Relevance of Risk Predictions Derived from a Chronic Species Sensitivity Distribution with Cadmium to Aquatic Populations and Ecosystems

Abstract

Criteria to protect aquatic life are intended to protect diverse ecosystems, but in practice are usually developed from compilations of single-species toxicity tests using standard test organisms that were tested in laboratory environments. Species sensitivity distributions (SSDs) developed from these compilations are extrapolated to set aquatic ecosystem criteria. The protectiveness of the approach was critically reviewed with a chronic SSD for cadmium comprising 27 species within 21 genera. Within the data set, one genus had lower cadmium effects concentrations than the SSD fifth percentile-based criterion, so in theory this genus, the amphipod Hyalella, could be lost or at least allowed some level of harm by this criteria approach. However, population matrix modeling projected only slightly increased extinction risks for a temperate Hyalella population under scenarios similar to the SSD fifth percentile criterion. The criterion value was further compared to cadmium effects concentrations in ecosystem experiments and field studies. Generally, few adverse effects were inferred from ecosystem experiments at concentrations less than the SSD fifth percentile criterion. Exceptions were behavioral impairments in simplified food web studies. No adverse effects were apparent in field studies under conditions that seldom exceeded the criterion. At concentrations greater than the SSD fifth percentile, the magnitudes of adverse effects in the field studies were roughly proportional to the laboratory-based fraction of species with adverse effects in the SSD. Overall, the modeling and field validation comparisons of the chronic criterion values generally supported the relevance and protectiveness of the SSD fifth percentile approach with cadmium.

Predicted mixture toxic pressure relates to observed fraction of benthic macrofauna species impacted by contaminant mixtures

“…31,2012 half-year); (2) area (sites within the more remote natural Biesbosch wetlands area [nature reserve] and sites outside that area, which are more subject to direct human influences); (3) channel classification (classification of use of water body for shipping; 0 ¼ no shipping, and values between 1 and 6 represent a range from extensive recreational shipping to extensive professional shipping with large 6-part push barge vessels); (4) fairway width classification (seven classes); (5) pH (KClextraction); (6) percentage of dry matter of sediment samples (%); (7) water depth (m); (8) sedimentation or erosion (cm/year, based on echo sounding and spatial interpolation in the context of a large sediment-balance assessment for the area); (9) sand fraction (% dry wt; particle size >210 mm); (10) tidal difference (cm); (11) lutum fraction (% dry wt; particle size <2 mm); (12) organic matter fraction (% dry wt); and (13) acute toxic pressure (msPAF-EC50). Toxicol.…”

Extrapolating Growth Reductions in Fish to Changes in Population Extinction Risks: Copper and Chinook Salmon

“…Here we try to consider some of these issues at the population level, assuming that these inferences have relevance to other scales of ecological organization that are too complex to directly consider in this analysis, such as metapopulations or ecosystems (Ferson and Ginzburg 1996). Some previous uses of demographic population models to estimate risk from contaminants to aquatic organisms include striped bass with several chemicals (Barnthouse et al 1989), larval estuarine fish, crustaceans, and hypoxia (USEPA 2000), brook trout and fathead minnow populations with endocrine disrupting chemicals (Brown et al 2003), contaminant effects on swimming speed and predator evasion behaviors in a juvenile marine fish (Rose et al 2003;Murphy et al 2008), coastal salmon populations with an ocean-type life history of limited freshwater residency exposed to a generic contaminant causing 10% reduction in mortality and reproduction Meador 2005, 2006), cutthroat trout populations and selenium ( Van Kirk and Hill 2007), and benthic crustacean populations and cadmium (Mebane 2010).…”

Handling Fish Mixture Exposures in Risk Assessment

“…Where probabilistic toxic effect predictions are matched with biological field observations, it has been noted that effects in the field are rarely observed at concentrations equivalent to lower centiles from chronic NOEC distributions such as the aforementioned PNEC-level, for example, the HC5 derived from an NOEC-SSD (Giddings, Solomon et al, 2001;Van den Brink, Blake et al, 2006;Mebane, 2010). Where probabilistic toxic effect predictions are matched with biological field observations, it has been noted that effects in the field are rarely observed at concentrations equivalent to lower centiles from chronic NOEC distributions such as the aforementioned PNEC-level, for example, the HC5 derived from an NOEC-SSD (Giddings, Solomon et al, 2001;Van den Brink, Blake et al, 2006;Mebane, 2010).…”