DOI: 10.5061/dryad.xksn02vr6
title: Phenotypic homogenization and potential fitness constraints following non-native introgression in an endemic sportfish

description: Introgressive hybridization may lead to contrasting evolutionary outcomes
 that are difficult to predict, since they depend on the fitness effects of
 endogenous genomic interactions and environmental factors. Conservation of
 endemic biodiversity may be more effective with direct measurement of
 introgressed ancestry and fitness in wild populations, especially for
 keystone taxa at risk of hybridization following species introductions. We
 assessed the relationship of non-native ancestry with growth and body
 condition in the basin-restricted Neosho Bass (Micropterus velox; NB),
 focusing on two streams in the NB native range that are admixed
 extensively with non-native Smallmouth Bass (M. dolomieu; SMB). We
 quantified genetic composition of 116 fish from Big Sugar Creek (N=46) and
 Elk River (N=70) at 14 microsatellite loci. Using back-calculated total
 length-at-age estimated from sagittal otoliths, we assessed whether
 genetic ancestry explained variation in von Bertalanffy growth model
 parameters, accounting for sex and stream effects. We then assessed the
 relationship of ancestry and body condition. We found no differences in
 growth parameters by sex, stream, or ancestry, suggesting phenotypic
 homogenization which could be mediated by selection on body size. We found
 a negative correlation between SMB ancestry and condition, including lower
 condition in Big Sugar Creek, possibly reflecting a trade-off between
 maximum length and condition with respect to overall fitness. We show that
 ongoing non-native introgression, which may be augmented by anthropogenic
 SMB introductions, may attenuate evolutionary differentiation between
 species and directly influence fitness, possibly having critical
 implications for long-term persistence and management of adaptive
 potential in a popular and ecologically important endemic sportfish.
description: Fish were collected by hook-and-line angling. Tissue samples
 (~25mg) for genetic analysis were removed from the upper caudal fin and
 stored in 95% ethanol until processing. All phenotypic measurements were
 obtained as described in the final publication.
description: # Reproduce analyses for Gunn et al. 2024 Below, you will find all
 data included in Gunn et al. (2024). These data can be used along with the
 associated project GitHub repository to work through all data analyses
 step-by-step. ## Project: Effects of admixture on fitness in Neosho Bass
 populations We assessed the effect of admixture on fitness in two stream
 populations within the native range of the Neosho Bass (*M. velox*; NB)
 which are known to have extensively hybridized with Smallmouth Bass
 (*Micropterus dolomieu*; SMB). Specifically, we used 14 microsatellite
 loci in a Bayesian analysis of population structure to estimate
 proportions of interspecific ancestry in individuals collected from Big
 Sugar Creek and the Elk River in southwestern Missouri (Central Interior
 Highlands ecoregion (CIH), North America). We used ancestry inference to
 estimate the proportion of ancestry derived from SMB and NB. For each
 individual, we measured age and total length and projected individual
 growth using the standard parameterization of the von Bertalanffy growth
 model. Finally, we used body condition as a proxy for fitness and
 generated an ancestry-condition correlation across the global dataset. We
 ultimately sought to understand the short-term genetic consequences of
 admixture for NB populations in order to better inform management and
 long-term viability of distinct, economically and ecologically important
 sportfish species in the CIH. ## Data In this repository, you will find
 three separate data files (each file is provided both in comma separate
 value format, i.e., .csv, and Excel format, i.e., .xlsx). Files in .csv
 format are provided for machine readability, but all corresponding code
 requires files in .xlsx format for analysis. We also provide one data
 folder. Files and folders are required to reproduce analyses for Gunn et
 al. (2024): 1. raw genotype data (genotype_data.csv; genotype_data.xlsx)
 2. raw phenotype data (phenotype_data.csv; phenotype_data.xlsx) 3.
 metadata (metadata.csv; metadata.xlsx) 4. raw otolith images
 (otolith_images.zip) Below, we provide a full description of each data
 file and folder, including names and descriptions of variables in each
 column. **genotype_data.csv (.xlsx)** 1. "*sample_id*": a
 unique, alphanumeric ID for each sample 2. "*structure_number*":
 the alphanumerical ID for each individual in the proper format for
 analysis with the program STRUCTURE 3. *locus**names: *for each
 microsatellite locus used in this study, there are two adjacent columns,
 the first labeled with the locus name (e.g.,* "mdo9",
 "mdo5", "mdo7", "**mdo10*", etc.) and the
 second with no header label (blank). Each column contains one of two
 alleles at that locus (three-digit numerical value, e.g., 146). *Note:
 columns with blank headers must remain blank, as they will be read and
 transformed using the R library employed for analysis of these data.
 **phenotype_data.csv (.xlsx)** 1. "*sample_id*": same as in
 metadata and genotype data (followed by "structure_number"; see
 Analysis 3, Phase 1, step 1e) 2. "*sex*": male or female,
 determined by internal gonad examination, 3. "*tl_alive*": total
 length (mm) of the fish immediately upon capture by rod-and-reel, as
 measured from the tip of the lower mandible to the posterior tip of the
 caudal fin squeezed together 4. "*tl_dead*": total length (mm)
 of the fish upon sample processeing (after approximately one month frozen)
 5. "*mass_dead*": mass (g) of the fish in grams at time of
 sample processing 6. "*meas_ints*": initials of the person doing
 the phenotypic measuring, measuring, or assessment, for later analysis of
 observer/measurement bias 7. "*eddie_age*": age estimate based
 on sagittal otolith annuli, performed by Eddie Sterling 8.
 "*joe_age*": age estimate performed by Joe Gunn 9.
 "*michael_age*": age estimate performed by Michael Moore 10.
 "*consensus_age*": ultimate age estimate based on consensus of
 three agers, Eddie, Joe, and Michael 11. "*sl*": standard length
 (mm) of the fish, as measured from the tip of the mandible to the hyperal
 plate, just anterior to the caudal fin 12. "*bd*": body depth
 (mm) , as measured at the thickest part of the fish 13. "*hl*":
 head length (mm), as measured from the tip of the lower mandible to the
 posterior end of the gill plate 14. "*ol*": orbital length (mm)
 , the diameter of the eye ball 15. "*sd_ray*": the number of
 soft dorsal fin rays. **metadata.csv (.xlsx)** 1. "*sample_id*":
 a unique, alphanumeric ID for each sample 2. "*river_code*": the
 alphabetical prefix for each sample id, where "FBS" stands for
 "Fitness, Big Sugar Creek" and "FER" stands for
 "Fitness, Elk River" 3. "*sample_number*": the
 numerical prefix for each sample (01, 02, 03,..., 71) 4.
 "*structure_number*": the alphanumerical ID for each individual
 in the proper format for analysis with the program STRUCTURE 5.
 "*easting*": easting UTM (ZONE 15S) measurement (GIS) 6.
 "*northing*": northing UTM (ZONE 15S) measurement (GIS) 7.
 "*dist_from_df*": distance (m) from Deep Ford Access (DFA) at
 which each sample was collected 8. "*range_id*": species native
 range from which each individual was collected 9. "*river*":
 river or stream from which each individual was collected 10.
 "*population*": designation for whether the sample was included
 in growth analyses ("sample") or was used solely as a reference
 for ancestry analysis ("reference") NA - not available
 **otolith_images.zip** Compressed (zip) folder containing all raw otolith
 images (PNG) analyzed in this study. Images are labeled as described for
 the "sample*id" column of each raw dataset (genotype_data
 phenotypes_data, metadata), i.e., with a unique alphanumeric ID
 representing the fish from which the otolith was collected. To reproduce
 analyses for Gunn et al. (2020), follow instructions in the README.md file
 on the project Zenodo repository
 ([https://doi.org/10.5281/zenodo.13983316](https://doi.org/10.5281/zenodo.13983316)). First, download these data into your `/raw_data` directory within the home working directory. You should then be ready to fully analyze data as described in the associated Rmarkdown files. If you have any questions or issues with data and/or code, please don't hesitate to contact me: [jcgunn@uvm.edu](mailto:jcgunn@uvm.edu) --- ## Versioning Oct 23, V2: Added image folder and csv versions of all Excel files