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MaxEnt (maximum entropy model) is an ecological distribution model used to predict species distribution. It is based on the principle of maximum entropy and estimates the probability distribution of species in different geographical areas by using known species distribution points and environmental variable data. The following is the principle, flow chart, usage scenarios and C language and Python code examples of MaxEnt.
Detailed explanation of the principle :
The core idea of the MaxEnt model is to select a probability distribution under given constraints. This distribution has the maximum entropy, that is, the most uncertain distribution, and is consistent with the known observation data (species distribution points) and environmental variable data.
Inputs to the MaxEnt model include the following:
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Known species distribution point data: These data describe the geographical locations where the species is known to exist.
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Environmental variable data: These data include environmental factors such as climate, topography, soil, etc., which may affect the distribution of species.
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Environmental variable data from unknown areas: Used to predict the distribution of species in new geographic areas.
The MaxEnt model uses known distribution point data and environmental variable data to estimate a probability distribution that is as uniform as possible at known points to maximize the distribution prediction accuracy of species in unknown areas.
Flowchart :
MaxEnt model working