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Diatom species responses along gradients of dissolved inorganic carbon, total phosphorus, and lake depth from lakes across Canada.

Katherine GriffithsMatthew P DudaDermot AntoniadesJohn P SmolIrene Gregory-Eaves
Published in: Journal of phycology (2024)
Diatoms are key components of freshwater ecosystems and are regularly used for paleolimnological reconstructions, in which defining species optima and tolerances is fundamental for interpreting assemblage shifts in a sediment record. Here, we examined responses of diatoms across three major environmental gradients-dissolved inorganic carbon (range: 0.1-230.5 mg · L -1 ), total phosphorus (range: 3-326 μg · L -1 ), and maximum lake depth (range: 0.9-55.0 m)-taken from 158 lakes from across Canada. The lakes were sampled as part of the LakePulse Network, which conducted a standardized sampling of lakes spanning 12 Canadian ecozones. Hierarchical logistic regression was used to model the species responses of 37 common taxa, and species optima and tolerances were calculated with weighted average modeling. The most common response detected was the symmetrical unimodal model, suggesting we likely captured the full environmental ranges for many species, although skewed unimodal responses were also common. Indicator species analyses identified taxa with high predictive values and fidelities to particular ecozones, with high-nutrient-adapted taxa such as Stephanodiscus spp. and Cyclotella meneghiniana characteristic of the agriculturally productive Prairie region. The Prairies stood out in the dataset as the region with the most unique flora from the local contribution to beta diversity analysis. Overall, the autecological data provided by our study will allow for improved interpretations of paleolimnological records and other biomonitoring efforts, addressing management concerns and contributing to a better understanding of our changing environment.
Keyphrases
  • genetic diversity
  • magnetic resonance
  • optical coherence tomography
  • climate change
  • organic matter
  • computed tomography
  • big data
  • artificial intelligence