Design Criteria and Derivation of Indicators for Ecological Position, Direction, and Risk

Abstract

The baseline specifications for indicators for ecological risk assessment and management are presented. These indicators may be related to the assessment space as determined by cultural values and laws. This assessment space is a multivariate expression of the assessment endpoints. The three types of indicators include current position, the probability of leaving the assessment space, and the probability of reentering the assessment space. The formulation of indicators must recognize that ecological systems are dynamically complex and composed of both deterministic and stochastic components. As has been demonstrated in laboratory ecological systems and field experiments, the best impact indicators change over time, eliminating the possibility of a single variable being an accurate measure of position and change. We set six specific requirements for developing a useful set of indicators, as follows. The indicators must be placed in the context of the assessment space. Because projections cause the loss of information, a variety of methods with different assumptions about the nature of the system should be used. The best attributes to measure alteration of the system change over time; to use just one set may result in missing the dynamic complexity of the system and subsequent impacts. Ecological systems are both deterministic and stochastic, and representation of each is important. The stochastic component can have a tremendous impact, changing the overall dynamics. Spatial context is critical in making accurate predictions and must be known.

Persistence of the Effects of Jet-a in a Microcosm With Releases From the Sediment

“…The specific results of this MFC multispecies experiment are distinctive from those obtained by the SAM protocol using the same and related jet fuels [22,23,42]. The SAM experiments use a defined set of organisms inoculated into a nutrientrich media.…”

“…This allowed the toxicant to persist longer than in experiments using the water-soluble fraction of Jet-A and other turbine fuels [22,23,42]. This allowed the toxicant to persist longer than in experiments using the water-soluble fraction of Jet-A and other turbine fuels [22,23,42].…”

“…Dealing with dynamic systems is difficult statistically with typical ecological data sets, although methods are under development [42]. These microcosm experiments, because of their relatively low variability and number of true replicates, can find treatment related impacts at very low concentrations of materials.…”

Dos and don'ts of spatially explicit ecological risk assessments

“…In addition to quantifying spatial patterns of contaminants, spatially explicit ERAs may also address spatial processes such as animal movement. Only very recently have spatially explicit models of animal movement and meta‐population dynamics begun to be used in ERAs [1–3,13,14]. For example, the effect of flounder foraging behavior and habitat preferences on exposure to polychlorinated biphenyls in sediments were assessed using a tractable individual‐based model [15].…”

“…Such spatial patchiness is probably a common characteristic of ecosystems and has been an active area of research in ecology [22] and particularly in landscape ecology [23,24]. Based on a review of investigations of toxicants in aquatic and terrestrial microcosms, computer simulations of meta‐populations, and field studies with patchy environments, Landis and McLaughlin [2] make the following four conclusions: Low levels of toxicity in one patch can affect populations in other patches that have no direct exposure to the toxicant; the spatial arrangement of patches strongly influences toxicity; effects of a toxicant persist after it has degraded due to effects on population dynamics; and several outcomes can occur with the same initial conditions. For these reasons, spatially explicit ERAs may need to confront complex spatial and temporal interactions and dynamics that complicate analyses and interpretations.…”