Context Habitat suitability models (HSM) can improve our understanding of a species’ ecology and are valuable tools for informing landscape-scale decisions. We can increase HSM predictive accuracy and derive more realistic conclusions by taking a multi-scale approach. However, this process is often statistically complex and computationally intensive. Objectives We provide an easily implemented, flexible framework for sequential multi-level, multi-scale HSM and compare it to two other commonly-applied approaches: single-level, multi-scale HSM and their post-hoc combinations. Methods Our framework implements scale optimisation and model tuning at each level in turn, from the highest (population range) to the lowest (e.g. foraging habitat) level, whilst incorporating output habitat suitability indices from a higher level as a predictor. We used MaxEnt and a species of conservation concern in Britain, the lesser horseshoe bat ( Rhinolophus hipposideros ), to demonstrate and compare multi-scale approaches. Results Integrating models across levels, either by applying our framework, or by multiplying single-level model predictions, improved predictive performance over single-level models. Moreover, differences in the importance and direction of the species-environment associations highlight the potential for false inferences from single-level models or their post-hoc combinations. The single-level summer range model incorrectly identified a positive influence of heathland cover, whereas sequential multi-level models made biological sense and underlined this species’ requirement for extensive broadleaf woodland cover, hedgerows and access to buildings for roosting in rural areas. Conclusions We conclude that multi-level HSM appear superior to single-level, multi-scale approaches; models should be sequentially integrated across levels if information on species-environment relationships is of importance.

中文翻译：

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改进栖息地适宜性建模的连续多级框架

上下文栖息地适宜性模型 (HSM) 可以提高我们对物种生态的理解，并且是为景观尺度决策提供信息的宝贵工具。我们可以通过采用多尺度方法来提高 HSM 预测准确性并得出更现实的结论。然而，这个过程通常在统计上很复杂并且计算量很大。目标我们为顺序多级、多尺度 HSM 提供了一个易于实现、灵活的框架，并将其与其他两种常用方法进行比较：单级、多尺度 HSM 及其事后组合。方法 我们的框架依次在每个级别实施规模优化和模型调整，从最高（种群范围）到最低（例如觅食栖息地）级别，同时将更高级别的输出栖息地适宜性指数作为预测指标。我们使用 MaxEnt 和英国的一种保护问题，即较小的马蹄蝠 (Rhinolophus hipposideros) 来演示和比较多尺度方法。结果 通过应用我们的框架或乘以单级模型预测，跨级别集成模型，提高了单级模型的预测性能。此外，物种-环境关联的重要性和方向的差异突出了单级模型或其事后组合的错误推断的可能性。单级夏季范围模型错误地确定了荒地覆盖的积极影响，而连续多级模型具有生物学意义，并强调了该物种对广泛的阔叶林地覆盖、树篱和进入农村地区栖息的建筑物的要求。结论 我们得出结论，多层次 HSM 似乎优于单层次、多尺度方法；如果物种-环境关系的信息很重要，模型应该跨层次顺序整合。