Population assessment without individual identification using camera-traps: A comparison of four methods

Santini, Giacomo; Abolaffio, Milo; Ossi, Federico; Franzetti, Barbara; Cagnacci, F.; Focardi, Stefano

doi:10.1016/j.baae.2022.03.007

Cited by 22 publications

(24 citation statements)

References 59 publications

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“…The viewshed density estimators are all sensitive to measurements of the sampled area in front of cameras (e.g., Santini et al., 2022). Even when cameras are carefully placed to representatively sample a region of interest, these cameras sample only a tiny fraction of the total study area.…”

Section: Introductionmentioning

confidence: 99%

Best practices to account for capture probability and viewable area in camera‐based abundance estimation

Moeller

Waller

DeCesare

et al. 2022

Remote Sens Ecol Conserv

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A suite of recently developed statistical methods to estimate the abundance and density of unmarked animals from camera traps require accurate estimates of the area sampled by each camera. Although viewshed area is fundamental to achieving accurate abundance estimates, there are no established guidelines for collecting this information in the field. Furthermore, while the complexities of the detection process from motion sensor photography are generally acknowledged, viewable area (the common factor between motion sensor and time lapse photography) on its own has been underemphasized. We establish a common set of terminology to identify the component parts of viewshed area, contrast the photographic capture process and area measurements for time lapse and motion sensor photography, and review methods for estimating viewable area in the field. We use a case study to demonstrate the importance of accurate estimates of viewable area on abundance estimates. Time lapse photography combined with accurate measurements of viewable area allow researchers to assume that capture probability equals 1. Motion sensor photography requires measuring distances to each animal and fitting a distance sampling curve to account for capture probability of <1.

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Section: Introductionmentioning

confidence: 99%

Best practices to account for capture probability and viewable area in camera‐based abundance estimation

Moeller

Waller

DeCesare

et al. 2022

Remote Sens Ecol Conserv

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“…Nakashima (2022) commented on our paper Santini et al (2022) recently published in this journal about the critical assessment of existing methods to estimate the abundance of unmarked animals using camera traps. The Author of the letter, while acknowledging the general value of the paper, raised a series of interesting elements for further discussion, pointing out presumed weaknesses in our approach.…”

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confidence: 99%

“…The recent technological improvement of camera traps, their decreasing cost and ease of use has induced researchers to develop camera trap-based methods to estimate population density for species without obvious visual patterns allowing individual identification (cf. Santini et al, 2022 andNakashima, 2022). Yet, wildlife managers are confronted with the difficult choice of what method to use in relation to the population to survey (e.g., a population of a presumably rare species) and the specific context of investigation (e.g., the number of available camera traps).…”

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confidence: 99%

“…Beyond the relative performance of the methods under the set of conditions we tested, i.e. their basic form as described in the literature, it is important to underline that improvements are possible and desirable, as comparisons like the one performed in Santini et al (2022) or critical comments in Nakashima (2022)…”

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confidence: 99%

“…Basic and Applied Ecology 65 (2022) 123À125 www.elsevier.com/locate/baae this suggestion represents an interesting avenue of research for future evaluations. However, as already stated, the aim ofSantini et al (2022) is to provide a comparison of the performance of the different methods applied as they have been proposed, i.e. as practitioners are likely to apply them, leaving the detailed examination of the flaws and possible improvements to further investigation as it would not fit in a synthetic piece of work.…”

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confidence: 99%

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Making the best of a hard job: A response to Nakashima (2022)

Santini

Abolaffio²,

Ossi

et al. 2022

Basic and Applied Ecology

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Assessing trends in population size of three unmarked species: A comparison of a multi‐species N‐mixture model and random encounter models

Bollen,

Palencia,

Vicente

et al. 2023

Ecology and Evolution

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Estimation of changes in abundances and densities is essential for the research, management, and conservation of animal populations. Recently, technological advances have facilitated the surveillance of animal populations through the adoption of passive sensors, such as camera traps (CT). Several methods, including the random encounter model (REM), have been developed for estimating densities of unmarked populations but require additional information. Hierarchical abundance models, such as the N‐mixture model (NMM), can estimate abundances without performing additional fieldwork but do not explicitly estimate the area effectively sampled. This obscures the interpretation of its densities and requires its users to focus on relative measures of abundance instead. Hence, the main objective of our study is to evaluate if REM and NMM yield consistent results qualitatively. Therefore, we compare relative trends: (i) between species, (ii) between years and (iii) across years obtained from annual density/abundance estimates of three species (fox, wild boar and red deer) in central Spain monitored by a camera trapping network for five consecutive winter periods. We reveal that NMM and REM provided density estimates in the same order of magnitude for wild boar, but not for foxes and red deer. Assuming a Poisson detection process in the NMM was important to control for inflation of abundance estimates for frequently detected species. Both methods consistently ranked density/abundance across species (between species trend), but did not always agree on relative ranks of yearly estimates within a single population (between years trend), nor on its linear population trends across years (across years trend). Our results suggest that relative trends are generally consistent when the range of variability is large, but can become inconsistent when the range of variability is smaller.

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Population assessment without individual identification using camera-traps: A comparison of four methods

Cited by 22 publications

References 59 publications

Best practices to account for capture probability and viewable area in camera‐based abundance estimation

Best practices to account for capture probability and viewable area in camera‐based abundance estimation

Making the best of a hard job: A response to Nakashima (2022)

Assessing trends in population size of three unmarked species: A comparison of a multi‐species N‐mixture model and random encounter models

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