SPECIES is an innovative platform used to explore and analyze large geospatial datasets, offering a new way of looking at traditional spatial databases. In SPECIES, spatial variables of arbitrary resolution may be used to determine any and all spatial relations between organisms and geospatial variables that represent their environment to construct models at both the niche and community levels. These models can be used to infer both biotic and abiotic interactions. With SPECIES the user may create and refine niche models that consider thousands of variables on the fly; SPECIES can also construct Complex Inference Networks using hundreds of variables that model the relations between species at the community or ecosystemic level.
The last few decades have seen an explosion in the amount and availability of spatio-temporal data, particularly in the areas of climate change and biodiversity. Much more challenging than obtaining the data however, is the task of understanding what it is telling us. In particular, how can we use it to build models of the Complex Adaptive Systems described by these data to better understand them and make predictive models that can be used to make smarter decisions?
For example:
What are the factors, both biotic and abiotic, that influence the distribution of species?
What is the nature of the key interactions that govern the distribution of a species?
What is the nature of the key interactions that govern the composition of a community or ecosystem?
How are these factors and interactions changing in time?
The ability to address these questions has important implications in many important areas, such as biodiversity, ecosystem services, emerging diseases and climate change, among others.
SPECIES is a publicly available platform, based on a tried and tested theoretical framework for spatial modelling, that can construct predictive models using data of arbitrary spatial resolution. This framework has been successfully applied in multiple areas, such as species distribution models, where it has been possible to include both environmental rasters and point distribution data in the description of the niche of a given species. It has also been used to predict and discover biotic interactions, such as those between vector and host in important zoonosis such as Leishmaniasis and Chagas.
