Hazen Elliott L, Abrahms Briana, Brodie Stephanie, Carroll Gemma, Welch Heather, Bograd Steven J
NOAA Southwest Fisheries Science Center, Environmental Research Division, Monterey, CA, USA.
Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, USA.
Mov Ecol. 2021 Feb 17;9(1):5. doi: 10.1186/s40462-021-00240-2.
Habitat suitability models give insight into the ecological drivers of species distributions and are increasingly common in management and conservation planning. Telemetry data can be used in habitat models to describe where animals were present, however this requires the use of presence-only modeling approaches or the generation of 'pseudo-absences' to simulate locations where animals did not go. To highlight considerations for generating pseudo-absences for telemetry-based habitat models, we explored how different methods of pseudo-absence generation affect model performance across species' movement strategies, model types, and environments.
We built habitat models for marine and terrestrial case studies, Northeast Pacific blue whales (Balaenoptera musculus) and African elephants (Loxodonta africana). We tested four pseudo-absence generation methods commonly used in telemetry-based habitat models: (1) background sampling; (2) sampling within a buffer zone around presence locations; (3) correlated random walks beginning at the tag release location; (4) reverse correlated random walks beginning at the last tag location. Habitat models were built using generalised linear mixed models, generalised additive mixed models, and boosted regression trees.
We found that the separation in environmental niche space between presences and pseudo-absences was the single most important driver of model explanatory power and predictive skill. This result was consistent across marine and terrestrial habitats, two species with vastly different movement syndromes, and three different model types. The best-performing pseudo-absence method depended on which created the greatest environmental separation: background sampling for blue whales and reverse correlated random walks for elephants. However, despite the fact that models with greater environmental separation performed better according to traditional predictive skill metrics, they did not always produce biologically realistic spatial predictions relative to known distributions.
Habitat model performance may be positively biased in cases where pseudo-absences are sampled from environments that are dissimilar to presences. This emphasizes the need to carefully consider spatial extent of the sampling domain and environmental heterogeneity of pseudo-absence samples when developing habitat models, and highlights the importance of scrutinizing spatial predictions to ensure that habitat models are biologically realistic and fit for modeling objectives.
栖息地适宜性模型有助于深入了解物种分布的生态驱动因素，在管理和保护规划中越来越常见。遥测数据可用于栖息地模型，以描述动物出现的地点，然而这需要使用仅存在建模方法或生成“伪缺失”来模拟动物未前往的地点。为了突出为基于遥测的栖息地模型生成伪缺失时的注意事项，我们探讨了不同的伪缺失生成方法如何影响跨物种移动策略、模型类型和环境的模型性能。
我们为海洋和陆地案例研究构建了栖息地模型，即东北太平洋蓝鲸（Balaenoptera musculus）和非洲象（Loxodonta africana）。我们测试了基于遥测的栖息地模型中常用的四种伪缺失生成方法：（1）背景采样；（2）在存在位置周围的缓冲区采样；（3）从标签释放位置开始的相关随机游走；（4）从最后一个标签位置开始的反向相关随机游走。栖息地模型使用广义线性混合模型、广义相加混合模型和增强回归树构建。
我们发现，存在和伪缺失之间环境生态位空间的分离是模型解释力和预测技能的最重要单一驱动因素。这一结果在海洋和陆地栖息地、两种具有截然不同移动模式的物种以及三种不同模型类型中都是一致的。表现最佳的伪缺失方法取决于哪种方法能产生最大的环境分离：蓝鲸采用背景采样，大象采用反向相关随机游走。然而，尽管根据传统预测技能指标，具有更大环境分离的模型表现更好，但相对于已知分布，它们并不总是能产生生物学上现实的空间预测。
在从与存在环境不同的环境中采样伪缺失的情况下，栖息地模型性能可能存在正偏差。这强调了在开发栖息地模型时仔细考虑采样域的空间范围和伪缺失样本的环境异质性的必要性，并突出了审查空间预测以确保栖息地模型在生物学上现实且适合建模目标的重要性。
