Evaluating AUSRIVAS predictive model performance for detecting simulated eutrophication effects on invertebrate assemblages

Sue NICHOLS, Trefor Reynoldson, Evan HARRISON

    Research output: Contribution to journalArticle

    5 Citations (Scopus)

    Abstract

    Confidence in any bioassessment method is related to its ability to detect ecological improvement or impairment. We evaluated Australian River Assessment (AUSRIVAS)-style predictive models built using reference site data sets from the Australian Capital Territory (ACT), the Yukon Territory (YT; Canada), and the Laurentian Great Lakes (GL; North America) area. We evaluated model performance as ability to correctly assign reference condition with independent reference-site data. Evaluating model ability to detect human disturbance is generally more problematic because the actual condition of test sites is usually unknown. Independent reference-site data underwent simulated impairment by varying the proportions of sensitive, intermediate, and tolerant taxa to simulate degrees of eutrophication. Model performance was related to differences in data sets, such as number and distribution of invertebrate taxa. Sensitive taxa tended to have lower expected probabilities of occurrence than more-tolerant taxa, but the distribution of taxa grouped by tolerance categories also differed by data set. Thus, the models differed in ability to detect the simulated impairment. The ACT model performed best with respect to Type 1 error rates (0%) and the GL model the worst (38%). The YT model performed best (10% error) for detecting moderate impairment, and the ACT model detected all severely impaired sites. AUSRIVAS did not assign most mildly impaired sites to below-reference condition, but a reduction in observed/expected values for some of the mildly impaired sites was observed. Models did not detect mild impairment that simply changed taxon abundances because presence–absence data were used for models. However, in comparison with other models described in this special issue (that did use abundance data), the AUSRIVAS model performance was comparable or better for detecting the simulated moderate and severe impairments.
    Original languageEnglish
    Pages (from-to)1212-1224
    Number of pages13
    JournalFreshwater Science
    Volume33
    Issue number4
    DOIs
    Publication statusPublished - 2014

    Fingerprint

    eutrophication
    invertebrate
    invertebrates
    rivers
    river
    Australian Capital Territory
    effect
    biological assessment
    Yukon Territory
    Great Lakes
    tolerance
    Canada
    disturbance

    Cite this

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    title = "Evaluating AUSRIVAS predictive model performance for detecting simulated eutrophication effects on invertebrate assemblages",
    abstract = "Confidence in any bioassessment method is related to its ability to detect ecological improvement or impairment. We evaluated Australian River Assessment (AUSRIVAS)-style predictive models built using reference site data sets from the Australian Capital Territory (ACT), the Yukon Territory (YT; Canada), and the Laurentian Great Lakes (GL; North America) area. We evaluated model performance as ability to correctly assign reference condition with independent reference-site data. Evaluating model ability to detect human disturbance is generally more problematic because the actual condition of test sites is usually unknown. Independent reference-site data underwent simulated impairment by varying the proportions of sensitive, intermediate, and tolerant taxa to simulate degrees of eutrophication. Model performance was related to differences in data sets, such as number and distribution of invertebrate taxa. Sensitive taxa tended to have lower expected probabilities of occurrence than more-tolerant taxa, but the distribution of taxa grouped by tolerance categories also differed by data set. Thus, the models differed in ability to detect the simulated impairment. The ACT model performed best with respect to Type 1 error rates (0{\%}) and the GL model the worst (38{\%}). The YT model performed best (10{\%} error) for detecting moderate impairment, and the ACT model detected all severely impaired sites. AUSRIVAS did not assign most mildly impaired sites to below-reference condition, but a reduction in observed/expected values for some of the mildly impaired sites was observed. Models did not detect mild impairment that simply changed taxon abundances because presence–absence data were used for models. However, in comparison with other models described in this special issue (that did use abundance data), the AUSRIVAS model performance was comparable or better for detecting the simulated moderate and severe impairments.",
    keywords = "reference condition approach, AUSRIVAS, simulated impairment, bioassessment, predictive modeling, macroinvertebrates.",
    author = "Sue NICHOLS and Trefor Reynoldson and Evan HARRISON",
    year = "2014",
    doi = "10.1086/678946",
    language = "English",
    volume = "33",
    pages = "1212--1224",
    journal = "Journal of the North American Benthological Society",
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    Evaluating AUSRIVAS predictive model performance for detecting simulated eutrophication effects on invertebrate assemblages. / NICHOLS, Sue; Reynoldson, Trefor; HARRISON, Evan.

    In: Freshwater Science, Vol. 33, No. 4, 2014, p. 1212-1224.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Evaluating AUSRIVAS predictive model performance for detecting simulated eutrophication effects on invertebrate assemblages

    AU - NICHOLS, Sue

    AU - Reynoldson, Trefor

    AU - HARRISON, Evan

    PY - 2014

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    N2 - Confidence in any bioassessment method is related to its ability to detect ecological improvement or impairment. We evaluated Australian River Assessment (AUSRIVAS)-style predictive models built using reference site data sets from the Australian Capital Territory (ACT), the Yukon Territory (YT; Canada), and the Laurentian Great Lakes (GL; North America) area. We evaluated model performance as ability to correctly assign reference condition with independent reference-site data. Evaluating model ability to detect human disturbance is generally more problematic because the actual condition of test sites is usually unknown. Independent reference-site data underwent simulated impairment by varying the proportions of sensitive, intermediate, and tolerant taxa to simulate degrees of eutrophication. Model performance was related to differences in data sets, such as number and distribution of invertebrate taxa. Sensitive taxa tended to have lower expected probabilities of occurrence than more-tolerant taxa, but the distribution of taxa grouped by tolerance categories also differed by data set. Thus, the models differed in ability to detect the simulated impairment. The ACT model performed best with respect to Type 1 error rates (0%) and the GL model the worst (38%). The YT model performed best (10% error) for detecting moderate impairment, and the ACT model detected all severely impaired sites. AUSRIVAS did not assign most mildly impaired sites to below-reference condition, but a reduction in observed/expected values for some of the mildly impaired sites was observed. Models did not detect mild impairment that simply changed taxon abundances because presence–absence data were used for models. However, in comparison with other models described in this special issue (that did use abundance data), the AUSRIVAS model performance was comparable or better for detecting the simulated moderate and severe impairments.

    AB - Confidence in any bioassessment method is related to its ability to detect ecological improvement or impairment. We evaluated Australian River Assessment (AUSRIVAS)-style predictive models built using reference site data sets from the Australian Capital Territory (ACT), the Yukon Territory (YT; Canada), and the Laurentian Great Lakes (GL; North America) area. We evaluated model performance as ability to correctly assign reference condition with independent reference-site data. Evaluating model ability to detect human disturbance is generally more problematic because the actual condition of test sites is usually unknown. Independent reference-site data underwent simulated impairment by varying the proportions of sensitive, intermediate, and tolerant taxa to simulate degrees of eutrophication. Model performance was related to differences in data sets, such as number and distribution of invertebrate taxa. Sensitive taxa tended to have lower expected probabilities of occurrence than more-tolerant taxa, but the distribution of taxa grouped by tolerance categories also differed by data set. Thus, the models differed in ability to detect the simulated impairment. The ACT model performed best with respect to Type 1 error rates (0%) and the GL model the worst (38%). The YT model performed best (10% error) for detecting moderate impairment, and the ACT model detected all severely impaired sites. AUSRIVAS did not assign most mildly impaired sites to below-reference condition, but a reduction in observed/expected values for some of the mildly impaired sites was observed. Models did not detect mild impairment that simply changed taxon abundances because presence–absence data were used for models. However, in comparison with other models described in this special issue (that did use abundance data), the AUSRIVAS model performance was comparable or better for detecting the simulated moderate and severe impairments.

    KW - reference condition approach

    KW - AUSRIVAS

    KW - simulated impairment

    KW - bioassessment

    KW - predictive modeling

    KW - macroinvertebrates.

    U2 - 10.1086/678946

    DO - 10.1086/678946

    M3 - Article

    VL - 33

    SP - 1212

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    JO - Journal of the North American Benthological Society

    JF - Journal of the North American Benthological Society

    SN - 2161-9565

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