Luciano Bosso

What Story Does Geographic Separation of Insular Bats Tell? A Case Study on Sardinian Rhinolophids

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.

Biosorption of pentachlorophenol by Anthracophyllum discolor in the form of live fungal pellets

Pentachlorophenol (PCP) is an extremely dangerous pollutant for every ecosystem. In this study we have detected how PCP concentration and pH levels can influence PCP adsorption by Anthracophyllum discolor in the form of live fungal pellets. PCP adsorption was evaluated after 24 hours in KCl 0.1 M electrolyte solution with initial PCP concentrations of 5 and 10 mg L (-1) and with pH values between 4 and 9 (at intervals of 0.5). Fourier Transform Infrared Spectroscopy (FTIR) was used to identify functional groups of fungal biomass that can interact with PCP. The amount of PCP that was adsorbed by A. discolor was >80% at pH values between 5 and 5.5, whatever the concentration tested. PCP adsorption significantly decreased in liquid medium of pH > 6.0. FTIR results showed that amides, alkanes, carboxylates, carboxyl and hydroxyl groups may be important to the PCP adsorption for pellets of A. discolor. Live fungal pellets of A. discolor may be used as a natural biosorbent for liquid solutions contaminated by PCP.

Habitat suitability and movement corridors of grey wolf (Canis lupus) in Northern Pakistan

Habitat suitability models are useful to understand species distribution and to guide management and conservation strategies. The grey wolf (Canis lupus) has been extirpated from most of its historic range in Pakistan primarily due to its impact on livestock and livelihoods. We used non-invasive survey data from camera traps and genetic sampling to develop a habitat suitability model for C. lupus in northern Pakistan and to explore the extent of connectivity among populations. We detected suitable habitat of grey wolf using a maximum entropy approach (Maxent ver. 3.4.0) and identified suitable movement corridors using the Circuitscape 4.0 tool. Our model showed high levels of predictive performances, as seen from the values of area under curve (0.971±0.002) and true skill statistics (0.886±0.021). The main predictors for habitat suitability for C. lupus were distances to road, mean temperature of the wettest quarter and distance to river. The model predicted ca. 23,129 km2 of suitable areas for wolf in Pakistan, with much of suitable habitat in remote and inaccessible areas that appeared to be well connected through vulnerable movement corridors. These movement corridors suggest that potentially the wolf range can expand in Pakistan’s Northern Areas. However, managing protected areas with stringent restrictions is challenging in northern Pakistan, in part due to heavy dependence of people on natural resources. The habitat suitability map provided by this study can inform future management strategies by helping authorities to identify key conservation areas.

Nature protection areas of Europe are insufficient to preserve the threatened beetle Rosalia alpina (Coleoptera: Cerambycidae): evidence from species distribution models and conservation gap analysis

1. Natura 2000 network (N2000) and national protected areas (NPAs) are recognised as the most important core ‘units’ for biological conservation in Europe.

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species’ ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1–3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10–12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species.

Ignoring seasonal changes in the ecological niche of non-migratory species may lead to biases in potential distribution models: lessons from bats

Phenology is a key feature in the description of species niches to capture seasonality in resource use and climate requirements. Species distribution models (SDMs) are widespread tools to evaluate a species’ potential distribution and identify its large-scale habitat preferences. Despite its chief importance, data phenology is often neglected in SDM development. Non-migratory bats of temperate regions are good model species to test the effect of data seasonality on SDM outputs because of their different roosting preferences between hibernation and reproduction. We hypothesized that (1) the output of SDMs developed for six non-migratory European bat species will differ between hibernation and reproduction; (2) models built from datasets encompassing both ecological stages will perform better than seasonal models. We employed a dataset of 470 independent occurrences of bat hibernacula and 400 independent records of nursery roosts of selected species and for each species we developed separate winter, summer and mixed (i.e. generated from both winter and summer occurrences) models. Seasonal and mixed potential ranges differed from each other and the direction of this difference was species-specific. Mixed models outperformed seasonal models in representing species niches. Our work highlights the importance of considering data seasonality in the development of SDMs for bats as well as many other organisms, including non-migratory species, otherwise the analysis will lead to significant biases whose consequences for conservation planning and landscape management may be detrimental.

Environmental drivers of parasite load and species richness in introduced parakeets in an urban landscape

Introduced species represent a threat to native wildlife worldwide, due to predation, competition, and disease transmission. Concurrent introduction of parasites may also add a new dimension of competition, i.e. parasite-mediated competition, through spillover and spillback dynamics. Urban areas are major hotspots of introduced species, but little is known about the effects of urban habitat structure on the parasite load and diversity of introduced species. Here, we investigated such environmental effects on the ectoparasite load, richness, and occurrence of spillback in two widespread invasive parakeets, Psittacula krameri and Myiopsitta monachus, in the metropolitan area of Rome, central Italy. We tested 231 parakeets and found that in both species parasite load was positively influenced by host abundance at local scale, while environmental features such as the amount of natural or urban habitats, as well as richness of native birds, influenced parasite occurrence, load, and richness differently in the two host species. Therefore, we highlight the importance of host population density and habitat composition in shaping the role of introduced parakeets in the spread of both native and introduced parasites, recommending the monitoring of urban populations of birds and their parasites to assess and manage the potential occurrence of parasite-mediated competition dynamics as well as potential spread of vector-borne diseases.