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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

Magnitude of projected vegetation change.Bray-Curtis distance between future and baseline vegetation distributions, based on frequency distributions illustrated in Fig 3, in relation to mean annual temperature of future climates. Colors indicate change in precipitation under future climates (red = negative, blue = positive).
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pone.0130629.g004: Magnitude of projected vegetation change.Bray-Curtis distance between future and baseline vegetation distributions, based on frequency distributions illustrated in Fig 3, in relation to mean annual temperature of future climates. Colors indicate change in precipitation under future climates (red = negative, blue = positive).

Mentions: Projections of the PVM model across a range of future climate scenarios suggest that substantial changes in the relative frequency of the various vegetation types would be expected in response to 21st century climate change (Fig 3, showing the average of relative probabilities across all pixels). Overall, under warmer and drier (e.g., higher CWD) climates, shrublands and oak woodlands expand or persist in extent, while grassland and montane and coniferous forest decline. (Results under alternative models are shown in S4 and S5 Figs see Methods). We emphasize that this is an equilibrium response model, so the model cannot address the question of how long it would take for these predicted responses to occur. The overall magnitude of change, measured as the Bray-Curtis dissimilarity index between the baseline frequency vector vs. future projections, increases linearly with increases in MAT and the absolute value of change in PPT, though temperature has a much stronger effect (multiple regression R2 = 0.95, Fig 4). This measure only assesses the overall frequencies of vegetation types, and not their predicted spatial locations, so it does not address the magnitude of local impacts or the projected distribution shifts of individual vegetation types (see below). Examples of projected vegetation distributions from this model, under selected future scenarios, are illustrated in Chornesky et al. [34].


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)

Magnitude of projected vegetation change.Bray-Curtis distance between future and baseline vegetation distributions, based on frequency distributions illustrated in Fig 3, in relation to mean annual temperature of future climates. Colors indicate change in precipitation under future climates (red = negative, blue = positive).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0130629.g004: Magnitude of projected vegetation change.Bray-Curtis distance between future and baseline vegetation distributions, based on frequency distributions illustrated in Fig 3, in relation to mean annual temperature of future climates. Colors indicate change in precipitation under future climates (red = negative, blue = positive).
Mentions: Projections of the PVM model across a range of future climate scenarios suggest that substantial changes in the relative frequency of the various vegetation types would be expected in response to 21st century climate change (Fig 3, showing the average of relative probabilities across all pixels). Overall, under warmer and drier (e.g., higher CWD) climates, shrublands and oak woodlands expand or persist in extent, while grassland and montane and coniferous forest decline. (Results under alternative models are shown in S4 and S5 Figs see Methods). We emphasize that this is an equilibrium response model, so the model cannot address the question of how long it would take for these predicted responses to occur. The overall magnitude of change, measured as the Bray-Curtis dissimilarity index between the baseline frequency vector vs. future projections, increases linearly with increases in MAT and the absolute value of change in PPT, though temperature has a much stronger effect (multiple regression R2 = 0.95, Fig 4). This measure only assesses the overall frequencies of vegetation types, and not their predicted spatial locations, so it does not address the magnitude of local impacts or the projected distribution shifts of individual vegetation types (see below). Examples of projected vegetation distributions from this model, under selected future scenarios, are illustrated in Chornesky et al. [34].

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