Environmental signals from leaves – a physiognomic analysis of European vegetation

Abstract

Summary• Leaf physiognomic traits vary predictably along climatic and environmental gradients. The relationships between leaf physiognomy and climate have been investigated on different continents, but so far an investigation based on European vegetation has been missing.• A grid data set (0.5 ° × 0.5 ° latitude/longitude) has been compiled in order to determine spatial patterns of leaf physiognomy across Europe. Based on distribution maps of native European hardwoods, synthetic chorologic flora lists were compiled for all grid cells. Every synthetic chorologic flora was characterised by 25 leaf physiognomic traits and correlated with 16 climatic parameters.• Clear spatial patterns of leaf physiognomy have been observed, which are statistically significant related to certain, temperature-related climate parameters. Transfer functions for several climatic parameters have been established, based on the observed relationships.• The study provides evidence that synthetically generated floras represent a powerful tool for analysing spatial patterns of leaf physiognomy and their relationships to climate. The transfer functions from the European data set indicate slightly different relationships of leaf physiognomy and environment compared with results obtained from other continents.

Spatial distribution and functional significance of leaf lamina shape in Amazonian forest trees

“…One of the aims of using these ordinal categories is to identify the distribution and potential environmental correlates of leaves at either end of the leaf shape continuum (broad→elongate). Traiser et al's (2005) categories may not do this effectively in the present study because they are based on European vegetation. For this reason, we also split the dataset based on the mean ratio of length to width for the sample.…”

“…Our main source for exploring spatial variation in leaf shape across the Amazon rainforest is the RAINFOR database (Peacock et al, 2007 1 ), which has been constructed using information from 115 permanent plots distributed across eight South American countries, to which we added 26 data plots from Guyanas and Suriname compiled by the ATDN network (ter Steege et al, 2003). At the time of data extraction for this study (August 2006) the RAINFOR database contained 108 720 individual trees ≥10 cm DBH (diameter at breast height) from 3324 species.…”

“…For example, if we were to use only the mean I 1,2 for each plot important trends may be missed because plots with different length-width distributions could display similar means. Thus, for the purpose of illustration and analysis, we follow the methodology of Traiser et al (2005), and divide our leaf shape index into three ordinal categories: leaves with an index of ≤1, between 1 and <3, and leaves with an index ≥3. One of the aims of using these ordinal categories is to identify the distribution and potential environmental correlates of leaves at either end of the leaf shape continuum (broad→elongate).…”

Computational biomes: The ecometrics of large mammal teeth

“…Different traits have been explored for ecometric analyses. For plants, leaf shapes have been considered (Wolfe, 1995;Traiser et al, 2005). For animals, considered traits include teeth (Eronen et al, 2010a;Liu et al, 2012;Meloro and Kovarovic, 2013;Polly and Head, 2015;Fortelius et al, 2016;Zliobaite et al, 2016), limbs and locomotion (Polly and Head, 2015;Barr, 2017;Levering et al, 2017), skeletal traits (Lawing et al, 2012), as well as body mass (Meloro and Kovarovic, 2013).…”

Regional constraints on leaf physiognomy and precipitation regression models: a case study from China

“…Moreover, in equatorial Africa, the proportion of untoothed margin species is positively correlated with wet months precipitation provided that precipitation is not less than 5 cm per month, while leaf size, particularly in mesophyll size, most strongly correlate with wet months and annual precipitation (Jacobs 1999). However, not all correlations between leaf physiognomy and precipitation are statistically significant, an observation that has been supported by a research from Europe: Traiser et al (2005) noticed that based on European vegetation data, none of these transfer functions can produce reliable precipitation prediction. Peppe et al (2011) studies data from 92 samples on a global scale, they found a low correlation between leaf size and MAP; even when the method of digital leaf physiognomy is used, this correlation increased only slightly.…”