Modeling the viability of the free‐ranging cheetah population in Namibia: an object‐oriented Bayesian network approach

Johnson, Sandra; Marker, Laurie; Mengersen, Kerrie; Gordon, Chris; Melzheimer, Jörg; Schmidt‐Küntzel, Anne; Nghikembua, Matti T.; Fabiano, Ezequiel; Henghali, Josephine N.; Wachter, Bettina

doi:10.1890/es12-00357.1

Cited by 27 publications

(18 citation statements)

References 48 publications

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“…Probabilistic approaches are needed to predict population trends and possible risks associated with management decisions. Typically, the uncertainty captured by these approaches relates only to the stochasticity of the modelled dynamics, but full consideration of demographic, environmental, and parameter uncertainties can reduce bias in estimates of population persistence (Melbourne & Hastings, 2008), lead to optimal conservation management (Johnson et al, 2013) and reveal specific data collection priorities to reduce that uncertainty (Maslo & Fefferman, 2015).…”

Section: Introductionmentioning

confidence: 99%

Evaluating population viability and efficacy of conservation management using integrated population models

Saunders

Cuthbert

Zipkin

2018

Journal of Applied Ecology

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Predicting population responses to environmental conditions or management scenarios is a fundamental challenge for conservation. Proper consideration of demographic, environmental and parameter uncertainties is essential for projecting population trends and optimal conservation strategies. We developed a coupled integrated population model‐Bayesian population viability analysis to assess the (1) impact of demographic rates (survival, fecundity, immigration) on past population dynamics; (2) population viability 10 years into the future; and (3) efficacy of possible management strategies for the federally endangered Great Lakes piping plover Charadrius melodus population. Our model synthesizes long‐term population survey, nest monitoring and mark–resight data, while accounting for multiple sources of uncertainty. We incorporated latent abundance of eastern North American merlins Falco columbarius, a primary predator of adult plovers, as a covariate on adult survival via a parallel state‐space model, accounting for the influence of an imperfectly observed process (i.e. predation pressure) on population viability. Mean plover abundance increased from 18 pairs in 1993 to 75 pairs in 2016, but annual population growth (λ¯t) was projected to be 0.95 (95% CI 0.72–1.12), suggesting a potential decline to 67 pairs within 10 years. Without accounting for an expanding merlin population, we would have concluded that the plover population was projected to increase (λ¯t = 1.02; 95% CI 0.94–1.09) to 91 pairs by 2026. We compared four conservation scenarios: (1) no proposed management; (2) increased control of chick predators (e.g. Corvidae, Laridae, mammals); (3) increased merlin control; and (4) simultaneous chick predator and merlin control. Compared to the null scenario, chick predator control reduced quasi‐extinction probability from 11.9% to 8.7%, merlin control more than halved (3.5%) the probability and simultaneous control reduced quasi‐extinction probability to 2.6%. Synthesis and applications. Piping plover recovery actions should consider systematic predator control, rather than current ad hoc protocols, especially given the predicted increase in regional merlin abundance. This approach of combining integrated population models with Bayesian population viability analysis to identify limiting components of the population cycle and evaluate alternative management strategies for conservation decision‐making shows great utility for aiding recovery of threatened populations.

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Section: Introductionmentioning

confidence: 99%

Evaluating population viability and efficacy of conservation management using integrated population models

Saunders

Cuthbert

Zipkin

2018

Journal of Applied Ecology

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“…Despite large amounts of uncertainty and incomplete data about rare species, wildlife managers have often been required to make the best possible management decision (Smith et al 2007, Johnson et al 2013) to maintain populations of interests. Although it is more than half a century since implementing conservation activities for the endangered Persian fallow deer, there has not yet been a strategic long-term planning or a comprehensive management support system to inform management decisions.…”

Section: The Bbn Model (Aim 2)mentioning

confidence: 99%

Assessing translocation success of the endangered Persian fallow deer using a Bayesian Belief Network

et al. 2015

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Abstract. For the past 50 years, the endangered Persian fallow deer (Dama mesopotamica) have been translocated to various sites throughout Iran. To better understand the varying degrees of success at the translocation sites, population growth rates were measured for all the sites, and factors believed to affect the growth rate, such as initial population structures of the translocated herds and habitat characteristics, were identified and modeled. The population growth rate was used as a proxy for translocation success. Quantitative ecological data for Persian fallow deer is scarce, but expert knowledge was readily available to inform and enhance fallow deer management options. We integrated the available quantitative data and qualitative information in a Bayesian Belief Network (BBN) model to predict Persian fallow deer translocation success. The BBN model was tested using scenarios based on previous translocations to 13 sites in Iran. It correctly predicted the success of translocated populations in 11 out of the 13 sites. This model may be used as a decision support tool for future translocations, and can assist in designing reintroduction programs of the Persian fallow deer. Moreover, it should be adapted to incorporate new knowledge as evidence of translocation successes and failures emerge. Although the BBN model was developed specifically for the translocation of Persian fallow deer, this approach can clearly be applied to design and assess the success of translocation programs of other endangered species, and may be extended to design and assess alternative conservation management strategies.

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“…Bayesian networks can provide a means to estimate and compare the efficacy of combinations of measures. Bayesian networks are well established as a sound statistical framework to integrate information from diverse sources such as empirical data, expert opinion, and model output, and have been used in a wide variety disciplines, including conservation, forensic science, health, environmental and resource management, and biosecurity …”

Section: Introductionmentioning

confidence: 99%

Bayesian Networks to Compare Pest Control Interventions on Commodities Along Agricultural Production Chains

et al. 2017

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The production of an agricultural commodity involves a sequence of processes: planting/growing, harvesting, sorting/grading, postharvest treatment, packing, and exporting. A Bayesian network has been developed to represent the level of potential infestation of an agricultural commodity by a specified pest along an agricultural production chain. It reflects the dependency of this infestation on the predicted level of pest challenge, the anticipated susceptibility of the commodity to the pest, the level of impact from pest control measures as designed, and any variation from that due to uncertainty in measure efficacy. The objective of this Bayesian network is to facilitate agreement between national governments of the exporters and importers on a set of phytosanitary measures to meet specific phytosanitary measure requirements to achieve target levels of protection against regulated pests. The model can be used to compare the performance of different combinations of measures under different scenarios of pest challenge, making use of available measure performance data. A case study is presented using a model developed for a fruit fly pest on dragon fruit in Vietnam; the model parameters and results are illustrative and do not imply a particular level of fruit fly infestation of these exports; rather, they provide the most likely, alternative, or worst-case scenarios of the impact of measures. As a means to facilitate agreement for trade, the model provides a framework to support communication between exporters and importers about any differences in perceptions of the risk reduction achieved by pest control measures deployed during the commodity production chain.

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Modeling the viability of the free‐ranging cheetah population in Namibia: an object‐oriented Bayesian network approach

Cited by 27 publications

References 48 publications

Evaluating population viability and efficacy of conservation management using integrated population models

Evaluating population viability and efficacy of conservation management using integrated population models

Assessing translocation success of the endangered Persian fallow deer using a Bayesian Belief Network

Bayesian Networks to Compare Pest Control Interventions on Commodities Along Agricultural Production Chains

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