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doi: 10.1111/j.1442-9993.1993.tb00432.xpmid: N/A
Abstract This paper concerns the ways that our philosophical attitudes to the environment can influence the appropriateness of methodologies for solving environmental problems. Sometimes a public perception is expressed that science takes scant regard of the concerns of the people affected. Is it possible for scientists and managers to respond to such concerns and still fulfil the logical and methodological rigour that their discipline demands? I believe we have to address fundamental issues of definitions and meaning before useful debate can occur among parties interested in environmental decision‐making. Delving into the ideas behind our everyday practices of environmental management should promote re‐evaluation of our beliefs, attitudes and concerns about nature.
doi: 10.1111/j.1442-9993.1993.tb00433.xpmid: N/A
Abstract I discuss briefly how the basic science of ecology contributed to the resolution of environmental problems in eight different cases in which I have been involved over the past few years. I then draw from 2 decades of experience as an academic marine ecologist and biological oceanographer to extract general lessons from each case on how environmental applications could be improved by better use of the wisdom of the field of ecology. The eight cases respresent four pairs of contrasts covering a spectrum of applications of ecological science to environmental problems: (i) two assessments of the environmental impacts from coastal ocean discharges, one evaluated by data collected under traditionally designed, self‐monitoring programmes largely mandated by the agency granting the discharge permit and the other employing an academically designed programme of explicit impact testing; (ii) two assessments of the effects of offshore oil and gas development, one done before exploration drilling to assess the adequacy of available environmental information on which to base decisions about drilling permits and the other an analysis of natural resource damages after a coastal oil spill had occurred; (iii) two evaluations of environmental problems conducted by special ad hoc panels created to resolve the environmental disputes; and (iv) two examples of how rule‐making commissions employed ecological insights in the establishment of regulations, in one case to preserve environmental quality and in the other to manage natural fishery resources.
doi: 10.1111/j.1442-9993.1993.tb00434.xpmid: N/A
Abstract Dynamic numerical models and field experiments play important roles in impact assessment and management. Unfortunately, extreme and simplistic views have developed about whether and how to use these tools, so their complementary values to the manager are often not recognized. We often hear the outrageous claim (or hope) that numerical models can synthesize ‘all’ relevant information for predicting the impact of policy choice, hence making experimental experience unnecessary. From experimentalists, we hear the equally naive criticism that ecological systems are so complex that nothing is predictable without experimental experience. What we usually get from the proponents of these extremes are either models that are dangerously unreliable, or experiments that provide nice scientific answers to the wrong questions. Wise use of modelling begins with the following points: (i) explicit modelling is an excellent way to clarify policy concerns and identify processes that are most likely to be important in making predictions about policy effects; (ii) we can do a very good job of modelling some processes and relationships, particularly those having to do with basic spatial and temporal scales of impact as related to physical transport, chemical transformations, and life history characteristics of indicator populations (longevity, delays and response times due to age structured rates of reproduction and mortality); and (iii) there are some important dynamic processes, such as long‐term accumulation of toxic materials in the environment, that unfold over such large space and time scales as to preclude direct experimental study (leaving only the issue of which models to use in making predictions, not whether to model ‐ unless the processes are simply ignored). But points (i) and (ii) represent steps that a good experimentalist will take anyway: be clear about what practical results an experiment is intended to produce, and do not waste effort on experiments to measure things that can be predicted reliably from existing knowledge. The key to successful use of modelling and experimentation in management is in making good judgements about the interface between points (ii) and (iii); that is, in making good judgements about both what variables cannot be reliably predicted, and of these, which to treat experimentally and which to gamble on predicting from models.
doi: 10.1111/j.1442-9993.1993.tb00435.xpmid: N/A
Abstract The measurement of changes in the structure of natural marine communities is widely used for the detection and monitoring of man's impact on the sea. Such studies are almost always a compromise between the scientific ideal and political, financial and logistical constraints. This paper considers a number of these constraints, suggests some possible solutions, and discusses the consequences (in terms of loss of information) of adopting strategies which might be considered suboptimal.
doi: 10.1111/j.1442-9993.1993.tb00436.xpmid: N/A
Abstract 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.
doi: 10.1111/j.1442-9993.1993.tb00437.xpmid: N/A
Abstract Procedures used to detect environmental impacts that occur as a result of planned disturbances are often inadequate. Widely used designs for univariate measures, such as the abundance of a population, lack proper spatial replication and have unjustified patterns of temporal sampling. Asymmetrical analyses of variance derived from repeated measures models can be used to detect many types of impact that are not identifiable using widely recommended BACI (Before/After, Control/Impact) sampling. These asymmetrical, beyond BACI designs are also more logical because of spatial replication. The mechanics of these procedures are discussed, including worked examples of calculations, considerations of their power to detect impacts of a specified magnitude and the integration of various temporal and spatial scales into the design. Related issues are briefly discussed concerning optimization of sampling and how to proceed when no data are available before a disturbance.
doi: 10.1111/j.1442-9993.1993.tb00438.xpmid: N/A
Abstract In the early 1980s, a strategy for graphical representation of multivariate (multi‐species) abundance data was introduced into marine ecology by, among others, Field, et al. (1982). A decade on, it is instructive to: (i) identify which elements of this often‐quoted strategy have proved most useful in practical assessment of community change resulting from pollution impact; and (ii) ask to what extent evolution of techniques in the intervening years has added self‐consistency and comprehensiveness to the approach. The pivotal concept has proved to be that of a biologically‐relevant definition of similarity of two samples, and its utilization mainly in simple rank form, for example ‘sample A is more similar to sample B than it is to sample C’. Statistical assumptions about the data are thus minimized and the resulting non‐parametric techniques will be of very general applicability. From such a starting point, a unified framework needs to encompass: (i) the display of community patterns through clustering and ordination of samples; (ii) identification of species principally responsible for determining sample groupings; (iii) statistical tests for differences in space and time (multivariate analogues of analysis of variance, based on rank similarities); and (iv) the linking of community differences to patterns in the physical and chemical environment (the latter also dictated by rank similarities between samples). Techniques are described that bring such a framework into place, and areas in which problems remain are identified. Accumulated practical experience with these methods is discussed, in particular applications to marine benthos, and it is concluded that they have much to offer practitioners of environmental impact studies on communities.
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