The king rail (Rallus elegans) is a secretive marsh bird that is threatened or endangered in eight of nine states and provinces in the Laurentian Great Lakes (Great Lakes) region. Available survey data suggests that this species has undergone population declines across this region and these are believed to have been driven by habitat loss and degradation. An improved understanding of the amount and type of habitat king rails require during the breeding season at sites within the Great Lakes region would inform and improve progress toward conservation goals. During 2019–2021, we caught and radio‐tagged 14 king rails in northwestern Ohio and southeastern Michigan within impounded coastal wetlands of western Lake Erie. We used radio telemetry to identify breeding season (May–August) home‐range characteristics and third order habitat selection within home ranges (hereafter microhabitat). For the birds whose home range stabilized (N = 10), we found a mean home‐range size of 8.8 ha (±1.63 [SE]; range = 1.9 to 15.8). We generated a classification tree to determine which habitat characteristics were associated with king rail presence within home ranges in our study. We found that vegetative density within home ranges was particularly associated with king rail presence. Phragmites australis was also associated with king rail presence, despite its invasiveness and negative ecological impacts in the region, and could be selectively maintained to benefit king rails. Our results suggest that managers may be able to provide microhabitat for king rails by maintaining water depths of 6 to 17 cm and by promoting native, robust vegetation in the genera Carex and Juncus. Our findings could help inform wetland managers and conservation planners in the Great Lakes region, particularly in western Lake Erie coastal marshes, of patch sizes, water depths, plant communities, and vegetative structure preferred by king rails.

Migratory bird populations frequently consist of individuals that overwinter variable distances from the breeding site. Seasonal changes in photoperiod, which varies with latitude, underlie seasonal changes in singing frequency in birds. Therefore, migratory populations that consist of individuals that overwinter at different latitudes with large overwintering ranges could experience within-population variation in seasonal production of song. To test the influence of overwintering latitude on intrapopulation variance in song production in the spring, we subjected two groups of Eastern Song Sparrows (Melospiza melodia melodia) from the same partially migratory breeding population to different photoperiodic schedules associated with a 1,300-km difference in overwintering location. One group remained on the natural photoperiodic schedule of the breeding site (resident group) while the other group experienced a nonbreeding photoperiod that mimicked a southern migration in the fall followed by a northern migration back to the breeding site in the spring (migratory group). We compared song output between the two groups in three different stages (nonbreeding, prebreeding, and breeding). Little singing occurred during nonbreeding stage sample dates (20 November, 6 December) for the resident group, and no singing occurred for the migrant group. During the prebreeding stage (27 January, 7 February), significantly more singing occurred in the resident group than in the migrant group. During the breeding stage (21 March, 4 April), after a simulated migration for the migrants, song output was similar in both groups. These results suggest that within-population variation in wintering latitude may contribute to variation in seasonal changes in singing behavior, which may covary with readiness to breed. Studies utilizing confirmed migrants and residents, rather than merely simulated migrants and residents, are also needed to better understand these processes. K E Y W O R D Sbirdsong, connectivity, partial migration, photoperiod, timing | 749 BREWER Et al.

Reliable and efficient avian monitoring tools are required to identify population change and then guide conservation initiatives. Autonomous recording units (ARUs) could increase both the amount and quality of monitoring data, though manual analysis of recordings is time consuming. Machine learning could help to analyze these audio data and identify focal species, though few ornithologists know how to cater this tool for their own projects. We present a workflow that exemplifies how machine learning can reduce the amount of expert review time required for analyzing audio recordings to detect a secretive focal species (Sora; Porzana carolina). The deep convolutional neural network that we trained achieved a precision of 97% and reduced the amount of audio for expert review by ~66% while still retaining 60% of Sora calls. Our study could be particularly useful, as an example, for those who wish to utilize machine learning to analyze audio recordings of a focal species that has not often been recorded. Such applications could help to facilitate the effective conservation of avian populations.

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