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USGS2022

Field testing a high-frequency acoustic attenuation system for measuring fine suspended sediments and algal movements

Acoustic measurements of suspended sediment have the potential to allow remote, autonomous monitoring of sediment movements at much higher temporal resolution than traditional manual sampling methods. Although suspended sands present a challenging measurement problem due to their logarithmic distribution with depth, fine clay sediments are distributed evenly throughout a stream cross section, making them amenable to point measurements. In order to improve measurement capabilities for fine sediments in stream channels, The National Center for Physical Acoustics at The University of Mississippi has developed a remote, autonomous acoustic system to monitor fine sediments transported in streams. The system was tested on the Middle Rio Grande near San Acacia, New Mexico, and in Goodwin Creek in Panola County, Mississippi. The acoustic instruments were compared to sediment concentrations from physical samples in both deployments. Diurnal patterns were found in the acoustic signals from the Middle Rio Grande, and a follow up experiment at The University of Mississippi Biological Field Station was used to investigate the potential effects of algal biomass on acoustic attenuation measurements. The results showed diurnal patterns in attenuation were associated with patterns in light, temperature, and dissolved oxygen. These results combined with information from the literature indicate diel movement of algal colonies in the water column of some water bodies may interfere with high-frequency acoustic measurements in natural environments and that acoustic methods have the potential to allow ecological researchers to evaluate mass algal movements in the field. Results from Goodwin Creek demonstrate that the acoustic system is able to provide measurements of sediment concentration with high temporal resolution that track well with expected sediment transport patterns in response to discharge hydrographs.

Applied Acoustics↗

USGS1997

Estimating temporary emigration using capture-recapture data with Pollock's robust design

Statistical inference for capture–recapture studies of open animal populations typically relies on the assumption that all emigration from the studied population is permanent. However, there are many instances in which this assumption is unlikely to be met. We define two general models for the process of temporary emigration: completely random and Markovian. We then consider effects of these two types of temporary emigration on Jolly–Seber estimators and on estimators arising from the full-likelihood approach to robust design data. Capture–recapture data arising from Pollock’s robust design provide the basis for obtaining unbiased estimates of demographic parameters in the presence of temporary emigration, and for estimating the probability of temporary emigration. We present a likelihood-based approach to dealing with temporary emigration that permits estimation under different models of temporary emigration and yields tests for completely random and Markovian emigration. In addition, we use the relationship between capture probability estimates based on closed and open models under completely random temporary emigration to derive three ad hoc estimators for the probability of temporary emigration. Two of these should be especially useful in situations where capture probabilities are heterogeneous among individual animals. Ad hoc and full-likelihood estimators are illustrated for small-mammal capture–recapture data sets. We believe that these models and estimators will be useful for testing hypotheses about the process of temporary emigration, for estimating demographic parameters in the presence of temporary emigration, and for estimating probabilities of temporary emigration. These latter estimates are frequently of ecological interest as indicators of animal movement and, in some sampling situations, as direct estimates of breeding probabilities and proportions.

Ecology↗

USGS2024

Estimating migration timing and abundance in partial migratory systems by integrating continuous antenna detections with physical captures

Many populations migrate between two different habitats (e.g. wintering/foraging to breeding area, mainstem–tributary, river–lake, river–ocean, river–side channel) as part of their life history. Detection technologies, such as passive integrated transponder (PIT) antennas or sonic receivers, can be placed at boundaries between habitats (e.g. near the confluence of rivers) to detect migratory movements of marked animals. Often, these detection systems have high detection probabilities and detect many individuals but are limited in their ability to make inferences about abundance because only marked individuals can be detected. Here, we introduce a mark–recapture modelling approach that uses detections from a double-array PIT antenna system to imply movement directionality from arrays and estimate migration timing. Additionally, when combined with physical captures, the model can be used to estimate abundances for both migratory and non-migratory groups and help quantify partial migration. We first test our approach using simulation, and results indicate our approach displayed negligible bias for total abundance (less than ±1%) and slight biases for state-specific abundance estimates (±1%–6%). We fit our model to array detections and physical captures of three native fishes (humpback chub [ Gila cypha ], flannelmouth sucker [ Catostomus latipinnis ] and bluehead sucker [ Catostomus discobolus ]) in the Little Colorado River (LCR) in Grand Canyon, AZ, a system that exhibits partial migration (i.e. includes residents and migrants). Abundance estimates from our model confirm that, for all three species, migratory individuals are much more numerous than residents. There was little difference in movement timing between 2021 (a year without preceding winter/spring floods) and 2022 (a year with a small flood occurring in early April). In both years, flannelmouth sucker arrived in mid-March whereas humpback chub and bluehead sucker arrivals occurred early- to mid-April. With humpback chub and flannelmouth sucker, movement timing was influenced by body size so that large individuals were more likely to arrive early compared to smaller individuals. With more years of data, this model framework could be used to evaluate ecological questions pertaining to flow cues and movement timing or intensity, relative trends in migrants versus residents and ecological drivers of skipped spawning.

Journal of Animal Ecology↗

USGS2021

Caution is warranted when using animal space-use and movement to infer behavioral states

Background Identifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals. Methods Using a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events. Results For white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual’s UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events. Conclusions Caution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors.

Movement Ecology↗

USGS2024

Movement behavior in a dominant ungulate underlies successful adjustment to a rapidly changing landscape following megafire

Background Movement plays a key role in allowing animal species to adapt to sudden environmental shifts. Anthropogenic climate and land use change have accelerated the frequency of some of these extreme disturbances, including megafire. These megafires dramatically alter ecosystems and challenge the capacity of several species to adjust to a rapidly changing landscape. Ungulates and their movement behaviors play a central role in the ecosystem functions of fire-prone ecosystems around the world. Previous work has shown behavioral plasticity is an important mechanism underlying whether large ungulates are able to adjust to recent changes in their environments effectively. Ungulates may respond to the immediate effects of megafire by adjusting their movement and behavior, but how these responses persist or change over time following disturbance is poorly understood. Methods We examined how an ecologically dominant ungulate with strong site fidelity, Columbian black-tailed deer ( Odocoileus hemionus columbianus ), adjusted its movement and behavior in response to an altered landscape following a megafire. To do so, we collected GPS data from 21 individual female deer over the course of a year to compare changes in home range size over time and used resource selection functions (RSFs) and hidden Markov movement models (HMMs) to assess changes in behavior and habitat selection. Results We found compelling evidence of adaptive capacity across individual deer in response to megafire. Deer avoided exposed and severely burned areas that lack forage and could be riskier for predation immediately following megafire, but they later altered these behaviors to select areas that burned at higher severities, potentially to take advantage of enhanced forage. Conclusions These results suggest that despite their high site fidelity, deer can navigate altered landscapes to track rapid shifts in encounter risk with predators and resource availability. This successful adjustment of movement and behavior following extreme disturbance could help facilitate resilience at broader ecological scales.

California↗

USGS2019

Brook trout (Salvelinus fontinalis) movement and survival after removal of two dams on the West Branch of the Wolf River, Wisconsin

Dam removals allow fish to access habitats that may provide ecological benefits and risks, but the extent of fish movements through former dam sites has not been thoroughly evaluated for many species. We installed stationary PIT antennas in 2016 and 2017 to evaluate movements and survival of brook trout Salvelinus fontinalis in the West Branch of the Wolf River (WBWR) in central Wisconsin following removal of two dams and channel modifications designed to promote fish movement. These changes provided access to lacustrine habitats that might provide suitable winter habitat or act as ecological sinks. We used multistate models to estimate transition probabilities between river sections, to determine whether brook trout: (a) moved between multiple river sections and (b) entered lacustrine habitats as seasonal refuges, but eventually returned to lotic habitat. We also used a Cormack-Jolly-Seber model to evaluate whether apparent survival of brook trout in the WBWR was comparable to other populations. Few fish moved among river sections or used lacustrine habitat (<5% of tagged fish); most brook trout remained in sections where they were initially tagged, potentially due to quality habitat located throughout the river. Like other studies, brook trout in the WBWR appear to experience high mortality based on low number of detections, few physical recaptures and an estimated eight-month apparent survival rate of 0.27. In scenarios where fish can already access suitable habitat, removal of dams may not result in substantial increases in fish movement and colonisation of newly accessible habitat may not occur immediately.

Wisconsin↗

USGS2013

Comparative spring-staging ecology of sympatric arctic-nesting geese in south-central Nebraska

The Rainwater Basin in Nebraska has been a historic staging area for midcontinent greater white-fronted geese ( Anser albifrons frontalis ) since the 1950s and, in the mid-1990s, millions of midcontinent lesser snow geese ( Chen caerulescens caerulescens ) expanded their spring migration route to include this region. In response to speculation that snow geese may be in direct competition with white-fronted geese, we compared staging ecology by quantifying diet, habitat use, movement patterns, and time budgets during springs 1998–1999. Collected white-fronted geese (n = 190) and snow geese (n = 203) consumed primarily corn ( Zea mays ; 97–98% aggregate dry mass) while staging in Nebraska; thus, diet overlap was nearly complete. Both species used cornfields most frequently during the morning (54–55%) and wetlands more during the afternoon (51–65%). When found grouped together, snow goose abundance was greater than white-fronted goose abundance by an average of 57 times ( se = 11, n = 131 groups) in crop fields and 28 times ( se = 9, n = 84 groups) in wetlands. Snow geese and white-fronted geese flew similar distances between roosting and feeding sites, leaving and returning to wetland roost sties at similar times in mornings and afternoons. Overlap in habitat-specific time budgets was high; resting was the most common behavior on wetlands, and foraging was a common behavior in fields. We observed 111 interspecific agonistic interactions while observing white-fronted and snow geese. White-fronted geese initiated and dominated more interactions with other waterfowl species than did snow geese (32 vs. 14%). Certain aspects of spring-staging niches ( i.e. , diet, habitat use, movement patterns, and habitat-specific behavior) of white-fronted and snow geese overlapped greatly at this mid-latitude staging site, creating opportunity for potential food- and habitat-based competition between species. Snow geese did not consistently dominate interactions with white-fronted geese; yet large differences in their numbers coupled with high degrees of spatial, temporal, and ecological overlap support potential for exploitative competition during years when waste corn may be in short supply and dry years when few wetlands are available for staging waterfowl.

Nebraska↗

USGS2012

Underestimating the effects of spatial heterogeneity due to individual movement and spatial scale: infectious disease as an example

Many ecological and epidemiological studies occur in systems with mobile individuals and heterogeneous landscapes. Using a simulation model, we show that the accuracy of inferring an underlying biological process from observational data depends on movement and spatial scale of the analysis. As an example, we focused on estimating the relationship between host density and pathogen transmission. Observational data can result in highly biased inference about the underlying process when individuals move among sampling areas. Even without sampling error, the effect of host density on disease transmission is underestimated by approximately 50 % when one in ten hosts move among sampling areas per lifetime. Aggregating data across larger regions causes minimal bias when host movement is low, and results in less biased inference when movement rates are high. However, increasing data aggregation reduces the observed spatial variation, which would lead to the misperception that a spatially targeted control effort may not be very effective. In addition, averaging over the local heterogeneity will result in underestimating the importance of spatial covariates. Minimizing the bias due to movement is not just about choosing the best spatial scale for analysis, but also about reducing the error associated with using the sampling location as a proxy for an individual’s spatial history. This error associated with the exposure covariate can be reduced by choosing sampling regions with less movement, including longitudinal information of individuals’ movements, or reducing the window of exposure by using repeated sampling or younger individuals.

Landscape Ecology↗

USGS2014

Movements and activity of juvenile Brown Treesnakes ( Boiga irregularis )

Understanding the spatial ecology and foraging strategy of invasive animals is essential for success in control or eradication. We studied movements and activity in juvenile Brown Treesnakes on Guam, as this population segment has proven particularly difficult to control. Distance between daytime refugia (from telemetry of 18 juveniles, 423-800 mm snout-vent length) ranged from 0-118 m ( n = 86), with a grand mean of 43 m. There were tendencies for shorter snake movements on nights directly following a full moon and on dry nights, but variation among snakes was of a larger magnitude and would greatly reduce chances to detect moon or rain effects unless corrected for. Snake activity was estimated from audio recordings of signals from “tipping” radio transmitters, analyzed for pulse period and amplitude. Activity was highest in the hours immediately after sunset, and gradually declined throughout the night before dropping abruptly in conjunction with sunrise. Snake activity was higher on rainy nights, and tended to be highest during waning moons and when the moon was below the horizon. We conclude that small Brown Treesnakes forage actively and appear to move far enough to regularly encounter the traps and bait used on Guam for control purposes, suggesting that alternative explanations are required for their low capture rates with these control tools.

Copeia↗

USGS2016

Overwintering strategies of migratory birds: a novel approach for estimating seasonal movement patterns of residents and transients

Our understanding of movement patterns in wildlife populations has played an important role in current ecological knowledge and can inform landscape conservation decisions. Direct measures of movement can be obtained using marked individuals, but this requires tracking individuals across a landscape or multiple sites. We demonstrate how movements can be estimated indirectly using single-site, capture–mark–recapture (CMR) data with a multi-state open robust design with state uncertainty model (MSORD-SU). We treat residence and transience as two phenotypic states of overwintering migrants and use time- and state-dependent probabilities of site entry and persistence as indirect measures of movement. We applied the MSORD-SU to data on eight species of overwintering Neotropical birds collected in 14 countries between 2002 and 2011. In addition to entry and persistence probabilities, we estimated the proportions of residents at a study site and mean residence times. We identified overwintering movement patterns and residence times that contrasted with prior categorizations of territoriality. Most species showed an evidence of residents entering sites at multiple time intervals, with transients tending to enter between peak resident movement times. Persistence and the proportion of residents varied by latitude, but were not always positively correlated for a given species. Synthesis and applications . Our results suggest that migratory songbirds commonly move among habitats during the overwintering period. Substantial proportions of populations appear to be comprised of transient individuals, and residents tend to persist at specific sites for relatively short periods of time. This information on persistence and movement patterns should be explored for specific habitats to guide landscape management on the wintering grounds, such as determining which habitats are conserved or restored as part of certification programmes of tropical agroforestry crops. We suggest that research and conservation efforts on Neotropical migrant songbirds focus on identifying landscape configurations and regional habitat networks that support these diverse overwintering strategies to secure full life cycle conservation.

Journal of Applied Ecology↗

USGS2022

A framework for integrating inferred movement behavior into disease risk models

Movement behavior is an important contributor to habitat selection and its incorporation in disease risk models has been somewhat neglected. The habitat preferences of host individuals affect their probability of exposure to pathogens. If preference behavior can be incorporated in ecological niche models (ENMs) when data on pathogen distributions are available, then variation in such behavior may dramatically impact exposure risk. Here we use data from the anthrax endemic system of Etosha National Park, Namibia, to demonstrate how integrating inferred movement behavior alters the construction of disease risk maps. We used a Maximum Entropy (MaxEnt) model that associated soil, bioclimatic, and vegetation variables with the best available pathogen presence data collected at anthrax carcass sites to map areas of most likely Bacillus anthracis (the causative bacterium of anthrax) persistence. We then used a hidden Markov model (HMM) to distinguish foraging and non-foraging behavioral states along the movement tracks of nine zebra ( Equus quagga ) during the 2009 and 2010 anthrax seasons. The resulting tracks, decomposed on the basis of the inferred behavioral state, formed the basis of step-selection functions (SSFs) that used the MaxEnt output as a potential predictor variable. Our analyses revealed different risks of exposure during different zebra behavioral states, which were obscured when the full movement tracks were analyzed without consideration of the underlying behavioral states of individuals. Pathogen (or vector) distribution models may be misleading with regard to the actual risk faced by host animal populations when specific behavioral states are not explicitly accounted for in selection analyses. To more accurately evaluate exposure risk, especially in the case of environmentally transmitted pathogens, selection functions could be built for each identified behavioral state and then used to assess the comparative exposure risk across relevant states. The scale of data collection and analysis, however, introduces complexities and limitations for consideration when interpreting results.

Etosha National Park↗

USGS2010

Migration of waterfowl in the east asian flyway and spatial relationship to HPAI H5N1 outbreaks

Poyang Lake is situated within the East Asian Flyway, a migratory corridor for waterfowl that also encompasses Guangdong Province, China, the epicenter of highly pathogenic avian influenza (HPAI) H5N1. The lake is the largest freshwater body in China and a significant congregation site for waterfowl; however, surrounding rice fields and poultry grazing have created an overlap with wild waterbirds, a situation conducive to avian influenza transmission. Reports of HPAI H5N1 in healthy wild ducks at Poyang Lake have raised concerns about the potential of resilient free-ranging birds to disseminate the virus. Yet the role wild ducks play in connecting regions of HPAI H5N1 outbreak in Asia is hindered by a lack of information about their migratory ecology. During 2007-08 we marked wild ducks at Poyang Lake with satellite transmitters to examine the location and timing of spring migration and identify any spatiotemporal relationship with HPAI H5N1 outbreaks. Species included the Eurasian wigeon ( Anas penelope ), northern pintail ( Anas acuta ), common teal ( Anas crecca ), falcated teal ( Anas falcata ), Baikal teal ( Anas formosa ), mallard ( Anas platyrhynchos ), garganey ( Anas querquedula ), and Chinese spotbill ( Anas poecilohyncha ). These wild ducks (excluding the resident mallard and Chinese spotbill ducks) followed the East Asian Flyway along the coast to breeding areas in northern China, eastern Mongolia, and eastern Russia. None migrated west toward Qinghai Lake (site of the largest wild bird epizootic), thus failing to demonstrate any migratory connection to the Central Asian Flyway. A newly developed Brownian bridge spatial analysis indicated that HPAI H5N1 outbreaks reported in the flyway were related to latitude and poultry density but not to the core migration corridor or to wetland habitats. Also, we found a temporal mismatch between timing of outbreaks and wild duck movements. These analyses depend on complete or representative reporting of outbreaks, but by documenting movements of wild waterfowl, we present ecological knowledge that better informs epidemiological investigations seeking to explain and predict the spread of avian influenza viruses. ?? 2010 American Association of Avian Pathologists.

Avian Diseases↗

USGS2009

Nocturnal movements of desert bighorn sheep in the Muddy Mountains, Nevada

Adequate management of a species requires complete knowledge of its ecology, including both nocturnal and diurnal behavior. Knowledge of the movement behavior of bighorn sheep can provide insight for understanding spatial population processes as the combined result of individual behavior, physiological constraints, and fine-scale environmental influences. However, because of past difficulties in tracking radio-collared animals in the dark, little is known about nocturnal movements of many ungulates, including desert bighorn sheep (Ovis canadensis nelsoni). The use of GPS collars that supply sequential location records over a 24-hour period has provided new opportunities for recording movements of animals. We report findings from a preliminary analysis of data on nocturnal movements of desert bighorn sheep wearing GPS collars in the Muddy Mountains, Nevada. The study is part of a larger research project on movement patterns aimed at assessing the connectivity of bighorn sheep populations that comprise the metapopulation in southern Nevada. We recorded nighttime locations at 2100 and 0400 PST and calculated a total of 8,758 nocturnal movement distances for 12 ewes and 16 rams. Nocturnal movements were characteristic of all individuals. Bighorn sheep varied in the magnitude of movement from night to night and there were significant differences among individuals in the mean distance moved. Females had a greater proportion of short movement distances when compared to males; males had the greatest proportion of long distance movements. Movement distances were affected by sex, season, and percent of the moon’s surface illumination. Mean movement distances of males were greater than those of females. For both males and females, mean movement distances were shortest during the late spring months (April-June) and greatest during summer (July–September). Mean movement distances were greatest when the moon was brightest for all seasons except late spring, when movement distances for both sexes were shortest. We also describe movement distances >1 km (19% ewes, 81% rams) relative to movements within or between 50% core use areas, and nighttime intermountain and intra-mountain travel.

Black Mountain, Muddy Mountain↗

USGS2024

Treed Gaussian processes for animal movement modeling

Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous-time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement. The machine learning formulation of TGPs enables modeling to be nearly automated, while their Bayesian formulation allows for the derivation of movement descriptors with associated uncertainty measures. We demonstrate the use of an existing R package to implement TGPs using the familiar Markov chain Monte Carlo algorithm. We then use estimated movement trajectories to derive movement descriptors that can be compared across individuals and populations. We applied the TGP model to a case study of lesser prairie-chickens ( Tympanuchus pallidicinctus ) to demonstrate the benefits of TGP modeling and compared distance traveled and residence times across lesser prairie-chicken individuals and populations. For broad usability, we outline all steps necessary for practitioners to specify relevant movement descriptors (e.g., turn angles, speed, contact points) and apply TGP modeling and trajectory comparison to their own telemetry datasets. Combining the predictive power of machine learning and the statistical inference of Bayesian methods to model movement trajectories allows for the estimation of statistically comparable movement descriptors from telemetry studies. Our use of an accessible R package allows practitioners to model trajectories and estimate movement descriptors, facilitating the use of telemetry data to answer applied management questions.

Ecology and Evolution↗

USGS2010

Movements of juvenile common ravens in an arid landscape

Movement patterns of juvenile birds are poorly understood, yet critically important ecological phenomena, especially for species with a prolonged juvenile period. We evaluated postfledging movements of juvenile common ravens (Corvus corax) in a western Mojave Desert landscape composed of a mosaic of natural and anthropogenic elements. Generally, ravens do not begin breeding until after their fourth year. We marked 2 annual cohorts of juvenile ravens and followed them from dispersal from their natal territory for up to 33 months. Movements of juvenile common ravens were similar for males and females. Conspecifics and confined livestock feeding operations represented important resources for juvenile ravens, and juveniles were rarely located in open desert. However, initial movements from the natal territory to the nearest communal point subsidy rather than the closest anthropogenic resource suggested juvenile dispersal was influenced by the combination of conspecifics and anthropogenic resources, rather than the distribution of those resources. Land managers concerned with growing raven populations should reduce access to concentrated anthropogenic resources such as landfills and dairies, which serve as important resources for juveniles. Because juvenile ravens rarely venture into open desert, reducing their numbers by lethal removal or other means is unlikely to lessen raven predation of desert tortoises (Gopherus agassizii).

Journal of Wildlife Management↗

USGS2018

Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements

Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground‐based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi‐state mark–recapture methods using a hierarchical N ‐mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state‐space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk ( Cervus elaphus nelsoni ) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N‐mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.

Ecological Applications↗

USGS2017

Imputation approaches for animal movement modeling

The analysis of telemetry data is common in animal ecological studies. While the collection of telemetry data for individual animals has improved dramatically, the methods to properly account for inherent uncertainties (e.g., measurement error, dependence, barriers to movement) have lagged behind. Still, many new statistical approaches have been developed to infer unknown quantities affecting animal movement or predict movement based on telemetry data. Hierarchical statistical models are useful to account for some of the aforementioned uncertainties, as well as provide population-level inference, but they often come with an increased computational burden. For certain types of statistical models, it is straightforward to provide inference if the latent true animal trajectory is known, but challenging otherwise. In these cases, approaches related to multiple imputation have been employed to account for the uncertainty associated with our knowledge of the latent trajectory. Despite the increasing use of imputation approaches for modeling animal movement, the general sensitivity and accuracy of these methods have not been explored in detail. We provide an introduction to animal movement modeling and describe how imputation approaches may be helpful for certain types of models. We also assess the performance of imputation approaches in two simulation studies. Our simulation studies suggests that inference for model parameters directly related to the location of an individual may be more accurate than inference for parameters associated with higher-order processes such as velocity or acceleration. Finally, we apply these methods to analyze a telemetry data set involving northern fur seals ( Callorhinus ursinus ) in the Bering Sea. Supplementary materials accompanying this paper appear online.

Journal of Agricultural, Biological, and Environme↗

USGS2018

Migratory coupling between predators and prey

Animal migrations act to couple ecosystems and are undertaken by some of the world’s most endangered taxa. Predators often exploit migrant prey, but the movements taken by these consumers are rarely studied or understood. We define such movements, where migrant prey induce large-scale movements of predators, as migratory coupling. Migratory coupling can have ecological consequences for the participating prey, predators and the communities they traverse across the landscape. We review examples of migratory coupling in the literature and provide hypotheses regarding conditions favourable for their occurrence. We also provide a framework for interactions induced by migratory coupling and demonstrate their potential community-level impacts by examining other forms of spatial shifts in predators. Migratory coupling integrates the fields of landscape, movement, food web and community ecologies, and represents an understudied frontier in ecology.

Nature Ecology & Evolution↗