Hierarchical multi-scale occupancy estimation for monitoring wildlife populations

Occupancy estimation is an effective analytic framework, but requires repeated surveys of a sample unit to estimate the probability of detection. Detection rates can be estimated from spatially replicated rather than temporally replicated surveys, but this may violate the closure assumption and result in biased estimates of occupancy. We present a new application of a multi-scale occupancy model that permits the simultaneous use of presence–absence data collected at 2 spatial scales and uses a removal design to estimate the probability of detection. Occupancy at the small scale corresponds to local territory occupancy, whereas occupancy at the large scale corresponds to regional occupancy of the sample units. Small-scale occupancy also corresponds to a spatial availability or coverage parameter where a species may be unavailable for sampling at a fraction of the survey stations. We applied the multi-scale occupancy model to a hierarchical sample design for 2 bird species in the Black Hills National Forest: brown creeper (Certhia americana) and lark sparrow (Chondestes grammacus). Our application of the multi-scale occupancy model is particularly well suited for hierarchical sample designs, such as spatially replicated survey stations within sample units that are typical of avian monitoring programs. The model appropriately accounts for the non-independence of the spatially replicated survey stations, addresses the closure assumption for the spatially replicated survey stations, and is useful for decomposing the observation process into detection and availability parameters. This analytic approach is likely to be useful for monitoring at local and regional scales, modeling multi-scale habitat relationships, and estimating population state variables for rare species of conservation concern. © 2011 The Wildlife Society.     

Estimating Detection Probabilities from Sign of Collared Peccary

ABSTRACT Determining presence or absence of collared peccaries (Pecari tajacu) from surveys of sign (tracks and feces) requires information on whether animals in sample units are detected. We estimated detection probabilities of collared peccary from sign surveys using occupancy models. Because it was unlikely that residence status of collared peccary in sampling units remained the same over a survey season, which is a primary assumption of occupancy models, we first determined the time interval for which to pool data. We then examined the influence of rainfall and peccary abundance on detection probabilities. We placed 90 sign stations (25-m-diam circular plots) throughout Chaparral Wildlife Management Area, south Texas, USA. We surveyed plots weekly for the presence or non-presence of collared peccary during 2 11-week sampling seasons in spring and fall 2003. We examined sign data weekly and we pooled the data in intervals from 2 weeks to 5 weeks. Estimates of detection probabilities increased from 1 week to 3 weeks of pooled data and leveled off thereafter. We needed a 3-week time interval to meet the assumption of unchanging residence status. Using sign data pooled in 3-week increments, detection probabilities were influenced by areas that differed in peccary abundance, but they were not influenced by rainfall. Estimates of detection probabilities ranged from 0.43 to 0.77 for 3-week time intervals. Sign surveys and occupancy modeling of data can be used to measure spatial patterns of collared peccary in south Texas as long as multiple 3-week time intervals are sampled.     

Estimating breeding season abundance of golden-cheeked warblers in Texas,USA

Population abundance estimates using predictive models are important for describing habitat use and responses to population-level impacts, evaluating conservation status of a species, and for establishing monitoring programs. The golden-cheeked warbler (Setophaga chrysoparia) is a neotropical migratory bird that was listed as federally endangered in 1990 because of threats related to loss and fragmentation of its woodland habitat. Since listing, abundance estimates for the species have mainly relied on localized population studies on public lands and qualitative-based methods. Our goal was to estimate breeding population size of male warblers using a predictive model based on metrics for patches of woodland habitat throughout the species' breeding range. We first conducted occupancy surveys to determine range-wide distribution. We then conducted standard point-count surveys on a subset of the initial sampling locations to estimate density of males. Mean observed patch-specific density was 0.23 males/ha (95% CI = 0.197–0.252, n = 301). We modeled the relationship between patch-specific density of males and woodland patch characteristics (size and landscape composition) and predicted patch occupancy. The probability of patch occupancy, derived from a model that used patch size and landscape composition as predictor variables while addressing effects of spatial relatedness, best predicted patch-specific density. We predicted patch-specific densities as a function of occupancy probability and estimated abundance of male warblers across 63,616 woodland patches accounting for 1.678 million ha of potential warbler habitat. Using a Monte Carlo simulation, our approach yielded a range-wide male warbler population estimate of 263,339 (95% CI: 223,927–302,620). Our results provide the first abundance estimate using habitat and count data from a sampling design focused on range-wide inference. Managers can use the resulting model as a tool to support conservation planning and guide recovery efforts. © 2012 The Wildlife Society.     

An evaluation of multiple‐pass seining to monitor blackstripe topminnow Fundulus notatus (Rafinesque, 1820) in the Sydenham River (Ontario,Canada)

Multiple‐pass (i.e. removal) sampling and mark‐recapture experiments were undertaken in the Sydenham River (Ontario, Canada) to assess the effectiveness of seining to detect and estimate the local abundance of blackstripe topminnow, Fundulus notatus (Rafinesque, 1820) as well as to compare catch characteristics from closed and open (with and without block nets) sample units. Probability of species detection using three‐pass seining was estimated to be 0.58 in closed units, and 0.51 in open units. To be 95% confident of occupancy status, a minimum of five repeat surveys is required. A decline in catch occurred in only half of the sample units, population size estimates were often imprecise, and attempts to generate mark‐recapture population estimates were unsuccessful. Mean capture probabilities were 0.48 in closed units and 0.65 in open units, when depletion occurred. For blackstripe topminnow and other fishes encountered, there were no significant differences between closed and open units in the frequency of depletion or capture probability. Compared to single‐pass surveys, monitoring programs that employ three seine hauls are more likely to detect the presence of the blackstripe topminnow and any decline in local abundance.     

Can Occupancy–Abundance Models Be Used to Monitor Wolf Abundance?

Estimating the abundance of wild carnivores is of foremost importance for conservation and management. However, given their elusive habits, direct observations of these animals are difficult to obtain, so abundance is more commonly estimated from sign surveys or radio-marked individuals. These methods can be costly and difficult, particularly in large areas with heavy forest cover. As an alternative, recent research has suggested that wolf abundance can be estimated from occupancy–abundance curves derived from “virtual” surveys of simulated wolf track networks. Although potentially more cost-effective, the utility of this approach hinges on its robustness to violations of its assumptions. We assessed the sensitivity of the occupancy–abundance approach to four assumptions: variation in wolf movement rates, changes in pack cohesion, presence of lone wolves, and size of survey units. Our simulations showed that occupancy rates and wolf pack abundances were biased high if track surveys were conducted when wolves made long compared to short movements, wolf packs were moving as multiple hunting units as opposed to a cohesive pack, and lone wolves were moving throughout the surveyed landscape. We also found that larger survey units (400 and 576 km²) were more robust to changes in these factors than smaller survey units (36 and 144 km²). However, occupancy rates derived from large survey units rapidly reached an asymptote at 100% occupancy, suggesting that these large units are inappropriate for areas with moderate to high wolf densities (>15 wolves/1,000 km²). Virtually-derived occupancy–abundance relationships can be a useful method for monitoring wolves and other elusive wildlife if applied within certain constraints, in particular biological knowledge of the surveyed species needs to be incorporated into the design of the occupancy surveys. Further, we suggest that the applicability of this method could be extended by directly incorporating some of its assumptions into the modelling framework.     

Encounter frequencies between GPS-collared wolves (Canis lupus) and moose (Alces alces) in a Scandinavian wolf territory

Over 6,000 GPS fixes from two wolves (Canis lupus) and 30,000 GPS fixes from five moose (Alces alces) in a wolf territory in southern Scandinavia were used to assess the static and dynamic interactions between predator and prey individuals. Our results showed that wolves were closer to some of the moose when inside their home ranges than expected if they had moved independently of each other, and we also found a higher number of close encounters (<500 m) than expected. This suggests that the wolves were actively seeking the individual moose within their territory. Furthermore, the wolves showed a preference for moving on gravel forest roads, which may be used as convenient travel routes when patrolling the territory and seeking areas where the moose are. However, due to the particularly large size of the wolf territory combined with relatively high moose densities, the wolves generally spent a very small proportion of their time inside the home range of each individual moose, and the frequency of encounters between the wolves and any particular moose was very low. We suggest that the high moose:wolf ratio in this large Scandinavian wolf territory compared to that typically occurring in North America, results in a relatively low encounter frequency and a low predation risk for individual moose, as the predation pressure is spread over a high number of prey individuals.     

Field evaluation of abundance estimates under binomial and multinomial N-mixture models

Assessing and modelling abundance from animal count data is a very common task in ecology and management. Detection is arguably never perfect, but modern hierarchical models can incorporate detection probability and yield abundance estimates that are corrected for imperfect detection. Two variants of these models rely on counts of unmarked individuals, or territories (binomial N-mixture models, or binmix), and on detection histories based on territory-mapping data (multinomial N-mixture models or multimix). However, calibration studies which evaluate these two N-mixture model approaches are needed. We analysed conventional territory-mapping data (three surveys in 2014 and four in 2015) using both binmix and multimix models to estimate abundance for two common avian cavity-nesting forest species (Great Tit Parus major and Eurasian Blue Tit Cyanistes caeruleus). In the same study area, we used two benchmarks: occupancy data from a dense nestbox scheme and total number of detected territories. To investigate variance in estimates due to the territory assignment, three independent ornithologists conducted territory assignments. Nestbox occupancy yields a minimum number of territories, as some natural cavities may have been used, and binmix model estimates were generally higher than this benchmark. Estimates using the multimix model were slightly more precise than binmix model estimates. Depending on the person assigning the territories, the multimix model estimates became quite different, either overestimating or underestimating the ‘truth’. We conclude that N-mixture models estimated abundance reliably, even for our very small sample sizes. Territory-mapping counts depended on territory assignment and this carried over to estimates under the multimix model. This limitation has to be taken into account when abundance estimates are compared between sites or years. Whenever possible, accounting for such hidden heterogeneity in the raw data of bird surveys, via including a ‘territory editor’ factor, is recommended. Distributing the surveys randomly (in time and space) to editors may also alleviate this problem.     

Home range,activity and sociality of a top predator,the dingo: a test of the Resource Dispersion Hypothesis

The idea that groups of individuals may develop around resource patches led to the formulation of the Resource Dispersion Hypothesis (RDH). We tested the predictions of the RDH, within a quasi‐experimental framework, using Australia’s largest terrestrial predator, the dingo Canis lupus dingo. Average dingo group sizes were higher in areas with abundant focal food sources around two mine sites compared with those in more distant areas. This supports the notion that resource richness favours larger group size, consistent with the RDH. Irrespective of season or sex, average home range estimates and daily activity for dingoes around the mine sites were significantly less than for dingoes that lived well away. Assuming that a territory is the defended part of the home range and that territory size is correlated with home range size, consistent with the RDH, the spatial dispersion of food patches therefore determined territory size for dingoes in our study. However, although sample size was small, some dingoes that accessed the supplementary food resource at the mines also spent a large proportion of their time away, suggesting a breakdown of territorial defence around the focal food resource. This, in combination with the large variation in home range size among dingoes that accessed the same supplementary food resource, limits the predictive capabilities of the RDH for this species. We hypothesize that constraints on exclusive home range occupancy will arise if a surfeit of food resources (in excess of requirements for homeostasis) is available in a small area, and that this will have further effects on access to mates and social structure. We present a conceptual model of facultative territorial defence where focal resources are available to demonstrate our findings.     

Optimising long‐term monitoring projects for species distribution modelling: how atlas data may help

Long‐term biodiversity monitoring data are mainly used to estimate changes in species occupancy or abundance over time, but they may also be incorporated into predictive models to document species distributions in space. Although changes in occupancy or abundance may be estimated from a relatively limited number of sampling units, small sample size may lead to inaccurate spatial models and maps of predicted species distributions. We provide a methodological approach to estimate the minimum sample size needed in monitoring projects to produce accurate species distribution models and maps. The method assumes that monitoring data are not yet available when sampling strategies are to be designed and is based on external distribution data from atlas projects. Atlas data are typically collected in a large number of sampling units during a restricted timeframe and are often similar in nature to the information gathered from long‐term monitoring projects. The large number of sampling units in atlas projects makes it possible to simulate a broad gradient of sample sizes in monitoring data and to examine how the number of sampling units influences the accuracy of the models. We apply the method to several bird species using data from a regional breeding bird atlas. We explore the effect of prevalence, range size and habitat specialization of the species on the sample size needed to generate accurate models. Model accuracy is sensitive to particularly small sample sizes and levels off beyond a sufficiently large number of sampling units that varies among species depending mainly on their prevalence. The integration of spatial modelling techniques into monitoring projects is a cost‐effective approach as it offers the possibility to estimate the dynamics of species distributions in space and over time. We believe our innovative method will help in the sampling design of future monitoring projects aiming to achieve such integration.     

Simulations inform design of regional occupancy‐based monitoring for a sparsely distributed,territorial species

Sparsely distributed species attract conservation concern, but insufficient information on population trends challenges conservation and funding prioritization. Occupancy‐based monitoring is attractive for these species, but appropriate sampling design and inference depend on particulars of the study system. We employed spatially explicit simulations to identify minimum levels of sampling effort for a regional occupancy monitoring study design, using white‐headed woodpeckers (Picoides albolvartus), a sparsely distributed, territorial species threatened by habitat decline and degradation, as a case study. We compared the original design with commonly proposed alternatives with varying targets of inference (i.e., species range, space use, or abundance) and spatial extent of sampling. Sampling effort needed to achieve adequate power to observe a long‐term population trend (≥80% chance to observe a 2% yearly decline over 20 years) with the previously used study design consisted of annually monitoring ≥120 transects using a single‐survey approach or ≥90 transects surveyed twice per year using a repeat‐survey approach. Designs that shifted inference toward finer‐resolution trends in abundance and extended the spatial extent of sampling by shortening transects, employing a single‐survey approach to monitoring, and incorporating a panel design (33% of units surveyed per year) improved power and reduced error in estimating abundance trends. In contrast, efforts to monitor coarse‐scale trends in species range or space use with repeat surveys provided extremely limited statistical power. Synthesis and applications. Sampling resolutions that approximate home range size, spatially extensive sampling, and designs that target inference of abundance trends rather than range dynamics are probably best suited and most feasible for broad‐scale occupancy‐based monitoring of sparsely distributed territorial animal species.     

Linking occupancy surveys with habitat characteristics to estimate abundance and distribution in an endangered cryptic bird

Accurate estimates of the distribution and abundance of endangered species are crucial to determine their status and plan recovery options, but such estimates are often difficult to obtain for species with low detection probabilities or that occur in inaccessible habitats. The Puaiohi (Myadestes palmeri) is a cryptic species endemic to Kaua?i, Hawai‘i, and restricted to high elevation ravines that are largely inaccessible. To improve current population estimates, we developed an approach to model distribution and abundance of Puaiohi across their range by linking occupancy surveys to habitat characteristics, territory density, and landscape attributes. Occupancy per station ranged from 0.17 to 0.82, and was best predicted by the number and vertical extent of cliffs, cliff slope, stream width, and elevation. To link occupancy estimates with abundance, we used territory mapping data to estimate the average number of territories per survey station (0.44 and 0.66 territories per station in low and high occupancy streams, respectively), and the average number of individuals per territory (1.9). We then modeled Puaiohi occupancy as a function of two remote-sensed measures of habitat (stream sinuosity and elevation) to predict occupancy across its entire range. We combined predicted occupancy with estimates of birds per station to produce a global population estimate of 494 (95% CI 414–580) individuals. Our approach is a model for using multiple independent sources of information to accurately track population trends, and we discuss future directions for modeling abundance of this, and other, rare species.     

Integrating sign surveys and telemetry data for estimating brown bear (Ursus arctos) density in the Romanian Carpathians

Accurate population size estimates are important information for sustainable wildlife management. The Romanian Carpathians harbor the largest brown bear (Ursus arctos) population in Europe, yet current management relies on estimates of density that lack statistical oversight and ignore uncertainty deriving from track surveys. In this study, we investigate an alternative approach to estimate brown bear density using sign surveys along transects within a novel integration of occupancy models and home range methods. We performed repeated surveys along 2‐km segments of forest roads during three distinct seasons: spring 2011, fall‐winter 2011, and spring 2012, within three game management units and a Natura 2000 site. We estimated bears abundances along transects using the number of unique tracks observed per survey occasion via N‐mixture hierarchical models, which account for imperfect detection. To obtain brown bear densities, we combined these abundances with the effective sampling area of the transects, that is, estimated as a function of the median (± bootstrapped SE) of the core home range (5.58 ± 1.08 km²) based on telemetry data from 17 bears tracked for 1‐month periods overlapping our surveys windows. Our analyses yielded average brown bear densities (and 95% confidence intervals) for the three seasons of: 11.5 (7.8–15.3), 11.3 (7.4–15.2), and 12.4 (8.6–16.3) individuals/100 km². Across game management units, mean densities ranged between 7.5 and 14.8 individuals/100 km². Our method incorporates multiple sources of uncertainty (e.g., effective sampling area, imperfect detection) to estimate brown bear density, but the inference fundamentally relies on unmarked individuals only. While useful as a temporary approach to monitor brown bears, we urge implementing DNA capture–recapture methods regionally to inform brown bear management and recommend increasing resources for GPS collars to improve estimates of effective sampling area.     

A field test of the distance sampling method using Golden‐cheeked Warblers

ABSTRACT Criteria for delisting Golden‐cheeked Warblers (Dendroica chrysoparia) include protection of sufficient breeding habitat to ensure the continued existence of 1000 to 3000 singing males in each of eight recovery regions for ≥10 consecutive years. Hence, accurate abundance estimation is an integral component in the recovery of this species. I conducted a field test to determine if the distance sampling method provided unbiased abundance estimates for Golden‐cheeked Warblers and develop recommendations to improve the accuracy of estimates by minimizing the effects of violating this method's assumptions. To determine if observers could satisfy the assumptions that birds are detected at the point with certainty and at their initial locations, I compared point‐transect sampling estimates from 2‐, 3‐, 4‐, and 5‐min time intervals to actual abundance determined by intensive territory monitoring. Point‐transect abundance estimates were 15%, 29%, 43%, and 59% greater than actual abundance (N= 156) for the 2‐, 3‐, 4‐, and 5‐min intervals, respectively. Point‐transect sampling produced unbiased estimates of Golden‐cheeked Warbler abundance when counts were limited to 2 min (N= 154–207). Abundance estimates derived from point‐transect sampling were likely greater than actual abundance because observers did not satisfy the assumption that birds were detected at their initial locations due to the frequent movements and large territory sizes of male Golden‐cheeked Warblers. To minimize the effect of movement on abundance estimates, I recommend limiting counts of singing males to 2‐min per point. Counts for other species in similar habitats with similar behavior and movement patterns also should be limited to 2 min when unbiased estimates are important and conducting field tests of the point‐transect distance sampling method is not possible.     

Spatial patch occupancy patterns of the Lower Keys marsh rabbit

Reliable estimates of presence or absence of a species can provide substantial information on management questions related to distribution and habitat use but should incorporate the probability of detection to reduce bias. We surveyed for the endangered Lower Keys marsh rabbit (Sylvilagus palustris hefneri) in habitat patches on 5 Florida Key islands, USA, to estimate occupancy and detection probabilities. We derived detection probabilities using spatial replication of plots and evaluated hypotheses that patch location (coastal or interior) and patch size influence occupancy and detection. Results demonstrate that detection probability, given rabbits were present, was <0.5 and suggest that naïve estimates (i.e., estimates without consideration of imperfect detection) of patch occupancy are negatively biased. We found that patch size and location influenced probability of occupancy but not detection. Our findings will be used by Refuge managers to evaluate population trends of Lower Keys marsh rabbits from historical data and to guide management decisions for species recovery. The sampling and analytical methods we used may be useful for researchers and managers of other endangered lagomorphs and cryptic or fossorial animals occupying diverse habitats. © 2011 The Wildlife Society.     

Effect of sampling scale on the assessment of epiphytic bacterial populations

Bacterial populations on above-ground plant surfaces were estimated at three different biological scales, including leaflet disks, entire leaflets, and whole plants. The influence of sample scale on the estimation of mean bacterial population size per unit and per gram and on the variability among sampling units was quantified at each scale. Populations were highly variable among sampling units at every scale examined, suggesting that there is no optimal scale at which sample variance is reduced. The distribution of population sizes among sample units was sometimes, but not consistently, described by the lognormal. Regardless of the sampling scale, expression of population sizes on a per gram basis may not reduce variance, because population size was not generally a function of sample unit weight within any single sampling scale. In addition, the data show that scaling populations on a per gram basis does not provide a useful means of comparing population estimates from samples taken at different scales. The implications of these results for designing sampling strategies to address specific issues in microbial ecology are discussed. Correspondence to: L.L. Kinkel     

How Reliable are Density Estimates for Diurnal Primates?

Primate population assessments provide the basis for comparative studies and are necessary prerequisites in determining conservation status. The most widely used assessment method is line transect sampling, which generates systematic data quickly and comparatively inexpensively. In contrast, the presumably most reliable method is long-term monitoring of known groups, which is both slow and costly. To assess the reliability of various analytical methods, we compared group and population densities for white-handed gibbons (Hylobates lar carpenteri) and Phayre’s leaf monkeys (Trachypithecus phayrei crepusculus) derived from transect walks with those from long-term group follows at Phu Khieo Wildlife Sanctuary, Thailand. Our assistants and we regularly walked a 4-km transect over 30 mo (480 km total), resulting in 155 gibbon sightings and 125 leaf monkey sightings. We then estimated densities via 1) DISTANCE and 2) the Kelker method based on perpendicular distances (PD) or animal-to-observer distances (AOD). We compared the 3 estimates to values based on known home ranges (95% kernels), accounting for home range overlap, combined with group size data. Analyses of line transect data consistently overestimated group densities for both species, while underestimating group size for leaf monkeys. Quality of results varied according to the group size and spread of each species. However, we found, in accordance with previous studies, that values derived via AOD (or its derivations) matched most closely with population estimates based on home range data.     

Abundance of rare and elusive species: Empirical investigation of closed versus spatially explicit capture–recapture models with lynx as a case study

Effective conservation and management require reliable monitoring methods and estimates of abundance to prioritize human and financial investments. Camera trapping is a non-invasive sampling method allowing the use of capture–recapture (CR) models to estimate abundance while accounting for the difficulty of detecting individuals in the wild. We investigated the relative performance of standard closed CR models and spatially explicit CR models (SECR) that incorporate spatial information in the data. Using simulations, we considered 4 scenarios comparing low versus high detection probability and small versus large populations and contrasted abundance estimates obtained from both approaches. Standard CR and SECR models both provided minimally biased abundance estimates, but precision was improved when using SECR models. The associated confidence intervals also provided better coverage than their non-spatial counterpart. We concluded SECR models exhibit better statistical performance than standard closed CR models and allow for sound management strategies based on density maps of activity centers. To illustrate the comparison, we considered the Eurasian lynx (Lynx lynx) as a case study that provided the first abundance estimates of a local population in France. © 2012 The Wildlife Society.     

Habitat occupancy by riparian muskrats reveals tolerance to urbanization and invasive vegetation

Wildlife communities are being altered by rapid environmental change including habitat loss and fragmentation, urbanization, and spread of invasive species. To predict consequences of these anthropogenic changes to landscapes, it is necessary to identify not only species that are negatively affected, but also species that are unaffected or even thrive. We used occupancy modeling to examine the spatial distribution of muskrats (Ondatra zibethicus) in riparian habitat within an agricultural region of east-central Illinois from 2007 to 2008. We examined whether site occupancy was related to local habitat conditions and anthropogenic landscape alterations including urbanization and dominance of invasive reed canary grass (Phalaris arundinacea). We sampled 90 study sites (200-m stream segments) for occupancy by muskrats based on presence of tracks, scat, and feeding sign. Per-survey detection probability was 0.79 (SE = 0.04) in 2007 and 0.76 (SE = 0.04) in 2008. Detection was related positively to Julian date and negatively to abundance of woody debris and emergent rocks. Site occupancy by muskrats was 0.59 (SE = 0.09) in 2007 and 0.69 (SE = 0.06) in 2008, a year with above-average precipitation. Occupancy was related positively to urban land cover surrounding sites, which could reflect higher baseflows and reduced risk from predation and trapping in urban areas. Occupancy was unrelated to site dominance by invasive reed canary grass, but muskrats occurred more often at larger, deeper streams and those with greater bank heights and less sandy bank soils. Turnover between years was driven by stream size and water availability. Muskrats exhibited tolerance to key aspects of environmental change, and muskrats might even be urban adapters when occupying riparian habitat that remains adequately connected in urbanizing landscapes. © 2011 The Wildlife Society.     

Evaluation of Bear Rub Surveys to Monitor Grizzly Bear Population Trends

Abstract: Wildlife managers need reliable estimates of population size, trend, and distribution to make informed decisions about how to recover at-risk populations, yet obtaining these estimates is costly and often imprecise. The grizzly bear (Ursus arctos) population in northwestern Montana, USA, has been managed for recovery since being listed under the United States Endangered Species Act in 1975, yet no rigorous data were available to evaluate the program's success. We used encounter data from 379 grizzly bears identified through bear rub surveys to parameterize a series of Pradel model simulations in Program MARK to assess the ability of noninvasive genetic sampling to estimate population growth rates. We evaluated model performance in terms of 1) power to detect gender-specific and population-wide declines in population abundance, 2) precision and relative bias of growth rate estimates, and 3) sampling effort required to achieve 80% power to detect a decline within 10 years. Simulations indicated that ecosystem-wide, annual bear rub surveys would exceed 80% power to detect a 3% annual decline within 6 years. Robust-design models with 2 simulated surveys per year provided precise and unbiased annual estimates of trend, abundance, and apparent survival. Designs incorporating one survey per year require less sampling effort but only yield trend and apparent survival estimates. Our results suggest that systematic, annual bear rub surveys may provide a viable complement or alternative to telemetry-based methods for monitoring trends in grizzly bear populations.     

Factors affecting detectability of river otters during sign surveys

Sign surveys are commonly used to study and monitor wildlife species but may be flawed when surveys are conducted only once and cover short distances, which can lead to a lack of accountability for false absences. Multiple observers surveyed for river otter (Lontra canadensis) scat and tracks along stream and reservoir shorelines at 110 randomly selected sites in eastern Kansas from January to April 2008 and 2009 to determine if detection probability differed among substrates, sign types, observers, survey lengths, and near access points. We estimated detection probabilities (p) of river otters using occupancy models in Program PRESENCE. Mean detection probability for a 400-m survey was highest in mud substrates (p = 0.60) and lowest in snow (p = 0.18) and leaf litter substrates (p = 0.27). Scat had a higher detection probability (p = 0.53) than tracks (p = 0.18), and experienced observers had higher detection probabilities (p > 0.71) than novice observers (p < 0.55). Detection probabilities increased almost 3-fold as survey length increased from 200 m to 1,000 m, and otter sign was not concentrated near access points. After accounting for imperfect detection, our estimates of otter site occupancy based on a 400-m survey increased >3-fold, providing further evidence of the potential negative bias that can occur in estimates from sign surveys when imperfect detection is not addressed. Our study identifies areas for improvement in sign survey methodologies and results are applicable for sign surveys commonly used for many species across a range of habitats. © 2010 The Wildlife Society