Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience

  • Joanna R. Blaszczak
  • , Charles B. Yackulic
  • , Robert K. Shriver
  • , Robert O. Hall

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Directly observing autotrophic biomass at ecologically relevant frequencies is difficult in many ecosystems, hampering our ability to predict productivity through time. Since disturbances can impart distinct reductions in river productivity through time by modifying underlying standing stocks of biomass, mechanistic models fit to productivity time series can infer underlying biomass dynamics. We incorporated biomass dynamics into a river ecosystem productivity model for six rivers to identify disturbance flow thresholds and understand the resilience of primary producers. The magnitude of flood necessary to disturb biomass and thereby reduce ecosystem productivity was consistently lower than the more commonly used disturbance flow threshold of the flood magnitude necessary to mobilize river bed sediment. The estimated daily maximum percent increase in biomass (a proxy for resilience) ranged from 5% to 42% across rivers. Our latent biomass model improves understanding of disturbance thresholds and recovery patterns of autotrophic biomass within river ecosystems.

Original languageEnglish
Pages (from-to)1510-1522
Number of pages13
JournalEcology Letters
Volume26
Issue number9
DOIs
StatePublished - Sep 2023

Funding

This research was supported by the StreamPulse project with funding from the National Science Foundation's Division of Environmental Biology (1834679) and the modelscape project with funding from the NSF Office of Integrative Activities (2019528). We thank Joel Scheingross for helpful comments regarding geomorphology and participants in the StreamPulse project for helpful comments on early iterations of this work. We also thank Nick Marzolf, Kathi Jo Jankowski, three anonymous reviewers and the editor for comments that greatly improved this manuscript. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This research was supported by the StreamPulse project with funding from the National Science Foundation's Division of Environmental Biology (1834679) and the modelscape project with funding from the NSF Office of Integrative Activities (2019528). We thank Joel Scheingross for helpful comments regarding geomorphology and participants in the StreamPulse project for helpful comments on early iterations of this work. We also thank Nick Marzolf, Kathi Jo Jankowski, three anonymous reviewers and the editor for comments that greatly improved this manuscript. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Funder number
1834679
2019528

    Keywords

    • autotrophic biomass
    • disturbance ecology
    • gross primary productivity
    • population dynamics
    • resilience
    • Carbon Cycle
    • Time Factors
    • Ecosystem
    • Biomass
    • Rivers

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