DOI: 10.5061/dryad.n5tb2rbtq
title: Quantifying thermal exposure for migratory riverine species: phenology of Chinook salmon populations predicts thermal stress

description: Migratory species are particularly vulnerable to climate change because
 habitat throughout their entire migration cycle must be suitable for the
 species to persist. For migratory species in rivers, predicting climate
 change impacts is especially difficult because there is a lack of
 spatially-continuous and seasonally-varying stream temperature data,
 habitat conditions can vary for an individual throughout its life cycle,
 and vulnerability can vary by life stage and season. To predict thermal
 impacts on migratory riverine populations, we first expanded a spatial
 stream network model to predict mean monthly temperature for 465,775 river
 km in the western U.S., and then applied simple yet plausible future
 stream-temperature change scenarios. We then joined stream temperature
 predictions to 44,396 spatial observations and life stage-specific
 phenology (timing) for 26 ecotypes (i.e. geographically distinct
 population groups expressing one of four distinct seasonal migration
 patterns) of Chinook salmon (Oncorhynchus tshawytscha), a phenotypically
 diverse anadromous salmonid that is ecologically and economically
 important but declining throughout its range. Thermal stress, assessed for
 each life stage and ecotype based on federal criteria, was influenced by
 migration timing rather than latitude, elevation, or migration distance,
 such that sympatric ecotypes often showed differential thermal exposure.
 Early-migration phenotypes were especially vulnerable due to prolonged
 residency in inland streams during the summer. We evaluated the thermal
 suitability of 31,699 stream km which are currently blocked by dams to
 explore reintroduction above dams as an option to mitigate the negative
 effects of our warmer stream temperature scenarios. Our results showed
 that negative impacts of stream temperature warming can be offset for
 almost all ecotypes if formerly occupied habitat above dams is made
 available. Our approach of combining spatial distribution and phenology
 data with spatially- and temporally-explicit temperature predictions
 enables researchers to examine thermal exposure of migrating populations
 that use seasonally-varying habitats.
description: Monthly stream temperature Seasonal stream
 temperature is essential for modelling differential vulnerability of
 contrasting ecotypes. We expanded a pre-existing spatial stream network
 (SSN) model, currently only available for summer months, to all months of
 the year in order to have a full picture of year-round thermal
 habitat. SSN models require a stream network, observed water temperatures
 at discrete locations, and spatially and/or temporally explicit
 covariates. This model uses the National Stream Internet (NSI) network,
 which was derived from the NHDPlus dataset and prepared for use with SSNs.
 We included observed stream temperatures from 1993 on, with the end year
 varying by region (2011-2015). Within each month, we averaged observed
 temperatures for sites that had multiple observations within a day and
 observations on at least 90% of days; we then used this monthly mean for
 the model. Mean monthly temperature was modeled as a function of ten
 spatial and two temporal covariates. The spatial covariates are elevation
 ('ELEV'; m), canopy ('CANOPY'; %), slope
 ('SLOPE'; m/m), annual precipitation ('PRECIP'; mm),
 cumulative drainage area ('CUMDRAINAG';
 km<sup>2</sup>), North American Albers northing coordinate
 ('Y_COORD'; m), upstream watershed area that is lake or
 reservoir ('NLCD11PC'; %), the amount of flow that is base flow
 ('BFI'; %), upstream watershed area that is glacier
 ('GLACIER'; %), and tailwater ('TAILWATER'; binary –
 0, 1). Each covariate was spatially linked with the stream network at a 1
 km interval. The temporal covariates are historical air temperature (°C)
 and flow (m<sup>3</sup>/s). We filled in gaps in the time
 series using an iterative PCA approach, then calculated monthly means and
 linked values to water temperature observations by year. Mean monthly
 temperature was modelled by fitting the SSN linear mixed model for each
 month in each of 8 sub-regional watersheds, spanning most of California,
 Oregon, and Washington. See our paper for additional modeling
 details. To validate each monthly model for each
 sub-regional watershed, we randomly split the water temperature data into
 a training dataset and a testing dataset based on spatial location, such
 that approximately 80% of the data were used for model fitting and 20% of
 the data were used for model validation. When a site had data in multiple
 years, we assigned all data for that site to either the training or
 testing dataset rather than being split between them so that our
 out-of-sample metrics would be an estimate of how the model performed in
 areas where we had no data. We performed leave-one-out cross validation on
 the training dataset and calculated three performance metrics on each
 training dataset and testing dataset: the square of the correlation
 coefficient between observations and predictions
 (<i>r<sup>2</sup></i>), the root mean square
 prediction error (RMSPE), and the mean absolute prediction error (MAPE).
 We then used the model to predict mean monthly water temperature at 1 km
 resolution for the period 2002-2011. The years 2002-2011 represent the
 most recent time period when most temperature records were collected.
 Predictions used the universal kriging equation which accounts for both
 the model predictors and spatial autocorrelation. Finally, we assigned
 temperatures in reaches representing manmade lakes and reservoirs
 ('FTYPE' = "ArtificialPath" &
 'WATERBODY' = 1) as "-9999"; these were not included
 in our analyses. Chinook salmon spatial distribution
 dataset We defined the spatial distributions of
 freshwater life stages for each Chinook salmon ecotype. Chinook salmon
 consist of federally recognized Ecologically Significant Units (ESU) based
 on geography and genetic relatedness at neutral markers, but an ESU can
 contain multiple run types, the trait that specifies the peak seasonal
 timing (i.e. spring, summer, fall, winter) of adult migration into
 freshwater and is used most frequently to define populations. Chinook
 salmon observations were extracted from eight field-observational and
 distributional data sources (1. Aquatic Species Observation Database,
 obtained via California Dept. of Fish and Wildlife; 2.
 http://www.calfish.org/DataandMaps/CalFishDataExplorer.aspx; 3.
 www.gbif.org; 4. www.iobis.org; 5. https://www.streamnet.org/; 6.
 https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys; 7. https://aquatic.biodata.usgs.gov; 8. http://vertnet.org) from August 2017-January 2018. We divided linear distribution datasets (StreamNet and CalFish) into points separated by 1 km and then added the points to the observational dataset. Observations have a georeferenced accuracy to 500 m, and each observation was linked with the appropriate ecotype, life stage, and month. We removed fish of unknown ecotype, unknown life stage, or known hatchery origin. To match the temporal limit of the stream temperature modeling project, we removed observations prior to 1993. We specified the ecotype and/or ESU (if not specified) by spatially merging observation locations with ESU distribution shapefiles (http://www.westcoast.fisheries.noaa.gov/maps_data/Species_Maps_Data.html). Due to low numbers and ambiguity of other life stages (e.g. “juvenile” could indicate rearing or outmigration), we focused our efforts on observations listing spawning location, such as those based on redd counts or spawner surveys. We supplemented our dataset with additional spawning and redd observations from the upriver bright fall-run ecotype in the ~90 km Hanford Reach of the Columbia River, digitized from several references. We removed duplicate observations from the entire spatial distribution dataset using R. Finally, we removed observations that georeferenced &gt; 500m from a stream. Ecotypes with fewer than 50 observations were not examined. This filtering resulted in a total of 44,396 spawning site observations. We defined pre-spawn holding habitat from spawning and redd locations. For additional details and a map figure, see the published paper. 
description: Attached are files for data reproduction. The stream temperature
 modeling results are attached as 8 shapefiles (.shp), corresponding to
 each sub-regional watershed. The Chinook salmon spatial distribution
 dataset and associated data is attached as a .csv. See associated
 README_StreamTemp and README_Chinook files.