©KORA

©KORA

Optimising the Value of By-catch from lynx Camera Trap Surveys in the Swiss Jura Region.

Fiona Anne Pamplin

6th August 2013

©KORA

A dissertation submitted to the University of East Anglia, Norwich for the Master of Science degree in Applied Ecology and Conservation 2012-2013.

©This copy of the dissertation has been supplied on the condition that copyright rests with the author and that no information derived therefrom may be published without the author’s written consent. Copyright for all wildlife camera trap photographs used in this document rests with KORA and may not be reproduced in any media without prior permission from KORA, Switzerland.

©KORA

Contents

1. Abstract ………………………………………………………………………………………. 4

2. Introduction………………………………………………………………………………… 5

3. Methods …………………………………………………………………………………….. 10

4. Results………………………………………………………………………………………… 18

5. Conclusion & Discussion………………...... 29

6. Recommendations……………………………………………………………………... 34

7. References…………………………………………………………………………………. 37

8. Appendix …………………………………………………………...……………………... 42

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Acknowledgements

I am extremely grateful to Dr. Urs Breitenmoser (KORA) for providing me with the wonderful opportunity to work at KORA Switzerland and for allowing me to use the camera trap data for the purpose of this study. Many thanks also to Dr. Fridolin Zimmermann for granting me access to his treasure trove of camera trap photos, providing lots of helpful ideas, editing suggestions and supporting references.

I am indebted to Danilo Foresti (KORA) without whom I would not even have cleared the first analytical hurdles! For his patience and guidance in helping me to master the basics of the

Presence program and for his continued and most generous support throughout the project.

Thank You!

Very special thanks to Dr. Jenny Gill (UEA supervisor) for her encouragement, sound advice and down-to-earth perspective – and for always being there to guide me back to safe waters when I was clearly out of depth! I would also like to mention three other people who have gallantly come to my rescue in addressing various tricky issues concerning ‘Presence’ modelling – UEA

PhD student Maira de Souza, Dr. James Hines (USGS Wildlife Research Center) and Dr. Darryl

MacKenzie (Proteus Wildlife Research Consultants, New Zealand).

A very BIG thank you to you all!

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Optimising the Value of By-catch from Lynx lynx Camera Trap Surveys in the Swiss Jura.

1. Abstract

In order to effectively manage wildlife populations and to evaluate the results of conservation interventions, wildlife managers must first identify ways of measuring population size and geographic distribution of target species. However, the expense and logistics of running surveillance programs for multiple species can be prohibitive. The aim of this study was to explore the potential of camera trap data that had been collected as part of an ongoing monitoring program for Lynx lynx in the Swiss Jura mountains, as a source of information about other wildlife species. Using a photographic data bank from the 60 day 2012-2103 winter survey, images were analysed to assess species richness, distribution and the feasibility of conducting occupancy modelling using PRESENCE software. Unlike camera trap surveys designed to estimate abundance within a capture-recapture framework, occupancy modelling does not rely on recognition of individual and may provide a reasonable alternative for assessing population status and trends. The findings of this study demonstrate the value of camera trap by-catch as a source of quantifiable information for species sympatric to lynx. Of a total 2902 wildlife images from 61 camera trap locations, 97.4% photos were of species not specifically targeted within the sampling protocol. The data set of photographs collected for secondary species; roe deer, boar, chamois, badger, fox and were sufficiently large to provide robust indicators of species distribution and richness for the study area. Considerably less data were captured for the smaller species, , beech and pine marten. Occupancy modelling was possible for those species with adequate sample size. However lack of model fit was a problem across all of the species, suggesting that the environmental covariates that had been selected for modelling purposes are not strong predictors of occupancy. This study demonstrates the value of camera traps as a tool for

4 multi-species monitoring programs, even when the original sampling protocol is designed around one target species.

2. Introduction

As camera technology has increased, at the same time becoming more affordable, camera traps have become an integral component of many ecology and conservation programs. ‘Camera trapping’ involves the use of fixed cameras, which are usually triggered by either heat and motion or infra-red remote sensors, to take photographs of passing animals. Camera trap protocols were originally developed for estimating tiger abundance (Karanth & Nichols 1998) using a capture- recapture statistical framework and this application has since been extended to a number of species where the identification of individual animals is possible (Dillon & Kelly 2007, Silver et al.

2004). As a research tool, camera traps are now used in a variety of applications: to inventory elusive and rare animals (Tobler et al. 2008, Watts et al. 2007), to explore species habitat use and distribution (Bowkett et al. 2007, Goulart et al. 2009), to collect data on population demographics

(Lopez-Parra et al. 2012) and to explore intra-guild competition (Davis et al. 2010). In all of these projects, it is inevitable that photographs are captured of animals other than the target species, resulting in very large wildlife data sets that frequently remain unanalysed (Linkie et al. 2013).

Given the investment required (both in terms of effort and materials) to conduct camera surveys and the paucity of information available for many wildlife species, this represents a significant opportunity cost.

Lynx lynx was reintroduced to the Jura Mountains in the early 1970’s and is fully protected in

Switzerland under the Bern Convention on the Conservation of European Wildlife and Habitats

(1979, Appendix III). It is also listed in Appendix II of CITES (1975). Since the late 1980’s, the progress of the lynx population in the Swiss Jura has been monitored by the non-profit

5 organisation KORA (Coordinated Research Projects for the Conservation and Management of

Carnivores in Switzerland). KORA is affiliated to the University of Berne and undertakes applied research on behalf of the Swiss government on the monitoring, ecology and conservation of carnivores in a human dominated landscape.

KORA has conducted deterministic camera trapping surveys targeting lynx in the Jura every year since 2008. The result of the latest 2012 -2013 winter survey in the northern part of this region reveals the presence of 14 individual lynx within the 882 km2 reference area (Zimmermann et al., in preparation) (Appendix: Figure A).

Most large carnivore monitoring projects of this kind are established either because of the funding available for such charismatic animals or because of issues of human-carnivore conflict and the need to identify and track problem animals. However, in addition to lynx, a large number of other species (‘by-catch’) are captured on camera – including European wildcat, meso- carnivores and various ungulate species. These animals all have an important role to play in natural ecological processes, such as predator-prey interactions, interspecies competition and in the case of herbivores, grazing pressure and competition for pasture with domestic livestock.

Equally relevant in terms of conservation management at the landscape level, they rarely attract the interest or funding for independent field studies to monitor population size, trends and distribution. In Switzerland, as in many other similar projects around the world, a vast bank of camera trap data has been accumulated that has yet to be fully exploited. To date, the only studies that have been undertaken by KORA for camera trap data collected in the Jura, concern the felid species (Eichholzer, 2010, Hercé, 2011, Zimmermann et al. 2007, 2010).

So how could we use all this data? With the exception of Felis sylvestris, identification of individuals is virtually impossible and we cannot therefore attempt to estimate absolute abundance of each species. However there are other indicators that are commonly used to

6 quantify the status of a wildlife population or community: these include species richness and occupancy. Species richness refers to the number of species in a location or its species ‘diversity’.

Species richness indicators are often used for measuring the impact of anthropogenic pressures on biodiversity and for assessing the results of management interventions (O’Brien et al. 2011).

Occupancy in single species population studies is defined as the “proportion of area, patches or sample units that is occupied” (MacKenzie et al. 2006). It is viewed as a surrogate for abundance, such that changes in the proportion of area occupied by a species (ψ) infer changes in its population size (MacKenzie & Nichols 2004). Furthermore, the problem of imperfect detection

(when a species is present but not detected during a survey) can be overcome by incorporating a function of detection probability into the occupancy model ( MacKenzie et al. 2002).

Research Objectives

The aim of this study is to conduct an evaluation of the Jura North camera trap by-catch in order to assess its value and application to wildlife monitoring programs in Switzerland (and elsewhere).

Specific areas that will be explored include: a) the quantity and quality of photos of non-target animals that are captured on camera b) the potential of these images to provide robust estimates of species richness (for ungulates and meso-carnivores) in the Jura region and c) an indication of geographic range for each species within the study area. The second part of the assessment involves modelling occupancy for a select number of species, with a focus on meso-carvivores.

Important questions that will be addressed include a) are there sufficient data to model occupancy? b) are the data suitable to explore relationships between environmental variables and species occupancy?

Based on the findings, I shall identify ways in which camera trap protocols for the Jura North study area can be optimized to increase the amount and quality of data collected for these secondary species.

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Study Site

The KORA Jura north 882 km2 camera trap reference area (Zimmermann et al. 2010) lies in the northern part of the Swiss Jura mountains – a low, secondary limestone mountain chain with altitude ranging from 484 – 1718 meters above sea level (Breitenmoser-Wϋrsten et al.

2007a)(Figure 1). The Jura mountains straddle the borders of Switzerland, France and southern

Germany. The landscape comprises a mosaic of fragmented forest and pasture with mixed deciduous forest (characterised by Fagus sylvatica and Quercus sp.) located on the lower south facing slopes, transitioning into boreal coniferous forest (mainly Abies alba and Picea abies) on the higher ridges and in cold depressions on the mountain plateau (Breitenmoser et.al 2007). Total forest coverage in the Swiss Jura is about 45%. Human settlements cover a further 4.1% of the landscape, which, along with associated agricultural activities, are located mainly along the valley basins and lower slopes. Depending on the canton, population density ranges between 60 – 491 inhabitants/km2 but an average figure for the entire Jura mountain region is estimated at 130-140 km inhabitants/km2 (Breitenmoser et al. 2007).

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France

Figure 1: Map of the study reference area (outlined in blue) in the Swiss Jura mountain range. Locations marked with red circles indicate camera trap sites. Areas outlined in orange show major towns. Black lines delineate cantons: BE - Bern, SO - Solothurn, JU - Jura, BL- Basel Land. Switzerland

Figure 2: Typical forest road along which a camera site is located. Right - Cuddeback© Capture camera in situ.

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3. Method

The analyses covered the Jura Nord winter season camera trap photographs taken between 1st

December 2012 - 29th January 2103. Photographs included carnivores (Lynx lynx, Felis silvestris), meso-carnivores (Vulpes vulpes, Meles meles, Martes foina, Martes martes), ungulates (Capreolus capreolus, Rupicapra rupicapra, Sus scrofa) and lagomorphe (Lepus europaeus). Occupancy modelling was conducted for all of the carnivores, meso-carnivores and roe deer, an important lynx prey species (Breitenmoser et al. 2007).

Camera Site Sampling Design

The survey protocol was originally designed to maximize the detection of Lynx lynx (Zimmermann et al. 2007). The 882 km2 study area was overlaid with a random generated 2.5km x 2.5 km sampling grid (Laass 1999). In every second grid cell, a site considered to have a high likelihood of lynx detection was selected. This was usually on a forest road, hiking trail or occasionally a wildlife trail or bridge known to be used by lynx (Figure 2). If it was not possible to find a suitable, accessible site, a location was chosen in one of the adjacent cells. A total of 61 camera trap sites were deployed, each with two opposing cameras. The use of two cameras makes it possible to photograph both flanks of a lynx and thus facilitates the identification of individuals. Cameras were mounted on trees or support poles at a height of approximately 70 cm from the ground and orientated to photograph any mid-large sized passing along the trail. They were off-set slightly so that the flash of one camera did not cause overexposure of the film in the opposite camera.

Motion activated, digital Cuddeback© Capture cameras (Non Typical Inc. American Blvd, De Pere,

WI, USA) were used. During darkness, these operate using ‘white flash’ which provides the high quality images necessary for lynx identification. Sensors were tripped by any movement within a range of about 3-4 meters and shutter delay (the time between two photos) was set at the

10 minimum value of 30 seconds. All day and night images were in colour. Cameras were serviced each week to change batteries and to change SD cards. In the event of fresh snow, cameras sometimes had to be cleared and raised. Likewise after melt, cameras had to be lowered again. No lures or bait were used.

Digital photographs were first sorted to eliminate non-wildlife material (photos of pet dogs, people, vehicles) or unusable images (corrupted or highly overexposed, blanks), corrected for inaccuracies of dates/times on camera settings and then catalogued in the KORA photo-library. All wildlife images and cats (domestic/hybrids) were then downloaded and processed in the new

KORA MySQL camera trap database. Expert advice was sought (Dr. Simon Capt, Centre Suisse de

Cartographie de la Faune and Christian Sutter, Wald, Jagd und Fischerei des Kantons Aargau) for identification of marten photos where the chest and head were not visible, making distinction between Martes martes and Martes foina difficult. The assimilation and data entry of photographic material took approximately 280 hours. Once all photos had been entered and the database had been cleaned and checked for missing information (associated with each photo entry), data were extracted in excel format for further analyses.

Differences in potential occupancy status (proportion of area occupied versus proportion of area used, Mackenzie, D. 2005) were verified for each of the species by calculating radius distances of home ranges (the latter obtained from previous studies in similar temperate/alpine environments, see Appendix 1 Table A). In all cases except Lynx, the radius was within the 2.5 km size of the camera trap cell. We can therefore assume that most sites were sampled independently. For the wide-ranging lynx however, this assumption is violated, with any one individual being detected at multiple sites (Appendix: Figure A).

Apart from lynx, it can therefore be reasonably assumed that the camera trap sampling protocol meets the assumptions required for occupancy analysis as defined by Mackenzie et al. 2006:

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i) Sites are closed to changes in occupancy (i.e. occupancy status at each site does not change over the survey season). ii) The probability of occupancy is constant across sites (or differences in occupancy probability are modelled using covariates). iii) Probability of detection is constant across all sites and surveys or is a function of site- survey covariates. iv) Detection of species and detection histories at each location are independent.

Modelling Framework

Using the program PRESENCE version 5.8 (USGS Wildlife Research Center/Proteus Wildlife

Research Consultants, New Zealand), likelihood-based occupancy modelling (MacKenzie et al.

2006) was used to estimate both the site occupancy (ψ: the probability that the species occurred at a site) and detection (p: the probability that the species was detected if present). The single-season model uses multiple surveys across a number of sites to build a likelihood of occupancy based on a series of probabilistic arguments. Imperfect detection can be corrected by estimating the probability of detection thereby improving the precision of site occupancy values (Mackenzie et al.

2002).

For the purpose of this study, a sampling occasion is defined as 5 consecutive trap days - to give a total of 12 pentad sampling occasions. A detection event is therefore defined as the photo-capture of a given species at a given camera trap station within a pentad sampling occasion. This approach is used by KORA for all their lynx monitoring studies (Zimmerman et al. 2007). Sampling occasions of about 10 is also considered acceptable for other medium sized (Ancrenaz et al. 2012.).

For some species (badger, wildcat, beech marten) the number of pentads included in the occupancy analyses was reduced to nine to account for inactivity during the extreme weather conditions experienced at the beginning of December 2012.

The encounter histories of the target species were constructed for each camera trap station over the 12 (or 9) sampling occasions using a standard ‘X matrix format’ (Otis et al. 1978), where ‘1’

12 indicated the detection of a species and ‘0’ indicated non-detection. In situations where there were incomplete survey histories (for example where cameras had stopped working) a missing value entry (-) was incorporated into the detection/non detection matrix. For any pentad where there was only partial camera coverage, a camera coverage index (‘camcov’) was entered in the detection sampling matrix. For example, 3 day coverage in a 5 day pentad is indexed as 0.6.

The effects of different covariates on probability of occupancy (drawn from eight environmental or habitat factors) and detection (constant, survey specific or camera coverage) were modelled for each camera trap station.

In order to limit the number of variables included in the models to those that are likely to be biologically relevant and to avoid the creation of over-complex models which lack the large data sets necessary to support robust modelling procedures (Burnham & Anderson 2002), a set of predictor variables related to ecological requirements for each species was developed. These are based on the findings of published studies on the natural history of each species in similar

European ecosystems (Table 1) and fall broadly into categories of habitat, landscape and anthropogenic factors. Landscape data was sourced from the Swiss Office Fédéral de Topographie

(Swiss Topo Vector 25 2008) and Office Fédéral de la Statistique (Geostat, Nomenclature 2004

NOLU04), courtesy of KORA. Geostat raster scaling is I hectare (100m x 100m) cells. Vector 25 accuracy ranges from 3 – 8 meters.

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Table 1. Definitions, data source and descriptions (including Swiss Topo or Geostat nomenclature in brackets) of the environmental factors used in the ‘Presence’ simulations for camera trap data collected in the Swiss Jura Nord reference area 2012-2013. The analysis column indicates which variables are already known to be important for a particular species and where applicable, the justification for inclusion and literature source.

Variable Unit Source Description Analysis Justification Reference Distance to meters Swiss Topo Distance to nearest motorway, Badger Avoidance of communication Obidzinski et al. 2013 Road/Railway line Vector 25 routes de 1er classe & 2ieme links classe (A & B roads), merged with distance to nearest surface railway line. Distance to settlement meters Swiss Topo Distance to nearest settlement Badger Avoidance of built up areas Obidzinski et al. 2013 Vector 25 (hamlet/village/town) Roe deer “ “ “ “ “ Mysterud et al. 1999 (Z_Siedl)* Beech Attracted to settlements Prigioni et al 2008 Baghli et al marten 2002 Distance to open meters GEOSTAT Distance to natural Badger Meadows are important Obidzinski et al. 2013 pastures/meadows Landuse meadows(42), farm pastures invertebrate food source (43,44) & Alpine grazing areas Roe deer Graze at night Bonnot et al. 2013 (45,46,47,48,49). Avoid open areas Prigioni et al 2008 Distance to Forest edge meters Swiss Topo Distance to nearest forest edge Roe deer During cold weather roe deer Said et al. 2005 Vector 25 (Waldrand & WaldO) prefer feeding sites with Bertolino et al. 2008 coverage. Tree & shrub browsers. In winter, badgers use forests & Badger woodlands more Do Linh San et al. 2007 Distance to Orchards meters Swiss Topo Distance to nearest orchard Badger Fruit is important food Mortelliti & Boitani 2008 Vector 25 (Z_ObstAn) Martens Prigioni et al . 2008 Distance to Rocky areas meters Swiss Topo Distance to nearest rocky Beech Like rocky, open areas Prigioni et al . 2008 Vector 25 outcrop (Z_Fels) marten Elevation meters GEOSTAT Meters above sea level at Lynx Correlated to location of suitable Zimmermann & Breitenmoser camera trap GPS point forest habitat 2002 Slope degrees GEOSTAT Gradient of land at camera trap Lynx Correlated to location of forest Zimmermann & Breitenmoser GPS point habitat 2002 Aspect 4 categories of GEOSTAT Directional facing of camera Roe Southern facing slopes warmer – Nesti et al. 2010 (Chamois) 90° trap station: NW-NE, NE-SE, SE- Badger snow thaws more quickly SW , SW-NW facilitating access to vegetation and invertebrates.

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Using the GPS location for each camera trap station, distance to nearest meadow, forest edge, settlement, orchard, communication link (roads and railway lines were merged into one variable) was calculated using Arc Toolbox Proximity – Near function. All distances were calculated following the Euclidean method and the circular ‘Aspect’ covariate was reclassified into one of four 90 degree categories. Manipulation of spatial data was run using ArcGIS ArcMap version 10.1 (ESRI

Inc., Redlands, CA. USA).

Frequency distributions of camera trap sites in relation to each of the environmental variables were checked to ensure that there was no bias. All continuous data were normally distributed except for ‘Distance to rocky areas’ which was positively skewed (Kolmogorov-Smirnov test p<0.05). Log10 transformation of the data set enabled it to meet the assumption of normal distribution.

Multi-collinearity among the environmental/habitat variables was also tested to avoid redundancy in the data (Aguilera et al. 2006). Variables can be considered as having a strong correlation when

Pearson coefficient r >0.7± (Fowler et al. 1998). All pairs had positive, but very low to moderate levels of collinearity, with the exception of distance to forest edge and distance to meadow (r=0.87 n=61 p<0.01). Depending on likely ecological relevance to a given species, only one of these two variables was selected for inclusion in the model sets. Similarly, distance to orchards was only used in simulations for beech marten.

Model development and selection

In building each model set to estimate probability of species occupancy (ψ) for the study area, a two step approach was initially explored (Linkie et al. 2007, Sarmento et al. 2011). First the effect of camera coverage and intercept on detection probability was evaluated, while keeping site occupancy constant (ψ [.] p [variable]). Then the best-fitting model for detection was combined with all the candidate models representing different combinations of biotic and abiotic site

15 covariates, including a global model that contained all potential covariate and a baseline model (ψ

[.] p[.]) where occupancy and detection probability remained constants.

However this approach did not provide consistent results when compared with a straight-forward full model approach and in keeping with the recommendation by MacKenzie et al. (2006), a full model approach was selected.

In all simulations, the sample size was defined and entered into the Presence model as the number of pentad detections (D. MacKenzie, personal communication, 2013). The sample size for each species in the Jura North data set is small compared to the number of parameters (described in the scientific literature as being where n/k <40 and k is the number of fitted parameters in the most complicated candidate model, Burnham & Anderson 2002, MacKenzie et al. 2006)) and hence the

Presence modelling for all species was conducted using AICC (a modified version of AIC).

The number of covariates (parameters) was kept within the n/10 rule (Anderson, D. 2008) to avoid over-fitting the modal. So for badgers, n=140 and therefore the maximum number of parameters that could be considered was 14. This also resulted in a wide variance in the number of modal sets that could be run across species.

Candidate models were ranked and evaluated using the Akaike Information Criteria (AIC) (Burnham

& Anderson 2002) , where the model with the lowest AIC value represents the ‘best approximating model’ (Symonds & Moussalli 2011). Following the ‘rule of thumb’ selection criteria recommendations (Burnham & Anderson 2002), top ranking models were considered to be those where the difference in AIC between two (delta value) was ≤ 2 on the grounds that both models have approximately equal weight in the data (i.e. they are considered to be as good as the best model). Richards et al. 2011 recommend selecting models with delta AIC values less than 6 in order to have approximately 95% chance of including the truly most parsimonious model in the candidate set. Models with Delta AIC≥6 were therefore discounted.

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AIC is affected by over dispersion in the data (which may reflect poor fit of a model). The single variance inflation factor c-hat (ĉ) can be estimated from the goodness of fit chi-squared statistic

(χ2) of the global model where ĉ = χ2/df (Cox and Snell 1989). When there is no over-dispersion, the single variance inflation factor ĉ = 1. Any values greater (or less) than 1 indicate over or under- dispersion. Goodness of fit testing was therefore conducted for the global modal (with greatest number of parameters) in each set using 1000 parametric bootstraps. Where the over-dispersion coefficient c-hat (ĉ) had a value> 3, the model was dismissed (Burnham & Anderson 2002, Cooch &

White 2013). If ĉ was between 1 and 3, the c-hat value adjustment was made within the Presence program (and because the over-dispersion coefficient is a parameter, k was also increased by 1

(Burnham & Anderson 2002) to give QAIC values for all candidate models.

Finally, for the Beech marten best model, the occupancy value for each site was calculated using the psi beta covariate values from the Presence output:-

Linear.occ(i) = A1 + ( A2 * slope) where A1 = untransformed beta covariate psi, A2 = slope beta value and slope = degrees of slope incline for each site. These were then averaged to give a psi value for the total reference area.

Where models showed evidence of over dispersion (e.g. badger ĉ = 2. 3872) the standard error for

Beta (β) value parameter ψ was multiplied by √ĉ. This step was done after logit transformation of

SE β value.

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4. Results

Over 7000 digital images were catalogued into the photo-library. Of these, 3146 wildlife images were entered onto the new KORA database. Elimination of photos that were captured outside the

60 day sampling window or were unidentifiable at species level, gave a total sample size of 2902 photos (including domestic cats/hybrids). The 61 sites x 60 days gave a potential sampling effort of

3660 trap days (24 hour), but due to technical failures with cameras and some problems with heavy snowfall and camera theft, the effective effort for the sampling period was reduced to 3480 trap days.

Species richness (Smax ).

Thirteen different wildlife species were detected over the 60 day sampling window. All had been photographed at least once by day 31 and, of these, the 6 larger mammals (fox, lynx, badger, boar, chamois and roe deer) by day 3 (Figure 3). The large gap between the early and later detections may be related to the adverse weather conditions in the first few weeks of December. It is not possible to calculate species diversity indices as we have no data on the actual abundance of each species.

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14

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st Species Days to 1 10 photo capture Fox 1 Roe deer 1 8 Chamois 1 Cumulative No. of species Hare 1 Lynx 2 6 Badger 3 Log. (Cumulative No. of species) Boar 3 4 Beech 15 marten Pine 20 marten No. of new species captured on camera on captured species new of No. 2 Hedgehog 20 Wildcat 25 0 Squirrel 31 1 11 21 31 41 51

Effort (camera trap days)

Figure 3: Species richness curve for Jura Nord 2012-2013 indicating the number of days (31) to accumulate a full inventory of target animal species. Speed of first detection for each species is given in the table (right).

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In their raw form, the photographic records (Figure 4) provide a useful ‘return on effort’ index but do not take into account replication of individuals (one animal may be photographed by one or both cameras at a site and the same animal may be photographed several times within a short timeframe (<10 minutes) as it moves within the catchment zone of the camera). The pattern of relative spatial and temporal abundance is therefore also presented based on the number of pentads that a species was detected over the survey period (Table 2). The highest frequencies of photo captures, detection pentads and naïve site occupancy (Figure 5) were for fox, badger, roe deer and hare. With the exception of hare, animals with a smaller body mass had considerably lower levels of photographic detection and site occupancy. Included in these preliminary results are data for domestic/feral cats (Felis catus) and possible domestic-wildcat hybrids which we could not positively identify as either hybrid or pure wildcat.

All species show broad geographic distribution (Figure 6) with little apparent spatial clustering.

Photos of smaller animals were generally of good quality and identification issues occurred only in the cases of marten (with partial images lacking the distinguishing head and throat areas) and Felis species due to the problems of differentiation between hybrids and true .

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Figure 4: A sample of camera trap photographs from the KORA Jura North winter season 2012-2013. From top left to right: Lynx (Lynx lynx), Wildcat (Felis silvestris), Hybrid domestic/wildcat (Felis catus/sylvestris), Badger (Meles meles), Fox (Vulpes vulpes), Beech marten (Martes foina), Pine marten (Martes martes), Chamois (Rupicapra rupicapra), Roe deer (Capreolus capreolus), Boar (Sus scrofa), Brown hare (Lepus europaeus), Grey squirrel (Sciurus vulgaris).

Note: All photographs are copyright KORA.

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Table 2: The number of photographs, pentads and sites in which each species were recorded in the Jura North study area between 1/12/12 – 29/01/13. The trapping effort per 100 trap days is expressed as the number of photographs/number of effective trap days x100.

Species No. of photo Record rate per No. of No. sites records 100 trap day effort pentads detected (R/3480x100) Fox 1673 48.07 433 60 Badger 304 8.74 141 45

Roe Deer 285 8.19 114 40 European Hare 252 7.24 121 35 Chamois 166 4.77 58 23

Lynx 76 2.18 36 25 Wild boar 57 1.64 31 23 Beech marten 33 0.95 29 13 Domestic/hybrid cats 31 0.89 24 11 Wild Cat 10 0.29 9 7

Pine marten 7 0.2 6 6 Hedgehog 5 0.14 3 1 Squirrel 2 0.06 2 2

Bird 1 0.03 1 1 Unidentified 28 - - -

100

80 No. of sites detected

Naïve occupancy (n=61) 60

40

20

detected species where sites of % & No. 0

Figure 5: Number of camera trap sites at which species were detected at least once (d) during the sampling period (1/12/12 – 29/01/13) and the naïve occupancy of each species, expressed as percentage (d/61 x100). Domestic and hybrid cats are consolidated as differentiation of species not possible in some cases.

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LYNX FOX

BADGER WILDCAT

HARE BOAR

CHAMOIS ROE DEER

PINE MARTEN BEECH MARTEN

Species detected Camera trap sites that did not detect the species during the survey period Forest cover

Figure 6: Camera trap locations in Jura North study area where species were detected (minimum 1 pentad detection) between 1/12/12-29/01/13.

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Occupancy Modelling

Badger

Badgers were detected at 45 of the 61 camera trap sites with a naïve site occupancy of 0.74 (Figure

5). Due to the relatively large sample size (n = 140) compared to some of the other species, over

60 permutations of occupancy and detection variables were simulated. Two models had ∆AICC ≤3 and a further 9 models had ∆AICC values between 3.43 – 5.64, indicating similar support for each of these models (Table 3). The second ranked model (wi = 0.12) was ψ (Aspect)p(.), indicating that there may be a relationship between badger occupancy and the direction in which the slope is facing. Review of the original occupancy data according to aspect category suggests that badgers prefer SW-SE facing slopes.

However, no single model clearly out-performed the other models (i.e. with wi >0.9). The occupancy model with the greatest support ( wi = 0.34) was ψ (.) p(.) which gave an estimate of ψ

= 0.767 (SE ± 0.075)(Figure 7). Given the large set of candidate models, the results suggest that the environmental covariates included in the modelling are not strong predictors of badger occupancy.

Fox

This species is ubiquitous and was detected at all sites except Innere Klus (and poor camera coverage at this site is the likely reason for imperfect detection). For this reason, modelling scenarios included only the five different detection parameters, not environmental covariates.

Naïve occupancy estimate was 0.98 (Figure 5). The top two ranking models had AIC weights of

0.325 and 0.323 but the ∆AICC values for all candidates <3 (Table 3). The top ranking model gives a predicted occupancy estimate of ψ = 0.9922 (SE ± 0.007)(Figure 7).

Lynx

In the case of lynx, it is more appropriate to describe occupancy status as the proportion of area that was used rather than the proportion of area occupied (MacKenzie 2005). Lynx were photographed at 25 sites with naïve occupancy of 0.41 (Figure 5). Due to small sample size 24

(number of pentads n = 36) the modelling was limited to a maximum of four parameters. Distance to orchards and elevation were excluded from simulations (as orchards do not present a likely food source and lynx are known to cross the landscape at all elevations). Most models suffered from under-dispersion and several were rejected as on the basis of non-convergence or simply that the modelling was unstable. The latter case included habitat variables such as distance to meadow, forest edge and distance to roads/railways. From the remaining candidate set, eight models had

∆AICC values< (Table 3). The model with the strongest support, with lynx detection and occupancy modelled as a constant function, had AIC weight of only 0.22. The predicted occupancy estimate for this model was 0.72 (SE ±0.11) (Figure 7). Of the environmental factors, only distance to Rocky areas, Aspect and Slope appeared in the eight top ranking models.

Wildcat

There were very few confirmed identifications of wildcat which was detected at only seven sites, and only once at each site. Even after reducing the number of sampling occasions to 9 pentads to allow for the heavy snow, the small detection sample size (n=7) meant that modelling was really limited to just 2 parameters. Incorporation of any of the environmental covariates (which meant k

≥3) resulted in under-dispersion of data and very high standard errors. Only two models were therefore considered in the final assessment with a combined AICwgt> 0.91 (Table 3). However the constant model psi(.)p(.) produced an inconclusive figure for occupancy. The best fit for the limited data was psi(.)p(camcov) with an estimated occupancy figure of ψ = 0.326 (SE ±0.202) (Figure 7).

This model suggests that the camera coverage index improved the detection component of the modelling process. In comparison, the naive estimate is just 0.1148 (Figure 5).

Martens

Beech marten was detected at 13 sites between the 4th – 12th pentad with 29 detection events.

Naïve occupancy is 0.2131 (Figure 5). Occupancy modelling was attempted using covariate combinations of 2 and 3 parameters but all of the models showed extensive over-dispersion with

25 goodness of fit c-hat values of between 3.8 – 6.16. This included the most simple constant model psi(.),p(.) with c-hat = 5.23. On that basis the models were rejected.

There were only 7 photographs of pine marten from a total of 6 sites to give a naïve occupancy figure of 0.098. Given that the pentad detection event sample size is also only 6, no occupancy simulations were attempted for pine martens. Both species of marten were detected at two sites -

Wolfschluct und Lauch.

Roe Deer

There were 114 detection events so simulations were run using a maximum of 11 parameters.

Again, most models showed evidence of over dispersion and after c-hat adjustment, the top ranking model was the constant psi(.), (p.) (Table 3) which gave ψ = 0.68 (SE ±0.096) (Figure 7). A further 10 models fell within the Delta ≤ 6 criterion for consideration (i.e. we can be 95% confident that the most parsimonious models were retained within the confidence set of 11 (Richards et al.

2011)). All environmental covariates appeared in the final candidate set of 11 models.

Sarcoptic Mange

One unexpected finding was the observation of sarcoptic mange (commonly known as canine scabies) in 70 fox photographs from 21 different camera trap sites. Sarcoptic mange is a highly contagious skin infection caused by the Sarcoptes scabiei mite (Balestrieri et al 2006). It was possible to detect quite early levels of infestation, with just small areas of tail or flank showing alopecia or skin lesions, as well as the more advanced cases where mange had affected large areas of the torso and legs (Figure 8). Mange was not detected in photographs of any of the other species.

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Table 3: Models of site occupancy ѱ and detection probability (p) for five mammal species based on camera trap data collected for Jura Nord study area between 1/12/12- 29/01/13. Parameters (p & ѱ) were fixed or allowed to vary using site or detection covariates. For each species, models are ranked according to their AICc (second order Akaike’s Information Criterion corrected for small sample sizes) or QAICc (quasi-AIC corrected for over dispersion) and AIC model weight. Only models that fell within the ∆QAICc <6 criterion for badger, lynx, fox, roe deer & ∆AICc <6 for wildcat, are presented. Based on this selection process, predicted occupancy estimates for the top ranking models are shown in figure 6.

MODEL QAICc DeltaQAICc AIC Model weight Likelihood Parameters BADGER psi(.),p(.) 252.46 0 0.337 1.000 2 psi(Aspect),p(.) 254.57 2.11 0.117 0.348 3 psi(DISTRdRail),p(camcov+intercept) 255.89 3.43 0.061 0.180 4 psi(Slope),p(camcov+intercept) 255.92 3.46 0.060 0.177 4 psi(DISTMeadow),p(camcov+intercept) 256.39 3.93 0.047 0.140 4 psi(Aspect),p(camcov+intercept) 256.40 3.94 0.047 0.140 4 psi(DISTSettlement),p(camcov+intercept) 256.41 3.95 0.047 0.139 4 psi(.),p(surveyspecific) 257.12 4.66 0.033 0.097 10 psi(DISTRdRail, DISTMeadow),p(camcov+intercept) 258.06 5.6 0.021 0.061 5 psi(DISTRdRail, Aspect),p(camcov+intercept) 258.06 5.6 0.021 0.061 5 psi(DISTMeadow, Slope),p(camcov+intercept) 258.1 5.64 0.020 0.060 5 FOX psi(.),p(surveyspecific) 331.29 0 0.325 1.000 13 psi(.),p(surveyspecific+camcov) 331.30 0.01 0.323 0.995 14 psi(.), p(camcov) 332.85 1.56 0.149 0.458 2 psi(.),p(camcov+intercept) 333.21 1.92 0.124 0.383 3 psi(.),p(.) 334.14 2.85 0.078 0.241 2

LYNX psi(.),p(.) 287.83 0 0.219 1.000 2 psi(Slope),p(.) 288.27 0.44 0.176 0.803 3 psi(.),p(camcov+intercept) 288.91 1.08 0.128 0.583 3 psi(DISTRocky),p(.) 289.01 1.18 0.121 0.554 3 psi(Aspect),p(.) 289.28 1.45 0.106 0.484 3 psi(Slope),p(camcov+intercept) 289.36 1.53 0.10 0.47 4 psi(DISTRocky),p(camcov+intercept) 290.32 2.49 0.063 0.288 4 psi(Aspect),p(camcov+intercept) 290.44 2.61 0.059 0.271 4 ROE DEER psi(.),p(.) 210.29 0 0.197 1.000 2 psi(.),p(camcov+intercept) 211.02 0.73 0.136 0.694 3 psi(DISTRocky),p(camcov+intercept) 211.38 1.09 0.114 0.580 4 psi(DISTRocky, Elevation),p(camcov+intercept) 211.47 1.18 0.109 0.554 5 psi(Elevation),p(camcov+intercept) 211.49 1.2 0.108 0.549 4 psi(DISTSettlement),p(camcov+intercept) 212.59 2.3 0.062 0.317 4 psi(DISTRdRail),p(camcov+intercept) 212.71 2.42 0.059 0.298 4 psi(DISTForedge),p(camcov+intercept) 213.07 2.78 0.049 0.025 4 psi(Aspect),p(camcov+intercept) 213.16 2.87 0.047 0.238 4 psi(DISTMeadow),p(camcov+intercept) 213.19 2.9 0.046 0.235 4 psi(Slope),p(camcov+intercept) 213.2 2.91 0.046 0.233 4

MODEL AICc DeltaAICc AIC Model weight Likelihood Parameters WILDCAT psi(.),p(.) 81.7 0 0.798 1 2 psi(.),p(camcov) 85.59 3.89 0.114 0.143 2 27

1.00 Naïve occupancy

Predicted occupancy ѱ 0.80

0.60

0.40

Occupancy Site 0.20

0.00 Fox Badger Lynx Roe deer Wildcat Beech marten

Figure 7: Occupancy estimates (ѱ) for mammal species in Jura North study area based on top ranking models (∆ AICc = 0 and ∆QAICc = 0). Bars represent ± 1SE adjusted for over-dispersion for fox, badger & roe deer. Fox ψ = 0.992 (±0.007), Badger ψ = 0.767 (±0.075), Lynx ψ = 0.721 (±0.107), Roe deer ψ = 0.684 (±0.096), Wildcat ψ = 0.326 (±0.20). Occupancy models for beech marten all had goodness of fit c-hat values >3.8 and were therefore rejected.

Figure 8: Examples of fox presenting symptoms of sarcoptic mange in a population from the Jura North study area, 2012-2013 winter camera trap survey.

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5. Conclusions and Discussion

This study has demonstrated the value of camera trap by-catch as a source of quantifiable information for species sympatric to lynx in the Swiss Jura. Secondary data collected in this way also represents a considerable increase in ‘return on investment’, given the costs of running large scale camera trap programs. Of a total 2902 wildlife images, there were 76 images of lynx; the remaining 97.4% of photos were of species not specifically targeted within the sampling protocol.

The data set of photographs collected for these secondary species; roe deer, boar, chamois, badger, fox and hare were sufficiently large to provide robust indicators of species distribution and richness for the study area. If the research objective is to establish a baseline ‘snapshot’ of species richness, this study suggests that a 35 day sampling window would be adequate to capture the diversity of mammals occupying any one camera trap catchment area. Considerably less data were captured for the smaller species, wildcat (ten photos across seven sites), beech martens (33 photos from 13 sites) and pine marten (seven photos from six sites). There were no photographs of (Mustela putorius) or stoat (Mustela ermine), which have been detected in other KORA studies in Switzerland. Even during the milder temperatures, only five hedgehog photographs (all from one site) and two squirrel photos were captured. Camera trapping within the current survey protocol (particularly during the winter months) is clearly not an appropriate tool for detecting the presence of squirrel and hedgehog.

A question of timing

The Swiss camera trap surveys are deliberately undertaken during winter months to optimise lynx detection rates. Lynx make more use of forest roads and trails during the winter and snow can provide clear tracks, thus helping to identify movement corridors (Zimmermann et al. 2012). Lynx tend to increase their ranging behaviours prior to and during the mating season in March

(Breitenmoser & Breitenmoser-Würsten, 2008) hereby increasing trap capture probability.

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Furthermore, there are no births during the winter (Breitenmoser-Würsten et al. 2001) and immigration and emigration are at their lowest point as the majority of juveniles only start to disperse around mid April (Zimmermann et al. 2005). These factors mean that the assumptions of a closed population are upheld. The channelling of wildlife to human-made paths and roads as a result of snow probably holds true for many of the other species, as does the assumption about closed populations. So in some respects, a winter survey does have advantages for the targeting of secondary species. However, the timing of the camera trap survey does not appear to be optimal in terms of its application to a multi-species monitoring program. The complete absence of marten, wildcat and badger photographs in the first 15 days of December is most likely due to the heavy snowfall in the Jura mountains at the beginning of the month. Cold winter weather is clearly an important factor which affects the detection of smaller species, which simply cannot move around in deep snow, may be hidden from view, or may reduce activity levels to conserve energy.

This is the case for badger, which remain inactive when temperatures fall below 5° C or when the ground is frozen and covered with snow (Do Linh San et al. 2006). Weather conditions therefore need to be taken into consideration when selecting sampling windows for analyses of data for badger and smaller sized species, wildcat and martens. The presence of snow appears to be less of a concern in terms of activity and detection levels for the ungulates (boar, chamois, roe deer), fox and hare.

Trends over time

The value of establishing occupancy estimates for any given species, lies primarily in monitoring changes over time. For those species where there is sufficient sample size (fox, roe, badger, chamois, hare and boar), this study now needs to be expanded to include camera trap data for earlier seasons – 2008/2009 and 2010/2011. This should provide some interesting trend data both in terms of distribution and occupancy status.

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Are there fewer pine martens than beech marten or is this a question of detection?

The number of photos captured for pine marten (7) was much lower than that of beech marten

(33). Possible explanations for this include: i) pine marten abundance is simply much lower than that of beech marten ii) pine martens may be more arboreal and spend more time outside the range of the camera iii) there is bias in the locations of camera trap according to fine scale habitat preferences between the two species, which at present, cannot be identified using Swiss

GEOSTAT/TOPO landscape profiling. Given the paucity of information about the ecology of these two species in the Swiss Alpine region (C. Breitenmoser, personal communication 2013), these questions could perhaps provide the theme for a future research study.

The threat of mange to wildlife populations

The prevalence of sarcoptic mange in fox populations at 34% of sites is of concern. Whilst the preferred hosts are members of the Canidae family (dogs, foxes, wolves), Sarcoptes scabiei infection has been reported in more than 100 species of wild and domestic animals (Balestrieri et al. 2006). The movement of the mite as it burrows through the skin, combined with the body’s massive allergic response, causes extensive scratching and self-made lesions, which in turn, can lead to secondary infections (Balestrieri et al 2006). In severe cases, extensive alopecia in an alpine environment can result in hypothermia. Unless treated, most cases of sarcoptic mange are fatal. If the Jura North study site is representative of the prevalence levels in Switzerland generally, these findings confirm the threat presented by Sarcoptes scabiei to wildlife populations of lynx (Lynx lynx), golden jackal (Canis aureus) and to the more recent return of the grey wolf (Canis lupus), as well as to ungulate species, boar and chamois (Pence & Ueckermann 2002, Rossi et al. 1995). This study also demonstrates the utility of camera traps as a tool for monitoring prevalence levels and geographic distribution of the disease.

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The application of occupancy analysis to photographic data.

Sampling programs designed to estimate occupancy do not rely on recognition of individual animals and may provide a reasonable alternative for assessing population status and trends, without the effort and expense of large scale multi-species monitoring programs designed to estimate abundance (MacKenzie & Nichols 2004). With this application in mind, the potential of occupancy modelling was tested on a selection of species from the camera trap program. There were no serious issues of large standard errors (due to inadequate sample size) or over/under dispersion for models based on roe deer, fox and badger data. That said, all of the species models suffered with some degree of over dispersion (where there is more variation in the observed data than expected by the model), even for the most simple models, with few parameters. For roe deer, goodness of fit for the top ranking models (Table 3) ranged from c-hat values of 2.6 - 3.1, for fox, from 1.7 - 2.9 and badger, from 1.4 - 2.2. As long as the model structure is correct, small over dispersion factors may be expected in the context of modelling ecological count data (MacKenzie et al. 2006). In such instances, over dispersion may be due to small violations of assumptions such as independence (i.e. there are correlations between individual animals that exist in groups/herds, or where the young continue to live with parents) and heterogeneity between individuals

(Burnham & Anderson 2002). However, in this case, over-dispersion is more likely to be an indication that the model structure is inadequate; that the variation in the data is not accounted for by the fitted model (Burnham & Anderson 2002).

Small sample sizes for lynx, wildcat and beech marten resulted in large or false standard errors and computational problems (non convergence and error ## signs in the Presence program outputs).

Under-dispersion (where there is less variation in the observed data than expected by the model) was evident in the modelling for all three species.

Poor model fit is most likely attributable to the inclusion of covariates which failed to account for differences in site occupancy due to environmental factors. In each set of selected candidate

32 models (with Delta QAIC<6)(Table 3), AIC values did not provide a clear top ranking model (AIC value >90), rather, a range of predictors all with similar weightings, all showing similar levels of support. Despite careful selection of biologically relevant environmental factors, these results are inconclusive in establishing predictors of occupancy for each species.

The homogeneity of the study camera sites in terms of their landscape characteristics (which is to be expected given that these sites have been deliberately selected as optimum lynx habitat) means that the selected psi occupancy covariates did not provide sufficient variability for modelling, regardless of data set size. Furthermore, in some instances, the range within the variable was low e.g. distance to meadows range was between 50 meters – 495 meters. Perhaps this is insignificant for larger mammals that can easily cover such distances within minutes.

Unlike the data available for the UK from sources such as UK Digimap and Centre for Ecology and

Hydrology, Swiss GEOSTAT/TOPO does not provide really fine-scale landscape and habitat data.

However forest type (coniferous, deciduous, mixed, density) and vegetation (acid grassland, bracken, shrub etc.) are important determinants of habitat use at the local scale and need to be built in to modelling parameters. Other factors that could be incorporated include proximity of surface water (streams/rivers) and soil type (Mortelliti & Boitani 2008). If fine scale descriptors of habitat types can be included for each set of species models, this may provide more definitive results. On the other hand, it may be the case that the relevant environmental predictors of occupancy for the secondary species of interest can only be identified using a separate random survey design. This would require camera trap sites to be located in a completely random fashion within each grid cell.

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Technical Issues associated with data analysis

A number of challenges arose in the technical aspects of data analysis: i) There is no agreement in the scientific literature on the definition of effective ‘sample

size’ in the context of multi-site, multi sampling occasion surveys. A sample can be the

number of sites, the number of detections or the number of surveys at occupied sites and

it may be different for occupancy and detection probabilities (Burnham & Anderson,

2002, MacKenzie et al. 2006). Interestingly, I could find no similar occupancy studies using

Presence software that covered this point in the method. ii) Similarly, there is inconsistency in approach regarding the acceptable cut-off point for the

c-hat over dispersion parameter. Burnham & Anderson (2002) suggest that an over

dispersion parameter is acceptable if 1≤ c-hat ≤ 4. Above this value, there is likely to be

some structural lack of fit and models should be rejected. However Cooch & White, 2013,

advocate chat ≤ 3 as acceptable.

In this study I have used the number of pentad detections as the sample size and I have used a c- hat value of 3 as the cut – off point for model fit. If Presence modelling is to be used for future studies in the Jura North and other KORA reference areas, it is important that there is consistency of method across species and seasons in order that data may be compared without bias.

6. Applied Research Recommendations

The aim of this study was to explore the potential of camera traps to collect data that can be used in wildlife monitoring of non-target species. Whilst large numbers of photographs were available for fox, badger and important prey species: roe deer and chamois, the quantity of photographs appears to follow a gradient of body size (with the exception of hare). Inadequate sample size meant that occupancy modelling was not possible for several of the smaller species. Attempts at modelling occupancy were further compromised by the environmental homogeneity of the camera

34 trap sites and the lack of fine scale descriptors of habitat. This led to inconclusive results in the identification of habitat predictors. Modifications to the spatial and temporal deployment of camera traps (with minimal disruption to the lynx survey effort) should improve the quantity of photographs available for wildcat, boar and marten species. More detailed evaluation of landscape characteristics and habitat resource in terms of food provisioning, may enhance the sensitivity of occupancy modelling. Mindful of the need to minimise disruption to the lynx camera trap survey protocol, the following recommendations are made:

i) Begin the camera trap survey one month earlier to give a total of 90 days sampling. Severe

weather is unlikely in November and animal activity levels or detection should not be

compromised during this time. It will also ensure that approximately 12 pentads of data are

available for analyses, even after exclusion of those pentads that cannot be used due to

heavy snow (more likely in December and January).

ii) Lower the height of camera traps during this extension period to 45cm. This should improve

the detection of smaller animals, particularly when they are close to the camera. Lowering

the camera equates to a change in the survey (detection) function parameter but can be

built in to the modelling process.

iii) Some animals may prefer to use undisturbed habitat or undergrowth rather than open

human-made tracks (Harmsen et al. 2010). If there is sufficient man-power, the ideal

would be to deploy the cameras (during the extension period) on a wildlife trail, away from

the human traffic associated with forest roads and hiking trails. This could compromise the

detection of lynx, but that should not be a problem given that the official lynx sampling

timeframe would not start until the end of November.

iv) Keep records of weekly weather conditions for each camera trap site on the servicing

protocol sheet. This will assist in the de-selection of pentads/sites for modelling purposes,

where detection is problematic due to deep snow and will also provide additional

35

environmental data which can be included as a covariate of detection (e.g snow,

temperature). v) Conduct habitat assessments at each camera trap station/grid cell. The survey should be

conducted at several scales (dependent on the species) and should include vegetation and

forest types, density of forest cover, structure and density of understory, soil type, and

distance to surface water. Consideration should be given to specific food resources such as

oak, horse-chestnut and pine, wild fruits and levels of invertebrate abundance. The surveys

need to be completed during the summer or autumn so that identification of plant species

is facilitated.

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8. APPENDIX

Table A: Mean home range for each species as defined from recent literature and calculated radius measurement (km).

Species Mean home range Radius km Reference Hectares Badger 320.5 1.01 Do Linh San et al. 2007 Wildcat 467 1.22 Biró, Z. et al. 2004 Boar 415 (winter) 1.15 Baubet et al. 1998 Chamois 32.2 (winter) 0.32 Nesti et al. 2010 Roe deer 88 (winter) 0.53 Ramanzin et al. 2007 Beech marten 115 (Switzerland) 0.61 HausserJ. 1995 European hare 53 (alpine) 0.41 Parkes 1984 Lynx 172.5 7.41 Breitenmoser-Wursten et al. 2007b

France

Figure A: Map showing location of study reference area (outlined in blue) in the Swiss Jura North mountain range. Circles with black dots indicate camera trap sites where lynx were detected. Circles without dots are camera traps with no lynx detections during the sampling period 1/12/12 – 29/01/13. Black lines delineate cantons: BE - Bern, SO - Solothurn, JU - Jura, BL- Basel Land. Eclipses indicate the estimated range of individual lynx that are identified as CARV, MATA, ADIN, JOLY, B301, B167, B310, B217 plus animals that were only detected at one or two sites B286, B293, R141, L139, B291. Source: KORA 2013

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