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USGS1995

A capture-recapture survival analysis model for radio-tagged animals

In recent years, survival analysis of radio-tagged animals has developed using methods based on the Kaplan-Meier method used in medical and engineering applications (Pollock et al., 1989a,b). An important assumption of this approach is that all tagged animals with a functioning radio can be relocated at each sampling time with probability 1. This assumption may not always be reasonable in practice. In this paper, we show how a general capture-recapture model can be derived which allows for some probability (less than one) for animals to be relocated. This model is not simply a Jolly-Seber model because it is possible to relocate both dead and live animals, unlike when traditional tagging is used. The model can also be viewed as a generalization of the Kaplan-Meier procedure, thus linking the Jolly-Seber and Kaplan-Meier approaches to survival estimation. We present maximum likelihood estimators and discuss testing between submodels. We also discuss model assumptions and their validity in practice. An example is presented based on canvasback data collected by G. M. Haramis of Patuxent Wildlife Research Center, Laurel, Maryland, USA.

Book chapter↗

USGS2001

Methods for estimating dispersal probabilities and related parameters using marked animals

Deriving valid inferences about the causes and consequences of dispersal from empirical studies depends largely on our ability reliably to estimate parameters associated with dispersal. Here, we present a review of the methods available for estimating dispersal and related parameters using marked individuals. We emphasize methods that place dispersal in a probabilistic framework. In this context, we define a dispersal event as a movement of a specified distance or from one predefined patch to another, the magnitude of the distance or the definition of a `patch? depending on the ecological or evolutionary question(s) being addressed. We have organized the chapter based on four general classes of data for animals that are captured, marked, and released alive: (1) recovery data, in which animals are recovered dead at a subsequent time, (2) recapture/resighting data, in which animals are either recaptured or resighted alive on subsequent sampling occasions, (3) known-status data, in which marked animals are reobserved alive or dead at specified times with probability 1.0, and (4) combined data, in which data are of more than one type (e.g., live recapture and ring recovery). For each data type, we discuss the data required, the estimation techniques, and the types of questions that might be addressed from studies conducted at single and multiple sites.

Book chapter↗

USGS2006

Hierarchical models of animal abundance and occurrence

Much of animal ecology is devoted to studies of abundance and occurrence of species, based on surveys of spatially referenced sample units. These surveys frequently yield sparse counts that are contaminated by imperfect detection, making direct inference about abundance or occurrence based on observational data infeasible. This article describes a flexible hierarchical modeling framework for estimation and inference about animal abundance and occurrence from survey data that are subject to imperfect detection. Within this framework, we specify models of abundance and detectability of animals at the level of the local populations defined by the sample units. Information at the level of the local population is aggregated by specifying models that describe variation in abundance and detection among sites. We describe likelihood-based and Bayesian methods for estimation and inference under the resulting hierarchical model. We provide two examples of the application of hierarchical models to animal survey data, the first based on removal counts of stream fish and the second based on avian quadrat counts. For both examples, we provide a Bayesian analysis of the models using the software WinBUGS.

Journal of Agricultural, Biological, and Environme↗

USGS2011

Rare and endangered species of Hawai`i Volcanoes National Park; endangered, threatened, and rare animal, plant, and community handbook

Introduction Hawai`i Volcanoes National Park (HAVO) is the largest area in the State of Hawai`i protected for its geology and landscapes and its native flora and fauna. The park covers approximately 135,000 hectares or 333,000 acres in all. These lands stretch from the seacoast of Kīlauea Volcano to far above timberline on the summit of Mauna Loa (Figure 1). This vast area includes expanses of forests, woodlands, shrublands, and barren lava flows that represent an array of native ecosystems. Contained within these communities are a great many species of rare animals and plants, most of them unique to the island of Hawai`i, and some of them surviving only in the park. These are the biological treasures of Hawai`i Volcanoes National Park. Our book is a guide to all animal and plant species in HAVO that are specially recognized as endangered species in the general sense. (The official designations at four levels and the unofficial designation of species of concern are explained later.) There are 23 such animal species and 71 plant species covered in the handbook, including six species planted in HAVO but not naturally occurring. In addition, we describe seven rare communities. In some cases, HAVO offers the best opportunity to save these species and communities from extinction. Increasingly, the park has attempted to restore rare populations by conducting surveys to locate them, controlling threats such as feral livestock, and bolstering existing populations or creating new ones by planting nursery stock. To aid such efforts, our original intent was to publish an identification guide for researchers and field management personnel. Particularly, we wanted to familiarize the reader with the many rare plant species which otherwise are known mainly from the technical literature. Because we soon came to realize that this handbook would be useful to a much larger, general readership, our aim is to make this information available to anyone interested in endangered animals and plants at Hawai`i Volcanoes National Park.

Hawaii↗

USGS2013

Estimating animal resource selection from telemetry data using point process models

Analyses of animal resource selection functions (RSF) using data collected from relocations of individuals via remote telemetry devices have become commonplace. Increasing technological advances, however, have produced statistical challenges in analysing such highly autocorrelated data. Weighted distribution methods have been proposed for analysing RSFs with telemetry data. However, they can be computationally challenging due to an intractable normalizing constant and cannot be aggregated (i.e. collapsed) over time to make space-only inference. In this study, we take a conceptually different approach to modelling animal telemetry data for making RSF inference. We consider the telemetry data to be a realization of a space–time point process. Under the point process paradigm, the times of the relocations are also considered to be random rather than fixed. We show the point process models we propose are a generalization of the weighted distribution telemetry models. By generalizing the weighted model, we can access several numerical techniques for evaluating point process likelihoods that make use of common statistical software. Thus, the analysis methods can be readily implemented by animal ecologists. In addition to ease of computation, the point process models can be aggregated over time by marginalizing over the temporal component of the model. This allows a full range of models to be constructed for RSF analysis at the individual movement level up to the study area level. To demonstrate the analysis of telemetry data with the point process approach, we analysed a data set of telemetry locations from northern fur seals (Callorhinus ursinus) in the Pribilof Islands, Alaska. Both a space–time and an aggregated space-only model were fitted. At the individual level, the space–time analysis showed little selection relative to the habitat covariates. However, at the study area level, the space-only model showed strong selection relative to the covariates.

Alaska↗

USGS1990

Pharmacokinetic modeling in aquatic animals. 1. Models and concepts

While clinical and toxicological applications of pharmacokinetics have continued to evolve both conceptually and experimentally, pharmacokinetics modeling in aquatic animals has not progressed accordingly. In this paper we present methods and concepts of pharmacokinetic modeling in aquatic animals using multicompartmental, clearance-based, non-compartmental and physiologically-based pharmacokinetic models. These models should be considered as alternatives to traditional approaches, which assume that the animal acts as a single homogeneous compartment based on apparent monoexponential elimination.

Aquatic Toxicology↗

USGS2016

Assessing the relationship between groundwater nitrate and animal feeding operations in Iowa (USA)

Nitrate-nitrogen is a common contaminant of drinking water in many agricultural areas of the United States of America (USA). Ingested nitrate from contaminated drinking water has been linked to an increased risk of several cancers, specific birth defects, and other diseases. In this research, we assessed the relationship between animal feeding operations (AFOs) and groundwater nitrate in private wells in Iowa. We characterized AFOs by swine and total animal units and type (open, confined, or mixed), and we evaluated the number and spatial intensities of AFOs in proximity to private wells. The types of AFO indicate the extent to which a facility is enclosed by a roof. Using linear regression models, we found significant positive associations between the total number of AFOs within 2 km of a well (p trend < 0.001), number of open AFOs within 5 km of a well (p trend < 0.001), and number of mixed AFOs within 30 km of a well (p trend < 0.001) and the log nitrate concentration. Additionally, we found significant increases in log nitrate in the top quartiles for AFO spatial intensity, open AFO spatial intensity, and mixed AFO spatial intensity compared to the bottom quartile (0.171 log(mg/L), 0.319 log(mg/L), and 0.541 log(mg/L), respectively; all p < 0.001). We also explored the spatial distribution of nitrate-nitrogen in drinking wells and found significant spatial clustering of high-nitrate wells (> 5 mg/L) compared with low-nitrate (≤ 5 mg/L) wells ( p = 0.001). A generalized additive model for high-nitrate status identified statistically significant areas of risk for high levels of nitrate. Adjustment for some AFO predictor variables explained a portion of the elevated nitrate risk. These results support a relationship between animal feeding operations and groundwater nitrate concentrations and differences in nitrate loss from confined AFOs vs. open or mixed types.

Iowa↗

USGS2020

Freshwater neurotoxins and concerns for human, animal, and ecosystemhealth: A review of anatoxin-a and saxitoxin

Toxic cyanobacteria are a concern worldwide because they can adversely affect humans, animals, and ecosystems. However, neurotoxins produced by freshwater cyanobacteria are understudied relative to microcystin. Thus, the objective of this critical review was to provide a comprehensive examination of the modes of action, production, fate, and occurrence of the freshwater neurotoxins anatoxin-a and saxitoxin as they relate to human, animal, and ecosystem health. Literature on freshwater anatoxin-a and saxitoxin was obtained and reviewed for both laboratory and field studies. Current (2020) research identifies as many as 41 anatoxin-a producing species and 15 saxitoxin-producing species of freshwater cyanobacteria. Field studies indicate that anatoxin-a and saxitoxin have widespread distribution, and examples are given from every continent except Antarctica. Human and animal health concerns can range from acute to chronic. However, few researchers studied chronic or sublethal effects of freshwater exposures to anatoxin-a or saxitoxin. Ecosystemhealth also is a concern, as the effects of toxicity may be far reaching and include consequences throughout the food web. Several gaps in knowledgewere identified for anatoxin-a and saxitoxin, including triggers of production and release, environmental fate and degradation, primary and secondary exposure routes, diel variation, food web effects, effects of cyanotoxin mixtures, and sublethal health effects on individual organisms and populations. Despite the gaps, this critical review facilitates our current understanding of freshwater neurotoxins and thus can serve to guide future research on anatoxin-a, saxitoxin, and other cyanotoxins.

Science of the Total Environment↗

USGS2020

Animal movement models with mechanistic selection functions

A suite of statistical methods are used to study animal movement. Most of these methods treat animal trajectory data in one of three ways: as discrete pro- cesses, as continuous processes, or as point processes. We brie y review each of these approaches and then focus in on the latter. In the context of point processes, so-called resource selection analyses are among the most common way to statis- tically treat animal trajectory data. However, most resource selection analyses provide inference based on approximations of point process models. The forms of these models have been limited to a few types of specications that provide infer- ence about relative resource use and, less commonly, probability of use. For more general spatio-temporal point process models, the most common type of analysis often proceeds with a data augmentation approach that is used to create a binary data set that can be analyzed with conditional logistic regression. We show that the conditional logistic regression likelihood can be generalized to accommodate a variety of alternative specications related to resource selection. We then provide an example of a case where a spatio-temporal point process model coincides with that implied by a mechanistic model for movement expressed as a partial dier- ential equation derived from rst principles of movement. We demonstrate that inference from this form of point process model is intuitive (and could be useful for management and conservation) by analyzing a set of telemetry data from a mountain lion in Colorado, USA, to understand the eects of spatially explicit environmental conditions on movement behavior of this species.

Spatial Statistics↗

USGS2012

Climate impacts on bird and plant communities from altered animal-plant interactions

The contribution of climate change to declining populations of organisms remains a question of outstanding concern. Much attention to declining populations has focused on how changing climate drives phenological mismatches between animals and their food. Effects of climate on plant communities may provide an alternative, but particularly powerful, influence on animal populations because plants provide their habitats. Here, we show that abundances of deciduous trees and associated songbirds have declined with decreasing snowfall over 22 years of study in montane Arizona, USA. We experimentally tested the hypothesis that declining snowfall indirectly influences plants and associated birds by allowing greater over-winter herbivory by elk (Cervus canadensis). We excluded elk from one of two paired snowmelt drainages (10 ha per drainage), and replicated this paired experiment across three distant canyons. Over six years, we reversed multi-decade declines in plant and bird populations by experimentally inhibiting heavy winter herbivory associated with declining snowfall. Moreover, predation rates on songbird nests decreased in exclosures, despite higher abundances of nest predators, demonstrating the over-riding importance of habitat quality to avian recruitment. Thus, our results suggest that climate impacts on plant–animal interactions can have forceful ramifying effects on plants, birds, and ecological interactions.

Arizona↗

USGS2017

Unraveling the disease consequences and mechanisms of modular structure in animal social networks

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

PNAS↗

USGS2017

Basis function models for animal movement

Advances in satellite-based data collection techniques have served as a catalyst for new statistical methodology to analyze these data. In wildlife ecological studies, satellite-based data and methodology have provided a wealth of information about animal space use and the investigation of individual-based animal–environment relationships. With the technology for data collection improving dramatically over time, we are left with massive archives of historical animal telemetry data of varying quality. While many contemporary statistical approaches for inferring movement behavior are specified in discrete time, we develop a flexible continuous-time stochastic integral equation framework that is amenable to reduced-rank second-order covariance parameterizations. We demonstrate how the associated first-order basis functions can be constructed to mimic behavioral characteristics in realistic trajectory processes using telemetry data from mule deer and mountain lion individuals in western North America. Our approach is parallelizable and provides inference for heterogenous trajectories using nonstationary spatial modeling techniques that are feasible for large telemetry datasets. Supplementary materials for this article are available online.

Journal of the American Statistical Association↗

USGS2003

Analysis of ecological context for identifying vegetation and animal conservation planning foci: An example from the arid South-western USA

In developing conservation strategies, it is important to maximize effects of conservation within a specified land tract and to maximize conservation effects on surrounding area (ecological context). The authors proposed two criteria to select biotic entities for conservation foci: (1) the relative occurrence of fauna or flora in a tract is greater than that of an ecological context region; and (2) occurrence of the fauna or flora is relatively limited in the ecological context region. Using extensive spatial data on vegetation and wildlife habitat distribution, the authors identified strategic vegetation and fauna conservation foci for the 400 000 ha Fort Bliss military reservation in New Mexico and Texas relative to a 164 km radius ecological context region intersecting seven ecological zones and the predicted habitat distribution of 616 animal species. The authors set two specific criteria: (1) predicted area of a species' occurrence is <50% of the ecological context region; and (2) percentage of Fort Bliss intersecting the species' or vegetation community predicted areas in the ecological context region is >5% (Fort Bliss is 4.2% of the region). These criteria selected one vegetation class and 40 animal species. Further, these vegetation and animal foci were primarily located in two areas of Fort Bliss. Sensitivity analyses with other analytical radii corroborated the context radius used. Conservation of the two areas and associated taxa will maximize the contribution of Fort Bliss's conservation efforts in its ecological proximity. This relatively simple but information-rich process represents economical and defensible preliminary contextual analysis for detailed conservation planning.

Journal of Environmental Planning and Management↗

USGS2024

Valuing wildlife sightings at the species-wide and individual animal levels: An approach and application to bear viewing in Yellowstone National Park

Wildlife viewing is a popular recreation activity in parks and protected areas around the world, yet information on the nonmarket value that visitors derive from viewing specific species, and how that value is affected by small-scale population changes, is lacking. We devised an approach to fill the gap. First, we applied the travel cost method to obtain the value of a wildlife viewing trip. Next, we estimated a value per animal sighting, which we aggregated by the number of visitors who experienced and valued a sighting. Finally, we incorporated the probability of viewing an animal to determine an individual animal’s contribution to sighting value, which varies by species type, visibility, and seasonality. We examined bear viewing in Yellowstone National Park. For grizzly and black bears, respectively, per-sighting values are $16 and $14, aggregate annual sighting values are $6.9 and $9.7 million, and annual per-bear viewing values are $46,000 and $15,000.

Idaho, Montana, Wyoming↗

USGS2015

A new method for discovering behavior patterns among animal movements

Advanced satellite tracking technologies enable biologists to track animal movements at fine spatial and temporal scales. The resultant data present opportunities and challenges for understanding animal behavioral mechanisms. In this paper, we develop a new method to elucidate animal movement patterns from tracking data. Here, we propose the notion of continuous behavior patterns as a concise representation of popular migration routes and underlying sequential behaviors during migration. Each stage in the pattern is characterized in terms of space (i.e., the places traversed during movements) and time (i.e. the time spent in those places); that is, the behavioral state corresponding to a stage is inferred according to the spatiotemporal and sequential context. Hence, the pattern may be interpreted predictably. We develop a candidate generation and refinement framework to derive all continuous behavior patterns from raw trajectories. In the framework, we first define the representative spots to denote the underlying potential behavioral states that are extracted from individual trajectories according to the similarity of relaxed continuous locations in certain distinct time intervals. We determine the common behaviors of multiple individuals according to the spatiotemporal proximity of representative spots and apply a projection-based extension approach to generate candidate sequential behavior sequences as candidate patterns. Finally, the candidate generation procedure is combined with a refinement procedure to derive continuous behavior patterns. We apply an ordered processing strategy to accelerate candidate refinement. The proposed patterns and discovery framework are evaluated through conceptual experiments on both real GPS-tracking and large synthetic datasets.

International Journal of Geographical Information ↗

USGS2010

Global positioning system and associated technologies in animal behaviour and ecological research

Biologists can equip animals with global positioning system (GPS) technology to obtain accurate (less than or equal to 30 m) locations that can be combined with sensor data to study animal behaviour and ecology. We provide the background of GPS techniques that have been used to gather data for wildlife studies. We review how GPS has been integrated into functional systems with data storage, data transfer, power supplies, packaging and sensor technologies to collect temperature, activity, proximity and mortality data from terrestrial species and birds. GPS 'rapid fixing' technologies combined with sensors provide location, dive frequency and duration profiles, and underwater acoustic information for the study of marine species. We examine how these rapid fixing technologies may be applied to terrestrial and avian applications. We discuss positional data quality and the capability for high-frequency sampling associated with GPS locations. We present alternatives for storing and retrieving data by using dataloggers (biologging), radio-frequency download systems (e.g. very high frequency, spread spectrum), integration of GPS with other satellite systems (e.g. Argos, Globalstar) and potential new data recovery technologies (e.g. network nodes). GPS is one component among many rapidly evolving technologies. Therefore, we recommend that users and suppliers interact to ensure the availability of appropriate equipment to meet animal research objectives.

Philosophical Transactions of the Royal Society B:↗

USGS2018

Evaluation of acoustic telemetry grids for determining aquatic animal movement and survival

Acoustic telemetry studies have frequently prioritized linear configurations of hydrophone receivers, such as perpendicular from shorelines or across rivers, to detect the presence of tagged aquatic animals. This approach introduces unknown bias when receivers are stationed for convenience at geographic bottlenecks (e.g., at the mouth of an embayment or between islands) as opposed to deployments following a statistical sampling design. We evaluated two-dimensional acoustic receiver arrays (grids: receivers spread uniformly across space) as an alternative approach to provide estimates of survival, movement, and habitat use. Performance of variably-spaced receiver grids (5–25 km spacing) was evaluated by simulating (1) animal tracks as correlated random walks (speed: 0.1–0.9 m/s; turning angle standard deviation: 5–30 degrees); (2) variable tag transmission intervals along each track (nominal delay: 15–300 seconds); and (3) probability of detection of each transmission based on logistic detection range curves (midpoint: 200–1500 m). From simulations, we quantified i) time between successive detections on any receiver (detection time), ii) time between successive detections on different receivers (transit time), and iii) distance between successive detections on different receivers (transit distance). In the most restrictive detection range scenario (200 m), the 95 th percentile of transit time was 3.2 days at 5 km grid spacing, 5.7 days at 7 km, and 15.2 days at 25 km; for the 1500 m detection range scenario, it was 0.1 days at 5 km, 0.5 days at 7 km, and 10.8 days at 25 km. These values represented upper bounds on the expected maximum time that an animal could go undetected. Comparison of the simulations with pilot studies on three fishes (walleye Sander vitreus , common carp Cyprinus carpio , and channel catfish Ictalurus punctatus ) from two independent large lake ecosystems (lakes Erie and Winnipeg) revealed shorter detection and transit times than what simulations predicted. By spreading effort uniformly across space, grids can improve understanding of fish migration over the commonly employed receiver line approach, but at increased time cost for maintaining grids.

Methods in Ecology and Evolution↗

USGS2018

Fear of feces? Trade-offs between disease risk and foraging drive animal activity around raccoon latrines

Fear of predation alters prey behavior, which can indirectly alter entire landscapes. A parasite-induced ecology of fear might also exist if animals avoid parasite-contaminated resources when infection costs outweigh foraging benefits. To investigate whether animals avoid parasite contaminated sites, and if such avoidance balances disease costs and foraging gains, we monitored animal behavior at raccoon latrines – sites that concentrate both seeds and pathogenic parasite eggs. Using wildlife cameras, we documented over 40 potentially susceptible vertebrate species in latrines and adjacent habitat. Latrine contact rates reflected background activity, diet preferences and disease risk. Disease-tolerant raccoons and rats displayed significant site attraction, while susceptible birds and small mammals avoided these high-risk sites. This suggests that parasites, like predators, might create a landscape of fear for vulnerable hosts. Such non-consumptive parasite effects could alter disease transmission, population dynamics, and even ecosystem structure.

California↗