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|CSDMS meeting email address=nlyons@tulane.edu
 
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|CSDMS meeting title presentation=Linking landscapes and lineages using models
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|CSDMS meeting title presentation=Linking landscapes and lineages with models
 
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Revision as of 06:51, 23 September 2019

CSDMS 2020: Linking Ecosphere and Geosphere


Linking landscapes and lineages with models



Nathan Lyons

Tulane University, United States
nlyons@tulane.edu

Abstract
The lineages of organismal populations are developed by environmental change, notably by topographic change. The evolution of landscape relief structure alters population spatial distribution, drives speciation where populations spatially fragment, and increases extinction susceptibility of species where its habitat shrinks. SpeciesEvolver is a new modeling tool that constructs lineages in response to environmental change at geologic, macroevolutionary, and landscape scales. The tool tracks species geographic range and evaluates macroevolutionary rules built into the tool or defined by the software user. SpeciesEvolver is built into the Landlab modeling toolkit that contains surface processes components. Meaning, software users can readily build models to explore landscape-life links. Here, I demonstrate SpeciesEvolver functionality with models that illustrate how a landscape with few species can evolve into a biodiversity hotspot following changes in climate and tectonics. Future studies can use SpeciesEvolver to pursue questions regarding the mechanisms by which lineages respond to the drivers and details of landscape evolution, taxon-specific and region-specific interactions between biotas and their environments, and biotic impacts on landscapes.

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Of interest for:
  • Terrestrial Working Group
  • Coastal Working Group
  • Education and Knowledge Transfer (EKT) Working Group
  • Cyberinformatics and Numerics Working Group
  • Hydrology Focus Research Group
  • Carbonates and Biogenics Focus Research Group
  • Critical Zone Focus Research Group
  • Ecosystem Dynamics Focus Research Group
  • Artificial Intelligence & Machine Learning Initiative