The International Tree‐Ring Data Bank (ITRDB) revisited: Data availability and global ecological representativity

Abstract

Aim:The International Tree-Ring Data Bank (ITRDB) is the most comprehensive database of tree growth. To evaluate its usefulness and improve its accessibility to the broad scientific community, we aimed to: (a) quantify its biases, (b) assess how well it represents global forests, (c) develop tools to identify priority areas to improve its representativity, and d) make available the corrected database.Location: Worldwide. Time period: Contributed datasets between 1974 and 2017.Major taxa studied: Trees. Methods:We identified and corrected formatting issues in all individual datasets of the ITRDB. We then calculated the representativity of the ITRDB with respect to species, spatial coverage, climatic regions, elevations, need for data update, climatic limitations on growth, vascular plant diversity, and associated animal diversity. We combined these metrics into a global Priority Sampling Index (PSI) to highlight ways to improve ITRDB representativity. Results:Our refined dataset provides access to a network of >52 million growth data points worldwide. We found, however, that the database is dominated by trees from forests with low diversity, in semi-arid climates, coniferous species, and in western North America. Conifers represented 81% of the ITRDB and even in wellsampled areas, broadleaves were poorly represented. Our PSI stressed the need to increase the database diversity in terms of broadleaf species and identified poorly represented regions that require scientific attention. Great gains will be made by increasing research and data sharing in African, Asian, and South American forests. Main conclusions:The extensive data and coverage of the ITRDB show great promise to address macroecological questions. To achieve this, however, we have to overcome the significant gaps in the representativity of the ITRDB. A strategic and organized group effort is required, and we hope the tools and data provided here can guide the efforts to improve this invaluable database. K E Y W O R D Sbias analysis, big data, data accessibility, Dendrochronology, dendroecology, meta-analysis, tree growth, tree-ring research

A global perspective on the climate‐driven growth synchrony of neighbouring trees

“…The ITRDB (Zhao et al, 2019) is the largest archive containing digital tree‐ring width measurements. As of June 2015, the ITRDB contained >4,000 ring‐width records from all continents except Antarctica.…”

Delineating Environmental Stresses to Primary Production of U.S. Forests From Tree Rings: Effects of Climate Seasonality, Soil, and Topography

“…A second potential limitation is bias within the ITRDB, both in site selection and within‐site sample design. There is a heavy skew in the spatial representativeness of the ITRDB (Zhao et al, ), limiting full application across diverse or undersampled regions. In particular, low‐diversity semiarid conifer forests of western North America are disproportionately represented in the ITRDB, while forests in other regions are underrepresented (Zhao et al, ).…”

“…The most common sample design therefore involves selecting large, dominant trees growing in stressful landscape positions such as steep, south facing rocky slopes (Cherubini et al, 1998;Nehrbass-Ahles et al, 2014). While other sample designs are also used (e.g., Davis et al, 2009;Dye et al, 2016;Nehrbass-Ahles et al, 2014), many of the sites in the ITRDB are likely biased toward old trees with relatively low growth rates that may not be representative of the entire stand or region (Zhao et al, 2019). This is especially problematic for studies using tree-ring data to directly calculate primary productivity or long-term growth trends (Bowman et al, 2013;Brienen et al, 2012;Cherubini et al, 1998;Nehrbass-Ahles et al, 2014), although inferences regarding the environmental drivers of growth are relatively robust to choice of sample design (Nehrbass-Ahles et al, 2014).…”

“…Formally testing the impact of sample design on the tree-ring environmental stress index remains a priority for future research. To fully realize the potential for tree rings to inform our understanding of the carbon cycle, we also need to continue collecting new tree-ring data that mitigate well-known sampling biases (Klesse et al, 2018;Nehrbass-Ahles et al, 2014), regional biases (Klesse et al, 2018;Zhao et al, 2019), and taxonomic biases (Zhao et al, 2019).…”

Examining Naturogenic Processes and Anthropogenic Influences on Tree Growth and Development via Stem Analysis: Data Processing and Computational Analytics

“…The standardization of data management protocols and methodologies in terms of cross-dating procedures and statistical approaches, inclusive of the provision of the prerequisite software analogues, are well developed for this analytical class (sensu Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA). Furthermore, as a consequence, these approaches have received considerable acceptance and deployment, leading to the creation of well-populated open-access global repositories of standardized tree ring chronologies with pertinent metadata attached (e.g., International Tree-ring Data Bank [21]). Analysis of these data sets within the context of climate change have significantly advanced the understanding of the key drivers of tree growth, leading to more informed predictions of the consequences of anthropogenic-induced climate change across the globe (e.g., [22]).…”