Abstract: A good overlap between offspring energetic requirements and availability of resources is required for successful reproduction. Accordingly, individuals from numerous species fine-tune their timing of breeding by integrating cues that predict environmental conditions during the offspring period. Besides acquiring information from their direct interaction with the environment (personal information), individuals can integrate information by observing the behaviours or performance of others (social information). The use of social information is often beneficial because the accumulated knowledge of conspecifics may represent a source of information more reliable than the intrinsically more limited personal information. However, although social information constitutes the major source of information in a wide range of contexts, studies investigating its use in the context of timing of breeding are scarce. We investigated whether black-legged kittiwakes (Rissa tridactyla) used social information to adjust the timing of egg-laying. We manipulated social information using a food-supplementation experiment, known to advance kittiwakes' reproductive phenology. We expected food-supplemented and unsupplemented pairs to delay and advance, respectively, their timing of laying when surrounded by a majority of neighbours from the opposite food-treatment. However, both unsupplemented and food-supplemented kittiwakes delayed egg-laying when surrounded by a higher proportion of neighbours from the opposite food-treatment. This result shows that kittiwakes use social information to time egg-laying, but that it is not used to match the seasonal peak of food availability. We suggest that when social and personal cues give contradictory environmental information, individuals may benefit from delaying laying to gather more information to make better decisions about investment into eggs. Further, we explored a potential proximate mechanism for the pattern we report. We show that baseline corticosterone, known to mediate reproductive decisions, was lower in unsupplemented females facing a higher proportion of food-supplemented neighbours. Altogether, our results suggest that to fine-tune their timing of laying, kittiwakes use complex decision-making processes in which social and personal information interplay.

Abstract: Fossorial locomotion is often considered as the most energetically costly of all terrestrial locomotion. Small arctic rodents, such as lemmings, dig tunnels not only in the soil but also through the snowpack, which is present for over 8 months of the year. Lemmings typically dig in the softest snow layer called the depth hoar but with climate change, melt-freeze and rain-on-snow (ROS) events are expected to increase in the Arctic, leading to a higher frequency of hardened snowpacks. We assessed the impacts of snow hardness on the locomotion of two lemming species showing different morphological adaptations for digging. We hypothesized that an increase in snow hardness would (1) decrease lemming performance and (2) increase their effort while digging, but those responses would differ between lemming species. We exposed four brown lemmings (Lemmus trimucronatus) and three collared lemmings (Dicrostonyx groenlandicus) to snow of different hardness (soft, hard, and ROS) during 30-min trials (n = 63 trials) in a cold room and filmed their behavior. We found that the digging speed and tunnel length of both species decreased with snow hardness and density, underlining the critical role of snow properties in affecting lemming digging performance. During the ROS trials, time spent digging by lemmings increased considerably and they also started using their incisors to help break the hard snow, validating our second hypothesis. Overall, digging performance was higher in collared lemmings, the species showing more morphological adaptations to digging, than in brown lemmings. We conclude that the digging performance of lemming is highly dependent on snowpack hardness and that the anticipated increase in ROS events may pose a critical energetic challenge for arctic rodent populations.

Abstract:

Due to recent climate change conditions, i.e. increasing temperatures and changing precipitation patterns, arctic snow cover dynamics exhibit strong changes in terms of extent and duration. Arctic amplification processes and impacts are well documented expected to strengthen in coming decades. In this context, innovative observation methods are helpful for a better comprehension of the spatial variability of snow properties relevant for climate research and hydrological applications.

Microwave remote sensing provides exceptional spatial and temporal performance in terms of all-weather application and target penetration. Time-series of Synthetic Active Radar images (SAR) are becoming more accessible at different frequencies and polarimetry has demonstrated a significant advantage for detecting changes in different media. Concerning arctic snow monitoring, SAR sensors can offer continuous time-series during the polar night and with cloud cover, providing a consequent advantage in regard of optical sensors.

The aim of this study is dedicated to the spatial/temporal variability of snow in the Ny-Ålesund area on the Br∅gger peninsula, Svalbard (N 78°55’ / E 11° 55’). The TerraSAR-X satellite (DLR, Germany) operated at X-band (3.1 cm, 9.6 GHz) with dual co-pol mode (HH/VV) at 5-m spatial resolution, and with high incidence angles (36° to 39°) poviding a better snow penetration and reducing topographic constraints. A dataset of 92 images (ascending and descending) is available since 2017, together with a high resolution DEM (NPI 5-m) and consistent in-situ measurements of meteorological data and snow profiles including glaciers sites.

Polarimetric processing is based on the Kennaugh matrix decomposition, copolar phase coherence (CCOH) and copolar phase difference (CPD). The Kennaugh matrix elements K₀, K₃, K_4, and K₇ are, respectively, the total intensity, phase ratio, intensity ratio, and shift between HH and VV phase center. Their interpretation allows analysing the structure of the snowpack linked to the near real time of in-situ measurements (snow profiles).

The X-band signal is strongly influenced by the snow stratigraphy: internal ice layers reduce or block the penetration of the signal into the snow pack. The best R² correlation performances between estimated and measured snow heights are ranging from 0.50 to 0.70 for dry snow conditions. Therefore, the use of the X-band for regular snow height estimations remains limited under these conditions.

Conversely, this study shows the benefit of TerraSAR-X thanks to the Kennaugh matrix elements analysis. A focus is set on the Copolar Phase Difference (CPD, Leinss 2016) between VV and HH polarization: Φ CPD = Φ _VV – Φ _HH. Our results indicate that the CPD values are related to the snow metamorphism: positive values correspond to dry snow (horizontal structures), negative values indicate recrystallization processes (vertical structures).

Backscattering evolution in time offer a good proxy for meteorological events detection, impacting on snow metamorphism. Fresh snowfalls or melting processes can then be retrieved at the regional scale and linked to air temperature or precipitation measurements at local scale. Polarimetric SAR time series is therefore of interest to complement satellite-based precipitation measurements in the Arctic.