DOI: 10.7280/d1hm6f
title: Data from: Unraveling the multiple facilitative effects of consumers on marine primary producers

description: The loss of consumers threatens the integrity of ecological systems, but
 the mechanisms underlying the effects on communities and ecosystems remain
 difficult to predict. This is, in part, due to the complex roles that
 consumers play in those systems. Here, we highlight this complexity by
 quantifying two mechanisms by which molluscan grazers – typically thought
 of as consumers of their algal resources – facilitate algae on rocky
 shores. Initial observations in high-zone tide pools revealed that both
 water-column ammonium concentrations and photosynthetic biomass were
 higher in pools containing higher densities of grazers, suggesting that
 local-scale nutrient recycling by the grazers could be enhancing algal
 biomass. We assessed this possibility by experimentally manipulating
 grazer abundances at the level of whole tide pools but controlling access
 of those grazers to experimental plots within each pool. Contrary to
 predictions that algal biomass inside grazer exclusions would increase as
 grazer abundances in the pools increased, we found that algal biomass
 inside grazer-exclusion fences was unaffected by grazer abundances.
 Instead, the consumptive effects of grazers that were evident at low
 grazer abundances transitioned to facilitative effects as experimentally
 manipulated grazer abundances increased. This finding suggested that these
 positive interactions were associated with the physical presence of
 grazers and not just grazers’ effects on nutrient availability. Subsequent
 experiments highlighted the potential role of “slime” – the pedal mucous
 trails left behind as the mollusks crawl on the substratum – in promoting
 recruitment of algae and thereby mediating a spatial subsidy of new
 organic matter into the system. Furthermore, different grazer groups
 contributed disproportionately to ammonium excretion (i.e., turban snails)
 versus slime production (i.e., littorine snails), suggesting a potential
 role for grazer diversity. Our work highlights the complex ways in which
 consumers affect their resources, including multiple, complementary
 mechanisms by which these grazers facilitate the algae they consume.
description: This work was conducted at Corona del Mar State Beach (33°
 35' 16.3" N, 117° 52' 1.5" W) on the coast of southern
 California USA. All research was conducted under California Department of
 Fish and Wildlife Scientific Collecting Permit SCP-13405. Except for some
 collections and short-term experiments in nearby tide pools to avoid
 affecting long-term experimental treatments, all measurements, surveys,
 and experiments were conducted in <em>n </em>= 18 tide pools
 in the rocky reefs at this location. Pool volumes averaged 22.6 ± 3.6
 (mean ± SE) L, and bottom surface areas averaged 0.46 ± 0.05
 m<sup>2</sup>. The pools were high on the shore; tidal
 elevations, determined using a self-leveling laser level (CST/berger,
 Watseka, Illinois, USA), averaged 1.58 ± 0.04 m above mean lower-low
 water. Based on comparisons with a nearby tide gauge (Newport Bay
 Entrance, Corona del Mar; 1.1 km from our study location) and typical wave
 heights, these pools were either splashed or covered for ~ 30% of the time
 during our measurements and experiments there.  
 Initial observations focused on the largest, most conspicuous
 grazers in the tide pools, turban snails (<em>Tegula
 funebralis</em> and <em>T. gallina</em>). Subsequent
 work also included littorine snails (<em>Littorina
 scutulata</em> and <em>L. plena</em>), limpets
 (<em>Lottia limatula</em>, <em>L. scabra</em>, and
 <em>L. strigatella</em>), and chitons (<em>Cyanoplax
 hartwegii</em> and <em>Nuttalina californica</em>).
 Collectively, these grazing mollusks comprised almost all the
 invertebrates in the tide pools, though pools also contained occasional
 mussels, hermit crabs, and sea anemones. None of these “other” species
 were abundant enough, relative to the grazers, to appreciably modify
 nutrient availability (Bracken et al. 2018). Other, smaller invertebrates
 (e.g., copepods, amphipods) were rare in the pools. Macroalgae were also
 virtually absent, and the grazers primarily consumed periphyton, including
 bentic microalgae and microscopic forms of macroalgae.
 We surveyed grazer abundances by spreading a piece of flexible
 nylon trawl net (10 cm × 10 cm squares) across the bottom of each pool
 (Foulweather Trawl Supply, Newport, Oregon, USA; Bracken and Nielsen
 2004). The net was used to count grazers and measure surface area. Grazer
 abundances were converted to ash-free dry biomass using values in Bracken
 et al. (2018). Initial surveys and measurements (relationships between
 turban snail biomass density, ammonium accumulation, and biofilm
 photosynthetic biomass) focused on <em>n</em> = 10 tide pools,
 but our subsequent experiment included <em>n</em> = 18 pools
 for greater replication of treatments. Water samples
 were collected from each pool as the pool was isolated by the receding
 tide and again ~3.5 hr (range 2.8 – 4.3, depending on the height of the
 pool) later. Samples were collected in acid-washed bottles, placed
 immediately on ice, and transported to the lab for ammonium
 (NH<sub>4</sub><sup>+</sup>) analyses. We used the
 difference in NH<sub>4</sub><sup>+</sup>
 concentrations and the elapsed time between samples to calculate the rate
 of NH<sub>4</sub><sup>+</sup> accumulation in each
 pool (μmol L<sup>-1</sup> hr<sup>-1</sup>).
 Samples were analyzed using the phenol-hypochlorite method (Solórzano
 1969) on a UV-1800 benchtop spectrophotometer (Shimadzu, Carlsbad,
 California, USA). Photosynthetic biomass associated
 with the benthic periphyton growing on the substratum was quantified
 non-destructively in each pool using pulse amplitude modulated (PAM)
 fluorometry. We covered each tide pool with an opaque plastic sheet for at
 least 30 min, then used a Diving-PAM-II underwater chlorophyll fluorometer
 (Heinz Walz GmbH, Effeltrich, Germany) to measure the dark-adapted minimal
 level of fluorescence (<em>F</em><sub>0</sub>;
 Murchie and Lawson 2013). This parameter is a frequently used
 non-destructive surrogate for extracted chlorophyll <em>a</em>
 (chl <em>a</em>) values (Honeywill, Paterson, and Hagerthey
 2002, LaScala-Gruenewald et al. 2016), and our measurements support this
 relationship. Chl <em>a</em> concentrations (mg per tile)
 extracted from travertine tiles placed in pools to quantify periphyton
 abundance were correlated with log(dark-adapted
 <em>F</em><sub>0</sub>) measurements taken on
 those tiles in the field, regardless of the gain setting (gain 9:
 <em>F</em><sub>1,58</sub> = 104.9,
 <em>P</em> &lt; 0.001,
 <em>R</em><sup>2</sup> = 0.64; gain 3:
 <em>F</em><sub>1,31</sub> = 92.0,
 <em>P</em> &lt; 0.001,
 <em>R</em><sup>2</sup> = 0.75), and we used these
 relationships to convert PAM measurements to chl <em>a</em>.
 Reported values for each pool are the average of <em>n</em> =
 5 measurements taken haphazardly across the rock substratum under the
 opaque covering. Because these pools lack macroalgae, the dark-adapted
 <em>F</em><sub>0</sub> measurements provided an
 estimate of the photosynthetic biomass in the biofilm in each
 pool. Travertine tiles (10 cm × 10 cm) were similar in
 composition, color, and rugosity to the rocky reefs at the study location
 but provided a consistent substratum that was easily fenced to limit
 grazer access. Three tiles were surrounded by 5-cm-high fences made from
 stainless steel wire cloth mesh (8 × 8 mesh size, 0.7 mm wire diameter,
 2.5 mm opening size; McMaster-Carr, Elmhurst, Illinois, USA) and attached
 to a wall anchor drilled into the bottom of each tide pool using lag
 bolts, fender washers, and neoprene washers for cushioning. Tiles were
 placed in each pool 2 months prior to the start of the experiment to allow
 microalgae to recruit and grow. At the beginning of the experiment,
 dark-adapted <em>F</em><sub>0</sub> values (gain
 setting 9) were measured on each tile, as described above, and one tile
 was randomly selected for removal and chl <em>a</em>
 extraction in 90% acetone. Extracted chl <em>a</em> was
 determined using a Trilogy benchtop fluorometer (Turner Designs,
 Sunnyvale, California, USA) to evaluate the relationship between initial
 chl <em>a</em> and
 <em>F</em><sub>0</sub> values.
 Within each pool, the remaining two tiles were randomly assigned
 to either a “fenced” treatment – the stainless steel fence was left
 surrounding the tile – or an “unfenced” treatment – the fence was removed.
 Fences were effective at limiting access by grazers. Whereas there was no
 effect of the herbivore manipulation treatment on grazer biomass on fenced
 tiles (generalized linear model [gamma distribution, log link]:
 X<sup>2</sup> = 3.4, d.f. = 2, <em>P </em>=
 0.186), the biomass of grazers on unfenced tiles depended on the treatment
 (X<sup>2</sup> = 13.3, d.f. = 2, <em>P </em>=
 0.001). Accordingly, the difference in grazer biomass between fenced and
 unfenced tiles depended on the treatment; effects were most apparent in
 tide pools where grazers were added and least apparent where grazers were
 removed (X<sup>2</sup> = 17.9, d.f. = 2, <em>P
 </em>&lt; 0.001), as differences between treatments were
 minimized where there were few grazers in the pools. We
 used a stratified random method to assign grazer manipulations at the
 level of tide pools. Pools were ranked by volume, generating 6 sets of
 <em>n</em> = 3 pools each. Within each set, pools were
 randomly assigned to one of three treatments: reduced, ambient, or
 addition. Every few days, all grazers were counted (and counts were
 subsequently converted to ash-free dry biomass [Bracken et al. 2018]),
 removed from each “removal” pool, and added to each “addition” pool.
 Grazers in “ambient” pools were removed from the substratum and replaced
 in the same pool to mimic disturbance associated with removals and
 additions. Herbivore manipulations were effective at modifying grazer
 biomass in tide pools (GENMOD: X<sup>2</sup> = 13.0, d.f. = 2,
 <em>P </em>= 0.002). Relative to unmanipulated control pools,
 additions resulted in an increase in grazer biomass (GENMOD:
 <em>z</em> = 2.4, <em>P</em> = 0.049 after Tukey
 adjustment). Rates of NH<sub>4</sub><sup>+</sup>
 accumulation in each pool were evaluated as described above.
 The experiment was maintained for ~ 6 weeks (26 April to 09 June
 2016), and dark-adapted <em>F</em><sub>0</sub>
 values were measured on each tile (the average of 4 measurements, 1 in
 each quadrant) every 2 – 7 days using the PAM fluorometer. Initially, the
 fluorometer was set to a more sensitive gain setting, but as algal biomass
 increased on the tiles, we reduced the setting from 9 to 3 to avoid
 overflow errors due to high signal intensity. At the end of the
 experiment, we removed all tiles, extracted accumulated chl
 <em>a</em> in 90% acetone, and evaluated the relationship
 between the final dark-adapted
 <em>F</em><sub>0</sub> readings (gain 3) and the
 extracted chl <em>a</em> concentrations. We used these
 relationships to convert <em>F</em><sub>0</sub> to
 chl <em>a</em> so that units were comparable regardless of the
 gain setting. We evaluated mechanisms underlying our
 experimental results by examining how the presence of grazers affected
 algal biomass. In the field, we anchored 10 cm × 10 cm travertine tiles
 (<em>n</em> = 10) in sets of two in a large tide pool adjacent
 to our experimental pools. Both tiles were enclosed in fences, but they
 were also covered with stainless steel wire cloth to prevent enclosed
 snails from escaping. One tile in each set was randomly assigned to a
 +Snail treatment (8 turban snails, corresponding to densities observed in
 nearby tide pools); the other ‑Snail tile in each set contained no snails.
 After seven days, we estimated photosynthetic biomass accumulation on each
 tile by covering the tide pool with black plastic sheeting for 30 minutes
 prior to measuring dark-adapted
 <em>F</em><sub>0</sub> values in situ using a PAM
 fluorometer. To assess effects of slime in the absence
 of snails, we quantified effects of snails on algal recruitment in
 <em>n</em> = 8 square microcosms in the lab. Each microcosm
 contained a 10 cm × 10 cm travertine tile and 200 ml of saltwater
 (salinity of 35; Instant Ocean® Sea Salt, Blacksburg, Virginia, USA). Half
 of the microcosms (<em>n</em> = 4) contained littorine snails
 (<em>Littorina scutulata</em> and <em>L.
 plena</em>), and the other half contained no snails. We focused on
 <em>Littorina</em> because our measurements indicated that
 littorines produced more slime than other grazer groups (see
 <em>Results</em>). Snail abundances in snail-addition
 microcosms were determined by ash-free dry biomass (0.91 ± 0.01 g),
 consistent with abundances in our assessments of grazer ammonium excretion
 (Bracken et al. 2018) and grazer abundances at the site (M. Bracken,
 <em>personal observation</em>). Microcosms were covered with
 nylon mesh filter screens to prevent escapees and placed in a
 temperature-controlled (16°C) room under lighting conditions programmed to
 mimic natural sunrise and sunset cycles. After 24 hours, 44 ml of f/2
 culture medium containing dense cultures of the phytoplankton
 <em>Tetraselmis chui</em> and <em>Dunaliella
 salina</em> (Algae Research and Supply, Carlsbad, California, USA)
 were added to each microcosm. Both phytoplankton species are found in
 nearshore ecosystems in the northeastern Pacific (Jensen and Tanner 1973;
 Hori et al. 1986), though we acknowledge that they are not perfect
 analogues of the species recruiting to tide pools. After 1 hour, we poured
 off the seawater and culture medium, removed the snails, and carefully
 added 200 ml of fresh seawater. We then covered the microcosms with an
 opaque plastic sheet for 30 minutes before measuring dark-adapted
 <em>F</em><sub>0</sub> values to estimate the
 photosynthetic biomass retained on each tile. Ammonium
 excretion and slime production rates were evaluated in microcosms
 containing 200 ml of saltwater (salinity of 35; Instant Ocean® Sea Salt,
 Blacksburg, Virginia, USA). Each microcosm contained a travertine tile as
 substrate. To simulate the still-water conditions in tide pools, the water
 was not stirred or aerated. Temperatures were maintained at 20 °C, which
 is representative of field conditions (19.51 ± 0.01 °C). Grazers were
 collected from the field immediately prior to measurements. The same
 ash-free dry biomass of grazers (0.91 ± 0.01 g) was added to each
 microcosm at the beginning of each trial based on average ash-free dry
 mass measurements of representative individuals of each grazer group
 (Bracken et al. 2018). Because individuals of the different groups were
 characterized by different masses, maintaining a constant mass across
 grazer groups necessitated different numbers of individuals for each group
 in each microcosm: 135 littorine snails, 45 limpets, 15 chitons, or 5
 turban snails. Microcosms were covered with nylon mesh filter screens to
 prevent escapes. Trials were run for ~19 h in the lab,
 with a light-dark cycle (92 ± 3 mmol photons m<sup>-2</sup>
 s<sup>-1</sup>) that mimicked outdoor conditions (13 hr light
 : 11 hr dark). This duration was between the median isolation time of
 experimental tide pools in the absence of wave splash (23.0 h) and after
 accounting for a 0.5 m swell height (8.3 h; Bracken et al. 2018). Initial
 water samples were taken from each microcosm prior to adding the grazers,
 and a second set of samples was taken at the end of the trial. Ammonium
 concentrations in the water samples were analyzed using the
 phenolhypochlorite method (Solórzano 1969) on a UV-1800 benchtop
 spectrophotometer (Shimadzu, Carlsbad, California, USA). Changes in
 ammonium concentrations in control microcosms without grazers were minimal
 and were accounted for when calculating ammonium accumulation rates.
 Initial ammonium concentrations averaged 0.9 ± 0.2 mmol/L.
 As molluscan grazers move across the substratum, they glide on a
 thin layer of a glycoprotein network (hereafter, “slime”) that adheres
 them to the surface and allows them to crawl (Denny 1980). Because of the
 potential for slime to affect algal recruitment (Connor and Quinn 1984),
 we quantified accumulation of slime associated with the different grazer
 groups. After the final ammonium samples were collected from each
 microcosm, we removed and rinsed each tile with 25 ml of artificial
 seawater, saving the rinse water. We then used a razor blade to carefully
 scrape the surface residue from the tile, cleaning the razor blade in the
 rinse water. Finally, we rinsed the tile again with 25 mL of seawater, for
 a total volume of 50 ml, which we poured into a 50 ml conical tube and
 vortexed. The contents of the tube were then trapped on an
 Isopore<sup>TM</sup> membrane filter (0.4 mm pore size
 hydrophylic polycarbonate membrane; MilliporeSigma, Merck KGaA, Darmstadt,
 Germany) and stained with Alcyan Blue (pH 2.5), a dye that complexes with
 acidic groups of polysaccharides and mucous glycoproteins (Hall et al.
 1980, Passow and Alldredge 1995). Filters were rinsed with deionized water
 then placed in 5 ml of 80% sulfuric acid for 2-4 hr while mixing gently.
 The resulting solution was then read at 787 nm on a UV-1800 benchtop
 spectrophotometer (Shimadzu, Carlsbad, California, USA; Passow and
 Alldredge 1995). The method was calibrated using xanthan gum as a standard
 (Passow and Alldredge 1995), so all slime accumulation values are
 xanthan-gum equivalents. Ammonium and slime accumulation rates were
 calculated on both a per-individual (umol h<sup>-1</sup>
 ind<sup>‑1 </sup>or mg h<sup>-1</sup>
 ind<sup>‑1</sup>) and per-biomass (umol
 h<sup>-1</sup> g<sup>‑1 </sup>or mg
 h<sup>-1</sup> g<sup>‑1</sup>) basis based on the
 change in ammonium concentrations over time or the slime that had
 accumulated on the substratum in <em>n</em> = 4 microcosms for
 each grazer group. To estimate contributions of each
 grazer group to ammonium and slime accumulation rates in the field, we
 combined lab-measured rates with field surveys of grazer abundances.
 Grazer counts in surveys of <em>n</em> = 18 tide pools were
 multiplied by the per-individual rates of ammonium and slime accumulation
 for each grazer group. These were then used to calculate the percentage of
 the total estimated ammonium and slime accumulation in each pool
 associated with each grazer group. References
 Bracken, Matthew E. S., and K. J. Nielsen. 2004. "Diversity
 of intertidal macroalgae increases with nutrient loading by
 invertebrates." <em>Ecology</em> 85 (10):2828-2836. doi:
 10.1890/03-0651. Bracken, Matthew E. S., Jill M. Oates,
 Alexander J. Badten, and Genevieve Bernatchez. 2018. "Predicting
 rates of consumer-mediated nutrient cycling by a diverse herbivore
 assemblage." <em>Marine Biology</em> 165 (10):165. doi:
 10.1007/s00227-018-3422-z. Connor, Valerie M., and
 James F. Quinn. 1984. "Stimulation of food species growth by limpet
 mucus." <em>Science</em> 225 (4664):843-844. doi:
 10.1126/science.225.4664.843. Denny, Mark. 1980.
 "The role of gastropod pedal mucus in locomotion."
 <em>Nature</em> 285 (5761):160-161. doi:
 10.1038/285160a0. Hall, Roderick L., Rosamund J.
 Miller, Anthony C. Peatfield, Paul S. Richarson, Isobel Williams, and
 Irvin Lampert. 1980. "A colorimetric assay for mucous glycoproteins
 using Alcyan Blue." <em>Biochemical Society
 Transactions</em> 8 (1):72. doi: 10.1042/bst0080072.
 Honeywill, Claire, David Paterson, and Scot Hagerthey. 2002.
 "Determination of microphytobenthic biomass using pulse-amplitude
 modulated minimum fluorescence." <em>European Journal of
 Phycology</em> 37 (4):485-492. doi:
 10.1017/S0967026202003888. Hori, Terumitsu, Richard E.
 Norris, and Mitsuo Chihara. 1986. "Studies on the ultrastructure and
 taxonomy of the genus <em>Tetraselmis</em>
 (Prasinophyceae)." <em>The Botanical Magazine, Tokyo</em>
 99 (1): 123-35. doi: 10.1007/BF02488627 Jensen, James
 B., and Sarah J. Tanner. 1973. “A preliminary check-list of the marine
 algae of the Moss Landing jetty: an annotated floristic compilation.”
 <em>Contributions from the Moss Landing Marine Laboratories No.
 38</em>. Technical Publication 73-7, CASUC-MLML-TP-73-07.
 https://repository.library.noaa.gov/view/noaa/10891.
 LaScala-Gruenewald, Diana E., Luke P. Miller, Matthew E. S.
 Bracken, Bengt J. Allen, and Mark W. Denny. 2016. "Quantifying the
 top-down effects of grazers on a rocky shore: selective grazing and the
 potential for competition." <em>Marine Ecology Progress
 Series</em> 553:49-66. doi: 10.3354/meps11774.
 Murchie, E. H., and T. Lawson. 2013. "Chlorophyll
 fluorescence analysis: a guide to good practice and understanding some new
 applications." <em>Journal of Experimental Botany</em> 64
 (13):3983-3998. doi: 10.1093/jxb/ert208. Passow, U.,
 and A. L. Alldredge. 1995. "A dye-binding assay for the
 spectrophotometric measurement of transparent exopolymer particles
 (TEP)." <em>Limnology and Oceanography</em> 40
 (7):1326-1335. doi: 10.4319/lo.1995.40.7.1326.
 Solórzano, L. 1969. "Determination of ammonia in natural
 waters by the phenolhypochlorite method." <em>Limnology and
 Oceanography</em> 14:799-801. doi:
 10.4319/lo.1969.14.5.0799.
description: Data files are provided as comma-delimited (.csv) text
 files.
description: # Data from: Unraveling the multiple effects of consumers on marine
 primary producers Data represent a series of observations and experimental
 results that highlight the roles of grazers on rocky shore primary
 producers. Observational datasets (chl_f0_gain_20230222.csv,
 tegula_ammonium_pam_20230222.csv) highlight relationships between
 variables based on field observations of algal biofilms, grazer
 abundances, and ammonium concentrations. Experimental datasets
 (field_algal_recruitment_20230222.csv,
 field_grazer_experiment_20230222.csv, lab_algal_recruitment_20230222.csv)
 highlight responses in manipulative field and lab experiments to evaluate
 effects of grazers on a variety of responses related to primary producer
 abundances. A final dataset (grazer_slime_ammon_20230222.csv) includes
 measurements of ammonium fluxes and pedal mucous "slime"
 accumulation associated with different types of grazers. \## Description
 of the data and file structure This dataset includes the following files,
 each with a unique set of measured attributes, described below: (1)
 chl_f0_gain_20230430.csv a. gain = the gain setting on the WALZ Diving PAM
 pulse amplitude modulated fluorometer. Values = 3 or 9. b. f0_avg = the
 average dark-adapted f0 value measured by the PAM fluorometer on the tile
 in fluorescence units. c. chla = the extracted chlorophyll a concentration
 measured on a Turner Trilogy benchtop fluorometer in micrograms per m2.
 (2) field_algal_recruitment_20230430.csv a. F0_mean = the average of four
 dark-adapted f0 values measured on that tile using the PAM fluorometer. b.
 treatment = the grazer manipulation treatment. Values = SNAIL (snails
 enclosed on the tile) or CONTROL (no snails enclosed on the tile). (3)
 field_grazer_experiment_20240430.csv a. tide_pool = a number code used to
 identify each tide pool on the shore. This number was etched into a label
 adhered to the rock adjacent to each pool and served as a unique
 identifier. Values are nonconsecutive (some pools were initally marked but
 could not be used for the experiment) and range from 1 to 23. b. treatment
 = the grazer manipulation treatment. Values = AMB (ambient, grazer
 abundance unmanipulated),  or CONTROL (no snails enclosed on the tile), NO
 (grazers removed and added to the matched addition pool), or ADD (grazers
 added). c. mean_grazer_biomass = the average biomass of grazers in the
 tide pool (grams per m2). d. mean_grazer_fence = the average difference in
 grazer biomass between fenced and unfenced tiles within a tide pool (grams
 per tile). e. mean_fenced_chla = the average chlorophyll a value measured
 on each fenced tile (micrograms per tile). f. initial_fenced_chla = the
 chlorophyll a value measured on each fenced tile at the beginning of the
 experiment (micrograms per tile). g. mean_unfenced_chla = the average
 chlorophyll a value measured on each unfenced tile (micrograms per tile).
 h. initial_unfenced_chla = the chlorophyll a value measured on each
 unfenced tile at the beginning of the experiment (micrograms per tile). i.
 mean_chla_fence_effect = the average difference in the chlorophyll a
 values measured on the fenced and unfenced tiles within each tide pool
 (micrograms per tile). j. initial_chla_fence_effect = the initial
 difference in the chlorophyll a values measured on the fenced and unfenced
 tiles within each tide pool (micrograms per tile). k. height = the
 elevation of the tide pool on the shore (m above mean lower-low water). l.
 vol = the volume of the tide pool (L) m. sa = the surface area of the tide
 pool (m2) n. sa_v = the surface-area-to-volume ratio of the tide pool (m2
 per L) (4) grazer_slime_ammon_20230430.csv a. ammon = the per-biomass
 ammonium accumulation rate (micromol per hr per g). b. slime = the
 uncorrected, per-biomass slime accumulation rate (units per hr per g). c.
 grazer_group = the type of grazer (teg = turban snail, lit = littorine
 snail, lim = limpet, chi = chiton). d. ammon_pct = the percentage of total
 ammonium accumulation attributable to that individual. e. slime_pct = the
 percentage of total slime production attributable to that individual. (5)
 lab_algal_recruitment_20230430.csv a. F0_mean = the average dark-adapted
 f0 value measured on that tile. b. treatment = whether snails had been
 present on that tile. Values = Snail or Control. (6)
 tegula_ammonium_pam_20230430.csv a. teg_m2 = the density of turban snails
 in the tide pool (number per m2). b. hrs = the length of time elapsed
 since the pool was isolated by the receding tide. c. ammon_flux = the
 change in ammonium concentrations in the tide pool (micromol per L per
 hr). d. f0 = the average dark-adapted f0 value measured in the tide pool
 using a PAM fluorometer. e. teg_g_m2 = the biomass of turban snails in the
 tide pool (grams per m2) f. chla = the calculated chlorophyll amount on
 the tile (micrograms per m2) \## Sharing/Access information Data are not
 archived or accessible elsewhere. For questions, please contact Matthew
 Bracken at [m.bracken@uci.edu](mailto:m.bracken@uci.edu). \##
 Code/Software This submission does not include code.