Despite predictions of poleward and upslope shifts in the distribution of breeding passerines under climate change, studies often report variable responses with some species shifting opposite of the expected direction and others showing range stability. While changes in climate could affect distribution directly, passerines show strong preferences for particular structural vegetation characteristics, suggesting that long‐term changes in vegetation may mediate a species' distributional response to climate, and consequently, may be responsible for the observed heterogeneity. We assessed changes in the elevational distribution and occupancy probability of 17 passerine species in Denali National Park, Alaska, from 1995 to 2013 across an elevational gradient containing multiple topographically defined ecotones (treeline and shrubline). An upward distributional shift was pervasive among shrub‐tundra species, corresponding with observed expansion of shrub cover at higher elevations. Forest‐associated passerines showed a weaker response as a group with some species showing range stability or retraction, while others exhibited modest expansions that were consistent with an advancing treeline ecotone. Denali's mean summer temperature increased significantly over the past century, but remained relatively stable over our study period, implying that longer term changes in climate indirectly influenced bird distribution through changes in woody vegetation. Further, heterogeneity in the response of forest‐associated species was consistent with a slower rate of forest development and expansion as compared with shrub colonization. Together, our results indicate that the elevational range dynamics of passerines may be related to species‐specific associations with different vegetation communities and variation across these vegetation communities in the timescale over which distributional change is occurring.

Model‐based distance sampling is commonly used to understand spatial variation in the density of wildlife species. The standard approach assumes that individuals are distributed uniformly and models spatial variation in density using plot‐level effects. Thinned point process (TPP) models for surveys of unmarked populations (spatial distance sampling) better leverage the spatial information underlying individual encounters, and in the presence of within‐plot variation in density, may explain a larger proportion of the spatial variation in density. However, existing spatial distance sampling approaches are conditioned on the assumption that all individuals are present and available for sampling. Temporary emigration of individuals can therefore result in biased estimates of abundance. We extended spatial distance sampling models to accommodate temporary emigration (TPP model). Using simulations of a thinned inhomogeneous point process, we assessed the performance of the TPP model relative to the temporary emigration distance sampling (TEDS) model, which implies a uniform distribution of individuals. In addition, we compared inferences between TPP and TEDS models using data for two passerine species in Alaska. Parameter estimates from the TPP model exhibited improved coverage probability and precision relative to the TEDS model including a 26% reduction in the coefficient of variation (CV) of the population size estimate. In the applied example, the TEDS model indicated weak relationships between abundance and habitat covariates, whereas the TPP model indicated strong relationships for those same effects, suggesting that spatial distance sampling models can provide considerably stronger inference in the presence of within‐plot variation in density. In addition, the CV of the population size estimates for the two passerine species were 32% and 4% smaller under the TPP model. Synthesis and applications. We expect our extension accommodating temporary emigration will be a critical specification for spatial distance sampling models, particularly for studies assessing changes in the distribution and abundance of highly mobile species including passerines.

. 2017. Subarctic-breeding passerines exhibit phenological resilience to extreme spring conditions. Ecosphere 8(2):e01680. 10.1002/ecs2.1680Abstract. There has been relatively little study of the capacity of subarctic passerines to adjust their phenologies to rapid changes on their breeding grounds. Here, we assess variation in passerine arrival timing in Denali National Park, Alaska, from 1995 to 2015, a period that included both the warmest and coldest recorded mean spring temperatures for the park. Using an open-population occupancy modeling approach in which arrival events are random variables, we investigated interannual variation in the arrival distribution for 10 Nearctic-Nearctic migrants, three Nearctic-Neotropical migrants, and one Palearctic migrant. Neotropical-Nearctic migrants varied in terms of the flexibility of their arrival timing, but generally showed plastic phenologies, suggesting resilience under extreme spring conditions. In comparison, Nearctic-Nearctic migrants showed similar or greater plasticity in arrival timing. A majority of species showed synchronous-asynchronous fluctuation in arrival (i.e., synchronous arrival in some years, asynchronous in others) in combination with various levels of the mean response (i.e., early, average, and late arrival), suggesting the presence of interactions between environmental conditions at multiple scales and inter-individual variation. The presence of synchronous-asynchronous fluctuation in arrival suggests that weakening of the north-south temperature gradient under continued Arctic amplification may strongly affect arrival variances. Our results also suggest that complex interactions between distributional and phenological changes may be possible. For example, the arrival distribution of Fox Sparrow (Passerella iliaca) became more synchronized over time, a pattern that coincided with a dramatic increase in occupancy probability through expansion of its elevational distribution. Overall, our findings suggest that monitoring of the mean-variance relationship may lead to a deeper understanding of the factors shaping phenological responses.

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The timing of life cycle events has strong fitness consequences, suggesting that monitoring of arrival and departure timing may help understand spatial and population dynamics. Several existing models with inference to arrival and departure in unmarked populations are applicable to detection/non‐detection data which is a reduced information summary of the underlying population and phenological dynamics. These models also do not directly address the dependence of seasonal variation in availability (e.g. song rate) on arrival timing, often treating the seasonal distribution of availability as fixed across years despite allowing variation in arrival. Model development in an abundance framework has largely occurred in the context of stopover populations, rather than populations that exhibit some period of closure between arrival and departure phases. We developed an N‐mixture model that accommodates the dependence of seasonal availability on arrival timing, providing inference about abundance and both arrival and departure timing based on repeated count data. The model is applicable to populations in which there exists some period of closure between the arrival and departure phases. We developed two general formulations of the model, both of which include a model for the arrival process, but differ in the model for seasonal availability. The first formulation is applicable to cases in which seasonal availability is a function of cue production. The other is applicable to situations where seasonal availability is a function of departure of individuals or their transition to a state in which they remain unavailable for detection. We demonstrated through simulation that both versions provide unbiased and precise estimates of phenology and abundance and illustrated the cue production formulation using data collected in Denali National Park, Alaska for three passerine species. We expect that our inference framework will be broadly applicable in studies of unmarked populations where joint assessment of population, spatial and phenological dynamics is of interest.