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Influence of large-scale food supplementation on diversity of rodent communities Kohler, Chris Mark 1993

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INFLUENCE OF LARGE-SCALE FOOD SUPPLEMENTATIONON DIVERSITY OF RODENT COMMUNITIESbyCHRIS MARK KOHLERB.Sc, The University of New Brunswick, 1987A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCE IN FORESTRYinTHE FACULTY OF GRADUATE STUDIES(Department of Forest Sciences)We accept this thesis as conformingto the required standardTHE UNIVERSITY OF BRITISH COLUMBIAOCTOBER, 1993© Chris Mark Kohier, 1993In presenting this thesis in partial fulfilment of the requirements for an advanceddegree at the University of British Columbia, I agree that the Library shall make itfreely available for reference and study. I further agree that permission for extensivecopying of this thesis for scholarly purposes may be granted by the head of mydepartment or by his or her representatives. It is understood that copying orpublication of this thesis for financial gain shall not be allowed without my writtenpermission.Department of c’es+ ScLeiAeSThe University of British ColumbiaVancouver, CanadaDate OcevJ I lDE-6 (2/88)11ABSTRACTRodents were monitored bi-weekly to determine seasonal responses of rodentdiversity to large scale supplemental feeding. Sunflower seeds were added byhelicopter to thinned, fertilized, pole-sized lodgepole pine stands in the Montane Spruce(MSdfl1) - Engelmann Spruce-Subalpine Fir (ESSFdC7)biogeoclimatic zone (transitionarea) of southern interior British Columbia. My objectives were: 1) to determine therate of seed consumption; 2) to determine the response of rodent communities tosupplemental feeding; and 3) to evaluate methods for expressing species diversity.Forty-eight 1-rn2 seed plots were used to estimate seed consumption rate. Consumptionrates did not differ among stands, or between inside (where trap bait was present) andoutside small mammal trap grids. Seed consumption rate increased as summerprogressed, and was faster during the second year than the first year of seeding. TheSimpson index indicated that diversity decreased in seeded areas, as a result ofdeclining evenness caused by increased deer mouse (Perornyscus inanicularus)population density. Species richness was maintained on seeded areas, and hence logseries alpha expressed higher diversity on one of the two seeded areas, when comparedto its respective control.I reviewed literature to assess the merits of using the Shannon-Wiener andSimpson indices, Shannon-Wiener and Simpson evenness, and log series alpha. Formy purposes, the Simpson index was the most useful for expressing changingdominance, and log series alpha was most useful for reflecting richness. Both indiceshave low sensitivity to sample size and are highly discriminatory. The ShannonWiener index and Simpson and Shannon-Wiener evenness were used for comparingresults among other studies.111TABLE OF CONTENTSPageABSTRACT.iiLIST OF TABLES vLIST OF FIGURES viiACKNOWLEDGMENTS ixGENERAL INTRODUCTION 1CHAPTER 1. INFLUENCE OF LARGE-SCALE FOOD SUPPLEMENTATION ONDIVERSITY OF RODENT COMMUNITIES 2INTRODUCTION 2Literature Review 2Objectives and Hypotheses 4METHODS AND MATERIALS 6Description of Study Area 6Experimental Design and Seed Distribution 6Seed Monitoring 7Rodent Populations and Diversity 8RESULTS 10Seed Consumption 10Rodent Populations and Diversity 13Populations 13Pooled Relative Abundances and Diversity 18Bi-weekly Diversities 23DISCUSSION 32Experimental Design 32Seed Consumption 32ivRodent Populations.33Diversity of Rodent Communities 34Control A- Treatment B Comparison 34Control C- Treatment D Comparison 37General Discussion 38CONCLUSIONS AND RECOMMENDATIONS 43CHAPTER 2. DIVERSITY: CRITIQUE AND FORMULA SELECTION 45INTRODUCTION 45BACKGROUND 45ASSESSMENT OF SOME POPULAR DIVERSITY MEASURES 47SPECIES ABUNDANCE MODELS 50INDICES BASED ON THE PROPORTIONAL ABUNDANCE OF SPECIES 51FOCUSING THE DIVERSITY CONCEPT 57LITERATURE CITED 59VLIST OF TABLESPageTable 1. ANOVA results showing no significant difference in seedconsumption rate among sunflower seed treatment grids B and D, orinside and outside small mammal grids. These data were collected on15 May and 14 June 1992, in the MSd1 - ESSFdC2 biogeoclimatic zonenear Ewer Creek (Vernon B.C.) 11Table 2. Bi-weekly population estimates (Jolly-Seber, except first and last*,which are MNA) for rodents on control grid A. (CHIP = chipmunks;PERO = deer mice; RBV = redback voles; PHEN = heather voles;M.LONG = long-tailed voles; ZAP = jumping mice) 14Table 3. Bi-weekly population estimates (Jolly-Seber, except first and last*,which are MNA) for rodents on treatment grid B. (CHIP chipmunks;PERO = deer mice; RBV = redback voles; PHEN = heather voles;M.LONG = long-tailed voles) 15Table 4. Bi-weekly population estimates (Jolly-Seber, except first and last*,which are MNA) for rodents on control grid C. (CHIP = chipmunks;PERO = deer mice; RBV = redback voles; PHEN = heather voles;M.LONG = long-tailed voles; ZAP = jumping mice) 16Table 5. Bi-weekly population estimates (Jolly-Seber, except first and last*,which are MNA) for rodents on treatment grid D. (CHIP chipmunks;viPERO = deer mice; RBV = redback voles; PHEN = heather voles;M.LONG = long-tailed voles) 17Table 6. Effect of sunflower seed treatment on total number of animals (N),number of species (R), and evenness (E). These are average values for1991, compared to those of 1992 (NC no change) 41viiLIST OF FIGURESPageFigure 1. Rate of sunflower seed germination and consumption by animals.Data were collected during 1991 and 1992, in thinned, fertilized,20-28 year old lodgepole pine stands in the MSdm - ESSFdC,biogeoclimatic zone of south central interior British Columbia 12Figure 2. Rank abundance plots for grid A control and grid B food supplementedcommunities. Summer data are pooled from mid-June to mid-August for1991 (and late May to 20 July for 1992), fall data are pooled from mid-August to mid-October 1991 (and 20 July to mid-October for 1992) 19Figure 3. Rank abundance plots for grid C control and grid D food supplementedcommunities. Summer data are pooled from mid-June to mid-August for1991 (and late May to 20 July for 1992), fall data are pooled from midAugust to mid-October (and 20 July to mid-October for 1992) 20Figure 4. Diversity of rodents in treated and control grids. Pooled populationdata for summer and fall. Summer data are pooled from mid-June to midAugust for 1991 (and late May to 20 July for 1992), fall data are pooledfrom mid-August to mid-October (and 20 July to mid-October for 1992).. 22Figure 5. Simpson indices of diversity and evenness for the control A-treatmentB comparison. Data were collected bi-weekly during 1991 and 1992 24vi”Figure 6. Simpson indices of diversity and evenness for the control C-treatmentD comparison. Data were collected bi-weekly during 1991 and 1992 26Figure 7. Shannon-Wiener indices of diversity and evenness for the control A-treatment B comparison. Data were collected bi-weekly during 1991 and1992 28Figure 8. Shannon-Wiener indices of diversity and evenness for the control C-treatment D comparison. Data were collected bi-weekly during 1991 and1992 29Figure 9. Log series alpha indices of diversity for both study replicates. Datawere collected bi-weekly during 1991 and 1992. Numbers near data pointsrepresent richness 30Figure 10. Hypothetical rank abundance plots illustrating the typical shape offour species abundance models: geometric series, log series, log normaland broken stick. Abundance of each species is plotted on a logarithmicscale against the species rank 48Figure 11. K-dominance curves ranking species for grid C control and grid Dfood supplemented communities, on July 5, 1991, and July 19, 1992.Species are ranked from most to least dominant 55ixACKNOWLEDGMENTSI was encouraged when Dr. Alton Harestad, Dr. Peter Marshall and Dr.Hamish Kinirnins became my supervisory committee. I thank Dr. Tom Sullivan, mysupervisor, for his patience and guidance on both personal and scientific levels.Thanks to Don Purdy from the B.C. Ministry of Forests, Vernon, for hisflexibility on sunflower seeding dates. All help in the form of time, materials andequipment, greatly improved the quality of this field research. Funding was obtainedthrough an NSERC operating grant to Dr. Sullivan, and several contracts from theB.C. Ministry of Forests Silviculture Section, Kamloops Region.There were contributions from a wide range of field assistants, and all of theirhelp was greatly appreciated. The Bolton family opened their doors, providing an idealbase for my project at the Bolton Ranch which saved time and money. Not only werethey very hospitable, but also their ranch provided us with countless, on-location,recreation and leisure opportunities. Special thanks to all Boltons, and especiallyEleanore’ s cookies.GENERAL INTRODUCTIONThere has been increasing concern about the degradation of our globalenvironment, and especially about the issue of decreasing biological diversity(biodiversity). Forestry practices which alter the environment are constantly underattack for various reasons, and they are blamed for decreasing biodiversity (Westman1990, Kimmins 1992). This claim is not always true, and frequently is not supportedby data. Accusations can result from a misinterpretation of statistics, because of thelarge variety of ways they can be used to describe diversity (Hunter 1990, Westman1990, Kimmins 1992).This study was designed to explore the validity of diversity as a concept forguiding present and future changes in forest management practices. Chapter 1 showshow supplemental feeding, as a forest stand protection technique, affects small mammaldiversity at the community level. Chapter 2 contains a review of conventional methodsof calculating diversity indices. Included with this ‘critique and formula selectionchapter?, are various opinions on the validity of these measures, and reasons forselection of the Simpson, Shannon-Wiener, and log-series alpha indices to summarizemy data.2CHAPTER 1INFLUENCE OF LARGE-SCALE FOOD SUPPLEMENTATIONON DIVERSITY OF RODENT COMMUNITIESINTRODUCTIONAdding sunflower seed to the forest as a diversionary food is a relatively newsilvicultural technique which reduces red squirrel (Tamiasciurus hurJsonicus) de-barkingdamage to juvenile lodgepole pine stands (Pinus conrorta var. iatfoiia) (Sullivan andKienner 1993). However, little is known of its effects on mammalian species diversity.Past evidence indicates that enrichment of all parts of the resource spectrum willincrease diversity, while an increase in only part of the resource spectrum will allowsuperior competitors to take advantage and exclude other species, thus decreasingdiversity (MacArthur 1972). The present study was designed to determine thepopulation density (abundance) response of each species in a small mammal communityto this resource enrichment, and to express the data in the context of diversity.LITERATURE REVIEWInformation on how supplemental food influences species diversity at the smallmammal community level (large scale) is scarce (Szaro and Saiwasser 1989).Abramsky (1978) reported an increase in species diversity when supplemental food was3added to a small mammal community. This supports MacArthur (1972) who suggestedthat: 1) an increase in the abundance of all resources caused by an overall increase in•production may lead to higher species diversity; and 2) an increase in only a part ofthe total resource spectrum may lead to a decrease in species diversity throughdominance by those species that are competitively superior in exploiting the augmentedresources. Abramsky (1978) holds that because he supplied “large amounts” of seed, aresponse in one group of organisms (i.e. immigrating Dipodomys ordii) would not haveprevented a similar response in other groups by intertaxon competition. This, hesuggests, supports MacArthur’s prediction #1 above. I do not agree, and offer that itactually rejects prediction #2. In this case, the “increase in part of the resourcespectrum” resulting from addition of a non-native seed is an increase in resourcediversity and spectrum in MacArthur’s (1972) model. Further complicatingAbramsky’s interpretation is his experimental technique, whereby he added a singlesupplemental food plot after monitoring his control and increased production plots for 3years. This is simple and temporal pseudoreplication (Hurlbert 1984).Whether an increase in a small part of the resource spectrum will decreasediversity should depend on: 1) size, behaviour and other interacting mechanisms ofinvading species (if any), and those already present and: 2) how their niche parametersoverlap with those of other resident species. Sullivan (unpubi.) found no difference indiversity between control and treatment areas supplemented with 2 applications ofsunflower seed at a rate of 22.7 kg/ha.A 2- to 3-fold increase in population density of terrestrial vertebrates is a typicaloutcome of small scale food supplementation experiments, which indicates that thesepopulations are frequently limited by food supply (Boutin 1990). Food is oftensupplied on a spatial scale that is relevant to the question of habitat choice byindividuals, but not to the questions of population regulation or community dynamics.Increased reproduction and survival usually occur in fed populations, but they do not4alter the pattern of population change. Food supplementation on a limited scale (1-2ha) merely creates small ‘hot spots’ which have an increased production of animals.• The ability to conduct large-scale (25 ha) experiments on food supplementationwas made possible by an operational program using diversionary food to protect foreststands. Aerial application of food, in a uniform distribution over large areas for a two-year period, was hypothesized to eliminate the population limitations caused by foodsupply. Based on this rationale, it was decided to investigate how rodent diversity wasaffected at this large-scale community level.OBJECTIVES AND HYPOTHESESThe challenge was to determine how to describe changing dynamics of smallmammal communities (order Rodentia) supplemented with a high energy food source,compared to dynamics of non-supplemented control communities. This was done byusing conventional diversity measures and interpreting the meaning of the diversitystatistics in the coitext of current silvicultural and small mammal communityparadigms.There were three main objectives: 1) to determine if sunflower seeds were eaten,and, if so, what the consumption rate was and how this rate changed over a two-yearperiod. This provided evidence that any change in diversity of rodents in the treatmentarea was probably due in part to sunflower seeds being eaten; 2) to transform rodentpopulation data, obtained by live-trapping bi-weekly for two years, into diversitystatistics; and 3) to discuss these data in the context of applied and theoretical science.Two main hypotheses were evaluated in the study: 1) that sunflower seedconsumption was the same on both supplementally-fed areas. Seed plots wereestablished outside and inside the trapping area (where trap bait was present).Therefore, it was necessary to test the hypothesis that seeds were consumed moreslowly where trap bait was present than where no bait was present; 2) and that overtime, rodent diversity would increase on food supplementation areas compared tocontrol areas where supplemental food was not added.56METHODS AND MATERIALSDESCRIPTION OF STUDY AREAField work was conducted 25 km west north-west of Vernon in the south centralinterior of British Columbia, between the latitudes of 50° 21.5’ to 50° 22.8’ N, andlongitudes of 119° 35.3’ to 119° 36.5’ W, near the northwest end of Okanagan Lake.Site elevation ranged from 1356 rn to 1478 m, in the MSd,T, - ESSFdC2 biogeoclimaticzone (transition area) (Meidinger and Pojar 1991). The climate is characterized bywarm, dry summers and cold, dry winters with mean July and January temperatures of+16 and -10° C, respectively, and a mean annual precipitation of 40 cm. In maturestands, the predominant coniferous species are western red cedar (Thuja plicata),Douglas-fir (Pseudotsuga nienziesii), subalpine fir (Abies lasiocarpa), Engel mannspruce (Picea engeiniannii) - white spruce (P. glauca) hybrid, western larch (Larixoccidentalis), and lodgepole pine.Two lodgepole pine stands (age 21 and 28 years), approximately 36 ha eachwhich had been thinned and fertilized, were selected for large-scale foodsupplementation. Two other thinned and fertilized stands of 17.8 and 38.8 ha, aged 28and 20 years, respectively, were used for control replicates. The average d.b.h. oftrees on control and treatment blocks ranged from 13.8 ± 0.2 cm to 16.8 ± 0.3 cm.EXPERIMENTAL DESIGN AND SEED DISTRIBUTIONA randomized block design was used, with two control and two treatment areas.Rodent sampling grids of 4 ha were within the center of each area. All treatment areaswere separated by at least 100 m to minimize animal movements between grid areas.7Treatment areas were selected on the basis of efficiency of helicopter seeding, ratherthan on the animal communities that were expected in each area. Treatment blocks hadsunflower seed applied once a month from early June to October in 1991 (5 times), andMay to October in 1992 (6 times). This provided a liberal amount of food to all rodentspecies living in the treatment areas.Sunflower seed was applied uniformly over the treatment areas at a rate of 20kg/ha/month, using a helicopter carrying a cone shaped hopper designed to spreadfertilizer or grass seed. Seed survival plots were established to test the hypothesis thatsunflower seed was not being eaten at the same rate inside the trap grids as outside, orat the same rate among both supplementally fed treatment areas. Each treatment areahad 48 1-rn2 seed plots in a square arrangement, 24 of which were within the trapgrids, the other 24 being in the surrounding seeded area. Seed plots within the trapgrids, where seed consumption rate may have been affected by the presence of trap bait(oats and peanut butter), were considered ‘different’ than those plots outside the trapgrids. Therefore, the four areas were compared by 2x2 factorial ANOVA, realizingthat the in-out of trap grid comparisons were not true replicates because of lack ofindependence (i.e. they were only separated by 28.6 m).SEED MONITORINGSix 1-rn2 seed plots were spaced 28.6 rn apart on each of 8 lines. On each line, 3plots were in, and 3 were out of the trap grid. All seeds landing in these plots duringaerial application were removed, and replaced with 15 seeds whose locations weremarked by toothpicks (aerial application approximated 15 seeds/rn2). The intention wasto record rate of consumption, and number of seeds germinated on a bi-weekly basis.Three checks were completed during 1991, and 10 checks during 1992. Data stated as8‘rate of consumption’ refers to number of seeds eaten from the date of the most recentseeding, to the sampling date indicated.RODENT POPULATIONS AND DIVERSITYIn each replicate stand, 225 Longworth live-traps were located at 14.3-rnintervals in a checkerboard pattern on 16 lines (16 x 15). One trap was placed at eachstation for the duration of the project, so animals would become accustomed to theirpresence, thus keeping trappability high. Trapping was conducted from May toOctober during 1991 and 1992 on a bi-weekly schedule. At each trap-session, trapswere set on the afternoon of day 1, checked on the morning and afternoon of day 2,and again on the morning of day 3. Traps were baited with oats, peanut butter mixedwith sunflower seed oil and a slice of carrot. Cotton was provided for bedding. Ateach check, all untagged animals were ear tagged with numbered fish fingerling tags.Information on sex, reproductive condition, species, body weight (using Pesola springscales), and tag number was recorded.This trapping technique provided data for calculating the change in populationdensities of each species over time, using the Jolly-Seber stochastic model (Seber1982). Also, minimum number alive (MNA) estimates (Krebs 1966) were made forthe first and last week of trapping. The Jolly-Seber estimates were used to inferabundances of each species for calculating diversity statistics.Bi-weekly population density estimates are presented, and pooled abundances arepresented as relative abundance graphs. The four pooled periods are summer 1991, fall1991, summer 1992, and fall 1992. For the control A- treatment B comparison,summer data are pooled from mid-June to mid-August for 1991 (late May to July 20for 1992), and fall data are pooled from mid-August to mid-October (July 20 to midOctober for 1992). The Simpson and Shannon-Wiener indices and log series alpha(described in Chapter 2) were used to estimate diversities using first the pooledabundances, and then the un-pooled, bi-weekly abundances.910RESULTSSEED CONSUMPTIONAn application density of 20 kg/ha of sunflower seed is the standard quantity foroperational application. Seed consumption will be described in detail, after anexplanation as to why it was possible to pool seed consumption plot data from bothtreatment areas, and plots from inside and outside the small mammal trapping grids.ANOVA (2x2 factorial) was used to test for different consumption rates amongboth treatment areas, and inside and outside the small mammal trap grids. Visualinspection of the mean consumption rates from the four areas suggested there were noobvious differences, so only the data from May 15 and June 14 of 1992 werestatistically analyzed. These data were collected one week following seed applicationsand are early enough in the season that less than half the applied seed had been eaten.ANOVA showed that there was no signitcant difference ( = 0.05) in consumptionrates among all four areas, at either date (Table 1).The consumption rate one week post-seeding in May 1992 was 6.06 kg/ha/week,which increased to 11.42 kg/ha/week one week after the June seeding (Figure 1). ByJune 5, 17.04 of the original 20 kg/ha had been eaten, leaving 2.96 kg/ha of uneaten orgerminated seeds. Thus, after the June application, 22.96 kg/ha was present, of which21.59 kg was consumed by July 3. It was not possible to get a 2-week post-seedingcheck in May, but June, July and August can be compared with 2- and 4-week checks.Their respective two-week rates of consumption were 17.80 (June) to 19.07 (July) to19.84 kg/ha/2 weeks (August). This two week rate decreased to 19.14 betweenSeptember 25 and October 12.11Table 1. ANOVA results showing no significant difference in seedconsumption rate among sunflower seed treatment grids B and D, or inside and outsidesmall mammal grids. These data were collected on 15 May and 14 June 1992, in theMSdfl - ESSFdC2 biogeoclimatic zone near Ewer Creek (Vernon) B.C. P = probability,(F005)9.= 3.95).a) May 15, 1991Source of DF SS MS F PvariationALL PLOTS 3 34.88GRID (A) 1 2.04 2.04 0.08 0.72IN/OUT (B) 1 0.17 0.17 0.01 0.93Ax B 1 32.64 32.64 1.32 0.25IN CELLS 92 2277.08 24.75TOTAL 95 2311.96b) June 14, 1991Source of DF SS MS F PvariationALL PLOTS 3 51.04GRID (A) 1 12.04 12.04 0.50 0.51IN/OUT (B) 1 37.50 37.50 1.55 0.22AxB 1 1.50 1.50 0.06 0.80IN CELLS 92 2232.58 24.27TOTAL 95 2283.63122520o 15UuJ0• 1050Figure 1. Rate of sunflower seed germination and consumption by animals. Datawere collected during 1991 and 1992, in thinned, fertilized, 20-28 year old lodgepolepine stands in the MSdfl - ESSFd, biogeoclimatic zone of south central interior BritishColumbia.* kg/ha EATEN kg/ha ADDED-kg/ha GERMINATED kg/ha NOT EATEN7 21A1991 199213The above consumption rates for 1992 can be compared to the 1991 rates. Notethat the mid-July 1991 germination rate of 5.52 kg/ha/2 weeks (27.6%, or 41,400seeds/ha/2 weeks) is substantially higher than the mid-July 1992 rate of 1.07 kg/ha/2weeks (5.4%, or 1605 seeds/ha/2 weeks), probably because there was much more rainin 1991 than 1992. The germinated seeds were mostly consumed before rooting, whilethe others were observed to have been clipped by the end. of the four week post-seedingperiod (data for germinant survival were not recorded). This is evident by noting thatall seeds, including germinants of July 26, 1991, were consumed by August 7, 2weeks later (Figure 1).Comparing 1991 to 1992, the July 26 (1991) 2-week consumption rate of 13.39kg/ha increased by 5.68 kg/ha to 19.07 kg/ha on July 19, 1992 (slightly earlier in theseason). The seed was all consumed by 4 weeks post-seeding during both years.Again, on August 21, 1991, the two-week rate was 18.85 kg/ha, compared to 19.84kg/ha on August 12, 1992. Germination rates on these dates were comparable at 0.56(1991) and 0.66 kg/ha (1992). In summary, seeds were eaten at a faster rate as theseason progressed, and faster in 1992 than 1991.RODENT POPULATIONS AND DIVERSITYPopulationsTo compute diversity indices on a bi-weekly interval, population densities of eachspecies were estimated (Tables 2-5). This brief section presents absolute abundances ofspecies on the study site (discussed in detail in Sullivan and Kohler [in prep.]).However, the relative abundances are more important for calculating and examiningdiversity.14Table 2. Bi-weekly population estimates (Jolly-Seber, except first and last*, which areMNA) for rodents on control grid A. (CHIP chipmunks; PERO = deer mice; RBV= redback voles; PHEN = heather voles; M.LONG = long-tailed voles; ZAP =jumping mice).WEEK CHIP PERO RBV PHEN M.LONG ZAPYEAR 1991*Jun07 30.0 7.0 10.0 0.0 0.0 1.0Jun-21 37.7 26.0 12.0 1.0 0.0 2.0Jul-05 30.9 37.2 10.7 0.0 0.0 2.0Jul-19 39.9 36.4 17.0 1.0 0.0 3.0Aug-02 40.2 31.9 14.4 1.0 0.0 0.0Aug-16 36.2 41.9 15.0 1.0 1.0 0.0Aug-30 34.5 37.1 12.8 3.0 0.0 0.0Sep-13 33.8 38.4 9.5 1.0 0.0 0.0Oct-li 76.8 36.2 16.0 1.0 0.0 0.0YEAR 1992May-22 32.5 26.1 27.0 2.0 0.0 — 0.0Jun-05 27.1 29.5 8.4 1.0 0.0 0.0Jun-19 27.1 39.9 13.6 0.0 0.0 0.0Jun-29 29.2 51.0 8.8 0.0 0.0 0.0Jul-13 33.5 56.0 16.7 0.0 0.0 0.0Jul-27 39.1 63.8 7.3 1.0 1.0 0.0Aug-10 37.9 53.0 8.7 0.0 9.0 0.0Aug-24 34.1 58.6 9.0 0.0 1.0 0.0Sep-07 21.9 46.2 12.0 0.0 0.0 0.0Sep-22 12.0 46.3 21.7 0.0 0.0 0.0Oct-06 7.0 44.7 11.0 0.0 0.0 0.0*Qct.i9 1.0 58.0 8.0 2.0 0.0 0.015Table 3. Bi-weekly population estimates (Jolly-Seber, except first and last*,which are MNA) for rodents on treatment grid B. (CHIP = chipmunks; PERO = deermice; RBV redback voles; PHEN = heather voles; M.LONG = long-tailed voles).WEEK CHIP PERO RBV PHEN M.LONGYEAR 1991*Jun07 21.0 11.0 16.0 0.0 0.0Jun-21 15.0 15.5 18.0 2.0 0.0Jul-05 18.8 29.0 20.4 0.0 0.0Jul-19 48.2 53.0 23.9 1.0 0.0Aug-02 48.6 69.4 21.1 0.0 0.0Aug-16 23.1 90.7 22.1 0.0 0.0Aug-30 50.5 103.2 27.4 0.0 0.0Sep-13 36.4 100.6 32.5 0.0 0.0Oct-11 24.1 115.5 57.5 0.0 0.0YEAR 1992May-22 23.4 60.0 22.9 1.0 0.0Jun-05 46.6 106.8 16.6 0.0 0.0Jun-19 19.0 132.6 20.2 0.0 0.0Jun-29 19.2 169.5 19.0 0.0 0.0Jul-13 32.2 177.6 19.7 0.0 0.0Jul-27 44.3 206.9 13.3 0.0 1.0Aug-10 46.0 171.6 12.6 0.0 1.0Aug-24 31.4 176.7 22.6 0.0 1.0Sep-07 12.0 167.5 34.9 1.0 0.0Sep-22 0.0 163.9 44.9 0.0 0.0Oct-06. 0.0 135.5 13.9 0.0 0.0*Oct.19 0.0 41.0 8.0 0.0 0.016Table 4. Bi-weekly population estimates (Jolly-Seber, except first and last*,which are MNA) for rodents on control grid C. (CHIP = chipmunks; PERO = deermice; RBV = redback voles; PHEN = heather voles; M.LONG = long-tailed voles;ZAP = jumping mice).WEEK CHIP PERO RBV PHEN M.LONG ZAPYEAR 1991 -*May.22 26.0 11.0 4.0 1.0 0.0 0.0Jun-07 33.6 18.2 8.0 2.0 0.0 0.0Jun-21 53.2 48.9 10.0 3.0 0.0 0.0Jul-05 50.4 63.9 11.0 2.0 0.0 0.0Jul-19 53.4 66.3 15.8 1.0 0.0 1.0Aug-02 63.3 82.5 20.9 2.0 0.0 0.0Aug-16 62.3 84.6 15.0 1.0 0.0 0.0Aug-30 57.9 71.1 9.6 1.0 0.0 0.0Sep-13 61.1 68.3 13.1 2.0 0.0 0.0Oct-11 67.5 74.3 33.0 0.0 0.0 0.0YEAR 1992May-22 56.8 33.8 28.9 0.0 0.0 0.0Jun-05 60.6 33.0 10.8 1.0 0.0 0.0Jun-19 61.0 51.6 16.4 2.0 0.0 0.0Jun-29 59.6 62.5 19.8 1.0 0.0 0.0Jul-13 51.6 70.4 13.9 5.0 0.0 0.0Jul-27 67.6 93.9 16.9 0.0 0.0 0.0Aug-10 36.0 81.1 12.0 0.0 0.0 0.0Aug-24 33.4 64.1 18.0 0.0 0.0 0.0Sep-07 21.4 51.0 13.7 0.0 0.0 0.0Sep-22 7.0 54.5 19.0 0.0 1.0 0.0Oct-06 2.0 45.9 26.2 1.0 2.0 0.0*Octl9 2.0 29.0 24.0 3.0 2.0 0.017Table 5. Bi-weekly population estimates (Jolly-Seber, except first and last*,which are MNA) for rodents on treatment grid D. (CHIP = chipmunks; PERO =deer mice; RBV = redback voles; PHEN = heather voles; M.LONG = long-tailedvoles).WEEK CHIP PERO RBV PHEN M.LONGYEAR 1991 -*May22 9.0 6.0 5.0 0.0 0.0Jun-07 17.0 11.0 5.0 4.0 0.0Jun-21 19.0 18.9 4.0 15.0 0.0Jul-05 21.5 39.5 6.3 22.0 0.0Jul-19 89.3 41.5 6.6 16.5 0.0Aug-02 63.5 52.2 14.1 17.0 0.0Aug-16 47.3 65.8 10.2 12.0 0.0Aug-30 63.7 89.0 7.5 8.0 0.0Sep-13 85.8 105.2 7.2 10.0 0.0Oct-11 84.3 102.9 5.3 4.0 0.0YEAR 1992May-22 29.9 38.3 9.3 9.0 2.0Jun-05 22.4 58.2 12.8 19.2 2.0Jun-19 21.5 79.8 8.0 16.5 4.0Jun-29 48.0 113.2 27.5 15.8 1.0Jul-13 30.9 143.1 10.7 10.9 1.0Jul-27 161.8 208.7 15.0 13.0 1.0Aug-10 60.4 185.0 27.5 12.0 1.0Aug-24 30.0 177.9 15.0 3.0 1.0Sep-07 12.1 221.7 3.0 3.0 1.0Sep-22 6.0 179.4 2.0 3.0 1.0Oct-06 0.0 109.8 4.0 2.0 1.0*Qct19 0.0 38.0 2.0 2.0 1.018The population data indicate that there was a large increase in abundance of deermice on both treatment grids (Tables 3,5), relative to the controls (Tables 2,4). Deermice were the most abundant species on all grids.Yellow pine chipmunks (Euranilas wnoenus) were, on average, the second mostabundant species on treatment and control areas, but it was difficult to determine theextent to which they responded to supplemental food. The largest resident numbers ofchipmunks were on control C (Table 4) and treatment B (Table 3). There were timeswhen treatment D had some apparently unstable but high numbers of chipmunks (e.g.> 160/4 ha; July 27, 1992). Chipmunk abundance on treatment areas fluctuated muchmore than that on control areas.On treatment D, northern redback voles (Clethrionomys gapped) and heathervoles (Phenaconiys intermedius) in creased in abundance, and long-tailed voles(Microlus longicauclus) were consistently present (Table 5). These less abundantspecies were also caught on the other 3 grids. The western jumping mouse (Zapusprinceps) was the least abundant species, and was trapped on the control grids only(Tab]es 2, 4).Pooled Relative Abundances and DiversityBefore discussing bi-weekly diversity, pooled relative abundance and diversityinformation is presented. In terms of relative abundances of each species, the datawere averaged for the summer and fall of each trapping season, providing foursuccessive graphs for each grid (Figures 2 and 3). The largest change in smallmammal community structure was increasing dominance by deer mice in the foodsupplemented areas. The most noticeable change in the control areas was a switchfrom a community dominated by chipmunks in 1991 to one dominated by deer mice in1992. The increase in deer mouse dominance in treatment areas was the cause of a0.40.4z0.,030.t\:jr ,.. L-iPERO RBVo. ;•__tsç-02Figure 2. Rank abundance plots for grid A control and grid B food supplementedcommunities. Summer data are pooled from mid-June to mid-August for 1991 (andlate May to 20 July for 1992), fall data are pooled from mid-August to mid-October1991 (and 20 July to mid-October for 1992). (CHIP = chipmunks; PERO = deermice; RBV = redback voles; PHEN heather voles; M.LONG = long-tailed voles;ZAP = jumping mice).CONTROL A19100_V ZA.P £101014L)zz0.£40s0.403£0_no 100_v £REN0.60.401000 0_WV CHIP MI.ONG P100_N20Figure 3. Rank abundance plots for grid C control and grid D food supplementedcommunities. Summer data are pooled from mid-June to mid-August for 1991 (andlate May to 20 July for 1992), fall data are pooled from mid-August to mid-October(and 20 July to mid-October for 1992). (CHIP = chipmunks; PERO = deer mice;RBV = redback voles; PHEN = heather voles; M.LONG = long-tailed voles; ZAP =jumping mice).(‘n,,yronT0.60,PEEN ZAP0.600403c-)zz=0.60.4-. IIPERO CHIP PHEN REV0.2REV PHVN0s0,0.203: IPERO CHIP PHEN REV M.LONG:L0.0.4PPRO COOl? REV PENN M.LONG21decrease in evenness, which is indicated by the increasing concavity of theaccompanying curves. In the controls during 1992, naturally increasing dominance by• deer mice had a similar but reduced effect on evenness. The two species (chipmunkand deer mouse) switched numerical positions in the community, having no effect ondiversity computational results (Figures 2 and 3).The three species caught consistently on the study area were redback voles, deermice and chipmunks. There were also three uncommon species appearing rarely tooccasionally. The rarest, the western jumping mouse, appeared during the first fourtrap sessions only, on control grid A, and the fifth session on control grid C. The nextrarest species after the jumping mouse was the long-tailed vole which in 1991 wascaught only once, on control C (August 16). This species was more common in 1992,and most prevalent on treatment D. Heather voles were caught occasionally on all 4grids during all four pooled periods (except summer 1991, treatment B). Thispopulation was especially prevalent on treatment grid D, where it became moredominant than redback voles during 1991 and summer 1992. Of the three rare species,absolute abundance of long-tailed and heather voles increased most on treatment D.Jumping mice were present only during the wet early summer of 1991.I used average relative abundances to calculate Simpson, Shannon-Wiener and logseries alpha diversity indices (Figure 4). The Simpson and Shannon-Wiener indicesindicated that diversity on the control grids was fairly constant over time. On treatmentD, Simpson indicates decreasing diversity (due to increasing deer mouse abundance),whereas Shannon-Wiener shows an increase due to the summer 1992 addition of therare long-tailed vole to the community. Immigration of this species is also reflected bythe rise of log series alpha from 0.73 to 1 .00. On treatment grid B, this increase inShannon-Wiener did not occur with the re-appearance of heather voles in summer 1992or the new appearance of long-tailed voles in fall 1992. During summer 1992 for thisgrid, there were only 4 species, compared to 5 in treatment D, and the absolute22SHANNON-WIENERo1.2LOG SERIES ALPHA0.20 ISUMMER 1991 FALL 1991 SUMMER 1992 FALL 1992C0NTA -±-TREATB -*-[ TREATDFigure 4. Diversity of rodents in treated and control grids. Pooled populationdata for summer and fall. Summer data are pooled from mid-June to mid-August for1991 (and late May to 20 July for 1992), faIl data are pooled from mid-August to midOctober (and 20 July to mid-October for 1992).23abundances of heather voles were much lower in treatment grid B than D. Shannon-Wiener and log series alpha, therefore, being sensitive to an increase in number of rare• species and their abundance, responded more positively for grid D in summer 1992.During faIl 1992, both treatment grids had 5 species which is reflected in the similarityof both the Shannon-Wiener and log series alpha indices for that time.Bi-weekly DiversitiesThe general overview presented by pooling the data into four seasonal categoriesis now followed by a more detailed examination of diversity, on a bi-weekly basis.SinipsonTsindexResults of Simpson’s indices of diversity and evenness indicated that treatment Bwas initially more diverse than control A (Figure 5a). On the June 7 pre-seeding trapsession, Simpson’s index was 0.556 on control A and 0.659 on treatment B, for adifference of 0. 103 (not on graph). The only measurement available for thiscomparison was MNA, whereas the remainder of these two diversity curves are basedstrictly on Jolly-Seber estimates. MNA was used here to indicate the magnitude ofchange that would be expected after an operational application of supplemental food.The difference decreased to 0.029 four weeks later, after seeding, when control Adiversity increased to 0.634. and treatment B increased to only 0.663. After this initialdecrease in treatment-control difference following one seeding, the declining trendcontinued in the treatment relative to the control area, with successive seedings.Evenness was relatively stable in 1991 on control A until after August 30(Figure 5b). Redhack voles then began decreasing, while the chipmunk population wasstill increasing, causing a decline in evenness and diversity. Evenness on treatment Bbegan a downward trend after June 5, 1992, but an upward fluctuation was measuredIz01*22 60CONTROL A 3 TREATMENT B240.80.60.47JI I I, I I t I I III2 221 5 19 2 16 30 13 27 11J A S 01991I I I II I2 R2 2 222 5 19 28 13 27 10 24 7N D J F M A MJ J A S19920.201.2C’,C,,zz0.87 21 5 19 2 16 30 13 27 11J J A S 0199122 5 19 28 13 27 10 24 7 22 6N D J F M A MJ J A S 0SUNFLOWER SEED 1992Figure 5. Simpson indices of diversity and evenness for the control A-treatmentB comparison. Data were collected bi-weekly during 1991 and 1992.25on September 22, 1992, when chipmunks (probably hibernating) and heather voleswere no longer captured. This left only 2 species, creating very low diversity, butrelatively high evenness.The patterns of Simpson diversity and evenness on control C and treatment D,were similar to those on control A and treatment B (Figure 6). Trapping began twoweeks earlier here than on grids A and B. A slight increase from May 22, 1991,(MNA data, not on graph) in treatment D diversity was evident after the first(operational level) seed application, but there was a greater increase during that periodon control C. The June 7 control C diversity was 0.610 and treatment D was 0.689 fora difference of 0.079. Four weeks after the first seeding (July 5), thr ordering of siteshad switched, because control C had become more diverse (0.688) than treatment D(0.588). There was a large decline in Simpson diversity on the treatment D grid byJuly 19, when the chipmunk population increased from 21 the previous trap session to89, and the less common heather voles decreased from 22 to 16. Diversity returned tothe control level by the following trap session as the chipmunk population dropped backto 63 and redback voles increased from 6.6 to 14.0. By October 11, 1991 however,the treatment area (0.543) was substantially lower than the control diversity (0.639),because deer mice increased their domination of the small mammal community.During two declines in the 1992 treatment diversity curve (June 19 and July13), a large increase in deer mouse abundance and a concomitant decrease in redbackvole abundance occurred relative to previous weeks. The largest declining trend intreatment D diversity occurred after August 10, when the relative abundance of 3 of the5 species present decreased to one-half their previous level, but deer mouse abundancedecreased only slightly.26>•z0rJzzz0Figure 6. Simpson indices of diversity and evenness for the control C-treatmentD comparison. Data were collected bi-weekly during 1991 and 1992.0.8CONTROL C v TREATMENT D5 19 2 16 30 13 27 11J A S 019910.40.20 __.L.II II liii I I I I II II II III_l_IL_]_7 21J10. 5 19 28 13 27 10 24 7 22 6N 0 J F M A MJ J A S 019927 21 5 19 2 16 30 13 27 11 19 28 13 27 10 24 7 226J J A S 0 N D J F M A MJ J A S027Shannon-WienerThe Shannon-Wiener function indicated an initial decrease in diversity for allgrids, except treatment D after first seeding (Figures 7, 8), as did Simpson’s index.Although treatment B is more diverse than control A (Shannon-Wiener index) beforeseeding started, by July 5, 1991, it became lower for the duration of the study. TheShannon-Wiener index in both replicates is similar to that of the Simpson index.However, the control C-treatment D comparison was different during 1992, due to aconsistent richness value of 5 species in treatment D for all but the last trap session,compared with only 3 to 4 species in control C (Figure 8). The Shannon-Wiener indexwas sensitive to this richness difference during the first half of the 1992 season. Thiswas also reflected in the pooled relative abundance graphs during this time, where bothShannon-Wiener and log series alpha were higher for treatment D (Figure 3). ByAugust 4, the absolute abundances of chipmunks, redback and heather voles relative todeer mice on treatment D were so low that Shannon-Wiener diversity also declined.Log series alphaThe difference in richness between control C and treatment D is evident whenexpressed by log series alpha. Log series alpha fluctuates greatly and synchronouslyfor control A and treatment B in early 1991 due to coincidental richness changesoccurring on each grid (Figure 9a). Richness (R) for the first 4 trap essions changedin a sequence of 5, 4, 5, 4 on control A and 4, 3, 4, 3 on treatment B. Thesefluctuations show the very strong dependence of log series alpha on the number ofspecies. Both the treatment and control replicates had a generally decreasing trend inlog series alpha until mid-August 1991, with the treatments decreasing more rapidlythan the controls.28rdz0zz1.2zi>08z0Z0.6ri 0.4CONTROL A - TREATMENT B21.50.5011991 19920.207 21 5 19 2 16 30 13 27 11 22 5 19 28 13 27 10 24 7 22 6J J A S 0 N D J F M A MJ J A S 01991 SUNFLOWER SEED 1992Figure 7. Shannon-Wiener indices of diversity and evenness for the control Atreatment B comparison. Data were collected hi-weekly during 1991 and 1992.291.S‘-.4z00.51010.8z>0.600AU)0.20CONTROL C C TREATMENT DFigure 8. Shannon-Wiener indices of diversity and evenness for the control C-treatment D comparison. Data were collected bi-weekly during 1991 and 1992.27 21 5 19 2 16 30 13 27 11 22 5 19 28 13 27 10 24 7 22 6J J A S 0 N D J F M A MJ J A S 01991 19927 21 5 19 2 16 30 13 27 11 22 5 19 28 13 27 10 24 7 22 6J J A S 0 N D J F M A MJ J A S 01991 SUNFLOWER SEED 19921.4 301.2Cl, 9. Log series alpha indices of diversity for both study replicates. Datawere collected bi-weekly during 1991 and 1992. Numbers near data points representrichness.1991 19921.41.20.87 21 5 19 2 16 30 13 27 11J J A S ON 0 J F M A M J1991 SUNFLOWER SEED 199231Treatment D starts decreasing in diversity at a faster rate than control C, after thefirst seeding. During 1992 however, treatment D had more species than control C,- until October 6, and similarly, log series alpha values for treatment D were higher.This is especially noticeable on May 22 when treatment and control alphas were 1. 149(R=5) and 0.558 (R=3), respectively. The curves for June 5 to July 13, 1992, reflectthe rapidly increasing deer mouse population on treatment grid D as its diversitydecreases slightly relative to the control (richness did not change for either grid). FromJuly 27 to September 7, treatment richness was 5, while control C was 3, giving Dhigher diversity again. To a greater extent than in the A-B replicate, treatment Dmaintained more species than control C during 1992.32DISCUSSIONEXPERIMENTAL DESIGNThe precision of the diversity calculations may be considered low, because thereare only 2 replicates. Accuracy may also be low, in the conventional sense of design,because there is only one sample unit (trap grid) per treatment area. This precludedrandomization of units, which is necessary to insure accuracy. Increased accuracyobtainable from randomization can be imagined as sampling over a range of lodgepolepine stands across the landscape. This scale of project was not possible because offinancial and logistical limitations. However, because the 4-ha grids were much largerthan necessary to estimate small mammal abundance, accuracy and precision areeffectively increased when compared with conventional 1-ha or smaller grids. Largegrids also increase the precision of the measuring instruments (trap grids) because thesample will be more representative of relative abundances of species in the smallmammal community.SEED CONSUMPTIONThis study was designed to show how rapidly sunflower seeds were consumedin the forest, and to investigate the response of small mammal diversity to this increasein one component of the resource spectrum. Seed plots were used to determine if seedswere consumed, and if so, how fast. The plots were not intended however, fordetermining what species consumed the seeds. Birds, insects, and mammals other thanthose caught in live-traps were also utilizing the added resource. Pine siskins(Garduelis pint(s) and evening grosbeaks (Goceorhrausres vespertina) were observed33eating seeds, and seeds were observed in black bear (Ursus americanus) feces, andcaches (likely red squirrel) excavated by bears.The general trend in both the 1991 and 1992 field seasons was that seeds wereconsumed at a faster rate following each successive application, and faster in 1992 than1991. This trend is similar to that of increasing abundances of rodents. Rates shouldtheoretically have declined on a negative exponential rate, as seeds were consumed andbecame harder to find. If there was a constant supply however, the consumption rateshould have increased exponentially because of increasing populations of rodents assummer progressed, until the seasonal population decline in early winter.Four of the species present in the community (chipmunks, deer mice, redbackand long-tailed voles) were held in captivity for approximately one month, andsurvived well on sunflower seeds, carrots and water. Chipmunks, deer mice and redsquirrels have been shown to benefit (increased birth rates, survival and immigration)from sunflower seeds used as supplemental foods (Sullivan 1979, Sullivan er al. 1983,Sullivan 1990). Combining this evidence with the live trapping data indicated that allthe species present probably benefited from the sunflower seed treatment. It is possiblethat some species such as deer mice, may specialize on sunflower seeds. This mightallow the rarer herbivores such as redback, long-tailed and heather voles, to utilizenatural plant products more efficiently because of reduced competition.RODENT POPULATIONSThe reason why chipmunk population density fluctuated more on treatment areasthan on control areas is not known, but it may be related to reduced trappability causedby the presence of sunflower seeds. In another study, provision of sunflower seedincreased and maintained Town send ch ipnl un k (Eutamias townsendii) populationdensity 50% above that of control areas in a coastal British Columbia forest (Sullivan er34al. 1983). Traps were baited with sunflower seeds which may have improvedtrappability in that study, but sunflower seeds were restricted to the treatment areas formy study, so high energy food was not available to control communities.If there were such large fluctuations in chipmunk density as indicated, the overalleffect of sunflower seed on community structure was obviously different than if nofluctuations occurred. The following two factors may have caused the apparentfluctuations: 1) reduced trappability due to sunflower seeds on the study site, causingdisinterest in the trap bait; 2) fluctuations in density caused by different immigrationand emigration patterns on treatment, than on control areas. These patterns could havebeen affected by seed availability, and by an increase in the competitive red squirrelpopulation, causing behavioral changes.Behaviour was likely altered by increased food availability. Alihough averagelitter sizes for redback, heather and long-tailed voles in the Kananaskis Valley, Alberta,were reported to be 5.2, 4.1, and 3.7, respectively (Norrie and Milr 1989), thesespecies have not been shown to specialize on certain plant foods, suggesting they areflexible in what they eat. It was suggested that the litter sizes were related todifferences in social organization and activity patterns, both of which could have beenaltered by sunflower seeding in my study, for mice, voles and chipmunks.DIVERSITY OF RODENT COMMUNITIESControl A- Treatment B ComparisonAlthough I showed that at least 20 kg/ha/month of sunflower seeds could beconsumed, the hypothesis that the index of rodent diversity would increase in foodsupplemented areas was not supported. Pooled relative abundances, the ShannonWiener and Simpson’s index, and log series alpha (except 1992, replicate C-D) all35indicate that diversity decreased in food-supplemented young lodgepole pine stands inthe MSdlfl - ESSFdC, biogeoclimatic transition zone of B.C.Simpson’s index is a Type II heterogeneity measure, being most affected by thedominant species. Deer mice were obviously the most successful in convertingsunflower seed into increased reproduction (Sullivan and Kohler, in prep.), and thuswere by far the most influential factor in causing a decline in the Simpson andShannon-Wiener indices over time.The diversity curve for treatment grid B showed three lows which reflect thisquality of Simpson’s index (Figure 5a). The first on August 16, 1991 occurred whenchipmunk abundance decreased to about one-half of the previous trap session value(and thus inflating relative deer mouse abundance). Chipmunks were the second mostabundant species at that time, and deer mouse abundance was low compared to 1992values. In 1992, diversity hit a second low on June 29, when the deer mouseabundance was rising (then at 169 animals) and chipmunk abundance had decreasedagain. These observations demonstrate the effect of dominance on irnpson’s index.The converse effect on Simpson’s index is evident by looking at two exampleswhere richness had more of an effect. The third low (October 6, 1992) was moreattributable to richness which had decreased to 2, from 4 in the previous trap session(September 22). This was buffered by a slight increase in redback vole abundance,which dropped to its lowest level on October 6.On control A (July 13, 1992), Simpson’s index had a major increase from 0.562to 0.604, which was mostly due to an increase in abundance of the rarest species (thenredback voles) from 9 to 17 animals. This indicated that as the most dominant speciesbecame more comparable to the rare species abundance (lower density of deer mice oncontrol A than treatment B), rare species abundance had more of an effect onSimpson’s index.36High evenness should not be considered high diversity, as shown by comparingthe A-B replicate grids (Figure 5b). When one or two animals of a rare species arecaught, which increases richness and likely diversity in comparison with the previoussampling period, a decrease in evenness is expressed. Good examples of this occurredon control A from trap session 1 to 6 where there were weekly fluctuations of richnesswhich caused the diversity curve to go up when R increased and down when itdecreased. The evenness curve A fluctuated in the opposite manner. For treatment B,the same opposite fluctuations occurred for the first four trap sessions. This effect wasmost dramatic when abundances of all species were relatively low.Another good example on control A was when R increased from 3 to 5 betweenJuly 13 and 27, 1992. Here, one redback vole and one heather vole each were caught.Diversity decreased here for both Simpson and Shannon-Wiener although Shannon-Wiener is a richness measure (Figures 5, 7). However, evenness decreased much morethan the diversity indices.These observations of simultaneously increasing diversity and decreasingevenness contradict the underlying theory of Simpson (1949). An increase in diversitytheoretically should be caused by an increase in evenness. For this and reasons givenin Chapter 2, evenness will not be considered as a reliable diversity index forinterpretation of my data.The trends for Simpson and Shannon-Wiener diversity followed the samesequence, but there were noticeable differences when R changes (Figures 7, 8). If Rincreases, the accompanying effect on diversity will be slightly more positive with theType I Shannon-Wiener index, which is sensitive to rare species (relative to Simpson’sindex).37Control C- Treatment D ComparisonThe control C-treatment D replicate followed a trend of decreasing treatmentdiversity (Simpson) through the 1991 and 1992 trapping. The main difference in thisreplicate, compared to the A-B replicate, is that the rare species persisted much betterin the treatment than in all other grids. There was considerable variation, however,which likely was partly caused by fluctuations in evenness created by non-synchronizedlitters among species, by immigration, and by inconsistent appearance of rare species.During 1992, for example, the decrease in treatment D diversity was buffered onJune 28, July 27, and August 10 by high chipmunk, redback and heather voleabundances, and consistent appearances of long-tailed voles. Simpson and Shannon-Wiener indices on treatment D were similar to control C during these weeks, even withtreatment area deer mice increasing to 206 to 208 anirnals!4 ha by July 27. The early1992 deer mouse abundance was lower, and chipmunk, heather vole and long-tailedvole abundances were much higher, on treatment D than B. This was the main factorcontributing to higher Simpson, Shannon-Wiener, and log series alpha indices fortreatment D compared to control C, for at least part of summer 1992. It was not untilabundances of voles and chipmunks decreased greatly on treatment D (September 7)that diversity also decreased substantially.It is not known what benefits the rare species on grid D may have obtained fromsunflower seeds. Similarly, I do not know if the rare jumping mice appearing on thecontrol grids in 1991 would have persisted during 1992 had sunflower seed beenpresent there. With respect to treatment D, a wet grassy area dominated by aspen(Populus rreniuloides) was where many heather and long-tailed voles were caught.Possibly this interspersion of habitats provided a refuge for the rare species, from themore seed-oriented deer mice (T. Sullivan, pers. comm.). It could also be that the38added food was more preferable than that naturally present, and in this habitat allowedthe rare species to benefit.The rare species likely benefited from the supplemental food as indicated byShannon-Wiener and especially the log series alpha indices. Treatment D diversity (logseries alpha) was higher or comparable to control C through the 1992 season untilAugust 24, when heather voles decreased dramatically (12 to 3) and chipmunks andredbacks also decreased. A similar occurrence 6 weeks earlier (July 13) also caused adecrease in the Shannon-Wiener index.Increased prevalence of heather and long-tailed voles in treatment D had animportant influence on richness-based indices, which log series alpha expresses muchbetter than Shannon-Wiener. Diversity is higher on the treatment than control at thestart of 1992, and stays higher until September 22, when all rare species exceptjumping mice reappeared on control C, and October 6 when heather and long-tailedvoles were still present on the control. On October 6, chipmunks were not caught onthe treatment area, but two were caught on the control.GENERAL DISCUSSIONAn additional hypothesis to be tested was that diversity would return to normalshortly after termination of seeding, with a time lag which was to be identified. Thefield work was terminated before this could be tested, but at another food supplementedsite in interior British Columbia (ICH11 biogeoclimatic zone), 2 of 7 species recordedas significantly increasing in abundance by Sullivan (unpubi.) and Sullivan and Klenner(1993)(red squirrel and meadow voles [Micrortis pennsyivamcus]) returned to controllevels by August. Sunflower seed was applied at a density of 22.7 kg/ha on fourtreatment blocks (2 manual and 2 by helicopter), once in each of May and June. This39suggests that diversity on my study site would likely return to control levels soon aftertermination of seeding.The response of the present rodent communities to supplemental food is clear andwould have been even more so had trappability been higher in the treatment areas.Changes to diversity however, are less clear, because of the dichotomy betweenrichness and dominance based measures apparent with these data. Less clear arecomparative diversities at times when treatment-control pairs were near the same levelof diversity. Even if a statistical comparison was used for these periods, another indexcould likely be used to contradict the conclusion made. The best example was the late1992 low (and decreasing) treatment area diversities as indicated by Simpson andShannon-Wiener, but higher diversities as indicated by log series alpha.Among the three indices used, concordance of rankings are most highlycorrelated between Shannon-Wiener and log series alpha (P < 0.05), but are notsignificantly correlated between log series alpha and Simpson, or Simpson andShannon-Wiener (Magurran 1988). In many cases, the Simpson and Shannon-Wienerindices will give inconsistent ranking of diversity among grids (Spearman rankcorrelation coefficient; Magurran 1988). This occurred with my data, as Shannon-Wiener and log series alpha expressed treatment D as being more diverse than did theSimpson index during early 1992. By using more than one measure, emphasis can beput on either more species, or a more even distribution of abundances of species,depending on which is regarded as having greater diversity.For my study, log series alpha could have been chosen as the sole indicator inattempts to argue that supplemental food increases rodent diversity. Likewise, Simpsoncould have been chosen to argue the contrary. Still another measure could have beenchosen, such as beta, which expresses greater difference among communities as beingmore diverse. This would make the treatment more diverse than the control since thereare greater differences among the entities (speèies populations) used in the diversity40formulae. Beta diversity contrasts differences among habitats, and would expresssunflower seeding as increasing diversity, because differences among forest stands areincreased across the landscape.By choosing to interpret my data in the context of diversity, value judgmentswere necessary to determine whether seeding improves or degrades the small mammalcommunity. The reality is that evenness decreased because the more common speciesresponded most positively (Table 6). This supports MacArthur’s (1972) prediction that‘an increase in only part of the resource spectrum may lead to a decrease in speciesdiversity through dominance by those species that are competitively superior inexploiting the augmented resources’. However, no rare species were excluded by thesecompetitively superior mammals, as predicted by MacArthur’s (1972) model. Thisindicates that the rare species may have benefited in some way (Table 6). This couldhave been from decreased pressure on natural foods by the common species, or fromthe rare species switching to sunflower seeds.The high deer mouse population density resulting from this large scalesupplementation of sunflower seed (far above operational stand protection levels; seeSullivan and Klenner 1993), has occurred elsewhere in nature. Densities of 55 deermice/ha (MNA; Sullivan and Sullivan 1982) were found where seeds were releasedfollowing harvesting of lodgepole pine stands, and should similarly be found afternatural fires (depending on stand age and fire severity) which release large quantities oflodgepole pine seed.Before deciding whether these results indicate that sunflower seeding is a good orbad practice, the treatment must be put into perspective. First, seeding is a techniquewhich benefits both trees (reduces squirrel debarking damage) and animals, so it is agood integrated forestry practice. Second, operational seeding occurs only once/year,in early to mid-May, depending on when the cambium becomes active, so populationeffects will be less than that observed for my research. Third, a whole array of species41Table 6. Effect of sunflower seed treatment on total number of animals (NUM),number of species (RICH), and evenness (EVEN). These are average values for 1991,compared to those of 1992 (NC = no change).CONTROL GRIDS TREATMENT GRIDSA C B DNUM f 1% t 8% f 50% t 70%RICH t t4.4 to 3.7 4.0 to 3.6 3.3 to 3.6 4.0 to 4.9EVEN NC NC42are affected which were not caught in Longworth traps, including birds, fungi, insects,rodents, and large herbivores which eat the germinated sunflowers as they develop intoplants. The research area was a multiple land use area which included logging,recreation and agriculture. Cows grazing in the area were observed feeding onsunflower plants growing at the helicopter landing where some seeds were left behind.Another group of animals of importance are the predators. Intuitively, it seemslikely that with a greatly increased prey base (total number of animals, Table 6),predators would benefit. Long-tailed weasels (Musrelafrenata) and short-tailed weasels(Musrela ermina) were caught and tagged during field work, but few recapturesoccurred on treatment or control areas, so population estimates could not be made.43CONCLUSIONS AND RECOMMENDATIONSAll the species caught on the study site (except heather voles) were held incaptivity for approximately I month, and survived on water, carrots, and sunflowerseeds. This showed that they would eat the supplemental food. Seed plots showed thatthe animal community could consume close to 20 kg/ha in one month in early summer,and at least 20 kg/ha in 2 weeks or less, by late summer and early fall.There were two important facts evident from the Jolly-Seber populationestimates. One was that in lodgepole pine stands with supplemental food, deer miceand chipmunk population densities increased the most. However, they also were thetwo most abundant species naturally (without sunflower seed). The other fact, was thatrare species were not excluded from the treatment areas, indicating that they may havebenefited from supplemental food. Evidence from other studies indicate that thesecommunity alterations would have returned to normal within a few months post-seeding. I suspect that with 6 months of feeding, there may be a residual effect thefollowing year caused by improved over winter survival and reproduction.Increases in the absolute abundances of deer mice and chipmunks producedlargely skewed relative abundances, causing decreasing diversity when indicesincorporating relative abundances of species were considered. The maximumpopulation density occurring for deer mice, has also been found in habitats with nosupplemental food.My data suggest that rare species were not excluded by the more commonspecies, but that all species benefited. Seeding creates forest stands with different smallmammal community structures across the landscape, possibly increasing beta diversity.These changes should rebound to normal relatively quickly after cessation of seeding.44Thus, it seems likely that seeding can safely be used at well above the 1 month/yearoperational level without a detrimental effect on small mammal populations.Sunflower seed as a supplemental food is likely very beneficial to the animalcommunity, because rodents are the prey base for carnivorous animals. However,carnivores have large home ranges, and hence the effect on them may be much lessthan that on the local population of rodents. Sampling to include carnivores may be thenext step in determining the full effect of sunflower seeding on the ecosystem. Withthis larger scale investigation, diversity across the landscape (beta) could also beexamined.45CHAPTER 2DIVERSITY: CRITIQUE AND FORMULA SELECTIONINTRODUCTIONBrowsing through an ecology textbook will reveal techniques for measuringsingle entity attributes of an ecological community such as population (of a single predefined taxocene), and for measuring community parameters which involve more thanone entity such as the concepts of niche and diversity. The fact that some diversitymeasures are a function of number of species present (richness), and evenness withwhich the individuals are distributed among these species, contributes only partly to thepresent confusion and debate concerning the validity of diversity as a concept (Huribert1971, Southwood 1978, Wood 1993), and its measurement (Magurran 1988).Calculating and comparing diversities seems initially like a simple task, but upon closeinvestigation, conclusions become less clear.BACKGROUNDOne fact that makes comparisons difficult is that, for some formulae, two varyingcomponents are sometimes involved, richness and relative abundance (relativeabundance is the same as evenness; and hence a source of confusion) (Magurran 1988).Magurran (1988) identifies richness as the number of taxonornic units in the area ofconcern. Relative abundance, in the case of species, refers to the absolute number ofeach species relative to the absolute number of each of the other species in a given46community. These two variables change in ways that are not consistently correlated, sowhen they are combined it becomes difficult to interpret the relevance of the measures.Huribert (1971) points out that the use, by some ecologists, of the separate parametersof richness or evenness to indicate diversity (and other parameters on which heelaborates), has contributed to criticism of diversity as a concept to the extent that hecalls species diversity a non-concept. However, Hill (1973) recommended thatdiversity indices be used if carefully defined in the context of the data being analyzedThe second fact that leads to confusion is that diversity measures are valuejudgements, on which two people are unlikely to agree perfectly. For example, thereality of having two choices (richness and evenness) with variable weightingdepending on the formula used, results in a value judgment. Similarly for evenness,there is a more complex, hidden value choice. This problem is discussed by Wood(1993), and relates to the truth that diversity is measured to attempt to determine thenumber or degree of differences among biological entities (Wood 1993). He explainshow degrees of difference can be a function of point of view, and that evenness fits thiscategory.By convention, maximum evenness is considered most diverse, because theprobability of interspecific encounters is high (Simpson 1949). Maxinnim evenness isgenerally not observed in natural systems. There is usually a hierarchy where somespecies are more or less dominant, with the range (differences) being dependent onfactors such as geographical scale of data collection and successional stage. A perfectlyeven community (all species with the same abundance) has no difference among itsentities, so it could be considered least diverse, if greater difference among entities ischosen to be most important (Wood 1993).Considering a perfectly even community, for example, there is:1) no diversity in actual abundances (for they are identical); and472) maximum diversity in terms of the perception of the observer in the field(Wood 1993).Here, there is a value judgment placed on evenness as a measure of diversity, becauseevenness was chosen to form the underlying theory of most diversity indices. Thisproblem is considered in the interpretation of the results of my study. Magurran (1988)disagrees with this criticism, but she also refers to Lloyd and Ghelardi’s (1964)suggestion to compare empirical data with the broken stick model (Figure 10), whichrepresents the most even state of affairs found in nature. Wood (1993) emphasizes thatmost ecologists do not claim the entities (species, genes, ecosystems etc.) as thediversity, but rather the variety among them is the diversity, and that number andfrequency of the attributes are key components of the variety.This confusion, arising from a number of sources, surrounding the concept ofdiversity, has led many to believe that biodiversity may not be reasonable objective innatural resources management. It is not my intent to discuss this part of the issuewhich is explained elsewhere (Magurran 1988, Hunter 1990, Burton et a!. 1992,Kimmins 1992).ASSESSMENT OF SOME POPULAR DIVERSITY MEASURESThere are prefixes used which help describe the geographical scale at whichmeasurements of diversity are being made. They are from large to small scalerespectively, landscape, beta (across ecosystems), and alpha (within ecosystem)diversity. My study was in the lDF-MS1zone of British Columbia, and is classifiedas alpha diversity (which is easily confused with the diversity statistic used in Chapter1, called log series alpha). There is also temporal diversity which retrs to the change48Figure 10. Hypothetical rank abundance plots illustrating the typical shape offour species abundance models: geometric series, log series, log normal and brokenstick. Abundance of each species is plotted on a logarithmic scale against the speciesrank in order from the most to least abundant species (Magurran 1988).100roken stick1.0.100C0C0III ‘II geometricseriesnormalSSSS0.01\ log series0.001species sequence49in diversity with respect to natural succession over time (Kimmins 1992). 1 suggestthere is another form of temporal diversity which is seasonal. This should be obviousin harsher seasonal environments. For example in this project, density of deer micecontinually increases as early spring progresses to fall, then winter mortality occurs, sodiversity has seasonal fluctuations within the process of successional change.For example, differences in diversity statistics for food supplemented and controlareas both show change over time, such as seasonal temporal diversity. But thestatistics also can be used to show how the actual difference between areas changesover two seasons. Whether the comparison changes positively or negatively, is subjectto opinion for the above reasons. Wood’s (1993) analysis suggests that what ismeasured as being less diverse in the sunflower treated areas over time, could either beconsidered more diverse because there are larger differences (in relative abundances)within the community after seeding, and between the treatment and control areas, orless diverse as evenness decreases. This becomes more clear upon considering theresults and discussion sections of Chapter 1.Confusion of different evenness/richness weightings can be avoided by usingjust N (number of animals), but this ignores very real differences in abundance betweenspecies (deer mice and heather voles for example), and also ignores the basic differenceof number of species. If number of species (R, same as S; another source ofconfusion; species density’), was chosen to interpret the present data, Wood’s (1993)measurement would be S-i because there are S-i differences between the entities. Thisuse of only S similarly eliminates ambiguity caused by combining S and relativeabundance. This brings up another source of conflict that Wood (1993) andBunnell(1990) elaborate on concerning species as an entity. The animals caught1 Typically species counts are called richness, unless specific areas are censused, which was done for thisstudy, in which case it is called species density (Hurlhert 1971). To avoid further confusion however, the results ofspecies counts in this study will be referred to as richness (R).50during field work are identifiable to non-interbreeding species, and are assumed to beunambiguous entities.The Margalef index (Djg=(S-l)/lnN) and Menhinick index (DMfl=SIVN) use Sand N, and these parameters are both used in the log series alpha formula, which wasused for my analysis (see Magurran [1988] for details of these and other formulae; acomputer program for calculating log series alpha is given in Krebs 1989). Log seriesalpha involves more complex calculation and is more discriminatory (identifies subtledifferences better) than the above two measurements. The Margalef index uses S - 1which is consistent with Wood’s (1993) expression of differences. Any index such aslog series alpha that uses S and N, but not relative proportions, has the problem ofmasking shifts in relative abundance (if present) when S and N do not change. Thereare very little temporal data in diversity studies, and as Magurran (1988) points out,having the same N and S values seldom occurs with real data. In my data there arelarge differences in N among grids, and within grids, over the two-week intervals ofmeasurement.SPECIES ABUNDANCE MODELSSpecies abundance models are theoretical species abundance distributions, towhich empirical data can be tested for the appropriate ‘fit’. Magurran (1988) statesthat “a species abundance distribution utilizes all the information gathered in acommunity and is the most complete mathematical description of the data”. It is usefulto learn how empirical data relate to these models, because the models provide a basisfor discussing relevant theories such as the type of community competition present.For example, the geometric series (Figure 10) representing a community having only afew very dominant species, is a condition of pre-emption, where a few species havepre-empted a large portion of the niche hyperspace. Results of my field study on51sunflower seeding indicated that the rodent community gradually progresses toward thisstate over a two-year period, although the number of species is not reduced.There are two main reasons why I have chosen not to use the models. One is thatthe difficult computations require complex computer analyses, and this would have tobe done repeatedly over time to identify when the community changed in underlyingtheoretical distribution. Secondly, it is difficult to justify conforming empirical data toa mathematical or biological theoretical distribution. As Southwood (1978) states: “forthe ecologist, the ability of a parameter or index to discriminate between changedconditions may be more relevant than, and is not identical to, the precision of the ‘fit’of the underlying model”. Similarly, “statistical models in general seem unhelpful,partly on theoretical grounds in that they test no biological criterion, but mainly onpractical grounds since rarely do biological data seem to fit statistical models to anyreasonable degree and often real data can be fitted to a variety of mathematical curvesequally badly” (Lambshead and Platt 1984). Other authors have made similarcriticisms, that relative abundance data often fit more than one underlying theoreticalcurve.The Q statistic may have been a good measure for the present study because itmeasures the interquartile slope of the species abundance curve, and thus does not giveweighting to either the rare or dominant species (Magurran 1988). However, duringmy study no more than 7 species were counted at any one census, so eliminating theupper and lower quartiles (50% of the species) would not leave sufficient data.INDICES BASED ON THE PROPORTIONAL ABUNDANCE OF SPECIESThese indices can be broken into two groups, information statistics (Type I) anddominance indices (Type II), and one has been chosen from each group for analyzingmy data. Magurran (1988) recommends using one of these indices in combination with52an index which is not based on proportional abundances, such as log series alpha. Forusing the first group of indices (of which the Shannon-Wiener function is used for thisstudy), one assumption is that the information being tested is analogous to informationin a code or message, and answers the question ‘how difficult would it be to predictcorrectly the species of the next individual collected?’. Shannon-Wiener (H’) iscalculated as follows:SH’=- p (In p)‘ Iwhere s = number of species, and the quantity p is the proportion ofindividuals found in the ith species. And evenness is calculated as:E = H’/Hiiaxwhere H.. is the maximum possible evenness, given S and N.Two further assumptions of Shannon—Wiener are that individuals are randomlysampled from an infinitely large population (Pielou 1975), and that all species in thecommunity of interest are represented in the sample. BrilloLlin’s index produces similarresults to Shannon-Wiener and is recommended if all the species are not known (Pie]ou1966), and if every individual is censused without replacement (Krebs 1989). Neitherof these two conditions applies to my data. Krebs (1989), recognizing Hurlbert’s(1971) and Washington’s (1984) criticisms of Shannon-Wiener, recommends using theindex on empirical rather than theoretical grounds. Krebs (1989) also cites Taylor etal. (1976) as stating log series alpha was better than the other indices that he tested,because it reduced the variance in his replicates. Taylor (1984) also found that intesting eight diversity measures on his Rothamsted Insect Survey data, log series alphawas the best discriminator, the next best being Shannon-Wiener. Peet (1974)53recommends N1 = eH (a transformation of Shannon-Wiener) as the best Type Iheterogeneity measure.It has been shown that estimates of Shannon-Wiener from field data are biased(Adams and M’Cune [1979] in Krebs 1989), but because in my study comparisons weremade among treatments and controls, the bias may be similar in each.The Berger-Parker index (N,ax/N), where Niax is the abundance of the mostdominant species and N is the abundance of all species combined, is also subject to biascaused by fluctuations in abundance of the most common species. It would expressincreasing deer mouse dominance (as occurred in my study) very well, but it isimportant to also be aware of the number of species present (Magurran 1988, Burton etal. 1992).Simpson’s index was the third measurement used for this study for the abovereasons, and it also has a higher discriminatory power than the Berger-Parker index. Itis calculated as follows:D = p12where D is diversity, and p1 is proportion of the ith species. Evenness iscalculated as:E = D/D,where Dimix is the maximum possible evenness, given S and N.The reciprocal of Simpson’s index (l/D, used for my analysis) theoretically gives“the probability that two individuals chosen at random and independently from thepopulation will be found to belong to the same group” (Simpson 1949). From this54point of view, lID could be of value as an indicator of the probability of interspecificencounters.May (1975) has shown that the underlying species abundance distribution (Figure10) strongly affects the outcome for Simpson’s index, if more than ten species arecollected in the sample. That would be important for this study (but there are < 10species) because the non-seeded control areas approach the log normal or broken stickdistributions (Figure 10). The sunflower seed treated areas start similarly, but progresstoward the geometric series. Therefore, the treatment area calculations of Simpson’sdiversity would be less influenced by richness as time progressed, which would biasresults toward dominance. There is bias inherent in Simpson’s index (toward evennessas a ‘human value’) because the formula is known as a dominance measure, and henceis sensitive to the most dominant species (e.g., deer mice in my study).Because two of the formulae selected for use here, Shannon-Wiener and Simpson,are biased towards richness and dominance, respectively, there is a good chance thatthey could give inconsistent ordering of communities. Constructing K-dominancecurves will detect if this error will occur. A K-dominance curve shows percentagecumulative ranked abundances by species, where K is the species rank on the X axis.If the curves for the areas being compared intersect, there is a high likelihood of non-concordance of site ranking when using Shannon-Wiener and Simpson indices (Platt etal. 1984).There are many cases where non-concordance of site ranking occurs for thepresent data (although for reasons already given and to follow, Simpson and ShannonWiener were both used in Chapter 1). For example, trapping results from July 5,1991, indicated the community structures of control C and treatment D grids were suchthat the K-dominance curves did not intersect (Figure 1 la). In this case, the Simpsonand Shannon-Wiener indices rank the two grids concordantly, indicating control C islower in diversity than treatment D (Figures 6a, 8a). Non-concordance of rankings55b)k-dominance curvesspecies rankFigure 11. K-dominance curves ranking species for grid C control and grid Dfood supplemented communities, on July 5, 1991, and July 19, 1992. Species areranked from most to least dominant.a) percent cumulative abundance1 2 3 41 2 3 4 556occurs on July 19 however, and the K-dominance curves for that date do intersect(Figure 1 lb). In this case, the Shannon-Wiener index ordered treatment D morediverse than control C (Figure 8a), while the Simpson index gave the opposite ordering(figure 6a).The ability to communicate in a universal language is desirable, and is onepositive result of common use of the Shannon-Wiener and Simpson indices, whetherthe reasons for their use are scientifically sound or not. Using them in my study forcomparative purposes is a recommended objective (Magurran 1988). Having chosenformulae which place value on both ends of the relative abundance spectrum (dominantand rare) permits a discussion of the data from both points of view. Log series alpha ispreferred over the Shannon-Wiener index for its higher discriminatory abilities, andbecause of recommendations in the literature for its usage, and non-use of Shannon-Wiener (Peet 1974, Goodman 1975, May 1975, Alatalo and Alatalo 1977, Southwood1978, Taylor 1978, Routledge 1979, Magurran 1988). In measuring response ofnematode species abundance patterns to pollution, where one or a few species usuallybecome very dominant, the Simpson index, which weights species by their abundance,is preferred (Platt el a!. 1984, Lambshead and Platt 1985). My data are similar andindicate a rapidly increasing deer mouse abundance.For the above reasons I used Simpson’s index for dominance, and log seriesalpha for a richness index. Despite the very strong increasing dominance of deer mice,log series alpha, with its high discriminatory ability, indicated that one of the treatmentareas which had two more species than its control counterpart during most of 1992,similarly had a higher diversity. Using the Simpson and Shannon-Wiener indices showhow areas which have very different communities, may be evaluated purely on whetherthe investigator places more value on richness or dominance. “It is a well known butnot sufficiently emphasized observation that numerical diversity indices conceal morethan they reveal. However, it still remains very attractive to be able to reduce a57complex set of data to just one figure, especially when the results are eventuallyintended to be assessed by a non-specialist” (Platt et al. 1984). My intention is to givethe best assessment of what the computed indices mean about the small mammalcommunities being compared. Evenness was included for comparative purposes, butfor reasons giveh, it is ignored in the discussion of results section.The indices used for analyses, log series alpha, Shannon-Wiener, the reciprocalof Simpson’s index (referred to as Simpson), and Shannon-Wiener and Simpsonevenness are all relatively highly discriminatory, and independent of sample size(number of animals caught. Other indices which are sensitive to sample size should notbe used here because of the larger sample sizes (more animals caught per trap period)in food supplemented than control areas.FOCUSING THE DIVERSITY CONCEPTAdding a prefix or modifier to diversity gives the concept boundaries, such asthe word ‘biodiversity’. It has a prefix ‘bio’ which means life, so the concept islimited to the diversity of living things2. From an anthropomorphic viewpoint, this is avery broad limit. By adding other prefixes the notion becomes more focused. Thefollowing sequence of prefixes (modifiers) focus the concept of diversity to thenarrower limits of the taxonomic order of Rodentia diversity: diversity, biodiversity,animal diversity, mammal diversity, Rodentia diversity (referred to simply as rodentdiversity in Chapter 1).Although rodent diversity has global connotations, the notion may be furthermodified to impose spatial limits. Spatial limits could be strictly political, as inpractice some people consider non-living things (dcad wood lr example) part of biodiversity. There are living things in thedead wood. Also, things such as structures and Ilinctions are supervenient (come along with), and not necessary in the description(Wood 1993).58British Columbia, or geographical as in Vancouver Island. Furthermore, vegetationlimits could be drawn, such as rodent diversity in an even-aged lodgepole pine stand.Thus, for the purposes of my study, using both geographical and vegetation limits, dataare used to determine the diversity of rodents in 21 to 28-year-old lodgepole pine standsat 1300 m elevation in the upper Ewer Creek watershed of central B.C. This is adefined community of mammals interacting in a specific environment. These pointsshow why when speaking of biodiversity, the context must be clearly defined.The very fact that there is conflict over how to measure diversity shows thestrong dedication and concern by people for the well-being of ecosystems. There isstill a basic weakness in the concept of diversity, because its measures are all value-laden. This is acceptable if it is very clearly indicated what type of diversity is beingshown, and what the basic data consist of so other indices can be used for a differentperspective. Species abundance models seem good only for theoretical discussion, butnot for classifying empirical data. There are many reasons for using Simpson and logseries alpha to calculate diversity statistics for my data. Most importantly, they areboth highly discriminatory, they are not unduly affected by sample size, and both arewidely used, thus my results can be compared to other studies.59LITERATURE CITEDAbramsky, Z. 1978. Small mammal community ecology changes in species diversity inresponse to manipulated productivity. Oecologia 34: 113-124.Adams, I.E. and McCune, E.D. 1979. Application of the generalized jack-knife toShannon’s measure of information used as an index of diversity. Grassle,J.F., Patil, G.P., Smith, W. and C. Taille. eds. 1979. Ecological diversity intheory and practice. International Co-operative PubI. House, Fairland, MD, Pp.117-13 1.Alatalo, R. and R. Alatalo. 1977. Components of diversity: multivariate analysis withinteraction, in Magurran, A.E. 1988. Ecological diversity and its measurement.Princeton University Press. Princeton, New Jersey. 179 pp.Bouti ii, S. 1990. Food supplementation experiments with terrestrial vertebrates:patterns, problems, and the future. Can. J. Zool. 68: 203-220.Bunnell, F.L. 1990. Biodiversity: what, where, why and how. Proceedings, WildlifeForestry Symposium: a workshop on resource integration for wildlife and forestmanagers. Prince George, B.C., March 1990. Pp. 29-45.Burton, P.J., Balisky, A.C., Coward, L.P., Cumming, S.G. and D.D. Kneeshaw.1992. The value of managing for biodiversity. For. Chron. 68(2): 225-237.Dennis, B. and P. Patil. 1989. Species abundance, diversity, and environmentalpredictability. Grassle, J.F., Patil, G.P., Smith W., and C. Taillie. eds.1979. Ecological diversity in theory and practice. International Co-operativePub]. House. Fairland, MD. U.S.A. Pp. 93-114.Goodman, D. 1975. The theory of diversity-stability relations in ecology. jn Magurran,A.E. 1988. Ecological diversity and its measurement. Princeton UniversityPress. Princeton, New Jersey. 179 pp.Hill, M.O. 1973. Diversity and evenness: a unifying notation and its consequences.Ecology 54(2): 427-432.Hunter, M.L. 1990. Wildlife, forests and forestry - principles of managing forests forbiological diversity. Prentice Hall Inc. Englewood Cliffs, New Jersey. 370 pp.Huribert, S.H. 1971. The non-concept of species diversity: a critique and alternativeparameters. Ecology 52: 577-586.60Huribert, S.H. 1984. Pseudoreplication and experimental design of ecological fieldexperiments. Ecol. Monogr. 54(2): 187-211.Hutcheson, K. 1970. A test for comparing diversities based on the Shannon-Wienerformula. J. Theor. Biol. 29: 151-154.Kimmins, H. 1992. Balancing act: environmental issues in forestry. UBC Press. 224pp.Krebs, C.J. 1966. Demographic changes in fluctuating populations of Microtuscaflfornicus. Ecol. Monogr. 36: 239-273.Krebs, C.J. 1989. Ecological methodology. Harper and Row Pubi., New York, NewYork. 654 pp.Lambshead, J. and H.M. Platt. 1985. Structural patterns of marine benthic assemblagesand their relationships with empirical statistical models. Gibbs, P.E. ed.1984. Proceedings of the 19th European marine biology symposium, Plymouth.Cambridge University Press, Cambridge. Pp. 371-380.Lloyd, M. and R.J. Ghelardi. 1964. A table for calculating the ‘equitabilitycomponent of species diversity. J. Anim. Ecol. 33: 2 17-255.MacArthur, R.H. 1972. Geographical ecology: Patterns in the distribution of species.Harper and Row. Publ., New York. 269 pp.Magurran, A.E. 1988. Ecological diversity and its measurement. Princeton UniversityPress. Princeton, New Jersey. 179 pp.May, R.M. 1975. Patterns of species abundance and diversity. n Cody, M.L., andJ.M. Diamond. eds. 1975. Ecology and evolution of communities. HarvardUniversity Press, Cambridge, MA. Pp. 81-120.Meidinger, D. and J. Pojar. eds. 1991. Ecosystems of B.C. Special report series, ISSN0843-6452; no. 6. Crown Publ. Inc. Victoria, B.C. 330 pp.Norrie, M.B. and J.S. Millar. 1989. Food resources and reproduction in four microtinerodents. Can. J. Zool. 68: 641-650.Patrick, R., Hohn, M. and J. Wallace. 1954. A new method of determining the patternof the diatom plora. in Abramsky, Z. 1978. Small mammal community ecologychanges in species diversity in response to manipulated productivity. Oecologia34: 113-124.61Peet, R.K. 1974. The measurement of species diversity. Ann. Rev. Ecol. System. 5:285-307.Pielou, E.C. 1966. Species-diversity and pattern-diversity in the study of ecologicalsuccession. J. Theoret. Biol. 10: 370-383.Pielou, E.C. 1975. Ecological diversity. Wiley, New York. 165 pp.Platt, H.M., Shaw, K.M., and P.J.D. Larnbshead. 1984. Nematode species abundancepatterns and their use in the detection of environiliental perturbations.Hydrobiolugia. 118: 59-66.Routledge, R.D. 1979. Diversity indicies: which ones are admissible. In Magurran,A.E. 1988. Ecological diversity and its measurement. Princeton UniversityPress. Princeton, New Jersey. 179 pp.Seber, G.A.F. 1982. The estimation of animal abundance. 2nd ed. Charles Griffin andCo., London. 654 pp.Simpson, E.H. 1949. Measurement of diversity. Nature 163: 688.Southwood, T.R.E. 1978. Ecological methods: With particular reference to insectpopulations. 2nd ed. Chapman and Hall, London. 524 pp.Sullivan, T.P. 1979. The use of alternative foods to reduce conifer seed predation bythe deer mouse, (Peroinyscus mcinicularus) J. Anim. Ecol. 16: 475—495.Sullivan, T.P. 1990. Responses of red squirrel (Tainiasciurus hudsonicus) populationsto supplemental food. J. Mammal. 71(4): 579-590.Sullivan, T.P. and W. Klenner. 1993. Influence of diversionary food on red squirrelpopulations and damage to crop trees in young lodgepole pine forests. Ecol.Applic. (in press).Sullivan, T.P. and D.S. Sullivan. 1982. The use of alternative foods to reducelodgepole pine seed predation by small mammals. J. Appi. Ecol. 19: 33-45.Sullivan, T.P., Sullivan, D.S. and C.J. Krebs. 1983. Demographic responses of achipmunk (Eurainias townsend/i) population with supplemental food. J. Ani m.Ecol. 52: 743-755.Szaro, R. and H. Salwasser. 1990. Conserving the heritage: The USDA forest serviceon biological diversity. In The Society. 1989. Forestry on the frontier. Proc.Soc. Am. For. NatI. Cony., Sept. 24-27. Spokane, Washington. Bethesda, Md.Pp. 12-1562Tamarin, R.H. 1985. Biology of new world Microtus. Special Pubi. No. 8. The Amer.Soc. of Mammal. 893 pp.Taylor, R.H. 1978. Bates, Williams, Hutchinson - a variety of diversities. in Mound,L.A. and N. Warloff. eds. 1978. Diversity of insect faunas: 9th symposium ofthe Royal Entomological Society. Blackwell, Oxford. Pp. 1-18.Taylor, L.R. 1984. Synoptic dynamics, migration and the Rothamsted insect survey. J.Anim. Ecol. 55: 1-38.Taylor, L.R., Kempton, R.A. and I.P. Woiwood. 1976. Diversity statistics and thelog-series model. J. Anim. Ecol. 45: 255-272.Tilman, D. 1982. Resource competition and community strucwre. Princeton UniversityPress, Princeton. 296 pp.Washington, H.G. 1984. Diversity, biotic and similarity indicies: a review with specialrelevance to aquatic ecosystems. Water Res. 18(6): 653-694.Westman, W.F. 1990. Managing for biodiversity: Unresolved science and policyquestions. Bioscience 40(1): 26-33.Wood, P.M. 1993. The priority of biological diversity conservation on forest land usedecision making. PhD. Dissert. U.B.C. (in progress). Chapter 4.


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