Traditional environmental studies have employed sampling at different times, but based on re-randomized 'replicate' samples taken at each time. For example, in a 4 year monitoring study of near-shore marine benthic communities there might be three box cores collected annually at each of three depths along each of three transects. Repeated measures designs, long used in medicine and the social sciences, are based on resampling replicates (e.g. sites) at a series of times. In such designs spatial sampling variability is not used for tests of environmental impact. Error for such tests is based on variability of time trends among similar sites (similar with respect to impact). For example in a tropical oil spill study five oiled and five unoiled coral reefs were studied over 5 years. Error for tests of oil impact was based on variability among reefs (within degree-of-oiling category) in the year-to-year trends of biological response variables. It was not based on variability among field samples within reefs at given times.The two approaches (univariate and multivariate) to repeated measures analysis of variance are described. The pros and cons of each are discussed, as are the assumptions and consequences of their violations. Emphasis is especially placed on the adequacy of error degrees of freedom in the two approaches, and some exploration of power to detect impact is presented. Examples of application of repeated measures designs to various impact and monitoring studies are presented and discussed, including (i) interpretation of significant effects; (ii) decomposition of effects by contrasts (e.g. before vs after impact); and (iii) modelling time trends by polynomial and cosine functions.

Many environmental studies generate multivariate data, often consisting of two or more conceptually different sets of variables. For example one may wish to relate species abundances to environmental variables. A recent concept gaining popularity in marine benthic impact studies is the "sediment quality triad", which relates benthic community composition as one set of variables to sediment chemistry (contaminents) as a second set, and to toxicities of sediment to test organisms in the laboratory as a third set. This is conceptually appealing, but the original applications of the concept rely heavily on indices (e.g. diversity indices, toxicity indices) rather than on multivariate statistics. Procedures for testing and describing relationships among variables are reviewed, with emphasis on environmental applications. We describe and exemplify several multivariate statistical approaches to effective application of the sediment quality triad concept, including Mantel's tests and related descriptive analyses; ordination on each set of variables followed by methods to relate ordination axes among the sets; canonical correlation analysis; fitting a general linear model; and cluster analysis on each set of variables followed by a three-way contingency table analysis.

The results of a preliminary organic budget for a small (1.13 h) dystrophic lake in Maritime Canada are presented. Wood's Pond is moderately autotrophically productive (395 mg C m-2 day-'). Allochthonous inputs overwhelmed autochthonous production by an approximate ratio of 9.7:1. Seepage through the surrounding Sphagnum mosses accounted for 89.9% of all organic carbon entering the lake while fluvial transport was responsible for over 90% of losses. The yearly balance was a net loss of approximately 1 820 kg C. The marked dominance of allochthonous inputs over autochthonous production obviously influences the physico-chemical limnology of Wood's Pond but it does not necessarily follow that this system is heterotrophic. The yearly autotrophic production, approximately 1466 kg C, essentially balances an annual community respiration and insect emergence of 1 481 kg C. This suggests that, at least in theory, the biological functioning of this dystrophic lake system could be driven almost entirely by autotrophic production.