Exploring intraspecific and interspecific variation of coral reef algae using a novel traitbased framework
Data files
Sep 05, 2024 version files 303.91 KB

Data_JEcol20240504.xlsx

README.md
Abstract
The development of traitbased 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 traitbased 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 traitbased framework in a taxon that is increasingly dominant on tropical reefs.
README: Exploring intraspecific and interspecific variation of coral reef algae using a novel traitbased framework
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 120) 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 subsample 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 subsamples 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_JEcol20240504.xlsx
Variables
Variables  Description  units/format 

species  20 samples of each species were collected at each of our sites  genus_species 
site  numbers 15 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  DDMonth 
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 (ShapiroWilks 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.
Methods
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 1015m between collected individuals. We only collected individuals that appeared healthy and intact. Collections were stored in flowthrough 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 < 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.
Statistical Analysis
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.