Editor Recommendation: A Multi-State Species Distribution Modelling Framework for Species Using Distinct Habitats

Post provided by Jana McPherson

© Amélie Augé

© Amélie Augé

Correlative distribution models have become essential tools in conservation, macroecology and ecology more generally. They help turn limited occurrence records into predictive maps that help us get a better sense of where species might be found, which areas might be critical for their protection, how large their range currently is, and how it might change with climate change, urban encroachment or other forms of habitat conversion.

It can be frustrating, however, when species distribution models (and the predictive maps they produce) don’t adequately capture what we already know about the habitat needs of a species. A major challenge to date has been to represent the environmental needs of species that require distinct habitats during different life stages or behavioural states. Rainbow parrotfish (Scarus guacamaia), for example, spend their youth sheltered from predators in mangrove areas before moving onto coral reefs, and European nightjars (Caprimulgus europaeus) breed in heathland but require access to grazed grassland for foraging. Correlative distribution models confronted with occurrence records from both life stages or behavioural modes tend to produce poor predictive maps because they confound these distinct requirements. Continue reading


Multi-State Species Distribution Models: What to do When Species Need Multiple Habitats

Post provided by Jan Engler, Veronica Frans and Amélie Augé

The north, south, east, and west boundaries of a species’ range tell us very little about what is happening inside…

― Robert H. MacArthur (1972, p. 149)

When You Enter the Matrix, Things Become Difficult!

New Zealand sea lion mother and pup. © Amélie Augé

New Zealand sea lion mother and pup. © Amélie Augé

Protecting wildlife calls for a profound understanding of species’ habitat demands to guide concrete conservation actions. Quantifying the relationships between species and their environment using species distribution models (SDMs) has attracted tremendous attention over the past two decades. Usually these species-environment relationships are estimated on coarse spatial scales, using globally-interpolated long-term climate data sets. While they’re useful for studies on large-scale species distributions, these environmental predictors have limited applications for conservation management.

Climatic data were the first environmental information available with global coverage, but a wide range of Earth observation techniques have increased the availability of much finer environmental information. This allows us to quantify species-environment relationships in unprecedented detail. We can now shift the scale that SDMs operate at, resulting in more useful applications in conservation – SDMs now enter the matrix.

This shift in scale brings new challenges, especially for species using multiple distinct habitat types to survive. The landscape matrix, which has been negligible at the broad (global) scale, is hugely important at the fine (local) scale. It is not only that we need to quantify certain habitat types but also need to consider their arrangement in the landscape, which is basically what the landscape matrix is about. But as we enter the matrix, things become difficult. Continue reading