Predicting The Potential Habitat Suitability Of Aporinellus Banks In China Under Future Climate Scenarios Using Maximum Entropy Model

Predicting The Potential Habitat Suitability Of Aporinellus Banks In China Under Future Climate Scenarios Using Maximum Entropy Model | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 September 2025 V1 Latest version Share on Predicting The Potential Habitat Suitability Of Aporinellus Banks In China Under Future Climate Scenarios Using Maximum Entropy Model Authors : Lili Dong , Pengling Mu , Li Ma 0000-0002-3436-1387 [email protected] , and Qiang Li Authors Info & Affiliations https://doi.org/10.22541/au.175822863.32139300/v1 190 views 129 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Climate change will significantly impact the ecological suitability and diversity of species. As a predatory natural enemy, Aporinellus Banks, 1911 (Hymenoptera: Pompilidae) regulates spiders populations through predation, thereby affecting downstream secondary producers, maintaining ecosystem balance, and providing important ecosystem services. In this research, the MaxEnt model was applied to simulate the potential distribution of habitats in China, considering current and future climate scenarios (ssp126, ssp245, ssp370, and ssp585). The analysis was based on 89 occurrence points and 55 environmental variables. The model achieved an average AUC value of 0.923, indicating excellent performance, with predicted results highly consistent with observed data. The findings show that the main factors influencing habitat distribution are the mean maximum temperature of January (44.3%) and total precipitation in November (10%). Under current climatic conditions, an estimated 52.97% of China’s landmass is predicted to provide suitable habitat for Aporinellus , with suitability predominantly concentrated in provinces including Yunnan, Guizhou, Guangxi, Guangdong, Fujian, Hainan, and Taiwan. However, under future climate change conditions, suitable habitats are projected to decrease, with the potential distribution in low-suitability areas showing a general downward trend. Under the ssp585 scenario, suitable habitats are expected to decline by 5.57% during 2021-2040 and by 12.45% during 2081-2100. Overall, under both current and future climatic conditions, the suitable habitats of Aporinellus are concentrated in warm and humid agroecological zones. These results advance our understanding of the mechanisms by which environmental variables govern the habitat suitability of Aporinellus , provide a foundation for the conservation and utilization of spider wasps species diversity, and offer valuable perspectives into the diversity, stability, and dynamic changes of the ecosystems they inhabit. 1. Introduction The Intergovernmental Panel on Climate Change (IPCC) emphasized the continued warming trend in its Fifth Assessment Report. Between 1880 and 2012, the global average temperature increased by 0.85°C. In the coming decades, global temperatures are projected to rise by 1.5°C to 2°C, with a possible increase of up to 4.8°C by the end of this century (Yu-Cheng et al. 2017, Intergovernmental Panel On Climate Change (Ipcc) 2023). As the largest and most diverse group within terrestrial ecosystems, insects are highly dependent on environmental temperature for their survival and reproduction (Damien and Tougeron 2019, Fan et al. 2024). Studies have demonstrated that insects—central components of many ecosystems—are among the most affected animal groups, with climate change exerting widespread impacts from individuals to entire communities. Climate change can not only drive species to extinction but also cause significant changes in their abundance, distribution, species assemblages, composition, and interactions with other species (Harvey et al., 2023). In ecosystems, predatory relationships not only shape the population dynamics of species but also play a vital role in the trophic structure, energy flow, and the maintenance of biodiversity (Estes et al. 2011). Evolving from an arachnid ancestor during the Devonian period (about 400 million years ago), spiders represent a highly diverse group characterized by wide dietary breadth. They are among the most abundant and widespread predators in terrestrial ecosystems and are believed to be the primary predators of insects (Nyffeler and Birkhofer, 2017). Spider wasps (Hymenoptera: Pompilidae), as high-level trophic predators that specialize in preying on spiders, are one of the most distinctive and easily recognizable families of aculeate insects in animal communities. They are widely distributed across six major zoogeographical regions of the world. Members of this family occupy an extensive range of habitats and climatic zones, spanning deserts, forests, coastal regions, mountains, and even tundra, with tropical regions harboring the greatest species diversity (Loktionov 2023). The extant fauna of Pompilidae approximately 5000 species in 217 genera (Loktionov, 2023). A common biological trait of Pompilidae is that female spider wasps construct nests in ground cavities, wood, or grass stems, and may also use clay to build nests on stones, branches, tree trunks, or the undersides of leaves. They provision their offspring with spiders (Rodriguez et al. 2016), although some groups within this family have evolved a kleptoparasitic lifestyle (Loktionov, 2014). The genus Aporinellus Banks, 1911, belongs to Pompilinae, Pompilini. It currently includes 45 species (Dong et al., 2023) and is present in all major zoogeographical regions with the exception of the Australian region (Day 1988) (Figure 1). Species of the genus frequently feed on nectar, and some also visit flowers. In addition, members of the group typically prey on spiders from the families Salticidae and Thomisidae (Evans 1951), which are important predatory arthropods widely distributed across diverse habitats and play a key role in regulating plant pest populations (Nyffeler and Birkhofer 2017). The interaction between predators and prey serves as a key link connecting different trophic levels (Chen et al. 2025), influencing multiple levels—from prey to primary producers—through predator-community dynamics. Ultimately, this interaction regulates ecosystem functions such as productivity (Miller et al. 2006, Schmitz 2009), pest control (Snyder et al. 2006), and decomposition (Hawlena et al. 2012, Chen et al. 2025). Therefore, from a trophic perspective, predatory interactions between spider wasps and spiders contribute not only to energy flow but also serve as a critical component of the ecological food web. Spider wasps play a role as predators of natural enemy insects within the food web, and their interactions with spiders reveal a complex and multidimensional ecological network. The predatory activities of predators reduce prey populations, thereby creating more ecological space for other organisms (such as plants and other insects) and promoting the maintenance of ecological diversity and the sustainability of ecosystem services (Snyder 2019). By preying on spiders, spider wasps influence and regulate the dynamic balance of food chain relationships among spiders, herbivorous insects, and plants, playing an important role in ecosystem functioning. This complex predatory interaction reflects the interdependence and dynamic equilibrium among different trophic levels within the ecosystem. Therefore, the species diversity, geographical distribution, and temporal dynamics of spider wasps also mirror the diversity and stability of the ecosystems they inhabit. As an important group of spider wasps, the genus Aporinellus plays a key predatory role in ecosystem functioning; however, its species diversity, distribution patterns, and responses to current and future climate change remain insufficiently studied. Under the trophic-rank hypothesis, species from upper trophic levels are expected to be more severely influenced by environmental disturbances than species from lower levels (Gilman et al. 2010). Therefore, forecasting the impacts of climate change on these relationships presents challenges, as it alters not only individual organisms but also the interactions between them (Damien and Tougeron 2019). At present, human-driven ecosystem modifications and climate change are altering spider wasp communities, inevitably leading to changes or losses in biodiversity. Therefore, studying the diversity of spider wasps and predicting changes in their suitable habitats are of great research and practical significance for conserving natural enemy insect resources and providing valuable insights into ecosystem diversity and its changing trends. Species distribution models (SDMs) can accurately represent the natural distribution of species within their current range, particularly when survey data and relevant functional predictors are analyzed using specified models for appropriate modeling analysis (Elith and Leathwick 2009). These models are widely used in conservation biology to forecast potential changes in species distributions under various scenarios (Rathore and Sharma 2023). By assessing the correlations between species distribution locations and different environmental factors, ecological niche models (ENMs) forecast areas that match the ecological requirements of species (Guisan and Zimmermann 2000, Yao et al. 2024). Among ENM algorithms, the MaxEnt model is particularly popular because of its fast computation and high predictive accuracy (Zhang, 2015). As species distribution prediction methods have advanced, the impacts of climate change are increasingly incorporated as crucial factors in model projections (Yao et al., 2024). China spans both the Palearctic and Oriental regions and exhibits highly diverse climatic types. In this study, MaxEnt was employed to estimate the potential distribution of Aporinellus in China under current climatic conditions and four future climate scenarios. The main objectives were to: (1) examine the species richness pattern of Aporinellus in China; (2) evaluate the relative importance of environmental factors influencing its distribution; and (3) predict its potential distribution and trends under various climate change scenarios. These research findings hold significant scientific and practical value for assessing the impact of climate change on the distribution of Aporinellus species, formulating conservation strategies, optimizing ecosystem service assessments, and advancing biodiversity conservation. They also provide valuable information for studying ecosystem diversity and its changing trends. 2. Materials and Methods 2.1. Occurrence records collection The occurrence data of Aporinellus were collected from the following sources: (1) published references related to Aporinellus (Tsuneki 1989, Dong et al., 2023), as well as unpublished records from fieldwork conducted between 2005 and 2024 investigating historical and potential occurrence points in China; (2) collection records of Aporinellus specimens deposited in South China Agricultural University (SCAU), Institute of Zoology, Guangdong Academy of Sciences (CAS). Initially, a total of 92 distribution points were collected. To ensure coordinate accuracy, we verified and compared the data using Google Earth 7.1. In addition, a buffer analysis was applied to filter out occurrence points that were too close to each other. A 2.5 km buffer radius was used to filter occurrence points. When points were within 5.0 km of each other, only one was retained. This procedure was implemented to reduce strong spatial correlation and avoid overfitting in the simulations (Gao et al. 2021). Ultimately, 89 distribution points were retained for use in the final model (Figure 2). 2.2. Environmental variables The distribution patterns of insect species are shaped by a range of environmental variables (Hortal et al. 2010). In this study, 19 bioclimatic variables with a resolution of 2.5 arc minutes were downloaded from WorldClim v2.1 (https://www.worldclim.org/). Additionally, the monthly mean minimum temperature, monthly mean maximum temperature, and monthly total precipitation were included. The future bioclimatic variables are based on the shared socioeconomic pathways (SSPs) SSP126, SSP245, SSP370, and SSP585 of CMIP6 (O’Neill et al. 2016), which represent low, medium, high, and higher emission scenarios, respectively. These variables were simulated using the global climate model (GCM) BCC-CSM2-MR for scenario modeling in various periods of the 21st century. We calculated the percentage contribution of each environmental variable using Jackknife analysis in MaxEnt software to address multicollinearity among bioclimatic variables, which may affect the predictive performance and accuracy of the maximum entropy model (Sillero, 2011; Verbruggen et al., 2013). In addition, we used SPSS software v27.0 (International Business Machines Corporation, Armonk, NY, USA) together with Pearson correlation analysis to examine the relationships among climate/environmental variables (International Business Machines Corporation, Armonk, NY, USA) (Phillips et al. 2004). 2.3. Optimized Maxent model When simulating species distributions using the Maxent model, overfitting and high complexity can occur, which may reduce the accuracy of the results (Phillips et al. 2004). Therefore, previous studies have suggested optimizing the parameter characteristic class (FC) and regularization multiplier (RM). The values of FC and RM can be combined to achieve optimal model complexity. Specifically, RM was tested at eight values ranging from 0.5 to 4.0, with increments of 0.5. Six types of FC combinations were tested: H, L, LQ, LQH, LQHP, and LQHPT (L: linear, Q: quadratic, H: hinge, P: product, T: threshold) (Merow et al. 2013, Radosavljevic and Anderson 2014, Morales et al. 2017). The modified Akaike Information Criterion (AICc) for different parameter settings was then calculated using the ENMeval tool. AICc is used to estimate the complexity of the maximum entropy model, with the lowest value indicating the best fit (Akaike 1998, Gao et al. 2021). The contribution percentages and permutation importance of different environmental variables were used to estimate the relative contributions in the final MaxEnt model. The distribution points of Aporinellus and related environmental data were imported into MaxEnt to establish the initial model, with the cloglog output format selected (Phillips et al. 2004). During model computation, the random test percentage was set to 25, meaning that 75% of the distribution data were used to train the model and 25% were used to validate it. Each setting was repeated 10 times, and the final result was reported as the average of these repetitions. The correlation of key environmental variables was assessed by cross-validation, and environmental response curves were generated to reveal the relationships between species occurrence probability and environmental factors. 2.4. Model Evaluation and Habitat Suitability Classification The performance of the MaxEnt model simulations was assessed using the area under the receiver operating characteristic curve (AUC). The area under the curve (AUC) metric spans from 0 to 1, where elevated values correspond to enhanced predictive performance of the maximum entropy model (Swets 1988, Bai et al. 2022). However, the AUC values may vary with the spatial extent of species’ geographical range; a wider spatial extent generally results in higher AUC values (Peterson et al. 2008). To mitigate this potential distortion, we adopted the partial Regional ROC (P-ROC) framework for model validation. Using R software (v3.0), we computed the AUC ratio (AUC_E / AUC_0.5) with a 5% omission tolerance (E = 0.05) according to the partial ROC protocol. A ratio exceeding 1.0 serves as statistical evidence that the model’s predictions surpass chance expectations (Qiao et al. 2016). The ASC output from MaxEnt was imported into ArcMap, converted into raster data, and overlaid on the administrative division map of China for visualization. The natural breaks (Jenks) method was used to classify habitat suitability into four categories: unsuitable (P < 0.10), less suitable (0.10 ≤ P < 0.28), moderately suitable (0.28 ≤ P < 0.58), and highly suitable (P ≥ 0.58) (Zhao et al. 2024). The area of suitable habitat was then calculated in R software based on these classifications. 3. Results 3.1. The major parameters of maximum entropy model Bioclimatic and environmental variables were chosen using jackknife analysis and Pearson’s correlation analysis. The selected environmental variables are presented in Table 1. The FC combination used in the maximum entropy model for this study was LQH, with an ’RM’ value of 1.5 (Figure 3). 3.2. The predictive accuracy of maximum entropy model Model accuracy under both current and future climate conditions was assessed through the AUC value and AUC ratio. For all 17 climate scenarios, AUC exceeded 0.88 and the AUC ratio was greater than 1.0, demonstrating the strong predictive performance of the Maxent model (Table 2, Figure 4). Owing to the limited sample size, the average omission curve derived from test data exhibits a minor discrepancy relative to the projected omission under near-present climate conditions (Figure 5). 3.3. The potential distribution of Aporinellus under the current climate condition This study applied the Maxent model to classify the potential suitable distribution area of Aporinellus into four levels: highly suitable, moderately suitable, low suitable, and unsuitable. Distribution statistics and area analyses of the different suitable habitats were also conducted (Figure 6). The results indicate that, under current climatic conditions, the total suitable area is 5.085 × 10⁶ km², accounting for 52.97% of China’s total land area. The range extends across 92.246°–121.547°E, 17.88°–28.66°N, 115.927°–116.787°E, 38.876°–39.498°N, and 117.533°–121.145°E, 47.755°–50.908°N. Among these regions, the highly suitable area is estimated at 6.9 × 10⁵ km², representing 7.19% of China’s land area. The distribution is concentrated in southeastern Tibet, Yunnan, southern Guangxi, southern Guangdong, southern Fujian, as well as the majority of Hainan and Taiwan, central Hebei, and northeastern Inner Mongolia. The moderately suitable area is predicted to cover 9.72 × 10⁵ km² (10.13% of the total land area) and is concentrated in northern Xinjiang, southeastern Sichuan, southern Guizhou, Guangxi, Guangdong, northern Fujian, southern Hunan, southern Jiangxi, central Hebei, and the western parts of Jilin and Heilongjiang. The low suitable area is the largest, covering 3.423 × 10⁶ km² (35.66% of the total land area), and is mainly distributed in northern Xinjiang, Southwest China, parts of North, South, East, and Northeast China. 3.4. Evaluation of environmental factors The Maxent model uses the Jackknife method, and the results reflect the influence weights of different environmental factors on the habitat suitability of Aporinellus (Figure 7). The vertical axis shows the selected environmental variables, while the horizontal axis indicates the score for each variable. The dark blue bar represents the model score when only that specific environmental factor is included, the light blue bar represents the sum of scores for all variables excluding that factor, and the red bar represents the total score of all variables. The Jackknife analysis showed that tmax1, tmax2, tmax3, and prec11 contributed the highest gains in the ”only this variable” test, demonstrating their strong individual explanatory ability. In contrast, bio15 and tmin7 had the lowest gains in the ”this variable excluded” test, indicating that they provide distinctive environmental information. Overall, our analysis identified five temperature-related variables and one precipitation-related variable as the key environmental factors shaping species distribution patterns. These findings confirm the predominant role of climatic factors and suggest that the distribution of Aporinellus is mainly driven by the combined influence of temperature variability and seasonal precipitation. Response curves constructed via the maximum entropy model, which depict relationships between principal environmental variables and species distribution probabilities, delineate the environmental parameter ranges corresponding to varying probability thresholds (Figure 8). The results indicate that the suitable temperature ranges for Aporinellus under tmax1 and tmax2 are 0–29.3°C and 0–32.5°C, with optimal values at 20°C and 21–32.5°C, respectively. When tmax1 and tmax2 are above 0°C, the probability of occurrence rises with temperature, peaking at 20°C and 21°C. For prec11, the suitable precipitation range is 0–245.8 mm, with occurrence probability increasing alongside precipitation, and the optimal range being 150–245.8 mm. Under tmax3, the suitable range is 0–25°C, with the most favorable conditions consistently above 25°C. When tmax3 exceeds 0°C, occurrence probability increases with temperature, reaching a maximum at 25°C. The optimal values for tmin5 and tmin7 are above 0°C and 5°C, respectively, and their probabilities rise with temperature, with 20°C being the most suitable (Because of poor model performance, tmax1–3 values below 0°C are not considered meaningful and are excluded from discussion in this study). 3.5. Potential distribution of Aporinellus under future climate conditions A comparative analysis of predictions under four CMIP6 climate scenarios and current data on suitable habitats indicates that, under future climate conditions, the suitable distribution area of Aporinellus in China will decrease throughout the 21st century. Specifically, the highly suitable and moderately suitable areas in northern China will shift to low-suitability and unsuitable areas (Figures 6, 9). The total predicted suitable area decreases in the periods 2021-2040 under ssp126 and 2081-2100 under ssp585, measuring 4.916 × 10⁶ km² and 4.452 × 10⁶ km², respectively. This represents a decrease of 3.32% in the former and 12.45% in the latter, compared to the current climate conditions. Similarly, the largest expansion of highly suitable habitat was projected for the period 2081-2100 under SSP245, reaching 7.19 × 10⁵ km² and representing a 4.2% increase relative to the current extent. In contrast, the minimum highly suitable area is predicted for the period 2061-2080 under ssp126, with an area of 6.66 × 10⁵ km², a 3.47% decrease from the current climate conditions. The moderately suitable area decreases in the period 2081-2100 under ssp126, reducing to 8.22 × 10⁵ km², a 15.43% decrease compared to current climate conditions. In the period 2041-2060 under ssp370, the area further decreases to 5.93 × 10⁵ km², reflecting a 39.05% reduction from the moderately suitable area under current conditions. The predicted low suitability area is largest in the period 2041-2060 under ssp245, reaching 3.554 × 10⁶ km², an increase of 3.83%. In contrast, the minimum low suitability area is predicted to be 3.101 × 10⁶ km² in the period 2081-2100 under ssp585, representing a 9.41% decrease compared to the current low suitability area (Figure 10). 4. Discussion MaxEnt (Maximum Entropy) model is a widely used tool in species distribution modeling (SDM), renowned for its capacity to delineate ecologically viable habitats for target species through the application of maximum entropy principles (Bonelli et al. 2021). This study utilized 89 georeferenced presence records spanning 12 provincial-level administrative regions in China (Figure 2), with 55 Worldclim-derived bioclimatic variables serving as key environmental predictors in the modeling framework . The MaxEnt model was then applied to simulate the potential suitable distribution area of Aporinellus in China. Due to its ability to handle small sample sizes and to accurately predict potential distributions based on environmental variables and current occurrence data under the principle of maximum entropy, MaxEnt is considered a highly reliable tool for predicting climatic suitability (Gao et al. 2021). Our results indicate that Aporinellus currently occupies a relatively extensive area of suitable habitat in China, accounting for 52.97% of the total land area. However, under future climate scenarios, the total suitable area is expected to decrease, and the medium- and high-suitability areas in northern China are projected to shift into low-suitability regions. The primary environmental variables influencing the distribution of spider wasps are temperature and precipitation, with the most suitable ranges being 20°-30°C and 150-245.8 mm, respectively. 4.1. Evaluation and prediction results of the MaxEnt model Based on the distribution data of Aporinellus and 55 climatic variables, this study employed the MaxEnt model to predict the potential suitable habitat areas for Aporinellus in China under both current and future climatic conditions. The model’s accuracy was assessed using the AUC (Area Under the Curve) metric. The average AUC value of the MaxEnt model was 0.923, indicating a high level of predictive performance and strong reliability in modeling the distribution of Aporinellus . The prediction results revealed that the current potential suitable habitats for Aporinellus are mainly concentrated in the southern regions of China, including Yunnan, Guizhou, Guangxi, Guangdong, Fujian, Hainan, and Taiwan. These areas are characterized by diverse landforms, such as mountains, plateaus, plains, and wetlands. Overall, the terrain is varied, and the regions are strongly influenced by the monsoon climate. They are typically warm, humid, and receive abundant precipitation, suggesting that Aporinellus is well-adapted to these ecological environments. 4.2. Main environmental variables affecting the distribution of Aporinellus The key environmental variables affecting the occurrence probability of Aporinellus were analyzed, and the corresponding response curves were produced. The results indicate that the occurrence probability of Aporinellus changes in response to variations in these key environmental factors (Figure 8). In this study, the seven main environmental variables influencing the suitability distribution of Aporinellus were tmax1, prec11, tmin7, tmax3, tmax2, tmin5, and bio15. Among these, temperature and precipitation were the most important contributors to its distribution. Temperature, in particular, was identified as the dominant factor, with a total contribution of 44.3%. Overall, climate plays a critical role in determining species distributions (Castex et al. 2018). The physiological activities of insects are highly sensitive to temperature fluctuations (Skendžić et al. 2021). Both extremely low and excessively high temperatures can inhibit essential functions such as reproduction, growth, and survival. In particular, excessively high temperatures can directly disrupt physiological processes (Liu et al. 2025). In this study, the most influential environmental factor determining Aporinellus distribution was the mean maximum temperature of January. When the temperature exceeded 20°C, the probability of occurrence increased with rising temperatures. In addition to temperature, precipitation also plays a significant role in shaping the distribution of Aporinellus . Drought is one of the key limiting factors for insect distribution (González-Tokman et al. 2020). Adequate precipitation creates a moist environment that is favorable for insect survival and helps mitigate the negative effects of drought (Skendžić et al. 2021). Our results indicate that the most suitable habitats for Aporinellus are located in the southern regions of China, which are characterized by a humid climate, abundant rainfall, and high temperatures. In these regions, seasonal precipitation has a marked influence on the foraging behavior of spider wasps. During the wet season, conditions are favorable for spider activity, leading to an increase in both the abundance and activity levels of spiders. This, in turn, provides a stable and plentiful prey base for spider wasps. As a result, Aporinellus shows heightened foraging activity during periods of increased precipitation. It contributes to a better understanding of their ecological niche, supports efforts to delineate their potential range, and offers important guidance for the conservation of spider wasp species diversity. 4.3. Changes in the adaptive distribution of Aporinellus Under current climate conditions, the highly and moderately suitable areas in Xinjiang, Hebei, Beijing, Inner Mongolia, Jilin, and Heilongjiang provinces in northern China will transform into low suitable area under future climate conditions. Therefore, under different future climate scenarios, the total suitable habitat area for Aporinellus will decrease over time. According to Figure 10, over time, the area of highly suitable area shows a gradual upward trend under all scenarios except ssp126. Studies have shown that the life cycle and reproductive activities of Dipogon sperconsus (Hymenoptera: Pompilidae) are significantly influenced by climatic factors. For example, warm climate conditions promote its reproductive activities, while cold seasons may lead to a decrease or cessation of its activities (Nishimoto et al. 2021), which is consistent with the results of this study. In the future, the highly suitable area of Aporinellus will expand, particularly in tropical rainforest climates and coastal areas, further confirming that temperature and precipitation are the main factors affecting Aporinellus . Aporinellus is suitable for growth in environments with high temperatures and abundant precipitation. In addition, Aporinellus provides important ecosystem services by preying on spiders, thereby influencing and regulating the dynamic balance among spiders, herbivorous insects, and plants within the food web. Climate change has impacted the geographical distribution of Aporinellus , along with associated shifts in the biological communities it inhabits. The research results on the dynamic change trend of the geographical distribution of the Aporinellus mentioned above also reflect one of the possible corresponding changes in the ecosystem in which it is located, and can provide useful information for studying the dynamic change trend of the corresponding ecosystem. 4.4. Limitations of the study Although the MaxEnt model offers advantages in terms of ease of use, handling limited sample sizes, and achieving high prediction accuracy, it also has inevitable limitations similar to those of other ecological niche prediction models (Ji et al. 2020). In this study, the current distribution of the Aporinellus was used as the occurrence data, and the values of 55 environmental variables were based on present-day climate extremes. As a result, potential future changes in distribution may have been overlooked. Additionally, several other important factors—such as topography, environmental heterogeneity, soil characteristics, the distribution of natural enemies, host spider associations, and interspecific interactions—were not considered (Iannella et al. 2019), which could introduce some errors into the model predictions. However, it is not possible to account for all environmental factors within a single model (Xu et al. 2022), thus, some errors are inevitable. This study primarily focused on the influence of climate on the distribution of the horned spider wasps. Future research should incorporate a broader range of variables and ecological factors to enhance the reliability and comprehensiveness of the model’s predictions. 5. Conclusion Spider wasps are an important type of hunting natural enemy and pollinating insect. Accurately predicting how biological and climatic variables influence the spatial distribution of Aporinellus is essential for their conservation. In this study, the MaxEnt model was employed to assess the potential impacts of current and future climate conditions on the habitat suitability of spider wasps. The results demonstrated that the MaxEnt model remains effective in predicting the potential distribution of Aporinellus in China, even with a limited sample size. By integrating current and future climate scenarios, the model provides more accurate predictions and successfully identifies key environmental variables that influence suitable habitats now and in the future from 2021 to 2100 (shared socioeconomic pathways SSP126, SSP245, SSP370, and SSP585). Temperature was found to be a critical factor in determining suitable habitats, likely due to its influence on the reproductive and developmental requirements of spider wasps. Under projected future climate conditions, both the total area and the extent of suitable habitats are expected to decline, with significant changes observed in regions such as Xinjiang, Inner Mongolia, Heilongjiang, Beijing, and Hebei. These findings offer valuable insights for monitoring Aporinellus populations, forecasting their spatial distribution, and developing effective long-term conservation strategies. References Akaike, H., 1998. Information theory and an extension of the maximum likelihood principle. In : E. Parzen, K. Tanabe, and G. Kitagawa, eds. Selected Papers of Hirotugu Akaike . New York, NY: Springer, 199–213.Bai, J., Hou, P., Jin, D., Zhai, J., Ma, Y., and Zhao, J., 2022. Habitat suitability assessment of black-necked crane ( Grus nigricollis ) in the zoige grassland wetland ecological function zone on the eastern Tibetan Plateau. Diversity , 14 (7), 579. https://doi.org/10.3390/d14070579.Bonelli, S., Barbero, F., Zampollo, A., Cerrato, C., Genovesi, P., and La Morgia, V., 2021. Scaling-up targets for a threatened butterfly: a method to define favourable reference values. Ecological Indicators , 133, 108356. https://doi.org/10.1016/j.ecolind.2021.108356.Castex, V., Beniston, M., Calanca, P., Fleury, D., and Moreau, J., 2018. Pest management under climate change: the importance of understanding tritrophic relations. Science of the Total Environment , 616, 397–407. https://doi.org/10.1016/j.scitotenv.2017.11.027.Chen, J., Wang, M., Luo, A., Zhang, F., Chesters, D., Liu, S., Li, Y., Von Oheimb, G., Kunz, M., Zhou, Q., Bruelheide, H., Liu, X., Ma, K., Schuldt, A., and Zhu, C., 2025. Bottom‐up and top‐down effects combine to drive predator–prey interactions in a forest biodiversity experiment. Journal of Animal Ecology , 1–12. Doi: 10.1111/1365-2656.70103.Damien, M. and Tougeron, K., 2019. Prey–predator phenological mismatch under climate change. Current Opinion in Insect Science , 35, 60–68. https://doi.org/10.1016/j.cois.2019.07.002.Day, M.C., 1988. Spider wasps: Hymenoptera: Pompilidae. Handbooks for the identification of British insects , 6 (4), 1–60.Dong, L.; Song, Z.; Li, Q.; Ma, L, 2023. Two new species of the genus Aporinellus Banks, 1911 from China, with a key to species from Palearctic and Oriental regions (Hymenoptera: Pompilidae). Oriental Insects , 1–19. https://doi.org/10.1080/00305316.2023.2277949.Elith, J. and Leathwick, J.R., 2009. Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics , 40 (1), 677–697. Doi: 10.1146/annurev.ecolsys.110308.120159.Estes, J.A., Terborgh, J., Brashares, J.S., Power, M.E., Berger, J., Bond, W.J., Carpenter, S.R., Essington, T.E., Holt, R.D., Jackson, J.B.C., Marquis, R.J., Oksanen, L., Oksanen, T., Paine, R.T., Pikitch, E.K., Ripple, W.J., Sandin, S.A., Scheffer, M., Schoener, T.W., Shurin, J.B., Sinclair, A.R.E., Soulé, M.E., Virtanen, R., and Wardle, D.A., 2011. Trophic downgrading of planet earth. Science , 333 (6040), 301–306.Evans, H.E., 1951. A taxonomic study of the nearctic spider wasps belonging to the tribe Pompilini (Hymenoptera: Pompilidae). Part III. Transactions of the American Entomological Society (1890-) , 77 (3/4), 203–330.Fan, H., Liu, T., Chen, Y., Liao, Z., Chen, J., Hu, Y., Qiao, G., and Wei, F., 2024. Geographical patterns and determinants of insect biodiversity in China. Science China Life Sciences , 67 (6), 1255–1265. https://doi.org/10.1007/s11427-023-2483-0.Gao, T., Xu, Q., Liu, Y., Zhao, J., and Shi, J., 2021. Predicting the potential geographic distribution of Sirex nitobei in China under climate change using maximum entropy model. Forests , 12 (2), 151. https://doi.org/10.3390/f12020151.Gilman, S.E., Urban, M.C., Tewksbury, J., Gilchrist, G.W., and Holt, R.D., 2010. A framework for community interactions under climate change. Trends in Ecology & Evolution , 25 (6), 325–331.González-Tokman, D., Córdoba-Aguilar, A., Dáttilo, W., Lira-Noriega, A., Sánchez-Guillén, R.A., and Villalobos, F., 2020. Insect responses to heat: physiological mechanisms, evolution and ecological implications in a warming world. Biological Reviews , 95 (3), 802–821. Doi: 10.1111/brv.12588.Guisan, A. and Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecological modelling , 135 (2–3), 147–186.Harvey, J.A., Tougeron, K., Gols, R., Heinen, R., Abarca, M., Abram, P.K., Basset, Y., Berg, M., Boggs, C., Brodeur, J., Cardoso, P., De Boer, J.G., De Snoo, G.R., Deacon, C., Dell, J.E., Desneux, N., Dillon, M.E., Duffy, G.A., Dyer, L.A., Ellers, J., Espíndola, A., Fordyce, J., Forister, M.L., Fukushima, C., Gage, M.J.G., García‐Robledo, C., Gely, C., Gobbi, M., Hallmann, C., Hance, T., Harte, J., Hochkirch, A., Hof, C., Hoffmann, A.A., Kingsolver, J.G., Lamarre, G.P.A., Laurance, W.F., Lavandero, B., Leather, S.R., Lehmann, P., Le Lann, C., López‐Uribe, M.M., Ma, C., Ma, G., Moiroux, J., Monticelli, L., Nice, C., Ode, P.J., Pincebourde, S., Ripple, W.J., Rowe, M., Samways, M.J., Sentis, A., Shah, A.A., Stork, N., Terblanche, J.S., Thakur, M.P., Thomas, M.B., Tylianakis, J.M., Van Baaren, J., Van De Pol, M., Van Der Putten, W.H., Van Dyck, H., Verberk, W.C.E.P., Wagner, D.L., Weisser, W.W., Wetzel, W.C., Woods, H.A., Wyckhuys, K.A.G., and Chown, S.L., 2023. Scientists’ warning on climate change and insects. Ecological Monographs , 93 (1), e1553. Doi: https://doi.org/10.1002/ecm.1553.Hawlena, D., Strickland, M.S., Bradford, M.A., and Schmitz, O.J., 2012. Fear of predation slows plant-litter decomposition. Science , 336 (6087), 1434–1438. Doi: 10.1126/science.1220097.Hortal, J., Roura‐Pascual, N., Sanders, N.J., and Rahbek, C., 2010. Understanding (insect) species distributions across spatial scales. Ecography , 33 (1), 51–53. Doi: 10.1111/j.1600-0587.2009.06428.x.Iannella, M., De Simone, W., D’Alessandro, P., Console, G., and Biondi, M., 2019. Investigating the current and future co-occurrence of Ambrosia artemisiifolia and Ophraella communa in Europe through ecological modelling and remote sensing data analysis. International Journal of Environmental Research and Public Health , 16 (18), 3416. https://doi.org/10.3390/ijerph16183416.Intergovernmental Panel On Climate Change (Ipcc), 2023. Climate change 2021 – the physical science basis: Working group I contribution to the sixth assessment report of the intergovernmental panel on climate change . 1st edn. Cambridge University Press.Ji, W., Han, K., Lu, Y., and Wei, J., 2020. Predicting the potential distribution of the vine mealybug, Planococcus ficus under climate change by MaxEnt. Crop Protection , 137, 105268. https://doi.org/10.1016/j.cropro.2020.105268.Liu, Q., Wang, Z., Xu, D., Peng, Y., Wu, J., Liu, Z., Li, X., and Zhuo, Z., 2025. Effects of climate change on the distribution of Papilio xuthus . Insects , 16 (2), 131. https://doi.org/ 10.3390/insects16020131.Loktionov, V.M., 2014. Spider wasps (Hymenoptera: Pompilidae) of the Russian far east. Dalnauka: Vladivostok, 1–472.Loktionov, V.M., 2023. Geographical distribution of the spider wasps (Hymenoptera: Pompilidae) of the world. Euroasian Entomological Journal , 32 (4), 394–402. Doi: 10.15298/rusentj.32.4.05. Doi: 10.15298/rusentj.32.4.05.Merow, C., Smith, M.J., and Silander, J.A., 2013. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography , 36 (10), 1058–1069. Doi: 10.1111/j.1600-0587.2013.07872.x.Miller, D.A., Grand, J.B., Fondell, T.F., and Anthony, M., 2006. Predator functional response and prey survival: direct and indirect interactions affecting a marked prey population. Journal of Animal Ecology , 75 (1), 101–110.Morales, N.S., Fernández, I.C., and Baca-González, V., 2017. MaxEnt’s parameter configuration and small samples: Are we paying attention to recommendations? A systematic review. PeerJ , 5, e3093. Doi: 10.7717/peerj.3093.Nishimoto, Y., Shimizu, A., Yoshimura, J., and Endo, T., 2021. Life history and nesting ecology of a japanese tube-nesting spider wasp Dipogon sperconsus (Hymenoptera: Pompilidae). Scientific Reports , 11 (1), 12810. Nyffeler, M. and Birkhofer, K., 2017. An estimated 400–800 million tons of prey are annually killed by the global spider community. Science of Nature , 104 (3–4), 30. Doi: 10.1007/s00114-017-1440-1.O’Neill, B.C., Tebaldi, C., Van Vuuren, D.P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G.A., Moss, R., Riahi, K., and Sanderson, B.M., 2016. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development , 9 (9), 3461–3482. Doi:10.5194/gmd-9-3461-2016.Peterson, A.T., Papeş, M., and Soberón, J., 2008. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological modelling , 213 (1), 63–72. https://doi.org/10.1016/j.ecolmodel.2007.11.008.Phillips, S.J., Dudík, M., and Schapire, R.E., 2004. A maximum entropy approach to species distribution modeling. Proceedings of the twenty-first international conference on Machine learning , 83.Qiao, H., Peterson, A.T., Campbell, L.P., Soberón, J., Ji, L., and Escobar, L.E., 2016. NicheA: Creating virtual species and ecological niches in multivariate environmental scenarios. Ecography , 39 (8), 805–813. Doi: 10.1111/ecog.01961.Radosavljevic, A. and Anderson, R.P., 2014. Making better Maxent models of species distributions: Complexity, over fitting and evaluation. Journal of Biogeography , 41 (4), 629–643. Doi:10.1111/jbi.12227.Rathore, M.K. and Sharma, L.K., 2023. Efficacy of species distribution models (SDMs) for ecological realms to ascertain biological conservation and practices. Biodiversity and Conservation , 32 (10), 3053–3087. https://doi.org/10.1007/s10531-023-02648-1.Rodriguez, J., Pitts, J.P., Florez, J.A., Bond, J.E., and Von Dohlen, C.D., 2016. Molecular phylogeny of Pompilinae (Hymenoptera: Pompilidae): Evidence for rapid diversification and host shifts in spider wasps. Molecular Phylogenetics and Evolution , 94, 55–64. http://dx.doi.org/10.1016/j.ympev.2015.08.014.Schmitz, O.J., 2009. Effects of predator functional diversity on grassland ecosystem function. Ecology , 90 (9), 2339–2345.Sillero, N., 2011. What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecological Modelling , 222 (8), 1343–1346. Doi: 10.1016/j.ecolmodel.2011.01.018.Skendžić, S., Zovko, M., Živković, I.P., Lešić, V., and Lemić, D., 2021. The impact of climate change on agricultural insect pests. Insects , 12 (5), 440. https://doi.org/10.3390/insects12050440.Snyder, W.E., 2019. Give predators a complement: conserving natural enemy biodiversity to improve biocontrol. Biological Control , 135, 73–82. https://doi.org/10.1016/j.biocontrol.2019.04.017.Snyder, W.E., Snyder, G.B., Finke, D.L., and Straub, C.S., 2006. Predator biodiversity strengthens herbivore suppression. Ecology Letters , 9 (7), 789–796. https://doi.org/10.1111/j.1461-0248.2006.00922.x. Sun, Y., Guo, H., Ge, F., 2017. Progress in research on the responses of insects to global climate change. Chinese Journal of Applied Entomology . 54 (4), 539–552. DOI: 10.7679/j.issn.2095-1353.2017.066Swets, J.A., 1988. Measuring the accuracy of diagnostic systems. Science , 240 (4857), 1285–1293.Tsuneki, K., 1989. A study of the Pompilidae of Taiwan (Hymenoptera). SPJHA , 35, 1–180.Verbruggen, H., Tyberghein, L., Belton, G.S., Mineur, F., Jueterbock, A., Hoarau, G., Gurgel, C.F.D., and De Clerck, O., 2013. Improving transferability of introduced species’ distribution models: New tools to forecast the spread of a highly invasive seaweed. PLoS One , 8 (6), e68337.Xu, D., Zhuo, Z., Li, X., and Wang, R., 2022. Distribution and invasion risk assessment of Oryctes rhinoceros (L.) in China under changing climate. Journal of Applied Entomology , 146 (4), 385–395. https://doi.org/10.1111/jen.12989.Yao, J., Zhou, C., Wang, W., Li, Y., Du, T., and Shi, L., 2024. Habitat suitability of Danaus genutia based on the optimized MaxEnt model. Insects , 15 (12), 971. https://doi.org/10.3390/insects15120971.Zhang, L., 2015. Application of MAXENT maximum entropy model in predicting the potential distribution range of species. Bulletin of Biology , 50 (11), 9–12.Zhao, Q., Li, H., Chen, C., Fan, S., Wei, J., Cai, B., and Zhang, H., 2024. Potential global distribution of Paracoccus marginatus , under climate change conditions, using MaxEnt. Insects , 15 (2), 98. https://doi.org/10.3390/insects15020098. Figure legends Figure 1. Aporinellus magnidentatus Dong et Ma Figure 2. Geographical map of the distribution point of Aporinellus . Figure 3. MaxEnt model parameter combination. Figure 4. ROC curves and AUC used to evaluate the performance of Aporinellus model. Figure 5. Output of the predictive accuracy of the maximum entropy model. Figure 6. Occurrence points and potential distribution of Aporinellus in China under near-current climate conditions. Figure 7. The Jackknife test results reveal the key bioclimatic factors influencing the potential distribution of Aporinellus in China. Figure 8. Response curves characterizing the relationship between environmental predictors and the predicted distribution probability of Aporinellus. Mean values (solid line) were derived from an ensemble of 10 model replicates, and the blue envelopes depict the uncertainty (± standard deviation) around the mean estimate. Figure 9. Suitable distribution of Aporinellus in China under four future climate scenarios (21st century). Figure 10. The suitable distribution area of Aporinellus across different periods under four future climate scenarios. Data Accessibilty Statement All the required data are uploaded as supplementary material. Competing Interests Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contributions Lili Dong and Qiang Li: Conceptualization; Pengling Mu: formal analysis; Lili Dong and Pengling Mu: visualization; Lili Dong: original draft preparation; Li Ma: writing—review and editing. Acknowledgments We are grateful to our colleagues in the laboratory for their assistance in collecting specimens for this study. This work was supported by the National Natural Science Foundation of China under Grant number 31960112 and the Agricultural Basic Research joint project of Yunnan Province under Grant number 202101BD070001-004. Supplementary Material File (tables.docx) Download 15.75 KB Information & Authors Information Version history V1 Version 1 18 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ecological experiment evolutionary ecology invertebrate terrestrial Authors Affiliations Lili Dong Yunnan Agricultural University View all articles by this author Pengling Mu Yunnan Agricultural University View all articles by this author Li Ma 0000-0002-3436-1387 [email protected] Yunnan Agricultural University View all articles by this author Qiang Li Yunnan Agricultural University View all articles by this author Metrics & Citations Metrics Article Usage 190 views 129 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lili Dong, Pengling Mu, Li Ma, et al. Predicting The Potential Habitat Suitability Of Aporinellus Banks In China Under Future Climate Scenarios Using Maximum Entropy Model. Authorea . 18 September 2025. DOI: https://doi.org/10.22541/au.175822863.32139300/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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