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At least 37 records · Page 2Linked to original sources
USGS2005

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↗
USGS2023

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↗
USGS2020

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↗
USGS2022

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↗
USGS2021

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↗
USGS2013

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↗
USGS2009

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↗
USGS2024

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↗