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A life‐history approach to predicting the recovery of aquatic invertebrate populations after exposure to xenobiotic chemicals
Author(s) -
Sherratt Thomas N.,
Roberts Gilbert,
Williams Penny,
Whitfield Mercia,
Biggs Jeremy,
Shillabeer Nigel,
Maund Stephen J.
Publication year - 1999
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.5620181118
Subject(s) - invertebrate , mesocosm , taxon , biology , cypermethrin , ecology , environmental science , pesticide , ecosystem
A combined empirical and theoretical study was conducted to evaluate the degree to which an understanding of the life histories of different freshwater invertebrate taxa could improve our ability to predict their relative rates of recovery after a toxic perturbation. Two chemicals, cypermethrin and 3,4‐dichloroaniline, were introduced separately into large freshwater tanks (mesocosms, 1.25 m diameter, 1.25 m depth) containing established freshwater invertebrate communities. Immigration was simulated in selected mesocosms by introducing particular taxa at predetermined intervals. For both chemical treatments, laboratory rank toxicity data successfully predicted the observed relative short‐term mortalities of species, whereas the relative times taken for taxa to recover from cypermethrin exposure also correlated with their relative susceptibilities to this compound. Crucially, a significant component of residual variance in rank recovery to cypermethrin was explained by variation in overall rates of reproduction among taxa. A simulation model was therefore developed to allow us to integrate the disparate information on invertebrate life histories into a predictive model. This model was broadly supported, particularly for the cypermethrin exposure data, where observed and predicted rank recovery times were highly correlated. Overall, this study broadly confirms the importance of life‐history characters in governing recovery and the validity of simple modeling approaches for predicting recovery.