Optimizing Segmentation In Occupancy Modelling Of Camera-Trap Data

O_LIAccurate estimation of species abundances is a common challenge in conservation biology, particularly when abundances are compared in space or time. Occupancy modelling provides relative abundance estimates from camera-trapping data without the need for individual recognition. This requires segmentation of continuous records into a series of intervals with either detection or non-detection. While the segmentation method may have profound effects on the accuracy of occupancy modelling, no form of segmentation optimization is yet available. C_LIO_LIWe assessed how segmentation, defined by interval length and number, influences the accuracy of predictions by the Royle-Nichols occupancy model and how this relationship depends on species density, study duration, and the number of sampling points. We simulated capture data using an individual-based model in which we varied the species densities between study locations, and then fitted models using different segmentations. Using the simulation results, we developed a simple tool for choosing optimal segmentation and the best minimum number of intervals to use. To provide an example, we used the optimization tool on actual data from a camera-trapping study in Western Equatorial Africa and compared relative wildlife abundances between two forest management types. C_LIO_LIWe found that the optimum interval length for the Royle-Nichols occupancy model varied with species density, study duration, and the number of sampling points. By analyzing the empirical data, we found that optimal segmentation and minimum number of intervals differed substantially between species. Modelling with optimized, species-specific interval numbers and lengths yielded more conservative outcomes (i.e. fewer significant effects) than did modelling with fixed numbers and lengths. Furthermore, the choice of interval length can affect the direction of relationships. C_LIO_LIOur results indicate that the interval length is by no means a parameter to be standardized at a given value but should be carefully chosen based on the properties of the data at hand. This study shows that the arbitrary segmentation that is commonly used in occupancy modelling may not be optimal. Our tool helps to optimize segmentation, increases the accuracy of relative abundance estimations, and thus facilitates the use of camera-trapping studies to evaluate conservation measures. C_LI