The fieldwork was carried out by Mark O'Donoghue assisted by Michael Cos- grove. We also thank Les Haylock and Peter Devine of Haylock Sheet Metal. Work ...
Aquatic Botany, 47 (1994) 277-291
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0304-3770/94/$07.00 © 1994 - Elsevier Science B.V. All rights reserved
A n efficient m e t h o d for estimating seagrass biomass • B.G. Long*, T.D. Skewes, I.R. Poiner CSIRO Marine Laboratories, P.O. Box 120, Cleveland, Qld. 4163, Australia (Accepted 9 November 1993)
Abstract Seagrass biomass was estimated in a 37.649 km2 area in Moreton Bay, Queensland. The study area was stratified by hand-digitisingseven strata that were identified by photo-interpretation of a colour aerial photograph. A pilot study was undertaken to calculate how many samples should be taken at each site and how best to allocate sampling effort to the seven strata. Sengrass samples taken with a modified 'orange-peel' grab and by coring were the same (P> 0.5). As the grab is easier to use, quicker, can be operated by one person from a small dinghy, and does not call for diving or wading, we used it for the main study. Four seagrasses were found in the study area. The dominant seagtass was Zostera capricorni Aschers., with smaller quantities of Halophila ovalis (R.Br.) Hook.f., Halophila spinulosa (R.Br.) Aschers. and Halodule uninervis (Forssk.) Aschers. The total seasrass biomass in the study area was estimated at 2145 + 568 t (95% confidence interval). Stratification improved the precision of the simple random sampling estimate by 68%. The use of a Geographic Information System (GIS) and Computer Aided Drafting (CAD) for sample design, a Global Positioning System (GPS) for locating sample sites in the field and the grab for taking samples substantiallyenhanced research productivity and accuracy. The experimental design is statistically robust and provides large samples for costeffective and reliable estimates of seagrass biomass.
Introduction Seagrasses are an important component of coastal systems: they provide nursery grounds, adult habitat and food for commercially important prawns and fish (Young, 1978; Bell and Howard, 1989; Blaber et al., 1992), wading birds and dugongs (Preen et al., 1992). They also produce large amounts of detritus and dissolved organic matter (Moriarty et al., 1984). Reliable and accurate estimates of the seagrass biomass are essential for estimating production and the links with the ecological system components that rely directly or indirectly on this resource. However, the traditional method of sampling seagrass, by taking cores and by quadrats, is time consuming and expensive, and in subtidal areas scuba is *Corresponding author.
SSDI 0 3 0 4 - 3 7 7 0 ( 93 ) 0 0 3 6 5 - F
278
B.G. Long et al. / Aquatic Botany 47 (1994) 277-291
t
2 km
J
I 153°18'E
Mud - - 2 7 ° 20' S
I~lanrl
,'\
Fisherman Islands
/ \
/
'o
St H e l e n a
Island o
/
o
........
0
/"
~
I'~',~ ' ;
-'2". " Q ~ ' l
Fig. 1. Map of the study area in Moreton Bay, showing locations sampled for seagrass.
usually also required (see studies described by Downing and Anderson ( 1985 ) ). Cost usually restricts the number of samples that can be taken, which can result in unreliable estimates. To improve precision, Downing and Anderson ( 1985 ) recommended harvesting many small quadrats in preference to a few large quadrats. To cut costs further, MeUors ( 1991 ) calibrated visually ranked quadrats against directly harvested quadrats. However, this method requires personnel with special skills and training. We have developed a sampling technique for estimating the biomass of seagrass that uses aerial photographs to stratify the study area and a Geographic Information System (GIS) to design the sampling. A Global Positioning System (GPS) was used to locate sites in the field, and cost-benefit analysis was used to optimise sampling. A seagrass grab was used for taking seagrass samples in the field rapidly and efficiently. The seagrass biomass and shoot density in a 38 kin 2 area of Moreton Bay (Fig. 1 ) was estimated by this method. Methods
GeographicInformation System A digital map of Fisherman Islands in AutoCad ® (Autodesk Incorporated); DXF format provided by the Port of Brisbane Authority was input
R G. Long et al./Aquatic Botany 47 (1994) 277-291
279
into the SPANS T M GIS (Interatydac Technologies, Nepean, Ont., Canada). This map did not cover the entire study area, so the coastline west of Fisherman Islands, Green Island, St Helena Island and Mud Island was digitised from topographic charts of the area (Wynnum Topographic Map 1:25 000, 1984; Bishop Island Topographic Map, 1:25 000, 1971 ) (Fig. 1 ).
Study area stratification The study area was stratified by hand-digitising seven ground-cover types identified from colour aerial photographs provided by the Port of Brisbane Authority. The photographs had been taken at low tide (0.2 m) at 14:30 h on 7 September 1991 at an altitude of 3000 m. The Hasselblad camera was fitted with a blue polarising f'tlter to reduce sun glare and highlight areas of seagrass. Six seagrass and one subtidal strata were identified. The subtidal stratum (channel stratum ) was defined as all areas in the aerial photograph where we could not see the bottom. The six seagrass strata were classified as: ( 1 ) intertidal dense seagrass beds on the mudflats off Fisherman Islands; (2) wormdigging areas (where anglers dig for nereid worms for fish bait) in dense seagrass beds; (3) patchy seagrass areas; (4) very patchy seagrass areas; (5) seagrass near Oyster Point; (6) dense seagrass along the west side of St Helena Island. Area estimates for the seven strata were measured with the GIS (Table 1 ). The study area was divided in the GIS into 60 m × 60 m squares, the smallest area that could reliably be located in the field using the GPS with only one receiver. A complete list of all potential sample sites for each stratum was generated by the GIS, put into a database, and a random subset selected.
Field sampling Sites were located in the field with the GPS receiver. At each site, a sample of the bottom was taken with a hand-operated seagrass grab. An aluminium Table 1 Descriptions of the seven strata identified from aerial photography and area estimates Stratum
I)cscription
Area (km 2)
1 2 3
Dense seagrass Worming area Patchy seagrass
4
Very patchy seagrass
5 6 7
St. Helena seagrass Oyster Point seagrass Channel subtidal Total area
8.0771 0.4544 1.7230 1.1720 0.1049 0.5574 25.5602 37.6491
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B.G. Long et al. /Aquatic Botany 47 (1994) 277-291
dinghy was used for transport to sample sites and as a platform from which to operate the grab. The seagrass grab was a modification of the 'orange-peer grab used commercially to excavate pipes (Fig. 2). Instead of a system of pulleys, four arms were used to lever the jaws closed. A short length of chain was attached to the ends of the two opposing arms, and rope was attached to a clamp which was fastened through the middle link of both chains. The grab was loaded by extending the arms and engaging a spring-loaded clamp. The spring disengaged the clamp and the levers were free to move when upward pressure on the arms was released. Pulling up on the rope levered the grab into the sediment. The top margin was weighted by 8 kg of lead to assist grab penetration. The upper part of each blade was scalloped so that seagrass would not be cut when the blades closed. Grab samples were taken by lowering the grab to the bottom, triggering the release clamp with a sharp jerk on the rope and then firmly and slowly hoisting it out of the sediment. The grab was emptied into a bin on deck and the seagrass was sieved in a lug-basket (10 mm holes) to remove the sediment from the rhizomes. At each site the sediment texture was categorised as mud, sandy mud, muddy sand or sand by squeezing a small handful of sediment. The seagrass was transferred to plastic bags, stored on ice, and later sorted and weighed in the laboratory.
Sampleprocessing The seagrasses were sorted into species and the shoots counted. The aboveground shoots and stems were separated from the below-ground rhizomes, dried at 60 °C for 8 h and weighed to the nearest 0.1 g.
V grab blades Fig. 2. Scz~,rassgrab used in the study.
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Seagrass grab As sediment type affected the digging efficiency of the grab, the diameter of the sediment plug, for at least three samples in the various sediment types, was measured with a ruler. The area sampled by the seagrass grab ranged from 0.016 m 2 (sdffi0.0052) in hard-packed sands, to 0.085 m 2 (sd=0.0091) in mud. In sandy muds the area sampled was 0.069 m 2 (sd-0.0025) and in muddy sands the area sampled was 0.047 m 2 (sd=0.0069). A correction factor was applied to seagrass biomass and shoot density to adjust all samples to 1 m 2. To test whether the grab gave a true sample, grab and core samples of a homogeneous Zostera capricorni Aschers. seagrass bed were compared. Core samples were taken by positioning the corer on the seagrass, gently running a finger around the inside edge to include all seagrass shoots, and twisting the corer into the sediment. A shovel was inserted under the corer and the core removed. An analysis of variance (ANOVA) of 30 grab and 23 core samples from 11 sites found no difference between the sample devices in the representativeness of their samples (P> 0.05 ) (Fig. 3).
Pilot study To direct sampling effort most effectively, we obtained variance estimates of the seagrass biomass in the seven strata. Seventy sites were alloeated to the seven strata in proportion to their area and to estimates of their above-ground seagrass biomass variance obtained from previous studies at Victoria Point, 20 km south of Fisherman Islands (Commonwealth Scientific and Industrial Research Organization (CSIRO), unpublished data, 1990). The samples were taken between 20 and 27 September 1991:27 in the dense seagrass, eight in the worming area, eight in the patchy seagrass, eight in the very patchy sea-
300
,2500
•
-i-
z,I
200-
-r-
7--
'2000 '1500
g m"2
NO. Shoots
.1000 100500 0
o grab
core
AGB
grab core
BGB
grab core
ShD
Fi~ 3. Bar graphs of above-ground biomass (AGB) and below-ground biomass ( BGB ) ( g m - 2), and shoot density (ShD), for grab and corer ( 1 se bar included).
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grass, eight in the St Helena seagrass bed, six in the Oyster Point seagrass and five in the channel strata. At ten sites where seagrass was dense, from three to five grab samples (mean, 3.9 per site) were taken to obtain within- and between-site variance estimates for cost-benefit analysis.
Cost-benefit analysis Cost-benefit analysis was used to determine the optimum number of grabs to take per site (Underwood, 1981 ). The time costs used in the formula were: the time to sample a site (cs), and the time required to take a grab at a site and to process the sample in the laboratory (Cw). The analysis indicated that the optimum number of samples per site was one (Table 2a). It also indicated that the ratio of within-site to between-site variance in dense seagrass would need to be of the order of 19:1 before it became cost-effective to take more than one sample per site.
Optimum allocation of sampling effort to strata Neyman allocation was used to minimise the variance for a fixed sample size (Cochran, 1977). Each stratum is allocated sampling effort according to the estimates of the variance (from the pilot study) and its area. The method Table 2 Cost-benefit analysis for (a) determining the optimum number of samples to take per site (s,2, between-site variance; s 2, within-site variance for seagrass biomass) and (b) optimum (Neyman) allocation of sampling effort and actual allocation after subjective assessment of strata size and variance
(a)
Source
Variance estimates (% of total)
Cost (min)
Site 233.4 (11%) 8 Within site 1820.7 (88%) 68 Grabs/siteffi [ (c,.s 2 ) / ( c w . s 2 ) ] t = [ (8X 1820.7)/(68 X 233.4) ]~ =0.96~ 1
(b) Stratum l 2 3 4 5 6 7
Neyman
Actual
nh
nh
164 7 If7 9 4 4 0
186 17 I06 18 lO 16 8
B.G. Long et al. / AquaticBotany 47 (1994) 277-291
283
was also used to calculate the number of samples required for 15% precision of the mean (as defined by Andrew and Mapstone, 1987). The estimate of sample size n for fixed variance v(y~t) was
N-
no 1+ no N
(1)
where N
L
E NhS, V(Ysdh = 1 (Cochran, 1977), and the variables are as defined below. The variance estimates from the pilot study indicated that, for a Neyman allocation of 305 samples to the seven strata, a 15% precision for seagrass biomass would be obtained (Table 2b). To be conservative, an additional 56 samples were allocated to the study area (Fig. 1 ). The fieldwork for the study proper was done over 6 weeks from 25 November 1991 to 8 January 1992.
Seagrass biomass estimates The stratified mean, Yst,was estimated as L =I
WHY',
(2)
and the estimated variance of the stratified mean, v(Yst), as --
WhS h
v(y.O=
~ h=l\
h
WhSh
/
~ h=l\
(3) /
where Nh is the total number of sampling units in stratum h, nh is the number of samples in stratum h, Y,i is the value obtained from the/th unit, Wh=Nh/ N is the stratum weight Yh = ( i ~ _ l Y h i / t t h )
is the stratum mean, and s~ is the sample estimate of stratum h variance. The precision of the stratified biomass estimate was compared with the precision expected from a simple random sample where the estimated variance of the mean of a simple random sample from the same population, Vr~, is
284
B.G. Long et al. / Aquatic Botany 47 (199#) 277-291
I
(a)
153 ° 18' E
-- 27 ° 20' S
/
\
/
\
/
\
/
\ /
\ \
I g m'~ -~
L o 1300
J
(b)
153 ° 18' E
-- 27 ° 20' S
/'\ \ /
\
/
\
\
.~ g
m "2
(2) 5o o
25
B.G. Longet al. /AquaticBotany47 (1994)277-291
285
I (C)
-
153 ° 18'E
27 ° 20' S
/
\
/ ,\
/ /
\
J
\ \
g m "2
0 0
(d)
100
1 5 3 ° 18' E
-- 27 ° 20' S
/
\ \
/
\
/
\
/
'\ \
g m "2
0 3o n
0 15
Fi~ 4. Maps of the study area, showing biomass at sample locations of (a) Zostera capricorni, (b) Halophila ovalis, (c) Halophila spinulosa and (d) Halodule uninervis. The radii of the circles are proportional to the biomass; legends indicate the relationship between bubble size and biomass for each species.
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B.G. Long et al. / Aquatic Botany 47 (1994) 277-291
r I L N nh "] Eran -- J~ -~7 ~ -S--h E Y h:j - Y -2 s t +v(Y~#I t_IVh-~ 1 tlhj..~ 1
I
(4)
where y~,j is the squared observation j in stratum h, y~t is the stratified mean, N is the total number of sampling units, L is the number of strata, n is the number of samples, Nh is the total number of samples in stratum h and nh is the number of samples taken in stratum h (Cochran, 1977, p. 136). Correlations between above- and below-ground biomass and shoot density were tested to measure the redundancy in these variables.
Area covered by seagrass The area covered by seagrass was calculated as the proportion of sites sampied where seagrass was present. This technique overestimates seagrass cover, as areas, not points, were sampled (Pielou, 1974), but the bias is likely to be insignificant because the grab area was small (0.016-0.085 m 2) relative to the study area (38 kin2). Results
Seagrass distribution Seagrass was found at 250 of the 361 sites and was widely distributed throughout the intertidal zone of the study area. Z. capricorni Aschers. and Halophila ovalis (R.Br.) Hook.f. were found throughout the intertidal regions (Figs. 4a and 4b), whereas Halophila spinulosa (R.Br.) Aschers. and Halodule uninervis (Forssk.) Asehers. were mainly found in a small area east of the Fisherman Islands mangroves (Figs. 4c and 4d).
Seagrass parameter estimates Z. capricorni, the most abundant seagrass in the study area, was found in all intertidal strata (Table 3). Its biomass ranged from 45 g m -2 in the very patchy seagrass strata to 364 g m -2 in the Oyster Point strata. Halophila ovalis was also found in all intertidal strata, its biomass ranging from 0.3 g m -2 in the St Helena seagrass bed to 11 g m - 2 in the dense seagrass stratum (Stratum 1 ). Halodule uninervis was sampled in only two strata and Halophila spinulosa in only one stratum. Correlations between above- and below-ground biomass and shoot density for all seagrasses were high and significant (Table 4). Thus, the distribution of shoot densities in the study area (Table 5 ) largely mirrored the distribution of seagrass biomass. The ratio of above-ground biomass to below-ground biomass ranged from 2.70 (se=0.694) for Halophila
B.G. Longet o2./AquaticBotany47 (1994) 277-291
287
Table 3 Mean biomass (gm -2) and coefficient of variation (%) for all seagrasses and pooled seagrass Stratum
Zostera capricorni
Halophila ovalis
Halodule uninervis
Halophila spinulosa
All species
1
117.04 255% 111.36 100% 120.55 405% 44.96 216% 258.24 81% 363.92 239% 0.0 0.0
11.45 304% 10.33 192% 7.11 384% 0.52 310% 0.30 257% 6.28 141% 0.0 0.0
2.38 504% 1.52 412% 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0.0
4.97 261% 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0.0
195.84 229% 123.21 92% 127.66 383% 45.48 214% 258.54 81% 370.19 235% 0.0 0.0
2 3 4 5 6 7
Table 4 Correlations between above-ground biomass (AGB), below-ground biomass (BGB) and shoot density (ShD) for seagrasses sampled in the Port of Brisbane study area Seagrass
n
AGB/BGB
AGB/ShD
BGB/ShD
Zostera capricorni Halophila ovalis Halodule uninervis Halophila spinulosa
188 157 16 43
0.82** 0.94** 0.81.* 0.89**
0.54** 0.71.* 0.82** 0.87"
0.68** 0.77** 0.45 0.84**
*P
