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At least 55 records · Page 3Linked to original sources
USGS2022

Risk assessment for bull trout introduction into Sullivan Lake and Harvey Creek, northeastern Washington

The Kalispel Tribe of Indians (KT), U. S. Fish and Wildlife Service, and Washington Department of Fish and Wildlife are engaged in conservation of bull trout ( Salvelinus confluentus ) in the Lake Pend Oreille (LPO) Core Area. The LPO is a complex habitat core area which falls within three states (Montana, Idaho, and Washington) and a tribal entity. As part of the conservation process, KT worked in cooperation with the U. S. Geological Survey (USGS) to complete a risk assessment for introduction of bull trout into Sullivan Lake/Harvey Creek, northeastern, Washington. The risk assessment was designed to evaluate potential risks to resident fish species, to bull trout introduced into Sullivan Lake, and to bull trout donor source populations. This risk assessment describes the potential risks associated with pathogens (introduction of pathogens and increased pathogen burden), genetics (such as risk to donor sources, straying and breeding with native bull trout, and introduction of bull-brook hybrids), and ecological interactions (such as predation and competition). Potential donor source populations were identified and evaluated using a qualitative approach based on expert opinion and a decision framework. Literature reviews were completed for fish species composition and abundance in Sullivan Lake basin to assess potential ecological interactions and risks to these populations and to the introduced bull trout. The USGS assessed pathogen risks through two major questions: (1) whether introduced bull trout might bring pathogens into the Sullivan Lake basin that were not previously present and (2) whether the health of introduced bull trout could be adversely affected by pathogens already present in the basin. Assessment of genetic risks included demographic risks to donor source populations, potential for hybridization with native bull trout, and the risk of introducing bull-brook hybrids. Literature reviews were used in conjunction with discussions among regional biologists to identify potential donor source populations and their population attributes. A decision framework was developed by USGS in collaboration with KT biologists that identified desirable population attributes (life history behavior, abundance, population viability, feasibility of collection, and environmental match) associated with donor source populations and established ranking criteria. The population attribute information was used with the (1) decision framework, (2) established ranking criteria, and (3) expert opinion of regional biologists, to assign scores for overall ranking of donor source populations. The LPO source population was the highest ranked and is considered a robust and stable population. The risk of introducing pathogens from LPO into Sullivan Lake via a bull trout introduction program seems low, and indirect pathogen burden risks to resident species can be mitigated using established pathogen surveillance methods. The likelihood that bull trout, introduced into Sullivan Lake, stray and spawn with native bull trout is low. Nearest-neighbor donor source populations, such as LPO, could minimize negative fitness impacts that might occur from straying and interbreeding of individuals that become entrained and help maintain natural patterns of genetic diversity in native populations. The ecological risk that a bull trout introduction presents to resident species seems to be low but with some uncertainty. Pygmy whitefish, a Washington State Sensitive species, is likely most vulnerable to extirpation with increased predation pressure with introduction of an additional piscivore into the ecosystem. The status of the pygmy whitefish in Sullivan Lake is unknown. The ecological risks most likely to reduce the viability of introduced bull trout are predation by burbot and an adequate forage base in Sullivan Lake. Prior fish surveys provided data on resident species abundance, provided an established baseline for effective monitoring, and identifying ecosystem changes post-bull trout introduction to inform future adaptive management decisions.

Washington↗
USGS2009

An introduction to standardized sampling

It was probably one of the oddest riots in the history of the United States. In Erie, Pennsylvania during 1853. federal marshals were called to restore order during bloody uprisings. A mob of women, equipped with sledgehammers, was tearing up railroad rack to protest standardization of track width (Nesmith 1985). All across the United States, standardization of rail gauges was talking place to improve transportation across the country,but many people did not want consistency. Jobs moving freight from, a train running on one gauge of track to a train running on another gauge were plentiful at this time, and standardization would mean these jobs would disappear. Fortunately, for us today, the riots were quelled and standardization of railroad tack gauges went ahead. The magnificent transportation system of North America was aided by the standardization of rails, contributing to robust economies. Standardization of industrial processes, languages, measurements, and data collection methods has been essential for world progress (Figure 1.1). Today , we are often unaware of the degree of standardization of the most basic elements of our society--from bolts and nuts where thread sizes are standard to computer components that can be used interchangeably to the standard sizes of photos we carry in our wallets or purses. Data collection and presentation are standardized in many disciplines, including medicine, meteorology, geology, and water chemistry. For example, our cholesterol, body temperature, and blood pressure are measured by standard medical tests and compared to averages calculated from the results of the same standard tests for many other people to determine if individuals are higher, lower, or average compared to the population in general. If these diagnostic tests were not standardized, it is unlikely that we would be able to evaluate eve the most basic data about our health. In fact, if standardization was not used in countless other facets of our society our lives would be much more difficult. For data collection purposes, standardization means to collect data in one way so comparisons can be easily made. Although routine data collection has been standardized in many other disciplines, data from freshwater fish sampling across North America have not. Previously, most data collection has been standardized only at local, state, and provincial levels (Bonar and Hubert 2002). Several years ago, when one of the authors (Bonar) was a biologist for a state agency, he was asked to compile as much data as he could about the state's warmwater fish communities to provide information to managers developing fishing regulations. These data had been collected by many biologists over time using different methods, including rotenone, electrofishing, gill netting, and hook-and-line sampling. Data were written carefully on detailed data sheets or in scribbled notes in a biologists's notebook. As you can imagine, these data were a nightmare to compile. However, they were even worse to interpret. How could length-frequency distributions be compared among lakes if the methods used to catch the fish were dissimilar with differing efficiencies in sampling fish of various species and lengths? How could catch per unit effort (CPUE), a common index of population density, be compared when samples were collected one year using fyke nets and the next year by electrofishing? Ultimately, how could one compare if fish population were high, low, or average in growth, body condition, or abundance if there was no compilation of distributions of standard data to facilitate comparison? Months were spent trying to interpret these data, and finally a body of comparable data gathered by similar methods was assimilated. However, much of the nonstandard data had to be discarded--data that had taken thousands of hours to collect but were essentially useless. If all data jhad been collected and recorded in a standard manner, whoch would have required very little extra work, all of these hours of survey effort would not have gone to naught and isights regardimng the fisheries would have been imporived by a larger number of samples.

Book chapter↗
USGS2009

Length frequency, condition, growth, and catch per effort indices for common North American fishes

One of the greatest advantages to using standard sampling is the ability of compare sample data to those data collected from an array of other populations over a wide geographic area using similar techniques. A biologist can then ascertain if the sampling data are within an expected range or are higher or lower than expected when compared to other populations.. When biologists collect data using different biased, unvalidated methods, it is often impossible to compare data among populations and evaluate the results. Standard sampling allows comparisons of data among sampling periods and locations. Until now, summaries of freshwater fisheries data collected in a standard manner across North America have not been available. Length at age, length-weight relationships, and other types of detailed summary data are available in Carlander (1696-1977). However, a variety of sampling methods were used to collect these data, allowing successful comparisons among length at age and length-weight data but making it difficult or impossible to compare size distribution and catch per unit effort (CPUE) data among populations. This chapter provides North American and ecoregion (area that contains a geographically distinct assemblage of natural communities and share similar environmental conditions) averages and distributions of several commonly used sampling techniques described in this book. Biologists using the standardized sampling techniques described in this book will be able to compare their data sets to the summaries provided in the tables that follow to determine if the data from their sampled populations of fish are average, below average, or above average for a given index in their ecoregion or across North America.

Book chapter↗
USGS1986

This week?s Citation Classic: 'Stickel, L. F. A comparison of certain methods of measuring ranges of small mammals. J. Mammalogy 35: 1-15, 1954.'

During my first employment as a junior biologist conducting food habits analyses at the Patuxent Research Refuge (now Patuxent Wildlife Research Center) of the US Fish and Wildlife Service, the first station director, Arnold L. Nelson, gave each staff biologist the opportunity to spend a little time conducting field studies on the 2,600-acre research area. These studies were to help in ecological evaluation of the area, but also, no doubt, functioned as a morale builder for biologists daily engaged in difficult, demanding and confining laboratory work. I undertook to measure the population density of small mammals in different habitats. The state-of-the-art methods recommended to me and prevalent in the literature worried me, especially after preliminary field work. Reading the classic, paper by W.H. Burt [Terrioriality and home range concepts as applied to mammals. J. Mammalogy 24:346-352, 1943] was a turning point for me in seeing that knowledge of home-range size was the key to measuring population density. My first papers employed these concepts in field evaluations. However, they could give-only limited attention to the question of how home ranges could be measured. Furthermore, the importance of knowledge of the home range to habitat evaluation and to understanding animal behavior was becoming increasingly apparent. Advocacy of many different methods of measuring home ranges appeared in the literature. It did not seem possible to evaluate them objectively. It occurred to me that artificial populations could be used to help in understanding the mechanisms of measuring home ranges. The effects of random trap-visiting, bias toward central traps, trap-spacing, range shape, and other factors could be considered. So grids of traps were inked onto oversize graph paper, and simulated ranges were cut from plastic to be tossed as randomly as possible on the trap-grids. It was a slow, laborious job. Analysis showed that random captures in artificial populations produced patterns that were similar in many ways to those observed in field studies; the necessity of appropriate trap-spacing in relation to range size became obvious. Some commonly used methods of expressing range size proved to be clearly superior to others. However, the artificial populations could not fully simulate actual populations, where use patterns changed as conditions changed and the ?boundaries? of ranges tended to shift continuously or with time. Progress was made, but problems remained. I suspect that the reasons the paper has been cited frequently rest on the continuing interest in home-range behavior and the continuing difficulty in field evaluations, despite the many advances in observational methodology. The sophisticated techniques that became possible with the use of computers [R.I. Jennrich and F.B. Turner. Measurement of non-circular home range. J. Theor. Biol 23:227-237, 1969] also presented new possibilities for analysis and understanding of home-range characteristics and created a resurgence of interest in home-range behavior and its biological significance.

Current Contents / Agriculture, Biology & Environm↗
USGS2007

Scale-dependent approaches to modeling spatial epidemiology of chronic wasting disease.

This e-book is the product of a second workshop that was funded and promoted by the United States Geological Survey to enhance cooperation between states for the management of chronic wasting disease (CWD). The first workshop addressed issues surrounding the statistical design and collection of surveillance data for CWD. The second workshop, from which this document arose, followed logically from the first workshop and focused on appropriate methods for analysis, interpretation, and use of CWD surveillance and related epidemiology data. Consequently, the emphasis of this e-book is on modeling approaches to describe and gain insight of the spatial epidemiology of CWD. We designed this e-book for wildlife managers and biologists who are responsible for the surveillance of CWD in their state or agency. We chose spatial methods that are popular or common in the spatial epidemiology literature and evaluated them for their relevance to modeling CWD. Our opinion of the usefulness and relevance of each method was based on the type of field data commonly collected as part of CWD surveillance programs and what we know about CWD biology, ecology, and epidemiology. Specifically, we expected the field data to consist primarily of the infection status of a harvested or culled sample along with its date of collection (not date of infection), location, and demographic status. We evaluated methods in light of the fact that CWD does not appear to spread rapidly through wild populations, relative to more highly contagious viruses, and can be spread directly from animal to animal or indirectly through environmental contamination. We discovered that many of the wellpublished methods were developed for fast-spreading human diseases, such as influenza and measles. While these methods are applicable to fast spreading wildlife diseases, such as foot-and-mouth disease or West Nile virus, many are not likely to work well for CWD. Only limited data exist to evaluate geographic and spatial spread because many locations where we find CWD tend to be locations where samples have just been taken or sample sizes have just become large enough to have a high probability of detecting a low prevalence. Consequently, methods that work well to describe or predict the spread of foot-and-mouth disease throughout England, which occurred within a year, do not work well for describing or predicting CWD spread. We did not exclude methods that we regarded as inappropriate; rather, we included methods that are commonly used for disease epidemiology and then discussed their applicability for modeling the spatial epidemiology of CWD. We hope including inappropriate methods with an explanation of why they are ill-suited for CWD will make it easier to drop them from consideration and explain to others why they were not recommended for spatial modeling of CWD. We organized the three chapters by scale and extent for which each method was developed or best suited. The first chapter covers methods appropriate to multi-jurisdictional or multi-state modeling, which we call “regional” scale. The second chapter covers methods appropriate for within state areas such as wildlife management units or metapopulations, which we call “landscape” scale. The third chapter covers methods appropriate for population or individual-based modeling, which we call “fine” scale. We know this rubric is somewhat artificial because many methods work at multiple scales. We hope, however, that this structure addresses some of the challenges faced by managers that work at local, regional, state, and national scales. Further, the resolution of empirical data often changes with spatial scale, which affects the utility of different modeling approaches. For example, individual-based models work best at modeling spread within populations, while risk analysis is most useful for summarizing data over larger scales such as a region. Because some methods are applicable at several scales, however, we included a graphic at the beginning of each method that indicates the range of scales for which it applies. For example, the graphic to the right indicates that the method is most applicable for regional-scale modeling. There is also a question of resolution as well as scale and extent for each method. CWD surveillance data have been collected over large areas, such as a wildlife management unit or state, but the resolution of the data may be fine scale with GPS locations for many samples. For each method, we described the required resolution of the data and describe the type of data required, as well as what questions the method could answer and how useful the method is, given typical CWD data. For each scale, we presented a focal approach that would be useful for understanding the spatial pattern and epidemiology of CWD, as well as being a useful tool for CWD management. The focal approaches include risk analysis and micromaps for the regional scale, cluster analysis for the landscape scale, and individual based modeling for the fine scale of within population. For each of these methods, we used simulated data and walked through the method step by step to fully illustrate the “how to”, with specifics about what is input and output, as well as what questions the method addresses. We also provided a summary table to, at a glance, describe the scale, questions that can be addressed, and general data required for each method described in this e-book. We hope that this review will be helpful to biologists and managers by increasing the utility of their surveillance data, and ultimately be useful for increasing our understanding of CWD and allowing wildlife biologists and managers to move beyond retroactive fire-fighting to proactive preventative action.

Book↗
USGS2016

Preserving reptiles for research

What are voucher specimens and why do we collect them? Voucher specimens are animals and/or their parts that are deposited in a research museum to document the occurrence of a taxon at a specific location in space and time (Pleijel et al., 2008; Reynolds and McDiarmid, 2012). For field biologists, vouchers are the repeatable element of a field study as they allow other biologists, now and in the future, to confirm the identity of species that were studied. The scientific importance of a voucher specimen or series of specimens is that other people are afforded the opportunity to examine the entire animal and confirm or correct identifications. A photographic record is somewhat useful for recording the occurrence of a species, but such records can be insufficient for reliable confirmation of specific identity. Even if a photo shows diagnostic characters of currently recognized taxa, it may not show characters that separate taxa that may be described in the future. Substantial cryptic biodiversity is being found in even relatively well-known herpetofaunas (Crawford et al., 2010), and specimens allow researchers to retroactively evaluate the true diversity in a study as understanding of taxonomy evolves. They enable biologists to study the systematic relationships of populations by quantifying variation in different traits. Specimens are also a source of biological data such as behaviour, ecology, epidemiology, and reproduction through examination of their anatomy, reproductive and digestive tracts, and parasites (Suarez and Tsutsui, 2004). Preserving reptiles as vouchers is not difficult, although doing it properly requires care, effort, and time. Poorly preserved vouchers can invalidate the results and conclusions of your study because of the inability to confirm the identity of your study animals. Good science requires repeatability of observations, and the absence of vouchers or poorly preserved ones prevents such confirmation. Due to space restrictions, we are unable to go into as much detail as we would like in this chapter. A number of publications give more details on some topics discussed in this chapter, such as Pisani (1973), Pisani and Villa (1974), Etheridge (1996), Karns (1986), McDiarmid (1994), Cortez et al. (2006), Foster (2012) (and subchapters therein), Reynolds and McDiarmid (2012), and Simmons (2015). Although some of these works focus on amphibians, they also apply to reptiles in many aspects.

Book chapter↗
USGS2005

Wildlife health and disease investigations

Wildlife population management requires knowledge of factors that affect population sustainability. Mortality is one of the most important of those factors. Without a clear understanding of the causes of mortality, decisions by managers of whether or how to intercede may be inappropriate. Wildlife biologists are usually the first to discover, assess, and respond to wildlife mortality. Biologists who make accurate, complete and timely field investigations, and proper collection and shipment of samples to a diagnostic facility are essential for an accurate diagnosis. In combination with wildlife disease specialists, biologists can identify causes of wildlife mortality, detect long-term patterns in factors that affect the survival of populations, and take appropriate corrective action to minimize the impact of some mortality factors on wildlife populations.

Book chapter↗
USGS1981

Waterfowl and their wintering grounds in Mexico, 1937-64

The U.S. Fish and Wildlife Service has been interested in migratory birds, especially waterfowl, in Mexico for many years, An early period of cooperation in waterfowl administration was culminated in 1937 with the final ratification of the Convention Between the United States and the United Mexican States for the Protection of Migratory Birds and Game Mammals, usually referred to as the Migratory Bird Treat. Management of waterfowl on this continent is primarily carried out by hunting regulations. Current information on the status of each species must be obtained each year to serve as a basis for any needed modifications in the regulations. In the United States and Canada, wildlife biologists of the U.S. Fish and Wildlife Service and the Canadian Wildlife Service annually make the surveys to obtain this basic information. But the Government of Mexico has made no comparable surveys. Mexicans are not interested in hunting waterfowl to the extent that citizens of the United States and Canada are. As a consequence, Mexico's Department of Game emphasizes activities other than waterfowl management. Waterfowl, especially ducks, winter in or migrate through Mexico in large numbers, so it is obvious that and continental surveys of the winter population should include Mexico. Some general investigations of waterfowl distribution there were made in 1926 and earlier by E. A. Goldman. He was familiar with much of Mexico because he and E. W. Nelson studied mammals and biota there for many years. In the 1930's, because of the greater emphasis on waterfowl conservation and management, more detailed surveys were made of the continental breeding and wintering populations. One of these activities was designated as the midwinter, or January, inventory. In the early 1940's the senior author, who had been the Central Flyaway Biologist since 1937, recommended that the waterfowl wintering grounds in Mexico be included in the coverage of the midwinter waterfowl inventory. This was arranged in 1947, and the first aerial coverage, which only included coastal localities, was made in January-February of that year. The information obtained confirmed the value and advisability of including the Mexican wintering grounds in the annual survey; beginning in 1951 the wintering grounds in the Mexican highlands also were included. The surveys of Mexico were continued, except in 1957, through 1965; after 1965 they were greatly reduced. Ground reconnaissance and surveys had begun in Mexico with the preliminary work of Goldman in 1926, but quantitative studies were not started until 1937. The ground surveys continued at intervals in various parts of the Republic until 1960. Ground surveys are invaluable for such purposes as securing ecological information, specimens of birds and plants, and historical data about the areas from local residents, but the only satisfactory method of obtaining accurate quantitative information on waterfowl populations and their distribution is by airplane. Consequently, the major emphasis since 1947 has been on the annual aerial surveys. Ground studies were made much less frequently and were carried out mainly to obtain correlative information on the waterfowl foods available, the ecology of habitats, and for liaison with wildlife officials and biologists in Mexico. An important reason for the surveys of the waterfowl wintering grounds in Mexico was to determine their adequacy for the population of birds using them, and their potential to accommodate additional waterfowl if the wintering grounds in the United States became inadequate and larger flights entered Mexico. Other advantages included providing accurate information to hunters and other interested persons in the United States regarding the waterfowl shooting in Mexico, and determining the current status of these birds and their habitats in that country. Some of these findings were previously summarized by us (Saunders and Saunders 1949; Saunders 1964).

Resource Publication↗
USGS2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion‐activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter‐out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty‐animal model.” Our species model and empty‐animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out‐of‐sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out‐of‐sample datasets) and the empty‐animal model achieved an accuracy of 91%–94% on out‐of‐sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty‐animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

Ecology and Evolution↗
USGS2022

Estimating wolf abundance from cameras

Monitoring the abundance of rare carnivores is a daunting task for wildlife biologists. Many carnivore populations persist at relatively low densities, public interest is high, and the need for population estimates is great. Recent advances in trail camera technology provide an unprecedented opportunity for biologists to monitor rare species economically. Few studies, however, have conducted rigorous analyses of our ability to estimate abundance of low-density carnivores with cameras. We used motion-triggered trail cameras and a space-to-event model to estimate gray wolf ( Canis lupus ) abundance across three study areas in Idaho, USA, 2016–2018. We compared abundance estimates between cameras and noninvasive genetic sampling that had been extensively tested in our study areas. Estimates of mean wolf abundance from camera and genetic surveys were within 22% of one another and 95% CIs overlapped in 2 of the 3 years. A single camera with many detections appeared to bias camera estimates high in 2018. A subsequent bootstrapping procedure produced a population estimate from cameras equal to that derived from genetic sampling, however. Camera surveys were less than half the cost of genetic surveys once initial camera purchases were made. Our results suggest that cameras can be a viable method for estimating wolf abundance across broad landscapes (>10,000 km 2 ).

Idaho↗
USGS2002

Large scale wildlife monitoring studies: Statistical methods for design and analysis

Techniques for estimation of absolute abundance of wildlife populations have received a lot of attention in recent years. The statistical research has been focused on intensive small-scale studies. Recently, however, wildlife biologists have desired to study populations of animals at very large scales for monitoring purposes. Population indices are widely used in these extensive monitoring programs because they are inexpensive compared to estimates of absolute abundance. A crucial underlying assumption is that the population index ( C ) is directly proportional to the population density ( D ). The proportionality constant, β , is simply the probability of 'detection' for animals in the survey. As spatial and temporal comparisons of indices are crucial, it is necessary to also assume that the probability of detection is constant over space and time. Biologists intuitively recognize this when they design rigid protocols for the studies where the indices are collected. Unfortunately, however in many field studios the assumption is clearly invalid. We believe that the estimation of detection probability should be built into the monitoring design through a double sampling approach. A large sample of points provides an abundance index, and a smaller sub-sample of the same points is used to estimate detection probability. There is an important need for statistical research on the design and analysis of these complex studies. Some basic concepts based on actual avian, amphibian, and fish monitoring studies are presented in this article.

Environmetrics↗
USGS2013

Demographics of reintroduced populations: estimation, modeling, and decision analysis

Reintroduction can be necessary for recovering populations of threatened species. However, the success of reintroduction efforts has been poorer than many biologists and managers would hope. To increase the benefits gained from reintroduction, management decision making should be couched within formal decision-analytic frameworks. Decision analysis is a structured process for informing decision making that recognizes that all decisions have a set of components—objectives, alternative management actions, predictive models, and optimization methods—that can be decomposed, analyzed, and recomposed to facilitate optimal, transparent decisions. Because the outcome of interest in reintroduction efforts is typically population viability or related metrics, models used in decision analysis efforts for reintroductions will need to include population models. In this special section of the Journal of Wildlife Management, we highlight examples of the construction and use of models for informing management decisions in reintroduced populations. In this introductory contribution, we review concepts in decision analysis, population modeling for analysis of decisions in reintroduction settings, and future directions. Increased use of formal decision analysis, including adaptive management, has great potential to inform reintroduction efforts. Adopting these practices will require close collaboration among managers, decision analysts, population modelers, and field biologists.

Journal of Wildlife Management↗
USGS2024

Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA

Objective Long‐term standardized monitoring programs are fundamental to assessing how fish populations respond to anthropogenic stressors. Standardized monitoring programs may need to adopt new methods to adapt to rapid environmental changes that are associated with a changing climate. In the upper Yellowstone River, Montana, biologists have used a standardized, mark–recapture monitoring protocol to annually estimate the abundance of trout since 1978 to assess population status and trends. However, within the past two decades, climate change has caused changes in discharge timing that have prevented standardized monitoring from occurring annually. Methods We investigated the feasibility of using two analytical methods, N‐mixture models and mean capture probability, for estimating the abundance of three trout species in the upper Yellowstone River using the historical long‐term data set; these methods allow abundance to be estimated when a mark–recapture estimate cannot be obtained due to hydrologic conditions. Result When compared with abundance estimates from mark–recapture methods, N‐mixture models most often resulted in negatively biased abundance estimates, whereas mean capture probability analyses resulted in positively biased abundance estimates. Additionally, N‐mixture models produced negatively biased estimates when tested against true abundance values from simulated data sets. The bias in the N‐mixture model estimates was caused by poor model fit and variation in capture probability. The bias in the mean capture probability estimates was caused by heterogeneity in capture probability, likely caused by variable environmental conditions, which were not accounted for in the models. Conclusion N‐mixture models and mean capture probability are not viable alternatives for estimating abundance in the upper Yellowstone River. Thus, exploring additional adaptations to sampling methodologies and analytical approaches, including models that require individually marked fish, will be valuable for this system. Climate change will undoubtedly necessitate changes to standardized sampling methods throughout the world; thus, developing alternative sampling and analytical methods will be important for maintaining the utility of long‐term data sets.

Montana↗
USGS2014

Biodiversity loss and infectious diseases

When conservation biologists think about infectious diseases, their thoughts are mostly negative. Infectious diseases have been associated with the extinction and endangerment of some species, though this is rare, and other factors like habitat loss and poorly regulated harvest still are the overwhelming drivers of endangerment. Parasites are pervasive and play important roles as natural enemies on par with top predators, from regulating population abundances to maintaining species diversity. Sometimes, parasites themselves can be endangered. However, it seems unlikely that humans will miss extinct parasites. Parasites are often sensitive to habitat loss and degradation, making them positive indicators of ecosystem “health”. Conservation biologists need to carefully consider infectious diseases when planning conservation actions. This can include minimizing the movement of domestic and invasive species, vaccination, and culling.

Book chapter↗
USGS2011

Behavioral, clinical, and pathological characterization of acid metalliferous water toxicity in mallards

From September to November 2000, United States Fish and Wildlife Service biologists investigated incidents involving 221 bird deaths at 3 mine sites located in New Mexico and Arizona. These bird deaths primarily involved passerine and waterfowl species and were assumed to be linked to consumption of acid metalliferous water (AMW). Because all of the carcasses were found in or near pregnant leach solution ponds, tailings ponds, and associated lakes or storm water retention basins, an acute-toxicity study was undertaken using a synthetic AMW (SAMW) formulation based on the contaminant profile of a representative pond believed to be responsible for avian mortalities. An acute oral-toxicity trial was performed with a mixed-sex group of mallards ( Anas platyrhynchos ). After a 24-h pretreatment food and water fast, gorge drinking was evident in both SAMW treatment and control groups, with water consumption rates greatest during the initial drinking periods. Seven of nine treated mallards were killed in extremis within 12 h after the initiation of dose. Total lethal doses of SAMW ranged from 69.8 to 270.1 mL/kg (mean ± SE 127.9 ± 27.1). Lethal doses of SAMW were consumed in as few as 20 to 40 min after first exposure. Clinical signs of SAMW toxicity included increased serum uric acid, aspartate aminotransferase, creatine kinase, potassium, and P levels. PCV values of SAMW-treated birds were also increased compared with control mallards. Histopathological lesions were observed in the esophagus, proventriculus, ventriculus, and duodenum of SAMW-treated mallards, with the most distinctive being erosion and ulceration of the kaolin of the ventriculus, ventricular hemorrhage and/or congestion, and duodenal hemorrhage. Clinical, pathological, and tissue-residue results from this study are consistent with literature documenting acute metal toxicosis, especially copper (Cu), in avian species and provide useful diagnostic profiles for AMW toxicity or mortality events. Blood and kidney Cu concentrations were 23- and 6-fold greater, respectively, in SAMW mortalities compared with controls, whereas Cu concentrations in liver were not nearly as increased, suggesting that blood and kidney concentrations may be more useful than liver concentrations for diagnosing Cu toxicosis in wild birds. Based on these findings and other reports of AMW toxicity events in wild birds, we conclude that AMW bodies pose a significant hazard to wildlife that come in contact with them.

New Mexico, Arizona↗
USGS2011

A comparison of approaches for estimating relative impacts of nonnative fishes

Lack of standard methods for quantifying impact has hindered risk assessments of high-impact invaders. To understand methodological strengths and weaknesses, we compared five approaches (in parentheses) for quantifying impact of nonnative fishes: reviewing documented impacts in a large-scale database (review); surveying fish biologists regarding three categories of impact (socioeconomic, ecological, abundance); and estimating frequency of occurrence from existing collection records (collection). In addition, we compared game and nongame biologists’ ratings of game and nongame species. Although mean species ratings were generally correlated among approaches, we documented important discrepancies. The review approach required little effort but often inaccurately estimated impact in our study region (Mid-Atlantic United States). Game fishes received lower ratings from the socioeconomic approach, which yielded the greatest consistency among respondents. The ecological approach exhibited lower respondent bias but was sensitive to pre-existing perceptions of high-impact invaders. The abundance approach provided the least-biased assessment of region-specific impact but did not account for differences in per-capita effects among species. The collection approach required the most effort and did not provide reliable estimates of impact. Multiple approaches to assessing a species’ impact are instructive, but impact ratings must be interpreted in the context of methodological strengths and weaknesses and key management issues. A combination of our ecological and abundance approaches may be most appropriate for assessing ecological impact, whereas our socioeconomic approach is more useful for understanding social dimensions. These approaches are readily transferrable to other regions and taxa; if refined, they can help standardize the assessment of impacts of nonnative species.

Environmental Management↗
USGS2020

Keeping up with the times: Mapping range-wide habitat suitability for endangered species in a changing environment

Biologists and policy-makers have the difficult task of allocating limited resources to habitat conservation and management for endangered species in the face of changing environmental conditions. Satellite remote sensing can inform conservation because it is an efficient means to obtain environmental data over broad spatial and temporal extents. Yet, the challenges of accessing, processing, and analyzing remote sensing data hinder wider application of these techniques in conservation planning. We used Landsat data and hierarchical statistical models to link satellite-derived habitat measurements with abundance of endangered Yuma Ridgway's rails ( Rallus obsoletus yumanensis ) within the Lower Colorado River Basin and Salton Sink, USA. We addressed many of the challenges facing the application of remote sensing techniques by using the web-based, freely-available Google Earth Engine to process Landsat datasets, apply habitat models, and generate maps to predict habitat suitability at a fine spatial grain (30 m) across the range of the species. These maps are shareable, interactive, and easy to update annually as habitat conditions change using a Google Earth Engine App we developed. Thus, we provide a framework for building habitat suitability models and maps to help target adaptive habitat management over broad extents for sensitive species, enabling biologists to improve conservation and restoration efforts regularly as conditions change in highly variable ecosystems. We demonstrate this approach for Yuma Ridgway's rails, but our methods for merging hierarchical statistical models with open-source mapping software to describe spatial-temporal heterogeneity in habitat quality are applicable to any species, and are especially helpful to species inhabiting highly variable ecosystems.

Arizona, California, Nevada↗
USGS2023

A regionally varying habitat model to inform management for greater sage-grouse persistence across their range

Identifying habitat needs for species with large distributions is challenging because species-habitat associations may vary across scales and regions (spatial nonstationarity). Furthermore, management efforts often cross jurisdictional boundaries, complicating the development of cohesive conservation strategies among management entities. The greater sage-grouse ( Centrocercus urophasianus ) is a rapidly declining species that spans 11 U.S. states and responds to habitat conditions across a wide range of spatial scales and regions. Allowing for regional variance in species-habitat associations and suitability predictions could systematically identify important habitats at levels relevant to management. We collaboratively developed a model with Bureau of Land Management (BLM) biologists that: (1) evaluated the scale of effect for different environmental covariates; (2) accounted for regional differences in population-level responses; and (3) predicted probabilities of persistence across the U.S. occupied range. We modeled range-wide lek persistence data (6615 communal breeding sites classified as active or inactive) as a function of environmental covariates. Environmental covariates included sagebrush cover, pinyon-juniper cover, topography, precipitation, point and line disturbance densities, and landscape configuration metrics. Our model treated habitat assessment areas – regionally delineated by BLM biologists – as random intercepts and slopes that allowed for geographic variation in species-habitat associations and predicted probabilities of lek persistence. Our final model indicated support for 12 environmental covariates predicting lek persistence at scales extending between 1- to 15-km radii from lek centers, and a covariate measuring distance to the occupied range boundary. Five of these covariates showed significant regionally varying responses: sagebrush clumpiness (a measure of habitat aggregation), pinyon-juniper cover, point disturbance of anthropogenic features such as energy infrastructure and communication towers, elevation, and a topographic index associated with mesic habitats. This spatial nonstationarity indicates unitary range-wide recommendations, or rules-of-thumb with respect to their effects on lek persistence, may be problematic for these environmental conditions. For covariates that did not include random slopes, and which were potentially amenable to management actions, we found that leks were predicted to become extirpated when sagebrush cover fell below 9.6 % (summarized at the 3.2-km radius extent), and the proportion of classified sagebrush habitat fell below 0.7 (1-km). We produced a continuous predictive probability surface of lek persistence which we binned based on model sensitivity thresholds to produce habitat quality categories. The highest quality habitat (capturing 50 % of active leks) covered 25.5 % of the occupied range, while the combined lowest through highest quality habitats (capturing 95 % of active leks) covered 65.0 %. Accommodating regional environmental differences in models that are relevant to habitat management planning will help ensure their applicability to targeted goals. Continuous collaboration between modelers and land managers early in the modeling process increases the likelihood of this outcome.

Global Ecology and Conservation↗