Dataset Information

Accumulating crop functional trait data with citizen science.

ABSTRACT: Trait-based ecology is greatly informed by large datasets for the analyses of inter- and intraspecific trait variation (ITV) in plants. This is especially true in trait-based agricultural research where crop ITV is high, yet crop trait data remains limited. Based on farmer-led collections, we developed and evaluated the first citizen science plant trait initiative. Here we generated a dataset of eight leaf traits for a commercially important crop species (Daucus carota), sampled from two distinct regions in Canada, which is 25-fold larger than datasets available in existing trait databases. Citizen-collected trait data supported analyses addressing theoretical and applied questions related to (i) intraspecific trait dimensionality, (ii) the extent and drivers of ITV, and (iii) the sampling intensity needed to derive accurate trait values. Citizen science is a viable means to enhance functional trait data coverage across terrestrial ecosystems, and in doing so, can directly support theoretical and applied trait-based analyses of plants.

SUBMITTER: Isaac ME

PROVIDER: S-EPMC6823441 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Accumulating crop functional trait data with citizen science.

Isaac Marney E ME Martin Adam R AR

Scientific reports 20191031 1

Trait-based ecology is greatly informed by large datasets for the analyses of inter- and intraspecific trait variation (ITV) in plants. This is especially true in trait-based agricultural research where crop ITV is high, yet crop trait data remains limited. Based on farmer-led collections, we developed and evaluated the first citizen science plant trait initiative. Here we generated a dataset of eight leaf traits for a commercially important crop species (Daucus carota), sampled from two distinc ...[more]

PMID: 31673016

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Dataset Information

Accumulating crop functional trait data with citizen science.

Publications

Accumulating crop functional trait data with citizen science.

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