DOI: 10.5061/dryad.f1vhhmh27
title: Data and code: Microgeographic variation in demography and thermal regimes stabilize regional abundance of a widespread freshwater fish

description: Predicting the persistence of species under climate change is an
 increasingly important objective in ecological research and management.
 However, biotic and abiotic heterogeneity can drive asynchrony in
 population responses at small spatial scales, complicating species-level
 assessments. For widely distributed species consisting of many fragmented
 populations, such as brook trout (Salvelinus fontinalis), understanding
 drivers of asynchrony in population dynamics can improve predictions of
 range-wide climate impacts. We analyzed demographic time-series from
 mark-recapture surveys of eleven natural brook trout populations in
 eastern Canada over 13 years to examine the extent, drivers, and
 consequences of fine-scale population variation. The focal populations
 were genetically differentiated, occupied a small area (~25 km2) with few
 human impacts, and experienced similar climate conditions. Recruitment was
 highly asynchronous, weakly related to climate variables, and showed
 population-specific relationships with other demographic processes,
 generating diverse population dynamics. In contrast, individual growth was
 mostly synchronized among populations and driven by a shared positive
 relationship with stream temperature. Outputs from population-specific
 models were unrelated to four of five hypothesized drivers (recruitment,
 growth, reproductive success, phylogenetic distance), but variation in
 groundwater inputs strongly influenced stream temperature regimes and
 stock-recruitment relationships. Finally, population asynchrony generated
 a portfolio effect that stabilized regional species abundance. Our results
 demonstrate that population demographic and habitat diversity at
 microgeographic scales can play a significant role in moderating species
 responses to climate change. Moreover, we suggest that the absence of
 human activities within study streams preserved natural habitat variation
 and contributed to asynchrony in brook trout abundance, while the small
 study area eased monitoring and increased the likelihood of detecting
 asynchrony. Therefore, anthropogenic habitat degradation, landscape
 context, and spatial scale must be considered when developing management
 strategies to monitor and maintain populations that are diverse, stable,
 and resilient to climate change.
description: There are three datasets included in this submission, but all are
 based on empirical or remotely-sensed data gathered from Cape Race
 (Newfoundland, Canada) for a study in Ecological Applications. Details for
 each dataset are listed below. File 1.) Recruitment
 and Growth Time Series_with DFA Covariates.csv This
 file contains time-series of recuitment (Var=Recruitment; age-1 census
 population size; units=number of individuals) and growth (Var=Growth;
 median age-1 growth rate; units=mm/year) for 11 populations of brook trout
 (column headers: BC, DY, HM, LC, LO, MC, STBC, UC, UO, WC, WN) and an
 additional metapopulation that combines data from LO and UO (column
 header: OB). Sampling_Year denotes years in which populations were sampled
 via mark-recapture, while Birth_Year denotes the year in which age-1
 individuals were born within each sampling year. Also provided are mean
 air temperature (column headers starting with T_; units=degrees Celsius)
 and total precipitation (column headers starting with P_; units=mm)
 calculated from the DayMet database for seven different time-periods.
 Time-periods are denoted by abbreviations at the end of each column header
 and include the growing season (GS; April-November), non-growing season
 (NonGS; December-March), reproduction (October 8-31), incubation
 (November-March), emergence (May), first summer (July-August), and first
 winter (December-February). Further details can be found in the Methods
 and Appendix S1. Missing data are included and represented by blank
 cells. File 2.) Age-Specific Abundance and Growth_with
 Stream Temperatures.csv This file contains data on
 total census population size (column header: Nc; units=number of
 individuals), recruitment (Nc_1), adult abundance (Nc_2), juvenile growth
 (Growth_1; units=mm/year), and adult growth (Growth_2; units=mm/year) from
 11 brook trout populations (Population=BC, DY, HM, LC, LO, MC, STBC, UC,
 UO, WC, or WN) and one meta-population that combines data from LO and UO
 (Population=OB). Data with 1- or 2-year lags are denoted by additional
 text (1YrAft, 2YrAft) at the end of the variable name, and confidence
 intervals for recruitment and adult abundance are denoted by the
 "LCI_" and "UCI_" columns. Year denotes years in which
 populations were sampled via mark-recapture, Brood_Year denotes the year
 in which age-1 individuals were born within each sampling year, and
 Proportion_Method denotes how the proportion of recruits (age-1) and
 adults (age-2 and older) was determined for each population during each
 year. Population-specific stream temperatures (column headers starting
 with StreamTemp_; units=degrees celsius) were estimated during the same
 periods outlined for File 1 (column headers ending in Rep=reproduction,
 Inc=incubation, Emrg=emergence, Sum=first summer, Win=first winter,
 GS=growing season). Accumulated degree-days were also estimated as the
 cumulative sum of daily stream temperatures from November-May 1st
 (DD_May1) and November-August 31st (DD_Aug31). Further details can be
 found in the Methods, Appendix S1, and Appendix S2. Missing data are
 included and represented by blank cells. File 3.)
 Reconstructed Stream Temperature_1980-2021 This file
 contains daily stream temperature data from 1980-2021, which were
 reconstructed based on daily air temperature records from DayMet and four
 parameters estimated in population-specific non-linear regressions that
 related observed stream temperature to air temperature using data
 collected between 2012 and 2021. Stream temperatures were estimated for 10
 populations of brook trout (column headers: BC, DY, HM, LC, LO, MC, STBC,
 UC, UO, WC, WN; units=degrees Celsius). Year and DayNum columns correspond
 to the year and ordinal day associated with each stream temperature
 estimate, while air temperature (column header: AirTemp; units=degrees
 Celsius) is also shown for each day and year. Winter_Year and
 Incubation_Year columns group together observations from the same
 incubation (November-March) and winter (December-February) period, which
 span different years. The Season column groups observations into winter
 (December-February), spring (March-May), summer (June-August) and fall
 (September-November) periods. The Life_Stage column groups observations
 into four time-periods used in File 1 (reproduction, incubation,
 emergence, first summer) and represents temperatures experienced by brook
 trout at various points in their ontogeny. The Growing_Season column
 groups observations from the growing season (Growing_Season=Yes;
 April-November) and non-growing season (Growing_Season=No). Further
 details can be found in the Methods and Appendix S2. Missing data are
 included and represented by blank cells.
description: These data files are designed to be analyzed using R Studio. The
 relevant R code for analysis is available on Zenodo.
description: # Title of Datasets File 1: Recruitment and Growth Time Series\_with DFA
 Covariates.csv Time-series of recruitment and growth for 11 brook trout
 populations, with relevant climate data used in correlation analyses and
 dynamic factor analysis. File 2: Age-Specific Abundance and Growth\_with
 Stream Temperatures.csv Demographic data for juvenile and adult brook
 trout across 11 populations, with relevant stream temperature data used to
 estimate GLMMs. File 3: Reconstructed Stream Temperature\_1980-2021.csv
 Population-specific stream temperature data estimated from daily air
 temperature used to characterize thermal regimes experienced by each
 population. ## Description of the data and file structure File 1: This
 file contains time-series of recuitment (Var=Recruitment; age-1 census
 population size; units=number of individuals) and growth (Var=Growth;
 median age-1 growth rate; units=mm/year) for 11 populations of brook trout
 (column headers: BC, DY, HM, LC, LO, MC, STBC, UC, UO, WC, WN) and an
 additional metapopulation that combines data from LO and UO (column
 header: OB). Sampling\_Year denotes years in which populations were
 sampled via mark-recapture, while Birth\_Year denotes the year in which
 age-1 individuals were born within each sampling year. Also provided are
 mean air temperature (column headers starting with T\_; units=degrees
 celsius) and total precipitation (column headers starting with P\_;
 units=mm) calculated from the DayMet database for seven different
 time-periods. Time-periods are denoted by abbreviations at the end of each
 column header and include the growing season (GS; April-November),
 non-growing season (NonGS; December-March), reproduction (October 8-31),
 incubation (November-March), emergence (May), first summer (July-August),
 and first winter (December-February). Further details can be found in the
 Methods and Appendix S1. Missing data are included and represented by
 blank cells. File 2: This file contains data on total census population
 size (column header: Nc; units=number of individuals), recruitment
 (Nc\_1), adult abundance (Nc\_2), juvenile growth (Growth\_1;
 units=mm/year), and adult growth (Growth\_2; units=mm/year) from 11 brook
 trout populations (Population=BC, DY, HM, LC, LO, MC, STBC, UC, UO, WC, or
 WN) and one meta-population that combines data from LO and UO
 (Population=OB). Data with 1- or 2-year lags are denoted by additional
 text (1YrAft, 2YrAft) at the end of the variable name, and confidence
 intervals for recruitment and adult abundance are denoted by the
 "LCI\_" and "UCI\_" columns. Year denotes years in
 which populations were sampled via mark-recapture, Brood\_Year denotes the
 year in which age-1 individuals were born within each sampling year, and
 Proportion\_Method denotes how the proportion of recruits (age-1) and
 adults (age-2 and older) was determined for each population during each
 year. Population-specific stream temperatures (column headers starting
 with StreamTemp\_; units=degrees celsius) were estimated during the same
 periods outlined for File 1 (column headers ending in Rep=reproduction,
 Inc=incubation, Emrg=emergence, Sum=first summer, Win=first winter,
 GS=growing season). Accumulated degree-days were also estimated as the
 cumulative sum of daily stream temperatures from November-May 1st
 (DD\_May1) and November-August 31st (DD\_Aug31). Further details can be
 found in the Methods, Appendix S1, and Appendix S2. Missing data are
 included and represented by blank cells. File 3: This file contains daily
 stream temperature data from 1980-2021, which were reconstructed based on
 daily air temperature records from DayMet and four parameters estimated in
 population-specific non-linear regressions that related observed stream
 temperature to air temperature using data collected between 2012 and 2021.
 Stream temperatures were estimated for 10 populations of brook trout
 (column headers: BC, DY, HM, LC, LO, MC, STBC, UC, UO, WC, WN;
 units=degrees celsius). Year and DayNum columns correspond to the year and
 ordinal day associated with each stream temperature estimate, while air
 temperature (column header: AirTemp; units=degrees celsius) is also shown
 for each day and year. Winter\_Year and Incubation\_Year columns group
 together observations from the same incubation (November-March) and winter
 (December-February) period, which span different years. The Season column
 groups observations into winter (December-February), spring (March-May),
 summer (June-August) and fall (September-November) periods. The
 Life\_Stage column groups observations into four time-periods used in File
 1 (reproduction, incubation, emergence, first summer) and represents
 temperatures experienced by brook trout at various points in their
 ontogeny. The Growing\_Season column groups observations from the growing
 season (Growing\_Season=Yes; April-November) and non-growing season
 (Growing\_Season=No). Further details can be found in the Methods and
 Appendix S2. Missing data are included and represented by blank cells. ##
 Sharing/Access information Data are only available on Dryad and code used
 to generate results is only available on Zenodo. Temperature and
 precipitation data were derived from the following sources: * DayMet
 (https://daymet.ornl.gov/) ## Code/Software All data were analyzed in R
 Studio with the code archived in Zenodo.