Biomass Allometry for Alder, Dwarf Birch, and Willow in Boreal Forest and Tundra Ecosystems of Far Northeastern Siberia and North-Central Alaska ⇑ Logan T

Forest Ecology and Management 337 (2015) 110–118

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Biomass allometry for alder, dwarf birch, and willow in boreal forest and tundra ecosystems of far northeastern Siberia and north-central Alaska ⇑ Logan T. Berner a,b, , Heather D. Alexander c, Michael M. Loranty d, Peter Ganzlin e,1, Michelle C. Mack e, Sergei P. Davydov f, Scott J. Goetz a a The Woods Hole Research Center, 149 Woods Hole Road, Falmouth, MA 02540-1644, USA b Department of Forest Ecosystems and Society, Oregon State University, 367 Richardson Hall, Corvallis, OR 97330, USA c Department of Biological Sciences, University of Texas – Brownsville, 80 Fort Brown, Brownsville, TX 78520, USA d Department of Geography, Colgate University, 13 Oak Drive, Hamilton, NY 13346, USA e Department of Biology, University of Florida, P.O. Box 118525, Gainesville, FL 32611, USA f North-East Scientiﬁc Station, Paciﬁc Institute for Geography, Russian Academy of Sciences, Cherskii, Sakha Republic, Russia article info abstract

Article history: Shrubs play an important ecological role in the Arctic system, and there is evidence from many Arctic Received 23 September 2014 regions of deciduous shrubs increasing in size and expanding into previously forb or graminoid-domi- Received in revised form 28 October 2014 nated ecosystems. There is thus a pressing need to accurately quantify regional and temporal variation Accepted 30 October 2014 in shrub biomass in Arctic regions, yet allometric equations needed for deriving biomass estimates from ﬁeld surveys are rare. We developed 66 allometric equations relating basal diameter (BD) to various aboveground plant characteristics for three tall, deciduous shrub genera growing in boreal and tundra Keywords: ecoregions in far northeastern Siberia (Yakutia) and north-central Alaska. We related BD to plant height Shrub and stem, branch, new growth (leaves + new twigs), and total aboveground biomass for alder (Alnus vir- Biometry Alnus idis subsp. crispa and Alnus fruticosa), dwarf birch (Betula nana subsp. exilis and divaricata), and willow Betula (Salix spp.). The equations were based on measurements of 358 shrubs harvested at 33 sites. Plant height 2 2 2 Salix (r = 0.48–0.95), total aboveground biomass (r = 0.46–0.99), and component biomass (r = 0.13–0.99) Carbon were signiﬁcantly (P < 0.01) related to shrub BD. Alder and willow populations exhibited differences in allometric relationships across ecoregions, but this was not the case for dwarf birch. The allometric relationships we developed provide a tool for researchers and land managers seeking to better quantify and monitor the form and function of shrubs across the Arctic landscape. Ó 2014 Elsevier B.V. All rights reserved.

1. Introduction are highly responsive to environmental change (Chapin et al., 1995; Bret-Harte et al., 2002) and there is mounting evidence Shrubs – multi-stemmed woody plants – are widely distributed derived from satellite imagery, repeat-photography, dendrochro- throughout the Arctic (Walker et al., 2005). These plants provide nology and long-term monitoring showing increased size and important forage for wildlife (White and Trudell, 1980; Sæther abundance of tall deciduous shrubs in many Arctic regions, includ- and Andersen, 1990) and inﬂuence many aspects of ecosystem ing parts of Alaska, Canada, Scandinavia and Russia (Sturm et al., function, including nutrient cycling (Sturm et al., 2005; Tape 2005; Tape et al., 2006; Berner et al., 2011; Myers-Smith et al., et al., 2006; DeMarco et al., 2011) surface energy balance (Chapin 2011; Frost et al., 2014; Frost and Epstein, 2014). Increased shrub et al., 2005; Loranty et al., 2011), permafrost thaw and stability cover has been linked to regional warming (Forbes et al., 2010; (Blok et al., 2010; Lawrence and Swenson, 2011), and carbon stor- Macias-Fauria et al., 2012; Berner et al., 2013), as well as to distur- age (Shaver and Chapin, 1991; Epstein et al., 2012). Arctic shrubs bances (Racine et al., 2004; Frost et al., 2013; Jones et al., 2013) and other processes (e.g. herbivory and anthropogenic activities, Myers-Smith et al., 2011). Shrub expansion and increased height ⇑ Corresponding author at: Department of Forest Ecosystems and Society, Oregon might lead to larger aboveground carbon pools; yet modeling State University, 367 Richardson Hall, Corvallis, OR 97330, USA. Tel.: +1 (702) 524 efforts suggest that these changes in shrub populations could act 3667. as a net positive feedback to regional warming by increasing E-mail address: [email protected] (L.T. Berner). 1 Present address: College of Forestry and Conservation, University of Montana, 32 energy absorbed by the land surface, enhancing evapotranspiration Campus Drive, Missoula, MT 59812, USA. (Lawrence and Swenson, 2011; Bonﬁls et al., 2012; Pearson et al., http://dx.doi.org/10.1016/j.foreco.2014.10.027 0378-1127/Ó 2014 Elsevier B.V. All rights reserved. L.T. Berner et al. / Forest Ecology and Management 337 (2015) 110–118 111

2013), and exacerbating permafrost thaw (Lawrence and Swenson, Yakutia in far northeastern Siberia (Table 1, Fig. 1). In Alaska, sam- 2011; Bonfils et al., 2012). Although details remain unresolved pling occurred near Fairbanks in the boreal zone, as detailed in (Loranty and Goetz, 2012), snow-shrub interactions will likely plan Alexander et al. (2012), and near the Toolik Field Station in a tun- an important role in shaping the seasonality and magnitude of dra ecosystem (Pizano et al., 2014). In Yakutia, sampling occurred feedbacks on the climate system (Sturm et al., 2005; Lawrence near Cherskii in the boreal zone and Ambarchik in tundra, both and Swenson, 2011; Bonfils et al., 2012). located in the northern portion of the Kolyma River watershed. Given the ongoing changes in Arctic shrub populations and the Deciduous shrubs are widespread and often phylogenetically importance of shrubs for wildlife, surface biophysics, and ecosys- similar in these four ecoregions (Petrovsky and Zaslavskaya, tem carbon balance, there is a need to quantify variations in shrub 1981; Krestov, 2003; Troeva et al., 2010). Many species of willow biomass and height across regions, ecosystems, and time in an (e.g. Salix alaxensis, Salix glauca, and Salix pulchra) occur in these accurate, repeatable, and rapid manner. Although destructive har- ecoregions and occupy a range of habitat types (e.g. upland, ripar- vests are generally the most accurate method, this approach is ian and tundra). Both Alaskan green alder (Alnus viridis subsp. cri- time-consuming and not suitable for long-term monitoring. Many spa) and Siberian alder (Alnus fruticosa), sometimes considered a alternative methods, therefore, have been developed for estimating subspecies of A. viridis, similarly occur over a variety of upland shrub aboveground biomass (Chojnacky and Milton, 2008) such as and lowland landscape positions. Arctic dwarf birch (Betula nana point-intercept sampling (Shaver and Chapin, 1991) and a combi- subsp. exilis) are found widely across northeastern Asia and north- nation of percent cover and plant height (Chen et al., 2009). In an ern North America, complementing Betula glandulosa in boreal evaluation of techniques for estimating shrub biomass pools, Alaska and B. nana subsp. divaricata in boreal Yakutia. Chojnacky and Milton (2008) noted that the most robust, albeit relatively time-consuming, approach involves measuring stem basal 2.2. Shrub collection, processing, and inventory diameter (BD) of individual shrubs in a known area and then applying allometric equations to convert stem BD to biomass. Allo- Samples used in this analysis were harvested as part of three metric equations are, however, often unavailable for species or independent projects; therefore, sampling strategies and process- regions of interest, particularly in Arctic regions. ing methods were similar but not completely identical. In Yakutia The objective of this study was to develop allometric equations and boreal Alaska, we sampled shrubs across a range of size classes relating shrub BD to height (H) and stem, branch, new growth, and and landscape positions, excluding riparian zones. We harvested total aboveground biomass (AGB) for three deciduous shrub genera plants so long as they did not exhibit severe damage (e.g. extensive or species found widely throughout the Arctic. In particular, we browse or large broken stems). On the Alaskan tundra, willow were focused on alder (Alnus spp.), dwarf birch (Betula spp.) and tall wil- harvested at random intervals along transects in and around ther- low species (Salix spp.) growing at boreal and tundra sites in far mokarst features. In all cases we clipped each shrub at the soil sur- northeastern Siberia (Yakutia) and north-central Alaska. We then face and then measured the stem BD using calipers. In the event examined variation in allometric relationships among ecoregions that the base of the stem did not appear circular, we measured for each species or generic group to determine whether generalized BD twice at perpendicular angles and then averaged the measure- equations could be applied to estimate biomass regardless of ecore- ments. B. nana often assumes a multi-stemmed growth form in gion. As an illustration of the influence of applying equations from which stems fuse beneath the soil surface. We considered each other ecoregions, we estimated willow AGB pools for boreal and individual stem protruding from the soil as a unit of observation tundra sites in Yakutia by combining site inventories with equa- (i.e. not the entire plant). For a subset of shrubs, we measured tions for willow developed both within and outside each of the standing height using a stadia rod or tape measure prior to two ecoregions. The shrub allometry presented herein should serve clipping. as a resource for researchers and land managers needing to quantify After harvesting, we partitioned the plants into tissues (stem, shrub biomass pools across space or time in select Arctic regions. branches, new growth), subsampled if necessary, and then oven- dried the material at 60 °C until it reached a constant mass. Stems 2. Methods and branches included both wood and bark. Alder (A. viridis subsp. crispa and A. fruticosa) were identified to the species-level, as were 2.1. Study area most tall willow from the Alaskan tundra (S. alaxensis, S. glauca, and S. pulchra) and Yakutian tundra (S. pulchra); however, we did Over three summers (2011–2013) we harvested 358 plants not classify tall willow harvested in other ecoregions beyond spread across 27 sites in north-central Alaska and six sites in genus. We did not differentiate between dwarf birch subspecies

Table 1 Summary of shrub samples collected in Alaska (USA) and Yakutia (Russia) for allometric analysis. The ranges in basal diameter (BD), stem length (L), and aboveground biomass (AGB) are provided for each genera (Alnus, Betula, and Salix) and ecoregion.

Genus Ecoregion General proximity Year sampled N sites N stems Range Species BD (cm) L (cm) AGB (g) Alder Boreal Alaska Fairbanks 2011 11 32 0.26–5.3 2–450 0.60–2510.27 A. viridis subsp. crispa Boreal Yakutia Cherskii 2012 2 22 0.18–9.52 3–475 0.05–9867.55 A. fruticosa Total 13 54 0.18–9.52 2–475 0.05–9867.55 Birch Boreal Yakutia Cherskii 2012 3 25 0.09–2.53 8–152 0.04–521.79 B. nana subsp. exilis B. nana subsp. divaricata Tundra Yakutia Ambarchik 2013 2 27 0.25–0.75 20–54 0.88–8.42 B. nana subsp. exilis Total 5 52 0.09–2.53 8–152 0.04–521.79 Willow Boreal Alaska Fairbanks 2011 13 39 0.11–3.9 4–390 0.08–1361.52 Salix spp. Boreal Yakutia Cherskii 2012 3 28 0.1–6.3 3–593 0.03–4819.84 Salix spp. Tundra Alaska Toolik 2011 2 30 0.2–3.17 4–225 0.48–563.51 S. alaxensis, S. glauca, S. pulchra Tundra Yakutia Ambarchik 2013 9 155 0.11–4.7 6–282 0.44–1409.67 S. pulchra Total 27 252 0.1–6.3 3–593 0.026–4819.84 112 L.T. Berner et al. / Forest Ecology and Management 337 (2015) 110–118

are a large, taxonomically-complex group for which species identi- ﬁcation is often quite challenging, in part due to retrogressive hybridization (Skvortsov, 1999; Hardig et al., 2000). As a result, willow are often grouped regardless of species when deriving allometric equations (Smith and Brand, 1983; Bond-Lamberty et al., 2002) and during subsequent ecological investigations (e.g. Mack et al., 2008; Alexander et al., 2012). In addition to harvesting shrubs, we conducted shrub inventories at boreal (n = 10) and tundra (n =6;Loranty et al., 2014) sites in northern Yakutia during the summers of 2012 and 2013. We established ﬁxed-area plots at each site and then measured the BD of every willow using calipers. Plot area depended on shrub density and ranged from 0.5 to 20 m2 (see Fig. 3).

2.3. Statistical analysis

For each shrub genus we developed ecoregion-speciﬁc and more generalized, cross-region allometric equations relating shrub BD to component biomass, AGB, and H. We used weighed nonlinear least squares regression (NLS), as implemented by the stats package in the statistical software R (R Core Team, 2013), to ﬁt power functions of the form

M ¼ aBDb Fig. 1. Map depicting the general sampling areas in northern Yakutia (Russia) and Alaska (USA). The approximate location of latitudinal treeline is shown as a black where M was shrub dry biomass (g) or height (cm); BD was shrub line, as per the Circumpolar Arctic Vegetation (CAVM, Walker et al., 2005). Number basal diameter (cm); and a and b were fitted coefficients. Regression of sample sites are shown as different sized circles, with sample site number residuals were weighted by y1/2 or y1 to correct for non-random increasing with circle size. residuals, especially the tendency to overpredict biomass or H of small BD shrubs. Power functions are commonly used when devel- (B. nana subsp. exilis and B. nana subsp. divaricata) at boreal sites in oping allometric equations, though are often fitted by applying lin- Yakutia, though at the tundra sites in that region only B. nana ear regression after logarithmic transformation of both dependent subsp. exilis is present (Petrovsky and Zaslavskaya, 1981). Willow and independent variables (e.g. Bond-Lamberty et al., 2002;

Fig. 2. Shrub total aboveground biomass (left panels) and new growth (leaf + twig) biomass (right panels) versus basal diameter for (a) alder, (b) dwarf birch, and (c) willow at boreal and tundra sites in northern Alaska (USA) and Yakutia (Russia). Regression coefﬁcients are given in Table 2. L.T. Berner et al. / Forest Ecology and Management 337 (2015) 110–118 113

Tundra ship between BD and biomass among ecoregions (Table 3). Alaskan alders tended to increase more in AGB, stem, and new 150 growth mass per unit increase in BD than plants in Yakutia. In

terms of AGB, this was particularly evident at BD < 2 cm. Although )

2 100 − we sampled alders with BD ranging from 0.18 to 9.52 cm, we note that these equations are likely most accurate at BD < 4 cm because g m

( 50 the majority of samples fell within this range. Our sampling did not 0 include Alder from tundra ecosystems. Boreal 3.1.2. Dwarf birch 150 Dwarf birch BD was a signiﬁcant predictor (P < 0.001) of AGB (r2 = 0.70–0.95, RMSE = 1.34–6.47 g) and mass of individual com- 100 ponents (r2 = 0.52–0.98, RMSE = 0.26–4.66 g) at boreal and tundra

Predicted willow AGB sites in northern Yakutia (Table 2, Fig. 2b). Model fits tended to 50 be better for boreal sites (r2 = 0.89–0.98, RMSE = 1.41–6.47 g) than 2 0 tundra sites (r = 0.52–0.70, RMSE = 0.26–0.79 g), despite the higher RMSE, which resulted from the larger range of BDs at the boreal sites. There were no significant differences in SSE between Genus the generalized and ecoregion-specific models for any of the plant components (Table 3), suggesting that there were similar relation- Boreal.Alaska Tundra.Alaska Boreal.Yakutia

Tundra.Yakutia ships between BD and the mass of plant components at the boreal Allometry model and tundra sites. The generalized allometric model explained much of the variation in plant mass based on BD (r2 = 0.90–0.98, Fig. 3. Willow aboveground biomass predicted for boreal (n = 10) and tundra (n =6) RMSE = 0.54–1.34 g). Dwarf birch was not sampled at Alaskan bor- sites in northern Yakutia using allometry models developed specifically for each of eal or tundra sites. these ecoregions, as well as using models developed for ecoregions in Alaska and a generic genus model. 3.1.3. Willow Jenkins et al., 2003). Fitting the power functions using NLS consis- For tall willow, BD was a significant predictor (P < 0.001) of AGB tently resulted in better fits (e.g. lower root mean square error (r2 = 0.45–0.99, RMSE = 3.49–25.58 g) and the mass of individual [RMSE], more random residuals) in comparison to fitting the models components (r2 = 0.13–0.99, RMSE = 1.22–20.97 g) at boreal and with log–log regression and the Sprugel (1983) correction (results tundra sites in both Alaska and Yakutia (Table 2, Fig. 2c); however, not shown). For each regression equation we provide the coefficient in some instances the models had little predictive power. Compar- of determination (r2), RMSE, and standard errors of the regression ison of SSE from the full and reduced models suggested some coefficients. We provide standard errors for the regression coeffi- regional differences in the relationship between BD and plant mass cients so that any uncertainty associated with the models can be (Table 3). Willow from boreal and tundra sites in Yakutia exhibited propagated into subsequent analyses using Monte Carlo simula- very similar relationships between BD and biomass, with no signif- tions or other techniques. icant difference in SSE between the generalized and ecoregion-spe- For each genus, we tested whether there were differences in cific models (Table 3). For Yakutia willow, BD explained between allometric models among ecoregions using extra sum-of-squares 86% and 99% of the variability in plant mass (P < 0.001, F-tests (Motulsky, 2004). This involved fitting a generalized model RMSE = 4.33–18.00 g). In contrast with willow from Yakutia, there to data for multiple ecoregions (‘‘full model’’) and comparing the were significant differences in the relationship between BD and error sum-of-squares (SSE) from this model (SSEF) against the both AGB and new growth mass at boreal and tundra sites in cumulative SSE that resulted from fitting a separate model to each Alaska (P < 0.001 for both, Table 3). In Alaska, willow from tundra ecoregion (‘‘reduced model’’; SSER). The F-statistic was calculated sites showed much greater variability in the relationships between as BD and both AGB (r2 = 0.45, RMSE = 8.11 g) and new growth mass (r2 = 013, RMSE = 10.75 g) than did plants at boreal sites (AGB: ðSSE SSE Þ SSE 2 2 F ¼ R F F r = 0.68, RMSE = 4.77 g; new growth: r = 0.72, RMSE = 6.81 g). dfR dfF dfF Comparison of models with measurements pooled by ecosystem, instead of by region, suggested similarities in the relationship where df and df are the degrees of freedom from the reduced and R F between BD and AGB at boreal sites (P > 0.05), but not at tundra full models, respectively. This tested the null hypothesis that there sites (P < 0.05). was no difference between SSER and SSEF, which would indicate that the generalized model performed as well as the ecoregion-spe- 3.2. Comparison of allometric models in predicting willow cific models. aboveground biomass

3. Results Willow AGB stocks averaged 15.28 ± 5.18 g m2 at boreal sites (±1 SE; n = 10; range: 0.28–42.00 g m2) and 70.07 ± 20.49 g m2 3.1. Shrub biomass and basal diameter relationships at tundra sites (n = 6; range: 5.99–131.20 g m2) when predicted using ecoregion-specific allometric models. By comparison, willow 3.1.1. Alder AGB stocks averaged 14.31 ± 4.69 g m2 at boreal sites and For alder, BD was a significant predictor (P < 0.001) of both AGB 62.46 ± 18.09 g m2 at tundra sites when predicted using the gen- (r2 = 0.94–0.99, RMSE = 20.22–25.24 g) and mass of individual eric genus model. These translate into average percent errors plant components (r2 = 0.85–0.99, RMSE = 5.10–27.20 g) at boreal (±1 SD) of 0 ± 9% and 10 ± 2% at boreal and tundra sites, respec- sites in Alaska and Yakutia (Table 2, Fig. 2a). Comparison of the tively. While predictions from the generic genus model showed generalized and ecoregion-specific models revealed significant dif- good agreement with predictions based on ecoregion-specific ferences (P < 0.001) in SSE, suggesting differences in the relation- models, applying models developed for Alaska to estimate willow 114 L.T. Berner et al. / Forest Ecology and Management 337 (2015) 110–118

Table 2 Allometric equations relating basal diameter (BD) to stem, branch, new growth, and total aboveground biomass (AGB) for alder, dwarf birch and tall willow at boreal and tundra sites in Alaska (USA) and Yakutia (Russia). Equations are of the form M = aBDb where units of M are in grams of dry weight and BD is in centimeters. All equations are statistically- signiﬁcant at P < 0.001. The range of BD used in model development are provided, as are the standard errors of the regression coefﬁcients. Alder and tall willow exhibit apical dominance and therefore we considered the stem to be the primary shoot supporting the apical bud and branches to be lateral growth off of the stem. Dwarf birch, on the other hand, often does not exhibit apical dominance and therefore we considered the stem to be the shoot with the largest diameter and branches to be all other shoots. New growth represented a combination of leaves and twigs produced the year of harvest.

Genus Ecoregion Component BD range (cm) aa[se] bb[se] r2 df RMSE (g) Alder Boreal Alaska Stem 0.33–5.30 4.28 2.35 3.68 0.35 0.90 2,26 27.20 Branch 0.60–5.30 2.23 0.75 3.19 0.22 0.96 2,20 6.94 New growth 0.26–5.30 10.88 1.73 1.55 0.13 0.85 2,30 5.10 AGB 0.26–5.30 13.31 4.30 3.15 0.21 0.94 2,30 25.24 Boreal Yakutia Stem 0.18–9.52 17.45 4.70 2.52 0.13 0.98 2,20 26.22 Branch 0.26–9.52 2.88 1.12 3.25 0.18 0.99 2,17 20.23 New growth 0.18–9.52 4.22 1.64 2.07 0.19 0.94 2,20 7.81 AGB 0.18–9.52 23.70 3.45 2.68 0.07 0.99 2,20 20.22 Boreal Pooled Stem 0.18–9.52 9.90 2.04 2.80 0.11 0.94 2,48 6.50 Branch 0.26–9.52 1.81 0.40 3.45 0.11 0.99 2,39 3.91 New growth 0.18–9.52 6.64 0.84 1.84 0.07 0.92 2,52 2.33 AGB 0.18–9.52 19.40 2.47 2.78 0.07 0.99 2,52 5.40 Birch Boreal Yakutia Stem 0.09–2.53 17.47 2.77 2.36 0.21 0.89 2,23 4.66 Branch 0.19–2.53 5.73 1.26 4.06 0.26 0.98 2,16 3.26 New growth 0.09–2.53 4.57 0.62 2.45 0.18 0.93 2,23 1.41 AGB 0.09–2.53 28.10 4.33 2.97 0.19 0.95 2,23 6.47 Tundra Yakutia Stem 0.25–0.75 4.89 0.96 1.49 0.27 0.52 2,25 0.45 Branch 0.25–0.75 9.86 2.04 2.56 0.32 0.69 2,25 0.53 New growth 0.25–0.75 2.52 0.50 1.66 0.28 0.55 2,25 0.26 AGB 0.25–0.75 16.38 2.70 1.92 0.24 0.70 2,25 0.79 Yakutia Pooled Stem 0.09–2.53 13.44 0.95 2.64 0.10 0.90 2,50 1.28 Branch 0.19–2.53 6.26 0.76 3.92 0.16 0.98 2,43 1.16 New growth 0.09–2.53 4.47 0.23 2.42 0.07 0.93 2,50 0.54 AGB 0.09–2.53 28.97 1.56 2.88 0.08 0.95 2,50 1.34 Willow Boreal Alaska Stem 0.35–3.90 20.62 7.66 2.29 0.31 0.64 2,31 20.97 Branch 0.35–3.90 5.19 2.12 2.37 0.34 0.77 2,23 7.22 New growth 0.11–3.90 10.54 2.40 1.71 0.20 0.72 2,35 6.81 AGB 0.11–3.90 27.58 4.63 2.36 0.15 0.68 2,37 4.77 Boreal Yakutia Stem 0.10–6.30 16.53 2.56 2.85 0.09 0.99 2,26 13.40 Branch 0.25–6.30 3.56 2.04 3.06 0.33 0.92 2,20 18.77 New growth 0.10–6.30 3.11 1.27 2.18 0.24 0.85 2,26 6.04 AGB 0.10–6.30 23.53 5.43 2.83 0.13 0.98 2,26 25.58 Tundra Yakutia Stem 0.20–3.17 12.87 0.81 2.93 0.06 0.99 2,28 1.96 Branch 0.20–3.17 6.97 0.97 2.65 0.14 0.94 2,27 2.71 New growth 0.20–3.17 4.51 0.41 2.04 0.10 0.94 2,28 1.22 AGB 0.20–3.17 25.28 1.74 2.70 0.07 0.99 2,28 3.49 Tundra Alaska New growth 0.35–4.70 6.50 2.20 1.23 0.33 0.13 2,85 10.75 AGB 0.11–4.70 27.72 4.24 2.30 0.14 0.45 2,153 8.11 Willow Alaska Pooled New growth 0.35–4.70 15.06 4.4 0.84 0.28 0.13 2,85 29.96 AGB 0.11–4.70 47.22 7.21 1.98 0.13 0.55 2,192 36.04 Yakutia Pooled Stem 0.10–6.30 15.09 1.45 2.90 0.06 0.99 2,56 9.65 Branch 0.20–6.30 4.20 1.32 2.97 0.18 0.93 2,49 12.61 New growth 0.10–6.30 3.72 0.77 2.08 0.13 0.86 2,56 4.33 AGB 0.10–6.30 23.19 3.18 2.84 0.08 0.98 2,56 18.00 Boreal Pooled Stem 0.10–6.30 9.26 1.92 3.16 0.12 0.97 2,59 21.61 Branch 0.25–6.30 1.86 0.71 3.41 0.22 0.91 2,45 14.82 New growth 0.10–6.30 7.38 1.56 1.72 0.14 0.73 2,63 7.38 AGB 0.10–6.30 16.25 3.19 3.03 0.12 0.96 2,65 27.85 Tundra Pooled New growth 0.20–4.70 5.85 1.52 1.34 0.25 0.13 2,115 9.34 AGB 0.11–4.70 46.46 7.19 1.99 0.14 0.54 2,183 35.37 Pooled Stem 0.10–6.30 10.20 1.37 3.07 0.09 0.96 2,89 4.10 Branch 0.20–6.30 2.33 0.56 3.23 0.15 0.90 2,74 3.36 New growth 0.10–6.30 2.11 0.50 2.07 0.21 0.24 2,180 3.60 AGB 0.10–6.30 21.80 2.52 2.64 0.09 0.75 2,250 7.41

AGB stocks in Yakutia tended to result in overestimating stocks by (P > 0.05, Table 3). Willow at boreal sites in Yakutia and Alaska 31–49%. had similar BD–H relationships (P > 0.05) and exhibited greater increases in H per unit increase in BD than did willow at Alaska tundra sites. Maximum observed heights were 526 cm, 461 cm, 3.3. Shrub basal diameter and height and relationships and 134 cm for willow, alder, and birch, respectively. Shrub BD was as signiﬁcant predictor (P < 0.001) of H for all three genera (r2 = 0.48–0.95, RMSE = 6.65–14.72 cm, Table 4, 4. Discussion Fig. 4). Alder tended to increase most in H per unit BD, followed by willow and then birch. There was no difference in the BD–H For tall-growing arctic shrubs, basal diameter was a strong pre- relationships between alder at boreal sites in Yakutia or Alaska, dictor of some aboveground characteristics (e.g. total aboveground as evident through comparison of full and reduced models biomass), yet an inconsistent predictor of others (e.g. new growth). L.T. Berner et al. / Forest Ecology and Management 337 (2015) 110–118 115

Table 3 Comparison generalized and ecoregion-specific allometric models. For each species, a generalized model was fit to data from multiple ecoregions (‘‘full model’’), and the error sum-of-squares (SSE) was compared against the cumulative SSE that resulted from fitting a separate model to each ecoregion (‘‘reduced model’’). Extra sum-of-squares F-tests indicated whether there were significant differences in SSE between full and reduced models. A significant difference suggests ecoregion-specific variation in the relationship between BD and the variable of interest.

Genus Model Component Full model Reduced model F-value P-value df SSE df SSE Alder Boreal Stem 48 1,778,701 46 1,130,900 8.741 0.001 Branch 39 241,646 37 256,486 1.198 0.313 New growth 52 29,330 50 23,343 5.308 0.008 AGB 52 988,117 50 742,722 6.457 0.003 Height 52 136,874 50 124,574 2.336 0.107 Birch Yakutia Stem 50 5430 48 5037 1.804 0.176 Branch 43 1787 41 1538 2.993 0.061 New growth 50 267 48 241 2.390 0.102 AGB 50 17,719 48 16,052 2.352 0.106 Willow Alaska New growth 122 123,237 120 104,319 9.364 <0.001 AGB 192 7,068,133 190 8,055,840 13.415 <0.001 Height 119 255,704 117 224,341 7.298 0.001 Yakutia Stem 56 162,913 54 158,654 0.732 0.486 Branch 49 164,391 47 164,555 0.024 0.976 New growth 56 12,364 54 12,218 0.329 0.721 AGB 56 772,062 54 768,551 0.127 0.881 Boreal Stem 59 634,321 57 529,298 4.884 0.011 Branch 45 194,790 43 175,287 2.253 0.117 New growth 63 36,516 61 27,609 7.683 0.001 AGB 65 1,618,711 63 1,511,016 2.162 0.124 Height 64 108,098 62 102,937 1.528 0.225 Tundra New growth 115 89,621 113 88,929 0.444 0.642 AGB 183 6,405,498 181 7,313,375 12.969 <0.001 All Pooled Stem 89 634,127 85 530,020 3.653 0.009 Branch 74 198,613 70 176,691 2.042 0.098 New growth 180 142,028 174 116,538 5.384 <0.001 AGB 250 10,736,827 244 8,824,391 7.422 <0.001 Height 147 368,841 143 254,902 11.352 <0.001

Table 4 Allometric equations relating basal diameter (BD) to plant height (H) for three shrub genera (alder, birch and willow) at boreal and tundra sites in Alaska (USA) and Yakutia (Russia). Equations are of the form H = aBDb, where H and BD are in centimeters. All equations are statistically-signiﬁcant at P < 0.001. The range in BDs used in model development are provided, as are the standard errors of the regression coefﬁcients.

Genus Ecoregion Range (cm) aa[se] bb[se] r2 df RMSE (cm) Alder Boreal Alaska 0.26–5.3 95.27 9.04 0.60 0.10 0.48 2,30 14.72 Boreal Yakutia 0.18–9.52 71.04 8.43 0.90 0.07 0.93 2,20 11.58 Boreal Pooled 0.18–9.52 64.19 5.51 0.95 0.06 0.73 2,52 4.68 Birch Boreal Yakutia 0.09–2.53 62.10 4.32 0.78 0.11 0.73 2,23 6.65 Willow Boreal Alaska 0.11–3.90 91.26 6.55 0.80 0.08 0.70 2,36 11.67 Boreal Yakutia 0.10–6.30 79.72 6.88 1.01 0.06 0.95 2,26 9.50 Tundra Alaska 0.35–4.7 65.91 5.50 0.81 0.09 0.52 2,81 12.82 Willow Alaska Pooled 0.11–4.7 73.33 4.41 0.75 0.07 0.55 2,119 13.27 Boreal Pooled 0.10–6.30 86.15 4.99 0.93 0.05 0.87 2,64 11.26 Pooled 0.10–6.30 63.08 3.27 0.92 0.05 0.65 2,147 4.16 Pooled Pooled 0.09–9.52 62.63 2.46 0.94 0.04 0.70 2,226 4.15

Previous studies on tall shrubs (Buech and Rugg, 1995) and trees mass and biomass components. Specifically, generalized (Gower et al., 1997; Bond-Lamberty et al., 2002) have similarly equations did a poorer job of predicting biomass of alder in boreal found that stem diameter tends to be a stronger predictor of stem Alaska and Yakutia and willow in all ecoregions except Yakutia. and total aboveground biomass than of foliage or new growth bio- Generalized equations developed for dwarf birch produced similar mass. This highlights that shrub basal diameter can generally be biomass estimates as ecoregion-specific equations, but we only used to estimate total aboveground biomass with relatively high sampled this species in Yakutia and not Alaska. The drivers of confidence, yet that caution is needed when estimating the bio- regional variation in the relationship between BD and shrub biomass of individual plant components, particularly those related mass are not entirely clear, yet could be related to a suite of factors, to foliage and new growth. including genetics, community dynamics (e.g. competition and Our analysis also showed that allometric equations derived in a herbivory pressure), growing season length, water and nutrient particular ecoregion did not always accurately predict biomass of availability (Waring et al., 1985; Cody, 1991; Mack et al., 2008; shrubs outside of that ecoregion, as has been demonstrated in Paul et al., 2013b). other studies (e.g. Grier et al., 1984; Bond-Lamberty et al., 2002; Regional variations may also relate to fire disturbances, which Jenkins et al., 2003; Paul et al., 2013a). In 50% of the comparisons are more common in boreal Alaska compared to the other ecore- made between ecoregion-specific and generalized equations, eco- gions (Beck et al., 2011; Berner et al., 2012). Shrubs sampled in bor- region-specific equations better predicted total aboveground bio- eal Alaska commonly grew in mid-successional stands (<60 year 116 L.T. Berner et al. / Forest Ecology and Management 337 (2015) 110–118

pling occurred as part of three separate studies, so shrubs were 500 (a) Alder ● ● not always collected in an entirely consistent manner, as is often 400 ● the case when compiling allometric data (Jenkins et al., 2003; 300 ● Paul et al., 2013b). As such, allometric equations derived from wil- ● ● ●● ● lows sampled in the Alaskan tundra may have produced different 200 ● ●

Height (cm) biomass estimates than generalized equations because sampling 100 Boreal.Alaska ● ●● ● ● Boreal.Yakutia was random and included shrubs with signs of damage (e.g. ●● ● 0 browse, insect, wind, etc.), while equations derived in other regions did not. Thus, equations produced from shrubs with little 500 (b) Birch damage should not be applied in areas where shrubs are exten- 400 sively damaged by herbivores or other activities. Another potential 300 limitation to our equations, as previously noted, is that we did not, 200 in most cases, identify willow or dwarf birch to the species-level ● except. Species identity could lead to differences in the BD versus Height (cm) ● ● ● ● ● 100 ● ● ● ● ● biomass relationships and therefore we limited this potential con- ●●●●●● Boreal.Yakutia ●●● 0 founding factor for willows by only harvesting tall-growing willows, and not smaller-stature or recumbent species like Salix ● 500 (c) Willow reticulata or Salix phlebophylla. Although we did not formally test ● 400 ● for differences among willow species, the equations for both boreal ● 300 ● ● and tundra Yakutia, which were produced using a suite of tall- 2 ● ● growing willows, consistently yielded r values >0.85, suggesting 200 ● Boreal.Alaska ● strong similarities among species. Model fits for willow were less Height (cm) ●● ● 100 ●● Boreal.Yakutia ●● robust for Alaska boreal and tundra ecoregions, which could be ●● ● Tundra.Alaska 0 ●●● related to differences in BD versus biomass relations among spe- 0246810 cies, as well as to variations in sampling protocol, fire disturbance Basal diameter (cm) history, or permafrost. Despite these challenges and caveats, our allometric equations contribute much needed data for Arctic Fig. 4. Shrub height plotted against basal diameter for (a) alder, (b) dwarf birch, and regions, especially northeastern Siberia. (c) willow growing in northern Yakutia and Alaska. Regression coefficients are given Regardless of the driver, using equations from one ecoregion to in Table 4. predict shrub biomass in another region may over- or underesti- mate biomass pools. For example, using Alaska tundra equations old) that initiated following fire (see Alexander et al., 2012 for for willow AGB to predict willow AGB in Yakutia resulted in an details), while shrubs in boreal Yakutia were acquired mainly from overestimation of biomass by 31–49%. This could lead to misinter- mature stands. Most fires in boreal Alaska are stand-replacing, thus pretations of the implications of shifts in shrub cover on Arctic eco- open the canopy, increase soil water and nitrogen availability, and system function, including carbon, energy, and water budgets. set the stage for secondary succession (Hart and Chen, 2006). All of Given this regional variation and potential for incorrectly predict- these fire-driven changes in resource availability may allow shrubs ing changes in shrub biomass, allometric equations developed in to allocate more aboveground biomass per unit BD, effectively one ecoregion should be used with caution in other ecoregions changing the shrub’s allometry. This may explain why alder at (Jenkins et al., 2003). If researchers working in other Arctic regions the Alaskan boreal sites tended to put on more biomass per unit do not have access to regional allometric equations and do not BD than alder at boreal sites in Yakutia leading to differences in have the necessary resources available to develop new equations, the ecoregion-specific and generalized models for this shrub spe- applying the pooled equations presented here may be appropriate cies. Thus, to prevent overestimates of shrub biomass and coinci- if efforts are made to validate the accuracy of these equations by dent changes in ecosystem function, equations for fire-prone harvesting and drying sample stems and comparing the observed ecoregions should be derived separately for other regions. and predicted plant mass. Differences in the presence and stability of permafrost may also Future efforts are needed to develop new biomass allometry contribute to differences in the relationship between BD and AGB equations for Arctic shrubs; expanding sampling into new regions, within and between ecoregions. Both Yakutian sites and the Alas- species and size classes. Our analysis used basal diameter as the kan tundra site are underlain by continuous permafrost, whereas predictor variable to estimate aboveground biomass, yet other pre- the boreal Alaska site lies within the discontinuous permafrost dictor variables such as percent cover, plant height or crown vol- zone (Brown et al., 2014). Additionally, the Alaskan tundra site ume (Chen et al., 2009; Paul et al., 2013b) could be explored in was affected by thermal erosion associated with permafrost degra- greater detail and compared against alternative methods, both in dation (Pizano et al., 2014), and thawing permafrost or deeper terms of prediction accuracy and ease of implementation. There active layers may increase soil nutrient availability (Natali et al., is also a need to collate existing equations and archive raw mea- 2012). The presence or absence of permafrost, or changes in active surements used in the development of the equations. One means layer dynamics associated with permafrost degradation may lead of collating existing allometric relationships, which will help spur to altered soil drainage or differences in nutrient availability understanding of variability in form and function, is for researchers within and between ecoregions, both of which can shift allocation to submit their equations to centralized online databases, such as patterns in shrubs (Bret-Harte et al., 2002; Pajunen, 2009). Of GlobAllomeTree (http://www.globallometree.org/; Henry et al., course such variability can also be observed at the level of individ- 2013). Furthermore, efforts are needed to archive raw field mea- ual research sites, and so developing allometric equations for dif- surements to prevent data loss through time. The loss of raw mea- ferent permafrost conditions is not realistic. However, the surements creates major obstacles to the development of presence and state of permafrost should be considered when using generalized allometric equations (Jenkins et al., 2003). We present these equations. our raw measurements in the Appendix and will furthermore Sampling protocol may also be important for determining the archive these data with the Long Term Ecological Research (LTER) relationship between BD and shrub biomass. In this study, sam- Network Data Portal (https://portal.lternet.edu/). L.T. Berner et al. / Forest Ecology and Management 337 (2015) 110–118 117

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