Elsevier

Water Research

Volume 124, 1 November 2017, Pages 108-128
Water Research

Review
Modelling food-web mediated effects of hydrological variability and environmental flows

https://doi.org/10.1016/j.watres.2017.07.031Get rights and content

Highlights

  • Variations in hydrologic regime affect food-web structure and function.

  • We review approaches to modelling and their utility in predicting food-web change.

  • 11 model features enable prediction of food-web outcomes of hydrological variability.

  • No current model includes all these features; a mix of approaches is indicated.

  • Considering food web-hydrology interactions will improve environmental flow planning.

Abstract

Environmental flows are designed to enhance aquatic ecosystems through a variety of mechanisms; however, to date most attention has been paid to the effects on habitat quality and life-history triggers, especially for fish and vegetation. The effects of environmental flows on food webs have so far received little attention, despite food-web thinking being fundamental to understanding of river ecosystems. Understanding environmental flows in a food-web context can help scientists and policy-makers better understand and manage outcomes of flow alteration and restoration. In this paper, we consider mechanisms by which flow variability can influence and alter food webs, and place these within a conceptual and numerical modelling framework. We also review the strengths and weaknesses of various approaches to modelling the effects of hydrological management on food webs. Although classic bioenergetic models such as Ecopath with Ecosim capture many of the key features required, other approaches, such as biogeochemical ecosystem modelling, end-to-end modelling, population dynamic models, individual-based models, graph theory models, and stock assessment models are also relevant. In many cases, a combination of approaches will be useful. We identify current challenges and new directions in modelling food-web responses to hydrological variability and environmental flow management. These include better integration of food-web and hydraulic models, taking physiologically-based approaches to food quality effects, and better representation of variations in space and time that may create ecosystem control points.

Introduction

The hydrological regimes of rivers across the globe have been drastically altered by river regulation and water extraction, leading to profound changes in the structure and function of aquatic ecosystems. To address this, environmental flows are increasingly used to restore elements of natural hydrological regimes that are important to maintaining ecosystem processes. Environmental flows (defined by Arthington et al. (2010) as the “quantity, timing and quality of water required to sustain ecosystems”) are deliberate modifications of altered hydrological regimes intended to maintain or restore ecosystem processes and ecological assets (Poff and Zimmerman, 2010).

Given legitimate competing demands for water, it is usually impractical to completely restore hydrological regimes in rivers to their natural state. Instead, decisions must be made about which elements of the overall flow regime (in space and/or time) should be re-instated to achieve environmental goals. Often, these interventions are designed to create positive outcomes for a particular species or ecological community such as commercially- or recreationally-important fish, waterbirds or riparian and floodplain vegetation (Baldwin et al., 2014, Yang et al., 2016). Interventions have tended to focus attention on population processes such as reproduction and on the provision of suitable habitat for particular species or communities. Ecosystem processes, particularly the movement of energy through food webs (Hladyz et al., 2011), have received much less attention.

A ‘food web’ is a description of the pathways through which energy and essential nutrients move through an ecosystem, from basal resources such as organic matter, through various trophic levels, to apex predators such as fish and waterbirds (Thompson et al., 2012a). The linkages within food webs are critical in determining the nature and magnitude of ecosystem responses, such as enhanced native fish and waterbird populations, to provision of environmental water (Naiman et al., 2012).

Aquatic ecosystems are characterised by complex interactions of abiotic and biotic processes, hydrological disturbances including floods or drought, and poorly understood feedback loops (Fisher et al., 2007, Lansing, 2003). The effects of changes in hydrological regimes are often difficult to predict due to nonlinearities, dependence on antecedent conditions, time lags and thresholds of response (Burkett et al., 2005, Parsons, 2006). An additional challenge for environmental flows research is that managed flow interventions tend to be large, costly and difficult (if not impossible) to replicate (Konrad et al., 2011). Therefore, aquatic ecosystems do not readily lend themselves to examination via replicated experimental approaches (Webb et al., 2010). Modelling and the use of scenario analysis can go some way towards addressing these difficulties (Burkett et al., 2005).

Models used to facilitate river management range from simple images guiding restoration objectives and qualitative conceptual models (e.g. Mika et al., 2010), to quantitative predictive models, both independent (Lester et al., 2013, McDonald et al., 2015) and integrated into decision support systems (Maloney et al., 2015). The outputs of quantitative models can be used to predict the outcomes of a given flow intervention, and the accuracy of these predictions can be empirically tested by monitoring the system during and immediately following an intervention (Konrad et al., 2011). Models can also be used to strengthen causal inference by modelling the ‘counterfactual’ (i.e. the predicted condition/state in the absence of an intervention).

Here, we seek to address the inter-related challenges of: (a) integrating a food-web perspective into environmental flows planning and evaluation; and (b) identifying appropriate modelling tools to support that integration. We begin by briefly discussing how hydrological regimes can affect food-web dynamics in riverine systems, with a focus on the knowledge needs of river planners and managers. We then identify the model features that would be needed to answer those food-web related questions, and summarise the common types of models that have those features. This information is used to identify modelling approaches suited to each question, describe how models have been used to answer such questions in the past, and identify future directions for the development of models to better assess food-web mediated effects of hydrological variability.

The primary intended audience of this paper is environmental flow scientists and river managers seeking a holistic approach to managing rivers for ecosystem outcomes. It provides a brief review and guidance for developing conceptual and quantitative models for environmental flows.

Section snippets

Questions to be addressed by food-web models

To facilitate the identification of the features required in models designed to address food web-related questions, we carried out a review of the recent (2009–2016) literature using the search terms ‘food web’ and ‘model’, occurring with ‘aquatic’, ‘marine’, or ‘freshwater’ in Google Scholar and Web of Science databases. This revealed 216 papers that contained those terms (see Section 6). We also included several earlier papers that described models used in the 2009–2016 papers. We found few

How hydrological regimes affect food webs

The hydrological regime of a river is of fundamental importance for the ecology of river–floodplain systems (Bunn and Arthington, 2002, Naiman et al., 2008, Poff et al., 1997). Variations in flow magnitude and flow event frequency lead to well-documented variations in the area and depth of inundated land, hydrological connectivity, substrate types and distribution, and water velocity (e.g. Sommer et al., 2004). The physical changes associated with hydrological change profoundly affect the type

Model features that capture the effects of hydrology on food webs

As outlined above, hydrology affects food webs through changes in the quantity, types and connectivity of physical habitats, through changes in basal resource supply as organic matter sources change, and as different producers and consumers thrive in different hydrological conditions. In addition, biological processes such as competition and reproduction are influenced by hydrology and can affect food webs. Thus, models need to represent a complex suite of processes to capture all possible

What types of models are suited to which questions?

Given this extensive list of model features, it is unlikely that any one model will be able to capture all possible food-web mediated effects of hydrological variability, nor should this usually be the goal. Nonetheless, a variety of approaches may be useful in answering environmental flow management questions. Table 1 summarises a range of modelling approaches that include some or all of the features listed above and that are therefore likely to be relevant to simulating food-web mediated

How have models in the literature approached environmental flow questions?

As described above, we conducted a brief review of the recent aquatic food-web literature (focusing on papers published between 2009 and 2016), which revealed some interesting trends. Of the 216 relevant papers that we identified, 124 were reviewed in detail (see Supplementary Material for a list). Review papers were excluded from the analysis, but papers applying new methods to previously published food webs and meta-analyses were included. Models were categorised based on the descriptions

Challenges and current directions

As is evident from the review above, there are a wealth of studies investigating the effects of environmental conditions and anthropogenic influences on food webs. Many of these focus on freshwater systems, including riverine systems, but some key challenges and future directions for the exploration of the impacts of environmental flows on food webs emerge. In this section, we will discuss three of these directions in more detail: investigating the influence of food quality explicitly;

The role of food-web models to inform environmental flows

Hydrological regimes, including environmental flows, have the capacity to influence many aspects of freshwater food webs. These effects have been inadequately recognised in the literature, with most studies of environmental flows focusing only on the direct effects of hydrology and connectivity on species of interest. In riverine ecosystems, there has so far been limited use of food-web models, and these applications rarely extend to include flow effects.

Without focusing on specific named model

Conclusion

Hydrological regimes, including environmental flows, influence many aspects of freshwater food webs. Modelling can inform management of river flows to enhance food webs. A range of modelling approaches may be useful, either separately or in combination, and care is needed to match the modelling approach to the research or management question. A particular challenge in bringing together hydrological and food-web models is in ensuring that the data used for each are comparable in terms of spatial

Declaration of authorship

BJR, REL, DSB, NRB, RJR, DSR and RMT conceived the paper. BJR and REL drafted the manuscript with input from DSB, NRB, RJR, DSR and RMT. RD completed the initial targeted review of food-web papers which was subsequently revised and updated by REL.

Acknowledgements

This study was funded by the Australian Department of Environment and Energy's Commonwealth Environmental Water Office (Project number M/BUS/465) through the Murray-Darling Basin Environmental Water Knowledge and Research project, administered by the Murray-Darling Freshwater Research Centre. RT was funded by an Australian Research Council Future Fellowship (FT110100957). Additional support for the preparation of this manuscript came from CSIRO, Deakin University, La Trobe University,

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