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Responses of rodent populations to spatial heterogeneity and successional changes within Sitka spruce (Picea sitchensis) plantations at Hamsterley Forest, County Durham.

Santos Fernandez, Fernando Antonio dos

How to cite: Santos Fernandez, Fernando Antonio dos (1993) Responses of rodent populations to spatial heterogeneity and successional changes within Sitka spruce (Picea sitchensis) plantations at Hamsterley Forest, County Durham., Durham theses, Durham University. Available at Durham E-Theses Online: http://etheses.dur.ac.uk/1200/

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2 RESPONSES OF RODENT POPULATIONS TO SPATIAL HETEROGENEITY AND SUCCESSIONAL CHANGES WTHIN SITKA SPRUCE (Picea sitchensis) PLANTATIONS AT HAMSTERLEY FOREST, COUNTY DURHAM

Fernando Antonio dos SantosFernandez, BSc (Rio de Janeiro), We (Campinas)

The copyright of this thesis rests with the author. No quotation from it should be published without his prior written consentand information derived from it should be acknowledged.

A thesispresented in candidaturefor the degreeof Doctor of Philosophyin the University of Durharn,April 1993

(D 2 JUL 1993 To my father,to my mother,and to my Rodentia.

""Miceire merelythe protusion into our own dimensionof vastlyhyýer. intelfigentq PanOtmensional.beings. The wholebusiness with the squealcingand the cheeseis just a front

DouglasAdams, "The 11itchhikees Guide to the Galaxy,,

ii ý heterogeneity ABSTRACT - Responses of rodent populations to spatial and successional changes within Sitka spruce (Picea sitchensis) plantations at Hamsterley Forest, County Durham. Populations of woodmice (Apodemus sylvaticus), bank voles (Clethrionomys glareolus) and field voles (Microtus agrestis) were studied by five-trapping in Sitka spruce plantations at Hamsterley Forest, northeast England, from February 1990 to June 1992. The study was carried out at two distinct spatial scales. At the coarser spatial scale,the effects of successionalchange and of spatial (inter- site) heterogeneitywithin the forest were comparedby censusingrodent commmunities in sites representing three successional stages: mature plantations (about 40 years after planting), clear-fellings and young plantations (5-8 years after planting). In, mature plantations woodmice and bank voles were both abundant, in clear-fellings the former , were usually dominant and in young plantations field voles were also abundant alongside' the other two species. Pooling all sites, in young plantations rodent communities had highest diversity and evenness,mostly becauseof an inter-site component @-diversity). On a site-by-site basis, in young plantations communities had neither higher a-diversity nor evenness than in mature plantations. Clear-fellings showed least diversity and evenness.Multivariate analyses revealed marked taxonomic and structural changes in vegetation during successionand how the rodents responded to such changes. Within young plantations bank voles were associatedwith dense ground cover, provided mostly by heather. Field voles. were associated with the non-palatable grass Deschwnpsia flýxuosa and to palatable grassesas well. Woodmice were habitat generalists, but their abundancewas negatively correlated with that of field voles. Spatial heterogeneity in soils explained much of the inter-site variation in ground vegetation which in turn explained much of the P-diversity in rodent communities in young plantations. At the finer spatial scale, populations of woodmice and bank voles were studied by monthly trapping in five 0.8 1 ha grids within an habitat mosaic produced by the felling of a plantation of mature Sitka spruce. Inter-grid movements were frequent for both species, but especially woodmice, which also had larger home ranges. Population dynamics of woodmice in the whole mosaic were apparently similar to patterns describedpreviously in other habitats, except that density-dependentreduction of survival and reproduction by late autumn was more severethan usual. Woodmice were more abundant in 1991 than in 1990, apparently due to increased seed supply. Bank voles, in contrast, did not show regular annual fluctuations in numbers. Although breeding stopped in both winters, population densities increased steadly during 1991 and remained high until spring 1992. Clear-felling of a part of the study area had little immediate effect on populations of either species; responses to clear-felfing were gradual rather than sudden. The experimental removal of tree brashings from recent clear-felfings scarcely affected the populations of woodmice, but made the clear-feUingsunsuitable for bank voles, apparently due to the reduced availability of shelter. Habitat selection within the whole habitat mosaic was density-independent in bank voles, but density-dependent in woodmice. In the later species,demographic differences among local subpopulations accounted for most of the observed density-dependentchanges in spatial distribution, although inter-grid movements also played an important part. The responsesof each rodent speciesto spatial and temporal heterogeneity and a possible* role of density-dependenthabitat selection in population regulation are discussed.

iii I certify that all material in this thesis which is not my own work has been identified and that no material is included for which a degree has previously been conferred upon me.

The copyright of this thesis rests with the author. No quotation from it should be Published without his prior written consent and information derived from it should be acknowledged.

iv ACKNOVaMGEMENTS .I I-

ProfessorPeter K Evans, my co-supervisor, got interested in my project with furry rather than feathered animals, provided lengthy and extremely useful discussionsand was always prompt in making careful and sensitive comments on the manuscript. I also much appreciatedhis attention to my deadlinesand his help to keep within them. Dr. Nigel Dunstone, my co-supervisor, cared to get me settled and going, helped me with several practical arrangementsand facilities (from a caravan in Hamsterley to computer hardware and software) and provided discussions along with extensive and detailed commentson the manuscript. Prof Eric Le Bouleng6 arranged facilities and assistedme in nmning his program CMR at the Uniti de Biomitrie of Universit6 Catholique de Louvain, supplied the ear-tags and discussedmany topics. I also thank him for his hospitality, his friendship, and for showing me 44 ±3 brands of the nicest Belgian beers. Back in Durham, Dr. Tusi Butterfield was helpful and good humoured all the way, provided discussionsfrom invertebratesto multivariate analysis, and had endlesspatience with my constant borrowings of everything. I also owe a big thank you to DrýýAlan Pearson for being always extremely helpfid and patient in discussing the soils and their relationship with the vegetation. Dr. Brian Huntley discussed vegetation sampling and Inultivariate analysis where his characteristic clear thinking was much appreciated. Dr. Murray Grant provided valuable discussions on analysis of home ranges and of habitat Preferences.Dr. Val Standen arranged help of the Durham University Conservation Volunteers to my field experiement. Dr. Phil Huhne provided several pieces of sensible advice, especially on regression analysis. Dr. J.C. Coulson provided advice on the chi- square analyses in Section 3.3.1. Among the Department technical staff, Mr. 'Eric Henderson was always helpful and reasonable(except in his support for Nigel Mansell) I and also thank Mr. M. Bone, Mr. D. (not G.E. ) Hutchinson, and the secretarial staff for their helpfulness. In Hamsterley Forest, first of all I thank Gordon Simpson and Brian Walker. Gordon'authorized me to work in the forest, and Brian showed it to me. Both helped identifying grasses,and delighted me with their encyclopedic knowledge of the forest and their infectious enthusiasm for forest life. Mr. Pritchard gave me authorization for the controlled burning brashings,in of spite of his concerns about the reputation of Brazilians regarding burning forest The Forest S. Commission rangers and secretarial staff were ' always helpfid. Specialthanks also-go to Mrs. j. S. peart and Mr. D. Swaindon of Mayland Hall Farm for kindly sendingme that mouse which had a ring in its ear.

V Severalsmall mammal ecologists contributed with adviceat differentstages of this work. Dr. JohnFlowerdew discussed the originalproject with me, gaveme several.useful later Prof Sam Erlinge piecesof adviceon methods,and provided encouragement, on. - - - providedadvice on field voles.Also, asa passengerin my'car,ý he valliantly escapedunhurt a crash againsta Land Rover, thus ýsparing me the'guilt of, killing ýsuch an illustrious scientist.Dr. Tom Tew patiently showedme radio-trackingtechniques and results,and discussedthe pros and cons of the technique as compared to five trapping, his encouragementwas, much appreciated.*ýDrs. - John Gurnell, W.I. - Montgomery and,Ken Ashbydiscussed some isolated aspects of the study.I alsothank Prof PeterJewell and Dr. K. Eltringham.for their hospitalityat the B.E. S. - Symposiumin Cambridge. The field experimentof removal of brashingscould not have been carried'out without the help of the Durham University ConservationVolunteers, the Hamsterley CommunityTask Force led by Bill Jordan,and eight inmatesof DurhamPrison. SusanGibson, Rhett Harrison,Sandeep Vadher andAdrian Burrows allowed me to use their unpublisheddata; Rhett and Sandeepwere also very nice,friends, and the former alsohelped in my field work in June1992. Among my roomates,Mark O'ConneUwas a specialfriend, and his infectious enthusiasmand endlessscientific and philosophicalcuriosity were a daily delight. Iain Downie likes animalswith too many legs, but this friendly fellow conjured clever suggestionsand bits of MacIntoshmagic with appearon Figures3.8,4.1 and 5.1. Digger Jackson,Rob Evans and Pablo Corcuerawere also a most gratifying daily company. Besidesmy roommates,I was especiallypleased with the companyof SarahWilson, StewartLowther, M6nica Calado,Karen Haysom,Lys Muirhead and Ian Scott in the Departmentalong these years. In GraduateSociety, Dr. Mike Richardsonmade me feel at home from day one, and Dr. Peter Grundy used his considerablepersuasive powers on my behalf in two importantoccasions. I alsothank Mrs. ElaineIreland and the staff of ShincliffeHall. Many other friendshelped making these last yearsmore enjoyable.In mentioning someof them, it is unavoidableto make someunfair ommissions;the ones so omitted pleaseblame it on my bad memoryand refrain from throwing the volume on my head.In Durham, I sharedespecially good momentswith, among others, Chrisanthos,Kostas (Frcnten1o), Mtz, Maria, Zi Luis, Mircia and Mariana(Ouro Preto is expected),Joan, Julia, JeanMarc and Isabelle,Naresh, Adam, mi hermanaElena Fernandez, Roberto (ah, que saudadesde Belim do Pari), Luis Teodoro (".. do -ji te cuido pelo 1"), Mike and Lourdes.Chris in andTama Melbourn,Carlos and Gislenein Oxford were the samegood fiiends in old throughout, spite of the distance.In Louvain-la-Neuve,I had a wonderful

vi time enjoyingthe hospitalityof the Le'Bouleng6s(Eric and his wife, PauleLe Bouleng6- Nguyen) and the companyof people like Gabriel, Marc, Fabienneand Vincent. In Scotland,I had the privilege of sharingfun, ideasand dreamswith Marcos Buckeridge (who is like a, brotherto me), Audrey,Marcelo Trinity Birth, Dora, Helen, Lulu, Selina, Ima, Naso, Marcelo Buzzi, Gisele,Manoel Clitudio, Fibio and Efiane.Maria Alice, first andforemost, supported me lovingly at all times,even at the most difficult moments,and sharedthe most specialones as well. I thank my motherIza and my brother Marcos, and somenice fiiends, especially Gabrieland Tise, for their continuoussupport and encouragement even across an oceanof distance.I also thank Dr. Rui Cerqueirafor startingall this, and Mr. C. Veloso, Ms. M. Monte, Mr. M. Nascimento,Mr. B. Guedes,,Mr. F. Venturini and Mr. 'A. Saterfor their inspirationand companyin the lonely momentsthroughout these years. And I thank Dr. MichaelRosenzweig for the ideaof quotingDouglas Adams. Last but not least,I thank CAPES(Coordenadoria de Aperfeigoamentode Pessoal do Ensino Superior),from the BrazilianMinistry of Education,for providing me a grant and paying all my University fees, without which- this thesis would never have been possible.

vii CONTENTS

Abstract Acknowledgements'- v Viii Contents .ýý )dfi List of Tables xv Ust of Figures -

I- ENTRODUCTION

temporal habitat heterogeneity, and 1.1 -Habitat fragmentation, spatial and small mammalpopulations for mammalsin shifting habitat 3 1.2 - Planted forests as a model studies on small mosaics in 5 1.3 - Sitka spruce Britain bank field voles: some contrasts 7 1.4 - The ecology of woodmice, voles and 8 1.5 - Thesis structure I1

2- STUDY AREA AND GENERAL METHODS

10 2.1- HamsterleyForest 11 2.2- Capture- mark - recapturemethods - 11 2.2.1 - Trappingand marking techniques 2.2.2'- Dealingwith tag losses 14 15 2.3 - The database 16 2.4 - Statisticalanalysis

3-, SUCCESSIONAL AND SPATIAL VARIATION, OF RODENT COMMUNITIES IN SITKA SPRUCE PLANTATIONS IN HAMSTERLEY FOREST

17 3.1- Introduction 3.1.1 - The chronosequenceapproach and its assumptions 17 3.1.2 - The chronosequenceapproach as appliedat HamsterleyForest is

viii 3.2- Methods 19 3.2.1 - Rodent censuses 19 distributional 3.2.2 - Analysis of rodent patterns 23 3.2.3 - Diversity and evenness 24 3.2.4 - Habitat description 24 3.3- Results 28 3.3.1 - Overall patterns mi species compositions, numbers and relative 28 abundances 3.3.2 - Temporal variation and spatial variation 32 3.3.3 -Correlations among the abundancesof the rodent species 34 3.3.4 - Diversity and evenness 34 3.3.5 - Patterns of successionalchange and their relationship with the rodents 37 3.4- Discussion 46 in 3.4.1 - Overall pattern of successionalchanges the rodent communities 46 3.4.2 - Spatial scalesof variation in the rodent communities 48 3.4.3 - Temporal variation versus spatial variation' 49 3.4.4 - Competition between'woodmice and field voles ? 51 3.4.5 - Habitat changesand the related changesin the rodent communities 52 3.4.6 - An overview: soil characteristics,vegetation, and the heterogeneity of 55 each successionalstage

4- RODENT POPULATIONS IN A HABITAT MOSAIC PRODUCED BY FELLING: I- POPULATION DYNAMICS IN THE MOSAIC AS A WHOLE

4.1 - Introduction 58 4.1 Rodent in habitat .1- populations a mosaic: scalesof analysis 58 4.1.2 - Aims of the study of rodent populations in the Comer Complex as a 58 whole 4.2 - Methods 59 4.2.1 - The Comer Complex: trapping design and trapping programme 59 4.2.2 - Estimates of population parameters 61 4.2.3 - Home"rangesand distancesmoved between captures 65

ix 4.3 - Results 466 4.3.1 Abundances of each, rodent species; corrected versus, uncorrected 66 estimates - 4.3.2 - Population dynamicsof woodmice 68 4.3.3 - Population dynamicsof bank voles 72 distances 79 4.3.4 - Home range sizesand average moved 84 4.4 - Discussion fluctuations 84 4.4.1 - Population dynamics'of woodmice: annual 4.4.2 - Differences betweenyears and their effects on woodmice populations 87 4.4.3 - Population dynamicsof bank voles 89 dynamics 91 4.4.4 - Home rangesand their relationship with population dynamics 4.4.5 - Woodmice in a Sitka.successional mosaic: usual population 93 in an unusual study area

5- RODENT POPULATIONS IN A HABITAT MOSAIC PRODUCED BY FELLING: H- HABITAT CHANGES,ASSOCIATED TO CLEAR- FELLING AND THEIR EFFECTSON THE LOCAL-ABUNDANCES OF RODENTS

5.1 - Introduction 95 5.1.1 - Clear-felling, tree brashings, and rodent populations: a field 95 experimenton the effectsof a drastichabitat change 5.1.2 - Testingpredicted effects of the habitatchange 96 5.2 - Methods 97 5.2.1 - Trappingdesign and trapping programme 97 5.2.2 -A field experiment:removal of tree brashingsby controlledburning 99 5.2.3 Estimatesof rodent abundancesin eachgrid and comparisonsbetween 100 grids 5.2.4 - Testingthe effectsof the removalof brashings:comparison between 100 rodentspatial distributions 5.3 - Results 101 5.3.1 - The effectsof clear-fellingon rodentabundances 101 5.3.2 - The effectsof the removalof brashings,on rodent abundances 107 5.3.3 - The effectsof the removalof brashingson spatialdistributions 110

x 5.4 Discussion 110 - . 5.4.1 - Validity of the controls:fine scalespatial heterogeneity as a problem 110 for experimentaldesign 5.4.2- Effectsof clear-fellingon the rodentpopulations 112 different, 5.4.3 - Removalof tree brashingsfrom clear-fellings: effects on 114 woodmiceand bank voles,

6- RODENT POPULATIONS IN A HABITAT MOSAIC PRODUCED BY FELLING: III DENSITY-DEPENDENCE IN HABITAT SELECTION

6.1 - Introduction 117 6.1.1-ý Density-dependent habitat selection: pattern and processes 117 6.1.2 - Assumptionsand aims of the presentstudy 118 6.2 -Methods -, 119 6.2.1 - Trappingdesign and trapping programme 119 6.2.2 - Estimation of demographic parameters of populations and 119 subpopulations 6.2.3- Habitat selectionand its relationshipwith populationsizes 120 6.2.4- Inter-gridmovements and the estimationof emigrationand immigration 122 rates 6.2.5 - Relationship between changes in spatial distribution and the 122 demographicparameters of the subpopulations 6.3 - Results 123 6.3.1 - The relationship between habitat selection and population sizes 123 6.3.2 - Frequency of inter-grid movements 131 6.3.3 - Demographic parametersof subpopulationsand their relationship with 137 variations in numbersat grid level 6.4 - Discussion 144 6.4.1 - Habitat selection and population densities: contrasting patterns for 144 woodmice and bank voles 6.4.2 -A comment on the methods availableto test density-dependenthabitat 145 selection 6.4.3 Density-dependent - habitat selection in woodmice: movements or 146 variable local demographies?

3d, 7- GENERAL DISCUSSION I 'I., ý

bank field 7.1 - Differential responsesof woodmice, ý voles and voles to spatial 150 and temporal habitat heterogeneity Sitka in increasing heterogeneity 153 7.2 -A possible role of mature spruce spatial in the young plantations habitat 154 7.3 - Flexibility of preferencesand population regulation

REFERENCES 159

Appendix I- Program AGECLASS 180 Appendix 2- Program FCOORD 182- Appendix 3- Correcting population -size estimates for the varying number of 184 grids: effects on the correlations between population sizes and other parameters Appendix 4ý Some long distancemovements of woodmice 186 Appendix 5- Dates of the trapping sessions 188

I:: -LIST OF TABLES

Table 2.1. The data base: number of individuals caught and number of captures 15 of eachrodent species. Table 3.1. Characteristicsof the 18 sites trapped during the study: successional 22 stages,National Grid Reference,dates of planting and soil types. - Table 3.2. Habitat variablesmeasured in 15 of the trapping grids. 26 Table 3.3. Abundancesof three speciesof rodents in the trapping sites, and totals 29 7recorded in each successionalstage. - Table 3.4. Comparison of the ratio of woodmice to bank voles in different 30 - successionalstages. Table 3.5. Averages of the total number of rodents caught in a surnmer standard 32 trapping session at each successionalstage and comparisons among the stages. Table, 3.6. ANOVAs comparing,abundances of rodents in different years, and 33 Kruskal-Wallis , tests comparing 'abundances of rodent - in different successionalstages. Table 3.7. Correlations between the abundancesof rodent species in different 34 sites in each successionalstage. Table 3.8. Shannon'seIr diversity indices for the rodent community at each site 35 Table 3.9. Hill's modified evennessindices for the rodent community at each site '36 Table 3.10. Comparison of the site diversity and site evennessindices of different 37 succýssionalstages Table 3.11. Average values for the 20 habitat variables at each of the 15 sites. 38 Table 4.1. Estimates of demographic parameters for woodmice in the Comer 69 Complex, using the Manlya-Parr'method. I-L. Table 4.2. Estimates of demographic parameters for woodmice in the Comer 71 Complex: survival rates for, each age class, juvenile recruitment and proportion of reproductive females. Table 4.3. Estimates of demographic parameters for bank voles in the Comer 74 Complex, using the Manly-Parr method. Table 4.4. Estimates of demographic parameters for bank voles in the Comer 76 Complex: survival rates for each age class, juvenile recruitment and proportion of reproductive females. Table 4.5. Home ranges and movementsof woodmice in the Comer Complex. 80

xiii Table 4.6. Home rangesand movementsof bank voles in the Comer Complex 81 Table 5.1. Monthly variations in the abundanceof woodmice in each grid of the 102 Comer Complex. Table 5.2. Monthly variations in the abundanceof bank voles in each grid of the 103 Comer Complex: Table 5.3. Effects of clear-felling and' of removal -of tree brashings on the 104 abundanceof woodmice and bank voles in different grids within Comer Complex. Table 6.1. Monthly variation of IvIevs habitat selection indices for woodmice. 124 Table 6.2. Monthly variation of IvIevs habitat selection indice for bank voles. 125 Table 6.3. Values for Rosenzweig and AbramsWs regression method applied to 129 habitat selectionin woodmice. Table 6.4. Values for Rosenzweig and AbramsWs regression method applied to 134 habitat selectionin bank voles. Table 6.5. Intergrid, movementsof woodmice within the Comer Complex. 135 Table 6.6 Intergrid bank Comer Complex. 136 , movementsof voles within'the Table 6.7. Numbers of movementsof woornice as a proportion of the number of 138 individuals availableto move. Table 6.8. Estimates of 30-day rates of variation in numbers of woodmice caught 140 in eachgrid within the Comer Complex. Table 6.9. Estimates of 30-day mortality and recruitment rates for woodmice in 141 each grid within the Comer Complex. Table 6.10. Estimates of 30-day emigration and immigration rates for woodmice 142 in eachgrid within the Comer Complex. Table 6.11. Parameters of multiple regression equations relating the variation in 143 numbers of woodmice in'each subpopulation (dependent variable) to mortality, ý',recruitment,, -emigration and immigration rates 'at the correspondinggrids (independentvariables). '

)dv LIST OF FIGURES

Figure 2.1. The habitatmosaic within HamsterleyForest: distributionof conifer '12 plantationsof differentages and clear-fellings in 1990. Figure3.1. Map of HarnsterleyForest showing the locationof all studysites. ;21 Figure 3.2. Diagram of a standardtrapping grid, showing trapping points and 27 habitatsampling points. Figure3.3. Pie chartsof the distributionof abundancesof the threerodent species 31 in plantationsof four successionalstages. Figure 3.4. DetrendedCorrespondence Analysis (DCA) diagram showing the 40 ordinationof the fifteen sitesaccording to the ten plant covervariables. - Figure 3.5.- DetrendedCorrespondence Analysis (DCA) -diagram showing the 41 ordinationof the ten plant covervariables according to the fifteen sites. Figure 3.6. DetrendedCorrespondence -Analysis (DCA) diagram,,showing the "42 ordinationof the fifteen sitesaccording to the ten structuralvariables. ,- Figure 33. - DetrendedCorrespondence Analysis (DCA) diagram showing the 43 ordinationof the ten structuralvariables according to the fifteen sites., Figure 3.8. CanonicalCorrespondence Analysis (CCA) ,diagram showing the 45 ordinationof sites,environmental variables and the threerodent species. Figure4.1. Positionof the five standardgrids trappedwithin Comer Complex. 60 Figure4.2. Monthly variationof populationsizes of woodmiceand bank voles in 67 the ComerComplex, as estimatedby the Manly-Parrmethod. Figure4.3. Monthly variationof woodmicesurvival rates: overall survivalrelated 70 to Populationsizes, and a comparisonof adult andjuvenile survival. Figure 4.4. Monthly variationof woodmicereproduction and recruitment,and its 73 relationshipwith the variationin populationsizes. Figure4.5. Monthly variationof bank vole survivalrates: overall survivalrelated 77 to populationsizes, and a comparisonof adult andjuvenile survival. Figure4.6. Monthly variationof bank vole reproductionand recruitment,and its 78 relationshipwith the variationin populationsizes. Figure 4.7. Relationshipbetween number of capturesper individual and the 82 correspondinghome range area estimates, for woodmiceand bank voles. Figure 4.8. Seasonalvariation of population sizes and its relationship with 83 averagehome range areas of woodmiceand averagedistances moved by bankvoles.

xv Figure 5.1. Position of the five standard grids trapped within Comer Complex, 98 plus additional trapping lines in grassyverges. Figure 5.2. Effects of clear-felling on rodent abundances: comparison of 106 population levels in the felled grid Comer 2 and the control Comer North. Figure 5.3. Effects of removal of tree brashings on rodent abundancesin clear- 108 fellings: first replicate. Figure 5.4. Effects of removal of tree brashings on rodent abundancesin clear-,,, '109 fellings: secondreplicate. -,, Effects brashings Figure 5.5. of the experimental removal of tree on the spatial ... III distribution of bank voles and woodmice. Figure 6.1. Variation of wood mouse population sizes (estimated by the Manly- 127 Parr method) and degree of habitat selection estimated by the modified IvIevs method. Figure 6.2., Scatterplot relating the variation inýwood mouse population sizes 128 (estimated by the Manly-Parr method) against degree of habitat selection estimatedby the modified IvIevs method. Figure 6.3. Relationship between population sizes and habitat selection in 130 woodmice, analysedby Rosenzweig and Abramsky's regressionmethod. Figure 6.4. Variation of bank vole population sizes (estimated by the Manly-Parr -132 method) and degree of habitat selection estimated by the modified IvIevs - method. Figure 6.5. Scatterplots showing the relationship between population sizes and 133 habitat selection in bank voles by the modified IvIevs method and by the Rosenzweig AbramsWs and method. -ý '41 . ". ý Figure 6.6. Histograms showing the seasonal variation in, the frequency of- 139 movementsof woodmice from grids with low numbers to grids with high numbersand vice-versa.

xvi CHAPTER I" U41RODUCTION

,I 'I- .,,, -- -ý1 .1-ia. ý, I. I

This thesispresents the resultsof my researchinto the responsesof smallrodent populationsto changesin time and spacein the structureof a plantedconifer forest. The presentChapter aims to insert my study in the context of previous studieson-related topics, to briefly introducethe biology of the main speciesinvolved, - and to presentýthe structureof the rest of the thesis.

1.1 - Habitat fragmentation, spatial and temporal habitat heterogeneity, and small fragmentation is mammal populations - The process of habitat, one of the'greatest that has to the biological in times. Many changes. man' caused world - modem natural habitats which were essentiallycontinuous a few generations ago have been reduced to a landscape habitat mosaic-like formed by scattered, "patches"- of ,the original ("habitat islands") -surrounded by transformed areas.- A few detailed examples of the process of fragmentation are described in Shafer, 1990, but other examples are increasingly easy to find. Intense - habitat fragmentationý is already characteristic- -of nearly all developed countries, and the same process is now well under way in most tropical regions (Harris, 1984). Using Hutchinson!s (1965) metaphor, the -evolutionary play is now ýstaged in a markedly different ecological theatre., In addition to spatial heterogeneity between the original and transformed habitats, the process of habitat fragmentation often tends to increase temporal heterogeneity as One well. of the main sources of temporal heterogenity is succession:in many casesthe Original continuous habitat was the local climax -vegetation, which is partly replaced by earlier successional'stageswhich are usually more unstable. Often such successionsare blocked in actually early stages(for example, where forest gives way to pastureland), but if to'proceed allowed they result in successional habitat mosaics, patchworks where dfferent habitat patches correspond to different successional' stages of the same Additionally, isolated habitat community. patches of small area can be very unstable and further in prone to changes,even the absenceof additional human disturbance. This was dramatically in isolates shown small of tropical forest in Amazonia, where the trees along the edges are very vulnerable to windthrow, so that isolation itself, virtually condemnsthe forest to (Lovejoy reEcts additional, changes et al., 1986). In, cases where ýhuman influences have increasedboth spatial and temporal habitat heterogeneity, landscapeshave

I often evolvedinto complex"shifting habitat mosaics", -patchwoTks of habitatstransformed in manydifferent ways and changing continually in time. Understandingthe effects-of habitat fragmentationon biological communitiesýis essentialas a basis for sound wildlife- conservationand managementpractices in the modemworld (Harris, 1984;Shafer, 1990). As mammalsin generalhave relativelylarge body sizes,low populationdensities, and less vagility than birds, for -example,mammal communitiesare objects of special concern, for - conservation.If areassurrounding preservedpatches are unsuitable for somespecies, as is often the case,then from the point of view of thosespecies the habitatpatches constitute true "islands"in a biogeographical sense.In suchislands the diversityof the mammalcommunities is likely to be reducedby the combinedeffects of reducedarea - which increasesextinction -rates of the original species- and increasedisolation - which reducesrates of colonizationby new speciesand also the geneticviability of the populationswithin the risland" (MacArthur and Wilson, 1967;Brown andKodric-Brown, - 1977; Gilpin and Soule','1986; Shafer, 'I 990). Large"sizedmammals are considerablymore vulnerableto extinction by habitat fragmentationthan small ones',because, of smallerpopulation sizeswhich increasethe probabilityof extinctionby either demographicstochasticity or loss of geneticvariability due to inbreeding,(Gilpin and Soule', 1986). Thus most reduction of diversity in fragmentedmammal communities is effectedby extirpationof the largest species,and/or the ones of highest-trophiclevel such as carnivores(Vrflcox, 1980).-However, ' habitat fragmentation;and in particular"shifting habitat mosaics" as definedabove, are also likely to haveseveral important effects on populationsof smallmammal species (i. e. rodentsand insectivoresaround most of the World, and marsupialsin Australiaand in the Neotropical Region). - Studyof the effectsof spatialheterogeneity on smallmammal populations has been focus a of considerablerecent research.One of the first reasonsfor this interest was theoretical:the realizationthat be important for spatial-heterogeneity could regulating non-cyclicalpopulations of microtinerodents (voles and lemmings)in Fennoscandia(e. g. 1977; Hansson, Stenseth, 1977,1980, -1985; Hestbeck,- 1982). More recently, conservationaspects of habitatfragmentation have been emphasized in studiesin a variety of tropical and subtropicalbiotas aroundthe world. For example,Bennett (1990) studied the importance habitat for of , corridors- - the 'conservation of marsupial- and rodent in fiagmented populations a Eucalyptusforest in, Australia, and Fonsecaand Robinson (1990) comparedthe smallmammal communities in patches-with and without mammalian predatorswithin the Brazilian'AtlanticForest., In Europe,where habitatfragmentation is havebeen widespreak manystudies carried out on responsesto spatialheterogeneity of

2 bank populations'of the commonestwoodland rodents (woodmice, Apodemus spp. -I and voles,Clethfionomys gZareolus). Several of the recentstudies have focused on population dynamicsin isolatedpatches and movementsbetween patches in differenttypes of habitat in (Geuse 1985, mosaics.Cases studied included fragmented woodlands Belgium et al. Doncaster, Bauchauand Le Bouleng6 1991), urban habitatsin Oxford, (Dickman and in 1987,1989) and in Warsaw (Szackiý and Liro;, 1991), and,an agriculturallandscape Yorkshire (Zhang and Usher, 1991). The responsesof these,species and -field voles been (Microtus agrestis)to temporal habitat variation, especiallysuccession, have also (as in Chapter3 analysedin severallocalities in Britain and continentalEurope reviewed of the presentstudy). Few studies so far have consideredsimultaneously the -effectsof spatial and temporalhabitat heterogeneityon the most commonBritish -rodents. ý Flowerdew, et al. (1977) studied habitat preferencesof four rodent speciesin a fenlandý habitat -mosaic how local subjectto flooding in East'Anglia,and Montgomeryet al. (1991) studied and interannualdifferences in seedavailability affected the distributionofA. sylvaticuswithin a forest (mostly coniferous)in Northern Ireland.,The presentstudy addressesthe effectsof in spatial heterogeneityand successionalchanges on rodent -populations --a-planted coniferousforest in northeastEngland.

in habitat 1.2 - Planted forests as a model for studies on small mammals shifting for the mosaics - Man-made forests can be especially suitable studying effects of spatial and temporal heterogeneity on small mammal populations and communities,,for several interconnectedreasons, as follows. Iý4, Planted forests, especially commercial ones,,-are often dominated by one or a few tree, species, so-that the mature plantations constitute a single, 'well-defined "climax" community. As mature plantations are felled and replaced by younger ones,,over large forest areas a typical successional habitat mosaic is formed, with different patches corresponding to older plantations, younger plantations, clear-fellings, and so on. This successionalhabitat mosaic resembles situations found ýin 'natural mono-species conifer forests in the taiga belt in Northern Europe. A major difference could be the clear-felling of some patchesof mature forest at chosen times in planted forests. However, the role of clear-fellings in restarting sucqessionin -a few patches at a time is parallelled by 'Wind- throws in natural conifer forests, with the dfference that the fallen trees stay in the wind- throws, while the trunks are removed in clear-fellings (although pieces of no commercial value are often left in situ). Another difference between planted and natural forests is that in the former the boundariesbetween the successionalpatches are sharply defined (by the

3 limits of eachplantation "compartment") and the successionalstage of eachpatch can be unambiguouslyassigned. This mosaicof sharplydistinct patchesprovides good opportunitiesto study small mammal population dynamics and spatial distributions in, heterogeneoushabitats. Additionally,the well-definedsuccessional habitat mosaics of plantedforests are suitable for studyof the effectsof successionon the mammalsby simultaneouslysampling sites of differentstages, without needto follow the long trajectory of successionin a singleplace (the so-calledchronosequence approach, Twigg et al., 1989,Fox, 1990). However, local variation in speciescomposition and relative abundancein small mammalcommunities within a singlevegetation type is often very marked (e.g., Brown and Kurzius, 1989) and successionalstage alone is unlikely to explain all the variation betweendfferent patches.In a monospeciesforest one importantvariable (the dominant tree species)is held,constant in all sites.This makesit easierto comparethe effects of temporal (successional)change with those of spatialheterogeneity as reflectedin local variationamong different sites representing the samesuccessional stage. ", I - As a whole,- planted forests can be seenas simplemodels in which the effectsof spatialand temporalheterogeneity on smallmammal populations and communitiescan be studied, where techniquesand ideas can be developedfor later -application in more complexnatural forests, either in the temperateor in tropical regions. Besidesproviding a good model system, planted forests merit considerable attentionbecause of the vast areasthey coverin the landscapetoday in both the developed and developingcountries around the World. in BritaK largeareas have been planted with conifertrees since the early 1920's,following the settingup of the ForestryCommission in 1919 (Rowan, 1986). Today conifers account for more than two thirds -of British woodland,covering over 1.3 million hectaresout of nearly 1.9 million hectaresof, high forest; approximately800/6 of the former arearefers to plantationsof introducedconifer (Pettyand Avery, 1990). -species 1 "1 1ýIIýI Many - of the early large scaleForestry Comminssionplantations have become for mature and ready felling during the last two decades.Thus, an important changeis happening large over, areasof the British coniferousforests (Rowan, 1986), namelythe start of the secondrotation, the replantingof conifers on sites previously occupiedby important similarplantations. An differencebetween first and secondrotation plantations is in latter the presence the of piles of tree brashings,branches and piecesof treeswithout commercialvalue which often are left in situ when the trees are removed after felling. Among brashings other effects,,-tree may hinder the colonization of the young second by rotation plantations-- some plant species. As -the tree brashings are gradually

4 decomposed,the differencebetween first and secondrotation shouldbecome smaller in mature than in young plantations.On the other hand, little is known as to whether plantationsof successiveconifer rotations in Britain will be affectedby cumulativeeffects of the trees on soils (1--liles,1986). Due to the recentintroduction of secondrotation in Britain, there are few studieson rodent populationsin British ýsecond rotation conifer plantations(as reviewed by Staines, 1986), although a'series of"recent studies,(e. g. Thomson,1986; Gibson, -1989; Vadher, 1990) are startingto fill this gap. ',, ý: ýý,

(Picea Carriere); 1.3 - Sitka spruce in ýBritain - Sitka spruce sitchensis the main tree speciesinvolved in the presentstudy, is a native of the westerncoast of North America, where its original rangelies betweenCalifornia and Alaska (Faulknerand Wood,- 1957). First introducedto Britain in 1831, P. sitcheiWsis now the most abundanttree speciesin the Britain Isles:it aloneaccounts for over half a million hectaresof British woodland,i. e., nearly as much as all broadleavedspecies put together (Petty and Avery, 4990). Sitka sprucein its native range is one of the tallest of -all living trees, as individualsseveral hundredyears old may reachup to 90 rn in British Columbia(Chiras, 1990). However, in short-rotationcommercial plantations this speciesis felled when 50 yearsold or less,long before it reachesits maximumsize. Thus British "mature" Sitka spruceplantations are actuallyforests of youngtrees, but throughout'thepresent study I use the word "mature" Sitkain its forestry to be for felling. meaning,i. e., old enough suitable -I ýt The native distributionof Sitka suggeststhat it has a high moisturerequirement. That was no barrierfor the establishmentof the speciesin most placesin Britain, whereit soon acquireda good reputationbecause of its rapid rate of growth,ý high volume of production, straight trunks, easeof establishmenteven in relatively poor soils,,and a considerabletolerance to exposure(Faulkner and Wood, 11957).Such qualities,shown in the earlierplantations, encouraged finther plantingof Sitka, which in the last few decades has for -accounted a progressivelyýlarger proportion of the conifers planted in Britain (Rowan, 1986).However, Sitka!s early successled to an underestimationof someof the it problems encounteredon poorer soilssuch as the leastfertile peats,and when plantedin heather sites where (Calluna spp.) is present.In the absenceof grazing heathermay becomedominant and Sitka grows slowly,,if at all; sometimespine hasto be plantedwith Sitkato helpin suppressionof heather(Faulkner and Wood, 1957). Although Sitka seedsbegin to be shedby October of the year in which they are produced(Brown andNeustein, 1972, quoted in Hill; 1986),a considerableproportion is 'retained overwinter in the cones at the,top of the trees and are dispersedonly in the Mowing in (B. Walker, Year, starting Spring ]ForestryCornmisssion, ýpers. comm.).

ý Thereforethe number'Of seedsdispersed -in -a given year is correlatedwith the seed productionfrom the previousyear. Sitka spruceis an especiallyinteresting model speciesfor study of the effectsof temporal heterogeneityon small.inanInIal populations,, because successional changes in plantationsof P. sitchensisare especiallyquick and clear-cut.Mature Sitka sprucetrees developvery densebranches which block the passageof light, especiallyin the commercial plantationswhere tree densitiesare usuallyhigher than in naturalforests. In matureSitka spruceplantations little fight reachesthe forest floor and consequentlythe ground is completelybare, exceptfor mosses;all other vegetationis shadowedout (Hiu, 1979). Although this trend is reversedlater in natural spruceforests (where senescenceof trees eventuallymakes'the canopy slightly more open and ground vegetation can invade), commercial spruce plantationsare harvestedbefore senescencestarts, -- thus ground vegetationis largelyabsent by the time the forest is clear-feUed.,After clear-felfingground vegetationstarts to colonizethe young plantations.,,Typically, from somefive yearsafter planting a rich ground layer of grasses,heather, brahible, bracken, rushes and herbs is found amidstthe smaUconifer trees (Gibson, 1989). This is a completelydifferent habitat for rodentsfrom the matureforest found in the samesites a few yearsbefore. However, this habitatis short-lived,as the canopy starts to close when the plantationsare about fifteenyears old. In other speciesof conifersthis stageof successioncan inducedramatic changesin rodentcommunities over a period of a few years(Ferns, 1979a). Due to Sitka!s rapid growth, all the markedhabitat changesin the successionalcycle take place within forty to fifty years,i. e. the lenghtof a rotation. As comparedto broadleavedtrees, the litter from Sitka spruceand other conifers decomposes slower. Litter under broadleavedstands tends to be transformedquickly by soil macro-decomposersinto smaUparticles which are readly mixed with the mineral horizons of the soil (as 'mull" humus) where it continues,to decay.!Conifer Utter in He contrasttends to on the soil surfacefor many years(as "mor" humus),being slowly degradedby microbialdecomposers before soU animals are ableto transformit into small particleswhich can mix with the soil; one reasonfor this is that conifer fitter tendsto be have more acid and'to -a higher tannin content, which makes it less palatable to earthwormsand other decomposers(SatchelL animal 1967, quoted-in Miles, 1986). As difference consequencesof this in decomposition,conifers in-general, when growing'on soils susceptibleto rapid change,tend to promote more surfaceacumulation of organic higherdegree matter,greater acidity, and a of soil podzolizationthan broadleavedspecies (review in Miles, 1986). Podzols are well-drained, very, acidic soils-Which are characterizedby an intermediatehorizon which is nutrient-poOrdue to leaching(Avery,

6 1990).Norway spruce(Picea abjes) has been widely studiedin continentalEurope where reportsfrom at leastten countriesshow that it reducessoil pH and causesor accelerates podzolization;although less well studied,the congenericspecies, Sitka spruce,apparently showssimilar effects in Britain (Wes, -1986). The rate of changein pH andin the speedof the processof podzolizationcan vary greatly from site to site, accordingto the local characteristicsof the soil.- Well-drained,poorly buffered soils are most vulnerableto change,conversely, water-logged, well bufferedsoils are quite resistantto changesin pH andto podzolization(Wes, 1986).

1.4 - The ecologyof woodmice,bank voles and field voles: somecontrasts - The small mammalsinvolved in the presentstudy are the three most-common British wild rodents, i.e., the wood mouse(Apodemus sylwficus L. ), the bank vole (Clethrionomysgkreolus Schreber) field (Microlus L. ). The and the -vole agrestis -1 three speciesare widely distributedthrough Europe and their ecologyhas been extensively studied in Britain and elsewhere,as reviewedby Flowerdew(1994,1991), Flowerdew et al. (1985), Alibhai and Gipps(199 1) and Gippsand Alibhai (1991). In the presentSection I use somefindings of previousstudies to positionthe three speciesalong continua defining possible strategies of feeding, movementsand -habitat , preferences.-I.. am aware that this, ýscheme is a, simplificationfor two reasons:first becauselinear continua are an artificial way of representingranges of possiblestrategies, and secondbecause within eachspecies there is' seasonaland/or geographicalvariation in severalof the parametersdescribed -below. However, I suggestthat in each casethere is a recognizableorder in which the three speciescan be arrangedalong the possiblerange of strategies,,and that theseorders are relevantto an understandingof the dfferent responsesof these rodents to spatial and temporalhabitat heterogeneity. .-, -II 1) Feeding strategies- Woodmiceare mostly seedeaters, and amongthe three species largest- they eat the proportion of animal food; bank voles eat a wider variety of plant foods suchas seeds,berries, roots, leavesand fung;L but less animalfood; field voles are mostlygrass specialists and seldomeat any animalfood (Watts, 1968;Evans, 1973; Ferns, Obrtel 1976; and Holisova, 1979;Hansson, 1985; Montgomery and Montgomery, 1990; Zubaid and Gorman, 1991)..As, pointed out by Holisova and Obrtel (1980), these differencesdefine a gradientof feedingstrategies: woodmice usually concentrateon high distributedfood, quality, sparsely bank voles are intermediate,and field voles dependon abundantfood of low energeticvalue.. 2) Home rangesand extentof movements- Wood[miceare highly vagile animals,using a large home for range an mfunalof its size and maldnglong dispersal,movements; bank

7 home voteshave home ranges of intermediate'size,and field voteshave very small ranges 1985, andrestricted movements (e. g. Brown, 1966,Watts, 1970,Wolton andFlowerdew, Attuquayefioel al., 1986,Gipps and Alibhai, 1991).This pattern is apparentlyrelated to the differencesin feedingstrategies discussed in the previousparagraph: the specieswhich usesthe most denselydistributed food hasthe smallesthome range, and vice-versa. --, - highly being 3) Degreeof habitatspecialization - Woodmiceare adaptable, commonin a habitatsbut by wide variety of habitats;bank voles-alsouse several are more restricted their dependenceon ground cover-,field votesare the most habitatspecialist of the three, living mostlyin rough grasslandand young forestry plantations with a lush growth of grass, in habitats,(Southern Lowe, and seldombeing found in appreciabledensities other and 1968, Gurnell, 1985, Flowerdew; 1991ýAlibahi and Gipps, 1991, Gipps and AlibhaL 1991).The ubiquitousdistribution of woodmiceapparently is related to their generalist habits, diet, plus strategiesof predatoravoidance that dependchiefly on nocturnal agility depend anduse of burrowingsystems; the voteswhich are partly diurnal more on ground ýI cover(Brown, 1956a;King, 1985). '-ý, _..III :

is in 1.5 - Thesisstructure - This thesis organized sevenchapters, starting with a general Introduction (presentChapter). Chapter 2 describesthe study area, HamsterleyForest, There and discussesmethodological aspects which are commonto all parts of the study. follow four semi-independentchapters'(3 to 6), which are the core of the thesis.Each of thesechapters has its own introduction, Methods,Results and Discussionsections. 'The specificgoals of the part of the studydescribed in eachChapter are statedin the respective Introductions.Conceptual and/or theoretical aspectswhich are relevant specificallyto eachChapter are reviewedin their respectiveIntroduction sections,or in their Discussion sectionsif this was more suitable (e.g., previous studies on population dynamicsof woodmiceand bank voles,are briefly reviewedin the Discussion-of Chapter4 to allow comparisonswith my own results).My resultsare presentedin Chapters3 to 6, and most discussionof their implicationsis carried out in the respectiveDiscussion sections. The contentsof thesechapters are briefly outlinedbelow. Chapter3 discussesthe variationin speciescomposition and relativeabundance of rodentcommunities in relationto successionalchange and to spatialheterogeneity in Sitka spruce plantationsin HamsterleyForest. The overall pattern of variation in rodent community composition with successionis characterized.In young plantations, the successionalstage in which maximumheterogeneity was found amongdifferent sites,the relative abundancesof rodent speciesare related to local vegetation composition and structure and to soil characteristics.This Chapter describes a study on a larger

8 geographicalscale (the forest as a whole) than the remainingchapters; it is also the only Chapterin which rodentsare studiedchiefly with 1acommunity, " rather than population, approach. Chapter4 discussesthe populationdynamics of woodmice and bank voles in a shifting habitatmosaic formed by matureplantations and clear-fellings.This Chapter,as well as.the two following ones, deals with a smaller spatial scale than Chapter 3: populationprocesses are studiedin an areawithin and arounda singlecompartment of the forest (the so-calledComer Complex).As rodent individualsoften are not restrictedto singlesites within this mosaic,Chapter 4 discussesrodent populationdynamics in Comer Complex as a whole. My results are comparedwith previous studies on population dynamicsof the two rodentspecies in otherhabitats. ----. -C, I 1ýý11 1 Chapter 5 describesthe responsesof woodmice-andbank vole populationsto clear-fellingof a matureplantation within Comer Complex.-It discusseshow that drastic habitat changeinfluences the distribution of rodents-among, subareas within, the area studiedin Chapter4. For this purposetwo field experimentswere used:first, the "natural" field experimentrepresented by the felling itself, second,the removalof tree brashingson recentclear-fellings to evaluatethe importanceof shelterin suchhabitats for eachrodent species.' Chapter 6 focuses on',density-dependence in, habitat selection:-it relates the variationsin rodent populationdensities to how widely they are distributedwithin the habitatsmosaic of Comer Complex.The differentpatterns shown by woodmiceand bank voles are contrasted,and two methodsto study density-dependenthabitat selectionare discussed. This Chapter also analyseshow important are movementsbetween sites4n promotion of changesin spatial distribution associatedwith changesin- density of woodmice. ,I-_. ýI 11ý The final Chapter (seven)is a generalDiscussion which connectsthe resultsof the Chapters. previous -It comparesthe speciesof rodentsregarding their responsesto spatial temporal heterogeneity, and discusseshow the planting of, Sitka may influence spatial heterogeneity forest, -within the and suggestsa, hypothesis relating the differential responsesof rodent populationsto spatial heterogeneitywith their distinct patterns of fluctuationsin numbers.

9 -i CHAPTER2, -- I STUDY AREA AND GENERAL METHODS

2.1 - HamsterleyForest'

is located - HamsterleyForest, owned and managedby the-ForestryCommission, in the, southwestpart of County Durham (longitude 01* 55' W, latitude 540,391N), approximately40 Km to the Southof the City of Durham.'It layson the easternside of the Pennines,between the rivers Teesand Wear. The forest itself coversa total areaof 2,020 ha, emcompassingthe catchmentarea of a tributary of the river Wear, BedbumBeck, plus Bedbum Beclesown major tributary, Euden Beck.,The lowest point in the forest (at Bedburn,in the east)lies at approximately150 m andthe land risesto the west; reachinga maximumaltitude of 430 in (Gibson,- 1989).The underlyinggeology of the areais sand andmudstone of the millstonegrit era(Malvido, 1989).The predominantsoil type is peaty gley, but there is considerablelocal variation in soil types within the forest, discussedin Chapter3., The areawhere HamsterleyForest lies today was a shootingestate and grouse moor before being purchasedby the Forestry Commissionin 1927.Planting of the first rotationlook place from the late 1920'suntil 1951. During these early decadesof the ForestryCommission, several different conifer species(most imported)were test-planted, which explainswhy a high numberof differentconifer speciesis found now in Harnsterley. In 1989,86% of HarnsterleyForest was coveredby conifer plantations,8% by pastures, recreationareas and roads and 4%'by broadleavedwoodland (Walker, 1989, quoted in Vadher, 1990).A farm (PenningtonFarni) is locatedwithin the forest and the recreation areasare mostlymanaged grasslands used for campingand picnics.The areassurrounding the forestare covered mostly by rough pasture,moorland and agricultural fields. Within the forest, Sitka spruce(Picea sitchensis)is the commonesttree species. In 1989it alonecovered 880 ha - approximately51.5 % of the total areaof conifers.The secondmost commontree in Hamsterleyis the nativeconifer Scotspine (Pinussyhvstris). The remainingconifer species,covering relatively small areaswithin the forest,,include Douglasfir (Pseudotsugamenziesfi), Norway spruce(Picea abies), European larch (Larix decidua);Japanese larch (L leptolepis)and Westernhemlock (Tsuga heteroPhYfla)- The deciduous relatively small area of woodland consistsmostly of a -few blocks of oak (Quercus robur) and trees plantedalong streanisidesand roadsides,especially oak, birch

10 (Betulapendula), beech(Fagus sylvatica) and sycamore(Acer Pseudoplatmms).Due to the commercialadvantages offered by Sitka spruce,the predominanceof this specieshas increasedsteadily in Hamsterleyduring the last few decades,as hashappened in Forestry Commissionplantations in general(Chapter 1). Nearly all conifersplanted in HamsterleY" from 1980to the presentday havebeen either predominantly or entirelySitka spruce. By the start of the present study in early 1990, most conifer plantationsin Hamsterleywere reaching the endof the first rotation (i. e. they were about40 yearsold or more), but comparativelyfew sites had alreadybeen felled and replanted.Figure 2.1, basedon ForestryCommission timber extractionmaps on the scaleof 1:10,000, shows the distributionof coniferplantations of dfferent agesand of clear-fellingswithin Hamsterley Forestin February1990. Mature plantationspredominated over youngerplantations (i. e. the two categories"planted from 1960to 1990"and "plantedafter 1980"in Figure 2.1) in the habitatmosaic in Hamsterleyat that time. Someof the recentclear-fellings, including all those sitesdescribed in the following Chapters,were left to natural regenerationand not replantedwithin the durationof the study.

2.2 - Capture - mark - recapture methods

for 2.2.1 - Trapping and marking techniques - Within each site chosen study a square grid of 49 points Qx 7) was marked, using a hand compassand metric tape. The spacing between points was 15 in, thus the area delimited by each grid was 0.81 ha. Points were marked with bamboo canes, except in the mature forest grids where plastic yellow tape attached to the trees was found to be preferable to canes. The shape and size of these grids were similar to those used in the Mammal Society Woodland Small Mammal Survey coordinated by LK Flowerdew. Unlike the Survey grids, in my grid design only one 'trap was placed at each point. I opted for using one trap per point as a compromise between my ability to operate the grids in times of high catchesand the.need to trap in several sites simultaneously,but arguably having onlý one trap available in each point n-dght lead to for competition traps among the rodents and consequent failure -to catch a p'art of the population. However, according to Southern (1? 73), -reasonably""consisteýf of rodent population sizes will be produced if at least 20% of the traps are unoccupied in every trapping night. During my study this condition failed to, be fulfilled in one occasion 'see'Chapters only (grid Comer 2 in October 1991; 5 and 6). ' Thus it, is likely that for competition the traps did not seriously affect the'population size'estimates in most occasions.T6 design I used is referred.to hereafter as_th6"standard grid".

11 «ci 1 0% ac e-

- Live-trapping was performed using Ungworth traps (Chitty and Kempson;" 1949; Gurnell and Flowerdew, 1982) baited with a few grams of whole wheat and crushed oats, and filled with hay to provide bedding.,Traps were set for three consecutivenights without is -a "standard I any pre-baiting; this procedure called'hereafter trapping session".- opted not to use pre-baiting becauseit is known that pre-baiting tends to increaseý the "edgeeffect" (trap-induced immigration of animals-from, the surroundingý areas into the grids); this effect causesserious problems in the estimation.of several demographic parameters (Southern, 1973; Smith el al, 1975; Flowerdew, 1976a;Tanaka, 1980). Bait was renewed whenevernecessary and bedding after every trapping session. - 1:1A, potential complication in five trapping is the effect of odours left, in traps by animals captured previously, which are likely to influence capture probabilities (Stoddart, 1982; Stoddart and Smith, 1986)."Stoddart (1982) found, that field volesýapparently prefeffed'clean Longworth traps to soiled ones; few new individuals were captured after the traps had beenused for nine consecutivedays without cleaning. On the other hand, the sameauthor (Stoddart'and Smith, 1986) found that previous captures of woodmice could actually increasethe efficiency of the traps for conspecifics and Gumell and Little (1992) found the same-for both woodmice and bank voles. Absence,of effects has also been reported in ýseveral cases; e.g., - Montgomery (1987) found no significant -difference between population, estimates obtained from,,,use of breakback traps (which are not affected by soiling) and of Longworth 'traps, and'Tew (1987) found no detectable difference in the capture efficiency of clean and dirty Longworths, forbank voles, and woodmice. In a review of the effects of trap odours, Gurnell and Little (1992: table VH) could find no consistent patterns but, concluded that in general odours do not affect significantly the - results of, trapping, studies, provided that, a reasonableý number of is Unoccupiedtraps available to the animals.'As'responses to odours seem t6 vary among (and sometimeswithin) species,in the present study the traps were not washed after every trapping sessionbut about once every three sessions. Rodents'-were I marked with ear-tags stamped with individual -numbers. Theývery light "Michel" surgical clips were used as ear-tags,-as introduced by Le Bouleng6-Nguyen Le Bouleng6 (1986). As and compared tojoe-clipping, which is the other most commonly used, method for, long-term marking, of. small mammals,, ear.tagging .,allows quicker identification marking, easier of the mark, and less suffering to the animals. Species, sex,,and weight were recorded for all individual rodents captured. Signs of (e. reproductive condition,,- g. , abdominal testes,ý, perforate vaginas, vaginal - plugs, lactation; pregnancy)were also recorded whenever observed.

13 2.2.2 - Dealing with tag losses - Loss of a NEchel,tag can be recognised easily from a characteristictear left in the skin of the ear. During the study I detected a rate of tag loss of 6.4% for woodmice, 6.0% for bank voles and zero for field voles (but only 16 recaptureswere obtained for this species).Rates of tag loss for woodmice and bank voles are slightly higher than the ones reported by Le Bouleng&Nguyen and Le Boulengi (1986). Such rates could introduce biases in the estimates of population size, survival, recruitment and trappability, which are all dependenton the assumptionthat all marks are retained (Seber 1982). ý,; All - lost tags zwere replaced in the field. For data analysis, nevertheless, two different procedures were *used to deal with the problem of tag loss, according to, the being parameters estimated.,- ý 1) For all estimatesof home ranges,movements and spatial distributions (Chapters 4-6), tag replacementswere ignored: individuals which had lost a tag were considered as new individuals. The effect of this procedure is that estimates of those parameters are likely to be conservative (e.g. some movements went undetected because of lost tags). Every location/movementquoted is basedon positive evidenceonly. - 2) For all estimates of demographic parameters, (population sizes, survival, recruitment and trappability; Chapters 3-6), tag replacementswere included: the code of the new (replaced) tag was applied retrospectively to, an individual which had disappeared , from the population previously. In other words, an individual was chosen as the "most likely" to be the one that had lost the tag. This choice was made by following, sequentially, a series of criteria, namely: 1, same species;2, same sex; 3, same grid; 4, most recently disappeared; 5, weight equal:-or nearest -lower-, 6, similar reproductive condition. Candidates for replacementwere chosen using "calendars of catches" (Petrucewicz and AndrzeJwS4 1962, quoted in Le Bouleng6,1997) in which each capture was labelled with the information relevant for each criterion. Such calendars were produced using the CMR (Le program Boulengi, 1985,1987). In the specific case of recruitment, the above in individual procedure resulted any which had lost a tag being excluded from the estimate, i. e., recruitment rates were basedon new individuals only. It is likely that in ýI some casesof replacementthe individual which actually lost the tag have been may misidentified. However, this does not seriously affect estimates of individual, population, rather than parameters.,Tor example, in a survival estimate what is the matters proportion of previously marked individuals which are still alive, not who they The fourth (most are. criterion recently disappeared) was especially relevant for its survival estimates: aim was to minimize the bias caused by "resuscitating" dead

14 individuals through tag replacement.As in the case of movements,I opted for a conservativeoption, which resultedin minimumestimates for survival.

2.3 - The data base,

The analyses,discussed in the following Chapters are based on the, results of 22,041 trap-nights during the From this trapping I - spent study as a whole., effort obtained 4,034 captures of, 1,270 individual rodents. The data base for each Chapter overlaps with that of other, Chapters; for example; the captures obtained in the ýComer Complex (Chapters 4-6) in certain months were included in the larger scale rodent censusescarried out in those months (Chapter 3). The distribution of captures among the three rodent speciesis shown in Table 2.1.

TABLE 2.1. The database: number of individualscaught and number of capturesof each rodent speciesduring the study as a whole. Data on numberof individualsare corrected for tag losses(see Section 2.2.2).

Species Individuals Males Females Unsexed Number of captures

Apodemussylvaticus 956 549 388 19 3,172 Clethrionomysglareolus 283 133 143 7 815 Microlus agreslis 31 15 16 0 47 1 1 1 1 1 11

In addition to the rodents, there were 46 capturesof common shrews (Sorex 13 araneus), of pigmy shrews(S. minutus)and four of weasels(Mustela nivalis). All figures the aboveexclude the datafrom the associatedstudies quoted with the i. authors'permission, e., those of Gibson (1989), Harrison (1990), Vadher (1990) and Burrows (1991).

15 2.4 - Statistical Analysis

The statisticalprocedures used in this studyfollow Zar (1984),,-except when stated otherwise.Preference was given to parametrictests, but in caseswhere data violated the necessaryassumptions I usednon-parametric tests instead.Calculations were carried out usingboth the mainftamecomputer and IBM PC's at the University of Durham plus the mainftamecomputer at Universit6Catholique de Louvain,Belgium. Commonerstatistical testswere carriedout usingthe PC programMicrostat (Ecosoft,Inc. ); the softwareused for more specificprocedures is givenin eachof the correspondingSections in Chapters3 to 6. Thoughoutthe text resultsare referredto as being significantor not; this concerns statisticalsignificance at the 5% level.

16 CHAPTER 3 SUCCESSIONAL AND SPATIAL VARIATION IN RODENT CONQAUNITIES IN" ý SITKA SPRUCE PLANTATIONS IN HAMSTERLEY FORESY -

3.1 -Introduction

The goalsof the study'describedin this Chapterwere as follows.- (1) To the characterize overall pattern of change-of speciescomposition, and relative abundancesin rodent,-communities -during succession-in, Sitka spruce plantations at HamsterleyForest. (2) To quantifythe spatial(inter-site) and temporal(interannual) variation foundý within eachsuccessional stage. 'ý,- (3) To characterizethe changesin vegetationcomposition and structureoccurring during succession. (4) To relatethe averageabundance of eachrodent speciesto the successionalchanges (3), to the variabilityin spaceand time found within eachstage (2) and to the distribution of the otherrodent species.

3.1.1 - The chronosequence approach and its assumptions

Successionalchanges in vegetationand their associatedmammalian communities happen may over time intervalslonger than most feasiblestudies, sometimes longer than the 'investigatoes own life span. One way'of tackling this problem is to sample simultaneouslyseveral sites at different successionalstages. This generalapproach has been called the (Hill, -- chronosequencemethod, ' ý1986; -,, - Twigg , el al. j- ' 1989). Chronosequenceshave beenused to study mammaliansuccessions in continentalEurope (e. Hansson,1978; g. ý Wolk arid,Wok 1982) and studiesin Australiahave supportedthe by validity of the method showing,that the, patterns revealedwere similar to those from long-term emerging studiesfollowing successionin a singlesite (Twigg et al., 1989; Fox,,.,1990).;, The Chapter'reports present the results of a chronosequencestudy of successionalchanges in rodentcommunities in Sitka spruceat HamsterleyForest. Implicit in ', the chronosequencemethod are two'aSSUmptions:-1) that sitesare true (i. replicatesof eachother e., given time enough,a young plantationin my study would

17 developinto a habitatsimilar to a matureplantation as found today);- 2) that samplingcan be regardedas instantaneousin relationto the time scaleof the successionalchange itself, i.e., a site selectedto representa given successionalstage'does not'change'to the following stagewithin the durationof the study. The first assumption,could be perfectly met only in a perfectly,homogeneous habitat(and with the additional-assumption that successionis completelydeterministic). However,chronosequences, can be usedin heterogeneoushabitats if severalreplicates of eachsuccessional stage are available.This enablesthe investigatorto comparethe effects of successionalchange and of local factorsin determiningthe specificcharacteristics of the habitatat any given site. As discussedin Chapter 1; it is the balancebetween these two competingeffects which determinesthe physiognomyof the habitatmosaic as a whole. ", Replicationis desirablein ecologicalstudies, as variation is such an all-pervasive characteristicof all ecological systems(Hairston, ' 1989). 'However, 'when only, one researcheris availableto work with small mammalsat'between-grids (site) level rather than within-grid level, there are practical limitations to the feasible- number of valid replicates.- The chronosequencemethod ý implies a time constraint when appliedý to fluctuatingpopulations: all replicategrids mustbe trappedat almostthe sametime if they are going to provide a- true replication (Hurlbert, 1984). Usually some degree of compromisehas to -be achievedbetween the statistically'desirable and the feasible; however,useful results-have been producedin most small mammalstudies even if they have had fewer replicatesthan some studies''on other taxa (Schoener; 1986). The compromisemust also take into accountsituations where greater or lesservariability is likely to be found:those demand more or lessreplication respectively.

3.1.2 The - chronosequenceapproach as applied at Hamsterley Forest"

For the studyreported in the presentChapter, I was ableto use a "core" of eleven regularly sampledreplicates representing three sucessionalstages (clear-fellings, young plantationsand matureplantations), plus sevenadditional replicates sampled occasionally (see below). Some additional,comparable data on a fourth stage, "closing" (the stage where the canopystarts to close and to shadowout the ground vegetation)were made by available an associatedstudy in Hamsterley(Burrowi; 1991).These successional stages were chosento' representthe most distinct Idnds,of habitatsfaced by rodents,during in succession Sitka spruce.,Numbers of replicates'differedbetween stages,'the highest being number allocated- to'young plantations (as high- variabilityý among them was

is suggestedby Gibson,1989) and the lowestnumber allocated to matureplantations, which were superficiallymost homogeneous to a humanobserver. - The secondassumption of the chronosequencemethod, that the samplingperiod is short in relation to the scale of, successionalchange, may be difficult to meet when studyingspecies with pronouncedinter-annual population fluctuations,, especially cyclic populations.In such casesit may be difficult to disintanglethe effects of year to year differencesin relativeabundances from the trendsassociated with succession.One way of tackling this problemis to samplefor severalyears so that year to year differenceswill averagethemselves out; but not for too manyyears or sitesmay changetoo much to be regarded as, representativeof the successionalstage they were - originally: meant to represent.In the presentstudy,, I sampledat a standardtime of the yearfor threeyears, but for the young plantationsa fourth year of comparabledata was availablefrom the same sites (Gibson, 1989; see Section3.2.1). Young plantationsare the favourite habitat of Microlus agresdswhich, in most British localities,reaches peak densitiesevery three or four years (Elton; 1942; Chitty, ' 1952;- Richards, 1981). Allocations of sites to given successionalstages are likely to haveremained valid throughoutthe study. Clear-felling siteswere zero to threeyears after felling, andnone of them hadbeen replanted by the end of the study-,young plantationswere five to eight yearsafter planting, and mature (first rotation) plantationswere about forty yearsold or more. Burrows (1991) two "closing" siteswere fourteenand twenty yearsold respectivelywhen sampled. As thereare no secondrotation matureplantations available in HamsterleyForest I characterizedsuccession using matureplantations of the first rotation, and clear-feffings, YoungPlantations and "closing"stage plantations of the secondrotation., This arrangement is representativeof mostplanted forests in Britain today.My studymay alsobe considered representativeof secondrotation succession,if the assumptionis made that in future mature second-rotationSitka spruceplantations will be sin-fflarto mature first-rotation ones.This assumptionwas discussedin ChapterI (Section1.2).

3.2 - Methods

3.2.1 Rodent censuses Censuses in .-, -* were performed early summer(early, June-mid July) in 1990,1991and 1992.As I could not trap all grids simultaneously,census trapping during was carriedout a 4-6 weeksperiod eachyear. Populationsof woodmicein most forest habitatsshow within-yearfluctuations by decline. . characterized a, spring -- numbers relativelystable in early summer(June-July), and a late sumer or autumnincrease (Watts

19 1969, Flowerdew 1985, Montgomery 1989a). Bank voles in Britain often show a similar pattern (Afibhai and Gipps,, 1985); in field voles the pattern varies dramatically from year to year according to the stage of the population "cycle" (e.g. Chitty, 1952). Thus early summer data are likely to be more comparable among grids than data collected at other seasons.One of the two annual samplings for the Mammal Society survey of woodland small rodents is also performed in June Q.IL Flowerdew, pers. comm.). For these reasons, early summer was chosen as the census period. An additional "pilot" census was performed in late winter (Febmary-March) 1990, chiefly to test whether some areaswere suitablefor inclusionin the regularcensuses. Standardgrids were sampledregularly at ten sites during the summercensuses. three clear-fellings("View", "Farm Felled" and "Comer South"), five young plantations ("Road", "Adder", "Stuart",Tarm Young" and "ScotsPine") and two matureplantations ("ComerNorth" and "FarmMature"). Sitka sprucewas the dominantconifer speciesin all sites, although in some mature sites a few Scots Pine (Pinus s)IwsftIs) were found (mostly deadtrees overshadowedby Sitka) and in someof the young plantationssmall amountsof Scots Pine had been planted alongsideSitka. The sites within the Comer Complex(see Chapters 4 and 5) which were clear-felledduring the study("Comer 1", "2" and "3") were includedin eachcensus according to the successionalstage they presented', at the time. Clear-fellingswhere brashings were experimentallyremoved (see Chapter5) were not included in the censusesafter the manipulation,because they were not consideredas representativeof "natural" clear-fellingsanymore. Some additional grids ("Hut Mature", "Comer East", "Comer Young") were trappedonly in the summer1990 in census, associationwith Vadhees(1990) study. One mature-site ("Adder Mature") included in the pilot censuswas not trappedthereafter due to frequenthuman disturbance to the plot. The positionof all siteswithin HamsterleyForest is shownin Figure3.1. The National Grid Referenceof eachsite, datesof plantingan' d/or felling, and son typesaccording to the Forest Confission'ssoil map of Hamsterleyare shownin Table 3.1. It be should noticedthat there is a great variation of soil.types within'each successional distribution stage,and that of soil typesis "fine-grained"spatially, so that most siteshave at leasttwo distincttypes/subtypes of soil. S. Gibsonkindly allowedme to use her original data on rodent abundancesin the in 1989.Gibson! young plantations s secondtrapping session (which I use) was performed I at the sametime of the year as my summer'-censuses,but she used five-day trapping sessionswhile I usedthree days.To correct for this, I havereworked her data to include the. first days'in her only three each of sessions.Even so, the data' are not wholly because:1) Gibsons comparable grids had 50 traps,while minehad 49; 2) sheused two

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22 days of pre-baiting, while I did not use pre-baiting; 3) she used rectangular (5 x 10 points) grids, while I used square(7 x 7) grids.:While the first'dfference is likely to be negligible, the other two might be important. Both of these methodological differences are likely to produce a greater edge effect in her, grids and thus inflated population estimates when compared with rnine. Edge effect is a recurrent problem for estimation of small mammal densitiesin general(Smith el al., 1975; Flowerdew, 1976).lFor this reason Gibsods results were used,only when comparing relative abundancesof the three rodent species.ýý In order to simplify, the interpretation of some comparisons, two data sets were defined. The "restricted data set",(shown in bold in all Tables) excludes the results of the pilot (late winter) census and all the "one-offs", (grids trapped just once in the summer censuses).The results of the pilot censusas well as the one-offs forin the "additional data , set". The two setsput together are referred to as "total data".

3.2.2, - Analysis of rodent distributional patterns The abundances.of the rodent speciesat the time of each censusin each site were estimated by-the number of different individuals captured. The-Pne-year time intervals between most censusesand the short duration of eachtrapping sessionprevented the use of probabilistic population estimators, which are dependenton survival and/or catchability estimates.Most individuals marked in one year would have died by the next. For each rodent speciesseparately, abundances in the three years (four for young plantations) were compared by means of analysis of variance (one-way ANOVA, Zar, 1984). In ANOVA! s terminology, the groups were defined by all sites trapped in each irrespective year, of successionalstage. The restricted data set was used in this analysis.., The frequency distributions of abundancesamongst the sites of two of the rodent species (Microtus agrestis and. Clethrionomys glareolus) showed significant positive skewness , ' and therefore they were normalised by ie log (x 1) transformations prior to the analysis. For each rodent species, abundancesin different sites within each successional stage,were compared as well. in ANOVA! s terminology, each group was defined by one site, the members of each group being the numbers captured in that site in each of the As years. above, the"'restricted data set was used. Given the low number of elements Cviars) in each group and'the consequentimpossibility of reliably assessingthe normality ýdata, of the the non-parametric Kruskal-Wallis test (Zar, 1994) was used. For comparing in different abundances mature plantations, of which only two replicates were available in data the restricted set, a non-parametric Mann-Whitney U test (Zar, 1984) was used instead of Kruskal-Wallis.

23 Pairwisecorrelations between the abundancesof the rodent specieswere tested usingPearsorfs product-moment correlations (Zar, 1984).-For this analysisthe total data set was used, as the aim was to test how the abundancesof eachpair of specieswere correlatedover a rangeof differentcircumstances. -

Shannon 3.2.3 - Diversity and Evenness - Diversity was estimated using the modified index; el-ý, wheree= basis of the natural logarithms, H=I pi. ln(pi) and pi ='proportion of the species 9" in the sample. Compared to the original Shannon index (H), ýwhich is proportional to the logarithm of the number of species, el'ý has'the advantage of being directly proportional to the number of speciesitself,, Whichmakes- infeipretaflon easier.For example, a community withelr= 3 has a diversity equivalent to three equally abundant species(Ludwig and Reynolds, '1988). Evenness,one of the componentsof diversity, wIas estimated s'eparitely using Hilrs modified ratio (Alatalo, 1991). The aim of this procedure was to separatethe effects of species richness' and of variations in the distribution of relative abundances when interpreting the diversity patterns. In relation to other evennessindices, 101's modified ratio has the considerable advantage that it tends to be independent of sample size (Ludwig and Reynolds, 1988) and it is givenby (terminology as above):

eir -I

Computations of diversity and evennessindices were performed using Ludwig and Reynolds' (1989) "Statistical Ecology" program. When Shannods and Hill's indices are calculated for a set.of sites many of which contain just one species,they tend to generate a strongly bimodal distribution, where one mode is the value corresponding to an Ione-species community - (Shanýnotfs index 71 and Mrs = 0). Such distributions cannot be made normal bý the usual data transformations, and therefore when comparing the 'indicesI used non-parametric Mann-Whitney U tests instead of t-tests. The data'from the winte'r census were discarded in these tests, to minimize the effects of population fluctuations on the comparisons of diversity and evenness.

analysis ýerfbrmid 3.2.4- Habitat Description,.,- All habitat were at among-grids level leveL i. rather thanMthin-grid e., although each grid is representedby the sum of several

24 sampling trapping points, the grid as a-whole was' the basic unit 'of analysisthroughout. The habitat variables measuredincluded those which were shown to be important for at least one of the axes identified through Principal Component Analysis of habitats in young second-rotation plantations by Gibson (1989). Several other variables which-I suspected to be important for other stageswere added as well, making a total of 20 habitat variables '10 divided into two iets: variables describing cover by dfferent plants in the associated vegetation, and 10 structural variables.1he variablesmeasured are listed in Table 3.2. The variable "palatable grasses"includes all the speciesof soft grassesfound in the study sites and known to be good food resourcesfor Microlus agrestis (Fems, 1976), but most of the records referýto three pecies only: Holcus lanatus, Agrosfis canina and H. mollis. Habitat variables were measuredin fifteen sites, including all sites in the restricted data set and four additional'ones, among them the two canopy closing sites (Burrows, 1991). Measurementswere taken in May-June 1991. At this time of the year many plants are in flower, making identifications easier. In each plot, most variables were measuredin nine I m2 quadrats distributed along the grid, one in the center, four making a cross around this first one, and one near each comer of the grid (Figure 3.2). This technique was adapted from Shirnwell (1971) and aims to provide homogeneouscover of areas where gradients may be present. For the plant cover variables, numerical values were attributed to eachusing the Domin scale,which varies from 0 to 10 with increasing percentagecover (Bannister, 1966; Chapman, 1976). The structural variables describing cover were measureddirectly in percentagesrather than on the Domin scale, and the measurementof a few other structural variables followed specific procedures. Height of undergrowth was sampled in the Im2 quadrats, but measuredin cm. For the four density variables (cones, Sitka trees, broadlea'vedtrees, ' Sitka saplings) and for cover by low branches it was felt that a larger sampling area was required to produce meaningful estimates.These variables A were measured in a circle with a ratio of 2.5 m (area 19.65 centered at the cane marking the point. Cones, trees and saplings were counted and percent cover by low branches estimated within such circles. Thi use of different scales to measure different variables does not matter for the ordination techniques, DCA and CCA (see below), because the variables are standardized during the calculations (see case studies in Ter Braak, 1986).

Analysis' of the successional changes and their relationships with the rodent abundancesfollowed two steps, using two complementary multivariate methods. First the changes in vegetation Were studied.. by means of ordinations using Detrended , Analysis , Correspondence or DCA and Gauch, 1980; Gauch, 1982). DCA ordinates sites or speciesalong environmentalgradients, by creating axes which are composites of

25 TABLE 3.2. Habitat variables measuredin fifteen of the Censusgrids. Asterisks (*) indicate those measuredalso by Gibson (1989). Variables were measuredusing the Domin scale (see text) except where specified otherwise.

PLANT COMPOSITION VARIABLES

1) Purple moor grass (Molinia caerula)*

2) Palatable grasses (Agrostis canina; A. capilkyris, Holcus lanalus, H mollis, Festuca ovina) *

3) Wavy hair grass (Deschampsiaflexuosa)*

4) Heather (Calluna vulgaris) *

5) Bracken (Pteridium aquilinum) 6) Bramble (Rubusftuticosus)

7) Bilberry (Vaccinium myrtillus)* . 8) Herbs (e.g. Chamaeneriumanguslifolium, Galium s=atile, Rumex spp)

9) Rushes(Juncus effusus,J squarosus) 10) Mosses

STRUCTURALVARIABLES

11) Height of groundvegetation (cm) 12) Total coverby groundvegetation (%) 13) Coverby baresoil (!/o)* 14) Coverby litter (0/6)

15) Densityof sprucecones on the ground(number/m2) 16) Coverby brashings* (%)

17)Density of Sitka sprucetrees* (numberin a Smdiameter circle) 18) Densityof broadleavedtrees * (as above) 19) Coverby low branches(%)

20) Densityof Sitka sprucesaplings (number/m2)

26 Figure3.2. Diagramof a standardtrapping grid, with 15mtrap spacing(see text, Chapter2). Trappingwas performedat all points, and habitatsampling was performedat the nine points markedby_ squares.

+ -i.- + +

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27 environmentalvariables (indirect gradient analysis).Calculations were performed using the program DECORANA (Hill, 1979a), separately for the two sets of measured habitat variables, i. e., the plant cover variables and the structural variables. For each set of variables, ordinations of the sites according to the habitat variables and of the variables according to the sites were calculated ("samples" ordination and "species" ordination respectively in DCA terminology). The sites were plotted in relation to the first two axes in a two-dimensional plot, using the "sample scores" produced by the program. Sites close together in the plot are similar in terms of the cover/structural variables measured;those far apart are dissimilar. Successionalstage and habitat data collected from the sites were then used to interpret the gradients. Reciprocally, habitat variables were ordinated by site using DCA! s "species scores". Variables close together in the plot have a similar distribution acrosssites. The second step was to relate the whole pattern of variation of vegetation (cover and structural)with the abundancesof the rodents,sitewise, to identify the environmental factorswhich are correlatedwith the observedabundance of rodentsat different stages. This analysisused Canonical Correspondence Analysis'or CCA (Gauch, 1982;Ter Braak, 1986).CCA hasthe advantagethat the ordinationaxes are chosenin the light of known environmentalvariables, by imposingthe extra constraintthat axesare linear combinations of known environmentalvariables (direct gradient analysis).In CCA terminology, the habitatvariables were treated as "environmentalconstraints" and the rodent abundances were treated as "biological response".Calculations were performed by the program CANOCO (CANOnical CommunityOrdination; Ter Braak, 1988). All variables(plant cover and structural)Were pooled together in the initial set of variables(allowing insights into the relativeimportance of plant cover and structuralvariables). Before running CCA itselfý the Variable Inflation Factors (VIF) and weighted correlation coefficientswere in analysed order to detectand removevariables which were too closelycorrelated to be usedtogether in the ordination(see Ter Braak, 1986).

3.3 - Results

3.3.1 OveraH patterns in ý, - species composition, umbers and i-ilative abundances - The different individuals numbers of caught for each species at each site during each censusare shown in Table 3.3. These data include S. Gibsores1989 data. The proportions of the three species in the restricted data set and the additional data did set not Mer significantly for young plantations and mature plantations (Table

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29 I 3.4). Therefore,the total data set was used to representthe proportions of ihe three speciesin both thesesuccessional stages. On the other hand,the proportionsin the two data sets differed significantly(X2 = 5.48, p<0.05; Table 3.3) in the clear-fellings. Therefore,the restricteddata set was used to representthe proportionsat this stage. Figure 3.3 showshow the individualscaptured are distributedamong the three rodent speciesin eachof the successionalstages, pooling data from all sitesrepresenting eachstage. In additionto the stagesdescribed above, Figure 3.3 includesthe "closing" stagedata from Burrows, 1991,who usedexactly the samestudy methodsas I did. Two patternsare imediately apparent: (1) field voleswere presentin considerableabundance in young plantations,but virtually absentin the other stages;and (2) the proportions of woodmiceto bank voles were quite variable among different stages.At clear-fellings, woodmicewere by far the dominantspecies and bank voles were much less abundant (throughoutthis study, "dominant"is used simply as a short-cut to "the most abundant species").In young plantationsas a whole abundancesof thesetwo specieswere quite equitative,the proportion of bank voles to woodmicebeing significantlyhigher in this stagethan in both clear-fellingsand mature plantations (see Table 3.4, below). The pattern for the "closing" stage(represented by much smallersample sizes) resembles the young plantations,with high abundanceof bankvoles; the proportionsof the two speciesdid not differ significantly from young plantations (Table 3.4). Mature plantations overall presenteda pattern which resemblesclear-fellings!, from which they did not differ significantly(Table 3.4).

TABLE 3.4. Comparisons of the ratio of woodmice to bank,voles in different successional Data from Figure stages. 3.3. Chi-square tests with one degree of freedom and Yates correction for continuity.

Successionalstages: X2 Significance comparison (2-tail) Clear-fellings Young x 16.39 p<0.001 Clear-fellings Closing x 0.69 p>0.25 Clear-fellings Mature x 0.04 p>0.75 Young x Closing 2.99 0.05 0.10

30 FIGURE 3.3. Pie charts of the distributions of abundances(number of different individuals captured) of the three rodent speciesin plantations of four successionalstages, pooling all the sites of each stage. Data from Table 3.3, except on the "closing" stage which are from Burrows (1991). The restricted data set was used for clear-fellings, and the total data set for (see the remaining stages text). I

1 156 75

...... FAIM 'AiM ....

OWk ve 170 85 30

Clear-Fellings Young,,,Plantations-

ocnk van vdm 20

"Cl ",, osin g stage -Mature Plantations -

31 Total numbersof 'rodents"ateach' site (the surn of the three numbers'-of 'the' sequencesin Table 3.3) were comparedacross stages. Analysis was basedon the total data set, but excluding Gibsods 1989 data becauseof the bias expectedfrom the methodologicaldifferences pointed out above,and the winter 1990pilot censusbecause of the effectsof seasonalvariation in numbers.Although the averagetotal numberof rodents was highestin matureplantations followed by young plantationsand clear-fellings,values were quite variablewithin eachsuccessional stage and consequentlyt-tests did not show any significantdifference between any of the stages(Table 3.5).

TABLE 3.5. Averagesof the total number of rodents caught in a summer standard trapping sessionat each successionalstage (top) and comparisonsamong the stages (bottom). Data from Table 3.3 with some deletions (see text). Logarithmic transformationswere usedto normalisethe-data prior to the tests.

Successionalstage Numberof -,- n s.d. -+ trappingsessions

Clear-fellings 13 9.31 ± 5.99 Young 17 12.00+ 9.72 Mature 09 15.22+ 12.36

Comparisons t- Significance(p) (2-tail)

Clear-fellingsx Young' -0.7869 0.2190' Clear-fellings,x Mature -0.9925 0.1664 Young x Mature -0.4563 0.3262

3.3.2 Temporal - - variation and spatial variation - The average abundancesof woodmiceand of field voleswere found to be significantlydifferent in the four yearsfrom 1989 1992 (ANOVA, to woodmice,F=5.640, p<0.01, and field voles F=5.640, P< 0.01)" However,' no significant difference was found amongst years in the average abundanceof bankvoles (Table3.6, top).

32 for in TABLE 3.6.- Top: ANOVAs comparing each speciesof rodent the numbers captured different years at all sites trapped. Bottom: Kruskal-Wallis test for each successionalstage comparing the numbers of each species captured per site' Each site was represented by numberscaught in all years when that site was trapped. Both analysesused the restricted data set (see text). For mature plantations (I dS ), Mann-Whitney's U-test was used; values of z (the normal apro)dmationto Mann-Whitney's U statistics) are shown.

Species Source Mean D. F. Mean F Significance of square square of F variation

-Apodemus Among 579.42 3 19.139 5.64 p<0.01 sylvalicus years Within 1129.56 33 34.229 years' Total 1708.97 36

Clethrionomys Among 0.13 3 0.043 0.17- p 0.92 glareolus years Within 8.47 33 `0.257 years Total 8.61 36

Microstus Among 1.70 0.566- 5.64 p<0.01 agrestis years Within 3.31 33 0.101 years Total 5.01 36

Species Successional D. F. Significance -H stages

Clear-fellings 1.076 3 0.7829 Apodemussylvaticus, Young 8.489 4 0.0752 Mature (Z) 1.09I_ 1 0.1376

Felled 7.746 3 0.0516 Clethrionomys glareolus Young 9.329 - 4 0.0534 Mature (z) 1.746 1 0.0404

Microtus Young 8.146,, agresds - -4 0.0864

33 Differences in rodent abundanceamong sites (within a given successionalstage) were found to be significant only for bank voles in mature plantations (Mann-Whitney test, 2-tafl, z=1.746, p<0.05; Table 3.6, bottom). However, the overall pattern shown in Table 3.6 (bottom) should be interpreted carefully: while only this difference was significant, 0.05

3.3.3 - Correlations among the abundances of the rodent species - No significant correlation was found between the abundanceof bank voles and either of the other two species (Table 3.7). On the other hand, a significant negative correlation between the abundanceof woodmice and of field voles was found in young plantations (r = -0.344, n= 27, p<0.05).

TABLE 3.7. Values of Pearson'scoefficients (r) for the correlations between the abundancesof rodent speciesin differentsites in eachsuccessional stage. Number of sites for eachstage is indicated(ds) and significanceof the valuesis shownfollowing eachr, n.s. = non significant; p <0.05.

Species: Clear-feffings Young Mature comparison (n = 15) (n = 27) (n = 12)

AsylvadcusxCglareolus -0.150(n. s.) 0.090 (n. s.) 0.271 (n. s.) A. sylvaticus x M. agrestis -0.413 (n. s.) - 0.344 C. glareolus x M. agresfis -0.132 (n. s.) -0.057 (n. s.) I I I II

3.3.4 - Diversity and evenness- When data from all sites at each successionalstage were pooled (as in Figure 3.3), young plantations have a higher rodent diversity, as measuredby Shannon index, than either clear-fellings or mature plantations.-, Those two latter stages diversities presented similar (Table 3.18,last However, ',when instead of pooling data diversity indices Icolumn). the were calculated on a ýite-by-site basis (Table 3.8), a very different picture arose. On a site-by-site basis Shannon indices were not significantly higher in young plantations than in either of the remaining two stages,while site diversities in mature plantations were si"cantly higher than in clear-fellings (Table 3.10).

34 b. 40. JD

CD W) It 00 coo

eq go I C4 N 'S ýo %0 t- M en ýM 1 on ý 47% in le

(L) 41) en C4 %0 r- 00 CN on t- 00 lwt .4 r- t- p 18

Ir- SIN C) t- C4 4n 00 4D en co ON 0 -4 ý %0 tlý tl% t": in C4

00 00 %0 on .q -4 En C4 coVII m 40.

W2mWU

35 4ý4ca 0

C13

1.0.

in 0ý 00

a. a

s W-600 t- e4 t-\0 (14 0 00 NO 4n 10

ell C4 00 -t 1ý 00 GZ V*4 00 R Zý 0 CK 0 00 C4 I.-01 OR0 tlý t- Co C-i tlý

00 -t C4 0 of) e4 en En

4.4 4). 9 00 \-o r- f- %n 47% CP% %D .91.0. =: co mr 00

ILI 61 w c, -"- - C4 1- 00 C5, 00 co m8 in -* 11

S" *81 11 .E VA 81 1

k- tol 36 The samecontrast of the patternsfor pooled and site-by-siteindices was found againwhen the evennesscomponent of diversitywas analysedseparately. When siteswere pooled,Hill's evennessindex was still highestfor young plantations,followed by mature - plantationsand clear-fellingsin this order (Table 3.9). However, when indices were calculatedon a site-by-sitebasis (Table 3.9), againHill's indiceswere not significantly higherin youngplantations than in eitherof the two other sta-ges,while matureplantations hada significantlyhigher site evennessthan clear-fellings(Table 3.10).

TABLE 3.10. Comparisonof the site diversity and site evennessindices of different successionalstages, by meansof Mann-WhitneysU tests.Data from Tables3.8 and 3.9, usingdata from the summercensuses only. The statisticsquoted are 1-6il valuesof Z' the normalaproximation to the Mann-Whitneydistribution, and their significance(see text).

Comparison Diversity indices: Evennessindices: z and significance z and significance

Clear-fellings x young -1.334 (p = 0.091) - 1.1563 (p = 0.125) Clear-fellings x mature -2.871 (p < 0.01) -2.538 (p < 0.01) Young x mature -0.901 (p = 0.184) -1.551 (p = 0.061)

3.3.5 Patterns of their - successional change and relationship with the rodents - The values measuredfor each habitat variable at each site are presented in Table 3.11. Each in Table 3.11 is value the average of the nine measurementstaken at each site, rounded to integer. From the nearest the examination of the table itself some differences among successionalstages can easily be noted. For example, the plant cover variables (1-9,12) had low in consistently values clear-fellings and in mature plantations, while cover by brashings(variable 16) high in was recent clear-fellings, intermediate in young plantations and low in the older sites. In the by DCA, analysis the eigenvaluesfor the four ordination axes for plant cover were 0.177,0.135,0.041 and 0.011, and for 0.520, structure were '0.285,0.076 and 0.019. These that in both results show casesthe first two axes accounted for most of the

,1 37 tic, rA

0 C% 0

th u0 U 61 o c 0 o o CD 0 o o (D en o 0 0 m t y= t , qt W.) 0 6B -4 u I r) 90 -* m 0 W) -41 N 0 en C4 V) %0 0% 15 , in en e4 .2 0 u -I(DI fl I . 4 --1 -4 onI. IT %n . en e4 c %,o %n - 0 - N !ý r4It o m r" W om fn -* wlon en 0 0 ;1 t- m a-, %n ID 1 0 %oll C4 r- C:) Q (D %01-* 2 0%c% I ' c' 40. I- I :ý -4 %0 91 in C:rl-n -11 -*I1ý - ." (7% I I 1 eq , -, ell Go Ch M

-00 en 4n C 0 0 an rn 1 12Wý.r NO .. be I I I I rd in V-drn t en I 'm on I"n a 0 0 0 - 01 0 00 ql, W,

L) 0

rl,

.0§- .2-2:1

l 13 a ce g C3 * Ef FA ts

r E l it En

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0.. 0-- % ý - % wl . 0. ý 0ý OPN .-I 0-ý t- 00 0% t- 00:E ý0% ýR 0.4 a - ma 8- .8. Im,in , ,

P 8. U.0 38 variation explainedby the ordination, while the remainingaxes explainedlittle of the variation and will be discussedfurther. The eigeývaluesalso show that DCA -not accountedfor a greaterproportion of the variationin structurethan in plant cover. The four DCA plots (ordination of sites and ordination of habitat variables,for eachset of variables)are presentedin Figures3.4 to 3.7. The respectiveDCA scoresare each to identification individual sites,/,, habitat variables in presentedunder plot allow lof the plots. Ordinationof the sitesclearly separates the stagesof succession,either using the plant cover variablesor the structuralvariables. In the plot of site ordination by plant cover (Figure 3.4), successioncan be visualizedas a circle going clockwisearound the diagrampassing by the clusterswhich correspondto the successivestages. Only one clear- felling lay to the left of the vertical axis, and was thus more similar to young plantations than to the rest of the clear-fellings.This point correspondsto the View site, the clear- felling wherecolonization by groundvegetation was quickest(see variables 1-10 in Table 3.11). This explainsits sirnilarityto young plantations.In the plot of plant cover variables ordinatedby sites(Figure 3.5), the position of the variablesin relation to the positionsof the successionalstages in the previousplot providesa picture of the vegetationchanges. Therefore,herbs are most associatedto clear-fellings;palatable grasses appear in clear- fellingsbut closerto the young plantationstage of which Deschwnpsiaflexuosa,heather, bracken,bramble and rush were most characteristic;bilberry was most associatedto the transitionbetween young and "closing" stages,Molinia caeruld to the "closing" sites,and mosseswere most associatedto matureplantations. In the plot of site ordination,.by structuralvariables'(Figuri'3.6), the patternfound in Figure 3.4 repeatsitself. successionalchange again appearsas a clockwisetrajectory aroundthe diagrarn,and againView is closer in characterto young plantationsthan to clear-fellings.In the plot of structuralvariables ordinated by site (Figure3.7), clear-fellings are most closely associatedwith bare soil and abundanceof brashingsbut young plantationsto increasedcover andheight of groundvegetation, and to increaseddensity of trees(both conifer and broadleaved) and of Sitka saplings.The "closing" stagewas most , closely associated,as one would -expect,to maximumdensity of low branches.Mature plantations were associated with high cover by litter (represented mostly by undecomposedSitka needles)and densityof Sitka cones. Before running the second stage of the analysis, Canonical Correspondence Analysis (CCA), sevenvariab les were dropped as a result of the analysisof Variable leaving , Inflation Factors, the remaining13 as the basicinput for the CCA analysisitself. The (see variablesremoved Table 3.11) were: cover by herbs,rush and Etter; total cover height and of undergrowth,density of conesand of Sitka sprucesaplings. Among the most

39 Figure 3.4. DetrendedCorrespondence Analysis (DCA) diagram showingthe ordination of different the fifteen sites accordingto the ten plant cover variables.Sites correspondingto DCA for successionalstages are representedby different symbols. scores eachsite are shown Iunder the diagram(see text, Section3.3.5).

luu

D C 40 A

5 20 c 0 13 C3 r0 e s -20 -+ -*

-40

-AA 0 50 100 150 200 DCA scores

Clear FeUingx + Young Plantations "' Closing Stage E3 Maiure PI W, tLans

Sites DCA scores Axis I - A)ds 2

1) Comer 1 -101 -16 2) Comer 2 127 -13 3) Comer 3 42 -31 4) Fann Felled 65 -14 5) Comer South 46 -19 6) View -5 -40 7) ScotsPine , -37 -20 8) Road -27 12 9) ýkddcr -5 -28 10) Stuart -15 -24 11) Farm Young -15 -24 12) Low Closing 9 95 13) High Closing -22 63 14) Comer North 191 9 15) Comer Mature 169 9

40 Figure 3.5. DCA diagram showing the ordination'of the ten plant -cover variables-according to the fifteen sites. DCA scores for each variable are shown under the' diagram; variable numberscorrespond to those in Table 3.11.

500 13

400

D 300 c A -C37 s 200 c 0 r e s 100

to 0 C36 E38 04 [33 9 132 -100 -200 -150 -100 -50 0 50 100 150 200 DCA - scores

C3 Plant cover var.

Plantcover variables DCA scores Axis I k-ds 2

I)Afolinia caerula -72 461 2) Palatable grasses 0 -76 3) Deschampsiaflexuosa -71 -60 4) Heather -58 -31 5) Bracken -157 119 6) Bramble -126 -12 7) Bilbcrry -60 216 8) Herbs 37 -15 9) Rush -106 -89 10) Mosses 191, 18

41, Figure 3.6. DCA diagram showing the ordination of the fifteen sites according to the ten structuralvariables. Symbols as in Figure 3.4. DCA scoresfor eachsite are shown under the diagram.

150

100 0

D 50 c A

-60

-100 13

-150,1 1ý Is II11 -150 -100 -50 0 50 100 ISO 200 DCA scores'

0 Clear FellLngs + Young Plantauans Closing Stage Mature PI-ntaUons

sites DCA scoTes A.,ds I Axis 2

1) Comer 1 -128 -2 2) Comer 2 -123 68 3) Comer 3 -105 77 4) Farm Felled -90 120 3) Comer South -99 26 6) View 48 102 7) ScotsPine 151 -21 8) Road 110 24 9) Adder - 158 -15 10) Stuart,"' 110 28 11) Fann Young 100 -14 12) Low Closing -23 -67 13) High Closing 6 -42 14) Comer North rl 12 Comer 15) Mature . 88 -54

42 Figure 3.7. DCA diagramshowing the ordination of the ten structural variablesaccording to the fifteen sites.DCA scoresfor eachvariable are shownunder the diagram;variable numbers correspondto thosein Table3.11.

300

200 C3

20 100 C313 (3

C3 0 C312 014

-100

1315 19 13 -200 -300, -200 -100 0,1 too ý00 300 DCA scores

Z3 Structural variables

Structural variables 'DCA scores' A-,ds I Axis 2

11) Height of undergrowth 127 13 12) Cover by undergrowth 202 13) Cover by bare -0 soil -144 100' 14) Cover by litter -151 -33 15) Densitv Sitka of cones -211 -159 16) Cover ýy brashings -81 205 17) Density Sitka of Um 147 -68 18) Density broadleaved of 229 -43 19)Density low branches of -Is- -181 20) Density Sitka saplings 165 103 ,

43 conspicuouslosses were total cover and height of undergrowth,two potentially very important structuralvariables, but which could not be kept becauseof being correlated with severalother variables.The variablekept in the analysiswhich showedthe highest correlationwith these structural variableswas cover by heather (r = 0.83 and 0.66, respectively;both, p<0.01). Thereforein the analysisthat follows heather,to a certain extent,represents total coverand height of undergrowth. CCA analysiswas carried out until the simplestpossible significant model was identified, in five habitat (Deschampsid heather, bramble, which variables jflexuosa, brackenand bare soil) were left to accountfor the variation in the three rodent species. The first canonicalaxis accountedfor 91.1 % of the variancerepresented in a species- environmentalvariables biplot basedon the first two axes (Figure 3.8). The sum of the canonicaleigenvalues, which indicatesthe proportion of communityvariation explained by the CCA axes,was 0.684.A Monte Carlo simulationshowed the first axisto be significant (p = 0.02,100 randompermutations). Only threeof the variables,however, had significant regressioncoefficients in relationto the first CCA axis:Deschmpsia, t=6.13; heather,t = 8.17; bramble,t=9.34; all three, p<0.001. Only thesethree significantvariables are representedby arrows in Figure 3.8. Regressioncoefficients were not significant for brackenand bare soil (t = 0.28 andt=0.14 respectively;both p>0.50). Curiously,in an alternativemodel with Deschwnpsia,heather and bramblealone, no regressioncoefficient though was significant. Bracken and bare soil, even they did not have significant regressioncoefficients by themselves,were necessaryto achievea model in which other variablesdid. Further examination of the CCA biplot suggeststhat the first Pais can be related to a soil fertility gradient.High valuesalong the first wds were achievedby bramble(a plant which is dependenton good soils)and the two young plantationswhich havegood brown gley / brown earthsoils: Road and ScotsPine (which he on either side of Deschwnpsiain, the biplot). At the other extremeof the first axis is bare soil. The secondaxis can be relatedto a sheltergradient, from bare soil to heather,a plant which providesgood cover and,as mentionedabove, was highly correlatedwith the estimatesof total cover. Among the rodent species,Apodemus sylvaticus was most closely associatedto bare soil (althoughthis was a non-significantenvironmental variable in the biplot) and to clear-fellingsamong the sites.However, all sitesof all stages,except for Road and Scots Pine mentionedabove, were, close to the position of woodmicein the biplot, and to the position of ClethHonomysglareolus as well. This latter specieswas found in closest associationwith heather(along both axes),with bramble(along axis two) and to a lesser extentwith bracken.Microtus qgresýswas found in closestassociation with Deschmpia

44 ci 2 Uý u >--- u = C'- Cý .2 13 0 0 4 -4

W, -, " cp V- :5 ,-

: -. U

45 flexuosa (alongboth axes)and with bramble(along axis one,the proposed"fertilityý a)ds). Amongsites, its positionwas closeto Road and ScotsPine only, - a patternopposite to the othertwo rodentspecies.

3.4 - Discussion

3.4.1 - Overall pattern of successionalchanges in the rodent communities - Pooling all replicatesof each stage, a pattern of successionalchange in rodent communities emergedwhich canbe describedas follows. 1) Mature plantations held two-species communitieswhere woodmice tend to be numericallydominant, but bank voles are found in considerableabundance. Therefore the two dominantrodent speciesin mature Sitka spruceplantations at, Hamsterleyare the sametwo which are typically dominantin deciduouswoodlands all aroundBritain, except in Apodemusflavicolfis (Gumell, 1985). the restrictedareas where occurs - - .1-- 11'ý' 2) Recentclear-fellings had a similarpattern, the ratio of bank voles to woodmicebeing just slightly, and, non-significantly,-smaller than in mature plantations. This pattern suggeststhat communitiesin the clear-fellingswere Me more than a continuationof the communitiespresent before the felling took place.Field voleswere very rare in this stage: only onewas evercaught mi 2,205 trap nights. 3) In the youngplantation stage, with its luxuriantground vegetation (see Table 3.11), the communitieschanged completely., -The third species- (field voles) becamenumerically importantin the community,while bankvoles became as abundantas woodmice. 4) Someinsight into what happenswhen the,canopy starts to dose was obtainedfrom Burrowe (1991) study.- His data includedjust -two replicates,and therefore must be interpretedwith care.However, t 1ýey suggest that bank voles were still very abundant at but this stage almost all field,voles had disappeared;although 1991, was an year when appreciablenumbers of field voles were found elsewhereat Hamsterley.The pattern for the "closing" stage,as one would expectýrepresented a transition betweenthe patterns found in youngand mature plantations. Few previous studiesof rodent communitiesin British Sitka spruceplantations provide resultsstrictly comparableto mine. Gibson!s (1989) and Burrows!.,(1991) results were discussed,above. In spring-summer1990, VadherArapped in Hamsterley three replicatesof eachof the samethree successionalstages I studiedand his resultson species (Vadher, compositions 1990)were consistentwith mine. Thomson(1986) studieda Sitka in North spruceplantation Walesand found bankvoles andwoodmice as dominantspecies

46 in the maturestands but, in sharpcontrast with my results,-field voles were the the most in As he did habitat abundantspecies recent clear-fellings. not provide a -detailed descriptionit is impossibleto know whetherhis clear-fellings(unlike mine) were quickly invadedby grasseswhich could explainthe observeddifference (see Section 3.4.5)., ý The resultsof the presentstudy are to some,extent similar to findings of some single-sitestudies in conifersuccessional mosaics in Britain and continentalEurope. Sharp andWilson (1987) studiedrodent populationsin an areareplanted with ScotsPine (Pinus sylvestris)in Yorkshire, where woodmicewere dominantfor the first three years after felling, bank voles increasedthereafter and remained abundant through the young plantationstage until the end of the study (thirteenyears after replanting)and field voles had a short-lived"pulse" of abundancebetween the fourth and the sixth year. AD their findingswere consistentwith the pattern I -found at Hamsterley.Ferns (1979a, 1979b), working in a japaneselarch (Larir kaempfen)plantation in Devon, found that M. -agrestis was very abundantin young (3-5 yearsold) plantations,but disappearedcompletely in the relativelyshort interval up to the "closing"stage (in his casewhen plantations were around 8 years old). Larch is a deciduousconifer, but neverthelessthe undergrowthis sparse under mature stands,because in Winter little light is availableto the ground-vegetation anyway.As Fernstrapped just one site and his trapping was interruptedfor three years (from the fifth to the eight year of the plantation),it is not clearto what extentcyclicity of the. field vole population may have- influenced his results. However,- Venables,and Venables(1965,1971) also reported a sudden(rather than gradual)decrease of field voles linkedwith the closingof the canopyat a mixedCorsican-Lodgepole-Jack Pine plantation in Wales. While someaspects of the successionalpattern are consistentover a'broad range of conditions(e. g. in conifer forestsM agrestis is nearly always restricted to young plantations;-Hansson, 1971; 1974, Charles,1981, Jensen,1984, Staines,' 1986), in other aspectssimilarities weaken as the geographicand taxonomic distancesincrease. For Clethrionomys example, glareolus is usually dominant over Apodemusspp in mature forestsof thoseconifer specieswhich allow the permanenceof denseundergrowth; such by as those studied, Hansson(1978) and Wolk andWolk (1982) in North Swedenand Polandrespectively. - (1990) Kirkland reviewedseveral studies of the effects of clear-fellingson small mammalcommunities in North America and in most casesfound ý increases in' both in diversities in densities. and population Neither -of these effects were ýdetected in Hamsterleyby the presentstudy. Increasesin diversity are hinderedin Hamsterleyby the restricted pool of small rodent speciesavaable in the North of England. The only

47 abundantspecies that could be added to the mature woodland 'Community,Microjus agrestis,does not colonizeunless a continuouslayer of grasses'develops (Section 3.4.5). In this situationof absenceof specialists,the most generalist,adaptable species is likely to reap most of the benefitsofthe new resourcesavailable in clear-fellingsand to become dominantthere. That was the patternfound by Morrison and Anthony (1989) in Oregon, the dominantspecies being the generalistwhite-footed mouse Peromyscus maniculaws. In the presentstudy woodmouse was the dominantrodent on most sites and occasions,and the actualextent of its dominancetends to be underestimatedby the pooling of data (see next Section). f- Overallrodent densitieswere not significantly'differentbetween any successional stages,despite young stagesof spruceforests having much higherproductivity than older stages(Hansson, -1974). Due to its grazing diet, field vole is the specieswhich benefits most from the increasein production and when that happensthey can be foundý in very high densitiesin young plantations(e. g. Hansson,1971,1974; Charles,1981). However, field voleswere not alwayspresent in youngplantations at Hamsterley,and abundancesof all specieswere highly variableboth in time and spacewithin each stage(see Sections 3.4.2,3.4.4 and 3.4.5). Both factors contribute to the finding,that no stage had total rodentdensities consistently higher than other stages.

Spatial 3.4.2 - scales of variation in the rodent communities - If instead of pooling sites as in the previous Section we analyse the data on a site-by-site basis,-the picture sketched above is incomplete in at least two points, as a second look at Table 3.3 win show.: First, bank - voles were much less abundantthan woodmice in clear-fellings in every case except at two sites in Summer 1992.,These were Comer South (where the first bank vole caught there was the, only individual present) and Comer 2, (where bank voles outnumbered woodmice tenfold). In this trapping sessionat Comer 2 alone two thirds of bank'voles,found all in all clear-fellings in the restricted data set were recorded: Indeed, data from the ýComer, 2, in Summer 1992 alone accounted for the failure to find a difference between significant the proportion of bank voles,to woodmice in clear-fellings and mature plantations (chi-square removing Comer 2 Summer 92: using restricted data X2 set, = 8.10; using total data, X2 = 9.82; either, p<0.01). Therefore, the pattern for clear-fellings, may be better, described by saying that woodmice are usually dominant (much in more so than mature plantations) and bank voles are usually rare, but exceptionally this pattern can be dramatically reversed. Possible reasons leading to the in Comer 2 in Summer unusual pattern 1992will be discussedin Chapter 5. ý--

48 hiding The-, second point similarly ýshows pooling of sites smaller scale heterogeneity. The pooled pattern in young plantations is three species with similar abundances. However, of the five young plantations trapped regularly, two ýwere by bank (Farm consistently dominated by woodn-dce (Adder and Stuart), one voles in by Young) and two by field voles when they were abundant Hamsterley as a whole, and Thus woodmice when field voles were absent (Road and Scots Pine). the three-species found community found for the young plantation stage as a whole was seldom at any given specific site. different different in the - Similar findings of patterns at spatial'scaleswere reflected diversity and evennessindices as well (see Section 3.3.4). With all sites of each stage pooled, diversity was highest in young plantations, as a straightforward consequenceof Mature the additional species plus the most even abundances found in this. stage. but lower plantations and clear-felfings had diversities similar to each other, than young diversity plantations. When instead of pooled the sites were averaged, at mature sites was less on average as high as in young plantations, while clear-fellings were significantly diverse than either of the two. When the evennesscomponent is analised separately, the pattern becomes more clear. As expected from Figure, 3.3.,ý with, pooled sites young in plantations had the highest evenness,followed by mature plantations and clear-fellings this order. However, in a site by site basis, averageevenness was at least as high in mature sites as in young plantations, with clear-fellings again coming last. When together, these that young plantations are more diverse - seen results suggest than mature forests because of an inter-site component (0, or differentiation, diversity - Magurran, 1988) rather than by within-site diversity (a, or point,, diversity _.Magurran, 1988). Differentiation diversity is a result of speciesreplacement: on average,,young sites had neither more species nor more even abundancesthan mature sites; but-in-young plantations different combinations of specieswere found at different sites. ,,, ý-, -ý :ý-

3.4.3 - Temporal variation versus spatial variation - In addition to the spatial variation, a considerableamount of temporal variation was found. ANOVA (Table 3.6) showed that in the four years of the study both 'woodmice and field vole abundance differed significantly. For woodn-fice,marked non-cyclic year to year fluctuations in abundanceare often found, due, for example,-to varying seed supplies. (Gurnell, - 1981). Within mature plantations numbers of woodmice were much higher in 1991 than in the other years. This probably was due to 1991 being an excellent seedyear in Hamsterley (see Chapter 4). In the case of field voles, the interannual variation found was probably associated with a

49 !'cyclic" populationfluctuation. Microtus agrestiswas found in high numbersin Road and ScotsPine andwas presentin two other grids in 1989,a year which could havebeen the peakof the cycle.This was followed by a crash:by Summer1990 the specieshad all but disappearedfrom all sites.A slow recoverywas apparentthrough 1991 (seealso Chapter 4) and 1992,although a secondpeak had not yet beenreached by the end of this study.It is well known that Miaotus agrestispopulations follow a 3-4 year cyclethroughout most of the distributionof this species(Elton, 1942; Chitty,, 1952; Myers andý Krebs, 1974; Marcstromet al., -1990), although some populations in Britain (Richards,-1981,1985) and SouthernFennoscandia (Erlinge et al.;, 1983;Hansson, 1984; Erlinge, 1987) do not show regularfluctuations. I At-first-sight, inter-site variation seemsto be smallerthan ýinterannual variation: Kruskal-Wallisor Mann Whitney tests showedsignificant inter-site differencesonly for bankvoles at mature forests. However,- in -four out, of six non-significanttests (young plantationsfor eachof the species,and clear-fellings for bankvoles) a value corresponding to P<0.10 was found (Table 3.6).- Non-parametric tests were usedbecause the data set did ýnot meet the criteria for'use of their parametricanalogues (Section 3.2.2), but the former haveless power (Zar, 1984).Therefore it seemsthat inter-sitevariation may have beenunderestimated by the testsused., , Both ANOVA and Kruskal-Wallis compareamong-groups variation (Ad) with within-group variation (WG). Inter-site variation was WG when comparingyears;, and converselyinterannual variation was WG when comparingsites. Thus thesetwo analyses can,be seenas reciprocalto each other. Therefore-it is not surprisingthat ýintemmual differencescould be demonstratedfor woodmice which showed the least inter-site variation, and inter-site differencescould be demonstratedonly for bank volesýwhich showedthe leastinterannual variation. -Field voles showedgreat variation both in time and space:It is perhapssurprising that significantinterannual variation could be demonstrated even in the presenceof such high inter-site variation, and converselythat for young plantationsinter-site variation was nearly significant(see previous paragraph) in spite of high interannual the variation. eI-, ý -,, -- 'In overview,each species has a distinct, characteristicplace in a-planedefined by population,variation - along two axes: time. and space. Woodmice, the most habitat generalistof the three species,shows little spatialvariation but -considerableinterannual Bank variation.ý voles, much more of a -habitat specialistthan woodmice, presentmore but have inter-sitevariation more constantpopulations. Field'voles populationspresent high both in very variation spaceand in time. Thesepatterns are roughly consistentwith previousideas on habitatuse (Gurnell, 1985)and population fluctuations (Richards, 1981;

50 AUbhaiand Gipps, 1995;Flowerdew, 1985) of the species'concerned,although it mustbe emphasizedthat cyclicity and intensity of -fluctuations - in ýboth voles vary considerably accrosstheir distributions (Alibhai and Gipps,-- 1985; Hanssonand Henttonen,- 1985;- Marcstrom 1990). et al., -- -.1 -ý 1 -1'. I ý-'"II. -I

3.4.4 Competition betweenWoodmice and Field Voles ?- The woodmouseApademus sylvaticuswas the most ubiquitousspecies in the presentstudy. -It was found in all sites and often in moderateto high densities;in only five trapping sessionswere no woodmice caught.Therefore they were conspicuousby their almost completeabsence in the three trapping sessionswhere highestnumbers of field voles were captured(Road, Summer 1989and Summer1992; Scots Pine, Summer,1989). In thesesame two sites,woodmice were the dominantspecies in 1990and 1991when Microtus agresfisin turn was almost In absent. clear-fellings,-just one field vole was capturedin the whole study period - coincidentallyor not, on anotherof the rare occasionswhen woodmicewere absent.This patternaccounts for the significantnegative correlation found betweenthe abundancesof thosetwo species(Section 3.3.3). A'negativeinteraction between M agrestisand Clethrionomysglareolus has been found in severalstudies in Scandinavia(MyllymaK 1977a;Hansson, 1979,1982,1993), bank tend to be from field where voles excluded grasslandwhen numbersof -voles are high, but the two speciescoexist when densitiesofM agrestisare low. Flowerdewet al. (1977) suggestedthat the reversepattern could be occurringbetween the two speciesin in fenland Cambridgeshire,where bank voles seemedto affect the distribution of field Competition voles. between field voles and woodmice has not often been proposed, althougha patternvery similarto the one I found in Hamsterleywas describedby Brown 166-8) in, Silwood (1954: Park, Berkshire.The pattern I found suggeststhat woodmice field in interact and voles Hamsterley in a way similar to the interactionof the two voles describedabove. The hypothesisis thavM agrestis (a grass specialist)would be the superiorcompetitor which, when in high densities,can excludeA sylvadcus(an habitat from, generalist) productive grassy sites in fertile soU (see Sections3.4.5 and 3.4.6). However,A. sylwdcus can opportunisticallyinvade when the numbersof field voles are low duringthe low stageof their populationcycle. hypothesis The above, of asimmetric competition is consistentwith the data far Hamsterley, gatheredso at althoughthe evidencein its supportis just correlativeand thereforedoes not excludealternative explanations. For example,conceivably the species have highly distinct habitat, could preferences,,and subtle habitat changesin time could make- the habitat favorable sometimesf or: one and for the This , sometimes other.

51 explanationdoes not seemlikely if one takesinto accounthow generalistwoodmice are in habitatuse (e.g., Gurnell,' 1985).,The hypothesisof asimmetriccompetition could best be testedusing an experimentalapproach. Field experimentsinvolving selectiveremoval of one specieshave often beenuseful to examineinterspecific interactions in smallmarnmals (e.g. Grant, 1972; Gliwicz, 1981; Montgomery, 1981; Brown etýaL, 1986; Schoener, 1983,1986). However, the most promisingfield experimentwas unfeasibleduring the time availablefor my fieldwork. -It would have involvedthe removal of M. -agrestis (the specieshypothesized to be competitivelysuperior) and subsequentmonitoring of possible changesin numbersor habitat-,use by A. sylvaticus (the specieshypothesized to be competitivelyinferior). To perform this experiment,considerable numbers of M agrestis would needto be removed(otherwise the effect on A. sy1mlicuswould be negligible),but this was not feasiblebecause populations of field voles had not yet reacheda peak in Harnsterleyby the end of this studyin June 1992.It would be highly desirableto perform this experimentat a future time whenhigh densitiesofM agrestiscan again be found. -, -

3.4.5 Habitat in - changes and the related changes the rodent communities - The patterns outlined in the previous Sections suggest that, young plantations are the most unpredictable stage of successionfor rodent communities in Sitka spruce, both in terms of speciescomposition and relative abundances.,,By unpredictable I mean that a local rodent community cannot be accurately predicted by the age of the plantation alone in the case of young plantations, while such a prediction is much'more likely to be accurate for a clear- felling or a mature, plantation. Rodent community succession-begins with, the usually in stereotyped composition clear-fellings, then it can follow nearly any path during, the but it young plantation stage, gradually moves again towards a more predictable outcome as plantations reach maturity. But how* does the "unpredictability" of rodent communities in the young ? plantations arise According to Gurnell (1985), quantity and species composition of the holds ground vegetation the key to small mammal community structure in planted forests. found in The ground vegetation each plantation is in turn affected by soil characteristics, seed bank and colonization history (Hill, 979b, 1986). To the high ýI explain . variation local in among communities the young plantation stage, one, has'first to show evidence is higher in that there variation, the vegetation of young plantations than in the other stages,and that such variation favours several distinct rodent communities. Results DCA of analysis,either by vegetation or structure (Figures 3.4 and 3.6) do not seemto provide such evidence. Young plantation sites are as clumped in the diagrams

52 as any other successional'stage.However, a more careful interpretation'suggeststhat sites are clumped due to the large gaps between stagesin several variables. For example,- most variables of plant cover have high values in young plantations,- and zero values in mature Sitka. Conversely,,densities of low branches,for example, are high in "closing" and mature plantations, very low in most young plantations, zero in clear-fellings (Table 3.1 1). The ordination pattern reflects chiefly those all-or-nothing variables, that is;, exactly the ones which changewith successionin most predictable ways (see species scoresunder Figures 3.5 and 3.7). Therefore the effect of such variables dwarfs other, variables which show happens more subtle within-stage variation - as with'most plant cover variables within young plantations (Table 3.11). When brought into by CCA (Figure, 3.8) , .- the rodents are the picture those previously hidden differencesbecome apparent.For example, there is an isolated cluster at the right of the biplot formed by three Idnds of entities: two young plantation sites (Whick are Road and Scots Pine), one habitat variable (the grass Descharnpsiaflexuosa) and one rodent species(Microlus agrestis). Field voles are also associatedwith bramble, along axis one. Road and Scots Pine were the sites with highest abundanceof field voles (Sections 3.4.2-3.4.4). The reason for the associationbetween field voles and the wavy hair grassD. flexuosa is not immediately clear: this grass is consideredunpalatable to field voles,(Ferns, 1976).- One would expect field voles to be more closely associated to-Ahe variable "palatablegrasses" instead, as Ferns found that Holcus lanatus and especiallyAgrosds spp are important food resourcesto Microtus agrestis. Palatable grasseswere excluded from the final CCA model precisely becausethey had a high correlation with D. flexuosa, and this'last better in models using variable explained the variation rodent communities.-This puzzling result seemsto be due to palatable grassesoccurring more often than D. flexuosa' field in sites were voles were not present.- Palatable grasseswere present in, all five M (and in agresfis sites abundant Road and Scots Pine), but they were found in eight other D. flexuosa sites as well. was also present in all M agrestis sites, but in only four others (Table 3.11). The presenceof palatable grassesis a necessarybut not a sufficient factor to explain the occurrence of field voles, -which need continuous ground cover, by grasses (Ferns, Hansson, -1976,1979b,, 1977). A sparse cover even of the most edible grassesis D. flexuosa, not enough. abundantin Road and Scots Pine (and paradwically contributing to good ground cover there), but with a distribution more restricted than palatable grasses and therefore more similar to the voles! own distributim, seems to be in a sense representingpalatable grasses in the CCA model. Bank found in voles were all young plantations, but,, they were consistently dominant in only the Farm Young site. The unique composition of the rodent community

53 at this site was matchedby an uniqueplant communityas well. Farm Young was the site where Sitka sprucewas found in by-far the highest density (mostly due to self-seeding ratherthan planting)and also the site with highestabundance of heather,which was the variable most closely associatedwith bank voles in CCA analysis(Figure 3.9). Farm Young was also the young plantationwith lowest overall abundanceof grassesand it showeda relativelyhigh abundanceof bramble,-which was also found to be associatedto bankvoles in the CCA biplot (Figure3.8)., An associationbetween bank voles and bramblehas been reported previouslyby Southernand Lowe (1968) and Gurnell,(1985: 388). Brambles provide edible berries which maybe a good food resourcefor bank voles; Hansson(1985: 151) found berriesto be an important food source for bank voles in conifer forests and reforestationsin Scandinavia.Heather, on the other hand,is not likely to be a good food sourcefor bank voles. Its seedsare very small and difficult to gather. Only the current year shoots and flowers,at the exposedtop of the plant, are availableto herbivores,while the bulk of the biomass, is (Miller, 1979). Those fitctors the woody part, not probably-explain why heatherdoes not appearusually in the diet of voles,(Hansson, 1985). However, heather does provide good cover and indeed it showeda very high correlation with the total amount of ground cover (Section 3.3.4). The position of bank voles in relation to the secondCCA axis in Fig. 3.8, which seemsto representa cover gradient(Section 3.3.5), is indicativeof their strong preferencefor good cover. Severalother studieshave shown bank voles to be associatedwith denseground cover (Fullagar el al., 1963; Kikkawa, 1964;- Jewell, 1966, Ashby, 1967; Flowerdew el al., 1977; Montgomery, 1979; Mazurkiewicz,1991), probablybecause of protection againstaerial predators(Southern and Lowe, 1968;Southern, 1970). Actually C. glareolus has more often beenassociated with brackenthan heather (Evans, 1942; Fullagar el al.; 1963,Jewell, 1966).Bracken was indeeda componentof the CCA model,but it was not significantlyrelated to the first axis, andits associationwith bankvoles was slightly lessthan heathers(Figure 3.8). Healingel al. (1983) argued that',what is preferred by bank voles, is dense cover with little understoreyso that there are open spacesunder the cover through which voles can move. in their studyarea (Skomer Island) they found that sparsebracken with an undercoverof grass(a situation similar to the Road and SootsPine sites) was a bad habitat for bank voles, while there was a trend (non-significant)for associationbetween bank voles and heather.Dense heather can provide the structural conditions neededfor bank voles: a closedground cover with plenty of open spaceunderneath. In the caseof Farm Young, coverwas further increasedby the exceptionallyhigh densityof young sprucetrees, which resultedin a densityof low branchesfive times higher than at any other young plantation

54 (Table3.11). Put togetherwith the heather,this makesFarm Young a densethicket with few exposedpatches, therefore fulfilling bank voles' cover requirementswell. Again, unsuitablefood plant(s)were found to havea structuralrole which is necessaryto explain the abundanceof the vole species. I ý-,ýI"Iý. , Abundanceof Apodemussylvaticus in young plantationswas lessvariable than the abundanceof voles:woodmice were at leastmoderately abundant in all sitesof that stage, except when field voles were numerous(previous Section). In the two sites where woodmice were consistentlydominant (Adder and Stuart) their abundancewas not exceptionallyhigh, but voleswere seldomfound (seeTable 3.3). Examinationof the CCA biplot doeslittle to clarify woodmice'shabitat preferences:they were closely associated only with the amountof bare soil, which seemsto reflect woodmicedominance in clear- fellingsrather than fine habitatpreferences within young plantations.The most abundant grassat Adder and Stuartsites is Holcus molfis - which, conhwy to H. lanalus, produces very low, prostratetussocks, under which field voles cannotmake their tunnels,and which would not providegood coverfor bankvoles (see discussion above in this Section).Dense patchesof heatherare found at the two sites,but the low densityof treesdoes not createa groundcover for bankvoles as good as in FarmYoung. Good food sourcesmay be fewer aswell: bramble,for example,is scarce(Table 3.11). Giventhat the specifichabitat factors which allow dominanceby eithervole in the remainingthree sites are not found at Adder and Stuart, and that woodmice in general are more habitat generalistthan the voles (Sections3.4.1,3.4.3; - Gurnell, ý 1985), it seemsthat ýat Adder and Stuart- woodmice are dominant"by default",in the absenceof the circumstancesthat favour eithervole. t -' -.

3.4.6 An - overview: soil characteristics, vegetation, -and the heterogeneity of each In five successional stage - all young plantations discussedabove, vegetation seemsto be determined to a extent by local characteristics of the soil. For -great example'ý the field bramble associationof voles with may reflect good soils: bramble is usually found on fertile M soils 1979b). Indeed, Road and Scots Pine sites have mostly nutrient-rich, brown well-drained gley/brown earth soils (Table 3.1) which allow a high productivity, indeed the highest overall abundance,of grasses (Table 3.11). Deschmpsiaflexuosa is likely be favoured to especially by the conditions at those sites, due to its preference for well-drained soils (Ciibson, 1989): 1 11 i The in both dominated soils woodmice sites, Adder and Stuart, are surface-water These be far gleys. can good soils as as mineral nutrients are concerned, but they are water-logged which makes them unfavourable to many plants which demand well-drained

55 soils.'Such plants include D. flexuosa,bramble and bilberry, which are scarceat'Adder and Stuart(Table 3.11). In Farm Young site, the soil is mostly podzolic (either podzolic gley or typical podzol),quite unlike the soil in any other young plantationwithin the restricteddata set but rathersimilar to ComerYoung, wherebank voles were also found in high abundance (Table3.3). As discussedin Chapter1, podzolic soils are highly acidic - indeedýVadher (1990) found an averagepH of 3.69 at Farm Young site, - and the superficial and intermediatehorizons are,poor in nutrients. Such conditions favour plants which, are tolerantto acidic conditions," while having relatively deeproot systems'inorder to reach the nutrientsavailable under the podzol.-Both requirementsare fulfilled by heatherand Sitka, plus some highly adaptableherbs as the rosebay willowherb Chamaenerium angustifolium(the most abundantherb in Farm Young), but not by grasses.Reasons for the relativelyhigh abundanceof bramble,normally found on good soils, are unclear.Farm Young is adjacentto PenningtonFarm where brambleis found in abundancegrowing alongpasture edges and verges.When suchan abundantsource of colonizersis available, bramblemay spreadvegetatively to areasto which is not especiallywell suited, and the absenceof grassesmay also help it to thrive in Farm Young QA Pearsonand G. Simpson,pers. comm. ). Soil characteristicsinteractwith colonizationhistory and seedbanks in determining the vegetationfound at any given site. In somecases the influenceof theselatter factors can be considerable.The case of bramble in Farm Young has just been mentioned. Bracken is almost totally dependenton vegetative spread W, 1986) and it Will not invadea newly openedhabitat unless a suitablesource of colonizersis availableat a short distance;therefore soil characteristicsalone cannot explain its distributionwell. Heatheris not usuallya dominantplant in first rotation young conifer plantations,and the fact that it dominant is sometimes in secondrotation may be partially explainedby its good survival in as seeds soil, allowing it to be more abundantand viable than most competitorsin seed bankswhen the first rotation is felled (FK 1979b,1986). However, the suitability or not for of the soil grassesmay play a part as well, not least becausesome grasses(like Agrostis canina) also survive for long periods in the seed bank M 1979b). The banks importance of seed and particularities of colonization history must not be underestimated.Nonetheless, soil is an essentiallimiting factor for plant communitiesin young plantationsand an understandingof its variationshelps considerablyto make this "unpredictable"stage more predictable. As following an overview, the conceptualmodel is suggestedto explain the interplay in of spatial and successionalvariations rodent communitiesin Sitka spruce

56 plantationsat Hamsterley.The high P-diversityof rodent communitiesin young second- rotationplantations arises because the markedlocal differencesin soil characteristicsresult in differentassociations of ground vegetationwhich in turn make eachsite most suitable for a Merent rodent species,through the mechanismsdiscussed in this and the previous Sections.Soil typesunder matureplantations in Harnsterleyare as varied as under young plantations,but this variationis "buffered"as the ground vegetation,whatever its identity, gets shadowedout. The habitatsat the various sites changegradually towards similar communities:single tree stands,bare ground exceptfor mosses,Sitka seedsas the main food resource.When trees are clear-felled,inter-site variation is smallat first but tendsto increaseas ground vegetation develops and soil differencesonce again are expressed.

57 CHAPTER 4 RODENTPOPULATIONS IN A HABITAT MOSAIC PRODUCEDBY FELLING: I: POPULATIONDYNAMICS IN THE MOSAICAS A WHOLE

4.1 - Introduction

4.1.1 - Rodent populations in an habitat mosaic: scalesof analysis - There has been considerableinterest recently in the responsesof small mammalpopulations to habitat heterogeneity(e. g. Geuseet al.,, 1985,Dickman and Doncaster, 1987,1989, Bauchau and Le Boulengi, 1991,Montgomery et al., 1991, Szackiand Liro, 1991,Zhang and Usher, 1991; see Chapter 1). The presentChapter and following two report the results of a Capture-Mark-Recapturestudy of rodent populationswithin an habitat mos6c (hereafter called "Comer Complex")produced by the felling of a block of mature Sitka sprucein HamsterleyForest. Comer Complexwas heterogeneousboth spatially(at any given time duringthe studythere were both matureplantations and clear-fellingswithin the area)and temporally(because felling of the block took placeduring the study). I Small mammalpopulations within the habitat mosaic in Comer Complex were studiedat two different spatialscales. At the coarserscale, population dynamicsof each rodent specieswere analysedin the mosaictaken as a whole. Such analysesassumed that populationssampled in dfferent grids were not isolatedfrom eachother, thus it shouldbe possibleto accountfor the temporalvariation in numbersin Comer Complexas a whole, in its habitat heterogeneity. spite of Analysesat this level are discussedin the present Chapter. At finer the scale, population processeswere analysedin each individual grid separately.Analyses at this scaleaimed to relatevariations in spatialdistribution within the rodent populationswith two factors, which are discussedin separateChapters although likely be independent. they are not to Chapter5 discussesthe effects of the spatial and habitat heterogeneitybrought temporal about by felling on the spatial distribution of Chapter6 rodents. relatesthe rodent population fluctuations, discussedin the present Chapter,to their spatialdistribution.

4.1.2 - Aims of the study of rodent populations in the Comer Complex as a whole - The aim of the study reported in the present Chapter are as Mows:

58 in Comer (1) To characterize'thepattern of annualfluctuations in the rodent populations Complex,and to analysethe populationdynamics of the most commonspecies. in different_ (2) To assessthe validity of the assumptionthat rodent "populations" grids within Comer Complexare highly interconnected,through the analysisof patternsof use of spaceby individualrodents within the area. (3) To estimatehome range sizes and averagedistance moved between captures of individuals of each species and sex, to characterize seasonalvariations in those parameters,and to relatethem to the populationdynamics studied under (1). (4) To comparethe populationdynamics of the rodents in Comer Complex with those describedfor the same species in other habitats, highlighting the similarities and differencesin the likely mechanisms.

4.2 - Methods

, 4.2.1 The Comer Complex: trapping design and trapping programme - Within a Sitka spruce block of six hectares, scheduled for felling in summer 1990, four standard grids (Section 2.2) were trapped from May, 1990 onwards. Two grids (Comer I and Comer 2) were located within the block to be felled. Therefore, they were initially mature forest (planted in 1946) and became clear-fellings when their trees were cut and removed in July-August 1990 (see Chapter 5). The two other grids (Comer South and Comer North) were adjacent to the block being felled and therefore their successionalstage did not changeduring the study. Comer North was a mature plantation (also planted in 1946) and Comer South an old (1989) clear-felling, which had not been replanted. A fifth grid (Comer 3) was added in September 1990 within the block that had just been felled. The positions of the five grids and habitat types within and around the Comer Complex are shown in Figure 4.1. Year of planting and soil characteristics of each grid are presented in Table 3.1, Chapter 3. From Table 3.1 it can be seenthat there were several soil types representedin which Comer Complex, is just one more example of the great spatial heterogeneity in soils in Hamsterley Forest (Chapters I and 3). Soils in the north of Comer Complex (including ' the entire grid Comer North and part of, the grid Comer 2) and the extreme south Comer. (including'part of Sout4) were peatýcovered by a Calluna vulgaris and Diophorum blanket,boi. vaonatum Most of the central part was a mixture of surface. water Sley and podzofic gley, and an irpnpan,occupied part of Comer South.

59 FIGURE 4. L Position of the five standard grids trapped within the Comer Complex. Within the mature plantation felled in July-August 1990, trapping staried in gnds I and 2 before felling, and ý_-omer in grid 3 in September 1990. North (N) and Comer South (S) were respectively a mature plantation and a clear-felling throughout the stud,.v (May 1990-June 1992).

Mature Plantaýon i loom Clear-felledJuly-August 1990 = Old Clcar-fcllcd 60 A standardtrapping session(Section 2.2) was carried out monthly (except for January)in all grids from,1990 until March 1992.An additionalstandard trapping session was carriedout in June1992; as part of the Censuses(Chapter 3). However,' Comer I and Comer 2 could not be trappedin July 1990, due to the felling. Comer 1 and Comer 3 could-not be trappedin October 1990and October-November1991 respectively because of brash removalfor a field experiment(Chapter 5). In December1990 and February 1991, becausedeep snow hindered trapping sessions, valid datawere obtainedonly for the new clear-felfinggrids (Comer 1,2 and3) but not for ComerNorth and South.

4.2.2, -'ýEstimates of Population Parameters - Population size, survival and daily trappability were estimatedusing the method of Manly and Parr (1968). This method has the advantage of not assuming homogeneity of survival rates among, individuals. As population 'dynamics-of woodmice and bank voles usually include marked differences between survival probabilities of adults and juveniles at some times of the year (as found by e.g.;, Watts, 1969,' Flowerdew, 1974, Gurnell, 1978), the Manly-Parr method is appropriate for these species. II The Manly-Parr estimate of population size at time t is given by

Nt nt Pt

And the estimateof survivalis givenby:

st at rt. (Pt + I) 41 1. Where:

nt = numberof individualscaught at the sampleat time t pt = estimatedproportion of the populationwhich is caughtat the sampleat t at = numberof individualscaptured on both samplest and t+I rt = numberof markedindividuals released at samplet (usually= nt) pt +I= proportionof rt which is recapturedat t+I

The key Manly-Parr .. stageof the procedureis the estimationof pt, which is the This however,is dependent trappabilityestimate. step, on the very unusualway in which

61 dataare tabulated for this methodand it cannotbe easilyexplained without referenceto a detailed example(Begon, 1979: 38). Therefore, for more details of the Manly-Parr procedure;the readeris referedto Begon(1979: 38-42) and Seber(1982: 233-7).,ý', Populationsize estimatesare likely to be negativelybiased in monthswhen less than five grids were trapped(fisted in Section4.2.1). Two caseswere distinguishedand treateddifferently, as follows. (1) Monthswhen only two grids were trapped(during felfing) or three grids were trapped,missing Comer North and South,the grids at eachextremity of Comer Complex. Suchestimates were not consideredrepresentative of the whole populationand thus they werenot usedwhen relating population sizes to any other parameters.However, estimates for thosemonths are included(in parentheses)in the Tablesbelow. (2) Months when four grids were trapped, missimgone of the central grids.- in thesemonths the negativebias was likely to be smallerthan the 20% the reduction in number of grids would suggest,for two reasons:First, becauseof frequent inter-grid movements,individuals not caught in the grid missingin a particular month t might be caughtelsewhere in the samemonth. ý Second, if they were not caughtin month t but were recapturedsubsequently, that would result in a lowered trappability estimatein month t and a correspondinglyincreased population size estimate.This would partly compensate for the missinggrid. The precisecorrection factor could not be estimatedfrom Manly-Parr formulae. Becauseof, this, the correlations between populationý sizes and- the other demographicparameters and spatialparameters (see next Sections)were testedusing two alternativesets of populationsize estimates(a) uncorrected;(b) correctedby multiplying by 1.25 the values obtainedin caseswhen four grids were trapped insteadof five (as indicatedabove, this probablyover-compensates for the reductionin the numberof grids trapped). I All demographicparameters which can, bedensity-dependent (e.g. survival, recruitment,reproductive rates) are likely to be relatedto populationdensity (i. e. number of individualsper unit area) rather than to population size. However,,the estimationof populationdensity of small mammalsis a complex problem. It is well known that small mammalsoften enterthe grids from adjacentareas, so the actual samplingareas are larger than thosecovered by the grids. However,it is difficult to estimatethe extent of this "edge effect". Severalmethods have been proposed to correct for edgeeffect (see Smith et al., 1975, Flowerdew,- 1976a, Tanaka;1980,, Schroeder; 1981, Seber, 1982), yet actual s=pling areascan seldombe estimatedwith great accuracy.Thus, insteadof trying to estimatedensities., I opted to correlatethe remainingdemographic parameters with my estimatesof populationsizes. Such analysesrely on the assumptionthat there is a high

62 correlationbetween population size and populationdensity, so that the former parameter canbe usedas an indexto the second. For estimatingsurvival of specific age classesinstead of the whole population, sample'sizes'oftenwere not enoughfor using the Manly-Parr procedures.Therefore the "minimum survival" method was used, following Chitty and Phipps (1966) and Montgomery(1980). *Minimum survival(Pmin) is givenby

'Prnin NI Nt-i

WhereNt is the numberof individualsstill alive in montht, out of Nt- I capturedin the previousmonth. When calculatingPmin for juvenile or subadults,individuals which had changedto the next ageclass by montht were includedin Nt. Per capitarecruitment rates at time t were estimatedsimply as the numberof new juvenileindividuals appearing in the populationbetween t -1 and t, divided by population size at t. Recruitmentestimates were calculated-using juveniles only, as recruitmentof individualsof older age classesis difficult to separatefrom immigration and/or from individualsactually recruited before month t but not capturedpreviously. The proportionof reproductivefemales in month t was estimatedas the numberof femalesshowing any signsof reproductiveactivity (seeChapter 2) divided by the number of subadultplus adult femalescaptured in that month. ý For each rodent species,correlations between - population sizes, survival rates, recruitment rates- and proportion of 'reproductive,females, were tested,by means of Pearsodsproduct-moment correlation coefficients. All possiblepairwise correlations were tested, except the ones involving pairs -which by definition are not independent-,(e. g. overall survivaland survival of one age class).Additionally, delayeddensity-dependence of all survival,recruitment and reproductiverates was testedby correlatingthe valuesof theseparameters in month t with populationsize estimatesin the previous month. Data were ýnormalised by either logarithm or arcsine transformations (Zar,, 1984) when necessary. In most casesthe intervalbetween trapping sessionswas not exactly 30 days,but all survival,mortality and recruitmentestimates were convertedto 30 day rates - Le rates expressingrespectively survival, mortality, and recruitmentover a period of 30 days.11iis convertionwas carried out using a logarithmic transformation,as follows (exemplified with a survivalrate, St):

63 d, s, = Nt =es. s=lnSd and- 'S30=00s: Nt-1,

Where:. ýI "I', I --

St = survivalrate estimatefor the montht Nt andNt. I= numbersat montht andat the previousmonth respectively s= daily survivalrate e"I d= actualnumber of daysseparating the trappingsessions at t andt-I In ande= naturallogarithm and its base,respectively S30= 30 day survivalrate

Body. weights were used as the criterion to separateage classes.The -weight chosento separatejuveniles from subadultswas the one at which it becamecommon to find individualswith signsof reproductiveactivity. Similar criteria for separationof age classeshave been used in previous studieson British rodents (e.g., Flowerdew, 1974; Gurnell,- 1978). The values-actually used in the present study were: for A. sylwdcus.' juvenilesup to 15g, subadults,16-20g; adults, 21g plus. For C. glareolus, the respective valueswere: up to 15g, 16-19g,-and 20g plus. Body weights are easyto measurein the field, but may not be a preciseindex of age of the rodents. However,- at least for A. sylvadcusthere is little increasein the accuracyof age estimateseven if severaladditional characterssuch as weight of eye lensesand tooth wear are also taken into account (Gurnell and'Knee, 1984). Since in -the present study weights were used to allocate individualsto just threebroad age classes, misallocations are likely to be few. '-" ,,, Another potentialpitfaU of using classesbased on body weights is that at certain timesof the yeargrowth may ceaseand animalsmay evenlose weight slightly as they get older, as happensin somebank vole populationsduring winter (Crawley, 1970).To avoid this problema SAS program(AGECLASS) was written with E. Le Bouleng6to allocate individuals to age classesirreversibly (Appendix 1). For example, if an individual progressedfrom juvenileto subadult,-it would not be consideredas a juvenile againeven if its weightfell backto the juveniles'weight range. Except for the separation the demographic - of age .classes, , all estimates of ý parametersdescribed in this Sectionwere calculatedby the program CMR (Le Bouleng6, 1985,1997).

64 4.2.3- Home rangesand distancesmoved between captures - Home rangeareas were estimatedusing the Minimum Convex Polygon method (MCP, Jennrich and Turner, 1969).This is the methodmost widely usedto estimatehome range areasfor mammals; thereforeresults are comparablewith most of the existingliterature (Harris el al., 1990). MCP home,range estimateswere calculatedusing the McPAAL' program (Stuweý and Blowowiak, 1985). Both - home range areas and -average distance moved -between successivecaptures were calculatedfrom the coordinatesof eachcapture point on a map of the whole Comer Complex.Any capturelocation in a grid was assignedto the nearest pair of coordinateson the overallmap, taking into accounttrap distancesand actualinter-' grid distancesand anglesas shown in Figure 4.1. This conversionwas performed,by a SASprogram (FCOORD) written with E. Le Boulengefor this purpose(Appendix 2). , 1, For eachspecies and sex, home range areaswere estimatedusing all capturesof each,individual ("total home ranges"). Separateestimates for: each seasonwere also calculated,re-running the programwith only the capturesof eachindividual in the period April-June (spring) or July-September(summer) or October-December(autumn) or February-March(winter)., All home range estimates(total and seasonal)were obtained only from those individualscaptured five or more times. This criterion follows Lidicker (1966)and MurCia et al. (1986), amongothers. Seasonalvariation in homerange area was testedusing the Kruskal-Walfistest, a non-parametricanalogue of ANOVA. I opted for Kruskal-Wallis becauseof marked differencesin the numberof home range estimatesavailable for each season.A similar numberof data in eachgroup is an assumptionof ANOVA (Zar, 1984). 1 also sought correlationsbetween average home range area in eachseason and populationsizes of the speciesconcerned. For this analysis.- population sizes in each seasonwere definedas the averagepopulation size in the monthscomposing each'season, as definedin the previous paragraph. I ý9ýý The accuracyof homerange area estimates is likely to be dependenton the munber of captures(or radio-trackingfixes) of the individual(Harris et al., 1990).If the estimated home range areasare plotted againstnumber of capturesper individual,,,the ýnumber of capturesat which the curvelevels off shouldcorrespond to the numberneeded to provide an accurateestimate of trap-revealedhome range size. In the presentstudy estimatesof intended home range areasare primarily for comparisonsbetween species,sexes and However, insight seasons. to provide an on how accuratelythe estimates-reflect actual home home range sizes,-total range areaswere plotted against number of captures, for separately eachspecies and sex, and the best fit third order polynomialwas drawn to help to describethe Third trends. order polynomialsWere, preferred becausefirst and

65 second order polynomials tend to produce monotonic functions which lose' much information, while higher order polyr6mials often are misleading becausea sharp_ inflectionin the curvecan be easilycaused by a singledata point. The averagedistance moved between successive captures was calculatedusing the programCMR, basedon the overall coordinatescalculated by FCOORD.This calculation was first performed for each speciesand sex using all captures of each individual. Estimateswere also calculatedseparately by season,as with the home range estimates. Seasonalvariation in averagedistance moved could not be tested statistically,because CMR outputsaverage distances moved by eachindividuaL without giving the varianceof these averages,i. e., the varianceof the distancemoved by each individual. Thus, the absencesof variancesand ranked orders by individual movementsprevented the use of ANOVA and Kruskal-Wallisrespectivelly. However, the correlation between average extensionof movementsin each seasonand the correspondingpopulation size was calculatedjust as done for seasonalhome ranges.This analysisinvolves, the --untested assumptionthat the distributionof individual movementsdid not differ significantlyfrom normal within each season,i. e., that the averageswere valid estimatorsof the central tendencyof the distributions.

4.3 - Results

4.3.1 Abundances - of each rodent species; corrected versus uncorrected estimates - Population size estimatesusing (1) data corrected for number of grids and (2) uncorrected data were calculated for the two most abundant rodent species, woodmice (Apodemus bank sylvaticus) and voles (Clethtionomys glareolus). Results of the correlations between demographic population size estimates and the other parameters, home range areas and movementswere compared across the two. types of population estimates. In every single case,correlations were statistically significant or not, regardlessof which set of population size estimateswas used. All the results that follow are based on the original (uncorrected) (the based estimates corresponding results on the corrected estimates are shown in Appendix 3). The in monthly variations population sizes of woodmi6e and bank voles are shown - in Figure 4.2. Data for June 1992 are number of different individuals caught, because Manly-Parr for estimates this month could not be calculated because of the three-month from the in Consequently, gap previous trapping all grids. population size estimates for that month were not used when calculating correlations with any demographic parameters,

66 FIGURE 4.2. Monthly variation of population sizes of woodmice and bank voles,in the Comer from Tables 4.1 Complex, as estimatedby the Manly-Parr method. Data and 4.3.1

Population Size ISO

i6o - 140 - 120 loo- so- 60- 40- 20- 0 AMJJASONDJFMAMJJASONDJFMAMJJ logo 1991- 1992 Time

Woodmics

Population size 60

40-

10-

0 AMJJABONDJFMIAMJJ, ASONDJFMAMJJ 1 1990 1 1991 1 1992 1 Time

Bank voles

67 but neverthelessthey are shownin Figure4.2 just to illustratethe populationtrends by the end of the study. From May 1990 to March 1992, population sizesof the two species werenot significantlycorrelated to eachother (r = 0.346,p>0.10,17 dS ). .-_ Field voles (Microtus agresfis) were not captured within the Comer Complex during most of the study. However, one to three individualswere capturedin autumn 1990 and spring 1991 and, after the specieshad been absentfor one year, a single individual was captured at the last trapping sessionin June 1992 (see Chapter 5). Calculationof demographicestimates was not feasiblefor this speciesdue to its rarity in the studyarea, and therefore its demographyis not discussedany fiuther in this Chapter.,

4.3.2 - Population dynamics of woodmice - Estimates of demographic parameters for Apodemus sybudcus are presented in Table 4.1. In both years of the study, woodmice populations had a phaseof low numbers in spring and early summer, followed by a marked increase by late summer and autumn, and remained high until December. However, in 1991 numbers were generally higher than in 1990 and the increase started earlier (July 1991, compared to August / September 1990). On the other hand numbers fell more sharply through the winter in 1991-92 than in 1990-91 (Figure 4.2). Estimated daily trappabilities for woodmice were above 0.8 in most cases. The main exceptionswere around and just after the time of felling in 1990 and in spring 1991 (Table 4.1). Total survival (Table 4.1) was highest at the start of the population increase in both years, but fell sharply by the time peak numbers were reached in October-November 4.3). Although (Figure total survival was not significantly correlated with population size in (r 0.252, the same month = p>0.20, n= 16), it was negatively correlated with in population size the previous month (r = -0.591, p<0.01, n= 15). This "delayed" for correlation was not significant any age class taken separately, the highest correlation being between adult survival at t and population size at t-I (r = -0.247, p>0.20, n= 16). Examination of survival rates separatedby age class showed contrasting patterns of variation for, different age classes (Table 4.2). Patterns for - . subadult, survival were erratic, sometimessimilar to adult survival (e.g. June 1990, May 1991) and sometimesto juvenile (e. October'1990, December survival g. 1991). A clearer pattern emerged when juvenile w Overall, and adult survival rates ere compared. there was only a positive, non- between juvenile significant correlation adult and survival (r = 0.183, p>0.20, n- 16), but there interesting in were contrasts the variation of the two rates along the study (Figure 4.3). Adult higher juvenile survival was than survival throughout 1990, except in September,during the increase.In population spring and summer 1991, survival of

69 TABLE 4.1. Estimates of demographic parameters for woodmice (Apodemus sylvaticus) in the Comer Complex, using the Manly-Parr method: population sizes, 30-day survival rates and daily trappabilities.

Time Population size Survival rate Daily trapp.

May 29.00+ 0.00 - 1.000 Jun 19.00 T 0.00 0.293 1.000 Jul (18.00± 6.71) (0.138) (0.667) 1990 Aug 48.00 + 18.97 0.735 0.667 Sep 72.00 18.59 0.590 0.625 Oct 145.71 34.06 0.391 0.583 Nov 96.00 5.66 0.296 0.938 Dec (78.00 + 30.59) (0.432) (0.500)

Feb (21.00 + 0.00) (0.444) (1.000) Mar 41.00± 0.00 0.301 1.000 Apr 50.60 ± 14.78 0.563 0.455 May 93.50 + 35.03 0.895 0.364 Jun 64.80+ 5.80 0.554 0.833 1991 Jul 100.36T 5.39 0.596 0.917 - Aug 106.11 T 4.13 0.541 0.905 Sep 132.00 ± 9.38 0.732 0.818 Oct 159.71 ±13.98 0.550 0.808 Nov 84.58+ 5.81 0.303 0.828 Dec 68.83:; 10.07 0.655 0.857

Feb 18.29 1.83 0.510 0.975 1992 Mar 20.00 0.00 - - Jun (9)

Note. Estimatesin parenthesesare not comparableto the others becausethey are basedon two or three grids only or, in the caseof June 1992,because the populationestimate for the last month is simply the number of different individuals caught (see text, Section 4.2.2). Traces(-) indicatecases where calculations were not feasiblewith the dataavailable.

69 FIGURE 4.3. Monthly variation of woodmice survival rates. Top: overall survival, estimated by the Manly-Parr method, and its relationship with the variation of population sizes. Bottom: comparisonof adult and juvenile survival, both estimated by Pmin. Data from Tables 4.1 and 4.2.

Survival Population she

P 160 0.8- 140 120 0.6 - 100

so 0.4- -60 13 -40 02- 13 20 0110 AMJJASONDJFMAMJJASONDJFMAMJ 1 1 1 1990 19911 -1 1992 Time

... Population size --I'- Total survival

Survival rates OA 0.7

0.6

0.5

0.4

0,3

0.1 n AMJJA80NDJFMAMJJA80NDJFMAMJ 1 1990 1991 1992 Time

Juvenilesurvival --4- Adult survival

70 TABLE 4.2. Estimates of demographic parameters for Apodemus Sylvaticus in the Comer (P for juvenile Complex: 30-day minimum survival rates n)m.-) each age class, 30-day recruiment rates and proportion of reproductive subadult or adult females.

Time Survival Survival Survival Recruitment Proportion juveniles subadults adults juveniles reproductive females(n) 1990 May 0.778(9) Jun 0.000 0.380 0.369 0.073 1.000(9) Jul '(0.000) (0.000) (0.226) (0.000) (0.750(4)) Aug - 0.375 0.599 0.112 0.857(14) Sep 0.535 0.690 0.450 0.180 1.000(10) Oct 0.260 0.150 0.570 0.220 0.917(12) Nov 0.226 0.520 0.455, 0.331 0.364(22) Dec (0.232) (0.232) (0.146) (0.147) (0.000(9))

1991 Feb (0.406) (0.000) (0.610) (0.017), (0.333(3)) Mar 0.000 0.540 0.289 0.105 0.667(12) Apr 0.405 0.491 0.313 0.052 0.800(5) May 0.000 0.648 0.688, 0.053 1.000(11) Jun 0.488 0.488 0.620 0.255 0.765(17) Jul 0.533 0.533 0.695 0.299 0.958(24) Aug 0.476 0.591 0.591 0.321 0.897(29) Sep 0.698 0.743' 0.589 0.240 0.708(24) Oct 0.502 0.491 0.491 0.260 0.061(33) Nov 0.217 0.375 0.285 0.089 0.000(21) Dec 0.603 0.627 0.111 0.040 0.000(21)

1992 Feb 0.693 0.4.63 0.,61 ,8 0.024 ' 0.167(6) Mar 0.651 0.537 0.476 0.107 0.625(8)

Jun 0.000 1.000(5)

Note. Numberof adult and subadultfemales captured at eachmonth is given in parentheses in the last column.Remaining symbols as'in Table 4.1.

71 1991 juveniles was again the poorer in all months except April. In autumn the two groups juveniles better had similar survival rates, and in contrast to the previous year, survived than adults through the winter 1991-92. high during The proportion of reproductive female woodmice (Table 4.2) was in both Decembers most of each year of study, but it fell in both autumns, reached zero 4.4). The and recovered gradually during the springs of 1991 and 1992 (Figure only females fell marked difference between the years was that proportion of reproductive earlier in 1991 than in 1990: by October 1990 more than 90% of the subadult or adult females in, the population were still in reproductive condition, while in October of the following year the proportion was only 6.1%. The proportion of reproductive femaleswas it not correlated with population size in the samemonth (r = -0.063, p>0.50, n= 17), but showed a"significantnegative correlation with population size in the previous month (r =- 0.583, p<0.01, n= 16; Figure 4.4). Among all the remaining demographic parameters measured for woodmice, the proportion of reproductive females showed a positive correlation only with adult survival (r = 0.478, p<0.05, n= 17). Variation in juvenile recruitment rates was closely coupled with variation in population size (Figure 4.4), -resulting in a significant positive correlation (r = 0.640, p< 0.0 1, n= 17). An interesting finding was that juvenile recruitment was zero by the end of the study in June 1992, in spite of the proportion of reproductive females at that month being 1-000 (five out of five). Juvenile recruitment also showed a significant negative correlation wit adult survival (r = -0.475, p<0.05, n= 17). All other correlations among 'h demographicparameters for woodmice were non-significant. Iý ý' ý'

43.3 - Population dynamics of bank voles - The variationin numbersof Clethrionomys glareolus was very different from that of A. sylvaticus. in 1990, bank voles were less consistently-- abundantthan woodmice, and the population peak was -reachedin summer(August) rather than autumn (Table 4.3, Figure 4.2). Bank vole populations remainedrelatively high in the winter of 1990-91,and fell only slightly in spring 1991. After that the increasein numbersstarted earlier (June) than the increasein woodmiceand high numberswere sustainedthrough the autumnleading to a Decemberpeak. In contrast to woodmice,bank voleswere still abundantby the end of the study in June 1992(Figure 4.2). Estimated daily trappabilitieswere lower for bank voles than for woodmice, lowest althoughthe valueswere found at a similartime for the two species-autumn 1990, just after felling, and spring 1991(Table 4.3).

72 FIGURE 4.4. Monthly variation of woodmice reproduction and recruitment, and its relationship with the variation in population sizes. Top: proportion of reproductive females; bottom: juvenile recruitment. Data from Tables 4.1 and 4.2.

Proportion Population size low 160 140

0.8 120 100 0.6 so

0.4 60 40 02 20

0 0 AMJJASONDJFMAMJJASONDJFMAMJJ 1990 1991 1992 Time

Populationsize Propieprod. females

Recruitment Population size U.ao _-lou

0.3 160 140 026 120 0.2 loo- 0.16 80 60 0.1 40 0.06 20 n n AMJJABONDJFMAMJJASONDJFMAMJJ 1 1990 1991 1992 1 Time

1111-Population &Us --ý- Recruitment

73 TABLE 4.3. Estimates of demographicparameters for bank voles (Clethrionomys gZareolus) in the Comer Complex, using the Manly-Parr method: population sizes, 30-day survival and daily trappability. Symbols as in Table 4.1.

Time Population size Survival Daily trapp.

May 4.00+ 0.00 Jun 7.00: ý 0.00 Jul (18.00T 12.00), (0.500) 1990 Aug 26.00 ':ý0.00 0.308 11.000 Sep 24.00: 8.94 0.485 0.333 Oct 21.60 -; 3.35 0.600 0.833 Nov 16.67 5.09 0.355 0.600 Dec (8.00 + 0.00)

Feb (20.00 ± 9.49) - (0.400) Mar 26.25 ± 7.36 1.000 0.571 Apr 22.50 + 10.87 1.000 0.400 May 15.00+ 0.00 0.503 1.000" Jun 21.00T 3.74 0.691 0.667 1991 Jul 24.75 ;: 3.41 0.803 0.727 Aug 32.00:; 5.66 0.490 0.750 Sep 3 7.50 7.02 0.532 0.533 Oct 37.71 7.60 0.555 0.583 Nov 30.00 3.87 0.522 0.667 Dec 43.75 8.35 1.000 0.571

Feb 33.60+ 4.34 0.810 0.714 1992 Mar 29.00T 0.00 - - Jun (28) Overall survival of bank yoles was in -most caseshigher in. 1991-- than in the correspondingmonths of the previousyear (Table4-3). No significantcorrelation could be found betweentotal survivaland population size, with or without a time lag (r = 0.168, n 15 andr=0.268, n =14 respectively;either, p>0.20; Figure4.5). -- Adult survival(Table 4.4) was significantlycorrelated with populationsize in the samemonth (r = 0.635, p<0.01, n= 17) and the previousmonth as well (r = 0.478, p< 0.05, n =16). Adult survival was very good through the winter of 1991-92,when high numberswere sustained(Figure 4.5). The correlationbetween population size and juvenile survival was marginallynon-significant (r = 0.368,0.05 0.50,n =47). Survivalof adult andjuvenile bank voles followed very similar patternsin the first yearof study,but very differentin the second(Table 4.4, Figure 4.5). Survivalof both age 1 classesincreased through summerand early autumn 1990, fell sharplyin November,but this recoveredby spring 1991;in period survival of the two age groups was positively correlated(r = 0.826, p<0.05, n= 6). From then on adult survival was higher than juvenilesurvival in everysingle month except September 1991 and February 1992, the gap betweenthe two ratesbeing greatest in autumn199 1. However, survivalwas very good in both groupsover the winter 1991-92,when high numberswere- sustained. The negative correlationbetween the survival of the two age groups in the secondyear was non. significant(r = -0.404,p>0.10, n= 8). Reproductivefemale bank voles were capturedin every month exceptDecember 1990 and February 1992 (Table 4.4). As in woodmice, there was evidenceof a short, interruptionof breedingin winter, followed by a quick recoveryto a high proportion of females reproductive by March in both years.A comparisonbetween the two yearsof the study showssome marked differences. The proportion of reproductivefemales in 1991 was alwayshigher than in the correspondingmonths of 1990 until August (Figure 4.6). during Thereafter, the early stagesof the period of high numbers(autumn 1991),a much smallerproportion of femaleswas reproductivethan during the correspondingmonths in 1990. On the other hand,breeding apparently stopped at least one month later in 1991 in 1990. The than proportion of reproductive females in bank voles was negatively correlatedwith populationsize at the samemonth (r = -0.419, p<0.05, n= 17) andwith in populationsize the previousmonth as well (r = -0.707, p<0.01, n= 16; Figure 4.6), but in positivelycorrelated with adult survival the previousmonth (r - 0.479, p<0.05, n = 16).

75 TABLE 4.4. Estimates of demographic parameters for Clethrionomys glareolus in the Complex: 30-day (P for Comer monthly variations of minimum survival rates nsugadultU-) each age class, 30-day juvenile recruiment rates and proportion of reproductive or adult females.Symbols as in Table 4.2.

Time Survival Survival Survival Recruitment Proportion juveniles subadults adults juveniles reproductive females 1990 May - - - 0.500(4) Jun 1.000 0.000 0.100 0.429(7) Jul (0.000) (0.476) (0.000) (0.119) (1.000(4)) Aug 0.000 1.000 0.305 0.165 0.667(18) Sep 0.623 0.470 0.410 0.088 0.500(14) Oct 0.500 1.000 0.710 0.231 0.500(6) Nov 0.141 0.430 0.106 0.800(5) Dec (0.205) _0.058(0.371) (0.371) (0.179) (0.000(l))

1991 Feb (0.000) (1.000) (0.902) (0.018) (0.333(3)) Mar 1.000 0.226 1.000 0.025 0.714(7) Apr 0.593 0.000' 0.834 ' 0.000 1.000(4) May - 0.648 0.648 0.000 0.875(8) Jun 0.743 0.630 0.197 1.000(8) Jul 0.533 1.000 0.835 0.147 1.000(10) Aug 0.287 0.389 0.688 0.469 1.000(9) Sep 0.933 0.315 0.617 0.133 0.400(5) Oct 0.449 0.555 1.000 0.214 0.286(7) Nov 0.246 0.893 1.000 0.214 0.125(8) Dec 0.713 0.879 1.000 0.249 0.333(9) 1 1992 Feb 0.908 0.801 0.671 0.078 0.000(6) Mar 0.693 0.735 1.000 0.148 0.667(6)

Jun 0.071 0.750(8)

76 FIGURE 4.5. Monthly variation of bank vole survival rates. Top: overall survival, estimated by the Manly-Parr method, and its relationship with the variation of population sizes.Bottom: comparisonof adult and juvenile survival, both estimated by Pmir'LData from Tables 4.3 and 4.4.

Survival Population sin Go

io 0.8 io 0.6 io 0.4

02 10

n n AMJJASONDJFMAMJJASONDJFMAMJ %f 1990 1 1991 1992 1 Time

Population size Total survival

survivalrates

0.0

0.4

0.2

0 AMJJASONDJFMAMJJASONDJFMAIAJ 1 1990 1 1991 1 1992 1 Time

Juvenilesurvival Adult'auirvival

77 FIGURE 4.6. Monthly variation of bank vole reproduction and recruitment, and its relationshipwith the variationin population sizes.Top: proportion of reproductivefemales; bottom:juvenile recruitment. Data from Tables4.3 and 4A.

Proportion Population ou

OA 30 os 20 '0.4

02 10

AMJJASON, DJFMAMJJASONDJFMAMJJ 1990 1991 1992 Time

Population size Propreprod. females

Recruitment Popu lation size

0.4 49.

0.3 30

02 20

0.1 10

0 n AMJ JASONDJFMAMJJASOND'JFMAMJJ%'' logo 1991 1992 Time,

Population size Recruitment

78 As in woodmice,bank vole population sizes until March 1992 were positively 17; Figure 4.6). correlatedwith juvenile recruitmentrates (r = 0.421, p<0.05, n= However,juvenile'recruitment was low in June 1992,by the time of the sustainedhigh numbersin the end of the study (Figure 4.6). Juvenile recruitment rates were not significantlycorrelated with the survivalrates of any ageclass.

distances high, the 4.3.4 - Home range areas and average moved -A proportion of home rangesfrom which areascould be calculated extended over more than one trapping (66.7%) 33 61 female grid. In woodmice, 70 out of 105 male home ranges and out of i 7 home ranges (54. O/o)included two or more grids. In bank voles, the proportions were out of 16 for males (43.8%) and 3 out of 17 for females (17.6%). In most of such cases, the grids being used by a single individual belonged to different habitat types. The mature forest grid Comer North and the clear-felling Comer 2 were the pair most often used by individuals captured in more than one grid (see analysis of movements by grids in Chapter 6). As a whole, the results show that individuals (especially woodmice) were often not restricted to a single grid within the Comer Complex, thus supporting the assumptionthat the population of the area can be treated as a whole. Estimates of total home range areas for individuals of each species and sex are presented in Tables 4.5 and 4.6 and plotted against number of captures in Figure 4.7. Home range area estimateslevelled at about 10 captures for male bank voles and at about 20 captures for male woodmice. For female woodmice estimates seemedto level at about 10 captures.The apparent exponential increase at the right of the graph (Fig. 4.7b) is due not to the individual captured most often having a large home range, but rather to the individual with the second highest number of captures having a small home range, thus causingthe upward bend of the polynomial function. For female bank voles there was little evidenceof the curve levelling at all. Thus the values I obtained for both speciesand both sexes, which were based on all individuals caught five or more times, probably underestimatedtrue home range sizesto some extent. Nevertheless those values are useful for comparisons between seasons and sexes, and indeed several patterns emerge from thesecomparisons, as fbUows. Male woodmice had significantly larger total home range areas than female woodmice(t = 2.461,p<0.01). This patternwas found in all seasons,but the difference was small in both autumns(Table 4.5). Female home ranges showed little seasonal variation(Kruskal-Wallis, H=2.053, p>0.90); but nudehome I ranges varied significantly with season(Kruskal-Walfis, H 15.461,p<0.05). Male homerange areas were larger at the time of lowest densities,i. e., springand summer1990, winter 1990-91and spring

79 TABLE 4.5. Home ranges and movementsof woodinice in the Comer Complex. Top: home Bottom: range areas as estimated by the Minimum Convex Polygon method. average distancesmoved between successivecaptures.

Home rangessizes Males Females (ha)

Overall 0.407+0.379(105) 0.294+0.358(61)

1990 Winter 0.029(l) - Spring 0.862 + 1.079 (2) 0.161 ± 0.194 (2) Sumnier 0.550: 0.465 (3) 0.198 ±0.082 (3) Autumn 0.242 -;± 0.217(19) 0.214 ±0.217 (9) 1991 Winter 0.533 ± 0.380 (7) 0.158 ±0.056 (4) Spring 0.621 ± 0.292 (6) 0.241 ± 0.272 (6) Summer 0.283 ± 0.272 (30) 0.177 + 0.177 (16) Autumn 0.277 ± 0.188 (28) 0.201 0.191 (15) 1992 Winter 0.378 ± 0.338 (3) 0.239 0.172 (3)

Average distancesmoved Males Females

Overall 48.4(1124) 39.9(646) 1990 Winter 34.5(22) 27.4(15) Spring 48.8(38) 24.5(38) Summer 57.1(49) 26.7(33) Autumn 39.4(162) 42.6(91) 1991 Winter 58.2(73) 38.4(27) Spring 71' 2(65) 40.0(47) * Summer 41.3(271) 42.4(156) Autumn 43.6(306) 34.2(151) 1992 Winter 57.0(37) 47.4(30)

INote. Values presented tor home range areas are means t s.d; sample sizes (numbe-r of individuals) in parentheses; for movements, values are the averages, with sample sizes (number of movements) in parentheses.

so TABLE 4.6. Home rangesand movements of bank voles in the Comer Complex. Top: home range areas as estimated by the Minimum Convex Polygon method. Bottom: averageý distancesmoved between successivecaptures.

Home range sizes Males Females (ha)

Overall 0.191 + 0.187 (16) 0.136 + 0.156 (17)

1990 Winter - Spring - Summer 0.075 + 0.093(3) 0.085 ± 0.035 (3) Autumn 6.' 059(1) 0.038(l) 1991 4.I"t ,:, Winter 0.189 + 0.174 (2) Spring 6 ' 055(1) 0.066(l) Summer . 0.041 0.023 (2) Autumn - 0.143 0.035 (3) 1992 Winter 0.039 + 0.017 (2) 0.036 + 0.009 (2)

Average distancemoved Males Females M)

Overall 33.2(150) 22.5(231) 1990 Winter 30.3(4) Spring 20.6(14) Summer 32.7(23) 22.8(35) Autumn 27.0(9) 20.4(17) 1991 Winter 32.3(14) 27.5(10) Spring 57.8(13) 19.9(23) Summer 26.6(12) 17.0(27) Autumn 16.6(16) 28.2(36) 1992 Winter 18.2(28) 24.5(26)

81 FIGURE 4.7. Relationshipbetween number of capturesper individual and the corresponding home range areaestimates (in hectares).a) Male woodmice. b) Femalewoodmice. c) Male bank voles. d) Femalebank voles. Curves are the third order polynomialswhich fit best the data.

HR (he) size Hill (be) 3.9 size 3..

210 2.0 -

1.0 I., -

0.0 10 20 so 40 10 to so kvmb*r of captufoo Nomberof capturos

MR size (ba) Hr size (he) .. r

0.5 - 0.6

0.4 0.5

0.4

O.s

0.2 ...... 0.1 0.1

0.0 -4- 0.0 0 le au au 4 Nombe; ei etoteret NVMDOFof captum (d)

112 FIGURE 4.8. Seasonalvariation of population sizes and its relationship with average home range areas of woodmice (top) and avmge distances moved by bank voles (bottom). Population sizesare given by the averagepopulation size during the season(Section 4.2.3).

Woodmice

opulation sizes Home range areas (ha) 140 1

-120- ...... -0.6 100- -0.8 so - /*

60- 0.4 40- 0.2 20

01 0 Wl sp au AU wl Ap Su Au wl sp I - - ,. Seasons

Population sizes Male home ranges Female home ranges

Bank voles POPUlation alias Distance* Mond (M) 4u ou

50 30 40

20 30

20 10 10

0 1% wi Sp Su Au wl sp su Au wl sp Seasons

--ftPulationsi3a -Q-M&jGMVvsMGnte -b- remsis movements

83 1991 (Table 4.5). Indeed,male home range- area showeda strong negativecorrelation with averagepopulation size* at eachseason (r = -0.706; p<0.05, n=8 seasons;-Figure 4.8). No significantcorrelation was found, however;between female home range area and seasonalpopulation size (r = 0.087,p>0.50, n=8; Figure4.8). Patternsshown by the averagedistances moved by woodmicebetween captures were consistentwith the variation in homerange areas (Table 4.5). Males movedfurther than femalesonly at-times of low population size. At times of high population size (autumn1990, summer and autumn 1991) the distancesmoved by malesand femaleswere siddar. The correlationbetween average distance moved and averagepopulation size at eachseason was negativebut marginallynon-significant for males(r = -0.5739'0.050.25). ýA smallernumber of estimatesof home range areawere obtainedfor bank voles than for woodmice,thus seasonalpatterns of variation in this parametercould not be testedstatistically. Although there was a trend for male bank voles to have bigger home rangesthan females(Table 4.6), the differencebetween sexes was not significant(t = 0.837, p>0.20). However, patterns regarding distancesmoved between successive capturesare clearer.Overall, males again moved finther than females.This differencewas apparentthroughout a whole year from summer1990 to summer199I. The pattern was apparentlyreversed in autumn1991 and winter 1992,the time of highestnumbers of bank voles, when femalesmoved further than males.As a, consequenceof these contrasting patternsfor males and females,average distance moved showed a significantnegative correlationwith populationsize for males(r = -0.724, p<0.05), but not for females(r 0.437, p>0.10; Figure4.8).

4.4 - Discussion

Population dynamics 4.4.1 - of woodmice: annual fluctuations - Populationdynamics of APOdemussYlvalicus have been extensivelystudied in Britain, mostly in deciduous woodland(e. g. Ashby, 1967,Watts, 1969,Flowerdew, 1972,1974,1976b,Gurnell, 1978, 1981,Montgomery 1979,1980). The resultsof theseand other studieswere reviewedby (1978,1985,1987) Flowerdew and Montgomery (1989a). The patterns -can be summarizedas follows. 1) Populationdensities usually fall in spring, are relativelly stablethrough early summer. but increaseby late start to summerand early autumnto reachan autumnor winter peak. Nun;bers high throughwinter decrease often remain and start to againin spring. ---

94 2) Food supplyis probablya major influenceon the peak densitiesreached in autumn/ winter, but factors-other than, food supply also control the -timing of increaseand determinepeak densities, especially density-dependent factors. It was suggestedby Watts (1969)that the timing of the start of the populationincrease is density-dependent,with an earlier increasein years when summerpopulation densitiesare low, so that autumn-/ winter numberstend to be less variable than summer numbers.However, Flowerdew (1985) found that winter densitieswere probablymore variablethan proposedby Watts and Montgomery(1989a) found that lessthan one third of the variation in peak numbers was accountedfor by density-dependentprocesses operating during the period of increase. Furthermorehe did not find evidenceof density-dependencein the timing of the start of the late increase. summer I -, III. -I _ý1 1.", 11,11- 3) The size of the spring decline is strongly influencedby overwinter food supply, but changesin the social and spatial structureof the populationat the start of the breeding season(late winter or early spring)are likely to play a part as well. Frorn'thistime of the year until early summer,juvenile survivalis very poor probablyas a result of aggression from overwinteredmales (Flowerdew, 1974,1978; Gurnell,,1978). Thus numbersremain stablebecause survival ofjuveniles is poor, not becauseof any limitation on numbersborn. Femaleterritoriality at this time of the year-may also limit recruitmentof juveniles as suggestedby Flowerdew(1985: 327). Indeed,the importanceof femalespacing behaviour in populationregulation in woodmicehas recently been emphazised by Montgomeryel al. (1991). ýI "'. '_ 'I11, "' -1 by 4) In autumn, the end of the breedingseason, food suppliesare often good; most overwinteredmales have died, the remainingones are less aggressivetowards juveniles and immigrants;female territoriality is reduced.As a result, survival of juvenilesimprove andthe populationgrows to a peak. high 5) In times of numbers,populations of woodmice can show a density-dependent in the reduction proportion of reproductivefemales (Watts, -, 1969; Smal and Fairley; 1982) or evenan early endto breeding(Watts, 1969;Montgomery; 198 1). On the other hand,in food yearswith a very good supply,breeding may extendwell into the winter (e.g. Smyth, 1966) density-dependent and as a result - regulationý may break,down and populations continueto grow throughthe (e. Gumell, 1981;Smal winter g. andFairley, 1982). -- The , patterns-found at the'Comer Complex can be compared-to,the picture by follows. sketchedabove, point point, as .111-I .ý 1) The fluctuation in pattern of seasonal numberswas consistentin most respectswith found for A. patterns previously sylvalicus.Population sizes were low in spring in both years, and did not show increase, any consistent until .early summer. In both years

93 populationincrease started by late summerand peaknumbers were found in October.,The only point which differedfrom the previousstudies was that in Comer Complexnumbers decreasedearlier than usual. There was a sharpfall betweenOctober and November and a quick overwinter declineby late winter in both years, as revealedby'the low February populationsizes. Thus, sustainedhigh numbersin winter, as found in severalprevious studies,were not foundin the presentstudy. 2) Spring / early summer densitieswere indeed highly variable from year to year: intermediatein 1990, considerablyhigher in 1991, lowest in 1992. In contrast, autumn peaknumbers were strikingly similar: estimatedpopulation sizeswere 145.71in October 1990 and 159.71in October 1991 (four grids trappedin both occasions).'Although my dataare not suitableto test the hypothesisof density-dependentregulation of the autumn numbers,they are consistentwith this hypothesis. 3) Generally,males had bigger home ranges and movementsin springand summerthan in autumnand winter. Such a differencewas not found in females.A high proportion of femaleswas reproductivein the populationin the spring and summerof both 1990 and 1991,and in the springof 1992 as well. However, during most of spring and summerin both 1990and 1991juvenile recruitmentwas low, andjuvenile survivalwas considerably lower The low juvenile in in than adult survival. recruitment spite of active reproduction spring/ early summerwas most dramaticallyillustrated by the June 1992data. All females capturedthat month (five) were reproductive,yet juvenile recruitmentwas zero. ,The explanationof this apparentparadox is that juvenile recruitmentand juvenile survival are unlikely to be independentparameters. An individualwill count as a recruit only when it appearsin the trappablepopulation of independentindividuals, seldom weighing less than 12g at the first capture.Whether it doesso dependson its survival during the early, non- trappableweeks of its fife. Therefore,.the low juvenile recruitmentin times when a high proportion of femaleswas reproductiveprobably indicates a low survival of juvenilesin the early stagesof their lifes. Putting togetherall parameters,the patternfound for wood mousepopulations in spring and summeris consistentwith the ideasthat adult malesuse largehome ranges in the searchfor reproductivefemales, and that there is a considerable productionof young individualswhich neverthelesshave very low, survival in the early stagesof their livest probablydue to competitionwith more successfuladult individuals. 4) In autumn,male home ranges and movementswere smallerthan in springand summer. Adult survival and the proportion of reproductivefemales were both high in the early increase phasesof population in both years.Estimated juvenile survivalwas alsovery high in late / in summer earlyautumn 1991, but not in 1990.However, juvenile recruitmentwas high by in very this time of the year both years, and this pattern is, likely to reflect an

86 increasein early juvenile survival as well (see previous paragraph).-The population increasewas significantlycorrelated with this increasein juvenile recruitment.Overall, 'the results were consistentwith the -idea that the late autumn/earlysummer population increaseis due to sustainedreproduction and improvedjuvenile survival,probably due to reducedinterference from the adults. 5) In woodmicein Comer Complexbreeding stopped in both winters and thus the annual pattern of population fluctuation was -not f'overidden in eithert year. There was ,also evidenceof a strong delayeddensity-dependence -in the reduction of the proportion of reproductivefemales by: late autumnin both ýyears., It seemsthat this wasý an important factor in limiting peak numbersand in determiningthe timing of start of the population decline.Evidence of density-dependenceduring the declinephase was found also in the negativecorrelation between total survivalin a given month and populationdensity in the previousmonth, so that total survival was-low by late autumn of, both years.- As total survivallumps together all age classes,this is not very informativeof the processesgoing on within the population.However, this result may reflect a density-dependent'reduction chiefly in adult survival,which may not havebeen detected due to the lower accuracyof I'min (usedto estimateage classsurvival) as comparedto Manly-Parr(used to estimate total survival).Indeed, among the age classestaken separately,adult survival showedthe strongest(although non-significant) delayed negative correlation with populationsize. rAll considered,the strong density-dependentreduction in reproductionand probably adult survivalas well seemsto be one of the most distinctivefeatures shown by the woodmice at Comer Complexwhen comparedwith the processesfound in previousstudies on this species. It is interesting also to comparemy resultswith Tantods (1965,ý 1969) suggestion that populationestimates for woodmicein summerare not reliablebecause trappability of is this species very low then. In Comer Complex, trappability'was comparativelylow 0.7) from (below July to October 1990 (Table 4.1), whicli'might seemto support his However, hypothesis. as felling happenedwithin this period it is impossibleto separatethe from effect of season a possibleadverse effect of the felling processon trappability.Bank vole trappabilitieswere also low in july and september1990, and woodmice trappabilities in low in 1991were only spring, but-not in surruner.,Thus, data obtainedin the present studydo not provideclear supportf or Tantoifs suggestion.

4.4.2 Differencesbetween their - years and effects on woodmice populations - Most discussedin, points the previous Section apply to the whole study, but some,marked dfferences are also evidentwhen the first and the secondyear are compared.In 1991the

117 populationincrease started earlier than in 1990, and high numberswere sustainedfor a much longer period, althoughpeak numberswere similar and reachedat a similar time. Thus, numbersincreased earlier in the year when earlysummer ýpopulation levels were higher. This result supportsMontgomery's (1989a) contention that density-dependefit control of the timing of autumnpeak is not a constantfeature in A. sylvadcuspopulation dynamicsas had been suggestedby Watts (1969). -As for the overvvinterpopulation decline,it was muchsharper following 1991than it had beenfollowing 1990,and numbers were still falling by the end of the study in June 1992.This final declinewas apparently linkedto an exacerbationof the commonannual pattern: a completefailure in recruitment; in spiteof considerablereproductive activity in the populationin spring 1992.'-, ý The highernumbers in 1991 seemedto be at least partly.due to better prospects for juveniles in that year than in 1990.,Throughout the study,,population size was significantlycorrelated with juvenile recruitment.Recruitment increased earlier in 1991 than in 1990,which explainsthe earlierpopulation increase. Additionaly; juvenile survival was high for a longerperiod in 1991than in 1990,which explainsthe high numbersbeing recordedfor a longer period in 1991. Survival of adults and proportion of reproductive females,on the other hand,decreased faster in 1991than in 1990.This is consistentwith the suggestionof depsity-dependentvariation of theseparameters made above, so that sharperdeclines in adult survivaland reproductionwere to be expectedin the year when numberswere slightlyhigher and sustained f or muchlonger. The differencesobserved between years can be attributedto variationsin the seed crop. Data on inter-annualvariation in productionof Sitka spruceseeds in Hamsterleywas availablefrom the ForestryCommission seed store in Alice Holt Lodge, Hampshire.Such data do not allow fully quantitativeestimates of seed-production, but years at a given locality can be comparedby the respectiveamounts of seedarriving from that locality to the seedstore. As discussedin Chapter1, althoughsome of the Sitka seedis droppedby the autumnof the sameyear it is produced,most is droppedin the following spring.Thus the availabilityof seedson the ground during most of the year is relatedto the production of seedson the trees in the previous year. Keeping this in mind, the information-on indicate Hamsterley that much more seedwas availableto rodentson the ground in 1991 than in 1990: "... 1990was a particularlygood in Sitka ( ) seedyear spruce ... there were large very numbersof seeddispersed during the autumnof 1990through to early springof 1991( ) the level in ... general of seedproduction 1988and 1989was lower than 1990 so the numberof seedson the ground during 1989 and 1990 would certainlybe less" (C.J. NLxon, Forestry Commission, pers. comm.). "... 1990 cone -crops were best, 1991

89 marginally worse and 1989 the poorest." (Dr. P. Gosling,: Forestry Commission, pers. comm.). food by in habitats have - Studies on the eaten woodmice a variety'of established that seedsare a major component of their diet (Watts, 1968; Hansson, 1985) although the mice can turn largely to invertebratesif seedsare scarce either seasonally(Watts, 1968) or locally (Obrtel and Holisova, 1979; Zubaid and Gorman, 1991). ýCorrespondingly, the dependenceof woodmice population fluctuations on seed supplies is well established.For example, Gumell (1981), working in oak woodland, found that woodmice were able to reach a population peak earlier and sustain high numbers for longer in a year following an autumn of good, seed production. That seems to be the case also -in 1991- in Comer Complex, as seed availability on the ground must have been better than in the previous year. However, as 199I's own crop started to be dropped by October,- it war, not good enough to break down density-dependent population regulation in woodmice; indeed, density-dependencein the timing of the end of reproduction ýseemed to be particularly strong in 1991 in this species.

4.43 - Population dynamics of bank voles - Fluctuations in bank vole populations have usually been categorized into two types: annual fluctuations found in the southern parts of the speciesýrange and multiannual cycles found in Northern Fennoscandia (Alibhai and Gipps, 1985; Stenseth, 1985). British populations were included in the non-cyclic category by Alibhai and Gipps (1985). In most cases this is probably true, but there are -a few apparent exceptions. Ashby, (1967) claimed three to four year cycles for bank vole in populations the deciduous Houghall Wood near Durham, Northern England, although Afibhai and Gipps did not include his study as an example of multi-annual fluctuations. Lowe (1982) Southern and trapped bank vole populations in Wytham Woods, near from 1948 oxford, to 1981 and suggestedthat until 1964 populations followed two and a half year cycles.-The pattern later broke down and probably due to this inconsistency Lowe's Southern and study is quoted by Alibhai and Gipps (1985: 287,, plus their Fig. 1) "non-cyclic as an example of fluctuations". Newson (1963) studied another population in Wytharn Woods where the typical annual fluctuation was overidden by a sustained increasein lasting numbers more than one year. ýIn my view, it not clear whether British bank vole populations may show multi-annual cycles in the sameway as field voles. Whether by affected cyclicity or not, what is clear is that the pattern of annual fluctuations in bank is voles certainly more variable than in woodmice, although average trends (Abbhai seasonal are roughly similar and Gipps, 1985). The average pattern in

99 "typical" years(i. e. yearswith springdecline) is peak densitiesin autumn,-a quick decUne throughwinter, a slowerdecline in springand an increasestarting in summertowards the autumn peak. In other years the spring decline may not happen and the population fluctuationsare erratic (Ahbhai and Gipps, 1985:Figs. 3 and 5, respectivelly). Annualtrapping in eighteensites within HamsterleyForest for three to four years between1989 to 1992failed to find evidenceof cyclicity in bank voles populationsin the forest as a whole (Chapter3). Nevertheless,in Comer Complexbetween year dfferences were greaterfor bankvoles than for woodmice.Although bankvoles were more abundant in Comer Complexin 1991than in 1990,just like woodmice,several additional between year dfferenceswere found in the formet speciesbut not in the later. First, seasonal fluctuationswerevery differenc-peak numberswere reachedin August in 1990 and in Decemberin 1991, i.e., the secondyear. did not correspondto the averagepattern for "typical".years described by Afibhai and Gipps (1985: Fig. 3). Second,-adult survivalwas very poor during most of 1990,but very high during 1991 and towards the end of the study.Third, an inverserelationship between adult andjuvenile survivalwas found only in the year of high numbers(199 1), but not in the year of low, numbers(1990). -ý Fourth, juvenile recruitmentwas much higher in 1991 than in 1990. As discussedabove for (Section 4.4.1), woodmice this last pattern probablyreflected an improvementýin early juvenile survival;ýgiven that reproductiveactivity in the two yearswas similar. Thus the "typical" annualfluctuation in 1990 seemsto have given place in 1991; to a long, steady population'increaseuntil the, end of -reproductiveseason, followed -by sustainedhigh numbersdue to high Survivaloverwinter and until spring 1992.The patternof variationin numberslooks strikingly similar to one found by Newson (1963) in Wytham Woods: a nearlycontinuous population growth over two years,with only a slight spring declinein the start of the secondyear. Arguably, the apparentcontinuous growth of bank vole populationsin Comer Complexthrough the winter 1991-92could havebeen just an artefactof the three month in between gap trapping March andJune 1992.In this hypothesis,a markedspring decline havehappened would after all, but it would havegone undetected because of the trapping High in June gap. numbers 1992would representa return to (rather than sustained)high levels,due population to recruitmenttowards the end of the gap. Recruitmentdata do not hypothesis, seemto supportthis -becauserecruitment was very low in June 1992 (Figure 4.6). Additionally, four only out of 28 bank voles capturedin that month were subadults, which could arguablyalso reflect recentrecruitment. It seemsthat a quick recoverydue to intense high recruitmentcannot explain the numbersin June 1992, and therefore any springdecline in 1991-92,if it happenedat all, was very slight.

90 A markeddifference between Newson' s results and mine is that he attributedthe anomalouspopulation increase to winter breedingbut -I ýfound evidencethat, breeding stopped(although very late) in the winter of 1991-92: none of six adult femaleswas reproductivein February1992. Due to this small samplesize, I can only suggestbut not test the hypothesisthat an overall trend of populationincrease spanning -more, than one year(as foundby Newson)is not necessarilydependent on winter breeding,provided that the interruptionof breedingis short and survival is high. In Comer'Complex,bank vole adult survivalwas indeedhigh until March 1992,although estimates are not availablelater in the spring. The relationshipbetween population size and seedsupplies is not likely to be'as straightforwardfor bank voles as for woodmice in Comerý Complex, -for two reasons. First, bank diet is Seeds less bank voles' usually more varied. are often -important,and voles use a, greater variety of alternativefood sources,especially green leaves,,fungi, berries,lichens and bark (Watts, 1968;Flowerdew and Gardner,ý1978; Smaland Fairley, 1980;Hansson, 1985). Second, as hintedin Chapter3 and discussedmore fully in the next two Chapters,the high abundanceof bank voles in Comer Complex in 1992 was concentratedin a singlegrid, the clear-fellingComer 2. The caseof this individual grid will be discussedin next Chapter.As a more generalremark, patterns at the level of the populationof the whole Comer Complexwere generallyclearer for woodmicethan for bankvoles. This was partially due to two non-independentfactors: (1) bank voles moved less often betweengrids than woodmice (Section 4.3-4) and (2) bank voles are more habitat-specialistthan woodmice in general (Chapter 3) and in Comer Complex-in particular (Chapter6). For these-two reasonsthe large spatial scale approachapplied betterto the latter speciesthan to the former.

4.4.4 Home ranges their - and relationship with population dynamics - The estimates home in Comer of range areas Complex (Tables 4.5 and 4.6) can be compared with previous studies on the same species using the Nfinimum Convex Polygon,, keeping in mind that some authors published their results in m2 and I presentedmine in hectares(I ha 10,000 The I found = m2). areas were bigger than most previous trap-based studies, for especially woodmice (compare for examplewith Wolton and Flowerdew, 1985: Tables 2 3). Home based and range estimates on radio-tracking have often resulted in values higher than but it is my corresponding estimates, well establishedthat radio-tracking yelds higher than estimates comparable trap-based studies (Wolton, 1985; Attuquayefio el al., 1986). Even the within set of trap-revealed home ranges, detailed numerical comparisons because home are not warranted range estimates are demonstrably dependent on trap

91 spacing,-which! varies among," studies (Gumell and Gipps.- 1989). However,-ý' the' comparisons-suggest that home rangesin Comer Complex were bigger than average valuesfor the respectivespecies, especially in the caseof woodmice., Both homerange areas and averagedistances moved were bigger for woodmice thanfor bankvoles in ComerComplex: Most previousstudies in a variety of habitatshave alsofound this dfferencebetween the two species(Brown, 1956b,,1966, Bergstedt, 1966, Crawley, 1969,Wolton and Flowerdew, 1985). Exceptionswere Kikkawa, 1964 (who found inconclusiveresults) and Smaland Fairley (1982) who found bigger home ranges for bankvoles than for woodmicein an oak-yewwoodland in Ireland. biggerfor ýý, -Withineach species, home ranges and movementsare consistently males than for females(e. g. Brown-1956b,1969,, MUer, 1958,Bergstedt, 1966,Montgomery, 1979,Wolton, 1985;Attuquayefio et al., 1986;,present study). ' However, severalstudies havefound this differenceonly in the early reproductiveseason (spring / summer)when malehome ranges are much bigger than in autumn/ winter (e.g. Kikkawa,- 1964, Crawley, 1969,Randolph, 1977; Cody 1982quoted by Wolton and Flowerdew,1995; Attuquayefio et al., 1986).By the end of the reproductiveseason and in winter, male and femalehome rangesmay dffer little in either species.This patternis quite evidentin Codys (1982) data (quotedby Wolton andFlowerdew, 1985; their Tables2 and 3). In the presentstudy there was evidencefor sucha patternin both species,but therewere someconsiderable between year dfferences,which are probably due to the betweenyear dfferences in population dynamics(Sections 4.4.1 to 4.4.3).,For both speciesa significantnegative correlation was found betweenactual populationsizes and male home rangesand -/ or distancesmoved. For example,woodmice male home rangeswere, much larger in summer 1990 than in summer1991, which seemsto be associatedwith the earlierstart of populationincrease in 1991.Distances moved by malebank voles were shorterfrom summer1991 onwards; the periodwhen numbers of this specieswere highest(distances moved are probably,although not necessarily,correlated with home range area). Thus,,it seemsthat for both species home if fluctuation male rangeareas varied accordingto season the population -followed its usualseasonal pattern, -but they reflectedactual numbers rather than seasonif the usual patternwas not followed. As notedby Wolton and Flowerdew(1985), patternsof spatial organizationin woodmiceand bankvoles appearto be remarkablysimilar, 'in spite of their phylogeneticand morphologicaldifferences. In both species,in the early breedingseason, malesconsiderably increase the size of their ranges,while femalesincrease theirs to a smallerextent, ^if at A andmay become territorial. In the, present study,ý the relationshipbetween spatial patterns and population regulationin bank voles cannotbe satisfactorilyanalysed, due to the marked dfferences

92 betweenyears in the populationdynamics of this species.However, seasonalvariation of spatialpatterns in woodmicecan be related to existing spatialmodels and to how they affect populationregulation. Ostfeld (1990) reviewed the evidenceon territoriality by small mammals,proposing that femaleshold territories in spring and--.summer which providefood enoughfor their Utters,provided that the quality and patchydistribution of the food make territories worth defending.Males; by their tum, distribute themselves spatiallyin order to maximizeaccess to femaleswhile excludingcompeting ýmales: The larger home rangesof malesin spring and summerallow their hom7eranges to overlap severalfemale home ranges. " This model seemsto be roughly consistentwith the spatial structureof woodmicepopulations as reviewedby Wolton and Flowerdew(1985)., Such spatialstructure, in turn; is one of the :fiictors involved in the mechanism'ofpopulation regulationin woodafice,as discussedin Section4.4.1. Malcingallowance for betweenyear differencesin the seasonalvariation of home'ranges,which seemto be' related to the between'year differencesin population fluctuations,,,the spatial patterns I found for woodmicein the Comer Complexwere consistentwith the onesdescribed by Wolton and Flowerdewand put ýinto a wider context by Ostfeld. Therefore,they provide additional evidencethat the processof populationregulation of woodmicein Comer Complexwas mostly Consistentwith the -views by Flowerdew (1985) and Montgomery (1989a).

4.4.5 - Woodmice in a Sitka successional mosaic: usual population dynamics in an unusual - study area -' At least three major Merences -were - observed -between woodmouse population dynamics in Comer Complex and the patterns found elsewhereby previous workers. 'AJI three were observed at a similar time of the year (late autumn) and be may well closely related to each other. Numbers started to fall earlier in autumn in Comer Complex in than most previous studies; 'overall survival suffered a, density- dependent bY late reduction autumn, apparently chiefly the result of reduced adult survival;'and reproduction was also severely reduced by late autumn, again'in a density- dependent All way. three pieces of evidence sugIgest that there was strong density- dependent regulation of wood mouse population size in Comer Complex as early as late to Montgomery's autunA contrary (1989a) view that density-dependent factors usually explain little of the decline phasein woodmice populations. It is tempting find to try to a single hypothesis to explain this early, density- dependent decline. One be reason may that many Sitka seedsare not dropped within the they As sameyear as are produced. a consequence,late autumn food supply in Sitka might be less than in forest is dropped plentiful a where all seed in the year it is produced, as

93 happensin deciduouswoodland where most woodmice population studieswere carried out. As a second,non-exclusive hypothesis, -it can be argued that density-dependence far would be felt more severelyin Sitka becausethis habitatis, as a whole, suboptimalas asfood suppliesare concerned.This conditionhas been hypothezised for a Norway spruce (Picea abies) monoculturein Czechoslovakiaby Holisova and Obrtel (1980). Their conclusionwas based on the scarcity of ground vegetation (which would provide alternativefood sources)under Norway spruce, plus the unusuallyhigh proportion of invertebratesfound in the diet of woodmice in their study area (Obrtel and Holisova, 1979).Woodmice would have to feed on invertebratesin habitatswhere seedsupply is in poor, a suggestionlater corroboratedby Montgomeryand Montgomery(1990) conifer plantationsin Irelandand by Zubaidand Gorman(199 1) on a sanddune in Scotland.Data on diet of the Comer Complexwoodmice are not availableat present,but the scarcityof ground vegetation under mature-,Sitka spruce and, recent clear-fellingswas clearly demonstratedby the habitat analysisdescribed in Chapter3., The large woodmicehome rangeareas in Comer Complexprovide finther supportfor the hypothesisof scarcefood resources;home range areasof, woodmice are inverselycorrelated with ýhabitat - quality (Attuquayefloet al., 1986). However, it is interestingto note that,, apart from this difference,in -timing and nature of the decline, all other population processesfor woodmice were remarkably similarto thosein other habitats,mostly deciduouswoodland, as found in previousstudies (Section4.4.1). - In most casesthose studieswere made deliberatelyin an homogeneous habitat,habitat heterogeneity being a possiblecomplicating factor in populationdynamics. in the presentstudy the habitat was known to be heterogeneousat the start. Yet, most featuresof wood mousepopulation dynamics as describedby previousinvestigators were easilyrecognizable. Therefore, habitat homogeneityis not a necessarycondition for the mechanismsof populationregulation in woodmicediscussed in Section4.4.1 to operate. On the contrary,habitat heterogeneity - evenif more subtlethan in Comer Complex- may be an important componentof the mechanismof regulation. This possibility has been discussedpreviously for field voles (Hansson,1977), bank voles,(Stenseth, 1985) and morerecently for woodmice(Montgomery, 1989b; Montgomery et al., 1991).The role of habitatheterogeneity in populationregulation within the Comer Complexis discussedin Chapter6.

94 CHAPTER5 RODENT POPULATIONSIN A HABITAT MOSAIC PRODUCEDBY FELLING: ASSOCIATEDWITH CLEAR-FELLING AND THEIR 11- HABITAT CHANGES EFFECTSON THE LOCAL ABUNDANCES OF RODENTS ,, -ý. ,-

5.1 - Introduction

brashings, field experiment on 5.1.1 - Clear-felling, tree and rodent populations: a forest is the effectsof a drastic habitat change- Strictly speaking,the clear-fellingof a damage not an eventin naturalforest succession;it most closely resemblesthe effectsof from wind-throw in which the successionalprocess would start afterwards.Nevertheless,, forest clear-fellingis one of the quickest and most,extensive "experimental". changes a habitatcan experience.The processitselt which involves cutting and removing all trees both fact with heavymachinery, is potentiallyhazardous to rodents.Considering this and the closerelationship often found betweenabundance of a particular rodent speciesýand structuralcharacteristics of the habitat(e. g.,, Chapter 3), one would expectclear-felling to be a time when rodent communitieswould suffer especiallyquick and dramaticchanges (Kirkland, 1977,1990,Morrison andAnthony, 1989). The magnitudeand direction of the-changes-inrodent communities-mayalso be linked to specificforestry practicesfor, felling. For example,after the trees are-cut, the trunks of all commerciallyvaluable trees are stripped of their branchesand taken away from the felled area. The branches,pieces of trees, and those trees which are not comerciallyvaluable (together forming what hereafterare called "tree brashings"),may simply be left to decomposein situ, or they may be-removed,usually by controlled burning.In ForestryCommission plantations it has beenmore commonrecently to leave the brashingsin situ; this is partly becauseof the cost involved in removing them and partly becausethey reducethe growth of plants which may competewith tree seedlings (Gibson,1989). 1.1,1 The two alternative practicesmarkedly affect the habitats created thereby for rodents. Colonization by ground vegetation should be quicker if the brashings are removed,which might improve the habitat for rodents..On the other hand, the -tree brashingsare almostthe only kind of ground cover availablein recent clear-fellings;their removalshould increase the vulnerabilityof the rodentsto predators,especially birds of prey. indeed, belts of cleared ground were used by Muftoz and Murfia (1990) in a

95 plantationof pine (Pinusradiata) in Chile to increasethe efficiencyof predationby barn owls (Tyto alba) on smallrodents. Arguably, the removalof brashingscould also improve the efficiencyof mustelidpredators in localizing their prey, as they orient primarily by sight, and only secondarilyby smell (King, 1985). In Harnsterley,there are short-eared owls, Asio flammeus, and tawny owls,' SMx aluco -(Gibson, 1989; B. Walker, pers. comm.), andduring the presentstudy I madefour capturesof weasels(Musteld nivalis) in Longworth traps, plus twelve sightingsof mustelids(weasels and stoats,M erminea). Thus,predators were availablein the studyarea to benefitfrom any increasedvulnerability of the rodent populations.However, it shouldbe noted that if the anti-predatordefences of differentrodent speciesdepend on ground cover to Merent extents,the removal of brashingscould make the habitat unsuitablefor some rodent speciesonly, and not for others. The presentChapter reports the resultsof two field experimentson the effectsof clear-fellingof a part of a Sitka spruceplantation in Comer Complex(Hamsterley Forest) on the local rodentpopulations. The first field experimentwas the clear-fellingitseK of a block of matureSitka spruce.The secondinvolved the removalof all the tree brashings from recent clear-fellings.In both cases,rodent populationswere trapped within the experimentalsites before and after the manipulation.Additionally, simultaneousrodent trapping was carried out in control (i. e. unmanipulated)sites. The results of the experimentswere evaluatedby comparingrodent ý abundances in the experimentalareas andin the controls,to try to separatethe effectsof the manipulationsfrom the seasonalor inter-annualfluctuations in density of eachrodent population (Chapter4). Additionally, pre-experimentalsimilarity betweencontrol and experimentalareas, - as: well as between experimentalreplicates, was alsotested whenever possible, following Hurlbert (1984).

5.1.2 Testing - predicted effects of the habitat changes - Hairston (1989) emphasizes for formulating the need explicity the hypothesesbeing tested in ecological experiments, by preferably making predictions which the outcome of the experiment could either Although confirm or reject. in most previous studies of clear-felling (reviewed by Kirkland, 1990) increase an in overall small mammal population densities occurred, the influenced patterns were significantly by geographical locality and characteristics of the forest being felled (Parker 1989). Thus, predictions of the results of the experiments be based found in locality should on patterns a particular - in the present case on those in Sitka spruce clear-fellings within Hamsterley Forest (see Chapter 3; Vadher, 1990). Such for predictions, the two experiments describedabove, are as follows.

96 (1) Effects of clear-felfing: overall rodent population densitie's'would reihain similarto the levelsfound in the matureforest before feffing. Woodmicewould become the dominantrodent speciesin the clear-fellings;bank voles, evenif presentin abundance in the matureforest, would becomescarcer than woodmice in the new habitat. (2) Effects of the removal of tree-brashingsfrom the clear-felling's:this manipulationwould -severely reduce the populationlevels of bank voles, as the abunilance of this speciesusually is dependenton ground,cover (Chapter3) and in particularcan be correlatedwith the amountof slashleft in reforestations(Jensen, 1984). Woodmice would be less affectedby the manipulationthan bank voles. As a consequence,dominance of woodmicein the clear-fellingswould be further increased,possibly forming single-species "communities". Besidestesting thesepredictions, the presentstudy aimedto investigatewhether the processof felling itself causesrapid changesin rodent abundancesor whether the changestake place gradually after felling is completed,.as rodent conununitiesare constrainedby the new habitat.

5.2 - Methods

5.2.1 Trapping design - and trapping programme - Within a Sitka spruce block of six hectares, planted in 1946 and scheduledfor felling in the summer of 1990, two standard grids (Comer I and Comer 2) were trapped from March, 1990 onwards. These two sites I initially were mature forest but their trees were cut and removed in July-August 1990. (i. Two control sites e. sites adjacent to the felled block, but which did not change successionalstage during the study) were trapped from May, 1990 onwards. The two Comer North, controls were a mature plantation (also planted 1946) not scheduled for felling, Comer South, and an area clear-felled in 1989. After the felling, all trapping points Comer I 2were within and rernirked in the original places with the help of reference to points external the grids. In September 1990, just after the felling was completed, an additional grid was marked within the'area'that had just been felled (Comer 3). The the five habitat positions of grids and types within and around the Comer Complex are in Figure 5.1. Year shown of planting and soil characteristics of each grid were'presented earlier, in Table 3.1, Chapter 3." A trap in standard ping sessionwas carried out each month (except in January of 1991 1992) in from 1990 and all grids until March 1992,,'plus an additional early summer in June trapping session 1992.,However, Comer I and 2 could not be trapped in July

97 FIGURE 5.1. Position of the five standard grids trapped within Comer Complex. Within the mature plantation felled in July-August 1990, trapping started in grids I and 2 before felling. and in grid 3 in September 1990. Grids North (N) and South (S) were respectivelly a mature plantation and a clear-felling throughout the study (May 1990-June 1992). Lines parallel to the edges of the felled block show the position of the additional trapping lines in grassy verges (trapped in May and August, 1990).

Mature Plantation I loom I Clear-felled July-August 1990 = Old Clear-felled

98 1990,due to the tree felling activitiesand Comer I and 3 could not be trappedin October 1990 and October-November1991 respectively,because of brash removal for the field experiment.In December1990 and February1991, because deep snow hinderedtrapping sessions,valid datawere obtainedonly for the experimentalgrids (Comer I to 3) but not for the controls. In addition to the grid trapping describedabove, 80 trap nights were spent in trappinglines in grassyverges surrounding the Comer Complexin May 1990and another 80 in August 1990, to check for the presenceof field voles,(Microlus agrestis) as a potentialinvader of the areaduring the time of felling. The position of theselines is shown in Figure5.1., The felling startedon the eastside of the six-hectareSitka spruceblock in'June, 1990. In July, the felling machinesreached the grids Comer I and 2, and no trapping could be carriedout in the two grids during that month.,However, in August the process of felling was discontinuedagain. ýBy this time all Comer I grid had beenfelled, but the treeshad not beenremoved from one half; 20 trapping points were remarkedin the half from which the trees had already been extracted. In Comer 2 most trees were still standing,and 45 trapping points could be used in August: 30 of the original 49 points which were still within standingforest, plus 15 which were remarkedin the part just felled. The estimatesof rodent abundancesin Comer I and 2 in August 1990 were basedon theseincomplete grids.

5.2.2 field brashings -A experiment: removal of tree by controlled burning - In two grids, Comer I and 3, removal of the tree brashingswas carried out by controlled buming. In each grid, all pieces of trees, from large logs to twigs as small as could be gathered, from, were removed an area of one hectare (the grid plus a strip 5m wide in each direction). The brashings were gathered in piles and each pile was bumt in turn, so that the fire would not get out of control. Care was taken to keep the fires alive as long as possible in bum order to out each pile completely. After buming trapping points -wereremarked in help the original places with the of reference points external to the grid. The controlled burning was carried out with the assistance of the Community Task ýýForce, based in Hamsterley. The Durham University Conservation Volunteers and inmates of the Durham Prison help. provided extm The first experiment was carried out in Comer I in October 1990P,Comer 2 i. remaining as a control, e., an adjacent site clear-felled at the same time but did have the brashings which not removed. In October-Novernber 1991 the experiment in was repeated Comer 3, Comer 2 again being the control.

99 5.2.3 - Estimates of rodent abundances in each grid and comparisons between grids - As the rodents captured in each grid did not form independent populations (Section 4.3.4), throughout this Chapter "population" refers to the rodents inhabiting -the whole Comer Complex, and "subpopulation" refers to those found in a single grid., Manly-Parr subroutinesfor estimation of population sizes, survival and trappability (as used in Chapter 4) were not applied to the subpopulations for two reasons, as follows. -First, sample sizes often were not large enough. Second,I was especially concernedwith fluxes of individuals between grids, which might be seasonally reversed (see next Chapter). A- method ýwas neededtherefore which treated an individual which had moved from grid A to B by month t, but returned later, as absentfrom A at t. Both Manly-Parr or the simple technique often used for small sample sizes,the MNA (Minimum Number Alive; Krebs,- 1966) would have counted the individual as present in A at month t if it returned there at a later date. Thus, subpopulation sizesfor individual grids at month t were estimated simply by the number of different individuals captured in each grid at t. This estimate is likely to be negatively biased; nevertheless,provided that a similar bias applies to all grids, this estimator should be useful for comparisonsbetween subpopulations. When comparing the number of individuals caught in different grids; I used paired- sampletests in order to separateseasonal variations in numbers*(which occur in all grids) inter-grid from the variations which were being tested. In several cases the available (i. sample sizes e. number'of months) were too small to assess the normality,- of the distributions, which is an assumption of the paired Studenfs t-tests often used for this kind Thus, of comparison. 'I opted for a cautious approach and tested differences between grids using the non-parametric analogue of the paired t-test, the Wdcoxon signed ranks test. This is less test powerful than the t-test, but on the other hand it does not assume normality of the distributions (Zar, 1984).

5.2.4- ting the effects of the removal of brashings: comparisonsbetween rodent distributions The spatial - removalof tree brashingswas performedonly within the grids 5m plus the strip aroundthem. Thus it is conceivablethat animalsfrom the surrounding haveinvaded areascould the grids and inflatedthe countsof animalswithin eachgrid;, this is the so-called"edge effect" (Smith et al., 1975;Flowerdew, 1976; Tanaka, 1980). The is edgeeffect a commonproblem when estimatingpopulation densities, but providedthat a grid coversonly part of the areaof the singlehabitat it samples,grid-based estimates of population sizes can be used as an index to population densities(Section 4.2.2)., If however'thehabitats within and around thi grid differ sharply, such an index could be It happenin misleading. might such a case that animalscaught -Aithin the grid come

100 mostly, or entirely, from the habitat surrounding the grid; in this, case-the rodent populationscaught in the grid would certainlynot be representativeof the habitatwithin it. Additionally, if different rodent speciesshow edge effects to different degrees,the relativeabundances of the specieswithin the communitycould be grosslymisrepresented by captureswithin the grid. To correct for this bias in the grids where brashingswere removed,I tried to estimateedge effect by comparingthe observedand expectedfrequencies of capturesin the outer and inner traps with Chi-squaretests. This method is based on Schroeder's (1981) method,which assumesthat an edgeeffect would inflate the numberof capturesin the outer traps more than in the inner ones and detects any effect by comparingthe trappingsuccess of traps locatedin dfferent positionsof a grid. In the presentstudy, the expectedproportions were 24/49 of the capturesin the 24 outer traps, and 25/49 of the capturesin the 25, ones.All comparisonswere carried out with contingencyChi- -inner squaretests with one degreeof freedom,using Yates! correction for continuity. When interpretingthe results,care was taken with the assymetricalimplications of two possible patterns,as follows. If capturesof a specieswere concentratedalong the edgesof a grid, there was evidenceof edgeeffect due to dependenceon shelter.On the other hand, if a patternof captureswas evenlydistributed the speciesdid not dependon shelter,but this doesnot necessarilymean that there was no edgeeffect. If animalsfrom the surroundings move considerabledistances in relation to the size of the grid and they do not dependon shelter,they could easilymove towards the center of the grid as well, to an extent that would makethe patternof capturesconcentrated in the edgesvanish completely.

5.3 - Results

The 53.1 - effects of clear-felling on rodent abundances- The numbersof different individuals of each rodent species caught each month before felling in the two experimentalgrids Comer I and Comer 2 are shownin Tables5.1 and 5.2, togetherwith the levels recordedin the two control grids, 'Comer North (mature) and Comer South (felled).A. sylwticus was found in all grids, and C. glareoluswas absentonly from the old clear-fellingcontrol Comer South. four During the monthsbefore felling, woodmicewere significantlymore abundant in in Comer 2 than Corner 1; a similar differencewas significantin bank vole abundance level only at the p=0.072 (Table 5.3). Thus, there was evidenceof some pre-felling heterogeneity amongstthese two experimentalgrids. No statisticalcomparison was

101

(91 TABLE 5.1. Monthly variations in the abundance of woodmice (Apodemus Sylvaticus) in different,individuals each grid of the Comer Complex. Values are numbers of actually caught.

CoN(M) ime Col(M-F) Co2(M-F) Co3(F) CoS(F)

Mar 1 10 Apr 4 13 May 5 11 - 8 9 Jun 3 9 - 9 7 5 1990 Jul Felling Felling - 3 " 6 Aug 3"' lo - 3 Sep 19 7 14 3 9 Oct BrashRem 24, 23 23 22 Nov 30 18 29 13 13 Dec 22 8 9 - -

Feb 3 10 10 - - Mar 11 13 9- 3 18- Apr 5 3 . 6 6 6 May 3 9 8 6 11 Jun 2 14 7 6 31 1991 Jul 14 26 20 16 33 Aug 14 25 22 18 32 Sep 12 27 23 30 28 Oct 32 42 BrashRem 44 27 Nov 22 23 BrashRem 29 is Dec 15 is 6 23 12

Feb 2 6 3 5 3 1992 4 5 9 5 3-- -Mar -Jun 4 2 3 0 4

Note. Periods when either clear-felling or the experimental removal of tree brashings was taking place ("Felfing" and "BrashRem" respectively) are indicated. Successionalstages of the grids are: K mature; F, clear-felling. Values for mature grids are shown in bold; clear- fellings in standard; experimental (brash removed) plots italics. Asterisks (*) for August, 1990 indicate samplings based on incomplete grids in Comer I and 2 at the time of felling (seetext).

102 TABLE 5.2..Montbly variations in the abundanceof bank voles (Clethrionomys glareolus) and field voles (Microtus agrestis, numbers in superscript) in each grid of the Comer Complex. Values are numbers of different individuals actually caught. AR symbols as in Table

Time Col(M-F) Co2(M-F) Co3(F) CoS(F) CoN(M)

Mar 0 2 Apr 0 2 - May 1 1 - 0 1 Jun 1 5 - .0 3 1990 Jul FeUing Felling - 0 6 Aug 1* 10* - 0 11 Sep 2 7 1 01 5 Oct BrashRem 8 0 03 11 Nov 2 3 0 -, 02 5 Dec 1 2 1

Feb 0 5 2 Mar 0 7 1 03 10 Apr 0 6 3 0 2 May 1 7 1- 0 7 1991 Jun 1 3 1 0 9 Jul 4 5 0 0 9 Aug 3 3 2 0 16 Sep 1 5 3 0 12 Oct 2 8 BrashRem, 0 12 Nov 1 12 BrashRem 1 7 Dec 1 21 0 1 3

Feb 2 19 0 1 3 1992 Mar 4 22 0 1 2 Jun 1 20 01 1 8

103 e, 0 -r,

9, 33 oo

2 -0,6. e4

8 .4- Cý 0% Cý 0% r , Iq 0 -4 W') -, ý -4 0 e4 cl en 0 C4m v c; c; v in = c: to - JD x ýo 0% S r4 8g:: ý C4 ý Go, ý. (21. t- an %D en -0r. = CD clý Co ý:: e 80Ev c:s , Aue

ID N . Co cis in 4w = S d - 2 = Iý 49 , I 1 I IE ý I luý LD .0 . 0

C4 0 C) e n en

C3 ni ený 9 ti-zý , to C3 W

tin r 4 A

U2 ý i` Z, 20.ZA. STI rz al 104 possiblebetween rodent abundancesin the maturecontrol Comer North and in Comer I or Comer 2 beforefelling. However,the numbersof woodmiceand especiallybank voles in ComerNorth were more similarto Comer 2 than to Comer I in this period (Tables5.1 and 5.2). Therefore,given the pre-felling heterogeneitybetween the two experimental grids, I choseto analysethe effects of felling by comparingComer North (the mature control) only with Comer2, the most similarof the experimentals. The effects of clear-fellingon the rodent populationsare shown in Figure 5.2, which comparesabundances of each speciesof rodent in Comer 2 and Comer North before, during and after felling. Consideringthat in August 1990 only 45 insteadof 49 trapswere placedin Comer 2, therewas no obviousreduction in the abundanceof either rodent speciesin Comer 2 comparedto previous months or to the mature control grid (seeFigure 5.2). After felling was completedand until the end of the study in June 1992, the abundancesof both rodent specieswere not significantlydifferent betweenComer 2 and the maturecontrol Comer North (Table 5.3). However, somefiner-scale pattern was apparentif this period was divided in two insteadof being consideredas a whole. In the first year after felling (until Septemberfor woodmiceand October 1991 for bank voles), variation in numbersof both rodent speciesfollowed parallel seasonaltrends in Comer North andComer 2 (Figure5.2); abundanceswere higherin the matureforest control than in the clear-fellingin most cases,although no significantdifference could be established for either species(Table 5.3). However, from October-November1991 onwards, the pattern was reversed:both woodmice and bank voles became more abundant, and significantlyso, in the clear-fellingComer 2 than in Comer North (Table 5.3). Wood mouseabundances in both grids still followed parallelseasonal fluctuations, but bank vole in numbers Comer North reacheda peak in late summer-earlyautumn in 1991 and fell sharplythereafter, whilst numberi in Comer 2 peakedlater (Much 1992) and were still high by the end Ofthe studyin June(Figure 5.2). The three new clear-fellingswere also found to differ from the older control clear- South)in felling (Comer terms of the presenceof bank voles,which were found in Comer 3 from just felling, I, 2 and after but were completelyabsent from comer south until November1991 (Table 5.2). On the other handýall but one of the field voles caughtwithin Complex Comer were capturedin Comer South and not in the newer clear-fellings,(Table 5.2). Thus, the new clear-fellingsapparently had not convergedby the end of the study to the samecommunity composition as shownby Comer South,and for this reasonthe latter grid was not usedas a clear-felledcontrol in comparisonsas originally planned. During trapping in the grassyverges around the Comer Complexin May 1990,3 field in voleswere captured the fines,but nonewas ever capturedelsewhere. In August

105 FIGURE 5.2. Effects of clear-fellingon rodent abundances:numbers of different individual woodmice(top) and bank voles (bottom) caughtin the control grid Comer North (a mature Sitka spruceplantation) and in the experimentalgrid Comer 2, where trees were felled in July-August1990 (time of clearfelling is indicatedas a vertical line). Woodmice

Numbers caught 60

-30 I 20

'10

w FMAMJJASONDJFMAMJJABONDJFMAMJJý- 90 91 92 Time

Control Experlmental

Bank voles

. Numbers caught

Clear-felling 20

cr tý ...... cl n -FMAMJJASONDJFMAMJJASONDJFMAMJJ , so 91 92 1 Time

Control., Experimental

106 1990no field'vole was'caughtamong 7 rodentscaptured in the lines. The populationsof field voles were apparently low in Comer Complex just as they were all around Hamsterleyat that the (Chapter3), and thus this specieswas scarcelyavailable to invade the new habitatafter clear-fellingwas finished.

53.2 - The effects of the removal of brashings on rodent abundances - Figure 5.3 shows the variations in the abundanceof woodmice and bank voles; in the experimental grid Comer, I and the control Comer 2 before and after the tree brashings were removed from theformer grid in October 1990. Before the manipulation, both species were more abundantin Comer 2 than in Comer I in most months (a significant difference was found although for bank voles, not for woodmice; Table 5.3). These results provide evidence of some heterogeneity between the two sites before the brashings were removed. Thus the validity of Comer 2 as a control to Comer I-is open to question and consequently the comparisonsshould be interpreted with care. Having said that, from the first trapping after the removal of brashings (November 1990) until the end of the study in June 1992, the abundanceof bank voles was much higher in Comer 2 than in Comer I (Figure 5.3); the difference was highly significant (Table 5.3). There was a short-lived "pulse" of abundance in of woodmice Comer I in the two months imediately after the brashings were burned, i. e., November and December 1990, but thereafter the species was significantly less in abundant the experimental grid Comer I than in the control Comer 2 (Figure 5.3 and Table 5.3).

The effects of the replication of the experimental removal of brashings, in Comer 3 in October-November 'Before 1991, are'shown in-Figuij 5.4. the experizient, bank voles were again significantly more abundant in the control Comer 2 than in Comer 3, although difference found between no significant was the abundanceof woodmice in the two grids (Table 5.3). Thus the samereservations which apply to Comer 2 as a control to Comer 1, to Comer 2 also apply as a control to Comer 3. After the brashings,were removed bank voles remained significantly more abundant in Comer 2 than in Comer 3 (Table 5.3), but difference the was more marked than before the experiment: bank voles became more in Comer 2 in abundant than any other grid, while none was caught in Comer 3 in the (Table 5.2). The ' same period removal of brashings produced no obviouS' effect on the just before abundanceof woodmice: as the manipulation, no significant difference was found between the in numbers of this species the two grids (Table 5.3). In contrast to had happenedin Comer I in what the'previous year, no short-lived "pulse" of abundance

107 FIGURE 5.3. Effects of removal of tree brashings from clear-fellings on the abundancesof rodents: first replicate. Graphs show the numbers of different individual woodmice (top) and bank voles (bottom) caught in the control grid Comer 2 and in the experimental grid Comer 1. Both sites were felled in July-August 1990, but brashings were removed from Comer I only, in October 1990 (time of removal of the brashings is indicated as a vertical line).

Woodmice

Nvabors eaught 60

Of brashings 40-

10 *a . 10

0 FMAMJJASONDJFMAMJJASONDJFMAMJJ 90 1 91 92 Time

Experimental Control

Bank voles Numbers oaught W.0

0 Of brashInge- il...... 20 »ý, '*«

10 " a ci. `0 .. 6 .

t-MAMJJASONDJFMAMJJABONDJFMAMJJ 90 91 92 Time

Experimental ....I'- Control

109 FIGURE 5.4. Effects of removalof tree brashingsfrom clear-fellingson the abundancesof rodents:second replicate. Graphs show the numbersof different individual woodmice(top) and bank voles (bottom) caughtin the control grid Comer 2 and in the experimentalgrid Comer 3. Both sites were felled in July-August 1990, but brashingswere removed from Comer3 only, in October-November1991 (time of removalof the brashingsis indicatedas a verticalline). Woodmice

Numbers oaught 50

n FMAMJJASONDJFMAMJJASONDJFMAM*J J so 91 Time

... Control -4 Experimental

Bank voles

Numbers oaught zo

0 FMAMJJASONDJFMAMJJASONDJFMAMJJ 1 1 90 Of 1 92 1, I Time

...Control -47 Experimental

109 of woodmice was detectedin Comer 3 in the first months after the. brashingswere, removed(Figure 5.4).

brashings distributions After the 5.3.3 - The effects of the removal of on sPatial - brashings were removed from Comer 1, woodmice and bank voles showed contrasting patterns of distribution within this grid. The distribution of captures of woodmice did not provide a clear evidence of edge cffect: the proportion of captures in the outer traps was higher than expected, but the difference was marginally non-significant (Figure 5.5; X2 = 3.10,0.05 0.50) or bank voles (X2 = 1.24, p>0.25; Figure 5.5). Thus, the concentration of captures of bank voles along the edges of the grids where brashings had been removed was not paralleled either by the same species in a clear-felling with brashings left in place, nor by woodmice in situations where brashings were left or were removed.

5.4 - Discussion

5.4.1 - Validity of the controls: fine scale spatial heterogeneity as a problem for experimental design -A standing Sitka spruce forest looks superficially one of the most homogeneous habitats one could find. Additionally, the study described in Chapter 3 inter-site found greatest heterogeneity amongst young plantations, not mature plantations in or clear-fellings. Yet, at least three cases I found unexpectedly large pre-experiment between dfferences either experimental and control areas or between areas which should have provided replicates of the sameexperimental treatment, as follows. , (1) Before the clear-felling of the two replicate grids Comer I and 2, the abundancesof the two rodent species differed in the two sites, although, the distance between them was only about one hundred meters (Figure 5.1). (2) After clear-felling, the rodent communities in the three recently felled grids (Comer 1-3) in and the older clear-felled Comer South grid differed: bank voles were

110 FIGURE 5.5. Effects of experimental removal of brashings on the spatial distribution of different individuals) in rodents: number of captures (not necessarily of each point of a in Comer I brashings'had been standard trapping grid. Top, bank voles after the removed (November 1990-June 1992); bottom left, woodmice in the same grid and period; bottom Comer 2, brashings during right, bank voles in the control grid where were.not removed,., the sameperiod. I

Bank voles Cornei' 1

12 2

Corner -Woodmice - I ý' Bank voles Corner 2

6 I, I, 17 9, " Iý ý6, -, 3 3 10 6 a

4 4

4 7 4 7 3 10 6 a

is 4 a 4

14 is at 7 4

8 9 a I

10 Is 7 14 7 to found only in the recentclear-fellings but not in Comer South, field voles were found (in' smallnumbers) in Comer South-only. The differencemight have been explicableby the time sinceclear-felling: for example,if bank voles were to becomegradually less abundant with time in the clear-fellings,the pattern would gradually convergeto that of, Comer South.That was not the case.Comer 2,,,where tree brashingswere not removed,,was supposedto be the grid most comparableto Comer South,and yet nearlytwo yearsafter beingfelled Comer 2 showeda very high abundanceof bankvoles. I (3) Bank voles were nearly awaysmore abundantin Comer 2 than in Comer 3 betweenSeptember 1990 and September1991. The two sites were located about one hundredmeters apart, they had beenfelled at the sametime, and in the period mentioned tree brashingswere still presentin both.-, .. -1 1 This patternof great heterogeneitybetween supposedly similar sites,which caused greatproblems for the interpretationof the experiments,,seems to be a consequenceof the fine scalespatial heterogeneity in Hamsterleyas a whole. The study on successionaland spatialvariation in rodent communitiesaround -the whole forest (Chapter 3), had been carried out simultaneouslywith the population study at Comer Complex. Spatial heterogeneityof the rodent communitieswas greatest,among young plantationý'sites, whereany one of the threerodent species(woodmice, bank voles and field voles) could be locally dominant.In matureplantations and clear-fellings,field voles were usually absent andwoodmice were usuallythe most abundantspecies. However, the absoluteabundances of eachspecies were quite variableamong mature plantations or amongclear-felling sites, and,in particular,bank voles were often absentf T orn clear-fellings(see Table 3.3). At Itea for young plantations, this variation, in rodent communities was associated with fine composition and structure of vegetation,, which responded to the i scale spatial heterogeneityof soil types in the forest (Section 3.4.6). Thus, the overall pattern of spatial variation of rodent communities revealed by the simultaneous study carried out in larger spatial scale may explain the first and the third cases of inadequacy of controls described above. The second, i. e., the marked numerical dominance of bank voles in the clear-fened Comer 2 towards the end of the study,-cannot be explained satisfactorily by these factors.

H 5.4.2 Effects - of clear-felling on the rodent populations - An unexpectedresult of this little study was how the sudden,drastic habitat change,produced when'the trees,were felled,seemed to affectthe rodent populations.No obviousreduction in rodent abundance detectedin couldbe Comer 2 in'August 1990,when part of the Comer 2 grid was already felled and part not. Consideringthat in August only 20 traps were set in Comer 1; from being which trees were still removed,rodent densitieswere not obviously reducedby

112 felling in Comer I either (see Tables5A and 5.2).,Harrison (1990) collectedadditional dataon short-termresponses to felling. He trappeda site within Comer Complex(but not within my grids) just after it was clear-felledin Juneý 1990. ý. He failed to capture any animalsin the first week-after clear-felling (in 56 trap nights),but startedto capturethem againas early as the secondweek. As in'the presentstudy, many of the animalsrecaptured by him soon after clear-fellinghad beenmarked originally in the sameplace before the forest was cut. Onemay ask whetherthey had movedout and cameback to their original homeranges after felling'was over, or whetherthey survivedfelling in situ, but were not capturedin the first week becausetheir trappability was reducedfor some reason.My results (Chapter4) suggestthat the trappability of both'woodmice and bank voles,in Comer Complexas a whole was lower than averagearound the time of felling. A, clear directionalpattern in the movements,first awayfrom the areasbeing felled and then back after,a short interval,-could not, be detectedby Harrison (1990). Thus it is not certain whetheror not the survivinganimals survived in situ. However, what is clear is -that the processof clear-felling" either- had - no effect or only a very short-lived effect- on the abundanceof rodents.'!, -ý -1 After the processof removalof the treeswas completed,the effectsof the drastic habitatchange were smallerand more gradualthan expected."In the first year after felling, seasonaltrends in the matureforest control grid Comer North and in the clear-fellinggrid Comer2 were similarfor both rodent species,abundances being only slightly higherin the control. - One,year after the clear-felling had taken place,marked differencesbegan to appear.Both rodent speciesbecame significantly more abundantin the clear-fellingthan in the control, the differencebeing much more obviousin bank voles. The direction of this changein abundancewas contrary to my expectation:I had predicted that woodmice become,dominant to bank in would voles- clear-fellings,-- yet the opposite, gradually in Comer 2. happened Thus the outcomeof the clear-feUingexperiment did not confirm my predictionon the relativeabundances of the two species(Section 5.1.2). in As pointed out Section 5.4.1, the high abundanceof bank,-voles in Comer 2 during first, half 1992 the of was atypicaL-even when comparedwith the ýother clear- fellings(with brashingsleft) trappedin HamsterleyForest at a similartime, i. e.- during the (Chapter June 1992 census 3). Apparentlythe high numbersin Comer 2 in that period were part of a long-termincrease of the bank vole populationin Comer Complexduring (Chapter more than an year 4), plus bank voles having quite rigid habitat preferences (Chapter6) Comer being and 2 a highly preferredgrid by the end of the study. However, the Comer questionremains of why 2 was preferredin relation to aUother grids, including

113 the adjacentmature control Comer North: bank voles were usually more abundantin matureSitka spruceforest than in clear-fellings,in Harnsterley(Chapter'3). It should be noticed that Comer 2 was atypical among clear-fellingsfor being partly located-on a Calluna-Eriophorumblanket bog. This substratummay well be more fertile than the soils of the remaining clear-fellings, which are mostly podzolic (Table 3.1); the soil characteristicscould haveallowed a good growth of ground vegetationin Comer 2 in the secondyear after felling, which could havefavoured the bank voles. However, nearlyone yearafter Comer2 was felled, the groundvegetation there was no more abundantthan in the remaining clear-fellings (Table 3.11). This result is not incompatible with the hypothesisabove, but it doesnot provide supportfor it either. An alternativeview is that the markeddominance of woodmicecould be a-characteristic of recent-clear-fellings, but bank voles would becomeincreasingly common a few years after clear-fellingas ground coverimproves. This view, however,is clearlynot supportedby the patternsshown by the oldestclear-fellings I studied,Comer South (presentChapter) and View (Chapter3). All thingsconsidered, the reasonsfor the uniquenessof Comer 2 are not clearas in this caseit is difficult to disintanglethe effectsof clear-fellingfrom the effectsof spatialvariability. ý

5.4.3 - Removal of tree brashings from clear-fellings: different effects on woodmici and bank voles - As predicted, the removal of brashings from the'recent clear-fellings causedlittle depressiveeffect on the abundanceof woodinice. In the first replicate of the experiment (Comer -1), there was actually a short-lived "pulse" of high abundance of woodmice in the two first months after the brashings were removed. J.L. Butterfield (pers: comm.) suggestedthat the "pulse" could have arisen becauseground invertebrates initially become easier to find on the bare ground, but soon become scarcer in the area by then devoid of most of the decaying" matter which originally supported the invertebrate communities. Woodmice switch their food habits opportunistically to prey mostly on invertebrateswhen seed is scarce either locally or seasonally(Obrtel and Holisova, 1979, Montgomery and Montgomery, 1990, Zubaid and Gorman; 1991; see also Section 4.4.5). Additionaly, any remaining edible seedscould also be easier to find after brash removal. In (1956) California, Tevis found that mice (mostly Peromyscus maniculatus) were more in finding efficient seedsof Douglas-fir (Ps6dotsuga Wifolia) in clear-fellings where the been by burning. slashhad removed Whatever the reason for the "pulse"; woodmice then becameless in Comer abundant I -than in the control Comer 2, but they remained present in the experimentalgrid in considerableabundance. - When the experiment was replicated in Comer 3 in 1991, the "pulse" of abundance did The for is but not occur. reason this not clear it may be linked to the overall wood

114 mousepopulation levels being lower in 1991than in the previousyear, Additionally, the removal of brashingstook longer in 1991 than in the previous,year,, finishing only in November,In November1991 the reproductiveseason of woodmicehad nearly finished andthus the potentialfor populationincrease was reduced(Chapter 4), andby this time of the yearthe activity of manyinvertebrates in clear-fellingsalso decreases(J. L. Butterfield, pers. comm.). From the manipulationuntil the end of the, study, the. abundancesof in Comer 3 lower in Comer woodmice: were not significantly than ,the control - 2. Consideringthe resultsof the two replicatestogether, it seemsthat the removalof the tree brashingsfrom recentclear-fellings did not makethese habitats unsuitable for Apodemus This finding is habitat described ,sylvaticus. consistentwith the preferencesof this species in Chapter3, as an associationbetween woodmice and bare soil was found in the CCA analysis(see Figure 3.8). Woodmiceapparently do not dependmuch on cover for anti- predatordefence. Being strictly nocturnaland avoidingbright moonlight (Kikkawa, 1964; Wolton," 1983),they rely on the cover of darkness,plus behaviouralresponses Eke leaping and freezing, to evade sight-orientedpredators. Furthermore, woodmice can make efficient use of their complexburrow systemsto escapefrom mustelid predators(King, 1985). In the bank depressed contrast-with woodmice, abundanceof voles was severely by the removalof tree brashings.In Comer 1, bank voles were much lessabundant than in the control from the time brashings-were c removed until, the end of the study. The differenceis clear, but ýit is necessaryto keep in mind that bank voles had alreadybeen significantlymore abundantin Comer 2 than in Comer I before the removal of the brashings.Thus, using numbers alone it is difficult to separatethe effects of the experimentalmanipulation from the effectsof spatialheterogeneity. However, the spatial distributionof capturesof bank voles in Comer I indicatesthat they usedthe core of this less grid much often than suggestedby abundanceestimates alone. Most capturesof bank in Comer I voles probablyrepresent edge effect: individualswhich inhabitedchiefly areas (wherebrashings aroundthe grid were left) finding peripheraltraps, but seldomventuring into the core of the grid, wherelittle cover was available. it be should noticed that the brashings-freeComer I grid was quickly invadedby from vegetation spring 1991,the dominantplants by the end of the study being herbslike (Chmnaenerium rosebaywillowherb angustifolium)and sorrel (Rumexspp). However, up the to the end of studythe cover providedby theseherbs was not by any meansas dense the by brashings by as coverprovided or cover plantsprefered by bank voles elsewherein Hamsterley,especially heather (Chapter 3).

115 When the experiment was replicated in Comer 3 in 1991, not a single bank vole was captured there after the brashings were removed. Although bank voles-had already been less abundant in'Comer 3 than in the control Comer 2 before the'experiment, this result reinforces the conclusion that the removal of the brashings makes a recent clear- felling unsuitablefor bank voles. Thus, most of the results of the experiment on removal of brashings, especially regarding rodent spatial distribution, upheld the prediction that the experimentalmanipulation would affect bank voles more than woodmice (Section 5.1.2). This finding is related to bank voles' marked preference for habitats with dense cover (Chapter 3), apparently becauseit provides shelter against aerial predators (Southern and, Lowe, 1968,1982, Southern, 1970). Being partly diumal, bank voles do not make full use of the protection provided by darkness. Additionally, bank voles, are not as agile - as woodmice, and they tend to be flushed out of their underground, burrows by mustelid predators more easily (King 1985). Thus, if no cover is available above ground to prevent bank voles being detected in the first place, they would be very vulnerable to. weasels and stoats as i well. Because of all -these features, the anti-predator defences of bank voles depend strongly on the shelter provided by ground cover, and in recent clear-fellings only the tree brashingscan provide such cover. A: .1 The findings'discussed in Section leaving . this suggest that the policy of tree brashings in situ favours diversity of rodent communities in recent clear-fellings because, although the brashings may be irrelevant for woodmice, they are a necessaryfactor, to allow bank voles to remain in any appreciabledensities in these habitats.

116 CHAPTER 6- RODENT POPULATIONS IN A HABITAT MOSAIC PRODUCED BY FELLING: IN HABITAT SELECTION III - DENSITY-DEPENDENCE

6.1 -Introduction ý-*"ý

The idea 6.1.1 - Density-dependent habitat selection: pattern and, processes-, of density-dependenthabitat selection was apparentlydeveloped independently by SvIrdson (1949),Morisita (1950ýquoted in Rosenzweig,1989) and Fretwell andLucas (1970), and was later given'a fuller theoreticaltreatment by Fretwell (1972), Rosenzweig(1981) and Morris (1987). The basicidea is that at low densitiesanimal populations can "afford" to selectjust the besthabitats but, at high densitiesof conspecifics,part of the populationWill be forced by competitionto utilize suboptimalhabitats as well. As a result, populations would be distributed-more widely among habitats at high population densities,,and conversely,"true" habitatpreferences would be evident only at low populationdensities. Density-dependenthabitat selection theory was originally devised for one-species situations,i. e., the models assumedinterspecific interactions to be unimportant.More recenttheoretical developments have extended density-dependent habitat selectiontheory to includethe effectsof interspecificinteractions (e. g., Rosenzweigand Abranisky, 1986; Rosenzweig,1989). Sincethe 198Us,small mammals have been one of the taxa most often usedin field studiestesting density-dependent habitat selection (e. g. Rosenzweigand Abramsky,1985, 1986; Morris, 1987; Abramsky, 1989; Montgomery, 1989c; Abramsky el al.; ý199010 Messierel al., 1990). The model has been found to apply to some speciesand not to others;even among congeneric species, some may be density-dependenthabitat selectors and othersnot (Rosenzweigand Abranisky,1985). The ýstudies listed above sought to detect density-dependenthabitat selectionby the patternit is expectedto produce,i. e., populationmore widely spreadamong different habitatsat high densitiesthan at low densities.Presumably due to the high mobility of many animals,theorists have usually assumedthat this pattern is brought about by non. random movementsof individuals. An "evolutionary astute" individual (Rosenzweig, 1981)would displaythe optimalbehaviour of moving towardsthe habitatwhich best suits it at any given population density; it, has been,assumed, that the directional population fluxes (emigration/immigration) resulting from this behaviourare the' most'important,or

117 the only cause of the observed pattern (e.g. FretweU, 1972; Abramsky and Van Dyne, 1980; Rosenzweig, 1981). Arguably, the pattern expected from density-dependenthabitat selection based on active movementsamong habitats could also be produced by an alternative model, arising from differences among the local (i. e. habitat level) demographies in an habitat mosaic. The reasoningis as follows. At times of low overall population'density, numbers are higWy variable spatially:, some habitats are "poor" and other "rich" (i. e., have low and high densities respectively). When the population increases, it increases more in the "poor" habitats than in the "rich", not because of emigrationrimmigration but because survival and/or recruitment improve more in the poor habitats. When the population decreases, conversely, survival and/or recruitment deteriorate more sharply in the grids which originally had low densities, so restoring the original pattern. This process would be expected if (1) there is a link between population density and abundanceof resources and (2) spatial distribution of resources is more aggregated when they are scarce than when they are abundant.This model would produce a pattern of animals more widely distributed among habitats at times of high population densities, i. e., the same pattern usually associatedwith density-dependenthabitat selection. In a strict sense,the pattern should be , called habitat selection only if it was generated by movements, but for convenience I follow the literature in calling the pattern "density-dependenthabitat selection" throughout this Chapter, regardlessof which process generatesit. To my knowledge it has never been tested which one of, the two alternative mechanisms described above, or which combination of the two (they are not mutually exclusive) causesthe observed pattern of , density-dependenthabitat selection in small mammals.

6.1.2 - Assumptions and aims of the present study - The present Chapter discussesthe results of a study on two rodent species- the wood mouse Apodemus sylvalicus and the bank vole Clethrionomys glareolus - within an habitat mosaic in Hamsterley Forest. As describedin Chapters 4 and 5, this habitat mosaic presented spatial variation (part of the area was composed of mature Sitka spruce plantations and part of clear-felfings) and temporal variation as well (part of the felling took place during the study). However, in the presentChapter I am not concernedwith the nature of the habitat variation, which was discussedin Chapter 5. Here I only make the assumption that habitat variation e)dsted throughout the study, and I try to measure how the population densities of each rodent ' speciesaffected its responseto this habitat variation. In dealing with each species separately, I make a second assumption, i. e., interspecific interactions between the two speciesare weak, so that habitat preferencesare

118 by density more likely to be affectedby the densityof conspecificsthan the of, the other bank (Geuse species.This is probablya reasonableassumption for woodmice and voles andBauchau, 1985; Gumell, 1985;present study, Chapter3). The aimsof the studydiscussed in the presentChapter were as follows. density- (1) To evaluatewhether habitat selectionby each of the rodent specieswas dependentor not. (2) If either speciesshowed density-dependent habitat selection,to establishwhether this by differencesin patternwas causedby directionalpopulation fluxes amonghabitats, or local demographies,or by a combinationof both.

6.2 -Methods

The Comer Complex, 6.2.1 - Trapping design and trapping programme - study area, (Comer North was described in Section 4.2.1. Five standard trapping grids 1,2,3, and South) were trapped within this area; the position of the grids in relation to each other and to the changing habitat types was shown in Figure 4.1. The habitat changeswhich took in place in Comer Complex during the study were discussed Chapter 5. Trapping was performed in Comer Complex monthly from May 1990 to June 1992. The trapping programme was describedin Section 4.2.1.

6.2.2 - Estimation of demographic parameters of populations and subpopulations - Population sizesfor the whole Comer Complex were estimated by the Manly-Parr method (Manly and Parr, 1969). Estimates corrected for number of grids and uncorrected estimateswere both calculated. Although habitat selection is expected to be related (if at all) to population density and not to population size, population size estimateswere used as an index of population density. All these aspectswere discussedin Section 4.2.2. Throughout this Chapter, "population" applies to animals living in the whole area of the Comer Complex, while "subpopulation" applies to animals living in one given grid. Estimates of size of subpopulitigns were given by number of different individuals dicussed in Section 512.2. Mortality for the, i captured, as . rates subpopulation of a grid (Mi) were estimatedby:

MI =I- Pinini'

119 Where Pmin is minimum survival, following Chitty 1and Phipps (1966), and Montgomery (1980), as describedin Section 4.2.2. Per ,, capita recruitment rates at time t were estimated as simply the number of new juvenile individuals appearing in the subpopulation between t -1 and t, divided by subpopulationsize at t, just as was done for the whole population (Section 4.2.2). Althoughthe intervalbetween trapping sessionswas not constant,all survivaland recruitmentrates were transformedto 30-day rates (i. e. survival and recruitmentover a periodof 30 days)using the logarithmictransformations described in Section4.2.2. ,,

6.23 - Habitat selectionand its relationship with population sizes- The relationship betweenhabitat selection and population sizes was studiedusing two differentmethods. (a) The first I derivedfrom IvIev's "electivity" index QvIev, 1961,quoted in Krebs, 1989) and hereafter,is called modified IvIev's method.- IvIevs approachwas devised originally for dietary preferences,but the unmodifiedindex has been found suitablefor studiesof habitatpreference as well (Bowden, 1990; Grant et aL, 1992). In the present studyI usedgrids as the basichabitat units; grids usuallyrepresented a singlehabitat type at a time, if habitatis definedeither by successionalstage or by experimentalmanipulation (Chapters4 and5). The value of the index for eachgrid is givenby:

Ei A- Mi) (Ri + Mi)

Where: -,,

Ei = IvIeVs index for grid L individuals Ri = percentageof caught in grid i among the total caught in the m grids. Mi = percentageof availability of grid i among the m grids.

The parameterRi is the subpopulationsize estimate for eachgrid; m is the number of grids that were trappedin the month under consideration;Mi is given by percentageof in trappingeffort spent grid i, in relationto total trapping effort spentin the m grids. In the presentstudy the trappingeffort was the samein eachgrid in everymonth exceptAugust, 1990(see Section 5.2.1). The index Ei is completelybased in proportions, and thereforethe index itself is density-independent. It varies from -1 (no individual caught at i) to I (all individuals i); caughtat the higherthe absolutevalue of Ei, the strongerthe preference/avoidance.

120 I modifiedIvIevs methodto evaluatethe relationshipbetween habitat selection and populationdensity by defining a new parameter,the overall degreeof habitat selection (Eo), which is givenby: , .1

Eo m

WhereI Ei I is the absolutevalue of Ei. Eo was relatedto populationsize, N, by,a simplelinear regression.A significant,negative slope of this regression,would indicate density-dependenthabitat selection. Conversely, a significantpositive slopewould indicate a morerestricted habitat utilization at high densities,i. e., inversedensity-dependence. (b) The data were also analysedusing the regression method proposed by Rosenzweigand Abramsky(1985) and used to study density-dependencein the habitat selectionby gerbilsin Israel'sNegev desert(Rosenzweig, 1989; Abramskyet al.,, 1990). Resultsobtained by this method and by the modified IvleVs method,were compared, Rosenzweigand Abramskys method is basedon their modifiedSimpson index, given by-

y= (m.N*. y) -m+I- N*

Where: y Simpsotfs diversity index n- 2- N*2 ni = population size at habitat i m= numberof habitats N* = Ym ni (calledsimply N by Rosenzweigand Abramsky-, here I call it N* to, differentiatefrom the Manly-Parrpopulation size estimate, N).

The Simpson'sindex, is modified y, regressedagainst N* - 1. The technique in createsa space which habitat selectivitiesare given by the slopesof either one single line, lines straight or severalstraight which intersectat the origin. There are three kinds of follows. possibleresults, as (1) If there is no habitat selectionat all (i. e. the speciesis distributed be at random),y' valueswill scatteredaround a line parallelto the x axis, i.e. (2) If is density-independent with slope zero. there habitat selection,the plot will, again diagram,but producea singlestraight-line scatter this time with a constant,positive slope. (3) If thereis density-dependenthabitat selection,the scatterdiagram will form a complex function fines comprisinga sequenceof three straight connectedto eachother. The points

121 follow a steep positive slope from -the origin until 'some threshold population size'is reached.The slope then changesto negative as habitat selection decreasesas a function of the increasedpopulation size. A second inflexion then occurs leading to a third straight line, which may reach a second, less steep positive slope reflecting the disparate ability of the habitats to support the species. This expected curve is shown in Rosenzweig and Abramsky (1985) and Rosenzweig (1989) and also in the present Chapter, where it is comparedwith my results (see Figure 6.3.,bottom). ' Rosenzweig and AbramsWs method assumesthat proportions of trapping, effort spent in each grid, each month are constant. Thus, estimates for August 1990, when trapping effort was reduced in grids Comer I and Comer 2 (Section 5.2.1), could not be calculatedusing this method.

6.2.4 - Inter-grid movements and the estimation of emigration and immigration rates - Movements between grids were detected by recaptures, in any grid, of individuals previously captured elsewhere.For. rodent species actually showing a pattern of density- dependent habitat selection, such inter-grid movements were used to test whether there was a relationship between the direction of movements and population densities in each grid, as expected if the pattern was causedmostly by non-random movements. Frequency of movementswas related to subpopulation sizes in the grids of origin and destination of each movement, by using IvIevs Ei indices to classify grids as holding either high numbers low (Hn) or numbers (Ln) at time t, if they had respectively positive or negative values of Ei at time t. Movements involving grids with average numbers (Ivlev = 0) were discarded from the analysis.For each season,the frequency of each possible type of movement (e.g from Hn a grid to a Ln grid) was calculated by dividing number of recorded movementsby individuals number of available to move. In the example given, this was done by dividing Hn Ln the number of to movements by the total number of dfferent individuals present in all Hn grids during the seasonunder consideration. Emigration immigration and rates for grid i at month t were calculated as follows: frequency inter-grid of movementsfrom and towards i respectivelly, in the period from t- I divided by in i to t, population size grid at W. Values were converted to 30-day rates the logarithmic transformation describedin Section using 4.2.2. ýII--I -I- I1 11

6.2.5 Relationship between - changes in spatial distribution, and the demographic the parameters of subpopulations - The processes accounting for the changes in the spatial distribution within the by population were studied relating variation in the size of

122 subpopulationswith estimates-of recruitment,mortality, intrnigration'andemigration for eachsubpopulation. Variation in the sizeof subpopulationswas givenby:

Nij- dNi,t = (Ni.t. - 1) Ni, t-I

The resulting values were converted to 3O-day rates,.using the, logarithmic , transformations describedin Section 4.2.2, and then related to the remaining demographic parametersfor subpopulationsusing the following multiple regressionmodel:

dN4.t = a.lý-, t - b.MLt + c.Ii, t - d.ELt +e

Where:

Rj't, MLt, Itt, El't = recruitment, mortality, immigration and emigration rates respectively, for grid i at time t (see Sections 6.2.2 and 6.2.4). a,b, c,d, e = fitted regressionconstants.

The relative importanceof the four demographicfactors can be assessedby comparingthe partial regressioncoefficients of eachfactor. As the goal was to understand the processeswhich accountedfor variation in the spatial distribution within.the whole Comer Complex,data were pooled for all grids. However, the regressionanalysis was performedseparately for casesin which subpopulationswere increasing(dNt > 0) and decreasing(dNt < 0), becausethe relative importanceof the demographicfactors is likely to be different in the two situations.Cases in which dNt =0 (i. e. absenceof variation in the dependentvariable) were discardedfrom the analysis.

6.3 - Results

63.1 The betweenhabitat - relationship selection and population sizes- The number of woodmiceand bank voles actually caughtin each grid and month is shown in Tables 5.1 and 5.2 respectively (Chapter 5). From those data IvIev indices for individual preferencelavoidanceof grids (Ei) plus overall degreeof habitat selection(Eo) for (Tables6.1 were calculated rodent species and 6.2). The Ei indices showedthat no by grid was consistentlypreferred or avoided woodmicethroughout the study,although in

123

A- habitat indices for TABLE 6.1 - Monthly variations of IvIevs selection individual trapping degree habitat, in (Eo, last grids , (E-)I plus the overall of selection the whole study area column) for Apodemussylvaticus.

Time Col(M-F) Co2(M-F), Co3(F) CoS(F) CoN(M) OveraU Ei Ej El Ei , Ei , (Eo) , _ 1990 May -0.25 0.14 -0.02 0.04 0.113 Jun -0.40 0.13 0.13 0.00 0.165 Jul FeUing Fefling - - w Aug 0.05* 0.24* - . 0.38 -0.05 0.180 Sep 0.29 -0.20 0.15 -0.55 -0.07 0.252 Oct BrashRem 0.02 0.00 0.00 -0.02 0.010 Nov 0.19, -0.07 0.17 -0.23 -0.23 0.178 1991 Mar 0.01 0.,09 -0.09 -0.57 0.25, 0.202 Apr -0.02 -0.27 0.07 0.07 0.07 0.100 May -0.42 0.10 0.04 -0.10 0.20 0.172 Jun -0.71 0.08 -0.26 -0.33 0.44 0.364 Jul -0.22 0.09 -0.04 -0.15 0.20 0.140 Aug -0.23 0.06 0.00 -0.10 0.18 0.114 Sep -0.33 0.06 -0.02 0.11 0.08 0.120 Oct -0.06 0.07 BrashRem 0.10 -0.15 0.095 Nov -0.02 0.00 'BrasbRem 0.12 -0.12 0.065 Dec 0.01 0.10 -0.42 0.22 -0.10 0.170 1992 Feb -0.31 0.22 -0.12 0.14 '-0.12' 0.182- Mar -0.13 -0.02 0.27 -0.02 -0.27 0.142

Jun 0.21 -0.13 0.07 -1.00 0.21 0.324

Note. Successionalstages of the grids are: M, mature; F, clear-felling.Values for mature grids are shownin bold; clear-fellingsin standard;experimental (brash removed) plots italics. Ei's are basedon data from Table 5.1. For each month, Eo is given by the averageof the absolutevalues of Eýs. Asterisks (*) indicatescases when incompletegrids were trapped duringfelling.

124 IvIevs habitat indices.for individual TABLE 6.2 - Monthly variations of selection trapping grids (Ei) plus the overall degree of habitat selection in the whole study area (Eo, last column) for ClethrionomysgIareolus-All symbols as in Table 6.1.

Time Col(M-F) Co2(M-F) Co3(F) CoS(F) CoN(M) Overall Ei Ei E, Ei E, (Eo)

1990 - May 0.14 0.14 -1.00 0.14 0.355 Jun 0.38 0.14 -0.38- -1.00 -0.475 Jul Felling Felling 0.25 0.488 Aug -0.46* 0.24* -1.00 A444 Sep -0.20 0.30 -0.58 -1.00 0.14 Oct BrashRem. 0.25 -1.00 -1.00 0.36 0.653 - Nov' 0.00 0.20 -1.00 -1.00 0.43 0.526 1991 , 1`6.60 Mar -1.60 0.32 -0.56 -1.0 0 0.47 Apr -0.41', 0.43 0.11 -1.00 -0.09 0.408 May -0.52 0.37 -0.52 -1.00 0.37 0.556 Jun -0.47 0.03 -0.47 -1.00 0.53 0.500 jul 0.05 ý-0.16 ý, -1.00 -1.00 0.33 , 0.508 Aug -0.23 -0.23 -0.41 4.00 0.54 0.482 Sep -0.62 0.09 -0.17 -1.00 0.48 0.472 Oct -0.47 0.19 BrashRem -1.00 0.37 0.508 Nov -0.68 0.39 BrashRem -0.68 0.14 0.473 Dec -0.68 0.60 -1.00 -0.68 --0.27 0.646 1992 Feb -0.43 0.58 -1.00 -0.67 -0.25 0.586 Mar -0.18 0.58 -1.00 -0.71 -0.49 0.592

Jun -0.71 0.54 -1.00 -0.71 0.14 0.620

Note: Basedon datafrom Table5.2.

125 most caseseach grid remainedin either categoryfor severalconsecutive months (Table 6.1). For bank voles preferences/avoidancesof individual grids, as shown by Ei, were stronger and more consistentthan for woodmice. Comer North and Comer 2 were consistentlyprefered, and Comer I and Comer South were consistentlyavoided (Table 6.2). Indeed,no bank vole was capturedin Comer South beforeNovember 1991. Habitat selectionby bankvoles was strongerthan by woodmice,as Eos were significantlyhigher for the formerspecies than for the later (t test, 19 d.f, t= -14,088,p-< 0.0001). For both species,overall degreeof habitat selection(Eo) was regressedagainst population sizesusing both corrected and uncorrectedpopulation size,estimates, and results using both types of estimateswere compared.In both cases,regressions were significantor not regardlessof which set of population size estimateswas used. All the resultsthat follow are basedon the original (uncorrected)estimates. The corresponding resultsbased on correctedestimates are presentedin Appendix3. For woodmice,variation in EOwas inverselyrelated to variation in populationsize (Figure 6.1). The linear regressionof Eo againstpopulation size was significant(Figure 6.2) andits slopewas negativeand significantlydifferent from zero (t = -2.273, p<0.05). Variation in populationsize explainednearly one quarter of the variancein the degreeof habitat selectivity(r2 = 0.233). Theseresults indicate that there was density-dependent habitatselection in woodmicewithin the ComerComplex. No conclusiveresult could be obtainedby applying Rosenzweigand Abramsky's methodto woodmice(Table 6.3). The linear regressionof the modified Simpson!s indices Y againstpopulation sizes (N* - 1) was not significant(Figure 6.3, top). Thus, there was no evidencefor either the hypothesisof no habitat selection at all', (which would correspondto a constantslope of zero) or the hypothesisof density-independenthabitat selection(constant, positive slope). However, the ýscatter of the points'was also not obviously correspondentto the shapeexpected for density-dependenthabitat selection (Figure 6.3, bottom). Comparingthe two halves of Figure 6.3, it can be' seenthat my results had a superficialsimilarity with the expectedpattern, but there were also three differences.First, there was no straight fine with steep positive slope starting from the Second, origin. there was little evidenceof a secondstraight line showing the gradual declineof the y's valuesafter they reachthe maximum,which would reflect the density- dependence.The , maximumy, looks more like an isolated point than like a threshold. Third, therewas no evidenceof a secondpositive slopeat all. In summary,for woodmice Rosenzweig Abramsky's and methoddid not allow me to distinguishconclusively between the three hypothesis: habitat competing no selection,densitY-independent habitat selection, anddensity-dependent habitat selection.

126 It 04 e7!;

1--ý Ici

LL x (a 0 4)

. tz Z 10 rA

*.= ti

0 r= .N

0 >

02 ci, M Ei 0. LL 0 0= e:.o Z CL IL0 15 ce .r ýe Co Co - -% e 0;; -<0 ý6 0 - .-30)

10 73 3 CL 0 CL IRMP NWF %-f %-0 %-# %»p 40 Ki Co CM 0 Co

127 1 CO

>%4) CM CD Iii IN iz = r- (D 10 10 1 E3. co 4) >%r. -, 7 4- JD 0

r= 0 CM 0 4) Zu .Nm

UJ r- .0Z Co Co m 0 ce 0 **-

43 CA rA

Co 0 CO 0 =. Q

-0.0 »-2 ce CO :3 > CL CL) cu 41 0

0 mCo x1 CD 1 *c3 i 0 CM

(D > -j (:) It tv) cm 0 c; 6 C;

129 TABLE 6.3. Values for Rosenzweig and AbramsWs regression method apphed to habitat selectionin woodmice. y are values for the Simpson index based on data from Table 5.1, and y' are the corresponding values of the modified Simpson index (see text). N* representsthe sum of the numbersof individuals in all grids.

Time y 3e N*- I

May 0.2672 -0.7296 32 Jun 0.2806 0.4276 27 1990 Jul Not calculable Aug Not calculable Sep 0.2574 10.9240 51 Oct 0.2502 -2.9624 91 Nov 0.2265 9.6475 102

Mar- 0.2414 7.1780 53 Apr 0.2101 -0.2687 25 May 0.2272 1.0320 36 Jun 0.3461 39.8300 59 1991 Jul 0.2203 7.0635 108 Aug 0.2153 4.4915 110 Sep 0.2143 4.5800 119 Oct 0.2594 2.4520 144 Nov 0.2573 -0.3136 91 Dec 0.2297 6.9890 73

Feb 0.2299 -1.1595 is 1992 Mar 0.2308 0.0004 25

Jun 0.2663 0.3095 13

129 FIGURE 6.3. Top: Scatterplot of the variation in wood mouse population sizes against breadth habitat by Simpsorfs indices; both of utilization measured modified - parameters estimated according to Rosenzweig and Abramsloys (1985) method. Bottom: an example of the typical pattern for a density-dependenthabitat selector-,-redrawn from Rosenzweig, 1989.

0.011IN - 1) *'4.063 40 13 F 0.042, p-0.841

30 -

10- 13 13 13 13 13 13 0u E3 13 13

so 100 120 140 0-, '20 , 40 60 - - 160

30 -

20-

10-

-io 0 60 100 160 200 Gerbillus allenbyi

130 For bank voles the m dified IvIevs index EO seemedto actually increaserather than decreasewith populationsize (Figure 6.4). This would produce a regressionwith a positiverather than negative slope; however, the regressionwas not significant(F = 3.321, p=0.086; Figure6.5, top). Furthermore,ornitting the May 1991point (which is basedon data from 3 individualsonly) the fit of the regressionwas very poor (F = 0.739, p 0.408).Thus, in contrastwith the findingsfor woodmice,the use of IvIev'smodified index did not provide evidencethat habitatselection in bank voles within Comer Complexwas density-dependent. Analysisof bank vole habitat selectionby Rosenzweigand AbramsWs method (Table 6.4) confirmedthe patternsrevealed by the modified IvIevs method.There was a highly significantregression between y' and (N* - 1); the slope of this regressionwas positive and significantlydifferent from zero (t = 6.790, p<0.001; Figure 6.5, bottom). Thus both methodsprovided evidence of strong density-independenthabitat selectionby bankvoles within the Cotne, Complex.

Out 1,931 6.3.2 - Frequency of inter-grid movements - of recaptures of woodmice within the Comer Complex, 294 revealed inter-grid movements. For bank voles, only 24 inter-grid movementswere detected, out of 430 recaptures of individuals of this species. Thus, woodmice moved between grids significantly more often than bank voles (X2 27.243, p<0.0001). The numbers of inter-grid movements recorded for each species in each,month, and with grid of origin and of destination, are shown in Tables 6.5 and 6.6. The first clear pattem is that movements between Comer North and Comer 2 were more frequent than between any other pair of grids. These two grids are among the closest to each other, although distancesbetween Comer I and 3 and between Comer I and Comer South were similar (Figure 4.1). Almost all inter-grid movements of bank voles were restricted to the Comer North-Comer 2 pair, these two grids being those where bank vole population sizes were highest; however, no significant difference was found between the directions moved within this pair (Table 6.6). As compared to bank voles, woodmice showed many more movements between pairs of grids other than Comer North-Comer 2 (Table 6.5). However, no significant Merence, was found in the frequency of movements in the two directions within any pair. As density-dependenthabitat selection could be demonstrated only for woodmice, directionality analysis of the of the movements and of their importance for spatial distribution was carried out for this species only. Frequencies of movements from low (Ln) high numbers to numbers (Hn) grids and vice-versa were compared by chi-square

131 Co Co le CM

M r_ «a

*C 0 < CM > E 4) L, > ce U.

x -0 -z -0, «o CA 2> Cf) .ce -

Q0 N 02 0=

jm = 92.-8 0 CD.4' Co 0 r= LL > cu -% .Ur_ c m LU JD .w c4.w = 00 CL z 0 IL

CO 0 - CO) <0 Co - 0 - -. 3 0)

'I-.

132 FIGURE 6.5. Scatterplotsshowing the relationshipbetween population sizes and habitat selectionin bankvoles as shownby the modifiedIvIeVs method(top) and by the Rosenzweig andAbramsWs method (bottom). The regressionline which best fits the datausing the latter methodis shown.Symbols as in Figures6.2 and 6.3 respectivelly.

IvIev's Index U. 1

0.6

0.5 13 1:3 13

0.4 Cl 0.3

0.2 Eo - 0.0034. N 0.4372 0.1 F, o 3.321,0.086

0 0 10 20 30 40 50 Population size

y. -- Du 7-7 C3 so Y's 2.123. (N - 1) - 16.316 Fa 46.110, Pc0.001 C3 0 40

20 13 13 E3 0 10 Cl

rl 0 13

-10 10 is 20 25 30 N* -I

133 TABLE 6.4. Values for Rosenzweig and AbramsWs regression method applied to habitat selectionin bank voles. Based on data from Table 5.2; all symbols as in Table 6.3.1

ime y Y, N* -i

May 0.3333 -2.0004 2 Jun 0.4321 3.5556 8 1990 Jul Not calculable Aug Not calculable Sep 0.3511 7.3325 14 Oct 0.5125 16.9500 18 Nov 0.3800 5.0000 9

Mar 0.4630 19.6700 17 Apr 0.4050 7.2750 10 May 0.3906 11.2480 15 Jun 0.4694 14.8580 13 1991 Jul 0.3765 11.8850 17 Aug 0.4826 29.9120 23 Sep 0.4059 17.6195 20 Oct 0.4380 13.5440 21 Nov 0.4422 13.1448 20 Dec 0.6686 56.9180 25

Feb 0.6000 46.0000 24 1992 Mar 0.6005 54.0725 28 Jun 0.5178 43.6700 29

134 ' Comer CompleTx. --7 TABLE 6.5. Inter-grid movementsofApodemus sylvaticus within the

Month of Pairs o grids N-2 N-3 N-S 1-3 I-S 2-3 2-S 3-S arrý ý N-1 -1-2 new grid 1990 Mar 1/0 Apr 0/1 May 2/0 0/1 Jun 1/1 Jul 0/2 - Aug 510 0/1 0/2 Sep 0/1 0/1 - 0/1 1/6 Oct 1/0 4/1 Nov 1/1 015 4/1 1/2 0/1 Doc 211 013

1991 Feb 0/2 2/2 Mar 0/8 3/1 1/0 0/1 Apr_ 1/1 2/3 3/0 May 213 1/0 0/1 0/1 Jun 2/5 1/1 0/3 Jul 7/11 2/0 113 2/1 Aug 0/1 8/8 0/1 1/1 1/0 3/1 2/2 Sep 510 1/1 1/1 1/2 213 0/1 Oct 0/1 8/14 0/1 013 9/0 0/1 1/0 2/0 Nov 8/11 1/0 0/1 10/1 Dec 8/6 4/9 1 1 1992 Feb 2/2 1/0 0/1 0/1 Oil Mar 3/2 0/1 1/1 1/0 1/0

Totals 0/3 63n5 0/1 2/1 4/6 14122 31/19 11/15 1/2 13/14

X2 NC 0.877 NC NC 0.100 1.361 2.420 0.183 'NC 0'.000 (P) (>.25) (>.75) (>. 10) (>. 10) (>.50) (>.90)

Note. The first numberrefers to movementsin tne direction in wtucn the grids are qýoted in the top fine of the table, and the secondnumber to movementsin the"oppositedirection. Blank spacesindicate cases where no movementwas detected;traces (-) indicatecases where no movementcould be detectedbecause either grid of the pair was not being trappedat the time. Last fine: resultsof the chi-squaretest comparingthe frequencyof movementsin each directionwithin eachpair of grids; all chi-squaretests with Yates!correction for continuity; NC = not calculable.

135 . TABLE 6.6. Inter-grid movements of Clethrionomys glareolus within the Comer complex. All symbolsas in Table 6.5.1

Month of Pairs grids 7N-IFT arrival in N-2 N-3 N-S 1-2 1-3 I-S 2-3 2-S 3-S new grid 1990 Mar Apr May Jun Jul Aug Sep Oct 1/0 0/1 Nov 0/1 Dec

1991 Feb Mar 0/2 Apr 2/1 1/0 May 1/0 Jun 0/1 Jul Aug Scp Oct 1/0 Nov 1/1 0/1 Dec 3/1

1992 Fcb 1/1 Mar 1/0

Total, 0/0 12/9 0/1 0/0 1/0 0/0 0/0 1/1 0/0 0/0

X2 NC 0.450 NC NC NC NC NC NC NC NC (>.50) 1 1

136 tests (Table 6.7) and shown as a histogram in Figure 6-6. In 1990, frequencies of movements in the two directions were similar in spring, but movements*from Ln to Hn grids were significantly,more common than movements in the opposite direction'during the summerof 1990 (X2 = 8.431, p<0.0 1), a time of low overall population size. During autumn 1990 and winter 1991, when overall population sizes were high for most of the time, movementsfrom Hn to Ln grids seemedto be more common than vice-versa, but the difference was not significant. In spring iggi, with low overall population sizes, movementsftom Ln to Hn grids were again significantly more common than movements . in the reversedirection (X2 = 5.222, p<0.05). This sametrend was apparent through the rest of 1991 (with high population sizes) but especially marked towards the end of the study in spring 1992 (with very low population sizes); however, in neither casewere there significant Merences.

6.3.3 -' Demographic parameters of subpopulations and their relationship with variations in numbers at grid level - Thirty-day per capita rates of variation in the number of woodmice caught at each grid in each month (dNi, t) are shown in Table 6.8. Corresponding mortality (Mi, t) and recruitment (Ri, t) rates are shown in Table 6.9, and emigration (EýO and immigration (Ii, t) rates in Table 6.10. Distributions of the demographic rates were not normal and a x! = log (x + -1) transformation was applied before regression analysis. The multivariate regression of log (dNi, t) against the logarithm of the four demographic rates produced highly significant' equations both at times of population increase (dNi, t > 0) and at times of population decrease(dNLt < 0). During population increase, recruitment and immigration showed significant positive partial regression coefficients (Table 6.11). Mortality had a significant negative coefficient and the partial coefficient for emigration was not significant. In order of proportion of variance explained (measured by r2) recruitment was by far the most important followed variable, by immigration and mortality in this order. During population decrease,mortality and emigration had significant negative partial regression coefficients (Table 6.11). Recruitment had a significant positive coefficient and the partial coefficient for immigration was not significant. Mortality explained most variance, followed by recruitment and emigration in this order.

137 TABLE 6.7. Numbers of movements of Apodemus sylvaticus as a proportion of number of individuals available to move. Movements classified according to the IvIevs indices (Ei) of the grids involved (prefered (+) or avoided (-)) and seasons.

Type of movement Season(n) -to- + to + -to+ + to - X2

1990 Spring(79) 0/24 2/55 -1/24 2/55 0.277NS Summer(I 10) 0/37 2/73 8/37 2/73 8.431 ** Autumn (247) 1/108 11/139 4/108 12/139 1.692 NS

1991 Winter (77) 0/35 8/42 2/35 4/42 0.038 NS Spring (177) 2/57 16/120 7/57 3/120 5.222 * Summer(400) 8/143 40/257 13/143 13/257ý 1.840 NS Autumn (432) 5/164 22/268 31/164 35/268 2.251 NS

1992 Winter (119) 7/43 4143 5n6 0.032 NS Spring (39) 2/19 in61/20 3/19 0/20 1.559 NS I

Note. For eachseason, (n) indicatetotal numberof individualsavailable to move (seetext). Movementdata from Table 6.5. Classificationof the grids is based on their IvIevs index (Table6.1). Chi-square frequencies testsare comparisonsof of individualsmoving - to + and + to - only (I d.f).

139 0V cy

>> CM ci >ýv 0 -0 Q c + Z0 .- CA J9

CL) CL) c 0 = -0 0 >> 0 (U 0-0 (1) 4n ». E Co ý+, - 4) *.m j--a c,. -, -0 CI) cri 0 b- .i-- r r= U.0 Cb ihm ýhCo -0 mU 0 < E

000 oft. 0 . 0 0) LU.2 cm0 r=-0 -4.d C -ci3N>< 0 CD-ý 0 cr

U-

lo 4 CY to C%jto C;

139 TABLE 6.8. Estimates of 30-day rates of variation in numbers of woodmice (Apodemus sylvaticus) caught in each grid within the Comer Complex. Each value expressesvariation in numbers (data from Table 5.1) over a period of 30 days, as a proportion of the numbers by the start of the period. Increases and declines in numbers have positive and negative signs respectively. Symbolsas in Table 6.1.

Time Col(M-F) Co2(M-F) Co3(F) CoS(F) CoN(M)

May +0.208 -0.128 - - Jun -0.600 -0.273 +0.075 -0.133 Jul Felling Felling - 1990 Aug - - 0.000 +0.142 Sep +1.090 -0.765 0.000 +0.500 Oct BrashRem +1.860 + 0.643 +6.667 +1.444 Nov +0.369 -0.441 +0.460 -0.543 -0.511

Apr -0.606 -0.525 -0.370 -+-1.580 -1.053 May -0.272 +1.363 +0.227 0.000 +0.568, Jun -0.343 +0.574 -0.129 0.000 +1.475 1991 Jul +5.455 +0.779 +1.689 +1.563 +0.091 Aug 0 -0.052 +0.137 +0.179 Sep -0.043 -0.102 +0.057 +0.032 +0.667 -0.125 Oct Z500 + +0.833 BrashRem +0.538 -0.033 Nov -0.335 -0.485 BrashRem Dec -0.284 -0.345 -0.434 -0.297 -0.292 - 0.3 10 -0.500

1992 Feb -0.377 -0.290 -0.238 -0.305 -0.293 Mar +1.070 -0.178 +2.143 0.000 0.000

140 TABLE 6.9. Estimates of 30-day mortality and recruitment rates (i. e mortality and in Comer over a penod of 30 days) for Apodemus sylvalicus each grid within the recruitment by first Complex. In each pair of values mortality and recruitment, are given the and second values respectively.

Time Col(M-F) Co2(M-F) Co3(F)_, CoS(f) CoN(M)

1990 may 1.000/0.418 1.000 /0.448 - Jun 0.748/0.300 0.858/0.682 1.00010.300 0.78410.467 Jul Felling Felling " 1ýf, Aug 0.937 /0.833 0.654/1-500 Sep 0.249/ 1.769 0.711/ 0.394 0.400 /0.667 0.117/0.167 Oct BrashRem 0.426/2.000 0.500/0.571 0.500 /6.333 0.667/1.667 Nov 0.06710.437 0.823/ 0.294 0.149/0.460 0.691/0.217 0.719 /0.170

1991 Mar Apr 0.76410.202 0.497/0.000 0.594 / 0.123 0.827 1.577 0.469/0.000 may 0.53010.410 0.000/1.360 0.377 / 0.568 0.532/0.568 0.265/0.908 Jun 0.20110.343 0.712/0.804 0.129/0.259 0.767 /0.675 0.222 /1.105 Jul 0.465IZ725 0.399/0.844 0.291 / 1.559 0.313 / 1.875 0.431/0.333 Aug 0.61110.584 0.394 0.420 0.61110.682 0.489 /0.536 0.39210.392 Sep 0.32910.255 0.333 0.372 0.313/ 0.260 0.316/0.889 0.303/0.219 Oct 0.51712.125 0.350/0.778 BrashRem 0.722 /1.000 0.40310.163 Nov 0.61910.201 0.785/ 0.076 BrashRem 0.685 /0.133 0.633/0.115 Dec 0.46010.124 0.442/ 0.093 0.18710.034 0.414 /0.103 0.757/0.167

1992 Feb 0.69210-000 0.615/0.024 0.41010.000 0.409/0.017 0.326/0.000 Mar 0.52511.605 0.695/0.357 0.35012-143 0.436/0.000 O.O(W / 0.000

Note. Traces (-) indicate cases where the method does not a or results are not comparable.Asterisks (*) indicate incomplete grids (during felling). iining symbolsas in Table 6.1.

141 TABLE 6.10. Estimates of 30-day emigration and immigration rates (Le emigration and immigratiot overap, iod of 30 days) for Apodemus sylvaticus in each grid within the Comer Complex. each pYof values mortality and recruitment are given by the first and second valuesrespectively.

Timc Col(M-F) Co2(M-F) C03(F) COS(F) CON(M)

1990 May 0.000 / 0.579 0.000 /0.355 Jun 0.140 / 0.140 0.064 /0.064 0.000/0.000 0-000/0.000 Jul Riling FeRing Aug 0.000/0.000 0.642/0.000 Scp 0.093 / 0.093 0.125 /0.000 0.68310.000 0.000 /0.227 Oct BrasbRem 0.000/0.143 0.143 /0.429 0.000/0.333 0.000/0.000 Nov 0.000/0.557 0.000 / 0.294 0.153 /0.153 0.460/0.614 0-0(10/0.080

1991 Mar Apr 0.00010365 0.402 /0.100 0.144 /0.289 0.000 / 1.303 0.000 /0.072 May 0.12610.000 0.417 / 0.627 0.105 /0.209 0.103 /0.000 0.000/0.313 Jun 069010.345 0.000 /0.574 0.259 /0.129 0.000/0.000 0.000/0.470 Jul 0.68011.365 0.059 /0.649 0.130 /0.390 0.000 / O.O(W 0.029 /0.323 Aug 0.09710.195 0.157 /0.472 0.068 /0.409 0.000/0.256 0.041/0.372 Sep 0.23810.179 0.033 /0.300 0.075 10.151 0.046/0.092 0.026 /0.026 Oct 000010.385 0.129 / 0.384 BrasbRcm 0.000 /0.462 0.124/0.618 Nov 0.033 10.067 0.000 /0.204 BrashRem 0.(99/0.268 0.079 / 0.437 Dec 006210.558 0.000 /0.474 0.000/0.000 0.188/0.047 0.303/0.454

1992 - Feb 0.00010.029 0.000 /0.072 000010.145 0.038 /0.000 0.036/0.072 Mar 0.53510.535 0.000 /0.535 000010357 0.000/0.428 0.000/1.070

142 TABLE 6.11.- Parameters of multiple regression equations relating the variation in number of woodmice in each subpopulation (dependent variable) to mortality, recruitment, emigration and immigration- rates at the corresponding grids (independent, variables). Separate,regressions were calculated for periods of subpopulation increase (top) and subpopulationdecline (bottom). -- -, -IýI- 1'1ý1.1

Case Partial regressioncocfficients t of reg.ý coeffs. - Variance Variance Equation!s (d.E) (a s.d. ) of eachindependent and significance explainedby explained F-value variable (P) eachvariable by and signiE (partial r2) equati6ý (P) (r2)

"'-'--0.501+0.195- Mortality . 2.573 (0.016) 0.197 0.889 53.844 Recruitment' 1.132+0.084' 13.507(<0.001) - '0.871 0.001) dNLt >0 Emigration -0 368 + 0.227 . 1.617 (0.118) - 0.088 (29) Immigration '0.590 + 0.159 3.705 (< 0 001) 0.337 . Constant (b) "'-0.068

Mortality '172 -0.676"+ 0. -3.936 (< 0.00 1) 0.341 0.428 5.604 Recruitment 0.573 + 0.167 '3.425 (0.002) 0.281 (0.002) '-0.459+0.187 dNLt

143 6.4 - Discussion

for 6.4.1 - Habitat selectionand population density: contrasting patterns woodmice and bank voles - For bankvoles, - the resultsof both the modified IvIevs method and the Rosenzweigand Abranisky's method were similar,and they show two points.Tirst, habitat selectionin bank voles was strong - much strongerthan in woodmice (as shown by the Eo's in IvIeVs method, and by the slope of the regressionline in Rosenzweigand Abramsky's).This is consistentwith the idea that bank voles have quite marked habitat preferences,'due to reasonsalready discussed in Chapters3 and 5. The secondfinding was that in bank voles habitat selectionwithin the Comer Complex,was independentof populationdensity. ý For woodmice,the two methodsgave different results. IvIevs Us showedthat woodmicewere mor-e widely distributedwhen numbersare high than when they were low (Figure 6.1)1'i. e., that habitat selection,was density-dependent.This conclusion was confirmed by- the results of the regression of EO on N. -However,,the results of Rosenzweigand Abranisky'smethod were inconclusive.None of two hypothesesimplying (no'habitat density-independent aýlinear - relationship-between ý y' and N* "selection, or habitatselection) were supportedby the results.It was also not clear whether my results fitted the pattern expectedby density-dependent-habitat selection; although there was some superficialresemblance (Figure 6.3).* Based-on the clear results of the modified IvIev'smethod; I believethat the most parsimonioushypothesis is that there was density- dependenthabitat selection by woodmice in'Comer Complex, but Rosenzweig and Abramsk-/smethod neither support nor disproveit. Habitat selectionwithin Comer Complexthus seemedto be density-independentin bank voles, but density-dependentin woodmice. ýThis result - implies that habitat preferencesof bank voles were rigid in the sensethat no matter how high population densitieswere, they did not increasetheir utilization of suboptimalhabitats. In contrast, the populationof woodmicewas quite flexible spatially and when their densitywas high they readily changedtheir habitat distribution making heavy use of grids which were underusedpreviously. Besides emphasizing how intensebank voles' habitat preferences are, this contrastbetween the two speciescan also be related to ýthe greater vagifity of woodmicewhen comparedto bank voles.-Among the two species,woodmice have the larger home ranges'(Chapter4) and, even,more important, they make long distance (e. Kikkawa, 1964; movementsmore often g. -Bergstedt,1966; Watts, 1970; Geuseet al., Section 4985; Harrison, 1990;present study,, 6.3.2). Woodmice often move distancesof severalhundred meters under naturalconditions (Hacker and ]Pearson,1952, Bovetý 1962,

144 Bergstedt, 1966); during the present study in Hamsterley, two movements longer than I Ian were detected (Appendix 4). All considered,, woodinice, tend to be less habitatspecialist and more vagile than bank voles. Although there may be other causesinvolved as well, these two factors surely contribute to the finding that populations of woodmice, are spatially fluid and able to respond to changesin population density by expanding their habitat utilization, while bank vole populations do not expand theirs.

density-dependent habitat 6.4.2 - A- comment on the methods available to test by selection- As Rosenzweigand Abranisk-Ysmethod was proposed them as a standard techniquefor the study of habitatselection in smallmammals, it is worth investigatingits apparentfailure in dealingwith the data on woodmicein Comer Complex.,I proposethis was for two reasons,as follows. (1) The method is based on Simpson!s index, which in turn is based on the summationof squared-proportions. This tends to under-representthe habitatswith the lowest populationsizes. For example,two habitatsA and B which had respectively0.5 and 0.1 of the total captureswill contribute0.25 and 0.01 to the summationwhich gives the value of the index. Thus, grid A had-numbersonly five times higher than B, but it contributes25 timesmore to the index. Therefore,the methodis sensitiveto variationsin habitats high but habitats low preference-among the with numbers, whether the -with - numbers(supposedly the sub-optirnalhabitats) are little usedor not usedat all makeslittle difference.,From Table 6.1 it can be, seenthat variations in numbersamong the least- preferredhabitats accounts for muchof the variation in the overall degreeof selectivity,as measuredby EOwithin the IvIevs modifiedmethod. . (2) The patternwhich correspondsto density-dependenthabitat selectionin a plot of Rosenzweigand AbramskysYs againstN* is not a single linear regression,but three linear functions connected to make a, complex function. The method predicts the aproximateshape of this complexfunction, but does not specifyan equationdescribing it which'is amenableto testing. Instead the validity of the prediction is tested by the independentfitting of three linear regressions.There is a considerabledegree of subjectivityin decidingwhich points to includewhen calculatingeach of the three linear regressions,which might allow very different patterns to be claimed as confirming the prediction.Thus, the method!s ability to falsify the hypothesisof density-dependenthabitat selectionis smaller than its ability to falsify the alternative hypothesesof no habitat selectionor density-independenthabitat selection (which canbe falsifiedby datanot fitting linear based data a single regression on all, points). Casessuch as that provided by my

145 woodmicedata, where it is debatablewhether the pattern doesor does not correspondto the expecteddensity-dependent function, are bound to arise. Whetherthe modified IvIevs method proposed here is reliable to test density- dependenthabitat selection in a variety of situationsremains to be demonstrated,but the methodoffers two advantagesover Rosenzweigand Abramsky's.First, it providesextra information,i. e., preferencesand avoidancesof individual grids (Ei). Second,the testing of the hypothesisof density-dependenceinvolves less subjectivity.As a disadvantage,the modified IvIevs methodassumes that the dependenceof habitat selectionon, density is linearalong all the rangeof densities,which may well not be true for manyspecies: It may be possible to expand the method to test alternative models without the linearity ý . assumption,i. e., modelswhere the relationshipbetween Eo and populationsizes would be expressedby a single,non-linear equation.

6.4.3 - Density-dependent habitat selection in woodmice: movements or variable local demographies ?-A considerablepractical problem in trap-based studies on the role' in demographic is how distinguish between' of movements processes . --to , true emigrationtimmigration movements and common within-home-range movements. Any for criterion used this distinction is bound to have shortcomings. For example, using only non-reversed movements would rule out true emigrationrimmigration movements which later kind is are reversed;this of movement likely to happen in a fluid population using an, habitat distribution where the spatial of resources changestemporally. Additionally, some between movements grids within. a home range can be perceived as non-reversed emigration/immigration movements becausetrapping is discontinuous in time. Thus, it is difficult from extremely to separate the signal the noise in this kind of data. Instead of dubious I did using criteria of value, not make any attempt to - distinguish immigration/emigration from within-home-range movements, and opted for an alternative approach. I analysed the -frequencies of., all inter-grid movements, hoping that fluxes in emigrationrunmigration would result significant differences in the frequencies of in movements two opposite directions. This approach seems to have been successful in some cases,but not in others. ,"I If density-dependent movements are to explain variations in the spatial patterns Comer Complex within as a whole, then one would expect primarily that movements from high to low grids with numbers grids with numbers would be more fi-equent than. vice- density is high (prediction versa when population 1). The complementary ýphenomenon happen: densities low, might also when population are movements from grids with low

146 numbersto gridswith high numberscould be more commonthan vice-versa,as individuals seekthe besthabitats (prediction 2). "In the presentstudy, - movementswere in the direction expectedby prediction I duringthe autumnof 1990and winter of 1990-91,but in neither casewas the difference significant.At sin-Martimes in the following year, movementsin the direction contrary to the predictedwere found slightly more often, but again,no significant differencewas found. ;--- Prediction2 was more sucessfidthan prediction 1. Movementsin the predicted direction were more commonthan vice-versain all seasonswhen population densities were,low., This dfference was significant,in summer 1990 and spring 1991, and,the differencein frequenciesfound in spring 1992could havebeen significant as well if it had not beenfor the smallsample sizes obtained in that season. I-II , Szackiand Liro (1991) testedwhat I called prediction I for Apodemusagrarius and Clethrionomysglareolus in aýsuburban habitat mosaic in Warsaw. In their study predictionI appliedto somecases, but they also found that "...In manycases directions of movementswere not consistentwith the densitygradient (i. e. more animalsmoved from less dense.,into more dense habitats than the opposite direction)" (Szac1dand Liro, 1991:222). ý That is, they found a patternsimilar to the one predictedin my prediction2 as well, but as they were not testingthis predictionthey did not attribute any meaningto this finding. Unfortunately,as Szac1dand Uiro did not presenttheir directionsof movements separatedby season,further comparisonsbetween their resultsand mine are not feasible. Overall,from my resultson woodmicein the Comer Complex it seemsthat there was some non-randomnessin the direction of movementswhich was related to the changesin populationdensity of this species.The actual pattern may be sharperthan I could detect,-as the abovementioned "noise" in thesedata must haveobscured the pattern to someextent. ., Supposingthat there is indeed pattern in the direction of the movements,the questionstill remains:how importantare they to explainthe changesin spatialdistribution within the population?ý , In the presentstudy, the resultsof the regressionanalysis for the increasephase of subpopulationsshowed two interestingpoints, as follows. 1) During the increasephase, mmgraflon was of someimportance in explainingthe growth of subpopulations(although lessthan recruitment),but emigrationwas not a significantfactor restrictingthe growth. This pattern suggeststhat during the increasein subpopulationsemigration was less directed immigration, i. than ý e., emigration rates were not related to variation in grid densities,but immigration intense was more towards the 'grids where numbers were

147 increasing most., 2) Whatever the trends in directions of movements, they were less important than differences 'among 'local demographies. Recruitment explained more variance in subpopulation sizesthan immigration, and mortality more than emigration. It is interesting to note that the most important single factor, juvenile recruitment, reflects to a great extent early juvenile survival, as discussedin Chapter 4. During the decline phase,the corresponding points were as follows. 1) Emigration was of importance in explaining the decline (although less so than mortality), but inunigration was not a significant factor counteracting the decline. Thus, following the same reasoning as in the previous paragraph, it seems that during the decline of subpopulations;emigration was more directed than imn-dgration:more individuals left grids where numberswere declining faster, but immigration rates were not related to variations in grid densities.2) Whatever the trends in ernigrationtinunigration, they were once again less important than local demographiesin explaining the population trends. In summary, immigration contributed significantly to the increase in subpopulations,and emigration contributed to their decline. Such patterns are consistent with the ones expected from the view that what appears to be density-dependent habitat selection is at least partly achieved by non-random movements of individuals among habitats. Nonetheless,the importance of movements was overwhelmed by the importance of local (grid-level) recruitment and mortality. A word of caution is necessary about the interpretation of these results. The importance relative of local demographies and movements is surely dependent on the spatial scale of the study. This is easily seen by looýing at the two extreme hypothetical situations. If grids were too far apart, no intergrid movement would be detected and size of subpopulations,would appear to be determined exclusively by local demographies. On if the other extreme, grids were too close to each other, true emigration/immigration be drowned movementswould amongst a mass of non-directional home range movements in be to a extent that all pattern movements would obliterated. This second case would lead frequency to an overestimation of the of movements, but to an underestimation of importance in determining their size of subpopulations, as patterns of direction of be Were movementswould not clear-cut. movements of paramount importance, probably be detected by this could only studies at an intermediate, optirnal spatial scale between the It (due two extremes outlined above. seems to me to the high overall frequency of intergrid design is movements) that my study closer to the second extreme than to the first, different importances and relative of movements and local dernographies might be by different As obtained studies at spatial scales. with other parameters in small mammal (population densities, Gurnell ecology and Gipps, 1989; habitat preferences, Morris,

148 1989),spatial scale influences the measurementsobtained, and other comparablestudies arenecessary to put into contextthe resultsof the one describedin this Chapter.

149 CHAPTER 7 GENERAL DISCUSSION

7.1 - Differential responses of woodmice, bank voles and field voles to spatial, and temporal habitat heterogeneity. The findings of the present study show that each of the three rodent species responds to spatial and temporal habitat heterogeneity in different ways. To examine these differences'further, ýit is convenient first to make a distinction between field voles on one hand and the woodland rodents (bank voles and woodmice) on the other. As a grassland species, field voles differ from the other two in the way they perceive the habitat mosaic. For woodland species, forests can be seený as, "islands" surrounded by a, rsea" of unsuitable habitats (Harris 1984, Bauchau and Le Bouleng6, 1991). For field voles, young plantations with abundance of grasses are the islands, and the mature forest is the surrounding matrix of unhospitable habitat. This spatial isolation of field vole populations is determined by the temporal isolation of the species within the succession:only the young plantations are a suitable habitat for field voles; the earlier and later successionalstages are not (Chapter 3). In Hamsterley Forest during - the present, study, - most conifer plantations were mature; young plantations and clear-fellings occupied a relatively small area (Figure 2.1). forest in a with this age structure the isolation of field vole populations in space and time be would expected to be particularly marked. Indeed, out of total of 54 standard trapping during sessionscarried out the censuses(including S. - Gibson!s data),, field voles were during just 11,10 in caught of which were young plantations (Chapter 3). At only one site' (Road) field Similarly, were voles always present. - within Comer Complex, where followed intervals for populations were at monthly two years, field voles were captured in only two out of five grids, and for only short periods of time in both (Chapter 5). Thus in Hamsterley field followed voles apparently and colonized ephemeral suitable habitats, a first for strategy proposed this speciesby Hansson (1977) and Stenseth (1977). Both these dispersal authors regard as essentialfor this strategy. However, the comparatively frequent long distance for dispersal movements required such would not be expected in a species whose home range and normal movements (Chapter are so restricted I). The - field of dispersal in field is discussed below evidence vole populations in Section 7.3.- As field have very habitat it is likely voles restricted preferences that in this species grassland important for corridors are connecting,populations which would otherwise be isolated-, habitat corridors are often important to connect small - mammal populations in other

150 fragmented habitats (e.g.. Bennett, 1990, Zhang and Usher, 1991). Charles (198 1) found that in old conifer standsin Scotland, densities of field voles were much higher in the rides between the mature plantations than in the plantations themselves. In Hamsterley, however, I failed to find evidence of high abundancesof field voles in the grassy rides of Comer Complex in 1990 (Chapter 5), but as population densities of this specieswere very low in the forest as a whole in that year (Chapter 3), my results may reflect the low overall for population density rather than inadequacy of the rides as habitat corridors M. agrestis in Hamsterley. be - At present, accessof field voles,to the young plantations I studied should not a located problem becauseall sites I trapped regularly (except Farm Young) are along the edges of the forest, adjacent to pastureland and/or to continuous grassy rides bordering is, main roads-,Farm Young is located near pastureland in Pennington Farm and also bordered by grassy rides along'a major road within the forest. -As the second rotation continues and more sites are clear-felled and replanted in the central areas of the forest, grassy rides along the internal roads should become increasingly important as habitat corridors for field voles. Looldrig further ahead, as the forest goes through further rotations the need for habitat corridors might be reduced again as the proportion of young plantations available within the forest increases,especially in the short rotations used for Sitka spruce. Indeed, a consequenceof short rotations is that the age distribution of the plantations in the forest eventually reachesan equilibrium characterized by a high proportion of young stages. A 45-55 years rotation results in,. an equilibrium proportion of about 4(Y.Yo young, plus "closingý plantations; in a 100 year rotation, however, as used in continental Europe, this proportion would be only about IM16 (Staines, 1986). Such an increase in the number of suitable patches may help colonization and persistence of local populations by providing "stepping stones" linldng the patches (Wilson and Willis, 1975, Shafer 1990), and also by assisting the colonization of the young plantations by suitable grasses. However, my results suggest that, at least in Hamsterley, a future decrease in the importance of the grassy corridors is by no means guaranteed. Several young plantations on the edge of the forest were not used by field voles becauseof habitat inadequacies,in spite of easy access to such peripherally distributed sites. An increased proportion of young plantations in Harnsterley will make field vole populations less insular only if the soil of most young plantation sites is fertile enough to allow the growth of grasseswhich are nutritionally and structurally suitable for field voles (Chapter 3). Among the two, rodents lumped together as "woodland" species in the first paragraph of this Section,-the responsesto habitat fragmentation also diftred. I found

151 woodmicemore widely distributedthan bank voles on the spatial scalesof Hamsterley Forest as a whole and within the Comer Complex. In the forest as a whole, woodmice were capturedin 5 1,out of 54 standardtrapping sessionsin the censusesbut bank voles only in 34 (Chapter3). Again,'on this geographicalscale part of the differencebetween the two speciesis explainedby how frequentlythey occur along the successionalscale. Woodmicewere commonin all successionalstages, but bank voles were seldomabundant in clear-fellings,with the exceptionof the site Comer 2 in 1992 (Chapter3). Another part of the difFerenceresulted from the more restrictedhabitat preferencesof bank voles than woodmicewithin the young plantation stage (Chapter 3), chiefly a result of the former species!greater dependence on ground cover, as discussedin ChaptersI and 3 and shown experimentallyin Chapter5. At the smaller spatial scale, within Comer Complex,, the same pattern-of differential responsesof woodmice and, bank voles to temporal heterogeneitywas observed.Woodmice were seldom absent,from any site at any time, while bank voles were absentfrom one or more sites during most of the study (Chapter 5). Wood mouse populationswere alsomore equitablydistributed among sites, as shownby their indicesof overall degreeof habitatselection, Eos (Chapter6). This pattern seemsto be relatednot only to the wider habitat preferencesof woodmice, but also to their greater vagifity., Within Comer Complex,woodmice had larger home rangesthan bank voles (Chapter4) andmoved between sites significantly more often (Chapter6). Additionally, the vagility of woodmicewas illustratedby the two long distancemovements discussed in Appendix 4. differencesin habitat In turn,, the preferencesand vagility are related ýto the other ecologicaldifferences between the two speciesdiscussed in Chapter 1. For example, woodmiceare relativelymore vagile becausetheir food is sparselydistributed, and also dependlittle becausethey on ground cover for anti-predatordefence. All theseaspects of their ecology interact with each other and constrain each other, forming an -adaptive suite" (senw Bartholomew,1972, quoted in Pianka, 1989).The differencesbetween their respectiveadaptive suites determine different and to a certainextent predictableresponses of woodmiceand bank voles to spatialand temporalhabitat heterogeneity. is If this view correct, the characteristicresponses of thesetwo speciesto spatial heterogeneityshould be similar in a wide variety of situations.In fragmenteddeciduous in Belgium,local woodland extinctionsand recolonizations;were commonfor bank voles, for but not woodmice; this result was attributed- to woodmice having wider -habitat preferencesand moving longer distancesthan bank voles (Geuseel al., 1985; Bauchau Le Bouleng6,1991). Dickman Doncaster and and (1997,1989) found that bank Voles from individual were absent patchessignificantly more often than woodmice in -anurban

152 habitatmosaic in Oxford. All thesefindings are similar to my findings in Hamsterley.On the other hand,Dickman and Doncaster(1989) and Szackiand Liro (1991) did noffind a significantdifference in the frequencyof, long movementsby, the two speciesin urban habitat mosaics,- neither did Zhang-and Usher (1991) in an agricultural landscapein Yorkshire.In my opinion,the most commonpattern of responsesof thesetwo speciesto spatialheterogeneity (at least in mosaicswith discontinouscover) is probably the one found in Belgiumand in Harnsterley:namely, that bank vole 'populationsare more patchily distributedand more isolatedfrom each other than wood mousepopulations. However, eventhough thesetwo rodent speciesare so well studied,it is not yet establishedhow general this 'distributional pattern is, and some open questions remain, especially concerningmovements. We needbetter knowledgeof the maximumdistances moved by thesespecies, how frequentare long distancemovements,, and what proportion of animals undertaking them return at a later date. -Only when such questions are answered satisfactorilyin a variety of habitatswe will be able to improve our ability to generalizeas to how thesespecies cope with habitatfragmentation.

increasing 7.2 -A possible role of mature Sitka spruce in spatial heterogeneity in the young plantations -As discussed in Chapter 1, Sitka spruce probably impoverishes the soils on which it is planted; particularly by acidification and podzolization,, although the extent of the impoverishment is still debatable. In the present study, I found that rodent communities were most variable in young plantations by comparison with other stages of Sitka spruce succession' this variation was associated with local differences in ground vegetation which in turn were associatedwith high local variation in soil types (Chapter 3). Some'of the local soil variation within Hamsterley Forest surely pre-dates the planting of Sitka spruce there and other plant species (e.g., heather) may also contribute to soil (Miles, acidification - 1986). However, many of the differences I found between soils were due either to different degreesof podzolization (going from typical gleys through podzolic gleys to typical podzols) and some were due to poor decomposition of organic matter under very acidic conditions; both these effects can be attributed, at least partly, to Sitka spruce (Miles, 1986).-More important, -soils under Sitka in different localities can suffer acidification and podzolization to different degrees according to date of planting, density length in of planting, of rotation, addition to the local variations in the susceptibility of the impoverishment (Chapter 1)., Thus it is likely soil to that planting of areas-with Sitka increase is,, heterogeneity spruce can spatial variation of soils, that soil is to some extent a product, of soils at different sites being modified by Sitka to'different degrees. If this is have homogeneous correct, we the paradox that the relatively mature forest where local

153 variationis "buffered"(Chapter 3) contributesto the considerableheterogeneity of both groundvegetation and rodent communities in the young plantationsthat will follow it. An extension,of this view is that locaLvariation -in rodent conununities,would increaseduring the first few rotations (at least from, first to second),but decreaseagain thereafteras all soilswithin the forest becomeequally poor. This view is dependenton the assumptionthat planting of Sitka results in a progressivelong-term impoverishmentof soils.The dataavailable so far on long-termimpacts of coniferson soils are not enoughto decidewhether this assumptionis valid or not (NMes,1986).

As by 7.3 - Flexibility in habitat preferencesand population regulation - emphasized Montgomery(1989a, -1989b) there are two kinds of density-dependencewhich can play a part in ýpopulation regulation: temporal density-dependenceand spatial density- dependence.Temporal density-dependence, i.e.; density-dependencein the time-seriesof abundancesjswill establishedas one important factor in the regulation of wood mouse populations(Watts, 1969,Flowerdew, 1985, Montgomery, 1989a;see Chapter 4). Spatial density-dependence,i.e. density-dependenthabitat selection,has been shown only recently for A. sylvalim by Montgomery(1989b,, 1989c) in two studiesin, respectively;conifer plantationsin Northern Ireland and mixed deciduouswoodland in southwestEngland. In the secondstudy,, woodmice showed a tendencyto expandtheir use of suboptimalhabitats when density of -conspecificswas high, but the'contrary happened,when a'superior competitor (the yellow-neckedmouse, A. - flaWcolfts), was present,at ýhigh densities.In Hamsterley,where A. flaWcolfis is, absent,,habitat selectionby A sylvaticuswas more clearlydependent on its own density(Chapter 6). ',,, -I 1ý - The interactionbetween the two forms of density-dependencehas seldom been studiedin smallmammals, but this is a promisingavenue of research,especially in relation to population,regulation. Fascinating- hypotheses,can be developed and tested. For example, it is known that some simple mathematicalmodels describing population variation, based on non-linear dfference equations,show a surprisingly rich army of dynamicbehaviour (May, 1976). A single model,can produce either stable populations, regular annuali fluctuations, two or four (or more) year regular cycles, or non.C, -ycfic, "chaotic" variation,as the value of a single parameter- r, the intrinsic rate of population growth - increasesabove successive thresholds (May, 1976;May and Oster, 1976).There is someterminological confusion about r, I use it here to mean,the'rate at which the populationwould grow, at a given'locality and time, in the absenceof density-dependent definition is effectsrestricting the growth. This consistentwith the most usual meaningof r (Begonand Mortimer, 1986:47). In mathematicalterms, the parameterr determinesthe

154 degreeof non-linearityof the populationgrowth. May and Oster'soriginal modelsapplied to populationsshowing reproductive and non-reproductiveperiods, and non-ýoverlapping generations.-The first conditionapplies to rodentsliving at high latitudes;the secondnot; but slightly more complexmodels with behavioursimilar to the ones describedby May and Oster can be obtained assumingoverlapping generationsas well. An interesting featureof suchmodels is that a singlespecies may show eitherregular annualfluctuations, or regular pluriannualcycles; or non-cyclic- pluriannual variations, without, the need to invoke extensivechanges in its populationprocesses: which mathematicalbehaviour the populationshows depends only on the value assumedby r. This meansthat there may not be a specificcause for cyclicity, as cyclicity in itself need not be an,.event requiring a specificexplanation, but only one behaviourwithin a continuum of possiblebehaviours expectedfrom a singlemodel. If the view expressedabove is true, it shouldbe possibleto find examplesof single microtinepopulations showing completely different dynamicbehaviours, at different times evenin the absenceof any obviouschanges (either in habitat or intrinsic to the population) which could be associatedwith a changein the regulatory mechanismsof the population There are severalpublished studies which seemto show examples,of this. Suchvariety of dynamicbehaviour in a single population has been found by Getz el al. (1987) who studied populations,of Microtus ochrogaver and M pemisy1wmicusin grasslandsin Illinois, U. S.A. - for fourteen years; their results are clearly consistent with the view expressedabove. Southernand Lowe (1982) and Taitt and Krebs (1985, quoted in Lidicker, 1988) provide additional examplesof a single microtine population showing eitherannual fluctuations or pluriannualcycles at different times. It is well establishedthat there are severalmarked demographicchanges (in survival rates, recruitment rates and spatial patterns) during population cycles; as reviewed, for example, by Myllymald for Microtus (1977b) agrestis.However, many of these changescould be effects of the drasticchanges in densityduring cycles,rather than a primary causeof the cycficity itselý which would be a result of the. same population mechanismsfound in the non-cyclic populations,only with a differentvalue for the parameterr., ý-, Thus,.the question,would reduce to: for a particular species,what makes the parameterr-i. e. the rate at which the populationwould grow in the absenceof density- dependent between different localities, effects - vary or at different times in the same locality ? While in in variations r as a result of changes the environmentalconditions are to be (Flowerdew, 1987: 115), determine expected to the causesof the variation in r in is because naturalconditions not a simplequestion, this Parameterrepresents a composite factors influencing of a seriesof population growth through their effects on birth and

155 , deathrates (Begon and Mortimer, . 1986). Thus, even if cyclicity and,non-cyclicity are dfferent behavioursshown by a single model, which behaviour the population shows could be influencedby a variety of causalfactors - as in the multifactorial view that W., Lidicker has advocatedwith a regular five-year cyclicity (Lidicker, 1973,-1978,1983, 1988). I propose,as an hypothesis,that density-dependencein habitat selectioncould be one of thesefactors, because by definition density-dependenteffects will be triggered by an increasein populationdensity (numbers per unit area) rather than in population i itself A given r would result in a much lower increasein population density it, when numbersincrease, the populationis spreadover a larger area.This would be equivalentto a lower valueof r, if consideredin relationto the numberspresent only in the original area of distribution. If a populationof a density-dependenthabitat selector starts to grow quickly in the optimalhabitat, the increasein populationdensity in this habitatis restrainedby increasing the use of suboptimalhabitats, and as a result r never exceedsthe thresholdvalue that would makethe populationbecome cyclic., If on the other hand the -speciesis a density- independenthabitat selector, it doesnot usethe "escapevalve" of suboptimalhabitats, and the increasein density- in the preferred habitat is not restrained:r may go above,the thresholdand the populationmay become cyclic. This hypothesisis analogousto the -"social fence" hypothesis proposed by Hestbeck(1982), but with a difference.Hestbeck suggestedthat, during a population "cycle", at low population density only the optimal habitat is, occupied, but when the populationgrows surplusindividuals disperse into suboptimalhabitats around the original habitat. (optimal) Dispersalcontinues until all vacant spacesare filled in the suboptimal Once habitats. such population density has been rea hed, the spacingbehaviour of the individuals habitats "fence" -in the suboptimal would the central population, preventing finiher dispersal.This would causethe populationin the optimal habitat to be "regulated by resourceexhaustion" resulting in cyclical pluriannualfluctuations (Hestbeck, 1982). 1 suggestthat for somepopulations habitats other than the optimal might simply not be occupied,- becausethe population showsý density-independent habitat selection. The imprisoning "fence" the populationwould not be a social one, but simply the rigidity of its own habitatpreferences. Several (seeHansson other recentmodels and Stenseth,1988) have also analysed the relationship between spatial heterogeneityand in population cyclicity - microtine rodents. Such modelswere reviewedby Gliwicz (1988) who pointed out that different haveled finding models to contradictoryconclusions, some that cyclicity should require

156 habitat heterogeneity,and some finding thit cyclic populations should 'occur' in more homogeneoushabitats. The hypothesisI suggesthere is not basedon spatialheterogeneity alone;it is basedalso on the different extentsto which eachlocal populationchanges its useof spacein responseto variationsin populationdensity. These differences depend not only on the spatial,heterogeneity found at a given place and time but also on how the peculiaritiesof the biology of eachspecies affect its responseto suchspatial heterogeneity, asdiscussed in Chapter6 andin Section7.1. - If we acceptthat some of the factors which, influence r vary latitudinally (e.g., lengthof breedingseason and intensityof breeding),the hypothesisI suggestis consistent with bankvoles and field voles being cyclic at higher latitudesbut not at lower latitudes. Any hypothesison' the causesof cyclicity should be able to explain such intraspecific gradientsof cyclicity (Hanssonand Henttonen,' 1985,ý Stenseth, 1985). Looking at the most commonsmall, British rodents,,woodmice are never cyclic across their range in Europe,bank voles are sometimescyclic and sometimesnot, and field voles,are cyclic in most areas.The few studiesof density-dependenthabitat selectionmade so far suggest that woodmiceare density-dependenthabitat selectors,and bank voles were found to be density-independenthabitat selectors in the present study- (Chapter 6). A testable prediction of ,my hypothesis-is that field voles should be density-independenthabitat selectors,at leastin most cases., As yet it is not clear whether this last point is true or not, as to my knowledge thereis no publishedstudy analysing specifically density-dependence in habitatselection in Microtus agresfis.As their habitat preferencesare even more restricted than bank voles' (Chapter 1), 1 would expect field voles to be density-independenthabitat selectors, in habitats especially such as conifer plantations,where sites are either favourable or (Chapter clearly unfavourable 3). On the other hand, several demographic models for field proposed voles,assume that they are actually density-dependenthabitat selectors. Suchmodels have attributed an important role to dispersalto suboptimalhabitats during the increasephase of populationcycles (e. g. Hansson,1977,1989, Stenseth,1977,1980, Stenseth 1985, el al., 1977,Hestbeck, 1982), but the strengthof the field evidenceon the actual importanceof dispersal,especially in cyclic populations,seems questionable. For example,in their recent review Gipps and Ah'bhai(1991) quote the studiesby Hansson Myllymald (1977c) Vfitala (1977), and (1977) to indicate the extent of dispersalin M. All agresds. these three studies provide only MiCidentalevidence of a few dispersive thmn dispersal (either frequency events;none of quantifies rates of movementsper unit of time, or as a proportion of the population size). The occurrence of some density- dependentdispersal, in itsel& does not necessarilymean that its intensity is enough to

157 regulatethe populationthrough a substantialrearrangement in the spatial distribution. There is someevidence that in Fennoscandiafield voles use some suboptimalhabitats when populationdensities are high (e.g. ýHansson, 1977, Erlinge et al.-,,: 1983), but their numbersin suchhabitats are usually low. Additionally, Stensethrecognizes that cyclicity is more likely in homogeneoushabitats, where there are less opportunities for dispersal. Thus,in my opinionthere is no conclusiveevidence yet of markeddensity-dependence in habitatselection in most field vole populations.: If field voles are found to be density-independenthabitat selectors,then among woodmice,bank voles and field voles there would be an inverserelationship between the degree of'flexibility in habitat,selection and, frequency of population cyclicity. This* relationshipwould be consistentwith the hypothesisI propose,although it would not, of course,prove it. Furthermore,I would expect density-dependenthabitat selectionto be only one of severalfactors influencingcyclicity,, as there are severalfactors which affect the valueof the parameterr (seeabove). I suggestthat the hypothesisI present here is worth testing, which could be achieved,-by comparingdensity-dependence in habitat selectionin cyclic and non-cyclic smallmammal populations in heterogeneoushabitats. Regardless of whetherthis particular hypothesisis true or not, it seemsclear that an understandingof smaUmammals! responses heterogeneity to spatial can help us to understandbetter their responsesto temporal heterogeneity,one of the oldestproblems of ecology.,

158 ýREFERENCES

1989. Communities in dunes Israel. Tex. ABRAMSKY,, -Z.,,- of gerbiline rodents sand of Tech Untv. Mus. Spec. Publ. 28: 205-17.

ABRAMSKY, Z., M. L. ROSENZWEIG, B. PINSHOW, J. S. BROWN, B. KOTLER & W. A. MITCBELL, 1990. Habitat selection: an experimental field test with two gerbil species.Ecology, '71(6): 2358-69.

ABRAMSKY, Z., & G.M. VAN DYNE, 1980. Field studies and a simulation model of small mammalsinhabiting a patchy environment. Oikos, 35: 80-92.

ALIBHAI, S.K., & J.H. W. GIPPS, 1985. The population dynamics of bank voles. Symp. Zool. Soc.'Lond, 55: 277-313.

ALIBHAI, S.K., & J.H. W. GIPPS, 1991. Bank vole. In G.B. Corbet & S. Harris (Eds.), 7he Handbook of British Mammals (Third Edition). Oxford: Blackwell, pp. 192-203.

ASHBY, K. R.,, 1967. Studies on the ecology of field mice and' voles (Apodemus sylvaticus, Clethrionomys glý2reolus and Microtus agrestis) in Houghall Wood, Durham. J. Zool. (Lond), 152: 389-513.

ALATALO, R.V., - 1981. Problems in the measurementof evennessin ecology. Oikos, 37: 199-204.

ATTUQUAYEFIO, D. K., M. L. GORMAN & PU WOLTON, 1986. Home range sizes in the wood mouse Apodemus sylvaticus: habitat, sex and seasonal differences. J. Zbol. '(Lond), 210: 45-53.

AVERY, B. W., 1990. Soils of the British Isles. Wallingford (U. K. ): C.kB. International.

BANNISTER, P., 1966.The use of subjectiveestimates of cover-abundanceas the basis for ordination.J. Ecol, 54: 665-74., ,-

159 BAUCHAU, V., &, E. LE BOULENGIL,1991. ýBiologie de, populations'de rongeurs forestiers,dans un espaceheterogene. In M. Le Berre & L. Le Guelte (Eds.), Le Rongeurel lEspace.Paris: K Chabaud,pp 275-83.

BEGON, M., 1979.Investigating Animal Abundance:Capture-Recapture for Biologists. London:Edward Arnold.

BEGON,M., & M. MORTIMER, 1986.Population Ecology: a Unified Studyof Animals and Plants (SecondEdition). Oxford: Blackwell.

BENNETT, A.F., 1990. Habitat corridors and the conservationof small mammalsin a fragmentedforest envirorunent.Lwdscape Ecology, 4(2-3): 109-22.

BERGSTEDT, B.,, 1966.A Home range movements of the rodent species Clethrionomys glareolus (Schr.), Apodemus flavicollis (Melchior) and A. sylvaticus(L. ) in southSweden. Oikos, 17: 150-7.

BOVET, J., 1962.Influence dun effet directionelsur le retour au gite des mulots fauve et sylvestre(Apodemusfiavicollis Melchior et A. sylvaticusL. ) et du campagnolroux (Clethrionomysgleveolus Schreber)(Mammalia,-Rodentia). Z Tierpsychoi0gie, 19(4):472-88.

BOWDEN, C.G. X, 1990.Selection of foraging habitatsby woodlarks,(Lullula arborea) nestingin pineplantations., J ýAppl. Ecol, 27: 410-9.

BROWN, J.H., &A. KODRIC-BROWN, 1977.Turnover rates in insular biogeography: effectof immigrationon extinction.Ecology, 58: 445-9.

BROWN, J.H., D.W. DAVIDSON,, J.C. MUNGER & R. S. INOUyE, 1986. Experimentalcommunity ecology: the desert granivOresystem. In J.M. Diamond & T.J. Case(Eds. ), CommunityEcoloSy. New York: Harper & Row, pp 41.6 1.

BROWN, J.H., & M. KURZIUS, 1989. Spatialand temporalvariation in guilds of North American granivorous desert rodents. Tex. Tech Univ. Mus. Spec. PubL, 28-.'71-90.

160 BROWN, L. E., 1954. Small mammal populations at Silwood Park Field Centre, J Mamm. 35(2): 161-76. Berkshire,England. .-

BROWN, L. E., 1956a. Field experimentson the activity of the small mammals, Apodemus,Clethrionomys and Microlus. Proc. Zool. Soc.-Lond., 126: 549-64.,ý,

BROWN, L. E., 1956b.Movements of someBritish small mammals.J Anim. Ecol., 25: 54-71.

-10 BROWN, L. E., 1966.Home rangeand movementsof small mammals.Symp. Zool. Soc. Lond., 19: 111-42.

BROWN, L. E., 1969.-Field experimentson'the movementsof Apodemussylvaticus L. usingtracking and trapping techniques. 0ecologia, 2: 198-222.

in BURROWS,A. W., -1991. - Ae associatedchanges rodent communitieswith succession in 2nd rotation Sitka spruce (Picea sitchencis) plantations. Undergraduate project,University of Durham,

CHAPNLAN,S. B.; 1976.Methods in Plant Ecology. Oxford: Blackwell.

CHARLES, W.N., 1981. Abundance of field vole (Microtus agrestis) in conifer plantations.-In F.T., Last & A. S.,Gardiner (Eds. ), Forest and WoodlandEcology. Cambridge:Institute of TerrestrialEcology, pp. 135-7.

CHORAS,D., 1990.Environmental Science (Aird Edition). Redwood City, Cafifomia: Benjamin/Cumniings.

CIRTTY,, *D., 1952. Mortality among voles, (Microtus agrestis) at Lake Vyrnwy, Montgomeryshire in 1936-39. Phil. Trans. Roy. Soc., 236B: 505-52.

CHrM, D., & DA KENIPSON,',1949. Prebaiting small numunalsand a new designof live trap. Ecology,30: 536-42.-

CHITTY, D., & PHUPPS,E., 1966.Seasonal changes in survival in mixed populationsof two speciesof vole. J Anim. Ecol., 35: 313-31.

161 CRAWLEY, M. C., 1969. Movements and home ranges of Clethfionomys glareolus Oikos, Schreberand Apodemus sylvaticus L. in north-east England. 20: 310-9.

CRAWLEY, M. C., 1970. Some,population dynamicsof the Bank vole, Clethrionomys in J. ZooL glarcolus andthe Wood mouse,Apodemus syhudcus mixed woodland. (Loncl), 160: 71-89.

DICKMAN, C.R., & C.P. DONCASTER, 1987.The ecology of small mammalsin urban habitats.I. Populationsin a patchyenvironment. J Anim. Ecol., 58: 119-27.

DICKMAN, C.R., & C.P. DONCASTER, 1989.The ecology of small mammalsin urban habitats.II. Demographyand dispersal.J. Anim. EcoL, 58: 119-27.

ELTON, C.H. S.; 1942. Voles,Mice and Lemmings.- Problems in Population Dpwmics. Oxford: ClarendonPress.

ERLINGE, S., G. GORANSSON,L. HANSSON, G. HOGSTEDT, 0. LIBERG, I. N. NILSSON, T. NILSSON, T. VON SCHANTZ & M. SYLVEN, 1993.Predation as a regulatingfactor on smallrodent populationsin southernSwederL Oikos, 40: 36-52.

ERLINGE, S., 1987. Predation,and noncyclicity in a inicrotine population in South Sweden.Oilbos, 50 (3): 347-52.

EVANS, D.M., 1973.Seasonal variation in the body compositionand nutrition of the vole Microtus agrestis.J Anim. EcoL, 42: 1-18.

EVANS, F.C., 1942.Studies on a smallmammal population in Bagley Wood, Berkshire. ', J'Anim. 'Ecol., 11: 182-97.

FAULKNER, R., & R.F. WOOD, 1957. Picea sitchensis (Bongard) Carriere. In MacDonald,J., R.F. Wood, M. V. Edwards& J.R. Aldhous (Eds.), Exotic Forest Treesin GreatBritain. Forestry CommissionBulletin, 30, pp 85-9 1.

162 FERNS,P. N. '' 1976.Diet of a Microlus agrestispopulation in south west Britain. Oikos, 27:-506-11,

FERNS, P.N., ý1979a. Sucessiohalchanges, in the small mammal community of a young larch plantation in South west Britain. Mammalia, 43(4): 439-52.

FERNS,'P. N'I' 1979b.Growth, ý reproduction and residencyin a declining'population of Microtus agrestis.,J Anim. Ecol., 48: 739-58.,

FLOWERDEW,J. R., 1972.The effect of supplementaryfood on a population of wood nýce(Apodemussylvaticus).J. Anim. EcoL, 41(3): 553-66.

FLOWERDEW,J. R., " 1974.Field and laboratoryexperiments on the socialbehaviour and populationdynamics of the wood mouseApodemus sylvaticus. J. Anim. Ecol, 43(2): 499-510.

FLOWERDEW, J.R., 1976a. Techniques in Mammalogy. 4: ecological methods. Mmnm. Rev., 6: 123-59.

FLOWERDEW,, J.R., 1976b. The effect of a local increase in food supply on the distribution of woodland mice and voles. J. Zool. (Lond), 190(4): 509-13.

FLOWERDEW9ýJJL, 1978. Residents and transients in wood mouse populations. In F.J. Ebling & D. M. Stoddart (Eds.), Pop ulation Control by Social Behaviour. London: Institute of Biology, pp. 49-66.

FLOWERDEW, J.R., '1984. Woo&nice and Yellow-Necked Mice. Mammal Society Booklet, Oswestry (UK): Anthony Nelson.

FLOVN. RDEW, J.R., 1985. The population dynamics of wood mice and yellow-necked mice. Symp. Zool, 'Soc. Lond, 55: 315-38.' "-,, I --

FLOWERDEW, J.F,, 1987. Mmmals. - 7heir Reproductive Biology and Population Ecology.London: EdwardAmold., -.i,., -'

163 FLOWERDEW, J.R., 1991. Wood mouse. In G.B. Corbet & S.-Harris (Eds.), 7he HwidbookofBritish Mwnmals (Third Edition). Oxford: BlackwelL pp. 220-9.

FLOWERDEW,J. FL, & G. GARDNER, 1978. Smallrodent populationsand food supply in a Derbyshireashwood. J Anim. Ecol., 47: 72540.

FLOWERDEW, J.K, I GURNELL & J.H. W. GIPPS (Eds.), 1985.77;e EcoloV of WoodlandRodents, Bank Poles and Wood Mice. Symp Zool Soc Lond, 55. Oxford: ClarendonPress.

FLOWTIMEW, JR, SIG. HALL & J. 'CLEVEDON BRONNN,1977. Smal rodents, their habitats,and the effectsof flooding at Wicken Fen, Cambridgeshire.J Zool. aDnd), 192: 32342.

FONSECA, G.A. B., & J.G. ' ROBINSON, 1990. Forest,size and structure: competitive andpredatory effects on smallmammal communities. Biological Conservadon,53: 265-94.

FOY, BI, 1990. Changesin the structure of mammalian communities over successional time scales.Oikos. 59: 321-9.

FRETWELL, S.D., 1972.Populations in a SeasonalEnvironment. Princeton:Princeton Univ. Press. I

FRETWELL, S.D., & H. L. LUCAS, Jr., 1970. On territorial behaviour and other factors influencing habitat -distribution in birds. I. Theoretical, development. Acla Biotheorefica, 19: 16-36.

FULLAGAR, P-J-. P.A-" JEWELL, R-M. LOCKLEY ,&I. W. ROWLANDS, 1963. The Skomervole (Clethrionomysglareohis skomerensis)and long-tailed field mouse (Apodemussylvaticus) on Skomer island, Pembrokeshire,in 1960. Proc. Z001. Soc.Lond, 140: 295-314.

GAUCIL Jr., H. G., 1982. Multivayiate-AnabWs in Community Ecology. Cwnbridge: CambridgeUniv. Press.

164 GETZ,,L. L., 'J.F. HOFMANN, B.J. KLATT, L. VERNER, F. RUSSELL,COLE & R.L. LINDROTH, 1987. Fourteen years of population fluctuations of Microtus ochrogaster and M. pennsylvanicusin east central Illinois. Cam J. Zool., 65(6): 1317-25.

GEUSE, P., & V. BAUCHAU, 1985. Apodemus sylvaticus (Rodentia: Muridae) et Clethrionomys glareolus (Rodentia: Nficrotidae): competition ou coexistence ? Annh. Soc. P, Zod BeIg., 115(2): 211-20.1 ýý v

GEUSE, P., V. BAUCHAU & E. LE BOULENGt, 1985. Distribution and population dynamicsof wood mice in a patchy woodland habitat in central Belgium. Acia Zool. Fennica,173: 65-68.

GIBSON, S.M., 1989. The Distribution and Abundanceof Small Mammals in Second. Rotation Sitka Spruce Plantations -in* Northern England MSc Dissertation, Universityof Durham.

GILPIN, M. E, & M. E. SOIJLIý 1986. Minimum viable populations: processes,of species In E. (Ed.), 1986. Conservation extinction. Souk, M. Biology - 7he Science of ,Scarcity wW Diversity. Sunderland (Mass., U. S.A, ): Sinauer, Associates, pp. 19-34.

GIPPS, J.H. W., & S.K. ALI13HAI, 1991.- Field vole. In G.B. Corbet & S. Harris (Eds.), Ae Handbook ofBrifish Mammals (Third Edition). Oxford: Blackwell, pp. 203-8.

GLIWICZ, J., 1981.Competitive interactions within a forest communityin centralPoland. Oikos,37: 353-62.,

GLMCZ, J., 1988.The role of dispersalin modelsof smallrodent populationdynamics. Oikos,52: 219-21.

GRANT, M. C., ' R.E. CHAMBERS,& P.R. EVANS, '1992. The effects of re-seeding heathland breeding Numenius on whimbrel phaeopusin Shetland.IR. Habitat use by broods.J. Appl. EcoL, 29: 516-23.

165 GRANT, P.R., 1972.lnterspecificý competition among rodents. Ann. Rev. EcoL Syst.,3: 79-106. --I

GURNELL, J., 1978.Seasonal changes in numbersand malebehavioural interactions in a populationof wood mice,Apodemus sylvaticus. J Anim. Ecol., 47(3): 741-55.

GURNELL, J., 1981.Woodland rodents and tree seed supply. In J.A- Chapman& D. Pursley(Eds. ), Ae WorMvide Furbearer ConferenceProceedings, Falls Chard: PLR.Donnelly &Sons, pp. 1191-214.

GURNELL, J., 1985.Woodland rodent conununities.Symp. Zool. Soc. Loncl, 55: 377- 411.

GURNELL, J, & J.R. ýFLOWERDEW, 1982. Live Trapping Small Mammals.* a Practical Guide.Reading: Mammal Society.

GURNELL, J., & J.H. W. GIPPS, 4989. Inter-trap movements-and estimating rodent densities.J Zool. (Lond), 217(2): 241-54.

J., & C.I. KNEE, 1984. Determining GURNELL, the age, of wood mice ý(Apodemus *Ivaficus). Folia Zoologica, 33(4): 33948.

GURNELL, J., & J. LITTLE, 1992. The influence of trap residual odour on catching woodland rodents. Anim. Behav., 43(4): 623-32.

H. S. 1951. Description HACKER, H. P., & PEARSON, of the long-tailed field mouse, Apodemus sylvaticus, on South Haven Peninsula, Dorset, in 1937, with some observationson its wandering and homing powers. J Lim Soc. (Zool.), 42: 1-17.

HACKER, H. P., & H. S. PEARSON, 1952. The growth, survival, wandering and variation long-tailed of the field mouse, Apodemus sylvaticus. III. Wandering powers and distribution. BiomeMka (Cwnbridge), 39: 389-413.

Sr., HAULSTON,N. G., 1989.Ecokgical Experiments- Purpose,Design, and Execution. Cambridge:Cambridge Univ. Press.

166 HANSSON,L., 1971.Estimates of the productivity of smallmammals'in a South Swedish spruceplantation. AnnIs. Zool. Fennid, 8: 118-26.

HANSSON,L., 1974.'SmaU mammid productivity and consumptionin spruceforest and reforestationin SouthSweden. Oikos, 25: 153-6.

HANSSON,L., 1977.Spatial dynamics of field volesMicrolus agrestis in heterogeneous habitat.Oikos, 29: 539-44.

HANSON, L., 1978.Small mammal abundance in relation to environmentalvariables in threeSwedish forest phases.-Stud Forest. Suedca, 147: 1-40.

HANSSON, L., 1979. 'Condition and diet in relation to habitat in bank voles Clethilonomysglareblus: population or communityapproach. Oikos; 33: 55-63. `

HANSSON, L., 1982. Experimentson habitat selection in voles: implications for the distributionof two commonEuropean species. Oecologia, 52: 246-52.

HANSSON, L., 1983. Competitionbetween rodents in successionalstages of a Taiga. forest:Microtus agresfisversus Clethrionomys glareolus. Oikos, 40: 258-60.

HANSSON,L.; 1984.Predation as the factor causingextended low densitiesin microtine cycles.Oikos, 43: 255-256:'t ,

HANSSON,L., 1985.The food of bankvoles, wood mice and yeUow-neckedmice., Symp. Zool. Soc.Lond, 55: 141-68.

HANSSON, L., 1989.Landscape and habitat dependencein cyclic and sernicyclicSmall rodents.Holarctic Ecol., 12: 345-50.

HANSSON, L., & H. HENTTONEN, 1985. Gradients in density variations of small rodents:the importanceof latitude and snow cover. Oecologia,67: 394402.

HANSSON, L., & N. C. STENSETH (Eds.), 1988. Modelling small rodent population dynamics.Oikos, 52: 137-229.

167 HARRIS, L. D., 1984.7he FragmentedForest. - Island Biogeographic 77yeoryand ýthe ConservationofBiotic Diversity. Chicago:Univ. ChicagoPress. -'

HARRIS, S., W.J. CRESSWELL,P. G. FORDE, W.J. TREWHELLA, T. WOOLLARD & S.VRAY, 1990.Home-range analysis using Tadio-tracidngdata -a review of problemsand techniquesparticularly as appliedto the study of mammals.Mw"m. Rev.,20: 97-123.

HARRISON, R.D., 1990. Me long distance movementsof Apodemussylvalicus and Clethrionomysglareolus, at the time offelling, in a mature Sitka Spruce (Picea sitchensis)plantation. UndergraduateProject, Univeretty of Durham

]HEALING, T.D., V. T. JEWELL, P.A. JEWELL, I. W. ROWLANDS & J.W. GIPPS, 1983.Populations of the bankvole (Clethrionomysglareolus) and long-tailedfield mouse(Apodemus sylvaticus) on Skomer Island, Dyfed. J'Zool. (Lond), 199: 447-60.

IHESTBECK, J.B., - 1982. Population regulation of cyclic mammals: the social fence hypothesis.Oikos, 39: 157-63.

HELL, M. O., 1979a.DECORANA: a FORMANprogram for detrendedcorrespondence analysis a?d reciprocal averaging. Ithaca (N.Y., U. S.A. ): Cornell University, Sectionof Ecology and Systematics.

HILL, M. O., 1979b. The development of a flora in even-aged plantations. In Ford, E.D., Malcolm, D. C. & J. Atterson, 7he Ecology of Even-aged Forest planlations. Cambridge: Institute of Terrestrial Ecology, pp. 175-92. -

HILL, M. O., 1986.Ground flora and successionin commercial forests. In Jenkins, D. (Ed.), Trees Wildlife Scottish; and in the, 'Uplands. Banchory: institute of TerrestrialEcologY, pp. - 71-8.

HELL, M. O., &- H. G. GAUCIL. Jr., 1980. Detrended , - - - CorrespondenceAnalysis, an improvedordination technique. Vegetatio, 42: 47-58.

168 HOLISOVA, V., & R- OBRTEL, 1980. Food resource partitioning among four forest. Folia my0morph rodent'populations coexisting in a spruce Zool., - 29(3): 193-207.

HURLBERT, S.H., 1984. PseýdoreplicatiOnand, the design of ecological field 9 - experiments.EcologicalMonog? Whs, 54(2): 187-211.

HUTCHINSON, G.E., 1965. Jhe Ecological Aeatre and the Evolutionmy Play. New Haven:Yale Univ. Press.

JENRICH, FLI., & F.B. TURNER, 1969. Measurementof non-circular home range. J Aeor. BioL, 22: 227-237.

JENSEN, T. S., 1984. Habitat distribution, home range and movementsof rodents in matureforests and reforestations.Actalool. Fennica; 171: 305-7. -

JEWELL, P., 1966.Breeding season and recruitmentin someBritish mammalsconfined on smallislands. Symp. Zool. Soc.Loncl, 15: 89-116.

KEKKAWA,, J., 1964.,ý Movement, - activity and distribution of the small rodents Clethrionomysgkweolus and Apodemussyhuticus in woodland. J. Anim. Ecol., 33: 259-99.

KING, C.M., 1985.Interactions between woodland rodents and their predators.Symp. Zool. Soc.Lond, 55: 219-47.

KIRKLAND, G.L., Jr., 1977.Responses of smallmammals to the clearcuttingof northern Appalachianforests. J. Mamm., 58(4): 600-9.

Patterns initial MUCLAND, G.L., Jr., 1990. of small mammalýcommunity changeafter clear-cuttingof temperateNorth Americanforests. Oikos, 59(3): 313-20.

KREBS, 'C. J., 1966: Demographic changes:,in fluctuating 'populations of Microtus c,alifomicus. Ecol. Monographs, 36: 239-73.

y.REBS, C.j., 1989.FcologicalMethodology. New York: Harper and Row.

169 LE BOULENGt, E., 1985.Computer package for the analysisof capture-mark-recapture data.Acta ZooL Fenn., 173: 69-72.

LE BOULENGt, E., 1987. Package C.M. R. AnalYsis of Capture-RecaptureData. PackageDescriPtion and User'sGuide (Version 6). Louvain-la-Neuve:Universite Catoliquede Louvain.

LE BOULENGE-NGUYEN,P., & E. LE BOULENGE, 1986. A new ear-tag for small mammals.J. Zool. (Lond), 209: 302-04.

LIDICKER, W. Z., Jr., 1966. Ecological observations on a feral house mouse population declining to extinction. EcoL Monographs, 36: 27-50.

LIDICKER, W2.2 Jr., 1973. Regulation of'numbers, in an-island population of the California vole, a problem in communitY dynamics. Ecol Monographs, 43: 271-302.

in LIDICKER, W. Z., -Jr., 1978.-Regulation of numbers small mammal populations historical reflections and a synthesis.Spec. Pub! Series, Pymatuning Laboratory of Ecology, Univ. Pittsburgh, 5: 1224 1.

LIDICKER, W. Z., Jr., 1983. Regulacion poblacional de mamiferos. Medio Ambiente, 6: 3-11.

LIDICKETý W. Z., Jr., 1988. Solving the enigma of microtine "cycles". J. Mamm., 69(2): 225-35.

LOVEJOY, T.E., R.O. BIERREGAARD, Jr., J.R. RYLANDS, J.R. MALCOUL C.E. QUINTELA, L. H. HARPER, KS. BROWN, Jr., A.H. - POWELL, G-V. H. POWELL, H. O.R. SCHUBART, & M. B. HAYS, 1986.Edge and other effects of E. Souli (Ed), Conservation isolationof Amazonforest fragments.In M. Biology.. Sunderland(Mass., U. 7,he Science of Scarcity and Diversity, S.A. ): Sinauer Associates,pp 251-85.

170 LUDWIG, J.X, & J.F. REYNOLDS, 1988- Statisfiral Ecology., a, blmer on Methods and Computing.New York: JohnWfley. -

MACARTHUR, R.H., & E.O. WILSON.- 1967. Ae Aeory of Island BiqgeqgriT*y. Princeton:Princeton Univ. Press.

MALVIDO, JR, 1989. Possible Conservation -implications for Invenebrales of UnplantedStreamsides in ConiferousPlantations. MS c Dissertation,University of Durham.

MANLY, B.F. J., & M. J. PARR, 1968.-A new method of estimating population size, survivorshipand birth rate from capture-recapturedata. Trans.Soc. Brit. Entomol 18: 81-89.

MARCSTROK V., N. HOGLUND & C.J. KREBS, 1990. Periodic fluctuationsin small mammalsat Boda, Swedenfrom ý1961 to 1988.J Anim. Ecol., 59: 753-61.

NlAGLTRRAN, A. E., 1988. Ecological Diversity and Its Measurement. Princeton: Princeton University Press.

MAY, R.M., 1976. Simple mathematicalmodels with very complicated dynamics. jVWure 261: 459-67.

MAY, R.M. & G.F. OSTER, 1976. Bifurcations and dynamic 9- complexity in simple ecological models.Amer. Natur., 110: 573-99.

MAZURKIEWICZ, M., 1991.Population dynamics and demograPhy bank 9 of the vole in diffierenttree stands.Acta 77jerjologica,36(3-4): 207-27.

1990.Density-dePendent MESSEER,F., JA VIRGL & L. MARINELLL habitat selection Oe'0109ia, in muskrats:a test of the idealfree distributionmodel. 84: 380-5.

L, what treeson soils ? In D. Jenkins (Ed.), MRES, 1986. are the effectsof Trees and Wildlife in Scottish Up1muls.Banchory: Institute of Terrestrial Ecology, the pp 55-62.

171- MJJJXI;ý G.R., 1979. Quantity'and quality of the annual production -of shoots and flowersby Calluna vulgaris in North-EastScotland. J EcoL, 67: 109-29.

MJUXJ;ý R.S., 1958. A study of a wood mouse population in Wytham Woods, Berkshire.J Mammalogy,39: 477-93.

MONTGOMERY, S.S. F., & W. I. MONTGOMERY, 1990. Intrapopulation variation in the diet of the wood mouseApodemus sylvaticus. J Zool. (LoncL),222: 641-51.

MONTGOMERY, W.I., 1979. Seasonalvariation in numbers of Apodemussyhwficus, Aporlemusflavicollisand CeMonomys glareolus.J ZooL (Lond), 188: 283-6.

MONTGOMERY,W. I., 1980.Population structure and dynamicsof sympatricApodemus species(Rodentia: Muridae). J ZooL aond), 192: 351-77.

MONTGOMIERY, - W. I., 1981. A removal exPeTiment'with sympatric populations of Apodemus sylvaticus (L. ) and A. flavicolfis (Melchior) (Rodentia: Muridae). Oecologia, 51: 123-32.

MONTGOMERY, W. I., 1987. The application of capture-mark-recapture methods to the enumerationof small mammal populations. Symp. Zool. Soc. Lond, 58: 25-57.

MONTGOMMY, W. I., 1989a. Population regulation in the wood mouse, Apoikmus sylvaticus. 1. Density dependencein the annual cycle of abundance.J Anim. Ecol, 58: 465-75.

MONTGOMEERY, W. L, ý1989b. Population regulation, in the, wood mouse, Apockmus distribution sylvaticus. Il. Density dependence in spatial and reproduction. J Anim. Ecol., 59: 477-94.

MONTGOMERY, W. I., 1989c. Population parameters,spatial division, and niche breadth in two ApodetitUSspecies sharing a woodland habitat. Tex.,Tech Univ. Mus. Spec. PubL 28: 45-57.

172 MONTGOMERY, W.I., W.L. WILSON, R- ILAMILTON & P. McCARTNEY, 1991. Dispersionin the Wood Mouse,Apodemus sylvalicus: variable resourcesin time andspace. J. Anim. Ecol,, 60: 179-92.

MORRIS, D.W., 41987. Tests of density-dependent,habitat selection in a patchy enviromnent.Ecol. Monographs,'57(4): 269-81.

MORRIS, D.W., '1989.The effect of spatial scale on patterns of habitat use: red-backed volesas an empiricalmodel of localabundance for Northernmarnmals To ex. T4ech Univ.Mus. Spec. PuM 28: 23-32.

MORRISON,, M. L., & R. G.- ANTHONY, 1989. Habitat use by small mammals on early- growth clear-cuttings in Western Oregon. Can. J Zool., 67(4): 805-11.

MUNOZ, A., & P, MURIýk 1990. Control 'of small mammals in a pine plantation (Central Chile) by modification of the habitat of predators (Tyto alba, Strigiforme, and Pseudalopexsp., Canidae).Acta Oecologica, 11(2): 251-6 1.

R., L. A., GONZALE & P.L. 1986. Population MURfJA, ý 'MESERVE, ecology of Oryzomys longicaudatus philipph (Rodentia, Cricetidae) in Southern Chile. j. 'Anim. Ecol., 55: 281-93.,

MYERS, J.H., & C.J. KREBS, 1974. Population cycles in rodents. Sd. Amer., 230(6): - -38-46.-'', '-I

field Microtus MYLLYMAKI, A, 1977a.Interactions between the, vole agresti., and its microtinecompetitors in Central-Scandinavianpopulations. Oikos, 29: 570-80.

in fluctuating MYLLYMAKI,, A,- 1977b.Demographic mechanisms the Populations of the field vole Microlus agrestis.Oikos, 29: 468-93.

A, Intraspecific and homerange dynamics in MYLLYMAKL .1977c., competition the field voleMicrotus agresfis.Oikos, 29: 553-69.

The food eatenby Apodemus sylucticu., OBRTEL,PL, & V. HOLISOVA, 1979. in a sprucemonoculture. Folia Zool., 28: 299-310.

173 OSTFELD, PLS., 1990. The ecology of terfitoriality in small mammals. Trends in Ecolpsy & Evoludon, 5(12): 411-5.

PARKER, G.R., 1989.Effects of reforestationupon smallmammal communities in New Brunswick.Can. FieldNatur., 103(4): 509-19.

PETTY, S.J., & M. I. AVERY, 1990.-Forest bird communities-a review of the ecology and managementofforest bird communitiesin relation to silvicultural practices in the British uplands.Edinburgh: Forest Commission.

PLANK& E.R., 1988. Evolutionary Ecology (Fourth Edition). New York: Harper & Row.

RANDOLPH,,S. E., 1977. Changingspatial relationshipsin a population of Apodemus sylvaticuswith the onsetof breeding.J. Anim. Ecol., 46(2): 653-76.

RICHARDS;C. G. J., 1981. SomeAspects of the PopulationEcology ofMicrotus agrestis, theField Pole.Unpublished D. Phil. Thesis,University of Oxford.

RICHARDS,,C. G. J., - - 1985. The population dynamicsof Microtus agrestis in Wytham, 1949to 1978.A cla Zool, Fennica, 173: 35-3 8.

ROSENZWEIG,M. L., 1981. A theory of habitatselection. Ecology, 62(2): 327-35.

ROSENZWEIG, M. L., 1989. Habitat selection, community organization, and small mammal studies. Tex. Tech. Univ. Mus. Spec. Publ., 28: 5-2 1.

ROSENZWEIG,M. L., & Z. ABRAMSKY, 1985. Detecting density-dependenthabitat selection.Amer. Natur., 126: 405-17.

ROSENZWEIG,M. L., & Z. ABRAMSKY, 1986. Centrifugal community organization. Oikos,46: 33948.

174 ROWAN. A. A., 1986. The nature of British upland forests in the 1990s: In D. Jenkins (Ed.), Trees and Wildlife in the Scottish Uplands. Banchory: Institute of Terrestrial Ecology, pp. 7-13.

SCHOENEK-T.W., 1983.,Field experimentson interspecificcompetition. -Amer. Natur., 122:240-85.

SCHOENER,T. W., 1986." When should a field experimentbe -counted ?: a, reply to Galindoand Krebs. -Oikos; 46(l): 119-21.

SCHROEDER,G. D., 1981. Using edge effect-to estimateanimal densities.J. Mmm.; - 62(3): 568-73.

SEBEK'G. A.F., 1982.7he Estimation of Animal Abundanceand Related Parivneters (SecondEdition). London: CharlesCniffin.

SHAFER,'C. L., 1990. Nature Reserves.Island Aeory and Conservation practice. Washington:Smithsonian Institution Press.

SHARP, - C. & K. A. WILSON, 1987. Small 11 mammal use of a managed secondary succession.ImpHnt (York), 9: 4-9.

SHINIWELL, D. W., 1971.7he Description and CZassifilcationof Vegetation. London: Sidewick and Jackson.

SMAL, M., &TS. FAHULEY, 'C. 1980. Food of woodmice (Apodemussylvaticus) and bankvoles (Clethrionomysglareolus) in oak and yew woods at Killarney, Ireland. -J Zool. (L6nd), 191:413-8.

SMAL, C.M., & J.S. FAIRLEY, 1982. The dynamicsand regulation of small rodent populationsin the woodland ecosystemsof Killarney, Ireland. J. Zool. (Lond), 196: 1-30.

SNHTK M. H., R.H. GARDNER, J.B. GENTRY, D.W. KAUFMAN & M. H. OTARRELL, -1975.,,Density estimationsof small mammal populations.In F.B. GoUey,'-'K. Petrucewicz and L. RYszkowsId (Eds.), Small Mammals: 7heir

175 Productivity and Population Dpýics. Cambridge: Cambridge Univ. Press, pp 25-53.

SMYTH, M., 1966.Wmter breedingin woodlandrnice, Apodemus sylvaticus, and voles, Clethrionomysglareolus andMicrotus agrestis, near Oxford. J Anim. EcoL, 35: 471-85.- 1- 11-

SOULt, M. E. (Ed.), 1986. Conservation Biology - 7he Science of Scarcity and Diversity. Sunderland(Mass., U. S.A. ): SinauerAssociates.

SOUTHERN, H.N., 1970. The natural control of a population of tawny owls (Strix aluco).J. Zool. amid), 162: 197-285.ý

SOUTHERN, H.N., 1973.,A yardstick for measuring populations of small rodents. Mamm.Rev.. 3: 1-10.

SOUTHERN,H. N., & V. P.W. LOWE, 1968. The pattern of distribution of prey and -" predationin tawny owl territories.J Anim. Ecol., 37: 75-97.

SOUTHERN, H. N., & V. P.W. LOWE, 1982. Predation by tawny owls (SlHx aluco) on bank voles (Clethrionomys glareolus) and wood mice (Apodemus sylvaticus). J. Zool. (Lond), 198: 83-102.

STAINES, B.W., 1986.Mammals of Scottishupland woods. In D. Jenkins(Ed. ), Trees and Wildlife in the Scottish Uplands.Banchory: Institute of TerrestrialEcology, pp. 112-120. -ý

STENSETH;N. C., 1977.'On the importanceof spatio-temporalheterogeneity for the populationý dynamics of -rodents: towards a theoretical foundation of rodent control. Oikos,29: 545-52.

STENSETK N. C., 1980.- Spatial heterogeneity and population stability: some evolutionaryconsequences. Oikos, 35: 165-84.

STENSETK N. C.', 1985.Models of bank vole and wood mousepopulations. Symp. Z001., Soc.Lond, 55: 339-76.

176 STENSETK N. C., L. HANSSON, A. MYLLYMAKI, M. ANDERSON & J. KATILA, 1977.- General models -for the population dynamics of the field vole Microtus agrestis in Central Scandinavia.Oikos, 29: 61642.

STODDART, -D.M., 1982. Does trap odour influence estimation of population size of the short-tailed vole, Microlus agrestis ? J. Anim. Ecol., 51: 375-86.

STODDART, D. M., & P.A. SNUTI-L 1986. Recognition of odour-induced bias in the live- trapping of Apodemus sylvaticus. Oikos, 46: 194-9.

STUWE, M., & C.E. BLOHOWTAK, 1985.'McPAAL: Micro-computer Program for the Analysis of Animal Locations. Computer Print-out, Fort Royal (VA, U. S.A. ): SmithsonianInstitution.

SVARDSON,G., 1949.Competition and habitat selection in birds. Oikos, 1: 157-74.

SZACK4 J., & A.'LIRO, 1991. Movementsof small mammalsin the heterogeneous landscape.Landscape Ecology, 5(4): 219-24.

TANAKA, FL, 1980. Controversial problems in advanced research on estimating population-densities, of small rodents.Res. Pop. EcoL (Kyoto University), sup. 2: 1-67. :ý-

TANTON, "M. T., 1965. Problems of five-trapping and population estimation for the woodmouse,Apodemus s)lvaficus. J. Anim. Ecol., 34: 1-22.

TANTON, M. T., 1969. The estimation and biology of populations of the bank vole (Clethrionomysglareolus (Schr.)) and woodmouse(Apodemus Sylvaticus (L. )). j. Anim. Ecol., 38: 511-29.

TER BRAAK, C.F. J., 1986. Canonical correspondenceanalysis: a new eigenvector techniquefor multivariatedirect gradientanalysis. Ecology, 67(5): 1167-79.

TER BRAAK, C.F. J., 1988. CANOCO - a FORTRAN Program for Canonical Community Or&nation by (Partial) (Defrended) (Canonical) Correspondence

177 Analysis,Principal ComponentsAnabaisandRedundancy Analysis (Version2.1). Wageningen(Holland): TechnicalReport LWA-89-02, Groep Landbowwiskund [AgriculturalEcology Group]

TEVIS, L.,,*., 1956,"Responses of smallmammal populations to logging of Douglas-fir. J. Mamm.,37(2): 189-96.

TEW, Tý,1987. A comparisonof small mammalresponses to clean and dirty traps. J Zool, (Lond), 212: 361-64.

THOMSON, A.G., " 1986. Anomalies in'estimations of smallmammal abundance in conifer plantations.J Zool. aond), 209: 287-90.

TWIGG," L. E.,.B' J.' FOX &I LUO, 1989. The modified primary successionfollowing sandmining: a validationof the use of chronosequen6eanalysis. Aust. J Ecol., 14: 441-7.

VADBERý S.A-, 1990.7he Distribution and Abundanceof Small Mammals In Second Rotation Sitka Spruce Plantations in Hamsterley Forest, South West County Durham.MSc Dissertation,University of Durham. """"

VENABLES, L. S.V., & U.M. VENABLES, 1965.Transect sampling of small mammals in coniferplantations, Newborough Warren, Anglesey. Nature Wales,9: 171-84.

VENABLES, L. S.V., U. M. 1971. Mammal ^'& -VENABLES, . population changes in'a young conifer plantation, 1960-70, Newborough Warren, Anglesey. Nature Wales, 12: 159-63.

VHTALA, J., 1977. Social organizationin cyclic subarctic populations of the voles Clethfionomys rufocoms (Sund.) and Microtus agrestis (L. ). AnnIs. Zool. ennid, 14: 53-93 -'"

WATTS, C.H. S.,' 1968. The foods eaten by Apodemuss3lvaficus and Clethrionomys glareolusin Wythain Woods,Berkslire. J Anim. EcoL. 37:25-41.

178 WATTS, C.H. S., 1969. The regulation of Wood Mice (Apodemus sylvaticus) numbers in Wytharn Woods, Berkshire. J. Anim. Ecol, 38: 285-304.

WATTS, C.H. S., 1970.Long distancemovements of Bank voles andWood mice.J. Zool. (Lond), 161: 247-56.

WILCOX, B.A., 1980.Insular ecologyand conservation.In M. E. Souk & B. A. Wilcox (Eds.), ConservationBiolqy, Sunderland(Mass): Sinauer Associates, pp. 95-117.

WILSON, E.O., & E.O. WRIIS, 1975. Applied biogeography.In NLL. Cody & J.M. Diamond (Eds.), EcoloV and Evolution of Communities.Cambridge, Mass, (U.S. A. ): BelknapPresS, pp. 522-34.

WOLK, E., &K WOM 1982.Responses of smallmammals to the forest managementin the BialowiezaPrimeval Forest. Acta Aeriologica, 27: 45-59.

WOLTON, R.J., 1983.The activity of free-rangingwood mice Apodemussylvaticus. J. Anim. Ecol., 52: 781-94.

WOLTON, PLJ., 1985. The ranging and nesting behaviour of Woodinice, Apodemus sylvaticus (Rodentia:Muridae) as revealedby radio-tracidng.J. Zool. (Lond), 206(2): 203-22.

WOLTON, R-J., & J-R- FLOWERDEW, 1985. Spatial distribution and movements of wood mice, yeHow necked mice and bank voles. Symp. ZooL Soc. Lond, 55: 249-75.

ZAR, J.R, 1984. Biostalistical AnabWs (Second Edition). Englewood Cliffs (Ni, U. S.A. ): PrenticeHall.

MANG, Z., & M. B. USHER, 1991. Dispersal of wood mice and bank voles;in an agriculturallandscape. Acta Aeriologica, 36: 239-45.

ZUBAID, A., & M. L. GORMAN, 1991. The diet of wood mice Apodemus*Iwficus living in a sanddune habitat in north-eastScotland. J. Zool. (Lond), 225: 227-32.

179 APPENDIX I- PROGRAM AGECLASS

Ageclass is a SAS program, written by E. Le Boulenge' and me, to allocate individuals irreversibly to age classesby body weight (see text, Section 4.2.2),

1 SAS procedure to create an ageclass (Agecl) based upon 2 the value of Weight (according to species) 3 and uith noni -decxeasing value - It replaces weiclas 4 cms liledef in disk f$indiv data a;

5 cms riladef soztie pun;

6 Data capt; infile in; input grid $ agegrid $ species $ tag $ 9 sex $ date line column ueight rep $ alive $ taglos $ 10 ntag $ weiclas $ glin gcol ueicl session;

12 /* ---Sozting file by Individual and Capture Date--

MOTE: The infile IX is: Filename=F$IHDIV DATA Al, Recfm=FB Lzecl=60, Blksi=e=1020

NOTE: 3058 records were read :Exom the infile IM. MOTE: The data set WORK.CAPT has 3058 observations and 18 variables.

13 PROC SORT; 14 BY TAG DATE; is 16 /* --Calculating Date of first capture of Individuals--- 17 /* --- and calculating the value of Ageci (not decreasing) -

HOTE: SAS sort uas used. HOTE: The data set UORK. CAPT has 3058 obsazvations and 18 variables.

DATA; SET; BY TAG DATE; 19 RETAIX PREM 0 ageclant 0; 20 if weight =. I weight =0 then agecl, = 0; 21 else if weight < 16 then agecl = 1; 22 else if species='CGI then do; 23 it weight < 20 then agecl=2; 24 else agecl=3; 25 end; 26 else do; 27 if weight < 21 then agecl=2; 28 else agecl=3; 29 end; 30 IF FIRST. TAG THEX PREn=DATE; 31 else if agecl

ISO I i

2 The SAS System

35 Proc sort; 36 by prem tag date; /* ---- Reoxder the :Eile 37

HOTEE:SAS sort uas used. HOTE: The d'ataý set IJORK'.DATAl has '3058 observations and 21 variables.

23 Proc lorinat; 39 /* Produce labels ior the values oi Agecl 40 value AGEFMT 41 0= 191 42 43 2 ="ISI 44 3 MOTE. Format AGEF11T has been output.

45 Data; Set; 46 tile sortie; 47 PUT grid SCHAR3.45 agegrid $CHARI. 0 species $CHARZ. aIO tag 48 $CHARIaN sex $CHAR1. a16 date 3. a2O line 2. a23 colwan 2. a26 49 weight 2. a29 rep $CHAR1. a3l alive $CHAR1. M taglos $CHAR4. 50 @38 ntag $CHARS. a45 agecl AGEFrIT1.4D47 glin 2. aSO gcol 2. 51 M weicl 1. aSS session 2.;

'61rjrE: The lile SORTIE is: .. Filename=PUHCH Racf"i=F Lrecl=80, Blksi=e=8O

191 APPENDIX 2- PROGRAM FCOORD

Fcoord is a SAS program,written by E. Le Boulengd'andme, to translatecapture locations(grid numberand trap number)into locations in a commonsystem of spatial coordinates,based on the actualdistances and angles between the grids.

SAS procedure to calculate "pooled" trap coordinates cms Aledel in disk Windiv data a;

3 cms :Eiledef soxtie pun;

4 Data capt; 5 infile in; 6 file sortie; 7 input grid S agegrid $ species $ tag $ a sex $ date line column weight rep $ alive $ taglos $ 9 ntag S uaiclas $ glin gcol session; 10 11 it grid='BL". ' and line <3 then do; 12 alpha=32; x=column; y=line; dx=0.666667; dy=dx; 13 if Iine=11then do; xO=5; yO=81; end; 14 else do; x0=8; yO=86; y=y-1; end; is end; 16 else if grid='CO. Ml then do; 17 alpha=32; x=column; y=line; dx=-l; dy=1; xO=42; yO=102; is end, 19 20 else if grid=IC02' then do; 21 alpha=35; 22 if agagrid='XI then do; 23 x=column; y=line; dx=0.666667; dy=-dx; xO=38; yO=91; 24 end; 25 else do; 26 x=line; yxcolumn; dx=-l; dy=dx; x0=4S; yO=95; 27 end; 28 end; 29 30 else if grid='CO11 then do; 31 alpha=18; 32' if agegrid='Xl then do; 33 x=column; y=line; dx=0.666667; dy=-J; xO=33; yO=44; 34 end; 35 else do; 36 x=lina; y=colu: -an; dx=l; dy=dx; XO=36; yO=34; 37 end; 38 end; 39 else if grid='C031 then do; ý40 41 alpha=23; x=column; Y21ine; dx=1; dy=dx; xO=62; yO=39; 42 end; 43 44 else if grid='CCSI then do; 45 alpha=33; x=lina; Y=Column; dx=l; dy=-I; x0=51; yOzZO; 46 end; 47 48 else x=.; 47 2 The SAS'System

il then do; 50 x ne . :-, 1 x= X-1; y= Y-1; /* Distance to origin c: E grid 52 alphazalpha * 3.14159 180; /* Alpha in radians 53 R11 = cos(alpha); RZZ. R11; /* Rotation matx4,.x 54 R21 = sin(al; ha); R12 -R21; 55 xo=xo 3; /* Origin of g=-44- in 15M units 56 yO=yo 3; -, 57 Heux = Ho + R11 :1x* dx + R12 *y * dv; rs Newy = yO + RZI *x dx + R22 *y * dy; 59 Neux = round(ANauX) + 60 Heuy = round(HeuY) + 1; 61 and; -7 else do; 11suX=.; XeuY=HeuX; end;

PUT grid $11."HAR3. @5 agegzid $CHAR1. 617 species $CHARZ. a110 tag $CH. date line, 65 '.'. R3. c314 sex XHAR1. -316 3. ZZO 2. a"13 coluýftfi 2. J26 6b weight 2.029 rep schazl. J31 alive $chazi. a33 taglos schaz4. 67 4138 ntag $chax5. Z45 ueiclas $ charl. Z47 XeuY 2. a5O HeuX 2. M sassion 2..;

"COTE: The infile. IX is: F-ilen=a=F$IXDIV DATA All Rec! ==FB #Lrecl=80, Blksi=a=960

NOTE: .?he lile SORTIE is: Filinane=MCH Recim=F Lxecl=00. Bl]xima=80

183 APPENDIX 3 CORRECTINGPOPULATION SIZE ESTIMATES FOR TBE VARYING NUMBER OF GRIDS:EFFECTS ON THE CORRELATIONSBETWEEN POPULATION SIZES AND OTBER PARAMETERS

TABLE APPENDIX 3. Pearsodscorrelation coefficientsbetween population size (as estimatedby the Manly-Parr method) and other demographicand spatial parameters, ' comparingpopulation size estimatescorrected for the numberof grids actually trapped, anduncorrected estimates (see text, Section4.2.2).

Comparison df. Uncorrected Corrected

r p r a) Demographicrates correlatedwith Nt Total survival, As 16 0.2520 > 0.20 0.0800 > 0.50 Total survival, Cg 15 0.2676 > 0.20 0.0943 > 0.50 Adult survival, As 17 0.3374 0.050.10 Adult survival, Cg 17 0.6347 < 0.01 0.6056 <0.01 Subadult survival, As 17 -0.0179 > 0.50 -0.1495 > 0.25 Subadultsurvival, Cg 17 -0.0201 > 0.50 0.0978 > 0.50 JuvenilesurvivaL As 16 0.1972 >0.10 0.0307 > 0.50 Juvenilesurvival, Cg 14 0.3675 0.03 0.25 Juvenilerecruitment, As 17 0.6404 <0.01 0.5788 <0.01 Juvenilerecruitment, Cg 17 0.4208 < 0.05 0.4653 < 0.05 Prop. females,As 17 rcprod. -0.0630 > 0.50 .0.1232 > 0.25 Prop. females,Cg 17 reprod. -0.4191 < 0.05 -0.5153 < 0.05

t =c> imm c3cm nT-2. nlumC5L - mmm nm3el-" 1P4Mum)

184 A-ýýr%c2l-2c 3f cscmoiml--: Lim%2mftALc5r%)

b) Demographicrates df. Uncorrected Corrected correlatedwith Nt -I r p r p Total surviv4 As 15 -0.5911 < 0.01 -0.4496 < 0.05 Total survival,Cg 14 0.1678 > 0.25 0.1010 > 0.50 Adult survival,As 16 -0.2472 > 0.20 -0.3416 0.05 0.50 -0.0372 > 0.50 Subadultsurvival, Cg 16 0.0157 > 0.50 0.0442 > 0.50 Juvenilesurvival, As 15 -0.1432 > 0.25 -0.1732 > 0.25 Juvenilesurvival, Cg 14 0.3870 0.05 0.10 Juvenilerecruitment, As 16 0.4117 0.05 0.50 0.0708 > 0.50 Prop.reprod. females, As 16 -0.5830 <0.01 -0.6248 < 0.01 Prop.rcprod. females. Cg 16 -0.7071 <0.01 -0.7613 <0.01 c) Home ranges/ distances moved (DM! ) x seasonalN homcranges, As 8 -0.7058 < 0.05 -0.6991 < 0.05 'Male, Male homeranges, Cg 5 0.1236 > 0.50 0.0505 > 0.50 Femalehome ranges, As 8 0.0872 > 0.50 0.0966 > 0.50 Femalehome ranges, Cg 6 0.4804 0.6510 > 0.10 .>0.10 Male DA4s,As 8 -0.5734 0.05 0.25 0.1706 > 0.25 DMs, Female Cg 8 1 0.4372 >0.10 0.4903 >0.10 d) Regressions,IvIev's F F modifiedmethod! s E,, xN E. x N, As 17 5.1650 < 0.05 * 7.0180 < 0.05 * 12.3940 E. xN, Cg 17 3.3210 0.05 0.10

Note. Syrpbols; As, Apodemus sylvaticus-,Cg, Clethrionomys gi, eolu I, degrees , or S-*,d of freedom; r, correlationcoefficient; F, Fishersratio; p, one-tail probability of eachvalue of r andF; asterisks indicatesignificant results: p<0.05 and p<0.0 1.

185 APPENDIX 4 SOMELONG DISTANCE MOVEMENTS OF WOODMICE

While many ecologistsbelieve that long distancemovements (i. e. longer than I Km) are commonin woodmice(W. I. Montgomery,pers. comm.), there seemsto be little publishedfield evidenceproving this point. During the presentstudy in HamsterleyForest, two exceptionallylong movementswere detected.These movements were longer than any natural movementpreviously recorded for Apodemussylvalicus (compare,for example, with Bovet, 1962, Kikkawa, 1964, Bergstedt, 1966, Watts, 1970, Geuseel al., 1985, Wolton and Flowerdew,1985); they exceedeven the 1.5 Km recentlyrecorded by Szacki and Liro (1991) for the congenericspecies A. agrarius. The characteristicsof the two movementsI recordedwere asfollows. 1) Movementby a fully reproductiveadult male, weighing 22 g. After being captured twice in a young Sitka spruceplantation (grid Comer East) on 18 and 20 June 1990, it was next capturedthree weekslater (I I July) in a clear-felfingin the North of the forest (grid View). The straightline distancebetween the two pointsis 1,750m (as estimated,to the nearest50 m, by plotting the displacementon a map of scale 1:10,000); the two sites are separatedby a stream,Euden Beck (Figure Appendix4). After this movement,it was recapturedtwice in View, on the two following days, before disappearingfrom the population. 2) Movementby a non-reproductivejuvenile male,weighing 14 g (beforethe movement). It was capturedthree times in October 1991 (the last on 17) in the grid Comer South, a clear-felfing.It was next found thirty-six dayslater (22 November),killed by a breakback trap set near the buildings of Mayland Hall Farm. Such buildings are surroundedby improved pastureland,and located5,100 m away in straightline from the original capture point, alsoacross a stream,Bedburn Beck (FigureAppendix 4). distances The quoted should be regardedas minimum estimatesof the actual distances- travelled,because they were measuredin a straight fine and it is most unlikely that the animalsthemselves followed such a route, as they had to cross streams.Judging by their distancesalone, the two displacementsI detected were probably dispersive Thesefindings movements. showthat Apodemussyhuncus is capableof makingvery long in Although is movements nature. this not unexpectedin view of the high vagility of this both its home (e. Wolton, rodent within range g. 1985; Attuquayefio et al., 1986) and during dispersal(see referencesquoted above),the presentnote is the first case,to my knowledge,in which natural movementsof such a length were actually recordedfor this species.

186 C13 E LL cc

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197 APPENDIX 5 DATES OF THE TRAPPING SESSIONS

The dates listed below refer to standard four-day trapping sessions,as describedin Chaptertwo; traps were placedin the first day and checkedin the three following days.

a) Datesof the censuses(Chapter 3)

Successional Site Wmter Summer Summer Summer stages 1990 1990 1991 1992 Farm Felled 20-23/02 02-05/07 09-12/07 15-18/06 View 05-08/03 11-14/07 10-13/07 16-19/06 Clear-fellings Comer South 25-28/06 27-30/06 08-11/06 Comer 2 21-24/06 09-12/06 Comer East 18-21/06 Comer 3 20-23/06 Hut 06-09/03 25-28/06 Adder 05-08/02 12-15/06 12-15/07 02-05/06 Young Farm 19-22/02 02-05/07 10-13/06 15-18/06 (5-8 yrs old) Scots Pine 06-09/03 02-05/07 09-12/07 02-05/07 Road 10-13/07 12/15/07 02-05/07 Stuart 02-05/07 10-13/07 01-04/07 Comer Young 18-21/06 Adder Mature 06-09/02 Comer 1 29-31/03 18-21/06 Mature Comer 2 28/31103 19-21/06 (about 40 yrs Hut Mature 25-28/06 old or more) Comer North 12-15/07 27-30/06 02-05/07 Farm Mature 02-05/07 10-13/06 15-18/06

188 APPENDIX 5 (cont.)

6). Symbols b) Datesof trappingof the populationstudy in Comer Complex(Chapters 4 to as in TableS. 1, Chapter5.

Month CoI(M-F) Co2(M-F) Co3(F) CoS(F) CoN(M)

Mar 28-31 28-31 - Apr 23-26 23-26 - - - May 28-31 28-31 - 07-10 07-10 Jun 18-21 18-21 - 25-28 25-28 1990 Jul Felling Felling - 28-31 12-15 Aug 15-18 15-18 - 14-17 14-17 Sep 26-29 25-28 25-28 26-29 26-29 Oct BrashRern 28-31 28-31 26-29 26-29 Nov 14-17 12-15 12-15 19-22 20-23 Dec 03-06 04-07 03-06 - -

Feb 25-28 25-28 25-28 - - Mar 12-15 12-15 12-15 19-22 19-22 Apr 08-11 08-11 08-11 09-12 09-12 May 21-24 21-24 21-24 22-25 21-24 1991 Jun 20-23 21-24 20-23 27-30 27-30 Jul 22-25 23-26 22-25 28-31 28-31 Aug 13-16 13-16 13-16 19-22 19-22 Sep 24-27 25-28 24-27 17-20 17-20 Oct 14-17 14-17 BrasbRem 16-19 22-25 Nov 11-14 11-14 BrasbRem 19-22 19-22 Dec 03-06 03-06 09-12 09-12 09-12

Feb 10-13 10-13 11-14 24-27 24-27 1992 Mar 09-12 09-12 09-12 16-19 16-19 Jun 08-11 08-11 08-11 09-12 16-19

189

004 S