Sx seed sources deployed in BC.  The artificial plantation data from RESULTS is more elaborate than NR data in terms of the opening frequency and area treated.  However, even if the NR data are updated with better integrity, NR would likely remain a minor component of reforestation records compared with plantation (Figure 21 and Figure 35).  The provincial historical context of forest recovery suggests that there are incremental increasing trends of ‘A’ class seed deployment over time especially after 1988 (Table 11and Figure 16).  One reason for this is that there is no seed orchard plans prior to 1988 (Figure 15).  Also there is a policy shift which requires seedlots to be registered for plantation at this time (Province of British Columbia, 2007c.).  Thus, it is possible that some of the ‘N’ class may be reported as ‘B’ class which causes ‘N’ class deployment to shrink after 1988 while B class use surpasses it (Figure 17).  However; there is no way to determine the exact amount of these deployments. 111 BVP, PG and PGN (BPP) are three adjacent SPZs which allow seed sources to be deployed interchangeably.  For the adjacent zones, where seeds are allowed to be deployed across boundaries, ‘A’ class increase gradually after 1995, while ‘B’ class is used frequently as well and its spatial distribution has two tails with 1995 as a peak.  ‘B’ class remains a major seed source representing 50.15% of the total silvicultural area in adjacent BPP zones (Figure 19 and Figure 20).  ‘B+’ is consistently a very small share of the total seed deployment according to its area and stem proportions, per year or accumulatively.  The ‘N’ class continues to shrink over time and represents a very small portion of total silviculture area.  These trends indicate the increasing genetic gain of Sx plantation at the landscape level in both BC and all BPP zones. 5.1.2 Prince George zone overview For the PG zone, the spatial openings reported represent 48.3% of the total area of all species in PG from 1995 to 2004.  The increasing trend of ‘A’ class in PG shows that the magnitude of high quality seed use keeps rising.  Also the deployment activities meet the 75 % selected seed use target of FGC business plan (2006/07) in the study area.  In order to evaluate the impact of Sx forest practices in PG zone after 1995, the dynamics of seed classes is useful to estimate the genetic resource recovery and forest resilience state.  In PG more forest recovery openings are trackable within the study period where there are more spatial opening records and higher ratio of ‘A’ class seed use (Table 24 and Table 25). PG seed orchard programs have been developed for 20 years, which ensures plenty of improved seed sources.  Tracking ‘A’ class seed source at seedlot and seed orchard level can help foresters to locate seedlings and monitor the progeny performance for further selection among improved stocks.  The increasing genetic quality of PG zone is the consequence of intensive deployment of seed orchard seeds with higher genetic worth, such as orchard 214 seeds (Table 28 and Figure 33), which have an average genetic worth of 23% (Woods Ed., 2006).  For orchard 214 seedlots, seed supply is cyclical, which may bring about fluctuations of seedling amount by year (Table 29 and Table 30).  Orchard 214 seeds are not evenly dispersed among BEC zones (Table 38 and Figure 41).  Thus, ‘A’ class seed deployment distributes unevenly overtime and 112 space though it keeps growing in total (Figure 30 and Figure 40).  However, repeating intensive use of a particular genetic source such as a specific seedlot may potentially affect the genetic diversity through oversimplification of forest management practices over time.  NR preserves the natural gene pool of Sx, while ‘A’ class use provides relatively higher genetic diversity than the local wild populations (Stoehr and El-Kassaby, 1996).  According to the FGC plan 2006/2007, the diversity of 214 seed is ensured by the relatively large size of effective population, which relates to its parent size (34).  High quality seed sources ensure higher genetic improvement of GRM and lower effects on the genetic diversity of natural populations (Yanchuk, 2001). The comparison of plantation status amongst BEC zones reveals the significant seed stock distribution of forest recovery in major BEC zones (Figure 38).  Also the biogeoclimatic conditions constrain the plantation within the dominant BEC zones.  The BEC zone system represents regional distribution of natural species in BC (Watts and Tolland, 2005).  Interior spruce ranges from alpine tundra, subalpine and montane boreal areas (e.g., ESSF SBS and SBPS zones), to cool temperate climatic areas (Watts and Tolland, 2005).  A similar trend may be seen in the dominant SPUs as well (Figure 46).  SPU PG low is one target area of improved seed deployment (Table 43 and Table 45), because of its elevation and it’s overlapping with the SBS zone.  SPU PG low is intensively replanted with ‘A’ class seed, where increasing amounts of improved seed stock is planted over time (Figure 47 and Figure 48).  For the 214 orchard seed, the total area in PG low and PG high also increases (Figure 49). The annual change of plantation frequency and intensity varies at different zonal scales.  Also the deployment area of different seed sources varies within each zonal unit (e.g., BEC zones and SPUs).  However, there is more frequent plantation within the SBS zone and the PG low unit which also shows a spatial clustering trend of plantation in PG by their biogeoclimatic and topographical conditions, such as PG low and the SBS zone.  PG low is the major seed production target region which contains orchard 214 (Woods, 2006).  The overall reforestation area has decreased over the past ten years, but the genetic quality of reforestation improves overall (Figure 30).  The spatial distribution of seed deployment at different zonal scale reveals 113 the harvest locations as well.  Thus this analysis is capable of zooming into different spatial scales to track different seed orchard seed over time and space. 5.2 Seed deployment by species From 1995 to 2004, eleven species are deployed in the PG Seed Planning Zone, of which the majorities are interior spruce and lodgepole pine (Figure 27).  Between 1999 and 2004, Pli is planted more broadly than Sx (Figure 22 and Figure 24).  5.9% of all BC openings are represented in this subset of PG zone.  The non-spatial openings are only about 23% of the total plantation area in PG, which are not able to be represented in map visualizations.  These openings are plantations in PG from 1995 to 2004 without spatial polygons assigned.  However, the magnitude of non-spatial area decreases (Figure 24), not only in the total area but also in each species.  There are 11 species recorded in spatial openings (Table 18) and 13 for the non- spatial (Table 20).  However, only Pli, Sx and Fdi are reported every year, but Pli have larger plantation area than Sx (Table 21 and Figure 25).  This suggests that the silvicultural operation for Pli is intensive but spatial information is not broadly assigned for the openings.  For interior lodgepole pine, PG low SPU_Pli uses four seed orchards (220, 222, 236 and 237) before 2015, and the adjacent SPUs also employ enough seed orchard for plantation.  The Douglas-fir seed orchard program is smaller in scale than both lodgepole pine and interior spruce (Forest Genetics Council of British Columbia, 2007). Despite the change of genetic composition in Sx in PG, the genetic class trends among different species do not follow the same pattern according to the multispecies report.  The seed production of Sx orchards is better developed when compared to Pli orchards according to the amount and quality of ‘A’ class seed deployed in PG zone (Table 24).  Between different opening types, the spatial and the non-spatial, the composition of species differs as well.  Sx and Pli both have large amounts of plantations in PG (Figure 27).  However, Sx has higher ratio of ‘A’ class seed use (Figure 28 and Figure 29).  This is also one of the reasons why Sx is selected for this exploratory study which has elaborate and trackable records within the technical scope of this research. 114  5.3 Spatio-temporal data overview and visualization Visualizing seed deployment dynamics allows the generation of insights into both spatial and temporal patterns that may have arisen due to specific policies or management practices.  The kernel density analysis depicts the spatial opening distribution over time.  Spatial distribution and spatial trend from 1995 to 2004 reveal north and south hotspots around the city of Prince George (Figure 54 and Figure 55).  At different geographic scales, the spatial and temporal distribution of genetic classes is not stable.  In Figure 52, tree numbers planted in spatial openings varies over space and time revealing temporal distribution patterns.  For different genetic classes, there are different spatial patterns as well (Figure 56).  The map representation shows how these clusters are spatially distributed over time in PG.  This spatial and temporal change can also be visualized for fractional components such as ‘A’ class seed (Figure 55) and orchard 214 seed (Figure 57). With the kernel density maps, it is easy to identify the hotspots of ‘A’ class in the north and south, where plantation is more intensive (Figure 57).  The variation of NR distribution each year is significant, of which there are two hotspots merged together in the south of Prince George city, but its shape does not change very much after 2000 (Figure 58 and Figure 59).  Most NR areas are near the city, but there is likely a buffer zone around the city to prevent the city from merging into the hotspot.  The reason for this may lie in people’s perceptions about clear cutting near their residence because of the visual impact of forest management (Sheppard, 2000) or for ownership issues.  So the plantation openings are distributed further away from the city area, removed from the more densely populated local community.  The aggregation of selected seeds may create potential risks for these improved forest trees, because 12,180 ha (1995-2004) of plantation are within 52,807 ha area, which is about 20% silvicultural openings (Figure 54).  If there are epidemics of diseases or pest that find a niche with these genetic sources, it may not be easy to minimize negative impacts on the improved forest stands.  A mosaic pattern of tree stands tends to have a lower probability of higher diversity loss in fire or insect epidemics (MacCleeryl, 1995). 115 5.4 For further studies This study is portable for multiple zones and species, which may be worthwhile for investigating the background GRM status for developing criteria and indicator systems for the purposes of decision support system construction.  In order to develop the criteria and indicators for GRM, the genetic gain and improvement indices need to be developed in the future.  The effective population size, genetic worth value and other quantitative genetics indicators are not included in this research but represent an area of potential extension of this work.  To support further spatial statistics modeling, it is necessary to increase the use of continuous variables and indices, such as landscape fragmentation metrics for the landscape level analysis and biodiversity indices to further evaluate the ecological impact of plantation (Barbaro, 2007).  For further integrated studies on forest genetic resources changes related to environmental conditions, it is also worthwhile to add in climate factors and tree performance variables.  Therefore, not only the forest inventory data, but also climate, pedology, ecology and hydrology data are necessary to enhance the potential development of a decision support system.  It is also important to improve the quality of long-term records of GRM as we are not sure what management questions we may have in the future.  For example, it is necessary to develop a unique Opening ID field as a key to join the spatial and aspatial datasets.  Opening ID is supposed to be a unique identification number to distinguish openings in FREP.  However, there are duplicates in the spatial data set, which are treated as data errors or multi-parts openings in the data preparation. In order to provide high quality spatial data for future studies, the denomination rules of openings should be well followed in the information management process. Also, there are non-spatial openings without spatial references, which shrink in frequency in the PG case (Figure 60). The ratio of non-spatial openings is close to 1% among all openings operated till 2004. However, these non-spatial openings bring in underestimation of the silviculture magnitude in the map representation of the results. For the construction of provincial forest genetic resource information service systems, it is note worthy to decrease the occurrence of such openings. 116  Figure 60 Non-spatial opening ratio in PG zone from 1995 to 2004 The map representation provides a powerful perspective on spatial and temporal trends of plantation within the study period.  In future applications, the preparation of new data input needs to be consistent, such as new SPZs, new species, and new report items.  The attribute data is supposed to be updated year by year.  For this research, the plantation records in 2005 is only recorded as 11.6 ha in PG, which is too low compared with its normal trend from 1995 to 2004. Thus there are no 2005 openings included here.  The integration of an improved NR dataset and plantation dataset is a possible option for data management as well.  Though for all scenarios the method needs to be refined and adjusted to other SPZs and species, the framework is generally supported by this research.  117 6 Conclusion Twenty-eight years ago, the BC Forest Service established the first record of silvicultural and reforestation activities.  These historical records have the potential to be utilized for the improved management of issues such as GRM monitoring, responding to the climate change and reforestation.  The results of this research provide spatio-temporal evidence of the effects of GRM policies and activities at the landscape level.  These insights can be useful for dealing with future uncertainties, understanding the relationship of geographical variation of species and GRM strategies, and illustrating historical changes in tree productivity.  This can be used to develop GIS decision support tools in the future that could integrate these multi-disciplinary concepts and information to uncover relationships between present phenomenon and future consequences. With the aid of GIS, it is easier to understand these environmental changes more thoroughly, as well as gain insights into how GRM may help use to adapt to changes in the future. The background report, which covers the whole GRM period for Sx, suggests that there is a significant gap in selected seed use between the two periods before and after 1987.  Since 1987, the gene resources have improved due to the implementation of a long-term seed orchard plan, which allows adequate seed stocks for reforestation.  The seed deployment in adjacent zones (BVP, PG and PGN) follows a similar increasing trend in Sx ‘A’ class seed use as in the interior of BC.  Natural regeneration plays a minor role in forest recovery processes compared with the silviculture areas however there are significant limitations in documenting this component of reforestation as previously discussed.  The multiple species analysis indentifies the major silviculture species in PG, Sx and Pli among whom Sx has more adequate seed orchard support. In PG openings, Sx genetic resources have improved significantly since 1995, with radical increases of ‘A’ class seed use particularly in the SBS zone.  The spatial clusters of ‘A’ class populations reveal hotspots of intensive management of Sx in PG and may represent potential areas of risk if not well managed in the future. This research indicates that GRM target of selected seed use ratio (75%) in Sx plantation is exceeded in PG zone in 2004 (90%) and it also illustrates the history of progression of GRM in 118 this region.  Such historical assessment of GRM activities can be applied to other species of interest or applied to other SPZs of interest.  GRM faces significant future challenges such as adaptation to climate change and this research lays the ground work for such projects to commence.  Long- term data management will be an issue for future studies, which involves integrity within a particular dataset over time as well as addressing issues of data interoperability across multiple databases.  An interdisciplinary approach to forest management will certainly be required to address future concerns and highlights the need for spatial data to be able to be brought together from diverse areas.  GIS tools can be more frequently applied in such analysis but are dependent on high quality data if we are able to use them to solve complex land management problems.   119 References Anderson, L.M. 1981. Land use designations affect perception of scenic beauty in forest landscapes. Forest Sciences 27: 392-400. Austin, M.A., D.A. Buffett, D.J. Nicolson, G.G.E. Scudder and V. Stevens (eds.) (2008). Taking Nature’s pulse: The status of biodiversity in British Columbia. Victoria, BC: Biodiversity BC. Baker, W. L., & Weisberg, P. J. (1997). Using GIS to model tree population parameters in the rocky mountain national park forest-tundra ecotone. Journal of Biogeography, 24(4), 513- 526. Barbaro, L., Rossi, J., Vetillard, F., Nezan, J., & Jactel, H. (2007). The spatial distribution of birds and carabid beetles in pine plantation forests: The role of landscape composition and structure. Journal of Biogeography, 34(4), 652-664. Boldstad, P. (2002). GIS fundamentals: A first text on geographic information systems (First ed.). White Bear Lake, Minnesota: Elder Press. Canadian Model Forest Network. (2008). Network initiatives--local level indicators > definitions. Retrieved Oct 2, 2008, from http://www.modelforest.net/cmfn/en/initiatives/indicators/definitions/default.aspx Centre for forest conservation genetics in UBC. (2007). ClimateBC: A program to generate climate normal data for genecology and climate change studies in western Canada. Retrieved May 28th, 2008, from http://www.genetics.forestry.ubc.ca/cfcg/climate- models.html Chamberlain, B. C., & Meitner, M. J. (2008). Automating the visual resource management and harvest design process. Landscape and Urban Planning (accepted).  120 Cleveland, W. S., & McGill, R. (1985). Graphical perception and graphical methods for analyzing scientific data. Science, 229(4716), 828-833. Dale, V. H., Joyce, L. A., Mcnulty, S., & Neilson, R.P. et al. (2001). Climate change and forest disturbances. Bioscience, 51(9), 723-734. Edgell M. C.R. (2007). Biogeoclimatic zone. Retrieved April 20th, 2007, from http://www.thecanadianencyclopedia.com/index EUFORGEN. (2007). The european forest genetic resources Programme . Retrieved Sept. 29th, 2008, 2008, from http://www.bioversityinternational.org/networks/euforgen Finstad, K., Bonfils, A. C., Shearer, W., & Macdonald, P. (2007). Trees with novel traits in Canada: Regulations and related scientific issues. Tree Genetics & Genomes, 3(2), 135-139. Forest Genetics Council of BC. (2007). The forest genetics council of British Columbia. Retrieved June 28th, 2008, from http://www.fgcouncil.bc.ca/ Forest Genetics Council of British Columbia (2007). Forest genetics council. Retrieved 2007 April 15th, 2004, from www.fgcouncil.bc.ca Forestry Agency, the Ministry of Agriculture, Forestry and Fisheries of Japan. (2007). Annual report on trends in forests and forestry fiscal year 2006 (summary). Japan: The Ministry of Agriculture, Forestry and Fisheries of Japan. Graham, A. (1999). Late cretaceous and cenozoic history of north American vegetation: North of mexico Oxford University Press, USA. Hamann, A., & Wang, T. (2006). Potential effects of climate change on ecosystem and tree species distribution in British Columbia. Ecology, 87(11), 2773-2786.  121 Hamann, A., Aitken, S. N., & Yanchuk, A. D. (2004). Cataloguing in situ protection of genetic resources for major commercial forest trees in British Columbia. Forest Ecology and Management, 197(1/3), 295-305. Jennings, M. D. (2000). Gap analysis: Concepts, methods, and recent results. Landscape Ecology, 15(1), 5-20. Ji, W. W., & Leberg, P. (2002). A GIS-based approach for assessing the regional conservation status of genetic diversity: An example from the southern appalachians. Environmental Management, 29(4), 531-544. Johnsen, K., Samuelson, L., Teskey, R., McNulty, S., & Fox, T. (2001). Process models as tools in forestry research and management. Forest Science, 47(1), 2-8. Johnson, E. A., & Fryer, G. I. (1989). Population dynamics in lodgepole pine-Engelmann spruce forests. Ecology, 70(5), 1335-1345. Jones, P. G., Beebe, S. E., Tohme, J., & Galwey, N. W. (1997). The use of geographical information systems in biodiversity exploration and conservation. Biodiversity and Conservation, 6(7), 947-958. Koskela, J., Buck, A., & and Teissier du Cros, E. (editors). (2007). EUFORGEN climate change and forest genetic diversity. Maccarese (Rome) Italy: Bioversity International. Ledig, T. F. (1986). Conservation strategies for forest gene resources. Forest Ecology and Management, 14(2), 77-90. Leite, S. M. M., Bonine, C. A., Mori, E. S., do Valle, C. F., & Marino, C. L. (2002). Genetic variability in a breeding population of eucalyptus urophylla STB lake. Silvae Genetica, 51(5/6), 253-255.  122 Lemes, M. R., Grattapaglia, D., Grogan, J., Proctor, J., & Gribel, R. (2007). Flexible mating system in a logged population of swietenia macrophylla king (meliaceae): Implications for the management of a threatened neotropical tree species. Plant Ecology, 192(2), 169- 179. Lipow, S. R., Vance-Borland, K., St Clair, J. B., Henderson, J., & McCain, C. (2004). Gap analysis of conserved genetic resources for forest trees. Conservation Biology, 18(2), 412-423. Liu Jiping, Lv Xiangguo, & Yin Shubai. (2005). GAP analysis: A geographic approach to protect biological diversity. Progress in Geography, 24(001), 41-51. Lobe, J. (2004). Hamburger consumption spurs Amazon deforestation. Retrieved May 31st, 2008, from http://www.commondreams.org/headlines04/0409-05.htm) Last accessed 8/29/04 Ministry of Forests (Ed.). (2004). The state of British Columbia’s forests, 2006. Victoria, British Columbia: Ministry of Forests and Range. Morgenstern, E. K. (1996). Geographic variation in forest trees: Genetic basis and application of knowledge in silviculture.Vancouver B.C.: University of British Columbia Press. Noss, R. F. (2001). Beyond Kyoto: Forest management in a time of rapid climate change. Conservation Biology, 15(3), 578-590. Province of British Columbia (Ed.). (2005). Chief forester’s standards for seed use. Victoria, BC: Province of British Columbia. Retrieved from http://www.for.gov.bc.ca/code/cfstandards/html/index.htm Province of British Columbia. (2007a). Indicator 6 – genetic diversity -- the state of British Columbia’s forests – 2006. Retrieved June 26th, 2008, from http://www.for.gov.bc.ca/hfp/sof/2006/06.htm  123 Province of British Columbia. (2007b). Forest and range evaluation program. Retrieved June 26th, 2008, from http://www.for.gov.bc.ca/hfp/frep/ Province of British Columbia. (2007c). Seed planning and registry application. Retrieved May 28th, 2008, from http://www.for.gov.bc.ca/HTI/spar/ Province of British Columbia. (2007d). Tree improvement branch. Retrieved May 28th, 2008, from http://www.for.gov.bc.ca/hti/ Province of British Columbia. (2008). RESULTS. Retrieved June 26th, 2008, from http://www.for.gov.bc.ca/his/results/index.htm Putz, F. E., Blate, G. M., Redford, K. H., Fimbel, R., & Robinson, J. (2001). Tropical forest management and conservation of biodiversity: An overview. Conservation Biology, 15(1), 7-20. Rajora, O. P., & Dancik, B. P. (2000). Population genetic variation, structure, and evolution in engelmann spruce, white spruce, and their natural hybrid complex in alberta. Can.J.Bot, 78(6), 768-780. Rehfeldt, G. E., Ying, C. C., Spittlehouse, D. L., & Hamilton Jr, D. A. (1999). Genetic responses to climate in pinus contorta: Niche breadth, climate change, and reforestation. Ecological Monographs, 69(3), 375-407. Research Branch, Tree Improvement Branch and Forest Genetics Council of British Columbia. (2007). Workshop synopsis: Forest tree genetic resource conservation and managment (GRM) in British Columbia (working document. Victoria, British Columbia: Tree Improvement Branch and Forest Genetics Council of British Columbia. Rice, R. E., Sugal, C. A., Ratay, S. M., & Fonseca, G. A. (2001). Sustainable forest management: A review of conventional wisdom. Advances in Applied Biodiversity Science, 3  124 Rusanen, M., Napola J., & Nikkanen, T. et al. (2004). Forest genetic resource management in Finland. Helsinki: Finnish Forest Research Institute and Ministry of Agriculture and Forestry. Shen, X. (2007). Conservation strategies of forest genetic resources. Forestry Science and Technology Development (LKKF), 21(003), 1-4. Sheppard, S. R. J. (2000). Beyond visual resource management: Emerging theories of an ecological aesthetic and visible stewardship. In Sheppard and Harshaw (Ed.), Forest and landscapes: Linking ecology, sustainability and aesthetics. IUFRO research series (pp. 149–172). Wallingford, UK: CABI Publishing. Smith, D. A., Ralls, K., Hurt, A., Adams, B., Parker, M., & Maldonado, J. E. (2006). Assessing reliability of microsatellite genotypes from kit fox faecal samples using genetic and GIS analyses. Molecular Ecology, 15(2), 387-406. Sorensen, F. C., Campbell, R. K., & Franklin, J. F. (1990). Geographic variation in growth and phenology of seedlings of the abies procera/A. magnifica complex. Forest Ecology and Management, 36(2), 205-232. Stoehr, M., & El-Kassaby, Y. (1997). Levels of genetic diversity at different stages of the domestication cycle of interior spruce in british columbia. Theoretical and Applied Genetics (Germany), 94(1), 83-90. Timberline Forest Inventory Consultants Ltd. (2006). Genetic Diversity for British Columbia (Draft- V1). Victoria, British Columbia: Timberline Forest Inventory Consultants Ltd. Timberline Forest Inventory Consultants Ltd. (2007). Benchmarking genetic diversity in British Columbia: 1970 – 2005 (draft). Victoria, BC: Timberline Forest Inventory Consultants Ltd. Tree Improvement Branch. (2006). Gene resource management. Retrieved April/15, 2007, from http://www.for.gov.bc.ca/hti/generesource.htm  125 Tree Improvement Branch  (2007). Forest tree gene resource management in British Columbia. In Tree Improvement Branch (Ed.), Tree improvement branch update extension note. Victoria, B.C.: Tree Improvement Branch. Tree Improvement Branch  (2007). SeedMap web map and reporting system. Retrieved April/15, 2007, from www.for.gov.bc.ca/hti/seedmap/index.htm Wang, T., Hamann, A., Spittlehouse, D. L., & Aitken, S. N. (2006). Development of scale-free climate data for western canada for use in resource management. Int.J.Climatol, 26, 383- 397. Watts S. and Tolland L. (2004). Forestry handbook for British Columbia (5th ed.). Vancouver , B.C.: The Forestry Undergraduate Society Faculty of Forestry University of British Columbia, Vancouver. Weisberg, P. J., & Baker, W. L. (1995). Spatial variation in tree seedling and krummholz growth in the forest-tundra ecotone of rocky mountain national park, colorado, USA. Arctic and Alpine Research, 27(2), 116-129. White, T. L., Adams, W. T., & Neale, D. B. (Eds.). (2006). Forest genetics. Wallingford, United Kingdom: CABI Publishing. Woods, J. H. (Ed.). (2006). Forest genetics council of BC business plan 2006 – 2007. Victoria, BC: Forest Genetics Council of BC. Retrieved from http://www.llbc.leg.bc.ca/public/PubDocs/bcdocs/343629/FGCBP_2006_07.pdf Ying, C. C., & Yanchuk, A. D. (2006). The development of British Columbia’s tree seed transfer guidelines: Purpose, concept, methodology, and implementation. Forest Ecology and Management, 227(1-2), 1-13.   126 Appendix 1 BGC Zones Zone Name Subzone Name examples ESSF Engelmann Spruce -- Subalpine Fir Dry Cold, Moist Mild, Dry Cool, etc. ICH Interior Cedar – Hemlock Dry Cool etc. IDF Interior Douglas-fir Dry Cool etc. SBPS Sub-Boreal Pine -- Spruce Dry Cold etc. SBS Sub-Boreal Spruce Dry Hot etc.   127 Appendix 2 Model graphics and geodatabase Step 1 SPZ model  Figure A2.1 Geodatabase of SPZ model  Figure A2.2 Model flow chart of Step 1-SPZ model.   128  Step 2 SPU model  Figure A2.3 Geodatabase of Step 2.  Figure A2.4 Model flow chart of Step 2-1   129  Figure A2.5 Model flow chart of Step 2-2.   130 Step 3-BECzone model  Figure A2.6 Geodatabase of Step 3.  131  Figure A2.7 Model flow chart of Step 3.   132  Figure A2.8 Geodatabase of Kernel density modeling  133  Figure A2.9 Model flow chart of Kernel density modeling.

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            data-media="{[{embed.selectedMedia}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
https://iiif.library.ubc.ca/presentation/dsp.24.1-0067090/manifest

Comment

Related Items