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A Geographic Mosaic of Climate Change Impacts on Terrestrial Vegetation: Which Areas Are Most at Risk?

Ackerly DD, Cornwell WK, Weiss SB, Flint LE, Flint AL - PLoS ONE (2015)

Bottom Line: The model was then projected for 54 future climate scenarios, spanning a representative range of temperature and precipitation projections from the CMIP3 and CMIP5 ensembles.Surprisingly, sensitivity to climate change is higher closer to the coast, on lower insolation, north-facing slopes and in areas of higher precipitation.The greater sensitivity of moist and low insolation sites is an unexpected outcome that challenges views on the location and stability of climate refugia.

View Article: PubMed Central - PubMed

Affiliation: Department of Integrative Biology, University of California, Berkeley, California, United States of America; Jepson Herbarium, University of California, Berkeley, California, United States of America.

ABSTRACT
Changes in climate projected for the 21st century are expected to trigger widespread and pervasive biotic impacts. Forecasting these changes and their implications for ecosystem services is a major research goal. Much of the research on biotic responses to climate change has focused on either projected shifts in individual species distributions or broad-scale changes in biome distributions. Here, we introduce a novel application of multinomial logistic regression as a powerful approach to model vegetation distributions and potential responses to 21st century climate change. We modeled the distribution of 22 major vegetation types, most defined by a single dominant woody species, across the San Francisco Bay Area. Predictor variables included climate and topographic variables. The novel aspect of our model is the output: a vector of relative probabilities for each vegetation type in each location within the study domain. The model was then projected for 54 future climate scenarios, spanning a representative range of temperature and precipitation projections from the CMIP3 and CMIP5 ensembles. We found that sensitivity of vegetation to climate change is highly heterogeneous across the region. Surprisingly, sensitivity to climate change is higher closer to the coast, on lower insolation, north-facing slopes and in areas of higher precipitation. While such sites may provide refugia for mesic and cool-adapted vegetation in the face of a warming climate, the model suggests they will still be highly dynamic and relatively sensitive to climate-driven vegetation transitions. The greater sensitivity of moist and low insolation sites is an unexpected outcome that challenges views on the location and stability of climate refugia. Projections provide a foundation for conservation planning and land management, and highlight the need for a greater understanding of the mechanisms and time scales of potential climate-driven vegetation transitions.

No MeSH data available.


Related in: MedlinePlus

Projected changes in vegetation location and abundance.Changes in the location and abundance of the 22 vegetation types illustrated for the GFDL-A2-2070-2099 future. a) Change in distance to coast vs. proportional change in abundance (sum of relative frequencies across all pixels); b) change in elevation vs. proportional change in abundance; c) Change in distance to coast vs. change in elevation.
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pone.0130629.g006: Projected changes in vegetation location and abundance.Changes in the location and abundance of the 22 vegetation types illustrated for the GFDL-A2-2070-2099 future. a) Change in distance to coast vs. proportional change in abundance (sum of relative frequencies across all pixels); b) change in elevation vs. proportional change in abundance; c) Change in distance to coast vs. change in elevation.

Mentions: Changes in the spatial distribution of each vegetation type were assessed by calculating the mean elevation and distance from the coast, weighting each pixel by the probability that it was occupied by the respective vegetation type. In general, vegetation types that increased in frequency under warming climates shifted towards the coast and to slightly lower elevations and deeper soils, while declining types shifted away from the coast and uphill, and sometimes onto more exposed sites (i.e. south-facing). This is illustrated under one future scenario, GFDL-A2-2070-2099, +3.94°C MAT (Fig 6). Note that projected shifts towards the coast, or inland and uphill, can offset rising summer temperatures by moving towards cooler summer locations along these geographic or elevational gradients. In contrast, winter temperatures are warmer near the coast, so vegetation shifting towards the coast would compound the exposure to winter warming due to the combined effects of the geographic shift and climate warming.


A Geographic Mosaic of Climate Change Impacts on Terrestrial Vegetation: Which Areas Are Most at Risk?

Ackerly DD, Cornwell WK, Weiss SB, Flint LE, Flint AL - PLoS ONE (2015)

Projected changes in vegetation location and abundance.Changes in the location and abundance of the 22 vegetation types illustrated for the GFDL-A2-2070-2099 future. a) Change in distance to coast vs. proportional change in abundance (sum of relative frequencies across all pixels); b) change in elevation vs. proportional change in abundance; c) Change in distance to coast vs. change in elevation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482696&req=5

pone.0130629.g006: Projected changes in vegetation location and abundance.Changes in the location and abundance of the 22 vegetation types illustrated for the GFDL-A2-2070-2099 future. a) Change in distance to coast vs. proportional change in abundance (sum of relative frequencies across all pixels); b) change in elevation vs. proportional change in abundance; c) Change in distance to coast vs. change in elevation.
Mentions: Changes in the spatial distribution of each vegetation type were assessed by calculating the mean elevation and distance from the coast, weighting each pixel by the probability that it was occupied by the respective vegetation type. In general, vegetation types that increased in frequency under warming climates shifted towards the coast and to slightly lower elevations and deeper soils, while declining types shifted away from the coast and uphill, and sometimes onto more exposed sites (i.e. south-facing). This is illustrated under one future scenario, GFDL-A2-2070-2099, +3.94°C MAT (Fig 6). Note that projected shifts towards the coast, or inland and uphill, can offset rising summer temperatures by moving towards cooler summer locations along these geographic or elevational gradients. In contrast, winter temperatures are warmer near the coast, so vegetation shifting towards the coast would compound the exposure to winter warming due to the combined effects of the geographic shift and climate warming.

Bottom Line: The model was then projected for 54 future climate scenarios, spanning a representative range of temperature and precipitation projections from the CMIP3 and CMIP5 ensembles.Surprisingly, sensitivity to climate change is higher closer to the coast, on lower insolation, north-facing slopes and in areas of higher precipitation.The greater sensitivity of moist and low insolation sites is an unexpected outcome that challenges views on the location and stability of climate refugia.

View Article: PubMed Central - PubMed

Affiliation: Department of Integrative Biology, University of California, Berkeley, California, United States of America; Jepson Herbarium, University of California, Berkeley, California, United States of America.

ABSTRACT
Changes in climate projected for the 21st century are expected to trigger widespread and pervasive biotic impacts. Forecasting these changes and their implications for ecosystem services is a major research goal. Much of the research on biotic responses to climate change has focused on either projected shifts in individual species distributions or broad-scale changes in biome distributions. Here, we introduce a novel application of multinomial logistic regression as a powerful approach to model vegetation distributions and potential responses to 21st century climate change. We modeled the distribution of 22 major vegetation types, most defined by a single dominant woody species, across the San Francisco Bay Area. Predictor variables included climate and topographic variables. The novel aspect of our model is the output: a vector of relative probabilities for each vegetation type in each location within the study domain. The model was then projected for 54 future climate scenarios, spanning a representative range of temperature and precipitation projections from the CMIP3 and CMIP5 ensembles. We found that sensitivity of vegetation to climate change is highly heterogeneous across the region. Surprisingly, sensitivity to climate change is higher closer to the coast, on lower insolation, north-facing slopes and in areas of higher precipitation. While such sites may provide refugia for mesic and cool-adapted vegetation in the face of a warming climate, the model suggests they will still be highly dynamic and relatively sensitive to climate-driven vegetation transitions. The greater sensitivity of moist and low insolation sites is an unexpected outcome that challenges views on the location and stability of climate refugia. Projections provide a foundation for conservation planning and land management, and highlight the need for a greater understanding of the mechanisms and time scales of potential climate-driven vegetation transitions.

No MeSH data available.


Related in: MedlinePlus