Predicting spatiotemporal abundance of breeding waterfowl across Canada: A Bayesian hierarchical modelling approach. Issue 10 (13th August 2020)

• Record Type: Journal Article
• Authors: Adde, Antoine; Darveau, Marcel; Barker, Nicole; Cumming, Steven
• Editors: López, Ana Benítez
• Is Part Of: Diversity & distributions. Volume 26:Issue 10(2020)
• Page Start: 1248
• Page End: 1263
• Publication Date: 2020-08-13
• Subjects: Bayesian hierarchical models -- breeding waterfowl -- Canada -- covariate selection -- habitat -- INLA‐SPDE -- spatiotemporal -- species distribution modelling; Biodiversity -- Periodicals; Biodiversity conservation -- Periodicals; 577
• DOI: 10.1111/ddi.13129
• Languages: English
• ISSNs: 1366-9516

Abstract

Abstract: 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.