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The loss of habitat for numerous organisms due to climate change has significantly accelerated the rate of species extinction. Unfortunately, there have been no studies conducted on the impact of climate change and other factors on the distribution patterns of cicada species in India. In the present study, we investigated the current and potential future distribution of two cicada species, Platypleura octoguttata and Lemuriana apicalis, using environmental variables and occurrence data through maximum entropy modelling. The distribution ranges of both species show some similarities under the current climatic conditions. According to predictions based on future climate scenarios, the distribution areas for P. octoguttata and L. apicalis are predicted to decrease to varying extents. However, the anticipated reduction of distribution areas for these two cicada species is different, indicating that both species have distinct responses to climate change. The changes in the distributional centroids show a consistent trend of moving in a north-westward direction across all future periods under the four climate scenarios (SSP126, SSP264, SSP370, and SSP585), except for SSP370 in the case of L. apicalis, which shows the direction of overall migration north-eastwards over time. The creation of a new protected area at the border of Bijnor District in Uttar Pradesh Province and Haridwar District in Uttarakhand Province would be greatly helpful in future for the conservation of these two species. Our findings highlight the impact of climate change on the distribution range of these two cicada species, offering valuable insights for conservation efforts in India.

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

To support conservation efforts, accurate mapping of marine organism community’ distribution has become more critical than ever before. While previous mapping endeavours have primarily focused on easily accessible shallow‐water habitats, there remains limited knowledge about the ecosystems lying beyond SCUBA diving depths, such as mesophotic coral ecosystems (MCEs, ~30–150 m). MCEs are important habitats from an ecological and conservation perspective, yet little is known about the environmental factors that shape these ecosystems and their distribution, particularly in the Indian Ocean region. The goals of this study are to (1) predict the spatial distribution and extent of distinct benthic communities and MCEs in the Chagos Archipelago, central Indian Ocean, (2) test the effectiveness of a range of environmental and topography derived variables to predict the location of MCEs around Egmont Atoll and the Archipelago, and (3) independently validate the models produced. In addition, we compared the MCEs predicted extent in the Archipelago for the models derived from high‐resolution multibeam and low‐resolution GEBCO bathymetry data. Using maximum entropy modelling, all models resulted in excellent (> 0.9) performances, for AUC and threshold‐dependent metrics, predicting extensive and previously undocumented MCEs across the entire Archipelago with, however, differences in the predicted extent between the high‐ and low‐resolution models. Independent validation resulted in fair (> 0.7 AUC) and poor (> 0.6 AUC) performances for the high‐resolution and low‐resolution models, respectively. Photosynthetically active radiation (PAR), temperature, chlorophyll‐a, and topographically derived variables were identified as the most influential predictors. In conclusion, this study provides the first prediction of the distribution of MCEs and their distinct benthic communities in the Archipelago. It highlights their significance in terms of potential extent and response to various environmental factors, supporting decision making for prioritising future survey sites to study MCEs across the Archipelago and targeting ecologically important areas for conservation. This study aims to map and predict the distribution of mesophotic coral ecosystems (MCEs) in the Chagos Archipelago, Indian Ocean, using high‐ and low‐resolution bathymetry data. The models, developed using environmental and topographical variables, showed excellent predictive performance, revealing extensive, previously undocumented MCEs; though independent validation showed stronger results for high‐resolution data. The findings offer valuable insights for conservation efforts by highlighting the ecological importance of MCEs and guiding future research and conservation priorities in the region.

Agriculture, global biodiversity and distribution of species are increasingly influenced by changing climate. Assessing the future distribution of biodiversity under different climate change scenarios is an essential step towards conservation planning and policy implementations. To understand the climate change impacts, the present study used Garcinia gummi - gutta cash crop species as a case study that is even exported, adding the nation’s foreign reserve. Given the importance of this crop for local and national economy, the main objectives of the study were to analyse the impact of present and future climates on ecologically susceptible G. gummi - gutta species in the Western Ghats based on maximum entropy model (MaxEnt). Future projections with RCP scenarios for 2050 and 2070 were made using the data of 84 species occurrence and climatic variables of three climate models from IPCC 5th assessment. The contribution of climatic variables was analysed by jackknife test, and 0.888 of AOC indicates high accuracy of the model results. It was found that annual precipitation, coldest quarter precipitation, and precipitation seasonality were the key determining factors for the suitability of this species. In addition, the results of all scenarios showed that the current suitability of the species would be dramatically decreased by 2050 and 2070. The study suggests how the MaxEnt approach can be an important tool for agricultural development, management of species habitats, conservation of biodiversity, and climate change rehabitation planning.

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.

There are significant challenges associated with mapping critical habitat for large, migratory species. The humpback whales of Oceania in the South Pacific are no exception, with their winter breeding grounds spanning >4000 km of ocean basin. This subpopulation is listed as endangered, but there are few systematic spatial data with which to prioritise specific areas for additional research or conservation. A few sites in Oceania have been the focus of long-term, non-systematic population surveys. Using the maximum entropy algorithm, we developed predictive habitat models for 2 such sites: American Samoa 2003–2010 (n = 300) and Tonga 1996–2007 (n = 475), using sightings of whale groups and environmental factors hypothesised to influence their space-use patterns. At both sites, shallow water was the best predictor of the spatial distribution of mother–calf pairs. In contrast, access to deep water was important for adult groups, and sea-floor slope and rugosity influenced habitat suitability for males engaged in acoustic breeding displays. Our study illustrates the value of predictive modelling for identifying habitat partitioning for specific sub-groups of a wider population. Similarities between habitat requirements predicted in our study to those identified for other populations suggest that the slow recovery of Oceania humpback whales cannot be attributed to unusual breeding-habitat needs; instead, there may be other factors influencing the slow increase in population size. We recommend that the modelling techniques utilised here be used to identify other breeding sites within Oceania for future research and conservation efforts across the South Pacific region.

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.

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.

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.