Migratory animals often go to extraordinary lengths to follow broad-scale changes in the spatial distributions of food resources over time. An example is the molt-migration system of songbirds in the central and western United States, wherein birds interrupt migratory movements to molt their feathers in the ephemerally productive North American monsoon region in Northwestern Mexico. Within this large-scale migration system, the efficacy of individual migration behaviors relative to dynamics of primary productivity en route is unknown. We performed a comparison of real migration tracks from a common molt migrant, the painted bunting (Passerina ciris), with the results from millions of random-walk simulations to determine if real birds demonstrate optimal migratory behavior with respect to seasonal changes in primary productivity across the landscape. We scored migration routes of both real and simulated birds using an index of new plant growth, wherein daily values for each day of the autumn migration period were summed to indicate the total exposure to primary productivity. We then compared the highest-scoring simulated routes with real bird tracks and observed a high degree of correspondence between them, with the monsoon region featuring prominently in most of the high-ranking simulations. However, real birds generally left the breeding grounds later than virtual birds, perhaps because of life-history constraints not represented in the simulations. Our work supports the contention that painted bunting migration maximizes exposure to new plant growth and provides a novel approach for exploring migratory behaviors in other species and other regions.

A multisensor data fusion approach via acoustics, infrared camera, and marine radar is proposed and described in the application of avian monitoring. The ultimate scope of avian monitoring is to preserve the population of birds and bats, especially those listed in the endangered list, by observing their activity and behavior over the migration period. With the significant attention toward the construction of off-shore/on-shore wind farms in recent decades, the wind turbines are more threatening the avian life with increasing the risk of birds/bats collision with turbine blades. In order to address this problem, this paper proposes a fuzzy Bayesian-based multisensory data fusion approach to provide the activity information regarding the targets in the application of avian monitoring. The developed technique is used to process the Spring and Fall 2011 migration period.

Complex behavioral traits, such as those making up a migratory phenotype, are regulated by multiple environmental factors and multiple genes. We investigated possible relationships between microsatellite variation at two candidate genes implicated in the control of migratory behavior, Clock and Adcyap1, and several aspects of migratory life-history and evolutionary divergence in the Painted Bunting (Passerina ciris), a species that shows wide variation in migratory and molting strategies across a disjunct distribution. We focused on Clock and Adcyap1 microsatellite variation across three Painted Bunting populations in Oklahoma, Louisiana, and North Carolina, and for the Oklahoma breeding population we used published migration tracking data on adult males to explore phenotypic variation in individual migratory behavior. We found no correlation between microsatellite allele size within either Clock and Adcyap1 relative to the initiation or duration of fall migration in adult males breeding in Oklahoma. We also show the lack of significant correlations with aspects of the migratory phenotype for the Louisiana population. Our research highlights the limitations of studying microsatellite allelic mutations that are of undetermined functional influence relative to complex behavioral phenotypes.

Small‐bodied terrestrial animals such as songbirds (Order Passeriformes) are especially vulnerable to hail‐induced mortality; yet, hail events are challenging to predict, and they often occur in locations where populations are not being studied. Focusing on nesting grassland songbirds, we demonstrate a novel approach to estimate hail‐induced mortality. We quantify the relationship between the probability of nests destroyed by hail and measured Level‐III Next Generation Radar (NEXRAD) data, including atmospheric base reflectivity, maximum estimated size of hail and maximum estimated azimuthal wind shear. On 22 June 2014, a hailstorm in northern Colorado destroyed 102 out of 203 known nests within our research site. Lark bunting (Calamospiza melanocorys) nests comprised most of the sample (n = 186). Destroyed nests were more likely to be found in areas of higher storm intensity, and distributions of NEXRAD variables differed between failed and surviving nests. For 133 ground nests where nest‐site vegetation was measured, we examined the ameliorative influence of woody vegetation, nest cover and vegetation density by comparing results for 13 different logistic regression models incorporating the independent and additive effects of weather and vegetation variables. The most parsimonious model used only the interactive effect of hail size and wind shear to predict the probability of nest survival, and the data provided no support for any of the models without this predictor. We conclude that vegetation structure may not mitigate mortality from severe hailstorms and that weather radar products can be used remotely to estimate potential for hail mortality of nesting grassland birds. These insights will improve the efficacy of grassland bird population models under predicted climate change scenarios.