[Talk] Using SVMs to model ranges of congeneric sloth species

Incorporating biotic information to guide range estimates of three species of *Bradypus*.

By Cecina Babich Morrow in species distribution modeling Bradypus machine learning

March 15, 2019

Date

March 15, 2019

Time

2:30 PM

Location

New York, NY

Event

Species distribution modeling (SDM) techniques are a common tool for estimating species ranges. These models typically rely only only on abiotic variables without accounting for biotic interactions, despite the fact that these interactions may impose important constraints on ranges. Distribution patterns in which closely-related parapatric species replace each other across geographic space are common in ecology. We sought to address whether incorporating biotic information into range estimates for three species of sloth (genus Bradypus) would improve distribution models for species demonstrating this parapatric pattern of distribution. We used support vector machines (SVMs) as masks to delineate the predicted boundaries between these three species’ ranges. We created two different kinds of SVMs: 1) spatial SVMs using only occurrence data, and 2) sp+env SVMs using occurrence data in conjunction with predicted habitat suitability from SDMs. We found that the sp+env SVM resulted in the most ecologically realistic distribution model, accounting for contact zones between species and the effects of climate.

Slides

Posted on:
March 15, 2019
Length:
1 minute read, 159 words
Categories:
species distribution modeling Bradypus machine learning
Tags:
species distribution modeling Bradypus machine learning
See Also:
Operationalizing expert knowledge in species’ range estimates using diverse data types
[Talk] Delineating parapatric ranges using species distribution models and support vector machines: An example with three-toed sloths (Bradypus)
[Poster] Improving species range estimates for an arboreal species group with a parapatric distribution
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