DOI: 10.5061/dryad.4j0zpc8m7
title: Data from: In situ decrease in rhodolith growth associated with Arctic climate change

description: Rhodoliths built by crustose coralline algae (CCA) are ecosystem engineers
 of global importance. In the Arctic photic zone, their three-dimensional
 growth emulates the habitat complexity of coral reefs but with a far
 slower growth rate, growing at micrometres per year rather than
 millimetres. While climate change is known to exert various impacts on the
 CCA’s calcite skeleton, including geochemical and structural alterations,
 field observations of net growth over decade-long timescales are lacking.
 Here, we use a temporally-explicit model to show that rising ocean
 temperatures over nearly 100 years were associated with reduced rhodolith
 growth at different depths in the Arctic. Over the past 90 years, the
 median growth rate was 85 µm yr-1 but each °C increase in summer seawater
 temperature decreased growth by a mean of 8.9 µm (95% CIs = 1.32 - 16.60
 µm °C-1, p &lt; 0.05). The decrease was expressed for rhodolith
 occurrences in 11 and 27 m water depth but not at 46 m, also having the
 shortest time series (1991 – 2015). Although increasing temperatures can
 spur plant growth, we suggest anthropogenic climate change has either
 exceeded the population thermal optimum for these CCA, or synergistic
 effects of warming, ocean acidification, and/or increasing turbidity
 impair rhodolith growth. Rhodoliths built by calcitic CCA are important
 habitat providers worldwide, so decreased growth would lead to yet another
 facet of anthropogenic habitat loss.
description: # Data from: In situ decrease in rhodolith growth associated with Arctic
 climate change
 [https://doi.org/10.5061/dryad.4j0zpc8m7](https://doi.org/10.5061/dryad.4j0zpc8m7) This dataset contains all data necessary to support the findings of the study **Methods** **Study area** The rhodolith samples were obtained during the MSM 55 expedition (ARCA) of *RV Maria S. Merian* from 11th to 29th June 2016 in Mosselbukta (‘shellfish bay’). The bay is 5 km long and up to 8 km wide, located at the mouth of the Wijdefjorden along the west shore of Mosselhalvøya peninsula at the northern end of Ny-Friesland, Spitsbergen Island, Svalbard archipelago (79°53’N, 15°55’E). Mosselbukta is a classical site for geo-biological research on carbonate production by benthic communities in Svalbard waters and was first surveyed in 1883, with reported rhodolith beds several square kilometres in extent. The carbonate production rates of the beds appear most pronounced at around 45 m water depth with 119.8 g (CaCO3) m-2 yr-1, where rhodoliths of up to 25 cm in diameter cover 60–80% of the seafloor. Glaciogenic material from pebble to boulder size dominates the seafloor in Mosselbukta, providing suitable hardground for CCA and subsequent rhodolith formation. *Boreolithothamnion glaciale* is the prevailing CCA species in this area and can be encountered down to a dysphotic 70 m water depth. Being of polar latitude, Mosselbukta is subject to extreme seasonality. Although there are no permanently moored data logging stations in the area, environmental data from repeated CTD (Conductivity, Temperature, Depth) profiling, autonomous temperature/salinity logging (over 15 months), and short-term deployments of a lander identified several distinct water masses affecting the area over the course of the year. Sea ice cover varies in Mosselbukta, but commonly forms in December/January and starts to break up between May and July, minimizing the photosynthetically active radiation significantly during that time. Daylight is absent during the polar night, which lasts 122 days at Mosselbukta. Generally, the levels of photosynthetically active radiation at 46 m water depth are low, with only few excursions above the detection limit in Mosselbukta, reaching a maximum of only 4.5 µmol m-2 s-1. Moreover, the observed turbidity at all water depths is considerable. **Sample collection and growth increment analysis** Rhodoliths were collected from three different water depths in Mosselbukta, using the manned submersible *JAGO* or a beam trawl. All rhodoliths were dried in cabinet desiccators at 30°C for 48 h and stored in sealed plastic bags containing silica gel as a drying agent. Three rhodoliths from each water depth (11, 27, 46 m) were selected for further analysis, thereby choosing specimens with differing weights and morphologies to capture natural variability within the rhodolith occurrences. All sampled rhodoliths were mainly built by the coralline algal species *B. glaciale*. In *B. glaciale*, the formation of protuberances is very common and they are the main production areas of skeletal material. Therefore, these areas of the rhodolith are most suitable for growth pattern analyses. Accordingly, three protuberances per rhodolith were haphazardly chosen from different areas of the rhodolith surface and sampled by a water-cooled, hollow diamond drill bit with an inner diameter of 4.5 mm. The drill bit was mounted to a Fein ASCM 18 QM electric drill operated in a Fein drill rig. Due to the varying thickness of the coralline algal skeleton in the respective samples, the lengths of the drill cores differ, giving time series with varying length. To visualize the growth patterns within the protuberances via X-ray micro-computed tomography (Micro-CT), the drill cores were mounted on borosilicate glass rods using hotmelt adhesive. Scans were performed on a General Electric Phoenix v|tome|x s 240 Micro-CT scanner) with a 180 kV Nanotube, using a 0.1 mm copper filter. During a full rotation of each sample, 1500 frames were taken at 200 ms exposure time at 100 kV and 100 mA of current. These settings were kept the same for all samples as these values gave the best results in terms of resolution and brightness, to identify growth rings. Depending on the size of the sample, the scanning time varied between 20 to 160 min. Raw data were reconstructed with the GE datos|x software version 2.4, using a Feldkamp algorithm based on a filtered back projection. For noise reduction of the reconstructed data set, a median filter (VolumeGraphics Studio MAX software version 3.0) within a local neighbourhood of 3 voxels (diameter of neighbourhood area) was used. All post-processing was performed with the VolumeGraphics software, resulting in longitudinal sections through the centre of each protuberance. For growth pattern analysis in ImageJ version 1.53c, each longitudinal section was exported to a TIFF-format file. In ImageJ, the annual growth increments, visible as an alternation of bright and dark bandings, were counted and their widths were measured at the longitudinal axis of each protuberance. The outermost growth increment of each protuberance was assigned to the sampling year (2016) and increments were only counted and measured back in time until the first growth interruption, or hiatus, which results from CCA dieback, including the potential erosion of skeletal material, and recolonization. Such a hiatus is evident from large and perpendicularly running primigenous cells. All growth increments below a hiatus were excluded from further analysis because the amount of time incorporated in a hiatus is unknown. Of the 27 protuberances, four (numbers 3, 5, 9, and 10) were not usable because of undeterminable growth patterns and were therefore excluded from further analysis. CCA growth data are compiled in SI 1. **Environmental data** To infer the impacts of the environmental parameters of temperature, light, and salinity on the skeletal growth of the rhodolith-forming CCA species *B. glaciale*, we retrieved timeseries data from several sources. Sea surface temperature (SST) data derive from the COBE dataset dating back to the year 1854, with a temporal resolution of one month and a spatial resolution of 1x1°, using the 80.5°N/15.5°E grid cell. Because we used SST as a proxy for the relative temperature experienced by CCAs deeper in the water column, the relationship between spatial grain and correlation strength may not be linear but unimodal. Some spatial-averaging or smoothing of local surface features of temperature may even be beneficial to observing a correlation with growth at depth. Nevertheless, we additionally analyzed growth against a dataset (ERA5) with a higher spatial resolution of 0.25x0.25°. However, according to the authors, there are significant issues for areas with sea-ice cover, leading to unrealistic temperature estimations. Therefore and because of the scaling considerations outlined above, we focused our analysis on the COBE dataset (correlation between the two summer SST timeseries, Spearman’s Rho = 0.73, *p* &lt; 0.0001). Assuming the months July and August as the main growing season for the rhodoliths and a temporal lag in water vs. air temperature, SST data were averaged for July and August. We additionally tested the effects of warming on growth by using the annual maximum SST, as well as SST averaged, over the interval from April to October (i.e., same as for sea-ice cover). Both of these variables gave higher SST values centered around the warmest month of September (see SI 2) and led to an insignificant correlation between CCA growth and SST (see SI 3 and SI 4). We suggest that temperature mostly affected growth close to the summer solstice, because daytime length and sun angle were then longest and highest, respectively. These light improvements may be most important given light attenuation at the habitat depths of the algae. Observational sea-ice concentration data derived from the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) data set, dating back to the year 1850, with a temporal resolution of one month and a spatial resolution of 1x1°, using the 79.5°N/15.5°E grid cell. Polar Night at our sampling sites begins October 22nd and ends February 19th, limiting the growth season, so ice cover was averaged between April and October, assuming a temporal lag of water behind air temperature. We also tested the number of ice-free months per year, i.e., the months where ice cover equals zero, as an alternative variable, which was highly collinear with sea-ice cover (Spearman’s Rho = 0.88, *p* &lt; 0.0001). Because it is binary whether a month is ice-free or not, we considered this integer variable to be coarser than sea-ice cover, and we decided to use sea-ice cover for our final model. Nevertheless, we provide the alternative model results using the ice-free months in SI 5, highlighting that usage of this variable does not alter the results significantly. Salinity data derive from Met Office Hadley Centre observations datasets, dating back to the year 1900, with a temporal resolution of one month and a spatial resolution of 1x1°, using the 80.0°N/15.0°E grid cell. As salinity is related to sea-ice formation, we averaged salinity for the same time period as sea-ice cover (April to October). All environmental data are compiled in SI 6. **Statistical analysis** The effect of summer seawater temperature and other environmental variables on growth was analysed using linear models with a Gaussian distribution and mixed effects, accounting for multiple measurements per sampled rhodolith, and rhodoliths nested within depth groups. The linear models included a temporal covariance structure, the optimal form of which was identified as standard by comparing Akaike information criterion (AIC) values among different modelled orders of autoregression, p, and moving average, q (settling on p = 2, q = 2). Inspection of residuals of models that included this temporal covariance structure confirmed there to be no residual temporal autocorrelation. Backwards model selection was used to assess model optimality, based on AIC. Distributions of growth rates were compared with Kolmogorov-Smirnov tests (null hypothesis is that the distributions are from the same population). Relationships between environmental were explored with Spearman’s rank correlations (Rho-values). Growth rates were compared among depths using Wilcoxon rank tests over the temporal extent shared by all depths, from 1991 to 2015. All statistical analyses were performed in R version 4.3.1, and with the packages lme4 and nlme. We provide descriptive overviews on the development of CCA growth increments via a matrix plot, a comparison plot between the mean growth rate and SST over time as well as frequency plots on the distribution of growth observations. The complete code for our analyses is provided in SI 7 with the data files in SI 8. ## Description of the data and file structure The file names of our dataset corresponds to the list of supplementary information, the application of these data is further outlined in the methods section of the paper. **SI 1** | Compilation of CCA growth data. **SI 2** | Mean SSTs per month. **SI 3** | Model results for SST maximum values. **SI 4** | Model results for SSTs in the April-October interval **SI 5** | Alternative model results with the variable “ice-free months”. **SI 6** | Compilation of environmental data. **SI 7** | R-code used for the analyses. **SI 8** | Data files for the R-code            SI_08-1_incwidth_long.csv are the thicknesses of the algal growth increments in µm with an annual resolution            SI_08-2_salinity_monthly.txt are the salinities (dimensionless) with a monthly resolution            SI_08-3_sea_ice_monthly.txt are are the sea ice concentrations with a monthly resolution            SI_08-4_sst4st.csv are the sea surface temperatures on a monthly resolution # Code/Software All statistical analyses were performed in R version 4.3.1 (R Core Team, 2023), and with the packages lme4 (Bates et al., 2015) and nlme (Pinheiro & Bates, 2000) Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. *Journal of Statistical Software*,* 67*(1), 1-48. Pinheiro, J., & Bates, D. (2000). *nlme: Linear and nonlinear mixed effects models*.* *In (Version 3.1-162) R Core Team. (2023). *R: A language and environment for statistical computing*.* *In R Foundation for Statistical Computing.