Cargando…

Developing demographic toxicity data: optimizing effort for predicting population outcomes

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 subleth...

Descripción completa

Detalles Bibliográficos
Autores principales: Stark, John D., Banks, John E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888283/
https://www.ncbi.nlm.nih.gov/pubmed/27257546
http://dx.doi.org/10.7717/peerj.2067
_version_ 1782434837507866624
author Stark, John D.
Banks, John E.
author_facet Stark, John D.
Banks, John E.
author_sort Stark, John D.
collection PubMed
description 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.
format Online
Article
Text
id pubmed-4888283
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher PeerJ Inc.
record_format MEDLINE/PubMed
spelling pubmed-48882832016-06-02 Developing demographic toxicity data: optimizing effort for predicting population outcomes Stark, John D. Banks, John E. PeerJ Ecology 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. PeerJ Inc. 2016-05-25 /pmc/articles/PMC4888283/ /pubmed/27257546 http://dx.doi.org/10.7717/peerj.2067 Text en ©2016 Stark and Banks http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Ecology
Stark, John D.
Banks, John E.
Developing demographic toxicity data: optimizing effort for predicting population outcomes
title Developing demographic toxicity data: optimizing effort for predicting population outcomes
title_full Developing demographic toxicity data: optimizing effort for predicting population outcomes
title_fullStr Developing demographic toxicity data: optimizing effort for predicting population outcomes
title_full_unstemmed Developing demographic toxicity data: optimizing effort for predicting population outcomes
title_short Developing demographic toxicity data: optimizing effort for predicting population outcomes
title_sort developing demographic toxicity data: optimizing effort for predicting population outcomes
topic Ecology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888283/
https://www.ncbi.nlm.nih.gov/pubmed/27257546
http://dx.doi.org/10.7717/peerj.2067
work_keys_str_mv AT starkjohnd developingdemographictoxicitydataoptimizingeffortforpredictingpopulationoutcomes
AT banksjohne developingdemographictoxicitydataoptimizingeffortforpredictingpopulationoutcomes