Monthly Archives: April 2019

Genetic evidence of a northward range expansion in the Eastern Bering Sea stock of Pacific cod

Ingrid Spies1
1NOAA
May 7th, 2019 10:00 (PST): FSH 203

Genetic evidence of a northward range expansion in the Eastern Bering Sea stock of Pacific cod

Abstract: Poleward species range shifts have been predicted to result from climate change, and many observations have confirmed such movement. The abundant center hypothesis predicts that range shifts will take place by movement of individuals from core habitat to marginal habitat. However, poleward shifts may also represent a homogeneous shift in distribution, northward movement of specific populations, or colonization processes at the poleward edge of the distribution. The ecosystem of the Bering Sea has been changing along with the climate, moving from an arctic to a subarctic system. Several fish species have been observed further north than previously and in increasing abundances. We examined one of these fish species, Pacific cod, in the northern Bering Sea to assess whether they migrated from another stock in the eastern Bering Sea, Gulf of Alaska, or Aleutian Islands, or whether they represent separate populations. Genetic analyses using 3,457 SNP markers indicated that non-spawning cod collected in August 2017 in the northern Bering Sea were similar to spawning stocks of cod in the eastern Bering Sea. This result suggests escalating northward movement of the large eastern Bering Sea stock of Pacific cod that may be consistent with the abundant center hypothesis.

PuntLab Celebrates a Successful Centennial

The Punt Lab had a wonderful week celebrating the 100-year anniversary of SAFS at the “Super-Bevan” that took place this week. Graduate students Kristin Privitera-Johnson, Grant Adams and Postdoc Megsie Siple (standing in for Caitlin Allen) all presented during the three-day symposium. We had a great opportunity to take a group photo with some current and past lab members.

Many thanks to the wonderful sponsors and all the speakers at the Bevan, and a big “kudos” to Andre for putting on such a fun celebration!

Spatiotemporal models of populations in stream networks

Merrill Rudd1 Jim Thorson2
1Scaleability LLC
2NOAA
April 2nd, 2019 9:00 (PST): FSH 203

Spatiotemporal models of populations in stream networks

Abstract: Spatial and spatiotemporal models of stream networks are vital in the management and conservation of freshwater and anadromous fishes, often threated by terrestrial development along connected waters upstream. An ideal approach to modeling stream networks would 1) allow for spatial correlations over time or autocorrelations as a function of distance, 2) distinguish between process and observation error, and 3) overcome computational challenges to analyze large networks while estimating covariance structures. The latter two issues for estimating spatiotemporal densities are addressed using the Vector Autoregressive Spatio-Temporal (VAST) model. VAST has many features that make it an accessible and reproducible tool for single and multivariate spatiotemporal models with covariates. Previous applications have only used Gaussian Markov random fields to approximate spatial and spatiotemporal variation. We added a stream network spatial model that uses the Ornstein-Uhlenbeck process, a stochastic process that implies a child node is correlated with its parent node as a function of distance, where variation includes an exponential rate of decay in correlation between child and parent nodes and an asymptotic variance for two sites that are far apart. This presentation will provide an overview of updates to the VAST R package considering stream networks as a spatial model and preliminary results of its application for longfin eels in the Waitaki River catchment in New Zealand. The Waitaki River catchment has been heavily affected by dams, the largest of which was installed in 1948. Longfin eels are a cultural keystone species for Maori. In the face of increasing terrestrial development, impacts of dams, and continued harvest, spatiotemporal modeling including multiple types of information is vital for estimating longfin eel densities. This work will contribute to ongoing studies of New Zealand longfin eel populations and later other freshwater species. This modeling approach is also being applied to model juvenile and adult densities of coho salmon in Oregon. This study could benefit from the perspectives of scientists with stream network modeling experience or spatiotemporal modeling experience in other contexts.