Guidelines for the use of spatially varying coefficients in species distribution models

Doser, Jeffrey W.; Kéry, Marc; Saunders, Sarah P.; Finley, Andrew O.; Bateman, Brooke L.; Grand, Joanna; Reault, Shannon; Weed, Aaron S.; Zipkin, Elise F.

doi:10.1111/geb.13814

Cited by 5 publications

References 60 publications

(86 reference statements)

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Uncovering habitat associations and thresholds—insights for managing breeding waterfowl in Eastern Canada

Frei,

Cox,

Brown

et al. 2024

Landsc Ecol

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Context Understanding how habitat influences species abundance is crucial in developing ecologically sound wildlife conservation management plans. Exploring habitat associations and ecological thresholds in species’ responses allows for better conservation and management on a landscape-scale. Objectives This work aimed to identify habitat drivers and response thresholds of waterfowl and waterbird species’ densities in eastern Canada to support key landscape-level decisions for habitat conservation and wetland management. Methods We developed predictive abundance models for 17 species across eastern Canada from 2001 to 2015 using data from four regional surveys and identified areas where prioritizing enhancement of wetlands would increase the breeding density of five priority waterfowl species. Results Habitat associations and spatial abundance patterns varied across species, but most species responded strongly to forest composition, agriculture, and wetland features. Threshold effects occurred and varied among species, yet generally once 14% of a plot was covered in wetlands, positive effects of increased wetland diminished for most species. Our results allow for the targeting of investments in increasing wetland area along portions of eastern Canada that provide the best opportunities to increase breeding densities for priority waterfowl species. Conclusions Understanding species-habitat associations and response thresholds allows for landscape management and planning and prioritization of limited resources. We suggest that management and wetland enhancement efforts for waterfowl in eastern Canada should be guided by predictive models and response thresholds of key habitat attributes to best prioritize actions that will have the biggest positive impact on multiple species.

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Uncovering habitat associations and thresholds—insights for managing breeding waterfowl in Eastern Canada

Frei,

Cox,

Brown

et al. 2024

Landsc Ecol

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Spatial ensemble learning for predicting the potential geographical distribution of invasive species

Yang,

Wan,

Deng

2024

International Journal of Geographical Information Science

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Spatio‐temporal occupancy models with INLA

Belmont,

Martino,

Illian

et al. 2024

Methods Ecol Evol

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Modern methods for quantifying, predicting and mapping species distributions have played a crucial part in biodiversity conservation. Occupancy models have become a popular choice for analysing species occurrence data due to their ability to separate out the observational error induced by imperfect detection of a species, and the sources of bias affecting the occupancy process. However, the spatial and temporal variation in occupancy that is not accounted for by environmental covariates is often ignored or modelled through simple spatial structures as the computational costs of fitting explicit spatio‐temporal models is too high. In this work, we demonstrate how Integrated Nested Laplace Approximation (INLA) may be used to fit complex spatio‐temporal occupancy models and how the R‐INLA package can provide a user‐friendly interface to make such complex models available to users. We show how occupancy models, provided some simplification on the detection process is assumed, can be framed as latent Gaussian models and, as such, benefit from the powerful INLA framework. A large selection of complex modelling features, and random effect models, including spatio‐temporal models, have already been implemented in R‐INLA. These also become available for occupancy models, providing the user with an efficient, reliable and flexible toolbox. We illustrate how INLA provides a flexible and computationally efficient framework for developing and fitting complex occupancy models using two complex case studies. Through these, we show how different spatio‐temporal models that include spatial‐varying trends, smooth terms and spatio‐temporal random effects can be fitted to aggregated detection/non‐detection data. At the cost of limiting the complexity of the detection model structure, INLA can incorporate a range of rather complex structures in the ecological process of interest and hence, extend the functionality of occupancy models. The limitations of occupancy models in terms of scalability for large spatio‐temporal data sets remain a challenge and an active area of research. INLA‐based occupancy models provide an alternative inferential and computational framework to fit complex spatio‐temporal occupancy models. The need for new and more flexible computationally efficient approaches to fit such models makes INLA an attractive option for addressing complex ecological problems, and a promising area of research.

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Guidelines for the use of spatially varying coefficients in species distribution models

Cited by 5 publications

References 60 publications

Uncovering habitat associations and thresholds—insights for managing breeding waterfowl in Eastern Canada

Uncovering habitat associations and thresholds—insights for managing breeding waterfowl in Eastern Canada

Spatial ensemble learning for predicting the potential geographical distribution of invasive species

Spatio‐temporal occupancy models with INLA

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