Social

Influence of climate and site variables over jack pine growth and development

Climate
Vegetation
kirtlands warbler
jack pine
Project Abstract: 
Management of jack pine ecosystems in northern Michigan has been shaped in large part by efforts to recover the Kirtland’s Warbler (KW; Setophaga kirtlandii), a neotropical migratory bird with a breeding range restricted almost entirely to northern Lower Michigan. This species is considered conservation reliant, meaning that without continuous regeneration of early-successional jack pine nesting habitat, the population would decline once again. The management scheme developed in the early 1980s by the interagency KW Recovery Team consists of using commercial timber harvests to support establishment of high-density jack pine plantations on planned 50-year rotations. After 50 years the goal was to use timber sales to harvest the stand and provide revenue for replanting new breeding habitat. It has been found, however that growth at 50 years remains insufficient to produce marketable roundwood volume on current markets. As such, it has been suggested by the Michigan DNR to extend rotations to 70 years, yet little is known about how jack pine develop at these older ages, particularly on xeric sites at the core of the KW management area. My research aims to address these unknowns about jack pine growth and development, as well as provide a better understanding of how fundamental forces of climate and plant physiology impact jack pine growth throughout its range.
Investigators: 
youngj98
Status of Research Project: 
Active
Years Active: 
2024
Methods: 
The primary growth and yield measures I will produce include site index curves, annual growth increment and radial accumulation curves, as well as an optimal harvest model which integrates height, basal area, and mortality contextualized by soil and climate. Site index curves will be generated using methods similar to the stand specific curves generated in chapter 2, aggregating data based on tree height growth, rather than aggregating based on stand. This has the added benefit of being directly comparable to methods used to generate site index curves for jack pine in northern Ontario several decades ago (Carmean, 1989). Basal area increment and radial accumulation will be modeled using data from breast height cookies collected for data chapter 2. I will fit a two parameter Richard’s function to each site, such that the parameterization of the Richard’s function is expressed in terms of climate, soil, and stand density estimates. To do this I will aggregate ring width data from all available sources for each site including both breast height discs and cores, providing a more comprehensive view of the variability in diameter growth from site to site (Sharma, 2023). Optimal harvest models will be made based on each of the unique management objectives currently proposed by the MI DNR, including management for biomass fuel production, maximum wood volume, and usable log metrics provided by mills local to jack pine management areas in Michigan. A unique consideration brought on by the collapse of the biomass market through Michigan as well is how rotation lengths may need to be changed to respond to these market changes, and how that rotation length may change under climate warming or existing geography related climate variability. To develop an ecosystem classification scheme, I will begin by revisiting all sites used to measure productivity for data chapter 2. At these sites, I will use a 1 square meter quadrat to measure the percent cover for all ground layer and shrub species at 5 points through each plot within a given stand, as well as a species presence list developed from a meandering transect through the stand. I will also include tree species abundances collected from initial stand inventories conducted for chapter 2. I will then put this field collected data into a site by species matrix to be used in a regression tree analysis, using community dissimilarity as well as climate and soil parameters for each site, generating a dichotomous tree (Brudvig, et. al., 2014). Splits on the output dichotomous tree will be based on community dissimilarity, soils, and climate, with each grouping becoming increasingly narrow for the similarity between site parameters of included stands. For each level of the regression tree, I will use ANOVA to effectively ‘trim the tree’, identifying the lowest splits on each branch which demonstrate meaningful differences in tree productivity. This will provide me with site groupings characterized by their differences in species composition, soil, climate and tree productivity. After initial analysis on the utility of my existing dataset for generating productivity classes however, it is likely that I will need to conduct additional sampling. For these add on sites however, I will focus on more easily measured estimates of productivity, using cores and heights to fit each site to my previously generated productivity indices, but carrying out identical floristic measurements to those described above.