Random versus Game Trail-Based Camera Trap Placement Strategy for Monitoring Terrestrial Mammal Communities

Cusack, Jeremy J.; Dickman, Amy; Rowcliffe, J. Marcus; Carbone, Chris; Macdonald, David W.; Coulson, Tim

doi:10.1371/journal.pone.0126373

Cited by 153 publications

(177 citation statements)

References 39 publications

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“…Some studies are using the stratified approach with a grid (Tobler et al, 2008), others apply random points or gametrail approach (Cusack et al, 2015). In some of the published papers the sampling approach was not reported (Carvalho et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Biodiversity estimates from different camera trap surveys: a case study from Osogovo Mt., Bulgaria

Zlatanova¹,

Popova²

2018

Nat. Cons. Res.

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Inventorying mammal assemblages is vital for their conservation and management, especially when they include rare or endangered species. However, obtaining a correct estimation of the species diversity in a particular area can be challenging due to uncertainties regarding study design and duration. In this paper, we present the biodiversity estimates derived from three unrelated camera trap studies in Osogovo Mt., Bulgaria. They have different duration and positioning schemes of the camera trap locations: Study 1 -grid based, 34 days; Study 2 -random points based, 138 days; Study 3 -locations based on expert opinion, 1437 days. Utilising EstimateS, we compare a number of estimators (Shannon diversity index, Coleman rarefaction curve, ACE (Abundance-based Coverage Estimator), ICE (Incidence-based Coverage Estimator), Chao 1, Chao 2 and Jackknife estimators) to the number of present and confirmed and/or potentially present mammals (excluding bats) in the mountains. A total of 17 mammal species were registered in the three studies, which represents around 76% of the permanently present mammals in the mountain that inhabit its forested area and can be detected by a camera trap. The results point to some guidelines that can aid future camera trap research in temperate forested areas. A grid-based design works best for very short study periods (e.g. 10 days), while the opportunistic expert-based positioning scheme provides good results for longer studies (approx. a month). However, the grid-based design needs to be further tested for longer periods. Generally, the random points approach does not yield satisfactory results. In agreement with other studies, analysis based on the Jackknife procedure (Jack 2) appears to result in the best estimate of species richness. When performing camera trap studies, special care should be taken to minimise the number of unidentifiable photos and to take into account «trap-shy» individuals. The results from this study emphasise the need for careful preliminary planning of camera trap studies depending on aims, duration and target species.

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Section: Discussionmentioning

confidence: 99%

Biodiversity estimates from different camera trap surveys: a case study from Osogovo Mt., Bulgaria

Zlatanova¹,

Popova²

2018

Nat. Cons. Res.

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“…Others have implemented non-random camera trap placement to increase detections, but this design can violate key assumptions of random sampling [6]. Further, research indicates even minor adjustments to camera trap placement at a determined sampling location can influence which species are detected during surveys [8, 11–13]. …”

Section: Introductionmentioning

confidence: 99%

Single-Camera Trap Survey Designs Miss Detections: Impacts on Estimates of Occupancy and Community Metrics

2016

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The use of camera traps as a tool for studying wildlife populations is commonplace. However, few have considered how the number of detections of wildlife differ depending upon the number of camera traps placed at cameras-sites, and how this impacts estimates of occupancy and community composition. During December 2015–February 2016, we deployed four camera traps per camera-site, separated into treatment groups of one, two, and four camera traps, in southern Illinois to compare whether estimates of wildlife community metrics and occupancy probabilities differed among survey methods. The overall number of species detected per camera-site was greatest with the four-camera survey method (P<0.0184). The four-camera survey method detected 1.25 additional species per camera-site than the one-camera survey method, and was the only survey method to completely detect the ground-dwelling silvicolous community. The four-camera survey method recorded individual species at 3.57 additional camera-sites (P = 0.003) and nearly doubled the number of camera-sites where white-tailed deer (Odocoileus virginianus) were detected compared to one- and two-camera survey methods. We also compared occupancy rates estimated by survey methods; as the number of cameras deployed per camera-site increased, occupancy estimates were closer to naïve estimates, detection probabilities increased, and standard errors of detection probabilities decreased. Additionally, each survey method resulted in differing top-ranked, species-specific occupancy models when habitat covariates were included. Underestimates of occurrence and misrepresented community metrics can have significant impacts on species of conservation concern, particularly in areas where habitat manipulation is likely. Having multiple camera traps per site revealed significant shortcomings with the common one-camera trap survey method. While we realize survey design is often constrained logistically, we suggest increasing effort to at least two camera traps facing opposite directions per camera-site in habitat association studies, and to utilize camera-trap arrays when restricted by equipment availability.

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“…(2) The main results and conclusions of this paper are based on the use of camera traps to estimate the relative occurrence of prey and predators. Design of camera trap surveys is essential because it strongly influences camera trap capture rates and thus affects inferences made at the community level [8]. …”

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confidence: 99%

Multi-trophic interactions in anthropogenic landscapes: the devil is in the detail

et al. 2016

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Comments to Dorresteijn et al. (2015)Dorresteijn et al. [1] recently addressed the question what influence humans have on predator-prey interactions in human-modified landscapes. Their study follows increasing evidence from relatively natural landscapes that large carnivores are central drivers of ecosystem structure and functioning through density-mediated and behaviourally mediated effects on their prey [2]. A key question is whether large carnivores will have similar ecological effects in human-dominated landscapes [3]. This is particularly relevant in the light of the carnivore recolonization of European landscapes [4]. We applaud Dorresteijn et al. [1] for highlighting that ecological effects of large carnivores may be quite different in human-modified landscapes. Unfortunately, we feel that there are crucial flaws with their study design, analyses and interpretation. These flaws seriously undermine the strong conclusions that they draw about the role of topdown and bottom-up processes in anthropogenic landscapes. Our main concerns include (i) lack of relevant bottom-up data, (ii) a camera trap design unsuitable for testing their hypotheses regarding community structure, and (iii) top-down and bottom-up processes that are studied at an inappropriate spatial scale.(1) Their main aim was 'to quantify the relative effects of top-down versus bottom-up processes in shaping predator and prey distributions'. It is surprising that the authors articulate this main objective without including actual bottom-up drivers in their analyses. Bottom-up processes were captured by variation in two land cover types, forest versus pasture cover, using the Corine Land Cover data. However, these very coarse categories do not reflect the variation in food resources (i.e. bottom-up processes) that drive the distributions of prey and predator. This variation occurs at finer spatial resolution, nested within the main land cover types that the authors used. For example, inside forest habitat (where all cameras were placed), one important fine-scale factor that drives deer distribution, and hence camera trapping rates, is variation in canopy openness, with deer strongly concentrating inside canopy gaps [5]. In addition, fox abundance (the mesopredator in this study) is driven by variation in prey availability that is linked to microhabitat variation [6]. Therefore, the Corine Land Cover data alone provide no basis for testing the importance of bottom-up effects in driving encounter rates of mesopredators and herbivores. In fact, we would argue that such land cover data may be a better proxy for top-down forces. Ungulate prey species avoid open or closed habitat depending on whether they respond to a large carnivore or human hunter [7]. Although the authors draw conclusions about bottomup effects, we argue that these are unfounded owing to an absence of relevant bottom-up variables in their models. (2) The main results and conclusions of this paper are based on the use of camera traps to estimate the relative occurrence of prey and pred...

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Random versus Game Trail-Based Camera Trap Placement Strategy for Monitoring Terrestrial Mammal Communities

Cited by 153 publications

References 39 publications

Biodiversity estimates from different camera trap surveys: a case study from Osogovo Mt., Bulgaria

Biodiversity estimates from different camera trap surveys: a case study from Osogovo Mt., Bulgaria

Single-Camera Trap Survey Designs Miss Detections: Impacts on Estimates of Occupancy and Community Metrics

Multi-trophic interactions in anthropogenic landscapes: the devil is in the detail

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