Extreme spring conditions in the Arctic delay spring phenology of long-distance migratory songbirds.

Arctic regions are warming rapidly, with extreme weather events increasing in frequency, duration, and intensity just as in other regions. Many studies have focused on how shifting seasonality in environmental conditions affects vegetation phenology, while far fewer have examined how the breeding phenology of arctic fauna responds. We studied two species of long-distance migratory songbirds, Lapland longspurs, Calcarius lapponicus, and white-crowned sparrows, Zonotrichia leucophrys gambelii, across five consecutive breeding seasons in northern Alaskan tundra. We aimed to understand how spring environmental conditions affected breeding cycle phenology, including the timing of arrival on breeding grounds, territory establishment, and clutch initiation. Spring temperatures, precipitation, and snow-free dates differed significantly among years, with 2013 characterized by unusually late snow cover. In response, we found a significant delay in breeding-cycle phenology for both study species in 2013 relative to other study years: the first bird observed was delayed by 6-10 days, with mean arrival by 3-6 days, territory establishment by 6-13 days, and clutch initiation by 4-10 days. Further, snow cover, temperature, and precipitation during the territory establishment period were important predictors of clutch initiation dates for both species. These findings suggest that Arctic-breeding passerine communities may have the flexibility required to adjust breeding phenology in response to the increasingly extreme and unpredictable environmental conditions-although future generations may encounter conditions that exceed their current range of phenological flexibility.

Breeding on the leading edge of a northward range expansion: differences in morphology and the stress response in the arctic Gambel's white-crowned sparrow.

Individuals at the forefront of a range shift are likely to exhibit phenotypic traits that distinguish them from the population breeding within the historic range. Recent studies have examined morphological, physiological and behavioral phenotypes of individuals at the edge of their range. Several studies have found differences in the hypothalamic-pituitary-adrenal (HPA) axis activity in response to acute restraint stress in individuals at the range limits. HPA axis activation leads to elevations in glucocorticoids that regulate physiology and behavior. Here we compare the hormonal profiles and morphometrics from Gambel's white-crowned sparrows (Zonotrichia leucophrys gambelii) breeding at the northern limit of the population's range to those birds breeding within the historic population range. Birds breeding at the northern limit experienced a harsher environment with colder temperatures; however, we found no differences in arthropod prey biomass between the northern limit and more southern (historic) sites. Males at the northern limit had higher body condition scores (mass corrected for body size) compared to individuals within the historic range, but no differences were found in beak and tarsus lengths, wing chord, muscle profile or fat stores. In males during the pre-parental stage, before breeding commenced, HPA axis activity was elevated in birds at the northern limit of the range, but no differences were found during the parental or molt stages. Females showed no differences in HPA axis activity during the parental stage. This study suggests that "pioneering" individuals at the limits of their breeding range exhibit physiology and morphology that are distinct from individuals within the historic range.

The effect of extreme spring weather on body condition and stress physiology in Lapland longspurs and white-crowned sparrows breeding in the Arctic.

Climate change is causing rapid shifts in temperature while also increasing the frequency, duration, and intensity of extreme weather. In the northern hemisphere, the spring of 2013 was characterized as extreme due to record high snow cover and low temperatures. Studies that describe the effects of extreme weather on phenology across taxa are limited while morphological and physiological responses remain poorly understood. Stress physiology, as measured through baseline and stress-induced concentrations of cortisol or corticosterone, has often been studied to understand how organisms respond to environmental stressors. We compared body condition and stress physiology of two long-distance migrants breeding in low arctic Alaska - the white-crowned sparrow (Zonotrichia leucophrys) and Lapland longspur (Calcarius lapponicus) - in 2013, an extreme weather year, with three more typical years (2011, 2012, and 2014). The extended snow cover in spring 2013 caused measureable changes in phenology, body condition and physiology. Arrival timing for both species was delayed 4-5days compared to the other three years. Lapland longspurs had reduced fat stores, pectoralis muscle profiles, body mass, and hematocrit levels, while stress-induced concentrations of corticosterone were increased. Similarly, white-crowned sparrows had reduced pectoralis muscle profiles and hematocrit levels, but in contrast to Lapland longspurs, had elevated fat stores and no difference in mass or stress physiology relative to other study years. An understanding of physiological mechanisms that regulate coping strategies is of critical importance for predicting how species will respond to the occurrence of extreme events in the future due to global climate change.

Greater deciduous shrub abundance extends tundra peak season and increases modeled net CO2 uptake.

Satellite studies of the terrestrial Arctic report increased summer greening and longer overall growing and peak seasons since the 1980s, which increases productivity and the period of carbon uptake. These trends are attributed to increasing air temperatures and reduced snow cover duration in spring and fall. Concurrently, deciduous shrubs are becoming increasingly abundant in tundra landscapes, which may also impact canopy phenology and productivity. Our aim was to determine the influence of greater deciduous shrub abundance on tundra canopy phenology and subsequent impacts on net ecosystem carbon exchange (NEE) during the growing and peak seasons in the arctic foothills region of Alaska. We compared deciduous shrub-dominated and evergreen/graminoid-dominated community-level canopy phenology throughout the growing season using the normalized difference vegetation index (NDVI). We used a tundra plant-community-specific leaf area index (LAI) model to estimate LAI throughout the green season and a tundra-specific NEE model to estimate the impact of greater deciduous shrub abundance and associated shifts in both leaf area and canopy phenology on tundra carbon flux. We found that deciduous shrub canopies reached the onset of peak greenness 13 days earlier and the onset of senescence 3 days earlier compared to evergreen/graminoid canopies, resulting in a 10-day extension of the peak season. The combined effect of the longer peak season and greater leaf area of deciduous shrub canopies almost tripled the modeled net carbon uptake of deciduous shrub communities compared to evergreen/graminoid communities, while the longer peak season alone resulted in 84% greater carbon uptake in deciduous shrub communities. These results suggest that greater deciduous shrub abundance increases carbon uptake not only due to greater leaf area, but also due to an extension of the period of peak greenness, which extends the period of maximum carbon uptake.

Greater shrub dominance alters breeding habitat and food resources for migratory songbirds in Alaskan arctic tundra.

Climate warming is affecting the Arctic in multiple ways, including via increased dominance of deciduous shrubs. Although many studies have focused on how this vegetation shift is altering nutrient cycling and energy balance, few have explicitly considered effects on tundra fauna, such as the millions of migratory songbirds that breed in northern regions every year. To understand how increasing deciduous shrub dominance may alter breeding songbird habitat, we quantified vegetation and arthropod community characteristics in both graminoid and shrub dominated tundra. We combined measurements of preferred nest site characteristics for Lapland longspurs (Calcarius lapponicus) and Gambel's White-crowned sparrows (Zonotrichia leucophrys gambelii) with modeled predictions for the distribution of plant community types in the Alaskan arctic foothills region for the year 2050. Lapland longspur nests were found in sedge-dominated tussock tundra where shrub height does not exceed 20 cm, whereas White-crowned sparrows nested only under shrubs between 20 cm and 1 m in height, with no preference for shrub species. Shrub canopies had higher canopy-dwelling arthropod availability (i.e. small flies and spiders) but lower ground-dwelling arthropod availability (i.e. large spiders and beetles). Since flies are the birds' preferred prey, increasing shrubs may result in a net enhancement in preferred prey availability. Acknowledging the coarse resolution of existing tundra vegetation models, we predict that by 2050 there will be a northward shift in current White-crowned sparrow habitat range and a 20-60% increase in their preferred habitat extent, while Lapland longspur habitat extent will be equivalently reduced. Our findings can be used to make first approximations of future habitat change for species with similar nesting requirements. However, we contend that as exemplified by this study's findings, existing tundra modeling tools cannot yet simulate the fine-scale habitat characteristics that are critical to accurately predicting future habitat extent for many wildlife species.

NDVI as a predictor of canopy arthropod biomass in the Alaskan arctic tundra.

The physical and biological responses to rapid arctic warming are proving acute, and as such, there is a need to monitor, understand, and predict ecological responses over large spatial and temporal scales. The use of the normalized difference vegetation index (NDVI) acquired from airborne and satellite sensors addresses this need, as it is widely used as a tool for detecting and quantifying spatial and temporal dynamics of tundra vegetation cover, productivity, and phenology. Such extensive use of the NDVI to quantify vegetation characteristics suggests that it may be similarly applied to characterizing primary and secondary consumer communities. Here, we develop empirical models to predict canopy arthropod biomass with canopy-level measurements of the NDVI both across and within distinct tundra vegetation communities over four growing seasons in the Arctic Foothills region of the Brooks Range, Alaska, USA. When canopy arthropod biomass is predicted with the NDVI across all four growing seasons, our overall model that includes all four vegetation communities explains 63% of the variance in canopy arthropod biomass, whereas our models specific to each of the four vegetation communities explain 74% (moist tussock tundra), 82% (erect shrub tundra), 84% (riparian shrub tundra), and 87% (dwarf shrub tundra) of the observed variation in canopy arthropod biomass. Our field-based study suggests that measurements of the NDVI made from air- and spaceborne sensors may be able to quantify spatial and temporal variation in canopy arthropod biomass at landscape to regional scales.

Eavesdropping on the Arctic: Automated bioacoustics reveal dynamics in songbird breeding phenology.

Bioacoustic networks could vastly expand the coverage of wildlife monitoring to complement satellite observations of climate and vegetation. This approach would enable global-scale understanding of how climate change influences phenomena such as migratory timing of avian species. The enormous data sets that autonomous recorders typically generate demand automated analyses that remain largely undeveloped. We devised automated signal processing and machine learning approaches to estimate dates on which songbird communities arrived at arctic breeding grounds. Acoustically estimated dates agreed well with those determined via traditional surveys and were strongly related to the landscape's snow-free dates. We found that environmental conditions heavily influenced daily variation in songbird vocal activity, especially before egg laying. Our novel approaches demonstrate that variation in avian migratory arrival can be detected autonomously. Large-scale deployment of this innovation in wildlife monitoring would enable the coverage necessary to assess and forecast changes in bird migration in the face of climate change.