Quantifying flow–ecology relationships with functional linear models

Stewart‐Koster, Ben; Olden, Julian D.; Gido, Keith B.

doi:10.1080/02626667.2013.860231

Cited by 40 publications

(28 citation statements)

References 49 publications

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“…Rather than use a suite of hydrological metrics as covariates for species density, we used functional regression models to incorporate antecedent flow as continuous functions spanning the days of the year (Ramsay & Silverman, ; Yen et al., ),

{Density}_{i} = {normalβ}_{0} + {false\sum}_{t = 1}^{365} {normalβ}_{1} false(t false) \cdot {Flow}_{i} false(t false) + {normalε}_{i}

where Density i is the sampled species’ density in year i , Flow i ( t ) is the measured daily discharge on calendar day t (ranging from day 1 to 365) in year i , β 0 is the estimated average density for that species, β 1 ( t ) is the estimated unit increase in species density per unit increase in discharge on calendar day t , and ε i is the model residual associated with year i . Such functional approaches are more appropriate than traditional linear regression methods for characterising flow regimes in a holistic manner (Stewart‐Koster, Olden, & Gido, ). Here, rather than a suite of single‐valued regression coefficients, flow–ecology relationships are represented by the function‐valued regression coefficients β 1 ( t ).…”

Section: Methodsmentioning

confidence: 99%

Evaluating transferability of flow–ecology relationships across space, time and taxonomy

Chen

Olden

2017

Freshwater Biology

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Environmental flow assessments are becoming increasingly central to ecologically‐sustainable river management. Rigorous evaluations of flow–ecology relationships serve a vital role in guiding these assessments to meet targeted ecosystem objectives. However, limited resources and widespread environmental change are outpacing the ability to gain knowledge of species’ flow responses and assess environmental needs for rivers individually. Successfully transferring flow–ecology relationships across space and time would facilitate regional‐scale environmental flow assessments, yet the necessary contexts for such success remains a knowledge gap. Here, we leverage long‐term, multi‐species datasets across multiple river basins in southwestern United States as a case study to explore whether relationships between species abundances and hydrological conditions are transferable across space and time. Additionally, we evaluate the potential for ecological guilds based on fluvial dependence and life‐history strategies to facilitate the transfer of flow–ecology knowledge across taxonomic boundaries. Species varied in the spatial transferability of their flow–ecology relationships. Spatial transferability was similar when comparing a species’ flow–ecology relationships within a river basin versus across different river basins, although transferability was considerably greater across free‐flowing rivers compared to regulated rivers. Species’ flow–ecology relationships transferred through time just as well as across space. Ecological guilds defined according to fluvial dependence and life‐history strategies offered just as much potential for transferring flow–ecology knowledge among species as transferring within species across space or time. Our study provides insights into transferring flow–ecology knowledge to support effective, regional‐scale environmental flows. Further research into developing transferable flow–ecology relationships for a wide range of environmental predictors and biological responses across different spatial scales and flow regimes will enable us to keep pace with the increasing demand for science to inform sustainable river management.

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{Density}_{i} = {normalβ}_{0} + {false\sum}_{t = 1}^{365} {normalβ}_{1} false(t false) \cdot {Flow}_{i} false(t false) + {normalε}_{i}

Section: Methodsmentioning

confidence: 99%

Evaluating transferability of flow–ecology relationships across space, time and taxonomy

Chen

Olden

2017

Freshwater Biology

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“…FPCA is a relatively new technique, and methods to handle jumps in time series are the topic of ongoing research. FPCA has been successfully used to relate temporal variation in river flow to ecological responses such as fish abundance (Ainsworth et al 2011, Stewart-Koster et al 2014, and thus could be useful for assessing effects of continuous sound exposure (e.g. offshore construction, vessel traffic) on marine mammal occurrence or abundance.…”

Section: Discussionmentioning

confidence: 99%

Gray whale densities during a seismic survey off Sakhalin Island, Russia

Muir¹,

Ainsworth²,

Racca³

et al. 2016

Endang. Species. Res.

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“…Broadly speaking, flow–ecology relationships can reflect the biological response to either flow magnitude or flow variation, both of which are potentially altered by water regulation. Ecological responses can be related to a wide variety of hydrologic drivers (Olden & Poff, ; Stewart‐Koster, Olden, & Gido, ); Buchanan et al., ; for brevity this review focuses on ecological response to flow magnitude at low discharge.…”

Section: Likelihood Of Nonlinearity For Different Ecological Indicatomentioning

confidence: 99%

Developing flow–ecology relationships: Implications of nonlinear biological responses for water management

Rosenfeld

2017

Freshwater Biology

109

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Empirical relationships between stream flow and ecological responses (flow–ecology relationships) are essential for establishing environmental flows and evaluating tradeoffs between instream values and out‐of‐stream uses. Establishing the shape of flow–ecology relationships (i.e. slope, linearity versus nonlinearity) is particularly important to avoid crossing ecological thresholds in water management. This review focuses on ecological responses to discharge at low summer flows when out‐of‐stream water demand is often highest, and identifying ecological contexts where nonlinearities are most likely. Most physical attributes (temperature, dissolved oxygen, available habitat) and ecological responses (energy flow, fish survival, recruitment, community structure) show at least some evidence of nonlinear relationships with flow, although assumptions of linearity may be reasonable across limited discharge ranges which may include low flows. Nonlinearities are most likely in systems that are near existing thresholds (e.g. cold‐water transitional fish communities that are close to upper thermal tolerances). The probability of nonlinearities is likely to increase under future landuse and climate change scenarios, particularly in combination with other stressors, such as eutrophication, which may greatly accelerate temperature‐related decline in dissolved oxygen under climate warming. Managers need to anticipate changes in flow–ecology relationships and develop management systems that are robust to change. Field programmes to establish the slope and linearity of local flow–ecology relationships are essential for regional management, but developing generalisable flow–ecology relationships that are transferrable to regions with limited resources also needs to be a priority. Generalised relationships can be generated through meta‐analysis of empirical flow–ecology relationships, and may prove especially useful if they can capture how environmental and ecological context (channel size and morphology, landuse, flow regime, antecedent conditions, habitat or taxonomic guild) affect flow–ecology relationships. For instance, linking empirical data from flow–ecology relationships to available habitat predicted by physical habitat simulation models (e.g. PHABSIM) may provide a better mechanistic basis for modelling ecological responses, while providing much needed validation for habitat simulation approaches. This would also help bridge the gap between emerging holistic environmental flow modelling approaches and more traditional habitat simulation methods.

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Quantifying flow–ecology relationships with functional linear models

Cited by 40 publications

References 49 publications

Evaluating transferability of flow–ecology relationships across space, time and taxonomy

Evaluating transferability of flow–ecology relationships across space, time and taxonomy

Gray whale densities during a seismic survey off Sakhalin Island, Russia

Developing flow–ecology relationships: Implications of nonlinear biological responses for water management

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