Space‐for‐time substitutions in climate change ecology and evolution

气候变化 生态学 地理 人口 空间生态学 代理(统计) 时间尺度 环境资源管理 生物 环境科学 计算机科学 机器学习 社会学 人口学
作者
Rebecca S. L. Lovell,Sinéad Collins,Simon H. Martin,Alex L. Pigot,Albert B. Phillimore
出处
期刊:Biological Reviews [Wiley]
卷期号:98 (6): 2243-2270 被引量:57
标识
DOI:10.1111/brv.13004
摘要

ABSTRACT In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is an urgent challenge. Since we seldom have sufficient long‐term biological data to use the past to anticipate the future, spatial climate–biotic relationships are often used as a proxy for predicting biotic responses to climate change over time. These ‘space‐for‐time substitutions’ (SFTS) have become near ubiquitous in global change biology, but with different subfields largely developing methods in isolation. We review how climate‐focussed SFTS are used in four subfields of ecology and evolution, each focussed on a different type of biotic variable – population phenotypes, population genotypes, species' distributions, and ecological communities. We then examine the similarities and differences between subfields in terms of methods, limitations and opportunities. While SFTS are used for a wide range of applications, two main approaches are applied across the four subfields: spatial in situ gradient methods and transplant experiments. We find that SFTS methods share common limitations relating to ( i ) the causality of identified spatial climate–biotic relationships and ( ii ) the transferability of these relationships, i.e. whether climate–biotic relationships observed over space are equivalent to those occurring over time. Moreover, despite widespread application of SFTS in climate change research, key assumptions remain largely untested. We highlight opportunities to enhance the robustness of SFTS by addressing key assumptions and limitations, with a particular emphasis on where approaches could be shared between the four subfields.
最长约 10秒,即可获得该文献文件

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
脑洞疼应助啦啦咔嘞采纳,获得10
1秒前
22222发布了新的文献求助10
1秒前
科研通AI2S应助科研通管家采纳,获得10
2秒前
一苇以航应助科研通管家采纳,获得20
2秒前
Ava应助科研通管家采纳,获得10
2秒前
思源应助科研通管家采纳,获得30
2秒前
共享精神应助科研通管家采纳,获得10
2秒前
科研通AI2S应助科研通管家采纳,获得10
3秒前
桐桐应助科研通管家采纳,获得10
3秒前
华仔应助科研通管家采纳,获得10
3秒前
3秒前
一个搞不懂晶体学的小牛马完成签到,获得积分10
3秒前
3秒前
4秒前
4秒前
彭于晏应助HWJ采纳,获得10
6秒前
7秒前
开朗万天完成签到,获得积分10
7秒前
书生意气发布了新的文献求助10
8秒前
8秒前
8秒前
10秒前
情怀应助tsq采纳,获得10
11秒前
spirit发布了新的文献求助10
11秒前
11秒前
13秒前
sunrise完成签到,获得积分10
13秒前
14秒前
Akim应助书生意气采纳,获得10
14秒前
着急的小松鼠完成签到,获得积分10
14秒前
打打应助海豚有海采纳,获得10
14秒前
chenll1988发布了新的文献求助10
15秒前
ZhuJY完成签到,获得积分10
16秒前
传奇3应助帅气的凡之采纳,获得10
16秒前
十三应助小宋采纳,获得10
16秒前
阿珊发布了新的文献求助10
17秒前
晚风发布了新的文献求助10
18秒前
ignih发布了新的文献求助10
19秒前
19秒前
开朗万天发布了新的文献求助20
21秒前
高分求助中
Technologies supporting mass customization of apparel: A pilot project 600
Izeltabart tapatansine - AdisInsight 500
Chinesen in Europa – Europäer in China: Journalisten, Spione, Studenten 500
Arthur Ewert: A Life for the Comintern 500
China's Relations With Japan 1945-83: The Role of Liao Chengzhi // Kurt Werner Radtke 500
Two Years in Peking 1965-1966: Book 1: Living and Teaching in Mao's China // Reginald Hunt 500
Epigenetic Drug Discovery 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3814536
求助须知:如何正确求助?哪些是违规求助? 3358651
关于积分的说明 10396766
捐赠科研通 3076017
什么是DOI,文献DOI怎么找? 1689648
邀请新用户注册赠送积分活动 813180
科研通“疑难数据库(出版商)”最低求助积分说明 767514