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• 1. [Article] Traits, neighbors, and species performance in prairie restoration

  Questions: Are traits related to the performance of plant species in restoration? Are the relationships between traits and performance consistent across the functional groups of annual forbs, perennial ...

  Citation × Citation Citation Abstract Copy Title: Traits, neighbors, and species performance in prairie restoration Author: Roberts, R. E., Wilson, Mark V., Clark, Deborah Louise, 1950- Questions: Are traits related to the performance of plant species in restoration? Are the relationships between traits and performance consistent across the functional groups of annual forbs, perennial forbs, and grasses? Do the relationships between traits and performance depend on neighboring functional groups? Location: A former agricultural field, being restored to native upland prairie, in the Willamette Valley of western Oregon USA. Methods: Twenty-eight native species, representing three functional groups, were sown in seven different combinations. The following summer the performance of each species was measured. Eleven functional traits were measured from plants in the laboratory and in the field. Correlations between individual traits and performance variables were measured and stepwise regression techniques were used to determine which sets of traits were most strongly related to performance. Results: Sets of traits explained up to 56% of variation in cover, and up to 48% of variation in establishment frequency. The relationships between traits and performance were influenced by functional group identity, and the functional group identity of neighboring species also influenced species’ cover and the relationships between traits and cover. Species’ establishment rate in monoculture was the trait most strongly correlated to both establishment and cover in mixtures. In multi-trait models, annual forb functional group identity was strongly related to establishment in mixtures, and height, leaf weight ratio at 7 days, and seed mass were strongly related to cover. Conclusions: Based on the strength of the statistical models, multiple-trait models should be a useful way of predicting the performance of species prior to sowing in restoration. The functional group identity of each species as well as the other species being sown may need to be taken into account when making predictions. Title: Traits, neighbors, and species performance in prairie restoration Author: Roberts, R. E., Wilson, Mark V., Clark, Deborah Louise, 1950- Questions: Are traits related to the performance of plant species in restoration? Are the relationships between traits and performance consistent across the functional groups of annual forbs, perennial forbs, and grasses? Do the relationships between traits and performance depend on neighboring functional groups? Location: A former agricultural field, being restored to native upland prairie, in the Willamette Valley of western Oregon USA. Methods: Twenty-eight native species, representing three functional groups, were sown in seven different combinations. The following summer the performance of each species was measured. Eleven functional traits were measured from plants in the laboratory and in the field. Correlations between individual traits and performance variables were measured and stepwise regression techniques were used to determine which sets of traits were most strongly related to performance. Results: Sets of traits explained up to 56% of variation in cover, and up to 48% of variation in establishment frequency. The relationships between traits and performance were influenced by functional group identity, and the functional group identity of neighboring species also influenced species’ cover and the relationships between traits and cover. Species’ establishment rate in monoculture was the trait most strongly correlated to both establishment and cover in mixtures. In multi-trait models, annual forb functional group identity was strongly related to establishment in mixtures, and height, leaf weight ratio at 7 days, and seed mass were strongly related to cover. Conclusions: Based on the strength of the statistical models, multiple-trait models should be a useful way of predicting the performance of species prior to sowing in restoration. The functional group identity of each species as well as the other species being sown may need to be taken into account when making predictions. Close

• 2. [Article] Plant traits – a tool for restoration?

  Question: Most results of restoration efforts are species-specific and/or site-specific and therefore are not general enough to be easily applied to other species and other sites. Our research addresses ...

  Citation × Citation Citation Abstract Copy Title: Plant traits – a tool for restoration? Author: Wilson, Mark V., Kaye, Thomas N., Roberts, Rachel, Dunwiddie, Peter W., Clark, Deborah Louise, 1950-, Stanley, Amanda Question: Most results of restoration efforts are species-specific and/or site-specific and therefore are not general enough to be easily applied to other species and other sites. Our research addresses the issue of species-specific results by investigating the feasibility of using plant traits instead of taxonomic species to characterize species responses to restoration treatments. Specifically, we test the explanatory power of plant traits, one necessary prerequisite for the development of predictive and general plant trait models. Location: Ten remnant bunchgrass prairie sites in the Pacific Northwest of North America (Oregon and Washington, USA; British Columbia, Canada). Methods: We developed two types of quantitative models for each of 10 prairie restoration sites: 1) plant trait models, which related plant traits to species field responses following restoration management treatments, and 2) species identity models, which related species taxonomic identity to species field responses following restoration management treatments. Species identity models determined the maximum amount of variability of field responses that can be explained by differences in individual species’ responses to management treatments. Plant trait models determined what proportion of this explanatory power can be attributed to plant traits. This approach contrasts with approaches often used in other plant trait studies that describe how traits vary with environmental conditions. Specifically, we used these two models to address the following questions: 1) How much of the variability in field responses of plants to restoration management treatments is explained by plant traits? 2) How well do plant traits explain the variability of field responses following restoration management treatments compared to models relating field responses to species identity? Our approach was to measure two aspects of explanatory power: R2 (variability explained) and AIC (a measure of model fit that accounts for parsimony, i.e., how well a model fits the data with relatively few explanatory variables). Results: 1) The plant trait models (relating plant traits with plant field responses) explained much of the variability within each of the ten restoration sites, with R2 values ranging between 31% and 69%. 2) The species identity models (relating species taxonomic identity with plant field responses) explained between 47% and 74% of variability in field performance (R2). Thus, the plant trait models explained nearly as much variability as the species identity models. In seven out of nine sites, the plant trait models were superior to the species identity models as measured by AIC; that is, the trait models did well at explaining variability with less model complexity (i.e., fewer explanatory variables). Conclusion: Development of general and predictive plant trait models is a multi-step process. Strong explanatory power by plant trait models, both on an absolute scale and as compared to species identity models, supports the feasibility of using plant traits instead of species taxonomic identity as a common language to characterize plant field responses to restoration treatments. Such high explanatory power is one necessary pre-requisite for the development of predictive and general plant trait models. Our results also indicate that the plant trait models are site-specific even though all sites were upland bunchgrass remnant prairies. We discuss the next steps in the development of more general and predictive models: incorporating environmental factors into the plant trait models to address the issue of site-specificity and testing the power of these models to predict vegetation responses. Title: Plant traits – a tool for restoration? Author: Wilson, Mark V., Kaye, Thomas N., Roberts, Rachel, Dunwiddie, Peter W., Clark, Deborah Louise, 1950-, Stanley, Amanda Question: Most results of restoration efforts are species-specific and/or site-specific and therefore are not general enough to be easily applied to other species and other sites. Our research addresses the issue of species-specific results by investigating the feasibility of using plant traits instead of taxonomic species to characterize species responses to restoration treatments. Specifically, we test the explanatory power of plant traits, one necessary prerequisite for the development of predictive and general plant trait models. Location: Ten remnant bunchgrass prairie sites in the Pacific Northwest of North America (Oregon and Washington, USA; British Columbia, Canada). Methods: We developed two types of quantitative models for each of 10 prairie restoration sites: 1) plant trait models, which related plant traits to species field responses following restoration management treatments, and 2) species identity models, which related species taxonomic identity to species field responses following restoration management treatments. Species identity models determined the maximum amount of variability of field responses that can be explained by differences in individual species’ responses to management treatments. Plant trait models determined what proportion of this explanatory power can be attributed to plant traits. This approach contrasts with approaches often used in other plant trait studies that describe how traits vary with environmental conditions. Specifically, we used these two models to address the following questions: 1) How much of the variability in field responses of plants to restoration management treatments is explained by plant traits? 2) How well do plant traits explain the variability of field responses following restoration management treatments compared to models relating field responses to species identity? Our approach was to measure two aspects of explanatory power: R2 (variability explained) and AIC (a measure of model fit that accounts for parsimony, i.e., how well a model fits the data with relatively few explanatory variables). Results: 1) The plant trait models (relating plant traits with plant field responses) explained much of the variability within each of the ten restoration sites, with R2 values ranging between 31% and 69%. 2) The species identity models (relating species taxonomic identity with plant field responses) explained between 47% and 74% of variability in field performance (R2). Thus, the plant trait models explained nearly as much variability as the species identity models. In seven out of nine sites, the plant trait models were superior to the species identity models as measured by AIC; that is, the trait models did well at explaining variability with less model complexity (i.e., fewer explanatory variables). Conclusion: Development of general and predictive plant trait models is a multi-step process. Strong explanatory power by plant trait models, both on an absolute scale and as compared to species identity models, supports the feasibility of using plant traits instead of species taxonomic identity as a common language to characterize plant field responses to restoration treatments. Such high explanatory power is one necessary pre-requisite for the development of predictive and general plant trait models. Our results also indicate that the plant trait models are site-specific even though all sites were upland bunchgrass remnant prairies. We discuss the next steps in the development of more general and predictive models: incorporating environmental factors into the plant trait models to address the issue of site-specificity and testing the power of these models to predict vegetation responses. Close