Leaf form–climate relationships on the global stage: an ensemble of characters

Abstract

Aim Early in their evolution, angiosperms evolved a diversity of leaf form far greater than that of any other group of land plants. Some of this diversity evolved in response to varying climate. Our aim is to test the global relationship between leaf form in woody dicot angiosperms and the climate in which they live. Location We have compiled a data set describing leaf form (using 31 standardized categorical characters) from 378 natural or naturalized vegetation sites from around the world. Our data include sites from all continents except Antarctica and encompass biomes from tropical to taiga, over a range of elevations from 0.5 m to over 3000 m. Methods We chose the Climate Leaf Analysis Multivariate Program sampling, scoring and analytical protocols to test the relationships between climate and leaf form, which is based on canonical correspondence analysis. Cluster analysis evaluates the role of historical factors in shaping the patterns, and pairwise Pearson correlations examine the relationships among leaf characters. Results Woody dicot leaf characters form a physiognomic spectrum that reflects local climate conditions. On a global scale, correlations between leaf form and climate are consistent, irrespective of climate regime, vegetation type or biogeographic history. Relationships with temperature variables are maintained even when leaf margin characters, regarded as being particularly well correlated with mean annual temperature, are removed. Main conclusions In natural woody dicot vegetation an integrated spectrum of leaf form has developed across multiple leaf character states and species. This spectrum appears more strongly influenced by prevailing climate than biogeographic history. The covariation of leaf traits across species suggests strong integration of leaf form. New methods of exploring structure in multidimensional physiognomic space enable better application of leaf form to palaeoclimate reconstruction.

Paleogene monsoons across India and South China: Drivers of biotic change

“…However no taxonomic identification is required for extracting climate signals from leaf form using the technique known as Climate Leaf Analysis Multivariate Program (CLAMP) Yang et al, 2015).…”

“…Species selection no doubt occurs over short timescales (decades to millenia) but evolutionary adaptation must also occur. This is evidenced by the finding of Yang et al (2015) that stands of vegetation in highly endemic and compositionally distinct floras such as those of South Africa, New Zealand and Japan, all of which due to latitude and altitude grow under similar climates, have leaf trait spectra more similar to one another than those of other stands that experience different climates (Yang et al, 2015, Figs. 3 and S2).…”

“…Although Jacques et al (2015) first demonstrated the utility of CLAMP to investigate monsoon climates in China, we use the Khan et al (2014) calibration because it additionally ACCEPTED MANUSCRIPT…”

How to interpret palaeoclimate CLAMP estimates - is it a number value or an interval range?

“…All the mentioned reference files and datasets can be freely downloadable from the CLAMP website (Spicer 2011-2016) -see Appendix 1. Recently Yang et al (2015) presented a new calibration dataset from 378 natural or naturalized vegetation sites from all continents except Antarctica including biomes from tropical to taiga, over a range of elevations from 0.5 m to over 3000 m a.s.l. The study verified the generally assumed correlation between leaf form and climate used by CLAMP and/or LMA.…”

MORPHYLL: A database of fossil leaves and their morphological traits

“…For example, Leaf Margin Analysis exploits the statistically highly significant negative correlation between frequency of taxa showing toothed leaf margin and mean annual temperature (MAT) (Wolfe and Upchurch 1987;Wilf, 1997;Greenwood et al, 2004;Peppe et al, 2011;Royer et al, 2012). Another approach, the Climate Leaf Analysis Multivariate Program (CLAMP), includes numerous additional correlations between various leaf traits and climate parameters (Wolfe, 1993;Wolfe and Spicer, 1999;Yang et al, 2015). As an example, leaf size (nanophyll, leptophyll, etc.…”