Sampling effort required for fitting spatially explicit models of species distribution dynamics

• Importance of study design for reliable predictions in spatial occupancy dynamics models. • Longer study durations and higher spatial coverage yielded more accurate predictions. • Ensuring high effective probability of detection is key for reliable model predictions. • Initial occupancy and occupancy temporal trends influence minimum sampling coverage needed. • Alternative sampling designs tested for improved model precision. Understanding and accurately predicting species distribution dynamics is essential for effective biodiversity conservation and management. Spatial dynamic occupancy models (SpDynOcc models) provide a valuable framework for analyzing temporal changes in species occurrence but require substantial data, making it critical to understand their data needs for reliable estimates. In this study, we use a simulation approach to investigate the role of survey effort, both in terms of study duration and spatial coverage, in obtaining accurate predictions from a generic SpDynOcc model. We also test the efficacy of two alternative sampling designs compared to random sampling. We varied multiple factors influencing species occurrence and detection (initial occupancy, occupancy dynamics, and probability of detection) to study the way in which they affect the data requirements for accurate parameter estimation. Models performed best with longer study durations, higher spatial coverage, and higher effective probability of detection (i.e., over all survey occasions). Nevertheless, the specific minimum sampling coverage needed notably varied based on initial occupancy and on occupancy dynamics scenarios. Preferential habitat sampling performed particularly well for low initial occupancy and high-decrease scenarios. These results indicate that tailored survey strategies are essential and must be informed by the specific ecological context. Our findings provide guidance on the survey designs needed to obtain accurate SpDynOcc model predictions, aiding researchers in the effective application of these models for studying species spatial occupancy dynamics.