Conserving migratory birds is made especially difficult because of movement among spatially disparate locations across the annual cycle. In light of challenges presented by the scale and ecology of migratory birds, successful conservation requires integrating objectives, management, and monitoring across scales, from local management units to ecoregional and flyway administrative boundaries. We present an integrated approach using a spatially explicit energetic-based mechanistic bird migration model useful to conservation decision-making across disparate scales and locations. This model moves a Mallard-like bird (Anas platyrhynchos), through spring and fall migration as a function of caloric gains and losses across a continental-scale energy landscape. We predicted with this model that fall migration, where birds moved from breeding to wintering habitat, took a mean of 27.5 d of flight with a mean seasonal survivorship of 90.5% (95% Cl = 89.2%, 91.9%), whereas spring migration took a mean of 23.5 d of flight with mean seasonal survivorship of 93.6% (95% CI = 92.5%, 94.7%). Sensitivity analyses suggested that survival during migration was sensitive to flight speed, flight cost, the amount of energy the animal could carry, and the spatial pattern of energy availability, but generally insensitive to total energy availability per se. Nevertheless, continental patterns in the bird-use days occurred principally in relation to wetland cover and agricultural habitat in the fall. Bird-use days were highest in both spring and fall in the Mississippi Alluvial Valley and along the coast and near-shore environments of South Carolina. Spatial sensitivity analyses suggested that locations nearer to migratory endpoints were less important to survivorship; for instance, removing energy from a 1036 km2 stopover site at a time from the Atlantic Flyway suggested coastal areas between New Jersey and North Carolina, including the Chesapeake Bay and the North Carolina piedmont, are essential locations for efficient migration and increasing survivorship during spring migration but not locations in Ontario and Massachusetts. This sort of spatially explicit information may allow decision-makers to prioritize their conservation actions toward locations most influential to migratory success. Thus, this mechanistic model of avian migration provides a decision-analytic medium integrating the potential consequences of local actions to flyway-scale phenomena.

We developed a continental energetics‐based model of daily mallard (Anas platyrhynchos) movement during the non‐breeding period (September to May) to predict year‐specific migration and overwinter occurrence. The model approximates movements and stopovers as functions of metabolism and weather, in terms of temperature and frozen precipitation (i.e., snow). The model is a Markov process operating at the population level and is parameterized through a review of literature. We applied the model to 62 years of daily weather data for the non‐breeding period. The average proportion of available habitat decreased as weather severity increased, with mortality decreasing as the proportion of available habitat increased. The most commonly used locations during the course of the non‐breeding period were generally consistent across years, with the most inter‐annual variation present in the overwintering area. Our model revealed that the distribution of mallards on the landscape changed more dramatically when the variation in daily available habitat was greater. The main routes for avian migration in North America were predicted by our simulations: the Atlantic, Mississippi, Central, and Pacific flyways. Our model predicted an average of 77.4% survivorship for the non‐breeding period across all years (range = 76.4%–78.4%), with lowest survivorship during autumn (90.5 ± 1.4%), intermediate survivorship in winter (91.8 ± 0.7%), and greatest survivorship in spring (93.6 ± 1.1%). We provide the parameters necessary for exploration within and among other taxa to leverage the generalizability of this migration model to a broader expanse of bird species, and across a range of climate change and land use/land cover change scenarios.

AimA key aspect of the ecology and management of biological invasions is the prevalence and duration of lag phases in population growth. Here, we explore the occurrence of lag phases in exotic bird populations using the Audubon Christmas Bird Count database.LocationHawaiian Island archipelago.MethodsWe expand on the use of piecewise model fitting techniques to detect lags in exotic bird populations on Hawaii. We searched for explanations as to the occurrence of these lags using five possible mechanisms (body size, niche breadth, propagule pressure, length of record and lag phase growth rate).ResultsWe found evidence of lag phases for 14 of 17 species we evaluated (range: 10–38 years, mean using observed data = 16 ± 12), and we discovered very rapid growth to maximum abundance following the end of the lag phase (mean using observed data = 8 ± 6 years). We found no evidence for any association between the possible mechanisms influencing the occurrence and duration of the lag phases.Main conclusionsOur results are the first to rigorously quantify lags in exotic animal populations; most existing evidence comes from plants. We show that lags are as common in birds as in plants, although we provide preliminary evidence that the duration of lags in birds is shorter than in plants. We highlight the need for continued efforts to elucidate lag phase occurrence and duration in biological invasions, and we demonstrate the expanded utility of piecewise model fitting approaches to quantify these lags using count data.

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Tree swallow, Tachycineta bicolor, nestlings were collected from 60 sites in the Great Lakes, which included multiple sites within 27 Areas of Concern (AOCs) and six sites not listed as AOCs from 2010 to 2014. Nestlings, approximately 12 days-of-age, were evaluated for ethoxyresorufin-O-dealkylase (EROD) activity, chromosomal damage, and six measures of oxidative stress. Data on each of these biomarkers were divided into four equal numbered groups from the highest to lowest values and the groups were compared to contaminant concentrations using multivariate analysis. Contaminant concentrations, from the same nestlings, included polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), perfluorinated compounds (PFCs), and 17 elements. Alkylated polycyclic aromatic hydrocarbons (aPAHs) and parent PAHs (pPAHs) were measured in pooled nestling dietary samples. Polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans, and pesticides were measured in sibling eggs. Concentrations of aPAHs, pPAHs, chlordane, dieldrin, heptachlor, and PCBs, in that order, were the major contributors to the significant differences between the lowest and highest EROD activities; PFCs, PBDEs, the remaining pesticides, and all elements were of secondary importance. The four categories of chromosomal damage did not separate out well based on the contaminants measured. Concentrations of aPAHs, pPAHs, heptachlor, PCBs, chlordane, and dieldrin were the major contributors to the significant differences between the lowest and highest activities of two oxidative stress measures, total sulfhydryl (TSH) activity and protein bound sulfhydryl (PBSH) activity. The four categories of thiobarbituric acid reacting substances (TBARS), oxidized glutathione (GSSG), reduced glutathione (GSH), and the ratio of GSSG/GSH did not separate well based on the contaminants measured.