Using long‐term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models

Schädel, Christina; Seyednasrollah, Bijan; Hanson, Paul J.; Hufkens, Koen; Pearson, Kyle J.; Warren, Jeffrey M.; Richardson, Andrew D.

doi:10.1002/pei3.10118

Title: Using long‐term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models

Journal Article · 28 June 2023 · Plant-Environment Interactions

DOI: https://doi.org/10.1002/pei3.10118 · OSTI ID:1987735

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Center for Ecosystem Science and Society Northern Arizona University Flagstaff Arizona USA, Woodwell Climate Research Center Falmouth Massachusetts USA
School of Informatics, Computing and Cyber Systems Northern Arizona University Flagstaff Arizona USA
Environmental Sciences Division and Climate Change Science Institute Oak Ridge National Laboratory Oak Ridge Tennessee USA
BlueGreen Labs 9120 Melsele Belgium
Center for Ecosystem Science and Society Northern Arizona University Flagstaff Arizona USA, School of Informatics, Computing and Cyber Systems Northern Arizona University Flagstaff Arizona USA

Abstract Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model structures that are best suited to predicting phenological changes under future climate scenarios. Here, we model spring and autumn phenological transition dates obtained from digital repeat photography in a boreal Picea ‐ Sphagnum bog in response to a gradient of whole ecosystem warming manipulations of up to +9°C, using five years of observational data. In spring, seven equally best‐performing models for Larix utilized the accumulation of growing degree days as a common driver for temperature forcing. For Picea , the best two models were sequential models requiring winter chilling before spring forcing temperature is accumulated. In shrub, parallel models with chilling and forcing requirements occurring simultaneously were identified as the best models. Autumn models were substantially improved when a CO ₂ parameter was included. Overall, the combination of experimental manipulations and multiple years of observations combined with variation in weather provided the framework to rule out a large number of candidate models and to identify best spring and autumn models for each plant functional type.

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Sponsoring Organization:: USDOE

OSTI ID:: 1987735

Journal Information:: Plant-Environment Interactions, Journal Name: Plant-Environment Interactions Journal Issue: 4 Vol. 4; ISSN 2575-6265

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: United States

Language:: English

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