RESUMO
Here we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits. Together, these traits -plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass - define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.
RESUMO
Abstract Directionality in coupling, defined as the linkage relating causes to their effects at a later time, can be used to explain the core dynamics of ecological systems by untangling direct and feedback relationships between the different components of the systems. Inferring causality from measured ecological variables sampled through time remains a formidable challenge further made difficult by the action of periodic drivers overlapping the natural dynamics of the system. Periodicity in the drivers can often mask the self-sustained oscillations originating from the autonomous dynamics. While linear and direct causal relationships are commonly addressed in the time domain, using the well-established machinery of Granger causality (G-causality), the presence of periodic forcing requires frequency-based statistics (e.g., the Fourier transform), able to distinguish coupling induced by oscillations in external drivers from genuine endogenous interactions. Recent nonparametric spectral extensions of G-causality to the frequency domain pave the way for the scale-by-scale decomposition of causality, which can improve our ability to link oscillatory behaviors of ecological networks to causal mechanisms. The performance of both spectral G-causality and its conditional extension for multivariate systems is explored in quantifying causal interactions within ecological networks. Through two case studies involving synthetic and actual time series, it is demonstrated that conditional G-causality outperforms standard G-causality in identifying causal links and their concomitant timescales.
Assuntos
Ecossistema , Modelos Teóricos , Fotossíntese , Solo , Estatísticas não Paramétricas , ÁrvoresRESUMO
Although there is increasing evidence of the temporal correlation between photosynthesis and soil CO(2) efflux, no study has so far tested its generality across the growing season at multiple study sites and across several time scales. Here, we used continuous (hourly) data and applied time series analysis (wavelet coherence analysis) to identify temporal correlations and time lags between photosynthesis and soil CO(2) efflux for three forests from different climates and a grassland. Results showed the existence of multi-temporal correlations at time periods that varied between 1 and 16 d during the growing seasons at all study sites. Temporal correlations were strongest at the 1 d time period, with longer time lags for forests relative to the grassland. The multi-temporal correlations were not continuous throughout the growing season, and were weakened when the effect of variations in soil temperature and CO(2) diffusivity on soil CO(2) efflux was taken into account. Multi-temporal correlations between photosynthesis and soil CO(2) efflux exist, and suggest that multiple biophysical drivers (i.e. photosynthesis, soil CO(2) diffusion, temperature) are likely to coexist for the regulation of allocation and transport speed of carbon during a growing season. Future studies should consider the multi-temporal influence of these biophysical drivers to investigate their effect on the transport of carbon through the soil-plant-atmosphere continuum.