
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39261484
71228
10.1038/s41598-024-71228-2
Article
Predicting possible distribution of rice leaf roller (Cnaphalocrocis medinalis) under climate change scenarios using MaxEnt model in China
Zhao Yuncheng
Zhang Lei
Wang Chunzhi wchunzhi@cma.gov.cn

grid.8658.3 0000 0001 2234 550X National Meteorological Center, 46 Zhongguancun South St., Beijing, 100081 China
11 9 2024
11 9 2024
2024
14 2124517 3 2024
26 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The relationship between climate conditions and pest life is a key determinant of their distribution. Cnaphalocrocis medinalis Guenee, a major rice pest, exhibits outbreaks and its distribution patterns closely linked to meteorological factors. By using 244 actual distribution and occurrence data of C. medinalis along with 8 bioclimatic data, and employing the MaxEnt model and ArcGIS, combined with the latest SSPs climate scenario data, this study evaluated the risk region distribution status in the current period and predicted changes in China from 2040 to 2100. The results indicate that an overall increase in the risk area for C. medinalis, particularly under SSP245 scenario during 2040–2060. While Low-risk areas are expected to decrease, Medium and High-risk areas are projected to increase significantly, with worsening pest infestations anticipated in southern Hubei, eastern Hunan, most of Jiangxi, central Fujian, northern Guangdong, and southern Jiangsu. Regions such as central Liaoning are expected to reach the minimum survival standard for C. medinalis in future, leading to the northward shift in risk areas. Difference plots highlighted areas of increased and decreased suitability, providing actionable insights for policymakers. Regions with increased suitability align with the predicted northward shift of many agricultural pests, necessitating enhanced monitoring, specific pest control measures, and updated agricultural policies to address changing risk profiles. Additionally, the centroid analysis showed a northwest shift direction in future, primarily located at the junction of Shaoyang City and Loudi City, situated around 27–28 °N degrees north latitude and 111–113 °E. The study underscores the significant impact of climate change on the distribution of rice leaf roller, providing valuable insights for agricultural planning and management. The northward and westward expansion of risk areas necessitates adaptive strategies to mitigate potential impacts on agriculture. Enhanced monitoring, integrated pest management, and the development of pest-resistant crops are essential for addressing future challenges posed by climate change.

Keywords

Climate change
MaxEnt
Cnaphalocrocis medinalis
Risk region
China
Subject terms

Climate change
Climate-change ecology
National Key R & D Program of China2022YFD1400400 2022YFD1400400 2022YFD1400400 Zhao Yuncheng Zhang Lei Wang Chunzhi issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

The Sixth Assessment Report (IPCC-AR6) from the Intergovernmental Panel on Climate Change highlights the persistent rise in greenhouse gas emissions, leading to a global surface temperature increase of 1.09 °C and a sea-level rise of 0.2 m over the past 120 years. China is significantly impacted by climate change, with the annual average temperature rising at a rate of 0.15 °C per decade since the twentieth century, surpassing the global average temperature increase1. The warming trend from 1951 to 2020 is more pronounced at 0.26 °C per decade, marking the last 20 years as the warmest period since 19012. Climate change profoundly affects geographic distribution and species diversity3, altering habitats for agricultural pests and diseases through climate warming, changes in precipitation patterns, increased agro-meteorological disasters, extreme weather events, and shifts in farming practices4. In recent years, the occurrence areas of crop diseases and insect pests in China exhibit an upward trajectory, with a 7.27-fold, and 4.72-fold increase in the area of diseases and pest from 1961 to 2010, respectively5. Concurrently, climate warming has accelerated the growth and development of migratory pests, resulting in earlier emergence, migration, and population peaks. In future climate warming scenarios, migratory pests are anticipated to have a broader distribution and inflict more harm than at present6.

Rice leaf roller (Cnaphalocrocis medinalis Guenee), a significant pest of rice, is prevalent in tropical and subtropical regions, including Asia, Australia, East Africa, and other regions. In China, it predominantly infests rice areas in the south and the middle and lower reaches of the Yangtze River and stands as one of the "two migratory" pests affecting rice cultivation7. Since the latter 1990s, the geographical range of rice leaf roller has expanded, with a notable increase in its harm intensity. From 2005 to 2015, the annual occurrence area of rice leaf roller reached 250 million mu, constituting 66.3% of the total rice planting area, resulting in an average annual loss of 7 million tons of rice8. The expansion of rice cultivation areas northward, particularly in Northeast China, due to increased heat resources from climate warming, has extended the damage range of rice leaf roller further north9,10. Consequently, the damage range of rice leaf roller has extended further north. Previous studies have paid more attention to migration routes and control strategies of the pest. However, despite having various control measures, information about the pest occurrence range, especially in the complex background climate change is lacking11,12. Notably, Jiang et al. 13 used the MaxEnt model and ArcGIS to evaluate the population distribution of rice leaf rollers under climate change scenarios in China, identifying key climatic factors but not integrating the latest SSP climate scenario data. Wang et al. (2023)14also employed the MaxEnt model to predict the potential distribution of three invasive scarab beetles in China but did not explore dynamic changes in risk areas or detailed management strategies for future climate scenarios. Hence, there is need to assess the impact of climate change on the future distribution of pests and diseases, especially the changes in risk regions, so as to understand the occurrence and development regularity in time and strengthen the monitoring, early warning, and prevention in rice production. This study aims to fill these gaps by incorporating the latest SSP climate scenario data and providing comprehensive predictions and management strategies for the future distribution of rice leaf rollers.

Species Distribution Modeling (SDM) is a crucial method for investigating the impact of climate change on species distribution15. It could simulate species distribution and ecological requirements by utilizing existing data and relevant environmental factors16. Prominent SDMs include the Genetic Algorithm Model based on Rule Set Prediction (GARP)17, Ecological Niche Factor Analysis Model (ENFA)18, and Maximum Entropy(MaxEnt) Model19. Among these, the MaxEnt Model, grounded in maximum entropy theory, predicts species distribution based solely on occurrence points and environmental data19. Its robust predictive capabilities have been widely employed in predicting crop suitability, evaluating potential suitability for animals and plants, assessing risks related to invasive species, and determining areas prone to the occurrence and development of pests and diseases in future climate20–22. Operating the model can not only deeply understand the main environmental factors that affect the distribution, but also help to simulate the potential species distribution areas and predicts the occurrence region of pests and diseases23–25. Combined with the latest climate scenario data, the MaxEnt model becomes instrumental in predicting species distribution changes in suitable areas in future. This predictive capability can offer valuable insights for crop planning, industrial development, and holds considerable reference significance for studying ecosystem sustainability and adaptation to climate change for policymakers26.

Currently, there is an imperative need to thoroughly investigate and refine the alterations in the risk area of the rice leaf roller amid the backdrop of climate change, so as to offering valuable insights for enhancing rice pest control strategies in China. Therefore, in this study, the MaxEnt model was combined with ArcGIS spatial analysis tool to utilize the occurrence data and environmental variables of rice leaf roller in China to predict the risk range and dynamics of rice leaf roller occurrence under various climatic scenarios. The objectives of this study are to: (1) map the potential risk region of Cnaphalocrocis medinalis under current climate situation in China, (2) predict potential risk region distribution of C. medinalis species under future climate change scenarios, (3) evaluate the effect of climate change on the suitable habitat area of C. medinalis and identify the shift range of these areas under future climate scenarios.

Meterials and methods

Occurrence data

The occurrence data for Cnaphalocrocis medinalis were downloaded from the Global Biodiversity Information Facility (GBIF, http://www.gbif.org, last accessed on 11 October 2023) and encompassed both data records and occurrence records from agricultural sites in China Meteorological Data Network (https://data.cma.cn, last accessed on 26 October 2023). In this study, 3921 samples were collected, with 2030 obtained from agricultural stations and 1891 sourced from GBIF. After the elimination of incomplete, ambiguous, and duplicate records, 244 accurate point data were retained (Fig. 1). The latitude and longitude coordinates of the point data are stored in ".csv" format under the header "Categories, Longitude, Latitude."Fig. 1 Geographical distribution of Cnaphalocrocis medinalis Guenee (HJL: Heilongjiang, JL: Jilin, LN: Liaoning, SX: Shaanxi, SD: Shandong, HN: He’nan, AH: Anhui, JS: Jiangsu, HB: Hubei, ZJ: Zhejiang, JX: Jiangxi, HUN: Hu’nan, FJ: Fujian, GD: Guangdong, GX: Guangxi, HAN: Hainan, SC: Sichuan, CQ: Chongqing, GZ: Guizhou, YN: Yunnan).

Bioclimatic data

The data of bioclimatic variables is the key factor to determine species distribution. In this study, 19 bioclimatic variables (bio01-bio19) with 2.5 arc-min spatial resolution from the WorldClim database version 2.1 (https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html, last accessed on 26 October 2023), were initially selected.

The future climate data utilized in this study originates from the Sixth phase of the Coupled Model Intercomparison Project (CMIP6) and is specifically generated by the BCC-CSM2-MR climate system model developed by the National Climate Center27. The data was downloaded from the World Climate Data Website (https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html, V2.1, last accessed on 26 October 2023). Three distinct climate scenarios, namely, SSP (shared socio-economic path) 126 (low-level greenhouse gas emissions), SSP245 (medium-level greenhouse gas emissions), and SSP580 (high-level greenhouse gas emissions), were selected for the time frames of 2041–2060 and 2081–2100. The spatial resolution of the data set is 2.5 arc-minutes. The downloaded data, in "tiff" format, underwent conversion into an ASCII file in ArcGIS through the "raster to ASCII" tool to facilitate compatibility with the MaxEnt software. All environmental variables in raster format were geographically aligned using WGS84 datum as the default parameters.

Preprocessing of bioclimatic variables

MaxEnt model is a mathematical model based on the principle of climate similarity, which explores the correlation between the geographical distribution of species and climate factors. The selection of climate factors is the key to determine the accuracy of simulation. Therefore, before modeling, it is necessary to evaluate the importance of climate factors to obtain key limiting factors, however, the multicollinearity among variables may lead to over-fitting of the model and misinterpretation of the modeled results15. To avoid the consequences due to multicollinearity among 19 bioclimate variables , a three-step screening method was adopted in this study. Firstly, in the MaxEnt model, by default, the contribution rate of each variable is evaluated by Jackknife, and the variables with low contribution rate (< 1%) are deleted28. Secondly, the Spatial-Analyst tool in ArcGIS software is used for correlation analysis to remove the factors with high collinearity (|r|≥ 0.8)29, if the absolute value of the correlation coefficient of two variables is greater than 0.8, only one variable with greater contribution is selected to participate in the simulation30. Finally, eight bioclimatic variables (bio2, bio7, bio8, bio10, bio11, bio15, bio18 and bio19) were selected to simulate the distribution of rice leaf roller under current and future climatic conditions (Table 1). The model ignores factors such as land use or land cover change, human interference, species diffusion or biological interaction.Table 1 Climate variables used for modelling climatic niches.

Code	Variable name	Unit	
bio1	Annual mean temperature	°C	
bio2	Mean diurnal range	°C	
bio3	Isothermality (BIO2/BIO7) (× 100)	–	
bio4	Temperature seasonality (standard deviation × 100)	C of V	
bio5	Max temperature of warmest month	°C	
bio6	Min temperature of coldest month	°C	
bio7	Temperature annual range (BIO5-BIO6)	°C	
bio8	Mean temperature of wettest quarter	°C	
bio9	Mean temperature of driest quarter	°C	
bio10	Mean temperature of warmest quarter	°C	
bio11	Mean temperature of coldest quarter	°C	
bio12	Annual precipitation	mm	
bio13	Precipitation of wettest month	mm	
bio14	Precipitation of driest month	mm	
bio15	Precipitation seasonality (coefficient of variation)	–	
bio16	Precipitation of wettest quarter	mm	
bio17	Precipitation of driest quarter	mm	
bio18	Precipitation of warmest quarter	mm	
bio19	Precipitation of coldest quarter	mm	
The bolded variables were used in modeling.

Risk region distribution modeling procedure of C. medinalis

In this study, we used the Maxent model (version 3.4.1, https://biodiversityinformatics.amnh.org/open_source/maxent/) to simulate the current and future risk region change of the C. medinalis. The model has good forecasting ability and stability, and will not be affected by small sample deviation and fewer samples31,32. Following the model's requirements, the ".csv" file containing the distribution information of C. medinalis and the pre-screened environmental variables were imported into the MaxEnt software for simulation. The output results are formatted as "Cloglog" with the output file type set as "asc". Additionally, the options for "create response curve" and "Do Jackknife" are selected. The model parameters include a regularization multiplier (β) of 0.5, a maximum of 10,000 background points, a maximum of 1000 iterations, and 10 replicates. The convergence threshold is set at 0.00001, random test percentage at 25%, and all other parameters are maintained as default values in the software.

The random testing percentage is set at 25%, signifying that 75% of the total database serves as random samples for training the models, while the remaining 25% is allocated for testing the model predictions33. To prevent overfitting of the test data, a regularization multiplier value of 0.5 is implemented in this study. The output format defaults to "cloglog" transformation, chosen for its stronger theoretical rationale compared to the logistic transform34. Research indicates that when the sample size exceeds 80, selecting "auto features"35,36 is recommended. Given that the number of samples in this study is 244 (which is greater than 80), "auto features" is appropriately chosen. The Jackknife test is employed to assess the contribution and importance of each bioclimatic variable to the model37.

After modeling the species distribution, the spatial changes were calculated for the future compared to the current by in ArcGIS 10 software. The prediction results of current and future were imported into ArcGIS by "ASCII to raster" and reclassified the risk areas to 5 levels by the Natural Breaks (Jenks) method: Unsuitable habitat, Low-risk region, Medium-risk region, High-risk region and Highest-risk region38. In order to compare the future and current area variation and distributions, Unsuitable habitat, Low-risk region, Medium-risk region, High-risk region and Highest-risk region was further reclassified as 1, 2, 3, 4 and 5. The current results layer was subtracted from the future results by the “subtraction” tool in Arctoolbox39. Among the results obtained, 0 means that the risk area has not changed, positive values mean that the risk area has expanded, filled in red, and negative values mean that the risk area has decreased, which is represented by green. Area calculation of the suitable habitat and the subtraction result were carried out in ArcGIS 10. The SDMtoolbox tool40 in ArcGIS software is used to calculate the area change and centroid position of the current and future suitable areas, describe the direction and distance of centroid migration in different climate scenarios, and clarify the dynamic migration path of rice leaf roller distribution in China risk area41.

Evaluating the model performance

The accuracy of the MaxEnt model is assessed using the AUC (Area Under the receiver operating characteristic (ROC) curve)19,32. A higher AUC value corresponds to high simulation accuracy. The AUC value falls within the range of 0 to 1, with categorizations as follows: invalid prediction (0.5–0.6), poor prediction (0.6–0.7), average prediction (0.7–0.8), good prediction (0.8–0.9), and excellent prediction (0.9–1). The closer the AUC value to 1, the more accurate the model's prediction results37,42.

Results

Model performance and variable contributions

The average AUC value for both the test and training data in this study was 0.912 (Fig. 2), signifying the model's excellent performance in modeling the distribution of risk regions for rice leaf roller in China. It is essential to note that AUC values may exhibit a tendency to be lower for species with a broad distribution range43,44.Fig. 2 ROC curve and AUC value under the current period (10 replicated runs).

The contribution rate of each environmental variable calculated by the MaxEnt model and the Jackknife test method are shown in Fig. 3. The top four contributors, with their respective contribution rates, were bio19 (62.9%), bio18 (17.3%), bio10 (9.5%), and bio2 (2.9%). Cumulatively, these four bioclimate variables accounted for 92.6% of the total contribution rate, signifying their primary role in model development. The Jackknife test showed that bio11, bio18, bio2, and bio19 provided very high gains (> 1.0) when used independently, indicating that these four variables contained more useful information than the other variables and were more useful for determining the risk regional distribution of the rice leaf roller (Fig. 4).Fig. 3 Percentage contribution and permutation importance of t each predictor variables to the MaxEnt model.

Fig. 4 Results of Jackknife evaluations of the relative importance of predictor variables and their per-centage contribution in Maxent model for Cnaphalocrocis medinalis Guenee in China.

Furthermore, the quantitative relationship between environmental variables and the logical probability of existence (also known as habitat suitability) is shown as the response curves in Fig. 5, providing additional insights into the species' ecological preferences and the potential impact of climate change39. The response curve of bio19 (Precipitation of Coldest Quarter) indicates a sharp increase in habitat suitability with increasing precipitation, plateauing around 200 mm. This suggests that areas with higher precipitation during the coldest quarter are more favorable for the rice leaf roller. For bio18 (Precipitation of Warmest Quarter), habitat suitability increases significantly with precipitation in the warmest quarter, peaking around 490 mm. Beyond this point, suitability decreases, indicating an optimal range of precipitation for the species' survival is 480 ~ 1000 mm. The response curve of bio15 (Precipitation Seasonality) shows a bell-shaped distribution, with the highest suitability around a precipitation seasonality index of 50, indicating that regions with moderate seasonal rainfall variability are most suitable for the rice leaf roller. Response to bio2 (Mean Diurnal Range) shows an optimal mean diurnal temperature range around 7 °C, suggesting that areas with this temperature variability between day and night are most conducive for the species' habitat. These insights highlight the critical thresholds and optimal conditions for the rice leaf roller's habitat, providing valuable information for predicting future risk regions under changing climate scenarios.Fig. 5 Response curves of climatic suitability for the major climate factors according to MaxEnt model.

Changes in the spatial distribution of risk region in the future

Figure 6 and Table 2 shown the potential future geographical risk distribution and comparisons with the current climate condition for rice leaf roller under CMIP6 projection. Comparative analysis indicates an overall increase in the total risk area for the occurrence and development of rice leaf roller across the next three climate scenarios. Among all the scenarios, SSP245 (2040–2060) is anticipated to exhibit the highest increase, reaching 249.81 × 104 km2, a rise of 2.17% from the present situation. The varying degrees of change in different risk areas highlight the need for careful consideration in future prevention and control strategies, especially the northward movement of the Medium-risk region, which shifts from southern Shandong (SD) to northern Shandong (SD) in all SSPs. Notably, the Highest-risk region experiences a rising trend in the SSP245 (2040–2060) and SSP585 (2080–2100) scenarios, while other SSPs demonstrate a decline trend, especially in SSP126 (2040–2060), where the Highest-risk area decreases by more than 10%.Fig. 6 The potential distribution area of risk region in current and future climate conditions (HJL: Heilongjiang, JL: Jilin, LN: Liaoning, SX: Shaanxi, SD: Shandong, HN: He’nan, AH: Anhui, JS: Jiangsu, HB: Hubei, ZJ: Zhejiang, JX: Jiangxi, HUN: Hu’nan, FJ: Fujian, GD: Guangdong, GX: Guangxi, HAN: Hainan, SC: Sichuan, CQ: Chongqing, GZ: Guizhou, YN: Yunnan).

Table 2 Changes in the distribution area of potential risk regions under current and future climate change scenatios/(× 104 km2).

Climate scenarios	Decade	Total risk regions	Regions of low risk region	Regions of medium risk region	Regions of high risk region	Regions of highest risk region	
Area (× 104 km2)	Area change (%)	Area (× 104 km2)	Area change (%)	Area (× 104 km2)	Area change (%)	Area (× 104 km2)	Area change (%)	Area (× 104 km2)	Area change (%)	
–	Current	244.51	–	54.16	–	89.88	–	55.77	–	44.7	–	
SSP126	2040–2060	245.57	0.43	50	− 7.68	96.66	7.54	58.98	5.76	39.94	− 10.65	
SSP126	2080–2100	243.59	− 0.38	48.96	− 9.60	100.21	11.49	51.39	− 7.85	43.02	− 3.76	
SSP245	2040–2060	249.81	2.17	54.35	0.35	88.37	− 1.68	60.18	7.91	46.91	4.94	
SSP245	2080–2100	246.26	0.72	47.64	− 12.04	99.73	10.96	55.04	− 1.31	43.85	− 1.90	
SSP585	2040–2060	247.88	1.38	50.53	− 6.70	96.05	6.86	56.81	1.86	44.5	− 0.45	
SSP585	2080–2100	242.6	− 0.78	48.8	− 9.89	95.37	6.11	52.79	− 5.34	45.65	2.13	

In terms of specific time periods and scenarios, the study estimates a total risk area of 245.57 × 104 km2 by SSP126 (2040–2060), representing a 0.43% increase from the current climate conditions. During this period, the High-risk and Medium-risk regions are projected to expand to58.98 × 104 km2 and 96.66 × 104 km2, with 8.76% and 7.54% increases, while the Highest-risk region will decrease by 10.65%, primarily in central Hubei, southwestern Jiangxi (JX), and southeastern Guangdong (GD) provinces. This period represents the greatest decrease in the extremely High-risk area and marks a significant shift in the pest range in Jiangxi (JX) province from southwest to northeast. By 2080–2100, the total risk areas will decrease, with only the Medium-risk region increasing by 11.49% to 100.21 × 104 km2. High-risk areas in eastern Sichuan (SC) and Hubei (HB) are predicted to intensify, while the Highest-risk area in southern Guangdong (GD) is expected to decrease. Simultaneously, the boundary of the Medium-risk area in Shandong (SD) province will shift northward, and the risk in Fujian (FJ) will diminish.

Under the SSP245 scenario from 2040 to 2060, the Low-risk region, High-risk region, and Highest-risk region will cover 54.35 × 104 km2, 60.18 × 104 km2, and 46.91 × 104 km2, respectively, marking increases of 0.35%, 7.91%, and 4.94% compared to the current climate, caused the highest area in all scenarios. During this period, the risk of pests in Fujian (FJ) is predicted to increase, the boundary of the High-risk area will shift northward, and the Low-risk area will expand in central Jilin (JL) province. By 2080–2100, the High-risk range in Guangxi (GX) is expected to expand, though the expansion range is smaller than that in Highest area. The area of the Highest-risk region in Hunan (HUN) and Jiangxi will increase, with the Highest-risk area in Jiangxi (JX) moving further north and east. The boundary of the Low-risk area will move from the northern part of Jilin (JL) Province to the central part of Liaoning (LN) province, reducing from 54.16 × 104 km2 to 47.64 × 104 km2.

Under the SSP585 scenario in 2040–2060, the Medium-risk and High-risk area exhibit an upward trend, increasing to 96.05 × 104 km2 and 56.81 × 104 km2. The High-risk area will show a north-moving trend from northern Shandong (SD) to northern Liaoning (LN). By 2080–2100, the total risk region will decrease to 242.6 × 104 km2, with the reduction mainly occurring in Liaoning (LN) and Yunnan (YN) provinces in Low-risk area and the High-risk area in eastern Jiangxi (JX) province. The Highest-risk area shows an increase trend in central Chongqing (CQ), eastern Hunan (HUN), Jiangxi (JX) and northern Fujian (FJ), causing the area increase from 44.7 × 104 km2 currently to 46.65 × 104 km2. However, the high-risk areas in Guangdong (GD) and Guangxi (GX) will be reduced to High-risk and Medium-risk regions.

Difference plots of risk region in the future

To provide more actionable insights for policymakers, we created difference plots by subtracting the current habitat suitability values from the future ones (Fig. 7). These plots clearly show where habitat suitability is increasing or decreasing, highlighting the significant impacts of climate change on the species' habitat across various regions. The difference plots use a color gradient, in which red indicates areas where the pest occurrence is expected to become more serious and green indicates areas where the occurrence is expected to become less serious. In the SSP126 scenario for 2041–2060, the Northern and Northeastern regions, including parts of Heilongjiang (HLJ) and Liaoning (LN) provinces, exhibit significant increases in habitat suitability for the rice leaf roller, indicating a northward expansion of the pest. In contrast, the Southern and Central regions, including parts of Hunan (HN) and Jiangxi (JX) provinces, show a decrease in habitat suitability, possibly due to changing precipitation patterns or other climatic factors. The SSP245 scenario for the same period shows a notable increase in habitat suitability across a broad swath of central and eastern China, including major rice-producing areas in Jiangsu (JS) and Anhui (AH) provinces, suggesting heightened pest-control pressures. Meanwhile, the southwest regions, including parts of Sichuan (SC) and Yunnan (YN) provinces, show decreased suitability, likely due to adverse climatic conditions impacting the pest's lifecycle. The SSP585 scenario for 2041–2060 highlights significant increases in the northern regions, with additional areas in Shandong (SD) and Hebei (HB) provinces becoming more susceptible to pest infestations, reinforcing the trend of northward pest expansion. Decreases in suitability are less pronounced, indicating that extreme climate scenarios may maintain or even increase overall pest viability in most areas. For the period 2081–2100, the SSP126 scenario shows further northward and intensified increases in suitability, including significant areas in Henan (HN) and Hubei (HB) provinces. Some areas in the south, like Guangdong (GD) and Guangxi (GX), exhibit decreased suitability, potentially due to less hospitable climatic conditions. The SSP245 scenario for 2081–2100 shows extensive increases in suitability across almost all eastern China, highlighting the need for robust pest management systems in these agriculturally important regions. Decreases are observed in a few isolated areas. In the SSP585 scenario for 2081–2100, most of central-China could become highly suitable for the rice leaf roller, necessitating comprehensive pest management strategies. Very few areas show decreased suitability, underscoring the extensive impact of severe climate change on pest distribution.Fig. 7 Difference plots Change of risk region in the future by the compare to the current (HJL: Heilongjiang, JL: Jilin, LN: Liaoning, SX: Shaanxi, SD: Shandong, HN: He’nan, AH: Anhui, JS: Jiangsu, HB: Hubei, ZJ: Zhejiang, JX: Jiangxi, HUN: Hu’nan, FJ: Fujian, GD: Guangdong, GX: Guangxi, HAN: Hainan, SC: Sichuan, CQ: Chongqing, GZ: Guizhou, YN: Yunnan).

Centroid changes in potential distribution

Under different SSP scenarios, the centroid position of the risk area of rice leaf roller is shown in Fig. 8. Under the current climate, the center of risk area is in the south-central part of Shaoyang City, Hunan Province (111.013 °E, 27.150 °N). Under the SSP126 scenario, the centroid of the risk area is predicted to shift northeastward to Loudi (111.888 °E, 27.613 °N) during 2040–2060, and further to the junction of Loudi City and Xiangtan City (112.314 °E, 27.644 °N) by 2080–2100, reflecting a consistent northeastward movement under milder climate change conditions. In the SSP245 scenario, the centroid will initially move northeastward to (112.269 °E, 27.554 °N) during 2040–2060, but will shift westward to the east of Shaoyang City in 2080–2100 (111.873 °E, 27.325 °N), indicating more complex shifts due to moderate climate change. Under the SSP585 scenario, the centroid will be located northeast (112.325 °E, 27.715 °N) in 2040–2060, will move further northeast to north of Xiangtan City (112.591 °E, 27.801 °N) by 2080–2100, marking the longest distance of migration among all scenarios. The consistent northeastward and higher latitude movement of the centroid across all scenarios highlights the potential northward expansion of the pest's habitat driven by rising temperatures and changing precipitation patterns. The varying degrees of centroid movement suggest that the severity of climate change will directly influence the extent of habitat shift, underscoring the need for policymakers and agricultural managers to incorporate these projections into strategic planning to mitigate the impact of climate change on pest distribution and crop production.Fig. 8 Centroid migration and change of Cnaphalocrocis medinalis risk region in future. (a) Total movement; (b) movement in SSP126; (c) movement in SSP245; (d) movement in SSP585.

Discussion

Rice leaf roller (Cnaphalocrocis medinalis Guenee) pose a significant threat to rice production, with a wide distribution spanning, encompassing tropical and subtropical rice-growing regions in Asia, Oceania, and Africa, from 48 °N to 24 °S and 0 °E to 172 °W. In China, it extends from Taiwan Province to Tibet in the east–west direction and from Hainan to Heilongjiang in the south-north direction, overwintering in regions such as Hainan Province, Guangxi Province, Guangdong Province, and Fujian Province7. The pest’s widespread occurrence in most rice planting areas, influenced by climate conditions, insect source bases, and planting conditions, results in large-scale or even extra-large-scale infestations45. The pest exhibits characteristics such as early migration, extensive occurrence, overlapping generations, high insect population in fields, and a prolonged occurrence period, leading to 60–70% leaf damage to paddy crop46. With the northward movement of rice planting areas, the occurrence of rice leaf roller is also expanding northward47. Understanding the changing trends in the occurrence and development of dangerous areas in the future becomes crucial for effective pest management. The MaxEnt model, a powerful tool for studying insect invasion and potential distribution in the future, provides us with an effective means to comprehensively understand the distribution of this pest and its possible future changing trend.

Climate variables and their impact

Based on the analysis of 244 occurrence and distribution records of the rice leaf roller and 8 environmental variables, this study investigated the potential distribution changes under current climatic conditions and various future climate scenarios. With an average AUC of 0.912 after 10 repetitions, the MaxEnt model demonstrated high accuracy. Eight key climatic factors (bio2, bio7, bio8, bio10, bio11, bio15, bio18, and bio19) closely associated with the distribution of the rice leaf roller were selected to predict the current and future risk area distribution. Notably, bio19 (coldest season rainfall), bio18 (warmest season rainfall), bio10 (warmest season average temperature), and bio2 (average daily temperature difference) emerged as influential variables, collectively contributing over 90% to the model. This underscores the significant impact of precipitation and temperature on the distribution of rice leaf roller, aligning with its biological characteristics. Prior studies highlighted the substantial influence of temperature and humidity on insect distribution, emphasizing temperature as a driving force for insect survival, growth, development, and reproduction48.Temperature fluctuations, especially rising temperatures, can alter the behavior and occurrence patterns of insects49. The rising temperature will advance the damage time of some insects, such as Nezaira viridula, a major alien invasive insect in stinkbug, and at the same time greatly reduce its overwintering mortality and increase the damage range50. The optimal temperature and humidity conditions for rice leaf roller larvae are within the range of 22 to 28℃ and above 80%, facilitating their survival, growth, and development. Anomalies in mating behavior may occur under short-term high temperatures51.Previous studies also revealed the positive correlation of rainfall and rainy days from late July to mid-August with the occurrence degree of pests. With the increase of temperature and the change of precipitation pattern, rice leaf roller may show different occurrence trends in different regions, which is very important for formulating regional control strategies52. The results of this study are consistent with those of Jiang et al.13 who used the MaxEnt and ArcGIS to evaluate the population distribution of rice leaf roller caused by climate change in China. The response curve of environmental variables (annual average temperature and precipitation) and distribution probability shows that the rice leaf roller thrives in areas with abundant rainfall and a small temperature difference between day and night, with its occurrence and development fluctuating with seasonal changes. Therefore, attention should be paid to the occurrence, development, and control of rice leaf roller in rainy, high temperature and humid areas.

Future predictions and implications

Evaluating the implications of climate change on pests’ distribution presents challenges due to the uncertainties associated with future climatic conditions53. However, understanding these implications can provide valuable insights for pest management and help mitigate potential negative impacts while capitalizing on any beneficial changes54. The results indicated a positive influence of climate change on expanding risk areas, especially under the SSP245 (2040–2060), with a projected increase to 249.81 × 104 km2, marking a 2.17% rise. Regions such as southern Hubei, eastern Hunan, Jiangxi, central Fujian, and southern Jiangsu are expected to face Highest risk levels. Rising CO2 concentrations and temperatures will likely enable regions like central Liaoning to meet the survival standard for C. medinalis, shifting risk areas northward. However, this expansion is not uniform, with southern Guangdong and parts of Guangxi potentially experiencing reduce risk, transitioning from the Highest to High-risk or Middle-risk levels. The difference plots in Fig. 7 offer valuable insights into the dynamic changes in pest distribution under future climate scenarios. The analysis shows that some regions, such as central and eastern China, will experience heightened pest pressures, while others, like southwestern China, may see a reduction. This variability underscores the importance of region-specific strategies and policies tailored to the unique climatic and ecological conditions of each area55. Besides, the analysis of center of gravity movement also supports this trend. In this study, the SDMToolsbox was employed to assess the changes in the centroid movement of C. medinalis in China under various climate scenarios19. Presently, the centroid is situated in the middle-east of Shaoyang City in Hunan province (111.013 °E, 26.97 °N). As we project into the 2050s and 2090s, the centroid of the total risk region demonstrates a west-northward shift from Shaoyang City towards Loudi and Xiangtan City, suggesting that climate warming may contribute to the migration of C. medinalis from low-latitude to high-latitude regions in the future. The migration directions of centroids exhibit variations under different climate scenarios, possibly attributed to uncertainties arising from distinct levels of human activities and climate warming. While the projections provide a valuable framework for pest-control planning, the inherent uncertainties in climate models necessitate a flexible approach in future. So, the agricultural policies should be adaptable to account for unexpected changes in pest dynamics and climatic conditions.

Adaptive management strategies in future

In the context of global warming and socio-economic development, the suitable area for rice will expand, indicating that more new suitable habitats will be created for the damage range of rice leaf roller in the future56. Higher temperatures generally accelerate the development of insects but can reduce reproductive rates and increase flight potential, leading to increased migration, highlighting the dynamic changes in pest distribution under future climate scenarios. The consistent northward shift of risk areas across all scenarios indicates a need for regional agricultural managers to anticipate and prepare for new pest challenges in traditionally less-affected northern regions. This includes enhancing monitoring systems and updating pest management strategies to address the shifting distribution. The increase in Medium and High-risk areas, particularly under the SSP245 and SSP585 scenarios, suggests that even regions currently experiencing lower pest pressures may see significant increases. This shift necessitates adaptive management strategies, including enhanced monitoring in regions projected to face higher pest suitability, implementing specific pest control measures in newly affected areas, updating agricultural policies to reflect the changing risk profiles and ensure resources are allocated to high-risk areas, and some proactive measures such as adopting integrated pest management (IPM) practices and developing pest-resistant crop varieties57.

It is crucial to recognize that the threshold range of each climatic factor analyzed in this study represents only the simulated suitable distribution of the rice leaf roller, and the events influencing its outbreak are the outcome of the synergistic effects of numerous factors. While the MaxEnt model accurately simulates the suitable area, it does not imply complete alignment with the actual distribution area58. The distribution of C. medinalis is shaped not only by climatic factors but also by non-biological elements such as topography, soil, and human activities, including socioeconomic development, human intervention, and policy. Future studies could further refine these projections by incorporating additional factors such as soil conditions, land use changes, and pest management practices. These insights provide a more detailed understanding of how climate change will affect the distribution of C. medinalis, enabling more effective planning and response strategies to mitigate potential impacts on agriculture.

Conclusion

In summary, this study results the current and future distribution patterns of C. medinalis, a major pest affecting rice crops. Leveraging the MaxEnt model and the occurred-pest datasets, we increased the accuracy of predicting the pest's potential habitat. Key climate variables, such as coldest season rainfall and warmest season rainfall, emerged as critical determinants of the pest's distribution. The projections under different climate scenarios (SSP126, SSP245, SSP585) reveal an alarming trend of expanding risk areas, particularly under the 2040–2060 period. This expansion is anticipated to affect regions in southern Hubei, eastern Hunan, most of Jiangxi, central Fujian, northern Guangdong, and southern Jiangsu, which are poised to transition into the highest-risk category. The difference plots revealed areas of increased and decreased suitability, providing actionable insights for policymakers. Regions showing increased suitability align with the predicted northward shift of many agricultural pests, necessitating enhanced monitoring, specific pest control measures, and updated agricultural policies to address changing risk profiles. The centroid analysis showed a northward and westward shift of risk areas, with variations in migration distances under different climate scenarios. The SSP585 scenario exhibited the longest migration distance, suggesting more substantial shifts in pest distribution due to severe climate change. It is crucial to note that while MaxEnt model provides a framework for understanding the pest's potential distribution, actual occurrences are influenced by multifaceted factors, including topography, soil conditions, and human activities. Additionally, the economic viability of pest management strategies may vary across regions. Overall, this study enhances our understanding of the distribution dynamics of rice leaf roller and its potential responses to climate change, providing valuable insights for formulating effective pest management strategies.

Supplementary Information

Supplementary Table S1.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71228-2.

Author contributions

Conceptualization, Y. Z. and C. W.; methodology, Y.Z. and L. Z.; software, Y. Z. and L. Z.; formal analysis, Y. Z. and C. W.; writing—original draft preparation, Y. Z.; writing—review and editing, L. Z. and C. W.; funding acquisition, C. W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Key R & D Program of China, 2022YFD1400400.

Data availability

The authors confirm that the data supporting the findings of this study are available within the article [and/or its supplementary materials].

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. IPCC Climate change 2021. The physical science basis 2021 Cambriage Univeraity Press
IPCC. Climate change 2021. The physical science basis (Cambriage Univeraity Press, 2021).
2. CMA Climate Change Centre Blue book on climate change in China 2021 Science Press
CMA Climate Change Centre. Blue book on climate change in China (Science Press, 2021).
3. NWilfried T Biodiversity: Climate change and the ecologist Nature 2007 448 550 552 10.1038/448550a 17671497
NWilfried, T. Biodiversity: Climate change and the ecologist. Nature 448, 550–552 (2007).17671497 10.1038/448550a
4. Kruger AP Mello Garcia FR Teixeira CM Nava DE Global potential distribution of Anastrepha grandis (Diptera, Tephritidae) under climate change scenarios Crop Prot. 2022 151 105836 10.1016/j.cropro.2021.105836
Kruger, A. P., Mello Garcia, F. R., Teixeira, C. M. & Nava, D. E. Global potential distribution of Anastrepha grandis (Diptera, Tephritidae) under climate change scenarios. Crop Prot. 151, 105836 (2022).10.1016/j.cropro.2021.105836
5. Guangsheng Z Research prospect on impact of climate change on agricultural production in China Meteorol. Environ. Sci. 2015 38 80 94
Guangsheng, Z. Research prospect on impact of climate change on agricultural production in China. Meteorol. Environ. Sci. 38, 80–94 (2015).
6. Wang C Fei M Meng L Harvey JA Li B Effects of elevated CO2 and temperature on survival and wing dimorphism of two species of rice planthoppers (Hemiptera: Delphacidae) under interaction Pest Manag. Sci. 2020 76 2087 2094 10.1002/ps.5747 31944534
Wang, C., Fei, M., Meng, L., Harvey, J. A. & Li, B. Effects of elevated CO2 and temperature on survival and wing dimorphism of two species of rice planthoppers (Hemiptera: Delphacidae) under interaction. Pest Manag. Sci. 76, 2087–2094 (2020).31944534 10.1002/ps.5747
7. Zheng X Ren X Su J Insecticide susceptibility of Cnaphalocrocis medinalis (Lepidoptera: Pyralidae) in China J. Econ. Entomol. 2011 104 653 658 10.1603/EC10419 21510218
Zheng, X., Ren, X. & Su, J. Insecticide susceptibility of Cnaphalocrocis medinalis (Lepidoptera: Pyralidae) in China. J. Econ. Entomol. 104, 653–658 (2011).21510218 10.1603/EC10419
8. Minghong L Gao H Baoping Z Anh Tuan H Hong Khanh D Analysis of the relationships of rice planthopper and rice leaf folder occurrence between China and Vietnam Plant Protect. 2018 3 31 36
Minghong, L., Gao, H., Baoping, Z., Anh Tuan, H. & Hong Khanh, D. Analysis of the relationships of rice planthopper and rice leaf folder occurrence between China and Vietnam. Plant Protect. 3, 31–36 (2018).
9. Liu Z Shifts in the extent and location of rice cropping areas match the climate change pattern in China during 1980–2010 Reg. Environ. Change. 2015 15 919 929 10.1007/s10113-014-0677-x
Liu, Z. et al. Shifts in the extent and location of rice cropping areas match the climate change pattern in China during 1980–2010. Reg. Environ. Change. 15, 919–929 (2015).10.1007/s10113-014-0677-x
10. Wang H Hijmans RJ Climate change and geographic shifts in rice production in China Environ. Res. Commun. 2019 1 11008 10.1088/2515-7620/ab0856
Wang, H. & Hijmans, R. J. Climate change and geographic shifts in rice production in China. Environ. Res. Commun. 1, 11008 (2019).10.1088/2515-7620/ab0856
11. Chun-yang L Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda (Lepidoptera:Noctuidae) based on computer vision J. Integr. Agr. 2023 22 2173 2187 10.1016/j.jia.2022.12.003
Chun-yang, L. et al. Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda (Lepidoptera:Noctuidae) based on computer vision. J. Integr. Agr. 22, 2173–2187 (2023).10.1016/j.jia.2022.12.003
12. Early R González-Moreno P Murphy S Day R Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm NeoBiota. 2018 40 25 50 10.3897/neobiota.40.28165
Early, R., González-Moreno, P., Murphy, S. & Day, R. Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm. NeoBiota. 40, 25–50 (2018).10.3897/neobiota.40.28165
13. Jiang G RuLin W Yanli W Zhanhong S Shan L Forecast and analysis of Cnaphalocrocis medinalis Guenee risk Region in China based on climate change Meteorol. Environ. Sci. 2017 40 21 27
Jiang, G., RuLin, W., Yanli, W., Zhanhong, S. & Shan, L. Forecast and analysis of Cnaphalocrocis medinalis Guenee risk Region in China based on climate change. Meteorol. Environ. Sci. 40, 21–27 (2017).
14. Wang Z Study on environmental factors affecting the quality of codonopsis radix based on MaxEnt model and all-in-one functional factor Sci. Rep. 2023 13 20726 10.1038/s41598-023-46546-6 38007505
Wang, Z. et al. Study on environmental factors affecting the quality of codonopsis radix based on MaxEnt model and all-in-one functional factor. Sci. Rep. 13, 20726 (2023).38007505 10.1038/s41598-023-46546-6
15. Sillero N What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods Ecol. Model. 2011 222 1343 1346 10.1016/j.ecolmodel.2011.01.018
Sillero, N. What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecol. Model. 222, 1343–1346 (2011).10.1016/j.ecolmodel.2011.01.018
16. Soberón J Arroyo-Peña B Are fundamental niches larger than the realized? Testing a 50-year-old prediction by Hutchinson PLoS ONE 2017 12 e175138 10.1371/journal.pone.0175138
Soberón, J. & Arroyo-Peña, B. Are fundamental niches larger than the realized? Testing a 50-year-old prediction by Hutchinson. PLoS ONE 12, e175138 (2017).10.1371/journal.pone.0175138
17. Stockwell D David P The GARP modelling system: Problems and solutions to automated spatial prediction Int. J. Geogr. Inf. Sci. 1999 13 143 158 10.1080/136588199241391
Stockwell, D. & David, P. The GARP modelling system: Problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci. 13, 143–158 (1999).10.1080/136588199241391
18. Hirzel AH Hausser J Chessel D Perrin N Ecological-niche factor analysis: How to compute habitat-suitability maps without absence data? Ecology. 2002 83 2027 2036 10.1890/0012-9658(2002)083[2027:ENFAHT]2.0.CO;2
Hirzel, A. H., Hausser, J., Chessel, D. & Perrin, N. Ecological-niche factor analysis: How to compute habitat-suitability maps without absence data?. Ecology. 83, 2027–2036 (2002).10.1890/0012-9658(2002)083[2027:ENFAHT]2.0.CO;2
19. Phillips SJ Anderson RP Schapire RE Maximum entropy modeling of species geographic distributions Ecol. Model. 2006 190 231 259 10.1016/j.ecolmodel.2005.03.026
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).10.1016/j.ecolmodel.2005.03.026
20. Al-Obaidi MJ Ali HB Effect of climate change on the distribution of zoonotic cutaneous Leishmaniasis in Iraq J. Phys. Conf. Ser. 2021 1818 12052 10.1088/1742-6596/1818/1/012052
Al-Obaidi, M. J. & Ali, H. B. Effect of climate change on the distribution of zoonotic cutaneous Leishmaniasis in Iraq. J. Phys. Conf. Ser. 1818, 12052 (2021).10.1088/1742-6596/1818/1/012052
21. Anand V Oinam B Singh IH Predicting the current and future potential spatial distribution of endangered Rucervus eldii (Sangai) Using MaxEnt model Environ. Monit. Assess. 2021 193 147 10.1007/s10661-021-08950-1 33638015
Anand, V., Oinam, B. & Singh, I. H. Predicting the current and future potential spatial distribution of endangered Rucervus eldii (Sangai) Using MaxEnt model. Environ. Monit. Assess. 193, 147 (2021).33638015 10.1007/s10661-021-08950-1
22. Karuppaiah V Predicting the potential geographical distribution of onion Thrips, Thrips Tabaci in India based on climate change projections using MaxEnt Sci. Rep. 2023 13 7934 10.1038/s41598-023-35012-y 37193780
Karuppaiah, V. et al. Predicting the potential geographical distribution of onion Thrips, Thrips Tabaci in India based on climate change projections using MaxEnt. Sci. Rep. 13, 7934 (2023).37193780 10.1038/s41598-023-35012-y
23. He Y Predicting potential global distribution and risk regions for potato cyst nematodes (Globodera rostochiensis and Globodera pallida) Sci. Rep. 2022 12 21843 10.1038/s41598-022-26443-0 36528656
He, Y. et al. Predicting potential global distribution and risk regions for potato cyst nematodes (Globodera rostochiensis and Globodera pallida). Sci. Rep. 12, 21843 (2022).36528656 10.1038/s41598-022-26443-0
24. Wei B Wang R Hou K Wang X Wu W Predicting the current and future cultivation regions of Carthamus tinctorius L. Using MaxEnt model under climate change in China Global Ecol. Conserv. 2018 16 e477
Wei, B., Wang, R., Hou, K., Wang, X. & Wu, W. Predicting the current and future cultivation regions of Carthamus tinctorius L. Using MaxEnt model under climate change in China. Global Ecol. Conserv. 16, e477 (2018).
25. Zhang X Li G Du S Simulating the potential distribution of Elaeagnus angustifolia L. based on climatic constraints in China Ecol. Eng. 2018 113 27 34 10.1016/j.ecoleng.2018.01.009
Zhang, X., Li, G. & Du, S. Simulating the potential distribution of Elaeagnus angustifolia L. based on climatic constraints in China. Ecol. Eng. 113, 27–34 (2018).10.1016/j.ecoleng.2018.01.009
26. Thuiller W Lavorel S Araújo MB Niche properties and geographical extent as predictors of species sensitivity to climate change Global Ecol. Biogeogr. 2005 14 347 357 10.1111/j.1466-822X.2005.00162.x
Thuiller, W., Lavorel, S. & Araújo, M. B. Niche properties and geographical extent as predictors of species sensitivity to climate change. Global Ecol. Biogeogr. 14, 347–357 (2005).10.1111/j.1466-822X.2005.00162.x
27. Wu T The Beijing climate center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6 Geosci. Model Dev. 2019 12 1573 1600 10.5194/gmd-12-1573-2019
Wu, T. et al. The Beijing climate center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci. Model Dev. 12, 1573–1600 (2019).10.5194/gmd-12-1573-2019
28. Rong Z Modeling the effect of climate change on the potential distribution of qinghai spruce (Picea crassifolia Kom) in qilian mountains Forests. 2019 10 62 10.3390/f10010062
Rong, Z. et al. Modeling the effect of climate change on the potential distribution of qinghai spruce (Picea crassifolia Kom) in qilian mountains. Forests. 10, 62 (2019).10.3390/f10010062
29. Ferrier, P. S. An Evaluation of Alternative Algorithms for Fitting Species Distribution Models Using Logistic Regression. Ecol. Model. (2000).
30. Yang X Kushwaha SPS Saran S Xu J Roy PS Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. In Lesser Himalayan Foothills Ecol. Eng. 2013 51 83 87 10.1016/j.ecoleng.2012.12.004
Yang, X., Kushwaha, S. P. S., Saran, S., Xu, J. & Roy, P. S. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. In Lesser Himalayan Foothills. Ecol. Eng. 51, 83–87 (2013).10.1016/j.ecoleng.2012.12.004
31. Pearson RG Raxworthy CJ Nakamura M Peterson AT Original Article: Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in madagascar J. Biogeogr. 2007 34 102 117 10.1111/j.1365-2699.2006.01594.x
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Peterson, A. T. Original Article: Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in madagascar. J. Biogeogr. 34, 102–117 (2007).10.1111/j.1365-2699.2006.01594.x
32. Elith J Graham CH Anderson RP Dudík M Zimmermann NE Novel methods improve prediction of species' distributions from occurence data Ecography. 2006 29 129 151 10.1111/j.2006.0906-7590.04596.x
Elith, J., Graham, C. H., Anderson, R. P., Dudík, M. & Zimmermann, N. E. Novel methods improve prediction of species’ distributions from occurence data. Ecography. 29, 129–151 (2006).10.1111/j.2006.0906-7590.04596.x
33. Zhang K Sun L Tao J Impact of climate change on the distribution of Euscaphis Japonica (Staphyleaceae) trees Forests. 2020 11 525 10.3390/f11050525
Zhang, K., Sun, L. & Tao, J. Impact of climate change on the distribution of Euscaphis Japonica (Staphyleaceae) trees. Forests. 11, 525 (2020).10.3390/f11050525
34. Phillips SJ Anderson RP Dudík M Schapire RE Blair ME Opening the black box: An open-source release of Maxent Ecography. 2017 40 887 893 10.1111/ecog.03049
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography. 40, 887–893 (2017).10.1111/ecog.03049
35. Phillips SJ Dudık M Modeling of species distributions with maxent: New extensions and a comprehensive evaluation Ecography 2008 31 161 175 10.1111/j.0906-7590.2008.5203.x
Phillips, S. J. & Dudık, M. Modeling of species distributions with maxent: New extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).10.1111/j.0906-7590.2008.5203.x
36. Li Y Li M Li C Liu Z Optimized maxent model predictions of climate change impacts on the suitable distribution of Cunninghamia lanceolata in China Forests. 2020 11 302 10.3390/f11030302
Li, Y., Li, M., Li, C. & Liu, Z. Optimized maxent model predictions of climate change impacts on the suitable distribution of Cunninghamia lanceolata in China. Forests. 11, 302 (2020).10.3390/f11030302
37. Hanley JA McNeil BJ The meaning under a receiver characteristic and use of the area operating (ROC) Curve Radiology. 1982 143 29 36 10.1148/radiology.143.1.7063747 7063747
Hanley, J. A. & McNeil, B. J. The meaning under a receiver characteristic and use of the area operating (ROC) Curve. Radiology. 143, 29–36 (1982).7063747 10.1148/radiology.143.1.7063747
38. Song P Potential global distribution of the guava root-knot nematode Meloidogyne Enterolobii under different climate change scenarios using MaxEnt ecological niche modeling J. Integr. Agric. 2023 22 2138 2150 10.1016/j.jia.2023.06.022
Song, P. et al. Potential global distribution of the guava root-knot nematode Meloidogyne Enterolobii under different climate change scenarios using MaxEnt ecological niche modeling. J. Integr. Agric. 22, 2138–2150 (2023).10.1016/j.jia.2023.06.022
39. Zhao Y Zhao M Zhang L Wang C Xu Y Predicting possible distribution of tea (Camellia sinensis L.) under climate change scenarios using MaxEnt model in China Agriculture 2021 11 1122 10.3390/agriculture11111122
Zhao, Y., Zhao, M., Zhang, L., Wang, C. & Xu, Y. Predicting possible distribution of tea (Camellia sinensis L.) under climate change scenarios using MaxEnt model in China. Agriculture 11, 1122 (2021).10.3390/agriculture11111122
40. Jovanovic S Reynoutria niche modelling and protected area prioritization for restoration and protection from invasion: A Southeastern Europe case study J. Nat. Conserv. 2018 41 1 15 10.1016/j.jnc.2017.10.011
Jovanovic, S. et al. Reynoutria niche modelling and protected area prioritization for restoration and protection from invasion: A Southeastern Europe case study. J. Nat. Conserv. 41, 1–15 (2018).10.1016/j.jnc.2017.10.011
41. Brown JL SDM toolbox: A Python-basedGIS Toolkit for landscape genetic, biogeographic and species distribution model analyses Methods Ecol. Evol. 2014 5 694 700 10.1111/2041-210X.12200
Brown, J. L. SDM toolbox: A Python-basedGIS Toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5, 694–700 (2014).10.1111/2041-210X.12200
42. Swets J Measuring the accuracy of diagnostic systems Science (American Association for the Advancement of Science). 1988 240 1285 1293 10.1126/science.3287615
Swets, J. Measuring the accuracy of diagnostic systems. Science (American Association for the Advancement of Science). 240, 1285–1293 (1988).10.1126/science.3287615
43. McPherson JM Jetz W Effects of species' ecology on the accuracy of distribution models Ecography. 2007 30 135 151
McPherson, J. M. & Jetz, W. Effects of species’ ecology on the accuracy of distribution models. Ecography. 30, 135–151 (2007).
44. Evangelista PH Modelling invasion for a habitat generalist and a specialist plant species Divers. Distrib. 2010 14 808 817 10.1111/j.1472-4642.2008.00486.x
Evangelista, P. H. et al. Modelling invasion for a habitat generalist and a specialist plant species. Divers. Distrib. 14, 808–817 (2010).10.1111/j.1472-4642.2008.00486.x
45. Bewke GB Review on integrated pest management of important disease and insect pest of rice (Oryzae sativa L.) World Sci. News 2018 100 184 196
Bewke, G. B. Review on integrated pest management of important disease and insect pest of rice (Oryzae sativa L.). World Sci. News 100, 184–196 (2018).
46. Jeer M Choudhary VK Dixit A Field efficacy of new pre-mix formulation of flonicamid 15% + fipronil 15% WG against major insect pests of rice J. Entomol. Zool. Stud. 2017 5 679 685
Jeer, M., Choudhary, V. K. & Dixit, A. Field efficacy of new pre-mix formulation of flonicamid 15% + fipronil 15% WG against major insect pests of rice. J. Entomol. Zool. Stud. 5, 679–685 (2017).
47. Wang BX Hof AR Chun-Sen MA Impacts of climate change on crop production, pests and pathogens of wheat and rice Front. Agric. Sci. Eng. 2022 9 4 18 10.15302/J-FASE-2021432
Wang, B. X., Hof, A. R. & Chun-Sen, M. A. Impacts of climate change on crop production, pests and pathogens of wheat and rice. Front. Agric. Sci. Eng. 9, 4–18 (2022).10.15302/J-FASE-2021432
48. Trebicki P Dader B Vassiliadis S Fereres A Insect-plant-pathogen interactions as shaped by future climate: Effects on biology, distribution, and implications for agriculture Insect Sci. 2017 24 975 989 10.1111/1744-7917.12531 28843026
Trebicki, P., Dader, B., Vassiliadis, S. & Fereres, A. Insect-plant-pathogen interactions as shaped by future climate: Effects on biology, distribution, and implications for agriculture. Insect Sci. 24, 975–989 (2017).28843026 10.1111/1744-7917.12531
49. Skendžić S Zovko M Živković IP Lešić V Lemić D The impact of climate change on agricultural insect pests Insects 2021 12 440 10.3390/insects12050440 34066138
Skendžić, S., Zovko, M., Živković, I. P., Lešić, V. & Lemić, D. The impact of climate change on agricultural insect pests. Insects 12, 440 (2021).34066138 10.3390/insects12050440
50. Kiritani K Impacts of global warming on nezara viridula and its native congeneric species J. Asia-Pac. Entomol. 2011 14 221 226 10.1016/j.aspen.2010.09.002
Kiritani, K. Impacts of global warming on nezara viridula and its native congeneric species. J. Asia-Pac. Entomol. 14, 221–226 (2011).10.1016/j.aspen.2010.09.002
51. Gangwar RK Life cycle and abundance of rice leaf folder, Cnaphalocrocis medinalis (Guenee)–A review J. Nat. Sci. Res. 2015 5 103 105
Gangwar, R. K. Life cycle and abundance of rice leaf folder, Cnaphalocrocis medinalis (Guenee)–A review. J. Nat. Sci. Res. 5, 103–105 (2015).
52. Chintalapati P Temperature thresholds and thermal requirements for the development of the rice leaf folder, Cnaphalocrocis medinalis J. Insect Sci. 2013 13 1 14 23879856
Chintalapati, P. et al. Temperature thresholds and thermal requirements for the development of the rice leaf folder, Cnaphalocrocis medinalis. J. Insect Sci. 13, 1–14 (2013).23879856
53. Rosenzweig C Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison Proc. Natl. Acad. Sci. 2014 111 3268 3273 10.1073/pnas.1222463110 24344314
Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl. Acad. Sci. 111, 3268–3273 (2014).24344314 10.1073/pnas.1222463110
54. van Valkengoed AM Steg L Meta-Analyses of factors motivating climate change adaptation behaviour Nat. Clim. Change. 2019 9 158 163 10.1038/s41558-018-0371-y
van Valkengoed, A. M. & Steg, L. Meta-Analyses of factors motivating climate change adaptation behaviour. Nat. Clim. Change. 9, 158–163 (2019).10.1038/s41558-018-0371-y
55. Bouri M Arslan KS Şahin F Climate-smart pest management in sustainable agriculture: Promises and challenges Sustainability 2023 15 4592 10.3390/su15054592
Bouri, M., Arslan, K. S. & Şahin, F. Climate-smart pest management in sustainable agriculture: Promises and challenges. Sustainability 15, 4592 (2023).10.3390/su15054592
56. Tong LV Qian G Yong-xia D Li L Shou-zhang P Predicting potential suitable planting area of rice in China under future climate change scenarios using the MaxEnt model Chin. J. Agrometeorol. 2022 43 262 275
Tong, L. V., Qian, G., Yong-xia, D., Li, L. & Shou-zhang, P. Predicting potential suitable planting area of rice in China under future climate change scenarios using the MaxEnt model. Chin. J. Agrometeorol. 43, 262–275 (2022).
57. Zayan, S. Impact of Climate Change on Plant Diseases and IPM Strategies., 2019.
58. Wiens JA Stralberg D Jongsomjit D Howell CA Snyder MA Niches, models, and climate change: Assessing the assumptions and uncertainties P. Natl. Acad. Sci. USA 2009 106 Suppl 2 19729 19736 10.1073/pnas.0901639106
Wiens, J. A., Stralberg, D., Jongsomjit, D., Howell, C. A. & Snyder, M. A. Niches, models, and climate change: Assessing the assumptions and uncertainties. P. Natl. Acad. Sci. USA 106(Suppl 2), 19729–19736 (2009).10.1073/pnas.0901639106
