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 We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology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.

Migratory animals often go to extraordinary lengths to follow broad-scale changes in the spatial distributions of food resources over time. An example is the molt-migration system of songbirds in the central and western United States, wherein birds interrupt migratory movements to molt their feathers in the ephemerally productive North American monsoon region in Northwestern Mexico. Within this large-scale migration system, the efficacy of individual migration behaviors relative to dynamics of primary productivity en route is unknown. We performed a comparison of real migration tracks from a common molt migrant, the painted bunting (Passerina ciris), with the results from millions of random-walk simulations to determine if real birds demonstrate optimal migratory behavior with respect to seasonal changes in primary productivity across the landscape. We scored migration routes of both real and simulated birds using an index of new plant growth, wherein daily values for each day of the autumn migration period were summed to indicate the total exposure to primary productivity. We then compared the highest-scoring simulated routes with real bird tracks and observed a high degree of correspondence between them, with the monsoon region featuring prominently in most of the high-ranking simulations. However, real birds generally left the breeding grounds later than virtual birds, perhaps because of life-history constraints not represented in the simulations. Our work supports the contention that painted bunting migration maximizes exposure to new plant growth and provides a novel approach for exploring migratory behaviors in other species and other regions.

Habitat availability determines the distribution of migratory waterfowl along their flyway, which further influences the transmission and spatial spread of avian influenza viruses (AIVs). The extensive habitat loss in the East Asian-Australasian Flyway (EAAF) may have potentially altered the virus spread and transmission, but those consequences are rarely studied. We constructed 6 fall migration networks that differed in their level of habitat loss, wherein an increase in habitat loss resulted in smaller networks with fewer sites and links. We integrated an agent-based model and a susceptible-infected-recovered model to simulate waterfowl migration and AIV transmission. We found that extensive habitat loss in the EAAF can 1) relocate the outbreaks northwards, responding to the distribution changes of wintering waterfowl geese, 2) increase the outbreak risk in remaining sites due to larger goose congregations, and 3) facilitate AIV transmission in the migratory population. In addition, our modeling output was in line with the predictions from the concept of “migratory escape”, i.e., the migration allows the geese to “escape” from the location where infection risk is high, affecting the pattern of infection prevalence in the waterfowl population. Our modeling shed light on the potential consequences of habitat loss in spreading and transmitting AIV at the flyway scale and suggested the driving mechanisms behind these effects, indicating the importance of conservation in changing spatial and temporal patterns of AIV outbreaks. What are the possible consequences of extensive habitat loss on the spread and transmission of avian influenza viruses (AIVs)? We used a logistic regression model to select the suitable habitats of Greater white-fronted goose Anser albifrons in the East Asian-Australasian Flyway, which has extensive habitat loss and seasonal passing waterfowl population. We treated these habitats as sites to construct 6 fall migration networks by removing sites in order of habitat loss (i.e., site removal). We then simulated geese migration in these networks and explored the impacts of habitat loss on AIV spread and transmission. We found that extensive habitat loss can cause relocation of the geese and outbreaks and promote the AIV spread and transmission. Our modeling outputs also suggested a “migratory escape” pattern for changing the spatial and temporal pattern of infection prevalence in the population. Overall, our study showed that habitat loss can affect AIV spread and transmission, suggesting the importance of habitat conservation in changing the spatial and temporal pattern of disease prevalence.