Biofilm responses to salmon carcass analog addition in central Idaho streams

Pacific salmon populations have declined due to human activity in the Pacific Northwest, resulting in decreased delivery of marine-derived nutrients to streams. Managers use artificial nutrient additions to increase juvenile salmon growth and survival and assume that added nutrients stimulate biofilm production, which propagates up the food web to juvenile salmon. We assessed biofilm responses (standing crop, nutrient limitation, and metabolism) to experimental additions of salmon carcass analog in tributaries of the Salmon River, Idaho in 2010 and 2011. Biofilm standing crop and nutrient limitation did not respond to analog, but primary productivity and respiration increased in the subset of streams where they were measured. Discrepancies between biofilm productivity and standing crop may occur if standing crop is constrained by physical and biological factors. Thus, conclusions about biofilm response to analog should not be based on standing crop alone and mitigation research may benefit from nutrient budgets of entire watersheds.

Finnish Miners versus Ivy League Capitalists

Finnish immigrants were recruited to work in the copper mines of Michigan by Boston capitalists intimately associated with Harvard. Boston capitalists defined the culture of capitalism in the United States. They extended their culture across the continent as they acquired mines and railroads. Finnish workers encountered Ivy League capitalism in Michigan, Minnesota, Idaho, Montana, Wyoming, Colorado, Utah and Arizona—often with the same interrelated group of capitalists. The two groups continually met in explosive confrontations in Montana, Michigan, Minnesota, and Arizona. To the Ivy League capitalists, Finns were terrorists who threatened the American social order. For the Finns, it was a war for social justice. In my presentation, I will trace the experience of Finnish workers in corporations controlled by Ivy League capitalists from the first strike at Calumet & Hecla in 1873 to the Red Scare of 1919-1921.

Effectiveness of a nondestructive evaluation technique for assessing standing timber quality

The research presented in this thesis was conducted to further the development of the stress wave method of nondestructively assessing the quality of wood in standing trees. The specific objective of this research was to examine, in the field, use of two stress wave nondestructive assessment techniques. The first technique examined utilizes a laboratory-built measurement system consisting of commercially available accelerometers and a digital storage oscilloscope. The second technique uses a commercially available tool that incorporates several technologies to determine speed of stress wave propagation in standing trees. Field measurements using both techniques were conducted on sixty red pine trees in south-central Wisconsin and 115 ponderosa pine trees in western Idaho. After in-situ measurements were taken, thirty tested red pine trees were felled and a 15-foot-long butt log was obtained from each tree, while all tested ponderosa pine trees were felled and an 8 1/2 -foot-long butt log was obtained, respectively. The butt logs were sent to the USDA Forest Products Laboratory and nondestructively tested using a resonance stress wave technique. Strong correlative relationships were observed between stress wave values obtained from both field measurement techniques. Excellent relationships were also observed between standing tree and log speed-of-sound values.

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.