Migration timing and routes, and wintering areas of Flammulated Owls

Determining patterns in annual movements of animals is an important component of population ecology, particularly for migratory birds where migration timing and routes, and wintering habitats have key bearing on population dynamics. From 2009 to 2011, we used light-level geolocators to document the migratory movements of Flammulated Owls (Psiloscops flammeolus). Four males departed from breeding areas in Colorado for fall migration between ≤5 and 21 October, arrived in wintering areas in Mexico between 11 October and 3 November, departed from wintering areas from ≤6 to 21 April, and returned to Colorado between 15 and 21 May. Core wintering areas for three males were located in the Trans-Mexican Volcanic Belt Mountains in the states of Jalisco, Michoacán, and Puebla in central and east-central Mexico, and the core area for the other male was in the Sierra Madre Oriental Mountains in Tamaulipas. The mean distance from breeding to wintering centroids was 2057 ± 128 km (SE). During fall migration, two males took a southeastern path to eastern Mexico, and two males took a path due south to central Mexico. In contrast, during spring migration, all four males traveled north from Mexico along the Sierra Madre Oriental Mountains to the Rio Grande Valley and north through New Mexico. The first stopovers in fall and last stopovers in spring were the longest in duration for all males and located 300–400 km from breeding areas. Final spring stopovers may have allowed male Flammulated Owls to fine tune the timing of their return to high-elevation breeding areas where late snows are not uncommon. One male tracked in both years had similar migration routes, timing, and wintering areas each year. Core wintering and final stopover areas were located primarily in coniferous forests and woodlands, particularly pine-oak forests, suggesting that these are important habitats for Flammulated Owls throughout their annual cycle

A modern method of multiple working hypotheses to improve inference in ecology

Science provides a method to learn about the relationships between observed patterns and the processes that generate them. However, inference can be confounded when an observed pattern cannot be clearly and wholly attributed to a hypothesized process. Over-reliance on traditional single-hypothesis methods (i.e. null hypothesis significance testing) has resulted in replication crises in several disciplines, and ecology exhibits features common to these fields (e.g. low-power study designs, questionable research practices, etc.). Considering multiple working hypotheses in combination with pre-data collection modelling can be an effective means to mitigate many of these problems. We present a framework for explicitly modelling systems in which relevant processes are commonly omitted, overlooked or not considered and provide a formal workflow for a pre-data collection analysis of multiple candidate hypotheses. We advocate for and suggest ways that pre-data collection modelling can be combined with consideration of multiple working hypotheses to improve the efficiency and accuracy of research in ecology

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

<p>Data From: Dynamic environments generate geographic fluctuations in population structure of an inland shorebird. Table S1. Samples IDs, age class (adult = after hatch year; AHY) and young (juvenile = hatch year; HY), δ²H values from feathers, and year of collection at the wintering ground (Imperial County, California, USA).</p&gt

Comment on “A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate”, author Coro

In this letter we present comments on the article “A global-scale ecological niche model to predict SARS-CoV-2 coronavirus” by Coro published in 2020.AC is supported by NSF grant DBI-1565128

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

Gould E, Fraser H, Parker T, et al. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology. 2023.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 (mostly social science) fields, and has found substantial variability among results, despite analysts having the same data and research question. We implemented an analogous study in ecology and evolutionary biology, fields in which there have been no empirical exploration of the variation in effect sizes or model predictions generated by the analytical decisions of different researchers. 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), and 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 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