The benefits and disbenefits associated with cultural ecosystem services of urban green spaces

Cultural ecosystem services (CES) and disservices shape landscape planning policy to a huge extent. We focus on the benefits and disbenefits associated with CES. The study aimed to explore the co-occurrence of the benefits and disbenefits associated with CES as well as the relationship between spatial and landscape characteristics and specific benefits and disbenefits. We conducted a map-based online questionnaire among visitors of two urban green spaces in Kraków, Poland (Wolski Forest and Jordan Park). Respondents were asked to map places visited and assign them benefits and disbenefits using indicator statements. We found three bundles of benefits (connection to nature, social bonding in nature and responsibility) and five to seven bundles of disbenefits (1). The experiences (e.g., strengthening social bonds) were more concentrated whereas the identities (e.g., reflection) were more blurred spatially owing to their individualistic nature (2). The relationship between benefits/disbenefits and landscape features showed a relatively weak correlation, with a more discernible pattern observed in the case of experiences and capabilities (3). Respondents perceived more human-related disbenefits (overcrowding, noise, rubbish), exhibiting a greater geographical concentration, especially in proximity to tourist attractions (4). The ecosystem-related disbenefits (insects, allergies) were less geographically concentrated (5). Furthermore, the study unveiled differences in the perception of disbenefits across seasons. Visitors of warmer months expressed concerns about overcrowding, insects, and allergies, while those exploring the study areas in winter indicated challenges associated with darkness and snow-covered paths (6). These are important implications for management to increase the comfort of visits to green spaces.

Habitat-specific conservation priorities of multidimensional diversity patterns of amphibians in China effectively contribute to the '3030' target

Amphibia is the most threatened animal group among all land vertebrates in the context of anthropogenic global change. Filling the conservation gaps for this taxonomic group could help achieve the ambitious target of covering 30 % of the land by 2030 ('3030' target) set by the 15-th meeting of the Conference of the Parties (COP15). In this study, we compiled the most up-to-date occurrence records and corresponding species-specific traits and phylogenies of amphibians in China (particularly those newly described in the past decade) to explore the spatial distribution patterns of multidimensional diversity (including taxonomic, functional, and phylogenetic) for different species groups (including all, endemic and threatened). Additionally, a new conservation gap index (CGI) was proposed and applied to the analysis of multi-objective conservation strategies. The results showed that the spatial distribution of taxonomic, functional and phylogenetic diversity of amphibians in China is markedly geographically diverse, with common hotspots for all three concentrated in the humid mountainous regions of southern China. The CGI, which is independent of arbitrary threshold selection and grid cell size, showed that the conservation gap for amphibians in China is largest in biomes such as tropical and subtropical moist broadleaf forests and temperate broadleaf and mixed forests. The multi-objective conservation analysis revealed that the Yangtze River basin, Pearl River basin and Southeast Basin in China have pivotal roles in achieving the '3030' target due to their high taxonomic, phylogenetic and functional diversity, relatively high proportion of threatened and endemic species, and low coverage of existing nature reserves. Notably, sustainable management of less-protected habitats, including farmlands and grasslands, can reduce the area requirement of strict protection for reaching the '3030' conservation goal. This study provides practical strategies for guiding amphibian conservation by systematically integrating multidimensional biodiversity information, habitat features and the spatial distributions of the existing natural reserves.

Using buffer analysis to determine urban park cooling intensity: Five estimation methods for Nanjing, China

Urban parks are part of the blue-green infrastructure of urban ecosystems. Although the cooling effect of urban parks has been widely recognized, the understanding of park cooling intensity (PCI) and its mechanisms remains incomplete. Applicable and accurate quantification could facilitate better design and management of urban parks. We used five methods (equal area method [EAM], equal radius method [ERM], fixed radius method [FRM], turning point method-maximum perspective [TPM-M], and turning point method-accumulation perspective [TPM-A]) to estimate PCI, and established the method selection mechanism, which we evaluated in terms of PCI amplitudes, spatial heterogeneity, and interactions with park landscape features. Using Nanjing as a case study, we employed spatial and statistical analyses to further assess the autocorrelation of PCI and its relationship with park landscape features. The results indicate the following: (1) 62.38 % of Nanjing's urban parks are located above the 90 % confidence level in cold spot areas. (2) Different methods had significant effects on the estimated PCI, were positively correlated, and had similar spatial heterogeneity. (3) All methods revealed that park area (PA), water area proportion (WAP), and the normalized difference vegetation index (NDVI) of the vegetated area (NDVI_veg) were the three dominant factors that influenced PCI; WAP and NDVI_veg that achieved more effective cooling. (4) The quantification of PCI using the ERM and TPM is recommended over other methods. These findings are essential for landscape planners to understand the formation of PCI and design cooler parks to mitigate the urban heat island (UHI) effect more systematically.

Robust spatial estimates of biomass carbon on farms

The drive for farm businesses to move towards net zero greenhouse gas emissions means that there is a need to develop robust methods to quantify the amount of biomass carbon (C) on farms. Direct measurements can be destructive and time-consuming and some prediction methods provide no assessment of uncertainty. This study describes the development, validation, and use of an integrated spatial approach, including the use of lidar data, and Bayesian Belief Networks (BBNs) to quantify total biomass carbon stocks (C_total) of i) land cover and ii) landscape features such as hedges and lone trees for five case study sites in lowland England. The results demonstrated that it was possible to develop and use a remote integrated approach to estimate biomass carbon at a farm scale. The highest achievable prediction accuracy was attained from models using the variables AGBC, BGBC, DOMC, age, height, species and land cover, derived from measured information and from literature review. The two BBN models successfully predicted the test values of the total biomass carbon with propagated error rates of 6.7 % and 4.3 % for the land cover and landscape features respectively. These error rates were lower than in other studies indicating that the seven predictors are strong determinants of biomass carbon. The lidar data also enabled the spatial presentation and calculation of the variable C stocks along the length of hedges and within woodlands.