Patch selection by wintering white-tailed deer : consequences for eastern hemlock regeneration at local and landscape scales

Tsuga canadensis (eastern hemlock) is a highly shade-tolerant, late-successional, and long-lived conifer species found throughout eastern North America. It is most often found in pure or nearly pure stands, because highly acidic and nutrient poor forest floor conditions are thought to favor T. canadensis regeneration while simultaneously limiting the establishment of some hardwood species with greater nutrient requirements. Once a common species, T. canadensis is currently experiencing widescale declines across its range. The hemlock woolly adelgid (Adelges tsugae) is decimating the population across its eastern distribution. Across the Upper Great Lakes region, where the adelgid is currently being held at bay by cold winter temperatures, T. canadensis has been experiencing failures in regeneration attributed, in part, to herbivory by white-tailed deer (Odocoileus virginianus). Deer utilize T. canadensis stands as winter habitat in areas of high snow depth. Tsuga canadensis, once a major component of these forests, currently exists at just a fraction of its pre-settlement abundance due to historic logging and contemporary forest management practices, and what remains is found in small remnant patches surrounded by second- and third-growth deciduous forests. The deer population across the region, however, is likely double that of pre-European settlement times. In this dissertation I explore the relationship between white-tailed deer use of T. canadensis as winter habitat and the effect this use is having on regeneration and forest succession. For this research I quantified stand composition and structure and abiotic variables of elevation and snow depth in 39 randomly selected T. canadensis stands from across the western Upper Peninsula of Michigan. I also quantified composition and the configuration of the landscapes surrounding these stands. I measured relative deer use of T. canadensis stands as pellet group piles deposited in each stand during each of three consecutive winters, 2005-06, 2006-07, and 2007-08. The results of this research suggest that deer use of T. canadensis stands as winter habitat is influenced primarily by snow depth, elevation, and the composition and configuration of the greater landscapes surrounding these stands. Specifically, stands with more heterogeneous landscapes surrounding them (i.e., a patchy mosaic of conifer, deciduous, and open cover) had higher relative deer use than stands surrounded by homogenous deciduous forest cover. Additionally, the intensity of use and the number of stands used was greater in years with higher average snow depth. Tsuga canadensis regeneration in these stands was negatively associated with deer use and Acer saccharum (sugar maple) basal area. Of the 39 stands, 17 and 22 stands had no T. canadensis regeneration in small and large sapling categories, respectively. Acer saccharum was the most common understory tree species, and the importance of A. saccharum in the understory (stems < 10 cm dbh) of the stands was positively associated with overstory A. saccharum dominance. Tsuga canadensis establishment was associated with high-decay coarse woody debris and moss, and deciduous leaf litter inputs in these stands may be limiting access to these important microsites. Furthermore, A. saccharum is more tolerant to the effects of deer herbivory than T. canadensis, giving A. saccharum a competitive advantage in stands being utilized as winter habitat by deer. My research suggests that limited microsite availability, in conjunction with deer herbivory, may be leading to an erosion in T. canadensis patch stability and an altered successional trajectory toward one of A. saccharum dominance, an alternately stable climax species.

Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further.