Recommendations for quantitative uncertainty consideration in ecology and evolution.

Ecological and evolutionary studies are currently failing to achieve complete and consistent reporting of model-related uncertainty. We identify three key barriers - a focus on parameter-related uncertainty, obscure uncertainty metrics, and limited recognition of uncertainty propagation - which have led to gaps in uncertainty consideration. However, these gaps can be closed.

Insights into the quantification and reporting of model-related uncertainty across different disciplines.

Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with real-world impacts in diverse spheres, including conservation, epidemiology, climate science, and policy. Despite these potentially damaging consequences, we still know little about how different fields quantify and report uncertainty.

Contrasting physiological responses to habitat degradation in two arboreal mammals.

To cope with the challenges presented by habitat degradation and loss, animals must often respond by adjusting physiological and behavioral mechanisms. Here we quantified physiological and behavioral traits, including body temperature and food consumption, of two mammals with differing thermoregulatory strategies in response to changes in climate and habitat. We show that both species responded to challenging climatic conditions by increasing torpor use to save energy, yet their responses were impacted by varying vegetation levels.

Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway.

Many publications make use of opportunistic data, such as citizen science observation data, to infer large-scale properties of species' distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them.

Strengthening the evidence base for temperature-mediated phenological asynchrony and its impacts.

Climate warming has caused the seasonal timing of many components of ecological food chains to advance. In the context of trophic interactions, the match-mismatch hypothesis postulates that differential shifts can lead to phenological asynchrony with negative impacts for consumers. However, at present there has been no consistent analysis of the links between temperature change, phenological asynchrony and individual-to-population-level impacts across taxa, trophic levels and biomes at a global scale.

Phenological asynchrony: a ticking time-bomb for seemingly stable populations?

Climate change has been shown to induce shifts in the timing of life-history events. As a result, interactions between species can become disrupted, with potentially detrimental effects. Predicting these consequences has proven challenging.

Data Integration for Large-Scale Models of Species Distributions.

With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies.

Cue identification in phenology: A case study of the predictive performance of current statistical tools.

Changes in the timing of life-history events (phenology) are a widespread consequence of climate change. Predicting population resilience requires knowledge of how phenology is likely to change over time, which can be gained by identifying the specific environmental cues that drive phenological events. Cue identification is often achieved with statistical testing of candidate cues.

Incubation behavior adjustments, driven by ambient temperature variation, improve synchrony between hatch dates and caterpillar peak in a wild bird population.

For organisms living in seasonal environments, synchronizing the peak energetic demands of reproduction with peak food availability is a key challenge. Understanding the extent to which animals can adjust behavior to optimize reproductive timing, and the cues they use to do this, is essential for predicting how they will respond to future climate change. In birds, the timing of peak energetic demand is largely determined by the timing of clutch initiation; however, considerable alterations can still occur once egg laying has begun.

Who escapes detection? Quantifying the causes and consequences of sampling biases in a long-term field study.

Inferences drawn from long-term field studies are vulnerable to biases in observability of different classes of individuals, which may lead to biases in the estimates of selection, or fitness. Population surveys that monitor breeding individuals can introduce such biases by not identifying individuals that fail early in their reproductive attempts. Here, we quantify how the standard protocol for detecting breeding females introduces bias in a long-term population study of the great tit, Parus major.