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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.

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.

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.

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.

Aim: Understanding the roles of allopatric fragmentation and niche divergence in intraspecific lineage diversification is central to our comprehension of how new species arise and evolve. Here we integrate an extensive multilocus dataset with species distribution models to address these topics in genetically differentiated population lineages of the common midwife toad (Alytes obstetricans). Location: Iberian Peninsula, Western Europe. Methods: We genotyped 965 individuals from 142 localities at 12 microsatellite loci to finely delineate the ranges of population lineages in A. obstetricans. We then built ecological niche-based models for each lineage and tested for niche overlap, equivalency and similarity based on PCA-env analyses. We also investigated the relationship between genetic and environmental distances across lineages. Results: We recovered seven differentiated genetic clusters, with mostly different (sometimes nonoverlapping) niches. However, niches are not more divergent than expected given the largely allopatric ranges of population lineages. We found no significant isolation by environment within lineages, with genetic distances mostly explained by geographical distances. Main conclusions: Allopatric fragmentation in A. obstetricans resulted in (sometimes highly) different niches in intraspecific population lineages. Within lineages, isolation by distance is the major driver of patterns of genetic variation. Our study highlights the importance of incorporating intraspecific genetic structure into species distribution models to explore spatial patterns of genetic diversity in terms of their underlying processes.

Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.

Question: Predictive vegetation modelling relies on the use of environmental variables, which are usually derived from abase data set with some level of error, and this error is propagated to any subsequently derived environmental variables. The question for this study is: What is the level of error and uncertainty in environmental variables based on the error propagated from a Digital Elevation Model (DEM) and how does it vary for both direct and indirect variables? Location: Kioloa region, New South Wales, Australia Methods: The level of error in a DEM is assessed and used to develop an error model for analysing error propagation to derived environmental variables. We tested both indirect (elevation, slope, aspect, topographic position) and direct (average air temperature, net solar radiation, and topographic wetness index) variables for their robustness to propagated error from the DEM. Results: It is shown that the direct environmental variable net solar radiation is less affected by error in the DEM than the indirect variables aspect and slope, but that regional conditions such as slope steepness and cloudiness can influence this outcome. However, the indirect environmental variable topographic position was less affected by error in the DEM than topographic wetness index. Interestingly, the results disagreed with the current assumption that indirect variables are necessarily less sensitive to propagated error because they are less derived. Conclusions: The results indicate that variables exhibit both systematic bias and instability under uncertainty. There is a clear need to consider the sensitivity of variables to error in their base data sets in addition to the question of whether to use direct or indirect variables.

AIM: The Hutchinsonian hypervolume is the conceptual foundation for many lines of ecological and evolutionary inquiry, including functional morphology, comparative biology, community ecology and niche theory. However, extant methods to sample from hypervolumes or measure their geometry perform poorly on high‐dimensional or holey datasets. INNOVATION: We first highlight the conceptual and computational issues that have prevented a more direct approach to measuring hypervolumes. Next, we present a new multivariate kernel density estimation method that resolves many of these problems in an arbitrary number of dimensions. MAIN CONCLUSIONS: We show that our method (implemented as the ‘hypervolume’ R package) can match several extant methods for hypervolume geometry and species distribution modelling. Tools to quantify high‐dimensional ecological hypervolumes will enable a wide range of fundamental descriptive, inferential and comparative questions to be addressed.

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.

[This corrects the article DOI: 10.3389/fpls.2023.1148828.].