Abstract
The stingless bees of the genus Melipona play an important role as pollinators of wild and cultivated plants in the Neotropics, contributing to forest maintenance and food production. Similar to other bees, meliponids are threatened by anthropogenic impacts on the environment, such as habitat conversion and climate change. Here, we apply ecological niche models to estimate the habitat suitability for ten stingless bee species from Brazil. We quantify the impacts of land use change and climate change on their distribution. Additionally, we assess the efficiency of the Brazilian protected areas (PAs) for the conservation of these species. We found that the predicted species range increased in agricultural lands and decreased in natural vegetation between 2000 and 2014. Our models predict that seven species will face a reduction and three species an increase in their suitable habitat in the coming years. The Brazilian PAs fail in safeguarding the majority of climatically suitable habitats for the stingless bee species studied. Our findings suggest that land use and climate change pose a serious risk for the conservation of stingless bees and their pollination services in Brazil.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agudelo-Hz WJ, Urbina-Cardona N, Armenteras-Pascual D (2019) Critical shifts on spatial traits and the risk of extinction of Andean anurans: an assessment of the combined effects of climate and land-use change in Colombia. Perspect Ecol Conserv 17:206–219. https://doi.org/10.1016/j.pecon.2019.11.002
Almpanidou V, Schofield G, Kallimanis AS, Türkozan O, Hays GC et al (2016) Using climatic suitability thresholds to identify past, present and future population viability. Ecol Indic 71:551–556. https://doi.org/10.1016/j.ecolind.2016.07.038
Araujo M, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22:42–47. https://doi.org/10.1016/j.tree.2006.09.010
Araújo MB, Pearson RG (2005) Equilibrium of species’ distributions with climate. Ecography 28:693–695. https://doi.org/10.1111/j.2005.0906-7590.04253.x
Araújo ED, Costa M, Chaud-Netto J, Fowler HG (2004) Body size and flight distance in stingless bees (Hymenoptera: Meliponini): inference of flight range and possible ecological implications. Braz J Biol 64:563–568. https://doi.org/10.1590/S1519-69842004000400003
Araujo MB, Pearson RG, Thuiller W, Erhard M (2005) Validation of species-climate impact models under climate change. Glob Chang Biol 11:1504–1513. https://doi.org/10.1111/j.1365-2486.2005.01000.x
Ashraf U, Peterson AT, Chaudhry MN, Ashraf I, Saquib Z et al (2017) Ecological niche model comparison under different climate scenarios: a case study of Olea spp. in Asia. Ecosphere 8:e01825. https://doi.org/10.1002/ecs2.1825
Assis J, Araújo MB, Serrão EA (2018) Projected climate changes threaten ancient refugia of kelp forests in the North Atlantic. Glob Chang Biol 24:e55–e66. https://doi.org/10.1111/gcb.13818
Balram S, Dragićević S, Meredith T (2004) A collaborative GIS method for integrating local and technical knowledge in establishing biodiversity conservation priorities. Biodivers Conserv 13:1195–1208. https://doi.org/10.1023/B:BIOC.0000018152.11643.9c
Bascompte J, García MB, Ortega R, Rezende EL, Pironon S et al (2019) Mutualistic interactions reshuffle the effects of climate change on plants across the tree of life. Sci Adv 5:eaav2539. https://doi.org/10.1126/sciadv.aav2539
Bates AJ, Sadler JP, Fairbrass AJ, Falk SJ, Hale JD et al (2011) Changing Bee and Hoverfly Pollinator Assemblages along an Urban-Rural Gradient. PLoS One 6:e23459. https://doi.org/10.1371/journal.pone.0023459
Bean WT, Stafford R, Brashares JS (2012) The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models. Ecography 35:250–258. https://doi.org/10.1111/j.1600-0587.2011.06545.x
Beaumont LJ, Graham E, Duursma DE, Wilson PD, Cabrelli A et al (2016) Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges? Ecol Model 342:135–146. https://doi.org/10.1016/j.ecolmodel.2016.10.004
Biesmeijer JC, Roberts SPM, Reemer M, Ohlemüller R, Edwards M et al (2006) Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313:351–354. https://doi.org/10.1126/science.1127863
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. https://doi.org/10.1016/j.ecolmodel.2013.12.012
Breeze TD, Gallai N, Garibaldi LA, Li XS (2016) Economic measures of pollination services: shortcomings and future directions. Trends Ecol Evol 31:927–939. https://doi.org/10.1016/j.tree.2016.09.002
Brown JL (2014) SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol Evol 5:694–700. https://doi.org/10.1111/2041-210X.12200
Cariveau DP, Williams NM, Benjamin FE, Winfree R (2013) Response diversity to land use occurs but does not consistently stabilise ecosystem services provided by native pollinators. Ecol Lett 16:903–911. https://doi.org/10.1111/ele.12126
de Freitas A d S, Vanderborght B, Barth OM (2018) Pollen resources used by Melipona quadrifasciata anthidioides Lepeletier in an urban forest in Rio de Janeiro city, Brazil. Palynology 42:392–399. https://doi.org/10.1080/01916122.2017.1363827
de Oliveira SN, de Carvalho Júnior OA, Gomes RAT, Guimarães RF, McManus CM (2017) Landscape-fragmentation change due to recent agricultural expansion in the Brazilian Savanna, Western Bahia, Brazil. Reg Environ Chang 17:411–423. https://doi.org/10.1007/s10113-016-0960-0
Diffenbaugh NS, Field CB (2013) Changes in ecologically critical terrestrial climate conditions. Science 341:486–492. https://doi.org/10.1126/science.1237123
Dupont YL, Damgaard C, Simonsen V (2011) Quantitative historical change in Bumblebee (Bombus spp.) Assemblages of Red Clover Fields. PLoS One 6:e25172. https://doi.org/10.1371/journal.pone.0025172
Elith J, Graham HC, Anderson RP, Dudík M, Ferrier S et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–342. https://doi.org/10.1111/j.2041-210x.2010.00036.x
Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE et al (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
Foley JA, Ramankutty N, Brauman KA, Cassidy ES, Gerber J et al (2011) Solutions for a cultivated planet. Nature 478:337–342. https://doi.org/10.1038/nature10452
Fourcade Y, Besnard AG, Secondi J (2018) Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Glob Ecol Biogeogr 27:245–256. https://doi.org/10.1111/geb.12684
Garibaldi LA, Carvalheiro LG, Vaissiere BE, Gemmill-Herren B, Hipolito J et al (2016) Mutually beneficial pollinator diversity and crop yield outcomes in small and large farms. Science 351:388–391. https://doi.org/10.1126/science.aac7287
Giannini TC, Acosta AL, Garófalo CA, Saraiva AM, Alves-dos-Santos I et al (2012) Pollination services at risk: bee habitats will decrease owing to climate change in Brazil. Ecol Model 244:127–131. https://doi.org/10.1016/j.ecolmodel.2012.06.035
Giannini TC, Cordeiro GD, Freitas BM, Saraiva AM, Imperatriz-Fonseca VL (2015) The dependence of crops for pollinators and the economic value of pollination in Brazil. J Econ Entomol 108:849–857. https://doi.org/10.1093/jee/tov093
Giannini TC, Maia-Silva C, Acosta AL, Jaffe R, Carvalho AT et al (2017) Protecting a managed bee pollinator against climate change: strategies for an area with extreme climatic conditions and socioeconomic vulnerability. Apidologie 48:784–794. https://doi.org/10.1007/s13592-017-0523-5
Giannini TC, Costa WF, Borges RC, Miranda L, da Costa CPW et al (2020) Climate change in the Eastern Amazon: crop-pollinator and occurrence-restricted bees are potentially more affected. Reg Environ Chang 20:9. https://doi.org/10.1007/s10113-020-01611-y
Gomes VHF, Vieira ICG, Salomão RP, ter Steege H (2019) Amazonian tree species threatened by deforestation and climate change. Nat Clim Chang 9:547–553. https://doi.org/10.1038/s41558-019-0500-2
Guillera-Arroita G, Lahoz-Monfort JJ, Elith J, Gordon A, Kujala H et al (2015) Is my species distribution model fit for purpose? Matching data and models to applications. Glob Ecol Biogeogr 24:276–292. https://doi.org/10.1111/geb.12268
Guo Y, Li X, Zhao Z, Wei H, Gao B et al (2017) Prediction of the potential geographic distribution of the ectomycorrhizal mushroom Tricholoma matsutake under multiple climate change scenarios. Nat Publ Gr 7:46221. https://doi.org/10.1038/srep46221
Haddad NM, Brudvig LA, Clobert J, Davies KF, Gonzalez A et al (2015) Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci Adv 1:e1500052. https://doi.org/10.1126/sciadv.1500052
Hao T, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2020) Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography 43:549–558. https://doi.org/10.1111/ecog.04890
Harsch MA, Phillips A, Zhou Y, Leung M, Rinnan DS et al (2017) Moving forward: insights and applications of moving-habitat models for climate change ecology. J Ecol 105:1169–1181. https://doi.org/10.1111/1365-2745.12724
Hautier Y, Tilman D, Isbell F, Seabloom EW, Borer ET et al (2015) Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348:336–340. https://doi.org/10.1126/science.aaa1788
Hernandez PA, Graham CH, 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. https://doi.org/10.1111/j.0906-7590.2006.04700.x
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. https://doi.org/10.1002/joc.1276
Hijmans RJ, Phillips S, Leathwick J, Elith J (2017) Package ‘dismo” - species distribution modeling.’ CRAN Repos
Hoffmann AA, Sgrò CM (2011) Climate change and evolutionary adaptation. Nature 470:479–485. https://doi.org/10.1038/nature09670
IUCN Standards and Petitions Committee (2019) Guidelines for using the IUCN red list categories and criteria. Version 14. Prep by Stand Petitions Comm
Jaffé R, Pope N, Carvalho AT, Maia UM, Blochein B et al (2015) Bees for development: brazilian survey reveals how to optimize stingless beekeeping. PLoS One 10:e0121157. https://doi.org/10.1371/journal.pone.0121157
Jha S, Kremen C (2013) Urban land use limits regional bumble bee gene flow. Mol Ecol 22:2483–2495. https://doi.org/10.1111/mec.12275
Khanum R, Mumtaz AS, Kumar S (2013) Predicting impacts of climate change on medicinal asclepiads of Pakistan using Maxent modeling. Acta Oecol 49:23–31. https://doi.org/10.1016/j.actao.2013.02.007
Klein A-M, Vaissière BE, Cane JH, Steffan-Dewenter I, Cunningham SA et al (2007) Importance of pollinators in changing landscapes for world crops. Proc R Soc B Biol Sci 274:303–313. https://doi.org/10.1098/rspb.2006.3721
Krechemer SF, Marchioro CA (2020) Past, present, and future distributions of bumble bees in South America: identifying priority species and areas for conservation. J Appl Ecol 00:1–11. https://doi.org/10.1111/1365-2664.13650
Lautenbach S, Seppelt R, Liebscher J, Dormann CF (2012) Spatial and temporal trends of global pollination benefit. PLoS One 7:e35954. https://doi.org/10.1371/journal.pone.0035954
Leroy B, Delsol R, Hugueny B, Meynard CN, Barhoumi C et al (2018) Without quality presence-absence data, discrimination metrics such as TSS can be misleading measures of model performance. J Biogeogr 45:1994–2002. https://doi.org/10.1111/jbi.13402
Lima VP, Marchioro CA, Joner F, ter Steege H, Siddique I (2020) Extinction threat to neglected Plinia edulis exacerbated by climate change, yet likely mitigated by conservation through sustainable use. Austral Ecol 45:376–383. https://doi.org/10.1111/aec.12867
Loiselle BA, Howell CA, Graham CH, Goerck JM, Brooks T et al (2003) Avoiding pitfalls of using species distribution models in conservation planning. Conserv Biol 17:1591–1600. https://doi.org/10.1111/j.1523-1739.2003.00233.x
Memmott J, Craze PG, Waser NM, Price MV (2007) Global warming and the disruption of plant-pollinator interactions. Ecol Lett 10:710–717. https://doi.org/10.1111/j.1461-0248.2007.01061.x
Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 36:1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x
Meyer ALS, Pie MR, Passos FC (2014) Assessing the exposure of lion tamarins (Leontopithecus spp.) to future climate change. Am J Primatol 76:551–562. https://doi.org/10.1002/ajp.22247
Millennium Ecosystem Assessment (2005) Ecosystems and human well-being: scenarios, vol 2. Island Press, Washington, DC
Mitchell EAD, Mulhauser B, Mulot M, Mutabazi A, Glauser G et al (2017) A worldwide survey of neonicotinoids in honey. Science 358:109–111. https://doi.org/10.1126/science.aan3684
MMA (2011) Monitoramento do desmatamento nos biomas brasileiros por Satélite. Acordo de cooperação técnica MMA/IBAMA. Monitoramento do Bioma Cerrado: 2009–2011. http://www.mma.gov.br/images/arquivo/80120/PPCerrado/Relatorio Tecnico_Bioma Cerrado_2011vfinal.pdf. Accessed 30 May 2018
Moreira PA, Lins J, Dequigiovanni G, Veasey EA, Clement CR (2015) The domestication of Annatto (Bixa orellana) from Bixa urucurana in Amazonia. Econ Bot 69:127–135. https://doi.org/10.1007/s12231-015-9304-0
Newbold T (2018) Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc R Soc B Biol Sci 285:20180792. https://doi.org/10.1098/rspb.2018.0792
Oliveira U, Paglia AP, Brescovit AD, de Carvalho CJB, Silva DP et al (2016) The strong influence of collection bias on biodiversity knowledge shortfalls of Brazilian terrestrial biodiversity. Divers Distrib 22:1232–1244. https://doi.org/10.1111/ddi.12489
Oliveira U, Soares-Filho BS, Paglia AP, Brescovit AD, de Carvalho CJB et al (2017) Biodiversity conservation gaps in the Brazilian protected areas. Sci Rep 7:9141. https://doi.org/10.1038/s41598-017-08707-2
Pecl GT, Araújo MB, Bell JD, Blanchard J, BoneBrake TC et al (2017) Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355:eaai9214. https://doi.org/10.1126/science.aai9214
Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography. https://doi.org/10.1111/j.0906-7590.2008.5203.x
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Pimm SL, Jenkins CN, Abell R, Brooks TM, Gittleman JL et al (2014) The biodiversity of species and their rates of extinction, distribution, and protection. Science 344:1246752. https://doi.org/10.1126/science.1246752
Potts SG, Imperatriz-fonseca V, Ngo HT, Aizen MA, Biesmeijer JC et al (2016) Safeguarding pollinators and their values to human well-being. Nat Publ Gr 540:220–229. https://doi.org/10.1038/nature20588
Proosdij ASJ, Sosef MSM, Wieringa JJ, Raes N (2016) Minimum required number of specimen records to develop accurate species distribution models. Ecography 39:542–552. https://doi.org/10.1111/ecog.01509
Qin A, Liu B, Guo Q, Bussmann RW, Ma F et al (2017) Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Glob Ecol Conserv 10:130–139. https://doi.org/10.1016/j.gecco.2017.02.004
Raes N, ter Steege H (2007) A null-model for significance testing of presence-only species distribution models. Ecography 30:727–736. https://doi.org/10.1111/j.2007.0906-7590.05041.x
Ramalho M (2004) Stingless bees and mass flowering trees in the canopy of Atlantic Forest: a tight relationship. Acta Bot Bras 18:37–47. https://doi.org/10.1590/S0102-33062004000100005
Rayner L, Lindenmayer DB, Wood JT, Gibbons P, Manning AD (2014) Are protected areas maintaining bird diversity? Ecography 37:43–53. https://doi.org/10.1111/j.1600-0587.2013.00388.x
Ribeiro BR, Sales LP, Loyola R (2018) Strategies for mammal conservation under climate change in the Amazon. Biodivers Conserv 27:1943–1959. https://doi.org/10.1007/s10531-018-1518-x
Santini L, Benítez-López A, Čengić M, Maiorano L, Huijbregts MAJ (2020) Assessing the reliability of species distribution projections in climate change research. bioRxiv. https://doi.org/10.1101/2020.06.10.143917
Silva M (2005) The Brazilian Protected Areas Program. Conserv Biol 19:608–611. https://doi.org/10.1111/j.1523-1739.2005.00707.x
Soares-Filho B, Moutinho P, Nepstad D, Anderson A, Rodrigues H et al (2010) Role of Brazilian Amazon protected areas in climate change mitigation. Proc Natl Acad Sci 107:10821–10826. https://doi.org/10.1073/pnas.0913048107
Soberon J, Peterson AT (2005) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers Inform 2:1–10. https://doi.org/10.17161/bi.v2i0.4
Sobral-Souza T, Vancine MH, Ribeiro MC, Lima-Ribeiro MS (2018) Efficiency of protected areas in Amazon and Atlantic Forest conservation: a spatio-temporal view. Acta Oecol 87:1–7. https://doi.org/10.1016/j.actao.2018.01.001
Thuiller W, Guéguen M, Renaud J, Karger DN, Zimmermann NE (2019) Uncertainty in ensembles of global biodiversity scenarios. Nat Commun 10:1446. https://doi.org/10.1038/s41467-019-09519-w
Uuemaa E, Antrop M, Roosaare J, Marja R, Mander U (2009) Landscape metrics and indices: an overview of their use in landscape research. Living Rev Landsc Res 3. https://doi.org/10.12942/lrlr-2009-1
Vitousek PM, Mooney HA, Lubchenco J, Melillo JM (1997) Human domination of Earth’s ecosystems. Science 277:494–499. https://doi.org/10.1126/science.277.5325.494
Warren DL, Seifert SN (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of models selection criteria. Ecol Appl 21:335–342. https://doi.org/10.1890/10-1171.1
Warren DL, Glor RE, Turelli M (2010) ENMTools: A toolbox for comparative studies of environmental niche models. Ecography 33:607–611. https://doi.org/10.1111/j.1600-0587.2009.06142.x
Warren R, VanDerWal J, Price J, Welbergen JA, Atkinson I et al (2013) Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nat Clim Chang 3:678–682. https://doi.org/10.1038/nclimate1887
Warren DL, Matzke NJ, Iglesias TL (2020) Evaluating presence-only species distribution models with discrimination accuracy is uninformative for many applications. J Biogeogr 47:167–180. https://doi.org/10.1111/jbi.13705
West AM, Kumar S, Wakie T, Brown CS, Shohlgren TJ et al (2015) Using high-resolution future climate scenarios to forecast Bromus tectorum invasion in Rocky Mountain National Park. PLoS One 10:e0117893. https://doi.org/10.1371/journal.pone.0117893
Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH et al (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:763–773. https://doi.org/10.1111/j.1472-4642.2008.00482.x
Woodcock BA, Bullock JM, Shore RF, Heard MS, Pereira MG et al (2017) Country-specific effects of neonicotinoid pesticides on honey bees and wild bees. Science 356:1393–1395. https://doi.org/10.1126/science.aaa1190
Zurell D, Franklin J, König C, Bouchet PJ, Dormann CF et al (2020) A standard protocol for reporting species distribution models. Ecography 43:1261–1277. https://doi.org/10.1111/ecog.04960
Acknowledgements
We sincerely thank Prof. Dr. Hans ter Steege and Prof. Dr. Koos Biesmeijer for carefully checking our manuscript, GEOPAM/UFMA on behalf of Prof. Dr. Murilo Drummond for providing occurrence data, Geomatic Lab/UFSC on behalf of Prof. Dr. Alexandre ten Caten for the technological resources, and two anonymous reviewers for their significant contribution in the improvement of this manuscript.
Funding
This study was partially financed by the Coordination for the Improvement of Higher Education Personnel (CAPES) - Finance Code 001.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interest.
Additional information
Communicated by Wolfgang Cramer
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
ESM 1
(DOCX 34.6 kb)
Rights and permissions
About this article
Cite this article
Lima, V.P., Marchioro, C.A. Brazilian stingless bees are threatened by habitat conversion and climate change. Reg Environ Change 21, 14 (2021). https://doi.org/10.1007/s10113-021-01751-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10113-021-01751-9