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Is forest net primary production resistance supported by structural legacies?

Project Abstract: 
With increasing globalization coupled with climate change, the spread of novel pests and pathogens in ecosystems is greater than ever before (Turner 2010, Raffa et al. 2008). These agents of disturbance have the potential to eliminate entire species and/or plant functional groups from forests (Atkins et al. 2020, Gough et al. in review). Yet, recent studies have indicated that forests have incredible capacity to maintain functional stability, including net primary production (NPP), despite significant disturbance-related losses in leaf area index (LAI) (Fahey et al. 2016, Grigri et al. 2020, Gough et al. 2020). Using a large-scale experiment at the University of Michigan Biological Station (UMBS) as a simulation of moderate to severe forest disturbance, the Forest Resilience Threshold Experiment (FoRTE) seeks to understand the mechanisms through which this stability is maintained in the face of such disturbances. Structures that remain after a disturbance may determine the stability of ecosystem processes following disturbance (Johnstone et al. 2016). Material legacies, or abiotic (e.g., nitrogen) and biotic (e.g., diversity) capital and structures (e.g., biomass) that persist after a disturbance are well studied in the context of plant community stability (Johnstone et al. 2016). Examples of such studies include analysis of: surviving tree species, abundance, and spatial arrangement (Seidl et al. 2014, Turner et al. 1998, Meigs and Keeton 2018); coarse downed and standing woody debris (Meigs and Keeton 2018); and soil seed bank, and seed persistence (Johnstone et al. 2016, Turner et al. 1998). Forest structural characteristics as functionally-relevant legacies of disturbance, however, have largely been excluded from prior studies at the ecosystem scale, despite growing evidence of their role in C cycling stability (Wales et al. 2020, Gough et al. 2020). Previous work at UMBS and elsewhere has established the relationship between structural characteristics and NPP in the absence of disturbance or immediately following disturbance, including measures of complexity such as rugosity (Hardiman et al. 2013b; Scheuerman et al. 2018; Gough et al. 2019), woody biomass (Gough et al. 2020; Haber et al. 2020) and, to a lesser degree, diversity (Gough et al. 2020; Gough et al. 2019; Gough et al. 2010; Scheuerman et al. 2018; Wales et al. 2020). In all of these cases, increased levels of each measure was tied to greater NPP. However, the question remains: how does the stability of multiple material legacies through disturbance affect the stability of ecosystem function? In this study, my overarching objective is to determine which of three categories of material legacies confer NPP stability, focusing on three-year resistance, as disturbance severity increases and in two disturbance types. Focusing on resistance, I will: 1.) Identify which structural and resource legacies with hypothesized linkages to NPP remain intact as disturbance severity increases and following two disturbance types; 2.) Evaluate how resource legacies relate to resistance as a dimension of NPP stability in two disturbance types, one affecting primarily large and the other small trees; 3.) Characterize which resource legacies convey NPP stability as disturbance severity increases from 0 to 85% gross LAI loss.
Years Active: 
2021
Research sites: 
Methods: 
Study Site The experimental forest used in this project is located at the University of Michigan Biological Station (UMBS) in Pellston, MI (45° 35’ N 84° 43’ W). This 100 year-old forest is comprised of early successional species in the upper canopy--birch (Betula spp.) and aspen (Populus spp.)--with later successional species--northern red oak (Quercus rubra), red maple (Acer rubrum), sugar maple (A. saccharum), American beech (Fagus grandifolia), white pine (Pinus strobus) and eastern hemlock (Tsuga canadensis), red pine (Pinus resinosa), striped maple (Acer pensylvanicum), and balsam fir (Abies balsamea)--in the subcanopy (Gough et al. 2007). In 2019, when the FoRTE was initiated, approximately 3,600 trees with a DBH >8 cm in four replicates were stem girdled to simulate a phloem-disrupting disturbance. Within each replicate there are four plots representing 0%, 45%, 65%, and 85% disturbance severity. Each plot is further subdivided into two treatment types: top-down and bottom-up, in which the trees with the highest LAI were stem-girdled (top-down) or where trees with the lowest LAI were girdled first (bottom-up). In sum, there are 32 sub-plots representing both top-down and bottom-up treatment types at each of the disturbance severities repeated four times. Data Collection Net Primary Production: Building on methods from previous work in this experiment (Grigri et al. 2020), net primary production will be measured by collecting diameter at breast height (DBH) measurements of 25% of the trees in each subplot—once at the beginning of the field season (June) and once at the end (August) as well as a third, dormant season measurement in November. Stems ≥8 cm have been fitted with dendrometer bands, and the measurements will continue to be collected using the existing bands. For trees < 8 cm in 2 x 2 meter nested subplots in the subcanopy, calipers will be used to collect tree diameter data. Allometric equations that are both site and species specific will be used to calculate changes in biomass in the plots, and NPP will be calculated based on biomass accumulation (Gough et al. 2008). Biomass and Diversity: Structural legacy indices will be recorded at the start of the growing season to represent post-disturbance levels of legacy persistence. Biomass will be calculated using the DBH measurements and species and site specific allometric equations described above. Likewise, diversity will be calculated using Shannon’s index. Canopy Rugosity: Rugosity measurements will be collected using a ground-based portable canopy LiDAR system (PCL). The system will be mounted on a frame and worn while walking transects through the center of each of the subplots (Gough et al. in press). Estimates of canopy structure using this raw data, binned vertically and horizontally into 1-m2 grids, will be analyzed using the forestr package in R (Atkins et al. 2018). For in depth mathematical derivations of rugosity, we refer readers to Atkins et al. 2018.