Close-Kin Mark-Recapture Models

Close-Kin Mark-Recapture (CKMR) is a recent extension of the ordinary mark–recapture methods used to estimate animal abundance and other population parameters. Where ordinary mark–recapture only consider the subsequent identification of the same animal a recapture, CKMR expands this by also viewing the genetic identification of a relatives as a recapture. One of the challenges of CKMR models compared to ordinary mark–recapture is that the recapture probabilities are tightly coupled to the life histories of the animals in questions. This thesis contains three different contributions to the CKMR literature. Firstly I develop a CKMR estimator for age structured populations, presented in Ruzzante (2019). Secondly, I develop theoretical background for half sibling CKMR analysis, and apply kin analysis to data from the River Etne. Thirdly, it expands on the results from Skaug (2017) and derives several new results for the case where age of both parent and offspring is unknown. The first part contains the method development of a parent–offspring CKMR model for brook trout populations, electrofished yearly in the period 2013-2018. I here develop a moment estimator for population size for an age structured model, related to the Lincoln–Petersen estimator. The estimator is applied under two different population assumptions, stable age structure, and variable recruitment and representative sampling. Special focus is on the small population situation, where large sample approximations used in previous CKMR studies cannot be assumed. A small sample bias correction for the estimator is developed and validated using parametric bootstrap simulations. Using the perspective that the parent marks the offspring instead of the commonly used offspring marks juvenile, a simple and general form of the estimator is derived. Viewing offspring as the marked part of the population also leads to an expression for the variance of the expected number of parent–offspring pairs in a sample, which is found to be less than the Poisson variance unless fecundity is very overdispersed. The second part contains theoretical background and model development for half sibling CKMR analysis, to examine the conditions under which same cohort siblings are suitable for CKMR analysis. A half sibling kinship analysis of single year data set of Atlantic salmon from the River Etne 2013 is performed to check if it is suitable for CKMR. In the third part, the probability that an individual has a living parent in an age structured population is discussed in detail. For the case where age information for both parent and offspring is unavailable, I derive two useful expressions for the probability of a living parent when mortality is constant, or constant after onset of maturity. With the additional assumption of constant population size, this probability is shown to be 1/2, similar to what is previously proved for constant fecundity.Masteroppgave i statistikkSTAT399MAMN-STA

Validation of close‐kin mark–recapture (CKMR) methods for estimating population abundance

Under embargo until: 2020-06-181. Knowing how many individuals there are in a population is a fundamental problem in the management and conservation of freshwater and marine fish. We compare abundance estimates (census size, Nc) in seven brook trout Salvelinus fontinalis populations using standard mark–recapture (MR) and the close‐kin mark–recapture (CKMR) method. Our purpose is to validate CKMR as a method for estimating population size. 2. Close‐kin mark–recapture is based on the principle that an individual's genotype can be considered a “recapture” of the genotypes of each of its parents. Assuming offspring and parents are sampled independently, the number of parent–offspring pairs (POPs) genetically identified in these samples can be used to estimate abundance. We genotyped (33 microsatellites) and aged c. 2,400 brook trout individuals collected over 5 consecutive years (2014–2018). 3. We provide an alternative interpretation of CKMR in terms of the Lincoln– Petersen estimator in which the parents are considered as tagging the offspring rather than the offspring “recapturing” the parents. 4. Despite various sources of uncertainty, we find close agreement between standard MR abundance estimates obtained through double‐pass electrofishing and CKMR estimates, which require information on age‐specific fecundity, and population‐ and age‐specific survival rates. Population sizes (N) are estimated to range between 300 and 6,000 adult individuals. Our study constitutes the first in situ validation of CKMR and establishes it as a useful method for estimating population size in aquatic systems where assumptions of random sampling and thorough mixing of individuals can be met.acceptedVersio

Close-Kin Mark-Recapture Models

Close-Kin Mark-Recapture (CKMR) is a recent extension of the ordinary mark–recapture methods used to estimate animal abundance and other population parameters. Where ordinary mark–recapture only consider the subsequent identification of the same animal a recapture, CKMR expands this by also viewing the genetic identification of a relatives as a recapture. One of the challenges of CKMR models compared to ordinary mark–recapture is that the recapture probabilities are tightly coupled to the life histories of the animals in questions. This thesis contains three different contributions to the CKMR literature. Firstly I develop a CKMR estimator for age structured populations, presented in Ruzzante (2019). Secondly, I develop theoretical background for half sibling CKMR analysis, and apply kin analysis to data from the River Etne. Thirdly, it expands on the results from Skaug (2017) and derives several new results for the case where age of both parent and offspring is unknown. The first part contains the method development of a parent–offspring CKMR model for brook trout populations, electrofished yearly in the period 2013-2018. I here develop a moment estimator for population size for an age structured model, related to the Lincoln–Petersen estimator. The estimator is applied under two different population assumptions, stable age structure, and variable recruitment and representative sampling. Special focus is on the small population situation, where large sample approximations used in previous CKMR studies cannot be assumed. A small sample bias correction for the estimator is developed and validated using parametric bootstrap simulations. Using the perspective that the parent marks the offspring instead of the commonly used offspring marks juvenile, a simple and general form of the estimator is derived. Viewing offspring as the marked part of the population also leads to an expression for the variance of the expected number of parent–offspring pairs in a sample, which is found to be less than the Poisson variance unless fecundity is very overdispersed. The second part contains theoretical background and model development for half sibling CKMR analysis, to examine the conditions under which same cohort siblings are suitable for CKMR analysis. A half sibling kinship analysis of single year data set of Atlantic salmon from the River Etne 2013 is performed to check if it is suitable for CKMR. In the third part, the probability that an individual has a living parent in an age structured population is discussed in detail. For the case where age information for both parent and offspring is unavailable, I derive two useful expressions for the probability of a living parent when mortality is constant, or constant after onset of maturity. With the additional assumption of constant population size, this probability is shown to be 1/2, similar to what is previously proved for constant fecundity

Fig 2 -

Simulated (left) and observed (right) distribution of the LODPO (top) and LODHS (bottom) scores. For the three simulated datasets (HS, PO, U) normalized densities (unit area) are shown, while for the real data the absolute frequency of each relationship category, as assigned by the maximum likelihood, is displayed.</p

Time intervals between detection of the first and second individuals of dyads with LOD_PO > 6 in each assignment category.

The square brackets in the y-axis show individual IDs for each pair. Dots and asterisks indicate the first and second encounters, respectively. Color represents the probability of close-related relationship inference for each dyad LOD score (the brighter the color, the higher the score).</p

Overview of the DNA samples for southern right whales collected in the Antarctic Ocean during dedicated sighting surveys in 1993–2019.

Overview of the DNA samples for southern right whales collected in the Antarctic Ocean during dedicated sighting surveys in 1993–2019.</p

Summary of information for the 28 dyads (D1,D2) identified with LOD_PO > 6.

Summary of information for the 28 dyads (D1,D2) identified with LODPO > 6.</p

Geographical connections for dyads with LOD_PO>6 indicating the degree of strength of the connection.

The inset shows a map focusing on the Indo sector of the Antarctic where most observations are concentrated. Maps were obtained by R [6] using rnaturalearth package [7].</p

Number of related pairs by different (maximum probability) kinship category (PO/HS/FS), for different cutoff LOD values.

Number of related pairs by different (maximum probability) kinship category (PO/HS/FS), for different cutoff LOD values.</p

Time intervals between detection of the first and second individuals of dyads with LOD_PO < 6 in each assignment category.

Time intervals between detection of the first and second individuals of dyads with LODPO < 6 in each assignment category.</p