Patrones de distribución de cerambícidos (Coleoptera: Cerambycidae) en México
DOI:
https://doi.org/10.22201/ib.20078706e.2025.96.5602Palabras clave:
Registros de colectas, Cerambycidae, Modelo de distribución, Variable de repuestaResumen
Para este estudio se elaboró una base de datos georreferenciados de especies de Cerambycidae rcolectadas en México para documentar sus patrones de distribución. Una muestra de 24 especies fue modelada para generar sus distribuciones potenciales mediante la aplicación de un enfoque de consenso. Se usaron 4 algoritmos de predicción: Maxent, Support Vector Machine, Generalized Linear Model y Artificial Neural Networks. Un total de 1,699 localidades fueron obtenidas después de aplicar procedimientos de limpiado y georreferenciación, lo que resultó en un total de 414 especies georreferenciadas. Especies con ≥ 20 registros incluyeron 9 géneros y 24 especies con 779 registros; especies con 5-20 registros incluyeron 41 géneros y 124 especies con 1,072 registros; especies con ˂ 5 registros incluyeron 94 géneros y 266 especies con 512 registros. Solamente las especies con ≥ 20 registros fueron
modeladas. De acuerdo con el algoritmo Maxent, existieron variables con altos porcentajes de contribución en las predicciones. Los valores más frecuentes de las variables ambientales (respuesta) indicaron cuáles dominaron la distribución de especies y el rango de tales variables provee un estimado de la amplitud de valores ambientales, donde las especies pueden estar presentes. Hace falta trabajo taxonómico para documentar la diversidad de Cerambycidae en México.
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Anderson, R. P., & Martı́nez-Meyer, E. (2004). Modeling species’ geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador. Biological Conservation, 116, 167–179. https://doi-org.pbidi.unam.mx:2443/10.1016/S0006-3207(03)00187-3
Araújo, M. B., & New, M. (2007). Ensemble forecasting of species distributions. Trends in Ecology & Evolution, 22, 42–47. https://doi.org/10.1016/j.tree.2006.09.010
Austin, M. P. (2002). Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157, 101–118. https://doi.org/10.1016/S0304-3800(02)00205-3
Ballesteros-Mejia, L., Kitching, I. J., Jetz, W., Nagel, P., & Beck, J. (2013). Mapping the biodiversity of tropical insects: species richness and inventory completeness of African sphingid moths. Global Ecology and Biogeography, 22, 586–595. https://doi.org/10.1111/geb.12039
Ballesteros-Mejia, L., Kitching, I. J., Jetz, W., & Beck, J. (2017). Putting insects on the map: near-global variation in sphingid moth richness along spatial and environmental gradients. Ecography, 40, 698–708. https://doi.org/10.1111/ecog.02438
Barredo, J. I., Strona, G., De Rigo, D., Caudullo, G., Stancanelli, G., & San-Miguel-
Ayanz, J. (2015). Assessing the potential distribution of insect pests: case studies on large pine weevil (Hylobius abietis L) and horse-chestnut leaf miner (Cameraria ohridella) under present and future climate conditions in European forests. EPPO Bulletin, 45, 273–281. https://doi.org/10.1111/epp.12208
Bezark, L. G., & Monné, M. A. (2013). Checklist of the Oxypeltidae, Vesperidae, Disteniidae and Cerambycidae, (Coleoptera) of the Western Hemisphere. Retrieved from: http://bezbycids.com/byciddb/checklists/WestHemiCerambycidae2024.pdf
Bosso, L., Smeraldo, S., Rapuzzi, P., Sama, G., & Garonna, A. P. (2018). Nature protection areas of Europe are insufficient to preserve the threatened beetle Rosalia alpina (Coleoptera: Cerambycidae): evidence from species distribution models and conservation gap analysis. Ecological Entomology, 43, 192–203. https://doi.org/10.1111/een.12485
Buse, J., Schröder, B., & Assmann, T. (2007). Modelling habitat and spatial distribution of an endangered longhorn beetle-A case study for saproxylic insect conservation. Biological Conservation, 137, 372–381. https://doi.org/10.1016/j.biocon.2007.02.025
Catalano, G. A., D’Urso, P. R., Maci, F., & Arcidiacono, C. (2023). Influence of parameters in SDM application on citrus presence in mediterranean area. Sustainability, 15, 7656. https://doi.org/10.3390/su15097656
Crawford, P. H. C., & Hoagland, B. W. (2010). Using species distribution models to guide conservation at the state level: the endangered American burying beetle (Nicrophorus americanus) in Oklahoma. Journal of Insect Conservation, 14, 511–521. https://doi.org/10.1007/s10841-010-9280-8
Colwell, R. K. (1996). Biota: the biodiversity database manager. Sinauer Associates, Sunderland, Massachusetts. Systematic Biology, 46, 574–575.
D’Amen, M., Pradervand, J. N., & Guisan, A. (2015). Predicting richness and composition in mountain insect communities at high resolution: a new test of the SESAM framework. Global Ecology and Biogeography, 24, 1443–1453. https://doi.org/10.1111/geb.12357
Drake, J. M., Randin, C., & Guisan, A. (2006). Modelling ecological niches with support vector machines. Journal of Applied Ecology, 43, 424–432. https://doi.org/10.1111/j.1365-2664.2006.01141.x
Elith, J., Graham, C. H., Anderson, R. P., Dudík, M., Ferrier, S., Guisan, A. et al. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
Eickermann, M., Junk, J., & Rapisarda, C. (2023). Climate change and insects. Insects, 14, 678. https://doi.org/10.3390/insects14080678
Elith, J., & Graham, C. H. (2009). Do they? How do they? Why do they differ? On finding reasons for differing performances of species distribution models. Ecography, 32, 66–77. https://doi.org/10.1111/j.1600-0587.2008.05505.x
Felicísimo, A. M., Armendáriz, I., & Alberdi, V. (2021). Modelling the potential effects of climate change in the distribution of Xylotrechus arvicola in Spain. Horticultural Science (Prague), 48, 38–46. https://doi.org/10.17221/85/2019-HORTSCI
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 4302–4315. https://doi.org/10.1002/joc.5086
Franklin, J. (2010). Mapping species distributions. Spatial inference and prediction. Cambridge UK: Cambridge University Press. https://doi.org/10.1017/CBO9780511810602
Graham, C. H., Ferrier, S., Huettman, F., Moritz, C., & Peterson, A. T. (2004). New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology & Evolution, 19, 497–503. https://doi.org/10.1016/j.tree.2004.07.006
Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147–186. https://doi.org/10.1016/S0304-3800(00)00354-9
Guisan, A., Edwards, T. C., & Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling, 157, 89–100. https://doi.org/10.1016/S0304-3800(02)00204-1
Guo, Q., Kelly, M., & Graham, C. H. (2005). Support vector machines for predicting distribution of Sudden Oak Death in California. Ecological Modelling, 182, 75–90. https://doi.org/10.1016/j.ecolmodel.2004.07.012
Guo, Q., & Liu, Y. (2010). ModEco: an integrated software package for ecological niche modeling. Ecography, 33, 637–642. https://doi.org/10.1111/j.1600-0587.2010.06416.x
Hassall, C. (2012). Predicting the distributions of under-recorded Odonata using species distribution models. Insect Conservation and Diversity, 5, 192–201. https://doi.org/10.1111/j.1752-4598.2011.00150.x
Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: data mining, inference, and prediction. New York: Springer. https://doi.org/10.1007/978-0-387-21606-5
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology: A Journal of the Royal Meteorological Society, 25, 1965–1978. https://doi.org/10.1002/joc.1276
Huntley, B., Green, R. E., Collingham, Y. C., Hill, J. K., Willis, S. G., Bartlein, P. J. et al. (2004). The performance of models relating species geographical distributions to climate is independent of trophic level. Ecology Letters, 7, 417–426. https://doi.org/10.1111/j.1461-0248.2004.00598.x
Jung, J. M., Lee, W. H., & Jung, S. (2016). Insect distribution in response to climate change based on a model: Review of function and use of CLIMEX. Entomological Research, 46, 223–235. https://doi.org/10.1111/1748-5967.12171
Kostova, R., Bekchiev, R., Popgeorgiev, G., & Kornilev, Y. V. (2023). First exhaustive distribution and habitat modelling of Morimus asper (Sulzer, 1776) sensu lato (Coleoptera, Cerambycidae) in Bulgaria. Nature Conservation, 53, 39–59. https://doi.org/10.3897/natureconservation.53.104243
Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., & Aulagnier, S. (1996). Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling, 90, 39–52. https://doi.org/10.1016/0304-3800(95)00142-5
Li, A., Wang, J., Wang, R., Yang, H., Yang, W., Yang, C. et al. (2020). MaxEnt modeling to predict current and future distributions of Batocera lineolata (Coleoptera: Cerambycidae) under climate change in China. Écoscience, 27, 23–31. https://doi.org/10.1080/11956860.2019.1673604
Linsley, E. G. (1961). The Cerambycidae of North America. Part I. Introduction. University of California Publications in Entomology, 18, 1–135.
Lobo, J. M. (2016). The use of occurrence data to predict the effects of climate change on insects. Current Opinion in Insect Science, 17, 62–68. https://doi.org/10.1016/j.cois.2016.07.003
Lobo, J. M., Lumaret, J. P., & Jay-Robert, P. (2002). Modelling the species richness distribution of French dung beetles (Coleoptera, Scarabaeidae) and delimiting the predictive capacity of different groups of explanatory variables. Global Ecology and Biogeography, 11, 265–277. https://doi.org/10.1046/j.1466-822X.2002.00291.x
Ma, G., & Ma, C. S. (2022). Potential distribution of invasive crop pests under climate change: incorporating mitigation responses of insects into prediction models. Current Opinion in Insect Science, 49, 15–21. https://doi.org/10.1016/j.cois.2021.10.006
Martínez-Hernández, J. G., Rös, M., Pérez-Flores, O., & Toledo-Hernández, V. H. (2024). Checklist of the Cerambycidae (Coleoptera: Chrysomeloidea) of Oaxaca, Mexico. Zootaxa, 5405, 185–208. https://doi.org/10.11646/zootaxa.5405.2.2
Monné, M. A. (2005). Catalogue of the Cerambycidae (Coleoptera) of the Neotropical region. Part I. Subfamily Cerambycinae. Zootaxa, 946, 17–65. https://doi.org/10.11646/zootaxa.946.1.1
Müller, K. R., Mika, S., Tsuda, K., & Schölkopf, K. (2002). An introduction to Kernel-based learning algorithms. In Yu Hen Hu, & Jenq-Neng Hwang (Eds.), Handbook of neural network signal processing. Boca-Raton: CRC Press. https://doi.org/10.1201/9781315220413
Nearns, E. H., Lord, N. P., Lingafelter, S. W., Santos, A., Miller, K. B., & Zaspel, J. M. (2017). LONGICORN ID. Retrieved from: https://cerambycids.com
Noguera, F. A. (2014). Biodiversidad de Cerambycidae (Coleoptera) en México. Revista Mexicana de Biodiversidad, 85 (Supl.), S290–S297. https://doi.org/10.7550/rmb.32966
Norberg, A., Abrego, N., Blanchet F. G., Adler, F. R., Anderson, B. J., Anttila, J. et al. (2019). A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecological Monographs, 89, e01370. https://doi.org/10.1002/ecm.1370
Peterson, A. T., Papeş, M., & Soberón, J. (2008). Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling, 213, 63–72. https://doi.org/10.1016/j.ecolmodel.2007.11.008
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., & Blair, M. E. (2017). Opening the black box: an open‐source release of Maxent. Ecography, 40, 887–893. https://doi.org/10.1111/ecog.03049
Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
Pouteau, R., Meyer, J. Y., Taputuarai, R., & Stoll, B. (2012). Support vector machines to map rare and endangered native plants in Pacific islands forests. Ecological Informatics, 9, 37–46. https://doi.org/10.1016/j.ecoinf.2012.03.003
Ruzzier, E., Lupi, D., Tirozzi, P., Dondina, O., Orioli V., Jucker, C. et al. (2024). A two‑step species distribution modeling to disentangle the effect of habitat and bioclimatic covariates on Psacothea hilaris, a potentially invasive species. Bio Invasions, 26, 1861–1881. https://doi.org/10.1007/s10530-024-03283-9
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13, 1443–1471. https://doi.org/10.1162/089976601750264965
Senay, S. D., & Worner, S. P. (2019). Multi-scenario species distribution modeling. Insects, 10, 65. https://doi.org/10.3390/insects10030065
Silva, D. P., Aguiar, A. G., & Simião-Ferreira, J. (2016). Assessing the distribution and conservation status of a long-horned beetle with species distribution models. Journal of Insect Conservation, 20, 611–620. https://doi.org/10.1007/s10841-016-9892-8
Toledo, V. H., & Corona, A. M. (2006). Patrones de distribución de la familia Cerambycidae (coleóptera). In J. J. Morrone, & J. Llorente Bosques (Eds.), Componentes bióticos principales de la entomofauna mexicana, México (pp. 425–474). México D.F.: Las Prensas de Ciencias, UNAM.
Ulrichs, C., & Hopper, K. R. (2008). Predicting insect distributions from climate and habitat data. BioControl, 53, 881–894. https://doi.org/10.1007/s10526-007-9143-8
Urbani, F., D’alessandro, P., & Biondi, M. (2017). Using Maximum Entropy Modeling (MaxEnt) to predict future trends in the distribution of high altitude endemic insects in response to climate change. Bulletin of Insectology, 70, 189–200.
Valavi, R., Guillera-Arroita, G., Lahoz-Monfort, J. J., & Elith, J. (2022). Predictive performance of presence-only species distribution models: a benchmark study with reproducible code. Ecological Monographs, 92, e01486. https://doi.org/10.1002/ecm.1486
Watts, M. J., & Worner, S. P. (2008). Comparing ensemble and cascaded neural networks that combine biotic and abiotic variables to predict insect species distribution. Ecological Informatics, 3, 354–366. https://doi.org/10.1016/j.ecoinf.2008.08.003
Williams, J. N., Seo, C., Thorne, J., Nelson, J. K., Erwin, S., O’Brien, J. M. et al. (2009). Using species distribution models to predict new occurrences for rare plants. Diversity and Distributions, 15, 565–576. https://doi.org/10.1111/j.1472-4642.2009.00567.x
Zaniewski, A. E., Lehmann, A., & Overton, J. M. (2002). Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecological Modelling, 157, 261–280. https://doi.org/10.1016/S0304-3800(02)00199-0
Zhao, J., Zou, X., Yuan, F., Luo, Y., & Shi, J. (2023). Predicting the current and future distribution of Monochamus carolinensis (Coleoptera: Cerambycidae) based on the maximum entropy model. Pest Management Science, 79, 5393–5404. https://doi.org/10.1002/ps.7753