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  1. Forest disturbances, such as an eastern spruce budworm ( Choristoneura fumiferana ) outbreak, impact the strength and persistence of forest carbon sinks. Salvage harvests are a typical management response to widespread tree mortality, but the decision to salvage mortality has large implications for the fate of carbon stocks (including forest carbon and harvested wood products) in the near and long terms. In this study, we created decision-support models for salvage harvesting based on carbon after an eastern spruce budworm outbreak. We used lasso regression to determine which stand characteristics (e.g., basal area) are the best predictors of carbon 40 years after an outbreak in both salvage and no salvage scenarios. We modeled carbon at year 40 for different treatment scenarios and discount rates. Treatment scenarios represent residual stand conditions that may be present when an outbreak occurs. Economic discount rates were applied to 40-year carbon values to account for near and long-term carbon storage aspects. We found that the volume and size of eastern spruce budworm host species are significant predictors of salvage preference based on carbon. We found overall that salvaging less volume is recommended to avoid major swings in carbon budgets and that discounting carbon values to apply weight to near or long-term sequestration greatly affects whether salvaging is preferred. Lasso models are constructed for the northeastern US, however, similar concepts may be applied beyond our study area and potentially for other insect outbreaks similar to spruce budworm, such as mountain pine beetle ( Dendroctonus ponderosae ) or hemlock woolly adelgid ( Adelges tsugae ). From a policy standpoint widespread salvaging could create a large carbon emissions deficit with the risk of not being fully replenished within a desired timeframe. Since salvaging is often financially driven, especially for private landowners, carbon market payments or incentives for not salvaging is a consideration for future policy. 
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  2. The ability to automatically delineate individual tree crowns using remote sensing data opens the possibility to collect detailed tree information over large geographic regions. While individual tree crown delineation (ITCD) methods have proven successful in conifer-dominated forests using Light Detection and Ranging (LiDAR) data, it remains unclear how well these methods can be applied in deciduous broadleaf-dominated forests. We applied five automated LiDAR-based ITCD methods across fifteen plots ranging from conifer- to broadleaf-dominated forest stands at Harvard Forest in Petersham, MA, USA, and assessed accuracy against manual delineation of crowns from unmanned aerial vehicle (UAV) imagery. We then identified tree- and plot-level factors influencing the success of automated delineation techniques. There was relatively little difference in accuracy between automated crown delineation methods (51–59% aggregated plot accuracy) and, despite parameter tuning, none of the methods produced high accuracy across all plots (27—90% range in plot-level accuracy). The accuracy of all methods was significantly higher with increased plot conifer fraction, and individual conifer trees were identified with higher accuracy (mean 64%) than broadleaf trees (42%) across methods. Further, while tree-level factors (e.g., diameter at breast height, height and crown area) strongly influenced the success of crown delineations, the influence of plot-level factors varied. The most important plot-level factor was species evenness, a metric of relative species abundance that is related to both conifer fraction and the degree to which trees can fill canopy space. As species evenness decreased (e.g., high conifer fraction and less efficient filling of canopy space), the probability of successful delineation increased. Overall, our work suggests that the tested LiDAR-based ITCD methods perform equally well in a mixed temperate forest, but that delineation success is driven by forest characteristics like functional group, tree size, diversity, and crown architecture. While LiDAR-based ITCD methods are well suited for stands with distinct canopy structure, we suggest that future work explore the integration of phenology and spectral characteristics with existing LiDAR as an approach to improve crown delineation in broadleaf-dominated stands. 
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  3. Abstract

    Linking biometric measurements of stand‐level biomass growth to tower‐based measurements of carbon uptake—gross primary productivity and net ecosystem productivity—has been the focus of numerous ecosystem‐level studies aimed to better understand the factors regulating carbon allocation to slow‐turnover wood biomass pools. However, few of these studies have investigated the importance of previous year uptake to growth. We tested the relationship between wood biomass increment (WBI) and different temporal periods of carbon uptake from the current and previous years to investigate the potential lagged allocation of fixed carbon to growth among six mature, temperate forests. We found WBI was strongly correlated to carbon uptake across space (i.e., long‐term averages at the different sites) but on annual timescales, WBI was much less related to carbon uptake, suggesting a temporal mismatch between C fixation and allocation to biomass. We detected lags in allocation of the previous year's carbon uptake to WBI at three of the six sites. Sites with higher annual WBI had overall stronger correlations to carbon uptake, with the strongest correlations to carbon uptake from the previous year. Only one site had WBI with strong positive relationships to current year uptake and not the previous year. Forests with low rates of WBI demonstrated weak correlations to carbon uptake from the previous year and stronger relationships to current year climate conditions. Our work shows an important, but not universal, role of lagged allocation of the previous year's carbon uptake to growth in temperate forests.

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