Discovery of SNPs for individual identification by reduced representation sequencing of moose (<i>Alces alces</i>)

<div><p>Monitoring of wild animal populations is challenging, yet reliable information about population processes is important for both management and conservation efforts. Access to molecular markers, such as SNPs, enables population monitoring through genotyping of various DNA sources. We have developed 96 high quality SNP markers for individual identification of moose (<i>Alces alces</i>), an economically and ecologically important top-herbivore in boreal regions. Reduced representation libraries constructed from 34 moose were high-throughput <i>de novo</i> sequenced, generating nearly 50 million read pairs. About 50 000 stacks of aligned reads containing one or more SNPs were discovered with the Stacks pipeline. Several quality criteria were applied on the candidate SNPs to find markers informative on the individual level and well representative for the population. An empirical validation by genotyping of sequenced individuals and additional moose, resulted in the selection of a final panel of 86 high quality autosomal SNPs. Additionally, five sex-specific SNPs and five SNPs for sympatric species diagnostics are included in the panel. The genotyping error rate was 0.002 for the total panel and probability of identities were low enough to separate individuals with high confidence. Moreover, the autosomal SNPs were highly informative also for population level analyses. The potential applications of this SNP panel are thus many including investigations of population size, sex ratios, relatedness, reproductive success and population structure. Ideally, SNP-based studies could improve today’s population monitoring and increase our knowledge about moose population dynamics.</p></div

SNP markers for sex-determination in moose.

<p>Five sex-specific SNPs located on the Y-chromosome are included in the panel.</p

Quantifying Migration Behaviour Using Net Squared Displacement Approach: Clarifications and Caveats

<div><p>Estimating migration parameters of individuals and populations is vital for their conservation and management. Studies on animal movements and migration often depend upon location data from tracked animals and it is important that such data are appropriately analyzed for reliable estimates of migration and effective management of moving animals. The Net Squared Displacement (NSD) approach for modelling animal movement is being increasingly used as it can objectively quantify migration characteristics and separate different types of movements from migration. However, the ability of NSD to properly classify the movement patterns of individuals has been criticized and issues related to study design arise with respect to starting locations of the data/animals, data sampling regime and extent of movement of species. We address the issues raised over NSD using tracking data from 319 moose (<i>Alces alces</i>) in Sweden. Moose is an ideal species to test this approach, as it can be sedentary, nomadic, dispersing or migratory and individuals vary in their extent, timing and duration of migration. We propose a two-step process of using the NSD approach by first classifying movement modes using mean squared displacement (MSD) instead of NSD and then estimating the extent, duration and timing of migration using NSD. We show that the NSD approach is robust to the choice of starting dates except when the start date occurs during the migratory phase. We also show that the starting location of the animal has a marginal influence on the correct quantification of migration characteristics. The number of locations per day (1–48) did not significantly affect the performance of non-linear mixed effects models, which correctly distinguished migration from other movement types, however, high-resolution data had a significant negative influence on estimates for the timing of migrations. The extent of movement, however, had an effect on the classification of movements, and individuals undertaking short- distance migrations can be misclassified as other movements such as sedentary or nomadic. Our study raises important considerations for designing, analysing and interpreting movement ecology studies, and how these should be determined by the biology of the species and the ecological and conservation questions in focus.</p></div

Filtering process for informative SNPs.

<p>Schematic overview of the selection process of autosomal SNPs for individual identification, starting at nearly 250 000 stacks of matching DNA sequences and resulting in the selection of 86 SNPs for final validation.</p

Probability of identity (PI) combining 83 autosomal SNPs.

<p>The PI decreases rapidly with increasing number of SNPs and reaches zero (PI < 0.01) with the five most informative SNPs. A combination of 10 SNPs is enough to correctly identify/separate first order relatives (PI_sibs).</p

Barplot showing the two clusters suggested by STRUCTURE.

<p>The SNP panel separate the 59 moose included in the SNP validation into two genetic clusters. Information about sampling location (south/north) was added to the figure after the analysis to visualize the concordance between assignment of genetic cluster and sampling location. Two individuals, SN8 (south-north 8) and NS8 (north-south 8), are pointed out since they show the most admixture between the two clusters.</p

Sampling locations.

<p>Moose (n = 34) included for <i>de novo</i> sequencing were sampled in the area around 16 locations (1–3 individuals per location) throughout Sweden. The name and geographic coordinates of the sampling locations are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197364#pone.0197364.s001" target="_blank">S1 Fig</a>.</p

The impact of “Starting Date” on the model outputs of migration parameters.

<p>Results are the averaged output for the 41 moose used in this study. The model outputs show estimates of distance (km²), timing (“From” is the start date of migration and “To” is the end date of migration) and duration (number of days) of the migrations. As per <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149594#pone.0149594.g001" target="_blank">Fig 1</a>, Migration 1 or 2 can be either a spring or autumn migration depending on the starting date of the data. For moose in northern Sweden, spring migrations occur in May and June and autumn migrations occur between November and January. For the quantification of start and end of migration, we used the formulas mentioned in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149594#pone.0149594.t001" target="_blank">Table 1</a>.</p

Comparison of migration model outputs at differing temporal resolutions of movement data and spatial resolution of starting locations using first recorded location and single location per day (SL), single location per day but a starting location that is the mean location during the first week (SLW), single location per day but a starting location that is the mean location during the first month (SLM) mean resolution data (MR) or high resolution data (HR).

<p>Comparison of migration model outputs at differing temporal resolutions of movement data and spatial resolution of starting locations using first recorded location and single location per day (SL), single location per day but a starting location that is the mean location during the first week (SLW), single location per day but a starting location that is the mean location during the first month (SLM) mean resolution data (MR) or high resolution data (HR).</p

Effect of selection of “Starting Date” for different months and the predictability of migration.

<p>The table shows the percentage of moose individuals (<i>n</i> = 26) that are in their winter or summer range.</p