DOI: 10.7280/d1kt2m
title: Climate change amelioration by marine producers: Does dominance predict impact?

description: Climate change threatens biodiversity worldwide, and assessing how those
 changes will impact communities will be critical for conservation.
 Dominant primary producers can alter local-scale environmental conditions,
 reducing temperature via shading and mitigating ocean acidification via
 photosynthesis, which could buffer communities from the impacts of climate
 change. We conducted two experiments on the coast of southeastern Alaska
 to assess the effects of a common seaweed species, Neorhodomela oregona,
 on temperature and pH in field tide pools and tide pool mesocosms. We
 found that N. oregona was numerically dominant in this system, covering
 &gt;60% of habitable space in the pools and accounting for &gt;40%
 of live cover. However, while N. oregona had a density-dependent effect on
 pH in isolated mesocosms, we did not find a consistent effect of N.
 oregona on either pH or water temperature in tide pools in the field.
 These results suggest that the amelioration of climate change impacts in
 immersed marine ecosystems by primary producers is not universal and
 likely depends on species’ functional attributes, including photosynthetic
 rate and physical structure, in addition to abundance or dominance.
description: <strong>Study Site</strong>: To evaluate the role of
 the abundant alga <em>Neorhodomela oregona</em> (Doty) Masuda
 in driving local climate conditions, we conducted “removal” and “mesocosm”
 experiments at John Brown’s Beach (57.05° N, 135.33° W) near Sitka, Alaska
 from 05 Jul 2019 to 27 Sep 2019. Southeast Alaska was an ideal location
 for this study as it has been subjected to relatively low levels of direct
 human disturbance yet is experiencing rapid environmental change (Stafford
 <em>et al</em>., 2000). Air temperature in Southeast Alaska
 has increased by ~0.11°C per decade since 1830 (Wendler <em>et
 al</em>., 2016; Jewett and Romanou, 2017), well above the global
 mean rate of 0.07°C per decade (since 1880; Blunden and Arndt, 2019). Sea
 surface ocean pH has declined by 0.03 units over a recent 15-year window
 (1991-2006) in the northeast Pacific waters off the Alaskan coast (Byrne
 <em>et al.</em>, 2010).
 <strong>Removal Experiment</strong>: We selected 10
 tide pools, which ranged from 2.5 - 23.5 L in volume and 2.49 - 3.29 m in
 tide height (i.e., vertical position within the intertidal zone), which
 were separated by an average distance of 4 m, for the removal experiment.
 We began by assessing the physical characteristics of the experimental
 tide pools. We measured volume by pumping the water from a tide pool into
 a graduated bucket, and we assessed the basal surface area of the pool, as
 well as <em>N. oregona</em> abundance, by placing a flexible
 mesh quadrat with 10 cm × 10 cm squares on the bottom of each tide pool
 (Bracken and Nielsen, 2004; Sorte and Bracken, 2015; Silbiger and Sorte,
 2018). Tide heights (in meters above mean lower low water) for each pool
 were measured using a sight level, a surveying rod, and tidal predictions
 from the National Oceanic and Atmospheric Administration (2019). We
 assigned pools to treatment and control groups (n = 5, removal or control)
 by randomizing assignments until various physical and biological metrics
 did not vary between treatment and control (based on a generalized linear
 model with a threshold of p &gt; 0.2). Metrics included tide height,
 volume, basal surface area, percent cover of <em>N.
 oregona</em>, and species richness. We removed <em>N.
 oregona</em> from the treatment pools using scissors and by cutting
 as close to the substratum as possible without removing the holdfasts to
 avoid damaging surrounding organisms. We measured the wet biomass of
 <em>N. oregona</em> from each removal pool in the field before
 using that algal biomass in the mesocosm experiment (described
 below). To assess the abundance of <em>N.
 oregona</em> and community composition in the tide pools, we
 conducted biodiversity surveys in the pools before and immediately after
 <em>N. oregona</em> removal (06 Jul – 19 Jul 2019), and then
 every two weeks until 27 Sep 2019 (for a total of seven surveys; Figures
 1B & A1). During the surveys, we pumped water out of each tide
 pool, laid down a flexible mesh quadrat with 10 cm × 10 cm squares along
 the bottom, recorded the surface area covered by each sessile species
 (algae and invertebrates; 0.1 square or 10 cm<sup>2</sup>
 being the minimum measurement assigned for a species present in trace
 amounts) and counted all mobile invertebrates present (Bracken and
 Nielsen, 2004; Silbiger and Sorte, 2018). We identified organisms to the
 lowest possible taxonomic level: species when possible and genus when
 species were impossible to differentiate in the field (as with, e.g.,
 <em>Littorina plena</em> and <em>Littorina
 scutulata</em>). In some cases, species were pooled and tallied
 together (e.g., “limpets” or “coralline algae”).  To
 assess the impacts of <em>N. oregona</em> removal on tide pool
 pH, we conducted time-series samplings in the tide pools during the
 daytime and nighttime both before and after <em>N.
 oregona</em> removal (10 Jul – 16 Jul 2019; Figure A1). We measured
 temperature and salinity with a ProDSS Multiparameter Water Quality Meter
 (YSI, Yellow Springs, Ohio), and light intensity with a MQ-210 Underwater
 Quantum Meter (Apogee, Logan, Utah). Over the four sampling periods (day
 and night, both before and after removal), we took physical measurements
 at five time points over a ~2.5 h time series during low tide, sampling
 once every 30 min beginning immediately following isolation of the tide
 pools from the ocean. We also collected water samples at the first, third
 and fifth time points. The water samples were collected by hand-pumping
 250 mL of water from the bottom of the tide pool into a vacuum flask, and
 then carefully siphoning the water into two 125 mL amber glass sample
 bottles to minimize gas exchange between the water sample and the
 atmosphere. All sample containers were rinsed three times with seawater
 prior to sample collection. We immediately preserved each water sample
 with 60 µL HgCl<sub>2</sub> and sealed them for later analyses
 to determine pH and total alkalinity. We analyzed pH in
 the water samples from both experiments on a UV-1800 benchtop
 spectrophotometer (Shimadzu, Carlsbad, California), following best
 practices as described in Dickson <em>et al</em>. (2007). We
 divided each water sample into triplicate subsamples and analyzed them
 separately. We took an initial reading of each subsample at three
 wavelengths, added 50 µL of m-cresol dye, and mixed and re-analyzed the
 subsample at the same three wavelengths (Liu and Chan, 2011). We
 calculated the difference between the initial reading and the dye-added
 measurement, which we then used to calculate the pH value of each
 subsample. We took the mean of all subsamples with &lt; 0.005 pH unit
 difference among them (subsamples outside that range were excluded) for
 each individual sample to produce a preliminary pH value. We then used
 CO2calc software (Robbins <em>et al</em>., 2010) to correct
 the preliminary pH value for total alkalinity (TA; analyzed as described
 below), salinity, temperature, and stoichiometric dissociation constants
 and calculate the final pH on the total scale (Mehrbach <em>et
 al</em>., 1973; Dickson and Millero, 1987; Kroeker <em>et
 al</em>., 2021). We analyzed the TA of the water
 samples with open-cell titrations (as in Silbiger and Sorte, 2018) on a
 T50 titrator with LabX software (Mettler-Toledo AG, Schwerzenbach,
 Switzerland). We measured a certified reference material (Marine Physical
 Laboratory, Scripps Institution of Oceanography, La Jolla, California) at
 the beginning of each session as a standard (acceptable range: ±1% error),
 following an established protocol for open-cell TA analysis (SOP 3b)
 (Dickson <em>et al</em>., 2007; Silbiger and Sorte,
 2018). We conducted two additional samplings using a
 light-dark incubation method (Noël <em>et al</em>., 2010;
 Bracken <em>et al</em>., 2022) to assess how pH in the tide
 pools responded to differing light conditions. During these trials, we
 measured pH values across three time points using a HI9829 multiparameter
 meter with a 7609829 glass pH electrode (Hanna Instruments, Woonsocket,
 Rhode Island), which was calibrated using a Tris solution according to the
 best practices specified in SOP 6a by Dickson <em>et al</em>.
 (2007). We measured initial pH, remeasured following a ~30 min dark
 incubation period under an opaque, black plastic sheet, and collected a
 final measurement after a ~30 min light incubation period following the
 removal of the sheet. Tide pool water temperatures were
 recorded every 5 min for the duration of the study by HOBO Pendant®
 Temperature/Light 64K Data Loggers (Onset Computer Corporation, Bourne,
 Massachusetts) anchored in the center of the pools. For comparison to our
 seawater temperature data, ambient air temperature data were sourced from
 the weather station at nearby Sitka Rocky Gutierrez Airport (Sitka,
 Alaska; &lt; 1 km from the site) via CustomWeather, Inc.
 (2021).  We conducted all statistical analyses in R
 (R-version 4.0.4; R Core Team, 2013) using a generalized linear mixed
 model (GLMM) repeated measures analyses and generalized linear models
 (GLM). We used a GLMM (‘lmer’ function; Bates <em>et al</em>.,
 2015) to evaluate the effect of the removal treatment on <em>N.
 oregona</em> abundance (cover) in the experimental tide pools and
 track recovery over time. <em>N. oregona</em> cover was
 modeled as a function of the fixed factors of treatment, time (bi-weekly
 surveys), and treatment × time, with the tide pool included as a random
 effect. We applied Kenward-Roger corrections to the GLMM to adjust the
 degrees of freedom to accurately reflect a repeated measures structure
 (Kenward and Roger, 1997; Kuznetsova <em>et al</em>., 2017)
 and conducted post hoc pairwise comparisons on<em> N.
 oregona</em> cover using Tukey’s HSD (‘emmeans’ function; Lenth,
 2018). To evaluate the effects of <em>N.
 oregona</em> on pH, we used the pH values at each of the three time
 points at which water was sampled to calculate the rate of pH change in
 tide pools (i.e., the slope of the relationship between pH and time), and
 we compared abundances of <em>N. oregona</em> to the
 calculated rate of pH change during the daytime and nighttime sampling
 periods. Similarly, we used the field pH measurements from the light-dark
 trials (which were subsequently converted from mV to pH units) to
 calculate the rate of pH change between the initial measurement and the
 measurement taken at the end of the dark incubation period to represent
 the rates of pH change during the night (Bracken <em>et
 al</em>., 2022), as well as the rate of pH change between the end of
 the dark incubation period and the final measurement (after a ~30 minute
 light incubation period) to correspond to the daytime water samplings. To
 assess the effects of <em>N. oregona</em> on water temperature
 in tide pools, we calculated the daily maximum water temperature for each
 tide pool over the full 11-week period following <em>N.
 oregona</em> removal. We used GLMs (‘glm’
 function in R) to assess the effects of<em> N. oregona</em> on
 pH. For intact pools prior to <em>N. oregona</em> removal, we
 evaluated the rate of pH change as a function of<em> N.
 oregona</em> area (in cm<sup>2</sup> of surface area per
 L of water volume), with the tide height of each pool, mean light in each
 pool (average of five time points; the light was not included in night
 analyses as it was uniformly measured as 0 at night), and mean water
 temperature in each pool during the sampling (across the five time points)
 included as covariates. Identical analyses were conducted on the pH data
 from the light-dark trials, with light intervals substituted for daytime
 samplings and dark intervals replacing nighttime samplings, except that
 individual temperature measurements were used rather than a mean value.
 This analysis of intact tide pools (before the removal) was also run with
 the assigned treatment group included as an additional factor, an analysis
 which confirmed that there was no initial difference in pH change between
 the treatment groups prior to removal (p &gt; 0.4).
 To test the effect of the <em>N. oregona</em> removal
 on pH, we evaluated the rate of pH change after removal as a function of
 treatment (removal vs. control), with tide height, mean water temperature,
 mean light, and pre-removal <em>N. oregona</em> area (in
 two-dimensional basal cover as measured in the biodiversity surveys)
 included as covariates, as well as an interaction between treatment and
 pre-removal <em>N. oregona</em> area. The interaction effect
 was included to assess whether the amount of <em>N.
 oregona</em> removed influenced the results, and we separately
 tested the effect of pre-removal <em>N. oregona</em> area in
 the removal and the control groups in the absence of other covariates to
 further investigate the role of initial <em>N. oregona</em>
 area as a potential driver of pH change. Finally, we conducted a combined
 analysis of the rates of pH change during day and night based on
 treatment, with pre-removal <em>N. oregona</em> area included
 as a covariate, as well as post-hoc tests comparing the treatment groups
 (‘emmeans’ function; Lenth, 2018). Assumptions of normality and
 homogeneity of variances were checked using the Shapiro-Wilk test and
 Levene’s tests, respectively. We evaluated the role of
 the total producer and consumer assemblage in driving pH by comparing the
 pH change in each pool to total consumer abundance and producer dominance.
 Total consumer abundance was calculated using the surface area of all
 basal invertebrate species and converting counts of mobile invertebrates
 to surface area (Table A1). We did this conversion using photographic
 image analysis (with ImageJ; Abràmoff <em>et al</em>., 2004)
 of ~10 individuals per species of mobile invertebrate to find a mean
 surface area for an individual of each species and then multiplying that
 value by the number of individuals in each pool. For the few species we
 could not collect in the field, we substituted the measurements of species
 known to be of similar size (Table A1). We used 10
 cm<sup>2</sup> as a minimum surface area for any mobile
 invertebrate species present, consistent with our methods used for the
 basal species in our community surveys. We then calculated consumer
 abundance as the total area per tide pool volume of non-photosynthetic
 species. Producer dominance, a metric used to represent the relative
 abundance of producers and consumers in an ecosystem, was calculated as
 the total abundance of all producer species (in two-dimensional basal
 cover from the biodiversity surveys) minus the total abundance of all
 consumers present. We modeled the rate of pH change as a function of total
 consumer abundance (cm<sup>2</sup> L<sup>-1</sup>;
 ‘glm’ function) with tide height, mean water temperature, and mean light
 included as covariates, and ran similar analyses (with the same covariates
 included) on pH and producer dominance. Additionally, to account for the
 potential effects of the highly productive producer
 <em>Ulva</em> spp. (Sand-Jensen, 1988; Israel <em>et
 al</em>., 1995), we also ran the pre-removal and post-removal
 analyses of <em>N. oregona</em> abundance and pH with
 <em>Ulva</em> spp. abundance included as an additional
 covariate. The GLMs used in the removal experiment used a Gaussian
 distribution (identity link) except for the models of total consumer
 abundance and nighttime pH, which used a Gaussian distribution with an
 inverse link after the model failed to pass the Shapiro-Wilk test using an
 identity link. To evaluate the effect of
 <em>N.</em> <em>oregona</em> removal on tide pool
 water temperature, we conducted a repeated measures analysis using a GLMM
 (‘lmer’ function, with Kenward-Roger corrections applied; Kenward and
 Roger, 1997; Bates <em>et al</em>., 2015; Kuznetsova
 <em>et al</em>., 2017) with data from the first month (prior
 to significant <em>N. oregona</em> recovery following the
 removal treatment; Figure 1) and, in a separate analysis, for the full
 11-week duration of the study. The temperature was modeled as a function
 of the fixed factors of treatment, time (days), ambient air temperature,
 and an interaction between treatment and time, with the tide pool included
 as a random effect. <strong>Mesocosm
 Experiment</strong>: We set up mesocosms on the beach adjacent to
 the experimental pools at our John Brown’s Beach study site on 13 Jul
 2019. Mesocosms (12-L plastic tubs, <em>n</em> = 5
 <em>N. oregona</em> addition, and <em>n</em> = 3
 control) were arrayed in two parallel lines of four, randomly arranged
 with regards to treatment. We added <em>N. oregona</em>
 biomass from one of the n = 5 removal tide pools to each of the
 <em>n</em> = 5 addition treatment mesocosms. Each mesocosm
 also contained the quantity of seawater equal to the volume of the pool
 from which the <em>N. oregona</em> was removed (except that 10
 L of seawater was added to the two mesocosms corresponding to the removal
 pools with &gt;10 L volume). We added 10 L of seawater but no
 <em>N. oregona</em> biomass to the control
 mesocosms. We conducted water sampling using a time
 series similar to the removal experiment (as described above), except that
 there was no “before” sample collection. We sampled the mesocosms after
 <em>N. oregona</em> addition during the daytime (4 h after
 algae were added to the mesocosms) and nighttime (10 h after addition;
 Figure A1). Prior to each time-series sampling, we simulated tidal
 inundation by flushing the mesocosms with seawater. We secured the algae
 in the mesocosms with wire mesh, poured the water out of the mesocosms,
 and used a graduated bucket to refill the mesocosms with the assigned
 volume of seawater. We took physical measurements at five time points over
 a ~2.5 h time series, sampling once every 30 min, and collected water
 samples on the first, third and fifth time points for later pH and TA
 analyses. To test the effect of <em>N.
 oregona</em> on the rate of pH change in isolation, we applied GLMs
 (‘glm’ function) to the data from the mesocosms, for which we used two
 metrics of <em>N. oregona</em> abundance: source pool
 <em>N. oregona</em> surface area per mesocosm volume
 (cm<sup>2</sup> L<sup>-1</sup>), which was the
 same metric we used for the algae in the field tide pools, and
 <em>N. oregona</em> biomass per mesocosm water volume (g
 L<sup>-1</sup>), values that were only available for the
 mesocosms populated with the detached algae. We included mean water
 temperature as a covariate. We also used two GLMs (‘glm’ function) to
 analyze the combined day and night rates of pH change by treatment, with
 <em>N. oregona</em> biomass or source pool surface area of
 <em>N. oregona</em> included as a covariate, as well as
 post-hoc tests comparing the treatments in each model. Light measurements
 were not available for these analyses; however, the mesocosms were
 situated in an area of the beach with relatively homogenous light
 conditions (S. Mahanes, pers. obs.). Assumptions of normality and
 homogeneity of variances were checked using the Shapiro-Wilk test and
 Levene’s tests, respectively. All GLMs for the mesocosm pH analyses used a
 Gaussian distribution (identity link) except the analyses on the daytime
 sampling using biomass, which used a gamma distribution (inverse link)
 after the model failed to pass the Shapiro-Wilk test using a Gaussian
 distribution.