2019 CSDMS meeting-065: Difference between revisions
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|CSDMS meeting abstract title= | |CSDMS meeting abstract title=Species Distribution Models (SDMs) for characterizing the ecology of modern foraminifera and how this can inform reconstructions of past oceanographic states from Earth’s climatic history | ||
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{{CSDMS meeting abstract template 2019 | {{CSDMS meeting abstract template 2019 | ||
|CSDMS meeting abstract= | |CSDMS meeting abstract=Planktic foraminifera abundances and distributions, i.e. faunal assemblages, have been used to reconstruct oceanographic conditions from Earth's ancient past. Here we examine the utility of Species Distribution Models (SDMs) in characterizing the ecology of modern foraminifera and how this can inform reconstructions of past oceanographic states from Earth’s climatic history. Standard faunal assemblage proxy reconstructions often reduce multidimensional environmental data into a single variable, typically temperature. However, when environmental covariates feature strong spatial autocorrelation, these traditional methods may incorrectly interpret information from other variables as a temperature signal. Using modern machine learning-based SDMs we show that while temperature is an unquestionably important control on foraminifera distribution, other environmental factors appear to play a non-trivial role. This has implications not only for temperature values derived from standard assemblage-based reconstructions, but also may help reconcile apparent mismatches between proxies and climate model simulations | ||
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Latest revision as of 08:55, 23 April 2019
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Species Distribution Models (SDMs) for characterizing the ecology of modern foraminifera and how this can inform reconstructions of past oceanographic states from Earth’s climatic history
Planktic foraminifera abundances and distributions, i.e. faunal assemblages, have been used to reconstruct oceanographic conditions from Earth's ancient past. Here we examine the utility of Species Distribution Models (SDMs) in characterizing the ecology of modern foraminifera and how this can inform reconstructions of past oceanographic states from Earth’s climatic history. Standard faunal assemblage proxy reconstructions often reduce multidimensional environmental data into a single variable, typically temperature. However, when environmental covariates feature strong spatial autocorrelation, these traditional methods may incorrectly interpret information from other variables as a temperature signal. Using modern machine learning-based SDMs we show that while temperature is an unquestionably important control on foraminifera distribution, other environmental factors appear to play a non-trivial role. This has implications not only for temperature values derived from standard assemblage-based reconstructions, but also may help reconcile apparent mismatches between proxies and climate model simulations
