Maximum Entropy Algorithm Research Articles

Ultrasonic Image Features under the Intelligent Algorithm in the Diagnosis of Severe Sepsis Complicated with Renal Injury.

This research was aimed at analyzing the diagnosis of severe sepsis complicated with acute kidney injury (AKI) by ultrasonic image information based on the artificial intelligence pulse-coupled neural network (PCNN) algorithm and at improving the diagnostic accuracy and efficiency of clinical severe sepsis complicated with AKI. In this research, 50 patients with sepsis complicated with AKI were collected as the observation group and 50 patients with sepsis as the control group. All patients underwent ultrasound examination. The clinical data of the two groups were collected, and the scores of acute physiology and chronic health assessment (APACHE II) and sequential organ failure assessment (SOFA) were compared. The ultrasonic image information enhancement algorithm based on artificial intelligence PCNN is constructed and simulated and is compared with the maximum between-class variance (OSTU) algorithm and the maximum entropy algorithm. The results showed that the PCNN algorithm was superior to the OSTU algorithm and maximum entropy algorithm in the segmentation results of severe sepsis combined with AKI in terms of regional consistency (UM), regional contrast (CM), and shape measure (SM). The acute physiology and chronic health evaluation (APACHE II) and sequential organ failure assessment (SOFA) scores in the observation group were substantially higher than those in the control group (P < 0.05). The interlobular artery resistance index (RI) in the observation group was substantially higher than that in the control group (P < 0.05). Moreover, the mean transit time (mTT) in the observation group was significantly higher than that in the control group (4.85 ± 1.27 vs. 3.42 ± 1.04), and the perfusion index (PI) was significantly lower than that in the control group (134.46 ± 17.29 vs. 168.37 ± 19.28), with statistical significance (P < 0.05). In summary, it can substantially increase ultrasonic image information based on the artificial intelligence PCNN algorithm. The RI, mTT, and PI of the renal interlobular artery level in ultrasound images can be used as indexes for the diagnosis of severe sepsis complicated with AKI.

Species distribution models applied to mosquitoes: Use, quality assessment, and recommendations for best practice

Mosquito borne diseases (MBD) are a major global health concern. To aid MBD management efforts, the distribution of mosquito species is frequently investigated through species distribution models (SDMs). However, the quality these SDMs for management use has not been examined. We evaluated 127 publications of mosquito SDMs published between 1998 and 2020 and assessed each against a set of recently-developed, best-practice standards pertaining to quality of the response variable, predictor variables, model building, and model evaluation aspects. Mosquito SDMs were predominantly trained with presence-background response variables (77% of studies), bioclimatic predictor variables (39-63%), maximum entropy algorithm (54%), and evaluated by area under the receiver operating curve (36%) or confusion matrix metrics (34%). Aedes were the best-studied genus (70 studies). Pan-African (20%) and global (16%) distribution studies dominated. All published studies had one or more unacceptable standards within considered aspects, but no aspect observed unacceptable standards in all publications. The highest proportion of unacceptable standards were observed within predictor variables (60%), followed by model building (53%), model evaluation (34%), and response variable (17%). Response variable and model building demonstrated 8% and 0.2% increases in quality over time, but predictor variables and model evaluation exhibited 6% and 2% decreases in quality, respectively. Quality of mosquito SDMs has not changed since introduction of best practice standards. Quality of mosquito SDMs can be improved by ensuring known species temperature and precipitation thresholds are represented within the response variable. Resolution of predictor variables must be justified from ecological knowledge or statistically approximated. SDMs of mosquitoes require improved evaluation against independent data or creation of geographically-structured data. We encourage future mosquito SDM applications to utilize the most recent SDM standards and recommendations to improve applicability.

Sparse online maximum entropy inverse reinforcement learning via proximal optimization and truncated gradient

Maximum entropy (ME) algorithms have been broadly studied towards the learning of rewards and obtaining of optimal policies for inverse reinforcement learning (IRL) problems. However, the tendency for the avoidance of maximum entropy IRL (ME IRL) algorithms, due to issues such as complicated computations, overfitting, and low convergence, has inspired studies aimed at the development of newer ME IRL algorithms of improved performance. Here, a new maximum entropy IRL based on proximal optimization is proposed. The follow-the-proximally-regularized-leader (FTPRL) method, with its good sparse solutions, is taken as proximal optimization to improve the generalization performance of ME IRL algorithm, resulting in ME-FTPRL IRL. With the help of l1/l22-regularization and adaptive per-state learning rates, our proposed algorithm can select features and correct the update direction of reward weights to reduce model complexity and avoid overfitting, which also speeds up convergence. During each iteration, the truncated gradient (TG) method is applied for the ME-FTPRL IRL (named ME-TFTPRL IRL) to update reward weights. This avoids the floating-point problem of the FTPRL method. Q-learning algorithm is then used to obtain the optimal strategies with the learned rewards. Subsequently, the sparsity and convergence of ME-TFTPRL IRL are proven based on regularization, TG method and regret bound. Simulation results demonstrate that our proposed ME-TFTPRL IRL has better sparsity, better generalization, and faster convergence than either existing or our own initially proposed ME IRL algorithms.

Morphological characterization and habitat suitability modeling of the goat population of Benin under climate change scenarios

BackgroundInsufficient knowledge of the genetic and phenotypic diversity in the local Beninese goat population combined with the lack of understanding of its adaptive capacity to ongoing environmental and societal changes hampers the development of strategies for better management and genetic improvement. The objective of this study was to establish the current geographical distribution of goats in Benin based on their morphology and model the potential habitat suitability of the three known main goat phenotypes (i.e., Djallonké goat or Type I, Sahelian goat or Type II, and their Crossbreeds or Type III) under climate change scenarios. Ten qualitative and 26 linear body measurements were taken on 2114 adult female goats sampled across the three vegetation zones of the country. Fifteen ratios were generated from the quantitative variables. The data were analyzed using generalized linear model procedures followed by multiple comparisons of least-squares means and multivariate analytical methods, including canonical discrimination analysis and hierarchical ascendant classification. Each goat was then assigned to one of the three aforementioned main goat phenotypes following its morphological characteristics and according to the a priori cluster membership defined in the previous step. The Maximum Entropy algorithm was used to model the current and future distribution of the three goat phenotypes under climate change scenario using the Representative Conservation Pathways 4.5 and 8.5.ResultsAll linear body measurements varied among vegetation zones. In the discriminant function analysis, 71% of the measured individuals were correctly classified in their vegetation zone of origin by seven measured variables and three ratios. The cluster procedure analysis revealed two groups of goats subdivided into the three main phenotypes. The modeling results showed that the currently highly favorable habitats were distributed in the South for Type I, in the North for Type II, and both South and North for Type III. However, under climate change scenarios, the favorable habitats for Type I decreased while those of Types II and III increased.ConclusionsThe results of this study confirm the spatial variation of the goat population in Benin. The habitat suitability model can be used to support decision-making toward better management of goat genetic diversity in Benin.

Analysis of the Rook (&lt;em&gt;Corvus frugilegus&lt;/em&gt;) wintering population in the Friuli Venezia Giulia region (Italy) using maximum entropy models

This research analyzed the wintering population of rooks (Corvus frugilegus) in the Friuli Venezia Giulia region (Italy) from 2010 to 2020. The goal was to determine the population size, trend, and distribution over the decade by assessing census data and evaluating the study area's environmental suitability. It was important to identify the relationship between the region’s environmental characteristics and the presence of the species by developing territory suitability models, processed using the maximum entropy algorithms and MaxEnt (maximum entropy) software. It was possible to determine the combinations of the factors potentially influencing habitat attendance, to develop environmental suitability maps, and to identify the area with the greater probability of the presence of the species, as well as those areas with potential suitability. Data for the presence of wintering rooks were derived from annual censuses, carried out by direct observation along pre-established transects in the Friuli Venezia Giulia region. Based on the census data, that total number of rooks in Friuli Venezia Giulia across the 10-year period had a maximum peak recorded in 2015 (1543) and a minimum in 2019 (853). The distribution of the species was concentrated in the central-southern areas of the region, with greater turnout in the areas characterized by agricultural land use, while the mountainous areas, located in the southern part of the region, were excluded. The environmental suitability models developed with MaxEnt on a regional scale demonstrated excellent predictive capacity, validating the species’ ecological needs. Although the rook is not an endangered species, its range has been drastically reduced in Italy, at least in the last century, and MaxEnt could be used in the future as a management tool in order to outline conservation actions for the species, especially for its habitats.

The impact of climate change on the distribution of Sphyrna lewini in the tropical eastern Pacific

Variability and climate change due to anthropic influence have brought about alterations to marine ecosystems, that, in turn, have affected the physiology and metabolism of ectotherm species, such as the common hammerhead shark (Sphyrna lewini). However, the impact that climate variability may have on this species’ distribution, particularly in the Eastern Tropical Pacific Marine Corridor, which is considered an area with great marine biodiversity, is unknown. The purpose of this research was to evaluate the effect of derivate impact of climate change on the oceanographic distribution of the hammerhead shark (Sphyrna lewini) in the Eastern Tropical Pacific Marine Corridor, contrasting the present and future scenarios for 2050. The methodology used was an ecological niche model based on the KUENM R package software that uses the maximum entropy algorithm (MaxEnt). The modelling was made for the year 2050 under RCP2.6 and RCP8.5 scenarios. A total of 952 models were made, out of which only one met the statistical parameters established as optimal, for future scenarios. The environmental suitability for S.lewini shows that this species would migrate to the south in the Chilean Pacific, associated with a possible warming that the equatorial zone will have and the possible cooling that the subtropical zone of the South Pacific will have by 2050, the product of changes in oceanographic dynamics.

Forecasting shifts in habitat suitability of three marine predators suggests a rapid decline in inter-specific overlap under future climate change.

Understanding how environmental and climate change can alter habitat overlap of marine predators has great value for the management and conservation of marine ecosystems. Here, we estimated spatiotemporal changes in habitat suitability and inter‐specific overlap among three marine predators: Baltic gray seals (Halichoerus grypus), harbor seals (Phoca vitulina), and harbor porpoises (Phocoena phocoena) under contemporary and future conditions. Location data (>200 tagged individuals) were collected in the southwestern region of the Baltic Sea; one of the fastest‐warming semi‐enclosed seas in the world. We used the maximum entropy (MaxEnt) algorithm to estimate changes in total area size and overlap of species‐specific habitat suitability between 1997–2020 and 2091–2100. Predictor variables included environmental and climate‐sensitive oceanographic conditions in the area. Sea‐level rise, sea surface temperature, and salinity data were taken from representative concentration pathways [RCPs] scenarios 6.0 and 8.5 to forecast potential climate change effects. Model output suggested that habitat suitability of Baltic gray seals will decline over space and time, driven by changes in sea surface salinity and a loss of currently available haulout sites following sea‐level rise in the future. A similar, although weaker, effect was observed for harbor seals, while suitability of habitat for harbor porpoises was predicted to increase slightly over space and time. Inter‐specific overlap in highly suitable habitats was also predicted to increase slightly under RCP scenario 6.0 when compared to contemporary conditions, but to disappear under RCP scenario 8.5. Our study suggests that marine predators in the southwestern Baltic Sea may respond differently to future climatic conditions, leading to divergent shifts in habitat suitability that are likely to decrease inter‐specific overlap over time and space. We conclude that climate change can lead to a marked redistribution of area use by marine predators in the region, which may influence local food‐web dynamics and ecosystem functioning.

Unstructured Road Detection Method Based on RGB Maximum Two-Dimensional Entropy and Fuzzy Entropy

To solve the problem that lane keeping function for automatic driving and vehicle assisted driving will not work reliably on unstructured road without lane lines or other guide markings, this article uses the characteristics of information entropy to generate the RGB entropy image to pre-segment the road region on unstructured road image. At the same time, the maximum two-dimensional entropy algorithm is introduced to achieve the joint segmentation using gray and neighborhood gray to effectively reduce the impact of interference on segmentation. After that, the fuzzy entropy algorithm is used to judge and determine the actual road boundary by combining the results of RGB and maximum two-dimensional entropy image. Finally, using the improved least square fitting quadratic curve model to build the road boundary. Our method could well and rapidly extract the lane from unstructured road image and fit out the lane line, which helps to achieve visual based lane keeping on unstructured road for autopilot and driver assistance system.

Climate Niche Modelling for Mapping Potential Distributions of Four Framework Tree Species: Implications for Planning Forest Restoration in Tropical and Subtropical Asia

Selecting tree species to plant for forest ecosystem restoration is critical but problematic. Knowing tree species’ climatic niches can help, but such information is limited for most tropical tree species. Consequently, the research presented here explored the use of climate and species record location data, to map the potential distribution of four tree species across tropical and subtropical Asia based on their modelled climatic niches. All were framework species, of proven effectiveness for forest restoration: Choerospondias axillaris (Roxb.) B.L. Burtt and A.W. Hill, Ficus hispida L.f., Hovenia dulcis Thunb., and Prunus cerasoides Buch.-Ham. ex D. Don. Potential species distributions were projected onto maps of known climatic conditions using the maximum entropy algorithm in Maxent software to predict where the climate is conducive for including each species in forest restoration trials. The models predicted species location very well for F. hispida and H. dulcis and fairly well for P. cerasoides and C. axillaris. Climatically suitable areas for C. axillaris were located mostly north of the equator, whilst those of F. hispida, H. dulcis and P. cerasoides extended south of the equator. The importance of each climatic niche variable differed among species. Driest-month precipitation was critical for F. hispida, as well as mean dry-season temperature for C. axillaris and P. cerasoides and cold-season precipitation for H. dulcis. In addition to aiding species selection for forest restoration, potential distribution maps based on climatic niche models can indicate where novel species for tree plantations might be successful and where species might become invasive. Applying such techniques to a large number of species will be needed to significantly improve species selection for forest ecosystem restoration.

A probability-based efficient assessment of seismic wave scattering in complex topography with geo-property uncertainty

There is great randomness in determining geotechnical parameters, resulting in an uncertain local site effect caused by seismic wave scattering such as valleys, hills, basins, etc. It is significant to develop the probability-based assessment method to quantify the local site effect considering the geo-property uncertainty. The multiplicative dimensional reduction method (M-DRM) is employed to approximate statistical moments of the response of the local topography with random parameters. A multi-dimensional random problem is converted into a series of deterministic analyses, solved by the boundary element method. The probability distribution estimation can be obtained based on the maximum entropy algorithm. This approach is used to solve the seismic wave scattering of fossil river courses under P-waves, and the applicability is verified compared to the Monte Carlo simulation. The results show that M-DRM can assess the seismic wave scattering by local topography with high efficiency. The seismic wave scattering inhomogeneously magnifies the geo-property uncertainty in wave propagation. The displacement variability is primarily related to the incident frequency, and the response under high-frequency P-waves has significant variability. It is the reason that the effect of the geo-property uncertainty on the peak ground acceleration is determined by the energy distribution of the incident wave.

Detection of Weak Pulse Signal under Chaotic Noise based on Fractional Maximum Correlation Entropy Algorithm

How to improve the detection accuracy of target weak signal is always the difficulty of signal processing. In this paper, based on fractional maximum correlation entropy algorithm and combined with the local linear model, a method for detecting weak pulse signal in chaotic noise background is proposed. Firstly, for the sensitivity of chaotic signal to initial values and short-term predictability, reconstruct the phase space of the observation signal, establish a local linear model, use the fractional maximum correlation entropy algorithm for parameter estimation, and perform a single-step prediction to obtain the prediction error. Then, in order to accurately detect the submerged weak pulse signal, a threshold is given. Finally, the simulation results show that the proposed model in this paper can effectively detect the weak pulse signal under the background of chaotic noise, and it is suitable for signals of different intensities, and the detection speed and accuracy are much better than other models.

Modeling the Potential Global Distribution of Honeybee Pest, Galleria mellonella under Changing Climate.

Simple SummaryThe greater wax moth (GWM) is a common pest of bee colonies throughout the world. This study highlighted the global habitat suitability of GWM using the statistical power of Maxent to model its current and future distribution under climate-change scenarios in 2050 and 2070. Our study shed light on the major environmental factors that manage the habitat suitability of GWM. To the best of our knowledge, this is the first modeling study of wax moths. In brief, this pest can cause severe indirect damage to the global honey market totalling millions of dollars; therefore, developing prompt monitoring or control strategies is advised. Beekeeping is essential for the global food supply, yet honeybee health and hive numbers are increasingly threatened by habitat alteration, climate change, agrochemical overuse, pathogens, diseases, and insect pests. However, pests and diseases that have unknown spatial distribution and influences are blamed for diminishing honeybee colonies over the world. The greater wax moth (GWM), Galleria mellonella, is a pervasive pest of the honeybee, Apis mellifera. It has an international distribution that causes severe loss to the beekeeping industry. The GWM larvae burrow into the edge of unsealed cells that have pollen, bee brood, and honey through to the midrib of the wax comb. Burrowing larvae leave behind masses of webs that cause honey to leak out and entangle emerging bees, resulting in death by starvation, a phenomenon called galleriasis. In this study, the maximum entropy algorithm implemented in (Maxent) model was used to predict the global spatial distribution of GWM throughout the world. Two representative concentration pathways (RCPs) 2.6 and 8.5 of three global climate models (GCMs), were used to forecast the global distribution of GWM in 2050 and 2070. The Maxent models for GWM provided a high value of the Area Under Curve equal to 0.8 ± 0.001, which was a satisfactory result. Furthermore, True Skilled Statistics assured the perfection of the resultant models with a value equal to 0.7. These values indicated a significant correlation between the models and the ecology of the pest species. The models also showed a very high habitat suitability for the GWM in hot-spot honey exporting and importing countries. Furthermore, we extrapolated the economic impact of such pests in both feral and wild honeybee populations and consequently the global market of the honeybee industry.

Spatial Distribution of Precise Suitability of Plantation: A Case Study of Main Coniferous Forests in Hubei Province, China

(1) Background. Conifers are the main plantation species in southern China, including Masson Pine (MP), Chinese fir (CF) and Chinese thuja (CT). Clarifying the suitable site conditions for these conifers is helpful for large-area afforestation, so as to manage forests to provide a higher level of ecosystem services. To achieve the research goals, we take the conifers in Hubei Province of southern China as a case study. (2) Methods. The situations of conifers, as well as environmental conditions of 448 sampling plots, were then investigated. The suitable growth environment of conifers in the studied area was determined by the maximum entropy algorithm, and the suitability spatial distribution of coniferous forests at the provincial level was also analyzed. (3) Results. The effect of the conifers suitability prediction model reached an accurate level, where AUC values of MP, CF and CT training set were 0.828, 0.856 and 0.970, respectively. Among multiple environmental factors, such as geography and climate, altitude is the most important factor affecting conifer growth. The contribution of altitude to the growth suitability of MP, CF and CT was 38.1%, 36.2% and 36.1%, respectively. Suitable areas of MP, CF and CT were 97,400 ha, 74,300 ha and 39,900 ha, accounting for 52.45%, 39.97% and 21.46% of the studied area, respectively. We concluded that the suitable site conditions of conifer plantations were 2800–5600 °C annual accumulated temperature, 40–1680 m a.s.l., and &lt;40° slopes. (4) Conclusions. The study suggests that accurate spatial suitability evaluation should be carried out to provide sufficient support for the large-area afforestation in southern China. However, due to our data and study area limitations, further studies are needed to explore the above findings for a full set of plantation species in an extensive area of southern China.

MaxEnt Modelling and Impact of Climate Change on Habitat Suitability Variations of Economically Important Chilgoza Pine (Pinus gerardiana Wall.) in South Asia

Chilgoza pine is an economically and ecologically important evergreen coniferous tree species of the dry and rocky temperate zone, and a native of south Asia. This species is rated as near threatened (NT) by the International Union for Conservation of Nature (IUCN). This study hypothesized that climatic, soil and topographic variations strongly influence the distribution pattern and potential habitat suitability prediction of Chilgoza pine. Accordingly, this study was aimed to document the potential habitat suitability variations of Chilgoza pine under varying environmental scenarios by using 37 different environmental variables. The maximum entropy (MaxEnt) algorithm in MaxEnt software was used to forecast the potential habitat suitability under current and future (i.e., 2050s and 2070s) climate change scenarios (i.e., Shared Socio-economic Pathways (SSPs): 245 and 585). A total of 238 species occurrence records were collected from Afghanistan, Pakistan and India, and employed to build the predictive distribution model. The results showed that normalized difference vegetation index, mean temperature of coldest quarter, isothermality, precipitation of driest month and volumetric fraction of the coarse soil fragments (&gt;2 mm) were the leading predictors of species presence prediction. High accuracy values (&gt;0.9) of predicted distribution models were recorded, and remarkable shrinkage of potentially suitable habitat of Chilgoza pine was followed by Afghanistan, India and China. The estimated extent of occurrence (EOO) of the species was about 84,938 km2, and the area of occupancy (AOO) was about 888 km2, with 54 major sub-populations. This study concluded that, as the total predicted suitable habitat under current climate scenario (138,782 km2) is reasonably higher than the existing EOO, this might represent a case of continuous range contraction. Hence, the outcomes of this research can be used to build the future conservation and management plans accordingly for this economically valuable species in the region.

Who will be where: Climate driven redistribution of fish habitat in southern Germany

To improve the robustness of projections of freshwater fish distributions under climate change, species distribution models (SDMs) were calculated for six fish species in southwestern Germany with different ecological requirements along an upstream-downstream gradient in a multi-general circulation model (GCM) approach. Using the maximum entropy (Maxent) algorithm and a high number of occurrence records (N = 4684), species distributions were projected to future climate conditions derived from 13 GCMs across the most likely representative carbon pathways (RCP4.5 and 8.5) and two time spans (near future 2050, and far future 2070), resulting in 104 distribution maps per species that were then used for the statistical analysis of future trends. Climate change is likely to affect the distribution of four of the six fish species. The potential ranges of salmonids are predicted to decline by up to 92% (brown trout) and 75% (grayling). In contrast, habitat suitability for perch and roach is predicted to increase by up to 108% and 53%, respectively. Even when accounting for broad variation in GCMs and realistic RCPs, these results suggest climate change will drive a significant redistribution of fish habitat. Salmonid-dominated communities in headwaters seem more sensitive to climate change than the fish communities of downstream sections. Because headwaters are a prevailing element of the hydrographic network in southwestern Germany, such changes may result in large-scale regressions of valuable fish communities that currently occupy broad geographic niches.

Predicting future urban waterlogging-prone areas by coupling the maximum entropy and FLUS model

• We aim to develop a robust method for predicting future waterlogging-prone areas. • Maximum entropy algorithm and Future Land Use Simulation model were combined. • Dominant driver of waterlogging (land use change) was considered in the prediction. • A high proportion of impervious surfaces may face huge waterlogging risks in 2030. • This method could support future urban design and waterlogging risk prevention. Urban waterlogging is a severe hazard that can directly damage environmental quality and human well-being. It would be desirable for hazard mitigation planning and sustainable urban design if potential waterlogging-prone areas under dynamic land use change could be appropriately predicted. However, previous related studies did not simultaneously consider the reliability of negative samples and the future influence of fine-scale land use change. To fill the knowledge gap, this research has developed a robust method for predicting future waterlogging-prone areas by coupling the maximum entropy (MAXENT) and the Future Land Use Simulation (FLUS) model. The former can ensure that no extra sampling bias will be introduced, while the latter can accurately forecast the spatio-temporal pattern of land use. This case study has confirmed the accuracy and feasibility of this method. It was found that the proportion of impervious surfaces, population density, and proportion of green areas are key spatial drivers behind urban waterlogging issues. In addition, the future hazard potential map provided by the MAXENT and FLUS implies that a large proportion of impervious surfaces will face huge waterlogging risks. Therefore, policymakers should focus more on places with a higher probability of urban waterlogging. In summary, this research is expected to offer a practical tool for future urban design and waterlogging risk prevention.

A joint distribution framework to improve presence‐only species distribution models by exploiting opportunistic surveys

AbstractAimThe availability of data related to species occurrences has favoured the development of species distribution models using only observations of presence. These data are intrinsically biased by the sampling effort. Presence‐only (PO) species distribution models (SDM) typically account for this effect by introducing additional data considered to be related with the sampling. This approach, however, does not allow the characterisation of the sampling effort and hinders the interpretation of the model. Here, we propose a Bayesian framework for PO SDMs that can explicitly model the sampling effect.LocationMexico.TaxonPines, flycatchers (family Tyranidae), birds and plants.MethodsThe framework defines a bivariate process separable into ecological and sampling effort processes. PO data are conceived of incomplete observations where some presences have been filtered out. A choosing principle is used to separate out presences, missing data and absences relative to the species of interest and the sampling observations. The framework provides three modelling alternatives to account for a spatial autocorrelation structure: independent latent variables (model I); common latent spatial random effect (model II) and correlated latent spatial random effects (model III). The framework was compared against the Maximum Entropy (MaxEnt) algorithm in two case studies: one for the prediction of pines and another for the prediction of flycatchers.ResultsIn both case studies, at least one of the proposed models achieved higher predictive accuracy than MaxEnt. The model III fit best when the sampling effort was informative, while model II was more suitable in cases with a high proportion of non‐sampled sites.Main ConclusionsOur approach provides a flexible framework for PO SDMs aided by a sampling effort process informed by the accumulated observations of independent and heterogeneous surveys. For the two case studies, the framework provided a model with a higher predictive accuracy than an optimised version of MaxEnt.

Ecological Niche Model of Bacillus cereus Group Isolates Containing a Homologue of the pXO1 Anthrax Toxin Genes Infecting Metalworkers in the United States.

While Bacillus cereus typically causes opportunistic infections in humans, within the last three decades, severe and fatal infections caused by isolates of the B. cereus group harboring anthrax toxin genes have been reported in the United States. From 1994 to 2020, seven cases of anthrax-like illness resulting from these isolates have been identified. With one exception, the cases have occurred in the Gulf States region of the United States among metalworkers. We aimed to develop an ecological niche model (ENM) to estimate a spatial area conducive to the survival of these organisms based on the presence of known human infections and environmental variables. The estimated ecological niche for B. cereus was modeled with the maximum entropy algorithm (Maxent). Environmental variables contributing most to the model were soil characteristics (cation exchange capacity, carbon content, soil pH), temperature, enhanced vegetation index (EVI), and land surface temperature (LST). Much of the suitable environments were located throughout the Gulf Coast Plain, Texas Backland Prairies, East Central Texas Plains, Edwards Plateau, Cross Timbers, Mississippi Alluvial Plain, and Central Great Plains. These findings may provide additional guidance to narrow potential risk areas to efficiently communicate messages to metalworkers and potentially identify individuals who may benefit from the anthrax vaccine.

Using Maximum Entropy Algorithm to Analyze Current and Future Distribution of the Asian hornet, Vespa velutina, in Europe and North Africa Under Climate Change Conditions

Using Maximum Entropy Algorithm to Analyze Current and Future Distribution of the Asian hornet, Vespa velutina, in Europe and North Africa Under Climate Change Conditions

Predicting the possibility of African horse sickness (AHS) introduction into China using spatial risk analysis and habitat connectivity of Culicoides

African horse sickness (AHS) is a devastating equine infectious disease. On 17 March 2020, it first appeared in Thailand and threatened all the South-East Asia equine industry security. Therefore, it is imperative to carry out risk warnings of the AHS in China. The maximum entropy algorithm was used to model AHS and Culicoides separately by using climate and non-climate variables. The least cost path (LCP) method was used to analyze the habitat connectivity of Culicoides with the reclassified land cover and altitude as cost factors. The models showed the mean area under the curve as 0.918 and 0.964 for AHS and Culicoides. The prediction result map shows that there is a high risk area in the southern part of China while the habitats of the Culicoides are connected to each other. Therefore, the risk of introducing AHS into China is high and control of the border area should be strengthened immediately.