Potential distribution of the howler monkey (Alouatta palliata) in cocoa agrosystems based on a niche model
DOI:
https://doi.org/10.5281/zenodo.7317180Keywords:
Canopy height, Maxent, Remote sensing, Vegetation indexAbstract
Anthropogenic activities have caused habitat fragmentation and loss, which are the main threats to primates. Because of this, ecological niche models have become a widely used tool to determine the potential habitat of species. These models rarely include biotic factors, although vegetation variables such as height and phenology, data derived from remote sensing, were integrated into this research. We developed a model to obtain the potential distribution of the primate A. palliata. Therefore, records of the presence of monkeys in the field were collected, and later data were obtained on the spectral index of vegetation and the height of the canopy, derived from remote sensing, bioclimatic variables were also used. Subsequently, these variables were analyzed using the Variance Inflation Factor to discriminate those with the highest correlation. Finally, we use a Maximum Entropy algorithm included in the Maxent software, together with the presence registration data, vegetation index, height, and bioclimatic data. The predicted distribution of A. palliata was strongly associated with canopy height, vegetation index (RVI), and warmest quarter precipitation (Bio18). The areas with the highest probability of the presence of A. palliata are strongly associated with cocoa agrosystems and certain spaces of natural vegetation such as mangroves. With the integration of the variables derived from remote sensing, the potential distribution model obtained an excellent evaluation, to predict cocoa agrosystems as available habitats of the howler monkey A. palliata, thus identifying areas with a high probability of the presence of this species of primate, and thus offer a tool to decision-makers, to plan future studies and then establish criteria for the creation of areas for the conservation of primates in Mexico.
References
Arroyo‐Rodríguez, V., y Dias, P. A. D. (2010). Effects of habitat fragmentation and disturbance on howler monkeys: a review. American Journal of Primatology, 72(1), 1-16. https://doi.org/10.1002/ajp.20753
Alkorta, I., Albizu, I., y Garbisu, C. (2003). Biodiversity and agroecosystems. Biodiversity and conservation, 12(12), 2521-2522.
Barbosa, A. M., Real, R., Olivero, J., y Vargas, J. M. (2003). Otter (Lutra lutra) distribution modeling at two resolution scales suited to conservation planning in the Iberian Peninsula. Biological conservation, 114(3), 377-387. https://doi.org/10.1016/S0006-3207(03)00066-1
Bernard, H., Matsuda, I., Hanya, G., & Ahmad, A. H. (2011). Characteristics of night sleeping trees of proboscis monkeys (Nasalis larvatus) in Sabah, Malaysia. International Journal of Primatology, 32(1), 259-267.
Boria, R. A., Olson, L. E., Goodman, S. M., y Anderson, R. P. (2014). Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275, 73-77. https://doi.org/10.1016/j.ecolmodel.2013.12.012
Calixto-Pérez, E., Alarcón-Guerrero, J., Ramos-Fernández, G., Dias, P. A. D., Rangel-Negrín, A., Améndola-Pimenta, M., & Urquiza-Haas, T. (2018). Integrating expert knowledge and ecological niche models to estimate Mexican primates’ distribution. Primates, 59(5), 451-467. https://doi.org/10.1007/s10329-018-0673-8
Chaves, Ó. M., y Bicca-Marques, J. C. (2017). Crop feeding by brown howlers (Alouatta guariba clamitans) in forest fragments: The conservation value of cultivated species. International Journal of Primatology, 38(2), 263-281.
Cuarón, A.D., Shedden, A., Rodríguez-Luna, E., de Grammont, P.C., Link, A., Palacios, E., Morales, A. y Cortés-Ortiz, L. (2008). Alouatta palliata. The IUCN Red List of Threatened Species 2008: e.T39960A10280447.
Escobedo-Morales, L. A., & Mandujano, S. (2007). Conservación del mono aullador en la reserva de la biosfera Los Tuxtlas, Veracruz: un enfoque metapoblacional. Hacia una cultura de conservación de la diversidad biológica, 131-140.
Estrada, A. (2015). Conservation of Alouatta: Social and economic drivers of habitat loss, information vacuum, and mitigating population declines. In Howler monkeys (pp. 383409). Springer, New York, NY.
Estrada, A., Saenz, J., Harvey, C., Naranjo, E., Muñoz, D., y Rosales-Meda, M. (2006). Primates in agroecosystems: conservation value of some agricultural practices in Mesoamerican landscapes. In New Perspectives in the Study of Mesoamerican Primates (pp. 437-470). Springer US.
Elith, J., Graham, C. H., Anderson, R. P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R. J., Huettmann, F., Leathwick, R., Lehmann, A., Li, J., Lohmann L. G., Loiselle, B. A., Manion, G., Moritz, C., Nakamura, M., akazawa, Y., Overton, J. M., Peterson, A. T., Phillips, S. J., Richardson, K., Scachetti-Pereira, R., Schapire, R. E., Soberón, J., Williams, S., Wisz, M. S., y Zimmermann N. E. (2006). Novel methods improve prediction of species' distributions from occurrence data. Ecography, 129-151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
Fourcade, Y., Engler, J. O., Rödder, D., y Secondi, J. (2014). Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PloS one, 9(5), e97122. https://doi.org/10.1371/journal.pone.0097122
Harding, L. E. (2015). Nasalis larvatus (Primates: Colobini). Mammalian Species, 47(926), 84-99. https://doi.org/10.1093/mspecies/sev009
Holzmann, I., Agostini, I., DeMatteo, K., Areta, J. I., Merino, M. L., y Di Bitetti, M. S. (2015). Using species distribution modeling to assess factors that determine the distribution of two parapatric Howlers (Alouatta spp.) in South America. International Journal of Primatology, 36(1), 18-32. https://doi.org/10.1007/s10764-014-9805-1
Halvorsen, R., Mazzoni, S., Dirksen, J. W., Næsset, E., Gobakken, T., y Ohlson, M. (2016). How important are choice of model selection method and spatial autocorrelation of presence data for distribution modelling by MaxEnt? Ecological modelling, 328, 108-118. https://doi.org/10.1016/j.ecolmodel.2016.02.021
INEGI (2017). Anuario estadístico y geográfico de Tabasco 2017. Instituto Nacional de Estadística y Geografía. Gobierno de la República Mexicana.
Li, Y. (2007). Terrestriality and tree stratum use in a group of Sichuan snub-nosed monkeys. Primates, 48(3), 197-207.
Luoto, M., Pöyry, J., Heikkinen, R. K., y Saarinen, K. (2005). Uncertainty of bioclimate envelope models based on the geographical distribution of species. Global Ecology and biogeography, 14(6), 575-584. https://doi.org/10.1111/j.1466-822X.2005.00186.x
Maciel-Mata, C. A., Manríquez-Morán, N., Octavio-Aguilar, P., & Sánchez-Rojas, G. (2015). El área de distribución de las especies: revisión del concepto. Acta universitaria, 25(2), 03-19.
Mateo, R. G., Felicísimo, Á. M., y Muñoz, J. (2011). Modelos de distribución de especies: Una revisión sintética. Revista chilena de historia natural, 84(2), 217-240. http://dx.doi.org/10.4067/S0716-078X2011000200008
Merckx, B., Steyaert, M., Vanreusel, A., Vincx, M., y Vanaverbeke, J. (2011). Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling. Ecological Modelling, 222(3), 588-597. https://doi.org/10.1016/j.ecolmodel.2010.11.016
Medeiros, K., Bastos, M., Jones, G., & Bezerra, B. (2019). Behavior, Diet, and Habitat Use by Blonde Capuchin Monkeys (Sapajus flavius) in a Coastal Area Prone to Flooding: Direct Observations and Camera Trapping. International Journal of Primatology, 40(4-5), 511-531.
Muñoz, D., Estrada, A., Naranjo, E., & Ochoa, S. (2006). Foraging ecology of howler monkeys in a cacao (Theobroma cacao) plantation in Comalcalco, Mexico. American Journal of Primatology: Official Journal of the American Society of Primatologists, 68(2), 127-142. https://doi.org/10.1002/ajp.20211
Mühlner, S., Kormann, U., Schmidt-Entling, M., Herzog, F., & Bailey, D. (2010). Structural versus functional habitat connectivity measures to explain bird diversity in fragmented orchards. Journal of Landscape Ecology, 3(1), 52-64.
Ordoñez Sierra R. (2014). Modelado espacio-temporal de desfase y amplitud de la variabilidad climática en la cuenca Lerma-Chapala-Santiago. Tesis de Maestría. Centro Interamericano de Recursos del Agua, Facultad de Ingeniería, Universidad Autónoma del Estado de México. Toluca, Estado de México. 155 p. http://hdl.handle.net/20.500.11799/109524
Pablos, N. S., Barbosa, A. M., Freiría, F. M., & Real, R. (2010). Los modelos de nicho ecológico en la herpetología ibérica: pasado, presente y futuro. Boletín de la Asociación Herpetológica Española, (21), 2-24.
Palma-López, D. J., Cisneros, D. J., Moreno, C. E., y Rincón-Ramírez, J. A. (2007). Suelos de Tabasco: su uso y manejo sustentable. Colegio de Postgraduados-ISPROTAB-FUPROTAB. Villahermosa, Tabasco, México. 195 pp.
Pratumchart, K., Suwannatrai, K., Sereewong, C., Thinkhamrop, K., Chaiyos, J., Boonmars, T., & Suwannatrai, A. T. (2019). Ecological Niche Model based on Maximum Entropy for mapping distribution of Bithynia siamensis goniomphalos, first intermediate host snail of Opisthorchis viverrini in Thailand. Acta tropica, 193, 183-191. https://doi.org/10.1016/j.actatropica.2019.03.004
Peterson, A. T., & Soberón, J. (2012). Species distribution modeling and ecological niche modeling: getting the concepts right. Natureza & Conservação, 10(2), 102-107.
Phillips, S. J., y Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2), 161-175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
Peterson A. T., Soberón J., Pearson R. G., Anderson R. P., Martínez-Meyer E., Nakamura M., Bastos-Araújo M. (2011) Ecological Niches and Geographic Distributions. Princeton University Press. Consulted: 20/May/2017.
Phillips, S. J., Anderson, R. P., y Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological modelling, 190(3-4), 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Polechová, J., & Storch, D. (2008). Ecological niche. Encyclopedia of ecology, 2, 1088-1097.
Pyritz, L. W., Büntge, A. B., Herzog, S. K., & Kessler, M. (2010). Effects of habitat structure and fragmentation on diversity and abundance of primates in tropical deciduous forests in Bolivia. International journal of primatology, 31(5), 796-812.
Ramos-Reyes, R., Sánchez-Hernández, R., y Gama-Campillo, L. M. (2016). Análisis de cambios de uso del suelo en el municipio costero de Comalcalco, Tabasco, México. Ecosistemas y recursos agropecuarios, 3(8), 151-160.
Rylands, A. B., Groves, C. P., Mittermeier, R. A., Cortés-Ortiz, L., y Hines, J. J. (2006). Taxonomy and distributions of Mesoamerican primates. In New perspectives in the study of Mesoamerican primates (pp. 29-79). Springer, Boston, MA. https://doi.org/10.1007/0-387-25872-8_3
Sánchez-Munguía, A. (2005). Uso del suelo agropecuario y deforestación en Tabasco 1950-2000. Universidad Juárez Autónoma de Tabasco. 123 pp.
San Vicente, M. G., & Valencia, P. J. L. (2012). Efectos de la fragmentación de hábitats y pérdida de conectividad ecológica dentro de la dinámica territorial. Polígonos. Revista de geografía, (16), 35-54. http://dx.doi.org/10.18002/pol.v0i16.410
Sillero, N., & Goncalves-Seco, L. (2014). Spatial structure analysis of a reptile community with airborne LiDAR data. International Journal of Geographical Information Science, 28(8), 1709-1722. http://dx.doi.org/10.1080/13658816.2014.902062
Sillero, N., Brito, J. C., Skidmore, A. K., & Toxopeus, A. G. (2009). Biogeographical patterns derived from remote sensing variables: the amphibians and reptiles of the Iberian Peninsula. Amphibia-Reptilia, 30(2), 185-206.
Sillero, N., Brito, J., Martín-Alfageme, S., García-Meléndez, E., Toxopeus, A., & Skidmore, A. (2012). The significance of using satellite imagery data only in Ecological Niche Modelling of Iberian herps. Acta herpetologica, 7(2), 221-237.
Schurr, F. M., Pagel, J., Cabral, J. S., Groeneveld, J., Bykova, O., O’Hara, R. B., & Schröder, B. (2012). How to understand species’ niches and range dynamics: a demographic research agenda for biogeography. Journal of Biogeography, 39(12), 2146-2162. https://doi.org/10.1111/j.1365-2699.2012.02737.x
Schooley, R. L., & Branch, L. C. (2011). Habitat quality of source patches and connectivity in fragmented landscapes. Biodiversity and Conservation, 20(8), 1611-1623.
Soley‐Guardia, M., Gutiérrez, E. E., Thomas, D. M., Ochoa‐G, J., Aguilera, M., & Anderson, R. P. (2016). Are we overestimating the niche? Removing marginal localities helps ecological niche models detect environmental barriers. Ecology and evolution, 6(5), 1267-1279. https://doi.org/10.1002/ece3.1900
Sweets, J. A. 1988. Measuring the accuracy of diagnostic systems. Science 240: 1285-1293. DOI: 10.1126/science.3287615
Title P.O., y Bemmels J.B. (2018). ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography. 41:291–307. https://doi.org/10.1111/ecog.02880
Valle, M., Borja, Á., Chust, G., Galparsoro, I., & Garmendia, J. M. (2011). Modelling suitable estuarine habitats for Zostera noltii, using ecological niche factor analysis and bathymetric LiDAR. Estuarine, Coastal and Shelf Science, 94(2), 144-154. https://doi.org/10.1016/j.ecss.2011.05.031
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