Alós Josep, Palmer Miquel, Balle Salvador, Arlinghaus Robert
Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany.
Instituto Mediterráneo de Estudios Avanzados, IMEDEA (CSIC-UIB), C/ Miquel Marqués 21, 07190, Esporles, Illes Balears, Spain.
PLoS One. 2016 Apr 27;11(4):e0154089. doi: 10.1371/journal.pone.0154089. eCollection 2016.
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.
状态空间模型（SSM）越来越多地应用于涉及生物遥测生成的位置数据的研究中，因为它们能够从未观测到或观测误差不可忽略的位置估计运动参数。海洋沿岸鱼类中常用的遥测系统由全向声学接收器阵列组成，这些接收器在跟踪期内生成检测事件的多元时间序列。在这里，我们报告了一种SSM应用的新颖贝叶斯拟合方法，该方法将活动范围内的机械运动特性（随机游走的一种特殊情况，由奥恩斯坦-乌伦贝克过程加权）与从声学接收器阵列获得的数据典型的观测误差模型相结合。我们通过模拟建模以及对位置间各种运动特性配置和时间步长影响的广泛敏感性分析，探索了该方法的性能和准确性。模型结果显示对运动参数的估计准确且无偏差，在大多数情况下，模拟的运动参数能够被正确恢复。只有在极端情况下（快速游泳速度与长时间步长上的检测数量合并时），模型才会产生一些偏差，这在实地应用中需要加以考虑。我们的方法随后应用于从小型海洋沿岸鱼类珍珠剃刀鱼（Xyrichtys novacula）收集的实际声学跟踪数据。我们在此提出的贝叶斯SSM为习惯贝叶斯推理方式的人提供了一种替代方法。我们的贝叶斯SSM可以轻松地适应和推广到任何物种，从而能够研究自由活动动物的活动范围和领地建立对生态和进化的影响，无论是鱼类还是其他类群。
