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The large increase in interest concerning the study of spatial patterns of species and communities has undoubtedly partly resulted from the expansion of techniques available for quantitatively and qualitatively assessing patterns. Most of these techniques, for example, quadrat-variance (Greig-Smith, 1952; Upton and Fingleton, 1985), spatial autocorrelation analysis (Cliff and Ord, 1973), mapping (Burrough, 1987), spatially-constrained clustering (Legendre and Fortin, 1989) and ordination (Wartenberg, 1985; Ter Braak, 1986) and semi-variogram analysis (see review by Legendre and Fortin, 1989) were developed by plant ecologists and have been adapted for use in other areas of ecology. Many of these techniques have been employed in the study of spatial patterns in the marine benthic environment. Pattern and observational scale are intrinsically linked (Addicott et al., 1987; Levin, 1992; Platt and Sathyendranath, 1992; Hall et al., 1993; Lawrie, 1996). Patterns evident at one scale can either disappear or appear as noise when viewed at other scales (Eckman, 1979; Valiela, 1984; McArdle et al., 1990; Dutilleul and Legendre, 1993; Thrush et al., 1997a). This is because the processes which give rise to patterns in nature are scale-dependent (Legendre et al., 1997; Thrush et al., 1997b). For example, non-overlapping distributions of two species at one scale may reflect interspecific competition while at larger scales, a positive association between the two species may result from habitat selection (Wiens, 1989). Consequently, studying the relationship between the pattern observed and the process creating that pattern is problematical (McArdle et al., 1997; Thrush et al., 1997a, 1997b). Levin (1992) and Thrush et al. (1997c) suggested that recognition that different processes are relevant at different scales precludes the reductionist approach of sampling at the 'right' scale (e.g., Wiens, 1989; Kotliar and Wiens, 1990). Consequently, they proposed that ecologists should ask the question of how we can draw conclusions from one scale to another. Often, however, the scale at which one samples depends upon the questions one is attempting to answer within a particular study or upon logistical constraints. McArdle and Blackwell (1989) and Legendre et al. (1997) suggested that a good starting point before planning an experiment is the identification of the patterns that can be detected at one or several spatial scales. 2
The scales of observation, or the scales at which one 'views' the environment, obviously impose a limit as to the processes which can be investigated in any one study. This limitation is particularly pertinent to soft-bottom marine benthic studies where the organisms in question are often found exclusively below the sediment surface (Thrush, 1991): since only relatively small areas can be sampled the range of scales studied are small. Observational scales are varied by changing one or more of three aspects of a survey (Kotliar and Wiens, 1989; Legendre and Fortin, 1989; Wiens, 1989; Allen and Hoekstra, 1991; He eta!., 1994; Thrush eta!., 1997b): • grain - the size of the sampling unit, limits the smallest scale which can be investigated; • lag - distance between sampling units; • extent - area covered by the study, determines the largest scale viewed. The greatest amount of information will undoubtedly be gained from an investigation with zero lag and a grain which approaches the size of the organism (i.e., small contiguous cores), but logistically this can only be achieved to a very small extent. The majority of ecological studies carried out in the marine benthic environment have been conducted at scales with grains and lags determined by practical, logistic or other factors and many of them have therefore tended to miss the most suitable scales at which to study. Furthermore, most studies are conducted without any prior knowledge of the spatial scales of patterning which Taylor (1984) suggested leads to non-viable results. In many environmental impact studies for example, patchiness at any spatial scale between that of the sampling units and the location sampled will not be revealed by the sampling design: within-location variation will not have been adequately estimated by the replicate samples thus preventing valid comparisons among locations (Morrisey et al., 1992). Marine soft-bottom infaunal species have been shown to exhibit patchiness at a range of scales. In general, clumped distributions tend to be most common while regular spacing is only apparent at the micro-scale. Studies which have been carried out to 3
- Page 1 and 2: AN INVESTIGATION INTO THE PROCESSES
- Page 3 and 4: ABSTRACT The spionid polychaete Pyg
- Page 5 and 6: ACKNOWLEDGEMENTS I am indebted to m
- Page 7 and 8: Results. . 65 Size distribution of
- Page 9 and 10: CHAPTER 9. GENERAL DISCUSSION . . 2
- Page 11 and 12: Figure 5.4 Figure 5.5 Figure 6.1 Fi
- Page 13 and 14: LIST OF TABLES Table 2.1 Statistica
- Page 15: BACKGROUND CHAPTER 1 INTRODUCTION A
- Page 19 and 20: systematic sampling design to inves
- Page 21 and 22: aised sediment within an otherwise
- Page 23 and 24: Fauchald and Jumars (1979) describe
- Page 25: Dalmeny House and sewage discharged
- Page 28 and 29: E 7— E 6-ac. MHWS MHWN t co 4 —
- Page 30 and 31: variance (TTLQV) techniques (see Lu
- Page 32 and 33: analysis using Moran's and Geary's
- Page 34 and 35: Holme and McIntyre (1984). Percenta
- Page 36 and 37: Pattern Analysis - Grid Surveys Sur
- Page 38 and 39: 57 64 1=1 0 0 0 0 0 0 0 O 0 0 0 0 0
- Page 40 and 41: Maps produced by kriging and other
- Page 42 and 43: 200 180 1160 140 100 1-3 80 g 60 c.
- Page 44 and 45: 2.5 1.5 0.5 0 3 T (i) % Silt/clay%
- Page 46 and 47: v : m pattern Id pattern Ip pattern
- Page 48 and 49: " v : m pattern Id pattern Ip patte
- Page 50 and 51: The results show that at the smalle
- Page 52 and 53: Nephtys hombergii's spatial distrib
- Page 54 and 55: (vii) G. duebeni (ix) % Organic con
- Page 56 and 57: 8m survey - spatial patterns Figure
- Page 58 and 59: (1) P. elegans (iii) L. conchilega
- Page 60 and 61: a) Ts 1.4 0.6 u 0.2 -0.2 1.4 'E5 0.
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The scales of observation, or the scales at which one 'views' the environment,<br />
obviously impose a limit as to the processes which can be investigated in any one<br />
study. This limitation is particularly pertinent to soft-bottom marine benthic studies<br />
where the organisms in question are often found exclusively below the sediment<br />
surface (Thrush, 1991): since only relatively small areas can be sampled the range of<br />
scales studied are small. Observational scales are varied by changing one or more of<br />
three aspects of a survey (Kotliar and Wiens, 1989; Legendre and Fortin, 1989;<br />
Wiens, 1989; Allen and Hoekstra, 1991; He eta!., 1994; Thrush eta!., 1997b):<br />
• grain - the size of the sampling unit, limits the smallest scale which can be<br />
investigated;<br />
• lag - distance between sampling units;<br />
• extent - area covered by the study, determines the largest scale viewed.<br />
The greatest amount of information will undoubtedly be gained from an investigation<br />
with zero lag and a grain which approaches the size of the organism (i.e., small<br />
contiguous cores), but logistically this can only be achieved to a very small extent.<br />
The majority of ecological studies carried out in the marine benthic environment have<br />
been conducted at scales with grains and lags determined by practical, logistic or other<br />
factors and many of them have therefore tended to miss the most suitable scales at<br />
which to study. Furthermore, most studies are conducted without any prior<br />
knowledge of the spatial scales of patterning which Taylor (1984) suggested leads to<br />
non-viable results. In many environmental impact studies for example, patchiness at<br />
any spatial scale between that of the sampling units and the location sampled will not<br />
be revealed by the sampling design: within-location variation will not have been<br />
adequately estimated by the replicate samples thus preventing valid comparisons<br />
among locations (Morrisey et al., 1992).<br />
Marine soft-bottom infaunal species have been shown to exhibit patchiness at a range<br />
of scales. In general, clumped distributions tend to be most common while regular<br />
spacing is only apparent at the micro-scale. Studies which have been carried out to<br />
3
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