Plant community structure and nitrogen inputs modulate the climate signal on leaf traits

Abstract

Aim Leaf traits strongly impact biogeochemical cycles in terrestrial ecosystems. Understanding leaf trait variation along environmental gradients is thus essential to improve the representation of vegetation in Earth system models. Our aims were to quantify relationships between leaf traits and climate in permanent grasslands at a biogeographical scale and to test whether these relationships were sensitive to (a) the level of nitrogen inputs and (b) the inclusion of information pertaining to plant community organization. Location Permanent grasslands throughout France. Methods We combined existing datasets on climate, soil, nitrogen inputs (fertilization and deposition), species composition and four traits, namely specific leaf area, leaf dry matter content and leaf nitrogen and phosphorus concentrations, for 15,865 French permanent grasslands. Trait–climate relationships were tested using the following four climatic variables available across 1,833 pixels (5 km × 5 km): mean annual temperature (MAT) and precipitation (MAP), and two indices accounting for the length of the growing season. We compared these relationships at the pixel level using either using community-level or species’ trait means. Results Our findings were as follows: (a) leaf traits related to plant nutrient economy shift consistently along a gradient of growing season length accounting for temperature and soil water limitations of plant growth (GSLtw); (b) weighting leaf traits by species abundance in local communities is pivotal to capture leaf trait–environment relationships correctly at a biogeographical scale; and (c) the relationships between traits and GSLtw weaken for grasslands with a high nitrogen input. Main conclusions The effects of climate on plant communities are better described using composite descriptors than coarse variables such as MAT or MAP, but appear weaker for high-nitrogen grasslands. Using information at the community level tends to strengthen trait–climate relationships. The interplay of land management, community assembly and bioclimate appears crucial to the prediction of leaf trait variations and their effects on biogeochemical cycles

Functional traits modulate the response of alien plants along abiotic and biotic gradients

“…This corroborates the prediction that unproductive environments in severe abiotic conditions should be dominated by conservative plants (Grime, 1977) and productive environments by strong competitors (Borgy, Violle, Choler, Denelle, et al, 2017;Díaz et al, 2004).…”

Late Quaternary climate legacies in contemporary plant functional composition

“…Non-climate factors may also be important drivers of functional composition over multiple timescales. For example, past human impacts on landscapes via active propagation, land clearance, or fire regimes (Bond & Keeley, 2005;Keeley, Pausas, Rundel, Bond, & Bradstock, 2011) are widely acknowledged throughout tropical (Levis et al, 2017;Malhi, 2018;Ross, 2011) and temperate (Abrams & Nowacki, 2008;Borgy, Violle, Choler, Denelle, et al, 2017;Feng, Mao, Benito, Swenson, & Svenning, 2017;Nowacki & Abrams, 2008) regions. Soil and surficial geology may also be important in determining plant species distributions (Ordoñez et al, 2009 Bintanja, Wal, and Oerlemans (2011), with deeper shading indicating warmer conditions.…”

Spatial patterns and climate relationships of major plant traits in the New World differ between woody and herbaceous species

“…As a result, rare species are given as much statistical weight as common species. We suspect that accounting for trait abundance by using weighted measures of trait means and variances would strengthen the relationships (Borgy, Violle, Choler, Denelle et al., ). Third, our analyses are based on a relatively coarse spatial resolution.…”

“…We separately mapped trait patterns based on species occurrences and on occurrences inferred from the range maps. Trait maps based on species occurrences per grid cell can be spatially biased because of differential sampling intensity and the presence of species with extreme trait values (Borgy, Violle, Choler, Garnier et al, 2017). To address this, we excluded grid cells with a higher variance than the 99% quantile for the respective traits ( Figure S2.3) and two grid cells of extremely high values of mean leaf N and SLA.…”

“…Results concerning trait variances diverge even more across studies (Šímová et al., , ; Swenson & Weiser, ; Swenson et al., ). These inconsistencies could be due to various factors such as differences in sampling scale, sparsity of data, methods of inference, historical legacies, sensitivity to land use and the specific growth forms studied (Borgy, Violle, Choler, Denelle et al., ; Borgy, Violle, Choler, Garnier et al., ). Many studies have combined woody and herbaceous species in single analyses (e.g.…”