**5.3 Algorithm**

356 The Dynamical Processes of Biodiversity – Case Studies of Evolution and Spatial Distribution

analyzed the genetic population structure of the endangered mayfly (*Ameletus inopinatus*) in its European range genotyping hundreds of individuals from different populations. They found variations in genetic diversity and also projected the distribution of species through SDM for the year 2080 finding some areas of regional habitat loss. By relating these range shifts to the population genetic results, they were able to identify conservation units that, if preserved, would maintain high levels of the present-day genetic diversity and continue to

Most ecological forecasting of future species ranges is based on models that generally ignore evolution and assume that the mechanistic relationship between species abundance and environmental characteristics is unchanged at the timescale of the projection (Lavergne et al., 2010). But there is accumulating evidence that evolution can proceed fast (Hairston et al., 2005) and genetic variation for adaptation - and more generally for traits defining species ecological niches - is common both between and within populations, suggesting a high level of local adaptation to climate at a fine scale (Pearman et al., 2008). Adaptation and dispersal are often presented as alternative mechanisms whereby a population can respond to changing environmental conditions playing a crucial role in tracking favorable environmental conditions through space (Pease et al., 1989). Thus migration of different genotypes could have important consequences for the evolution of geographical distribution

Addressing the main aspects discussed here about distribution of species, it was suggested that the new trends on SDM, regarding the impacts of global changes on species diversity, are niche evolution, phylogeographic and phylogenetic research (Zimmermann et al., 2010). As pointed out by Gilman et al. (2010), the key question is not the effects resulting from global change on individual species, but the stability of the system as a whole. Integrated fields of research will allow novel analysis of both historical and contemporary drivers of species ranges, and will likely provide new possibilities to understand present day species

Species distribution modeling presents some steps and requires expertise knowledge about the focal species and also ecology, geography and clime. The following summarized steps

It is necessary to prepare a database with presence and absence points of the focal species. This step can include the georeferencing of points (latitude x longitude) and the exclusion of doubtful or inaccurate information. Occurrence data can be obtained from biodiversity data providers such as Global Biodiversity Information Facility (GBIF) and The Inter-American Biodiversity Information Network (IABIN). Also, to search occurrences in the literature or

This step aims to obtain the environmental layers to be used. Usually, they must be on 'raster' format, in which a matrix of cells is used to build the image. Generally, a Geographic Information System (GIS) software is necessary, such as ArcGIS (ESRI Inc.) or DIVA GIS (LizardTech, Inc. and University of California) to prepare them for SDM. Climate data sets can be found in the WordClim (Hijmans et al., 2005) and in the International Center of Tropical Agriculture (CIAT) websites. Categorical data sets can be found in World Wild Life

provide long-term suitable habitat under future climate change scenarios.

limits (Davis et al., 2005).

are suggested.

**5.1 Occurrence points** 

**5.2 Environmental data sets** 

distributions and project them to the future.

perform new local surveys can provide additional information.

Algorithms are finite sequences of instructions for calculating a function. There are lots of algorithms for SDM. Two important ones are Maxent (Maximum Entropy - Phillips et al., 2006) and Genetic Algorithm for Rule set Production (GARP - Stockwell and Peters, 1999) that have been successfully applied to small data sets with presence-only occurrence points (Wisz et al., 2008). GARP and other algorithms can be found in openModeller (Santana et al., 2008), a computational system to perform SDM. Another system available to SDM is BIOMOD (Biodiversity Modelling – Thuiller, 2003) developed to R (The R Foundation for Statistical Computing) that presents nine algorithms.
