Robert S Harbert1, Kevin C Nixon1. 1. Section of Plant Biology, 412 Mann Library, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA L.H. Bailey Hortorium, Cornell University, Ithaca, New York 14853, USA.
Abstract
UNLABELLED: • PREMISE OF THE STUDY: Plant distributions have long been understood to be correlated with the environmental conditions to which species are adapted. Climate is one of the major components driving species distributions. Therefore, it is expected that the plants coexisting in a community are reflective of the local environment, particularly climate.• METHODS: Presented here is a method for the estimation of climate from local plant species coexistence data. The method, Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE), is a likelihood-based method that employs specimen collection data at a global scale for the inference of species climate tolerance. CRACLE calculates the maximum joint likelihood of coexistence given individual species climate tolerance characterization to estimate the expected climate.• KEY RESULTS: Plant distribution data for more than 4000 species were used to show that this method accurately infers expected climate profiles for 165 sites with diverse climatic conditions. Estimates differ from the WorldClim global climate model by less than 1.5°C on average for mean annual temperature and less than ∼250 mm for mean annual precipitation. This is a significant improvement upon other plant-based climate-proxy methods.• CONCLUSIONS: CRACLE validates long hypothesized interactions between climate and local associations of plant species. Furthermore, CRACLE successfully estimates climate that is consistent with the widely used WorldClim model and therefore may be applied to the quantitative estimation of paleoclimate in future studies.
UNLABELLED: • PREMISE OF THE STUDY: Plant distributions have long been understood to be correlated with the environmental conditions to which species are adapted. Climate is one of the major components driving species distributions. Therefore, it is expected that the plants coexisting in a community are reflective of the local environment, particularly climate.• METHODS: Presented here is a method for the estimation of climate from local plant species coexistence data. The method, Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE), is a likelihood-based method that employs specimen collection data at a global scale for the inference of species climate tolerance. CRACLE calculates the maximum joint likelihood of coexistence given individual species climate tolerance characterization to estimate the expected climate.• KEY RESULTS: Plant distribution data for more than 4000 species were used to show that this method accurately infers expected climate profiles for 165 sites with diverse climatic conditions. Estimates differ from the WorldClim global climate model by less than 1.5°C on average for mean annual temperature and less than ∼250 mm for mean annual precipitation. This is a significant improvement upon other plant-based climate-proxy methods.• CONCLUSIONS: CRACLE validates long hypothesized interactions between climate and local associations of plant species. Furthermore, CRACLE successfully estimates climate that is consistent with the widely used WorldClim model and therefore may be applied to the quantitative estimation of paleoclimate in future studies.
Authors: Johann P Klages; Ulrich Salzmann; Torsten Bickert; Claus-Dieter Hillenbrand; Karsten Gohl; Gerhard Kuhn; Steven M Bohaty; Jürgen Titschack; Juliane Müller; Thomas Frederichs; Thorsten Bauersachs; Werner Ehrmann; Tina van de Flierdt; Patric Simões Pereira; Robert D Larter; Gerrit Lohmann; Igor Niezgodzki; Gabriele Uenzelmann-Neben; Maximilian Zundel; Cornelia Spiegel; Chris Mark; David Chew; Jane E Francis; Gernot Nehrke; Florian Schwarz; James A Smith; Tim Freudenthal; Oliver Esper; Heiko Pälike; Thomas A Ronge; Ricarda Dziadek Journal: Nature Date: 2020-04-01 Impact factor: 49.962
Authors: Renata D Alquezar; Fabio J V Costa; João Paulo Sena-Souza; Gabriela B Nardoto; Keith A Hobson Journal: PLoS One Date: 2022-08-03 Impact factor: 3.752
Authors: C Martínez; C Jaramillo; A Correa-Metrío; W Crepet; J E Moreno; A Aliaga; F Moreno; M Ibañez-Mejia; M B Bush Journal: Sci Adv Date: 2020-08-28 Impact factor: 14.136