Johannes Rick

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.

The moth catcher vine, Araujia sericifera Brotero (Apocynaceae), is a prevalent invader in many countries worldwide, where it has been reported to be a significant threat to biodiversity and agriculture. However, limited knowledge exists surrounding the ecology, invasion and impacts of A. sericifera in South Africa, challenging the implementation of management efforts. Therefore, this study sought to understand the vine's invasion within a South African context. To determine the density of A. sericifera in response to ecological and socio-economic predictors, its abundance along roads in 42 Johannesburg suburbs was measured. Additionally, A. sericifera abundance was recorded on foot within two nature reserves in Johannesburg, where density estimates were found to be 29 times higher than those obtained via roadside surveys. At a local scale, there was no evidence to indicate that vegetation, urban cover, and median household income influence A. sericifera density, suggesting that the vine establishes indiscriminately across Johannesburg. To determine whether this opportunistic behaviour was reflected in the vine's recruitment, its emergence rates in response to shaded and full-sun conditions were investigated. Seedling emergence was higher under shaded conditions (47.8%) than under full-sun (11%). Using MaxEnt models, environmental variables affecting the plant's distribution in South Africa were identified. Human disturbance had a permutation importance of 85.6%, indicating that A. sericifera's establishment is largely driven by disturbance. While A. sericifera appears to be in its lag phase of invasion and is unlikely to invade natural ecosystems in the short term, its abundance in the two nature reserves suggests that management should be prioritised during this lag phase, prior to its potential expansion into less-disturbed areas.

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.

AbstractIn July 2022, we collected three groups of Clathrus columnatus Bosc (Clathraceae) specimens (four mature and eight immature basidiomata) from Santa Cruz Island, Galapagos. We report these specimens as the first confirmed records of this species from the Galapagos Archipelago and Ecuador. We hypothesize that C. columnatus constitutes a recent introduction to the islands. We provide macro- and microscopic descriptions, including photographs of fresh and fluid-preserved basidiomata, and comments on the species’ taxonomy, ecology, and distribution.

Changes in climate, which can be understood as fluctuations in climate patterns as a reflection of natural or anthropic interventions, can generate changes in the environment and consequently affect the diversity of organisms. Fungi are extremely important in organic matter cycling in different environments, mainly forest areas, decomposing dead wood. To better understand the effects of climate change on two wood-degrading Agaricomycetes, their potential neotropical distribution was modeled using known occurrence data, available in the GBIF database and in specific literature, and associated with predictor variables extracted from Worldclim. A modeling package in R environment was used to analyze the present and future suitability for the optimistic and pessimistic scenarios. The results indicate the climate as an important factor in the distribution of Auricularia brasiliana and Megasporoporia neosetulosa. The suitability factors for the metrics used indicate that the models can be used to analyze climatic areas and that temperature and precipitation strongly influence the permanence of species in these locations. The results also indicate areas that can be affected by climatic effects, consequently causing a decrease in the occurrence and permanence of these fungi in the Neotropics. Our models can be useful as future guidelines in conservation studies for fungi.

Sclerotinia sclerotiorum, a fungal pathogen, causes world-wide crop losses and additional disease management strategies are needed. Modeling the climate niche of this fungus may offer a tool for the selection of biological control organisms and cultural methods of control. Maxent, a modeling technique, was used to characterize the climate niche for the fungus. The technique requires disease occurrence data, bioclimatic data layers, and geospatial analysis. A cross-correlation was performed with ArcGIS 10.8.1, to reduce nineteen bioclimatic variables (WorldClim) to nine variables. The model results were evaluated by AUC (area under the curve). A final model was created with the random seed procedure of Maxent and gave an average AUC of 0.935 with an AUC difference of −0.008. The most critical variables included annual precipitation (importance: 14.1%) with a range of 450 mm to 2500 mm and the mean temperature of the coldest quarter0 (importance: 55.6%) with a range of −16 °C to 24 °C, which contributed the most to the final model. A habitat suitability map was generated in ArcGIS 10.8.1 from the final Maxent model. The final model was validated by comparing results with another occurrence dataset. A Z-Score statistical test confirmed no significant differences between the two datasets for all suitability areas.

The popularization of citizen-science platforms has increased the amount of data available in a fine spatial and temporal resolution, which can be used to fill distribution knowledge gaps through probabilistic maps. In this study, we gathered expert-based information and used species distribution models to produce two independent maps of the greater rhea ( Rhea americana , Rheiformes, Rheidae) distribution in the state of Rio Grande do Sul, Brazil. We integrated municipality level detection/non-detection data from five citizen-science datasets into a Bayesian site occupancy model, accounting for false negatives, sampling effort, habitat covariates, and spatial autocorrelation. We addressed whether habitat (grassland and crop field cover, number of rural properties) and spatial autocorrelation explains the realized occurrence of the species and compared model-based and expert-based occurrence maps. The mean estimated percentage of occupied municipalities was 48% (239 out of 497 municipalities), whereas experts declared 21% of the municipalities (103) as occupied by the species. While both mapping approaches showed greater rhea presence in most municipalities of the Pampa biome, they disagreed in the majority of the municipalities in the Atlantic Forest, where more fieldwork must be undertaken. The greater rhea distribution was exclusively explained by the spatial autocorrelation component, suggesting that the species expanded its distribution towards the north of the state, reaching the Atlantic Forest, following deforestation and agriculture expansion.

To clarify biogeographic patterns of two mushroom species (Phallus merulinus and Geastrum courtecuissei) previously reported from Myanmar, sequence data of the internal transcribed spacer (ITS) region of nuclear ribosomal DNA were retrieved from GenBank. The BLAST search and phylogenetic analyses of Phallus indicated that P. merulinus and P. atrovolvatus from wide areas, including Australia, Myanmar, Thailand, Brazil, and French Guiana, cannot be distinguished molecularly. The species was, therefore, considered widespread across tropical to subtropical regions. In contrast, G. courtecuissei from Myanmar was tightly clustered exclusively with G. courtecuissei from Central and South America, supporting the idea of its disjunct distribution between Southeast Asia (Myanmar) and Central-South Americas.