TL;DR - Had to re-run Oly RAD-seq data for effective population size after outliers were filtered out. Had to convert from genind to GENEPOP, which was much more annoying than I expected.

Converting a genind Object to GENEPOP Format

This code shows how to export a genind object (oly.genind) to a GENEPOP‐format file for use in NeEstimator2.

1. Install and load graph4lg

if (!requireNamespace("graph4lg", quietly = TRUE)) {
  install.packages("graph4lg")
}
library(graph4lg)

2. Load the .Rdata File and Identify the genind Object

load("/Users/zacharybengtsson/Downloads/oly.no23.Rdata")
ls()
# [1] "oly.genind"  # your genind object

3. Export to GENEPOP Format (.txt)

genind_to_genepop(
  oly.genind,
  output = "/Users/zacharybengtsson/Desktop/NeEstimator2.X/oly_no23.txt"
)

4. (Optional) Rename to .gen Extension

file.rename(
  "/Users/zacharybengtsson/Desktop/NeEstimator2.X/oly_no23.txt",
  "/Users/zacharybengtsson/Desktop/NeEstimator2.X/oly_no23.gen"
)

5. (Optional) Fix Missing‐Data Padding with sed

In case missing genotypes are six-digit codes (000000), collapse them to four digits (0000):

sed 's/000000/0000/g' \
  /Users/zacharybengtsson/Desktop/NeEstimator2.X/oly_no23.gen \
  > /Users/zacharybengtsson/Desktop/NeEstimator2.X/oly_no23_fixed.gen

Now /Users/.../oly_no23_fixed.gen is ready for NeEstimator2.

6. Run in NeEstimator2

The file is now ready for NeEstimator2 which can be run with the linked instructions in my lab notebook.

The result is update effective population sizes (Ne) for wild and hatchery individuals in the North, Central, and South Sound.

Here’s the table in Markdown format:

Population N LD Ne (Pcrit 0.05–0.01; parametric CI; jackknife CI) HE Nb (range; upper CI) MC Nb (95% CI)
CSMB17W 95 2 994–3 466 (2 476–4 383; 1 417–∞) 219–447 (upper ∞) 12.4 (10.1–15.0)
CSMB18H 95 485–531 (469–548; 278–1 590) 7.1 (6.0–8.4)
NSFB16H 89 331–374 (323–384; 239–586) 22.5 (16.4–29.7)
NSFB18W2 95 1 716–1 880 (1 540–2 115; 1 301–2 725) 32.4 (23.0–43.3)
SSNB18H 94 298–333 (291–341; 200–623) 1 051–2 797 (upper ∞) 2.6 (2.3–2.9)
SSNB18W 93 3 338–4 054 (2 714–5 361; 1 698–∞) 7.6 (6.5–8.6)