Limits...
Hotspots of Community Change: Temporal Dynamics Are Spatially Variable in Understory Plant Composition of a California Oak Woodland.

Spotswood EN, Bartolome JW, Allen-Diaz B - PLoS ONE (2015)

Bottom Line: Temporal dynamics are often assumed to be relatively static at the landscape scale, provided disturbance and climate are continuous.This study shows that local and landscape scale factors jointly influence temporal dynamics creating hotspots that are particularly responsive to climate and disturbance.Thus, adequate predictions of response to disturbance or to changing climate will only be achieved by considering how factors at multiple spatial scales influence community resilience and recovery.

View Article: PubMed Central - PubMed

Affiliation: Department of Environmental Science, Policy and Management, University of California, Berkeley, California, United States of America.

ABSTRACT
Community response to external drivers such climate and disturbance can lead to fluctuations in community composition, or to directional change. Temporal dynamics can be influenced by a combination of drivers operating at multiple spatial scales, including external landscape scale drivers, local abiotic conditions, and local species pools. We hypothesized that spatial variation in these factors can create heterogeneity in temporal dynamics within landscapes. We used understory plant species composition from an 11 year dataset from a California oak woodland to compare plots where disturbance was experimentally manipulated with the removal of livestock grazing and a prescribed burn. We quantified three properties of temporal variation: compositional change (reflecting the appearance and disappearance of species), temporal fluctuation, and directional change. Directional change was related most strongly to disturbance type, and was highest at plots where grazing was removed during the study. Temporal fluctuations, compositional change, and directional change were all related to intrinsic abiotic factors, suggesting that some locations are more responsive to external drivers than others. Temporal fluctuations and compositional change were linked to local functional composition, indicating that environmental filters can create subsets of the local species pool that do not respond in the same way to external drivers. Temporal dynamics are often assumed to be relatively static at the landscape scale, provided disturbance and climate are continuous. This study shows that local and landscape scale factors jointly influence temporal dynamics creating hotspots that are particularly responsive to climate and disturbance. Thus, adequate predictions of response to disturbance or to changing climate will only be achieved by considering how factors at multiple spatial scales influence community resilience and recovery.

No MeSH data available.


Related in: MedlinePlus

Conceptual framework for temporal dynamics.In a) a conceptual framework shows how landscape and local factors can affect temporal dynamics. Landscape scale external drivers can influence temporal dynamics directly, or can interact with local scale abiotic factors, creating spatial variation in temporal dynamics. Furthermore, abiotic factors can influence temporal dynamics directly, or indirectly via environmental filters that drive local compositional patterns that are functionally distinct subsets of the regional species pool. In b) a framework (modified from [26]) for quantifying different types of temporal variation shows how change can be either directional or stable, and stable changes can occur either via high compositional change in composition, or via large temporal fluctuations in the amount of compositional dissimilarity. Directional change is quantified using the regression slope of the relationship between compositional dissimilarity and the square root of the time lag (or interval between sampling points) and compositional change is quantified using the average dissimilarity values for a plot. Temporal fluctuation is quantified using the root mean square of the residuals from the linear regression fit between dissimilarity and time lag. Temporal fluctuation reflects the amount of change in composition that occurs irrespective of directional change. This metric summarizes the variability in dissimilarity, and can have a large value even when the slope of the regression relationship is zero. Small values indicate high predictability in temporal variation which could occur either due to predictable directional change or constant compositional change where the amount of community change is similar regardless of time lag between sampling points. Large values indicate large fluctuations that are not well predicted by the time interval between sampling points.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4519272&req=5

pone.0133501.g001: Conceptual framework for temporal dynamics.In a) a conceptual framework shows how landscape and local factors can affect temporal dynamics. Landscape scale external drivers can influence temporal dynamics directly, or can interact with local scale abiotic factors, creating spatial variation in temporal dynamics. Furthermore, abiotic factors can influence temporal dynamics directly, or indirectly via environmental filters that drive local compositional patterns that are functionally distinct subsets of the regional species pool. In b) a framework (modified from [26]) for quantifying different types of temporal variation shows how change can be either directional or stable, and stable changes can occur either via high compositional change in composition, or via large temporal fluctuations in the amount of compositional dissimilarity. Directional change is quantified using the regression slope of the relationship between compositional dissimilarity and the square root of the time lag (or interval between sampling points) and compositional change is quantified using the average dissimilarity values for a plot. Temporal fluctuation is quantified using the root mean square of the residuals from the linear regression fit between dissimilarity and time lag. Temporal fluctuation reflects the amount of change in composition that occurs irrespective of directional change. This metric summarizes the variability in dissimilarity, and can have a large value even when the slope of the regression relationship is zero. Small values indicate high predictability in temporal variation which could occur either due to predictable directional change or constant compositional change where the amount of community change is similar regardless of time lag between sampling points. Large values indicate large fluctuations that are not well predicted by the time interval between sampling points.

Mentions: Three primary mechanisms could generate spatial variability in temporal dynamics. First, spatial variation in external drivers may influence temporal dynamics directly at the landscape scale (Fig 1a). Second, local abiotic factors can interact with external drivers to either buffer or magnify their effects, producing spatial variability in how plant communities experience similar climate or disturbance. Finally, abiotic factors can act as strong environmental filters that limit the pool of species that is likely to occur [19,20], and variation in plant responses to climate and disturbance can alter how communities shift through time [21,22]. For example, recent evidence suggests that plant communities on infertile soils may be relatively resistant to climate variation because these communities contain ‘stress tolerant’ functional traits that constrain their ability to respond to climate [22,23]. Differences in functional composition across sites are more likely to reflect environmental filtering than other community assembly mechanisms because they are caused by differences in the presence or abundance of multiple species. For example, dispersal and demographic stochasticity [19,20,24] can alter the presence or abundance of a single species at a site, but are unlikely to affect suites of species in the same way.


Hotspots of Community Change: Temporal Dynamics Are Spatially Variable in Understory Plant Composition of a California Oak Woodland.

Spotswood EN, Bartolome JW, Allen-Diaz B - PLoS ONE (2015)

Conceptual framework for temporal dynamics.In a) a conceptual framework shows how landscape and local factors can affect temporal dynamics. Landscape scale external drivers can influence temporal dynamics directly, or can interact with local scale abiotic factors, creating spatial variation in temporal dynamics. Furthermore, abiotic factors can influence temporal dynamics directly, or indirectly via environmental filters that drive local compositional patterns that are functionally distinct subsets of the regional species pool. In b) a framework (modified from [26]) for quantifying different types of temporal variation shows how change can be either directional or stable, and stable changes can occur either via high compositional change in composition, or via large temporal fluctuations in the amount of compositional dissimilarity. Directional change is quantified using the regression slope of the relationship between compositional dissimilarity and the square root of the time lag (or interval between sampling points) and compositional change is quantified using the average dissimilarity values for a plot. Temporal fluctuation is quantified using the root mean square of the residuals from the linear regression fit between dissimilarity and time lag. Temporal fluctuation reflects the amount of change in composition that occurs irrespective of directional change. This metric summarizes the variability in dissimilarity, and can have a large value even when the slope of the regression relationship is zero. Small values indicate high predictability in temporal variation which could occur either due to predictable directional change or constant compositional change where the amount of community change is similar regardless of time lag between sampling points. Large values indicate large fluctuations that are not well predicted by the time interval between sampling points.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4519272&req=5

pone.0133501.g001: Conceptual framework for temporal dynamics.In a) a conceptual framework shows how landscape and local factors can affect temporal dynamics. Landscape scale external drivers can influence temporal dynamics directly, or can interact with local scale abiotic factors, creating spatial variation in temporal dynamics. Furthermore, abiotic factors can influence temporal dynamics directly, or indirectly via environmental filters that drive local compositional patterns that are functionally distinct subsets of the regional species pool. In b) a framework (modified from [26]) for quantifying different types of temporal variation shows how change can be either directional or stable, and stable changes can occur either via high compositional change in composition, or via large temporal fluctuations in the amount of compositional dissimilarity. Directional change is quantified using the regression slope of the relationship between compositional dissimilarity and the square root of the time lag (or interval between sampling points) and compositional change is quantified using the average dissimilarity values for a plot. Temporal fluctuation is quantified using the root mean square of the residuals from the linear regression fit between dissimilarity and time lag. Temporal fluctuation reflects the amount of change in composition that occurs irrespective of directional change. This metric summarizes the variability in dissimilarity, and can have a large value even when the slope of the regression relationship is zero. Small values indicate high predictability in temporal variation which could occur either due to predictable directional change or constant compositional change where the amount of community change is similar regardless of time lag between sampling points. Large values indicate large fluctuations that are not well predicted by the time interval between sampling points.
Mentions: Three primary mechanisms could generate spatial variability in temporal dynamics. First, spatial variation in external drivers may influence temporal dynamics directly at the landscape scale (Fig 1a). Second, local abiotic factors can interact with external drivers to either buffer or magnify their effects, producing spatial variability in how plant communities experience similar climate or disturbance. Finally, abiotic factors can act as strong environmental filters that limit the pool of species that is likely to occur [19,20], and variation in plant responses to climate and disturbance can alter how communities shift through time [21,22]. For example, recent evidence suggests that plant communities on infertile soils may be relatively resistant to climate variation because these communities contain ‘stress tolerant’ functional traits that constrain their ability to respond to climate [22,23]. Differences in functional composition across sites are more likely to reflect environmental filtering than other community assembly mechanisms because they are caused by differences in the presence or abundance of multiple species. For example, dispersal and demographic stochasticity [19,20,24] can alter the presence or abundance of a single species at a site, but are unlikely to affect suites of species in the same way.

Bottom Line: Temporal dynamics are often assumed to be relatively static at the landscape scale, provided disturbance and climate are continuous.This study shows that local and landscape scale factors jointly influence temporal dynamics creating hotspots that are particularly responsive to climate and disturbance.Thus, adequate predictions of response to disturbance or to changing climate will only be achieved by considering how factors at multiple spatial scales influence community resilience and recovery.

View Article: PubMed Central - PubMed

Affiliation: Department of Environmental Science, Policy and Management, University of California, Berkeley, California, United States of America.

ABSTRACT
Community response to external drivers such climate and disturbance can lead to fluctuations in community composition, or to directional change. Temporal dynamics can be influenced by a combination of drivers operating at multiple spatial scales, including external landscape scale drivers, local abiotic conditions, and local species pools. We hypothesized that spatial variation in these factors can create heterogeneity in temporal dynamics within landscapes. We used understory plant species composition from an 11 year dataset from a California oak woodland to compare plots where disturbance was experimentally manipulated with the removal of livestock grazing and a prescribed burn. We quantified three properties of temporal variation: compositional change (reflecting the appearance and disappearance of species), temporal fluctuation, and directional change. Directional change was related most strongly to disturbance type, and was highest at plots where grazing was removed during the study. Temporal fluctuations, compositional change, and directional change were all related to intrinsic abiotic factors, suggesting that some locations are more responsive to external drivers than others. Temporal fluctuations and compositional change were linked to local functional composition, indicating that environmental filters can create subsets of the local species pool that do not respond in the same way to external drivers. Temporal dynamics are often assumed to be relatively static at the landscape scale, provided disturbance and climate are continuous. This study shows that local and landscape scale factors jointly influence temporal dynamics creating hotspots that are particularly responsive to climate and disturbance. Thus, adequate predictions of response to disturbance or to changing climate will only be achieved by considering how factors at multiple spatial scales influence community resilience and recovery.

No MeSH data available.


Related in: MedlinePlus