Number Of Patches Research Articles

Forecasting Suitable Habitats of the Clouded Leopard (Neofelis nebulosa) in Asia: Insights into the Present and Future Climate Projections Within and Beyond Extant Boundaries

Terrestrial carnivores, such as the clouded leopard (Neofelis nebulosa), are ‘vulnerable’ and experiencing significant population declines in mainland Asia. Considering the assessed threats, the IUCN has repetitively revised the range of this felid and now characterized it into four distinct categories (extant, possibly extant, possibly extinct, and extinct). Although several ecological works have been accomplished on this enigmatic carnivore, the overall view of its habitat suitability, fragmentation, and corridor connectivity in both present and historical ranges is lacking. Thus, achieving this ecological information under present and future climate scenarios is crucial. The model identified merely 44,033 sq. km falling within the extant range (representing 31.66%) and 20,034 sq. km (8.13%) in the possibly extant range. Fascinatingly, within the historical ranges, an additional 15,264 sq. km (6.58%) has been identified as suitable habitat in the possibly extinct range and 14,022 sq. km (2.38%) in the extinct range. Notably, a total of 25,614 sq. km of suitable habitat is found within designated protected areas across the entire range. Nevertheless, climate change is expected to drive habitat loss of up to 41% (overall IUCN range) for N. nebulosa in both present and historical extent, with habitat patches becoming increasingly fragmented. This is reflected in a projected decline in the number of viable habitat patches (NP) by up to 23.29% in the future. This study also identified 18 transboundary biological corridors for N. nebulosa, with Southeast Asian countries expected to experience the most significant declines in corridor connectivity. In contrast, the South Asian countries (Bhutan, Nepal, and India) are projected to maintain relatively higher connectivity in the future. Nevertheless, a substantial decline in overall mean corridor connectivity is projected in the near future due to the impacts of climate change. This study underscores the urgent need for a coordinated and multifaceted conservation strategy for N. nebulosa, focusing on mitigating habitat loss and fragmentation. Practical measures must be implemented to protect the species’ shrinking range, considering its declining corridor networks and heightened vulnerability to inbreeding depression. Moreover, the assessment of habitat suitability both within and beyond the extant range, alongside corridor connectivity measures, provides valuable insights into potential translocation and reintroduction sites for this species. These findings provide a critical foundation for developing a strategic conservation plan tailored to the specific needs of this felid species across South and Southeast Asia, ensuring enhanced climate resilience and mitigating associated threats.

Assessing climatic change impacts on mangrove structural dynamics on the northern coasts of the Persian Gulf

Abstract. Monitoring long-term trends in structural changes within mangrove ecosystems is essential for understanding their response to climate change and devising effective adaptation strategies in coastal regions. This study examines changes in mangrove patchiness within the Hara Biosphere Reserve (HBR) along the northern coasts of the Persian Gulf (PG) over a 31-year period (1986-2017), focusing on variations in rainfall patterns and drought occurrences. Employing a 35-year time series of monthly Standardized Precipitation Index (SPI) values alongside satellite imagery analysis, trends in both the number of patches (NP) and the Largest Patch Index (LPI) were assessed at the mangrove stand level. The analysis reveals a significant correlation between structural changes in mangroves and drought events. Pre-1998, characterized by wetter conditions, witnessed a decrease in both NP and LPI, indicating patch expansion and habitat extension. Conversely, post-1998, during drought periods, both indices increased, indicating habitat degradation due to heightened drought intensity. Pre-1998 structural changes in the HBR signify habitat expansion, with increased patch extent and core areas reflecting enhanced structural integrity. However, post-1998, a concerning trend of habitat degradation emerged, with increased NP and LPI attributed to intensified droughts. These findings highlight a transitional period marked by favourable conditions for mangrove growth followed by habitat degradation linked to increased drought intensity. This underscores the vulnerability of mangrove ecosystems to climate change impacts, particularly exacerbated droughts, necessitating urgent efforts for conservation and management to preserve biodiversity and ecosystem services in coastal regions.

Nonlinear Radiative Response to Patterned Global Warming due to Convection Aggregation and Nonlinear Tropical Dynamics

Abstract Climate responses to global warming exhibit large dependence on the spatial pattern of sea surface temperature (SST) changes. A Green’s function (GF) approach—predicting the global climate response to a complex SST pattern change as the linear superposition of the response to finite-area SST perturbations evaluated in isolation—has been used to systematically evaluate and understand the top of atmosphere (TOA) radiation response to patterned warming. However, in general, the linear GF approach fails for TOA radiation reconstruction under future global warming scenarios in coupled models. Here, we show that the linear superposition of global mean TOA radiation responses to large SST warming perturbations at multiple patches (the GF approach prediction) overestimates the actual response to simultaneous multipatch perturbation. This is because the linear superposition overestimates tropical large-scale convection aggregation strengthening upon localized heating that enhances longwave radiative cooling, which is further explained by the overestimation of circulation response and associated horizontal water vapor transport. The nonadditivity of TOA radiation response is caused by the nonadditivity of convection aggregation, ultimately rooted in nonlinear tropical dynamics. We also demonstrate that the prediction error of the GF approach grows with decreasing patch size (equivalently, increasing patch number). We conclude that using the GF approach to predict future climate change could overestimate longwave radiative cooling and underestimate (effective) climate sensitivity. Numerical experiments may be used to identify specific perturbation patterns where the errors are smaller than the signal. Our research also highlights that an increase in the degree of large-scale convection aggregation has a nonlinear and negative feedback for mean warming.

VLFATRollout: Fully transformer-based classifier for retinal OCT volumes

Background and Objective:Despite the promising capabilities of 3D transformer architectures in video analysis, their application to high-resolution 3D medical volumes encounters several challenges. One major limitation is the high number of 3D patches, which reduces the efficiency of the global self-attention mechanisms of transformers. Additionally, background information can distract vision transformers from focusing on crucial areas of the input image, thereby introducing noise into the final representation. Moreover, the variability in the number of slices per volume complicates the development of models capable of processing input volumes of any resolution while simple solutions like subsampling may risk losing essential diagnostic details. Methods:To address these challenges, we introduce an end-to-end transformer-based framework, variable length feature aggregator transformer rollout (VLFATRollout), to classify volumetric data. The proposed VLFATRollout enjoys several merits. First, the proposed VLFATRollout can effectively mine slice-level fore-background information with the help of transformer’s attention matrices. Second, randomization of volume-wise resolution (i.e. the number of slices) during training enhances the learning capacity of the learnable positional embedding (PE) assigned to each volume slice. This technique allows the PEs to generalize across neighboring slices, facilitating the handling of high-resolution volumes at the test time. Results:VLFATRollout was thoroughly tested on the retinal optical coherence tomography (OCT) volume classification task, demonstrating a notable average improvement of 5.47% in balanced accuracy over the leading convolutional models for a 5-class diagnostic task. These results emphasize the effectiveness of our framework in enhancing slice-level representation and its adaptability across different volume resolutions, paving the way for advanced transformer applications in medical image analysis. The code is available at https://github.com/marziehoghbaie/VLFATRollout/.

Analysis of the Spatial Pattern and Driving Forces of Linpan in Western Sichuan

Abstract. This study aims to explore the landscape pattern and its driving forces of Linpan in western Sichuan, aiming to gain a deeper understanding of the spatial distribution of this unique rural settlement form. The landscape indices and redundancy analysis method were employed to investigate the landscape pattern and driving forces of Linpan in the western Sichuan region. By extracting the spatial information of Linpan in the study area, quantitative analysis of their landscape pattern was conducted using landscape indices. The results reveal certain characteristics of the landscape pattern of Linpan in the study area, where Longquanyi exhibits the highest degree of landscape fragmentation, with the highest patch density and smaller average patch size, yet the highest landscape diversity, while Wenjiang shows the lowest diversity, and Xinjin exhibits a more dispersed distribution of patches, and Dujiangyan has the highest number of patches. Using redundancy analysis, this study reveals the influence of natural and social driving forces on the landscape pattern of Linpan in Western Sichuan. The results indicate that urbanization rate, patch density, mean patch size, and average elevation are significant factors influencing the distribution and morphology of Linpan. These factors affect the spatial layout and connectivity of Linpan, driving changes in the land use structure, and profoundly impacting the landscape pattern of Linpan. To achieve the ecological sustainability, it is essential to comprehensively consider the impacts of both natural and human factors, scientifically plan and manage Linpan, and promote their harmonious coexistence with the surrounding environment.

Quantitative Evaluation of Soil Erosion in Loess Hilly Area of Western Henan Based on Sampling Approach

The terrain in the loess hilly area of western Henan is fragmented, with steep slopes and weak soil erosion resistance. The substantial soil erosion in this region results in plenty of problems, including decreased soil productivity and ecological degradation. These problems significantly hinder the social and economic development in the region. Soil conservation planning and ecological development require accurate soil erosion surveys. However, the studies of spatio-temporal patterns, evolution, and the driving force of soil erosion in this region are insufficient. Therefore, based on a multi-stage, unequal probability, systematic area sampling method and field investigation, the soil erosion of the loess hilly area of western Henan was quantitatively evaluated by the Chinese Soil Loss Equation (CSLE) in 2022. The impact forces of soil erosion were analyzed by means of a geographic detector and multiple linear regression analysis, and the key driving factors of the spatio-temporal evolution of soil erosion in this region were revealed. The results were as follows. (1) The average soil erosion rate of the loess hilly area in western Henan in 2022 was 5.94 t⋅ha−1⋅a−1, with a percentage of soil erosion area of 29.10%. (2) High soil erosion rates mainly appeared in the west of Shangjie, Xingyang, and Jiyuan, which are related to the development of production and construction projects in these areas. The areas with a high percentage of soil erosion area were in the north (Xinan and Yima), west (Lushi), and southeast (Songxian and Ruyang) of the study area. Moreover, areas with the most erosion were found in forest land, cultivated land, and areas with a slope above 25°. (3) At the landscape level, the number and density of patches of all land types, except orchard land, increased significantly, and the boundary perimeter, landscape pattern segmentation, and degree of fragmentation increased. (4) The geographical detector and multiple linear regression analysis indicated that the driving forces of soil erosion are mainly topographic and climatic (slope length, elevation, precipitation, and temperature). Soil erosion was significantly influenced by the density of landscape patches. These maps and factors influencing soil erosion can serve as valuable sources of information for regional soil conservation plans and ecological environment improvements.

Effects of Bt-cotton cultivation on Helicoverpa armigera activity-density in agricultural landscapes in northwestern China.

Cotton bollworm, Helicoverpa armigera (Hübner), threatens many crops. Bacillus thuringiensis (Bt) cotton has been planted to control this severe pest in northern Xinjiang, China. In 2021 and 2022, we monitored the activity densities of H. armigera males using sex pheromone traps in Bt cotton and non-Bt maize fields. We assessed how much of the population variation of cotton bollworm in the fields within the Bt cotton planting area could be explained by (i) landscape composition [including the proportion of Bt cotton among total area of host crops (cotton, maize, wheat and vegetables)], (ii) landscape configuration (patch density - i.e. the number of patches within a given area) across 0.5-2.0 km scales, or (iii) the population density of the previous pest generation. Cotton bollworm activity-density exhibited two distinct peaks annually (mid- to late May and mid-July each year), with the number of males caught during the second peak significantly and positively correlated with the first peak's numbers. The suppressive effect of the proportion of Bt cotton in the landscape on bollworms was more pronounced at larger scales, and patch density had a significant positive effect on bollworm activity density. These findings support (i) the promotion of Bt cotton in northwestern China has reduced and suppressed the occurrence of cotton bollworms at the landscape scale and (ii) the importance of controlling spring populations for effective summer outbreak management, and (iii) that cotton bollworm control should be coordinated at a large scale across multiple crop fields. © 2024 Society of Chemical Industry.

A piezoelectric acoustic black hole absorber for rail vibration reduction and energy harvesting: Semi-analytical model

Theoretically, vibration energy can be converted and harvested during the process of vibration reduction, thus enabling a self-powered supply for sensor nodes. However, little research was reported on rail absorbers that combine vibration reduction and energy harvesting. Accordingly, this study aims to design an absorber based on the acoustic black hole (ABH) effect, termed the P-ABH absorber, that can simultaneously achieve broadband vibration reduction and energy harvesting. Firstly, the P-ABH absorber was preliminarily designed, and the semi-analytical model of the track with the P-ABH absorber was established by the Rayleigh-Ritz method. Secondly, the semi-analytical model was verified by the 3D finite element method, and the vibration reduction and energy harvesting performance were initially evaluated. The dimensions of the ABH structure, along with the sizes of the piezoelectric patches, were then determined. Finally, the vibration reduction and energy harvesting performances of the P-ABH absorbers fully deployed between each fastener were evaluated. The results demonstrated that the P-ABH absorber exhibited a wider vibration reduction bandwidth than the uniform piezoelectric absorber and the piezoelectric patches, with an increase in energy harvesting power by 27.47 % and 115.26 %, respectively. The vibration reduction and energy harvesting performance were significantly affected by the number of piezoelectric patches but little affected by the thickness and total length. When the P-ABH absorbers were fully attached to the rail, the overall mean square velocities at the three resonant frequencies, which required emphatic control, were reduced by 36.77 %, 98.37 %, and 3.52 %, respectively. Meanwhile, the energy harvesting performance was excellent. The P-ABH absorber presents a promising solution for improving the sustainability and cost-effectiveness of track systems.

Climate gradient‐driven intraspecific aggregation propensity linked to interpatch modulation in grassland communities

AbstractThe response of vegetation to climate change on a large scale should be studied at the community level rather than the species level. This necessitates a focused exploration of emerging spatial patterns. Here, we surveyed 264 sites in the Inner Mongolia typical steppe, using the “needling” method to investigate 39,600 clumps formed through the coexistence relationships of dominant species. We found that the effects of slow climate change on grassland communities can be categorized into two general trends: (1) a monotone relationship, characterized by changes in the number of dominant species, compositional diversity, and optimal patch area, and (2) a unimodal relationship, reflected in variations in the number of patches and interspecific associations. The two distinct trends, connected by optimal patch area, concurrently support both the habitat amount hypothesis and the intermediate disturbance hypothesis. These findings suggest that climate change indirectly influences the area and amount of vegetation patches by regulating the arrangement of clumps. Moreover, they indicate that it is the distribution, rather than the number, of species that serves as the front line for plant communities adapting to climate change.

Automating field‐based floral surveys with machine learning

Abstract The abundance and diversity of flowering plant species are important indicators of pollinator habitat quality, but traditional field‐based surveying techniques are time‐intensive. Therefore, they are often biased due to under‐sampling and are difficult to scale. Aerial photography was collected across 10 sites located in and around Rouge National Urban Park, Toronto, Canada using a consumer‐grade drone. A convolutional neural network (CNN) was trained to semantically segment, or identify and categorize, pixel clusters which represent flowers in the collected aerial imagery. Specifically, flowers of the dominant taxa found in the depauperate fall flowering plant community were surveyed. This included yellow flowering Solidago spp., white Symphyotrichum ericoides/lanceolatum and purple Symphyotrichum novae‐angliae. The CNN was trained using 930 m2 of manually annotated data, ~1% of the mapped landscape. The trained CNN was tested on 20% of the manually annotated data concealed during training. In addition, it was externally validated by comparing the predicted drone‐derived floral abundance metrics (i.e. floral area (m2) and the number of floral patches) to the field‐based count of floral units estimated for 34 4 m2 plots. The CNN returned accurate multiclassification when evaluated against the testing data. It obtained a precision score of 0.769, a recall of 0.849, and an F1 score of 0.807. The automated floral abundance counting yielded estimates that were strongly correlated with field‐based manual counting. In addition, flower segmentation using the trained CNN was time‐efficient. On average, it took roughly the same amount of time to segment the flowers occurring in an entire drone scene as it took to complete the abundance count of a single quadrat. However, the training process, particularly manual data annotation, was the most time‐consuming component of the study. Practical implication: Overall, the analysis provided valuable insights into automated flower classification and abundance estimation using drone imagery and machine learning. The results demonstrate that these tools can be used to provide accurate and scalable estimates of pollinator habitat quality. Further research should consider diverse wildflower systems to develop the generalizability of the methods.

Monitoring urban growth and landscape fragmentation in Kaduna, Nigeria, using remote sensing approach

This study assessed urban growth in Kaduna using remote sensing indices, landscape metrics, and Landsat images (2003, 2013, and 2023). Land use/land cover was carried out using the decision tree algorithm, while the transition matrix was computed using ArcGIS 10.8. Landscape fragmentation was assessed using Fragstat 4.3. The results revealed that between 2003 and 2023, bare land, built-up areas, and water bodies increased by 596 km2, 121.04 km2, and 0.22 km2, respectively, while cultivated land and tree cover decreased by 525.54 km2 and 191.91 km2, respectively. For the rate of change, bare lands, built-up areas, and water bodies increased by 29.81 km2, 6.05 km2, and 0.01 km2 annually, respectively, while cultivated lands and water bodies decreased by 26.28 km2 and 9.60 km2, respectively. For the land-use transition, 112.71 km2 of the built-up areas remained persistent. Cultivated lands, tree cover, and water bodies transitioned to built-up areas, losing 85.43 km2, 13.91 km2, and 0.91 km2, respectively. Landscape fragmentation analysis revealed that the number of patches increased from 405 in 2003 to 614 in 2023. Patch density, largest patch index, total edge, edge density, area-weighted mean shape index, and fractal dimension index decreased between 2003 and 2013 but increased between 2013 and 2023, indicating urban sprawl. However, the area-weighted mean contiguity index consistently increased between 2003 and 2023. The study concluded that urbanization is the leading driver of landscape fragmentation. This study recommends that development should be controlled to reduce encroachment on agricultural lands and habitats, thereby reducing landscape fragmentation and land degradation.

THE ROLE AND IMPACT OF VEGETATION ON THE URBAN FABRIC. CASE OF GUELMA CITY

In cities, land use changes caused by various human activities can affect how natural ecosystems function. In this context, it is increasingly important for cities to consider the role of vegetation in preserving a sustainable environment. A diachronic analysis of landscape changes was applied to assess the presence and distribution of vegetation to determine whether it was evolving or regressing. This phenomenon was studied within the current administrative boundaries of the city of Guelma (Algeria). For this purpose, as an approach based on landscape metrics, several Landsat TM and ETM+ remote sensing satellite images were used throughout the period 1987-2019. A set of landscape indices, including NP, AREA_MN, LPI, PLAND, AI and LSI, were calculated to map land cover, the mechanism of land cover and vegetation change, and their impact on the urban ecosystem. The geo-statistical procedure was carried out using a geographic information system Qgis combined by statistical software using Fragstats to calculate various landscape metrics at class level for the analysis of fragmentation . The results of the landscape metric analysis show that the decrease in average area and the increase in the number of green patches are important indicators of land degradation, meaning that the mechanism of landscape degradation and transformation is progressive. This underlines the need to give particular attention to land use and land cover in the region to ensure the sustainable allocation of natural resources.

Soil organic carbon sequestration can be promoted through the improvement of landscape configuration heterogeneity in typical agricultural regions of northeast China

Landscape heterogeneity is considered a promising option for building resilient and sustainable agroecosystems. Understanding the relationships between farmland soil organic carbon (SOC) and landscape heterogeneity can support soil carbon sequestration and better serve food security and climate change. However, the influence extent and optimal scale of farmland landscape heterogeneity (i.e. landscape composition and landscape configuration heterogeneity) on SOC remains unclear. In this study, we established the relationships between multi-scale landscape heterogeneity and SOC in a typical grain-production county of northeast China. Stepwise regression results showed that when the buffer radius was 2000 m, the interpretation of SOC by landscape heterogeneity was the largest. The effects of landscape composition and landscape configuration on SOC were further decomposed by Variance Partitioning Analysis, and we found that independent interpretation ability of landscape configuration (8%) exceeded landscape composition (7%). The result of soil mapping combined with landscape indexes also showed that landscape configuration contributed more to the increased accuracy. Moreover, we found that correlation between configuration indexes and SOC at the class level was less related than that at the landscape level, among which the two most important indexes were Mean Fractal Dimension Index (FRAC_MN) and Number of Patches (NP). FRAC_MN was even more important than natural factors, indicating the validity of landscape as an indicator of human activities should not be ignored when considering farmland SOC. Overall, the results of this study revealed that the negative effects of agricultural intensification on SOC can be buffered to a certain extent by increasing the complexity of patch shape and reducing the degree of landscape fragmentation at the landscape level, providing hope for the sustainable development in intensive agricultural areas. In addition, due to the scale effect of landscape heterogeneity on farmland SOC, we suggest that decision makers should consider the spatial scale in landscape allocation and planning. This study provides a scientific reference for realizing the balance between grain production and ecological function in intensive agricultural areas.

Driving the Evolution of Land Use Patterns: The Impact of Urban Agglomeration Construction Land in the Yangtze River Delta, China

The rapid increase in population and economic activities has greatly influenced land use and spatial development. In urban agglomerations where socioeconomic activities are densely concentrated, the clash between ecological protection and economic growth is becoming more evident. Therefore, a thorough quantitative assessment of spatial changes driven by land use dynamics, alongside an examination of temporal and spatial driving factors, is crucial in offering scientific backing for the long-term and sustainable growth of urban agglomerations. This paper focuses on the major urban agglomerations in China’s Yangtze River Delta region, examining the spatiotemporal evolution of land use and landscape patterns from 2000 to 2020. By employing the standard deviation ellipse technique, coupled with multiple linear regression and the geographical detector model, we conduct a quantitative assessment of the directional trends in urban construction land expansion as well as the diverse impacts of temporal and spatial factors on this expansion across various periods and regions. The findings indicate that over the past 20 years, construction land in the Yangtze River Delta Urban Agglomeration expanded in concentrated patches, showing significant scale effects with relatively intact farmland and forest land being increasingly encroached upon. Landscape-type transitions predominantly occurred in cities around Taihu Lake and Hangzhou Bay, with the most significant transition being farmland converted to construction land, resulting in a greater number of patches and more pronounced land fragmentation. Throughout the 20 years, the standard deviation ellipse of construction land in the Yangtze River Delta Urban Agglomeration expanded and shifted, with the predominant expansion trending from the northwest toward the southeast, and the EN orientation being the most intense expansion area, covering 1641.24 km2. The influence of temporal and spatial driving factors on the expansion of urban construction land differed across various periods and regions. This study thoroughly examines the driving factors that affect the evolution of urban construction land in the region, offering valuable scientific evidence and references for future planning and development of the Yangtze River Delta Urban Agglomeration, aiding in the formulation of more precise and efficient urban management and land use strategies.

Spatiotemporal Dynamics and Drivers of Coastal Wetlands in Tianjin–Hebei over the Past 80 Years

Coastal wetland ecosystems are critical due to their diverse ecological and economic benefits, yet they have been significantly affected by human activities over the past century. Understanding the spatiotemporal changes and underlying factors influencing these ecosystems is crucial for developing effective ecological protection and restoration strategies. This study examines the Tianjin–Hebei coastal wetlands using topographic maps from the 1940s and Landsat satellite imagery from 1975, 2000, and 2020, supplemented by historical literature and field surveys. The aim is to analyze the distribution and classification of coastal wetlands across various temporal intervals. The findings indicate an expansion of the Tianjin–Hebei coastal wetlands from 7301.34 km2 in the 1940s to 8041.73 km2 in 2020. However, natural wetlands have declined by approximately 44.36 km2/year, while constructed wetlands have increased by around 53.61 km2/year. The wetlands have also become increasingly fragmented, with higher numbers of patches and densities. The analysis of driving factors points to human activities—such as urban construction, cultivated land reclamation, sea aquaculture, and land reclamation—as the primary contributors to these changes. Furthermore, the study addresses the ecological and environmental issues stemming from wetland changes and proposes strategies for wetland conservation. This research aims to enhance the understanding among researchers and policymakers of the dynamics and drivers of coastal wetland changes, as well as the major challenges in their protection, and to serve as a foundation for developing evidence-based conservation and restoration strategies.

Impacts of forest extent, configuration and landscape context on presence of declining breeding Eurasian curlew Numenius arquata and implications for planning new woodland

In response to the combined impacts of the climate and biodiversity crises, as well as for timber security and increased recreational access to green spaces, there is a global drive to increase tree cover. In the UK, an estimated 1.5 million ha of afforestation are required to meet its carbon net-zero emissions targets (Committee on Climate Change, 2018). Despite the potential benefits, careful consideration must be given to the impacts of woodland creation on species adapted to open habitats. To investigate potential risks and mitigation for the IUCN Near Threatened Eurasian curlew Numenius arquata, a national spatially extensive field-scale dataset was used to investigate the relationships between curlew presence during the breeding season and a range of forest and landscape variables at two different spatial scales (0.5 km and 1 km). Variables included forest extent and configuration, and interaction between forests and extent of semi-natural open habitats, moorland management and topography. At both spatial scales, a negative relationship existed between extent of forest and the probability of curlew presence, and at 1 km, between probability of presence and the number of forest patches. However, these negative patterns depended on landscape context and were reduced where there was a greater quantity of semi-natural open habitat, such as moorland or rough grassland, and moorland management present. Overall, the findings emphasise the need to consider the impacts of woodland creation projects on species adapted to open habitats. However, the results suggest that these impacts can be influenced by landscape. These results could help inform decisions regarding the appropriateness of woodland creation in different landscapes and possible mitigation measures that could be applied against the risks created by afforestation.

Forest encroachment in Eastern European forest-steppes at a decadal time scale

Abstract In the Eurasian forest-steppe, with increasing aridity, the balance between naturally co-existing forest and grassland patches is expected to shift towards grassland dominance in the long run, although feedback mechanisms and changes in land-use may alter this process. In this study, we compared old and recent aerial photographs of Hungarian forest-steppes to find out whether and how the forest proportion and the number of forest patches change at the decadal time scale. The percentage area covered by forest significantly increased in all study sites. The observed forest encroachment may be a legacy from earlier land-use: due to ceased or reduced grazing pressure, forests are invading grasslands until the potential forest cover allowed by climate and soil is reached. The number of forest patches significantly increased at one site (Fülöpháza), while it decreased at two sites (Bugac and Orgovány) and showed no significant change at the fourth site (Tázlár). This indicates that forest encroachment can happen at least in two different ways: through the emergence of new forest patches in the grassland, and through the extension and coalescence of already existing forest patches. Though the present work revealed increasing tree cover at a decadal time scale, the dynamic process should be monitored in the future to see how the vegetation reacts to further aridification. This could help devise a conservation strategy, as the woody/non-woody balance has a profound influence on basic ecosystem properties.

RAPID: Robust multi-pAtch masker using channel-wise Pooled varIance with two-stage patch Detection

Recently, adversarial patches have become frequently used in adversarial attacks in real-world settings, evolving into various shapes and numbers. However, existing defense methods often exhibit limitations in addressing specific attacks, datasets, or conditions. This underscores the demand for versatile and robust defenses capable of operating across diverse scenarios. In this paper, we propose the RAPID (Robust multi-pAtch masker using channel-wise Pooled varIance with two-stage patch Detection) framework, a stable solution to restore detection efficacy in the presence of multiple patches. The RAPID framework excels in defending against attacks regardless of patch number or shape, offering a versatile defense adaptable to diverse adversarial scenarios. RAPID employs a two-stage strategy to identify and mask coordinates associated with patch attacks. In the first stage, we propose the ‘channel-wise pooled variance’ to detect candidate patch regions. In the second step, upon detecting these regions, we identify dense areas as patches and mask them accordingly. This framework easily integrates into the preprocessing stage of any object detection model due to its independent structure, requiring no modifications to the model itself. Evaluation indicates that RAPID enhances robustness by up to 60% compared to other defenses. RAPID achieves mAP50 and mAP@50-95 values of 0.696 and 0.479, respectively.

Spatio-Temporal Heterogeneity of the Urban Heat Effect and Its Socio-Ecological Drivers in Yangzhou City, China

Rapid urbanization and land-use changes may affect the intensity of urban heat islands (UHIs). However, research on the eastern Chinese city of Yangzhou is lacking. Using land cover data and the InVest Urban Cooling model, this study evaluated the spatiotemporal heterogeneity of the UHI effect from 1990 to 2020 and its socioecological drivers in Yangzhou City. Landscape pattern indices such as patch area (CA), percentage of landscape (PLAND), number of patches, patch density, and aggregation index were created using Fragstats 4.2 software. Several social indicators, such as gross domestic product (GDP), night-light index, and population density, were considered to explore their correlation with UHI indicators. During the past three decades, rapid urbanization in Yangzhou has intensified the UHI effect, with the cooling capacity (cc park) and heat mitigation index (HMI) decreasing by ~9.6%; however, the mixed air temperature (T air) has increased by 0.14 °C. The main heat island areas are concentrated in southern Yangzhou, including the Hanjiang and Guangling districts, and have expanded over time. T air was positively correlated with GDP, night-light index, and population density. Moreover, for the impervious land use type, cc park and HMI were negatively correlated with CA and PLAND (p < 0.01). This study contributes to a deeper understanding of the dynamics of UHIs and provides valuable insights for policymakers, urban planners, and researchers striving to create sustainable and climate-resilient cities in Yangzhou.

Piecewise Developable Modeling via Implicit Neural Deformation and Feature-Guided Cutting.

We propose a novel and automatic method to model shapes using a small set of discrete developable patches. Central to our approach is using implicit neural shape representation that makes our algorithm independent of tessellation and allows us to obtain the Gaussian curvature of each point analytically. With this powerful representation, we first deform the input shape to be an almost developable shape with clear and sparse salient feature curves. Then, we convert the deformed implicit field to a triangle mesh, which is further cut to disk topology along parts of the sparse feature curves. Finally, we achieve the resulting piecewise developable mesh by alternatingly optimizing discrete developability, enforcing manufacturability constraints, and merging patches. The feasibility and practicability of our method are demonstrated over various shapes. Compared to the state-of-the-art methods, our method achieves a better tradeoff between the number of developable patches and the approximation error.