DOI: 10.5061/dryad.h70rxwdtc
title: Exploring intraspecific and interspecific variation of coral reef algae using a novel trait-based framework

description: The development of trait-based approaches has accelerated our
 understanding of how communities assemble, respond to environmental
 change, and may best be managed in the Anthropocene. Understanding the
 magnitude and pattern of interspecific variability forms a critical
 underpinning of trait-based approaches while exploring intraspecific
 variability can identify the potential of species to adapt to changing
 environmental drivers. Our work is motivated by the critical need for a
 novel conceptual framework for understanding the functional ecology of
 macroalgae, as the current paradigm is still mired in functional group
 models developed in the 1980s. Our objective was to quantify interspecific
 and intraspecific functional trait variability in three common and
 morphologically diverse species of tropical marine macroalgae by exploring
 traits relating to the ecological functions of resource acquisition,
 resistance to herbivory, and resistance to physical disturbance and the
 tradeoffs between them. We quantified intraspecific and interspecific
 variability of 11 functional traits for three common and morphologically
 diverse species of tropical macroalgae from five fringing reefs of
 Mo’orea, French Polynesia that were likely to capture a wide range of
 environmental variability. Differences in traits among species and sites
 were determined with PERMANOVA, visualized with NMDS, and tradeoffs
 between pairs of traits explored with correlation. Finally, spatial
 patterns among select traits across all species were quantified. Species
 clustered together in distinctly different trait spaces driven by
 tradeoffs among suites of functional traits. Two of three species had
 considerable intraspecific variability, though this variability occurred
 at different scales, while one clustered tightly. Exploration of
 individual traits across species and sites revealed tradeoffs between two
 strategies for resource acquisition, growing tall and strong vs investing
 in large surface area. Synthesis: We captured novel patterns of
 interspecific and intraspecific variability for tropical marine
 macroalgae. We found fundamental differences in traits between species
 that may represent ecological strategies while considerable intraspecific
 variability demonstrates a wide range in abilities to respond to
 environmental drivers. Overall, our work provides novel insights into
 intra and interspecific trait variation that form an essential
 underpinning for using a trait-based framework in a taxon that is
 increasingly dominant on tropical reefs.
description: We chose three macroalgal species from Class Phaeophyceae, Padina
 boryana, Sargassum pacificum, and Turbinaria ornata (hereafter referred to
 by genus name) due to their abundance on fringing reefs of Mo’orea. We
 chose five fringing reefs along the north shore of Mo’orea, French
 Polynesia that differ in their structure and orientation to wind and
 current. Three reefs (Public Beach Ta’ahiamanu East and West and the Gump
 Station) are patch reefs within bays that are more sheltered from wave
 action. Two other reefs (Hilton Resort (patch reef) and Maharepa
 (continuous reef)) are along the more exposed north shore. Surface flows
 in the lagoon are driven by waves generated by the prevailing
 northeasterly trade winds and the diurnal sea breeze.  
 All macroalgal individuals were collected between 20 January and
 5 February at each of the five fringing reef sites at depths ranging from
 approximately 0.25 to 3m. Twenty samples of each species were collected
 from each site (N=300). To capture the full range of inter and
 intraspecific variability in traits, individual macroalgal thalli were
 randomly collected across all habitats in this depth range, including hard
 bottom, coral rubble, and the shallow tops of dead coral heads on the reef
 slope, crest, flat, and backreef. When possible, we ensured a minimum
 distance of 10-15m between collected individuals. We only collected
 individuals that appeared healthy and intact. Collections were stored in
 flow-through water tables and traits were measured within 12
 hours.  TH was measured with a ruler from the bottom of
 the holdfast to the top of the longest branch. WW was measured on a scale
 after spinning thalli for 60 seconds in a salad spinner to remove a
 consistent amount of surface water. V was measured as the displacement of
 water in a graduated cylinder. SA of Padina and Sargassum was calculated
 on Adobe Photoshop from photographs of individuals or subsamples laid flat
 on a white background with a ruler; subsamples were scaled to whole
 individuals. Turbinaria SA was approximated by splitting the thallus into
 simple geometric shapes, taking measurements with a ruler, calculating
 appropriate surface areas of each shape, and adding them together. For DW,
 thalli were rinsed of salts and dried in a drying oven at &lt; 60oC
 until constant weight. BN was counted on whole or subsamples of
 individuals, subsamples were scaled to whole individuals. For Sargassum
 and Turbinaria BWW, five randomly selected blades were weighted, averaged,
 and multiplied by blade number for the final value. For Padina, BWW was
 the W after the holdfast was removed. BT was measured as resistance to
 piercing by a penetrometer. For Padina, measurements were taken 1cm from
 the apical ridge. For Turbinaria and Sargassum, five blades were randomly
 chosen and pierced and these values were averaged. TS was measured by
 attaching the thallus to a spring scale and pulling on the thallus until
 it broke. <em>Statistical
 Analysis </em> We conducted a PERMANOVA
 (dissimilarity method: Bray, 999 permutations) to detect differences in
 trait distributions due to species, location, and interaction. NMDS
 visualised the distribution of individuals in trait space and a loading
 plot illustrated how the functional traits drove this distribution.
 Ellipses enclose groups of individuals separated by site and species using
 the 50% confidence interval of the SD. We used Pearson’s correlation to
 explore bivariate tradeoffs.   We conducted post hoc
 univariate analysis on five selected traits: TH, SA, SA:DW, BT and TS. We
 were unable to normalise our data through transformation. Thus, we used
 GLMs with a gamma distribution to test for differences among traits with
 species and site as fixed effects.
description: # Exploring intraspecific and interspecific variation of coral reef algae
 using a novel trait-based framework
 [https://doi.org/10.5061/dryad.h70rxwdtc](https://doi.org/10.5061/dryad.h70rxwdtc) ## Description of the data and file structure Data contains trait values of three common tropical macroalgae from five sampling sites in Mo'orea, French Polynesia. These data were used to approximate the variation in trait values between different species and the variation in trait values between different sites. Data is structured in a spreadsheet with six tabs. * **Metadata** lists the variable names used throughout the spreadsheet, provides a brief description of each, and indicates the units of measurement. * **OriginalData** contains the raw data which was collected and recorded. * It is organised into three sections, one for each of the species sampled (sargassum_mangarevense, padina_boryana, and turbinaria_ornata). * Each species is further split into the five sites (1 - Sailing School 1; 2 - Sailing School 2; 3 - Hilton; 4 - Gump; 5 - Maharepa) from which samples were collected. * From there it is split into the 20 individual samples (numbered 1-20) collected from each site. * Tests were conducted on each sample to determine or calculate values for 11 different traits (Thallus Height (TH), Tensile Strength (TS), Blade Toughness (tot_avg_P), Surface Area (SA), Blade Number (num_B), TH:WW, TH:V, SA:DW, SA:V, DW:WW, BWW:WW). * Some traits required multiple measurements to determine. For example, the average of a set of measurements may have been required to accurately estimate certain traits, while other traits were ratios of two measurements. Additionally, certain samples were too large for some of their traits to be measured directly, so sub samples were measured and scaled up. Examples of each case are listed below: * Blade Wet Weight and Penetration Weight were calculated as the average of measurements taken from 5 random blades of a sample * TH:WW was calculated as the ratio between Thallus Height and Wet Weight * Total Surface Area and Blade Number were calculated using Wet Weight, a sub sample Wet Weight, and that sub sample's Surface Area and Blade Number * **DataFlippedRatios** contains the same information as OriginalData, only with some of the ratios (TH:WW, TH:V) calculated as their reverse (WW:TH, V:TH) in order to draw more meaningful conclusions from the data. * **CleanDataFlippedRatios** contains the key measurements from DataFlippedRatios which are used in analysis and removes the sub-sample measurements and individual measurements used to calculate averages. This tab also has outliers and measurement errors removed. * **Means** contains the calculated averages of each measurement for each of the three species at each of the five sites. * **TurbSA** contains data collected to calculate the surface area of *Turbinaria ornata* which could not be calculated by the same method as the other species due to its unique shape. The various length measurements included in this tab are used to geometrically estimate the surface area of samples. A detailed diagram is provided in the supplement of the associated paper. **Note on empty cells:** Cells containing "n/a" are areas where the measurement is not applicable to the sample. This is primarily due to differences between species of algae or differences in the size of samples. For example, while *Padina* and *Turbinaria* generally had a manageable number of blades which could be counted by hand, *Sargassum* often had hundreds of blades. As a result, blade number was calculated using sub-samples which were scaled to the whole sample for *sargassum*, but these measurements were not necessary for *Padina* or *Turbinaria*.  Another case concerns the difference in size between samples from different sites. Certain samples of *Padina* had fewer than 5 blades. Thus, measurements which were taken as the average of 5 blades, like average blade wet weight and average weight to penetrate, had some empty cells.  ### Files and variables #### File: Data\_JEcol-2024-0504.xlsx ##### Variables | Variables | Description | units/format | | :--------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------- | | species | 20 samples of each species were collected at each of our sites | genus\_species | | site | numbers 1-5 represent 1 of our 5 sites | 1 - Sailing School 1; 2 - Sailing School 2; 3 - Hilton; 4 - Gump; 5 - Maharepa | | Date | The date of collection and processing - it is the same day | DD-Month | | individual | species sample number from a specific site | 1 through 20 | | V | volume - measured by submerging algae specimen into a graduated cylinder and measuring the volume displaced from the initial amount | milliliter | | WW | wet weight - measured by putting into a salad spinner until the weight stops changing (\~60s per spin cycle) to remove excess water then measured on a digital scale | grams | | TH | thallus height - from the beginning of the branch to the end of the growing thallus | centimeters | | TS | Tensile strength | kilograms | | SUB\_W | Wet weight of subsection - portion of sample chosen to be representative of the full sample, measured on a digital scale | grams | | SUB\_SA | Surface area of subsection - portion of sample chosen to be representative of the full sample, spread flat on a white background and photographed, analyzed in photoshop to determine surface area | centimeters^2 | | SA | surface area - a subsection is taken from the specimen and wet weight is measured to indicate a proportion the subsection represents of the entire specimen; surface area of the subsection is then measured on Photoshop and multiplied by that proportion factor to find the total surface area | centimeters^2 | | BN | number of blades - a subsection is taken from the specimen and wet weight is measured to indicate a proportion the subsection represents of the entire specimen; the number of blades of the subsection is then counted and multiplied by that proportion factor to find the total number of blades | number of blades | | BWW1 | blade wet weight for first randomly chosen- wet weight was found by spinning in a salad spinner (\~60 s per spin cycle) until the found weight measured in one cycle equaled the weight found in the next cycle | grams | | BWW2 | blade wet weight for second randomly chosen- wet weight was found by spinning in a salad spinner until the found weight measured in one cycle equaled the weight found in the next cycle | grams | | BWW3 | blade wet weight for third randomly chosen- wet weight was found by spinning in a salad spinner until the found weight measured in one cycle equaled the weight found in the next cycle | grams | | BWW4 | blade wet weight for fourth randomly chosen- wet weight was found by spinning in a salad spinner until the found weight measured in one cycle equaled the weight found in the next cycle | grams | | BWW5 | blade wet weight for fifth randomly chosen | grams | | avg\_BWW | the average blade wet weight from the 5 samples | grams | | PW | The weight of the penentrometer | grams | | P1 | the weight required to penetrate the first randomly chosen blade | grams | | P2 | the weight required to penetrate the second randomly chosen blade | grams | | P3 | the weight required to penetrate the third randomly chosen blade | grams | | P4 | the weight required to penetrate the fourth randomly chosen blade | grams | | P5 | the weight required to penetrate the fifth randomly chosen blade | grams | | BT | the average weight required to penetrate the 5 samples | grams | | AW | the aluminum weight - the weight of the aluminum before holding the algae | grams | | sum\_AWDW | the sum of aluminum wet weight and the dry weight - the weight after being in the oven | grams | | DW | the dry weight - the difference between sum and AW | grams | | SA:DW | the ratio between surface area and dry weight | centimeter^2/grams | | TH:WW | the ratio between thallus height and wet weight | centimeter/grams | | SA:V | the ratio between surface area and volume | centimeter^2/milliliters | | WW:DW | the ratio between wet weight and dry weight | | | TH:V | the ratio between height and volume | cm/mL | | BWW:WW | the ratio between blade wet weight and wet weight | | | **Thallus\_H** | the height of the sample thallus | centimeters | | :-------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------- | :------------ | | Thallus\_D | the diameter of the sample thallus | centimeters | | Thallus\_r | the radius of the sample thallus, calculated as half the diameter | centimeters | | Blade\_cyl\_H | the height of the sample blade cylinder | centimeters | | Blade\_cyl\_D | the diameter of the sample blade cylinder | centimeters | | Blade\_cyl\_r | the radius of the sample blade cylinder, calculated as half the diameter | centimeters | | Blade\_cone\_s\_length | the side length of the sample blade cone | centimeters | | Blade\_cone\_D | the diameter of the sample blade cone | centimeters | | Blade\_cone\_r | the radius of the sample blade cone, calculated as half the diameter | centimeters | | Blade\_disk\_D | the diameter of the sample blade disk | centimeters | | Blade\_disk\_r | the radius of the sample blade disk, calculated as half the diameter | centimeters | | Blade\_lit\_cone\_s\_length | the side length of the sample little blade cone | centimeters | | Blade\_lit\_cone\_D | the diameter of the sample little blade cone | centimeters | | Blade\_lit\_cone\_r | the radius of the sample little blade cone, calculated as half the diameter | centimeters | | Area\_of\_stalk | the area of the sample stalk, calculated using thallus height and radius | centimeters^2 | | Area\_of\_big\_blade | the area of the sample big blade, calculated using blade cylinder height and radius, blade cone side length and radius | centimeters^2 | | Area\_of\_lit\_blade | the area of the sample little blade, calculated using little blade cylinder height and radius, little blade cone side length and radius | centimeters^2 | ## Code/software All analyses were conducted in R (version 4.3.1; R Core Team 2023) with Rstudio (2023.06.2). The vegan package (Oksanen et al., 2022) was used for NMDS and PERMANOVA. The car package (Fox et al., 2023) tested the assumptions of parametric statistics (Shapiro-Wilks and Levene’s tests). The package reshape2 (Wickham, 2020) was used to transform data. RColorBrewer (Neuwirth, 2022) was used to create the correlation matrix, and all plots were created in ggplot2 (Wickham et al., 2024). Svglite (Wickham et al., 2023) and insight (Lüdecke et al., 2019) exported our work in proper formats.