Potential distribution of Croton guatemalensis: a model with reproductive biology data

keywords: Habitat suitability, multipurpose species, phenology, physiology, MaxEnt 4.4.4.

Abstract

Background: The inclusion of information on the phenology of any given species can significantly improve the resulting of potential distribution models. Scientific literature does not provide up-to-date information on the abiotic and biotic factors that determine the distribution of Croton guatemalensis, a species native to communities in south Mexico. For the first time, the potential distribution of C. guatemalensis was determined using a model which includes reproductive biology data.

Questions: Which bioclimatic and climatic variables most contribute to the distribution of C. guatemalensis? Does reproductive biology data contribute significantly to the prediction of the species distribution?

Studied species/Mathematical model: Croton guatemalensis/ Maximum Entropy Modeling

Study area and dates: Chiapas, Mexico, January - December 2020.

Methods: The MaxEnt 4.4.4 algorithm was used, incorporating 16 variables, including bioclimatic, climatic and elevation. In addition, a habitat suitability layer was built.

Results: The model presented a precision of AUC = 0.964 ± 0.004. Eight variables contributed to explain 86.5 % of the potential distribution of the species. According to their contribution to the model, the most important were the seasonality of precipitation, habitat suitability, elevation and April solar radiation. The species was found in the physiographic regions Central America South Mountain Range Subprovince, Central Depression of Chiapas Discontinuity, and Altos de Chiapas Subprovince.

Conclusions: The inclusion reproductive biology data of C. guatemalensis contributed to improve the model. This information allows the development of more effective management and conservation plans by identifying the precise regions in which the species is found.

Downloads

Download data is not yet available.
Potential distribution of <em>Croton guatemalensis</em>: a model with reproductive biology data

References

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. DOI: https://doi.org/10.1111/ecog.01132

Andreu ILG, Mora I, Martínez-Casas JL. 2006. Morfometría, viabilidad y variabilidad de las semillas de la población de Pinus hartwegii del Cofre de Perote, Veracruz, México. Cuadernos de Biodiversidad 1: 14-18. DOI: https://doi.org/10.14198/cdbio.2006.19.03

Arias-Aguilar D, Acosta-Vargas LG, Rodríguez-Gonzalez A, Quesada-Quirós M. 2016. Efecto del cambio climático sobre el patrón de distribución de las especies de plantas en el Parque Nacional Volcán Irazú (PNVI) basado en simulaciones a mediano y largo plazo. BSc Thesis. Instituto Tecnológico de Costa Rica.

Bañuelos-Revilla JE, Palacio-Núñez J, Martínez-Montoya JF, Olmos-Oropeza G, Flores-Cano JA. 2019. Distribución potencial y abundancia de candelilla (Euphorbia antisyphilitica) en el norte de Zacatecas, México. Madera y Bosques 25: e2511657. DOI: https://doi.org/10.21829/myb.201 9.2511657

Chuine I. 2010. Why does phenology drive species distribution? Philosophical Transactions of the Royal Society B: Biological Sciences 365: 3149-3160. DOI: https://doi.org/10.1098/rstb.2010.0142

Chuine I, Beaubien EG. 2001. Phenology is a major determinant of tree species range. Ecology Letters 4: 500-510. DOI: https://doi.org/10.1046/j.1461-0248.2001.00261.x

COMPADRE and COMADRE Matrix Databases. 2019. COMPADRE and COMADRE Matrix Databases. https://compadredb.wordpress.com/ (accessed October 23, 2020)

CONABIO. 2019. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad. https://www.gob.mx/conabio (accessed January 23, 2020).

Cortés-Flores J, Cornejo-Tenorio G, Ibarra-Manríquez G. 2011. Fenología reproductiva de las especies arbóreas de un bosque neotropical. Interciencia: Revista de ciencia y tecnología de América 36: 608-613.

Ecosur [El Colegio de la Frontera Sur]. 2005. Subprovincias fisiográficas, conjunto de datos del Programa de Ordenamiento Territorial Estatal. Chiapas, México: Secretaría de Obras Públicas del Gobierno del Estado Chiapas. https://www.ecosur.mx (accessed January 20, 2020).

Fischer G, Orduz J. 2012. Ecofisiología en frutales. In: Fischer G, ed. Manual para el Cultivo de Frutales en el Trópico. Bogotá: Produmedios, pp. 54-72. ISBN: 978-958-99892-5-8.

Giraldo-Cañas DG. 2000. Variación de la diversidad florística en un mosaico sucesional en la cordillera Central andina (Antioquia, Colombia). Darwiniana 38: 33-42. DOI: https://doi.org/10.14522/darwiniana.2014.381-2.159

Godoy-Burki AC. 2016. Efectos del cambio climático sobre especies de plantas vasculares del sur de los Andes Centrales: Un estudio en el noroeste de Argentina (NOA). Ecología Austral 26: 83-94.

González-Hernández A, Morales-Villafaña R, Romero-Sánchez ME, Islas-Trejo B, Pérez-Miranda R. 2018. Modelling potential distribution of a pine bark beetle in Mexican temperate forests using forecast data and spatial analysis tools. Journal of Forestry Research 31: 649-659. DOI: https://doi.org/10.1007/s11676-018-0858-4

Gormley AM, Forsyth DM, Griffioen P, Lindeman M, Ramsey DSL, Scroggie MP, Woodford L. 2011. Using presence-only and presence-absence data to estimate the current and potential distributions of established invasive species. Journal of Applied Ecology 48: 25-34. DOI: https://doi.org/10.1111/j.1365-2664.2010.01911.x

Hernández-Ruíz J, Herrera-Cabrera BE, Delgado-Alvarado A, Salazar-Rojas VM, Bustamante-Gonzalez Á, Campos-Contreras JE, Ramírez-Juarez J. 2016. Potential distribution and geographic characteristics of wild populations of Vanilla planifolia (Orchidaceae) Oaxaca, Mexico. Revista de Biología Tropical 64: 235-246. DOI: https://doi.org/10.15517/rbt.v64i1.17854

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. DOI: https://doi.org/10.1002/joc.1276

Lira-Noriega A, Soberón J, Miller CP. 2013. Process-based and correlative modeling of desert mistletoe distribution: A multiscalar approach. Ecosphere 4: 1-23. DOI: https://doi.org/10.1890/ES13-00155.1

Liu C, Berry PM, Dawson TP, Pearson RG. 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28: 385-393. DOI: https://doi.org/10.1111/j.0906-7590.2005.03957.x

Martinez B, Arenas F, Trilla A, Viejo R, Carreño F. 2014. Combining physiological threshold knowledge to species distribution models is key to improving forecasts of the future niche for macroalgae. Global Change Biology 21: 1-13. DOI: https://doi.org/10.1111/gcb.12655

Martínez-Pastur G, Cellini JM, Barrera MD, Lencinas MV, Soler R, Peri PL. 2017. Influencia de factores bióticos y abióticos en el crecimiento de la regeneración pre- y post-cosecha en un bosque de Nothofagus pumilio. Bosque (Valdivia) 38: 247-257. DOI: https://doi.org/10.4067/S0717-92002017000200003

Mittermeier RA, Myers N, Thomsen JB, Da Fonseca GAB, Oliveiri S. 1998. Biodiversity hotspots and major tropical wilderness areas: Approaches to setting conservation priorities. Conservation Biology 12: 516-520. DOI: https://doi.org/10.1046/j.1523-1739.1998.012003516.x

Moles AT, Falster DS, Leishman MR, Westoby M. 2004. Small-seeded species produce more seeds per square metre of canopy per year, but not per individual per lifetime. Journal of Ecology 92: 384-396. DOI: https://doi.org/10.1111/j.0022-0477.2004.00880.x

Morellato LPC, Talora DC, Takahasi A, Bencke CC, Romera EC, Zipparro VB. 2000. Phenology of Atlantic rain forest trees: A comparative study. BIOTROPICA 32: 811-823.

Orantes-García C, Moreno-Moreno RA, Verdugo-Valdez AG, Farrera-Sarmiento O. 2015. Plantas útiles en comunidades campesinas de la Selva Zoque-Chiapas. Chiapas, México: Universidad de Ciencias y Artes de Chiapas. ISBN: 978-607-8410-47-7

Palma-Ordaz S, Delgadillo-Rodríguez J. 2014. Distribución potencial de ocho especies exóticas de carácter invasor en el estado de Baja California, México. Botanical Sciences 92: 587-597. DOI: https://doi.org/10.17129/botsci.135

Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259. DOI: https://doi.org/10.1016/j.ecolmodel.2005.03.026

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

Pozo-Gómez DM, Orantes-García C, Rioja-Paradela TM, Moreno-Moreno RA, Carrillo-Reyes A. 2020. Croton guatemalensis (Euphorbiaceae) phenology at the Zoque Tropical Forest Biological Corridor. Madera y Bosques 26: e 2621969. DOI: https://doi.org/10.21829/myb.2020.2621969

Pozo-Gómez DM, Orantes-García C, Rioja-Paradela TM, Moreno-Moreno RA, Farrera-Sarmiento O. 2019. Diferencias en morfometría y germinación de semillas de Croton guatemalensis (Euphorbiaceae), procedentes de poblaciones silvestres de la Selva Zoque, Chiapas, México. Acta Botanica Mexicana 126: e1384 DOI: https://doi.org/10.21829/abm126.2019.1384

Quirós MQ, Vargas LGA, Aguilar DA, González AR. 2017. Modelación de nichos ecológicos basado en tres escenarios de cambio climático para cinco especies de plantas en zonas altas de Costa Rica. Revista Forestal Mesoamericana Kurú 14: 1-12. DOI: https://doi.org/10.18845/rfmk.v14i34.2991

R Core Team. 2018. R: A Language and Environment for StatisticalComputing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Radosavljevic A, Anderson RP. 2014. Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography 41: 629-643. DOI: https://doi.org/10.1111/jbi.12227

SEMARNAT [Secretaría del Medio Ambiente y Recursos Naturales]. 2010. Norma Oficial Mexicana NOM-059-SEMARNAT-2010, Protección ambiental - Especies nativas de México de flora y fauna silvestres - Categorías de riesgo y especificaciones para su inclusión, exclusión o cambio - Lista de especies en riesgo. Diario Oficial de la Federación. 2da Sección, 30 de diciembre de 2010.

Vera ML. 1995. Efecto de la altitud en la fenología de la floración en especies arbustivas del norte de España. Lagascalia 18: 3-4.

Visser ME, Both C. 2005. Shifts in phenology due to global climate change: The need for a yardstick. Proceedings of the Rosyal Society B Biological Sciences 272: 2561-2569. DOI: https://doi.org/10.1098/rspb.2005.3356

Wright SJ, Van Schaik CP. 1994. Light and the phenology of tropical trees. The American Naturalist 143: 192-199. DOI: https://doi.org/10.1086/285600

Published
2021-12-15
How to Cite
Pozo-Gómez, D. M., Orantes-García, C., Sánchez-Cortéz, M. S., Rioja-Paradela, T., & Carrillo-Reyes, A. (2021). Potential distribution of Croton guatemalensis: a model with reproductive biology data. Botanical Sciences, 100(2), 291-299. https://doi.org/10.17129/botsci.2865
Section
ECOLOGY / ECOLOGÍA