Combination of factors rather than single disturbance drives perturbation of the nitrogen cycle in a temperate forest

IF 3.9 3区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Biogeochemistry Pub Date : 2023-12-03 DOI:10.1007/s10533-023-01105-z
Mark B. Green, Linda H. Pardo, John L. Campbell, Emma Rosi, Emily S. Bernhardt, Charles T. Driscoll, Timothy J. Fahey, Nicholas LoRusso, Jackie Matthes, Pamela H. Templer
{"title":"Combination of factors rather than single disturbance drives perturbation of the nitrogen cycle in a temperate forest","authors":"Mark B. Green,&nbsp;Linda H. Pardo,&nbsp;John L. Campbell,&nbsp;Emma Rosi,&nbsp;Emily S. Bernhardt,&nbsp;Charles T. Driscoll,&nbsp;Timothy J. Fahey,&nbsp;Nicholas LoRusso,&nbsp;Jackie Matthes,&nbsp;Pamela H. Templer","doi":"10.1007/s10533-023-01105-z","DOIUrl":null,"url":null,"abstract":"<div><p>Nitrogen (N) is a critical element in many ecological and biogeochemical processes in forest ecosystems. Cycling of N is sensitive to changes in climate, atmospheric carbon dioxide (CO<sub>2</sub>) concentrations, and air pollution. Streamwater nitrate draining a forested ecosystem can indicate how an ecosystem is responding to these changes. We observed a pulse in streamwater nitrate concentration and export at a long-term forest research site in eastern North America that resulted in a 10-fold increase in nitrate export compared to observations over the prior decade. The pulse in streamwater nitrate occurred in a reference catchment in the 2013 water year, but was not associated with a distinct disturbance event. We analyzed a suite of environmental variables to explore possible causes. The correlation between each environmental variable and streamwater nitrate concentration was consistently higher when we accounted for the antecedent conditions of the variable prior to a given streamwater observation. In most cases, the optimal antecedent period exceeded two years. We assessed the most important variables for predicting streamwater nitrate concentration by training a machine learning model to predict streamwater nitrate concentration in the years preceding and during the streamwater nitrate pulse. The results of the correlation and machine learning analyses suggest that the pulsed increase in streamwater nitrate resulted from both (1) decreased plant uptake due to lower terrestrial gross primary production, possibly due to increased soil frost or reduced solar radiation or both; and (2) increased net N mineralization and nitrification due to warm temperatures from 2010 to 2013. Additionally, variables associated with hydrological transport of nitrate, such as maximum stream discharge, emerged as important, suggesting that hydrology played a role in the pulse. Overall, our analyses indicate that the streamwater nitrate pulse was caused by a combination of factors that occurred in the years prior to the pulse, not a single disturbance event.</p></div>","PeriodicalId":8901,"journal":{"name":"Biogeochemistry","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biogeochemistry","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s10533-023-01105-z","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Nitrogen (N) is a critical element in many ecological and biogeochemical processes in forest ecosystems. Cycling of N is sensitive to changes in climate, atmospheric carbon dioxide (CO2) concentrations, and air pollution. Streamwater nitrate draining a forested ecosystem can indicate how an ecosystem is responding to these changes. We observed a pulse in streamwater nitrate concentration and export at a long-term forest research site in eastern North America that resulted in a 10-fold increase in nitrate export compared to observations over the prior decade. The pulse in streamwater nitrate occurred in a reference catchment in the 2013 water year, but was not associated with a distinct disturbance event. We analyzed a suite of environmental variables to explore possible causes. The correlation between each environmental variable and streamwater nitrate concentration was consistently higher when we accounted for the antecedent conditions of the variable prior to a given streamwater observation. In most cases, the optimal antecedent period exceeded two years. We assessed the most important variables for predicting streamwater nitrate concentration by training a machine learning model to predict streamwater nitrate concentration in the years preceding and during the streamwater nitrate pulse. The results of the correlation and machine learning analyses suggest that the pulsed increase in streamwater nitrate resulted from both (1) decreased plant uptake due to lower terrestrial gross primary production, possibly due to increased soil frost or reduced solar radiation or both; and (2) increased net N mineralization and nitrification due to warm temperatures from 2010 to 2013. Additionally, variables associated with hydrological transport of nitrate, such as maximum stream discharge, emerged as important, suggesting that hydrology played a role in the pulse. Overall, our analyses indicate that the streamwater nitrate pulse was caused by a combination of factors that occurred in the years prior to the pulse, not a single disturbance event.

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
因子的组合而不是单一的扰动驱动了温带森林氮循环的扰动
氮(N)是森林生态系统中许多生态和生物地球化学过程的关键元素。氮的循环对气候、大气二氧化碳(CO2)浓度和空气污染的变化非常敏感。流经森林生态系统的水流硝酸盐可以表明生态系统如何对这些变化作出反应。我们在北美东部的一个长期森林研究地点观察到水流硝酸盐浓度和出口的脉冲,导致硝酸盐出口与前十年的观测结果相比增加了10倍。2013水年某参考流域出现了径流硝酸盐的脉动,但与明显的扰动事件无关。我们分析了一系列环境变量来探索可能的原因。当我们在给定的河流观测之前考虑变量的先决条件时,每个环境变量与河流硝酸盐浓度之间的相关性始终较高。在大多数情况下,最佳的前期期超过两年。我们通过训练机器学习模型来预测河流硝酸盐脉冲前几年和期间的河流硝酸盐浓度,评估了预测河流硝酸盐浓度的最重要变量。相关分析和机器学习分析的结果表明,径流硝酸盐的脉冲增加是由以下两方面造成的:(1)由于陆地初级生产总量降低,植物吸收减少,可能是由于土壤霜冻增加或太阳辐射减少,或两者兼而有之;(2) 2010 ~ 2013年暖化导致净氮矿化和硝化作用增加。此外,与硝酸盐水文运输相关的变量,如最大水流流量,也变得很重要,这表明水文在脉冲中发挥了作用。总的来说,我们的分析表明,水流硝酸盐脉冲是由脉冲前几年发生的各种因素的组合引起的,而不是单一的干扰事件。
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 去求助
来源期刊
Biogeochemistry
Biogeochemistry 环境科学-地球科学综合
CiteScore
7.10
自引率
5.00%
发文量
112
审稿时长
3.2 months
期刊介绍: Biogeochemistry publishes original and synthetic papers dealing with biotic controls on the chemistry of the environment, or with the geochemical control of the structure and function of ecosystems. Cycles are considered, either of individual elements or of specific classes of natural or anthropogenic compounds in ecosystems. Particular emphasis is given to coupled interactions of element cycles. The journal spans from the molecular to global scales to elucidate the mechanisms driving patterns in biogeochemical cycles through space and time. Studies on both natural and artificial ecosystems are published when they contribute to a general understanding of biogeochemistry.
期刊最新文献
Regional differences in soil stable isotopes and vibrational features at depth in three California grasslands High spatial variability in wetland methane fluxes is tied to vegetation patch types Calcium sorption and isotope fractionation in Bacillus subtilis and Pseudomonas aeruginosa Forest types control the contribution of litter and roots to labile and persistent soil organic carbon Response of Fe(III)-reducing kinetics, microbial community structure and Fe(III)-related functional genes to Fe(III)-organic matter complexes and ferrihydrite in lake sediment
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1