Abstract
The ever-increasing requirement of land for food production causes habitat loss and biodiversity decline. Human activities like agriculture are responsible for increases in global temperature, which may preclude species’ survival if they cannot adapt to new climatic conditions or track suitable ones. Although negative impacts of climate change may act in synergy with agriculture when dispersion routes are blocked by croplands, agriculture is important to local economies. Therefore, the demand for land conversion causes conflict among stakeholders and decision makers. But can we benefit both economy and environment? Here we propose an approach to help find a balance between agriculture expansion and biodiversity conservation. We used suitable areas for agriculture to identify priority places to implement monocultures. We modeled species distributions to avoid sites with high conservation value and used species dispersal ability to minimize the distance between present-day and future suitable areas for species persistence. We used a decision-support tool to find a balance between economic development and species conservation, and we conclude that land use conversion is a threat for species persistence given that negative impacts caused by crops could be exacerbated by climate change. Unguided agriculture expansion into future species distribution areas is possible due to severe decreases in the areas for species to persist in the future. Facing this scenario, applying ecological knowledge to guide agriculture expansion is urgent if we want to spare species future distribution area in the Cerrado.
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References
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 Chang 145:131–143. https://doi.org/10.1007/s10584-017-2082-1
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
Amatulli G, Domisch S, Tuanmu MN et al (2018) Data descriptor: a suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Nat Sci Data 5:1–15. https://doi.org/10.1038/sdata.2018.40
Anderson RP, 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. J Biogeogr 37:1378–1393. https://doi.org/10.1111/j.1365-2699.2010.02290.x
Asner GP, Loarie SR, Heyder U (2010) Combined effects of climate and land-use change on the future of humid tropical forests. Conserv Lett 3:395–403. https://doi.org/10.1111/j.1755-263X.2010.00133.x
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 Model 222:1810–1819. https://doi.org/10.1016/j.ecolmodel.2011.02.011
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
Bell G, Gonzalez A (2009) Evolutionary rescue can prevent extinction following environmental change. Ecol Lett 12:942–948. https://doi.org/10.1111/j.1461-0248.2009.01350.x
Bellard C, Bertelsmeier C, Leadley P et al (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377. https://doi.org/10.1111/j.1461-0248.2011.01736.x
Bonvicino CR, Lima JFS, Almeida FC (2003) A new species of Calomys Waterhouse (Rodentia, Sigmodontinae) from the Cerrado of Central Brazil. Rev Bras Zool 20:301–307
Bowman J, Jaeger JAG, Fahrig L (2002) Dispersal distance of mammals is proportional to home range size. Ecology 83:2049–2055
Boyce MS, Vernier PR, Nielsen SE, Schmiegelow FKA (2002) Evaluating resource selection functions. Ecol Model 157:281–300. https://doi.org/10.1016/S0304-3800(02)00200-4
Brar B, Singh J, Singh G, Kaur G (2015) Effects of long term application of inorganic and organic fertilizers on soil organic carbon and physical properties in maize-wheat rotation. Agronomy 5:220–238. https://doi.org/10.3390/agronomy5020220
Brasil (2018) Projeções do Agronegócio. Ministério da Agricultura, Pecuária e Abastecimento, Brasília
Breiman L (2001) Random forest. Mach Learn 45:5–32
Brown JL, Yoder AD (2015) Shifting ranges and conservation challenges for lemurs in the face of climate change. Ecol Evol 5:1131–1142. https://doi.org/10.1002/ece3.1418
Buainain AM, Garcia R (2015) Recent development patterns and challenges of Brazilian agriculture. In: Shome P, Sharma P (eds) Emerging economies: food and energy security, and technology and innovation. Springer, New Delhi, pp 41–66
Buol SW (2009) Soils and agriculture in Central-West and North Brazil. Sci Agric 66:697–707
Charmantier A, McCleery RH, Cole LR et al (2008) Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320:800–803. https://doi.org/10.1126/science.1157174
Chen I-C, Hill JK, Ohlemüller R et al (2011) Rapid range shifts of species associated with high levels of climate warming. Science 333:1024–1026. https://doi.org/10.1126/science.1206432
Cortes C, Vapnik V (1995) Suppot-vector networks. Mach Learn 20:273–297. https://doi.org/10.1023/A:1022627411411
Costa WJEM (2017) Three new species of the killifish genus Melanorivulus from the central Brazilian Cerrado savanna (Cyprinodontiformes, Aplocheilidae). Zookeys 2017:51–70. https://doi.org/10.3897/zookeys.645.10920
Devillers R, Pressey RL, Grech A et al (2015) Reinventing residual reserves in the sea: are we favouring ease of establishment over need for protection? Aquat Conserv Mar Freshw Ecosyst 25:480–504. https://doi.org/10.1002/aqc.2445
Diniz-Filho JAF, Mauricio Bini L, Fernando Rangel T et al (2009) Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography 32:897–906. https://doi.org/10.1111/j.1600-0587.2009.06196.x
Dobrovolski R, Diniz-Filho JAF, Loyola RD, Marco Júnior P (2011a) Agricultural expansion and the fate of global conservation priorities. Biodivers Conserv 20:2445–2459. https://doi.org/10.1007/s10531-011-9997-z
Dobrovolski R, Loyola RD, De Marco Júnior P, Diniz-Filho JAF (2011b) Agricultural expansion can menace brazilian protected areas during the 21st century. Nat Conserv 9:208–213. https://doi.org/10.4322/natcon.2011.027
Dobrovolski R, Loyola R, Da Fonseca GAB et al (2014) Globalizing conservation efforts to save species and enhance food production. Bioscience 64:539–545. https://doi.org/10.1093/biosci/biu064
Faleiro FV, Machado RB, Loyola RD (2013) Defining spatial conservation priorities in the face of land-use and climate change. Biol Conserv 158:248–257. https://doi.org/10.1016/j.biocon.2012.09.020
FAO (2010) Global forest resources assessment 2010. In: Food and Agriculture Organization of the United Nations. pp 18–31
Françoso RD, Brandão R, Nogueira CC et al (2015) Habitat loss and the effectiveness of protected areas in the Cerrado biodiversity hotspot. Nat Conserv 3:35–40
García-Valdés R, Svenning J-C, Zavala MA et al (2015) Evaluating the combined effects of climate and land-use change on tree species distributions. J Appl Ecol 52:902–912. https://doi.org/10.1111/1365-2664.12453
Giordano AJ (2016) Ecology and status of the jaguarundi Puma yagouaroundi: a synthesis of existing knowledge. Mamm Rev 46:30–43. https://doi.org/10.1111/mam.12051
Golding N (2014) GRaF: Species distribution modelling using latent Gaussian random fields. R Package version 0.1-12
Golding N, Purse BV (2016) Fast and flexible Bayesian species distribution modelling using Gaussian processes. Methods Ecol Evol 7:598–608. https://doi.org/10.1111/2041-210X.12523
Gonçalvez PR, Almeida FC, Bonvicino CR (2003) A new species of Wiedomys (Rodentia: sigmodontinae) from Brazilian Cerrado. Mamm Biol 29:250–251. https://doi.org/10.1097/WNO.0b013e3181b56a3d
Guisan A, Edwards TC Jr, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157:89–100. https://doi.org/10.1016/S0304-3800(02)00204-1
Hannah L, Midgley G, Andelman S et al (2007) Protected area needs in a changing climate. Front Ecol Environ 5:131–138
Hannah L, Roehrdanz PR, Ikegami M et al (2013) Climate change, wine, and conservation. Proc Natl Acad Sci USA 110:6907–6912. https://doi.org/10.1073/pnas.1210127110
Hastie T, Tibshirani R (1986) Generalized additive models. Stat Sci 1:297–318. https://doi.org/10.1214/ss/1177013604
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, Maintainer JE (2017) Package “dismo” species distribution modeling. R Packag version 1.1-4. https://doi.org/10.1002/abio.370020112
Hirzel AH, Le Lay G, Helfer V et al (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecol Model 199:142–152. https://doi.org/10.1016/j.ecolmodel.2006.05.017
Intergovernmental Panel on Climate Change (2000) Summary for policymakers. Emissions scenarios. IPCC, Geneva
Iturbide M, Bedia J, Herrera S et al (2015) A framework for species distribution modelling with improved pseudo-absence generation. Ecol Model 312:166–174. https://doi.org/10.1016/j.ecolmodel.2015.05.018
Jackson HB, Fahrig L (2013) Habitat loss and fragmentation. Encycl Biodivers 4:50–58. https://doi.org/10.1016/B978-0-12-384719-5.00399-3
Jezkova T, Wiens JJ (2016) Rates of change in climatic niches in plant and animal populations are much slower than projected climate change. Proc R Soc B 283:1–9. https://doi.org/10.1098/rspb.2016.2104
Jiménez-Valverde A, Lobo JM, Hortal J (2008) Not as good as they seem: the importance of concepts in species distribution modelling. Divers Distrib 14:885–890. https://doi.org/10.1111/j.1472-4642.2008.00496.x
Jorge MSP (2005) Population density and home range size of red-rumped agoutis (Dasyprocta leporina). Within and outside a natural Brazil nut stand in Southeastern Amazonia. Biotropica 37:317–321
Karatzoglou A, Smola A, Hornik K, Zeileis A (2004) kernlab: an S4 package for kernel methods in R. J Stat Softw 11:1–20. https://doi.org/10.1016/j.csda.2009.09.023
Kelt DA, Van Vuren DH (2001) The ecology and macroecology of mammalian home range area. Am Nat 157:637–645
Kennedy JD, Borregaard MK, Jønsson KA et al (2016) The influence of wing morphology upon the dispersal, geographical distributions and diversification of the corvides (Aves; passeriformes). Proc R Soc B 283:20161922. https://doi.org/10.1098/rspb.2016.1922
Klink CA, Machado RB (2005) Conservation of the Brazilian Cerrado. Conserv Biol 19:707–713. https://doi.org/10.1111/j.1523-1739.2005.00702.x
Lambin EF, Meyfroidt P (2011) Global land use change, economic globalization, and the looming land scarcity. Proc Natl Acad Sci 108:3465–3472. https://doi.org/10.1073/pnas.1100480108
Langham GM, Schuetz JG, Distler T et al (2015) Conservation status of North American birds in the face of future climate change. PLoS ONE 10:e0135350. https://doi.org/10.1371/journal.pone.0135350
Lehtomäki J, Moilanen A (2013) Methods and workflow for spatial conservation prioritization using Zonation. Environ Model Softw 47:128–137
Lemoine NP (2015) Climate change may alter breeding ground distributions of eastern migratory monarchs (Danaus plexippus) via range expansion of Asclepias host plants. PLoS ONE 10:1–22. https://doi.org/10.1371/journal.pone.0118614
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
Li F, Zhang S, Bu K et al (2015) The relationships between land use change and demographic dynamics in western Jilin province. J Geogr Sci 25:617–636. https://doi.org/10.1007/s11442-015-1191-x
Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2:18–22
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
Lobo JM, Jiménez-Valverde A, Hortal J (2010) The uncertain nature of absences and their importance in species distribution modelling. Ecography 33:103–114. https://doi.org/10.1111/j.1600-0587.2009.06039.x
Loyola RD, Lemes P, Faleiro FV et al (2012) Severe loss of suitable climatic conditions for marsupial species in Brazil: challenges and opportunities for conservation. PLoS ONE 7:e46257. https://doi.org/10.1371/journal.pone.0046257
Maiorano L, Falcucci A, Zimmermann NE et al (2011) The future of terrestrial mammals in the Mediterranean basin under climate change. Philos Trans R Soc 366:2681–2692. https://doi.org/10.1098/rstb.2011.0121
Manna MC, Swarup A, Wanjari RH et al (2007) Long-term fertilization, manure and liming effects on soil organic matter and crop yields. Soil Tillage Res 94:397–409. https://doi.org/10.1016/j.still.2006.08.013
Mantyka-pringle CS, Martin TG, Rhodes JR (2012) Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Glob Chang Biol 18:1239–1252. https://doi.org/10.1111/j.1365-2486.2011.02593.x
Mantyka-Pringle CS, Visconti P, Di Marco M et al (2015) Climate change modifies risk of global biodiversity loss due to land-cover change. Biol Conserv 187:103–111. https://doi.org/10.1016/j.biocon.2015.04.016
Mcsorley R, Gallaher RN (1996) Effect of yard waste compost on nematode densities and maize yield. Suppl J Nematol 28:655–660
Meng L, Ding W, Cai Z (2005) Long-term application of organic manure and nitrogen fertilizer on N2O emissions, soil quality and crop production in a sandy loam soil. Soil Biol Biochem 37:2037–2045. https://doi.org/10.1016/j.soilbio.2005.03.007
MMA (2015) Plano de Ação para Prevenção e Controle do Desmatamento e das Queimadas. Brasília
Moilanen A, Pouzols FM, Meller L, et al (2014) Spatial conservation planning methods and software Zonation. Version 4 User manual. C-BIG Conservation Biology, Helsinki
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 <scp> Maxent </scp> ecological niche models. Methods Ecol Evol 5:1198–1205. https://doi.org/10.1111/2041-210X.12261
Olson DM, Dinerstein E, Wikramanayake ED et al (2001) Terrestrial ecoregions of the world: a new map of life on earth. Bioscience 51:933. https://doi.org/10.1641/0006-3568(2001)051%5b0933:TEOTWA%5d2.0.CO;2
Pereira HM, Leadley PW, Proença V et al (2010) Scenarios for global biodiversity in the 21st century. Science 330:1496–1501. https://doi.org/10.1126/science.1196624
Phillips S (2017) maxnet: fitting “Maxent” species distribution models with “glmnet”
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Modell 190:231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Polasky S, Fackler P, Lonsdorf E et al (2008) Where to put things? Spatial land management to sustain biodiversity and economic returns. Biol Conserv 141:1505–1524
R Core Development Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
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
Roberts DR, Bahn V, Ciuti S et al (2017) Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40:913–929. https://doi.org/10.1111/ecog.02881
Rodrigues MT, Pavan D, Curcio FF (2007) Two new species of Lizards of the genus Bachia (Squamata, Gymnophthalmidae) from Central Brazil. J Herpetol 41:545–553. https://doi.org/10.1670/06-103.1
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. https://doi.org/10.1111/j.2041-210X.2011.00182.x
Sala OE, Iii FSC, Armesto JJ et al (2000) Global biodiversity scenarios for the year 2100. Science 287:1770–1775
Salvador MA, de Brito JIB (2018) Trend of annual temperature and frequency of extreme events in the MATOPIBA region of Brazil. Theor Appl Climatol 133:253–261. https://doi.org/10.1007/s00704-017-2179-5
Schloss CA, Nuñez TA, Lawler JJ (2012) Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc Natl Acad Sci USA 109:8596–8611. https://doi.org/10.1073/pnas.1116791109
Silva DP, Gonzalez VH, Melo GAR et al (2014) Seeking the flowers for the bees: integrating biotic interactions into niche models to assess the distribution of the exotic bee species Lithurgus huberi in South America. Ecol Model 273:200–209. https://doi.org/10.1016/j.ecolmodel.2013.11.016
Soares-Filho B, Rajâo R, Merry F et al (2016) Brazil’s market for trading forest certificates. PLoS ONE 11:1–17. https://doi.org/10.1371/journal.pone.0152311
Soberón J, Peterson AT (2005) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers Inform 2:1–10. https://doi.org/10.1093/wber/lhm022
Srivastava JP, Alderman H (1993) Poverty and agricultural resource management. In: Agriculture and environmental challenges, pp 197–214
Strassburg BBN, Brooks T, Feltran-Barbieri R et al (2017) Moment of truth for the Cerrado hotspot. Nat Ecol Evol 1:1–3. https://doi.org/10.1038/s41559-017-0099
Teixeira MJ, Recoder RS, Camacho A et al (2013) A new species of Bachia Gray, 1845 (Squamata: gymnophthalmidae) from the Eastern Brazilian Cerrado, and data on its ecology, physiology and behavior. Zootaxa 3616:173–189
Tester M, Langridge P (2010) Breeding technologies to increase crop production in a changing world. Science 327:818–822
Thomas CD, Williamson M (2012) Extinction and climate change. Nature 482:E4–E5. https://doi.org/10.1038/nature10858
Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018) blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. bioRxiv. https://doi.org/10.1101/357798
VanDerWal J, Shoo LP, Graham C, Williams SE (2009) Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol Model 220:589–594. https://doi.org/10.1016/j.ecolmodel.2008.11.010
Vieira RRS, Ribeiro BR, Resende FM et al (2018) Compliance to Brazil’s Forest Code will not protect biodiversity and ecosystem services. Divers Distrib 24:434–438. https://doi.org/10.1111/ddi.12700
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 9:1–13. https://doi.org/10.1186/1472-6785-9-8
Zabel F, Putzenlechner B, Mauser W (2014) Global agricultural land resources: a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions. PLoS ONE 9:1–12. https://doi.org/10.1371/journal.pone.0107522
Zhu GP, Peterson AT (2017) Do consensus models outperform individual models? Transferability evaluations of diverse modeling approaches for an invasive moth. Biol Invasions 19:2519–2532. https://doi.org/10.1007/s10530-017-1460-y
Acknowledgements
We thank two anonymous reviewers for comments and suggestion that improved the paper. RL research is funded by CNPq (Grant #306694/2018-2). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. This paper is a contribution of the INCT in Ecology, Evolution and Biodiversity Conservation founded by MCTIC/CNPq/FAPEG (Grant 465610/2014-5).
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Lemes, L., de Andrade, A.F.A. & Loyola, R. Spatial priorities for agricultural development in the Brazilian Cerrado: may economy and conservation coexist?. Biodivers Conserv 29, 1683–1700 (2020). https://doi.org/10.1007/s10531-019-01719-6
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DOI: https://doi.org/10.1007/s10531-019-01719-6