Ant-gardens: a specialized ant-epiphyte mutualism capable of facing the effects of climate change

Abstract

It is suggested that specialized mutualisms are more vulnerable to climate change. Ant-gardens (AGs) are a complex and specialized mutualistic system represented by epiphytic plants that specifically inhabit the arboreal nest built by canopy ants in tropical forests. Different ant-epiphyte ensembles constitute the AGs throughout the Neotropics. However, neither the environmental factors that determine their geographical distribution nor the effects of climate change on this canopy biological system are known. Here, we estimated the ecological niche and elevational distribution of the Neotropical AGs as an entity (regardless of species composition), and individually for six AG ant and 16 AG epiphyte species in order to determine and compare their current and future distributions (vulnerability), using two unrelated Global Circulation Models for the year 2070 under two Representative Concentration Pathways (RCP4.5: optimistic and RCP8.5: pessimistic). The current potential distribution of the AGs is discontinuous from Tamaulipas, Mexico, to Rio Grande do Sul, Brazil, in low elevation areas with high mean annual temperatures (> 25 °C) and precipitation (> 2400 mm). In contrast, the individual distributions of the AG ants and epiphytes tended not to follow to this climatic profile and were segregated by both latitude and elevation. The geographic distribution of most AG ant and epiphyte species diminished under climate change, while that of the AGs increased, even under the pessimistic scenario. This suggests that AGs allow the species that comprise them to broaden their ecological niche and be more resistant to climate change than they would be outside of this system.

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Data availability

Occurrence records of the Ant-gardens (AGs) and the AG ant and epiphyte species are available at: https://doi.org/10.6084/m9.figshare.13627349.v1.

Code availability

The R codes are available from the corresponding author on request.

References

  1. Abrahamczyk S, Poretschkin C, Renner SS (2017) Evolutionary flexibility in five hummingbird/plant mutualistic systems: testing temporal and geographic matching. J Biogeogr 44:1847–1855. https://doi.org/10.1111/jbi.12962

    Article  Google Scholar 

  2. Afkhami ME, McIntyre PJ, Strauss SY (2014) Mutualist-mediated effects on species’ range limits across large geographic scales. Ecol Lett 17:1265–1273. https://doi.org/10.1111/ele.12332

    Article  PubMed  PubMed Central  Google Scholar 

  3. Aiello-Lammens ME, Boria RA, Radosavljevic A, Vilela B, Anderson RP (2015) spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38:541–545. https://doi.org/10.1111/ecog.01132

    Article  Google 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. https://doi.org/10.1111/jbi.12825

    Article  Google Scholar 

  5. Anderson RP (2013) A framework for using niche models to estimate impacts of climate change on species distributions. Ann N Y Acad Sci 1297:8–28. https://doi.org/10.1111/nyas.12264

    Article  PubMed  Google Scholar 

  6. Araújo MB, Luoto M (2007) The importance of biotic interactions for modelling species distributions under climate change. Global Ecol Biogeogr 16:743–753. https://doi.org/10.1111/j.1466-8238.2007.00359.x

    Article  Google Scholar 

  7. Araújo MB, Rozenfeld A (2014) The geographic scaling of biotic interactions. Ecography 37:406–415. https://doi.org/10.1111/j.1600-0587.2013.00643.x

    Article  Google Scholar 

  8. Barbosa AM (2015) fuzzySim: applying fuzzy logic to binary similarity indices in ecology. Methods Ecol Evol 6:853–858. https://doi.org/10.1111/2041-210x.12372

    Article  Google Scholar 

  9. Barve N, Barve V (2013) ENMGadgets: Tools for pre and post processing in ENM workflow (R package version 0.1.0). Retrieved from https://github.com/vijaybarve/ENMGadgets

  10. Barve N et al (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Model 222:1810–1819. https://doi.org/10.1016/j.ecolmodel.2011.02.011

    Article  Google Scholar 

  11. Batstone RT, Carscadden KA, Afkhami ME, Frederickson ME (2018) Using niche breadth theory to explain generalization in mutualisms. Ecology 99:1039–1050. https://doi.org/10.1002/ecy.2188

    Article  PubMed  Google Scholar 

  12. Bishop TR et al (2019) Thermoregulatory traits combine with range shifts to alter the future of montane ant assemblages. Global Change Biol 25:2162–2173. https://doi.org/10.1111/gcb.14622

    Article  Google Scholar 

  13. Blois JL, Zarnetske PL, Fitzpatrick MC, Finnegan S (2013) Climate change and the past, present, and future of biotic interactions. Science 341:499–504. https://doi.org/10.1126/science.1237184

    CAS  Article  PubMed  Google Scholar 

  14. Blüthgen N, Schmit-Neuerburg V, Engwald S, Barthlott W (2001) Ants as epiphyte gardeners: comparing the nutrient quality of ant and termite canopy substrates in a Venezuelan lowland rain forest. J Trop Ecol 17:887–894. https://doi.org/10.1017/S0266467401001651

    Article  Google Scholar 

  15. 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

    Article  Google Scholar 

  16. Bowman A, Azzalini A (2019) sm: smoothing methods for nonparametric regression and density estimation. Version 2.2-5.6. Available at http://CRAN.R-project.org/package=sm

  17. Burnham RJ, Johnson KR (2004) South American palaeobotany and the origins of Neotropical rainforests. Phil Trans R Soc Lond B 359:1595–1610. https://doi.org/10.1098/rstb.2004.1531

    Article  Google Scholar 

  18. Catling PM (1995) Evidence for partitioning of Belezean ant nest substrate by a characteristic flora. Biotropica 27:535–537

    Article  Google Scholar 

  19. Chomicki G, Janda M, Renner SS (2017) The assembly of ant-farmed gardens: mutualism specialization following host broadening. Proc R Soc B 284:20161759. https://doi.org/10.1098/rspb.2016.1759

    CAS  Article  PubMed  Google Scholar 

  20. Colwell RK, Brehm G, Cardelús CL, Gilman AC, Longino JT (2008) Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322:258–261. https://doi.org/10.1126/science.1162547

    CAS  Article  PubMed  Google Scholar 

  21. Cunha HF, Ferreira ÉD, Tessarolo G, Nabout JC (2018) Host plant distributions and climate interact to affect the predicted geographic distribution of a Neotropical termite. Biotropica 50:625–632. https://doi.org/10.1111/btp.12555

    Article  Google Scholar 

  22. Danielson JJ, Gesch DB (2011) Global multi-resolution terrain elevation data 2010 (GMTED2010). Report 2011-1073. https://doi.org/10.3133/ofr20111073

  23. Davidson DW (1988) Ecological studies of Neotropical ant gardens. Ecology 69:1138–1152. https://doi.org/10.2307/1941268

    Article  Google Scholar 

  24. Diamond SE et al (2012) Who likes it hot? A global analysis of the climatic, ecological, and evolutionary determinants of warming tolerance in ants. Global Change Biol 18:448–456. https://doi.org/10.1111/j.1365-2486.2011.02542.x

    Article  Google Scholar 

  25. Dormann CF et al (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x

    Article  Google Scholar 

  26. Dunn RR, Harris NC, Colwell RK, Koh LP, Sodhi NS (2009) The sixth mass coextinction: are most endangered species parasites or mutualists? Proc R Soc B 276:3037–3045. https://doi.org/10.1098/rspb.2009.0413

    Article  PubMed  Google Scholar 

  27. Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159

    Article  Google Scholar 

  28. Fayle TM, Edwards DP, Foster WA, Yusah KM, Turner EC (2015) An ant–plant by-product mutualism is robust to selective logging of rain forest and conversion to oil palm plantation. Oecologia 178:441–450. https://doi.org/10.1007/s00442-014-3208-z

    Article  PubMed  PubMed Central  Google Scholar 

  29. Fernandes IO, de Souza JLP (2018) Dataset of long-term monitoring of ground-dwelling ants (Hymenoptera: Formicidae) in the influence areas of a hydroelectric power plant on the Madeira River in the Amazon Basin. Biodivers Data J. https://doi.org/10.3897/BDJ.6.e24375

    Article  PubMed  PubMed Central  Google Scholar 

  30. Flores-Palacios A, Ortiz-Pulido R (2005) Epiphyte orchid establishment on termite carton trails. Biotropica 37:457–461. https://doi.org/10.1111/j.1744-7429.2005.00060.x

    Article  Google Scholar 

  31. Filazzola A, Sotomayor DA, Lortie CJ (2018) Modelling the niche space of desert annuals needs to include positive interactions. Oikos 127:264–273. https://doi.org/10.1111/oik.04688

    Article  Google Scholar 

  32. GBIF.org (2017) GBIF Home Page. Available from: https://www.gbif.org [10 November 2017]

  33. Giannini TC, Chapman DS, Saraiva AM, Alves-dos-Santos I, Biesmeijer JC (2013) Improving species distribution models using biotic interactions: a case study of parasites, pollinators and plants. Ecography 36:649–656. https://doi.org/10.1111/j.1600-0587.2012.07191.x

    Article  Google Scholar 

  34. Givnish TJ et al (2011) Phylogeny, adaptive radiation, and historical biogeography in Bromeliaceae: insights from an eight-locus plastid phylogeny. Am J Bot 98:872–895. https://doi.org/10.3732/ajb.1000059

    Article  PubMed  Google Scholar 

  35. Guo F, Lenoir J, Bonebrake TC (2018) Land-use change interacts with climate to determine elevational species redistribution. Nat Commun 9:1315. https://doi.org/10.1038/s41467-018-03786-9

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. Harris RMB, Grose MR, Lee G, Bindoff NL, Porfirio LL, Fox-Hughes P (2014) Climate projections for ecologists. WIREs Clim Change 5:621–637. https://doi.org/10.1002/wcc.291

    Article  Google Scholar 

  37. Helms JA IV (2018) The flight ecology of ants (Hymenoptera: Formicidae). Myrmecol News 26:19–30. https://doi.org/10.25849/myrmecol.news_026:019

    Article  Google Scholar 

  38. Hijmans RJ, Graham CH (2006) The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biol 12:2272–2281. https://doi.org/10.1111/j.1365-2486.2006.01256.x

    Article  Google Scholar 

  39. Hijmans RJ et al. (2016) raster: geographic data analysis and modeling. Version 2.5-8. Available at http://CRAN.R-project.org/package=raster

  40. Hsu RC-C, Tamis WLM, Raes N, de Snoo GR, Wolf JHD, Oostermeijer G, Lin S-H (2012) Simulating climate change impacts on forests and associated vascular epiphytes in a subtropical island of East Asia. Divers Distrib 18:334–347. https://doi.org/10.1111/j.1472-4642.2011.00819.x

    Article  Google Scholar 

  41. Hutchinson GE (1957) Concluding remarks. Cold Spring Harbor Symp Quant Biol 22:415–427. https://doi.org/10.1101/sqb.1957.022.01.039

    Article  Google Scholar 

  42. Janicki J, Narula N, Ziegler M, Guénard B, Economo EP (2016) Visualizing and interacting with large-volume biodiversity data using client–server web-mapping applications: the design and implementation of antmaps.org. Ecol Inf 32:185–193. https://doi.org/10.1016/j.ecoinf.2016.02.006

    Article  Google Scholar 

  43. Jaramillo C, Cárdenas A (2013) Global warming and neotropical rainforests: a historical perspective. Annu Rev Earth Planet Sci 41:741–766. https://doi.org/10.1146/annurev-earth-042711-105403

    CAS  Article  Google Scholar 

  44. Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53:457–481. https://doi.org/10.2307/2281868

    Article  Google Scholar 

  45. Karger DN et al (2017) Climatologies at high resolution for the earth’s land surface areas. Sci Data 4:170122. https://doi.org/10.1038/sdata.2017.122

    Article  PubMed  PubMed Central  Google Scholar 

  46. Kassambara A, Kosinski M (2018) survminer: Drawing Survival Curves using 'ggplot2'. Version 0.4.3. Available at https://CRAN.R-project.org/package=survminer.

  47. Kaufmann E, Maschwitz U (2006) Ant-gardens of tropical Asian rainforests. Naturwissenschaften 93:216–227. https://doi.org/10.1007/s00114-005-0081-y

    CAS  Article  PubMed  Google Scholar 

  48. Kiers TE, Palmer TM, Ives AR, Bruno JF, Bronstein JL (2010) Mutualisms in a changing world: an evolutionary perspective. Ecol Lett 13:1459–1474. https://doi.org/10.1111/j.1461-0248.2010.01538.x

    Article  Google Scholar 

  49. Kleinfeldt SE (1978) Ant-gardens: the interaction of Codonanthe crassifolia (Gesneriaceae) and Crematogaster longispina (Formicidae). Ecology 59:449–456. https://doi.org/10.2307/1936574

    Article  Google Scholar 

  50. Leroy C, Petitclerc F, Orivel J, Corbara B, Carrias JF, Dejean A, Céréghino R (2017) The influence of light, substrate and seed origin on the germination and establishment of an ant-garden bromeliad. Plant Biol 19:70–78. https://doi.org/10.1111/plb.12452

    CAS  Article  PubMed  Google Scholar 

  51. Liu C, White M, Newell G (2013) Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr 40:778–789. https://doi.org/10.1111/jbi.12058

    Article  Google Scholar 

  52. Löwenberg-Neto P (2014) Neotropical region: a shapefile of Morrone’s (2014) biogeographical regionalisation. Zootaxa 3802: 300. https://doi.org/10.11646/zootaxa.3802.2.12

  53. Luebert F, Weigend M (2014) Phylogenetic insights into Andean plant diversification. Front Ecol Environ. https://doi.org/10.3389/fevo.2014.00027

    Article  Google Scholar 

  54. Madison M (1979) Additional observations on ant-gardens in Amazonas. Selbyana 5:107–115

    Google Scholar 

  55. Magrin GO et al. (2014) Central and South America. In: Barros VR et al. (eds) Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York, pp 1499–1566

  56. Morales-Linares J, García-Franco JG, Flores-Palacios A, Valenzuela-González JE, Mata-Rosas M, Díaz-Castelazo C (2016) Vascular epiphytes and host trees of ant-gardens in an anthropic landscape in southeastern Mexico. Sci Nat 103:96. https://doi.org/10.1007/s00114-016-1421-9

    CAS  Article  Google Scholar 

  57. Morales-Linares J, García-Franco JG, Flores-Palacios A, Valenzuela-González JE, Mata-Rosas M, Díaz-Castelazo C (2018) Orchid seed removal by ants in Neotropical ant-gardens. Plant Biol 20:525–530. https://doi.org/10.1111/plb.12715

    CAS  Article  PubMed  Google Scholar 

  58. Müller L-LB, Albach DC, Zotz G (2018) Growth responses to elevated temperatures and the importance of ontogenetic niche shifts in Bromeliaceae. New Phytol 217:127–139. https://doi.org/10.1111/nph.14732

    Article  PubMed  Google Scholar 

  59. Muscarella R, Galante PJ, Soley-Guardia M, Boria RA, Kass JM, Uriarte M, Anderson RP (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. https://doi.org/10.1111/2041-210X.12261

    Article  Google Scholar 

  60. Norberg A et al (2019) A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecol Monogr 89:e01370. https://doi.org/10.1002/ecm.1370

    Article  Google Scholar 

  61. Olden JD, Rooney TP (2006) On defining and quantifying biotic homogenization. Global Ecol Biogeogr 15:113–120. https://doi.org/10.1111/j.1466-822X.2006.00214.x

    Article  Google Scholar 

  62. Orivel J, Dejean A (1999) Selection of epiphyte seeds by ant-garden ants. Ecoscience 6:51–55

    Article  Google Scholar 

  63. Orivel J, Leroy C (2011) The diversity and ecology of ant gardens (Hymenoptera: Formicidae; Spermatophyta: Angiospermae). Myrmecol News 14:73–85

    Google Scholar 

  64. Ovaskainen O et al (2017) How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol Lett 20:561–576. https://doi.org/10.1111/ele.12757

    Article  PubMed  Google Scholar 

  65. Pearson RG, Dawson TP (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecol Biogeogr 12:361–371. https://doi.org/10.1046/j.1466-822X.2003.00042.x

    Article  Google Scholar 

  66. Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102–117. https://doi.org/10.1111/j.1365-2699.2006.01594.x

    Article  Google Scholar 

  67. Pérez-Escobar OA et al (2017) Recent origin and rapid speciation of Neotropical orchids in the world’s richest plant biodiversity hotspot. New Phytol 215:891–905. https://doi.org/10.1111/nph.14629

    Article  PubMed  PubMed Central  Google Scholar 

  68. Peterson AT (2012) Niche modeling—model evaluation. Biodivers Inf 8:41. https://doi.org/10.17161/bi.v8i1.4300

  69. Peterson AT, Papeş M, Soberón J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Model 213:63–72. https://doi.org/10.1016/j.ecolmodel.2007.11.008

    Article  Google Scholar 

  70. Phillips SJ, Anderson RP, Dudík M, Schapire RE, Blair ME (2017) Opening the black box: an open-source release of Maxent. Ecography 40:887–893. https://doi.org/10.1111/ecog.03049

    Article  Google Scholar 

  71. 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

    Article  Google Scholar 

  72. Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x

    Article  Google Scholar 

  73. R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  74. Ramos FN et al (2019) ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest. Ecology 100:e02541. https://doi.org/10.1002/ecy.2541

    Article  PubMed  Google Scholar 

  75. Sánchez-Bayo F, Wyckhuys KAG (2019) Worldwide decline of the entomofauna: A review of its drivers. Biol Conserv 232:8–27. https://doi.org/10.1016/j.biocon.2019.01.020

    Article  Google Scholar 

  76. Sanderson BM, Knutti R, Caldwell P (2015) A representative democracy to reduce interdependency in a multimodel ensemble. J Clim 28:5171–5194. https://doi.org/10.1175/jcli-d-14-00362.1

    Article  Google Scholar 

  77. Schoener TW (1968) The Anolis lizards of Bimini: Resource partitioning in a complex fauna. Ecology 49:704–726. https://doi.org/10.2307/1935534

    Article  Google Scholar 

  78. Soberón J, Nakamura M (2009) Niches and distributional areas: concepts, methods, and assumptions. Proc Natl Acad Sci USA 106:19644–19650. https://doi.org/10.1073/pnas.0901637106

    Article  PubMed  Google Scholar 

  79. Stadler B, Dixon AFG (2008) Mutualism: ants and their insect partners. Cambridge University Press, New York

    Google Scholar 

  80. Therneau TM, Lumley T (2018) survival: Survival Analysis. Version 2.43-3. Available at http://CRAN.R-project.org/package=survival.

  81. Valiente-Banuet A et al (2015) Beyond species loss: the extinction of ecological interactions in a changing world. Funct Ecol 29:299–307. https://doi.org/10.1111/1365-2435.12356

    Article  Google Scholar 

  82. Van der Putten WH, Macel M, Visser ME (2010) Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels. Philos Trans R Soc B 365:2025–2034. https://doi.org/10.1098/rstb.2010.0037

    Article  Google Scholar 

  83. van 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

    Article  Google Scholar 

  84. Warren DL, Beaumont LJ, Dinnage R, Baumgartner JB (2019) New methods for measuring ENM breadth and overlap in environmental space. Ecography 42:444–446. https://doi.org/10.1111/ecog.03900

    Article  Google Scholar 

  85. Warren DL, Glor RE, Turelli M (2008) Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62:2868–2883. https://doi.org/10.1111/j.1558-5646.2008.00482.x

    Article  Google Scholar 

  86. 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

    Article  Google Scholar 

  87. Warren R 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

    Article  Google Scholar 

  88. Wiens D, Slaton MR (2012) The mechanism of background extinction. Biol J Linn Soc 105:255–268. https://doi.org/10.1111/j.1095-8312.2011.01819.x

    Article  Google Scholar 

  89. Wisz MS et al (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biol Rev 88:15–30. https://doi.org/10.1111/j.1469-185X.2012.00235.x

    Article  PubMed  Google Scholar 

  90. Youngsteadt E, Nojima S, Häberlein C, Schulz S, Schal C (2008) Seed odor mediates an obligate ant-plant mutualism in Amazonian rainforests. Proc Natl Acad Sci USA 105:4571–4575. https://doi.org/10.1073/pnas.0708643105

    Article  PubMed  Google Scholar 

  91. Zotz G, Bader MY (2009) Epiphytic plants in a changing world-global: change effects on vascular and non-vascular epiphytes. In: Lüttge U, Beyschlag W, Büdel B, Francis D (eds) Progress in botany. Springer, Berlin, Heidelberg, pp 147–170. https://doi.org/10.1007/978-3-540-68421-3_7

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Acknowledgements

We thank Michelle I. Ramos-Robles for assistance in the field and comments regarding the ecological niche modeling, and Miguel Vásquez Bolaños for identification of the AG ants of southeastern Mexico. The comments and criticism of K. MacMillan and three anonymous reviewers improved the manuscript.

Funding

The first author received the support of the Programa para el Desarrollo Profesional Docente en Educación Superior (PRODEP Grant 511-6/17-8702) and the fieldwork was funded by the Cuerpo Académico Biología del Dosel.

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JML conceived and designed the research. JML analyzed the data. JML, VHTH, AMCL and AFP wrote the manuscript.

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Correspondence to Jonas Morales-Linares.

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Morales-Linares, J., Corona-López, A.M., Toledo-Hernández, V.H. et al. Ant-gardens: a specialized ant-epiphyte mutualism capable of facing the effects of climate change. Biodivers Conserv (2021). https://doi.org/10.1007/s10531-021-02138-2

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Keywords

  • Ecological niche modeling
  • Elevational distribution
  • MaxEnt
  • Neotropics
  • Niche equivalence
  • Species distribution models