DOI: 10.5061/dryad.bcc2fqzqz
title: Data for: Kelp forest loss and emergence of turf algae reshapes energy flow to predators in a rapidly warming ecosystem

description: Climate change is decimating habitat-forming species in ecosystems around
 the world. Yet the impacts of habitat loss on the energetics of the wider
 food web remain uncertain for many iconic ecosystems, including cold-water
 kelp forests. Here, we assessed how the loss of kelp forests and
 subsequent proliferation of low-lying turf algae in the Gulf of Maine has
 altered the trophic niches of, and energy acquired by, predatory reef
 fishes. Bulk tissue δ13C and δ15N analysis of fish muscle showed that
 fishes in kelp forests had larger trophic niches and greater interspecific
 niche separation than did fishes on turf reefs. Moreover, δ13C analysis of
 essential amino acids revealed that kelp-derived energy accounted for the
 majority of energy used by forest fishes (&gt;50 % on average),
 whereas fishes on turf reefs compensated for kelp decline via greater
 reliance on a phytoplankton-based energy channel. Therefore, ecosystem
 state shifts to turf algae – now a global phenomenon – may have
 far-reaching impacts on food web energy channels and resilience.
description: <strong>Section 1:</strong>
 <strong>Underwater surveys</strong> 
 <em>Data1_macroalgae_biomass.csv</em>
 <em>Data2_roving_fishes.csv</em>
 Macroalgae surveys. During the summers of
 2021 and 2022, we characterized macroalgae assemblages on subtidal rocky
 reefs via scuba (n = 11-16 sites per year, 3-5 per subregion). At each
 site, divers laid a 40 m transect along the isobath at 5-7 m depth,
 parallel to shore, then placed 1 m<sup>2</sup> quadrats (n =
 4-6 quadrats per site) at predetermined intervals along the transect. We
 collected all kelps (i.e., canopy-forming brown macroalgae in the order
 Laminariales) from the 1 m<sup>2</sup> quadrat and all
 understory macroalgae (i.e., the diverse consortium of bladed, foliose,
 and filamentous seaweed taxa residing under the canopy) from a ¼
 m<sup>2</sup> area of the quadrat. In the lab, we sorted
 macroalgae by species, identified them to the lowest possible taxonomic
 level, dried them by spinning, and weighed each. We performed these
 surveys between July and mid-September (oceanographic summer) of both
 years. Fish surveys. To
 characterize the mobile reef fish assemblage at each site, we conducted
 visual fish censuses. While swimming along 40 m transects (n = 3 per
 site), a diver identified, counted, and estimated the size (total length,
 2.5 cm size bins) of each fish that they observed within a 2 m wide band.
 We calculated the biomass of each fish observation via published
 length-weight relationships from FishBase (<em>70</em>). Fish
 surveys were performed at the same time as macroalgae surveys in each
 subregion (n = 11-16 sites per year, 3-5 sites per subregion) between July
 and September of each year. <strong>Section
 2:</strong> <strong>Isotope sample collection &
 processing</strong> 
 <em>Data3_bulk_CN_stable_isotopes.csv</em>
 <em>Data4_AA_d13C_values.csv</em>
 <em>Data5_FDA_consumer_means.csv</em>
 <em>Data6_POM_18S.csv</em>
 Collecting primary producer samples. Based
 on our macroalgae data and observations of the ecosystem, we determined
 that two potential benthic energy channels (i.e., bottom-associated,
 functionally distinct primary producer groups) could exist on shallow,
 subtidal rocky reefs in the Gulf of Maine: one based on kelps
 (canopy-forming brown macroalgae, including subtidal Laminariales and
 intertidal fucoids) and another based on red macroalgae (seaweeds in the
 phylum Rhodophyta). To characterize the isotopic values of these possible
 energy sources, abundant macroalgae were collected by hand via scuba,
 concurrent with macroalgae biomass collections (see above). For subtidal
 kelps (n = 19, e.g., <em>Laminaria digitata</em> and
 <em>Saccharina latissima</em>), we took samples from clean
 parts of the interior blade, approximately 15 cm distal to the growth
 meristem. For all other macroalgae (n = 44), we selected whole thalli that
 were free of fouling. In order to comprehensively characterize the
 isotopic signature of all abundant basal energy resources, we supplemented
 our subtidal kelp samples with fucoidian brown macroalgae
 (<em>Ascophyllum nodosum</em> and <em>Fucus</em>
 spp., n = 6) that were ubiquitous in intertidal habitats adjacent to our
 subtidal study sites. Since fucoids and kelps share some ecological
 functions and can have similar isotopic signatures, we included the fucoid
 samples in the “kelp” isotope endmember group. All macroalgae samples were
 carefully rinsed with DI water to remove epifauna. To
 characterize the isotopic signature of local phytoplankton (which
 constitute a pelagic energy channel), we collected particulate organic
 matter (POM) from 1 m depth with a Niskin sampler at sites ~1 to 5 km
 offshore in all subregions (n = 27). We prefiltered water through an
 inline 300 µM nitex mesh filter to remove zooplankton or large
 particulates. In the lab, we collected POM by passing 2.5-4.4 L of the
 water through pre-combusted GF/F filters (0.7 µM mesh size) with a vacuum
 filter. We sequenced the DNA from a portion of several POM filters (18S
 metabarcoding) to confirm that phytoplankton comprised the bulk of the
 organic material trapped on the filter (Supplementary Text, Fig. S5, Fig.
 S6). Hence, we refer to POM as “phytoplankton” here and elsewhere, for
 simplicity. Collecting consumer tissue
 samples. To characterize the isotopic signatures of
 common primary consumers, we collected samples of invertebrates that
 represented three distinct primary consumer guilds and may serve as fish
 prey. For this study, we focused on filter-feeding bivalves (blue mussels
 <em>Mytilus edulis,</em> and rock borer clams
 <em>Hiatella arctica</em>), grazing snails (<em>Lacuna
 vincta</em> and <em>Margarites helicinus</em>), and
 amphipods (which comprise a variety of families but are largely
 detritivores or surface suspension feeders) (<em>71</em>).
 These organisms (n = 3-43 samples per group per subregion) were collected
 from survey sites in late summer of 2022. Divers collected mussels by hand
 via scuba, and all small, epifaunal mesoinvertebrates were collected from
 macroalgae by rinsing the macroalgae in DI water in the lab and catching
 the contents in a 500 µM sieve. To isolate mesoinvertebrates, we looked
 through the contents of the sieve under a dissecting microscope and sorted
 mesoinvertebrates based on the lowest identifiable taxonomic level (often
 species level for bivalves and snails, and family or superfamily for
 amphipods). We were careful to remove pieces of detritus or macroalgae
 from mesoinvertebrates. Invertebrates were frozen after
 collection. Tissue samples from <em>Mytilus</em> were obtained
 by dissecting a piece of the adductor muscle. We processed whole snails
 and <em>Hiatella</em> as bulk samples and subjected them to
 demineralization in weak (0.5M) hydrochloric acid for 12 to 24 hours to
 remove calcium from their shells. We then extracted lipids from all
 invertebrate samples by soaking them for 24 hours in a 2:1
 chloroform-methanol solution. After three rounds of soaking (72 hours
 total), exchanging the chloroform:methanol solution between rounds, we
 rinsed samples thoroughly in DI water. We collected
 fish from rocky reefs in Casco Bay, Midcoast, Penobscot Bay, and Downeast
 subregions between September and November of 2022. We used hook &
 line fishing to target pollock (n = 10-14 per subregion) and deployed
 minnow traps baited with mussels to catch cunner (n = 5-15 per subregion).
 Fishes were euthanized in the field using an MS-222 seawater solution in
 accordance with the University of Maine IACUC (protocol A2022-08-02).
 Specimens were temporarily stored on ice, then dissected in the lab the
 same day they were caught. We excised a piece of dorsal muscle tissue for
 isotopic analysis, which was rinsed with DI water and stored frozen. As
 with invertebrate tissues, we lipid-extracted fish tissues with a 2:1
 chloroform:methanol solution for 72 hours and then rinsed them with DI
 water. We kept samples in muffled 20 ml scintillation
 vials (borosilicate glass, with foil-lined caps). All samples were stored
 frozen at -20 °C until they were lyophilized. 
 <strong>Section 3:</strong> <strong>Bulk tissue
 <em>δ</em><sup>13</sup>C and
 <em>δ</em><sup>15</sup>N analysis </strong>
 
 <em>Data3_bulk_CN_stable_isotopes.csv</em>
 We packed tissue samples into 3.5 x 5 mm tin capsules (Analytics,
 Valencia, CA; USA Analytics, Anaheim, CA) to prepare them for bulk tissue
 <em>δ</em><sup>13</sup>C and
 <em>δ</em><sup>15</sup>N analysis. We weighed kelp
 and other macroalgae samples to ~3.5 mg, and invertebrate samples and fish
 muscle to ~1 mg. For phytoplankton samples, we packed ~¼ to ½ of each GF/F
 filter into 5 x 9 mm tin capsules. Bulk tissue
 <em>δ</em><sup>13</sup>C and
 <em>δ</em><sup>15</sup>N values were measured via
 continuous flow on a Costech 4010 elemental analyzer coupled to a Thermo
 Scientific Delta V Plus isotope ratio mass spectrometer (EA-IRMS) at the
 University of New Mexico Center for Stable Isotopes (UNM-CSI, Albuquerque,
 New Mexico, USA). We report all isotope results as <em>δ</em>
 values with units of per mil (‰):
 <em>δ</em><sup>13</sup>C or
 <em>δ</em><sup>15</sup>N =
 [(R<sub>sample</sub>/R<sub>standard</sub>) - 1] ×
 1000, where R represents the
 <sup>13</sup>C:<sup>12</sup>C or
 <sup>15</sup>N:<sup>14</sup>N ratios
 (<em>72</em>). The internationally accepted standards are
 Vienna-Pee Dee Belemnite for
 <em>δ</em><sup>13</sup>C and atmospheric
 N<sub>2</sub> for
 <em>δ</em><sup>15</sup>N. The isotopic values of
 our samples were corrected and calibrated to these international standards
 based on analysis of in-house reference materials (casein and tuna for
 protein, green chile and blue gramma for plants/algae), which had standard
 deviations below 0.2 ‰ within all runs.
 <strong>Section 4: Stable carbon isotope analysis of
 essential amino acids
 (<em>δ</em><sup>13</sup>C<sub>EAA</sub>) </strong> <em>Data4_AA_d13C_values.csv</em> <em>Data5_FDA_consumer_means.csv</em> We used <em>δ</em><sup>13</sup>C<sub>EAA</sub> fingerprinting to assess the energy (carbon) contribution of basal resources to fish from the four subregions across the coast of Maine. To prepare samples for <em>δ</em><sup>13</sup>C<sub>EAA</sub> analysis, we subjected them to hydrolysis, derivatization, and analysis in a gas-chromatography combustion unit coupled to an isotope-ratio mass spectrometer (GC-C-IRMS). First, we hydrolyzed macroalgae samples (4-5 mg), POM filters (~¼ to ½ filter), and lipid-extracted fish muscle (5-6 mg) in 6M hydrochloric acid. We flushed hydrolysis tubes with N<sub>2</sub> gas before sealing and incubated them for 20 hours at 110 °C. Hydrolyzed amino acids from primary producer samples were subsequently filtered through muffled quartz wool to remove any remaining particulates. We derivatized amino acids into <em>N-</em>trifluoroacetyl isopropyl esters following Silfer et al. (<em>73</em>). Hydrolysates were subsequently dried under N<sub>2</sub>, esterified in 1 mL of 4:1 isopropanol:acetyl chloride (105°C, 1 h), dried again under N<sub>2</sub> with two rinse cycles using dichloromethane (DCM), and then acetylated in 1 mL of 1:1 trifluoracetic anhydride:DCM (105 °C, 10 min). All samples for this project were derivatized in batches of 8-25 samples, along with an in-house reference material containing amino acids with known <em>δ</em><sup>13</sup>C values measured via EA-IRMS at UNM-CSI (Table S5). The in-house reference material was a mixture of 12 purified and powdered amino acids (Sigma Aldrich, Saint Louis, MO, USA): alanine, aspartic acid, glutamic acid, glycine, isoleucine, leucine, lysine, phenylalanine, proline, serine, threonine, and valine. To make the reference material, we dissolved amino acid powders into weak hydrochloric acid (&lt; 0.01 M) at a concentration of ~125 mM. Individual amino acid solutions were then mixed together, and a small aliquot of this mixture was dried under N<sub>2</sub> gas and derivatized alongside each batch of unknown samples. We analyzed our derivatized samples and in-house reference material via a GC-C-IRMS at UNM-CSI. Briefly, we injected 1-1.5 µL of each sample into a 60 m BPX5 gas chromatograph column (0.32 mm ID, 1 µm film thickness, SGE Analytical Science) within a Trace 1310 GC, where amino acid separation was completed, then combusted into CO<sub>2</sub> within a high temperature furnace (1000 °C) of a Thermo-Scientific Isolink II, and finally analyzed in a Thermo Scientific Delta V Plus isotope ratio mass spectrometer. We ran all samples in duplicate or triplicate injections, taking the average isotopic values of each sample. The within-run standard deviations of all amino acid <em>δ</em><sup>13</sup>C values for the in-house reference material measured in this study ranged from 0.2 ‰ (alanine) to 0.5 ‰ (lysine) but averaged &lt; 0.5 ‰ per day, as averaged across 3 to 7 injections. The global standard deviations ranged from 0.5 ‰ (valine) to 1.3 ‰ (phenylalanine) across all runs and standard injections (Table S5). <em>δ</em><sup>13</sup>C values were then corrected based on measurements of the in-house reference material (Table S5), which was analyzed bracketing each sample and at the beginning and end of each run. The reagents used during derivatization add carbon to the side chains of amino acids, and <em>δ</em><sup>13</sup>C values measured via GC-C-IRMS are thus a combination of intrinsic amino acid carbon and reagent carbon. However, because amino acid reference materials of known <em>δ</em><sup>13</sup>C composition were derivatized and run with each batch of samples, we were able to correct for this carbon addition for each amino acid using the following equation: <em>δ</em><sup>13</sup>C<em><sub>sample</sub></em> = (<em>δ</em><sup>13</sup>C<em><sub>dsa</sub></em> - <em>δ</em><sup>13</sup>C<em><sub>dst</sub></em> + <em>δ</em><sup>13</sup>C<em><sub>std </sub></em> × p<em><sub>std</sub></em>) / p<em><sub>std</sub></em> Here, <em>δ</em><sup>13</sup>C<sub>dsa</sub> refers to the measured value of a derivatized amino acid within the sample, and <em>δ</em><sup>13</sup>C<sub>dst</sub> is the measured value of the derivatized amino acid within the in-house reference material. The term <em>δ</em><sup>13</sup>C<sub>std</sub> reflects the un-derivatized, or intrinsic, <em>δ</em><sup>13</sup>C value of that amino acid in the reference material; these values were determined via EA-IRMS at UNM-CSI  (Table S5). Finally, p<sub>std</sub> is the proportion of carbon in the measured derivative that was originally sourced from the amino acid, which varies among amino acids. These corrections were done on a daily basis, or on a ‘per run’ basis, which means unknown samples were corrected using four to six bracketed injections of the reference material. We generated <em>δ</em><sup>13</sup>C values for 12 amino acids, however, our analyses focused on five of the six EAA that we could reliably measure in our primary producers: threonine, valine, leucine, isoleucine, and phenylalanine. These compounds are useful for tracing the movement of organic matter across food webs, as they are not synthesized or modified by consumers and therefore do not undergo discrimination as they move between trophic levels (<em>51</em>, <em>52</em>). We did not use data for amino acids in samples where standard deviations exceeded 1.0 ‰ among injections. In addition, although lysine is considered essential and was measured, this EAA exhibited significant coelution with tyrosine in many of our primary producer samples, resulting in variable <em>δ</em><sup>13</sup>C values. Hence, we did not include lysine data in any statistical analyses. <strong>Section 5: Flexible discriminant analysis (FDA) to quantify consumer energy channel use</strong> <em>Data4_AA_d13C_values.csv</em> <em>Data5_FDA_consumer_means.csv</em> Partitioning primary producer groups with a classification model. To determine whether <em>δ</em><sup>13</sup>C<sub>EAA</sub> could distinguish kelp (n = 16), red macroalgae (n = 17), and phytoplankton (n = 11) – the three primary producer groups that represent distinct energy channels in our system – we employed a flexible discriminant analysis (FDA), a non-parametric classification model. We applied the FDA [R package ‘<em>mda’ </em>(<em>79</em>)] using <em>δ</em><sup>13</sup>C<sub>EAA</sub> from five essential amino acids (isoleucine, leucine, phenylalanine, threonine, valine) as predictors, and assessed the robustness of producer ‘fingerprints’ based on successful reclassification in a leave-one-out cross-validation test. Kelps, red macroalgae, and phytoplankton were sufficiently separated in multivariate <em>δ</em><sup>13</sup>C<sub>EAA</sub> space (see results, Fig. 3, Table S6), and thus we used this FDA model for quantifying energy contributions to consumers. Quantifying consumers’ basal energy sources. To quantify the proportion of energy from each producer group used by fishes, we used a bootstrap resampling approach to run 10,000 iterations of the FDA model with a random subset of 10 members of each producer group each time (sampled with replacement). We used each iteration of the model to classify fish based on their <em>δ</em><sup>13</sup>C<sub>EAA</sub>, with the model returning a probability that each individual fish belonged to each producer group. We averaged these posterior probability estimates from all 10,000 iterations to get a mean estimate for each individual (Table S4) and used those mean probabilities of classification as the estimated proportion of energy derived from each producer group (<em>18</em>). We chose to use this multivariate statistical method (FDA) rather than traditional Bayesian mixing models [e.g., ‘<em>MixSIAR’</em> (<em>82</em>)] since they can more effectively utilize multiple EAA to partition the proportional contribution of multiple basal resource groups (<em>18</em>). <em>δ</em><sup>13</sup>C<sub>EAA </sub>data naturally avoids the common issues encountered with bulk tissue isotopic data that make mixing models so useful for these studies. Notably, multivariate isotopic patterns of marine algae are conserved across broad spatiotemporal scales (<em>52</em>), and EAA have negligible isotopic offsets with trophic transfer in most cases [e.g., (<em>51</em>, <em>83</em>)].  <strong>Section 6: Particulate organic matter (POM) as a proxy for phytoplankton</strong> <em>Data6_POM_18S.csv</em> To understand the isotopic signature of phytoplankton, we collected water samples (2.5-4.4 L, n = 6-8 per region) and filtered the associated particulate organic matter (POM) onto pre-combusted 0.7 µM pore size GF/F filters. We cut filters into halves, thirds, or quarters to use for both bulk tissue and compound-specific stable isotope analysis. To verify whether the identity of “POM” was phytoplankton species, we sequenced the 18S gene (see below) from a portion of 17 POM filters (n = 3-6 per region). We extracted the DNA from POM filters using a Qiagen PowerSoil DNA extraction kit following the manufacturer's quick-start protocols, except for the first step, where we added the filter, beads, and lysis buffer to a 5 mL tube for manual lysis. We sent extracts to be indexed and sequenced with a one-step PCR using eukaryote-specific primers targeting the 18S rRNA V4 gene region (E572F & E1009R primers, (<em>84</em>). Amplicon sequencing was carried out on a NextSeq instrument at the Integrated Microbiome Resource (Dalhousie University, Halifax, Nova Scotia, CA). We processed the dereplicated sequences using a dada2 pipeline (<em>85</em>). Our quality filtering parameters were maxEE = (2,2), auto truncLen, trunQ = 2, and quantile_min = 0.8. We used a minOverlap of 20 bp when merging paired-end reads. We assigned taxonomy to amplicon sequence variants [ASVs (<em>86</em>)] with the PR2 database (<em>87</em>). From 16 samples, we received 625,616 total reads. Since we were interested in the most abundant components of the POM community, we filtered out all reads assigned to species that made up less than 1 % of the total reads in each sample. We also removed two samples with &lt; 1000 reads (P2: 24 reads, and P10: 365 reads). After filtering, we were left with 14 samples with 34,410 ± 5,419 reads (mean ± SE). We combined the reads that matched to common microalgae or other broad taxonomic groups and reveal that 86 % of reads did indeed come from phytoplankton (a group of single celled microalgae reliant at least in large part on autotrophy; Fig. S5) and that the major phytoplankton groups across the coast of Maine are diatoms and dinoflagellates (Fig. S6).
description: # Data for: Kelp forest loss and emergence of turf algae reshapes energy
 flow to predators in a rapidly warming ecosystem Dataset DOI:
 [10.5061/dryad.bcc2fqzqz](10.5061/dryad.bcc2fqzqz) ## Description of the
 data and file structure Our study took place in four subregions of the
 Maine coast that span a gradient of temperature and kelp forest habitat
 condition. At each study site, we conducted surveys to quantify available
 basal resources (macroalgae) and characterize the fish assemblage. We then
 collected representative primary producers and the two dominant fish
 species for stable isotope analysis. We analyzed these fish using both
 bulk tissue and compound-specific stable isotope analyses to document fish
 isotopic niches and energy use on kelp-dominated vs. red turf
 algae-dominated reefs. ## Files and variables ### File:
 Data1\_macroalgae\_biomass.csv **Description:** This dataset contains
 measurements of seaweed biomass (i.e., kelp, red macroalgae, and other
 seaweeds) collected from replicate 1 square meter quadrats (n = 4-6 per
 site) at multiple sites and subregions in coastal Maine. Macroalgae
 biomass surveys were conducted during the summers of 2021 and 2022 on
 shallow, subtidal rocky reefs (5-7 m depth MLLW) across Maine’s exposed
 outer coast. * See “Section 1: Underwater surveys” in the methods for
 details. ##### Variables * **survey_date**: the date on which the survey
 was conducted * **site**: the name of the location where the survey took
 place, corresponds to **lat** and **long** * **region**: the subregion of
 coastal Maine encompassing the study **site** (*Casco Bay*, *Midcoast*,
 *Penobscot* *Bay*, or *Downeast*) * **lat**: latitude coordinate of survey
 **site** (*decimal degrees* of transect, meter mark 0) * **long**:
 longitude coordinate of survey **site** (*decimal degrees* of transect,
 meter mark 0) * **meter_mark**: the distance (in *meters*) along a given
 transect where the quadrat was placed for sample collection *
 **taxa**: the lowest taxonomic classification to which the sample was
 identified in the lab. This is either a species, a genus, or a
 morphological grouping. Note: in 2021, *Vertebrata* species were
 identified to species level (*V. fucoides* or *V. nigra*) when possible,
 whereas in 2022, they were always combined as *Vertebrata* spp. *
 **group**: a broader classification based on higher-level taxonomy and
 morphology (*kelp, brown_other, green,  red_turf,  red_other*) *
 **mass_g**: the mass (biomass) of the sampled macroalgae, scaled to *grams
 per square meter*. Samples were spun (in a salad spinner or mesh bag) to
 remove excess water before weighing. ‘NA’ = biomass measurement is missing
 ### File: Data2\_roving\_fishes.csv **Description:** This dataset contains
 fish count and length (in 2.5 cm size bins) estimates recorded during
 surveys of replicate 40 x 2 m transects on subtidal rocky reefs across the
 coast of Maine. Biomass (in grams) of each individual was computed using a
 known, species-specific length-weight relationship. We used 2.5 cm “size
 bins” to estimate fish total length. The smallest size bin was 1-2.5 cm,
 and then bins increased in increments of 2.5 cm (2.5-5, 5-7.5, 7.5-10,
 etc.). However, some estimates are integrated across multiple bins; for
 example, when sizing fish from a distance, or schools with many
 individuals of differing sizes. Note: each row is an observation, and in
 many cases, multiple observations were made per transect. ‘na’ = not
 applicable. * See “Section 1: Underwater surveys” in the methods for more
 details. ##### Variables * **survey_date**: the date when the fish survey
 was conducted * **region**: the subregion of Maine encompassing the study
 **site** (*Casco Bay*, *Midcoast*, *Penobscot Bay*, or *Downeast*) *
 **site**: the name of the location where the survey took place,
 corresponds to **lat** and **long** * **lat**: latitude coordinate of
 survey **site** (*decimal degrees* of transect 1, meter mark 0) *
 **long**: longitude coordinate of survey **site** (*decimal degrees* of
 transect 1, meter mark 0) * **visibility_m**: water visibility at the site
 in *meters*, measured by divers as the maximum horizontal distance from
 which they could see a Secchi disk. A single measurement was taken per
 survey. ‘NR’ indicates that visibility was not recorded during the survey
 * **transect**: identifier for the transect replicate along which fish
 were observed and measured (1, 2, or 3). Transects were 40 m x 2 m and ran
 parallel to the reef at ~5-7 m depth. There was a minimum of 10 m between
 transects * **taxa**: the identity (common name) of the fish observed.
 ‘no_fish’ indicates no fish were observed along the transect. Fish taxa
 include *pollock* (*Pollachius virens*), *cunner* (*Tautogolabrus
 adspersus*), *gunnel* (*Pholis gunnellus*), *cod* (*Gadus morhua*),
 sculpin (*Myoxocephalus* spp.), and winter_flounder (*Pseudopleuronectes
 americanus*) * **length_min**: the minimum end of the size bin for the
 observed fish, in *centimeters* * **length_max**: the upper end of the
 size bin for the observed fish, in *centimeters* * **length_cm**: length
 estimate in *centimeters*, based on average of **length_min** and
 **length_max** * **count**: counted or estimated number of fish in a given
 observation * **a**: species-specific coefficient ‘a’ for calculating
 biomass from length, derived from length-weight parameters on
 [FishBase](https://www.fishbase.se/search.php) * **b**: species-specific
 coefficient ‘b’ for calculating biomass from length, derived from
 length-weight parameters on [FishBase](https://www.fishbase.se/search.php)
 * **biomass_g**: the estimated biomass of fish (single or multiple
 individuals) in a given observation in *grams*, calculated from: (***a***
 × **length_cm**^**b** ) ×
  **count**![](file:////Users/dara/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image002.png) ### File: Data3\_bulk\_CN\_stable\_isotopes.csv **Description:** This dataset contains bulk tissue δ¹³C and δ¹⁵N values for primary producers, invertebrates, and fish collected across the coast of Maine in 2022. * See “Section 2: Isotope sample collection & processing” in the methods for details about sample collection. * See “Section 3:  Bulk tissue δ¹³C and δ¹⁵N analysis” in the methods for details about sample analysis. ##### Variables * **sample_code**: unique identifier for each sample. The first letter of the code denotes sample type (‘P’ = particulate organic matter (POM), ‘A’ = macroalgae, “I” = invertebrate whole bodies or tissues, ‘F’ = fish muscle tissue) * **taxa**: the identity of the sample to the lowest taxonomic level (species, genus, higher taxonomic grouping, or POM) * **group**: broader taxonomic or functional group, based on **taxa** * **type**: broader groupings to categorize the sample (*POM*, *macroalgae*, *invertebrate*, or *fish*) * **region**: the subregion of Maine encompassing the sample collection **site** (*Casco Bay*, *Midcoast*, *Penobscot Bay*, or *Downeast*) * **site**: the name of the sample collection location * **lat**: latitude coordinate of the sample collection location (in *decimal degrees*). Some POM collection sites are approximate; these are noted in **notes** * **long**: longitude coordinate of the sample collection location (in *decimal degrees*). Some POM collection sites are approximate; these are noted in **notes** * **collection_date**: the date the sample was collected * **length_mm**: length of the sampled organism in *millimeters*, if applicable. ‘na’ if not measured * **wet_weight_g**: the measured weight of the sampled organism in *grams.* ‘na’ if not measured. 'nr' = weight measurement is missing * **d13C**: the stable carbon isotope ratio (δ¹³C) of the sample, measured by IRMS and corrected based on internal standards. Units are per mil (‰) * **d15N**: the stable nitrogen isotope ratio (δ¹⁵N) of the sample, measured by IRMS and corrected based on internal standards. Units are per mil (‰) * **C.N**: the carbon-to-nitrogen ratio of the sample, measured by IRMS * **notes**: additional observations or metadata related to the sample. ‘na’ if no notes were made ### File: Data4\_AA\_d13C\_values.csv **Description:** This dataset contains stable carbon isotope (δ¹³C) values of individual amino acids for primary producers and predatory fishes following sample hydrolysis, derivatization, and analysis in a gas-chromatography combustion unit coupled to an isotope-ratio mass spectrometer (GC-C-IRMS). The values have been corrected based on internal reference materials. Data about each sample, reference material, and GC-C-IRMS run are included. The corresponding bulk tissue δ¹³C and δ¹⁵N values for these samples are in *Data3_bulk_CN_stable_isotopes.csv*; however, this data file includes seven additional samples that were collected in 2023.  Columns with a three-letter amino acid code contain δ¹³C values obtained by GC-C-IRMS, then corrected based on their reference material, or their standard deviation between injections for that amino acid (denoted by ‘SD’). The three-letter code specifies the amino acid (see below). δ¹³C values may be ‘na’ if we did not obtain data for the particular amino acid in that sample. SD values may be ‘na’ if no usable δ¹³C values were collected or only one injection produced usable δ¹³C values. Units are per mil (‰). * See “Section 2: Isotope sample collection & processing” in the methods for details about sample collection. * See “Section 4: Stable carbon isotope analysis of essential amino acids (*δ*¹³C<sub>EAA</sub>)” in the methods for details about sample analysis. * See “Section 5: Flexible discriminant analysis (FDA) to quantify consumer energy channel use” for details on how we use these data to quantify the proportional contribution of various energy channels to consumers. ##### Variables * **sample_code**: unique identifier for each sample. First letter of code denotes sample type (‘*P*’= particulate organic matter (POM), ‘*A*’ = macroalgae, ‘*F*’ = fish muscle tissue) * **taxa**: the identity of the sample to the lowest taxonomic level (species, genus, higher taxonomic grouping, or POM) * **group**: broader taxonomic or functional group, based on **taxa** * **type**: broader groupings to categorize the sample (POM, macroalgae, or fish) * **site**: the name of the sample collection location * **region**: the subregion of Maine encompassing the sample collection **site** (*Casco Bay*, *Midcoast*, *Penobscot* *Bay*, or *Downeast*) * **collection_date**: the date the sample was collected * **length_mm**: length of the sample organism in *millimeters*, if applicable. ‘na’ if not measured * **hydrolysis_mass_mg**: mass of the sample used for hydrolysis in *milligrams*. ‘na’ if not applicable, as for POM samples, since the samples were on a filter * **ref_1**: identifier for one internal reference material with which the sample was derivatized. The three-letter code denotes standard type (UNM or STD), the second three-number code denotes standard batch * **ref_2**: identifier for a second internal reference material with which the sample was derivatized. The three-letter code denotes standard type (UNM or STD), the second three-number code denotes standard batch. ‘na’ if only one internal reference material was made with the respective hydrolysis batch * **runID**: numeric identifier for the GC-C-IRMS run * **run_date**: date of the GC-C-IRMS run * **STD**: identifier number of the reference material which was run on the GC-C-IRMS alongside the sample in question and used to correct the δ¹³C values for each amino acid (3-number code corresponding to **ref_1** or **ref_2**) * **STD.t**: type of the reference material which was run on the GC-C-IRMS alongside the sample in question and used to correct the δ¹³C values for each amino acid (*UNM* or *STD*, corresponding to number code in **ref_1** or **ref_2**) * **N.inj**: number of injections made for the sample during analysis, from which the following values were derived * **Ala13C**: alanine δ¹³C value, ‰ * **Ala13C.SD**: alanine δ¹³C standard deviation between injections * **Gly13C**: glycine δ¹³C value, ‰ * **Gly13C.SD**: glycine δ¹³C standard deviation between injections * **Thr13C**: threonine δ¹³C value, ‰. This is an essential amino acid * **Thr13C.SD**: threonine δ¹³C standard deviation between injections * **Ser13C**: serine δ¹³C value, ‰ * **Ser13C.SD**: serine δ¹³C standard deviation between injections * **Val13C**: valine δ¹³C value, ‰. This is an essential amino acid * **Val13C.SD**: valine δ¹³C standard deviation between injections * **Leu13C**: leucine δ¹³C value, ‰. This is an essential amino acid * **Leu13C.SD**: leucine δ¹³C standard deviation between injections * **Ile13C**: isoleucine δ¹³C value, ‰. This is an essential amino acid * **Ile13C.SD**: isoleucine δ¹³C standard deviation between injections * **Pro13C**: proline δ¹³C value, ‰ * **Pro13C.SD**: proline δ¹³C standard deviation between injections * **Asp13C**: aspartic acid δ¹³C value, ‰ * **Asp13C.SD**: aspartic acid δ¹³C standard deviation between injections * **Glu13C**: glutamic acid δ¹³C value, ‰ * **Glu13C.SD**: glutamic acid δ¹³C standard deviation between injections * **Phe13C**: phenylalanine δ¹³C value, ‰. This is an essential amino acid * **Phe13C.SD**: phenylalanine δ¹³C standard deviation between injections * **Tyr13C**: tyrosine δ¹³C value, ‰ * **Tyr13C.SD**: tyrosine δ¹³C standard deviation between injections * **Lys13C**: lysine δ¹³C value, ‰. This is an essential amino acid * **Lys13C.SD**: lysine δ¹³C standard deviation between injections * **Arg13C**: arginine δ¹³C value, ‰ * **Arg13C.SD**: arginine δ¹³C standard deviation between injections ### File: Data5\_FDA\_consumer\_means.csv **Description:** This dataset is the output from a bootstrap resampling of a flexible discriminant analysis (FDA) classification of consumer (predatory fish) energy channels. This dataset can be directly derived from δ¹³C data of five essential amino acids (Ile, Leu, Thr, Val, and Phe) from *Data4_AA_d13C_values.csv*; however, because resampling randomly subsets the primary producer data, there may be slight deviations in each iteration. This output was used in our analyses and figure generation.    * See “Section 4: Stable carbon isotope analysis of essential amino acids (*δ*¹³C<sub>EAA</sub>)” in the methods for how the original *δ*¹³C<sub>EAA</sub> measurements were made. * See “Section 5: Flexible discriminant analysis (FDA) to quantify consumer energy channel use” for details on how this dataset was derived. ##### Variables * **sample_code**: unique identifier for the sample. All samples are predatory fish (pollock or cunner), and their sample metadata (including which subregion they are from) and original EAA δ¹³C values can be found in *Data4_AA_d13C_values.csv* * **K.mean**: mean probability (from **N** bootstrap iterations) that the EAA of this fish was derived from *kelp* * **K.sd**: standard deviation of *kelp* estimates * **P.mean**: mean probability (from **N** bootstrap iterations) that the EAA of this fish was derived from *phytoplankton* * **P.sd**: standard deviation of *phytoplankton* estimates * **R.mean**: mean probability (from **N** bootstrap iterations) that the EAA of this fish was derived from *red macroalgae* * **R.sd**: standard deviation of *red macroalgae* estimates * **N**: number of iterations used to calculate the means and standard deviations. Set to 10,000 ### File: Data6\_POM\_18S.csv **Description:** This dataset is derived from amplicon sequencing of the 18SV4 gene region, and taxonomic assignments were made using a *dada2* bioinformatics pipeline with the PR2 database. We retained all ASVs that were among the 99% most abundant in each sample, and grouped them here based on species-level taxonomic assignments. * See “Section 2: Isotope sample collection & processing” in the methods for details about POM sample collection. * See “Section 6: Particulate organic matter (POM) as a proxy for phytoplankton” in the methods for details about POM sample sequencing and data processing. ##### Variables * **sample_code**: unique identifier for the POM sample, which matches those in the bulk (*Data3_bulk_CN_stable_isotopes.csv*) and amino acid (*Data4_AA_d13C_values.csv*) stable isotope datasets * **region**: subregion of Maine where the POM sample was collected. Samples were collected at a depth of ~1m below the surface and ~ 1km offshore of the nearest coastline * **tax.Kingdom**: kingdom-level taxonomic classification, from a *dada2* assignment based on the PR2 database * **tax.Supergroup**: supergroup-level taxonomic classification, from a *dada2* assignment based on the PR2 database * **tax.Division**: division-level taxonomic classification, from a *dada2* assignment based on the PR2 database * **tax.Class**: class-level taxonomic classification, from a *dada2* assignment based on the PR2 database * **tax.Order**: order-level taxonomic classification, from a *dada2* assignment based on the PR2 database * **tax.Family**: family-level taxonomic classification, from a *dada2* assignment based on the PR2 database * **tax.Genus**: genus-level taxonomic classification, from a *dada2* assignment based on the PR2 database * **tax.Species**: species- or lowest taxonomic-level classification, from a *dada2* assignment based on the PR2 database * **group**: manually assigned ecological or functional grouping based on *dada2* taxonomic assignments * **phytoplankton**: categorical indicator of whether the taxon is phytoplankton. ‘*yes’* if the group is an autotrophic microalgae. ‘*no’* if other (e.g., microheterotrophs or animals) * **read_abundance**: total number of sequence reads for the taxon in the sample * **read_proportion**: proportion of reads for the taxon relative to the total reads in the sample