Corneille Antoine Jean Abraham Oudemans

Without appropriate and ongoing management interventions, weeds will continue to economically and environmentally disadvantage agricultural and natural ecosystems. For these management strategies to have long‐term sustained success, they need to carefully consider the biological aspects of the targeted weed. These strategies will also need to consider potential adaptations evolved by the targeted weed in response to a range of selection pressures imposed by anthropogenetic factors, climate change, changing environmental conditions, and inappropriate or unsuccessful management regimes. One group of weeds that has been observed to readily adapt to a wide range of conditions and has shown considerable challenges in their management is invasive grasses. Adding to these challenges is that several invasive grasses have also developed resistance to a range of herbicide modes of action, which, to date, has been one of the most commonly used methods of control. To address these challenges, this review explores the biology and ecology of the globally invasive annuals Echinochloa crus‐galli (Barnyard grass) and Panicum capillare (Witchgrass), and the perennial Sorghum halepense (Johnson grass) to identify (i) the most suitable management options for their control and (ii) potential research gaps that may assist in the future management direction of these species. Based on the findings of this review, it is clear that an integrated management approach that targets different aspects of the plant's biology, in combination with early detection and treatment and ongoing surveillance, is necessary for the long‐term control of these species. Although a combination of methods appears promising, further investigation still is required to evaluate their efficiency and long‐term success in a changing environment, all of which are further discussed within this review.

Understanding the drivers of species richness patterns is a major goal of ecology and evolutionary biology, and the drivers vary across regions and taxa. Here, we assessed the influence of environmental factors and evolutionary history on the pattern of species richness in the genus Sorbus (110 species). We mapped the global species richness pattern of Sorbus at a spatial resolution of 200 × 200 km, using 10,652 specimen records. We used stepwise regression to assess the relationship between 23 environmental predictors and species richness and estimated the diversification rate of Sorbus based on chloroplast genome data. The effects of environmental factors were explained by adjusted R2, and evolutionary factors were inferred based on differences in diversification rates. We found that the species richness of Sorbus was highest in the Hengduan Mountains (HDM), which is probably the center of diversity. Among the selected environmental predictors, the integrated model including all environmental predictors had the largest explanatory power for species richness. The determinants of species richness show regional differences. On the global and continental scale, energy and water availability become the main driving factors. In contrast, climate seasonality is the primary factor in the HDM. The diversification rate results showed no significant differences between HDM and non-HDM, suggesting that evolutionary history may have limited impact on the pattern of Sorbus species richness. We conclude that environmental factors play an important role in shaping the global pattern of Sorbus species richness, while diversification rates have a lesser impact.

Abstract After many decades of effective control of stem rust caused by the Puccinia graminis f.sp. tritici, (hereafter Pgt) the reported emergence of race TTKSK/Ug99 of Pgt in Uganda reignited concerns about epidemics worldwide because ∼90% of world wheat cultivars had no resistance to the new race. Since it was initially detected in Uganda in 1998, Ug99 variants have now been identified in thirteen countries in Africa and the Middle East. Stem rust has been a major problem in the past, and concern is increasing about the risk of return to Central and East Asia. Whilst control programs in North America and Europe relied on the use of resistant cultivars in combination with eradication of barberry (Berberis spp.), the alternate host required for the stem rust pathogen to complete its full lifecycle, the focus in East Asia was principally on the use of resistant wheat cultivars. Here, we investigate potential airborne transmission pathways for stem rust outbreaks in the Middle East to reach East Asia using an integrated modelling framework combining estimates of fungal spore deposition from an atmospheric dispersion model, environmental suitability for spore germination, and crop calendar information. We consider the role of mountain ranges in restricting transmission pathways, and we incorporate a representation of a generic barberry species into the lifecycle. We find viable transmission pathways to East Asia from the Middle East to the north via Central Asia and to the south via South Asia and that an initial infection in the Middle East could persist in East Asia for up to three years due to the presence of the alternate host. Our results indicate the need for further assessment of barberry species distributions in East Asia and appropriate methods for targeted surveillance and mitigation strategies should stem rust incidence increase in the Middle East region.

To address the remaining knowledge gap regarding the distribution of seagrasses in Ireland, this study aimed a) to create an updated seagrass (Zostera spp.) distribution map, and b) to evaluate the environmental quality to which seagrass meadows are exposed. To achieve the first objective, we (i) combined the available data on seagrass distribution published to date, and (ii) mapped additional meadows by implementing an integrated method based on species distribution models, satellite-derived images, and snorkelling-based surveys. We mapped 209 new seagrass meadows (14.98 km2), representing a 37.03 % increase over previously reported extents. Consequently, the total extent of Irish seagrass meadows is estimated to be at least 54.85 km2. To address the second objective, we assessed the level of anthropogenic pressure of seagrass meadows based on the index provided by the Water Framework Directive of the European Environment Agency. This study demonstrates that Irish meadows are primarily located in areas with ‘HIGH’ and ‘GOOD’ water status.

Polyploidy, the result of whole-genome duplication (WGD), is a major driver of eukaryote evolution. Yet WGDs are hugely disruptive mutations, and we still lack a clear understanding of their fitness consequences. Here, we study whether WGDs result in greater diversity of genomic structural variants (SVs) and how they influence evolutionary dynamics in a plant genus, Cochlearia (Brassicaceae). By using long-read sequencing and a graph-based pangenome, we find both negative and positive interactions between WGDs and SVs. Masking of recessive mutations due to WGDs leads to a progressive accumulation of deleterious SVs across four ploidal levels (from diploids to octoploids), likely reducing the adaptive potential of polyploid populations. However, we also discover putative benefits arising from SV accumulation, as more ploidy-specific SVs harbor signals of local adaptation in polyploids than in diploids. Together, our results suggest that SVs play diverse and contrasting roles in the evolutionary trajectories of young polyploids. Cochlearia is an evolutionarily dynamic genus with different base chromosome numbers and ploidal levels. Here, the authors construct a graph-based pangenome for Cochlearia and reveal that whole-genome duplication associated with greater diversity of genomic structural variation and their possible function in adaptation.

Parasitic weeds in the genera Orobanche, Phelipanche (broomrapes) and Striga (witchweeds) have a devastating impact on food security across much of Africa, Asia and the Mediterranean Basin. Yet, how climatic factors might affect the range expansion of these weeds in the context of global environmental change remains unexplored. We examined satellite‐based environmental variables such as surface temperature, root zone soil moisture, and elevation, in relation to parasitic weed distribution and environmental conditions over time, in combination with observational data from the Global Biodiversity Information Facility (GBIF). Our analysis reveals contrasting environmental and altitude preferences in the genera Striga and Orobanche. Asiatic witchweed (Striga asiatica), which infests corn, rice, sorghum, and sugar cane crops, appears to be expanding its range in high elevation habitats. It also shows a significant association with heat‐moisture coupling events, the frequency of which is rising in such environments. These results point to geographical shifts in distribution and abundance in parasitic weeds due to climate change.

Fungal pathogens are responsible for 30% of emerging infectious diseases (EIDs) in plants. The risk of a pathogen emerging on a new host is strongly tied to its host breadth; however, the determinants of host range are still poorly understood. Here, we explore the factors that shape host breadth of plant pathogens within Botryosphaeriaceae, a fungal family associated with several devastating diseases in economically important crops. While most host plants are associated with just one or a few fungal species, some hosts appear to be susceptible to infection by multiple fungi. However, the variation in the number of fungal taxa recorded across hosts is not easily explained by heritable plant traits. Nevertheless, we reveal strong evolutionary conservatism in host breadth, with most fungi infecting closely related host plants, but with some notable exceptions that seem to have escaped phylogenetic constraints on host range. Recent anthropogenic movement of plants, including widespread planting of crops, has provided new opportunities for pathogen spillover. We suggest that constraints to pathogen distributions will likely be further disrupted by climate change, and we may see future emergence events in regions where hosts are present but current climate is unfavorable.

AbstractAimSpreading infectious diseases associated with introduced pathogens can have devastating effects on native biota and human livelihoods. We analyse the global distribution of 100 major alien fungal and oomycete pathogens with substantial socio‐economic and environmental impacts and examine their taxonomy, ecological characteristics, temporal accumulation trajectories, regional hot‐ and coldspots of taxon richness and taxon flows between continents.LocationGlobal.TaxonAlien/cryptogenic fungi and fungus‐like oomycetes, pathogenic to plants or animals.MethodsTo identify over/underrepresented classes and phyla, we performed Chi2 tests of independence. To describe spatial patterns, we calculated the region‐wise richness and identified hot‐ and coldspots, defined as residuals after correcting taxon richness for region area and sampling effort via a quasi‐Poisson regression. We examined the relationship with environmental and socio‐economic drivers with a multiple linear regression and evaluated a potential island effect. Regional first records were pooled over 20‐year periods, and for global flows the links between the native range to the alien regions were mapped.ResultsPeronosporomycetes (Oomycota) were overrepresented among taxa and regional taxon richness was positively correlated with area and sampling effort. While no island effect was found, likely due to host limitations, hotspots were correlated with human modification of terrestrial land, per capita gross domestic product, temperate and tropical forest biomes, and orobiomes. Regional first records have increased steeply in recent decades. While Europe and Northern America were major recipients, about half of the taxa originate from Asia.Main ConclusionsWe highlight the putative importance of anthropogenic drivers, such as land use providing a conducive environment, contact opportunities and susceptible hosts, as well as economic wealth likely increasing colonisation pressure. While most taxa were associated with socio‐economic impacts, possibly partly due to a bias in research focus, about a third show substantial impacts to both socio‐economy and the environment, underscoring the importance of maintaining a wholescale perspective across natural and managed systems.

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.

Predicting the likelihood that non-native species will be introduced into new areas remains one of conservation’s greatest challenges and, consequently, it is necessary to adopt adequate management measures to mitigate the effects of future biological invasions. At present, not much information is available on the areas in which non-native aquatic plant species could establish themselves in the Iberian Peninsula. Species distribution models were used to predict the potential invasion risk of (1) non-native aquatic plant species already established in the peninsula (32 species) and (2) those with the potential to invade the peninsula (40 species). The results revealed that the Iberian Peninsula contains a number of areas capable of hosting non-native aquatic plant species. Areas under anthropogenic pressure are at the greatest risk of invasion, and the variable most related to invasion risk is temperature. The results of this work were used to create the Invasion Risk Atlas for Alien Aquatic Plants in the Iberian Peninsula, a novel online resource that provides information about the potential distribution of non-native aquatic plant species. The atlas and this article are intended to serve as reference tools for the development of public policies, management regimes, and control strategies aimed at the prevention, mitigation, and eradication of non-native aquatic plant species.