Species Distribution Models

Development of species distribution models (SDMs) and their application has expanded 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 historical 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.

Mapped modeled probabilities of species occurrences can be viewed and model outputs formatted for analysis may be downloaded via the model class table below.

When using models, please cite as shown below.

How to Cite Models:

Citation of models will be dependent on model class as model development will involve specific personnel contingent on application. There are many published applications using the maximum entropy algorithm and similar protocols as employed here (see SDM Background and Literature), but the most relevant publication to use for methodology citation would be:

Labay, B. J., A. E. Cohen, B. Sissel, D. A. Hendrickson, F.D. Martin, and S. Sarkar., 2011. Assessing historical fish community composition using surveys, historical collection data, and species distribution models. PLoS ONE 6, e25145.

Citation for Using Class 01 Models

Labay, B. J., D. A. Hendrickson, and A. E. Cohen. 2012. Fishes of Texas Project Class 01 Species Distributions Models (models). Published by Texas Natural History Collection, a division of Texas Natural Science Center, University of Texas at Austin. Accessed (insert date of data access).

Citation for Using Class 02 Models

Labay, B. J., D. A. Hendrickson, and A. E. Cohen. 2015. Fishes of Texas Project Class 02 Species Distributions Models (models). Published by Texas Natural History Collection, a division of Texas Natural Science Center, University of Texas at Austin. Accessed (insert date of data access).

SDM Background

Advances in information technology and worldwide efforts to compile, digitize, and make biodiversity data available (e.g., NatureServe [www.natureserve.org], Global Biodiversity Information Facility [www.gbif.org]) have recently improved our perception of the diverse scales of anthropogenic alteration of the environment. Simultaneously, development of new tools and techniques help summarize and utilize these biodiversity datasets. Species distribution modeling (SDM) is one such tool that is increasingly used in many disciplines, including applied fields of systematic conservation planning, climate change studies, disease ecology (Sarkar et al. 2010, Peterson et al. 2008, Gonzales et al. 2010, Moffett et al. 2007), and invasive species research.

Our publication (Labay et al. 2011) demonstrates a probabilistic approach to fill gaps in existing collection data as a means to establish historical baseline conditions. The number of publications on SDM’s utility in the field of conservation have increased recently, and include applications towards invasive plant spread (Merow et al. 2011), mammalian conservation (Lopez-Arevalo et al. 2011), fish species conservation (Sindt et al. 2011), conservation planning protocols (Lawler et al. 2011; Carvalho et al. 2011), forest management (Falk & Mellert 2011), and species or system response to climate change (Graham et al. 2011; Falk & Mellert 2011). We are encouraged by the continued growth and utility of this tool for use in conservation, and have intentions of continuing research with these models to further our understanding about stream fishes in Texas.