Malchow, Anne-Kathleen and Fandos, Guillermo and Kormann, Urs G. and Grueebler, Martin U. and Kery, Marc and Hartig, Florian and Zurell, Damaris (2024) Fitting individual-based models of spatial population dynamics to long-term monitoring data. ECOLOGICAL APPLICATIONS, 34 (4). e2966. ISSN 1051-0761, 1939-5582
Full text not available from this repository. (Request a copy)Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Bayesian inference; cross-validation; dispersal; inverse calibration; process-based model; range dynamics; spatially explicit; species distribution model (SDM) |
| Subjects: | 500 Science > 580 Botanical sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Pflanzenwissenschaften > Group Theoretical Ecology (Prof. Dr. Florian Hartig) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 02 Dec 2025 07:38 |
| Last Modified: | 02 Dec 2025 07:38 |
| URI: | https://pred.uni-regensburg.de/id/eprint/64598 |
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