Development of species distribution models (SDMs) and application of them has been expanding very rapidly over the past few years. Often based on simple occurrence data like that provided by the Fishes of Texas project, they summarize and make these data sets useful in new ways and across large spatial extents. They have proven useful in diverse applications such as conservation planning, climate change studies, disease ecology, invasive species research, and community ecology.

As a first step toward many future landscape-scale geospatial analyses using Fishes of Texas data, we developed powerful predictive computer models of species’ distributions using commonly accepted practices and modeling algorithms and provide them here so that others may use them in their own research and applications. Our models provide continuous coverages of probabilities of species occurrences across all cells of a fine-scale grid extending across all of Texas, thus effectively “filling in the blanks” between the actual occurrences that we know to be distributed in non-random ways as a result of diverse historic factors such as collectors' interests, gears, landowner permission, etc.

We developed these models using only the most precisely located recent occurrence records in the Fishes of Texas database together with recent climate and physical environmental data. These models have now been thoroughly tested and demonstrated to be powerful predictors of actual occurrences under current conditions. They were constructed in such a way that the probability values in the models can be interpreted as indicators of suitability of habitat that are mostly independent of large scale land and water development influences such as diversions or dams.