Forest Region
Scott A. Parsons1,3, Robert A. Congdon1, Luke P. Shoo2, Vanessa Valdez-Ramirez1, and Stephen E. Williams1
1
Centre for Tropical Biodiversity and Climate Change, School of Marine and Tropical Biology, James Cook University, Townsville, Qld, 4811, Australia
2School of Biological Sciences, The University of Queensland, St Lucia, Qld, 4072, Australia
ABSTRACT
Understanding the spatial variability in plant litter processes is essential for accurate comprehension of biogeochemical cycles and ecosys-tem function. We assessed spatial patterns in litter processes from local to regional scales, at sites throughout the wet tropical rain for-ests of northern Australia. We aimed to determine the controls (e.g., climate, soil, plant community composition) on annual litter standing crop, annual litterfall rate and in situ leaf litter decomposability. The level of spatial variance in these components, and leaf litter N, P, Ca, lignin,a-cellulose and total phenolics, was determined from within the scale of subregion, to site (1 km transects) to local/plot (~30 m2). Overall, standing crop was modeled with litterfall and its chemical composition, in situ decomposability, soil Na, and topogra-phy (r2= 0.69, 36 plots). Litterfall was most closely aligned with plant species richness and stem density (negative correlation); leaf decomposability with leaf-P and lignin, soil Na, and dry season moisture (r2= 0.89, 40 plots). The predominant scale of variability in lit-terfall rates was local (plot), while litter standing crop and a-cellulose variability was more evenly distributed across spatial scales. Litter decomposability, N, P and phenolics were more aligned with subregional differences. Leaf litter C, lignin and Ca varied most at the site level, suggesting more local controls. We show that variability in litter quality and decomposability are more easily accounted for spatially than litterfall rates, which vary widely over short distances possibly in response to idiosyncratic patterns of disturbance.
Key words: Australia; climate; decomposition; litter quality; litter standing crop; litterfall; spatial variability; tropical rain forest.
AN UNDERSTANDING OF THE AMOUNT OF DECAYING LITTER ON THE FOREST FLOOR, and its retention, is critical in comprehending bio-geochemical and ecosystem models and climate change (Chapin et al. 2002, Grime 2002). Due to variation in litter inputs and decomposition, the litter layer is often heterogeneous (Proctor et al. 1983). This is especially so for tropical forests, which have high species diversity and heterogeneity in canopy and soil com-position, and rates of localized disturbance (Silver et al. 1994, Townsend et al. 2008). The amount and chemical composition of litter on the ground has a large bearing on soil respiration and carbon cycling (Davidson & Janssens 2006), and understanding the spatial variability in litter is an important factor in accounting for errors in ecosystem and biogeochemical models (Zhou et al. 2009, Norby & Zak 2011).
Around 80 percent of terrestrial carbon has been estimated to be stored in plant litter and soils (Wang et al. 2010), and car-bon dynamics are influenced by complex interactions between plant communities, climate and the soil biota that feedback to the entire biosphere (Facelli & Pickett 1991). These interactions include litterfall production, the accumulation of litter on the soil, decomposition, the formation of soil organic matter and fluxes of C to the atmosphere (Swift et al. 1979, Vitousek & Sanford 1986). In tropical forests, plant traits and litter chemical quality (e.g., the chemical potential of the material for decay), which is
often the most important control on decay rates (Cornwell et al. 2008), can vary substantially at small scales (H€attenschwiler et al. 2008). For instance, the chemical composition of litter (e.g., chem-ical quality for decay) can vary up to sevenfold between species at small scales (e.g., for litter nutrients such as P; H€attenschwiler et al.2008). In addition, natural and anthropogenic disturbance in the tropics is common, but can be spatially patchy (e.g., reduced-impact logging, localized wind) or widespread (e.g., cyclones, intense logging). Disturbance is an influential driver of ecosystem structure and function in rain forests and can have varying effects on litter processes (Herbohn & Congdon 1993, Gleason et al. 2010). Variability in the quantity and composition of litterfall and litter on the surface leads to variation (spatial and also seasonally) in nutrient availability, with direct effects on ecosystem processes (Silver 1994, Townsend et al. 2008). Assessing this variability at both local and broad (e.g., regional) scales is integral to fully com-prehending ecosystem processes, as patchiness in litter processes leads to variability in carbon and nutrient cycles, plant productiv-ity, soil fertility and decomposition (Clark et al. 2001b, Townsend et al.2011).
The body of information on litter decomposition is immense for most biomes (Liski et al. 2003, Adair et al. 2008, Zhang et al. 2008, Powers et al. 2009, Wieder et al. 2009), yet there is limited information on spatial variability, potentially due to difficulties in quantifying large areas. In the tropics, exceptionally high spatial variance in litter processes is likely, due to high numbers of spe-cies and the diversity of landforms (H€attenschwiler et al. 2008).
Received 5 December 2013; revision accepted 12 February 2014.
3Corresponding author; e-mail: scottanthonyparsons@gmail.com
Also, spatial variability in litterfall rates has been shown to be the major determinant in spatial heterogeneity in organic matter and element dynamics in tropical rain forests (Burghouts et al. 1998), however data linking this in detail to decomposition, litter quality and litter standing crop (LSC) at increasing spatial levels are also sparse.
Here, we use data from a detailed regional study of tropical rain forest litterfall, litter chemical quality, leaf decomposition rates and LSC, to better understand spatial variance. We sampled sites distributed throughout the wet tropics of Australia to ask the following: (1) what variables determine the amount of LSC, litterfall rates and decomposability? (2) how do litterfall rates, lit-ter chemical quality and decay rates vary spatially (from local communities to region-wide patterns)? and, (3) how does regional variance compare to local variance? We ask these questions to gain a better understanding of the controls on the amount of lit-ter on the soil surface and the inherent variability in this compo-nent of forest dynamics. We develop models of LSC, leaf in situ decomposability (predicted decomposition rate, determined from previous modeling work) and litterfall rates, with environment (climate, soil, topography), plant community composition, and lit-ter chemical quality to explain what delit-termines this variation throughout our region. We anticipated that both local and regio-nal spatial variability would be very high, especially due to vari-ability in litterfall rates (Burghouts et al. 1998), but also due to site-specific litter dynamics, spatial variation in decomposition and the controls on decay.
METHODS
STUDY SITES.—Sites were located in tropical rain forest of dif-fering complexity in North Queensland Australia, on four mountain ranges (subregions—Carbine, Spec, Windsor and Atherton uplands), from near sea level to 1,300 m elevation. Our plots contained a variety of geologies and soil types (mes-otrophic soils on basalt to very poor old olig(mes-otrophic soils on granite, rhyolite and mudstone), along with varying climates, due to elevational effects on temperature and different rainfall patterns from more aseasonal very wet sites (both upland and lower elevations) to seasonally wet/drier sites (Appendix S1 for full details of the study plots). There is a partial association between rainfall and soil nutrients in the region, with some of the wettest sites being also on richer basaltic soils in the Atherton region (see Appendices S1 and S2, and see follow-ing). This cross-correlation was taken into account into all analyses.
Our sites have been the location of detailed studies into plant litter processes, including quantification of leaf litter decay rates and the controls on decay (Parsons et al. 2012) and model-ling of leaf litter quality and decomposition (Parsons et al. 2011) and other studies into litter processes (Parsons 2010). The cur-rent study examined 20 sites, of which 19 were used for LSC and 20 for litterfall, decomposability and litter chemical quality. Each site comprised single plots at six points located at 200 m intervals along a 1-km transect (although some sites contained
fewer points). For comparisons involving litterfall and litter qual-ity, we established two points per site as ‘detailed monitoring plots’ (Parsons et al. 2011), and used them for measurement of variables to model drivers of LSC, litterfall and leaf litter decom-posability (generally, plots 2 and 5, Appendix S1). Each plot was square shaped and approximately 30 m2. To improve spatial cov-erage of LSC, plots at the remaining points were sampled on fewer occasions to determine spatial variance in litter on the ground (see following). We determined all litter variables over 2 yr between May 2007 and August 2009. We sampled litter chemical composition monthly in the first year and bimonthly in the second, and determined litterfall rates generally from monthly collections. Most sites had experienced varying forms of distur-bance in the past, especially selective logging prior to World Heri-tage listing in 1988 and cyclonic activity. Four of the Atherton sites were affected by a recent cyclone (severe category 5 cyclone Larry, March 2006, see Appendix S1, CS). Canopy coverage at these sites recovered over the study period, however the litterfall rates from these sites should be treated tentatively, which was considered in the analysis.
SITE DESCRIPTIONS.—An estimate was determined of plant species richness and community composition along 209 2 m belt tran-sects at each plot sampled directly through the ‘star’ (see follow-ing) of the litter collection traps. This involved identification of all trees and shrubs greater than 1.6 m tall, with abundances recorded and heights estimated. Vines and lianas were not sam-pled due to difficulties in collection. Despite this, the survey pro-duced a standardized measure of number of species and the density of individuals for comparisons between the plots, espe-cially relating to the material impacting on the litter processes. We used plant species richness, tree richness (≥5 m tall) and the overall plant stem density (number of individuals) in analyses. To partially assess local disturbance history, we used the abundance of obligate pioneer/gap colonizing species on the transects, from the natural history notes within Hyland et al. (2002). We consid-ered species ‘favoured by disturbance’ and ‘characteristic compo-nent(s) of rain forest regrowth’ to be general pioneers/gap colonizers or very early secondary species suggesting disturbance (Denslow 1987) (% disturbance species). Previous observations noted increased species richness and stem density in sites with histories of disturbance (i.e., intermediate disturbance effects on vegetation structure, e.g., Collins et al. 1995), for instance, from selective logging, snig roads and cyclonic activity of varying ages (Parsons 2010).
We determined soil chemistry from the mean of three ran-domly selected, auger cores taken at each plot, based on thefine earth fraction (<2 mm). We used the mean of 0–10 cm and 20–30 cm depth samples. Nutrients were determined using the single digest method and sodium salicylate (total N) and molyb-date (total P) colorimetric methods (Anderson & Ingram 1989, Baethgen & Alley 1989), and atomic absorbance spectrometry (Ca, Mg and Na). Soil particle analysis (sand, silt, clay) followed the method of Rhoades (1982). We included topography as the slope of the site (in degrees).
AMOUNT AND COMPOSITION OF LITTERFALL AND LITTER ON THE GROUND.—We determined litter standing crop and litterfall as fine litter only, i.e., not including woody material>2 cm diameter. For litter standing crop we used the volumetric method of Parsons et al. (2009), and calibrations therein. In this method, volume is measured in situ (using a volumetric cylinder and measuring device, Parsons et al. 2009), and LSC (t/ha) is calculated with a linear model to convert volume to mass. In thefield, the method works by placing all the litter within the quadrat area (0.25 m2), from randomly selected locations within the plot, into the cylin-der and flattening with the measuring device until constant vol-ume (Parsons et al. 2009). The benefits of the volvol-umetric method in sampling numerous sites are discussed in detail in Parsons et al.(2009), here with many measurements taken to cover spatial variability of each plot. The approach allows for high-resolution data to be collected without removing samples, lowering distur-bance and saving time and effort (Parsons et al. 2009). To deter-mine mean annual LSC for the detailed monitoring plots, sampling intensity varied between sites, however we calculated means from 10–20 quadrats measured every 2 to 3 mo, over the 2 yr.
We measured litterfall using ten circular leaf litter traps of 0.25 m2at each site, withfive per detailed monitoring plot. Traps were made from a circular steel hoop with a fiberglass (1 mm) mesh basket, fixed in place approximately 1 m from the ground (Newbould 1967, Parsons et al. 2011). We placed five traps in a ‘star’ formation, each separated by around 5 m within the plots (Parsons et al. 2011). After collection samples were dried, sepa-rated into components (wood, leaf, reproductive and unclassified materials <2 mm) and weighed. The leaf samples were also used to study leaf litter decomposition in Parsons et al. (2012) and to model litter in situ decomposability and determine litter chemical composition in Parsons et al. (2011) and in this study.
We determined LSC means for all plots sampled (detailed monitoring plots and those sampled on fewer occasions). For LSC, we used all data collected in the 24 mo to determine spatial variability (see statistics sections below). We sampled a total of 93 plots for LSC across all sites (19 sites in total), including 36 detailed monitoring plots. For litterfall and litter chemistry, we utilized 40 detailed monitoring plots at 20 sites.
We used a modeled variable of the potential in situ decom-position rate of leaf litter based on initial litter comdecom-position, obtained with near infrared spectrometry (model validated r2= 0.78, SE = 0.23, from N = 85 initial litterbag samples) in Parsons et al. 2011 and from the litterbag study of Parsons et al. 2012. This decomposability measure is a prediction of the subse-quent (in situ) decomposition rate of leaf litterfall samples (i.e., at litterfall), based on the chemical composition of the material (i.e., initial litter quality), and is represented as exponential decay quo-tient (k from the equation mass= Ae kt) (Parsons et al. 2011, 2012), here termed in situ leaf litterfall decomposability or knirs (per yr). The predicted value contains more information than simpler litter quality measures e.g., lignin, N, P etc. (i.e., all possi-ble combinations of interest within the NIR range). This is a
prediction of in situ decay rate (potential decomposition rate), taking into account the initial leaf litter quality (at litterfall), climate, and soil of the site (i.e., the conditions that contributed to the decay rates in Parsons et al. 2012 along with the variability in the litter composition of the predictions). This value is limited however, in assessing the broader plot level variability in other factors controlling decomposition (i.e., outside of the conditions of the experiment in Parsons et al. 2012), such as local variability in soil biota. We also used values obtained with NIRS in Parsons et al. 2011 of leaf litter chemical compositions (total N, P, Ca, phenolics, acid detergent lignin, anda-cellulose), to determine the spatial variation in litter chemical quality. We utilized all leaf litter collected in litterfall traps over the 2 yr to determine the decom-posability and litter quality values, within the accurate bounds of the NIRS models (Parsons et al. 2011). The total number of accurate samples varied for each variable (Decomposability N = 2452; N N = 2514; P N = 2409, Ca N = 2514, lignin N = 2512; cellulose N = 2512 and phenolics N = 2510, from 19 separate months over 2 yr from the mean for 5 traps/plot). For spatial variability, we used a total of 3417 litterfall samples, span-ning 24 mo (20 sites, 40 plots), and 6559 LSC samples (19 sites, 93 plots, 24 mo collection).
STATISTICAL ANALYSES.—To test for differences in LSC, litterfall rate and litter decomposability between plots at the same site (local differences vs. regional differences), we used nested ANO-VA, with site (using the whole transect for standing crop, and the detailed monitoring plots for litterfall and knirs) as the fixed fac-tor, and plots within sites as random factors (e.g., plots within the transect). To explore spatial variance, we calculated restricted maximum likelihood (RML) models (R-project software, library= lme4) to partition the regional variance into spatial hier-archies (i.e., subregion-site-plot). RML allows for variance parti-tioning in unbalanced sampling designs (Corbeil & Searle 1976), which was the makeup of our data. For LSC we did this using the raw data (samples converted to t/ha) with subregion, site and plot as random factors, and month as a fixed factor. To show spatial variability in litterfall, decomposability, and the five litter chemical variables, we determined the average values over the 2 yr per litter trap, i.e., RML model calculated from the mean value per trap as a function of subregion, site and plot.
To determine environmental controls on litter processes, this study used a combination of correlation analysis (Spearman Rank correlations) and best sub-set linear regressions (R software, library: leaps). We made comparisons of the annual mean values from the detailed monitoring plots with plant community compo-sition, litterfall, climate and soil variables, along with litter chemi-cal quality and decomposability. The ‘leaps’ package does not allow significantly cross-correlated variables in the best subset models. We used this function along with the results of the corre-lation analysis to produce the best model with non-confounded results from cross correlations.
Other environmental variables tested in the models were as follows: mean annual litterfall rate, percent wood in litterfall,
mean annual precipitation (MAP), mean annual temperature (MAT), coefficient of variation in monthly rainfall totals/rainfall seasonality (MAPCV); average leaf wetness (moisture condensa-tion) in the dry season (dry season moisture). MAP, MAT and MAPCV came from BIOCLIM modelling and dry season mois-ture from on-site data loggers (HOBO type, Onset computer corporation USA). For soil we used the following: total N, P, organic C, clay, sand, silt, Ca and Na. We also used litter chemical composition and decomposability in the models, as the mean annual value for the detailed monitoring plots.
RESULTS
AMOUNT OF LITTERFALL AND LITTER STANDING CROP.—For the detailed monitoring plots, 2 yr average LSC ranged from 10.94 t/ ha (seasonally dry rain forest on rhyolite) to 3.70 t/ha (recently cyclone-damaged upland site on basalt) (regional mean 6.21 t/ha, monthly mean SEM = 1.55 t/ha) (Appendix S1). Generally the Windsor, Spec and Carbine subregions had higher annual LSCs than the Atherton subregion (Fig S1A. and Appendix S1). The range of mean litterfall rates in the 2 yr was 11.29 t/ha/yr (mid-land Acacia sp. closed forest on granite) to 5.44 t/ha/yr (cyclone-damaged upland site on basalt) (regional mean 7.46, monthly mean SEM= 1.74 t/ha/yr), Fig. S1B and Appendix S1. The range in leaf litterfall decomposability was 2.25 per yr (low-land a-seasonal forest on basalt) to 0.32 per yr (seasonally dry rain forest on rhyolite) (regional mean= 1.27 monthly SEM= 0.48 per yr, Fig. S1C and Appendix S1). The range of individual trap litterfall rates and decomposability were 1.3–14.7 t/ha/yr and 0.23–2.6 per yr respectively. Higher leaf litter total N and P mostly occurred on soils derived from basalt (Fig. S1). Regional mean ( monthly SEM) leaf litter chemical concentra-tions were as follows: N= 1.30 0.24%; P = 0.031 0.02%; Ca = 1.01 0.43%; C = 48.5 1.5%; lignin = 35.32 2.2%; a-cellulose = 20.26 1.39%; phenolics = 0.50 0.16% (Fig. S1 D–J and Appendix S1).
DRIVERS OF VARIABILITY IN LITTER.—When comparing regional to local variance, all litter variables varied by plot when controlling for site (Fig. S1, LSC: nested ANOVA, df = 18, F = 8.42, P< 0.0001; litterfall: df = 19, F = 13.46, P < 0.001; decompos-ability: df= 19, F = 10.82, P < 0.0001; N: df = 19, F = 19.08, P< 0.0001; phosphorus: df = 19, F = 14.99, P < 0.0001; Ca: df= 19, F = 20.47 P < 0.0001; C: df = 19, F = 13.75 P< 0.0001; lignin: df = 19, F = 8.05, P < 0.0001; phenolics: df= 19, F = 11.52, P < 0.0001). There was a raw regional vari-ance in LSC for the detailed monitoring plots of 12.64 t/ha (i.e., including monthly variance). By partitioning this to include month/time as a factor in the model, it showed that mean local variance among sites represented 9.0 percent of this regional mean (1.17 t/ha, taking month as a factor) and within plot vari-ance 6.7 percent of the regional mean (0.82 t/ha), and within subregion 5.7 percent (N= 93 plots, 19 sites) (Table 1). There was a variance in litterfall of 4.56 t/ha/yr for the whole region, with 28.1 percent of this variance explained within plots, and
<0.1 percent of the regional variance explained by site and subre-gion (Table 1). This meant that most of the variance in litterfall in the region occurred within individual plots (Fig. S1). We calcu-lated regional variance in leaf litterfall decomposability as 0.28 per yr. Our plots contained 10.4 percent of the regional variance in decomposability, with 30.5 percent partitioned to sites, and 52.7 percent to subregion (subregional = 0.15 per yr, site = 0.09 per yr and plot 0.03 per yr).
The subregional scale contained the largest portion of the regional variance for the litter chemical quality indices, N, P and total phenolics (58.4%, 53.3% and 58.8% of the regional variance respectively), while the site level contained the majority of the
TABLE 1. Partitioning of the spatial variance in litter standing crop, annual litterfall rate and leaf litterfall decomposability in Australian tropical rainforests, as determined from restricted maximum likelihood models. The proportion of the explained regional variance is partitioned into plots (~ 30 m2), sites
(1 km transects where standing crop was measured, plots separated by 400 m for litterfall andknirs) and subregion and plots. The percent of the
regional variance and numbers of groups in each hierarchy are also shown. Region Subregion Site Plot Residual Litter standing crop (t/ha) (mean) Var 12.64 0.72 1.13 0.85 9.94 % 5.7 9.0 6.7 – n (6559) 4 19 93 – Litterfall (t/ha/yr) (mean) Var 4.56 <0.001 <0.001 1.28 3.3 % – <0.01 <0.01 28.1 – n (200) 4 20 40 – Litter decomposability knirs(per yr)
(mean) Var 0.28 0.15 0.09 0.03 0.02 % – 52.7 30.5 10.4 – n (200) 4 20 40 – Leaf litter N (%) Var 0.06 0.04 0.02 0.01 0.003 % – 58.4 25.5 11.3 – n (200) 4 20 40 – Leaf litter P (%) Var 0.0003 0.0002 0.0001 0.00003 0.00003 % – 53.32 28.47 9.11 – n (200) 4 20 40 – Leaf litter C (%) Var 2.49 0.64 1.32 0.36 0.17 % – 25.8 53.0 14.4 – n (200) 4 20 40 – Leaf litter Ca (%) Var 0.19 0.05 0.10 0.03 0.01 % – 24.0 54.7 17.3 – n (200) 4 20 40 – Leaf litter lignin (%) Var 6.28 0.34 3.00 2.11 0.84 % – 5.3 47.7 33.6 – n (200) 4 20 40 – Leaf litter a-cellulose (%) Var 2.33 0.70 0.74 0.68 0.21 % – 30.1 31.7 29.3 – n (200) 4 20 40 – Leaf litter total phenolics (%) Var 0.03 0.02 0.01 0.00 0.001 % – 58.8 23.7 13.2 – N (200) 4 20 40 –
regional variance for C, Ca and lignin (53%, 54.7% ,and 47.7% respectively). The regional variance was contained for a-cellulose fairly evenly at all of the spatial scales (Table 1).
Litter standing crop correlated most strongly with (in order of Spearman correlations, Table 2 and Appendix S2): leaf litter C ( 0.75, P< 0.0001); leaf litterfall decomposability ( 0.74, P< 0.0001); leaf litter phenolics (0.68, P < 0.0001); dry season moisture ( 0.66, P< 0.0001); soil Na ( 0.58, P < 0.0001); leaf litter P ( 0.55, P< 0.0001); soil P ( 0.47, P = 0.001); rainfall seasonality (0.45, P= 0.005); and mean annual temperature ( 0.44, P= 0.007) (Table 2 and Appendix S2 for full correlation table). There were also generally lower LSCs on the cyclone-dam-aged sites ( 0.48, P= 0.001, Table 2), however, this relationship was potentially spurious and largely driven by these sites’ gener-ally fertile characteristics (Appendix S2). Alone, we did not find litterfall rate to be significant correlate with LSC (P = 0.17). Our best model explaining LSC means included (in order of signifi-cance) the decomposability of the leaf litter (negative relation-ship), soil Na (negative), the proportion of wood (< 2 cm) in the litterfall (positive), slope of the site (positive) (detailed monitoring plots, model adj. r2= 0.69, Table 3, Appendix S3 for partial plots).
There were poor correlations for litterfall rate with most environmental variables (Table 2). Plant species richness was the only variable tested to correlate significantly with litterfall, with highest richness at sites with lower litterfall (Spearman: 0.36, P= 0.03) (Table 1). There was similarly a correlation between plant stem density and litterfall rate ( 0.32, P = 0.06). A weak negative relationship between cyclone damage and litterfall rate ( 0.27, P = 0.09), suggested that our damaged sites had not fully recovered from cyclone Larry over the study. Our best model explaining litterfall was a simple linear regression with plant stem density, however, the relationship was not significantly linear (P= 0.11, Table 3 and Appendix S3). There were no improve-ments in the litterfall relationships through non-linear transforma-tions (log) of stem density and plant richness (P> 0.05). Our four recently cyclone-damaged sites generally had higher plant species richness and proportions of disturbance species (correla-tion= 0.49 and 0.36, P < 0.05 respectively, Appendix S2)
The best model for the decomposability of leaf litterfall included (in order of significance): leaf litter P, lignin, soil Na, and dry season moisture (model adj. r2= 0.89, P < 0.0001, Table 3). There were also significant correlations between leaf decomposability and moisture indices (MAP, MAPCV, dry season
TABLE 2. Correlations (Spearman rank) for mean annual litter standing crop (t/ha), litterfall rate (t/ha/yr) and leaf decomposability (per yr) with environmental variables for plots in Australian tropical rain forest.
Variable Mean annual litter standing crop Litterfall rate Leaf decomposability
Litter Litter standing crop – 0.236 0.738***
Litterfall rate 0.236 – 0.212
% wood (litterfall) 0.338* 0.146 0.150
Litter decomposability 0.740*** 0.212 –
Climate Mean annual temperature 0.438** 0.026 0.195
Mean annual rainfall 0.356* 0.278 0.618***
Rainfall seasonality 0.454** 0.309 0.691***
Dry season moisture 0.655*** 0.139 0.620***
Mean annual radiation 0.289 0.278 0.631***
Soil Soil Ca 0.280 0.008 0.475**
Soil Na 0.575*** 0.068 0.310
Soil N 0.353* 0.06 0.231
Soil P 0.472** 0.104 0.697***
Silt 0.19 0.245 0.057
Community composition Plant species richness 0.283 0.360* 0.179
Tree species richness 0.082 0.238 0.054
Plant stem density 0.155 0.316 0.049
% Disturbance species 0.046 0.072 0.229
Cyclone damaged 0.481** 0.276 0.569**
Leaf litter quality Nitrogen 0.486** 0.242 0.831***
Lignin 0.041 0.141 0.203 a-cellulose (%) 0.259 0.089 0.408** Carbon 0.747*** 0.137 0.784*** Phosphorus 0.549*** 0.18 0.879*** Total Phenolics 0.675*** 0.106 0.804*** *P < 0.05, **P < 0.01, ***P < 0.001.
moisture), soil P and Ca, leaf litter N,a-cellulose, C, P, phenolics and cyclone damage (P< 0.01, Table 2).
DISCUSSION
We found high local variability for litterfall rates and to a lesser extent, litter standing crop. In addition, variation in leaf litter lig-nin, C and Ca was greatest locally, i.e., between sites and plots, as opposed to subregions. However, our study explained variability in leaf litterfall decomposability, litter N, P and phenolics better at larger spatial scales (>1 km apart).
Litterfall showed a spatially idiosyncratic pattern when com-pared to most other variables we studied. In contrast to other studies, we did not find litterfall to be strongly associated with environmental variables (e.g., climate and soil). For instance, vari-ability in South American tropical forest litterfall rates is broadly controlled by soil N and P (Chave et al. 2009). With some of our richer nutrient sites having experienced recent cyclonic activity (although not exclusively), reducing canopy coverage and litterfall inputs, it is not surprising that we found few environmental cor-relates with litterfall. The data here suggests that although recov-ery of litterfall rates had occurred at the damaged sites since the 2006 cyclone, valid comparisons of litterfall rates could not be made across the full region. This may also explain why our regio-nal mean (7.5 t/ha/yr) was slightly lower than South American rain forests (8.6 t/ha/yr)(Chave et al. 2009). Similarly, previous work in the Australian wet tropics (Brasell et al. 1980, Spain 1984, Herbohn & Congdon 1993) noted up to twofold higher lit-terfall rates in the wetter and more fertile Atherton region, com-pared to sites on more oligotrophic soils (Herbohn & Congdon 1993, Stocker et al. 1995).
The negative relationship of litterfall with plant density and species richness also points to disturbance effects on litter
processes. That is, thinning of the canopy creates higher densities of younger individuals, but reduced coverage lowers litterfall. It has been shown in North Queensland that litterfall rates do not differ between >25 yr old selectively logged and nearby primary rain forest (Herbohn & Congdon 1993). Thus, in the present study this pattern was more likely a result of recent cyclone events rather than logging, considering the extended time since anthropogenic disturbance. This may also be a symptom of inter-mediate disturbance effects (Collins et al. 1995) on plant species richness and litterfall rates, with higher species richness and stem density in damaged areas but lower litter inputs (Parsons 2010 and Appendix S2). The time since major disturbance differed greatly between our sites (e.g., < 2 yr for cyclone damage to in many cases>50 yr for logging). Such spatially varied disturbance patterns over the landscape, along with even patchier disturbance events, (e.g., idiosyncratic wind events in topographically diverse areas), adds to already high spatial variance in patterns of litter-fall, e.g. undisturbed rain forests (Burghouts et al. 1998).
Litter decomposability may vary within a small area, mostly due to environmental heterogeneity, differences in traits between neighboring species and also leaves at different stages of decay falling to the ground (Herbohn & Congdon 1993, Townsend et al. 2008). Despite this, we did not find strong controls from plant community composition or species richness on decompos-ability variance. Also, we noted relatively even decomposdecompos-ability at plot level, i.e., representative of the plot (≥approx. 5 m apart). As we increased scale to comparisons between sites (~400 m apart), there was an increase in the variability in decomposability from ~10 to ~30 percent of the regional variance. This is probably due to greater similarity in soil and climate (including micro-climates), and potentially species composition at the plot scale. The subre-gions here generally show this control, as mountain ranges of dis-tinct soils/geologies. Our work supports that some aspects of
TABLE 3. Model parameters from best sub-set linear regression on forest litter standing crop, litterfall rate and leaf litter decomposability.
Model Est. Std. err t P
Residuals
BIC
Res. SE df Adj. R2 F Model P
Litter standing crop (Intercept) 6.9 1.4 4.9 <0.001 1.04 35 0.69 16.9 <0.0001 26.8
Slope of site 0.07 0.04 2.0 0.06
Litterfall rate 0.21 0.10 2.0 0.06
% Wood in litterfall 0.08 0.03 2.3 0.03
Leaf decomposability 2.02 0.47 4.3 0.0002
Soil Na 44.09 11.3 3.9 0.001
Litterfall rate (Intercept) 8.52 0.7 11.8 <0.001 1.74 35 0.05 2.7 0.108 2.5
Plant stem density 0.03 0.02 1.6 0.11
Leaf decomposability (Intercept) 1.18 0.43 2.8 0.009 0.16 35 0.89 80.02 <0.0001 69.5
Dry season moisture 0.008 0.004 1.8 0.07
Soil Na 4.53 1.7 2.7 0.01
Litterfall P 24.7 2.12 11.7 <0.001
litter quality are more a function of these broader differences. For example, leaf litter P and to a lesser extent N, are correlated with soil fertility/geology in tropical forests (Vitousek & Sanford 1986). Also, phenolics in leaf litter, which are important inhibitors of decay at these sites (Parsons et al. 2012), showed similar pat-terns to N and P. Phenolics are partly controlled by climate (especially annual radiation) and soil composition (Close & McAr-thur 2002). More aligned litter chemical concentrations at small spatial scales come about due to the broad correlations of litter quality with climate and soil fertility.
In contrast, we showed somewhat idiosyncratic spatial vari-ability in leaf litter lignin and Ca. Leaf litter lignin contents may be higher in plant species with longer lived tissues (e.g., later suc-cessional species) (Mediavilla et al. 2008), although this may not be simply related to successional status as opposite trends (i.e., poorer quality litter) also exist in rain forest pioneers (Parsons & Congdon 2008), that potentially turnover leaves faster (Reich et al. 1991). Our data suggest that high local variance in lignin may be partially driven by disturbance, with positive correlations between lignin and plant species richness, and a negative correlation between lignin and Ca (Parsons 2010, Appendix S2 for correla-tion matrix). A similar trend has been noted in Amazon ecosys-tems (Vasconcelos & Laurance 2005). Our results provide additional evidence that in areas with high rates of disturbance, variance in litter compositions, and thus processes, can be height-ened due to the effects on plant community composition.
Litter chemical composition is a commonly determined con-trol on litter dynamics on soil at most scales (Cornwell et al. 2008). The results here support this, like similar studies on litter decay rates in this region (Parsons & Congdon 2008, Parsons et al.2012), with the alignment of the decomposability of leaf lit-ter (e.g., nutrients like P, lignocellulose and phenolic contents), and the amount of wood in litterfall, with LSC. Thus, a more chemically recalcitrant litter layer (e.g., poor leaf quality, large amounts of woody material) leads to more litter build up, and also less seasonal fluctuations in amount (Parsons 2010 and un-publ. data). This trend is further supported by wood decompos-ability being generally correlated with leaf litter decomposdecompos-ability (Weedon et al. 2009).
The drivers of variance in the amount of litter on the ground in seasonally wet tropical forests can be spatially varying litterfall quantities, litterfall composition, micro-climates, soil com-position, and topography. At our sites, disturbance may have also lead to lower LSCs after cyclone damage (e.g., due to decreased litter inputs), however our data did not allow us to confirm this due to the higher decay rates also occurring in these locations (Parsons et al. 2012). As we noted also, sodium shortage is a strong determinant of carbon cycling in tropical forests (Kaspari et al. 2009). In addition, P limitation in soils and on litter pro-cesses is common in tropical forests, including our sites (Parsons et al. 2012), although in general, multiple nutrients are likely to limit litterfall and decomposition (Kaspari et al. 2008). We also observed temperature sensitivity of the amount of litter on the ground (Davidson & Janssens 2006), with cooler upland sites generally having deeper litter layers. Regionally however, spatially
varying litterfall rates may explain the bulk of variability in LSC at small spatial scales (Burghouts et al. 1998). This further emphasizes the importance of accurate quantification of litterfall rates for ecosystem modelling (Clark et al. 2001a).
It is important to note, we did not assess spatial variability in the soil biota, which can also influence decomposition. Because the distributions of soil organisms often show predictable rela-tionships with climate, soil, and vegetation type (Ettema & War-dle 2002), we likely included a diversity of the soil biota, at least at larger spatial scales. Future studies should consider this biota explicitly, as well as quantify variability within litter traps (e.g., between neighboring trees).
ACKNOWLEDGMENTS
We thank the following people and funding bodies in the produc-tion of this manuscript: Collin Storlie, Ivan Lawler, Yvette Wil-liams, numerous volunteers for help with collecting the litter volume data, Earthwatch Institute, Skyrail Rainforest Foundation, Queensland government Smart-State funding program, The Mar-ine and Tropical Sciences Research Facility, James Cook Univer-sity School of Marine and Tropical Biology, Queensland Parks and Wildlife Service and three anonymous reviewers for helping improve the manuscript.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online version of this article:
FIGURE S1. Plots showing the spatial distribution of litter components in Australian tropical rain forests.
APPENDIX S1. Raw data used in analyses.
APPENDIX S2. Spearman rank correlations of all variables used in analyses.
APPENDIX S3. Partial plots of regression analyses.
LITERATURE CITED
ADAIR, E. C., W. J. PARTON, S. J. D. DELGROSSO, W. L. SILVER, M. E. H AR-MON, S. A. HALL, I. C. BURKE,ANDS. C. HART. 2008. Simple three-pool model accurately describes patterns of long-term litter decompo-sition in diverse climates. Glob. Change Biol. 14: 2636–2660. ANDERSON, J. M.,ANDJ. S. I. INGRAM. 1989. Tropical soil biology and fertility.
C.A.B International, Cambridge.
BAETHGEN, W. E.,ANDM. M. ALLEY. 1989. A manual colorimetric procedure for measuring ammonium nitrogen in soil and plant Kjeldahl digest. Commun. Soil Sci. Plant Anal. 20: 961–969.
BRASELL, H. M., G. L. UNWIN,ANDG. C. STOCKER. 1980. The quantity tempo-ral distribution and minetempo-ral element content of litterfall in 2 forest types at 2 sites in tropical Australia. J. Ecol. 68: 123–140.
BURGHOUTS, T. B. A., N. M. V. STRAALEN,ANDL. A. BURIJNZEL. 1998. Spatial
heterogeneity of element and litter turnover in a Bornean rainforest. J. Trop. Ecol. 14: 477–506.
CHAPIN, F. S., P. A. MATSON,ANDH. A. MOONEY. 2002. Principles of
terres-trial ecosystem ecology. Springer Verlag, New York.
CHAVE, J., D. NAVARRETE, S. ALMEIDA, E. ALVAREZ, L. ARAGAO, D. BONAL, P. CHATELET, J. SILVA-ESPEJO, J. Y. GORET, P. VON HILDERBRAND, E.
JIMENEZ, S. PATINO, M. C. PE ~NUELA, O. L. PHILLIPS, P. STEVENSON,AND
Y. MALHI. 2009. Regional and temporal patterns of litterfall in tropical
South America. Biogeosci. Dis. 6: 7565–7597.
CLARK, D. A., S. BROWN, D. W. KICKLIGHTER, J. Q. CHAMBERS, J. R. T HOMLIN-SON,ANDJ. NI. 2001a. Measuring net primary production in forests: Concepts andfield methods. Ecol. Appl. 11: 356–370.
CLARK, D. A., S. BROWN, D. W. KICKLIGHTER, J. Q. CHAMBERS, J. R. T HOMLIN-SON, J. NI,ANDE. A. HOLLAND. 2001b. Net primary production in tropical forests: An evaluation and synthesis of existing field data. Ecol. Appl. 11: 371–384.
CLOSE, D. C.,AND C. MCARTHUR. 2002. Rethinking the role of many plant phenolics- protection from photodamage not herbivores? Oikos 99: 166–172.
COLLINS, S. L., S. M. GLENN,ANDD. J. GIBSON. 1995. Experimental analysis of intermediate disturbance and initial floristic composition: Decou-pling cause and effect. Ecology 76: 486–492.
CORBEIL, R. R., AND S. R. SEARLE. 1976. Restricted maximum likelihood
(REML) estimation of variance components in the mixed model. Technometrics 18: 31–38.
CORNWELL, W. K., J. H. CORNELISSEN, K. AMATANGELO, E. DORREPAAL, V. T.
EVINER, O. GODOY, S. E. HOBBIE, B. HOORENS, H. KUROKAWA, N. PEREZ-HARGUINDEGUY, H. M. QUESTED, L. S. SANTIAGO, D. A. W AR-DLE, I. J. WRIGHT, R. AERTS, S. D. ALLISON, P. VANBODEGOM, V. B ROV-KIN, A. CHATAIN, T. V. CALLAGHAN, S. DIAZ, E. GARNIER, D. E. GURVICH, E. KAZAKOU, J. A. KLEIN, J. READ, P. B. REICH, N. A. S OU-DZILOVSKAIA, M. V. VAIERETTI,ANDM. WESTOBY. 2008. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11: 1065–1071.
DAVIDSON, E. A.,ANDI. A. JANSSENS2006. Temperature sensitivity of soil car-bon decomposition and feedbacks to climate change. Nature 440: 165–173.
DENSLOW, J. S. 1987. Tropical rainforest gaps and tree species diversity. Annu.
Rev. Ecol. Syst. 18: 431–451.
ETTEMA, C. H.,ANDD. A. WARDLE. 2002. Spatial soil ecology. Trends Ecol.
Evol. 17: 177–183.
FACELLI, J. M.,AND S. T. A. PICKETT. 1991. Plant litter: Its dynamics and
effects on plant community structure. Bot. Rev. 57: 1–32.
GLEASON, S. M., J. READ, A. ARES,ANDD. J. METCALFE. 2010. Species–soil associations, disturbance, and nutrient cycling in an Australian tropical rainforest. Oecologia, 162: 1047–1058.
GRIME, J. P. 2002. Plant strategies, vegetation processes, and ecosystem prop-erties. John Wiley & Sons Inc, New York.
H€ATTENSCHWILER, S., B. AESCHLIMANN, M. M. CO ^UTEAUX, J. ROY,ANDD. B O-NAL. 2008. High variation in foliage and leaf litter chemistry among 45 tree species of a neotropical rainforest community. New Phytol. 179: 165–175.
HERBOHN, J. L.,ANDR. A. CONGDON. 1993. Ecosystem dynamics at disturbed and undisturbed sites in north Queensland wet tropical rain forest: II. Litterfall. J. Trop. Ecol. 9: 365–379.
HYLAND, B. P. M., D. C. WHIFFEN, C. B. GRAY,ANDR. W. ELICK. 2002.
Aus-tralian tropical rainforest plants – trees, shrubs and vines. CSIRO, Melbourne.
KASPARI, M., N. MILTON, K. E. GARCIA, M. SANTANA, S. J. WRIGHT,ANDJ. B.
YAVITT. 2008. Multiple nutrients limit litterfall and decomposition in a
tropical Forest. Ecol. Lett. 11: 35–43.
KASPARI, M., S. P. YANOKIAK, R. DUDLEY, M. YUAN,ANDN. A. CLAY. 2009. Sodium shortage as a constraint on the carbon cycle in an inland trop-ical rainforest. Proc. Natl Acad. Sci. USA 106: 1–5.
LISKI, J., A. R. I. NISSINEN, M. ERHARD, AND O. TASKININ. 2003. Climatic effects on litter decomposition from arctic tundra to tropical rainfor-est. Glob. Change Biol. 9: 575–584.
MEDIAVILLA, S., A. GARCIA-CIUDAD, B. GARCIA-CRIADO, AND A. ESCUDERO. 2008. Testing the correlations between leaf life span and leaf structural reinforcement in 13 species of European Mediterranean woody plants. Funct. Ecol. 22: 787–793.
NEWBOULD, P. J. 1967. Methods for estimating the primary production of
for-ests. Blackwell Scientific Publications, Oxford.
NORBY, R. J., AND D. R. ZAK. 2011. Ecological lessons from free-air CO2
enrichment (FACE) experiments. Annu. Rev. Ecol. Evol. Syst. 42: 181–203.
PARSONS, S. A. 2010. Decomposition, nutrient cycling and climate change in Australian tropical rainforests. PhD dissertation, James Cook University.
PARSONS, S. A., ANDR. A. CONGDON. 2008. Plant litter decomposition and nutrient cycling in north Queensland tropical rainforest communities of differing successional status. J. Trop. Ecol. 24: 317–327.
PARSONS, S. A., R. A. CONGDON, C. J. STORLIE, L. P. SHOO,ANDS. E. W IL-LIAMS. 2012. Regional patterns and controls on leaf decomposition in Australian tropical rainforests. Austral. Ecol. 37: 845–854.
PARSONS, S. A., I. R. LAWLER, R.A. CONGDON,ANDS. E. WILLIAMS. 2011.
Rain-forest litter quality and chemical controls on leaf decomposition with near infrared spectrometry. J. Plant Nutr. Soil Sci. 174: 710–720. PARSONS, S. A., L. P. SHOO,ANDS. E. WILLIAMS. 2009. Volume measurements
for quicker determination of forest litter standing crop. J. Trop. Ecol. 25: 665–669.
POWERS, J. S., R. A. MONTGOMERY, E. C. ADAIR, F. Q. BREARLEY, S. J. DEWALT, C. T. CASTANHO, J. CHAVE, E. DEINERT, J. U. GANZHORN, M. E. G IL-BERT, J. ANTONIO-GONZALEZ, S. BUNYAVEJCHEWIN, H. R. GRAU, K. E. HARMS, A. HIREMATH, S. IRIARTE-VIVAR, E. MANZANE, A. A. de O LIVE-IRA, L. POORTER, J. B. RAMANAMANJATO, C. SALK, A. VARELA, G. D. WEIBLEN,ANDM. T. LERDAU. 2009. Decomposition in tropical forests: A pan-tropical study of the effects of litter type, litter placement and mesofaunal exclusion across a precipitation gradient. J. Ecol. 97: 801–811.
PROCTOR, J., J. M. ANDERSON, S. C. L. FOGDEN,ANDH. W. VALLACK. 1983. Ecological studies in four contrasting lowland rain forests in Gunung Mulu national park, Sarawak: II. Litterfall, litter standing crop and pre-liminary observations on herbivory. J. Ecol. 71: 261–283.
REICH, P.B., C. UHL, M. B. WALTERS,ANDD. S. ELLSWORTH. 1991. Leaf
life-span as a determinant of leaf structure and function among 23 Ama-zonian tree species. Oecologia 86:16–24.
RHOADES, J. D. 1982. Mechanical analysis of soils. In A. L. Page, R. H. Miller,
and D. R. Keeney (Eds). Methods of soil analysis, Part 2. chemical and microbiological properties, pp. 1692. American Society of Agron-omy, Madison, USA.
SILVER, W. L. 1994. Is nutrient availability related to plant nutrient use in humid tropical forests? Oecologia 98: 336–343.
SILVER, W. L., F. N. SCATENA, A. H. JOHNSON, T. G. SICCAMA, ANDM. J. SANCHEZ. 1994. Nutrient availability in a montane wet tropical forest: Spatial patterns and methodological considerations. Plant Soil 164: 129–145.
SPAIN, A. V. 1984. Litterfall and the standing crop of litter in three Australian tropical rainforests. J. Ecol. 72: 947–961.
STOCKER, G.C., W. A. THOMPSON, A. K. IRVINE, J. D. FITZSIMON, ANDP. R. THOMAS. 1995. Annual patterns of litterfall in a lowland and tableland
rainforest in tropical Australia. Biotropica 27:412–420.
SWIFT, M. J., O. W. HEAL,ANDJ. M. ANDERSON. 1979. Decomposition in
ter-restrial ecosystems. In D. J. Anderson, P. Greig-Smith, and F. A. Pit-elka (Eds). Studies in ecology, vol. 5, pp. 1–266. Blackwell Scientific Publications, Melbourne.
TOWNSEND, A. R., G. P. ASNER,ANDC.C. CLEVELAND. 2008. The biogeochemi-cal heterogeneity of tropibiogeochemi-cal forests. Trends Ecol. Evol. 23: 424–431. TOWNSEND, A. R., C. C. CLEVELAND, B. Z. HOULTON, C. B. ALDEN,ANDJ. W.
WHITE. 2011. Multi-element regulation of the tropical forest carbon cycle. Front. Ecol. Environ. 9: 9–17.
VASCONCELOS, H. L.,ANDW. F. LAURANCE 2005. Influence of habitat, litter
type, and soil invertebrates on leaf-litter decomposition in a fragmented Amazonian landscape. Oecologia. 144: 456–462. VITOUSEK, P. M.,ANDR. L. SANFORD. 1986. Nutrient cycling in moist tropical
WANG, Y. P., R. M. LAW,ANDB. PAK. 2010. A global model of carbon, nitrogen
and phosphorus cycles for the terrestrial biosphere. Biogeosciences 7: 2261–2282.
WEEDON, J. T., W. K. CORNWELL, J. H. CORNELISSEN, A. E. ZANNE, C. WIRTH,
ANDD. A. COOMES. 2009. Global meta-analysis of wood decomposition rates: A role for trait variation among tree species? Ecol. Lett. 12: 45–56. WIEDER, W. R., C. C. CLEVELAND,ANDA. R. TOWNSEND. 2009. Controls over leaf
litter decomposition in wet tropical forests. Ecology 90: 3333–3341.
ZHANG, D., D. HUI, Y. LUO,ANDG. ZHOU. 2008. Rates of litter decomposition
in terrestrial ecosystems: Global patterns and controlling factors. J. Plant Ecol. 1: 1–9.
ZHOU, T., P. SHI, D. HUI,ANDY. LUO. 2009. Global pattern of temperature sensitivity of soil heterotrophic respiration (Q10) and its implications for carbon-climate feedback. J. Geophys. Res. 114: 1–9.
