Variation across space, species and methods in models of spring phenology

C.J. Chamberlain , E.M. Wolkovich
{"title":"Variation across space, species and methods in models of spring phenology","authors":"C.J. Chamberlain ,&nbsp;E.M. Wolkovich","doi":"10.1016/j.ecochg.2023.100071","DOIUrl":null,"url":null,"abstract":"<div><p>Predicting spring phenology in temperate forests is critical for forecasting important processes such as carbon storage. One major forecasting method for phenology is the growing degree day (GDD) model, which tracks heat accumulation. Forecasts using GDD models typically assume that the GDD threshold for a species is constant across diverse landscapes, but increasing evidence suggests otherwise. Shifts in climate with anthropogenic warming may change the required GDD. Variation in climate across space may also lead to variation in GDD requirements, with recent studies suggesting that fine-scale spatial variation in climate may matter to phenology. Here, we combine simulations, observations from an urban and a rural site, and Bayesian hierarchical models to assess how consistent GDD models of budburst are across species and space. We built GDD models using two different methods to measure climate data: on-site weather stations and local dataloggers. We find that estimated GDD thresholds can vary up to 20% across sites and methods. Our results suggest our studied urban site requires fewer GDDs until budburst and may have stronger microclimate effects than the studied rural site, though these effects depend on the method used to measure climate. Further, we find that GDD models are less accurate for early-active species and may become less accurate with warming. Our results suggest that local-scale forecasts based on GDD models for spring phenology should incorporate these inherent accuracy issues of GDD models, alongside the variations we found across space, species and warming. Testing whether these issues persist at larger spatial scales could improve forecasts for temperate forests.</p></div>","PeriodicalId":100260,"journal":{"name":"Climate Change Ecology","volume":"5 ","pages":"Article 100071"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Climate Change Ecology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666900523000072","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Predicting spring phenology in temperate forests is critical for forecasting important processes such as carbon storage. One major forecasting method for phenology is the growing degree day (GDD) model, which tracks heat accumulation. Forecasts using GDD models typically assume that the GDD threshold for a species is constant across diverse landscapes, but increasing evidence suggests otherwise. Shifts in climate with anthropogenic warming may change the required GDD. Variation in climate across space may also lead to variation in GDD requirements, with recent studies suggesting that fine-scale spatial variation in climate may matter to phenology. Here, we combine simulations, observations from an urban and a rural site, and Bayesian hierarchical models to assess how consistent GDD models of budburst are across species and space. We built GDD models using two different methods to measure climate data: on-site weather stations and local dataloggers. We find that estimated GDD thresholds can vary up to 20% across sites and methods. Our results suggest our studied urban site requires fewer GDDs until budburst and may have stronger microclimate effects than the studied rural site, though these effects depend on the method used to measure climate. Further, we find that GDD models are less accurate for early-active species and may become less accurate with warming. Our results suggest that local-scale forecasts based on GDD models for spring phenology should incorporate these inherent accuracy issues of GDD models, alongside the variations we found across space, species and warming. Testing whether these issues persist at larger spatial scales could improve forecasts for temperate forests.

春季表型模型中空间、物种和方法的变化
预测温带森林的春季酚学对于预测碳储存等重要过程至关重要。一种主要的酚学预测方法是生长度日(GDD)模型,该模型跟踪热量积累。使用GDD模型的预测通常假设一个物种的GDD阈值在不同的景观中是恒定的,但越来越多的证据表明情况并非如此。气候随人为变暖的变化可能会改变所需的GDD。空间气候的变化也可能导致GDD要求的变化,最近的研究表明,气候的精细尺度空间变化可能对酚学很重要。在这里,我们将模拟、城市和农村地区的观测结果以及贝叶斯层次模型相结合,以评估芽突的GDD模型在物种和空间上的一致性。我们使用两种不同的方法来测量气候数据:现场气象站和本地数据记录器,建立了GDD模型。我们发现,估计的GDD阈值在不同的站点和方法中可能会有高达20%的差异。我们的研究结果表明,我们研究的城市地区在芽突之前需要更少的GDD,并且可能比研究的农村地区具有更强的小气候影响,尽管这些影响取决于用于测量气候的方法。此外,我们发现GDD模型对早期活跃物种的准确性较低,并且可能随着气候变暖而变得不那么准确。我们的研究结果表明,基于GDD春季气象学模型的地方尺度预测应该包括GDD模型的这些固有准确性问题,以及我们在空间、物种和变暖方面发现的变化。测试这些问题是否在更大的空间尺度上持续存在,可以改善对温带森林的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.00
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信