Particle Backtracking Improves Breeding Subpopulation Discrimination and Natal-Source Identification in Mixed Populations

Abstract

We provide a novel method to improve the use of natural tagging approaches for subpopulation discrimination and source-origin identification in aquatic and terrestrial animals with a passive dispersive phase. Our method integrates observed site-referenced biological information on individuals in mixed populations with a particle-tracking model to retrace likely dispersal histories prior to capture (i.e., particle backtracking). To illustrate and test our approach, we focus on western Lake Erie’s yellow perch (Perca flavescens) population during 2006–2007, using microsatellite DNA and otolith microchemistry from larvae and juveniles as natural tags. Particle backtracking showed that not all larvae collected near a presumed hatching location may have originated there, owing to passive drift during the larval stage that was influenced by strong river- and wind-driven water circulation. Re-assigning larvae to their most probable hatching site (based on probabilistic dispersal trajectories from the particle backtracking model) improved the use of genetics and otolith microchemistry to discriminate among local breeding subpopulations. This enhancement, in turn, altered (and likely improved) the estimated contributions of each breeding subpopulation to the mixed population of juvenile recruits. Our findings indicate that particle backtracking can complement existing tools used to identify the origin of individuals in mixed populations, especially in flow-dominated systems.

Independent estimates of marine population connectivity are more concordant when accounting for uncertainties in larval origins

“…The empirical methods used by previous studies assign larvae or recruits to putative parental populations on a probabilistic fashion (based on assumptions of probability distributions of alleles or elements, number and size of populations, and other demographic processes), and have intrinsic uncertainties 57 . Three studies that did estimate a connectivity matrix based on genetics or elemental fingerprinting did explicitly incorporate this uncertainty into the decision of allocating larvae or recruits to parental populations, by specifying a posterior probability threshold for correct assignment (from 0.70 to 0.95 32 , 47 , 50 ), while five studies simply allocated larvae or recruits to a given population when the posterior probability of pertaining to this population was higher than that of pertaining to any other population 15 , 19 , 30 , 44 , 52 . Numerical biophysical models also have intrinsic uncertainties associated with different biological and oceanographic causes 13 , 14 .…”

“…None of the studies presented the models in a framework of model validation against observations, nor were they concerned with the uncertainty inherent to the empirical measurements of connectivity when comparing predictions of the models to empirical observations 59 , 60 . Only three studies explicitly accounted for uncertainty into the decision of allocating larvae or recruits to parental populations 32 , 47 , 50 , and only 32 attempted a formal quantitative comparison between model predictions and observations. This uncertainty can be very large and probably depends on the number of populations.…”

Monitoring Spawning Activity in a Southern California Marine Protected Area Using Molecular Identification of Fish Eggs

“…Following the study, several collection dates were identified for their high diversity or notable species. Focusing on these dates, probability exposure maps were produced to show where the eggs were most likely to have been spawned based on the surface currents on the days prior to collection [ 30 ]. Most of these maps showed a high probability of local retention within the MPA, indicating its utilization as a spawning site for many of the fish species we identified.…”

Converging approaches for modeling the dispersal of propagules in air and sea

“…However, Ecological Modelling published 6 papers citing Bartsch et al (1989) or Miller (2007) and 18 papers citing Nathan et al (2002), Kuparinen (2006) or Nathan et al (2011). Among the 685 publications that cited at least one of these 5 papers, only one (Fraker et al, 2015) cited Bartsch et al (1989) or Miller (2007) and Nathan et al (2002), Kuparinen (2006) or Nathan et al (2011). Fraker et al (2015 indeed briefly discussed the applicability of dispersal models to both marine and terrestrial organisms.…”