|Brodeur, Richard D.
|Duffy-Anderson, Janet Theresa
|Koslow, Julian Anthony
Ecological time series are critical to understanding and managing ocean ecosystem response to climate variability, climate change, and other stressors. NOAA has maintained an extensive network of ecological observations through ichthyoplankton (larval fish) survey programs providing fishery-independent time series data from southern California to Alaska. These data provide a robust framework for evaluating west coast-wide, cross-ecosystem shifts in fish populations that that may have occurred in response to climate variability and/or anthropogenic activities.
Project tasks are to (1) assemble ichthyoplankton data sets from the Bering Sea (20 years), Gulf of Alaska (30+ years), and the northern and southern California Current (Oregon 20 years, California 63 years); (2) assess coast-wide shared and unshared patterns of change in key species and assemblages and evaluate potential relationships with environmental drivers; and (3) evaluate which populations or assemblages may serve as indicators of ecosystem status and harbingers of climate change.
The project will test several key hypotheses: (1) Coast-wide shifts in ichthyoplankton community structure and abundance have occurred in response to changing ocean conditions. (2) Larval fishes along the west coast with cool-water affinities exhibit a decadal-scale decline at the southern edge and a possible increase at the northern end of their range, leading to the appearance of large-scale coherence along with a possible inverse response in Alaskan waters (Gulf of Alaska/Bering Sea) versus the California Current. (3) Multispecies complexes can be identified in each region to serve as indicators of biological impacts of changing ocean conditions (e.g. deoxygenation, warming).
We seek a quantitative fisheries ecologist with prior experience with time series analyses and working with large data sets (physical and biological) to join our multidisciplinary team of physical oceanographers, fisheries ecologists, stock assessment scientists, and fisheries statisticians to address the questions outlined above. Candidates with robust statistical modeling expertise that may include traditional multivariate analyses (PCA, hierarchical clustering, ordination), GAM, dynamic factor analysis, and/or Random Forest and Gradient Forest Models are preferred.
Koslow JA, Goericke R, Watson W: Fish assemblages in the Southern California Current: relationships with climate, 1951-2008. Fisheries Oceanography 22: 207-219, 2013
Brodeur RD, et al: Abundance and diversity of ichthyoplankton as indicators of recent climate change in an upwelling area off Oregon. Marine Ecology Progress Series 366: 187-202, 2008
McClatchie S, et al: Long time series in US fisheries oceanography. Oceanography 27: 48-67, 2014
Climate change; Fisheries; Ichthyoplankton; Ecosystem indicators; Time series; Statistical modeling; Recruitment;