DOI: 10.5061/dryad.47d7wm3pb
title: Data from: Estimating a physiologically-based threshold to oxygen and temperature from marine monitoring data reveals challenges and opportunities for forecasting distribution shifts

description: Species distribution modeling is increasingly used to describe and
 anticipate consequences of a warming ocean. These models often identify
 statistical associations between distribution and environmental conditions
 such as temperature and oxygen, but rarely consider the mechanisms by
 which these environmental variables affect metabolism. Oxygen and
 temperature jointly govern the balance of oxygen supply to oxygen demand,
 and theory predicts thresholds below which population densities are
 diminished. However, parameterizing models with this joint dependence is
 challenging because of the paucity of experimental work for most species,
 and the limited applicability of experimental findings in situ. Here we
 ask whether the joint effects of temperature and oxygen can be reliably
 inferred from species distribution observations in the field, using the
 U.S. Pacific Coast as a model system. We developed a statistical model
 that adapted the metabolic index—a compound metric that incorporates these
 joint effects on the ratio of oxygen supply and oxygen demand by applying
 an Arrhenius equation—and used a non-linear threshold function to link the
 index to fish distribution. Through simulation testing, we found that our
 statistical model could not precisely estimate the parameters due to
 inherent features of the distribution data. However, the model reliably
 estimated an overall metabolic index threshold effect. When applied to
 case studies of real data, it provided a better fit to sablefish
 (Anoplopoma fimbria) spatial distribution than previously used models.
 This physiological framework may improve predictions of species
 distribution, even in novel environmental conditions. Further efforts to
 combine insights from physiology and realized species distributions will
 improve forecasts of species’ responses to future environmental changes.
description: # Estimating a physiologically-based threshold to oxygen and temperature
 from marine monitoring data reveals challenges and opportunities for
 forecasting distribution shifts Dataset for the two case study
 applications from "*Estimating a physiologically-based threshold to
 oxygen and temperature from marine monitoring data reveals challenges and
 opportunities for forecasting distribution shifts*."
 `dat_longspine.rds` contains the bottom trawl survey data for longspine
 thornyhead, and `dat_sablefish.rds` contains the bottom trawl survey data
 for sablefish. This data is a subset of the NOAA West Coast Bottom Trawl
 Survey Data:
 [https://www.fisheries.noaa.gov/west-coast/science-data/us-west-coast-groundfish-bottom-trawl-survey](https://www.fisheries.noaa.gov/west-coast/science-data/us-west-coast-groundfish-bottom-trawl-survey). The full NOAA West Coast  Bottom Trawl data is publicly available from NOAA for other users, and this data file is not intended to be the comprehensive and used for other analysis purposes. These data files are included here for convenience to include only the years and species used in the manuscript, if a user is re-running the analysis code for this manuscript. There is additionally a zip file for the GitHub repository with all code to conduct analysis and create figures from the manuscript. See more details at  [https://github.com/jindivero/estimating_mi_from_distribution2](https://github.com/jindivero/estimating_mi_from_distribution2) The columns in the dat_longspine.rds and dat_sablefish.rds dataframes are: * trawl_id: the unique NOAA id for the trawl * species: common name for the species * year: year of survey * cpue_kg_km2: the trawl catch biomass in kilogram per kilometer squared * o2: measured dissolved oxygen in mg/L  * temp: measured temperature in Celsius * depth: measured depth in m * mi: calculated metabolic index * pO2: partial pressure of dissolved oxygen in kPa (converted from o2 column) * julian_day: the day of the year of the trawl * pass: NOAA metadata on the trawl * p1: proportion of trawl catch in the first size quartile * p2: proportion of trawl catch in the second size quartile * p3: proportion of trawl catch in the third size quartile * p4: proportion of trawl catch in the fourth size quartile * longitude: longitude of trawl * latitude: latitude of trawl * X: longitude converted into UTM * Y: latitude converted into UTM * temp_s: scaled temperature * log_depth_scaled: log of depth, scaled * log_depth_scaled2: log_depth_scaled squared, for quadratic polynomial in fish distribution model * jday_scaled2: julian_day, scaled and squared for model fitting * jday_scaled: julian_day, scaled * invtemp: inverse of temperature * cpue_s: cpue_kg_km2, scaled