Urban Landscape Complexity as a Driver of Urban Evolution in White Clover (Trifolium repens). Malesis, A. Ph.D. Thesis, 2023. Accepted: 2023-09-27T17:22:19Z![Urban Landscape Complexity as a Driver of Urban Evolution in White Clover (Trifolium repens) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Cities are influencing evolution on contemporary timescales, but the mechanisms driving evolutionary change are not well understood. Previous studies on urban eco-evolutionary dynamics generally assume that urban structures predictably evolve from a dense core to less intensive peripheries, discounting their spatial complexities. Cities are mosaics of patches, each governed by a unique set of parameters that interact to create a unique set of ecological conditions and stressors distributed unevenly across the landscape. Using an urban-rural transects approach, prior studies identified variance in Hydrogen Cyanide (HCN) production in Trifolium repens (white clover) contingent on the distance from city centers. This investigation introduces a refined approach via the urban complexity framework, incorporating measures of landscape heterogeneity, connectivity, and historical contingency to explicate variability in HCN production across 20 North American cities. A model selection approach was employed to comparatively evaluate the explanatory power of these predictors to distance from city center along an urban-rural transect in multi-city and single city models. The results reveal that multivariate models incorporating urban complexity variables, notably the connectivity of cropland, demonstrated higher adjusted R2 values than univariate distance-based models in multi-city contexts. Although this finding does not significantly augment the predictive efficacy of single city models, it underscores the shared explanatory contribution of these variables. In conclusion, this investigation posits urban complexity as a critical determinant of urban eco-evolutionary dynamics, warranting further exploration to effectively inform the practice of urban planning.
@phdthesis{malesis_urban_2023,
type = {Thesis},
title = {Urban {Landscape} {Complexity} as a {Driver} of {Urban} {Evolution} in {White} {Clover} ({Trifolium} repens)},
copyright = {none},
url = {https://digital.lib.washington.edu:443/researchworks/handle/1773/50932},
abstract = {Cities are influencing evolution on contemporary timescales, but the mechanisms driving evolutionary change are not well understood. Previous studies on urban eco-evolutionary dynamics generally assume that urban structures predictably evolve from a dense core to less intensive peripheries, discounting their spatial complexities. Cities are mosaics of patches, each governed by a unique set of parameters that interact to create a unique set of ecological conditions and stressors distributed unevenly across the landscape. Using an urban-rural transects approach, prior studies identified variance in Hydrogen Cyanide (HCN) production in Trifolium repens (white clover) contingent on the distance from city centers. This investigation introduces a refined approach via the urban complexity framework, incorporating measures of landscape heterogeneity, connectivity, and historical contingency to explicate variability in HCN production across 20 North American cities. A model selection approach was employed to comparatively evaluate the explanatory power of these predictors to distance from city center along an urban-rural transect in multi-city and single city models. The results reveal that multivariate models incorporating urban complexity variables, notably the connectivity of cropland, demonstrated higher adjusted R2 values than univariate distance-based models in multi-city contexts. Although this finding does not significantly augment the predictive efficacy of single city models, it underscores the shared explanatory contribution of these variables. In conclusion, this investigation posits urban complexity as a critical determinant of urban eco-evolutionary dynamics, warranting further exploration to effectively inform the practice of urban planning.},
language = {en\_US},
urldate = {2024-01-09},
author = {Malesis, Anna},
year = {2023},
note = {Accepted: 2023-09-27T17:22:19Z},
keywords = {NALCMS, Terrestrial Ecoregions (CEC 1997)},
}
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{"_id":"3TjNJP5dyQEPhNJuG","bibbaseid":"malesis-urbanlandscapecomplexityasadriverofurbanevolutioninwhiteclovertrifoliumrepens-2023","author_short":["Malesis, A."],"bibdata":{"bibtype":"phdthesis","type":"Thesis","title":"Urban Landscape Complexity as a Driver of Urban Evolution in White Clover (Trifolium repens)","copyright":"none","url":"https://digital.lib.washington.edu:443/researchworks/handle/1773/50932","abstract":"Cities are influencing evolution on contemporary timescales, but the mechanisms driving evolutionary change are not well understood. Previous studies on urban eco-evolutionary dynamics generally assume that urban structures predictably evolve from a dense core to less intensive peripheries, discounting their spatial complexities. Cities are mosaics of patches, each governed by a unique set of parameters that interact to create a unique set of ecological conditions and stressors distributed unevenly across the landscape. Using an urban-rural transects approach, prior studies identified variance in Hydrogen Cyanide (HCN) production in Trifolium repens (white clover) contingent on the distance from city centers. This investigation introduces a refined approach via the urban complexity framework, incorporating measures of landscape heterogeneity, connectivity, and historical contingency to explicate variability in HCN production across 20 North American cities. A model selection approach was employed to comparatively evaluate the explanatory power of these predictors to distance from city center along an urban-rural transect in multi-city and single city models. The results reveal that multivariate models incorporating urban complexity variables, notably the connectivity of cropland, demonstrated higher adjusted R2 values than univariate distance-based models in multi-city contexts. Although this finding does not significantly augment the predictive efficacy of single city models, it underscores the shared explanatory contribution of these variables. In conclusion, this investigation posits urban complexity as a critical determinant of urban eco-evolutionary dynamics, warranting further exploration to effectively inform the practice of urban planning.","language":"en_US","urldate":"2024-01-09","author":[{"propositions":[],"lastnames":["Malesis"],"firstnames":["Anna"],"suffixes":[]}],"year":"2023","note":"Accepted: 2023-09-27T17:22:19Z","keywords":"NALCMS, Terrestrial Ecoregions (CEC 1997)","bibtex":"@phdthesis{malesis_urban_2023,\n\ttype = {Thesis},\n\ttitle = {Urban {Landscape} {Complexity} as a {Driver} of {Urban} {Evolution} in {White} {Clover} ({Trifolium} repens)},\n\tcopyright = {none},\n\turl = {https://digital.lib.washington.edu:443/researchworks/handle/1773/50932},\n\tabstract = {Cities are influencing evolution on contemporary timescales, but the mechanisms driving evolutionary change are not well understood. Previous studies on urban eco-evolutionary dynamics generally assume that urban structures predictably evolve from a dense core to less intensive peripheries, discounting their spatial complexities. Cities are mosaics of patches, each governed by a unique set of parameters that interact to create a unique set of ecological conditions and stressors distributed unevenly across the landscape. Using an urban-rural transects approach, prior studies identified variance in Hydrogen Cyanide (HCN) production in Trifolium repens (white clover) contingent on the distance from city centers. This investigation introduces a refined approach via the urban complexity framework, incorporating measures of landscape heterogeneity, connectivity, and historical contingency to explicate variability in HCN production across 20 North American cities. A model selection approach was employed to comparatively evaluate the explanatory power of these predictors to distance from city center along an urban-rural transect in multi-city and single city models. The results reveal that multivariate models incorporating urban complexity variables, notably the connectivity of cropland, demonstrated higher adjusted R2 values than univariate distance-based models in multi-city contexts. Although this finding does not significantly augment the predictive efficacy of single city models, it underscores the shared explanatory contribution of these variables. 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