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Risk of spread of the Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) in Ghana

Published online by Cambridge University Press:  03 May 2024

Kodwo Dadzie Ninsin
Affiliation:
Department of Biological Sciences, School of Natural and Environmental Sciences, University of Environment and Sustainable Development, PMB, Somanya, E/R, Ghana
Philipe Guilherme Corcino Souza
Affiliation:
Department of Agronomy, Instituto Federal de Ciência e Tecnologia do Triângulo Mineiro (IFTM Campus Uberlândia), Uberlândia, MG 38400-970, Brazil
George Correa Amaro
Affiliation:
Embrapa Roraima, Boa Vista, Roraima 69301-970, Brazil
Owusu Fordjour Aidoo*
Affiliation:
Department of Biological Sciences, School of Natural and Environmental Sciences, University of Environment and Sustainable Development, PMB, Somanya, E/R, Ghana Department of Entomology, College of Agricultural, Human, and Natural Resource Sciences, Washington State University, Pullman, WA 99164, USA
Edmond Joseph Djibril Victor Barry
Affiliation:
Department of Agronomy, Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM), Diamantina, MG 39100-000, Brazil
Ricardo Siqueira da Silva
Affiliation:
Department of Agronomy, Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM), Diamantina, MG 39100-000, Brazil
Jonathan Osei-Owusu
Affiliation:
Department of Physical and Mathematical Sciences, School of Natural and Environmental Sciences, University of Environment and Sustainable Development, PMB, Somanya, E/R, Ghana
Aboagye Kwarteng Dofuor
Affiliation:
Department of Biological Sciences, School of Natural and Environmental Sciences, University of Environment and Sustainable Development, PMB, Somanya, E/R, Ghana
Fred Kormla Ablormeti
Affiliation:
Council for Scientific and Industrial Research (CSIR), P. O. Box 245, Sekondi, W/R, Ghana
William K. Heve
Affiliation:
Department of Biological Sciences, School of Natural and Environmental Sciences, University of Environment and Sustainable Development, PMB, Somanya, E/R, Ghana
George Edusei
Affiliation:
Department of Physical and Mathematical Sciences, School of Natural and Environmental Sciences, University of Environment and Sustainable Development, PMB, Somanya, E/R, Ghana
Lakpo Koku Agboyi
Affiliation:
Centre for Agriculture and Biosciences International (CABI), CSIR Campus, No. 6 Agostino Neto Road, Airport Residential Area, P. O. Box CT 8630, Cantonments, Ghana
Patrick Beseh
Affiliation:
Plant Protection and Regulatory Services Directorate. P. O. Box M37, Accra, Ghana
Hettie Arwoh Boafo
Affiliation:
Centre for Agriculture and Biosciences International (CABI), CSIR Campus, No. 6 Agostino Neto Road, Airport Residential Area, P. O. Box CT 8630, Cantonments, Ghana
Christian Borgemeister
Affiliation:
Centre for Development Research (ZEF), University of Bonn, Genscherallee 3, 53113 Bonn, Germany
Mamoudou Sétamou
Affiliation:
Citrus Center, Texas A & M University-Kingsville, 312 N. International Blvd., Weslaco, TX 78599, USA
*
Corresponding author: Owusu Fordjour Aidoo; Email: ofaidoo@uesd.edu.gh; owusufordjour.aidoo@wsu.edu

Abstract

The impact of invasive species on biodiversity, food security and economy is increasingly noticeable in various regions of the globe as a consequence of climate change. Yet, there is limited research on how climate change affects the distribution of the invasive Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera:Liviidae) in Ghana. Using maxnet package to fit the Maxent model in R software, we answered the following questions; (i) what are the main drivers for D. citri distribution, (ii) what are the D. citri-specific habitat requirements and (iii) how well do the risk maps fit with what we know to be correctly based on the available evidence?. We found that temperature seasonality (Bio04), mean temperature of warmest quarter (Bio10), precipitation of driest quarter (Bio17), moderate resolution imaging spectroradiometer land cover and precipitation seasonality (Bio15), were the most important drivers of D. citri distribution. The results follow the known distribution records of the pest with potential expansion of habitat suitability in the future. Because many invasive species, including D. citri, can adapt to the changing climates, our findings can serve as a guide for surveillance, tracking and prevention of D. citri spread in Ghana.

Type
Research Paper
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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References

Aarts, G, Fieberg, J and Matthiopoulos, J (2012) Comparative interpretation of count, presence–absence and point methods for species distribution models. Methods in Ecology and Evolution 3, 177187.10.1111/j.2041-210X.2011.00141.xCrossRefGoogle Scholar
Aidoo, OF, Tanga, CM, Mohamed, SA, Rasowo, BA, Khamis, FM, Rwomushana, I, Kimani, J, Agyakwa, AK, Daisy, S, Sétamou, M, Ekesi, S and Borgemeister, C (2019) Distribution, degree of damage and risk of spread of Trioza erytreae (Hemiptera: Triozidae) in Kenya. Journal of Applied Entomology 143, 822833.10.1111/jen.12668CrossRefGoogle Scholar
Aidoo, OF, Cunze, S, Guimapi, RA, Arhin, L, Ablormeti, FK, Tettey, E, Dampare, F, Afram, Y, Bonsu, O, Obeng, J, Lutuf, H, Dickinson, M and Yankey, N (2021) Lethal yellowing disease: insights from predicting potential distribution under different climate change scenarios. Journal of Plant Diseases and Protection 128, 13131325.10.1007/s41348-021-00488-1CrossRefGoogle Scholar
Aidoo, OF, Souza, PG, da Silva, RS, Santana, PA Jr., Picanço, MC, Kyerematen, R, Sètamou, M, Ekesi, S and Borgemeister, C (2022) Climate-induced range shifts of invasive species (Diaphorina citri Kuwayama). Pest Management Science 78, 25342549.10.1002/ps.6886CrossRefGoogle ScholarPubMed
Aidoo, OF, Souza, PG, Silva, RS, Júnior, PA, Picanço, MC, Heve, WK, Duker, RQ, Ablormeti, FK, Sétamou, M and Borgemeister, C (2023a) Modeling climate change impacts on potential global distribution of Tamarixia radiata Waterston (Hymenoptera: Eulophidae). Science of The Total Environment 864, 160962.10.1016/j.scitotenv.2022.160962CrossRefGoogle ScholarPubMed
Aidoo, OF, Ablormeti, FK, Ninsin, KD, Antwi-Agyakwa, AK, Osei-Owusu, J, Heve, WK, Dofuor, AK, Soto, YL, Edusei, G, Osabutey, AF, Sossah, FL, Aryee, CO, Alabi, OJ and Sétamou, M (2023b) First report on the presence of huanglongbing vectors (Diaphorina citri and Trioza erytreae) in Ghana. Scientific Reports 13, 11366.10.1038/s41598-023-37625-9CrossRefGoogle ScholarPubMed
Allouche, O, Tsoar, A and Kadmon, R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal Applied Ecology 43, 12231232.10.1111/j.1365-2664.2006.01214.xCrossRefGoogle Scholar
Amaro, G, Fidelis, EG, da Silva, RS and 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). Ecological Modelling 483, 110454.10.1016/j.ecolmodel.2023.110454CrossRefGoogle Scholar
Anderson, RP and Raza, A (2010) The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela: effect of study region on models of distributions. Journal of Biogeography 37, 13781393.10.1111/j.1365-2699.2010.02290.xCrossRefGoogle Scholar
Antolínez, CA, Olarte-Castillo, XA, Martini, X and Rivera, MJ (2022) Influence of daily temperature maximums on the development and short-distance movement of the Asian citrus psyllid. Journal of Thermal Biology 110, 103354.10.1016/j.jtherbio.2022.103354CrossRefGoogle ScholarPubMed
Araújo, MB and Guisan, A (2006) Five (or so) challenges for species distribution modelling. Journal Biogeography 33, 16771688.10.1111/j.1365-2699.2006.01584.xCrossRefGoogle Scholar
Araújo, MB, Anderson, RP, Márcia Barbosa, A, Beale, CM, Dormann, CF, Early, R, Garcia, RA, Guisan, A, Maiorano, L, Naimi, B, O'Hara, RB, Zimmermann, NE and Rahbek, C (2019) Standards for distribution models in biodiversity assessments. Science Advances 5, eaat4858.10.1126/sciadv.aat4858CrossRefGoogle ScholarPubMed
Asare-Bediako, E, Addo-Quaye, AA, Tetteh, JP, Buah, JN, Van Der Puije, GC and Acheampong, RA (2013) Prevalence of mistletoe on citrus trees in the Abura-Asebu-Kwamankese district of the Central Region of Ghana. International Journal of Scientific & Technology Research 2, 122127.Google Scholar
Austin, MP and Van Niel, KP (2011) Improving species distribution models for climate change studies: variable selection and scale. Journal of Biogeography 38, 18.10.1111/j.1365-2699.2010.02416.xCrossRefGoogle Scholar
Barbet-Massin, M, Jiguet, F, Albert, CH and Thuiller, W (2012) Selecting pseudo-absences for species distribution models: how, where and how many?: how to use pseudo-absences in niche modelling? Methods in Ecology and Evolution 3, 327338.10.1111/j.2041-210X.2011.00172.xCrossRefGoogle Scholar
Barrett, SCH (2000) Microevolutionary influences of global change on plant invasions. In Mooney, HA and Hobbs, RK (eds), The Impact of Global Change on Invasive Species. Covelo, CA: Island Press, pp. 115139.Google Scholar
Barve, N, Barve, V, Jiménez-Valverde, A, Lira-Noriega, A, Maher, SP, Peterson, AT, Soberón, J and Villalobos, F (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling 222, 18101819.10.1016/j.ecolmodel.2011.02.011CrossRefGoogle Scholar
Bayles, BR, Thomas, SM, Simmons, GS, Grafton-Cardwell, EE and Daugherty, MP (2017) Spatiotemporal dynamics of the Southern California Asian citrus psyllid (Diaphorina citri) invasion. PLoS ONE 12, e0173226.10.1371/journal.pone.0173226CrossRefGoogle ScholarPubMed
Beattie, GA (2020) 12 Management of the Asian Citrus Psyllid in Asia. In Asian Citrus Psyllid: Biology, Ecology and Management of the Huanglongbing Vector, p. 179.Google Scholar
Beaumont, LJ, Gallagher, RV, Thuiller, W, Downey, PO, Leishman, MR and Hughes, L (2009) Different climatic envelopes among invasive populations may lead to underestimations of current and future bio- logical invasions. Diversity and Distribution, 15, 409420.10.1111/j.1472-4642.2008.00547.xCrossRefGoogle Scholar
Bové, JM (2006) Huanglongbing: a destructive, newly-emerging, century-old disease of citrus. Journal of Plant Pathology 88, 737.Google Scholar
Bové, JM (2014) Huanglongbing or yellow shoot, a disease of Gondwanan origin: will it destroy citrus worldwide? Phytoparasitica 42, 579583.10.1007/s12600-014-0415-4CrossRefGoogle Scholar
Bradley, AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30, 11451159.10.1016/S0031-3203(96)00142-2CrossRefGoogle Scholar
Brentu, FC, Oduro, KA, Offei, SK, Odamtten, GT, Vicent, A, Peres, NA and Timmer, LW (2012) Crop loss, aetiology, and epidemiology of citrus black spot in Ghana. European Journal of Plant Pathology 133, 657670.10.1007/s10658-012-9944-1CrossRefGoogle Scholar
Broennimann, O and Guisan, A (2008) Predicting current and future biological invasions: both native and invaded ranges matter. Biology Letters, 4, 585589.10.1098/rsbl.2008.0254CrossRefGoogle ScholarPubMed
Broennimann, O, Fitzpatrick, MC, Pearman, PB, Petitpierre, B, Pellissier, L, Yoccoz, NG, Thuiller, W, Fortin, M-J, Randin, C, Zimmermann, NE, Graham, CH and Guisan, A (2012) Measuring ecological niche overlap from occurrence and spatial environmental data: measuring niche overlap. Global Ecology and Biogeography 21, 481497.10.1111/j.1466-8238.2011.00698.xCrossRefGoogle Scholar
Broennimann, O, Di Cola, V and Guisan, A (2022) ecospat: spatial ecology miscellaneous methods, R package ver. 3.2.1. https://CRAN.R-project.org/package=ecospatGoogle Scholar
Castellanos, AA, Huntley, JW, Voelker, G and Lawing, AM (2019) Environmental filtering improves ecological niche models across multiple scales. Methods in Ecology and Evolution, 10, 481492.10.1111/2041-210X.13142CrossRefGoogle Scholar
Chamberlain, S, Barve, V, Mcglinn, D, Oldoni, D, Desmet, P, Geffert, L and Ram, K (2023) rgbif: Interface to the Global Biodiversity Information Facility API. Website: https://CRAN.R-project.org/package=rgbif (accessed 13 March 2024).Google Scholar
Cooper, JC and Soberón, J (2018) Creating individual accessible area hypotheses improves stacked species distribution model performance. Global Ecology and Biogeography 27, 156165.10.1111/geb.12678CrossRefGoogle Scholar
Devi, HS and Sharma, DR (2014) Impact of abiotic factors on build-up of citrus psylla, Diaphorina citri Kuwayama population in Punjab, India. Journal of Applied and Natural Science 6, 371376.10.31018/jans.v6i2.430CrossRefGoogle Scholar
Dray, S and Dufour, A-B (2007) The ade4 package: implementing the duality diagram for ecologists. Journal of Statistical Software 22, 120.10.18637/jss.v022.i04CrossRefGoogle Scholar
Elith, J, Graham, CH, Anderson, RP, Dudik, M, Ferrier, S, Guisan, A, Hijmans, RJ, Huettmann, F, Leathwick, JR, Lehmann, A, Li, J, Lohmann, LG, Loiselle, BA, Manion, G, Moritz, C, Nakamura, M, Nakazawa, Y, Overton, JM, Peterson, AT, Phillips, SJ, Richardson, K, Scachetti-Pereira, R, Schapire, RE, Soberon, J, Williams, S, Wisz, MS and Zimmermann, NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129151.10.1111/j.2006.0906-7590.04596.xCrossRefGoogle Scholar
Elith, J, Phillips, SJ, Hastie, T, Dudik, M, Chee, YE and Yates, CJ (2011) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17, 4357.10.1111/j.1472-4642.2010.00725.xCrossRefGoogle Scholar
Fick, SE and Hijmans, RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37, 43024315.10.1002/joc.5086CrossRefGoogle Scholar
Finch, DM, Butler, JL, Runyon, JB, Fettig, CJ, Kilkenny, FF, Jose, S, Frankel, SJ, Cushman, SA, Cobb, RC, Dukes, JS, Hicke, JA and Amelon, SK (2021) Effects of climate change on invasive species. Invasive species in forests and rangelands of the United States: a comprehensive science synthesis for the United States forest sector, 57-83.10.1007/978-3-030-45367-1_4CrossRefGoogle Scholar
Fithian, W and Hastie, T (2013) Finite-sample equivalence in statistical models for presence-only data. The Annals of Applied Statistics 7, 1917.10.1214/13-AOAS667CrossRefGoogle ScholarPubMed
Fitzpatrick, MC and Hargrove, WW (2009) The projection of species distribution models and the problem of non-analog climate. Biodiversity and Conservation 18, 22552261.10.1007/s10531-009-9584-8CrossRefGoogle Scholar
Friedl, M and Sulla-Menashe, D (2022) MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2023-10-11 from https://doi.org/10.5067/MODIS/MCD12C1.061CrossRefGoogle Scholar
Friedman, J, Hastie, T and Tibshirani, R (2010) Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33, 1.10.18637/jss.v033.i01CrossRefGoogle ScholarPubMed
Guillera-Arroita, G, Lahoz-Monfort, JJ, Elith, J, Gordon, A, Kujala, H, Lentini, PE, McCarthy, MA, Tingley, R and Wintle, BA (2015) Is my species distribution model fit for purpose? Matching data and models to applications: matching distribution models to applications. Global Ecology and Biogeography 24, 276292.10.1111/geb.12268CrossRefGoogle Scholar
Guisan, A, Petitpierre, B, Broennimann, O, Daehler, C and Kueffer, C (2014) Unifying niche shift studies: insights from biological invasions. Trends in Ecology and Evolution 29, 260269.10.1016/j.tree.2014.02.009CrossRefGoogle ScholarPubMed
Hall, DG, Wenninger, EJ and Hentz, MG (2011) Temperature studies with the Asian citrus psyllid, Diaphorina citri: cold hardiness and temperature thresholds for oviposition. Journal of Insect Science 11, 83.10.1673/031.011.8301CrossRefGoogle ScholarPubMed
Hall, DG, Hentz, MG, Meyer, JM, Kriss, AB, Gottwald, TR and Boucias, DG (2012) Observations on the entomopathogenic fungus Hirsutella citriformis attacking adult Diaphorina citri (Hemiptera: Psyllidae) in a managed citrus grove. BioControl 57, 663675.10.1007/s10526-012-9448-0CrossRefGoogle Scholar
Hall, DG, Richardson, ML, Ammar, ED and Halbert, SE (2013) Asian citrus psyllid, Diaphorina citri, vector of citrus huanglongbing disease. Entomologia Experimentalis et Applicata 146, 207223.10.1111/eea.12025CrossRefGoogle Scholar
Heikkinen, RK, Marmion, M and Luoto, M (2012) Does the interpolation accuracy of species distribution models come at the expense of transfer- ability? Ecography (Cop.) 35, 276288.10.1111/j.1600-0587.2011.06999.xCrossRefGoogle Scholar
Helmstetter, NA, Conway, CJ, Stevens, BS and Goldberg, AR (2021) Balancing transferability and complexity of species distribution models for rare species conservation. Diversity and Distributions 27, 95108.10.1111/ddi.13174CrossRefGoogle Scholar
Hijmans, RJ (2012) Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology 93, 679688.10.1890/11-0826.1CrossRefGoogle ScholarPubMed
Hijmans, RJ (2023) terra: Spatial data analysis. https://CRAN.R-project.org/package=terraGoogle Scholar
Hijmans, RJ, Barbosa, M, Ghosh, A and Mandel, A (2023a) geodata: Download geographic data.Google Scholar
Hill, MP, Gallardo, B, Terblanche, JS (2017) A global assessment of climatic niche shifts and human influence in insect invasions: HILL et al. Global Ecology Biogeography 26, 679689.10.1111/geb.12578CrossRefGoogle Scholar
Hosmer, DW, Lemeshow, S and Sturdivant, RX (2013) Applied Logistic Regression, Applied Logistic Regression, 3rd Edn. Wiley. https://doi.org/10.1002/9781118548387. Wiley series in probability and statistics.CrossRefGoogle Scholar
Jarnevich, CS, Stohlgren, TJ, Kumar, S, Morisette, JT and Holcombe, TR (2015) Caveats for correlative species distribution modeling. Ecological Informatics 29, 615.10.1016/j.ecoinf.2015.06.007CrossRefGoogle Scholar
Jiang, Y, Metz, CE and Nishikawa, RM (1996) A receiver operating characteristic partial area index for highly sensitive diagnostic tests. Radiology 201, 745750.10.1148/radiology.201.3.8939225CrossRefGoogle ScholarPubMed
Kassambara, A and Mundt, F (2022) factoextra: Extract and visualize the results of multivariate data analyses (1.0.7) [R]. https://cran.rproject.org/web/packages/factoextra/index.htmlGoogle Scholar
Khan, AM, Li, Q, Saqib, Z, Khan, N, Habib, T, Khalid, N, Majeed, M and Tariq, A (2022) MaxEnt modelling and impact of climate change on habitat suitability variations of economically important chilgoza pine (Pinus gerardiana wall.) in South Asia. Forests 13, 715.10.3390/f13050715CrossRefGoogle Scholar
Khosravi, R, Hemani, MR, Malekian, M, Flint, A and Flint, L (2016) Maxent modeling for predicting potential distribution of goitered gazelle in central Iran: the effect of extent and grain size on performance of the model. Turkish Journal of Zoology 40, 574585.10.3906/zoo-1505-38CrossRefGoogle Scholar
Landis, JR and Koch, GG (1977) The measurement of observer agreement for categorical data. Biometrics 33, 159.10.2307/2529310CrossRefGoogle ScholarPubMed
Lawson, CR, Hodgson, JA, Wilson, RJ and Richards, SA (2014) Prevalence, thresholds and the performance of presence–absence models. Methods in Ecology and Evolution 5, 5464.10.1111/2041-210X.12123CrossRefGoogle Scholar
Lee, JA, Halbert, SE, Dawson, WO, Robertson, CJ, Keesling, JE and Singer, BH (2015) Asymptomatic spread of huanglongbing and implications for disease control. Proceedings of the National Academy of Sciences, 112, 76057610.10.1073/pnas.1508253112CrossRefGoogle ScholarPubMed
Liu, C, Berry, PM, Dawson, TP and Pearson, RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28, 385393.10.1111/j.0906-7590.2005.03957.xCrossRefGoogle Scholar
Liu, C, White, M and Newell, G (2013) Selecting thresholds for the prediction of species occurrence with presence-only data. Journal of Biogeography 40, 778789.10.1111/jbi.12058CrossRefGoogle Scholar
Liu, C, Newell, G and White, M (2016) On the selection of thresholds for predicting species occurrence with presence-only data. Ecology and Evolution 6, 337348.10.1002/ece3.1878CrossRefGoogle ScholarPubMed
López-Collado, J, López-Arroyo, JI, Robles-García, PL and Márquez-Santos, M (2013) Geographic distribution of habitat, development, and population growth rates of the Asian citrus psyllid, Diaphorina citri, in Mexico. Journal of Insect Science 13.10.1673/031.013.11401CrossRefGoogle ScholarPubMed
Low, BW, Zeng, Y, Tan, HH and Yeo, DC (2021) Predictor complexity and feature selection affect Maxent model transferability: evidence from global freshwater invasive species. Diversity and Distributions 27, 497511.10.1111/ddi.13211CrossRefGoogle Scholar
Luo, Y and Agnarsson, I (2018) Global mt DNA genetic structure and hypothesized invasion history of a major pest of citrus, Diaphorina citri (Hemiptera: Liviidae). Ecology and Evolution, 8, 257265.10.1002/ece3.3680CrossRefGoogle Scholar
Machado-Stredel, F, Cobos, ME and Peterson, AT (2021) A simulation-based method for selecting calibration areas for ecological niche models and species distribution models. Frontiers of Biogeography 13. https://doi.org/10.21425/F5FBG48814.CrossRefGoogle Scholar
Massicotte, P and South, A (2023) rnaturalearth: World Map Data from Natural Earth. R package version 0.3.3.9000. Available from https://docs.ropensci.org/rnaturalearth/index.html (accessed April 2023).Google Scholar
McClish, DK (1989) Analyzing a portion of the ROC curve. Medical Decision Making 9, 190195.10.1177/0272989X8900900307CrossRefGoogle ScholarPubMed
Merow, C, Smith, MJ and Silander, JA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography (Cop.). 36, 10581069.10.1111/j.1600-0587.2013.07872.xCrossRefGoogle Scholar
Milosavljević, I, McCalla, KA, Morgan, DJ and Hoddle, MS (2020) The effects of constant and fluctuating temperatures on development of Diaphorina citri (Hemiptera: Liviidae), the Asian citrus psyllid. Journal of Economic Entomology 113, 633645.10.1093/jee/toz320CrossRefGoogle ScholarPubMed
Naeem, A, Freed, S, Jin, FL, Akmal, M and Mehmood, M (2016) Monitoring of insecticide resistance in Diaphorina citri Kuwayama (Hemiptera: Psyllidae) from citrus groves of Punjab, Pakistan. Crop Protection, 86, 6268.10.1016/j.cropro.2016.04.010CrossRefGoogle Scholar
Nahrung, HF, Liebhold, AM, Brockerhoff, EG and Rassati, D (2023) Forest insect biosecurity: processes, patterns, predictions, pitfalls. Annual Review of Entomology, 68, 211229.10.1146/annurev-ento-120220-010854CrossRefGoogle ScholarPubMed
Northrup, JM, Hooten, MB, Anderson, CR Jr. and Wittemyer, G (2013) Practical guidance on characterizing availability in resource selection functions under a use–availability design. Ecology 94, 14561463.10.1890/12-1688.1CrossRefGoogle Scholar
O'Donnell, MS and Ignizio, DA (2012) Bioclimatic predictors for supporting ecological applications in the conterminous United States: U.S. Geological Survey Data Series 691, 10.Google Scholar
Oduro, C, Shuoben, B, Ayugi, B, Beibei, L, Babaousmail, H, Sarfo, I, Ullah, S and Ngoma, H (2021) Observed and Coupled Model Intercomparison Project 6 multimodel simulated changes in near-surface temperature properties over Ghana during the 20th century. International Journal of Climatology, 42, 121.Google Scholar
Owens, HL, Campbell, LP, Dornak, LL, Saupe, EE, Barve, N, Soberón, J, Ingenloff, K, Lira-Noriega, A, Hensz, CM, Myers, CE and Peterson, AT (2013) Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecological Modelling 263, 1018.10.1016/j.ecolmodel.2013.04.011CrossRefGoogle Scholar
Padayachee, AL, Irlich, UM, Faulkner, KT, Gaertner, M, Procheş, Ş, Wilson, JR and Rouget, M (2017) How do invasive species travel to and through urban environments?. Biological Invasions, 19, 35573570.10.1007/s10530-017-1596-9CrossRefGoogle Scholar
Paris, TM, Allan, SA, Hall, DG, Hentz, MG, Croxton, SD, Ainpudi, N and Stansly, PA (2017) Effects of temperature, photoperiod, and rainfall on morphometric variation of Diaphorina citri (Hemiptera: Liviidae). Environmental Entomology 46, 143158.Google ScholarPubMed
Parravicini, V, Azzurro, E, Kulbicki, M and Belmaker, J (2015) Niche shift can impair the ability to predict invasion risk in the marine realm: an illustration using Mediterranean fish invaders. Ecology Letters 18, 246253.10.1111/ele.12401CrossRefGoogle ScholarPubMed
Pebesma, EJ (2018) Simple features for R: standardized support for spatial vector data. R Journal 10, 439.10.32614/RJ-2018-009CrossRefGoogle Scholar
Perkins-Taylor, IE and Frey, JK (2020) Predicting the distribution of a rare chipmunk (Neotamias quadrivittatus oscuraensis): comparing MaxEnt and occupancy models. Journal of Mammalogy 101, 10351048.10.1093/jmammal/gyaa057CrossRefGoogle ScholarPubMed
Peterson, AT (2006) Uses and requirements of ecological niche models and related distributional models. Biodiversity Informatics 3, 5972.10.17161/bi.v3i0.29CrossRefGoogle Scholar
Phillips, SJ (2017) A brief tutorial on Maxent. Disponível: http://biodiversityinformatics.amnh.org/open_source/maxent/. Acesso em: 20-nov-2018.Google Scholar
Phillips, SJ and Dudík, M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161175.10.1111/j.0906-7590.2008.5203.xCrossRefGoogle Scholar
Phillips, S and Phillips, MS (2021) Package ‘maxnet’. Version 0.1, 4. https://github.com/mrmaxent/maxnet (Last accessed 2nd February 2024).Google Scholar
Phillips, SJ, Dudík, M and Schapire, RE (2004) A maximum entropy approach to species distribution modeling. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), Banff, Alberta, Canada, July 4–8, 2004, p. 83.10.1145/1015330.1015412CrossRefGoogle Scholar
Phillips, SJ, Anderson, RP and Schapire, RE (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231259.10.1016/j.ecolmodel.2005.03.026CrossRefGoogle Scholar
Phillips, SJ, Dudík, M, Elith, J, Graham, CH, Lehmann, A, Leathwick, J and Ferrier, S (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19, 181197.10.1890/07-2153.1CrossRefGoogle ScholarPubMed
Phillips, SJ, Anderson, RP, Dudík, M, Schapire, RE and Blair, ME (2017) Opening the black box: an open-source release of Maxent. Ecography 40, 887893.10.1111/ecog.03049CrossRefGoogle Scholar
Radosavljevic, A and Anderson, RP (2014) Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of biogeography 41, 629643.10.1111/jbi.12227CrossRefGoogle Scholar
Randin, CF, Dirnböck, T, Dullinger, S, Zimmermann, NE, Zappa, M and Guisan, A (2006) Are niche-based species distribution models transferable in space? Journal of Biogeography 33, 16891703.10.1111/j.1365-2699.2006.01466.xCrossRefGoogle Scholar
R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Available at https://www.R-project.org/Google Scholar
Renner, IW and Warton, DI (2013) Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology: equivalence of MAXENT and Poisson point process models. BIOM 69, 274281.10.1111/j.1541-0420.2012.01824.xCrossRefGoogle ScholarPubMed
Renner, IW, Elith, J, Baddeley, A, Fithian, W, Hastie, T, Phillips, SJ, Popovic, G and Warton, DI (2015) Point process models for presence-only analysis. Methods in Ecology and Evolution 6, 366379.10.1111/2041-210X.12352CrossRefGoogle Scholar
Roberts, DR, Bahn, V, Ciuti, S, Boyce, MS, Elith, J, Guillera-Arroita, G, Hauenstein, S, Lahoz-Monfort, JJ, Schröder, B, Thuiller, W, Warton, DI, Wintle, BA, Hartig, F and Dormann, CF (2017) Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913929.10.1111/ecog.02881CrossRefGoogle Scholar
Robin, X, Turck, N, Hainard, A, Lisacek, F, Sanchez, J-C and Müller, M (2009) Bioinformatics for protein biomarker panel classification: what is needed to bring biomarker panels into in vitro diagnostics? Expert Review of Proteomics 6, 675689.10.1586/epr.09.83CrossRefGoogle ScholarPubMed
Robin, X, Turck, N, Hainard, A, Tiberti, N, Lisacek, F, Sanchez, J-C and Müller, M (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12, 77.10.1186/1471-2105-12-77CrossRefGoogle Scholar
Rubel, F and Kottek, M (2010) Observed and projected climate shifts 1901–2100 depicted by world maps of the Köppen-Geiger climate classification. metz 19, 135141.10.1127/0941-2948/2010/0430CrossRefGoogle Scholar
Santini, L, Benítez-López, A, Maiorano, L, Čengić, M and Huijbregts, MAJ (2021) Assessing the reliability of species distribution projections in climate change research. Diversity and Distribution 27, 10351050.10.1111/ddi.13252CrossRefGoogle Scholar
Schneider, L, Rebetez, M and Rasmann, S (2022) The effect of climate change on invasive crop pests across biomes. Current Opinion in Insect Science, 50, 100895.10.1016/j.cois.2022.100895CrossRefGoogle ScholarPubMed
Sétamou, M, Soto, YL, Tachin, M and Alabi, OJ (2023) Report on the first detection of Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) in the Republic of Benin, West Africa. Scientific Reports, 13, 801.10.1038/s41598-023-28030-3CrossRefGoogle ScholarPubMed
Shabani, F, Kumar, L and Ahmadi, M (2018) Assessing accuracy methods of species distribution models: AUC, specificity, sensitivity and the true skill statistic. Global Journal of Human-Social Science: B Geography, Geo-Sciences, Environmental Science & Disaster Management 18.Google Scholar
Shcheglovitova, M and Anderson, RP (2013) Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes. Ecological Modelling 269, 917.10.1016/j.ecolmodel.2013.08.011CrossRefGoogle Scholar
Simberloff, D and Gibbons, L (2004) Now you see them, now you don't! – population crashes of established introduced species. Biological Invasions, 6, 161172.10.1023/B:BINV.0000022133.49752.46CrossRefGoogle Scholar
Skendžić, S, Zovko, M, Pajač Živković, I, Lešić, V and Lemić, D (2021) Effect of climate change on introduced and native agricultural invasive insect pests in Europe. Insects 12, 985.10.3390/insects12110985CrossRefGoogle ScholarPubMed
Smith, AB (2013) On evaluating species distribution models with random background sites in place of absences when test presences disproportionately sample suitable habitat. Diversity and Distributions 19, 867872.10.1111/ddi.12031CrossRefGoogle Scholar
Streiner, DL and Cairney, J (2007) What's under the ROC? An introduction to receiver operating characteristics curves. Canadian Journal of Psychiatry, 121128. doi: 10.1177/070674370705200210, PMID: 17375868.CrossRefGoogle ScholarPubMed
Tennekes, M (2018) tmap: Thematic maps in R. Journal of Statistical Software 84, 139.10.18637/jss.v084.i06CrossRefGoogle Scholar
Tesfamariam, BG, Gessesse, B and Melgani, F (2022) MaxEnt-based modeling of suitable habitat for rehabilitation of Podocarpus forest at landscape-scale. Environmental Systems Research 11, 112.10.1186/s40068-022-00248-6CrossRefGoogle Scholar
Tsai, JH and Liu, YH (2000) Biology of Diaphorina citri (Homoptera: Psyllidae) on four host plants. Journal of Economic Entomology 93, 17211725.10.1603/0022-0493-93.6.1721CrossRefGoogle ScholarPubMed
Uden, DR, Mech, AM, Havill, NP, Schulz, AN, Ayres, MP, Herms, DA, Hoover, AM, Gandhi, KJ, Hufbauer, RA, Liebhold, AM and Marsico, TD (2023) Phylogenetic risk assessment is robust for forecasting the impact of European insects on North American conifers. Ecological Applications, 33, e2761.10.1002/eap.2761CrossRefGoogle ScholarPubMed
Valavi, R, Guillera-Arroita, G, Lahoz-Monfort, JJ and Elith, J (2022) Predictive performance of presence-only species distribution models: a benchmark study with reproducible code. Ecological Monographs 92. https://doi.org/10.1002/ecm.1486.CrossRefGoogle Scholar
VanDerWal, J, Shoo, LP, Johnson, CN and Williams, SE (2009) Abundance and the environmental niche: environmental suitability estimated from niche models predicts the upper limit of local abundance. The American Naturalist 174, 282291.10.1086/600087CrossRefGoogle ScholarPubMed
Varela, S, Anderson, RP, Garcia-Valdes, R and Fernandez-Gonzalez, F (2014) Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models. Ecography, 37, 10841091.10.1111/j.1600-0587.2013.00441.xCrossRefGoogle Scholar
Velazco, SJE, Villalobos, F, Galvão, F and De Marco Júnior, P (2019) A dark scenario for Cerrado plant species: effects of future climate, land use and protected areas ineffectiveness. Diversity and Distributions 25, 660673.10.1111/ddi.12886CrossRefGoogle Scholar
Velazco, SJE, Svenning, J-C, Ribeiro, BR and Laureto, LMO (2020) On opportunities and threats to conserve the phylogenetic diversity of Neotropical palms. Diversity and Distributions, 27, 512523.10.1111/ddi.13215CrossRefGoogle Scholar
Velazco, SJE, Rose, MB, de Andrade, AFA, Minoli, I and Franklin, J (2022) flexsdm: An R package for supporting a comprehensive and flexible species distribution modelling workflow. Methods Ecology and Evolution 13, 16611669.10.1111/2041-210X.13874CrossRefGoogle Scholar
Venette, RC (2017) Climate analyses to assess risks from invasive forest insects: simple matching to advanced models. Current Forestry Reports 3, 255268.10.1007/s40725-017-0061-4CrossRefGoogle Scholar
Wang, S, Xiao, Y and Zhang, H (2015) Studies of the past, current and future potential distributions of Diaphorina citri Kuwayama (Homoptera: Psyllidae) in China. Chinese Journal of Applied Entomology 52, 11401148.Google Scholar
Wang, R, Yang, H, Luo, W, Wang, M, Lu, X, Huang, T, Zhao, J and Li, Q (2019) Predicting the potential distribution of the Asian citrus psyllid, Diaphorina citri (Kuwayama), in China using the MaxEnt model. PeerJ 7, e7323.10.7717/peerj.7323CrossRefGoogle Scholar
Wang, R, Yang, H, Wang, M, Zhang, Z, Huang, T, Wen, G and Li, Q (2020) Predictions of potential geographical distribution of Diaphorina citri (Kuwayama) in China under climate change scenarios. Scientific Reports 10, 9202.10.1038/s41598-020-66274-5CrossRefGoogle Scholar
Warren, DL, Matzke, NJ and Iglesias, TL (2020) Evaluating presence-only species distribution models with discrimination accuracy is uninformative for many applications. Journal of Biogeography 47, 167180.10.1111/jbi.13705CrossRefGoogle Scholar
Webber, BL, Yates, CJ, Le Maitre, DC, Scott, JK, Kriticos, DJ, Ota, N, McNeill, A, Le Roux, JJ, Midgley, GF (2011) Modelling horses for novel climate courses: insights from projecting potential distributions of native and alien Australian acacias with correlative and mechanistic models. Diversity and Distributions 17, 9781000.10.1111/j.1472-4642.2011.00811.xCrossRefGoogle Scholar
Wei, T and Simko, V (2021) R package ‘corrplot’: Visualization of a Correlation Matrix. (Version 0.92). https://github.com/taiyun/corrplotGoogle Scholar
Welker, S, Pierre, M, Santiago, JP, Dutt, M, Vincent, C and Levy, A (2022) Phloem transport limitation in Huanglongbing-affected sweet orange is dependent on phloem-limited bacteria and callose. Tree Physiology, 42, 379390.10.1093/treephys/tpab134CrossRefGoogle ScholarPubMed
Williams, JW, Jackson, ST and Kutzbach, JE (2007) Projected distributions of novel and disappearing climates by 2100 AD. Proceedings of the National Academy of Sciences 104, 57385742.10.1073/pnas.0606292104CrossRefGoogle ScholarPubMed
Woodman, SM, Forney, KA, Becker, EA, DeAngelis, ML, Hazen, EL, Palacios, DM and Redfern, JV (2019) Esdm: a tool for creating and exploring ensembles of predictions from species distribution and abundance models. Methods in Ecology and Evolution 10, 19231933.10.1111/2041-210X.13283CrossRefGoogle Scholar
Yates, KL, Bouchet, PJ, Caley, MJ, Mengersen, K, Randin, CF, Parnell, S, Fielding, AH, Bamford, AJ, Ban, S, Barbosa, AM, Dormann, CF, Elith, J, Embling, CB, Ervin, GN, Fisher, R, Gould, S, Graf, RF, Gregr, EJ, Halpin, PN, Heikkinen, RK, Heinänen, S, Jones, AR, Krishnakumar, PK, Lauria, V, Lozano-Montes, H, Mannocci, L, Mellin, C, Mesgaran, MB, Moreno-Amat, E, Mormede, S, Novaczek, E, Oppel, S, Ortuño Crespo, G, Peterson, AT, Rapacciuolo, G, Roberts, JJ, Ross, RE, Scales, KL, Schoeman, D, Snelgrove, P, Sundblad, G, Thuiller, W, Torres, LG, Verbruggen, H, Wang, L, Wenger, S, Whittingham, MJ, Zharikov, Y, Zurell, D and Sequeira, AMM (2018) Outstanding challenges in the transferability of ecological models. Trends in Ecology and Evolution 33, 790802.10.1016/j.tree.2018.08.001CrossRefGoogle ScholarPubMed
Zavala-Zapata, V, Lázaro-Dzul, MO, Sánchez-Borja, M, Vargas-Tovar, JA, Álvarez-Ramos, R and Azuara-Domínguez, A (2022) Abundance of Diaphorina citri Kuwayama1 associated with temperature and precipitation at Tamaulipas, Mexico. Southwestern Entomologist 47, 713722.10.3958/059.047.0321CrossRefGoogle Scholar
Zhang, Z, Mammola, S, McLay, CL, Capinha, C and Yokota, M (2020) To invade or not to invade? Exploring the niche-based processes underlying the failure of a biological invasion using the invasive Chinese mitten crab. Total Environment, 728, 110.10.1016/j.scitotenv.2020.138815CrossRefGoogle ScholarPubMed
Zhu, GP, Liu, Q and Gao, YB (2014) Improving ecological niche model transferability to predict the potential distribution of invasive exotic species. Biodiversity Science 22, 223230.Google Scholar
Zizka, A, Silvestro, D, Andermann, T, Azevedo, J, Duarte Ritter, C, Edler, D, Farooq, H, Herdean, A, Ariza, M, Scharn, R, Svantesson, S, Wengström, N, Zizka, V and Antonelli, A (2019) CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution, 10, 744751.10.1111/2041-210X.13152CrossRefGoogle Scholar
Zorzenon, FP, Tomaseto, AF, Daugherty, MP, Lopes, JR and Miranda, MP (2021) Factors associated with Diaphorina citri immigration into commercial citrus orchards in São Paulo State, Brazil. Journal of Applied Entomology 145, 326335.10.1111/jen.12851CrossRefGoogle Scholar
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