DOI: 10.5061/dryad.4qrfj6qj5
title: Temporal variability in effective size (Ne) identifies potential sources of discrepancies between mark recapture and close kin mark recapture estimates of population abundance

description: We non-lethally sampled N=5400 brook trout (Salvelinus fontinalis) from
 seven populations during 6 consecutive years (2014-2019) and genotyped
 them at 33 microsatellites to examine variation in Ne, Nc, and in their
 ratio. Although efforts to estimate effective population size, census
 size, and their ratio in wild populations are expanding, few empirical
 studies investigate interannual changes in these parameters. Hence, we do
 not know how repeatable or representative many estimates may be. Answering
 this question requires studies of long-term population dynamics. Here we
 took advantage of a rich dataset of seven brook trout (Salvelinus
 fontinalis) populations, 5 consecutive years and 5400 individuals
 genotyped at 33 microsatellites to examine variation in estimates of
 effective and census size and in their ratio. We first estimated the
 annual effective number of breeders (Nb) using individuals aged 1+. We
 then adjusted these estimates using two life history traits, to obtain
 Nb(adj2) and subsequently Ne(adj2),  following Waples et al.
 (2013). Ne(adj2) was estimated for the years 2014 to 2019. Census
 size was estimated by mark recapture using double-pass electrofishing
 (Nc(MR)) (years 2014-2018) as well as by the Close Kin Mark Recapture
 approach (Nc(CKMR)) (years 2015-2017). Within populations, annual
 variation in Ne(adj2), (ratio of maximum to minimum
 Ne(adj2) ranged from 1.6-fold to 58-fold. Over all 7 populations,
 the median annual variation in Ne(adj2),was around 5-fold. These results
 reflect important interannual changes in the variance in reproductive
 success and more generally in population dynamics. Within population
 Nc(MR) varied between years by a (median) factor of 2.7 with a range from
 2 to 4.3. Thus, estimated effective size varied nearly twice as much as
 did estimated census size. Our results therefore suggest that, at least in
 small populations like those examined in the present study, any single
 annual estimate of Ne(adj2) is unlikely to be representative of
 long-term dynamics. At least 3-4 annual estimates may be required for an
 estimate of contemporary effective size to be truly representative. We
 then compared Nc(MR) to Nc(CKMR). For five of the seven
 populations, the estimates of population abundance based on mark recapture
 (Nc(MR) ) were indistinguishable from those based on close kin mark
 recapture (Nc(CKMR)). The two populations with discordant Nc(MR) and
 Nc(CKMR) exhibited extremely low Ne(adj2) /
 Nc(MR)  ratios and the largest annual variation in
 (58-fold and 35.4-fold respectively). These results suggest that sampling
 effort in these two streams may have been insufficient to properly capture
 the genetic diversity of the entire population and that individuals
 sampled were not representative of the population. Our study further
 validates CKMR as a method for estimating abundance in wild populations
 and it demonstrates how knowledge of the temporal variation in the
 estimate of Ne can be used to identify potential sources of
 discrepancies between the estimates of Nc(MR) and  those of
 Nc(CKMR).
description: Non lethal sampling. Data collected by electrofishing. DNA
 extracted from finclip tissue samples.
description: # Temporal variability in effective size (Ne) identifies potential sources
 of discrepancies between mark recapture and close kin mark recapture
 estimates of population abundance
 [https://doi.org/10.5061/dryad.4qrfj6qj5](https://doi.org/10.5061/dryad.4qrfj6qj5) ## Description of the data and file structure Microsatellite DNA genotype data (33 loci) for 7 brook trout populations for years 2014-2019. Individuals aged 1-3 ### Files and variables #### File: BROOK\_TROUT\_GENOTYPES\_2014-2015-2016-2017 DRYAD.xlsx **Description:** **Genotypic data (microsatellite DNA) for seven brook trout populations sampled non-lethally in years 2014 to 2017.ach microsatellite locus has 2 columns , the second column is denoted with a "-2" at the end of the label. Age is in years, length is in cm.** ##### Variables: ##### Sample ID, Population, Length (cm), Age (years), Ssa-04.d56, Ssa-04.d56-2 , Ssa-01.12, Ssa-01.12-2, SFOC113, SFOC113-2, SFOC88, SFOC88-2, SFOD129, SFOD129-2, Ssa-10.3, Ssa-10.3-2, Ssa-03.7 , Ssa-03.7-2, Ssa-06.8, Ssa-06.8-2, Ssa-09.12, Ssa-09.12-2, Ssa-10.2, Ssa-10.2-2, Ssa-12.2 , Ssa-12.2-2, Ssa-14.10, Ssa-14.10-2, Ssa-16.2, Ssa-16.2-2, Ssa-20.3, Ssa-20.3-2, Ssa-29.2, Ssa-29.2-2, SFOC28, SFOC28-2, Ssa-05.10, Ssa-05.10-2, SFOC24, SFOC24-2, Ssa-15.1, Ssa-15.1-2, Ssa-21.5, Ssa-21.5-2 , 1\_Ssa-1.14, 1\_Ssa-1.14-2, 1\_Ssa-15.9, 1\_Ssa-15.9-2, 1\_Ssa-20.d16, 1\_Ssa-20.d16-2, 1\_Ssa-23.9, 1\_Ssa-23.9-2 , 1\_Ssa-26.d06, 1\_Ssa-26.d06-2, 1\_Ssa-27.d07, 1\_Ssa-27.d07-2, 1\_Ssa-27.d19, 1\_Ssa-27.d19-2, 1\_Ssa-28.d08, 1\_Ssa-28.d08-2, 1\_Ssa-4.9, 1\_Ssa-4.9-2, 2\_Ssa-1.7, 2\_Ssa-1.7-2, 2\_Ssa-11.1, 2\_Ssa-11.1-2, 2\_Ssa-13.6, 2\_Ssa-13.6-2, 2\_Ssa-27.1, 2\_Ssa-27.1-2 ##### File: BROOK\_TROUT\_GENOTYPES\_2018\_DRYAD.xlsx **Description:** **Genotypic data (microsatellite DNA) for seven brook trout populations sampled non-lethally in 2018. Each microsatellite locus has 2 columns , the second column is denoted with a "-b" at the end of the label. Age is in years, length is in cm.** ##### Variables: Sample ID, Population, Length (cm), Age (years), Ssa-04.d56, Ssa-04.d56-b, Ssa-01.12, Ssa-01.12-b, SFOC113, SFOC113-b, SFOC88, SFOC88-b, SFOD129, SFOD129-b, Ssa-10.3, Ssa-10.3-b, Ssa-03.7, Ssa-03.7-b, Ssa-06.8, Ssa-06.8-b, Ssa-09.12, Ssa-09.12-b, Ssa-10.2, Ssa-10.2-b, Ssa-12.2, Ssa-12.2-b, Ssa-14.10, Ssa-14.10-b, Ssa-16.2, Ssa-16.2-b, Ssa-20.3, Ssa-20.3-b, Ssa-29.2, Ssa-29.2-b, SFOC28, SFOC28-b, Ssa-05.10, Ssa-05.10-b, SFOC24, SFOC24-b, Ssa-15.1, Ssa-15.1-b, Ssa-21.5, Ssa-21.5-b, 1_Ssa-1.14, 1_Ssa-1.14-b, 1_Ssa-15.9, 1_Ssa-15.9-b, 1_Ssa-20.d16, 1_Ssa-20.d16-b,1_Ssa-23.9, 1_Ssa-23.9-b, 1_Ssa-26.d06, 1_Ssa-26.d06-b, 1_Ssa-27.d07, 1_Ssa-27.d07-b, 1_Ssa-27.d19, 1_Ssa-27.d19-b, 1_Ssa-28.d08, 1_Ssa-28.d08-b, 1_Ssa-4.9, 1_Ssa-4.9-b, 2_Ssa-1.7, 2_Ssa-1.7-b, 2_Ssa-11.1, 2_Ssa-11.1-b, 2_Ssa-13.6, 2_Ssa-13.6-b, 2_Ssa-27.1, 2_Ssa-27.1-b **File: BROOK_TROUT_GENOTYPES_2019_-_ALSO_2018_YOY_DRYAD.xlsx** **Description:** **Genotypic data (microsatellite DNA) for seven brook trout populations sampled non-lethally in 2019 (includes Young of the Year (YOY) sampled in 2018) Each microsatellite locus has 2 columns , the second column is denoted with a "-b" at the end of the label** ##### Variables: Sample ID, Population, Length (cm), Ssa-04.d56, Ssa-04.d56-b, Ssa-01.12, Ssa-01.12-b, SFOC113, SFOC113-b, SFOC88, SFOC88-b, SFOD129, SFOD129-b, Ssa-10.3, Ssa-10.3-b, Ssa-03.7, Ssa-03.7-b, Ssa-06.8, Ssa-06.8-b, Ssa-09.12, Ssa-09.12-b, Ssa-10.2, Ssa-10.2-b, Ssa-12.2, Ssa-12.2-b, Ssa-14.10, Ssa-14.10-b, Ssa-16.2, Ssa-16.2-b, Ssa-20.3, Ssa-20.3-b, Ssa-29.2, Ssa-29.2-b, SFOC28, SFOC28-b, Ssa-05.10, Ssa-05.10-b, SFOC24, SFOC24-b, Ssa-15.1, Ssa-15.1-b, Ssa-21.5, Ssa-21.5-b, 1_Ssa-1.14, 1_Ssa-1.14-b, 1_Ssa-15.9, 1_Ssa-15.9-b, 1_Ssa-20.d16, 1_Ssa-20.d16-b,1_Ssa-23.9, 1_Ssa-23.9-b, 1_Ssa-26.d06, 1_Ssa-26.d06-b, 1_Ssa-27.d07, 1_Ssa-27.d07-b, 1_Ssa-27.d19, 1_Ssa-27.d19-b, 1_Ssa-28.d08, 1_Ssa-28.d08-b, 1_Ssa-4.9, 1_Ssa-4.9-b, 2_Ssa-1.7, 2_Ssa-1.7-b, 2_Ssa-11.1, 2_Ssa-11.1-b, 2_Ssa-13.6, 2_Ssa-13.6-b, 2_Ssa-27.1, 2_Ssa-27.1-b ## Code/software EXCEL