Multimethod, multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

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.

Wolf population genetics inEurope: a systematic review, meta‐analysis and suggestions for conservation and management

“…Additionally, genetic assessement is not considered for management planning (but see Godinho et al., ). Although there has been an increasing effort to homogenize census methods over recent years (Llaneza, Garcia & Lopez‐Bao, ; Jiménez et al., ), better coordination between different Spanish autonomous regions and between both countries is required.…”

Wild boar in focus: Review of existing models on spatial distribution and density of wild boar and proposal for next steps

“…Broadly there are four main classifications: profile models such as Bioclim, Domain and Mahalanobis distance which are fitted using strictly presence-only data; presence-background models such as MaxEnt (Phillips et al, 2006) which contrast the environmental space occupied by presences with a background sample of the available landscape; presence-absence models which can be sub-divided into regressions techniques such as Generalised Linear Models (GLM) and machine learning such as Random Forest, Support Vector Machines and Boosted Regression Trees (BRT); and occupancy style models based on a binomial response describing successful detections over a given number of visits typically parameterised using a Bayesian framework (INLA, hSDM). An extension of the latter is to consider a hierarchy of processes describing explicitly both the underlying ecological processes and the observational processes thereby separating any bias in detection which may otherwise influence predictions of the species distribution (Gelfand et al, 2003;Latimer et al, 2006;Jiménez et al, 2016). Each model type has different strengths and limitations and there is no consensus as to which model type is best (Croft et al, 2017).…”

Toward reliable population estimates of wolves by combining spatial capture-recapture models and non-invasive DNA monitoring

“…However, this conversion remains a challenge 20 and, in most cases, the data required to calculate conversion factors properly is not available. While the number of packs or reproductions are a reasonable target for wolf monitoring at regional scales 17 , apart from mismatches between management goals (i.e., often based on the number of individuals, for instance, to establish hunting quotas) and monitoring targets (i.e., packs or reproductions), there are cases where estimating the number of wolves may be important. Examples include small and endangered wolf populations, such as the Sierra Morena 12 or Mexican 21 wolf populations, and populations under game management 6 .…”

“…Different field methods and analytical approaches have been used to address the challenge of surveying large carnivore populations at regional scales 17 , 22 – 26 , including non-invasive DNA monitoring 21 , 23 , 27 . This method, combined with traditional capture-recapture procedures, is often presented as a promising strategy to achieve robust, feasible and economically affordable population size estimates.…”