亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Towards a more dynamic metabolic theory of ecology to predict climate change effects on biological systems

生态学 气候变化 生物 环境科学
作者
Keila Stark,Tom Clegg,Joey R. Bernhardt,Tess Nahanni Grainger,Christopher P. Kempes,Van M. Savage,Mary I. O’Connor,Samraat Pawar
出处
期刊:The American Naturalist [University of Chicago Press]
卷期号:205 (3): 285-305 被引量:2
标识
DOI:10.1086/733197
摘要

AbstractThe metabolic theory of ecology (MTE) aims to link biophysical constraints on individual metabolic rates to the emergence of patterns at the population and ecosystem scales. Because MTE links temperature's kinetic effects on individual metabolism to ecological processes at higher levels of organization, it holds great potential to mechanistically predict how complex ecological systems respond to warming and increased temperature fluctuations under climate change. To scale up from individuals to ecosystems, applications of classical MTE implicitly assume that focusing on steady-state dynamics and averaging temperature responses across individuals and populations adequately capture the dominant attributes of biological systems. However, in the context of climate change, frequent perturbations from steady state and rapid changes in thermal performance curves via plasticity and evolution are almost guaranteed. Here, we explain how some of the assumptions made when applying MTE's simplest canonical expression can lead to blind spots in understanding how temperature change affects biological systems and how this presents an opportunity for formal expansion of the theory. We review existing advances in this direction and provide a decision tree for identifying when dynamic modifications to classical MTE are needed for certain research questions. We conclude with empirical and theoretical challenges to be addressed in a more dynamic MTE for understanding biological change in an increasingly uncertain world.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI6.2应助Muth采纳,获得10
3秒前
3秒前
宁ning发布了新的文献求助10
3秒前
李爱国应助liberty采纳,获得10
3秒前
冯昊完成签到,获得积分10
4秒前
4秒前
辛艺完成签到,获得积分10
6秒前
8秒前
英勇问晴完成签到,获得积分10
12秒前
樱木灰发布了新的文献求助10
13秒前
14秒前
14秒前
lkk发布了新的文献求助10
15秒前
温暖伟祺完成签到,获得积分10
23秒前
七七完成签到 ,获得积分10
25秒前
诚心巧曼完成签到 ,获得积分10
28秒前
29秒前
29秒前
36秒前
41秒前
NI完成签到 ,获得积分10
41秒前
牛奶完成签到 ,获得积分10
41秒前
42秒前
成就云朵完成签到,获得积分10
43秒前
44秒前
生动的鱼发布了新的文献求助10
51秒前
53秒前
坚强的钻石完成签到,获得积分10
54秒前
54秒前
59秒前
1分钟前
哈哈哈完成签到,获得积分20
1分钟前
cc321发布了新的文献求助10
1分钟前
迷人的幻嫣完成签到,获得积分10
1分钟前
儿儿发布了新的文献求助10
1分钟前
1分钟前
1分钟前
1分钟前
1分钟前
1分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Rosenblum, Global Change Biology 800
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Physiologic specialization in Peronospora manshurica 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 化学工程 工程类 有机化学 物理 复合材料 生物化学 内科学 细胞生物学 基因 遗传学 免疫学 冶金 光电子学 癌症研究
热门帖子
关注 科研通微信公众号,转发送积分 7777904
求助须知:如何正确求助?哪些是违规求助? 9318680
关于积分的说明 20365449
捐赠科研通 7365023
什么是DOI,文献DOI怎么找? 3319128
关于科研通互助平台的介绍 2466789
邀请新用户注册赠送积分活动 2334387