Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity

Enquist, Brian J.; Condit, Rick; Peet, Robert K.; Schildhauer, Mark; Thiers, Barbara M.

doi:10.7287/peerj.preprints.2615v2

Cited by 102 publications

(95 citation statements)

References 20 publications

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“…Most trait data come from Floras and checklists for which annotated information on traits have been extracted, but also large trait compilations with or without spatial context are incorporated (e.g. Enquist et al, 2016;. The range of functional traits currently covered by GIFT reflects different aspects of plant morphology, life history, reproduction, physiology, genetics and ecology (Table S1).…”

Section: Functional Traitsmentioning

confidence: 99%

GIFT – A Global Inventory of Floras and Traits for macroecology and biogeography

Weigelt

König

Kreft

2019

Journal of Biogeography

164

189

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Aim:To understand how functional traits and evolutionary history shape the geographic distribution of plant life on Earth, we need to integrate high-quality and global-scale distribution data with functional and phylogenetic information. Largescale distribution data for plants are, however, often restricted to either certain taxonomic groups or geographic regions. Range maps only exist for a small subset of all plant species and digitally available point-occurrence information is biased both geographically and taxonomically. Floras and checklists represent an alternative, yet rarely used potential source of information. They contain highly curated information about the species composition of a clearly defined area, and together virtually cover the entire global land surface. Here, we report on our recent efforts to mobilize this information for macroecological and biogeographical analyses in the GIFT database, the Global Inventory of Floras and Traits. Location: Global. Taxon: Land plants (Embryophyta).Methods: GIFT integrates plant distributions from regional Floras and checklists with functional traits, phylogenetic information, and region-level geographic, environmental and socio-economic data. It contains information about the floristic status (native, endemic, alien and naturalized) and takes advantage of the wealth of trait information in the regional Floras, complemented by data from global trait databases.Results: GIFT 1.0 holds species lists for 2,893 regions across the whole globe including ~315,000 taxonomically standardized species names (i.e. c. 80% of all known land plant species) and ~3 million species-by-region occurrences. Based on a hierarchical and taxonomical derivation scheme, GIFT contains information for 83 functional traits and more than 2.3 million trait-by-species combinations and achieves unprecedented coverage in categorical traits such as woodiness (~233,000 spp.) or growth form (~213,000 spp.). Main conclusions:Here, we present the structure, content and automated workflows of GIFT and a corresponding web-interface (http://gift.uni-goett ingen.de) as proof of concept for the feasibility and potential of mobilizing aggregated biodiversity data for global macroecological and biogeographical research.

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Section: Functional Traitsmentioning

confidence: 99%

GIFT – A Global Inventory of Floras and Traits for macroecology and biogeography

Weigelt

König

Kreft

2019

Journal of Biogeography

164

189

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“…Palmer, 2000;Tebaldi & Knutti, 2007), and different species distribution models (SDMs) as well as random subsets of initial distribution data in macroecology (see, e.g. For example, if a macroecologist were to download occurrence data of Carex bohemica for France from BIEN through the BIEN Rpackage (Enquist et al, 2016), she might be happy to find 245 georeferenced observations (Figure 3b). Ensembles in macroecology aim at accessing the uncertainties stemming from a range of variable conditions such as input data, type, structure and complexity of the models, their parameters, or climate scenarios (Dormann et al, 2018 Another source of uncertainty is linked to data: all data sets contain limitations of some sort.…”

Section: Un Certaint Y Propag Ati On and B Ia S In Datamentioning

confidence: 99%

Macroecology in the age of Big Data – Where to go from here?

Wüest

Zimmermann

Zurell

et al. 2019

Journal of Biogeography

116

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Recent years have seen an exponential increase in the amount of data available in all sciences and application domains. Macroecology is part of this “Big Data” trend, with a strong rise in the volume of data that we are using for our research. Here, we summarize the most recent developments in macroecology in the age of Big Data that were presented at the 2018 annual meeting of the Specialist Group Macroecology of the Ecological Society of Germany, Austria and Switzerland (GfÖ). Supported by computational advances, macroecology has been a rapidly developing field over recent years. Our meeting highlighted important avenues for further progress in terms of standardized data collection, data integration, method development and process integration. In particular, we focus on (a) important data gaps and new initiatives to close them, for example through space‐ and airborne sensors, (b) how various data sources and types can be integrated, (c) how uncertainty can be assessed in data‐driven analyses and (d) how Big Data and machine learning approaches have opened new ways of investigating processes rather than simply describing patterns. We discuss how Big Data opens up new opportunities, but also poses new challenges to macroecological research. In the future, it will be essential to carefully assess data quality, the reproducibility of data compilation and analytical methods, and the communication of uncertainties. Major progress in the field will depend on the definition of data standards and workflows for macroecology, such that scientific quality and integrity are guaranteed, and collaboration in research projects is made easier.

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“…Despite such concerns related to geographic coverage and sampling effort, another key challenge is the quality of the data being used (Boyle et al 2013;Enquist et al 2016;Franklin et al 2017). For instance, Wiser (2016) found that a common issue in vegetation plot data bases is incorrect or missing geo-coordinates.…”

Section: Introductionmentioning

confidence: 99%

“…Other challenges generally found in large aggregations of occurrence records are related to taxonomic miss-identification and taxonomic shifts (Thessen and Patterson 2011), for which tools have been specifically designed to harmonize naming conventions (Boyle et al 2013). A formal protocol for quality assessment and quality control is still a challenge (Franklin et al 2017), although key recommendations are in place (Costello et al 2014;Enquist et al 2016;Anderson et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Monitoring and understanding the distribution of tree species in the world is a major research agenda in plant ecology, especially under ongoing rapid global change (Enquist et al 2016;Franklin et al 2016). Estimating tree species distributions is ultimately needed in order to provide better understanding of tree diversity, a key driver of forest functioning (Paquette and Messier 2011;Pichancourt et al 2014) and forest ecosystem service provisioning (Gamfeldt et al 2013; Thompson et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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Big data of tree species distributions: how big and how good?

et al. 2017

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Background: Trees play crucial roles in the biosphere and societies worldwide, with a total of 60,065 tree species currently identified. Increasingly, a large amount of data on tree species occurrences is being generated worldwide: from inventories to pressed plants. While many of these data are currently available in big databases, several challenges hamper their use, notably geolocation problems and taxonomic uncertainty. Further, we lack a complete picture of the data coverage and quality assessment for open/public databases of tree occurrences. Methods: We combined data from five major aggregators of occurrence data (e.g. Global Biodiversity Information Facility, Botanical Information and Ecological Network v.3, DRYFLOR, RAINBIO and Atlas of Living Australia) by creating a workflow to integrate, assess and control data quality of tree species occurrences for species distribution modeling. We further assessed the coverage -the extent of geographical data -of five economically important tree families (Arecaceae, Dipterocarpaceae, Fagaceae, Myrtaceae, Pinaceae). Results: Globally, we identified 49,206 tree species (84.69% of total tree species pool) with occurrence records. The total number of occurrence records was 36.69 M, among which 6.40 M could be considered high quality records for species distribution modeling. The results show that Europe, North America and Australia have a considerable spatial coverage of tree occurrence data. Conversely, key biodiverse regions such as South-East Asia and central Africa and parts of the Amazon are still characterized by geographical open-public data gaps. Such gaps are also found even for economically important families of trees, although their overall ranges are covered. Only 15,140 species (26.05%) had at least 20 records of high quality. Conclusions: Our geographical coverage analysis shows that a wealth of easily accessible data exist on tree species occurrences worldwide, but regional gaps and coordinate errors are abundant. Thus, assessment of tree distributions will need accurate occurrence quality control protocols and key collaborations and data aggregation, especially from national forest inventory programs, to improve the current publicly available data.

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Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity

Abstract: ; bthiers@nybg.org. Research in the application of information technology to herbarium management, and outreach efforts to increase access to specimen-based data for the scientific community.

Cited by 102 publications

References 20 publications

GIFT – A Global Inventory of Floras and Traits for macroecology and biogeography

GIFT – A Global Inventory of Floras and Traits for macroecology and biogeography

Macroecology in the age of Big Data – Where to go from here?

Big data of tree species distributions: how big and how good?

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