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Identification and assessment of habitat suitability are essential to the conservation of threatened species such as the Asiatic black bear (Ursus thibetanus) in Pakistan. Regionally, for example, in the Hindu Kush Mountains, there has been growing public concern regarding negative impacts on the bears' natural habitats due to land use and climate change. Many of the efforts to identify and conserve suitable habitats are based on limited data and have been unable to accurately predict habitat preferences. This study aims to fill this gap by developing predictive models for U. thibetanus based on the integration of new occurrences and climate and land cover data. We installed camera traps in 81 different locations across a gradient of elevation. Over the duration of 413 trap nights, we collected 110 different bear detections at 31 camera stations. The bear favored densely forested regions between 1,835 m and 3,348 m above sea level, with a catch rate of 26.6/100 trap nights. Our models demonstrated high levels of prediction accuracy (AUC > 0.97) and predicted that 43% of the total area would make a good habitat for bears. The mean temperature of coldest quarter, normalized difference vegetation index, and annual mean temperature were the main determinants of habitat suitability. The findings of this study, which is the first to map the current distribution and suitable habitat of the Asiatic black bear in the Hindu Kush Mountain Range, contribute new local-scale habitat suitability data to the study of bears in Swat Valley, Pakistan. Our results may be used to provide important conservation information for U. thibetanus that is useful to policymakers for improving future management planning. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Application of species distribution models to estimate and manage the Asiatic black bear (Ursus thibetanus) habitat in the Hindu Kush Mountains, Pakistan" published in the European Journal of Wildlife Research. The correction addresses an error in the Funding section of the original article. The corrected text acknowledges the support of the Khyber Pakhtunkhwa Wildlife Department and Luciano Bosso's support from the National Recovery and Resilience Plan. The publisher, Springer Nature, maintains a neutral stance on jurisdictional claims and institutional affiliations. [Extracted from the article]

Climate change is among the key anthropogenic factors affecting species' distribution, with important consequences for conservation. However, little is known concerning the consequences of distributional changes on community‐level interactions, and responses by generalist species might have many ecological implications in terms of novel interactions with resident species.In this study, we applied Ecological Niche Models and niche analysis to three generalist bat species, Hypsugo savii, Pipistrellus kuhlii, and Pipistrellus pipistrellus, which share similar ecological traits and are sympatric in parts of their ranges. Our aims were to investigate how predicted climate change will affect species' distribution and to analyse the degree of climatic niche overlap between the three species, in both the current and the future scenarios (2050 and 2070; Representative Concentration Pathways 4.5 and 8.5).Temperatures were the most important predictors influencing species' range expansion in future. According to our models, Pipistrellus kuhlii and Hypsugo savii may expand their geographic ranges towards northern latitudes, whereas the geographic range of the less thermophilous Pipistrellus will shift northwards, resulting in it losing the southern portion in Europe. The already considerable degree of climatic niche overlap between the three species will increase further in future.On the basis of our findings, within the new areas potentially colonised by all three species in future, alterations in community‐level balance might occur, bringing about effects that are only partially predictable. In view of this, we highlight the need for further research and improved monitoring of bat communities in areas that are predicted to be particularly vulnerable to climate change. [ABSTRACT FROM AUTHOR]

Because of the high risk of going unnoticed, cryptic species represent a major challenge to biodiversity assessments, and this is particularly true for taxa that include many such species, for example, bats. Long‐eared bats from the genus Plecotus comprise numerous cryptic species occurring in the Mediterranean Region and present complex phylogenetic relationships and often unclear distributions, particularly at the edge of their known ranges and on islands. Here, we combine Species Distribution Models (SDMs), field surveys and molecular analyses to shed light on the presence of a cryptic long‐eared bat species from North Africa, Plecotus gaisleri, on the islands of the Sicily Channel, providing strong evidence that this species also occurs in Europe, at least on the islands of the Western Mediterranean Sea that act as a crossroad between the Old Continent and Africa. Species Distribution Models built using African records of P. gaisleri and projected to the Sicily Channel Islands showed that all these islands are potentially suitable for the species. Molecular identification of Plecotus captured on Pantelleria, and recent data from Malta and Gozo, confirmed the species' presence on two of the islands in question. Besides confirming that P. gaisleri occurs on Pantelleria, haplotype network reconstructions highlighted moderate structuring between insular and continental populations of this species. Our results remark the role of Italy as a bat diversity hotspot in the Mediterranean and also highlight the need to include P. gaisleri in European faunal checklists and conservation directives, confirming the usefulness of combining different approaches to explore the presence of cryptic species outside their known ranges—a fundamental step to informing conservation. [ABSTRACT FROM AUTHOR]

Fire is a major disturbance that affects ecological communities, and when fire events increase in frequency or extent, they may jeopardise biodiversity. Although long-term studies are irreplaceable to understand how biological communities respond to wildfires, a rapid, efficient assessment of the consequences of wildfire is paramount to inform habitat management and restoration. Although Species Distribution Models (SDMs) may be applied to achieve this goal, they have not yet been used in that way. In summer 2017, during an extended drought that affected Italy, a severe wildfire occurred in the Vesuvius National Park (southern Italy). We applied SDMs to assess how much potential habitat was lost by the 12 bat species occurring in the area because of the wildfire, and whether habitat fragmentation increased following the event. Our analysis supported the hypotheses we tested (i.e. that the fire event potentially affected all species through habitat reduction and fragmentation) and that the bat species potentially most affected were those adapted to foraging in cluttered habitat (forest). We show that SDMs are a valuable tool for a first, rapid assessment of the effects of large-scale wildfires, and that they may help identify the areas that need to be monitored for animal activity and phenology, and to assist in saving human and financial resources. We show that 'Species Distribution Models' are a useful tool to assess the loss of potential bat habitat following a severe wildfire. Such models may therefore be employed for a preliminary evaluation of wildlife damage to bat populations and inform management and monitoring. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) provide realistic scenarios to explain the influence of bioclimatic variables on plant pathogen distribution. Diplodia sapinea is most harmful to plantations of both exotic and native pine species in Italy, causing economic consequences expecially to edible seed production. In this study, we developed maximum entropy models for D. sapinea in Italy to reach the following goals: (i) to carry out the pathogen’s first geographical distribution analysis in Italy and determine which eco-geographical variables (EGVs) may influence its outbreaks; (ii) to detect the effect of climate change on the potential occurrence of disease outbreaks by 2050 and 2070. We used Maxent ver. 3.4.0 to develop SDMs. We used six global climate models (BCC-CSM1-1, CCSM4, GISS-E2-R, MIROC5, HadGEM2-ES and MPI-ESM-LR) for two representative concentration pathways (4.5 and 8.5) and two time projections (2050 and 2070) to detect future climate projections of D. sapinea . The most important EGVs influencing outbreaks were land cover, altitude, mean temperature of driest and wettest quarter, precipitation of wettest quarter, precipitation seasonality and minimum temperature of coldest month. The distribution of D. sapinea mostly expanded in central and southern Italy and shifted in altitude upwards on average by ca. 93m a.s.l. Moreover the fungus expanded the range where disease outbreaks may be recorded in response to an increase in the mean temperature of wettest and driest quarter by ca. 1.9 °C and 5.8 °C, respectively in all climate change scenarios. Precipitation of wettest quarter did not differ between current and any of future models. Under different climate change scenarios D. sapinea 's disease outbreaks will be likely to affect larger areas of pine forests in the country, probably causing heavy effects on the dynamics and evolution of these stands or perhaps constraining their survival. [ABSTRACT FROM AUTHOR]

We generated a risk map to forecast the potential effects of the spreading of zebra mussels Dreissena polymorpha across the Italian territory. We assessed the invader's potential impact on rivers, lakes, watersheds and dams at a fine-grained scale and detected those more at risk that should be targeted with appropriate monitoring. We developed a MaxEnt model and employed weighted overlay analyses to detect the species' potential distribution and generate risk maps for Italy. D. polymorpha has a greater probability of occurring at low to medium altitudes in areas characterised by fluviatile deposits of major streams. Northern and central Italy appear more at risk. Some hydroelectric power dams are at high risk, while most dams for irrigation, drinkable water reservoirs and other dam types are at medium to low risk. The lakes and rivers reaches (representing likely expansion pathways) at medium-high or high risk mostly occur in northern and central Italy. We highlight the importance of modelling potential invasions on a country scale to achieve the sufficient resolution needed to develop appropriate monitoring plans and prevent the invader's harmful effects. Further high-resolution risk maps are needed for other regions partly or not yet colonised by the zebra mussel. [ABSTRACT FROM AUTHOR]

Species Distribution Models (SDMs) may provide important information for the follow-up phase of reintroduction operations by identifying the main areas most likely to be colonized by the reintroduced species. We used SDMs to identify the potential distribution of Eurasian beavers ( Castor fiber ) reintroduced to Serbia and Bosnia and Herzegovina in 2004–2006 after being historically driven to extinction by overhunting. Models were also used to carry out a gap analysis to assess the degree of protection granted by the national reserve networks to the potentially expanding population. Distances from hydrographic network, broadleaved forest, main watercourses and farmland were the main factors influencing model performance. We estimated that suitable habitat covers 14.0% (31,000 km 2 ) of the whole study area. In Serbia, in 2004–2013 beavers expanded their range at a mean colonization speed of 70.9 ± 12.8 km/year (mean ± SD). Only 2.89% of and 9.72% of beaver’s suitable habitat lie within the national network of protected areas of Bosnia and Serbia respectively. We detected new potential areas where beavers will likely settle in the near future, advising on where further monitoring should be carried out. We also identified low suitability areas to be targeted with appropriate management to improve their conditions as well as important regions falling outside reserve boundaries to which protection should be granted. [ABSTRACT FROM AUTHOR]

Species distribution models may provide realistic scenarios to explain the influence of bioclimatic variables in the context of emerging plant pathogens. Xylella fastidiosa is a xylem-limited Gram-negative bacterium causing severe diseases in man}' plant species. We developed a maximum entropy model for X. fastidiosa in Italy. Our objectives were to carry out a preliminary analysis of the species' potential geographical distribution and determine which eco-geographical variables ma}' favour its presence in other Italian regions besides Apulia. The analysis of single variable contribution showed that precipitation of the driest (40.3%) and wettest (30.4%) months were the main factors influencing model performance. Altitude, precipitation of warmest quarter, mean temperature of coldest quarter, and land cover provided a total contribution of 19.5%. Based on the model predictions, X. fastidiosa has a high probability (> 0.8) of colonizing areas characterized by: i) low altitude (0-150 m a.s.L); ii) precipitations iii the driest month < 10 mm, in the wettest month ranging between 80-110 mm and during the warmest quarter < 60 mm; iii) mean temperature of coldest quarter ⩾ 8°C; iv) agricultural areas comprising intensive agriculture, complex cultivation patterns, olive groves, annual crops associated with permanent crops, orchards and vineyards; forest (essentially oak woodland); and Mediterranean shrubland. Species distribution models showed a high probability of X fastidiosa occurrence in the regions of Apulia, Calabria, Rasiiicata, Sicily, Sardinia and coastal areas of Campania, Lazio and south Tuscany. Maxent models achieved excellent levels of predictive performance according to area under curve (AUC), true skill statistic (TSS) and minimum difference between training and testing AUC data (AUCdiff). Our study indicated that X. fastidiosa has the potential to overcome the current boundaries of distribution and affect areas of Italy outside Apuiia. [ABSTRACT FROM AUTHOR]

Competition may lead to changes in a species’ environmental niche in areas of sympatry and shifts in the niche of weaker competitors to occupy areas where stronger ones are rarer. Although mainland Mediterranean (Rhinolophus euryale) and Mehely’s (R. mehelyi) horseshoe bats mitigate competition by habitat partitioning, this may not be true on resource-limited systems such as islands. We hypothesize that Sardinian R. euryale (SAR) have a distinct ecological niche suited to persist in the south of Sardinia where R. mehelyi is rarer. Assuming that SAR originated from other Italian populations (PES) – mostly allopatric with R. mehelyi – once on Sardinia the former may have undergone niche displacement driven by R. mehelyi. Alternatively, its niche could have been inherited from a Maghrebian source population. We: a) generated Maxent Species Distribution Models (SDM) for Sardinian populations; b) calibrated a model with PES occurrences and projected it to Sardinia to see whether PES niche would increase R. euryale’s sympatry with R. mehelyi; and c) tested for niche similarity between R. mehelyi and PES, PES and SAR, and R. mehelyi and SAR. Finally we predicted R. euryale’s range in Northern Africa both in the present and during the Last Glacial Maximum (LGM) by calibrating SDMs respectively with SAR and PES occurrences and projecting them to the Maghreb. R. mehelyi and PES showed niche similarity potentially leading to competition. According to PES’ niche, R. euryale would show a larger sympatry with R. mehelyi on Sardinia than according to SAR niche. Such niches have null similarity. The current and LGM Maghrebian ranges of R. euryale were predicted to be wide according to SAR’s niche, negligible according to PES’ niche. SAR’s niche allows R. euryale to persist where R. mehelyi is rarer and competition probably mild. Possible explanations may be competition-driven niche displacement or Maghrebian origin. [ABSTRACT FROM AUTHOR]

Artificial light at night (ALAN) is a pervasive form of pollution largely affecting wildlife, from individual behaviour to community structure and dynamics. As nocturnal mammals, bats are often adversely affected by ALAN, yet some "light-opportunistic" species exploit it by hunting insects swarming near lights. Here we used two potentially competing pipistrelle species as models, Kuhl's (Pipistrellus kuhlii) and common (Pipistrellus pipistrellus) pipistrelles, both known to forage in artificially illuminated areas. We set our study in a mountainous area of central Italy, where only recently did the two species become syntopic. We applied spatial modelling and radiotracking to contrast potential vs. actual environmental preferences by the two pipistrelles. Species distribution models and niche analysis showed a large interspecific niche overlap, including a preference for illuminated areas, presenting a potential competition scenario. Pipistrellus pipistrellus association with ALAN, however, was weakened by adding P. kuhlii as a biotic variable to the model. Radiotracking showed that the two species segregated habitats at a small spatial scale and that P. kuhlii used artificially illuminated sites much more frequently than P. pipistrellus , despite both species potentially being streetlamp foragers. We demonstrate that ALAN influences niche segregation between two potentially competing species, confirming its pervasive effects on species and community dynamics, and provide an example of how light pollution and species' habitat preferences may weave a tapestry of complex ecological interactions. [Display omitted] • Two pipistrelles recently became syntopic in a mountain region. • We used spatial modelling and radiotracking to explore bat habitat preferences. • Models supported an interspecific competition scenario. • In the field, one bat mostly fed near streetlamps, the other often hunted in dark habitat. • Light pollution influences species interactions and niche segregation. [ABSTRACT FROM AUTHOR]