Drivers of deforestation in the basin of the Usumacinta River: Inference on process from pattern analysis using generalised additive models

Autoři: Raúl Abel Vaca aff001;  Duncan John Golicher aff002;  Rocío Rodiles-Hernández aff001;  Miguel Ángel Castillo-Santiago aff003;  Marylin Bejarano aff004;  Darío Alejandro Navarrete-Gutiérrez aff003
Působiště autorů: CONACYT—Consorcio de Investigación, Innovación y Desarrollo para las Zonas Áridas (CIIDZA), El Colegio de San Luis (COLSAN), Fraccionamiento Colinas del Parque, San Luis Potosi, S.L.P., México aff001;  Department of Life and Environmental Sciences, Bournemouth University, Poole, Dorset, United Kingdom aff002;  Laboratorio de Análisis de Información Geográfica y Estadística, El Colegio de la Frontera Sur, San Cristóbal de Las Casas, Chiapas, México aff003;  Pronatura Sur A.C., San Cristóbal de Las Casas, Chiapas, México aff004
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222908


Quantifying patterns of deforestation and linking these patterns to potentially influencing variables is a key component of modelling and projecting land use change. Statistical methods based on null hypothesis testing are only partially successful for interpreting deforestation in the context of the processes that have led to their formation. Simplifications of cause-consequence relationships that are difficult to support empirically may influence environment and development policies because they suggest simple solutions to complex problems. Deforestation is a complex process driven by multiple proximate and underlying factors and a range of scales. In this study we use a multivariate statistical analysis to provide contextual explanation for deforestation in the Usumacinta River Basin based on partial pattern matching. Our approach avoided testing trivial null hypotheses of lack of association and investigated the strength and form of the response to drivers. As not all factors involved in deforestation are easily mapped as GIS layers, analytical challenges arise due to lack of a one to one correspondence between mappable attributes and drivers. We avoided testing simple statistical hypotheses such as the detectability of a significant linear relationship between deforestation and proximity to roads or water. We developed a series of informative generalised additive models based on combinations of layers that corresponded to hypotheses regarding processes. The importance of the variables representing accessibility was emphasised by the analysis. We provide evidence that land tenure is a critical factor in shaping the decision to deforest and that direct beam insolation has an effect associated with fire frequency and intensity. The effect of winter insolation was found to have many applied implications for land management. The methodology was useful for interpreting the relative importance of sets of variables representing drivers of deforestation. It was an informative approach, thus allowing the construction of a comprehensive understanding of its causes.

Klíčová slova:

Agricultural workers – Agriculture – Deforestation – Forests – Livestock – Population density – Urban areas – Insolation


1. Lambin EF, Turner BL, Geist HJ, Agbola SB, Angelsen A, Bruce JW et al. The causes of land-use and land-cover change: moving beyond the myths. Global Environ Chang. 2001; 11: 261–269.

2. Turner MG, Gardner RH, O’Neill RV. Landscape ecology in theory and practice: Pattern and process. Springer, New York; 2001.

3. Cochrane MA. Synergistic interactions between habitat fragmentation and fire in evergreen tropical forests. Conserv Biol. 2001; 15: 1515–1521.

4. Cochrane MA. Fire science for rainforests. Nature. 2003; 421: 913919.

5. Pechony O, Shindell DT. Driving forces of global wildfires over the past millennium and the forthcoming century. P Natl Acad Sci USA. 2010; 107: 19167–19170.

6. Brotons L, Aquilué N, de Cáceres M, Fortin M-J, Fall A. How fire history, fire suppression practices and climate change affect wildfire regimes in Mediterranean landscapes. PLoS ONE. 2013; 8(5): e62392. doi: 10.1371/journal.pone.0062392 23658726

7. Senft RL, Rittenhouse LR, Woodmansee RG. The Use of Regression Models to Predict Spatial Patterns of Cattle Behavior. J Range Manage. 1983; 36(5): 553–557.

8. Bailey DK, Gross JE, Laca EA, Rittenhouse LR, Coughenour MB, Swift DM et al. Mechanisms that result in large herbivore grazing distribution patterns. J Range Manage. 1996; 49(5):386–400.

9. Curtin CG. Livestock grazing, rest, and restoration in arid landscapes. Conserv Biol. 2002; 16(3): 840–842.

10. DeFries R, Herold M, Verchot L, Macedo MN, Shimabukuro Y. Export-oriented deforestation in Mato Grosso: harbinger or exception for other tropical forests? Philos T R Soc A. 2013; 368: 20120173

11. Fletes HB, Rangel F, Oliva-Velas A, Ocampo-Guzmán G. Pequeños productores, reestructuración y expansión de la palma africana en Chiapas. Región y Sociedad. 2013; 57: 203–239.

12. Mena CF, Bilsborrow RE, McClain ME. Socioeconomic drivers of deforestation in the northern Ecuadorian Amazon. Environ Manage. 2006; 37(6): 802–815. doi: 10.1007/s00267-003-0230-z 16555027

13. Alix-Garcia J, de Janvry A, Sadoulet E. A tale of two communities: Explaining deforestation in Mexico. World Dev. 2005; 33(2): 219–235.

14. Geist HJ, Lambin EF. Proximate causes and underlying driving forces of tropical deforestation. BioScience. 2002; 52(2): 143–150.

15. Holland MB, de Koning F, Morales M, Naughton-Treves L, Robinson BE, Suárez L. Complex tenure and deforestation: Implications for conservation incentives in the Ecuadorian Amazon. World Dev. 2014; 55: 21–36.

16. Geist HJ, Lambin EF. What drives tropical deforestation? A meta-analysis of proximate and underlying causes of deforestation based on subnational case study evidence. Louvain-la-Neuve, Belgium; 2001.

17. Lambin EF, Geist HJ, Lepers E. Dynamics of Land-Use and Land-Cover Change in Tropical Regions. Annu Rev Env Resour. 2003; 28: 205–241.

18. Hosonuma N, Herold M, de Sy V, DeFries RS, Brockhaus M, Verchot L et al. An assessment of deforestation and forest degradation drivers in developing countries. Environ Res Lett. 2012; 7(4): 4009.

19. Swart RJ, Raskin P, Robinson J. The problem of the future: sustainability science and scenario analysis. Global Environ Chang. 2004; 14: 137–146.

20. Geoghegan J, Schneider L, Vance C. Temporal dynamics and spatial scales: Modeling deforestation in the southern Yucatán peninsular region. GeoJournal. 2004; 61: 353–363.

21. Mather A, Needle C. The relationships of population and forest trends. Geogr J. 2000; 166(1): 2–13.

22. Geist H, Lambin E. Is poverty the cause of tropical deforestation? The International Forestry Review. 2003; 5(1): 64–67.

23. Burnham K, Anderson D. Kullback-Leibler information as a basis for strong inference in ecological studies. Wildlife Res. 2001; 28(2): 111–120.

24. Anderson DR, Burnham KP, Thompson WL. Null hypothesis testing: Problems, prevalence, and an alternative. J Wildlife Manage. 2000; 64(4): 912–923.

25. Cherry S. Statistical tests in publications of the Wildlife Society. Wildlife Soc B. 1998; 26: 947–953.

26. Johnson DH. The insignificance of statistical significance testing. J Wildlife Manage. 1999; 63(3): 763–772.

27. Nester MR. An applied statistician’s creed. Applied Statistics. 1996; 45(4): 401–410.

28. Stephens PA, Buskirk SW, Hayward GD, Martínez del Río C. Information theory and hypothesis testing: A call for pluralism. J Appl Ecol. 2005; 42(1): 4–12.

29. Mifsut IM, Castro M. La cuenca del río Usumacinta: Perfil y perspectivas para su conservación y desarrollo sustentable. In: Cotler Ávalos H., editor. Las cuencas hidrográficas de México. Diagnóstico y priorización. México D.F.: SEMARNAT-INE-Gonzálo Río Arronte, I.A.P; 2010. pp. 193–197.

30. Hudson PF, Hendrickson DA, Benke AC, Varela-Romero A, Rodiles-Hernández R, Minckley WL. Rivers of Mexico. In Benke C., Cushing CE, editors. Rivers of North America. Elsevier; 2005. pp. 1031–1084.

31. Challenger A. Utilización y conservación de los ecosistemas terrestres de México: Pasado, prresente y futuro. Universidad Autónoma de México, Instituto de Biología, México; 1998.

32. Rzedowski J. Vegetación de México. Limusa, México; 1978.

33. Vaca RA, Golicher DJ, Cayuela L, Hewson J, Steininger M. Evidence of Incipient Forest Transition in Southern Mexico. PLoS ONE. 2012; 7(8): e42309. doi: 10.1371/journal.pone.0042309 22905123

34. INEGI, CONABIO, & INE. Ecorregiones terrestres de México. México. 2008. Scale 1:1000000.

35. Isaac-Márquez R, de Jong B, Eastmond A, Ochoa-Gaona S, Hernández S, Sandoval JL. Factors affecting land use and peasant production strategies in Eastern of Tabasco, Mexico. Universidad y Ciencia. 2005; 21(42): 56–72.

36. Isaac-Márquez R, de Jong B, Eastmond A, Ochoa-Gaona S, Hernández S, Sandoval JL. Programas gubernamentales y respuestas campesinas en el uso del suelo: El caso de la zona oriente de Tabasco, México. Región y Sociedad. 2008; 43: 97–129.

37. Capdepont-Ballina JL, Marín-Olán P. Tabasco’s Economy and its Impact over the Urban Sprawl of Villahermosa City (1960–2010). Revista LiminaR. Estudios Sociales y Humanísticos. 2014; 12(1): 144–160.

38. Hamann R, Ankersen T. The Usumacinta river, Building a Framework for Cooperation between Mexico and Guatemala, paper submitted to the roundtable meeting held in San Cristobal de Las Casas (Mexico), on July 25 and 26, 1996.

39. Gandin J. Social Perceptions of Environmental Changes and Local Development within the Usumacinta River Basin. APCBEE Procedia. 2012; 1:239–244.

40. Sánchez-Munguía A. Uso del suelo agropecuario y deforestación en Tabasco 1950–2000. Universidad Juárez Autónoma de Tabasco, México; 2005.

41. Alvarado E. Poverty and Inequality in Mexico after NAFTA: Challenges, Setbacks and Implications. Estudios Fronterizos. 2008; 9(17): 73–105.

42. GRASS Development Team. Geographic Resources Analysis Support System (GRASS) Software, Version 7.2. Open Source Geospatial Foundation; 2017.

43. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2008.

44. Wood SN. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J Am Stat Assoc. 2004; 99: 673–686.

45. Beale CM, Lennon JJ, Elston DA, Brewer MJ, Yearsley JM. Red herrings remain in geographical ecology: A reply to Hawkins et al. (2007). Ecography. 2007; 30(6): 845–847.

46. Pebesma EJ. Multivariable geostatistics in S: The gstat package. Comput Geosci. 2004; 30(7): 683–691.

47. Therneau TM, Atkinson B, Ripley R. rpart: Recursive Partitioning. R package version 3.1–44; 2009.

48. Díaz-Gallegos JR, Mass JF, Velázquez A. Trends of tropical deforestation in Southeast Mexico. Singapore J Trop Geo. 2010; 31(2): 180–196.

49. Trejo I., Dirzo R. Deforestation of seasonally dry tropical forest: a national and local analysis in Mexico. Biol Conserv. 2000; 94: 133–142.

50. Ramírez-Delgado JP, Christman Z, Schmook B. Deforestation and fragmentation of seasonal tropical forests in the southern Yucatán, Mexico (1990–2006). Geocarto Int. 2014; 6049: 1–38.

51. Vaca RA, Rodiles-Hernández R, Ochoa-Gaona S, Taylor-Aquino NE, Obregón-Viloria R, Díaz-García DA et al. Evaluating and supporting conservation action in agricultural landscapes of the Usumacinta River Basin. Journal of Environmental Management. 2019; 230: 392–404. doi: 10.1016/j.jenvman.2018.09.055 30296677

52. Barbier EB, Burgess JC. Econommic analysis of deforestation in Mexico. Environment and Development Economics. 1996; 1: 203–239.

53. Guerra-Martínez V, Ochoa-Gaona S. Evaluación espacio-temporal de la vegetación y uso del suelo en la Reserva de la Biosfera Pantanos de Centla, Tabasco (1990–2000). Boletín Del Instituto de Geografía UNAM. 2006; 59: 7–25.

54. Pignataro AG, Levy-Tacher SI, Aguirre-Rivera JR, Nahed-Toral J, González-Espinoza M, Redón-Carmona N. Silvopastoral systems of the Chol Mayan ethnic group in southern Mexico: Strategies with a traditional basis. Journal of Environ Manage. 2016; 181: 363–373.

55. DeFries RS, Rudel T, Uriarte M, Hansen M. Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nat Geosci. 2010; 3: 178–181.

56. García-Barrios L, Galván-Miyoshi YM, Valdivieso-Pérez IA, Masera OR, Bocco G, Vandermeer J. Neotropical forest conservation, agricultural intensification, and rural out-migration: The Mexican experience. BioScience. 2009; 59(10): 863–873.

57. Flores-Santiago A. La modernización de la agricultura en el trópico húmedo mexicano: veinte años de experiencia en la Chontalpa, Tabasco. Revista de Geografía Agrícola. 1986; 13–14: 104–115.

58. Mendoza E, Dirzo R. Deforestation in Lacandonia (southeast Mexico): evidence for the declaration of the northernmost tropical hot-spot. Biodiversity and Conservation. 1999; 8: 1621–1641.

59. Chowdhury RR. Landscape change in the Calakmul Biosphere Reserve, Mexico: Modeling the driving forces of smallholder deforestation in land parcels. Appl Geogr. 2006; 26: 129–152.

60. Nahed-Toral J, Sanchez-Muñoz B, Mena Y, Ruiz-Rojas J, Aguilar-Jimenez R, Castel JM et al. Feasibility of converting agrosilvopastoral systems of dairy cattle to the organic production model in southeastern Mexico. J Clean Prod. 2013; 43: 136–145.

61. Wyman MS, Stein TV. Modeling social and land-use/land-cover change data to assess drivers of smallholder deforestation in Belize. Appl Geogr. 2010; 30: 329–342.

62. Laurance WF, Albernaz AKM, Schroth G, Fearnside PM, Bergen S, Venticinque EM et al. Predictors of deforestation in the Brazilian Amazon. J Biogeogr. 2002; 29(5–6): 737–748.

63. Rudel TK, DeFries R, Asner GP, Laurance WF. Changing drivers of deforestation and new opportunities for conservation. Conserv Biol. 2009; 23(6): 1396–1405. doi: 10.1111/j.1523-1739.2009.01332.x 20078640

64. Ellis EA, Romero Montero JA, Hernández Gómez IU, Porter-Bolland L, Ellis PW. Private property and Mennonites are major drivers of forest cover loss in central Yucatan Peninsula, Mexico. Land Use Policy. 2017; 69: 474–484.

65. Sanfiorenzo-Barnhard C, García-Barrios L, Meléndez-Ackerman E, Trujillo-Vásquez R. Woody cover and local farmers’ perceptions of active pasturelands in La Sepultura Biosphere Reserve buffer zone, Mexico. Mt Res Dev. 2009; 29(4): 320–327.

66. Rodríguez-Trejo DA. Fire regimes, fire ecology, and fire management in Mexico. AMBIO: A Journal of the Human Environment. 2008; 37(7): 548–556.

67. Ressl R, Lopez G, Cruz I, Colditz RR, Schmidt M, Ressl S et al. Operational active fire mapping and burnt area identification applicable to Mexican Nature Protection Areas using MODIS and NOAA-AVHRR direct readout data. Remote Sens Environ. 2009; 113(6): 1113–1126.

68. Román-Cuesta RM, Martínez-Vilalta J. Effectiveness of Protected Areas in Mitigating Fire within Their Boundaries: Case Study of Chiapas, Mexico. Conserv Biol. 2006; 20(4): 1074–1086. 16922224

Článek vyšel v časopise


2019 Číslo 9

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…


Zvyšte si kvalifikaci online z pohodlí domova

Ulcerative colitis_muž_břicho_střeva
Ulcerózní kolitida
nový kurz

Blokátory angiotenzinových receptorů (sartany)
Autoři: MUDr. Jiří Krupička, Ph.D.

Antiseptika a prevence ve stomatologii
Autoři: MUDr. Ladislav Korábek, CSc., MBA

Citikolin v neuroprotekci a neuroregeneraci: od výzkumu do klinické praxe nejen očních lékařů
Autoři: MUDr. Petr Výborný, CSc., FEBO

Zánětlivá bolest zad a axiální spondylartritida – Diagnostika a referenční strategie
Autoři: MUDr. Monika Gregová, Ph.D., MUDr. Kristýna Bubová

Všechny kurzy
Kurzy Doporučená témata