English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Ecological studies of the highly diverse groups of organisms, such as algae, can be particularly prone to taxonomic opinion bias. There is mounting pressure to recognize and report this bias across studies to facilitate reproducible science and data re-use; however, there are fewer studies which link taxonomic bias to loss of precision in modelling community responses or in bioassessments. We use long-term phytoplankton monitoring data to evaluate effects of taxonomic bias on relevant ecological metrics including algal biovolume, cell counts, diversity, and the performance of a species-based weighted-averaging model for inferring phosphorus concentrations. Despite the non-zero taxonomic bias between paired original and quality assurance samples, several important indicators derived from these data, including model-inferred phosphorus concentrations, total algal biovolume and cell density, and diversity were mostly unaffected. We discuss this as an example of using ecologically relevant model performance criteria for making recommendations about the acceptable level of taxonomic bias.

Taxonomic bias in freshwater phytoplankton communities and its effect on environmental assessment metrics