A camera‐based method for estimating absolute density in animals displaying home range behaviour

Consistent between‐individual differences in movement are widely recognised across taxa. In addition, foraging plasticity at the within‐individual level suggests a behavioural dependency on the internal energy demand. Because behaviour co‐varies with fast‐slow life history (LH) strategies in an adaptive context, as theoretically predicted by the pace‐of‐life syndrome hypothesis, mass/energy fluxes should link behaviour and its plasticity with physiology at both between‐ and within‐individual levels. However, a mechanistic framework driving these links in a fluctuating ecological context is lacking. Focusing on home range behaviour, we propose a novel behavioural‐bioenergetics theoretical model to address such complexities at the individual level based on energy balance. We propose explicit mechanistic links between behaviour, physiology/metabolism and LH by merging two well‐founded theories, the movement ecology paradigm and the dynamic energetic budget theory. Overall, our behavioural‐bioenergetics model integrates the mechanisms explaining how (1) behavioural between‐ and within‐individual variabilities connect with internal state variable dynamics, (2) physiology and behaviour are explicitly interconnected by mass/energy fluxes, and (3) different LHs may arise from both behavioural and physiological variabilities in a given ecological context. Our novel theoretical model reveals encouraging opportunities for empiricists and theoreticians to delve into the eco‐evolutionary processes that favour or hinder the development of between‐individual differences in behaviour and the evolution of personality‐dependent movement syndromes.

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“…; Campos‐Candela et al . ). Within our proposed model, disentangling

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and γ will allow one to explore different dimensions of the HR behaviour.…”

Section: Main Insights From Our Behavioural‐bioenergetics Model and Pmentioning

confidence: 97%

A mechanistic theory of personality‐dependent movement behaviour based on dynamic energy budgets

et al. 2018

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“…), or require continuous or video monitoring of sampling units (Campos‐Candela et al. , Nakashima et al. ).…”

Section: Introductionmentioning

confidence: 99%

The influence of movement on the occupancy–density relationship at small spatial scales

et al. 2019

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The global decline of large carnivores demands effective and efficient methods to monitor population status, particularly using non-invasive methods. Density is among the most useful metrics of population status because it is directly comparable across space and time. Unfortunately, density is difficult to measure reliably, especially for mobile, cryptic species. Recently, efforts have turned to approximating density based on its relationship to more readily estimable indices of occurrence. However, the relationship between density and such indices is contingent on several key assumptions that field studies often violate. Recent research has shown that these relationships are unreliable where sampling units are not independent, as is often the case when estimating density or occurrence of large carnivores. Here, we use the largest data set thus far collected for leopards (Panthera pardus)-88 camera-trap surveys undertaken in 24 protected areas between 2013 and 2018-to explore how density and other population characteristics relate to parameter estimates in occupancy and Royle-Nichols abundance models. We show how home-range size confounds underlying relationships, with larger home ranges inflating the proportion of area used (PAU) and resulting in double counting in abundance models. Relativizing estimates of occupancy and abundance by home-range size improved their relationship with density, but the relationship remained weak and largely uninformative for management. Our findings illustrate the pitfalls of using the PAU or abundance as implicit proxies for density and highlight the challenges of assessing population status for wide-ranging, cryptic species across fragmented landscapes.

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“…In summary, we have proposed a method for estimating density in three dimensions using data from remote detectors, which can be used in ecological and conservation research and as a monitoring tool. The description of detection zones provided will be useful in extending other density estimation methods that are also based on the ideal gas model (Campos-Candela et al 2018;Moeller et al 2018). There is an extensive and growing literature characterizing sampling requirements for camera-traps in two dimensions, but such considerations are practically non-existent for species moving in three dimensions (O'Connell et al 2010;Burton et al 2015).…”

Section: Discussionmentioning

confidence: 99%

Estimating animal density in three dimensions using capture‐frequency data from remote detectors

Soto

Castañeda

Mandrak

et al. 2020

Remote Sens Ecol Conserv

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Remote detectors are being used increasingly often to study aquatic and aerial species, which move significantly differently from terrestrial species. Camera-trapping studies have used frameworks based on animal movement patterns to show that a species' detection frequency, along with movement speed and detector specifications, can be used to estimate absolute population density. This approach, however, has not yet been adapted to cases where movement is threedimensional. Here we adapt one such framework to three-dimensional movement, to characterize the relationship between population density, animal speed, characteristics of a remote sensor's detection zone and detection frequency. The derivation involves defining the detection zone mathematically and calculating the mean area of the profile it presents to approaching individuals. We developed two variants of the model-one assuming random movement of all individuals, and one allowing for different probabilities for each approach direction (e.g. that animals more often swim/fly horizontally than vertically). We used computer simulations to evaluate model performance for a wide range of animal and detector densities. Simulations show that in ideal conditions the method approximates true density well, and that estimates become increasingly accurate using more detectors, or sampling for longer. Moreover the method is robust to violations of assumptions, accuracy is decreased only in extreme cases where all detectors are facing the same way. We provide equations for estimating population density from detection frequency and outline how to estimate the necessary parameters. We discuss how environmental variables and species-specific characteristics affect parameter estimates and how to account for these differences in density estimations. Our method can be applied to common remote detection methods (cameras and acoustic detectors), which are currently being used to study a diversity of species and environments. Therefore, our work may significantly expand the number and diversity of species for which density can be estimated.

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A camera‐based method for estimating absolute density in animals displaying home range behaviour

Cited by 49 publications

References 55 publications

A mechanistic theory of personality‐dependent movement behaviour based on dynamic energy budgets

A mechanistic theory of personality‐dependent movement behaviour based on dynamic energy budgets

The influence of movement on the occupancy–density relationship at small spatial scales

Estimating animal density in three dimensions using capture‐frequency data from remote detectors

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