Scholarly article on topic 'Investigation into survey techniques of large mammals: surveyor competence and camera-trapping vs. transect-sampling'

Investigation into survey techniques of large mammals: surveyor competence and camera-trapping vs. transect-sampling Academic research paper on "Biological sciences"

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Academic research paper on topic "Investigation into survey techniques of large mammals: surveyor competence and camera-trapping vs. transect-sampling"

BioscienceHonzons Volume 4 • Number 1 • March 2011 10.1093/biohorizons/hzr006

Advance Access publication 23 February 2011

Research article

Investigation into survey techniques of large mammals: surveyor competence and camera-trapping vs. transect-sampling

Nathan James Roberts*

Centre for Wildlife Conservation, National School of Forestry, University of Cumbria, Newton Rigg, Penrith, Cumbria CA11 0AH, UK. * Corresponding author: 13 Church Road, Catsfield, Battle, East Sussex, TN33 9DP. Email: njr304@hotmail.co.uk

Supervisors: Dr Owen Nevin and Dr Ian Convery, National School of Forestry, University of Cumbria, Newton Rigg, Penrith, Cumbria, CA11 0AH, UK.

Rigorous and cost-effective methods are essential to efficiently assess wildlife populations and obtain accurate data to inform conserva- h

tion and management decisions. In the UK, available data on terrestrial mammal species are distinctly lacking, many populations are in z

decline and survey methods are technically demanding and labour-intensive. There is, therefore, much need to investigate alternative §

methodologies to ensure that resource use is efficient and data are reliable. Camera-trapping presents a relatively new approach for f

surveying mammals, though in the UK, the extent to which camera traps have been used has not been quantified and their performance jj

has not yet been compared relative to existing methods. This study uses biological parameters and economic and logistic costs to assess r

the efficiency and reliability of camera-trapping and transect-sampling during winter field trials. Tracks and sign surveys and sightings §

surveys were conducted simultaneously and where appropriate, investigated independently. In addition, a nationally-distributed g

questionnaire was used to investigate surveyor competence and identify temporal trends in method use in the UK. Field trials concluded §

that camera-trapping was the most labour-efficient method for producing a species inventory, and frequently recorded more species per °

sampling site than did transect-sampling. However, when the total sampling period was limited, species were encountered at a faster u

rate by the detection of tracks and signs than by the alternative methods investigated. The single density estimate derived from camera V

trap data was higher than that from transect-sampling, and no differences were observed within the three alpha diversity index i

estimates derived by each survey method. The questionnaire suggests that the reliability of species presence/absence data derived L

from tracks and signs surveys is probably compromised by surveyor confidence of species identification. A multi-evidence approach ?

is, therefore, recommended for less-competent surveyors. Despite greater initial economic costs, it is advocated that camera-trapping §

may be an efficient, rigorous and cost-effective method for large-scale long-term monitoring programmes. Furthermore, data §

suggest that camera trap use will become increasingly frequent in the UK. More research is required to investigate the relationships u

between method efficiency and season, species density and habitat, and to assess the accuracy of species density estimates. t

Key words: camera-trapping, efficiency, mammals, reliability, transect-sampling, UK. 2

Submitted July 2010; accepted on 20 January 2011

Introduction

Accurate assessments of species distributions, population densities and species richness are essential to effectively direct conservation strategies and management practices. -Monitoring species distributions and abundance also provides important data to evaluate whether favourable conservation status has been achieved,5 and supports the legal obligation towards species protection and conservation.

However, available data on terrestrial mammalian fauna are distinctly lacking in the UK, and many populations are in decline.8 Furthermore, surveying mammals is laborious and technically demanding.6'9

Numerous survey methodologies are currently practised,10 each with specific advantages and disadvantages, particularly in terms of detectability, labour and financial costs, and usability by surveyors.11,12 In the UK, mammal

© The Author 2011. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 40

surveying is dependent on a body of full-time professionals supported by a strong foundation of volunteers. The ability of trained volunteers is virtually comparable to that of professionals in some instances, though the experience and skill of the latter may compromise the relative performance of volunteers.13 It is, therefore, essential that field techniques can be readily implemented by people with varying levels of expertise,11 or else recorded species distributions may rather reflect those of skilled surveyors.

Transect-sampling is a widely used survey technique, 5 and considered volunteer-friendly, visual counts are the simplest method to survey mammals. Indirect sampling techniques that rely on the detection of tracks and signs along transects may also be implemented by surveyors with limited training. Conversely, camera-trapping is a relatively new methodological advancement 7 that uses specialized equipment to detect and 'trap' photographs of passing animals.

It is recommended that new methodologies are assessed relative to existing knowledge.19 To date, numerous field tests have been conducted to compare the efficiency, detect-ability and accuracy of camera-trapping and transect-sampling, ' - though none have so far been performed in the UK. Previous comparisons have concluded that transect-sampling may provide a more complete species inventory,23 and obtain a greater frequency of records than alternative methods. However, conflicting results have been reported for the efficiency of species detection in terms of sampling effort. ' Furthermore, questionnaires indicate that field sign surveys are often considered more difficult than sightings surveys, and camera-trapping has been reported to allow for more accurate species identification than can be achieved by the identification of tracks. 5

The most appropriate method for a given survey may be determined by, inter alia, the objectives and biological questions asked, characteristics and conservation status of the target species and dependability of the method. Ultimately however, it is often resource availability that determines method selection. ' ' The efficient use of resources is paramount in conservation,27 and financial and labour costs should, therefore, be key considerations in the selection of the most feasible sampling method.16'28'29 Furthermore, the application of an efficient, reliable and cost-effective method may maximize the validity of species assessments. , , ,

Despite escalating global popularity in the use of camera traps, no camera-trapping studies in the UK natural environment have been published in the literature [web of science search: TS = (camera-trapping) and CU = (United Kingdom or England or Scotland or Wales or Northern Ireland or Great Britain or Ireland)]. However, it is anticipated that camera traps will become of greater use by biologists as technologies and methodologies advance.

The principal objective of the present study was to compare the rigour of camera-trapping and transect-sampling as techniques for recording species, estimating

Table 1. Species considered by the field trials and questionnaire

Species Common name

Cetartiodactyla

Cervidae Capreolus capreolus Roe deer

Cervus elaphus Red deer

Cervus nippon Sika deer

Dama dama Fallow deer

Hydropotes inermis Chinese water deer

Muntiacus reevesi Muntjac

Carnivora

Canidae Vulpes vulpes Fox

Felidae Felis silvestris Wildcat

Mustelidae Lutra lutra Otter

Martes martes Pine marten

Meles meles Badger

Mustela erminea Stoat

Mustela nivalis Weasel

Mustela putorius Polecat

Neovison vison Mink

Eulipotyphla

Erinaceidae Erinaceus europaeus Hedgehog

Talpidae Talpa europaea Mole

Lagomorpha

Leporidae Lepus europaeus Brown hare

Lepus timidus Mountain/Irish hare

Oryctolagus cuniculus Rabbit

Rodentia

Sciuridae Sciurus carolinensis Grey squirrel

Sciurus vulgaris Red squirrel

species density and assessing alpha diversity. Specifically, the study performed field trials and used a questionnaire to investigate the efficiency and reliability of surveying terrestrial woodland mammals in the UK. Both components of the study were concerned with a target suite of species (Table 1); the majority of small mammals were not considered. Field trials primarily assessed the labour and economic efficiency of each sampling technique and the questionnaire investigated surveyor competence.

Materials and methods

Field trials

Study area

Field trials were conducted in Edenbrows Wood in Cumbria (OS Reference: NY 497 498; Fig. 1), a mixed Plantation on Ancient Woodland Site adjacent to the River Eden, a Special Area of Conservation and Site of Special Scientific Interest. The study area is ~0.28 km2 and composed of mixed

Figure 1. Edenbrows Wood, Cumbria (OS Reference: NY 497 498) with sampling sites defined and camera trap stations and transect locations marked.

conifers and ash (Fraxinus excelsior)/oak (Quercus petraea) woodland. Floral composition was used to divide the study area into six sampling sites: Grand Fir (Abies grandis); Mixed Conifers; Hybrid Larch (Larix x eurolepis); Sitka Spruce (Picea sitchensis); Mixed Broadleaves and Mixed Broadleaves/Hybrid Larch.

The mean daily temperature for the period December 2009 to February 2010—recorded at Brampton climate station 11 km north of the study area—was 0.7°C with a range from —10.5 to 7.5°C. The mean daily rainfall and snow depth for the same period was 2.2 mm and 3 cm, respectively.

The site was selected on the basis of accessibility, size, floral diversity and the high level of recreational use, which represents the effects of disturbance associated with publicly accessible land, including potential equipment security threats.

Field trials were conducted from December to February as field signs were less likely to be obscured by vegetation,24 species observation would also, therefore, be less affected by visual background noise,31 and this methodology also acknowledges recommendations of the Winter Mammal Monitoring (WMM) pilot study.24 Furthermore, the sampling period was minimal to meet the assumptions of density estimate calculations.18

Camera-trapping

The general framework of the camera-trapping survey followed a previously trialled methodology;18 the methodology was refined during preliminary trials in Borneo in September

2009 and Cumbria in December 2009. Three camera trap stations were assigned to each sampling site and coordinates were random. Inter-camera distance ranged between 43 and 288 m. Each camera was operational for between 5 and 57 consecutive trap nights, sampling from December 2009 to January 2010.

Sixteen Reconyx (models RC55 RapidFire and PC85 RapidFire Pro; Reconyx Inc., WI, USA) and two CamTrakker (model MK-8; CamTrakker, GA, USA) passive infrared camera traps were used, with automatic infrared flash and strobe flash, respectively. All units were programmed to capture maximum photographs per trigger, configured to minimal latency periods between triggers and secured to trees at a height of ~0.3 m to maximize capture probability.32 Assuming functionality, cameras were operational 24 h a day, and date and time were imprinted on all photographs. Traps were checked infrequently, usually replacing memory cards. The total sampling effort was 693 trap nights (i.e. sum of sampling nights per camera).

Sensor detection parameters were estimated for each camera model,18 and figures weighted by the total sampling effort for each model to determine the values used in analysis (J. Rowcliffe, personal communication). Roe deer (Capreolus capreolus) group size (g) and average day range (v) were provided by Robin Gill (personal communication) and a primary literature source,33 respectively.

Transect-sampling

Ten linear transects of mean length ( + SD) 127.6 + 68.5 m were established, directly connecting camera trap stations

within each sampling site and extending ~30 m before the first station and after the last (Fig. 1). All mammals in the target suite of species were registered according to standardized techniques,34 collecting sightings and tracks and signs data simultaneously. Animal- and sign-observer distances were estimated without the use of equipment. Transects were walked between 0820 and 1430 h, and sampling was conducted between January and February 2010. Each transect was walked either four or five times, achieving a total of 46 transect surveys, thereby accumulating 5.7 km of transects walked.

Signs of fox (Vulpes vulpes), badger (Meles meles), mole (Talpa europaea), rabbit (Oryctolagus cuniculus), otter (Lutra lutra) and roe deer were registered; only fox, badger and otter tracks were considered. Deer faecal pellets were recorded according to standardized methods,35 and were not cleared from transects.36 Tracks and signs which could not be confidently identified were photographed for ex situ two-way agreement.

Questionnaire

Between February and March 2010, a seven-part web-based questionnaire was used to investigate the temporal trends in the use of survey techniques and the reliability of species identification and distance estimation by mammal surveyors (see Supplementary material online). The first three parts investigated the respondents' previous surveying experience, including reporting the period during which they first implemented each technique. Respondents were then asked to quantify their confidence of estimating distance and identifying species from photograph, sight and tracks and signs. The fifth part investigated the training the respondent had received and would consider in the future. In the penultimate part, respondents were asked to quantify their anticipated future involvement in mammal surveying in the UK with regards to method use. Questions pertaining to demographics were included in the final part of the questionnaire for the purposes of describing the sample.

To achieve comprehensive sampling, the questionnaire was distributed to virtually all organizations collaborating with the Tracking Mammals Partnership,37 supplemented by additional non-governmental and governmental organizations, volunteer organizations and independent ecologists. Each unit of the sampling frame (n = 134) was authorized responsible questionnaire dissemination within the respective organizations and among appropriate contacts. In addition to direct email invitations, the questionnaire was accessible via: Devon Wildlife Trust website; Lincolnshire Wildlife Trust newsletter, February 2010; The Mammal Society (TMS) website and e-bulletin, March 2010 and the public forum Wild About Britain, directing respondents to the appropriate TMS web page.

Previously validated questions used during the WMM pilot study24 were included to allow for direct comparisons

where appropriate, and the three hypothetical training courses were considered theoretically feasible (A. Dunlop, personal communication). The questionnaire was programmed with complex routing logic to automatically direct respondents based on individual responses, and piloted on nine individuals representing the five typology groups (i.e. academic, mammal interest, student, etc.).

Data analysis Field trials

To evaluate the rigour of camera-trapping for surveying the target suite of species, a subset of all photographs were used, excluding domestic animals, humans, birds and small mammals (with the exception of Sciurus spp.). Blank captures and exposures in which the species was unidentifiable were also excluded from analysis. Independent records were defined as (i) consecutive photographs of the same species taken at an interval of >0.5 h or (ii) photographs of different species irrespective of interval length. In analyses of transect-sampling, tracks and signs data and sightings data were pooled.

Transect-sampling data and camera-trap data were pooled to estimate total species richness by Chao Presence/Absence in DIVERSITY.38 Species accumulation curves plot Sobs (Mao Tau) values computed in EstimateS,39 pooling data by sampling day and performing 100 randomizations without replacement to eliminate bias associated with unequal sampling effort per day.

Relative efficiency of species detection12 was valued and subsequently compared by paired t-test. Differences between methods in the mean latency to first detection (LTD)12 were analysed by paired t-test.

Labour investments were calculated from daily records of time expended in data collection, excluding general field time. No randomizations were performed for species accumulation plots per human hour of field investment and are, therefore, presented chronologically. Economic costs included equipment and theft compensatory costs only; general project costs were omitted. Cost comparisons were performed by chi-square test, incorporating Yates' correction for continuity.

Photographic capture rates were used to estimate species density;18 variance and precision were not estimated. Roe deer density was estimated from faecal pellet counts,35 and variance calculated as the SD of density estimates from each sampling day.36

Alpha diversity indices were calculated in DIVERSITY,38 defining an individual as an independent record plus additional animals observed in a single instance by sight or photograph.

All variables were tested for normality by Kolmogorov-Smirnov test and statistical comparisons performed in

SPSS 15.0 (SPSS, Inc., IL, USA), measuring significance at P = 0.05.

Questionnaire

Statistical analyses were performed in SPSS 15.0 (SPSS, Inc., IL, USA), considering significance at P = 0.05. All usable data were analysed, thereby including the individual responses from questionnaires that were partially complete. Descriptive and statistical analyses of the survey data were performed. Confidence of species identification from different forms of evidence was assessed by Kruskal-Wallis and post-hoc analysis. Friedman tests were used to investigate the frequency of method use and preference of hypothetical training courses. Comparisons of temporal trends in method use were performed by chi-square test, incorporating Yates' correction for continuity.

Results

Field trials

Eight target species were recorded during the field trials: badger; fox; grey squirrel (Sciurus carolinensis); mole; otter; rabbit; red squirrel (Sciurus vulgaris) and roe deer. In 117 independent records, camera traps registered all of the above species, excluding mole. A total of 142 records were obtained during transect-sampling; sightings data constituted 3 of these records. Excluding squirrels, all of the above species were detected by tracks and signs, and only rabbit and red squirrel were encountered by direct observation. Camera-trapping consistently recorded equal or more species per sampling site than did transect-sampling, yielding a greater value of relative efficiency (t = 3.3, df = 5, P = 0.021; Table 2). Species richness ( + SD) of the entire study site was estimated as 9 + 3.01. Of those species detected

Table 2. Observed richness of sampling sites and relative efficiency of each survey method per sampling site (i.e. number of species detected by individual method/total number of species detected in each sampling site)

Sampling site Camera-trapping Transect- Richness

sampling

Sitka Spruce 0.8 0.4 5

Mixed Broadleaves 0.86 0.71 7

Mixed Broadleaves/ 0.67 0.67 3

Hybrid Larch

Mixed Conifers 1 0.5 6

Grand Fir 0.83 0.67 6

Hybrid Larch 1 0.5 4

Mean 0.86 0.57

SD 0.13 0.12

by both camera-trapping and transect-sampling, the latter detected each species in fewer days (i.e. lower mean value of LTD; t = 4.264, df = 5, P = 0.008). Specifically, species were accumulated by the detection of tracks and signs at a faster rate than any other detection method when sampling effort <5 d (Fig. 2). However, in terms of field labour investments, less than 1 h was required to achieve an asymptote of accumulative species detected by camera-trapping (Fig. 3). This labour investment was significantly less than the amount expended for tracks and signs (x2 = 45.15, df = 1, P < 0.001) and sightings (/ = 36.20, df = 1, P < 0.001); no difference between transect-based methods was observed (x = 1, df = 1, P > 0.25). Total field labour investments were equitable between methods; financial costs were significantly greater for camera-trapping (P < 0.001; Table 3).

The weighted mean camera trap detection arc and distance were 0.704 6 and 0.005 km, respectively. An average roe deer group size of 1.6 and average day range of 2.19 km day-1 was used to calculate density from camera-trapping rates. Roe deer density was estimated by transect-sampling ( + SD) as 3.85 + 1.60 km" , a significantly lower density than derived by camera-trapping (15.47 km" ; P < 0.001).

Figure 2. Cumulative number of species observed as a function of increased sampling effort;Sobs (Mao Tau) presented.

Figure 3. Cumulative number of species detected with increasing field labour investments, presented chronologically.

Table 3. Cost comparison of survey methods

Method

Financial (£)

Time (h)

Camera-trapping

Transect-sampling

8423 20 <0.001

12.23 14.64 NS

Table 4. Alpha diversity indices of Edenbrows Wood, estimated by two survey methods

Method Speciesa Individuals'1 Diversityc Abundanced Richnesse

Camera-trapping 7 123 1.37 + 0.01 2.8б 1.25

Transect-sampling 7 143 0.84 + 0.01 1.58 1.21

P NS NS NS

aObserved value.

bObserved value (independent records plus additional animals observed in a single instance).

cShannon-Wiener.

dSimpson's diversity index.

eMargalef.

Camera-trap data and transect data derived alpha diversity estimates that were neither significantly different for abundance, richness or diversity (Table 4).

Questionnaire

A total of 79 responses were received at a completion rate of 73.4% (n = 58); the remaining 21 were partially complete. Each region of the UK was represented by at least one respondent (except London) and the modal demographics were the following: male (53.4%); aged 25-34 (25.9%); professional country worker (27.6%) and employee or member of TMS (43.1%). The sample was comparable to the respondents involved in the WMM pilot study, in terms of typology (e.g. academic, student, etc.; t = 1.49, df = 5, P = 0.197) and gender (t = 4.28, df = 1, P = 0.146). However, respondents were typically younger in the present study (t = 3.37, df = 7, P = 0.012).

Questionnaire respondents indicated that camera-trapping was implemented less frequently than both transect-based sampling methods in 2009 (Friedman Test, df = 2, P = 0.000). This relationship was also anticipated for the period 2010-2015 (Friedman test, df = 2, P = 0.000). The differences between 2009 and 2010-2015 were not significant within methods (Fig. 4).

Of those respondents with camera-trapping experience (24.1%), nearly 85% first used camera traps for wildlife studies in the UK between 2006 and 2009, and the earliest

recorded use was during the period 1996-2000 (5.3%). Conversely, in the period 2006-2009, 24.2% and 19.4% of all respondents first conducted sightings surveys and tracks and signs surveys, respectively. For both transect-based methods, 21% of respondents first conducted each survey method pre-1990; no respondents first used either method in 2010.

The frequency of respondents who stated they could confidently identify the target suite of species by photograph (i.e. the product of camera-trapping), sight and tracks and signs differed significantly between forms of evidence (Kruskal-Wallis, H = 29.044, df = 2, P = 0.000). Fewer respondents were confident in the identification of species from tracks and signs than from photographs (Mann-Whitney, U = 66.00, P = 0.000) and sightings (Mann-Whitney, U = 42.50, P = 0.000). Furthermore, fewer respondents were able to confidently identify species from photographs than from sight (Mann-Whitney, U = 337.50, P = 0.024). Concerning the eight species recorded during field trials only, the median number of respondents who stated they could confidently identify each species by photograph, sight and tracks and signs was 54, 58 and 47, respectively; the interquartile ranges were 0, 1 and 27.75, respectively (Fig. 5).

Respondents were asked to quantify their confidence of estimating animal-observer distance on a five-point scale. Only 22.2% of respondents could estimate distance to the highest degree of accuracy. The modal value (35.2%) of quantified confidence was '4'.

Figure 4. Friedman test mean rank of survey method use and anticipated future use, where integers represent the scale of annual frequency of surveys per respondent: never (0 surveys;1);rarely (1-2 surveys;2); occasionally (3-4 surveys;3);frequently (5+ surveys;4).

Figure 5. Frequency of respondents able to confidently identify a range of mammal species (badger;fox;grey squirrel;mole;otter;rabbit;red squirrel; roe deer) from different forms of evidence.

Of those respondents who had not been trained in the use of camera traps (91.5%), most (74.1%) would consider attending a camera-trapping training course. When presented with three hypothetical training courses, respondents indicated that preference was varied (Friedman test, df = 2, P = 0.000). The 1-day course was most preferred and was considered by 79.3% of respondents to provide adequate training to become competent in camera trap use.

Discussion

Despite the relocation of two camera traps due to accessibility concerns of original placement, and subsequent low sampling effort of these cameras, camera-trapping produced a more complete inventory than alternative methods, as concluded by other studies.23'40 In terms of sampling days, species were detected at the fastest rate by tracks and signs, also supporting previous conclusions.20 However, labour efficiency of transect-sampling was comparatively limited due to the high investments required to establish transects before sampling could commence. High variability in surveyor competence of species identification from field signs across species and the evident inability of most mammal surveyors to confidently estimate animal-observer distance raises concerns over the reliability of field sign data and distance-sampling-derived density estimates, respectively.

This study has emphasized that failure to detect species may not be indicative of species absence, rather insufficient effort or use of an inappropriate method.41 Due to the ambiguity of squirrel field signs,24 only sightings were considered during field trials, for which no records of grey squirrel were obtained; this result was disputed by camera trap evidence of species presence. As the grey squirrel is a direct threat to the native red squirrel,42 this result also highlights the conservation significance of differences in detectability between methods.

Total field labour investments were equitable between methods to ensure efficiency was directly comparable. However, it should be noted that camera-trapping labour investments were probably lower than in other circumstances as standard detection zone checks were not performed due to logistical constraints. Approximately optimal positioning was determined during preliminary trials, which considering the above, could simulate training. The field trials, therefore, demonstrate the performance of camera-trapping when a trained surveyor is limited by logistics. Alternatively, when the survey objectives do not include density estimation, it is proposed that the 9 h otherwise invested in estimating detection zone parameters would probably be sufficient to perform these checks.

To estimate species density from camera trap data, it is recommended that the animal-related parameters are estimated simultaneously to camera-trapping;18 this was not logistically feasible in the present study due to the substantial

associated costs.21 It is, therefore, probable that bias has been incurred as a result of using external information. However, it is proposed that the roe deer density estimate derived from camera trap data is more accurate than that derived from faecal pellet counts, as a greater number of individuals were observed by independent photographic captures than was estimated by the latter.

Density estimate comparisons are limited in the present study as transects were not conducted on consecutive days, and the age of species tracks could, therefore, not be accurately determined as required. Furthermore, the absence of data of animal-related parameters limited the use of camera trap data, and frequency of sightings records were considered insufficient to accurately calculate species density by distance-sampling.

The assumption of distance-sampling that states recorded distances are exact may be violated by the diverse mammal surveying community, as suggested by the questionnaire respondents. The modal class of confidence (i.e. '4') also concurs with the WMM pilot study sample. Although density may be reliably calculated from inaccurate field data, tendency to under- or over-estimate distance may bias results.

The questionnaire sample also indicates that the mammal surveying community is less able to identify species from tracks and signs than from sightings and photographs. Misidentification may result in erroneous population estimates, and non-representative presence/absence data that may misguide conservation and management initiatives, potentially with severe financial implications.14 Respondents further indicated that species identification from photographs is more difficult than from sightings, though it should be noted that the latter is largely dependent on clear observations, and is biased towards large-bodied diurnal species. Although there is also a positive relationship between body weight and detection probability by camera traps, camera-trapping may overcome the temporal limitations of transect-sampling by surveying at night. Furthermore, unambiguous photographs obtained by camera traps provide indisputable evidence of species pres-ence,29 considerably reducing observer inaccuracies and bias common with other methods. It is, therefore, recommended that less-competent surveyors supplement field sign surveys with camera trap data, where possible.29

Assuming surveyor competence, when the aim of a survey is to produce a species inventory and the surveyor is limited by total sampling period and not human labour investments, it is proposed that tracks and signs surveys may offer the best approach for producing rapid faunal assessments. However, if the reverse limitations are applied, camera-trapping may be the most efficient method as labour investments are comparatively minimal,22,44 unlike transect-sampling which required ~5 h of field labour to establish transects only. Furthermore, it is proposed that camera-trapping may

provide a cost-effective approach for monitoring programmes at large temporal and spatial scales, 5 as the substantial equipment costs may be offset by frequent and repeated use.

Alpha diversity estimates were not different between survey methods, suggesting that either method may be used to derive accurate estimates. Furthermore, it is proposed that Shannon-Wiener is the most appropriate index to use in future assessments as it recognizes the number of species and the evenness of species abundance.

It is suggested that transect-sampling is a well-practiced methodology historically and that the annual frequency of transect-based surveys may remain approximately constant in the future. Conversely, data suggest that camera trap use will increase, which is further supported by the respondents' interest in receiving camera-trapping training. This study also provides an insight into the training interests of mammal surveyors and a foundation from which to further investigate and design future training courses.

Conclusion

This is the first published study to compare the efficiency and reliability of camera-trapping and transect-sampling as techniques for surveying terrestrial mammals in the UK. As reported by non- UK studies, ' this investigation concludes that the best balance between cost-effectiveness, labour efficiency and rigour can be achieved by camera-trapping. It has highlighted that despite lower initial economic costs, transect-sampling rigour may be compromised by surveyor competence of identifying tracks and signs, and the comparatively slow rate of species accumulation per human hour of labour investment.

This study also presents the first attempt to identify temporal trends in survey method use in the UK. It is evident that camera-trapping is currently implemented less frequently than transect-sampling, and data further suggest that the former will increase in the future, complementing the global trend.47

In a brief comparison of survey methods at a small spatial and temporal scale, it is foolhardy to assert too much in the way of definitive conclusions. Instead, the results should be considered the inferences of a single case study only, when environmental conditions were favourable for infrared camera trap performance,36 and detectability of field signs.48 Furthermore, it should be acknowledged that

(i) survey efficiency may differ in space and time,11'20'22'49

(ii) capture success may vary within and between camera trap models,29,50,51 (iii) flash-induced trap-shy may reduce trapping rates52 and (iv) detection efficiency may be density

dependent.22

It is, therefore, advocated that further studies are conducted to investigate the spatial and temporal factors affecting method efficiency and rigour, including the effects of

season, species density and habitat. Furthermore, density estimates derived by camera-trapping and transect-sampling should be validated against direct counts. ,5

Supplementary data

Supplementary data is available at Bioscience Horizons online.

Acknowledgements

I would like to thank my principal supervisor Dr Owen Nevin, co-supervisor Dr Ian Convery, and acknowledge the logistical support given by Dr Andrew Ramsey, Ellie Lindsay, Melanie Clapham and Robbie Hawkins. I would also like to thank Dr Marcus Rowcliffe, Dr Robin Gill, Dr Mathew Crowther and Dr Philip Stevens. I am also thankful to Marina Pacheco, Laura Drake, Alex Dunlop, Simon Poulton, Rowena Staff and the anonymous individuals who responded to the questionnaire and assisted with distribution. Finally, I would like to thank my family and friends.

Funding

Financial support was kindly received from The Clouded Leopard Project (USA) and Penrith Lions Club (UK).

Author biography

N.J.R. studied BSc. (Hons) Animal Conservation Science at the Centre for Wildlife Conservation, University of Cumbria, UK, where he acquired substantial knowledge and expertise. Furthermore, he has received training in the use of advanced tools of modelling and analysis of spatial data, completing two courses in Geographic Information Systems. Academic achievements are complemented by numerous independent and supportive research projects and familiarity of working in temperate and tropical environments. Independent research projects have included investigations of species ecology, protected area management and animal behaviour. His principal interest is wild cat conservation and research, with a specific focus on human-felid conflict and species ecology. As a hobbyist wildlife and landscape photographer, he is also interested in the applications of camera traps in wildlife research. He aims to integrate his passion for photography into future research endeavours as he aspires to pursue a career in wild cat conservation and ecological research.

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