Deadwood in New Zealand's indigenous forests

Forest Ecology and Management 258 (2009) 2456–2466

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Deadwood in New Zealand’s indigenous forests Sarah J. Richardson a,*, Duane A. Peltzer a, Jennifer M. Hurst a, Robert B. Allen a, Peter J. Bellingham a, Fiona E. Carswell a, Peter W. Clinton b, Alan D. Griffiths c, Susan K. Wiser a, Elaine F. Wright d a

Landcare Research, P.O. Box 40, Lincoln 7640, New Zealand Scion, P.O. Box 29237, Fendalton, Christchurch, New Zealand c Indigenous Forestry Unit, Ministry of Agriculture and Forestry, 14 Sir William Pickering Drive, P.O. Box 20 280, Christchurch 8053, New Zealand d Department of Conservation, P.O. Box 13049, Christchurch, New Zealand b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 22 June 2009 Received in revised form 18 August 2009 Accepted 18 August 2009

We present the results of a systematic, unbiased national survey of deadwood volume and biomass in New Zealand’s remaining indigenous forests based on an 8-km grid of 894 permanent plots. New Zealand’s old growth evergreen temperate forests are largely comprised of long-lived, slow-growing tree species typically growing in cool, humid conditions; collectively these conditions are thought to promote accumulation of high deadwood stocks. We estimated deadwood biomass and volume in New Zealand’s forests and compared these stocks with published values from other broadleaved evergreen temperate forests. Mean deadwood biomass in New Zealand was 54 Mg ha1 but ranged across plots from 0 to 550 Mg ha1. Mean deadwood volume was 158 m3 ha1 and ranged across plots from 0 to 1890 m3 ha1. Fallen logs accounted for 63% of total deadwood volume and 65% of total deadwood biomass, with standing dead trees being the remainder. Each piece of deadwood was classified into one of three broad decay classes and >40% of deadwood was fallen logs of the intermediate decay class. Deadwood biomass and volume varied 1.8- and 1.9-fold, respectively, among forest types and was greatest in broadleaved forests, dominated by Weinmannia racemosa (Cunoniaceae), Metrosideros umbellata (Myrtaceae) and Metrosideros robusta, and broadleaf-Nothofagus (Nothofagaceae) forests supporting the large tree species Nothofagus fusca. Deadwood biomass and volume were least in broadleaf-conifer admixtures. We used structural equation models to determine whether deadwood biomass could be predicted from climate and environment (vapor pressure deficit, elevation and slope), live tree biomass, forest composition (captured by two ordination axes), wood density of live trees, and tree size (a proxy for stand age). The model that best fit the data retained only vapor pressure deficit, live tree biomass and the first ordination axis as predictors of deadwood biomass. However, this model predicted just 2.4% of the variation in deadwood biomass, suggesting that additional factors not captured by this dataset, such as disturbance dynamics, may control deadwood abundance. Comparisons with other temperate and tropical forests did not support the hypothesis that New Zealand’s cool temperate rainforests support higher than expected biomass or volume of deadwood. ß 2009 Elsevier B.V. All rights reserved.

Keywords: Carbon CWD Deadwood Evergreen temperate rainforest Forest inventory

1. Introduction Deadwood, or coarse woody debris (CWD), is a significant structural component of old growth forests that performs a wide range of critical ecological and biogeochemical functions (Harmon et al., 1986). In addition to being an important store of aboveground biomass, carbon and nutrients (e.g., Turner et al., 1995) deadwood provides habitat and food sources for a vast range of forest species including microbes and fungi (Allen et al., 2000; Norde´n et al., 2004), bryophytes and lichens (Paltto et al., 2008),

* Corresponding author. Tel.: +64 3321 9788; fax: +64 3321 9998. E-mail address: [email protected] (S.J. Richardson). 0378-1127/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2009.08.022

vascular plants (Scheller and Mladenoff, 2002) and vertebrate species (Sedgeley and O’Donnell, 1999; Spiering and Knight, 2005). Fallen deadwood additionally provides a critical establishment site for canopy tree seedlings, away from soil pathogens, herbivores, root competition, and the intense shade of understorey vegetation (O’Hanlon-Manners and Kotanen, 2004; Royo and Carson, 2006). Maintenance of deadwood in old growth forests is therefore a significant component of sustainable forest management (Grove, 2001). The amount of deadwood in forests varies greatly around the world, from none to >600 Mg ha1 (Nonaka et al., 2007; Schlegel and Donoso, 2008), but most range from 30 to 200 Mg ha1 (Harmon et al., 1986; Tyrrell and Crow, 1994; Carmona et al., 2002; Baker et al., 2007; Jo¨nsson and Jonsson, 2007). Variation among

S.J. Richardson et al. / Forest Ecology and Management 258 (2009) 2456–2466

biomes, forest types and individual sites is partly determined by the spatial scale over which the estimate is made, but to a larger extent by ecological factors that influence the rates of input to and export from the deadwood pool, and the residence time of biomass in the deadwood pool (Harmon et al., 1986). These ecological factors include rates of tree mortality and the effects of natural disturbance (Harmon et al., 1986; Carmona et al., 2002), management history and depletion of the live tree biomass pool (Jo¨nsson and Jonsson, 2007), climate (Kennedy et al., 2008) and the traits of the tree species involved such as maximum tree height and diameter, wood density and chemistry, and rate of decay (DeDeyn et al., 2008; Cornwell et al., 2009; Weedon et al., 2009). Collectively, the influence of these factors suggest that cool, humid forests with large, slow-growing tree species should support the greatest volumes and biomass of deadwood. Such conditions are widespread in New Zealand’s remaining indigenous forests, which are predominantly montane, temperate broadleaved evergreen rainforests (Wardle et al., 1983), in which fires are naturally very rare (Ogden et al., 1998). Temperate broadleaved evergreen forests generally might be expected to have high deadwood biomass because broadleaved species frequently have higher wood densities than conifer species (Hacke et al., 2001; Weedon et al., 2009), and because species with long leaf lifespans (evergreen species) typically have higher wood densities than deciduous species (Ishida et al., 2008). In this article we report on the first systematic and objective national survey of deadwood biomass and volume conducted in New Zealand’s remaining indigenous forests. The data presented here are one of few large-scale objective deadwood inventories (e.g., Bo¨hl and Bra¨ndli, 2007); such surveys contrast with the majority of published estimates where study sites have been subjectively selected to examine anthropogenically depleted or naturally abundant deadwood stocks. We use our data to determine whether the biomass of deadwood can be predicted from environment and climate, live tree biomass and tree composition. Climate is thought to have a direct influence on deadwood biomass by affecting decomposition rates (Zell et al., 2009) but will also indirectly affect deadwood biomass by determining tree community composition and live tree biomass. Furthermore, oscillation of forest biomass between live tree and deadwood pools following disturbance and during stand recovery (Carmona et al., 2002; Clinton et al., 2002) should underpin a negative relationship between live tree biomass and deadwood biomass within any given forest type. Here we determine the relative contribution made by each of these effects using a structural equation model. Lastly we use these data to test whether New Zealand rainforests support unusually high amounts of deadwood, relative to other temperate broadleaved evergreen forests, and more broadly, relative to other temperate and tropical forests.

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they include Beilschmiedia tawa, Griselinia littoralis, Melicytus ramiflorus and species of treeferns (Cyathea and Dicksonia). Comprehensive descriptions of New Zealand’s indigenous forests are available in Wardle et al. (1983), Wardle (1984) and Wardle (1991). In this analysis we summarize deadwood using five broad forest types (after Wiser and Hurst, 2008) that capture these broad-scale patterns; Nothofagus forest, Nothofagus-broadleavedconifer forest, Nothofagus-broadleaved forest; broadleaved-conifer forest and broadleaved forest. 2.2. Sampling New Zealand’s indigenous forests These forests were sampled using a systematic 8-km grid of permanent plots (20 m  20 m) across area mapped as indigenous forest (Fig. 1) following the sampling approach described in Coomes et al. (2002). These plots were established under the auspices of the New Zealand Carbon Monitoring System, with the primary focus of monitoring carbon stocks. Plots sampled from sea level to 1400 m, encompassing the full elevational range of indigenous forests in New Zealand (Wardle, 1984; Wardle, 1991). From that grid, we summarized data from 894 plots that were measured as forest vegetation and for which data were publicly available (Fig. 1). Sampling all plots required five years (2001–2006 inclusive) with data collected between October and April each year. 2.3. Calculation of live tree volume and biomass We briefly describe how live tree and deadwood biomass and volume were measured and calculated on each plot; a full

2. Methods 2.1. Description of New Zealand’s indigenous forests New Zealand’s remaining indigenous forests cover approximately 6.4 million ha or 23% of the land surface area (Wardle et al., 1983). More than half these forests are dominated by one or more species of southern beech (Nothofagus; plant names follow the Allan Herbarium, 2002–2006). Elsewhere, Nothofagus forms admixtures with conifer species (e.g., Dacrydium cupressinum, Dacrycarpus dacrydioides, Podocarpus spp., Prumnopitys spp., Libocedrus spp.) and dominant broadleaved species such as Weinmannia racemosa, Metrosideros umbellata and Metrosideros robusta. Admixtures without Nothofagus are diverse and in addition to the conifer and broadleaved species already mentioned

Fig. 1. Map of New Zealand showing the extent of remaining indigenous forest (pale grey) and the 894 study plots (black circles).

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description of how volume and biomass formulae were developed is provided in Coomes et al. (2002). The diameter at breast height (dbh, cm) was measured on all live trees with a dbh  10 cm and tree heights (m) were measured on a subset of live individuals of each species. The relationship between dbh and height was modeled for each species using a log–log regression which included elevation as a covariate (after Coomes et al., 2002). These regressions were used to estimate height for all individuals. Using measured diameter (dbh, cm) and estimated height (ht, m), we calculated live tree volume (V, m3) as: 2

0:946

V ¼ 0:0000598ðdbh  htÞ

(1)

and converted live tree volume (V) to live tree biomass using one of two formulas depending on whether the tree was recorded as sound: Stem mass ¼ b  V

(2)

or hollow: Stem mass ¼ b ð1:0  0:0019  dbhÞ  VÞ

(3)

where b is the species-specific wood density. Wood density values were assembled from Hinds and Reid (1957), Clifton (1994) and unpublished estimates collected by the authors. The biomass of foliage and branch mass (>10 cm diameter) was calculated as: 1:53

Foliage mass ðkgÞ ¼ 0:0406  dbh

2:33

Branch mass ðkgÞ ¼ 0:03  dbh

(4) (5)

2.4. Calculation of deadwood volume and biomass Each piece of deadwood with a diameter 10 cm was measured on each plot. For standing dead trees, the diameter at breast height and the tree height were measured. For stumps (which are nearly all naturally occurring rather than a result of harvesting) and fallen trees, the length was measured along with two sets of orthogonal widths at either end. Each piece of deadwood was ascribed one of three decay classes according to Allen et al. (1997): I = wood hard but not stained, growth rings visible and bark intact; II = wood hard but stained, growth rings indistinguishable, bark usually sloughing off, log core remains solid; III = advanced wood decay with some loss of original form; bark may remain intact in some species, wood friable and core no longer solid. The volume and biomass of standing dead trees followed that of living stems without allowance for branches and leaves. The volume (V, m3) of stumps and fallen logs was estimated using the two pairs of orthogonal widths (a and b, and c and d, cm) and the length of the stump or log (l, m)   pl 2 2 ½ða þ bÞ þ ðc þ dÞ  V¼ (6) 32 Fallen log and stump volumes were converted to biomass using Eq. (2) for sound logs and stumps and Eq. (3) for hollow logs and stumps. Wood density (b) was adjusted for each log to account for its recorded decay class using the values in Coomes et al. (2002) for four dominant tree species. Deadwood identified to one of these four species took a species-specific value and these four species were used to calculate an average that was used for all other deadwood pieces. The relative abundance of each of the four dominant tree species in the total deadwood pool was used to calculate a weighted average for each decay class. 2.5. Summarizing deadwood biomass and volume The biomass and volume of all live trees and deadwood pieces were summed per plot and these data were slope corrected using

the cosine of the plot-level slope. The biomass and volume of deadwood were standardized by expressing them as a percentage of aboveground biomass (the sum of live tree biomass and deadwood biomass). We summarized actual and standardized deadwood biomass and volume according to taxonomic resolution (whether it was identified to species, to genus or left unidentified), stature (fallen or standing), by decay class, by size class and by forest type (Wiser and Hurst, 2008). Regressions between deadwood and live tree biomass used log-transformed data with an allowance for zero values (McCune et al., 2002). 2.6. Modeling deadwood biomass We used structural equation modeling (SEM) to determine how among-plot variation in deadwood biomass was controlled both directly and indirectly by climate and environment, live tree biomass, forest composition, live tree wood density and live tree size (as a proxy for stand age). In order for live tree size to act as a proxy for stand age in mixed species forests, we took every live tree stem and expressed it as the percentile size for its species nationally. We averaged these percentiles per plot to obtain an estimate of plot maturity, or stand age, while correcting for varied species composition within and among plots. SEM is a general multivariate statistical technique that can account for direct and indirect effects of variables and complex relationships among them (e.g., Shipley, 2000; Grace et al., 2007). Our hypothesized model allows environmental variables to influence both vegetation abundance and composition and these in turn to influence deadwood abundance. In addition, environment can also influence deadwood abundance directly, e.g., through processing rates of decomposers. We constructed SEM models to predict deadwood abundance (endogenous or latent variable predicted from deadwood biomass and volume) from live tree abundance (endogenous variable predicted from live tree biomass and volume), vegetation composition (endogenous variable predicted from two non-metric multidimensional scaling (NMS) ordination axes using live tree biomass by species), live tree wood density, live tree size (average size percentile across all stems on the plot) and environmental effects (elevation and slope were measured directly on each plot and vapor pressure deficit (VPD) was estimated for each plot using geographic location; Leathwick, 2001; Leathwick et al., 2003). Because only a few environmental variables were available for analysis (i.e., elevation, slope, VPD), this reduced the potential for model overfitting. We calculated standardized path coefficients for this model, and the model fit to the data were assessed by standard criteria including analyses of heterogeneity using Chi-square tests to judge significance. All analyses were completed in R v 2.8.1 (R Development Core Team, 2008) except for the structural equation modeling, conducted in AMOS 5 (Arbuckle and Wothke, 2000). 2.7. Comparisons with temperate broadleaved evergreen forests globally Deadwood biomass was compared with estimates from other temperate broadleaved evergreen forests (sensu Ovington, 1986). Published values range in their granularity (e.g., individual data for single plots, means from many plots, ranges across plots) and the spatial scale of estimates (e.g., individual plots of varying sizes, stands, catchments or regions). Individual data from small plots are more likely to include very high or very low values of deadwood biomass because small plots capture small-scale heterogeneity and large plots homogenize spatial variation (Krebs, 1998). Our individual plot data (400 m2) are therefore more likely to contain very low or very high values of deadwood biomass compared with published means taken across many plots. These differences

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cannot be overcome but to minimize data manipulation, we present our data and published values in the minimum granularity possible and acknowledge that each datum is not necessarily equivalent. Likewise, we compare our estimates with those from plot-based and transect sampling methods presented together. Comparisons among studies could be distorted by variation in the diameter of deadwood included (ranging from 1 to 10 cm) but as small diameter deadwood typically has little influence on total biomass, these differences are relatively minor. 3. Results 3.1. Biomass and volume of deadwood 1

Mean deadwood biomass was 54  2 Mg ha and mean deadwood volume was 158  6 m3 ha1 (Table 1). Fallen deadwood was 65% of deadwood biomass and 63% of deadwood volume. Eight plots or 1% nationally had no deadwood (Fig. 2). The proportion of aboveground volume and biomass as deadwood varied from 0% to 100% across all plots and the mean was 16% for biomass and 27% for volume (Table 1). There were weak positive relationships between live tree biomass and deadwood biomass (Fig. 3a) and live tree volume and deadwood volume (Fig. 3b). The relationships between the % aboveground biomass or volume as deadwood and live tree biomass or volume were non-linear and negative (Fig. 3c and d). Plots with high live tree biomass or volume had less aboveground biomass

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Table 1 Deadwood and live tree biomass and volume in New Zealand’s indigenous forests. Data are calculated from 894 representative permanent forest plots (20 m  20 m). Pool Deadwood biomass (Mg ha1) Fallen deadwood biomass (Mg ha1) Standing deadwood biomass (Mg ha1)

Mean  1SE

Median

54  2 35  2 19  1

36 19 8

Live tree biomass (Mg ha1)

315  8

267

Deadwood volume (m3 ha1) Fallen deadwood volume (m3 ha1) Standing deadwood volume (m3 ha1)

158  6 99  4 59  4

106 56 26

Live tree volume (m3 ha1)

429  12

350

16  0.5 27  0.7

11 23

% Aboveground biomass in deadwood % Aboveground volume in deadwood All values are slope corrected.

or volume in deadwood but plots with low live tree biomass or volume had a wide range of aboveground biomass or volume in deadwood (Fig. 3a and b). 3.2. Predicting deadwood biomass using structural equation models Our hypothesized model, which allowed climate and environment to influence live tree abundance, forest composition, wood

Fig. 2. Biomass and volume of deadwood in 894 plots across New Zealand’s remaining indigenous forests (a) biomass (t ha1), (b) volume (m3 ha1), (c) % aboveground biomass in deadwood and (d) % aboveground volume in deadwood.

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Fig. 3. Relationships between (a) live tree and deadwood biomass, (b) live tree and deadwood volume, (c) % aboveground biomass as deadwood and live tree biomass and (d) % aboveground volume as deadwood and live tree volume for 894 systematically located plots in New Zealand’s remaining indigenous forests. Generalized linear regression for (a) deadwood biomass = 1.24 + 0.51 (live tree biomass) x2 = 32.4, P < 0.0001, r2 = 0.11; (b) deadwood volume = 0.80 + 0.46 (live tree volume) x2 = 30.3, P < 0.0001, r2 = 0.12; (c) % aboveground biomass as deadwood = 1.01 e0.42(live tree biomass) x2 = 6.21, P = 0.0127, r2 = 0.20; (d) % aboveground volume as deadwood = 1.13 e0.31(live tree biomass) x2 = 6.93, P = 0.0085, r2 = 0.19. Deadwood and live tree biomass and volume were log-transformed with an allowance for zero values (McCune et al., 2002) and the % aboveground biomass and volume were transformed using arcsin(Hx).

density and tree sizes, and deadwood abundance through both direct and indirect pathways, did not provide a good fit to the data, and neither did slightly simpler versions of this model (x215 ¼ 54:2, P < 0.001). After stepwise reduction of the measurement models, a much simpler model that retained only measured (exogenous) variables and not latent (endogenous) variables was found that fit the data well (x21 ¼ 1:82, P = 0.177; P = 0.324 using permutation tests). This model retained only VPD as a predictor of live tree biomass and the first axis of the NMS ordination (NMS1), and all three variables as predictors of deadwood biomass (Fig. 4). Temperature and precipitation did not provide any additional explanatory power over and above VPD and were excluded from the final SEM. The first axis of the NMS ordination was positively correlated with live tree wood density (Electronic Supplementary Table 1) and each variable had a similar influence on the model, although the ordination axis was marginally better and was retained. The standardized total effects (regression weight, partial r2) of modeled variables on deadwood biomass are as follows: standardized regression weights of VPD were all weak to biomass (0.143), NMS1 (0.450) and deadwood biomass (0.068); live tree biomass and NMS1 to deadwood biomass were 0.132 and 0.01, respectively. Even though VPD predicted ca. 20% of the variation in forest composition, this effect was not carried through to deadwood biomass because forest composition was a very weak predictor of deadwood biomass. This final model predicted ca. 2.4%

of the variation in deadwood biomass, suggesting either that deadwood biomass is highly variable or stochastic, or that additional variables not included in the models determine deadwood biomass.

Fig. 4. Path diagram for final structural equation model predicting deadwood abundance. Boxes are measured exogenous variables (n = 894 plots). Lines with arrows show paths of effects between pairs of variables. Numbers associated with lines are path coefficients (standardized partial r2) and numbers above boxes (in bold) are total model r2 for that variable. Other statistical details are in Section 3.

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Fig. 5. Distribution of (a) deadwood biomass, (b) deadwood pieces and (c) deadwood volume among fallen and standing pools in three ordinal decay classes in New Zealand’s remaining indigenous forests.

3.3. Decay states and stature of deadwood Fallen deadwood was more abundant than standing deadwood (Fig. 5). Fallen deadwood accounted for 66% of deadwood biomass, 74% of deadwood pieces and 64% of deadwood volume (Fig. 5). Partially decayed (decay class II) fallen deadwood was the greatest contributor to total deadwood biomass (44%), abundance (37%) and volume (38%). Well-decayed (decay class III) deadwood pieces accounted for 45% of all deadwood pieces but were only 24% of biomass and 31% of volume (Fig. 5). 3.4. Size class structure of individual deadwood pieces The biomass of individual deadwood pieces varied widely from 0.0001 Mg for well decayed treefern stems to 15.1 Mg for a partially decayed stem of M. robusta. Most deadwood pieces (88%) were 0.1 Mg (Table 2). Likewise, the volume of individual deadwood pieces ranged from 0.0004 to 48.64 m3 and 96% were 1 m3 (Table 2). The overall density of deadwood pieces was 723 ha1 (Table 2). 3.5. Species identities of deadwood

identified to species, Nothofagus fusca accounted for the greatest volume and biomass of deadwood across all plots (13.5% of all deadwood volume and 11.3% of all deadwood biomass) and Nothofagus species collectively accounted for 42.1% of all deadwood volume and 39.8% of all deadwood biomass nationally (Table 3). 3.6. Deadwood biomass and volume by forest type Deadwood biomass and volume varied 1.8- and 1.9-fold, respectively, among forest types (Table 4, Figs. 7 and 8) and were greatest in broadleaved forests, dominated by W. racemosa, M. umbellata and M. robusta, and in broadleaf-Nothofagus forests supporting the large tree species N. fusca. Biomass and volume were least in broadleaf-conifer admixtures. Variation among forest types was determined more by variation in the fallen pool (2.1-fold for volume and 2.4-fold for biomass) than the standing dead pool (1.7fold for volume and 1.4-fold for biomass; Fig. 7). Standing deadwood was more consistent across forest types (16–22 Mg ha1 and 46– 77 m3 ha1) than fallen deadwood biomass (24–59 Mg ha1 and 74–159 m3 ha1). There were weak positive relationships between live tree biomass and deadwood biomass within four of the five forest types (generalized linear regressions; Nothofagus-broad-

In total 26,054 pieces of deadwood were sampled of which 54% were identified to species and 10% were identified either to genus or to a functional group of species (e.g., tree ferns). The precision with which deadwood was identified either to species or genus declined as logs decayed (Fig. 6) and was greatest in compositionally simple Nothofagus forests (81%) and least in broadleaved forests (34%). Of those deadwood pieces Table 2 Density (no ha1) of deadwood pieces in New Zealand forests summarized by biomass size classes and volume size classes. Metric

Size class

Density (no ha1)

% All deadwood pieces

Biomass (Mg)

<0.01 0.01–<0.1 0.1–<1 1–<5 5

326 311 80 6.0 0.3

45.1 42.9 11.1 0.83 0.04

Volume (m3)

<0.1 0.1–<0.5 0.5–<1 1–<5 5–<10 10

529 142 27 24 2 0.6

73.1 19.6 3.68 3.36 0.25 0.08

Fig. 6. Relative contribution made to each decay class by deadwood identified to one of three levels of taxonomic resolution. Genus includes instances where a piece of deadwood was identified to a functional group such as ‘‘treeferns’’.

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Table 3 Total biomass volume of deadwood for those species that collectively make up 98% of deadwood biomass or volume in New Zealand’s indigenous forests. Species

Family

Mean wood density of live wood (kg m3)

Total volume (m3)

Total biomass (Mg)

% Total volume

% Total biomass

Unknown species Nothofagus fusca Nothofagus species Nothofagus menziesii Weinmannia racemosa Nothofagus solandri var. cliffortioides Dacrydium cupressinum Metrosideros robusta Beilschmiedia tawa Nothofagus truncata Metrosideros umbellata Podocarpus hallii Libocedrus bidwillii Nothofagus solandri Dicksonia squarrosa Griselinia littoralis Prumnopitys ferruginea Knightia excelsa Cyathea smithii Cyathea medullaris Prumnopitys taxifolia Cyathea dealbata Podocarpus species Ixerba brexioides Podocarpus totara Dacrycarpus dacrydioides Kunzea ericoides Elaeocarpus dentatus Litsea calicaris Elaeocarpus hookerianus Weinmannia silvicola Melicytus ramiflorus Quintinia serrata Hedycarya arborea

Unknown Nothofagaceae Nothofagaceae Nothofagaceae Cunoniaceae Nothofagaceae Podocarpaceae Myrtaceae Lauraceae Nothofagaceae Myrtaceae Podocarpaceae Cupressaceae Nothofagaceae Dicksoniaceae Cornaceae Podocarpaceae Proteaceae Cyatheaceae Cyatheaceae Podocarpaceae Cyatheaceae Podocarpaceae Grossulariaceae Podocarpaceae Podocarpaceae Myrtaceae Elaeocarpaceae Lauraceae Elaeocarpaceae Cunoniaceae Violaceae Grossulariaceae Monimiaceae

600 587 610 552 636 578 543 880 648 668 1013 502 389 562 189 687 576 642 199 225 569 225 485 623 460 413 788 622 559 537 627 583 488 585

1424 672.2 541.0 465.3 247.8 246.5 238.5 187.7 134.4 134.1 130.9 89.6 39.9 39.2 33.7 27.0 26.7 26.4 25.1 20.9 17.7 17.1 14.7 11.6 11.2 11.1 9.8 7.9 7.7 7.5 7.5 7.5 7.4 7.2

455.0 191.4 182.1 157.1 114.3 78.7 78.1 92.7 52.3 55.0 74.5 26.1 9.4 14.1 3.8 12.2 9.9 9.6 2.7 2.7 5.8 2.2 3.8 4.1 2.6 3.0 5.0 3.2 2.7 2.4 2.5 2.8 2.3 2.5

28.6 13.5 10.9 9.3 5.0 4.9 4.8 3.8 2.7 2.7 2.6 1.8 0.8 0.8 0.7 0.5 0.5 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1

26.8 11.3 10.7 9.2 6.7 4.6 4.6 5.5 3.1 3.2 4.4 1.5 0.6 0.8 0.2 0.7 0.6 0.6 0.2 0.2 0.3 0.1 0.2 0.2 0.2 0.2 0.3 0.2 0.2 0.1 0.1 0.2 0.1 0.1

Sources for wood density estimates: Hinds and Reid (1957), Clifton (1994) and unpublished data of the authors.

leaved-conifer forest deadwood biomass = 0.10 + 0.58 (live tree biomass) x2 = 38.7, P < 0.0001, r2 = 0.23; Nothofagus-broadleaved forest deadwood biomass = 0.37 + 0.79 (live tree biomass) x2 = 48.0, P < 0.0001, r2 = 0.28; Nothofagus forest deadwood biomass = 0.04 + 0.63 (live tree biomass) x2 = 38.8, P < 0.0001, r2 = 0.13; broadleaved-conifer forest deadwood biomass = 0.57 + 0.33 (live tree biomass) x2 = 127.4, P = 0.0003, r2 = 0.04) but not for the fifth forest type (broadleaved forest deadwood biomass = 1.39 + 0.12 (live tree biomass)).

3.7. Comparisons with temperate broadleaved evergreen forests globally Deadwood biomass in New Zealand’s forests was comparable to other estimates from temperate broadleaved evergreen forests (Fig. 8). High deadwood biomass (>200 Mg ha1) examples from New Zealand forests were similar to wet sclerophyll forest in Australia and Nothofagus-broadleaved-conifer forests in Chile (Fig. 8). Many of the published estimates presented in Fig. 8 are

Fig. 7. Deadwood biomass and volume in fallen and standing pools for the five major forest types in New Zealand. Data are arithmetic means with 1SE.

For biomass and volume, data are mean  1SE. For live tree diameters, data are the mean geometric mean diameter per plot, and the mean 98th percentile diameter per plot. For mean annual temperature (MAT), mean annual rainfall (MAR), slope, vapor pressure deficit (VPD) and elevation, data are the mean and range in parentheses.

(160–1403) (5–820) (22–1157) (4–1356) (7–965) 867 328 626 605 419 (0–0.5) (0.1–0.6) (0.01–0.5) (0–0.4) (0–0.4) 0.3 0.3 0.2 0.2 0.2 (0–54) (0–57) (0–58) (0–61) (0–57) 26 24 21 27 25 (842–4480) (1033–7316) (1158–6136) (931–9450) (847–7346) 1977 2355 2554 2668 4074 (5.2–11.3) (8.3–15.5) (6.7–11.8) (6.1–11.8) (6.5–11.2) 7.6 11.4 9.0 8.8 8.9 9.9 9.0 7.7 8.7 9.1 344  16 379  17 436  28 489  25 508  42 125 351 156 182 80 Beech Broadleaved-conifer Beech-broadleaved-conifer Beech-broadleaved Broadleaved

44  4 45  3 53  4 66  5 79  9

138  12 123  7 164  14 197  14 235  30

255  11 284  11 321  18 357  16 394  34

(45.8) (41.6) (44.6) (51.2) (52.9)

Elevation (m) VPD (kPa) Slope (8) MAR (mm) MAT (8C) Live tree diameter (cm) Live tree volume (Mg ha1) Live tree biomass (Mg ha1) Deadwood volume (m3 ha1) Deadwood biomass (Mg ha1) N plots Forest type

Table 4 Deadwood and live tree biomass and volume in New Zealand’s five indigenous forest types (after Wiser and Hurst, 2008). Data are calculated from 894 representative permanent forest plots (20 m  20 m). All values are slope corrected.

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averages and the range of values between 0 and 200 Mg ha1 corresponded well with the averages for New Zealand’s five forest types (44–79 Mg ha1). 4. Discussion 4.1. Comparisons of New Zealand deadwood stocks with other regions The biomass and volume of deadwood in New Zealand’s indigenous forests varied widely but the mean values were similar to other temperate broadleaved evergreen forests (reviewed in Fig. 8). More broadly, the range of deadwood abundance values was comparable with other types of temperate forests (60– 1189 m3 ha1 and 3–269 Mg ha1; Harmon et al., 1986) and appeared to be slightly higher than in tropical broadleaved evergreen forests (0–96 Mg ha1; reviewed in Baker et al., 2007; Chao et al., 2008; Palace et al., 2007). The proportion of aboveground biomass stored as deadwood (16%) was within the range reported from other temperate forests (1–45%; Harmon and Hua, 1991). While there were some individual plots that had high biomass (>200 Mg ha1) or volume (>400 m3 ha1) these presumably reflected localized stand disturbance at the stand-level (400 m2) scale at which forests were measured. While many of the conditions promoting deadwood occur in New Zealand (e.g., tall tree species with large diameters; cool, humid conditions; frequent tectonic disturbance and tree mortality events) their influence on deadwood biomass and volume is manifest only locally. One of the benefits of conducting an objective national-scale survey is to demonstrate that large areas of forest have modest amounts of deadwood and are dominated by short-statured tree species with small diameters (McGlone et al., 2010). Several previous studies of deadwood in New Zealand have targeted areas with large amounts of deadwood (>300 Mg ha1; Stewart and Burrows, 1994; Bellingham et al., 1999) for specific purposes and it would be misleading to assume that they represent New Zealand more broadly. 4.2. Predicting deadwood biomass from environment, live tree biomass and forest tree composition We originally hypothesized that deadwood abundance (biomass and volume) would be predicted by complex interactions among climate and environment, live tree abundance (biomass and volume), forest composition, live tree wood density and live tree sizes; such a model was not supported by the data. Instead, a much simpler model was supported using only VPD, live tree biomass and a single ordination axis describing a major gradient of forest composition, but this model was a poor predictor of deadwood biomass (ca. 3% of variation; Fig. 4). This is somewhat surprising given the gradients captured by our dataset (e.g., the full elevational range of indigenous forests in New Zealand). Poor predictive power is probably not due to overfitting of the data, but rather to missing predictive variables. The two most likely predictors not captured are stand age or forest developmental stage, and disturbance history; collectively, these strongly influence the amount of deadwood and its duration (Harmon et al., 1986). We attempted to capture information on stand age using live tree biomass and live tree sizes. Live tree biomass was retained in our final model but contrary to our expectations, it did not act as a proxy for stand age. At a small scale, in even-aged stands, there should be a negative relationship between live tree biomass and deadwood biomass reflecting the reciprocal oscillation of forest biomass between live and dead pools (Lambert et al., 1980; Allen et al., 1997). However, in this national-scale analysis, live tree and deadwood biomass were weakly positively correlated because plots containing large-sized tree species produced larger

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Fig. 8. Deadwood biomass (Mg ha1) in temperate broadleaved evergreen forests. NZ1 New Zealand Nothofagus-broadleaved-conifer forest (this study); NZ2 New Zealand Nothofagus-broadleaved forest (this study); NZ3 New Zealand Nothofagus forest (this study); NZ4 New Zealand broadleaved-conifer forest (this study); NZ5 New Zealand broadleaved forest (this study); AU1 Australia dry sclerophyll forest (Woldendorp and Keenan, 2005); AU2 Australia rainforest (Woldendorp and Keenan, 2005); AU3 Australia tall eucalyptus forest (Roxburgh et al., 2006); AU4 Australia wet sclerophyll (Woldendorp and Keenan, 2005); AU5 Australia woodland (Woldendorp and Keenan, 2005); CHL Chile Nothofagus forest and Nothofagus-broadleaved-conifer forest (Carmona et al., 2002; Schlegel and Donoso, 2008); CHN China Lithocarpus-Castanopsis forest (Liu et al., 2002; Yan et al., 2007; Lipan et al., 2008); USA North America Lithocarpus-Kalmia forest (Woodall et al., 2008).

pieces of deadwood. This positive relationship between live tree and deadwood biomass was also retained within forest types because our broad forest types all contain a wide range of tree sizes and environments (Table 4). Tree size, adjusted by species to reflect the size percentile for that species nationally, was not retained in the model. We had anticipated that this variable would reflect stand maturity and capture the gradient from recently disturbed plots with high deadwood abundance and relatively small live trees, to mature stands with large individuals and little deadwood. Given that our plots systematically sample the entire forested area of New Zealand, we are confident that we sampled a wide gradient of disturbance regimes and successional states so the absence of a relationship between tree size and deadwood suggests that pointin-time measures of stand maturity fail to capture information on past disturbance and consequently, on deadwood. Once these permanent plots have been remeasured, stand age and disturbance history can be estimated more accurately (e.g., Coomes and Allen, 2007) but deadwood biomass would still reflect the compound influence of disturbance events over varying time scales that could not be estimated from a remeasurement. For decay-resistant species, deadwood biomass may reflect disturbance over the last century or more (e.g., Allen and Rose, 1983) whereas for rapidly decaying species, it may reflect only the last decade or two. These results highlight that models predicting deadwood require information on temporal dynamics, either through incorporation of long-term repeated measures of plots or through the use of process-based models of forest dynamics such as SORTIE (Papaik and Canham, 2006). 4.3. Management of deadwood Most of New Zealand’s indigenous forests are managed for conservation goals and under current policy no deadwood can be extracted from those conservation forests. A small proportion (<5%) of those conservation forests were logged in the past and part of the management of deadwood is to restore both live and dead biomass. A small area of privately owned indigenous forest is actively managed for timber production and the majority of harvested deadwood comes large stems of a few commercially valuable podocarp conifer species such as Prumnopitys taxifolia, Podocarpus totara, Podocarpus hallii and Dacrycarpus dacrydioides (Indigenous Forestry Unit, Ministry of Agriculture and Forests, New Zealand, unpublished data 2004). The consequences for nutrient cycling of selectively harvesting conifer deadwood are unclear but given the substantial differences in wood chemistry

between angiosperm and gymnosperm wood, such as greater carbon and lignin concentrations and lower nitrogen and phosphorus concentrations in gymnosperms (Weedon et al., 2009), we suggest that such removals could accelerate stand-level decomposition rates. Further, the impacts will be size-specific as large stems are typically targeted for deadwood recovery. Deadwood is a significant store of calcium (Hart et al., 2003) and a site of free-living nitrogen fixation (Matzek and Vitousek, 2003; Clinton et al., 2005) and nutrients removed during deadwood recovery are likely to exceed rates of nutrient supply in those nutrient-impoverished sites where conifer species are most abundant (Richardson et al., 2004) and targeted for harvesting. The consequences of deadwood removal for biodiversity are unknown as there are no data on the functional responses of biodiversity to deadwood abundance in New Zealand forests (cf. Australia, MacNally et al., 2002). Information on deadwood habitat utilization exist for bat and bird species (O’Donnell and Dilks, 1994; Warburton et al., 1992; Sedgeley and O’Donnell, 1999) and to a much lesser extent for invertebrate groups (Evans et al., 2003) and fungi in Nothofagus forests (Allen et al., 2000) but substantially more information is required to develop ecologically meaningful harvesting guidelines that reflect the impact of removal on biodiversity. 5. Conclusions We present the first results of a systematic nationwide survey of deadwood biomass and volume in New Zealand’s remaining indigenous forests. Despite utilizing this large-scale dataset that captures major environmental, productivity and forest composition gradients, the best model incorporating these gradients only predicted ca. 3% of the variation in deadwood abundance. This is most likely due to point-in-time data failing to capture disturbance dynamics which control the rate and frequency of deadwood production across sites. Contrary to our expectations, the amount of deadwood was typical for temperate evergreen broadleaved forests worldwide, despite New Zealand’s cool, humid environment. Broad differences among forest types provide initial cautionary guidelines for maintaining deadwood in forests managed for timber production. These can be applied alongside the guiding principle that deadwood biomass is, on average, 16% of live tree biomass or 27% of live tree volume. However, the development of more refined sustainable harvesting guidelines requires quantitative data on the functional responses of nutrient cycling and biodiversity to progressive deadwood removal of

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different species, particularly after disturbance and pulsed inputs of deadwood (Zimmerman et al., 1995). Acknowledgements We acknowledge the use of data drawn from the Land Use and Carbon Analysis System (LUCAS) project administered by the New Zealand Ministry for the Environment (MfE). Staff at the National Vegetation Survey (NVS) databank and Meredith McKay assisted with data retrieval and interpretation. Mark Smale reviewed an earlier draft. The authors were funded by the Policy Unit of the Ministry of Agriculture and Forests (SJR, JMH, PWC, ADG), by Landcare Research (FEC, DAP), or the New Zealand Foundation for Research, Science and Technology, either through the Ecosystem Resilience Outcome-Based Investment (Contract ID C09X0502; RBA, PJB, EFW) or the Sustainable Indigenous Forestry program (Contract ID C09X0802; SKW). The order of authorship is alphabetical after the first three.

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