Underlying trends confound estimates of fish population responses to river discharge

Yen, Jian D. L.; Thomson, James Robertson; Lyon, Jarod; Koster, Wayne; Kitchingman, Adrian; Raymond, Scott; Stamation, Kasey; Tonkin, Zeb

doi:10.1111/fwb.13793

Cited by 5 publications

(4 citation statements)

References 65 publications

(128 reference statements)

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“…flow alteration, fragmentation, over-fishing) and interventions (e.g. fish stocking and habitat enhancements) 59 .…”

Section: Discussionmentioning

confidence: 99%

Accurate, non-destructive, and high-throughput age estimation for Golden perch (Macquaria ambigua spp.) using DNA methylation

Mayne,

Espinoza,

Crook

et al. 2023

Sci Rep

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Age structure information of animal populations is fundamental to their conservation and management. In fisheries, age is routinely obtained by counting daily or annual increments in calcified structures (e.g., otoliths) which requires lethal sampling. Recently, DNA methylation has been shown to estimate age using DNA extracted from fin tissue without the need to kill the fish. In this study we used conserved known age-associated sites from the zebrafish (Danio rerio) genome to predict the age of golden perch (Macquaria ambigua), a large-bodied native fish from eastern Australia. Individuals aged using validated otolith techniques from across the species’ distribution were used to calibrate three epigenetic clocks. One clock was calibrated using daily (daily clock) and another with annual (annual clock) otolith increment counts, respectively. A third used both daily and annual increments (universal clock). We found a high correlation between the otolith and epigenetic age (Pearson correlation > 0.94) across all clocks. The median absolute error was 2.4 days in the daily clock, 184.6 days in the annual clock, and 74.5 days in the universal clock. Our study demonstrates the emerging utility of epigenetic clocks as non-lethal and high-throughput tools for obtaining age estimates to support the management of fish populations and fisheries.

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“…flow alteration, fragmentation, over-fishing) and interventions (e.g. fish stocking and habitat enhancements) 59 .…”

Section: Discussionmentioning

confidence: 99%

Accurate, non-destructive, and high-throughput age estimation for Golden perch (Macquaria ambigua spp.) using DNA methylation

Mayne,

Espinoza,

Crook

et al. 2023

Sci Rep

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“…The fact that model C revealed relationships not evident in models A and B is an important result and is a further rationale to fit multiple models. Such discrepancies among models can provide insight into the mechanisms giving rise to observed population patterns, and (potentially) evidence in favor of one or more alternative hypotheses (Yen et al, 2021). Case Study 1 provides an illustration: evidence for hypothesis 2 (populations increase in years following exceptionally high flows) is only provided by model C. Models A and B do not reveal this relationship because they use abundance rather than growth rate as a response variable, and abundances tend to be low in years following exceptionally high flows because populations are still recovering from the even lower levels in the preceding year.…”

Section: Discussionmentioning

confidence: 99%

Simple statistical models can be sufficient for testing hypotheses with population time‐series data

Wenger

Stowe

Gido

et al. 2022

Ecology and Evolution

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Time‐series data offer wide‐ranging opportunities to test hypotheses about the physical and biological factors that influence species abundances. Although sophisticated models have been developed and applied to analyze abundance time series, they require information about species detectability that is often unavailable. We propose that in many cases, simpler models are adequate for testing hypotheses. We consider three relatively simple regression models for time series, using simulated and empirical (fish and mammal) datasets. Model A is a conventional generalized linear model of abundance, model B adds a temporal autoregressive term, and model C uses an estimate of population growth rate as a response variable, with the option of including a term for density dependence. All models can be fit using Bayesian and non‐Bayesian methods. Simulation results demonstrated that model C tended to have greater support for long‐lived, lower‐fecundity organisms (K life‐history strategists), while model A, the simplest, tended to be supported for shorter‐lived, high‐fecundity organisms (r life‐history strategists). Analysis of real‐world fish and mammal datasets found that models A, B, and C each enjoyed support for at least some species, but sometimes yielded different insights. In particular, model C indicated effects of predictor variables that were not evident in analyses with models A and B. Bayesian and frequentist models yielded similar parameter estimates and performance. We conclude that relatively simple models are useful for testing hypotheses about the factors that influence abundance in time‐series data, and can be appropriate choices for datasets that lack the information needed to fit more complicated models. When feasible, we advise fitting datasets with multiple models because they can provide complementary information.

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“…When source populations are absent, recovery may take substantially longer, or not occur at all (Langford et al, 2009; Sundermann et al, 2011). Fish populations can also be highly variable through time due to climatic and other governing factors (Tonkin et al, 2019), making it difficult to reliably assess responses to interventions, especially in the short‐term, against these background trends (Höckendorff et al, 2017; Yen et al, 2021). Other factors, such as anthropogenic pressures from the adjacent catchment area and environmental factors (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…As a consequence, it is also not yet possible to set definitive benchmarks and targets in relation to fish responses. Two approaches, not mutually exclusive, could be useful to improve our understanding: (1) to collect long‐term datasets, to disentangle responses to interventions from background trends (Höckendorff et al, 2017; Yen et al, 2021); or (2) to collate data from future intervention projects as it becomes available. The second approach would help support a more detailed set of analyses to better quantify responses and explore the potential influence of a wider range of covariates (describing both abiotic and biotic factors, including the traits of different species) that might explain variability in fish responses to interventions.…”

Section: Discussionmentioning

confidence: 99%

A synthesis of 15 years of instream woody habitat management: Progress towards benchmarks and assessing fish responses

et al. 2022

Self Cite

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There is increasing focus globally on restoration actions to mitigate the effects of humans on freshwater ecosystems. Given the substantial investments in these actions, there is a need to determine if they meet their intended ecological outcomes. Setting benchmarks and rigorous monitoring of responses to actions are essential but are often not undertaken. Addition of instream woody habitat (IWH) is one of the most common stream rehabilitation techniques globally, particularly with the aim of enhancing fish populations. We present a synthesis of IWH interventions undertaken over a 15‐year period across the state of Victoria, in south‐eastern Australia. We collated data from 275 rivers on past IWH densities and, using benchmarks that had been set in earlier work, we examined how much current IWH levels deviated from this benchmark (Aim 1), as a way of assessing where IWH is degraded relative to estimated natural levels, and quantified progress towards achieving IWH benchmarks (Aim 2). We then used data from 25 intervention projects to assess fish responses to IWH additions using a meta‐analysis (Aim 3), including exploring the role of several predictors (time since intervention, baseline habitat density) on the magnitude of these responses. Many rivers had lower IWH densities than their benchmarks. The density of IWH had increased by less than 20% in many waterways where IWH had been added. However, at some locations, IWH additions had led to density increases of more than 40%. Fish responses to IWH additions were mixed overall but we found evidence that positive effects are more likely at locations older than 8 years post‐intervention, and those in areas with higher IWH density prior to additions. Our study highlighted: (1) deriving benchmarks can help set clear management targets, and facilitate meaningful evaluation of progress; and (2) fish responses to IWH additions may take several years (at least) to occur, and response trajectories can be non‐linear, including the potential for short‐term negative outcomes. Therefore, a long‐term commitment to monitoring and evaluation is needed. Consideration of these points will help river managers decide where to focus their efforts to maximise ecological responses to IWH additions and other restoration actions more broadly.

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Underlying trends confound estimates of fish population responses to river discharge

Cited by 5 publications

References 65 publications

Accurate, non-destructive, and high-throughput age estimation for Golden perch (Macquaria ambigua spp.) using DNA methylation

Accurate, non-destructive, and high-throughput age estimation for Golden perch (Macquaria ambigua spp.) using DNA methylation

Simple statistical models can be sufficient for testing hypotheses with population time‐series data

A synthesis of 15 years of instream woody habitat management: Progress towards benchmarks and assessing fish responses

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