Geology Reports⌕ Search
Home/Archive/USGS

USGS · 70192614

A functional model for characterizing long-distance movement behaviour

Frances E. Buderman·Mevin Hooten·Jacob S. Ivan·Tanya M. Shenk
View original source ↗Export metadata ↓

Abstract

Advancements in wildlife telemetry techniques have made it possible to collect large data sets of highly accurate animal locations at a fine temporal resolution. These data sets have prompted the development of a number of statistical methodologies for modelling animal movement. Telemetry data sets are often collected for purposes other than fine-scale movement analysis. These data sets may differ substantially from those that are collected with technologies suitable for fine-scale movement modelling and may consist of locations that are irregular in time, are temporally coarse or have large measurement error. These data sets are time-consuming and costly to collect but may still provide valuable information about movement behaviour. We developed a Bayesian movement model that accounts for error from multiple data sources as well as movement behaviour at different temporal scales. The Bayesian framework allows us to calculate derived quantities that describe temporally varying movement behaviour, such as residence time, speed and persistence in direction. The model is flexible, easy to implement and computationally efficient. We apply this model to data from Colorado Canada lynx ( Lynx canadensis ) and use derived quantities to identify changes in movement behaviour.

Explore related subjects

Methods in Ecology and EvolutionArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Frances E. Buderman, Mevin Hooten, Jacob S. Ivan, Tanya M. Shenk. 2015-10-24. A functional model for characterizing long-distance movement behaviour. https://doi.org/10.1111/2041-210x.12465

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

A systematic review of avian influenza virus transmission models: Gaps and opportunities

Highly pathogenic avian influenza virus (HPAIV) increasingly threatens wild birds, poultry, non-human mammals and humans, yet gaps and opportunities in avian influenza virus (AIV) transmission modelling have not been comprehensively synthesized. We reviewed 182 articles published through 2025, categorized dimensions, including modelling approaches, data, host types, biological levels, spatial scales, temporal scales and model objectives, examined associations among these dimensions, and synthesized author-stated limitations and recommendations. Statistical and mechanistic models dominated. Model data centred on population dynamics and demography, disease surveillance, host epidemiology and competence, meteorology and land use and land cover. Most studies focused on poultry or wild birds at regional spatial scales, seasonal/annual and multi-year temporal scales, population or multi-population levels, and were retrospective. Machine learning and decision- making models were less common. Immunity, behaviour and life history, community composition, morphology, viral genetics and viral persistence received less attention. Non-human mammals, individual and community biological levels, local and broader spatial scales, daily temporal scales and prospective modelling were also less frequently modelled. The authors frequently reported limitations related to data availability, surveillance sampling and reporting bias, and model simplification, while commonly recommending improved data quality and greater model complexity and realism.

Methods in Ecology and Evolution↗
USGS2026

Spatially realistic dynamic N-mixture models

Dispersal is a complex ecological process that can be driven by the abiotic environment, density-dependent processes and species mobility. Traditionally, dispersal has been studied with capture-based data that are often limited in space and time. More recently, spatial dynamic N-mixture models (i.e. N-mixture models accounting for dispersal among local populations and changes in abundance over time) allow the study of dispersal with count data. However, current spatial dynamic N-mixture models focus mainly on distance-based dispersal, limiting our ability to evaluate realistic patterns and drivers of dispersal at relevant spatial and temporal scales. We developed a spatially realistic dynamic N-mixture model that allows dispersal to be driven by environmental conditions and population density. Using simulations, we evaluated the inferential performance of this model when large proportions of sites are unsurveyed and the survey period is short, situations that are typical of real-world studies. We then used two case studies to illustrate the applications of this model, one representing a species with limited mobility in fragmented habitat (Chiricahua Leopard Frogs, Lithobates chiricahuensis , in isolated ponds) and the other representing a species with great mobility in continuous heterogeneous habitat (Common Nighthawks, Chordeiles minor , in grasslands). Our simulation study showed that the model provides valid inference even when the number of surveyed sites is small (𝑛 =10) or when the survey period is short for dynamic models (5 years), indicating that the model could have great practical value in real-world studies. The case study of Chiricahua Leopard Frogs showed that the model is capable of revealing pond-type-specific dispersal and predicting dispersal among ponds. The case study of Common Nighthawks showed the ability of the model to reveal the effect of primary productivity on dispersal and to map dispersal across the study area. Our effort demonstrates the potential of using count data to gain insights about complex yet realistic dispersal processes. The model that we introduce is flexible and adaptable for a broad range of situations in population and conservation ecology

Arizona, Kansas↗
USGS2025

Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity

1. The biodiversity crisis necessitates spatially extensive methods to monitor multiple taxonomic groups for evidence of change in response to evolving environmental conditions. Programs that combine passive acoustic monitoring and machine learning are increasingly used to meet this need. These methods require large, annotated datasets, which are time-consuming and expensive to produce, creating potential barriers to adoption in data- and funding-poor regions. Recently released pre-trained avian acoustic classification models provide opportunities to reduce the need for manual labelling and accelerate the development of new acoustic classification algorithms through transfer learning. Transfer learning is a strategy for developing algorithms under data scarcity that uses pre-trained models from related tasks to adapt to new tasks. 2. Our primary objective was to develop a transfer learning strategy using the feature embeddings of a pre-trained avian classification model to train custom acoustic classification models in data-scarce contexts. We used three annotated avian acoustic datasets to test whether transfer learning and soundscape simulation-based data augmentation could substantially reduce the annotated training data necessary to develop performant custom acoustic classifiers. We also conducted a sensitivity analysis for hyperparameter choice and model architecture. We then assessed the generalizability of our strategy to increasingly novel non-avian classification tasks. 3. With as few as two training examples per class, our soundscape simulation data augmentation approach consistently yielded new classifiers with improved performance relative to the pre-trained classification model and transfer learning classifiers trained with other augmentation approaches. Performance increases were evident for three avian test datasets, including single-class and multi-label contexts. We observed that the relative performance among our data augmentation approaches varied for the avian datasets and nearly converged for one dataset when we included more training examples. 4. We demonstrate an efficient approach to developing new acoustic classifiers leveraging open-source sound repositories and pre-trained networks to reduce manual labelling. With very few examples, our soundscape simulation approach to data augmentation yielded classifiers with performance equivalent to those trained with many more examples, showing it is possible to reduce manual label-ling while still achieving high-performance classifiers and, in turn, expanding the potential for passive acoustic monitoring to address rising biodiversity monitoring needs.

Methods in Ecology and Evolution↗