Assessing and mitigating privacy risks of sparse, noisy genotypes by local alignment to haplotype databases.

Single nucleotide polymorphisms (SNPs) from omics data create a reidentification risk for individuals and their relatives. Although the ability of thousands of SNPs (especially rare ones) to identify individuals has been repeatedly shown, the availability of small sets of noisy genotypes, from environmental DNA samples or functional genomics data, motivated us to quantify their informativeness. We present a computational tool suite, termed Privacy Leakage by Inference across Genotypic HMM Trajectories (PLIGHT), using population-genetics-based hidden Markov models (HMMs) of recombination and mutation to find piecewise alignment of small, noisy SNP sets to reference haplotype databases. We explore cases in which query individuals are either known to be in the database, or not, and consider several genotype queries, including those from environmental sample swabs from known individuals and from simulated "mosaics" (two-individual composites). Using PLIGHT on a database with ∼5000 haplotypes, we find for common, noise-free SNPs that only ten are sufficient to identify individuals, ∼20 can identify both components in two-individual mosaics, and 20-30 can identify first-order relatives. Using noisy environmental-sample-derived SNPs, PLIGHT identifies individuals in a database using ∼30 SNPs. Even when the individuals are not in the database, local genotype matches allow for some phenotypic information leakage based on coarse-grained SNP imputation. Finally, by quantifying privacy leakage from sparse SNP sets, PLIGHT helps determine the value of selectively sanitizing released SNPs without explicit assumptions about population membership or allele frequency. To make this practical, we provide a sanitization tool to remove the most identifying SNPs from genomic data.

Fine-scale habitat heterogeneity and vole runways influence seed dispersal in Plagiobothrys nothofulvus.

PREMISE: Seed dispersal allows plants to colonize new sites and contributes to gene flow among populations. Despite its fundamental importance to ecological and evolutionary processes, our understanding of seed dispersal is limited due to the difficulty of directly observing dispersal events. This is particularly true for the majority of plant species that are considered to have gravity as their primary dispersal mechanism. The potential for long-distance movement of gravity-dispersed seeds by secondary dispersal vectors is rarely evaluated. METHODS: We employ whole-genome assays of maternally inherited cpDNA in Plagiobothrys nothofulvus to resolve patterns of genetic variation due to effective (realized) seed dispersal within a 16 hectare prairie that is characterized by a mosaic of habitat types. We evaluate the effects of microgeographic landscape features extracted from micro-UAV aerial surveys on patterns of seed dispersal using landscape genetics methods. RESULTS: We found evidence of high resistance to seed-mediated gene flow (effective dispersal) within patches of Plagiobothrys nothofulvus, and strong genetic structure over distances of less than 20 m. Geographic distance was a poor predictor of dispersal distance, while landscape features had stronger influences on patterns of dispersal (distance and direction of seed movement). Patterns of dispersal were best predicted by the combined distribution of flower patches, habitat type, and the network of vole runways, with the latter explaining the largest proportion of variation in the model. CONCLUSIONS: Our results suggest that primary dispersal occurs mostly within microhabitats and infrequent secondary dispersal may occur over longer distances due to the activity of small mammals and other vertebrates.

Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology.

PREMISE OF THE STUDY: Low-elevation surveys with small aerial drones (micro-unmanned aerial vehicles [UAVs]) may be used for a wide variety of applications in plant ecology, including mapping vegetation over small- to medium-sized regions. We provide an overview of methods and procedures for conducting surveys and illustrate some of these applications. METHODS: Aerial images were obtained by flying a small drone along transects over the area of interest. Images were used to create a composite image (orthomosaic) and a digital surface model (DSM). Vegetation classification was conducted manually and using an automated routine. Coverage of an individual species was estimated from aerial images. RESULTS: We created a vegetation map for the entire region from the orthomosaic and DSM, and mapped the density of one species. Comparison of our manual and automated habitat classification confirmed that our mapping methods were accurate. A species with high contrast to the background matrix allowed adequate estimate of its coverage. DISCUSSION: The example surveys demonstrate that small aerial drones are capable of gathering large amounts of information on the distribution of vegetation and individual species with minimal impact to sensitive habitats. Low-elevation aerial surveys have potential for a wide range of applications in plant ecology.