John Fieberg

Fuel economy matters to both animals and machines, as the cost of transport can determine where, when, and how far locomotion is possible. However, established estimates of these values for animal flight come from laboratories and treat the atmosphere as constant, even though free-flying animals routinely extract energy from atmospheric flows. Using high-resolution movement data, we estimate the cost of transport for 50 bird and bat species flying in the wild. Our results revise the classic ranking of locomotion costs, suggesting that flying can be cheaper than swimming though almost as costly as running, depending on interactions between physical environment, morphology, and behavior. Linking transport costs to atmospheric conditions provides a mechanistic basis for predicting how changing windscapes will reshape the energetics, movements, and distributions of flying animals.

To make forests more resilient to our modern fire regime, landowners and managers are increasingly using stand-thinning and burning treatments to reduce available fuels. These treatments alter the landscape and affect species who depend on structurally complex forests, yet actionable guidelines for forest managers looking to both restore forests and mitigate potential negative impacts of restoration on wildlife remain scant. We evaluated how Pacific martens (Martes caurina) responded to forest structure, including thinned stands, at multiple spatial scales using GPS collar data from 33 individuals in Lassen National Forest, California from 2010 to 2020. We classified forest patches as Open, Simple (naturally sparse or thinned), or Complex (dense, multistory conifer), then analyzed home range composition, within-home range space use, and fine-scale movement using autocorrelated kernel density estimation, zero-inflated negative binomial models, and integrated step-selection function, respectively. Martens selected home ranges dominated by large Complex patches, but included Simple patches at frequencies proportional to, and sizes lesser than, landscape availability. Within home ranges, martens used Simple and Complex patches at similar rates once we accounted for patch area, with preference for Complex patches emerging only above ~40 ha. At the step scale, martens strongly selected Complex patches, lightly selected patch edges in Simple patches, and avoided Open structure. These results suggest marten habitat selection is expressed most strongly during home range establishment and at the scale of the step. For management, we recommend thinning treatments remain below ~14 ha and preserve large, connected Complex forest, while recognizing that long-term fire resilience may ultimately protect the complex structure on which martens depend.

Ecology has undergone a profound methodological transformation through the rapid growth in new technologies, data volume, statistical innovation and analytical sophistication. These developments create unprecedented opportunities for novel ecological insights, but have also magnified longstanding concerns about quantitative training, which persist despite more than a century of debate and repeated calls for reform. Here, we posit why, despite enduring and widespread agreement on educational goals, progress in quantitative ecology education has remained limited. We argue that limited progress reflects a mismatch between widely supported reforms and the conditions needed to implement them at scale. While discussions of quantitative education have often focused on curricular content—what ecologists should learn—progress is also shaped by pedagogical, interpersonal, institutional and disciplinary factors. We discuss these interconnected components and argue that their combined influence helps explain the inertia that continues to characterise quantitative ecology education. Building on this diagnosis, we offer an integrated vision for quantitative ecology education. We argue that durable quantitative literacy is best developed through inclusive, context‐rich and people‐centred pedagogies that address barriers to learning, support diverse learners and ensure that procedural competence is grounded in strong conceptual understanding. We further advocate for coordinated action from disciplinary leaders, stronger institutional support for educators and learners, recognition of heterogeneous cohorts as a pedagogical strength, greater interdisciplinary collaboration between ecologists and quantitative specialists, and a shift from expectations of universal fluency towards a model of distributed expertise underpinned by foundational quantitative literacy. As ecology evolves, the need for quantitative literacy is greater than ever. Progress will depend not simply on identifying what should be taught and how, but on addressing the broader conditions that constrain the pace and scale of implementation of widely supported educational aspirations. We argue that reframing persistent challenges as interconnected problems of implementation, rather than aspiration, will help in overcoming a century of inertia in quantitative ecology education.

Understanding how animals acquire and use information from the environment is critical for linking movement to population dynamics, species distributions and conservation. Advances in tracking technologies and growing interest in learning processes have opened opportunities to study behaviours such as habitat exploration in translocated animals or ontogeny of migration and dispersal movements. However, accessible statistical methods for studying these behavioural processes are lacking. We present a learning-explicit step-selection analysis (SSA) that integrates movement, habitat selection, and learning into a single evolving process. At the start of the movement trajectory, the animal is assumed to have no knowledge of the landscape, i.e. its internal habitat quality map is initialized to a constant. As the animal moves, this map is updated dynamically: with each step, only the habitat within its perceptual range becomes known and contributes to future decisions. Through simulations, we show that this method separates true habitat preferences from learning effects and reveals when large-scale behaviours, such as attraction to resources or avoidance of risks, emerge as knowledge accumulates. Critically, we demonstrate that ignoring learning, as in traditional SSA, can lead to biased estimators of habitat selection. Finally, we apply our approach to a real-world GPS dataset of naïve individuals, consisting of 10 juvenile red kites dispersing in Switzerland. Using leave-one-individual-out cross-validation, we show that learner SSFs consistently outperform traditional SSFs in predicting movement decisions. We conclude by discussing methodological considerations and future directions for integrating learning into movement ecology. By explicitly modelling the formation of memory that integrates both spatial information and habitat quality, our approach advances process-based movement ecology while remaining compatible with standard SSA workflows, offering practical tools for both theoretical research and applied wildlife management.

Detecting infectious disease in wildlife is critical for conservation, management of reintroduction programmes, and to reduce the risk of spillover into livestock and humans. However, collecting diagnostic samples from free‐ranging animals is logistically difficult and costly. Many pathogens alter host behaviour, including reductions in movement, suggesting that animal tracking data could offer a way to infer infection status. We develop a modelling framework that links animal movement to disease processes using hidden Markov models (HMMs). Infection status is treated as a hidden (unobserved) state, while movement patterns, such as step lengths and turning angles, serve as state‐dependent observations. This structure is similar to epidemiological compartmental models (‘Susceptible‐Infected‐Recovered; SIR’), allowing movement data to be formally connected to disease progression. We test multiple model formulations, including (1) constrained state transition probabilities to preclude or include recovery, (2) covariate effects to test whether ecological factors influence infection risk and (3) hierarchically structured HMMs (HHMMs) to distinguish movement responses at short and long temporal scales. We apply this framework to GPS movement data from 84 reintroduced scimitar‐horned oryx ( Oryx dammah ) in Chad. During the study period, 38 individuals were confirmed dead and 6 were sampled for pathogens (e.g. Rift Valley Fever, Peste des Petits Ruminants, Babesiosis). Models that included biologically realistic constraints on transitions among infection states successfully detected disease‐associated reductions in movement and aligned with veterinary necropsy and diagnostic reports. Unconstrained models performed poorly, misclassifying individuals and failing to detect disease progression. Simulation results provided further evidence that constrained HMMs can infer susceptible, infected and recovered states under appropriate model specifications. Synthesis and applications . We demonstrate how (H)HMMs can be tailored to different epidemiological scenarios and provide a template workflow for developing and selecting Hidden Markov models to infer disease status from animal movement data. Identifying infection before mortality occurs offers a valuable early‐warning tool for population managers, reduces reliance on difficult and costly field testing and improves surveillance strategies for vulnerable and reintroduced populations.

In ecology, causal questions are ubiquitous, yet the literature describing systematic approaches to answering these questions is vast and fragmented across different traditions (e.g., randomization, structural equation modeling, convergent cross mapping). In our Perspective, we connect the causal assumptions, tasks, frameworks, and methods across these traditions, thereby providing a synthesis of the concepts and methodological advances for detecting and quantifying causal relationships in ecological systems. Through a newly developed workflow, we emphasize how ecologists’ choices among empirical approaches are guided by the pre-existing knowledge that ecologists have and the causal assumptions that ecologists are willing to make.

Lead (Pb) is a toxic heavy metal that has detrimental effects on wildlife. The toxicant is particularly relevant for species that use a muscular gizzard to digest food and that forage in areas with accumulated sediment at the bottom of lakes and streams. Poisoning from ingested spent lead ammunition or fishing tackle has been a major source of mortality of Trumpeter Swans ( Cygnus buccinator ) across North America. However, virtually all lead testing has been conducted on carcasses or swans brought to wildlife rehabilitation centers, and there are no baseline data from free-ranging swans. We estimated blood lead concentrations for 115 free-flying Trumpeter Swans throughout the breeding range of the Interior Population that inhabits central North America. We detected lead in all samples, but 90% of swans had blood lead levels in the ‘background’ range (< 200 ppb), 8% of swans had blood lead levels in the ‘sub-clinical’ range (200–500 ppb), and 2% of swans had blood lead levels in the ‘clinical’ (500–1,000 ppb) or ‘severe’ (> 1,000 ppb) range of lead toxicity. The swan with the highest blood lead level (1,076 ppb) was captured 3 km from a major coal power plant, although we were unable to definitively trace the source of lead (e.g., to coal ash or fishing tackle) in that individual. Our study provides useful baseline information on the prevalence and magnitude of blood lead levels among Interior Population trumpeter swans and demonstrates the apparent continued wide-scale exposure of wetland-feeding waterfowl to lead.

The Poweshiek skipperling Oarisma poweshiek (Parker, 1870) (Lepidoptera: Hesperiidae) is an endangered prairie obligate butterfly native to the north central United States and southern Canada. Conservation efforts for this species rely on ex situ approaches for population augmentation and reintroductions. As such, improving our understanding of the behaviors of Poweshiek skipperlings and maximizing their reproductive output in an ex situ setting are critical for the success of associated conservation initiatives. In this study, we examined the frequency of breeding behaviors exhibited by Poweshiek skipperlings in relation to various environmental factors: sunlight intensity (measured in lux), ambient temperature, and age. Sunlight intensity was a significant predictor of breeding behavior frequency, but we did not detect an effect of ambient temperature on breeding behavior. We also documented a generally negative relationship between age and breeding behavior frequency for both sexes. The results of our study underscore the importance of natural sunlight in encouraging breeding behaviors in an ex situ conservation environment. Ex situ observations also can help identify environmental conditions that promote high levels of Poweshiek skipperling activity, which could be used to optimize the timing of in situ population surveys.

Animal movements contribute to the spread of infectious diseases and are driven in part by environmental conditions. We investigated the links among the environment, animal movement, and infectious disease dynamics in waterfowl, which are among the primary wildlife hosts of avian influenza viruses. By combining telemetry data on 4606 individuals from 26 waterfowl species with data on land cover, weather, and vegetation, we found that waterfowl moved less in areas of higher land cover heterogeneity and higher human population density. Moreover, predicted waterfowl movement distances were weakly but positively correlated with distances between detections of H5N1 highly pathogenic avian influenza in wild waterfowl, suggesting that environmental conditions might contribute to the spread of this disease via their effects on bird movements. By considering wildlife movements alongside other drivers of infectious disease dynamics, such as livestock production and human mobility, we move closer to predicting outbreaks and informing interventions.