Data_Sheet_1_A Dynamic Stress-Scape Framework to Evaluate Potential Effects of Multiple Environmental Stressors on Gulf of Alaska Juvenile Pacific Cod.docx

• Main Authors: Josiah Blaisdell, Hillary L. Thalmann, Willem Klajbor, Yue Zhang, Jessica A. Miller, Benjamin J. Laurel, Maria T. Kavanaugh
• Format: Dataset
• Language: unknown
• Published: 2021
• Subjects: Oceanography; Marine Biology; Marine Geoscience; Biological Oceanography; Chemical Oceanography; Physical Oceanography; Marine Engineering; stress-scapes; Gulf of Alaska; machine learning; visualization; Pacific cod; multiple environmental stressors; Pacific; Ocean acidification; Alaska
• Online Access: https://doi.org/10.3389/fmars.2021.656088.s001; https://figshare.com/articles/dataset/Data_Sheet_1_A_Dynamic_Stress-Scape_Framework_to_Evaluate_Potential_Effects_of_Multiple_Environmental_Stressors_on_Gulf_of_Alaska_Juvenile_Pacific_Cod_docx/14577627

Quantifying the spatial and temporal footprint of multiple environmental stressors on marine fisheries is imperative to understanding the effects of changing ocean conditions on living marine resources. Pacific Cod (Gadus macrocephalus), an important marine species in the Gulf of Alaska ecosystem, has declined dramatically in recent years, likely in response to extreme environmental variability in the Gulf of Alaska related to anomalous marine heatwave conditions in 2014–2016 and 2019. Here, we evaluate the effects of two potential environmental stressors, temperature variability and ocean acidification, on the growth of juvenile Pacific Cod in the Gulf of Alaska using a novel machine-learning framework called “stress-scapes,” which applies the fundamentals of dynamic seascape classification to both environmental and biological data. Stress-scapes apply a probabilistic self-organizing map (prSOM) machine learning algorithm and Hierarchical Agglomerative Clustering (HAC) analysis to produce distinct, dynamic patches of the ocean that share similar environmental variability and Pacific Cod growth characteristics, preserve the topology of the underlying data, and are robust to non-linear biological patterns. We then compare stress-scape output classes to Pacific Cod growth rates in the field using otolith increment analysis. Our work successfully resolved five dynamic stress-scapes in the coastal Gulf of Alaska ecosystem from 2010 to 2016. We utilized stress-scapes to compare conditions during the 2014–2016 marine heatwave to cooler years immediately prior and found that the stress-scapes captured distinct heatwave and non-heatwave classes, which highlighted high juvenile Pacific Cod growth and anomalous environmental conditions during heatwave conditions. Dominant stress-scapes underestimated juvenile Pacific Cod growth across all study years when compared to otolith-derived field growth rates, highlighting the potential for selective mortality or biological parameters currently missing in the stress-scape model ...