Leah Beesley

Improving ecological response monitoring of environmental flows.

AbstractEnvironmental flows are now an important restoration technique in flow-degraded rivers, and with the increasing public scrutiny of their effectiveness and value, the importance of undertaking scientifically robust monitoring is now even more critical. Many existing environmental flow monitoring programs have poorly defined objectives, nonjustified indicator choices, weak experimental designs, poor statistical strength, and often focus on outcomes from a single event. These negative attributes make them difficult to learn from. We provide practical recommendations that aim to improve the performance, scientific robustness, and defensibility of environmental flow monitoring programs. We draw on the literature and knowledge gained from working with stakeholders and managers to design, implement, and monitor a range of environmental flow types. We recommend that (1) environmental flow monitoring programs should be implemented within an adaptive management framework; (2) objectives of environmental flow programs should be well defined, attainable, and based on an agreed conceptual understanding of the system; (3) program and intervention targets should be attainable, measurable, and inform program objectives; (4) intervention monitoring programs should improve our understanding of flow-ecological responses and related conceptual models; (5) indicator selection should be based on conceptual models, objectives, and prioritization approaches; (6) appropriate monitoring designs and statistical tools should be used to measure and determine ecological response; (7) responses should be measured within timeframes that are relevant to the indicator(s); (8) watering events should be treated as replicates of a larger experiment; (9) environmental flow outcomes should be reported using a standard suite of metadata. Incorporating these attributes into future monitoring programs should ensure their outcomes are transferable and measured with high scientific credibility.

A Bayesian Belief Network Decision Support Tool for Watering Wetlands to Maximise Native Fish Outcomes.

Wetlands are productive and diverse habitats for native fish but can be highly degraded, particularly in the Murray-Darling Basin (MDB), south-eastern Australia. Wetland management requires tools and processes that facilitate the synthesis and application of knowledge for decisions concerning the allocation of environmental water to wetlands to improve environmental outcomes. This paper describes the development of a Decision Support Tool (DST), based on a Bayesian Network designed to provide the best available science and support adaptive management of environmental flows into wetlands. The DST predicts the probability of improvements in fish population health as defined by abundance, population structure and fish condition for introduced common carp and three native species of fish: carp gudgeon, Australian smelt, and golden perch. Model sensitivity and validation showed that fish response varied depending on model inputs, but that responses from the DST were an accurate reflection of fish responses in wetlands based on field data. Ultimately, the success of this DST is dependent on its adoption by wetland managers. Throughout the entire development process, adoption of the DST has been promoted through engagement with managers and subsequently, through initiatives to integrate it into current management initiatives.

Using abiotic drivers of fish spawning to inform environmental flow management

Summary 1. Environmental flows are a key restoration technique for conserving ecological function in flow-degraded rivers. Species-specific, flow–biota relationships are increasingly being used to determine environmental flow needs and manage their use; however, many of these relationships are poorly described. 2. We evaluate relationships between environmental variables and spawning intensity for a fish assemblage from the Murray River, Australia, over a ten-year period. We developed a hierarchical multispecies model that accounted for incomplete detection to compare spawning outcomes of native and non-native species using realistic, alternative, water management scenarios. 3. Temperature was an important predictor of spawning intensity for all seven species studied, while both concurrent and antecedent flow conditions were important for many species. Our water management scenario testing accounted for these relationships and indicated that increasing the magnitude of smaller floods following lower antecedent flow conditions, at water temperatures of 18–20C, achieves the greatest spawning outcome for native fish. 4. Synthesis and applications. Our results indicate that principally temperature, and flow as a secondary variable, influence the timing and strength of fish spawning. The synthesis of these spawning relationships predicts that managers will achieve the greatest spawning return per unit of environmental water when flows are applied on top of an existing flow pulse. This study highlights the importance of considering a range of abiotic factors and the use of modelling scenarios to improve environmental flow outcomes.

Model development of a Bayesian Belief Network for managing inundation events for wetland fish

Wetlands are essential components of floodplain-river ecosystems that often suffer degradation due to river regulation. To this end, the application of environmental water is increasingly being seen as an important amelioration strategy. However, decisions regarding the delivery of water to maximise environmental benefits, including native fish population health, are complex and difficult. This paper describes the development of a Bayesian Belief Network (BBN) model as part of a Decision Support Tool for assessing inundation strategies to benefit native wetland fish. Separate, albeit closely related, BBNs were developed for three native (golden perch Macquaria ambigua, carp gudgeon Hypseleotris spp., Australian smelt Retropinna semoni) and one alien fish species (common carp Cyprinus carpio carpio). The model structure was based on a conceptualisation of the relationships between wetland habitats, hydrology and fish responses, with emphasis on the types of inundation activities undertaken by managers. Conditional probability tables for fish responses were constructed from expert opinion and the model was validated against field data. The predictive ability and sensitivity of the model reflected the inherent high variability in relationships between wetland characteristics, hydrology and fish responses, but was nonetheless able to address satisfactorily such complexities within a holistic framework. As the model was designed in conjunction with managers and evaluated by them, its application will be enhanced by on-going engagement between managers and scientists.

Evaluating estimators of species richness: the importance of considering statistical error rates

Summary The performance of species richness estimators can be highly variable. Evaluating the accuracy and precision of different estimators for different assemblages is common in the ecological literature, but estimator performance is rarely measured in terms of research goals such as detecting patterns in diversity. We evaluated the efficacy of nonparametric richness estimators to detect changes (i.e. type-I and type-II error rates) in species richness using two experimental designs: a block design and a trend analysis. We also evaluated estimator efficacy across a variety of species-abundance distributions, species number and sample sizes. The evaluation was performed using simulated data that mimicked the qualities of real data to ensure real-world relevance. We found that the bias and precision of all estimators evaluated had high sensitivity to sample size and shifts in the species-abundance distribution. Importantly, all estimators demonstrated elevated type-I error rates when the species-abundance distribution varied. These inflated type-I error rates resulted in spurious conclusions about patterns in species richness. Results suggest that caution should be taken when using nonparametric estimators to detect pattern in species richness. Furthermore, estimator evaluations should always include measures of type-I and type-II error rates. These quantities can reveal the inference consequences of the dependency of estimator bias and precision on community and sampling characteristics.

Hierarchical multi‐taxa models inform riparian vs. hydrologic restoration of urban streams in a permeable landscape

The degradation of streams caused by urbanization tends to follow predictable patterns; however, there is a growing appreciation for heterogeneity in stream response to urbanization due to the local geoclimatic context. Furthermore, there is building evidence that streams in mildly sloped, permeable landscapes respond uncharacteristically to urban stress calling for a more nuanced approach to restoration. We evaluated the relative influence of local-scale riparian characteristics and catchment-scale imperviousness on the macroinvertebrate assemblages of streams in the flat, permeable urban landscape of Perth, Western Australia. Using a hierarchical multi-taxa model, we predicted the outcomes of stylized stream restoration strategies to increase the riparian integrity at the local scale or decrease the influences of imperviousness at the catchment scale. In the urban streams of Perth, we show that local-scale riparian restoration can influence the structure of macroinvertebrate assemblages to a greater degree than managing the influences of catchment-scale imperviousness. We also observed an interaction between the effect of riparian integrity and imperviousness such that the effect of increased riparian integrity was enhanced at lower levels of catchment imperviousness. This study represents one of few conducted in flat, permeable landscapes and the first aimed at informing urban stream restoration in Perth, adding to the growing appreciation for heterogeneity of the Urban Stream Syndrome and its importance for urban stream restoration.

Macroinvertebrate and covariate data

Tab-1: Macroinvertebrate catch data at sample sites. Tab-2: Covariate data at sample sites.