Entropy Model Research Articles

Understanding sustainability of woody species suitability zones on the Loess Plateau for optimal creation zone selection in response to future climate change.

Climate change has profound implications for the distribution of suitable habitats for woody species. In this study, we assessed the optimal distribution thresholds for twelve woody species on the Loess Plateau using the Maximum Entropy (MaxEnt) model, incorporating sample points of tree species alongside relevant environmental variables. We analyzed the sustainability of potentially suitable zones and proposed a framework for selecting a regulatory model to establish the most suitable creation zones in response to future climate change. The results indicated that: (1) The distributions potentially suitable for Platycladus orientalis and Pinus tabuliformis were predominantly influenced by mean annual temperatures, whereas Pinus armandii and Quercus aliena var. acutiserrata exhibited optimal conditions at temperatures around -4°C. Both Hippophae rhamnoides and Larix gmelinii var. principis-rupprechtii had suitable threshold precipitation levels exceeding 200mm, with optimal thresholds surpassing 250mm. (2) Most high-suitability zones for woody species across various future climate scenarios were primarily located in southern regions, including examples such as Betula platyphylla Sukaczev, Platycladus orientalis, Pinus sylvestris var. mongholica. Some of these high-suitability areas displayed insular and linear distributions, notably Larix gmelinii var. principis-rupprechtii, Quercus aliena var. acutiserrata, Salix cheilophila. (3) There was no southward shift in the northern boundary of the sustainability zones for any woody species across the different scenarios. Betula platyphylla and Salix babylonica exhibited the broadest distribution of sustainability zones. (4) The most suitable areas for the establishment of woody species were primarily found in the western, southern, and eastern regions, whereas the northern and central areas were less favorable for tree growth. Among the scenarios analyzed, SSP585 presents the most extensive distribution area. This study is expected to improve the distribution structure of woody species and the implementation of management policies.

Distribution and habitat suitability of Amorphophallus gigas with MaxEnt modeling in north Sumatra, Indonesia

Amorphophallus gigas is exclusively found in community agroforestry gardens within the northern Sumatra region, Indonesia. This species faces various threats including land conversion and tuber extraction for economic purposes. Despite its unique habitat characteristics, the conservation status remains unrecorded on the IUCN Red List. Effective conservation requires comprehensive data, including distribution and habitat conditions in the field. Therefore, this study aimed to analyze the variables affecting the distribution of A. gigas in northern Sumatra and predict the size of the area with the potential for spread. The variables examined included height, slope, slope direction, climatic conditions, and land cover. Coordinate points were taken directly in the field using GPS, while Maximum Entropy (MaxEnt) modeling was used to predict the suitability of the habitat of the species. MaxEnt modeling of variables affecting the distribution of A. gigas showed that soil type played an important role (contribution 55%), followed by the average monthly temperature range (16%), and altitude (7.9%). The most suitable area was found to be located in the southern part of the province. The results of this research are useful for formulating conservation strategies for A. gigas.

Sustainability Benefit Evaluation and Optimization of Rural Public Spaces Under Self-Organization Theory

Agriculture-oriented rural areas represent one of the forms of specialized agricultural practices and economic development. Public spaces serve as critical carriers within the rural spatial system. Rural public spaces are divided into two forms: explicit spaces and implicit spaces. The interaction between these forms significantly influences the morphological evolution of rural public spaces. This study takes the ancient village cluster in Anyi, Nanchang City, China as a case study. By collecting POI (Point of Interest) data and conducting surveys on visitors’ landscape preferences, it employs a life circle spatial division method and the VEISD (Village Evaluation Indicators for Sustainable Development) entropy model to evaluate the sustainability benefits of rural public spaces. Based on the evaluation results, the study proposes a control and guidance method for public spaces under self-organization theory. This method leverages the interference effects of explicit rural public spaces on implicit spaces to optimize rural public spaces. The study focuses on the planning and renovation of public space nodes in Luotian Village. By adjusting the sub-indicator “Village Public Environment Construction D22”, it validates the scientific robustness of the systems analysis theory and the VEISD framework. By adjusting the spatial layout and attributes of a critical spatial node—the Ancient Camphor Tree Square in Luotian Village—within rural public space planning, the study advances the guidance and control of public spaces during the self-organization evolution of rural areas. It enhances the openness of spatial forms and the functional integration of public space nodes. The results demonstrate that this method can analyze the vitality characteristics of factors within subsystems through the layout and indicator system of rural public spaces. It also validates the findings via correlation tests with the demands for POI and landscape preferences, ultimately constructing the VEISD framework for rural public spaces. This research provides theoretical support for optimizing the resource transformation and utilization of rural public spaces, offering a reference model for the sustainable development of rural areas.

Towards an Efficient Remote Sensing Image Compression Network with Visual State Space Model

In the past few years, deep learning has achieved remarkable advancements in the area of image compression. Remote sensing image compression networks focus on enhancing the similarity between the input and reconstructed images, effectively reducing the storage and bandwidth requirements for high-resolution remote sensing images. As the network’s effective receptive field (ERF) expands, it can capture more feature information across the remote sensing images, thereby reducing spatial redundancy and improving compression efficiency. However, the majority of these learned image compression (LIC) techniques are primarily CNN-based and transformer-based, often failing to balance the global ERF and computational complexity optimally. To alleviate this issue, we propose a learned remote sensing image compression network with visual state space model named VMIC to achieve a better trade-off between computational complexity and performance. Specifically, instead of stacking small convolution kernels or heavy self-attention mechanisms, we employ a 2D-bidirectional selective scan mechanism. Every element within the feature map aggregates data from multiple spatial positions, establishing a globally effective receptive field with linear computational complexity. We extend it to an omni-selective scan for the global-spatial correlations within our Channel and Global Context Entropy Model (CGCM), enabling the integration of spatial and channel priors to minimize redundancy across slices. Experimental results demonstrate that the proposed method achieves superior trade-off between rate-distortion performance and complexity. Furthermore, in comparison to traditional codecs and learned image compression algorithms, our model achieves BD-rate reductions of −4.48%, −9.80% over the state-of-the-art VTM on the AID and NWPU VHR-10 datasets, respectively, as well as −6.73% and −7.93% on the panchromatic and multispectral images of the WorldView-3 remote sensing dataset.

Risk effects of environmental factors on human brucellosis in Aksu Prefecture, Xinjiang, China, 2014–2023

The context of rapid global environmental change underscores the pressing necessity to investigate the environmental factors and high-risk areas that contribute to the occurrence of brucellosis. In this study, a maximum entropy (MaxEnt) model was employed to analyze the factors influencing brucellosis in the Aksu Prefecture from 2014 to 2023. A distributed lag nonlinear model (DLNM) was employed to investigate the lagged effect of meteorological factors on the occurrence of brucellosis. A total of 17 environmental factors were identified as affecting the distribution of brucellosis to varying degrees. The largest contributing was the normalized difference vegetation index (NDVI), followed by gross domestic product (GDP), and then meteorological factors such as average temperature, average relative humidity, and average wind speed. The receiver operating characteristic (ROC) curve demonstrated that the MaxEnt model exhibited a high degree of predictive efficacy, with an area under the curve (AUC) value of 0.921. The impact of high temperature (25℃ with a 2-month lag, RR = 3.130, 95% CI 1.642 ~ 5.965), low relative humidity (28% with a 2.5-month lag, RR = 1.795, 95% CI 1.298 ~ 2.483), and low wind speed (1.9 m/s with a 0-month lag, RR = 2.408, 95% CI 1.360 ~ 4.264) are the most significant meteorological factors associated with the incidence of brucellosis. The trends in the impact of extreme meteorological conditions on the spread of brucellosis were found to be generally consistent. Stratified analyses indicated that males were more affected by meteorological factors than females. The prevalence of brucellosis is influenced by a range of socio-economic and meteorological factors, and a multifaceted approach is necessary to prevent and control brucellosis.

Distribution changes of Ormosia microphylla under different climatic scenarios

Ormosia microphylla is a nationally prioritized wild plant in China but effects of likely future climate change have been poorly studied. Here distribution data of O. microphylla and environmental data with an optimized MaxEnt maximum entropy model were used to predict potentially suitable areas under current and future climate scenarios. The results showed that with future climate warming, the total area suitable for O. microphylla might gradually increase. In the three future periods (2030s (2021–2040), 2050s (2041–2060) and 2090s (2081–2100)), the medium and high suitable areas under different climate scenarios generally showed an expanding trend, while the low suitable areas mostly showed a decreasing trend. At the same time, the potential suitable areas for O. microphylla in China have shown a certain degree of migration trend towards higher latitudes in the north and northwest, as well as a trend towards higher altitudes. The research results will provide data support for the protection of germplasm resources and the development of artificial cultivation techniques for O. microphylla, and provide a theoretical basis for the protection of other rare and endangered plants.

Adapting distribution patterns of desert locusts, Schistocerca gregaria in response to global climate change.

The desert locust (Schistocerca gregaria) is a destructive migratory pest, posing great threat to over 60 countries globally. In the backdrop of climate change, the habitat suitability of desert locusts is poised to undergo alterations. Hence, investigating the shifting dynamics of desert locust habitats holds profound significance in ensuring global agricultural resilience and food security. In this study, we combined the maximum entropy modelling and geographic information system technology to conduct a comprehensive analysis of the impact of climate change on the distribution patterns and habitat adaptability of desert locusts. The results indicate that the suitable areas for desert locusts (0.2976 × 108 km2) are concentrated in northern Africa and southwestern Asia, accounting for 19.97% of the total global land area. Key environmental variables affecting the desert locust distribution include temperature annual range, mean temperature of the coldest quarter, average temperature of February, and precipitation of the driest month. Under the SSP1-2.6 and SSP5-8.5 climate scenarios, potential suitable areas for desert locusts are estimated to increase from 2030 (2021-2040) to 2090 (2081-2100). By 2090, highly suitable areas for SSP1-2.6 and SSP5-8.5 are projected to be 0.0606 × 108 and 0.0891 × 108 km2, respectively, reflecting an expansion of 1.84 and 2.77% compared to existing ones. These research findings provide a theoretical basis for adopting prevention and control strategies for desert locusts.

Phylogenetic relationships of Smilax astrosperma (Smilacaceae) with other Smilax species and its potential distribution patterns in karst areas of south China

The genus Smilax is adapted for growth in karst areas of south China, likely because of the karst habitat heterogeneity of the area and climate factors. However, the phylogenetic relationships among different species in this genus and impact of environmental changes on its geographic distribution patterns remain unclear. Therefore, we assembled the 11 chloroplast genomes of the genus Smilax and reconstructed its phylogeny using the maximum‐likelihood method to confirm the phylogenetic relationships of Smilax astrosperma among 28 other Smilax genus species. In addition, a maximum entropy (MaxEnt) model was applied to evaluate and predict the potential distribution of S. astrosperma using bioclimatic variables, soil environment, elevation, slope degree and aspect, and human footprints under present and predicted future climate scenarios. The results show that S. astrosperma has a close relationship with S. glabra in the genus Smilax. The average temperature of the coldest quarter is a crucial factor influencing the distribution of S. astrosperma in karst areas. The potential distribution range of S. astrosperma increases and decreases under the SSP1‐2.6 and SSP5‐8.5 scenarios, respectively. These findings highlight that S. astrosperma and other species of the karst areas of south China need special attention under climate warming since both warming itself and extreme weather events threaten their survival.

A Framework for Developing Biodiversity Conservation Networks Based on Morphological Spatial Pattern Analysis and the Maximum Entropy Model: A Case Study of the Jianghan Plain, China

Constructing ecological networks in urban areas improves ecosystem stability and biodiversity protection. However, most studies focus on optimizing ecological environments through objective assessments, often neglecting species diversity. This study developed a biodiversity grading framework for the Jianghan Plain using species observation and ecosystem diversity data. Supported by ArcGIS, ecological sources were identified via MSPA and graded using the Guidelines and MaxEnt model. The MCR model was used to simulate connectivity barriers between ecological sources and calculate the minimum cumulative resistance distance, thereby generating corridors and ultimately constructing a hierarchical biodiversity conservation network for the Jianghan Plain. Our findings indicated the following: (1) The Jianghan Plain hosts 21 major ecological sources, primarily natural water bodies at the plain’s edge, which can be classified into five primary and 16 secondary sources based on biodiversity grades. (2) The recessive corridors, comprising 10 primary and 95 secondary ones, are mainly concentrated in the central Jianghan Plain, with primary corridors located centrally and westward, characterized by a large overall span. (3) Changhu Lake and Honghu Lake, two critical water bodies with high-quality habitats and significant biodiversity, were identified as key ecological nodes from the ecological sources, bridging and guiding the central and southern corridors. (4) Based on the ecological network distribution and key nodes and corridors, a “three zones, three belts, and two points” strategy was proposed for optimizing the Jianghan Plain’s ecological network. This study provides a novel framework and theoretical support for regional habitat, biodiversity conservation, and sustainable development.

Identification of Environmental Determinants Involved in the Distribution of Burkholderia pseudomallei in Southeast Asia using MaxEnt software.

Burkholderia pseudomallei (Bp), causing melioidosis, is becoming a major global public health concern. It is highly endemic in Southeast Asia (SEA) and Northern Australia and is persisting beyond the established areas of endemicity. This study aimed to determine the environmental variables that would predict the most suitable ecological niche for this pathogenic bacterium in SEA by maximum entropy (MaxEnt) modeling. Systematic review and meta-analysis of data for melioidosis were obtained from public databases such as PubMed, Harmonized World Soil (HWSD) and WorldClim. The potential map showing the environmental layers was processed by ArcGIS, and the prediction for the probability of habitat suitability using MaxEnt software (version 3·4·4) and ENMeval R-based modeling tools was utilized to generate the distribution map with the best-fit model. Both bioclimatic and edaphic predictors were found to be the most important niche-determining environmental variables affecting the geographical distribution of Bp. The highest probability of suitability was predicted in areas with mean temperature of the wettest quarter at ≥26°C, annual precipitation of <2300 mm and Acrisol soil type. Combining those significantly influential variables, our predictive modeling generated a potential distribution map showing the concentration of areas and its location names with high suitability for Bp presence. The predicted distribution of Bp is extensive in the mainland part of SEA. This can be used to draw appropriate measures to safeguard public health and address the true disease burden of melioidosis in the region under the current climate scenario.

Presettlement Tree Distributions and Forest Types of Northeast Ohio, USA, Mapped With Species Distribution Models

ABSTRACTQuestionsEuropean colonization radically transformed the landscapes of eastern North America. Understanding this legacy is vital to managing current ecological communities. To do so requires spatially explicit information about presettlement vegetation. Here we test the ability of species distribution models, which have rarely been used with historical data like land survey records, to generate useful predictions of presettlement tree distributions. These models also allow us to assess pre‐disturbance vegetation‐environment relationships.LocationCuyahoga County, Ohio, USA.MethodsGeneralized linear models, generalized boosting models, random forests, and maximum entropy models related the distributions of 17 tree taxa to elevation, slope, aspect, and soil type, based on 4234 tree observations from circa‐1800 surveys. Cluster analysis defined forest types and created a prediction map of forest types using random forests.ResultsThis study generated high‐resolution predictions of presettlement tree distributions. Fagus grandifolia and Carya spp. were predicted to have found suitable habitat in over half the area. Elevation was by far the most important predictor, followed by slope. Many taxa were more likely to occur at higher elevations, corresponding to the Allegheny Plateau, while others followed river valleys. Broadly, 51% of the county was predicted to support forest types with Fagus grandifolia and/or Acer spp., and 48% of the county was predicted to support forest types with Quercus spp., Carya spp., and/or Castanea dentata.ConclusionsThis study demonstrates that species distribution modeling with historical data provides insights into presettlement vegetation and vegetation‐environment relationships. Our results reveal striking ecological patterns not apparent in today's landscape, such as the sharp difference in vegetation between the Central Lowland and Allegheny Plateau. The maps created here offer a historical perspective that can inform conservation, education, and further research.

Comparative Study between AR-MEM Model and Decision Tree

Predictive modeling is understanding complex sys- tems and decisionmaking informed by the parameter space of various domains. The aim of this study is to compare two predictive models, the Auto-Regressive Maximum Entropy Model (ARMEM) and the Decision Tree model, for predicting the performance of a specific variable, Surface Ocean Direction, from a dataset. The dataset, obtained through High-Frequency Radar (HFR) measurements around Koko Head, was taken as a case study to test these models. ARMEM is a time-series model that merges the autoregressive methods with the principle of maximum entropy, thus being highly appropriate for high-resolution spectral analysis and noisy or incomplete data. On the other hand, the Decision Tree model works through recursive partitioning of data and thereby provides intuitive, interpretable predictions through capturing the underlying linear and nonlinear relationships.

Energy of Functional Brain States Correlates With Cognition in Adolescent-Onset Schizophrenia and Healthy Persons.

Adolescent-onset schizophrenia (AOS) is relatively rare, under-studied, and associated with more severe cognitive impairments and poorer outcomes than adult-onset schizophrenia. Neuroimaging has shown altered regional activations (first-order effects) and functional connectivity (second-order effects) in AOS compared to controls. The pairwise maximum entropy model (MEM) integrates first- and second-order factors into a single quantity called energy, which is inversely related to probability of occurrence of brain activity patterns. We take a combinatorial approach to study multiple brain-wide MEMs of task-associated components; hundreds of independent MEMs for various sub-systems were fit to 7 Tesla functional MRI scans. Acquisitions were collected from 23 AOS individuals and 53 healthy controls while performing the Penn Conditional Exclusion Test (PCET) for executive function, which is known to be impaired in AOS. Accuracy of PCET performance was significantly reduced among AOS compared with controls. A majority of the models showed significant negative correlation between PCET scores and the total energy attained over the fMRI. Severity of psychopathology was correlated positively with energy. Across all instantiations, the AOS group was associated with significantly more frequent occurrence of states of higher energy, assessed with a mixed effects model. An example MEM instance was investigated further using energy landscapes, which visualize high and low energy states on a low-dimensional plane, and trajectory analysis, which quantify the evolution of brain states throughout this landscape. Both supported patient-control differences in the energy profiles. The MEM's integrated representation of energy in task-associated systems can help characterize pathophysiology of AOS, cognitive impairments, and psychopathology.

Remotely-Sensed Game Trails Are a Behavioral Footprint That Explains Patterns of Herbivore Habitat Use.

Trade-offs between food acquisition and predator avoidance shape the landscape-scale movements of herbivores. These movements create landscape features, such as game trails, which are paths that animals use repeatedly to traverse the landscape. As such, these trails integrate behavioral trade-offs over space and time. Here, we used remotely sensed imagery to analyze the density of game trails with spatial environmental variables to understand landscape-scale patterns of herbivore habitat use in an African savanna. Woody plant cover was the best predictor of game trail density, with the highest densities correlating with intermediate woody plant cover. We also explored how patterns of game trail density compared to two known measures of herbivore habitat use (i.e., dung counts and maximum entropy modeling) and found strong quantitative fits. To understand the patterns revealed by the density of game trails, we explored the trade-off between food acquisition and perceived predation risk across a woody plant cover gradient. Using behavioral observations, we found that the relationship between woody plant cover and the distribution of game trails was likely driven by the risk and reward trade-off, with less vigilance and more feeding occurring in areas with a high density of game trails and intermediate woody cover. Ultimately, we show that game trails are a novel data source that can be used to identify broadly-occurring patterns of herbivore habitat use over large spatial scales.

GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India

GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India

AGRICULTURAL OASIS EXPANSION AND URBAN GROWTH IN TOLGA SOUTHEASTERN ALGERIA: A GEOSPATIAL STUDY

The assessment of land use and land cover (LULC) changes is crucial to understanding its impacts on the natural environment and resources. The dynamics of LULC might be a result of national legislation or unplanned development. This study utilizes remote sensing data to evaluate the LULC in Tolga Oasis resulting from the promulgation of agricultural development law in 1983. Four Landsat images from 1985, 2000, 2015, and 2023 were classified using the Support Vector Machine (SVM) and ArcGIS Pro software. The findings showed a continuous change in the built-up area and vegetation area. The increase in built-up area was in conjunction with the rise in vegetation area. A spatial direction approach and concentric circle approach were used to assess the change in each direction and to identify the zone experiencing the most change in the built-up and agricultural oasis expansion. Shannon’s entropy model was used to measure the dispersion and the compactness of urban growth. The overall outcomes revealed that all directions showed an increase in built-up and vegetation area. Total Shannon’s entropy values showed compact urban growth in 1985, while, a dispersed development was recorded in 2000, 2015, and 2023. Statistical analysis demonstrated a high correlation between date palm plantations and vegetation area by 0,928%, as well as a significant correlation between built-up areas and population growth by 0,926%. These results can be helpful for the local authorities and planners in order to make a sustainable urban development and protect the fragile oasis ecosystem.

Assessing Wildfire Risk in South Korea Under Climate Change Using the Maximum Entropy Model and Shared Socioeconomic Pathway Scenarios

For effective management and prevention, wildfire risk prediction needs to consider the substantial impacts of climate change on wildfire patterns. This study analyzed the probability of wildfire occurrence in South Korea using the Maximum Entropy (MaxEnt) model and predicted future wildfire occurrence under shared socioeconomic pathway (SSP) climate change scenarios. The model utilized historical fire occurrence data and was trained using 12 environmental variables encompassing climate, topography, vegetation, and socioeconomic factors. Future wildfire risk was predicted under the SSP2-4.5 and SSP5-8.5 scenarios for 2041–2060 and 2081–2100. Increased average temperature and solar radiation were key drivers of elevated wildfire risk, whereas increased precipitation and relative humidity reduced this risk. Under current conditions, 367,027 ha (6.52%) within the study area were classified as high-risk based on the MaxEnt model output (p &gt; 0.6). Under both SSP scenarios, a decline in the at-risk area was observed over time. This study provides fundamental data for wildfire management and prevention strategies in South Korea and provides quantitative evidence on the potential impact of climate-related environmental changes on wildfires.

Predicting the Potential Distribution of Cheirotonus jansoni (Coleoptera: Scarabaeidae) Under Climate Change.

Cheirotonus jansoni (Jordan, 1898), a beetle species of ecological and ornamental significance, is predominantly found in southern China. With limited dispersal ability, it is classified as a Class 2 protected species in China. In this study, the widely employed maximum entropy (MaxEnt) model and the ensemble Biomod2 model were applied to simulate C. jansoni habitat suitability in China under current environmental conditions based on available distribution data and multiple environmental variables. The optimized MaxEnt model demonstrated improved accuracy and robust predictive capabilities, making it the preferred choice for simulating dynamic changes in potentially suitable habitats for C. jansoni under future climate scenarios. Protection gaps were further identified through analyses of the overlap between nature reserves and highly suitable areas for C. jansoni. The established models indicated that this species primarily resides in southeastern mountainous regions of China below 2000 m, with a preferred altitude of 1000-2000 m. Future climate scenarios suggest a reduction in the overall suitable habitat for C. jansoni with an increase in temperature, underscoring the urgent need for enhanced conservation efforts for this beetle species.

Using maximum entropy modeling to predict the potential distributions of genus Copaifera L. in a conservation unit in the Brazilian Amazon

Using maximum entropy modeling to predict the potential distributions of genus Copaifera L. in a conservation unit in the Brazilian Amazon

Phylogeny, antiquity, and niche occupancy of Trinomia (Hymenoptera: Halictidae), an Afrotropical endemic genus of Nomiinae.

The Afrotropical region is home to many endemic bee groups, yet almost none have been studied from an integrated, holistic perspective. Among them, the halictid subfamily Nomiinae contains exceptional African diversity with variable distributions and life histories. Here, we combine phylogenomics, molecular dating, and distributional modelling to explore the evolutionary ecology of the genus Trinomia. We analyzed a matrix of 59 species of Nomiinae using ultraconserved element (UCE) and whole genome data, including all six species of Trinomia, and estimated divergence times for the subfamily. We then generated distribution models for all six species of Trinomia using Maximum Entropy models (MaxEnt) and 671 spatial data points. From these methods, we discovered a monophyletic Trinomia with an unexpected sister group relationship to the Asian-endemic genus Gnathonomia, as well as a recent origin of Trinomia in the late Miocene (∼5.8 million years ago). From our results, we found hints of phylogenetic conservatism in distribution among sister-groups of Trinomia, however, our results also highlight the need for additional efforts inventorying, identifying, and sharing data on African bees. This study represents an exemplary first step into studying bee spatial phylogenomics of African endemic bees.