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Fig. 4 in The global distribution of known and undiscovered ant biodiversity

Fig. 4. Empirical and predicted raritycenters of the Western Hemisphere. Rarity centersbased on currentknowledge and projectedbya Random Forestmodelunder a "universal high sampling" scenario. See Fig. 3 for more explanation.

Fig. 1 in The global distribution of known and undiscovered ant biodiversity

Fig. 1. Globalantspeciesrichness patternsincomparison withterrestrialvertebrates. (A) Species richnesscenters (top 10% of area) for amphibians, birds, mammals, reptiles, and ants, indicating areas of congruence and incongruence of biodiversity centers across taxa. (B) Species richness maps based on stacking individual species range estimates for ants and vertebrates. (C) Spearman's correlation matrix for grid cell–level species richness across taxa.

Fig. 5 in The global distribution of known and undiscovered ant biodiversity

Fig. 5. Empirical and predicted rarity centers of Europe, Africa, and West Asia. Rarity centers based on current knowledge and projected by a Random Forest model under a "universal high sampling" scenario. See Fig.3 for more explanation.

Global aquatic biodiversity impacts of nitrogen and phosphorus fertiliser use for major crops

Abstract Purpose The intensive application of nitrogen and phosphorus fertilisers on agricultural land to fertilise crops has caused eutrophication, the nutrient enrichment of waterbodies leading to excessive growth of algae, deoxygenation and loss of aquatic biodiversity. Life cycle impact assessments (LCIA) are often used to determine the environmental impacts of fertiliser use. However, the lack of suitable methodologies to estimate the fate and transport of nutrients from soils makes crop and regional impact comparisons challenging. Using a newly devised, spatially explicit nutrient fate and transport model (fate factor, FF) within an LCIA framework, this study estimates the global spatial-variability of nutrient loss from fertilisation of crops and their relative impact on aquatic biodiversity, specifically species richness. Method The newly devised FFs are based on the global spatially explicit nutrient model IMAGE-GNM. The FF’s enable us to assess N and P’s fate and transport from indirect soil emissions (arable land, grassland and natural land) to freshwater environments. We additionally improve the spatial resolution of existing soil FFs for N within marine environments from basin scale to 5 arcmin resolution. We applied our FF’s within current LCIA methodologies to assess the nutrient loading (midpoint indicator) and final aquatic biodiversity impact (endpoint indicator) from 17 crops. Results and discussion Our results identify strong variability in inputs, loadings and impacts due to differences in the fate, transport and impact of nutrients within the local environmental context. Such variability is translated into large differences between the popularly used nutrient use efficiency (NUE) indicator and final aquatic impacts caused by specific crops. Heavily produced crops (maize, rice, wheat, sugarcane and soybean) with the highest loading rates to receptors did not necessarily have the highest aquatic impacts. We identified rank variability existed at different metric stages (fertiliser inputs, receptor loadings, aquatic impacts) specifically for wheat and sugarcane. Our results showed high spatial variability in high aquatic impacts with significant biodiversity loss outside of the highest production regions. Conclusion Our study identified global hotspots for biodiversity impacts depend on the local context that exist beyond the field (e.g. the fate and transport of nutrients to receptor environments, and the receptor environment's vulnerability). Aquatic impacts from fertiliser use for specific crop commodities should be considered in strategic fertiliser pollution control decision-making and environmentally sustainable crop-commodity trade sourcing decisions. The development of the improved FFs should be used to aid spatially explicit and site-specific LCIA nutrient studies from soils. Here we provide the fate factors developed and used in our study to estimate biodiversity impacts from nitrogen and phosphorus fertiliser use.

opencc-zeroMay 2024View details →

Multinational evaluation of genetic diversity indicators for the Kunming-Montreal Global Biodiversity Framework

Under the recently adopted Kunming-Montreal Global Biodiversity Framework, 196 Parties committed to report the status of genetic diversity for all species. To facilitate reporting, three genetic diversity indicators were developed, two of which focus on processes contributing to genetic diversity conservation: maintaining genetically distinct populations and ensuring populations are large enough to maintain genetic diversity. The major advantage of these indicators is that they can be estimated with or without DNA-based data. However, demonstrating their feasibility requires addressing the methodological challenges of using data gathered from diverse sources, across diverse taxonomic groups, and for countries of varying socioeconomic status and biodiversity levels. Here, we assess the genetic indicators for 919 taxa, representing 5,271 populations across nine countries, including megadiverse countries and developing economies. Eighty-three percent of taxa assessed had data available to calculate at least one indicator. Our results show that although the majority of species maintain most populations, 58% of species have populations too small to maintain genetic diversity. Moreover, genetic indicator values suggest that IUCN Red List status and other initiatives fail to assess genetic status, highlighting the critical importance of genetic indicators.

opencc-zeroOct 2022View details →

Data from: Pathways to global-change effects on biodiversity: New opportunities for dynamically forecasting demography and species interactions

In structured populations, persistence under environmental change is threatened when abiotic factors simultaneously negatively affect survival and reproduction of several life-cycle stages. Such effects can then be exacerbated when species interactions generate reciprocal feedbacks between the demographic rates of the different species. Despite the importance of such demographic feedbacks, forecasts that account for them are severely limited as individual-based data on interacting species are perceived to be essential for such mechanistic forecasting - but are rarely available. This dataset is the input to showcase a state-of-the-art Bayesian method to infer and project stage-specific survival and reproduction from abundance data for several interacting species in a Mediterranean shrub community.

Fig. 5 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 5. Data sources according to epidemiological level and scale. Representation of the data sources (left column) used for each epidemiological level (central column), and the scale of the corresponding data sources (right). Colors of the left column correspond to general data-sources categories; for example, green corresponds to biological/biodiversity data sources (e.g., Biodiversity repositories and biological general source). Health-related sources are represented in purple (Health gov: governmental, init-program: initiative or programs). Using this broad categorization, most of the sources contribute with data related to the three epidemiological levels, although with an unpaired flow. For example, scientific literature has a lower contribution for hosts/ reservoirs, and biodiversity-biological sources have a minor contribution for pathogens. Most data sources have a global scale meanwhile governmental sources have a relevant contribution to pathogen data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 3. Diseases explored in the studies according to the use of GBIF and the taxa class of the causal pathogen. In the right panel: positive studies (i.e., those studies that used GBIF-mediated data for at least one of the variables explored), negative studies in the left. Bars represent the number of studies exploring each disease, and filling colors represent the corresponding taxa class of the disease agent or causal pathogen (Purple scale, with lighter coloration for fungal diseases, followed by parasites, bacteria, and viruses with the darker purple). Abbreviations: the abbreviation Oth (Fungal Oth, Parasite Oth, Bacteria Oth, Virus Oth) represents a category with multiple species, merged to simplify the figure due to the low number of studies of each disease. Ricket-related: diseases related to Rickettsia species; Paras: parasites; Schistos: Schistosomiases; Leishm: Leishmaniases (both cutaneous and visceral); Dis: disease; Bact: bacteria; Fev: fever; V: virus; CoronaV: Coronavirus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 2. Research areas identified in the studies. Research areas subcategories are represented in the left axis, and general research area groups in the right axis. Orange circles represent the number of positive studies, the blue circles the negatives, and the black lines between them represent the differences in the number of studies, in which larger lines represent larger differences between positive and negatives. Orange icons correspond to research areas with larger number of positive studies, i.e., positive studies were more related to Biology (Bio), Ecology (Ecol) and Other (Hum Soc: Human society; Phy Env Geo: Physical environmental geology; Earth Atm: Earth and atmospheric sciences). Negative studies were more frequent in research areas with blue icons, including Medical (Med) and Veterinary sciences (Vet: Veterinarian and agriculture). In the green icon (Eng Inf Mat: Engineering, informatics, and mathematics) there was no major differences between groups. Subcategories: Bio Zoo: Biology and zoology; Bio Evo Gen: Biology, evolution, and genetics; Bio Bioch: Biology and biochemistry; Env Mang: Environmental management and sciences; Eco App: Ecological applications; Math Stat: Mathematical statistics; Inf Comp: informatics and computing; Eng Geom: Engineer and geometrics; Microb: Microbiology; Pub Heal: Public health; Med Micro: Medical microbiology; Med Clin Heal: Medical clinical and health. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 1 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 1. General framework of analyses at study- and variable-levels. In the upper section (study-level, in grey), studies are divided in those that used GBIF-mediated data (positives, in orange) and those that did not (negatives, in blue). Positive studies were group according if GBIF was used as the only data source for all variables (2 studies), or if the variables were based on GBIF together with other data sources (105 studies). In the variable-level section (bottom, white background) the total 358 variables extracted from the positive and negative studies were categorized according to the specific use of GBIF, resulting in five types of variables, four of them extracted from the positive studies. Note that in those studies based on GBIF, the different variables could be based on GBIF alone (33 variables), GBIF together with other sources (85), or specific variables may not be based on GBIF-mediated data at all (81 variables). Finally, each variable was related to different epidemiological roles, resulting in a larger number hosts/reservoirs variables, mostly based on GBIF-mediated data, and a higher presence of pathogen species-variables not using GBIF. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 4. Variables according to taxon class (Y- axis) epidemiological level (bar colour) and the use of GBIF- mediated data. Bars represent the number of variables by each taxon class (Y-axis), separated in two panels according to the use of GBIF-mediated data. In the right panel, variables in which GBIF-mediated data was used (Used_GBIF), in the left panel variables in which data was not obtained from GBIF (NonGBIF). Next to the bars, the number of variables by each epidemi- ological level, and percentage in rela- tion to the total number of variables of each group (Used_GBIF: 120 and Non- GBIF: 238). Taxon classes are grouped by taxonomic associations (e.g., birds, primates, ticks, mosquitoes); however, some were merged to simplify the figure. For example, mamm/oth/var includes multiple mammal species which were sparsely mentioned; simi- larly, hosts/res var, vector other and path other grouped several species participating as hosts/reservoirs, vec- tors, and pathogens, respectively. Bar colors represent epidemiological levels (pathogens, vectors, hosts/reservoirs), and Other (in sienna) includes species participating as hosts' regulator, predators, among others. GBIF-mediated data was only used in three pathogen variables (purple), representing only 2.5% of the variables in which GBIF-mediated data was used, resulting in a remarkable difference with other sources (NonGBIF), in which pathogens represented a 59.7%. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

opencc-zeroFeb 2023View details →

Global warming leads to habitat loss and genetic erosion of alpine biodiversity

Aim: Species living on steep environmental gradients are expected to be especially sensitive to global climate change. Here, we combined genetic, ecological niche modelling and climatic niche comparisons to investigate the influence of climate on the biogeography of three alpine species with overlapping ranges. Location: Te Waipounamu (South Island) Aotearoa–New Zealand. Taxon: Endemic alpine-adapted Cataontopinae grasshoppers. Methods: We used niche modelling to estimate and project the potential niche of three focal species under past and future climate scenarios. Vulnerability assessments were performed using niche factor analyses. Demographic trends and phylogeographic structure were investigated using samples from 15 mountain tops to generate mitochondrial DNA haplotype networks and population genetic statistics. Results: Niche models and genetic data suggest suitable habitat for all three alpine species was more widespread and contiguous in the past than today. Demographic analyses indicate in situ survival rather than post-Pleistocene colonisation of current habitat. Population structuring and genetic divergence suggest that mountain uplift during the Pliocene and environmental barriers during Pleistocene glacial and interglacial stages shaped contemporary population structure of each species. Though geographically overlapping, niche analyses suggest these alpine species are not ecologically identical, and each shows similar but distinct responses to environmental change, but all will lose intraspecific diversity through population extinction. Main conclusions: Climatic, biological and geophysical factors controlled population structuring of three cold-adapted species during the Pleistocene with a legacy of spatially separate intraspecific lineages. Ecological niche models for each species emphasise distinct combinations of environmental proxies, but all are expected to experience severe habitat reduction during climate warming. Increased global temperatures drive available habitat to higher elevation resulting in population contractions, range shifts, habitat fragmentation, local extinctions, and genetic impoverishment. Despite alpine species not being ecologically identical, we predict all mountain biota will lose significant genetic diversity due to global warming.