Department of Environment and Geography, Wentworth Way, University of York, Heslington, York, United Kingdom.
PLoS One. 2019 Jul 10;14(7):e0219357. doi: 10.1371/journal.pone.0219357. eCollection 2019.
Despite advances in technology, there are still constraints on the use of some tracking devices for small species when gathering high temporal and spatial resolution data on movement and resource use. For small species, weight limits imposed on GPS loggers and the consequent impacts on battery life, restrict the volume of data that can be collected. Research on home range and habitat selection for these species should therefore incorporate a consideration of how different sampling parameters and methods may affect the structure of the data and the conclusions drawn. However, factors such as these are seldom explicitly considered. We applied two commonly-used methods of home range estimation, Movement-based Kernel Density Estimation (MKDE) and Kernel Density Estimation (KDE) to investigate the influence of fix rate, tracking duration and method on home range size and habitat selection, using GPS tracking data collected at two different fix rates from a small, aerially-insectivorous bird, the European nightjar Caprimulgus europaeus. Effects of tracking parameters varied with home range estimation method. Fix rate and tracking duration most strongly explained change in MKDE and KDE home range size respectively. Total number of fixes and tracking duration had the strongest impact on habitat selection. High between- and within-individual variation strongly influenced outcomes and was most evident when exploring the effects of varying tracking duration. To reduce skew and bias in home range size estimation and especially habitat selection caused by individual variation and estimation method, we recommend tracking animals for the longest period possible even if this results in a reduced fix rate. If accurate movement properties, (e.g. trajectory length and turning angle) and biologically-representative movement occurrence ranges are more important, then a higher fix rate should be used, but priority habitats can still be identified with an infrequent sampling strategy.
尽管技术在不断进步,但在收集小物种的运动和资源利用的高时间和空间分辨率数据时,仍存在一些跟踪设备使用上的限制。对于小物种,GPS 记录器上的重量限制以及对电池寿命的相应影响,限制了可以收集的数据量。因此,对于这些物种的家域和栖息地选择研究,应考虑不同的采样参数和方法如何影响数据结构和得出的结论。然而,这些因素很少被明确考虑。我们应用了两种常用的家域估计方法,基于运动的核密度估计(MKDE)和核密度估计(KDE),以调查固定速率、跟踪持续时间和方法对家域大小和栖息地选择的影响,使用在两个不同固定速率下从欧洲夜鹰(Caprimulgus europaeus)收集的 GPS 跟踪数据。跟踪参数的影响因家域估计方法而异。固定速率和跟踪持续时间分别对 MKDE 和 KDE 家域大小的变化解释最强。固定点数和跟踪持续时间对栖息地选择的影响最大。个体间和个体内的高度变异性强烈影响结果,在探索不同跟踪持续时间的影响时最为明显。为了减少由个体变异和估计方法引起的家域大小估计和特别是栖息地选择的偏斜和偏差,我们建议尽可能长时间地跟踪动物,即使这会导致固定速率降低。如果更准确的运动特性(例如轨迹长度和转弯角度)和具有代表性的运动出现范围更为重要,则应使用更高的固定速率,但仍可以使用不频繁的采样策略来确定优先栖息地。