Quantifying effects of tracking data bias on species distribution models
Quantifying effects of tracking data bias on species distribution models
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DOI：
10.1111/2041-210x.13507
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发表时间：
2020-10-20
影响因子：
6.6
通讯作者：
Sequeira, Ana M. M.
中科院分区：
文献类型：
--
作者：
O'Toole, Malcolm;Queiroz, Nuno;Sequeira, Ana M. M.
遥测数据集正在变得越来越大，使用不同的技术（全球定位系统、Argos、光基地理定位）覆盖更广泛的物种。总之，这些数据集具有巨大的潜力，可以通过开发物种分布或栖息地适宜性模型（SDM）来预测物种在空间和时间上的环境依赖性，从而在广泛的空间尺度上了解物种的空间使用。然而，跟踪数据集可能存在严重偏差，并且缺乏对此类偏差如何影响SDM预测的评估，因此，我们对动物分布的解释缺乏。我们根据预先确定的环境值为随机捕食者和中央觅食者生成模拟轨迹，然后根据跟踪数据集中的五种常见偏差的组合从这些轨迹中采样位置：（a）标记位置;（B）跟踪设备;（c）轨迹内的数据间隙;（d）过早的标签分离（或失败）和（e）不同的处理方法。然后，我们使用了240个有偏模拟数据集的组合，开发了二项式广义线性（GLM）和加法（GAM）模型，以估计不同环境集（凉爽的深层，凉爽的沿海，温暖的深层和温暖的沿海环境）的栖息地适宜性。我们的研究结果表明，标记位置和轨道长度对降低模型性能的影响最大，但这些偏差可以通过向数据集添加一小部分额外的、相对偏差较小的轨道来克服。相比之下，所有其他偏差的影响几乎可以忽略不计，包括有足够轨迹可用的低分辨率跟踪数据集。我们还强调了在使用可能引入其他偏差（例如内插位置）的处理方法时需要谨慎的方法。随机捕食者和中心位置觅食者得到了类似的趋势，但后者的模型性能相对较低。我们提供的证据表明，即使是非GPS跟踪数据集可以很容易地用于提高物种的大规模空间使用的知识，而不需要详细的处理和跟踪重建。在当前数据采集迅速增加和迫切需要解决全球变化的大空间尺度生态后果的背景下，这一点尤其重要。
Telemetry datasets are becoming increasingly large and covering a wider range of species using different technologies (GPS, Argos, light-based geolocation). Together, such datasets hold tremendous potential to understand species' space use at broad spatial scale, through the development of species distribution or habitat suitability models (SDMs) to predict environmental dependencies of species across space and time. However, tracking datasets can be heavily biased and an assessment of how such biases affect SDM predictions, and therefore, our interpretation of animal distributions is lacking. We generated simulated tracks based on predetermined environmental values for a random predator and a central place forager, and then sampled positions from those tracks based on a combination of five common biases in tracking datasets: (a) tagging location; (b) tracking device; (c) data gaps within tracks; (d) premature tag detachment (or failure) and (e) different processing methods. We then used 240 combinations of the resulting biased simulated datasets to develop binomial generalised linear (GLM) and additive (GAM) models to estimate habitat suitability in different environmental sets (cool deep, cool coastal, warm deep and warm coastal environments). Our results show that tagging location and length of tracks have the largest effects in decreasing model performance, but that these biases can be overcome by adding a small percentage of additional, relatively less biased tracks to the dataset. In comparison, the effects from all other biases were almost negligible, including for low resolution tracking datasets for which sufficient tracks are available. We also highlight the need for a cautionary approach when using processing methods that can introduce other biases (e.g. interpolated locations). Similar trends were obtained for the random predator and the central place forager, but with relatively lower model performance for the latter. We provide evidence that even non-GPS tracking datasets can be readily used to improve the knowledge of large-scale space use by species without the need for detailed processing and tracking reconstruction. This is especially relevant in the current context of rapid increase in data acquisition and the urgent need to address the large spatial scale ecological consequences of global change.