Additional information that may be collected to support modelling density

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Assumptions, Pros, Cons

Assumptions

• Demographic closure (i.e., no births or deaths) (Wearn & Glover-Kapfer, 2017)

• Detection probability. of different individuals is equal (Wearn & Glover-Kapfer, 2017)

  • or, for SECR., individuals have equal Detection probability. at a given distance from the centre of their home range. (Wearn & Glover-Kapfer, 2017)

• Detections of different individuals are independent (Wearn & Glover-Kapfer, 2017)

• Behaviour is unaffected by cameras and marking. (Wearn & Glover-Kapfer, 2017)

• Individuals do not lose marks (Wearn & Glover-Kapfer, 2017)

• Individuals are not misidentified (Wearn & Glover-Kapfer, 2017)

• Surveys. are independent (Wearn & Glover-Kapfer, 2017)

• For conventional models, geographic closure (i.e., no immigration or emigration) (Wearn & Glover-Kapfer, 2017)

• Spatially explicit models have further assumptions. about animal movement (Wearn & Glover-Kapfer, 2017; Rowcliffe et al., 2008; Royle et al., 2009; O’Brien et al., 2011); these include:

  • Home ranges. are stable (Wearn & Glover-Kapfer, 2017)

  • Movement is unaffected by the cameras (Wearn & Glover-Kapfer, 2017)

  • Camera locations. are randomly placed. with respect to the distribution and orientation of home ranges. (Wearn & Glover-Kapfer, 2017)

  • Distribution of home range. centres follows a defined distribution (Poisson, or other, e.g., negative binomial) (Wearn & Glover-Kapfer, 2017)

Pros

• Produces direct estimates of density. or population size for explicit spatial regions (Chandler & Royle, 2013)

• Allows researchers to mark a subset of the population / to take advantage of natural markings (Wearn & Glover-Kapfer, 2017)

• Estimates are fully comparable across space, time, species and studies (Wearn & Glover-Kapfer, 2017)

• density. estimates obtained in a single model, fully incorporate spatial information of locations and individuals (Wearn & Glover-Kapfer, 2017)

• Both likelihood-based and Bayesian versions of the model have been implemented in relatively easy-to-use software DENSITY and SPACECAP, respectively, as well as associated R packages) (Wearn & Glover-Kapfer, 2017)

• Flexibility in study design (e.g., ‘holes’ in the trapping grid) (Wearn & Glover-Kapfer, 2017)

• Open SECR. (Efford, 2004; Borchers & Efford, 2008; Royle & Young, 2008; Royle et al., 2009) models exist that allow for estimation of recruitment and survival rates (Wearn & Glover-Kapfer, 2017)

• Avoid ad-hoc definitions of study area and edge effects (Doran-Myers, 2018)

• SECR. (Efford, 2004; Borchers & Efford, 2008; Royle & Young, 2008; Royle et al., 2009) accounts for variation in individual Detection probability.; can produce spatial variation in density.; SECR. (Efford, 2004; Borchers & Efford, 2008; Royle & Young, 2008; Royle et al., 2009) more sensitive to detect moderate-to-major populations changes (+/-20-80%) (Royle & Young, 2008; Royle et al., 2009)

Cons

• Requires that individuals are identifiable (Wearn & Glover-Kapfer, 2017)

• Requires that a minimum number of individuals are trapped (each recaptured multiple times ideally) (Wearn & Glover-Kapfer, 2017)

• Requires that each individual is captured at a number of camera locations. (Wearn & Glover-Kapfer, 2017)

• Multiple cameras per station may be required to identify individuals; difficult to implement at large spatial scales as it requires a high density. of cameras (Morin et al., 2022)

• May not be precise. enough for long-term monitoring (Green et al., 2020)

• Cameras must be close enough that animals are detected at multiple camera locations. (Wearn & Glover-Kapfer, 2017) (may be challenging to implement at large scales as many cameras are needed)’ (Chandler & Royle, 2013)

• ½ MMDM (Mean Maximum Distance Moved) will usually lead to an underestimation of home range. size and thus overestimation of density. (Parmenter et al., 2003; Noss et al., 2012; Wearn & Glover-Kapfer, 2017)

Overview

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In-depth

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Visual resources

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Shiny apps/Widgets

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Analytical tools & Resources

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References

Clarke, J., Bohm, H., Burton, C., Constantinou, A. (2023). Using Camera Traps to Estimate Medium and Large Mammal Density: Comparison of Methods and Recommendations for Wildlife Managers. https://doi.org/10.13140/RG.2.2.18364.72320