Quantifying uncertainty in environmental indices: an application to an estuarine health index

Dobbie, Melissa J.; Clifford, David

doi:10.1071/mf13065

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…Most indices of ecosystem 'health' are derived from variables measuring environmental condition, which are used as a surrogate for biological diversity (e.g. Kwok, Eldridge & Oliver 2011;Rodgers et al 2012;Dobbie & Cllifford 2015). The strength of the relations between environmental condition and species abundances can be tested using basic survey data (sites 9 species abundances 9 environmental variables: e.g.…”

Section: Introductionmentioning

confidence: 99%

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A fresh approach reveals how dispersal shapes metacommunity structure in a human‐altered landscape

Downes

Lancaster

Glaister

et al. 2016

Journal of Applied Ecology

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1.To understand species losses from disturbed landscapes, it is important to distinguish the effects of degraded environmental conditions from those caused by barriers to dispersal between habitat patches. To assess the relative importance of these effects, we developed a new approach using permutation and association tests applied to rank abundance data, using the invertebrate fauna of two rivers in two seasons. 2. Our study streams were Hughes Creek and Seven Creeks, in south-eastern Australia, which have both been degraded by agriculture in downstream sections. We collected benthic invertebrates and also dispersing individuals (drift, terrestrial adults) during two seasons in [2007][2008]. Study sites spanned strong environmental gradients as well as the main dispersal route (up-and down-channel). Environmental data were analysed to set up permutation tests on rank abundances. Survey and disperser data were contrasted using contingency table analyses. 3. The results suggest dispersal plays a strong role in community structure. Environmental effects were evident and strongest upstream, but evidence of environmental effects was weak over much of the gradient. Many species had different distributions in different data sets or dispersers that were abundant at locations distant from centres of benthic distribution. 4. Our results differ from many studies, but few have been able to evaluate dispersal effects directly. Our method provides a practical approach for evaluating the role dispersal plays in driving species abundance patterns across landscapes, thus bridging a gap between theory and practice. 5. Synthesis and applications. Managers typically use indices of ecosystem health that assume environmental conditions largely determine species diversity and abundance. Dispersal between habitat patches is known to be important, but there are no reliable methods to assess the role dispersal may play. We provide an approach that allows both dispersal and environmental effects on species distributions to be evaluated from survey data. This may open the way for dispersal information to be incorporated into management actions. Additionally, the approach should allow improved siting of restoration projects that depend greatly on successful dispersal of individuals for successful outcomes.

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Section: Introductionmentioning

confidence: 99%

“…Kwok, Eldridge & Oliver ; Rodgers et al . ; Dobbie & Cllifford ). The strength of the relations between environmental condition and species abundances can be tested using basic survey data (sites × species abundances × environmental variables: e.g.…”

Section: Introductionmentioning

confidence: 99%

A fresh approach reveals how dispersal shapes metacommunity structure in a human‐altered landscape

Downes

Lancaster

Glaister

et al. 2016

Journal of Applied Ecology

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“…For composite indicator models, however, dealing with uncertainty is more complicated, and consequently, rarely implemented. Despite the challenges, it is possible to quantify uncertainty in composite indicators [ 14 , 15 , 20 , 21 , 26 , 27 ]. One of our goals here was to inform gapfilling efforts during future indicator construction.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, for composite indicators, the overall framework may fail to accurately represent an ecosystem if indicators are not well selected or the weights used to aggregate indicators do not reflect their relative importance in the system. Methods of quantifying uncertainty for composite indicators have been developed [ 3 , 15 , 20 , 21 ] and have been applied to some composite indicators [ 24 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Mapping Uncertainty Due to Missing Data in the Global Ocean Health Index

2016

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Indicators are increasingly used to measure environmental systems; however, they are often criticized for failing to measure and describe uncertainty. Uncertainty is particularly difficult to evaluate and communicate in the case of composite indicators which aggregate many indicators of ecosystem condition. One of the ongoing goals of the Ocean Health Index (OHI) has been to improve our approach to dealing with missing data, which is a major source of uncertainty. Here we: (1) quantify the potential influence of gapfilled data on index scores from the 2015 global OHI assessment; (2) develop effective methods of tracking, quantifying, and communicating this information; and (3) provide general guidance for implementing gapfilling procedures for existing and emerging indicators, including regional OHI assessments. For the overall OHI global index score, the percent contribution of gapfilled data was relatively small (18.5%); however, it varied substantially among regions and goals. In general, smaller territorial jurisdictions and the food provision and tourism and recreation goals required the most gapfilling. We found the best approach for managing gapfilled data was to mirror the general framework used to organize, calculate, and communicate the Index data and scores. Quantifying gapfilling provides a measure of the reliability of the scores for different regions and components of an indicator. Importantly, this information highlights the importance of the underlying datasets used to calculate composite indicators and can inform and incentivize future data collection.

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How is ecosystem health defined and measured? A critical review of freshwater and estuarine studies

O’Brien

Townsend

Hale

et al. 2016

Ecological Indicators

131

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Quantifying uncertainty in environmental indices: an application to an estuarine health index

Cited by 8 publications

References 24 publications

A fresh approach reveals how dispersal shapes metacommunity structure in a human‐altered landscape

A fresh approach reveals how dispersal shapes metacommunity structure in a human‐altered landscape

Mapping Uncertainty Due to Missing Data in the Global Ocean Health Index

How is ecosystem health defined and measured? A critical review of freshwater and estuarine studies

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