Controls on Lake Pelagic Primary Productivity: Formalizing the Nutrient‐Color Paradigm

Abstract Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. In lakes, pelagic gross primary productivity (GPP) is strongly controlled by inputs of nutrients and dissolved organic matter. Although past studies have developed process models of this nutrient‐color paradigm (NCP), broad empirical tests of these models are scarce. We used data from 58 globally distributed, mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. The model includes three state variables–dissolved phosphorus, terrestrial dissolved organic carbon (DOC), and phytoplankton biomass–and generates realistic predictions for equilibrium rates of pelagic GPP. We calibrated our model using a Bayesian data assimilation technique on a subset of lakes where DOC and total phosphorus (TP) loads were known. We then asked how well the calibrated model performed with a larger set of lakes. Revised parameter estimates from the updated model aligned well with existing literature values. Observed GPP varied nonlinearly with both inflow DOC and TP concentrations in a manner consistent with increasing light limitation as DOC inputs increased and decreasing nutrient limitation as TP inputs increased. Furthermore, across these diverse lake ecosystems, model predictions of GPP were highly correlated with observed values derived from high‐frequency sensor data. The GPP predictions using the updated parameters improved upon previous estimates, expanding the utility of a process model with simplified assumptions for water column mixing. Our analysis provides a model structure that may be broadly useful for understanding current and future patterns in lake primary production.

Authors

Institutions

Publication Details

Journal
Journal of Geophysical Research Biogeosciences
Published
2024-12-01
DOI
https://doi.org/10.1029/2024jg008140
Citations
6
Primary Topic
Water Quality and Pollution Assessment
Type
article
Field-Weighted Citation Impact
1.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Controls on Lake Pelagic Primary Productivity: Formalizing the Nutrient‐Color Paradigm

Michael J. Vanni, Matthew J. Farragher, Alo Laas, Jennifer L. Klug et al.
6 citations
Journal of Geophysical Research Biogeosciences
Water Quality and Pollution Assessment
1.21
article

Controls on Lake Pelagic Primary Productivity: Formalizing the Nutrient‐Color Paradigm

Michael J. Vanni, Matthew J. Farragher, Alo Laas, Jennifer L. Klug, Piet Verburg, Lars G. Rudstam, Stuart E. Jones, Sudeep Chandra, Soren Brothers, Gesa A. Weyhenmeyer, Kristen T. Holeck, Hsiu‐Mei Chou, Marcus Klaus, Jill S. Baron, Facundo Scordo, Carly R. Olson, Isabella A. Oleksy, Christopher T. Solomon, Garabet Kazanjian, Joshua Culpepper, Sabine Hilt, Erik Lundin, Jan Köhler, Sheel Bansal, James A. Rusak, Rita Adrian, Brittni L. Bertolet, William Colom, Elvira de Eyto, Kevin C. Rose, Paul A. del Giorgio
article en
6 citations

Abstract

Abstract Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. In lakes, pelagic gross primary productivity (GPP) is strongly controlled by inputs of nutrients and dissolved organic matter. Although past studies have developed process models of this nutrient‐color paradigm (NCP), broad empirical tests of these models are scarce. We used data from 58 globally distributed, mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. The model includes three state variables–dissolved phosphorus, terrestrial dissolved organic carbon (DOC), and phytoplankton biomass–and generates realistic predictions for equilibrium rates of pelagic GPP. We calibrated our model using a Bayesian data assimilation technique on a subset of lakes where DOC and total phosphorus (TP) loads were known. We then asked how well the calibrated model performed with a larger set of lakes. Revised parameter estimates from the updated model aligned well with existing literature values. Observed GPP varied nonlinearly with both inflow DOC and TP concentrations in a manner consistent with increasing light limitation as DOC inputs increased and decreasing nutrient limitation as TP inputs increased. Furthermore, across these diverse lake ecosystems, model predictions of GPP were highly correlated with observed values derived from high‐frequency sensor data. The GPP predictions using the updated parameters improved upon previous estimates, expanding the utility of a process model with simplified assumptions for water column mixing. Our analysis provides a model structure that may be broadly useful for understanding current and future patterns in lake primary production.

Journal of Geophysical Research BiogeosciencesVol. 129(12)
University of Notre Dame (US), University of Nebraska–Lincoln (US), Uppsala University (SE), Fairfield University (US), United States Geological Survey (US), Ministry of the Environment, Conservation and Parks (CA), University of Nevada, Reno (US), Université du Québec à Montréal (CA), Rensselaer Polytechnic Institute (US), Institute of Arctic and Alpine Research (US), University of Toronto (CA), American University of Armenia (AM), University of Colorado Boulder (US), York University (CA), Estonian University of Life Sciences (EE), University of California, Irvine (US), Queen's University (CA), Cornell University (US), Swedish Polar Research Secretariat (SE), Abisko Scientific Research Station (SE), Royal Ontario Museum (CA), Swedish University of Agricultural Sciences (SE), Cary Institute of Ecosystem Studies (US), Victoria University of Wellington (NZ), National Center for High-Performance Computing (TW), Leibniz Institute of Freshwater Ecology and Inland Fisheries (DE), Centro Científico Tecnológico - Bahía Blanca (AR), Louisiana Department of Natural Resources (US), Marine Institute (IE), Fort Collins Science Center, Northern Prairie Wildlife Research Center, Universidad Nacional del Sur (AR), University of Maine (US), Miami University (US)
National Science Foundation, U.S. Department of Energy, Biodiversa+, Vetenskapsrådet, U.S. Geological Survey, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 22%
Water Quality and Pollution Assessment
1.21
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.