Disturbance-Based Silviculture for Habitat Diversity Thom 2020.Pdf

Forest Ecology and Management 469 (2020) 118132

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Forest Ecology and Management

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Disturbance-based silviculture for habitat diversification: Effects on forest T structure, dynamics, and carbon storage ⁎ Dominik Thoma,b,c,d, William S. Keetona,b, a Rubenstein School of Environment and Natural Resources, University of Vermont, 81 Carrigan Drive, Burlington, VT 05405, USA b Gund Institute for Environment, University of Vermont, 617 Main Street, Burlington, VT 05405, USA c Institute of Silviculture, Department of Forest- and Soil Sciences, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter-Jordan-Straße 82, 1190 Vienna, Austria d Ecosystem Dynamics and Forest Management Group, School of Life Sciences, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany

ARTICLE INFO ABSTRACT

Keywords: Disturbance-based silviculture is of increasing interest as an approach to provide multiple ecosystem services and Silviculture with Birds in Mind beta diversity in habitat conditions. One such approach increasingly employed in the eastern U.S. is a set of CARBON storage forestry practices developed to diversify forested bird habitats, called Silviculture with Birds in Mind (SBM). Disturbance-based forestry While strongly appealing to many private landowners, empirical data have not yet been reported regarding the Forest management effects of SBM treatments on forest structure and dynamics and how they compare to natural disturbances. Forest structure Moreover, the potential of bird-oriented silviculture like SBM to enhance co-benefits, for instance, by retaining Habitat Beta diversity high carbon stocks in managed forests, has not been investigated. The objectives of our study were thus (i) to Silviculture analyze the effects of SBM treatments on forests and compare them with natural disturbances, and (ii)toassess Wind disturbance the co-benefits of multiple habitat indicators and carbon storage within three years of silvicultural treatmentin mature northern hardwood-conifer forests. We derived 14 stand structural variables as well as carbon storage from 217 SBM inventory plots, and compared them with the effects of intermediate-severity wind disturbance using non-metric multidimensional scaling (NMDS). Subsequently, we applied multi-hierarchical Bayesian models to investigate SBM treatment effects on aboveground carbon storage, as well as on four key habitat indicators. We also used Bayesianmodels to derive the relationships between habitat indicators and carbon storage. SBM treatments created a diversity of post-harvest stand conditions and, while having lower values for some structural characteristics in comparison to controls, significantly enhanced the variation in individual structural elements. Moreover, the treatments were closer in ordinal space to the irregular structure associated with intermediate-severity wind disturbance than untreated control plots. However, the NMDS indicated that SBM treatments did not fully approximate partial wind disturbances. Carbon storage was positively associated with stand structural complexity. Disturbance-based approaches like SBM help diversify habitat conditions and we expect these effects to become more pronounced as stands respond to the treatments over time. If applied more broadly, treatments targeted at diversifying habitats would also help maintain high carbon stocking at landscape scales. However, as the treatments do not fully emulate the region’s natural disturbance regime, we propose widening the portfolio of multi-cohort, retention, and gap-based silvicultural approaches in landscape-scale management.

1. Introduction ‘disturbance-based’, ‘nature-oriented’, ‘ecological’, ‘close-to-nature’, ‘multi-functional’ or ‘retention’ forestry (Puettmann et al., 2015), share Recent decades have witnessed growing interest in the development the objective of perpetuating the full range of stand scale structures and of silvicultural approaches designed to mimic natural disturbances in landscape patterns which organisms require, assuming adaptation to many parts of the world (Brang et al., 2014; Franklin et al., 2007; the natural disturbance regimes driving stand and landscape dynamics Seymour et al., 2002). These approaches, often referred to as (Franklin et al., 2007). A further goal is to provide a broader array of

⁎ Corresponding author at: Rubenstein School of Environment and Natural Resources, University of Vermont, 81 Carrigan Drive, Burlington, VT 05405, USA. E-mail address: [email protected] (W.S. Keeton). https://doi.org/10.1016/j.foreco.2020.118132 Received 11 December 2019; Received in revised form 31 March 2020; Accepted 1 April 2020 0378-1127/ © 2020 Elsevier B.V. All rights reserved. D. Thom and W.S. Keeton Forest Ecology and Management 469 (2020) 118132 habitat conditions and ecosystem services than may be associated with breeding population in the region; and (iv) are relatively easy to practices, such as short rotation industrial forestry or over reliance on identify by non-professional observers. Habitat conditions supporting even-aged management, that tend to simplify and homogenize stand feeding and breeding of different species include, for instance, irregu- and landscape structures (Kuuluvainen and Grenfell, 2012; Puettmann larly sized and structured canopy gaps with early successional vegeta- and Tappeiner, 2014). Some disturbance-based silvicultural systems tion (e.g., American woodcock (Scolopax minor [Gmel.])), tall trees were specifically designed to encourage both stand structural com- (e.g., scarlet tanager (Piranga olivacea [Gmel.])), and downed (e.g., plexity and landscape patch diversity (Keeton, 2006; Bauhus et al., Canada warbler (Cardellina canadensis [L.])) and standing deadwood 2009; Stanturf et al., 2014). These approaches have great potential to (e.g., yellow-bellied sapsucker (Sphyrapicus varius [L.])) (Hagenbuch balance the interests of society in biodiversity and the supply of mul- et al., 2012). SBM treatments have been designed to collectively create tiple ecosystem services. For instance, management for structural these habitat conditions by mimicking disturbances of different seve- complexity in mixed northern hardwood forests of eastern North rities and sizes. America has been found to increase elements of late-successional bio- Some of the silvicultural approaches investigated here, such as diversity (Dove and Keeton, 2015; Gottesman and Keeton, 2017; variable density thinning (in SBM termed “variable retention thinning” McKenny et al., 2006) and carbon storage (Ford and Keeton, 2017) or VRT), have been employed in many different forest systems in North while, at the same time, providing wood products from timber harvests America (Carey, 2003; Stanturf et al., 2014), but have been poorly (Nunery and Keeton, 2010). studied in the eastern U.S. Others are better understood in terms of Disturbance-based approaches have struggled to gain traction implications for long-term stand development trajectories (see, for ex- within the forestry community in the eastern United States (Fahey ample, Halpin et al., 2017) but have not been assessed relative to other et al., 2018). However, recently interest has developed around silvi- disturbance based approaches. Though the subject of on-going mon- cultural approaches specifically tailored to promote a diversity of bird itoring, effects of the SBM treatments on bird populations are still lar- habitats (Sallabanks and Arnett, 2005), reflecting the nation-wide in- gely uncertain. Stand improvement thinning, a variant of one of the terest among non-industrial private landowners in birds as a primary SBM treatments, was successful at fostering songbird occupancy and motivation for owning forestland (Butler et al., 2007). A leading ex- abundance (Rankin and Perlut, 2015). In addition, Nareff et al. (2019) ample is a set of approaches collectively called Silviculture with Birds in found that territory densities and population abundances for Cerulean Mind (SBM), currently being demonstrated on a variety of ownerships Warblers responded positively to a range of retention-based, bird-or- in northeastern North America (Hagenbuch et al., 2012). Variants of iented silvicultural practices in Central Appalachian hardwoods. How- SBM are under development for other regions of North America as well, ever, the success of bird-oriented treatments at emulating natural dis- such as the U.S. Southeast and Pacific Northwest (see, for example, turbances, and their ability to achieve management objectives other Wood et al., 2013). In the Northeast, six innovative silvicultural treat- than bird habitat, have not been previously assessed. This also holds ments adapted to three stand development conditions have been pro- true for associations between silviculturally enhanced bird habitat di- moted, of which five had been implemented at the time of this study. versity and ecosystem services such as carbon storage. The treatments are largely based on disturbance-oriented concepts (see In this study, we quantify the co-benefits of disturbance-based Frelich and Lorimer, 1991; Seymour et al., 2002; Keeton, 2006; treatments oriented towards birds in terms of their effects on habitat Franklin et al., 2007), such as gap creation, legacy tree retention, conditions, stand structure associated with natural disturbance impacts, variable density thinning, and dead wood enhancement, and are ap- and carbon storage, the latter being a service fundamentally linked to plied to deciduous hardwood, coniferous, and mixed forest types. Some climate regulation (Bonan, 2008; Schwaab et al., 2015; Thom et al., of the treatments are explicitly intended to emulate the multi-aged (or 2017). The objectives of our study are to: (i) describe the outcome of “multi-cohort”) stand structures associated with partial disturbance SBM treatments and compare them with natural disturbances typical events, particularly intermediate (or moderate) intensity windthrow for the region; and (ii) analyze the co-benefits of multiple habitat in- (Hanson and Lorimer, 2007; Meigs and Keeton, 2018). Multi-cohort dicators and carbon storage within three years of silvicultural treat- systems, such as the irregular shelterwood method and expanding gaps ment. We hypothesize that SBM treatments have varying effects on with retention, are of increasing interest and usage in the region individual structural indicators, and as a suite of techniques emulate a (D’Amato et al., 2018; Kern et al., 2017; Raymond et al., 2009). range of low to intermediate severity natural disturbance influences on The disturbance-based concepts guiding SBM postulate that by ad- forest structure (Morrissey et al., 2014). Hence, we expect that these justing silvicultural systems to benefit a broader array of taxa, including treatments enhance the availability of key structural attributes sup- avian diversity, other co-benefits may accrue. These include ecosystem porting habitat conditions for numerous forest-dwelling species. services associated with a diversity of successional and stand develop- Moreover, we expect that carbon density remains high at most SBM ment conditions (Swanson et al., 2011), including a greater portion of stands after treatment which has been observed after low severity forested area in late-successional or old-growth conditions (Thom et al., natural disturbance (Reinikainen et al., 2013). We also anticipate a 2019). These conditions are often associated with a high structural positive relationship between key structural variables and carbon sto- complexity and high carbon density (Gunn et al., 2014; Keeton et al., rage as structurally complex northern hardwood-conifer forests have 2011; McGarvey et al., 2015; Urbano and Keeton, 2017). been found to exhibit a high carbon density (Thom and Keeton, 2019; To date the SBM approaches have had broad appeal (Keeton, un- Urbano and Keeton, 2017). published landowner survey data), both because they integrate wildlife goals (game and non-game) with economic objectives and because 2. Methods landowners overwhelmingly support bird conservation on non-industrial private forests (Butler et al., 2007). In northern New England, 2.1. Study region the prescriptions target 12 indicator species as proxies for the diversity of habitat conditions required by 40 songbird species identified as high Silvicultural manipulations were conducted as trials on 1–4 stands priorities for conservation (Hagenbuch et al., 2012). A strong suit is the at seven locations distributed across the state of Vermont in the incorporation of explicit operational considerations rendering the northeastern United States (Fig. 1). The number of treatments (in- guidelines readily implementable across a variety of ownerships, including controls) per location varied between two and four according to cluding private non-industrial forestlands (Hagenbuch et al., 2012). the specific goals of the forest owners. The locations include a rangeof The 12 indicator species in SBM were selected because they (i) re- biophysical regions spanning eastern temperate and hemi-boreal quire a wide range of forests types and conditions; (ii) are declining in biomes (Table 1), and this range of starting conditions thus provided a population size or are at risk; (iii) have a large portion of their global robust test of treatment outcomes. Starting conditions were similar

2 D. Thom and W.S. Keeton Forest Ecology and Management 469 (2020) 118132 Unknown past, but very likely similar to other locations Natural development • • St. Albans 66 4 10 Native American use Agriculture Natural development • • • NW Solid Waste 62 33 11 northern hardwood northern hardwood Native American use Agriculture Forest regrowth Plantation Logging Natural development

Fig. 1. Study region. Presented are inventory locations in Vermont collected for • • • • • • Northwoods 597 21 4 northeastern highlands Champlain valleyhardwoods Champlain valley this study as well as windthrown sites in Vermont and New York State published in Meigs and Keeton (2018). within locations, allowing treatment areas to be matched with untreated portions of the same stands (hereafter referred to as “controls”). Northern hardwood forests in this region are dominated by sugar maple (Acer saccharum [Marsh.]), American beech (Fagus grandifolia Agriculture Forest regrowth Logging Natural development • • • • Mud Pond [Ehrh.]), and yellow birch (Betula alleghenensis [Britt.]), with minor 146 19 22 piedmont components of red maple (Acer rubrum [L.]), paper birch (Betula pa- pyrifera [L.]), stripped maple (Acer pensylvanicum [L.]), and other deciduous species. Mixed northern hardwood forests have an elevated conifer component including eastern hemlock (Tsuga canadensis [L.]), red spruce (Picea rubens [Sarg.]), and sometimes eastern white pine (Pinus strobus [L.]). These secondary forests have developed after Agriculture Forest regrowth Logging Natural development

clearing and later agricultural abandonment as a consequence of out- • • • • Merck 1,253 11 7 northern hardwood northern hardwood mixed, northern migration and industrialization starting in the mid-19th century (Foster et al., 1998). Most sites have had a history of periodic partial harvesting, ranging from firewood cutting to low intensity thinnings, but not regeneration harvests. All forests at the seven study locations have developed through natural regeneration and secondary succession in the recent past (60–100 years), and are now in an early- to mid-seral development stage (Thompson et al., 2013). Fig. 1 . The information included was obtained from the forest management plans of each location. Agriculture Forest regrowth Logging Plantation Logging Natural development

2.2. Treatments, sampling, and data processing • • • • • • Coolidge 21 20 7 northern hardwood; red spruce component

Treatments examined in this study include five silvicultural approaches designed to emulate low-intermediate severity disturbances for the purpose of diversifying habitat conditions (Table 2). These approaches have been poorly evaluated in the northeastern U.S. in terms of their utility for creating diverse habitat conditions and producing an array of ecosystem services (Fahey et al., 2018; Kern et al., 2017). The

treatments were implemented between 2012 and 2015; all stands at a Agriculture Natural development • • Audubon given location were treated in the same year. We then collected forest 27 hardwood data within 1–3 years of manipulation, depending on location, in both treated and untreated control plots (see above). At each stand, we established 6–41 randomly distributed inventory plots (median 12), resulting in a total of 217 plots for the study overall. Plots were spaced 50 to 200 m apart. Employing a nested plot design, we measured a number of variables enabling the assessment of treatment effects on forest structure and composition, including attributes Land use history Hectares Number of treated plotsNumber of control 23 plotsBio-physical Region 25 Forest composition northern Green Mountains southern Eastern Green hemlock; Mountains northern Taconic mountains northern Vermont related to standing live and dead trees, variability in tree dimensions, Table 1 Descriptions of the seven inventory locations presented in

3 D. Thom and W.S. Keeton Forest Ecology and Management 469 (2020) 118132

Table 2 Silviculture with Birds in Mind (SBM) prescription choices and descriptions. Example shown is for northern hardwoods. Adapted from (Hagenbuch et al., 2012).

SBM treatment Objectives Purpose Differences

Crop Tree Release and Canopy Select 74–173 crop trees ha−1 Increase growth and vigor of selected dominant Improvement thinning promoting growth Gap Formation (CTRG) • Remove competing dominant and co- • and co-dominant trees in the highest quality, most vigorous • dominant trees if their crowns touch a Accelerate crown release associated with individuals crop tree • density-dependent tree mortality and fine- scaled disturbances Expanding-Gap Group Long (20 + years) or indefinite Establish new cohorts/release advanced Regeneration treatment on a variable entry Shelterwood (EGGS) • regeneration periods • regeneration cycle; may create two-aged, multi-aged, or Regeneration in groups and/or Emulate the dynamics of gradually expanding uneven-aged stands • patches • natural gaps, including legacy tree retention Gradually expand groups and/or within gaps • patches over successive entries Crop tree release and thinning in • matrix between gaps Small-group and Single-tree- 12–15 year cutting cycle Remove low quality and mature trees in the Regeneration treatment that will create an selection (SGSTS) • Variable-sized openings from 0.04 to • matrix to promote regeneration uneven-aged stand • 0.81 ha Establish a wide variety of age classes Single-tree selection and crop tree • Increase vigor of remaining trees • release (free thinning) in matrix • Increase understory vegetation • Emulate a range of density-dependent and • density-independent mortality processes Shelterwood with Reserves Use seed cutting to remove 40–60% of Harvest commercially-mature canopy trees Regeneration treatment that will create a (SWR) • original basal area • through a series of partial cuts two-or multi-aged stand Leave residual basal area of 11–14 m2 Regenerate all or portions of a stand with • ha−1 in saw timber • intermediate to shade tolerant species Removal cut when approx. 12,350 Emulate the biological legacies associated with • saplings ha−1 are well established • natural disturbance effects by retaining live Retain large-diameter trees for > 25% trees over multiple rotations • of rotation or indefinitely Variable Retention Thinning Variable density marking of cull trees Remove low quality trees in matrix Intermediate treatment or stand (VRT)* • Remove trees of low-vigor/poor • Reduce tree density per ha to improve growing improvement thinning • quality • conditions for remaining trees Reduce small pole crown cover to Enhance canopy vigor • 70–75% • Enhance horizontal variation in stand structure Reduce large pole crown cover to • • 75–80% Remove 50–60% of intermediate • crown class • Remove 10–25% co-dominant crown * In SBM, the more commonly used term “variable density thinning” is called “variable retention thinning”. coarse woody debris (CWD), canopy cover, and sapling diversity. the volumetric approach of Woodall et al. (2011). Specifically, we de- Live and dead trees were inventoried within variable radius (2.3 rived the volume of each tree using equations and coefficients for tree metric basal-area factor) prism plots. We recorded species, diameter at species of the ‘Northeastern region’; converted the volume to biomass breast height (dbh; 1.37 m), height, and decay class (1–9) of all considering, among others, the specific gravity of the bole and bark; trees ≥ 5 cm in dbh. Saplings (≥1m in height, < 5 cm dbh) were and computed the top and branch biomass (for details, see Woodall identified to species and tallied in a 30 × 30 m square aroundplot et al., 2011). In addition, we reduced standing dead biomass based on center. The species and decay class (1–5) of CWD was estimated using decay- and species-group (i.e., softwood and hardwood) specific density the line-intercept method following Shiver and Borders (1996) for all reduction factors (Harmon et al., 2011). Finally, we divided biomass by downed logs ≥ 1 m in length and ≥ 10 cm diameter along two 60 m two to obtain carbon (Neumann et al., 2016; Smith et al., 2013). CWD transects. We obtained canopy cover estimates at 35.0% of the plots carbon computations were based on Ford and Keeton (2017). We used a using digital hemispheric photography (Easter and Spies, 1994); extent combined factor to reduce density based on decay class and species- of sub-sampling was limited by equipment availability. However, as the group, and then converted biomass into carbon (Harmon et al., 2008). canopy cover data were well distributed across locations (only North- Ultimately, we combined the three pools (live trees, snags, and CWD) woods was missing) and treatments (only CTRG was missing), we re- into total aboveground carbon. tained these data for the first comparison of treatment effects on stand structure (see below). Photographic data were processed using Hemi- 2.3. Treatment effects on forest structure and carbon View 2.1 SR5 Canopy Analysis Software (2014). We used the field data to derive 14 indicators of forest structure; After aggregating all tree data to forest plots, we compared forest these were selected because of their utility in differentiating seral structure and carbon among treatments. To enable a comparison of conditions and habitat functionality (Thom and Keeton, 2019). We forest structure between the five SBM treatments and natural dis- obtained the H’-Index of structural and compositional diversity using turbances, we added data from 27 recently inventoried plots from four the ‘post-hoc method’ (Staudhammer and LeMay, 2001). For this pur- locations affected by windthrow in the states of Vermont and NewYork pose, we computed a Shannon Index (H’) for dbh, height, and species (Meigs and Keeton, 2018)(Fig. 1). Plot size, layout, and design were diversity, respectively, and averaged across these three indicators. In exactly the same in the comparison study. Wind disturbance events, doing so, the method weights horizontal, vertical, and species diversity including tornadoes, hurricanes, and microbursts, occur frequently the equally. northeastern U.S. (Kosiba et al., 2018), and constitute a strong driver of Subsequently, we calculated the carbon storage in live and dead forest structure (Fischer et al., 2013; Konôpka et al., 2016; Meigs and aboveground pools. Carbon in standing trees was estimated following Keeton, 2018). The windthrow events in Meigs and Keeton (2018) were

4 D. Thom and W.S. Keeton Forest Ecology and Management 469 (2020) 118132 of intermediate severity, and represent the upper spectrum of severities spatial autocorrelation (i.e., Moran’s I statistics were between 0 and the SBM treatments aim to mimic (i.e., in particular, SWR). −0.091 and all p > 0.05). We evaluated the goodness-of-fit by ana- We employed the vegan package (Oksanen et al., 2019) to perform lyzing the Bayesian R2 and posterior predictive checks. Afterwards, we non-metric multidimensional scaling (NMDS) ordinations in which we extracted the effect sizes of treatments on the response variables and visualized the differences between treatment effects on forest structure. scaled them by dividing through the maximum value of each response As canopy openness was measured on 103 of the 244 plots (42.2%) only variable. Ultimately, we averaged throughout the five indicators to (including the windthrown plots), and the effective number of sapling derive their combined average performance. In doing so, the maximum species was not available for the windthrown plots, we used the cluster possible performance was scaled to 100% with each indicator having a package (Maechler et al., 2019) to divide each entry by the range of its potential maximum of 20%. variable and subtracted the minimum value to standardize the data before deriving a Gower distance matrix. A Gower distance matrix is a 2.4. Correlation between structural elements and carbon storage non-estimation method that is robust even if a high proportion of data is missing (Brown et al., 2012). Then we fitted NMDS models with upto After assessing the effect of SBM treatments on management goals, six dimensions including the 14 structural variables for the 244 plots we analyzed the relationships of three key structural elements for ha- (217 plots derived for this study and 27 windthrown plots of Meigs and bitat conditions (H’-Index, large trees, and deadwood) with above- Keeton (2018)), and a maximum of 500 iterations and 100 random ground carbon storage. Consistent with previous studies (Thom and starts. We interpreted the models’ stress visually with a screeplot (Fig. Keeton, 2019; Urbano and Keeton, 2017), we expected a higher amount S1). Aiming to obtain a model with low stress while being interpretable, of carbon stored in structurally and compositionally more complex we selected the model with the first three dimensions. A stress value of forests as indicated by the H’-index and the amount of large trees with a 0.153, and a high correlation between observed dissimilarity and or- dbh > 50 cm. In the longer term, structural and functional niche dination distance (non-metric fit R2 = 0.977; linear fit R2 =0.879) complementarity will likely increase photosynthesis rates and nutrient (Fig. S2) indicated a good model fit. Subsequently, we performed a cycling (Fotis et al., 2018; Mensah et al., 2016; Williams et al., 2017). multilevel permutation-based analysis of similarities (ANOSIM) to test We also expected that deadwood will have a positive relationship with for significant differences between all groups controlling for location carbon storage (Pan et al., 2011; Thom and Keeton, 2019). Live biomass and stand conditions. The R test statistic of ANOSIM range between 0 is reduced immediately after a disturbance and is transferred into the (no difference between treatments) and 1 (no similarity between dead biomass pool. While decomposition of deadwood in forests of the treatments). Moreover, we tested for significant differences (α = 0.05) study region takes decades (Russell et al., 2014), tree growth at mul- among treatments, control areas, and forests affected by windthrow for tiple canopy positions responds to newly available growing space (i.e. the 14 structural attributes, as well as total aboveground carbon, in- the “release effect”) following low to intermediate severity disturbances dividually using pairwise independence tests with a Benjamini-Hoch- (Fraver and White, 2005). berg p-value adjustment using the rcompanion package (Mangiafico, We log transformed carbon storage to normalize the data distribu- 2017). tion and standardized each predictor, as they were on different scales, Next, we investigated the effect of silvicultural treatments on key by subtracting the mean value and dividing by the standard deviation variables of interest for forest management in more detail. In particular, (z-score transformation). The highest VIF across predictor variables was we investigated treatment effects on aboveground carbon storage, 1.2. This time we fit Bayesian multi-hierarchical models with location, which constitutes a key climate regulating service in temperate forest stand, and treatment as random effects. Including treatment as addi- ecosystems (Bonan, 2008; Thom et al., 2017), as well as four key in- tional random effect controls for management impacts (e.g., the amount dicators of structural and compositional diversity: (i) The H’-index of of timber harvested) on carbon storage. We fit three models with and structural and compositional diversity indicates heterogeneity in ha- without weakly informative priors (Gelman, 2006): (i) assuming linear bitat conditions for forest-dwelling species (Staudhammer and LeMay, responses for all carbon predictors; (ii) with splines (i.e., piecewise 2001). (ii) Deadwood provides habitat for a number of species such as polynomial functions) of all predictors accounting for non-linear re- saproxylic beetles and wood-inhabiting fungi decomposing organic sponses; and (iii) including splines for H’-index only, following our material which is important for ecosystem functioning (Thorn et al., hypothesis that structural diversity effects on carbon storage are 2018). (iii) Large live and dead trees provide habitat for a number of stronger in structurally less diverse forests (i.e., a logarithmic re- animal species, such as cavity-nesting birds (Runde and Capen, 1987). lationship between H’-index and carbon storage). We compared all (iv) The sapling diversity determines the potential for the future tree models using leave-one-out cross-validation (LOO) based on the pos- species diversity (Gottesman and Keeton, 2017). We investigated the terior likelihood (Vehtari et al., 2017). LOO indicated that the model correlation structure between the four predictors of structural and with weakly informative priors and splines for all predictors had the compositional diversity variables using a variance inflation factor (VIF) highest predictive accuracy. Spatial autocorrelation of the final model and a correlation matrix. The highest VIF and Pearson correlation were was low (Moran’s I statistics = −0.001 and p = 0.536). Moreover, we 1.3 and 0.364 (Fig. S3), respectively, indicating a low correlation visually inspected posterior predictive checks to confirm the accuracy among predictors (Dormann et al., 2013), and thus a highly in- of this model. dependent information value of each habitat indicator. After log-transformation of response variables, we used the brms 3. Results package (Bürkner, 2018) to fit a multi-hierarchical Bayesian model employing location and stand as random effects and treatment as the 3.1. Varying effects of treatments on forest structure only fixed effect predicting each of the six variables. Bayesian approaches capture parameter uncertainty and thus determine the prob- The NMDS shows that SBM treatments overall increased structural ability of treatment effect sizes on the selected response variable. Using diversity as indicated by an extension of the ordinal space compared to a Bayesian model in combination with Markov Chain Monte Carlo control plots only (Fig. 2). In part, treatments were similar to other (MCMC) sampling is straightforward when obtaining posterior dis- treatments or to the unmanaged control group, but disparities were tributions, and is not limited by the degrees of freedom (Rossi and evident for individual structural elements. Most closely related were Allenby, 2003). Including location and stand as random effects con- “crop tree release and canopy gap formation” (CTRG) and “variable trolled for differences in site (e.g., climate and soils) and stand condi- retention thinning” (VRT). There were followed by “expanding-gap tions (e.g., differences in forest age and type). Using the spdep package group shelterwood” (EGGS) and “small-group and single-tree selection” (Bivand et al., 2019), we confirmed that models were not affected by (SGSTS) (see Table 2 for treatment definitions). “Shelterwood with

5 D. Thom and W.S. Keeton Forest Ecology and Management 469 (2020) 118132

highest for SWR. Dead BA as well as the density of live and dead trees with dbh > 50 cm were highest for EGGS. Conifer share as well as SD height and SD dbh were highest for SGSTS. Only live and dead tree densities as well as canopy openness did not differ significantly across treatments, while the latter was restricted to a comparison of only three treatments due to data limitations. The control group had a significantly higher live BA in comparison to all treatments. Other structural variables were significantly different between controls and some treatments. For instance, control plots had a comparably high structural complexity as indicated by the H’-Index, SD height, and SD dbh.

3.2. Different effects of treatments and natural intermediate severity disturbance

To determine how closely the SBM treatments emulated disturbance Fig. 2. NMDS presenting differences in forest structure across the different treatments. Presented are the first two axes of a three dimensional ordination. effects, we compared against previously published data on an inter- Ellipses present the standard deviation of plots (points) around the centroids mediate severity windthrow event in the study area (Meigs and Keeton, (crosses). Colors visualize the total extent of structural variation within each 2018). While the effects of windthrow on forest structure was highly group. One EGGS outlier was omitted in the visualization. variable, the centroid and its standard deviation clearly distinguished intermediate severity wind disturbance from the SBM treatment effects (Fig. 2). SWR was closest whereas the control group was furthest away from windthrown plots in ordination space. We confirmed the differences between all groups with a multilevel permutation-based ANOSIM controlling for location and stand conditions. The ANOSIM indicated moderate (R statistic: 0.340), but highly significant (p < 0.001) differences between groups. The distance between windthrown plots and treatments in ordinal space was strongly driven by conifer share (Fig. 3, Table 3). But also CWD differed markedly between treatment and windthrow effects with volumes being between 3 and almost 18times higher at windthrown plots (p < 0.001) (Table 3). In contrast, the H’- Index was significantly lower at windthrown plots in comparison to treatments (p < 0.050) with exception of SWR (p = 0.549). In particular, the H’-Index of SGSTS plots was even twice as high as at windthrown plots. Moreover, we identified significant differences between treatments and intermediate severity disturbance effects on carbon storage (Table 3). Carbon storage after windthrow was lower than after any Fig. 3. Associations between treatments and forest structural variables. Presented are variables with a significant (α = 0.05) impact on the ordination. treatment with the exception of SWR. This is a result of both low Crosses represent the centroids of treatments. One outlier of EGGS was omitted deadwood amounts and residual live BA after SWR. in the visualization. 3.3. Treatment effects on habitat characteristics and carbon storage reserves” (SWR) and SGSTS exhibited the largest distance between centroids, indicating the lowest similarity in structural diversity of We found significant differences between treatments in their effects forest plots. VRT was closest to untreated plots, whereas the distance on structural elements (H’-Index, Large Trees, Sapling diversity, and between SWR and control was highest. The total extent of structural deadwood indicators) determining habitat conditions for birds and outcomes is represented by the spread of individual plots in ordinal many other forest-dwelling species as well as on carbon storage space. They also define the standard deviations around the centroids, (Table 3). The multi- hierarchical Bayesian models revealed that EGGS indicating the uncertainty of treatment outcomes. In particular, there and SGSTS resulted in the highest values for key habitat and carbon was a high uncertainty for the EGGS centroid, for which the number of storage indicators, assessed here as a relative performance percentage plots was most limited. The ellipses also overlapped with other treat- (Fig. 4, Fig. 5). However, shortly after treatments control plots per- ments considerably. In contrast, uncertainty was lowest for the SGSTS formed slightly better than EGGS (+4.9%), and SGSTS (+5.7%). centroid, while some variation across the ordinal space remained. The CTRG, VRT and SWR performed notably worse than untreated plots first ordination axis (NMDS1) was strongly driven by conifer share, with −11.4%, −21.7%, and −34.8%. Control plots exhibited the followed by live and dead trees with dbh > 50 cm (Fig. 3). Live BA and highest carbon storage, H’-Index, and number of large trees, but were H’-Index had a great influence on the second ordination axis (NMDS2). low in deadwood volume and intermediate in sapling diversity which Adding a finer resolution to the analysis of treatment effects, we were most strongly associated with EGGS and SWR, respectively. Ac- identified significant differences between treatments for structural at- counting for forest structure only (i.e., without accounting for carbon tributes (Table 3). H’-Index and CWD volume exhibited the most pro- storage), EGGS performed slightly better than control plots (+5.1%). nounced disparities across treatments. They were defined by three SGSTS and EGGS were most balanced across the investigated manage- statistically significant distinct groups. The highest median values for ment objectives with a minimum performance of 54.9% and 49.8% per H′-Index and the amount of CWD were found for SGSTS and EGGS, indicator. The Bayesian models explained on average 31.2% of the respectively. Several other indicators were categorized into two sig- variation, ranging between 20.8% for sapling diversity and 43.4% for nificantly different groups across treatments. Live BA and snag volume carbon storage. Hence, uncertainty remains in the prediction of treat- were particularly low, while the effective number of sapling species was ment effects on these indicators (Fig. S4).

6 D. Thom and W.S. Keeton Forest Ecology and Management 469 (2020) 118132

Table 3 Comparison of treatment effects on forest structure and total aboveground carbon storage with reference conditions and windthrown plots. Presented aremedianand interquartile ranges (in parentheses). Letters indicate statistically significant differences (α = 0.05) based on pairwise independence tests with a Benjamini-Hochberg p-value adjustment. Note that the number of plots differs with data availability (n) across treatments and forest structure variables.

Structural attribute Unit CTRG EGGS SGSTS SWR VRT Control Windthrow* (n = 28) (n = 13) (n = 26) (n = 16) (n = 48) (n = 86) (n = 27)

Live BA (n = 244) m2 ha−1 23.0 a 23.0 ab 27.6 a 8.0 b 23.0 a 29.8 c 16.1 b (15.5;30.4) (13.8;36.7) (20.7;29.8) (1.7;25.3) (16.1;28.1) (25.3;36.7) (9.2;18.4) Dead BA (n = 244) m2 ha−1 0.0 a 4.6 b 0.0 a 0.0 ac 0.0 abc 2.3 bc 6.9 b (0.0;2.3) (4.6;6.9) (0.0;2.3) (0.0;2.3) (0.0;2.3) (0.0;6.9) (2.3;10.3) Live tree density (n = 244) n ha−1 479 ns 331 ns 748 ns 404 ns 477 ns 606 ns 237 ns (239;945) (124;579) (385;1080) (4;1005) (244;912) (345;1098) (135;568) Dead tree density (n = 244) n ha−1 0 ab 29 ab 0 ab 0 ab 0 ab 0 a 57 b (0;70) (0;39) (0;41) (0;0) (0;0) (0;28) (22;143) Canopy openness (n = 103) % NA 32.7 ab 8.4NA 9.6 ac 5.3 a 9.0c 37.0b (31.9;35.3) (8.4;8.4) (5.8;19.0) (3.4;32.0) (4.5;12.3) (16.5;47.5) H’-Index (n = 221) dimensionless 1.1 ab 1.3 abcd 1.4 c 0.8 ad 1.2 b 1.4 c 0.7 d (0.9;1.3) (0.7;1.6) (1.3;1.6) (0.0;1.3) (1.0;1.4) (1.2;1.5) (0.4;0.8) Conifer share (n = 235) % 0.0 a 33.6 bc 50.6 bd 50.0 bd 0.0 ac 4.2 c 69.0 d (0.0;1.7) (12.5;51.6) (27.3;74.4) (40.3;72.5) (0.0;28.6) (0.0;35.5) (50.0;90.0) Live trees with dbh > 50 cm (n = 237) n ha−1 0 a 19 b 16 b 0 a 0 a 10 b 18 b (0;10) (0;24) (6;27) (0;7) (0,9) (0,22) (10;34) Dead trees with dbh > 50 cm (n = 237) n ha−1 0 ab 6 a 0 ab 0 b 0 b 0 b 0 b (0;0) (0;13) (0;0) (0;0) (0;0) (0;0) (0;0) Coarse woody debris (CWD) volume (n = 243) m3 ha−1 80.7 ab 146.5 a 59.9 b 54.2 bc 27.0 c 31.6 c 479.2 d (43.7;129.4) (73.5;186.6) (31.4;116.4) (22.7;84.9) (0.0;59.0) (6.3;58.1) (441.2;962.9) Snag volume (n = 221) m3 ha−1 10.5 abc 27.2 a 16.2 abc 0.0 b 5.9 bc 6.2 bc 25.7 ac (0.0;33.0) (11.6;43.7) (0.0;30.6) (0.0;15.2) (0.0;21.4) (0.0;23.7) (21.4;32.2) SD height (n = 220) m 3.8 a 5.2 abc 5.4 bc 2.5 a 4.4 ab 5.4c 3.0 abc (3.0;5.0) (3.7;7.6) (4.3;6.8) (0.0;4.4) (2.7;6.6) (4.3;6.8) (2.3;5.3) SD dbh (n = 242) cm 8.9 a 12.0 abc 12.0 bc 4.6 ab 9.4 a 11.7 c 13.3 bc (5.3;10.2) (8.6;15.3) (9.6;15.2) (0.0;9.9) (7.7;11.4) (10.0;13.9) (8.1;17.6) Effective number of sapling species (n = 217)−1 nha 2.4 ab 2.0 ab 1.9 a 3.3 b 2.8 ab 2.3 a NA (1.2;3.8) (1.0;3.7) (1.0;2.6) (1.8;4.5) (2.2;3.9) (1.5;2.9) Aboveground carbon storage (n = 244) Mg ha−1 110.5 ab 131.8 ab 119.4 a 31.0c 96.5b 150.7 d 53.1 e (80.3;134.1) (71.4;151.3) (90.9;138.6) (22.2;56.1) (61.8;123.3) (126.0;166.8) (35.5;91.2)

* Meigs and Keeton (2018).

Fig. 5. Stacked barplot for the combined relative performance of each response Fig. 4. Treatment effect on four key variables for habitat conditions and variable across the treatments. The theoretical maximum is 100% if all vari- aboveground carbon storage based on multi-hierarchical Bayesian models with ables were at their maximum values in the same group (i.e., each individual location and stand as random effects. Model outputs were back-transformed to variable has a maximum value of 20% corresponding to their effect presented in original values and scaled by predicted maximum values. Fig. 4).

3.4. Carbon storage increases with structural complexity (Fig. 6a), while the positive associations of deadwood volume (Fig. 6b) and large tree density (Fig. 6c) with carbon storage were only moderate We identified positive associations between key habitat variables and almost linear. (i.e., measures of stand structural complexity) and carbon storage (Fig. 6, Fig. S3). A pairwise correlation test indicated a strong positive association between carbon storage and the H’-Index (r = 0.675) and, 4. Discussion to a smaller degree, a positive relationship between carbon storage and large tree density (r = 0.335). Deadwood volume (r = 0.084) was only 4.1. Habitat diversification in mature forests weakly positively correlated with carbon storage. The multi-hierarchical Bayesian model confirmed these results, but also revealed in- Our study rests squarely within the context of debate over how best creasing uncertainty in the relationship between forest structure and to manage secondary eastern forests redeveloped since agricultural carbon storage with increasing complexity (Fig. 6). The model identi- abandonment in the late 19th and early 20th centuries. Today’s forests fied a logarithmic relationship between H’-Index and carbon storage are predominately in mid-successional stages of development (Foster et al., 1998; Thom et al., 2019). While a number of species, including

7 D. Thom and W.S. Keeton Forest Ecology and Management 469 (2020) 118132

Fig. 6. Relationship between forest structure and aboveground carbon. Variables are ordered according to their effect size on carbon. Note that x-axes denote thez- scores (scaled and centered) of predictors while carbon storage (including carbon of live trees as well as of standing and downed deadwood) on the y-axes is log + 1- transformed. birds that require dense mature forests for breeding and feeding, benefit history and, in some cases, conventional forestry practices (Messier from these conditions (Hagenbuch et al., 2012), others require forest et al., 2013; Urbano and Keeton, 2017). structures that are less common in these forests, such as larger openings or large trees – living and dead, standing and downed (McGee, 2019). 4.2. Short vs. long-term treatment effects For example, the suite of 12 indicator birds in SBM for northern New England includes two species that require larger gaps; American Our results showed that disturbance-based silvicultural treatments Woodcock and Chestnut-sided warbler (Setophaga pensylvanica [L.]). In are effective at creating habitat conditions that differ from untreated addition, White Throated Sparrow (Zonotrichia albicollis [Gmel.]) ha- controls (Fig. 2), for instance, by providing higher amounts of standing bitat is patchy, encompassing some areas with less than 50% tree cover. and downed deadwood and increasing sapling diversity (Table 3, What to do with this “bubble” of mature forest – entirely an artifact of Fig. 3). Thus these treatments enhance the variation of structural ele- land-use history – is thus at the core of the debate. Most would agree ments required by different groups of birds and other forest dwelling that a desirable objective is diversifying forest structure and age class species (Drapeau et al., 2009; Hagenbuch et al., 2012; Thorn et al., distributions at landscape scales to produce greater beta diversity in 2018). In addition, we found that these effects will – where structural habitat conditions for birds and other species (Ćosović et al., 2020; complexity is enhanced – have co-benefits in terms of carbon storage. García-Tejero et al., 2018; McGee, 2019; Swanson et al., 2011; Veech Supporting our hypothesis, the SBM treatment effects differed sig- and Crist, 2007). But views differ as to whether management for early nificantly in several structural elements associated with biodiversity seral habitats should be emphasized, or whether recovery of late-suc- and ecosystem functions (Table 3, Fig. S4). As such, SBM provides cessional habitats might also be beneficial on some portion of the foresters with a broader set of tools for managing a diverse array of landscape (Keeton et al., 2018). The former view favors early-succes- biodiversity and co-varying ecosystem functions. However, while some sional species whose populations are declining due to forest maturation SBM treatments retained high levels of carbon (in particular, SGSTS and as well as habitat displacement by agriculture and sub-urban/ex-urban CTRG) and one treatment (EGGS) provided higher outputs for key development (Greenberg et al., 2011). Early seral habitats can be pro- structural elements indicating diverse habitat conditions, soon after moted by forestry practices such as patch cutting (King et al., 2001). For harvest (i.e., before lagged responses to re-allocation of growing space), example, in the SBM program for the U.S. state of Massachusetts, op- none of the treatments had higher combined outputs of key structural tions for Chestnut Sided Warbler and White Throated Sparrow include elements and carbon in comparison to controls (Figs. 3, 4). Control both patch cutting and clearcutting (Massachusetts Audubon, undated). plots maintained the highest carbon storage, H’-Index, and large tree However, neither of these treatments was emphasized within the SBM density. However, the higher values in controls were largely a function program at the time of this study. Promotion of late-seral habitat uti- of their initial starting condition and the time lag required for silvi- lizes both passive and active silvicultural methods to promote re- cultural treatments to achieve responses through regeneration, release, development of the complex stand structures and ecosystem services and enhanced growth rates. associated with older and primary forest landscapes (Ford and Keeton, And yet the SBM treatments retained, on average, higher carbon 2017; Keeton et al., 2018). densities than are typical under more intensive silvicultural practices These approaches are not mutually exclusive; silviculture is never a (see Nunery and Keeton, 2010; Duveneck et al., 2014), such as clear one-size-fits-all endeavor. But forest managers will need a variety of cutting, large group selection or patch cutting lacking within group/ new silvicultural approaches to achieve the full range of biodiversity patch retention, and conventional shelterwoods employing a full re- objectives – including enhancing beta diversity of forest landscapes moval cut. In this respect, broader use of disturbance-based approaches, (Fahey et al., 2018). The silvicultural practices employed within the like SBM, could be an effective means for maintaining and even in- SBM framework will address this need based on our findings. In par- creasing carbon stocking at large scales as a critical element of carbon tially emulating a range of natural disturbance effects, while also very forestry (Kerchner and Keeton, 2015; McKinley et al., 2011). Moreover, intentionally targeting the habitat conditions needed to support the full SWR and VRT had a higher sapling diversity, and all other treatments complement of native bird guilds (i.e. successional and stand structure (CTRG, EGGS, and SGSTS) increased the amount of both standing and associations), the outcome, if applied more widely, would be a di- downed deadwood (Table 3). These findings suggest that the treatments versified mosaic landscape. The spatial variation we found instand are likely to be effective at altering or redirecting stand development structure outcomes at multiple scales (e.g. plot and stand levels) signals trajectories over the longer term, as well as having enhanced deadwood great potential for SBM in enhancing patch diversity within stands and habitat elements and other aspects of stand complexity, such as gap as well as structural diversity among stands. Promoting continued mosaics and patch diversity, over the short term. structural diversification across multiple spatial scales is a key element Our findings and their relevance for forest management depend on of adaptive management in secondary forests simplified by land-use several factors, including the choice of ecosystem services and habitat

8 D. Thom and W.S. Keeton Forest Ecology and Management 469 (2020) 118132 indicators, their weighting (here all variables were equally weighted), unrelated to forest composition. as well as the specific management goals. Most importantly, the out- Our comparison between SBM treatments and natural disturbance comes of our study may differ notably when assessed over a longer time effects on forest structure was restricted to intermediate severity frame. Here we investigated the short-term effects of SBM treatments, windthrow. However, the disturbance regime of northern hardwood whereas forest management objectives span decades to centuries. Based and mixed northern hardwood-conifer forests is far more complex on the treatment effects, however, we can infer possible future stand (Kosiba et al., 2018; Seymour et al., 2002), ranging from low severity development trajectories. For example, the SWR treatment was corre- events, such as small gap formation, to high severity events, such as lated with significantly higher sapling diversity. We expect the release stand replacing hurricanes or insect outbreaks (e.g. eastern spruce effect of this and other treatments will become even more evident inthe budworm). And yet, recent evidence has shown that a common feature future, leading to a more diverse forest composition and likely also of almost all disturbances in the northern forest region is persistence of enhanced structural complexity in the long term (D’Amato et al., 2011; legacy structure, live and dead, standing and down, dispersed or ag- Meigs et al., 2017; Keeton et al., 2018). As control plots showed only gregated in clumps (D’Amato et al., 2018; Lorimer and Halpin, 2014; moderate variation in canopy openness (4.5%−12.3%, see Table 3), we Meigs and Keeton, 2018; Sass et al., 2018). This carryover, often re- predict that some treatments, in particular EGGS, will improve the ferred to as biological legacy (Franklin et al., 2002), occurs across a abundance of light-demanding and intermediate shade-tolerant species wide range of variation, giving silviculturists great flexibility in terms of on the landscape. Treatments with high variation in canopy openness, the different types of structures that can be promoted for a widerange most notably VRT, may provide a diverse mix of species with diverging of objectives (North and Keeton, 2008). Hence, disturbance-based light requirements. Moreover, as some species, such as eastern hemlock treatments offer great potential for introducing greater complexity at and red spruce, use downed trees as nurse logs, treatments may increase multiple scales on managed landscapes. SBM offers one such approach the abundance of these species into the future, depending on other based on our results, but will need to be employed in conjunction with variables such as deer browse and climate (Gottesman and Keeton, other silvicultural systems where objectives include emulating the full 2017). Higher tree species diversity as well as higher proportions of range of natural disturbance effects and interactions with stand devel- eastern hemlock in mixed woods stands, has recently been shown to opment pathways. Most importantly, those other silvicultural systems enhance carbon density in northern hardwood-conifer forests (Thom should focus on leaving higher amounts of deadwood in forest ecosys- and Keeton, 2019). Promoting such species mixes may thus comprise a tems as CWD volume differed most strongly between windthrow and useful carbon forestry approach on appropriate sites (Chen et al., 2018; SBM treatments (Table 3), and this structure provides critical habitat Wang et al., 2011). The complex long-term dynamics induced by SBM for a number of species (Latty et al., 2006; Vítková et al., 2018). On the treatments should be examined in the future, either empirically or other hand, avoiding salvaging timber after natural disturbances is through simulation modeling, to test our hypothesis of alternative de- likely a more cost-effective strategy to foster habitat conditions of those velopment pathways and their effects on carbon storage. species, and should be incorporated into holistic forest management plans (Thorn et al., 2018). Storm-created legacies, such as tip-up 4.3. Emulation of natural disturbance effects mounds and snags, have also been shown to be important for foraging and nesting of rare bird species (Thorn et al., 2016) as well as other All SBM treatments were to some degree structurally distinct from biota (Dove and Keeton, 2015) and may thus be of direct concern to control plots (Table 3) and were closer in ordinal space to intermediate improve the SBM guidelines. severity windthrow as compared to untreated plots (Fig. 2). Hence, they were partially successful in mimicking intermediate intensity natural 4.4. Co-variation of carbon and structural complexity disturbances. However, not entirely supporting our hypothesis, they did not constitute a full emulation of this type of disturbance. Our findings supported the hypothesis that key structural elements Overall, the direction of treatment effects on forest structure in- for habitat conditions are positively associated with carbon storage. dicates that they well reproduced low-severity gap dynamics which are This was most evident for the H’-Index of structural and compositional most commonly observed in northern hardwood and mixed northern diversity. In particular, carbon storage increased the most with an in- hardwood-conifer forests (Dahir and Lorimer, 1996). However, at the crease in H’-Index at forests with low structural and compositional di- stand scales assessed here, they fell short in mimicking disturbances of versity. This finding has been supported by previous studies (e.g., Silva intermediate severity, such as partial mortality events like microbursts, Pedro et al., 2017, Urbano and Keeton, 2017, Fotis et al., 2018, Thom tornadoes, and moderate severity hurricanes (Kosiba et al., 2018; and Keeton, 2019), and can be harnessed to develop management Lorimer and Frelich, 1994; Meigs and Keeton, 2018). With a Live BA strategies with carbon storage as primary goal. Directly after a silvi- target reduction of 40–60%, SWR was the highest severity treatment in cultural treatment, the effect of a high structural complexity on carbon this study (Table 2). The reduction of Live BA to 8 m2 ha−1 was even storage is mostly related to the lower removal of biomass of those below the expected 11–14 m2 ha−1. Although SWR was most similar to treatments. However, fostering structural complexity, such as canopy intermediate severity windthrow, there were distinct disparities be- heterogeneity, can increase carbon uptake efficiency in the long term tween treatment and natural disturbance effects on forest structure (Fotis et al., 2018; Gough et al., 2019; Hardiman et al., 2011). Thus, (Fig. 2). As a result, neither one individual nor the combination of all employing SBM in conjunction with other disturbance-based silvi- treatments emulated all aspects of the natural disturbance regime. cultural approaches (D’Amato et al., 2018) expands the available op- However, the contrasts in stand structure between natural disturbances tions for multi-functional forest management. and silvicultural practices derived here could partly be the result of differing pre-disturbance structure and composition. In particular, the 5. Conclusions conifer share of windthrown plots was higher which could, for instance, explain the low SD height coupled with high SD dbh of conifer domi- SBM treatments will be useful additions to the portfolio of silvi- nated forests. Variation in tree height and dbh is positively correlated in cultural options, helping to increase beta diversity in habitat conditions hardwood dominated forests, as shown here for stands managed with at landscape scales. While the combined short-term outcomes for key low-intensity (Table 3) and by our previous work investigating sec- structural elements were highest in control plots, these values were ondary forest development (Urbano and Keeton, 2017; Thom and likely an artifact of starting condition and the long-term nature of re- Keeton, 2019). However, overall trends in the divergence in forest sponses to silvicultural treatment. We anticipate that the treatments structure between treatments and windthrow presented here should be will alter future developmental trajectories based on the initial out- robust, as contrasts were driven strongly by deadwood indicators comes we found as well as previous investigations of long-term

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