Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves |
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| Authors: | José Jiménez Emilio J García Luis Llaneza Vicente Palacios Luis Mariano González Francisco García‐Domínguez Jaime Múñoz‐Igualada José Vicente López‐Bao |
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| Affiliation: | 1. Institute of Research in Game Resources‐CSIC, Ciudad Real, Spain;2. A.RE.NA. Asesores en Recursos Naturales, Lugo, Spain;3. Subdirección General de Medio Natural, Ministerio de Agricultura, Alimentación y Medio Ambiente de Espa?a, Madrid, Spain;4. Tragsatec, Gerencia de Calidad, Evaluación Ambiental y Biodiversidad, Madrid, Spain;5. Research Unit of Biodiversity (UO‐CSIC‐PA), Oviedo University, Mieres, Spain |
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| Abstract: | In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters. |
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| Keywords: | Bayesian hierarchical‐site‐occupancy models Canis lupus howling landscape‐level decision making monitoring optimization wolf management wolf marks aullidos gestió n de lobos marcaje territorial modelos bayesianos jerá rquicos de ocupació n de sitio optimizació n del monitoreo toma de decisiones a escala del paisaje Canis lupus |
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