On the importance of estimating detection probabilities from at‐sea surveys of flying seabirds

The primary and accepted method used to estimate seabird densities at sea from ships is the strip transect method, designed to correct for the effect of random directional bird movement relative to that of the ship. However, this method relies on the critical assumption that all of the birds within the survey strip are detected. We used the distance sampling method from line‐transects to estimate detection probability of a number of species of flying seabirds, and to test whether distance from the ship and bird body size affected detectability. Detection probability decreased from 0.987 (SE=0.029) to 0.269 (SE=0.035) with increasing strip half‐width from 100 to 1400 m. Detection probability also varied between size‐groups of species with strip half‐width. For all size‐groups, this probability was close to 1 for strip half‐width of 100 m, but was 0.869 (SE=0.115), 0.725 (SE=0.096) and 0.693 (SE=0.091) for strip half‐width of 300 m, a typical strip width used in seabird surveys, for respectively large, medium and small size flying seabirds. For larger strip half‐width, detection probability was higher for large sized species, intermediate for medium sized species and lower for smaller sized species. For strip half‐width larger than 100 m we suggest that more attention should be paid to testing the assumption of perfect detectability, because abundance estimates may be underestimated when this assumption is violated. Finally, the effect of the speed of travel of flying seabird on the detection probability was estimated in a simulation study, which suggests that detection probability was underestimated with increasing flying speed.     

Using time‐of‐detection to evaluate detectability assumptions in temporally replicated aural count indices: an example with Ring‐necked Pheasants

ABSTRACT The validity of treating counts as indices to abundance is based on the assumption that the expected detection probability, E(p), is constant over time or comparison groups or, more realistically, that variation in p is small relative to variation in population size that investigators seek to detect. Unfortunately, reliable estimates of E(p) and var(p) are lacking for most index methods. As a case study, we applied the time‐of‐detection method to temporally replicated (within season) aural counts of crowing male Ring‐necked Pheasants (Phasianus colchicus) at 18 sites in southern Minnesota in 2007 to evaluate the detectability assumptions. More specifically, we used the time‐of‐detection method to estimate E(p) and var(p), and then used these estimates in a Monte Carlo simulation to evaluate bias‐variance tradeoffs associated with adjusting count indices for imperfect detection. The estimated mean detection probability in our case study was 0.533 (SE = 0.030) and estimated spatial variation in E(p) was 0.081 (95% CI: 0.057–0.126). On average, both adjusted (for) and unadjusted counts of crowing males qualitatively described the simulated relationship between pheasant abundance and grassland abundance, but the bias‐variance tradeoff was smaller for adjusted counts (MSE = 0.003 vs. 0.045, respectively). Our case study supports the general recommendation to use, whenever feasible, formal population‐estimation procedures (e.g., mark‐recapture, distance sampling, double sampling) to account for imperfect detection. However, we caution that interpreting estimates of absolute abundance can be complicated, even if formal estimation methods are used. For example, the time‐of‐detection method was useful for evaluating detectability assumptions in our case study and the method could be used to adjust aural count indices for imperfect detection. Conversely, using the time‐of‐detection method to estimate absolute abundances in our case study was problematic because the biological populations and sampling coverage could not be clearly delineated. These estimation and inference challenges may also be important in other avian surveys that involve mobile species (whose home ranges may overlap several sampling sites), temporally replicated counts, and inexact sampling coverage.     

Detectability in Audio-Visual Surveys of Tropical Rainforest Birds: The Influence of Species,Weather and Habitat Characteristics

Indices of relative abundance do not control for variation in detectability, which can bias density estimates such that ecological processes are difficult to infer. Distance sampling methods can be used to correct for detectability, but in rainforest, where dense vegetation and diverse assemblages complicate sampling, information is lacking about factors affecting their application. Rare species present an additional challenge, as data may be too sparse to fit detection functions. We present analyses of distance sampling data collected for a diverse tropical rainforest bird assemblage across broad elevational and latitudinal gradients in North Queensland, Australia. Using audio and visual detections, we assessed the influence of various factors on Effective Strip Width (ESW), an intuitively useful parameter, since it can be used to calculate an estimate of density from count data. Body size and species exerted the most important influence on ESW, with larger species detectable over greater distances than smaller species. Secondarily, wet weather and high shrub density decreased ESW for most species. ESW for several species also differed between summer and winter, possibly due to seasonal differences in calling behavior. Distance sampling proved logistically intensive in these environments, but large differences in ESW between species confirmed the need to correct for detection probability to obtain accurate density estimates. Our results suggest an evidence-based approach to controlling for factors influencing detectability, and avenues for further work including modeling detectability as a function of species characteristics such as body size and call characteristics. Such models may be useful in developing a calibration for non-distance sampling data and for estimating detectability of rare species.     

Using spotlight observations to predict resource selection and abundance for white-tailed deer

Spotlight surveys for white-tailed deer (Odocoileus virginianus) can yield large presence-only datasets applicable to a variety of resource selection modeling procedures. By understanding how populations distribute according to a given resource for a reference area, density and abundance can be predicted across new areas assuming the relationship between habitat quality (measured by an index of selection) and species distribution are equivalent. Habitat-based density estimators have been applied to wildlife species and are useful for addressing conservation and management concerns. Although achieving reliable population estimates is a primary goal for spotlighting studies, presence-only models have yet to be applied to spotlight data for estimating habitat selection and abundance for deer. From 2012 to 2017, we conducted spring spotlight surveys in each of 99 counties in Iowa, USA, and collected spatial locations for 20,149 groups of deer (n = 71,323 individuals). We used a resource selection function (RSF) based on deer locations to predict the relative probability of use for deer at the population level and to estimate statewide abundance. The number of deer observed statewide increased significantly with increasing RSF value for all years and the mean RSF value along survey transects explained 59% of the variability in county-level deer counts, indicating that a functional response between habitat quality and deer distribution existed at landscape scales. We applied our RSF to a habitat-based density estimator (extrapolation) and zero-inflated Poisson (ZIP) and negative binomial (ZINB) count models to predict statewide abundance from spotlight counts. Population estimates for 2012 were variable, indicating that atypical weather conditions may affect spotlight counts and population estimates in some years. For 2013–2017, we predicted a mean population of 439,129 (95% CI ∼ ± 55,926), 440,360 (∼ ± 43,676), and 465,959 (∼ ± 51,242) deer across years for extrapolation, ZIP, and ZINB models, respectively. Estimates from all models were not significantly different than estimates from an existing deer population accounting model in Iowa for 2013 and 2016, and differed by <76,000 deer for all models from 2013–2017. Extrapolation and ZIP models performed similarly and differed by <2,897 deer across all years, whereas ZINB models showed inconsistencies in model convergence and precision of estimates. Our results indicate that presence-only models are capable of producing reliable and precise estimates of resource selection and abundance for deer at broad landscape scales in Iowa and provide a tool for estimating deer abundance in a spatially explicit manner. © 2019 The Wildlife Society.     

Abundance estimates to inform butterfly management: double-observer versus distance sampling

Abundance estimates are used to establish baselines, set recovery targets, and assess management actions, all of which are essential aspects of evidence-based natural resource management. For many rare butterflies, these estimates do not exist, and conservation decisions rely instead on expert opinion. Using Bartram’s scrub-hairstreak (Strymon acis bartrami, US Endangered) as a case study, we present a novel comparison of two methods that permit the incorporation of detection probabilities into abundance estimates, distance sampling and double-observer surveys. Additionally we provide a framework for establishing a systematic sampling scheme for monitoring very rare butterflies. We surveyed butterflies monthly in 2013, increasing intensity to weekly when butterflies were detected. We conducted 19 complete, island-wide surveys on Big Pine Key in the Florida Keys, detecting a total of 59 Bartram’s scrub-hairstreaks across all surveys. Peak daily abundances were similar as estimated with distance sampling, 156 butterflies (95 % CI 65–247), and double-observer, 169 butterflies (95 % CI 65–269). Selecting a method for estimating abundance of rare species involves evaluating trade-offs between methods. Distance sampling requires at least 40 detections, but only one observer, while double-observer requires only 10 detections, but two observers. Double-observer abundance estimates agreed with distance sampling estimates, which suggests that double-observer is a reasonable alternative method to use for estimating detection probability and abundance for rare species that cannot be surveyed with other, more commonly used methods.     

Modelling occurrence and abundance of species when detection is imperfect

Relationships between species abundance and occupancy are of considerable interest in metapopulation biology and in macroecology. Such relationships may be described concisely using probability models that characterize variation in abundance of a species. However, estimation of the parameters of these models in most ecological problems is impaired by imperfect detection. When organisms are detected imperfectly, observed counts are biased estimates of true abundance, and this induces bias in stated occupancy or occurrence probability. In this paper we consider a class of models that enable estimation of abundance/occupancy relationships from counts of organisms that result from surveys in which detection is imperfect. Under such models, parameter estimation and inference are based on conventional likelihood methods. We provide an application of these models to geographically extensive breeding bird survey data in which alternative models of abundance are considered that include factors that influence variation in abundance and detectability. Using these models, we produce estimates of abundance and occupancy maps that honor important sources of spatial variation in avian abundance and provide clearly interpretable characterizations of abundance and occupancy adjusted for imperfect detection.     

Evaluation of three methods to estimate density and detectability from roadside point counts

Roadside point counts are often used to estimate trends of bird populations. The use of aural counts of birds without adjustment for detection probability, however, can lead to incorrect population trend estimates. We compared precision of estimates of density and detectability of whistling northern bobwhites (Colinus virginianus) using distance sampling, independent double-observer, and removal methods from roadside surveys. Two observers independently recorded each whistling bird heard, distance from the observer, and time of first detection at 362 call-count stops in Ohio. We examined models that included covariates for year and observer effects for each method and distance from observer effects for the double-observer and removal methods using Akaike's Information Criterion (AIC). The best model of detectability from distance sampling included observer and year effects. The best models from the removal and double-observer techniques included observer and distance effects. All 3 methods provided precise estimates of detection probability (CV = 2.4–4.4%) with a range of detectability of 0.44–0.95 for a 6-min survey. Density estimates from double-observer surveys had the lowest coefficient of variation (2005 = 3.2%, 2006 = 1.7%), but the removal method also provided precise estimates of density (2005 CV = 3.4%, 2006 CV = 4.8%), and density estimates from distance sampling were less precise (2005 CV = 9.6%, 2006 CV = 7.9%). Assumptions of distance sampling were violated in our study because probability of detecting bobwhites near the observer was <1 or the roadside survey points were not randomly distributed with respect to the birds. Distances also were not consistently recorded by individual members of observer pairs. Although double-observer surveys provided more precise estimates, we recommend using the removal method to estimate detectability and abundance of bobwhites. The removal method provided precise estimates of density and detection probability and requires half the personnel time as double-observer surveys. Furthermore, the likelihood of meeting model assumptions is higher for the removal survey than with independent double-observers. © 2011 The Wildlife Society.     

Pooling robustness in distance sampling: Avoiding bias when there is unmodelled heterogeneity

The pooling robustness property of distance sampling results in unbiased abundance estimation even when sources of variation in detection probability are not modeled. However, this property cannot be relied upon to produce unbiased subpopulation abundance estimates when using a single pooled detection function that ignores subpopulations. We investigate by simulation the effect of differences in subpopulation detectability upon bias in subpopulation abundance estimates. We contrast subpopulation abundance estimates using a pooled detection function with estimates derived using a detection function model employing a subpopulation covariate. Using point transect survey data from a multispecies songbird study, species-specific abundance estimates are compared using pooled detection functions with and without a small number of adjustment terms, and a detection function with species as a covariate. With simulation, we demonstrate the bias of subpopulation abundance estimates when a pooled detection function is employed. The magnitude of the bias is positively related to the magnitude of disparity between the subpopulation detection functions. However, the abundance estimate for the entire population remains unbiased except when there is extreme heterogeneity in detection functions. Inclusion of a detection function model with a subpopulation covariate essentially removes the bias of the subpopulation abundance estimates. The analysis of the songbird point count surveys shows some bias in species-specific abundance estimates when a pooled detection function is used. Pooling robustness is a unique property of distance sampling, producing unbiased abundance estimates at the level of the study area even in the presence of large differences in detectability between subpopulations. In situations where subpopulation abundance estimates are required for data-poor subpopulations and where the subpopulations can be identified, we recommend the use of subpopulation as a covariate to reduce bias induced in subpopulation abundance estimates.     

Partitioning Detectability Components in Populations Subject to Within-Season Temporary Emigration Using Binomial Mixture Models

Detectability of individual animals is highly variable and nearly always < 1; imperfect detection must be accounted for to reliably estimate population sizes and trends. Hierarchical models can simultaneously estimate abundance and effective detection probability, but there are several different mechanisms that cause variation in detectability. Neglecting temporary emigration can lead to biased population estimates because availability and conditional detection probability are confounded. In this study, we extend previous hierarchical binomial mixture models to account for multiple sources of variation in detectability. The state process of the hierarchical model describes ecological mechanisms that generate spatial and temporal patterns in abundance, while the observation model accounts for the imperfect nature of counting individuals due to temporary emigration and false absences. We illustrate our model’s potential advantages, including the allowance of temporary emigration between sampling periods, with a case study of southern red-backed salamanders Plethodon serratus. We fit our model and a standard binomial mixture model to counts of terrestrial salamanders surveyed at 40 sites during 3–5 surveys each spring and fall 2010–2012. Our models generated similar parameter estimates to standard binomial mixture models. Aspect was the best predictor of salamander abundance in our case study; abundance increased as aspect became more northeasterly. Increased time-since-rainfall strongly decreased salamander surface activity (i.e. availability for sampling), while higher amounts of woody cover objects and rocks increased conditional detection probability (i.e. probability of capture, given an animal is exposed to sampling). By explicitly accounting for both components of detectability, we increased congruence between our statistical modeling and our ecological understanding of the system. We stress the importance of choosing survey locations and protocols that maximize species availability and conditional detection probability to increase population parameter estimate reliability.     

Studying Large Mammals With Imperfect Detection: Status and Habitat Preferences of Wild Cattle and Large Carnivores in Eastern Cambodia

Studying large mammal species in tropical forests is a conservation challenge with species’ behavior and ecology often increasing the probability of non‐detection during surveys. Consequently, knowledge of the distribution, status, and natural history of many large mammal species in Southeast Asia is limited. I developed occupancy models from camera‐trapping data, thereby accounting for imperfect detection at sampling sites, to clarify the status and habitat requirements of four globally threatened or near threatened large mammals (banteng Bos javanicus, gaur Bos gaurus, dhole Cuon alpinus, and leopard Panthera pardus) in Mondulkiri Protected Forest, eastern Cambodia. Camera traps were operational for >3500 trap nights with 202 photographic encounters of the four study species. Model averaged occupancy estimates were between 5 percent (leopard) and 140 percent (gaur) higher than naive estimates (i.e., proportion of camera‐trap sites species recorded from) thus highlighting the importance of accounting for detectability during conservation surveys. I recommend the use of an occupancy framework when using camera‐trap data to study the status, ecology, and habitat preferences of poorly known and elusive species. The results highlight the importance of mixed deciduous and semi‐evergreen forest for wild cattle in eastern Cambodia and I emphasize that these habitats must be considered in conservation planning across the Lower Mekong Dry Forest Ecoregion.     

Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling

Models and data used to describe species–area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species–area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species‐level Poisson processes and estimate patch‐level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early‐successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species–area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density–area relationships and occurrence probability–area relationships can alter the form of species–area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.     

Comparison of Methods for Estimating Bird Abundance and Trends From Historical Count Data

Abstract: The use of bird counts as indices has come under increasing scrutiny because assumptions concerning detection probabilities may not be met, but there also seems to be some resistance to use of model-based approaches to estimating abundance. We used data from the United States Forest Service, Southern Region bird monitoring program to compare several common approaches for estimating annual abundance or indices and population trends from point-count data. We compared indices of abundance estimated as annual means of counts and from a mixed-Poisson model to abundance estimates from a count-removal model with 3 time intervals and a distance model with 3 distance bands. We compared trend estimates calculated from an autoregressive, exponential model fit to annual abundance estimates from the above methods and also by estimating trend directly by treating year as a continuous covariate in the mixed-Poisson model. We produced estimates for 6 forest songbirds based on an average of 621 and 459 points in 2 physiographic areas from 1997 to 2004. There was strong evidence that detection probabilities varied among species and years. Nevertheless, there was good overall agreement across trend estimates from the 5 methods for 9 of 12 comparisons. In 3 of 12 comparisons, however, patterns in detection probabilities potentially confounded interpretation of uncorrected counts. Estimates of detection probabilities differed greatly between removal and distance models, likely because the methods estimated different components of detection probability and the data collection was not optimally designed for either method. Given that detection probabilities often vary among species, years, and observers investigators should address detection probability in their surveys, whether it be by estimation of probability of detection and abundance, estimation of effects of key covariates when modeling count as an index of abundance, or through design-based methods to standardize these effects.     

Species richness estimation and determinants of species detectability in butterfly monitoring programmes

Abstract 1. Species richness is the most widely used biodiversity index, but can be hard to measure. Many species remain undetected, hence raw species counts will often underestimate true species richness. In contrast, capture–recapture methods estimate true species richness and correct for imperfect and varying detectability. 2. Detectability is a crucial quantity that provides the link between a species count and true species richness. For insects, it has hardly ever been estimated, although this is required for the interpretation of species counts. 3. In the Swiss butterfly monitoring programme about 100 transect routes are surveyed seven times a year using a highly standardised protocol. In July 2003, control observers made two additional surveys on 38 transects. Data from these 38 quadrats were analysed to see whether currently available capture–recapture models can provide quadrat‐specific estimates of species richness, and to estimate species detectability in relation to transect, observer, survey, region, and abundance. 4. Species richness over the entire season cannot be estimated using current capture–recapture methods. The species pool was open, preventing use of closed population models, and detectability varied by species, preventing use of current open population models. Assuming a closed species pool during two mid‐season (July) surveys, a Jackknife capture–recapture method was used that accounts for heterogeneity to estimate mean detectability and species richness. 5. In every case, more species were present than were counted. Mean species detectability was 0.61 (SE 0.01) with significant differences between observers (range 0.37–0.83). Species‐specific detection at time t+ 1 was then modelled for those species seen at t for three mid‐season surveys. Detectability averaged 0.50 (range 0.17–0.81) for individual species and 0.65, 0.44, and 0.42 for surveys. Abundant species were detected more easily, although this relationship explained only 5% of variation in species detectability. 6. These are important, although not entirely unexpected, results for species richness estimation of short‐lived animals. Raw counts of species may be misleading species richness indicators unless many surveys are conducted. Monitoring programmes should be calibrated, i.e. the assumption of constant detectability over dimensions of interest needs to be tested. The development of capture–recapture or similar models that can cope with both open populations and heterogeneous species detectability to estimate species richness should be a research priority.     

Does accounting for imperfect detection improve species distribution models?

Models of species distributions are increasingly being used to address a variety of problems in conservation biology. In many applications, perfect or constant detectability of species, given presence, is assumed. While this problem has been acknowledged and addressed through the development of occupancy models, we still know little regarding whether addressing the potential for imperfect detection improves the predictive performance of species distribution models in nature. Here, we contrast logistic regression models of species occurrence that do not correct for detectability to hierarchical occupancy models that explicitly estimate and adjust for detectability, and maximum entropy models that attempt to circumvent the detectability problem by using data from known presence locations only. We use a large‐scale, long‐term monitoring database across western Montana and northern Idaho to contrast these models for nine landbird species that cover a broad spectrum in detectability. Overall, occupancy models were similar to or better than other approaches in terms of predictive accuracy, as measured by the Area Under the ROC Curve (AUC) and Kappa, with maximum entropy tending to provide the lowest predictive accuracy. Models varied in the types of errors associated with predictions, such that some model approaches may be preferred over others in certain situations. As expected, predictive performance varied across a gradient in species detectability, with logistic regression providing lower relative performance for less detectable species and Maxent providing lower performance for highly detectable species. We conclude by discussing the advantages and limitations to each approach for developing large‐scale species distribution models.     

Effort d’échantillonnage et atlas floristiques – exhaustivité des mailles et caractérisation des lacunes dans la connaissance

Floristic atlases have an important input to flora conservation planning even though their data quality varied greatly across countries. This study aimed to assess survey completeness of cells of floristic atlases. Then, a surveying guide is designed to overcome as efficiently as possible sampling biases. A review and analyses on a wide dataset were carried out to select an estimator of the true species richness of surveyed cells. The Jackknife 1, a non-parametric estimator, appeared as the best compromise for regional floristic atlases. The number of records in each cell was used as an estimator of sampling effort. The ratio between the observed species richness and the estimated species richness measures the completeness of inventories in each surveyed cell. Eighteen variables were selected to describe current inventories and design new surveys. These variables highlight locations, periods and species to be given priority in future studies.     

Spatially explicit estimation of occupancy,detection probability and survey effort needed to inform conservation planning

Aim It is increasingly recognized the importance of accounting for imperfect detection in species distribution modelling and conservation planning. However, the integration of detectability into a spatially explicit frame has received little attention. We aim (1) to show how to develop distribution maps of both detection probability and survey effort required to reliably determine a species presence/absence and (2) to increase awareness of the spatial variation of detection error inherent in studies of species occurrence. Location North‐western Spain. Methods  We registered the presence/absence of the endangered Egyptian vulture (Neophron percnopterus) in 213 surveys performed in 40 of 104 territories once known to be occupied. We model simultaneously both detection probability and occurrence, using site occupancy modelling. With the resulting regression equations, we developed distribution maps of both detection probability and required sampling effort throughout the area. Results Of the studied territories, 72.5% were detected as occupied, but after accounting for imperfect detection, the proportion of sites truly occupied was 79%. Detectability decreased in territories with higher topographical irregularity and increased with both the time of day of the survey and the progress of the season. Spatial distribution of detectability showed a mainly north–south gradient following the distribution of slope in the area. The likelihood of occupancy increased with rockier, less forested surface and less topographical irregularity within the territory. A minimum of five surveys, on average, are needed to assess, with 95% probability, the occupancy status of a site, ranging from ≤ 3 to > 24 visits/territory depending on survey‐ and site‐specific features. Main conclusions Accounting for detectability and its sources of variation allows us to elaborate distribution maps of detectability‐based survey effort. These maps are useful tools to reliably assess (e.g. with 95% probability) occupancy status throughout a landscape and provide guidance for species conservation planning.     

Modelling broad‐scale wolverine occupancy in a remote boreal region using multi‐year aerial survey data

Aim

We used data from aerial surveys of wolverine tracks collected in seven winters over a 10‐year period (2003–2012) within a 574,287 km² study area to evaluate the broad‐scale pattern of wolverine occurrence across a remote northern boreal forest region, identifying areas of high and low occupancy.

Location

Northern Ontario, Canada.

Taxon

Wolverine (Gulo gulo Linnaeus, 1758).

Methods

We collected wolverine tracks and observations in 100‐km² hexagonal survey units, making a total of 6,664 visits to 3,039 units, visiting each 1–9 times. We used hierarchical Bayesian occupancy modelling to model wolverine occurrence, and included covariates with the potential to affect detection and/or occupancy probability of wolverines.

Results

we detected wolverines on 946 visits, 14.2% of total visits. Probability of detecting a wolverine varied among years and between the two ecozones in the study area. Wolverine occupancy was negatively related to two important covariates, the geographical coordinate Easting and thawing degree‐days. A site occupancy probability map indicated that wolverine occupancy probabilities were highest, and standard error lowest, in the western and northern portions of the study area.

Main conclusions

The occupancy framework enabled us to use observation data from tracks of this elusive, wide‐ranging carnivore over a vast, remote area while explicitly considering detectability and spatial autocorrelation, yielding a map of probable wolverine distribution in northern Ontario that would not be possible using other methods of detection across a large region. With resource development pressures increasing in this globally significant region in the face of a changing climate, it is important to monitor changes in distribution of species like wolverines that have low population growth rates, large spatial requirements and sensitivity to human disturbance. This study demonstrates a relatively cost‐effective and non‐invasive alternative to monitoring based on wolverine harvest records, which have not been available since 2009 in Ontario due to changes in the provincial regulatory regime for this threatened species.     

Landscape-scale distribution and density of raptor populations wintering in anthropogenic-dominated desert landscapes

Anthropogenic development has great potential to affect fragile desert environments. Large-scale development of renewable energy infrastructure is planned for many desert ecosystems. Development plans should account for anthropogenic effects to distributions and abundance of rare or sensitive wildlife; however, baseline data on abundance and distribution of such wildlife are often lacking. We surveyed for predatory birds in the Sonoran and Mojave Deserts of southern California, USA, in an area designated for protection under the “Desert Renewable Energy Conservation Plan”, to determine how these birds are distributed across the landscape and how this distribution is affected by existing development. We developed species-specific models of resight probability to adjust estimates of abundance and density of each individual common species. Second, we developed combined-species models of resight probability for common and rare species so that we could make use of sparse data on the latter. We determined that many common species, such as red-tailed hawks, loggerhead shrikes, and especially common ravens, are associated with human development and likely subsidized by human activity. Species-specific and combined-species models of resight probability performed similarly, although the former model type provided higher quality information. Comparing abundance estimates with past surveys in the Mojave Desert suggests numbers of predatory birds associated with human development have increased while other sensitive species not associated with development have decreased. This approach gave us information beyond what we would have collected by focusing either on common or rare species, thus it provides a low-cost framework for others conducting surveys in similar desert environments outside of California.     

Acoustic fish biomass assessment in a deep Tunisian reservoir: effects of season and diel rhythm on survey results

To determine the best acoustic sampling period for obtaining fish biomass estimates of a Mediterranean deep reservoir in Tunisia, day and night surveys were performed in spring (April), summer (September), autumn (December) and winter (March). A Simrad EK60 echosounder, equipped with two 120 kHz split-beam transducers for simultaneous horizontal and vertical beaming, was used to sample the entire water column. Data collected in December were not usable, because fish merged with methane gas bubbles. However, fish abundance varied across the other seasons, with a peak in acoustic biomass during summer nighttime hours associated with high water temperatures. Across seasons, the fish occupied the entire water column and fish schools were rarely observed. The preferential timeframe (i.e. maximum fish detectability and low gas flux) for acoustic sampling was nighttime hours in summer and daytime hours during spring and winter. Our findings highlight the importance of collecting data across seasons and photoperiods when determining an acoustic sampling strategy.     

A multispecies dependent double‐observer model: A new method for estimating multispecies abundance

Conservation of biological communities requires accurate estimates of abundance for multiple species. Recent advances in estimating abundance of multiple species, such as Bayesian multispecies N‐mixture models, account for multiple sources of variation, including detection error. However, false‐positive errors (misidentification or double counts), which are prevalent in multispecies data sets, remain largely unaddressed. The dependent‐double observer (DDO) method is an emerging method that both accounts for detection error and is suggested to reduce the occurrence of false positives because it relies on two observers working collaboratively to identify individuals. To date, the DDO method has not been combined with advantages of multispecies N‐mixture models. Here, we derive an extension of a multispecies N‐mixture model using the DDO survey method to create a multispecies dependent double‐observer abundance model (MDAM). The MDAM uses a hierarchical framework to account for biological and observational processes in a statistically consistent framework while using the accurate observation data from the DDO survey method. We demonstrate that the MDAM accurately estimates abundance of multiple species with simulated and real multispecies data sets. Simulations showed that the model provides both precise and accurate abundance estimates, with average credible interval coverage across 100 repeated simulations of 94.5% for abundance estimates and 92.5% for detection estimates. In addition, 92.2% of abundance estimates had a mean absolute percent error between 0% and 20%, with a mean of 7.7%. We present the MDAM as an important step forward in expanding the applicability of the DDO method to a multispecies setting. Previous implementation of the DDO method suggests the MDAM can be applied to a broad array of biological communities. We suggest that researchers interested in assessing biological communities consider the MDAM as a tool for deriving accurate, multispecies abundance estimates.