4.1 Sampling design All measurements will be made at the plot scale (Table 1). Each Burn Plot is a ~1 ha treatment area to which the experimental manipulation (clearcut bole removal and burn) was applied in 1936, 1948, 1954, 1980, or 1998. In addition, there are 10 Cut-Only Plots established within 3 km of the the Burn Plots; these are 0.04-1 ha in size and allow sampling of near-identical forest stands that regenerated after the same post-logging fires as the Burn Plots in 1911, but which have subsequently been cut once in 1952, 1972, or 1980-1985.. The primary difference between Burn Plots and these Cut-only plots in the 1911-origin forest is, therefore, their integrated disturbance severity, with the latter disturbed by fire only once during the prior century (at the time of stand establishment). Nearby old-growth forests on UMBS property (Colonial Point and the Forestry Plots, Table 1) have historically established plots, ranging in size from 0.04 to 0.1 ha, representing potential ‘end member’ communities of forest succession in the area. To maximize comparability with the Burn Plots, and with other components of the C cycle research program at UMBS, we will standardize the size of these plots at 0.1 ha, enlarging existing plots and installing additional 0.1 ha plots as necessary to sample across the range of environmental conditions and forest communities within these old forests (which range in size from 10 to 100 ha). Specifically, the largest of these forests, Colonial Point, has stands dominated by mixtures of hemlock, white pine, red oak, beech, and sugar maple distributed across a range of landscape positions from outwash plain to moraine; installing and sampling several plots within each of the major landscape ecosystems at this site will allow us to constrain forest structure C storage for multiple possible ‘end-members’ (e.g., conifer v. mixed deciduous) of succession in the area. 4.2 Measuring NPP and NEP components and soil properties The hypotheses we propose to test pertain to two measures of forest C storage: NEP (H1, H3) and NPP (H2, H3), both of which we will measure using well-established, validated approaches that yield high confidence estimates, particularly over multi-year intervals (Gough et al. 2008b). NEP produces a quantitatively more complete assessment of ecosystem C storage by accounting for both NPP and C losses from heterotrophic respiration. Therefore, we will use NEP as the primary response variable for all hypotheses to be tested with contemporary data collections on the Burn Plot chronosequence and in the old-growth stands. However, NPP is a major term in the calculation of NEP, is highly correlated with NEP, and is estimated with high confidence at our site (Gough et al. 2008b). As such, we will use NPP as a secondary response metric of C storage in testing H1 and H3. When using historic datasets to address hypotheses, we will use NPP as the sole metric of forest C storage because archived data do not permit estimation of heterotrophic respiration or precisely constrained estimates of soil C change. For contemporary measurements, we will calculate annual total NPP as the sum of above- and belowground woody biomass accumulation (estimated from band dendrometers or tree cores and allometric equations; TerMikaelian & Korzukhin 1997; Cairns et al. 1997), leaf and fine debris production (quantified with litter traps), coarse woody debris inputs (measured on subplots), and fine root production (described below). NPP calculations based on historic datasets will focus only on the primary production of wood, which requires conversion of forest inventory data (live and dead stem counts, height, dbh) from the Burn Plots and other long-term measurement plots into aboveground woody biomass densities (kg ha-1) using allometric equations. Wood NPP from historical datasets will be calculated as the incremental change in aboveground woody biomass between resampling dates, supplemented with increment boring and dendrochronology to reconstruct historic trends at finer temporal resolution. We will conduct new plot-based data collections on the Burn Plots, the Cut-only Plots, and in selected plots in old-growth stands to quantify the terms necessary for estimating NEP, which we will calculate using two different approaches as a way of constraining estimates: 1) NEP = total NPP – heterotrophic respiration; and 2) NEP = NPPwood + Δ soil C mass (Gough et al. 2007a). We will use a LI-COR 6400 portable photosynthesis system fitted with a soil cuvette to measure soil respiration on shallow collars (2-cm soil depth), and constrain the fraction derived from heterotrophic activity by measuring deep collars (40-cm soil depth) that extend well below the predominant rooting zone (Haynes & Gower 1995). Coarse woody debris respiration, the other main component of heterotrophic respiration, will be estimated from temperature and moisture-driven models of this process developed on site (Gough et al. 2007b). We will estimate the contribution of fine root production and turnover to total NPP and NEP using two methods: first, by combining direct measurements of aboveground litterfall, soil respiration, and soil C (at 5-yr intervals) with the total belowground C allocation/mass balance method of Raich & Nadelhoffer (1989). This method estimates C allocated to root production as the difference between soil respiration and aboveground litter inputs over a common time interval, assuming the soil C pool is at steady state. We will test the steady-state soil C assumption directly as part of this work, using new soil collections and archived soil samples from the Burn Plot and Cut-only chronosequences and old-growth stands to detect and account as necessary for hypothesized, directional changes in the soil C stock (Gough et al. 2007a). For the second method of constraining fine root production as an NEP component, we will validate our mass balance estimates of C allocation to roots against a subset of plots sampled with root ingrowth cores (Vogt & Persson 1991) deployed to 20 cm depth, where most fine roots are distributed in these soils. Detailed measurements of soil properties will be an important component of H1. We will use ion-exchange resin bag (IERB) incubations as a quantitative index of N availability across the Burn Plots and nearby old-growth stands. Our experience with IERBs at UMBS demonstrates that they provide a highly informative measure of soil inorganic N cycling processes (Nave et al., in review) and availability to plants and mycorrhizal fungi (Nave et al. 2011; Nave et al. 2013). Because work from this area suggests that soil properties other than N availability affect forest biomass production rates (Roberts & Richardson 1985; Zak et al. 1989), we will also measure Melich-extractable phosphorous, water holding capacity, base saturation, and cation exchange capacity of solid-phase soil samples (Sparks 1996). These latter soil chemical and physical properties will be quantified on samples collected according to standard protocols that we have used to quantify soil C and N pools in depth increments of: Oe and A horizons, 0-10 cm, 10-20 cm, 20-40 cm, 40-60 cm, and 60-100 cm mineral increments (note that the top two mineral increments correspond to E and Bs horizons in the sandy Spodosols of the area; Nave et al. 2011a; Nave et al. in review). Lastly, in addition to soil C and N pool characterization, we will construct depth profiles of 13C and 15N to infer differences in soil organic matter stabilization across plots of different ages. If stable isotope data suggest important differences in organic matter sources or cycling across these plots, we will develop 14C depth profiles for the plots by submitting samples to the International Soil Carbon Network Radiocarbon Collaborative, an avenue for subsidized 14C analysis and interpretation with which project Co-PI Lucas Nave is closely involved. The availability of archived soils from the Burn Plots in 1980, 1993, 2004 and 2012 for solid phase analysis (C and N content and isotope signatures) provide an additional level of confidence for understanding soil changes over time. Specifically, while our proposed new measurements of soil C and N pools across the Burn Plots will use a tightly constrained space-for-time substitutions to quantify soil change over the course of forest development, having access to archived samples from the same locations allows direct re-measurement of the same soils and the opportunity for validation of changes in soil chemistry inferred from the chronosequence. Archived Burn Plot soils from the 1980 and 1993 collections will be directly comparable to the Oe, A horizon and 0-10 cm increments we propose for new data collections, while the soils collected in 2004, 2012 and 2013 for some of the Burn Plots and old-growth sites comprise a deeper inventory of the soil profile (up to 100 cm). Altogether, archived samples across this range of dates also afford the opportunity for 14C ‘bomb-curve’ work (Hua & Barbetti 2004) to quantify changes in soil C turnover during forest succession, should this prove a compelling direction in the soils component of the proposed research. 4.3 Quantifying forest structure with LIDAR and understory/ground flora inventories To test H1 and H2, we will quantify canopy physical structure from ground-based measurements with a sub-canopy Portable Canopy LIDAR (PCL: Model LD90-3100VHS-FLP, mfd. by RIEGL USA) along 2 perpendicular transects crossing each plot. We will use sampling and analytical methods successfully used to link forest structure and function at UMBS (Hardiman et al. 2011). The PCL unit is a pack-mounted device carried in a fixed position, which vertically emits and subse-quen¬tly detects light reflected by overhead surfaces (such as a forest canopy). The high frequency of light emission pulses and return counts (up to 2000 hz) allow continuous characterization of the height (and variability of height) of the surface above operator-traversed transects. A data logger and post-processing software convert raw data into measures of surface height and its spatial variability; for the latter, canopy rugosity, a metric of canopy structural complexity, is positively correlated with forest NPP at UMBS, a demonstrably better predictor of NPP than LAI (Hardiman et al. 2011; 2013). In addition, we will characterize the distribution and size of understory trees and the diversity and distribution of ground layer plant species in the Burn Plots and in old-growth forest plots to test how upper canopy physical structure influences quantities such as seedling regeneration, recruitment into the understory for key tree species, and herbaceous flora diversity. These distributional and compositional metrics will then be linked to concurrent estimates of annual NPP within these lower strata (sapling growth rates and ground-layer herbaceous biomass from clipped plots), allowing us to link below-canopy structural dynamics to overall forest C storage through ecological succession. 4.4 Extending long-term datasets and integration with new data collections The most important of our existing and new datasets for which we request LTREB support derive from the experimental Burn Plot chronosequence. Importantly, however, both existing and new data from UMBS old-growth stands (e.g. Colonial Point, Table 1) and from Cut-only chronosequence plots (not burned after 1911) will be useful for comparing to Burn Plot data. Altogether, these datasets share a common set of response parameters that we will use to construct historic trajectories of forest biomass accumulation, soil development, and accumulation of ecosystem C and N stocks over successional development. These parameters include standard forest inventory data, such as the species, diameter (and often height) of all trees in permanent inventory plots. We will use this information, plus additional existing long-term datasets on the diversity and spatial distribution of understory and ground-layer plants, to test our hypothesis (H1) about the linkages between forest biological and physical complexity on C storage. To test hypotheses about NPP and NEP across stages of forest community and ecosystem development (H2, H3), we will apply allometric models (Koerper 1977, Koerper & Richardson 1980, TerMikaelian & Korzukhin 1997) to our forest inventory data to estimate historic above- and belowground biomass C stocks across the years of measurement specified in Table 1. These datasets, which have been collected by faculty and student investigators since the 1930s, share common metrics such as the species composition, abundance, frequency and density of plants in these lower strata of the forest. Augmenting these forest inventory and understory floristic datasets with our proposed new data collections will add strength to our testing of hypotheses H1-H3. This is especially the case for H1, which requires linking PCL (Section 4.3) measurements of current forest structure with more classical measures of vegetation spatial distribution and composition to test the effects of forest physical structure on C storage. Within the context of this design, understanding structural drivers of NPP in forests varying in age will involve direct measures of stand-level biological diversity (e.g., understory composition and structure) available from long-term inventory datasets, and inferences based on contemporary, measured relationships between biological diversity and structural complexity (i.e., the relationship between canopy structural complexity and understory composition/structure).We will utilize long-term climate data collected at UMBS since 1912 (e.g., Figure 5) to test the hypothesis that multi-year or decadal-scale climate trends mediate rates of change in biomass C pools between historic forest inventory data collection events (H2). In addition, we will apply a higher-resolution approach to the same hypothesis by combining long-term UMBS weather data with tree coring and dendrochronological analysis to test whether structurally simpler young stands show greater relative short-term (1-2 yr) NPP decline in response to discrete, weather-related disturbances (e.g., droughts or ice storms). Furthermore, as yearly NPP and NEP measurements on the Burn Plots and nearby old-growth forest plots are major components of our proposed research, and weather and climate data collection is ongoing at UMBS, we will use contemporary data collections to assess whether NPP and NEP in older stands are more resistant to variations in weather and climate than in younger stands during the LTREB period of observation. Finally, we propose the use of dendrochronological measures to investigate climatic controls on forest C balances. Specifically, we will use diameter increments of dominant trees in stands of different ages (Burn Plots and other plots) to determine whether growth responses to climate patterns in decades of the 20th century differ from more recent and current responses. We acknowledge, as noted by a reviewer of our prior LTREB proposal, that although chrono-sequences can and have been used to provide transformative insights in ecosystem processes (e.g., Crocker & Major 1955, Reiners et al. 1971, Pardo et al. 1995, Veldkamp et al. 1999, Jussy et al. 2000, Law et al. 2003, White et al. 2004), their use is subject to some limitations. For example, starting conditions (such as soil organic matter content and nutrient capital) might have varied at different treatment dates, and climatic conditions during similar stages of ecosystem development across plots with different stand initiation dates could differ enough across decades to influence C accumulation and soil fertility. However, we are positioned to minimize such limitations as archived soils and data from the Burn Plots and Cut-only Plots (data only) going back to 1980 are available for our analysis and use (Table 1). Also, studies of soil carbon and nitrogen stocks and processes (White 2000, White et al. 2004, Hockaday et al. 2006) have been done over the past 13 years and will become increasingly valuable through the 5 to 10 years of our proposed study. Importantly, UMBS climate records (daily max-min temperatures and precipitation) dating back to the time of initiation of the 1911 Forest (Figure 5) and throughout though course of burning and cutting treatments will be useful for comparing climate regimes across similar stages of different aged experimental plots.