Experimental Investigation of Observation Error in Anuran Call Surveys

ABSTRACT Occupancy models that account for imperfect detection are often used to monitor anuran and songbird species occurrence. However, presence—absence data arising from auditory detections may be more prone to observation error (e.g., false-positive detections) than are sampling approaches utilizing physical captures or sightings of individuals. We conducted realistic, replicated field experiments using a remote broadcasting system to simulate simple anuran call surveys and to investigate potential factors affecting observation error in these studies. Distance, time, ambient noise, and observer abilities were the most important factors explaining false-negative detections. Distance and observer ability were the best overall predictors of false-positive errors, but ambient noise and competing species also affected error rates for some species. False-positive errors made up 5% of all positive detections, with individual observers exhibiting false-positive rates between 0.5% and 14%. Previous research suggests false-positive errors of these magnitudes would induce substantial positive biases in standard estimators of species occurrence, and we recommend practices to mitigate for false positives when developing occupancy monitoring protocols that rely on auditory detections. These recommendations include additional observer training, limiting the number of target species, and establishing distance and ambient noise thresholds during surveys.     

Occupancy and detectability of Snowy Plovers in western Utah: an application to a low density population

Occupancy modeling can be used to identify habitat characteristics associated with species occurrence. Additionally, occupancy sampling can provide measures of detection probability, increasing confidence in monitoring efforts. Little is known about the distribution and habitat preferences of a small population of Snowy Plovers (Charadrius nivosus) in western Utah. We conducted a study to estimate occupancy and detection probability of Snowy Plovers in western Utah during 2011 and 2012. We made repeated visits to randomly selected survey plots during the breeding period, sampling 84 64‐ha plots in 2011 and 100 64‐ha plots in 2012 and recording the number of adults and habitat characteristics in each plot. We then modeled the relationship between detection, occupancy, and covariates that included distance to water, distance to roads, land cover types, and characteristics of the vegetation. We also included covariates for observer, Julian date, temperature, cloud cover, and wind speed when modeling detection probability. Detection probability was high (0.74, 95% CI = 0.57–0.86) and positively influenced by temperature. Occupancy of 64‐ha plots was low (0.27, 95% CI = 0.18–0.39) and did not vary by year. Occupancy of Snowy Plovers was negatively associated with distance to water (β = ?0.62 ± 0.31, 95% CI = ?1.23 to ?0.01) and percent shrub cover (β = ?0.28 ± 0.02, 95% CI = ?0.58 to ?0.01). Land cover types also influenced plot occupancy. Management actions that conserve shallow water and adjacent habitats or minimize disturbance in these areas are likely to have conservation benefits for Snowy Plovers where water is scarce. Because our detection probabilities were high, investigators involved in future monitoring efforts can achieve reasonable precision with limited revisits to sample plots.     

Ranked species occupancy curves reveal common patterns among diverse metacommunities

Aim Community ecologists often compare assemblages. Alternatively, one may compare species distributions among assemblages for macroecological comparisons of species niche traits and dispersal abilities, which are consistent with metacommunity theory and a regional community concept. The aim of this meta‐analysis is to use regressions of ranked species occupancy curves (RSOCs) among diverse metacommunities and to consider the common patterns observed. Location Diverse data sets from four continents are analysed. Methods Six regression models were translated from traditional occupancy frequency distributions (OFDs) and are distributed among four equation families. Each regression model was fitted to each of 24 data sets and compared using the Akaike information criterion. The analysed data sets encompass a wide range of spatial scales (5 cm–50 km grain, 2–7000 km extent), study scales (11–590 species, 6–5114 sites) and taxa. Observed RSOC regressions were tested for the differences in scale and taxa. Results Three RSOC models within two equation families (exponential and sigmoidal) are required to describe the very different data sets. This result is generally consistent with OFD research, but unlike OFD‐based expectations the simple RSOC patterns are not related to spatial scale or other factors. Species occupancy in diverse metacommunities is efficiently summarized with RSOCs, and multi‐model inference reliably distinguishes among alternative RSOCs. Main conclusions RSOCs are simple to generate and analyse and clearly identified surprisingly similar patterns among very different metacommunities. Species‐specific hypotheses (e.g. niche‐based factors and dispersal abilities) that depend on spatial scale may not translate to diverse metacommunities that sample regional communities. A novel set of three metacommunity succession and disturbance hypotheses potentially explain RSOC patterns and should be tested in subsequent research. RSOCs are an operational approach to the regional community concept and should be useful in macroecology and metacommunity ecology.     

Impact of removal on occupancy patterns of the invasive rainbow lorikeet (Trichoglossus moluccanus) in Tasmania

Introduced species may threaten both biodiversity and agriculture, necessitating an understanding on the factors that influence their distribution, and the efficacy of control measures. In Tasmania, Australia, the introduced rainbow lorikeet (Trichoglossus moluccanus) may be widespread, but data on where they occur and the efficacy of control methods are limited. We used an occupancy modelling framework (presence–absence data) to undertake a survey of two populations of invasive rainbow lorikeets to: (i) understand their distribution across the north and south of the island, and (ii) evaluate the impact of removing birds from the southern population by quantifying occupancy before (2016) and after (2018) removal. The best model explaining occupancy in both populations included a negative relationship with distance from central urban areas. We found no change in site occupancy or detectability in the southern population after removal of 208 birds (potentially comprising >50% of their original population size). This result may be explained by one of three possibilities: (i) the population is larger than previously thought, (ii) the population recovered quickly after reduction, or (iii) removal of birds reduced population density but not area of occupancy. We highlight the importance of urban habitats for the invasive rainbow lorikeet and suggest that alternative methods (e.g. abundance/density-based monitoring) may better detect impacts of removal.     

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.     

A definite measure of occupancy exposures,seeking with non-radiolabeled in vivo 5-HT2A receptor occupancy and in vitro free fractions

Abstract

Unbound drug concentration in the brain would be the true exposure responsible for specific target occupancy. Drug exposures from preclinical are total concentrations of those over/underestimate the clinical dose projection. With the application of mass spectrometry, the current work proposes a definite measure of test drug exposures at serotonin-2A occupancy. The 5-HT_2A occupancy of antagonist in the rat brain has determined with non-radiolabeled tracer MDL-100,907 at an optimized dose (3?µg/kg) and treatment time (30?min). Equilibrium dialysis method determines the in vitro free fraction of the test antagonist in untreated rat brain homogenates and plasma. Drug-free fractions derived the unbound concentration (EC₅₀) in plasma and brain at test doses. The corresponding binding affinities (Ki) correlated with the unbound concentrations. Except for quetiapine, the ED₅₀ values in the dose-occupancy curves of antagonists are close and ranged from 1 to 3?mg/kg. The test drug quetiapine, eplivanserin, and clozapine showed high free fractions in plasma, but for ketanserin and olanzapine, the brain free fraction was higher. The correlation between the unbound EC₅₀ of the antagonists and corresponding Ki values was good (r²=0.828). The improved EC₅₀ accuracy with unbound concentrations was 10–250 folds in plasma and 10–170 folds in the brain. Further, the free fractions (f_{u, plasma}/f_{u, brain}) of test drugs had shown a correlation of ～83% with brain permeability (C_{total brain}/C_{total plasma}), a limiting factor. Thus, correlating the occupancy with unbound exposure and pharmacology would result in an accurate measurement of drug potency and optimizes in selecting the clinical dose.