Evidences for a shared history for spectacled salamanders, haplotypes and climate

Abstract

The so-called glacial refugia, formed during the Pleistocene climatic oscillations, played a major role in shaping the distribution of European species, triggering migrations or isolating populations. Many of these events were recently investigated by genetic data, mainly for the European Last Glacial stage, in the Iberic, Italian and Greek-Balkan peninsulas. The amphibian genus Salamandrina, the most ancient living salamandrid lineage, was widespread in Europe until the climatic oscillations of Miocene probably forced it to shelter in the only suitable territory at that time, the Apennines. Nowadays this genus is endemic of peninsular Italy with two parapatric species, S. perspicillata and S. terdigitata, sharing an area of secondary contact formed after the Last Glacial Maximum. Climate is generally identified as the key factor for the interpretation of genetic data. In this research, we directly measure climate influences on the two Salamandrina known species through Ensemble Modelling techniques and post-modelling GIS analyses, integrating updated genetic data in this process. Our results confirm the hypotheses of southwards (and subsequent northwards) shifts, identify glacial refugia and corridors used for the post-glacial re-colonization. Finally, we map a contact zone deserving more sampling effort to disentangle the introgression and hybridization observed.

Assessing influence in biofuel production and ecosystem services when environmental changes affect plant–pest relationships

“…ENM were built in R environment (R Core Team, 2016), using the ‘biomod2’ modelling package, which permits to obtain the so‐called ‘Ensemble Models’ (EMs), the models obtained by merging single ENMs calculated by the different algorithms available within (Thuiller, Georges, & Engler, 2016). The ‘BIOMOD_EnsembleModeling’ function was used for this purpose, with single models obtained from Generalized Linear Models (set to type = ‘quadratic’ and interaction level = 3), Multiple Adaptive Regression Splines (set to type = ‘quadratic’ and interaction level = 3), Generalized Boosting Models (sometimes named BRTs, with number of trees set to 10,000, interaction depth = 3 and 10‐fold cross‐validation) and Maxent (Maxent.Phillips, maximum interactions = 5,000 and betamultiplier = 2), an approach often used to encompass and take advantage of different modelling techniques (Cerasoli et al., 2019; D'Alessandro, Iannella, Frasca, & Biondi, 2018; Iannella, D’Alessandro, & Biondi, 2018). Five sets of 1,000 pseudoabsences each were generated through the ‘sre’ (Surface Range Envelope, set to 0.05) algorithm, which calculates a linear envelope on the basis of selected predictors and selects pseudoabsences outside the set quantile, for all the reasons reported in Iannella, Cerasoli, D’Alessandro, Console, and Biondi (2018) and all the references within.…”

Phylogeography and species distribution modelling of Cryptocephalus barii (Coleoptera: Chrysomelidae): is this alpine endemic species close to extinction?

“…Nineteen bioclimatic variables were downloaded from the web repository Worldclim.org (Hijmans et al 2005) at 30” spatial resolution and cut to the extent of the European Alps (sensu Biondi et al 2013) through the ‘Extract by Mask’ tool in ArcMap 10.0 (ESRI, 2010). After this process, variables were tested for possible multicollinearity through the ‘Band Collection Statistics’ tool in ArcMap 10.0 (ESRI, 2010), a correlation matrix was calculated and variables’ pairs exceeding the Pearson’s r value of 0.85 were discarded (Elith et al 2006; Iannella et al 2018a; Iannella et al 2018b).…”

Investigating the Current and Future Co-Occurrence of Ambrosia artemisiifolia and Ophraella communa in Europe through Ecological Modelling and Remote Sensing Data Analysis

“…Recently, many papers investigated the response of IAS to climate change, with several studies indicating an increase of potential invasiveness, which goes along with the global change [14,15,16]. In this context, ecological niche models (ENMs) are a tool that can be used to infer the distribution of species in different spaces or times (e.g., [17,18,19,20,21]), even when dealing with IAS. In this case, models can be calibrated on native areas of the IAS and subsequently projected to the environmental conditions of the invaded ranges; this can also be achieved for future climatic conditions [1,22].…”