Abstract. The use of presence/absence data in wildlife management and biological surveys is widespread. There is a growing interest in quantifying the sources of error associated with these data. We show that false-negative errors (failure to record a species when in fact it is present) can have a significant impact on statistical estimation of habitat models using simulated data. Then we introduce an extension of logistic modeling, the zero-inflated binomial (ZIB) model that permits the estimation of the rate of false-negative errors and the correction of estimates of the probability of occurrence for false-negative errors by using repeated visits to the same site. Our simulations show that even relatively low rates of false negatives bias statistical estimates of habitat effects. The method with three repeated visits eliminates the bias, but estimates are relatively imprecise. Six repeated visits improve precision of estimates to levels comparable to that achieved with conventional statistics in the absence of false-negative errors. In general, when error rates are Յ50% greater efficiency is gained by adding more sites, whereas when error rates are Ͼ50% it is better to increase the number of repeated visits. We highlight the flexibility of the method with three case studies, clearly demonstrating the effect of false-negative errors for a range of commonly used survey methods.

An evaluation was made of the influence of aphidophagous syrphid larvae on the population dynamics of cereal aphids in winter wheat in Germany, using both field records and published information. The peak density of aphid populations was significantly dependent on aphid abundance at the time syrphid larvae became active, a fact indicating the high predation potential of the beneficials as well as the importance of synchronization. The level of aphid infestation was generally lower in southern than in northern Germany. This difference may result from syrphid larvae being 2-3 weeks earlier in southern Germany, when aphid numbers were only half those found in northern Germany.

Abstract. There is a wealth of literature documenting a directional change of body size in heavily harvested populations. Most of this work concentrates on aquatic systems, but terrestrial populations are equally at risk. This paper explores the capacity of harvest refuges to counteract potential effects of size-selective harvesting on the allele frequency of populations. We constructed a stochastic, individual-based model parameterized with data on red kangaroos. Because we do not know which part of individual growth would change in the course of natural selection, we explored the effects of two alternative models of individual growth in which alleles affect either the growth rate or the maximum size. The model results show that size-selective harvesting can result in significantly smaller kangaroos for a given age when the entire population is subject to harvesting. In contrast, in scenarios that include dispersal from harvest refuges, the initial allele frequency remains virtually unchanged.

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...