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.
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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).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:
Citation for Using Class 02 Models
Labay, B. J., A. E. Cohen, B. Sissel, D. A. Hendrickson, Fand A. D. Martin, and S. Sarkar., 2011. Assessing historical fish community composition using surveys, historical collection data, and species distribution models. PLoS ONE 6, e25145E. 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.