Distinct responses and range shifts of lizard populations across an elevational gradient under climate change

气候变化 生态学 航程(航空) 蜥蜴 栖息地 环境梯度 全球变化 生物 环境科学 地理 复合材料 材料科学
作者
Zhong‐Wen Jiang,Liang Ma,Chunrong Mi,Shi‐ang Tao,Fengyi Guo,Wei‐Guo Du
出处
期刊:Global Change Biology [Wiley]
卷期号:29 (10): 2669-2680 被引量:10
标识
DOI:10.1111/gcb.16656
摘要

Abstract Ongoing climate change has profoundly affected global biodiversity, but its impacts on populations across elevations remain understudied. Using mechanistic niche models incorporating species traits, we predicted ecophysiological responses (activity times, oxygen consumption and evaporative water loss) for lizard populations at high‐elevation (<3600 m asl) and extra‐high‐elevation (≥3600 m asl) under recent (1970–2000) and future (2081–2100) climates. Compared with their high‐elevation counterparts, lizards from extra‐high‐elevation are predicted to experience a greater increase in activity time and oxygen consumption. By integrating these ecophysiological responses into hybrid species distribution models (HSDMs), we were able to make the following predictions under two warming scenarios (SSP1‐2.6, SSP5‐8.5). By 2081–2100, we predict that lizards at both high‐ and extra‐high‐elevation will shift upslope; lizards at extra‐high‐elevation will gain more and lose less habitat than will their high‐elevation congeners. We therefore advocate the conservation of high‐elevation species in the context of climate change, especially for those populations living close to their lower elevational range limits. In addition, by comparing the results from HSDMs and traditional species distribution models, we highlight the importance of considering intraspecific variation and local adaptation in physiological traits along elevational gradients when forecasting species' future distributions under climate change.
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
诚心的大炮完成签到,获得积分10
1秒前
默默的芙完成签到,获得积分10
1秒前
天天快乐应助kekao采纳,获得10
2秒前
3秒前
July发布了新的文献求助10
3秒前
4秒前
123发布了新的文献求助10
4秒前
煜cy发布了新的文献求助10
5秒前
7秒前
金金金金发布了新的文献求助200
7秒前
我是老大应助温暖的书白采纳,获得10
7秒前
传奇3应助kk采纳,获得10
8秒前
9秒前
9秒前
刻苦海露发布了新的文献求助10
10秒前
简单完成签到,获得积分20
10秒前
nannan发布了新的文献求助10
11秒前
HHHHH发布了新的文献求助10
12秒前
Tong完成签到,获得积分10
12秒前
橘子完成签到,获得积分10
13秒前
贤惠的白开水完成签到 ,获得积分10
13秒前
kimk发布了新的文献求助10
14秒前
14秒前
16秒前
17秒前
勤恳的不悔完成签到,获得积分10
19秒前
mike5492发布了新的文献求助10
19秒前
kk发布了新的文献求助10
19秒前
20秒前
20秒前
20秒前
NexusExplorer应助kekao采纳,获得10
21秒前
NexusExplorer应助nannan采纳,获得10
21秒前
wqx完成签到,获得积分10
21秒前
Symbol完成签到,获得积分20
21秒前
丰富采波完成签到 ,获得积分10
22秒前
悦耳白山完成签到,获得积分20
22秒前
23秒前
情怀应助99v587采纳,获得10
23秒前
豆子发布了新的文献求助10
23秒前
高分求助中
All the Birds of the World 4000
Production Logging: Theoretical and Interpretive Elements 3000
Les Mantodea de Guyane Insecta, Polyneoptera 2000
Machine Learning Methods in Geoscience 1000
Resilience of a Nation: A History of the Military in Rwanda 888
Evaluating the Cardiometabolic Efficacy and Safety of Lipoprotein Lipase Pathway Targets in Combination With Approved Lipid-Lowering Targets: A Drug Target Mendelian Randomization Study 500
Crystal Nonlinear Optics: with SNLO examples (Second Edition) 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 3733087
求助须知:如何正确求助?哪些是违规求助? 3277283
关于积分的说明 10001426
捐赠科研通 2992973
什么是DOI,文献DOI怎么找? 1642490
邀请新用户注册赠送积分活动 780441
科研通“疑难数据库(出版商)”最低求助积分说明 748844