Cretois Benjamin, Simmonds Emily G, Linnell John D C, van Moorter Bram, Rolandsen Christer M, Solberg Erling J, Strand Olav, Gundersen Vegard, Roer Ole, Rød Jan Ketil
Department of Geography Norwegian University of Science and Technology Trondheim Norway.
Norwegian Institute for Nature Research Trondheim Norway.
Ecol Evol. 2021 Oct 5;11(21):15191-15204. doi: 10.1002/ece3.8200. eCollection 2021 Nov.
Many publications make use of opportunistic data, such as citizen science observation data, to infer large-scale properties of species' distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species' home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS-telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species' ecology. Models naïvely using opportunistic observations in habitat-use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species' RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat-use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat-use studies.
许多出版物利用机会性数据，如公民科学观测数据，来推断物种分布的大规模特征。然而，少数使用机会性公民科学数据在栖息地层面研究动物生态学的出版物，在这样做时并未考虑机会性记录中的空间偏差，或使用难以推广的方法。在本研究中，我们探讨了机会性观测中存在的偏差，并提出了一种校正这些偏差的方法。我们首先通过将挪威三种野生有蹄类动物的机会性公民科学观测与GPS遥测数据进行比较，研究了偏差的程度。然后，我们通过指定偏差模型来量化偏差的程度。根据偏差模型，我们在物种的活动范围内对可用位置进行采样。连同机会性观测一起，我们使用校正后的可用位置来估计资源选择函数（RSF）。我们用这三个物种的模拟和实证数据集对该方法进行了测试。我们将校正方法的结果与未校正的机会性观测得到的RSF以及使用GPS遥测数据得到的RSF进行了比较。最后，我们比较了使用这些模型各自得到的栖息地适宜性地图。机会性观测比来自GPS遥测的位置更容易受到人类可达性和能见度的影响。这对推断物种生态学有影响。在栖息地利用研究中单纯使用机会性观测的模型可能会导致虚假推断。然而，根据机会性数据中的空间偏差对可用位置进行采样，在某些情况下可以改善对物种RSF的估计以及预测的栖息地适宜性地图。这项研究突出了在栖息地利用研究中使用机会性观测的挑战和机遇。虽然我们的方法并非万无一失，但它是朝着释放机会性公民科学数据在栖息地利用研究中的潜力迈出的第一步。
