2014
DOI: 10.1111/1365-2664.12342
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Juvenile fish response to wetland inundation: how antecedent conditions can inform environmental flow policies for native fish

Abstract: Summary1. One of the greatest threats to lotic ecosystems is flow regulation. The impacts of flow regulation on native fish are often mitigated using periodic water allocations, termed environmental flows. The effectiveness of environmental flows has been studied in some systems, but the role of flow conditions prior to water allocation (i.e. antecedent conditions) has rarely been quantified. 2. We evaluated the effects of floodplain inundation due to variable river flows on fish abundance in wetlands of the m… Show more

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Cited by 33 publications
(42 citation statements)
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“…; Beesley et al . ). This procedure gives similar results as more complex methods such as modelling parameter inclusion probabilities with Bayesian mixture models.…”
Section: Methodsmentioning
confidence: 97%
See 1 more Smart Citation
“…; Beesley et al . ). This procedure gives similar results as more complex methods such as modelling parameter inclusion probabilities with Bayesian mixture models.…”
Section: Methodsmentioning
confidence: 97%
“…Furthermore, few studies explicitly link flow–biota relationships to the decision process experienced by flow managers, hindering the development of targeted management strategies (but see Beesley et al . ). These limitations reduce managers' ability to predict the outcomes of environmental flow strategies and, thus, the effectiveness of management actions, exposing the discipline to criticism (Poff et al .…”
Section: Introductionmentioning
confidence: 97%
“…There are several benefits of this modeling approach over traditional and more common multi-variate methods (e.g., Olden 2003, Iknayan et al 2014, Warton et al 2015a. Finally, taxa-specific models as opposed to traditional multivariate methods (e.g., Legendre and Legendre 2012) can be used to resolve uncertainty among competing management options through explicit prediction, providing a direct link between ecological research and management (e.g., Beesley et al 2014, King et al 2016. This model behavior occurs because the community-level hyper-distributions act as priors for taxa-specific estimates.…”
Section: Model Overviewmentioning
confidence: 99%
“…Information theoretic approaches to variable selection, such as AIC and DIC, are often unreliable when applied to complex Bayesian hierarchical models with multiple random effects (Celeux et al 2006, Millar 2009, Hooten and Hobbs 2015, Broms et al 2016); therefore, we applied a simplified procedure based on credible interval overlap with zero that is common in the ecological literature (e.g., White et al 2013, Beesley et al 2014, King et al 2016. Information theoretic approaches to variable selection, such as AIC and DIC, are often unreliable when applied to complex Bayesian hierarchical models with multiple random effects (Celeux et al 2006, Millar 2009, Hooten and Hobbs 2015, Broms et al 2016); therefore, we applied a simplified procedure based on credible interval overlap with zero that is common in the ecological literature (e.g., White et al 2013, Beesley et al 2014, King et al 2016.…”
Section: Model Evaluationmentioning
confidence: 99%
“…Mechanistically, the long-term flow regime imposes evolutionary selective pressures that may favour or select for particular life-history traits, community structure, and behaviours that exploit particular aspects of the flow regime, such as predictable seasonal flow patterns (Hoeinghaus, Winemiller, & Birnbaum, 2007;Lamouroux, Poff, & Angermeier, 2002;Lytle & Poff, 2004;Poff & Allan, 1995). floods), altered habitat availability and connectivity, resource provision, and by providing opportunities for movement and migration, and conditions suitable for spawning and recruitment (Beesley et al, 2014;Kennard et al, 2007). floods), altered habitat availability and connectivity, resource provision, and by providing opportunities for movement and migration, and conditions suitable for spawning and recruitment (Beesley et al, 2014;Kennard et al, 2007).…”
Section: Introductionmentioning
confidence: 99%