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

Avaliação de modelo de extração da água do solo por sistemas radiculares divididos entre camadas de solo com propriedades hidráulicas distintas Evaluation of a root-soil water extraction model by root systems divided over soil layers with distinct hydraulic properties

A avaliação da capacidade de raízes de plantas em extrair água do solo é de grande importância na modelagem da taxa de transpiração e, para entender o crescimento e rendimento vegetal e o balanço de água e de solutos no solo. Para testar um modelo de extração radicular macroscópico baseado no processo em escala microscópica, descreveram-se os resultados de um experimento com plantas cujo sistema radicular foi dividido entre camadas de solo com propriedades hidráulicas contrastantes. Um experimento de lisímetro dividido com plantas de sorgo foi realizado em Piracicaba-SP. Quatro lisímetros com dois compartimentos separados fisicamente (split-pot) foram construídos e preenchidos com material de dois tipos de solo de diferentes classes texturais (um solo de textura média - AR e outro de textura argilosa - AG). Durante um mês e meio foi imposto um regime hídrico, alternando a irrigação entre os compartimentos. O teor de água nos compartimentos dos lisímetros foi monitorado com TDR e tensiômetros. O material dos dois solos foi analisado conforme método-padrão quanto às suas propriedades de retenção e condução da água. A densidade radicular foi determinada por pesagem no fim do experimento, tendo ficado em torno de duas vezes maior no solo AR do que no AG. Observou-se que a extração de água ocorreu preferencialmente do compartimento do lisímetro com maior potencial de fluxo matricial. Em certas ocasiões houve transferência de água do lado de maior para o de menor potencial de fluxo matricial, com a liberação da água ao solo pelo sistema radicular (hydraulic lift). Para compensar o efeito da heterogeneidade da distribuição radicular e da atividade radicular, incluiu-se, no modelo, um fator empírico f de correção. O modelo testado descreveu bem 80 % das observações com a utilização de valores de f de 0,01506 e 0,003713, para os solos AR e AG, respectivamente. O modelo simulou a liberação de água ao solo mais frequentemente do que ela ocorreu no experimento. Esse fato pode indicar que a resistência interna do sistema radicular, não contabilizada pelo modelo, pode ter papel importante nas relações hídricas na rizosfera.<br>Evaluating plant root capacity in extrating water from the soil is important for transpiration modeling and to understand crop growth and yield and soil water and nutrient balance. Aiming to test a macroscopic root water extraction model based on the microscopic process description, an experiment was described in which the root system of plants penetrated different soil layers with contrasting hydraulic properties. Four lysimeters containing two physically divided compartments were built and filled with material of two soils with different texture (a medium textured soil - AR and a clayey soil - AG). During a month and a half a water regime was imposed alternating the irrigation among the compartments. The soil water content in the compartments was measured with TDR and tensiometers. Soil hydraulic properties - retention and conductivity - were analyzed by standard methods. Root density was determined by weighing at the end of the experiment, resulting in values twice as high in AR than in AG soil. It was observed that water extraction occurred preferentially from the lysimeter compartments with the highest matric flux potential. Occasionally, water transfer from the compartment with higher matric flux potential to the lower one was observed, releasing water from root to soil (hydraulic lift). To compensate for the effect of heterogeneity of root distribution and root activity and soil-root contact, an empirical factor f was added to the model. Its value was determined by a numerical fitting procedure aiming at the highest correlation between model and observation in the four lysimeters. The model described 80% of the observations satisfactorily by using these f values, which were 0.01506 and 0.003713, respectively, for AR and AG. Model predictions indicated a much more frequent water release from roots to soil than observed in the experiment. This may suggest internal root resistance, not considered by the model, may play an important role in root-water relations

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