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Article

Mapping the Potential Presence of the Spotted Wing Drosophila Under Current and Future Scenario: An Update of the Distribution Modeling and Ecological Perspectives

by
Lenon Morales Abeijon
1,
Jesús Hernando Gómez Llano
1,
Lizandra Jaqueline Robe
2,
Sergio Marcelo Ovruski
3 and
Flávio Roberto Mello Garcia
4,*
1
Programa de Pós-Graduação em Fitossanidade, Universidade Federal de Pelotas, Pelotas 96010-000, Rio Grande do Sul, Brazil
2
Departamento de Ecologia e Evolução, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria 97010-000, Rio Grande do Sul, Brazil
3
Pilot Plant for Microbiological Industrial Processes and Biotechnology (PROIMI-CONICET), Biological Control Division, Tucumán T4001MVB, Argentina
4
Departamento de Ecologia, Zoologia e Genética, Instituto de Biologia, Universidade Federal de Pelotas, Capão do Leão 96160-000, Rio Grande do Sul, Brazil
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(4), 838; https://doi.org/10.3390/agronomy15040838
Submission received: 21 February 2025 / Revised: 18 March 2025 / Accepted: 24 March 2025 / Published: 28 March 2025
(This article belongs to the Special Issue Ecological Aspects as a Basis for Future Pest Integrated Management—2nd Edition)
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Abstract

:
The article addresses the current and future potential distribution of Drosophila suzukii (Diptera: Drosophilidae), commonly known as spotted wing Drosophila (SWD). This invasive pest affects various fruit crops worldwide. Native to Southeast Asia, the species has rapidly expanded due to its high adaptability to climates and ability to infest ripe fruits. SWD occurrence data were collected from multiple databases, pseudo-absences were selected from the background area, and climatic variables were downloaded from WorldClim. The Random Forest algorithm was employed to model the current distribution and project future scenarios, categorizing environmental suitability into high, moderate, and low levels. The analysis of bioclimatic variables indicated that factors such as isothermality, maximum temperature of the warmest month, and precipitation of the driest month are the most significant for pest distribution. The results revealed high climatic suitability for the species in North America, Europe, and Asia, with projections indicating expansion under climate change scenarios in the Northern Hemisphere, including new areas in Europe and North America. Regions with higher suitability are expected to require management and monitoring strategies, particularly in vulnerable agricultural areas. Furthermore, the study underscores the importance of climatic data in predicting pest distribution and formulating effective control and mitigation policies.

1. Introduction

The spotted wing drosophila (SWD), Drosophila suzukii (Matsumura) (Diptera: Drosophilidae), has become a significant global pest over the past decade, posing potential harm to agricultural crops [1,2,3,4]. Native to Southeast Asia, this fly is recognized for its ability to invade territories across all continents [1,5,6,7,8,9,10], driven by its high potential for geographic dispersal [11], tolerance to a wide range of climatic conditions [12], and capacity to infest a variety of intact, ripened fruits [13] piercing the epicarp of fresh fruits for oviposition, c causes significant economic losses [2,12,14].
In recent decades, agricultural production has significantly increased worldwide. However, this progress has often been hindered by the presence of various insect pests, which have reduced crop productivity [15,16]. Given their influence on anthropogenic agricultural ecosystems, a comprehensive assessment of agricultural productivity and food security must carefully consider the impact of insect pests [16,17].
Global warming and economic globalization have accelerated the spread of new pests through multiple invasion routes, posing additional challenges to [18,19]. Examining potential changes in the abundance and distribution of pest species due to climate changes is crucial for economic and food security [20]. Given the serious threat pests pose to global systems [21], various strategies have been developed for their management, each with advantages and disadvantages [22].
Several authors highlight the importance of Species Distribution Modeling (SDM) in elucidating dispersion patterns and establishing integrated management strategies [1,23]. Research on SWD distribution modeling has significantly advanced over the past two decades, reflecting growing concerns about the impact of this pest on global agriculture [1,6,24,25,26,27]. These studies emphasize the complexity of the factors influencing SWD’s ecology and distribution, including climatic variables, habitat, and interactions with plant hosts. These approaches are essential for developing effective pest control measures [26].
In recent years, both correlative and mechanistic models have been applied to understand the continental and global spatial distribution of SWD [6,24]. Correlative models suggest high environmental suitability, particularly in temperate and subtropical areas [6]. Conversely, mechanistic models capture the notion that cold winter temperatures in the northern distribution limit and high temperatures combined with low relative humidity in arid regions are the main limiting factors affecting SWD phenology [24].
In addition, SDM can also predict the niche shift in invasive species compared to their native habitat [28], forecast species distribution patterns according to their physiological tolerances [25], and indicate the effects of global warming through projections of suitable or unsuitable areas for species establishment [29]. Correlative models, such as the MaxEnt model, rely on occurrence data and climatic variables, applied in regions like North America, Europe, and Asia, aiming to predict areas susceptible to pest invasion and support the development of integrated management strategies [25]. However, recent studies analyzing the SDM of pest insects have demonstrated that the models generated using Random Forest perform better in predicting the potential distribution of the species [30].
Climatic variables, such as temperature and humidity, have been identified as key abiotic factors determining species distribution limits, highlighting the importance of a detailed understanding of these factors for implementing measures to control and mitigate damages caused by this pest [25]. Modeling conducted with the MaxEnt and Genetic Algorithm for Ruleset Production (GARP) algorithms indicated that D. suzukii has a higher invasion potential in temperate and subtropical climate areas [6]. Additionally, in future scenarios, the pest may expand to new suitable areas in the Northern Hemisphere due to rising temperatures [29]. Thus, modeling has proven a valuable tool not only for predicting risk areas but also for guiding monitoring and control policies, particularly in vulnerable regions to new invasions [24], and even allowing natural enemies, such as parasitoids, to be introduced effectively through sufficient overlap with the pest’s distribution [27].
Therefore, this article aims to determine the current and future distribution of SWD through distribution models derived from the species occurrence data. These models will enable the prediction of distribution with potential risk of establishment, providing important data for future pest management strategies.

2. Materials and Methods

2.1. Data Collection for the Occurrence of D. suzukii

Occurrences of D. suzukii were primarily obtained from the TaxoDros database v. 1.04 (University of Zurich, Zurich, Switzerland; https://www.taxodros.uzh.ch, accessed on 14 December 2023) [31] up to January 2024. In addition, five other databases were consulted: the Global Biodiversity Information Facility (GBIF, Copenhagen, Dernmark; https://www.gbif.org/, accessed on 26 November 2023), iNaturalist (Carlifornia Academy of Sciences, San Francisco, CA, USA; https://www.inaturalist.org/, accessed on 26 November 2023), VertNet (University of Florida, Gainesville, FL, USA; http://www.vertnet.org/, accessed on 26 November 2023), Berkeley Ecoinformatics Engine (Ecoengine, University of California, Berkeley, CA, USA), and Integrated Digitized Biocollections (iDigBio, University of Florida, Gainesville; https://www.idigbio.org/, accessed on 26 November 2023). The ‘occ’ function from the ‘spocc’ package v.1.2.2 [32] was employed in R v. 4.3.2 [33] to retrieve these data. Subsequently, the occurrence dataset was refined using the ‘clean_coordinates’ function from the ‘CoordinateCleaner’ package v.3.0.1.
Due to the absence of accurate absence data, ‘pseudo-absences’ were generated for the target species at a ratio of twice the number of occurrences [34] using the ‘randomPoints’ function from the ‘dismo’ package v.1.3-14 in R v.4.3.2 [33]. According to Barbet-Massin et al. [34], when running models using machine learning methods with the Random Forest algorithm, the number of pseudo-absences should be equal to the number of true presences to avoid data imbalance. These pseudo-absences were randomly distributed throughout the background area.

2.2. Download and Selection of WorldClim Climatic Layers

Climatic data for current and future projections were downloaded from WorldClim (Global Climate Data; Fick [35]) as ‘raster’ files. These data included 19 bioclimatic variables, covering temperature and precipitation variables (Table 1), with a spatial resolution of 2.5 arc minutes (approximately 4.5 km). We used historical climate data from WorldClim v. 2.1 for the current projections, covering 1970 to 2000.
For future climate projections, we used a set of three global climate models (GCMs) from the ‘Coupled Model Intercomparison Project Phase 6’ (CMIP6) (available on WorldClim (https://www.worldclim.org/data/cmip6/cmip6climate.html, accessed on 29 May 2022); [36]); [‘ACESS-ESM1-5’, ‘HadGEM3-GC31-MM’, and ‘MIROC6’]. Under the SSP585 shared socioeconomic pathway (SSP), which closely reflects the current trajectory of greenhouse gas emissions, we adopted a pessimistic outlook, assuming an energy-intensive economy reliant on fossil fuels with increasing greenhouse gas emissions over time [37,38]. Projections were performed for two time intervals: 2021–2040 and 2041–2060.
To mitigate multicollinearity in our models, occurrence data were used to extract values of the bioclimatic variables. Subsequently, a collinearity test among the bioclimatic variables was conducted using the ‘vifstep’ function from the ‘usdm’ package (v.1.1-18; [39]). Variables with VIF (Variance Inflation Factor) values < 10 were selected for further analysis [40].

2.3. Current and Future Distribution Modeling

We employed the Random Forest algorithm for modeling, as implemented by the RandomForest package v. 4.7-1.1 [41]. The parameters ‘ntree’ and ‘mtry’ were tuned using the Caret package v. 7.0-1 [42,43], resulting in values of 2500 for ‘ntree’ and 3 and 25 for ‘mtry’. During model fitting, 75% of the occurrence data were allocated for training and 25% for testing. A threshold based on the minimum training presence (MTP) was applied to delineate suitable areas for the species under different periods. This approach allowed us to classify suitability ranges into four intervals: values close to 1 (green in our maps) identified high suitability areas, values around 0.5 (yellow in our maps) represented moderate suitability, areas near the species MTP threshold (0.13) were considered as low suitability regions, whereas areas below this threshold were classified as unsuitable.

2.4. Model Evaluation and Importance of Each Bioclimatic Variable

To evaluate the model’s performance, we employed the ROC (receiver operating characteristic) curve analysis, focusing on the area under the curve (AUC) [44] and the true skill statistic (TSS) to assess the predictive performance of the modeling technique [45]. AUC values are a standard metric for evaluating the accuracy of predictive distribution models, with values below 0.7 indicating poor performance, 0.7 to 0.9 moderate performance, and >0.9 good performance [46]. For each species, the relative importance (Mean Square Error and Node Purity) of the selected bioclimatic variables was determined based on multicollinearity analysis and the AUC metric, generated from the Random Forest model through hundreds of classification and regression trees, each trained by randomly selecting a bootstrap subset and a random set of predictor variables. Thus, the contribution of each predictor variable was assessed based on how much it reduced node impurity or by the frequency of successful predictions in the classification or regression forest [47].

3. Results

The occurrence dataset compiled in this study included 3561 occurrence points of D. suzukii (Figure 1). After trimming, this dataset was reduced to 1309 occurrence points (Supplementary File S1). Among the 19 bioclimatic variables, nine presented VIF values < 10 and were employed in the modeling strategy: BIO 2 (mean diurnal range monthly); BIO 3 (isothermality); BIO 5 (max temperature of warmest month); BIO 8 (mean temperature of wettest quarter); BIO 9 (mean temperature of driest quarter); BIO 14 (precipitation of driest month); BIO 15 (precipitation seasonality); BIO 18 (precipitation of warmest quarter); and BIO 19 (precipitation of coldest quarter).

3.1. Current Distribution of D. suzukii

The current SWD distribution model demonstrated a high predictive performance TSS value of 0.54 and AUC value of 0.991 (Supplementary File S2) and enabled the identification of areas with varying suitability levels for the target species (Figure 2A). This worldwide projection indicated that the northeastern, southern, and southeastern regions of the United States, as well as parts of the Pacific Coast and the southeastern portion of Canada, are highly climatically suitable for the pest’s establishment. Similarly, countries such as the United Kingdom, Netherlands, and South Korea were identified as potential areas for SWD occupation.
Currently, the known occurrence records of the SWD are concentrated along the Atlantic coast of the United States and Canada and near the Pacific coast of North America. Additionally, the species is widely distributed across much of Europe’s western and eastern portions (Figure 2A). Countries with recorded pest occurrences in Latin America include Argentina, Brazil, Chile, Colombia, Mexico, and Costa Rica. In Asia, the pest’s endemic region, SWD is found in Southeast Asia in countries such as China, South Korea, Japan, and Mongolia.
In Europe, the spotted wing drosophila is widely recorded along the western coasts of the Iberian and Italian Peninsulas, the British Isles and Mediterranean islands, Central-Western and Central Europe, the Scandinavian region, the Balkans, and Eastern Europe. Beyond these regions, the fly has also been recorded in East Africa, including Kenya, Comoros, Madagascar, and the Canary Islands. In Oceania, the fly has not yet been recorded as invasive; however, the western and eastern coasts of Australia and New Zealand show moderate suitability for SWD.

3.2. Future Distribution of D. suzukii

Under the future scenario conditions, the projected models indicated a significant expansion of the pest into regions of the Asian continent and parts of North America (Figure 2B,C). However, the regions currently classified as highly suitable in Europe and North America are expected to experience a reduction in their suitability. Interestingly, South America is also projected to become less climatically suitable for the SWD within the next 50 years, primarily due to a slight reduction in suitability in the Chilean region. Additionally, the southwestern and southeastern regions of Australia, as well as the islands of New Zealand, will remain moderately suitable climatic areas.

3.3. Climatic Variables Determining the Distribution of D. suzukii

The environmental variables that contributed the highest to the modeling strategy (Table 1 and Figure 3) were isothermality (BIO3; 16.45% contribution), precipitation of the driest month (BIO14; 15.33% contribution), maximum temperature of the warmest month (BIO5; 13.00% contribution), precipitation of the coldest quarter (BIO19; 12.80% contribution), and mean temperature of the driest quarter (BIO9; 11.02% contribution).
The patterns of the curves depicting the effects of each bioclimatic variable on the modeling strategy indicate that the probability of species occurrence did not vary considerably regarding the mean diurnal ranges (BIO2), mean temperature of the wettest quarter (BIO8), and precipitation seasonality (BIO15) variations. Otherwise, the results suggest that the pest is more likely to occur in regions where the maximum temperature of the warmest month (BIO5) ranges between 20 °C and 30 °C and isothermality (BIO3) values around 30 °C. This finding implies that the species may possess mechanisms to cope with daily temperature variations in certain regions, thriving in areas with high isothermality by rapidly adapting and regulating its body temperature in response to thermal changes.
Furthermore, the probability of occurrence of the SWD also increases in regions experiencing mean temperatures during the driest quarter (BIO9) between 5 °C and 38 °C and decreases in regions where the mean temperature of the driest quarter (BIO9) is below 0 °C. This probability of occurrence also increases in regions with precipitation levels starting at 100 mm during the driest month (BIO14) and above 500 mm regarding precipitation during the warmest quarter (BIO18).

4. Discussion

The rapid global expansion of the SWD has raised significant concerns for agriculture practices due to the substantial damage it causes to various small fruit crops [14,48,49]. The fly has a broad range of hosts, spanning 13 families of angiosperms [50]. Since its larvae develop and feed inside the fruits [51], the export trade of fruits enforces a strict zero-tolerance policy for infestations [9]. As a result, many of these high-value small fruits become non-marketable each year.
In this study, we updated the distribution of SWD using a machine learning method with the Random Forest algorithm, which offers greater robustness and accuracy [52], incorporating extensive data collection from six databases to determine the distribution of the current and future climate-suitable areas for the pest. Additionally, the models generated by Random Forest exhibited a high AUC value, indicating strong model performance with the sample size used. Mi et al. [52], comparing four machine learning algorithms for SDM, such as TreeNet (Stochastic Gradient Boosting and Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree), and MaxEnt (Maximum Entropy), found that the robustness and accuracy of species distribution models can be improved using Random Forest. Moreover, Random Forest provides a better model fit to the dataset size, contrasting with previous studies that employed other algorithms for SWD distribution modeling, such as MaxEnt [6,26,29].
Overall, our findings align with the global results of dos Santos et al. [6], Ørsted and Ørsted [28], and Reyes and Lira-Noriega [29], identifying the eastern (northeastern) coast of the United States and Canada as the most favorable areas for the SWD establishment. Nevertheless, they contrast with the models generated by Gutierrez, Ponti, and Dalton [24] for North and Latin America, indicating the most favorable areas for pest presence are located on the northwestern coast of the United States.
In America, the pest has also spread across several other countries, including Argentina, Brazil, Uruguay, Chile, Colombia, Mexico, and Costa Rica, which exhibited only mild climatic suitability values in our models. These findings are consistent with previous studies that demonstrated the pest’s high capacity for geographic dispersal, including its expansion into South America [5,53] and its ability to tolerate a wide range of climatic conditions [12], which facilitates its rapid colonization of new territories [11].
Additionally, the SWD has been spreading rapidly across Europe, where it has been detected in regions along the western coasts of the Iberian and Italian Peninsulas, the British Isles, the Mediterranean, Central Europe, Scandinavia, the Balkans, and Eastern Europe. This expansion in Europe is particularly concerning due to the diversity of the affected crops and the economic importance of the invaded regions. Our results indicate that countries such as the United Kingdom and the Netherlands have high climatic suitability for the pest. Reyes and Lira-Noriega [29], in a study comparing areas of climatic suitability for the SWD establishment, found that these regions feature a prevalence of cultivated and managed vegetation lands, which could amplify the extent of at-risk areas in Europe. These authors also presented models under two climate change scenarios predicting a significant expansion of SWD in the Northern Hemisphere, particularly in northern and eastern Europe, temperate North America, and eastern Asia.
In Asia, the SWD is endemic to countries such as China, South Korea, Japan, and Mongolia but has continuously expanded into new areas. Considering the importance of abiotic variables, our evaluation of potential distribution under the current and future climatic conditions aligns with recent information on the pest’s presence across North and South America, Europe, Asia, and Oceania [6,28,29]. However, according to our model, beyond its traditional economic damage in Southeast Asia [12] and the predicted moderate suitability in China and Japan [28], the SWD could potentially impact at least one-third of Russian territory, with a moderate likelihood of establishment. According to Nair and Peterson [27], D. suzukii could affect approximately 3.7 million km2 of Russian territory.
In Oceania, Dos Santos et al. [6], Ørsted and Ørsted [28], and Nair and Peterson [27] generated models that highlight suitable conditions for the SWD invasion in substantial regions of southeastern Australia and much of New Zealand. However, our model suggests low climatic suitability along the western and eastern coasts of Australia and New Zealand. In Australia, Maino, Schouten, and Umina [54], analyzing the potential spread of the SWD, identified the eastern coastal regions around major cities as the likely entry points for the pest into the country. Nevertheless, these countries have implemented quarantine restrictions on the domestic and international trade of several SWD host crops, including cherries, peaches, plums, strawberries, and grapes [12,55]. In our study, although the suitability is lower compared to other regions, its presence still poses a significant threat to the local crops in these countries.
Furthermore, the projections generated in our study using Random Forest models indicated a future expansion of the pest into new regions of the Asian and North American continents. In contrast, some areas currently suitable in Europe and North America are expected to experience a reduction in suitability scores. These patterns align with various other pest modeling studies. For instance, Dos Santos et al. [6] used MaxEnt (Maximum Entropy algorithm) to detect areas with high suitability in temperate regions, including North America, Europe, and some areas in South America and Asia. Similar results were previously found by Nair and Peterson [27]. Although low climatic suitability values are generally reported for South America, particularly Chile and Uruguay, Nair and Peterson [27] suggest that the potential distribution of the SWD in Uruguay could reach an area of 176,465.55 km2.
Range expansions and contractions are widely driven by the climatic tolerances of each species, playing a key role in historical biogeography, dispersal capacity of invasive species, and responses to global climate change [56]. In this research, we found that the probability of occurrence of the SWD increases in regions where the temperature of the driest quarter ranges between 5 °C and 38 °C and decreases in regions where the mean temperature of the wettest quarter is below 10 °C. This suggests that temperate to cold climates, particularly during the wet season, may represent a significant thermal limit for the fly’s distribution. However, the pest can spread into regions with colder climates as long as temperatures do not drop below the lethal minimum threshold [25].
Our analysis also observed that the SWD thrives in regions with a minimum moisture level during the driest months and in regions with high humidity during the warmest quarter (above 500 mm). These findings align with the results of Dos Santos et al. [6] and Ørsted and Ørsted [28], who identified the annual precipitation and precipitation during the driest quarter as key factors influencing suitability levels and, thus, species establishment.
From this perspective, the Köppen–Geiger climate classification, widely used to categorize terrestrial climates based on temperature and precipitation parameters, is essential for understanding the potential distribution of species [57]. This classification identifies specific climatic regions that may favor or limit the pest, taking into account seasonal variations in temperature and humidity. According to this classification, temperate climates with warm summers and moderate winters, such as Cf (temperate mesothermal without a dry season) and Df (cold continental without a dry season), provide suitable conditions for the survival and proliferation of the target species. These regions, characterized by a uniform distribution of precipitation throughout the year and temperatures that do not exceed the thermal limits tolerated by D. suzukii, can combine climatic factors in ecological modeling to predict the geographic expansion or contraction of invasive species under a scenario of climate change.

5. Conclusions

Based on the analyses conducted here and the potential distribution maps reconstructed for Drosophila suzukii, it is evident that the pest has a high potential for global expansion, affecting various agricultural regions worldwide. Our results indicate that under climate change scenarios, D. suzukii could establish itself in new areas, particularly in the Northern Hemisphere, including parts of northern and eastern Europe, temperate North America, and eastern Asia. Given the pest’s high climatic adaptability and dispersal capacity, expansion into these regions poses a significant risk to agriculture.
Moreover, identifying regions with high climatic suitability for D. suzukii is important for developing management and control strategies. Future studies should focus on monitoring these areas, particularly those without known invasive populations, such as Peru in South America. Additionally, it is recommended that integrated management programs be implemented that consider the interaction between D. suzukii and its biological control agents and the influence of climatic variables on the pest’s distribution. These measures will be essential to mitigate the economic and ecological impacts caused by the expansion of this invasive pest.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15040838/s1, Supplementary File S1: Drosophila suzukii occurrence records, and Supplementary File S2: AUC values.

Author Contributions

Conceptualization, L.M.A., J.H.G.L., L.J.R. and F.R.M.G.; methodology, L.M.A., J.H.G.L. and L.J.R.; software, L.M.A. and J.H.G.L.; formal analysis, L.J.R., S.M.O. and F.R.M.G.; investigation, L.M.A., J.H.G.L., S.M.O. and F.R.M.G.; resources, F.R.M.G.; data curation, L.M.A.; writing—original draft preparation, L.M.A., S.M.O. and F.R.M.G.; writing—review and editing, L.M.A. and L.J.R.; supervision, F.R.M.G.; funding acquisition, F.R.M.G. All the authors have read and agreed to the published version of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

We thank Coordination for the Improvement of Higher Education Personnel (CAPES); we also thank the Council of Technological and Scientific Development (CNPq) for a productivity grant to F.R.M.G. (Grant number: 408479/2021-3) and L.J.R (Grant number: 314206/2021-3).

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author(s).

Acknowledgments

We thank the National Council of Technological and Scientific Development of Brazil (CNPq) for the Scholarship of Research Productivity of F.R.M.G. and L.J.R.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Current distribution of Drosophila suzukii. The spotted wing Drosophila known occurrence points employed to reconstruct the predictive models are represented in red.
Figure 1. Current distribution of Drosophila suzukii. The spotted wing Drosophila known occurrence points employed to reconstruct the predictive models are represented in red.
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Figure 2. Potential suitability maps modeled for Drosophila suzukii under the current (A) and future conditions (B,C). The future climatic suitability maps were based on the SSP585 scenario for (B) 2021–2040 and (C) 2041–2060. The suitability values varied according to the color scale from red to green, with red representing low suitability and green high suitability.
Figure 2. Potential suitability maps modeled for Drosophila suzukii under the current (A) and future conditions (B,C). The future climatic suitability maps were based on the SSP585 scenario for (B) 2021–2040 and (C) 2041–2060. The suitability values varied according to the color scale from red to green, with red representing low suitability and green high suitability.
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Figure 3. Effect of each of the employed climatic variables in the Drosophila suzukii potential distribution models generated by the Random Forest algorithm.
Figure 3. Effect of each of the employed climatic variables in the Drosophila suzukii potential distribution models generated by the Random Forest algorithm.
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Table 1. Relative importance (% Increase Mean Square Error and Increase in Node Purity) and percentual contribution of climatic variables on suitability in Drosophila suzukii distribution models.
Table 1. Relative importance (% Increase Mean Square Error and Increase in Node Purity) and percentual contribution of climatic variables on suitability in Drosophila suzukii distribution models.
Climatic Variables% Increase Mean Square ErrorIncrease in Node Purity% Contribution
BIO30.1258113.5016.45
BIO140.1173175.2915.33
BIO50.099482.3913.00
BIO190.0979148.5712.80
BIO90.084365.2511.02
BIO150.0741122.479.70
BIO80.073359.139.59
BIO180.052543.286.86
BIO20.040126.355.24
Legend: BIO2 (mean diurnal range monthly); BIO3 (isothermality); BIO5 (max temperature of warmest month); BIO8 (mean temperature of wettest quarter); BIO9 (mean temperature of driest quarter); BIO14 (precipitation of driest month); BIO15 (precipitation seasonality); BIO18 (precipitation of warmest quarter); and BIO19 (precipitation of coldest quarter).
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Abeijon, L.M.; Gómez Llano, J.H.; Robe, L.J.; Ovruski, S.M.; Garcia, F.R.M. Mapping the Potential Presence of the Spotted Wing Drosophila Under Current and Future Scenario: An Update of the Distribution Modeling and Ecological Perspectives. Agronomy 2025, 15, 838. https://doi.org/10.3390/agronomy15040838

AMA Style

Abeijon LM, Gómez Llano JH, Robe LJ, Ovruski SM, Garcia FRM. Mapping the Potential Presence of the Spotted Wing Drosophila Under Current and Future Scenario: An Update of the Distribution Modeling and Ecological Perspectives. Agronomy. 2025; 15(4):838. https://doi.org/10.3390/agronomy15040838

Chicago/Turabian Style

Abeijon, Lenon Morales, Jesús Hernando Gómez Llano, Lizandra Jaqueline Robe, Sergio Marcelo Ovruski, and Flávio Roberto Mello Garcia. 2025. "Mapping the Potential Presence of the Spotted Wing Drosophila Under Current and Future Scenario: An Update of the Distribution Modeling and Ecological Perspectives" Agronomy 15, no. 4: 838. https://doi.org/10.3390/agronomy15040838

APA Style

Abeijon, L. M., Gómez Llano, J. H., Robe, L. J., Ovruski, S. M., & Garcia, F. R. M. (2025). Mapping the Potential Presence of the Spotted Wing Drosophila Under Current and Future Scenario: An Update of the Distribution Modeling and Ecological Perspectives. Agronomy, 15(4), 838. https://doi.org/10.3390/agronomy15040838

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