Publications by authors named "John Fieberg"

28 Publications

  • Page 1 of 1

Estimating the movements of terrestrial animal populations using broad-scale occurrence data.

Mov Ecol 2021 Dec 11;9(1):60. Epub 2021 Dec 11.

Cornell Lab of Ornithology, Cornell University, Ithaca, NY, 14850, USA.

As human and automated sensor networks collect increasingly massive volumes of animal observations, new opportunities have arisen to use these data to infer or track species movements. Sources of broad scale occurrence datasets include crowdsourced databases, such as eBird and iNaturalist, weather surveillance radars, and passive automated sensors including acoustic monitoring units and camera trap networks. Such data resources represent static observations, typically at the species level, at a given location. Nonetheless, by combining multiple observations across many locations and times it is possible to infer spatially continuous population-level movements. Population-level movement characterizes the aggregated movement of individuals comprising a population, such as range contractions, expansions, climate tracking, or migration, that can result from physical, behavioral, or demographic processes. A desire to model population movements from such forms of occurrence data has led to an evolving field that has created new analytical and statistical approaches that can account for spatial and temporal sampling bias in the observations. The insights generated from the growth of population-level movement research can complement the insights from focal tracking studies, and elucidate mechanisms driving changes in population distributions at potentially larger spatial and temporal scales. This review will summarize current broad-scale occurrence datasets, discuss the latest approaches for utilizing them in population-level movement analyses, and highlight studies where such analyses have provided ecological insights. We outline the conceptual approaches and common methodological steps to infer movements from spatially distributed occurrence data that currently exist for terrestrial animals, though similar approaches may be applicable to plants, freshwater, or marine organisms.
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http://dx.doi.org/10.1186/s40462-021-00294-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665594PMC
December 2021

Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution.

Ecol Appl 2022 Jan 28;32(1):e02470. Epub 2021 Nov 28.

Department of Natural Resources Science, University of Rhode Island, Kingston, Rhode Island, USA.

Habitat selection is a fundamental animal behavior that shapes a wide range of ecological processes, including animal movement, nutrient transfer, trophic dynamics and population distribution. Although habitat selection has been a focus of ecological studies for decades, technological, conceptual and methodological advances over the last 20 yr have led to a surge in studies addressing this process. Despite the substantial literature focused on quantifying the habitat-selection patterns of animals, there is a marked lack of guidance on best analytical practices. The conceptual foundations of the most commonly applied modeling frameworks can be confusing even to those well versed in their application. Furthermore, there has yet to be a synthesis of the advances made over the last 20 yr. Therefore, there is a need for both synthesis of the current state of knowledge on habitat selection, and guidance for those seeking to study this process. Here, we provide an approachable overview and synthesis of the literature on habitat-selection analyses (HSAs) conducted using selection functions, which are by far the most applied modeling framework for understanding the habitat-selection process. This review is purposefully non-technical and focused on understanding without heavy mathematical and statistical notation, which can confuse many practitioners. We offer an overview and history of HSAs, describing the tortuous conceptual path to our current understanding. Through this overview, we also aim to address the areas of greatest confusion in the literature. We synthesize the literature outlining the most exciting conceptual advances in the field of habitat-selection modeling, discussing the substantial ecological and evolutionary inference that can be made using contemporary techniques. We aim for this paper to provide clarity for those navigating the complex literature on HSAs while acting as a reference and best practices guide for practitioners.
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http://dx.doi.org/10.1002/eap.2470DOI Listing
January 2022

Individual-Level Memory Is Sufficient to Create Spatial Segregation among Neighboring Colonies of Central Place Foragers.

Am Nat 2021 08 24;198(2):E37-E52. Epub 2021 Jun 24.

AbstractCentral place foragers often segregate in space, even without signs of direct agonistic interactions. Using parsimonious individual-based simulations, we show that for species with spatial cognitive abilities, individual-level memory of resource availability can be sufficient to cause spatial segregation in the foraging ranges of colonial animals. The shapes of the foraging distributions are governed by commuting costs, the emerging distribution of depleted resources, and the fidelity of foragers to their colonies. When colony fidelity is weak and foragers can easily switch to colonies located closer to favorable foraging grounds, this leads to space partitioning with equidistant borders between neighboring colonies. In contrast, when colony fidelity is strong-for example, because larger colonies provide safety in numbers or individuals are unable to leave-it can create a regional imbalance between resource requirements and resource availability. This leads to nontrivial space-use patterns that propagate through the landscape. Interestingly, while better spatial memory creates more defined boundaries between neighboring colonies, it can lower the average intake rate of the population, suggesting a potential trade-off between an individual's attempt for increased intake and population growth rates.
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http://dx.doi.org/10.1086/715014DOI Listing
August 2021

A fresh look at an old concept: home-range estimation in a tidy world.

PeerJ 2021 19;9:e11031. Epub 2021 Mar 19.

Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN, USA.

A rich set of statistical techniques has been developed over the last several decades to estimate the spatial extent of animal home ranges from telemetry data, and new methods to estimate home ranges continue to be developed. Here we investigate home-range estimation from a computational point of view and aim to provide a general framework for computing home ranges, independent of specific estimators. We show how such a workflow can help to make home-range estimation easier and more intuitive, and we provide a series of examples illustrating how different estimators can be compared easily. This allows one to perform a sensitivity analysis to determine the degree to which the choice of estimator influences qualitative and quantitative conclusions. By providing a standardized implementation of home-range estimators, we hope to equip researchers with the tools needed to explore how estimator choice influences answers to biologically meaningful questions.
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http://dx.doi.org/10.7717/peerj.11031DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8048401PMC
March 2021

A 'How to' guide for interpreting parameters in habitat-selection analyses.

J Anim Ecol 2021 05 12;90(5):1027-1043. Epub 2021 Mar 12.

Department of Wildland Resources and Ecology Center, Utah State University, Logan, UT, USA.

Habitat-selection analyses allow researchers to link animals to their environment via habitat-selection or step-selection functions, and are commonly used to address questions related to wildlife management and conservation efforts. Habitat-selection analyses that incorporate movement characteristics, referred to as integrated step-selection analyses, are particularly appealing because they allow modelling of both movement and habitat-selection processes. Despite their popularity, many users struggle with interpreting parameters in habitat-selection and step-selection functions. Integrated step-selection analyses also require several additional steps to translate model parameters into a full-fledged movement model, and the mathematics supporting this approach can be challenging for many to understand. Using simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point process can facilitate parameter interpretation in habitat-selection analyses. Furthermore, we provide a 'how to' guide illustrating the steps required to implement integrated step-selection analyses using the amt package By providing clear examples with open-source code, we hope to make habitat-selection analyses more understandable and accessible to end users.
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http://dx.doi.org/10.1111/1365-2656.13441DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8251592PMC
May 2021

Within Reach? Habitat Availability as a Function of Individual Mobility and Spatial Structuring.

Am Nat 2020 06 21;195(6):1009-1026. Epub 2020 Apr 21.

Organisms need access to particular habitats for their survival and reproduction. However, even if all necessary habitats are available within the broader environment, they may not all be easily reachable from the position of a single individual. Many species distribution models consider populations in environmental (or niche) space, hence overlooking this fundamental aspect of geographical accessibility. Here, we develop a formal way of thinking about habitat availability in environmental spaces by describing how limitations in accessibility can cause animals to experience a more limited or simply different mixture of habitats than those more broadly available. We develop an analytical framework for characterizing constrained habitat availability based on the statistical properties of movement and environmental autocorrelation. Using simulation experiments, we show that our general statistical representation of constrained availability is a good approximation of habitat availability for particular realizations of landscape-organism interactions. We present two applications of our approach, one to the statistical analysis of habitat preference (using step-selection functions to analyze harbor seal telemetry data) and a second that derives theoretical insights about population viability from knowledge of the underlying environment. Analytical expressions for habitat availability, such as those we develop here, can yield gains in analytical speed, biological realism, and conceptual generality by allowing us to formulate models that are habitat sensitive without needing to be spatially explicit.
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http://dx.doi.org/10.1086/708519DOI Listing
June 2020

Resampling-based methods for biologists.

PeerJ 2020 7;8:e9089. Epub 2020 May 7.

Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN, USA.

Ecological data often violate common assumptions of traditional parametric statistics (e.g., that residuals are Normally distributed, have constant variance, and cases are independent). Modern statistical methods are well equipped to handle these complications, but they can be challenging for non-statisticians to understand and implement. Rather than default to increasingly complex statistical methods, resampling-based methods can sometimes provide an alternative method for performing statistical inference, while also facilitating a deeper understanding of foundational concepts in frequentist statistics (e.g., sampling distributions, confidence intervals, -values). Using simple examples and case studies, we demonstrate how resampling-based methods can help elucidate core statistical concepts and provide alternative methods for tackling challenging problems across a broad range of ecological applications.
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http://dx.doi.org/10.7717/peerj.9089DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7211410PMC
May 2020

Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation.

J Anim Ecol 2020 01 9;89(1):80-92. Epub 2019 Sep 9.

Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN, USA.

Popular frameworks for studying habitat selection include resource-selection functions (RSFs) and step-selection functions (SSFs), estimated using logistic and conditional logistic regression, respectively. Both frameworks compare environmental covariates associated with locations animals visit with environmental covariates at a set of locations assumed available to the animals. Conceptually, slopes that vary by individual, that is, random coefficient models, could be used to accommodate inter-individual heterogeneity with either approach. While fitting such models for RSFs is possible with standard software for generalized linear mixed-effects models (GLMMs), straightforward and efficient one-step procedures for fitting SSFs with random coefficients are currently lacking. To close this gap, we take advantage of the fact that the conditional logistic regression model (i.e. the SSF) is likelihood-equivalent to a Poisson model with stratum-specific fixed intercepts. By interpreting the intercepts as a random effect with a large (fixed) variance, inference for random-slope models becomes feasible with standard Bayesian techniques, or with frequentist methods that allow one to fix the variance of a random effect. We compare this approach to other commonly applied alternatives, including models without random slopes and mixed conditional regression models fit using a two-step algorithm. Using data from mountain goats (Oreamnos americanus) and Eurasian otters (Lutra lutra), we illustrate that our models lead to valid and feasible inference. In addition, we conduct a simulation study to compare different estimation approaches for SSFs and to demonstrate the importance of including individual-specific slopes when estimating individual- and population-level habitat-selection parameters. By providing coded examples using integrated nested Laplace approximations (INLA) and Template Model Builder (TMB) for Bayesian and frequentist analysis via the R packages R-INLA and glmmTMB, we hope to make efficient estimation of RSFs and SSFs with random effects accessible to anyone in the field. SSFs with individual-specific coefficients are particularly attractive since they can provide insights into movement and habitat-selection processes at fine-spatial and temporal scales, but these models had previously been very challenging to fit.
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http://dx.doi.org/10.1111/1365-2656.13087DOI Listing
January 2020

Revisiting the benefits of active approaches for restoring damaged ecosystems. A Comment on Jones HP et al. 2018 Restoration and repair of Earth's damaged ecosystems.

Proc Biol Sci 2019 07 17;286(1907):20182928. Epub 2019 Jul 17.

Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, St Paul, MN 55108, USA.

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http://dx.doi.org/10.1098/rspb.2018.2928DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6661343PMC
July 2019

Impact of prey occupancy and other ecological and anthropogenic factors on tiger distribution in Thailand's western forest complex.

Ecol Evol 2019 03 18;9(5):2449-2458. Epub 2019 Feb 18.

University of Minnesota Saint Paul Minnesota.

Despite conservation efforts, large mammals such as tigers () and their main prey, gaur (), banteng (), and sambar (), are highly threatened and declining across their entire range. The only large viable source population of tigers in mainland Southeast Asia occurs in Thailand's Western Forest Complex (WEFCOM), an approximately 19,000 km landscape of 17 contiguous protected areas.We used an occupancy modeling framework, which accounts for imperfect detection, to identify the factors that affect tiger distribution at the approximate scale of a female tiger's home range, 64 km, and site use at a scale of 1-km. At the larger scale, we estimated the proportion of sites at WEFCOM that were occupied by tigers; at the finer scale, we identified the key variables that influence site-use and developed a predictive distribution map. At both scales, we examined key anthropogenic and ecological factors that help explain tiger distribution and habitat use, including probabilities of gaur, banteng, and sambar occurrence from a companion study.Occupancy estimated at the 64-km scale was primarily influenced by the combined presence of all three large prey species, and 37% or 5,858 km of the landscape was predicted to be occupied by tigers. In contrast, site use estimated at the scale of 1 km was most strongly influenced by the presence of sambar.By modeling occupancy while accounting for imperfect probability of detection, we established reliable benchmark data on the distribution of tigers in WEFCOM. This study also identified factors that limit tiger distributions; which managers can then target to expand tiger distribution and guide recovery elsewhere in Southeast Asia.
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http://dx.doi.org/10.1002/ece3.4845DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6405490PMC
March 2019

Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses.

Ecol Evol 2019 Jan 5;9(2):880-890. Epub 2019 Feb 5.

Department of Integrative Biology University of Guelph Guelph Ontario Canada.

Advances in tracking technology have led to an exponential increase in animal location data, greatly enhancing our ability to address interesting questions in movement ecology, but also presenting new challenges related to data management and analysis. Step-selection functions (SSFs) are commonly used to link environmental covariates to animal location data collected at fine temporal resolution. SSFs are estimated by comparing observed steps connecting successive animal locations to random steps, using a likelihood equivalent of a Cox proportional hazards model. By using common statistical distributions to model step length and turn angle distributions, and including habitat- and movement-related covariates (functions of distances between points, angular deviations), it is possible to make inference regarding habitat selection and movement processes or to control one process while investigating the other. The fitted model can also be used to estimate utilization distributions and mechanistic home ranges. Here, we present the R package amt (animal movement tools) that allows users to fit SSFs to data and to simulate space use of animals from fitted models. The amt package also provides tools for managing telemetry data. Using fisher () data as a case study, we illustrate a four-step approach to the analysis of animal movement data, consisting of data management, exploratory data analysis, fitting of models, and simulating from fitted models.
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http://dx.doi.org/10.1002/ece3.4823DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362447PMC
January 2019

Moose movement rates are altered by wolf presence in two ecosystems.

Ecol Evol 2018 Sep 19;8(17):9017-9033. Epub 2018 Aug 19.

Conservation Department Minnesota Zoo Apple Valley Minnesota.

Predators directly impact prey populations through lethal encounters, but understanding nonlethal, indirect effects is also critical because foraging animals often face trade-offs between predator avoidance and energy intake. Quantifying these indirect effects can be difficult even when it is possible to monitor individuals that regularly interact. Our goal was to understand how movement and resource selection of a predator (wolves; ) influence the movement behavior of a prey species (moose; ). We tested whether moose avoided areas with high predicted wolf resource use in two study areas with differing prey compositions, whether avoidance patterns varied seasonally, and whether daily activity budgets of moose and wolves aligned temporally. We deployed GPS collars on both species at two sites in northern Minnesota. We created seasonal resource selection functions (RSF) for wolves and modeled the relationship between moose first-passage time (FPT), a method that discerns alterations in movement rates, and wolf RSF values. Larger FPT values suggest rest/foraging, whereas shorter FPT values indicate travel/fleeing. We found that the movements of moose and wolves peaked at similar times of day in both study areas. Moose FPTs were 45% lower in areas most selected for by wolves relative to those avoided. The relationship between wolf RSF and moose FPT was nonlinear and varied seasonally. Differences in FPT between low and high RSF values were greatest in winter (-82.1%) and spring (-57.6%) in northeastern Minnesota and similar for all seasons in the Voyageurs National Park ecosystem. In northeastern Minnesota, where moose comprise a larger percentage of wolf diet, the relationship between moose FPT and wolf RSF was more pronounced (ave. across seasons: -60.1%) than the Voyageurs National Park ecosystem (-30.4%). These findings highlight the role wolves can play in determining moose behavior, whereby moose spend less time in areas with higher predicted likelihood of wolf resource selection.
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http://dx.doi.org/10.1002/ece3.4402DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157672PMC
September 2018

Time series sightability modeling of animal populations.

PLoS One 2018 12;13(1):e0190706. Epub 2018 Jan 12.

Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, St. Paul, MN, United States of America.

Logistic regression models-or "sightability models"-fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190706PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5766105PMC
February 2018

Uncovering state-dependent relationships in shallow lakes using Bayesian latent variable regression.

Ecol Appl 2018 03 19;28(2):309-322. Epub 2018 Jan 19.

Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, St. Paul, Minnesota, 55108, USA.

Ecosystems sometimes undergo dramatic shifts between contrasting regimes. Shallow lakes, for instance, can transition between two alternative stable states: a clear state dominated by submerged aquatic vegetation and a turbid state dominated by phytoplankton. Theoretical models suggest that critical nutrient thresholds differentiate three lake types: highly resilient clear lakes, lakes that may switch between clear and turbid states following perturbations, and highly resilient turbid lakes. For effective and efficient management of shallow lakes and other systems, managers need tools to identify critical thresholds and state-dependent relationships between driving variables and key system features. Using shallow lakes as a model system for which alternative stable states have been demonstrated, we developed an integrated framework using Bayesian latent variable regression (BLR) to classify lake states, identify critical total phosphorus (TP) thresholds, and estimate steady state relationships between TP and chlorophyll a (chl a) using cross-sectional data. We evaluated the method using data simulated from a stochastic differential equation model and compared its performance to k-means clustering with regression (KMR). We also applied the framework to data comprising 130 shallow lakes. For simulated data sets, BLR had high state classification rates (median/mean accuracy >97%) and accurately estimated TP thresholds and state-dependent TP-chl a relationships. Classification and estimation improved with increasing sample size and decreasing noise levels. Compared to KMR, BLR had higher classification rates and better approximated the TP-chl a steady state relationships and TP thresholds. We fit the BLR model to three different years of empirical shallow lake data, and managers can use the estimated bifurcation diagrams to prioritize lakes for management according to their proximity to thresholds and chance of successful rehabilitation. Our model improves upon previous methods for shallow lakes because it allows classification and regression to occur simultaneously and inform one another, directly estimates TP thresholds and the uncertainty associated with thresholds and state classifications, and enables meaningful constraints to be built into models. The BLR framework is broadly applicable to other ecosystems known to exhibit alternative stable states in which regression can be used to establish relationships between driving variables and state variables.
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http://dx.doi.org/10.1002/eap.1645DOI Listing
March 2018

Projecting range-wide sun bear population trends using tree cover and camera-trap bycatch data.

PLoS One 2017 29;12(9):e0185336. Epub 2017 Sep 29.

Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, St Paul, Minnesota, United States of America.

Monitoring population trends of threatened species requires standardized techniques that can be applied over broad areas and repeated through time. Sun bears Helarctos malayanus are a forest dependent tropical bear found throughout most of Southeast Asia. Previous estimates of global population trends have relied on expert opinion and cannot be systematically replicated. We combined data from 1,463 camera traps within 31 field sites across sun bear range to model the relationship between photo catch rates of sun bears and tree cover. Sun bears were detected in all levels of tree cover above 20%, and the probability of presence was positively associated with the amount of tree cover within a 6-km2 buffer of the camera traps. We used the relationship between catch rates and tree cover across space to infer temporal trends in sun bear abundance in response to tree cover loss at country and global-scales. Our model-based projections based on this "space for time" substitution suggested that sun bear population declines associated with tree cover loss between 2000-2014 in mainland southeast Asia were ~9%, with declines highest in Cambodia and lowest in Myanmar. During the same period, sun bear populations in insular southeast Asia (Malaysia, Indonesia and Brunei) were projected to have declined at a much higher rate (22%). Cast forward over 30-years, from the year 2000, by assuming a constant rate of change in tree cover, we projected population declines in the insular region that surpassed 50%, meeting the IUCN criteria for endangered if sun bears were listed on the population level. Although this approach requires several assumptions, most notably that trends in abundance across space can be used to infer temporal trends, population projections using remotely sensed tree cover data may serve as a useful alternative (or supplement) to expert opinion. The advantages of this approach is that it is objective, data-driven, repeatable, and it requires that all assumptions be clearly stated.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0185336PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5621681PMC
November 2017

Bears Show a Physiological but Limited Behavioral Response to Unmanned Aerial Vehicles.

Curr Biol 2015 Aug 13;25(17):2278-83. Epub 2015 Aug 13.

Department of Fisheries, Wildlife & Conservation Biology, University of Minnesota, St. Paul, MN 55108, USA.

Unmanned aerial vehicles (UAVs) have the potential to revolutionize the way research is conducted in many scientific fields. UAVs can access remote or difficult terrain, collect large amounts of data for lower cost than traditional aerial methods, and facilitate observations of species that are wary of human presence. Currently, despite large regulatory hurdles, UAVs are being deployed by researchers and conservationists to monitor threats to biodiversity, collect frequent aerial imagery, estimate population abundance, and deter poaching. Studies have examined the behavioral responses of wildlife to aircraft (including UAVs), but with the widespread increase in UAV flights, it is critical to understand whether UAVs act as stressors to wildlife and to quantify that impact. Biologger technology allows for the remote monitoring of stress responses in free-roaming individuals, and when linked to locational information, it can be used to determine events or components of an animal's environment that elicit a physiological response not apparent based on behavior alone. We assessed effects of UAV flights on movements and heart rate responses of free-roaming American black bears. We observed consistently strong physiological responses but infrequent behavioral changes. All bears, including an individual denned for hibernation, responded to UAV flights with elevated heart rates, rising as much as 123 beats per minute above the pre-flight baseline. It is important to consider the additional stress on wildlife from UAV flights when developing regulations and best scientific practices.
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http://dx.doi.org/10.1016/j.cub.2015.07.024DOI Listing
August 2015

A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies.

Ecol Evol 2014 May 19;4(10):1903-12. Epub 2014 Apr 19.

National Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA National Marine Fisheries Service 7600 Sand Point Way NE, Seattle, Washington, 98115.

An important assumption in observational studies is that sampled individuals are representative of some larger study population. Yet, this assumption is often unrealistic. Notable examples include online public-opinion polls, publication biases associated with statistically significant results, and in ecology, telemetry studies with significant habitat-induced probabilities of missed locations. This problem can be overcome by modeling selection probabilities simultaneously with other predictor-response relationships or by weighting observations by inverse selection probabilities. We illustrate the problem and a solution when modeling mixed migration strategies of northern white-tailed deer (Odocoileus virginianus). Captures occur on winter yards where deer migrate in response to changing environmental conditions. Yet, not all deer migrate in all years, and captures during mild years are more likely to target deer that migrate every year (i.e., obligate migrators). Characterizing deer as conditional or obligate migrators is also challenging unless deer are observed for many years and under a variety of winter conditions. We developed a hidden Markov model where the probability of capture depends on each individual's migration strategy (conditional versus obligate migrator), a partially latent variable that depends on winter severity in the year of capture. In a 15-year study, involving 168 white-tailed deer, the estimated probability of migrating for conditional migrators increased nonlinearly with an index of winter severity. We estimated a higher proportion of obligates in the study cohort than in the population, except during a span of 3 years surrounding back-to-back severe winters. These results support the hypothesis that selection biases occur as a result of capturing deer on winter yards, with the magnitude of bias depending on the severity of winter weather. Hidden Markov models offer an attractive framework for addressing selection biases due to their ability to incorporate latent variables and model direct and indirect links between state variables and capture probabilities.
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http://dx.doi.org/10.1002/ece3.1066DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4063483PMC
May 2014

Thinking like a duck: fall lake use and movement patterns of juvenile ring-necked ducks before migration.

PLoS One 2014 14;9(2):e88597. Epub 2014 Feb 14.

Minnesota Department of Natural Resources, Bemidji, Minnesota, United States of America.

The post-fledging period is one of the least studied portions of the annual cycle in waterfowl. Yet, recruitment into the breeding population requires that young birds have sufficient resources to survive this period. We used radio-telemetry and generalized estimating equations to examine support for four hypotheses regarding the drivers of landscape scale habitat use and movements made by juvenile ring-necked ducks between the pre-fledging period and departure for migration. Our response variables included the probability of movement, distances moved, and use of different lake types: brood-rearing lakes, staging lakes, and lakes with low potential for disturbance. Birds increased their use of staging areas and lakes with low potential for disturbance (i.e., without houses or boat accesses, >100 m from roads, or big lakes with areas where birds could sit undisturbed) throughout the fall, but these changes began before the start of the hunting season and their trajectory was not changed by the onset of hunting. Males and females moved similar distances and had similar probabilities of movements each week. However, females were more likely than males to use brood-rearing lakes later in the fall. Our findings suggest juvenile ring-necked ducks require different lake types throughout the fall, and managing solely for breeding habitat will be insufficient for meeting needs during the post-fledging period. Maintaining areas with low potential for disturbance and areas suitable for staging will ensure that ring-necked ducks have access to habitat throughout the fall.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0088597PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3925105PMC
October 2014

A long-term assessment of the variability in winter use of dense conifer cover by female white-tailed deer.

PLoS One 2013 13;8(6):e65368. Epub 2013 Jun 13.

Forest Wildlife Populations and Research Group, Minnesota Department of Natural Resources, Forest Lake, Minnesota, United States of America.

Background: Long-term studies allow capture of a wide breadth of environmental variability and a broader context within which to maximize our understanding of relationships to specific aspects of wildlife behavior. The goal of our study was to improve our understanding of the biological value of dense conifer cover to deer on winter range relative to snow depth and ambient temperature.

Methodology/principal Findings: We examined variation among deer in their use of dense conifer cover during a 12-year study period as potentially influenced by winter severity and cover availability. Female deer were fitted with a mixture of very high frequency (VHF, n = 267) and Global Positioning System (GPS, n = 24) collars for monitoring use of specific cover types at the population and individual levels, respectively. We developed habitat composites for four study sites. We fit multinomial response models to VHF (daytime) data to describe population-level use patterns as a function of snow depth, ambient temperature, and cover availability. To develop alternative hypotheses regarding expected spatio-temporal patterns in the use of dense conifer cover, we considered two sets of competing sub-hypotheses. The first set addressed whether or not dense conifer cover was limiting on the four study sites. The second set considered four alternative sub-hypotheses regarding the potential influence of snow depth and ambient temperature on space use patterns. Deer use of dense conifer cover increased the most with increasing snow depth and most abruptly on the two sites where it was most available, suggestive of an energy conservation strategy. Deer use of dense cover decreased the most with decreasing temperatures on the sites where it was most available. At all four sites deer made greater daytime use (55 to >80% probability of use) of open vegetation types at the lowest daily minimum temperatures indicating the importance of thermal benefits afforded from increased exposure to solar radiation. Date-time plots of GPS data (24 hr) allowed us to explore individual diurnal and seasonal patterns of habitat use relative to changes in snow depth. There was significant among-animal variability in their propensity to be found in three density classes of conifer cover and other open types, but little difference between diurnal and nocturnal patterns of habitat use.

Conclusions/significance: Consistent with our findings reported elsewhere that snow depth has a greater impact on deer survival than ambient temperature, herein our population-level results highlight the importance of dense conifer cover as snow shelter rather than thermal cover. Collectively, our findings suggest that maximizing availability of dense conifer cover in an energetically beneficial arrangement with quality feeding sites should be a prominent component of habitat management for deer.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0065368PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3681817PMC
January 2014

Quantifying the effect of habitat availability on species distributions.

J Anim Ecol 2013 Nov 2;82(6):1135-45. Epub 2013 Apr 2.

IMARES Wageningen UR, PO Box 167, 1790AD, Den Burg, The Netherlands; Department of Aquatic Ecology and Water quality Management, Wageningen UR, PO Box 47, 6700AA, Wageningen, the Netherlands.

1. If animals moved randomly in space, the use of different habitats would be proportional to their availability. Hence, deviations from proportionality between use and availability are considered the tell-tale sign of preference. This principle forms the basis for most habitat selection and species distribution models fitted to use-availability or count data (e.g. MaxEnt and Resource Selection Functions). 2. Yet, once an essential habitat type is sufficiently abundant to meet an individual's needs, increased availability of this habitat type may lead to a decrease in the use/availability ratio. Accordingly, habitat selection functions may estimate negative coefficients when habitats are superabundant, incorrectly suggesting an apparent avoidance. Furthermore, not accounting for the effects of availability on habitat use may lead to poor predictions, particularly when applied to habitats that differ considerably from those for which data have been collected. 3. Using simulations, we show that habitat use varies non-linearly with habitat availability, even when individuals follow simple movement rules to acquire food and avoid risk. The results show that the impact of availability strongly depends on the type of habitat (e.g. whether it is essential or substitutable) and how it interacts with the distribution and availability of other habitats. 4. We demonstrate the utility of a variety of existing and new methods that enable the influence of habitat availability to be explicitly estimated. Models that allow for non-linear effects (using b-spline smoothers) and interactions between environmental covariates defining habitats and measures of their availability were best able to capture simulated patterns of habitat use across a range of environments. 5. An appealing aspect of some of the methods we discuss is that the relative influence of availability is not defined a priori, but directly estimated by the model. This feature is likely to improve model prediction, hint at the mechanism of habitat selection, and may signpost habitats that are critical for the organism's fitness.
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http://dx.doi.org/10.1111/1365-2656.12061DOI Listing
November 2013

Comparing effects of lake- and watershed-scale influences on communities of aquatic invertebrates in shallow lakes.

PLoS One 2012 6;7(9):e44644. Epub 2012 Sep 6.

Wetland Wildlife Populations and Research Group, Minnesota Department of Natural Resources, Bemidji, Minnesota, United States of America.

Constraints on lake communities are complex and are usually studied by using limited combinations of variables derived from measurements within or adjacent to study waters. While informative, results often provide limited insight about magnitude of simultaneous influences operating at multiple scales, such as lake- vs. watershed-scale. To formulate comparisons of such contrasting influences, we explored factors controlling the abundance of predominant aquatic invertebrates in 75 shallow lakes in western Minnesota, USA. Using robust regression techniques, we modeled relative abundance of Amphipoda, small and large cladocera, Corixidae, aquatic Diptera, and an aggregate taxon that combined Ephemeroptera-Trichoptera-Odonata (ETO) in response to lake- and watershed-scale characteristics. Predictor variables included fish and submerged plant abundance, linear distance to the nearest wetland or lake, watershed size, and proportion of the watershed in agricultural production. Among-lake variability in invertebrate abundance was more often explained by lake-scale predictors than by variables based on watershed characteristics. For example, we identified significant associations between fish presence and community type and abundance of small and large cladocera, Amphipoda, Diptera, and ETO. Abundance of Amphipoda, Diptera, and Corixidae were also positively correlated with submerged plant abundance. We observed no associations between lake-watershed variables and abundance of our invertebrate taxa. Broadly, our results seem to indicate preeminence of lake-level influences on aquatic invertebrates in shallow lakes, but historical land-use legacies may mask important relationships.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0044644PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3435286PMC
March 2013

Generalized functional responses for species distributions.

Ecology 2011 Mar;92(3):583-9

Scottish Oceans Institute, School of Biology, University of St. Andrews, East Sands, St. Andrews, Fife KY168LB Scotland, United Kingdom.

Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different conditions because organisms respond nonlinearly to changes in habitat availability. This phenomenon, known as a functional response in resource selection, has been debated extensively in the RSF literature but continues to be ignored by practitioners for lack of a practical treatment. We therefore extend the RSF approach to enable it to estimate generalized functional responses (GFRs) from spatial data. GFRs employ data from several sampling instances characterized by diverse profiles of habitat availability. By modeling the regression coefficients of the underlying RSF as functions of availability, GFRs can account for environmental change and thus predict population distributions in new environments. We formulate the approach as a mixed-effects model so that it is estimable by readily available statistical software. We illustrate its application using (1) simulation and (2) wolf home-range telemetry. Our results indicate that GFRs can offer considerable improvements in estimation speed and predictive ability over existing mixed-effects approaches.
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http://dx.doi.org/10.1890/10-0751.1DOI Listing
March 2011

Integrated population modeling of black bears in Minnesota: implications for monitoring and management.

PLoS One 2010 Aug 12;5(8):e12114. Epub 2010 Aug 12.

Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, Minnesota, United States of America.

Background: Wildlife populations are difficult to monitor directly because of costs and logistical challenges associated with collecting informative abundance data from live animals. By contrast, data on harvested individuals (e.g., age and sex) are often readily available. Increasingly, integrated population models are used for natural resource management because they synthesize various relevant data into a single analysis.

Methodology/principal Findings: We investigated the performance of integrated population models applied to black bears (Ursus americanus) in Minnesota, USA. Models were constructed using sex-specific age-at-harvest matrices (1980-2008), data on hunting effort and natural food supplies (which affects hunting success), and statewide mark-recapture estimates of abundance (1991, 1997, 2002). We compared this approach to Downing reconstruction, a commonly used population monitoring method that utilizes only age-at-harvest data. We first conducted a large-scale simulation study, in which our integrated models provided more accurate estimates of population trends than did Downing reconstruction. Estimates of trends were robust to various forms of model misspecification, including incorrectly specified cub and yearling survival parameters, age-related reporting biases in harvest data, and unmodeled temporal variability in survival and harvest rates. When applied to actual data on Minnesota black bears, the model predicted that harvest rates were negatively correlated with food availability and positively correlated with hunting effort, consistent with independent telemetry data. With no direct data on fertility, the model also correctly predicted 2-point cycles in cub production. Model-derived estimates of abundance for the most recent years provided a reasonable match to an empirical population estimate obtained after modeling efforts were completed.

Conclusions/significance: Integrated population modeling provided a reasonable framework for synthesizing age-at-harvest data, periodic large-scale abundance estimates, and measured covariates thought to affect harvest rates of black bears in Minnesota. Collection and analysis of these data appear to form the basis of a robust and viable population monitoring program.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012114PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2920827PMC
August 2010

Correlation and studies of habitat selection: problem, red herring or opportunity?

Philos Trans R Soc Lond B Biol Sci 2010 Jul;365(1550):2233-44

Biometrics Unit, Minnesota Department of Natural Resources, 5463-C W. Broadway, Forest Lake, MN 55434, USA.

With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, 'two-stage' approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use-availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. Sophisticated analyses that explicitly model correlation rather than consider it a nuisance, like mixed effects and state-space models, offer potentially novel insights into the process of resource selection, but additional work is needed to make them more generally applicable to large datasets based on the use-availability designs. Until then, variance inflation techniques and two-stage approaches should offer pragmatic and flexible approaches to modelling correlated data.
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http://dx.doi.org/10.1098/rstb.2010.0079DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894958PMC
July 2010

The home-range concept: are traditional estimators still relevant with modern telemetry technology?

Philos Trans R Soc Lond B Biol Sci 2010 Jul;365(1550):2221-31

Department of Biological Sciences, Idaho State University, 921 South 8th Avenue, Stop 8007, Pocatello, ID 83209, USA.

Recent advances in animal tracking and telemetry technology have allowed the collection of location data at an ever-increasing rate and accuracy, and these advances have been accompanied by the development of new methods of data analysis for portraying space use, home ranges and utilization distributions. New statistical approaches include data-intensive techniques such as kriging and nonlinear generalized regression models for habitat use. In addition, mechanistic home-range models, derived from models of animal movement behaviour, promise to offer new insights into how home ranges emerge as the result of specific patterns of movements by individuals in response to their environment. Traditional methods such as kernel density estimators are likely to remain popular because of their ease of use. Large datasets make it possible to apply these methods over relatively short periods of time such as weeks or months, and these estimates may be analysed using mixed effects models, offering another approach to studying temporal variation in space-use patterns. Although new technologies open new avenues in ecological research, our knowledge of why animals use space in the ways we observe will only advance by researchers using these new technologies and asking new and innovative questions about the empirical patterns they observe.
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http://dx.doi.org/10.1098/rstb.2010.0093DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894967PMC
July 2010

Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data.

Philos Trans R Soc Lond B Biol Sci 2010 Jul;365(1550):2187-200

SUNY College of Environmental Science and Forestry, 1 Forestry Drive, Syracuse, NY 13210, USA.

Global positioning system (GPS) technologies collect unprecedented volumes of animal location data, providing ever greater insight into animal behaviour. Despite a certain degree of inherent imprecision and bias in GPS locations, little synthesis regarding the predominant causes of these errors, their implications for ecological analysis or solutions exists. Terrestrial deployments report 37 per cent or less non-random data loss and location precision 30 m or less on average, with canopy closure having the predominant effect, and animal behaviour interacting with local habitat conditions to affect errors in unpredictable ways. Home-range estimates appear generally robust to contemporary levels of location imprecision and bias, whereas movement paths and inferences of habitat selection may readily become misleading. There is a critical need for greater understanding of the additive or compounding effects of location imprecision, fix-rate bias, and, in the case of resource selection, map error on ecological insights. Technological advances will help, but at present analysts have a suite of ad hoc statistical corrections and modelling approaches available-tools that vary greatly in analytical complexity and utility. The success of these solutions depends critically on understanding the error-inducing mechanisms, and the biggest gap in our current understanding involves species-specific behavioural effects on GPS performance.
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http://dx.doi.org/10.1098/rstb.2010.0084DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894963PMC
July 2010

What time is it? Choice of time origin and scale in extended proportional hazards models.

Ecology 2009 Jun;90(6):1687-97

Biometrics Unit, Minnesota Department of Natural Resources, 5463-C West Broadway, Forest Lake, Minnesota 55025, USA.

The analysis of telemetry data offers many unique challenges due to both the observation process and the complexity of the underlying system (e.g., risk of mortality may be influenced by both age and a wide range of environmental variables). Although semi-parametric proportional hazards (SPPH) models have been proposed for analyzing ecological data, recent applications have failed to address the importance of choosing an appropriate time origin and scale for analysis. We compared models fit to a long-term deer (Odocoileus spp.) survival data set using three alternative survival timescales: age, time since start of study, and time since 6 June (with a seasonally recurrent timescale). Temporal variability in risk resulted from multiple sources (e.g., changes in hunting pressure, winter severity), and the risk of mortality varied nonlinearly with age (highest risk for young and older individuals). Age-varying hazards were represented well using regression splines, but temporal variability was more difficult to model using parametric assumptions. Annual survival estimates using the three timescales differed considerably. The model using a study-based timescale most closely tracked temporal patterns in risk. Given the difficulties in modeling temporal variability using parametric assumptions, we recommend this approach over an age-based or recurrent timescale when using SPPH models to evaluate the impact of large (naturally occurring or experimental) disturbances or to estimate annual age-specific survival rates. Lastly, we discuss the strengths and limitations of SPPH models relative to fully parametric approaches.
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http://dx.doi.org/10.1890/08-0724.1DOI Listing
June 2009

Kernel density estimators of home range: smoothing and the autocorrelation red herring.

Authors:
John Fieberg

Ecology 2007 Apr;88(4):1059-66

Minnesota Department of Natural Resources, Biometrics Unit, 5463-C W. Broadway, Forest Lake 55025, USA.

Two oft-cited drawbacks of kernel density estimators (KDEs) of home range are their sensitivity to the choice of smoothing parameter(s) and their need for independent data. Several simulation studies have been conducted to compare the performance of objective, data-based methods of choosing optimal smoothing parameters in the context of home range and utilization distribution (UD) estimation. Lost in this discussion of choice of smoothing parameters is the general role of smoothing in data analysis, namely, that smoothing serves to increase precision at the cost of increased bias. A primary goal of this paper is to illustrate this bias-variance trade-off by applying KDEs to sampled locations from simulated movement paths. These simulations will also be used to explore the role of autocorrelation in estimating UDs. Autocorrelation can be reduced (1) by increasing study duration (for a fixed sample size) or (2) by decreasing the sampling rate. While the first option will often be reasonable, for a fixed study duration higher sampling rates should always result in improved estimates of space use. Further, KDEs with typical data-based methods of choosing smoothing parameters should provide competitive estimates of space use for fixed study periods unless autocorrelation substantially alters the optimal level of smoothing.
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http://dx.doi.org/10.1890/06-0930DOI Listing
April 2007
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