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. 2026 Apr 10;17:1786710. doi: 10.3389/fpls.2026.1786710

Predicting the expansion of Gephyraulus lycantha as a key pest of goji berry in China under climate change

Zhongkang Song 1,†, Jianling Li 1,†, Changrong Deng 1, Guozhen Duan 1, Guanghui Fan 1,*
PMCID: PMC13106313  PMID: 42040295

Abstract

Background

The gall midge, Gephyraulus lycantha Jiao & Kolesik (Diptera: Cecidomyiidae), causes abnormal enlargement of goji berry, Lycium barbarum L, buds during its larval stage, forming galls and resulting in a significantly decrease in yield in China. Identifying the distribution of the midge in China under current and future climate change scenarios will provide guidance for the scientific prevention and control of this pest.

Methods

The MaxEnt model was used to predict the current and future potential suitable habitats for the midge in China based on the filtered 56 distribution points and 11 environmental factors, and the ArcGIS software was used to analyze the changes in its suitable region.

Results

The results showed that when the parameters were feature combination (FC) = HP and regularization multiplier (RM) = 1, the MaxEnt model was optimal, and the AUC and TSS values were greater than 0.90. The mean temperature of driest quarter (suitable range was -9.36–4.43 °C) was the most critical factor influencing the distribution of the midge. Under current climate conditions, the area of suitable habitat for the midge was 112.73 × 104 km2, primarily distributed in Xinjiang (29.03 × 104 km2), Inner Mongolia (26.44 × 104 km2), Gansu (18.36 × 104 km2), Qinghai (10.46 × 104 km2), and Ningxia (3.90 × 104 km2) Provinces. Under the 2050s and 2070s climate scenarios, the area of suitable habitats was larger than current ones (except for SSP126), reaching its maximum under the SSP585 (119.06 × 104 km2) and SSP245 scenarios (135.25 × 104 km2), respectively.

Conclusion

In addition, climate warming would cause the suitable habitat of the midge to expand northeastward. Therefore, it is necessary to strengthen monitoring, early warning, and control measures for the pest to ensure the production of goji berry.

Keywords: gall midge, goji berry, maxent, distribution, climate change

1. Introduction

Lycium barbarum L., commonly known as the goji berry, is a medicinal and edible herb indigenous to China. It has the effects of nourishing the liver and kidney, benefiting essence, and improving eyesight (Ma et al., 2023). Currently, the annual demand for goji berry is nearly 300,000 tons, with a cultivation area exceeding 1,200 km2 (Li et al., 2015). The entire industrial chain generates an income of over 32 billion RMB, making it a vital economic source for farmers and herdsmen in Northwest China (Song et al., 2024). However, goji berry undergoes simultaneous vegetative and reproductive growth with luxuriant branches and leaves, making it more susceptible to pests compared with other medicinal plants. The gall midge, Gephyraulus lycantha Jiao & Kolesik (Diptera: Cecidomyiidae), is a severe gall-forming pest that inflictes devastating damage on goji berry production (Zhang et al., 2023). Adults of the midge oviposit in the young buds of goji berry, and the hatched larvae bore into and feed on the ovaries (Figures 1A-C). This induces ovarian hypertrophy leading to gall formation, which inhibits the affected flower buds from normal anthesis and fructification. Mature larvae then burrow into the soil to pupate, completing the life cycle (Figure 1D) (Li et al., 2015). Due to the concealed nature of the larvae within the flower bud, the control efficacy of conventional pesticides, such as Deltamethrin and Imidacloprid, was poor because of their limited systemic absorption (Li et al., 2017). In recent years, with the continuous expansion of goji berry cultivation area, the distribution and damage range of the midge have increased annually. In severe cases, the infestation rate of the midge exceeds 60%, and the annual yield loss can reach 45%–65% (Wu and Li, 2014; Liu et al., 2020).

Figure 1.

Panel A shows goji berry flower buds parasitized by Gephyraulus lycantha. Panel B displays normal, healthy goji berry flower buds. Panel C presents parasitized, unhealthy goji berry flower buds. Panel D is a circular diagram illustrating the life cycle of Gephyraulus lycantha, with four labeled stages: adult laying egg (a), eggs hatching (b), larva with gall formation (c), and pupa emerging (d). Scale bars are visible in panels A and C for size reference.

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(A) The flower buds parasitized by G. lycantha; (B) Normal flower buds; (C) Parasitized flower buds; (D) The life history of G. lycantha. Photographs by Jianling Li and Haili Qiao.

Over the past century, rising greenhouse gas emissions have driven significant global climate warming, with its impacts on ecosystems and the natural environment becoming increasingly pronounced (Pawan, 2014). As ectothermic animals, insects lack the ability to regulate their own body temperature, and their vital activities depend entirely on environmental temperature, making them highly sensitive to changes in ambient temperature (Bai et al., 2022). To cope with variations in environmental temperature, some pests can avoid unfavorable conditions through diapause regulation and seasonal migration (Peng et al., 2023; Zhang et al., 2025), whereas most insects adapt to climate change by modulating their internal metabolism (Durak et al., 2021). Within suitable temperature ranges, the growth and development of the midge are significantly promoted, while temperature fluctuations exert an impact on its life cycle. Changes in temperature regimes not only affect the growth, development, reproduction, and survival of the pests, but may also alter their population sizes and geographical distributions, and promote their expansion to higher latitudes and elevations (Nedvěd, 2009; Colinet et al., 2015). Therefore, studying the distribution of suitable habitats of the midge in China under climate change can provide a scientific basis for the prevention and control of this pest.

In the context of global climate change, Species Distribution Models (SDMs) are mathematical models based on niche theory, widely applied in pest prevention and monitoring, protection of rare species, biodiversity research, and assessments of the potential impact of climate change on species distribution (Santana et al., 2019; Rathore and Sharma, 2023). Among the available SDMs, the maximum entropy model (MaxEnt) has been used extensively both regionally and internationally in many fields owing to its the advantages of accurate prediction, simple operation, and high efficiency (Elith et al., 2006). This model comprehensively analyzes the distribution data of species and environmental factors to simulate suitable habitats in different regions (Sillero, 2011). In recent years, the model has been widely applied to predict the geographical distribution of the goji fruit fly Neoceratitis asiatica Becker (Song et al., 2024), the leaf beetle Lema decempunctata Gebler (Li et al., 2025b), Aphis sp (Ma et al., 2019), and their host plant L. barbarum (Wang et al., 2023).

Currently, the damage caused by the midge to its host plant is becoming increasingly severe. However, existing research on this pest has mainly focused on its biological characteristics (Zhang et al., 2021), behavior (Sun et al., 2023), and control strategies (Zhang et al., 2019). Studies on the geographical distribution and changes in suitable habitats of the midge under future climate scenarios remain unreported. The main objectives of this study are to: (1) identify the key environmental factors influencing its geographical distribution; (2) predict the distribution of potentially suitable areas for the midge in China under current climate conditions; and (3) forecast and compare the potentially suitable areas for the midge and the changing trends under different future (2050s, 2070s) climate scenarios (SSP126, SSP245, SSP370, SSP585). The findings will provide a theoretical basis for the monitoring and forecasting of the midge and guide the site selection of organic goji berry bases.

2. Materials and methods

2.1. Study area

In China, the primary goji berry production areas were concentrated in the Northwestern region (35°05′–42°50′ N, 92°13′–108°46′ E), at an altitude of approximately 1500 m, including Ningxia, Gansu, Qinghai, Xinjiang and Inner Mongolia (Figure 2). The study area has a temperate continental climate, with an annual average temperature of 3–10 °C and annual precipitation of 100–400 mm. Sufficient sunlight and large diurnal temperature differences are conducive to the synthesis and accumulation of active ingredients in goji berry (Song et al., 2025).

Figure 2.

Colored topographic map of China showing altitude gradient from high elevations in red in the west to low elevations in blue in the east, with major rivers and provinces labeled; red dots indicate specific distribution points, concentrated in Gansu and Ningxia.

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The filtered distribution points of G. lycantha in China.

2.2. Distributed data acquisition and processing

The distribution points of the midge were primarily obtained from the Medicinal Plant Pests Database (https://www.pests.cn/index/tu?pic_name=%E6%9E%B8%E6%9D%9E%E7%BA%A2%E7%98%BF%E8%9A%8A, accessed 19 August 2024), published literature (Xu et al., 2014), and field investigations (2023–2024), with a total of 79 distribution points collated. To reduce the influence of sampling bias on prediction results, these distribution points were filtered using the buffer analysis method in ArcGIS 10.7 (https://www.esri.com/). Only one point was retained when the distance between two points was less than 10 km (Wu et al., 2025). Finally, 56 distribution points were retained for subsequent modelling (Figure 2, Supplementary Table 1). The geographical base map of China was from the Standard Map Service System of the Ministry of Natural Resources (http://bzdt.ch.mnr.gov.cn/download.html?searchText=GS%2520(2019)%25201822) [Inspection number: GS (2019) 1822].

2.3. Environmental data acquisition and processing

Nineteen climate factors and 17 soil factors were utilized to predict the current and future suitable habitats of the midge (Table 1). For consistency, the spatial resolution of the 36 environmental factors was 2.5 arc-minutes (5 km). Climate factors were obtained from the WorldClim database (WorldClim v2.1, https://www.worldclim.org/), covering the time period from 1970 to 2000. Future 2050s (averages for 2041–2060) and 2070s (averages for 2061–2080) climate data were selected from four Shared Socioeconomic Pathways (SSPs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6), which had a good predictive effect in China. These included low greenhouse gas emission (SSP126), medium greenhouse gas emission (SSP245), high greenhouse gas emission (SSP370), and extremely high greenhouse gas emission (SSP585) (Eyring et al., 2016; Tan et al., 2022). Due to the habit of pupating in the soil of the midge, soil characteristics might influence its emergence and survival (Deng et al., 2025). Soil factors were obtained from the Harmonized World Soil Database (HWSD v1.2, http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/). Given the present lack of future soil data, this study assumed that soil conditions would remain unchanged over time (Li et al., 2024a; Song et al., 2024).

Table 1.

Detailed information on the 36 environmental factors.

Factor Description (Unit) Factor Description (Unit)
bio1 Annual mean temperature (°C) bio19 Precipitation of coldest quarter (mm)
bio2 Mean diurnal temperature range (°C) t_gravel Volume percentage of gravel (%)
bio3 Isothermality t_sand Percentage of sand (%)
bio4 Temperature seasonality t_silt Percentage of silt (%)
bio5 Maximum temperature of warmest month (°C) t_clay Percentage of clay (%)
bio6 Minimum temperature of coldest month (°C) t_usda_tex Texture class name and code
bio7 Temperature annual range (°C) t_ref_bulk_density Cation exchange capacity (g/cm³)
bio8 Mean temperature of wettest quarter (°C) t_oc Percentage of organic carbon (%)
bio9 Mean temperature of driest quarter (°C) t_ph_h2o Soil reaction
bio10 Mean temperature of warmest quarter (°C) t_cec_clay Cation exchange capacity of the clay fraction (cmol/kg)
bio11 Mean temperature of coldest quarter (°C) t_cec_soil Cation exchange capacity (cmol/kg)
bio12 Annual precipitation (mm) t_bs Base saturation (%)
bio13 Precipitation of wettest month (mm) t_teb Total exchangeable bases (cmol/kg)
bio14 Precipitation of driest month (mm) t_caco3 Calcium carbonate content (%)
bio15 Precipitation seasonality (mm) t_caso4 Calcium sulfate content (%)
bio16 Precipitation of wettest quarter (mm) t_esp Exchangeable sodium percentage (%)
bio17 Precipitation of driest quarter (mm) t_ece Electrical conductivity (dS/m)
bio18 Precipitation of warmest quarter (mm) t_texture Topsoil texture
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To reduce multicollinearity and improve the prediction accuracy of the MaxEnt model (Han et al., 2025), Pearson correlation analysis was conducted on 36 environmental factors using ENMTool (https://github.com/) (Supplementary Figure 1). The default parameter settings of the MaxEnt model v3.4.1 (https://biodiversityinformatics.amnh.org/open_source/maxent/) were adopted, and the environmental factors and distribution points were imported into the model, and the jackknife analysis was used to evaluate the contribution rates of the 36 environmental factors. The environmental factors with zero contribution were excluded. Factors with an absolute correlation coefficient greater than 0.8 were retained only when they reached the highest contribution in the jackknife analysis (Li et al., 2024a). Finally, 11 environmental factors for model construction and prediction were screened out.

2.4. Model parameter optimization

Since the feature combinations (FC) and regularization multiplier (RM) significantly influence the stability of the MaxEnt model, this study utilized the “kuenm” package (https://github.com/) in R v3.6.3 (https://www.r-project.org/) to optimize the two parameters. During the optimization process, a total of 1,240 parameter combinations were evaluated by combining 31 feature combinations (L, Q, H, P, and T) with regularization multipliers ranging from 0.1 to 4.0 (0.1 increments). 75% of the occurrence data were randomly selected as training data, and the remaining 25% as testing data. The parameters associated with the minimum values of both the omission rate and delta AICc were selected as the optimal model parameters (Deng et al., 2024).

The screened distribution data of the midge and environmental factors were imported into MaxEnt. The optimal parameter combination of FC and RM was selected, with 75% of the distribution points as training data and the remaining 25% as testing data. The jackknife method was applied to evaluate the contribution of key environmental factors. The output format was set to Logistic, and the repeated run type was Subsample, with the simulation repeated 10 times. All other parameters were set to default.

The area under the receiver operation characteristic curve (AUC) and true skill statistic (TSS) could be used to evaluate the accuracy of prediction results. The closer the AUC and TSS values were to 1.0, the higher the predictive accuracy. When the AUC and TSS values were greater than 0.90 and 0.75, respectively, the model was considered to exhibit excellent predictive performance (Shi et al., 2023; Song et al., 2025).

2.5. Classification and area calculation of suitable habitats

The MaxEnt prediction results were reclassified and visualized using ArcGIS. The Maximum Test Sensitivity Plus Specificity (MTSPS) threshold was employed as a standard criterion for defining the boundaries of species’ suitable habitats (Wang et al., 2025). According to this threshold, the suitable habitats of the midge were divided into unsuitability (0–MTSPS), low suitability (MTSPS–0.4), moderate suitability (0.4–0.6), and high suitability (0.6–1.0) (Li et al., 2024a).

The number of grids occupied by each suitable habitat and the total number of grids in China were counted. The area of each habitat in China and in the primary goji berry cultivation provinces (Ningxia, Gansu, Inner Mongolia, Xinjiang, and Qinghai) under current and future (2050s, 2070s) climate scenarios was calculated based on the proportion of their grids to China’s total land area (Song et al., 2024).

2.6. Spatiotemporal changes and centroid analysis

To display the changes in potential suitable habitats of the midge under current and future climate scenarios, the SDM Toolbox (v2.5, http://www.sdmtoolbox.org/) was utilized. Utilizing the “Distribution Changes Between Binary SDMs” function, and the values were defined as 0 for unsuitability, –1 for expansion, 1 for stable, and 2 for reduce, and the areas of these four types of changes were calculated using the method described in Section 2.5. Furthermore, the “Centroid Changes (Lines)” function was used to calculate the migration distance and location of the habitat centroids (Li et al., 2024b).

3. Results

3.1. Model optimization results and evaluation

After optimization, the values of omission rate and delta AICc reached their minimum. Under this condition, the optimal parameters for the MaxEnt model were identified as FC = HP (Hinge + Product) and RM = 1 (Figure 3). Based on the optimized model parameter settings, the MaxEnt model prediction results showed a AUC value as high as 0.981 and a TSS value of 0.974, indicating that the model was accurate and could effectively predict the suitable habitats of the midge.

Figure 3.

Scatterplot showing omission rates at five percent threshold value on the vertical axis and natural logarithm of AICc on the horizontal axis. Open circles represent all candidate models, and black triangles represent selected models. Legend in the bottom left identifies shapes. Most candidate models have omission rates clustered at discrete values between zero point one five and zero point four zero with selected models grouped at the lower left.

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Optimal model parameter combination.

3.2. Dominant environmental factors influencing the distribution of G. lycantha

The environmental factors influencing the distribution of the midge were comprehensively analyzed based on the jackknife of regularized training gain and the contribution rate. When only this factor was considered (Figure 4), the mean temperature of driest quarter (bio9) had the greatest impact on the regularized training gain, followed by precipitation seasonality (bio15) and precipitation of coldest quarter (bio19).

Figure 4.

Horizontal bar chart comparing regularization training gain for various environmental factors, with blue bars for “Only this factor,” light green for “Except this factor,” and red for “All factors.” The “ALL” category reaches the highest gain, labeled in red at the bottom. Each factor's individual and excluded contributions are visually contrasted.

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Jackknife test result of environmental factors.

From the perspective of environmental factor contribution rates (Table 2), the environmental factors with the highest contribution rate to the model were mean temperature of driest quarter (bio9, 40.5%), precipitation of coldest quarter (bio19, 26.1%), and precipitation seasonality (bio15, 15.4%), with a cumulative contribution rate of 82.0%.

Table 2.

Importance of environmental factors.

Factor Contribution rate (%)
bio9 40.5
bio19 26.1
bio15 15.4
t_bs 5.5
bio12 4.9
bio18 3.1
bio14 2.5
t_caco3 1.4
t_caso4 0.5
t_gravel 0.1
t_texture 0.1
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To visualize the response curves of the main environmental factors, factors with a logistic output value exceeding 0.1693 (MTSPS) were selected, indicating that the corresponding environmental conditions were favorable for the growth and development of the midge. Under this threshold, the suitable response ranges of the key environmental factors were bio09 (-9.36–4.43 °C), bio19 (≤7.98 mm), and bio15 (87.85%–99.43%) (Figure 5, Supplementary Figure 2).

Figure 5.

Three line graphs show logistic output for mean temperature of driest quarter, precipitation of coldest quarter, and precipitation seasonality. Peaks are present near zero for temperature and near one hundred for seasonality, with MTSPs threshold marked.

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Response curve of dominant environmental factor.

3.3. Suitable habitats under different climate scenarios

3.3.1. Current suitable habitats

The suitable habitats of the midge were mainly concentrated in northwestern China (Figure 6). The total area of the suitable habitats was approximately 112.73 × 104 km2, accounting for 11.74% of China’s land area (Figure 7). The area of the highly suitable habitats was approximately 8.79 × 104 km2, accounting for 7.80% of the total suitable habitat area. It was primarily distributed in Ningxia (3.90 × 104 km2), central Gansu (2.71 × 104 km2), southern Inner Mongolia (1.59 × 104 km2), and scattered areas such as eastern Qinghai and central Xinjiang (0.59 × 104 km2). The area of moderately suitable habitat was approximately 15.47 × 104 km2, accounting for 13.72% of the total suitable habitat area. It was mainly distributed in the eastern and northwestern regions adjacent to the highly suitable habitats. In China, the suitable habitats for the midge were predominantly classified as low-suitability areas, covering approximately 88.47 × 104 km2 and accounting for 9.22% of the country’s total land area. These areas were primarily distributed in North China and Northwest China.

Figure 6.

Choropleth map of China showing habitat suitability, with areas classified as unsuitability in gray, low suitability in green, moderate suitability in yellow, and high suitability in red. Provincial borders are marked in black and the Yangtze and Yellow Rivers are marked in blue. High suitability zones are concentrated along the middle reaches of the rivers in central and eastern China. Inset map at bottom right provides context for outlying territories.

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Distribution of suitable habitats of G. lycantha in China under current climate.

Figure 7.

Grouped bar chart comparing areas of suitable habitats in China and five regions (Xinjiang, Inner Mongolia, Gansu, Qinghai, Ningxia) under current and future climate scenarios (SSP126, SSP245, SSP370, SSP585) for 2050s and 2070s. Bars show low (blue), moderate (yellow), and high (red) suitability, with low suitability dominating overall except Ningxia, where high suitability is consistently higher.

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Area of the suitable habitats of G. lycantha under current and future climate scenarios.

3.3.2. Future suitable habitats

Under different future (2050s and 2070s) climate scenarios, the distribution of the suitable habitats of the midge remained largely consistent with the current distribution, primarily concentrated in North China and Northwest China.

In the 2050s and 2070s, with increased greenhouse gas emissions, the area of suitable habitats for the midge under SSP245, SSP370, and SSP585 climate scenarios expanded, except for the SSP126 scenario. Among the four climate scenarios in the future, the suitable area under SSP585-2050s (119.06 × 104 km2) and SSP245-2070s (135.25 × 104 km2) reached their maximum, with an increase of 5.62% and 19.98% compared with the current area, respectively (Figure 7).

Under future climate scenarios, Xinjiang was the province most severely affected by the midge, followed by Inner Mongolia, Gansu, Qinghai, and Ningxia (Figure 8). The area of suitable habitats in Xinjiang (42.64 × 104 km2) and Gansu (20.94 × 104 km2) reached their maximum under SSP245-2070s, with increases of 46.89% and 14.08%, respectively. In Inner Mongolia, the suitable area reached its maximum (34.98 × 104 km2) under SSP126-2050s, increasing by 32.29%. In Qinghai, the suitable area reached its maximum (12.39 × 104 km2) under SSP585-2070s, increasing by 18.38%. In Ningxia, the suitable area reached its maximum (5.67 × 104 km2) under SSP585-2050s, increasing by 5.24%.

Figure 8.

Grid of eight color-coded maps showing projected suitability for a species or condition across China from 2050s to 2070s under four climate scenarios (SSP126, SSP245, SSP370, SSP585), with suitability categories: unsuitability (gray), low (green), moderate (yellow), and high (red). Central and northern regions consistently display more yellow and red, indicating higher suitability, with increasing expansion and intensity from 2050s to 2070s across all scenarios. Major river systems are outlined in blue. Insets depict surrounding islands.

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Distribution of suitable habitats of G. lycantha in China under different future climate scenarios.

3.4. Spatiotemporal and centroid changes in the suitable habitats of G. lycantha

Compared with the current climatic conditions, 76.26%–86.00% of suitable habitats remained stable in the 2050s, with the expansion area reaching a maximum of 29.77 × 104 km2 in the SSP585 scenario. In the 2070s, 75.40%–89.37% of suitable habitats remained stable, with the expansion area reaching a maximum of 34.51 × 104 km2 in the SSP245 scenario (Figure 9). Notably, the expansion regions were mainly concentrated in southern Xinjiang Province, Haixi Mongolian and Tibetan Autonomous Prefecture of Qinghai Province, and northwestern Gansu Province (Figure 10).

Figure 9.

Bar chart comparing changes in suitable area by category—expansion, stable, and reduction—for four SSP scenarios (SSP126, SSP245, SSP370, SSP585) across the 2050s and 2070s. Yellow stable bars are consistently the highest, followed by teal expansion and red reduction bars.

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Spatiotemporal changes in the area of suitable habitats under different future climate scenarios compared with the current.

Figure 10.

Panel of eight maps of China compares habitat suitability changes under four SSP climate scenarios (SSP126, 245, 370, 585) for the 2050s and 2070s, with color-coded areas for unsuitability, reduction, stability, and expansion. Blue lines mark major rivers, and a legend identifies gray, green, yellow, and red for unsuitability, reduction, stable, and expansion zones respectively. Maps include latitudinal and longitudinal labels, and each panel is individually labeled with its scenario and decade.

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Dynamic changes in suitable habitats of G. lycantha under different climate scenarios.

The movement of the centroid reflected variations in environmental factors influencing habitat suitability, and future changes in climate, soil, and other environmental conditions would influence the potential distribution of the midge. The centroid of the suitable habitats under current climate conditions was in Zhangye prefecture-level city (99.55° E, 38.91° N), Gansu Province, bordering Qinghai (Figure 11). Compared with the current situation, the centroid of the suitable habitat shifted eastward (under SSP126 and SSP585) and westward (under SSP245 and SSP370) in the 2050s, with a migration distance of 59.50–155.36 km. Compared with the 2050s, the centroid of suitable habitats shifted northeastward in the 2070s, with a migration distance of 17.45–178.96 km.

Figure 11.

Map illustrating projected changes in centroids under four climate scenarios (SSP126, SSP245, SSP370, SSP585) for the 2050s and 2070s in northwestern China, with colored lines and points indicating centroid shifts from the current position across Inner Mongolia, Qinghai, and Gansu provinces; inset map shows study region within China.

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Changes in the centroid of suitable habitat of G. lycantha in China.

4. Discussion

4.1. Analysis of key environmental factors in geographic distribution of G. lycantha

Since the midge had the habit of pupating in soil, Deng et al. (2025) demonstrated that different soil factors significantly affected the emergence of adult the midge. The most favorable conditions for its emergence were soil moisture content of 15%, soil bulk density of 1.0 g/cm³, and soil depth of 5 cm. However, the results of this study indicated that the total contribution rate of soil factors to the distribution of the midge was 7.5%, while that of climatic factors was 92.5%. This discrepancy highlighted that while soil factors played a significant role in the pupation process of the midge, climate factors remained the primary driver of the species’ geographical distribution at the macro-scale. For insects, the rates of key physiological processes in their life cycles (development, eclosion, reproduction) were determined by environmental conditions, especially temperature and precipitation factors that render insect distributions highly sensitive to changes in climatic factors (Régnière, 2009). For instance, Li et al. (2025b) reported that climatic factors had a significantly higher contribution rate than soil factors in influencing the distribution of L. decempunctata, which was consistent with the results of this study.

The interaction among the environment and host plant directly affected the spatiotemporal distribution and dynamics of pests (Wang and Qin, 2007). According to the contribution rates and regularized training gain, the mean temperature of the driest quarter was identified as the dominant climatic factor affecting the distribution of the midge, with a suitable range of -9.36 to -4.43 °C. Although no studies had been reported on the minimum lethal temperature of the midge, combined with its biological characteristic of overwintering in soil, it could be inferred that excessively low temperatures during the overwintering period might prevent overwintering pupae from surviving due to cold stress. Conversely, unusually high winter temperatures might break the diapause of overwintering pupae, leading to premature development. However, the emerged adults would struggle to survive and reproduce as they lacked host flower buds for oviposition (Li et al., 2025b). Existing research showed that the mean temperature of the driest quarter was one of the key environmental factors affecting the distribution of its host plant, L. barbarum (Wang et al., 2023). When this temperature ranged from -7.4 to -3.3 °C, the suitability probability of L. barbarum exceeded 56% (Shen et al., 2023). Notably, the suitability range of the midge corresponded closely to that of its host plant. This synchronization ensured that the midge’s reproductive cycle aligned with critical growth stages of the host, thereby ensuring the stability of its population.

4.2. Potential distribution of G. lycantha under climate change

Climate change could profoundly affect species’ potential distribution patterns and biodiversity (Bellard et al., 2012). For example, plants adapted to new environmental conditions and coped with climate change by adjusting their geographical distribution (Wei et al., 2018). Similarly, pests faced pressures from climate change, leading to shifts in their suitable habitats. This study found that the suitable habitat area of the midge in China exhibited an overall expanding trend, indicating that climate change had a significant positive impact on its distribution. Under future climatic conditions, the hydrothermal conditions in high-latitude regions underwent significant changes. The environmental stress that originally restricted the distribution of the midge in high-latitude regions gradually diminished, thereby promoting the northeastward migration of its population. Li et al. (2025a) found that the suitable habitats of Russian box thorn, Lycium ruthenicum, gradually shifted northeastward, which was consistent with the results of this study. Furthermore, similar distribution dynamics had been extensively documented across various other pests. For instance, research indicated that the potential suitable habitat of the Japanese orange fly, Bactrocera tsuneonis (Miyake), in China was continuously expanding northward, facilitated by the species’ rapid adaptive evolution to localized thermal extremes (Mao et al., 2024). The northward shifts observed in Monolepta signata Oliver (Liu et al., 2024) and N. asiatica (Song et al., 2024) further corroborate that climate change was transforming previously unsuitable regions into suitable habitats for pest colonization. These shared migration patterns across diverse agricultural pests further substantiated that climate change was the primary driver changing their geographical distributions, while also posing an increasingly severe threat to agricultural security in high-latitude regions. Under the low greenhouse gas emission scenario (SSP126), the suitable habitat area of the midge exhibited an overall reduction trend. However, under medium and high greenhouse gas emission scenarios (SSP245, SSP370 and SSP585), it showed a general expansion trend. We speculated that temperature rise caused by the accumulation of carbon dioxide emissions was conducive to the population growth of the midge, and mitigating carbon dioxide emissions could inhibit its population expansion to a certain extent.

4.3. Potential limitations

Although the MaxEnt simulation results employed in this study demonstrated high accuracy, the method itself possessed inherent limitations. The predicted results reflected the theoretical distribution of the midge under various future climate scenarios, whereas actual population dynamics might be constrained by modeling uncertainties and other confounding factors. This study used soil factors derived from the HWSD, with the assumption that these factors remained constant under both current and future climate scenarios. This assumption did not account for the fact that climate change-driven shifts in soil hydrothermal conditions (such as soil moisture fluctuations and intensified salinization) might affect the survival and development of pupae in the topsoil. Additionally, the geographical distribution of the midge was influenced not only by environmental factors (climate and soil) but also by other biotic and abiotic factors, including human activities, terrain differences, natural enemies, as well as its own adaptability and evolutionary capacity (Pearson and Dawson, 2003; Zhao et al., 2022). All these factors might lead to certain discrepancies between the model’s predicted results and the actual future geographical distribution of the midge. Future work should integrate these elements to better predict habitat availability in a changing environment.

4.4. Control strategy

In practice, prevention and control zones can be delineated based on the simulation results, and targeted precision control measures can be implemented accordingly. For the highly suitable habitats of the midge in China, such as Xinjiang, Inner Mongolia, Gansu, Qinghai, and Ningxia, stricter control measures (deep plowing to kill pupae, physical trapping and spraying of pesticides) should be adopted to reduce the degree of damage to the host plant (Li et al., 2015). On the other hand, as the suitable habitats of the midge expand northeastward, regions such as Xinjiang, Qinghai, and Liaoning should strengthen the dynamic monitoring of the midge, and formulate effective monitoring and control strategies to prevent widespread outbreaks.

5. Conclusion

This study systematically predicted and analyzed the potential distribution of the midge, G. lycantha, in China under current and future climate scenarios using an optimized MaxEnt model. According to the prediction results, the suitable habitats of the midge were predominantly located in Northwest China, covering the provinces of Xinjiang, Inner Mongolia, Gansu, Qinghai, and Ningxia, with the mean temperature of the driest quarter being the dominant environmental factor limiting its distribution. Under future climate scenarios, the suitable habitats of the midge were projected to expand further (except for the SSP126), with a distribution centroid shifting toward the northeast. This study provides a scientific basis for the standardized management and site selection of goji berry cultivation, and offers data support for stakeholders to develop targeted pest control strategies, thereby mitigating the impact of the midge on the sustainable development of the goji berry industry. Furthermore, future studies should integrate multiple biotic and abiotic factors, and combine population genetics with ecological niche modeling to improve the accuracy of predictions regarding the midge’s distribution.

Funding Statement

The author(s) declared that financial support was received for this work and/or its publication. This research was supported by the Natural Science Foundation of Qinghai Province (grant number 2024-ZJ-981).

Footnotes

Edited by: Xiao Ming Zhang, Yunnan Agricultural University, China

Reviewed by: Zhengxue Zhao, Anshun University, China

Mohamed M. El-Khalafy, Kafrelsheikh University, Egypt

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

ZS: Writing – review & editing, Methodology, Formal analysis, Writing – original draft, Software, Conceptualization, Data curation, Investigation. JL: Funding acquisition, Formal analysis, Visualization, Methodology, Conceptualization, Writing – review & editing, Investigation. CD: Investigation, Writing – review & editing, Formal analysis. GD: Writing – review & editing, Formal analysis, Investigation. GF: Formal analysis, Writing – review & editing, Investigation, Funding acquisition, Methodology.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2026.1786710/full#supplementary-material

DataSheet1.zip (10.2MB, zip)

References

  1. Bai Y., Zhou W., Zhu Z. (2022). Effects of global warming on insect natural enemies. J. Zhejiang Univ. 48, 269–278. doi:  10.3785/j.issn.1008-9209.2021.05.171 [DOI] [Google Scholar]
  2. Bellard C., Bertelsmeier C., Leadley P., Thuiller W., Courchamp F. (2012). Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377. doi:  10.1111/j.1461-0248.2011.01736.x. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Colinet H., Sinclair B. J., Vernon P., Renault D. (2015). Insects in fluctuating thermal environments. Annu. Rev. Entomol. 60, 123–140. doi:  10.1146/annurev-ento-010814-021017. PMID: [DOI] [PubMed] [Google Scholar]
  4. Deng C., Feng J., Wang J., Li H., Qiao H., Xu C., et al. (2025). Effects of soil factors on the emergence and eclosion of gephyraulus lycantha. J. Environ. Entomol. 47, 483–490. doi:  10.3969/j.issn.1674-0858.2025.02.15. PMID: 35900448 [DOI] [Google Scholar]
  5. Deng C., Zhong Q., Shao D., Ren Y., Li Q., Wen J., et al. (2024). Potential suitable habitats of chili pepper in China under climate change. Plants 13, 1027. doi:  10.3390/plants13071027. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Durak R., Dampc J., Kula-Maximenko M., Mołoń M., Durak T. (2021). Changes in antioxidative, oxidoreductive and detoxification enzymes during development of aphids and temperature increase. Antioxidants 10, 1181. doi:  10.3390/antiox10081181. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Elith J., Graham C. H., Anderson R. P., Dudík M., Ferrier S., Guisan A., et al. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29 (2), 129–151. doi:  10.32614/cran.package.netassoc, PMID: 41351832 [DOI] [Google Scholar]
  8. Eyring V., Bony S., Meehl G. A., Senior C. A., Stevens B., Stouffer R. J., et al. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model. Dev. 9, 1937–1958. doi:  10.5194/gmd-9-1937-2016. PMID: 38859159 [DOI] [Google Scholar]
  9. Han H., Liu L., Gao J., Lu M. (2025). Prediction of the potentially risk areas for the invasive plant solanum aculeatissimum jacq. In China under the background of climate change based on Maxent optimization model. Open J. Nat. Sci. 13, 261. doi:  10.12677/ojns.2025.132027 [DOI] [Google Scholar]
  10. Li F., Duan G., Fan G., Li J. (2025. b). Predicting geographical distribution of lema decempunctata with Maxent modeling. Chin. J. Ecol. 44, 2096. doi:  10.13292/j.1000-4890.202506.019 [DOI] [Google Scholar]
  11. Li J., Deng C., Duan G., Wang Z., Zhang Y., Fan G. (2024. a). Potentially suitable habitats of daodi goji berry in China under climate change. Front. Plant Sci. 14. doi:  10.3389/fpls.2023.1279019. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Li C., Gu Y., Liu B., Chun K. P., Octavianti T., Tan M. L., et al. (2025. a). Assessing the impacts of climate change on the potential geographical distribution of lycium ruthenicum in China. Biology 14, 1379. doi:  10.3390/biology14101379. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Li J., Liu S., Xu C., Zhu X., Qiao H., Guo K., et al. (2017). Population dynamics and control strategies of major pests of wolfberry, lycium barbarum. Mod. Chin. Med. 19, 1599–1604. doi:  10.13313/j.issn.1673-4890.2017.11.021 [DOI] [Google Scholar]
  14. Li Z., Ma Y., Li Y., Liu Y., Wang Y., Wang X. (2024. b). Spatial and temporal variations of the potential habitat of asterothamnus centraliasiaticus on the Qinghai-Tibet Plateau under climate change. Chin. J. Ecol. 43, 1566. doi:  10.13292/j.1000-4890.202406.025 [DOI] [Google Scholar]
  15. Li J., Xu C., Qiao L., Liu S., Chen J., Qiao H., et al. (2015). The occurrence characteristics and control strategies of jaapiella sp.of lycium barbarum l. Mod. Chin. Med. 17, 840–843, 850. doi:  10.13313/j.issn.1673-4890.2015.8.018 [DOI] [Google Scholar]
  16. Liu S., Lei J., Chen J., Xu C., Lu P., Qiao H. (2020). Biological characteristics and occurrence patterns of the gall midge jaapiella sp. In Zhongning county, Ningxia Hui Autonomous region. J. Plant Protect. 47, 446–454. doi:  10.13802/j.cnki.zwbhxb.2020.2019067 [DOI] [Google Scholar]
  17. Liu Q., Zhao J., Hu C., Ma J., Deng C., Ma L., et al. (2024). Predicting the current and future distribution of monolepta signata (coleoptera: chrysomelidae) based on the maximum entropy model. Insects 15, 575. doi:  10.3390/insects15080575. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Ma J., Liu G., Zhang X. (2019). Prediction on potential establishment area of aphis sp. In Ningxia based on Maxent model. Geogr. Geoinf. Sci. 35, 40–45. doi:  10.3969/j.issn.1672-0504.2019.05.007. PMID: 35900448 [DOI] [Google Scholar]
  19. Ma R., Zhang X., Ni Z., Thakur K., Wang W., Yan Y., et al. (2023). Lycium barbarum (goji) as functional food: a review of its nutrition, phytochemical structure, biological features, and food industry prospects. Crit. Rev. Food Sci. Nutr. 63, 10621–10635. doi:  10.1080/10408398.2022.2078788. PMID: [DOI] [PubMed] [Google Scholar]
  20. Mao J., Meng F., Song Y., Li D., Ji Q., Hong Y., et al. (2024). Forecasting the expansion of bactrocera tsuneonis (miyake) (diptera: tephritidae) in China using the Maxent model. Insects 15, 417. doi:  10.3390/insects15060417. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Nedvěd O. (2009). “ Temperature, effects on development and growth,” in Encycl. insets ( Elsevier, Amsterdam: ), 990–993. doi:  10.1016/B978-0-12-374144-8.00261-7, PMID: [DOI] [Google Scholar]
  22. Pawan M. (2014). Impacts of global warming on environment. Int. Res. J. Environment. Sci. 3, 72–78. doi:  10.4135/9781412963893.n329. PMID: 34907622 [DOI] [Google Scholar]
  23. Pearson R. G., Dawson T. P. (2003). Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371. doi:  10.1046/j.1466-822X.2003.00042.x. PMID: 41717205 [DOI] [Google Scholar]
  24. Peng Y., Jin M., Li Z., Li H., Zhang L., Yu S., et al. (2023). Population genomics provide insights into the evolution and adaptation of the asia corn borer. Mol. Biol. Evol. 40, msad112. doi:  10.1093/molbev/msad112. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Rathore M. K., Sharma L. K. (2023). Efficacy of species distribution models (SDMs) for ecological realms to ascertain biological conservation and practices. Biodivers. Conserv. 32, 3053–3087. doi:  10.1007/s10531-023-02648-1. PMID: 41841152 [DOI] [Google Scholar]
  26. Régnière J. (2009). Predicting insect continental distributions from species physiology. Unasylva 60, 37–42. doi:  10.1111/j.1752-4598.2011.00150.x. PMID: 41834780 [DOI] [Google Scholar]
  27. Santana P. A., Jr., Kumar L., Da Silva R. S., Pereira J. L., Picanço M. C. (2019). Assessing the impact of climate change on the worldwide distribution of dalbulus maidis (delong) using Maxent. Pest. Manage. Sci. 75, 2706–2715. doi:  10.1002/ps.5379. PMID: [DOI] [PubMed] [Google Scholar]
  28. Shen Z., Li J., He J., Yang H., Deng X., Zhang X. (2023). Prediction of potential distribution area for lycium barbarum by the Maxent model. Chin. Wild Plant Resour. 42, 101–107. doi:  10.3969/j.issn.1006-9690.2023.11.015. PMID: 35900448 [DOI] [Google Scholar]
  29. Shi X., Wang J., Zhang L., Chen S., Zhao A., Ning X., et al. (2023). Prediction of the potentially suitable areas of litsea cubeba in China based on future climate change using the optimized Maxent model. Ecol. Indic. 148, 110093. doi:  10.1016/j.ecolind.2023.110093. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Sillero N. (2011). What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecol. Model. 222, 1343–1346. doi:  10.1016/j.ecolmodel.2011.01.018. PMID: 41850414 [DOI] [Google Scholar]
  31. Song Z., Fan G., Deng C., Duan G., Li J. (2024). Predicting the distribution of neoceratitis asiatica (diptera: tephritidae), a primary pest of goji berry in China, under climate change. Insects 15, 558. doi:  10.3390/insects15080558. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Song Z., Liu Z., Deng C., Duan G., Fan G., Li J. (2025). Predicting suitable habitats of high-yield and-quality lycium barbarum based on climate characteristics in production area of Qinghai, China. Chin. J. Appl. Ecol. 36, 1118–1126. doi:  10.13287/j.1001-9332.202504.025. PMID: [DOI] [PubMed] [Google Scholar]
  33. Sun H., Zhang Y., Zhang F., Lei J., Ma M., Xu C., et al. (2023). Factors influencing the mating behavior of gephyraulus lycantha (diptera: Cecidomyiidae). Chin. J. Appl. Entomol. 60, 860–871. doi:  10.7679/j.issn.2095-1353.2023.080 [DOI] [Google Scholar]
  34. Tan J., Huang A., Shi X., Zhang Y., Zhang Y., Cao L., et al. (2022). Evaluating the performance of bcc-csm2-mr model in simulating the land surface processes in China. Plateau Meteorol. 41, 1335–1347. doi:  10.7522/j.issn.1000-0534.2021.00057 [DOI] [Google Scholar]
  35. Wang L., Jiang P., Xu D., Wang R., Sun Q. (2023). Analysis of geographic distribution patterns of lycium barbarum in the context of climate oscillations. Acta Bot. Boreali-Occident. Sin. 42, 2133–2142. doi:  10.7606/j.issn.1000-4025.2022.12.2133 [DOI] [Google Scholar]
  36. Wang C., Qin J. (2007). Insect-plant co-evolution: multitrophic interactions concerning helicoverpa species. Chin. Bull. Entomol. 44, 311–319. doi:  10.1360/zc2009-39-6-525. PMID: 30890139 [DOI] [Google Scholar]
  37. Wang J., Xu M., Ngawang N., Chen Y., Bonjor N., Jia X., et al. (2025). The potential habitat of phlomoides rotata in tibet was based on an optimized maxent model. Front. Plant Sci. 161560603. doi:  10.3389/fpls.2025.1560603. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Wei B., Wang R., Hou K., Wang X., Wu W. (2018). Predicting the current and future cultivation regions of carthamus tinctorius l. Using Maxent model under climate change in China. Glob. Ecol. Conserv. 16, e477. doi:  10.1016/j.gecco.2018.e00477. PMID: 41850414 [DOI] [Google Scholar]
  39. Wu G., Li C. (2014). Occurrence and control of gephyraulus lycantha. Nongcun Keji 8, 36–37. doi:  10.1177/01445987251371069. PMID: 41836481 [DOI] [Google Scholar]
  40. Wu K., Zhao C., Lu H., Li X., Lu T., Wu T., et al. (2025). Potential suitable distribution of gymnodiplocheilus pachycheilus based on Maxent model. Acta Hydrobiol. Sin. 49, 32501. doi:  10.7541/2025.2024.0283 [DOI] [Google Scholar]
  41. Xu C., Liu S., Xu R., Chen J., Qiao H., Jin H., et al. (2014). Investigation of production status in major wolfberry producing areas of China and some suggestions. China J. Chin. Mater. Med. 39 (11), 1979–1984. doi:  10.4268/cjcmm20141106 [DOI] [PubMed] [Google Scholar]
  42. Zhang F., Chen J., Ma M., Lu P., Liu S., Guo K., et al. (2021). Morphology and distribution of antennal sensilla in the gall midge gephyraulus lycantha (diptera: cecidomyiidae). Micron 145, 103061. doi:  10.1016/j.micron.2021.103061. PMID: [DOI] [PubMed] [Google Scholar]
  43. Zhang F., Li J., Liu S., Haili Q., Zhu X., Yang M., et al. (2019). Application of weeding cloth to control wolfberry gall midge, jaapiella sp. Mod. Chin. Med. 21, 937–940. doi:  10.13313/j.issn.1673-4890.20181023008 [DOI] [Google Scholar]
  44. Zhang Y., Sun H., Feng J., Xu C., Liu S., Guo K., et al. (2023). Oviposition preferences of gephyraulus lycantha (diptera: cecidomyiidae) females on three lycium host plants. Chin. J. Appl. Entomol. 60, 629–640. doi:  10.7679/j.issn.2095-1353.2023.060 [DOI] [Google Scholar]
  45. Zhang S., Zhang Y., Yang F., Zhou C., Shen H., Wang B., et al. (2025). Climate change is leading to an ecological trap in a migratory insect. Proc. Natl. Acad. Sci. 122, e1872372174. doi:  10.1073/pnas.2422595122. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Zhao X., Lei M., Wei C., Guo X. (2022). Assessing the suitable regions and the key factors for three cd-accumulating plants (sedum alfredii, phytolacca americana, and hylotelephium spectabile) in China using maxent model. Sci. Total Environ. 852, 158202. doi:  10.1016/j.scitotenv.2022.158202. PMID: [DOI] [PubMed] [Google Scholar]

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Data Availability Statement

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