Species distribution modelling
These models can be used to understand how environmental conditions influence the occurrence or abundance of a species, and for predictive purposes (ecological forecasting).
Predictions of current and/or future habitat suitability can be useful for management applications (e.g. reintroduction or translocation of vulnerable species, reserve placement in anticipation of climate change).
[2] The extent to which such modelled data reflect real-world species distributions will depend on a number of factors, including the nature, complexity, and accuracy of the models used and the quality of the available environmental data layers; the availability of sufficient and reliable species distribution data as model input; and the influence of various factors such as barriers to dispersal, geologic history, or biotic interactions, that increase the difference between the realized niche and the fundamental niche.
Correlative SDMs model the observed distribution of a species as a function of geographically referenced climatic predictor variables using multiple regression approaches.
Correlative SDMs assume that species are at equilibrium with their environment and that the relevant environmental variables have been adequately sampled.
However, they are more labor-intensive to create than correlational models and require the collection and validation of a lot of physiological data, which may not be readily available.
Dispersal, biotic interactions, and evolutionary processes present challenges, as they aren’t usually incorporated into either correlative or mechanistic models.
SPACES is an online Environmental niche modeling platform that allows users to design and run dozens of the most prominent methods in a high performance, multi-platform, browser-based environment.
It connects the research community to Australia's national computational infrastructure by integrating a suite of tools in a coherent online environment.
[11] This database system can also project crop yields and evaluate the impact of environmental factors such as climate change on plant growth and suitability.
[12] Most niche modelling methods are available in the R packages 'dismo', 'biomod2' and 'mopa'.. Software developers may want to build on the openModeller project.
The Collaboratory for Adaptation to Climate Change adapt.nd.edu Archived 2012-08-06 at the Wayback Machine has implemented an online version of openModeller that allows users to design and run openModeller in a high-performance, browser-based environment to allow for multiple parallel experiments without the limitations of local processor power.
