July 3 rd, 2014 Charlotte Germain-Aubrey ECOLOGICAL NICHE MODELING: PRACTICAL

ECOLOGICAL NICHE MODELS Look at a set of conditions under which a species occurs naturally (presence data) When possible, also look at conditions under which the species does NOT occur (absence data)  BUILD THE MODEL Apply the model in space (around where it occurs naturally, or in another area), or in time (where did it use to occur, or where will it occur in the future)  PROJECT THE MODEL Occurrence data: where the species is present, but not where it is absent Distribution map: where the species is absent, but not where it is present

Step 1: gather points Occurrence points Specimens from museum Observation points Distribution map (absence map) species-distribution-modeling-part-two-what-is-niche-modeling/

Step 2: gather environmental data Climatic Geological Soil Water Chemicals Species interaction positive: obligate host negative: excluding competitor species-distribution-modeling-part-two-what-is-niche-modeling/

Step 3: Extract environmental values at points of occurrence species-distribution-modeling-part-two-what-is-niche-modeling/

Step 4: Build a model Here, a very simple model: 95% of the species distribution for each environmental variable species-distribution-modeling-part-two-what-is-niche-modeling/

Step 4: Build a model Here, a machine learning model comparing the presence and absence MaxEnt species-distribution-modeling-part-two-what-is-niche-modeling/

Step 5: project the model Here projection in the space and time of occurrence species-distribution-modeling-part-two-what-is-niche-modeling/

Step 5: project the model Here, a projection into a new area

Maximum Entropy algorithm used (machine-learning) to build and project niche models. 25% of dataset set aside for testing the model and giving feedback on how well it performs (machine learning process) MAXENT

species,longitude,latitude bradypus_variegatus,-65.4, bradypus_variegatus, , bradypus_variegatus, ,-16.8

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