Decision analysis for conservation breeding: Maximizing production for reintroduction of whooping cranes

Abstract

Captive breeding is key to management of severely endangered species, but maximizing captive production can be challenging because of poor knowledge of species breeding biology and the complexity of evaluating different management options. In the face of uncertainty and complexity, decision‐analytic approaches can be used to identify optimal management options for maximizing captive production. Building decision‐analytic models requires iterations of model conception, data analysis, model building and evaluation, identification of remaining uncertainty, further research and monitoring to reduce uncertainty, and integration of new data into the model. We initiated such a process to maximize captive production of the whooping crane (Grus americana), the world's most endangered crane, which is managed through captive breeding and reintroduction. We collected 15 years of captive breeding data from 3 institutions and used Bayesian analysis and model selection to identify predictors of whooping crane hatching success. The strongest predictor, and that with clear management relevance, was incubation environment. The incubation period of whooping crane eggs is split across two environments: crane nests and artificial incubators. Although artificial incubators are useful for allowing breeding pairs to produce multiple clutches, our results indicate that crane incubation is most effective at promoting hatching success. Hatching probability increased the longer an egg spent in a crane nest, from 40% hatching probability for eggs receiving 1 day of crane incubation to 95% for those receiving 30 days (time incubated in each environment varied independently of total incubation period). Because birds will lay fewer eggs when they are incubating longer, a tradeoff exists between the number of clutches produced and egg hatching probability. We developed a decision‐analytic model that estimated 16 to be the optimal number of days of crane incubation needed to maximize the number of offspring produced. These results show that using decision‐analytic tools to account for uncertainty in captive breeding can improve the rate at which such programs contribute to wildlife reintroductions. © 2011 The Wildlife Society.

Effects of inbreeding and parental incubation on captive breeding success in Hawaiian crows

“…In brown kiwis, risk of hatching failure was high if artificial incubation began before the second half of incubation but then leveled off (Robertson et al, 2006). Similarly, a study on whooping cranes found that 16 days of parental incubation (of a 30 day incubation period) provided the most productive trade-off between egg production and hatching success (Smith et al, 2011), but that hatching probability continued to increase if eggs were naturally incubated throughout the entire incubation period. We found a steep linear increase in the proportion of eggs hatched with parental incubation time (F 1,10 = 57.1, R 2 = 0.87, b = 0.05, p < 0.001) which did not level off.…”

“…Since little is known about the factors that cause hatching failure in captive bird populations (Smith et al, 2011) and since offspring survival can be an important limitation on the contribution of captive populations to ex situ conservation efforts, our study is not only of importance to the conservation of 'alala, but may also be of value to the captive management of other species.…”

“…Rebke et al, 2010), may explain variation in embryo and chick survival. Furthermore, since natural incubation preceding artificial incubation of eggs has been shown to improve hatching success in some captive bird populations (Burnham, 1983;Robertson et al, 2006;Smith et al, 2011), we also tested for the effect of initial parental incubation on embryo and chick survival.…”

Genetic rescue, the greater prairie chicken and the problem of conservation reliance in the Anthropocene

“…Given these caveats, an ex situ propagation strategy may be a potential solution (e.g. whooping crane, Grus americana ; [75]).…”

A Bayesian multinomial logistic exposure model for estimating probabilities of competing sources of nest failure

“…We used the estimated w k to calculate the Bayes’ factor (BF) for each covariate, where the BF represents the odds ratio of inclusion (Smith et al . ). Following the recommendations of Link and Barker (, p. 2632) and the example of Converse et al .…”