Multiple axes of ecological vulnerability to climate change. (7th February 2020)
- Record Type:
- Journal Article
- Title:
- Multiple axes of ecological vulnerability to climate change. (7th February 2020)
- Main Title:
- Multiple axes of ecological vulnerability to climate change
- Authors:
- Kling, Matthew M.
Auer, Stephanie L.
Comer, Patrick J.
Ackerly, David D.
Hamilton, Healy - Abstract:
- Abstract: Observed ecological responses to climate change are highly individualistic across species and locations, and understanding the drivers of this variability is essential for management and conservation efforts. While it is clear that differences in exposure, sensitivity, and adaptive capacity all contribute to heterogeneity in climate change vulnerability, predicting these features at macroecological scales remains a critical challenge. We explore multiple drivers of heterogeneous vulnerability across the distributions of 96 vegetation types of the ecologically diverse western US, using data on observed climate trends from 1948 to 2014 to highlight emerging patterns of change. We ask three novel questions about factors potentially shaping vulnerability across the region: (a) How does sensitivity to different climate variables vary geographically and across vegetation classes? (b) How do multivariate climate exposure patterns interact with these sensitivities to shape vulnerability patterns? (c) How different are these vulnerability patterns according to three widely implemented vulnerability paradigms—niche novelty (decline in modeled suitability), temporal novelty (standardized anomaly), and spatial novelty (inbound climate velocity)—each of which uses a distinct frame of reference to quantify climate departure? We propose that considering these three novelty paradigms in combination could help improve our understanding and prediction of heterogeneous climate changeAbstract: Observed ecological responses to climate change are highly individualistic across species and locations, and understanding the drivers of this variability is essential for management and conservation efforts. While it is clear that differences in exposure, sensitivity, and adaptive capacity all contribute to heterogeneity in climate change vulnerability, predicting these features at macroecological scales remains a critical challenge. We explore multiple drivers of heterogeneous vulnerability across the distributions of 96 vegetation types of the ecologically diverse western US, using data on observed climate trends from 1948 to 2014 to highlight emerging patterns of change. We ask three novel questions about factors potentially shaping vulnerability across the region: (a) How does sensitivity to different climate variables vary geographically and across vegetation classes? (b) How do multivariate climate exposure patterns interact with these sensitivities to shape vulnerability patterns? (c) How different are these vulnerability patterns according to three widely implemented vulnerability paradigms—niche novelty (decline in modeled suitability), temporal novelty (standardized anomaly), and spatial novelty (inbound climate velocity)—each of which uses a distinct frame of reference to quantify climate departure? We propose that considering these three novelty paradigms in combination could help improve our understanding and prediction of heterogeneous climate change responses, and we discuss the distinct climate adaptation strategies connected with different combinations of high and low novelty across the three metrics. Our results reveal a diverse mosaic of climate change vulnerability signatures across the region's plant communities. Each of the above factors contributes strongly to this heterogeneity: climate variable sensitivity exhibits clear patterns across vegetation types, multivariate climate change data reveal highly diverse exposure signatures across locations, and the three novelty paradigms diverge widely in their climate change vulnerability predictions. Together, these results shed light on potential drivers of individualistic climate change responses and may help to inform effective management strategies. Abstract : Predicting large‐scale climate change vulnerability patterns is a critical challenge. We introduce a framework integrating three distinct paradigms of climate threat—ecological niche modeling, temporal climate anomalies, and spatial climate velocity—and discuss the distinct management strategies connected with different combinations of these metrics. We show that under recent climate change, patterns in these three axes of vulnerability are highly divergent across vegetation of the western US, and that vegetation types differ strongly in their sensitivity to different climate variables; these results reveal a diverse mosaic of climate change signatures across the region and may help inform climate adaptation strategies. See also the Commentary on this article by Alejandro Ordonez, 26, 2734–2736 . … (more)
- Is Part Of:
- Global change biology. Volume 26:Number 5(2020)
- Journal:
- Global change biology
- Issue:
- Volume 26:Number 5(2020)
- Issue Display:
- Volume 26, Issue 5 (2020)
- Year:
- 2020
- Volume:
- 26
- Issue:
- 5
- Issue Sort Value:
- 2020-0026-0005-0000
- Page Start:
- 2798
- Page End:
- 2813
- Publication Date:
- 2020-02-07
- Subjects:
- biogeography -- climate change -- climate departure -- climate velocity -- niche model -- novel climate -- resource management -- vegetation -- vulnerability
Climatic changes -- Environmental aspects -- Periodicals
Troposphere -- Environmental aspects -- Periodicals
Biodiversity conservation -- Periodicals
Eutrophication -- Periodicals
551.5 - Journal URLs:
- http://www.blackwell-synergy.com/member/institutions/issuelist.asp?journal=gcb ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1111/gcb.15008 ↗
- Languages:
- English
- ISSNs:
- 1354-1013
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4195.358330
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 21972.xml