MaxEnt Approach Research Articles

Potential distribution of Detarium microcarpum under different climate change scenarios in Burkina Faso

Climate change and human activities are major drivers influencing species distribution, posing significant challenges for woody species that provide essential ecosystem goods and services. Therefore, a thorough understanding of climate impacts on species distribution is crucial for more effective management. This study aims to assess the effect of climate change on the current and future distribution of Detarium microcarpum. This study was conducted in Burkina Faso. The maximum entropy (MaxEnt) approach was used to simulate the current and future distributions of the species. The future distribution of the species was assessed through four global climate models: HadGEM3-GC3.1-LL, ACCESS-CM2, CNRM-CM6-1 and MIROC6 under two Shared Socioeconomic Pathways (SSPs) scenarios, SSP2-4.5 and SSP5-8.5 at the horizons 2061–2080 and 2081–2100. The area under the curve (AUC ˃ 0.80) and True Skill Statistic (TSS=0.7) revealed a high level of prediction for the current and future scenarios. The results showed that the mean temperature of the Wettest Quarter (bio8) and Isothermality (bio3) were the most important environmental variables which affect D. microcarpum distribution. The results show that the current suitability area for the species covers 65.6 % of the country area with respectively high suitability (36.73 %) and low suitability (29.16 %). Based on the outcomes of the prediction, the high suitable areas for D. microcarpum conservation will decrease on average by 5,92 % of their current area under future climate change, regardless of the scenario and model used, except for the MIROC6 model under the SSP5-8.5 scenario, which will increase its current area from 0.56 % to 1.7 %. The result of this study underscores the significant effect of the future climate on the suitable area of D. microcarpum. Specific actions should be taken to ensure the suitable conservation of this valuable multipurpose species.

Habitat suitability and flood risk modeling for Gazella conservation actions in Mond protected area

Extreme floods are recognized as a major factor contributing to high mortality rates in mammalian populations. The primary objective of this study is to map flood hazards and evaluate habitat suitability within the Mond Protected Area (MPA), near the Mond River. This region is particularly important as it serves as a habitat for the vulnerable gazelle (Gazella subgutturosa). Through field surveys and information gathering, the study examines the effects of flooding on the mortality, distribution, and abundance of this gazelle population.The MaxEnt approach was used to assess habitat suitability, while flood hazard zoning was conducted using the HEC-RAS model. Field studies and the resulting flood hazard map identified three specific locations "Barid," "Rudbor," and "Cham Sheikh" where floodwaters infiltrate the protected area. This infiltration is attributed to riverbank erosion and the absence of protective vegetation in the above areas. The extreme flood on March 20, 2017, significantly increased gazelle mortality rates.By analyzing the maps integrating flood zoning, gazelle distribution, and population aggregation within the MPA, along with field surveys, practical and significant operational measures to enhance the gazelle habitat have been identified. One key measure includes establishing low-elevation platforms in areas where gazelles congregate. Six platforms have been designated, with three marked as "priority one" and three as "priority two," as the most effective solution for reducing gazelle casualties within the MPA. This decision considers factors such as the flood zoning map, gazelle presence, and historical data on locations with a substantial casualty of this species during floods.

Modeling the distribution of pine wilt disease in China using the ensemble models MaxEnt and CLIMEX.

Pine wilt disease (PWD) is a devastating plant disease caused by the pinewood nematode (PWN, Bursaphelenchus xylophilus) that is transmitted by several beetle species in the genus, Monochamus. Once present, the disease is difficult to control. Prevention rather than control is regarded as an effective strategy for PWD management. Central to this prevention strategy is the ability to predict the potential distribution of the disease. Here, we employed an integrated MaxEnt and CLIMEX approach to model the potential distribution of PWD under various climate-change scenarios. Our results indicate that rising temperatures and lower humidity under climate change will render some of the northern regions of China more suitable for the nematode and these beetles, causing the gradual northward movement of PWD. Furthermore, suitable habitats for three pine species, Pinus massoniana, P. taiwanensis and P. shurbergia, overlap with PWN and Monochamus, suggesting that these three species are potentially at high risk of PWD. Thus, PWD management should target the northern regions of China and the three pine species that are most susceptible to PWD.

Assessment of Climate Change Impact on Future Distribution of Palm Trees in Niger, West Africa

Palm trees are multi-purpose species that are involved in several socio-economic and cultural aspects of Niger people. Indeed, palm trees significantly improve local population life. This study aims to assess the impact of future climate change on the geographical distribution of Borassus aethiopum Mart., Hyphaene thebaica (L.) Mart. and Phoenix dactylifera L. and to determine the suitable habitats for the conservation and sustainable management of these species in Niger. The maxEnt approach was used to model the species distribution. Four climatic models of CHELSA V2.1 were used: gfdl-esm4, ipsl-cm6a-lr, mpi-esm1-2h, mri-esm2-0 under two scenarios: ssp1-2.6 and ssp5-8.5 by periods 2011–2040 and 2041–2070. The mean AUC was 0.93 for Borassus aethiopum (0.93), 0.86 for Hyphaene thebaica, and 0.87 for Phoenix dactylifera. TSS test mean were 0.85, 0.69, and 0.75 respectively for Borassus aethiopum, Hyphaene thebaica, and Phoenix dactylifera. The current distribution for highly suitable habitats was 33,223 km2 (2.22%), 159,495 km2 (10.66%), and 108,189 km2 (7%) respectively for Borassus aethiopum, Hyphaene thebaica, and Phoenix dactylifera. The moderately suitable habitats were 83,019 km2 (5.55%) for Borassus aethiopum, 86,239 km2 (5.76%) for Hyphaene thebaica and 66,426 km2 (4%) Phoenix dactylifera. The lowly suitable habitats were 137,859 km2 (9.21%), 201,609 km2 (13.48%), and 333,181 km2 (22%) respectively Borassus aethiopum, Hyphaene thebaica, and Phoenix dactylifera. The highly and moderately suitable habitats for Borassus aethiopum will increase over the period 2011–2040 and 2041–2070. The highly suitable habitats of Hyphaene thebaica will be highly decreased by the period 2011–2040 and 2041–2070 than the other suitable habitats. All suitable habitats and all models of Phoenix dactylifera will decrease under the period 2041–2070. Phoenix dactylifera and Hyphaene thebaica will be much more affected by future climate change. This will also affect production rates for farmers in Niger.

Potential impacts of climate change on the geographic distribution of Achillea eriophora DC., a medicinal species endemic to Iran in southwestern Asia.

Climate change is considered to rank among the most important global issues affecting species' geographic distributions and biodiversity. Understanding effects of climate change on species can enhance conservation efficacy. In this study, we applied ecological niche modeling (ENM) using maximum entropy (MaxEnt) approaches to predict the potential geographic distribution of Achillea eriophora DC., a medicinal plant species to Iran in southwestern Asia, under current and future climate scenarios. We evaluated potential distributional areas of the species, under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) for the period 2041-2060. Most current potential suitable areas were identified for A. eriophora in montane regions. Our results anticipated that the potential distribution of A. eriophora will expand geographically toward higher elevations and northward. However, the species is expected to experience relatively high losses of suitability in its actual habitats under future climate scenarios. Consequently, we recommend regional-to-national conservation action plans for A. eriophora in its natural habitats.

Mapping an endangered living fossil's distribution, threats, and protection coverage

AbstractSpecies Distribution Models have emerged in recent decades as a powerful tool for biodiversity research, as they allow, for example, assessing the status of species in their whole distributions. These models have been particularly helpful in demonstrating that amphibian populations are at high risk, with many declining and others already extinct. Here, we assessed the potential distribution of the helmeted water toad (Calyptocephalella gayi), an endemic Chilean amphibian considered a living fossil and cataloged as vulnerable, which requires significant conservation efforts. We modeled the species' potential distribution using the Maximum Entropy (Maxent) approach and determined the overlap with national protected areas. In addition, we conducted a geospatial risk analysis to estimate the threat level to which this toad is being subjected. The potential distribution of C. gayi ranges from 28°S to 44°S, mainly explained by altitude, mean diurnal temperature range, slope, and distance to water bodies. Protected areas only cover 3.55% of the species' potential geographic distribution, which is of concern, considering that the geospatial risk analysis showed that 60.61% of C. gayi's distribution is subjected to extreme and high risks. We discuss how these results are relevant to focusing and directing efficient protection and conservation efforts for this species shortly.

Modelling spatial patterns of forest fire occurrence in the Northwestern region of Vietnam

Forest fires present a significant threat to the tropical forest ecosystem in the northwestern region of Vietnam. Our study aimed to assess the impacts of environmental factors on forest fire occurrence and to map forest fire probability for the whole region. The forest fire occurrence data over the period 2003–2016, environmental factors (climate, fuel condition, topography, and human activity), and the MaxEnt approach were used for this study. The MaxEnt model performed better than the random model (AUC>0.88). Climatic factors (especially climatic seasonality: annual temperature range (bio_07), isothermality (bio_03), and precipitation of warmest quarter (bio_18)) had the highest contribution to the model, followed by population density and elevation. In contrast, fuel condition (Land cover type) had a small contribution to the model. While medium, high, and very high probabilities of forest fire occurred at medium to high elevations (e.g., Dien Bien, Son La, and Lai Chau provinces) throughout southern to northern and western areas, very low and low probability concentrated southeastern areas at lower elevations (mainly in Hoa Bình province). Our results may be helpful references for fire managers and policymakers to establish more effective fire management strategies for the region's forest.

Effect of environmental and anthropogenic factors on the distribution and co-occurrence of cold-water corals

Cold-water corals (CWCs) are bioengineering species that can increase habitat heterogeneity and improve the deep sea’s biological diversity and ecosystem functioning. Knowledge of their distribution provides a critical baseline for assessing the effect of natural and anthropogenic impacts on these important deep-sea habitats. The aims of this study are: i) provide new data on the spatial distribution of six CWCs species in the Strait of Sicily, ii) describe the principal environmental and anthropogenic variables that play a role in shaping their distribution, iii) identify hotspots in which individuals belonging to the various species co-occur. Presence-only data of six CWCs species, ten environmental variables (depth, slope, rugosity, aspect, flowdir, temperature, salinity, north bottom current, east bottom current, chlorophyll-a), and one variable relating to bottom trawling effort (Automatic Information System – AIS) were used to predict the suitable habitats. We used Maximum Entropy modelling (MaxEnt) approach and used the AUC (area under the receiver operating characteristic curve) and TSS (true skill statistics) to evaluate the model performance. The results showed excellent AUC, TSS and AUC’s standard deviation mean values for all six species. The validation show high predictive performance. MaxEnt identified slope, depth, and rugosity as the most important predictors, showing the highest percentage contribution for all six species considered. Throughout the study area, highlyinterspecific persistent density hotspot of CWCs co-occurrence were discovered, with a total extension of 4.05 km2 where all species co-occur. Although studies on the effect of environmental and anthropogenic factors that impact the distribution of these species of conservation interest remain scarce, the results of this study offer useful guidance for decision-makers to develop necessary conservation measures.

Filling the maize yield gap based on precision agriculture – A MaxEnt approach

Precision agriculture (PA) and yield gap (Yg) analysis are promising strategies to achieve the desired sustainable intensification of agricultural production systems. Current crop Yg approaches do not consider the internal field yield variability caused by soil properties. Topographic and edaphic characteristics causing consistent high and low yield patterns in time and space can be interpreted as an ecological niche and used as proxies for potential yield (Yp) and Yg. Ecological niche models (ENMs) are statistical models originally developed to forecast a species’ niche. However, its application to analyse crop yield spatio-temporal variability has never been made. This study aimed to fill this void by developing a novel approach: i) to quantify the magnitude and spatio-temporal distribution of Yp and Yg, ii) to identify the main factors that cause the Yg, and iii) to provide statistical and agronomical interpretation of the data to reduce the Yg. We performed this work using high-resolution maize yield maps from three seasons, with an ancillary dataset composed of soil electrical conductivity, soil properties and digital elevation models provided by “Quinta da Cholda”, Portugal. The yield maps were averaged, resulting in a standardised multiyear yield map. The 90th and 10th yield percentiles were interpreted as proxies for Yp and Yg, and analysed by an ENM machine learning algorithm – maximum entropy (MaxEnt). The average Yg and Yp were quantified as 1.5 and 19.1 ton/ha. Yp was characterised by having silty, richer soils and lower elevations, with several nutritional factors above the critical limits to maintain higher yields. Yg had loam soils coupled with higher relative elevations and lower nutrition content. This innovative modelling approach can efficiently manage high-dimensional spatio-temporal data to support advanced PA solutions, allowing detailed support for narrowing the Yg.

Uncertainties in the effectiveness of biological control of stem borers under different climate change scenarios in Eastern Africa

Climate change (CC) is expected to significantly affect biodiversity and ecosystem services. Adverse impacts from CC in the Global South are likely to be exacerbated by limited capacities to take adequate adaptation measures and existing developmental challenges. Insect pests today are already causing considerable yield losses in agricultural crop production in East Africa. Studies have shown that insects are strongly responding to CC by proliferation, shift in distribution, and by altering their phenology, which is why an impact on agriculture can be expected. Biological control (BC) has been proposed as an alternative measure to sustainably contain insect pests, but few studies predict its efficacy under future CC. Using the species maximum entropy modeling (Maxent) approach, we predict the current and future distribution of three important lepidopteran stem borer pests of maize in Eastern Africa, i.e., Busseola fusca (Fuller, 1901), Chilo partellus (Swinhoe, 1885), and Sesamia calamistis (Hampson, 1910), and two parasitoids that are currently used for BC, i.e., Cotesia flavipes (Cameron, 1891) and Cotesia sesamiae (Cameron, 1906). Based on these potential distributions and data collected during household surveys with local farmers in Kenya and Tanzania, also future maize yield losses are predicted for a business-as-usual scenario and a sustainable development scenario. We found that BC of the stem borer pests by C. flavipes and C. sesamiae will be less effective under more severe CC resulting in a reduced ability to curb maize yield losses caused by the stem borers. These results highlight the need to adapt BC measures to future CC to maintain its potential for environmentally friendly pest management strategies. The findings of this research are thus of particular relevance to policymakers, extension officers, and farmers in the region and will aid the adaptation of smallholder agricultural practices to the impacts of CC.

Projection of Robusta Coffee's Climate Suitability for Sustainable Indonesian Coffee Production

This study aims to analyze the impact of climate change on the climate suitability of Robusta coffee in five main Indonesian coffee production centers namely Aceh, North Sumatera, South Sumatera, Bengkulu and Lampung using the Maxent approach. The study used climate data, and climate projections from Worldclim and coffee location data from maps of Indonesia's main agricultural commodities. The results showed that Maxent had good performance in modeling the climatic suitability of Robusta coffee at the provincial level, and the corresponding production areas shifted with different patterns between provinces. The areas with suitable and highly suitable climates for Robusta coffee were projected to decrease in all provinces except for Bengkulu. The findings suggest a future challenge for Robusta coffee sustainability in Indonesia. Aceh, North Sumatera, South Sumatera, and Lampung need to develop adaptation strategies to anticipate the increasingly unsuitable environment. On the other hand, Bengkulu can be considered a new area for coffee plantation. The projection of the suitability of the coffee climate is crucial in determining the future coffee development areas and for the rejuvenation of the existing coffee plantations, highlighting the significance of the study's findings for policymakers, farmers, and other stakeholders.

Habitat suitability mapping for a high-value non-timber forest product: A case study of <i>Rauvolfia serpentina</i>

Non-timber forest products (NTFPs) are essential for rural livelihoods but are often threatened by deforestation and overexploitation. Rauvolfia serpentina is a critical NTFP species; however, there have been no studies on the impact of future climate change on its potential habitat distribution. We used the Maxent modeling approach to address this knowledge gap and determine this species’ current and future suitability under climate change scenarios. The areas more suitable to its occurrence were mainly located near the southwestern coastal regions of Myanmar and India. Based on our predictions, these ‘very high’ suitability areas are likely to decrease in 2070 under both low and high CO2 emission scenarios, indicating the future vulnerability of this species’ habitat to the impact of climate change. This species is a highly valued NTFP species in the study region; therefore, such a reduction in habitat suitability may result in reductions in NTFP production and economic benefits for the dependent populations. Our findings can help define and conserve areas containing the natural habitats of this high-valued NTFP species.

Different Modelling Approaches to Determine Suitable Areas for Conserving Egg-Cone Pine (Pinus oocarpa Schiede) Plus Trees in the Central Part of Mexico

Various spatial modelling methods and tools have been used in ecology and biogeography. The application of these options serves a dual function: first, they offer information about the potential distribution of species to understand the richness and diversity of unassessed areas. Second, spatial modelling methods employ these predictions to select relevant sites to determine natural conservation areas. In this study, we compared three methods for modelling the spatial distribution of Egg-cone Pine (Pinus oocarpa Schiede), an important non-timber pine in Mexico. The final goal is to estimate suitable areas for the conservation and reproduction of superior individuals (plus trees) of P. oocarpa as a conservation strategy outside the known distribution since this species possesses a high ecological and economic value. The model used were a generalised linear model (GLM) as a parametric regression method, random forest (RF) as a machine-learning method, and the MaxEnt model, a standard procedure, implemented using the Kuenm R package. The results suggest that the models used performed well since the AUROC was between 0.95 and 0.98 in all cases. MaxEnt and random forest approaches provided more conservative predictions for the distribution of suitable areas of plus trees of P. oocarpa than the generalised linear model, but the random forest algorithm achieved the best performance. The results of the study allowed the determination of ex situ conservation areas for P. oocarpa plus trees outside of their known distribution.

Bioclimatic projection of the ecological niche of curly mallow (Malva verticillata) based on the forecast of the dynamics of the geographical range in the context of global climate change

Curly mallow (Malva verticillata L.) is a promising species for cultivation to obtain valuable compounds for the application in medicine, and this species can be used in the bioenergy system to provide industry with alternative energy sources. For the highest economic efficiency, the practical use of this species requires the development of complex measures related to both agrotechnologies and selective breeding. Such measures require resources and there is an urgent problem of assessing the prospects of such investments taking into account the global climate change. Therefore, the problem that we aimed to solve was the assessment of how the global climate change would impact the curly mallow in general in the global context, as well as in the conditions of Ukraine in the next 50–70 years. The database of the Global Biodiversity Information Facility (GBIF) contains 2,104 records of curly-leaved mallow. This species is found on all the continents except Antarctica. Asia accounts for 39.1% of the species’ range, Europe – 53.3%, Africa – 3.6%, North America – 3.2%, South America – 0.1%, Australia – 0.8%. The modelling of M. verticillata response to the climatic factors showed that the best response models were V (in 31.6% of cases) and VII (in 36.8% of cases). Model V characterizes unimodal bell-shaped asymmetric response, and model VII – bimodal asymmetric response. The species response to the mean annual temperature is asymmetric bell-shaped with a shift to the right. The optimal average annual temperature for this species is 9.1 °C. Comparing the distribution of available resources and their use is the basis for identifying the features of the ecological niche of the species. The MaxEnt approach indicates that Southeast Asia and Europe have the most favourable conditions for the existence of this species. Changes in the climatic conditions over the next 50–70 years will make the conditions for the life of M. verticillata in the southern hemisphere unfavourable, and the favourable conditions for it in the northern hemisphere will shift significantly to the north. At the same time, conditions in the autochthonous range of the species will become unfavourable. Obviously, if not for the significant potential of the species to disperse, it would have died out as a result of the significant climate change. The area where favourable conditions for the species will remain unchanged is Central Europe. Conditions in Eastern Europe, including Ukraine, will moderately improve. The results indicate the perspective of the cultivation of curly mallow in Ukraine in the future.

Species Distribution Model for the Asian Tapir and Vegetation Characteristics of Batang Gadis National Park, North Sumatra, Indonesia.

The Asian tapir is a primitive mammal whose habitat is heavily fragmented due to human activities. Studies on the Asian tapirs in Sumatra are very few, thereby basic information for developing tapir conservation programmes is limited. This study aimed to develop the species distribution model to map the potential distribution of tapirs in Batang Gadis National Park (BGNP), investigate the characteristic of tapir habitat, and identify tapir feed plants around BGNP. The model was developed using the Maximum Entropy (Maxent) approach, based on the existing information on tapir occurrence in BGNP and environmental variables. Vegetation characteristics in different land cover (primary forests, secondary forests, and open fields) were investigated using the strip transect method. This study found that zonal classification, temperature and precipitation have the greatest percentage contribution to the model. The model estimated that around 24.45% of BGNP areas are suitable for tapir habitat, and tapirs distribute near community gardens. Our results also showed that plant diversity at study sites was categorised as moderate-high. About 23 plant species dominated by the Moraceae family were identified as feed plants for tapirs. In developing tapir conservation programmes, BGNP management needs to consider tapir distribution that is closed to community gardens. We propose BGNP to enrich feed plants in open fields of the wilderness and traditional zones; reduce the canopy cover in the wilderness and utilisation zones to stimulate the growth of feed plants; facilitate local people to live harmoniously with tapirs; involve local communities in tapir conservation programmes; encourage local communities to plant non-palatable crops for tapirs; and provide a compensation scheme.

Assessment of Climate Change and Land Use Effects on Water Lily (Nymphaea L.) Habitat Suitability in South America

Many aquatic species have restricted dispersal capabilities, making them the most vulnerable organisms to climate change and land use change patterns. These factors deplete Nymphaea species’ suitable habitats, threatening their populations and survival. In addition, the species are poorly documented, which may indicate how scarce they are or will become. Members of Nymphaea are ecologically important as well as having cultural and economic value, making them of conservation interest. Therefore, using the maximum entropy (MaxEnt) approach, climatic variables, land use, and presence points were modeled for seven Nymphaea species in South America, using three general circulation models (CCSM4, HADGEM2-AO, and MIROC5) and in two representative concentration pathways (RCPs 4.5 and 8.5) and two scenarios (2050 and 2070). Our results indicated that mean diurnal range (bio2), precipitation of the wettest month (bio13), temperature seasonality (bio15), and land use (dom_lu) were the main influencing factors. For all species, suitable areas were concentrated east of Brazil, and they were variable in northern parts of the continent. Besides, inconsistent expansion and contraction of suitable habitats were noticed among the species. For example, N. amazonum, N. rudgeana, and N. lasiophylla future habitat expansions declined and habitat contraction increased, while for N. ampla and N. jamesoniana, both future habitat expansion and contraction increased, and for N. pulchella and N. rudgeana it varied in the RCPs. Moreover, the largest projected suitable habitats were projected outside protected areas, characterized by high human impacts, despite our analysis indicating no significant change between protected and unprotected areas in suitable habitat change. Finally, understanding how climate change and land use affect species distribution is critical to developing conservation measures for aquatic species.

Contrasting occupancy models with presence-only models: Does accounting for detection lead to better predictions?

Species distribution models are popular statistical tools for inferring potential distribution range of species across space and time and are extensively used in conservation planning. Models based on presence-only data (e.g., MaxEnt) are widely used; however, these models assume perfect species detectability. Occupancy modelling is considered a better modelling technique since it accounts for species detectability. Presence-only models are relatively simpler, requiring only presence locations, while occupancy models are data hungry models requiring detection/non-detection data from multiple visits to the survey sites. We utilized data from the Kerala Bird Atlas (India) and modelled current distribution for 109 species using MaxEnt and occupancy approaches. MaxEnt performed well even with less occurrences, while occupancy model failed for species with fewer than 40 records. In terms of evaluation metrics, AUC and Root Mean Square Error, both models performed relatively better for species with low occurrences than those with high occurrences (generalist species). The comparison metrics (Relative-rank scores, Root Mean Square Error, Hellinger distance and Expectation of Shared Presences) were significantly correlated with the number of occurrences; MaxEnt and occupancy based SDMs for widespread species had more concordance than SDMs of narrowly distributed species. There was some discordance between algorithms with regards to diversity hotspot. Selection of best combination of variables and correction for overprediction can help improve performances of both models and improve consistency between them. Given the data hungry nature of occupancy models and marginal difference with the MaxEnt models; it appears that latter is better suited for predicting the distribution of rare species and studies dealing with cumulative data from multiple species.

Culex pipiens distribution in Tunisia: Identification of suitable areas through Random Forest and MaxEnt approaches

Tunisia has experienced several West Nile virus (WNV) outbreaks since 1997. Yet, there is limited information on the spatial distribution of the main WNV mosquito vector Culex pipiens suitability at the national level. In the present study, our aim was to predict and evaluate the potential and current distribution of Cx. pipiens in Tunisia. To this end, two species distribution models were used, i.e. MaxEnt and Random Forest. Occurrence records for Cx. pipiens were obtained from adult and larvae sampled in Tunisia from 2014 to 2017. Climatic and human factors were used as predictors to model the Cx. pipiens geographical distribution. Mean decrease accuracy and mean decrease Gini indices were calculated to evaluate the importance of the impact of different environmental and human variables on the probability distribution of Cx. pipiens. Suitable habitats were mainly distributed next to oases, in the north and eastern part of the country. The most important predictor was the population density in both models. The study found out that the governorates of Monastir, Nabeul, Manouba, Ariana, Bizerte, Gabes, Medenine and Kairouan are at highest epidemic risk. The potential distribution of Cx. pipiens coincides geographically with the observed distribution of the disease in humans in Tunisia. Our study has the potential for driving control effort in the fight against West Nile vector in Tunisia.

Predicting the potential geographic distribution of Nepeta septemcrenata Benth. and Phlomis aurea Decne in St. Katherine protectorate, South Sinai, Egypt, by using the maxent technique

The threatened and endangered species distribution data are frequently clustered, and modeling their suitable habitat distribution by commonly utilized modeling methods is difficult. Therefore, this work aimed to assess the potential geographic distribution of Nepeta septemcrenata and Phlomis aurea in the St. Katherine protectorate using a new maxent approach. In addition to identifying the principal environmental parameters, which highly affect the potential geographic distribution of Nepeta septemcrenata and Phlomis aurea in the St. Katherine protectorate. The findings presented that the assessment distribution of Nepeta septemcrenata and Phlomis aurea is concentrated in the high-mountain region in SKP. While the driest quarter's precipitation, the coldest quarter's precipitation, the warmest quarter's mean temperature, the coldest quarter's mean temperature, and elevation all had a role in Nepeta septemcrenata distribution. The coldest quarter's mean temperature, the driest quarter's mean temperature, precipitation seasonality, the warmest quarter's mean temperature, all had a role in the distribution of P. aurea. Additionally, the highly suitable ecological conditions for Nepeta septemcrenata cover about 200.5 km2 from the total protectorate area, while the highly suitable ecological conditions for Phlomis aurea cover only 104.1 km2. This work may help in conservation planning for these threatened and endemic species.

An ecological niche model to predict the geographic distribution of Haemagogus janthinomys, Dyar, 1921 a yellow fever and Mayaro virus vector, in South America.

Yellow fever virus (YFV) has a long history of impacting human health in South America. Mayaro virus (MAYV) is an emerging arbovirus of public health concern in the Neotropics and its full impact is yet unknown. Both YFV and MAYV are primarily maintained via a sylvatic transmission cycle but can be opportunistically transmitted to humans by the bites of infected forest dwelling Haemagogus janthinomys Dyar, 1921. To better understand the potential risk of YFV and MAYV transmission to humans, a more detailed understanding of this vector species’ distribution is critical. This study compiled a comprehensive database of 177 unique Hg. janthinomys collection sites retrieved from the published literature, digitized museum specimens and publicly accessible mosquito surveillance data. Covariate analysis was performed to optimize a selection of environmental (topographic and bioclimatic) variables associated with predicting habitat suitability, and species distributions modelled across South America using a maximum entropy (MaxEnt) approach. Our results indicate that suitable habitat for Hg. janthinomys can be found across forested regions of South America including the Atlantic forests and interior Amazon.