DOI: 10.5061/dryad.sxksn036w
title: Marine primary producers in a darker future – a meta-analysis of light effects on pelagic and benthic autotrophs

description: The availability of underwater light, as the primary energy source for all
 aquatic photoautotrophs, is (and will further be) altered by changing
 precipitation, water turbidity, mixing depth, and terrestrial input of
 chromophoric dissolved organic matter (CDOM). While experimental
 manipulations of CDOM input and turbidity are frequent, they often involve
 multiple interdependent changes (light, nutrients, C-supply). To create a
 baseline for the expected effects of light reduction alone, we performed a
 weighted meta-analysis on 240 published experiments (from 108 studies
 yielding 2,500 effect sizes) that directly reduced light availability and
 measured marine autotroph responses. Across all organisms, habitats, and
 response variables, reduced light led to an average 23% reduction in
 biomass-related performance, whereas the effect sizes on physiological
 performance did not significantly differ from zero. Especially pigment
 content increased with reduced light, which indicated strong physiological
 plasticity in response to diminished light. This acclimation potential was
 also indicated by light reduction effects minimized if experiments lasted
 longer. Nevertheless, performance (especially biomass accrual) was reduced
 the more the less light intensity remained available. Light reduction
 effects were also more negative at higher temperatures if ambient light
 conditions were poor. Macrophytes or benthic systems were more negatively
 affected by light reduction than microalgae or plankton systems,
 especially in physiological responses where microalgae and plankton showed
 slightly positive responses. Otherwise, effect magnitudes remained
 surprisingly consistent across habitats and aspects of experimental
 design. Therefore, the strong observed log-linear relationship between
 remaining light and autotrophic performance can be used as a baseline to
 predict marine primary production in future light climate.
description: We followed most recent
 recommendations for performing research synthesis and reporting
 meta-analysis in the setup, conduction, and documentation of this
 meta-analysis (Koricheva
 and Gurevitch 2014, Nakagawa et al. 2017). We amend the description of the method below
 with a table of compliance to recommendations (Appendix A) and a detailed
 assessment of study selection (Appendix B) and publication bias (Appendix
 C). <em>Data
 extraction</em> Relevant studies for the search were identified
 on ISI Web of Science (WOS) using the search term: “(light* OR irradianc*
 OR shadin* OR light treatmen*) AND (experimen* OR manipul* OR field
 experimen* OR enclosure* mesocosm* OR treatment*) AND (phytoplankt* OR
 macroalga* OR microphytobenth*”) AND (marin* NOT freshwater)”. The search
 performed in December 2020 resulted in 1599 published studies. From these,
 we selected studies manipulating light availability with a clear
 definition of treatment and control (Appendix B). Consequently, 252
 studies were selected for in-depth screening based on the abstracts. From
 this pre-selection, studies were excluded when i) freshwater systems were
 addressed, or ii) they did not report mean and a measure of variance
 (standard deviation, standard error of mean) for an autotroph biomass or a
 physiological response to light availability. 108 studies remained
 reporting results of 240 experiments, for which data extraction was done
 directly from the manuscript or from the incorporated figures using
 WebPlot Digitizer (Rohatgi
 2019). Because single
 studies reported multiple experiments and single experiments were sampled
 more than once, data extraction yielded 2500 effect sizes (k). Multiple
 effect sizes from a single experiment create non-independence in the data,
 but as detailed below, we accounted for this by using multilevel
 statistical analyses. In the context of our hypotheses, this approach was
 needed as we explicitly test for difference in different response
 variables (hypotheses H1b), at different levels of light reduction (H2a)
 and with different timing and thus chance for acclimation (H3b).
 Each
 experiment resulted in one or several estimates of the mean and standard
 deviation of the response variable for both treatment (reduced light) and
 control (ambient or higher light levels) as well as the respective number
 of replicates. Additionally, we extracted the following set of categorical
 and continuous variables (highlighted in bold) that detail the experiment,
 the habitat and organism group, potential covariates, and the response
 variables: <em>Experiment: </em>Experiments
 were either done in the lab (k = 1824) or in the field (k = 676)
 (<strong>lab</strong>.<strong>field</strong>), the
 latter including all outdoor manipulations. More precisely,
 <strong>experimental</strong>
 <strong>types</strong> distinguished field measurements (k =
 228), incubation experiments (k = 177, mostly <em>in</em>
 <em>situ</em>), mesocosms (k = 363), and microcosms
 (predominantly in the lab, often with cultures, k = 1,832). The
 <strong>type of light treatment </strong>by three categories
 depended on the used methodology: (i) shading screens (k = 753), (ii)
 irradiance reduction by e.g. dimming light (k = 1,152), and (iii)
 gradients (k = 568). A fourth category “other” (k = 27) covered a variety
 of rarely used approaches for light change. We quantified the strength of
 the light treatment by the <strong>remaining % light
 </strong>as continuous variable, ranging from 91% to almost complete
 darkness. We also recorded the <strong>duration</strong> of
 the experiment in days as continuous, ln-transformed moderator. The
 <strong>size</strong> of the experiment and the ambient
 <strong>irradiance</strong> were reported using different
 measures in the studies (area and volume in the former, instantaneous
 photon flux and daily light doses in the latter). To be able to use both
 aspects, we calculated instantaneous photon flux from the daily light
 doses assuming 12 hr daylight and converted area-based sized to volume
 assuming a third dimension of 1m, thus an area of 1 m² corresponds to 1000
 L. The spatial distribution of the studies was surprisingly broad
 (Appendix D, Figure D1), covering a range from 73.21° N to 77.52° S. We
 used absolute <strong>latitude</strong> as a predictor in our
 analyses. We followed recommendations (Appendix A) in testing whether
 effect sizes changed with <strong>year</strong> of publication
 (publication year) to see whether there were systematic biases in the
 evolution of this research field. 
 <em>Organisms and
 habit</em><em>ats:</em> We differentiated
 studies into pelagic (k = 1,844) or benthic (k = 656)
 <strong>systems</strong>. Most studies were from coastal
 <strong>habitats</strong> (k = 1,467, both pelagic and
 benthic), which was contrasted to offshore (k = 278) and “culture” for
 experiments dealing with laboratory cultures (k = 755) categories. With
 respect to <strong>organism</strong>
 <strong>groups</strong>, we reduced the comparison to
 macrophytes (seagrass, benthic macroalgae, k = 550) and microalgae (k =
 1,950, microphytobenthos and phytoplankton).  
 <em>Covariates</em>: The
 <strong>temperature</strong> (°C) measured during the
 experiment was used as a continuous moderator. Only a subset of studies
 reported nutrient availability in the form of total nitrogen and/or total
 phosphorus, which were highly correlated (r = 0.90, p &lt; 0.001). We
 therefore only used ln-transformed concentration of
 <strong>TN</strong> (µmol
 L<sup>-1</sup>). <em>Responses</em>: The variables
 used for quantifying the treatment effects were divided into two
 <strong>response</strong> <strong>types</strong>,
 biomass (k = 1,655; standing stock in the form of abundance, biomass,
 biovolume, or dry mass, primary production per area or volume, growth
 rates) and physiological responses (k = 845; cellular content of pigments,
 or primary productivity per unit biomass, or maximum electron transport
 rate). We used a second variable <strong>response</strong>
 <strong>category</strong> that detailed these types into four
 categories each: the response type biomass included i) abundance (k =
 421), ii) biomass (including biovolume, mass, chlorophyll per volume or
 area) (k = 920), iii) growth rate (k = 155), and iv) absolute productivity
 (per unit area or volume) (k = 159). The response type physiology
 comprised i) specific productivity (per unit biomass) (k = 219), ii)
 cellular content of pigments (k = 145), iii) cellular content of other
 molecules (storage molecules, nutrients) (k = 49), and iv) the maximum
 quantum yield or electron transport (k = 432). 
 <em>Effect
 Sizes</em> We calculated the log response ratio (LRR),
 which is among the most widely used effect size metrics used to quantify
 differences between treatment responses (LaJeunesse and Forbes 2003, Koricheva et al.
 2013). Specifically, LRR
 represents relative changes in the response variable, as the treatment
 mean T is expressed as ln-transformed ratio to the control mean C.
 For each effect size, we used the inverse of the
 sampling variance var.LRR for weighting, which is based on the standard
 deviation (SD), number of replicates (N) and means of treatment and
 control, respectively. <em>Statistical
 methods</em> All analyses were performed in R
 (R Development Core Team
 2018) using the package
 metafor (Viechtbauer
 2010). Following
 recommendations on how to handle non-independent effect sizes
 (Konstantopoulos 2011,
 Cheung 2019), we performed
 a multi-level meta-analysis, with a nested random effect structure with
 unique experiments nested in unique studies. We chose this approach as
 studies differed in how many experiments they reported.
 We
 performed this multi-level weighted analysis without moderators (k =
 2,500) to test H1a (evaluating general light reduction effects on
 autotrophic performance) and as a multivariate weighted metaregression
 using moderators to test all other hypotheses. For the latter, we chose an
 additive model without interactions, equivalent to a main effect analysis.
 A fully interactive model was not possible as in our dataset, like in most
 meta-analyses, the distribution of studies across categories was highly
 unequal and non-orthogonal. This reflects that certain level combinations
 are either not possible or not used in experimental designs.
 To
 produce unbiased average effect sizes and their confidence intervals, we
 use the multi-level weighted analysis without moderators for a) all data,
 b) the biomass and physiology responses separately, and c) for all
 categorical groups in each predictor variable separately for physiology
 and biomass. Here, confidence intervals not including zero indicated
 significant positive or negative effects overall. For the complete model,
 the same random effect structure (experiments within studies) was used and
 amended by fixed effect moderators, for which we included
 <strong>response type, response category, lab.field, experiment unit
 type, habitat, system, organism group</strong>, and<strong>
 type of light treatment</strong> as categorical variables and
 absolute <strong>temperature, duration, publication
 year</strong> and <strong>remaining % light </strong>as
 continuous variables. Significance of predictors rejected the null
 hypotheses corresponding to H2 and H3). 
 As not all studies reported
 all moderator values, the additive model comprised 2078 effect sizes. In
 order to evaluate whether this change in the data set and the presence of
 other moderators mattered, we also performed univariate multi-level
 meta-analyses and compared the outcome to the effect of the same moderator
 in the additive model (Appendix E). This comparison served three
 additional purposes. First, it allowed investigating whether collinearity
 between predictors changed the sign and significance of single moderators.
 As detailed there, the effects were highly congruent between univariate
 and multivariate explanatory models. From 22 estimates in the complete
 model, only 2 estimates were significant in one analysis and changed sign
 in the other coinciding with becoming non-significant. Both these cases
 are detailed in the Results but overall the complete model was not
 strongly affected by collinearity or the reduction in k.  Additionally,
 the univariate models provided predictor-specific intercepts in contrast
 to the complete model, which gives a single intercept for a certain
 combination of predictor groups at 0 (or 1 if log-transformed) values for
 continuous predictors. For four variables, the amount of missing
 information was so high that their inclusion in the additive model would
 have caused massive reduction in the database by &gt;500 effect sizes.
 Therefore, we tested the moderating effects of incoming irradiance,
 experiment size, latitude and TN concentrations only in the univariate
 analyses (Appendix E).  
 <strong>Appendix B:
 Systematic Literature Review</strong>
 We used the following keywords
 for literature search for ISI Web of Science (WOS), with access in
 December 2020: “(light* OR irradianc* OR shadin* OR light treatmen*) AND
 (experimen* OR manipul* OR field experimen* OR enclosure* mesocosm* OR
 treatment*) AND (phytoplankt* OR macroalga* OR microphytobenth*”) AND
 (marin* NOT freshwater)”. The search resulted in 1,599 studies. 25
 (default, from newest to oldest, not sorted by relevance) of the first
 abstracts of these studies were looked at by 3 researchers (LK, JW, MS)
 independently, to see if they would pick the same articles to be included
 in the meta-analysis. As the results were consistent, the remaining
 studies were divided between three of the authors. The initial exclusion
 criteria were  1) freshwater studies 
 2) no presence of light
 manipulation as a treatment 3) no reporting of primary production / biomass
 of marine primary producers, including phytoplankton, microphytobenthos,
 macroalgae and seagrass (but not heterotrophic
 bacteria) 4) have at least 1-day duration of the
 experiment to allow response to light treatment.
 Based on the abstracts, 252
 studies passed these criteria and were treated as the primary database for
 in-depth inspection. From these, further exclusion had to be performed as
 studies lacked control data for irradiance manipulation (i.e. a light
 reduction treatment had been performed but no control was conducted), data
 were not extractable (neither present in text, figures or appendix), or
 replication was missing. For this secondary database, data were extracted
 using the free online software Webplot Digitizer. However, some studies
 were flagged if data were presented in a way not allowing extraction
 (e.g., cluttered scatterplots) or standard deviations were missing. These
 reasons are stated in the database and the R-script provides all steps
 taken. These steps reduced the database to 108 studies, which contained
 240 unique experiments.