Effect of climate change on the distribution of temperate climate species in Oaxaca, Mexico

keywords: conservation strategy, climate models, ecological niche, Pinus, Quercus

Abstract

Background: Climate change is becoming more evident, and distribution models are useful tools to predict the effect it might cause on biodiversity.

Hypotheses: Under climate change scenarios, temperate forests species of the genus Pinus and Quercus will undergo reductions in their distribution area and changes in their spatial pattern.

Studied species: Arbutus xalapensis, Clethra mexicana, Pinus devoniana, Pinus oocarpa, Pinus teocote, Quercus acutifolia, Quercus castanea, Quercus crassifolia, Quercus elliptica, Quercus magnoliifolia and Quercus rugosa.

Study site: Oaxaca

Methods: Two scenarios were constructed, an optimistic one (SSP-1 and RCP 2-6) and a pessimistic one (SSP-5 and RCP 8.5) for the years 2030 and 2090. A total of 1,383 records and eight bioclimatic variables were used, along with seven learning algorithms, evaluated using ROC and TSS metrics.

Results: An ensemble model was obtained, in which the most important contributing variables were precipitation of the wettest quarter, mean annual temperature, minimum temperature of the coldest month and annual temperature range. The species that showed the highest ROC values were Clethra mexicana (0.91) and Arbutus xalapensis (0.89) with TSS values of 0.68 and 0.60, respectively.

Conclusions: Regardless of the scenario, by the year 2090 all species of Pinus and Quercus will reduce their potential distribution. Therefore, it is urgent to establish conservation policies.

Downloads

Download data is not yet available.
Effect of climate change on the distribution of temperate climate species in Oaxaca, Mexico

References

Allouche O, Tsoar A, Kadmon R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43: 1223-1232.DOI: https://doi.org/10.1111/j.1365-2664.2006.01214.x

Altamirano del Carmen MA, Estrada F, Gay-García C. 2021. A new method for assessing the performance of general circulation models based on their ability to simulate the response to observed forcing. Journal of Climate 34: 5385-5402. DOI: https://doi.org/10.1175/JCLI-D-20-0510.1

Álvarez LR. 1994. Geografía general del estado de Oaxaca. 2a. ed., Oaxaca: Carteles Editores, pp. 14-26. ISBN: 9709128604, 9789709128604.

Anderegg WR, Kane JM, Anderegg LD. 2013. Consequences of widespread tree mortality triggered by drought and temperature stress. Nature Climate Change 3: 30-36. DOI: https://doi.org/10.1038/nclimate1635

Barbet‐Massin M, Jiguet F, Albert CH, Thuiller W. 2012. Selecting pseudo‐absences for species distribution models: How, where and how many? Methods in Ecology and Evolution 3: 327-338. DOI: https://doi.org/10.1111/j.2041-210X.2011.00172.x

Booth TH, Nix H A, Busby JR, Hutchinson MF. 2014. BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Diversity and Distributions 20: 1-9.DOI: https://doi.org/10.1111/ddi.12144

Busby JR. 1991. BIOCLIM-a bioclimate analysis and prediction system. Plant Protection Quarterly 6: 8-9.

Cassini MH. 2011. Ranking threats using species distribution models in the IUCN Red List assessment process. Biodiversity and Conservation 20: 3689-3692. DOI: https://doi.org/10.1007/s10531-011-0126-9

CEF [Comisión Estatal Forestal]. 2023. Superficie Forestal Estatal. https://www.oaxaca.gob.mx/coesfo/superficie-forestal-estatal/ (accessed August 06, 2022).

CONAFOR [Comisión Nacional Forestal]. 2022. Inventario Nacional Forestal y de Suelos. https://snmf.cnf.gob.mx/datos-del-inventario/ (accessed December 05, 2022).

Corona-Núñez RO, Campo JE. 2023. Climate and socioeconomic drivers of biomass burning and carbon emissions from fires in tropical dry forests: A Pantropical analysis. Global Change Biology 29: 1062-1079. DOI: https://doi.org/10.1111/gcb.16516

Corona-Núñez RO, Li F, Campo JE, 2020. Fires represent an important source of carbon emissions in Mexico. Global Biogeochemical Cycles 34: e2020GB006815. DOI: https://doi.org/10.1029/2020GB006815

Corona-Núñez RO, Mendoza-Ponce A, López-Martínez R, 2017. Model selection changes the spatial heterogeneity and total potential carbon in a tropical dry forest. Forest Ecology and Management 405: 69-80. DOI: https://doi.org/10.1016/j.foreco.2017.09.018

Corona-Núñez RO, Mendoza-Ponce AV, Campo J. 2021. Assessment of above-ground biomass and carbon loss from a tropical dry forest in Mexico. Journal of Environmental Management 282: 111973. DOI: https://doi.org/10.1016/j.jenvman.2021.111973

CONABIO [Comisión Nacional pata el Conocimiento y Uso de la Biodiversidad]. 2022. La biodiversidad en Oaxaca. Estudio de Estado. México. https://www.biodiversidad.gob.mx/region/EEB/estudios/ee_oaxaca (accessed May 18, 2023).

Cruz-Cárdenas G, López-Mata L, Silva JT, Bernal-Santana N, Estrada-Godoy F, López-Sandoval JA. 2016. Modelado de la distribución potencial de especies de Pinaceae bajo escenarios de cambio climático en Michoacán. Revista Chapingo, Serie Ciencias Forestales y del Ambiente, 22: 135-148.

Dawson TP, Jackson ST, House JI, Prentice IC, Mace GM. 2011. Beyond predictions: biodiversity conservation in a changing climate. Science, 332: 53-58. DOI: https://doi.org/10.1126/science.1200303

Del Castillo RF, Pérez-De la Rosa JA, Vargas-Amado G, Rivera-García R. 2004. Coníferas. In: García-Mendoza A, Ordoñez MJ, Briones-Salas M, eds. Biodiversidad de Oaxaca. Instituto de Biología UNAM Fondo Oaxaqueño para la Conservación de la Naturaleza World Wildlife Fundation, México, pp. 141-158. https://web.ciidiroaxaca.ipn.mx/fsanchez/sites/web.ciidiroaxaca.ipn.mx.fsanchez/files/pdf/coniferas.pdf

Fick SE, Hijmans RJ. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37: 4302-4315. DOI: https://doi.org/10.1002/joc.5086

Freeman BG, Lee‐Yaw JA, Sunday JM, Hargreaves AL. 2018. Expanding, shifting and shrinking: The impact of global warming on species’ elevational distributions. Global Ecology and Biogeography, 27: 1268-1276.DOI: https://doi.org/10.1111/geb.12774

García-Aranda MA, Méndez-González J, Hernández-Arizmendi JY. 2018. Distribución potencial de Pinus cembroides, Pinus nelsonii y Pinus culminicola en el Noreste de México. Ecosistemas y Recursos Agropecuarios, 5: 3-13. DOI: http://dx.doi.org/10.19136/era.a5n13.1396

GBIF [Global Biodiversity Information Facility]. 2023. Bases de datos geográficos disponibles para las 11 especies en estudio. https://www.gbif.org/what-is-gbif (accessed December 09, 2022).

Gernandt DS, Pérez-De la Rosa JA. 2014. Biodiversidad de Pinophyta (coníferas) en México. Revista Mexicana de Biodiversidad 85: 126-133. DOI: https://doi.org/10.7550/rmb.32195

Gómez-Guerrero A, Correa-Díaz A, Castruita-Esparza LU. 2021. Cambio climático y dinámica de los ecosistemas forestales. Revista Fitotecnia Mexicana 44: 673-673. DOI: https://doi.org/10.35196/rfm.2021.4.673

Gómez-Ruiz PA, Lindig-Cisneros R. 2017. La restauración ecológica clásica y los retos de la actualidad: La migración asistida como estrategia de adaptación al cambio climático. Revista de Ciencias Ambientales, 51: 31-51.DOI: https://doi.org/10.15359/rca.51-2.2

Guisan A, Zimmermann NE. 2000. Predictive habitat distribution models in ecology. Ecological Modelling, 135: 147-186. DOI: https://doi.org/10.1016/S0304-3800(00)00354-9

Guitérrez E, Trejo I. 2014. Efecto del cambio climático en la distribución potencial de cinco especies arbóreas de bosque templado en México. Revista Mexicana de Biodiversidad, 85: 179-188. DOI: https://doi.org/10.7550/rmb.37737

INEGI [Instituto Nacional de Estadística y Geografía Información geográfica]. 2023. División por entidad federativa con base en el marco geoestadístico. https://www.inegi.gob.mx/geo/informaciongeografica/oaxaca (accessed February 12, 2023).

IPCC [Panel Intergubernamental sobre el Cambio Climático]. 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. (accessed April 25, 2023).

Jiménez-García D, Peterson AT. 2019. Climate change impact on endangered cloud forest tree species in Mexico. Revista Mexicana de Biodiversidad, 90: e902781. DOI: https://doi.org/10.22201/ib.20078706e.2019.90.2781.

Lenoir J, Gégout JC, Marquet PA, de Ruffray P, Brisse H. 2008. A significant upward shift in plant species optimum elevation during the 20th century. Science 320: 1768-1771. DOI: https://doi.org/10.1126/science.1156831

Manzanilla-Quijada GE, Treviño-Garza EJ, Aguirre-Calderón OA, Yerena-Yamallel JI, Manzanilla-Quiñones U. 2020. Current and future potential distribution and identification of suitable areas for the conservation of Cedrela odorata L. in the Yucatan Peninsula. Revista Chapingo Serie Ciencias Forestales y del Ambiente 26: 391-408. DOI: https://doi.org/10.5154/r.rchscfa.2019.10.075

Mendoza-Ponce A, Corona-Núñez RO, Galicia L, Kraxner F. 2019. Identifying hotspots of land use cover change under socioeconomic and climate change scenarios in Mexico. Ambio 48: 336-349. https://doi.org/10.1007/s13280-018-1085-0

Mendoza-Ponce AV, Corona-Núñez RO, Kraxner F, Estrada F. 2020. Spatial prioritization for biodiversity conservation in a megadiverse country. Anthropocene 32: 100267. DOI: https://doi.org/10.1016/j.ancene.2020.100267

Mendoza-Ponce A, Corona-Núñez RO, Kraxner F, Leduc S, Patrizio P. 2018. Identifying effects of land use cover changes and climate change on terrestrial ecosystems and carbon stocks in Mexico. Global Environmental Change 53: 12-23. DOI: https://doi.org/10.1016/j.gloenvcha.2018.08.004

Mendoza-Ponce A, Corona-Núñez RO, Nava LF, Estrada F, Calderón-Bustamante O, Martínez-Meyer E, Carabias J, Larralde-Corona AH, Barrios M, Pardo-Villegas PD. 2021. Impacts of land management and climate change in a developing and socioenvironmental challenging transboundary region. Journal of Environmental Management 300: 113748. DOI: https://doi.org/10.1016/j.jenvman.2021.113748

Mirhashemi H, Heydari M, Karami O, Ahmadi K, Mosavi A. 2023. Modeling climate change effects on the distribution of oak forests with machine learning. Forests 14: 469. DOI: https://doi.org/10.3390/f14030469

Nieves VA. 2020. Modelado de distribución de especies en los bosques de los andes meridionales. Papeles de Geografía 66: 195-207. DOI: https://doi.org/10.6018/geografia.409051

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. Journal of Biogeography 34: 102-117. DOI: https://doi.org/10.1111/j.1365-2699.2006.01594.x

Phillips SJ, Dudík M, Schapire RE. 2004. A maximum entropy approach to species distribution modeling. Proceedings of the Twenty- First International Conference on Machine Learning - ICML ’04, 83.DOI: https://doi.org/10.1145/1015330.1015412

R Core Team. 2022. R: a language and environment for statistical computing. R Foundation for Statistical Computing. Viena, Austria. Versión 4.2.2. www.r-project.org

Riahi K, Van-Vuuren, DP, Kriegler E, Edmonds J, O’Neill BC, Fujimori SM. 2017. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change 42: 153-168. DOI: https://doi.org/10.1016/j.gloenvcha.2016.05.009

Rivera ER, Amador JA. 2008. Predicción estacional del clima en Centroamérica mediante la reducción de escala dinámica. Parte I: Evaluación de los modelos de circulación general CCM3. 6 y ECHAM4. 5. Revista de Matemática: Teoría y Aplicaciones 15: 131-173.

Romero C, Lindig-Cisneros RA, Joyce DG, Beaulieu J, Bradley J St C, Jaquish BC. 2016. Assisted migration of forest populations for adapting trees to climate change. Revista Chapingo Serie Ciencias Forestales y del Ambiente 22: 303-323.

Saatchi SS, Harris NL, Brown S, Lefsky M, Mitchard ETA, Salas W, Zutta BR, Buermann W, Lewis SL, Hagen S, Petrova S, White L, Silman M, Morel A. 2011. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences 108: 9899-9904. DOI: https://doi.org/10.1073/pnas.1019576108

Sáenz-Romero C, Rehfeldt GE, Crookston GE, Pierre, NL, St-Amant D, Beaulieu J, Richardson, B. 2010. Contemporary and projected spline climate surfaces for Mexico and their use in understanding climate-plant relationships. Climatic Change 102: 595-623. DOI: https://doi.org/10.1007/s10584-009-9753-5

Safaei M, Rezayan H, Firouzabadi PZ, Sadidi J. 2021. Optimization of species distribution models using a genetic algorithm for simulating climate change effects on Zagros forests in Iran. Ecological Informatics 63: 101288. DOI: https://doi.org/10.1016/j.ecoinf.2021.101288

Stockwell D. 1999. The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science 13: 143-158. DOI: https://doi.org/10.1080/136588199241391

Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, NCEAS Predicting Species Distributions Working Group. 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions 14: 763-773. https://doi.org/10.1111/j.1472-4642.2008.00482.x

Xu Y, Huang Y, Zhao H, Yang M, Zhuang Y, Ye X. 2021. Modelling the effects of climate change on the distribution of endangered Cypripedium japonicum in China. Forests 12: 429.DOI: https://doi.org/10.3390/f12040429

Published
2023-12-05
How to Cite
Guzmán-Santiago, J. C., De los Santos-Posadas, H. M., Ángeles-Pérez, G., Vargas-Larreta, B., Gómez-Cárdenas, M., Rodríguez-Ortiz, G., & Corona-Núñez, R. O. (2023). Effect of climate change on the distribution of temperate climate species in Oaxaca, Mexico. Botanical Sciences, 102(1), 39-53. https://doi.org/10.17129/botsci.3355
Section
ECOLOGY / ECOLOGÍA