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Species distribution modeling (SDM) has become an increasingly common approach to explore questions about ecology, geography, outbreak risk, and global change as they relate to infectious disease vectors. Here, we conducted a systematic review of the scientific literature, screening 563 abstracts and identifying 204 studies that used SDMs to produce distribution estimates for mosquito species. While the number of studies employing SDM methods has increased markedly over the past decade, the overwhelming majority used a single method (maximum entropy modeling; MaxEnt) and focused on human infectious disease vectors or their close relatives. The majority of regional models were developed for areas in Africa and Asia, while more localized modeling efforts were most common for North America and Europe. Findings from this study highlight gaps in taxonomic, geographic, and methodological foci of current SDM literature for mosquitoes that can guide future efforts to study the geography of mosquito-borne disease risk. Graphical Abstract

The similarity in the three-dimensional structures of homologous proteins imposes strong constraints on their sequence variability. It has long been suggested that the resulting correlations among amino acid compositions at different sequence positions can be exploited to infer spatial contacts within the tertiary protein structure. Crucial to this inference is the ability to disentangle direct and indirect correlations, as accomplished by the recently introduced direct-coupling analysis (DCA). Here we develop a computationally efficient implementation of DCA, which allows us to evaluate the accuracy of contact prediction by DCA for a large number of protein domains, based purely on sequence information. DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global structure of the contact map for the majority of the protein domains examined. Furthermore, our analysis captures clear signals beyond intradomain residue contacts, arising, e.g., from alternative protein conformations, ligand-mediated residue couplings, and interdomain interactions in protein oligomers. Our findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, contingent on the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing.

The brown planthopper, Nilaparvata lugens (Stål) is the most serious pest of rice across the world. It is also known to transmit stunted viral disease; the insect alone or in combination with a virus causes the breakdown of rice vascular system, leading to economic losses in commercial rice production. Despite its immense economic importance, information on its potential distribution and factors governing the present and future distribution patterns is limited. Thus, in the present study we used maximum entropy modelling with bioclimatic variables to predict the present and future potential distribution of N. lugens in India as an indicator of risk. The predictions were mapped for spatio-temporal variation and area was analysed under suitability ranges. Jackknife analysis indicated that N. lugens geographic distribution was mostly influenced by temperature-based variables that explain up to 68.7% of the distribution, with precipitation factors explaining the rest. Among individual factors, the most important for distribution of N. lugens was annual mean temperature followed by precipitation of coldest quarter and precipitation seasonality. Our results highlight that the highly suitable areas under current climate conditions are 7.3%, whereas all projections show an increase under changing climatic conditions with time up to 2090, and with emission scenarios and a corresponding decrease in low-risk areas. We conclude that climate change increases the risk of N. lugens with increased temperature as it is likely to spread to the previously unsuitable areas in India, demanding adaptation strategies.

Building monuments was one way that past societies reconfigured their landscapes in response to shifting social and ecological factors. Understanding the connections between those factors and monument construction is critical, especially when multiple types of monuments were constructed across the same landscape. Geospatial technologies enable past cultural activities and environmental variables to be examined together at large scales. Many geospatial modeling approaches, however, are not designed for presence-only (occurrence) data, which can be limiting given that many archaeological site records are presence only. We use maximum entropy modeling (MaxEnt), which works with presence-only data, to predict the distribution of monuments across large landscapes, and we analyze MaxEnt output to quantify the contributions of spatioenvironmental variables to predicted distributions. We apply our approach to co-occurring Late Precontact (ca. A.D. 1000–1600)monuments in Michigan: (i) mounds and (ii) earthwork enclosures. Many of these features have been destroyed by modern development, and therefore, we conducted archival research to develop our monument occurrence database. We modeled each monument type separately using the same input variables. Analyzing variable contribution to MaxEnt output, we show that mound and enclosure landscape suitability was driven by contrasting variables. Proximity to inland lakes was key to mound placement, and proximity to rivers was key to sacred enclosures. This juxtaposition suggests that mounds met local needs for resource procurement success, whereas enclosures filled broader regional needs for intergroup exchange and shared ritual. Our study shows how MaxEnt can be used to develop sophisticated models of past cultural processes, including monument building, with imperfect, limited, presence-only data.

Background Despite the increasing number of cases of scrub typhus and its expanding geographical distribution in China, its potential distribution in Fujian Province, which is endemic for the disease, has yet to be investigated. Methods A negative binomial regression model for panel data mainly comprising meteorological, socioeconomic and land cover variables was used to determine the risk factors for the occurrence of scrub typhus. Maximum entropy modeling was used to identify the key predictive variables of scrub typhus and their ranges, map the suitability of different environments for the disease, and estimate the proportion of the population at different levels of infection risk. Results The final multivariate negative binomial regression model for panel data showed that the annual mean normalized difference vegetation index had the strongest correlation with the number of scrub typhus cases. With each 0.1% rise in shrubland and 1% rise in barren land there was a 75.0% and 37.0% increase in monthly scrub typhus cases, respectively. In contrast, each unit rise in mean wind speed in the previous 2 months and each 1% increase in water bodies corresponded to a decrease of 40.0% and 4.0% in monthly scrub typhus cases, respectively. The predictions of the maximum entropy model were robust, and the average area under the curve value was as high as 0.864. The best predictive variables for scrub typhus occurrence were population density, annual mean normalized difference vegetation index, and land cover types. The projected potentially most suitable areas for scrub typhus were widely distributed across the eastern coastal area of Fujian Province, with highly suitable and moderately suitable areas accounting for 16.14% and 9.42%, respectively. Of the total human population of the province, 81.63% reside in highly suitable areas for scrub typhus. Conclusions These findings could help deepen our understanding of the risk factors of scrub typhus, and provide information for public health authorities in Fujian Province to develop more effective surveillance and control strategies in identified high risk areas in Fujian Province.

A rapidly growing population and ongoing urbanization continue to strain agriculture's capacity to maintain a stable food supply, both through direct impacts such as land reclamation and indirect effects driven by accelerating climate change. One of the major consequences of climate change is the shifting geographic range of infectious plant pathogens, particularly , the causative agent of bacterial wilt. This pathogen poses a significant threat to several economically important crops including tomatoes, bananas, eggplants, and tobacco. To assess the current and future potential distribution of under various climate scenarios, maximum entropy (MaxEnt) modeling was applied. This method was used to construct predictive maps based on environmental variables influencing the pathogen's distribution. The predictive models demonstrated high accuracy and performance, with an area under the curve (AUC) of 0.89 and a true skill statistic (TSS) of 0.94. Annual mean temperature was identified as the most significant environmental predictor. The present-day distribution map revealed an almost cosmopolitan range, while future climate change scenarios indicated substantial shifts in distribution across all continents. These findings highlight the urgent need for implementing sustainable agricultural practices and developing novel, environmentally friendly methods to control the spread of . This is especially critical in developing countries where agriculture is most vulnerable, to ensure food security under changing climate conditions.

Schisandrae Sphenantherae Fructus (SSF), the dry ripe fruit of Schisandra sphenanthera Rehd. et Wils., is a traditional Chinese medicine with wide application potential. The quality of SSF indicated by the composition and contents of secondary metabolites is closely related to environmental factors, such as regional climate and soil conditions. The aims of this study were to predict the distribution patterns of potentially suitable areas for S. sphenanthera in China and pinpoint the major environmental factors influencing its accumulation of medicinal components. An optimized maximum entropy model was developed and applied under current and future climate scenarios (SSP1-RCP2.6, SSP3-RCP7, and SSP5-RCP8.5). Results show that the total suitable areas for S. sphenanthera (179.58×10 4 km 2 ) cover 18.71% of China’s territory under the current climatic conditions (1981–2010). Poorly, moderately, and highly suitable areas are 119.00×10 4 km 2 , 49.61×10 4 km 2 , and 10.98×10 4 km 2 , respectively. The potentially suitable areas for S. sphenanthera are predicted to shrink and shift westward under the future climatic conditions (2041–2070 and 2071–2100). The areas of low climate impact are located in southern Shaanxi, northwestern Guizhou, southeastern Chongqing, and western Hubei Provinces (or Municipality), which exhibit stable and high suitability under different climate scenarios. The contents of volatile oils, lignans, and polysaccharides in SSF are correlated with various environmental factors. The accumulation of major secondary metabolites is primarily influenced by temperature variation, seasonal precipitation, and annual precipitation. This study depicts the potential distribution of S. sphenanthera in China and its spatial change in the future. Our findings decipher the influence of habitat environment on the geographical distribution and medicinal quality of S. sphenanthera , which could have great implications for natural resource conservation and artificial cultivation.

The identification of significant habitats for highly mobile marine vertebrates is essential for their conservation. Evidence is often difficult to obtain for deep-diving species such as sperm whales (Physeter macrocephalus), where standard visual survey methods are not sufficient to detect the species. Sperm whales rely on sound for most of their activities, so acoustics is a crucial tool to locate them in the environment and collect information about their daily life. We used a maximum entropy (MaxEnt) modeling approach to predict potential habitats for sperm whales during 2007–2015 in an area of the Mediterranean Sea (characterized by submarine canyon systems) where sperm whale singletons, social units of females and calves, and clusters with immature males, were regularly encountered in sympatry. Models to test species’ distribution and the potential differences between groups of varying composition and life stages were based on 3 independent variables (depth, slope, and Euclidean distance from the nearest coast) and a combination of presence-only visual and acoustic data from boat-based surveys. One variable (depth) was the strongest predictor in all encounters (pooled data) and clusters, whereas distance from coast and slope best predicted encounters with singletons and social units, respectively. The model predicted suitable locations in areas that were well-known sperm whale habitat and in new regions of previously overlooked habitat, which possibly represent key areas for this endangered species in the Mediterranean. This study highlights that consideration should be taken regarding type of social aggregation when using modeling techniques for generating suitable habitat maps for conservation purposes.

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 quarter (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.

Tigers have seen significant population losses due to the degradation and fragmentation of their habitat ranges worldwide. Thus, habitat suitability assessment of such predators is essential for restoring their numbers and devising strategies for their protection. This paper aims to assess the habitat suitability and potential corridors for Bengal tiger species (Panthera tigris tigris) in the Valmiki Tiger Reserve (VTR) located in the West Champaran district of Bihar, India. Nine suitability conditioning factors (tree cover, prey richness, drainage density, vegetation types, elevation, slope, aspect, temperature, and rainfall) and seven threatening factors (forest fragmentation, land use land cover, distance from roads, railway tracks, settlement, range offices, and forest fire points) were selected for emphasizing species-environment association in VTR. The spatial layers of all the factors and presence location data of tigers were integrated into the MaxEnt model to prepare a habitat suitability map. The model was validated utilizing the receiver operating characteristic (ROC) curve (0.822), which was found in good agreement. The least-cost corridor modeling based on surface resistance was utilized to identify the cost-effective pathways and prioritize dispersal routes and potential corridors for this species. The findings revealed that the largest area of the Reserve was found to be moderately suitable (41.92%), followed by low suitable (22.98%), highly suitable (19.34%), and unsuitable areas (15.76%). The potential causes for low suitability and unsuitable habitats included human-induced disturbances, especially in the buffer zone of VTR. The core habitats and their connectivity, particularly in the eastern and central parts of the Reserve, facilitated the dispersal of the Bengal tiger population. This study offers significant insights for identifying crucial habitats and establishing corridors between them. The study calls for suitable measures for restricting human encroachment and increasing predator movements from the adjacent corridors of the protected reserves of Nepal and Uttar Pradesh. The findings may help forest managers and stakeholders for suggesting suitable conservation and restoration practices as well as regulating strategies for the self-sustenance of reintroduced tigers in the Reserve.