Date of Award

Winter 12-2016

Level of Access Assigned by Author

Open-Access Thesis

Degree Name

Master of Science (MS)

Department

Forest Resources

Advisor

Aaron R. Weiskittel

Second Committee Member

Anthony D’Amato

Third Committee Member

Erin Simons-Legaard

Abstract

The spruce-fir (Picea-Abies) forest type of the Acadian Region is at risk of disappearing from the United States and parts of Canada due to climate change and associated impacts. Managing for the ecosystem services provided by this forest type requires accurate forecasting of forest metrics across this broad international region in the face of the expected redistribution of tree species. This analysis linked species specific data with climate and topographic variables using the nonparametric random forest algorithm, to generate models that accurately predicted changes in species distribution due to climate change. A comprehensive dataset, consisting of 10,493,619 observations from twenty-two agencies, including historical inventories, assured accurate assignation of species distribution at a finer resolution (1 km2) than previous analyses. Different dependent variables were utilized, including presence/absence, a likelihood value, abundance variables (i.e. basal area, stem density, and importance value), and predicted maximum stand density index (SDImax), in order to inspect the difference in results in regards to their conservation management utility, as well as the effects of inherent species life history traits on outcomes.

Using linear quantile mixed models, predictions of SDImax were estimated for spruce or fir-dominated plots across the Acadian Region. Model performance was strong and estimates of SDImax from these models were similar to previous regional studies. The establishment of an individual constant slope of self-thinning for plots dominated by each spruce or fir species reinforces previous research that Reineke’s slope is not universal for all species, and that the differences in slope are telling of different species’ life history patterns. Individual plot estimates of SDImax, achieved through a varying intercept, allowed for the assessment of each stand’s potential and limitations in regards to the impact that climate, nutrient availability, site quality, and other factors might have on SDI.

A high association with environmental variables was exhibited for all dependent variables. Area under receiver operator curve values for presence/absence models averaged 0.99 ± 0.01 (mean ± SD) well above the accepted standard for excellent model performance. The addition of historical tree data revealed supplementary suitable habitat along the southern edge of species’ ranges, due to marginal dynamics potentially overlooked by approaches relying solely on current inventories. The likelihood models provided an adequate surrogate to abundance models, reflecting gradients of suitable habitat. The SDImax variables performed the best of the continuous variables inspected in regards to climate associations, likely because of the selection of spruce or fir-dominated plots and the ability to capture core ranges. Black spruce (Picea mariana (Miller) B.S.P.) responded the best to abundance modeling, due to this species’ uniform range. White spruce (Picea glauca (Moench) Voss) consistently performed the worst among all species for each model, due to this species’ wide distribution at low abundances. Presence/absence models assist in understanding the full range of climatically suitable habitats, abundance values provide the ability to prioritize suitable habitat based upon higher abundance, and SDImax models can be utilized for the construction of Density Management Diagrams and the active management of future landscapes based on size-density relationships.

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