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. Geosphere-Biosphere Program (IGBP) and DIVERSITAS, the TRY database (TRY-not an acronym, rather a statement of sentiment; https ://www.try-db.org; Kattge et al., 2011) was proposed with the explicit assignment to improve the availability and accessibility of plant trait data for ecology and earth system sciences. The Max Planck Institute for Biogeochemistry (MPI-BGC) offered to host the database and the different groups joined forces for this community-driven program. Two factors were key to the success of TRY: the support and trust of leaders in the field of functional plant ecology submitting large databases and the long-term funding by the Max Planck Society, the MPI-BGC and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, which has enabled the continuous development of the TRY database.

Abstract. Nurlaeni Y, Iskandar J, Junaedi DI. 2021. Ethnoecology of Zanthoxylum acanthopodium by local communities around Lake Toba, North Sumatra, Indonesia. Biodiversitas 22: 1806-1818. Zanthoxylum acanthopodium DC., locally known as andaliman, is a typical spice of traditional cuisine of communities living around Lake Toba, North Sumatra, Indonesia. Despite the extensive uses, the ethnoecological study on this plant is relatively scarce. This study aims to conduct ethnoecological studies of the management and cultivation of andaliman plants based on Traditional Ecological Knowledge of local communities of Salaon Dolok Village, a village nearby Lake Toba which has close relationship and intensively cultivated/utilized andaliman in their daily life. This study applied qualitative methods using the combination of direct observation and interviews with 46 selected respondents using snowball sampling. The results of this study showed that the cultivation of andaliman in Salaon Dolok Village was carried out using traditional ways with simple farming practices. These traditional farming models relied on manual farming practices in all farming aspects. The community has local knowledge related to the management of reliable agricultural systems from generation to generation. Andaliman cultivation in Salaon Dolok Village does not need maintenance costs for fertilizers and pesticides. In general, andaliman planting is mostly done in the forest. In general, the people of this village only recognize two kinds of andaliman cultivars, namely Andaliman 'Simanuk' and Andaliman 'Sihorbo'. The planting is usually carried out during the rainy season to reduce the mortality rate of the planted seedlings. The activity of seedling preparation is carried out in two simple ways. First, most farmers were collected andaliman seeds that grow wild around the existing andaliman plants. The second method is by collecting the seedling that grows from the burning land with some remains of old andaliman plants collected at the edge of the land. Sustainable agriculture should minimize the external input from outside of the agro-ecosystem. Low External Input Sustainable Agriculture (LEISA) emphasizes the efficient use of existing production factors to create sustainable agriculture, including for andaliman agriculture system.

Detecting exotic plant species is essential for invasive species management. By accounting for factors likely to affect species’ detection rates (e.g. survey conditions, observer experience), detectability models can help choose search methods and allocate search effort. Integrating information on species’ traits can refine detectability models, and might be particularly valuable if these traits can help improve estimates of detectability where data on particular species are rare. Analysing data collected during line transect distance sampling surveys in Indonesia, we used a multi-species hierarchical distance sampling model to evaluate how plant height, leaf size, leaf shape, and survey location influenced plant species detectability in secondary tropical rainforests. Detectability of the exotic plant species increased with plant height and leaf size. Detectability varied among the different survey locations. We failed to detect a clear effect of leaf shape on detectability. This study indicates that information on traits might improve predictions about exotic species detection, which can then be used to optimise the allocation of search effort for efficient species management. The innovation of the study lies in the multi-species distance sampling model, where the distance-detection function depends on leaf traits and height. The method can be applied elsewhere, including for different traits that may be relevant in other contexts. Trait-based multispecies distance sampling can be a practical approach for sampling exotic shrubs, herbs, or grasses species in the understorey of tropical forests.