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      Understanding Intraspecific Trait Variability Using Digital Herbarium Specimen Images

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      Biodiversity Information Science and Standards

      Pensoft Publishers

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          Abstract

          Plant traits are vital to quantify, understand and predict plant and vegetation ecology, including responses to environmental and climate change. Leaf traits are among the best sampled, with more than 200,000 records for individual traits. Nevertheless, their coverage is still strongly limited, especially with respect to characterizing variation within species and across longer time scales. However, to date, more than 3000 herbaria worldwide have collected 390 million plant specimens, dating from the 16th century. At present, the herbarium specimens are rapidly digitized and the images are made openly available to facilitate research and biodiversity conservation. In this study, we determined the potential of the digitized herbarium specimens images to:overcome limitations of data availability for quantitative leaf traits such as the area, length, width along with petiole length anduse the trait values to understand the intraspecific variability across spatio-temporal scales. For the study, initially, specimen metadata was analysed from various online resources such as the Global Plants Database, Natural History Museum Paris, iDigBio and Global Biodiversity Information Facility (GBIF).  Based on the completeness of the metadata, image availability, and the ease of measuring the leaf traits, we selected Salix bebbiana, Alnus incana, Viola canina, Salix glauca, Impatiens capensis, Chenopodium album, and Solanum dulcamara for the study. The semi-automated tool TraitEx (Gaikwad et al. 2019) was used to measure quantitative leaf traits such as the leaf area, perimeter, width, length and petiole length. Finally, excluding duplicates, we downloaded 17383 digital herbarium specimen images from iDigBio and GBIF, which included specimens from the 17th century to the present. However, about 5000 had insufficient information or quality issues, including not-yet-identified duplicates, or no intact leaves. For each selected image we measured four leaf traits - area, length, width and perimeter of the leaf blade -  on up to 5 leaves.  In sum, we collected about 120,000 trait records from 32009 leaves. Comparison of measured leaf traits to data from the TRY Plant Trait database (Kattge et al. 2019) revealed that we could improve the database for studying intraspecific trait variability by several orders of magnitude (from less than 10-100 records per species to >1000). The variation of trait records within the seven species shows reasonable patterns, which improves trust in the data quality. The extracted trait measurements were used to analyse the intraspecific variability for the species across different spatio-temporal resolutions. Machine learning method (random forest) was used to perform the analysis and the results revealed the imprint of spatial and temporal climate variation, including long term trends and climate change as well as seasonality effects, on leaf area. Through this study, we demonstrate the high benefits of digitizing herbarium specimens and reusing it for research studies to improve ecological knowledge and predictability of size-related leaf traits. 

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          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.
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            Measuring Morphological Functional Leaf Traits From Digitized Herbarium Specimens Using TraitEx Software

            Herbarium specimens are of vital importance for understanding biodiversity. There are more than 350 million specimens stored in herbaria worldwide (Thiers 2018) Globally, many herbaria have undertaken digitization projects of plant specimens, on a massive scale, to preserve them and to make the images easily accessible to users. Over the past years, with significant advances in the field of computer vision, new potential uses of digitized specimens have emerged, such as automated species identification using qualitative morphological traits. However, due to lack of efficient tools, efforts to extract functional (quantitative) morphological traits from digitized herbarium specimens are lagging behind. Functional trait data is of significant importance to understand the functioning of the ecosystem and interactions between biotic and abiotic factors. It is currently fragmented and initiatives such as TRY Trait database (https://www.try-db.org) are making efforts to fill the gaps in the observed trait matrix (Schrodt et al. 2015). In order to complement the global efforts, we have developed a software tool, TraitEx , which can measure quantitative traits such as the length, area, width and perimeter of leaves along with the petiole length from digitized herbarium specimens. TraitEx is a standalone Java-based open source tool developed after extensive interactions with biodiversity researchers. The main features of the tool are: (1) efficiently handling high-resolution specimen images, (2) accurately extracting measurements from specimens with varied leaf shapes that are mounted using white tape, (3) integrating ImageJ functionality (https://imagej.net/Welcome) to pre-process and edit the images, (4) measuring trait values to export in comma separated values (CSV) format along with original image and (5) reducing potential damage of fragile specimens, which might occur while physically measuring the traits. Along with user guide and documentation, TraitEx tool is available at https://bitbucket.org/traitExTool/traitextool. The tool is made available under the BSD-2-Clause License.
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              Author and article information

              Contributors
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              Journal
              Biodiversity Information Science and Standards
              BISS
              Pensoft Publishers
              2535-0897
              October 01 2020
              October 01 2020
              : 4
              Article
              10.3897/biss.4.59061
              © 2020

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