There is a long history in quantifying the relationship between species distribution and your interactions with biotic and abiotic environmental variables in ecological research. In addition, it is now an integral tool in providing biogeographic data in species assembly, particularly in cases where data is lacking.
The SDM approach is based on the principles of ecological niche theory, including the âfundamental nicheâ, which is primarily a function of physiological tolerance and ecosystem limitations; and ârealized nicheâ, which comprises to the effects of biotic interactions and competitive exclusion. An ecological niche is defined as follows: the set of conditions and resources in which individuals of specie survive, grow, and reproduce; and the environmental variables and ecological interactions that control the species distribution. Consequently, the SDM approach was developed as a probability distribution in geographic space, predicting complete spatial coverage of a particular species distribution, including locations where no event data is available. Therefore, if understanding species geographic distributions are fundamental ecological questions, these predictions are essential for species conservation and management, particularly for endangered species. SDM has broad applicability, including the capacity to assess the status of conservation reserves; efficiently locate areas of conservation priority; establish rare and endangered species distribution; biogeographic studies; analyze climate change affects on species distribution; and SDM serves to guide in efficient field data and specimen collection. Despite widespread application, points out that even for well-studied groups such as birds, some fundamental approaches (e.g. geographic ecology) remain poorly understood. It must be noted that since the inception of modeling in the 1970s enormous advances have occurred, and technological achievements have aided the fieldâs progression, which shows continued development. The most problematic issues identified in SDMs are the predominant use of biotic variables to improve prediction efficiency. Another issue discussed often is data quality (location accuracy) versus quantity (occurrence number), where efforts to create efficient models from incomplete data are questioned. There is no doubt that model success depends critically on the available data. [Pereira IM, Groppo M (2012) Ecological Niche Modeling: Using Satellite Imagery and New Field Data to Support Ecological Theory and its Applicability in the Brazilian Cerrado]
Last date updated on June, 2014