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An estimation of where forest fragmentation is likely to occur is critically important for improving the integrity of the forest landscape. We prepare a forest fragmentation susceptibility map for the first time by developing an integrated model and identify its causative factors in the forest landscape. Our proposed model is based upon the synergistic use of the earth observation data, forest fragmentation approach, patch forests, causative factors, and the weight-of-evidence (WOE) method in a Geographical Information System (GIS) platform. We evaluate the applicability of the proposed model in the Indian Himalayan region, a region of rich biodiversity and environmental significance in the Indian subcontinent. To obtain a forest fragmentation susceptibility map, we used patch forests as past evidence of completely degraded forests. Subsequently, we used these patch forests in the WOE method to assign the standardized weight value to each class of causative factors tested by the Variance Inflation Factor (VIF) method. Finally, we prepare a forest fragmentation susceptibility map and classify it into five levels: very low, low, medium, high, and very high and test its validity using 30% randomly selected patch forests. Our study reveals that around 40% of the study area is highly susceptible to forest fragmentation. This study identifies that forest fragmentation is more likely to occur if proximity to built-up areas, roads, agricultural lands, and streams is low, whereas it is less likely to occur in higher altitude zones (more than 2000 m a.s.l.). Additionally, forest fragmentation will likely occur in areas mainly facing south, east, southwest, and southeast directions and on very gentle and gentle slopes (less than 25 degrees). This study identifies Himalayan moist temperate and pine forests as being likely to be most affected by forest fragmentation in the future. The results suggest that the study area would experience more forest fragmentation in the future, meaning loss of forest landscape integrity and rich biodiversity in the Indian Himalayan region. Our integrated model achieved a prediction accuracy of 88.7%, indicating good accuracy of the model. This study will be helpful to minimize forest fragmentation and improve the integrity of the forest landscape by implementing forest restoration and reforestation schemes.

The onset of plant cultivation is one of the most important cultural transitions in human history 1 – 4 . Southwestern Amazonia has previously been proposed as an early centre of plant domestication, on the basis of molecular markers that show genetic similarities between domesticated plants and wild relatives 4 – 6 . However, the nature of the early human occupation of southwestern Amazonia, and the history of plant cultivation in this region, are poorly understood. Here we document the cultivation of squash ( Cucurbita sp.) at about 10,250 calibrated years before present (cal. yr bp ), manioc ( Manihot sp.) at about 10,350 cal. yr bp and maize ( Zea mays ) at about 6,850 cal. yr bp , in the Llanos de Moxos (Bolivia). We show that, starting at around 10,850 cal. yr bp , inhabitants of this region began to create a landscape that ultimately comprised approximately 4,700 artificial forest islands within a treeless, seasonally flooded savannah. Our results confirm that the Llanos de Moxos is a hotspot for early plant cultivation and demonstrate that—ever since their arrival in Amazonia—humans have markedly altered the landscape, with lasting repercussions for habitat heterogeneity and species conservation. Archaeological evidence that anthropic landscape changes and crop cultivation in southwestern Amazonia began about 10,000–11,000 years ago confirms that the region is a centre of early plant domestication.

Context The study of landscape over different years through the analysis of different sources (cadasters, aerophotos, orthophotos, satellite images) is commonly used in landscape planning and in researches focusing on landscape and land use changes and transformations. Most of these studies, despite the scale and the period analyzed, tend to apply different methodologies, making it difficult to compare results and trends among different landscapes. The aim of the paper is to present the details of the Historical and Environmental Evaluation (VASA) methodology, highlighting the possible applications for landscape changes assessment, by presenting a specific study case as well as its use across different situations. VASA has been developed within the Department of Agricultural, Food, Environmental and Forestry Science and Technology (DAGRI) of the University of Florence to create a standard methodology for the monitoring of landscape transformations, and it was initially applied for the Regional Government of Tuscany (Italy). In 2012, VASA has been chosen by the Italian Ministry of Agriculture, Food Sovereignty and Forests for the assessment of the rural landscapes to be included in the official list of the National Register of Rural Landscapes of Historical Interest often representing the first step for rural landscapes to be proposed for the recognition by international programmes such as the UNESCO WHL (cultural landscapes) and the FAO GIAHS (Globally Important Agricultural Heritage Systems). Results This methodology is based on the photointerpretation of the same area in different years through the Geographic Information Systems (GIS) software, for creating detailed maps and databases of land uses. In addition, various metrics are calculated for evaluating the structure of the landscape mosaic and its transformations. Conclusions Compared to other multitemporal analyses, the VASA methodology is capable of providing reliable, measurable, and comparable data regarding land use characteristics, land use changes, landscape mosaic structure, main vulnerabilities, landscape trends, linear features presence and changes. This approach can be applied to different geographical contexts and for different aims, allowing to compare the results more accurately in different environmental and cultural situations, or for establishing landscape monitoring systems.

Forest fires have caused considerable losses to ecologies, societies, and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, we proposed a spatial prediction model for forest fire susceptibility using a CNN. Past forest fire locations in Yunnan Province, China, from 2002 to 2010, and a set of 14 forest fire influencing factors were mapped using a geographic information system. Oversampling was applied to eliminate the class imbalance, and proportional stratified sampling was used to construct the training/validation sample libraries. A CNN architecture that is suitable for the prediction of forest fire susceptibility was designed and hyperparameters were optimized to improve the prediction accuracy. Then, the test dataset was fed into the trained model to construct the spatial prediction map of forest fire susceptibility in Yunnan Province. Finally, the prediction performance of the proposed model was assessed using several statistical measures—Wilcoxon signed-rank test, receiver operating characteristic curve, and area under the curve (AUC). The results confirmed the higher accuracy of the proposed CNN model (AUC 0.86) than those of the random forests, support vector machine, multilayer perceptron neural network, and kernel logistic regression benchmark classifiers. The CNN has stronger fitting and classification abilities and can make full use of neighborhood information, which is a promising alternative for the spatial prediction of forest fire susceptibility. This research extends the application of CNN to the prediction of forest fire susceptibility.

Recent growth of investments in wind energy and power industries has increased concerns about the associated adverse impacts on wildlife. In particular, flying vertebrates are especially at risk, both directly, through an extra mortality rate due to collision with turbines and electrocution, and indirectly through habitat loss or fragmentation. In this study, we propose a modelling approach that combines species distribution models and data managed in geographic information systems to predict and quantify the effects of wind turbines and power lines on the breeding habitat of a soaring migratory bird, the black stork Ciconia nigra, in Italy. The species is recolonizing the country, where it had been driven to extinction in the Middle Age by human persecution. Today, infrastructures such as those considered in our study might in fact hamper this recolonization. Our results show a high probability of presence of the species in several areas in Italy. The most important variables in influencing habitat suitability for C. nigra are the mean temperature of May followed by the distance from urban areas, inland wetlands and hydrographic network. Exposure to wind turbine collision and electrocution resulted to be potentially high. In particular, in Northern Italy the main potential risk of mortality for C. nigra is posed by power lines, whereas in southern regions the species might be mostly threatened by wind turbines. Our approach makes it possible to detect suitable areas that, although not yet colonized by the species, would imply a high mortality risk should the species colonize them in the future. The tool we provide may therefore prove useful to conservationists and landscape planners in order to mitigate the impact of human infrastructures on this species and encourage a more sustainable planning.

The identity of the dominant root-associated microbial symbionts in a forest determines the ability of trees to access limiting nutrients from atmospheric or soil pools 1 , 2 , sequester carbon 3 , 4 and withstand the effects of climate change 5 , 6 . Characterizing the global distribution of these symbioses and identifying the factors that control this distribution are thus integral to understanding the present and future functioning of forest ecosystems. Here we generate a spatially explicit global map of the symbiotic status of forests, using a database of over 1.1 million forest inventory plots that collectively contain over 28,000 tree species. Our analyses indicate that climate variables—in particular, climatically controlled variation in the rate of decomposition—are the primary drivers of the global distribution of major symbioses. We estimate that ectomycorrhizal trees, which represent only 2% of all plant species 7 , constitute approximately 60% of tree stems on Earth. Ectomycorrhizal symbiosis dominates forests in which seasonally cold and dry climates inhibit decomposition, and is the predominant form of symbiosis at high latitudes and elevation. By contrast, arbuscular mycorrhizal trees dominate in aseasonal, warm tropical forests, and occur with ectomycorrhizal trees in temperate biomes in which seasonally warm-and-wet climates enhance decomposition. Continental transitions between forests dominated by ectomycorrhizal or arbuscular mycorrhizal trees occur relatively abruptly along climate-driven decomposition gradients; these transitions are probably caused by positive feedback effects between plants and microorganisms. Symbiotic nitrogen fixers—which are insensitive to climatic controls on decomposition (compared with mycorrhizal fungi)—are most abundant in arid biomes with alkaline soils and high maximum temperatures. The climatically driven global symbiosis gradient that we document provides a spatially explicit quantitative understanding of microbial symbioses at the global scale, and demonstrates the critical role of microbial mutualisms in shaping the distribution of plant species. A spatially explicit global map of tree symbioses with nitrogen-fixing bacteria and mycorrhizal fungi reveals that climate variables are the primary drivers of the distribution of different types of symbiosis.

Detailed analysis of the evolution characteristics of landscape ecological risk is crucial for coastal sustainable management and for understanding the potential environmental impacts of a man-made landform landscapes (MMLL). As a typical open coastal wetland, large-scale human activities (e.g., tidal reclamation, fishery activities, wind farm construction, and port construction) have substantially affected the evolution of the coastal ecological environment. Previous landscape ecological risk assessment studies have documented the effectiveness of assessing the quality of ecological environment processes. However, these studies have either focused on the noncoastal zone, or they have not considered the evolution of the spatial characteristics and ecological risk evolution of the landscape at an optimal scale. Here, we present a landscape ecological risk pattern (LERP) evolution model, based on two successive steps: first, we constructed an optimal scale method with an appropriate extent and grain using multi–temporal Landsat TM/OLI images acquired in the years 2000, 2004, 2008, 2013 and 2017, and then we calculated landscape ecological risk indices. Based on this model, the entire process of the spatiotemporal evolution of ecological risk patterns of the open coastal wetlands in Jiangsu, China, was determined. The principal findings are as follows: (1) The main landscape types in the study area are tidal flats and farmland, and the main features of the landscape evolution are a significant increase in aquafarming and a substantial decrease in the tidal flat area, while the landscape heterogeneity increased; (2) In the past 20 years, the areas of low and relatively low ecological risk in the study region were greatly reduced, while the areas of medium, relatively high, and high ecological risk greatly increased; the areas of high-grade ecological risk areas are mainly around Dongtai and Dafeng; (3) The area of ecological risk from low-grade to high-grade occupied 71.75% of the study area during 2000–2017. During the previous periods (2000–2004 and 2004–2008), the areas of low-grade ecological risk were transformed to areas of middle-grade ecological risk area, while during the later periods (2008–2013 and 2013–2017) there was a substantial increase in the proportion of areas of high-grade ecological risk. Our results complement the official database of coastal landscape planning, and provide important information for assessing the potential effects of MMLL processes on coastal environments.

Landscape connectivity is crucial for many ecological processes, including dispersal, gene flow, demographic rescue, and movement in response to climate change. As a result, governmental and non-governmental organizations are focusing efforts to map and conserve areas that facilitate movement to maintain population connectivity and promote climate adaptation. In contrast, little focus has been placed on identifying barriers-landscape features which impede movement between ecologically important areas-where restoration could most improve connectivity. Yet knowing where barriers most strongly reduce connectivity can complement traditional analyses aimed at mapping best movement routes. We introduce a novel method to detect important barriers and provide example applications. Our method uses GIS neighborhood analyses in conjunction with effective distance analyses to detect barriers that, if removed, would significantly improve connectivity. Applicable in least-cost, circuit-theoretic, and simulation modeling frameworks, the method detects both complete (impermeable) barriers and those that impede but do not completely block movement. Barrier mapping complements corridor mapping by broadening the range of connectivity conservation alternatives available to practitioners. The method can help practitioners move beyond maintaining currently important areas to restoring and enhancing connectivity through active barrier removal. It can inform decisions on trade-offs between restoration and protection; for example, purchasing an intact corridor may be substantially more costly than restoring a barrier that blocks an alternative corridor. And it extends the concept of centrality to barriers, highlighting areas that most diminish connectivity across broad networks. Identifying which modeled barriers have the greatest impact can also help prioritize error checking of land cover data and collection of field data to improve connectivity maps. Barrier detection provides a different way to view the landscape, broadening thinking about connectivity and fragmentation while increasing conservation options.

Aim The ongoing global change makes landscape planning and management of ecological corridors crucial to preserve biodiversity. We propose a workflow optimizing the use of different data sources to convert ecological niche models (ENMs) into landscape‐focused species distribution models (SDMs), using these latter to compute ecological corridors. We infer corridors connecting present occurrence localities to future climatic refugia as well as to localities where extinct populations occurred. Also, a continuous connectivity change index is proposed to assess current–future differences. Finally, we discuss possible applications of our workflow to conservation, assessing the capability of established protected areas to preserve ecological corridors. Location Europe. Methods As case study to illustrate our framework, we use a database comprising occurrence localities of Vipera ursinii, one of the most endangered European reptiles. We obtain weighted SDMs for each of the four V. ursinii subspecies by coupling climate‐based ENMs with standardized occurrence frequencies along land use and altitude gradients through weighted averaging in GIS. We calculate current and future landscape connectivity for each subspecies based on the corresponding weighted SDM. We compare predictive performance of “traditional” ENMs, including climate, land use and topography as predictors and weighted SDMs. Results Weighted SDMs outperform ENMs, according to Boyce index. SDMs are used to infer connectivity, predicted to decrease in all future scenarios for V. ursinii, and assess where connections may favour movements of individuals to, for example, future suitable areas. Generally, protected areas are predicted to cover low‐connectivity territories. Main conclusions The proposed “couple‐and‐weigh” approach could represent a helpful tool to investigate biogeography, conservation and landscape planning topics, as it permits to capitalize on occurrence records and accessible environmental predictors by narrowing the target species’ potential distribution, estimated within “traditional” ENMs, to the realized one through post‐modelling GIS analyses, which in turn improves estimation of friction maps used to infer connectivity.