DOI: 10.5061/dryad.rfj6q57pb
title: Physiological and survival data of intertidal mollusks from contrasting upwelling environments

description: This dataset contains physiological measurements and survival records for
 two intertidal mollusk species (Chiton granosus and Scurria zebrina)
 collected from two contrasting coastal upwelling environments in central
 Chile (semi-permanent and seasonal). Data include capture–recapture
 records for survival estimation, metabolic rate and heart rate
 measurements obtained through laboratory assays, and calcification rate
 estimates based on buoyant weight. Sampling was conducted in situ and
 complemented with controlled laboratory experiments to assess
 physiological responses to natural environmental variability. Results
 indicate variation in metabolism linked to upwelling regimes, with higher
 survival in semi-permanent upwelling environments. These findings provide
 insights into phenotypic plasticity and adaptation in marine invertebrates
 under changing coastal conditions, offering valuable data for future
 ecological and conservation research.
description: # **Physiological and Survival Data of Intertidal Mollusks from
 Contrasting Upwelling Environments** **Dataset DOI:**
 10.5061/dryad.rfj6q57pb --- ## **Description of the Data and File
 Structure** This dataset was generated from a multi-year field and
 laboratory study assessing the influence of contrasting upwelling systems
 on the physiology, morphology, and survival of two intertidal mollusk
 species in central-northern Chile. * **Field data:** Monthly
 capture–mark–recapture surveys over 26 months at two sites (semi-permanent
 vs. seasonal upwelling), environmental variables, and individual
 morphological traits. * **Laboratory experiments:** Conducted under
 controlled conditions to measure metabolic rate and heart rate. *
 **Survival analysis:** Estimated using Cormack–Jolly–Seber models within a
 Bayesian framework. * **Physiological & morphological analysis:**
 Linear mixed models. * **Environmental context:** Satellite sea surface
 temperature (SST) data from ERA5 were used to characterize long-term
 thermal patterns and upwelling intensity. The compressed file
 **Upwelling_Mollusks_Data.zip** contains four main directories, each
 corresponding to a type of data collected and analyzed. Each folder
 contains raw data and R scripts. --- ## **Directory and File
 Descriptions** ### **1. physiology_morphology** Contains individual-level
 physiological and morphological data, plus R scripts for analysis.
 **Files:** * `cg_data.csv` → Raw data for *Chiton granosus* *
 `sz_data.csv` → Raw data for *Scurria zebrina* * `cg_analysis.R` → R
 script used to analyze *C. granosus* data * `sz_analysis.R` → R script
 used to analyze *S. zebrina* data **Variables in** **`cg_data.csv`**
 **and** **`sz_data.csv`:** * **Location:** Sampling site (Quintay =
 seasonal upwelling; Talcaruca = semi-permanent upwelling) * **Date:**
 Sampling date (MM-DD-YYYY) * **Species:** Scientific name (*Chiton
 granosus* or *Scurria zebrina*) * **Length:** Shell length (mm) *
 **Width:** Shell width (mm) * **Height:** Shell height (mm) * **Weight:**
 Buoyant weight (g) * **Met:** Oxygen consumption rate (mgO₂ h⁻¹ g⁻¹) *
 **HR:** Heart rate (NA if not measured) --- ### **2. survival** Contains
 capture–mark–recapture data for both species at both sites, plus model
 outputs. **Files:** * `data_cg_q.csv` → Raw data for *C. granosus*,
 Quintay site * `data_cg_t.csv` → Raw data for *C. granosus*, Talcaruca
 site * `data_sz_q.csv` → Raw data for *S. zebrina*, Quintay site *
 `data_sz_t.csv` → Raw data for *S. zebrina*, Talcaruca site **Variables in
 survival files:** * **Capture_history:** Capture–mark–recapture history,
 with presence (1) or absence (0) of each individual at each sampling
 occasion (nov_19, dec_19, jan_20, …) **Subfolder: models**\ Contains
 survival model analyses (Cormack–Jolly–Seber models). Four models
 included: * **p(t)phi(.):** Apparent survival is constant during the study
 * **p(t)phi(F):** Apparent survival varies with physiological performance
 * **p(t)phi(L):** Apparent survival varies with body length *
 ***p(t)phi(t)*****:** Apparent survival varies monthly at each location
 --- #### **Model: p(t)phi(.)** **Files included:** * `cg_q_p(t)phi(.).R` →
 R script used to analyze *C. granosus*, Quintay * `cg_t_p(t)phi(.).R` → R
 script used to analyze *C. granosus*, Talcaruca * `sz_q_p(t)phi(.).R` → R
 script used to analyze *S. zebrina*, Quintay * `sz_t_p(t)phi(.).R` → R
 script used to analyze *S. zebrina*, Talcaruca **Subfolder: graphic** *
 `data.txt` → Summary statistics of the model outputs *
 `graphic_p(t)phi(.).R` → R script for generating plots of the effect of
 locality on survival --- #### **Model: p(t)phi(F)** **Files included:** *
 `cg_q_p(t)phi(F).R` → R script used to analyze *C. granosus*, Quintay *
 `cg_t_p(t)phi(F).R` → R script used to analyze *C. granosus*, Talcaruca *
 `sz_q_p(t)phi(F).R` → R script used to analyze *S. zebrina*, Quintay *
 `sz_t_p(t)phi(F).R` → R script used to analyze *S. zebrina*, Talcaruca
 **Subfolder: graphic** * `data_1-6_months.txt` → Summary statistics for
 the first 6 months of capture–recapture * `graphic_met.R` → R script for
 generating plots of the relationship between physiological yield and
 survival probability --- #### **Model: p(t)phi(L)** **Files included:** *
 `cg_q_p(t)phi(L).R` → R script used to analyze *C. granosus*, Quintay *
 `cg_t_p(t)phi(L).R` → R script used to analyze *C. granosus*, Talcaruca *
 `sz_q_p(t)phi(L).R` → R script used to analyze *S. zebrina*, Quintay *
 `sz_t_p(t)phi(L).R` → R script used to analyze *S. zebrina*, Talcaruca
 **Subfolder: graphic** * `data_1-6_months.txt` → Summary statistics for
 the first 6 months of capture–recapture * `graphic_large.R` → R script for
 generating plots of relationship between body length and survival
 probability --- #### **Model: p(t)phi(t)** This model assumes both capture
 probability and survival vary over time. **Files included:** *
 `cg_q_p(t)phi(t).R` → R script used to analyze *C. granosus*, Quintay *
 `cg_t_p(t)phi(t).R` → R script used to analyze *C. granosus*, Talcaruca *
 `sz_q_p(t)phi(t).R` → R script used to analyze *S. zebrina*, Quintay *
 `sz_t_p(t)phi(t).R` → R script used to analyze *S. zebrina*, Talcaruca
 **Subfolder: graphic** * `cg_q.csv` → Summary statistics for *C.
 granosus*, Quintay * `cg_t.csv` → Summary statistics for *C. granosus*,
 Talcaruca * `sv_q.csv` → Summary statistics for *S. zebrina*, Quintay *
 `sv_t.csv` → Summary statistics for *S. zebrina*, Talcaruca *
 `graphic_time.R` → R script for generating plots of monthly survival
 probability --- **Variables contained in graphic data files (for all
 models)** * **meanPhi:** Estimated mean survival probability (appears in
 models where *ϕ* varies with time) * **mean:** Posterior mean of the
 parameter * **sd:** Standard deviation of the posterior distribution *
 **min:** Minimum value of the posterior distribution * **25%:** First
 quartile (25th percentile) * **50%:** Median (50th percentile) * **75%:**
 Third quartile (75th percentile) * **max:** Maximum value of the posterior
 distribution * **Rhat:** Gelman-Rubin convergence diagnostic * **n.eff:**
 Effective sample size * **overlap0:** Proportion of posterior distribution
 overlapping zero * **f:** Factor/indicator variable --- ### **3.
 temperature_pH** Contains in situ environmental monitoring data and
 analysis scripts. **Files:** * `tem_pH_q.csv` → Temperature and pH data
 from Quintay * `tem_pH_t.csv` → Temperature and pH data from Talcaruca *
 `tem_pH_boxplot.R` → R script for generating boxplots * `tem_vs_pH.R` → R
 script analyzing temperature–pH relationship **Variables:** * **Date:**
 Sampling date (MM-DD-YYYY) * **Temperature:** Seawater temperature (°C) *
 **pH:** Seawater pH * **Season:** Sampling season (Summer, Winter, …) ---
 ### **4. satellite_SST** Contains satellite-derived sea surface
 temperature data (ERA5) and processing scripts. **Files:** *
 `quintay_sst_2019_2021.nc` → NetCDF SST for Quintay *
 `talcaruca_sst_2019_2021.nc` → NetCDF SST for Talcaruca * `sst_netcdf.R` →
 R script for reading, processing, and plotting SST **Usage notes for .nc
 files:** The `.nc` files can be accessed using **Panoply**, a free NASA
 application that allows inspection and plotting of NetCDF data without
 coding. The files can also be read and processed in **R** using the
 `ncdf4` package. --- ### **Abbreviations Used** * **cg:** Chiton granosus
 * **sz:** Scurria zebrina * **t:** Talcaruca * **q:** Quintay * **sst:**
 Sea surface temperature (°C) * **met:** Metabolism * **tem:** Temperature
 (°C) * **phi** **(*ϕ*):** Apparent survival probability * **p:** Encounter
 probability * **F:** Physiological performance, covariate included in some
 models * **L:** Body length, covariate included in some models * **t:**
 Full temporal variation of survival * **. (dot in model notation)** **:**
 Constancy over time of survival --- ## **Code/software** * All analyses
 were conducted in **R version 4.2.3** * Survival models were fitted in a
 Bayesian framework using **JAGS** through the R package **jagsUI** *
 NetCDF files were processed using the **ncdf4** R package