Seasonal occurrence and abundance of dabbling ducks across the continental United States: Joint spatio‐temporal modelling for the Genus<i>Anas</i>

Humphreys, John; Murrow, Jennifer L.; Sullivan, Jeffery D.; Prosser, Diann J.

doi:10.1111/ddi.12960

Cited by 25 publications

(35 citation statements)

References 81 publications

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“…These datasets could be combined with the WBPHS through the use of integrated SDMs (Fletcher et al., 2019; Isaac et al., 2020; Miller, Pacifici, Sanderlin, & Reich, 2019). In addition, the recent incorporation of waterfowl data from citizen science within advanced spatiotemporal modelling framework has shown promising results (Humphreys et al., 2019).…”

Section: Discussionmentioning

confidence: 99%

Predicting spatiotemporal abundance of breeding waterfowl across Canada: A Bayesian hierarchical modelling approach

Adde

Darveau

Barker

et al. 2020

Diversity and Distributions

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Aim Our aim was to develop predictive statistical models for mapping the abundance of 18 waterfowl species at a pan‐Canadian level. We refined the previous generation of national waterfowl models by (a) developing new, more interpretable statistical models that (b) explicitly account for spatiotemporal variations in waterfowl abundance, while (c) testing for associations with an updated suite of habitat covariates. Location All of Canada, excluding the Northern Arctic ecozone. Methods Our response variables were annual species counts on 2,227 aerial‐survey segments over a period of 25 years (1990–2015). Combining machine‐learning and hierarchical regression modelling, we devised an innovative covariate selection strategy to select for each species the best subset of a panel of 232 candidate habitat covariates. With the selected covariates, we implemented hierarchical generalized linear models in a Bayesian framework, using the integrated nested Laplace approximation and stochastic partial differential equation approaches. Results On average, our models explained 47% of the observed variance for spatiotemporal predictions and 74% for temporally averaged spatial predictions. The 18 species models included 94 significant waterfowl‐habitat associations involving 42 distinct habitat covariates, with an average of 5.3 covariates per model. Covariates for forest attributes were the most represented in our models. The proportional biomass of Populus tremuloides was the most frequently selected covariate (10/94 associations in 10/18 species). Model predictions generated spatial and spatiotemporal maps of species abundances over almost all of Canada. Main conclusions We showed that it is possible to efficiently combine machine‐learning, variable selection and hierarchical Bayesian methods that exploit high‐dimensional covariate spaces. Our approach yielded powerful and easily interpretable species distribution models with very few covariates, while accounting for residual autocorrelation. Possible applications of the resulting models and maps include the development of biodiversity indicators, the evaluation and execution of conservation planning strategies, and ecosystem services monitoring.

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Section: Discussionmentioning

confidence: 99%

Predicting spatiotemporal abundance of breeding waterfowl across Canada: A Bayesian hierarchical modelling approach

Adde

Darveau

Barker

et al. 2020

Diversity and Distributions

View full text Add to dashboard Cite

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“…In another example, Guélat and Kéry (2018) described residual spatial autocorrelation in the context of species distribution modelling in the presence of misclassifications. Several models that account for spatial dependency have also been developed within the Bayesian framework for citizen science data (Humphreys et al., 2019). Conditional autoregressive (CAR) priors have been found to adequately capture spatial variability in some studies (Arab & Courter, 2015; Arab et al., 2016; Pagel et al., 2014; Purse et al., 2015), while Gaussian random fields (Humphreys et al., 2019) and stochastic partial differential equations (SPDE) (Peterson et al., 2020) have been successfully used in others.…”

Section: Introductionmentioning

confidence: 99%

“…Several models that account for spatial dependency have also been developed within the Bayesian framework for citizen science data (Humphreys et al., 2019). Conditional autoregressive (CAR) priors have been found to adequately capture spatial variability in some studies (Arab & Courter, 2015; Arab et al., 2016; Pagel et al., 2014; Purse et al., 2015), while Gaussian random fields (Humphreys et al., 2019) and stochastic partial differential equations (SPDE) (Peterson et al., 2020) have been successfully used in others. To our knowledge, no one has addressed the issue of misclassification in citizen science accounting for spatial dependence.…”

Section: Introductionmentioning

confidence: 99%

Correcting Misclassification Errors in Crowdsourced Ecological Data: A Bayesian Perspective

Santos–Fernández

Peterson

Vercelloni

et al. 2021

Journal of the Royal Statistical Society Series C: Applied Statistics

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Many research domains use data elicited from ‘citizen scientists’ when a direct measure of a process is expensive or infeasible. However, participants may report incorrect estimates or classifications due to their lack of skill. We demonstrate how Bayesian hierarchical models can be used to learn about latent variables of interest, while accounting for the participants’ abilities. The model is described in the context of an ecological application that involves crowdsourced classifications of georeferenced coral‐reef images from the Great Barrier Reef, Australia. The latent variable of interest is the proportion of coral cover, which is a common indicator of coral reef health. The participants’ abilities are expressed in terms of sensitivity and specificity of a correctly classified set of points on the images. The model also incorporates a spatial component, which allows prediction of the latent variable in locations that have not been surveyed. We show that the model outperforms traditional weighted‐regression approaches used to account for uncertainty in citizen science data. Our approach produces more accurate regression coefficients and provides a better characterisation of the latent process of interest. This new method is implemented in the probabilistic programming language Stan and can be applied to a wide number of problems that rely on uncertain citizen science data.

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“…Blue-winged teal were chosen as a representative waterfowl species due to their widespread distribution and suspected role in redistributing avian influenza viruses throughout breeding grounds in the U.S. Northern Great Plains and Canada and overwintering habitats in Mexico 17 , the Caribbean 37 , and Central and northern South America 16,18,38 . Because migratory behavior and habitat preferences can differ between duck species 39 , the selection of blue-winged teal as an archetypal dabbling duck species necessitates that several important caveats be considered. As examples, blue-winged teal have a tendency to start the spring migration later, undertake the fall migration earlier, and fly greater distances than other dabbling ducks 40,41 .…”

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confidence: 99%

Waterfowl occurrence and residence time as indicators of H5 and H7 avian influenza in North American Poultry

Humphreys

Ramey

Douglas

et al. 2020

Sci Rep

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Avian influenza (AI) affects wild aquatic birds and poses hazards to human health, food security, and wildlife conservation globally. Accordingly, there is a recognized need for new methods and tools to help quantify the dynamic interaction between wild bird hosts and commercial poultry. Using satellite-marked waterfowl, we applied Bayesian joint hierarchical modeling to concurrently model species distributions, residency times, migration timing, and disease occurrence probability under an integrated animal movement and disease distribution modeling framework. Our results indicate that migratory waterfowl are positively related to AI occurrence over North America such that as waterfowl occurrence probability or residence time increase at a given location, so too does the chance of a commercial poultry AI outbreak. Analyses also suggest that AI occurrence probability is greatest during our observed waterfowl northward migration, and less during the southward migration. Methodologically, we found that when modeling disparate facets of disease systems at the wildlifeagriculture interface, it is essential that multiscale spatial patterns be addressed to avoid mistakenly inferring a disease process or disease-environment relationship from a pattern evaluated at the improper spatial scale. The study offers important insights into migratory waterfowl ecology and AI disease dynamics that aid in better preparing for future outbreaks. Avian influenza is a global concern and poses hazards to human health, food security, and wildlife conservation worldwide 1,2. Domestic poultry operations are particularly vulnerable to avian influenza viruses maintained in wild bird hosts 3-6 as the viruses may be spread to poultry 7-10 via direct contact or by way of environmental contamination. Once introduced into a poultry operation, avian influenza viruses of the H5 and H7 hemagglutinin subtype can rapidly propagate through commercial flocks and mutate to the Highly Pathogenic Avian Influenza (HPAI) pathotype with increased virulence 1,11,12. HPAI outbreaks can inflict direct stock mortality or necessitate that culling protocols be implemented to minimize the risk of disease spread. In rare instances, control efforts may also reduce the spread of viruses potentially lethal to humans, as was shown for Goose Guangdong (GsGD) lineage HPAI H5N1 and H7N9 in China 13,14. Avian influenza viruses circulate among wild aquatic birds globally and taxa such as migratory waterfowl are considered to be natural biologic reservoirs 15. Waterfowl are infected with varying virus subtypes, including those with the H5 and H7 hemagglutinin protein that have the potential to mutate to HPAI in poultry throughout the Neotropics and Nearctic 16-19. During 2014 and 2015, it appears migratory waterfowl contributed to the introduction of GsGD lineage HPAI H5 viruses into North America 20-22 and subsequent spread in Canada and the U.S. 23-26. This outbreak was the largest in U.S. history, affected wild and domestic birds in 21 U.S. States, resulted in the loss of a...

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Seasonal occurrence and abundance of dabbling ducks across the continental United States: Joint spatio‐temporal modelling for the GenusAnas

Cited by 25 publications

References 81 publications

Predicting spatiotemporal abundance of breeding waterfowl across Canada: A Bayesian hierarchical modelling approach

Predicting spatiotemporal abundance of breeding waterfowl across Canada: A Bayesian hierarchical modelling approach

Correcting Misclassification Errors in Crowdsourced Ecological Data: A Bayesian Perspective

Waterfowl occurrence and residence time as indicators of H5 and H7 avian influenza in North American Poultry

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