Abstract
Camera trap applications range from studying wildlife habits to detecting rare species, which are difficult to capture by more traditional techniques. In this work, we aimed at finding the best model to predict the distribution pattern of wildlife and to explain the relationship between environmental conditions with the species detected by camera traps. We applied two types of statistical models in a specific Mediterranean landscape case. The results of both models shown adjustments over 80 %. First, we ran a Principal Components Analysis (PCA). Discriminant, and logistic analyses were performed for ungulates in general, and three species in particular: Barbary sheep, mouflon, and wild boar. The same environmental conditions explained the presence of these species in all the proposed models. Hence, we proved the generally positive influence of patch size on the presence of ungulates and negative influence of the fractal dimension and density edge. We quantified the relationships between a suite of landscape metrics measured in different grids to test whether spatial heterogeneity plays a major role in determining the distribution of ungulates. We explained much of the variation in distribution with metrics, specifically related to habitat heterogeneity. That outcome highlighted the potential importance of spatial heterogeneity in determining the distribution of large herbivores. We discussed our results in the forestry conservation practices context and discuss potential ways to integrate ungulate management and forestry practices better.
Highlights
Camera traps, and the images they generate are becoming an essential tool for field biologist studies and for monitoring terrestrial animals (Fegraus et al 2011)
Total Landscape Area (TLA) and Number of Patches (NUMP) metrics were not significant, so they were excluded from the model
The application of landscape metrics provided excellent results when included in monitoring wildlife studies (Smith et al 2004)
Summary
The images they generate are becoming an essential tool for field biologist studies and for monitoring terrestrial animals (Fegraus et al 2011). Camera traps are very convenient for detecting cryptic and rare species, which are difficult to capture by more traditional techniques This method is important to study endangered species, when capture or collection is restricted or prohibited (Botello et al 2007). Because of the extensive data collection, camera-trapping studies usually record abundant information about non-targeted species, but, that data has been marginalized and rarely published. Those extra stored images may provide critical information for certain types of research, for example: (1) measuring biodiversity, (2) finding new evidence about the efficacy of management actions taken in different protected areas, and (3) studying species thought to be locally extinct (Can and Togan 2009). Camera-trapping data usefulness can be determined from indirect methods (Rowcliffe et al 2008), for example, underlying detection probabilities from camera-trapping data can be estimated by combining occupancy models (Mackenzie et al 2002) and population size measurements (Royle and Nichols 2003, Stanley and Royle 2005)
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