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Article

Predicting the Potential Suitable Area of the Invasive Ant Linepithema humile in China under Future Climatic Scenarios Based on Optimized MaxEnt

by
Ming Li
1,†,
Xiaoqing Xian
1,†,
Haoxiang Zhao
1,
Lin Xue
2,
Baoxiong Chen
2,
Hongkun Huang
2,
Fanghao Wan
1 and
Wanxue Liu
1,*
1
State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Science, Beijing 100193, China
2
Rural Energy and Environment Agency, Ministry of Agriculture and Rural Affairs, Beijing 100125, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diversity 2022, 14(11), 921; https://doi.org/10.3390/d14110921
Submission received: 8 September 2022 / Revised: 24 October 2022 / Accepted: 26 October 2022 / Published: 28 October 2022
(This article belongs to the Special Issue Diversity, Biogeography and Community Ecology of Ants II)
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Abstract

:
Linepithema humile (Mayr, 1868) (Hymenoptera: Formicidae) is one of “100 of the world’s worst invasive alien species” listed by the International Union for Conservation of Nature and Natural Resources (IUCN). Although native to South America, this ant has spread worldwide via international trade. Currently, L. humile has not been found in China, and if it invades China, it might pose a potential risk to the native invertebrates, vertebrates, plants, and human livelihoods. Based on 2432 global occurrence records and ten bioclimatic variables, the optimized MaxEnt model was used to predict the potential suitable areas of L. humile in China. We analyzed the important bioclimatic variables affecting the potential suitable areas, and determined the changes in potential suitable areas under future climatic scenarios. Our results indicated that the mean temperature of the coldest quarter (Bio11), precipitation of the coldest quarter (Bio19), mean temperature of the wettest quarter (Bio8), and precipitation of the warmest quarter (Bio18) were the most important bioclimatic variables. Under the current climatic scenarios, the potential suitable area of L. humile in China is 80.31 × 104 km2, which is mainly located in Fujian, Zhejiang, Hunan, Jiangxi, Guangxi, Yunnan, and Hubei. Under future climate scenarios over coming decades, the potential suitable areas of L. humile showed an overall increase and a shift to higher latitudes, which indicated the invasion risk of L. humile in China will increase under climate change. Our findings provide the theoretical guidance for the early warning and monitoring of L. humile in China.

1. Introduction

Biological invasion is the process whereby non-native species are introduced into new areas in which they survive, reproduce, establish, and potentially expand their range [1]. Global trade has been the main driver of biological invasion [2], and with the rapid development of international trade over recent decades, the numbers of invasive alien species (IAS) and the detrimental impacts of invasion have been on the rise [3]. Biological invasions have a considerable effect on the biodiversity, ecological environment, and agricultural production of countries worldwide, and have accordingly become established as one of the five major global environmental issues of the 21st century [4]. Estimates based on recent global studies have put economic losses caused by IAS at up to $1.4 trillion per year, accounting for more than 5% of the global gross domestic product [5]. Therefore, biological invasion control should become one of the main concerns and work centers of all countries.
Ecologically, ants are among the most important insect species. Their small size and multifaceted nesting habits are conducive to human-mediated translocation [6], and as a consequence of the increasing growth in global trade, transportation, and tourism in recent decades, ants have accordingly been anthropogenically distributed beyond their native ranges. Five invasive alien ants (Linepithema humile, Solenopsis invicta, Anoplolepis gracilipes, Wasmannia auropunctata, and Pheidole megacephala) were listed among “100 of the world’s worst invasive alien species” [7].
Linepithema humile is native to South America (Argentina, Uruguay, Brazil, and Paraguay), and has been introduced to six continents and several oceanic islands [8]. Currently, there are no occurrence records of L. humile in most of East Asia, including China. The social structure of L. humile is polygamous, and many workers and queens form large, high-density nests, thus promoting the spread to new areas of the species [9]. These factors have accordingly contributed to the success of L. humile as a strong competitor and invader [9]. Linepithema humile is a well-known agricultural and urban pest that can spread rapidly throughout suitable areas, reducing species richness, affecting the growth and development of plants, and causing economic losses in invaded areas. For example, L. humile has been found to compete with native ants in San Luis Obispo, California, causing significant declines in the populations of 10 native ant species [10]. Similarly, L, humile has been observed to reduce the numbers of arthropods (the main pollinators of inflorescences) in the Western Cape of South Africa by 32% to 62%, thereby severely affecting plant reproduction and growth [11,12]. Moreover, in New Zealand, the cost of controlling L. humile has reached millions of dollars [13]. Although to date there have been no reports of L. humile populations in China, this invasive ant has biological characteristics similar to those of S. invicta. Accordingly, to prevent any future introduction and establishment of L. humile, which poses a threat to biodiversity, agricultural production, and human health.
According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), global warming will rise by 1.5 °C or more in the coming two decades [14]. Insects are poikilothermic temperature animals, which was particularly sensitive to temperature variables [15]. Rapid climate warming affected the growth and development, egg laying, overwintering, distribution patterns, and reproductive rates of insects [16]. Previous studies have shown that global climate warming will affect the distribution of many species worldwide, including invasive alien ants, causing changes in distribution patterns and promoting migration to higher latitudes [17]. For instance, the potential suitable areas of S. invicta will increase and move to higher latitudes in China under climate change [18]. Consequently, paying more attention to the response of L. humile to climate change is helpful not only in understanding the changes in the distribution pattern but also in formulating early warning and management strategies to prevent the invasion of L. humile in China.
Species distribution models (SDMs) are numerical tools that integrate environmental data with data of a species’ occurrence or abundance, and can predict the current and future potential suitable areas of the species [19,20,21]. The MaxEnt model has become the most commonly applied species distribution model, because of its small sample size requirement, simple operation, stable computing results, and short computing time [22,23,24]. Moreover, it performs well in predicting the regionally suitable distribution of IAS [25,26]. For instance, MaxEnt models were used to study the potential suitable area of the invasive ants S. invicta in China [27], and A. gracilipes in China and the world [28]. Using the default parameters to build a MaxEnt model is prone to overfitting, and results in less accurate model predictions [29,30]. Therefore, the ENMeval data package in the R software is used to optimize the parameters of the MaxEnt model to avoid model overfitting and improve the rationality and accuracy of species predictions [31].
In this study, we used the optimized MaxEnt model to predict the invasion risk of L. humile in China under current and future climatic scenarios. The main objectives of this study were: (1) to determine the important bioclimatic variables affecting the potential suitable area of L. humile in China, (2) to assess potential suitable areas of L. humile in China under current climatic scenarios, and (3) to determine changes in the potential suitable areas of L. humile in China under future climatic scenarios, and centroid shifts of potential suitable areas between current and future climatic scenarios. Our results would provide a scientific basis for continued monitoring and early warning of L. humile in China.

2. Materials and Methods

2.1. Global Occurrence Records of Linepithema humile

First, 10,371 global occurrence records of L. humile worldwide were obtained from the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/, accessed on 27 June 2022) and the Invasive Species Compendium of the Center for Agriculture and Bioscience International (CABI, https://www.cabi.org/, accessed on 28 June 2022). Only one occurrence record was kept for each 5 × 5 km raster interval to prevent overfitting of the model using the ENMtools software for the species occurrence records. Once filtering was complete, 2432 occurrence records were retained for model construction (Figure 1).

2.2. Bioclimatic Variables

A total of 19 bioclimatic variables were downloaded from the World Climate Database (http://www.worldclim.org/ accessed on 27 June 2022) at a resolution of 2.5′ under current and future climatic scenarios (Table 1). The bioclimatic climate variables under future climate scenarios included three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5 and SSP5-8.5) for two periods (the 2030s and 2050s) under the BCC-CSM2-MR developed by the global climate model of the National Climate Center (Table S1).
In order to avoid the result of the MaxEnt model overfitting due to the multicollinearity between the 19 bioclimatic variables. First, 19 bioclimatic variables were correlated in the spatial analyst tool in ArcGIS software; then the 19 bioclimatic variables and species occurrence records were imported into the MaxEnt model for 10 repetitions. If the two bioclimatic variables had a correlation coefficient with absolute values greater than 0.8 (|r| > 0.8), the bioclimatic variable with the higher contribution was retained (Figure S1) [32]. Finally, ten bioclimatic variables were used to construct the MaxEnt model (Table 1).

2.3. Optimized MaxEnt Modeling

The results of the MaxEnt model are prone to overfitting when modeling using the program’s default settings. Feature combinations (FCs) and regularization multiplier (RM) are the two most important parameters in MaxEnt models [33]. FCs included linear-L, quadratic-Q, hinge-H, product-P and threshold-T. The RM was set 0.5 to 4, at intervals of 0.5. In this study, we used the ENMeval package in R software to adjust the parameters of FCs and RM choose to use the delta AICc minimum parameter combination as the optimal parameter setting for the model [34]. FCs and RM were set to LQHPT and 0.5, respectively.

2.4. Model Evaluation and Potential Suitable Area Delineation

The FC and RM were set up according to the optimal model, and 75% of the occurrence records were selected for simulation training and 25% for model testing. In the MaxEnt model, the maximum number of iterations was set to 500, the background points to 10,000, and the output format to Cloglog, and cross-validated by running 10 replicates. The jackknife method was chosen to test and create response curves to assess the effects of bioclimatic variables on the potential suitable area of L. humile in China. The accuracy of the model was examined using the area enclosed by the area of under receiver operating characteristic (ROC) curve (AUC) [35]. The model prediction accuracy evaluation is classified as excellent for 0.8–1, usable for 0.5–0.8, and poor for 0–0.5 [36].
The maximum value of 10 repetitions in the MaxEnt model was selected as the result of this study, and the potential suitable area of L. humile in China was cropped according to map of the country. The results were generated based on the presence probability of L. humile, with values in the range of 0–1, with larger values indicating higher species presence probability. We used the reclassify tool in ArcGIS software to classify the suitable habitat into four levels, ‘highly suitable area’ (0.5~1), ‘moderately suitable area’ (0.3–0.5), ‘poorly suitable area’ (0.1–0.3) and ‘unsuitable area (0–0.1)’.
The spatial distribution of geographical objects can be described by the centroid, and the displacement of geographical objects over a period of time can be expressed using the centroid [37]. In this study, the regularity of potential habitat area displacement of L. humile was reflected by the change in the period of the potential habitat area. First, the habitat raster map was transformed into a vector map using the ArcGIS software. Then, the centroid of the potential habitat area was then calculated using the Statistical Analysis Zonal [37].

3. Results

3.1. Model Optimization and Accuracy Evaluation

Our results showed that delat.AICc is lowest when FCs are LQHPT and RM is 0.5, when Mean AUC is greater than the value at default parameters (Figure 2). Under the optimal parameter settings, the mean AUC value of the ten simulations based on the MaxEnt model for the current potential habitat area of L. humile was 0.883, indicating excellent model prediction accuracy and high stability (Figure 3).

3.2. Bioclimatic Variables Affecting the Potential Suitable Area of Linepithema humile

The mean temperature of the coldest quarter (Bio11), precipitation of the coldest quarter (Bio19), mean temperature of the wettest quarter (Bio8), and precipitation of the warmest quarter (Bio18) were the most important bioclimatic variables based on jackknife and percent contribution (Figure 4 and Figure S2). The important bioclimatic variables affecting the potential suitable area of L. humile are two temperature (mean temperature of the coldest quarter, mean temperature of the wettest quarter) and two precipitation variables (precipitation of the coldest quarter, precipitation of the warmest quarter).
The relationship between the probability of presence of L. humile and bioclimatic variables showed in Figure 5. When the probability of presence values of L. humile was greater than 0.5, it indicated that the corresponding interval of bioclimatic variables was suitable for the species. For temperature variables, the mean temperature of the coldest quarter (Bio11) and mean temperature of the wettest quarter (Bio8) ranged from 6.83 °C to 19.28 °C, and from 6.97 °C to 28.36 °C, respectively. For precipitation variables, the precipitation of the coldest quarter (Bio19) and precipitation of the warmest quarter (Bio18) ranged from 98.89 mm to 882.65 mm, and from 1.51 mm to 373.27 mm, respectively.

3.3. Current Potential Suitable Areas of Linepithema humile in China

The prediction of the potential suitable areas of L. humile in China under the current climate is shown in Figure 6. The total suitable area was 80.31 × 104 km2 (Table S2), accounting for 8.36% of the total area of Chinese mainland, which was mainly located in southeast and southwest of China. The highly suitable area was 0.18 × 104 km2, accounting for 0.02% of the total area of the Chinese mainland, which was mainly located in Fujian. The moderately suitable area was 6.37 × 104 km2, accounting for 0.66% of the total area of the Chinese mainland, which was mainly located in Zhejiang, Fujian, Hunan, and Guangxi. The poorly suitable area was 73.76 × 104 km2, accounting for 7.68% of the total area of the Chinese mainland, which was mainly located in Zhejiang, Fujian, Jiangxi, Hunan, Yunnan, and Hubei.

3.4. The Changes Potential Suitable Areas of Linepithema humile under Future Climatic Scenarios

The potential suitable areas of L. humile under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, in the 2030s and 2050s are shown in Figure 7 and Figure 8. The potential suitable areas were mainly located in Jiangsu, Zhejiang, Fujian, Jiangxi, Anhui, Hunan, Hubei, and Yunnan. The results showed that the potential suitable areas increased under the future climatic scenarios. The loss areas were mainly located in Fujian, Jiangxi, Guangxi, Yunnan, and Anhui. The gain areas were mainly located in Jiangsu, Anhui, Hubei, Sichuan, and Henan.
Under the SSP1-2.6 scenario in 2030s, the total, highly, moderately, and poorly suitable areas of L. humile were 104.60 × 104 km2, 0.54 × 104 km2, 9.22 × 104 km2, and 94.84 × 104 km2 (Table S2), respectively, accounting for 10.90%, 0.06%, 0.96%, and 9.88% of the area of Chinese mainland, respectively. Under the SSP1-2.6 scenario in 2050s, the total, highly, moderately, and poorly suitable areas of L. humile were 116.10 × 104 km2, 0.76 × 104 km2, 13.43 × 104 km2, and 101.91 × 104 km2, respectively, accounting for 12.09%, 0.08%, 1.40%, and 10.61% of the area of Chinese mainland, respectively. In the 2030s and 2050s, under SSP1-2.6, the total, highly, moderately, and poorly suitable areas gradually increased (Figure 7).
Under the SSP1-2.6 scenario in 2030s and 2050s, the loss areas were 9.24 × 104 km2, and 7.59 × 104 km2 (Table S3), respectively, which was mainly located in Anhui, Jiangxi, Fujian, Guangxi, Chongqing, and Sichuan. The gain areas were 33.21 × 104 km2, and 43.10 × 104 km2, respectively, which were mainly located in Anhui, Jiangsu, Hubei, Sichuan, Henan, and Guangdong (Figure 8).
Under the SSP2-4.5 scenario in 2030s, the total, highly, moderately, and poorly suitable areas of L. humile were 102.50 × 104 km2, 0.59 × 104 km2, 11.07 × 104 km2, and 90.84 × 104 km2, respectively, accounting for 10.67%, 0.06%, 1.15%, and 9.46% of the area of the Chinese mainland, respectively. Under the SSP2-4.5 scenario in 2050s, the total, highly, moderately, and poorly suitable areas of L. humile were 95.03 × 104 km2, 0.49 × 104 km2, 15.73 × 104 km2, and 78.81 × 104 km2, respectively, accounting for 9.90%, 0.05%, 1.64%, and 8.21% of the area of Chinese mainland, respectively. In the 2030s and 2050s, under SSP2-4.5, the total, highly, moderately, and poorly suitable areas gradually increased.
Under the SSP2-4.5 scenario in 2030s and 2050s, the loss areas were 9.45 × 104 km2, and 16.56 × 104 km2, respectively, which were mainly located in Anhui, Chongqing, Yunnan, Guangxi, Guangdong, and Sichuan. The gain areas were 31.56 × 104 km2, and 31.23 × 104 km2, respectively, which were mainly located in Anhui, Jiangsu, Hubei, Sichuan, Henan, and Guizhou.
Under the SSP5-8.5 scenario in 2030s, the total, highly, moderately, and poorly suitable areas of L. humile were 97.69 × 104 km2, 0.87 × 104 km2, 7.22 × 104 km2, and 89.60 × 104 km2, respectively, accounting for 10.17%, 0.09%, 0.75%, and 9.33% of the area of Chinese mainland, respectively. Under the SSP5-8.5 scenario in 2050s, the total, highly, moderately, and poorly suitable areas of L. humile were 73.04 × 104 km2, 1.12 × 104 km2, 4.26 × 104 km2, and 67.66 × 104 km2, respectively, accounting for 7.61%, 0.12%, 0.44%, and 7.05% of the area of Chinese mainland, respectively. In the 2030s and 2050s, under SSP5-8.5, the total, moderately, and poorly suitable areas gradually decreased, while the highly suitable area increased.
Under the SSP5-8.5 scenario in 2030s and 2050s, the loss areas were 11.78 × 104 km2, and 26.11 × 104 km2, respectively, which were mainly located in Anhui, Jiangxi, Yunnan, Chongqing, Fujian, and Guangxi. The gain areas were 28.91 × 104 km2, and 18.92 × 104 km2, respectively, which were mainly located in Anhui, Jiangsu, Hubei, Sichuan, and Henan.

3.5. The Centroid Migration of Potential Suitable Areas for Linepithema humile

The centroids of potential suitable areas of L. humile in China under current and future climate scenarios are shown in Figure 9. The centroid of potential suitable areas of L. humile shifted to northward and high-latitude areas under future climate scenarios.
Under the current climate, the centroid of the potential suitable area was located at the point (112.38° E, 27.28° N). Under SSP1-2.6 scenario, the suitable areas of 2030s and 2050s centroids were to the point (112.70° E, 28.33° N) and the point (112.59° E, 28.11° N), it shifted 0.32° E and 1.05° N from the current to the 2030s, 0.11° E and 0.22° N from the 2030s to the 2050s. Under SSP2-4.5 scenario, the suitable areas of 2030s and 2050s centroids were to the point (112.96° E, 28.13° N) and the point (112.94° E, 28.53° N), it shifted 0.58° E and 0.85° N from the current to the 2030s, 0.02° E and 0.40° N from the 2030s to the 2050s. Under SSP5-8.5 scenario, the suitable areas of 2030s and 2050s centroids were to the point (112.92° E, 28.19° N) and the point (112.54° E, 27.96° N), it shifted 0.54° E and 0.91° N from the current to the 2030s, 0.38° E and 0.23° N from the 2030s to the 2050s.

4. Discussion

The important bioclimatic variables affecting the potential suitable areas of L. humile in China were mean temperature of the coldest quarter (Bio11) and mean temperature of the wettest quarter (Bio8), precipitation of the coldest quarter (Bio19), and precipitation of the warmest quarter (Bio18). Our results showed that the presence probability of L. humile gradually increased when the mean temperature of the coldest quarter (Bio11) was −3.42–11.45 °C, and gradually decreased when it was 11.45–27.79 °C. The probability of L. humile was less change when the mean temperature of the wettest quarter (Bio8) was 6.63–18.34 °C, gradually increased when it was 18.41–27.88 °C, and gradually decreased when it was 27.88–29.91 °C. This would suggest that they prefer warm, wet climates over cold, wet conditions. This is consistent with previous studies. Temperature within a range from 18 to 32 °C has been established to influence all stages of L. humile development from eggs to adults, with development time being shortened at higher temperatures [38]. Linepithema humile stops feeding when the temperature is consistently below 5 °C, and the colony eventually starves to death [39]. The oviposition rate of L. humile was affected at laboratory temperatures ranging between 10 and 34 °C, and under different polygamous conditions, which oviposition highest at 28 °C [40].
Precipitation variables also had direct and indirect effects on the distribution patterns of L. humile. Changes in precipitation affect the humidity and temperature of L. humile habitats, and thereby affect growth and development, survival, reproduction, and overwintering [41,42,43]. Our results indicate that the probability of the presence of L. humile was greatly affected by the precipitation of the coldest quarter. Previous study showed that elevated soil moisture increased the populations of L. humile, and their ability to compete with native ant species [44]. Cessation of irrigation resulted in a population decline of L. humile in San Diego, USA [44].
The prediction of potential suitable areas of IAS is an important element for invasion risk assessment, identifying the potential suitable areas of L. humile in China can provide scientific guidance for the early monitoring, prevention, and control of L. humile. Our results showed that the potential suitable areas for L. humile under the current climate were mainly located in eastern, central and southwestern China. Under future climate scenario, the potential suitable areas generally showed an increasing trend. The mild climate and abundant moisture in the coastal areas of southeastern China provide suitable conditions for the invasion of L. humile [45]. Previous studies have indicated that climate warming increases the distribution areas of IAS. For instance, the global distribution areas of Zeuzera pyrina and Ceroplastes cirripediformis will increase under future climate scenarios and will expand to temperate regions worldwide [46,47]. The prediction of distribution areas of the 13 most invasive termites worldwide, indicated that the distribution areas of 12 species would likely increase under future climate scenarios [48]. There is some consistency between the results of the above studies and the those of this study.
As global climate continues to warm, many insects have shifted to high-latitude areas [49]. Our results showed that the centroid of potential suitable area of L. humile in China shifted to high-latitude areas under future climate scenarios. Previous studies showed that the potential suitable areas of S. invicta and Hyphantria cunea in China also shifted to high-latitude areas under all future climate scenarios [50,51]. These studies also verified our results and the hypothesis that insects will move to high-latitude areas with increasing global warming [52,53].
The global dispersal pathways of L. humile are mainly natural dispersal, global trade, and accidental dispersal [54,55]. The maximum possible invasion in China would be due to accidental dispersal. There are many vectors for accidental spread, including containers and packaging-wood, human-related waste, sod and mulch, plants or parts of plants, soil, and gravel [56]. When L. humile was introduced into a new habitat, they spread rapidly by natural dispersal and vectorial transport. Eastern, central and southwestern coastal areas of China were the potential suitable areas of L. humile, which is also the area at the highest risk of invasion, due to the large number of ports receiving international cargo. Zhejiang, Fujian, Jiangxi, Anhui, Hunan, Hubei, Yunnan, and Sichaun areas of China were the potential suitable areas of L. humile, which is also the area at the highest risk of invasion, due to the large number of ports receiving international cargo. Therefore, Hangzhou, Fuzhou, Nanchang, Hefei, Changsha, Wuhan, Kunming, and Chengdu customs should strengthen the quarantine of the logs, wooden packaging, and containers that was from Europe and America. If a wild population of L. humile is found in China, it should immediately be eradicated using cultural, biological and chemical control measures [56]. Cultural control measures mainly involve applying powder barriers and limiting access to water sources or nesting sites [44]. Biological control measures are more difficult, because of the lack of natural enemies of the species [57]. Chemical control measures are more widely used to prevent L. humile from entering an area, such as barrier treatment with contact-residual insecticides and baits, but these only kill actively feeding ants and barely affect the larvae or queen [58]. Finally, a comprehensive risk assessment system for the early warning, quarantine, and control of L. humile invasion was established.

5. Conclusions

We are the first to use an optimized MaxEnt models to explore the distribution pattern and limiting bioclimatic variables of L. humile in China. The high invasion risk areas were mainly located in Zhejiang, Fujian, Jiangxi, Anhui, Hunan, Hubei, Yunnan, and Sichaun. Moreover, the risk of invasion of L. humile in China will further increase under future climate scenarios. The mean temperature of the coldest quarter was the most significant bioclimatic variable affecting the distribution pattern of L. humile in China. Our study highlights the importance of climate change on the invasion of L. humile and could help formulate targeted early warning, prevention and control policies for L. humile in China.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d14110921/s1, Figure S1: Correlation coefficient between Bioclimatic variables; Figure S2: Contribution of different bioclimatic variables to MaxEnt model for Linepithema humile; Table S1: Three emission scenarios; Table S2: Potential suitable areas for Linepithema humile under different climate change scenarios (104 km2); Table S3: Future changes in suitable area (104 km2).

Author Contributions

M.L., X.X. and W.L.: conception and design of the research. M.L. and X.X.: acquisition of data. M.L., X.X. and H.Z.: analysis and interpretation of data. M.L. and X.X.: statistical analysis. M.L., X.X. and H.Z.: drafting the manuscript. L.X., H.H., B.C., W.L., and F.W.: manuscript revision. All authors have read and agreed to the published version of the manuscript.

Funding

This project was funded by the National Key R&D Program of China (grant no. 2021YFC2600400), the Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences (grant no. caascx-2017-2022-IAS).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in this article.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Global occurrence records of the Linepithema humile.
Figure 1. Global occurrence records of the Linepithema humile.
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Figure 2. Optimal parameter results for the MaxEnt model.
Figure 2. Optimal parameter results for the MaxEnt model.
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Figure 3. Mean AUC values of the Linepithema humile MaxEnt model.
Figure 3. Mean AUC values of the Linepithema humile MaxEnt model.
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Figure 4. Jackknife test results for Linepithema humile bioclimatic variables.
Figure 4. Jackknife test results for Linepithema humile bioclimatic variables.
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Figure 5. Probability of presence for important bioclimatic variables for Linepithema humile.
Figure 5. Probability of presence for important bioclimatic variables for Linepithema humile.
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Figure 6. Potential suitable area for Linepithema humile under current climatic scenarios in China.
Figure 6. Potential suitable area for Linepithema humile under current climatic scenarios in China.
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Figure 7. Potential suitable area for Linepithema humile under future climate scenarios in China.
Figure 7. Potential suitable area for Linepithema humile under future climate scenarios in China.
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Figure 8. Changes in potential suitable areas for Linepithema humile between the current and future climatic scenarios in China.
Figure 8. Changes in potential suitable areas for Linepithema humile between the current and future climatic scenarios in China.
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Figure 9. Distribution centroids of Linepithema humile under current and future climatic scenarios in China.
Figure 9. Distribution centroids of Linepithema humile under current and future climatic scenarios in China.
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Table
Table 1. Bioclimatic variables are related to the distribution of Linepithema humile.
Table 1. Bioclimatic variables are related to the distribution of Linepithema humile.
VariableDescriptionUnitWhether to Use for Modeling
Bio1Annual mean temperature°CNo
Bio2Mean diurnal air temperature area°CYes
Bio3Isothermality (bio2/bio7) (*100)—No
Bio4Temperature seasonality (standard deviation *100)—No
Bio5Max temperature of warmest month°CYes
Bio6Min temperature of coldest month°CNo
Bio7Temperature annual area (bio5-bio6)°CYes
Bio8Mean temperature of wettest quarter°CYes
Bio9Mean temperature of driest quarter°CNo
Bio10Mean temperature of warmest quarter°CNo
Bio11Mean temperature of coldest quarter°CYes
Bio12Annual precipitationmmNo
Bio13Precipitation of wettest monthmmNo
Bio14Precipitation of driest monthmmNo
Bio15Precipitation seasonality (coefficient of variation)—Yes
Bio16Precipitation of wettest quartermmYes
Bio17Precipitation of driest quartermmYes
Bio18Precipitation of warmest quartermmYes
Bio19Precipitation of coldest quartermmYes
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Li, M.; Xian, X.; Zhao, H.; Xue, L.; Chen, B.; Huang, H.; Wan, F.; Liu, W. Predicting the Potential Suitable Area of the Invasive Ant Linepithema humile in China under Future Climatic Scenarios Based on Optimized MaxEnt. Diversity 2022, 14, 921. https://doi.org/10.3390/d14110921

AMA Style

Li M, Xian X, Zhao H, Xue L, Chen B, Huang H, Wan F, Liu W. Predicting the Potential Suitable Area of the Invasive Ant Linepithema humile in China under Future Climatic Scenarios Based on Optimized MaxEnt. Diversity. 2022; 14(11):921. https://doi.org/10.3390/d14110921

Chicago/Turabian Style

Li, Ming, Xiaoqing Xian, Haoxiang Zhao, Lin Xue, Baoxiong Chen, Hongkun Huang, Fanghao Wan, and Wanxue Liu. 2022. "Predicting the Potential Suitable Area of the Invasive Ant Linepithema humile in China under Future Climatic Scenarios Based on Optimized MaxEnt" Diversity 14, no. 11: 921. https://doi.org/10.3390/d14110921

APA Style

Li, M., Xian, X., Zhao, H., Xue, L., Chen, B., Huang, H., Wan, F., & Liu, W. (2022). Predicting the Potential Suitable Area of the Invasive Ant Linepithema humile in China under Future Climatic Scenarios Based on Optimized MaxEnt. Diversity, 14(11), 921. https://doi.org/10.3390/d14110921

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