How can my research paper be useful for future meta-analyses on forest restoration plantations?
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Statistical meta-analysis is a powerful and useful tool to quantitatively synthesize the information conveyed in published studies on a particular topic. It allows identifying and quantifying overall patterns and exploring causes of variation. The inclusion of published works in meta-analyses requires, however, a minimum quality standard of the reported data and information on the methodology used. Our experience with conducting a meta-analysis on the relationship between seedling quality and field performance is that nearly one third of the apparently relevant publications had to be discarded because essential data, usually statistical dispersion parameters, were not properly reported. In addition, we encountered substantial difficulty to explore the effect of covariates due to the poor description of nursery cultivation methods, plantation location, and management in a significant proportion of the selected primary studies. Thus, we present guidelines for improving methodology detail and data presentation so that future forest restoration-oriented research can be more readily incorporated into meta-analyses. In general, research studies should report data on means, sample size, and any measure of variation even if they are not statistically significant. The online availability of raw data is the best practice to facilitate the inclusion of primary research on meta-analyses. Providing full information about the production of nursery seedlings, such as plant material and experimental conditions, is essential to test whether these procedures might have an effect on seedling quality. In addition, detailed information about field trials such as site climate, soil preparation techniques, previous land use, or post-plantation management, is needed to elucidate whether seedling quality is context-dependent. Thus, we provide a detailed checklist of important information that should be included when reporting forest restoration research involving the use of nursery-produced seedlings. All this will help to quantitatively synthetize current state-of-knowledge and thus contribute to the advancement of the forest restoration discipline.
KeywordsData quality Data reporting Meta-analysis Methodology guideline Seedling quality Research synthesis
This work was supported by the network Remedinal-3 (S2013/MAE-2719) of the Community of Madrid and Project CGL2014-53308-P SERAVI of the MINECO. EA was supported by the postdoctoral Grant “Ayudas para contratos para la formación postdoctoral” (FPDI-2013-15573) from the Ministry of Economy of the Spanish Government. We thank the suggestions and comments of Douglass F. Jacobs and two anonymous reviewers.
- Côté IM, Curtis PS, Rothstein HR, Stewart GB (2013) Gathering data: searching literature and selection criteria. In: Koricheva J, Gurevitch J, Mengersen K (eds) Handbook of meta-analysis in ecology and evolution, Princeton, pp 37–51Google Scholar
- Dumroese RK, Luna T, Landis TD (2008) Nursery manual for native plants: a guide for tribal nurseries. Agriculture Handbook 730, US Department of Agriculture, Forest Service, Washington, DCGoogle Scholar
- Duryea ML (ed) (1985) Evaluating seedling quality: principles, procedures, and predictive abilities of major tests. Forest Research Laboratory, Oregon State University, CorvallisGoogle Scholar
- Hainds MJ (2004) Determining the correct planting depth for container-grown longleaf pine seedlings. Gen Tech Rep SRS‒71, US Department of Agriculture, Forest Service, South Res Sta, Asheville NC, pp 317–318Google Scholar
- Koricheva J, Gurevitch J, Mengersen K (eds) (2013) Handbook of meta-analysis in ecology and evolution. Princeton University Press, PrincetonGoogle Scholar
- Lajeunesse MJ (2013) Recovering missing or partial data from studies: a survey of conversions and imputations for meta-analysis. In: Koricheva J, Gurevitch J, Mengersen K (eds) Handbook of meta-analysis in ecology and evolution, Princeton, pp 195–206Google Scholar
- Landis TD (1989) Mineral nutrients and fertilization. In: Landis TD, Tinus RW, McDonald SE, Barnett JP (eds) The container tree nursery manual, vol 4, seedling nutrition and irrigation. US Department of Agriculture, Forest Service, Washington, pp 1–67Google Scholar
- McCreary DD, Tecklin J (2001) The effects of different sizes of tree shelters on blue oak (Quercus douglasii) growth. West J Appl For 16:153–158Google Scholar
- Minnemeyer S, Laestadius L, Sizer N et al (2011) A world of opportunity. World Resource Institute, WashingtonGoogle Scholar
- Mollá S, Villar-Salvador P, García-Fayos P, Peñuelas Rubira JL (2006) Physiological and transplanting performance of Quercus ilex L. (holm oak) seedlings grown in nurseries with different winter conditions. For Ecol Manage 237:218–226. https://doi.org/10.1016/j.foreco.2006.09.047 CrossRefGoogle Scholar
- Pigott TD (1994) Methods for handling missing data in research synthesis. The handbook of research synthesis. Russell Sage Foundation, New York, pp 163–175Google Scholar
- Pinto JR, Dumroese RK, Davis AS, Landis TD (2011) Conducting seedling stocktype trials: a new approach to an old question. J For 109:293–299Google Scholar
- Ritchie GA, Dunlap JR (1980) Root growth potential: its development and expression in forest tree seedlings. NZJ For Sci 10:218–248Google Scholar
- Rosenberg MS, Rothstein HR, Gurevitch J (2013) Effect sizes: conventional choices and calculations. In: Koricheva J, Gurevitch J, Mengersen K (eds) Handbook of meta-analysis in ecology and evolution, Princeton, pp 61–71Google Scholar
- Villar-Salvador P, Puértolas J, Cuesta B et al (2012) Increase in size and nitrogen concentration enhances seedling survival in Mediterranean plantations. Insights from an ecophysiological conceptual model of plant survival. New For 43:755–770. https://doi.org/10.1007/s11056-012-9328-6 CrossRefGoogle Scholar
- Wakeley PC (1954) Planting the southern pines. Agricultural Monograph 18, US Department of Agriculture, Forest Service, WashingtonGoogle Scholar