Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process‐based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long‐term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual‐based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial‐block cross‐validation scheme and assess predictive performance with a rank‐correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

中文翻译：

---

将基于个体的空间人口动态模型与长期监测数据进行拟合

生成物种分布的空间预测是研究和政策的核心任务。目前，相关物种分布模型（cSDM）是用于此目的最广泛使用的工具之一。然而，cSDM 的一个基本假设，即物种分布与其环境保持平衡，在实际数据中很少得到满足，并且限制了 cSDM 动态预测的适用性。基于过程的动态 SDM (dSDM) 有望克服这些限制，因为它们明确地表示瞬态动态并增强时空可转移性。用于实现 dSDM 的软件工具变得越来越可用，但它们的参数估计可能很复杂。在这里，我们测试使用瑞士红风筝的长期监测数据校准和验证 dSDM 的可行性（米尔乌斯）。在过去的几十年里，这个种群的数量出现了强劲增长，范围也逐渐扩大，表明了一种非平衡的情况。我们使用 RangeShiftR 建模平台构建基于个体的模型，并使用贝叶斯推理进行模型校准。这允许整合异构数据源，例如来自已发表文献的参数估计和来自监测方案的观测数据，以及对参数不确定性的一致评估。我们的监测数据涵盖了 22 年来瑞士 267 个地点的繁殖对数量。我们使用空间块交叉验证方案验证我们的模型，并使用秩相关系数评估预测性能。我们的模型显示了非常好的空间投影预测准确性，并很好地代表了过去二十年观察到的人口动态。结果表明，繁殖成功是推动观察到的范围扩大的关键因素。根据我们的模型，瑞士红鸢种群占据了其当前范围的大部分，但密度有进一步增加的潜力。我们展示了使用 RangeShiftR 进行 dSDM 数据集成和验证的实用性。与 cSDM 相比，这种方法可以提高预测性能。此处介绍的工作流程可适用于任何具有人口统计和扩散参数的先验知识以及丰度或存在/不存在的时空观察的群体。拟合模型提供了对物种生态的改进的定量见解，这可以极大地帮助保护和管理工作。