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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 vegetation change at selected sites.Illustration of Bray-Curtis distances between baseline and future vegetation vectors (similar to Fig 3) for two selected pixels across our model domain. The slope of this relationship was used as a measure of the sensitivity of projected vegetation change in relation to climate, with mean annual temperature as a proxy for changes in JJA, DJF, and CWD. Much of the scatter around each regression line represents additional effects of PPT. Red illustrates a site with high sensitivity (slope = 0.298) and blue a site with low sensitivity (slope = 0.104).
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pone.0130629.g007: Projected vegetation change at selected sites.Illustration of Bray-Curtis distances between baseline and future vegetation vectors (similar to Fig 3) for two selected pixels across our model domain. The slope of this relationship was used as a measure of the sensitivity of projected vegetation change in relation to climate, with mean annual temperature as a proxy for changes in JJA, DJF, and CWD. Much of the scatter around each regression line represents additional effects of PPT. Red illustrates a site with high sensitivity (slope = 0.298) and blue a site with low sensitivity (slope = 0.104).

Mentions: Analysis of the Bray-Curtis distances between baseline and future vegetation vectors for each pixel reflects the magnitude of projected change at each location across the range of future climates. Local sensitivity of vegetation to climate change was assessed using linear regression as the slope (forced through the origin) of these dissimilarity measures relative to increase in MAT in that pixel (a proxy for DJF, JJA, and CWD change). These slopes exhibited a wide range from 0.002 to 0.4 (Fig 7) and variation across the region was distributed in a fairly patchy pattern (Fig 8). The relative exposure or magnitude of climate change (slope of local change in MAT relative to the regional average) varied only slightly, from 0.8 to 1.2, with higher values along the eastern boundary of the SFBA and some coastal locations, and lower values at higher elevations and along the north coast (S7 Fig). The overall projected impact of climate on vegetation (the product of local sensitivity and exposure) was thus primarily driven by variation in local sensitivity (Fig 9), so the spatial patterns in impact were similar to sensitivity (S8 Fig).


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 vegetation change at selected sites.Illustration of Bray-Curtis distances between baseline and future vegetation vectors (similar to Fig 3) for two selected pixels across our model domain. The slope of this relationship was used as a measure of the sensitivity of projected vegetation change in relation to climate, with mean annual temperature as a proxy for changes in JJA, DJF, and CWD. Much of the scatter around each regression line represents additional effects of PPT. Red illustrates a site with high sensitivity (slope = 0.298) and blue a site with low sensitivity (slope = 0.104).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0130629.g007: Projected vegetation change at selected sites.Illustration of Bray-Curtis distances between baseline and future vegetation vectors (similar to Fig 3) for two selected pixels across our model domain. The slope of this relationship was used as a measure of the sensitivity of projected vegetation change in relation to climate, with mean annual temperature as a proxy for changes in JJA, DJF, and CWD. Much of the scatter around each regression line represents additional effects of PPT. Red illustrates a site with high sensitivity (slope = 0.298) and blue a site with low sensitivity (slope = 0.104).
Mentions: Analysis of the Bray-Curtis distances between baseline and future vegetation vectors for each pixel reflects the magnitude of projected change at each location across the range of future climates. Local sensitivity of vegetation to climate change was assessed using linear regression as the slope (forced through the origin) of these dissimilarity measures relative to increase in MAT in that pixel (a proxy for DJF, JJA, and CWD change). These slopes exhibited a wide range from 0.002 to 0.4 (Fig 7) and variation across the region was distributed in a fairly patchy pattern (Fig 8). The relative exposure or magnitude of climate change (slope of local change in MAT relative to the regional average) varied only slightly, from 0.8 to 1.2, with higher values along the eastern boundary of the SFBA and some coastal locations, and lower values at higher elevations and along the north coast (S7 Fig). The overall projected impact of climate on vegetation (the product of local sensitivity and exposure) was thus primarily driven by variation in local sensitivity (Fig 9), so the spatial patterns in impact were similar to sensitivity (S8 Fig).

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