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Summary Grasses are exceptionally productive, yet their hydraulic adaptation is paradoxical. Among C3grasses, a high photosynthetic rate (Aarea) may depend on higher vein density (Dv) and hydraulic conductance (Kleaf). However, the higherDvof C4grasses suggests a hydraulic surplus, given their reduced need for highKleafresulting from lower stomatal conductance (gs).Combining hydraulic and photosynthetic physiological data for diverse common garden C3and C4species with data for 332 species from the published literature, and mechanistic modeling, we validated a framework for linkages of photosynthesis with hydraulic transport, anatomy, and adaptation to aridity.C3and C4grasses had similarKleafin our common garden, but C4grasses had higherKleafthan C3species in our meta‐analysis. Variation inKleafdepended on outside‐xylem pathways. C4grasses have highKleaf : gs, which modeling shows is essential to achieve their photosynthetic advantage.Across C3grasses, higherAareawas associated with higherKleaf, and adaptation to aridity, whereas for C4species, adaptation to aridity was associated with higherKleaf : gs. These associations are consistent with adaptation for stress avoidance.Hydraulic traits are a critical element of evolutionary and ecological success in C3and C4grasses and are crucial avenues for crop design and ecological forecasting.
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Comparing traditional and Bayesian approaches to ecological meta‐analysis
Abstract Despite the wide application of meta‐analysis in ecology, some of the traditional methods used for meta‐analysis may not perform well given the type of data characteristic of ecological meta‐analyses.We reviewed published meta‐analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies (ni) and the number of studies or records (k) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta‐analysis methods for estimating a mean effect size and its uncertainty interval.Our literature review showed thatniandkwere highly variable, distributions were right‐skewed and were generally small (medianni = 5, mediank = 44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). Whenkwas low (<40), 95% coverage was achieved by a confidence interval (CI) based on thetdistribution that uses an adjusted standard error (the Hartung–Knapp–Sidik–Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap orzdistribution CIs had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta‐analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults.In general, for a simple random‐effects meta‐analysis, the performance of the best frequentist and Bayesian methods was similar for the same combinations of factors (kand mean replication), though the Bayesian approach had higher than nominal (>95%) coverage for the mean effect whenkwas very low (k < 15). Our literature review suggests that many meta‐analyses that usedzdistribution or bootstrapping CIs may have overestimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta‐analyses.
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- PAR ID:
- 10456800
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 11
- Issue:
- 10
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- p. 1286-1295
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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