Alexander Croall

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.

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.

Rubus (Rosaceae), one of the most complicated angiosperm genera, contains about 863 species, and is notorious for its taxonomic difficulty. The most recent (1910–1914) global taxonomic treatment of the genus was conducted by Focke, who defined 12 subgenera. Phylogenetic results over the past 25 years suggest that Focke's subdivisions of Rubus are not monophyletic, and large‐scale taxonomic revisions are necessary. Our objective was to provide a comprehensive phylogenetic analysis of the genus based on an integrative evidence approach. Morphological characters, obtained from our own investigation of living plants and examination of herbarium specimens are combined with chloroplast genomic data. Our dataset comprised 196 accessions representing 145 Rubus species (including cultivars and hybrids) and all of Focke's subgenera, including 60 endemic Chinese species. Maximum likelihood analyses inferred phylogenetic relationships. Our analyses concur with previous molecular studies, but with modifications. Our data strongly support the reclassification of several subgenera within Rubus. Our molecular analyses agree with others that only R. subg. Anoplobatus forms a monophyletic group. Other subgenera are para‐ or polyphyletic. We suggest a revised subgeneric framework to accommodate monophyletic groups. Character evolution is reconstructed, and diagnostic morphological characters for different clades are identified and discussed. Based on morphological and molecular evidence, we propose a new classification system with 10 subgenera: R. subg. Anoplobatus, R. subg. Batothamnus, R. subg. Chamaerubus, R. subg. Cylactis, R. subg. Dalibarda, R. subg. Idaeobatus, R. subg. Lineati, R. subg. Malachobatus, R. subg. Melanobatus, and R. subg. Rubus. The revised infrageneric nomenclature inferred from our analyses is provided along with synonymy and type citations. Our new taxonomic backbone is the first systematic and complete global revision of Rubus since Focke's treatment. It offers new insights into deep phylogenetic relationships of Rubus and has important theoretical and practical significance for the development and utilization of these important agronomic crops.

Increasing demand for energy and also escalating environmental pollution show that industries cannot rely on fossil fuels, and it is necessary to adopt an alternative. In recent decades, algal bioenergy has emerged as a renewable energy source in different industries. However, algal bioenergy production is costly and faces different challenges and unknown aspects that need to be addressed. Experimental and theoretical research works have revealed that the efficiency of algal bioenergy production is influenced by several factors, including algae species, temperature, light, CO2, cultivation method, and available nutrients. Algal bioenergy production on commercial scales in cost-effective ways is the main aim of industries to compete with fossil fuels. Hence, it is vital to have a comprehensive knowledge of the previous findings and attain a suitable pathway for future studies/activities. In the present review paper, the potential of microalgae bioenergy production, influential parameters, previous experimental and theoretical studies, and different methods for microalgae biofuel production from cultivation stage to utilization are reviewed. Moreover, this work discusses the engineering activities and economic analysis of microalgae cultivation to utilization, and also useful suggestions are made for future research works. The outcomes of the present work confirm that innovative engineering methods can overcome scale-up challenging, increase the rate of production, and decrease the cost of algae bioenergy production. Hence, there is no long way to produce cost-effective algae bioenergy on commercial scales.

Today plants often flower earlier due to climate warming. Herbarium specimens are excellent witnesses of such long‐term changes. However, the magnitude of phenological shifts may vary geographically, and the data are often clustered. Therefore, large‐scale analyses of herbarium data are prone to pseudoreplication and geographical biases.We studied over 6000 herbarium specimens of 20 spring‐flowering forest understory herbs from Europe to understand how their phenology had changed during the last century. We estimated phenology trends with or without taking spatial autocorrelation into account.On average plants now flowered over 6 d earlier than at the beginning of the last century. These changes were strongly associated with warmer spring temperatures. Flowering time advanced 3.6 d per 1°C warming. Spatial modelling showed that, in some parts of Europe, plants flowered earlier or later than expected. Without accounting for this, the estimates of phenological shifts were biased and model fits were poor.Our study indicates that forest wildflowers in Europe strongly advanced their phenology in response to climate change. However, these phenological shifts differ geographically. This shows that it is crucial to combine the analysis of herbarium data with spatial modelling when testing for long‐term phenology trends across large spatial scales.

Abstract. Statistical climate reconstruction techniques are fundamental tools to study past climate variability from fossil proxy data. In particular, the methods based on probability density functions (or PDFs) can be used in various environments and with different climate proxies because they rely on elementary calibration data (i.e. modern geolocalised presence data). However, the difficulty of accessing and curating these calibration data and the complexity of interpreting probabilistic results have often limited their use in palaeoclimatological studies. Here, I introduce a new R package (crestr) to apply the PDF-based method CREST (Climate REconstruction SofTware) on diverse palaeoecological datasets and address these problems. crestr includes a globally curated calibration dataset for six common climate proxies (i.e. plants, beetles, chironomids, rodents, foraminifera, and dinoflagellate cysts) associated with an extensive range of climate variables (20 terrestrial and 19 marine variables) that enables its use in most terrestrial and marine environments. Private data collections can also be used instead of, or in combination with, the provided calibration dataset. The package includes a suite of graphical diagnostic tools to represent the data at each step of the reconstruction process and provide insights into the effect of the different modelling assumptions and external factors that underlie a reconstruction. With this R package, the CREST method can now be used in a scriptable environment and thus be more easily integrated with existing workflows. It is hoped that crestr will be used to produce the much-needed quantified climate reconstructions from the many regions where they are currently lacking, despite the availability of suitable fossil records. To support this development, the use of the package is illustrated with a step-by-step replication of a 790 000-year-long mean annual temperature reconstruction based on a pollen record from southeastern Africa.

The local climate in forest understories can deviate substantially from ambient conditions. Moreover, forest microclimates are often characterized by cyclic changes driven by management activities such as clear-cutting and subsequent planting. To understand how and why understorey plant communities …

Colobanthus (23 species) and Sagina (30–33 species) together are sister to Facchinia. Whereas Facchinia is distributed in western Eurasia, Colobanthus is almost exclusively distributed in the Southern Hemisphere, and Sagina is distributed in both hemispheres with the highest species diversity in wes…

The Qinghai-Tibetan Plateau (QTP) harbors abundant and diverse plant life owing to its high habitat heterogeneity. However, the distribution pattern of biodiversity hotspots and their conservation status remain unclear. Based on 148,283 high-resolution occurrence coordinates of 13,450 seed plants, w…

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…