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USGS1993

Managing biological diversity

Biological diversity is the variety of life and accompanying ecological processes (Off. Technol. Assess. 1987, Wilcove and Samson 1987, Keystone 1991). Conservation of biological diversity is a major environmental issue (Wilson 1988, Counc. Environ. Quality 1991). The health and future of the earth's ecological systems (Lubchenco et al. 1991), global climate change (Botkin 1990), and an ever-increasing rate in loss of species, communities, and ecological systems (Myers 1990) are among issues drawing biological diversity to the mainstream of conservation worldwide (Int. Union Conserv. Nat. and Nat. Resour. [IUCN] et al. 1991). The legal mandate for conserving biological diversity is now in place (Carlson 1988, Doremus 1991). More than 19 federal laws govern the use of biological resources in the United States (Rein 1991). The proposed National Biological Diversity Conservation and Environmental Research Act (H.R. 585 and S.58) notes the need for a national biological diversity policy, would create a national center for biological diversity research, and recommends a federal interagency strategy for ecosystem conservation. There are, however, hard choices ahead for the conservation of biological diversity, and biologists are grappling with how to set priorities in research and management (Roberts 1988). We sense disillusion among field biologists and managers relative to how to operationally approach the seemingly overwhelming charge of conserving biological diversity. Biologists also need to respond to critics like Hunt (1991) who suggest a tree farm has more biological diversity than an equal area of old-growth forest. At present, science has played only a minor role in the conservation of biological diversity (Weston 1992) with no unified approach available to evaluate strategies and programs that address the quality and quantity of biological diversity (Murphy 1990, Erwin 1992). Although actions to conserve biological diversity need to be clearly defined by viewing issues across biological, spatial, and temporal scales (Knopf and Smith 1992), natural resource managers find much conflicting information in the literature on strategies and programs for the conservation of biological diversity (Ehrlich 1992). Moreover, recommendations provided in much of the published information available for planning or decisions not only can be debated but may prove counterproductive if implemented. Current operational efforts beg for clearer focus on fundamental concepts central to daily decisions that impact native biological diversity. Recognizing that many biologists would provide different council and at the risk of oversimplification, we offer the following 4 topical issues as fundamental guidance to wise conservation action. These recommendations are based on our collective experiences working within conservation agencies since our original, collaborative essay (Samson and Knopf 1982). They are offered as initial, rather than authoritative, steps to better align research and management decisions with what we perceive as the critical issues in conserving biological diversity at the landscape and ecosystem levels of resolution.

Wildlife Society Bulletin↗
USGS2015

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

Wildlife habitat mapping has evolved at a rapid pace over the last few decades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls ( Strix occidentalis caurina ), whose geographic covers about 23 million ha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as predictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map. Recent increases in the use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. Instead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetation height for use in SDMs. We expect a SDM habitat map produced from directly measured predictor variables to be more accurate than one produced from modeled predictors. We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and estimates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo-interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC = 0.809 ± 0.011), but the performance of the Landsat-based map was within acceptable limits (AUC = 0.717 ± 0.021). As is common with photo-interpreted maps, there was no accuracy assessment available for comparison. The photo-interpreted map produced the highest and lowest estimates of habitat area, depending on which habitat classes were included (nesting, roosting, and foraging habitat = 9962 ha, nesting habitat only = 6036 ha). The Landsat-based map produced an estimate of habitat area that was within this range (95% CI: 6679–9592 ha), while the lidar-based map produced an area estimate similar to what was interpreted by local wildlife biologists as nesting (i.e., high quality) habitat using aerial imagery (95% CI: 5453–7216). Confidence intervals of habitat area estimates from the SDMs based on Landsat and lidar overlapped. We concluded that both Landsat- and lidar-based SDMs produced reasonable maps and area estimates for northern spotted owl habitat within the study area. The lidar-based map was more precise and spatially similar to what local wildlife biologists considered spotted owl nesting habitat. The Landsat-based map provided a less precise spatial representation of habitat within the relatively small geographic confines of the study area, but habitat area estimates were similar to both the photo-interpreted and lidar-based maps. Photo-interpreted maps are time consuming to produce, subjective in nature, and difficult to replicate. SDMs provide a framework for efficiently producing habitat maps that can be replicated as habitat conditions change over time, provided that comparable remotely sensed data are available. When the SDM uses predictor variables extracted from lidar data, it can produce a habitat map that is both accurate and useful at large and small spatial scales. In comparison, SDMs using Landsat-based data are more appropriate for large scale analyses of amounts and general spatial patterns of habitat at regional scales.

Remote Sensing of Environment↗
USGS2001

Sperm-cell ultrastructure of North American sturgeons. IV. The pallid sturgeon (Scaphirhynchus albus Forbes and Richardson, 1905)

Sperm-cell morphology and ultrastructure in the pallid sturgeon (Scaphirhynchus albus) were examined using transmission and scanning electron microscopy. Metrics and structure were compared with similar metrics obtained from other published descriptions of sturgeon sperm cells. General morphology was found to be similar to that of sperm cells of the white (Acipenser transmontanus), lake (A. fulvescens), stellate (A. stellatus), Chinese (A. sinensis), Russian (A. gueldenstaedti colchicus), and shortnose (A. brevirostrum) sturgeons, which all shared a gradual tapering of the nuclear diameter from posterior to anterior, unlike that of the Atlantic sturgeon (A. oxyrhynchus). The sperm cell of the pallid sturgeon was similar in size to that of the Atlantic sturgeon, being only slightly larger. The sperm cell of the pallid sturgeon differed from those of other sturgeons chiefly in the acrosomal region, where the posterolateral projections (PLP) have the shape of an acute triangle and are arranged in a spiral about the longitudinal axis of the cell. The PLP were longer than those of other sturgeons, being twice the length of those of the Atlantic sturgeon and 58% longer than those of the lake sturgeon. Also, in cross section the acrosome had the shape of a hollow cone rather than the cap of an oak tree acorn, as was found in ultrastructural studies of other sturgeons. In addition, we were able to confirm that the structural arrangement of the distal centriole of the midpiece is identical with that of the proximal centriole: nine sets of microtubular triplets around the periphery of the centriole. This information is of potential use to fishery biologists, forensic biologists, zoologists, reproductive physiologists, taxonomists, evolutionary biologists, and aquaculturists.

Canadian Journal of Zoology↗
USGS2012

An algal model for predicting attainment of tiered biological criteria of Maine's streams and rivers

State water-quality professionals developing new biological assessment methods often have difficulty relating assessment results to narrative criteria in water-quality standards. An alternative to selecting index thresholds arbitrarily is to include the Biological Condition Gradient (BCG) in the development of the assessment method. The BCG describes tiers of biological community condition to help identify and communicate the position of a water body along a gradient of water quality ranging from natural to degraded. Although originally developed for fish and macroinvertebrate communities of streams and rivers, the BCG is easily adapted to other habitats and taxonomic groups. We developed a discriminant analysis model with stream algal data to predict attainment of tiered aquatic-life uses in Maine's water-quality standards. We modified the BCG framework for Maine stream algae, related the BCG tiers to Maine's tiered aquatic-life uses, and identified appropriate algal metrics for describing BCG tiers. Using a modified Delphi method, 5 aquatic biologists independently evaluated algal community metrics for 230 samples from streams and rivers across the state and assigned a BCG tier (1–6) and Maine water quality class (AA/A, B, C, nonattainment of any class) to each sample. We used minimally disturbed reference sites to approximate natural conditions (Tier 1). Biologist class assignments were unanimous for 53% of samples, and 42% of samples differed by 1 class. The biologists debated and developed consensus class assignments. A linear discriminant model built to replicate a priori class assignments correctly classified 95% of 150 samples in the model training set and 91% of 80 samples in the model validation set. Locally derived metrics based on BCG taxon tolerance groupings (e.g., sensitive, intermediate, tolerant) were more effective than were metrics developed in other regions. Adding the algal discriminant model to Maine's existing macroinvertebrate discriminant model will broaden detection of biological impairment and further diagnose sources of impairment. The algal discriminant model is specific to Maine, but our approach of explicitly tying an assessment tool to tiered aquatic-life goals is widely transferrable to other regions, taxonomic groups, and waterbody types.

Freshwater Science↗
USGS2010

What you should know about land-cover data

Wildlife biologists are using land-characteristics data sets for a variety of applications. Many kinds of landscape variables have been characterized and the resultant data sets or maps are readily accessible. Often, too little consideration is given to the accuracy or traits of these data sets, most likely because biologists do not know how such data are compiled and rendered, or the potential pitfalls that can be encountered when applying these data. To increase understanding of the nature of land-characteristics data sets, I introduce aspects of source information and data-handling methodology that include the following: ambiguity of land characteristics; temporal considerations and the dynamic nature of the landscape; type of source data versus landscape features of interest; data resolution, scale, and geographic extent; data entry and positional problems; rare landscape features; and interpreter variation. I also include guidance for determining the quality of land-characteristics data sets through metadata or published documentation, visual clues, and independent information. The quality or suitability of the data sets for wildlife applications may be improved with thematic or spatial generalization, avoidance of transitional areas on maps, and merging of multiple data sources. Knowledge of the underlying challenges in compiling such data sets will help wildlife biologists to better assess the strengths and limitations and determine how best to use these data.

Journal of Wildlife Management↗
USGS2003

Monitoring amphibians in Great Smoky Mountains National Park

Amphibian species have inexplicably declined or disappeared in many regions of the world, and in some instances, serious malformations have been observed. In the United States, amphibian declines frequently have occurred even in protected areas. Causes for the declines and malformations probably are varied and may not even be related. The seemingly sudden declines in widely separated areas, however, suggests a need to monitor amphibian populations as well as identify the causes when declines or malformations are discovered. In 2000, the President of the United States and Congress directed Department of the Interior (DOI) agencies to develop a plan to monitor the trends in amphibian populations on DOI lands and to conduct causes of declines. The DOI has stewardship responsibilities over vast land holdings in the United States, much of it occupied by, or potential habitat for, amphibians. The U.S. Geological Survey (USGS) was given lead responsibility for planning and organizing this program, named the Amphibian Research and Monitoring Initiative (ARMI). Authorization carried the mandate to set up a national amphibian monitoring program on Federal lands, to develop the sampling techniques and biometrical analyses necessary to determine status and trends, and to identify possible causes of amphibian declines and malformations. The biological importance of Great Smoky Mountains National Park has been recognized by its designation as an International Biosphere Reserve. As such, it is clearly the leading region of significance for amphibian research. Although no other region shares the wealth of amphibians as found in the Great Smokies (31 species of salamanders, and 13 of frogs), the entire southern and mid-section of the Appalachian Mountain chain is characterized by a high diversity of amphibians, and inventories and monitoring protocols developed in the Smokies likely will be applicable to other Appalachian National Park Service properties. From 1998 to 2001, USGS biologists carried out a pilot inventory and monitoring research project in Great Smoky Mountains National Park. A variety of inventory, sampling, and monitoring techniques were employed and tested. These included wide-scale visual encounter surveys of amphibians at terrestrial and aquatic sites, intensive monitoring of selected plots, randomly placed small-grid plot sampling, litterbag sampling in streams, monitoring nesting females of selected species, call surveys, and monitoring specialized habitats, such as caves. Coupled with information derived from amphibian surveys on Federal lands using various other techniques (automated frog call data loggers, PVC pipes, drift fences, terrestrial and aquatic traps), an amphibian monitoring program was designed to best meet the needs of biologists and natural resource managers after taking into consideration the logistics, terrain, and life histories of the species found within Great Smoky Mountains National Park. This report provides an overview of the Park’s amphibians, the factors affecting their distribution, a review of important areas of biodiversity, and a summary of amphibian life history in the Southern Appalachians. In addition, survey techniques are described as well as examples of how the techniques are set up, a critique of what the results tell the observer, and a discussion of the limitations of the techniques and the data. The report reviews considerations for site selection, outlines steps for biosecurity and for processing diseased or dying animals, and provides resource managers with a decision tree on how to monitor the Park’s amphibians based on different levels of available resources. It concludes with an extensive list of references for inventorying and monitoring amphibians. USGS and Great Smoky Mountains National Park biologists need to establish cooperative efforts and training to ensure that congressionally mandated amphibian surveys are performed in a statistically rigorous and biologically meaningful manner, and that amphibian populations on Federal lands are monitored to ensure their long-term survival. The research detailed in this report will aid these cooperative efforts.

North Carolina, Tennessee↗
USGS2005

Advancing migratory bird conservation and management by using radar: An interagency collaboration

Migratory birds face many changes to the landscapes they traverse and the habitats they use. Wind turbines and communications towers, which pose hazards to birds and bats in flight, are being erected or proposed across the United States and offshore. Human activities can also destroy or threaten habitats critical to birds during migratory passage, and climate change appears to be altering migratory patterns. The U.S. Fish and Wildlife Service (USFWS) and other agencies are under increasing pressure to identify and evaluate movement patterns and habitats used during migration and other times. Few tools for deciphering migratory travels exist, but radar-based studies of movements and habitat use patterns in songbirds, waterfowl, and bats hold promise. The U.S. system of over 150 Doppler weather radars provides continental coverage, similar to the scale of bird migration. Although data stored from weather radar represent perhaps the second largest biological data archive in the world, use of those data is currently limited to technically savvy biologists who can handle the obscure data formats. Complementary mobile radar units and thermal and acoustic monitoring are also used in site-specific studies. Efforts to advance bird conservation and management through the use of radar arose independently in several USFWS/USGS collaborations. Recently, this coalition of scientists and resource managers identified the need to work together more closely to foster radar-related research and software development. U.S. Geological Survey (USGS) scientists at Fort Collins Science Center, National Wetlands Research Center, Northern Rocky Mountain Science Center, and Patuxent Wildlife Research Center, as well as USFWS Migratory Bird biologists across the country, are collaborating with university partners to develop a suite of products for managers. The goals are to identify migratory pathways and stopover sites for conservation, mitigation, and landscape planning; convey the importance of functional landscapes and unobstructed airspaces for migrating wildlife; enable use of radar by the wider biological, wind power, and related communities; and simplify the analysis of radar data. The long term focus is to use radar technologies to better understand movement patterns and habitat associations of migratory birds and other wildlife. Land managers and industry may use the knowledge and tools developed to optimize the siting of energy projects, other facilities, and migratory bird habitat projects. The complementary endeavors, not all of which are funded, concentrate on four fundamentals: (1) develop software, in collaboration with National Oceanic and Atmospheric Administration (NOAA) scientists, that enables biologists to access unfiltered weather data and integrate it into standard geographic information systems; (2) develop artificial intelligence-based filters that separate bird from nonbird radar echoes; (3) determine characteristics of bird migration in terms of altitude, speed and direction, daily movements, seasonality, and associations with habitats and landforms; and (4) examine specific movement patterns in relation to towers, wind generation facilities, and tall obstructions. Many technical issues make this work difficult, including complex data structures, massive data sets, digital recognition of birds, large areas not covered by weather radar, and model validation; however, progress will only be furthered by tackling the challenge. The new coalition will meets its goals by: (1) facilitating a productive collaboration with NOAA, Department of the Interior bureaus, state wildlife agencies, universities, power companies, and other potential partners; (2) building and strengthening scientific capabilities within USGS; (3) addressing key migratory bird management issues; and (4) ensuring full funding for the collaborative effort.

Open-File Report↗
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↗
USGS1981

Wildlife survey: Cache Creek and Little Granite Creek, Bridger-Teton National Forest

The results of the wildlife survey are drawn from 410 hours of field observations by the senior wildlife biologist and two biologist technicians, Terry Peterson and John Hoak. Additional observations were made by other project personnel, coincidental with other assigned duties, and by the senior biologist earlier in the season. An additional 23 hours were spent reviewing the available literature (17 hours) and talking with various individuals who have an intimate and respected knowledge of the study area (6 hours).

Bridger-Teton National Forest, Cache Creek and Lit↗
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↗