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Biology subjects

Dawson, M. N.

Publications and source records attributed to Dawson, M. N..

2 recordsLinked to original sources

Genomic separation of Salish Sea and Pacific outer coast populations of the keystone sea star Pisaster ochraceus

Understanding dispersal and population structure in marine species is critical to forecasting ecological responses to climate change. The ochre sea star (Pisaster ochraceus), an intertidal keystone predator along the North American Pacific coast, has long been considered to have little spatial genetic structure due to its extended larval dispersal period and thus high potential for gene flow. Here we characterize the spatial genomic architecture of P. ochraceus using whole-genome sequencing (WGS) data from individuals spanning over 3,000km of coastline from Alaska to southern California. Our analyses of putatively neutral SNPs evince considerable mixing throughout the latitudinal range yet reveal substantial population structure between outer Pacific coast populations and those residing within the semi-enclosed estuary of the Salish Sea, suggesting restricted gene flow and demographic divergence between these regions. Genomic divergence is further supported by evidence of selection, with outlier loci highlighting extended regions of low diversity in the Salish Sea, consistent with recent selective sweeps and potential local adaptation to the distinct estuarine conditions. These findings are consistent with the role of oceanographic barriers or environmental heterogeneity in shaping population structure in P. ochraceus, challenging an earlier notion of range-wide homogeneity. By exploring neutral and adaptive genetic diversity in this keystone marine species, we provide valuable insights for its capacity to persist in a rapidly changing world.

evolutionary biology↗

A Biodiversity Composition Map of California Derived from Environmental DNA Metabarcoding and Earth Observation

Unique ecosystems globally are under threat from ongoing anthropogenic environmental change. Effective conservation management requires more thorough biodiversity surveys that can reveal system-level patterns and that can be applied rapidly across space and time. We offer a way to use environmental DNA, community science and remote sensing together as methods to reduce the discrepancy between the magnitude of change and historical approaches to measure it. Taking advantages of modern ecological models, we integrate environmental DNA and Earth observations to evaluate regional biodiversity patterns for a snapshot of time, and provide critical community-level characterization. We collected 278 samples in Spring 2017 from coastal, shrub and lowland forest sites in California, a large-scale biodiversity hotspot. We applied gradient forest to model 915 family occurrences and community composition together with environmental variables and multi-scalar habitat classifications to produce a statewide biodiversity-based map. 16,118 taxonomic entries recovered were associated with environmental variables to test their predictive strength on alpha, beta, and zeta diversity. Local habitat classification was diagnostic of community composition, illuminating a characteristic of biodiversity hotspots. Using gradient forest models, environmental variables predicted 35% of the variance in eDNA patterns at the family level, with elevation, sand percentage, and greenness (NDVI32) as the top predictors. This predictive power was higher than we found in published literature at global scale. In addition to this indication of substantial environmental filtering, we also found a positive relationship between environmentally predicted families and their numbers of biotic interactions. In aggregate, these analyses showed that strong eDNA community-environment correlation is a general characteristic of temperate ecosystems, and may explain why communities easily destabilize under disturbances. Our study provides the first example of integrating citizen science based eDNA with biodiversity mapping across the tree of life, with promises to produce large scale, high resolution assessments that promote a more comprehensive and predictive understanding of the factors that influence biodiversity and enhance its maintenance.

ecology↗