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 We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology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

Protected areas (PAs) are a core component of conservation policy and practice. However, many species for which they are designated are highly mobile, and may move among sites within and beyond PA boundaries. Environmental impacts on sites beyond those boundaries could thus impact the PA's protected populations, with the risk of adversely affecting its conservation objectives. Conservationists therefore urgently need tools to assess impacts on PAs and their populations of developments beyond their boundaries. We present a framework for using network analysis of observations of marked individuals to assess the protection footprint of PAs in the wider landscape and the impact footprint of developments within or neighboring PAs. We illustrate the use of this framework by assessing the impact of a current airport development proposal on a partially protected wetland, the Tagus estuary in Portugal, specifically by evaluating the extent of noise disturbance on the PA's population of Black‐tailed Godwits, a protected migratory wader species. By analyzing individual movements between sites and across seasons, we find disturbance impacts on up to 68.3% of individual Black‐tailed Godwits overall, greatly exceeding the estimates of 0.46–5.5% in the airport's Environmental Impact Assessments which derived from count data. We then compared the pre‐development network with simulated networks that represented two levels of site avoidance by removing sites predicted to receive noise in excess of two thresholds, 55 or 65 dB(A). Avoidance by Godwits of the potentially‐impacted sites is predicted to have a larger effect on network structure than removing the same number of sites at random, suggesting that an airport in the proposed location would operate as a barrier to connectivity in this wetland. We also highlight the role that network analyses of locations of marked individuals can play in assessing environmental impacts on protected sites and populations.

Birds are a frequently chosen group for biodiversity monitoring as they are comparatively straightforward and inexpensive to sample and often perform well as ecological indicators. Two commonly used techniques for monitoring tropical forest bird communities are point counts and mist nets. General strengths and weaknesses of these techniques have been well-defined; however little research has examined how their effectiveness is mediated by the ecology of bird communities and their habitats. We examine how the overall performance of these methodologies differs between two widely separated tropical forests–Cusuco National Park (CNP), a Honduran cloud forest, and the lowland forests of Buton Forest Reserves (BFR) located on Buton Island, Indonesia. Consistent survey protocols were employed at both sites, with 77 point count stations and 22 mist netting stations being surveyed in each location. We found the effectiveness of both methods varied considerably between ecosystems. Point counts performed better in BFR than in CNP, detecting a greater percentage of known community richness (60% versus 41%) and generating more accurate species richness estimates. Conversely, mist netting performed better in CNP than in BFR, detecting a much higher percentage of known community richness (31% versus 7%). Indeed, mist netting proved overall to be highly ineffective within BFR. Best Akaike's Information Criterion models indicate differences in the effectiveness of methodologies between study sites relate to bird community composition, which in turn relates to ecological and biogeographical influences unique to each forest ecosystem. Results therefore suggest that, while generalized strengths and weaknesses of both methodologies can be defined, their overall effectiveness is also influenced by local characteristics specific to individual study sites. While this study focusses on ornithological surveys, the concept of local factors influencing effectiveness of field methodologies may also hold true for techniques targeting a wide range of taxonomic groups; this requires further research.

Capsule Wing length and age independently predicted spring arrival timing in a long-distance migratory warbler, but there was no association with mass or tarsus length. Aims To test a range of biometric features and other measures of fitness for their importance in determining spring arrival patterns in a long-distance migratory landbird, and to investigate why these features may be important. Methods Fifty-one males with known spring arrival dates were caught and key biometrics were measured at breeding sites across Norfolk, UK, during April and May 2012. Blood samples were also taken in order to consider any effect of infection with blood parasites. Results Early-arriving males were more likely to be adults and to have longer wings. Mass and tarsus length had no significant relationship with arrival timing. Haemosporidian prevalence was low (∼10%) compared to other studies of this species, and so it was not possible to determine the effect of infection on arrival date with confidence. Conclusions Wing length, rather than other measures that simply measure structural body size, is probably important in arrival timing due to either the advantage it confers by increasing flight efficiency or because it reflects past condition during feather growth.

Advancing breeding phenology is a commonly observed response to climate warming among bird species, potentially in response to shifts in the phenology of key resources. However, for migratory birds breeding at high latitudes, their capacity to breed earlier may be constrained by the time available between arrival on the breeding grounds and nesting, especially for later‐arriving individuals. This may have consequences for productivity, as early laying is often linked to higher breeding success, particularly in such seasonal environments. We investigated how migratory arrival timing influences subsequent timing of breeding, in particular whether the time between arrival and laying (arrival–laying gap) varies with arrival date, and if later‐arriving individuals are able to catch up with early‐arriving conspecifics. We tracked individual Icelandic Black‐tailed Godwit Limosa limosa islandica with GPS and PTT tags for one to two complete breeding seasons between 2013 and 2022. After arrival in Iceland, most Godwits visited their breeding territory within 5 days, though this interval was longer for earlier‐arriving birds. The total gap between arrival and laying was also longer for earlier‐arriving birds, such that laying date did not vary significantly with arrival date. These results suggest that, despite individual consistency in migratory phenology, subsequent timing of nesting is probably influenced by other factors, such as mate arrival timing and/or annual variation in environmental conditions. Regular pre‐nesting visits to the breeding territory may indicate that Godwits are able to monitor such factors closely and to nest early when conditions allow, but a larger sample of individuals and years is needed to assess whether early‐arriving individuals contribute disproportionately to population‐level advances in breeding phenology. Widespread tracking with high temporal and spatial resolution helps improve our understanding of phenological variation during the breeding season and its consequences for productivity and variation in juvenile phenology.