Frances Louisa Foord-Kelcey

The Khanka Lowland forest-steppe is the most eastern outpost of the Eurasian steppe biome. It includes unique grassland plant communities with rare steppe species. These coenosis have changed under the influence of anthropogenic activity, especially during the last 100 years and included both typical steppe species and nemoral mesophytic species. To distinguish these ecological groups of plants the random forest method with three datasets of environmental variables was applied. Specifically, a model of classification with the most important bioindices to predict a mesophytic ecological group of plants with a sensitivity greater than 80% was constructed. The data demonstrated the presence of steppe species that arrived at different times in the Primorye Territory. Most of these species are associated with the Mongolian-Daurian relict steppe complex and habit in the Khanka Lowland. Other species occur only in mountains in Primorye Territory and do not persist in the Khanka Lowland. These findings emphasize the presence of relict steppe communities with a complex of true steppe species in the Khanka Lowland. Steppe communities exhibit features of anthropogenic influence definitely through the long land use period but are not anthropogenic in origin. The most steppe species are located at the eastern border of distribution in the Khanka Lowlands and are valuable in terms of conservation and sources of information about steppe species origin and the emergence of the steppe biome as a whole.

How to effectively obtain species‐related low‐dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionality of high‐dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes.

Aim Patterns of individual variation are key to testing hypotheses about the mechanisms underlying biogeographic patterns. If species distributions are determined by environmental constraints, then populations near range margins may have reduced performance and be adapted to harsher environments. Model organisms are potentially important systems for biogeographical studies, given the available range‐wide natural history collections, and the importance of providing biogeographical context to their genetic and phenotypic diversity.LocationGlobal.TaxonArabidopsis thaliana (‘Arabidopsis’).MethodsWe fit occurrence records to climate data, and then projected the distribution of Arabidopsis under last glacial maximum, current and future climates. We confronted model predictions with individual performance measured on 2194 herbarium specimens, and we asked whether predicted suitability was associated with life history and genomic variation measured on ~900 natural accessions.ResultsThe most important climate variables constraining the Arabidopsis distribution were winter cold in northern and high elevation regions and summer heat in southern regions. Herbarium specimens from regions with lower habitat suitability in both northern and southern regions were smaller, supporting the hypothesis that the distribution of Arabidopsis is constrained by climate‐associated factors. Climate anomalies partly explained interannual variation in herbarium specimen size, but these did not closely correspond to local limiting factors identified in the distribution model. Late‐flowering genotypes were absent from the lowest suitability regions, suggesting slower life histories are only viable closer to the centre of the realized niche. We identified glacial refugia farther north than previously recognized, as well as refugia concordant with previous population genetic findings. Lower latitude populations, known to be genetically distinct, are most threatened by future climate change. The recently colonized range of Arabidopsis was well‐predicted by our native‐range model applied to certain regions but not others, suggesting it has colonized novel climates.Main ConclusionsIntegration of distribution models with performance data from vast natural history collections is a route forward for testing biogeographical hypotheses about species distributions and their relationship with evolutionary fitness across large scales.

The benefits of early detection of biological invasions are widely recognized, especially for protected areas (PAs). However, research on incipient invasive plant species is scarce compared to species with a recognized history of invasion. Here, we characterized the invasion status of the non-native conifer Juniperus communis in PAs and interface areas of Andean Patagonia, Argentina. We mapped its distribution and described both the invasion and the environments this species inhabits through field studies, a literature review, and a citizen science initiative. We also modeled the species’ potential distribution by comparing the climatic characteristics of its native range with those of the introduced ranges studied. The results show that J. communis is now widely distributed in the region, occurring naturally in diverse habitats, and frequently within and close to PAs. This species can be considered an incipient invader with a high potential for expansion in its regional distribution range, largely due to its high reproductive potential and the high habitat suitability of this environment. Early detection of a plant invasion affords a valuable opportunity to inform citizens of the potential risks to high conservation value ecosystems before the invader is perceived as a natural component of the landscape.

Numerous orchid species around the world have already been affected by the ongoing climate change, displaying phenological alterations and considerable changes to their distributions. The fly orchid (Ophrys insectifera L.) is a well-known and distinctive Ophrys species in Europe, with a broad distribution across the continent. This study explores the effects of climate change on the range of O. insectifera, using a species distribution models (SDMs) framework that encompasses different climatic models and scenarios for the near- and long-term future. The species’ environmentally suitable area is projected to shift northwards (as expected) but downhill (contrary to usual expectations) in the future. In addition, an overall range contraction is predicted under all investigated combinations of climatic models and scenarios. While this is moderate overall, it includes some regions of severe loss and other areas with major gains. Specifically, O. insectifera is projected to experience major area loss in its southern reaches (the Balkans, Italy and Spain), while it will expand its northern limits to North Europe, with the UK, Scandinavia, and the Baltic countries exhibiting the largest gains.View Full-Text

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Every species experiences limits to its geographic distribution. Some evolutionary models predict that populations at range edges are less well‐adapted to their local environments due to drift, expansion load, or swamping gene flow from the range interior. Alternatively, populations near range edges…

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