Linking Wild and Captive Populations to Maximize Species Persistence: Optimal Translocation Strategies

Abstract

Captive breeding of animals is widely used to manage endangered species, frequently with the ambition of future reintroduction into the wild. Because this conservation measure is very expensive, we need to optimize decisions, such as when to capture wild animals or release captive-bred individuals into the wild. It is unlikely that one particular strategy will always work best; instead, we expect the best decision to depend on the number of individuals in the wild and in captivity. We constructed a first-order Markov-chain population model for two populations, one captive and one wild, and we used stochastic dynamic programming to identify optimal state-dependent strategies. The model recommends unique sequences of optimal management actions over several years. A robust rule of thumb for species that can increase faster in captivity than in the wild is to capture the entire wild population whenever the wild population is below a threshold size of 20 females. This rule applies even if the wild population is growing and under a broad range of different parameter values. Once a captive population is established, it should be maintained as a safety net and animals should be released only if the captive population is close to its carrying capacity. We illustrate the utility of this model by applying it to the Arabian oryx (Oryx leucoryx). The threshold for capturing the entire Arabian oryx population in the wild is 36 females, and captive-bred individuals should not be released before the captive facilities are at least 85% full. ResumenLa reproducción de animales en cautiverio es utilizada ampliamente para manejar especies en peligro, frecuentemente con la ambición de reintroducirlos al medio natural. Debido a que esta medida de conservación es muy costosa necesitamos optimizar decisiones, tales como cuando capturar animales silvestres o liberar individuos criados en cautiverio. Es poco probable que una estrategia particular siempre funcione mejor; más bien, esperamos que la mejor decisión dependa del número de individuos silvestres y en cautiverio. Construimos un modelo poblacional de cadena de Markov de primer orden para dos poblaciones, una en cautiverio y otra silvestre, y usamos programación dinámica estocástica para identificar estrategias estado-dependientes óptimas. El modelo recomienda secuencias úni-cas de acciones de manejo óptimo durante varios años. Una regla básica robusta para especies que pueden incrementar más rápidamente en cautiverio que en su medio natural es la captura de toda la población silvestre, cuando ésta se encuentre debajo del umbral de 20 hembras. Esta regla aplica aun si la población silvestre está creciendo y bajo una amplia gama de valores de diferentes parámetros. Una vez que se establece una población en cautiverio, debe ser mantenida como una red de seguridad y los animales deben ser liberados solo si la población en cautiverio se aproxima a su capacidad de carga. Ilustramos la utilidad de este modelo aplicándolo al Oryx leucoryx. El umbral para la captura de toda la pobl...

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