Quantifying aboveground biomass and estimating net aboveground primary production for wetland macrophytes using a non-destructive phenometric technique

Aquatic Botany 62 (1998) 115±133

Quantifying aboveground biomass and estimating net aboveground primary production for wetland macrophytes using a non-destructive phenometric technique Robert J. Daoust*, Daniel L. Childers Department of Biological Sciences and Southeast Environmental Research Program, Florida International University, University Park, Miami, FL 33199, USA Received 27 June 1997; accepted 18 April 1998

Abstract A non-destructive net aboveground primary production (NAPP) estimation technique which reduces the amount of labour required without sacrificing method accuracy was developed using phenometric models for nine species from freshwater Everglades wetlands. The predictive power of these models ranged from 90% to 97% and all were highly significant (p<0.0001). Five species' models were tested in other habitats. Three of these yielded non-destructively estimated biomass values that were not significantly different from harvested values. We also determined that a small sample of the total plot population could be measured and still yield accurate live standing crop (LSC) values using a technique similar to rarefaction ± only 32% of the total number of individuals within any given plot must be regularly monitored. We tested the accuracy of our method by comparing NAPP rates computed using both estimated and destructively obtained LSC values. Calculated NAPP rates were not significantly different than those produced from the more traditional destructive technique. NAPP rates were also computed (1) using a variety of computation methods and (2) using different sampling frequencies. Both computation method and sampling frequency had an effect on NAPP rates and should be considered when conducting NAPP studies. # 1998 Elsevier Science B.V. All rights reserved. Keywords: Allometric techniques; Everglades; Florida; Live standing crop; Morphometric

* Corresponding author. Department of Biological Sciences, University of South Carolina, Columbia, SC 29208. Tel.: +803 777 4141; fax: +803 777 4002; e-mail: [email protected] 0304-3770/98/$ ± see front matter # 1998 Elsevier Science B.V. All rights reserved. PII: S 0 3 0 4 - 3 7 7 0 ( 9 8 ) 0 0 0 7 8 - 3

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1. Introduction The importance of assessing net aboveground primary productivity (NAPP) has long been recognised for the central role it plays in the carbon and energy cycles of a diverse range of systems (e.g. Teal, 1962; Ovington et al., 1963; Odum and Heald, 1975; McNaugton, 1985; Howes et al., 1986; de Leeuw et al., 1990). Consequently, numerous computational methods have been devised to calculate NAPP based on some measurement of changes in standing live and dead biomass over a year. Of these numerous methods, those presented by Smalley (1959), Wiegert and Evans (1964), Milner and Hughes (1968), Williams and Murdoch (1972) and Valiela et al. (1975) are most commonly employed in coastal, estuarine, wetland and prairie systems. Comparisons of these computational methods have been reported, yet there is no consensus as to which method is the most accurate (Kirby and Gosselink, 1976; Linthurst and Reimold, 1978a; Shew et al., 1981; Dickerman et al., 1986; Kaswadji et al., 1990). All of these computational methods use estimates of the amount of standing live (B) and dead (M) biomass to generate NAPP, yet differ in their underlying assumptions and how each uses these values to calculate NAPP. Since they all rely on measurements of B and M, the way in which these numbers are generated is fundamental to any study of NAPP. Early studies required destructive harvesting of both live and dead plant material in order to obtain values for B and M (Smalley, 1959; Wiegert and Evans, 1964; Milner and Hughes, 1968; Kruczynski et al., 1978). This can be problematic, however, in instances in which it is desirable to follow specific plant populations. Furthermore, destructive sampling introduces spatial variability into calculations of NAPP by precluding repeated measurements (Dai and Wiegert, 1996). In highly heterogeneous areas this may be especially troublesome. Other studies have attempted to address this issue using non-destructive, or partially non-destructive techniques (Table 1). Typically such methods involve following individual, tagged plants for changes in biomass, which are estimated using regressionbased phenometric relationships. Culm biomass values are then summed to determine the value of B which is then used in NAPP computations. There are numerous advantages associated with the employment of these techniques; notably, spatial heterogeneity problems are mitigated, mortality estimates can be derived, temporal sensitivity is increased, and long-term continuous monitoring is possible (Dai and Wiegert, 1996). Unfortunately, non-destructive techniques tend to be highly labour intensive and have, therefore, usually only been employed in areas where small sample sizes and simple censuses are appropriate. Partially, non-destructive techniques combine periodic harvests with non-destructive monitoring and reduce some labour (de Leeuw et al., 1996). With the exception of Hsieh's (1996) study, however, partially non-destructive techniques are restricted to areas with limited growing seasons where end-of-season biomass can be harvested (de Leeuw et al., 1996). Hsieh's (1996) modified Williams and Murdoch (1972) technique is dependent on published growth curves and could be problematic in highly diverse systems. The purpose of this paper is multifold. First, and foremost, we present a nondestructive technique which addresses many of the problems associated with NAPP studies. While our technique is similar to those of Dickerman et al. (1986) and Morris and

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Table 1 Summary of literature calculating NAPP for a number of systems using a variety of techniques Authors

Smalley (1959) Wiegert and Evans (1964) Milner and Hughes (1968) Williams and Murdoch (1972) Valiela et al. (1975) Kruczynski et al. (1978)

NAPP eqn. P P PB‡PM B‡ M P P

B

P

P B‡ M

P

M P B‡ M

Hopkinson et al. (1980) Shew et al. (1981) Smith and Kadlec (1985)

P P PB‡PM B‡ M P P B‡ M

Dickerman et al. (1986) Morris and Haskin (1990) Hsieh (1996)

P P PB‡ M B P P B‡ M

de Leeuw et al. (1996) Dai and Wiegert (1996)

P

P B‡ M

P

P B‡ M

Calculation method

Live biomass estimation technique

Dead biomass estimation technique

Type

Smalley (1959) Wiegert and Evans (1964) Milner and Hughes (1968) Williams and Murdoch (1972) Valiela et al. (1975) Wiegert and Evans (1964) Multiple Multiple Wiegert and Evans (1964) Multiple Smalley (1959) Williams and Murdoch (1972) de Leeuw et al. (1996) Multiple

Destructive Destructive

Destructive Destructive

D D

Destructive

Ð

D

Non-destructive

Non-destructive

ND

Ð

Destructive

D

Destructive

Destructive

D

Non-destructive Destructive Destructive

Non-destructive Destructive Non-destructive

ND D P

Non-destructive Non-destructive Destructive

Non-destructive Ð Non-destructive

ND ND P

Destructive

Non-destructive

P

Non-destructive

Non-destructive

ND

Note: NAPP P eqn. indicates whether the calculation method P employed uses only the P sum Pof changes in live biomass ( B), only the sum of changes in dead biomass ( M), or the sum of both ( B‡ M). Type indicates whether the method used by the investigators is destructive, (D), non-destructive, (ND), or partially destructive, (P).

Haskin (1990), it is an improvement over these same techniques in many ways. It not only reduces the amount of labour involved in non-destructive biomass estimation by eliminating the necessity of tagging and following individual culms, but also permits the use of larger study plots, allowing for a more statistically valid calculation of NAPP. Furthermore, this technique has been employed in a diverse, subtropical system; thus, it expands the range of applicability of earlier methods. Secondly, we compute NAPP rates using several computation methods and sampling intervals to determine how they differ and whether NAPP rates vary with changes in sampling intensity. Finally, this paper includes NAPP calculations for an area of relatively pristine freshwater Everglades wetlands which have not been reported elsewhere in the literature. 2. Methods 2.1. Study area This study was conducted in the Everglades, an area of extensive subtropical freshwater wetlands in south Florida. The Everglades wetland complex, consisting of a

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mix of freshwater marshes, cypress domes, and tropical hammocks, covers approximately 7900 km2 stretching from south of Lake Okeechobee to the mangrove coasts of the Gulf of Mexico and Florida Bay (Light and Dineen, 1994). It is an oligotrophic wetland system which receives the majority of its rainfall during the summer and fall (Davis, 1943). Most samples for this study were collected from an area covering about 1.0 km2 in northeastern Everglades National Park (ENP), approximately 10 km south of its northern boundary (25842.50 N, 808400 W). Additional samples were collected from a restored wetland in central ENP (258220 N, 808400 W) and from Taylor Slough, in the southeastern ENP (258200 N, 808380 W). This ensured that our method could be employed over a wide gradient. Everglades wetlands are composed of an assemblage of plant communities, which Davis (1943) and Loveless (1959) classified as sawgrass marsh, wet prairie, aquatic slough, and tree islands; these communities closely mimic the hydrologic regime of the system (Gunderson, 1994). Our northeastern ENP site is a mosaic of sawgrass marsh and wet prairie interspersed with tree islands. We monitored biomass changes and calculated NAPP in both sawgrass marsh and wet prairie communities. Sawgrass marsh covers approximately 65±70% of freshwater of Everglades wetlands and is dominated by the emergent aquatic sedge Cladium jamaicense Crantz (Loveless, 1959). Wet prairies are more diverse, often consisting of a mix of 6±8 co-occurring emergent macrophyte species. The most common wet prairie species at our northeast ENP site are Eleocharis cellulosa Torr. (spikerush), Sagittaria lancifolia L. (arrowhead), Peltandra virginica (L.) Schott and Endl. (spoon flowers), Pontederia cordata L. (pickerelweed), Crinum americanum L. (swamp lily), Hymenocallis palmeri S. Wats. (spider lily), Panicum hemitomon Schult. (maidencane), and Paspalidium geminatum (Forsk.) Stapf (Egyptian paspalidium). 2.2. The non-destructive NAPP estimation technique The primary goal of this paper was to present a non-destructive technique for determining NAPP. Our approach was based on the idea that individual biomass values can be non-destructively estimated for a small, randomly selected subset of the total plot population using phenometric, morphologically-based relationships. These individual biomass values can then be used to determine whole plot live standing crop (LSC) values by multiplying the average species-specific individual biomass values by the species stem density for the entire plot. Once the whole plot LSC values are estimated, NAPP rates can be calculated using a variety of computational methods. The development of our technique involved four distinct phases: first, phenometric models to predict individual culm biomass were determined; second, these phenometric models were tested in several Everglades habitats to assess versatility and portability; third, we tested our underlying assumption that a small, randomly selected number of individuals could be used to estimate total plot LSC using a technique similar to rarefaction and; fourth, we used LSC values from both non-destructive and destructive techniques to test the accuracy of our method.

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Fig. 1. Botanical sketches indicating which morphological variables were selected for use in the phenometric models to estimate live aboveground biomass. The nine species of interest have been divided into four groups based on similar morphologies. Group I: Cladium jamaicense, Crinum americanum, and Hymenocallis palmeri (shown); Group II: Peltandra virginica (shown), Pontederia cordata, and Sagittaria lancifolia; Group III: Eleocharis cellulosa (shown); Group IV: The grasses, Panicum hemitomon, and Paspalidium geminatum (shown). Due to the simplistic morphological structure of E. cellulosa, and the fact that the basal rosette is usually buried beneath the soil surface, a single leaf is considered for all purposes to be a single culm. For each of the grasses, P. hemitomon and P. geminatum, leaf length to base is measured as the length from the base to the last node. Sketches are not to scale.

2.2.1. Phenometric models For one year, from June 1995 to June 1996, we harvested live (when some green tissue was present) samples of the nine species listed above by clipping them at the soil surface and returning them to the lab for measurement. A minimum of 10 individuals of each species were collected every month. Phenometric variables were selected for measurement based on both the morphology of each species and common morphologies among all species (Fig. 1). Some variables were based on averages or sums of measurements for each leaf on a culm (leaf length to the base, leaf length of the lamina, leaf width at the base of the lamina, width at the mid-point of the leaf, leaf width at the top of the lamina, leaf thickness, and axillary shoot length). Total culm height was recorded as the leaf length from the tip to the base of the tallest leaf on the culm. After being measured, all individuals were tagged and dried at 708C to a constant dry weight.

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Table 2 List of complex interaction terms included in step-wise regression analysis Complex interaction term

Calculation equation

Species

Volume on base

[(0.5CDB)2]LLB

Volume on tip Leaf surface area

[(0.5CDT)2]LLB 0.5LWBLLT

Culm surface area

2(0.5CDB)LLB

Leaf volume

0.5LWMTHKLLB

Eleocharis cellulosa, Panicum hemitomon, Paspalidium geminatum Eleocharis cellulosa Peltandra virginica, Pontederia cordata, Sagittaria lancifolia Eleocharis cellulosa, Panicum hemitomon, Paspalidium geminatum Cladium jamaicense, Crinum americanum, Hymenocallis palmeri

Averaged and summed

  

All interaction terms represent volumetric or aerial measures. Equations include codes for each phenometric variable: CDB, culm diameter at base; CDT, culm diameter at tip; LLB, leaf length to base; LLT, leaf length at tip; LWB, leaf width at base; LWM, leaf width at mid. Those interaction terms which were averaged or summed for leaves on the culm are indicated.

All data were analysed to ensure normality. Those variables that failed standard normality tests were transformed using standard techniques (McClave and Dietrich, 1988). Standard multivariate stepwise regression techniques determined which of the measured phenometric variables contributed significantly to a model predicting individual plant biomass. We chose stepwise regression since it reduced collinearity, by discriminating among variables based on their contribution to overall predictive power, while simultaneously, maximising model power and minimising the number of variables included (Neter et al., 1990). All regressions were forced through the origin since morphometric and biomass values approach zero together. For some species, complex interaction terms, such as those representing volumetric measurements, were computed and included (Table 2). Interaction terms, other than those representing volumetric measures, were not considered important. To determine if exponential terms could possibly increase the predictive power of otherwise linear models, we also performed univariate polynomial regressions on each species dataset. Where polynomial regressions were significant, the appropriate exponential terms were subsequently introduced into the stepwise regression analysis. We used ANCOVA with time (by month) as the independent variable and multiple covariates (those phenometric variables selected from the whole year regression analysis) to determine if the predictive power of our models for either C. jamaicense or E. cellulosa would increase if we also incorporated temporal dynamics. de Leeuw et al. (1996) and Morris and Haskin (1990) both found that predictive power of their models increased when their samples were grouped using some temporal criteria (e.g. months or seasons). Such an analysis was appropriate for both C. jamaicense and E. cellulosa because of their large total samples sizes. We did not perform ANCOVA on any of the other seven species studied since we believed that any increase in predictive power would be offset by a complimentary reduction in sample size.

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Where ANCOVA indicated that monthly differences did significantly contribute to model variance for C. jamaicense or E. cellulosa, we used a procedure similar to ANOVA pairwise comparisons to determine which monthly groupings best benefited predictive power. Final submodels were selected based on two criteria: first, model predictive power was maximised (i.e. subsequent introduction of earlier or later months to the group caused the regression coefficient to decrease), and; second, ANCOVA analysis determined that time did not influence submodel variability. We did not test our final submodels with subsequent multivariate stepwise regression analyses, since further reduction in the number of phenometric parameters included in the submodels could possibly reintroduce time as a significant factor in explaining model variability. 2.2.2. Model extension to other Everglades habitats We also wanted to ensure that the models we developed from a small area (1.0 km2) of the ENP could also be used in a range of Everglades habitats. To accomplish this, we collected individuals of the same species from two other areas in ENP (Upper Taylor Slough, 258220 N, 808400 W and a restored wetland site, 258200 N, 808380 W). Both of these sites experience longer periods of dry-down and contain a more diverse flora. We were unable to locate all nine of the species listed earlier. For those species which were found, however, we collected a minimum 10 individuals in the late dry season (May) and again in the mid-wet season (August). We used independent Student's t-test to statistically compare estimated and harvested biomass values for these individuals. 2.2.3. Rarefaction tests We used a technique similar to rarefaction (Montgomery, 1991) to accomplish two tasks: first, to ensure that a small subset of individuals could be used to accurately predict LSC for an entire plot, thus eliminating the need to tag and monitor every culm within a quadrat and; second, to determine how large this subset must be to accomplish this goal. We sampled 1.0 m2 quadrats semi-annually (June 1996 and November 1996) in both communities. In each plot we determined the exact stem density of each species present and then non-destructively measured the appropriate phenometric variables on all individuals. Once this was completed, we harvested the entire plot and dried all plants to a constant weight at 708C to determine actual LSC (BA) present. We used the collected phenometric data to calculate biomass values for each individual culm. The estimated biomass for each species was calculated based on the equation:  Pn  i be i n Be ˆ n where Be is the estimated species biomass, be is the estimated individual biomass (derived from the species-specific phenometric regression models), i is the first randomly selected individual in the dataset, and n is the total number of individuals included in the subset. Each successive calculation randomly included an additional individual until all individuals were accounted for. The total estimated LSC for each iteration, BT, was then calculated by summing Be for each species. The rarefaction curve plotted the number

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of individuals (n) included in the calculation of BT against the ratio of BT to BA. This ratio determined predictive power ± as the accuracy of LSC estimates improved it approached unity. The point at which the rarefaction curve reached a horizontal asymptote indicated the minimum number of individuals required to estimate LSC. Where the curve did not asymptote, a subset of the total population was deemed insufficient to accurately estimate LSC. This procedure was repeated five times for each dataset using different random assignments of sampled individuals to determine if multiple iterations yielded the same end-point. 2.2.4. Method accuracy We validated NAPP rates generated using the non-destructive, or `estimated' technique with comparisons from those obtained using a more destructive, or `actual', method. This was accomplished by employing our non-destructive technique in triplicate, randomly selected 0.25 m2 plots in both communities at our northeastern ENP wetland site. These quadrats were also destructively harvested every month and dried at 708C for 48 h to a constant dry weight. New quadrats were selected every month. While this method reintroduced the spatial variability problems inherent in destructive sampling, it was the most efficient method for determining whether the NAPP rates calculated using our estimated LSC values were valid. We computed NAPP using the Smalley (1959), Milner and Hughes (1968), and modified Wiegert and Evans (Dickerman et al., 1986) methods. The modified Wiegert and Evans (Dickerman et al., 1986) method also requires an estimate of mortality, M, which we obtained by harvesting and drying dead material from each quadrat every month. While we did not non-destructively follow changes in mortality, our method may be extrapolated to include such estimates by recording the number of dead and live leaves on randomly selected individuals each month and using the proportion of dead to live leaves to estimate the amount of dead standing biomass. Such a method is appropriate for plants similar to those found in the Everglades, where dead leaves remain attached and identifiable for extended periods of time. This is similar to the Hopkinson et al. (1980) technique. Standard Student's t-tests determined if significant differences existed between NAPP rates generated using the non-destructive and destructive methods. 2.3. Net aboveground primary production Another purpose of this paper was to compare NAPP rates using various established computational methods and to determine how NAPP rates differed with changes in sampling intensity. We utilised the data generated from our triplicate 0.25 m2 plots harvested at our northeastern ENP wetland site to accomplish this. We calculated NAPP rates and tested for significant differences among three NAPP methods (Smalley, 1959; Milner and Hughes, 1968; Dickerman et al., 1986) using standard one-way ANOVA techniques. We then determined how NAPP rates varied with sampling intensity by manipulating our monthly data and calculating NAPP rates based on monthly, bimonthly, quarterly, ternate, and semi-annual intervals. Again, we used standard one-way ANOVA techniques to identify significant differences.

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3. Results 3.1. The non-destructive NAPP estimation technique 3.1.1. Phenometric models All of the phenometric variables we collected were normally distributed, with the exception of number of nodes present on the two graminoid species, Panicum hemitomon and Paspalidium geminatum. These data were normalized using standard square-root transformation techniques. Univariate polynomial regressions proved to be significant for some variables for six of the nine species; Cladium jamaicense, Hymenocallis palmeri, P. hemitomon, Pontederia cordata, Peltandra virginica, and Sagittaria lancifolia. As such, we included the appropriate exponential terms into the stepwise regression analysis for these six species. The stepwise regression analysis eliminated a number of variables for eight of the nine species. Notably, the majority of leaf-specific variables were eliminated for species such as C. jamaicense, H. palmeri, P. virginica, P. cordata, and S. lancifolia, thus greatly reducing the actual number of measurements required per plant. The only complex interaction term that was ever selected by the stepwise regression was volume for use in the models of both E. cellulosa and P. geminatum. All models were highly significant and regression coefficients varied from a low of 0.909 for H. palmeri to a high of 0.970 for P. cordata (Table 3). The ANCOVA with multiple covariates was significant for Eleocharis cellulosa but not Cladium jamaicense (Table 4). As such, the annual model for E. cellulosa was subdivided into seasonal groupings: wet season (May±October); and, dry season (November±April). This increased the predictive power of the E. cellulosa model from 90% to 95%. Subsequent addition or removal of months from these seasonal submodels caused predictive power to decrease (Table 4). 3.1.2. Model extension to other Everglades habitats We were able to collect five of our nine selected species in the Upper Taylor Slough and restored wetland sites (Crinum americanum, Cladium jamaicense, Eleocharis cellulosa, Hymenocallis palmeri, and Sagittaria lancifolia). Plant biomass did not differ significantly between the two sites (two sample independent z-test: zˆ0.404, z0.025ˆ1.960, n1ˆ51, n2ˆ98), thus further analysis was conducted on pooled datasets. Of the five species, three did not have significant differences between actual individual culm biomass values and those estimated using our non-destructive phenometric models (pˆ0.058, 0.943, 0.758 for C. americanum, H. palmeri, S. lancifolia, respectively). The estimated biomass values for Cladium jamaicense and Eleocharis cellulosa, however, were significantly different from actual values (oneway ANOVA; pˆ0.0005, Fˆ13.32 and pˆ0.0027, Fˆ9.58, respectively). The mean estimated individual culm biomass for C. jamaicense was 13.19 g. This overestimated the actual mean biomass of 7.96 g by approximately 39%. Conversely, our model underestimated by 44% the mean actual biomass of Eleocharis spp. collected from these two sites (mean biomass values of 0.131 g and 0.091 g for actual and estimated, respectively).

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Table 3 Mutlivariate models for each of the nine wetland species as selected through stepwise regression Species

Modela

r2

p-value

C. americanum

Biomassˆÿ0.953(#lvs)ÿ0.047(TCH)‡ 1.821(CDB)‡0.040(SLLB)

0.950

<0.0001

189

44

C. jamaicense

Biomassˆ0.016085(SLLB)‡6.05910ÿ6 (SLLB2)ÿ0.074517(ALLB)‡ 6.186748(CDB)

0.939

<0.0001

884

232

E. cellulosa

Wet season biomassˆ0.000383(TCH) ÿ0.271381(CDB)‡0.596701(CDT)‡ 0.052861(VB) Dry season biomassˆ0.000579(TCH) ÿ0.333268(CDB)‡0.403090(CDT) ‡0.067375(VB)

0.950

<0.0001

0.950

<0.0001

F value

1251

N

270

969

209

H. palmeri

Biomassˆÿ23.232(ALT)‡28.877(ALT2) ‡0.027(SLLB)‡1.348(ALWM2)

0.909

<0.0001

212

89

P. hemitomon

Biomassˆ0.249(#nodes0.5)‡0.076(VB) ‡0.0001138(TCH2)

0.923

<0.0001

565

145

P. geminatum

Biomassˆ0.094(#lvs)‡0.057(VB)

0.939

<0.0001

735

98

P. virginica

Biomassˆ0.0002056(TCH2)‡0.211(CDB2) ‡0.001(SLLB2)

0.955

<0.0001

718

104

P. cordata

Biomassˆÿ1.440(CDB)‡0.474(CDB2) ‡0.068(ALLT)‡0.255(ALWM) ‡0.010(SLLB)

0.970

<0.0001

684

103

S. lancifolia

Biomassˆ0.901(CDB)ÿ0.012(ALLB) ‡0.0001111(ALLB2)

0.945

<0.0001

861

152

a Models include codes for each of the phenometric variables used: #lvs, total number of green leaves on culm; TCH, the length of the tallest leaf on the culm from tip to base; CDB, culm diameter at base of plant; CDT, culm diameter at tip of plant; SLLB, sum of leaf lengths to base; ALLB, average leaf length to base; ALLT, average length of the lamina; ALWM, average width at the mid-point; ALT, average leaf thickness; #nodes, total number of nodes on plant; VB, volume of plant calculated using TCH and CDB.

3.1.3. Rarefaction tests The results of the model rarefaction tests confirm that a small randomly selected subset of the population found within a 1.0 m2 plot may be used to predict the whole plot LSC. For wet prairie communities, the rarefaction curves reached asymptotes after approximately 24 and 50 Eleocharis cellulosa stems had been measured during the dry and wet season, respectively (Fig. 2). In sawgrass marsh communities, seasonal differences were negligible and only eight individuals were required to estimate the whole plot biomass (Fig. 2). Variation between the five iterations for the sawgrass community, however, were greater than that for the wet prairie (Fig. 2). Our method

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Table 4 Regression and ANCOVA results for Eleocharis cellulosa and Cladium jamaicense. The grouping column lists those months that were included in the analysis (1ˆJanuary to 12ˆDecember) Species

Grouping

Regression n r2

p-value

F value

ANCOVA df p-value

F value

Eleocharis cellulosa

Whole year

479

0.907

<0.0001

2122.0

11

0.0001

42.43

5,6,7,8,9,10 6,7,8,9,10 5,6,7,8,9 4,5,6,7,8,9,10 5,6,7,8,9,10,11 1,2,3,4,11,12 1,2,3,4,12 1,2,3,11,12 1,2,3,4,10,11,12 1,2,3,4,5,11,12

270 219 240 330 299 209 180 149 239 260

0.950 0.943 0.934 0.948 0.933 0.950 0.927 0.942 0.938 0.933

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

1251.4 1112.0 1227.8 1490.1 1478.7 969.6 572.8 725.8 896.9 1189.6

5 ND ND ND ND 5 ND ND ND ND

0.5625 ND ND ND ND 0.7594 ND ND ND ND

0.34 ND ND ND ND 0.09 ND ND ND ND

Cladium jamaicense

Whole year

232

0.939

<0.0001

883.8

11

0.0605

2.55

Bold indicates the best regression for estimating live biomass based on two criteria; the model has the highest regression coefficient and ANCOVA results were insignificant (p-value>0.05). NDˆnot determined.

underestimated, by approximately 10%, the amount of actual LSC present (BA) during the dry season in both communities. Conversely, during the wet season our method overestimated BA by approximately 10%. This variation is likely caused by use of whole year models (hence, regression coefficient `averaged' over the year) for all species except Eleocharis cellulosa. 3.1.4. Method accuracy There were no significant differences in actual or estimated LSC values among the triplicate 0.25 m2 plots from our northeast ENP site; consequently, these were averaged for all subsequent analyses (all p-values>0.75). NAPP rates based on both actual and estimated LSC values were not significantly different in the sawgrass community regardless of which computation method was used (dfˆ19, tˆ1.040, t0.025ˆ2.093, pˆ0.3114; Table 5). This was also true for the wet prairie community (dfˆ19, tˆÿ0.127, t0.025ˆ2.093, pˆ0.9003). 3.2. Net aboveground primary production One-way ANOVA comparisons of the three NAPP computation methods indicated that in both community types there were significant differences among them (p<0.0001, Fˆ7538.37 for sawgrass; pˆ0.0003, Fˆ348.05 for wet prairie). The modified Wiegert and Evans (Dickerman et al., 1986) method consistently yielded higher NAPP rates than either the Smalley (1959) or Milner and Hughes (1968) methods (Tukey's pairwise

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Fig. 2. Rarefaction curves for (A) wet prairie community in the dry season (B) wet prairie community in the wet season (C) sawgrass community in the dry season and (D) sawgrass community in the wet season. The arrow indicates the end point at which all iterations of the rarefaction curve reach a relatively stable asymptote. The number below indicates the number of individuals which must be sampled in order to achieve an accurate estimate of whole plot live crop values.

b

a

2361 945 3620

(g dry wt m

ÿ2

Actual NAPP

a

yr )

ÿ1

Sawgrass community

2315 944 3593

(g dry wt m

ÿ2

ÿ1

yr )

Estimated NAPP

b

ÿ1.9 ÿ0.7 ÿ0.8

(%)

Difference

296 140 522

(g dry wt m

ÿ2

Actual NAPPa yr )

ÿ1

Wet prairie community

319 165 531

(g dry wt m

ÿ2

yr )

ÿ1

Estimated NAPPb

Actual NAPP rates have been generated using live standing crop values obtained from destructive monthly 0.25 m2 harvests. Estimated NAPP rates are based on live standing crop values obtained through the use of our non-destructive phenometric models.

Smalley (1959) Milner and Hughes (1968) Modified Wiegert and Evans (Dickerman et al., 1986)

Computation method

7.0 15.0 1.8

(%)

Difference

Table 5 Net aboveground primary production rates computed using the Smalley (1959), Milner and Hughes (1968), and modified Wiegert and Evans (Dickerman et al., 1986) methods for both sawgrass and wet prairie communities in freshwater Everglades wetlands

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Fig. 3. Plots of the differences in net aboveground primary production values caused by decreasing sampling frequency. Sampling intensity represents the number of months between sampling events. Values have been calculated using both destructive (A) and non-destructive (B) estimates of live standing biomass in both sawgrass (1) and wet prairie communities (2) of the Florida Everglades. Calculations have also been done using a number of computational methods: *, Milner and Hughes (1968); &, Smalley (1959); , modified Wiegert and Evans (Dickerman et al., 1986). Note that all NAPP values are lowest using ternate sampling periods and highest using monthly sampling periods.

comparisons; t0.05ˆ5.910, dfˆ3). We also found significant differences between NAPP rates with longer intervals between sampling events in both communities (pˆ0.0026, Fˆ6.74; Fig. 3). When NAPP means computed using all three methods were compared, monthly and bi-monthly sampling intervals were not significantly different from one another, but these were both significantly higher than those generated from quarterly, ternate and semi-annual sampling (Tukey's pairwise comparisons; t0.05ˆ4.367, dfˆ15). These results, however, changed for the wet prairie community when NAPP rates generated from the Milner and Hughes (1968) method were excluded from the analysis. In this instance, monthly sampling produced NAPP rates which were significantly different than those obtained using any other sampling intensity (Tukey's pairwise comparisons; t0.05ˆ4.64, dfˆ10).

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4. Discussion We obtained highly significant and predictive phenometric models for all nine of the major emergent macrophyte species found at our northeastern ENP site. Three of these models (Crinum americanum, Hymenocallis palmeri, and Sagittaria lancifolia) were also able to predict live individual biomass for specimens collected from different locations in ENP which experience dramatically different environmental conditions throughout the year. Our phenometric models for Cladium jamaicense and Eleocharis cellulosa were not as portable. Morris and Haskin (1990) and Hopkinson et al. (1980) also found that it was necessary to develop two separate phenometric models to estimate biomass for Spartina alterniflora, since this species has growth forms which vary in height in relation to its environment. It is likely that C. jamaicense exhibits a similar, environmentally-induced plasticity in its growth form; those individuals collected from the short hydroperiod mesic prairies at the restored wetland and upper Taylor Slough sites were considerably shorter than specimens collected from our northeastern ENP site. This type of growth plasticity is unlikely in Eleocharis cellulosa, since this species is structurally simple. The lack of model portability for this species may have arisen from the fact that we were measuring one of the other co-occuring Eleocharis species rather than E. cellulosa. Specific members of the genus Eleocharis are often difficult to distinguish from one another. The inability of our phenometric models to adequately estimate biomass for C. jamaicense and E. cellulosa in these drier wetland habitats, however, does not invalidate our general approach. Rather, it indicates that habitat-specific modifications of model parameters may be necessary for species with high morphological plasticity or subtle intrageneric morphological differences. Once such species are identified, and the appropriate models are calibrated, all that remains to estimate ALSC and NAPP is to ensure that the predictive power of the models is strong enough to guarantee that model error does not introduce a large degree of variation. The strong predictive power of our models (all greater than 90%) suggest that they satisfy this criteria. The rarefaction analysis supports our assumption that the amount of labour required in non-destructive NAPP estimation may be reduced by measuring only a small sample of a total plot. We were able to estimate LSC to within 10% of the actual biomass in both sawgrass marsh and wet prairie communities by measuring only 32% of the total number of individuals contained within a 1.0 m2 plot. Three of the four plots required 32% of the total number of individuals present to be measured in order to accurately estimate LSC. The wet prairie harvest sampled in June required only 24%. This plot, however, also contained the largest total number of individuals. Montgomery (1991) states that, sample size fraction should decrease as total population size increases, thus the June findings are not surprising. More important, however, is the fact that three of the curves produced sampling end points after 32% of the total plot population had been measured, suggesting that this represents a reliable fraction upon which LSC values can be estimated. Compared to either the Williams and Murdoch (1972) or the modified Wiegert and Evans (Dickerman et al., 1986) methods this represents a major reduction in labour. The final phase of technique development was a statistical comparison of NAPP rates generated using our non-destructive technique with those obtained using a destructive harvest method. We found no significant differences, regardless of the NAPP

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computational method employed, supporting the high accuracy of our technique (Table 5). NAPP rates varied among the three methods from 944±3620 g mÿ2 yrÿ1 for sawgrass marsh and 140±530 g mÿ2 yrÿ1 for the wet prairie community. Dai and Wiegert (1996) suggest that average NAPP rates based on only the Smalley (1959) and modified Wiegert and Evans (Dickerman et al., 1986) methods, however, probably provide the best estimate of this value. Our data support this supposition for two reasons. First, NAPP rates generated using the Milner and Hughes (1968) method were significantly different from the other two methods (Tukey's pairwise comparisons; t0.05ˆ5.91, dfˆ3). Secondly, our comparisons of NAPP rates from a range of sampling frequencies further suggests that the Milner and Hughes (1968) method may be unreliable, especially for estimating NAPP in Everglades wet prairie wetlands. In the wet prairie community, NAPP rates based on monthly and bi-monthly sampling were not significantly different when the Milner and Hughes (1968) method was included. These ANOVA results differed, however, when the Milner and Hughes (1968) method was excluded ± monthly and bi-monthly sampling intervals then produced NAPP rates which were significantly different from one another. The Milner and Hughes (1968) method calculates NAPP using only the sum of positive changes in LSC per unit time. As such, this method does not account for mortality between sampling events which may occur at high rates in systems with high turnover, such as Everglades wet prairies. Linthurst and Reimold (1978a) suggest caution when using the Milner and Hughes (1968) method to compute NAPP and our analysis supports this conclusion. We estimated that NAPP in Everglades wetlands is approximately 2991 891 g mÿ2 yrÿ1 and 409160 g mÿ2 yrÿ1 for sawgrass marsh and wet prairie communities, respectively, using the Smalley (1959) and modified Wiegert and Evans (Dickerman et al., 1986) methods. Davis (1989) used the Williams and Murdoch (1972) technique and reported NAPP values in Everglades wetlands along a nutrient gradient which ranged from 802 to 3035 g mÿ2 yrÿ1 based on the Williams and Murdoch (1972) technique. While, we did not calculate NAPP using the Williams and Murdoch technique, our values for sawgrass marsh are similar. The NAPP rates we calculated for the wet prairie community are lower than those reported for other systems in the literature (see review by Dai and Wiegert, 1996), and are similar only to those reported for Spartina alterniflora marshes along the northeast Atlantic coast (Linthurst and Reimold, 1978b; Houghton, 1985; Cranford et al., 1989). This is surprising due to the subtropical nature of Everglades wetlands, but this community is basically a sparsely-vegetated, open water system with relatively low plant densities making comparison with other, more macrophyte rich systems difficult or inappropriate. 5. Conclusions This paper had three objectives: (1) present a non-destructive repeatable sampling technique which reduces the amount of labour required to quantify aboveground biomass in wetlands; (2) compare NAPP rates using a variety of computation methods and sampling intervals; and, (3) report NAPP rates for freshwater Everglades wetlands. The predictive power of our multivariate phenometric models ranged from a high of 97% to a

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low of 90.1%. The non-destructive NAPP estimation technique we present here clearly reduces the amount of labour involved without sacrificing accuracy. Other nondestructive estimation techniques, such as those developed by Williams and Murdoch, 1972; Hopkinson et al., 1980; Dickerman et al., 1986; Morris and Haskin, 1990; Dai and Wiegert, 1996, require that a large number of plants be tagged and continually monitored in order to assess NAPP. In contrast, our technique allows investigators to census only 32% of the total plot population to accomplish this task. Furthermore, our technique may be applied in systems with either continuous or limited growing systems since it does not rely on collection of harvested end-of-season LSC values. This technique offers a number of benefits: (1) problems with spatial heterogeneity are mitigated; (2) temporal sensitivity is increased; (3) long-term monitoring is possible; (4) floristically diverse systems can be studied; and, (5) any of the currently accepted NAPP computation methods can be used. The second goal was to compare NAPP rates calculated using three different computation techniques. Our results mirrored earlier studies which found that the Smalley (1959) method underestimates NAPP while the Wiegert and Evans (1964) method overestimates it (Dai and Wiegert, 1996). Furthermore, the Milner and Hughes (1968) method consistently yielded NAPP rates which were significantly different from those produced by either of the other two methods. Linthurst and Reimold (1978b) also showed this trend for NAPP rates calculated for nine species from three wetland locations along the US east coast suggesting that, this method may not be reliable. Finally, we reported total community NAPP rates of 2991891 g mÿ2 yrÿ1 for sawgrass marsh and 409160 g mÿ2 yrÿ1 for wet prairies. The value reported for the sawgrass marsh community is similar to those in Davis (1989), and is higher than those reported for other systems, suggesting that sawgrass marsh communities in Everglades wetlands are among the most productive wetland systems along the Atlantic and Gulf coasts (Linthurst and Reimold, 1978b; Gallagher et al., 1980; Hopkinson et al., 1980; Kaswadji et al., 1990; Morris and Haskin, 1990). In contrast, the NAPP rate we calculated for the wet prairie community was generally lower than those for other North American wetlands. That Everglades wetlands form a mosaic of highly productive sawgrass communities lying adjacent to low productivity wet prairie communities suggests that, further research on the ecological factors which affect NAPP in this ecosystem are required. Employment of the non-destructive NAPP technique we present here is ideal for pursuit of such studies. Acknowledgements The authors thank C. Ehringhaus for the botanical sketches, C. Danger, D. Rodriguez, B. Sparkman, L. Flynn and members of the Wetland Ecosystem Ecology Lab at FIU for field and laboratory assistance, and J. O'Brien and J. Richards for taxonomic help. Also many thanks must go to A. Turner, J. Trexler, and S. Gulati for guidance and advice on statistical analysis. Partial support for this work was received from the South Florida Natural Resources Center, Everglades National Park, Florida. Southeast Environmental Research Program Contribution No. 72.

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