William V. DeLuca

Transoceanic migration by a 12 g songbird.

Many fundamental aspects of migration remain a mystery, largely due to our inability to follow small animals over vast spatial areas. For more than 50 years, it has been hypothesized that, during autumn migration, blackpoll warblers (Setophaga striata) depart northeastern North America and undertake a non-stop flight over the Atlantic Ocean to either the Greater Antilles or the northeastern coast of South America. Using miniaturized light-level geolocators, we provide the first irrefutable evidence that the blackpoll warbler, a 12 g boreal forest songbird, completes an autumn transoceanic migration ranging from 2270 to 2770 km (mean ± s.d.: 2540 ± 257) and requiring up to 3 days (62 h ± 10) of non-stop flight. This is one of the longest non-stop overwater flights recorded for a songbird and confirms what has long been believed to be one of the most extraordinary migratory feats on the planet.

Land cover and rainfall interact to shape waterbird community composition.

Human land cover can degrade estuaries directly through habitat loss and fragmentation or indirectly through nutrient inputs that reduce water quality. Strong precipitation events are occurring more frequently, causing greater hydrological connectivity between watersheds and estuaries. Nutrient enrichment and dissolved oxygen depletion that occur following these events are known to limit populations of benthic macroinvertebrates and commercially harvested species, but the consequences for top consumers such as birds remain largely unknown. We used non-metric multidimensional scaling (MDS) and structural equation modeling (SEM) to understand how land cover and annual variation in rainfall interact to shape waterbird community composition in Chesapeake Bay, USA. The MDS ordination indicated that urban subestuaries shifted from a mixed generalist-specialist community in 2002, a year of severe drought, to generalist-dominated community in 2003, of year of high rainfall. The SEM revealed that this change was concurrent with a sixfold increase in nitrate-N concentration in subestuaries. In the drought year of 2002, waterbird community composition depended only on the direct effect of urban development in watersheds. In the wet year of 2003, community composition depended both on this direct effect and on indirect effects associated with high nitrate-N inputs to northern parts of the Bay, particularly in urban subestuaries. Our findings suggest that increased runoff during periods of high rainfall can depress water quality enough to alter the composition of estuarine waterbird communities, and that this effect is compounded in subestuaries dominated by urban development. Estuarine restoration programs often chart progress by monitoring stressors and indicators, but rarely assess multivariate relationships among them. Estuarine management planning could be improved by tracking the structure of relationships among land cover, water quality, and waterbirds. Unraveling these complex relationships may help managers identify and mitigate ecological thresholds that occur with increasing human land cover.

Increasing nest success in the yellow-shouldered blackbird Agelaius xanthomus in southwest Puerto Rico

The yellow-shouldered blackbird, Agelaius xanthomus, is endemic to Puerto Rico and Mona Island, and endangered since 1976 mainly because of brood parasitism by the shiny cowbird, Molothrus bonariensis. In 1984 an artificial nest structures program was initiated, combined with the removal of M. bonariensis, to improve the reproductive success of A. xanthomus. The nesting success was high in artificial nest structures. From 1996 to 1999, 22 out of 804 nests in artificial structures and one of 203 nests in natural substrates were parasitized. The use of natural substrates increased from less than 1% (1996) to 35% (1999). Predation was the main cause of egg and chick loss and was high in natural substrates. The increase in the use of natural substrates for nesting is a trend favorable for the recovery of this species. Continued management is still needed to augment the breeding population and reproductive output.

Landscape capability models as a tool to predict fine-scale forest bird occupancy and abundance

ContextSpecies-specific models of landscape capability (LC) can inform landscape conservation design. Landscape capability is “the ability of the landscape to provide the environment […] and the local resources […] needed for survival and reproduction […] in sufficient quantity, quality and accessibility to meet the life history requirements of individuals and local populations.” Landscape capability incorporates species’ life histories, ecologies, and distributions to model habitat for current and future landscapes and climates as a proactive strategy for conservation planning.ObjectivesWe tested the ability of a set of LC models to explain variation in point occupancy and abundance for seven bird species representative of spruce-fir, mixed conifer-hardwood, and riparian and wooded wetland macrohabitats.MethodsWe compiled point count data sets used for biological inventory, species monitoring, and field studies across the northeastern United States to create an independent validation data set. Our validation explicitly accounted for underestimation in validation data using joint distance and time removal sampling.ResultsBlackpoll warbler (Setophaga striata), wood thrush (Hylocichla mustelina), and Louisiana (Parkesia motacilla) and northern waterthrush (P. noveboracensis) models were validated as predicting variation in abundance, although this varied from not biologically meaningful (1%) to strongly meaningful (59%). We verified all seven species models [including ovenbird (Seiurus aurocapilla), blackburnian (Setophaga fusca) and cerulean warbler (Setophaga cerulea)], as all were positively related to occupancy data.ConclusionsLC models represent a useful tool for conservation planning owing to their predictive ability over a regional extent. As improved remote-sensed data become available, LC layers are updated, which will improve predictions.