Abstract
Many species that occur at low densities are not accurately estimated using capture–recapture methods as such techniques assume that populations are well–defined in space. To solve this bias, spatially explicit capture–recapture (SECR) models have recently been developed. These models incorporate movement and can identify areas where it is more likely for individuals to concentrate their activity. In this study, we used data from camera–trap surveys of common genets (Genetta genetta) in Serra da Malcata (Portugal), designed to compare abundance estimates produced by SECR models with traditional closed–capture models. Using the SECR models, we observed spatial heterogeneity in genet distribution and density estimates were approximately two times lower than those obtained from the closed population models. The non–spatial model estimates were constrained to sampling grid size and likely underestimated movements, thereby overestimating density. Future research should consider the incorporation of cost–weighed models that can include explicit hypothesis on how environmental variables influence the distance metric.
Highlights
Researchers seeking to understand population proc esses need methods and models that provide accurate inferences on population status
We studied the distribution and abundance of common genets from October 2005 to November 2007
The most common approach is to combine this technique with standard closed population models
Summary
Researchers seeking to understand population proc esses need methods and models that provide accurate inferences on population status. The usual method to analyse density is to apply closed population mod els (White & Burnham, 1999), and convert these to densities using fundamentally ad hoc methods (Trolle et al, 2007). This approach presents two important difficulties: (1) the assumption of geographic closure of the population, i.e., no movement in and out of the sampling grid (White et al, 1982) which is frequently violated (Karanth & Nichols, 1998), and (2) the difficulty of estimating the effective sampling area (Balme et al, 2009a). Other approaches to estimate the buffer width include the full MMDM (Trolle et al, 2007), and the radius of an average home range (Sarmento et al, 2009).These approaches present major problems: (1) They lack theoretical justification, being mostly ad hoc methods (Bochers & Efford, 2008); and (2) comparisons of es timates from different methodologies become difficult (Sollmann et al, 2011)
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