Some current thoughts of wildlife biologists concerning mosquito control
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Proceedings of the Annual Meeting of the New Jerse↗
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Training biologists and resource managers in lesser developed countries
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Interciencia↗
Handbook of capture-recapture analysis
Every day, biologists in parkas, raincoats, and rubber boots go into the field to capture and mark a variety of animal species. Back in the office, statisticians create analytical models for the field biologists' data. But many times, representatives of the two professions do not fully understand one another's roles. This book bridges this gap by helping biologists understand state-of-the-art statistical methods for analyzing capture-recapture data. In so doing, statisticians will also become more familiar with the design of field studies and with the real-life issues facing biologists. Reliable outcomes of capture-recapture studies are vital to answering key ecological questions. Is the population increasing or decreasing? Do more or fewer animals have a particular characteristic? In answering these questions, biologists cannot hope to capture and mark entire populations. And frequently, the populations change unpredictably during a study. Thus, increasingly sophisticated models have been employed to convert data into answers to ecological questions. This book, by experts in capture-recapture analysis, introduces the most up-to-date methods for data analysis while explaining the theory behind those methods. Thorough, concise, and portable, it will be immensely useful to biologists, biometricians, and statisticians, students in both fields, and anyone else engaged in the capture-recapture process.
Book↗
Integrating community science and agency-collected monitoring data to expand monitoring capacity at large spatial scales
Monitoring species to better understand their status, ecology, and management needs is a major expense for agencies tasked with biodiversity conservation. Community science data have the potential to improve monitoring for minimal cost, given appropriate analytical frameworks. We describe a framework for integrating data from the eBird community science platform with agency-collected monitoring data using a multistate occupancy model. Our model accounts for the structural differences across datasets and allows for estimation of both occupancy and breeding probabilities. The framework was applied to Common Loons ( Gavia immer ) in Washington State. A total of 766 sites had observation effort, of which 713 sites had only eBird effort, 26 sites had only Washington Department of Fish and Wildlife (WDFW) effort, and 27 sites had both. We predicted that the probability of occupancy was only 0.07 (95% Bayesian credible interval, BCI = 0.02–0.51) at the 2324 sites in our sampling frame, though the probability that Common Loons were breeding at occupied sites was 0.95 (95% BCI = 0.71–1.00). We found that probability of occupancy was positively related to waterbody size (probability of a positive effect = 0.88) and negatively related to an index of human influence (probability of a negative effect = 0.94). We found that probability of breeding at occupied sites was positively related to tree canopy cover (0.86), negatively related to elevation (0.99), and negatively related to barren, scrub/shrub, and herbaceous land cover (0.98). We found that state agency biologists were 16 times more likely to detect breeding Common Loons at a site than were eBird users (0.94, 95% BCI = 0.78–0.99 for agency biologists vs. 0.08, 95% BCI = 0.06–0.10 for eBird users). However, the amount of effort expended by eBird users meant that they confirmed Common Loons at 94 sites while agency biologists confirmed them at just 24 sites, although evidence of reproduction was only contributed by agency biologists. Our results provide a better understanding of the distribution of Common Loons in Washington, while further demonstrating that community science data can be a valuable complement to agency-collected data, if appropriate frameworks are developed to integrate these data sources.
Ecosphere↗
What processes must we understand to forecast regional-scale population dynamics?
An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.
Proceedings of the Royal Society B: Biological Sci↗
A practical guide to understanding and validating complex models using data simulations
Biologists routinely fit novel and complex statistical models to push the limits of our understanding. Examples include, but are not limited to, flexible Bayesian approaches (e.g. BUGS, stan), frequentist and likelihood-based approaches (e.g. packages lme4 ) and machine learning methods. These software and programs afford the user greater control and flexibility in tailoring complex hierarchical models. However, this level of control and flexibility places a higher degree of responsibility on the user to evaluate the robustness of their statistical inference. To determine how often biologists are running model diagnostics on hierarchical models, we reviewed 50 recently published papers in 2021 in the journal Nature Ecology & Evolution , and we found that the majority of published papers did not report any validation of their hierarchical models, making it difficult for the reader to assess the robustness of their inference. This lack of reporting likely stems from a lack of standardized guidance for best practices and standard methods. Here, we provide a guide to understanding and validating complex models using data simulations. To determine how often biologists use data simulation techniques, we also reviewed 50 recently published papers in 2021 in the journal Methods Ecology & Evolution . We found that 78% of the papers that proposed a new estimation technique, package or model used simulations or generated data in some capacity (18 of 23 papers); but very few of those papers (5 of 23 papers) included either a demonstration that the code could recover realistic estimates for a dataset with known parameters or a demonstration of the statistical properties of the approach. To distil the variety of simulations techniques and their uses, we provide a taxonomy of simulation studies based on the intended inference. We also encourage authors to include a basic validation study whenever novel statistical models are used, which in general, is easy to implement. Simulating data helps a researcher gain a deeper understanding of the models and their assumptions and establish the reliability of their estimation approaches. Wider adoption of data simulations by biologists can improve statistical inference, reliability and open science practices.
Methods in Ecology and Evolution↗
Supporting the adaptive capacity of species through more effective knowledge exchange with conservation practitioners
There is an imperative for conservation practitioners to facilitate the ability of biodiversity to adapt to accelerating environmental change. Evolutionary biologists are well-positioned to inform the development of evidence-based management strategies that will effectively support the adaptive capacity of species and ecosystems. Conservation practitioners increasingly accept that management practices must change, but harbor concerns about how to apply recommended changes to their management contexts. Given the goodwill from both conservation practitioners and evolutionary biologists, we believe there is opportunity to accelerate the required changes by promoting closer collaboration between these two groups. We highlight how evolutionary biologists can harness lessons learned via the evidence-based conservation movement to make a substantive contribution to the development of effective conservation management practices. These lessons include: 1) recognising why practitioners do and do not use scientific evidence; 2) building an evidence base that will influence management decisions; 3) translating theory into a format that conservation practitioners can use to inform management practices; and 4) developing strategies for effective knowledge exchange. Although efforts will be required on both sides, we believe there are rewards for both practitioners and evolutionary biologists, not least of which is fostering practices that will help support the long-term persistence of species.
Evolutionary Applications↗
Legal, ethical, and procedural bases for the use of aseptic techniques to implant electronic devices
The popularity of implanting electronic devices such as transmitters and data loggers into captive and free-ranging animals has increased greatly in the past two decades. The devices have become smaller, more reliable, and more capable (Printz 2004; Wilson and Gifford 2005; Metcalfe et al. 2012). Compared with externally mounted devices, implanted devices are largely invisible to external viewers such as tourists and predators; exist in a physically protected, thermally stable environment in mammals and birds; and greatly reduce drag and risk of entanglement. An implanted animal does not outgrow its device or attachment method as can happen with collars and harnesses, which allows young animals to be more safely equipped. However, compared with mounting external devices, implantation requires greater technical ability to perform the necessary anesthesia, analgesia, and surgery. More than 83% of publications in the 1990s that used radiotelemetry on animals assumed that there were no adverse effects on the animal (Godfrey and Bryant 2003). It is likely that some studies using implanted electronic devices have not been published due to a high level of unexpected mortality or to aberrant behavior or disappearance of the implanted animals, a phenomenon known as the “file drawer” problem (Rosenthal 1979; Scargle 2000). The near absence of such studies from the published record may be providing a false sense of security that procedures being used are more innocuous than they actually are. Similarly, authors sometimes state that it was unlikely that device implantation was problematic because study animals appeared to behave normally, or authors state that previous investigators used the same technique and saw no problems. Such statements are suppositions if no supporting data are provided or if the animals were equipped because there was no other way to follow their activity. Moreover, such suppositions ignore other adverse effects that affect behavior indirectly, and animals often mask the signs of infection to avoid attracting predators (Wobeser 2006). Guidance specific to sterilization of electronic devices for implantation is limited in the wildlife record (Burger et al. 1994; Mulcahy 2003). Few biologists have been formally trained in aseptic technique, but most biologists know that electronic devices should be treated in some way to reduce the chance for infection of the host animal by bacteria, viruses, parasites, and fungi. Most biologists (73%) who implant devices into fishes believe aseptic techniques are important (Wagner and Cooke 2005). However, I maintain that many biologists find it difficult to place the concept of asepsis into practice in their work because of confusion about what constitutes aseptic technique, a lack of surgical knowledge and training, the perception of increased costs, or the belief that aseptic surgeries are impractical or unnecessary for their application. Some have even argued that, while compromising surgical techniques in the field might result in complications or mortalities, the money saved would allow for a compensatory increase in sample size (Anderson and Talcott 2006). In this paper I define aseptic surgical techniques, document the legal and professional guidance for performing aseptic surgeries on wild animals, and present options for sterilizing electronic devices and surgical instruments for field use.
Journal of Fish and Wildlife Management↗
Converting nonstandard fish sampling data to standardized data
Fishery biologists spend considerable effort over multiple years collecting data on fish population and community status using a particular sampling method or set of methods. However, new (and often more effective) sampling methods and technologies are continuously being developed. To incorporate these new sampling techniques, fishery biologists need a means for converting sample data collected using old methods so they can be compared with data collected using new methods. Similarly, fishery biologists often need a means to compare fish sample data collected using the same method over time (e.g., from year to year) and space (e.g., between sample sites). If fish abundance, species presence, or richness are estimated using an unbiased statistical estimator, the estimates can be validly compared, even if the fish sample data were collected with different methods. However, if unbiased statistical estimators were not used, biologists need methods for adjusting fish sampling data collected using different methods or using the same method collected under different sampling conditions. In this chapter, we describe and provide examples of statistical techniques for converting nonstandard fish sampling data to standardized data and for making comparisons of fish sampling data collected at different times or at different locations. We define standard fish sampling data as data collected using the standardized fish sampling methods described throughout this book. Any other sampling methods and associated data are thus defined as nonstandard. Before delving into the details of the statistical modeling techniques, we describe the nature of fish sample data, their uses, and their limitations. Catch-effort measures, such as relative abundance and catch per unit effort (CPUE), are more formally described as indices. Here, we define an index as any measure or count of a species or community (e.g., species richness) based on direct observation without an estimate of the ability to count individuals or species. Indices have some very desirable characteristics for use in fisheries research and management. In general (but not always), indices require less effort to collect and are usually more precise than unbiased population estimators (e.g., CPUE versus capture–recapture estimates of abundance). The proper use of indices for assessment of fish populations or communities, however, requires that the relationship between an index and the true value (e.g., fish density, species richness) is relatively constant (1) across the observable range of true values, (2) through time when evaluating trends at a single location, and (3) across space when making comparisons among locations.
Book chapter↗
Converting non-standard data to standardized data
Fishery biologists spend considerable effort over multiple years collecting data on fish population and community status using a particular sampling method or set of methods. However, new (and often more effective) sampling methods and technologies are continuously being developed. To incorporate these new sampling techniques, fishery biologists need a means for converting fish sampling data collected using old methods so that they can be compared with data collected using new sampling methods. Similarly, fishery biologists often need a means to compare fish sampling data collected using the same method over time (e.g., from year to year) and space (e.g., between sample sites). If fish abundance, species presence, or richness are estimated using an unbiased statistical estimator (e.g., occupancy estimation, capture-recapture estimation), the estimates can be validly compared even if the fish sample data were collected with different methods. However, if unbiased statistical estimators were not used, biologists need methods for adjusting fish sampling data collected using different methods or using the same method collected under different sampling conditions. In this chapter, we describe and provide examples of statistical techniques for converting nonstandard fish sampling data to American Fisheries Society (AFS) standardized data and for making comparisons of fish sampling data collected at different times or at different locations. We define standard fish sampling data as data collected using the standardized fish sampling methods described throughout this book. Any other sampling methods and associated data are thus defined as nonstandard. Before delving into the details of the techniques that can be used to convert data, we describe the nature of fish sample data, their uses, and their limitations.
Book chapter↗
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↗
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↗
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↗
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↗
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↗
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↗