Tree Functional Trait Application Project V2

Tree Functional Trait Application Project v1. Corresponding author : [e-mail] [e-mail] [e-mail] <br> Our dataset was originally created to offer scientists and the public a “ready-to-use” functional trait database for trees. This dataset is the first step within a larger project to create a standardized tree functional trait database for applied projects. <br> We aim to offer a clean and uniform database of traits with clear selection criteria for its retained values : that the trait value is original and can be traced back to a single publication (duplicates are removed); that the trait was measured on trees growing in their natural environment (not experimental); that measurement units are correct; that all remaining possible outliers are verified manually by going back to the original publication for confirmation. <br> The dataset consists (currently) of 268 tree species (based on the common species found in east of Quebec and Montreal, Canada), of which 159 had values for all four traits. For the remaining species, 39 had only a single missing trait value, 42 had more than one missing trait value and 28 had no data available at all. In this first version of the database, we selected four functional traits commonly used in ecology, known for their biological and ecological importance with regards to ecological strategies : specific leaf area, seed mass, leaf nitrogen content and wood density (Chave et al., 2009; Díaz et al., 2016; Reich, 2014; Wright et al., 2004). The data were obtained from the TRY database (Kattge et al., 2011), the Kew Seed Information Database, and from the literature, and were triaged to ensure that only values which matched our criteria (see above) were retained. <br> In adition to these four functional traits, we added tolerance indices, based on the wors of Niinemets &amp; Valladares (2006), Brandt et al. (2017), Matthews et al. (2011), NIACS (2021) et Rutledge (2021). Habitat (https://www.habitat-nature.com/) conducted an online litterature searches with the following search criteria &lt;"LATIN NAME" + toler**&gt; and &lt;"LATIN NAME"&gt; alone as well as synonyms of the Latin names. These new informations were merged with existing tolerance values to create new tolerance indicies. Indices from all sources were translated into categories : low, low-medium, medium, medium-high, high and their corresponding values (1-5). These values are added to the functional trait database. <br> Data for these four traits, in addition to a further binary distinction between gymnosperms and angiosperms, were used for a functional grouping analysis project on Montreal’s tree diversity. The aim of this project was to create functionally relevant species groups in order to improve functional diversity in urban areas. For this project, some data imputations were first performed to complete the dataset, adding trait values for the 39 species missing only a single value. Missing values were calculated using the multivariate imputation by chained equations (MICE) procedure (Azur, Stuart, Frangakis, &amp; Leaf, 2011). Note that other approaches can be used for filling-in missing values. In this new version (V2), these imputed data are not provided to avoid any missusage. <br> To calculate trait dissimilarity matrices, we used Gower’s distance for each pairwise combination of the 155 tree species in the dataset (110 complete species + 45 imputed species). We chose this distance metric because it can handle both quantitative and qualitative variables (Pavoine, Vallet, Dufour, Gachet, &amp; Daniel, 2009). We then analyzed the dissimilarity matrices using an agglomerative hierarchical cluster analysis and Ward’s method. Hierarchical clustering examines the similarity between pairs of data points whereas the Ward D method seeks to minimize the within-cluster variance (in order to obtain the most homogeneous clusters possible) and, as a consequence, to maximize the between-cluster variation (in order to obtain the most dissimilar clusters) at each clustering stage. Tolerance indicies were weighted in the clustering analysis because of the precision of these subjective values and the presence of missing values causing a perception bias. The Drought and Shade tolerances were assigned a weighting of 0.3. The Flood tolerance was assigned a weighting of 0.25. Flood stress is a stress that is less common than drought or shade. Thus, few species experience this stress and in few species are resistant to it, resulting in a shift in the distribution of flood tolerance compared to other tolerances. This is both a sampling and perception bias. The cut-off for the number of clusters was determined by an average silhouette width analysis followed by a biological interpretation of the clusters. Five clusters (classes) of functionally distinct tree species were thus retained and four of these classes could then be divided into sub-classes, giving eight groups. Again, other methods can be used to achieve functional groups. <br> An additional 42 species found in Montreal had two or three missing trait values (out of four) and could therefore not be included in the multivariate imputation method (due to the large uncertainty in the imputation method). In this new version (V2), we decided to not assign species to functional classes or groups but still make the traits values available. <br> All calculations were performed in R version 3.5.2 (R Core Team, 2019) using the function mice from the mice package (van Buuren &amp; Groothuis-Oudshoorn, 2011), the functions agnes, daisy and pam implemented within the cluster package (Maechler et al. 2019). <br> References <br> Azur, M. J., Stuart, E. A., Frangakis, C., &amp; Leaf, P. J. (2011). Multiple imputation by chained equations: What is it and how does it work? International Journal of Methods in Psychiatric Research, 20(1), 40–49. https://doi.org/10.1002/mpr.329 <br> Brandt, L. L. A., Lewis, A. D., Scott, L., Darling, L., Fahey, R. T., Iverson, L. R., ... &amp; Swanston, C. W. 2017. Chicago wilderness region urban forest vulnerability assessment and synthesis. General technical report NRS; 168. <br> Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., &amp; Zanne, A. E. (2009). Towards a worldwide wood economics spectrum. Ecology Letters, 12(4), 351–366. https://doi.org/10.1111/j.1461-0248.2009.01285.x <br> Díaz, S., Kattge, J., Cornelissen, J. H. C., Wright, I. J., Lavorel, S., Dray, S., … Gorné, L. D. (2016). The global spectrum of plant form and function. Nature, 529(7585), 167–171. https://doi.org/10.1038/nature16489 <br> Kattge, J., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., … Wirth, C. (2011). TRY - a global database of plant traits. Global Change Biology, 17(9), 2905–2935. https://doi.org/10.1111/j.1365-2486.2011.02451.x <br> Matthews, S.N., Iverson, L.R., Prasad, A.M., Peters, M.P., Rodewald, P.G. 2011. Modifying climate change habitat models using tree species-specific assessments of model uncertainty and life history-factors. Forest Ecology and Management. 262(8): 1460- 1472. <br> Niinemets, Ü., &amp; Valladares, F. (2006). Tolerance to shade, drought, and waterlogging of temperate northern hemisphere trees and shrubs. <em>Ecological monographs</em>, <em>76</em>(4), 521-547. <br> Northern Institute of Applied Climate Science (NIACS). 2021. Climate change vulnerability of urban trees. Seattle, Washington. https://forestadaptation.org/assess/ecosystem-vulnerability/urban/seattle <br> Pavoine, S., Vallet, J., Dufour, A. B., Gachet, S., &amp; Daniel, H. (2009). On the challenge of treating various types of variables: Application for improving the measurement of functional diversity. Oikos, 118(3), 391–402. https://doi.org/10.1111/j.1600-0706.2008.16668.x <br> R Core Team. (2019). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.r-project.org/ <br> Reich, P. B. (2014). The world-wide “fast-slow” plant economics spectrum: A traits manifesto. Journal of Ecology, 102(2), 275–301. https://doi.org/10.1111/1365-2745.12211 <br> Rutledge, A. 2021. Seattle Region: Climate Projections &amp; Tree Species Vulnerability. Summary Report from the Northern Institute of Applied Climate Science (NIACS). 56 p. https://forestadaptation.org/assess/ecosystem-vulnerability/urban/seattle <br> van Buuren, S., &amp; Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03 <br> Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z., Bongers, F., … Villar, R. (2004). The worldwide leaf economics spectrum. Nature, 428(6985), 821–827. https://doi.org/10.1038/nature02403 <br>