Modelling the impacts of agriculture in mixed-use landscapes: a review and case study involving two species of dabbling ducks

Lieske, David J.; MacIntosh, Megan; Millet, Lee; Bondrup‐Nielsen, Søren; Pollard, J. Bruce; Parsons, Glen J.; McLellan, Nic R.; Milton, G. Randy; MacKinnon, Frances; Connor, Kevin; Banks, Lauren K.

doi:10.1007/s10980-017-0579-7

Cited by 4 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This must reflect, in at least some cases, underlying processes related to feeding and/or nesting behaviour. For example, the positive association we found between cropland abundance and mallard TIP has previously been related to their foraging preferences (Forcey, Linz, Thogmartin, & Bleier, 2007; Lieske et al., 2018; Newbold & Eadie, 2004). Similarly, the positive association between deadwood biomass and goldeneye TIPs is easily interpreted in light of their cavity‐nesting behaviour, although not, to our knowledge, previously demonstrated.…”

Section: Discussionsupporting

confidence: 57%

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

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.

show abstract

Section: Discussionsupporting

confidence: 57%