scholarly journals virtualspecies, an R package to generate virtual species distributions

Ecography ◽  
2015 ◽  
Vol 39 (6) ◽  
pp. 599-607 ◽  
Author(s):  
Boris Leroy ◽  
Christine N. Meynard ◽  
Céline Bellard ◽  
Franck Courchamp
Keyword(s):  
Species Distributions ◽  
R Package ◽  
Virtual Species

Correlative climatic niche models predict real and virtual species distributions equally well

Ecology ◽  
2019 ◽  
Vol 101 (1) ◽  
Author(s):  
Valentin Journé ◽  
Jean‐Yves Barnagaud ◽  
Cyril Bernard ◽  
Pierre‐André Crochet ◽  
Xavier Morin
Keyword(s):  
Species Distributions ◽  
Climatic Niche ◽  
Niche Models ◽  
Virtual Species

Marine halogenated compound analysis: from an R package to the isolation of new griseophenone derivatives

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
C Roullier ◽  
Y Guitton ◽  
S Prado ◽  
O Grovel ◽  
YF Pouchus
Keyword(s):  
R Package ◽  
Halogenated Compound

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Sign in / Sign up

Export Citation Format

Share Document