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Migrating birds often fly in group formations during the daytime, whereas at night, it is generally presumed that they fly singly. However, it is difficult to quantify group behavior during nocturnal migration as there are few means of directly observing interactions among individuals. We employed an automated form of moonwatching to estimate percentages of birds that appear to migrate in groups during the night within the Central Flyway of North America. We compared percentages of birds in groups across the spring and fall and examined overnight temporal patterns of group behavior. We found groups were rare in both seasons, never exceeding 10% of birds observed, and were almost nonexistent during the fall. We also observed an overnight pattern of group behavior in the spring wherein groups were more commonly detected early in the night and again just before migration activity ceased. This finding may be related to changes in species composition of migrants throughout the night, or alternatively, it suggests that group formation may be associated with flocking activity on the ground as groups are most prevalent when birds begin and end a night of migration.

While researchers have investigated mating decisions for decades, gaps remain in our understanding of how behaviour influences social mate choice. We compared spatial cognitive performance and food caching propensity within social pairs of mountain chickadees inhabiting differentially harsh winter climates to understand how these measures contribute to social mate choice. Chickadees rely on specialized spatial cognitive abilities to recover food stores and survive harsh winters, and females can discriminate among males with varying spatial cognition. Because spatial cognition and caching propensity are critical for survival and likely heritable, pairing with a mate with such enhanced traits may provide indirect benefits to offspring. Comparing the behaviour of social mates, we found that spatial cognitive performance approached a significant correlation within pairs at low, but not at high elevation. We found no correlation within pairs in spatial reversal cognitive performance at either elevation; however, females at high elevation tended to perform better than their social mates. Finally, we found that caching propensity correlated within pairs at low, while males cached significantly more food than their social mates at high elevations. These results suggest that cognition and caching propensity may influence social mating decisions, but only in certain environments and for some aspects of cognition.

Social animals may use alternative strategies when foraging, with producer–scrounger being one stable dichotomy of strategies. While ‘producers’ search and discover new food sources, ‘scroungers’ obtain food discovered by producers. Previous work suggests that differences in cognitive abilities may influence tendencies toward being either a producer or a scrounger, but scrounging behaviour in the context of specialized cognitive abilities is less understood. We investigated whether food-caching mountain chickadees, which rely on spatial cognition to retrieve food caches, engage in scrounging when learning a spatial task. We analysed data from seven seasons of spatial cognition testing, using arrays of radio frequency identification-enabled bird feeders, to identify and quantify potential scrounging behaviour. Chickadees rarely engaged in scrounging, scrounging was not repeatable within individuals and nearly all scrounging events occurred before the bird learned the ‘producer’ strategy. Scrounging was less frequent in harsher winters, but adults scrounged more than juveniles, and birds at higher elevations scrounged more than chickadees at lower elevations. There was no clear association between spatial cognitive abilities and scrounging frequency. Overall, our study suggests that food-caching species with specialized spatial cognition do not use scrounging as a stable strategy when learning a spatial task, instead relying on learning abilities.

Climate change is increasing aridity in grassland and desert habitats across the southwestern United States, reducing available resources and drastically changing the breeding habitat of many bird species. Increases in aridity reduce sound propagation distances, potentially impacting habitat soundscapes, and could lead to a breakdown of the avian soundscapes in the form of loss of vocal culture, reduced mating opportunities, and local population extinctions. We developed an agent‐based model to examine how changes in aridity will affect both sound propagation and the ability of territorial birds to audibly contact their neighbors. We simulated vocal signal attenuation under a variety of environmental scenarios for the south, central semi‐arid prairies of the United States, ranging from contemporary weather conditions to predicted droughts under climate change. We also simulated how changes in physiological conditions, mainly evaporative water loss (EWL), would affect singing behavior. Under contemporary and climate change‐induced drought conditions, we found that significantly fewer individuals successfully contacted all adjacent neighbors than did individuals in either the contemporary or predicted climate change conditions. We also found that at higher sound frequencies and higher EWL, fewer individuals were able to successfully contact all their neighbors, particularly in drought and climate change drought conditions. These results indicate that climate change‐mediated aridification may alter the avian soundscape, such that vocal communication no longer effectively functions for mate attraction or territorial defense. As climate change progresses, increased aridity in current grasslands may favor shifts toward low‐frequency songs, colonial resource use, and altered songbird community compositions.

Avian migration has fascinated humans for centuries. Insights into the lives of migrant birds are often elusive; however, recent, standalone technological innovations have revolutionized our understanding of this complex biological phenomenon. A future challenge for following these highly mobile animals is the necessity of bringing multiple technologies together to capture a more complete understanding of their movements. Here, we designed a proof-of-concept multi-sensor array consisting of two weather surveillance radars (WSRs), one local and one regional, an autonomous moon-watching sensor capable of detecting birds flying in front of the moon, and an autonomous recording unit (ARU) capable of recording avian nocturnal flight calls. We deployed this array at a field site in central Oklahoma on select nights in March, April, and May of 2021 and integrated data from this array with wind data corresponding to this site to examine the influence of wind on the movements of spring migrants aloft across these spring nights. We found that regional avian migration intensity is statistically significantly negatively correlated with wind velocity, in line with previous research. Furthermore, we found evidence suggesting that when faced with strong, southerly winds, migrants take advantage of these conditions by adjusting their flight direction by drifting. Importantly, we found that most of the migration intensities detected by the sensors were intercorrelated, except when this correlation could not be ascertained because we lacked the sample size to do so. This study demonstrates the potential for multi-sensor arrays to reveal the detailed ways in which avian migrants move in response to changing atmospheric conditions while in flight.

Quantification of nocturnal migration of birds through moon watching is a technique ripe for modernization with superior computational power. In this paper, collected by a motorized telescope mount was data analyzed using both video observations by trained observers and modernized approaches using computer vision. The more advanced data extraction used the OpenCV library of computer vision tools to identify bird silhouettes by means of image stabilization and background subtraction. The silhouettes were sanitized and analyzed in sequence to produce stacked relationships between temporally close contours, discriminating birds from noise based on the assumption that birds migrate in stable paths. The flight ceiling of the birds was determined by extracting relevant correlation coefficient data from doppler radar co-located with the LunAero instrument in Norman, OK, USA using a method with low-computational overhead. The bird paths and flight ceiling were combined with lunar ephemera to provide input for the original method used for nocturnal migration quantification as well as an enhanced version of the same method with more advanced computational tools. We found that the manual quantification of migration activity detected 16,300 birds/km•h heading northwest from 110°, whereas the automated analysis reported a density of 43,794 birds/km•h heading northwest from 106.67°. Hence, there was agreement with regard to flight direction, but the automated method overestimated migration density by approximately three times. The reasons for the discrepancy between flight path detection appeared to be due to a substantial amount of noise in the video data as well as a tendency for the computer vision analysis to split single flight paths into two or more segments. The authors discuss ongoing innovations aimed at addressing these methodological challenges.