Data from: Estimating a physiologically-based threshold to oxygen and temperature from marine monitoring data reveals challenges and opportunities for forecasting distribution shifts
Abstract
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.
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. 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
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