Abstract: Coarse woody debris (CWD) is a poorly understood component of the carbon cycle. We report the first measurements of both CWD density (specific gravity) and necromass in northwestern Amazonia, using both line-intersect and plot-based methods. Average CWD densities were similar in clay-rich and white sand unflooded forests, but lower in floodplain forest (p ≤ 0.001). Necromass of CWD lying on the ground was also lower in the floodplain (10.2 ± 6.0 Mg ha-1, mean ± SE) than in the clay-rich (30.9 ± 5.4) and white sand (45.8 ± 7.3) forests (p ≤ 0.001). These patterns are likely driven by disturbance history, floristic composition, and decomposition rates. Plot-based data showed that standing and fallen CWD together accounted for 6.4 to 15.4 % of total aboveground vegetative mass (trees ≥10 cm diameter). Total necromass in the floodplain forest in this landscape is relatively low, whereas the unflooded sites are more typical of other neotropical studies. Across humid, lowland neotropical forests, densities of intact and partially decayed CWD are significantly correlated with live wood density at the same site (p = 0.026 and 0.003, respectively). These relationships can be applied to estimate CWD density for forests where destructive sampling has not been attempted.
Introduction
A third of the carbon pool in forest ecosystems is in neotropical forests (Dixon et al. 1994). However, coarse woody debris (CWD), one of the key components of this pool (Harmon et al. 1986), is still poorly understood (Clark et al. 2002; Keller et al. 2004; Palace et al. 2007). Published studies demonstrate that stocks of CWD (necromass) can account for 6 to 25 % of total aboveground living and dead vegetative mass (Delaney et al. 1998; Clark et al. 2002; Nascimento and Laurance 2002), and up to 25 % of the aboveground carbon content (Rice et al. 2004). However, most plot-based estimates of carbon balance typically focus only on the living aboveground biomass (e.g., Phillips et al. 1998; Baker et al. 2004b).
This study aims to contribute to the long-term goal of improving estimates of the carbon stocks and balances of lowland Amazonian forests. In particular, in western Amazonia few detailed studies of forest carbon pool have been conducted. Western Amazonia accounts for one quarter of the total 6 M km2 Amazonian rainforest, if defined conservatively as the Amazon basin forests of Colombia, Ecuador, Peru, Bolivia, and Acre (Brazil) (data adapted from FAO 2000). It is also likely to include 20 Pg carbon for aboveground live woody biomass (Malhi et al. 2006). To our knowledge, in the humid lowland forests of this region, there is only one study of necromass (southern Peru, Baker et al. in press) and one of CWD volume (Ecuador, Gale 2000). Here, we report for the first time for northwestern Amazonian values for CWD density and stocks.
In contrast to well-established protocols for measuring CWD volume such as line-intersect sampling (Warren and Olsen 1964; van Wagner 1968), there is no standard protocol for sampling the density of CWD. For example, Gerwing (2002) assumed that the densities of intact, partially decayed, and rotten logs are 100, 88, and 60 % that of living trees respectively, whereas Brown et al. (1995) used a random sample of 20 pieces of CWD. Most humid, lowland neotropical studies use a subjective classification of the degree of CWD decay (e.g., Kauffman et al. 1988; Keller et al. 2004), with two to six classes, depending on researchers' definitions, and the density of each class is calibrated by direct density measurement of sub-samples (Kauffman et al. 1988; Delaney et al. 1998; Clark et al. 2002; Keller et al. 2004). In total, there are only five other CWD density studies that use a direct sampling method to produce at least three decay classes, none of which is located in northwestern Amazonia (, (a)). Also, direct sampling is a destructive method which is not suitable for some protected reserves. An indirect, non-destructive method is needed, to allow rapid assessment of CWD stocks from measurements of CWD volume, and especially for some regions where at present only measurements of CWD volume are available (e.g., Gale 2000).
We conducted an investigation of CWD densities and stocks in three forest types at Jenaro Herrera in Amazonian Peru. The line-intersect (also known as line-intercept) method (Warren and Olsen 1964; van Wagner 1968) was applied to sample CWD pieces for density and necromass measurements in this landscape. Additional plot-based (Harmon and Sexton 1996) measurements were made to measure the quantities of both standing and fallen CWD, and also to compare directly the carbon pools in CWD and living trees in the same area.
Specifically, we ask: (1) What are the wood densities and stocks of CWD in the northwestern Amazonia? (2) Are there differences in the wood densities and stocks of CWD between forest types in this region? (3) Is there a pattern of CWD density across Amazonia?
Methods
Study sites
The field work was undertaken in northern Peru at the Centro de Investigaciones de Jenaro Herrera (400 55′ S, 730 44′ W), located 200 km upstream of Iquitos, and administered by the Peruvian Institute for Amazonian Research (IIAP) ((b)). Annual rainfall in this landscape ranges from 2500 to 2700 mm, with mean monthly precipitation from 140 to 309 mm, and mean annual temperature between 26 and 27 0C (Spichiger et al. 1996; Kvist and Nebel 2001). CWD densities and stocks were studied in one seasonally-flooded and two unflooded lowland forests. The unflooded forests are located on clay-rich and white sand soils, and the floodplain forest is located in high restinga forest, which on average is inundated for one month per year (Kvist and Nebel 2001). The sampling area surrounded four permanent plots which were established in the different forest types. In the unflooded forest, two 1-ha plots, established by the RAINFOR project (http://www.geog.leeds.ac.uk/projects/rainfor/index.html), were randomly located in mature forest on two different soil types: clay-rich (RAINFOR code: JEN-11) and white sand soil (JEN-12). Every living individual tree ≥ 10 cm in diameter in the plots was measured, tagged and identified in March 2005 and subsequently recensused in April 2006. In the seasonally flooded forest, two 1-ha plots were established in 1993 by Nebel et al. (2001b) (Plot 2 and Plot 3, coded JEN-02 and JEN-03, respectively).
Line-intersect based CWD measurements
We located two to four transects using the line-intersect method (van Wagner 1968) in March 2005 in the three types of forests. Each transect was started at least 10 m or more away from the corner of each permanent plot to avoid possible anthropogenic impacts, and was oriented in parallel to the two perpendicular border directions of each plot in order to reduce any effect of orientation bias (Bell et al. 1996). These transects were 160 to 400 m in length depending on the patch size of the selected forest type. When the selected angle was not suitable for transect setup (e.g., the line would cross a stream, trail, or enter a different forest type), the transect line orientation was shifted by 20 degrees if possible. When a trail could not be avoided, the transect stopped 20 m before and started 20 m after, continuing in the same direction. In total, we inventoried 800 m of transect in the forest on clay-rich soils, 610 m in white sand forest, and 470 m in floodplain forest, encountering a total of 249 fallen CWD.
CWD is defined as all dead woody material, including lianas and palm trees, with diameter ≥ 10 cm. For every piece of CWD we measured diameter, recorded decay class (see section: Density of CWD), and sampled part of the log for further density calibration. All CWD diameter measurements from the transects were used to estimate total CWD volume and necromass in each forest, except for data from the short (50 m) transect in the white sand forest. The CWD volume was calculated as:
[]
where V is the CWD volume per unit area, Di is the diameter (cm) of log i and L (m) is the length of the transect line (van Wagner 1968). The variance of the volume (σ2) for n transects was weighted by transect lengths (Lj), as recommended by De Vries (1986, p. 256, cited in Keller et al. 2004):
[]
Density of CWD
Density samples were taken from the pieces of CWD intersected by the transect lines, and four standing dead trunks adjacent to the transects. If a tree was intersected more than once by a sampling line, only one branch from the tree was sampled. Sampling methods depended on softness of logs. For hard pieces, a chain saw was used to cut a cylindrical radial section, and rectangular or cylinder solid wood plugs were taken from each radial section by a machete. Power drill sampling methods (Keller et al. 2004) were not suitable in this region because most samples disintegrated before extraction, which could be due to relatively soft wood in western Amazonia (Baker et al. 2004). Samples were removed randomly in one of the three radii through a plane perpendicular to the ground (upper vertical, 0°; middle horizontal, 90°/270°; lower vertical, 180°), and at 5 cm intervals outwards from the centre of each wood section.
For heavily decayed pieces, we collected a portion of the material by filling a known-volume (7 cm3) clear plastic cylinder (n = 68). The contents of each cylinder were removed to a known-weight envelope to permit measurement of dry weight in the laboratory. In some cases, the material was too fragile to extract a coherent rectangular shape by machete, but too solid to use the plastic cylinder method. Here, irregular shaped wood samples were taken. Where the decay classes of bark and heartwood were different, both parts were sampled. Digital photos were taken for each woody radial section with a ruler in order to calculate void space proportion for the log.
Fresh volumes were determined by calipers in three length dimensions (l1, l2 and l3) for a rectangular solid shape, or radius (r) and length (l) for a cylindrical sample. Volumes of irregular shape samples were determined by water displacement measurement inside a graduated plastic cylinder to the nearest 0.5 mm. All samples (n = 381, from 252 sampled trees) were then oven dried at 60°C. Dry weight was measured using a Oertling HB 63 2/3 db balance. Density was calculated as oven dry weight divided by fresh volume (Fearnside 1997).
'Original density' was calculated by averaging the density samples within each log, and then averaging these densities within the same decay class. Void space in a wood section can affect CWD volume and therefore density calculations. In our study, we defined void space as hollows surrounded entirely by solid wood and was determined by counting digital pixels of field photos using ImageJ (http://rsb.info.nih.gov/ij/). 'Revised density' values were then calculated by adjusting the original density values by average percentage of void space in each decay class. Except where specified, 'density' of CWD in this paper indicates the 'revised density'.
Decay classes of CWD were originally categorized into five classes, following Keller et al. (2004). Class 1 material was recently fallen with more than 75 % of the wood intact and hard, and sometimes still with fine twigs attached. Class 1.5 material was solid wood with slightly damaged bark. Material in class 2 was damaged, the log had experienced some decay, between the decay degree of class 1.5 and 2.5, and was also applied to pieces of wood where the bark had gone but the heartwood remained solid. Class 2.5 was applied to somewhat rotten material, with part of the wood friable and easily broken when kicked. Class 3 material was at least 75 % soft and rotten, which a machete blade could enter easily, and which collapsed when stepped on. Where state of decay of the bark and heartwood were very different, decay class of the log was attributed separately and treated as separate samples. To facilitate comparison within our landscape and with published studies, we also recomputed the 'five decay class' results into three major decay classes, by combining class 1.5 with class 1, and class 2.5 with class 3.
As the sample size in 'decay class one' (n = 1) of the original 'five decay class' classification design was insufficient, mean stand-level living wood density values were applied for this class. Living wood density averages were estimated on a basal area, rather than per-stem basis, because large trees are likely to disproportionately contribute to quantities of CWD, as tree size correlates with biomass (Chambers et al. 2001) and decomposition rate (Chambers et al. 2000). There was no such sample size problem in the 'three decay class' classification, so our actual measurements were used.
Average living wood density (ρBA), weighted by basal area, was estimated using the floristic composition of nearby permanent plots data (Nebel et al. 2001b; Peacock et al. in press) and a species wood density database (Baker et al. 2004a; Chave et al. 2006; Lopez-Gonzalez et al. 2006) (). Wood density data were matched to the plot data on a tree-by-tree basis. In cases where no species wood density was available, the average for the genus (24 % of 12 025 individuals), or family (4 %) was used. For unidentified trees, or individuals in families lacking wood density data, the average wood density of the available species in the plot, on a stems basis, was used (1 %).
Plot-based CWD measurements
We also quantified CWD volume in March 2005 using the plot-based method (Harmon and Sexton 1996) within the two unflooded permanent plots. We recorded diameter, length, and decay class (see section Density of CWD) of every CWD, whether standing or prone, in the whole clay-rich plot (1 ha) and half of the white sand plot (0.5 ha). For fallen CWD, diameters at both ends were estimated by two perpendicular directions to the nearest cm. Where accessible due to hollowing, the thickness of bark was recorded and used to adjust the volume of CWD. For 'standing' stumps the diameters of smaller ends and the length (height) were estimated. For logs tapering to less then 10 cm diameter, measurements were only made up to the point of 10 cm diameter. For buttressed trunks, diameters were taken above the buttresses.
The volume of each CWD piece by the plot-based method was calculated using Smalian's formula (Phillip 1994):
[]
where LCWD (m) is the length of the CWD piece, and D is the geometric mean of trunk diameter measurements (m) at either end 1 or 2. For CWD with bark thickness measurements, volumes were calculated by subtracting the inner volume from the outer volume.
Stocks of CWD and biomass
Stocks of CWD are termed as necromass. Necromass (N) in each decay class k was calculated as the product of the volume (Vk, by either the plot-based or intersect-based method) and the density (ρk) for all material in that class in each forest type.
The sampling error (EN) of necromass Nk (k = 1 to 3) by the line-intersect method was calculated as:
[]
where and EV represent the errors in density (ρk) and volume (Vk), respectively. This is a conservative approach which accounts for the possible interaction between errors in CWD density and volume (Taylor 1997). The total error in the necromass was estimated by summing the component errors in each decay class.
AGBcoarse (Mg) is defined as aboveground living trunks or branches ≥ 10 cm in diameter within plots. It is estimated by multiplying aboveground biomass (AGB, kg) by a coarse wood correction factor of 0.85 (Higuchi, unpublished data, cited in Chambers et al. 2000).
[] AGBcoarse = AGB - 0.85 / 1000
Estimates of AGB of the two unflooded forests (clay-rich and white sand plots) were derived from two different allometric models. The first is based on a one-site neotropical study, developed from trees larger than 5 cm in diameter at 1.3 m or above the buttresses (n = 315, near Manaus, Brazil, Chambers et al. 2001). We adjusted the equation using species-level wood density values following Baker et al. (2004a).
[]
where ρi (g cm-3) is the species-level wood density of each individual i, and Di (cm) is the diameter at 1.3 m of the same tree.
The second model is a multi-site pan-tropical 'moist forest' (n = 15) study, included all available biomass measurements of trees larger than 5 cm (Chave et al. 2005).
[]
AGB of the floodplain forest was estimated as the average of biomass results reported by Malhi et al. (2006) for JEN-02 and JEN-03. In Malhi et al. (2006), biomass was calculated using known plot basal area and structural conversion factors. The structural conversion factors were interpolated using distance-weighted kriging and soil-type weighted methods.
Results
Density of CWD
Revised CWD densities showed significant differences among decay classes and forest types (), both for the five class classification (ln-transformed, two-way ANOVA, decay class, F3, 239 = 15.212, p ≤ 0.001; forest type, F2, 239 = 5.883, p = 0.003; no interaction, p = 0.109) and the three class classification (decay class, F2, 243 = 19.754, p ≤ 0.001; forest type, F2, 243 = 7.624, p ≤ 0.001; no interaction, p = 0.063). Density declined monotonically with increasing levels of decay (). However, the densities of decay class 1 and 2 in the floodplain forest were similar which is due to the low CWD stocks and therefore small sample size in decay class 1. Densities of CWD were indistinguishable between forests on clay-rich soil and on the white sand, but both were significantly higher than densities of the floodplain forest ().
More than one fifth (21.3 %) of the sampled pieces had a void space in the wood, but the void area means were only 3 to 4 % in each decay class (). There were no significant effects of diameter size class (class 1: 10 ~ 20 cm, class 2: 20 ~ 40 cm, and class 3: ≥ 40 cm) on CWD densities among the decay classes (ln-transformed CWD densities, two-way ANOVA, diameter class, F2, 239 = 0.240, p = 0.787 for the five classes; F2, 243 = 0.224, p = 0.800 for the three classes). Moreover, the density of fallen wood was not affected by either radial position (Kruskal-Wallis test, p = 0.995), or by the distance from centre (linear regression, difference in density with the piece in the central of the same log against its distance from the centre, r2 = 0.001, p = 0.723).
Because the three class classification method revealed comparable patterns to the five class classification and is less susceptible to potential problems of small sample sizes, hereafter we only report results from the three class classification.
Stocks of CWD
Necromass also varied between forest types and decay classes by the line-intersect method estimation (two-way ANOVA, forest type, F2, 15 = 11.318, p ≤ 0.001; decay class, F2, 15 = 13.155, p ≤ 0.001; no interaction, p = 0.423). Based on the line-intersect method, there was no detectable difference in necromass between forests on clay-rich soils (30.9 Mg ha-1) and white sand (45.8 Mg ha-1), but necromass was significantly lower in the floodplain forest (10.2 Mg ha-1) (). Also, necromass in decay class 2 was significantly greater than in classes 1 and 3 ().
By the plot-based methods, the quantity of total necromass, including both fallen and standing CWD was 20.3 Mg ha-1 in the clay-rich forest, and 41.1 Mg ha-1 in the white sand forest (). Necromass of standing CWD accounted for 29 to 32 % of total necromass. In other words, the necromass of standing CWD was almost half as much (41−47 %) as the necromass of fallen CWD. The quantities of fallen necromass measured by the plot-based method were generally lower than those measured by the line-intersect method.
As standing CWD was not measured in the floodplain, we applied the average proportion of standing CWD (44 %), derived from the unflooded forests, as a preliminary estimate of total CWD in the floodplain forest. As a proportion of AGBcoarse in the three forests, CWD contributes proportionally most in the white sand forest (17.8−18.2 %), followed by the clay-rich forest (7.8−8.0 %), and least in the floodplain forest (6.8−6.9 %, ). As a proportion of total aboveground vegetative mass, CWD contributes 15.1−15.4 % in the white sand forest, 7.2−7.4 % in the clay-rich forest, and 6.4−6.5 % in the floodplain forest ().
Discussion
Stocks of CWD
CWD stocks were lowest in the floodplain forest and greatest in the white sand forest, even when the floodplain forest value was corrected for standing CWD (). Baker et al. (in press) reviewed other neotropical rainforest studies and showed that CWD stocks range from 96.1 Mg ha-1 on a nutrient-poor oxisol forest, Brazil (Rice et al. 2004) to only 2.5 Mg ha-1 on a spodosol (white sand) forest, Venezuela (Kauffman et al. 1988). Our floodplain forest is located toward the lower end, whereas the unflooded forests, on clay-rich and white sand soils, are in the middle of this range. The broad range of CWD reported for the neotropical forests implies large local and regional variations in disturbance history, decomposition rates, and/or input rates (i.e., mortality and branch fall), and may also be affected by differences in sampling methods used by researchers.
The low stocks of CWD in the floodplain forest probably result from flooding transportation and higher decomposition rates. A plot-based study in a white-water floodplain forest (várzea) in Brazil showed that flooding redistributed CWD from higher to lower forests, and that the cycle of wetting and drying enhanced the rate of decomposition (Martius 1997). In that study, dead wood was relatively insignificant (2.7 % of the living wood mass), an even lower proportion than we found. Low necromass in our study is also driven by low CWD density in this forest type (), as necromass is a product of the volume and density of CWD.
Quantities of CWD were not significantly different between the two unflooded forests, but CWD represented a higher proportion of AGBcoarse in the white sand than in the clay-rich forest. This phenomenon could be explained by either higher rates of CWD input, or lower decomposition rates of CWD in the white sand forest. Although we lack decomposition experiments, our short-term census results provide preliminary estimates of the CWD dynamics in this region. Mortality inputs (Mg ha-1 yr-1) were calculated for trees dying between 2005 and 2006 by summing their AGBcoarse at the first census and dividing by the census interval. Inputs for the clay-rich forest range from 4.5 to 4.6 (Mg ha-1 yr-1, based on the Chave and the Chambers models, respectively). Estimated mortality inputs of the white sand forest were much lower, ranging from 0.5 to 0.6 (Mg ha-1 yr-1). As a result these data do not support the suggestion that CWD inputs are higher in white sand forest. If CWD stocks are assumed to be at steady state, the estimated decomposition rate (yr-1) equals the mortality input (Mg ha-1 yr-1) divided by CWD stocks (Mg ha-1). Under this assumption, estimated decomposition rates are 0.22 yr-1 in the clay-rich forest and 0.01 yr-1 in the white sand forest. Our estimated decay rates are comparable (but in the white sand plot is slightly lower) to a central Amazon study which showed that the decomposition rate of dead trees ranged from 0.015 to 0.67 yr-1 with an average ± SE of 0.19 ± 0.03 yr-1 (Chambers et al. 2000). Low decomposition rates in our white sand forest are a plausible explanation for the relatively high CWD stocks in this forest type. However, longer term observations, including mortality inputs, direct measurements of decomposition rates, flooding disturbance effects, and variation in standing CWD stock are needed to better interpret the necromass balance of this landscape.
Density of CWD
Void space was not an important feature of CWD in this study. By contrast, a study in eastern Brazilian Amazonia reported that the void area of CWD ranged from 2 to 21 % (Keller et al. 2004) and Fearnside (1997) suggested that about 20 to 30 % of living trees (≥ 10 cm diameter) in Brazil have a hollow space in the centre. One reason for this lack of void space phenomenon in NW Amazonia could be due to the smaller average tree size (Malhi et al. 2002), and faster turnover rates and consequent shorter life-spans (Phillips et al. 2004) in this region. Thus, trees in western Amazonia may simply not grow large enough or live long enough to develop void space. Difference in hollow space proportions across Amazonia could therefore affect both biomass and necromass estimates. Studies based on allometric equations from eastern Amazonian, where the proportion of hollow section is larger, could underestimate the biomass of trees in these northwestern Amazonian forests. Consequently, the biomass results in our study should be treated as lower bound estimates.
Our study shows that within a relatively small area forest may differ markedly in CWD density. A plausible explanation is the distinctive floristic composition of floodplain forests (Terborgh and Andresen 1998; ter Steege et al. 2000). At Jenaro Herrera, the dominant families in the floodplain forest are Moraceae, Fabaceae, and Arecaceae (Nebel et al. 2001a) with mean wood density 0.60, 0.73, and 0.43 g cm-3, respectively (Lopez-Gonzalez et al. 2006). In the nearby clay-rich forest the dominant families are Fabaceae (0.73 g cm-3), Lecythidaceae (0.63), and Sapotaceae (0.75) (Lopez-Gonzalez et al. 2006; Peacock et al. in press). In the white sand forest, the dominant families also differ, including Fabaceae (0.73), Clusiaceae (0.66) and Euphorbiaceae (0.61) (Lopez-Gonzalez et al. 2006; Peacock et al. in press). As wood density is a phylogenetically conserved trait (Baker et al. 2004a) and the floodplain forest is composed of families with relatively low wood density, variation in floristic composition may explain the landscape-scale living and therefore CWD density differences.
However, the question remains, why should density of living trees be lower in floodplain forest? Soil fertility gradients may affect stand level wood density values. For example, higher fertility may favour low wood density species that grow fast, whereas low soil fertility slows tree growth and favours high wood density, longer-lived species (Muller-Landau 2004). Pan-Amazonian terra firma (unflooded) forest research shows that wood density is typically lower on the more fertile soils in western Amazonia than on the less fertile soils in central and eastern Amazonia (Baker et al. 2004a). At Jenaro Herrera, the floodplain forest grows on a fertile soil (concentration of cations (ECEC) = 20.9 cmol+/kg in horizon A, Nebel et al. 2001b) while the clay-rich (oxisols, Spichiger et al. 1996) and the white sand forests (ECEC = 1.84 cmol+/kg in horizon A, unpublished RAINFOR data) are located on much poorer soils. Together, the poorer soil fertility and therefore higher living wood density could explain why CWD densities were higher in the unflooded forests.
CWD and living tree density in the neotropics
To explore how important living wood density may be in determining CWD density, we constructed regression models for humid, lowland, neotropical forests from available data (, except for Juruena, Mato Grosso, Brazil where living wood density data was not available). We found significant relationships between wood densities of living trees (ρBA, weighted by basal area) and CWD, both in decay class one (ρk=1, r2 = 0.661, p = 0.026) and two (ρk=2, r2 = 0.860, p = 0.003), but not for decay class three (p = 0.324) (). The equations are as follows.
[] ρk=1 = 1.17 - ρBA - 0.21 and
[] ρk=2 = 1.17 - ρBA - 0.31
where ρBA (g cm-3) is the mean wood density of living trees weighted by basal area in the same area, and ρk=1 and ρk=2 (g cm-3) represent the CWD densities in decay class one and two, respectively. For the CWD density in decay class three, we suggest applying the average 0.29 g cm-3 (± 0.04, SE) for all humid, lowland neotropical forests. On average, CWD density in decay class one is 82 ± 6 % of ρBA, decay class 2 is 66 ± 4 % of ρBA, and decay class 3 is 46 ± 6 % of ρBA. However, as shows, plot-average densities (ρBA) between 0.57 and 0.66 g cm-3 are poorly represented in available neotropical studies. Investigations of the relationships within this range are needed to gain a better understanding of the overall relationships.
Measurement of CWD density
Although decay classes of CWD are classified subjectively, the direct measured densities correlated well with that of live trees which suggests cross-site comparison between classes is feasible. How many density classes are needed to accurately estimate necromass? Based on our results, the three class classification has similar patterns to the five class classification method and is less susceptible to sample size problems. When using the five class classification, the fallen necromass was estimated as 31.5 ± 6.6 Mg ha-1 in the clay-rich forest, 45.3 ± 13.2 in the white sand forest, and 10.7 ± 6.1 in the floodplain forest. All of these values are within the standard error ranges of the results using the three class classification. Thus, we believe that the three class classification is sufficient for necromass estimation.
Careful examination of decay class in dead wood is also important. For example, the densities of the intact and partially decayed CWD are indistinguishable in the Venezuelan plots (Delaney et al. 1998) (). Delaney et al. (1998) suggest that this pattern is because logs classified as partially decayed to rotten still had relatively sound heartwood or sapwood. Therefore, when the decay classes of heartwood and sapwood are different, we recommend recording them separately.
The densities in fallen CWD in our study did not vary significantly with radial positions and distance from centre. Keller et al. (2004) found that CWD density was significantly higher on the upper compared to the lower part of the log, and declined with distance from the log centre. These patterns are probably due to microbe availability and activity which vary within a log depending on moisture content and the substrate (Harmon et al. 1986). Our 'insignificant' results may be caused by a mixture of species-specific decomposition mechanisms. For example, the centre of palm boles decomposes quicker than its exterior parts, whereas the sapwood of many dicotyledonous species appears to decompose faster than heartwood. A chronosequence study of wood decomposition in boreal forests of Russia (Yatskov et al. 2003) suggests there are four types of CWD density decay patterns. (1) Linear decreasing with decay classes. (2) No density change until late decay stages due to decay-resistant heartwood. (3) Fast decreasing in density at early decay stages but a levelling off due to a high sapwood-to-heartwood ratio or intermediate decay resistant heartwood. (4) Complex decomposition processes proceeding simultaneously from both the outside and inside due to heart rot. Therefore, in order to examine the effects of radial position and distance to the centre, a better method would be to sample the same logs of known species at different radial positions and distances from the centre, rather than comparing these effects using different logs from unknown species.
Conclusions
Both density and stocks of CWD vary between forest types within a northwestern Amazonian landscape. The CWD density and stock of the floodplain forest in this study are relatively low, whereas the unflooded sites are more typical of other neotropical studies. Low CWD density and necromass in the floodplain forest were possibly due to the distinctive floristic composition and therefore low living wood density, and the flooding disturbance regime. In comparison, both CWD density and necromass were indistinguishable between the two unflooded forests. However, based on the higher quantity of CWD as a proportion of the living, aboveground biomass, and using short-term census data, we suggest that the decomposition rate is lower in the white sand plot than in the clay-rich forest. Longer term observations are needed to better interpret the necromass balance of this landscape. In a broader context of humid, lowland neotropical forests, CWD densities in decay class one and two were significantly correlated with the wood density of the living forest. Therefore, differences in floristic composition appear to provide a partial, generally applicable, explanation for the variation in CWD density. Also, for areas where destructive measurements are unavailable or not possible, the direct sampling method of CWD density may be replaced by estimates based on living wood density.
Acknowledgements
We thank Eurídice Honorio, Julio Pacaya, and other assistants for their important help in the field work, Michael Keller for his methodological suggestions, and Julie Peacock for database assistance. We also acknowledge Instituto de Investigaciones de la Amazonía Peruana (IIAP) for permission to access their facilities. This research is part of the PhD study of Kuo-Jung Chao, supported by the Overseas Research Students Award (Universities UK), the School of Geography and the University of Leeds. Field work was funded through a Natural Environment Research Council (NERC) standard grant to Oliver Phillips.
References
Table . Densities (average ± SE, g cm-3) of living and coarse woody debris (CWD, diameter ≥ 10 cm) in humid, lowland neotropical forests (rainfall ≥ 2000 mm yr-1). Living wood density (ρBA) is the plot-average of living trees, weighted by basal area. CWD densities (ρk) are classified into three decay classes (k), including intact (k=1), partially decayed (k=2), and rotten (k=3).
Table . Densities (average ± SE, g cm-3) of CWD in three forest types in Jenaro Herrera, northern Peru.
Table . Necromass (average ± SE, Mg ha-1) of fallen CWD in three forest types in Jenaro Herrera, northern Peru, based on the line-intersect method.
Table . Necromass (Mg ha-1) of both standing and fallen CWD in two unflooded forests in Jenaro Herrera, northern Peru, based on the plot-based method.
Table . Biomass (Mg ha-1) and necromass (Mg ha-1, both standing and fallen) in three forest types in Jenaro Herrera, northern Peru.
No
Location
Rainfall
Soil type
Living (ρBA)
CWD (ρk)
