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The genetic and demographic consequences of population subdivision have received considerable attention from conservation biologists. In particular, losses of genetic variability and reduced viability and fecundity due to inbreeding (inbreeding depression) are of concern. Studies of domestic, laboratory and zoo populations have shown inbreeding depression in a variety of traits related to fitness. Consequently, inbreeding depression is widely accepted as a fact. Recently, however, the relative impact of inbreeding on the viability of natural populations has been questioned. Work on the cheetah (Acinonyx jubatus), for example, has emphasized the overwhelming importance of environmental factors on mortality in the wild. Here we report that song sparrows (Melospiza melodia) that survived a severe population bottleneck were a non-random subset of the pre-crash population with respect to inbreeding, and that natural selection favoured outbred individuals. Thus, inbreeding depression was expressed in the face of an environmental challenge. Such challenges are also likely to be faced by inbred populations of endangered species. We suggest that environmental and genetic effects on survival may interact and, as a consequence, that their effects on individuals and populations should not be considered independently.

The distributions of animal populations change and evolve through time. Migratory species exploit different habitats at different times of the year. Biotic and abiotic features that determine where a species lives vary due to natural and anthropogenic factors. This spatiotemporal variation needs to be accounted for in any modeling of species' distributions. In this paper we introduce a semiparametric model that provides a flexible framework for analyzing dynamic patterns of species occurrence and abundance from broad-scale survey data. The spatiotemporal exploratory model (STEM) adds essential spatiotemporal structure to existing techniques for developing species distribution models through a simple parametric structure without requiring a detailed understanding of the underlying dynamic processes. STEMs use a multi-scale strategy to differentiate between local and global-scale spatiotemporal structure. A user-specified species distribution model accounts for spatial and temporal patterning at the local level. These local patterns are then allowed to "scale up" via ensemble averaging to larger scales. This makes STEMs especially well suited for exploring distributional dynamics arising from a variety of processes. Using data from eBird, an online citizen science bird-monitoring project, we demonstrate that monthly changes in distribution of a migratory species, the Tree Swallow (Tachycineta bicolor), can be more accurately described with a STEM than a conventional bagged decision tree model in which spatiotemporal structure has not been imposed. We also demonstrate that there is no loss of model predictive power when a STEM is used to describe a spatiotemporal distribution with very little spatiotemporal variation; the distribution of a nonmigratory species, the Northern Cardinal (Cardinalis cardinalis).

Month by month we tracked the spread of the epizootic of an apparently novel strain of a widespread poultry pathogen, Mycoplasma gallisepticum, through a previously unknown host, the house finch, whose abundance has been monitored over past decades. Here we are able to demonstrate a causal relationship between high disease prevalence and declining house finch abundance throughout the eastern half of North America because the epizootic reached different parts of the house finch range at different times. Three years after the epizootic arrived, house finch abundance stabilized at similar levels, although house finch abundance had been high and stable in some areas but low and rapidly increasing in others. This result, not previously documented in wild populations, is as expected from theory if transmission of the disease was density dependent.