DOI: 10.5061/dryad.1ns1rn90m
title: Data: Cumulative heat stress in fluctuating temperatures and implications for the distribution of freshwater fish

description: Predicting how rising temperatures will impact different species and
 communities is imperative and increasingly urgent with ongoing global
 warming. Here, we describe how thermal-death time curves obtained in the
 laboratory can be combined with an envelope model to predict the mortality
 of freshwater fish under field conditions and their distribution limits.
 We analyze the heat tolerance and distribution of 22 fish species
 distributed across North America and demonstrate that high temperatures
 imposed a distribution boundary for eleven of them, employing a null
 model. Importantly, predicted thermal boundaries closely match the warmest
 suitable locality of the envelope model. Simulated warming suggests that
 the distribution of fish species with lower heat tolerances will be
 disproportionately affected by rising temperatures, and the rate of local
 extinctions will be higher across fish communities in warmer localities.
 Ultimately, our analyses illustrate how physiological information can be
 combined with distribution models to forecast how warming temperatures are
 expected to impact different species and ecological communities.
description: # Data: Cumulative heat stress in fluctuating temperatures and
 implications for the distribution of freshwater fish Rezende and Carter
 (2024) Cumulative heat stress in fluctuating temperatures and implications
 for the distribution of freshwater fish. Global Change Biology (1)
 Datasets from Troia 2023 (`Data_Troia` - zip folder/data_nwis_v01) -
 Thermal profile from reference monitoring station across 66 river in US.
 '97 (Figures 1 and 2) - USGS National Water Information System:
 15-minute water temperature data from 66 study sites. File names are Site
 ID from National Water Information System (NWIS)
 - [https://waterdata.usgs.gov/nwis](https://waterdata.usgs.gov/nwis)
 Columns: * Source of data (USGS) * Site ID from National Water Information
 System (NWIS) * Date time * Time zone * Temperature in degrees Celsius *
 Measurement status (approved) (2) Species list (sheet = `species_list`) \
  Full list of fish species from the Integrated Taxonomic Information
 System. Columns: * common_name * scientific_name: Yellow highlighted
 indicated the species with thermal tolerance data available for
 distribution thermal boundary analysis. * iris_tsn: Taxonomic Serial
 Number from the Integrated Taxonomic Information System Species list
 (sheet = species_list_TDT) \ Heat tolerance parameters from the 22 species
 uses for distribution thermal boundary analysis (please see Table 1 main
 text). Columns: * common_name * scientific_name * iris_tsn: Taxonomic
 Serial Number from the Integrated Taxonomic Information System * CTmax:
 Upper critical thermal limit (°C) * z: Thermal sensitivity * guild:
 Thermal guild from Lyons et al. 2009, see table 1 in the main text * ref:
 see table 1 in the main text * sp_id: species id used in
 output_isoclines.txt, output_isoclines10.txt and output_sdm.txt (3) The
 `output_isoclines` derived from the analysis of mortality isoclines using
 data on fish thermal tolerance from the literature Columns: * ta50 -
 quantile 50% of temperature (ºC) * ta75 - quantile 75% of temperature (ºC)
 * ta95 - quantile 95% of temperature (ºC) * ta99 - quantile 99% of
 temperature (ºC) * sp1.rec - Mortality predicted (proportion) with daily
 recovery (1 to 0) * sp2.rec - Mortality predicted (proportion) with daily
 recovery (1 to 0) * ... * ... * sp22.rec - Mortality predicted
 (proportion) with daily recovery (1 to 0) (4) The `output_isoclines10`.
 Using the same script but raising 10ºC to obtain mortality in heat
 tolerant species (5) The `output_sdm` data were obtained for each species,
 indicating whether they occupied or remained unoccupied, utilizing the
 bioclimatic envelope model implemented by Troia (2023)\ Columns: * sp1.obs
 - Mortality observed\ 1 occupied - 0 unoccupied * sp2.obs - Mortality
 observed\ 1 occupied - 0 unoccupied * ... * ... * sp22.obs - Mortality
 observed\ 1 occupied - 0 unoccupied * lat - Latitude from the reference
 monitoring station * lon - Longitude from the reference monitoring station
 (6) References: Rezende, E. L., Bozinovic, F., Szilágyi, A. and Santos, M.
 2020. Predicting temperature mortality and selection in natural Drosophila
 populations. - Science 369: 1242–1245. Troia, M. J. 2023.
 Magnitude–duration relationships of physiological sensitivity and
 environmental exposure improve climate change vulnerability assessments. -
 Ecography. 2023: e06217.