Maini Chen, Anrong Dang, Xiangyu Li, Jingxiong Huang, Yang Weng
{"title":"为城市环境识别适应气候的植物:将机器学习与传统植物选择工具相结合","authors":"Maini Chen, Anrong Dang, Xiangyu Li, Jingxiong Huang, Yang Weng","doi":"10.1016/j.ufug.2024.128559","DOIUrl":null,"url":null,"abstract":"<div><div>Climate change has intensified the urban heat island effect and increased extreme weather conditions, posing risks to public health and urban vegetation. To address these challenges, selecting climate-ready urban plant species is crucial. Traditional climate niche-based methods often fall short in urban contexts due to neglecting anthropogenic factors. Our study addresses this research gap by introducing an innovative urban plant selection method that integrates vulnerability metrics, expert consensus, and plant introduction records through machine learning. We identified eight climatic variables essential for the survival of urban plants in Beijing, China, and established safety margins for eight climate variables of 1070 urban plant species across two periods: the baseline (1981–2010) and the future (2041–2070). Based on existing assessment data, expert consensus and plant introduction records, 247 plant species were classified into three levels of adaptability: backbone (highly adaptable, prevalent in Beijing), general (moderately adaptable, requiring specific care), and maladapted (poorly adaptable). Subsequently, we investigated the dynamic relationship between safety margins for eight climate variables across two time periods and the adaptability levels of plants by constructing an optimal machine learning model to predict urban plant adaptability levels, enhancing its accuracy through model comparisons and hyperparameter tuning. Our findings indicate that nearly half (49.0 %) of the plant species in Beijing may face reduced adaptability to future climate conditions. However, a majority (75.9 %) perform well under baseline climate conditions and are expected to adapt to future climate conditions. The results reaffirm that the species can grow well out of the niche limit, suggesting that the traditional climate niche-based method may be limited in urban contexts. Our approach overcomes the limitations of binary classification of traditional niche-based methods and the neglect of anthropogenic factors by incorporating an urban plant adaptability classification schema and ground truth derived from expert consensus and records of plant introductions through machine learning methods. This study provides a method for selecting climate-ready plant species for urban environments and supports evidence-based urban forestry management amidst climate change.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identifying climate-ready plant for urban environment: Integrating machine learning with traditional plant selection tools\",\"authors\":\"Maini Chen, Anrong Dang, Xiangyu Li, Jingxiong Huang, Yang Weng\",\"doi\":\"10.1016/j.ufug.2024.128559\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Climate change has intensified the urban heat island effect and increased extreme weather conditions, posing risks to public health and urban vegetation. To address these challenges, selecting climate-ready urban plant species is crucial. Traditional climate niche-based methods often fall short in urban contexts due to neglecting anthropogenic factors. Our study addresses this research gap by introducing an innovative urban plant selection method that integrates vulnerability metrics, expert consensus, and plant introduction records through machine learning. We identified eight climatic variables essential for the survival of urban plants in Beijing, China, and established safety margins for eight climate variables of 1070 urban plant species across two periods: the baseline (1981–2010) and the future (2041–2070). Based on existing assessment data, expert consensus and plant introduction records, 247 plant species were classified into three levels of adaptability: backbone (highly adaptable, prevalent in Beijing), general (moderately adaptable, requiring specific care), and maladapted (poorly adaptable). Subsequently, we investigated the dynamic relationship between safety margins for eight climate variables across two time periods and the adaptability levels of plants by constructing an optimal machine learning model to predict urban plant adaptability levels, enhancing its accuracy through model comparisons and hyperparameter tuning. Our findings indicate that nearly half (49.0 %) of the plant species in Beijing may face reduced adaptability to future climate conditions. However, a majority (75.9 %) perform well under baseline climate conditions and are expected to adapt to future climate conditions. The results reaffirm that the species can grow well out of the niche limit, suggesting that the traditional climate niche-based method may be limited in urban contexts. Our approach overcomes the limitations of binary classification of traditional niche-based methods and the neglect of anthropogenic factors by incorporating an urban plant adaptability classification schema and ground truth derived from expert consensus and records of plant introductions through machine learning methods. This study provides a method for selecting climate-ready plant species for urban environments and supports evidence-based urban forestry management amidst climate change.</div></div>\",\"PeriodicalId\":49394,\"journal\":{\"name\":\"Urban Forestry & Urban Greening\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Urban Forestry & Urban Greening\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1618866724003571\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Urban Forestry & Urban Greening","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1618866724003571","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
Identifying climate-ready plant for urban environment: Integrating machine learning with traditional plant selection tools
Climate change has intensified the urban heat island effect and increased extreme weather conditions, posing risks to public health and urban vegetation. To address these challenges, selecting climate-ready urban plant species is crucial. Traditional climate niche-based methods often fall short in urban contexts due to neglecting anthropogenic factors. Our study addresses this research gap by introducing an innovative urban plant selection method that integrates vulnerability metrics, expert consensus, and plant introduction records through machine learning. We identified eight climatic variables essential for the survival of urban plants in Beijing, China, and established safety margins for eight climate variables of 1070 urban plant species across two periods: the baseline (1981–2010) and the future (2041–2070). Based on existing assessment data, expert consensus and plant introduction records, 247 plant species were classified into three levels of adaptability: backbone (highly adaptable, prevalent in Beijing), general (moderately adaptable, requiring specific care), and maladapted (poorly adaptable). Subsequently, we investigated the dynamic relationship between safety margins for eight climate variables across two time periods and the adaptability levels of plants by constructing an optimal machine learning model to predict urban plant adaptability levels, enhancing its accuracy through model comparisons and hyperparameter tuning. Our findings indicate that nearly half (49.0 %) of the plant species in Beijing may face reduced adaptability to future climate conditions. However, a majority (75.9 %) perform well under baseline climate conditions and are expected to adapt to future climate conditions. The results reaffirm that the species can grow well out of the niche limit, suggesting that the traditional climate niche-based method may be limited in urban contexts. Our approach overcomes the limitations of binary classification of traditional niche-based methods and the neglect of anthropogenic factors by incorporating an urban plant adaptability classification schema and ground truth derived from expert consensus and records of plant introductions through machine learning methods. This study provides a method for selecting climate-ready plant species for urban environments and supports evidence-based urban forestry management amidst climate change.
期刊介绍:
Urban Forestry and Urban Greening is a refereed, international journal aimed at presenting high-quality research with urban and peri-urban woody and non-woody vegetation and its use, planning, design, establishment and management as its main topics. Urban Forestry and Urban Greening concentrates on all tree-dominated (as joint together in the urban forest) as well as other green resources in and around urban areas, such as woodlands, public and private urban parks and gardens, urban nature areas, street tree and square plantations, botanical gardens and cemeteries.
The journal welcomes basic and applied research papers, as well as review papers and short communications. Contributions should focus on one or more of the following aspects:
-Form and functions of urban forests and other vegetation, including aspects of urban ecology.
-Policy-making, planning and design related to urban forests and other vegetation.
-Selection and establishment of tree resources and other vegetation for urban environments.
-Management of urban forests and other vegetation.
Original contributions of a high academic standard are invited from a wide range of disciplines and fields, including forestry, biology, horticulture, arboriculture, landscape ecology, pathology, soil science, hydrology, landscape architecture, landscape planning, urban planning and design, economics, sociology, environmental psychology, public health, and education.