Meta-analysis is the quantitative, scientific synthesis of research results. Since the term and modern approaches to research synthesis were first introduced in the 1970s, meta-analysis has had a ...revolutionary effect in many scientific fields, helping to establish evidence-based practice and to resolve seemingly contradictory research outcomes. At the same time, its implementation has engendered criticism and controversy, in some cases general and others specific to particular disciplines. Here we take the opportunity provided by the recent fortieth anniversary of meta-analysis to reflect on the accomplishments, limitations, recent advances and directions for future developments in the field of research synthesis.
Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies. This is the first comprehensive handbook of meta-analysis written specifically for ...ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-analysts.
The chapters, written by renowned experts, walk readers through every step of meta-analysis, from problem formulation to the presentation of the results. The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used). Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets. This one-of-a-kind resource is uniquely tailored to the biological sciences, and will provide an invaluable text for practitioners from graduate students and senior scientists to policymakers in conservation and environmental management.
Walks you through every step of carrying out a meta-analysis in ecology and evolutionary biology, from problem formulation to result presentationBrings together experts from a broad range of fieldsShows how to avoid, minimize, or resolve pitfalls such as missing data, publication bias, varying data quality, nonindependence of observations, and phylogenetic dependencies among speciesHelps you choose the right softwareDraws on numerous examples based on real biological datasets
The number of published meta‐analyses in plant ecology has increased greatly over the last two decades. Meta‐analysis has made a significant contribution to the field, allowing review of evidence for ...various ecological hypotheses and theories, estimation of effects of major environmental drivers (climate change, habitat fragmentation, invasive species, air pollution), assessment of management and conservation strategies, and comparison of effects across different temporal and spatial scales, taxa and ecosystems, as well as research gap identification. We identified 322 meta‐analyses published in the field of plant ecology between 1996 and 2013 in 95 different journals and assessed their methodological and reporting quality according to standard criteria. Despite significant recent developments in the methodology of meta‐analysis, the quality of published meta‐analyses was uneven and showed little improvement over time. We found many cases of imprecise and inaccurate usage of the term ‘meta‐analysis’ in plant ecology, particularly confusion between meta‐analysis and vote counting and incorrect application of statistical techniques designed for primary studies to meta‐analytical data, without recognition of the violation of statistical assumptions of the analyses. Methodological issues for meta‐analyses in plant ecology include incomplete reporting of search strategy used to retrieve primary studies, failure to test for possible publication bias and to conduct sensitivity analysis to test the robustness of the results, as well as lack of availability of the data set used for the analyses. The use of meta‐analysis is particularly common in community ecology, ecophysiology and ecosystem ecology, but meta‐analyses in ecophysiology are more likely not to meet standard quality criteria than papers in other subdisciplines. Fewer meta‐analyses have been conducted in plant population ecology. Synthesis. Over the past two decades, plant ecologists have embraced meta‐analysis as a statistical tool to combine results across studies, and much has been learned as a result. However, as the popularity and usage of meta‐analysis in the field of plant ecology has grown, establishment of quality standards, as has been done in other disciplines, becomes increasingly important. In order to improve the quality of future meta‐analyses in plant ecology, we suggest adoption of a checklist of quality criteria for meta‐analysis for use by research synthesists, peer reviewers and journal editors.
Most current research on land‐use intensification addresses its potential to either threaten biodiversity or to boost agricultural production. However, little is known about the simultaneous effects ...of intensification on biodiversity and yield. To determine the responses of species richness and yield to conventional intensification, we conducted a global meta‐analysis synthesizing 115 studies which collected data for both variables at the same locations. We extracted 449 cases that cover a variety of areas used for agricultural (crops, fodder) and silvicultural (wood) production. We found that, across all production systems and species groups, conventional intensification is successful in increasing yield (grand mean + 20.3%), but it also results in a loss of species richness (−8.9%). However, analysis of sub‐groups revealed inconsistent results. For example, small intensification steps within low intensity systems did not affect yield or species richness. Within high‐intensity systems species losses were non‐significant but yield gains were substantial (+15.2%). Conventional intensification within medium intensity systems revealed the highest yield increase (+84.9%) and showed the largest loss in species richness (−22.9%). Production systems differed in their magnitude of richness response, with insignificant changes in silvicultural systems and substantial losses in crop systems (−21.2%). In addition, this meta‐analysis identifies a lack of studies that collect robust biodiversity (i.e. beyond species richness) and yield data at the same sites and that provide quantitative information on land‐use intensity. Our findings suggest that, in many cases, conventional land‐use intensification drives a trade‐off between species richness and production. However, species richness losses were often not significantly different from zero, suggesting even conventional intensification can result in yield increases without coming at the expense of biodiversity loss. These results should guide future research to close existing research gaps and to understand the circumstances required to achieve such win‐win or win‐no‐harm situations in conventional agriculture.
To determine the responses of species richness and yield to conventional land‐use intensification, we conducted a global meta‐analysis. Across all production systems (food, fodder, wood), intensification increases yield (+20.3%), but also leads to a loss of species (−8.9%). Within low intensity systems, intensification did not affect yield or richness, while within medium intensity systems, the highest yield increase (+84.9%) and largest richness loss (−22.9%) were found. Conventional intensification often drives a trade‐off between richness and production. However, this meta‐analysis also highlights that—even conventional—intensification can result in yield increases without coming at the expense of biodiversity loss.
Food waste has major consequences for social, nutritional, economic, and environmental issues, and yet the amount of food waste disposed in the U.S. has not been accurately quantified. We introduce ...the transparent and repeatable methods of meta-analysis and systematic reviewing to determine how much food is discarded in the U.S., and to determine if specific factors drive increased disposal. The aggregate proportion of food waste in U.S. municipal solid waste from 1995 to 2013 was found to be 0.147 (95% CI 0.137–0.157) of total disposed waste, which is lower than that estimated by U.S. Environmental Protection Agency for the same period (0.176). The proportion of food waste increased significantly with time, with the western U.S. region having consistently and significantly higher proportions of food waste than other regions. There were no significant differences in food waste between rural and urban samples, or between commercial/institutional and residential samples. The aggregate disposal rate for food waste was 0.615 pounds (0.279 kg) (95% CI 0.565–0.664) of food waste disposed per person per day, which equates to over 35.5 million tons (32.2 million tonnes) of food waste disposed annually in the U.S.
To make progress scientists need to know what other researchers have found and how they found it. However, transparency is often insufficient across much of ecology and evolution. Researchers often ...fail to report results and methods in detail sufficient to permit interpretation and meta-analysis, and many results go entirely unreported. Further, these unreported results are often a biased subset. Thus the conclusions we can draw from the published literature are themselves often biased and sometimes might be entirely incorrect. Fortunately there is a movement across empirical disciplines, and now within ecology and evolution, to shape editorial policies to better promote transparency. This can be done by either requiring more disclosure by scientists or by developing incentives to encourage disclosure.
Evidence suggests that insufficient transparency is a problem across much of ecology and evolution. Results and methods are often reported in insufficient detail or go entirely unreported. Further, these unreported results are often a biased subset, thus substantially hampering interpretation and meta-analysis.
Journals and other institutions, such as funding agencies, influence researchers’ decisions about disseminating results. There is a movement across empirical disciplines, including ecology and evolution, to shape institutional policies to better promote transparency.
Institutions can promote transparency by requiring or encouraging more disclosure, as with the now-familiar data archiving, or by developing an incentive structure promoting disclosure, such as preregistration of studies and analysis plans.
Dissertations are a foundational scientific product; they are the formative product that early‐career scientists create and share original knowledge. The methodological approaches used in ...dissertations vary with the research field. In plant ecology, these approaches include observations, experiments (field or controlled environment), literature reviews, theoretical approaches, or analyses of existing data (including “big data”). Recently, concerns have been raised about the rise of “big data” studies and the loss of observational and field‐based studies in ecology, but such trends have not been formally quantified. Therefore, we examined how the emphasis on each of these categories has changed over time and whether male and female authors differ in the methods employed. We found remarkable temporal consistency, with observational studies being dominant over the entire time span examined. There was an increase in the number of approaches employed per dissertation, with increases in analyses of databases and theoretical studies adding to rather than replacing traditional methodologies (like observations and field experiments). The representation of women increased over time. There were some differences in the approaches taken by men and women, which requires further investigation.
Recently, some researchers have raised concerns over the apparent rise of big data studies and the decline of field observations in ecology. But to what extent is this change occurring? Using plant ecology dissertations as a test case, we quantified trends in research methodologies and found that observational studies continue to dominate, even though newer methods have nudged their way in.
Invasion biologists often suggest that phenotypic plasticity plays an important role in successful plant invasions. Assuming that plasticity enhances ecological niche breadth and therefore confers a ...fitness advantage, recent studies have posed two main hypotheses: (1) invasive species are more plastic than non-invasive or native ones; (2) populations in the introduced range of an invasive species have evolved greater plasticity than populations in the native range. These two hypotheses largely reflect the disparate interests of ecologists and evolutionary biologists. Because these sciences are typically interested in different temporal and spatial scales, we describe what is required to assess phenotypic plasticity at different levels. We explore the inevitable tradeoffs of experiments conducted at the genotype vs. species level, outline components of experimental design required to identify plasticity at different levels, and review some examples from the recent literature. Moreover, we suggest that a successful invader may benefit from plasticity as either (1) a Jack-of-all-trades, better able to maintain fitness in unfavourable environments; (2) a Master-of-some, better able to increase fitness in favourable environments; or (3) a Jack-and-master that combines some level of both abilities. This new framework can be applied when testing both ecological or evolutionary oriented hypotheses, and therefore promises to bridge the gap between the two perspectives.
Managing forests for competing goals Gurevitch, Jessica
Science (American Association for the Advancement of Science),
2022-May-20, 2022-05-20, 20220520, Letnik:
376, Številka:
6595
Journal Article
Recenzirano
Tree plantations face difficult trade-offs between production and ecological goals.
Meta‐analyses often encounter studies with incompletely reported variance measures (e.g., standard deviation values) or sample sizes, both needed to conduct weighted meta‐analyses. Here, we first ...present a systematic literature survey on the frequency and treatment of missing data in published ecological meta‐analyses showing that the majority of meta‐analyses encountered incompletely reported studies. We then simulated meta‐analysis data sets to investigate the performance of 14 options to treat or impute missing SDs and/or SSs. Performance was thereby assessed using results from fully informed weighted analyses on (hypothetically) complete data sets. We show that the omission of incompletely reported studies is not a viable solution. Unweighted and sample size‐based variance approximation can yield unbiased grand means if effect sizes are independent of their corresponding SDs and SSs. The performance of different imputation methods depends on the structure of the meta‐analysis data set, especially in the case of correlated effect sizes and standard deviations or sample sizes. In a best‐case scenario, which assumes that SDs and/or SSs are both missing at random and are unrelated to effect sizes, our simulations show that the imputation of up to 90% of missing data still yields grand means and confidence intervals that are similar to those obtained with fully informed weighted analyses. We conclude that multiple imputation of missing variance measures and sample sizes could help overcome the problem of incompletely reported primary studies, not only in the field of ecological meta‐analyses. Still, caution must be exercised in consideration of potential correlations and pattern of missingness.
Meta‐analyses often encounter studies with incompletely reported variance measures (e.g., standard deviation values) or sample sizes, both needed to conduct weighted meta‐analyses. We present a systematic literature survey on the frequency and treatment of missing data in published ecological meta‐analyses. Simulating the effect of 14 different options to treat missing data in meta‐analysis, we show that multiple imputation of missing variance measures and sample sizes could help overcome the problem of incompletely reported primary studies.
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