DOI: 10.5061/dryad.pnvx0k6n6
title: Integrating ecosystem metabolism and consumer allochthony reveals nonlinear drivers in lake organic matter processing

description: 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.
description: <strong>Ecosystem Metabolism Dataset: </strong>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
 O<sub>2</sub> 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 O<sub>2</sub> 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
 O<sub>2 </sub>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 &gt; 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. <strong>Zooplankton Allochthony Dataset:
 </strong>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
 δ<sup>13</sup>C and δ<sup>15</sup>N, 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.