Theoretical predictions of observed to expected ratios in RIVPACS-type predictive model assessments of stream biological condition

Yuan, Lester L.

doi:10.1899/0887-3593(2006)025[0841:tpoote]2.0.co;2

Cited by 8 publications

(2 citation statements)

References 98 publications

(117 reference statements)

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“…The O/E index also has a structural weakness in that the detection of taxa with a low modelled probability of occurrence can counter the non-detection of taxa with a high probability of occurrence, leading to an under-representation of the difference between the predicted and observed assemblages (Van Sickle 2008). Furthermore, the choice of a threshold of predicted probability for including taxa in the calculation of the O/E index affects index values, even for reference sites (Yuan 2006). For assessment sites, the choice of a high threshold excludes taxa that are infrequent at reference sites but may, nevertheless, be adversely affected by anthropogenic stress (Clarke and Murphy 2006;Mazor et al 2016), or alternatively may benefit from certain types of anthropogenic stress (Edward et al 2000).…”

Section: Limitations Of O/e Indicesmentioning

confidence: 99%

What’s wrong with the Australian River Assessment System (AUSRIVAS)?

Chessman¹

2021

Mar. Freshwater Res.

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The Australian River Assessment System (AUSRIVAS or AusRivAS) is a national biomonitoring scheme that supposedly assesses the 'biological health' of rivers. AUSRIVAS outputs observed-over-expected (O/E) indices derived from macroinvertebrate survey data obtained both at a site to be assessed and at designated reference sites. However, AUSRIVAS reference sites lack any consistent or quantified status, and, therefore, AUSRIVAS O/E indices have no particular meaning. Moreover, many studies have found AUSRIVAS O/E to be a weak or inconsistent indicator of exposure to anthropogenic or human-influenced stressors. Poor performance by AUSRIVAS may relate to numerous factors including the following: (1) variable reference-site status, (2) inappropriate model predictors, (3) limitations of O/E indices, (4) inconstant sampling methods, and (5) neglect of non-seasonal temporal variability. The indices Ephemeroptera-Plecoptera-Trichoptera (EPT) and stream invertebrate grade number-average level (SIGNAL) provide alternatives that have often outperformed AUSRIVAS O/E in comparative tests. In addition, bioassessment of Australian rivers might be advanced by the development of diagnostic methods to identify the stressors causing ecological impact rather than merely to infer impact intensity and assign quality ratings to assessment sites.

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Section: Limitations Of O/e Indicesmentioning

confidence: 99%

What’s wrong with the Australian River Assessment System (AUSRIVAS)?

Chessman¹

2021

Mar. Freshwater Res.

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“…All models slightly underestimated observed sample richness (cf. mean O/E values in Table 3); a likely consequence of the use of p i values > 0 and the relatively small numbers of reference sites within the clusters used in each RIVPACS model (Yuan, 2006). Although the niche models based on measured and modelled stream temperature performed similarly, the models differed in terms of the non-thermal predictors that were selected.…”

Section: Niche Model Performancementioning

confidence: 99%

Using modelled stream temperatures to predict macro‐spatial patterns of stream invertebrate biodiversity

Hill

Hawkins

2014

Freshwater Biology

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Summary Stream temperature is thought to be a primary determinant of the macro‐spatial distribution of stream biota, but we lack temperature data for most streams. Past research often relied on surrogates, such as altitude, latitude, catchment area (ALCA) and air temperature to study biota–temperature relationships. However, temperature surrogates may not accurately represent the thermal environments experienced by the biota and could thus produce misleading inferences regarding such relationships. In the absence of observations, modelled stream temperature could improve both predictions and interpretations of stream biodiversity patterns. We tested this hypothesis by relating stream benthic invertebrate assemblage structure and composition at 92 reference‐quality streams to ALCA, air temperature, modelled stream temperature and measured stream temperature, that is a progression from a coarse surrogate to directly measured water temperature. Modelled stream temperatures were obtained from a U.S.A.‐wide model developed previously with data from 569 reference‐quality sites. Variation in taxonomic composition, measured with an ordination, was strongly and almost identically associated with both modelled and measured stream temperature, but was less strongly associated with ALCA and air temperature. We also built predictive niche models to assess how choice of thermal metrics affected model performance. Model performance was measured as the precision with which each model predicted the number of taxa at a site (i.e. observed‐to‐expected taxon ratios). Niche models that contained modelled or measured stream temperature were more precise than those based on air temperature and ALCA. In addition, we compared the predicted probabilities of occurrence using niche models based on both modelled and measured stream temperatures. The two produced predicted probabilities of occurrence that were statistically indistinguishable for most (79%) taxa. Finally, we calculated thermal optima for each taxon as the abundance‐weighted average of stream temperature at sites where each taxon occurred. Thermal optima produced with modelled and measured stream temperatures were almost identical. Large‐scale models of stream temperature can be sufficiently accurate and precise to detect temperature‐driven patterns in stream biodiversity and should be useful in predicting the effects of climate change and other human‐caused thermal alterations on stream biodiversity.

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