• Energy Research

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. 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. These data files are designed to be analyzed using R Studio. The relevant R code for analysis is available on Zenodo. Funding provided by: Natural Sciences and Engineering Research CouncilCrossref Funder Registry ID: https://ror.org/01h531d29Award Number: Funding provided by: Groupe de Recherché Interuniversitaire en Limnologie*Crossref Funder Registry ID: Award Number: Funding provided by: Fulbright CanadaCrossref Funder Registry ID: https://ror.org/031fh2e54Award Number: Funding provided by: Concordia UniversityCrossref Funder Registry ID: https://ror.org/0420zvk78Award Number:

AbstractSalmonids are of immense socio‐economic importance in much of the world, but are threatened by climate change. This has generated a substantial literature documenting the effects of climate variation on salmonid productivity in freshwater ecosystems, but there has been no global quantitative synthesis across studies. We conducted a systematic review and meta‐analysis to gain quantitative insight into key factors shaping the effects of climate on salmonid productivity, ultimately collecting 1321 correlations from 156 studies, representing 23 species across 24 countries. Fisher's Z was used as the standardized effect size, and a series of weighted mixed‐effects models were compared to identify covariates that best explained variation in effects. Patterns in climate effects were complex and were driven by spatial (latitude, elevation), temporal (time‐period, age‐class), and biological (range, habitat type, anadromy) variation within and among study populations. These trends were often consistent with predictions based on salmonid thermal tolerances. Namely, warming and decreased precipitation tended to reduce productivity when high temperatures challenged upper thermal limits, while opposite patterns were common when cold temperatures limited productivity. Overall, variable climate impacts on salmonids suggest that future declines in some locations may be counterbalanced by gains in others. In particular, we suggest that future warming should (1) increase salmonid productivity at high latitudes and elevations (especially >60° and >1500 m), (2) reduce productivity in populations experiencing hotter and dryer growing season conditions, (3) favor non‐native over native salmonids, and (4) impact lentic populations less negatively than lotic ones. These patterns should help conservation and management organizations identify populations most vulnerable to climate change, which can then be prioritized for protective measures. Our framework enables broad inferences about future productivity that can inform decision‐making under climate change for salmonids and other taxa, but more widespread, standardized, and hypothesis‐driven research is needed to expand current knowledge.

Salmonids are of immense socio-economic importance in much of the world but are threatened by climate change. This has generated a substantial literature documenting effects of climate variation on salmonid productivity in freshwater ecosystems, but there has been no global quantitative synthesis across studies. We conducted a systematic review and meta-analysis to gain quantitative insight into key factors shaping the effects of climate on salmonid productivity, ultimately collecting 1,321 correlations from 156 studies, representing 23 species across 24 countries. Fisher's Z was used as the standardized effect size, and a series of weighted mixed-effects models were compared to identify covariates that best explained variation in effects. Patterns in climate effects were complex, and were driven by spatial (latitude, elevation), temporal (time-period, age-class), and biological (range, habitat type, anadromy) variation within and among study populations. These trends were often consistent with predictions based on salmonid thermal tolerances. Namely, warming and decreased precipitation tended to reduce productivity when high temperatures challenged upper thermal limits, while opposite patterns were common when cold temperatures limited productivity. Overall, variable climate impacts on salmonids suggest that future declines in some locations may be counterbalanced by gains in others. In particular, we suggest that future warming should (1) increase salmonid productivity at high latitudes and elevations (especially >60° and >1,500m), (2) reduce productivity in populations experiencing hotter and dryer growing season conditions, (3) favor non-native over native salmonids, and (4) impact lentic populations less negatively than lotic ones. These patterns should help conservation and management organizations identify populations most vulnerable to climate change, which can then be prioritized for protective measures. Our framework enables broad inferences about future productivity that can inform decision-making under climate change for salmonids and other taxa, but more widespread, standardized, and hypothesis-driven research is needed to expand current knowledge. See README document and R code.Funding provided by: Groupe de Recherché Interuniversitaire en Limnologie*Crossref Funder Registry ID: Award Number: PhD FellowshipFunding provided by: Fulbright CanadaCrossref Funder Registry ID: http://dx.doi.org/10.13039/100010081Award Number: Student AwardFunding provided by: Eco-Canada*Crossref Funder Registry ID: Award Number: Magnet Student Work Placement ProgramFunding provided by: Natural Sciences and Engineering Research Council of CanadaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000038Award Number: Discovery GrantFunding provided by: Concordia UniversityCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100002914Award Number: Research Chair in Population Biodiversity and ConservationFunding provided by: Concordia UniversityCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100002914Award Number: Graduate Doctoral Fellowship See README document.

# 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. 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. 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. These data files are designed to be analyzed using R Studio. The relevant R code for analysis is available on Zenodo.

Salmonids are of immense socio-economic importance in much of the world but are threatened by climate change. This has generated a substantial literature documenting effects of climate variation on salmonid productivity in freshwater ecosystems, but there has been no global quantitative synthesis across studies. We conducted a systematic review and meta-analysis to gain quantitative insight into key factors shaping the effects of climate on salmonid productivity, ultimately collecting 1,321 correlations from 156 studies, representing 23 species across 24 countries. Fisher’s Z was used as the standardized effect size, and a series of weighted mixed-effects models were compared to identify covariates that best explained variation in effects. Patterns in climate effects were complex, and were driven by spatial (latitude, elevation), temporal (time-period, age-class), and biological (range, habitat type, anadromy) variation within and among study populations. These trends were often consistent with predictions based on salmonid thermal tolerances. Namely, warming and decreased precipitation tended to reduce productivity when high temperatures challenged upper thermal limits, while opposite patterns were common when cold temperatures limited productivity. Overall, variable climate impacts on salmonids suggest that future declines in some locations may be counterbalanced by gains in others. In particular, we suggest that future warming should (1) increase salmonid productivity at high latitudes and elevations (especially >60° and >1,500m), (2) reduce productivity in populations experiencing hotter and dryer growing season conditions, (3) favor non-native over native salmonids, and (4) impact lentic populations less negatively than lotic ones. These patterns should help conservation and management organizations identify populations most vulnerable to climate change, which can then be prioritized for protective measures. Our framework enables broad inferences about future productivity that can inform decision-making under climate change for salmonids and other taxa, but more widespread, standardized, and hypothesis-driven research is needed to expand current knowledge. See README document and R code. See README document.