Multifaceted Investigation into the Absence and Potential Invasion of Spotted Lanternfly (Lycorma delicatula) in Taiwan

doi:10.21203/rs.3.rs-4832573/v1

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Multifaceted Investigation into the Absence and Potential Invasion of Spotted Lanternfly (Lycorma delicatula) in Taiwan

https://doi.org/10.21203/rs.3.rs-4832573/v1

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BACKGROUND

The spotted lanternfly (Lycorma delicatula), a significant agricultural and forestry pest, poses concerns due to its potential for rapid spread in favorable environments. Originating from East Asia, its distribution and ecological impacts in various regions, including Taiwan, remain subject to uncertainty. This study employed a comprehensive methodology combining field surveys, museum specimen analysis, and ecological niche modeling to assess the presence and potential invasion risks of L. delicatula in Taiwan.

RESULTS

Despite extensive surveys over two years and thorough reviews of museum collections, which were estimated by iNEXT3D analysis to ensure survey completeness, we found no conclusive evidence for the presence of the species in Taiwan, challenging previous controversial records. Ecological niche models indicate that, under current climate conditions, L. delicatula might find suitable habitats primarily in specific high-altitude areas, while coastal and lower elevation zones generally show low suitability. Additionally, in Taiwan, most regions where the host plants of L. delicatula were recorded exhibited extremely low habitat suitability. Global climate change predictions suggest a possible northward shift in suitable habitats, and the existing limited suitable habitats in Taiwan may further decrease as climate change progresses.

CONCLUSION

However, concerning the relatively higher suitability for the species in particular mountainous areas, continuous monitoring and further research are recommended to conduct for better understanding the risk and preparing for potential future scenarios.

Entomology

invasive species

field survey

ecological niche modeling

biodiversity conservation

agriculture

climate change

The spotted lanternfly, Lycorma delicatula (White) (Hemiptera: Fulgoridae), a phloem-feeding planthopper native to China, India,¹ Japan,² and Vietnam,³ has emerged as a significant invasive threat in the United States and South Korea.⁴ Despite its current occurrence being limited to these countries, its potential for rapid spread and severe economic impact on agriculture and forestry make it global concern (Fig. 1, Table S1). Notably polyphagous, it feeds on over 100 host plants, causing considerable ecological and economic impacts.⁵ Besides affecting vineyards, it also damages ornamental and woody plants, impacting diverse industries such as landscape nurseries, timber, and Christmas tree production.⁶ This infestation leads to reduced yields and increased insecticide costs, estimated to result in a potential loss of 7.9 million US dollar in grape revenue in Pennsylvania alone and a worst-case scenario of 50% vine damage.⁷ The rapid spread and high-density populations of L. delicatula in residential areas further exacerbate its nuisance.⁸ Originating from southwestern China, the species diversified into six phylogeographic lineages and expanded northward across the Yangtze River in the late Pleistocene. This expansion brought it to South Korea and subsequently to the U.S., making South Korea a key bridgehead in its global invasion.⁹ Jones et al.¹⁰ emphasize the urgency of addressing this invasive threat, noting that without effective management, L. delicatula could rapidly spread across the United States, potentially establishing itself widely by 2037. This expansion, primarily driven by accidental human-mediated transport such as railroads, underscores the need for strategic planning and proactive measures. Surveillance and targeted management strategies are vital to prevent or slow down this spread and mitigate potential damage to economically significant agricultural commodities.

Biological invasions represent a major environmental challenge, as evidenced by their role in causing rapid and large-scale losses in community structure and biodiversity.^11–14 This problem is increasingly being exacerbated by climate change, characterized by rising temperatures, alterations in precipitation patterns, and shifts in atmospheric carbon dioxide.^15,16 Additionally, increasing global population and expanding trade networks are expected to further increase opportunities for species migration and spread in the coming decades.¹⁷ These factors collectively pose greater challenges to biodiversity, ecosystem stability, and human activities. Understanding the multifaceted ways in which climate change and human-driven processes are intensifying biological invasions is critical for developing effective management and mitigation strategies, making this a top priority for ecological research and conservation efforts.^14–16,18

The existence of L. delicatula in Taiwan has been a topic of debate. Previous literature (e.g., Chou et al.¹⁹ ; Li et al.²⁰) claimed its presence in Taiwan, with the records of particular host plants such as Acacia sp. and Toona sinensis. However, none of these authors have provided clear information regarding the depository of voucher specimens, which pose challenges to verification due to potential misidentifications. L. delicatula has long been considered a native species of Taiwan until more rigorous taxonomic reviews of local fauna were conducted. Lin et al.²¹ revised the fauna of Fulgoridae in Taiwan, brining significant doubts regarding the previous records of L. delicatula to light. Through climate change intensifies, these uncertainties take on new significance. Climate change can alter habitats and ecological niches, potentially facilitating the establishment and spread of invasive species¹⁸ such as L. delicatula. Taiwan, with its varying microclimates, could become increasingly hospitable to this pest under changing climatic conditions.

Our study aimed to resolve the uncertainty surrounding the presence of L. delicatula in Taiwan and to assess its potential invasion risks by different aspects. We conducted extensive field surveys and re-examined historical records, seeking definitive evidence of the species' existence in the region. Additionally, we aimed to evaluate the potential distribution of L. delicatula in global and endemic scales under current and future climatic conditions, with the distribution of important host plants taken into account. Our objectives are to clarify the status of L. delicatula in Taiwan and to understand its invasion potential in the face of climate change. This comprehensive analysis is essential for developing informed strategies to mitigate the risks associated with this invasive species.

2.1 Lanternfly survey and museum specimen examination

To systematically explore the presence of L. delicatula in Taiwan, we conducted a comprehensive field survey targeting the family Fulgoridae across the main island and its outlying islands. Our site selection was strategic, focusing primarily on diverse habitats with a wide range of lanternfly host plants, such as Ailanthus altissima, Toona sinensis, and Vitis spp., which are widely distributed across Taiwan. Priority was given to agricultural lands and shallow mountainous areas due to their higher likelihood of lanternfly presence. Figure 2 illustrates the locations where lanternflies were found or not found during our surveys. For specimen collection, we focused on the lanternfly’s host plants for direct searches and used light traps, including mercury bulbs and HID bulbs, for two-hour sessions to attract specific species. This targeted approach achieved an efficient collection of various lanternfly species, augmenting the robustness of our field data. Collected specimens were preserved either as dry pinned specimens for morphological examination or in 95% ethanol for future molecular studies. We adapted the dissection method for male genitalia from Constant and Pham²², involving heating in a potassium hydroxide solution and preservation in glycerol. Identification of specimens was carried out using the key and diagnostic characters provided by Lin et al.²¹, with detailed examination under a microscope Leica EZ4 (Leica, Germany). To estimate the sample completeness of our survey, sample completeness curve was generated using iNEXT.3D Online with individual-based abundance data and 1,000 bootstraps.^23,24

We have examined all Taiwanese lanternfly specimens available in officially recognized collections, including those in National Chung Hsing University (NCHU, Taichung, Taiwan), National Museum of Natural Sciences (NMNS, Taichung, Taiwan), National Taiwan University (NTU, Taipei, Taiwan), Taiwan Agricultural Research Institute (TARI, Taichung, Taiwan), and Taiwan Forestry Research Institute (TFRI, Taipei, Taiwan). These museums house extensive collections from comprehensive surveys conducted by entomologists over decades. It is important to note that while our survey was comprehensive and covered most known collections, the possibility of unexamined specimens in private or amateur collections cannot be entirely ruled out. However, given the large size and high visibility of L delicatula, it is unlikely that it would be overlooked if present. The collecting sites of the specimens were shown in Fig. 2 and Tables S2, S3, S4.

Maps were generated using Quantum GIS²⁵ for marking the collecting sites of the specimens from comprehensive surveys and museum collections in Taiwan as well as delineating the presence and absence of lanternflies across regions.

2.2 Prediction of habitat suitability of L. delicatula

2.2.1 Species occurrence dataset and environmental variables

The initial step in compiling the global occurrence dataset for L. delicatula involved sourcing data from iNaturalist.org, an informative database of georeferenced species observations often accompanied by photographic evidence. This platform allows preliminary validation of species identifications by a diverse global community, including both amateurs and experts.^26,27 To ensure the accuracy of species identification, we rigorously verified each photograph, selectively incorporating records with high confidence levels. During the data aggregation phase, we encountered significant challenges related to dataset imbalance (including duplicate records and overly dense data), predominantly due to an over-representation of observations from the United States, where the species is notably invasive. The imbalanced dataset can exacerbate the over-representation of certain regions within a study area, leading to severe spatial and environmental biases. This is often due to the dominance of certain environmental features from more accessible and extensively surveyed areas.^28,29 To address this problem, we implemented stringent filtering measures using Quantum GIS.²⁵ We carefully selected observations, ensuring a balanced representation by reducing the number of overly dense data points and augmenting data from underrepresented regions. Furthermore, to augment the accuracy of our occurrence data, we conducted a cross-verification with the FLOW database,³⁰ to ensure the precision of these records (Fig. 1; Table S1). Totally, 404 unique occurrences were compiled in Microsoft Excel and saved as CSV files for MaxEnt software for further analysis.³¹ This dataset adheres to best practices for data transparency and reproducibility by recommended by Liao et al.³². Employing nineteen bioclimatic variables from the WorldClim database (version 2.1), we focused on factors critical to arthropod survival and distribution, such as temperature and precipitation patterns from historical data (1970–2000).³³ Projections of future habitat suitability utilized the GISS-E2-1-G (Coupled Model Inter-comparison Project Phase VI, CMIP6) climate model under four distinct Shared Socioeconomic Pathways (SSPs) (SSP1-26, SSP2-45, SSP3-70, and SSP5-85) for 2050 and 2090 timeframes, ranging from optimistic climate mitigation scenarios to pathways anticipating significant global warming. The GISS-E2-1-G climate model from NASA is renowned for its enhanced climate sensitivity, making it apt for studying future warming pattern.^34,35 This comprehensive approach allowed for nuanced predictions of L. delicatula's distribution shifts under varying future climates, incorporating both broad global analyses (at 10-minute resolution) and detailed local studies within Taiwan (at 30-second resolution).

2.2.2 Distribution modeling

In response to insights by Amaro et al.³⁶ on the influence of geographic extent on model accuracy, we incorporated global distribution data at a 10-minute resolution along with current climatic variables to calculate maximum entropy. This method facilitated the projection of L. delicatula's potential global distribution, which was subsequently refined for Taiwan using a finer 30-second resolution. We projected these models under different climate scenarios at future time periods. To perform these analyses, we utilized MaxEnt (v.3.4.4)³⁷ in R (version 4.3.2)³⁸ and RStudio (version 2023.09.01 + 494)³⁹ and the R package 'dismo'.⁴⁰

Pseudo-absences were generated using presence-only data, setting the MaxEnt model to select 10,000 background points (default setting) and iterating until achieving 500 iterations or a convergence threshold of 0.00001. Concerned with issues of multicollinearity and the ratio of sample size to features, we first ran an initial model to identify the top ten variables based on their contribution and permutation impact. These variables then underwent a variance inflation factor (VIF) analysis to reduce multicollinearity. Only variables that met the VIF criteria were retained for final modeling, ensuring a more robust and interpretable model.^31,41 The key five variables chosen for further modeling include bio09, bio18, bio19, bio14, and bio15 (Table 1).

Table 1

Contribution rates and permutation importance for initial model and final model
							Final five variables
		Percent contribution	Permutation importance	Percent contribution	Permutation importance	VIF value	VIF value
bio01	Annual mean temperature (°C)	1.4	9.3			1189.104415
bio02	Mean diurnal range (°C)	0.3	1			12.305075
bio03	Mean diurnal range (°C)	0	0
bio04	Mean diurnal range (°C)	34.3	53.2			438.878154
bio05	Mean diurnal range (°C)	0.2	0.4			617.881303
bio06	Min temperature of coldest month (°C)	0.9	0
bio07	Temperature annual range (°C)	0.2	0.2			313.378983
bio08	Mean temperature of wettest quarter (°C)	0	0.3
bio09	Mean temperature of driest quarter (°C)	0.7	0.2	54.4	60.2	12.573447	1.130358
bio10	Mean temperature of warmest quarter (°C)	0.4	0
bio11	Mean temperature of coldest quarter (°C)	17.3	8.2			3819.751566
bio12	Annual precipitation (mm)	5.1	1.9			14.362106
bio13	Precipitation of wettest month (mm)	0.1	0.2
bio14	Precipitation of driest month (mm)	0	0.1	0.4	5.1	3.332012	2.951239
bio15	Precipitation seasonality (mm)	2.8	0.9	4.3	5.2	2.020947	1.447742
bio16	Precipitation of wettest quarter (mm)	0	0.3
bio17	Precipitation of driest quarter (mm)	0	0
bio18	Precipitation of warmest quarter (mm)	35.3	22.8	40.2	26	6.521328	1.554209
bio19	Precipitation of coldest quarter (mm)	0.9	0.9	0.7	3.5	4.232354	1.909103

We explored various combinations of feature classes and regularization multipliers as recommended in recent studies to avoid overfitting.^36,42 The parameter tuning process was automated using the 'ENMeval',⁴³ which enabled a systematic evaluation of diverse model configurations. Each configuration underwent ten-fold cross-validation to not only enhance its transferability across different scenarios but also help identify non-analogous conditions.³⁶ After completing the model runs, we selected the parameters that minimized the delta AICc. This approach balances model complexity and data fit, ensuring robustness without overfitting. Our final selection of MaxEnt models utilized five feature types—linear, quadratic, hinge, product, and threshold—with a regularization multiplier set at 1.5, signifying the best-fit model among those tested.

2.2.3 Model validation

The effectiveness of our models was assessed using the area under the curve (AUC) of the receiver operating characteristic ROC curve,⁴⁴ which plots the true positive rate against the false positive rate. AUC values, ranging from 0 to 1, with higher values indicating superior predictive capability.^36,45–48 Additionally, we calculated the difference between the training and testing AUC values (AUC.diff) as an indicator of the model’s generalization ability.⁴⁹ A smaller AUC.diff suggests that the model is consistent across both datasets, indicating good generalization, whereas a larger AUC.diff could imply overfitting to the training data. The continuous Boyce index (CBI) was also employed⁵⁰ to measure how well the model predictions align with the actual distribution of presences in the evaluation dataset. The CBI ranges from − 1 to 1, where positive values suggest the model's predictions are consistent with the observed distribution, while values close to zero suggest the performance is comparable to a random model. We further analyzed omission rates (OR) at sensitivities of 0% and 10% as additional performance indicators, focusing particularly on the 10% training omission rate (or.10p) to evaluate the model's accuracy in predicting known species locations.^51–53 The model's performance is considered suboptimal if the default OR exceeds the predicted rate, suggesting potential underprediction of known presences.⁴⁶ The alignment of the training set OR (represented by a blue line) with the predicted OR (black line) further helped us evaluate accuracy, with a close alignment indicating accurate model predictions (Figure S1).⁵⁴

2.2.4 Visualization of habitat suitability for L. delicatula and related host plants

The logistic model output, which predicts the presence probability of species conditioned by environmental variables, is depicted as a continuous gradient of habitat suitability on the map for each grid cell, with values ranging from 0 (unsuitable) to 1 (highly suitable). The color representation on the map adheres to this gradient, avoiding the imposition of artificial thresholds.³² This visualization approach ensures that the map reflects the full spectrum of habitat suitability from unsuitable to optimal conditions for the species across various regions and under different climate scenarios (Figs. 1B, 4A, 6, S2–S15). Furthermore, the analysis of dominant environmental variables was conducted through techniques such as jackknifing and response curve generation (Figures S16, S17),⁵⁵ providing a comprehensive understanding of the factors influencing species presence.

In our analysis, we prioritized host plants known to be significant for L. delicatula, selecting them based on their documented frequency of occurrence in the collected data. The occurrence data, sourced from iNaturalist, showed a varied distribution across the selected host plants, indicating potential preferences or prevalence in different habitats. Specifically, Ailanthus altissima was represented by 69 points, Vitis spp. by 158 points, Melia azedarach by 2,233 points, Acacia spp. by 2,820 points, and Toona sinensis by 87 points (Fig. 4B). These data points were instrumental in assessing the habitat suitability for L. delicatula in relation to each host plant, guiding our subsequent analysis and interpretation. Histograms generated from this data provided a visual representation of habitat suitability across various crop locations (Fig. 5), offering insights into potential risk areas for L. delicatula infestation.

3.1 Survey findings and museum collection insights

In our extensive field survey, we explored 27 distinct habitats across Taiwan's main island and its outlying islands, as shown in Fig. 2, Table S2, S3. Among these, lanternfly species, excluding L. delicatula, were detected in 11 habitats, predominantly in the northern regions of Taiwan. The species identified during the field surveys include Dichoptera similis Schumacher, Limois westwoodii (Hope), Pyrops watanabei (Matsumura), Saiva formosana Kato, and several others, reflecting the diversity of lanternflies in these areas. Notably, in the 16 remaining habitats, no lanternflies were detected. The sample completeness curve suggests our survey was exhaustive, as it plateaus when the sample size exceeds 2,000 individuals, indicating most Taiwanese lanternfly species have been sampled (Fig. 3). Complementing our field data, we examined lanternfly specimens from 48 collecting sites archived in five domestic museums and institutes (referenced in Figure S2, Table S4). This examination led to the identification of seven lanternfly species, such as Limois kikuchii Kato, L. meliae Kato, Penthicodes pulchella (Guérin-Méneville), and others. Crucially, in none of these museum collections was a voucher specimen of L. delicatula from Taiwan found, reaffirming our field survey observations and underscoring the absence of this invasive species in the region.

3.2 Ecological niche model outcomes under current climatic scenarios

Based on the delta.AICc of the result of cross-validation, linear, quadratic, hinge, product, and threshold (LQHPT) types were permitted among the five feature types, with regularization multiplier of 1.5 with evaluation metrics of delta.AICc = 0, AICc = 3657.363, AUC.val.avg = 0.9916206, AUC.diff.avg = 0.004633429, or.10p.avg = 0.1281579. The MaxEnt model with random sampling predicted the potential distribution of L. delicatula with high accuracy AUC values of 0.9941469 and 0.9916206, and CBI of 0.937 and 0.8796 for training and testing, respectively. The OR of L. delicatula for distribution prediction is presented in Figure S1. The average OR of the training set for L. delicatula was 0.002. These results of MaxEnt model indicating excellent predictive performance according to standard evaluation criteria.^39–41 Potential distribution maps for L. delicatula are presented at global and Taiwanese scales (Figs. 3, 4). High-suitability regions are indicated in green, while areas of moderate suitability are shown in yellow to orange. Regions unsuitable for habitation are represented in white. Globally, prominent high-suitability areas are observed in Asia (particularly Northern China, Japan, and Korea) and in the US (from the Eastern coast to the central region). In Europe, areas of very low suitability are noted. Taiwan primarily exhibits white, denoting unsuitability, with only a few mountainous regions showing marginal suitability.

3.3 Ecological niche model outcomes under future climatic scenarios

Figure 5 and supplementary Figures S2 display the projected changes in L. delicatula's habitat suitability across global and Taiwanese scales under various SSP climate scenarios for two future time periods. The global-scale projections are segmented into three key regions: Asia, Europe, and North America. In Asia, the projected habitat suitability shows a northward shift and contraction of highly suitable areas within China, relatively unchanged conditions in Korea, and a potential expansion in Japan. European projections mostly continue to show low suitability, with some mountainous regions in Central Europe potentially becoming more suitable. In North America, the distribution is predicted to expand northeastward with more severe climate change. Focusing on Taiwan, the projections predominantly indicate unsuitability, as evidenced by the predominance of white areas on the map.

4.1 Interpretation of survey and museum data

Our comprehensive investigation, which included a detailed review of field survey data and an examination of specimens from five prominent insect collections in Taiwan (Fig. 2), found no evidence supporting historical claims regarding the presence of L. delicatula in Taiwan.^19,20 Despite extensive searches, no voucher specimens of L. delicatula were discovered in any collections, nor were any living specimens detected across the diverse habitats surveyed. These results make us cast doubt on the validity of previous records, suggesting potential misidentification.²¹ Furthermore, a nymph specimen used in the phylogenetic analysis by Yeh et al.⁵⁶ was confirmed to belong to L. delicatula based on its molecular data. This specimen was collected in Taiwan, while detailed collection data was missing (Yeh, personal communication). This unique finding, lacking complete collection data, limits our ability to reasonably incorporate it into our overall conclusions. The application of iNEXT3D provided a statistical measure of the coverage of our survey. The sample completeness curve approaches an asymptote as over 2,000 individuals have been sampled, indicating that our field surveys have likely encompassed the range of most lanternfly species in Taiwan, including regions that were historically believed to be inhabited by L. delicatula (Fig. 3). This statistical validation suggests that the non-detection of L. delicatula is not due to inadequate sampling but rather reflects its true absence or extreme rarity in the region.

Additionally, during our examination, morphological similarities between certain individuals of L. meliae and L. delicatula were noted, further supporting the misidentification hypothesis. Comparative studies involving detailed morphological and genetic analysis are required to clarify this point conclusively. Furthermore, it is noteworthy that our review of iNaturalist database did not reveal any observation data of L. delicatula from either India or Vietnam, despite claims of its presence in these countries by previous researches.^1,3 Instead, we identified several observation records of L. imperialis from India, with a few individuals morphologically similar to L. delicatula. The absence of concrete occurrence data for L. delicatula and the morphological similarities between L. delicatula and L. imperialis raises questions about the accuracy of the records in these two countries. Similar to the situation in Taiwan, we speculate that records of L. delicatula in these countries could have resulted from the misidentification of species that are similar in appearance. Nonetheless, these conclusions necessitate further validation through future discoveries and the examinations of voucher specimens. Although our survey was extensive, we recognize that it did not encompass potential private or amateur collections which might not be accessible or held to the same curatorial standards as institutional collections. While such collections could theoretically contain additional specimens, our findings reflect the accessible and scientifically curated records, which suggest a high degree of survey completeness within the acknowledged limits.

4.2 Ecological niche modeling reveals unitability and potential shift for Lycorma delicatula

Our predictions confirm that most areas in Taiwan are unsuitable for L. delicatula (Fig. 4A), aligning with the findings of Wakie et al.⁵⁷ but contrasting with those of Jung et al.⁵⁸ Globally, we observe notable discrepancies in regions such as Europe, South Australia, Southern Africa, and Argentina (Fig. 1B), where Wakie et al.⁵⁷ indicated higher suitability. These discrepancies may stem from differences in the environmental variables considered, notably elevation, which was excluded from our model but may have been included in theirs. Jung et al.⁵⁸ presented a scenario favoring tropical regions for L. delicatula, contrary to the species' known distribution patterns (Fig. 1A).³⁰ This suggests their model may overestimate suitability in these climates, potentially due to differing assumptions or model inputs. In the United States, our model identifies the Northeast as a suitable habitat for L. delicatula (Fig. 1B), consistent with the research by Wakie et al.⁵⁷ and contrasting with that by Jung et al.⁵⁸, who suggested higher suitability in the Southeastern United States. This variation underscores the significant impact of selecting model parameters and the climatic data underpinning the analysis. Regarding the Korean Peninsula, our model suggests high suitability across the region (Fig. 1B), mostly aligning with the conclusions drawn by Wakie et al.⁵⁷, but there is a reduction in suitability in northern areas. In stark contrast, Jung et al.⁵⁸ highlight only southern coastal areas as highly suitable, and Namgung et al.⁵⁹ point to a greater suitability in lower elevation areas.

Our results provide insights into the potential distribution of L. delicatula under current and future climate scenarios (Figs. 1B, 4A, 6, S2–S15). We predict a contraction of suitable habitats in Asia, especially in China, with a northward shift in Europe, potentially making Central European mountainous areas new niches for the species. Japan is identified as an area for potential expansion, indicating the species' adaptability to changing climates, while Korea is expected to maintain its current suitability levels. Contrary to the situation in Korea and Taiwan, North America presents a different scenario. Our model forecasts a northeastward expansion of suitable habitats, which could potentially expose new regions to invasion risks as the climate warms. This finding contrast sharply with the westward expansion predicted by Jones et al.¹⁰, which emphasizes the role of human activities in the species' dispersal across the United States, including a likely emergence in California by 2033.

The comparison between our static habitat suitability models and the dynamic spread models from Jones et al.¹⁰ highlights the complexities of predicting invasive species movements. While our models focus on environmental suitability, the approach by Jones et al.¹⁰ integrated temporal dynamics of dispersal, emphasizing the limitations of relying solely on climatic data for forecasting changes in species distribution. For the U.S., the potential for new invasion fronts necessitates enhanced surveillance and proactive management strategies to address the anticipated spread, particularly in newly vulnerable areas. This comprehensive approach is crucial for developing effective biosecurity measures tailored to the specific challenges posed by changing climates and human influences on invasive species dynamics.

4.3 Implications for Taiwan’s biodiversity and agriculture

The comprehensive field surveys and detailed examinations of museum specimens show no evidence for the presence of L. delicatula in Taiwan. Our ecological niche modeling also indicates that under current climatic conditions, the majority of Taiwan offers low habitat suitability for L. delicatula. Based on the expected changes in climate, our models suggest alterations in the suitability of habitats, which may lead to a further decrease in the already limited areas that are marginally suitable. A comparison with the iNaturalist records of host plants associated with L. delicatula reveals that in most locations where these plants are present, the habitat suitability remains uniformly low, with no scores exceeding 0.2 (Fig. 5). This suggests that despite the presence of necessary flora, the overall environmental conditions of Taiwan are unsuitable for L. delicatula’s survival, indicating that this pest is currently unlikely to exist in Taiwan.

However, our analysis also found that some high-altitude regions such as Lishan, noted for their valuable agricultural outputs, may present marginal conditions favorable for L. delicatula. These areas, therefore, demand targeted monitoring to prevent any accidental introduction and establishment that could severely impact local ecosystems and economies. The threat of competitive exclusion, particularly affecting native related species like L. meliae, which is already in decline,²¹ could be a potential concern, because the development of L. delicatula from first instars to adults can be supported by chinaberry (Melia azedarach L.),⁶⁰ the major host of L. meliae. Moreover, the introduction of L. delicatula could have a negative influence on the vineyards in Taiwan as grapes (Vitis spp.) are particularly vulnerable to this pest and could suffer significant production losses.⁶¹ Some major growing regions of grapes in Taiwan, such as Taichung and Nantou,⁶² are at high risk due to their overlap with the habitats of L. meliae and relatively higher suitability for L. delicatula in particular areas. These findings underscore the necessity for continuous monitoring and proactive biosecurity strategies to mitigate the potential future establishment of L. delicatula. Developing a comprehensive understanding of both the current ecological potential and the anticipated pathways of this invasive species is crucial to formulating effective protective measures against the dynamic threats posed by invasive species under evolving climatic conditions.

4.4 Reflections and future work

While both our practical surveys and ecological niche modeling indicated the absence of L. delicatula in Taiwan at this moment, the model revealed that partial higher-altitude areas such as Lishan, known for their unique microclimatic conditions and valuable agricultural yields,³² could potentially support the populations of L. delicatula if there is any accidental introduction in the future. As a result, it is crucial to maintain continuous monitoring and public awareness, especially in high-altitude agricultural zones. Multifactorial models that consider a range of ecological and human factors (e.g., Jones et al.¹⁰), are essential for accurately predicting potential invasion pathways and for establishing effective biosecurity measures. Future research should further explore how invasive species might adapt to new environments and assess their potential ecological impacts using advanced modeling techniques. This approach is essential for developing targeted strategies to protect Taiwan’s biodiversity and agricultural resources from invasive threats under shifting climatic conditions. On the other hand, this study emphasizes the importance of robust data validation and adaptive research strategies, as highlighted by contributions from citizen science initiatives such as iNaturalist.^26,27 Although these platforms play a crucial role in advancing scientific knowledge, their data must be approached with caution. For instance, problems like misidentifications can drastically distort species distribution models, potentially resulting in incorrect assessments of ecological impact.^63,64

This comprehensive study refutes the previous records of L. delicatula in Taiwan, marking a pivotal moment in our understanding of the geographic range of this invasive species. While L. delicatula poses significant risks to biodiversity and agriculture globally, our research, utilizing multidimensional approaches, conclusively demonstrates that Taiwan's current and projected climatic conditions are inhospitable for this species. The global occurrence data of L. delicatula and the ecological niche modeling for the species also made us cast doubts on previous distribution records in other tropical regions. Our findings highlight the indispensable role of accurate species identification and the preservation of voucher specimens in validating distribution records, emphasizing the importance of taxonomic expertise in ecological studies.

SUPPORTING INFORMATION

Supporting information may be found in the online version of this article.

ACKNOWLEDGEMENTS

Comprehensive survey of Taiwanese lanternfly was supported by the Ministry of Science and Technology, Taiwan (now National Science and Technology Council) under grant number MOST108-2621-B-002-005-MY3 and the Bureau of Animal and Plant Health Inspection and Quarantine, Taiwan (now Animal and Plant Health Inspection Agency) under grant number 109-RA-BQ-09. We thank Shiuh-Feng Shiao (NTU, Taiwan), Yun Hsiao (NTU, Taiwan), Hui-Yun Tseng (NTU, Taiwan), Yu-Feng Hsu (NTNU, Taiwan), Hsien-Tzung Shih (TARI, Taiwan), Katsuyuki Eguchi (Tokyo Metropolitan University, Japan), and Francesco Ballarin (Tokyo Metropolitan University, Japan) for their advices. We appreciate the museum curators Chi-Wei Tsai (NTU, Taiwan), Chi-Feng Lee (TARI, Taiwan), Jing-Fu Tsai (NMNS, Taiwan), Sheng-Feng Lin (NCHU, Taiwan), and Ling-Mu Jaung (TFRI, Taiwan) for their permission of examining specimens. We are grateful to Shun-Chern Tsaur (NTU, Taiwan), Yu-Hsiu Lin (NTU, Taiwan; TAMU, USA), Kai-Wei Chan (NTU, Taiwan), Ming-Chung Chiu (NTU, Taiwan), Ching-Feng Lin (Taiwan), Li-Cheng Shih (Taiwan), Bin-Hong Ho (NCHU, Taiwan), Jian-Yi Cho (NCHU, Taiwan), Yu-Hsiang Ho (NCHU, Taiwan) and Hsin-Ting Yeh (NTU, Taiwan) for their assistance with field surveys. We thank anonymous reviewers for help in improving the manuscript. The second author was supported by the YF2022 Japan Society for the Promotion of Science (JSPS KAKENHI n°22P22380) Postdoctoral Fellowships, Japan. This study is dedicated to the late Chiun-Cheng Ko (NTU, Taiwan) for his unwavering support to lanternfly study in Taiwan

Distant WL (1906) The fauna of British India, including Ceylon and Burma. Rhynchota. Vol. 3: Heteroptera–Homoptera. Taylor & Francis, London, p 503
Kato M (1931) Fulgoridae of Japan. Dobutsugaku Zasshi 43:595–598
Pham HT (2011) A checklist of the family Fulgoridae (Homoptera: Auchenorrhyncha: Fulgoroidea) from Vietnam. Proceedings of the 3rd National Scientific Conference on Ecology and Biological Resources Hanoi, 22 October 2009:317–321
Urban JM, Leach H (2023) Biology and management of the spotted lanternfly, Lycorma delicatula (Hemiptera: Fulgoridae), in the United States. Annu Rev Entomol 68:151–167
Barringer L, Ciafré CM (2020) Worldwide feeding host plants of spotted lanternfly, with significant additions from North America. Environ Entomol 49:999–1011
Leach H, Walsh B, Swackhamer E, Korman A (2021) Spotted lanternfly management for landscape professionals. PennState Extension, May 19. https://extension.psu.edu/spotted-lanternfly-management-for-landscape-professionals [accessed 2 September 2023]
Harper JK, Stone W, Kelsey TW, Kime LF (2019) Potential economic impact of the spotted lanternfly on agriculture and forestry in Pennsylvania. Rep. 84, Cent. Rural Pa., Harrisburg
Urban JM, Calvin D, Hills-Stevenson J, Early response (2021) (2018–2020) to the threat of spotted lanternfly, Lycorma delicatula (Hemiptera: Fulgoridae) in Pennsylvania. Ann Entomol Soc Am 114:709–718
Du Z, Wu Y, Chen Z, Cao L, Ishikawa T, Kamitani S, Sota T, Song F, Tian L, Cai W (2021) Global phylogeography and invasion history of the spotted lanternfly revealed by mitochondrial phylogenomics. Evol Appl 14:915–930
Jones C, Skrip MM, Seliger BJ, Jones S, Wakie T, Takeuchi Y, Petras V, Petrasova A, Meentemeyer RK (2022) Spotted lanternfly predicted to establish in California by 2033 without preventative management. Commun Biol 5:558
Charles H, Dukes JS (2007) Impacts of Invasive Species on Ecosystem Services. Biol Invasions 193:217–237
Kehoe R, Frago E, Sanders D (2020) Cascading extinctions as a hidden driver of insect decline. Ecol Entomol 46:743–756
Zina V, Branco M, Franco JC (2020) Impact of the invasive argentine ant in citrus agroecosystems: Effects on the diversity and frequency of native ant species foraging on tree canopy. Insects 11:1–17
Bellard C, Thuiller W, Leroy B, Genovesi P, Bakkenes M, Courchamp F (2013) Will climate change promote future invasions? Glob Change Biol 19:3740–3748
Bertelsmeier C, Guénard B, Courchamp F (2013) Climate Change May Boost the Invasion of the Asian Needle Ant. PLoS ONE 8:e75438
Bertelsmeier C, Luque GM, Hoffmann BD, Courchamp F (2015) Worldwide ant invasions under climate change. Biodivers Conserv 24:117–128
Sardain A, Sardain E, Leung B (2019) Global forecasts of shipping traffic and biological invasions to 2050. Nat Sustain 2:274–282
Hulme PE (2016) Climate change and biological invasions: evidence, expectations, and response options. Biol Rev 92:1297–1313
Chou I, Lu JS, Huang J, Wang SZ (1985) Economic insect fauna of China Fascicle 36. Homoptera, Fulgoroidea. Science, Beijing. (in Chinese)
Li LY, Wang R, Waterhouse DF (1997) The Distribution and Importance of Arthropod Pests and Weeds of Agriculture and Forestry Plantations in Southern China. Chinese Academy of Agricultural Sciences, Guangdong Entomological Institute, and Australian Centre for International Agricultural Research
Lin YS, Liao JR, Shiao SF, Ko CC (2023) Lanternflies (Hemiptera: Fulgoridae) of Taiwan. Zool Stud 62:07
Constant J, Pham HT (2017) Review of the clavatus group of the lanternfly genus Pyrops (Hemiptera: Fulgoromorpha: Fulgoridae). Eur J Taxon 305:1–26
Chao A, Hu K-H (2024) iNEXT.3D Online: interpolation and extrapolation for three dimensions of biodiversity. https://chao.shinyapps.io/iNEXT_3D
Chao A, Henderson PA, Chiu C-H, Moyes F, Hu K-H, Dornelas M, Magurran AE (2021) Measuring temporal change in alpha diversity: a framework integrating taxonomic, phylogenetic and functional diversity and the iNEXT.3D standardization. Methods Ecol Evol 12:1926–1940
QGIS Development Team, QGIS geographic information system Open Source Geospatial Foundation Project. http://qgis.osgeo.org/ [accessed 20 December 2023]
Nugent J (2018) iNaturalist: Citizen science for 21st-century naturalists. Sci Scope 41:12
Garretson A, Cuddy T, Duffy AG (2023) iNaturalist Citizen Scientists and Forkner RE, Citizen science data reveal regional heterogeneity in phenological response to climate in the large milkweed bug, Oncopeltus fasciatus. Ecol Evol 13:e10213
Kramer-Schadt S, Niedballa J, Pilgrim JD, Schröder B, Lindenborn J et al (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Diver Distrib 19:1366–1379
Merow C, Smith MJ, Silander, JAJr (2013) A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069
Bourgoin T, FLOW (Fulgoromorpha Lists on the Web) (2024) : a world knowledge base dedicated to Fulgoromorpha. Database, Sorbonne Univ., Paris, Version 8, updated January 4. http://flow.hemiptera-databases.org [accessed 20 January 2024]
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259
Liao JR, Chiu MC, Kuo MH (2023) Reassessing the presence of alien predatory mites and their prospects in the face of future climate change. Pest Manag Sci 79:5186–5196
Fick SE, Hijmans RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302–4315
Kelley M, Schmidt GA, Nazarenko LS, Bauer SE, Ruedy R, Russell GL, Ackerman AS, Aleinov I, Bauer M, Bleck R, Canuto V, Cesana G, Cheng Y, Clune TL, Cook BI, Cruz CA, Del Genio AD, Elsaesser GS, Faluvegi G, Kiang NY, Kim D, Lacis AA, Leboissetier A, LeGrande AN, Lo KK, Marshall J, Matthews EE, McDermid S, Mezuman K, Miller RL, Murray LT, Oinas V, Orbe C, García-Pando CP, Perlwitz JP, Puma MJ, Rind D, Romanou A, Shindell DT, Sun S, Tausnev N, Tsigaridis K, Tselioudis G, Weng E, Wu J, Yao M-S (2019) GISS-E2.1: Configurations and climatology. J Adv Model Earth Syst 12:eMS002025 (2020)
Nazarenko LS, Tausnev N, Russell GL, Rind D, Miller RL, Schmidt GA, Bauer SE, Kelley M, Ruedy R, Ackerman AS, Aleinov I, Bauer M, Bleck R, Canuto V, Cesana G, Cheng Y, Clune TL, Cook BI, Cruz CA, Del Genio AD, Elsaesser GS, Faluvegi G, Kiang NY, Kim D, Lacis AA, Leboissetier A, LeGrande AN, Lo KK, Marshall J, Matthews EE, McDermid S, Mezuman K, Murray LT, Oinas V, Orbe C, García-Pando CP, Perlwitz JP, Puma MJ, Romanou A, Shindell DT, Sun S, Tsigaridis K, Tselioudis G, Weng E, Wu J, Yao M-S (2021) Future climate change under SSP emission scenarios with GISS-E2.1. J Adv Model Earth Syst 14:eMS002871 (2022)
Amaro G, Fidelis EG, da Silva RS, Marchioro CA (2023) Effect of study area extent on the potential distribution of species: a case study with models for Raoiella indica Hirst (Acari: Tenuipalpidae). Ecol Modell 483:110454
Phillips SJ, Dudik M, Schapire RE (2023) Maxent software for modeling species niches and distribution (Version 3.4.4). https://biodiversityinformatics.amnh.org/open_source/maxent/ [accessed 20 December 2023]
Core Team R R: a language and environment for statistical computing. Vienna, Austria. https://www.R-project.org/. [accessed 20 September 2023]
RStudio, Team, RStudio: integrated development environment for R., Boston MA. http://www.rstudio.com/ [accessed 20 December 2023]
Hijmans RJ, Phillips S, Leathwick J, Elith J Package ‘dismo’. http://cran.r-project.org/web/packages/dismo/index.html [accessed 20 December 2023]
Thompson CG, Kim RS, Aloe AM, Becker BJ (2017) Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic Appl Soc Psych 39:81–90
Low BW, Zeng Y, Tan HH, Yeo DCJ (2021) Predictor complexity and feature selection affect Maxent model transferability: evidence from global freshwater invasive species. Divers Distrib 27:497–511
Kass JM, Muscarella R, Galante PJ, Bohl C, Buitrago-Pinilla GE, Boria RA, Soley-Guardia M, Anderson RP Package ‘ENMeval’. http://cran.r-project.org/web/packages/ENMeval/index.html [accessed 20 December 2023].
Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49
Franklin J (2010) Mapping species distributions: spatial inference and prediction. Cambridge University Press, UK
Peterson AT, Soberón J, Anderson RP, Pearson RG, Martínez-Meyer E, Nakamura M, Araújo MB (2011) Ecological niches and geographic distributions: a modeling perspective. Princeton University Press, Princeton, NJ, USA
Velasco JA, Gonzalez-Salazar C (2019) Akaike information criterion should not be a test of geographical prediction accuracy in ecological niche modelling. Ecol Inf 51:25–32
Hernandez PA, Graham C, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773–785
Warren DL, Seifert SN (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335–342
Boyce MS, Vernier PR, Nielsen SE, Schmiegelow FKA (2002) Evaluating resource selection functions. Ecol Model 157:281–300
Pearson RG, Raxworthy CJ, Nakamura M, Peterson AT (2006) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102–117
Peterson AT, Papeş M, Soberón J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Model 213:63–72
Liu C, White MT, Newell G (2013) Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr 40:778–789
Radosavljevi A, Anderson RP (2014) Making better Maxent models of species distributions: complexity, overfitting and evaluation. J Biogeog 41:629–643
Boria RA, Olson LE, Goodman SM, Anderson RP (2014) Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol Model 275:73–77
Yeh WB, Yang CT, Hui CF (2005) A molecular phylogeny of planthoppers (Hemiptetra: Fulgoridae) inferred from Mitochondrial 16S rDNA sequences. Zool Stud 44:519–535
Wakie TT, Neven LG, Yee WL, Lu Z (2019) The establishment risk of Lycorma delicatula (Hemiptera: Fulgoridae) in the United States and globally. J Eco Entomol 113:306–314
Jung JM, Jung S, Byeon DH, Lee WH (2017) Model-based prediction of potential distribution of the invasive insect pest, spotted lanternfly Lycorma delicatula (Hemiptera: Fulgoridae), by using CLIMEX. J Asia-Pac Biodivers 10:532–538
Namgung H, Kim MJ, Baek S, Lee JH, Kim H (2020) Predicting potential current distribution of Lycorma delicatula (Hemiptera: Fulgoridae) using MaxEnt model in South Korea. J Asia Pac Entomol 23:291–297
Murman K, Setliff GP, Pugh CV, Toolan MJ, Canlas I et al (2020) Distribution, survival, and development of spotted lanternfly on host plants found in North America. Environ Entomol 49:1270–1281
Leach A, Leach H (2020) Characterizing the spatial distributions of spotted lanternfly (Hemiptera: Fulgoridae) in Pennsylvania vineyards. Sci Rep 10:20588
Seo HJ, Yang SJ, Song JH, Ma KB, Chen IZ, Roan SF (2020) Current status and prospects of small fruit production in Taiwan. J Korean Soc Int Agric 32:31–37
Kholia BS, Fraser-Jenkins CR (2011) Misidentification makes scientific publications worthless – save our taxonomy and taxonomists. Curr Sci 100:458–461
Costa H, Foody GM, Jiménez S, Silva L (2015) Impacts of species misidentification on species distribution modeling with presence-only data. ISPRS Int J Geo-Inf 4:2496–2518

The authors declare no competing interests.

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Multifaceted Investigation into the Absence and Potential Invasion of Spotted Lanternfly (Lycorma delicatula) in Taiwan

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Abstract

Figures

1 INTRODUCTION

2 MATERIALS AND METHODS

2.1 Lanternfly survey and museum specimen examination

2.2 Prediction of habitat suitability of L. delicatula

2.2.1 Species occurrence dataset and environmental variables

2.2.2 Distribution modeling

2.2.3 Model validation

2.2.4 Visualization of habitat suitability for L. delicatula and related host plants

3 RESULTS

3.1 Survey findings and museum collection insights

3.2 Ecological niche model outcomes under current climatic scenarios

3.3 Ecological niche model outcomes under future climatic scenarios

4 DISCUSSION

4.1 Interpretation of survey and museum data

4.2 Ecological niche modeling reveals unitability and potential shift for Lycorma delicatula

4.3 Implications for Taiwan’s biodiversity and agriculture

4.4 Reflections and future work

5 CONCLUSION

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SUPPORTING INFORMATION

ACKNOWLEDGEMENTS

References

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