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      Reconceptualizing synergism and antagonism among multiple stressors

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          Abstract

          The potential for complex synergistic or antagonistic interactions between multiple stressors presents one of the largest uncertainties when predicting ecological change but, despite common use of the terms in the scientific literature, a consensus on their operational definition is still lacking. The identification of synergism or antagonism is generally straightforward when stressors operate in the same direction, but if individual stressor effects oppose each other, the definition of synergism is paradoxical because what is synergistic to one stressor's effect direction is antagonistic to the others. In their highly cited meta-analysis, Crain et al. ( Ecology Letters, 11, 2008: 1304) assumed in situations with opposing individual effects that synergy only occurs when the cumulative effect is more negative than the additive sum of the opposing individual effects. We argue against this and propose a new systematic classification based on an additive effects model that combines the magnitude and response direction of the cumulative effect and the interaction effect. A new class of “mitigating synergism” is identified, where cumulative effects are reversed and enhanced. We applied our directional classification to the dataset compiled by Crain et al. ( Ecology Letters, 11, 2008: 1304) to determine the prevalence of synergistic, antagonistic, and additive interactions. Compared to their original analysis, we report differences in the representation of interaction classes by interaction type and we document examples of mitigating synergism, highlighting the importance of incorporating individual stressor effect directions in the determination of synergisms and antagonisms. This is particularly pertinent given a general bias in ecology toward investigating and reporting adverse multiple stressor effects (double negative). We emphasize the need for reconsideration by the ecological community of the interpretation of synergism and antagonism in situations where individual stressor effects oppose each other or where cumulative effects are reversed and enhanced.

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          Effect size, confidence interval and statistical significance: a practical guide for biologists.

          Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
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            Global biodiversity scenarios for the year 2100.

            Scenarios of changes in biodiversity for the year 2100 can now be developed based on scenarios of changes in atmospheric carbon dioxide, climate, vegetation, and land use and the known sensitivity of biodiversity to these changes. This study identified a ranking of the importance of drivers of change, a ranking of the biomes with respect to expected changes, and the major sources of uncertainties. For terrestrial ecosystems, land-use change probably will have the largest effect, followed by climate change, nitrogen deposition, biotic exchange, and elevated carbon dioxide concentration. For freshwater ecosystems, biotic exchange is much more important. Mediterranean climate and grassland ecosystems likely will experience the greatest proportional change in biodiversity because of the substantial influence of all drivers of biodiversity change. Northern temperate ecosystems are estimated to experience the least biodiversity change because major land-use change has already occurred. Plausible changes in biodiversity in other biomes depend on interactions among the causes of biodiversity change. These interactions represent one of the largest uncertainties in projections of future biodiversity change.
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              Quantifying the evidence for ecological synergies.

              There is increasing concern that multiple drivers of ecological change will interact synergistically to accelerate biodiversity loss. However, the prevalence and magnitude of these interactions remain one of the largest uncertainties in projections of future ecological change. We address this uncertainty by performing a meta-analysis of 112 published factorial experiments that evaluated the impacts of multiple stressors on animal mortality in freshwater, marine and terrestrial communities. We found that, on average, mortalities from the combined action of two stressors were not synergistic and this result was consistent across studies investigating different stressors, study organisms and life-history stages. Furthermore, only one-third of relevant experiments displayed truly synergistic effects, which does not support the prevailing ecological paradigm that synergies are rampant. However, in more than three-quarters of relevant experiments, the outcome of multiple stressor interactions was non-additive (i.e. synergies or antagonisms), suggesting that ecological surprises may be more common than simple additive effects.
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                Author and article information

                Journal
                Ecol Evol
                Ecol Evol
                ece3
                Ecology and Evolution
                BlackWell Publishing Ltd (Oxford, UK )
                2045-7758
                2045-7758
                April 2015
                11 March 2015
                : 5
                : 7
                : 1538-1547
                Affiliations
                Department of Zoology, University of Otago P.O. Box 56, Dunedin, 9054, New Zealand
                Author notes
                Correspondence Jeremy J. Piggott, Department of Zoology, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand., Tel: +64 3 4795855;, Fax: +64 3 4797584;, E-mail: jeremy.piggott@ 123456otago.ac.nz

                Funding Information Funding was provided by New Zealand's Ministry of Business, Innovation and Employment contract C01X1005.

                Article
                10.1002/ece3.1465
                4395182
                25897392
                36d8997f-141b-49e5-afd0-f1cbe0aff879
                © 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 02 February 2015
                : 22 February 2015
                Categories
                Original Research

                Evolutionary Biology
                antagonism,ecological surprise,interaction,stressor,synergism
                Evolutionary Biology
                antagonism, ecological surprise, interaction, stressor, synergism

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