Distribution Model (SDM) can be defined as a statistical/analytical algorithm
that predicts either actual or potential distribution of a species, given field
observations and auxiliary maps, as well as expert knowledge. A special group
of SDM focuses on the so called “occurrence
only records” containing pure records of locations where a species
occurred (Engler et al., 2004; Tsoar
et al., 2007). In simple way, we can
say that species distribution models (SDMs) are used to understand how the
presence of a species is associated to the environment and how a species may
response to change in its atmospheric environment. This can be helpful to find
out new locations where a rare species might be found or understand the
potential threats to particular species due to urban encroachment, climate
change or other causes (Warton and Leach, 2010). The contribution is required from
interdisciplinary subjects such as statisticians, computer scientist and
ecologist to construct species distribution models.  Example of SDMs is DISTRIB, Maxent, GARP and
SHIFT. The developmental objective of SDMs is to predict potential tree species
occurrence and potential colonization using statistical models based on
climate, site and soil variables. The main concept behind the SDMs is to focus
on statistical relationships; predicts habitat suitability based on historical
and current climate/site. These kinds of models do not consider sensitivity to
CO2 concentration and new competitive relationships. The scale and
range of SDMs is generally 20 km or it may depend on sampling scale of coarsest
site variable. The key components for SDMs are to use data mining statistical
methods (e.g. random forests, maximum likelihood, Artificial Neural Network,
boosted regression tree, support vector machines, etc.) for robust predictive
habitat models. The output of SDMs can tell about potentiality about habitat
suitability of species and also predict importance of potential species

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