Two Species Delimitation of Pseudaulacaspis (Hemiptera: Diaspididae) Based on Morphology, Molecular Clustering, and Niche Differentiation
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Sample Collection
2.2. DNA Extraction and PCR
2.3. Sequence Analysis and Molecular Systematics
2.4. Species Occurrence Data
2.5. Climatic Variables
2.6. Morphometric Analyses
2.7. Climatic Niche Modeling
2.8. Model Evaluation
2.9. Niche Comparison
3. Results
3.1. Statistical Analyses of the Morphological Dataset
3.2. Phylogenetic Relationship
3.3. ENMeval Optimized Parameters and Model Performance
3.4. Impact Analysis of Key Climatic Variables
3.5. Comparison of Current Potential Distribution Areas and Niche
3.6. Redescription
3.6.1. Pseudaulacaspis Pentagona (Figure 5)
3.6.2. Pseudaulacaspis Prunicola (Figure 6)
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Oberemok, V.V.; Gal’chinsky, N.V.; Useinov, R.Z.; Novikov, I.A.; Puzanova, Y.V.; Filatov, R.I.; Kouakou, N.J.; Kouame, K.F.; Kra, K.D.; Laikova, K.V. Four Most Pathogenic Superfamilies of Insect Pests of Suborder Sternorrhyncha: Invisible Superplunderers of Plant Vitality. Insects 2023, 14, 462. [Google Scholar] [CrossRef] [PubMed]
- Normark, B.B.; Okusu, A.; Morse, G.E.; Peterson, D.A.; Itioka, T.; Schneider, S.A. Phylogeny and Classification of Armored Scale Insects (Hemiptera: Coccomorpha: Diaspididae). Zootaxa 2019, 4616, 1–98. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, D.; Niu, M.; Lu, Y.; Wei, J.; Zhang, H. Taxon-Specific Ultraconserved Element Probe Design for Phylogenetic Analyses of Scale Insects (Hemiptera: Sternorrhyncha: Coccoidea). Front. Ecol. Evol. 2022, 10, 984396. [Google Scholar] [CrossRef]
- García Morales, M.; Denno, B.D.; Miller, D.R.; Miller, G.L.; Ben-Dov, Y.; Hardy, N.B. ScaleNet: A Literature-Based Model of Scale Insect Biology and Systematics. Database 2016, 2016, bav118. [Google Scholar] [PubMed]
- Hanks, L.M.; Denno, R.F. Local Adaptation in the Armored Scale Insect Pseudaulacaspis pentagona (Homoptera: Diaspididae). Ecology 1994, 75, 2301–2310. [Google Scholar] [CrossRef]
- Hank, L.M.; Denno, R.F. The white peach scale, Pseudaulacaspis pentagona (Targioni-Tozzetti) (Homoptera: Diaspididae): Life history in Maryland, host plants, and natural enemies. Proc. Entomol. Soc. Wash. 1993, 95, 79–98. [Google Scholar]
- Ülgentürk, S.; Çanakçioğlu, H. Scale insect pests on ornamental plants in urban habitats in Turkey. J. Pest. Sci. 2004, 77, 79–84. [Google Scholar]
- Takeda, M. Effects of temperature on oviposition in overwintering females and hatch in first-generation larvae of Pseudaulacaspis pentagona (Hemiptera: Diaspididae). Appl. Entomol. Zool. 2004, 39, 15–26. [Google Scholar] [CrossRef]
- Abbasipour, H. Developmental time and fecundity of white peach scale Pseudaulacaspis pentagona (Targioni-Tozzetti) (Homoptera: Diaspididae), on potato, kiwi and mulberry hosts in Iran. Pak. J. Biol. Sci. 2007, 10, 3220–3223. [Google Scholar] [CrossRef]
- Erkiliç, L.B.; Uygun, N. Development time and fecundity of the white peach scale, Pseudaulacaspis pentagona, in Turkey. Phytoparasitica 1997, 25, 9–16. [Google Scholar] [CrossRef]
- Kosztarab, M. Armored scale insects: Their biology, natural enemies and control. Econ. Importance 1990, 4, 307–311. [Google Scholar]
- Lu, Y.; Zhao, Q.; Cheng, L.; Zhao, L.; Zhang, H.; Wei, J. The Potential Global Distribution of the White Peach Scale Pseudaulacaspis pentagona (Targioni Tozzetti) under Climate Change. Forests 2020, 11, 192. [Google Scholar] [CrossRef] [Green Version]
- Kawai, S. Scale Insects of Japan in Colors; Zenkoku Nôson Kyôiku Kyôkai: Tokyo, Japan, 1980; 455p. (In Japanese) [Google Scholar]
- Davidson, J.A.; Miller, D.R.; Nakahara, S. The White Peach Scale, Pseudaulacaspis pentagona (Targioni-Tozzetti) (Homoptera: Diaspididae): Evidence That Current Concepts Include Two Species. Proc. Entomol. Soc. Wash. 1983, 85, 753–761. [Google Scholar]
- Tang, F.T. The Scale Insects of Horticulture and Forest of China; Shanxi Agricultural University Press: Shanxi, China, 1986; Volume III, pp. 144–146. [Google Scholar]
- Hebert, P.D.N.; Cywinska, A.; Ball, S.L.; de Waard, J.R. Biological Identifications through DNA Barcodes. Proc. R. Soc. Lond. B 2003, 270, 313–321. [Google Scholar] [CrossRef] [PubMed]
- Dunn, C.P. Keeping Taxonomy Based in Morphology. Trends Ecol. Evol. 2003, 18, 270–271. [Google Scholar] [CrossRef]
- Hodgson, C.J.; Hardy, N.B. The Phylogeny of the Superfamily Coccoidea (Hemiptera: Sternorrhyncha) Based on the Morphology of Extant and Extinct Macropterous Males: Phylogeny of Coccoidea via Macropterous Males. Syst. Entomol. 2013, 38, 794–804. [Google Scholar] [CrossRef]
- Mongue, A.J.; Michaelides, S.; Coombe, O.; Tena, A.; Kim, D.; Normark, B.B.; Gardner, A.; Hoddle, M.S.; Ross, L. Sex, Males, and Hermaphrodites in the Scale Insect Icerya Purchasi *. Evolution 2021, 75, 2972–2983. [Google Scholar] [CrossRef]
- Panzavolta, T.; Bracalini, M.; Benigno, A.; Moricca, S. Alien Invasive Pathogens and Pests Harming Trees, Forests, and Plantations: Pathways, Global Consequences and Management. Forests 2021, 12, 1364. [Google Scholar] [CrossRef]
- Cavender-Bares, J.; Pahlich, A. Molecular, Morphological, and Ecological Niche Differentiation of Sympatric Sister Oak Species, Quercus Virginiana and Q. Geminata (Fagaceae). Am. J. Bot. 2009, 96, 1690–1702. [Google Scholar] [CrossRef] [Green Version]
- Li, Y.; Wen, J.; Ren, Y.; Zhang, J. From Seven to Three: Integrative Species Delimitation Supports Major Reduction in Species Number in Rhodiola Section Trifida (Crassulaceae) on the Qinghai-Tibetan Plateau. Taxon 2019, 68, 268–279. [Google Scholar] [CrossRef]
- Moradmand, M.; Yousefi, M. Ecological Niche Modelling and Climate Change in Two Species Groups of Huntsman Spider Genus Eusparassus in the Western Palearctic. Sci. Rep. 2022, 12, 4138. [Google Scholar] [CrossRef] [PubMed]
- Van Valen, L. Ecological Species, Multispecies, and Oaks. Taxon 1976, 25, 233–239. [Google Scholar] [CrossRef] [Green Version]
- Andersson, L. The Driving Force: Species Concepts and Ecology. Taxon 1990, 39, 375–382. [Google Scholar] [CrossRef]
- Park, D.-S.; Suh, S.-J.; Oh, H.-W.; Hebert, P.D. Recovery of the mitochondrial COI barcode region in diverse Hexapoda through tRNA-based primers. BMC Genom. 2010, 11, 423. [Google Scholar] [CrossRef] [Green Version]
- Koo, H.N.; Kim, S.; Lee, J.S.; Kang, W.J.; Cho, W.S.; Kyung, Y.; Seo, J.H.; Kim, H.K.; Cho, S. Pseudococcus orchidicola (Hemiptera: Pseudococcidae), a newly found mealybug pest, confused with P. longispinus in Korea. Entomol. Res. 2017, 47, 185–193. [Google Scholar] [CrossRef]
- Katoh, K.; Standley, D.M. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol. Biol. Evol. 2013, 30, 772–780. [Google Scholar] [CrossRef] [Green Version]
- Castresana, J. Selection of Conserved Blocks from Multiple Alignments for Their Use in Phylogenetic Analysis. Mol. Biol. Evol. 2000, 17, 540–552. [Google Scholar] [CrossRef] [Green Version]
- Nelson, L.A.; Wallman, J.F.; Dowton, M. Using COI Barcodes to Identify Forensically and Medically Important Blowflies. Med. Vet. Entomol. 2007, 21, 44–52. [Google Scholar] [CrossRef]
- Posada, D. JModelTest: Phylogenetic Model Averaging. Mol. Biol. Evol. 2008, 25, 1253–1256. [Google Scholar] [CrossRef]
- Kadmon, R.; Farber, O.; Danin, A. Effect of Roadside Bias on the Accuracy of Predictive Maps Produced by Bioclimatic Models. Ecol. Appl. 2004, 14, 401–413. [Google Scholar] [CrossRef]
- Rodríguez-Castañeda, G.; Hof, A.R.; Jansson, R.; Harding, L.E. Predicting the Fate of Biodiversity Using Species’ Distribution Models: Enhancing Model Comparability and Repeatability. PLoS ONE 2012, 7, e44402. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Skendžić, S.; Zovko, M.; Živković, I.P.; Lešić, V.; Lemić, D. The Impact of Climate Change on Agricultural Insect Pests. Insects 2021, 12, 440. [Google Scholar] [CrossRef]
- Hijmans, R.J.; Cameron, S.E.; Parra, J.L.; Jones, P.G.; Jarvis, A. Very High Resolution Interpolated Climate Surfaces for Global Land Areas. Int. J. Climatol. 2005, 25, 1965–1978. [Google Scholar] [CrossRef]
- Heikkinen, R.K.; Luoto, M.; Araújo, M.B.; Virkkala, R.; Thuiller, W.; Sykes, M.T. Methods and Uncertainties in Bioclimatic Envelope Modelling under Climate Change. Prog. Phys. Geog. 2006, 30, 751–777. [Google Scholar] [CrossRef] [Green Version]
- Bosso, L.; Di Febbraro, M.; Cristinzio, G.; Zoina, A.; Russo, D. Shedding Light on the Effects of Climate Change on the Potential Distribution of Xylella Fastidiosa in the Mediterranean Basin. Biol. Invasions 2016, 18, 1759–1768. [Google Scholar] [CrossRef]
- Vea, I.; Gwiazdowski, R.; Normark, B. Corroborating Molecular Species Discovery: Four New Pine-Feeding Species of Chionaspis (Hemiptera, Diaspididae). ZooKeys 2013, 270, 37–58. [Google Scholar] [CrossRef] [Green Version]
- Lê, S.; Josse, J.; Husson, F. FactoMineR: An R Package for Multivariate Analysis. J. Stat. Softw. 2008, 25, 1–18. [Google Scholar] [CrossRef] [Green Version]
- Lemenkova, P. K-Means Clustering in R Libraries {cluster} and {factoextra} for Grouping Oceanographic Data. IJIAM 2019, 2, 1–26. [Google Scholar]
- Ramirez-Villegas, J.; Cuesta, F.; Devenish, C.; Peralvo, M.; Jarvis, A.; Arnillas, C.A. Using Species Distributions Models for Designing Conservation Strategies of Tropical Andean Biodiversity under Climate Change. J. Nat. Conserv. 2014, 22, 391–404. [Google Scholar] [CrossRef] [Green Version]
- Johnson, K.M.; Peterson, A.T.; Lash, R.R.; Carroll, D.S. Geographic Potential for Outbreaks of Marburg Hemorrhagic Fever. Am. J. Trop. Med. Hyg. 2006, 75, 9–15. [Google Scholar]
- Samy, A.M.; Thomas, S.M.; Wahed, A.A.E.; Cohoon, K.P.; Peterson, A.T. Mapping the Global Geographic Potential of Zika Virus Spread. Mem. Inst. Oswaldo Cruz 2016, 111, 559–560. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Roura-Pascual, N.; Suarez, A.V.; Gómez, C.; Pons, P.; Touyama, Y.; Wild, A.L.; Peterson, A.T. Geographical Potential of Argentine Ants ( Linepithema Humile Mayr) in the Face of Global Climate Change. Proc. R. Soc. Lond. B 2004, 271, 2527–2535. [Google Scholar] [CrossRef]
- Kozak, K.H.; Graham, C.H.; Wiens, J.J. Integrating GIS-Based Environmental Data into Evolutionary Biology. Trends Ecol. Evol. 2008, 23, 141–148. [Google Scholar] [CrossRef]
- Stockwell, D.R.B.; Beach, J.H.; Stewart, A.; Vorontsov, G.; Vieglais, D.; Pereira, R.S. The Use of the GARP Genetic Algorithm and Internet Grid Computing in the Lifemapper World Atlas of Species Biodiversity. Ecol. Modell. 2006, 195, 139–145. [Google Scholar] [CrossRef] [Green Version]
- Farashi, A.; Kaboli, M.; Karami, M. Predicting Range Expansion of Invasive Raccoons in Northern Iran Using ENFA Model at Two Different Scales. Ecol. Inform. 2013, 15, 96–102. [Google Scholar] [CrossRef]
- De Brogniez, D.; Ballabio, C.; Stevens, A.; Jones, R.J.A.; Montanarella, L.; van Wesemael, B. A Map of the Topsoil Organic Carbon Content of Europe Generated by a Generalized Additive Model. Eur. J. Soil Sci. 2015, 66, 121–134. [Google Scholar] [CrossRef] [Green Version]
- Booth, T.H.; Nix, H.A.; Busby, J.R.; Hutchinson, M.F. Bioclim: The First Species Distribution Modelling Package, Its Early Applications and Relevance to Most Current MaxEnt Studies. Divers. Distrib. 2014, 20, 1–9. [Google Scholar] [CrossRef]
- Elith, J.; Graham, C.H.; Anderson, R.P.; Dudík, M.; Ferrier, S.; Guisan, A.; Hijmans, R.J.; Huettmann, F.; Leathwick, J.R.; Lehmann, A.; et al. Novel Methods Improve Prediction of Species’ Distributions from Occurrence Data. Ecography 2006, 29, 129–151. [Google Scholar] [CrossRef] [Green Version]
- Jung, J.-M.; Lee, W.-H.; Jung, S. Insect Distribution in Response to Climate Change Based on a Model: Review of Function and Use of CLIMEX: Review of CLIMEX Functions and Its Applications. Entomol. Res. 2016, 46, 223–235. [Google Scholar] [CrossRef]
- Phillips, S.J.; Dudík, M. Modeling of Species Distributions with Maxent: New Extensions and a Comprehensive Evaluation. Ecography 2008, 31, 161–175. [Google Scholar] [CrossRef]
- Warren, D.L.; Seifert, S.N. Ecological Niche Modeling in Maxent: The Importance of Model Complexity and the Performance of Model Selection Criteria. Ecol. Appl. 2011, 21, 335–342. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Muscarella, R.; Galante, P.J.; Soley-Guardia, M.; Boria, R.A.; Kass, J.M.; Uriarte, M.; Anderson, R.P. ENMeval: An R Package for Conducting Spatially Independent Evaluations and Estimating Optimal Model Complexity for Maxent Ecological Niche Models. Methods Ecol. Evol. 2014, 5, 1198–1205. [Google Scholar] [CrossRef] [Green Version]
- Wang, D.; Cui, B.; Duan, S.; Chen, J.; Fan, H.; Lu, B.; Zheng, J. Moving North in China: The Habitat of Pedicularis Kansuensis in the Context of Climate Change. Sci. Total Environ. 2019, 697, 133979. [Google Scholar] [CrossRef]
- Lobo, J.M.; Jiménez-Valverde, A.; Real, R. AUC: A Misleading Measure of the Performance of Predictive Distribution Models. Global Ecol. Biogeogr. 2008, 17, 145–151. [Google Scholar] [CrossRef]
- Peterson, A.T.; Papeş, M.; Soberón, J. Rethinking Receiver Operating Characteristic Analysis Applications in Ecological Niche Modeling. Ecol. Modell. 2008, 213, 63–72. [Google Scholar] [CrossRef]
- Tang, X.; Yuan, Y.; Liu, X.; Zhang, J. Potential Range Expansion and Niche Shift of the Invasive Hyphantria cunea between Native and Invasive Countries. Ecol. Entomol. 2021, 46, 910–925. [Google Scholar] [CrossRef]
- Di Cola, V.; Broennimann, O.; Petitpierre, B.; Randin, C.; Engler, R.; Dubuis, A.; D’Amen, M.; Pellissier, L.; Pottier, J.; Pio, D.; et al. Ecospat: An R Package to Support Spatial Analyses and Modeling of Species Niches and Distributions. Ecography 2017, 40, 774–787. [Google Scholar] [CrossRef]
- Miller, D.R.; Davidson, J.A. Armored Scale Insect Pests of Trees and Shrubs (Hemiptera: Diaspididae); Cornell University Press: Ithaca, NY, USA, 2005. [Google Scholar]
- McCabe, M.F.; Tester, M. Digital Insights: Bridging the Phenotype-to-Genotype Divide. J. Exp. Bot. 2021, 72, 2807–2810. [Google Scholar] [CrossRef]
- Wang, F.; Wang, D.; Guo, G.; Hu, Y.; Wei, J.; Liu, J. Species Delimitation of the Dermacentor Ticks Based on Phylogenetic Clustering and Niche Modeling. PeerJ 2019, 7, e6911. [Google Scholar] [CrossRef] [Green Version]
- Fan, X.K.; Wu, J.; Comes, H.P.; Feng, Y.; Wang, T.; Yang, S.Z.; Iwasaki, T.; Zhu, H.; Jiang, Y.; Lee, J.; et al. Phylogenomic, morphological, and niche differentiation analyses unveil species delimitation and evolutionary history of endangered maples in Acer series Campestria (Sapindaceae). J. Syst. Evol. 2023, 61, 287–298. [Google Scholar] [CrossRef]
- Hemami, M.R.; Khosravi, R.; Groves, C.; Ahmadi, M. Morphological diversity and ecological niche divergence in goitered and sand gazelles. Ecol. Evol. 2020, 10, 11535–11548. [Google Scholar] [CrossRef]
- Lin, H.; Gu, K.; Li, W.; Zhao, Y. Integrating Coalescent-based Species Delimitation with Ecological Niche Modeling Delimited Two Species within the Stewartia Sinensis Complex (Theaceae). J. Syst. Evol. 2022, 60, 1037–1048. [Google Scholar] [CrossRef]
- Sethusa, M.; Yessoufou, K.; Van der Bank, M.; Van der Bank, H.; Millar, I.; Jacobs, A. DNA Barcode Efficacy for the Identification of Economically Important Scale Insects (Hemiptera: Coccoidea) in South Africa. Afr. Entomol. 2014, 22, 257–266. [Google Scholar] [CrossRef]
- Morse, G.E.; Normark, B.B. A Molecular Phylogenetic Study of Armoured Scale Insects (Hemiptera: Diaspididae): Phylogeny of the Diaspididae. Syst. Entomol. 2005, 31, 338–349. [Google Scholar] [CrossRef]
- Andersen, J.C.; Wu, J.; Gruwell, M.E.; Gwiazdowski, R.; Santana, S.E.; Feliciano, N.M.; Morse, G.E.; Normark, B.B. A Phylogenetic Analysis of Armored Scale Insects (Hemiptera: Diaspididae), Based upon Nuclear, Mitochondrial, and Endosymbiont Gene Sequences. Mol. Phylogenet. Evol. 2010, 57, 992–1003. [Google Scholar] [CrossRef] [PubMed]
- Zajac, B.K.; Martin-Vega, D.; Feddern, N.; Fremdt, H.; e Castro, C.P.; Szpila, K.; Reckel, F.; Schütt, S.; Verhoff, M.A.; Amendt, J.; et al. Molecular Identification and Phylogenetic Analysis of the Forensically Important Family Piophilidae (Diptera) from Different European Locations. Forensic Sci. Int. 2016, 259, 77–84. [Google Scholar] [CrossRef] [PubMed]
- Lemoine, N.P. Climate Change May Alter Breeding Ground Distributions of Eastern Migratory Monarchs (Danaus Plexippus) via Range Expansion of Asclepias Host Plants. PLoS ONE 2015, 10, e0118614. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, J.; Fan, G.; He, Y. Predicting the Current and Future Distribution of Three Coptis Herbs in China under Climate Change Conditions, Using the MaxEnt Model and Chemical Analysis. Sci. Total Environ. 2020, 698, 134141. [Google Scholar] [CrossRef] [PubMed]
- Azrag, A.G.; Mohamed, S.A.; Ndlela, S.; Ekesi, S. Predicting the Habitat Suitability of the Invasive White Mango Scale, Aulacaspis tubercularis; Newstead, 1906 (Hemiptera: Diaspididae) Using Bioclimatic Variables. Pest Manag. Sci. 2022, 78, 4114–4126. [Google Scholar] [CrossRef] [PubMed]
- Wei, J.; Peng, L.; He, Z.; Lu, Y.; Wang, F. Potential Distribution of Two Invasive Pineapple Pests under Climate Change. Pest Manag. Sci. 2020, 76, 1652–1663. [Google Scholar] [CrossRef]
- De Queiroz, K. Species Concepts and Species Delimitation. Syst. Biol. 2007, 56, 879–886. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Population | Sampling Sites | Host Plant | Species | Date | Lon/Lat |
---|---|---|---|---|---|
Ingroup | |||||
1 | Taigu, Shanxi | Amygdalus persica | P. prunicola | April 2021 | 112.55/37.35 |
2 | Yinchuan, Ningxia | Amygdalus persica | P. prunicola | October 2021 | 106.26/38.47 |
3 | Linfen, Shanxi | Amygdalus persica | P. prunicola | September 2021 | 111.48/36.09 |
4 | Dingbian, Shaanxi | Cerasus jamasakura | P. prunicola | July 2021 | 107.5/37.58 |
5 | Taiyuan, Shanxi | Amygdalus persica | P. prunicola | August 2021 | 112.5405/37.93527 |
6 | Fuzhou, Fujian | Aucuba japonica | P. pentagona | July 2021 | 119.311353/26.066559 |
Outgroup | |||||
1 | Wanning, Hainan | Trachycarpus fortunei | C. aonidum | June 2022 | 110.394186/18.803442 |
Species | Geographic Origin | Host Plant | Number of Individuals |
---|---|---|---|
P. pentagona | Fuzhou, Fujian | Trachycarpus fortunei | 6 |
Kunming, Yunnan | Phoebe zhennan | 5 | |
Nanning, Guangxi | Hibiscus mutabilis | 2 | |
Wuhan, Hubei | Trachycarpus fortunei | 4 | |
Dalian, Liaoning | Fraxinus rhynchophylla | 4 | |
Shatou, Guangzhou | Ricinus communis | 3 | |
Emeishan, Sichuan | Cycas revoluta | 3 | |
Emeishan, Sichuan | Sapium sebiferum | 2 | |
The Summer Palace, Beijing | Prunus armeniaca | 4 | |
Tianjin | Ailanthus altissima | 1 | |
Huangyan, Zhejiang | Ricinus communis | 3 | |
Hangzhou, Zhejiang | Morus alba | 3 | |
Guangzhou, Guangdong | Morus alba | 2 | |
Shuyang, Fujian | Prunus persica | 3 | |
Taiyuan, Shanxi | Osmanthus fragrans | 5 | |
P. prunicola | Taigu, Shanxi | Amygdalus persica | 3 |
Yinchuan, Ningxia | Amygdalus persica | 3 | |
Linfen, Shanxi | Amygdalus persica | 3 | |
Dingbian, Shaanxi | Cerasus jamasakura | 3 | |
Taiyuan, Shanxi | Amygdalus persica | 3 | |
Shanxi Agricultural University | Prunus davidiana | 3 | |
Baotou, Neimenggu | Prunus spp. | 4 | |
Kunming, Yunnan | Rhododendron simsii | 3 | |
Kunming, Yunnan | Firmiana simplex | 2 | |
Kunming, Yunnan | Camellia sinensis | 1 | |
Kunming, Yunnan | Sapium sebiferum | 2 | |
Kunming, Yunnan | Ligustrum lucidum | 3 | |
Guangdong Botanical Garden | Phoebe zhennan | 3 | |
Huangyan, Zhejiang | Prunus persica | 5 | |
Lanzhou, Gansu | Ulmus pumila | 3 | |
Chengdu, Sichuan | Osmanthus | 2 | |
Hangzhou, Zhejiang | Prunus subgen.Cerasus | 3 | |
Liaoning | Syringa oblate Lindl. | 3 | |
Tianjin | Cerasus sargentii | 3 | |
Emeishan, Sichuan | Prunus davidiana | 2 | |
Suzhou, Jiangsu | Prunus mume | 3 |
Variables | PC1 | PC2 |
---|---|---|
% of variance | 25.3% | 18.6% |
the number of disc pores | 0.015436 | / |
The number of dorsal macroducts in abdomen segment Ⅱ | −0.24286 | −0.30082 |
The number of dorsal macroducts in abdomen segment Ⅲ | −0.25793 | −0.29952 |
The number of dorsal macroducts in abdomen segment Ⅳ | −0.21502 | −0.31464 |
The number of dorsal macroducts in abdomen segment Ⅴ | −0.25672 | −0.20047 |
The number of dorsal macroducts in abdomen segment Ⅵ | −0.03553 | −0.09817 |
The number of perivulvar pores | −0.16445 | / |
The number of gland spines in the first space | 0.044814 | −0.04746 |
First space gland spine branched or not | 0.151556 | −0.25767 |
The number of gland spines in the second space | 0.016068 | 0.032757 |
Second space gland spine branched or not | 0.193984 | −0.30188 |
The number of gland spines in the third space | 0.105998 | −0.2109 |
Third space gland spine branched or not | 0.21454 | −0.31626 |
The number of gland spines in the fourth space | 0.087949 | −0.32941 |
Fourth space gland spine branched or not | 0.211909 | −0.28993 |
Insect body width | −0.22905 | −0.09553 |
Insect body length | −0.2585 | −0.09193 |
The ratio of insect body width: length | 0.00973 | −0.0246 |
Distance of anal opening to L1 | −0.33608 | −0.04948 |
The ratio of anal opening to L1: insect body length | 0.097384 | 0.088406 |
L1 width | −0.21788 | 0.000709 |
Distance between L1 | −0.25517 | 0.197385 |
The ratio of width between L1: L1 width | −0.16028 | 0.266761 |
Distance between antennae | −0.21577 | 0.084799 |
Climatic Variables | P. pentagona | P. prunicola |
---|---|---|
Mean diurnal range (Bio2) | 11.9 | 24.6 |
Isothermality (Bio3) | 35.4 | 23.1 |
Mean temperature of wettest quarter (Bio8) | 34.8 | 45.7 |
Precipitation seasonality (Bio15) | 18.2 | 4.6 |
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Lu, Y.; Deng, S.; Niu, M.; Li, H.; Zhao, Q.; Zhang, H.; Wei, J. Two Species Delimitation of Pseudaulacaspis (Hemiptera: Diaspididae) Based on Morphology, Molecular Clustering, and Niche Differentiation. Insects 2023, 14, 666. https://doi.org/10.3390/insects14080666
Lu Y, Deng S, Niu M, Li H, Zhao Q, Zhang H, Wei J. Two Species Delimitation of Pseudaulacaspis (Hemiptera: Diaspididae) Based on Morphology, Molecular Clustering, and Niche Differentiation. Insects. 2023; 14(8):666. https://doi.org/10.3390/insects14080666
Chicago/Turabian StyleLu, Yunyun, Shuqun Deng, Minmin Niu, Huiping Li, Qing Zhao, Hufang Zhang, and Jiufeng Wei. 2023. "Two Species Delimitation of Pseudaulacaspis (Hemiptera: Diaspididae) Based on Morphology, Molecular Clustering, and Niche Differentiation" Insects 14, no. 8: 666. https://doi.org/10.3390/insects14080666