Spatially-explicit conservation decisions rely on having information on where biodiversity assets are (e.g. threatened species), but also the location of threats and habitat condition (e.g. degraded vs pristine habitats). Another important consideration is the cost of acting at different locations in a landscape: the costs of reserving a piece of land for conservation can vary by orders of magnitude depending on where that land is!
Ecological modellers keep claiming that we need to improve the methods we use to estimate the spatial distribution of biodiversity, but the way biodiversity, threats, conditions and costs are used in spatial conservation prioritisation is such that some of these ‘spatial layers’ may have a much stronger influence that others.
In our recent paper (Kujala, Lahoz-Monfort, Elith, Moilanen (2018) Not all data are equal: Influence of data type and amount in spatial conservation prioritization. Methods in Ecology and Evolution, 9, 2249–2261) we explore how many layers of each kind are typically used in a spatial conservation prioritisation, and what influence individual layers of different types have on a spatial priority ranking. And, – surprise, surprise! – cost layers tend the be rather uncertain or incomplete, while having a strong influence on the outcome. Should we pay more attention to how we create cost layers?
If you find this question relevant and interesting, you might want to have a look at the recent blog post that Heini Kujala and I recently published in the journal Methods in Ecology & Evolution (MEE).
Esta entrada de blog en MEE está también disponible en español: En la planificación de la conservación, algunos datos son más importantes que otros.