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At least 19 recordsLinked to original sources
USGS2017

A comment on “temporal variation in survival and recovery rates of lesser scaup”

Concerns about declines in the abundance of lesser scaup ( Aythya affinis ) have promoted a number of analyses to understand reasons for this decline. Unfortunately, most of these analyses, including that of Arnold et al. (2016 Journal of Wildlife Management 80: 850–861), are based on observational studies leading to weak inference. Although we commend the efforts of Arnold et al. (2016 Journal of Wildlife Management 80: 850–861), we think their conclusions are over-stated given their retrospective analysis. Further, we note a number of inconsistencies in their reasoning and offer alternative conclusions that can be drawn from their analysis. Given the uncertainty still surrounding management of lesser scaup, we do not believe it is prudent to abandon or greatly modify adaptive management approaches designed specifically to make optimal decisions in the face of uncertainty. The current learning-based and recursive approach to management appears to be providing adequate guidance for harvest without punctuated changes to harvest levels, as Arnold et al. (2016 Journal of Wildlife Management 80: 850–861) recommend.

Journal of Wildlife Management↗
USGS2011

Multimodel inference and adaptive management

Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study’s inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.

Journal of Environmental Management↗
USGS2013

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

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

Journal of Wildlife Management↗
USGS2018

Increased scientific rigor will improve reliability of research and effectiveness of management

Rigorous science that produces reliable knowledge is critical to wildlife management because it increases accurate understanding of the natural world and informs management decisions effectively. Application of a rigorous scientific method based on hypothesis testing minimizes unreliable knowledge produced by research. To evaluate the prevalence of scientific rigor in wildlife research, we examined 24 issues of the Journal of Wildlife Management from August 2013 through July 2016. We found 43.9% of studies did not state or imply a priori hypotheses, which are necessary to produce reliable knowledge. We posit that this is due, at least in part, to a lack of common understanding of what rigorous science entails, how it produces more reliable knowledge than other forms of interpreting observations, and how research should be designed to maximize inferential strength and usefulness of application. Current primary literature does not provide succinct explanations of the logic behind a rigorous scientific method or readily applicable guidance for employing it, particularly in wildlife biology; we therefore synthesized an overview of the history, philosophy, and logic that define scientific rigor for biological studies. A rigorous scientific method includes 1) generating a research question from theory and prior observations, 2) developing hypotheses (i.e., plausible biological answers to the question), 3) formulating predictions (i.e., facts that must be true if the hypothesis is true), 4) designing and implementing research to collect data potentially consistent with predictions, 5) evaluating whether predictions are consistent with collected data, and 6) drawing inferences based on the evaluation. Explicitly testing a priori hypotheses reduces overall uncertainty by reducing the number of plausible biological explanations to only those that are logically well supported. Such research also draws inferences that are robust to idiosyncratic observations and unavoidable human biases. Offering only post hoc interpretations of statistical patterns (i.e., a posteriori hypotheses) adds to uncertainty because it increases the number of plausible biological explanations without determining which have the greatest support. Further, post hoc interpretations are strongly subject to human biases. Testing hypotheses maximizes the credibility of research findings, makes the strongest contributions to theory and management, and improves reproducibility of research. Management decisions based on rigorous research are most likely to result in effective conservation of wildlife resources.

Journal of Wildlife Management↗
USGS2021

Sage-grouse population dynamics are adversely impacted by overabundant feral horses

In recent decades, feral horse ( Equus caballus ; horse) populations increased in sagebrush ( Artimesia spp.) ecosystems, especially within the Great Basin, to the point of exceeding maximum appropriate management levels (AML max ), which were set by land administrators to balance resource use by feral horses, livestock, and wildlife. Concomitantly, greater sage-grouse ( Centrocercus urophasianus ; sage-grouse) are sagebrush obligates that have experienced population declines within these same arid environments as a result of steady and continued loss of seasonal habitats. Although a strong body of research indicates that overabundant populations of horses degrade sagebrush ecosystems, empirical evidence linking horse abundance to sage-grouse population dynamics is missing. Within a Bayesian framework, we employed state-space models to estimate population rate of change ( λ ) using 15 years (2005–2019) of count surveys of male sage-grouse at traditional breeding grounds (i.e., leks) as a function of horse abundance relative to AML max and other environmental covariates (e.g., wildfire, precipitation, % sagebrush cover). Additionally, we employed a post hoc impact-control design to validate existing AML max values as related to sage-grouse population responses, and to help control for environmental stochasticity and broad-scale oscillations in sage-grouse abundance. On average, for every 50% increase in horse abundance over AML max , our model predicted an annual decline in sage-grouse abundance by 2.6%. Horse abundance at or below AML max coincided with sage-grouse λ estimates that were consistent with trends at non-horse areas elsewhere in the study region. Thus, AML max , as a whole, appeared to be set adequately in preventing adverse effects to sage-grouse populations. Results indicated 76%, 97%, and >99% probability of sage-grouse population decline relative to controls when horse numbers are 2, 2.5, and ≥3 times over AML max , respectively. As of 2019, horse herds exceeded AML max in Nevada, USA, by >4 times on average across all horse management areas. If feral horse populations continue to grow at current rates unabated, model projections indicate sage-grouse populations will be reduced within horse-occupied areas by >70.0% by 2034 (15-year projection), on average compared to 21.2% estimated for control sites. A monitoring framework that improves on estimating horse abundance and identifying responses of sage-grouse and other key indicator species (plant and animal) would be beneficial to guide management decisions that promote co-occurrence of horses with sensitive wildlife and livestock within landscapes subjected to multiple uses. Published 2021. This article is a U.S. Government work and is in the public domain in the USA. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.

Nevada↗
USGS1999

Effects of sample size on kernel home range estimates

Kernel methods for estimating home range are being used increasingly in wildlife research, but the effect of sample size on their accuracy is not known. We used computer simulations of 10-200 points/home range and compared accuracy of home range estimates produced by fixed and adaptive kernels with the reference (REF) and least-squares cross-validation (LSCV) methods for determining the amount of smoothing. Simulated home ranges varied from simple to complex shapes created by mixing bivariate normal distributions. We used the size of the 95% home range area and the relative mean squared error of the surface fit to assess the accuracy of the kernel home range estimates. For both measures, the bias and variance approached an asymptote at about 50 observations/home range. The fixed kernel with smoothing selected by LSCV provided the least-biased estimates of the 95% home range area. All kernel methods produced similar surface fit for most simulations, but the fixed kernel with LSCV had the lowest frequency and magnitude of very poor estimates. We reviewed 101 papers published in The Journal of Wildlife Management (JWM) between 1980 and 1997 that estimated animal home ranges. A minority of these papers used nonparametric utilization distribution (UD) estimators, and most did not adequately report sample sizes. We recommend that home range studies using kernel estimates use LSCV to determine the amount of smoothing, obtain a minimum of 30 observations per animal (but preferably a?Y50), and report sample sizes in published results.

Journal of Wildlife Management↗
USGS1999

The insignificance of statistical significance testing

Despite their use in scientific journals such as The Journal of Wildlife Management , statistical hypothesis tests add very little value to the products of research. Indeed, they frequently confuse the interpretation of data. This paper describes how statistical hypothesis tests are often viewed, and then contrasts that interpretation with the correct one. I discuss the arbitrariness of P-values, conclusions that the null hypothesis is true, power analysis, and distinctions between statistical and biological significance. Statistical hypothesis testing, in which the null hypothesis about the properties of a population is almost always known a priori to be false, is contrasted with scientific hypothesis testing, which examines a credible null hypothesis about phenomena in nature. More meaningful alternatives are briefly outlined, including estimation and confidence intervals for determining the importance of factors, decision theory for guiding actions in the face of uncertainty, and Bayesian approaches to hypothesis testing and other statistical practices.

Journal of Wildlife Management↗
USGS1983

[Book review] Waterfowl ecology and management: Selected readings

This book is a compilation of papers from the extensive and varied published literature on the ecology and management of waterfowl. The 125 technical papers reprinted in this book are arranged in eight major sections and are from 21 journals, government reports, several books, and proceedings of symposia and annual conferences. The most frequent sources of papers are The Journal of Wildlife Management, The Auk, Transactions North American Wildlife and Natural Resources Conference, and Wildfowl. These readings span the period from 1937 through 1981. A majority of the technical papers was published during the sixties and seventies, reflecting the expansion of waterfowl research during that period. The front cover and the introductory page of each section are illustrated with black-and-white drawings of North American waterfowl by D. R. Barrick.

The Auk↗
USGS2007

Yellowstone grizzly bear investigations: Annual report of the Interagency Grizzly Bear Study Team, 2006

The contents of this Annual Report summarize results of monitoring and research from the 2006 field season. The report also contains a summary of nuisance grizzly bear ( Ursus arctos horribilis ) management actions. The Interagency Grizzly Bear Study Team (IGBST) continues to work on issues associated with counts of unduplicated females with cubs-of- the-year (COY). These counts are used to establish a minimum population size, which is then used to establish mortality thresholds for the Recovery Plan (U.S. Fish and Wildlife Service [USFWS] 1993). After considerable delays due to programming issues, a computer program that defines the rule set used by Knight et al. (1995) to differentiate unique family groups was development and tested in 2005 and 2006. Simulations using observations of collared females with COY were randomly sampled to generate datasets of observations of random females with COY. These datasets were then run though the simulations program to test the accuracy of the rules. Data are currently being summarized. This project has been completed and a manuscript was submitted to the Journal of Wildlife Management. The grizzly bear recovery plan (USFWS 1993) established human-caused mortality quotas. We used the latest information on reproduction and survival to estimate population trajectory in the same simulation model originally used by Harris (1984). A Wildlife Monographs was published in 2006. Additionally, the study team, in cooperation with several quantitative experts, reassessed how population size is indexed and how sustainable mortality rates are established. A draft report was presented to the Yellowstone Ecosystem Subcommittee in spring 2005. It was published as part of the USFWS Delisting Rule (Federal Register Vol. 70, No. 221, Nov. 17, 2005, 69853–69884) and subjected to public comment. This workshop document can be found at http://www.fws.gov/mountain-prairie/species/mammals/grizzly/yellowstone.htm. During the summer of 2006, a second workshop was held to address public comment and professional peer review. The result of this workshop was a supplement to the 2005 workshop document. This supplement can be found at http://www.fws.gov/mountain-prairie/species/mammals/grizzly/yellowstone.htm under the link Revised Methods to Estimate Population Size and Sustainable Mortality Limits. Results of those estimates are provided in Appendix A. Our project addressing the potential application of stable isotopes and trace elements to quantify consumption rates of whitebark pine ( Pinus albicaulis ) and cutthroat trout ( Oncorhynchus clarki ) by grizzly bears was completed. Our manuscript on consumption rates of whitebark pine was published in the Canadian Journal of Zoology 81:763-770. Results of the mercury studies were also published in the Canadian Journal of Zoology 82:493–501. Copies can be found on the IGBST website http://www.nrmsc.usgs.gov/research/igbst-home.htm. Based upon this work, we submitted a proposal to analyze all historic tissue samples from grizzly bears in the ecosystem. That proposal was funded and samples have been sent to a lab for isotopic analysis. We hope to have those results in early 2008. Results of DNA hair snaring work conducted on Yellowstone Lake were submitted and published in the Journal Ursus (Haroldson et al. 2005). Results of this study conducted from 1997–2000 showed a decline in fish use by grizzly bears when compared to earlier work conducted by Reinhart (1990) in 1985–1987. As a consequence, the IGBST submitted a proposal to the National Park Service and received 3 years funding to repeat that work. This project began in 2007. There are 2 graduate students and several field technicians working on the program. We completed the final field season in Grand Teton National Park evaluating habitat use both temporally and spatially between grizzly and black bears ( Ursus americanus ). We continue to use GPS technology that incorporates a spread spectrum communication system. Spread spectrum allows for transfer of stored GPS locations from the collar to a remote receiving station. Results of the 2006 field season are reported here. We plan to complete the final report in late 2007. We continued to monitor the health of whitebark pine in the Greater Yellowstone Ecosystem (GYE) in cooperation with the Greater Yellowstone Whitebark Pine Monitoring Working Group. A summary of the 2006 monitoring is also presented (Appendix B). The IGBST uses counts of winter-killed ungulates to index spring carcass abundance for grizzly bears. Likewise, we use wier counts and stream surveys to index cutthroat trout abundance. We ask Dr. Steve Cherry, Department of Mathematical Sciences, Montana State University-Bozeman, to review the protocols and make recommendations for improving them. That review and recommendations are presented in Appendix C. Finally, the state of Wyoming, following recommendations from the Yellowstone Ecosystem Subcommittee and the IGBST, launched the Bear Wise Community Effort. The focus is to minimize human/bear conflicts, minimize human-caused bear mortalities associated with conflicts, and safeguard the human community. Results of these efforts are detailed in Appendix D. The annual reports of the IGBST summarize annual data collection. Because additional information can be obtained after publication, data summaries are subject to change. For that reason, data analyses and summaries presented in this report supersede all previously published data. The study area and sampling techniques are reported by Blanchard (1985), Mattson et al. (1991 a), and Haroldson et al. (1998).

Idaho, Montana, Wyoming↗
USGS2004

Combining band recovery data and Pollock's robust design to model temporary and permanent emigration

Capture-recapture models are widely used to estimate demographic parameters of marked populations. Recently, this statistical theory has been extended to modeling dispersal of open populations. Multistate models can be used to estimate movement probabilities among subdivided populations if multiple sites are sampled. Frequently, however, sampling is limited to a single site, Models described by Burnham (1993, in Marked Individuals in the Study of Bird Populations , 199–213), which combined open population capture-recapture and band-recovery models, can be used to estimate permanent emigration when sampling is limited to a single population. Similarly, Kendall, Nichols, and Hines (1997, Ecology 51 , 563–578) developed models to estimate temporary emigration under Pollock's (1982, Journal of Wildlife Management 46 , 757–760) robust design. We describe a likelihood-based approach to simultaneously estimate temporary and permanent emigration when sampling is limited to a single population. We use a sampling design that combines the robust design and recoveries of individuals obtained immediately following each sampling period. We present a general form for our model where temporary emigration is a first-order Markov process, and we discuss more restrictive models. We illustrate these models with analysis of data on marked Canvasback ducks. Our analysis indicates that probability of permanent emigration for adult female Canvasbacks was 0.193 ( ) and that birds that were present at the study area in year i — 1 had a higher probability of presence in year i than birds that were not present in year i — 1.

Biometrics↗
USGS2007

Supplemental materials for the analysis of capture-recapture data for polar bears in Western Hudson Bay, Canada, 1984-2004

Regehr and others (2007, Survival and population size of polar bears in western Hudson Bay in relation to earlier sea ice breakup: Journal of Wildlife Management, v. 71, no. 8) evaluated survival in relation to climatic conditions and estimated population size for polar bears (Ursus maritimus) in western Hudson Bay, Canada. Here, we provide supplemental materials for the analyses in Regehr and others (2007). We demonstrate how tag-return data from harvested polar bears were used to adjust estimates of total survival for human-caused mortality. We describe the sex and age composition of the capture and harvest samples and provide results for goodness-of-fit tests applied to capture-recapture models. We also describe the capture-recapture model selection procedure and the structure of the most supported model, which was used to estimate survival and population size.

Data Series↗
USGS1989

Estimating carcass fat and protein in northern pintails during the nonbreeding season

I used northern pintails ( Anas acuta ) collected from August through March 1979-82 in the Sacramento Valley, California to derive equations to predict ether-extracted carcass fat, carcass protein, and skeletal lean dry weight. Ether-extracted carcass fat was best predicted by total fat depot weight (wet skin, abdominal fat, and intestinal fat) (r 2 = 0.94) and estimates based on carcass water content (r 2 = 0.93-0.98). Measured carcass protein was best predicted by a multiple regression including total protein depot weight (breast muscles, leg muscles, and gizzard) and tarsus length (R 2 = 0.79). I predicted skeletal lean dry weight by a multiple regression incorporating culmen, tarsus, and wing length (R 2 = 0.77). Predicted carcass fat agreed well with measured carcass fat in an independent data set of 30 pintails using total fat depot (r 2 = 0.92-0.96) and carcass water (r 2 = 0.97-0.99), but predicted carcass protein agreed less well with measured protein.

California↗
USGS1990

Indices used to assess status of sea otter populations: A reply

The California sea otter ( Enhydra lutris ) population, after increasing for more than half a century, stabilized and probably declined from the mid-1970's to the mid-1980's. Estes et al. (1986) suggested that the stabilization and decline were not due to food limitation. Garshelis et al. (1990) challenged this suggestion, although in doing so they misrepresented arguments made by Estes et al. (1986), provided no evidence for alternative hypotheses, and offered no constructive recommendations for a better means of population assessment. While acknowledging some of the points made by Garshelis et al. (1990), I believe the collective evidence presented by Estes et al. (1986) provided a reasonable basis for rejecting the food-limitation hypothesis, and point out that recent increases in the California sea otter population following a legislated reduction in net entanglement mortality is strong evidence against the food-limitation hypothesis.

California↗
USGS1992

Evaluating habitat selection with radio-telemetry triangulation error

Radio-telemetry triangulation errors result in the mislocation of animals and misclassification of habitat use. We present analytical methods that provide improved estimates of habitat use when misclassification probabilities can be determined. When misclassification probabilities cannot be determined, we use random subsamples from the error distribution of an estimated animal location to improve habitat use estimates. We conducted Monte Carlo simulations to evaluate the effects of this subsampling method, triangulation error, number of animal locations, habitat availability, and habitat complexity on bias and variation in habitat use estimates. Results for the subsampling method are illustrated using habitat selection by redhead ducks ( Aythya americana ). We recommend the subsampling method with a minimum of 50 random points to reduce problems associated with habitat misclassification.

Wisconsin↗