Making species salinity sensitivity distributions reflective of naturally occurring communities: using rapid testing and Bayesian statistics.
Species sensitivity distributions (SSDs) may accurately predict the proportion of species in a community that are at hazard from environmental contaminants only if they contain sensitivity data from a large sample of species representative of the mix of species present in the locality or habitat of interest. With current widely accepted ecotoxicological methods, however, this rarely occurs. Two recent suggestions address this problem. First, use rapid toxicity tests, which are less rigorous than conventional tests, to approximate experimentally the sensitivity of many species quickly and in approximate proportion to naturally occurring communities. Second, use expert judgements regarding the sensitivity of higher taxonomic groups (e.g., orders) and Bayesian statistical methods to construct SSDs that reflect the richness (or perceived importance) of these groups. Here, we describe and analyze several models from a Bayesian perspective to construct SSDs from data derived using rapid toxicity testing, combining both rapid test data and expert opinion. We compare these new models with two frequentist approaches, Kaplan-Meier and a log-normal distribution, using a large data set on the salinity sensitivity of freshwater macroinvertebrates from Victoria (Australia). The frequentist log-normal analysis produced a SSD that overestimated the hazard to species relative to the Kaplan-Meier and Bayesian analyses. Of the Bayesian analyses investigated, the introduction of a weighting factor to account for the richness (or importance) of taxonomic groups influenced the calculated hazard to species. Furthermore, Bayesian methods allowed us to determine credible intervals representing SSD uncertainty. We recommend that rapid tests, expert judgements, and novel Bayesian statistical methods be used so that SSDs reflect communities of organisms found in nature.
Hickey
Graeme L
GL
Department of Mathematical Sciences, Durham University, South Road, Durham DHI 3LE, United Kingdom. g.l.hickey@durham.ac.uk
Kefford
Ben J
BJ
Dunlop
Jason E
JE
Craig
Peter S
PS
D000818:Animals; D001499:Bayes Theorem; D004463:Ecology; D004785:Environmental Pollutants / Q000633:toxicity*; D012965:Sodium Chloride / Q000633:toxicity*; D013045:Species Specificity; D023382:Toxicity Tests, Acute / Q000379:methods*
D004785:Environmental Pollutants; D012965:Sodium Chloride