Aspects of the conservation biology of the

noctule bat (Nyctalus noctula)

lain James Mackie B.Sc.(Hons) University of Aberdeen

A thesis presented for the degree of Doctor of Philosophy. Department of Zoology. University of Aberdeen.

2002 I declare that this thesis was composed by myself and only includes work carried out by myself, except where otherwise indicated. It has not been submitted in any previous application for a higher degree. All verbatim quotations are identified with quotation marks and all sources of information have been fully acknowledged.

lain Mackie

2002

ii Abstract

The present study primarily examined habitat selection in the noctule bat by comparing used resources with samples of available resources. The distribution of individual bats of different reproductive status was also investigated, in relation to resources, to enable the prediction of future events under different habitat management scenarios. Individual bats were radio tracked to determine foraging and roosting habitat preferences in a cultural landscape.

Noctules consistently preferred to forage over broadleaved woodland and pasture rather than arable land and moorland. A comparison of habitat use and foraging activity demonstrated that non-lactating bats used less preferred habitats significantly more than lactating bats. However, there was little difference in the timing of foraging activity or in the distances traveled to foraging grounds between the two groups.

Roosting requirements were identified using data from three separate study sites and intraspecific roosting behaviour was investigated at the radio-tracking site. Noctules consistently selected old woodpecker holes that were larger, further from the ground and in more open situations. Lactating bats changed roosts less frequently and generally occupied one specific roost, which was larger than the other roosts used by the same colony.

The echolocation calls used by noctules are particularly suited to monitoring using bat detectors. Formal evidence that noctule calls could be accurately identified from field recordings was obtained by comparing the calls from tracked bats with calls recorded from Leisler's bats.

iii Summary

Characterising the habitat requirements of animals of conservation concern is a primary aim in conservation biology. Key habitats for bats are roosting and foraging areas. The present study primarily examined habitat selection in the noctule bat by comparing used resources with samples of available habitats. In order to control for methodological problems associated with correctly defining available resources, and statistical conflicts resulting from multiple testing, available resources were estimated at several different levels and both univariate and multivariate statistics were employed to measure the degree of selection. Further evidence that habitats were essential for population survival was investigated by intraspecific comparisons of foraging and roosting behaviour between reproducing and non-reproducing female noctules.

Compositional analysis, comparing the proportion of time spent foraging in available habitats with the proportional availability of those habitats, was used to determine foraging habitat preferences in a cultural landscape. To control for errors in defining available habitats two analyses were conducted, the first using proportions of each habitat found within the colony 2 Minimum Convex Polygon (MCP) (62.75km ), the second using proportions from individuals' 2 2 2 MCPs (mean 9.51km , range 0.53km to 24.57km ). Female noctules (n=20) preferentially foraged over broad leaved woodland and pasture in both analyses. Significantly less preferred or marginal habitats, which were underused relative to their availability, were arable land and moorland. A further compositional analysis comparing relative use between reproductive (n= I 0) and non-reproductive (n= 10) female noctules demonstrated that non-reproductive bats used marginal habitats significantly more than reproductive bats, suggesting the number of breeding bats may be limited by the area of preferred habitats. There was little difference in the timing of foraging activity or in the distances traveled to foraging grounds between the two reproductive categories.

Roost cavity requirements were assessed by comparing roosts (n=31) to random available cavities in three different types of woodland; plantation, lowland and river valley. Available cavities were determined at two spatial scales, random cavities in the same wood (n=31) and random cavities adjacent to roost trees (within 10m) (n=3}). In order to control for error in defining available cavity resources cavities were confirmed as available by close inspection. Noctules consistently selected old woodpecker cavities that were larger (mean volume 0.33m\ further from the ground (mean entrance height 7m) and in more open situations than random or available cavities across

iv woodland types. Intraspecific differences in roosting behaviour, at the river valley site, between reproductive categories found lactating bats changed roosts less frequently and generally occupied one roost. This main roost cavity was significantly larger and in a significantly larger tree than others used by the same colony. It was also the only dead tree used at that site.

As noctules relied on woodpeckers to provide suitable roost cavities a novel habitat creation technique was developed. Because woodpeckers prefer to excavate roosts in decayed heartwood, wood cores (n= 13) were removed from recently excavated woodpecker holes and placed in culture to identify the fungal species causing decay. One suitable heart-rot fungus (Inono(us hispidus) was isolated from wood cores. Future work will involve infecting trees with cultured fungi and monitoring for subsequent decay and woodpecker activity.

The echolocation calls used by noctules are particularly suited to monitoring using bat detectors. However, in order to model population abundance or change, probability based systems must be developed that control or quantify observer and sampling error. Multivariate models were constructed, using randomly sampled calls, to separate sympatric Nyctalus species. Observer error was minimised by automatic measurement of echolocation parameters and the level of misclassification was used to estimate the probability of correctly identifying individual "search phase" calls (75% correctly classified to species) and call sequences (98.5% correctly classified).

The study concludes by discussing a mechanistic framework demonstrating how availability of suitable habitats effects noctule populations, provides conservation advice and indicates possible directions for future research.

v Acknow ledgements

I am particularly in debted to my supervisor Paul Racey for his support and advice throughout this study. Tony Hutson also contributed advice regarding this study.

In 1997 the Endangered British Mammals Fund, administered by the Peoples Trust for Endangered Species, provided the Bat Conservation Trust with funding for a two year project on the conservation biology of the noctule bat. Further funding was provided by The Peoples Trust for Endangered Species, the Watt Fund, the Principles Fund and through a scholarship from Bat Conservation International for which I am very grateful.

Each chapter of this thesis required the assistance to a greater or lesser extent of many people including Peter Smith, Sheila Wright, Jessica Rushton, Nigel Hester, Helen Wells, Keith the Thatcher, Dave Cove, Shirley Thompson, Major David Counsel, Jon Russ, Frank Clarke, Steve Woodward, Ken Smith and without their input would have been significantly worse.

Special thanks go to Peter Smith for his hospitality, willingness to discuss complex issues and programme writing abilities. Nigel Hester from the National Trust provided me with logistics above and beyond the call of duty, making me feel quite welcome. Dave Cove and his family were especially friendly and regularly helped me out of a jam. Jon Russ provided the Leilser's bat echolocation calls and technical advice on bioacoustics without question. Ian Patterson provided the sound analysis software. Finally Ken Smith took me to look in his woodpecker holes and Steve Woodward is culturing the fungi.

During this study my home life has been particularly interesting and revolves around two people, my daughter Robyn and my Julie. They have supported me through some dark moments and although the holidays haven't been long they have maintained a refreshing ability to make me laugh or cry at their merest whim.

vi Finally I would like to thank my brothers and mother for never telling me to get a real job and occasionally seeming interested. My baby brother Gordon was a real saviour in the field for which I still owe him a great debt and £60.

I thank my father for many things.

vii Contents

CHAPTER 1. GENERAL INTRODUCTION 5

I.1 CONSERVATION BIOLOGY 6

1.1.1 ApPROACHES TO CONSERVATION BIOLOGY 6 1.1.2 FROM INDIVIDUAL BEHA VIOUR TO POPULATION ECOLOGY 7 1.2 BATS 7

1.2.1 TAXONOMY 7 1.2.2 ECOLOGY 9 1.2.3 CONTRIBUTION TO BIODIVERSITY 9 1.3 THE NOCTULE BAT (NYCTALUS NOCTULA) 10

1.3.1 LIFE CYCLE 11 1.3.2 DIET 14 1.3.3 FORAGING ACTIVITY AND HABITAT PREFERENCFS 14 1.3.4 ROOSTING PREFERENCES 15 1.4 ROOST SWITCHING 16

104.1 PREDATION 17 1.4.2 COMMUTING COSTS 17 1.4.3 ECTOPARASITE REDUCTION 18 1.4.4 ROOST MICROCLIMATE 18 1.5 BAT CONSERVATION RESEARCH 18

1.6 THE UK SITUATION 20

1.7 THE CONSERVATION OF THE NOCTULE BAT (NYCTALUS NOCTULA) 21

1.8 RECORDING AND MONITORING 22

1.8.1 MONITORING BATS 22 1.9 AIMS 24

CHAPTER 2. HABITAT PREFERENCE, SPACE USE AND FORAGING ACTIVITY OF NOCTULE BATS NYCTALUS NOCTULA IN A CULTURAL LANDSCAPE. 25

2.1 INTRODUCTION 26

2.2 METHOD 30

2.2.1 STUDY SITE AND STUDY COLONY 30 2.2.2 RADIO-TRACKING 30 2.2.3 HABITAT AVAILABILITY AND TIME BUDGET QUANTIFICATION 33 2.3 ANALYSIS 37

2.3.1 HABITAT PREFERENCES 37 2.4 RESULTS 38

2.4.1 TRACKING 38 2.4.2 HABITAT AVAILABILITY AND USE 39 2.4.3 HABITAT PREFERENCE 41 2.4.4 INDIVIDUAL HABITAT AVAILABILITY 43 2.4.5 DIFFERENCES IN HABITAT USE BETWEEN REPRODUCTIVE CATEGORIES 45 2.4.6 REPRODUCTIVE CATEGORY ACTIVITY COMPARISONS 49 2.4.6.1 Foraging activity 49 2.4.6.2 Time use 50 2.4.6.3 Space use 50 2.5 DISCUSSION 51

2.5 .1 HABITAT PREFERENCE 51 2.5.2 FORAGING ACTIVITY 53 2.5.3 DIFFERENTIAL HABITAT USE 54 2.5.4 CONSERVATION RECOMMENDATIONS 56 2.6 SUMMARY 57

CHAPTER 3. ROOST SELECTION AND ROOSTING BEHAVIOUR OF TREE DWELLING NOCTULE BATS (NYCTALUS NOCTULA). 58

3.1 INTRODUCTION 59

3.1.2 ROOSTING BEHAVIOUR 62 3.2 METHOD 64

3.2.1 ROOST LOCATION 64 3.2.2 STUDY AREAS 64 3.2.3 ROOST HOLES 65 3.2.4 RANDOM TREE HOLES 65 3.2.5 ADJACENT TREE HOLES 65 3.2.6 VARIABLES 66 3. 2. 6.1 Cavity variables 66 3.2.6.2 Tree and tree location variables 67 3.2.7 BEHAVIOUR 67 3.2.8 STATISTICS 68 3.3 RESULTS 69

3.3.1 BETWEEN WOODLAND COMPARISON 69 3.3.1.1 Continuous variables 69 3.3.2 BETWEEN CAVITY GROUP COMPARISONS 70 3.3.2.1 Cavity variables 70 3.3.2.2 Tree and habitat variables 70 3.3.2.3 Categorical variables 7l 3.3.3 COMPARISON OF WOODPECKER AND ROT HOLES 74 3.3.4 MULTIVARIATE RESULTS 75 3.3.5 ROOSTING BEHAVIOUR 76 3.3.5.1 Comparisons ofdifferent reproductive categories at a single site 76 NON LACTATING 77

3.6 DISCUSSION 78

3.6.1 PROBLEMS ASSOCIATED WITH HABITAT SELECTION STUDIES 78 3.6.2 THE NATURAL HISTORY OF NOCTULE BATS AND ITS EFFECT ON CAVITY AVAILABILITY 78 3.6.3 PROBLEMS ASSOCIATED WITH MULTIPLE TESTING 79 3.6.4 ROOST HABITAT SELECTION 79 3.6.5 THERMOREGULATION 80 3.6.6 PREDATION PRESSURE 81 3.6.6.1 Avian predation 81 3.6.6.2 Terrestrial predation 81

2 3.6.7 CONCLUSIONS 82 3.7 SUMMARY 84

CHAPTER 4. CAN BAT DETECTORS BE USED TO MONITOR NOCTULE BAT POPULA TIONS? 85

4.1 INTRODUCTION 86

4.1.1 WHY SHOULD DIFFERENT BAT SPECIES USE ECHOLOCATION CALLS WITH DIFFERENT CHARACTERISTICS? 89 4.1.2 SOUND PRODUCTION AND EMISSION 89 4.1.3 SOUND PROPAGATION IN AIR AND VARIATION OF SOUND PARAMETERS 90 4.1.4 PREDICTIONS FOR THE ECHOLOCATION CALL VARIATION OF NrCTALUS BATS 91 4.1.5 INTRASPECIFIC VARIATION 91 4.1.6 RANDOM CALL ACQUISITION 92 4.1.6 STATISTICS 93 4.2 METHOD 93 4.2.1 SAMPLING METHOD 93 4.2.2 ANALYSES AND EQUIPMENT 94 4.3 RESULTS 95

4.3.1 NrCTALUS CALLS 95 4.3.2 SINGLE RANDOM CALL COMPARISON 97 4.3.2.1 Interspecific variation 97 4.3.2.2 Multivariate analysis 98 4.3.3 AVERAGE CALL COMPARISON 104 4.3.3.1 Univariate comparison ofsingle and average call parameters 104 4.3.3.2 Multivariate analysis 105 4.4 DISCUSSION 106 4.4.1 ALTERNATING CALLS WITH DIFFERENT SOUND PARAMETERS 106 4.4.2 PREDICTED DIFFERENCES AND RELATIVE CONTRIBUTION OF CALL PARAMETERS 107 4.4.3 MONITORING BAT POPULATIONS 108 4.5 SUMMARY 110

CHAPTER 5. CREATING ROOST HOLES FOR NOCTULE BATS 111

5.1 INTRODUCTION 112

5.2 METHOD 114

5.3 RESULTS 115

5.4 DISCUSSION 115

5.5 SUMMARY 118

CHAPTER 6. GENERAL DISCUSSION 119

6.1 THE CONSERVATION BIOLOGY OF THE NOCTULE BAT 120

6.2 WHAT LIMITS BAT ABUNDANCE? 121

6.3 FORAGING HABIT A T USE 123

3 6.3.1 FORAGING RESOURCES AS A REGULATING FACTOR 124 6.3.1.1 How couldforaging resources regulate birth rate? 125 6.3.1.2 How could foraging resources regulate mortality:) 127 6.4 ROOST USE 128

6.4.1 ROOSTS AS A REGULATING fACTOR 129

~5THEALLEEEFFECTANDBATS 132

6.6 THE DECLINE OF THE NOCTULE 133

6.7 HABITAT LOSS AND NOCTULE BATS 134

6.8 CONSERVATION IMPLICATIONS OF FORAGING HABITAT LOSS 135

6.8.1 SPECIFIC CONSERVATION RECOMMENDATIONS 137 6.9 CONSERV ATION IMPLICATIONS OF ROOST LOSS 137

6.9.1 SPECIFIC CONSERVATION RECOMMENDATIONS 137 6.10 CONSERVATION IMPLICATIONS OF CLIMATE CHANGE 138

6.11 FUTURE RESEARCH 140

6.11.1 SPECIFIC RESEARCH RECOMMENDATIONS 140 REFERENCES 142

4 Chapter 1. General introduction

5 1.1 Conservation biology

Conservation biology has two primary aims - to investigate the impact of human activities on biological diversity and to develop practical approaches to prevent extinctions (Wilson 1992,

Caughley and Gunn 1996, Primak 1995). It provides principles and new approaches for the field of applied resource management. The key difference between conservation biology and more academic disciplines is that conservation biology seeks to provide answers to specific questions that can be applied in actual fields situations (Caughley and Gunn 1996), incorporating the best strategies for species protection or land management (Primak 1995).

There has been a precipitous decline in bat numbers over the last half century throughout

Europe (Stebbings and Griffith 1986) and observations of the noctule bat (Nyctalus noctula) suggest substantial and rapid decline (Hutson 1993). This study was undertaken to establish guidelines to conserve and enhance UK populations of the noctule bat.

1.1.1 Approaches to conservation biology

Previous studies of population ecology have concentrated on measuring demographic parameters (Sinclair 1989, Caughley and Gunn 1996). This type of study has also been carried out on the more synanthropic bat species (Thompson 1987, Entwistle 1994, Hoyle et at. 2001) but is particularly difficult in bats which are hard to catch, live for a long time and reproduce slowly. The evolutionary basis for these demographic parameters is not fully understood, which precludes the prediction of future events (Sutherland 1996). In addition, many of the population parameters required to measure the intrinsic rate of increase of populations and to carry out population viability analysis are not available for bats (Walsh et al. 200 I). Recent theoretical frameworks have been proposed whereby a behavioural basis for population ecology is developed from studying individual behaviour and this allows the

6 extrapolation to changing conditions, for example predicting the response of populations to habitat loss (Sutherland 1996, Sutherland 1998, Goss-Custard 1996).

1.1.2 From individual behaviour to population ecology

Population size is theoretically determined by the combined effect of density-dependent and density-independent influences on mortality and productivity (Figure 1.1). If a density­ dependent nature of fecundity or mortality can be derived from individual behaviour and related to habitat use this framework can be used to predict the effects of different types of habitat loss or change on populations. This model is the basis for my investigation into how changes in land cover and habitat management have and will effect noctule populations.

1.2 Bats

1.2.1 Taxonomy

Depending on the taxonomic authority used there are just over 1000 bats species (Order

Chiroptera). Koopman (1993) classified 925 species with a further 40 species discovered in the last decade, and some reclassification as a result of molecular evidence, giving an up-to­ date total of 1095 species (Simmons in press). This increase is proportional to that found in other mammalian orders over the same time period (Simmons in press). The Order is divided into two suborders, the Megachiroptera (1 family Pteropodidae, 181 species, confined to the

Old World tropics and subtropics) and the Microchiroptera (approximately 914 species)(Simmons in press).

7 Interference Depletion ~ /' Density . dependent stal"lstion * / PrOdallOn disease etc Mortality

::===. POPULATION SIZE

Breeding output

i " weather food etc. DensIty . dependent prOductMty i Territory size

Floaters" i /' Temlory qualIty Temtorial behaviour

Figure 1.1 A general framework linking individual behaviour to population size (adapted from Sutherland 1996)

8 The integrity of the order Chiroptera remains contentious, the earliest fossil remains provide little evidence of the group's earlier ancestry (Jepsen 1970) and some have suggested it is diphyletic with the Pteropodidae being more closely related to the primates as indicated by similarities of optical and neural pathways (Pettigrew et al. 1989). Molecular evidence and recent phylogenetic analysis in general supports monophyly (Bailey et al. 1992, Hutcheon et al. 1998) but methodological disagreements still allow for the possibility that bats are diphyletic (Pettigrew and Kirsch 1998).

1.2.2 Ecology

The Microchiroptera are distributed worldwide over terrestrial systems but are absent from the Polar Regions and more exposed and isolated land (Hutson et al. 2001). All

Microchiroptera echolocate to orientate. Approximately 75% feed on invertebrates. A few other species specialise in taking fish, amphibians, small vertebrates or blood (Norberg and

Fenton 1988, Turner 1975). The remainder, mainly from the Neotropical family

Phyllostomidae feed on fruit, nectar or pollen (Gould 1978, Fleming 1991). The largest family is the predominantly insectivorous Vespertilionidae with 390 species, of which 22% are listed as threatened i.e. Critically Endangered, Endangered or Vulnerable - IUCN classifications (Mickleburgh et al. 2002). Fourteen species of vespertilionid bats are found in the UK along with two species of Rhinolophidae, all are insectivorous (Corbet and Harris

1991).

1.2.3 Contribution to biodiversity

Bats constitute approximately one quarter of the mammal species worldwide as well as in the

UK (Hutson et al. 2001, Corbet and Harris 1991). They provide excellent models for global

9 and regional patterns in biodiversity (Ceballos and Brown 1995) and as biological indicators

(Medellin et al. 2000), they illustrate the diversity consequences of habitat fragmentation

(Cosson et af. 1999) and management (Estrada and Coates Estrada 200 I). Pteropidid and

Neotropical bats play keystone functional roles in the regeneration of forests (Humphrey and

Bonaccorso 1979, Rainey et al. 1995) and maintenance of biodiversity in arid habitats (A rita and Wilson 1987). Insectivorous bats are the primary nocturnal insect consumers (Kalko

1998, Hutson et al. 2001) and by foraging over large heterogeneous areas, they play a

valuable role in transporting nutrients into the forest ecosystem (Rainey et al. 1992).

1.3 The noctule bat (Nyctalus noctula)

There are eight species in the Palaearctic genus Nycta/us (Vespertilionidae) (Simmons in press), three of which occur in Europe, with two found in the UK, the noctule and Leisler's

bat (Nyctalus leisleri) (Hutson et al. 2001). Both are characterised by long narrow wings with

pointed tips and are distinguishable by size alone (Racey 1991). The common noctule is the

largest (ca. 30g), highest flying British bat, catching insects in flight by fast aerial hawking

(Cranbrook and Barrett 1965, Jones 1995). Its ability to exploit this niche is associated with wingshape (Norberg and Rayner 1987) and echolocation call design (Neuweiler 1989).

Noctules have large narrow wings and relatively high wing loading necessitating fast flight in

relatively open spaces. However, they have lower aspect ratios than the more extreme open­

air chiropteran foragers suggesting use of open habitats close to vegetation (Norberg and

Rayner 1987, Vogler and Neuweiler 1983). Noctules usually commute in a straight line or

large curve at heights of over twenty metres, but come closer to the ground at foraging areas,

rarely, however descending below five metres (Baagee 1987, Kronwitter 1988). World

distribution covers most of Europe and Asia to China and north Vietnam, although several

10 recognised subspecies (Corbet and Hill 1992) are considered by some to be separate species

(Yoshiyuki 1989, Simmons in press). Only the nominate form is found in Europe where it is widespread from the Iberian Peninsula to the limes norrlandicus (60-61 ON) in Sweden

(Figure 1.2).

\.3.1 Life Cycle

As with most Temperate Zone bat species (e.g. Pipistrellus pipistrellus Avery 1985, Plecotus auritus Hays el al. 1992, Rhinolophus jerrumequinum Ransome 1990) noctules arouse periodically throughout the hibernation period, with foraging activity observed but greatly reduced (Avery 1986). On mainland Europe several large mixed sex hibernation colonies are known (Trappman and Ropling 1996) and in the UK small groups of around a dozen hibernating bats are occasionally found during tree felling operations (Strachan 1986, Clark

1987).

Genetic analysis of the large European hibernacula has demonstrated that they are composed of individuals from several different summer colonies (Petit and Mayer 2000). General differences in migration distance and direction are observed between different age classes and sexes with juvenile males hibernating with juvenile and adult females from the same maternity colony (Petit and Mayer 2000, Petit et al. 200 I) but adult males dispersing randomly to occupy mating territories (Petit and Mayer 1999). Ringing recaptures have also demonstrated that not all adult females from the same maternity colony hibernate together

(Sluiter and van Heerdt 1966).

II -

Study site B

S udy site A ----,I

-.

Figure 1.2 The European distribution of the noctule bat Nyc/a/us nactu/a shaded (adapted from Mitchell-Jones et at. 1999) and the geographical locations of study sites.

12 After emergence from hibernacula in spring, ovulation occurs and females migrate back to their place of birth (Sluiter and Heerdt 1966, Stutz and Haffner 1986, Kronwitter 1988).

Large transient prematernity colonies are formed, of mixed sex and reproductive state, during the spring migration (HaUssler and Nagel 1984). Both banding studies and mtDNA analysis have demonstrated that females are faithful to a single maternity colony (Bels 1952, Petit et al. 1999). Typically, pregnancy lasts for 70-73 days (Racey 1991). Parturition takes place from the beginning of June (Gaisler et al. 1979, Sluiter and van Heerdt 1966) in exclusively female maternity colonies, which are nomadic. Twins are not uncommon (Sluiter and van

Heerdt 1966) and triplets have been reported (Gaisler et al. 1979). Roosts change frequently throughout summer and females can be observed carrying young during lactation (Gaisler et al. 1979, Sluiter and van Heerdt 1966).

At this time males are solitary or form small groups which also switch roosts frequently

(Kronwitter 1988). Recently volant young, about a month old, are observed in July (Heise

1993) and weaned during August (Sluiter and van Heerdt 1966). Females may become sexually mature in the year of their birth in captivity (Racey and Kleiman 1970) and in the wild (Cranbrook and Barrett 1965, Gaisler et al. 1979). but males do not form mating territories in their first year (Gaisler et al. 1979) although they may be capable of insemination (Kozhurina and Morozov 1994). During August, as a result of elevated testosterone levels (Racey 1974). individual adult males establish territorial mating roosts, emitting distinctive calls which attract conspecifics (Sluiter and van Heerdt 1966, Zingg

1988, Wied 1994). Harems of eighteen females have been found with a single male in Europe

(Sluiter and van Heerdt 1966) and the UK (I. Mackie unpublished info.). Repetition of the differential migration with large transient mixed sex colonies then occurs (HaUssler et al.

13 1997) culminating in large mixed sex hibernation colonies (Strelkov 1969, Strelkov 1997,

Trappman and Ropling 1996).

1.3.2 Diet

Faecal analysis has identified true flies (Diptera), beetles (Coleoptera) and moths

(Lepidoptera) to be comparably prevalent in the diet of noctules throughout Europe (Beck

1995, Gloor et al. 1995, Jones 1995, Mackenzie and Oxford 1995, Vaughan 1997, Rydell and

Petersons 1998). The representation of these three groups in faecal pellets varies temporally, reflecting their seasonal abundance (Gloor et al. 1995, Jones 1995). However, caddis flies

(Trichoptera) do not appear in the diet of noctules in England and yet they frequently appear

in faecal pellets (40-60%) in Switzerland and Latvia (Beck 1995, Gloor et al. 1995, Rydell and Petersons 1998). Rydell and Petersons (1998) suggested this might be due to the scarcity of large riverine and lacustrine habitats in England, although one of the English roosts studied is located near a major river (Mackenzie and Oxford 1995). Among the dietary

studies only that of Gloor et af. (1995) in Switzerland, trapped insects at foraging sites and the availability of caddis flies in the British studies is not known. Several observations of noctules concentrating their efforts specifically on one prey type at a foraging area have been

reported (Cranbrook and Barrett 1965, Barrett and Cranbrook 1964, Gloor et al. 1995). It has

been suggested that different hunting strategies can be adopted for different prey (Barrett and

Cranbrook 1964) and there is limited evidence that prey may be taken from substrates

(Barrett and Cranbrook 1964, Howes 1979, Kronwitter 1988).

1.3.3 Foraging activity and habitat preferences

The wing morphology and echolocation call design of the noctule are consistent with the way

it uses space (Norberg and Rayner 1987, Neuweiler 1989). Noctules are rarely seen foraging

14 under the canopy in woodland areas (Kronwitter 1988, Rachwald 1992). Substantially more foraging activity occurs over agricultural land next to woodland, riparian and lacustrine habitats, and bright artificially illuminated areas (Kronwitter 1988, Rachwald 1992, Gloor et af. 1995, Stutz and Haffner 1986). Kronwitter (1988) conducted a radio-tracking study of an all-male population in Germany. The noctules spent about sixty-five percent of their foraging time, over two years, in only two areas; above a lake and over a large, brightly illuminated asphalt area. Group foraging, with up to thirty individuals, was observed at both these sites.

In Cambridgeshire, radio tracked noctules extensively used artificially lit railway sidings, now an equally well lit high security jail (A. J. Mitchell-Jones pers. com.).

Noctules are the first bats to emerge at dusk in northern Europe (Jones and Rydell 1994), often before sunset in male (Stutz and Haffner 1986, Kronwitter 1988) and female populations (Jones 1995). Kronwitter (1988) reported noctules foraging at dusk over the lake and when light levels were sufficiently low for insects to be attracted to artificial lighting, the bats switched to foraging over the illuminated area. Individual bats frequently had three distinct foraging periods, of varying duration each night and this corresponds with detector studies where noctule population activity is continuous throughout the night with concentrations at dusk and dawn (Stutz and Haffner 1986, Rachwald 1992). In Kronwitter's

(1988) study seventy five percent of the second foraging period was spent over the illuminated area.

1.3.4 Roosting preferences

Tree holes predominate as the natural roost site of noctules in all seasons (Gaisler et al. 1979,

Sluiter and van Heerdt 1966) and this bat appears reluctant to accept anthropogenic alternatives, particularly in the UK (Racey 1991), especially when nursing. Although

15 noctules are widespread and relatively common in Europe, as a result of their tree dwelling nature, there is a substantial gap in knowledge of the basic roosting requirements and roosting ecology of this species. Noctules are particularly faithful to their traditional spring gathering roosts where they congregate in high numbers prior to moving to maternity roosts

(Sluiter and van Heerdt 1966, Briggs 1998). Sluiter and van Heerdt (1966) suggested that the fidelity exhibited for the spring gathering roost resulted from the seasonal lack of alternative tree holes due to competition from hole-nesting birds. The tree hollow concerned in their study extended to ground level rendering it unsuitable for tree-hole nesting birds. Similarly,

Cerveny and BUrger (\989) reported large numbers of occupied roosts from July onwards, corresponding to the end of the breeding season for hole-nesting birds. However, spring gathering roosts in the UK appear to be no different to other tree holes except they are traditionally used by noctules (Briggs 1998, T. Lane pers. comm.). This fidelity confers a certain amount of vulnerability to the bats using these sites especially as they are related to migratory behaviour. Throughout summer noctules are frequently observed to switch roosts

(Gaisler e/ at. 1979).

1.4 Roost Switching

The phenomenon of roost switching is observed in many bat species and noctules frequently change diurnal roost sites whilst maintaining social group stability (Sluiter and van Heerdt

1966). Movement from an established living area has several potential costs for bats; including time and energy in locating a new roost, predator exposure while searching, and additional costs of carrying young. Despite the potential costs, short-term roost changes are observed in bats throughout temperate (Brigham 1991, Brigham et al. 1997a, Whitaker Jr.

1998) and tropical zones (Fenton 1983a, Morrison 1980). Investigations of the benefits of

16 roost switching in bats have so far been unrewarding. Several possible hypotheses to explain roost switching have been proposed (reviewed by Lewis 1995), the most plausible of which include: predator avoidance, reduced commuting costs to foraging sites, lower ectoparasite loads, and energy saving roost microclimates. There has been some progress in clarifying how these hypotheses should be ranked.

1.4.1 Predation

Several studies have suggested that predation may have a significant selective pressure on bat behaviour. Speakman (1991) estimated that predatory birds accounted for over 11 % of bat mortality in Great Britain. However, this study was based on extrapolation from thirty-six reported predation occurrences as bat remains are rarely found in UK predatory bird pellets.

Direct observation of bat roosts in South Africa found that three species of raptors (Falco subbuteo, Accipiter tachiro and Aquila wahlberg) attained a 51 % capture success rate on two species of bats (Tadarida pumila and T. condylura) emerging at dusk (Fenton et al. 1994).

However, studies examining the effect of predation on roost fidelity have produced conflicting results. Screech owls (Otus asio) caused desertion of a roost by Myotis lucifugus

(Barclay 1982). Conversely. raptor attacks at a tree hole entrance did not discourage use by

Rhinolophus hildebrandti (Fenton and Rautenbach 1986).

1.4.2 Commuting costs

Most radio-tracking studies of insectivorous bats have been unable to establish a relationship between roost movements and the location of foraging areas (Brigham 1991, Fenton 1983,

Kronwitter 1988, Smith 200 I ).

17 1.4.3 Ectoparasite reduction

Limited success has been achieved concerning ectoparasite numbers and roost switching.

Lewis (1996) found high ectoparasite load in Antrozous pal/idus was correlated with lower body weights, and roost switching was positively correlated with ectoparasite number. Her study concerned a small population however, and was purely correlative. Nevertheless, ectoparasites have been shown to have severe energetic costs under controlled conditions

(Giorgi et al. 200 I) although this may not mimic natural situations.

1.4.4 Roost microclimate

The microclimate of diurnal roosts is of great importance to bats (Kunz 1982), particularly

Temperate Zone species at the northern border of their distribution. Prolonged periods of torpor increase gestation length (Racey 1973) and slow neonatal growth (Tuttle and

Stevenson 1982). Therefore the choice of roost type and location is likely to influence fitness and survival (Entwistle et a/. 1997). Bats occupying larger roosts such as attics or caves may encounter the required microclimate variability within a single roost (Licht and Leitner

1967). However, species that occupy smaller cavities may have to change roosts to avoid microclimate extremes and balance energy budgets.

1.5 Bat Conservation Research

The ability to fly and echolocate allows microchiropteran bats to forage at night over large areas at relatively low energetic costs (Norberg 1987, de la Cueva Salcedo et al. 1995). The rest of their lives are usually spent in a variety of shelters, generally termed roosts (Kunz

1982). Roosting and foraging habitat are key ecological factors influencing survival, abundance and distribution of bats (Humphrey 1975, Fenton 1970, Stebbings 1988, Entwistle

18 et a/. 1996, Hoyle et al. 2001) and research into bat habitat requirements has been identified as a conservation priority (Lunney 1989, Hutson 1993, Fenton 1997) receiving legislative backing from government directives such as the Eurobats Agreement (part of the Convention on Migratory Species) and the EC Habitats Directive (Racey 2000).

Previous studies which have resulted in conservation recommendations have directly studied roosting (Campbell et al. 1996, Entwistle et a/. 1997, Brigham et a/. 1997a, O'Donnell and

Sedge ley 1999) and foraging behaviour (Clark et al. 1993, Jones et al. 1995, Entwistle et al.

1996) or examined diet (Svennson and Rydell 1998, Arlettaz et a/. 2000), heterothermy

(Lewis 1993, Wilde el al. 1999), population genetics (Petri et al. 1997, Burland et al. 2001,

Rossiter et al. 2000, Petit et af. 1999), migration (Strelkov 1997a, Petit and Mayer 2000), predation (Fenton et al. 1994) or demographic parameters (Boyd and Stebbings 1989,

Entwistle 2000, Hoyle et al. 2001).

Most of these studies are purely descriptive, measuring specific parameters or establishing which habitats are preferred or how the animals behave. In order to predict the effects of different habitat management regimens, it is necessary to establish a relationship between behaviour, demography and environment. The underlying theory of behavioural ecology can then be used as a theoretical base to explain population processes and allow predictions about habitat management and thus conservation advice (Sutherland 1996). Relationships between bat populations and their environment have previously been based on between site comparisons (Brigham 1991, Gerell and Lundberg 1993, Geggie and Fenton 1985, Racey et al. 1998) or have examined productivity or survival in relation to activity and energy budgets without relating them to environmental factors (Adam 1994, Shiel 1999, Wilkinson and

Barclay 1997). An alternative approach is to base relationships between productivity and

19 habitat use on differences between individuals in a single highly variable area (Goss-Custard

1996). Evidence that habitats are essential to sustain populations can be obtained by investigating relationships between survival, productivity or other aspects of demography and the use of spatial resources (Kenward 2001).

1.6 The UK Situation

In Europe and the UK there has been a huge reduction in the number and range of plant and animal species over the last 50 years (Department of the Environment 1996). Key land cover changes thought to be responsible for these reductions are the loss of diversifying features

(hedges and ponds), an increase in intensive monoculture farming and forestry, fragmentation and destruction of natural habitats and industrial and urban development (Department of the

Environment 1996). The response of Europe's bat community to habitat modification is dynamic. Several species have shown localised increase in numbers (Pipistrellus pipistrellus,

Stebbings 1988, Rhinolophus hippisoderos, Walsh et at. 2001) or large-scale population increases (Myotis daubentonii, Kokurewicz 1995) and range expansion (P. nathusii, Russ

1998). Others have suffered massive population declines with corresponding range contraction (R. ferrumequinum, Ransome 1990) and even extinction (M myotis, Stebbings

1992).

Many species benefit from man-made factors by utilising buildings as roosts to increase their range (Plecotus austriacus Swift 2000, Eptesicus nilssonii Rydell 1989) or exploiting insect swarms produced by eutrophication (Myotis daubentonii Kokurewicz 1995, Racey et a/.

1998) and artificial lighting (Rydell and Racey 1995). These benefits are usually restricted to

20 a few generalist species and may even be detrimental to other species by, for example,

restricting access to an already limited food supply (Arlettaz et al. 2000). The propensity of

noctules to exploit swarms of insects particularly over street lamps (Kronwitter 1988, Rydell and Racey 1995) has obvious benefits as such artificially illuminated areas are increasing

(Racey 1998). However, it is of great concern that of the species able to exploit this resource only noctules are presently threatened (Hutson 1993).

1.7 The conservation of the noctule bat (Nyctalus noctula)

Specific aspects of the ecology of the noctule bat require additional investigation. Gaps in our current understanding are particularly evident in noctule roosting and foraging ecology.

Although there is some information on the roost requirements of noctules in plantation woodland (Boonman 2000) loss of roosting habitat, i.e. mature and post mature trees with rot and woodpecker holes, has been highlighted as a probable cause of population decline and restricted distribution (Hutson 1993, Racey 1998, Gaisler et al. 1998). Several studies have suggested that the availability of suitable roosts may be a primary constraint on the

population size and distribution of bats in general (Fenton 1970, Humphrey 1975, Bell et al.

1986) and for the noctule in particular (Limpens et al. 1997, Ahlen and Gerell 1989, Gaisler et al. 1998).

Factors considered important to the conservation of other British species such as habitat connectivity (P. pipistrellus Verboom and Huitema 1997, M daubentonii Limpens and

Kapteyn 1991, R. hippisoderos Schofield 1996) and loss of insect diversity (R. ferrumequinum Duverge and Jones 1994) may have a more minor effect on noctules which do not require linear landscape features to facilitate movement and have a broad dietary range

(de long and Ahlen 1991, Gaisler and Kolibac 1992, Vaughan 1997). However, there is no

21 current information on habitat selection by female noctules and there have been no studies of habitat use in more heterogeneous landscapes not dominated by large aquatic features.

Reliable information on bat population trends is sparse (Yalden 1999) and particularly so for noctules as one of the least synanthropic bat species found in the UK. At present the ability to monitor changes in their populations is limited and ultimately hinders conservation advice.

1.8 Recording and Monitoring

The effectiveness of any conservation guidelines is best measured in terms of the numbers and distribution of species they seek to conserve. Formulating effective policies and legislation, as well as ground level decisions, relies on sound scientific information. In particular wildlife managers need accurate information about an animal's range and abundance. To formulate a conservation management system without attempting to accurately monitor its performance is counter productive. Authoritative advice and effective action will only be possible if threats to and changes in populations can be measured.

1.8.1 Monitoring bats

Bats remain one of the most difficult groups of mammals to survey effectively and there are few historical records, none with standardised methods (Yalden 1999), although recently attempts have been made to produce effective bat monitoring systems (Walsh et al. 2001).

Roost and hibernacula counts are the longest established methods for monitoring bat populations (Ransome 1989, Speakman et af. 1991, Kokurewicz 1995) but local variation in estimates may invalidate extrapolation of this information, although this is rarely taken into consideration (e.g. Harris et al. 1995). These methods are only suitable for certain visible and

22 more synanthropic species or members of the population and as no concurrent probability­ based sampling (e.g. Haines and Pollock 1998) has been undertaken there is little evidence to counter arguments that severe declines have resulted from bats simply moving to other roost sites (Yoccoz e/ af. 2001). There are few known noctule hibernacula in the UK and summer colonies are rarely found usually in tree cavities and they often switch roosts, which may render roost counts impractical.

The increased availability of bat detectors has allowed the comparison of bat activity in different habitats, the plotting of species distributions and the identification of species/habitat associations (Walsh and Harris 1996, Fenton 1997, Ahlen and Baag0e 1999). Species identification studies have provided encouraging evidence that bat detectors could be used to identify individual species by their echolocation calls (Vaughan et al. 1997a, Parsons and

Jones 2000). However, there is still considerable debate about the efficacy of using bat detectors in the field to identify bats to species and hence us them as a tool to monitor population changes (Barclay 1999, O'Farrell et al. I 999). The high intensity, low duty cycle echolocation calls produced by noctules to orientate and find food are particularly suited to monitoring using bat detectors but formal evidence that they can be used to accurately monitor populations in the field is lacking. Specifically, there has been no estimation of the bias or magnitude of error associated with species identification in field estimation of bat populations using bat detectors (Walsh et al. 200 I). Unquantified, these problems introduce major error in population monitoring schemes (Yoccoz et al. 200 I).

23 1.9 Aims

• Investigate the foraging ecology of the noctule bat to identify preferred foraging habitat,

distances traveled when foraging and possible association between foraging behaviour

and breeding success (Chapter 2).

• Investigate the roosting ecology of the noctule bat to identify what factors make tree

holes suitable as roosts, how often noctules switch roosts and the possible association

between roosting behaviour and reproductive success (Chapter 3).

• Investigate the suitability of using bat detectors to monitor change in noctule populations

(Chapter 4).

24 Chapter 2. Habitat preference, space use and foraging activity of

noctule bats Nyctalus noctula in a cultural landscape.

25 2.1 Introduction

If wild animal populations use resources disproportionately. this should be particularly evident in resources that are of critical value to their survival and reproduction. Foraging habitat is of vital importance to Temperate Zone insectivorous bats (Kunz 1982) and its loss is a primary threat to the survival of bat species (Jones et al. 1995. Kunz and Racey 1998).

Degradation of habitat leads to increased foraging activity (Geggie and Fenton 1984). reduced growth of young (Tuttle 1976) and ultimately population decline (Gerell and

Lundberg 1993). Considerable recent attention has been directed towards the preservation of foraging habitats within commuting distance of maternity colonies of threatened bat species through the European Bat Agreement 1992 and E.U. Habitats and Species Directive 1992

(Racey 2000). Information concerning habitats and feeding areas that are important for a species is often crucial for wildlife conservation in general (Caughley and Gunn 1996,

Kenward 200 I) and specifically for bats (Jones et al. 1995, Racey 1998).

Bats are the most speciose order of mammals found on the British Isles (Corbet and Harris

1991) and as the major predators of night flying insects they perform a valuable role in terrestrial ecosystems (Kunz and Racey 1998, Kalko 1998). Being extremely long-lived and slow to reproduce, relative to their size. bats are thought to be particularly vulnerable to human-induced environmental change (Findley 1993). However, bat populations in Europe are far from static with several species currently increasing in numbers and undergoing range expansion while others, such as the noctule, suffer serious decline (Hutson 1993).

The noctule is the largest extant vespertilionid in Britain (Corbet and Harris 1991).

Morphologically predisposed to fast flight with a high aspect ratio and high wing loading

26 (Norberg 1987) the noctule has relatively small ears and echo locates through its open mouth when hawking for insects. With a broad diet (Beck 1995, Gloor et al. 1995, Mackenzie and

Oxford 1995, Vaughan 1997, Rydell and Petersons 1998) the noctule is capable of exploiting a range of prey from chironomid swarms produced by nitrogen enrichment (Jones 1995) and

Lepidoptera attracted by artificial lighting (Kronwitter 1988, Mitchell-Jones, 1990, Rydell

1992) to large emergent Coleoptera (Barrett and Cranbrook 1964, Catto et al. 1996).

Although locally abundant throughout its Palaearctic distribution and very apparent in the sky at dusk there are few individual-based studies of the noctule bat (Kronwitter 1988) and there is a lack of knowledge concerning the movements and habitat selection of maternity colonies of this species (Hutson 1993).

Previous studies of noctules have been conducted on an individual basis by radio-tracking an all male population in Germany (Kronwitter 1988) or at the population level with ultra-sound receivers in other parts of Europe (Rachwald 1992, Stutz and Haffner 1986a). There is little published information concerning the habitat requirements of noctule bats in the UK

(Mitchell-Jones 1993, Vaughan et al. 1997b). The British countryside is an intensively managed mosaic of different habitat fragments of varying sizes (Rackham 1986) and several studies have associated bat presence with land class (Walsh and Harris 1996, Wunder and

Carey 1996, Schofield 1996) however, there are few studies which quantify the habitat selection of specific bat species (e.g. Duverge 1996, Smith 2001, Russo et al. 2002).

The study of bat habitat use began with direct observation (Gould 1978, Nyholm 1965,

Dwyer 1970) and sampling with mist nets in open areas (Wallin 1961, Waldien and Hayes

1999). Cranbrook and Barrett (1965) demonstrated that noctules remained faithful to a foraging area for three successive years using this technique. Generally, however, noctules

27 fly above the height of mist nets but in this case they were feeding on house crickets low over a domestic refuse tip. Next chemiluminescent tags were used alone (Buchler 1976) and in combination with ultrasound receivers (Fenton and Thomas 1980) to study bat habitat use.

However, with a commuting flight speed of over 50 km h· 1 (Gaisler el at. 1979) and frequent high altitude flights (Kronwitter 1988) chemiluminescent tags would be wholly inadequate for making systematic observations of noctules.

Ultrasound receivers are now widely used to study bats. Since the 1970's receivers have been used to compare activity over different habitats and around landscape features in large scale or community studies (Fenton 1970, Limpens and Kapteyn 1991. Walsh and Harris 1996).

Although many bat species can be identified by their echolocation calls it is not possible to identify individual bats in free flight and density may not indicate habitat quality (van Home

1983). Furthermore bats alter their call structure, rate and intensity in different habitats

(Kalko and Schnitzler 1993, Jensen 1999) which affects detection distance and identification, confounding comparisons of activity between very different habitats. Therefore radio­ tracking is currently the only technique available to collect data systematically on individual noctules whilst minimising the bias associated with studies of fast flying nocturnal bats.

Radio-tracking has mostly been used to find an animal and observe its foraging (Morrison

1980, Brigham and Brigham 1989, Arlettaz 1993) and roosting behaviour (O'Donnell and

Sedge ley 1999, Brigham et a/. 1997a) or for descriptive studies of activity patterns (Geggie and Fenton 1985, Park et at. 1999) and identifying roosts to measure their characteristics

(Morrison 1980, Vonhof and Barclay 1996, Crampton and Barclay 1998, Sedge ley and

O'Donnell 1999, Fenton et at. 2000, Menzel 2001). Far less work has been carried out on foraging habitat preference and most studies merely noted habitats visited by bats (Fenton et

28 al. 1986, Hickey and Fenton 1990, Jones et al. 1995, Catto et al. 1996, Sheil et af. 1999), stated the proportion of time bats spent in different habitats (Brigham and Brigham 1989,

Jones and Morton 1992, Wilkinson and Barclay 1997) or displayed percentage of available habitat and percentage used without any statistical demonstration of habitat preference

(Robinson and Stebbings 1997). Relational studies linking distribution to environment are rare and have concentrated on plecotine bats or others specialised for gleaning prey from substrate (Clark et af. 1993, Adam et aJ. 1994, Entwistle et af. 1996, Arlettaz et al.1999).

Although a few rigorous studies of aerial insectivores have been carried out (Duverge 1996,

Smith 200 I) no quantified study relating habitat availability to bat distribution has been conducted for the noctule bat.

Determining which habitats are preferred provides fundamental information on the environmental resources necessary for sustaining populations (Caughley and Gunn 1996).

Individual-based demographic modeling identifies differences between individuals in a single highly variable environment and these differences are used to provide information on population demography (Sutherland 1996). Furthermore, evidence that habitats are essential can be obtained by relationships between survival, productivity or other aspects of demography and use of spatial resources (Kenward 2001).

Noctule bats are ideal models for habitat selection studies, using compositional analysis, as they are fast flying aerial hawking bats, which forage over large distances and do not require corridors to facilitate movement, unlike most other bat species (M nattereri de Jong 1994, P. pipistrellus Verboom and Huitema 1997, M daubentonii Limpens and Kapteyn 1991, R. hippisoderos Schofield 1996). The present study used compositional analysis (Aitchinson

29 1986, Aebischer et af. 1993a) to compare the time noctule bats spent foraging in different

habitats with the availability of those habitats to investigate whether overall habitat use was

random. Where movements were nonrandom, habitats were ranked in order of preference

following Aebischer et af. (1993a). Then intraspecific habitat use was compared for lactating

and non-lactating female bats following Aebisher et af. (l993b), to investigate intraspecific

habitat selection and target key habitats for lactating females, which may be essential to

sustain populations.

2.2 Method

2.2.1 Study site and study colony

The study was conducted during the summers of 1999 and 2000 in and around Horner

Woods, Somerset (51 0 II north, 30 34 west; Study Site A figure 1.2). Horner wood comprises ancient and semi-natural woodland in compartments of varying density and species composition. The surrounding countryside is a mosaic of lowland pastoral and arable

farmland rising to upland woods occupying the slopes between the enclosed fields of the valley bottom and extensive pasture and moors above (Figure 2.1). Members of the study colony roosted in a minimum of seven trees over anyone summer but females generally remained faithful to one main roost in a large pollarded ash (Fraxinus excelsior), which

frequently contained in excess of 80 bats (chapter 3). All bats were caught, under licence

from English Nature, using pole-nets, except a male that was mist netted.

2.2.2 Radio-tracking

30 Usually two or three bats were fitted with radio transmitters simultaneously. Each animal was sexed, weighed and the forearm lengths measured. Pregnant and immature bats were identified and were not tracked. Lactating bats were identified by expressing milk from enlarged nipples, which were surrounded by noticeable bare patches. Reduced nipples and absence of bare patches identified non-parous and parous females that had not produced young or may have lost their young in the current year. Tracked bats were therefore classified as lactating or non-lactating. Random adult female bats from each class were retained and after hair clipping, between the scapulae, 1.2g temperature-sensitive radio transmitters

(Biotrack, Essex, England) were attached using Skin-Bond ® (Canadian Howmedica, Guelph,

Ont.) surgical adhesive. Usually bats from both classes were tagged simultaneously and tracked on alternate days to control for season differences. Transmitters were mostly under

5% of bat body weight and never exceeded 6%. Bats were released into the air and tracked although data from the night of tagging were not used in any analysis. Three Mariner 57 receivers (Mariner Radar, Lowestoft, England) attached to three element fixed Vagi antennas were used to locate bats. Radio-tracked bats were followed when commuting to foraging areas in a car with roof mounted antennas (Kenward 2001) and were located at foraging sites on foot using the "close approach" method (White and Garrott 1990). An ultrasound receiver

(D240x Pettersson, Uppsala, Sweden) was also used to assist location at foraging sites. Data on habitat types used and time spent in each habitat type were recorded on a dictaphone in the field.

31 a)

b)

Figure 2.1 Photographs of the radio tracking study site a) Homer Wood, a steep sided wooded river valley around the main roost b) Porlock Vale, showing the mosaic of habitat types.

32 2.2.3 Habitat availability and time budget quantification

Arcview GIS was used to provide all spatial measurements and specifically to measure available habitats for both the colony Minimum Area Convex Polygon (MCP; Mohr 1947) and individual bat MCP's. Digitised o.s. Land-Line tiles (I: 10,000) of the total study area were downloaded from the Internet through Edinburgh Data and Information Access's

Digimap facility (http://www.edina.ac.uk) and converted to Arcview shape files using

MapManager version 6.1. These files were overlaid with a polygon shape file of different habitat types recorded from a field survey of the study area. Each bat's location data were then marked with a mouse click on individual shape files and habitat time budgets were recorded in the table of attributes for that point. The areas of each habitat type within individual bat MCP's were obtained by overlaying the outermost location fixes with a MCP shape file which was then used to cut through the habitat shapefile. This produced an individual bat's MCP composed of different habitat areas. Colony MCP was produced in the same way but overlaying the outermost points of all bats (Figure 2.2-2.3). Individual bat's attributes tables were then queried for total foraging time and total foraging time spent in each habitat. This allowed the calculation of percentage use of each habitat for each bat and the percentage availability of each habitat at two levels, individual MCP and colony MCP.

33 Main Roost

.. Woodland Moorland .. Pasture [:=J Arable c=J Other

o 5 10 Kilometers ~~~~~~------~

Figure 2.2 Noctule bat (Nyctafus noctufa) Colony MCP and individual MCPs for 10 lactating female bats.

34 • Main roost .. Woodland Moorland .. Pasture c=J Arable c=J Other

o 5 10 Kilometers

Figure 2.3 Noctule bat (Nyelafus noelufa) Colony MCP and individual MCPs for 10 non- lactating female bats.

35 Habitat categories used for the analysis were woodland, moorland, pasture, arable and other. Woodland was primarily broad-leaved (67%) and mixed (22%) with some conifer plantation (11 %). Arable included cropland and recently reseeded grassland that was not grazed (i.e. for silage or hay production) with all other areas of grass being classified as pasture. The other habitat category consisted of any habitat not included in the previously described categories such as small towns, villages and aquatic areas (saltmarsh, reservoir, and ponds).

Data for habitat use were for all foraging periods where individual bats used at least two habitat categories, so preference between habitats could be assessed, and contact was not lost for over 30 minutes. This excluded periods of inclement weather where extreme wind and precipitation restricted foraging flights to the woodland surrounding the roost. Where contact was lost for less than 30 minutes, the missing time was allocated to the habitat in which the bat was found. All bats used in the analysis were tracked for at least two complete nights.

Mep size was not related to the amount of tracking time (rs=0.17S, P=0.46, n=20) therefore it is assumed no bias resulted from varying amounts of data for each bat (White and Garrot

1990).

36 2.3 Analysis

2.3.1 Habitat preferences

Compositional analysis following Aebisher et aJ. (\ 993a) was selected to investigate habitat

preferences as the data recorded describe the proportion of time allocated to each habitat and the proportional area of habitat in each Mep. Individual bats were the sample units used, thus minimising problems associated with pseudoreplication (Hurlbert 1984). All other current analyses of habitat preference are less sensitive or have confounding sources of bias (see

Aebisher et af. 1993a, Kenward 2001). Analysis was conducted using a commercially available program (Smith 2001) specifically written to carry out all calculations described in

Aebisher et af. (1993a). Matched pairs of percentage time in each habitat with percentage area available were constructed for each bat. Log ratio transformations of time use in each

habitat relative to the other habitats were calculated, as were equivalent log ratios of area available relative to other habitats. Subtracting the paired log ratio transformations gives a matrix of log ratio differences. These differences are theoretically independent and, assuming the animal movements are random, normally distributed around zero. If the overall test of

non-random use is significant (MANOVA, Wilk's 1\) ranking of habitats proceeds by

multiple pairwise comparisons (Aebisher et at. 1993a). Log ratios did not appear to be

normally distributed so assumptions of normality were avoided by using randomisation tests

involving 5000 permutations (Manly 1997). To enable calculations of logarithms, missing

values for use were replaced by 0.01, as suggested by Aebisher et at. (1993a), which was at

least an order of magnitude less that the smallest measure and represented use too small to be

detected.

37 Comparison between lactating and non-lactating bats followed the same procedure starting by matching pairs of percentage time in each habitat between reproductive categories, similar to the survival analysis carried out by Aebisher et al. (1993b) but with a balanced data set and using time budgets instead of locational fixes. Moorland and arable categories were combined for this analysis, to form a new composite habitat labeled marginal, in order to remove a large number of null proportions as suggested by Aebisher et al. (1993b). This analysis pinpointed differences in relative habitat use between the two groups but does not correspond to overall differences in preference.

Non-parametric tests were used to analyse univariate measures of foraging activity, where assumptions of normality could not be met. A statistical significance level of a = 0.05 was used as the nominal rejection criterion for all statistical tests and P values are quoted as exact probability values.

2.4 Results

2.4.1 Tracking

Radio transmitters were attached to a total of 22 bats, 21 adult females and I adult male.

Sufficient data to be confident about the animal's movements were obtained from 20 female bats (10 lactating and 10 non lactating). The 10 nonlactating bats consisted of 6 nulliparous and 4 parous females. Contact was lost with the remaining female before a full night tracking had been possible therefore she was excluded from the analysis. The adult male was only tracked for one full night and intermittently while tracking simultaneously tagged females, therefore these data were not considered for analysis. The male often foraged in the same

38 areas as females but never roosted with them. All other females were tracked for at least two full nights across 73 full nights tracking.

2.4.2 Habitat availability and use

2 2 The colony Mep spread over seventy I km O.S. tiles and was 62.75 km . It consisted of

11.21 km 2 of woodland, 23.97km2 of moorland. 17.63km2 of pasture, 8.16 km 2 of arable land and 1.78 km 2 classified as other (Figure 2.2-2.3). The maximum distance a bat was recorded foraging from the roost tree was 6.3 km however the maximum straight line distance that a bat traveled in one foraging period exceeded 23.5km.

Foraging areas were generally associated with woodland and its pastoral surrounds or areas with high insect abundance such as ponds and saltmarsh. Bats foraged at a range of different heights; low over pasture rising to follow woodland edge and field boundaries or above canopy height flying over woodland and enclosed pasture.

Proportion of time spent over each habitat was graphically compared to the proportional availability of each habitat (Figure 2.4).

39 0.5 Area Proportion Available 0.4 I of eac h habitat ~ Time ~ Spent type 0.3

0.2

0.1

0.0 Pasture Woodland Moorland Arable Other Habitat

Figure 2.4. The proportion of time spent in each habitat, averaged over twenty female noctules, compared to the proportion of each habitat within the colony MCP.

40 2.4.3 Habitat preference

To demonstrate statistically that habitats in the colony Mep were used disproportionately and that movements were not random across the twenty bats, compositional analysis was used

(Table 2.1). From the resulting matrix, Wilk's A =0.1067, for which P=0.0002 (by randomisation) indicates strongly significant non-random use of habitats. After confirmation of non-random use, habitats were ranked by summing positive log ratio differences.

Randomisation was used to test whether the preference differed significantly from zero for each habitat pair (Table 2.2).

Female noctule habitat preference is expressed below from most to least preferred, with sequential significant differences in preference illustrated by»> and a reduction in size.

Woodland> Pasture »> Other »> Moorland> Arable

41 Table 2.1 . Compositional analysis of percentage use twenty noctule bats made of each habitat and percentage habiat area available in the colony MCP. L= lactating bat, NL= nonlactating bat. Tracking dates given for simultaneously tagged individuals.

lL lNL 2L 2NL 3NL 4NL SNL 31.. SNL 4L 51.. 6L 7l 7NL 8L 8NL 91.. 10l 9NL 10N\... Datesl (1817/99-21nt99) 1(25n199-2917/99) 1 (0218199-09{8I99 1 (15161O().2516100) (01171OO-11ntOO) 1 (1217100-1617/00) 1 (1717100.23f7/00) (2517100-3117100) Use Available

Woodland 79 61 51 31 12 76 61 67 ~ 34 31 32 68 36 50 39 52 24 26 14 17.87 Moorland 0.01 3 0.01 2 0.01 5 0.01 2 4 0.01 0.01 0.01 0.01 2 0.01 3 0.01 2 0.01 2 38.21 Pasture 18 27 42 59 00 19 39 28 ~ 61 60 68 30 48 50 45 48 69 00 56 28.08 Arable 0.01 0.01 0.01 0.01 4 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 14 0.01 3 0.01 0.01 0.01 9 13.01 other 3 9 7 8 18 0.01 0.01 3 0.Q1 5 9 0.01 2 0.01 0.01 10 0.01 5 8 19 2.64

Log ratios Wood/moor 8.97 301 &~ 2.74 7.09 2.72 8.72 3.51 2.76 8.13 8.04 8.W 8.82 2.89 8.52 2.56 8.56 2.48 7.86 1.95 -0.76 Wood/pasture 1.48 0.82 019 -0.64 -1.70 1.39 0.45 0.87 0.65 -0.58 -0.66 -0.75 0.82 -0.29 0.00 -0.14 0.08 -1.06 -0.93 -1.39 -0.45 Wood/arable 8.97 8.72 &~ 8.04 1.10 894 8.72 8.81 8.75 8.13 8.04 8.W 8.82 0.94 8.52 2.56 8.56 7.78 7.86 0.44 0.32 Wood/other 3.27 1.91 1.99 1.35 -0.41 8.94 8.72 3.11 8.75 1.92 1.24 8.07 3.53 8.19 8.52 1.36 8.56 1.57 1.18 -0.31 1.84 Moor/pasture -7.50 -2.20 ~~ -3.38 -8.79 -1.34 -8.27 -2.64 -2.11 -8.72 -8.70 -8.82 -8.01 -3.18 -8.52 -2.71 -8.48 -3.54 -8.79 -3.33 0.31 Moor/arable 0.00 5.70 000 5.~ -5.99 6.21 0.00 5.~ 5.99 0.00 0.00 0.00 0.00 -1.95 0.00 0.00 0.00 5.~ 0.00 -1.50 1.08 Moor/other -5.70 -1.10 4~ -1.39 -7.50 6.21 0.00 -0.41 5.99 -6.21 -6.80 0.00 -5.30 5.30 0.00 -1.20 0.00 -0.92 -6.~ -2.25 2.60 Pasture/arable 7.50 7.90 &~ 8.68 2.80 7.55 8.27 7.94 8.10 8.72 8.70 8.82 8.01 1.n 8.52 2.71 8.48 8.84 8.79 1.83 o.n Pasture/other 1.79 110 1.~ 2.00 1.~ 7.55 8.27 2.n 8.10 2.50 1.90 8.82 2.71 8.48 8.52 1.50 8.48 2.62 2.11 1.08 2.29 Arable/other -5.70 -6.80 4~ -6.68 -1.50 0.00 0.00 -5.70 0.00 -6.21 -6.80 0.00 -5.30 7.24 0.00 -1.20 0.00 -6.21 -6.~ -0.75 1.52

Log ratio difference Mean (s.e., Wood/moor an ~n 9.30 3.50 7.85 3.48 9.48 4.27 3.52 8.89 8.80 8.83 a58 3.65 a~ 3.32 9.32 3.24 8.62 2.71 6.56 (0.64) Wood/pasture 1.~ 1.V 0.65 -0.19 -1.25 1.84 0.90 1.32 1.10 -0.13 -0.21 -0.30 1.V 0.16 045 0.31 0.53 -0.60 -0.48 -0.93 0.38 (.20) Wood/arable &00 &~ 8.22 7.72 0.78 8.62 8.~ 8.49 8.~ 7.81 7.72 7.75 &~ 0.63 &20 2.25 8.24 7.47 7.55 0.12 6.70 (0.67) Wood/other 1.~ OW 0.15 -0.49 -2.25 7.10 6.88 1.27 6.91 0.08 -0.60 6.n 1.69 6.35 a~ -0.48 6.72 -0.27 -0.66 -2.15 2.23 (0.78) Moor/pasture ~~ ~50 -8.65 -3.69 -9.10 -1.64 -8.58 -2.95 -2.42 -9.02 -9.01 -9.13 ~~ -3.49 ~82 -3.02 -8.78 -3.85 -9.10 -3.64 -6.18 (0.67) Moor/arable -1.08 ~~ -1.08 4.22 -7.07 5.14 -1.08 4.22 4.91 -1.08 -1.08 -1.08 ~.08 -3.02 ~~ -1.08 -1.08 4.~ -1.08 -2.58 0.14 (0.73) Moor/other ~~ ~m -9.15 -3.99 -10.10 3.61 -2.60 -3.01 3.39 -8.81 -9.~ -2.60 ~.90 2.70 ~ro -3.80 -2.60 -3.52 -9.28 -4.85 -4.~(0.95) Pasture/arable an 7.13 7.57 7.91 2.03 6.78 7.50 7.17 7.~ 7.95 7.~ 8.05 7~ 0.46 7~ 1.94 7.71 8.07 8.03 1.06 6.32 (0.58) Pasture/other 450 -119 -0.50 -0.29 -0.99 5.26 5.98 -0.06 5.81 0.21 -O.~ 6.53 OG 6.18 a~ -0.79 6.18 O.~ -0.18 -1.21 1.85 (0.71) Arable/other ~.n ~~ -8.07 -8.21 -3.03 -1.52 -1.52 -7.23 -1.52 -7.74 -8.~ -1.52 ~82 5.72 -1.52 -2.73 -1.52 -7.74 -8.21 -2.27 -4.47 (0.86) Table 2.2. Habitat preference ranking matrix. Pairwise comparison of log ratio differences between percentage of available habitat in the colony MCP and percentage use of each habitat by all bats (+ indicates positive log ratio difference between habitat category in the corresponding row and habitat category in the corresponding column, +++ indicates significant difference, P

Woodland Moorland Pasture Arable Other Rank

Woodland +++ + +++ +++ 5

Moorland + 2

Pasture +++ +++ +++ 4

Arable

Other +++ +++ 3

2.4.4 Individual habitat availability

A further compositional analysis was carried out where each bat's available habitat was determined by the percentage area of each habitat within individual bat MCP's (Table 2.3)

43 and this was again compared to percentage use for each bat. Where habitats were not present in individual bat MCPs, the weighted mean A for that habitat was used for the analysis.

Table 2.3. Percentage area of each habitat within individual bat MCP's

Bat woodland moorland pasture arable other 1L 39.23 7.26 38.50 12.35 2.66 2L 21.16 19.30 38.28 18.97 2.29 3L 68.80 26.40 4.00 0.40 0.40 4L 27.37 7.65 32.17 32.17 0.65 5L 13.21 0 54.72 24.53 7.55 6L 35.15 5.98 38.31 17.40 3.16 7L 30.44 14.66 32.14 20.65 2.11 8L 21.53 51.60 26.87 0 0 9L 80.38 12.44 6.70 0 0.48 10L 24.26 48.47 23.16 3.62 0.48 1NL 23.61 44.79 23.78 6.60 1.22 2NL 29.12 44.99 22.65 2.40 0.84 3NL 29.41 34.18 29.63 4.55 2.22 4NL 65.33 28.39 4.27 1.51 0.50 5NL 62.16 2.70 35.14 0 0 6NL 66.67 2.15 31.18 0 0 7NL 25.64 48.81 19.73 5.82 a 8NL 23.73 34.64 29.79 9.94 1.90 9NL 24.78 38.95 29.15 6.20 0.92 10NL 23.16 42.78 24.75 6.19 3.13

From the resulting matrix, Wilk's A =0.094, for which P=0.0002 (by randomisation), again indicates a strong significant deviation of relative use from that expected assuming random dispersal and ranking of habitat preference proceeded by pairwise comparison of log ratio differences (Table 2.4).

44 Table 2.4. Habitat preference ranking matrix. Pairwise comparison of log ratio differences between percentage of available habitat in individual MCPs and percentage use of each habitat (+ indicates positive log ratio difference between habitat category in the corresponding row and habitat category in the corresponding column, +++ indicates significant difference, P

woodland moorland pasture arable other Rank woodland +++ +++ + 4 moorland pasture + +++ +++ + 5 arable + 2 other +++ +++ 3

Individual Mep based noctule habitat preference is expressed below, from most too least preferred, with significant differences in preference illustrated by»> and a reduction in size.

Pasture> Woodland> Other »> arable> moorland

2.4.5 Differences in habitat use between reproductive categories

Time budgets of lactating and non-lactating bats were compared to identify any differences in habitat use that may be related to breeding success. Proportion of time spent in each habitat

45 was compared between the two categories with moorland and arable categories combined to form a new composite habitat labeled marginal (Figure 2.5). This was to remove a large number of null proportions, as the categories concerned were the two least used habitats.

0.5 • Lactatirg Bats Proportion ~ No~lactatirg of time 0.4 Bats spent in each 0.3 habitat

0.2

0.1

0.0

Pastt.re VVoodlaoo Otrer Marginal Habitat

Figure 2.5. Comparison of the proportion of time spent in each habitat between lactating and non-lactating bats.

46 In order to test statistically whether there was a difference in habitat use between the two reproductive categories, compositional analysis was again used. The calculations were based upon habitat time budgets. The two least preferred categories in the previous compositional analysis were combined to avoid excessive null percentages. All remaining null percentages were replaced with 0.0 I which was at least an order of magnitude less than the smallest non­ null percentage and represented use too small to be detected (Table 2.5).

From the resulting matrix Wilk's 1\ =0.2889, for which P=0.024 (by randomisation), indicating a significant deviation of relative use.

After confirmation of significantly different relative use, habitats were again ranked by summing positive log ratio differences and randomisation was used to test whether relative use differed significantly for each habitat pair (Table 2.6).

47 Table 2.5. Compositional analysis comparing the relative use lactating and non-lactating female noctules made of different habitats. L= lactating bat, NL= non lactating bat.

Noctule lL 2L 3L 4L 5L 6L 7L 8L 9L 10L Use Woodland 79 51 67 34 31 32 68 50 52 24 Pasture 18 42 28 61 60 68 30 50 48 69 Other 3 7 3 5 9 0.01 2 0.01 0.01 5 Marginal 0.Q1 0.01 2 0.01 0.01 0.01 0.01 0.01 0.01 2

Noctule lNL 2NL 3NL 4NL 5NL 6NL 7NL 8NL 9NL lONL Use Woodland 61 31 12 76 61 63 36 39 26 14 Pasture 27 59 66 19 39 33 48 45 66 56 Other 9 8 18 0.01 0.01 0.01 0.01 10 8 19 Marginal 3 2 4 5 0.01 4 16 6 0.01 11

lNL 2NL 3NL 4NL 5NL 6NL 7NL 8NL 9NL lONL Log ratio Wood/pasture 0.82 -0.64 -1.70 1.39 0.45 0.65 -0.29 -0.14 -0.93 -1.39 Wood/other 1.91 1.35 -0.41 8.94 8.72 8.75 8.19 1.36 1.18 -0.31 Wood/Marginal 3.01 2.74 1.10 2.72 8.72 2.76 0.81 1.87 7.86 0.24 Pasture/other 1.10 2.00 1.30 7.55 8.27 8.10 8.48 1.50 2.11 1.08 Pasture/Marginal 2.20 3.38 2.80 1.34 8.27 2.11 1.10 2.01 8.79 1.63 Other/Marginal 1.10 1.39 1.50 -6.21 0.00 -5.99 -7.38 0.51 6.68 0.55

lL 2L 3L 4L 5L 6L 7L 8L 9L lOL Log ratio Wood/pasture 1.48 0.19 0.87 -0.58 -0.66 -0.75 0.82 0.00 0.08 -1.06 Wood/other 3.27 1.99 3.11 1.92 1.24 8.07 3.53 8.52 8.56 1.57 Wood/Marginal 8.97 8.54 3.51 8.13 8.04 8.07 8.82 8.52 8.56 2.48 Pasture/other 1.79 1.79 2.23 2.50 1.90 8.82 2.71 8.52 8.48 2.62 Pasture/Marginal 7.50 8.34 2.64 8.72 8.70 8.82 8.01 8.52 8.48 3.54 Other/Marginal 5.70 6.55 0.41 6.21 6.80 0.00 5.30 0.00 0.00 0.92

Log ratio differences wood/pasture -0.66 -0.84 -2.58 1.97 1.11 1.40 -1.11 -0.14 -1.01 -0.33 wood/other -1 .36 -0.63 -3.51 7.02 7.48 0.68 4.66 -7.16 -7.38 -1.87 Wood/Marginal -5.96 -5.80 -2.41 -5.41 0.68 -5.31 -8.01 -6.65 -0.69 -2.24 Pasture/other -0.69 0.21 -0.93 5.05 6.37 -0.72 5.77 -7.01 -6.37 -1.54 Pasture/Marginal -5.30 -4.96 0.16 -7.38 -0.43 -6.71 -6.91 -6.50 0.32 -1.91 Other/Marginal -4.61 -5.16 1.10 -12.43 -6.80 -5.99 -12.68 0.51 6.68 -0.37 Table 2.6. Relative habitat use ranking matrix. Comparisons of pairwise log ratio differences

between the percentage of time spent in different habitats by lactating and non-lactating bats

(+ indicates positive difference, +++ indicates mean was significantly different from zero,

P

lactating bats relative to non-lactating bats.

Woodland Pasture Other Marginal Rank

Woodland + + +++ 4

Pasture +++ 2

Other + + 3

Marginal 1

Ranking of habitats demonstrates that lactating noctules made significantly less use of

Marginal habitats, relative to woodland and pasture habitats, than non-lactating female

noctules. Preference of relative habitat use for lactating bats is expressed below, with »>

and a reduction of size denoting a significant difference between previous ranks.

Woodland> Other> Pasture »> Marginal

2.4.6 Reproductive category activity comparisons

2.4.6.1 Foraging activity

Nightly foraging activity was generally bimodal with the first, significantly longer flight,

around dusk and the second flight around dawn (median dusk 92mins, median dawn 37mins,

49 U=547, P

2.4.6.2 Time use

Different use of spatial resources may reflect temporal differences in the onset and duration of foraging periods. However, there was no significant difference in the duration of nightly foraging periods, between lactating and non-lactating bats. Furthermore interval between dusk emergence and sunset did not differ significantly between reproductive categories

(Table 2.7). However, lactating bats made significantly more foraging flights per night.

Table 2.7. Comparison of emergence time and average nightly foraging duration between reproductive categories. Mann-Whitney U test, n= 10 for each category.

Median Median U significance

(lactating) (non-lactating) emergence (mins after sunset) 7 11 86 0.16

Number of flights (per night) 3 2 129 0.043 foraging duration (mins) 113 117 98 0.62

2.4.6.3 Space use

Different use of spacial resources could result from non-lactating bats foraging further from the roost area. However there was no significant difference in the area of individual MCPs between reproductive categories. Furthermore no significant difference was found between the maximum distance traveled by reproductive categories (Table 2.8).

50 Table 2.8. Comparison of individual bats MCP and maximum flight distance between reproductive categories. Mann-Whitney U test, n= 10 for each category.

Median Median u significance

(lactating) (non-lactating)

2 MCP area (km ) 6.07 9.03 105 n.s. maximum distance (km) 3.82 4.70 107 n.s.

Broadleaved woodland and pasture were the foraging habitats consistently preferred by noctule bats. Although there was little difference in foraging activity non-lactating bats used marginal habitats significantly more than lactating bats.

2.5 Discussion

2.5.1 Habitat preference

Manly et al. (1993) defines resource selection as the process whereby an animal chooses to use a resource disproportionately to its availability. Conversely, Hutto (1985) suggests any non-random use is selection and need not involve choice. However, when studying selection it is assumed that the population of resource available to the animal can be correctly identified (White and Garrott 1990) and the animal is free to choose between them (Johnson

1980). Available resource is distinguished from abundance of resource by defining abundance as the total present in the environment. Territoriality, disturbance or interspecific interactions may prevent specific areas from being used (White and Garrott 1990), as may landscape structure (Hansson et al. 1995). Can we assume that all habitats measured in the

MCPs are available?

51 Specific aspects of the natural history of bats warrant particular attention to be paid to landscape structure. Many bat species are highly reliant on linear landscape features which facilitate movement to and from foraging areas (M nallereri de long 1994, P. pipistrellus

Verboom and Huitema 1997, M. daubentonii Limpens and Kapteyn 1991, R. hippisoderos

Schofield 1996) and in a highly fragmented landscape large areas of seemingly available habitats may be unavailable because bats are unable to travel between them due to the distribution of these features. Great care should therefore be taken before defining available habitats as all areas within a MCP for these and similar species, or serious errors in classifying habitat preference may result (White and Garrott 1990). Alternatively, a more path-centred analysis could be conducted on these species where the decisions animals make when faced with different habitats encountered along linear landscape features are taken into consideration. However, these considerations are not applicable to noctules which readily cross open habitats. For example they are often the only bat species found at highly isolated forest fragments (de Jong and Ahlen 1991, Gaisler and Kolibac 1992).

Individual behaviour such as territoriality or avoidance of another species may also prevent seemingly available habitat from being used (Kenward 200 I). Several bat species are reluctant to share foraging areas, particularly at low prey densities or at times of increased energy expenditure (P. pipistrellus Racey and Swift 1985, E. nilssonii Rydell 1989).

Furthermore, Kronwitter (1988) observed that adjacent local populations of noctules did not share foraging areas. By determining available habitat at two levels, colony MCP and

individual bat MCP, the threat of error in correctly defining available habitat has been minimised.

52 Both analyses highlighted woodland and pasture habitat categories as preferred and arable and moorland as avoided. The preference for woodland was under-stated, as this was often the only habitat used during dawn and supplementary foraging flights, as well as during inclement weather, which were not included in the analysis because the bats remained in one habitat type. Reliance on woodland and pasture has been well documented for other similar sized bat species (Rhinolophus ferrumequinum Jones and Morton 1992, E. serotinus CaUo et al. 1996) and the opportunistic use of other habitats with localised ephemeral insect concentrations corresponds with studies of the morphologically similar Leisler's bat N. leislen' (Shiel et al. 1999).

2.5.2 Foraging activity

Bimodal activity patterns during summer are common to many bat species (Swift 1980,

Erkert 1982, Catto et at. 1995) and follow the nocturnal activity patterns of flying insects

(Swift 1980), where reduced prey availability during the middle of the night renders foraging non-profitable. Extra foraging flights are thought to be a response to the increased energetic requirements of lactation (Barclay 1989, Rydell 1993). However in noctules, they may simply reflect increased foraging opportunity as all male populations increase the number of nightly foraging flights when high temperature increases insect concentrations in the middle of the night (Kronwitter 1988, Stutz and Haffner 1986), Although non-lactating bats also made extra foraging flights, lactating bats made significantly more and this extension of foraging time probably enabled females to meet increased energetic requirements in late lactation.

53 2.5.3 Differential habitat use

Non-lactating bats did not travel over a wider area or for greater distances than lactating bats and displayed the same habitat preferences. There was, however, evidence of resource partitioning with non-lactating bats using marginal habitats significantly more than lactating bats, although these habitats were still less preferred. Seasonal effects were controlled for by simultaneous tracking lactating and non-lactating bats and age effects are unlikely as both nUlliparous and parous non-lactating bats were tracked. Spatial separation of foraging sites between bats of different sex has been documented previously (P. auritus Entwistle et al.

1996, P. townsendii ingens Clarke et a/. 1993, M. daubentonii, Altringham pers. comm.) however, this is the first study to demonstrate differential intraspecific habitat use.

Furthermore, whereas fitness variables have been associated with roost selection (Entwistle

1994, Brigham and Fenton 1986) they have only rarely been related to bat habitat use

(Bradbury and Vehrencamp 1976). A density-dependent relationship with suitable habitat raises conservation considerations if the area of breeding habitat is under threat and allows the prediction of land use consequences on population distribution and abundance

(Sutherland 1996).

Several bat species respond aggressively to conspecifics while foraging (M daubentonii

Wallin 1961, P. pipistrellus, Swift and Racey 1985; E. nilssonii, Rydell 1989) and aggressive interactions between noctules were observed and vocalisations recorded (Figure 2.6) in the second half of July when there is an increase in volant young and many females are in the final stages of lactation.

54 100 kHz

Frequency

50~z

v \ -J

0050 0100 o I~O 0'00 o 'SO 0)00 0)50 al( Time

Figure 2.6. Noctule social cal1 sonogram of a recording made during an aggressive interaction over pasture. The first and last calls in the sequence are echolocation calls. For recording method refer to Chapter 4.

Barlow and Jones (1997) concluded that similarly structured calls of pipistrelle bats were used as a territorial spacing mechanism. This suggests that increased costs from interference by conspecifics may reduce the suitability of preferred habitat types (Sutherland 1996) and less dominant individuals are excluded. Furthermore, reproductive females may have more to gain from defending a preferred foraging area through learning benefits to offspring

(Brigham and Brigham 1989).

55 2.5.4 Conservation recommendations

Broadleaved woodland and surrounding pasture land are key foraging areas for noctule bats in cultural landscapes. These areas should be enlarged where possible and/or augmented by locating them in close proximity to each other. Farm practices that reduce the number and diversity of insects on pastureland, such as reseeding, applying fertilisers, hedge removal and the use of systemic insecticides should be minimised.

Habitats that encourage large amounts of insects, such as ponds and marsh areas should also be maintained or permitted to develop to supplement key habitats.

56 2.6 Summary

1. Noctule bats are ideal models for habitat selection studies. using compositional analysis. as they are long lived. forage over large distances and do not require corridors to facilitate movement. This allows the statistically defensible demonstration of habitat preferences in a highly variable environment.

2. Noctule bats preferentially foraged over broad leaved woodland and pasture.

3. Less preferred or marginal habitats were arable land and moorland.

4. However, these habitats were used significantly more by non-lactating female noctules when compared to lactating bats.

5. There was no temporal difference in foraging activity between the two reproductive categories and no significant difference in the distances traveled to foraging grounds.

6. This observed distribution suggests dominance-mediated spatial partitioning that allows theoretical prediction of the consequences of changing land use.

57 Chapter 3. Roost selection and roosting behaviour of tree dwelling

noctule bats (Nyetafus noetufa).

S8 3.1 Introduction

Roost choice is of vital importance to temperate lone insectivorous bat species (Kunl 1982).

Loss of suitable roosts may render local populations functIonally extinct (Brigham and

Fenton 1(86). limit population size and distribution (Fenton 1970. Humphrey 1975. Bell rI

(I/. 1(86) or reduce local abundance (Thomas 1988. Lunney 1(88). During the slimmer months. when the noctule bat (NycllIllI.\· nO(:llIla) is most active and females give birth. they spend over 85% of their time roosting in tree holes (Chapter 2). The observed geographical distribution of noctule bats on mainland Europe and the U.K. has been linked to the availability of suitable roost sites in broad leaved woodland (Ahlen and Gerell 1989. Racey

1991. Strclkov 1969. 1997b).

Bats are the most speciose order of mammals found on the British Isles (Corbet and Harris

1(91) and as the major predators of night flying insects. they perform a valuable role in terrestrial ecosystems (Kalko 1998. Kunz and Racey 1998). Tree roosting bats. in particular. transport large amounts of nutrients from riparian and non-wooded areas into the forest ecosystem (Rainey el al. 1992). The noctule bat is the least synanthropic and also the largest vespertilionid found in Britain. regularly foraging across different habitat types and over

large distances (Chapter 2). Although it has a broad dietary niche (Beck 1995; Gloor el aJ.

1995; Mackenzie and Oxford 1995; Vaughan 1997; Rydell and Petersons 1998) and is able to exploit the insect swarms produced by increased nitrogen enrichment of freshwaters and

artificial lighting (Kronwitter 1988. Mitchell-Jones pers. comm.). precipitous local declines

have been widely reported (Stebbings 1988. Hutson 1993). However. these declines have not

S9 been found in syntopic vespertilionids that are less reliant on trees for roosting (Hutson 1(93) suggesting the availability of suitable roosts may be a factor in the decline ofnoctule bats.

Noctules are reluctant to occupy roof spaces in man-made structures preferring instead natural roosts in tree holes (Racey 1991. Hutson 1993). Forestry practice in both temperate and tropical regions has promoted sanitation felling of dead and diseased trees (Kirby 1(98) and selective harvesting of large diameter timber. resulting in a huge reduction of rotting timber and tree holes (Pattanavibool and Edge 1996). particularly in the UK (Kirby 1998). If noctule bats are highly selective in their roosting requirements only a small proportion of available sites will be suitable. ultimately limiting population size. Although a great deal of advice has been ofTered to woodland managers concerning practices which enhance roosting opportunities for bats (Mayle 1990. Briggs 1998) there have been few rigorous European studies of what bats actually require.

To date most studies of roosting ecology in temperate zone bat species have been concerned with cave dwelling or synanthropic species (Kunz 1982. Fenton 1983). Interest in temperate zone woodland species began with the description of noctule tree roosts (reviewed by Gaisler el oJ. 1979) and anecdotal descriptions of single tree colonies of other species (reviewed by

Kunz 1982). However. as the bat species studied did not construct the roosts cavities but occupied a sample of available holes it is not possible to identify features selected by the bats unless a comparison is made with other available sites. Therefore studies began to examine specific tree characteristics and compare them to a sample of random available trees. but only a small group of parameters were measured providing an incomplete picture of the characteristics selected by bats (Lasionycleris noclivagam Barclay el al. 1988. Taylor and

Savva 1988. Nyclophilus gould; Lunney el al. 1988. N. noclula and M. daubenlonii Boonman

60 2000). More recently North American and Antipodean studies have examined a suite of roost tree and site characteristics and compared them to random samples of available tree holes to determine the degree of selection (l.(l.'iionycteri.~ noclil'i}.:{lI1s Mattson el al. 1996. Jv~}'()lis caIU()rnicus Brigham el (II. 1997a. Jvl. sot/alis Callahan el al. 1997. Lasion)'cteris noctivi}.:(lI1s

Betts 1998. (·halinolohll.'l tilhercillatu.\· Sedgeley and O'Donnell 1999). And this mirrored renewed interest in characterising roosting preferences for synanthropic bats using similar methods (Plec:otus Clilrilus Entwistle el al. 1997. Pipistre"u.~ pipi.'itrellll.'I Jenkins et al. 1998.

P. pipislrel/us Oakeley and Jones 1998. Eptesicusfu.'Icus Williams and Brittingham 1997).

No comparable tree roosting studies have been conducted in Europe and few studies have as yet examined internal tree cavity characteristics of roost and available tree holes (Sedgeley and O·Donnell).

In North America tree roosts have been examined in relation to the intensive logging of old growth forests (Barclay and Brigham 1996. Vonhof and Barc lay 1996) after Thomas (1988) and Lunney et al. (1988) raised concerns about the effects of such logging on bat populations. However these studies have limited applicability to the UK forestry situation, where forests are smaller. highly fragmented and all have been managed to some degree

(Peterkin 1998). Others have adopted a community-based approach by investigating roost use in several species simultaneously (Vonhof and Barclay 1996, Boonman 2000. Kalko et al.

1999). This approach gives a general indication of the main roost characteristics selected by all bat species and also provides information on cavity resource partitioning between species but may fail to identify important intraspecific factors. Further studies have investigated cavity selection by a single species at one site (e.g. Myolis sodalis Kurta el al. 1993), and comparisons with previous studies at different sites have identified slight differences in

61 selected characteristics at ditlerent geographical locations ralsmg questions about the applkaoility ofsinglc site investigations (Callahan 1.'1 al. 1993).

Detennining which attributes are important in roost choice provides fundamental infonnation about the selection pressures bats are under and how they meet their requirements for survival. The factors most important to survival and fecundity in temperate zone bat species are widely regarded to be thermoregulatory costs (e.g. Racey 1973. Tuttle and Stevenson

1982. Kurta et al. 1989. Entwistle et a/. 1997) and predation (e.g. Barclay 1982. Fenton et 01.

1994. Speakman 19(1). Tree roost temperature will be affected by surrounding habitat and the structure of the roost. Predation risks associated with terrestrial predators (Dunn 1979.

Sonerud 1985) may be minimised if mosts are hard to locate (by being higher or hidden) and risks associated with aerial predators may be reduced where roosts can be entered and exited at speed (because they are uncluttered). Characteristics should therefore be measured at three levels: tree location in the surrounding habitat. roost location on the tree and structural dimensions of the cavity. Furthermore. essential characteristics of roost cavities should be consistent across woodland types. This is the first study investigating roost selection. by comparing a suite of cavity variables across different woodland types. in any tree roosting

Palaearctic bat.

3.1.2 Roosting behaviour

Evidence that habitats are critical can be obtained by establishing relationships between survival or productivity and use (Kenward 200 I). The miniturisation of radio transmitters now allows individual bats to be followed and their behaviour monitored. All previous studies examining the behaviour of bats using tree roosts have concentrated on the time bats

62 spent in each tree before changing roosts and the distances traveled between roosts (Brigham

et al. 1997, Vonhof and Barclay 19(6). Further studies have compared these parameters

between species at the same site (Foster and Kurta 1999). However. only one study to date

has examined intraspecitic ditTerences in these parameters statistically and related these

differences to breeding success (O'Donnell 1999). furthermore. although emergence patterns

in large roosts in man-made structures have been investigated in relation to perceived

predation pressure (Speakman ct al. 1995. Fenton et al. 1994) there have been no studies to

determine clustering behaviour on emergence in tree roosting bats.

The present study measured location. tree and cavity characteristics of roost holes used by

noctules in ditTerent woodland types and compared these with a randomly selected sample of

tree holes at two spatial scales. random holes in the same wood and random holes adjacent to

roost holes. The aim was to detect any non-random use of available holes and identify

characteristics that ditTered significantly between groups. Then intraspecific roosting

behaviour was investigated at a single site in order to determine whether there was a link

between roosting behaviour and breeding success, and to determine whether bats clustered on

emergence. The ultimate objective was to produce a general resource probability function

that can be used by woodland managers, to identify those trees and holes which potentially

could be roosts (particularly those used by reproducing bats). for monitoring or protection.

63 3.2 Method

3.2.1 Roost location

Roosts were located using two methods. A network of local bat group volunteers intensively surveyed local woodlands at dusk and dawn. The noctule bat is the largest (ca. 30g). highest flying vespertilionid found in Britain. It is the first bat to emerge. often before dusk (Jones and Rydell 1(94) and this facilitates spotting bats when they commute to evening foraging grounds. Additionally the social calls of noctules are audible up to 100m from the roost

(Gaisler t!I al. 1979) and when returning to roosts at dawn. swanning behaviour can be readily observed and used to locate roosts. Identification was con finned in the hand.

A concurrent radio-tracking study resulted in further roost locations as noctules change roosts frequently (Kronwitter 1988. Sluiter and van Heerdt 1966). Radio-transmitters (1.2g

Biotrack. Dorset. England) were attached to twenty-one female bats (II lactating and 10 non­ lactating) and one male bat. Transmitters were attached between the scapulae of individual bats using Skin-Bond ® (Canadian Howmedica. Guelph. Ont.) surgical adhesive. Radio tracked bats were followed back to roosts using a receiver (Mariner. Suffolk. England) and three clement Vagi antennae. Visual observations of emergence were then made of all roost holes to confinn the presence of bats. The capture of bats was licenced by English Nature.

3.2.2 Study areas

Three separate study areas across the UK range were identified. each with five or more known noctule roosts (range 5-17). In order to make the study as representative as possible, plantation. lowland and river valley broad leaved woodlands were studied. The plantation was

64 located in Nottingham. in the English midlands (520 56 north. 10 12 west. study site B figure

1.2). and consisted of sweet chestnut (Castanea .m/iva). oak (Quercus robur) and ash

(f'raxinu.\· excelsior). Broadlcavcd lowland woodland composed primarily of oak and ash was studied in southeast England (51 0 21 north. 00 26 west. study site C figure 1.2). The radio tracking site was Horner wood NNR (National Nature Reserve) in Somerset. southwest

England (51 0 II north. 3° 34 west study site A figure 1.2). a wooded river valley again composed primarily of oak and ash.

3.2.3 Roost holes

A total of 31 noctulc day roosts were identified in trees from the three study areas and all were used in the analysis. Volunteers. between 1995 and 1998. identified twenty roosts.

Eleven were identified by radio tracking or by observing roost switching at study sites between 1998 and 2000.

3.2.4 Random tree holes

A random sample of available tree cavities was identified to compare with roost cavities.

These cavities were the closest cavities to a random point within the same wood that was a randomly generated distance from the roost tree in a randomly generated compass direction.

Binoculars were then used to locate the nearest cavity to this point by surveying nearby trees.

3.2.5 Adjacent tree holes

A random sample of adjacent tree cavities was also recorded for a further comparison to control for aspects of location that maybe linked to use. This allows the removal of tree location variables from the analysis of roost and adjacent cavities. A circle with a 10m radius centred on the roost tree was established and all cavity trees within this circle were

65 numbered. A random cavity tree was then selected by picking a numbered marble from a pocket. Irthe selected tree contained more than one cavity these were numbered and another numbered marble was chosen.

3.2.6 Variables

Each noetule roost tree (n=31). random tree (n=31) and adjacent tree (n=31) was climbed using single rope climbing techniques or a ladder and attributes of habitat tree and location were recorded. This was carried out initially to verify that the cavity extended to the heart wood and was large enough to contain a small colony of noctules. A minimum internal volume of IOOOcm.l was chosen to classify the cavity as available to noctules. The minimum volume of any noctule roost was over six times this arbitrary limit which eliminated small cavities that were obviously not available for use from the analysis. On seven occasions that random trees were climbed. it became clear that the perceived cavity was only the thickness of the bark or a cone shaped woodpecker excavation, which tapered to a point about ten centimetres from the entrance and they were therefore not available for use.

3.2.6.1 Cavity variables

The method of cavity fonnation i.e. whether the cavity was excavated by woodpeckers or fonned by rot was recorded. The cavity was then inspected visually by inserting an angled extendable mirror. A torch with fibre optic attachment provided supplementary light if required. Evidence of other species using the cavity was recorded. The internal dimensions of each cavity were measured using 1.5m of 3mm fencing wire. This could be shaped to measure length, breadth and height of the cavity. From these measurements, the volume of the cavity was estimated, assuming a cuboid shape. Other measurements recorded at the cavity entrance were; aspect (detennined with a compass), entrance diameter, wall thickness.

66 entrance height and clutter. Clutter was a measure of the structural complexity of branches

arollnd the roost entrance and was measured using a device consisting of two 2m canes joined perpendicular to each other at one end. This end was placed horizontally in the centre

of the cavity entrance. The other ends of the canes were then rotated downward in an arc until

the device was vertical. Any branches within the area enclosed by the rotating canes were

counted and their diameters recorded. providing a measure of vegetation around the entrance

of the roost.

3.2.6.2 Tree and tree lucation variables

The tree variables recorded were; status (live or dead), tree species, tree height (measured

using a clinometer) and diameter at breast height (DBH). Location variables recorded were;

distance to edge of wood (or clearing> I OOm~), nearest tree, density (DBH) (which was the

total diameter at breast height of trees around the tree containing the roost or cavity within a

5m radius circle centred on the focal tree) and distance to nearest tree in a 90° arc from the

entrance to the tree hole. The latter measure provides an indication of the closest object a bat

could fly into as it leaves or enters its potential roost site.

3.2.7 Behaviour

The number of times bats switched roosts and the distances between roost trees were

recorded. As catching the bats outside roosts to attach transmitters may have resulted in roost

switching which would not otherwise have taken place, data from the night of capture were

excluded from the analysis. Two or three bats were tagged simultaneously and it was not

possible to locate every bat every night. Only data from bats that were continually monitored

was used in the analysis. Bats were video taped emerging from the main roost using an

67 infrared digital camcorder (Sony TVR 100). The recording was subsequently played back and the time of emergence of individual bats \\'as entered on the CLUSTAN event recorder downloaded from the internet (http://www.abdn.ac.ukJ-nhiI58/c1ustan.htm).

3.2.8 Statistics

Tree cavities were selected as the resource unit and measured only once to minimise pseudoreplication (Hurlbert 1984). Non-parametric tests were used, when comparing single characteristics between sites and groups, as the characteristics investigated were not normally distributed and no satisfactory transformation could be made. Similarly, non-parametric tests were used tt)r the behavioural data and corrected for ties. To simultaneously determine which chardcteristics of cavities, trees and sites noctules selected relative to those available. and in keeping with the multivariate nature of selectivity data. logistic regression was used. No significant parameters were highly correlated (r>0.7) therefore all were included in the

regression (McGarigal el al. 2000). Categorical variables were analysed separately using G­ tests with William's corrections (Sokal and Rohlf 1995). Special t-tests comparing a single observation with the mean of a sample (Sokal and Rohlf 1995, p228) were used to compare

features of the main roost with others at the same site. Clustering on emergence was assessed

using G-tests and optimized chi-squared test computed by "CLUSTAN" software. A statistical significance level of a = 0.05 was used as the nominal rejection criterion for all statistical tests and P values are quoted as exact probability values where possible or estimates are given to qualify evidence of significance (Samuels 1991).

68 3.3 Results

.1 . .1.1 Iktween woodland wmparison

3.3.1.1 Continllolls variables

Of all the cavity and cavity tree variables recorded, from random cavities, only distance to the edge of the wood ditlered significantly between sites (Table 3.1). This was due to the large difTcrence in size between the plantation, lowland and river valley woodlands studied.

2 lIowever, distance from random cavities to the nearest large clearing (> 100m ) was not significantly different between sites and this measure was subsequently used to indicate the proximity of the cavity to what may be perceived by the bats as an edge. Subsequent analysis continued with data pooled for all sites.

Table 3.1. Comparison of measured features of random cavity trees (n=31) between plantation, lowland and river valley broad leaved woodland. using Kruskal Wallis tests.

Variable Median Median Median OF H P value Lowland Plantation River valle~ Tree Height (m) 19.2 24.3 18.8 2 0.4 0.817 DBH (m) 0.48 0.6 0.S6 2 0.6 0.740 Distance to Edge (m) 32 20.3 220 2 IB.SB <0.001 Distance to Clearing (m) 14 20.3 18.2 2 2.B2 0.245 Distance 90° (m) 3.S 5.4 5 2 4.BI 0.090 Nearest Tree (m) 3.2 3.1 3 2 0.12 0.943

Density DBH (m) 0.92 0.54 0.66 2 0.26 0.8BO Hole Height (m) 4.9 4.8 4.7 2 0.21 0.899 Hole Diameter (cm) 6 6 6 2 0.09 0.9S6 Wall Thickness (cm) 4.5 5 6 2 0.37 0.830 3 Cavity Volume (cm ) 8960 S272 8372 2 4.24 0.12 Clutter Diameter (em) 9 9 6 2 1.02 O.S99

69 .,.3.2 Between cavity group ':omparisons

).).2.1 ('avily variahlcs

Roost cavities were consistently larger in internal volume, further from the ground and their access less cluttered than both random and adjacent cavities. In addition, the walls of roost cavities were significantly thicker than the walls of random cavities Crable 3.2).

Table 3.2. Comparison of measured features of roost cavities (n=31) with features from a sample of random tree cavities in the same wood (n=31) and to a sample of adjacent tree cavities in the immediate vicinity of roosts trees (n=31). using Mann-Whitney U-tests. • P

Variables Median Median U Median U Roosts Random Roost -Random Adjacent Roost-Adjacent

1I0ie Height (m) 7.0 4.8 683"· 4.4 693.5"· lIole Diameter (em) 6.S 6.0 924.5 6.0 839.5 Wall Thickness (cm) 7.0 5.5 836.5· 6.0 856.5 Volume (cml) 33992 8372 756·· 12320 762" Clutter Diameter (cm) 0 7.5 1277.5·" 7.0 1241·"

3.3.2.2 Tree and habitat variables

Of all the tree and habitat variables recorded. only the distance to the nearest tree within a

90° arc from the cavity entrance was significantly different between roost cavities and both adjacent cavities and randomly selected cavities in the same wood. Distance to the woodland edge or clearing was highly significantly different between roost and random tree cavities.

and tree density around cavity trees was also significantly. different with roost trees being in

less dense areas of woodland (Table 3.3).

70 Table 3.3. Comparison of measured features of roost trees and their surrounding habitat (n=31) with features from a sample of random trees with cavities in the same wood (n=31) and to a sample of adjacent trees with cavities in the immediate vicinity of roost trees (n=31), lIsing Mann- Whitney U-tests. • P

Tree Height (m) 21.3 19.2 862.5 18.9 860.5 DBH (m) 0.63 0.53 861 0.58 936.5 Distance Edge (m) 6 17 1249··· Distance nearest tree (m) 9.1 4.2 646··· 5.4 760.5" Density (DBH) (m) 0.25 0.66 1186.5·· Nearest Tree (m) 3.7 3.0 884 3.1 953.5

3.3.2.3 Categorical variables

Noctules roosted at random with respect to the availability of cavity tree species when roost cavities were compared to both adjacent cavity trees (GIdJ 2.54. 3df. P>0.5; Figure 3.1) and random cavity trees in the same wood (Gldj 0.785, 3df, P>O.5; Figure 3.1: birch was removed from analysis due to low occurrence).

71 07

06

05

Roost f o p roportion. 0 0 4 ClRandom Troos 03 . EJAdJBcent 02

0 1

0 Oak Ash Beech Sweet Birch chestnut Troo Sp clo

Fi gure .1 . The pr p rti n fr t. ra nd m and ndja em tree pc Ie

imilarl n b er d fI r li ve and end tre \ ith th e f dim rent tatu bein g u ed in pr p rti n t their a il abil it in b th JJ 0.151, Idf, P> .5, roos rand m; dJ 0.151, I d .. P> .5. r ndjn ent; igurc ._.

72 .1

01 .

0'

01

Proportion of 0 ~ . Troos

D Roost RandOm Adjacent LIVE DEAD

Figure .2. The pr p rti 11 of r o t. ramI m and djn ent a it)' tree th t wer Ii e r dead .

tules \ ere . ele ti l! \ ith rc pc t t the melh d f nvity ~ rm Ii n when mpnred t a ailable trees in the immediate area f the r t ( ad) I . 5. I df, P< 0 I; Figure . and rand m tree fr m the ame \ d tule preferr d t ra st in large r \: dpe ker h Ie that were I r t r th dge r the \ d and

10 ated hi gher n tree , in Ie den e • rea \ ilh Ie tru turnl mplexi t .

7 OG

08

01

oe

os I Proportion of Cavltlos o. I

OJ

RooS I Random Adjacenl Roosl Random Adj cenl ROT WOODPECKER

figur . . The pr p rti n or r 1 rand III and adj cnl a ilic ~ rllled b fungal r 1 r w dpe ker e. avoli n.

mpari n f\

In rder t identi r harn leri li a iOl d wilh \ pc k r h Ie thol m a ialcd \ ilh the b cr 'd pr er n e, mpnri. n \ III d III ur d attribute dpe ker a ili ond lh rand m mple f a ilnbl en ilic ignifi ant dif cren c \ a ~ und bel\ een n mea ur d ri, ble lhat \ •

ignifi anti difTer nl in the Illp fI n nd r nd m ilie bl

7 Table 3.4. Comparison llfmeasured features from woodpecker (n==14) and natural rot cavities

(n= 19) from randomly selected trce cavities.

Median Median

Variable Woodpecker Rot LJ P value

Distance to edge (m) 20.25 13.45 321 0.348

Distance to nearest tree (m) 6.8 5.2 362 0.804

Density (m) 0.57 0.51 357.5 0.652

Hole height (m) 5.1 4.4 360 0.735

Wall thickness (cm) 6.5 6 368 0.867

3 Volume (cm ) 24.75 34 400.5 0.149

Clutter diameter (cm) 13 6 355 0.5725

3.3.4 Multivariate results

Of the II characteristics entered into the logistic regression analysis only two explained significant proportions of variation between roost trees and available trees adjacent to the

roost. The nearest tree within a 90° arc from the cavity tended to be further away from tree

cavities used as roosts (z=2.18, P= 0.03, odds ratio 1.65, coefficient 0.5) and roost cavities

were less cluttered (z=2.0 I, P=O.04, odds ratio 0.75, coefficient -0.28). Distance to the

nearest woodland edge (or clearing) and amount of clutter explained significant proportions

of the variation between roost and randomly available cavities. Roost cavities were closer to

the edge of the wood or clearing (z=-1.97, P=0.043, odds ratio 0.76, coefficient 0.28) and

were less cluttered (z=-2.15, P=0.038, odds ratio 0.53, coefficient -0.64). P-values ranging

from 0.999 - 0.462 for five goodness of fit tests: Pearson, Deviance, Hosmer-Lemeshow,

75 Brown (j~n~ral and Brown Symmetric. sugge!st that the! modd tits the! data well. Roosts cavitie!s were c1assitied corre!ctly 77% of the time!. and available cavities 81 %.

3.3.5 Roosting behaviour

All roosts w~re in trees. Three roosts of two othe!r bat spt!cies (Plecollls llllrilllS and A~l'otis

IIlll/ereri) were tound in the sample of random tree holes. Other species occupying non-roost cavities included ants, birds (Paru!) sp., stock doves ('olumba oenas), grey squirrels (Sciuru!) caro/inensi.'l). No noctule roosts were found in the random sample of available tree holes. At two colonies noctules were found to be co-roosting with Myoli... dauhenlonii. If they were present, noctules could generally be seen through the cavily entrance on close inspection and were audible from the ground on warm days. They usually roosted in the cavity above the entrance. adjacent to one another and with each individual in direct contact with the cavity wall.

3.3.5.1 Comparisons of different reproductive categories at a single site

How lactating and non-lactating female noctules used roosting habitat was compared to identify difTerences in habitat use relating to reproductive success. Lactating bats changed roosts significantly less often than non-lactating bats and consistently used one main tree roost (Table 3.5). Transmitters remained attached to non-lactating bats for a longer duration that resulted in slightly longer tracking times however, this difference was not significant

(Table 3.5).

76 Table 3.5. Comparison of roosting switching behaviour between lactating (n=6) and non- lactating (n:=:6) female noctules. using Mann-Whitney U-tests.

Lactating Non Lactating U P value (adj) Median (range) Median (range) Numher of switches 0(0-2) 3 (0-6) 26 0.0365 Numher of trees used I ( 1-2) 3 ( 1-3) 26 0.033 Time Tracked (days) 5 (3-6) 6(5-13) 29 0.113

When the main roost (the roost consistently used by lactating females) was compared to the other roosts used by the colony it was found to be significantly larger in volume and in a significantly larger tree (Table 3.6). Over eighty bats consitently emerged from this main roost.

Table 3.6. Characteristics of the main roost at the radiotracking site and means and standard deviations of characteristics from other roost trees at the same site with results of special t- tests comparing characteristics of the main roost to this sample of roost trees. Dist. 90° = distance to nearest tree in a 90° arc from the entrance to the roost hole.

Variables Main Mean Standard t-value Significance Roost Deviation or P value Tree height (m) 22.6 18.775 4.519 0.789 N.S DBH (m) 1.9 0.75 0.204 5.323 <0.001 Distance to Edge (m) 0 20.1 15.27 -1.24 N.S Dist. 90° (m) 17.5 12.3875 3.762 1.28 N.S Nearest tree (m) 3.6 4.85 2.823 -0.417 N.S Density DBH (em) 20 22.375 35.64 -0.063 N.S Hole height (m) 7.8 9.4375 3.299 -0.468 N.S Hole diameter (cm) 7.5 6.625 0.443 1.861 N.S Wall thickness (cm) 6.5 8.5 3.928 -0.48 N.S 3 Volume (em ) 329472 60786.75 69144.23 3.664 <0.01 Clutter diameter (em) I 4.9375 5.415 -0.686 N.S

At all other study sites lactating female noctules changed roosts throughout summer and

colonies consisted of less than 50 individuals.

77 No,tules at the rnuin roost were ti.lunJ to duster significantly on emergen,e with 73 buts taking 4mins and 32 seconds to emerge with a total chi-squared estimate for clustering of

23.07 and G 01'23.94 (6 d.L P

3.6 Discussion

3.6.1 Problems associated with habitat selection studies

There are many theoretical problems associated with the study of resource selection. the primary consideration being the ability to identify used and available habitat appropriately. as discussed in Chapter 2. Can it be assumed that all tree cavities in the present study are available or was an inappropriate random sample obtained?

3.6.2 The natural history of noctule bats and its effect on cavity availability

Noctules have long narrow wings and relatively high wing loading enabling fast flight in open space but reducing maneuverability at slow speeds (Norberg and Rayner 1987. Baagee

1987. Vogler and Neuweiler 1983. Neuweiler \989). This may restrict access to cavities if noctules are reluctant to fly amongst trees. However. they have lower aspect ratios than the more obligate open-air bats suggesting that they may be able to use open habitats close to vegetation (Vogler and Neuweiler 1983) and they could often be seen commuting through woodland to foraging areas. By determining available habitat at two spatial levels. adjacent tree cavities and random tree cavities in the same wood. the threat of error in characterising available resources has been minimised. particularly as there was consistency in the features selected at both levels. Furthermore. accessibility is easier to justify when the quantity of resource used is small in comparison to that available (Manly el 01. 1993).

78 3.6.3 Problems associated with multiple testing

Another source of error may arise from the use of multiple tests carried out on several variables which are measured from an individual record. Bonferroni or sequential Bonterroni corre(;tions can be applied to the u level to maintain protection against Type I errors (Rice

1989) but result in a drastic reduction in power (Samuels 1991). No correction was applied in the current analysis even though the number of variables recorded was large. as the study was based on a priori hypotheses even though it was the first such study of a Palaearctic bat in the highly modi tied woodlands of the UK. As such. the number of variables recorded is guided by knowledge of the species' natural history and there is no known method of ordering the variables to apply a suitable sequential correction. Furthennore the reason why a particular resource is selected is not directly revealed by the estimation of use (Manly el al. 1993). Any departure from random use is therefore a starting point for further study and a guide for conservation advice with trends observed being tested by subsequent study and analysis.

3.6.4 Roost habitat selection

The evolution of torpor has allowed bats to prosper in marginal habitats that periodically supply an over abundance of prey (Fenton 1983). Shaped by natural selection. the behaviour of temperate zone bat species is strongly influenced by temperature. Noctules spend over

85% of their summer in tree roosts therefore roost and site characteristics should reflect selection pressures. Roost characteristics are associated with fitness variables in some bat species (Brigham and Fenton 1986. Entwistle 1994).

79 3.6.5 lhennoregulalion

Bats in maternity colonies are predicted to maxImIse the amount of time they remain euthermic during the summer. Low body temperature slows foetal development and retards juvenile growth (Racey 1973. Tuttle 1975. Racey and Swift 1981. Zahn 1999). Presumably

more time to accumulate fat reserves before hibernation increases over-winter survival.

Similarly. low temperatures throughout the breeding season are correlated with reduced numbers of reproductive females and volant juveniles (Grindal el al. 1992. Lewis 1993.

Lewis 1994). Noctules preferred cavities closer to woodland edges in areas with lower tree density. Tree cavities closer to the edge of woodland and trees in less dense woodland may

reach higher diurnal temperature through solar exposure than those in the middle of dense

woodland (Kurta el al. 1993. Vonhof and Barclay 1996. Brigham el aJ. I 997a). Cavity temperature may also increase with height. resulting in active selection of high cavities by

noctules. The larger volume of roost cavities observed may also relate to temperature

regulation. as colonial bats can experience significant thermal benefit from clustering (Kurta

1986). Larger roosts will allow larger colonies and more thermogenic movement to actively

maximise thermoregulatory benefit (Licht and Leitner 1967) and this corresponds with

limited experimental evidence where only larger sized bat boxes were used by noctules

(Heise and Blohm 1998).

Thermoregulatory benefits appear to be the driving force in the selection of a roost cavity for

reproductive females. as the main roost in the present study was larger in volume.

consistently contained a large number of bats (>80) and was more exposed to solar radiation.

being the only dead tree that was used at that site. An experimental study of Myolis

bechsleinii demonstrated that lactating bats selected dark coloured boxes. which absorbed

more heat than the light coloured boxes used by non-lactating bats (Kerth el 01. 200 I).

80 3.6.6 Predation pressure

3.6.6.1 A vian predation

The remaining signilicant characteristics relate to structural complexity around the roost.

Distance to the nearest tree in a 90° arc from the roost entrance and the number of branches within two metres below the roost entrance are both measures of obstacles to direct flight.

Although increased solar radiation may be able to reach less cluttered cavities it is also probable that bats prefer easily accessible cavities to reduce exposure to aerial predators

(Vonhof and Barclay 1996). Noctules emerge to feed early. often before dusk (Chapter 2.

Jones 1995) risking predation by diurnal av ian predators (Kroymann 1994). Furthermore. in a recent review noctules were found to be the second most common bat species preyed upon by owls in Europe (Petrzelkova 1999). The short duration. highly clustered emergence is consistent with anti-predator response theory where emergence times that are less than predator handling times limit the number of possible captures (Fenton el al. 1994) and emergence outbursts dilute individual predation probability (Speakman el al. 1995).

3.6.6.2 Terrestrial predation

Higher roost entrances and larger cavities may reduce detection from terrestrial predators

(Dunn 1979, Sonerud 1985) and facilitate escape if the cavity is entered. as with a larger space available more bats will be able to escape before they are predated. Predation has been suggested as the pressure driving selection of tall and large diameter trees in other studies

(Vonhof and Barclay 1996, Brigham el a/. 1997a, Lunney el al. 1988, Taylor and Savva

1988). Predation rates are twice as high for black woodpeckers nesting in old cavities when compared to newly excavated cavities (Nilsson el al. 1991) and Sonerud (1985) considered this is the reason that woodpeckers usually excavate a new cavity each year. Noctules cannot

81 excavate cavities in trees and thereti.lre may select the highest available suitable cavities to compensate.

3.6.7 Conclusions

Selection for woodpecker holes by any bat species. across woodland types. has not been demonstrated prior to this study. Boonman el al. (2000) found selection of woodpecker holes by noctules in forest plantations in the Netherlands but this study demonstrates this selection even in highly structured forests with many possible roosting alternatives. Similarly. only six maternity colonies of LlIsioTly(:It'ris 'lO(:li\'iK"TI.... a migratory North American species which is morphologically similar to the noctule. have been described and all were in abandoned woodpecker holes (Vonhof and Barclay 1996). Small bat species appear to have no such preferences for the type of cavity they occupy (Vonhof and Barclay 1996). As no measured feature of cavities consistently ditTered in relation to its method of formation. the reason why noctules preferred woodpecker cavities remains unclear. Woodpeckers generally prefer trees with decayed heartwood but relatively hard sapwood (Harestad and Keisler 1989) as trees in this condition are relatively easy to excavate but still provide protection from predators and retain heat. Alternatively. woodpecker holes may be easier to locate as young arc highly vocal before tledging (Glue and Boswell 1994). Nevertheless. noctules seem to be reliant on woodpeckers to provide a range of suitable roosts and conservation recommendations should include the encouragement of woodpecker activity.

Understanding the roosting requirements. especially the maternity roost requirements of noctule bats is essential for their conservation. In forested landscapes loss of suitable roost trees has reduced bat abundance (Thomas 1988. Lunney el at. 1988). No woodland in

England has remained unaltered by man; consequently available cavities may already be

82 limited or sub-optimal. resulting in the observed similarity in cavity characteristics between ditlcrcnt \"oodland types, Noctulc:s are restricted to broadleaf woodland and like other woodland species (IJarhas/e/la barbm/ellu,\', .\~ro/i.\' bechs/dnii) this may be a major factor in their decline (Hutson 1993), Enhancing fungal decay and increasing woodpecker abundance in British woodland may ultimately lead to an increase in the abundance and distribution of bat species in general. and noctules in particular (Chapter 5),

83 3.7 Summary

I. Noctule tree roost cavity selection was assessed. by comparison with available cavities.

across three woodland types (plantation. 10'\ land. riparian) to identify any

characteristics. i.e. not specific to one woodland type. that noctules selected which may

be essential for survival.

2. Available cavities were assessed at two spatial scales. random and adjacent. and

confirmed as available by close inspection. to control for error in defining available

cavity resources.

3. Noctules consistently selected old woodpecker cavities that were larger. further from the

ground and in more open situations than random or available cavities across woodland

types.

4. Monitoring intraspecific roosting behaviour by radio telemetry at one site demonstrated

that lactating females switched roosts less frequently and favoured one main tree roost

when compared to non-lactating females.

5. This main roost cavity was in the only dead tree used by this colony and was significantly

larger in volume and in a significantly larger tree (DBH) than the other roosts used by

this colony.

84 Chapter 4. Can bat detectors be used to monitor noctule bat populations?

8S 4.1 Introduction

Ultrasound detectors are used to study the geographical distribution. habitat associations and activity patterns of microchiropteran bats (Walsh and Harris 1996. Limpens el al. 1997.

Fenton 1997. Ahlen and Baague 19(9). Bats whose calls have a low duty cycle (when sound is produced <20% of the time) and high intensity are particularly suitable for monitoring studies involving the passive detection of such calls. Interspecitic studies of sympatric species using ultrasound detectors rely on the ability to accurately identify each species from the echolocation calls it produces. However. there is general disagreement about the accuracy of species identification by echolocation calls

No single parameter of echolocation call can be used to separate all species of bat and multivariate models are required to separate interspecific echolocation calls. The models developed are limited by the range of calls with which they are calibrated. a measure of the intraspecific variation. and the degree of interspecific overlap. Traditionally. in order to accurately record calls of known species identity. bats have been recorded flying towards the microphone whilst leaving roosts (Parsons and Jones 2000). when released from the hand

(Vaughan el al. 1997). inside flight tents (Lance el al. 1996) or at foraging sites where they can be easily observed and unambiguously identified to species (Ahlen and Baag0C 1999).

These methods of call acquisition are biased towards a subsample of calls that bats use under

86 these \:in;umstances (Vaughan c:t al. 1997. Ahlen and 8aagoe 19(9). Using models calibrated with these calls for further ultrasound detector studies will result in bias towards overestimation of activity in environments similar to those in which the calibrated calls were recorded and under-representation in very ditTerent environments. Furthermore their value to licld ewlogists recording hats on transects across heterogeneous lands\:apes using bat detectors is questionable. The probability of corre\:tly classifying calls recorded from this subsample may be very different from the probability of correctly classifying calls recorded under field monitoring situations. Furthermore. this subsample does not represent a random sample of e\:holocation calls (Sokal and Rohlf 1(95) and were recorded in a controlled way.

For example Vaughan e/ al. (1997a) classified calls. recorded from bats exiting roosts. from 3 species (Nyc/alus noclula, N. leisler;, Eplesicus .verolinus) which constitute the low frequency

FM/Cf group of bats found in the UK and achieved an overall correct classification of 82%.

When calls recorded on a subsequent field study were analysed using models from these controlled recordings 48% of calls from this group were unidentifiable (Vaughan el al.

1997b). Classification studies provide information on the probability of correctly identifying bat calls to species. This probability can then be included in models estimating population abundance. where it replaces the detection probability used for other wildlife species (Barker and Sauer 1992). However. using probabilities generated from a small subsample of calls. which may not be used at survey locations. assumes that probabilities are equal across habitats (Y occoz el al. 200 I ).

The UK wide National Bat Monitoring Programme (NBMP) incorporates roost counts and field surveys to monitor bat populations and the field surveys rely on the ability of volunteers to accurately identify bats in the field by their echolocation calls. However. there has been no attempt to quantify the probability of correct identification or estimate observer error in these

87 surveys (Walsh el al. 200 I). Both are major sources of error in wildlife monitoring programmes and severely limit the inference that can be gained about the populations monitored (Yoccoz el at. 200 I). Can bat detectors really be used to monitor noctule populations?

One of the species being monitored by the NBMP is the noctule bat (N,l'cwlus noc.:tula) as it is currently of conservation concern (Hutson 1993). Two species from the genus Nyc/a/us are found sympatrically in Britain, the noctule and Leisler's bat (lV. leis/ail. Only the Leisler's bat is found in Ireland. Both species are commonly associated with trees (Corbet and Harris

1991) which makes roost counts more problematic and while the noctule is thought to be declining (Hutson 1993) the number and distribution of Leisler's bats found in bat boxes in

Britain and mainland Europe is increasing (I. Mackie unpublished, Dondini and Vergari

1995). The most recent study to date incorporating multivariate discriminant analysis of echolocation call parameters correctly classified noctules and Leisler's bats 82% of the time, from their echolocation calls (Parsons and Jones 2000). Here bats of both species were recorded at roost locations and noctules at foraging grounds when flying towards a detector on a fixed tripod. These methods may not record the full range of natural variation in call structure (Vaughan el al. I 997a), calls are not randomly sampled (Sokal and Rohlf 1995) and the techniques used do not simulate call capture by field biologists. Quantifying the accuracy in separating noctules and Leisler's by their echolocation calls, i.e. the probability of correct identification, recorded in field conditions would validate field survey techniques designed to monitor population change,

88 4.1.1 Why should different bat species use echolocation calls with ditTerent characteristics?

The echolocation calls of bats are shaped primarily by natural selection (Jones 1995). As an active orientation system with sender and receiver being the same individual. there is no connict of interest and the maximum incentive to pay costs (Bradbury and Vehrcamp 1998) therefore echolocation ca11s are expected to be honest signals (Fenton eI lIl. 1998). The evolutionary outcome of this system. and as a result of the physics of sound production and

propagation in air. are a number of echolocation strategies and interspecific differences in echolocation call structures that vary with bat morphology and behavioural ecology

(Neuweiler 1989). Further. as echolocation is used to acquire food. niche separation through

resource partitioning should result in call ditTerentiation (Findley 1993).

4.1.2 Sound production and emission

The acoustic orientation system used by microchiropteran bats is ultimately constrained by

features of their anatomy: the larynx. pharynx and the auditory system. The echolocation calls they produce show structured changes in frequency over time. Signal frequency is thought to depend on the tension of the non-muscular vocal membrane when vibrated

(Neuweiler 2000). The resonant properties of this vibrator not only determine which

frequencies will be produced. but also the relative amplitude and phases of these frequencies

(Bradbury and Vehrencamp 1998), Thus the anatomy of the larynx determines the frequency

and phase spectra of the sounds it produces.

Echolocation calls are emitted through the open mouth in most vespertilionid bats and as

there is little specialisation of the supraglottic air passages, the emitted sound spectrum is

equivalent to that produced by the larynx (Neuweiler 2000). Open-area foragers of larger

body size tend to use calls with most energy in lower frequencies than do smaller bats

89 (Waters e/ III. 19(5) which is expected as sound producing structures produce lower

frequency sounds as their size increases (Pye 1979). Bat species of a similar morphotype but very different in size should therefore have similarly structured calls that ditTer in frequencies

(Figure 4.1).

4.1.3 Sound propagation in air and variation of sound parameters

Echolocating bats constantly maximize the information they obtain from echolocation by changing design of the outgoing signal (Bradbury and Vehrencamp 1998). By changing the time and frequency parameters of their calls, bats can improve the information they obtain in different environments and when carrying out ditlerent tasks (Kalko and Schnitzler 1993).

Calls which vary over a large frequency bandwidth provide more detailed information about the texture of the environment and narrow bandwidth, long duration calls contain more energy, extending operational range (Simmons and Stein 1980). However, high frequency sound sufTers higher energy loss through atmospheric attenuation (Lawrence and Simmons

1982) and low frequencies may be unable to resolve small targets (Pye 1979, but see Waters el aJ. 1995). Thus most vespertilionids put maximum energy into the 20-60kHz frequency

range which probably reflects the trade-otT between detail and range (Fenton el aJ. 1998).

Low duty cycle bats separate call and echo in time and are thought to be unable to process

temporally overlapping call and echo (Schnitzler and Kalko 1998) although, some bats have

been observed to tolerate overlap (M nalterer; Siemers et 01. 2000. E. nilssonii Jenson el 01.

2001). Nevertheless, if overlap is a problem then call duration sets the minimum range of

prey detection as the echo cannot be detected while a call is being produced. Maximum range

is inversely related to repetition rate. as echoes must return before the next call emission

(Schnitzler and Kalko 1998). Therefore the duration of the call is greatly effected by

90 ecological context and environment (Neuweiler t 989). Doppler-tolerant call shape is also

important for fast flying low duty cycle bats. The accuracy of range measurement is reduced when bat and target move relative to each other because Doppler shifts change the frequencies of the emitted calls resulting in the echo having a slightly different frequency composition. This prohlem can be minimised hy increasing the period (reducing the

frequency) of successive sound waves orthe call as a linear function of time and the optimal

result is a hyperbolic decrease in the frequency of the call (Altes and Titlebaum 1970).

4.1.4 Predictions for the echolocation call variation of Nyc/a/u.t; bats

Noctules and Leisler's bats are similar morphologically but very different in size. The noctule

is Britain's largest bat weighing circa 30g it is around 109 heavier than Leisler's bat (Racey

19(1). The larger bat will theoretically have a larger vocal apparatus and a higher wing

loading, requiring faster night. Noctules are therefore predicted to have a longer call. that

more closely approximates a hyperbolic decrease, with a correspondingly lower repetition

rate, which starts, ends and has peak energy at lower frequencies than Leisler's in a similar

environment (Figure 4.1).

4.1.5 Intraspecific variation

A problem with identification arises where one species alters its call structure in response to

change in habitat structure and the parameters of these calls converge with those of another

species in a slightly different habitat. As noctules shorten their call duration and increase

bandwidth, to obtain more information about a cluttered environment and avoid call/echo

overlap, some call parameters converge with measured parameters of Leisler's calls.

Distinction between calls is further complicated by distortion of signals en route to the

91 l1linophone in tiekl situations. If population abundance is to be measun:d the probability of wrrectly identifying these calls must be estimated.

4.1.6 Random call acquisition

Obtaining calls from foraging bats of known species identity under natural conditions is diflicult and has previously been achieved where only one species is present (Rydell 1990) or where bats are captured, marked with chemiluminescent tags and then released and followed

(Fenton and Bell 1979). Marking with chemiluminescent tags is more applicable to the study of less mobile, low flying species. Both noctules and Leisler's have fast commuting flight and frequently fly at high altitude (Shiel et til. 19Q9, Chapter 2). Radiotracking is the only technique currently available which can be used to collect data systematically on individual bats whilst minimising the bias introduced by visual and acoustic methods.

The present study aimed to quantify the accuracy of field identification of N.vcra/us species using their echolocation call structure. This information can then be included in population monitoring schemes as probability of identification. Calls were recorded in all main habitat types for NyctaJus species found in Britain. To standardise measurement and control for observer error call parameters were measured automatically by commercially available software. The parameters were then used to build models which best classify the calls to species, and the relationships between parameters and their importance in classification were identified. Two different classification models were developed, the first calibrated with parameters measured from single calls recorded from each species. The second model was calibrated with average values for each parameter recorded from a call sequence. Single calls and average call sequences (averages used to control for sources of heterogeneity) were then

92 classified using the best models to obtain estimates of the accuracy of species identification by field recording methods.

4.1.6 Statistics

Non-parametric statistical tests were used tor univariate comparisons of call parameters as none was normally distributed and no suitable transformation could be made. Quadratic discriminant function analysis was used to classify calls to species as distributions of measured characteristics were not identical. but overlapped (Sakal and Rohlf 1995).

4.2 Method

4.2.1 Sampling method

Random stratified sampling: The echolocation calls of noctules were recorded from four

intensively studied locations across England. At two sites. individual bats (n=22) were radiotracked and calls were recorded in five different habitat types (broadleaved woodland. pasture. aquatic. arable and moor). Ten more bats were recorded at the other two study sites where they were followed to similar habitat types. The number of calls that were analysed for each habitat was proportional to the average time all radiotracked bats spent foraging in that

habitat (see Chapter two). One random sequence of search phase echolocation calls (Griffin

1958) was selected for each bat. Therefore thirty-two random. independent call sequences

were recorded which were stratifie<.! by foraging habitat and the proportion analysed for each

habitat was equivalent to the proportion of time spent foraging in that habitat.

Leisler's bats can be unambiguously identified by echolocation calls in Ireland (Russ 1999)

where it occurs allopatrically. A balanced data set was achieved by randomly selecting thirty-

93 two call sequences from a library of calls recorded from randomly selected I km squares in

Northern Ireland. No calls were recorded less than 500m from any known roost.

Calls wt!re recorded in a "proactive" lield-surveying manner by pointing the microphone at the passing bat when possible. Rarely was the bat flying directly at the microphone. and topography and environmental conditions were highly variable though weather conditions were never extreme.

4.2.2 Analyses and equipment

All calls were sampled using time-expansion bat detectors (D-980 Operational range 15-

200kHz. flat response (~3dB) in active range. Petersson Elektronik AB. Uppsala. Sweden).

Three seconds of real time was digitally stored in the detector at a sampling frequency of

350kHz and a resolution of 8 bits. then the stored sequence was time-expanded by ten times and recorded onto metal tape via a Sony WM-D6C Professional Walkman. Call parameters recorded from the same bat by ditTerent detector/observers were almost identical and this source of variation is assumed to be minimal.

The time-expanded signals were digitized on a PC at a sampling rate of 22.05kHz processed through a Fast Fourier Transformation (FFT) using 5 J 2 points and 50% time overlap and a

Hamming window. the result being displayed as sonograms with a resolution of 430Hz and

1.161 ms (A visoft° SASLab Pro. R. Specht. Berlin). Call parameters were recorded using

SASLab Pro's automatic parameter measurement facility. All parameters were measured from the fundamental. Maximum frequency was measured at the start. minimum frequency at the end. The peak frequency (frequency of maximum energy) as also measured. as was the peak frequnecy at the centre of the call (located at half the duration). The software locates start and end of each call by back tracking a set amount from the peak amplitude, in this case

-15dB. This facilitated objective measurement of calls with overlapping echoes and dillerent signal-tn-noise ratios. Call duration (time from start to end of the call) and inter call interval

(time from the end of the call to start of the next) .... ere also measured. Each automatic measurement was visually checked lor obvious errors. On six occasions the software measured environmental noise or calls from other bat species. These errors were easily identified and deleted. Call sequences were then rcmeasurcd and visually checked.

4.3 Results

4.3.1 Nyc/alliS calls

Both noctules and Leisler's often alternate between steeply frequency modulated (FM) calls and almost constant frequency (CF) calls when flying at high altitude (> t Om) (Figure 4. t).

This alternation can still he seen at lower altitudes in noctules (less than 5m) where successive calls difler in frequency parameters. Noctules alternate between calls (both calls used in 97% of call sequences) significantly more than Leisler's (50% of call sequences) (Gad)

20.18. t d.l'., P

9S a)

Frequency

\

-~-- :-00 . 00 ----6ilO 800 Time

b)

60 Id-f1 Spectrogram. r,T .'le 10]. , ~lannlno wmdnw

Frequency

30",Hz \ \ \

100 200 JOO ' 00 ~OOm. Time

Figure 4.1 Sonograms of a) noctule and b) Leisler's bat echolocation call sequences to illustrate the similarity in call shape but difference in frequencies used by each species. The two types of search call produced by each species are shown with two more frequency modulated calls between two more constant frequency calls. The two call types are usually alternated.

96 4.3.2 Single random \.:all wmparison

".3.2.1 Interspecific variation

A great deal of interspecilic variation can he seen when call parameters are compared hetwcen spe\.:ies (Table 4.1 ).

Table 4.1. Measured parameters of random echolocation calls re\.:orded from noctule and

Leisler's bats as means2:,standard deviations, with results and P values of Mann-Whitney U comparison.

Noctulc Leisler's U P value

Duration (ms) 14.62:,1.0 10.22:,0.52 809.5 0.002

Inter Call Interval (ms) 2462:,15.7 234.42:,15.3 983 0.445

Start Frequen9 (kHz) 29.76::1.342 32.72:,1.19 1186.5 0.049

End Frequency (kill) 20.69;!".0.45 24.012:.0.33 1374 <0.001

Centre Frequency (kHz) 22.422:,0.52 25.792:.0.42 1344 <0.001

Peak Frequency (kHz) 21.972:.0.46 2S.3±0.35 1372 <0.001

Although there are significant differences between all echolocation call parameters. except inter pulse interval. there is also a great deal of overlap. In order to visualise inter and intraspecific variation of echolocation call parameters more clearly and to identify situations where overlap is more likely to occur. the relationship between call duration and each of the other call parameters was investigated seperately by graphical illustration (Figures 4.3. 4.5,

4.7.4.9.4.11). Leisler's bats show consistently higher values for all frequency parameters of their calls (except start frequency). with respect to the call duration. than noctules. However. intraspecitic variation results in overlap across all frequency/duration relationships.

97 4.3.2.2 Multivariate analysis

Box's M-test (F = 1.79\, P == 0.014) indicated that the covanance matrices were heterogeneous, compelling the use of quadratic discriminant analysis. First measurements from thirty-two random calls f(x each species were used for the analysis and resulted in an overall correct identification rate of 75%, with cross validation (72% noctules correctly identified, 78~~ Leisler's correctly identitied). The individual contribution of each parameter to the functions was assessed by Wilks' A. Crable 4.2).

Table 4.2. The relative contribution to c1assitication of each echolocation parameter expressed as Wilks' A. The lower the value for Wilks' A the larger the contribution made to group separation by that parameter.

Wilks' A.

Duration 0808

Inter Call Interval 0.996

Start Frequency 0.958

End Frequency 0.635

Centre Frequency 0.706

Peak Frequency 0.652

98 0.5 0.45 j • 0.4 1 - 0.35 0.3 " Inter Pulse Interval 0 25 (s) . l 0.2 " 0.15 " 0.1 ., .- • 0.05 . - o L - o 0.005 0.01 0.015 0.02 0.025 0.03 Duration (s)

I • Noctule - Leisler's I L

Figure 4.3 . Intraspecific comparison of the relationship between inter pulse interval and call duration, with least squares fit trend lines (all relationships are significant p

0450.4 j • 0.35 • • • • 0.3 Average inter pulse 0 25 j interval (s) . 0.2 0.15 • 0.1 • • 0.05 0 -,- ---, 0 0.005 0.01 0.015 0.02 0.025 0.03 Average duration (s) l!£loctule • Lei~ler's J ------J Figure 4.4. Intraspecific comparison of the relationship between average inter pulse interval and average call duration, with least squares fit trend lines (all relationships are significant p

99 55000

50000 _. 45000 - .-. 40000 - • Start Frequency 35000 • (kHz) .- •• 30000 • I:' - - 25000 ~ - .- -. 20000 1

15000 -+ 0 0.005 0.01 0.015 0.02 0.025 0.03 Duration (5) • Noctule _ Leisler's

Figure 4.5. Intraspecific comparison of the relationship between call start frequency and call duration, with least squares fit trend lines (all relationships are significant p

55000

50000 4 • 45000 -I -- .. 40000 -I Average Start 35000 Frequency (Hz) I 30000 1

25000 -I

20000 ~

15000 -l-­ o 0.005 0.01 0.015 0.02 0.025 0.03 Average Duration (5) • Noctule _ Leisler'sl L

Figure 4.6. Intraspecific comparison of the relationship between average call start frequency and average call duration, with least squares fit trend lines (all relationships are significant p

100 31000 29000 27000 J - 25000 End Frequency (Hz) 23000 1 21000 19000 17000 '

15000 r o 0.005 0.01 0.Q15 0.02 0.025 0.03 Duration (5)

• Noctule _ Leisler's

L

Figure 4.7. Interspecific comparison of the relationship between call end frequency and call duration, with least squares fit trend lines (all relationships are significant p

31000 l 29000 1 1 27000 i - 25000 J - Average End 23000 -1 Frequency (Hz)

21000 ~

I 19000 ~ 17000 1 ., I 15000 -­ "T o 0.005 0.01 0.015 0.02 0.025 0.03 Average Duration (s) l ~Noctule _ LeiSler' ~

Figure 4.8. Interspecific comparison of the relationship between average call end frequency and average call duration, with least squares fit trend lines (all relationships are significant p

101 33000

31000 1 29000 -- 27000 I - Centre Frequency 25000 ~ (Hz) 23000 1

21000 I 19000

17000 ~

15000 -,-----,------,- T .------,---,------, o 0.005 0.01 0.015 0.02 0.025 0.03 Duration (5) l , • Noctule _ Leisler's I Figure 4.9. Intraspecific comparison of the relationship between call centre frequency and caB duration, with least squares fit trend lines (all relationships are significant p

33000 -, 31000 j 29000 --- 27000

Average Centre 25000 Frequency (Hz) 23000

21000 19000 17000

15000 +----,-----.----,----,----,----~ o 0.005 0.01 0.015 0.02 0.025 0.03 Average Duration (5) I. Noctule - Leisler's 1

Figure 4.10. Intraspecific comparison of the relationship between average call centre frequency and average call duration, with least squares fit trend lines (all relationships are significant p

102 31000

j • 29000 • • 27000 1 •

25000 ~

Peak Frequency (Hz) 23000 ,

21000 j

19000 ~

17000 l

15000 r T 0 0.005 0.01 0.015 0.02 0.025 0.03 Duration (5) r ' L! Noctule • Leisler's j

Figure 4.11. Intraspecific comparison of the relationship between call peak frequency and call duration, with least squares fit trend lines (all relationships are significant p

31000 1 29000 ~ • • 27000

25000 Average Peak 23000 Frequency (Hz) 21000

19000

17000

15000 +------,­ o 0.005 0.01 0.015 0.02 0.025 0.03 Average Duration (5) EOBoctu,e • Leisleii] J

Figure 4.12. Intraspecific comparison of the relationship between average call peak frequency and average call duration, with least squares fit trend lines (all relationships are significant p

103 ~.3.3 Average call comparison

Inspcction of thc rnisdassitication hlble revealed that rnany of the noctule culls that were wrongly classilied as Leisler's calls were the more FM calls that noctules consistently altcrnatc with more CF calls which had been selected I()r measurement by the random selcction prOl:ess. It appeared that other calls in these sequences might have been classified correctly. In order to control t()r this sampling elTect all search phase calls from random sequences wcre mC

64 average calls, 32 for each species. Each data point is still independent, each point measured from a ditlcrent bat. The enect this has on the variation of call parameters and overlap between populations can be compared to that of random calls (Figures 4.4, 4.6, 4.8,

4.10, 4.12). In all cases the intraspecific variation has been reduced and the interspecific seperation increased.

-t.3.3.1 Univariate comparison of single and average call parameters

A paired comparison was carried out to identify whether averaging had a significant intraspecific effect on parameter measurement for any individual parameter Cfable 4.3).

104 Table 4.3. Means2:,standard deviations tor average parameters and P values tor intraspecific

paired t-tests \:omparing individual parameters measured from single \:alls and average

parameters rc\:onJcd from sequel1\:cs of calls.

Average Noctule P Average Leisler's Leisler's P Nm:tule \:311s value calls value Duration (ms) 14.72:,4.64 0.815 tJ.862:,2.81 0.288

Inter call interval (ms) 254.42:,60.5 0.5 243.4:!:,71.1 0.05

Start Frequency (kllz) 26.02:,4.60 0.744 29.32:,4.34 0.492

Lnd Frequency (kHz) 21.22:,1.92 0.322 24. 99:!:,1. 97 0.852

Centre Frequency (kHz) 22.22:,2.27 0.4tJS 26.05.:.2.45 0.98

Peak Frequency (kHz) 22.02:,2.0 0.17 25.84:!:,2.07 0.96

The only parameter significantly cflected by averaging was inter call interval in Leisler's bat.

4.3.3.2 Multivariate analysis

A further discriminant function analysis was then carried out, again with cross validation, on

average call parameters recorded from all search calls in random sequences. Box's M-test (F

= 3.44, P ::: <0.00 I) again indicated the use of a quadratic function. The quadratic

discriminant function analysis from average call parameters significantly improved the

classification rate (G iklJ 17.47. Id.f.. P

one sequence wrongly classified. 100% Leisler's correctly classified). Again the individual

contribution of each average parameter was assessed by Wilks' )., (Table 4.4).

lOS Table 4.4. The relative contribution to classification functions of each average echolocation parameter expressed as Wilks' A. a likelihood ratio statistic. The lower the value for Wilks' A. the larger the contribution made to group separation hy that parameter.

Wilks' A.

Duration 0717

Inter call Interval 0995

Start Frequency 0956

End Frequency 0494

Centre Frequency 0603

Peak Frequency 0.519

No different combination of call parameters could match or increase this classification rate.

4.4 Discussion

4.4.1 Alternating calls with ditlercnt sound parameters

The use of alternating calls with diOerent structural parameters has been observed in several bat spec ies (e .g. Kl)ssl el al. 1999) and although the function remains contentious it seems likely that non-overlapping calls allow low duty cycle bats to use two separate frequency channels simultaneously which allows them to overcome problems associated with temporal overlap.

106 4.4.2 Predicted differences and relative contribution of call parameters

The end, centre and peak frequency parameters were highly significantly different between species and contributed consistently more to species identification than the other parameters.

They matched predictions and previous studies (Vaughan el al. I 997a, Parsons and Jones

2000) where the larger bat emitted lower frequencies. Start frequency contributed relatively less than the other frequency parameters, probably due to less accurate measurement as a result of increased attenuation of high frequencies (Lawrence and Simmons 1982) and due to different methods for obtaining random calls for the two groups. Inter call interval was consistently the least important classifying parameter, which may be due to bats sometimes missing a call per wingbeat (Jones 1995) but more probably is due to noctules alternating calls to avoid overlap instead of lengthening call intervals. However, the inter call interval measurement from Leisler's bats was the only parameter that was significantly affected by averaging. Therefore it may be particlllarly important to accurately measure call intervals, or screen sequences for missed calls, before classification in multivariate models using parameters measured from individual calls recorded from bat species that alternate call types.

Rarely are average call parameters used in classification studies but it seems intuitive as a method for reducing heterogeneity where echolocation behaviour cannot be standardised. In the field, bats scan from side to side (Kalka 1995. Jensen and Miller 1999) and are constantly changing direction and altering call structure in response to environment (Kalka and

Schnitzler 1993). In order to classify calls as "search phase" for analysis. a call sequence must be recorded which demonstrates that the bat has not begun to track prey. This facilitates the use of averaging search call parameters. Average call classification (98.5% of calls correctly classified to species) was far more accurate and provides the overall probability of

107 correct identification needed to measure population trends. The classification rate may have been higher than previous studies (Vaughan et al. 1997, Parsons and Jones 2000) because echolocation calls were randomly sampled and not restricted to a subsample at roost areas.

Identification studies based on nonrandom calls may result in higher misclassification rates because calls are disproportionately sampled from cluttered environments, therefore producing erroneous identification probabilities. It would be interesting to see what classification rate could be achieved for the FM Myotis spp. group using models derived from random field recorded calls.

4.4.3 Monitoring bat populations

Counting bats at roost sites is a long established method for monitoring bat populations

(Ransome 1989, Speakman et af. 1991, Jones and Altringham 1996). However, there is marked variation in local population estimates in the UK, which may invalidate extrapolation of this information to cover larger areas (Harris el aJ. 1995). Furthermore roost counts concentrate on synanthropic species or only on members of the population that form colonies that roost in houses and no studies have been conducted to quantify the rest of the population.

More recently community studies have been carried out on a large-scale regional basis using heterodyne bat detectors (Walsh et af. 1995, Walsh and Harris 1996) but these studies did not differentiate between species and concentrated on habitat associations. There then followed a series of call identification studies (Obrist 1995, Vaughan et al. 1997, Parsons and Jones

2000) designed to differentiate between the calls produced by different species as they left their roosts. Although identification rates are improving, with more sophisticated

measurement techniques, they are always recorded under standard conditions (Parsons and

Jones 2000). The present study is the first to demonstrate that measured calls from field

108 recordings result in high identification rates which implies bat detectors can be used to monitor bat population trends.

Bat detectors are commonly used to monitor bat activity in different habitats and infer species

preferences (Fenton 1970, Walsh and Harris 1996. Rachwald 1992). This practice is otten

confounded because bats, when changing the structure of their calls. alter their detectable

range. In addition different species produce calls of different intensity and species

identification through nonstandardised methods has introduced unquantitiable observer error.

The advances in bat detector technology now offer the opportunity for bat detectors to be

used for standardised studies of population change in individual species. for which they are

more suited. The major benefit of using bat detectors is that. with the correct sampling

design, they can obtain a representative sample of the whole population. Further. using

standardised techniques for call capture and analysis. the degree of precision and bias can be

quantified. Bat detectors are particularly suitable for monitoring trends in bats using high

intensity echolocation calls such as noctules.

109 4.5 Summary

1. The probability of correctly identifying N. noctula and N. leisleri was estimated from

randomly sampled echolocation calls recorded at many different sites and habitats.

2. Echolocation call parameters were automatically measured to control for observer effects

and multivariate models were generated using parameters from random calls and average

parameters from random call sequences.

3. Noctules alternated between different call types significantly more often than Leisler's

bat.

4. Quadratic discriminant analysis correctly classified 75% of random calls to speCies,

whereas 98.5% of call sequences were correctly classified to species from average call

parameters.

5. The present study provides evidence that bat detectors can be used to passively monitor

population trends of echolocating bats where formal quantification of the probability that

bats can be correctly identified from their echolocation calls is obtained.

110 Chapter 5. Creating roost holes for noctule bats

III 5.1 Introduction

Tree holes are a keystone resource (Primack 1995) that limit the abundance and community structure of many bird and mammal populations (Newton 1994. Shuttleworth 1999). In managed woodland lack of available tree holes can render local populations functionally extinct in otherwise suitable habitat (Newton 1994). Societal demands for sustainable forestry practice have resulted in forest managers requiring techniques to mitigate any adverse affects and increase any positive impacts that their forests have on the environment.

Twenty-two European bird species are obligate cavity nesters and sixty are known to use or prefer cavities (Newton 1994). Many of these species are protected in some way or have suffered severe population declines in recent years. Tree cavities are the natural roost site of almost all non-cave dwelling bat species and are used when rearing young by many other small and arboreal mammals (Corbet and Harris 1991). Consequently conservation measures for all these species generally advocate supplementary hole provision and several studies have demonstrated the extensive use some bat species make of bat boxes (e.g. Boyd and

Stebbings 1989). Recent research has shown noctule bats are highly selective in their choice of roost holes (Chapter 3) and availability of suitable roosts is likely to limit distribution and population abundance (Racey 1991, Ahlen and Gerell 1989, Strelkov 1997).

To compensate for this paucity of natural cavities, nest box schemes have been initiated throughout Europe. In many circumstances nest box provision has led to an immediate rise in the density of cavity breeding birds and experimental manipulation of nest boxes has

112 provided unequivocal evidence that number of sites limits their densities (Newton 1994).

Provision of boxes however, has only been possible on a local scale and is difficult to achieve as a long-term, large-scale conservation strategy. In addition. it requires a long term commitment of time and money, which is often forgotten, to maintain boxes in good condition. For example, although thousands of boxes were erected in a nationwide scheme in the 1980's a recent survey of these boxes found most were destroyed or unusable and had not been replaced (I. Mackie, unpublished information). Artificial sites are often species-specific and there is also some suggestion that highly visible nest boxes may place the occupants at a greater risk from predation, in particular from mammalian predators (Dunn 1977, Sonerud

1985).

Physically excavating cavities in trees has also been attempted with specialised drilling equipment and although successful is highly labour intensive (Copeyon 1990). More recently, research has been initiated to accelerate wood decay in forests and thus provide woodpeckers with habitat suitable for the excavation of their nest holes (Parks el a/. 1996).

Woodpeckers typically excavate one or more holes per year and previous holes are well used by other cavity nesters (Bonar 2000) and preferred by noctule bats (Chapter 3). Woodpeckers actively select trees with thinner sapwood and more decayed heartwood (Hooper el al. 1991,

Conner et a/. 1994) and the presence of suitable trees is thought to be the most important factor in the abundance of certain woodpeckers (Azevedo el al. 2000. Pasinelli 2000).

A number of techniques have been employed to accelerate decay which involve various different methods of killing trees (Conner el al. 1983, Bull and Partridge 1986, Parks el al.

1999) or the inoculation of heartwood rotting fungi (Parks el al. 1996). Killing trees, by girdling or injecting chemicals (Conner et al. 1983), results in a large quantity of standing

113 dead timber which tends to decay from the outside to the heartwood. This is unlike natural decay progression and the tree will usually suffer windsnap before its potential as a "wildlife tree" has been realised (Parks et aJ. 1999). Fungal inoculation has been more successful as it mimics natural stem decay, producing a vertical decay column in the heartwood of live trees, that is subsequently excavated by woodpeckers, and may remain in use for centuries (Parks el af. 1997). Preliminary results after 6 years demonstrated the viability of the technique with all inoculated trees (n = 60) containing decayed wood and 14% being used by woodpeckers or subsequent cavity nesters (Parks et aJ. 1998).

All previous research on inoculation to induce artificial decay has been carried out on the vast conifer forests of the Pacific North West, which has little relevance to European broad leaved woodland. This study therefore aimed to identify the fungal causes of decay in recently excavated woodpecker holes and noctule bat roosts in the UK and culture the identified fungi for inoculation into live trees.

5.2 Method

Trees were climbed using a ladder or single rope climbing techniques. Wood cores were removed from heart wood within 12 inches below the floor of known bat roosts (n=5) at the radio-tracking site in Somerset (chapter 2). A further 15 cores were removed below newly excavated woodpecker nests (n=13) and nearby non-excavated trees (n=2) at an RSPB study site in south east England. All cores were removed using an increment borer (Sunami,

Netherlands) which was sterilised in 70% ethanol between trees. Cores were removed from the borer using disposable latex rubber gloves (gloves used once) and placed in sterile universal tubes. Tubes were transported to the laboratory within 24 hours where they were

114 placed in 50ml standard nutrient agar and maintained at 20·C for 2 months or until fungal growth could be identified.

5.3 Results

Cores were removed from five bat roosts. one in an ash and the other four in oak. Eight newly excavated woodpecker nests were in oak, two were in birch and three in ash. Control trees were birch and oak. One juvenile noctule (aged by pelage colour) was found roosting in a newly excavated woodpecker cavity in an oak tree at the RSPB study site.

Two cores produced fungal growth under culture, each core containing a different fungal species. One suitable heart rotting Basidiomycete fungi was identified from a newly excavated woodpecker nest in an ash tree - lnonotus hispidus (Fr.) Karst - the other fungi was an unidentified ascomycete probably not responsible for heart rot decay.

Dowels containing inoculate are currently in culture.

5.4 Discussion

The discovery of a juvenile noctule in a woodpecker cavity that had been excavated the same

year provides anecdotal evidence that noctules may rely on woodpeckers to provide suitable

roost cavities. It also provides further information on the short time lag between woodpeckers

vacating their nest cavity and noctules adopting them as roosts.

The low incidence of successful fungal cultures may be partially explained by the very dry

summer in the south east of England at the time the cores were removed. No fungi were

115 cultured from the bat roosts although the wood was visibly discoloured. The cores were removed below bat roosts and the discolouration was probably a result of staining from urine.

It is possible that the urine has a sterilising effect and prevents further wood decay below

roosts. Further study comparing cores taken above and below roosts would clarify this

hypothesis.

Inonotus hi5pidus is a basidiomycete polypore, which preferentially occurs on common ash

and London plane, more rarely on elm, sycamore and lime (Kreisel 1961). Campbell (1931)

classified it as a white rot but more recent study has demonstrated its capacity for soft rot

(Schwarze et al. 2000). Both these types of rot break down cellulose, hemicellulose and

lignin and are associated with broad leaved trees and not conifers (Watling 1982). Inonotus

hispidus forms particularly extensive decay in ash, which can only weakly compartmentalise

the decay (Schwarze et al. 2000). The major difference in the way Inonotus hispidus decays

ash, when compared to other tree species, is its selective delignification of the middle lamella

between horizontal cells of the xylem rays (Schwarze et a/. 2000). This results in cracks

forming in the radial and circumferential directions, a cubical degradation of the wood

(Schwarze et al. 2000) which may be particularly easy for woodpeckers to excavate.

The fruit bodies form large brackets directly on the stem surface and appear between July and

September (Nuss 1986). Several Inonotus hispidus brackets were noted on noctule roosts in

ash trees from other sites. Sporulation occurs from August to October and requires high

temperatures which determine the UK distribution of this species (Nuss 1986 in Schwarze el

al. 2000). Interestingly this correlates well with green woodpecker and noctule distributions

(Figure 1.2). With 2.8million basidiospores produced per mm2 per day (McCracken and

Toole 1974) dispersal is by wind and infection occurs where injury allows penetration of the

116 heart wood. Once established, decay progresses for extensive periods of time as InonolliS hi!;pidus has few competitors and there is little successional change in heart rot fungi (Raynor and Boddy 1988).

Future work

Dowels containing inoculate will be inserted in random live nonvisibly infected ash trees after cores have been removed. The removed cores will be placed in culture to identify any

decay fungi present. A control sample will have cores removed and replaced. Inoculated and

control trees will subsequently be monitored for woodpecker activity. It is impossible to

predict the growth rates of decay fungi in living trees. Even under controlled conditions.

different strains of the same fungal species will exhibit considerable differences in the rate at

which they decay trees (Schwarze 1992). However, one year after inoculation. cores will be

removed from a sample of infected trees to verify transmission of Inonolus hispidus from the

dowels to the live tree.

This future project will assess how useful this technique is to provide holes for cavity

wildlife. lnonotus hispidus is very common in the forests and woodlands of Britain. Their

spores are naturally abundant in the airspora, although only very small proportions of these

spores colonise suitable host substrate. The actual increase in abundance of Inonolus

hispidus, if any would be negligible. Current forestry guidelines promote sustainable forestry

practice. However, forestry managers are at present limited in their capacity to maintain or

enhance cavity wildlife populations. If this technique offers a viable, economic, large-scale

method that accelerates cavity formation it will have widespread application. The proposed

methods are widely accepted and are not considered to pose any threat to the environment.

117 5.5 Summary

I. Wood cores were removed from below known noctule roosts, and below woodpecker

cavities that had been excavated the same year.

2. Inonotus hispidus was the only suitable heartwood rotting decay fungi isolated from

cultured wood cores.

3. Future research will involve inoculating live ash trees with dowels containing Inonotus

hispidus.

118 Chapter 6. General discussion

119 6.1 The conservation biology of the noctule bat

During the summer months noctule bats are at their most active and roosting and foraging habitats are fundamental to their survival and fecundity. In the varied environment of the main study site noctule bats were highly selective of the areas in which they foraged and

roosted. Strong preference was demonstrated for particular habitat types and this preference could be linked to reproductive success, at both foraging and roosting sites. Identifying differences in the behaviour of individual bats of different reproductive status and relating those differences to habitat use allows the theoretical prediction of distribution within and

between sites. This framework can then be used to predict the consequences of land

management and land use change on popUlations of noctule bats. Further, identifying the

characteristics associated with a preferred or critical habitat allows clarification of the

selection pressures and constraints noctule populations are under. This clarification can

highlight potential problems that may arise where noctules must utilise degraded or sub­

optimal habitats, but it can also guide conservation management or habitat restoration.

The high intensity and relatively low frequency echolocation calls used by noctule bats to

orientate and detect prey make them particularly suitable for bat detector surveys and because

their calls are distinct from other syntopic bats in the UK a nationwide bat detector survey

monitoring population trends should be initiated.

120 6.2 What limits bat abundance?

Temperate zone bat species are ultimately limited by the number of offspring they can produce in the highly seasonal temperate latitudes (Racey and Entwistle 2000). For approximately six months of the year energy intake exceeds that required for maintenance and, within this period, bats must gestate, lactate, mate and acquire enough fat reserves to see them through winter hibernation (Racey 1982). Slow foetal growth rate and extended gestation length limit the amount of time available to complete lactation, weaning and recovery of condition (Racey and Entwistle 2000). The major cost of powered flight in temperate zone bats is thought to be low fecundity with a large energy investment required to produce a single annual litter, usually consisting of a single offspring (Racey and Entwistle

2000).

Within this seasonal framework foraging resources during high summer, the two months of late pregnancy, lactation and weaning when energy requirements are highest, have been generally thought of as unlimited (Fenton 1983). The major benefit of living in or migrating to a highly seasonal environment is the superabundance of insect prey in summer, which consequently results in shorter gestation lengths, faster prenatal growth, larger litter size

(Jones 1998) and faster postnatal growth rates of young in more northern latitudes (Kunz and

Stern 1995). However, even during mid-summer bats seem to be reliant on the thermoregulatory energy saving benefits of clustering (McDonald et a/. 1990, Roverund and

Chappell 1991) and heterothermy (Kurta 1986, Racey and Speakman 1987) in order to

balance energy budgets, suggesting energy is in fact limited.

121 An explanation for this limit to energy intake is that aerial hawking bats are restricted in the amount of time when foraging resources are profitable. Insect availability varies temporally with large peaks in abundance around dusk and dawn (Lewis and Taylor 1963). Further more, huge reductions in availability result from temperatures of less than 10°C, which effectively prevent insects from flying on cold nights (Taylor 1963). By increasing foraging time bats also increase the energetic costs incurred by flight (Kurta et af. 1989), therefore bats can only extend foraging periods on warm nights when insect availability makes further foraging profitable (Rydell 1989). If this is the case, food availability is governed primarily by climate, which limits the amount of time bats can feed, and may be only weakly related to available habitat. Birth rate would therefore be independent of the density of bats, or availability of suitable habitat, instead being controlled by climate.

The regulation of bat populations specifically by ambient temperature has been proposed by

Ransome (1994, 1995) who demonstrated low spring temperature resulted in fewer offspring and later birth dates, which affect survival in Rhinolophus !errumequinum. Rydell (1989) also demonstrated a negative relationship between spring temperature and birth date in Eptesicus nillsonii and related foraging activity to the density of aerial insects with the minimum

3 density (0.1 per m ) required for foraging activity occurring on evenings over 10°C.

However, both these studies deal with colonies at the northern border of their distributions and Ransome (1994, 1995) used data from a severe population crash during an exceptionally cold year as evidence for regulation. Whether colonies more central in their geographic distribution are under the same population constraints has not yet been investigated. More importantly conservation recommendations seem redundant where there is no link between population regulation and availability of suitable habitat.

122 Similarly, although bats often select warm roost conditions (Entwistle et af. 1997, Zahn 1999,

Sedge ley 2001) the use of torpor in roosts has again usually been related to ambient temperature which dictates foraging resources and energy intake (Hickey and Fenton 1996,

Audet and Fenton 1988). Thus, although availability of resources may effectively limit the distribution of bat species the relative importance of foraging habitat and roost availability in regulating bat populations remains unclear (Fenton 1997).

6.3 Foraging habitat use

Radiotracking demonstrated that noctule bats did not forage randomly. Noctules consistently spent proportionately more time foraging over preferred habitat types of broadleaved woodland, pasture and other (villages and aquatic areas) habitat categories. Less preferred or marginal habitats were used proportionately less than their availability and consisted of moorland and arable fields. Bats confined their foraging to woodland areas during excessively windy and rainy nights. Generally foraging activity was bimodal with the main

period of foraging beginning at approximately sunset and a shorter period just prior to sunrise. As summer progressed extra foraging bouts between these periods sometimes occurred.

A comparison between the foraging behaviour of lactating and non-lactating females

indicated resource partitioning at the habitat level where non-lactating bats used marginal

habitats significantly more than lactating females. There was little evidence that foraging

resources were partitioned temporally or that different habitat use was linked to foraging

distances. This differential space use corresponds to the ideal despotic distribution model

123 (Fretwell 1972) and suggests population regulation through density-dependent birth rate or

mortality (Sutherland 1996). At high population levels there will be reduced intake due to

increased levels of interference or depletion and increased use of poorer quality habitats by

less effective competitors.

6.3.1 Foraging resources as a regulating factor

Only rarely have studies directly related bat abundance to food or habitat availability

(Bradbury and Verhencamp 1976, Entwistle 1994). Instead delays in parturition have been

found to correlate with climatic perturbations which effectively deplete all foraging resources

(Rydell 1989a, Ransome 1994). In addition, failure to breed has been related to climatic

variability linking breeding output to extrinsic factors (Grindal et af. 1992, Lewis 1993).

However, recent evidence suggests that habitat use may be an important behavioural

mechanism in partitioning food acquisition even leading to speciation in sibling species

(Arlettaz 1999), and it has been long established that habitats are partitioned by syntopic bat

species (Aldridge and Rautenbach 1987, Heller and von Helversen 1989).

Spatial partitioning of foraging grounds between male and female bats has been observed in

several bat species (Myotis lucifugus Barclay 1991, Nyctalus noctula Kronwitter 1988, M

daubentonii 1. Altringham pers. com.). The spatial segregation observed between

reproductive and non-reproductive noctules in the present study associated habitat use with

fecundity, and implies a density-dependent spacing mechanism with breeding area effectively

represented by the preferred habitat types.

Bradbury and Verhencamp (1976) observed non-reproductive bats foraging in areas of lower

insect abundance. The suitability of habitat can be considered equivalent to the fitness of its

124 occupants and breeding success is often used as a substitute for fitness (Sutherland 1996).

The partitioning observed in the present study resembles the "butTer effect" of Brown (1969) with non-breeding individuals, approximately equivalent to the "floaters" of Krebs (1971) and M0ller (1991), occupying poorer qual ity foraging habitat. As population size increases, poorer quality individuals spend more time over poorer quality habitats. This density­ dependent spacing is likely to be mediated by aggression with dominant individuals aggressively excluding sub-dominants from preferred foraging areas. Similar aggressive interactions have been observed in other aerial hawking bat species (M daubentonii Wallin

1961, Lasiurus cinereus semotus Belwood and Fullard 1984, P. pipistrellus Racey and Swift

1985, E. nillsonii Rydell 1989b) and aggressive encounters between noctules were occasionally seen and heard over pasture (Figure 2.6). Barlow and Jones (1997) demonstrated that similarly structured calls of P. pipistrellus were used as a territorial spacing mechanism and Racey and Swift (1985) related these calls to food availability.

6.3.1.1 How could foraging resources regulate birth rate?

Bats have evolved a number of reproductive strategies that could be used to mediate density­ dependent birth rate (Krutzsh and Crichton 2000) including failure to conceive, resorption and abortion of foetuses, multiple births or deferment of breeding (reviewed by Racey and

Entwistle 2000). Reproductive delay mechanisms such as delayed fertilisation, delayed implantation and delayed development may allow bats the opportunity to sample available foraging resources and manipulate birth rate through the above mechanisms. It has been proposed that the hibernating animals that mate in autumn do so because this is when animals are in good condition and can select high quality mates (Birkhead and Meller 1993).

However, prehibemal mating could have evolved in temperate bats to allow females the

125 opportunity to sample available foraging resources in spring before committing to reproduction.

Whether conception can be prevented in the face of unfavourable foraging resources is

unknown, however, in several bat species far fewer ova become implanted than are shed

(Pipistrellus subJlavus Wimsatt 1945, Chalinolobus gould;; Kitchener 1975) and resorption is

common (e.g. Wimsatt 1945) and can even occur at an advanced stage (Lasiurus cinereus

Bouchard et af. 2001). All hibernating female P. pipistreflus and N. noctula were found to be

inseminated by Racey (1972) but usually only 80% of bats in maternity colonies give birth in

anyone year (Racey 1979, Thompson 1987, Gaisler et al. 1979) suggesting deferment of

breeding may be decided in spring.

The proportion of females breeding can be related to food availability in the same year

(Myotis yumanensis Grindal et al. 1992, Antrozous pallidus Lewis 1993) and unfavourable

spring weather reduces the number of early breeding females recruited into the breeding

population (R. ferrumequinum Ransome 1995). Furthermore, Schowalter et al. (1979) found

three out of eight nulliparous M lucifugus caught during adverse weather conditions were

resorbing embryos. Finally, evidence that birth rate can be manipulated by multiple births

has been documented by Rakhmatulina (1972) who found a marked reduction in twinning

rate in P. pipistrellus in successive years which was associated with cold spring temperature.

In addition, differential resorption of a multiple pregnancy has recently been documented

(Lasiurus cinereus Bouchard et al. 2001).

Alternatively, many studies have shown that younger individuals tend to breed if population

density is reduced (e.g. in ospreys, Pandion haliaetus Poole 1989; and sparrow hawks,

126 Accipiter nisus Wyllie and Newton 1991) and Ransome (1995) observed an increased number of early breeders, in R. ferrumequinum, which he considered a population recovery response.

Noctules commonly produce twins and triplets are not uncommon (reviewed by Gaisler et al.

1979) which would enable them to vary their birth rate considerably depending on available resources. Kleiman and Racey (1969) considered that increased provision of food to captive noctules increased their twinning rate. Furthermore, although most non-breeders in this study were nulliparous, which may have been due to failure to mate, four parous non-breeders were captured and radiotracked. Unfortunately it was not possible to catch all the bats in this population to ascertain the proportion of females failing to reproduce and ultimately relate this to availability of preferred habitat.

6.3.1.2 How could foraging resources regulate mortality?

The largest factor in temperate zone bat mortality is generally considered to be overwinter survival (reviewed by Tuttle and Stevenson 1982). Sub-dominant or non-reproductive individuals are able to compensate for a lower energy intake in summer by using facultative torpor more frequently and for longer periods (E. fuscus Hamilton and Barclay 1994, E. fuscus Audet and Fenton 1988). However, during the winter months when prey resources are much more restricted, sub-dominant individuals will have smaller fat reserves necessitating more frequent, less profitable foraging flights, which may not meet the energetic demands of maintenance resulting in starvation. Density-dependent starvation may occur as a result of interference without significant depletion of resources (Goss-Custard et al. 200 I) which could result from the spatial partitioning of preferred habitats.

127 Ransome (1994) found that late breeding R. ferrumequinum and breeders that missed years were less efficient foragers, assessed from droppings produced after foraging flights. When comparing Eptesicusfuscus that were frequently active during winter, with conspecifics that predominately hibernated, Barclay (1986) found the active bats were far lighter and had reduced fat stores. Poorer quality bats may also be forced to forage more often during daylight and may die as a result of predation (Speakman 1991). Noctules were the second most common bat found in owl pellets in a recent review of European literature with a pronounced peak in records around autumn and early winter (Petrzelkova 1999).

Additionally, in order to balance energy budgets, sub-dominant bats may be forced to alter their roosting behaviour, increasing their chance of being preyed upon (Kokurewicz 1999). In a study on M daubentonii, Kokurewicz (1999) found poorer quality individuals and juveniles, hibernating in mines, were forced to roost in a colder microclimate closer to the ground in order to save energy, therefore increasing their exposure to terrestrial predation.

Thus mortality from predation may result from a complex interaction between foraging habitat availability and roost use.

6.4 Roost use

Across three broad leaved woodland types (lowland, river valley, plantation) noctule bats did not roost randomly in available tree holes. Tree cavities occupied by noctule bats were larger in volume, further from the ground and less cluttered than cavities found on trees in the immediate vicinity of the roost tree. When compared to random tree cavities in the same wood noctules roosted further from the ground in larger holes in trees that were closer to

128 clearings, in less dense areas of the wood. When compared to both random and adjacent cavities, noctules roosted more often in abandoned woodpecker cavities.

A comparison between the roosting behaviour of lactating and non-lactating female noctules at the main study site indicated that lactating females preferred to roost in one particular tree roost. This roost cavity was significantly larger and more exposed than the other roosts used by the rest of the colony. In addition, lactating bats switched roosts less often than non­ lactating bats. A comparison between maternity colonies found that only the largest colony remained faithful to one specific roost.

6.4.1 Roosts as a regulating factor

Availability of suitable roosts is considered a major factor in the geographic distribution of bat species (Humphrey 1975, Bell et af. 1986, Hoyle et af. 2001) but their relative importance in local bat abundance remains unclear. Bats form the largest aggregations of any mammal with colonies of Brazilian free tailed bats, Tadarida brasiliensis, numbering over 20 million

(Davis et al. 1962). Colonial roosting seems particularly important for temperate zone bat species and essential for reproducing individuals (Kunz 1982). Although many bat species have readily adopted man-made structures as roosts, the high degree of specialisation and reliance on trees by species such as noctules implies that a reduction in abundance of suitable holes may increase the risk of local extinctions or render populations functionally extinct.

Published studies have concentrated on identifying characteristics of roosts and suggesting

probable reasons for the selection of these characteristics (e.g. Vonhof and Barclay 1996,

Waldien et at. 2000, Lance et al. 2001). In general bats seem to select warmer roosts in

buildings (Entwistle et ai. 1997, Zahn 1999) and in trees (Sedgely 2001). In addition, tree

129 roosts tend to be further from the ground and relatively exposed when compared to a random sample of available cavities (Vonhof et al. 1997, Mager and Nelson 2001). The benefits of these attributes are thought to relate to energy saving through reduced thermoregulatory costs and reduced risk of predation (Brigham et a/. I 997a).

The different use of roosting habitat observed between reproductive and non-reproductive noctules at the main study site associated a specific roost with fecundity and implies that roosts suitable for reproduction may have more specialised characteristics and may be a limiting factor. The difference in roosting behaviour seen between colonies of different sizes at different sites, and groups of different sizes at the same site, suggests there are factors relating the density of bats to how they use roosts.

The relationship between population regulation and roost availability is not as intuitive as with foraging habitat. It seems logical to assume that if bats are able to actively assess roost temperature, there would be a warmer roost particularly favoured for remaining homeothermic and a cooler roost, which would facilitate torpor. Therefore the warmer the roost the greater the thermoregulatory energy saving which would be largely independent of the density of bats. However, the thermoregulatory benefits of clustering increase with group size (Tuttle 1975) and would be directly related to the density of bats, which may only be limited by the size of the cavity. Alternatively, the added costs of increased social interaction

(McNab 1982) or parasite exposure (Giorgi et al. 2001) may negate the thermoregulatory benefits of clustering in increased group sizes (McNab 1982).

The observed variation in roost behaviour was related, however, to the density of bats with smaller groups and colonies switching more frequently. Fenton et al. (1994) in a study of the

130 predation of Tadarida pumila and T. condy/ura by raptors, suggested through modeling that individuals in smaller colonies reduced their risk of predation by switching more frequently and that individuals in larger colonies could dilute their probability of being predated to acceptable levels by clustering on emergence. However. they provided little empirical hevidence for this with both small and large roosts being highly variable in the number of bats that emerged. This variability may have resulted in smaller roosts often containing no bats by chance rather than through different anti predation tactics. Furthermore, individual switching behaviour of bats from colonies of different sizes was not investigated.

Nevertheless, different density-dependent anti-predator tactics have been observed and related to habitat selection in pollock (Pollachius virens) which hide in sea weed at low densities and shoal at high densities (Rangeley and Kramer 1998). The increased energy being expended by smaller noctule colonies in order to continuously switch roosts. compounded by sub-optimal roost availability in areas where many cavity trees have been removed, may prevent females from reproducing or reduce survival and birth rate.

Increasing colony size may reduce costs through clustering, reduce costs assocated with switching behaviour and allow breeding bats to remain in the wannest roost for longer. On the other hand, small colonies may be restricted in the type of roost they can use for reproduction, and may be more likely to require a number of roosts with naturally warm microclimates. This raises the questions of how severely effected population growth rate is by reduced colony size and if there is a minimum colony size required for reproduction?

131 6.S The Allee Effect and bats

Any benefit an individual gains from the presence of conspecifics is broadly defined as the

Allee effect and it can be observed as any aggregation of animals over and above the concentration of resources (Allee et af. 1949). The large gains obtained from living with conspecifics means that at low population densities the rate of survival or reproduction may decline, resulting in increased likelihood of local extinction at low population levels. Small group size can reduce anything from how effectively predators hunt (e.g. in lions Panthera leo, Caraeo and Wolf 1975) to how well prey avoid predation (e.g. in lapwings Vanellus vanellus, Berg et af. 1992). The changing value of costs and benefits associated with group size can result in dramatic shifts in theoretical distribution and this has been used to explain why survival of some species is greatest at intermediate densities (e.g. in the dickcissel Spiza americana, Fretwell 1986) and to suggest that optimal flock size may be unstable (Sibly

1983). There is increasing appreciation that the Allee effect must be incorporated into models of population dynamics (Fowler and Baker 1991) and habitat use, with serious implications for conservation (Stephens and Sutherland 1999).

In almost all temperate zone bat species females form maternity colonies to raise their young

(Bradbury 1977). There is often little social interaction between adjacent maternity colonies

(N. noctula Sluiter and van Heert 1966, Plecotus auritus Entwistle et af. 2000, Chalinolobus tuberculatus O'Donnell 2000) and recent research has demonstrated some colony members are closely related (R. ferrumequinum Rossiter et al. 2000, Myotis bechsteinii Kerth et af.

2000). Furthermore spatial distance between colonies correlates with genetic distance

(Rossiter et af. 2000). Within this genetically structured distribution individual colonies often split into subgroups and reform in a manner that suggests individuals "actively" seek others

132 with which they have previously roosted with although they are not necessarily the most closely related (N. noctula Kozhurina 1993, M bechsteinii Kerth and Konig 1999).

In patchy environments this distribution of self contained colonies seems to correspond well with modern metapopulation theory (Hanski and Gilpin 1997). However, social isolation has also been found between adjacent colonies which roost in a large continuous, homogeneous environment questioning the applicability of classical metapopulation models (Chalinolobus tuberculatus O'Donnell 2000). In O'Donnell's (2000) study, different colonies had overlapping foraging ranges but roosted separately within this range. This suggests social partitioning of kin selected groups mediated by behaviour and resulting in distinct colonies with little or no social contact except perhaps between colony mating associations. This hypothesis remains to be tested. However in a recent genetic study of local P. aurilus colonies Burland et al. (2001) considered kin selection an unlikely factor in colony cohesion as low annual reproductive rates and equal male reproductive success result in little genetic relatedness between colony members. Nevertheless, if the Allee effect is severe and small colonies are behaviourally isolated, population declines may operate at the colony level.

Persistence of small populations is known to be lower (Lande 1988) and if population growth rate is inhibited at low colony size, small popUlations may be unable to increase fast enough to prevent extinction. Habitat loss may therefore reduce colony size to below viable levels

and prevent further colonisation from surrounding source populations.

6.6 The decline of the noctule

The factors leading to decline in British bat species are hard to pinpoint since loss of roosts,

insect prey and foraging habitat, as well as poisoning through pesticides and timber

133 treatment, have been so wide spread and catastrophic (Racey and Stebbings I 972). Whole colonies of bats were lost through remedial timber treatment (Stebbings 1988) and the effects of large scale agricultural and forestry pesticide application on bats are largely unknown

(Mayle 1990). Jefferies (1972) considered organochlorides to have caused bat population declines by demonstrating that bats were more heavily contaminated with residues of DDT and more susceptible to mortality, because of fat mobilisation during hibernation, than either insectivorous or carnivorous birds and Swanepool et 01. (1999) have more recently demonstrated sublethal effects of organochlorides on pipistrelle bats.

The threat of chemical poisoning has been greatly reduced in recent years through legislation or replacement with less harmful alternatives (Racey 1998). When coupled with the slow reproductive rate of bat species, it could be argued that available foraging area is not limited as UK populations have not yet replaced losses associated with large scale poisoning. The present study however offers evidence that availability of foraging area may limit noctule bat

populations and outlines a framework on which this operates. The highly specialised roosting and foraging requirements of noctules demonstrate where conservation guidelines can operate to ameliorate population declines.

6.7 Habitat loss and noctule bats

After the second world war, agricultural intensification brought about the reduction in area of

many natural habitats in Britain, which has been compounded in recent years by the Common

Agricultural Policy (CAP). An estimated 95% of lowland unimproved grassland was lost in

England and Wales between 1947 and 1985 (Lowe et 01. 1986) and 49% of ancient

woodland was lost from eleven English counties between 1939 and 1983 (Grove 1983). A

134 reduction in roost availability has been brought about through loss of woodland, sanitation felling of diseased trees and the conversion of broad leaved woodland into monospecific exotic conifer plantations (Kirby et al. \998). The general framework developed to explain noctule distribution within and between habitat types can be used to predict the consequences

of habitat change on the survival and reproduction of populations sustained by these habitats.

6.8 Conservation implications of foraging habitat loss

A reduction in preferred habitat is likely to result in at least one of three possibilities:

increased use of marginal habitats, greater number of non-breeding females or a reduction in

an individual's feeding area. All of these outcomes have consequences for survival and birth

rate. As preferred sites are lost, a greater proportion of noctules will forage over marginal

sites and therefore be unable to meet the energetic requirements of reproduction or

overwinter survival. In addition, non-breeders may persist for many years and dillute the

dominance of breeding individuals, further reducing breeding output. Non-breeders will be

forced to roost away from the main colony, increasing predation risk for themselves and

breeders and reducing numbers of clustering individuals in the breeding colony.

Although the habitat types used by a species can be many and varied, the distribution and

availability of breeding habitat is of primary importance for long-term population survival.

The results of the present study can therefore be used to produce authoritative conservation

guidelines. The identification of a link between reproductive success and the use of preferred

habitats has confirmed results from previous studies that broadleaved woodland, pasture and

aquatic areas are important habitats for bats (e.g. de Jong and Ahlen 1991, de Jong 1994,

Walsh and Harris 1996) and suggests their availability limits bat populations. Conservation

135 recommendations should attempt to preserve and enhance the area of preferred habitat types available to noctule populations.

Previous studies of habitats used by noctule bats have been carried out in areas with a large riverine or lacustrine component and highlighted the use noctules make of aquatic areas for foraging (Kronwitter 1988, Rachwald 1992). The present study also found that noctules preferentially foraged over aquatic areas, however, as these areas made up only a small proportion of available habitat it was possible to establish how noctules use landscapes that are not dominated by aquatic features. Broadleaved woodland is a critical habitat for many bat species and especially for noctules, which roost and forage there. Land management that increases the area of woodland will be beneficial to noctule populations. Although an in­ depth study of how noctules foraged within woodland areas was not carried out, sympathetic woodland management for noctule bats should involve consideration of the aerodynamic constraints imposed by long narrow wing morphology by maintaining open areas, vegetation of varying sizes and areas of low tree density.

The extensive use noctule bats made of pasture, particularly pasture with livestock, suggests reproducing noctules may be reliant on the associated arthropod fauna of this habitat type, as

are other syntopic bat species (R. ferrumequinum Jones and Morton 1992, Eptesicus serotinus

Catto et al. 1996, Robinson and Stebbings 1997). Maintaining pastoral areas and increasing

the area of permanent grassland with livestock are therefore to be encouraged.

136 6.8.1 Specific conservation recommendations

• Promote the planting and enlargement of broad leaved woodland, deconiferisation of

existing plantations and the sympathetic management of existing woodland by opening

clearings and rides.

• Promote the construction and maintenance of aquatic habitats such as ponds and

marshland as insect rich foraging areas.

• Encourage farmers to maintain unimproved pasture with livestock, particularly when

adjacent to woodland.

• Parkland trees should be conserved and additional trees planted to encourage a more

varied age structure.

6.9 Conservation implications of roost loss

The removal of a large number of possible roosts and in particular large exposed tree cavities will have a number of possible effects on the noctule populations that use them. A reduction in the average ambient temperature of roosts will lengthen gestation, prolong postnatal growth, increase the time to weaning and reduce overwinter survival. Fewer roosts will also increase the risk of predation.

6.9.1 Specific conservation recommendations

• Roosts should be protected

137 • Dead and decaying trees should be retained where possible.

• Tree surgery should be attempted before felling of cavity trees.

• Where roost trees must be felled the cavity should be retained and strapped to a nearby

tree at an appropriate height.

• Larger sized bat boxes should be used where roost supplementation for noctules is

necessary.

• Inoculating live trees with heartwood rotting fungi should be attempted. to speed up the

decay process and encourage woodpeckers. in managed woodlands.

6.10 Conservation implications of climate change

Apart from raising average temperature global warming is considered to have many other effects which include increase and decrease in rainfall and changes in the frequency and intensity of extreme climatic events (Harrison et al. 200 I). The probable effects of global warming on European birds have been reviewed by Moss (1998) and Berthold (1993), who concluded that global warming would more likely benefit residents as their populations are usually depressed by severe winters. Furthermore, the success of late-arriving, long-distance migrants would depend upon the degree of competition from resident species that had already benefited from milder weather. Partial migrants will become increasingly composed of

138 sedentary individuals and short to medium migrants should be selected to migrate less far.

Earlier springs and warmer drier summers may allow more bats to survive winter but will reduce marshland and terrestrial aquatic habitats.

Although it is not known whether many noctules from Britain migrate long distances, there was a definite reduction in activity at the main study site around the time of autumn migration and bats caught at the main roost in September were almost always sub-adults. If milder winters result in an increased number of noctules remaining in or around their natal sites then preferred habitat loss at these sites may have a more serious effect on overwinter survival and subsequent birth rate. If noctules continue to migrate, other more sedentary species such as Eptesicus serotinus and R. ferrumequinum may benefit from milder winters and result in increased competition for similar foraging resources. Noctules were tracked foraging in almost all weather conditions so the effect of increased frequency of severe precipitation or severe drought may be minor unless the frequency becomes extreme. During nights of heavy rain noctules were, however, restricted to using woodland areas and loss of this habitat may have severe consequences in the face of increasingly unpredictable weather conditions. The effect of drought on UK bat species has rarely been studied though Roche

(1997) found bat activity again restricted to woodland during the exceptionally dry summer of 1996. Any reduction in aquatic habitats may have serious consequences for noctule populations especially in areas where they rely on these features.

Alternatively, as the area ofnoctule climate space available in the UK will increase (Harrison et al. 2001) noctules may increase their range further north similar to the avifauna (Thomas and Lennon 1999). It is worth bearing in mind that none of the above senarios anticipate that

139 UK temperatures will fall in which case range would probably contract and local habitat quality would become more important.

6.11 Future research

The unique natural history of noctules, with complex socially structured behaviour and the

ability to travel large distances, requires landscape scale spatially explicit modeling of

population processes. For example how local colonies are distributed in relation to landscape

features and how this relates to roosting and foraging areas should be investigated as well as

the population dynamics of these colonies and how they interact. Clarification by

experimentation is the next step in determining the factors important in habitat selection.

6.11.1 Specific research recommendations

• Landscape ecology - relate the use noctules make of specific landscape features to

indices of feature quality.

• Spatially explicit population models - create models of colony dynamics which

incorporate spatial relationship between colonies and measures of landscape structure.

• Experimental studies of the effect of habitat loss and supplementation on noctule colonies

should be carried out.

• Bat boxes should be used to experimentally clarify roost selection factors.

140 • A bat detector monitoring system designed specifically to monitor noctule population

trends should be developed.

• Genetic analysis should be carried out to identify how local colonies are founded, and

whether they are structured as metapopulations and to indicate whether UK populations

migrate to mainland Europe.

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