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Invasive species can encounter environments different from their source populations, which may trigger rapid adaptive changes after introduction (niche shift hypothesis). To test this hypothesis, we investigated whether postintroduction evolution is correlated with contrasting environmental conditions between the European invasive and source ranges in the Asian tiger mosquito Aedes albopictus. The comparison of environmental niches occupied in European and source population ranges revealed more than 96% overlap between invasive and source niches, supporting niche conservatism. However, we found evidence for postintroduction genetic evolution by reanalyzing a published ddRADseq genomic dataset from 90 European invasive populations using genotype–environment association (GEA) methods and generalized dissimilarity modeling (GDM). Three loci, among which a putative heat-shock protein, exhibited significant allelic turnover along the gradient of winter precipitation that could be associated with ongoing range expansion. Wing morphometric traits weakly correlated with environmental gradients within Europe, but wing size differed between invasive and source populations located in different climatic areas. Niche similarities between source and invasive ranges might have facilitated the establishment of populations. Nonetheless, we found evidence for environmental-induced adaptive changes after introduction. The ability to rapidly evolve observed in invasive populations (genetic shift) together with a large proportion of unfilled potential suitable areas (80%) pave the way to further spread of Ae. albopictus in Europe.

In modeling species distributions and population dynamics, spatially‐interpolated climatic data are often used as proxies for real, on‐the‐ground measurements. For shallow freshwater systems, this practice may be problematic as interpolations used for surface waters are generated from terrestrial sensor networks measuring air temperatures. Using these may therefore bias statistical estimates of species' environmental tolerances or population projections – particularly among pleustonic and epilimnetic organisms. Using a global database of millions of daily satellite‐derived lake surface water temperatures (LSWT), I trained machine learning models to correct for the correspondence between air and LSWT as a function of atmospheric and topographic predictors, resulting in the creation of monthly high‐resolution global maps of air‐LSWT offsets, corresponding uncertainty measures and derived LSWT‐based bioclimatic layers for use by the scientific community. I then compared the performance of these LSWT layers and air temperature‐based layers in population dynamic and ecological niche models (ENM). While generally high, the correspondence between air temperature and LSWT was quite variable and often nonlinear depending on the spatial context. These LSWT predictions were better able to capture the modeled population dynamics and geographic distributions of two common aquatic plant species. Further, ENM models trained with LSWT predictors more accurately captured lab‐measured thermal response curves. I conclude that these predicted LSWT temperatures perform better than raw air temperatures when used for population projections and environmental niche modeling, and should be used by practitioners to derive more biologically‐meaningful results. These global LSWT predictions and corresponding error estimates and bioclimatic layers have been made freely available to all researchers in a permanent archive.

Future climate change may affect in the environmental suitability, which refers to the set of conditions necessary for a species to establish itself in a given ecosystem. The species Adenium obesum has a native distribution in desert regions, suggesting its adaptation to hot and arid climates. Thus, this study aimed to model the species environmental suitability for hot and dry climates, considering the premise of global temperature increase and the consequent possibility of expanding its distribution area. Occurrence data for Adenium obesum were obtained from the Global Biodiversity Information Facility (GBIF), while climate variables were sourced from the WorldClim database (version 2.1), with a spatial resolution of 5 arc-minutes. The modeling process employed the following algorithms: BRT, GAM, GLMPoly, MARS, MaxEnt, RF, and RPart. The models were evaluated using statistical metrics, considering those with AUC > 0.9 as potentially useful. For the True Skill Statistic (TSS) method, values above 0.7 were classified as good. To quantify, in km², the difference between the species' current potential distribution and the environmentally suitable areas projected for the future, the results were binarized, allowing the calculation of expansion, contraction, stability, and absence percentages. The future projections indicate an expansion of Adenium obesum , confirming the hypothesis that, due to its ecological and environmental characteristics, the species may achieve greater success in colonizing new areas under future climatic conditions characterized by higher temperatures and lower precipitation.

As climate change progresses rapidly, biodiversity declines, and ecosystems shift, it is becoming increasingly difficult to document dynamic populations, track fluctuations, and predict responses to climate change. Concurrently, publicly available databases and tools are improving scientific accessibility, increasing collaboration, and generating more data than ever before. One of the most successful projects is iNaturalist, an AI-driven social network doubling as a public database designed to allow citizen scientists to report personal biodiversity reports with accuracy. iNaturalist is especially useful for the research of rare, dangerous, and charismatic organisms, but requires better integration into the marine system. Despite their abundance and ecological relevance, there are few long-term, high-sample datasets for jellyfish, which makes management difficult. To provide some high-sample datasets and demonstrate the utility of publicly collected data, we synthesized two global datasets for ten genera of jellyfishes in the order Rhizostomeae containing 8412 curated datapoints from both iNaturalist (n = 7807) and the published literature (n = 605). We then used these reports in conjunction with publicly available environmental data to predict global niche partitioning and distributions. Initial niche models inferred that only two of ten genera have distinct niche spaces; however, the application of machine learning-based random forest models suggests genus-specific variation in the relevance of abiotic environmental variables used to predict jellyfish occurrence. Our approach to incorporating reports from the literature with iNaturalist data helped evaluate the quality of the models and, more importantly, the quality of the underlying data. We find that free, accessible online data is valuable, yet subject to biases through limited taxonomic, geographic, and environmental resolution. To improve data resolution, and in turn its informative power, we recommend increasing global participation through collaboration with experts, public figures, and hobbyists in underrepresented regions capable of implementing regionally coordinated projects.

Background & objectives: Cutaneous leishmaniasis (CL) is widespread in the tropical and subtropical regions of the world including, Tukey. Environmental determinants for the CL endemic areas in Turkey are relatively poorly understood. The aim of the present study was to develop a model based on ecological niche modeling (ENM) to predict the distribution of CL in endemic areas of Adana Province in Turkey. Methods: The environmental data from different sources were extracted and information on 1831 native CL cases, obtained from the Provincial Health Directorate of Adana were recorded. The location information obtained from the Ministry of Health database were used for modeling the current probability of CL occurrence and predicting its future distribution using ENM analyses. ArcGIS and MaxEnt models were used to explore the ecological conditions of the disease. Results: According to the MaxEnt model, the area under the curve (AUC) values for the current and projected future of CL were 0.868 and 0.867, respectively. The environmental variables, Bio1 (Annual mean temperature), Bio4 (Temperature seasonality) and DEM (Digital elevation model) were found to be associated with the presence of human cases of Leishmania infantum for both the time periods in the study area. Interpretation & conclusion: The AUC curves and risk map generated by the ENM model indicate that the future status of CL is likely to be stable in the northern part of Adana, but the southern part will be affected by climate changes (change of temperature) with a large number of patient-reporting. The results of the study could be used as a reference for CL and vector control studies. The ENM could be useful for researchers in vector control studies and better understanding of the epidemiology of vector-borne diseases in a specific area.

Climate-change scenarios project significant temperature changes for most of South America. We studied the potential impacts of predicted climate-driven change on the distribution and conservation of 26 broad-range birds from South America Cerrado biome (a savanna that also encompass tracts of grasslands and forests). We used 12 temperature or precipitation-related bioclimatic variables, nine niche modeling techniques, three general circulation models, and two climate scenarios (for 2030, 2065, 2099) for each species to model distribution ranges. To reach a consensus scenario, we used an ensemble-forecasting approach to obtain an average distribution for each species at each time interval. We estimated the range extent and shift of each species. Changes in range size varied across species and according to habitat dependency; future predicted range extent was negatively correlated with current predicted range extent in all scenarios. Evolution of range size under full or null dispersal scenarios varied among species from a 5% increase to an 80% decrease. The mean expected range shifts under null and full-dispersal scenarios were 175 and 200 km, respectively (range 15-399 km), and the shift was usually toward southeastern Brazil. We predicted larger range contractions and longer range shifts for forest- and grassland-dependent species than for savanna-dependent birds. A negative correlation between current range extent and predicted range loss revealed that geographically restricted species may face stronger threat and become even rarer. The predicted southeasterly direction of range changes is cause for concern because ranges are predicted to shift to the most developed and populated region of Brazil. Also, southeastern Brazil is the least likely region to contain significant dispersal corridors, to allow expansion of Cerrado vegetation types, or to accommodate creation of new reserves.

Rapeseed is one of the most important agricultural crops and is used in many ways. Due to the advancing climate crisis, the yield potential of rapeseed is increasingly impaired. In addition to changing environmental conditions, the expansion of cultivated areas also favours the infestation of rapeseed with various pests and pathogens. This results in the need for continuous further development of rapeseed varieties. To this end, the potential of the rapeseed gene pool should be exploited, as the various species included in it contain promising resistance alleles against pests and pathogens. In general, the biodiversity of crops and their wild relatives is increasingly endangered. In order to conserve them and to provide impulses for breeding activities as well, strategies for the conservation of plant genetic resources are necessary. In this study, we investigated to what extent the different species of the rapeseed gene pool are conserved in European genebanks and what gaps exist. In addition, a niche modelling approach was used to investigate how the natural distribution ranges of these species are expected to change by the end of the century, assuming different climate change scenarios. It was found that most species of the rapeseed gene pool are significantly underrepresented in European genebanks, especially regarding representation of the natural distribution areas. The situation is exacerbated by the fact that the natural distributions are expected to change, in some cases significantly, as a result of ongoing climate change. It is therefore necessary to further develop strategies to prevent the loss of wild relatives of rapeseed. Based on the results of the study, as a first step we have proposed a priority list of species that should be targeted for collecting in order to conserve the biodiversity of the rapeseed gene pool in the long term.

Aim: We combined genetic sequence data and ecological niche modelling to resolve the impacts of past climatic fluctuations on the distribution, genetic diversification, and demographic dynamics of an East Asian montane bird, the green-backed tit (Parus monticolus). Location: East Asia. Methods: Phylogenetic analyses were carried out using four mitochondrial fragments and seven nuclear loci from 161 birds sampled from 29 localities spanning the entire geographical range of the green-backed tit. We used BEAST to estimate the species tree and calculate divergence times. Extended Bayesian skyline plots were used to infer potential historical shifts in population size. We used MaxEnt to predict potential distributions during three periods: the present day, the Last Glacial Maximum and the Last Interglacial. Results: The mitochondrial DNA (mtDNA) gene tree showed strong support for three reciprocally monophyletic groups: a south-western clade, a central clade and a Taiwanese clade. Taiwanese and Vietnamese samples had fixed differences at several nuclear loci, but the south-western and central samples shared haplotypes at all nuclear loci. The mtDNA gene tree topology differed from the species tree topology. The species tree suggested sister relationships between Taiwanese and Vietnamese operational taxonomic units (OTUs) and between south-western and central OTUs. Diversification within the green-backed tit was relatively recent, probably within the last 0.9 million years. Extended Bayesian skyline plots suggested rapid population expansion in the south-western and central phylogroups after the Last Interglacial, and this result was consistent with ecological niche models. Main conclusions: Our results suggest that genetic diversification within the green-backed tit was affected by the later Pleistocene climate fluctuations. Ecological niche models indicated that the present-day vegetation distribution was, in many ways, more similar to that of the Last Glacial Maximum than it was to that of the Last Interglacial. Continental populations of the green-backed tit experienced unusual demographic and range expansion that is likely to have occurred during the cooling transition between the Last Interglacial and the Last Glacial Maximum. We found incongruence between the mtDNA gene tree and the species tree, which underscores the importance of using both mitochondrial and nuclear markers when estimating the evolutionary history of populations.

Background: Leishmaniases are parasitic diseases caused by Leishmania species and transmitted by the bite of sand flies. The genus Lutzomyia and Phlebotomus of sand flies are known to be the responsible vector for transmitting almost all Leishmania species to humans. The detection of Leishmania DNA in species of the genus Sergentomyia, in different regions, suggests their likely role in Leishmania transmission. Methods: Our objective was to determine the potential geographical distribution of Sergentomyia minuta, the most dominant Sergentomyia species in Morocco, using ecological niche modeling. Results: The results showed the widespread geographical distribution of S. minuta in Morocco, specifically in northern and central Morocco where visceral and cutaneous leishmaniasis foci occur. There were six abiotic factors affecting the distribution of S. minuta whose annual precipitation, precipitation seasonality and precipitation of driest month as the most important ecological variables of the model. Conclusion: A positive statistical correlation between human leishmaniasis cases and S. minuta abundance was noted suggesting the potential involvement of S. minuta in local Leishmania transmission cycles.

Background & objectives: Zoonotic cutaneous leishmaniasis (ZCL) is among the most endemic zoonotic diseases in Golestan Province of Iran. The aim of this study was to find the high risk areas of this infection by considering the distribution of reservoirs and human infection. Methods: A cross-sectional study was conducted, in which Rhombomys opimus (Gerbils) were captured from different collection sites across the Golestan Province, Iran. records about the occurrence of Rh. opimus in the Province was obtained from earlier studies and were gathered in a database. Furthermore, records about the disease existence were also obtained from the health system database of Golestan Province. Villages with at least three cases of ZcL were considered endemic foci and used as presence sites for Leishmania major. ArcGIS and MaxEnt model were used to map and predict the best ecological niches for both reservoir and parasite. Results: According to the MaxEnt model, the area under Roc curve for Rh. opimus and L. major was 0.92 and 0.89, respectively. The probability of presence for both species in the northeastern part of Golestan Province was more than the other parts. The Jackknife test indicated that factors like temperature and altitude plays significant role in predicting the environmental suitability for ZcL reservoir and parasite, respectively. Interpretation & conclusion: this modeling approach predicted the areas suitable for reservoir host and circulation of parasite to human. These findings can be used in proper mapping, surveillance and control of the CL.