Abstract
Brosimum rubescens, a tree species with Neotropical distribution, can achieve local monodominance in Southern Amazonian forests. Understanding how and why this species varies across space and time is important because the monodominance of some species alters ecosystem complexity. Here we evaluated the fundamental ecological niche of B. rubescens by species distribution models (SDM), combining predictive environmental variables with occurrence points, and determined the temporal persistence and how the spatial distribution patterns of this species vary with different environmental predictive variables. To generate the SDMs, we incorporated predictive environmental variables as main components of climatic, hydric and edaphic variables. All algorithms showed higher performance in spatial predictions for hydric variables and for the combination of climatic, hydric and edaphic variables. We identified that the potential niches of B. rubescens seem to be defined by climatic fluctuations, with the edaphic conditions not limiting the presence of this species in the evaluated spatial scale. From the last glacial maximum to the present, this species seems to have increased its spatial amplitude; however, from the present to the future, predictions suggest that B. rubescens will experience a considerable loss of its range. Our findings showed independent and combined effects of different environmental variables, allowing us to identify which are limiting or facilitating the spatial distribution of B. rubescens. We corroborate the spatial persistence and geographical fidelity of the species’ distribution patterns over time.
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References
Aiello-Lammens ME, Boria RA, Radosavljevic A et al (2015) spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography (Cop) 38:541–545. https://doi.org/10.1111/ecog.01132
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 43:1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x
Alvarez F, Gerhard P, Paiva Silva D, Spacek Godoy B, Montag LFDA (2020) Effects of different variable sets on the potential distribution of fish species in the Amazon Basin. Ecol Freshw Fish 29(4):764–778. https://doi.org/10.1111/eff.12552
Anderegg WRL, Konings AG, Trugman AT et al (2018) Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561:538–541. https://doi.org/10.1038/s41586-018-0539-7
Andrade-Lima AD (1966) Contribuição ao estudo do paralelismo da flora Amazônico-Nordestina (1st edn). Secretaria de Agricultura, Indústria e Comércio, Recife
Araújo I, Marimon BS, Scalon MC, Fauset S, Junior BHM, Tiwari R, Gloor MU (2021) Trees at the Amazonia-Cerrado transition are approaching high temperature thresholds. Environ Res Lett 16(3):034047. https://doi.org/10.1088/1748-9326/abe3b9
Arieira J, Penha J, Nunes da Cunha C, Couto EG (2016) Ontogenetic shifts in habitat-association of tree species in a neotropical wetland. Plant Soil 404:219–236. https://doi.org/10.1007/s11104-016-2844-y
Beck HE, Zimmermann NE, McVicar TR et al (2018) Present and future köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5:180214. https://doi.org/10.1038/sdata.2018.214
Berg CC (1972) Flora Neotropica - monograph no. 7: Olmedieae, Brosimeae (moraceae), reprinted. Hafner Publishing Company, Inc.: New York
Bittencourt PRL, Oliveira RS, da Costa ACL et al (2020) Amazonia trees have limited capacity to acclimate plant hydraulic properties in response to long-term drought. Glob Chang Biol 26:3569–3584. https://doi.org/10.1111/gcb.15040
Borchert R (1994) Soil and stem water storage determine phenology and distribution of tropical dry Forest trees. Ecology 75:1437–1449. https://doi.org/10.2307/1937467
Brito-Rocha E, Schilling AC, Dos Anjos L et al (2016) Regression models for estimating leaf area of seedlings and adult individuals of Neotropical rainforest tree species. Braz J Biol 76:983–989. https://doi.org/10.1590/1519-6984.05515
Bruijnzeel LA (2004) Hydrological functions of tropical forests: not seeing the soil for the trees? Agric Ecosyst Environ 104:185–228. https://doi.org/10.1016/j.agee.2004.01.015
Butler A, Marimon-Junior BH, Maracahipes L, Marimon BS, Silvério DV, Oliveira EA, Lenza E, Feldpausch TR, Meir P, Grace J (2013) Absorbing roots areas and transpiring leaf areas at the tropical Forest and savanna boundary in Brazil. 2013. In: Perrault C, Bellamy (eds) Savannas: climate, biodiversity and ecological significance. Nova Science Publishers, Exeter, pp 107–126
Carnicer C, Eisenlohr PV, Jácomo A et al (2020) Running to the mountains: mammal species will find potentially suitable areas on the Andes. Biodivers Conserv 29:1855–1869. https://doi.org/10.1007/s10531-020-01951-5
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):1–27
Clark DA, Clark DB (1994) Climate-induced annual variation in canopy tree growth in a Costa Rican tropical RainForest. J Ecol 82:865. https://doi.org/10.2307/2261450
De Marco P, Nóbrega CC (2018) Evaluating collinearity effects on species distribution models: an approach based on virtual species simulation. PLoS One 13:202403. https://doi.org/10.1371/journal.pone.0202403
de Oliveira G, Rangel TF, Lima-Ribeiro MS et al (2014) Evaluating, partitioning, and mapping the spatial autocorrelation component in ecological niche modeling: a new approach based on environmentally equidistant records. Ecography (Cop) 37:637–647. https://doi.org/10.1111/j.1600-0587.2013.00564.x
Domisch S, Jähnig SC, Simaika JP et al (2015) Application of species distribution models in stream ecosystems: the challenges of spatial and temporal scale, environmental predictors and species occurrence data. Fundam Appl Limnol 186:45–61. https://doi.org/10.1127/fal/2015/0627
Dubreuil V, Fante KP, Planchon O, Neto JLSA (2018) Os tipos de climas anuais no Brasil: uma aplicação da classificação de Köppen de 1961 a 2015. Confins. Revue franco-brésilienne de géographie/Revista franco-brasilera de geografia, (37). https://doi.org/10.4000/confins.15738
Elias F, Marimon BS, Marimon-Junior BH et al (2018) Idiosyncratic soil-tree species associations and their relationships with drought in a monodominant Amazon forest. Acta Oecol 91:127–136. https://doi.org/10.1016/j.actao.2018.07.004
Elias F, Marimon Junior BH, de Oliveira FJM et al (2019) Soil and topographic variation as a key factor driving the distribution of tree flora in the Amazonia/Cerrado transition. Acta Oecol 100:103467. https://doi.org/10.1016/j.actao.2019.103467
Elith J (2000) Quantitative methods for modelling species habitat: comparative performance and an application to Australian plants. In: Ferson S, Burgman M (eds) Quantitative methods for conservation biology, 1st edn. Springer, New York, pp 39–58
Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17(1):43–57
Feldpausch TR, Banin L, Phillips OL et al (2011) Height-diameter allometry of tropical forest trees. Biogeosciences 8:1081–1106. https://doi.org/10.5194/bg-8-1081-2011
Feldpausch TR, Phillips OL, Brienen RJWW et al (2016) Amazon forest response to repeated droughts. Glob Biogeochem Cycles 30:964–982. https://doi.org/10.1002/2015GB005133
Felfili JM, Rezende AV, Da Silva J, Silva MA (2000) Changes in the floristic composition of cerrado sensu stricto in Brazil over a nine-year period. J Trop Ecol 16:579–590. https://doi.org/10.1017/S0266467400001589
Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49. https://doi.org/10.1017/S0376892997000088
Gentry AH (1982) Neotropical floristic diversity: phytogeographical connections between central and South America, Pleistocene climatic fluctuations, or an accident of the Andean orogeny? Ann - Missouri Bot Gard 69:557–593. https://doi.org/10.2307/2399084
Gower JC (1971) A general coefficient of similarity and some of its properties. Biometrics 27:623–637
Hallé F, Oldeman RAA, Tomlinson PB (1978) Tropical trees and forests, 1st edn. Springer, Berlin
Hart TB, Hart JA, Murphy PG (1989) Monodominant and species-rich forests of the humid tropics: causes for their co-occurrence. Am Nat 133:613–633. https://doi.org/10.1086/284941
Hastie T, Qian J, Tay K (2016) An introduction to glmnet
Hijmans RJ, Graham CH (2006) Testing the ability of climate envelope models to predict the effect of climate change on species distributions. Glob Chang Biol 12:2272–2281. https://doi.org/10.1111/j.1365-2486.2006.01256.x
Hijmans RJ, Cameron SE, Parra JL et al (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. (2015) dismo: species distribution modeling. R package
IPCC (2013) Summary for Policymakers. In: Stocker T.F., Qin G-K D., Plattner MT, et al. (eds) Climate Change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press: Cambridge, pp. 3–29
Joly CA, Assis MA, Bernacci LC et al (2012) Florística e fitossociologia em parcelas permanentes da Mata Atlântica do sudeste do Brasil ao longo de um gradiente altitudinal. Biota Neotrop 12:123–145. https://doi.org/10.1590/s1676-06032012000100012
Karatzoglou A, Hornik K, Smola A, Zeileis A (2004) Kernlab - An S4 package for kernel methods in R. J Stat Softw 11:1–20. https://doi.org/10.18637/jss.v011.i09
Laurance WF (2003) Slow burn: the insidious effects of surface fires on tropical forests. Trends Ecol Evol 18:209–212. https://doi.org/10.1016/S0169-5347(03)00064-8
Laurance WF, Nascimento HEM, Laurance SG et al (2004) Inferred longevity of Amazonian rainforest trees based on a long-term demographic study. For Ecol Manag 190:131–143. https://doi.org/10.1016/j.foreco.2003.09.011
Leroy B, Delsol R, Hugueny B 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
Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2:18–22
Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography (Cop) 28:385–393. https://doi.org/10.1111/j.0906-7590.2005.03957.x
Marimon BS, Felfili JM (2006) Chuva de sementes em uma floresta monodominante de Brosimum rubescens Taub. e em uma floresta mista adjacente no Vale do Araguaia, MT, Brasil. Acta Bot Bras 20:423–432. https://doi.org/10.1590/S0102-33062006000200017
Marimon BS, Felfili JM, Haridasan M (2001) Studies in monodominant forests in eastern Mato Grosso, Brazil: I. a Forest of Brosimum rubescens taub. Edinb J Bot 58:123–137. https://doi.org/10.1017/S096042860100049X
Marimon BS, Felfili JM, Marimon-Junior BH et al (2008) Desenvolvimento inicial e partição de biomassa de Brosimum rubescens Taub. (Moraceae) sob diferentes níveis de sombreamento. Acta Bot Bras 22:941–953. https://doi.org/10.1590/s0102-33062008000400005
Marimon BS, Felfili JM, Fagg CW et al (2012) Monodominance in a forest of Brosimum rubescens Taub. (Moraceae): structure and dynamics of natural regeneration. Acta Oecol 43:134–139. https://doi.org/10.1016/j.actao.2012.07.001
Marimon BS, Felfili JM, Marimon BH et al (2014a) Leaf herbivory and monodominance in a Cerrado–Amazonia transitional forest, Mato Grosso, Brazil. Plant Biosyst Int J Deal Asp Plant Biol 150:124–130. https://doi.org/10.1080/11263504.2014.983577
Marimon BS, Marimon-Junior BH, Feldpausch TR, Oliveira-Santos C, Mews HA, Lopez-Gonzalez G et al (2014b) Disequilibrium and hyperdynamic tree turnover at the forest–cerrado transition zone in southern Amazonia. Plant Ecol Divers 7(1–2):281–292. https://doi.org/10.1080/17550874.2013.818072
Marimon BS, Oliveira-Santos C, Marimon-Junior BH et al (2020) Drought generates large, long-term changes in tree and liana regeneration in a monodominant Amazon forest. Plant Ecol 221:733–747. https://doi.org/10.1007/s11258-020-01047-8
Marimon-Junior BH (2007) Relação entre diversidade arbórea e aspectos do ciclo biogeoquímico de uma floresta monodominante de Brosimum rubescens Taub. e uma floresta mista adjacente no Leste Mato-grossense. PhD Thesis, Universidade de Brasília
Marimon-Junior BH, Hay JDV, Oliveras I et al (2019) Soil water-holding capacity and monodominance in southern Amazon tropical forests. Plant Soil 450:65–79. https://doi.org/10.1007/s11104-019-04257-w
Murphy BP, Bowman DMJS (2012) What controls the distribution of tropical forest and savanna? Ecol Lett 15:748–758. https://doi.org/10.1111/j.1461-0248.2012.01771.x
Nunes Da Cunha C, Junk WJ (2001) Distribution of Woody plant communities along the flood gradient in the Pantanal of Poconé, Mato Grosso, Brazil. Int J Ecol Environ Sci 27:63–70
Palacios PA (2005) Patrones estructurales y distribución espacial de poblaciones de Brosimum rubescens taub. En relación con la variabilidad fisiográfica en la ribera colombiana del río amazonas. Universidad Nacional de Colombia-Sede Amazonia
Parreira MR, Nabout JC, Tessarolo G et al (2019) Disentangling uncertainties from niche modeling in freshwater ecosystems. Ecol Model 391:1–8. https://doi.org/10.1016/j.ecolmodel.2018.10.024
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
Peh KSH, Lewis SL, Lloyd J (2011) Mechanisms of monodominance in diverse tropical tree-dominated systems. J Ecol 99:891–898. https://doi.org/10.1111/j.1365-2745.2011.01827.x
Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31(2):161–175
Phillips OL, van der Heijden G, Lewis SL et al (2010) Drought-mortality relationships for tropical forests. New Phytol 187:631–646. https://doi.org/10.1111/j.1469-8137.2010.03359.x
QGIS.org (2020) QGIS geographic information system. Open Source Geospatial Foundation Project. http://qgis.org
Quesada CA, Lloyd J, Schwarz M et al (2010) Variations in chemical and physical properties of Amazon forest soils in relation to their genesis. Biogeosciences 7:1515–1541. https://doi.org/10.5194/bg-7-1515-2010
R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3–900051-07- 0. http://www.R-project.org/
Riahi K, Rao S, Krey V et al (2011) RCP 8.5-a scenario of comparatively high greenhouse gas emissions. Clim Chang 109:33–57. https://doi.org/10.1007/s10584-011-0149-y
Ribeiro JF, Walter BMT (2008) As principais fitofisionomias do bioma Cerrado. In: Sano SM, de Almeida SP, Ribeiro JF (eds) Cerrado: Ecologia e flora, 1st edn. Embrapa Cerrados/Embrapa Informação Tecnológica, Brazil, pp 152–212
Rivera LE, Peãuela MC, Moreno F (2014) Intra annual seed production and availability of two morphotypes of Brosimum rubescens taubert in forests of the Colombian Amazon. Biota Neotrop 14:e20130073. https://doi.org/10.1590/1676-06032014007313
Schwalm CR, Glendon S, Duffy PB (2020) RCP 8.5 tracks cumulative CO2 emissions. Proc Natl Acad Sci. https://doi.org/10.1073/pnas.2007117117
Silverman BW (2018) Density estimation: for statistics and data analysis, 1st edn. Routledge, New York
Soberón JM (2010) Niche and area of distribution modeling: a population ecology perspective. Ecography (Cop) 33:159–167. https://doi.org/10.1111/j.1600-0587.2009.06074.x
Sobral-Souza T, Lima-Ribeiro MS, Solferini VN (2015) Biogeography of Neotropical rainforests: past connections between Amazon and Atlantic Forest detected by ecological niche modeling. Evol Ecol 29:643–655. https://doi.org/10.1007/s10682-015-9780-9
Soong JL, Janssens IA, Grau O et al (2020) Soil properties explain tree growth and mortality, but not biomass, across phosphorus-depleted tropical forests. Sci Rep 10:1–13. https://doi.org/10.1038/s41598-020-58913-8
ter Steege H, Henkel TW, Helal N et al (2019) Rarity of monodominance in hyperdiverse Amazonian forests. Sci Rep 9:13822. https://doi.org/10.1038/s41598-019-50323-9
Torti SD, Coley PD, Kursar TA (2001) Causes and consequences of monodominance in tropical lowland forests. Am Nat 157:141–153. https://doi.org/10.1086/318629
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. https://doi.org/10.1016/j.palaeo.2011.07.021
Velazco SJE, Galvão F, Villalobos F, De Marco P (2017) Using worldwide edaphic data to model plant species niches: an assessment at a continental extent. PLoS One 12:1–24. https://doi.org/10.1371/journal.pone.0186025
Wiens JJ, Donoghue MJ (2004) Historical biogeography, ecology and species richness. Trends Ecol Evol 19:639–644. https://doi.org/10.1016/j.tree.2004.09.011
Acknowledgments
A special thanks for the contributions and suggestions of Maria Eduarda Maldaner (Dudita), as well as Angélica Faria de Resende and Naraiana Loureiro Benone, which helped to substantially improve this work. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brazil (CAPES) - Finance Code 001. We also thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico/Projetos Ecológicos de Longa Duração-CNPq/PELD (441244/2016-5) and FAPEMAT/ReFlor (0589267/2016), for financial support. B.S. Marimon and B.H. Marimon-Junior acknowledge CNPq for their PQ-1D productivity grants (301153/2018-3 and 311027/2019-9), and P.S. Morandi acknowledges CAPES for his post-doc grant (88887.185186/2018-00). T.R. Feldpausch was supported by the UK Natural Environment Research Council (NERC, NE/N011570/1).
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This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brazil (CAPES) - Finance Code 001. We also thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico/Projetos Ecológicos de Longa Duração-CNPq/PELD (441244/2016–5 and 441572/2020–0) and FAPEMAT/ReFlor (0589267/2016), for financial support. B.S. Marimon and B.H. Marimon-Junior acknowledge CNPq for their productivity grants (301153/2018–3 and 311027/2019–9), and P.S. Morandi acknowledges CAPES for his post-doc grant (88887.185186/2018–00). T.R. Feldpausch was supported by the UK Natural Environment Research Council (NERC, NE/N011570/1).
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Conceived and designed the investigation: FA, PSM, BHM-J, RE, IA, LHM, AOM, BSM. Performed field and laboratory work: FA, PSM, IA, LHM, AOM. Analyzed the data: FA, RE. Contributed materials and analysis tools: FA, RE, BHM-J, TRF, BSM. Wrote the paper: FA, PSM, BHM-J, RE, IA, LHM, AOM, TRF, BSM.
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Alvarez, F., Morandi, P.S., Marimon-Junior, B.H. et al. Climate defined but not soil-restricted: the distribution of a Neotropical tree through space and time. Plant Soil 471, 175–191 (2022). https://doi.org/10.1007/s11104-021-05202-6
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DOI: https://doi.org/10.1007/s11104-021-05202-6