1. A computer-implemented method comprising: aggregating a plurality of disparate datasets into a document store with semi-structured attributes, each document in the document store specifying a geospatial location and a value of an environmental parameter at the geospatial location, the document store comprising a plurality of documents specifying a plurality of different geospatial locations and different environmental parameters, the environmental parameters including a climatological parameter, wherein at least two of the disparate datasets refer to a same geospatial location using different terminology and the aggregating includes correlating the different terminology and assigning a common label, for use in the document store, to the same geospatial location; generating niche model layers for the environmental parameters at the geospatial locations, the niche model layers including a model layer for each of the different environmental parameters, the generating comprising, for each of the different environmental parameters: identifying documents in the document store that contain information related to the geospatial locations and to the environmental parameter; iterating through the identified documents to determine a value of the environmental parameter at each of the geospatial locations; creating a grid with cells representing the geospatial locations and corresponding determined values of the environmental parameter; and storing the grid as a niche model layer for the environmental parameter; predicting a future geospatial location of a species based on environmental attributes of the species and contents of the niche model layers; and outputting the predicted future geospatial location of the species overlaid on a geographic map.