Johann Büttikofer

Rodents, a diverse, globally distributed and ecologically important order of mammals are nevertheless important reservoirs of known and novel zoonotic pathogens. Ongoing anthropogenic land use change is altering these species’ abundance and distribution, which among zoonotic host species may increase the risk of zoonoses spillover events. A better understanding of the current distribution of rodent species is required to guide attempts to mitigate against potentially increased zoonotic disease hazard and risk. However, available species distribution and host-pathogen association datasets (e.g. IUCN, GBIF, CLOVER) are often taxonomically and spatially biased. Here, we synthesise data from West Africa from 127 rodent trapping studies, published between 1964–2022, as an additional source of information to characterise the range and presence of rodent species and identify the subgroup of species that are potential or known pathogen hosts. We identify that these rodent trapping studies, although biased towards human dominated landscapes across West Africa, can usefully complement current rodent species distribution datasets and we calculate the discrepancies between these datasets. For five regionally important zoonotic pathogens (Arenaviridae spp., Borrelia spp., Lassa mammarenavirus, Leptospira spp. and Toxoplasma gondii), we identify host-pathogen associations that have not been previously reported in host-association datasets. Finally, for these five pathogen groups, we find that the proportion of a rodent hosts range that have been sampled remains small with geographic clustering. A priority should be to sample rodent hosts across a greater geographic range to better characterise current and future risk of zoonotic spillover events. In the interim, studies of spatial pathogen risk informed by rodent distributions must incorporate a measure of the current sampling biases. The current synthesis of contextually rich rodent trapping data enriches available information from IUCN, GBIF and CLOVER which can support a more complete understanding of the hazard of zoonotic spillover events.

Past responses to environmental change provide vital baseline data for estimating the potential resilience of extant taxa to future change. Here, we investigate the latitudinal range contraction that terrestrial and freshwater turtles (Testudinata) experienced from the Late Cretaceous to the Paleogene (100.5–23.03 mya) in response to major climatic changes. We apply ecological niche modeling (ENM) to reconstruct turtle niches, using ancient and modern distribution data, paleogeographic reconstructions, and the HadCM3L climate model to quantify their range shifts in the Cretaceous and late Eocene. We then use the insights provided by these models to infer their probable ecological responses to future climate scenarios at different representative concentration pathways (RCPs 4.5 and 8.5 for 2100), which project globally increased temperatures and spreading arid biomes at lower to mid-latitudes. We show that turtle ranges are predicted to expand poleward in the Northern Hemisphere, with decreased habitat suitability at lower latitudes, inverting a trend of latitudinal range contraction that has been prevalent since the Eocene. Trionychids and freshwater turtles can more easily track their niches than Testudinidae and other terrestrial groups. However, habitat destruction and fragmentation at higher latitudes will probably reduce the capability of turtles and tortoises to cope with future climate changes.

Our understanding of broad-scale biodiversity and functional trait patterns is largely based on plants, and relatively little information is available on soil arthropods. Here, we investigated the distribution of termite diversity globally and morphological traits and diversity across China. Our analyses showed increasing termite species richness with decreasing latitude at both the globally, and within-China. Additionally, we detected obvious latitudinal trends in the mean community value of termite morphological traits on average, with body size and leg length decreasing with increasing latitude. Furthermore, temperature, NDVI and water variables were the most important drivers controlling the variation in termite richness, and temperature and soil properties were key drivers of the geographic distribution of termite morphological traits. Our global termite richness map is one of the first high resolution maps for any arthropod group and especially given the functional importance of termites, our work provides a useful baseline for further ecological analysis.

Understanding the status and abundance of species is essential for effective conservation decision-making. However, the availability of species data varies across space, taxonomic groups and data types. A case study was therefore conducted in a high biodiversity region—East Africa—to evaluate data biases, the factors influencing data availability, and the consequences for conservation. In each of the eleven target countries, priority animal species were identified as threatened species that are protected by national governments, international conventions or conservation NGOs. We assessed data gaps and biases in the IUCN Red List of Threatened Species, the Global Biodiversity Information Facility and the Living Planet Index. A survey of practitioners and decision makers was conducted to confirm and assess consequences of these biases on biodiversity conservation efforts. Our results showed data on species occurrence and population trends were available for a significantly higher proportion of vertebrates than invertebrates. We observed a geographical bias, with higher tourism income countries having more priority species and more species with data than lower tourism income countries. Conservationists surveyed felt that, of the 40 types of data investigated, those data that are most important to conservation projects are the most difficult to access. The main challenges to data accessibility are excessive expense, technological challenges, and a lack of resources to process and analyse data. With this information, practitioners and decision makers can prioritise how and where to fill gaps to improve data availability and use, and ensure biodiversity monitoring is improved and conservation impacts enhanced.

Emerging diseases caused by coronaviruses of likely bat origin (e.g., SARS, MERS, SADS, COVID-19) have disrupted global health and economies for two decades. Evidence suggests that some bat SARS-related coronaviruses (SARSr-CoVs) could infect people directly, and that their spillover is more frequent than previously recognized. Each zoonotic spillover of a novel virus represents an opportunity for evolutionary adaptation and further spread; therefore, quantifying the extent of this spillover may help target prevention programs. We derive current range distributions for known bat SARSr-CoV hosts and quantify their overlap with human populations. We then use probabilistic risk assessment and data on human-bat contact, human viral seroprevalence, and antibody duration to estimate that a median of 66,280 people (95% CI: 65,351–67,131) are infected with SARSr-CoVs annually in Southeast Asia. These data on the geography and scale of spillover can be used to target surveillance and prevention programs for potential future bat-CoV emergence. Coronaviruses may spill over from bats to humans. This study uses epidemiological data, species distribution models, and probabilistic risk assessment to map overlap among people and SARSr-CoV bat hosts and estimate how many people are infected with bat-origin SARSr-CoVs in Southeast Asia annually.

Speciation through niche divergence often occurs as lineages of organisms colonize and adapt to new environments with novel ecological opportunities that facilitate the evolution of ecologically different phenotypes. In snakes, adaptive diversification may be driven by the evolution of traits relating to changes in their diets. Accordingly, habitatmediated differences in prey available to ancestral snakes as they colonized and occupied novel dynamic landscapes are likely to have been a strong selective agent behind the divergence and radiation of snakes across the globe. Using an ancestral reconstruction approach that considers the multivariate nature of ecological phenotypes while accounting for sampling variation between taxa, we explored how diet and macro-habitat use coevolved across a phylogeny of 67 species of Afro-Asian colubrine snakes. Our results show that the most recent common ancestor of this clade was likely a dietary generalist that occupied tropical forests in Asia. Deviations from this generalist diet to a variety of specialist diets each dominated by the utilization of single prey types repeatedly occurred as ancestral colubrines shifted from tropical forests to savanna and grassland habitats across Africa. We additionally found that dietary specialist species were on average smaller in maximum length than dietary generalists, congruent with established predator-size, preydiversity dynamics in snakes. We speculate that adaptive divergence in ancestral colubrines arose as a result of a selective regime that favored diets comprised of terrestrial prey, and that partitioning of different prey types led to the various forms of dietary specialization evident in these lineages today. Our findings provide new insights into the ecological correlates associated with the evolution of diet in snakes, thereby furthering our understanding of the driving forces behind patterns of snake diversification.

Aims Human-induced pressures such as deforestation cause anthropogenic range contractions (ARCs). Such contractions present dynamic distributions that may engender data misrepresentations within species distribution models. The temporal bias of occurrence data—where occurrences represent distributions before (past bias) or after (recent bias) ARCs—underpins these data misrepresentations. Occurrence–habitat mismatching results when occurrences sampled before contractions are modelled with contemporary anthropogenic variables; niche truncation results when occurrences sampled after contractions are modelled without anthropogenic variables. Our understanding of their independent and interactive effects on model performance remains incomplete but is vital for developing good modelling protocols. Through a virtual ecologist approach, we demonstrate how these data misrepresentations manifest and investigate their effects on model performance. Location Virtual Southeast Asia. Methods Using 100 virtual species, we simulated ARCs with 100-year land-use data and generated temporally biased (past and recent) occurrence datasets. We modelled datasets with and without a contemporary land-use variable (conventional modelling protocols) and with a temporally dynamic land-use variable. We evaluated each model's ability to predict historical and contemporary distributions. Results Greater ARC resulted in greater occurrence–habitat mismatching for datasets with past bias and greater niche truncation for datasets with recent bias. Occurrence–habitat mismatching prevented models with the contemporary land-use variable from predicting anthropogenic-related absences, causing overpredictions of contemporary distributions. Although niche truncation caused underpredictions of historical distributions (environmentally suitable habitats), incorporating the contemporary land-use variable resolved these underpredictions, even when mismatching occurred. Models with the temporally dynamic land-use variable consistently outperformed models without. Main conclusions We showed how these data misrepresentations can degrade model performance, undermining their use for empirical research and conservation science. Given the ubiquity of ARCs, these data misrepresentations are likely inherent to most datasets. Therefore, we present a three-step strategy for handling data misrepresentations: maximize the temporal range of anthropogenic predictors, exclude mismatched occurrences and test for residual data misrepresentations.

Species distribution models (SDM) have been proposed as valuable first screening tools for predicting species responses to new environmental conditions. SDMs are usually conducted at the species level, assuming that species-environment relationships are a species-specific feature that do not evolve …

Global climate change caused by fossil energy consumption is strongly threatening the species diversity of mammals. In particular, changes in temperature and precipitation have affected the habitat of pangolins. Thus, we employed the MaxEnt modeling approach to simulate the potential habitat distrib…

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…