{"title":"城市繁华还是城市萧条?加拿大蒙特利尔养蜂的挑战、适宜性和生存见解","authors":"Navid Mahdizadeh Gharakhanlou , Julien Vadnais , Liliana Perez , Nico Coallier","doi":"10.1016/j.ecoinf.2025.103296","DOIUrl":null,"url":null,"abstract":"<div><div>The rising interest in urban beekeeping underscores the need to investigate whether urban habitats are sustainable for managed honeybee populations. This study, conducted on the island of Montreal, Canada, aimed to i) assess honeybee colony survival within an urban environment, ii) determine the primary drivers affecting honeybee colony survival, and iii) explore the potential of urban areas to support beekeeping activities. This study applied two distinct survival analysis methods, namely random survival forests (RSF) and time-dependent Cox models, incorporating both static and dynamic geospatial variables including normalized difference vegetation index (NDVI), digital elevation model (DEM), percentages of urban areas and water, floral source diversity, road density, proximity to roads, surrounding hive count, ozone (O₃) concentration, fine particulate matter (PM2.5) levels, maximum temperature, and precipitation. To reflect typical honeybee foraging ranges, two buffer distances (1 km and 3 km) were analyzed, and model performance was assessed using the concordance index (C-index) and integrated Brier score (IBS). For the 1 km buffer, the RSF model achieved a C-index of 0.90 (training) and 0.82 (test) with IBS scores of 0.06 and 0.10, outperforming the Cox model, which showed a C-index of 0.56 (both training and test) and IBS values of 0.19 and 0.18. At 3 km, RSF further improved (C-index: 0.93 (training) and 0.84 (test); IBS: 0.05 (training) and 0.08 (test)), while the Cox model remained lower (C-index: 0.58 (training) and 0.60 (test); IBS: 0.19 (training) and 0.18 (test)). These results confirm RSF's superior performance and suggest that broader spatial context may enhance prediction accuracy. Additionally, our findings revealed that the surrounding hive count was the strongest predictor of beehive survival in both buffer scenarios. At 1 km, road proximity and elevation (i.e., DEM) followed in importance, while at 3 km, elevation and vegetation density (i.e., NDVI) were more influential. A primary outcome of this study was the generation of spatially explicit beehive habitat suitability maps for Montreal. Averaged over 2017–2021, these maps showed that large portions of the island are favorable for urban beekeeping, with 30.94 % of land classified as highly suitable and 38.28 % as moderately suitable, demonstrating strong potential for sustainable apiculture in urban environments. This study contributes to providing insights into urban planning and managed honeybee conservation through suitability mapping and predictor analysis.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103296"},"PeriodicalIF":5.8000,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Urban buzz or urban bust? Beekeeping challenges, suitability, and survival insights in Montreal, Canada\",\"authors\":\"Navid Mahdizadeh Gharakhanlou , Julien Vadnais , Liliana Perez , Nico Coallier\",\"doi\":\"10.1016/j.ecoinf.2025.103296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The rising interest in urban beekeeping underscores the need to investigate whether urban habitats are sustainable for managed honeybee populations. This study, conducted on the island of Montreal, Canada, aimed to i) assess honeybee colony survival within an urban environment, ii) determine the primary drivers affecting honeybee colony survival, and iii) explore the potential of urban areas to support beekeeping activities. This study applied two distinct survival analysis methods, namely random survival forests (RSF) and time-dependent Cox models, incorporating both static and dynamic geospatial variables including normalized difference vegetation index (NDVI), digital elevation model (DEM), percentages of urban areas and water, floral source diversity, road density, proximity to roads, surrounding hive count, ozone (O₃) concentration, fine particulate matter (PM2.5) levels, maximum temperature, and precipitation. To reflect typical honeybee foraging ranges, two buffer distances (1 km and 3 km) were analyzed, and model performance was assessed using the concordance index (C-index) and integrated Brier score (IBS). For the 1 km buffer, the RSF model achieved a C-index of 0.90 (training) and 0.82 (test) with IBS scores of 0.06 and 0.10, outperforming the Cox model, which showed a C-index of 0.56 (both training and test) and IBS values of 0.19 and 0.18. At 3 km, RSF further improved (C-index: 0.93 (training) and 0.84 (test); IBS: 0.05 (training) and 0.08 (test)), while the Cox model remained lower (C-index: 0.58 (training) and 0.60 (test); IBS: 0.19 (training) and 0.18 (test)). These results confirm RSF's superior performance and suggest that broader spatial context may enhance prediction accuracy. Additionally, our findings revealed that the surrounding hive count was the strongest predictor of beehive survival in both buffer scenarios. At 1 km, road proximity and elevation (i.e., DEM) followed in importance, while at 3 km, elevation and vegetation density (i.e., NDVI) were more influential. A primary outcome of this study was the generation of spatially explicit beehive habitat suitability maps for Montreal. Averaged over 2017–2021, these maps showed that large portions of the island are favorable for urban beekeeping, with 30.94 % of land classified as highly suitable and 38.28 % as moderately suitable, demonstrating strong potential for sustainable apiculture in urban environments. This study contributes to providing insights into urban planning and managed honeybee conservation through suitability mapping and predictor analysis.</div></div>\",\"PeriodicalId\":51024,\"journal\":{\"name\":\"Ecological Informatics\",\"volume\":\"90 \",\"pages\":\"Article 103296\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2025-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecological Informatics\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S157495412500305X\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Informatics","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S157495412500305X","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
Urban buzz or urban bust? Beekeeping challenges, suitability, and survival insights in Montreal, Canada
The rising interest in urban beekeeping underscores the need to investigate whether urban habitats are sustainable for managed honeybee populations. This study, conducted on the island of Montreal, Canada, aimed to i) assess honeybee colony survival within an urban environment, ii) determine the primary drivers affecting honeybee colony survival, and iii) explore the potential of urban areas to support beekeeping activities. This study applied two distinct survival analysis methods, namely random survival forests (RSF) and time-dependent Cox models, incorporating both static and dynamic geospatial variables including normalized difference vegetation index (NDVI), digital elevation model (DEM), percentages of urban areas and water, floral source diversity, road density, proximity to roads, surrounding hive count, ozone (O₃) concentration, fine particulate matter (PM2.5) levels, maximum temperature, and precipitation. To reflect typical honeybee foraging ranges, two buffer distances (1 km and 3 km) were analyzed, and model performance was assessed using the concordance index (C-index) and integrated Brier score (IBS). For the 1 km buffer, the RSF model achieved a C-index of 0.90 (training) and 0.82 (test) with IBS scores of 0.06 and 0.10, outperforming the Cox model, which showed a C-index of 0.56 (both training and test) and IBS values of 0.19 and 0.18. At 3 km, RSF further improved (C-index: 0.93 (training) and 0.84 (test); IBS: 0.05 (training) and 0.08 (test)), while the Cox model remained lower (C-index: 0.58 (training) and 0.60 (test); IBS: 0.19 (training) and 0.18 (test)). These results confirm RSF's superior performance and suggest that broader spatial context may enhance prediction accuracy. Additionally, our findings revealed that the surrounding hive count was the strongest predictor of beehive survival in both buffer scenarios. At 1 km, road proximity and elevation (i.e., DEM) followed in importance, while at 3 km, elevation and vegetation density (i.e., NDVI) were more influential. A primary outcome of this study was the generation of spatially explicit beehive habitat suitability maps for Montreal. Averaged over 2017–2021, these maps showed that large portions of the island are favorable for urban beekeeping, with 30.94 % of land classified as highly suitable and 38.28 % as moderately suitable, demonstrating strong potential for sustainable apiculture in urban environments. This study contributes to providing insights into urban planning and managed honeybee conservation through suitability mapping and predictor analysis.
期刊介绍:
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.