Large-scale movement behavior in a reintroduced predator population

Understanding movement behavior and identifying areas of landscape connectivity is critical for the conservation of many species. However, collecting fine-scale movement data can be prohibitively time consuming and costly, especially for rare or endangered species, whereas existing data sets may provide the best available information on animal movement. Contemporary movement models may not be an option for modeling existing data due to low temporal resolution and large or unusual error structures, but inference can still be obtained using a functional movement modeling approach. We use a functional movement model to perform a population-level analysis of telemetry data collected during the reintroduction of Canada lynx to Colorado. Little is known about southern lynx populations compared to those in Canada and Alaska, and inference is often limited to a few individuals due to their low densities. Our analysis of a population of Canada lynx fills significant gaps in the knowledge of Canada lynx behavior at the southern edge of its historical range. We analyzed functions of individual-level movement paths, such as speed, residence time, and tortuosity, and identified a region of connectivity that extended north from the San Juan Mountains, along the continental divide, and terminated in Wyoming at the northern edge of the Southern Rocky Mountains. Individuals were able to traverse large distances across non-boreal habitat, including exploratory movements to the Greater Yellowstone area and beyond. We found evidence for an effect of seasonality and breeding status on many of the movement quantities and documented a potential reintroduction effect. Our findings provide the first analysis of Canada lynx movement in Colorado and substantially augment the information available for conservation and management decisions. Th e functional movement framework can be extended to other species and demonstrates that information on movement behavior can be obtained using existing data sets

Statistical methods for modeling the movement and space-use of carnivores

2017 Summer.Includes bibliographical references.Recent advancements in the ability to monitor animal locations through time has led to a rapidly expanding field focused on statistical models for animal movement. However, many of the existing methods are computationally time-consuming to fit, restricting their application to a few individuals, and inaccessible to wildlife management practitioners. In addition, existing movement models were developed for contemporary animal location data. Many previously collected telemetry data sets may provide important information on animal movement, but there may be additional challenges that are not present in data collected explicitly for movement modeling. For example, telemetry data collected for survival studies may have large temporal gaps, and long-term studies may have used multiple data collection methods, resulting in data points with different error structures. My goal is to develop and expand on methods for modeling individual- and population-level animal movement in a flexible and computationally accessible framework. In Chapter 1, I discuss the role of carnivores in natural resource management and the habitat associations and movement ecology of two carnivores native to Colorado, Canada lynx and cougars. I describe the existing data sets, collected by Colorado Parks and Wildlife, that are available for analyzing Canada lynx and cougar movement ecology. I also discuss contemporary statistical methods for analyzing animal telemetry data. Finally, I conclude with my research objectives. Chapter 2 presents a new framework for modeling the unobserved paths of telemetered individuals while accounting for measurement error. Many available telemetry data sets were not collected for the purposes of movement modeling, making the use of existing methods challenging due to large temporal gaps and varying monitoring protocols. In contrast to the more traditional mechanistic movement models that appear in the literature, I propose a phenomenological functional model for animal movement. The movement process is approximated with basis functions (e.g., splines), which are an extremely flexible statistical tool that allows for complex, non-linear movement patterns at different temporal scales. In addition, the observed data contains complicated error structures that vary across telemetry type. I then apply this model to a case-study of two Canada lynx that were reintroduced to Colorado and show that inference about spatio-temporal movement behaviors can be obtained from the unobserved paths. For Chapter 3, I apply a population-level version of the functional movement model, developed in Chapter 1, to 153 Canada lynx that were released in Colorado as part of a state reintroduction program. Twelve offspring of the reintroduced individuals were also included in the analysis. I perform a post hoc analysis of movement paths using spatial visualizations and linear mixed models, allowing the different movement behaviors to vary as a function of season, sex, reproductive status, and reintroduction timeline. This chapter represents one of the most comprehensive analyses of Canada lynx movement in the continental United States. In Chapter 4, I discuss the fine-scale movement of cougars in the Colorado Front Range using a continuous-time discrete-space (CTDS) framework. The CTDS framework is computationally fast, flexible, and easily implemented in standard statistical programs. This chapter focuses on a population-level extension of the CTDS framework that can be used to model the population- and individual-level effect of landscape variables on movement rates and directionality. I use this model to determine potential drivers of cougar movement in the Colorado Front Range, a rapidly urbanizing area in the foothills of the Rocky Mountains. This work also uses the functional model I developed in Chapter 1, but with an error structure more appropriate for small-error GPS data. I conclude with a summary of findings, overarching themes, and potential future research directions in Chapter 5

Cluster Investigations

This is the input file used any components of the paper using GPS collared cougar kill-site information ("Sampled Collared Cougar Case Histories and Domestic Prey"). This processed version has all geographic coordinate information removed to protect the precise locations of the subject puma and to avoid providing specific types of location data of animals on private lands per state of Colorado statutes. Domestic prey presence, Julian calendar day number, cougar identifier, cougar age, cougar sex, housing density, and prey species eaten are identified respectively by the columns: "DomesticPrey", "Days", "Entity_ID", "Entity_age", "Entity_sex", "HDM100_fs4", and "Species". Other columns used in data processing include the unique GPS location cluster identifier ("ClusterID_8day"), start data of cluster ("ClusterStart"), identification of whether the species identified was the primary prey item ("Primary_Use"). Processing code is available upon request

SightPoints

This file contains the data for the reported cougar sightings used in the selection function (RSF) to determine if cougar sighting locations differed from cougar use. This processed version has all geographic coordinate information removed as many of these locations are on private property and cannot be given out in raw form. Variables included are Canopy, Distance.to.Canopy, Heat.Loading, Elevation, Housing.Density, Topographic.Wetness, Distance.to.Road, Distance.to.Structure and Density.Class

Data from: Accounting for tagging-to-harvest mortality in a Brownie tag-recovery model by incorporating radio-telemetry data

The Brownie tag-recovery model is useful for estimating harvest rates but assumes all tagged individuals survive to the first hunting season; otherwise, mortality between time of tagging and the hunting season will cause the Brownie estimator to be negatively biased. Alternatively, fitting animals with radio transmitters can be used to accurately estimate harvest rate but may be more costly. We developed a joint model to estimate harvest and annual survival rates that combines known-fate data from animals fitted with transmitters to estimate the probability of surviving the period from capture to the first hunting season, and data from reward-tagged animals in a Brownie tag-recovery model. We evaluated bias and precision of the joint estimator, and how to optimally allocate effort between animals fitted with radio transmitters and inexpensive ear tags or leg bands. Tagging-to-harvest survival rates from >20 individuals with radio transmitters combined with 50–100 reward tags resulted in an unbiased and precise estimator of harvest rates. In addition, the joint model can test whether transmitters affect an individual's probability of being harvested. We illustrate application of the model using data from wild turkey, Meleagris gallapavo, to estimate harvest rates, and data from white-tailed deer, Odocoileus virginianus, to evaluate whether the presence of a visible radio transmitter is related to the probability of a deer being harvested. The joint known-fate tag-recovery model eliminates the requirement to capture and mark animals immediately prior to the hunting season to obtain accurate and precise estimates of harvest rate. In addition, the joint model can assess whether marking animals with radio transmitters affects the individual's probability of being harvested, caused by hunter selectivity or changes in a marked animal's behavior

Data_Sheet_1_The effects of exploratory behavior on physical activity in a common animal model of human disease, zebrafish (Danio rerio).docx

Zebrafish (Danio rerio) are widely accepted as a multidisciplinary vertebrate model for neurobehavioral and clinical studies, and more recently have become established as a model for exercise physiology and behavior. Individual differences in activity level (e.g., exploration) have been characterized in zebrafish, however, how different levels of exploration correspond to differences in motivation to engage in swimming behavior has not yet been explored. We screened individual zebrafish in two tests of exploration: the open field and novel tank diving tests. The fish were then exposed to a tank in which they could choose to enter a compartment with a flow of water (as a means of testing voluntary motivation to exercise). After a 2-day habituation period, behavioral observations were conducted. We used correlative analyses to investigate the robustness of the different exploration tests. Due to the complexity of dependent behavioral variables, we used machine learning to determine the personality variables that were best at predicting swimming behavior. Our results show that contrary to our predictions, the correlation between novel tank diving test variables and open field test variables was relatively weak. Novel tank diving variables were more correlated with themselves than open field variables were to each other. Males exhibited stronger relationships between behavioral variables than did females. In terms of swimming behavior, fish that spent more time in the swimming zone spent more time actively swimming, however, swimming behavior was inconsistent across the time of the study. All relationships between swimming variables and exploration tests were relatively weak, though novel tank diving test variables had stronger correlations. Machine learning showed that three novel tank diving variables (entries top/bottom, movement rate, average top entry duration) and one open field variable (proportion of time spent frozen) were the best predictors of swimming behavior, demonstrating that the novel tank diving test is a powerful tool to investigate exploration. Increased knowledge about how individual differences in exploration may play a role in swimming behavior in zebrafish is fundamental to their utility as a model of exercise physiology and behavior.</p

Turkey Case Study

Very-high frequency (VHF) collar and reward-tag data for a case study on wild turkey in Pennsylvania from 2010-2012. The data is in the format required by program SURVIV, and the additional code to run the integrated Brownie tag-recovery and known-fate model is included

White-tailed Deer Case Study

Very-high frequency (VHF) collar and reward-tag data for a case study on white-tailed deer in Pennsylvania from 2009-2011. The data is in the format required by program SURVIV, and the additional code to run the integrated Brownie tag-recovery and known-fate model is included

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

Abstract 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