Data and code: Microgeographic variation in demography and thermal regimes stabilize regional abundance of a widespread freshwater fish

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