Environmental variability affects flexibility in activity onset at large geographic scales
Data files
Feb 18, 2026 version files 68.19 KB
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pele_data.csv
55.40 KB
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README.md
12.06 KB
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site_variance.csv
739 B
Abstract
Timing is an essential component of the phenotype, influencing organism fitness and fundamentally shaping ecological interactions. Although variation in the timing of activity can be substantial, the degree to which this variation stems from behavioral flexibility (within-individual variation) vs. consistent behavioral differences (among-individual variation) is poorly understood and represents a key knowledge gap as these have different ecological and evolutionary implications. Whereas flexibility in timing may be advantageous in novel circumstances, individuals could also make mistakes. Alternatively, consistent among-individual differences in timing may lead certain individuals to exhibit maladaptive behavior when confronting novel conditions (e.g., light pollution or novel predators). We estimated activity onset from 1050 captures of 483 individual white-footed mice, Peromyscus leucopus, across the upper Midwest, Northeast, Mid-Atlantic, and southern Appalachia regions of the United States. In each locale, we quantified the total phenotypic variation in activity onset and partitioned this into its within- and among-individual components. We then examined whether mice at locations experiencing more variable climate or residing in more structurally variable habitats exhibited differences in within- or among-individual variation in timing. We found that timing flexibility was always greater than consistency, and individuals from populations at cooler, drier sites with variable seasonal precipitation and temperature were more flexible than mice at warmer, wetter sites with less seasonal change in precipitation and temperature. Behavioral flexibility was not significantly associated with habitat structure heterogeneity. Our results reveal that most variation in timing across the range of this species derives from within-individual variation.
Overview
This dataset accompanies the manuscript Environmental variability affects individual variation in activity onset at large geographic scales. It contains two CSV files:
pele_data.csv— individual-level capture data for white-footed mice (Peromyscus leucopus) collected from March – October 2022 across 8 NEON sites across the upper Midwest, Northeast, Mid-Atlantic, and southern Appalachian regions of the United States as part of the National Ecological Observatory Network (NEON) monitoring protocol (NEON Doc #: NEON.DOC.000481). NEON sites used included BLAN: Blandy Experimental Farm, Virginia, HARV: Harvard Experimental Forest & Quabbin Watershed, Massachusetts, MLBS: Mountain Lake Biological Station, Virginia, ORNL: Oak Ridge National Laboratory, Tennessee, SCBI: Smithsonian Conservation Biology Institute, Virginia, SERC: Smithsonian Environmental Research Center, Maryland, STEI: Steigerwalt-Chequamegon, Wisconsin, and UNDE: University of Notre Dame Environmental Research Center, Michigan. These data were used in linear mixed-effects models to partition and estimate among- and within-individual variance in activity timing.site_variance.csv— site-level summaries including among- and within-individual variance estimates, mean activity timing, mean-scaled variance metrics, and measures of environmental and climatic variability at each NEON site. Raw rodent capture data and raw environmental data (habitat and climate) are available in NEON data repositories (see 'Data Availability). These data were used in linear models specifying variation in activity onset (Ii or Ir) as the response variable and climate or habitat variability as the predictor variable.
File descriptions
1. pele_data.csv
Each row represents a capture event for an individual mouse.
| Column name | Description | Units / Notes |
|---|---|---|
siteID |
NEON site identifier | Character |
process_date |
Date capture was processed | mm/dd/yyyy |
sex |
Biological sex of individual | M = male, F = female |
month |
Month of capture | 1–12 |
IDunique |
Unique identifier for each individual | Integer |
capture_date |
Date of capture | mm/dd/yyyy |
capture_time |
Time of capture. All times are given in Central Daylight Time (CDT) | hh:mm:ss |
hps |
Hours past sunset at capture | Numeric |
prop_elapsed |
Proportion of nighttime elapsed at capture | 0 = sunset, 1 = sunrise |
capnum |
Sequential capture number for the individual | Integer |
2. site_variance.csv
Each row represents a NEON site, summarizing site-level variance estimates and environmental characteristics.
| Column name | Description | Units / Notes |
|---|---|---|
siteID |
NEON site identifier | Character |
Vi |
Among-individual variance in capture timing | Derived from mixed-effects model |
Vr |
Within-individual (residual) variance in capture timing | Derived from mixed-effects model |
totalVar |
Total variance in capture timing (Vi + Vr) |
Numeric |
mean_timing |
Mean capture time at the site | Proportion of nighttime elapsed (0 = sunset, 1 = sunrise) |
Ii |
Mean-scaled individual variance | 100 × (Vi / mean_timing²) or equivalent |
Ir |
Mean-scaled residual variance | 100 × (Vr / mean_timing²) or equivalent |
PC1 |
Climate principal component summarizing mean annual temperature, relative humidity, precipitation, and monthly variation in these variables. High values = warmer, wetter sites with lower average relative humidity, less seasonal variation in precipitation/temperature, and more seasonal variation in relative humidity | Numeric |
buffer_cv |
Coefficient of variation in canopy height within trapping grid + 100 m buffer | Index of vegetation structural heterogeneity |
buffer_meanTRI |
Mean terrain ruggedness index (TRI) within grid + 100 m buffer | Index of topographic heterogeneity |
num_ids |
Number of unique individuals captured at the site | Integer |
num_obs |
Total number of capture events at the site | Integer |
num_obs_id |
Average number of captures per individual (num_obs / num_ids) |
Numeric |
Methods summary
Data were collected via NEON small mammal box trapping protocols across 8 sites. From March – October 2022, small mammal sampling was conducted at eight sites across the upper Midwest, Northeast, Mid-Atlantic, and southern Appalachian regions of the United States as part of the National Ecological Observatory Network (NEON) monitoring protocol (NEON Doc #: NEON.DOC.000481). We determined capture timing using paired iButton Thermochron temperature loggers (Maxim Integrated Inc., San Jose, CA) deployed in each live trap. In this method, one logger measures temperature inside the trap, while the other measures just outside the trap door. The captured rodent’s body heat raises the temperature inside the trap and the time at which the temperature diverges indicates the time of capture. Linear mixed-effects models were used to partition among- and within-individual variation in the timing of animal capture. Individual capture times were standardized relative to sunset and expressed both as hours past sunset and proportion of night elapsed. Site-level environmental and climatic variables were derived from NEON climate data and NEON Lidar data, canopy height models, and digital terrain models. See 'Data Availability' for links to these data available in NEON data repositories.
Data availability
All data files (pele_data.csv and site_variance.csv) will be made publicly available in the Dryad repository upon manuscript acceptance. Additional datasets are archived by NEON and available in the following NEON data repositories:
- Small mammal box trapping: https://doi.org/10.48443/p4re-p954
- Small mammal DNA extracts: https://doi.org/10.15468/6mxmvr
- Precipitation: https://doi.org/10.48443/zj63-h785
- Temperature and relative humidity: https://doi.org/10.48443/k9vk-5k27
- Canopy height models: https://doi.org/10.48443/zzz8-pr54
- Digital terrain models: https://doi.org/10.48443/hj77-kf64
Acknowledgments
This work was supported by the National Science Foundation (Award No. IOS-2110031). We thank the NEON small mammal teams and biorepository staff, Savannah Bartel, Amelia Weidemann, Elizabeth Locke, and Mark Fuka for assistance with iButton data collection, and Cristian D’Ambros for help with R code. NEON is sponsored by the NSF and operated under cooperative agreement by Battelle Memorial Institute.