Getting absence from presence

When modelling species distributions based on occurrences, absence is often derived from lack of presence (pseudo-absence), but absence could be due to poor surveying or species behaviour.  This paper presents a novel methodology to predict absence information from presence-only information.

Data resources used via GBIF : 1.7 million occurences
Atlantic cod (Gadus morhua) by bathyporeia licensed under CC BY-NC-ND 2.0

Atlantic cod (Gadus morhua) by bathyporeia licensed under CC BY-NC-ND 2.0

The lack of a presence doesn’t necessarily mean absence. When modelling species distributions based on occurrences, absence is often derived from lack of presence (pseudo-absence), but absence could be due to poor surveying or species behaviour. This paper presents a novel methodology to predict absence information from presence-only information. Using Marine Species Survey Reports from the Ocean Biogeographic Information System (OBIS), the authors developed a method which relies on the principle of predicting the absence of one species by the presence of others, thereby avoiding some sampling bias. They evaluated the performance of the method using the Atlantic cod (Gadus morhua) as a case study, and finally benchmarked against 1.7 million GBIF-mediated occurrences of 280 marine species. Although the method does not produce an exhaustive set of absences, it performs well and can be scaled to work regionally as well as globally.

Citations

Coro G, Magliozzi C, Vanden Berghe E, Bailly N, Ellenbroek A & Pagano P. (2016). Estimating absence locations of marine species from data of scientific surveys in OBIS. Ecological Modelling, 323, 61–76. doi:10.1016/j.ecolmodel.2015.12.008

Subject