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Effective wildlife management relies on rigorous estimates of population parameters, although data for small populations are often sparse, limiting inference. Integrated population models (IPMs) offer a potential solution by formally combining data sets in a unified analysis, thereby improving precision and allowing the estimation of latent parameters. We expected that incorporating open-population distance sampling models into an IPM framework would provide further advantages for assessing population dynamics, particularly for rare species. We present an open-distance IPM combining separate sources of abundance, composition, survival, and harvest data to better understand the dynamics of a small (~200 individuals) muskox (Ovibos moschatus) population in northwestern Alaska, USA. There was a 75% chance the muskox population in our study area was declining (λ < 1.0), primarily because of a −4.3%/year decline in adult females, and estimated survival probabilities were 0.70, 0.87, and 0.89 for yearlings, adult females, and adult males (harvest excluded), respectively. Insufficient numbers of recruits drove the decline in adult females, and harvest likely limited the adult male component of the population, accounting for up to 50% of mortalities. Together, these results suggest more conservative harvest management might be appropriate moving forward. In contrast, the results from a more conventional analysis were largely ambiguous, which would inevitably lead to delays in the application of appropriate management actions. Our work furthers the development of open-population distance sampling models and IPMs and demonstrates an efficient approach for managing small populations when extensive marking of individuals is not possible.

Species conservation and management require reliable information about animal distribution and population size. Better management actions within a species’ range can be achieved by identifying the location and timing of population changes. In the Greater Mahale Ecosystem (GME), western Tanzania, deforestation due to the expansion of human settlements and agriculture, annual burning, and logging are known threats to wildlife. For one of the most charismatic species, the endangered eastern chimpanzee (Pan troglodytes schweinfurthii), approximately 75% of the individuals are distributed outside national park boundaries, requiring monitoring and protection efforts over a vast landscape of various protection statuses. These efforts are especially challenging when we lack data on trends in density and population size. To predict spatio-temporal chimpanzee density and abundance across the GME, we used density surface modeling, fitting a generalized additive model to a 10-year time-series data set of nest counts based on line-transect surveys. The chimpanzee population declined at an annual rate of 2.41%, including declines of 1.72% in riparian forests (from this point forward, forests), 2.05% in miombo woodlands (from this point forward, woodlands) and 3.45% in nonforests. These population declines were accompanied by ecosystem-wide declines in vegetation types of 1.36% and 0.32% per year for forests and woodlands, respectively; we estimated an annual increase of 1.35% for nonforests. Our model predicted the highest chimpanzee density in forests (0.86 chimpanzees/km², 95% confidence intervals (CIs) 0.60–1.23; as of 2020), followed by woodlands (0.19, 95% CI 0.12–0.30) and nonforests (0.18, 95% CI 0.10–1.33). Although forests represent only 6% of the landscape, they support nearly one-quarter of the chimpanzee population (769 chimpanzees, 95% CI 536–1103). Woodlands dominate the landscape (71%) and therefore support more than a half of the chimpanzee population (2294; 95% CI 1420–3707). The remaining quarter of the landscape is represented by nonforests andsupports another quarter of the chimpanzee population (750; 95% CI 408–1381). Given the pressures on the remaining suitable habitat in Tanzania, and the need of chimpanzees to access both forest and woodland vegetation to survive, we urge future management actions to increase resources and expand the efforts to protect critical forest and woodland habitat and promote strategies and policies that more effectively prevent irreversible losses. We suggest that regular monitoring programs implement a systematic random design to effectively inform and allocate conservation actions and facilitate interannual comparisons for trend monitoring, measuring conservation success, and guiding adaptive management.

Polar bears have experienced a rapid loss of sea-ice habitat in the Barents Sea. Monitoring this subpopulation focuses on the effects on polar bear demography. In August 2015, we conducted a survey in the Norwegian Arctic to estimate polar bear numbers and reveal population substructure. DNA profiles from biopsy samples and ear tags identified on photographs revealed that about half of the bears in Svalbard, compared to only 4.5% in the pack ice north of the archipelago, were recognized recaptures. The recaptured bears had originally been marked in Svalbard, mostly in spring. The existence of a local Svalbard stock, and another ecotype of bears using the pack ice in autumn with low likelihood of visiting Svalbard, support separate population size estimation for the two areas. Mainly by aerial survey line transect distance sampling methods, we estimated that 264 (95% CI = 199 - 363) bears were in Svalbard, close to 241 bears estimated for August 2004. The pack ice area had an estimated 709 bears (95% CI = 334 - 1026). The pack ice and the total (Svalbard + pack ice, 973 bears, 95% CI = 665 - 1884) both had higher estimates compared to August 2004 (444 and 685 bears, respectively), but the increase was not significant. There is no evidence that the fast reduction of sea-ice habitat in the area has yet led to a reduction in population size. The carrying capacity is likely reduced significantly, but recovery from earlier depletion up to 1973 may still be ongoing.

Obtaining precise and unbiased estimates of feral burro (Equus asinus) abundance in the western United States is challenging due to their cryptic pelage and the rugged terrain they inhabit. Management agencies employ helicopter-based, simultaneous double-observer sightability surveys (hereafter denoted as DOS) to estimate abundance of burros; but the DOS method routinely produces negatively biased estimates due to residual heterogeneity in detection probability. Consequently, testing alternative methods to improve upon current procedures is warranted. Residual heterogeneity in DOS surveys can be minimized by including radio-collared individuals in the population. Alternatively, if distance measurements are recorded, residual heterogeneity can also be reduced via a mark-recapture distance sampling (MRDS) approach. Aerial infrared (IR) surveys offer a safer alternative than helicopter-based surveys because they can be flown at a higher altitude and require fewer observers in the aircraft. Further, IR surveys using a distance sampling approach have been shown to generate accurate and precise estimates of feral horse (E. caballus) populations. Accordingly, we compared results of surveys using aerial IR distance sampling, the standard DOS survey, a DOS survey incorporating detections of radio-collared individuals, and an MRDS analysis of a feral burro population with a known minimum population size in central Utah, winter 2015–2016 and spring 2016. The minimum number of burros known alive during the winter and spring surveys were 236 and 136, respectively. The average detection probability of IR surveys was P = 0.88 (SE = 0.16) and distance models produced estimates of 127 burros (95% CIs = 99–175) for the winter survey, and 94 burros (CIs = 72–134) for the spring survey. Mean detection probability of the standard DOS surveys was P = 0.78 (SE = 0.09), and model-generated abundance estimates were 155 burros (CIs = 133–227) in winter, and 92 burros (CIs = 79–139) in spring. Incorporating detections of radio-collared individuals in the DOS survey resulted in a decreased detection probability (P = 0.46; SE = 0.06) and increased abundance estimates to 267 (CIs = 169–571) and 155 (CIs = 128–263) for winter and spring, respectively. Mark-recapture distance sampling produced a mean detection probability of P = 0.48 (SE = 0.12) and resulted in estimates of 282 (CIs = 178–385) and 169 (CIs = 73–310) burros in winter and spring, respectively. Our study demonstrated that aerial IR surveys conducted using standard distance sampling can produce precise estimates of burro population sizes; however, estimates were negatively biased relative to the known population size. Small sample size limits generalization of our results, but the IR-based distance approach did not improve upon DOS surveys. Accounting for residual heterogeneity through use of radio-collars and mark-recapture distance sampling eliminated the negative bias from the standard DOS survey but decreased survey precision. Managers will need to decide whether unbiased but less precise abundance estimates are preferable compared to a more precise, but biased, estimate.

If animals are independently detected during surveys, many methods exist for estimating animal abundance despite detection probabilities <1. Common estimators include double‐observer models, distance sampling models and combined double‐observer and distance sampling models (known as mark‐recapture‐distance‐sampling models; MRDS). When animals reside in groups, however, the assumption of independent detection is violated. In this case, the standard approach is to account for imperfect detection of groups, while assuming that individuals within groups are detected perfectly. However, this assumption is often unsupported. We introduce an abundance estimator for grouped animals when detection of groups is imperfect and group size may be under‐counted, but not over‐counted. The estimator combines an MRDS model with an N‐mixture model to account for imperfect detection of individuals. The new MRDS‐Nmix model requires the same data as an MRDS model (independent detection histories, an estimate of distance to transect, and an estimate of group size), plus a second estimate of group size provided by the second observer. We extend the model to situations in which detection of individuals within groups declines with distance. We simulated 12 data sets and used Bayesian methods to compare the performance of the new MRDS‐Nmix model to an MRDS model. Abundance estimates generated by the MRDS‐Nmix model exhibited minimal bias and nominal coverage levels. In contrast, MRDS abundance estimates were biased low and exhibited poor coverage. Many species of conservation interest reside in groups and could benefit from an estimator that better accounts for imperfect detection. Furthermore, the ability to relax the assumption of perfect detection of individuals within detected groups may allow surveyors to re‐allocate resources toward detection of new groups instead of extensive surveys of known groups. We believe the proposed estimator is feasible because the only additional field data required are a second estimate of group size. We introduce an abundance estimator for grouped animals when detection of groups is imperfect and group size may be under‐counted. Using a second observation of group size, this estimator significantly reduces bias and increases coverage, relative to a mark‐recapture‐distance‐sampling model.

1. Private lands contain much of the world's biodiversity. Conservation of private land, especially agricultural land, is urgent yet challenging because of the diverse priorities of landowners. Local effects of farmland conservation programmes have been evaluated thoroughly, but population-level response to these programmes may depend on effects that extend beyond targeted land parcels. We investigated the landscape-scale effects of a grassland conservation initiative, the Conservation Reserve Enhancement Program (CREP), on a socially and economically important game bird, the Northern Bobwhite Colinus virginianus. 2. Barriers to assessing population-level response to conservation include determining the spatial scale at which a species responds to environmental change (the scale of effect) and untangling density-dependent processes. We performed point counts over 6 years at 247 sites with similar local CREP density but varying landscape-scale CREP density. We used an open-population distance sampling model to evaluate population response to landscape-level CREP density and to forecast population densities under differing re-enrolment scenarios. Our model included kernel smoothing techniques to estimate scale of effect and an estimator of the strength of density dependence. 3. Density dependence moderated the effectiveness of the CREP, but overall populations responded positively to increasing landscape-scale CREP density. We estimated that at least 5% of the landscape needs to be in CREP to meet population density goals of 0.25 bobwhite/ha. Conservatively, we recommend 10% of the landscape to be in CREP. Our percent cover recommendations are based on a distance-weighted average of CREP around focal sites. 4. Landscape-scale effects diminished with distance. For example, assuming all else is equal, a CREP field 3,000 m away had 88% less of an effect on local abundance than a field 1,000 m away. Fields farther than 5,000 m away had no effect on local abundance. 5. Synthesis and applications. Our study underscores the importance of a landscapescale approach to farmland conservation. Benefits of these programmes to wildlife can extend beyond the local scale, but their importance to local populations diminishes with distance. Estimating this relationship and incorporating it into a decision framework could help practitioners target land enrolment to meet broadscale population objectives.

Coastal whaling targeting Baird’s beaked whales has a long history, and more than a quarter of a century has passed since the last abundance estimation of this species was conducted for management purposes. Here, we estimated the latest and time series abundances of Baird’s beaked whales in the waters off the Pacific coast of Japan since 2008 using standard line transect analyses. Sighting surveys dedicated to estimating the abundance of Baird’s beaked whales were conducted four times. Additionally, we used the Baird’s beaked whale sighting dataset from a sighting survey targeting baleen whale in 2016. Two types of detection functions with multiple covariates were fitted to sighting data from these surveys. Abundances were estimated using the half-normal model to be 1524 (coefficient of variation, CV = 0.72) in 2008, 1546 (CV = 0.81) in 2009, 1093 (CV = 0.54) in 2015, 1034 (CV = 0.51) in 2016, and 3596 (CV = 0.82) in 2017. Some of these estimates had imperfect coverage, but all estimates sufficiently represented abundances in the main habitat in the study region. Overall, our abundance estimates were smaller than past estimates from the early 1990s, implying that further monitoring of the abundance are needed to manage and conserve populations of Baird’s beaked whales in this region.

Estimating the abundance of wide-ranging wildlife, difficult under any circumstances, is particularly challenging when detection is low and affected by factors that also influence density and distribution. In northeastern Washington, moose (Alces alces) have evidently increased since the 1970s but spend most of their time under coniferous cover that makes detection from the air difficult. We used a Bayesian hierarchical approach to incorporate habitat use (in the form of availability as a function of canopy closure) into a detection model within a mark-recapture distance sampling framework to estimate moose density. Our model of availability used a latent density surface employing habitat use data obtained from 17 adult female moose wearing global positioning system (GPS) collars. Distance sampling data, obtained from helicopter surveys in winters 2014, 2015, and 2016, consisted of double-observer detections of 166 moose groups along 2,241 km of systematically placed line transects within 29 survey blocks selected using a stratified-random design. We estimated moose density over the entire survey area as 0.49/km² (95% credible interval = 0.33–0.67/km²). Extrapolated to the 10,513-km² survey area, we estimated 5,169 moose (95% credible interval = 3,510–7,034). Our methodology allowed us to adjust for availability bias and produce an estimate even where detection was difficult but required many hours of helicopter flights, acceptable weather conditions, and the availability of GPS collared-moose.

Populations of common ravens (Corvus corax; ravens) have increased rapidly within sagebrush (Artemisia spp.) ecosystems between 1960 and 2020. Although ravens are native to North America, their population densities have expanded to levels that negatively influence the population dynamics of other wildlife species of conservation concern, such as greater sage-grouse (Centrocercus urophasianus) and desert tortoises (Gopherus agassizii). For this reason, lethal removal, such as the application of the avicide DRC-1339, has been used to manage raven numbers at local scales and under certain circumstances. Because the relative effectiveness of DRC-1339 in reducing raven populations densities is not thoroughly understood, we completed 2 case studies using a before-after-control-impact experimental design of density estimates generated from point count data within a Bayesian hierarchical distance sampling framework. Specifically, we analyzed >16,000 point count surveys collected during 2009–2019 and split into 2 study designs covering multiple field sites within the Great Basin region. The first experiment evaluated intra-annual changes in density by comparing before and after treatment time periods within a single breeding season for multiple treatment regions compared to 2 control regions. The other experiment focused on inter-annual differences by comparing time periods across years before and after the onset of annual avicide application for a single treatment region compared to multiple control regions. Our models estimated a 100% probability of decline in density relative to control sites for both the intra- and inter-annual model designs. At treatment sites, expected densities of ravens varied but were reduced by 43% (95% CRI: 33–49%) and 54% (95% CRI: 24–71%) according to intra- and inter-annual analyses, respectively, whereas densities increased by 42% (95% CRI: 27–60%) and 15% (95% CRI: -17 to 58%) at control sites. Although population densities were reduced with treatments, trends indicated that sustained effort would likely be needed to maintain densities at acceptable levels within regions of interest. Effectively reducing the adverse effects of raven populations on other native species likely will depend on a variety of targeted management actions such as improving habitat quality for prey species, possibly reducing ravens’ population density, and treating the cause of increased raven abundance to reduce future carrying capacity and prevent rebounds.

Cetacean abundance estimation often relies on distance sampling methods using shipboard visual line-transect surveys, which assumes that all animals on the trackline are detected and that the detection of animals decreases with increasing distance from the trackline. Mark–Recapture Distance Sampling (MRDS) typically employs a secondary visual observation team and may be used to identify the fraction of animals detected on the trackline when it is suspected that animals may have been missed. For species that are difficult to detect using visual observation methods, such as deep-diving species or those with cryptic surfacing behavior, this secondary team may be prone to the same limitations in detection as the primary observation team and alternative modes of detection may improve estimates. Here we examine the potential use of passive acoustic detection as a secondary platform for MRDS of rough-toothed dolphins (Steno bredanensis) during a combined visual and acoustic shipboard line-transect survey. The average trackline detection probability for rough-toothed dolphins was less than one for both the trial configuration (average p0=0.45 for the visual team) and independent observer configuration (average p0=0.37 for the visual, p0=0.77 for the acoustic and p0=0.84 for both teams combined). This study, while limited in scope, strongly suggests that passive acoustic methods may be an effective alternative for estimating p0 for some cetaceans species.