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    5753 research outputs found

    Butterfly distribution along altitudinal gradients: temporal changes over a short time period.

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    Mountain ecosystems are particularly sensitive to changes in climate and land cover, but at the same time, they can offer important refuges for species on the opposite of the more altered lowlands. To explore the potential role of mountain ecosystems in butterfly conservation and to assess the vulnerability of the alpine species, we analyzed the short-term changes (2006-2008 vs. 2012-2013) of butterflies\u27 distribution along altitudinal gradients in the NW Italian Alps. We sampled butterfly communities once a month (62 sampling stations, 3 seasonal replicates per year, from June to August) by semi-quantitative sampling techniques. The monitored gradient ranges from the montane to the alpine belt (600-2700 m a.s.l.) within three protected areas: Gran Paradiso National Park (LTER, Sitecode: LTER_EU_IT_109), Orsiera Rocciavr? Natural Park and Veglia Devero Natural Park. We investigated butterflies\u27 temporal changes in accordance with a hierarchical approach to assess potential relationships between species and community level. As a first step, we characterized each species in terms of habitat requirements, elevational range and temperature preferences and we compared plot occupancy and altitudinal range changes between time periods (2006-2008 vs. 2012-2013). Secondly, we focused on community level, analyzing species richness and community composition temporal changes. The species level analysis highlighted a general increase in mean occupancy level and significant changes at both altitudinal boundaries. Looking at the ecological groups, we observed an increase of generalist and highly mobile species at the expense of the specialist and less mobile ones. For the community level, we noticed a significant increase in species richness, in the community temperature index and a tendency towards homogenization within communities. Besides the short time period considered, butterflies species distribution and communities changed considerably. In light of these results, it is fundamental to continue monitoring activities to understand if we are facing transient changes or first signals of an imminent trend

    Earth observations for sustainable development goals monitoring based on essential variables and driver-pressure-state-impact-response indicators

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    In recent years, researchers of different communities have increased their efforts in formalizing a set of measurements regularly collected for analysing changes in Drivers, States, Impacts and Responses of a given discipline. In some cases, different actors have converged in a minimum set of Essential Variables (EVs), such as for Climate, Biodiversity or Oceans. The definition of such EVs is an ongoing evolution and in extension (e.g. EVs for water) although some communities have not even started (e.g. agriculture and energy). This paper characterizes the Earth Observation (EO) networks and creates a graph representation of their relations. Secondly, this graph is enriched with the EVs produced by each network creating a knowledge base. Finally, an effort has been done to identify links between EVs and Sustainable Development Goals (SDG) indicators in a way that they indirectly connect the EO. An analysis to detect gaps in EO variables due to a lack of observational networks is performed. Several suggestions for improving SDG indicators framework by considering EVs are exposed, as well as proposing new necessary EVs and suggesting new EO based indicators. The complete graph is available in the ENEON website (

    Domain Decomposition strategies for modelling survivability conditions of WECs

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    The increasing TRL of WECs requires that their survivability both in Ultimate Limit States (ULS) and Accidental Limit States (ALS) should be assessed. However, the definition of these conditions is not easy because they depend largely on the deployment site and on the kind of WEC. In fact, because of the use of resonance conditions for the amplification of the waves, the largest response in terms of motions and/or loads is not always triggered by the largest waves [1]. Generally, nonlinear free-surface effects and important flow-separation phenomena take place. To guarantee accuracy and preserve computational efficiency, the use of multi-methods numerical simulations can become very useful. We have already experience with Time and Spatial Domain Decompositions (DD): a potential-flow and a full Navier-Stokes solvers were coupled to investigate violent wave-body interaction and occurrence of green-water events [2] and a Harmonic Polynomial Cell Method (HPC) and OpenFOAM were coupled to model the behavior of a damaged ship section [3]. We propose to apply these kind of DD strategies to WECs and to study the local non linear and viscous effect by a Navier-Stokes solver around the WEC and couple it with a method that can accurately and efficiently describe the flow field afar. For the latter, we propose also the use of a Depth-Semi-Averaged model [4] to accurately describe the WEC motion in the deployment site

    Modeling plastics exposure for the marine biota: Risk maps for Fin Whales in the Pelagos Sanctuary (North-Western Mediterranean)

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    Several anthropogenic stressors threaten the Mediterranean basin, which is currently regarded as one of the most impacted marine ecoregions globally. Among those stressors, marine plastic litter is causing increasing concern about its environmental and biological consequences, the latter being largely unknown. To improve the understanding of these aspects, here we provide a mapped indicator of the risk of plastic ingestion by the fin whale Balaenoptera physalus, an endangered cetacean whose feeding grounds are located within the Pelagos Sanctuary for Mediterranean Marine Mammals, in the north-western Mediterranean Sea. We analyse a decade (2000-2010) of advection patterns of marine plastic litter, modeled as Lagrangian particles and released from the three major sources: untreated waste along coasts, plastic discharged from rivers and along maritime shipping routes. Risk of exposure to microplastics via food ingestion for fin whales is then evaluated by interlacing the plastic litter distribution obtained via particle tracking with maps of habitat suitability based on bathymetry and satellite-derived estimates of chlorophyll-a. Our modeling results locate the highest risk values in the Central Ligurian Sea, and show that all the three main sources of plastic litter taken into account clearly contribute to impacting cetaceans in the Sanctuary, yet with spatial and interannual variability of patterns. The procedure formalized with our approach can be extended to assess the risk caused by ingestion of plastics by other taxa and/or in other MPAs, as we suggest by providing an application on the whole ecosystem of Pelagos, thus informing targeted actions to tackle the complex issue of marine litter

    Identifying vegetation in arid regions using object-based image analysis with RGB-only aerial imagery.

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    Vegetation state is usually assessed by calculating vegetation indices (VIs) derived from remote sensing systems where the near infrared (NIR) band is used to enhance the vegetation signal. However VIs are pixel-based and require both visible and NIR bands. Yet, most archived photographs were obtained with cameras that record only the three visible bands. Attempts to construct VIs with the visible bands alone have shown only limited success, especially in drylands. The current study identifies vegetation patches in the hyperarid Israeli desert using only the visible bands from aerial photographs by adapting an alternative geospatial object-based image analysis (GEOBIA) routine, together with recent improvements in preprocessing. The preprocessing step selects a balanced threshold value for image segmentation using unsupervised parameter optimization. Then the images undergo two processes: segmentation and classification. After tallying modeled vegetation patches that overlap true tree locations, both true positive and false positive rates are obtained from the classification and receiver operating characteristic (ROC) curves are plotted. The results show successful identification of vegetation patches in multiple zones from each study area, with area under the ROC curve values between 0.72 and 0.8

    Effects of aspect and altitude on carbon cycling processes in a temperate mountain forest catchment

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    Context Varying altitudes and aspects within small distances are typically found in mountainous areas. Such a complex topography complicates the accurate quantification of forest C dynamics at larger scales. Objectives We determined the effects of altitude and aspect on forest C cycling in a typical, mountainous catchment in the Northern Limestone Alps. Methods Forest C pools and fluxes were measured along two altitudinal gradients (650-900 m a.s.l.) at south-west (SW) and north-east (NE) facing slopes. Net ecosystem production (NEP) was estimated using a biometric approach combining field measurements of aboveground biomass and soil CO2 efflux (SR) with allometric functions, root:shoot ratios and empirical SR modeling. Results NEP was higher at the SW facing slope (6.60 ? 3.01 t C ha-1 year-1), when compared to the NE facing slope (4.36 ? 2.61 t C ha-1 year-1). SR was higher at the SW facing slope too, balancing out any difference in NEP between aspects (NE: 1.30 ? 3.23 t C ha-1 year-1, SW: 1.65 ? 3.34 t C ha-1 year-1). Soil organic C stocks significantly decreased with altitude. Forest NPP and NEP did not show clear altitudinal trends within the catchment. Conclusions Under current climate conditions, altitude and aspect adversely affect C sequestering and releasing processes, resulting in a relatively uniform forest NEP in the catchment. Hence, including detailed climatic and soil conditions, which are driven by altitude and aspect, will unlikely improve forest NEP estimates at the scale of the studied catchment. In a future climate, however, shifts in temperature and precipitation may disproportionally affect forest C cycling at the southward slopes through increased water limitation

    A Bayesian approach to ecosystem service trade-off analysis utilizing expert knowledge

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    The concept of ecosystem services is gaining attention in the context of sustainable resource management. However, it is inherently difficult to account for tangible and intangible services in a combined model. The aim of this study is to extend the definition of ecosystem service trade-offs by using Bayesian Networks to capture the relationship between tangible and intangible ecosystem services. Tested is the potential of creating such a network based on existing literature and enhancement via expert elicitation. This study discusses the significance of expert elicitation to enhance the value of a Bayesian Network in data-restricted case studies, underlines the importance of inclusion of experts\u27 certainty, and demonstrates how multiple sources of knowledge can be combined into one model accounting for both tangible and intangible ecosystem services. Bayesian Networks appear to be a promising tool in this context, nevertheless, this approach is still in need of further refinement in structure and applicable guidelines for expert involvement and elicitation for a more unified methodology

    Optimal dates for assessing long-term changes in tree cover in the semi-arid biomes of South Africa using MODIS NDVI time series (2001-2018)

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    The varying proportions of tree and herbaceous cover in the grassland and savanna biomes of Southern Africa determine their capacity to provide ecosystem services. The asynchronous phenologies e.g. annual NDVI profiles of grasses and trees in these semi-arid landscapes provide an opportunity to estimate percentage tree-cover by determining the period of maximum contrast between grasses and trees. First, a 16-day NDVI time series was generated from MODIS NDVI data, i.e. MOD13A2 16-day NDVI composite data. Secondly, percentage tree-cover data for 100 sample polygons (4?4) pixels for areas that have not undergone change in tree cover between 2001 and 2018 were derived using high resolution Google Earth imagery. Next, a time series consisting of the coefficients of determination (R2) for the NDVI/tree-cover linear regression were computed for the 100 polygons. Lastly, a threshold R2>0.5 was used to determine the optimal period of the year for mapping tree-cover. It emerged that the narrow period from Julian day 161?177 (June 10?26) was the most consistent period with R2>0.5 in the region. 18 tree-cover maps (2001?2018) were generated using linear regression model coefficients derived from Julian day 161 for each year. Kendall correlation coefficient (tau) was used to determine areas of significant (p < 0.05 and p < 0.01) increasing or decreasing trend in tree-cover. Areas (polygons) that showed increasing tree-cover appeared to be more widespread in the trend map as compared to areas of decreasing tree-cover. An accuracy assessment of the map of increasing tree-cover was conducted using Google Earth high resolution images. Out of 330 and 200 mapped polygons verified using p < 0.05 and 0.01 thresholds, respectively, 180 (54% accuracy) and 132 (65% accuracy) showed evidence of tree recruitment. Farm abandonment appeared to have been the most important factor contributing to increasing tree-cover in the region

    Data on alpine grassland diversity in Gran Paradiso National Park, Italy

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    The diversity of alpine grassland species and their functional traits constitute alpine ecosystem functioning and services that support human-wellbeing. However, alpine grassland diversity is threatened by land use and climate change. Field surveys and monitoring are necessary to understand and preserve such endangered ecosystems. Here we describe data on abundances (percentage cover) of 247 alpine plant species (including mosses and lichens) inside nine 20 m by 20 m plots that were subdivided into 2 m by 2 m subplots. The nine plots are located in Gran Paradiso National Park, Italy. They cover three distinct alpine vegetation subtypes (\u27pure\u27 natural grassland, sparsely vegetated \u27rocky\u27 grassland, and wetland) in each of three valleys (Bardoney, Colle de Nivolet and Levionaz) between 2200 and 2700 m a.s.l., i.e. above the treeline. The vegetation survey was conducted in 2015 at the peak of vegetation development during August. The dataset is provided as supplementary material and associated with the research article "Optimizing sampling effort and information content of biodiversity surveys: a case study of alpine grassland" [1]. See [1] for data interpretation

    Differences in the spatial structure of two Pinus cembra L. populations in the Carpathian Mountains

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    Pinus cembra L. is a key species of high elevation forest ecosystems in Europe. However, in most mountain ranges, its importance has declined considerably. Remnant populations are often isolated and their dynamics and functioning are not well understood. Here, we apply novel approaches in pattern analysis to two P. cembra populations in the Carpathian Mountains in order to identify commonalities and divergences in their spatial structure and dynamics. Four study sites (1.2 ha each) were investigated within the treeline ecotone in two protected areas that dier in terms of protection status. Based on height and diameter, the individuals were classified into three size-classes: sapling, intermediate and adult trees. Spatial distribution and interactions between tree sizes were analyzed using point pattern analysis. The overall structure of all trees was aggregated at a small distance and regular at a greater distance in the population from the Natura 2000 site (p = 0.002), while in the National Park population it was a random pattern. However, the general patterns do not apply to tree size classes and the relationship among them. In the Natura 2000 site, there was no correlation, all the trees were mixed, regardless of their size. In the National Park, the sapling and intermediate were strongly clustered (p = 0.001), but the adult trees were spatially separated from all juveniles, forming patches at a lower elevation. In both areas, spatial patterns indicate the dynamics of the P. cembra population. Whereas in the National Park population, there is evidence of an upward shift, which cannot be confirmed in Natura 2000, where size classes are completely mixed and the dynamic does not translate into an expansion of the population area. The spatial dierences between the two populations indicate that conservation strategies need to be developed more individually to support the regeneration of these isolated populations


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