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Shortleaf research, newspaper articles, fact sheets, conference proceedings, literature reviews, and brochures.
Modeling
Estimating the probability of survival of individual shortleaf pine (Pinus echinata Mill.) trees
Shrestha, S. (2010). Estimating the probability of survival of individual shortleaf pine (Pinus echinata Mill.) trees. Proceedings of the 16th biennial southern silvicultural research conference. e-Gen. Tech. Rep. SRS-156. Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research Station. 314-315. Retrieved from https://www.srs.fs.fed.us/pubs/gtr/gtr_srs156/gtr_srs156_314.pdf
Individual tree growth models for natural even-aged shortleaf pine
Budhathoki, C. B., Lynch, T. B., & Guldin, J. M. (2006). Individual tree growth models for natural even-aged shortleaf pine. Southern Research Station, General Technical Report SRS-92. Retrieved from https://www.srs.fs.usda.gov/pubs/gtr/gtr_srs092/gtr_srs092-085-budhathokl.pdf 
Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia
La Manna, L., Matteucci, S., & Kitzberger, T. (2012). Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia. European Journal of Forest Research, 131(2), 323-337. Retrieved from http://link.springer.com/article/10.1007/s10342-011-0503-7#page-1
Nonlinear mixed modeling of basal area growth for shortleaf pine
Budhathoki, C. B., Lynch, T. B., & Guldin, J. M. (2007). An individual-tree dbh-total height model with random plot effects for shortleaf pine. Northern Research Station, General Technical Report NRS-P-15, p. 201. Retrieved from http://www.nrs.fs.fed.us/pubs/gtr/gtr_nrs-p-15.pdf#page=201
Nonlinear programming models to optimize uneven-aged shortleaf pine management
Schulte, B. J., & Buongiorno, J. (2002). Nonlinear programming models to optimize uneven-aged shortleaf pine management. Southern Research Station, General Technical Report SRS-48 pg. 448-453. Retrieved from https://www.srs.fs.usda.gov/pubs/gtr/gtr_srs048/article/gtr_srs048-schuite01.pdf
Spatial model for predicting the presence of cinnamon fungus (Phytophthora cinnamomi) in sclerophyll vegetation communities in south‐eastern Australia
Wilson, B., Lewis, A., & Aberton, J. (2003). Spatial model for predicting the presence of cinnamon fungus (Phytophthora cinnamomi) in sclerophyll vegetation communities in south‐eastern Australia. Austral ecology, 28(2), 108-115. Retrieved from http://onlinelibrary.wiley.com/doi/10.1046/j.1442-9993.2003.01253.x/full |
