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Culling large herbivores can reduce browsing damage. Our objective was to verify the effect of culling by considering spatial changes in browsing damage to test the hypothesis that the benefits of spatially biased culling extend to the spatial scale of culling. Culling sika deer (Cervus nippon) in Kumamoto Prefecture, Japan, increased from 2009–2017, but browsing damage remained stable across the prefecture; regional damage trends differed among grid meshes (i.e., 5 km × 5 km). Meshes with browsing damage reduction received higher culling pressure and a decline in the deer population. Browsing damage reduction from culling was not uniform across regions and was biased by spatial bias in culling. This study highlights the importance of spatial scale in large herbivore management and evaluation of its effect.

Regional land-use change is the leading cause of ecosystem carbon stock change; it is essential to investigate the response of LUCC to carbon stock to achieve the strategic goal of “double carbon” in a region. This paper proposes a residual network algorithm, the Residual Multi-module Fusion Network (R-MFNet), to address the problems of blurred feature boundary information, low classification accuracy, and high noise, which are often encountered in traditional classification methods. The network algorithm uses an R-ASPP module to expand the receptive field of the feature map to extract sufficient and multi-scale target features; it uses the attention mechanism to assign weights to the multi-scale information of each channel and space. It can fully preserve the remote sensing image features extracted by the convolutional layer through the residual connection. Using this classification network method, the classification of three Landsat-TM/OLI images of Zhengzhou City (the capital of Henan Province) from 2001 to 2020 was realized (the years that the three images were taken are 2001, 2009, and 2020). Compared with SVM, 2D-CNN, and deep residual networks (ResNet), the overall accuracy of the test dataset is increased by 10.07%, 3.96%, and 1.33%, respectively. The classification achieved using this method is closer to the real land surface, and its accuracy is higher than that of the finished product data obtained using the traditional classification method, providing high-precision land-use classification data for the subsequent carbon storage estimation research. Based on the land-use classification data and the carbon density data corrected by meteorological data (temperature and precipitation data), the InVEST model is used to analyze the land-use change and its impact on carbon storage in the region. The results showed that, from 2001 to 2020, the carbon stock in the study area showed a downward trend, with a total decrease of 1.48 × 107 t. Over the course of this 19-year period, the farmland area in Zhengzhou decreased by 1101.72 km2, and the built land area increased sharply by 936.16 km2. The area of land transfer accounted for 29.26% of the total area of Zhengzhou City from 2001 to 2009, and 31.20% from 2009 to 2020. The conversion of farmland to built land is the primary type of land transfer and the most important reason for decreasing carbon stock. The research results can provide support, in the form of scientific data, for land-use management decisions and carbon storage function protections in Zhengzhou and other cities around the world undergoing rapid urbanization.

Balancing economic growth with environmental protection is vital for the sustainable development of cities and regions. However, urban greening has rarely been considered in extensive studies. This study incorporates urban greening into a coupling coordination degree (CCD) model, in order to evaluate its coordination with economic performance. A total of 286 cities in China between 2005 and 2019 were selected as specific study subjects. Meanwhile, clustering method was used to classify different clusters based on CCD values, the Gini coefficient analysis was applied to discover the CCD values inequality characteristics and the exploratory spatial data analysis (ESDA) method was employed to study the CCD values spatial aggregation features. The results indicate that the CCD values presented significant spatial heterogeneity. Spatially, the CCD values were divided into eight clusters, with those in the eastern region generally being higher than in the central and western regions. Temporally, the CCD in all cities showed an increasing trend, but more than 60% of cities were still in the uncoordinated or low-level coordination stage. In addition, inequality and spatial aggregation characteristics were observed in CCD values, both of which presented decreasing trends. Greening has a stronger influence on the linked and coordinated growth of the two systems; therefore, we propose policy recommendations for pursuing the development of environmentally friendly cities from different aspects. In summary, this research allows for a better understanding of economic and environmental relationships, thus contributing to the objective of creating sustainable cities and communities.

Aim: Changes in climate and land use practices have been found to affect animal populations in different parts of the world. These studies have typically been conducted during the breeding season, whereas the non-breeding season (hereafter 'winter') has received much less attention. Changes in regional winter abundances could be caused by changes in overall population sizes and/or redistribution of populations. We tested these mechanisms for terrestrial winter bird population changes in Northern Europe and explored the role of climate change and species habitat preference. Location: The Netherlands, Denmark, Sweden, Finland. Methods: We used winter bird counts from four countries conducted annually between 15 December and 20 January in 1980/1981–2013/2014. We report national population trends for 50 species for which a trend could be calculated in at least three of the countries. We analysed country-specific population growth rates in relation to species' climatic summer and winter niches, habitat preference and migratory behaviour. Results: Species breeding in colder (typically northern) areas showed more negative winter population trends than species breeding in warmer areas. Regional winter population trends were negatively correlated with characteristics of their winter climatic niche: populations in the colder part of their winter distribution increased in abundance, whereas populations in the warmer part of their winter distribution decreased. Woodland species tended to do better than farmland species. Migratory behaviour did not explain variation in population trends. Main conclusions: The generally decreasing winter population trends of colddwelling breeding species probably reflect the general decline in population sizes of these species. In contrast, increasing winter population trends for populations in the colder parts of the winter distribution indicate a redistribution of wintering individuals towards the north-east. Both these patterns are likely caused by climate change.

The holy city of Ratanpur is situated in the Bilaspur district of the Central Indian state of Chhattisgarh. In the past few decades, the wetlands and water bodies of Ratanpur have been subjected to various anthropogenic pressures and undergone changes in land use land cover (LULC) patterns. The paper focuses on assessing the changes in land use and land cover in and around Ratanpur city from 1989 to 2015 using LANDSAT satellite imageries. The processing of satellite imageries and quantitative assessment of LULC data was done using ArcGIS and ERDAS Imagine software. From the current study, it is evident that the numbers and quality of ponds have decreased resulting in decreased numbers and frequency of avian fauna in the area. Earlier water bodies covered an area of 3.76% which has decreased to 2.06%. The reduction in areas covered under water bodies has increased in the dry watercourse area (3%) and river bed area (0.80%). As seen from 2015 data the built-up land areas have expanded by 2.22% as compared to 1989. A considerable decrease in open forest area (8.21 %) and agricultural land (3.97%) has been witnessed, whereas the area occupied by scrubland (6.42%), wasteland (1.18%), and built-up land (1.99%) has increased. The Spatio-temporal LULC changes of the study area can be used to monitor, plan, and implement proper town and country planning to maintain the sustainable environment of Ratanpur city. The adverse impact of urban growth in the surface water bodies/ponds must be regulated by taking suitable conservation measures at the individual and community level for maintaining the biodiversity and aesthetic beauty of the area.

For the first time an international acoustic survey dataset covering three decades was used to investigate the factors shaping the spatial and temporal patterns in the condition of sprat and herring in the Baltic Proper. Generalized additive models showed that the spatial and temporal fluctuations in sprat density have been the main drivers of the spatio-temporal changes of both sprat and herring condition, evidencing intra- and inter-specific density dependence mediated by the size and distribution of the sprat population. Salinity was also an important predictor of herring condition, whereas temperature explained only a minor part of sprat model deviance. Herring density was an additional albeit weak significant predictor for herring condition, evidencing also intra-specific density dependence within the herring population. For both species, condition was high and similar in all areas of the Baltic Proper until the early 1990s, coincident with low sprat densities. Afterwards, a drop in condition occurred and a clear south–north pattern emerged. The drop in condition after the early 1990s was stronger in the northern areas, where sprat population increased the most. We suggest that the increase in sprat density in the northern areas, and the consequent spatial differentiation in clupeid condition, have been triggered by the almost total disappearance of the predator cod from the northern Baltic Proper. This study provides a step forward in understanding clupeid condition in the Baltic Sea, presenting evidence that density-dependent mechanisms also operate at the spatial scale within stock units. This stresses the importance of spatio-temporal considerations in the management of exploited fish.

Climate change is a worldwide problem caused by various anthropogenic activities, leading to changes in hydroclimatic variables like temperature, rainfall, riverine flow, and extreme hydrometeorological events. In India, significant changes are noted in its natural resources and agriculture sectors. In this study, we analysed the long-term spatio-temporal change in rainfall patterns of Madhya Pradesh, Central India, using Indian Meteorological Department high-resolution gridded data from 439 grid points. The coefficient of variance analysis showed low variability in annual and monsoon rainfall but significant variability in pre-monsoon, post-monsoon, and winter seasons, indicating considerable seasonal variation. Pre-monsoon rainfall exhibited an increasing trend (0.018 mm annually), while annual, monsoon, post-monsoon, and winter rainfall showed decreasing trends. Change point analysis identified shifts in rainfall patterns in 1998 (monsoon, annual), 1955 (pre-monsoon), 1987 (post-monsoon), and 1986 (winter). Spatio-temporal distribution maps depicted irregular rainfall, with some areas experiencing drastic declines in precipitation after 1998. The maximum average annual rainfall reduced from 1,769 to 1,401 mm after 1998 affecting water availability. The study's findings highlight a significant shift in Madhya Pradesh's seasonal rainfall distribution after 1998, urging researchers and policymakers to address water-intensive cropping practices and foster climate resilience for a sustainable future in the region.

Understanding changes in urban vegetation is essential for ensuring sustainable and healthy cities, mitigating disturbances due to climate change, sustaining urban biodiversity, and supporting human health and wellbeing. This study investigates and describes the distribution and dynamic changes in urban vegetation over a 15-year period in Greater Melbourne, Australia. The study investigates how vegetation cover across Melbourne has changed at five-yearly intervals from 2001 to 2016 using the newly proposed dynamic change approach that extends the net change approach to quantify the amount of vegetation gain as well as loss. We examine this question at two spatial resolutions: (1) at the municipal landscape scale to capture broadscale change regardless of land tenure; and (2) at the scale of designated public open spaces within the municipalities to investigate the extent to which the loss of vegetation has occurred on lands that are intended to provide public access to vegetated areas in the city. Vegetation was quantified at four different times (2001, 2006, 2011, 2016), using the normalized difference vegetation index (NDVI). Dynamic changes of gain and loss in urban vegetation between the three periods were quantified for six local government areas (LGAs) and their associated public open spaces using a change matrix. The results showed an overall net loss of 64.5 square kilometres of urban vegetation from 2001 to 2016 in six LGAs. When extrapolated to the Greater Melbourne Area, this is approximately equivalent to 109 times the size of Central Park in New York City.

This study examines the dynamic mapping of impervious surface changes in optimising urban spatial structures and fostering sustainable development. A novel deep learning model and time-spectral-texture combination optimisation method were employed to identify pixel-based land-cover change trajectories. A piecewise linear regression model was also utilised to determine the time nodes of urban expansion. This methodology was applied to Panjin City, a resource-based city in China, to analyse temporal and spatial morphological changes related to urban expansion. The results reveal that the combination optimisation method achieved a trajectory classification accuracy of 93.10% and macro F1-score of 92.44%, with an urban expansion time identification accuracy of 84.24%. Panjin City’s built-up area increased from 312.75 to 489.49 km² between 1990 and 2020, reflecting a growth rate of 56.51% and an average expansion speed of 5.89 km²/year. Furthermore, the spatial compactness of impervious surfaces declined, with urban expansion patterns shifting from leapfrog and edge expansion to infilling after 2016. These findings emphasise the need for strategic urban planning to enhance land-use efficiency and promote sustainable development, offering valuable insights for urban expansion mapping in other cities.

Cropland ecosystems, which are most affected by human activities, are dual carriers of carbon sources and sinks. It has significant implications for the achievement of the “two-carbon” objective. The Middle and Lower Yangtze River Plain (MLYRP) is the principal grain-producing area of China, which is a great agricultural country. The development of green agriculture in this plain is of vital importance. Nevertheless, there is a lack of attention to the dynamics of the carbon footprints of cropland. Hence, this study was conducted with the help of carbon emission coefficient method. It investigated the spatio-temporal variations of carbon sources, sinks and carbon footprints of cropland ecosystems in this plain from 2013 to 2022. The findings suggest that (1) Carbon uptake was fluctuating up during the study period. Carbon uptake was higher in paddy and wheat. (2) Carbon emissions were declining year by year. Fertilizer and irrigated agriculture produced more carbon emissions. The top four for both indicators were Anhui, Jiangsu, Hubei and Hunan provinces. (3) The carbon footprint declined in fluctuations. This indicator ranked the top four in Hubei, Anhui, Zhejiang and Jiangsu provinces. The spatial distribution pattern of the above three indicators was more in the north and less in the south. (4) Cropland ecosystems exhibited carbon sinks. There were relatively large carbon eco-surplus and high carbon eco-efficiency. Nevertheless, the carbon ecological surplus was decreasing in fluctuation. Consequently, MLYRP should keep popularizing new technologies such as green manure crops and precision agriculture.