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9.6 Example: Fal estuary copepods
and present biotic and environmental data from five creeks of the Fal estuary, SW England, whose sediments can contain high heavy metal levels resulting from historic tin and copper mining in the surrounding valleys ({f}, Fig. 9.3). Fig. 9.3 Fal estuary cop...
9.7 Variability weighting
describe a similar idea to dispersion weighting for use when the data are continuous biological variables, such as diversity indices or other measures of ecological health of an assemblage. For such non-quantity data, for which zero plays no special role (and...
10.1 Species aggregation
Fig. 10.1a repeats the multivariate ordination (nMDS) seen in Fig. 1.7 for the macrofaunal data from Frierfjord, based on 4th-root transformed species counts and Bray-Curtis similarities among the 24 samples (at 6 sites, A-E,G). The assemblage consisted of 110...
10.2 Examples
Multivariate examples Nutrient-enrichment experiment In the soft-bottom mesocosms at Solbergstrand, Norway {N}, box-cores of sublittoral sediment were subjected to three levels of particulate organic enrichment (L = low dose, H = high dose and C = control), ...
10.3 Recommendation
Clearly the operational taxonomic level for environmental impact studies is another factor to be considered when planning such a survey, along with decisions about the number of stations to be sampled, number of replicates, types of statistical analysis to be ...
11.1 Introduction
Approach In many studies, the biotic data is matched by a suite of environmental variables measured at the same set of sites. These could be natural variables describing the physical properties of the substrate (or water) from which the samples were taken, e....
11.2 Example: Garroch Head macrofauna
For the 12 sampling stations (Fig. 8.3) across the sewage-sludge dump ground at Garroch Head {G}, the biotic information was supplemented by sediment chemical data on metal concentrations (Cu, Mn, Co, ...) and organic loading (% carbon and nitrogen); also reco...
11.3 Linking biota to univariate environmental measures (and examples)
Univariate community measures If the biotic data are best summarised by one, or a few, simple univariate measures (such as diversity indices), one possibility is to attempt to correlate these with a similarly small number of environmental variables, taken one ...
11.4 Linking biota to multivariate environmental patterns
The intuitive premise adopted here is that if the suite of environmental variables responsible for structuring the community were known¶, then samples having rather similar values for these variables would be expected to have rather similar species composition...
11.5 Further ‘BEST’ variations
Entering variables in groups In some contexts, it makes good sense to utilise an a priori group structure for the explanatory variables and enter or drop all variables within a single group simultaneously, e.g. if locations of sites expressed in latitude and l...
11.6 Linkage trees (and example)
The idea of linkage trees¶ is most easily understood in the context of a particular example, so Fig. 11.12 redisplays some of the nMDS bubble plots for the 17 Exe estuary sites used to illustrate the BEST/Bio-Env procedure, earlier in this chapter. Bio-Env sho...
11.7 Concluding remarks
For this chapter as a whole, two final points need to be made. The topic of experimental and field survey design for ecologists is a large one, addressed to some extent in the accompanying PERMANOVA+ manual ()¶, but this is a problematic area for all multivari...
12.1 Introduction
In Chapter 11 we have seen how both the univariate and multivariate community attributes can be correlated with natural and anthropogenic environmental variables. With careful sampling design, these methods can provide strong evidence as to which environmenta...
12.2 `Natural experiments’
It is doubtful whether so called natural experiments deserve to be called ‘experiments’ at all, and not simply well-designed field surveys, since they make comparisons of places or times which differ in the intensity of the particular environmental factor unde...
12.3 Field experiments
Field manipulative experiments include, for example, caging experiments to exclude or include predators, controlled pollution of experimental plots, and big-bag experiments with plankton. Their use was historically (unsurprisingly) predominantly for univariate...
12.4 Laboratory experiments
More or less natural communities of some components of the biota can be maintained in laboratory (and also outdoor) experimental containers and subjected to a variety of manipulations. Many types of experimental systems have been used for marine studies, rang...
13.1 Components
The biological effects of pollutants can be studied on assemblages of a wide variety of marine organisms: Pelagos plankton (both phytoplankton and zooplankton) fish (pelagic and demersal) Benthos (soft-bottom) macrobenthos meiobenthos microbenthos, not mu...
13.2 Plankton and fish
Plankton The advantages of plankton are that: a) Long tows over relatively large distances result in community samples which reflect integrated ecological conditions over large areas. They are therefore useful in monitoring more global changes. b) Identificat...
13.3 Macrobenthos and meiobenthos
Macrobenthos The advantages of soft-bottom macrobenthos are that: a) They are relatively non-mobile and are therefore useful for studying the local effects of pollutants. b) Their taxonomy is relatively easy. c) Quantitative sampling is relatively easy. d) The...
13.4 Hard-bottom epifauna and hard-bottom motile fauna
Hard-bottom epifauna The advantages of using hard-bottom encrusting faunas, reef-corals etc. are: a) They are immobile and therefore good for local effects studies. b) A major advantage over sedimentary faunas is that non-destructive (visual/photographic) samp...