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Following severe anthropogenic pressure from rapid economic development, wetland biodiversity is now decreasing alarmingly, thus leading to adverse effects. Protected areas (PAs) can be crucial conservation tools to secure wetland biodiversity. However, whether these PAs exhibit high conservation efficiency in buffering wildlife and habitats from human pressures needs to be understood. Given their sensitivity to habitat quality and regional resource changes, threatened waterfowl could be suitable wetland ecosystem indicators. This study examined the conservation effectiveness of Henan Yellow River Wetland National Nature Reserve (HYRWNNR), which is a crucial region on the East Asia–Australia route for global bird migration. We performed Maximum Entropy (MaxEnt) modeling based on field survey data of the 19 threatened waterfowl species, and Human Impact Index (HII) was further mapped with waterfowls distribution to identify the conservation gap and priorities of the HYRWNNR. The results indicated that threatened waterfowl distribution were affected by both environmental factors and human pressure, and a conservation gap existed in the HYRWNNR. Two conservation scenarios were generated based on the spatial pattern of conservation priorities, and their corresponding management strategies were suggested. This study identifies conservation priorities from a novel perspective by synthesizing habitat suitability and human pressure, which can present basic information regarding the HYRWNNR management while supporting waterfowl conservation planning, ultimately promoting wetland habitats sustainability. [ABSTRACT FROM AUTHOR]

Application of LiDAR technology has greatly enhanced tree segmentation and phenotypic analysis. There are few studies in urban green spaces using tree segmentation methods. Our aim is to improve the single-plant segmentation accuracy in tree and shrub communities through segmenting algorithm optimisation based on TLS LiDAR data of the urban green space. We developed a multi-round comparative shortest-path algorithm (M-CSP) to achieve the objectives: a) tree and shrub plant layer pre-division (TSPD); b) shrub type classifications (STC) into spherical, cylindrical, and rectangular shapes. The overall detection kappa value using M-CSP is 0.933, which is 18% higher than the CSP value of 0.790. M-CSP-based overall segmentation accuracy value (F-score) is 0.886, which is 13% higher than the CSP value of 0.783. The shrub F-score using M-CSP is 0.817, which is 26% higher than the CSP (0.646). M-CSP should provide a more accurate, faster, and less costly tool to study plant communities in urban green spaces. [ABSTRACT FROM AUTHOR]