Gastrointestinal microbiota play a vital role in maintaining organismal health, through facilitating nutrient uptake, detoxification and interactions with the immune system. The gastrointestinal microbiota of birds has been poorly studied, especially in wild species under natural conditions. Studies of avian gut microbiota are outnumbered ten to one by studies of mammals, and are dominated by research on domestic poultry. Unlike domestic poultry, wild birds vary widely in environmental preferences, physiology, and life‐history traits, such as migratory behavior and mating systems. Species characteristics result in a vast diversity in gut microbiota and its composition and function. Avian life‐history characteristics pose selection pressures on the gut microbiota, and ultimately affect host health. Here, we review current knowledge of the gut microbiota of wild birds, including: partitioning of digestive function and microbiota among different gastrointestinal compartments, microbial diversity and function in the context of host diet, energetics and behavior, and the intrinsic and extrinsic factors impacting gut microbiota in free‐living birds. The shared core microbiota of wild bird species is dominated by members of four major phyla: Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria. However, microbial communities varies inter‐ and intra‐specifically, and among gastrointestinal tract sections. To conclude, we identify three key research areas that warrant further investigation: 1) expanding the range of avian host taxa investigated, 2) identifying the function of avian gut microbiota in physiology and immunology, and 3) transitioning from observational studies to experimental manipulations to identify key determinants of wild bird gut microbiota composition.

Summary1. The effects of harvest on the annual and seasonal survival of willow ptarmigan Lagopus lagopus L. were tested in a large-scale harvest experiment. Management units were randomly assigned to one of three experimental treatments: 0%, 15% or 30% harvest. Seasonal quotas were based on the experimental treatment and estimates of bird density before the hunting season. Survival rates and hazard functions for radio-marked ptarmigan were then estimated under the competing risks of harvest and natural mortality. 2. The partially compensatory mortality hypothesis was supported: annual survival of ptarmigan was 0AE54 ± 0AE08 SE under 0% harvest, 0AE47 ± 0AE06 under 15% harvest, and was reduced to 0AE30 ± 0AE05 under 30% harvest. Harvest mortality increased linearly from 0AE08 ± 0AE05, 0AE27 ± 0AE05 and 0AE42 ± 0AE06 from 0% to 30% harvest, whereas natural mortality was 0AE38 ± 0AE08, 0AE25 ± 0AE05 and 0AE28 ± 0AE06 under the same treatments. 3. Realized risk of harvest mortality was 0AE08-0AE12 points higher than our set harvest treatments of 0-30% because birds were exposed to risk if they moved out of protected areas. The superadditive hypothesis was supported because birds in the 30% harvest treatment had higher natural mortality during winter after the hunting season. 4. Natural mortality was mainly because of raptor predation, with two seasonal peaks in fall and spring. Natural and harvest mortality coincided during early autumn with little potential for compensation during winter months. Peak risk of harvest mortality was 5· higher than natural mortality. Low natural mortality during winter suggests that most late season harvest would be additive mortality. 5. Environmental correlates of natural mortality of ptarmigan included seasonal changes in snow cover, onset of juvenile dispersal, and periods of territorial activity. Natural mortality of ptarmigan was highest during autumn movements and nesting by gyrfalcons Falco rusticolus L. Mortality was low when gyrfalcons had departed for coastal wintering sites, and during summer when ptarmigan were attending nests and broods. 6. Our experimental results have important implications for harvest management of upland gamebirds. Seasonal quotas based on proportional harvest were effective and should be set at £15% of August populations for regional management plans. Under threshold harvest of a reproductive surplus, 15% harvest would be sustainable at productivity rates ‡2AE5 young per pair. Impacts of winter harvest could be minimized by closing the hunting season in early November or by reducing late season quotas.

Estimation of demographic parameters is central to research questions in wildlife management, conservation, and evolutionary ecology. I review the 7 major classes of mark–recapture models that investigators can use to estimate apparent survival and other parameters from live‐encounter data. Return rates are the product of 4 probabilities: true survival (S), site fidelity (F), site propensity (δ), and true detection (p*). Cormack‐Jolly‐Seber (CJS) models improve upon return rates by separating apparent survival (ϕ = S × F) from the probability of encounter (p = δ × p*). The main drawback to mark–recapture models based on live‐encounter data is that the complement of apparent survival (1 – ϕ) includes losses to mortality and to permanent emigration, and these 2 ecological processes are difficult to disentangle. Advanced mark–recapture models require additional sampling effort but estimate apparent survival with greater precision and less bias, and they also offer estimates of other useful demographic parameters. Time‐since‐marking or transient models control for individuals not encountered after the occasion they are first marked, a common feature of wildlife populations. Temporal symmetry models combine forward‐ and reverse‐time modeling to estimate recruitment (f) and the finite rate of population change (k). Multi‐strata models include dynamic categorical information and offer state‐specific estimates of apparent survival and encounter rates, as well as probabilities of changing states (ψ). Robust design models subdivide sampling occasions into shorter periods, and they partition encounter rates (p) into estimates of temporary emigration (γ = 1 – δ) and true detection (p*). Joint models combine live encounters with other sources of information, including dead‐recovery data, and decompose apparent survival into estimates of true survival (S) and site fidelity (F). Cormack‐Jolly‐Seber and multi‐strata models have a large literature, but many of the advanced models have not yet received widespread use. In the future, wildlife ecologists should design field studies that take advantage of the best possible statistical procedures now that a range of alternative models and software tools are available.