Use of stochastic patch occupancy models in the California red-legged frog for Bayesian inference regarding past events and future persistence

Assessing causes of population decline is critically important to management of threatened species. Stochastic patch occupancy models (SPOMs) are popular tools for examining spatial and temporal dynamics of populations when presence–absence data in multiple habitat patches are available. We developed a Bayesian Markov chain method that extends existing SPOMs by focusing on past environmental changes that may have altered occupancy patterns prior to the beginning of data collection. Using occupancy data from 3 creeks, we applied the method to assess 2 hypothesized causes of population decline—in situ die-off and residual impact of past source population loss—in the California red-legged frog. Despite having no data for the 20–30 years between the hypothetical event leading to population decline and the first data collected, we were able to discriminate among hypotheses, finding evidence that in situ die-off increased in 2 of the creeks. Although the creeks had comparable numbers of occupied segments, owing to different extinction–colonization dynamics, our model predicted an 8-fold difference in persistence probabilities of their populations to 2030. Adding a source population led to a greater predicted persistence probability than did decreasing the in situ die-off, emphasizing that reversing the deleterious impacts of a disturbance may not be the most efficient management strategy. We expect our method will be useful for studying dynamics and evaluating management strategies of many species. La evaluación de las causas de la declinación poblacional es de importancia crítica para el manejo de especies amenazadas. Los modelos estocásticos de ocupación de parches (SPOMs, en inglés) son herramientas populares para examinar las dinámicas espaciales y temporales de las poblaciones cuando están disponibles los datos de presencia-ausencia para múltiples parches de hábitat. Desarrollamos un método bayesiano de cadena de Markov que extiende a los SPOMs existentes al enfocarse en los cambios ambientales pasados que podrían haber alterado los patrones de ocupación previos al inicio de la recolección de datos. Con los datos de ocupación de tres arroyos, aplicamos este método para evaluar dos causas hipotéticas de la declinación poblacional – muerte in situ e impacto residual de causas anteriores de pérdida de una poblacion fuente – de la rana californiana de patas rojas. A pesar de no tener datos para 20 – 30 años entre el evento hipotético que derivó en la declinació