From mapping to modeling: Integrating multi-temporal remote sensing data into species distributions models at different scales

In times of global change and rapid biodiversity loss, making spatially explicit assessments of species’ distributions is a core challenge in conservation planning. Thus, field surveys are essential to either collect target species or to conduct biological inventories in a given area. For sustainable management, large areas need to be covered - too costly in terms of time, effort and money to be done with field work alone. In the last two decades, correlative Species Distribution Models (SDMs) - that statistically link species records or abundances to environmental data - and remote sensing (RS) have become standard tools to interpolate between scattered field data using environmental information. Several SDM studies used land cover data, one of the standard RS products, to act as proxy for habitat availability. However, the combination of both techniques is still under-utilized – due to their different scientific background and remaining skepticism on both sides. This study summarizes several examples where multi-temporal RS imagery (representing differences in the vegetation seasonality) was directly integrated into SDMs (Maxent). As mentioned above, scale is a key factor in biodiversity assessments. When predicting species’ presence with remote sensing data as a mixed signal of the (plant) species itself and its environment we observe a continuous transition from “mapping” to “modeling”. What do we detect at which cell size? – This is the core question to be discussed