Data from: Modelling the fundamental niche to predict the effects of climate warming on interactions between native and invasive mussels in South Africa
Data files
Sep 23, 2026 version files 315.23 MB
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.Rhistory
530 B
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.Rproj.user
0 B
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CMENS_Chla_sites_extract.zip
1.51 MB
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Correct_MUR_SST_based_robomussels.R
10.36 KB
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correlation_DEB_output_Real_data_GSI_Lw.R
10.60 KB
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DEB_model_app_master_-_Mussel_Zonation_-_Climate_change_predictions.R
20.72 KB
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DEB_model_app_master_-_Mussel_Zonation.R
17.58 KB
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DEB_model_app_master_Zonation_optim_Xk.R
14.18 KB
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DEB_model_application_-_Mussel_Zonation.Rproj
218 B
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DEB_out_Perna_20231227.csv
64.74 MB
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DEB_pars_Mytilus.R
2.43 KB
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DEB_pars_Perna.R
2.56 KB
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DEB-model-application-Mussel-zonation-master
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env_mussel_biogeography.csv
25.69 MB
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env_mussel_biogeography.Rda
18.70 MB
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env_mussel_biogeography.zip
1.92 MB
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env.Rda
464.60 KB
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extracted_chla_MODISA_SA_15km_offshore.csv
12.10 MB
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gr_pars_site_species_season.csv
18.29 KB
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gr_pars_site_species.csv
2.23 KB
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LinMod_robo_MUR.csv
1.87 KB
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models_SST_Chla_vs_vBpars_GSI_CI.R
74.63 KB
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MUR_SST_df_daily_SA_15km_offshore.rds
1 MB
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Mussel_biogeography_-_South_Africa.Rproj
218 B
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Mussel_Condition_Index_SOUTH_AFRICA_final.csv
290.28 KB
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Mussel_Growth_South_Africa_FINAL.csv
862.15 KB
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offshore_coords_selct.csv
15.40 KB
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offshore_pxls_15km_SA.csv
18.69 KB
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Plot_map_sites_south_SouthAfrica.R
11.06 KB
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Prepare_Environmental_Data_CC_proj.R
50.80 KB
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README.md
24.51 KB
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scale.bar.func.R
9.86 KB
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site_list.csv
324 B
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sites_SA.csv
299 B
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south_africa_coast_hi_res.Rdata
58.01 KB
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sspALL_mean_SD_daily.Rds
103.78 MB
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SST_Chla_extract_SA_coastline.csv
22.13 MB
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SST_projection_data_DEB.zip
48.58 MB
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summ_DEB_out_GSI_L_20231227.csv
2.14 KB
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summ_GSI_data.csv
595 B
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TC_fun.R
734 B
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temp_all_SA.csv
12.70 MB
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Temp_HighTide_MUR_robo.csv
381.57 KB
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time_interval.csv
1.90 KB
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user_input.R
1.36 KB
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von_Bertalanffy_-_Notched_mussels_South_Africa.R
15.59 KB
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Xk_optim_values.csv
639 B
Abstract
Aim: Climate change is reshuffling the distribution of species globally and threatening to modify the structure and functioning of natural and managed ecosystems. Most studies predicting species distributions rely on statistical approaches that do not capture the species’ fundamental niche. In contrast, energy budget models include lethal and sublethal levels of stress to explore traits, such as growth and reproduction, that drive ecological responses. We used models parameterised for two competing intertidal mussels, the native Perna perna and the invasive Mytilus galloprovincialis, to investigate their current and projected distributions.
Location: South African rocky shores.
Taxa: Mussels, Mytilus galloprovincialis and Perna perna.
Methods: We used previously parameterised Dynamic Energy Budget (DEB) models. Model predictions were validated using a 2-year dataset describing current conditions (body temperature and chlorophyll-a concentration) and fitness-related traits (growth and reproductive effort) at nine sites: three western sites inhabited by Mytilus alone, three eastern sites occupied by Perna, and three southern sites where the species coexist. We then tested the effects of three climate change scenarios on the model predictions of maximum size, growth rate, and gonadosomatic index around South Africa.
Results: Current spatial patterns are likely to be maintained until the century’s end, and the physiological mechanisms presently excluding Mytilus from the east coast (impaired growth due to food limitation) and Perna from the west coast (impaired reproduction due to cold temperatures) will continue to operate until the end of the century. Our model also revealed a possible change in the competitive hierarchy between these species, as warming will favour the reproductive potential of Perna more than that of Mytilus.
Main conclusion: Our approach of exploring the fundamental niche rather than using a statistical approach suggests that the effects of projected temperature conditions on the distributions of these species will be less severe than expected.
Dataset DOI: 10.5061/dryad.8931zcs2q
Description of the data and file structure
Study locations
The coastline of South Africa comprises three broad zoogeographic regions: a cool temperate west coast, a warm temperate south coast and a subtropical east coast. To describe the current environmental conditions and quantify fitness-related traits of Perna and Mytilus, we sampled nine locations along this coast: three on the west (HB = Hondeklip Bay, DB = Doring Bay, PA = Paternoster), three on the south (JF = Jongensfontein, BR = Brenton-on-Sea, SF = Saint Francis Bay), and three on the east (MB = Morgans Bay, PE = Port Edward, BA = Ballito). Sampling was done approximately every three months, between April 2015 and December 2017. The chosen locations have similar topographies with moderate to intense wave activity. The regional climate and oceanographic characteristics, however, change gradually along the west-to-east axis. While the Benguela Current drives cold, eutrophic, productive conditions along the west coast, the influence of the Agulhas Current in the east generally results in warmer and nutrient-poor conditions.
In situ observations of present-day fitness-related traits
Fitness-related traits (i.e., growth rate, maximum size, condition index, and gonadosomatic index) were quantified using animals collected seasonally between April 2015 and May 2017 from the study locations where they occurred (i.e., Mytilus from the west coast sites, Perna from the east coast and both species from the south coast). The two species co-exist on the mid-shore on the south coast, and individuals were collected from this shore height at all locations.
We described the growth trajectories using the von Bertalanffy growth model. To estimate the von Bertalanffy model parameters (i.e., the growth coefficient and the asymptotic length Linf), we used the Ford-Walford plot method. The successive shell length measurements were taken seasonally at each location. Every three months, we notched the ventral shell margin of ~200 individuals, without dislodging them, using a battery-powered rotary tool (Dremel 8240) spanning the size range available at each site. Notched animals were retrieved after three months and preserved in 70% ethanol. In the laboratory, we measured the final and initial shell lengths with Vernier callipers to the nearest 0.01 mm. Growth increments were standardized per month. Because and Linf are known to be correlated in von Bertalanffy models, and therefore provide biased comparisons between populations with contrasting maximum lengths, we calculated overall growth rates (ω, mm year-1) by multiplying these two parameters following.
To assess condition index (CI) and gonadosomatic index (GSI) at each season and location, we collected 20 individuals per species (shell length ranging between 35 and 45 mm) and preserved them in 70% ethanol. In the laboratory, we removed epibionts, measured shell lengths using Vernier callipers (0.01-mm precision), and dissected out the soft tissue, discriminating between mantle (gonads) and soma. Tissues were dried for 48 h at 60°C and weighed to the nearest 0.01 mg. We calculated the condition index as soma dry weight divided by shell dry weight. Mussel gonadosomatic index was calculated as gonad dry weight divided by soma dry weight.
Relationship between environmental predictors and observed fitness-related traits
We used linear models computed in R v.4.3.3 (R Core Team, 2024) to examine the role of present-day (April 2015 – April 2017) environmental predictors on the fitness-related traits (Linf, ω, CI, and GSI) of Mytilus and Perna quantified seasonally across the coast of South Africa. Our predictors were temperature and chlorophyll-a (see below). The influence of these predictors was assessed based on the performance of competing models that included all combinations of the environmental variables tested and the null, intercept-only model. Model performance was quantified based on the coefficient of determination (r2), the Akaike information criteria (AIC), Bayesian information criterion (BIC), and their weights (wAIC, wBIC) to provide a relative probability of model ‘truth’, assuming that the true model is part of the model set. The environmental predictor data were scaled (z-transformation) for better interpretation of model outputs. The assumptions of normality and homogeneity of variances were confirmed for the top-ranked model based on visual interpretation of QQ-plots and model residuals vs. predictions plots.
Present-day and projected environmental data
The current (April 2015 – April 2017) environmental data included mussel body temperatures recorded locally at the nine study locations, and regional SST and Chl-*a *estimates derived from satellite products. Body temperatures were recorded (30 min sampling frequency, 0.5°C resolution) using three biomimetic temperature loggers (i.e., 'robomussels') per location, deployed within mid-shore mussel beds using Z-spar Splash Compound (Kopper’s Co.). We built robomussels by placing loggers (iButton DS1922, Maxim Integrated) in empty, 4-cm length shells of each species filled with waterproofing electrical resin (3M Scotchcast 48FR). Regional, alongshore SST data were extracted from the MUR (Multi-scale Ultra-high-Resolution SST Analysis fv04.1) dataset. We downloaded these data (daily estimates at a 1-km spatial resolution) from the NOAA server ERDDAP (Environmental Research Division’s Data Access Program) using the R package rerdapp. The Chl-a data (daily, cloud-free estimates at 4-km spatial resolution) were downloaded from the CMENS (Copernicus Marine Environmental Monitoring Service) repositories. To extract the SST and Chl-a data along the South African coastline, we generated 376 alongshore geographic points located 15 km offshore (number of locations = 113 west, 163 south, and 100 east coast). None of these points fell within shared environmental data pixels. We used the R package raster to extract the daily data at each geographic point, with a 10-km buffer zone. We corrected the MUR dataset based on linear regressions between the raw downloaded values and the in situ robomussel data recorded during high-tide periods at the corresponding study location. Location-specific corrections were then applied to each of the 376 alongshore locations based on the closest in situ study location.
We derived SST projections between 2016 and 2100 under three Shared Socioeconomic Pathway (SSP) emission scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5 extracted from the output of eight General Circulation Models (GCMs). SSP1-2.6 and SSP2-4.5 assume strong and moderate measures, respectively, to reduce consumption and increase energy efficiency, leading to an initial increase in radiative forcing, followed by stabilization at 2.6 and 4.5 W m-2 by 2100. SSP5-8.5 corresponds to a fossil-fuelled development case, which leads to stabilization at 8.5 W m-2 in 2100. We used daily SST projections produced for the Coupled Model-Inter-comparison Project Phase 6 (Eyring et al., 2016) by the following GCMs: (a) ACCESS, version CM2 (Australian Community Climate and Earth System Simulator), (b) BCC, version CSM2-MR (Beijing Climate Center), (c) CanESM5 (Canadian Earth System Model), (d) CNRM-CM6-1 (Centre National de Recherches Météorologiques and Cerfacs version 6), (e) EC-Earth3 (European community), (f) MIROC-6 (Model for Interdisciplinary Research on Climate version 6), (g) MRI-ESM2 (Meteorological Research Institute Earth System Model version 2), and (h) NorESM2-MM (Norwegian Earth System Model version 2, medium atmosphere-land resolution). The daily SST estimates from these models, relative to the reference year 2015, were then averaged for each of the 376 locations along the South African coastline. We then calculated daily averages of SST for each of the 376 locations along the South African coastline based on the projections of these eight GCMs.
DEB model predictions of present-day and future growth and reproductive effort
To predict the physiological performance of Mytilus and Perna individuals under present-day and future temperature scenarios, we estimated growth and reproduction using dynamic energy budget (DEB) theory. DEB theory provides a mechanistic framework for modelling dynamics in energy acquisition (feeding and assimilation to reserves) and utilisation (allocation to growth, reproduction, maturity, and maintenance). DEB theory assumptions are generalisable across all taxa, and consistent with foundational empirical models in biology, notably the von Bertalanffy growth model. Here we used DEB models previously parameterized for Perna and Mytilus. The models explicitly consider the effect of tidal dynamics and the associated fluctuations in body temperature and filter feeding. Additionally, we account for dynamics in thermal sensitivity associated with tidal cycles, as these species can undergo metabolic depression during aerial exposure. Variation in food quality and quantity can drive differences in the feeding of bivalves across populations. We accounted for this by initially optimising the half-saturation coefficient parameter, which defines the feeding functional response at each of our sites.
We ran model simulations for each site using present-day and climate change-projected SSTs (see Present-day and projected environmental data) as model input data. Simulations began with a 4-cm mussel and ran for two years with daily time steps. Under present-day conditions, the data included the period April 2015 to April 2017. For the different SSP scenarios, simulations were run for the first two years of each decade between 2020 and 2090.
To evaluate the DEB model skill for both species, we compared the model predictions under present-day conditions and our in situ growth and reproduction data. Comparisons were done based on the mean absolute error (MAE) and mean absolute percent error (MAPE). To examine the model’s ability to predict variation in fitness-related traits across locations for each species, we computed Pearson’s correlation coefficients (ρ) and associated P-values.
Files and variables
File: .Rhistory
Description: Rhistory file supporting the .RProj file
File: Correct_MUR_SST_based_robomussels.R
Description: MUR SST data corrected based on in situ robomussel data.
File: correlation_DEB_output_Real_data_GSI_Lw.R
Description: Correlations between DEB output and real data (GSI and Linf).
File: DEB_model_app_master_Zonation_optim_Xk.R
Description: Script for estimating the optimised values of parameter Xk.
File: DEB_pars_Mytilus.R
Description: DEB parameter values for Mytilus galloprovincialis.
File: DEB_pars_Perna.R
Description: DEB parameter values for Perna perna.
File: Mussel_biogeography_-_South_Africa.Rproj
Description: Rproj file
File: models_SST_Chla_vs_vBpars_GSI_CI.R
Description: Script to estimate linear regression models between SST and chl-a and the life history traits (von Bertalanffy growth parameters, GSI and CI).
File: Prepare_Environmental_Data_CC_proj.R
Description: Script to prepare the environmental data file, including climate change projection SST data.
File: summ_DEB_out_GSI_L_20231227.csv
Description: Summary data for the DEB model runs.
Variables
- species: species column.
- site: site column.
- max_GSI: Maximum gonado-somatic index value estimated through the DEB model.
- mean_GSI: Mean gonado-somatic index value estimated through the DEB model.
- pct90_GSI: 90th percentile gonado-somatic index value estimated through the DEB model.
- max_L: Maximum shell length estimated through the DEB model.
- mean_L: Mean shell length estimated through the DEB model.
- pct90_L: 90th percentile shell length estimated through the DEB model.
File: summ_GSI_data.csv
Description: Summary data for the GSI data.
Variables
- Species: species column.
- Site: site column.
- Mean_GSI: Mean gonado-somatic index value estimated through DEB model.
- sd_GSI: Standard deviation gonado-somatic index value estimated through DEB model.
- n: number of values used to estimate descriptive statistiques.
- max_GSI: Maximum gonado-somatic index value estimated through DEB model.
- min_GSI: Minimum gonado-somatic index value estimated through DEB model.
- pct90_GSI: 90th percentile gonado-somatic index value estimated through DEB model.
File: TC_fun.R
Description: Script for the temperature correction function.
File: von_Bertalanffy_-_Notched_mussels_South_Africa.R
Description: Script to estimate von Bertalanffy growth models.
File: .Rproj.user
Description: Rproj file
File: DEB-model-application-Mussel-zonation-master
Description: Folder containing DEB model scripts
File: extracted_chla_MODISA_SA_15km_offshore.csv
Description: MODIS_Aqua chla data extracted 15 km ofshore along the coast of South Africa.
Variables
- date: date column.
- site: site column.
- meanChla: mean chlorophyll-a.
- Year: year column.
File: gr_pars_site_species_season.csv
Description: Growth rate parameters by site, species, and season.
Variables
- site: site column.
- species: species column.
- Season: season column.
- intercept: Ford-Walford linear regression, intercept estimated.
- slope: Ford-Walford linear regression, slope estimated.
- int.CIlow: Ford-Walford linear regression, intercept low confidence interval estimated.
- int.CIhigh: Ford-Walford linear regression, intercept high confidence interval estimated.
- slope.CIlow: Ford-Walford linear regression, slope low confidence interval estimated.
- slope.CIhigh: Ford-Walford linear regression, slope high confidence interval estimated.
- r2: Ford-Walford linear regression, coefficient of determination.
- Linf: von Bertalanffy, infinite shell length estimated.
- K: von Bertalanffy, growth coefficient estimated.
File: gr_pars_site_species.csv
Description: Growth rate parameters by site and species.
Variables
- site: site column.
- species: species column.
- intercept: Ford-Walford linear regression, intercept estimated.
- slope: Ford-Walford linear regression, slope estimated.
- int.CIlow: Ford-Walford linear regression, intercept low confidence interval estimated.
- int.CIhigh: Ford-Walford linear regression, intercept high confidence interval estimated.
- slope.CIlow: Ford-Walford linear regression, slope low confidence interval estimated.
- slope.CIhigh: Ford-Walford linear regression, slope high confidence interval estimated.
- r2: Ford-Walford linear regression, coefficient of determination.
- Linf: von Bertalanffy, infinite shell length estimated.
- K: von Bertalanffy, growth coefficient estimated.
File: Temp_HighTide_MUR_robo.csv
Description: Temperature data during high tide periods, either from MUR or in situ robomussel loggers.
Variables
- date: date column.
- SST_MUR: sea-surface temperature extracted from the MUR (Multi-scale Ultra-high-Resolution SST Analysis fv04.1) dataset
- SST_Robo: body temperature data from robo-mussels.
- Site: site column.
File: sites_SA.csv
Description: Geographic coordinates of study sites in South Africa.
Variables
- Name: site name.
- lon: longitude
- lat: latitude
File: MUR_SST_df_daily_SA_15km_offshore.rds
Description: MUR SST data extracted 15 km offshore.
File: sspALL_mean_SD_daily.Rds
Description: SST differences from SSP climate change scenarios.
File: time_interval.csv
Description: Time interval for DEB model runs.
Variables
- site: site column.
- date_ini: initial date.
- date_fin: final date.
File: temp_all_SA.csv
Description: Temperature data from South Africa.
Variables
- time: time column.
- site: site column.
- temp: temperature °C.
File: offshore_pxls_15km_SA.csv
Description: Pixels used to extract chl-a and temperature data, 15 km offshore.
Variables
- lon: longitude.
- lat: latitude.
- coast: biogeographical coastal region: east, south, west.
File: offshore_coords_selct.csv
Description: Coordinates of the sites matching our study locations.
Variables
- site: site column.
- lon: longitude.
- lat: latitude.
- coast: biogeographical coastal region: east, south, west.
File: Mussel_Growth_South_Africa_FINAL.csv
Description: Mussel growth data.
Variables
- Number: Correlative number, per site.
- Site: Site column.
- Species: Species column.
- Season: Season column.
- Date_notched: Date when mussel was notched.
- Date_collect: Date when mussel was collected.
- Date_notched_strng: Date when mussel was notched, string format.
- Date_collect_strng: Date when mussel was collected, string format.
- Date_notched: Date when mussel was notched (repeat).
- Date_collect: Date when mussel was collected (repeat).
- Length1_mm: Initial shell length (mm).
- Length2_mm: Final shell length (mm).
- Ldiff_mm: Difference between initial and final shell length (mm).
File: south_africa_coast_hi_res.Rdata
Description: South Africa coast raster data for maps.
File: Plot_map_sites_south_SouthAfrica.R
Description: Script to plot a map of South Africa.
File: scale.bar.func.R
Description: Script to draw a scale bar.
File: site_list.csv
Description: List of sites.
Variables
- Name: Site name.
- lon: longitude.
- lat: latitude.
File: CMENS_Chla_sites_extract.Rda
Description: Extract Chl-a data.
File: SST_Chla_extract_SA_coastline.csv
Description: SST and Chl-a data extracted.
Variables
- date: date column.
- site: site column.
- meanSST: daily mean sea-surface temperature °C.
- meanChla: daily mean chlorophyll-a concentration mg/m3.
File: env_mussel_biogeography.csv
Description: Compiled environmental data for the coast of Soth Africa.
Variables
- Time: Date and time column.
- Robo_temp: Robo-mussel temperature °C.
- coast: biogeographical coastal region: east, south, west.
- tide_m: tide height (m).
- source: data source, robomussel.
- site: site column.
- date: date column.
- site_number: correlative site number.
- meanSST: mean sea-surface temperature data, from MUR.
- meanChla: mean chlorophyll-a concentration data.
- lon: longitude.
- lat: latitude.
File: DEB_model_application_-_Mussel_Zonation.Rproj
Description: Rproj file for DEB model applications
File: DEB_model_app_master_-Mussel_Zonation-_Climate_change_predictions.R
Description: R script file for DEB model applications considering climate change.
File: DEB_model_app_master_-_Mussel_Zonation.R
Description: R script file for DEB model applications under present-day conditions.
File: LinMod_robo_MUR.csv
Description: Model characterizing the relationship MUR vs robomussel temperature
Variables
- Site: site column.
- term: model term.
- estimate: mean estimate of coefficient.
- std.error: SD estimate of coefficient.
- statistic: statistic parameter.
- p.value: p-value.
File: user_input.R
Description: user input file
File: env.Rda
Description: Environmental data
File: Xk_optim_values.csv
Description: Estimated Xk optimised values
Variables
- species: species column.
- Xk_optim: half-saturation coefficient estimate.
- diff_target_pred: difference between observed and predicted values.
- site: site column.
File: env_mussel_biogeography.Rda
Description: environmental data compiled.
File: DEB_out_Perna_20231227.csv
Description: DEB model output data for Perna.
Variables
- Time: time column.
- Robo_temp: robomussel temperature data °C.
- coast: biogeographical coastal region: east, south, west.
- tide_m: tide height (m).
- site: site column.
- site_number: correlative site number.
- meanSST: mean sea-surface temperature °C.
- meanChla: mean chlorophyll-a concentration.
- lon: longitude.
- lat: latitude.
- tide_threshold: tide height threshold.
- f: scaled functional response.
- E.dens: reserve energy density, J/cm3
- sM: acceleration factor, -.
- v: energy conductance, cm/d
- pA: energy flux of assimilation process, J/d.
- pC: energy flux of mobilization process, J/d.
- pG: energy flux of structural growth process, J/d.
- pM: energy flux of somatic maintenance process, J/d.
- pJ: energy flux of maturity maintenance process, J/d.
- pR: energy flux of the reproduction/maturation process, J/d.
- E_egg: energy in egg, J.
- L: shell length, cm.
- Www: total physical wet weight, g.
- Wdw: total physical dry weight, g.
- Wsdw: soma dry weight, g.
- Wgdw: gonad dry weight, g.
- GSI: gonado-somatic index, -.
- E[1:length(E) - 1]: reserve energy on the previous day.
- V[1:length(V) - 1]: structural volume on the previous day.
- ER[1:length(ER) - 1]: reproductive buffer energy on the previous day.
- eggs: number of eggs.
File: ssp4.5_AllSites_2050_2051.Rda
Description: SSP4.5 data 2050-2051
File: ssp4.5_AllSites_2060_2061.Rda
Description: SSP4.5 data 2060-2061
File: ssp4.5_AllSites_2070_2071.Rda
Description: SSP4.5 data 2070-2071
File: ssp4.5_AllSites_2080_2081.Rda
Description: SSP4.5 data 2080-2081
File: ssp4.5_AllSites_2090_2091.Rda
Description: SSP4.5 data 2090-2091
File: ssp8.5_AllSites_2020_2021.Rda
Description: SSP8.5 data 2020-2091
File: ssp8.5_AllSites_2030_2031.Rda
**Description: **SSP8.5 data 2030-2031
File: ssp8.5_AllSites_2050_2051.Rda
Description: SSP8.5 data 2050-2051
File: ssp8.5_AllSites_2040_2041.Rda
Description: SSP8.5 data 2040-2041
File: ssp8.5_AllSites_2060_2061.Rda
Description: SSP8.5 data 2060-2061
File: ssp8.5_AllSites_2070_2071.Rda
Description: SSP8.5 data 2070-2071
File: ssp8.5_AllSites_2080_2081.Rda
Description: SSP8.5 data 2080-2081
File: ssp2.6_AllSites_2020_2021.Rda
Description: SSP2.6 data 2020-2021
File: ssp8.5_AllSites_2090_2091.Rda
Description: SSP8.5 data 2090-2091
File: ssp2.6_AllSites_2030_2031.Rda
Description: SSP2.6 data 2030-2031
File: ssp2.6_AllSites_2040_2041.Rda
Description: SSP2.6 data 2040-2041
File: ssp2.6_AllSites_2050_2051.Rda
Description: SSP2.6 data 2050-2051
File: ssp2.6_AllSites_2060_2061.Rda
Description: SSP2.6 data 2060-2061
File: ssp2.6_AllSites_2070_2071.Rda
Description: SSP2.6 data 2070-2071
File: ssp2.6_AllSites_2080_2081.Rda
Description: SSP2.6 data 2080-2081
File: ssp2.6_AllSites_2090_2091.Rda
Description: SSP2.6 data 2080-2081
File: Mussel_Condition_Index_SOUTH_AFRICA_final.csv
Description: Mussel condition index data.
Variables
- Number: correlative row number, per site.
- Site: site column.
- Species: species column.
- Season: season column.
- Dates: date column.
- Date_strng: date column, string format.
- Date_collect: collection date column.
- Length_mm: shell length, mm.
- Width_mm: shell width, mm.
- Tray_shell: mussel shell alluminium weighing tray weight, g.
- Tray_soma: mussel somatic tissue alluminium weighing tray weight, g.
- Tray_gonad: mussel gonad tissue alluminium weighing tray weight, g.
- Shell&Tray: alluminium weighing tray weight plus shell dry weight, g.
- Soma&Tray: alluminium weighing tray weight plus somatic dry weight, g.
- Gonad&Tray: alluminium weighing tray weight plus godad dry weight, g.
- ShellDW: shell dry weight, g.
- SomaDW: soma dry weight, g.
- GonadDW: gonad dry weight, g.
- GSI: gonado-somatic index, -.
- CI: body condition index, -.
File: ssp4.5_AllSites_2020_2021.Rda
Description: SSP4.5 data 2020-2021
File: ssp4.5_AllSites_2030_2031.Rda
Description: SSP4.5 data 2030-2031
File: ssp4.5_AllSites_2040_2041.Rda
Description: SSP4.5 data 2040-2041
Code/software
R v.4.3.3
