Habitat-specific demography and source-sink dynamics in a population of Siberian jays

P>1. There are a number of models describing population structure, many of which have the capacity to incorporate spatial habitat effects. One such model is the source-sink model, that describes a system where some habitats have a natality that is higher than mortality (source) and others have a mortality that exceeds natality (sink). A source can be maintained in the absence of migration, whereas a sink will go extinct. 2. However, the interaction between population dynamics and habitat quality is complex, and concerns have been raised about the validity of published empirical studies addressing source-sink dynamics. In particular, some of these studies fail to provide data on survival, a significant component in disentangling a sink from a low quality source. Moreover, failing to account for a density-dependent increase in mortality, or decrease in fecundity, can result in a territory being falsely assigned as a sink, when in fact, this density-dependent suppression only decreases the population size to a lower level, hence indicating a 'pseudo-sink'. 3. In this study, we investigate a long-term data set for key components of territory-specific demography (mortality and reproduction) and their relationship to habitat characteristics in the territorial, group-living Siberian jay (Perisoreus infaustus). We also assess territory-specific population growth rates (r), to test whether spatial population dynamics are consistent with the ideas of source-sink dynamics. 4. Although average mortality did not differ between sexes, habitat-specific mortality did. Female mortality was higher in older forests, a pattern not observed in males. Male mortality only increased with an increasing amount of open areas. Moreover, reproductive success was higher further away from human settlement, indicating a strong effect of human-associated nest predators. 5. Averaged over all years, 76% of the territories were sources. These territories generally consisted of less open areas, and were located further away from human settlement. 6. The source-sink model provides a tool for modelling demography in distinct habitat patches of different quality, which can aid in identifying key habitats within the landscape, and thus, reduce the risk of implementing unsound management decisions.

Does published orthodontic research account for clustering effects during statistical data analysis?

In orthodontics, multiple site observations within patients or multiple observations collected at consecutive time points are often encountered. Clustered designs require larger sample sizes compared to individual randomized trials and special statistical analyses that account for the fact that observations within clusters are correlated. It is the purpose of this study to assess to what degree clustering effects are considered during design and data analysis in the three major orthodontic journals. The contents of the most recent 24 issues of the American Journal of Orthodontics and Dentofacial Orthopedics (AJODO), Angle Orthodontist (AO), and European Journal of Orthodontics (EJO) from December 2010 backwards were hand searched. Articles with clustering effects and whether the authors accounted for clustering effects were identified. Additionally, information was collected on: involvement of a statistician, single or multicenter study, number of authors in the publication, geographical area, and statistical significance. From the 1584 articles, after exclusions, 1062 were assessed for clustering effects from which 250 (23.5 per cent) were considered to have clustering effects in the design (kappa = 0.92, 95 per cent CI: 0.67-0.99 for inter rater agreement). From the studies with clustering effects only, 63 (25.20 per cent) had indicated accounting for clustering effects. There was evidence that the studies published in the AO have higher odds of accounting for clustering effects [AO versus AJODO: odds ratio (OR) = 2.17, 95 per cent confidence interval (CI): 1.06-4.43, P = 0.03; EJO versus AJODO: OR = 1.90, 95 per cent CI: 0.84-4.24, non-significant; and EJO versus AO: OR = 1.15, 95 per cent CI: 0.57-2.33, non-significant). The results of this study indicate that only about a quarter of the studies with clustering effects account for this in statistical data analysis.