Laura P. Leites Ph.D.
B.S., Universidad de la Republica, Uruguay (1997)
M.S., University of Idaho (2001)
Ph.D., University of Idaho (2009)
Application of quantitative methods and statistical modeling techniques to natural resources issues.
Statistical modeling of forest communities and species responses to climate and global climate change.
Development of quantitative tools that improve on-the-ground planning of adaptation strategies in forest ecosystems.
FOR 350 – Forest Biometrics
Resource Modeling Association, Board of Directors
American Statistical Association, Member
Ecological Society of America, Member
Society of American Foresters, Member
Recent Research/Educational Projects:
Modeling forest tree species growth responses to changes in climate using provenance tests data.
Development of high spatial resolution bioclimatic models for important Pennsylvania forest communities.
Modeling regeneration of Pennsylvania forest ecosystems.
Modeling the allometry of loblolly pine in Uruguay.
Leites, L., A. Zubizarreta-Gerendiain, A. Robinson. 2013. Modeling the
mensurational relationships of plantation-grown loblolly pine (Pinus taeda L.)
in Uruguay. Forest Ecology and Management. 289:455-462.
Leites, L., G. Rehfeldt, A. Robinson, N. Crookston, B. Jaquish. 2012 . Possibilities and limitations of using historic provenance tests to infer forest species growth responses to climate change. Natural Resource Modeling. 25(3): 409-433.
Leites, L., A. Robinson, G. Rehfeldt, J. Marshall, N. Crookston. 2012. Height-growth response to climatic changes differ among populations of Douglas-fir: a novel analysis of historic data. Ecological Applications. 22(1): 154-165.
Leites, L., A. Robinson, N. Crookston. 2009. Accuracy and equivalence testing of crown ratio models and assessment of their impact on diameter growth and basal area increment predictions of two variants of the Forest Vegetation Simulator. Canadian Journal of Forest Research 39:655-665.
Leites, L., A. Robinson. 2004. Improving taper equations of loblolly pine with crown dimensions in a mixed-effects modeling framework. Forest Science. 50(2):204-212.