Estimating risks in declining populations with poor data

Holmes, Elizabeth E.

doi:10.1073/pnas.081055898

Cited by 132 publications

(184 citation statements)

References 16 publications

(14 reference statements)

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“…However, the results suggest that it is not the primary cause of high variability in estimates of Pacific salmon population abundance. The result contradicts findings for other species where estimates of environmental variance are usually small compared with sampling error variance (Holmes 2001). This genuinely high variability, in turn, limits the utility of these types of models to predict the future.…”

Section: Discussioncontrasting

confidence: 61%

“…This occurs because modeled population trajectories can reach zero (i.e., become extinct), or fall below a quasiextinction threshold, simply at random. Because this high variability appears to be unique to Pacific salmon populations (when compared with other ESA-listed vertebrates), much effort has recently been expended exploring methods to help understand and correct for high population variability (e.g., Holmes 2001;Staples et al 2004). If the apparent high variability is primarily a problem with counting methods it could be reduced by correcting for measurement error, sampling error, or both, and other possible problems.…”

mentioning

confidence: 99%

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Measure Twice, Estimate Once: Pacific Salmon Population Viability Analysis for Highly Variable Populations

Paulsen

Hinrichsen²,

Fisher

2007

Trans Am Fish Soc

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Abstract.-Because many stocks of Pacific salmon Oncorhynchus spp. are listed under the U.S. Endangered Species Act (ESA), research has focused on predicting the future population dynamics for these low-abundance stocks. One method used to make predictions is known as population viability analysis. Pacific salmon populations exhibit much higher apparent variability than other ESA-listed vertebrates, and high variability increases the probability of extinction. If the high variability is primarily due to counting methods, it could be reduced in model predictions by using methods that correct for measurement error, sampling error, or both. Using data from British Columbia pink salmon O. gorbuscha and Snake River springor summer-run Chinook salmon O. tshawytscha and several modeling approaches (Ricker, Dennis, and statespace models), we compared repeated counts of the same population (e.g., spawner and fry, dam and redd counts). We applied the methods to the first half of the time series and compared the predictions with the last half of the time series. The results demonstrated that having counts of all life stages of a Pacific salmon population is no guarantee that variability will be markedly reduced. Measurement error is not the primary cause of high variability in empirical estimates of abundance or in predicted future abundance for the stocks analyzed. The very wide bounds on predicted abundance limit the utility of the model predictions for making management decisions. Furthermore, obtaining more accurate or complete measurements of population abundance is unlikely to reduce the wide error bounds in predictions of future abundances.Over the past 15 years many stocks of Pacific salmon Oncorhynchus spp. have been listed as threatened or endangered under the U.S. Endangered Species Act (ESA) (NOAA Fisheries 2005). This has focused researchers' attention on a difficult problem: how best to predict the future course of population dynamics for listed stocks whose abundances are often greatly reduced from historic levels. One method used to make such predictions is known as population viability analysis (PVA). Several PVA methods have been used for modeling listed salmon populations, beginning with spawner-recruit (SR) models familiar to many fisheries biologists (e.g., Marmorek et al. 1998;Paulsen and Hinrichsen 2002). More recently, researchers, drawing on methods used for other ESAlisted vertebrate species (e.g., northern spotted owl Strix occidentalis caurina, loggerhead sea turtle Caretta caretta, snail kite Rostrhamus sociabilis, and Bachman's sparrow Aimophifa aestivulis; Morris et al. 2002), have employed diffusion approximation methods (e.g., Holmes 2004;Holmes and Semmens 2004). These methods have also been applied for long-term projections by management agencies at the evolutionarily significant unit (ESU) and population levels (NOAA 2000). Unlike spawner-recruit models diffusion approximation methods do not require information on the age of returning progeny (recruits) and, therefore, can be employed for...

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Section: Discussioncontrasting

confidence: 61%

mentioning

confidence: 99%

Measure Twice, Estimate Once: Pacific Salmon Population Viability Analysis for Highly Variable Populations

Paulsen

Hinrichsen²,

Fisher

2007

Trans Am Fish Soc

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“…Numerous authors have documented the impacts of observation errors on naive analyses which ignore it, either through simulations or analytically (see Meier and Fagan 2000, Holmes 2001. show that the observation error here generally leads to overestimation of k in Eq.…”

Section: Naive Analysesmentioning

confidence: 99%

“…The effects of observation error on analyses which ignore it are now fairly well understood (e.g., Ludwig 1999, Meier and Fagan 2000, Holmes 2001, Holmes and Fagan 2002, Parysow and Tazik 2002). There has been recent progress in developing and comparing different estimators that correct for observation error, but techniques for inference (i.e., standard errors, confidence intervals, and so on) have not been fully developed.…”

Section: Introductionmentioning

confidence: 99%

Statistical methods to correct for observation error in a density‐independent population model

Buonaccorsi¹,

Staudenmayer²

2009

Ecological Monographs

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Abstract. The problem of observation error in assessing the dynamics of populations over time has received increasing attention of late. Of particular interest has been a densityindependent dynamic model, which allows a trend and is commonly employed in population viability analysis (PVA). Most of the recent work in this area has focused on assessing the impact of the observation error and on finding corrected estimators, primarily under normal models with the observation errors assumed to have a constant variance. This paper provides a comprehensive overview of statistical methods for this problem and evaluates them through simulations. This includes the development and assessment of simple and practical ways to obtain standard errors and confidence intervals for the basic parameters in the model and functions of them, such as the intrinsic rate of increase or the probability of eventual extinction. We allow for unequally spaced data and possibly changing observation error variances, and we also discuss how to employ standard errors that often accompany the estimated abundances. Both likelihood techniques under normality and methods allowing non-normal observation errors are discussed, and we describe how the various likelihoodbased techniques can be implement using standard mixed-model software, such as PROC MIXED in SAS. The projection technique we use to handle functions of the basic parameters is also valuable when there is no observation error. The methods are motivated and illustrated using data for grizzly bears, Whooping Cranes, California Condors, and Puerto Rican Parrots. We present a simulation experiment to evaluate the performance of the some of the methods.

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“…Abundance estimates are the most common quantitative criterion in recovery plans (Gerber and Hatch 2002); however, they are often imprecise, error-ridden, or based on guesses (Holmes 2001, Campbell et al 2002. In some cases, insufficient or erroneous data can directly influence how management efforts are prioritized and may result in misallocation of finite conservation resources (McKelvey et al 2008).…”

Section: Monitoring Populations With Noninvasive Genetic Samplingmentioning

confidence: 99%

Demography and Genetic Structure of a Recovering Grizzly Bear Population

Kendall

Stetz

Boulanger³

et al. 2009

J Wildl Manag

255

359

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Grizzly bears (brown bears; Ursus arctos) are imperiled in the southern extent of their range worldwide. The threatened population in northwestern Montana, USA, has been managed for recovery since 1975; yet, no rigorous data were available to monitor program success. We used data from a large noninvasive genetic sampling effort conducted in 2004 and 33 years of physical captures to assess abundance, distribution, and genetic health of this population. We combined data from our 3 sampling methods (hair trap, bear rub, and physical capture) to construct individual bear encounter histories for use in Huggins—Pledger closed mark—recapture models. Our population estimate, Ň = 765 (95% CI = 715–831) was more than double the existing estimate derived from sightings of females with young. Based on our results, the estimated known, human‐caused mortality rate in 2004 was 4.6% (95% CI = 4.2–4.9%), slightly above the 4% considered sustainable; however, the high proportion of female mortalities raises concern. We used location data from telemetry, confirmed sightings, and genetic sampling to estimate occupied habitat. We found that grizzly bears occupied 33,480 km2 in the Northern Continental Divide Ecosystem (NCDE) during 1994–2007, including 10,340 km2 beyond the Recovery Zone. We used factorial correspondence analysis to identify potential barriers to gene flow within this population. Our results suggested that genetic interchange recently increased in areas with low gene flow in the past; however, we also detected evidence of incipient fragmentation across the major transportation corridor in this ecosystem. Our results suggest that the NCDE population is faring better than previously thought, and they highlight the need for a more rigorous monitoring program.

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Estimating risks in declining populations with poor data

Cited by 132 publications

References 16 publications

Measure Twice, Estimate Once: Pacific Salmon Population Viability Analysis for Highly Variable Populations

Measure Twice, Estimate Once: Pacific Salmon Population Viability Analysis for Highly Variable Populations

Statistical methods to correct for observation error in a density‐independent population model

Demography and Genetic Structure of a Recovering Grizzly Bear Population

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