Developing demographic toxicity data: optimizing effort for predicting population outcomes

  • Stark J
  • Banks J
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Abstract

Mounting evidence suggests that population endpoints in risk assessment are far more accurate than static assessments. Complete demographic toxicity data based on full life tables are eminently useful in predicting population outcomes in many applications because they capture both lethal and sublethal effects; however, developing these life tables is extremely costly. In this study we investigated the efficiency of partial life cycle tests as a substitute for full life cycles in parameterizing population models. Life table data were developed for three species of Daphniids, Ceriodaphnia dubia , Daphnia magna , and D. pulex , weekly throughout the life span of these species. Population growth rates ( λ ) and a series of other demographic parameters generated from the complete life cycle were compared to those calculated from cumulative weeks of the life cycle in order to determine the minimum number of weeks needed to generate an accurate population projection. Results showed that for C. dubia and D. pulex , λ values developed at >4 weeks (44.4% of the life cycle) were not significantly different from λ developed for the full life cycle (9 weeks) of each species. For D. magna , λ values developed at >7 weeks (70% of the life cycle) were not significantly different from λ developed for the full life cycle (10 weeks). Furthermore, these cutoff points for λ were not the same for other demographic parameters, with no clear pattern emerging. Our results indicate that for C. dubia, D. magna , and D. pulex , partial life tables can be used to generate population growth rates in lieu of full life tables. However, the implications of differences in cutoff points for different demographic parameters need to be investigated further.

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Stark, J. D., & Banks, J. E. (2016). Developing demographic toxicity data: optimizing effort for predicting population outcomes. PeerJ, 4, e2067. https://doi.org/10.7717/peerj.2067

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