Data from: Island biogeography and competition drive rapid venom complexity evolution across rattlesnakes
Data files
Apr 07, 2025 version files 77.77 KB
-
.Rhistory
282 B
-
Binned_peaks_for_RDA.csv
8.38 KB
-
fig3_script.R
17.13 KB
-
RDA.R
3.60 KB
-
README.md
3.70 KB
-
Spatial_Modeling_SRH_MAB_Scaled.Rmd
10.54 KB
-
Spatial_Modeling_SRH_MAB_Unscaled.Rmd
19.24 KB
-
Supplemental_Dataset_S1.csv
14.90 KB
Abstract
Understanding how human-mediated environmental change affects biodiversity and the timescale of an evolutionary response is key for conserving evolvability. Islands are proxies for fragmented landscapes and allow us to use historical changes in biodiversity under Island Biogeography Theory (IBT) to predict the consequences of immediate anthropogenic impacts on trait evolution. Rattlesnake venoms are molecular phenotypes that mediate interactions with prey, and diet and venom complexity are positively correlated. Consequently, rattlesnake venoms allow us to investigate how functional traits co-vary with changes in biodiversity according to IBT. We collected venom from 83 rattlesnakes across multiple species and 11 islands in the Gulf of California and estimated venom complexity using the Shannon Diversity Index. Using a mixed effects modeling approach, we found that the number of congeneric competitors, island isolation, and island area best predicted variation in venom complexity. All variables exhibited a negative relationship with venom complexity, contrary to predictions for island area under IBT. Larger islands with more congeneric competitors exhibited reduced trait complexity, likely reflecting niche partitioning and venom specialization driven by interspecific competition and/or increases in habitat heterogeneity. Ultimately, we used a synthetic eco-evolutionary framework to predict functional trait evolution across fragmented and isolated habitats.
Files and Descriptions
- Spatial_Modeling_SRH_MAB_Unscaled.rmd
- This script performs mixed-effects modeling using unscaled Shannon_H values to analyze venom complexity. The model incorporates spatial and ecological variables to assess their effects on venom complexity without scaling the Shannon_H metric.
- Note, geographic coordinates (latitude and longitude) of are not included in the dataset used in this analysis; therefore, models using spacial autocorrelation will not function. See note for the dataset for more information.
- Spatial_Modeling_SRH_MAB_Scaled.rmd
- This script performs the same mixed-effects modeling as the unscaled version but uses scaled Shannon_H values for venom complexity.
- Note, geographic coordinates (latitude and longitude) of are not included in the dataset used in this analysis; therefore, models using spacial autocorrelation will not function. See note for the dataset for more information.
- fig3_script.R
- This script generates Figure 3 for the study. It includes the necessary data processing, data simulation, visualization steps, and formatting for publication-quality output.
- RDA.R
- This script conducts a conditional redundancy analysis (RDA) on the binned HPLC peak data. The analysis evaluates how much variance in venom complexity is explained by specific predictor variables while accounting for covariates.
- Supplemental_Datasets_S1.csv
- Note: Geographic coordinates (latitude and longitude) of sample collection sites are not included in this dataset to protect the sensitive habitats and populations of island-dwelling rattlesnake species. Many of these islands are ecologically fragile and host endemic or vulnerable taxa. To prevent disturbance, unauthorized collecting, or potential exploitation, we have withheld precise location data in accordance with best practices for conservation-focused research. Researchers with legitimate scientific inquiries or conservation interests may request access to the location data by contacting the corresponding author.
- This csv dataset contains all metadata necessary to perform the analyses presented in this study.
- Binned_peaks_for_RDA.csv
- This csv dataset contains binned HPLC peak abundance data for all snakes and is necesarry for the RDA conducted in the study.
For both datasets, column names are as follow:
-
Tissue_ID - ID from researcher’s dataset used to distinguish specimens
-
Species - Scientific species name for each specimen. Genus of all species is* Crotalus *
-
Island - Name of the island where specimen was collected
-
Area - Island area (Km^2)
-
Distance - Island distance from nearest mainland (Km)
-
Age - Estimated age of island (MYA) based on gelogical epoch when formed
-
Rcomp - Number of other rattlesnake species found on the island where specimen was collected
-
Osnake- Number of all other snake species found on the island where specimen was collected
-
Scomp - Number of other potential competitor snake species (based on vertebrate diet) found on the island where specimen was collected
-
SVL_cm - Snout-vent-length (cm)
-
Shannon_H - Unscaled venom complexity metric calculated from HPLC peak data
-
Shannon_H_stand - Scaled venom complexity metric constrained from 0-1 for each species based on min and max Shannon_H values for each species
-
Peak1 - PeakX- Relative abundance of each RP-HPLC peak estimated as area under the peak relative to all other peaks
-
Bin1 - BinX - Relative abundance of each RP-HPLC peak estimated as area under the peak relative to all other peaks. Peaks are binned according to retention time for consistency among all species