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Spatial autocorrelation analysis of meiofaunal and microalgal populations on an intertidal sandflat: Scale linkage between consumers and resources
Estuarine,
Coastal
and Shelf
Science
(1990) 30,341-353
Spatial Autocorrelation Analysis of Meiofaunal and Microalgal Populations an Intertidal Sandflat: Scale Linkage Between Consumers and Resources
J. Pinckney
and R. Sandulli”
Department of Biological Sciences, University South Carolina 29208, U.S.A. and “Universita Dipartimento Di Genetica, Biologia Generale 8,80134 Napoli, Italy Received
24 April
on
1989 and in revised
form
of South Carolina, Columbia, Delgi Studi Di Napoli, E Molecolare, Via Mezzocannone 4 September
1989
Meiobenthos;microphytobenthos;spatialdistribution; Nematoda; Copepoda;Massachusetts Keywords:
Spatialautocorrelationanalysisprocedureswereusedto characterizethe spatial pattern of meiofaunaland microalgal populationson an intertidal sandflat located in Barnstable Harbor, Massachusetts.Simple correlation analyses betweenthe abundanceof meiofaunalgroupsand microalgalbiomassdid not revealsignificantcorrelations.However, the resultsof autocorrelationanalyses suggestedthat total meiofauna,nematodes,copepods,ostracods,Chl a, and pheopigmentsexhibit significantspatialautocorrelation.Further analysis,using correlograms,showedthat thesegroupssharenearly identicial spatialpatterns andhave similarpatchsizes,suggestinga commonspatiallinkagebetweenthese grazersand microalgalresources.
Introduction
For many years, ecologistshave commented on the nonrandom distribution of organisms. One type of nonrandom pattern is the organization of speciesor speciesassemblagesinto aggregations resulting in clumps or patches of abundance. Mosaics are formed when a number of patches are located within a relatively homogeneous background (Levin & Paine, 1974). In plankton communities, patchy distributions may be created by physical processessuch asupwelling and surface convergence in frontal zones(Dustan & Pinckney, 1989). In marine benthic systems,patchesmay be related to local environment, geological, and hydrodynamic conditions (Bell et al., 1978; E&man, 1979; Fleeger et al., 1984). Biotic factors, such ascompetition and predation, may also produce patchy distributions (Whittaker & Levin, 1977; Fleeger & Gee, 1986; Palmer, 1988). For example, predators may more readily locate prey items in some habitats than others, resulting in patches where prey densities are higher than the background levels (Southern & Lowe, 1968). Whittaker and Levin (1977) have studied patch phenomena in terms of successionand evaluated their importance in community structure. In their models, patches are formed 0272-7714/90/040341+ 13 $03.00/O @1990 AcademicPressLimited
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-7. Pinckneyt3 R. Sandulli
by disturbance, resulting in a community of successionalmosaics.The central idea is that patches promote and reinforce more complex community structure. Numerous methods have been developed to quantify and statistically analysethe distribution of organisms in their environment (Pielou, 1969). Spatial autocorrelation techniques have recently beenapplied to the measurementof pattern (Cliff & Ord, 1973,198l; Upton & Fingleton, 1985). For abundance data, spatial autocorrelation can be used to test whether the abundance at one locality is independent of abundancesin neighboring localities (Sokal & Oden, 1978a). Several recent studies have demonstrated the value of spatial autocorrelation techniques for examining pattern in benthic and planktonic communities (Jumars et al., 1977; Jumars, 1978; Decho & Fleeger, 1988; Eckman & Thistle, 1988; Legendre & Troussellier, 1988). Several researchershave studied the spatial distribution of meiofaunal populations. Bell et al. (1978) recognized that biogenic structures (fiddler crab burrows and Spartina roots) in estuarine mudflats promote patchy distributions of nematodes and copepods. Some meiofaunal populations exhibit patchiness at scalesthat coincide with potential food sources (Findlay, 1981; Montagna et al., 1983; Decho & Castenholz, 1986; Decho & Fleeger, 1988). Although there is some indication that inter-specific competition may reinforce patchy distributions (Hogue, 1978), habitat type is also an important factor producing patchiness(Warwick, 1981;Wilson, 1984). Only a few researchershave applied spatial autocorrelation techniques to analyse benthic distribution patterns (Jumars et al., 1977; Jumars, 1978; Decho & Fleeger, 1988; Eckman & Thistle, 1988). The general conclusions drawn by these studies are that speciespatches are highly variable in size and frequency, and that benthic communities are complex mosaicswhich interact with varying degreesof intensity. Benthic microalgae constitute the primary food source for a wide variety of meiofaunal organisms. Nematodes, harpacticoid copepods, protistans, oligochaetes, turbellarians, polychaetes, and amphipods actively graze on microalgae (Sanders et al., 1962; Coull, 1971; Admiraal, 1984). In someareas,the abundance of microalgae may limit the distribution of herbivores (Admiraal et al., 1983)and grazing pressuremay be intense enough to limit microalgal biomass(Davis & Lee, 1983; Montagna, 1984). The purpose of this study was to examine the microscale distribution of meiofaunal groups in relation to the pattern of benthic microalgae using spatial autocorrelation techniques. Specifically, the authors wanted to measure and compare patch sizes for meiofauna1groups and microalgae to determine the spatial relations between these consumers and one potential food resource.
Materials
and methods
Sampleswere collected during low tide from an intertidal sandfIat located in Barnstable Harbor, Massachusetts, U.S.A., on 4 August 1988. Barnstable Harbor is a high salinity estuary which receives little freshwater runoff and hasa mean tidal range of approximately 3 m (Ayers, 1959). This marsh hasnumerous sandflatswhich are exposedat low tide along with wide expansesof Spurtinu ulternifloru (Sanders et al., 1962). The intertidal sandflats have a rich benthic microalgal flora which is grazed by a variety of deposit feeders(Moul & Mason, 1957; Sanderset al., 1962; Round, 1979). Numerous polychaete (Nereis cuuduta) burrows aswell assnails (Hydrobiu, Nussurius,Littorinu) and feeding trails were found in the sampling area. Horseshoe crab (Limulus) body pits, formed during feeding, were also
Meiofaunal
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frequently encountered on the sandflat. Some small, shallow depressions (l-20 cm”) retained water during the period of mudlfat exposure. The sampling strategy was to collect a large number of contiguous cores over a small sampling area. The sampling apparatus consisted of modified tissue culture plates (Corning 25820-24). Each plate contained a 6 x 4 arrangement of 24 wells, each with a diameter of 15.5 mm and a depth of 17.6 mm (3.4 ml). Samples were collected by inserting four plates into the sediment, giving a 12 x 8 grid of 96 contiguous cores. Three replicates were collected within a 1 m2 sampling area. Using this methodology, 96 contiguous cores (area = 180.5 cm2) were collected in triplicate, giving a total sample area of 541.55 cm2 to a depth of 1.8 cm. In addition, three cores were collected to characterize the sediment in the sampling area. Although this methodology was replicated within the limits of the sampling area, our collections were not replicated in time. In the laboratory, each of the plate wells were randomly subsampled using a plastic drinking straw (0.3 cm2). Subsamples were fixed in formalin and stained with rose bengal, sorted under a dissecting microscope and major meiofaunal groups enumerated. The remainder of the sediment in each plate well was used for chlorophyl (Chl u) determinations. Sediment containing benthic microalgae was removed from each well and placed in plastic scintillation vials. The pigment extraction procedure consisted of adding 15 ml of 1001,, acetone (with MgCO,) to each vial and subsequent freezing for a period of 48 h. Samples were agitated twice daily to facilitate pigment extraction. Samples were then cleared by centrifugation (3000 rpm, 0 “C, 4 min), and read on a Perkin-Elmer Lambda 3B spectrophotometer. The concentration of Chl a and total pheopigments were determined using the methods and equations of Strickland and Parsons (1972). Spatial autocorrelation analysis is used to measure the relationship between abundances in adjacent cores. The theory and uses of spatial autocorrelation procedures to analyse spatial associations have been previously discussed (Cliff & Ord, 1973; 1981; Sokal & Oden, 1978a,b) and the reader is referred to the above references for more detailed descriptions. Spatial correlograms are constructed by calculating values of Moran’s I for fixed interpoint distances and plotting interval distance OS. Z (Sokal & Oden, 1978~). Significance values are computed for each value of I. Correlograms are used to illustrate how the autocorrelation coefficient changes as a function of interpoint distance (Sokal & Oden, 1978a). Correlograms were constructed for each of the groups within each sampling site using distance categories which correspond to core diameter distances. As intercore distance increases, the number of cores which satisfy the distance criterion decreases, leading to progressively smaller sample sizes at longer interval distances. For spatial correlograms, sample sizes less than 100 are unreliable, and have been excluded from this analysis (Sokal & Oden, 1978a). Binary weighting, rather than distancee2 weighting is used to calculate Z values at each of the fixed distance intervals. Patch sizes can be determined using spatial correlograms. For abundance data, the minimum Z value corresponds to the patch radius (Sokal & Oden, 1978~). Spatial autocorrelation procedures were performed using the Spatial Autocorrelation Analysis Package (SAAP), distributed by D. Wartenburg (R. W. Johnson Medical School, Piscataway, NJ, U.S.A.). Results The sediment within the sample area was mostly sand, mixed with a small amount of silt and clay. The mean sand:silt-clay ratio was 92.4% (1 SD = 0.85, IZ= 3).
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TABLE 1. Summary table of meiofaunal abundances, pigments. Abundances are in terms of individuals pg cm-’ to a depth of 1.76 cm. Max and Min refer obtained in each replicate Group
Replicate
Ciliata
1 2 3
Copepoda 2 3 Nematoda 2 3 Oligochaeta 2 3 Ostracoda
Polychaeta
Turbellaria
Total
Meiofauna 2 3
Chlorophyll
a 2 3
Pheopigments 2 3
Mean 5.0 1.0 1.7 26.8 39.3 28.1 814.6 920.9 908.7 1.5 1.5 0.8 29.8 27.3 21.1 5.3 5.8 2.6 14.1 13.8 10.8 902.0 1009.6 973.8 22.02 19.57 20.87 10.80 10.65 10.48
benthic microalgal Chl a and pheo10 cm-” and pigments in terms of to maximum and minimum values
*lSD
MaX
Min
8.04 3.92 3.73 17.11 19.69 20.13 275.12 400.90 282.72 2.75 3-12 2.12 16.66 16.56 12.95 8.66 5.80 3.96 11.08 13.00 9.51 306.21 421.97 309.30 2.932 2.929 3.766 2,686 2.115 2,152
31 31 20 71 82 87 2036 2765 1653 10 15 10 71 77 71 56 20 20 46 71 41 2219 2949 1786 30.1 30.1 39.2 15.9 16.8 14.4
0 0 0 0 0 0 250 383 270 0 0 0 0 0 0 0 0 0 0 0 0 291 403 291 15.2 14.4 12.5 3.5 4.1 2.6
The meiofaunal populations were divided into seven groups for analysis. The abundances of each of these groups, as well as Chl a and pheopigments, are given in Table 1. Due to high nematode abundance, the category of total meiofauna essentially reflects the numbers of nematodes. Given the small core sizes and small total sampling area, the variability, asdemonstrated by the SD, demonstratesthe remarkable heterogeneity within this microhabitat. The distributions ofnematodes and Chl a at Site 1have been plotted to illustrate the microscale variability within this habitat (Figure 1). In these two plots, no clear correlations between meiofaunal abundancesand Chl a are evident. Plots of the other groups at other sites demonstrate the samedegree of microscale heterogeneity (not shown). Spearman rank correlation coefficients were calculated for the entire data set to determine if there were any direct correlations between meiofauna and Chl a or pigment degradation products (pheopigments) (Table 2). Although a few significant values were detected, none were high enough to suggest strong correlations between the groups. Three dimensional plots (similar to Figure 1) were visually compared for groups found to be significantly correlated, but the relation wasnot evident. The general conclusion for the non-parametric correlation analysis is that microalgal biomassand pheopigments are not strongly correlated with meiofaunal abundances.
Meiofaunal
and microalgal
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345
25
Figure
1. A comparison
of the distribution
of Chl a (A) and nematodes
(B) at replicate
1.
All groups were subjected to spatial autocorrelation analysis. Moran’s I, Geary’s c and variance to mean ratio were determined for all groups within each replicate (Table 3). For each replicate, a single autocorrelation value was calculated using distance-’ weighting, following the suggestions of Jumars et al. (1977). All three values (Z,c,s”/x) were used to classify each group into one of six possible distribution type categories (Table 4). Nematodes, total meiofauna, Chl a, pheopigments, and possibly copepods and ostracods exhibit similar distribution patterns (type A). These groups are each positively spatially autocorrelated with patch sizes greater than 1 core area (1.9 cm’) but contained within the sampling area. Similarly, turbellarians, polychaetes, oligochaetes and ciliates exhibited aggregation, primarily due to high abundances in single isolated cores rather than a
346
J. Pinckney & R. Sand&i
TABLE 2. Spearman rank correlation coefficients for benthic microalgae and meiofauna. (n = 288 for each group)
Chlorophyll a Ciliata Copepoda Nematoda Oligochaeta Ostracoda Polychaeta Turbellaria Total Meiofauna
l r
-0.0218 -0.1270* -0.0448 -0.0119 0.0290 0.0499 0.0489 - 0.0485
Pheopigments -0.1242* - 0.0343 -0.1491** 0.0212 0.0305 - 0.0584 0.0592 -0.1360*
**p
gradient in abundance (type El). In general these groups did not show spatial autocorrelation within our sampling scale and the patch sizes of these groups could not be directly assessed.Patches sizes for these groups are larger than 318 cm2 or less than 1.9 cm2. Correlograms were constructed for each group in each replicate to determine spatial autocorrelation as a function of increasing distance between cores. A mean and SD were calculated for the three Zvalues at eachdistance interval to construct an ‘ average ’ correlogram for each meiofaunal and microalgal group (Figures 2-4). The Chl a, pheopigments, and nematodes correlograms exhibit a similar trend of decreasing spatial autocorrelation with increasing intercore distance. The similarity between these groups is evident when the average correlograms are plotted together (Figure 2). Copepods and ostracods show a similar trend, but Z values are generally closer to the expected value [e(Z)] (Figure 3). For Moran’s Z, the expected value [e(Z)] is usedin testing the null hypothesis that the observed spatial arrangement is due to random permutations among the variates (Jumars et al., 1977). Values which are significantly different from the expected value result in the rejection of the null hypothesis of no spatial autocorrelation. In the bottom panel, the correlograms are compared with Chl a, again showing the close agreement between the spatial distributions of copepods, ostracods and microalgae. The remaining groups exhibit quite different correlograms (Figure 4). As indicated by the results in Table 3, these groups did not yield significant Z values when an overall autocorrelation coefficient was calculated. The correlograms for ciliates, oligochaetes, polychaetes, and turbellarians closely follow the expected value [e(Z)] line, further illustrating the absence of spatial autocorrelation. In general, the Zvalues are lower for these groups and oscillate around the expected value line. Based on the correlograms, two types of distribution patterns emerge from the abundance data. One general type of correlogram, characteristic of Chl a, pheopigments, nematodes, copepods, and ostracods, has a gradual decrease with increasing interval distance to a break point at which the slopeof the line is minimized (w 6-8 cm). This break point can be interpreted as the patch radius for these groups. Chl a, pheopigments, nematodes, copepods, and ostracods all sharesimilar types of spatial distributions (Table 4) and patch sizes. The second type of correlogram doesnot have a common break point for all the groups and Zvalues closely follow the expected value line. Ciliates, oligochaetes, polychaetes, and turbellarians do not exhibit significant autocorrelation patterns and patch sizescan not be determined using correlograms at our sampling scale.
Meiofaunal
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TABLE 3. Results of spatial autocorrelation analyses of benthic microalgae and meiofauna1 groups. Moran’s I, Geary’s c, variance to mean ratio, and distribution type are given below. See Table 4 for a description of distribution types. Autocorrelation values were calculated for each replicate (96 cores) using inverse distanced weighting and significant values were assigned using the randomization assumption (Cliff & Ord 1973) Group
Replicate
Ciliata
Copepoda
Nematoda
Oligochaeta
Ostracoda
Polychaeta
Turbellaria
Total
Meiofauna
Chlorophyll
a
Pheopigments
*p
l *p
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
I @118*** -0.015 0.031 0.103*** 0.020 0.084*** 0.170*** 0.139*** 0.236*** - 0.005 - 0.006 0.043* 0,064** 0.108*** 0.085*** 0.025 - 0.005 0.011 0.018 0.071** 0.016 0.158*** 0.134*** 0.235*** 0.250*** 0.215*** o-149*** o-350*** 0.205*** 0.019
c 0.957 1.178 0.978 0.887*** O-989 0.900** 0.810*** 0,847** 0.759*** 1.012 1.044 0.983 0.970 0.898*** 0,875** 1.032 0.992 0.962 0.975 0.951 0.979 0.810*** 0,858** 0,759*** 0,703*** 0,716*** 0.956 0.638*** 0,778*** 0,997
Type 12.97*** 16,22*** 8.27*** 11,03*** 9.98*** 14.61*** 93,90*** 176,37*** 88,88*** 5,13*** 6.76*** 5.73*** 9.42*** 10,17*** 8.04*** 14.42*** 5.81*** 15,89*** 8,78*** 12.37*** 8,48*** 105.05*** 178.23*** 99.27*** 0,394*** 0.443*** 0.687** 0.675** 0.425*** 0,446***
C El El A El A A A A El El c C A A El El El El C El A A A A A A A A A
l **p
Discussion The arrangement of organisms can be assignedtwo distinctly different attributes. Spatial pattern is the geometric arrangement of organismsand can be described in terms of patch sizes and distance between patches. Spatial pattern is independent of a fixed reference point. Distribution pattern, on the other hand, is the arrangement of organisms with respect to a fixed reference point. If the reference point is moved, the distribution pattern changes. Distribution pattern is the real-world orientation of a spatial pattern. Correlograms provide a method for assessingthe spatial pattern of a particular group of organisms. Identical distribution patterns may or may not produce identical correlograms, while different spatial patterns result in different correlograms (Sokal & Oden, 19783). For example, A and B in Figure 5 have identical distribution and spatial patterns, and both will produce identical correlograms. If B is rotated 180” relative to A and and the distributions of A and B are superimposed, the resulting pattern is shown in C. In C, the distribution patterns of A and B are different, but the spatial patterns and correlograms for both are still identical.
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J. Pinckney & R. Sandulli
TABLE 4. Types of distribution patterns based on values of Moran’s I, Geary’s c, and variance to mean ratio. S indicates that the value is significantly different from the expected value, while NS denotes no significant difference. The + and - signs indicate whether the value is higher or lower than the expected value, respectively. Conclusions are based on discussions contained in Cliff and Ord (1973), Jumars et al. (1977), Sokal and Oden (1978a) and Decho and Fleeger (1988) Type
I
c
sZ/x
A
s
s
-
B C D El
SS NS NS
SNS S NS
SS+
E2
NS
NS
NS
Conclusions Patchy and patch size > 1 core area. Transition from high to low abundance occurs within sampling area. Spatial association between extreme and similar per core abundances. Regular (uniform) distribution or spacing mechanism. Aggregation based on a few extreme peaks in abundance. Aggregation based on a several similar peaks in abundance. Aggregation due to high abundance in a single, isolated core. Patch size smaller than two adjacent cores. Patches not clustered within sample area. Random distribution or spacing mechanism
(b)
-0.4’
0
r 4
L 8 Interval
distance
8 12
6
(cm)
Figure 2. Correlograms for (a) chlorophyl a, (b) pheopigments, and(c) nematodes. Each point is the mean 2 for all three replicates. Error bars are + lSD, (d) is an overlay of (a)-(c) three panels (0, chlorophyl a; A, pheopigments; n , nematodes). Error bars have been excluded for clarity. The expected value line is denoted by s(I). Values were calculated using binary weighting (Sokal& Oden 19783).
Meiofaunal
and microalgal
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349
sandpat
I
-0.q -0.4}
-0.41 0
I
I
I
I 4
I 8
I 12
Interval
dlslance
_1 16
hn)
Figure 3. Correlograms for (a) copepods and (b) ostracods. and (b) plus 2(a) (0, copepods; A, ostracods; n , chlorophyl the same as for Figure 2.
Part (c) is an overlay of (a) n). Otherwise description
(a)
-0.2 -0.4. o-2
’
’
0.0. -0.2.
I (b)
--
c (/I
-_-
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I
I
--
I
8
3
I
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I
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0*20-o-c -0.2 -0.4
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--
’
--
-----
7
-f ’
. (e)
0.2
0
4
8 Interval
distance
12
16
km)
Figure 4. Correlograms for (a) ciliates, (b) oligochaetes, (c) polychaetes, and (d) turbellarians. Part (e) is an overlay of (a)-(d) (0, cilates; A, oligochaetes; n , polychaetes; +, turbellarians). Otherwise description the same as for Figure 2.
350
3. Pinckney t.9 R. Sandulli
A
B
@p y.y .
C
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5. Illustration demonstrating the difference pattern. See text for explanation.
between
distribution
pattern
and
Exogenous forces, such as current patterns, sediment type, or other abiotic factors, should promote identical distribution and spatial patterns. Endogenous forces, such as competition, grazing, predation, or other biotic factors, may lead to different distribution patterns between speciesgroups, resulting in either similar or dissimilar spatial patterns (Sokal & Oden, 19786). If the correlograms are similar between different groups, a common linkage to spatial scalesis implied, regardlessof the apparent distribution pattern of each species. This property of spatial autocorrelation analysis can be applied to the meiofaunal and microalgal populations in Barnstable Harbor. In general, thesepopulations exhibit patchy distributions along a gradient of patch sizes. Similarity in patch size infers a common endogenousstructuring force for populations that sharethe samespatial pattern. Copepods, ostracods, nematodes, Chl a, and pheopigments appear to have nearly identical spatial patterns, but do not have the samedistribution pattern. This posesthe question of what endogenousfactors promote the spatial pattern of thesegroups. Our results suggest that some endogenous factor is responsible for structuring the spatial pattern of somemeiofaunal groups in this sandflat community. The observation that nematodes, ostracods, and copepods share a common pattern type with the benthic microalgae suggests that all are influenced by a common endogenous factor. Pheopigments, produced by Chl a breakdown during digestion, may represent previously grazed patches of microalgae. However, pheopigments may also be produced by natural degradation of microalgae, macroalgae, and macrophytic vegetation. Although we do not have any direct evidence for the nature of this structuring force, the results of this study coupled with field observations can be used to propose a mechanism. As a speculation, physical disturbance by feeding Limulus produces body pits which are comparable in size to microalgal-meiofaunal patches, suggesting that disturbance may be the primary structuring force.
Meiofaunal
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Time
Figure predator
6. Classical predator-prey curves illustrating to prey (RATIO). See text for explanation.
the variability
in the ratio
of
Herbivory, primarily by the numerically dominant nematodes, may regulate the distribution of microalgae. The absenceof a significant positive correlation between nematode abundance and microalgal biomassmay be explained using a classicalpredator-prey type system (Figure 6). When a successionalmosaic is sampled at a single point in time, the abundance of predator and prey are measured for a fixed point in time. In a dynamic and spatially heterogeneous habitat, replicate samplesare usually randomly collected over a large sample area. With respect to predator-prey dynamics, this procedure would be analogousto randomly selecting points in time and determining the abundance of predator and prey. In highly dynamic communities, the ratio of predator to prey is not fixed, but rather a range of values dependent on the ageor successionalstageof the samplelocation. In a community of successionalmosaics, each patch has its own distinct age and consequently a different predator-prey ratio. When a large number of samplesare collected, the predator-prey ratios represent a wide range of values and simple correlation analyses would be expected to yield non-significant results. The time difference between maximum prey abundance and maximum predator abundance can be defined aslag time. In caseswhere this lag time is0, predator abundance matches prey abundance and correlation analyses will yield highly significant positive correlations. At intermediate lag times, correlation analyses will give non-significant correlations between predator and prey. Further increasesin lag time would produce negative correlations between predator and prey. When looking for trophic relations between consumers and resources, the standard approach seemsto be to look for significant positive correlations between consumers and resources.The underlying idea is that the ratio of consumer to resourceis nearly constant. At the ecosystem level, this is a valid assumption (when the system is at equilibrium). However, the relative abundance of consumers and resources are usually measured at somescale lessthan the ecosystem. In dynamic, heterogeneoushabitats, consumers and resources are usually not present in fixed ratios and consequently, correlation analyses lead to non-significant results. Several studies have reported significant correlations between benthic microalgal biomassand potential meiofaunal herbivores (Montagna et al., 1983; Decho & Castenholz,
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1986; Bianchi 81 Rice, 1988; Decho & Fleeger, 1988). Likewise, others have found no significant correlations between the two (Giere, 1975; Alongi, 1988; Decho & Fleeger, 1988) and some have suggested that meiofaunal grazers are utilizing some other food resource (Joint et al., 1982). We suggest that simple correlation analyses can not and should not be used to infer trophic relations in meiofaunal food webs. Rather, meiofaunal communities should be examined in terms of successional mosaics and herbivore-primary producer (predator-prey) dynamics. Meiofaunal and microalgal communities are extremely dynamic and operate at scales which are difficult to study. We have conducted a detailed examination of spatial distributions of meiofauna and microalgae over a very small area at a single point in time. Using spatial autocorrelation procedures, we have documented the similarities and differences between the spatial patterns of the numerically dominant groups within this microhabitat. Several groups share a common spatial pattern, but the mechanism(s) promoting this have not been thoroughly examined. As demonstrated in this paper, spatial autocorrelation procedures can be used to generate a number of directly testable hypotheses concerning spatial patterns in meiofaunal and microalgal communites. Future research should examine the population dynamics within these microscale patches. Acknowledgements This research was conducted while the authors were attending the Marine Ecology Summer Course at the Marine Biological Laboratory in Woods Hole, Mass. We thank the instructors of this course for their guidance and inspiration. We also thank R. Whitlatch and K. Foreman for helpful suggestions and assistance, and B. Coull, R. Lovell, and two anonymous reviewers for their critical evaluations of earlier drafts of this manuscript. Partial funding was provided by the Slocum-Lunz Foundation. References Admiraal, W., Bouwman, L., Hoekstra, L. & Romeyn, K. 1983 Qualitative and quantitative interactions between microphytobenthos and herbivorous meiofauna on a brackish intertidal mudflat. Int Revue Ges. Hydrobiol68,175-191. Admiraal, W. 1984 The ecology of estuarine sediment-inhabiting diatoms. Progress in PhycoZogoZy Research 3, 269-322. Alongi, D. 1988 Microbial-meiofaunal interrelationships in some tropical intertidal sediments. Journal of Marine Research 46,349-365. Ayers, J. 1959 The hydrography of Barnstable Harbor, Massachusetts. Limnology and Oceanography 4, 448-462. Bell, S., Watzin, M. St Coull, B. 1978 Biogenic structure and its effect on the spatial heterogeneity of meiofauna in a salt marsh. Journal of Exp&imental Marine Biology and Ecology 3$99-107. Bianchi, T. 81 Rice, D. 1988 Feeding ecology of Leitoscoloplosfragil. II. Effects of worm density on benthic diatom production. Marine Biology 99,123-131. Cliff, A. & Ord, J. 1973 Spatial Autocorrelation Pion: London. Cliff, A. & Ord, J. 1981 Spatial Processes: Models and Applications Pion: London. Coull, B. 1971 Estuarine meiofauna interactions. In Estuarine Microbial Ecology (Stevenson, L. & Colwell R. eds). University of South Carolina Press: Columbia, pp. 499-512. Davis, M. &Lee, H. 1983 Recolonization of sediment-associated microalgae and effects of estuarine infauna on microalgal production. Marine Ecology Progress Series 11,227-232. Decho, A. & Castenholz, R. 1986 Spatial patterns and feeding of meiobenthic harpacticoid copepods in relation to resident microbial flora. Hydrobiologia 131,87-96. Decho, A. & Fleeger, J. 1988 Microscale dispersion of meiobenthic copepods in response to food-resource patchiness. Journal of Experimental Marine Biology and Ecology l&229-243. Dustan, P. & Pinckney, J. 1989 Tidally induced estuarine phytoplankton patchiness. Limnology and Oceanography 34,408417.
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sandjat
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Eckman, J. 1979 Small-scale patterns and processes in a soft-substratum, intertidal community. 3ournal of Marine Research 437-457. Eckman, J. & Thistle, D. 1988 Small-scale spatial pattern in meiobenthos in the San Diego Trough. Deep-Sea Research 35,1565-1578. Findlay, S. 1981 Small-scale spatial distribution of meiofauna on a mud and sandflat. Estuarine, Coastal and Shelf Science 12,471-484. Fleeger, J. & Gee, J. 1986 Does interference competition determine the vertical distribution of meiobenthic copepods? Journal of Experimental Marine Biology and Ecology 95, 173-l 8 1. Fleeger, J., Chandler, G., Fitzhugh, G. & Phillips, F. 1984 Effects of tidal currents on meiofauna densities in vegetated salt marsh sediments. Marine EcoZogy Progress Series 19,49-53. Giere, 0. 1975 Population structure, food relations, and ecological role of marine oligochaetes, with special reference to meiobenthic species. Marine Biology 31,139-156. Hogue, E. 1978 Spatial and temporal dynamics of a subtidal estuarine gastrotrich assemblage. Marine Biologv 49,21 l-222. Joint, L., Gee, J. & Warwick, R. 1982 Determination of fine-scale vertical distribution of microbes and meiofauna in an intertidal sediment. Marine Biology 72,157-164. Jumars, P. 1978 Spatial autocorrelation with RUM (Remote Underwater Manipulator): Vertical and horizontal structure of a bathyl benthic community. Deep-Sea Research 25,589-604. Jumars, P., Thistle, D. & Jones, M. 1977 Detecting two-dimensional spatial structure in biological data. Oecologia 28, 109-123. Legendre, P. & Troussellier, M. 1988 Aquatic heterotrophic bacteria: Modelling in the presence of spatial autocorrelation. Limnology Oceanography 33,1055-1067. Levin, S. & Paine, R. 1974 Disturbance, patch formation and community structure. Proceedings of the National Academy of Sciences (USA) 71,2744-2747. Montagna, P. 1984 In situ measurement of meiobenthic grazing rates on sediment bacteria and edaphic diatoms. Marine Ecology Progress Series 18,119-130. Montagna, P., Coull, B. Herring, T. & Dudley, B. 1983 The relationship between abundances of meiofauna and their suspected microbial food (diatoms and bacteria). Estuarine, Coastal and Shelf Science 17, 381-394. Moul, E. & Mason, D. 1957 Study of diatom populations on sand and mud flats in the Woods Hole area. Biology Bulletin 13, 351. Palmer, M. 1988 Epibenthic predators and marine meiofauna: Separating predation, disturbance, and hydrodynamic effects. Ecology 69,1251-1259. Pielou, E. 1969 An Introduction to Mathematical Ecology Wiley: New York. Round, F. 1979 A diatom assemblage living below the surface of intertidal sand flats. Marine Biology 54, 219-223. Sanders, H., Goudsmit, E., Mills, E. & Hampson, G. 1962 A study of the intertidal fauna of Barnstable Harbor, Massachusetts. Limnology and Oceanography 7,63-79. Sokal, R. & Oden, N. 1978a Spatial autocorrelation in biology. I. Methodology. BiologicalJournalofLinnear Society 10, 199-228. Sokal, R. & Oden, N. 19786 Spatial autocorrelation in biology. II. Some biological implications anf four applications of evolutionary and ecological interest. BiologicalJournalof the Linnean Society 10,229-249. Southern, H. & Lowe, V. 1968 The pattern of distribution of prey and predation in tawny owl territories. Journal of Animal Ecology 37,75-97. Strickland, J. & Parsons, T. 1972 A Practical Handbook of Seawater Analysis. Fisheries Research Board of Canada Bulletin 167. Upton, G. & Fingleton, B. 1985 Spatial Data Analysis by Example. J. Wiley, New York. Warwick, R. 1981 Survival strategies of meiofauna. In Feeding and Survival Strategies of Estuarine Organisms (Jones, N. & Wolff, W. eds). Pleunum, New York, pp. 39-52. Whittaker, R. & Levin, S. 1977 The role of mosaic phenomena in natural communites. Theoretical Population Biology 12, 117-139. Wilson, W. 1984 Non-overlapping distributions of spionid polychaetes: The relative importance of habitat and competition. Journal of Experimental Marine Biology and Ecology 75,119-127.
Coastal
and Shelf
Science
(1990) 30,341-353
Spatial Autocorrelation Analysis of Meiofaunal and Microalgal Populations an Intertidal Sandflat: Scale Linkage Between Consumers and Resources
J. Pinckney
and R. Sandulli”
Department of Biological Sciences, University South Carolina 29208, U.S.A. and “Universita Dipartimento Di Genetica, Biologia Generale 8,80134 Napoli, Italy Received
24 April
on
1989 and in revised
form
of South Carolina, Columbia, Delgi Studi Di Napoli, E Molecolare, Via Mezzocannone 4 September
1989
Meiobenthos;microphytobenthos;spatialdistribution; Nematoda; Copepoda;Massachusetts Keywords:
Spatialautocorrelationanalysisprocedureswereusedto characterizethe spatial pattern of meiofaunaland microalgal populationson an intertidal sandflat located in Barnstable Harbor, Massachusetts.Simple correlation analyses betweenthe abundanceof meiofaunalgroupsand microalgalbiomassdid not revealsignificantcorrelations.However, the resultsof autocorrelationanalyses suggestedthat total meiofauna,nematodes,copepods,ostracods,Chl a, and pheopigmentsexhibit significantspatialautocorrelation.Further analysis,using correlograms,showedthat thesegroupssharenearly identicial spatialpatterns andhave similarpatchsizes,suggestinga commonspatiallinkagebetweenthese grazersand microalgalresources.
Introduction
For many years, ecologistshave commented on the nonrandom distribution of organisms. One type of nonrandom pattern is the organization of speciesor speciesassemblagesinto aggregations resulting in clumps or patches of abundance. Mosaics are formed when a number of patches are located within a relatively homogeneous background (Levin & Paine, 1974). In plankton communities, patchy distributions may be created by physical processessuch asupwelling and surface convergence in frontal zones(Dustan & Pinckney, 1989). In marine benthic systems,patchesmay be related to local environment, geological, and hydrodynamic conditions (Bell et al., 1978; E&man, 1979; Fleeger et al., 1984). Biotic factors, such ascompetition and predation, may also produce patchy distributions (Whittaker & Levin, 1977; Fleeger & Gee, 1986; Palmer, 1988). For example, predators may more readily locate prey items in some habitats than others, resulting in patches where prey densities are higher than the background levels (Southern & Lowe, 1968). Whittaker and Levin (1977) have studied patch phenomena in terms of successionand evaluated their importance in community structure. In their models, patches are formed 0272-7714/90/040341+ 13 $03.00/O @1990 AcademicPressLimited
342
-7. Pinckneyt3 R. Sandulli
by disturbance, resulting in a community of successionalmosaics.The central idea is that patches promote and reinforce more complex community structure. Numerous methods have been developed to quantify and statistically analysethe distribution of organisms in their environment (Pielou, 1969). Spatial autocorrelation techniques have recently beenapplied to the measurementof pattern (Cliff & Ord, 1973,198l; Upton & Fingleton, 1985). For abundance data, spatial autocorrelation can be used to test whether the abundance at one locality is independent of abundancesin neighboring localities (Sokal & Oden, 1978a). Several recent studies have demonstrated the value of spatial autocorrelation techniques for examining pattern in benthic and planktonic communities (Jumars et al., 1977; Jumars, 1978; Decho & Fleeger, 1988; Eckman & Thistle, 1988; Legendre & Troussellier, 1988). Several researchershave studied the spatial distribution of meiofaunal populations. Bell et al. (1978) recognized that biogenic structures (fiddler crab burrows and Spartina roots) in estuarine mudflats promote patchy distributions of nematodes and copepods. Some meiofaunal populations exhibit patchiness at scalesthat coincide with potential food sources (Findlay, 1981; Montagna et al., 1983; Decho & Castenholz, 1986; Decho & Fleeger, 1988). Although there is some indication that inter-specific competition may reinforce patchy distributions (Hogue, 1978), habitat type is also an important factor producing patchiness(Warwick, 1981;Wilson, 1984). Only a few researchershave applied spatial autocorrelation techniques to analyse benthic distribution patterns (Jumars et al., 1977; Jumars, 1978; Decho & Fleeger, 1988; Eckman & Thistle, 1988). The general conclusions drawn by these studies are that speciespatches are highly variable in size and frequency, and that benthic communities are complex mosaicswhich interact with varying degreesof intensity. Benthic microalgae constitute the primary food source for a wide variety of meiofaunal organisms. Nematodes, harpacticoid copepods, protistans, oligochaetes, turbellarians, polychaetes, and amphipods actively graze on microalgae (Sanders et al., 1962; Coull, 1971; Admiraal, 1984). In someareas,the abundance of microalgae may limit the distribution of herbivores (Admiraal et al., 1983)and grazing pressuremay be intense enough to limit microalgal biomass(Davis & Lee, 1983; Montagna, 1984). The purpose of this study was to examine the microscale distribution of meiofaunal groups in relation to the pattern of benthic microalgae using spatial autocorrelation techniques. Specifically, the authors wanted to measure and compare patch sizes for meiofauna1groups and microalgae to determine the spatial relations between these consumers and one potential food resource.
Materials
and methods
Sampleswere collected during low tide from an intertidal sandfIat located in Barnstable Harbor, Massachusetts, U.S.A., on 4 August 1988. Barnstable Harbor is a high salinity estuary which receives little freshwater runoff and hasa mean tidal range of approximately 3 m (Ayers, 1959). This marsh hasnumerous sandflatswhich are exposedat low tide along with wide expansesof Spurtinu ulternifloru (Sanders et al., 1962). The intertidal sandflats have a rich benthic microalgal flora which is grazed by a variety of deposit feeders(Moul & Mason, 1957; Sanderset al., 1962; Round, 1979). Numerous polychaete (Nereis cuuduta) burrows aswell assnails (Hydrobiu, Nussurius,Littorinu) and feeding trails were found in the sampling area. Horseshoe crab (Limulus) body pits, formed during feeding, were also
Meiofaunal
and microalgal
populations
on an intertidal
sandflat
343
frequently encountered on the sandflat. Some small, shallow depressions (l-20 cm”) retained water during the period of mudlfat exposure. The sampling strategy was to collect a large number of contiguous cores over a small sampling area. The sampling apparatus consisted of modified tissue culture plates (Corning 25820-24). Each plate contained a 6 x 4 arrangement of 24 wells, each with a diameter of 15.5 mm and a depth of 17.6 mm (3.4 ml). Samples were collected by inserting four plates into the sediment, giving a 12 x 8 grid of 96 contiguous cores. Three replicates were collected within a 1 m2 sampling area. Using this methodology, 96 contiguous cores (area = 180.5 cm2) were collected in triplicate, giving a total sample area of 541.55 cm2 to a depth of 1.8 cm. In addition, three cores were collected to characterize the sediment in the sampling area. Although this methodology was replicated within the limits of the sampling area, our collections were not replicated in time. In the laboratory, each of the plate wells were randomly subsampled using a plastic drinking straw (0.3 cm2). Subsamples were fixed in formalin and stained with rose bengal, sorted under a dissecting microscope and major meiofaunal groups enumerated. The remainder of the sediment in each plate well was used for chlorophyl (Chl u) determinations. Sediment containing benthic microalgae was removed from each well and placed in plastic scintillation vials. The pigment extraction procedure consisted of adding 15 ml of 1001,, acetone (with MgCO,) to each vial and subsequent freezing for a period of 48 h. Samples were agitated twice daily to facilitate pigment extraction. Samples were then cleared by centrifugation (3000 rpm, 0 “C, 4 min), and read on a Perkin-Elmer Lambda 3B spectrophotometer. The concentration of Chl a and total pheopigments were determined using the methods and equations of Strickland and Parsons (1972). Spatial autocorrelation analysis is used to measure the relationship between abundances in adjacent cores. The theory and uses of spatial autocorrelation procedures to analyse spatial associations have been previously discussed (Cliff & Ord, 1973; 1981; Sokal & Oden, 1978a,b) and the reader is referred to the above references for more detailed descriptions. Spatial correlograms are constructed by calculating values of Moran’s I for fixed interpoint distances and plotting interval distance OS. Z (Sokal & Oden, 1978~). Significance values are computed for each value of I. Correlograms are used to illustrate how the autocorrelation coefficient changes as a function of interpoint distance (Sokal & Oden, 1978a). Correlograms were constructed for each of the groups within each sampling site using distance categories which correspond to core diameter distances. As intercore distance increases, the number of cores which satisfy the distance criterion decreases, leading to progressively smaller sample sizes at longer interval distances. For spatial correlograms, sample sizes less than 100 are unreliable, and have been excluded from this analysis (Sokal & Oden, 1978a). Binary weighting, rather than distancee2 weighting is used to calculate Z values at each of the fixed distance intervals. Patch sizes can be determined using spatial correlograms. For abundance data, the minimum Z value corresponds to the patch radius (Sokal & Oden, 1978~). Spatial autocorrelation procedures were performed using the Spatial Autocorrelation Analysis Package (SAAP), distributed by D. Wartenburg (R. W. Johnson Medical School, Piscataway, NJ, U.S.A.). Results The sediment within the sample area was mostly sand, mixed with a small amount of silt and clay. The mean sand:silt-clay ratio was 92.4% (1 SD = 0.85, IZ= 3).
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TABLE 1. Summary table of meiofaunal abundances, pigments. Abundances are in terms of individuals pg cm-’ to a depth of 1.76 cm. Max and Min refer obtained in each replicate Group
Replicate
Ciliata
1 2 3
Copepoda 2 3 Nematoda 2 3 Oligochaeta 2 3 Ostracoda
Polychaeta
Turbellaria
Total
Meiofauna 2 3
Chlorophyll
a 2 3
Pheopigments 2 3
Mean 5.0 1.0 1.7 26.8 39.3 28.1 814.6 920.9 908.7 1.5 1.5 0.8 29.8 27.3 21.1 5.3 5.8 2.6 14.1 13.8 10.8 902.0 1009.6 973.8 22.02 19.57 20.87 10.80 10.65 10.48
benthic microalgal Chl a and pheo10 cm-” and pigments in terms of to maximum and minimum values
*lSD
MaX
Min
8.04 3.92 3.73 17.11 19.69 20.13 275.12 400.90 282.72 2.75 3-12 2.12 16.66 16.56 12.95 8.66 5.80 3.96 11.08 13.00 9.51 306.21 421.97 309.30 2.932 2.929 3.766 2,686 2.115 2,152
31 31 20 71 82 87 2036 2765 1653 10 15 10 71 77 71 56 20 20 46 71 41 2219 2949 1786 30.1 30.1 39.2 15.9 16.8 14.4
0 0 0 0 0 0 250 383 270 0 0 0 0 0 0 0 0 0 0 0 0 291 403 291 15.2 14.4 12.5 3.5 4.1 2.6
The meiofaunal populations were divided into seven groups for analysis. The abundances of each of these groups, as well as Chl a and pheopigments, are given in Table 1. Due to high nematode abundance, the category of total meiofauna essentially reflects the numbers of nematodes. Given the small core sizes and small total sampling area, the variability, asdemonstrated by the SD, demonstratesthe remarkable heterogeneity within this microhabitat. The distributions ofnematodes and Chl a at Site 1have been plotted to illustrate the microscale variability within this habitat (Figure 1). In these two plots, no clear correlations between meiofaunal abundancesand Chl a are evident. Plots of the other groups at other sites demonstrate the samedegree of microscale heterogeneity (not shown). Spearman rank correlation coefficients were calculated for the entire data set to determine if there were any direct correlations between meiofauna and Chl a or pigment degradation products (pheopigments) (Table 2). Although a few significant values were detected, none were high enough to suggest strong correlations between the groups. Three dimensional plots (similar to Figure 1) were visually compared for groups found to be significantly correlated, but the relation wasnot evident. The general conclusion for the non-parametric correlation analysis is that microalgal biomassand pheopigments are not strongly correlated with meiofaunal abundances.
Meiofaunal
and microalgal
populations
on an intertidal
sandflat
345
25
Figure
1. A comparison
of the distribution
of Chl a (A) and nematodes
(B) at replicate
1.
All groups were subjected to spatial autocorrelation analysis. Moran’s I, Geary’s c and variance to mean ratio were determined for all groups within each replicate (Table 3). For each replicate, a single autocorrelation value was calculated using distance-’ weighting, following the suggestions of Jumars et al. (1977). All three values (Z,c,s”/x) were used to classify each group into one of six possible distribution type categories (Table 4). Nematodes, total meiofauna, Chl a, pheopigments, and possibly copepods and ostracods exhibit similar distribution patterns (type A). These groups are each positively spatially autocorrelated with patch sizes greater than 1 core area (1.9 cm’) but contained within the sampling area. Similarly, turbellarians, polychaetes, oligochaetes and ciliates exhibited aggregation, primarily due to high abundances in single isolated cores rather than a
346
J. Pinckney & R. Sand&i
TABLE 2. Spearman rank correlation coefficients for benthic microalgae and meiofauna. (n = 288 for each group)
Chlorophyll a Ciliata Copepoda Nematoda Oligochaeta Ostracoda Polychaeta Turbellaria Total Meiofauna
l r
-0.0218 -0.1270* -0.0448 -0.0119 0.0290 0.0499 0.0489 - 0.0485
Pheopigments -0.1242* - 0.0343 -0.1491** 0.0212 0.0305 - 0.0584 0.0592 -0.1360*
**p
gradient in abundance (type El). In general these groups did not show spatial autocorrelation within our sampling scale and the patch sizes of these groups could not be directly assessed.Patches sizes for these groups are larger than 318 cm2 or less than 1.9 cm2. Correlograms were constructed for each group in each replicate to determine spatial autocorrelation as a function of increasing distance between cores. A mean and SD were calculated for the three Zvalues at eachdistance interval to construct an ‘ average ’ correlogram for each meiofaunal and microalgal group (Figures 2-4). The Chl a, pheopigments, and nematodes correlograms exhibit a similar trend of decreasing spatial autocorrelation with increasing intercore distance. The similarity between these groups is evident when the average correlograms are plotted together (Figure 2). Copepods and ostracods show a similar trend, but Z values are generally closer to the expected value [e(Z)] (Figure 3). For Moran’s Z, the expected value [e(Z)] is usedin testing the null hypothesis that the observed spatial arrangement is due to random permutations among the variates (Jumars et al., 1977). Values which are significantly different from the expected value result in the rejection of the null hypothesis of no spatial autocorrelation. In the bottom panel, the correlograms are compared with Chl a, again showing the close agreement between the spatial distributions of copepods, ostracods and microalgae. The remaining groups exhibit quite different correlograms (Figure 4). As indicated by the results in Table 3, these groups did not yield significant Z values when an overall autocorrelation coefficient was calculated. The correlograms for ciliates, oligochaetes, polychaetes, and turbellarians closely follow the expected value [e(Z)] line, further illustrating the absence of spatial autocorrelation. In general, the Zvalues are lower for these groups and oscillate around the expected value line. Based on the correlograms, two types of distribution patterns emerge from the abundance data. One general type of correlogram, characteristic of Chl a, pheopigments, nematodes, copepods, and ostracods, has a gradual decrease with increasing interval distance to a break point at which the slopeof the line is minimized (w 6-8 cm). This break point can be interpreted as the patch radius for these groups. Chl a, pheopigments, nematodes, copepods, and ostracods all sharesimilar types of spatial distributions (Table 4) and patch sizes. The second type of correlogram doesnot have a common break point for all the groups and Zvalues closely follow the expected value line. Ciliates, oligochaetes, polychaetes, and turbellarians do not exhibit significant autocorrelation patterns and patch sizescan not be determined using correlograms at our sampling scale.
Meiofaunal
and microalgal populations on an intertidal
sandflat
347
TABLE 3. Results of spatial autocorrelation analyses of benthic microalgae and meiofauna1 groups. Moran’s I, Geary’s c, variance to mean ratio, and distribution type are given below. See Table 4 for a description of distribution types. Autocorrelation values were calculated for each replicate (96 cores) using inverse distanced weighting and significant values were assigned using the randomization assumption (Cliff & Ord 1973) Group
Replicate
Ciliata
Copepoda
Nematoda
Oligochaeta
Ostracoda
Polychaeta
Turbellaria
Total
Meiofauna
Chlorophyll
a
Pheopigments
*p
l *p
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
I @118*** -0.015 0.031 0.103*** 0.020 0.084*** 0.170*** 0.139*** 0.236*** - 0.005 - 0.006 0.043* 0,064** 0.108*** 0.085*** 0.025 - 0.005 0.011 0.018 0.071** 0.016 0.158*** 0.134*** 0.235*** 0.250*** 0.215*** o-149*** o-350*** 0.205*** 0.019
c 0.957 1.178 0.978 0.887*** O-989 0.900** 0.810*** 0,847** 0.759*** 1.012 1.044 0.983 0.970 0.898*** 0,875** 1.032 0.992 0.962 0.975 0.951 0.979 0.810*** 0,858** 0,759*** 0,703*** 0,716*** 0.956 0.638*** 0,778*** 0,997
Type 12.97*** 16,22*** 8.27*** 11,03*** 9.98*** 14.61*** 93,90*** 176,37*** 88,88*** 5,13*** 6.76*** 5.73*** 9.42*** 10,17*** 8.04*** 14.42*** 5.81*** 15,89*** 8,78*** 12.37*** 8,48*** 105.05*** 178.23*** 99.27*** 0,394*** 0.443*** 0.687** 0.675** 0.425*** 0,446***
C El El A El A A A A El El c C A A El El El El C El A A A A A A A A A
l **p
Discussion The arrangement of organisms can be assignedtwo distinctly different attributes. Spatial pattern is the geometric arrangement of organismsand can be described in terms of patch sizes and distance between patches. Spatial pattern is independent of a fixed reference point. Distribution pattern, on the other hand, is the arrangement of organisms with respect to a fixed reference point. If the reference point is moved, the distribution pattern changes. Distribution pattern is the real-world orientation of a spatial pattern. Correlograms provide a method for assessingthe spatial pattern of a particular group of organisms. Identical distribution patterns may or may not produce identical correlograms, while different spatial patterns result in different correlograms (Sokal & Oden, 19783). For example, A and B in Figure 5 have identical distribution and spatial patterns, and both will produce identical correlograms. If B is rotated 180” relative to A and and the distributions of A and B are superimposed, the resulting pattern is shown in C. In C, the distribution patterns of A and B are different, but the spatial patterns and correlograms for both are still identical.
348
J. Pinckney & R. Sandulli
TABLE 4. Types of distribution patterns based on values of Moran’s I, Geary’s c, and variance to mean ratio. S indicates that the value is significantly different from the expected value, while NS denotes no significant difference. The + and - signs indicate whether the value is higher or lower than the expected value, respectively. Conclusions are based on discussions contained in Cliff and Ord (1973), Jumars et al. (1977), Sokal and Oden (1978a) and Decho and Fleeger (1988) Type
I
c
sZ/x
A
s
s
-
B C D El
SS NS NS
SNS S NS
SS+
E2
NS
NS
NS
Conclusions Patchy and patch size > 1 core area. Transition from high to low abundance occurs within sampling area. Spatial association between extreme and similar per core abundances. Regular (uniform) distribution or spacing mechanism. Aggregation based on a few extreme peaks in abundance. Aggregation based on a several similar peaks in abundance. Aggregation due to high abundance in a single, isolated core. Patch size smaller than two adjacent cores. Patches not clustered within sample area. Random distribution or spacing mechanism
(b)
-0.4’
0
r 4
L 8 Interval
distance
8 12
6
(cm)
Figure 2. Correlograms for (a) chlorophyl a, (b) pheopigments, and(c) nematodes. Each point is the mean 2 for all three replicates. Error bars are + lSD, (d) is an overlay of (a)-(c) three panels (0, chlorophyl a; A, pheopigments; n , nematodes). Error bars have been excluded for clarity. The expected value line is denoted by s(I). Values were calculated using binary weighting (Sokal& Oden 19783).
Meiofaunal
and microalgal
populations
on an intertidal
349
sandpat
I
-0.q -0.4}
-0.41 0
I
I
I
I 4
I 8
I 12
Interval
dlslance
_1 16
hn)
Figure 3. Correlograms for (a) copepods and (b) ostracods. and (b) plus 2(a) (0, copepods; A, ostracods; n , chlorophyl the same as for Figure 2.
Part (c) is an overlay of (a) n). Otherwise description
(a)
-0.2 -0.4. o-2
’
’
0.0. -0.2.
I (b)
--
c (/I
-_-
-o-44
I
I
--
I
8
3
I
L (cl
0.2. --0.4
I
I
I
I
,_ Cd)
0*20-o-c -0.2 -0.4
(/I f
-
-:-
-
:
I
--
’
--
-----
7
-f ’
. (e)
0.2
0
4
8 Interval
distance
12
16
km)
Figure 4. Correlograms for (a) ciliates, (b) oligochaetes, (c) polychaetes, and (d) turbellarians. Part (e) is an overlay of (a)-(d) (0, cilates; A, oligochaetes; n , polychaetes; +, turbellarians). Otherwise description the same as for Figure 2.
350
3. Pinckney t.9 R. Sandulli
A
B
@p y.y .
C
(l-9 Figure spatial
:a.:. *.::::y .
888. .,y.:.: $2 @
5. Illustration demonstrating the difference pattern. See text for explanation.
between
distribution
pattern
and
Exogenous forces, such as current patterns, sediment type, or other abiotic factors, should promote identical distribution and spatial patterns. Endogenous forces, such as competition, grazing, predation, or other biotic factors, may lead to different distribution patterns between speciesgroups, resulting in either similar or dissimilar spatial patterns (Sokal & Oden, 19786). If the correlograms are similar between different groups, a common linkage to spatial scalesis implied, regardlessof the apparent distribution pattern of each species. This property of spatial autocorrelation analysis can be applied to the meiofaunal and microalgal populations in Barnstable Harbor. In general, thesepopulations exhibit patchy distributions along a gradient of patch sizes. Similarity in patch size infers a common endogenousstructuring force for populations that sharethe samespatial pattern. Copepods, ostracods, nematodes, Chl a, and pheopigments appear to have nearly identical spatial patterns, but do not have the samedistribution pattern. This posesthe question of what endogenousfactors promote the spatial pattern of thesegroups. Our results suggest that some endogenous factor is responsible for structuring the spatial pattern of somemeiofaunal groups in this sandflat community. The observation that nematodes, ostracods, and copepods share a common pattern type with the benthic microalgae suggests that all are influenced by a common endogenous factor. Pheopigments, produced by Chl a breakdown during digestion, may represent previously grazed patches of microalgae. However, pheopigments may also be produced by natural degradation of microalgae, macroalgae, and macrophytic vegetation. Although we do not have any direct evidence for the nature of this structuring force, the results of this study coupled with field observations can be used to propose a mechanism. As a speculation, physical disturbance by feeding Limulus produces body pits which are comparable in size to microalgal-meiofaunal patches, suggesting that disturbance may be the primary structuring force.
Meiofaunal
and microalgal populations on an intertidal
sandflat
351
Time
Figure predator
6. Classical predator-prey curves illustrating to prey (RATIO). See text for explanation.
the variability
in the ratio
of
Herbivory, primarily by the numerically dominant nematodes, may regulate the distribution of microalgae. The absenceof a significant positive correlation between nematode abundance and microalgal biomassmay be explained using a classicalpredator-prey type system (Figure 6). When a successionalmosaic is sampled at a single point in time, the abundance of predator and prey are measured for a fixed point in time. In a dynamic and spatially heterogeneous habitat, replicate samplesare usually randomly collected over a large sample area. With respect to predator-prey dynamics, this procedure would be analogousto randomly selecting points in time and determining the abundance of predator and prey. In highly dynamic communities, the ratio of predator to prey is not fixed, but rather a range of values dependent on the ageor successionalstageof the samplelocation. In a community of successionalmosaics, each patch has its own distinct age and consequently a different predator-prey ratio. When a large number of samplesare collected, the predator-prey ratios represent a wide range of values and simple correlation analyses would be expected to yield non-significant results. The time difference between maximum prey abundance and maximum predator abundance can be defined aslag time. In caseswhere this lag time is0, predator abundance matches prey abundance and correlation analyses will yield highly significant positive correlations. At intermediate lag times, correlation analyses will give non-significant correlations between predator and prey. Further increasesin lag time would produce negative correlations between predator and prey. When looking for trophic relations between consumers and resources, the standard approach seemsto be to look for significant positive correlations between consumers and resources.The underlying idea is that the ratio of consumer to resourceis nearly constant. At the ecosystem level, this is a valid assumption (when the system is at equilibrium). However, the relative abundance of consumers and resources are usually measured at somescale lessthan the ecosystem. In dynamic, heterogeneoushabitats, consumers and resources are usually not present in fixed ratios and consequently, correlation analyses lead to non-significant results. Several studies have reported significant correlations between benthic microalgal biomassand potential meiofaunal herbivores (Montagna et al., 1983; Decho & Castenholz,
352
3. Pinckney
& R. Sandulli
1986; Bianchi 81 Rice, 1988; Decho & Fleeger, 1988). Likewise, others have found no significant correlations between the two (Giere, 1975; Alongi, 1988; Decho & Fleeger, 1988) and some have suggested that meiofaunal grazers are utilizing some other food resource (Joint et al., 1982). We suggest that simple correlation analyses can not and should not be used to infer trophic relations in meiofaunal food webs. Rather, meiofaunal communities should be examined in terms of successional mosaics and herbivore-primary producer (predator-prey) dynamics. Meiofaunal and microalgal communities are extremely dynamic and operate at scales which are difficult to study. We have conducted a detailed examination of spatial distributions of meiofauna and microalgae over a very small area at a single point in time. Using spatial autocorrelation procedures, we have documented the similarities and differences between the spatial patterns of the numerically dominant groups within this microhabitat. Several groups share a common spatial pattern, but the mechanism(s) promoting this have not been thoroughly examined. As demonstrated in this paper, spatial autocorrelation procedures can be used to generate a number of directly testable hypotheses concerning spatial patterns in meiofaunal and microalgal communites. Future research should examine the population dynamics within these microscale patches. Acknowledgements This research was conducted while the authors were attending the Marine Ecology Summer Course at the Marine Biological Laboratory in Woods Hole, Mass. We thank the instructors of this course for their guidance and inspiration. We also thank R. Whitlatch and K. Foreman for helpful suggestions and assistance, and B. Coull, R. Lovell, and two anonymous reviewers for their critical evaluations of earlier drafts of this manuscript. Partial funding was provided by the Slocum-Lunz Foundation. References Admiraal, W., Bouwman, L., Hoekstra, L. & Romeyn, K. 1983 Qualitative and quantitative interactions between microphytobenthos and herbivorous meiofauna on a brackish intertidal mudflat. Int Revue Ges. Hydrobiol68,175-191. Admiraal, W. 1984 The ecology of estuarine sediment-inhabiting diatoms. Progress in PhycoZogoZy Research 3, 269-322. Alongi, D. 1988 Microbial-meiofaunal interrelationships in some tropical intertidal sediments. Journal of Marine Research 46,349-365. Ayers, J. 1959 The hydrography of Barnstable Harbor, Massachusetts. Limnology and Oceanography 4, 448-462. Bell, S., Watzin, M. St Coull, B. 1978 Biogenic structure and its effect on the spatial heterogeneity of meiofauna in a salt marsh. Journal of Exp&imental Marine Biology and Ecology 3$99-107. Bianchi, T. 81 Rice, D. 1988 Feeding ecology of Leitoscoloplosfragil. II. Effects of worm density on benthic diatom production. Marine Biology 99,123-131. Cliff, A. & Ord, J. 1973 Spatial Autocorrelation Pion: London. Cliff, A. & Ord, J. 1981 Spatial Processes: Models and Applications Pion: London. Coull, B. 1971 Estuarine meiofauna interactions. In Estuarine Microbial Ecology (Stevenson, L. & Colwell R. eds). University of South Carolina Press: Columbia, pp. 499-512. Davis, M. &Lee, H. 1983 Recolonization of sediment-associated microalgae and effects of estuarine infauna on microalgal production. Marine Ecology Progress Series 11,227-232. Decho, A. & Castenholz, R. 1986 Spatial patterns and feeding of meiobenthic harpacticoid copepods in relation to resident microbial flora. Hydrobiologia 131,87-96. Decho, A. & Fleeger, J. 1988 Microscale dispersion of meiobenthic copepods in response to food-resource patchiness. Journal of Experimental Marine Biology and Ecology l&229-243. Dustan, P. & Pinckney, J. 1989 Tidally induced estuarine phytoplankton patchiness. Limnology and Oceanography 34,408417.
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