Current and future ranges of an elusive North American insect using species distribution models

  • Daniel P. SilvaEmail author
  • André F. A. Andrade
  • João P. J. Oliveira
  • Danielle M. Morais
  • Julya E. A. Vieira
  • Michael S. Engel


Nearly all of Earth’s ecosystems are suffering rapid and intense environmental changes, pushing species extinction rates to levels higher than those previously observed in past mass-extinction events. In this context, the ongoing effects of climate change are expected to cause severe impacts on biodiversity in the near- to medium-term future. Yet, the lack of knowledge concerning the geographic distributions of species is an important drawback to the efficacy of practical actions towards species conservation. Species distribution models (SDMs) may help to overcome these knowledge shortfalls and evaluate the potential effects of climate change upon species distributions. Here, we made use of these tools to measure the potential effects of future climate change upon the distribution of Merope tuber Newman (Mecoptera: Meropeidae). Our SDM results show that the range of the species is expected to increase under almost all modeling methods employed. Such a change in range is mainly related to a poleward shift. Practically nothing is known about M. tuber’s ecology, but nonetheless, the future climate changes are expected to affect the species’ ecological features. This reinforces the need to increase resources for field surveys of this (and other) insect lineages. Such measures will provide more robust information on the biological and ecological attributes of species, allowing stakeholders to design more efficient tools to protect this species before human-related activities impose irreversible negative impacts.


Climate change Models Range change Sampling bias Information shortfalls 



AFAA received a fellowship from Coordenação para o Aperfeiçoamento de Pessoal de Nível Superior—CAPES. DMM and JEAV would like to thank Instituto Federal Goiano (IFGoiano), campus Urutaí, for an undergraduate scholarship offered to them during the development of this study. JPJO would like to thank the Programa de Educação Tutorial (PET/MEC/SESu) from IFGoiano for a scholarship offered during his undergraduate course.


  1. Aars J, Andersen M, Breniére A, Blanc S (2015) White-beaked dolphins trapped in the ice and eaten by polar bears. Polar Res 34:26612. CrossRefGoogle Scholar
  2. Acevedo P, Jiménez-Valverde A, Lobo JM, Real R (2017) Predictor weighting and geographical background delimitation: two synergetic sources of uncertainty when assessing species sensitivity to climate change. Clim Change 145:131–143. CrossRefGoogle Scholar
  3. Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, Kappa and the True Skill Statistic (TSS). J Appl Ecol 43:1223–1232CrossRefGoogle Scholar
  4. Anderson RP (2017) When and how should biotic interactions be considered in models of species niches and distributions? J Biogeogr 44:8–17. CrossRefGoogle Scholar
  5. Araújo MB, Pearson RG (2005) Equilibrium of species’ distributions with climate. Ecography 28:693–695CrossRefGoogle Scholar
  6. Barnosky AD, Matzke N, Tomiya S et al (2011) Has the Earth’s sixth mass extinction already arrived? Nature 471:51–57CrossRefPubMedGoogle Scholar
  7. Barrientos R, Kvist L, Barbosa A et al (2014) Refugia, colonization and diversification of an arid-adapted bird: coincident patterns between genetic data and ecological niche modelling. Mol Ecol 23:390–407. CrossRefPubMedGoogle Scholar
  8. Barry S, Elith J (2006) Error and uncertainty in habitat models. J Appl Ecol 43:413–423CrossRefGoogle Scholar
  9. Barve N, Barve V, Jiménez-Valverde A et al (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Modell 222:1810–1819. CrossRefGoogle Scholar
  10. Bawa KS, Kress WJ, Nadkarni NM (2004) Beyond paradise—meeting the challenges in tropical biology in the 21st century. Biotropica 36:276–284CrossRefGoogle Scholar
  11. Bellard C, Bertelsmeier C, Leadley P et al (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377. CrossRefPubMedPubMedCentralGoogle Scholar
  12. Biella P, Bogliani G, Cornalba M et al (2017) Distribution patterns of the cold adapted bumblebee Bombus alpinus in the Alps and hints of an uphill shift (Insecta: Hymenoptera: Apidae). J Insect Conserv 21:357–366. CrossRefGoogle Scholar
  13. Bini LM, Diniz-Filho JAF, Rangel TF et al (2006) Challenging Wallacean and Linnean shortfalls: knowledge gradients and conservation planning in a biodiversity hotspot. Divers Distrib 12:475–482CrossRefGoogle Scholar
  14. Both C, van Asch M, Bijlsma RG et al (2009) Climate change and unequal phenological changes across four trophic levels: constraints or adaptations? J Anim Ecol 78:73–83. CrossRefPubMedGoogle Scholar
  15. Breiman L (2001) Random forest. Mach Learn 45:5–32CrossRefGoogle Scholar
  16. Byers GW (1965) New and uncommon Neotropical Mecoptera. J Kansas Entomol Soc 38:135–144Google Scholar
  17. Cardoso P, Erwin TL, Borges PAV, New TR (2011) The seven impediments in invertebrate conservation and how to overcome them. Biol Conserv 144:2647–2655CrossRefGoogle Scholar
  18. Carneiro LRDA, Lima AP, Machado RB, Magnusson WE (2016) Limitations to the use of species-distribution models for environmental-impact assessments in the Amazon. PLoS ONE 11:1–17. CrossRefGoogle Scholar
  19. Ceballos G, Ehrlich PR, Barnosky AD et al (2015) Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci Adv 1:e1400253. CrossRefPubMedPubMedCentralGoogle Scholar
  20. Ceballos G, Ehrlich PR, Dirzo R (2017) Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proc Natl Acad Sci USA. CrossRefPubMedGoogle Scholar
  21. Cheung DKB, Marshall SA, Webb DW (2006) Mecoptera of Ontario. Can J Arthropod Identif 1:1–13. CrossRefGoogle Scholar
  22. De Marco Jr P, De Siqueira MF (2009) Como determinar a distribuição potencial de espécies sob uma abordagem conservacionista? Megadiversidade 5:65–76Google Scholar
  23. De Marco P, Nóbrega CC (2018) Evaluating collinearity effects on species distribution models: an approach based on virtual species simulation. PLoS ONE 13:e0202403. CrossRefGoogle Scholar
  24. De Siqueira MF, Durigan G, De Marco Jr P, Peterson AT (2009) Something from nothing: using landscape similarity and ecological niche modeling to find rare plant species. J Nat Conserv 17:25–32. CrossRefGoogle Scholar
  25. De Marco P, Villén S, Mendes P et al (2018) Vulnerability of Cerrado threatened mammals: an integrative landscape and climate modeling approach. Biodivers Conserv. CrossRefGoogle Scholar
  26. Diniz-Filho JAF, De Marco Jr P, Hawkins BA (2010) Defying the curse of ignorance: perspectives in insect macroecology and conservation biogeography. Insect Conserv Divers 3:172–179Google Scholar
  27. Dormann CF, Schymanski SJ, Cabral J et al (2012) Correlation and process in species distribution models: bridging a dichotomy. J Biogeogr 39:2119–2131. CrossRefGoogle Scholar
  28. Dormann CF, Elith J, Bacher S et al (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27–46. CrossRefGoogle Scholar
  29. Dunford JC, Kovarik PW, Somma LA, Serrano D (2007) First state records for Merope tuber (Mecoptera: Meropeidae) in Florida and biogeographical implications. Florida Entomol 90:581–584CrossRefGoogle Scholar
  30. Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697CrossRefGoogle Scholar
  31. Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–342CrossRefGoogle Scholar
  32. Friedrich F, Pohl H, Beckmann F, Beutel RG (2013) The head of Merope tuber (Meropeidae) and the phylogeny of Mecoptera (Hexapoda). Arthropod Struct Dev 42:69–88. CrossRefPubMedGoogle Scholar
  33. Gallien L, Muenkemueller T, Albert CH et al (2010) Predicting potential distributions of invasive species: where to go from here? Divers Distrib 16:331–342. CrossRefGoogle Scholar
  34. Giovanelli JG, de Siqueira MF, Haddad CF, Alexandrino J (2010) Modeling a spatially restricted distribution in the Neotropics: how the size of calibration area affects the performance of five presence-only methods. Ecol Modell 221:215–224CrossRefGoogle Scholar
  35. Godoy O, Bartomeus I, Rohr RP, Saavedra S (2018) Towards the integration of Niche and network theories. Trends Ecol Evol 33:287–300. CrossRefPubMedGoogle Scholar
  36. Google Inc (2018) Google Earth, version Scholar
  37. Graham MH (2003) Confronting multicollinearity in ecological multiple regression. Ecology 84:2809–2815CrossRefGoogle Scholar
  38. Graham CH, Ferrier S, Huettman F et al (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol Evol 19:497–503CrossRefPubMedGoogle Scholar
  39. Grimaldi DA, Engel MS (2013) The relict scorpionfly family Meropeidae (Mecoptera) in cretaceous amber. J Kansas Entomol Soc 86:253–263. CrossRefGoogle Scholar
  40. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009. CrossRefGoogle Scholar
  41. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Modell 135:147–186CrossRefGoogle Scholar
  42. Guisan A, Edwards TC, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Modell 157:89–100CrossRefGoogle Scholar
  43. Guisan A, Tingley R, Baumgartner JB et al (2013) Predicting species distributions for conservation decisions. Ecol Lett 16:1424–1435. CrossRefPubMedPubMedCentralGoogle Scholar
  44. Hallmann CA, Sorg M, Jongejans E et al (2017) More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12:e0185809. CrossRefPubMedPubMedCentralGoogle Scholar
  45. Hastie T, Tibshirani R (1986) Generalized additive models. Stat Sci 1:297–310CrossRefGoogle Scholar
  46. Hijmans RJ, Cameron SE, Parra JL et al (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  47. Hirzel AH, Le Lay G, Helfer V et al (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecol Modell 199:142–152. CrossRefGoogle Scholar
  48. Hortal J, Borges PA V, Jiménez-Valverde A, et al (2010) Assessing the areas under risk of invasion within islands through potential distribution modelling: The case of < i> Pittosporum undulatum < i> in São Miguel, Azores. J Nat Conserv 18:247–257. CrossRefGoogle Scholar
  49. Hortal J, Diniz-Filho JAF, Bini LM et al (2011) Ice age climate, evolutionary constraints and diversity patterns of European dung beetles. Ecol Lett 14:741–748. CrossRefPubMedGoogle Scholar
  50. Hortal J, de Bello F, Diniz-Filho JAF et al (2015) Seven shortfalls that beset large-scale knowledge of biodiversity. Annu Rev Ecol Evol Syst 46:523–549. CrossRefGoogle Scholar
  51. Hughes L (2000) Biological consequences of global warming: Is the signal already apparent? Trends Ecol Evol 15:56–61. CrossRefPubMedGoogle Scholar
  52. Hutchinson GE (1957) Concluding remarks. Cold Spring Harb Symp Quant Biol 22:415–427. CrossRefGoogle Scholar
  53. Hutchinson GE (1978) An introduction to population ecology, 1st edn. Yale University Press, New HavenGoogle Scholar
  54. IPCC (2013) Climate change 2013: the physical science basis. Working group I. Contribution to the IPCC 5th Assessment ReportGoogle Scholar
  55. Jiménez-Valverde A, Lobo JM (2006) The ghost of unbalanced species distribution data in geographical model predictions. Divers Distrib 12:521–524. CrossRefGoogle Scholar
  56. Jiménez-Valverde A, Lobo JM (2007) Threshold criteria for conversion of probability of species presence to either-or presence-absence. Acta Oecol 31:361–369CrossRefGoogle Scholar
  57. Johansson J, Kristensen NP, Nilsson J-Å, Jonzén N (2015) The eco-evolutionary consequences of interspecific phenological asynchrony—a theoretical perspective. Oikos 124:102–112. CrossRefGoogle Scholar
  58. Kramer-Schadt S, Niedballa J, Pilgrim JD et al (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib 19:1366–1379. CrossRefGoogle Scholar
  59. Lima-Ribeiro MS, Varela S, Nógues-Bravo D, Diniz-Filho JAF (2012) Potential suitable areas of Giant Ground Sloths dropped before its extinction in South America: the evidences from bioclimatic envelope modeling. Nat Conserv 10:145–151CrossRefGoogle Scholar
  60. Lobo JM (2016) The use of occurrence data to predict the effects of climate change on insects. Curr Opin Insect Sci 17:62–68. CrossRefPubMedGoogle Scholar
  61. Lobo JM, Tognelli MF (2011) Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data. J Nat Conserv 19:1–7CrossRefGoogle Scholar
  62. Lobo JM, Jiménez-Valverde A, Hortal J (2010) The uncertain nature of absences and their importance in species distribution modelling. Ecography 33:103–114CrossRefGoogle Scholar
  63. Losey JE, Vaughan M (2006) The economic value of ecological services provided by insects. Bioscience 56:311–323CrossRefGoogle Scholar
  64. Machado RJP, Kawada R, Rafael JA (2013) New continental record and new species of Austromerope (Mecoptera, Meropeidae) from Brazil. Zookeys 269:51–65. CrossRefGoogle Scholar
  65. Martins AC, Silva DP, De Marco Jr P, Melo GAR (2015) Species conservation under future climate change: the case of Bombus bellicosus, a potentially threatened South American bumblebee species. J Insect Conserv 19:33–43. CrossRefGoogle Scholar
  66. Mendes P, De Marco P (2018) Bat species vulnerability in Cerrado: integrating climatic suitability with sensitivity to land-use changes. Environ Conserv 45:67–74. CrossRefGoogle Scholar
  67. Merow C, Silander JA Jr (2014) A comparison of Maxlike and Maxent for modelling species distributions. Methods Ecol Evol 5:215–225. CrossRefGoogle Scholar
  68. Muscarella R, Galante PJ, Soley-Guardia M et al (2014) ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol Evol 5:1198–1205. CrossRefGoogle Scholar
  69. Newbold T (2010) Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models. Prog Phys Geogr 34:3–22. CrossRefGoogle Scholar
  70. Nóbrega CC, De Marco Jr P (2011) Unprotecting the rare species: a niche-based gap analysis for odonates in a core Cerrado area. Divers Distrib 17:491–505CrossRefGoogle Scholar
  71. Oliveira U, Paglia AP, Brescovit AD et al (2016) The strong influence of collection bias on biodiversity knowledge shortfalls of Brazilian terrestrial biodiversity. Divers Distrib 22:1232–1244. CrossRefGoogle Scholar
  72. Owens HL, Campbell LP, Dornak LL et al (2013) Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecol Modell 263:10–18. CrossRefGoogle Scholar
  73. Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37:637–669CrossRefGoogle Scholar
  74. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42CrossRefPubMedGoogle Scholar
  75. Pearson RG, Raxworthy CJ, Nakamura M, Peterson AT (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102–117CrossRefGoogle Scholar
  76. Pecl GT, Araújo MB, Bell JD et al (2017) Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355:eaai9214. CrossRefPubMedGoogle Scholar
  77. Peres EA, Sobral-Souza T, Perez MF et al (2015) Pleistocene Niche stability and lineage diversification in the subtropical spider Araneus omnicolor (Araneidae). PLoS ONE 10:e0121543. CrossRefPubMedPubMedCentralGoogle Scholar
  78. Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175CrossRefGoogle Scholar
  79. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Modell 190:231–259CrossRefGoogle Scholar
  80. Pyke GH, Ehrlich PR (2010) Biological collections and ecological/environmental research: a review, some observations and a look to the future. Biol Rev 85:247–266CrossRefPubMedGoogle Scholar
  81. Rangel TF, Loyola RD (2012) Labeling ecological niche models. Nat Conserv 10:119–126CrossRefGoogle Scholar
  82. Rasmont P, Franzén M, Lecocq T et al (2015) Climatic risk and distribution atlas of European bumblebees, 1st edn. Pensoft, SofiaGoogle Scholar
  83. Raxworthy CJ, Martínez-Meyer E, Horning N et al (2003) Predicting distributions of known and unknown reptile species in Madagascar. Nature 426:837–841CrossRefPubMedGoogle Scholar
  84. Reddy S, Dávalos LM (2003) Geographical sampling bias and its implications for conservation priorities in Africa. J Biogeogr 30:1719–1727CrossRefGoogle Scholar
  85. Riahi K, Rao S, Krey V et al (2011) RCP 8.5-A scenario of comparatively high greenhouse gas emissions. Clim Change 109:33–57. CrossRefGoogle Scholar
  86. Rödder D, Schmidtlein S, Veith M, Lötters S (2009) Alien invasive slider turtle in unpredicted habitat: a matter of niche shift or of predictors studied? PLoS ONE. CrossRefPubMedPubMedCentralGoogle Scholar
  87. Royle JA, Chandler RB, Yackulic C, Nichols JD (2012) Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions. Methods Ecol Evol 3:545–554. CrossRefGoogle Scholar
  88. Sastre P, Lobo JM (2009) Taxonomist survey biases and the unveiling of biodiversity patterns. Biol Conserv 142:462–467. CrossRefGoogle Scholar
  89. Schleuning M, Fründ J, Schweiger O et al (2016) Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat Commun 7:13965. CrossRefPubMedPubMedCentralGoogle Scholar
  90. Schweiger O, Biesmeijer JC, Bommarco R et al (2010) Multiple stressors on biotic interactions: how climate change and alien species interact to affect pollination. Biol Rev 85:777–795. CrossRefPubMedGoogle Scholar
  91. Schweiger O, Heikkinen RK, Harpke A et al (2012) Increasing range mismatching of interacting species under global change is related to their ecological characteristics. Glob Ecol Biogeogr 21:88–99. CrossRefGoogle Scholar
  92. Silva DP, Aguiar AJC, Melo GAR et al (2013) Amazonian species within the Cerrado savanna: new records and potential distribution for Aglae caerulea (Apidae: Euglossini). Apidologie 44:673–683. CrossRefGoogle Scholar
  93. Silva DP, Spigoloni ZA, Camargos LM et al (2016) Distributional modeling of Mantophasmatodea (Insecta: Notoptera): A preliminary application and the need for future sampling. Org Divers Evol 16:259–268. CrossRefGoogle Scholar
  94. Skvarla MJ, Hartshorn JA, Dowling APG (2014) Report on a large collection of Merope tuber Newman, 1838 (Mecoptera: Meropeidae), from Arkansas, with notes on collection technique, sex ratio, and male clasper size. Psyche. CrossRefGoogle Scholar
  95. Soberón J (2007) Grinnellian and Eltonian niches and geographic distributions of species. Ecol Lett 10:1115–1123. CrossRefPubMedGoogle Scholar
  96. Soberón J, Peterson AT (2005) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers Inform 2:1–10CrossRefGoogle Scholar
  97. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. CrossRefGoogle Scholar
  98. Thomas CD, Cameron A, Green RE et al (2004) Extinction risk from climate change. Nature 427:145–148CrossRefPubMedGoogle Scholar
  99. Tylianakis JM, Didham RK, Bascompte J, Wardle DA (2008) Global change and species interactions in terrestrial ecosystems. Ecol Lett 11:1351–1363. CrossRefPubMedGoogle Scholar
  100. VanDerWal J, Shoo LP, Graham C et al (2009) Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol Modell 220:589–594. CrossRefGoogle Scholar
  101. Vanhatalo J, Veneranta L, Hudd R (2012) Species distribution modeling with Gaussian processes: a case study with the youngest stages of sea spawning whitefish (Coregonus lavaretus L. s.l.) larvae. Ecol Modell 228:49–58. CrossRefGoogle Scholar
  102. Varela S, Lobo JM, Hortal J (2011) Using species distribution models in paleobiogeography: a matter of data, predictors and concepts. Palaeogeogr Palaeoclimatol Palaeoecol 310:451–463. CrossRefGoogle Scholar
  103. Walther G-R, Post E, Convey P et al (2002) Ecological responses to recent climate change. Nature 416:389–395. CrossRefPubMedGoogle Scholar
  104. Whittaker RJ, Araújo MB, Jepson P et al (2005) Conservation biogeography: assessment and prospect. Divers Distrib 11:3–23CrossRefGoogle Scholar
  105. Wilson EO (1987) The little things that run the world (the importance and conservation of invertebrates). Conserv Biol 1:344–346. CrossRefGoogle Scholar
  106. Wisz MS, Guisan A (2009) Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data. BMC Ecol 9:8. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.COBIMA Lab, Departamento de Ciências BiológicasInstituto Federal GoianoUrutaíBrazil
  2. 2.Theory, Metapopulation, and Landscape Ecology Lab, Instituto de Ciências BiológicasUniversidade Federal de GoiásGoiâniaBrazil
  3. 3.Programa de Pós-Graduação em Ecologia e Evolução, Instituto de Ciências BiológicasUniversidade Federal de GoiásGoiâniaBrazil
  4. 4.Biological Research Laboratory, Instituto de Ciências BiológicasInstituto Federal GoianoUrutaíBrazil
  5. 5.Division of Entomology, Natural History Museum, and Department of Ecology & Evolutionary BiologyUniversity of KansasLawrenceUSA
  6. 6.Division of Invertebrate ZoologyAmerican Museum of Natural HistoryNew YorkUSA

Personalised recommendations