The interactions of environmental, geographic, socio-demographic, and epidemiological factors in shaping mosquito-borne disease transmission dynamics are complex and changeable, influencing the abundance and distribution of vectors and the pathogens they transmit. In this study, 27 years of cross-sectional malaria survey data (1990-2017) were used to examine the effects of these factors on Plasmodium falciparum and Plasmodium vivax malaria presence at the community level in Africa and Asia. Monthly long-term, open-source data for each factor were compiled and analyzed using generalized linear models and classification and regression trees. Both temperature and precipitation exhibited unimodal relationships with malaria, with a positive effect up to a point after which a negative effect was observed as temperature and precipitation increased. Overall decline in malaria from 2000 to 2012 was well captured by the models, as was the resurgence after that. The models also indicated higher malaria in regions with lower economic and development indicators. Malaria is driven by a combination of environmental, geographic, socioeconomic, and epidemiological factors, and in this study, we demonstrated two approaches to capturing this complexity of drivers within models. Identifying these key drivers, and describing their associations with malaria, provides key information to inform planning and prevention strategies and interventions to reduce malaria burden.

The Americas hold the greatest bird diversity worldwide. Likewise, ectoparasite diversity is remarkable, including ticks of the Argasidae and Ixodidae families - commonly associated with birds. Considering that ticks have potential health implications for humans, animals, and ecosystems, we conducted a systematic review to evaluate the effects of bioclimatic, geographic variables, and bird species richness on tick infestation on wild birds across the Americas. We identified 72 articles that met our inclusion criteria and provided data on tick prevalence in wild birds. Using Generalized Additive Models, we assessed the effect of environmental factors, such as habitat type, climatic conditions, bird species richness, and geographic location, on tick infestation. Our findings show that most bird infestation case studies involved immature ticks, such as larvae or nymphs, while adult ticks represented only 13% of case studies. We found birds infested by ticks of the genera Amblyomma (68%), Ixodes (22%), Haemaphysalis (5%), Dermacentor (1%), and Rhipicephalus (0.8%) in twelve countries across the Americas. Our findings revealed that temperature variation and bird species richness were negatively associated with tick infestation, which also varied with geographic location, increasing in mid-latitudes but declining in extreme latitudes. Our results highlight the importance of understanding how environmental and bird community factors influence tick infestation in wild birds across the Americas and the dynamics of tick-borne diseases and their impact on biodiversity.

Cabomba caroliniana A. Gray, an ornamental submerged plant indigenous to tropical America, has been introduced to numerous countries in Europe, Asia, and Oceania, impacting native aquatic ecosystems. Given this species is a popular aquarium plant and widely traded, there is a high risk of introduction and invasion into other environments. In the current study the potential global geographic distribution of C. caroliniana was predicted under the effects of climate change and human influence in an optimised MaxEnt model. The model used rigorously screened occurrence records of C. caroliniana from hydro informatic datasets and 20 associated influencing factors. The findings indicate that temperature and human-mediated activities significantly influenced the distribution of C. caroliniana. At present, C. caroliniana covers an area of approximately 1531×104 km2 of appropriate habitat, especially in the south-eastern parts of South, central and North America, Southeast Asia, eastern Australia, and most of Europe. The suitable regions are anticipated to expand under future climate scenarios; however, the dynamics of the changes vary between different extents of climate change. For example, C. caroliniana is expected to expand to higher latitudes, following global temperature increases under SSP1-2.6 and SSP2-4.5 scenarios, however, intolerance to temperature extremes may mediate invasion at higher latitudes under future extreme climate scenarios, e.g., SSP5-8.5. Owing to the severe impacts its invasion causes, early warning and stringent border quarantine processes are required to guard against the introduction of C. caroliniana especially in the invasion hotspots such as, Peru, Italy, and South Korea.

Climate change majorly impacts biodiversity in diverse regions across the world, including South Asia, a megadiverse area with heterogeneous climatic and vegetation regions. However, climate impacts on bats in this region are not well-studied, and it is unclear whether climate effects will follow patterns predicted in other regions. We address this by assessing projected near-future changes in climatically suitable areas for 110 bat species from South Asia. We used ensemble ecological niche modelling with four algorithms (random forests, artificial neural networks, multivariate adaptive regression splines and maximum entropy) to define climatically suitable areas under current conditions (1970-2000). We then extrapolated near future (2041-2060) suitable areas under four projected scenarios (combining two global climate models and two shared socioeconomic pathways, SSP2: middle-of-the-road and SSP5: fossil-fuelled development). Projected future changes in suitable areas varied across species, with most species predicted to retain most of the current area or lose small amounts. When shifts occurred due to projected climate change, new areas were generally northward of current suitable areas. Suitability hotspots, defined as regions suitable for >30% of species, were generally predicted to become smaller and more fragmented. Overall, climate change in the near future may not lead to dramatic shifts in the distribution of bat species in South Asia, but local hotspots of biodiversity may be lost. Our results offer insight into climate change effects in less studied areas and can inform conservation planning, motivating reappraisals of conservation priorities and strategies for bats in South Asia.

The creation of protected areas (PAs) is not always based on science; consequently, some aquatic species may not receive the same level of protection as terrestrial ones. The objective of this study was to identify priority areas for the conservation of chelonians in the Brazilian Amazon basin and assess the contribution of PAs, distinguishing between Full Protection Areas, Sustainable Use Areas, and Indigenous Lands for group protection. The entire species modeling procedure was carried out using Species Distribution Models. Location records were obtained from platforms such as SpeciesLink, GBIF, the Hydroatlas database, and WorldClim for bioclimatic variables adjusted with algorithms like Maximum Entropy, Random Forest, Support Vector Machine, and Gaussian-Bayesian. Indigenous lands cover more than 50% of the distribution areas of chelonian species in the Brazilian Amazon. Protected areas with higher conservation importance (Full Protection Areas and Sustainable Use Areas) hold less than 15% of the combined species distribution. Researchers face significant challenges when making decisions with models, especially in conservation efforts involving diverse taxa that differ significantly from one another within a group of individuals.

Spatial information about the location and suitability of areas for native plant and animal species under different climate futures is an important input to land use and conservation planning and management. Australia, renowned for its abundant species diversity and endemism, often relies on modeled data to assess species distributions due to the country\'s vast size and the challenges associated with conducting on-ground surveys on such a large scale. The objective of this article is to develop habitat suitability maps for Australian flora and fauna under different climate futures.
Using MaxEnt, we produced Australia-wide habitat suitability maps under RCP2.6-SSP1, RCP4.5-SSP2, RCP7.0-SSP3, and RCP8.5-SSP5 climate futures for 1,382 terrestrial vertebrates and 9,251 vascular plants vascular plants at 5 km2 for open access. This represents 60% of all Australian mammal species, 77% of amphibian species, 50% of reptile species, 71% of bird species, and 44% of vascular plant species. We also include tabular data, which include summaries of total quality-weighted habitat area of species under different climate scenarios and time periods.
The spatial data supplied can help identify important and sensitive locations for species under various climate futures. Additionally, the supplied tabular data can provide insights into the impacts of climate change on biodiversity in Australia. These habitat suitability maps can be used as input data for landscape and conservation planning or species management, particularly under different climate change scenarios in Australia.

BACKGROUND: In Zimbabwe, anthrax is endemic with outbreaks being reported almost annually in livestock, wildlife, and humans over the past 40 years. Accurate modelling of its spatial distribution is key in formulating effective control strategies. In this study, an Ensemble Species Distribution Model was used to model the current and future distribution of anthrax occurrence in Zimbabwe.
METHODS: Bioclimatic variables derived from the Beijing Climate Centre Climate System Model were used to model the disease. Collinearity testing was conducted on the 19 bioclimatic variables and elevation to remove redundancy. Variables that had no collinearity were used for anthrax habitat suitability modelling. Two future climate change scenarios for different Representative Concentration Pathways (RCP), RCP4.5 and RCP8.5 were used. Model evaluation was done using true skill, Kappa statistics and receiver operating characteristics.
RESULTS: The results showed that under current bioclimatic conditions, eastern and western districts of Zimbabwe were modelled as highly suitable, central districts moderately suitable and southern parts marginally suitable for anthrax occurrence. Future predictions demonstrated that the suitable (8%) and highly suitable (7%) areas for anthrax occurrence would increase under RCP4.5 scenario. In contrast, a respective decrease (11%) and marginal increase (0.6%) of suitable and highly suitable areas for anthrax occurrence were predicted under the RCP8.5 scenario. The percentage contribution of the predictors varied for the different scenarios; Bio6 and Bio18 for the current scenario, Bio2, Bio4 and Bio9 for the RCP4.5 and Bio3 and Bio15 for the RCP8.5 scenarios.
CONCLUSIONS: The study revealed that areas currently suitable for anthrax should be targeted for surveillance and prevention. The predicted future anthrax distribution can be used to guide and prioritise surveillance and control activities and optimise allocation of limited resources. In the marginally to moderately suitable areas, effective disease surveillance systems and awareness need to be put in place for early detection of outbreaks. Targeted vaccinations and other control measures including collaborative \'One Health\' strategies need to be implemented in the predicted highly suitable areas. In the southern part where a high decrease in suitability was predicted, continued monitoring would be necessary to detect incursions early.

BACKGROUND: Antarctica and its unique biodiversity are increasingly at risk from the effects of global climate change and other human influences. A significant recent element underpinning strategies for Antarctic conservation has been the development of a system of Antarctic Conservation Biogeographic Regions (ACBRs). The datasets supporting this classification are, however, dominated by eukaryotic taxa, with contributions from the bacterial domain restricted to Actinomycetota and Cyanobacteriota. Nevertheless, the ice-free areas of the Antarctic continent and the sub-Antarctic islands are dominated in terms of diversity by bacteria. Our study aims to generate a comprehensive phylogenetic dataset of Antarctic bacteria with wide geographical coverage on the continent and sub-Antarctic islands, to investigate whether bacterial diversity and distribution is reflected in the current ACBRs.
RESULTS: Soil bacterial diversity and community composition did not fully conform with the ACBR classification. Although 19% of the variability was explained by this classification, the largest differences in bacterial community composition were between the broader continental and maritime Antarctic regions, where a degree of structural overlapping within continental and maritime bacterial communities was apparent, not fully reflecting the division into separate ACBRs. Strong divergence in soil bacterial community composition was also apparent between the Antarctic/sub-Antarctic islands and the Antarctic mainland. Bacterial communities were partially shaped by bioclimatic conditions, with 28% of dominant genera showing habitat preferences connected to at least one of the bioclimatic variables included in our analyses. These genera were also reported as indicator taxa for the ACBRs.
CONCLUSIONS: Overall, our data indicate that the current ACBR subdivision of the Antarctic continent does not fully reflect bacterial distribution and diversity in Antarctica. We observed considerable overlap in the structure of soil bacterial communities within the maritime Antarctic region and within the continental Antarctic region. Our results also suggest that bacterial communities might be impacted by regional climatic and other environmental changes. The dataset developed in this study provides a comprehensive baseline that will provide a valuable tool for biodiversity conservation efforts on the continent. Further studies are clearly required, and we emphasize the need for more extensive campaigns to systematically sample and characterize Antarctic and sub-Antarctic soil microbial communities. Video Abstract.

Rhodiola L. has high nutritional and medicinal value. Little is known about the properties of its habitat distribution and the important eco-environmental factors shaping its suitability. Rhodiola coccinea (Royle) Boriss., Rhodiola gelida Schrenk, Rhodiola kirilowii (Regel) Maxim., and Rhodiola quadrifida (Pall.) Fisch. et Mey., which are National Grade II Protected Plants, were selected for this research. Based on high-resolution environmental data for the past, current, and future climate scenarios, we modeled the suitable habitat for four species by MaxEnt, evaluated the importance of environmental factors in shaping their distribution, and identified distribution shifts under climate change scenarios. The results indicate that the growth distribution of R. coccinea, R. kirilowii, and R. quadrifida is most affected by bio10 (mean temperature of warmest quarter), bio3 (isothermality), and bio12 (annual precipitation), whereas that of R. gelida is most affected by bio8 (mean temperature of wettest quarter), bio13 (precipitation of wettest month), and bio16 (precipitation of wettest quarter). Under the current climate scenario, R. coccinea and R. quadrifida are primarily distributed in Tibet, eastern Qinghai, Sichuan, northern Yunnan, and southern Gansu in China, and according to the 2070 climate scenario, the suitable habitats for both species are expected to expand. On the other hand, the suitable habitats for R. gelida and R. kirilowii, which are primarily concentrated in southwestern Xinjiang, Tibet, eastern Qinghai, Sichuan, northern Yunnan, and southern Gansu in China, are projected to decrease under the 2070 climate scenario. Given these results, the four species included in our study urgently need to be subjected to targeted observation management to ensure the renewal of Rhodiola communities. In particular, R. gelida and R. kirilowii should be given more attention. This study provides a useful reference with valuable insights for developing effective management and conservation strategies for these four nationally protected plant species.

Bioclimatic variables (BCVs) are the most widely used predictors within the field of species distribution modeling, but recent studies imply that BCVs alone are not sufficient to describe these limits. Unfortunately, the most popular database, WorldClim, offers only a limited selection of bioclimatological predictors; thus, other climatological datasets should be considered, and, for data consistency, the BCVs should also be derived from the respective datasets. Here, we investigate how well the BCVs are represented by different datasets for the extended Mediterranean area within the period 1970-2020, how different calculation schemes affect the representation of BCVs, and how deviations among the datasets differ regionally. We consider different calculation schemes for quarters/months, the annual mean temperature (BCV-1), and the maximum temperature of the warmest month (BCV-5). Additionally, we analyzed the effect of different temporal resolutions for BCV-1 and BCV-5. Differences resulting from different calculation schemes are presented for ERA5-Land. Selected BCVs are analyzed to show differences between WorldClim, ERA5-Land, E-OBS, and CRU. Our results show that (a) differences between the two calculation schemes for BCV-1 diminish as the temporal resolution decreases, while the differences for BCV-5 increase; (b) with respect to the definition of the respective month/quarter, intra-annual shifts induced by the calculation schemes can have substantially different effects on the BCVs; (c) all datasets represent the different BCVs similarly, but with partly large differences in some subregions; and (d) the largest differences occur when specific month/quarters are defined by precipitation. In summary, (a) since the definition of BCVs matches different calculation schemes, transparent communication of the BCVs calculation schemes is required; (b) the calculation, integration, or elimination of BCVs has to be examined carefully for each dataset, region, period, or species; and (c) the evaluated datasets provide, except in some areas, a consistent representation of BCVs within the extended Mediterranean region.