Influence of climatic conditions, topography and soil attributes on the spatial distribution of site productivity index of the species rich forests of Jalisco, Mexico

Journal of Forestry Research

Volumn 25, Issue 1, 2014, Pages 87-95

  Mohamed, Adel ^a   Reich, Robin M ^b   Khosla, Raj ^b   Aguirre Bravo, C ^c   Briseño, Martin Mendoza ^d  

^a DESERT RESEARCH CENTER   (Egypt)

^b Veterinary Dentistry and Oral Surgery   (United States)

^c USDA FOREST SERVICE   (United States)

^d Colegio de Postgraduados ^*  (Mexico)

Author keywords

Best management practices; climate change; spatial predictions; tropical dry forests; weighted least squares

Indexed keywords

BEST MANAGEMENT PRACTICE; CLIMATE CHANGE; CLIMATE EFFECT; COST-BENEFIT ANALYSIS; DECISION MAKING; DRY FOREST; FOREST MANAGEMENT; FOREST RESOURCE; HYPOTHESIS TESTING; MAP; PRODUCTIVITY; SOIL PROPERTY; SPATIAL DISTRIBUTION; SPECIES RICHNESS; TOPOGRAPHIC EFFECT;

JALISCO; MEXICO [NORTH AMERICA];

EID: 84893984758     PISSN: 1007662X     EISSN: 19930607     Source Type: Journal    
DOI: 10.1007/s11676-014-0434-5     Document Type: Article

References (39)

1. Acharya T, Ray AK. 2005. Image processing: principles and applications. New York: Wiley, p. 452.
2. Agramont ARE, Maass SF, Bernal GN, Hernández JIV, Fredericksen TS. 2012. Effect of human disturbance on the structure and regeneration of forests in the Nevado de Toluca National Park, Mexico. Journal of Forestry Research. 23: 39-44.
3. Akaike H. 1969. Fitting autoregressive models for regression. Annals of the Institute of Statistical Mathematics, 21: 243-247.
4. Avery, T. E. and Burkhart, H. E. 2002. Forest Measurements (5th Ed). Madison: McGraw Hill, 456 pp.
5. Bradford JB. 2011. Divergence in forest-type response to climate and weather: evidence for regional links between forest-type evenness and net primary productivity. Ecosystems, 14: 975-986.
6. Brienen RJW, Lebrija-Trejos E, Zuidema PA, Martinez-Ramos M. 2010. Climate-growth analysis for a Mexican dry forest tree shows strong impact of sea surface temperatures and predicts future growth declines. Global Change Biology, 16: 2001-2012.
7. Challenger A. 1998. Utilización y conservación de los ecosistemas terrestres de México. Pasado, presente y futuro. Conabio, IBUNAM y Agrupacion Sierra Madre, México. pp. 375-442.
8. Cressie N. 1991. Statistics for spatial data. New York: John Wiley and Sons, p. 928.
9. Condit R, Aguilar S, Hernandez A, Perez R, Lao S, Angehr G, Hubbell SP. Foster RB. 2004. Tropical forest dynamics across a rainfall gradient and the impact of an El Nino dry season. Journal Tropical Ecology, 20: 51-72.
10. Elith J, Leathwick JR. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics40: 677-697.
11. Ercanli I, Gunlu A, Altun L, Baskent E. 2008. Relationship between site index of oriental spruce [Picea orientalis (L.) Link] and ecological variables in Mac?ka, Turkey. Scandinavian Journal Forestry Research, 23: 319-329.
12. Edenius L, Vencatasawmy CP, Sandstrom P, Dahlberg U. 2003. Combining satellite imagery and ancillary data to map snowbed vegetation important to Reindeer Rangifer tarandus. Arctic, Antarctic and Alpine Research, 35: 150-157.
13. Efron, B. and Tibshirani, R. J. 1993. An introduction to the bootstrap. New York: Chapman and Hall, p. 456.
14. ESRI. 2008. Environmental Systems Research Institute, Inc., 380 New York St., Readlands, CA 97393. USA.
15. Foster D, Swanson F, Aber J, Burke I, Brokaw N, Tilman D, Knapp A. 2003. The importance of land-use legacies to ecology and conservation. Bioscience53: 77-88.
16. Gesch D, Oimoen M, Greenlee S, Nelson C, Steuck M, Tyler D. 2002. The national elevation dataset. Photogrammetric Engineering & Remote Sensing, 68: 5-32.
17. Gilba EK, Kayombo CJ, Chirenje LI, Musamba EB. 2011. The influence of socio-economic factors on deforestation: a case study of the Bereku Forest Reserve in Tanzania. Biodiversity, 2: 31-39.
18. Gough CM, Vogel CS, Schmid HP, Curtis PS. 2008. Controls on annual forest carbon storage: lessons from the past and predictions for the future. Bioscience, 58: 609-22.
19. Gustafson EJ, Lietz SM, Wright JL. 2003. Predicting the spatial distribution of aspen growth potential in the upper great Lakes regions. Forest Science, 49: 499-508.
20. Huang S, Titus SJ. 1992. Comparison of nonlinear height-diameter functions for major Alberta tree species. Canadian Journal Forest Research, 22: 1297-1304.
21. Huang S, Titus SJ. 1993. An index of site productivity for uneven-aged or mixed-species stands. Canadian Journal Forest Research, 23: 558-562.
22. Louw JH, Scholes MC. 2006. Site index functions using site descriptors for Pinus patula plantations in South Africa. Forest Ecology and Management, 225: 94-103.
23. Ma MD, Jiang H, Liu SR, Zu CQ, Liu Yj, Wang JX. 2006. Estimation of forest-ecosystem site index using remote sensed data. Acta Ecologica Sinica, 26: 2810-2816.
24. Mohamed A, Reich RM, Khosla R, Aguirre-Bravo C, Mendoza Briseño M. 2012. Site productivity curves for the diverse forest types of Jalisco, Mexico. Madera y Bosques (in press).
25. Moreno-Sanchez R, Juan Manuel Torres-Rojo JM, Moreno-Sanchez F, Hawkins S, Little J, McPartland S. 2012. National assessment of the fragmentation, accessibility and anthropogenic pressure on the forests in Mexico. Journal of Forestry Research, 23: 529-541.
26. Nixon KC. 1993. El género Quercus en México. In: Ramamoorthy, T. P., R. Bye, A. Lot, y J. Fa (eds), Diversidad Biológica de México. Orígenes y Distribución. Instituto de Biología, UNAM. pp. 435-448.
27. Pande PK. 2005. Biomass and productivity in some disturbed tropical dry deciduous teak forests of Satpura plateau, Madhya Pradesh. Tropical Ecology, 46: 229-239.
28. Peters EB, Wythers KR, Bradford JB, Reich PB. 2013. Influence of disturbance on temperate forest productivity. Ecosystems, 16: 95-110.
29. Pokharel B, Dech JH. 2011. An ecological land classification approach to modeling the production of forest biomass. The Forestry Chronicle, 87: 23-32.
30. Pongpattananurak N, Reich RM, Khosla R, Aguirre-Bravo C. 2012. Modeling the spatial distribution of soil attributes at a regional level: A case study in the State of Jalisco, Mexico. Soil Science Society of America Journal, 76: 199-209.
31. Reich RM, Lundquist JE, Bravo VA. 2004. Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA. International Journal of Wildland Fire, 13: 119-129.
32. Reich RM, Aguirrie-Bravo C, Mendoza Briseno, M. 2008a. An innovative approach to inventory and monitoring of natural resources in the Mexican State of Jalisco. Environmental Monitoring and Assessments, 146: 383-396.
33. Reich RM, Aguirrie-Bravo C, Bravo VA. 2008b. New approach for modeling climatic data with applications in modeling tree species distributions in the states of Jalisco and Colima, Mexico. Journal Arid Environments, 72: 1343-1357.
34. Reich RM, Bonham DC, Aguirrie-Bravo C, Chazaro-Basañeza M. 2010. Patterns of tree species richness in Jalisco, Mexico: relation to topography, climate and forest structure. Plant Ecology, 210: 67-84.
35. Vanclay JK. 1992. Assessing site productivity in tropical moist forests: a review. Forest Ecology and Management, 54: 257-287.
36. Vanclay JK, Henry NB, 1988. Assessing site productivity of indigenous cypress pine forest in southern Queensland. Commonwealth Forestry Review, 67: 53-64.
37. th Ed.). New York: Springer, p. 495.
38. Wang Y, Frederic R, Chhun H. 2005. Evaluation of spatial predictions of site index obtained by parametric and nonparametric methods-A case study of Lodgepole pine productivity. Forest Ecology and Management, 214: 201-211.
39. Watt M, David P, Heidi D, Mark K. 2009. Predicting the spatial distribution of Cupressus lusitanica productivity in New Zealand. Forest Ecology and Management, 258: 27-223.