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Abstract A prominent challenge for managing migratory species is the development of conservation plans that accommodate spatiotemporally varying distributions throughout the year. Migratory networks are spatially‐explicit models that incorporate migratory assignment and seasonal abundance data to define patterns of connectivity between stages of the annual cycle. These models are particularly useful for widespread application because different types of migratory data can be used to quantify individual and population‐level movement across the annual cycle of migratory species. While there are clear benefits of combining migratory assignment and abundance data for the development of conservation strategies, there is a concurrent need for corresponding user‐friendly software to facilitate the integration of these data for conservation.Here, we presentmignette(migratory network tools ensemble), an R package for developing migratory network models to estimate network connectivity among migratory populations. We demonstrate the functionality ofmignettewith three empirical examples that highlight the use of different types of tracking data for migratory assignment.mignettefacilitates the modelling of migratory networks by providing R functions to: (1) define breeding and nonbreeding nodes, (2) assemble abundance and assignment data and (3) model the migratory network. Additionally,mignetteprovides R functions to visualize modelled migratory networks.With increasing availability of migratory assignment and abundance data,mignetterepresents a valuable tool for developing effective conservation strategies for migratory species.
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Population assignment from genotype likelihoods for low‐coverage whole‐genome sequencing data
Abstract Low‐coverage whole‐genome sequencing (WGS) is increasingly used for the study of evolution and ecology in both model and non‐model organisms; however, effective application of low‐coverage WGS data requires the implementation of probabilistic frameworks to account for the uncertainties in genotype likelihoods.Here, we present a probabilistic framework for using genotype likelihoods for standard population assignment applications. Additionally, we derive the Fisher information for allele frequency from genotype likelihoods and use that to describe a novel metric, theeffective sample size, which figures heavily in assignment accuracy. We make these developments available for application through WGSassign, an open‐source software package that is computationally efficient for working with whole‐genome data.Using simulated and empirical data sets, we demonstrate the behaviour of our assignment method across a range of population structures, sample sizes and read depths. Through these results, we show that WGSassign can provide highly accurate assignment, even for samples with low average read depths (<0.01X) and among weakly differentiated populations.Our simulation results highlight the importance of equalizing the effective sample sizes among source populations in order to achieve accurate population assignment with low‐coverage WGS data. We further provide study design recommendations for population assignment studies and discuss the broad utility of effective sample size for studies using low‐coverage WGS data.
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- Award ID(s):
- 1942313
- PAR ID:
- 10510114
- Publisher / Repository:
- Methods in Ecology and Evolution
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 15
- Issue:
- 3
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- 493 to 510
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/2041-210X.14286
