An unidentified TeV source in the vicinity of Cygnus OB2

Deep observation (∼113 hrs) of the Cygnus region at TeV energies using the HEGRA stereoscopic system of air Čerenkov telescopes has serendipitously revealed a signal positionally inside the core of the OB association Cygnus OB2, at the edge of the 95% error circle of the EGRET source 3EG J2033+4118, and ∼0.5° north of Cyg X-3. The source centre of gravity is RA αJ2000: 20hr32m07s± 9.2stats±2.2syss, Dec δJ2000: +41°30′30″2.0stat±0.4′sys. The source is steady, has a post-trial significance of +4.6σ, indication for extension with radius 5.6′ at the ∼3σ level, and has a differential power-law flux with hard photon index of - 1.9 ± 0.3stat ± 0.3sys. The integral flux above 1 TeV amounts ∼3% that of the Crab. No counterpart for the TeV source at other wavelengths is presently identified, and its extension would disfavour an exclusive pulsar or AGN origin. If associated with Cygnus OB2, this dense concentration of young, massive stars provides an environment conducive to multi-TeV particle acceleration and likely subsequent interaction with a nearby gas cloud. Alternatively, one could envisage γ-ray production via a jet-driven termination shock.F. A. Aharonian, ... G. P. Rowell, ... [et al

Dynamic biospeckle analysis, a new tool for the fast screening of plant nematicide selectivity

Background: Plant feeding, free-living nematodes cause extensive damage to plant roots by direct feeding and, in the case of some trichodorid and longidorid species, through the transmission of viruses. Developing more environmentally friendly, target-specific nematicides is currently impeded by slow and laborious methods of toxicity testing. Here, we developed a bioactivity assay based on the dynamics of light 'speckle' generated by living cells and we demonstrate its application by assessing chemicals' toxicity to different nematode trophic groups.Results: Free-living nematode populations extracted from soil were exposed to methanol and phenyl isothiocyanate (PEITC). Biospeckle analysis revealed differing behavioural responses as a function of nematode feeding groups. Trichodorus nematodes were less sensitive than were bacterial feeding nematodes or non-trichodorid plant feeding nematodes. Following 24 h of exposure to PEITC, bioactivity significantly decreased for plant and bacterial feeders but not for Trichodorus nematodes. Decreases in movement for plant and bacterial feeders in the presence of PEITC also led to measurable changes to the morphology of biospeckle patterns.Conclusions: Biospeckle analysis can be used to accelerate the screening of nematode bioactivity, thereby providing a fast way of testing the specificity of potential nematicidal compounds. With nematodes' distinctive movement and activity levels being visible in the biospeckle pattern, the technique has potential to screen the behavioural responses of diverse trophic nematode communities. The method discriminates both behavioural responses, morphological traits and activity levels and hence could be used to assess the specificity of nematicidal compounds.</p

A tail of plumage colouration: disentangling geographic, seasonal and dietary effects on plumage colour in a migratory songbird

Plumage ornamentation in birds serves critical inter- and intra-sexual signaling functions. While carotenoid-based plumage colouration is often viewed as a classic condition-dependent sexually selected trait, plumage colouration can be influenced by a wide array of both intrinsic and extrinsic factors. Understanding the mechanisms underlying variation in colouration is especially important for species where the signaling function of ornamental traits is complex or when the literature is conflicting. Here, we examined variation in the yellow/orange tail feathers of American redstarts Setophaga ruticilla passing through two migratory stopover sites in eastern North America during both spring and fall migration to assess the role of geographic variation and seasonality in influencing feather colouration. In addition, we investigated whether diet during moult (inferred via stable isotope analysis of feather δ15N and δ13C) influenced plumage colouration. Our findings indicate that geographic variation, season and diet all influence individual differences in American redstart colouration, represented by both traditional and tetrahedral colour variables. The extent to which these factors influence colour expression however is largely dependent on the colour metric under study, likely because different colour metrics reflect different attributes of the feather (e.g. structural components versus pigment deposition). The effects of diet (δ15N) and season were pronounced for brightness, suggesting a strong effect of diet and feather wear/degradation on feather structure. Though hue, a metric that should strongly reflect pigment deposition, also changed from spring to fall, that effect was dependent on age, with only adults experiencing a reduction in ornamentation. Taken together, our results highlight the numerous sources of variation behind plumage coloration and underscores the difficulty of unraveling complex visual signaling systems, such as those in American redstarts

Challenges and opportunities for data integration to improve estimation of migratory connectivity

Understanding migratory connectivity, or the linkage of populations between seasons, is critical for effective conservation and management of migratory wildlife. A growing number of tools are available for understanding where migratory individuals and populations occur throughout the annual cycle. Integration of the diverse measures of migratory movements can help elucidate migratory connectivity patterns with methodology that accounts for differences in sampling design, directionality, effort, precision and bias inherent to each data type. The R package MigConnectivity was developed to estimate population-specific connectivity and the range-wide strength of those connections. New functions allow users to integrate intrinsic markers, tracking and long-distance reencounter data, collected from the same or different individuals, to estimate population-specific transition probabilities (estTransition) and the range-wide strength of those transition probabilities (estStrength). We used simulation and real-world case studies to explore the challenges and limitations of data integration based on data from three migratory bird species, Painted Bunting (Passerina ciris), Yellow Warbler (Setophaga petechia) and Bald Eagle (Haliaeetus leucocephalus), two of which had bidirectional data. We found data integration is useful for quantifying migratory connectivity, as single data sources are less likely to be available across the species range. Furthermore, accurate strength estimates can be obtained from either breeding-to-nonbreeding or nonbreeding-to-breeding data. For bidirectional data, integration can lead to more accurate estimates when data are available from all regions in at least one season. The ability to conduct combined analyses that account for the unique limitations and biases of each data type is a promising possibility for overcoming the challenge of range-wide coverage that has been hard to achieve using single data types. The best-case scenario for data integration is to have data from all regions, especially if the question is range-wide or data are bidirectional. Multiple data types on animal movements are becoming increasingly available and integration of these growing datasets will lead to a better understanding of the full annual cycle of migratory animals

The strength of migratory connectivity in Painted Buntings is spatial scale dependent and shaped by molting behavior

As migratory species move through the stages of their annual cycle, individuals often display variation in the degree to which they remain in proximity to one another, a phenomenon called migratory connectivity. We show scale dependence in the strength of migratory connectivity in Painted Buntings (Passerina ciris), a North American passerine with disjunct eastern and interior breeding populations. Based on light-level geolocator data from 112 individuals at 11 breeding sites, migratory connectivity between breeding and wintering grounds was strong at the range-wide scale, with interior and eastern Painted Buntings remaining separated throughout the annual cycle. Conversely, migratory connectivity within the eastern and interior populations was weak, with individuals from different breeding areas mixing extensively on winter quarters. We found weak migratory connectivity within populations as birds moved from the breeding grounds to the wintering grounds (breeding-to-winter), with individuals from different regions of each population mixing extensively on the wintering grounds. The interior population, however, displayed strong migratory connectivity as birds moved from the breeding grounds to the intermediate molting grounds (breeding-to-molting), with birds from different breeding sites showing contrasting migratory strategies during the molting period. Our results suggest that spatial scale dependence of migratory connectivity is likely to be a pervasive phenomenon, given that migratory routes and the likelihood of molt migration often differ among populations. When possible, researchers should be deliberate about the spatial design of tracking studies to reduce potential biases that can result from spatial scale-dependent migratory connectivity

Dynamic environments generate geographic fluctuations in population structure of an inland shorebird

Species distributions depend on fine-scale ecological processes and population growth trajectories and are influenced by climate and weather changes. However, the characterization of inter-population dynamics underlying the geographic distributions of migratory organisms remains challenging. We adopted a stable isotope approach to investigate the dynamic population geography of a terrestrial migratory bird across multiple generations. We found that the age-specific geographic source of Mountain Plovers sampled during winter shifted over four years across a latitudinal gradient. Moreover, our results show that differential effects of climate on the probability of occurrence at the wintering ground could be a driver of population turnover in a migratory species adapted to extreme environmental stochasticity (i.e., drought occurrence). We propose a framework for the identification of spatial and temporal climate and weather components and respective effects on population composition and recruitment into migratory wintering populations. Our approach is useful to reveal population compositional shifts through hydrogen stable isotope analysis while accounting for cumulative drought effects

Same data, different analysts: Variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small "many analyst" study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future