In Malawi, smallholder households bordering forest reserves are structurally vulnerable to weather shocks with limited institutional coping strategies. In the context of climate change, forest co-management, defined as a shared governance arrangement in which local communities and the state jointly manage forest resources, often under formal or semi‑formal agreements, may provide a key mechanism for improving household welfare and reducing inequalities among riparian communities. Based on data from a field survey of 743 smallholder households coupled with georeferenced rainfall and forest cover data across seven forest reserves in Malawi, this study empirically analyses the impact of participation in Village Natural Resource Management Committees (VRNMCs) on household welfare, income inequality, as well as its safety net role in the aftermath of rainfall shocks. Employing IPWRA, PSM, and quantile regression models, the study shows the following results: First, socioeconomic, biophysical, and institutional factors significantly influence the likelihood of household participation in VNRMCs. Second, despite the disparities within forest reserves, VNRMC participation significantly increases forestry income, non-forestry income, the consumer durable asset index, and food consumption score. Third, household participation in VNRMCs helps safeguard forestry income from rainfall shocks, although the effect remains spatially heterogeneous. Fourth, in the context of weather shocks, participation in forest co-management significantly reduces per capita income inequality within households bordering forest reserves in Malawi, particularly for the lowest-income households. This study highlights the need for the Malawian Department of Forestry, donors, and local communities to strengthen participatory management of natural resources for pro-poor conservation purposes in the context of weather shocks. Longitudinal and panel studies are required for further field-based evidence.
{"title":"Who benefits from forest co-management? Heterogeneous welfare effects under rainfall shocks in Malawi","authors":"Prosper Salumu Kimwanga , Mavuto Tembo , Kelvin Mulungu , Bruno Kokouvi Kokou , Ulemu Msiska , Fanuel Kapute","doi":"10.1016/j.tfp.2026.101185","DOIUrl":"10.1016/j.tfp.2026.101185","url":null,"abstract":"<div><div>In Malawi, smallholder households bordering forest reserves are structurally vulnerable to weather shocks with limited institutional coping strategies. In the context of climate change, forest co-management, defined as a shared governance arrangement in which local communities and the state jointly manage forest resources, often under formal or semi‑formal agreements, may provide a key mechanism for improving household welfare and reducing inequalities among riparian communities. Based on data from a field survey of 743 smallholder households coupled with georeferenced rainfall and forest cover data across seven forest reserves in Malawi, this study empirically analyses the impact of participation in Village Natural Resource Management Committees (VRNMCs) on household welfare, income inequality, as well as its safety net role in the aftermath of rainfall shocks. Employing IPWRA, PSM, and quantile regression models, the study shows the following results: First, socioeconomic, biophysical, and institutional factors significantly influence the likelihood of household participation in VNRMCs. Second, despite the disparities within forest reserves, VNRMC participation significantly increases forestry income, non-forestry income, the consumer durable asset index, and food consumption score. Third, household participation in VNRMCs helps safeguard forestry income from rainfall shocks, although the effect remains spatially heterogeneous. Fourth, in the context of weather shocks, participation in forest co-management significantly reduces per capita income inequality within households bordering forest reserves in Malawi, particularly for the lowest-income households. This study highlights the need for the Malawian Department of Forestry, donors, and local communities to strengthen participatory management of natural resources for pro-poor conservation purposes in the context of weather shocks. Longitudinal and panel studies are required for further field-based evidence.</div></div>","PeriodicalId":36104,"journal":{"name":"Trees, Forests and People","volume":"24 ","pages":"Article 101185"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147400704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-11DOI: 10.1016/j.tfp.2026.101186
Rajasree Nandi , Mohammed Jashimuddin
Quantifying carbon stocks and sequestration in urban forests is essential for understanding their contribution to the global carbon cycle. Despite increasing attention to urban carbon accounting, limited studies have examined tropical urban forests using the i-Tree Eco Model, particularly in rapidly urbanizing cities such as Chattogram, Bangladesh. This study aimed to estimate carbon stocks in Chattogram’s urban forests using the i-Tree Eco Model, validated with pantropical allometric equations. Field data were collected from 208 circular plots (totaling 8.4032 ha; each 0.0404 ha) across three urban zones: Trees, Built-up with Trees, and Others. Total carbon stock estimated by i-Tree Eco (229,330 t) was 16.6 % lower than that from the Chave equation (275,002 t) and 21.3 % lower than the Brown equation (291,520 t). Carbon density estimates from i-Tree were 17–28 % lower than pantropical equations, although differences were generally not statistically significant (p ≥ 0.05) under two-tailed tests with varying assumptions for belowground biomass allocation and carbon conversions. Carbon density varied significantly among the three zones (p ≤ 0.05), with the Trees zone showing the highest values (41.26–52.57 t C ha⁻¹) and the Others zone the lowest. This variation reflects differences in tree size and density. Moreover, carbon density showed strong positive correlations with stand structural attributes e.g. dbh, basal area. The total annual carbon sequestration by Chattogram’s urban forests was estimated at 33,240 t CO₂, emphasizing their role in mitigating atmospheric carbon. These findings provide valuable insights into the carbon storage potential of urban forests and underscore their importance for climate change mitigation and strategic urban forest management in tropical cities.
量化城市森林的碳储量和固存对于了解它们对全球碳循环的贡献至关重要。尽管越来越多的人关注城市碳核算，但利用i-Tree生态模型对热带城市森林进行的研究有限，特别是在孟加拉国Chattogram等快速城市化的城市。本研究旨在利用i-Tree生态模型估计Chattogram城市森林的碳储量，并用泛热带异速生长方程进行验证。实地数据收集于208个圆形地块（总计8.4032公顷，每个地块0.0404公顷），分布在三个城区：树木区、树木区和其他区。i-Tree Eco估算的总碳储量（229,330 t）比Chave方程（275,002 t）低16.6%，比Brown方程（291,520 t）低21.3%。i-Tree估算的碳密度比pantropical方程低17 - 28%，尽管在不同地下生物量分配和碳转换假设的双尾检验中，差异通常没有统计学意义（p≥0.05）。3个区域的碳密度差异显著（p≤0.05），其中Trees区域的碳密度最高（41.26-52.57 t C ha⁻¹），other区域的碳密度最低。这种差异反映了树木大小和密度的差异。碳密度与林分结构属性（胸径、基底面积）呈显著正相关。Chattogram的城市森林的年固碳总量估计为33,240 t CO₂，强调了它们在缓解大气碳方面的作用。这些发现为了解城市森林的碳储存潜力提供了宝贵的见解，并强调了它们对热带城市减缓气候变化和战略性城市森林管理的重要性。
{"title":"Modelling carbon stock in a tropical urban forest: Insights from i-Tree Eco and pantropical allometric equations in Chattogram City, Bangladesh","authors":"Rajasree Nandi , Mohammed Jashimuddin","doi":"10.1016/j.tfp.2026.101186","DOIUrl":"10.1016/j.tfp.2026.101186","url":null,"abstract":"<div><div>Quantifying carbon stocks and sequestration in urban forests is essential for understanding their contribution to the global carbon cycle. Despite increasing attention to urban carbon accounting, limited studies have examined tropical urban forests using the i-Tree Eco Model, particularly in rapidly urbanizing cities such as Chattogram, Bangladesh. This study aimed to estimate carbon stocks in Chattogram’s urban forests using the i-Tree Eco Model, validated with pantropical allometric equations. Field data were collected from 208 circular plots (totaling 8.4032 ha; each 0.0404 ha) across three urban zones: Trees, Built-up with Trees, and Others. Total carbon stock estimated by i-Tree Eco (229,330 t) was 16.6 % lower than that from the Chave equation (275,002 t) and 21.3 % lower than the Brown equation (291,520 t). Carbon density estimates from i-Tree were 17–28 % lower than pantropical equations, although differences were generally not statistically significant (<em>p</em> ≥ 0.05) under two-tailed tests with varying assumptions for belowground biomass allocation and carbon conversions. Carbon density varied significantly among the three zones (<em>p</em> ≤ 0.05), with the Trees zone showing the highest values (41.26–52.57 t C ha⁻¹) and the Others zone the lowest. This variation reflects differences in tree size and density. Moreover, carbon density showed strong positive correlations with stand structural attributes e.g. dbh, basal area. The total annual carbon sequestration by Chattogram’s urban forests was estimated at 33,240 t CO₂, emphasizing their role in mitigating atmospheric carbon. These findings provide valuable insights into the carbon storage potential of urban forests and underscore their importance for climate change mitigation and strategic urban forest management in tropical cities.</div></div>","PeriodicalId":36104,"journal":{"name":"Trees, Forests and People","volume":"24 ","pages":"Article 101186"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147400705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-07DOI: 10.1016/j.tfp.2026.101154
Sina Irannejad, Hossein Bagheri
Accurate estimation of aboveground biomass (AGB) is essential for sustainable forest management and climate change monitoring; however, conventional remote sensing approaches often rely on a single data type and provide limited interpretability. This study introduces a novel framework that systematically integrates multi-sensor remote sensing (Sentinel-1 SAR, Sentinel-2, and Landsat-8 optical data), process-based climate variables (Climate-FVS), and LiDAR-derived topographic indices into deep learning models. By coupling these fusion scenarios with explainable AI (XAI) analyses, the framework not only improves predictive accuracy but also reveals the relative contribution of ecological drivers such as precipitation, temperature, and short-wave infrared bands. Seven U-Net-based architectures, including U-Net3+, TransU-Net, and Attention U-Net, were trained and evaluated using RMSE, MAE, and R² metrics. Results showed that U-Net3+ achieved the highest performance under the complete fusion scenario, with an RMSE of 28.10 Mg/ha, an MAE of 17.49 Mg/ha, and an R² of 0.89. XAI analyses highlighted that SWIR1, SWIR2, and Red bands were the most influential predictors, while climatic variables significantly improved model generalization in topographically complex areas. The highest errors occurred at vegetation boundaries and steep terrain. These findings demonstrate that multi-source data fusion combined with interpretable deep learning provides a robust pathway for both accurate AGB estimation and a deeper understanding of its environmental drivers, directly supporting carbon accounting and sustainable forest management.
{"title":"Explaining ecological drivers and management implications of forest biomass: An explainable deep learning fusion of remote sensing and climate data","authors":"Sina Irannejad, Hossein Bagheri","doi":"10.1016/j.tfp.2026.101154","DOIUrl":"10.1016/j.tfp.2026.101154","url":null,"abstract":"<div><div>Accurate estimation of aboveground biomass (AGB) is essential for sustainable forest management and climate change monitoring; however, conventional remote sensing approaches often rely on a single data type and provide limited interpretability. This study introduces a novel framework that systematically integrates multi-sensor remote sensing (Sentinel-1 SAR, Sentinel-2, and Landsat-8 optical data), process-based climate variables (Climate-FVS), and LiDAR-derived topographic indices into deep learning models. By coupling these fusion scenarios with explainable AI (XAI) analyses, the framework not only improves predictive accuracy but also reveals the relative contribution of ecological drivers such as precipitation, temperature, and short-wave infrared bands. Seven U-Net-based architectures, including U-Net3+, TransU-Net, and Attention U-Net, were trained and evaluated using RMSE, MAE, and R² metrics. Results showed that U-Net3+ achieved the highest performance under the complete fusion scenario, with an RMSE of 28.10 Mg/ha, an MAE of 17.49 Mg/ha, and an R² of 0.89. XAI analyses highlighted that SWIR1, SWIR2, and Red bands were the most influential predictors, while climatic variables significantly improved model generalization in topographically complex areas. The highest errors occurred at vegetation boundaries and steep terrain. These findings demonstrate that multi-source data fusion combined with interpretable deep learning provides a robust pathway for both accurate AGB estimation and a deeper understanding of its environmental drivers, directly supporting carbon accounting and sustainable forest management.</div></div>","PeriodicalId":36104,"journal":{"name":"Trees, Forests and People","volume":"24 ","pages":"Article 101154"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate carbon stock estimation is essential for supporting forest management and climate-related decision making, yet most regional assessments continue to apply uniform carbon concentration values. To improve estimation accuracy, we analyzed over 5,600+ samples from 194 destructively harvested Changbai larch (Larix olgensis) trees to quantify intra- and inter-component variation across three components: sapwood, heartwood, and bark. Significant differences in carbon concentration were found across components (p < 0.05). And the stem showed significant vertical variation in carbon concentration. Tree diameter at breast height (DBH), Relative DBH (Rd), tree age (Age) and mean DBH of dominant trees (Ddom) exerted significant influences on the carbon concentration across all tree components. A multivariate beta mixed-effects model (MBMM) was developed to simultaneously modeling carbon concentrations of multiple components while accounting for the nested sampling design. Incorporating tree- and stand-level random effects greatly improved model fit (R² increased from 0.5 to 0.8 roughly) and resulting in a reduction in mean absolute prediction error by 15% for sapwood and heartwood, 10% for bark, nearly, when compared to the fixed-effects models. These findings provide empirically based carbon parameters for L. olgensis plantations and highlight the importance of component-specific values in forest carbon accounting. Adoption of refined component-specific carbon concentrations can enhance the accuracy of carbon offset assessments, forest asset valuation, and monitoring frameworks, supporting policy implementation under national carbon neutrality goals.
{"title":"A multivariate beta mixed-effects approach to modeling stem carbon concentration of Larix olgensis in northeastern China","authors":"Longfei Xie , Zheng Miao , Yuanshuo Hao , Fengri Li , Lihu Dong","doi":"10.1016/j.tfp.2026.101174","DOIUrl":"10.1016/j.tfp.2026.101174","url":null,"abstract":"<div><div>Accurate carbon stock estimation is essential for supporting forest management and climate-related decision making, yet most regional assessments continue to apply uniform carbon concentration values. To improve estimation accuracy, we analyzed over 5,600+ samples from 194 destructively harvested Changbai larch (<em>Larix olgensis</em>) trees to quantify intra- and inter-component variation across three components: sapwood, heartwood, and bark. Significant differences in carbon concentration were found across components (<em>p</em> < 0.05). And the stem showed significant vertical variation in carbon concentration. Tree diameter at breast height (DBH), Relative DBH (Rd), tree age (Age) and mean DBH of dominant trees (Ddom) exerted significant influences on the carbon concentration across all tree components. A multivariate beta mixed-effects model (MBMM) was developed to simultaneously modeling carbon concentrations of multiple components while accounting for the nested sampling design. Incorporating tree- and stand-level random effects greatly improved model fit (<em>R</em>² increased from 0.5 to 0.8 roughly) and resulting in a reduction in mean absolute prediction error by 15% for sapwood and heartwood, 10% for bark, nearly, when compared to the fixed-effects models. These findings provide empirically based carbon parameters for <em>L. olgensis</em> plantations and highlight the importance of component-specific values in forest carbon accounting. Adoption of refined component-specific carbon concentrations can enhance the accuracy of carbon offset assessments, forest asset valuation, and monitoring frameworks, supporting policy implementation under national carbon neutrality goals.</div></div>","PeriodicalId":36104,"journal":{"name":"Trees, Forests and People","volume":"24 ","pages":"Article 101174"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Coffea arabica holds utmost economic and cultural significance for Ethiopians, being one of the major sources of national income and rural livelihoods. However, slight warming or changes in rainfall have catastrophic effects on the land suitability for cultivation and threaten the species. This study aimed to assess climate change impacts on a species’ land suitability across tropic agro-ecological zones (Warm/sub-humid, Warm/Humid, Cool/Sub-humid, and Cool/Sub-humid) in the Western Highlands of Ethiopia, using baseline (1981–2010) and future (2041-2070) climate conditions under the Shared Socioeconomic Pathways (SSP1–2.6, SSP3–7.0, and SSP5–8.5). Climate data were obtained from CHELSA, species occurrence data were collected through field surveys for the present, and from the Global Biodiversity Information Facility (GBIF) for the last 30 years. A digital elevation model was also sourced from the USGS Website. Data quality control (multicollinearity) was tested to ensure variable independence. Primarly, MaxEnt model was used to predict the potential distribution and habitat suitability of Coffea arabica, and then, Artificial Neural Network Model (ANNM) was used to polish Model's inherent uncertainties and capture Arabica Coffea response to environmental predictors. Results show that C. arabica suitability exhibits varied changes by agro-ecological zones, increasing under SSP1–2.6 (18.9%) and SSP3–7.0 (9.0%) but marginally decreasing under SSP5–8.5 scenario (2.0%). Future coffee suitability shows gains in tropical-warm/humid zones but major losses in tropical-warm/sub-humid areas. Suitability increases with altitude but decreases with higher precipitation seasonality across all scenarios. Farmers should integrate shade trees, adopt drought-tolerant varieties, and conserve soil moisture while gradually shifting coffee to higher altitudes.
{"title":"Geospatial modelling of Agro-climatic suitability for Coffea arabica under CMIP6 scenarios in the Western Highlands of Ethiopia","authors":"Fedhasa Benti Chalchissa , Yohanis Asfaw Wakene , Bayissa Leta Danno , Birhanu Kebede Kuris , Zenebe Reta Roba , Mitiku Badasa Moisa","doi":"10.1016/j.tfp.2026.101216","DOIUrl":"10.1016/j.tfp.2026.101216","url":null,"abstract":"<div><div><em>Coffea arabica</em> holds utmost economic and cultural significance for Ethiopians, being one of the major sources of national income and rural livelihoods. However, slight warming or changes in rainfall have catastrophic effects on the land suitability for cultivation and threaten the species. This study aimed to assess climate change impacts on a species’ land suitability across tropic agro-ecological zones (Warm/sub-humid, Warm/Humid, Cool/Sub-humid, and Cool/Sub-humid) in the Western Highlands of Ethiopia, using baseline (1981–2010) and future (2041-2070) climate conditions under the Shared Socioeconomic Pathways (SSP1–2.6, SSP3–7.0, and SSP5–8.5). Climate data were obtained from CHELSA, species occurrence data were collected through field surveys for the present, and from the Global Biodiversity Information Facility (GBIF) for the last 30 years. A digital elevation model was also sourced from the USGS Website. Data quality control (multicollinearity) was tested to ensure variable independence. Primarly, MaxEnt model was used to predict the potential distribution and habitat suitability of <em>Coffea arabica</em>, and then, Artificial Neural Network Model (ANNM) was used to polish Model's inherent uncertainties and capture Arabica Coffea response to environmental predictors. Results show that C. arabica suitability exhibits varied changes by agro-ecological zones, increasing under SSP1–2.6 (18.9%) and SSP3–7.0 (9.0%) but marginally decreasing under SSP5–8.5 scenario (2.0%). Future coffee suitability shows gains in tropical-warm/humid zones but major losses in tropical-warm/sub-humid areas. Suitability increases with altitude but decreases with higher precipitation seasonality across all scenarios. Farmers should integrate shade trees, adopt drought-tolerant varieties, and conserve soil moisture while gradually shifting coffee to higher altitudes.</div></div>","PeriodicalId":36104,"journal":{"name":"Trees, Forests and People","volume":"24 ","pages":"Article 101216"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147448587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-14DOI: 10.1016/j.tfp.2026.101158
Chaohang Zhang , Ying Zhang , Haofeng Bao , Yufeng Chen , Wenbo Li , Fengying Guan , Xianhua Wang , Zhihong Xu , Shahla Hosseini Bai , Chaomao Hui , Weiyi Liu
<div><div>The traditional harvesting practice for bamboo forests is selective cutting, which targets culms older than four years. In order to reduce labor and harvesting costs, new methods have been proposed in recent years, including strip harvesting for monopodial bamboos and clump harvesting for sympodial bamboos. These approaches not only lower management inputs but also emphasize the integrity and sustainability of bamboo forest ecosystems by retaining a certain number of bamboo clumps to promote natural regeneration and ecological restoration. <em>Dendrocalamus giganteus</em>, commonly known as Dragon bamboo, is a large, clump-forming bamboo species widely cultivated in southwestern Yunnan, China. It serves as an important raw material for construction, papermaking, and furniture manufacturing, with substantial practical and economic value. In previous studies, the research team focused on <em>D. giganteus</em> and investigated the growth conditions of bamboo stands under four different harvesting intensities (conventional selective harvesting, 1/3 clump harvesting, 1/2 clump harvesting, and complete clump harvesting). The results showed that 1/2 clump harvesting had the highest overall evaluation. To investigate the variation patterns of rhizosphere soil characteristics and their effects on organ nutrient distribution in <em>D. giganteus</em> with different stand ages after 1/2 clump harvesting restoration, 1- to 4-year-old <em>D. giganteus</em> were selected as the research objects. The nutrient content in plant organs, soil chemical properties, enzyme activity levels, and rhizosphere soil microbial community structure were measured. Variation patterns were analyzed, the key soil chemical factors driving changes in soil bacterial and fungal communities were identified, and a model of the influencing factors of soil chemical properties and microbial community structure on organ nutrient distribution in <em>D. giganteus</em> was constructed using partial least squares path modeling (PLS-PM). The main findings were as follows: (1) Rhizosphere soil nutrient contents of older bamboo culms were higher than those of younger ones after 1/2 clump harvesting recovery. (2) The dominant bacterial phyla were Proteobacteria, Acidobacteriota, and Chloroflexi, while the dominant fungal phyla were Basidiomycota and Ascomycota, 1/2 clump harvesting altered the composition of dominant fungal taxa across stand ages. (3) Soil pH, SOM, and NH<sub>4</sub><sup>+</sup>-N were the primary factors influencing bacterial community assembly (p < 0.01), whereas fungal communities were primarily regulated by AP, pH, NH<sub>4</sub><sup>+</sup>-N, and NO<sub>3</sub><sup>−</sup>-N (p < 0.01). (4) Nutrient contents in roots and culms were mainly influenced by bacterial communities, while branch nutrients were more affected by soil chemical properties, and leaf nutrients were largely governed by fungal communities. The variation patterns of organ nutrient content, rhiz
{"title":"Rhizosphere soil characteristics at different stand ages affect on plant organs’ nutrient distribution of Dendrocalamus giganteus forests following recovery of half-clump harvesting","authors":"Chaohang Zhang , Ying Zhang , Haofeng Bao , Yufeng Chen , Wenbo Li , Fengying Guan , Xianhua Wang , Zhihong Xu , Shahla Hosseini Bai , Chaomao Hui , Weiyi Liu","doi":"10.1016/j.tfp.2026.101158","DOIUrl":"10.1016/j.tfp.2026.101158","url":null,"abstract":"<div><div>The traditional harvesting practice for bamboo forests is selective cutting, which targets culms older than four years. In order to reduce labor and harvesting costs, new methods have been proposed in recent years, including strip harvesting for monopodial bamboos and clump harvesting for sympodial bamboos. These approaches not only lower management inputs but also emphasize the integrity and sustainability of bamboo forest ecosystems by retaining a certain number of bamboo clumps to promote natural regeneration and ecological restoration. <em>Dendrocalamus giganteus</em>, commonly known as Dragon bamboo, is a large, clump-forming bamboo species widely cultivated in southwestern Yunnan, China. It serves as an important raw material for construction, papermaking, and furniture manufacturing, with substantial practical and economic value. In previous studies, the research team focused on <em>D. giganteus</em> and investigated the growth conditions of bamboo stands under four different harvesting intensities (conventional selective harvesting, 1/3 clump harvesting, 1/2 clump harvesting, and complete clump harvesting). The results showed that 1/2 clump harvesting had the highest overall evaluation. To investigate the variation patterns of rhizosphere soil characteristics and their effects on organ nutrient distribution in <em>D. giganteus</em> with different stand ages after 1/2 clump harvesting restoration, 1- to 4-year-old <em>D. giganteus</em> were selected as the research objects. The nutrient content in plant organs, soil chemical properties, enzyme activity levels, and rhizosphere soil microbial community structure were measured. Variation patterns were analyzed, the key soil chemical factors driving changes in soil bacterial and fungal communities were identified, and a model of the influencing factors of soil chemical properties and microbial community structure on organ nutrient distribution in <em>D. giganteus</em> was constructed using partial least squares path modeling (PLS-PM). The main findings were as follows: (1) Rhizosphere soil nutrient contents of older bamboo culms were higher than those of younger ones after 1/2 clump harvesting recovery. (2) The dominant bacterial phyla were Proteobacteria, Acidobacteriota, and Chloroflexi, while the dominant fungal phyla were Basidiomycota and Ascomycota, 1/2 clump harvesting altered the composition of dominant fungal taxa across stand ages. (3) Soil pH, SOM, and NH<sub>4</sub><sup>+</sup>-N were the primary factors influencing bacterial community assembly (p < 0.01), whereas fungal communities were primarily regulated by AP, pH, NH<sub>4</sub><sup>+</sup>-N, and NO<sub>3</sub><sup>−</sup>-N (p < 0.01). (4) Nutrient contents in roots and culms were mainly influenced by bacterial communities, while branch nutrients were more affected by soil chemical properties, and leaf nutrients were largely governed by fungal communities. The variation patterns of organ nutrient content, rhiz","PeriodicalId":36104,"journal":{"name":"Trees, Forests and People","volume":"24 ","pages":"Article 101158"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-20DOI: 10.1016/j.tfp.2026.101198
Willams Oliveira, Jéssica Luiza S. Silva, Marcelo Tabarelli, Ariadna V. Lopes
Urbanization threatens biodiversity, ecological resilience, and human well-being. Currently, the critical role of urban trees in human well-being and mitigating environmental challenges is increasingly recognized. Nowadays, a major challenge is to combine urban development with sustainability. Therefore, this paper reviews and highlights the benefits of urban trees to people and discusses how urban trees contribute to achieving the Sustainable Development Goals. We also provide a discussion on the negative impacts of urbanization, including habitat degradation, climate change, and pollution, while emphasizing the potential of urban trees to deliver several ecosystem services and mitigate these challenges. Urban trees offer numerous ecosystem services (i.e., provisioning, regulating, cultural, and supporting), such as air purification, flood control, carbon sequestration, food provisioning, pollination, economic and aesthetic benefits, and mental health. However, challenges remain, including the integration of sustainable green spaces in urban planning and addressing social inequities in access to green spaces. In addition, the strategic use of native tree species in urban planning is critical to addressing environmental challenges in cities posed by urbanization. Furthermore, we additionally highlight that urban trees contribute directly or indirectly to achieving all the 17 Sustainable Development Goals by promoting environmental justice, reducing social and gender disparities, and enhancing the quality of life for city dwellers. We argue that cities managed with a sustainable perspective have the potential to act as a nature-based solution, contributing to the provision of multiple ecosystem services in urban areas, which extends from environmental benefits to the sphere of social equity.
{"title":"Benefits of urban trees to people and their potential contribution to all the 17 sustainable development goals","authors":"Willams Oliveira, Jéssica Luiza S. Silva, Marcelo Tabarelli, Ariadna V. Lopes","doi":"10.1016/j.tfp.2026.101198","DOIUrl":"10.1016/j.tfp.2026.101198","url":null,"abstract":"<div><div>Urbanization threatens biodiversity, ecological resilience, and human well-being. Currently, the critical role of urban trees in human well-being and mitigating environmental challenges is increasingly recognized. Nowadays, a major challenge is to combine urban development with sustainability. Therefore, this paper reviews and highlights the benefits of urban trees to people and discusses how urban trees contribute to achieving the Sustainable Development Goals. We also provide a discussion on the negative impacts of urbanization, including habitat degradation, climate change, and pollution, while emphasizing the potential of urban trees to deliver several ecosystem services and mitigate these challenges. Urban trees offer numerous ecosystem services (i.e., provisioning, regulating, cultural, and supporting), such as air purification, flood control, carbon sequestration, food provisioning, pollination, economic and aesthetic benefits, and mental health. However, challenges remain, including the integration of sustainable green spaces in urban planning and addressing social inequities in access to green spaces. In addition, the strategic use of native tree species in urban planning is critical to addressing environmental challenges in cities posed by urbanization. Furthermore, we additionally highlight that urban trees contribute directly or indirectly to achieving all the 17 Sustainable Development Goals by promoting environmental justice, reducing social and gender disparities, and enhancing the quality of life for city dwellers. We argue that cities managed with a sustainable perspective have the potential to act as a nature-based solution, contributing to the provision of multiple ecosystem services in urban areas, which extends from environmental benefits to the sphere of social equity.</div></div>","PeriodicalId":36104,"journal":{"name":"Trees, Forests and People","volume":"24 ","pages":"Article 101198"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-13DOI: 10.1016/j.tfp.2025.101123
Hugo Silva , Joana M.S. Cardoso , Bruno Manadas , Luís Fonseca
Pine wilt disease (PWD), caused by the pinewood nematode, Bursaphelenchus xylophilus, is a major ecological and economic threat to pine forests worldwide. Native to North America, B. xylophilus, considered a quarantine species by the European and Mediterranean Plant Protection Organisation, has spread to Asia and Europe, causing severe damage to pine species and resulting in substantial economic and ecological losses. The Bursaphelenchus genus, also includes the species B. mucronatus, the B. xylophilus closest related species, sharing similar morphological and ecological characteristics. To date, no major economic or ecological damage has been caused by B. mucronatus. However, over the years, some studies have suggested that it has a certain degree of pathogenicity in stressed trees, particularly those under drought and heat stress. This review provides a comprehensive synthesis of current knowledge on both species, covering their biology, distribution, life cycle, and methods for morphological and molecular identification. We further examine the known pathogenicity mechanisms, drawing from transcriptomic, genomic, and proteomic studies. By integrating recent advances across multiple disciplines, this review aims to clarify the similarities and distinctions between these two species, identify knowledge gaps, and contribute to future research and management strategies.
{"title":"Insights into pine wilt disease: a review on biology and pathogenicity of Bursaphelenchus xylophilus and B. mucronatus","authors":"Hugo Silva , Joana M.S. Cardoso , Bruno Manadas , Luís Fonseca","doi":"10.1016/j.tfp.2025.101123","DOIUrl":"10.1016/j.tfp.2025.101123","url":null,"abstract":"<div><div>Pine wilt disease (PWD), caused by the pinewood nematode, <em>Bursaphelenchus xylophilus</em>, is a major ecological and economic threat to pine forests worldwide. Native to North America, <em>B. xylophilus</em>, considered a quarantine species by the European and Mediterranean Plant Protection Organisation, has spread to Asia and Europe, causing severe damage to pine species and resulting in substantial economic and ecological losses. The <em>Bursaphelenchus</em> genus, also includes the species <em>B. mucronatus</em>, the <em>B. xylophilus</em> closest related species, sharing similar morphological and ecological characteristics. To date, no major economic or ecological damage has been caused by <em>B. mucronatus</em>. However, over the years, some studies have suggested that it has a certain degree of pathogenicity in stressed trees, particularly those under drought and heat stress. This review provides a comprehensive synthesis of current knowledge on both species, covering their biology, distribution, life cycle, and methods for morphological and molecular identification. We further examine the known pathogenicity mechanisms, drawing from transcriptomic, genomic, and proteomic studies. By integrating recent advances across multiple disciplines, this review aims to clarify the similarities and distinctions between these two species, identify knowledge gaps, and contribute to future research and management strategies.</div></div>","PeriodicalId":36104,"journal":{"name":"Trees, Forests and People","volume":"24 ","pages":"Article 101123"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-12DOI: 10.1016/j.tfp.2026.101190
Xi Zeng , Bo Wang
Insect herbivory is a key driver of forest regeneration, yet its effects on woody seedlings in subtropical forests remain inadequately quantified. We conducted a three-year study (2022–2024) in the Ailao Mountains, a subtropical montane evergreen broadleaved forest in Yunnan, China, which represents a globally significant biodiversity hotspot. Our results showed that mean community-level cumulative herbivory remained relatively stable across years (5.4–6.3%). However, the effects of herbivory on seedling growth exhibited strong interannual variation: negligible in 2022, negative in 2023, and divergent in 2024 (positively correlated with height growth but negatively with leaf number growth). These temporal shifts were closely linked to interannual variability in early rainy-season precipitation, whereas temperature played a negligible role. Growth responses also varied substantially among species, even under comparable herbivory pressure. Notably, herbivory had no significant effect on seedling survival in any year. Our findings demonstrate that herbivory effects on subtropical seedlings are context-dependent (mediated by rainfall), growth-metric-specific, and species-specific. We conclude that subtropical montane seedlings are resilient to moderate herbivory, providing scientific support for the non-intervention approach to herbivore management in protected forests. Nevertheless, climate-driven alterations in rainfall seasonality could disrupt these ecological relationships, underscoring the importance of long-term monitoring for informing adaptive forest management strategies.
{"title":"Dynamics of insect herbivory on seedling growth: A three-year study reveals metric-specific effects and resilience in a subtropical forest","authors":"Xi Zeng , Bo Wang","doi":"10.1016/j.tfp.2026.101190","DOIUrl":"10.1016/j.tfp.2026.101190","url":null,"abstract":"<div><div>Insect herbivory is a key driver of forest regeneration, yet its effects on woody seedlings in subtropical forests remain inadequately quantified. We conducted a three-year study (2022–2024) in the Ailao Mountains, a subtropical montane evergreen broadleaved forest in Yunnan, China, which represents a globally significant biodiversity hotspot. Our results showed that mean community-level cumulative herbivory remained relatively stable across years (5.4–6.3%). However, the effects of herbivory on seedling growth exhibited strong interannual variation: negligible in 2022, negative in 2023, and divergent in 2024 (positively correlated with height growth but negatively with leaf number growth). These temporal shifts were closely linked to interannual variability in early rainy-season precipitation, whereas temperature played a negligible role. Growth responses also varied substantially among species, even under comparable herbivory pressure. Notably, herbivory had no significant effect on seedling survival in any year. Our findings demonstrate that herbivory effects on subtropical seedlings are context-dependent (mediated by rainfall), growth-metric-specific, and species-specific. We conclude that subtropical montane seedlings are resilient to moderate herbivory, providing scientific support for the non-intervention approach to herbivore management in protected forests. Nevertheless, climate-driven alterations in rainfall seasonality could disrupt these ecological relationships, underscoring the importance of long-term monitoring for informing adaptive forest management strategies.</div></div>","PeriodicalId":36104,"journal":{"name":"Trees, Forests and People","volume":"24 ","pages":"Article 101190"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147400230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-24DOI: 10.1016/j.tfp.2026.101207
Luiz Antonio Martinelli , Rodrigo Figueiredo Almeida , Maria Gabriella da Silva Araújo , Deoclecio Jardim Amorim , Ana Claudia Gama Batista , Isabela Maria Souza-Silva , Edmar Mazzi , Evelyn Soares da Mata Ferrari , Clément Bataille , Caspar Christian Cedric Chater , Fábio José Viana Costa , Victor Deklerck , Paulo José Duarte-Neto , Niro Higuchi , Adriano José Nogueira Lima , Gabriela Bielefeld Nardoto , João Paulo Sena-Souza , Gabriel J. Bowen
Açaí (Euterpe spp.) is a flagship product of the Amazon bioeconomy, recognized for its high nutritional value and economic importance. The commercialization of açaí could benefit from a straightforward certification or traceability system that verifies geographic origin and product authenticity. This study introduces the first isotopic assignment model for açaí berries from the Brazilian Amazon, utilizing stable isotopes of oxygen (δ¹⁸O) and hydrogen (δD). We developed Random Forest-based isoscapes and employed Bayesian assignment techniques to create spatial posterior probability surfaces for 59 samples with known origins. The model’s evaluation included spatial performance metrics at sample level: posterior quantile rank, distance to the highest posterior cell, and the size of the 95% credible area. These metrics were combined into a composite score to assess individual sample performance and overall model accuracy. The model demonstrated moderate to good discriminatory ability. Although actual origins often fell outside the peak posterior zones, the median 95% credible region covered only 2.0% of the Brazilian Amazon, indicating a strong capacity for exclusion. Sixty-eight percent of samples had quantile ranks below 0.10, and 65% were within their respective 95% credible areas. The overall composite score was 0.29 (95% CI: 0.25–0.34), classifying the model as “good”. We conclude that this model is best suited for exclusion-based applications like isotopic provenance certification rather than for exploratory geographic assignments lacking prior location information. This research demonstrates the practical feasibility of using stable isotopes for traceability in tropical forest products and introduces transferable spatial metrics for future isotopic provenance modeling.
{"title":"Isotope fingerprinting for traceability in the amazon bioeconomy: A Bayesian assignment approach with açaí","authors":"Luiz Antonio Martinelli , Rodrigo Figueiredo Almeida , Maria Gabriella da Silva Araújo , Deoclecio Jardim Amorim , Ana Claudia Gama Batista , Isabela Maria Souza-Silva , Edmar Mazzi , Evelyn Soares da Mata Ferrari , Clément Bataille , Caspar Christian Cedric Chater , Fábio José Viana Costa , Victor Deklerck , Paulo José Duarte-Neto , Niro Higuchi , Adriano José Nogueira Lima , Gabriela Bielefeld Nardoto , João Paulo Sena-Souza , Gabriel J. Bowen","doi":"10.1016/j.tfp.2026.101207","DOIUrl":"10.1016/j.tfp.2026.101207","url":null,"abstract":"<div><div>Açaí (<em>Euterpe spp.</em>) is a flagship product of the Amazon bioeconomy, recognized for its high nutritional value and economic importance. The commercialization of açaí could benefit from a straightforward certification or traceability system that verifies geographic origin and product authenticity. This study introduces the first isotopic assignment model for açaí berries from the Brazilian Amazon, utilizing stable isotopes of oxygen (δ¹⁸O) and hydrogen (δD). We developed Random Forest-based isoscapes and employed Bayesian assignment techniques to create spatial posterior probability surfaces for 59 samples with known origins. The model’s evaluation included spatial performance metrics at sample level: posterior quantile rank, distance to the highest posterior cell, and the size of the 95% credible area. These metrics were combined into a composite score to assess individual sample performance and overall model accuracy. The model demonstrated moderate to good discriminatory ability. Although actual origins often fell outside the peak posterior zones, the median 95% credible region covered only 2.0% of the Brazilian Amazon, indicating a strong capacity for exclusion. Sixty-eight percent of samples had quantile ranks below 0.10, and 65% were within their respective 95% credible areas. The overall composite score was 0.29 (95% CI: 0.25–0.34), classifying the model as “good”. We conclude that this model is best suited for exclusion-based applications like isotopic provenance certification rather than for exploratory geographic assignments lacking prior location information. This research demonstrates the practical feasibility of using stable isotopes for traceability in tropical forest products and introduces transferable spatial metrics for future isotopic provenance modeling.</div></div>","PeriodicalId":36104,"journal":{"name":"Trees, Forests and People","volume":"24 ","pages":"Article 101207"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147400715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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