Mariette Rousseau

The use of non-pollen palynomorphs (NPP), particularly fossil fungi and algae, as palaeobiological proxies for Late Cretaceous palaeoenvironmental and palaeoclimatic reconstructions of warm-to-hot greenhouse conditions, can enhance our understanding of climate change impacts on modern Patagonian environments. This study aimed to reconstruct the Maastrichtian palaeoenvironment and palaeoclimate in the Cañadón Asfalto Basin (CAB, Chubut Province) by testing these NPPs as proxies using the Nearest Living Relative method (NLR). Moreover, using modern ecological requirements from open-source databases, such as GBIF and processing it with an open-source, cross-platform tool like QGIS, linked with Köppen-Geiger shapefiles, provided evidence of climate-driven palaeo-distribution patterns of fungal and algal diversity at CAB. Applying modern ecological requirements and biogeographic distribution data, we reconstructed the palaeoclimate as temperate with evenly distributed precipitation and warm summers, corresponding to the Cfb climate zone in Köppen-Geiger classifications. Additionally, our methodology produced reliable results regarding Cenozoic floras’ physiognomies based on fossil fungi, revealing a transition from sparsely wooded areas with palms and prairies to complex forest ecosystems with palms, deciduous trees, and shrubland. Furthermore, testing Cretaceous algae with the NLR method, for the first time, provided comprehensive insights into past water body characteristics, including trophic state and water quality.

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.

It would be advantageous to grow legume forage crops in order to increase the productivity and sustainability of sloped croplands in Hamkyongbukdo. In particular, the identification of potential cultivation areas for alfalfa in the given region could aid decision-making on policies and management related to forage crop production in the future. This study aimed to analyze the climate suitability of alfalfa in Hamkyongbukdo under current and future climate conditions using the Fuzzy Union model. The climate suitability predicted by the Fuzzy Union model was compared with the actual alfalfa cultivation area in the northern United States. Climate data obtained from 11 global climate models were used as input data for calculation of climate suitability in the study region to examine the uncertainty of projections under future climate conditions. The area where the climate suitability index was greater than a threshold value (22.6) explained about 44% of the variation in actual alfalfa cultivation areas by state in the northern United States. The climatic suitability of alfalfa was projected to decrease in most areas of Hamkyongbukdo under future climate scenarios. The climatic suitability in Onseong and Gyeongwon County was analyzed to be over 88 in the current climate conditions. However, it was projected to decrease by about 66% in the given areas by the 2090s. Our study illustrated that the impact of climate change on suitable cultivation areas was highly variable when different climate data were used as inputs to the Fuzzy Union model. Still, the ensemble of the climate suitability projections for alfalfa was projected to decrease considerably due to summer depression in Hamkyongbukdo. It would be advantageous to predict suitable cultivation areas by adding soil conditions or to predict the climate suitability of other leguminous crops such as hairy vetch, which merits further studies.

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.

Camelina [Camelina sativa (L.) Crantz] is a Brassicaceae oilseed that is gaining interest worldwide as low‐maintenance crop for diverse biobased applications. One of the most important factors determining its productivity is climate. We conducted a bioclimate analysis in order to analyze the relationship between climatic factors and the productivity of spring‐type camelina seeded in the spring, and to identify regions of the world with potential for camelina in this scenario. Using the modelling tool CLIMEX, a bioclimatic model was developed for spring‐seeded spring‐type camelina to match distribution, reported seed yields and phenology records in North America. Distribution, yield, and phenology data from outside of North America were used as independent datasets for model validation and demonstrated that model projections agreed with published distribution records, reported spring‐seeded camelina yields, and closely predicted crop phenology in Europe, South America, and Asia. Sensitivity analysis, used to quantify the response of camelina to changes in precipitation and temperature, indicated that crop performance was more sensitive to moisture than temperature index parameters, suggesting that the yield potential of spring‐seeded camelina may be more strongly impacted by water‐limited conditions than by high temperatures. Incremental climate scenarios also revealed that spring‐seeded camelina production will exhibit yield shifts at the continental scale as temperature and precipitation deviate from current conditions. Yield data were compared with indices of climatic suitability to provide estimates of potential worldwide camelina productivity. This information was used to identify new areas where spring‐seeded camelina could be grown and areas that may permit expanded production, including eastern Europe, China, eastern Russia, Australia and New Zealand. Our model is the first to have taken a systematic approach to determine suitable regions for potential worldwide production of spring‐seeded camelina.

Species Distribution Modelling (SDM) involves utilizing observations of a given species and its surrounding environment to produce a sound approximation of the species' potential distribution. The intricate relationships between organisms and their surroundings, coupled with the profusion of data, have captured the attention of ecologists and statisticians alike. Consequently, they have directed their efforts towards exploring the potential of machine learning techniques. Our study employs an ensemble machine learning approach to simulate the global ecological niche modelling of Ganoderma lucidum fungus. This involves the utilization of various environmental predictors and the averaging of multiple algorithms to achieve a comprehensive analysis. 563 spatially thinned presence points of G. lucidum were projected with three bio-climatic time frames, namely current, 2050, and 2070, and four Representative Concentration Pathways (RCPs), namely 2.6, 4.5, 6.0, and 8.5, as well as non-climatic variables (surface soil features, land use, rooting depth and water storage capacity at rooting zone). We observed excellent model qualities as the Area Under the receiver operating Curve (AUC) approached 0.90. Random Forest was identified as the best individual algorithm, while the Maxent entropy was identified as the least effective for Ecological Niche Modelling (ENM) of G. lucidum. Globally, under the current bio-climatic and non-bioclimatic projection, optimum habitat for this fungus covers 12510876.3 km2 area while, maximum area (13248546.9 Sq. km.) under this habitat class with future projections was recorded with RCP of 8.5 in 2070. The primary determinants of its current global distribution were ecosystem rooting depth, water storage capacity, and precipitation seasonality. While, with two future bioclimatic time frames and RCPs, Isothermality was identified as the most influential predictor. Based on our assessment, it has been determined that this particular fungus is exhibiting a persistent pattern of proliferation across the regions of Europe, America, and certain areas of India. The present investigation sought to underscore the importance of discerning the native habitats of this species, taking into account both current and anticipated climatic shifts. This knowledge is essential for effectively coordinating the artificial cultivation and natural harvesting of G. lucidum, which is necessary to meet the ever-increasing industrial demands.

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.

Pisolithus is a genus of gasteroid mycorrhizal symbionts associated with trees of several families of angiosperms and gymnosperms and distributed almost worldwide. Here we report a new record of Pisolithus arhizus from Tashkent, Uzbekistan, the first record of this species in Central Asia. The fruit bodies of P. arhizus were collected in several locations within the city and identified based on morphological characters. The ectomycorrhizal fungus formed symbiotic relationships with Juniperus sp. and Quercus sp. We provide its morphological description and photographs and also discuss our findings in the context of previously known records of this species.

Climate change is one of the most serious threats to global crops production at present and it will continue to be the largest threat in the future worldwide. Knowing how climate change affects crop productivity might help sustainability and crop improvement efforts. Under existing and projected climate change scenarios (2050s and 2070s in Ethiopia), the effect of global warming on the distribution of V. radiata and V. unguiculata was investigated. MaxEnt models were used to predict the current and future distribution pattern changes of these crops in Ethiopia using different climate change scenarios (i.e., lowest (RCP 2.6), moderate (RCP 4.5), and extreme (RCP 8.5)) for the years 2050s and 2070s. The study includes 81 and 68 occurrence points for V. radiata and V. unguiculata, respectively, along with 22 environmental variables. The suitability maps indicate that the Beneshangul Gumuz, Oromia, Amhara, SNNPR, and Tigray regions are the major Ethiopian regions with the potential to produce V. radiata, while Amhara, Gambella, Oromia, SNNPR, and Tigray are suitable for producing V. unguiculata. The model prediction for V. radiata habitat ranges distribution in Ethiopia indicated that 1.69%, 4.27%, 11.25% and 82.79% are estimated to be highly suitable, moderately suitable, less suitable, and unsuitable, respectively. On the other hand, the distribution of V. unguiculata is predicted to have 1.27%, 3.07%, 5.22%, and 90.44% habitat ranges that are highly suitable, moderately suitable, less suitable, and unsuitable, respectively, under the current climate change scenario by the year (2050s and 2070s) in Ethiopia. Among the environmental variables, precipitation of the wettest quarter (Bio16), solar radiation index (SRI), temperature seasonality (Bio4), and precipitation seasonality (Bio15) are discovered to be the most effective factors for defining habitat suitability for V. radiata, while precipitation of the wettest quarter (Bio16), temperature annual range (Bio7) and precipitation of the driest quarter (Bio17) found to be better habitat suitability indicator for V. unguiculata in Ethiopia. The result indicates that these variables were more relevant in predicting suitable habitat for these crops in Ethiopia. A future projection predicts that the suitable distribution region will become increasingly fragmented. In general, the study provides a scientific basis of suitable agro-ecological habitat for V. radiata and V. unguiculata for long-term crop management and production improvement in Ethiopia. Therefore, projections of current and future climate change impacts on such crops are vital to reduce the risk of crop failure and to identify the potential productive areas in the country.