Optimising the Value of By-catch from Lynx lynx Camera

Trap Surveys in the Swiss Jura Region.

Fiona Anne Pamplin

6th August 2013

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

©KORA

©KORA

2

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

©KORA

3

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!

4

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 animals 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 hare 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, wildcat,

beech marten 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

5

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

6

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

mammal 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

7

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.

8

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).

9

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

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

France

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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 animal 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

11

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 mammals (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’

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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.

14

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 Road/Railway line

meters Swiss Topo Vector 25

Distance to nearest motorway, routes de 1

er classe & 2

ieme

classe (A & B roads), merged with distance to nearest surface railway line.

Badger

Avoidance of communication links

Obidzinski et al. 2013

Distance to settlement meters Swiss Topo Vector 25

Distance to nearest settlement (hamlet/village/town) (Z_Siedl)*

Badger Roe deer Beech marten

Avoidance of built up areas “ “ “ “ “ Attracted to settlements

Obidzinski et al. 2013 Mysterud et al. 1999 Prigioni et al 2008 Baghli et al 2002

Distance to open pastures/meadows

meters GEOSTAT Landuse

Distance to natural meadows(42), farm pastures (43,44) & Alpine grazing areas (45,46,47,48,49).

Badger Roe deer Martens

Meadows are important invertebrate food source Graze at night Avoid open areas

Obidzinski et al. 2013 Bonnot et al. 2013 Prigioni et al 2008

Distance to Forest edge meters Swiss Topo Vector 25

Distance to nearest forest edge (Waldrand & WaldO)

Roe deer Badger

During cold weather roe deer prefer feeding sites with coverage. Tree & shrub browsers. In winter, badgers use forests & woodlands more

Said et al. 2005 Bertolino et al. 2008 Do Linh San et al. 2007

Distance to Orchards meters Swiss Topo Vector 25

Distance to nearest orchard (Z_ObstAn)

Badger Martens

Fruit is important food Mortelliti & Boitani 2008 Prigioni et al . 2008

Distance to Rocky areas meters Swiss Topo Vector 25

Distance to nearest rocky outcrop (Z_Fels)

Beech marten

Like rocky, open areas Prigioni et al . 2008

Elevation meters GEOSTAT

Meters above sea level at camera trap GPS point

Lynx Correlated to location of suitable forest habitat

Zimmermann & Breitenmoser 2002

Slope degrees GEOSTAT

Gradient of land at camera trap GPS point

Lynx Correlated to location of forest habitat

Zimmermann & Breitenmoser 2002

Aspect 4 categories of 90°

GEOSTAT

Directional facing of camera trap station: NW-NE, NE-SE, SE-SW , SW-NW

Roe Badger

Southern facing slopes warmer – snow thaws more quickly facilitating access to vegetation and invertebrates.

Nesti et al. 2010 (Chamois)

15

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

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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.

17

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.

19

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).

Species Days to 1st

photo capture

Fox 1 Roe deer 1 Chamois 1 Hare 1 Lynx 2 Badger 3 Boar 3 Beech marten

15

Pine marten

20

Hedgehog 20 Wildcat 25 Squirrel 31 0

2

4

6

8

10

12

14

1 11 21 31 41 51

Cumulative No. of species

Log. (Cumulative No. of species)

No

. of

new

sp

eci

es

cap

ture

d o

n c

ame

ra

Effort (camera trap days)

20

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 wildcats.

21

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.

22

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.

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.

Species No. of photo records

Record rate per 100 trap day effort (R/3480x100)

No. of pentads

No. sites detected

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 - - -

0

20

40

60

80

100

No. of sites detected

Naïve occupancy (n=61)

No

. & %

of

site

s w

her

e sp

eci

es

de

tect

ed

23

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.

LYNX

BADGER

FOX

BEECH MARTEN

BOAR

WILDCAT

ROE DEER

PINE MARTEN

CHAMOIS

HARE

24

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

25

(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

26

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.

27

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 weight

Model 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

weight Model

Likelihood

Parameters

WILDCAT

psi(.),p(.) 81.7 0 0.798 1 2

psi(.),p(camcov) 85.59 3.89 0.114 0.143 2

28

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.

0.00

0.20

0.40

0.60

0.80

1.00

Fox Badger Lynx Roe deer Wildcat Beech marten

Naïve occupancy

Predicted occupancy ѱ

Site

Occ

up

ancy

29

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 European polecat (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.

30

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.

31

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.

32

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

33

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.

34

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

35

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

36

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.

37

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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 Hectares

Radius km Reference

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

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

France