1. 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.
2. 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).
3. 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.
4. 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.

中文翻译：

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跟踪数据偏差对物种分布模型的量化影响

1. 遥测数据集变得越来越大，并使用不同的技术（GPS，Argos，基于光的地理位置）覆盖了更广泛的物种。这些数据集在一起，通过开发物种分布或栖息地适应性模型（SDM）来预测物种在空间和时间上的环境依赖性，在广泛的空间尺度上理解物种的空间使用具有巨大的潜力。但是，跟踪数据集可能存在严重偏差，并且无法评估此类偏差如何影响SDM预测，因此，我们缺乏对动物分布的解释。
2. 我们根据随机捕食者和中央觅食者的预定环境值生成了模拟轨迹，然后基于跟踪数据集中的五个常见偏差，从这些轨迹中采样了位置：（a）标记位置；（b）追踪装置；（c）轨道内的数据空白；（d）标签过早脱离（或失效）和（e）不同的处理方法。然后，我们使用所产生的有偏差的模拟数据集的240个组合来开发二项式广义线性（GLM）和加性（GAM）模型，以估计不同环境集（凉深，凉爽沿海，温暖的深海和温暖的沿海环境）中的栖息地适宜性。
3. 我们的结果表明，标记位置和轨道长度在降低模型性能方面具有最大的影响，但是可以通过向数据集中添加少量的附加，相对较少偏移的轨道来克服这些偏差。相比之下，来自所有其他偏差的影响几乎可以忽略不计，包括对于具有足够轨道的低分辨率跟踪数据集。当使用可能引入其他偏差（例如插值位置）的处理方法时，我们还强调了一种警告方法的必要性。随机捕食者和中央捕食者获得了相似的趋势，但后者的模型性能相对较低。
4. 我们提供的证据表明，即使不需要GPS跟踪数据集，也可以轻松用于提高物种大规模利用空间的知识，而无需进行详细的处理和跟踪重建。在当前数据获取快速增长的背景下，以及迫切需要解决全球变化带来的巨大空间尺度生态后果的情况下，这一点尤其重要。