Bennington Steph, Dillingham Peter W, Bourke Scott D, Dawson Stephen M, Slooten Elisabeth, Rayment William J
Department of Marine Science University of Otago Dunedin New Zealand.
Department of Mathematics and Statistics University of Otago Dunedin New Zealand.
Ecol Evol. 2024 Jul 22;14(7):e70074. doi: 10.1002/ece3.70074. eCollection 2024 Jul.
Species distribution models (SDMs) can be used to predict distributions in novel times or space (termed transferability) and fill knowledge gaps for areas that are data poor. In conservation, this can be used to determine the extent of spatial protection required. To understand how well a model transfers spatially, it needs to be independently tested, using data from novel habitats. Here, we test the transferability of SDMs for Hector's dolphin (), a culturally important (taonga) and endangered, coastal delphinid, endemic to Aotearoa New Zealand. We collected summer distribution data from three populations from 2021 to 2023. Using Generalised Additive Models, we built presence/absence SDMs for each population and validated the predictive ability of the top models (with TSS and AUC). Then, we tested the transferability of each top model by predicting the distribution of the remaining two populations. SDMs for two populations showed useful performance within their respective areas (Banks Peninsula and Otago), but when used to predict the two areas outside the models' source data, performance declined markedly. SDMs from the third area (Timaru) performed poorly, both for prediction within the source area and when transferred spatially. When data for model building were combined from two areas, results were mixed. Model interpolation was better when presence/absence data from Otago, an area of low density, were combined with data from areas of higher density, but was otherwise poor. The overall poor transferability of SDMs suggests that habitat preferences of Hector's dolphins vary between areas. For these dolphins, population-specific distribution data should be used for conservation planning. More generally, we demonstrate that a one model fits all approach is not always suitable. When SDMs are used to predict distribution in data-poor areas an assessment of performance in the new habitat is required, and results should be interpreted with caution.
物种分布模型（SDMs）可用于预测新的时间或空间中的分布情况（称为可转移性），并填补数据匮乏地区的知识空白。在保护工作中，这可用于确定所需的空间保护范围。为了解模型在空间上的转移效果如何，需要使用来自新栖息地的数据进行独立测试。在此，我们测试了赫氏海豚（）的物种分布模型的可转移性，赫氏海豚是新西兰特有的一种具有文化重要性（珍宝）且濒危的沿海海豚，在新西兰具有重要意义。我们收集了2021年至2023年来自三个种群的夏季分布数据。使用广义相加模型，我们为每个种群构建了存在/不存在的物种分布模型，并验证了顶级模型的预测能力（通过真技能统计和曲线下面积）。然后，我们通过预测其余两个种群的分布来测试每个顶级模型的可转移性。两个种群的物种分布模型在其各自区域（班克斯半岛和奥塔哥）内表现出有用的性能，但当用于预测模型源数据之外的两个区域时，性能显著下降。来自第三个区域（蒂马鲁）的物种分布模型在源区域内进行预测以及在空间转移时表现都很差。当将来自两个区域的模型构建数据合并时，结果好坏参半。当低密度区域奥塔哥的存在/不存在数据与高密度区域的数据合并时，模型插值效果较好，否则效果不佳。物种分布模型总体较差的可转移性表明，赫氏海豚的栖息地偏好因区域而异。对于这些海豚，应使用特定种群的分布数据进行保护规划。更普遍地说，我们证明了一种适用于所有情况的单一模型方法并不总是合适的。当使用物种分布模型来预测数据匮乏地区的分布时，需要评估其在新栖息地的性能，并且对结果的解释应谨慎。
