Entropy Model Research Articles

An entropy analysis model and irreversibility assessment of solid oxide fuel cells based on the Gibbs relation of charged particles

Energy conversion is the main purpose of various fuel cells, but the irreversibility of thermodynamic processes – including electrochemical reactions and heat and mass transfer in fuel cells – lead to a decrease in energy efficiency. In this paper, we develop a systemic theoretical model to evaluate the irreversibility of solid oxide fuel cells (SOFCs). By combining the conservation equations for energy, mass, and momentum with the Gibbs relation for charged particles, we establish the energy-balance equation for SOFCs and derive the entropy generation rates (EGRs) associated with electrochemical reactions and charged particle transfer processes. We solve the coupling multiphysics model in a three-dimensional computational unit of a SOFC, and calculate the EGRs and entropy flux rate based on the proposed thermodynamic model and the numerical results. We analyse the conversion and transfer mechanisms of energy based on the distributions of different local EGRs and the entropy flux rate, and the influence of various thermodynamic processes, such as mass transfer, heat transfer, and flow, on the overall operation of SOFCs. The results show that the EGR due to electrochemical reactions in the SOFC is the largest and is mainly due to activation and concentration polarisation. Complex heat and mass transfer processes occur in the corner region formed at the intersection of the channel, the current junction, and the electrode, leading to a higher localised EGR. We also evaluate the effects of variations in the anode gas mass flow rate, the oxygen fraction, and the channel length on thermodynamic efficiency. This study contributes to the understanding of the complex mechanisms of irreversible losses in SOFCs and provides guidance for further improving the thermodynamic efficiency of SOFCs.

Functional renormalization group approach for signal detection

This review paper utilizes renormalization group techniques for signal detection in nearly continuous positive spectra. We emphasize the universal aspects of the analogue field-theory approach. The primary objective is to present an extended self-consistent construction of the analogue effective field-theory framework for data, which can be interpreted as a maximum entropy model. In particular, we leverage universality arguments to justify the \mathbb{Z}_2ℤ2 symmetry of the classical action, highlighting the existence of both a large-scale (local) regime and a small-scale (nonlocal) regime. Secondly, in relation to noise models, we observe the universal relationship between phase transitions and symmetry breaking near the detection threshold. Finally, we address the challenge of defining the covariance matrix for tensor-like data. Based on the cutting graph prescription, we note the superiority of definitions that rely on complete graphs of large size for data analysis.

Combined topological and spatial constraints are required to capture the structure of neural connectomes

Abstract Volumetric brain reconstructions provide an unprecedented opportunity to gain insights into the complex connectivity patterns of neurons in an increasing number of organisms. Here, we model and quantify the complexity of the resulting neural connectomes in the fruit fly, mouse, and human and unveil a simple set of shared organizing principles across these organisms. To put the connectomes in a physical context, we also construct contactomes, the network of neurons in physical contact in each organism. With these, we establish that physical constraints—either given by pairwise distances or the contactome—play a crucial role in shaping the network structure. For example, neuron positions are highly optimal in terms of distance from their neighbors. Yet, spatial constraints alone cannot capture the network topology, including the broad degree distribution. Conversely, the degree sequence alone is insufficient to recover the spatial structure. We resolve this apparent mismatch by formulating scalable maximum entropy models, incorporating both types of constraints. The resulting generative models have predictive power beyond the input data, as they capture several additional biological and network characteristics, like synaptic weights and graphlet statistics.

Modeling of the Habitat Characteristics and Ecological Niche of the Asian Tiger Mosquito in a Fine-Scale Area of a Primate Research Center Using the Maximum Entropy Model.

Aedes-borne diseases, such as Zika and Chikungunya, originate from an enzootic cycle in which non-human primates (NHPs) function as reservoirs. This study aimed to analyze the characteristic habitat and ecological niche models of Aedes albopictus within the confines of a Primate Research Center (PRC), to assess its potential as a site for zoonotic arbovirus transmission. Additionally, this study aimed to construct a comprehensive map to delineate the risks of arbovirus transmission. A 1-year direct field survey was conducted from January to December 2022 in the PRC to obtain comprehensive data on the presence of larvae, including their conditions, habitat types, and physicochemical characteristics. Larval collection was meticulously performed at potential breeding sites using a 350 ml dipper and pipette. Information on the ecological niche was compiled based on a combination of general environmental variables and mosquito presence data obtained from direct field surveys using the Maximum Entropy (MaxEnt) model. In total, 120 presence points for Ae. albopictus larvae were obtained from the PRC area, with 23.02% of the larvae found in buckets as artificial habitats, and 18.25% found in bromeliad plants as natural habitat types. Larvae of Ae. albopictus occupy artificial habitats that are not turbid, exposed to direct sunlight, and devoid of predators. The abundances and occurrences of Ae. albopictus larvae was found to be significantly influenced by pH and total dissolved solids. This study showed that the PRC was a suitable habitat for breeding Ae. albopictus larvae, with the distance to buildings emerging as a significant environmental variable in the species distribution model CONCLUSIONS: The fine-scale empirical model developed for Ae. albopictus and its habitat characteristics not only provide insights into the suitability of vector habitats, but can also be used assess the risk of arbovirus transmission, potentially informing strategies for controlling mosquito breeding sites within the PRC.

The Thermodynamics of the Van Der Waals Black Hole Within Kaniadakis Entropy

In this work, we have studied the thermodynamic properties of the Van der Waals black hole in the framework of the relativistic Kaniadakis entropy. We have shown that the black hole properties, such as the mass and temperature, differ from those obtained by using the the Boltzmann–Gibbs approach. Moreover, the deformation κ-parameter changes the behavior of the Gibbs free energy via introduced thermodynamic instabilities, whereas the emission rate is influenced by κ only at low frequencies. Nonetheless, the pressure–volume (P(V)) characteristics are found independent of κ and the entropy form, unlike in other anti-de Sitter (AdS) black hole models. In summary, the presented findings partially support the previous arguments of Gohar and Salzano that, under certain circumstances, all entropic models are equivalent and indistinguishable.

Predictive Analysis of Soil Textural Fractions Using Entropy and Regression Models in Digital Soil Mapping

Predictive Analysis of Soil Textural Fractions Using Entropy and Regression Models in Digital Soil Mapping

Study on the Impact of Spatiotemporal Changes in the Ecological Environment on Grain Crops in the Subtropical Monsoon Climate Zone

Global warming and land-use type shifting lead to the degradation of natural habitats. The research on the ecological and environmental impact of the subtropical monsoon climate zone on food crop cultivation is not systematic enough. An Integrated Valuation of Ecosystem Services and Trade-offs (InVEST)–Patch-generating Land Use Simulation (PLUS)–Maximum Entropy (MaxEnt) model was created to provide a comprehensive assessment of the spatiotemporal variations for food crop habitat quality (HQ) in China’s subtropical monsoon climate zone from 2010 to 2030. The HQ degradation trend was obvious during 2010–2030 under the influence of land-use change. The expansion of lower habitat areas was larger than that of medium and higher habitat areas. The shared socio-economic pathways SSP-CP and SSP-EP increased the mean total area of suitable areas compared with the SSP-ND scenario for food crops by 9% and 17.8%, respectively. Land-use shifts increased the suitable range of food crops and mitigated the negative impacts of urban expansion on food growth. This research has theoretical guidance for land-use planning for food crop production in subtropical monsoon climate zones.

Prediction of the Potentially Suitable Areas of Sesame in China Under Climate Change Scenarios Using MaxEnt Model

Sesame (Sesamum indicum L, flora of China) is an essential oil crop in China, but its growth and development are affected by climate change. To cope with the impacts of climate change on sesame cultivation, we used the Maximum Entropy (MaxEnt) model to analyze the bioclimatic variables of climate suitability of sesame in China and predicted the suitable area and trend of sesame in China under current and future climate scenarios. The results showed that the MaxEnt model prediction was excellent. The most crucial bioclimatic variable influencing the distribution of sesame was max temperature in the warmest month, followed by annual mean temperature, annual precipitation, mean diurnal range, and precipitation of the driest month. Under the current climate scenario, the suitable areas of sesame were widely distributed in China, from south (Hainan) to north (Heilongjiang) and from east (Yellow Sea) to west (Tibet). The area of highly suitable areas was 64.51 × 104 km2, accounting for 6.69% of the total land area in China, and was primarily located in mainly located in southern central Henan, eastern central Hubei, northern central Anhui, northern central Jiangxi, and eastern central Hunan. The area of moderately suitable areas and lowly suitable areas accounted for 17.45% and 25.82%, respectively. Compared with the current climate scenario, the area of highly and lowly suitable areas under future climate scenarios increased by 0.10%–11.48% and 0.08%–8.67%, while the area of moderately suitable areas decreased by 0.31%–23.03%. In addition, the increased highly suitable areas were mainly distributed in northern Henan. The decreased moderately suitable areas were mainly distributed in Heilongjiang, Jilin, and Liaoning. This work is practically significant for optimizing the regional layout of sesame cultivation in response to future climate conditions.

The Prediction of Locusta migratoria (Linnaeus, 1758) Outbreak under Climate Change Scenario in Indonesia

Locusta migratoria (Linnaeus, 1758) is one of the locusts known as important pests of food crops. Outbreaks of this species can cause catastrophic damage to maize, paddy, and many other crops. A species distribution model was used to identify the probability of the locust's current and future potential distribution in the Indonesian archipelago. The study relied on the machine learning method Maximum Entropy (Maxent) Model to forecast the future spread of the species in the Indonesian archipelago and to find the climate variable that influenced the distribution of Locusta migratoria. The results showed an Area Under Curve (AUC) value of 0.956 for the Locusta migratoria model, indicating a highly reliable model. The important variable for the distribution of this species was precipitation, especially during the dry season. A low amount of rainfall increases the possibility of the species existing and being distributed. Maxent prediction models showed the potential distribution in the southern part of the Indonesian archipelago under both middle and worst-case scenarios for 2070. This model can become one of the baselines for early warning systems, targeted monitoring and surveillance, and the use of specific pesticides or biological control agents to prevent or minimize the harm of Locusta migratoria outbreak to agricultural lands in the future.

Top priority crop wild relatives exhibit different resilience responses to climate change in Benin (West Africa)

Climate change exacerbates biodiversity loss and contributes to disrupting ecosystem functioning, diminishing ecosystem services, and further endangering vulnerable populations of species. This study explores how different climate scenarios may impede the probable resilience of top-priority crop wild relatives in Benin. Records of the species were combined with climate and soil layers, in a maximum entropy modeling algorithm. For the future models, two shared socio-economic trajectories were considered: Shared Socioeconomic Pathway SSP2-4.5 and Shared Socioeconomic Pathway SSP5-8.5 from the CMPI6 scenarios for the period 2040–2070. Niche dynamics as well as similarity of the niches were assessed for all species for the proposition of sustainable conservation strategies. Results showed that all models yielded very good performance, as revealed by the AUC and TSS values. For most species, much of the distribution will remain stable across time, though some expansion or retraction will be observed in some cases. Most of the pairs of species display low similarity in the suitable areas for conservation. However, a higher similarity of the background was observed among some groups including Ipomoea and Dioscorea species. Further development of these models could help to get a complete view of the distribution of the species and set long-term conservation strategies for this group.

Spatiotemporal distribution prediction of the relict and endangered plant Tetraena mongolica in inner Mongolia, China under climate change

Climate change significantly affects the distribution of plant species, particularly that of relict plants. Tetraena mongolica Maxim. is a first-class endangered relict plant in China, primarily found in Inner Mongolia. This study explored the impact of multiple factors on its potential distribution under climate change. Considering a comprehensive set of 42 potential influencing variables, including climate, soil, net primary productivity (NPP), human activities, and topography, 29 variables were selected. The maximum entropy (MaxEnt) model was used to construct separate climate and soil niche models, and an “overlay function” was employed to construct a dual-suitability model. By establishing five different scenarios, we analyzed the effects of climate, human activities, and NPP on T. mongolica distribution. The results showed that climate is the most significant factor, soil constraints limit its distribution, and human activities reduce its suitable habitats. Although the direct influence of NPP is limited, it may indirectly affect T. mongolica distribution by improving habitat conditions. Future climate change is expected to sharply reduce suitable habitat areas, with the center of distribution migrating eastward. The study’s findings imply that climate change, human activities, and soil conditions significantly impact the distribution and survival of the endangered plant T. mongolica, necessitating comprehensive conservation measures to mitigate habitat loss and ensure its preservation.

Predicted Spatial Patterns of Suitable Habitats for Troides aeacus Under Different Climate Scenarios.

Troides aeacus is the largest butterfly in China and is highly valued for its ornamental beauty. Due to T. aeacus being classified as a national second-class protected species in China, studying its spatial distribution is crucial for developing effective conservation measures. In this study, a total of 490 distribution points were obtained, and the potential distribution areas of the golden-sheathed T. aeacus were analyzed by using the maximum entropy model (MaxEnt) based on three different greenhouse gas emission scenarios, namely, SSP1-2.6, SSP2-4.5, and SSP5-8.5, in combination with nine important environmental variables. The results indicate that temperature and precipitation are the primary environmental factors influencing the suitable habitat of T. aeacus, with key variables including the minimum temperature of the coldest month (bio6), temperature annual range (bio7), mean temperature of the warmest quarter (bio10), annual precipitation (bio12), precipitation of the coldest quarter (bio19), and slope. The height distribution of T. aeacus in my country is in the area south of the Huaihe River in the Qinling Mountains, with a total area of 270.96 × 104 km2, accounting for 28.23% of the total area of China. According to future climate change conditions, as climate warming progresses, both low- and high-suitability areas show an expansion trend in most scenarios, particularly under the SSP5-8.5 scenario, where highly suitable areas increase significantly while moderately suitable areas gradually shrink. To address future climate change, conservation strategies should focus on protecting highly suitable areas and strengthening the management of marginal habitats to enhance the adaptability and survival chances of T. aeacus.

Cyberbullying Detection and Prevention System for Enhancing Online Platform Safety Using Maximum Entropy Model

ABSTRACTThe widespread proliferation of social media platforms in the 21st century has undoubtedly brought about a significant shift in how individuals establish connections. However, it is essential to acknowledge that this technological innovation has also given rise to negative repercussions. The proliferation of online abuse, harassment, cyberbullying, and trolling has emerged as a significant concern, overshadowing the otherwise advantageous aspects of digital networking platforms. The detrimental effect of cyberbullying is of considerable concern, which often causes profound psychological and physiological anguish among its targets, sometimes resulting in contemplation of suicide. The proliferation of online harassment occurrences, including disclosing confidential dialogues and spreading detrimental rumors, has engendered extensive apprehension. The present research aims to address the pressing need to identify and mitigate cyberbullying within the context of social media platforms. A rigorous approach was used to gather data from Kaggle, which was further processed utilizing sophisticated methods like tokenization, stemming, and lemmatization. The research used the Maximum Entropy Model to develop a detection system to identify instances of bullying in texts or messages shared on social media sites. The algorithm exhibited a praiseworthy mean accuracy rate of 77% in detecting cases of cyberbullying. The results highlight the capacity of the algorithm to anticipate if remarks entered by individuals on social media might be classified as cyberbullying, representing a noteworthy advancement in addressing this widespread problem. This study supports the broader deployment of the created cyberbullying detection system across all social media platforms. The proactive use of this novel technology enables online platforms to effectively detect and mitigate cyberbullying, cultivating a digital environment characterized by enhanced safety and respect. This research illuminates the significant issue of cyberbullying. It presents a concrete remedy to protect the welfare of those who engage in online activities, particularly those more susceptible to harm, such as women and children.

The Future Migration Direction of Deer and Japanese Yew Is Consistent Under Climate Change

Climate change is becoming an important driver of biodiversity loss by altering the habitat, distribution and interspecific relationships of species. Japanese yew (Taxus cuspidata) is a first class protected plant in China, which has important ecological significance and occupies a certain position in the feeding habit of wapiti (Cervus elaphus) and Siberian roe deer (Capreolus pygargus). Due to human and animal damage, the number of Japanese yew has gradually decreased. Therefore, understanding the potential distribution of Japanese yew and the suitable areas for deer to browse on it under climate change will help to further protect these three species in Northeast China, especially migrate to more suitable areas in different scenarios in the future. From July 2021 to July 2024, we collected the information of species distribution and the variables associated with the species’ ecological limits in Muling National Nature Reserve to cross-reflect the current and future distribution and feeding area of the two species to assess each other’s impacts with Maximum entropy model (MaxEnt). The results showed that under the SSP2-4.5 and SSP5-8.5 scenarios, feeding pressure, driest quarter precipitation (BIO17) and seasonal temperature variation coefficient (BIO4) were the main variables affecting the distribution of Japanese yew, and the driest quarter precipitation (BIO17) and annual precipitation (BIO12) were the main variables affecting wapiti and Siberian roe deer foraging them. Under SSP2-4.5 and SSP5-8.5 scenarios, the suitable area of Japanese yew and the feeding area of the two species of deer gradually decreased from 2041 to 2100. Compared with wapiti, Siberian roe deer has a greater impact on the distribution range of Japanese yew, and the suitable feeding area is wider. It is expected that the potential centroid of Japanese yew, wapiti and Siberian roe deer will migrate to higher latitudes in the future. These findings provide a scientific basis for the reserve to develop relevant measures and plans and effectively protect the three species.

Set pair analysis and system dynamics coupling approach for structure simulation and variation trend evaluation of water resources spatial equilibrium system

AbstractThe implement of water resources spatial equilibrium (WRSE) schemes is fundamental task of integrated water resources management in China, in which, the evaluation and simulation analysis of WRSE system is of great concern for understanding the overall variation and feedback characteristics of WRSE system. Therefore, we utilized the ordered degree, entropy and connection number coupling model to evaluate the variation of WRSE system, and also employed system dynamics (SD) and scenario simulation integrated method to reveal the feedback characteristics between different equilibrium variables and subsystems, and thus the set pair analysis‐SD based approach for structure simulation and variation trend evaluation of WRSE system was constructed. The application results in Anhui province, China demonstrated that, the overall variation of provincial spatial equilibrium situation of WRSE system presented obvious improving trend, 2009–2019, the index of ordered degree and connection number entropy in Hefei, increased from the minimum of 0.6434 (Grade 3, unequilibrium) to maximum of 0.9985 (Grade 1, equilibrium). Moreover, the future variation of WRSE system will display stable improving trend during 2020 to 2029, in which, annual water resources availability and water resources utilization efficiency will have significant influence on WRSE system. And the research findings can be favorable to formulate water resources development and utilization strategies.

RGB-D Data Compression via Bi-Directional Cross-Modal Prior Transfer and Enhanced Entropy Modeling

RGB-D data, being homogeneous cross-modal data, demonstrates significant correlations among data elements. However, current research focuses only on a unidirectional pattern of cross-modal contextual information, neglecting the exploration of bidirectional relationships in the compression field. Thus, we propose a joint RGB-D compression scheme, which is combined with Bi-directional Cross-modal Prior Transfer (Bi-CPT) modules and a Bi-directional Cross-modal Enhanced Entropy (Bi-CEE) model. The Bi-CPT module is designed for compact representations of cross-modal features, effectively eliminating spatial and modality redundancies at different granularity levels. In contrast to the traditional entropy models, our proposed Bi-CEE model not only achieves spatial-channel contextual adaptation through partitioning RGB and depth features but also incorporates information from other modalities as prior to enhance the accuracy of probability estimation for latent variables. Furthermore, this model enables parallel multi-stage processing to accelerate coding. Experimental results demonstrate the superiority of our proposed framework over the current compression scheme, outperforming both rate-distortion performance and downstream tasks, including surface reconstruction and semantic segmentation. The source code will be available at https://github.com/xyy7/Learning-based-RGB-D-Image-Compression .

Habitat Associated with Ramps/Wild Leeks (Allium tricoccum Ait.) in Pennsylvania, USA: Guidance for Forest Farming Site Selection

Ramps or wild leeks (Allium tricoccum) are a popular foraged non-timber forest product in North America consumed for their edible bulbs and leaves. The agroforestry practice of forest farming is a possible solution to conservation challenges surrounding the wild exploitation of this species, but it requires proper site selection to be successful. In this study, maximum entropy (Maxent) modeling using 163 occurrence points and field data collected at 30 wild populations were combined to determine the characteristics of the ramp habitat in Pennsylvania. Both Maxent modeling and field measurements highlighted moist, lower slope positions with base-rich bedrock types as suitable for ramps. Sites shared 50% of their floristic associates on average, with 252 species documented in total. Forest communities associated with ramps included many species indicative of base-rich mesic soil conditions, but the relative abundance of some indicator species differed by region. The confirmation of model variables by field measurements and forest community types points to the usefulness of these characteristics in identifying suitable forest farming sites. When used in tandem, these results can help to guide site selection for forest farming and other conservation strategies.

Distinct Ecological Habits and Habitat Responses to Future Climate Change in Two Subspecies of Magnolia sieboldii K. Koch, a Tree Endemic to East Asia.

Magnolia sieboldii, an important ornamental tree native to East Asia, comprises two subspecies in distinct regions, with wild populations facing suboptimal survival. This study aimed to understand the potential habitat distribution of these subspecies under future climate-change conditions to support climate-adaptive conservation. The maximum entropy (MaxEnt) model was used with occurrence and environmental data to simulate the current and future suitable habitats under various climate scenarios. Precipitation in the warmest quarter played a crucial role in shaping the potential habitats of both subspecies; however, they exhibited different sensitivities to temperature-related variables and altitude. Magnolia sieboldii subsp. sieboldii is more sensitive to temperature seasonality and annual mean temperature, whereas Magnolia sieboldii subsp. japonica is more affected by altitude, mean temperature in the driest quarter, and isothermality. Currently, the subsp. sieboldii is predicted to have larger, more contiguous suitable habitats across northeastern China, the Korean Peninsula, and Japan, whereas the subsp. japonica occupies smaller, more disjunct habitats scattered in central and western Japan and the southern Chinese mountains. These two subspecies will respond differently to future climate change. Potentially suitable habitats for subsp. sieboldii are expected to expand significantly northward over time, especially under the SSP585 scenario compared with the SSP126 scenario. In contrast, moderately and highly suitable habitats for the subsp. japonica are projected to contract southward significantly. Therefore, we recommend prioritizing the conservation of the subsp. japonica over that of the subsp. sieboldii. Strategies include in situ and ex situ protection, introduction and cultivation, regional hybridization, and international cooperation. Our study offers valuable insights for the development of targeted conservation strategies for both subspecies of M. sieboldii to counteract the effects of climate change.

Enhanced GDM Algorithms for College Mathematics Teaching Quality Evaluation Under Interval-Valued Intuitionistic Fuzzy Sets

The college mathematics teaching quality evaluation is the multiple-attribute group decision-making (MAGDM). Currently, the Exponential TODIM (ExpTODIM) and MABAC was executed to put forward MAGDM. The interval-valued intuitionistic fuzzy sets (IVIFSs) are executed for portraying fuzzy data during the college mathematics teaching quality evaluation. In this study, the interval-valued intuitionistic fuzzy number ExpTODIM-MABAC (IVIFN-ExpTODIM-MABAC) approach is put forward the MAGDM with IVIFSs. At last, numerical example for college mathematics teaching quality evaluation is executed to verify the IVIFN-ExpTODIM-MABAC. The major contributions are executed: (1) the ExpTODIM-MABAC based was extended to IVIFSs along with Entropy model; (2) the entropy is employed to execute the weight under IVIFSs. (3) the IVIFN-ExpTODIM-MABAC approach is put forward MAGDM with IVIFSs; (4) numerical example for college mathematics teaching quality evaluation and comparative analysis is executed to proof the IVIFN-ExpTODIM-MABAC approach.

Mapping Biodiversity Conservation Priorities for Protected Areas for Spatial Optimization: A Case Study in the Songnen Plain, China.

The decline in biodiversity poses a serious threat to natural ecosystems and has become one of the most pressing global environmental issues. Establishing conservation priorities for protected areas (PAs) is one of the most direct and effective biodiversity conservation measures. However, conservation gaps arise as a result of existing problems in spatial layout of PAs, including overlapping protection scopes, artificial fragmentation of natural ecological regions, as well as "over-protection" and "over-exploitation." To optimize the spatial layout of PAs and improve the efficiency of biodiversity conservation, we employed the Habitat Quality module of the Integrated Assessment of Ecosystem Services and Tradeoffs (InVEST) model and the Maximum Entropy (MaxEnt) model to assess the PAs in the Songnen Plain, China. The combined model (MaxEnt-InVEST) revealed that the conservation priorities for PAs in the Songnen Plain occupied a total area of 14,764.14 km2 (10.24% of the total area of the Songnen Plain). The conservation priorities outside PAs occupied a total area of 7858.45 km2 (5.45% of the total area of the Songnen Plain) and were primarily distributed in the northeastern, central, and southwestern regions of the Songnen Plain. This indicated that existing PAs did not offer adequate protection for local biodiversity. The consistency of our combined modeling framework was 72.11%, which enabled a more accurate assessment of biodiversity hotspots and respects the land uses of the Songnen Plain. In addition, the modeling framework successfully created maps of conservation gaps of biodiversity hotspots based on actual species distribution data and considers current land uses. Our study was aimed at optimizing the spatial conservation efficiency of the Songnen Plain by assessing the conservation gaps in the Songnen Plain. It could provide a reference for the future development of a PA system centering on national parks.