skip to main content

Search for: All records

Creators/Authors contains: "James, Chase C."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    The ecological and oceanographic processes that drive the response of pelagic ocean microbiomes to environmental changes remain poorly understood, particularly in coastal upwelling ecosystems. Here we show that seasonal and interannual variability in coastal upwelling predicts pelagic ocean microbiome diversity and community structure in the Southern California Current region. Ribosomal RNA gene sequencing, targeting prokaryotic and eukaryotic microbes, from samples collected seasonally during 2014-2020 indicate that nitracline depth is the most robust predictor of spatial microbial community structure and biodiversity in this region. Striking ecological changes occurred due to the transition from a warm anomaly during 2014-2016, characterized by intense stratification, to cooler conditions in 2017-2018, representative of more typical upwelling conditions, with photosynthetic eukaryotes, especially diatoms, changing most strongly. The regional slope of nitracline depth exerts strong control on the relative proportion of highly diverse offshore communities and low biodiversity, but highly productive nearshore communities.

  2. Abstract The systematic substitution of direct observational data with synthesized data derived from models during the stock assessment process has emerged as a low-cost alternative to direct data collection efforts. What is not widely appreciated, however, is how the use of such synthesized data can overestimate predictive skill when forecasting recruitment is part of the assessment process. Using a global database of stock assessments, we show that Standard Fisheries Models (SFMs) can successfully predict synthesized data based on presumed stock-recruitment relationships, however, they are generally less skillful at predicting observational data that are either raw or minimally filtered (denoised without using explicit stock-recruitment models). Additionally, we find that an equation-free approach that does not presume a specific stock-recruitment relationship is better than SFMs at predicting synthesized data, and moreover it can also predict observational recruitment data very well. Thus, while synthesized datasets are cheaper in the short term, they carry costs that can limit their utility in predicting real world recruitment.