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MTEM Thesis - Jenni RISLER s910718

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Optimising camera trap survey effort

to reliably detect a threatened species,

the black-footed tree-rat, Mesembriomys gouldii,

in open forest and woodland of tropical savannas

of the Top End, Northern Territory

Jennifer Anne Risler BSc

(also published as J. Low Choy)

Research Institute for the Environment and Livelihoods

School of Environment Charles Darwin University

Submitted in partial fulfilment of the Masters of Tropical Environmental Management

19 June 2017


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Statement of authorship

I declare that this thesis is my own work and has not been submitted in any form for any

other degree or diploma at any university or other institute of tertiary education. Information

derived from the published and unpublished work of others has been acknowledged in the

text and list of references.

19 June 2017

Me checking images from a camera trap at Fish River Station, 2015

The remote camera trapping component of this research was conducted under the Charles

Darwin University Animal Ethics Committee approved project A13026 Ongoing monitoring

and biodiversity surveys in the Northern Territory held by the Flora and Fauna Division, NT

Department of Environment and Natural Resources.


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Acknowledgements

Firstly I would like to thank Penny Wurm for her constant support, enthusiasm and

guidance through my Masters of Tropical Environmental Management (MTEM)

journey. I would also like to thank all the lecturers who have contributed to my

knowledge and experience in the research realm; in particular I thank Karen Joyce,

Natalie Rossitor-Rachor and Keith McGuinness. Thank you to Bernie from the Charles

Darwin University Library for your constant patience and knowledge with Endnote.

A huge thank you to Mum and Dad for reading my papers throughout all my studies,

and for making dinners for the kids and for helping financially those times when

needed. Thank you to Alaric Fisher for financial and moral support at critical times

through my studies. Thank you to Daniel Low Choy for the valuable child free time

required during fieldwork and close to submission deadlines.

Thank you to my thesis supervisors. A massive thank you to Christine Schlesinger who

has made this thesis possible with your patience, guidance and questions in addition

to the support material you have provided me. Thanks to Dani Stokeld for the

invaluable statistical advice and R code, often while conducting arduous fieldwork.

Thank you to Graeme Gillespie for access to corporate data, valuable comments and

for keeping me focused. Thank you Brydie Hill for your support and valuable

comments on the final run.

Thank you to everyone who set camera traps, identified animals and managed the

datasets that formed the basis for my research. Thanks to the Indigenous rangers,

Parks and Wildlife rangers, Kakadu National Park staff, Flora and Fauna scientists and

field staff; and the friends and volunteers who have spent many hours and days both

on country and in the office to capture the high quality data I used.

Thanks to David Lindsay for valuable guidance in writing my thesis through his

thoroughly readable book “Scientific Writing = Thinking in Words” (Lindsay 2011). The

tools and inspiration I have gleaned from this book have made my writing more

concise and structured and even enjoyable.

Thanks to Hugh Davies and Jayne Brimbox who read and commented on drafts, an

amazing process in itself.


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Table of Contents

Statement of authorship .......................................................................................................... 2

Acknowledgements .................................................................................................................. 3

List of Tables ........................................................................................................................ 5

List of Figures ....................................................................................................................... 6

Abstract .................................................................................................................................... 7

Introduction ............................................................................................................................. 8

Camera traps ........................................................................................................................ 9

Occupancy analyses ............................................................................................................. 9

Survey effort and sampling design..................................................................................... 11

My research ....................................................................................................................... 13

Methods ................................................................................................................................. 15

Study area and data acquisition......................................................................................... 15

Camera trap survey design ................................................................................................ 17

Mammal Data .................................................................................................................... 19

Analyses ............................................................................................................................. 20

Results .................................................................................................................................... 22

Discussion............................................................................................................................... 26

Management implications ................................................................................................. 29

References ............................................................................................................................. 31

Appendix 1. R code developed by Danielle Stokeld 2016 ..................................................... 38

Appendix 2. Camera trap orientation to bait station ............................................................ 44

Appendix 3. Variation in detection due to camera trap set up ............................................ 46

Distance between camera trap and bait station ......................................................... 46

Model of camera (Infra-red flash and White light flash) ............................................. 46

Camera Trap setting (1080 Pixels versus 3.1 Mega Pixels) .......................................... 46


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List of Tables

Table 1. Regions for which camera trap data were available with the number of sites

sampled, my (JR) contribution to surveys and sites at which black-footed tree-rats

(BFTR) were present. Only regions 1-8 were used in final analyses. ..................... 16

Table 2. Detection probability (p) and occupancy (ψ) estimates with standard error (SE) for

the black-footed tree-rat, listed by region in descending order of occurrence. The

number of study sites per region is represented by n. .......................................... 25


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List of Figures

Figure 1. Distribution of the black-footed tree-rat represented by, pre 2000 (red dots) and

from 2000 onwards (green dots), atlas records overlayed on the study regions. . 14

Figure 2. Eight study regions ................................................................................................. 16

Figure 3. Camera trap placement of the five camera array deployed at each site. .............. 17

Figure 4. Set up of an individual camera trap and bait station with cleared understory

vegetation Fish River Station 2015 ......................................................................... 18

Figure 5. Camera trap images from the six regions in which they were detected. ............... 20

Figure 6. Estimates of detection probability (p) per number of camera traps (1-5) deployed

per site. Dots represent mean estimates and bars represent the 95% confidence

interval. ................................................................................................................... 22

Figure 7. Estimates of occupancy (ψ) per number of camera traps (1-5) deployed per site.

Dots represent mean estimates and bars represent the 95% confidence interval.

................................................................................................................................ 23

Figure 8. The probability of detecting a BFTR at least once at an occupied site when using 1-

5 cameras with increasing numbers of cameras from top to bottom (numbers of

cameras shown in grey boxes) over four weeks (28 nights). The shaded area

represents the 95% confidence interval around the detection probability estimate.

The red lines indicate the time at which a reliable detection probability estimate is

reached (lower limit of 95% confidence interval = 0.85). Numerical values are

presented in Appendix 1. ....................................................................................... 24

Figure 9. Camera score v1 template showing scoring zones (0-4) used to score Fish River

data by JR.. .............................................................................................................. 44

Figure 10. Camera trap images showing the difference between a camera trap set at 2.5 m

and 1.5 m from the bait station ............................................................................. 47

Figure 11. Camera trap images showing the difference between flash types used by

different camera trap models. ............................................................................... 48

Figure 12. Camera trap images showing the difference between the two resolution settings

available by the same camera trap model. ............................................................ 49


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Abstract

Context. Imperfect detection, where a species remains undetected in surveys even though

it is present, occurs in all wildlife surveys. The probability of detecting a species with different

sampling designs can be assessed using occupancy modelling, then improved through

employing the most appropriate survey effort and methods.

Aims. My research applies analytical methods to determine optimal survey effort, by

varying the number of camera traps per site and deployment time, for the detection of the

threatened black-footed tree-rat, Mesembriomys gouldii.

Methods. My analysis was applied to camera trap data collected by myself and others for

a range of research objectives from 224 sites across eight regions in the Top End, Northern

Territory, between 2013 and 2016. All data were collected using a five camera array and over

a minimum six week period. Single-season occupancy models were applied to daily detection

data over 42 days to assess the effect of altering the number of camera traps set per site on

detection probability. Cumulative detection curves with 95% confidence intervals were

calculated to determine the optimal length of deployment for each scenario of deploying one

to five camera traps per site.

Key Results. The detection probability for black-footed tree-rats was low (p = 0.15) for the

one camera scenario, but increased with the number of cameras set per site, as did the

precision around the detection probability estimate. The optimal length of deployment with a

predefined precision (p* > 0.85) dropped from 21 days to 11 days with the addition of a

second camera trap. The optimal survey design, that should reliably determine the presence

of black-footed tree-rats if they are present, was identified as two camera traps per site for

two weeks.

Conclusions. The optimal survey effort required to reliably detect a target species can be

derived from pre-existing data. Additional variation due to environmental, ecological and

detection method, in this case using camera traps, will also influence detection probability

and needs to be considered.

Implications. The outcomes of this research will be incorporated into impact assessment

guidelines to improve detectability and provide definitive and achievable methods for

detecting the black-footed tree-rat prior to development activities. The guidelines will serve

to standardise survey design and methods, providing a greater knowledge base for natural

resource management and species conservation. The analytical methods outlined can also be

applied to determine optimal survey design for other species using pre-existing data from

past surveys.


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Introduction

Wildlife surveys make a key contribution to biodiversity conservation worldwide by

providing valuable data for biodiversity inventory (Smith et al. 2016), habitat modelling

(Tyre et al. 2003; Gu and Swihart 2004; Stephens and Anderson 2014), ecological research

(Hamel et al. 2012) and environmental monitoring and management studies (Pellet and

Schmidt 2005; Woinarski et al. 2010; Durso et al. 2011; Dostine et al. 2013; Ziembicki et al.

2015). The value of these data relies on surveys being conducted with clear objectives

(MacKenzie and Royle 2005) and appropriate sampling design and methods. The most

effective survey methods to detect wildlife will vary depending on the target species.

Evaluating and optimising sampling design and survey effort is particularly important when

target species are rare or elusive and competing priorities for funding and resources exist

(Durso et al. 2011; Shannon et al. 2014; Gálvez et al. 2016). Rare and elusive animals are

often classified as threatened and so are afforded protection through state and national

legislation (TPWCA 2000; EPBCA 1999). Significant resources are invested from both

public and private sectors to improve the information regarding the occurrence of

threatened species prior to either conservation or development activities. It is important

to be able to accurately determine the presence of threatened species in an area to

ensure appropriate environmental management is applied. This is especially important

when natural resources, such as native vegetation, experience significant impact by

agricultural and mining development. The consequence of non-detection of threatened

species in an area may lead to inappropriate environmental management decisions which

result in local extinction events.

Australia has suffered from the highest rate of modern mammal extinctions in the world,

with 30 mammal species becoming extinct in the past 200 years (Woinarski et al. 2010;

Woinarski et al. 2011). Multiple threats have been identified with little known about how

they operate together or vary regionally (Ziembicki et al. 2015). As a result of these threats

traditional inventory methods such as live trapping, return too few captures to effectively

assess or monitor the presence of these species. Camera trapping has been widely

adopted as an alternative method for surveying threatened species because it enables

data collection over long periods, with minimum disturbance to the target species and can

optimise species detection.


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Camera traps

Camera trapping has fast become the most widely used, cost effective, low impact means

to reliably detect terrestrial mammal species because it can provide systematic and

accurate data over prolonged survey periods without the requirement of live trapping

(Gálvez et al. 2016). The advantages of camera trapping over conventional methods for

species inventory, ecological and monitoring studies are well recognised (Meek et al.

2014; Smith et al. 2016). One very important advantage is that camera traps allow efficient

survey of remote locations with minimal disturbance (Rovero et al. 2013; Smith et al.

2016). Camera traps can continuously operate for weeks at a time and, following the initial

effort of site preparation, require no subsequent attention. For longer deployments

potential disturbance to animals and the need to access study areas is limited to

occasional changing of batteries and SD cards, and minor clearing where vegetation has

grown. However, camera trap data can be compromised by poor sampling design and or

misdirected survey effort. Conversely appropriate guidelines aimed to standardise

methods and optimise the detection of target species can serve to enhance the value of

data and their application to management outcomes (Meek et al. 2014; Pease et al. 2016).

Occupancy analyses

The presence/absence data, hereafter detection/non-detection data, acquired through

camera trapping is particularly suited to estimating and modelling occupancy. Occupancy

is defined as the proportion of sampling units where a target species is present

(MacKenzie et al. 2002), and being able to determine occupancy with reasonable accuracy

is integral to monitoring studies and environmental management. Occupancy analysis or

occupancy modelling are terms used to describe the analysis of detection/non-detection

data to investigate occupancy in relation to ecological and environmental variation. Two

critical considerations of occupancy analyses are the spatial variation in a species’

distribution and the detectability of the species (MacKenzie et al. 2006); the latter is the

primary focus of my research. The concept of imperfect detection is key to occupancy

modelling, and recognises that even when a species occupies an area it may not be

detected during a particular survey (MacKenzie et al. 2002; Nichols et al. 2008; Shannon et

al. 2014). Imperfect detection results in ‘false absences’ which can lead to

underestimation of occupancy (Wintle et al. 2005; Guillera-Arroita et al. 2010). Therefore

to increase the accuracy of occupancy estimates it is important to incorporate the

probability of detecting a species (Royle 2006). Furthermore, if probability of detection


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differs between sites, failure to account for this heterogeneity can bias estimates of

species occurrence and cause misinterpretation of the factors and their influence on

patterns of occurrence (Tyre et al. 2003; MacKenzie et al. 2006; Stephens and Anderson

2014; Pease et al. 2016).

Detection probabilities are also likely to change over time, by region, for different species,

and may also vary according to the detection method used. For example, Dostine et al.

(2013) showed that detection of multiple frog species in tropical northern Australia varied

over time with increased detection when water temperature was lower during rainfall

events. Shannon et al. (2014) conducted multispecies assessment of mammals in

Colorado, USA, with camera trap data. They truncated the daily detection history for black

bears by inserting ‘missing data’ for the time when bears began hibernation during the

survey period, to avoid skewing their detection probability estimates.

Regional variation in detection may exist between locations depending on vegetation

structure or ecological adaptations of broad ranging species (O’Connell Jr. et al. 2006;

Pease et al. 2016). Durso et al. (2011) identified interspecific variability in detection

probabilities of North American aquatic snakes and provide many more examples of

interspecific differences in the probability of detection for a range of taxa from around the

world. Detection probabilities have also been shown to vary greatly among different

methods of detection (Nichols et al. 2008; Durso et al. 2011). To account for so many

sources of variability in detection probability MacKenzie et al. (2006) and Durso et al.

(2011) emphasize the importance of estimating detection probabilities at every

opportunity.

Occupancy modelling explicitly accounts for imperfect detection by using data from repeat

surveys at sites calculate detection probability (MacKenzie et al. 2004; MacKenzie and

Royle 2005). Detection probability may be expressed either as constant (p) or cumulative

(p*) over multiple surveys at each site. Cumulative detection probability incorporates

knowledge from previous surveys in repeat sampling so that the cumulative detection

probability value increases exponentially with each survey. Acceptable probability values

vary depending on the type of detection probability and analyses being applied. By current

convention, confidence in results is achieved where p > 0.2 for constant detection

(MacKenzie et al. 2006) or p > 0.3 when allowing for constant detection with additional

covariate information and missing observations (MacKenzie et al. 2002). When considering

cumulative detection a p* > 0.85 is desirable (MacKenzie and Royle 2005; Tyre et al. 2003).


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Occupancy metrics and modelling are valuable to ecological and management studies

because they account for imperfect detection (MacKenzie et al. 2006; Nichols et al. 2008).

However, the increase in use of occupancy modelling for a wide range of ecological and

management studies has highlighted the importance of guidance towards improved

sampling design to optimise detection probability (Hamel et al. 2012; Guillera-Arroita et al.

2014; Shannon et al. 2014; Gálvez et al. 2016; Pease et al. 2016; Smith 2016).

Existing data are valuable for informing future sampling design and survey effort because

they can provide preliminary estimates of detection probability and occupancy and can be

used as the basis for simulations that optimise accuracy and precision of these estimates

for future sampling designs (Hamel et al. 2012; Shannon et al. 2014; Geyle 2015).

However, to be useful for determining detection probability and occupancy estimates that

can be generalised over an entire study area, the pre-existing data must meet several

principles and assumptions. These principles and assumptions aim to reduce variability in

the data set and are: (i) sites must be randomly selected if representing a subset of the

study area, (ii) sites are closed with respect to changes in occupancy within the sample

period, (iii) model parameters are constant across sites, and (iv) species detections within

and between sites are independent. The consequences of relaxing these principles and the

subsequent resolutions required are provided and discussed in MacKenzie et al. (2004;

2005).

Survey effort and sampling design

The fundamental reasons for considering occupancy modelling to investigate occurrence

of rare and elusive animals derived from wildlife surveys, are cost and efficiency

(Mackenzie et al. 2004). To optimise the value of wildlife surveys for monitoring and

environmental management, it is paramount to consider the trade-offs between expense,

survey effort and the accuracy and precision of results. One of the most important trade-

off in relation to survey effort is how many sites to survey versus the level of effort per site

(Shannon et al. 2014; Pease et al. 2016; Smith et al. 2016). Following this, decisions around

what sampling methods to use, how many devices per site and how long to survey are

necessary. MacKenzie and Royle (2005) used a theoretical approach to determine the

survey effort required to either optimise the precision of occupancy for a given level of

survey effort, or to achieve a desired level of precision with minimal effort. Assuming

detection probability and occupancy were constant, they found that for rare species, in

general, it may be more efficient to survey more sites less intensively, while for common


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species fewer sites should be surveyed with more repeat surveys. Shannon et al. (2014)

used a simulation approach based on camera trap data from Colorado, USA, and

considered both occupancy and detection probability to explore trade-offs in sampling

design and survey effort. Shannon et al. (2014) found that increasing the total survey

effort generally decreases the error associated with occupancy estimates, as would be

expected. However, changing the number of sites or camera trap deployment length had

very different outcomes depending on whether a species was common or rare (its

occupancy) and how easy or hard to detect it was (its detection probability). Shannon et

al. (2014) provided recommended survey designs for ten mammal species and three

virtual species with varying occupancy and detection probability estimates, based on

simulations with three levels of acceptable error. They demonstrated that for rare species

with low detection probability an unfeasible number of sites and length of deployment

would be required to achieve sufficient levels of overall detectability. However, for

common species with low detection probability, increasing the length of deployment was

most efficient.

Smith et al. (2016) assessed survey effort for a finite set of resources, in this case cameras,

for biodiversity inventory surveys. They used data they collected from north-western

Australia to evaluate the number of sites and camera traps per site required to optimise

multiple species detection. They found that increasing the number of sites increased

species richness estimates in a shorter time than sampling fewer sites, which required

longer deployments. Increasing the number of camera traps per site also increased species

richness estimates, but not to the same extent, indicating that with a limited number of

camera traps, increasing the number of sites rather than camera traps per site was

favourable. They also found that fewer sites or fewer camera traps per site required more

sample occasions (trap nights) to achieve similar estimates. This study showed that most

detections of mammals occurred within the first seven days with limited value in leaving

camera traps deployed beyond two weeks (Smith et al. 2016). The value of optimising

sampling design to wildlife surveys lies in formulating a clear research objective,

identifying target species and detection methods, selecting sampling units and appropriate

survey effort within the available budget and acceptable error rate (MacKenzie et al. 2005;

Shannon et al. 2014).


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My research

At present there are no guidance statements available that standardise and optimise the

ability to detect the presence of individual threatened fauna species in the Northern

Territory. My research had two main aims: The first was to outline analytical methods for

determining optimal survey effort to reliably detect a particular target species, when pre-

existing data are available. My second aim was to contribute to the information required

to prepare a guidance statement for the target species, the threatened black-footed tree-

rat, Mesembriomys gouldii.

Specifically, my objective was to determine how to optimise detection through survey

effort, with the number of camera traps per site and the length of time camera traps should

be deployed as the key variables considered. I also discuss some considerations of sampling

design and survey methods to improve detection in a study area in the context of the

dataset used for analyses.

The black-footed tree-rat is classified as Vulnerable in the Northern Territory by the

Territory Parks and Wildlife Conservation Act (TPWCA) 2000 and nationally as Endangered

by the Environment Protection and Biodiversity Conservation Act (EPBCA) 1999. These

classifications represent International Union for Conservation of Nature (IUCN) categories

and the Northern Territory classification is based upon the IUCN criterion which recognises

“a population reduction of greater than 30 per cent over the last ten years where the cause

of reduction may not have ceased” (Hill 2012). Potential sources for the decline of the

black-footed tree-rat include habitat modification through increased fire frequency and

fragmentation (Hill 2012).

Black-footed tree-rats occupy open forest and woodland of Australian tropical savannas

where they den in tree hollows or Pandanus during the day and forage on the ground and

in trees at night (Pittman 2003; Rankmore 2006). Three disjunct subspecies are recognised

across northern Australia; M. gouldii gouldii on the northwest Kimberley and Northern

Territory mainland, M. gouldii melvillensis on Melville Island and M. gouldii rattoides on

Cape York Peninsula. While black-footed tree-rats are considered uncommon to rare in the

Kimberley and Queensland they are still common but patchily distributed across the Top

End of the Northern Territory (Figure 1). As a result of the decline of this species across its

range coupled with increasing habitat modification through small and large scale clearing, it

is imperative that the presence of the black-footed tree-rat can be accurately determined


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prior to the approval of development projects that could impact the native vegetation in

tropical woodlands.

Figure 1. Distribution of the black-footed tree-rat represented by, pre 2000 (red dots) and from 2000 onwards,

(green dots) atlas records overlayed on the study regions.

In the Northern Territory the impact of development on the environment is assessed by

the Environmental Protection Authority (EPA) through the environmental impact

assessment process (EIA). Consideration of impact on threatened species and the

environmental management required to mitigate these impacts is required by national

and local government legislation (EPA 2006; DEWHA 2010; EPA 2017). Guidance

statements generally provide advice and guidelines to proponents, consultants and the

general public regarding information requirements for an environmental impact

assessment. This context provides the scope for my second aim, to contribute to the

information required to prepare the guidance statement for the detection and ultimate

protection of the black-footed tree-rat in the Northern Territory.

I predicted that by using pre-existing camera trap data and a precision around detection

probability of p* > 0.85, I would be able to identify the number of camera traps per site

and the length of deployment required to reliably detect the black-footed tree-rat, my

test case, in tropical savanna woodland of the Top End.


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Methods

Study area and data acquisition

The camera trap data used in this study were collected in the central north mainland of

the Top End in the Northern Territory (NT). This area represents the western and southern

range of the black-footed tree-rat in the NT, excluding two known isolated populations, on

Melville Island and in north east Arnhem Land (NT Department of Environment and

Natural Resources Fauna Atlas). The study area experiences a tropical monsoonal climate

with highly seasonal rainfall. Rainfall is almost exclusively limited to the humid wet-season

months between November and April (Friend and Taylor 1985; Price et al. 2005; Firth et al.

2006; Murphy et al. 2014; Davies et al. 2016). The study area experiences an annual

rainfall gradient from c. 1700 mm in the north-west (Greater Darwin Region) to c. 600 mm

in the south-east (Nitmiluk National Park) (Bureau of Meteorology 2017). Temperatures

are high throughout the year with mean monthly minima and maxima during the survey

period ranging from 25.4 to 33.3 oC in November in the north and 13.2 to 30.9 oC in July in

the south (Bureau of Meteorology 2017). The most widespread vegetation type in

northern Australia landscapes is savanna; eucalypt dominated (Eucalyptus and Corymbia

spp.) forests and woodlands with a grassy understorey. Within the study area savanna

vegetation varies subtly across the rainfall gradient from north to south with a reduction

of tree height, canopy cover, structural complexity and shift in species composition with

decreasing rainfall (Woinarski et al. 2000; Ziembicki et al. 2015). Eucalyptus tetrodonta

and E. miniata (Darwin Stringybark and Darwin Woolybutt) are the most common trees in

savanna forests and woodlands, however, large areas comprising mixed eucalypt

woodlands and sandstone specific woodlands also occur in the Top End (Dunlop and Webb

1991; Woinarski et al. 2007).

Camera trap data available for this study had been collected for a range of research

objectives between 2013 and 2016, across 11 regions (Table 1). These data were collected

and managed by staff of the Flora and Fauna Division including myself. I was responsible

for all fieldwork and data management for the Fish River Project which included field

deployment and retrieval of camera traps; live trapping; site, fauna and habitat metadata

collection and management; downloading of images and species identification. I was also

lead scientist for managing field sites on a number of other surveys as noted in Table 1.


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Table 1. Regions for which camera trap data were available with the number of sites sampled, my (JR)

contribution to surveys and sites at which black-footed tree-rats (BFTR) were present. Only regions 1-

8 were used in final analyses.

Region Location Total # Sites

Number of sites surveyed by JR

Sites with BFTR

1 Garig Gunak Barlu (Cobourg) National Park (2014) 25 - 22

2 Djelk IPA (2013 - 2015) 30 - 0

3 Warddeken IPA (2013 - 2015) 46 - 0

4 Greater Darwin Region (2014) 31 8 12

5 Kakadu National Park (2014) 19 - 5

6 Nitmiluk National Park - Fire Plots (2015) 34 4 1

7 Litchfield National Park - Fire Plots (2016) 20 2 1

8 Fish River Station (2014) 19 19 7

9 Judbarra (Gregory) National Park - Bush Blitz 15 - 0

10 Wardaman IPA 9 - 0

11 Groote Eylandt 20 - 0

Data from three regions were excluded from analyses because they were outside the

current known range of the black-footed tree-rat and no black-footed tree-rats were

detected on images (Table 1). The data analysed in this study comprise detection/non-

detection data of the black-footed tree-rat derived from images from camera traps set at

224 sites across the eight remaining regions (Figure 2). All sites are broadly classified as

woodland and open forest (Wilson et al. 1990; Brocklehurst et al. 2007; DENR 2016).

Figure 2. Eight study regions; 1 = Garig Gunak Barlu National Park, 2 =Djelk Indigenous Protected Area,

3 = Warddeken Indigenous Protected Area, 4 = Greater Darwin Region, 5 = Kakadu National Park,

6 = Nitmiluk National Park, 7 = Litchfield National Park and 8 = Fish River Station.


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Camera trap survey design

Five cameras were deployed per site in a diamond configuration with one camera placed

in the centre. The four remaining cameras were placed with a minimum and maximum

spacing of 30 and 100 m and a maximum distance of 200 m between the outer camera

traps (Figure 3). The variation in location and distance between cameras is due to camera

traps being attached to trees, which do not grow uniformly in the environment. However

the placement of camera traps is standardised to maintain a sample footprint of

approximately 0.5 ha. The broad aim of the surveys was to maximise detection of small

native mammals and larger native and feral mammals. Three models of Reconyx infrared

motion triggered camera traps were used (HC600 - infra-red flash, HC550 and PC850 -

white light flash, http://www.reconyx.com/) Different camera models were spread

amongst sites such that one infra-red flash camera was used per site with four white light

flash cameras to ensure that trapping effort would be equal between sites with respect to

any differences in detection efficiency among camera types. All cameras were set to

record three still images per trigger, one second apart with no delay between subsequent

triggers and sensitivity was set to high. Deployment periods varied in length but were at

minimum six weeks long across all data sets. Camera traps were active during January to

September inclusive.

Figure 3. Camera trap placement of the five camera array deployed at each site.

Min distance 30 m

Distance 30-100 m (ideal 50 m)

Max distance between outside camera traps 200 m

http://www.reconyx.com/


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Cameras were attached to trees greater than 20 cm diameter at breast height (DBH) at a

height of 70 cm to the top of the camera trap housing. In areas with no suitable trees,

cameras were mounted to star pickets. Cameras were set south facing to enhance the

temperature differential required to detect animal movements by the passive infrared

sensor, especially during dawn and dusk. Camera orientation was angled downward from

horizontal so the bait station was central to the field of view. All cameras used with bait

stations comprising 50 mm polyvinyl chloride (PVC) pipe with removable vent caps at

either end and mounted by cable ties on a post at a height of 30 cm. Bait comprising oats,

peanut butter and honey was placed in each bait station. At each site, bait stations were

placed 2.5 m from three of the cameras and at 1.5 m from the other two cameras. The

vegetation surrounding the camera and bait station was cleared to a perimeter distance of

1 m and 1.5 m respectively (Figure 4).

Figure 4. Set up of an individual camera trap and bait station with cleared understory vegetation Fish River

Station 2015

1 m 1.5 m


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Mammal Data

As part of the original data collection images from camera traps were downloaded and

viewed using a variety of image processing programs. The presence of all vertebrate fauna

species was identified independently by two observers and recorded in Microsoft Excel or

Microsoft Access for each project area. Upon sourcing this data for 11 regions and

confirming the absence of black-footed tree-rat records in the three data sets that were

subsequently excluded, data for black-footed tree-rat detections and absences were

collated for the remaining eight regions. Figure 5 shows example images of black-footed

tree-rats from each of the six regions where they were detected. Detection periods (day to

night), site names and camera trap number (1-5) were standardised across the regions,

where differences had occurred between projects. Camera trap failures were identified

and recorded and all information was consolidated into a single excel spreadsheet. Some

detection data were recorded by date (midnight to midnight) rather than the active,

overnight, period for the black-footed tree-rat (midday to midday). These data were

converted to ensure detection events from the same night were not split over two dates.

Individual detections were converted into daily detection histories over a consecutive six

week survey period (42 nights) for each camera trap. When camera trap failures occurred

these affected either part of the deployment period (e.g. camera trap moved by animal,

SD card filled with false triggers) or the entire deployment period (e.g. camera trap left off

at set up). Occupancy modelling can account for missing data so all data from all camera

traps at all sites, including where cameras had failed, were used in analyses to account for

these eventualities in future sampling. The resultant detection histories for each camera

trap (1120 camera traps) at every site (224 sites) were represented by 1 when black-

footed tree-rats were present for the trap night, 0 when black-footed tree-rats were

absent for the trap night and NA where there was no data due to camera trap failure or no

survey undertaken that day.


20

Figure 5. Camera trap images from the six regions in which they were detected. No detections were recorded for two regions

Analyses

To assess the effect of the number of camera traps per site on the detectability of black-

footed tree-rats, single-season occupancy models were run using the package “unmarked”

(Fiske and Chandler 2011 and 2012) in R version 3.1.0 ( R Core Team 2014) using code

developed by Danielle Stokeld (Appendix 1). Analyses used the entire dataset, with

detection histories from all camera traps from each site within the eight regions,

Garig Gunak Barlu National Park Greater Darwin Region

Kakadu National Park Nitmiluk National Park

Litchfield National Park Fish River Station


21

irrespective of whether black-footed tree-rats were detected at an individual site. The

estimate of occupancy and overall probability of detecting a black-footed tree-rat from at

least one of the five camera traps set at a site, during a single sampling occasion, in this

case a night, was determined for the entire dataset. Detection probability estimates were

then calculated for different numbers of camera traps per site by subsequently dropping

one camera trap randomly from the five camera array down to a single camera trap (5

cams, 4 cams, 3 cams, 2 cams, 1 cam) using 100 bootstrap iterations. The output file

contained estimates of occupancy and detection probability for each scenario with

standard errors and upper and lower 95% confidence limits (Appendix 1). The mean

estimated detection probability and upper and lower confidence limits for each scenario

were then calculated.

To investigate the effect of the length of deployment on the probability of detection,

cumulative detection probabilities were calculated using the mean estimated detection

probability (p) and the number of survey nights (K) using the equation 1 - (1 - p)K

(MacKenzie and Royle 2005). Cumulative detection represents the probability of detecting

the target species on at least one of K nights if the site is known to be occupied.

Cumulative detection curves with 95% confidence intervals were then graphed in R for

each scenario of using one to five camera traps per site using code developed by Danielle

Stokeld (Appendix 1). The number of nights required to obtain reliable probability of

overall detection (p* = 0.85), reflecting an acceptable false negative rate of 0.15 (Tyre et

al. 2003), was determined for each scenario of one, two, three, four and five camera traps

deployed per site. The detection curves were truncated from the six week deployment

length, for which the data were available to four weeks when it became evident that the

reliable detection estimates were reached well within this time frame.

As regions had been pooled for previous analyses, a final analysis was conducted to

determine whether there were regional variations in detection probability and occupancy

estimates. Data from the five camera traps at a site were collapsed to derive a single

detection history per site to maintain the independence assumption. Then a single-season

model, using region as a covariate, was run in Presence version 12.1 (Hines 2006; US

Geological Survey 2017) to obtain regional detection probability and occupancy estimates.


22

Results

Black-footed tree-rats were detected at 48 of the 224 sites, with more than 974 detections

recorded from 163 camera traps over 47 040 trap-nights (naïve occupancy = 0.21). Some

camera trap failures occurred at set up (32 of 1120 camera traps) and a further 271

camera traps were moved or damaged during the six week deployment through

disturbance by animals or fire.

The probability of detecting a black-footed tree-rat at any site increased with the number

of camera traps deployed at a site (Figure 6). The accuracy of the estimate relative to the

true value, and precision, demonstrating the confidence in the detection probability

estimate, also increased. The precision around the probability of detecting a black-footed

tree-rat at any site is represented by a 95% confidence interval. Deploying five camera

traps per site (p5 = 0.31, range: 0.29-0.33) doubled the probability of detection compared

to deploying a single camera trap per site (p1 = 0.15, range: 0.09-0.22). Deploying more

than two cameras per site provided a reliable detection probability (p > 0.2) based on

analysis with no covariate information (MacKenzie et al. 2006). However if consideration is

given to the lower confidence limit, three camera traps per site provides a more reliable

estimate (p3 = 0.25, range: 0.20-0.30). The precision around detection estimates also

increased dramatically with the inclusion of the fifth camera trap.

Figure 6. Estimates of detection probability (p) per number of camera traps (1-5) deployed per site. Dots represent mean estimates and bars represent the 95% confidence interval.

Det

ecti

on

pro

bab

ility

est

imat

es (

95

% c

on

fid

ence

inte

rval

)

Number of camera traps per site


23

Estimates of occupancy and their accuracy also increased with the number of camera traps

per site. Using one and two camera traps per site resulted in low occupancy estimates (ψ1

= 0.15, range: 0.09-0.24 and ψ2 = 0.19, range: 0.12-0.28) which increased as more cameras

were added to the model (ψ3 = 0.21, range: 0.13-0.29; ψ4 = 0.22, range: 0.15-0.30 and ψ5 =

0.24, range: 0.18-0.30) (Figure 7). The precision around the occupancy estimates also

increased with increasing number of cameras per site, although not as dramatically as with

the detection probability estimates (Figures 6 and 7).

Figure 7. Estimates of occupancy (ψ) per number of camera traps (1-5) deployed per site. Dots represent

mean estimates and bars represent the 95% confidence interval.

The precision of cumulative detection probability estimates increased considerably with

each additional camera trap deployed per site (Figure 8). The time required to reliably

detect (p* ≥ 0.85) the presence of black-footed tree-rats at occupied sites was reduced

with each additional camera trap deployed per site (red line in Figure 8). The modelling

showed that deploying a single camera trap for 21 nights (3 weeks) would result in an 85%

chance of detecting a black-footed tree-rat at least once. If using a second camera trap,

the deployment time required to achieve the same precision is nearly halved at 11 nights.

If four and five camera traps are used, the time of deployment required to maintain

precision is further reduced to one week and 6 days respectively.

Occ

up

ancy

est

imat

es (

95

% c

on

fid

en

ce in

terv

al)

Number of camera traps per site


24

Figure 8. The probability of detecting a BFTR at least once at an occupied site when using 1-5 cameras with

increasing numbers of cameras from top to bottom (numbers of cameras shown in grey boxes)

over four weeks (28 nights). The shaded area represents the 95% confidence interval around the

detection probability estimate. The red lines indicate the time at which a reliable detection

probability estimate is reached (lower limit of 95% confidence interval = 0.85). Numerical values

are presented in Appendix 1.

Based on the analyses conducted, reliable detection (p* ≥ 0.85) of black-footed tree-rats, if

they are present in the landscape, can be achieved by using two camera traps for 11 nights

or three cameras over 9 nights (Figure 8), provided future sampling is undertaken in

similar conditions to those under which pre-existing data used in these analyses were

collected.

Number of trap nights

Pro

bab

ility

of

det

ecti

ng

a B

lack

-fo

ote

d t

ree

-rat


25

The core analyses for this project used data pooled across eight regions, however data

were also analysed separately for each region using a single detection history per site

based on five cameras per site over 42 nights. There was notable variation in both

occupancy and detectability of the black-footed tree-rat across regions (Table 2). The

highest occupancy was recorded at Garig Gunak Barlu National Park (ψ = 0.88 ± 0.06) with

moderate detectability (p = 0.37 ± 0.02). Litchfield National Park exhibited the lowest

occupancy (ψ = 0.03 ± 0.03) of all regions that detected black-footed tree-rats, but with a

relatively high probability of detection (p = 0.29 ± 0.07). Kakadu National Park showed

moderately low occupancy (ψ = 0.26 ± 0.10) with the highest rate of detection (p = 0.43 ±

0.03).

Table 2. Detection probability (p) and occupancy (ψ) estimates with standard error (SE) for the black-footed

tree-rat, listed by region in descending order of occurrence. The number of study sites per region is represented

by n.

Regions sampled n p SE ψ SE

Garig Gunak Barlu NP 25 0.37 ± 0.02 0.88 ± 0.06

Greater Darwin Region 31 0.13 ± 0.02 0.39 ± 0.09

Fish River Station 19 0.26 ± 0.03 0.37 ± 0.11

Kakadu NP 19 0.43 ± 0.03 0.26 ± 0.10

Nitmiluk NP 34 0.07 ± 0.04 0.05 ± 0.05

Litchfield NP 20 0.29 ± 0.07 0.03 ± 0.03

Djelk IPA* 30 0.50 ± 2.50 0.00 ± 0.00

Warddeken IPA* 46 0.50 ± 2.50 0.00 ± 0.00

*These regions had no detections of black-footed tree-rats


26

Discussion

My analyses demonstrate the effects of modifying survey effort on the ability to obtain

reliable detection of a single target species. I showed that the sampling design of a target

species can be effectively informed by modelling detection probabilities and occupancy

that result from different survey efforts. The data used for modelling were collected at

survey sites within the known distribution range of the black-footed tree-rat and provided

the information from which detection of this threatened species could be optimised in the

future. Multi-method occupancy modelling, incorporating imperfect detection, provided

the framework to investigate variation in the detection probability for differing numbers

of camera traps per site and the length of deployment. Deploying a second camera trap

per site reduced the required survey time to 11 days, a reduction of 10 days from a single

camera trap deployment. Increasing the number of camera traps per site beyond two only

reduced the deployment time by two days for each additional camera, which is not

enough to offset the increase in deployment effort and camera traps required. Based on

these results, I recommend that surveys targeting black-footed tree-rats should use two

camera traps per site for a minimum of two weeks, a survey effort that should reliably

detect the presence of this species.

The five-camera array described by Gillespie et al. (2015) is useful for monitoring and

evaluating the diversity of mammals present within an area, as it optimises detection of

multiple species. For this reason it has become standard for many wildlife surveys in the

Top End of the Northern Territory. The detection probability and occupancy estimates for

the research presented here were derived from wildlife surveys that used the five-camera

array. However the resources required to deploy five camera traps per survey site are

considerable, and the results of my analyses suggest that this level of survey effort is not

necessary if the purpose is solely to determine the presence or absence of the black-

footed tree-rat. The use of more than one camera trap per site, resulted in increases in

detection probability, which is consistent with other recent work that has demonstrated

that multiple cameras increase detection of single and multiple species (Stokeld et al.

2015; Pease 2016). The use of multiple camera traps per site also provides a contingency

for camera failure. The failure of one camera due to animal interference, environmental

damage or operator error will not result in complete loss of data for a site when multiple

cameras are used (Stokeld et al. 2015). For black-footed tree-rats, reliable overall

detection probability estimates (p > 0.20) were also higher for three camera traps per site


27

(0.25, range 0.20-0.30) than two camera traps per site (0.21, range 0.16-0.26), particularly

considering the lower confidence limit. However, the cumulative detection probability

curves, which also account for length of deployment show that deploying two cameras per

site rather than three should still enable reliable occupancy estimates.

Other mammals of northern Australia that are less conspicuous, more elusive, or wider

ranging than the black-footed tree-rat may require a greater effort in the number of

cameras or length of deployed per site. For example Stokeld et al. (2016) showed that five

to six camera traps per site coupled with deployed for a minimum of eight weeks were

required to adequately detect the presence of highly mobile feral cats, Felis catus. Pease

et al. (2016) found that detection probabilities of individual species varied as the number

of camera traps increased per site, which is not surprising given the variability in how

individual species utilise and move through the environment. Habitat use varies for

different species both seasonally and spatially resulting in variation in detection through

time and space (Shannon et al. 2014; Stokeld et al. 2015; Pease et al. 2016). Given the

variation in detectability between species, there is a need to investigate and refine survey

effort on a case-by-case basis, using similar analyses to those presented here for the black-

footed tree-rat, to optimise detection when single species are being targeted.

My analyses were limited to investigating the variation around the overall and cumulative

detection probability for survey effort. However detectability of target species can also be

affected by timing of surveys, regional variation, environmental and habitat factors, inter-

specific responses to camera trap set up and different camera models. For example, black-

footed tree-rat detection probabilities and occupancy estimates were shown to differ

across regions (Table 2). While occupancy modelling can incorporate these sources of

variability in multivariate analyses, this was outside the scope of the current study.

However, the implications of regional variation in detection probability and other sources

of variation that existed in the dataset used for my analyses, and that are likely to affect

detection probabilities of black-footed tree-rats, are discussed below.

The variation in dominant habitat type sampled per region and structural variation across

the rainfall, productivity gradient are the most likely factors that could account for the

observed regional variation in both occupancy and detection probability estimates (Table

2). An attempt to classify habitat at the survey sites in a way that could be incorporated

into analyses was made using two vegetation datasets. These datasets were broad enough

to encompass all sites, but resulted in the classification of all sites as various forms of


28

woodland and open forest (Wilson et al. 1990; Brocklehurst et al. 2007 in NR Maps 2016).

This broad classification indicates that all sites occur in suitable habitat for black-footed

tree-rats. However, some sites occurred in small-scale habitat types that occur patchily

within the savanna matrix, such as monsoon vine thicket, riparian, sandstone heath,

wetlands or open grasslands (Woinarski et al. 2000). With the exception of the riparian

areas it is unlikely that these habitats contain the target species. Previous research has

shown that the presence of species specific den trees and the patchy distribution of shrub

species that provide food resources are important determinants of habitat selection by

black-footed tree-rats (Friend and Taylor 1985; Friend 1987; Morton 1992; Rankmore

2006; Firth et al. 2006). Fine scale vegetation data were collected at survey sites, but

accessing and analysing these data was beyond the scope of my research. These data,

however, would enable the survey sites and regions to be classified according to the

predominant vegetation type in which camera traps were placed. For example, the low

detection and occupancy estimates of Nitmiluk National Park are likely to be a result of the

high proportion of sites sampled in sandstone heath or sandstone woodlands that lack the

mid-storey shrubs identified as important to black-footed tree-rats. Removing sites

located in unsuitable habitat from analyses, may enable more accurate estimates of

detectability. The implication of considering a subset of a larger study area is that

estimates and inferences regarding occupancy and detection will only be valid for the

subset of the landscape sampled (MacKenzie et al. 2006).

The timing of surveys can also affect the likelihood of detecting a species and has

implications for modelling the data. The timing of surveys to maximise detection should be

informed by the activity cycles of the target species, including breeding, aestivation,

hibernation or activity based on resource availability (Dostine et al. 2013; Shannon et al.

2014; Geyle 2015). The data used in the current study were obtained from camera trap

detections of black-footed tree-rats between January and September. However Friend

(1987), observed peak activity and trapability when live trapping black-footed tree-rats in

May to June and in October to November, due to the influx of young animals at these

times. More recently Geyle (2015) also identified bias in detectability due to seasonal

activity patterns of the closely related brush-tailed rabbit-rat, Conilurus penicillatus, on the

Tiwi Islands in northern Australia, and stressed the importance of timing surveys to

optimise detectability of this species. Further investigation of the current dataset may

provide direction to the optimal timing for camera trap surveys to improve detectability of

black-footed tree-rats.


29

Finally, detection of target species may be further optimised by considering the camera

trap set-up. Variations in placement of camera traps in the landscape, orientation of the

camera trap to bait station, type and or model of camera trap used, height of the camera

trap from the ground, distance between the camera trap and bait station, camera trap

settings, and flash type used by the camera trap have been shown to affect detection of

target species (Hamel et al. 2013; Meek et al. 2014; Gálvez 2016; Pease et al: 2016).

Preliminary exploratory analyses from Fish River Station indicated that detectability of

black-footed tree-rats and northern brown bandicoots, but not dingoes, may decrease

when the bait station was not centralised in the camera field of view (J. Risler unpublished

data presented in Appendix 2). Some sources of variation may be mitigated or managed

prior to data acquisition through clearly defined survey protocols to optimise detection of

target species. Where variation cannot be avoided occupancy modelling can account for

these sources of variation through the inclusion of covariate data in analyses. Further

guidance on survey design to optimise detection of black-footed tree-rats would be

achieved by incorporating these other factors in subsequent analyses.

Management implications

The recommended survey effort for black-footed tree-rats, of two traps deployed for a

minimum of two weeks, represents the most efficient survey effort identified in my

analyses. Optimal detectability for this species should be achievable with this design

provided that future sampling is conducted with the same methods and environmental

conditions as the original dataset. Along with deliberate consideration of other factors

that are likely to influence detectability of the species, the results presented here will be

incorporated into environmental impact assessment guidelines for the black-footed tree-

rat, to be endorsed by the Northern Territory Environmental Protection Authority. The

development of these guidelines will adopt a conservative approach to ensure that the

final survey design will optimise detection and account for the multiple sources of

variation to detectability. The guidelines will be available to proponents of development,

environmental consultants and the general public to guide and enable them to effectively

assess an area for the presence of black-footed tree-rat.

The ability to reliably determine the presence or absence of this species will become ever

more crucial. This research and the ensuing impact assessment guidelines are timely as

there is a high risk that future small and large scale clearing will further modify or reduce

the availability of suitable habitat for the black-footed tree-rat. Standardisation of survey


30

effort as proposed by this study will ensure that there is reliable and rigorous information

to guide future management decisions and protect the black-footed tree-rat from further

decline. Furthermore, the systematic stepwise approach to analysing pre-existing data to

determine optimal survey design, presented here, may be applied more broadly to other

species and detection methods, to improve the efficacy of surveys for other threatened

species.


31

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38

Appendix 1. R code developed by Danielle Stokeld 2016

Single-season occupancy models were implemented using the package “unmarked” in R version 3.1.0 (R Core Team 2014). The following code ran the occupancy models to;

determine detection probability (p) and occupancy (ψ) estimates for each scenario of one to five cameras per site,

create five output files, one for each scenario of one to five cameras, with ψ and p and their associated standard error, lower and upper confidence limit for each of 100 bootstrap iterations

graph ψ and p with upper and lower confidence intervals based on mean values of from the output files

#Set the working file directory

setwd("D:\MASTERS 2016-17\Data\rcode version2 and final dataset")

#Install required packages

#Install <- TRUE

#toInstall <- c("ggplot2","plyr","dplyr","unmarked")

#if(Install){

# install.packages(toInstall,

# dependencies = TRUE,

# repos = "http://cran.us.r-project.org")

#}

#Load packages

library(plyr)

library(dplyr)

library(unmarked)

library(ggplot2)

##Load csv file

dat <- read.csv("BFTR20160928.csv") #change file name

head(dat)

str(dat)

#S <- unique(dat$Site)

#S

#D <- dat[ sample( which( dat$Site == "CADELL_RTC_10"), 4, replace=F), ]

#D %>% summarise_each(funs(sum),3:44)

#######################################

#4 Cameras

#set up matrix for data to be entered into

output <- matrix(data=NA,nrow=100,ncol=8)

colnames(output) <- c("psi","psi.SE","LCL.psi","UCL.psi","p","p.SE","LCL.p","UCL.p")

#Run random selection of data 100 times

for(i in 1:100){

D <- dat[unlist(tapply(1:nrow(dat),dat$Site,function(x) sample(x,4))),] ## Replace 4 to 3, 2, 1 for random

choosing

DH<- D %>%


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group_by(Site) %>%

summarise_each(funs(sum),3:44)

Sp <- DH[,2:43]

umf <- unmarkedFrameOccu(y = Sp)

md <- occu(~1 ~1, umf)

psi <- backTransform(md, 'state')

p <- backTransform(md, type="det")

output[i,1] <- coef(psi)

output[i,2] <- SE(psi)

output[i,3] <- min(confint(psi, level = 0.95))

output[i,4] <- max(confint(psi, level = 0.95))

output[i,5] <- coef(p)

output[i,6] <- SE(p)

output[i,7] <- min(confint(p, level = 0.95))

output[i,8] <- max(confint(p, level = 0.95))

}

#save output

head(output)

output2 <- data.frame(output)

#Name file relevant of number of cameras randomly chosen

write.csv(output2, "BFTR4.csv") #rename file for number of cameras randomly chosen

(bftr4 <- c(4,mean(output2$psi),min(output2$LCL.psi),max(output2$UCL.psi),

+ mean(output2$p),min(output2$LCL.p),max(output2$UCL.p)))

###########################################################################

# 3 cameras




#Run random selection of data 100 times - 3 cams

for(i in 1:100){


choosing

DH<- D %>%

group_by(Site) %>%


Sp <- DH[,2:43]













}

#save output

head(output)



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write.csv(output2, "BFTR3.csv") #rename file for cameras randomly chose


+ mean(output2$p),min(output2$LCL.p),max(output2$UCL.p)))

###########################################################################

#2 cameras




#Run random selection of data 100 times - 3 cams

for(i in 1:100){


choosing

DH<- D %>%

group_by(Site) %>%


Sp <- DH[,2:43]













}

#save output for each species

head(output)



write.csv(output2, "BFTR2.csv") #rename file for cameras randomly chose

head(output2)

str(output2)


mean(output2$p),min(output2$LCL.p),max(output2$UCL.p)))

###########################################################################

#1 camera





for(i in 1:100){


choosing

DH<- D %>%


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group_by(Site) %>%


Sp <- DH[,2:43]













}

#save output

head(output)






###########################################################################

#5 cameras





for(i in 1:100){


choosing

DH<- D %>%

group_by(Site) %>%


Sp <- DH[,2:43]













}

#save output

head(output)



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######################################################

######################################################

#GRAPH THE RESULTS

#############################

#Create matrix

names <- c("cameras","m.psi","LCL.psi","UCL.psi","m.p","LCL.p","UCL.p")

results <- rbind(names,bftr5,bftr4,bftr3,bftr2,bftr1)

write.csv(results,"SamplingResults.csv")

######################################################

######################################################

#Plot estimates

r <- read.csv("BFTR_SamplingResults.csv")

str(r)

r$CT <- factor(r$cameras) #set the num of cameras to a factor

#Plot PSI and 95% confidence interval

p1 <- ggplot(r, aes(x=CT,y=psi)) + geom_point(size = 2.5) + theme_bw()

p1 +

facet_grid(~ Species) +

geom_errorbar(aes(ymin=LCL.psi, ymax=UCL.psi),alpha=0.5) +

xlab("Number of Cameras") + ylab("psi (95% confidence interval)") +

theme(axis.title.y=element_text(face="italic"))

#Plot P and 95% confidence interval

p2 <- ggplot(r, aes(x=CT,y=p)) + geom_point(size = 2.5) + theme_bw()

p2 +

facet_grid(~ Species) +

geom_errorbar(aes(ymin=LCL.p, ymax=UCL.p),alpha=0.5) +

xlab("Number of Cameras") + ylab("p (95% confidence interval)") +



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I then calculated a single mean detection probability and mean upper and lower 95%

confidence limits from the 100 output values for each scenario of one to five camera traps

per site. I converted these to cumulative detection values for days one to 28 days. I then

transposed the values in the following format to graph in R with the following code

developed by Danielle Stokeld. Highlighted cells show the days after which the precision of

p* > 0.85 was reached by the lower limit of the 95% confidence interval.

Q = read.csv("pCurves.csv")

head(Q)

names(Q)

str(Q)

###########################################

#Plot estimates

Q$Camera <- factor(Q$Camera) #set the num of cameras to a factor

p1 <- ggplot(Q, aes(x=Day,y=Mean)) + geom_line(size = 1) + theme_bw()

p1 +

facet_grid(Camera ~ .) +

geom_errorbar(aes(ymin=LL, ymax=UL),alpha=0.3) +

xlab("Number of Days") + ylab("p (95% confidence interval)") +


p1 +

facet_grid(Camera ~ .) +

geom_ribbon(aes(ymin=LL, ymax=UL),alpha=0.3) +

xlab("Number of Days") + ylab("p (95% confidence interval)") +

theme(axis.title.y=element_text(face="italic")) +

scale_x_continuous(limits=c(1,28), breaks = c(1,7,14,21,28)) +

scale_y_continuous(limits=c(0,1.00), breaks= c(.2,.4,.6,.8,1))


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Appendix 2. Camera trap orientation to bait station

Figure 9. Camera score v1 template showing scoring zones (0-4) used to score Fish River Data from 2014 and 2015 deployments by JR. This version was used in Gillespie et al. (2016) to account for variation in camera trap orientation to bait station.

Single-season occupancy models were implemented using Presence version 6.2 (Hines 2006) prior to field deployment of the five camera array at Fish River Station in 2015 by Jenni Risler. Results are shown on the following page. The sample size favours the more optimal camera trap orientation to the bait station. And while there is a perceived effect on camera score to detectability of the target species the precision around the detection probability estimates are great and show that the variation is not statistical significant.


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Black-footed tree-rat

score # sites n = 154 p SE

1 10 0.1014 0.1111

2 5 0.3094 0.1995

3 54 0.4941 0.0665

4 78 0.5687 0.0697

0 10 - -

Northern brown bandicoot


1 10 0.1000 0.1100

2 5 0.1622 0.1653

3 54 0.6372 0.0812

4 78 0.4746 0.0647

0 10 - -

Dingo


0 10 0.1791 0.0791

1 10 0.2800 0.1205

2 5 0.3723 0.1671

3 54 0.2078 0.0538

4 78 0.2312 0.0398

Northern brown

bandicoot

Black-footed tree-rat

Northern brown bandico

ot

Dingo


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Appendix 3. Variation in detection due to camera trap

set up

Distance between camera trap and bait station

Model of camera (Infra-red flash and White light flash)

Camera Trap setting (1080 Pixels versus 3.1 Mega Pixels)


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Figure 10. Camera trap images from Fish River Station showing the difference between a camera trap set 2.5 m from the bait station (TOP) and 1.5 m from the bait station (BOTTOM).


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Figure 11. Camera trap images showing the difference between flash types used by different camera trap models. In the infra-red (IR) flash image from Fish River Station (TOP) it is much harder to see the definition of the animal compared to the white led flash used at Garig Gunak Barlu National Park (BOTTOM).


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Figure 12. Camera trap images showing the difference between the two resolution settings available by the same camera trap model. The 1080 Pixel setting returns a ‘cropped’ image as seen in the image from Fish River Station (TOP) where the 3.1 Mega Pixel setting provides a greater field of view as seen in the image from Litchfield National Park (BOTTOM).

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