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Integrating ecosystem metabolism and consumer allochthony reveals nonlinear drivers in lake organic matter processing

Citation

Holgerson, Meredith et al. (2021), Integrating ecosystem metabolism and consumer allochthony reveals nonlinear drivers in lake organic matter processing, Dryad, Dataset, https://doi.org/10.5061/dryad.pnvx0k6n6

Abstract

Lakes process both terrestrial and aquatic organic matter, and the relative contribution from each source is often measured via ecosystem metabolism and terrestrial resource use in the food web (i.e., consumer allochthony). Yet, ecosystem metabolism and consumer allochthony are rarely considered together, despite possible interactions and potential for them to respond to the same lake characteristics. In this study, we compiled global datasets of lake gross primary production (GPP), ecosystem respiration (ER), and zooplankton allochthony to compare the strength and shape of relationships with physicochemical characteristics across a broad set of lakes. GPP was positively related to total phosphorus (TP) in lakes with intermediate TP concentrations (11 - 75 μg L-1) and was highest in lakes with intermediate dissolved organic carbon (DOC) concentrations. While ER and GPP were strongly positively correlated, decoupling occurred at high DOC concentrations. Lastly, allochthony had a unimodal relationship with TP and related variably to DOC. By integrating metabolism and allochthony, we identified similar change points in GPP and zooplankton allochthony at intermediate DOC (4.5 - 10 mg L-1) and TP (8 - 20 μg L-1) concentrations, indicating that allochthony and GPP may be coupled and inversely related. The ratio of DOC : nutrients also helped to identify conditions where lake organic matter processing responded more to autochthonous or allochthonous organic matter sources. As lakes globally face eutrophication and browning, predicting how lake organic matter processing will respond requires an updated paradigm that incorporates nonlinear dynamics and interactions.

Methods

Ecosystem Metabolism Dataset: We started with an ecosystem metabolism dataset compiled and published by Hoellein et al. (2013). Their dataset included studies published through part of 2012 that measured ecosystem metabolism using open-water diel O2 measurements in the summer months for freshwater and estuarine ecosystems; we included their freshwater pond and lake data in our dataset. In November 2019, we searched the literature for additional studies published between 2011 and 2019 that measured metabolism with diel O2 as in Hoellein et al. (2013). We searched Web of Science using the terms: “ecosystem AND (metabolism OR primary production OR respiration) AND (oxygen OR O-2 OR O2) AND (pond* OR lake* OR pool*).” We included search terms for pools, ponds, and lakes to capture water bodies of different sizes, but subsequently refer to all categories as “lakes.” Our search yielded 344 results, which we filtered for relevant titles down to 146 studies that we investigated for possible inclusion in the dataset. We included studies if they reported lake-specific estimates of GPP and ER. We had no requirement for the minimum number of days measured, and the number of days per study ranged from 3 to 760 (mean: 79 days). If open-water diel O2 metabolism was measured, but not reported for individual lakes, we contacted authors for more details (including for some studies in the Hoellein et al. 2013 dataset). We reported areal metabolic rates following Hoellein et al. (2013). When only volumetric metabolic rates were reported, we converted them to areal rates using mean lake depth or mixing depth if it was available. In some instances, multiple metabolism estimates were available for the same lake. In cases where a single study measured multiple habitats within a lake, we used the open water rate. If a lake had metabolic rates reported in multiple publications, the following rules were used to calculate or select a single value for the lake. If two or more studies encompassed overlapping study dates, the metabolism estimate from the study with the greater number of study days was used. If studies did not have overlapping timelines and the durations of the studies were all > 5 days, we calculated a weighted mean based on the study length. If one of the studies lasted ≤ 5 days, only values from longer studies were used.  If all studies lasted ≤ 5 days, metabolism estimates were averaged. The 5-day threshold was selected based on the distribution of the data, and effectively separated out short-term studies when longer, more robust data were available. Ultimately, we compiled a metabolism dataset that included GPP and ER estimates from 27 studies, representing 92 unique lakes.

Zooplankton Allochthony Dataset: We examined consumer allochthony using zooplankton due to (1) their ability to assimilate allochthonous and autochthonous organic matter directly and indirectly via the microbial loop, (2) their importance in linking basal resources to higher trophic levels, and (3) their widespread use for measuring consumer allochthony across lake sizes, allowing us to compile a large dataset. We created a zooplankton allochthony dataset by searching Web of Science in November 2019 with the following terms: “(lake* OR pond* OR pool*) AND “stable isotop*” AND (allochth* OR terrestrial)”. Our search yielded 1,131 results, which we filtered for relevant titles to 244 results to further investigate. We included studies that reported lake-specific estimates of zooplankton allochthony based on stable isotope analysis (usually δ13C and δ15N, but also δD). If allochthony was measured, but was not reported for each lake, we contacted authors for more details. We included one dataset that did not come up in our literature search, which was a study reporting zooplankton allochthony in 10 lakes in the northern Midwest of the United States (Kelly et al. 2014). Our final dataset included 173 zooplankton allochthony estimates from 18 studies, representing 92 unique lakes. Our dataset maintained the taxonomic resolution provided by authors (from as coarse as “mixed zooplankton” to as fine as genus). We grouped taxa into one of three groups: mixed zooplankton, copepods, or cladocerans. When multiple estimates of zooplankton allochthony were provided for the same lake and taxa type, we used the following rules to determine allochthony. First, if allochthony was reported for the same lake and taxa, we took the average across the ice-free season. Second, if estimates came from both natural abundance stable isotopes as well as an isotope tracer study, we used natural abundance estimates for more consistency with the rest of our dataset. Our resulting taxa-specific datasets included allochthony estimates in 69, 49, and 32 unique lakes for mixed zooplankton, copepods, and cladocerans, respectively.

Funding

National Science Foundation, Award: OCE‐1356192 and OCE‐1925796