DOI: 10.5061/dryad.280gb5msj
title: Balsam fir seedling bank in Whiteface Mountain (2014–2015)

description: The persistence of future forests depends on the success of tree seedlings
 which are experiencing increasing physiological stress from changing
 climate and air pollution. Although the moss layer can serve as an
 important substrate for tree seedlings, its potential for reducing
 environmental stress and enhancing the establishment of seedlings remains
 poorly understood. We tested if the moss layer decreased environmental
 stress and increased the abundance of balsam fir seedlings dominant in
 high-elevation forests of the northeastern United States that are
 sensitive to changing climate and mercury deposition. We surveyed balsam
 fir seedling density by substrate (moss, litter, other) on 120 quadrats
 (1×1 m) in two contrasting canopy environments (in gaps and under
 canopies), measured seedling stress, and quantified mercury content in
 seedlings and substrates. We observed that, in both canopy environments,
 tree seedlings established on moss exhibited (i) increased density, (ii)
 decreased physiological stress, and (iii) higher potential to recruit into
 larger size class, compared to seedlings established in litter. Regardless
 of canopy environment, seedling foliar mercury levels did not correspond
 to substrate mercury despite large differences in substrate mercury
 concentrations (relative to moss, litter concentrations were ~4-times
 greater and soil concentrations were ~6-times greater), likely reflecting
 the dominance of foliar over root uptake of mercury. Since the moss layer
 appeared to mitigate seedling drought stress and to increase seedling
 establishment and recruitment compared to other substrates, these
 microsite effects should be considered in models predicting forest
 regeneration and dynamics under increased drought stress associated with
 ongoing climate warming.
description: Study
 area This study was conducted in the high-elevation
 fir-dominated forests on Whiteface Mountain (44.22° N and 73.54° W) in the
 northeastern United States. Details of the study area were previously
 described in Berdugo and Dovciak (2019) and elsewhere (e.g., Battles and
 others 2003; Wason and others 2017b, 2021). Briefly, Whiteface Mountain is
 an isolated massif with a summit at 1,485 m above sea level (asl) and
 well-developed elevational climatic and vegetation zones whose ecology has
 been extensively studied over the past half century (e.g., Sprugel 1978;
 Battles and others 2003; Aleksic and others 2009; Wason and others 2017b).
 The vegetation of the area belongs to the Adirondack-New England Highlands
 ecosystem province (Bailey 2014) and is representative of regional forest
 communities with northern hardwood forests occurring below ~800 m asl,
 spruce-fir forests between ~800 m and ~1,300 m asl, and alpine communities
 above ~1,350 m asl (Battles and others 2003). Balsam fir
 (<em>Abies</em> <em>balsamea</em> (L.)
 Mill.) is a dominant tree species between ~1,100 and
 ~1,250 m asl where recurring linear openings in the forest canopy, known
 as fir waves, facilitate fir regeneration (Sprugel 1976; Silvertown and
 Dodd 1999). Soils in the area are mainly Histosols derived from the
 accumulation of organic material due to slow decomposition and slow
 weathering of anorthosite (Witty and Arnold, 1970; Sprugel,
 1976). The character of the moss layer in
 high-elevation fir forests of Whiteface Mountain is relatively
 unresponsive to the microclimatic and other environmental changes caused
 by gap dynamics (Berdugo and Dovciak, 2019). The moss layer here typically
 covers 57.5 + 2.2 % of the forest floor surface, is ~2.5 cm thick, and
 tends to contain ca. 5 species per square meter both in gaps and under
 closed canopies (Berdugo and Dovciak, 2019). Notwithstanding, two aspects
 of the moss layer differed between canopy environments:
 <em>Dicranum</em> <em>fuscescens</em> Turner,
 <em>Hypnum</em> <em>imponens</em> Hedw., and
 <em>Tetraphis</em> <em>pellucida</em> Hedw., are
 more abundant under closed canopies than in gaps, making these moss
 species indicator species for closed canopies in the area; and functional
 dispersion (derived from growth form and the Ellenberg indicator values
 for soil fertility and acidity) is higher under closed canopies than in
 gaps (Berdugo and Dovciak, 2019). While the moss layer in the study area
 includes up to 25 bryophyte species (Table S2.2 in Berdugo and Dovciak,
 2019), those identified as seedling substrates in the current study
 corresponded to the liverwort <em>Lophozia</em>
 <em>ventricosa</em> (Dicks.) Dumort. and four mosses,
 <em>D</em>. <em>fuscescens,</em>
 <em>H</em>. <em>imponens</em>,
 <em>Pleurozium</em> <em>schreberi</em> (Brid.)
 Mitt., and <em>Polytrichastrum</em>
 <em>alpinum</em> (Hedw) G. L. Sm. In all our observations,
 mats of <em>H</em>. <em>imponens</em> held sparse
 fertile shoots of <em>Pohlia</em>
 <em>nutants</em> (Hedw.) Lindb.
 The climate on Whiteface
 Mountain has been characterized in several studies (Table 1) and it can be
 considered a high-elevation form of a modified continental climate (McNab
 and others 2007) experiencing the warming climate trend typical of the
 northeastern United States (Huntington and others 2009; Wason and others
 2017a). The climate records specific for the fir wave zone (1,100–1,250 m
 asl) are rather sporadic compared to those at the summit (1,485 m asl) and
 the shoulder (~600 m asl) of the mountain where climate stations are
 located (Schwab and others 2015). The weather on Whiteface Mountain during the
 study period (in September 2015, see Study design and field data
 collection) was mild, but
 the month was considerably drier in 2015 (48.0 mm of precipitation) than
 the long-term average (93.6 mm, mean September precipitation for 1985–2019
 period) (National Atmospheric Deposition Program
 2020). Study design and field data
 collection  To assess the relevance of the moss layer for
 forest regeneration, we selected a total of 40 study plots using a
 stratified sampling design with two strata (forest gaps, closed canopy)
 that represented the two typical canopy environments in these
 high-elevation forests (cf. fir waves; Sprugel 1976). We selected 20 fir waves and placed one plot
 in each canopy gap and another plot under the forest canopy nearby (on
 average within 50.2 ± 3.9 m, mean ± SE, from the closest gap). Each plot
 consisted of three 1-m<sup>2</sup> survey quadrats placed
 along a transect in the gap center and parallel to the gap length; minimum
 spacing between survey quadrats was 5.6 m. Transect lengths varied between
 ca. 14 m and 25 m long, depending on the gap length while transects under
 forest canopy were consistently 25 m long. The total number of surveyed
 quadrats was 120 (3 quadrats × 40 plots).
 <em>Seedling bank
 surveys: </em>We
 characterized tree seedling abundance and composition on all quadrats in
 2014 (May to September) by counting the number of tree seedlings (≥5 and ≤
 25 cm tall) by species, height class, and substrate. Based on specific
 aging methods developed to describe the seedling bank structure of balsam
 fir (Parent and others 2003), this height range likely captures ~98% of
 balsam fir seedling bank, composed of seedlings recruited in, at least,
 the previous 10 years. Balsam fir also dominated the seedling bank as it
 was found in all plots and represented ~95 % of all surveyed seedlings.
 The remaining 5% of tree seedlings was split among other four species
 (<em>Picea</em> <em>rubens</em>,
 <em>Betula</em> <em>papyrifera</em> var.
 <em>cordifolia</em>, <em>B</em>.
 <em>alleghaniensis</em>, and <em>Populus</em>
 <em>tremuloides</em>) that were present on less than half of
 the plots. Seedlings were divided into two height classes: small (≥5 and ≤
 10 cm tall) and large (&gt;10 and ≤ 25 cm tall). Seedling substrate,
 characterized as moss, litter, and other (decaying logs, bare soil, peat,
 and rocks), was sampled to a depth ≤ 5 cm to collect the substrate under
 the moss layer (with thickness of 2.5 ± 0.3 cm, mean ± SE; Berdugo and
 Dovciak 2019) while accommodating the generally shallow soil depth
 (&lt; 10 cm deep; Gerson and others 2017). The two most common seedling substrates were
 moss and litter (Figure S1). <em>Microsite characteristics:</em>
 In addition to seedling substrate (see above), other environmental
 characteristics potentially affecting tree seedlings were also measured on
 all quadrats. Canopy cover (%) was measured with a concave spherical
 densiometer (Forestry
 Supplies Inc., Jackson, Mississippi, USA) at 50 cm above the ground. Despite lower
 accuracy under closed canopies, densiometer measurements of canopy cover
 (or openness) were shown to provide satisfactory contrast between closed
 forest canopies and open canopies such as those in forest gaps (Russavage
 and others. 2021). We also measured tree stem (≥2 m tall) density and height by species,
 density of standing dead trees, tree diameter at the breast height (DBH)
 by species, and the density of balsam fir saplings (individuals &gt;25
 cm but &lt; 2 m tall). We estimated the cover (%) of understory
 vegetation, coarse woody debris (CWD; the largest diameter &gt;5 cm),
 and fine woody debris (FWD; the largest diameter &lt; 5 cm) within
 &lt; 30 cm height from the ground surface. We also estimated the
 overall cover (%) for seedling substrates for each quadrat (substrate
 details are given in the section Seedling surveys above). Balsam fir does
 not build persistent seed banks (Houle 1992), but its seedling banks can
 instead stagnate under closed canopies and rapidly grow under canopy gaps
 (Sprugel 1976, Parent, Morin, and Messier 2000; Parent, Morin, and Messier
 2001; Parent and others 2003). Given these dynamics, mast years do not
 seem to have a strong effect on the structure of fir seedling banks
 (Parent and others 2003).
 <em>Seedling
 physiological stress: </em>To estimate the instantaneous physiological
 stress that seedlings experienced in the two main substrates (moss,
 litter) under both canopy environments (closed canopies, gaps), we
 contrasted the efficiency of heat dissipation (non-photochemical quenching
 – NPQ, Maxwell and Johnson 2000) in seedling needles. We calculated NPQ for a subsample of 186
 seedlings from the seedling bank survey to achieve comparable seedling
 numbers on the two dominant substrates (95 on moss, 91 on litter) and in
 the two canopy environments (98 under forest canopy, 88 in gaps) by
 selecting the most accessible ten study plots (five under the canopy, five
 in gaps) among the selected fir waves (see the section of Study design and
 field data collection). The efficiency of heat dissipation, NPQ, is
 calculated from non-invasive fluorescence measurements relative to
 dark-adapted conditions (Murchie and Lawson 2013). Therefore, we recorded
 chlorophyll fluorescence on seedling needles in both dark and light
 conditions. Dark-adapted seedling needles (Figure S2a) were excited with a
 pulse of 100 steps, each lasting 1.8 μs, of an intense saturating light
 (20,000 μmol m<sup>-2</sup> s<sup>-1</sup>) and
 light-adapted seedling needles (Figure S2b) were excited with this light
 pulse while recording ambient photosynthetically active radiation (PAR;
 μmol m<sup>-2</sup>
 s<sup>-1</sup>). Using the Pulse Modulated Chlorophyll
 Fluorometer Model FMS2 (Hansatech Instruments Ltd, Northfork, United
 Kingdom), we completed these instantaneous measurements
 before
 noon between September 7
 and 11, 2015. NPQ was calculated as
 (F<sub>m</sub>-
 F’<sub>m</sub>)/F’<sub>m</sub> in light-adapted
 samples, where F’<sub>m</sub> is the maximum fluorescence yield in
 light-adapted conditions, and F<sub>m</sub> is the maximum fluorescence yield in
 dark-adapted conditions (Murchie and Lawson 2013). We used a single
 F<sub>m</sub> value for seedlings in equivalent conditions
 (substrate, canopy environment) by averaging the measurements. Long-term
 seedling stress was avoided by targeting only healthy seedlings and was
 verified with a ratio between variable and maximum fluorescence
 (F<sub>v</sub>/F<sub>m</sub>) in dark-adapted needles &gt; 0.8 (Maxwell
 and Johnson 2000, Murchie & Lawson 2013) as the average Fv/Fm of
 dark-adapted needles of seedling established in both substrates and in
 both environments (n=74) was 0.84 ± 0.05 (mean ± SD). Ruling out long-term
 seedling stress allows us to assume that differences in efficiency of heat
 dissipation (NPQ) may result from short-term stress drivers (Murchie
 & Lawson 2013), such as climate.
 <em>Climatic
 stressors:</em> To
 identify potentially climatically stressful conditions for tree seedlings,
 we monitored instantaneous microclimate during the time when seedling
 instantaneous physiological stress (chlorophyll fluorescence) was measured
 (i.e., September 7–11,
 2015). Air temperature (T)
 and relative humidity (RH) were measured using iButton data loggers (Model
 DS1923; Maxim Integrated Products, Inc., Sunnyvale, California) on each
 plot every 15 minutes with 0.5°C resolution for temperature and 0.6% for
 relative humidity. Each iButton was placed at 30 cm above the ground
 within a handmade gill shield (~12 cm height × 9 cm diameter) to measure
 ambient microclimate (T, RH) while shielding the sensor against solar
 radiation and allowing for adequate ventilation (Tarara and Hoheisel
 2007). <em>Exposure to
 mercury:</em><em> </em>We quantified total
 mercury in seedling needles and dominant substrates (moss, litter) on the
 plots used for monitoring seedling instantaneous physiological stress (see
 above). Assuming mercury accumulation in conifer needles over time
 (Blackwell and others 2014) by quantifying total mercury, we indirectly
 measured seedling exposure to mercury (Parent and others 2003). We
 selected 53 seedlings using stratified random sampling to represent the
 combinations of substrate (litter, moss) and canopy environment (forest
 canopy, gap) and collected samples of both seedling needles and associated
 substrates (both surface and subsurface). Sample collection followed the
 protocol for environmental sampling of low-level trace metals (EPA Method
 1669; US EPA 1995) adapted for solid samples. Briefly, a two-member team
 wearing clean nitrile gloves split sampling tasks. “Clean hands” only
 handled the plastic bag containing the final sample. “Dirty hands”
 prepared sample containers, collected samples by operating pruning
 scissors and a soil sampling tube, cleaned these tools by rinsing them
 with trace metal grade HCl, and handled secondary container bags and
 shipping containers. All needles were collected from each seedling along
 with the associated seedling substrate(s). A substrate sample was
 collected when a single substrate was present within the 5 cm depth from
 the surface. Another separate substrate sample was collected if a
 different substrate was present within 5 cm of the substrate surface
 (i.e., subsurface substrate included moss, litter, or soil A horizon;
 Figure S1). Seedling roots and substrate sections contaminated by the
 adjacent substrates were discarded. Thus, a total of 125 samples were
 collected and transported on ice to the Center for Environmental Systems
 Engineering (CESE) at Syracuse University where they were kept frozen
 until processed. Samples were freeze-dried to a constant weight and hand
 homogenized to a fine powder (any twigs, stems, or rocks were removed from
 the samples). Homogenized samples were analyzed for total mercury
 concentration following the EPA Method 7473 (US EPA 2007) with an Advance
 Mercury Analyzer AMA 254 (Leco Corporation, Saint Joseph, Michigan);
 details on quality control according to the EPA Method 7473 (US EPA 2007)
 are described in Appendix S1 in supporting
 information. Data analyses and hypothesis
 testing To test our hypothesis H1, whether the moss
 layer had a positive effect on tree seedling density, we used a
 multivariate approach by fitting generalized linear models (GLM). We
 modeled the maximum count of fir seedlings per square meter for two
 seedling height classes, small (≥5 and ≤ 10 cm tall) and large (&gt;10 cm and ≤ 25
 cm tall) at the plot level (n=40) with a Poisson error distribution and
 ‘log’ link function. These two height classes allowed us to target
 seedlings established in, at least, the last decade (Parent and others
 2003). The full model predictor set included the percentage moss cover,
 four uncorrelated forest structural metrics (P&gt;0.05 and rho
 &lt; |0.300|; see Appendix S2)—canopy openness, fir sapling density,
 fir tree density, and density of other tree species—and the interaction
 between the moss cover and canopy openness. Because the forest structural
 metrics accounted for the structural differences among the plots, neither
 random nor categorical effects by canopy environment (i.e., gap vs.
 canopy) were included in the model; consistently including plot as a
 random effect did not change the model selection. We did not include the
 cover of litter (the other dominant substrate) in the model to avoid
 collinearity since moss and litter covers were highly correlated (Pearson
 correlation coefficient = -0.538, p&lt;0.001). We fitted all possible
 models and the best model was selected using delta AIC corrected for small
 sample size (DAICc; Anderson and Burnham 2002); models within
 DAICc&lt;2 were considered
 equivalent. To test our hypothesis H2, whether the moss layer had positive effects on seedling
 recruitment into the larger size class, we calculated an index of seedling recruitment
 potential. The index was calculated as a demographic ratio between large
 (&gt;10 and ≤ 25 cm tall) and small seedlings (≥5 and ≤10 cm tall) for
 those quadrats where both seedling height classes were present. The
 differences in seedling recruitment potential between the two dominant
 substrates were analyzed with the Kruskal-Wallis tests as data did not
 follow a normal distribution. To test our hypothesis H3, whether tree seedling
 stress varied by substrate, we performed several comparisons.
 We assessed the difference
 of the instant desiccation potential of the air between canopy
 environments, the difference of instant physiological stress of seedlings
 between substrates within each canopy environment, and the interaction
 between substrate and canopy environment on the instant physiological
 stress. Instant microclimate and instant physiological stress,
 synchronized by their time record, were positively correlated (Pearson’s
 correlation coefficient between instant air desiccation potential – VPD –
 and efficiency of heat dissipation – NPQ –: rho = 0.21, p&lt;0.001, n
 = 186). Field measurements
 of temperature (T) and relative humidity (RH) were combined to calculate
 instant vapor pressure deficit (VPD), a measure of the desiccation
 potential (Will and others, 2013). VPD was calculated as the difference
 between saturation vapor pressure (SVP) and actual vapor pressure (AVP) at
 a given temperature following Ward and Trimble (2003), where SVP = exp
 (16.78 × T - 116.9)/ T + 237.3, and AVP = SVP × RH/100. Seedling
 physiological stress was estimated as non-photochemical quenching (NPQ,
 see section Seedling
 physiological stress).
 Since NPQ data were non-normal (Shapiro-Wilk: W = 0.40, P &lt; 0.001)
 and variance homogeneity was not met for either substrate (Bartlett: x2(1)
 = 112.9, P &lt; 0.001) or canopy environment (Bartlett: x2(1) = 11.2,
 P &lt; 0.001), we used non-parametric Kruskal-Wallis test followed by
 Friedman test (Friedman 1937) for the interaction between substrate and
 canopy environment. Finally, to assess whether exposure to mercury
 differs between seedling substrates (H4), we tested the differences in
 total mercury concentrations (THg) both in (i) seedling needles (by
 substrate and canopy environment) and (ii) among the sampled substrates
 (moss, litter, and soil by canopy environment). Unlike instant
 physiological stress, exposure to mercury is cumulative as mercury
 concentration in plant tissues increases over time (Driscoll and others
 2013). Samples from quadrats were averaged at the plot level for each
 sample type (seedling needles, moss, litter, and soil). Since seedling
 total mercury concentrations met the assumptions of normality
 (Shapiro-Wilk: W=0.92; P=0.150) and variance homogeneity (between
 substrates, Bartlett: x2(1) = 0.00; P = 0.958; and between environments
 Bartlett: x2(1) = 0.43, P = 0.512), we used two-way ANOVA to test whether
 tree seedling total mercury differed by substrate in the forest as a whole
 and between canopy environments, while considering also the interaction
 between substrate and canopy environment. Finally, we tested for the
 differences in total mercury among seedling substrates using a
 Kruskal-Wallis test since substrate total mercury concentrations were not
 normally distributed (Shapiro-Wilk: W = 0.90; P = 0.01) and their
 variances were not homogeneous (x2(1) = 12.03, P = 0.003). We evaluated
 the main effect of canopy environment on the total mercury concentration
 in each substrate using one-way ANOVA since the data for each substrate
 separately complied with normality (Shapiro-Wilk: W&lt;0.93;
 P&gt;0.05). All analyses were performed in R (R Core Team,
 2018) with the ‘stats’ package as well as with additional specific
 packages; ‘lme4’ (Bates and others 2015), ‘MuMIn’ (Barton 2020), and ‘arm’
 (Gelman and Su 2020) for model fit and selection.