Using maximum topology matching to explore differences in species distribution models

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Species distribution models (SDM) are used to help understand what drives the distribution of various plant and animal species. These models are typically high dimensional scalar functions, where the dimensions of the domain correspond to predictor variables of the model algorithm. Understanding and exploring the differences between models help ecologists understand areas where their data or understanding of the system is incomplete and will help guide further investigation in these regions. These differences can also indicate an important source of model to model uncertainty. However, it is cumbersome and often impractical to perform this analysis using existing tools, which allows for manual exploration of the models usually as 1-dimensional curves. In this paper, we propose a topology-based framework to help ecologists explore the differences in various SDMs directly in the high dimensional domain. In order to accomplish this, we introduce the concept of maximum topology matching that computes a locality-aware correspondence between similar extrema of two scalar functions. The matching is then used to compute the similarity between two functions. We also design a visualization interface that allows ecologists to explore SDMs using their topological features and to study the differences between pairs of models found using maximum topological matching. We demonstrate the utility of the proposed framework through several use cases using different data sets and report the feedback obtained from ecologists.

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Publication type Conference Paper
Publication Subtype Conference Paper
Title Using maximum topology matching to explore differences in species distribution models
Year Published 2015
Language English
Publisher IEEE
Contributing office(s) North Central Climate Science Center
Description 8 p.
Larger Work Type Conference Paper
Conference Title VIS 2015
Conference Date October 25, 2015
Country United States
Online Only (Y/N) N
Additional Online Files (Y/N) N
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