2 resultados para Mean-variance.
Resumo:
To identify the causes of population decline in migratory birds, researchers must determine the relative influence of environmental changes on population dynamics while the birds are on breeding grounds, wintering grounds, and en route between the two. This is problematic when the wintering areas of specific populations are unknown. Here, we first identified the putative wintering areas of Common House-Martin (Delichon urbicum) and Common Swift (Apus apus) populations breeding in northern Italy as those areas, within the wintering ranges of these species, where the winter Normalized Difference Vegetation Index (NDVI), which may affect winter survival, best predicted annual variation in population indices observed in the breeding grounds in 1992–2009. In these analyses, we controlled for the potentially confounding effects of rainfall in the breeding grounds during the previous year, which may affect reproductive success; the North Atlantic Oscillation Index (NAO), which may account for climatic conditions faced by birds during migration; and the linear and squared term of year, which account for nonlinear population trends. The areas thus identified ranged from Guinea to Nigeria for the Common House-Martin, and were located in southern Ghana for the Common Swift. We then regressed annual population indices on mean NDVI values in the putative wintering areas and on the other variables, and used Bayesian model averaging (BMA) and hierarchical partitioning (HP) of variance to assess their relative contribution to population dynamics. We re-ran all the analyses using NDVI values at different spatial scales, and consistently found that our population of Common House-Martin was primarily affected by spring rainfall (43%–47.7% explained variance) and NDVI (24%–26.9%), while the Common Swift population was primarily affected by the NDVI (22.7%–34.8%). Although these results must be further validated, currently they are the only hypotheses about the wintering grounds of the Italian populations of these species, as no Common House-Martin and Common Swift ringed in Italy have been recovered in their wintering ranges.
Resumo:
Population models are essential components of large-scale conservation and management plans for the federally endangered Golden-cheeked Warbler (Setophaga chrysoparia; hereafter GCWA). However, existing models are based on vital rate estimates calculated using relatively small data sets that are now more than a decade old. We estimated more current, precise adult and juvenile apparent survival (Φ) probabilities and their associated variances for male GCWAs. In addition to providing estimates for use in population modeling, we tested hypotheses about spatial and temporal variation in Φ. We assessed whether a linear trend in Φ or a change in the overall mean Φ corresponded to an observed increase in GCWA abundance during 1992-2000 and if Φ varied among study plots. To accomplish these objectives, we analyzed long-term GCWA capture-resight data from 1992 through 2011, collected across seven study plots on the Fort Hood Military Reservation using a Cormack-Jolly-Seber model structure within program MARK. We also estimated Φ process and sampling variances using a variance-components approach. Our results did not provide evidence of site-specific variation in adult Φ on the installation. Because of a lack of data, we could not assess whether juvenile Φ varied spatially. We did not detect a strong temporal association between GCWA abundance and Φ. Mean estimates of Φ for adult and juvenile male GCWAs for all years analyzed were 0.47 with a process variance of 0.0120 and a sampling variance of 0.0113 and 0.28 with a process variance of 0.0076 and a sampling variance of 0.0149, respectively. Although juvenile Φ did not differ greatly from previous estimates, our adult Φ estimate suggests previous GCWA population models were overly optimistic with respect to adult survival. These updated Φ probabilities and their associated variances will be incorporated into new population models to assist with GCWA conservation decision making.