The need for validation of ecological indices

Moriarty, Pamela E.; Hodgson, Emma E.; Froehlich, Halley E.; Hennessey, Shannon M.; Marshall, Kristin N.; Oken, Kiva L.; Siple, Margaret C.; Ko, San; Koehn, Laura E.; Pierce, Becky; Stawitz, Christine C.

doi:10.1016/j.ecolind.2017.09.028

Cited by 23 publications

(14 citation statements)

References 45 publications

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“…Our general approach to considering the role of uncertainty, here applied to the OHI fisheries goal, is applicable to a variety of sustainability indices. As noted elsewhere (Burgass et al., ; Moriarty et al., ), very few indicator efforts currently account for uncertainty. The nature of composite indicators and indices make accounting for uncertainty difficult, but it is still possible (some examples provided in Frazier et al.…”

Section: Resultsmentioning

confidence: 99%

Quantifying uncertainty in the wild‐caught fisheries goal of the Ocean Health Index

et al. 2019

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Sustainability indices are proliferating, both to help synthesize scientific understanding and inform policy. However, it remains poorly understood how such indices are affected by underlying assumptions of the data and modelling approaches used to compute indicator values. Here, we focus on one such indicator, the fisheries goal within the Ocean Health Index (OHI), which evaluates the sustainable provision of food from wild fisheries. We quantify uncertainty in the fisheries goal status arising from the (a) approach for estimating missing data (i.e., fish stocks with no status) and (b) reliance on a data‐limited method (catch‐MSY) to estimate stock status (i.e., B/BMSY). We also compare several other models to estimate B/BMSY, including an ensemble approach, to determine whether alternative models might reduce uncertainty and bias. We find that the current OHI fisheries goal model results in overly optimistic fisheries goal statuses. Uncertainty and bias can be reduced by (a) using a mean (vs. median) gap‐filling approach to estimate missing stock scores and (b) estimating fisheries status using the central tendency from a simulated distribution of status scores generated by a bootstrap approach that incorporates error in B/BMSY. This multitiered approach to measure and describe uncertainty improves the transparency and interpretation of the indicator and allows us to better understand uncertainty around our OHI fisheries model and outputs for country‐level interpretation and use.

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Section: Resultsmentioning

confidence: 99%

Quantifying uncertainty in the wild‐caught fisheries goal of the Ocean Health Index

et al. 2019

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“…or levels (e.g., low versus high) can add further refinement to these data-filling approaches, noting an equal weight is still an assumed weighting scheme. 60 All else being equal, however, the most detailed data should be used when possible, and it is usually preferable to fill data gaps through estimation than to not account for a known pressure. 58 There are many approaches to estimating missing data, and using cross-validation methods to estimate error can be used to inform the best approach for gap filling and estimating uncertainty in models.…”

Section: Ll Open Accessmentioning

confidence: 99%

Integrating Life Cycle and Impact Assessments to Map Food's Cumulative Environmental Footprint

et al. 2020

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Feeding a growing, increasingly affluent population while limiting environmental pressures of food production is a central challenge for society. Understanding the location and magnitude of food production is key to addressing this challenge because pressures vary substantially across food production types. Applying data and models from life cycle assessment with the methodologies for mapping cumulative environmental impacts of human activities (hereafter cumulative impact mapping) provides a powerful approach to spatially map the cumulative environmental pressure of food production in a way that is consistent and comprehensive across food types. However, these methodologies have yet to be combined. By synthesizing life cycle assessment and cumulative impact mapping methodologies, we provide guidance for comprehensively and cumulatively mapping the environmental pressures (e.g., greenhouse gas emissions, spatial occupancy, and freshwater use) associated with food production systems. This spatial approach enables quantification of current and potential future environmental pressures, which is needed for decision makers to create more sustainable food policies and practices. ll

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“…There is a contradiction in the fact that whilst managers claim there is a lack of suitable indicators to assess the environmental status of marine systems, to apply the criteria from the European Commission (2017), we have plenty of indicators available to be used (HELCOM, 2010;Pereira et al, 2013;UNEP, 2014;Hummel et al, 2015;Teixeira et al, 2016;Miloslavich et al, 2018). However, it is true that in some cases there is a lack of rigorous testing and validation (Moriarty et al, 2018), which have been mitigated in recent times in Europe with the intercalibration of some of them within the WFD (European Commission, 2018). At the same time, there are plenty of indicators (e.g., contaminants, those related with fish stocks management) for which a long history of application and development allows us to use them with more confidence (Froese et al, 2018).…”

Section: Lack Of Tested and Validated Indicatorsmentioning

confidence: 99%

“…Hence, although substantial progress has been made in the last few years in assessing marine health in an integrative way, at global scale (Borja et al, 2016;Inniss et al, 2016), managers are still reluctant in undertaking such assessments, for different reasons: (i) the supposed lack of indicators able to be used at large scale (Hummel et al, 2015;Teixeira et al, 2016), including its rigorous testing and validation (Moriarty et al, 2018); (ii) the absence of suitable reference conditions or targets for those indicators (Borja et al, 2012); (iii) the difficulty of aggregating indicators from different ecosystem components, habitats, areas, etc. (Borja et al, 2014;Langhans et al, 2014;Probst and Lynam, 2016); (iv) the absence of criteria on the number of indicators to be used for an adequate assessment; (v) the discussion of whether integration should be done using the principle "one-out, all-out" (OOAO) (Borja and Rodríguez, 2010), in which the worst status at any level (indicator, ecosystem component, assessment unit, etc.…”

Section: Introductionmentioning

confidence: 99%

Yes, We Can! Large-Scale Integrative Assessment of European Regional Seas, Using Open Access Databases

et al. 2019

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Substantial progress has been made in assessing marine health in an integrative way. However, managers are still reluctant in undertaking such assessments, because: (i) lack of indicators; (ii) absence of targets; (iii) difficulty of aggregating indicators from different ecosystem components, habitats, and areas; (iv) absence of criteria on the number of indicators to be used; (v) discussion on the use of "one-out, all-out" (OOAO) principle in aggregating; and (vi) lack of traceability when integrating data. Our objective was, using open access databases with indicators across all the European seas with agreed targets, to demonstrate if the Nested Environmental status Assessment Tool (NEAT), can be used at the European scale, serving to managers and policy-makers as a tool to assess the environmental status under the Marine Strategy Framework Directive (MSFD). We have used MSFD Descriptor D3 (commercial fish) from 341 stocks, 119 species and two indicators from each of them (years 2013-2015); D5 (eutrophication) with 90th percentile of Chlorophyll-a (years 2009-2014); and D8 (contaminants), with Anthracene, Fluoranthene, Naphthalene, Cadmium, Nickel, and Lead as indicators (years 2009-2013). We have calculated the environmental status for each European subdivision, subregion, and regional sea, nested at different levels. The analyses include weighting and no-weighting by each assessment area; for ecosystem component (water column, phytoplankton, fish, crustaceans, and molluscs); descriptor (three), and habitat (pelagic, demersal/benthic), with the confidence value of the status. A sensitivity analysis was undertaken to determine a minimum number of indicators to include for a robust assessment. We demonstrated that using NEAT in assessing the status of large marine areas, by aggregating indicators, ecosystem components and descriptors, at different spatial scales, can remove at least four out of the six barriers that managers and policy-makers confront when undertaking such assessments. This can be done by using open-access databases and already established targets. Aggregating indicators of different origin is possible. Around 40 indicators seem to be enough to obtain robust assessments. It is better to integrate the assessment items using an ecosystem-based approach, rather than using the OOAO principle. Using NEAT, this approach supports identifying the problematic environmental issues needing management attention and measures.

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The need for validation of ecological indices

Cited by 23 publications

References 45 publications

Quantifying uncertainty in the wild‐caught fisheries goal of the Ocean Health Index

Quantifying uncertainty in the wild‐caught fisheries goal of the Ocean Health Index

Integrating Life Cycle and Impact Assessments to Map Food's Cumulative Environmental Footprint

Yes, We Can! Large-Scale Integrative Assessment of European Regional Seas, Using Open Access Databases

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