Avian Behavior, Ecology, and Evolution

INTRODUCTION

Throughout the annual cycle, migratory birds face pressures that have shaped aspects of their morphology, physiology, and behavior (Rappole 1995). Balancing migration, molt, and breeding during each annual cycle, selection should favor those individuals able to adapt to novel and challenging situations (Hedenström 2008). In addition, birds face continual pressure from dynamic factors such as changes in weather, competition, predation, and human development. Of these many challenges, perhaps the omnipresent threat to survival is the ability to obtain and maintain sufficient energy reserves (Jenni and Jenni-Eiermann 1998). This is particularly true of small migratory passerines that overwinter in continental climates at higher latitudes (Bonter et al. 2013) that may lack familiarity with local food resources, foraging locations, and predators (McCabe and Olsen 2015).

In northern temperate regions, small passerines employ several strategies to deal with resource scarcity during the winter period. Some resident species, such as corvids (Corvidae), nuthatches (Sittidae), titmice, and chickadees (Paridae) employ caching or hoarding to store food for later use (Roberts 1979, Petit et al. 1989, Daw et al. 2025). Migratory birds tend not to extrinsically cache food and typically compensate for variation in resources by increasing body mass or fat reserves during winter, a strategy commonly known as winter fattening (Lima 1986, Blem 1990, Broggi et al. 2019). Both strategies may be adaptive when reduced day length limits the time available for foraging and long nights increase the fasting period, creating a seasonal bottleneck for energy acquisition (Carey and Dawson 1999). Minimizing starvation risk alone would be suboptimal if not balanced against the competing need to avoid predation (Houston et al. 1993). Winter fattening would be costly if increasing energy reserves requires additional foraging time, thereby exposing birds to an increased risk of predation (Lima 1986, Rogers 1987). Similarly, increased mass may negatively impact flight performance, whereby higher wing loading reduces take-off time or maneuverability during a predatory attack (Lilliendahl 1997, Rogers 2015). Therefore, individual foragers must balance this starvation-predation trade-off to maximize overwinter survival (Lima 1986, Houston and McNamara 1993). With minimal predation pressure, birds should theoretically rebuild reserves immediately after, and immediately prior to, nocturnal fasting, largely through bimodal feeding at dawn and dusk (Houston et al. 1993, Bonter et al. 2013). Recent studies of wild birds, especially non-hoarding species, suggest that birds tend to forage at a steady rate throughout the day as a means of risk-spreading in the face of predation pressure (Lilliendahl 1997, Gentle and Gosler 2001, Bonter et al. 2013).

To further balance this predation-starvation trade-off many species of birds can fine-tune energy reserves at small temporal and spatial scales (Houston et al. 1993, McNamara et al. 1994) and in direct response to variation in winter weather (see Pravosudov and Grubb 1998). Adverse winter weather conditions, marked by prolonged periods of cold temperatures, wind, and snow, reduce time available for foraging while, in the case of seed-eating passerines, simultaneously reduces access to available food resources (Rogers 2015). Winter fattening is thought to vary inversely with food predictability, especially for ground-foraging species facing reduced access to potential foraging sites due to periodic or unexpected snowfall (Rogers 1987, Lehikoinen 1987). Numerous winter field studies have demonstrated that the lower predictability of foraging success in ground-foraging passerines results in higher overall fat reserves than tree-feeding species (Lehikoinen 1986, Lilliendahl 2002). Variability in winter weather conditions could therefore have cascading effects on migration success, reproduction, and survival, making it critical to understand how environmental factors influence seasonal changes in body condition (Broggi et al. 2022). Winter fattening may be particularly important when the nonbreeding season overlaps with prebreeding migration because birds must accumulate sufficient reserves to fuel their initial migratory flights (Cooper et al. 2015, McKinnon et al. 2015).

American Tree Sparrows (Spizelloides arborea) breed near the treeline and spend the nonbreeding period in the southern Great Lakes and midwestern United States, where they inhabit open scrubby, early successional habitat and are frequent visitors to backyard bird feeders (Naugler et al. 2020). Based on tracking data in the southern Great Lakes, prebreeding migration occurs from early February to late April, and postbreeding migration occurs from mid-October to late November, meaning birds spend the peak winter months in this region (Morbey et al. 2025). Generally considered open cover foragers during winter, the species often forages on the ground in open fields and hedgerows, spending considerable time moving through trees and shrubs between foraging areas (Rogers 2015, Naugler et al. 2020). In winter, individual American Tree Sparrows generally forage alone or in small dynamic groups that can vary in number and species composition over time (Naugler et al. 2020).

Despite the importance of winter fattening for survival and migration, few studies have quantified how environmental factors influence seasonal changes in body condition in temperate passerines over-wintering at higher latitudes (Broggi et al. 2019, McKinnon et al. 2019). In particular, the relative effects of weather, habitat conditions, and timing within the nonbreeding period on energy reserves in American Tree Sparrows remain poorly understood. Understanding these patterns is critical for species experiencing steep population declines, as reductions in winter survival or body condition could exacerbate population-level impacts during migration and the breeding season. Here, we assess how environmental variables and seasonal timing influence body condition in American Tree Sparrows across multiple sites with varying winter climates in southern and eastern Ontario. Specifically, we focused on distinguishing among three competing hypotheses: (H₀) American Tree Sparrow body condition will show no consistent trend related to nonlinear seasonal patterns or short-term weather effects. (H₁) American Tree Sparrow body condition will show a predictable, nonlinear seasonal pattern regardless of short-term weather conditions, consistent with compensatory fattening, with birds showing the highest body condition in mid-winter and lowest at the start and end of winter; (H₂) American Tree Sparrow body condition will respond plastically to short-term weather conditions that impact food access and energetic costs, such that body condition varies with environmental variables like temperature and snow depth after accounting for seasonal trends. By integrating morphometric measurements with local weather data, we aim to distinguish between these three hypotheses and identify the key drivers of winter fattening to inform conservation strategies for this declining species.

METHODS

Study areas

American Tree Sparrows were captured at two locations in southern Ontario (Long Point Bird Observatory–Old Cut Research Station, 42.58°N, -80.39°W; Guelph Lake Nature Centre, 43.59°N, -80.26°W) and one location in eastern Ontario (Reveler Conservation Area, 45.21°N, -75.22°W). Old Cut is a lower elevation site (~180 m above sea level) on the north shore of Lake Erie, while Guelph Lake is a higher elevation inland site (~360 m above sea level) about 50 km west of Lake Ontario. Reveler Conservation Area is a low elevation inland site (~87 m above sea level) located between the Ottawa and St. Lawrence Rivers. Old Cut is within the Lake Erie–Lake Ontario Ecoregion classified as having a humid temperate climate with average annual temperatures of 6.3–9.4 °C and precipitation of 770–1020 mm (Ecoregions Working Group 1989). The Guelph Lake and Reveler sites are within the Lake Simcoe–Rideau Ecoregion with average annual temperatures of 4.9–7.8 °C and precipitation of 776–1018 mm (Ecoregions Working Group 1989).

Field methods and capture data

We captured birds (n = 565) using mist nets or baited ground traps from 18 February 2019 to 17 March 2021. We were unable to reliably capture birds until after the first significant snowfall event at the start of each winter, whereupon birds became more reliant on supplemental food and utilized feeders more regularly. These captures represented 291 unique individuals, and 130 birds were recaptured one or multiple times leading to 274 total recapture events. Capture dates overlapped with the nonbreeding and prebreeding migration periods, as defined by eBird Status and Trends (Fink et al. 2023). Birds were aged based on plumage (e.g., rectrix shape; Pyle 2008), but because of monomorphic plumage we were unable to sex birds. We measured unflattened wing length using a wing ruler and weighed birds to within ± 0.1 gram using digital scales. We grouped hatch year (HY, before 31 Dec) and second year (SY, after 31 Dec), and after hatch year (AHY, before 31 Dec) and after second year (ASY, after 31 Dec) age classes into “HY” and “AHY,” respectively (Macdonald et al. 2016).

Environmental variables

In addition to the demographic and morphometric data that were collected along with banding data, the date and time of capture were used to calculate three additional environmental variables: time since midnight, night duration, and days since winter start. Time since midnight is simply the number of minutes between midnight and the capture time of the bird. We included this to evaluate the effect of capture time on mass. Night duration was the number of minutes from sunset to sunrise on the capture date. This was included to capture the effect of changing day length on mass. Finally, days since winter start was used rather than day of year because the overwintering period of American Tree Sparrow covers the late fall of one year and the winter of the following year. We defined “winter” as 1 November to 30 April, with the continuous variable of days since winter start beginning on 1 November.

Weather data were obtained from the Environment Canada historic weather data archives (https://weather.gc.ca/forecast/canada/index_e.html?id=ON). Weather stations were selected to be as close as possible to the capture location with daily data collected for 2018, 2019, 2020, and 2021. Hourly data was also collected for certain weather variables for which daily means were calculated.

The closest weather station to Reveler Conservation Area was the St. Albert, Ontario, station (~15 km from the study area). Hourly values for dew point temperature, relative humidity, and wind speed and daily values for mean daily temperature, maximum daily temperature, minimum daily temperature and daily total precipitation were available for this station. Daily snow depth data was not available from St. Albert, so we obtained this data from the next nearest station at Moose Creek (~20 km from the study area).

The closest weather station to Guelph Nature Centre was the Guelph Turfgrass station (~8 km from the study area). Hourly values for dew point temperature, relative humidity and wind speed and daily values for mean daily temperature, maximum daily temperature, and minimum daily temperature were available for this station, however for daily snow depth and daily total precipitation there was no data and so data were used from nearest station, which was Fergus MOE (~14 km from the study area).

The closest weather station to Long Point Bird Observatory was the Delhi station (~30 km from the study area). Hourly values for dew point temperature, relative humidity, and wind speed, and daily values for mean daily temperature, maximum daily temperature, minimum daily temperature, and daily total precipitation were available for this station, however for daily snow depth there was no data and so data were used from nearest station, which was Tillsonburg (~40 km from the study area).

Statistical analysis

We performed all analyses and plotting in R (version 4.3.3). To ensure comparability and reduce the influence of differing units and scales, all continuous environmental and temporal predictors (e.g., temperature, precipitation, wind speed, humidity, night duration, and time-related variables) were standardized prior to analysis. Standardization was performed using the scale() function.

Weather variable selection

To evaluate at what temporal scale these weather variables might impact bird mass, we calculated mean values for each weather variable across multiple “weather windows” spanning 5, 10, 15, 20, 25, and 30 days prior to the capture date. This multi-window approach allowed us to test whether body mass responds primarily to short-term, more unpredictable environmental conditions (e.g., sudden heavy rainfall or snow accumulation) or longer-term cumulative effects. To identify the most informative “window,” we then used Conditional Inference Tree analysis to assess which of these windows were most important for predicting bird mass. This approach allowed us to determine which weather window provided the strongest explanatory power for variable in body mass. Specifically, windows were considered more informative when they were selected as primary or early splitting variables in the analysis and produced significant tree partitions in predicting body mass. The selected windows were then used as predictors in our final dataset. This process identified minimum daily temperature over the past 30 days, mean daily snow depth over the past 5 days, and mean humidity over the past 5 days as the most informative predictors.

As minimum temperature and days since winter start were correlated, we residualized temperature against the seasonal trend to isolate anomalous weather from predictable seasonal changes. Specifically, we fit a linear model of minimum daily temperature as a function of a quadratic polynomial of days since winter start and used the residuals as a measure of temperature deviation from the seasonal expectation. Negative values indicate colder-than-expected conditions for that time of winter, while positive values indicate warmer-than-expected conditions.

Adjusted mass measurements

We adjusted the obtained weights of individual birds to account for the presence of Motus (Taylor et al. 2017) tags on a small number of individuals each year. The mean weight of the Motus tags used was determined by calculating the average difference in weight between a bird caught without a tag and later with a tag. This average difference was then subtracted from the 82 birds that had tags.

In addition, rather than using raw weight in our analysis, we calculated the Scaled Mass Index (Peig and Green 2009) for each bird caught. Scaled Mass Index (SMI) is a size-corrected measure of body condition using weight and wing chord (we did not include measurements of tarsus length, or other body traits, because these were not available for all individuals and repeatability of other traits is generally lower than wing length; see Subasinghe et al. 2021). We interpreted higher SMI as indicative of greater energy reserves (including fat reserves), while recognizing that body mass also includes lean tissue.

To account for diurnal and photoperiodic effects on body condition, we further corrected SMI by fitting a linear model of SMI as a function of time since midnight (time of capture) and night duration (day length). Residuals from this model were used as a corrected measure of body condition in further analyses. We operationally termed this measure as “adjusted mass” to represent a more accurate measurement of energy reserves than mass alone. We used both first captures and recaptured birds in our analysis and accounted for this by including Band ID as a random effect in our models below.

Modeling framework

We used paired models with and without a smooth seasonal term to explicitly test whether compensatory fattening explained additional variation in adjusted mass beyond environmental predictors alone. This approach allowed us to separate predictable seasonal changes from short-term responses to weather conditions. We identified these predictable seasonal processes as smooth, repeatable changes in adjusted mass over winter driven by physiological cycles and reliable season cues and used a smooth function of days since winter start to represent this in our models. Short-term environmental conditions were represented using residual temperature, snow depth, and humidity.

To assess the relative importance of seasonal compensatory fattening and environmental variables in predicting individual adjusted mass, we constructed three nested generalized additive mixed models (GAMMs) using the gamm function in the mgcv package (Wood 2000). Our first model (1) included daily humidity, snow depth, age category, and minimum daily temperature as fixed effects with Location and Band ID as random effects and adjusted mass as a response variable. Our second model (2) included only the smooth term for days since winters start and age as fixed effects with Location and Band ID as random effects and adjusted mass as a response variable. Finally, our third model (3) included all weather effects (daily humidity, snow depth, daily minimum temperature), age, and the smooth term as fixed effects, Location and Band ID as random effects and adjusted mass as a response variable. Band ID was included as a random effect to account for repeated measures on the same birds. We included Location as a random effect to account for non-independence of observations within sites and to treat our three study sites as a sample of potential locations. Because our aim was to understand the effects of seasonal and weather effects rather than differences among specific study locations, location was not included as a fixed effect. We evaluated performance of the three different models using comparison of Akaike’s Information Criterion corrected for small sample size (AICc) and comparing marginal and conditional R2 values to estimate the proportion of variance explained in each model. This allowed us to quantify the unique and shared contributions of both seasonal changes and weather predictors on adjusted mass.

Model diagnostics

Model assumptions were assessed by examining the residuals from the linear mixed-effects (LME) component of each GAMM. We extracted normalized residuals and fitted values using the resid() and fitted() functions from the nlme package. To assess normality, we generated Q-Q plots with reference lines, and to evaluate homoscedasticity, we plotted normalized residuals against fitted values. These diagnostic plots did not reveal major deviations from model assumptions. These methods were used for all models.

RESULTS

Across the wintering period, American Tree Sparrow body mass exhibited a pronounced seasonal pattern. Adjusted mass increased from early winter through mid-winter, followed by a decrease toward late winter and early spring, forming an inverted U-shaped relationship with days since winter start (Fig. 1). This pattern remained evident after accounting for environmental variables and age.

Seasonal and weather effects on body condition

In our models assessing the relative importance of seasonal compensatory fattening and environmental variables on adjusted mass, we found that none of the environmental predictors or age classes had a statistically significant linear effect on adjusted mass (adjusted mass; all p > 0.15). However, there was a significant nonlinear seasonal trend in body condition across the winter period, captured by a smooth function of days since winter start (effective degrees of freedom = 6.32, F = 12.18, p < 0.001; Fig. 2). This indicates that adjusted mass varied over time, independent of temperature, snow depth, humidity, or age.

Relative contributions of seasonal and weather predictors

We compared these three nested models: a seasonal-only model (smooth term for days since winter + age), a weather-only model (temperature residuals, snow depth, humidity + age), and the full model (seasonal + weather + age).

The seasonal-only model explained 32.6% of the variance in individual adjusted mass (marginal R² = 0.326), whereas the weather-only model explained 11.7% (marginal R² = 0.117). The full model including both seasonal and weather predictors explained 17.9% of the variance (marginal R² = 0.179). Conditional R² values (including random effects for band number and location) were 0.938, 0.805, and 0.935 for the seasonal-only, weather-only, and full models, respectively. The lower marginal R² of the full model relative to the seasonal-only model indicates substantial overlap between seasonal and weather effects, consistent with weather being partly structured by season.

DISCUSSION

Our results highlight that seasonal dynamics exert a stronger influence on adjusted mass in American Tree Sparrows wintering in southern and eastern Ontario than did short-term environmental fluctuations or age-related differences. Despite testing several climatic variables across multiple temporal scales, none of the environmental predictors or age classes had significant linear effects on adjusted mass. Instead, we observed a pronounced nonlinear seasonal trend, suggesting that American Tree Sparrows exhibit predictable changes in adjusted mass over the course of winter independent of temperature, snow depth, or humidity (Fig. 2). Such phenologically driven patterns have been documented in other temperate-zone passerines (Lehikoinen 1987) and likely reflect adaptive cycles of compensatory fattening and depletion in response to predictable seasonal shifts in food resources.

The seasonal trajectory in adjusted mass we observed is consistent with our hypothesis that American Tree Sparrows undergo compensatory fattening in early and mid-winter to buffer against prolonged cold and limited foraging time, followed by a reduction in fat stores as conditions moderate and spring migration approaches. Cues related to photoperiod are known to regulate many physiological processes in birds (MacDougall-Shackleton et al. 2009, Sharp 2005) and may underlie the nonlinear patterns detected in our smooth terms for days since winter start. The lack of significant age effects suggests that both first-year and adult American Tree Sparrows follow similar overwintering strategies, perhaps reflecting the necessity of maintaining sufficient reserves to survive cold nights and unpredictable weather regardless of experience or dominance status (Laplante et al. 2019).

Maximizing winter fat reserves may be an optimal strategy for addressing starvation risk, but energetic needs must be balanced with reduced escape flight performance and increased foraging exposure leading to increased predation (Macleod et al. 2008, Bonter et al. 2013). Avian predators are likely responsible for much of the predation on wintering American Tree Sparrows, with Cooper’s Hawk (Astur cooperii) and American Kestrel (Falco sparverius) being documented predators in our study area (Prescott 1987). The Northern Shrike (Lanius borealis), although relatively uncommon during winter, is also a known predator of American Tree Sparrows (Hannah 2005) and was detected several times at our study sites. Although predation rates were not measured, predator diversity and abundance decline with increasing latitude (Diaz et al. 2013), suggesting predation pressure overall may be relatively low at our study latitude, especially given harsh abiotic factors during mid-winter. Similarly, of the avian predators that remain at high latitudes during winter, most are adapted to prey on species with larger body masses than the American Tree Sparrow (Miller et al. 2022). Overall, individual American Tree Sparrows may be able to balance the counteracting risks of predation and starvation and may be biased toward avoiding starvation in winter.

The strong nonlinear seasonal component on adjusted mass (Fig. 1) underscores the importance of internal or predictable seasonal drivers operating independently of daily weather. This may reflect inherent rhythms in fattening linked to photoperiod, changes in food availability through the winter, or social and behavioral dynamics within mixed-species flocks (Pakanen et al. 2018, Laplante et al. 2019, Keyser et al. 2023). Although the models explained only a modest portion of the variation in adjusted mass (adjusted R² = 0.15), such values are typical for field studies of free-living birds where unmeasured individual-level differences, such as dominance rank, microhabitat selection, and foraging efficiency, could play a large role in determining body mass.

By incorporating multiple spatially distinct sites across southern and eastern Ontario and accounting for tag mass and time-of-day effects, our analyses demonstrate that the observed patterns are robust to methodological and spatial variation. Although we did control for time-of-day effects, we cannot rule out possible capture bias, wherein colder or snowier conditions could impact which birds are more likely to enter our traps seeking food. Despite this, the consistent seasonal trends across sites suggest that compensatory fattening in American Tree Sparrows is a widespread and intrinsic component of their overwintering strategy. Looking forward, projected increases in winter temperature and declines in snow cover across Ontario (Graham et al. 2017, Ettinger et al. 2020) may alter both the magnitude and timing of fattening cycles. Such changes could influence overwinter survival probabilities and migration readiness, with potential population-level implications for this and other Arctic-breeding passerines.

In summary, American Tree Sparrows wintering in Ontario exhibit predictable seasonal shifts in body condition possibly linked to changes in photoperiod and food availability as winter progresses. Under historically stable winter weather patterns, this integrated strategy likely allowed individuals to balance starvation risk against the costs of increased mass, maximizing survival through long cold periods. However, ongoing climate-driven changes in winter weather patterns, including increased temperature variability (Williams et al. 2015), more frequent freeze-thaw cycles (Henry 2008), and unpredictable snow cover (Zhu et al. 2019), may disrupt the reliability of these cues (Horton et al. 2015, La Sorte et al. 2016). Periods of reduced snow cover followed by sudden record snowfall events or extreme cold may limit access to ground-based food resources while simultaneously increasing energetic demands, potentially overwhelming the buffering capacity of compensatory fattening strategies (Carey 2009, Aguilar-Kirigin and Naya 2013). For species such as the American Tree Sparrow, which already exhibits steep population declines and relies on winter survival to sustain populations (Naugler et al. 2020), such mismatches between energetic strategy and environmental conditions could contribute to reduced overwinter survival and have carry-over effects into migration and breeding. Understanding how these strategies equip this species to respond to ongoing climatic change will be critical for predicting overwinter survival and population persistence in this species, along with other arctic-breeding passerines.

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