A multitude of different statistical models are commonly used to monitor trends in wildlife populations. Most are used to estimate abundance or survival (or both), and these estimates are then examined over time to infer trends in a population. The choice of which model to use is influenced by the key research question of interest and the types of data available. The accuracy and precision of any estimate from a population model are determined by whether the data meet the model assumptions. We assessed the performance of both closed and open capture–recapture models for determining trends in abundance and survival of River Cooters, Pseudemys concinna, in the Santa Fe River, Florida from 2009–2019. We fit three closed models to estimate abundance, one open model to estimate survival, and two robust design models to estimate both abundance and survival. We then used simulation to generate three datasets that represented different sampling designs, including one that mimics our field data, to assess model performance and compare tradeoffs in sampling design. We recommend using the robust design framework when possible as this design and model estimation returned accurate and precise estimates of abundance and survival. This model estimated survival ranging from 0.69–0.95 and capture probability from 0.21–0.25. This design requires consistent sampling of at least three events per year during a closed period, repeated over at least five years, to estimate survival between years. In situations where samples could not be repeated across years, closed population models are likely the most reliable framework in terms of model precision and accuracy. Overall, sampling designs that allow for repeated sampling and align the biology of the study species and the assumptions of the statistical model are likely the most informative approaches for sampling River Cooters and similar species.

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