Multi-species occupancy modelling of mammal and ground bird communities in rangeland in the Karoo: A case for dryland systems globally

Drouilly, Marine; Clark, Allan; O’Riain, M. Justin

doi:10.1016/j.biocon.2018.05.013

Cited by 51 publications

(29 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spatial generalized linear mixed models (SGLMM) are an extension of the general linear model (Nelder & Wedderburn, 1972) that allows the link function of the expected value of the random variable under investigation to be modeled as a function of a spatial random variable/s. The formulation was first developed by Besag, York, and Mollié (1991) and has been extensively used in areas such as agriculture (Besag & Higdon, 1999), biostatistics (Gelfand & Vounatsou, 2003;Waller & Gotway, 2004), ecology (Lichstein, Simons, Shriner, & Franzreb, 2002) and species distribution modelling (Drouilly, Clark, & O'Riain, 2018;Gelfand et al, 2005;Hooten et al, 2003;Latimer, Wu, Gelfand, & Silander, 2006).…”

Section: Bayesian Spatial Sso Modelsmentioning

confidence: 99%

Efficient Bayesian analysis of occupancy models with logit link functions

Clark

Altwegg

2019

Ecology and Evolution

View full text Add to dashboard Cite

Occupancy models (Ecology, 2002; 83: 2248) were developed to infer the probability that a species under investigation occupies a site. Bayesian analysis of these models can be undertaken using statistical packages such as WinBUGS, OpenBUGS, JAGS, and more recently Stan, however, since these packages were not developed specifically to fit occupancy models, one often experiences long run times when undertaking an analysis. Bayesian spatial single‐season occupancy models can also be fit using the R package stocc. The approach assumes that the detection and occupancy regression effects are modeled using probit link functions. The use of the logistic link function, however, is algebraically more tractable and allows one to easily interpret the coefficient effects of an estimated model by using odds ratios, which is not easily done for a probit link function for models that do not include spatial random effects. We develop a Gibbs sampler to obtain posterior samples from the posterior distribution of the parameters of various occupancy models (nonspatial and spatial) when logit link functions are used to model the regression effects of the detection and occupancy processes. We apply our methods to data extracted from the 2nd Southern African Bird Atlas Project to produce a species distribution map of the Cape weaver (Ploceus capensis) and helmeted guineafowl (Numida meleagris) for South Africa. We found that the Gibbs sampling algorithm developed produces posterior samples that are identical to those obtained when using JAGS and Stan and that in certain cases the posterior chains mix much faster than those obtained when using JAGS, stocc, and Stan. Our algorithms are implemented in the R package, Rcppocc. The software is freely available and stored on GitHub (https://github.com/AllanClark/Rcppocc).

show abstract

Section: Bayesian Spatial Sso Modelsmentioning

confidence: 99%

Efficient Bayesian analysis of occupancy models with logit link functions

Clark

Altwegg

2019

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

“…This shortfall represents a major issue because the rarest species are often the species of highest conservation concern. Multispecies occupancy models offer an analytical framework to address this challenge, as species with few detections borrow information from more abundant species, which improves precision of the parameter estimates for rare species (Drouilly, Clark, & O'Riain, ; Li, Bleisch, & Jiang, ; Tobler, Hartley, Carrillo‐Percastegui, & Powell, ). Because species‐specific responses to covariates can be projected to unsampled areas, this approach can be used to generate maps of species potential occurrence (MacKenzie et al, ; Sollmann et al, ).…”

Section: Introductionmentioning

confidence: 99%

Identifying conservation priorities in a defaunated tropical biodiversity hotspot

Tilker

Abrams

Nguyen

et al. 2020

Diversity and Distributions

View full text Add to dashboard Cite

Aim Unsustainable hunting is leading to widespread defaunation across the tropics. To mitigate against this threat with limited conservation resources, stakeholders must make decisions on where to focus anti‐poaching activities. Identifying priority areas in a robust way allows decision‐makers to target areas of conservation importance, therefore maximizing the impact of conservation interventions. Location Annamite mountains, Vietnam and Laos. Methods We conducted systematic landscape‐scale surveys across five study sites (four protected areas, one unprotected area) using camera‐trapping and leech‐derived environmental DNA. We analysed detections within a Bayesian multispecies occupancy framework to evaluate species responses to environmental and anthropogenic influences. Species responses were then used to predict occurrence to unsampled regions. We used predicted species richness maps and occurrence of endemic species to identify areas of conservation importance for targeted conservation interventions. Results Analyses showed that habitat‐based covariates were uninformative. Our final model therefore incorporated three anthropogenic covariates as well as elevation, which reflects both ecological and anthropogenic factors. Conservation‐priority species tended to found in areas that are more remote now or have been less accessible in the past, and at higher elevations. Predicted species richness was low and broadly similar across the sites, but slightly higher in the more remote site. Occupancy of the three endemic species showed a similar trend. Main conclusion Identifying spatial patterns of biodiversity in heavily defaunated landscapes may require novel methodological and analytical approaches. Our results indicate that to build robust prediction maps it is beneficial to sample over large spatial scales, use multiple detection methods to increase detections for rare species, include anthropogenic covariates that capture different aspects of hunting pressure and analyse data within a Bayesian multispecies framework. Our models further suggest that more remote areas should be prioritized for anti‐poaching efforts to prevent the loss of rare and endemic species.

show abstract

“…This shortfall represents a major issue because the rarest species are often the species of highest conservation concern. Multi-species occupancy models offer an analytical framework to address this challenge, as species with few detections borrow information from more abundant species, which allows parameter estimation for rare species (Tobler et al, 2015; Drouilly et al, 2018; Li et al, 2018). Because species-specific responses to covariates can be projected to unsampled areas, this approach can be used to generate maps of species potential occurrence (MacKenzie et al, 2017; Sollmann et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Identifying conservation priorities in a defaunated tropical biodiversity hotspot

Tilker

Abrams

Nguyen

et al. 2019

Preprint

View full text Add to dashboard Cite

Aim: Unsustainable hunting is leading to widespread defaunation across the tropics. To 21 mitigate against this threat with limited conservation resources, stakeholders must 22 make decisions on where to focus anti-poaching activities. Identifying priority areas in a 23 robust way allows decision-makers to target areas of conservation importance, 24 therefore maximizing the impact of conservation interventions. 25 Location: Annamite mountains, Vietnam and Laos.26 2 Methods: We conducted systematic landscape-scale surveys across five study sites (four 27 protected areas, one unprotected area) using camera-trapping and leech-derived 28 environmental DNA. We analyzed detections within a Bayesian multi-species occupancy 29 framework to evaluate species responses to environmental and anthropogenic 30 influences. Species responses were then used to predict occurrence to unsampled 31 regions. We used predicted species richness maps and occurrence of endemic species to 32 identify areas of conservation importance for targeted conservation interventions.33 Results: Analyses showed that habitat-based covariates were uninformative. Our final 34 model therefore incorporated three anthropogenic covariates as well as elevation, which 35 reflects both ecological and anthropogenic factors. Conservation-priority species tended 36to found in areas that are more remote now or have been less accessible in the past, and 37 at higher elevations. Predicted species richness was low and broadly similar across the 38 sites, but slightly higher in the more remote site. Occupancy of the three endemic species 39 showed a similar trend. 40Main conclusion: Identifying spatial patterns of biodiversity in heavily-defaunated 41 landscapes may require novel methodological and analytical approaches. Our results 42 indicate to build robust prediction maps it is beneficial to sample over large spatial 43 scales, use multiple detection methods to increase detections for rare species, include 44 anthropogenic covariates that capture different aspects of hunting pressure, and analyze 45 data within a Bayesian multi-species framework. Our models further suggest that more 46 remote areas should be prioritized for anti-poaching efforts to prevent the loss of rare 47 and endemic species. 48

show abstract

Multi-species occupancy modelling of mammal and ground bird communities in rangeland in the Karoo: A case for dryland systems globally

Cited by 51 publications

References 58 publications

Efficient Bayesian analysis of occupancy models with logit link functions

Efficient Bayesian analysis of occupancy models with logit link functions

Identifying conservation priorities in a defaunated tropical biodiversity hotspot

Identifying conservation priorities in a defaunated tropical biodiversity hotspot

Contact Info

Product

Resources

About