Multidimensional biases, gaps and uncertainties in global plant occurrence information.

By Carsten Meyer, Patrick Weigelt, H. Kreft. Ecology Letters, 2016

532 citations recorded by the source corpus.

Plants are a hyperdiverse clade that plays a key role in maintaining ecological and evolutionary processes as well as human livelihoods. Biases, gaps and uncertainties in plant occurrence information remain a central problem in ecology and conservation, but these limitations remain largely unassessed globally. In this synthesis, we propose a conceptual framework for analysing gaps in information coverage, information uncertainties and biases in these metrics along taxonomic, geographical and temporal dimensions, and apply it to all c. 370 000 species of land plants. To this end, we integrated 120 million point-occurrence records with independent databases on plant taxonomy, distributions and conservation status. We find that different data limitations are prevalent in each dimension. Different metrics of information coverage and uncertainty are largely uncorrelated, and reducing taxonomic, spatial or temporal uncertainty by filtering out records would usually come at great costs to coverage. In light of these multidimensional data limitations, we discuss prospects for global plant ecological and biogeographical research, monitoring and conservation and outline critical next steps towards more effective information usage and mobilisation. Our study provides an empirical baseline for evaluating and improving global floristic knowledge, along with a conceptual framework that can be applied to study other hyperdiverse clades.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/7e2e07bc430fe06be0dcdaa4f8e58f6f790fc811

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Citations in the corpus

GIFT – A Global Inventory of Floras and Traits for macroecology and biogeography (2019, methodology): “However, taxonomic, geographical and temporal biases (Hortal et al., 2015; Meyer et al., 2016), as well as limited curation and standardization efforts and the lack of important meta-information, like, for example, the floristic status at a given location (native, non-native, naturalized, etc.),…”
A standard protocol for reporting species distribution models (2020, background): “For example, data may vary in terms of spatial and temporal precision (Meyer et al. 2016), which could significantly affect model accuracy (Park and Davis 2017).”
Machine learning and deep learning—A review for ecologists (2022, unknown): “Biased data may be a significant problem for E&E, as geographic (Martin et al., 2012; Meyer et al., 2016) and taxonomic (Pyšek et al., 2008; Trimble & van Aarde, 2012 ) sampling biases are common.”
What we (don’t) know about global plant diversity (2018, unknown): “A great deal of resources go into conservation actions for some well-studied species that are at risk; in contrast, poorly-studied species may be going extinct before conservation biologists are able to interrogate a geographic database to determine where they are found or a trait database to…”
The unrealized potential of herbaria for global change biology (2017, methodology): “reviewed recently by Meyer et al. (2016) and Daru et al. (2018), who analyze biases represented in herbarium specimens and observational data.”
Sampling biases shape our view of the natural world (2020, background): “…some of these analyses, including geographic, political and accessibility biases (Kadmon et al. 2004, Boakes et al. 2010, Amano and Sutherland 2013, Meyer et al. 2016, Moudrý and Devillers 2020) most of these focus on limited regions or taxa (the largest analyzing terrestrial vertebrates only).”