Exploring new approaches and methodologies to characterize species distribution dynamics, instead of solely relying on static spatial patterns, should be a priority for species distribution modelling research. Dynamic occupancy models (here, ‘dynocc models') are a promising tool to capture temporal patterns of distribution change but their spatial accuracy has been shown to be limited. In this study, we evaluated the effectiveness of incorporating neighborhood connectivity effects into the colonization and extinction functions of dynocc models. To accomplish this, we compared dynocc models accounting either for neighborhood connectivity only, for site-level habitat covariates only, or combining both neighborhood and habitat explanations in the models for species extinction and colonization. All models were evaluated for a total of 46 bird species typical of forests and shrublands using data at 1 km² scale from two Catalan breeding bird atlases (CBBA2: 1999–2002 and CBBA3: 2015–2018). Models' predictive performance varied across species between dynocc models incorporating habitat covariates alone and those considering neighborhood connectivity alone. Among species, 68% exhibited a predominant response to habitat effects, 24% showed similar responses for habitat and connectivity effects, and 9% were mostly associated with connectivity effects. Dynocc models combining connectivity and habitat covariates achieved the best predictive performance for most species, with bigger gains for species with similar results from habitat-only and connectivity-only models. However, relative performance gains compared to dynocc models using only habitat or connectivity variables were generally modest for most species. This study shows the benefits of considering more spatially explicit formulations in dynocc models, specifically incorporating neighborhood connectivity into the extinction and colonization processes. Our work also highlights the importance of evaluating different model formulations and assessing which aspects of the model are more important depending on the study species.

中文翻译：

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评估动态占用物种分布模型中邻域连通性和栖息地效应的影响


探索新的途径和方法来表征物种分布动态，而不是仅仅依赖静态空间模式，应该成为物种分布建模研究的首要任务。动态占用模型（此处为“dynocc 模型”）是捕获分布变化时间模式的有前途的工具，但其空间精度已被证明是有限的。在这项研究中，我们评估了将邻域连通性效应纳入 dynocc 模型的殖民和灭绝函数中的有效性。为了实现这一目标，我们比较了仅考虑邻域连通性、仅考虑站点级栖息地协变量的 dynocc 模型，或者在物种灭绝和殖民化模型中将邻域和栖息地解释结合起来。所有模型均使用两份加泰罗尼亚繁殖鸟类地图集（CBBA2：1999-2002 年和 CBBA3：2015-2018）1 km ²规模的数据对森林和灌木丛中的总共 46 种典型鸟类进行了评估。在仅考虑栖息地协变量的 dynocc 模型和仅考虑邻域连通性的 dynocc 模型之间，模型的预测性能因物种而异。在物种中，68% 对栖息地影响表现出主要反应，24% 对栖息地和连通性影响表现出类似的反应，9% 主要与连通性影响相关。结合连通性和栖息地协变量的 Dynocc 模型对大多数物种实现了最佳的预测性能，对于仅栖息地模型和仅连通性模型具有类似结果的物种来说，获得了更大的收益。然而，与仅使用栖息地或连通性变量的 dynocc 模型相比，对于大多数物种来说，相对性能增益通常不大。 这项研究显示了在 dynocc 模型中考虑更明确的空间公式的好处，特别是将邻域连通性纳入灭绝和殖民过程中。我们的工作还强调了评估不同模型配方以及根据研究物种评估模型的哪些方面更重要的重要性。