TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Litterfall production and leaf area index in a virgin European beech (Fagus sylvatica L.) – Silver fir (Abies alba Mill.) forest

Because of their role in carbon and nutrient exchange, litterfall and leaf area have been increas- ingly studied in the last few decades. However, most existing information comes from managed forests, while comparable data for virgin forests is scarce. To address this scarcity, we investigated a mixed beech – silver fir virgin forest located in the Southern Carpathian Mountains, using 78 litter traps to measure the annual litterfall production, litter composition and leaf area index (LAI). The LAI was calculated in two ways: directly, by using litter traps, and indirectly, based on hemispherical photographs. Furthermore, we investigated the influence of different stand and environmental characteristics on litter production, total foliar mass and LAIs. Annual litter productivity ranged from 1.8 to 8.3 t ha−1 with a mean of 3.5 t ha−1. Litter was composed mainly of beech leaves (66%) along with a lower percentage of silver fir needles (16%). The total foliar dry mass (sum of beech leaves and silver fir needles) increased significantly with the proportion of beeches and decreased with the median stand age. The LAI determined by using litter traps had a mean value of 5.06 m2 m−2, ranging from 3.52 to 8.22, and was characterised by a higher variability than the LAI estimated indirectly using the hemispherical approach (which had a mean value of 3.65 and a range of 2.30–5.28). The two indices did not correlate with each other. We found no significant relation between the LAIs and any stand or environmental variables. We conclude that in the more complex forests, such as the virgin beech – silver fir mixed forest we studied, annual foliar dry mass is more closely related to stand characteristics than is LAI. We also note significant limitations of both LAI estimation methods, which indicate that a more elaborate approach to estimating LAI is needed

Jet stream position explains regional anomalies in European beech forest productivity and tree growth

The mechanistic pathways connecting ocean-atmosphere variability and terrestrial productivity are well-established theoretically, but remain challenging to quantify empirically. Such quantification will greatly improve the assessment and prediction of changes in terrestrial carbon sequestration in response to dynamically induced climatic extremes. The jet stream latitude (JSL) over the North Atlantic-European domain provides a synthetic and robust physical framework that integrates climate variability not accounted for by atmospheric circulation patterns alone. Surface climate impacts of north-south summer JSL displacements are not uniform across Europe, but rather create a northwestern-southeastern dipole in forest productivity and radial-growth anomalies. Summer JSL variability over the eastern North Atlantic-European domain (5-40E) exerts the strongest impact on European beech, inducing anomalies of up to 30% in modelled gross primary productivity and 50% in radial tree growth. The net effects of JSL movements on terrestrial carbon fluxes depend on forest density, carbon stocks, and productivity imbalances across biogeographic regions. © 2022, The Author(s).Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

TRY plant trait database, enhanced coverage and open access

Plant traits-the morphological, ahawnatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives