Context Species habitat suitability models rarely incorporate multiple spatial scales or functional shapes of a species’ response to covariates. Optimizing models for these factors may produce more robust, reliable, and informative habitat suitability models, which can be beneficial for the conservation of rare and endangered species, such as tigers ( Panthera tigris ). Objectives We provide the first formal assessment of the relative impacts of scale-optimization and shape-optimization on model performance and habitat suitability predictions. We explored how optimization influences conclusions regarding habitat selection and mapped probability of occurrence. Methods We collated environmental variables expected to affect tiger occurrence, calculating focal statistics and landscape metrics at spatial scales ranging from 250 m to 16 km. We then constructed a set of presence–absence generalized linear models including: (1) single-scale optimized models (SSO); (2) a multi-scale optimized model (MSO); (3) single-scale shape-optimized models (SSSO) and (4) a multi-scale- and shape-optimized model (MSSO). We compared performance and resulting prediction maps for top performing models. Results The SSO (16 km), SSSO (16 km), MSO, and MSSO models performed equally well (AUC > 0.9). However, these differed substantially in prediction and mapped habitat suitability, leading to different ecological understanding and potentially divergent conservation recommendations. Habitat selection was highly scale-dependent and the strongest relationships with environmental variables were at the broadest scales analysed. Modelling approach had a substantial influence in variable importance among top models. Conclusions Our results suggest that optimization of the scale of resource selection is crucial in modelling tiger habitat selection. However, in this analysis, shape-optimization did not improve model performance.

中文翻译：

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空间尺度而非功能形状的优化影响栖息地适宜性模型的性能：泰国老虎（Panthera tigris）的案例研究

上下文 物种栖息地适宜性模型很少包含物种对协变量响应的多个空间尺度或功能形状。针对这些因素优化模型可能会产生更稳健、可靠和信息丰富的栖息地适宜性模型，这对保护稀有和濒危物种，如老虎 (Panthera tigris) 是有益的。目标我们首次正式评估规模优化和形状优化对模型性能和栖息地适宜性预测的相对影响。我们探讨了优化如何影响关于栖息地选择和映射发生概率的结论。方法我们整理了预期影响老虎发生的环境变量，计算了 250 m 到 16 km 空间尺度上的焦点统计数据和景观指标。然后我们构建了一组存在-不存在广义线性模型，包括：（1）单尺度优化模型（SSO）；（2）多尺度优化模型（MSO）；(3) 单尺度形状优化模型 (SSSO) 和 (4) 多尺度和形状优化模型 (MSSO)。我们比较了性能最佳的模型的性能和结果预测图。结果 SSO（16 公里）、SSSO（16 公里）、MSO 和 MSSO 模型表现同样出色（AUC > 0.9）。然而，这些在预测和绘制的栖息地适宜性方面存在很大差异，导致不同的生态理解和潜在的不同保护建议。栖息地选择高度依赖于规模，与环境变量的最强关系是在最广泛的分析范围内。建模方法对顶级模型中的变量重要性有重大影响。结论 我们的结果表明，资源选择规模的优化对于模拟老虎栖息地选择至关重要。然而，在这个分析中，形状优化并没有提高模型性能。