Francis Petter

How climate change alters persistence and distribution of endangered species is an urgent question in current ecological research. However, many species distribution models do not consider consumers in the context of their resources. The distribution and survival of the West Indian manatee (Trichechus manatus), listed as a Vulnerable species on the IUCN Red List, critically depend on seagrass resources and freshwater sources for drinking. We parameterized Maxent models with Bio-ORACLE environmental layers, freshwater proximity data, and modelled seagrass distance layers, to determine manatee and seagrass distributions under future climate change scenarios. We used two plausible IPCC Representative Concentration Pathways (RCP45 and RCP26, respectively) for the year 2050. The model fits had high accuracies and predicted a marked decline in seagrass coverage (RCP26: -1.9%, RCP45: -6%), coinciding with declines in manatee ranges (RCP26: -9%, RCP45: -11.8%). We also found that over 94% of the projected manatee distribution for all scenarios fell within the seagrass distribution. The analysis showed a decline in seagrass coverage to significantly impact manatee distributions, since the distance to seagrass ecological layer contributed significantly to manatee distributions, along with distance to freshwater sources. Our findings suggest that manatees will lose substantial range due to future climate change, but the extent and direction of this change will be mediated by the degree of warming and its impact on the resources manatees depend on.

Climate change is a potential human-induced threat to rhino populations and their habitat. Information on the effects of climate change on rhinoceros species can help manage and develop conservation plans to adapt to these changes. In this study, two climate change scenarios were used to predict temperature and precipitation changes in national parks in southern Africa and the effect those changes would have on black (Diceros bicornis) and white (Ceratotherium simum) rhinoceros populations. The study used the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs) 4.5 and 8.5, atmospheric CO2 concentrations of 650 and 1370 ppm, for the years 2055 and 2085 to explore the temperature and precipitation changes. All spatial information was processed using Geographic Information Systems and statistical analysis. Results show the changing climate will have significant negative impacts on the probability of occurrence of both species. Temperature changes will affect these probabilities more than precipitation changes. All study parks will have zero probability of occurrence for the species throughout their ranges should conditions reach those represented by the RCP 8.5 scenario late in the century. Conservation activities for the rhinoceros should take into consideration the potential for temperature and precipitation changes modelled in this study.

Climate change is one of the main threats to biodiversity in the 21st century. However, the effects that it may have on different mammal species are unknown, making it difficult to implement conservation strategies. In this paper, we used species distribution models (SDM) to assess the effect of global climate change on the potential distribution of the 8 of the 9 marsupial species in Mexico, and analyzed their distribution in the current system of natural protected areas (NPAs). We used presence records for each species and bioclimatic variables from the present and the future (2050 and 2080) with 2 contrasting possible scenarios (representative concentration pathways RCP 4.5 and 8.5). We found that Tlacuatzin canescens would have the most stable potential range under any climate change scenario, while the remaining species (Caluromys derbianus, Chironectes minimus, Didelphis marsupialis, D. virginiana, Philander opossum, Marmosa mexicana, and Metachirus nudicaudatus) would undergo notable range losses in the future, though there would not only be losses—according to our SDMs, for all species there would be some range gain under the different climate scenarios, assuming the vegetation cover remained. The current system of NPAs in Mexico currently protects and under the 2 future scenarios would protect less than 20% of the potential range of marsupials, so a reevaluation of their areas beyond the NPAs is highly recommended for the long-term conservation of this group. Our results provide relevant information on the estimated effects of global climate change on marsupials, allowing us to design more effective methodologies for the protection of this portion of the mammalian fauna in Mexico.

Background Carnivore mammals are animals vulnerable to human interference, such as climate change and deforestation. Their distribution and persistence are affected by such impacts, mainly in tropical regions such as the Amazon. Due to the importance of carnivores in the maintenance and functioning of the ecosystem, they are extremely important animals for conservation. We evaluated the impact of climate change on the geographic distribution of carnivores in the Amazon using Species Distribution Models (SDMs). Do we seek to answer the following questions: (1) What is the effect of climate change on the distribution of carnivores in the Amazon? (2) Will carnivore species lose or gain representation within the Protected Areas (PAs) of the Amazon in the future? Methods We evaluated the distribution area of 16 species of carnivores mammals in the Amazon, based on two future climate scenarios (RCP 4.5 and RCP 8.5) for the year 2070. For the construction of the SDMs we used bioclimatic and vegetation cover variables (land type). Based on these models, we calculated the area loss and climate suitability of the species, as well as the effectiveness of the protected areas inserted in the Amazon. We estimated the effectiveness of PAs on the individual persistence of carnivores in the future, for this, we used the SDMs to perform the gap analysis. Finally, we analyze the effectiveness of PAs in protecting taxonomic richness in future scenarios. Results The SDMs showed satisfactory predictive performance, with Jaccard values above 0.85 and AUC above 0.91 for all species. In the present and for the future climate scenarios, we observe a reduction of potencial distribution in both future scenarios (RCP4.5 and RCP8.5), where five species will be negatively affected by climate change in the RCP 4.5 future scenario and eight in the RCP 8.5 scenario. The remaining species stay stable in terms of total area. All species in the study showed a loss of climatic suitability. Some species lost almost all climatic suitability in the RCP 8.5 scenario. According to the GAP analysis, all species are protected within the PAs both in the current scenario and in both future climate scenarios. From the null models, we found that in all climate scenarios, the PAs are not efficient in protecting species richness.

The distribution and transmission of Yersinia pestis , the bacterial agent of plague, responds dynamically to climate, both within wildlife reservoirs and human populations. The exact mechanisms mediating plague's response to climate are still poorly understood, particularly across large environmentally heterogeneous regions encompassing several reservoir species. A heterogeneous response to precipitation was observed in plague intensity across northern and southern China during the Third Pandemic. This has been attributed to the response of reservoir species in each region. We use environmental niche modelling and hindcasting methods to test the response of a broad range of reservoir species to precipitation. We find little support for the hypothesis that the response of reservoir species to precipitation mediated the impact of precipitation on plague intensity. We instead observed that precipitation variables were of limited importance in defining species niches and rarely showed the expected response to precipitation across northern and southern China. These findings do not suggest that precipitation–reservoir species dynamics never influence plague intensity but that instead, the response of reservoir species to precipitation across a single biome cannot be assumed and that limited numbers of reservoir species may have a disproportional impact upon plague intensity.

Abiotic factors are usually considered key drivers of species distribution at macro scales, while biotic interactions are mostly used at local scales. A few studies have explored the role of biotic interactions at macro scales, but all considered a limited number of species and obligate interactions. We examine the role of biotic interactions in large‐scale SDMs by testing two main hypotheses: (1) biotic factors in SDMs can have an important role at continental scale; (2) the inclusion of biotic factors in large‐scale SDMs is important also for generalist species. We used a maximum entropy algorithm to model the distribution of 177 bat species in Africa calibrating two SDMs for each species: one considering only abiotic variables (noBIO‐SDMs) and the other (BIO‐SDMs) including also biotic variables (trophic resource richness). We focused the interpretation of our results on variable importance and response curves. For each species, we also compared the potential distribution measuring the percentage of change between the two models in each pixel of the study area. All models gave AUC >0.7, with values on average higher in BIO‐SDMs compared to noBIO‐SDMs. Trophic resources showed an importance overall higher level than all abiotic predictors in most of the species (~68%), including generalist species. Response curves were highly interpretable in all models, confirming the ecological reliability of our models. Model comparison between the two models showed a change in potential distribution for more than 80% of the species, particularly in tropical forests and shrublands. Our results highlight the importance of considering biotic interactions in SDMs at macro scales. We demonstrated that a generic biotic proxy can be important for modeling species distribution when species‐specific data are not available, but we envision that a multi‐scale analysis combined with a better knowledge of the species might provide a better understanding of the role of biotic interactions.

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.

Population fluctuations are widespread across the animal kingdom, especially in the order Rodentia, which includes many globally important reservoir species for zoonotic pathogens. The implications of these fluctuations for zoonotic spillover remain poorly understood. Here, we report a global empirical analysis of data describing the linkages between habitat use, population fluctuations and zoonotic reservoir status in rodents. Our quantitative synthesis is based on data collated from papers and databases. We show that the magnitude of population fluctuations combined with species’ synanthropy and degree of human exploitation together distinguish most rodent reservoirs at a global scale, a result that was consistent across all pathogen types and pathogen transmission modes. Our spatial analyses identified hotspots of high transmission risk, including regions where reservoir species dominate the rodent community. Beyond rodents, these generalities inform our understanding of how natural and anthropogenic factors interact to increase the risk of zoonotic spillover in a rapidly changing world. Many rodent species are known as hosts of zoonotic pathogens, but the ecological conditions that trigger spillover are not well-understood. Here, the authors show that population fluctuations and association with human-dominated habitats explain the zoonotic reservoir status of rodents globally.

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.