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

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Conditional Occupancy Design Explained

Occupancy surveys are widely used in ecology to study wildlife and plant habitat use. To account for imperfect detection probability many researchers use occupancy models. But occupancy probability estimates for rare species tend to be biased because we’re unlikely to observe the animals at all and as a result, the data aren’t very informative.

In their new article – ‘Occupancy surveys with conditional replicates: An alternative sampling design for rare species‘ – Specht et al. developed a new “conditional” occupancy survey design to improve occupancy estimates for rare species, They also compare it to standard and removal occupancy study designs. In this video two of the authors, Hannah Specht and Henry Reich, explain how their new conditional occupancy survey design works. 

This video is based on the article ‘Occupancy surveys with conditional replicates: An alternative sampling design for rare species‘ by Specht et al.