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The dominant paradigm for assessing ecological responses to climate change assumes that future states of individuals and populations can be predicted by current, species-wide performance variation across spatial climatic gradients. However, if the fates of ecological systems are better predicted by past responses to in situ climatic variation through time, this current analytical paradigm may be severely misleading. Empirically testing whether spatial or temporal climate responses better predict how species respond to climate change has been elusive, largely due to restrictive data requirements. Here, we leverage a newly collected network of ponderosa pine tree-ring time series to test whether statistically inferred responses to spatial versus temporal climatic variation better predict how trees have responded to recent climate change. When compared to observed tree growth responses to climate change since 1980, predictions derived from spatial climatic variation were wrong in both magnitude and direction. This was not the case for predictions derived from climatic variation through time, which were able to replicate observed responses well. Future climate scenarios through the end of the 21st century exacerbated these disparities. These results suggest that the currently dominant paradigm of forecasting the ecological impacts of climate change based on spatial climatic variation may be severely misleading over decadal to centennial timescales.
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Increased spatial and temporal autocorrelation of temperature under climate change
Abstract Understanding spatiotemporal variation in environmental conditions is important to determine how climate change will impact ecological communities. The spatial and temporal autocorrelation of temperature can have strong impacts on community structure and persistence by increasing the duration and the magnitude of unfavorable conditions in sink populations and disrupting spatial rescue effects by synchronizing spatially segregated populations. Although increases in spatial and temporal autocorrelation of temperature have been documented in historical data, little is known about how climate change will impact these trends. We examined daily air temperature data from 21 General Circulation Models under the business-as-usual carbon emission scenario to quantify patterns of spatial and temporal autocorrelation between 1871 and 2099. Although both spatial and temporal autocorrelation increased over time, there was significant regional variation in the temporal autocorrelation trends. Additionally, we found a consistent breakpoint in the relationship between spatial autocorrelation and time around the year 2030, indicating an acceleration in the rate of increase of the spatial autocorrelation over the second half of the 21stcentury. Overall, our results suggest that ecological populations might experience elevated extinction risk under climate change because increased spatial and temporal autocorrelation of temperature is expected to erode both spatial and temporal refugia.
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- Award ID(s):
- 1635989
- PAR ID:
- 10153218
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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