Integrating distance sampling with minimum counts to improve monitoring

Minimum counts are commonly used to estimate population size and trend for wildlife conservation and management; however, the scope of inference based on such data is limited by untestable assumptions regarding the detection process. Alternative approaches, such as distance sampling, occupancy surveys, and repeated counts, can be employed to produce detection-corrected estimates of population parameters. Unfortunately, these approaches can be more complicated and costly to implement, potentially limiting their use. We explored a conceptual framework linking datasets collected at different spatial scales under different survey designs, with the goal of improving inference. Specifically, we link landscape-scale distance sampling surveys with local-scale minimum counts in an integrated modeling framework to estimate mountain goat (Oreamnos americanus) abundance at both the local and regional scale in south-central Alaska, USA, and provide an estimate of detection probability (i.e., sightability) for the minimum counts. Estimated sightability for the minimum count surveys was 0.67 (95% credible interval [CrI] = 0.52–0.83) and abundance for the entire area was 5,600 goats (CV = 9%), both in broad agreement with estimates from previous studies. Abundance estimates at the local scale (i.e., individual min. count unit) were reasonably precise ( = 18%), suggesting the integrated approach can increase the amount of information produced at both spatial scales by linking minimum count approaches with more rigorous survey designs. We propose that our integrated approach may be implemented in the context of a modified split-panel monitoring design by altering survey protocols to include frequent minimum counts within local count units and intermittent but more rigorous survey designs with inference to the entire study area or population of interest. Doing so would provide estimates of abundance with appropriate measures of uncertainty at multiple spatial scales, thereby improving inference for population monitoring and management. © 2019 The Wildlife Society.     

Survival of adult mountain goats in Washington: effects of season,translocation, snow,and precipitation

Elucidating patterns of adult survival rates is key to understanding population dynamics of large mammals. We used data from 7 separate studies of mountain goats (Oreamnos americanus) conducted from 2002 through 2022 in western Washington, USA, to quantify survival rates in relation to key biotic and abiotic factors using known fate models implemented in program MARK. We monitored 324 (206 females, 118 males) radio-marked mountain goats for 178,339 days. A substantial number of mountain goats in our sample (n = 217) had been translocated from the Olympic Peninsula to the Cascade Mountains on Washington's mainland, providing an added opportunity to examine translocation effects. We adopted a sequential modeling approach, first building a set of models to examine fundamental survival patterns by age, sex, season, study area, and translocated status. We used variables retained from the top model in a second set to investigate relationships between annual survival and local weather covariates hypothesized by previous studies to influence mountain goat behavior, habitat selection, and vital rates. Survival among adult females in spring was slightly lower than other sex and age categories, but seasonal patterns were otherwise not evident. There were significant negative relationships between survival and winter snow depth, an index of the previous year's drought, and mean daily temperature during the previous May, and a positive relationship with previous year's precipitation. Weather effects were similar among resident and translocated animals. By the end of the study period, weather effects had evidently contributed to reductions in mountain goat survival to a level low enough that population stability was unlikely. Because the frequency of droughts and warm spring temperatures are expected to increase with climate change, mountain goat populations in Washington will likely be increasingly challenged as the atmosphere warms.     

Availability bias in population survey of Northern Hudson Bay narwhal (Monodon monoceros)

Population estimates are important for the development of management plans of harvested species and thereby ultimately important for species sustainability. Aerial surveys are one of the methods used in preparing population estimates. For marine mammals, aerial population surveys require that animal biology is understood in order to account for availability bias. Availability bias in this case derives from animals that are invisible to the survey due to diving behavior. In order to understand diving behavior of the Northern Hudson Bay narwhal (Monodon monoceros), nine whales were tagged with satellite tracking devices in the Repulse Bay, Nunavut area in August 2006 (n = 5) and 2007 (n = 4). Of specific interest was time at depth of 0–2 m of water, the depth at which studies have shown that whales could be distinguished at the species level during an aerial survey. The proportion of time spent in 0–2 m of water can then be used to correct the population estimate from aerial survey. This research found that narwhals spent approximately 32 % of their time at the surface where they would be available to be seen by an aerial survey. This paper provides raw data that can be used to correct population survey estimates.     

Measurement error and survey design in sightability model development

Measurement error of explanatory variables used in sightability models can result in biased population estimates and associated measures of precision. We developed a Monte Carlo simulation procedure that can be implemented within the sightability model framework when measurement error is present. Additionally, we developed simulation and sample survey methods, for determining the optimal allocation of survey effort to maximize precision of population estimates for a fixed survey cost, when a complete survey of a study area is not feasible. We used data from aerial surveys of elk during 2004–2006 in Michigan to demonstrate the application of these techniques. By accounting for measurement error and applying appropriate survey design practices, managers employing sightability models may be able to generate more accurate and cost-effective population estimates and accompanying measures of precision than is possible if these techniques are ignored. © 2011 The Wildlife Society.     

Estimating mountain goat abundance using DNA from fecal pellets

Non-invasive collection of tissue samples to obtain DNA for microsatellite genotyping required to estimate population size has been used for many wildlife species but rarely for ungulates. We estimated mountain goat (Oreamnos americanus) population size on a mountain complex in southwestern British Columbia by identification of individuals using DNA obtained from fecal pellet and hair samples collected during 3 sampling sessions. We identified 55 individuals from 170 samples that were successfully genotyped, and estimated a population of 77 mountain goats (SE = 7.4). Mean capture probability was 0.38 (SE = 0.037) per session. Our technique provides one of the first statistically rigorous estimates of abundance of an ungulate species using DNA derived primarily from fecal pellets. Our technique enables managers to obtain minimum counts or population estimates of ungulates in areas of low sightability that can be used for conservation and management. © 2011 The Wildlife Society.     

Estimation and Correction of Visibility Bias in Aerial Surveys of Wintering Ducks

Abstract: Incomplete detection of all individuals leading to negative bias in abundance estimates is a pervasive source of error in aerial surveys of wildlife, and correcting that bias is a critical step in improving surveys. We conducted experiments using duck decoys as surrogates for live ducks to estimate bias associated with surveys of wintering ducks in Mississippi, USA. We found detection of decoy groups was related to wetland cover type (open vs. forested), group size (1–100 decoys), and interaction of these variables. Observers who detected decoy groups reported counts that averaged 78% of the decoys actually present, and this counting bias was not influenced by either covariate cited above. We integrated this sightability model into estimation procedures for our sample surveys with weight adjustments derived from probabilities of group detection (estimated by logistic regression) and count bias. To estimate variances of abundance estimates, we used bootstrap resampling of transects included in aerial surveys and data from the bias-correction experiment. When we implemented bias correction procedures on data from a field survey conducted in January 2004, we found bias-corrected estimates of abundance increased 36–42%, and associated standard errors increased 38–55%, depending on species or group estimated. We deemed our method successful for integrating correction of visibility bias in an existing sample survey design for wintering ducks in Mississippi, and we believe this procedure could be implemented in a variety of sampling problems for other locations and species. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):808–813; 2008)     

Modelling heterogeneity in detection probabilities in land and aerial abundance surveys in humpback whales (<Emphasis Type="Italic">Megaptera novaeangliae</Emphasis>)

The effective management and conservation of animal populations relies on statistically-sound and replicable surveys to obtain estimates of abundance and assess trends. Surveys of cetaceans, such as humpback whales Megaptera novaeangliae, are difficult to conduct and are particularly affected by bias in detection probability. For example, the probability of detection of whales from land decreases substantially with increased distance from the platform. This distance effect is also true for aerial surveys, combined with the problem that animals are unavailable for detection (underwater) whilst in the field of view. We present a novel approach that combines corrected double-platform land surveys with corrected aerial surveys to obtain a robust estimate of g(0), the probability of detection on the survey line, for aerial surveys of migrating humpback whales. Several sources of heterogeneity in detection probabilities were identified within the land and aerial surveys (including group composition, bearing of first sighting, number of groups being tracked simultaneously and cloud cover). After including these into our estimate of ĝ(0), we found that only 29% of available whales are being detected on the survey line (ĝ(0) = 0.288), which is a considerably smaller estimate than many available for humpback whales using other methods. Incorporating heterogeneity into the population surveys shows that we are likely to be underestimating the population size of whales on the east coast of Australia. The implications of this result for their conservation and management in light of increased whale-human conflict is discussed.     

Evaluating habitat selection models to predict critical habitat for mountain goats in northwest British Columbia

Anthropogenic activity imposes increasing pressure on wildlife populations globally; these pressures can affect habitat suitability and function, modify wildlife space use, and influence population viability. Native mountain goat (Oreamnos americanus) populations can be negatively affected by anthropogenic disturbance and modify their space use in response to land development and recreational activity. From 2018 to 2020, we studied space use of mountain goats northeast of Smithers, British Columbia, Canada, an area that is subject to increasing anthropogenic development and yearlong recreational activities. We aimed to generate models that would improve our ability to identify habitat for mountain goats relative to existing survey data and established ungulate winter ranges. Using resource selection function (RSF) analyses generated from global positioning system (GPS) collar data, we identified influential habitat covariates and compared these covariates and RSF values to existing habitat models. Additionally, we compared the extent to which our models were congruent with existing resource selection probability functions, were congruent with aerial survey data, and overlapped existing ungulate winter ranges previously derived from predictive models inside and outside of the study area. Overall, our models noted higher RSF values among GPS data relative to aerial survey data for winter months, while results for summer habitats were comparable. In extending our RSFs outside of the study area and evaluating the overlap with ungulate winter ranges in adjacent areas, values were similar, albeit lower, as is expected given that the models were developed elsewhere. Ultimately, these models, combined with existing methods, improve the accuracy and reliability of identified, important areas of habitat for mountain goats. We recommend that the RSF models generated here be used in conjunction with aerial survey data and existing methods to delineate ungulate winter ranges for mountain goats in similar eco-regions in British Columbia. The models developed here support existing methods that have been used to delineate or validate ungulate winter ranges for mountain goats in British Columbia and help facilitate mitigation measures to support the continued use of important winter habitat and significant landscape features that play a role in ensuring population viability and resilience through time.     

Bias in aerial estimates of the number of nests in White Ibis and Great Egret colonies

ABSTRACT Estimating detection error, as well as the magnitude of other potential survey biases, is essential when sampling efforts play a role in the estimation of population size and management of wildlife populations. We quantified visual biases in aerial surveys of nesting wading birds (Ciconiiformes) in colonies in the Florida Everglades using a negative binomial count regression model to compare numbers of nests in quadrats counted on the ground with numbers estimated from aerial photographs of the same quadrats. The model also allowed the determination of degree of difference between monitoring results based upon such factors as nest density, vegetative cover, and nest turnover rates. Aerial surveys of White Ibis (Eudocimus albus) colonies underestimated the true number of nests found during ground counts by 11.1%, and underestimates were significantly greater (P= 0.047) in a colony with high nest turnover. Error rates did not differ for quadrats that varied in the density of White Ibis nests did not differ, and visual bias did not increase with vegetative complexity (P= 0.73). Estimates of nest density in colonies of Great Egrets (Ardea alba) based on aerial surveys were higher than ground counts for 38% of the quadrats sampled, and mean visual bias was 23.1%. Species misidentification likely contributed to visibility bias for Great Egrets in our study, with some Snowy Egrets almost certainly mistaken for Great Egrets in aerial photos. Biases of the magnitude we observed fro Great Egrets and White Ibises can mask true population trends in long‐term monitoring and, therefore, we recommend that detection probability be explicitly evaluated when conducting aerial surveys of nesting birds.     

Incorporating Estimates of Group Size in Sightability Models for Wildlife

ABSTRACT Sightability models have been used to estimate population size of many wildlife species; however, a limitation of these models is an assumption that groups of animals observed and counted during aerial surveys are enumerated completely. Replacing these unknown counts with maximum observed counts, as is typically done, produces population size estimates that are negatively biased. This bias can be substantial depending on the degree of undercounting occurring. We first investigated a method-of-moments estimator of group sizes. We then defined a population size estimator using the method-of-moments estimator of group sizes in place of maximum counts in the traditional sightability models, thereby correcting for bias associated with undercounting group size. We also provide associated equations for calculating the variance of our estimator. This estimator is an improvement over existing sightability model techniques because it significantly reduces bias, and variance estimates provide near nominal confidence interval coverage. The data needed for this estimator can be easily collected and implemented by wildlife managers with a field crew of only 3 individuals and little additional flight or personnel time beyond the normal requirements for developing sightability models.     

A comparison of methods used to count the elephant population of the Addo Elephant National Park, South Africa

Evaluation of alternative techniques used to estimate elephant population sizes is important in order to assess the accuracy of the results obtained, upon which management decisions may be based. Data from annual helicopter surveys carried out in the Addo Elephant National Park (1978–97) are compared with registration counts obtained from intensive ground surveys in which all elephants within the park are individually known. On average, total population size estimated in aerial surveys is 8.0% lower than registration counts (n₁₉₉₇ = 251), aerial calf (< 1 year) counts are 48.8% lower than registration counts, and aerial carcass counts are 50% below the total number of deaths documented in ground surveys. Registration counts provide more accurate demographic data than aerial surveys, the results of which are shown to vary widely and unpredictably, thus compromising their value. Where population estimates rely exclusively on aerial surveys, replicate counts are recommended in order to generate confidence intervals.     

Aerial survey of waterbirds on wetlands as a measure of river and floodplain health

1. This study highlights the use of waterbird communities as potential measures of river and floodplain health at a landscape scale. 2. The abundance and diversity of a waterbird community (54 species) was measured over 15 trips with four aerial and three ground counts per trip on a 300-ha lake in arid Australia. 3. Aerial survey estimates of individual species were significantly less precise (SE/mean) than ground counts across two (11–100 and > 1000) out of four abundance classes of waterbirds: 0–10, 11–100, 101–1000 and > 1000. Standard error/mean as a percentage decreased with increasing abundance from about 60% for the lowest abundance class to 18% for the largest abundance class. 4. Aerial survey estimates were negatively biased for species in numbers of less than 10 and greater than 5000 but unbiased compared to ground counts for other abundance classes. Aerial surveys underestimated numbers of waterbirds by 50% when there were 40 000 waterbirds. Three ground counts found about seven more waterbird species than four aerial surveys. One ground count took about 150 times longer than two aerial surveys and cost 14 times more. 5. Regression models were derived, comparing aerial survey estimates to ground counts for 31 of 36 species for which there were sufficient data. Aerial survey estimates were unbiased for most of these species (67%), negatively biased for six species and positively biased for one species. Estimates were negatively biased in species that occurred in small numbers or that dived in response to the aircraft. 6. River system health encompasses the state of floodplain wetlands. Waterbirds on an entire wetland or floodplain may be estimated by aerial survey of waterbirds; this is a coarse but effective measure of waterbird abundance. Aerial survey is considerably less costly than ground survey and potentially provides a method for measuring river and floodplain health over long periods of time at the same scale as river management.     

Accounting for residual heterogeneity in double-observer sightability models decreases bias in burro abundance estimates

Feral burros (Equus asinus) and horses (E. ferus caballus) inhabiting public land in the western United States are intended to be managed at population levels established to promote a thriving, natural ecological balance. Double-observer sightability (M_DS) models, which use detection records from multiple observers and sighting covariates, perform well for estimating feral horse abundances, but their effectiveness for use in burro populations is less understood. These M_DS models help minimize detection bias, yet bias can be further reduced with models that account for unmodeled variation, or residual heterogeneity, in detection probability. In populations containing radio-marked individuals, residual heterogeneity can be estimated with M_DS models by including a covariate that corresponds to the marked status of a group (M_H models). Another approach is to use information from detections missed by both observers to account for the characteristics that make groups more or less likely to be detected, or recaptured, by the second observer (M_R models). We used aerial survey data from 3 burro populations (Sinbad Herd Management Area, UT [2016–2018], Lake Pleasant Herd Management Area, AZ [2017], and Fort Irwin National Training Center, CA [2016–2017]) to develop M_DS models applicable for feral burros in the southwestern United States. Our objectives were to quantify precision and bias of standard M_DS surveys for feral burros and to examine which model type for incorporating residual heterogeneity (M_H or M_R) would result in the least-biased estimates of burro populations relative to the minimum number known alive (MNKA) within the Sinbad Herd Management Area. Standard M_DS model estimates achieved a mean coefficient of variation of 0.08, while underestimating MNKA by an average of 27.1%. Accounting for residual heterogeneity through recapture probability in M_R models resulted in estimates closer to MNKA than M_H models (9.5% vs. 16.5% less than MNKA). Our results indicate that M_DS models can achieve precise enough estimates to monitor feral burro populations, but they routinely produce negatively biased estimates. We encourage the use of radio-collars to reduce bias in future burro surveys by accounting for residual heterogeneity through M_R models.     

Visual and acoustic surveys for North Atlantic right whales,Eubalaena glacialis,in Cape Cod Bay,Massachusetts, 2001–2005: Management implications

North Atlantic right whales, Eubalaena glacialis, remain endangered, primarily due to excessive anthropogenic mortality. Current management protocols in US waters are triggered by identifying the presence of at least one right whale in a management area. We assessed whether acoustic detection of right whale contact calls can work as an alternative to visual aerial surveys for establishing their presence. Aerial survey and acoustic monitoring were conducted in Cape Cod Bay, Massachusetts, in 2001–2005 and used to evaluate and compare right whale detections. Over the 58 d with simultaneous aerial and acoustic coverage, aerial surveys saw whales on approximately two-thirds of the days during which acoustic monitoring heard whales. There was no strong relationship between numbers of whales seen during aerial surveys and numbers of contact calls detected on survey days. Results indicate acoustic monitoring is a more reliable mechanism than aerial survey for detecting right whales. Because simple detection is sufficient to trigger current management protocols, continuous, autonomous acoustic monitoring provides information of immediate management utility more reliably than aerial surveillance. Aerial surveys are still required to provide data for estimating population parameters and for visually assessing the frequency and severity of injuries from shipping and fishing and detecting injured and entangled right whales.     

Evaluation of an Aerial Survey to Estimate Abundance of Wintering Ducks in Mississippi

Abstract: Researchers have successfully designed aerial surveys that provided precise estimates of wintering populations of ducks over large physiographic regions, yet few conservation agencies have adopted these probability-based sampling designs for their surveys. We designed and evaluated an aerial survey to estimate abundance of wintering mallards (Anas platyrhynchos), dabbling ducks (tribe Anatini) other than mallards, diving ducks (tribes Aythini, Mergini, and Oxyurini), and total ducks in western Mississippi, USA. We used design-based sampling of fixed width transects to estimate population indices (Ǐ), and we used model-based methods to correct population indices for visibility bias and estimate population abundance (Ň) for 14 surveys during winters 2002–2004. Correcting for bias increased estimates of mallards, other dabbling ducks, and diving ducks by an average of 40–48% among all surveys and contributed 48–61% of the estimated variance of Ň. However, mean-squared errors were consistently less for Ň than Ǐ. Estimates of Ň met our goals for precision (CV ≤ 15%) in 7 of 14 surveys for mallards, 5 surveys for other dabbling ducks, no surveys for diving ducks, and 10 surveys for total ducks. Generally, we estimated more mallards and other dabbling ducks in mid- and late winter (Jan-Feb) than early winter (Nov-Dec) and determined that population indices from the late 1980s were nearly 3 times greater than those from our study. We developed a method to display relative densities of ducks spatially as an additional application of survey data. Our study advanced methods of estimating abundance of wintering waterfowl, and we recommend this design for continued monitoring of wintering ducks in western Mississippi and similar physiographic regions.     

Aerial Surveys for Estimating Wild Turkey Abundance in the Texas Rolling Plains

Abstract: Aerial surveys have been used to estimate abundance of several wild bird species including wild turkeys (Meleagris gallopavo). We used inflatable turkey decoys at 3 study sites in the Texas Rolling Plains to simulate Rio Grande wild turkey (M. g. intermedia) flocks. We evaluated detectability of flocks and errors in counting flock size during fixed-wing (Cessna 172) aerial surveys using logistic and linear regression models. Flock detectability was primarily influenced by flock size and vegetative cover, and errors in counting flock size were primarily influenced by size of flocks. We conducted computer simulations to evaluate the accuracy and precision of fixed-wing aerial surveys and examined power to detect trends in population change. Our simulations suggested abundance estimates from fixed-wing aerial surveys may be underestimated by 10-15% (2.0-4.8% CV). Power analyses suggested that fixed-wing aerial surveys can provide sufficient power (>0.80) to detect a population change of 10-25% over a 4-5-year period. We concluded fixed-wing aerial surveys are feasible on ecoregion scales.     

Bayesian estimation of group sizes for a coastal cetacean using aerial survey data

Many small cetacean, sirenian, and pinniped species aggregate in groups of large or variable size. Accurate estimation of group sizes is essential for estimating the abundance and distribution of these species, but is challenging as individuals are highly mobile and only partially visible. We developed a Bayesian approach for estimating group sizes using wide‐angle aerial photographic or video imagery. Our approach accounts for both availability and perception bias, including a new method (analogous to distance sampling) for estimating perception bias due to small image size in wide‐angle images. We demonstrate our approach through an application to aerial survey data for an endangered population of beluga whales (Delphinapterus leucas) in Cook Inlet, Alaska. Our results strengthen understanding of variation in group size estimates and allow for probabilistic statements about the size of detected groups. Aerial surveys are a standard tool for estimating the abundance and distribution of various marine mammal species. The role of aerial photographic and video data in wildlife assessment is expected to increase substantially with the widespread uptake of unmanned aerial vehicle technology. Key aspects of our approach are relevant to group size estimation for a broad range of marine mammal, seabird, other waterfowl, and terrestrial ungulate species.     

Development of a Sightability Model for Low-Density Elk Populations in Ontario,Canada

ABSTRACT The status of recolonizing elk (Cervus elaphus) populations in Ontario, Canada, is unclear and there is a need for effective population survey methods that can be applied locally. We sought to develop a sightability model that could account for both low densities of elk and dense forest cover in elk-release areas in Ontario. We corrected winter aerial survey counts for sightability based on radiocollared animals known to be within observable distance of the aircraft. The multivariate model with the highest Akaike's Information Criterion corrected for sample size weight (w_i = 0.427) revealed that elk group size, elk activity, dominant tree type, percent canopy cover, and percent conifer cover were significant predictors of elk sightability. The group-size effect indicated that odds of sighting an elk increased by 1.353 (95% CI = 0.874-3.689) for every additional elk. Standing elk were 5.033 (95% CI = 0.936-15.541) times more likely to be observed than were resting elk, and those located in conifer cover were 0.013 (95% CI = 0.001-0.278) times less likely to be sighted than elk in deciduous cover. Furthermore, elk located in >50% canopy cover and >50% conifer cover were 0.041 (95% CI = 0.003-0.619) times and 0.484 (95% CI = 0.024-9.721) times less likely to be sighted than elk in more open habitat, respectively. During model validation, observers detected 79% (113/143) of known elk in any given area, and population and sightability model predictions (±90% CI) overlapped with the population estimate, implying that our predictive model was robust. Unsurprisingly, large groups of elk in open habitat increased model precision, which highlights difficulties of counting Ontario elk in their northern range. We conclude that our model provided increased reliability for estimating elk numbers in Ontario compared to existing methods, and that the estimator may be useful in other areas where elk density is low and sightability is poor due to dense forest cover.     

Mark-resight and sightability modeling of a western Washington elk population

The North Cascades (Nooksack) elk (Cervus elaphus) population declined during the 1980s, prompting a closure to state and tribal hunting in 1997 and an effort to restore the herd to former abundance. In 2005, we began a study to assess the size of the elk population, judge the effectiveness of restoration efforts, and develop a practical monitoring strategy. We concurrently evaluated 2 monitoring approaches: sightability correction modeling and mark-resight modeling. We collected data during February–April helicopter surveys and fit logistic regression models to predict the sightability of elk groups based on group and environmental variables. We used an information-theoretic criterion to compare 9 models of varying complexity; the best model predicted sightability of elk groups based on 1) transformed (log₂) group size, 2) forest canopy cover (%), and 3) a categorical activity variable (active vs. bedded). The sightability model indicated relatively steady and modest herd growth during 2006–2011, but estimates were less than minimum-known-alive counts. We also used the logit-normal mixed effects (LNME) mark-resight model to generate estimates of total elk population size and the sizes of the adult female and branch-antlered male subpopulations. We explored 15 LNME models to predict total population size and 12 models to predict subpopulations. Our results indicated individual heterogeneity in resighting probabilities and variation in resighting probabilities across sexes and some years. Model-averaged estimates of total population size increased from 639 (95% CI = 570–706) in spring 2006 to 1,248 (95% CI = 1,094–1,401) in 2011. We estimated the adult female subpopulation increased from 381 (95% CI = 338–424) in spring 2006 to 573 (95% CI = 507–639) by 2011. The branch-antlered male subpopulation estimates increased from 87 (95% CI = 54–119) to 180 (95% CI = 118–241) from spring 2006 to spring 2011. The LNME model estimates were greater than sightability model estimates and minimum-known-alive counts. We concluded that mark-resight performed better and was a viable approach for monitoring this small elk population and possibly others with similar characteristics (i.e., small population and landscape scales), but this approach requires periodic marking of elk; we estimated mark-resight costs would be about 40% greater than sightability model application costs. The utility of sightability-correction modeling was limited by a high proportion of groups with low detectability on our densely forested landscape. © 2012 The Wildlife Society.