Using a newly introduced framework to measure ecological stressor interactions

Tekin, Elif; Diamant, Eleanor S.; Cruz-Loya, Mauricio; Enriquez, Vivien; Singh, Nina; Savage, Van M.; Yeh, Pamela J.

doi:10.1111/ele.13533

Cited by 51 publications

(60 citation statements)

References 71 publications

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“…Multiple stressors research has accordingly grown in recent years (Orr et al, 2020 ), providing invaluable information on stressors interactions and unravelling mechanisms behind the observed responses. Meta‐analyses have tried to summarise common stressors interactions across ecosystems (Birk et al, 2020 ; Crain et al, 2008 ; Jackson et al, 2016 ) and new methods have been proposed to understand and classify non‐additive interactions (Piggott et al, 2015c ; Tekin et al, 2020 ; Thompson et al, 2018 ). However, the multidimensional conception of stability has never been applied in multiple stressors’ research.…”

Section: Introductionmentioning

confidence: 99%

Effects of multiple stressors on the dimensionality of ecological stability

Polazzo

Rico

2021

Ecology Letters

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Ecological stability is a multidimensional construct. Investigating multiple stability dimensions is key to understand how ecosystems respond to disturbance. Here, we evaluated the single and combined effects of common agricultural stressors (insecticide, herbicide and nutrients) on four dimensions of stability (resistance, resilience, recovery and invariability) and on the overall dimensionality of stability (DS) using the results of a freshwater mesocosm experiment. Functional recovery and resilience to pesticides were enhanced in nutrient‐enriched systems, whereas compositional recovery was generally not achieved. Pesticides did not affect compositional DS, whereas functional DS was significantly increased by the insecticide only in non‐enriched systems. Stressor interactions acted non‐additively on single stability dimensions as well as on functional DS. Moreover we demonstrate that pesticides can modify the correlation between functional and compositional aspects of stability. Our study shows that different disturbance types, and their interactions, require specific management actions to promote ecosystem stability.

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Section: Introductionmentioning

confidence: 99%

Effects of multiple stressors on the dimensionality of ecological stability

Polazzo

Rico

2021

Ecology Letters

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“…Historically, when researchers have explored the impact of stressors on the biology and ecology of organisms, they have looked at stressors in isolation [5]. However, stressors are most likely to occur concurrently and this co-occurrence can lead to additive, antagonistic or synergistic effects [6][7][8][9][10][11]. Accordingly, combined stressor effects have become an increasing focus in recent years [12,13].…”

Section: Introductionmentioning

confidence: 99%

Microplastic exposure interacts with habitat degradation to affect behaviour and survival of juvenile fish in the field

et al. 2020

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Coral reefs are degrading globally due to increased environmental stressors including warming and elevated levels of pollutants. These stressors affect not only habitat-forming organisms, such as corals, but they may also directly affect the organisms that inhabit these ecosystems. Here, we explore how the dual threat of habitat degradation and microplastic exposure may affect the behaviour and survival of coral reef fish in the field. Fish were caught prior to settlement and pulse-fed polystyrene microplastics six times over 4 days, then placed in the field on live or dead-degraded coral patches. Exposure to microplastics or dead coral led fish to be bolder, more active and stray further from shelter compared to control fish. Effect sizes indicated that plastic exposure had a greater effect on behaviour than degraded habitat, and we found no evidence of synergistic effects. This pattern was also displayed in their survival in the field. Our results highlight that attaining low concentrations of microplastic in the environment will be a useful management strategy, since minimizing microplastic intake by fishes may work concurrently with reef restoration strategies to enhance the resilience of coral reef populations.

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“…While the majority of studies currently apply the additive null model there is a growing body of literature suggesting alternative null models ( e.g., Tekin et al, 2020; Thompson et al, 2018 ) that may potentially be more appropriate when investigating the impacts of multiple stressors ( Dey & Koops, 2021; Schäfer & Piggott, 2018 ). The greatest issue with the additive null model is that it can lead to biologically meaningless expectations.…”

Section: Introductionmentioning

confidence: 99%

Are experiment sample sizes adequate to detect biologically important interactions between multiple stressors?

Burgess

Jackson

Murrell

2021

Preprint

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1. Most ecosystems are subject to co-occurring, anthropogenically driven changes and understanding how these multiple stressors interact is a pressing concern. Stressor interactions are typically studied using null models, with the additive and multiplicative null expectation being those most widely applied. Such approaches classify interactions as being synergistic, antagonistic, reversal, or indistinguishable from the null expectation. Despite their wide-spread use, there has been no thorough analysis of these null models, nor a systematic test of the robustness of their results to sample size or sampling error in the estimates of the responses to stressors. 2. We use data simulated from food web models where the true stressor interactions are known, and analytical results based on the null model equations to uncover how (i) sample size, (ii) variation in biological responses to the stressors and (iii) statistical significance, affect the ability to detect non-null interactions. 3. Our analyses lead to three main results. Firstly, it is clear the additive and multiplicative null models are not directly comparable, and over one third of all simulated interactions had classifications that were model dependent. Secondly, both null models have weak power to correctly classify interactions at commonly implemented sample sizes (i.e., ≤6 replicates), unless data uncertainty is unrealistically low. This means all but the most extreme interactions are indistinguishable from the null model expectation. Thirdly, we show that increasing sample size increases the power to detect the true interactions but only very slowly. However, the biggest gains come from increasing replicates from 3 up to 25 and we provide an R function for users to determine sample sizes required to detect a critical effect size of biological interest for the additive model. 4. Our results will aid researchers in the design of their experiments and the subsequent interpretation of results. We find no clear statistical advantage of using one null model over the other and argue null model choice should be based on biological relevance rather than statistical properties. However, there is a pressing need to increase experiment sample sizes otherwise many biologically important synergistic and antagonistic stressor interactions will continue to be missed.

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Using a newly introduced framework to measure ecological stressor interactions

Cited by 51 publications

References 71 publications

Effects of multiple stressors on the dimensionality of ecological stability

Effects of multiple stressors on the dimensionality of ecological stability

Microplastic exposure interacts with habitat degradation to affect behaviour and survival of juvenile fish in the field

Are experiment sample sizes adequate to detect biologically important interactions between multiple stressors?

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