Abstract
State-space models (SSM) are increasingly applied in studies involving biotelemetry-generated positional data because they are able to estimate movement parameters from positions that are unobserved or have been observed with non-negligible observational error. Popular telemetry systems in marine coastal fish consist of arrays of omnidirectional acoustic receivers, which generate a multivariate time-series of detection events across the tracking period. Here we report a novel Bayesian fitting of a SSM application that couples mechanistic movement properties within a home range (a specific case of random walk weighted by an Ornstein-Uhlenbeck process) with a model of observational error typical for data obtained from acoustic receiver arrays. We explored the performance and accuracy of the approach through simulation modelling and extensive sensitivity analyses of the effects of various configurations of movement properties and time-steps among positions. Model results show an accurate and unbiased estimation of the movement parameters, and in most cases the simulated movement parameters were properly retrieved. Only in extreme situations (when fast swimming speeds are combined with pooling the number of detections over long time-steps) the model produced some bias that needs to be accounted for in field applications. Our method was subsequently applied to real acoustic tracking data collected from a small marine coastal fish species, the pearly razorfish, Xyrichtys novacula. The Bayesian SSM we present here constitutes an alternative for those used to the Bayesian way of reasoning. Our Bayesian SSM can be easily adapted and generalized to any species, thereby allowing studies in freely roaming animals on the ecological and evolutionary consequences of home ranges and territory establishment, both in fishes and in other taxa.
Highlights
The home range is defined as the area used by an animal during its normal activities [1]
The Bayesian State-space models (SSM) retrieved the movement parameters of the simulated fish trajectories with acceptable precision and accuracy in most of the combinations of movement parameters and for the six time-steps that were considered (Fig 4)
Given that the data collected by arrays of omnidirectional acoustic receivers are only indirectly related to the fish’s position, the estimation of the movement parameters is often imprecise
Summary
The home range is defined as the area used by an animal during its normal activities [1]. Establishment of spatially confined home ranges, which may define an actively defended territory, is a widely observed pattern in nature [2]. The mechanistic idea behind the home range concept is that an animal moves following random stimuli (i.e., diffusion movement) but with an added tendency to remain around a specific point, which constrains the fraction of the available potentially suitable habitat to one that is used [7,8]. Among the different mechanistic movement models that have been proposed for describing home range behavior in animals, biased random walks are probably the most widespread [9,10]. Describing the drift that constrains the animal around the center of the home range by a bivariate Ornstein—Uhlenbeck (OU) process dates back at least to 1997 [11], and this specific implementation has been repeatedly used since providing mechanistic descriptors of home range behaviour for a range of wild-living animals (e.g., [12,13,14])
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