Grazing vs. mowing: A meta-analysis of biodiversity benefits for grassland management

This overview examines research synthesis in applied ecology and conservation. Vote counting and pooling unweighted averages are widespread despite the superiority of syntheses based on weighted combination of effects. Such analyses allow exploration of methodological uncertainty in addition to consistency of effects across species, space and time, but exploring heterogeneity remains controversial. Meta-analyses are required to generalize in ecology, and to inform evidence-based decision-making, but the more sophisticated statistical techniques and registers of research used in other disciplines must be employed in ecology to fully realize their benefits.

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Meta-analysis in applied ecology.

metaanalYSiS uSeS SummarY StatiSticS like effect sizes to combine information from multiple studies. Yet a common problem encountered when collecting information for calculating effect sizes is the absence of data from published studies. The incomplete reporting of means, correlations, variances, and sample sizes can bias metaanalysis in many ways: reviews will have smaller sample sizes because studies with missing data are often excluded (Orwin and Cordray 1985, Follmann et al. 1992); effect size metrics like Hedges’ d are disfavored because they require too many withinstudy statistics; approaches to pooling effect sizes will use less restrictive statistical models such as unweighted analyses (Kelley et al. 2004, Furukawa et al. 2006); and metaanalysis may yield spurious results because excluding studies with missing information could further exacerbate publication bias. In this chapter, I discuss possible solutions for dealing with partial information and missing data from published studies (Box 13.1). These solutions can improve the amount of the information extracted from individual studies, and increase the representation of data for metaanalysis. I rely heavily on advances and observations from the medical literature; this is necessary given that discussion relating to missing information has received limited attention in ecological and evolutionary metaanalysis (Lajeunesse and Forbes 2003). I begin with a description of the mechanisms that generate missing information within studies, followed by a discussion of how gaps of information can influence metaanalysis and the way studies are quantitatively reviewed. I then suggest some practical solutions to recovering missing statistics from published studies. These include statistical acrobatics to convert available information (e.g., ttest) into those that are more useful to compute effect sizes, as well as a few heuristic approaches that impute (fill gaps) missing information when pooling effect sizes (e.g., Follmann et al. 1992, Yuan and Little 2009). Finally, I discuss multipleimputation methods that account for the uncertainty associated with filling gaps of information when performing metaanalysis.

13. Recovering Missing or Partial Data from Studies: A Survey of Conversions and Imputations for Meta-analysis

A systematic review and meta-analysis were used to assess the current state of knowledge and quantify the effects of land use change (LUC) to second generation (2G), non-food bioenergy crops on soil organic carbon (SOC) and greenhouse gas (GHG) emissions of relevance to temperate zone agriculture. Following analysis from 138 original studies, transitions from arable to short rotation coppice (SRC, poplar or willow) or perennial grasses (mostly Miscanthus or switchgrass) resulted in increased SOC (+5.0 ± 7.8% and +25.7 ± 6.7% respectively). Transitions from grassland to SRC were broadly neutral (+3.7 ± 14.6%), whilst grassland to perennial grass transitions and forest to SRC both showed a decrease in SOC (−10.9 ± 4.3% and −11.4 ± 23.4% respectively). There were insufficient paired data to conduct a strict meta-analysis for GHG emissions but summary figures of general trends in GHGs from 188 original studies revealed increased and decreased soil CO2 emissions following transition from forests and arable to perennial grasses. We demonstrate that significant knowledge gaps exist surrounding the effects of land use change to bioenergy on greenhouse gas balance, particularly for CH4. There is also large uncertainty in quantifying transitions from grasslands and transitions to short rotation forestry. A striking finding of this review is the lack of empirical studies that are available to validate modelled data. Given that models are extensively use in the development of bioenergy LCA and sustainability criteria, this is an area where further long-term data sets are required.

Land use change to bioenergy: A meta-analysis of soil carbon and GHG emissions

Heathlands confronting global change: drivers of biodiversity loss from past to future scenarios

Impacts of grazing on lowland heathland in north-west Europe.

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Sophie Lake

Papers

Impacts of grazing on lowland heathland in north-west Europe.

Number 422 Impacts of livestock grazing on lowland heathland in the UK