Heterogeneity In Detection Probability Research Articles

Evaluating genetic capture‐recapture using a chimpanzee population of known size

ABSTRACTGenetic capture‐recapture (CR) estimates of population size have potential for aiding the conservation and management of rare or elusive animals. To date, few studies have explored the performance of genetic CR estimates by implementing them in a population of known size. We evaluated the accuracy and precision of genetic CR estimates by genotyping fecal samples collected opportunistically over the territory of a well‐studied group of approximately 190 previously identified and genotyped eastern chimpanzees (Pan troglodytes schweinfurthii) in Kibale National Park, Uganda. We compared the performance of genetic CR estimates based on 3‐month and 3‐year sampling periods to explore the impact of lengthened sample periods, which are expected to increase accuracy and precision of estimates but also increase the chances of violating population closure assumptions. We compared the effects of using spatial and non‐spatial models and equal or heterogeneous detection probabilities upon estimates. Over the 3‐year period, we detected 54% of the group members and produced population size estimates with more accuracy and narrower confidence intervals than the 3‐month sampling period. The population remained effectively closed over the 3 years and detection heterogeneity was linked to age but not sex. Non‐spatial methods estimated group size more accurately than spatially explicit methods, which had a stronger tendency to underestimate population size. This study suggests that genetic CR may produce accurate and precise population size estimates if substantial effort is allocated to sample collection and genotyping. © 2016 The Wildlife Society.

Intrinsic heterogeneity in detection probability and its effect on N‐mixture models

Summary Estimating the abundance or density of animal populations is often a fundamental task in ecological research and species conservation. N‐mixture models are widely used to estimate the detection probability of individual organisms that thusly leads to more accurate estimates of a species' true abundance. However, individuals likely vary in their probabilities of being detected. During a survey, heterogeneity (variation) in individual detection probability might arise due to conditions of the surveying process; this form of extrinsic heterogeneity can be accounted for by the use of appropriate covariates in the models. In contrast, intrinsic heterogeneity in the detection probabilities of individuals arises when intraspecific variation in behaviour results in individual organisms differing in their latent (inherent) probabilities of being detected. This form of heterogeneity is not tractable by the use of covariates and its possible effects on model performance have not been investigated to date. Using simulated data, we evaluated the performance of Poisson, negative binomial and zero‐inflated Poisson versions of N‐mixture models under the conditions of intrinsic heterogeneity in individual detection probability. Most versions of N‐mixture models performed well in estimating abundance as indicated by relatively low root‐mean‐square‐error values (RMSE < 1). Error distributions indicated a lack of substantial bias and relatively high precision and accuracy when simulated detection probabilities of individuals were high (>0·5) and heterogeneity was random. Otherwise, with structured heterogeneity (particularly positive density dependence) and low detection probabilities (<0·5), model performance was reduced (RMSE > 2). The poorest performing model was the zero‐inflated Poisson version of N‐mixture model applied to data from low survey effort. Our results suggest that N‐mixture models are robust to intrinsic heterogeneity in individual detection probabilities except when the detection probabilities are low. When model‐estimated detection probabilities are low (<0·5), model users should be aware that estimates of abundance could be erroneous if there was non‐random intrinsic heterogeneity in individual detection probabilities during the surveys. Remedying this situation might require redesigning the basic survey protocol such that it does not rely on behavioural traits (as cues to detection) that are intrinsically variable among individuals.

A Full-Capture Hierarchical Bayesian Model of Pollock's Closed Robust Design and Application to Dolphins

We present a Hierarchical Bayesian version of Pollock's Closed Robust Design for studying the survival, temporary-migration, and abundance of marked animals. Through simulations and analyses of a bottlenose dolphin photo-identification dataset, we compare several estimation frameworks, including Maximum Likelihood estimation (ML), model-averaging by AICc, as well as Bayesian and Hierarchical Bayesian (HB) procedures. Our results demonstrate a number of advantages of the Bayesian framework over other popular methods. First, for simple fixed-effect models, we show the near-equivalence of Bayesian and ML point-estimates and confidence/credibility intervals. Second, we demonstrate how there is an inherent correlation among temporary-migration and survival parameter estimates in the PCRD, and while this can lead to serious convergence issues and singularities among MLEs, we show that the Bayesian estimates were more reliable. Third, we demonstrate that a Hierarchical Bayesian model with carefully thought-out hyperpriors, can lead to similar parameter estimates and conclusions as multi-model inference by AICc model-averaging. This latter point is especially interesting for mark-recapture practitioners, for whom model-uncertainty and multi-model inference have become a major preoccupation. Lastly, we extend the Hierarchical Bayesian PCRD to include full-capture histories (i.e., by modelling a recruitment process) and individual-level heterogeneity in detection probabilities, which can have important consequences for the range of phenomena studied by the PCRD, as well as lead to large differences in abundance estimates. For example, we estimate 8%-24% more bottlenose dolphins in the western gulf of Shark Bay than previously estimated by ML and AICc-based model-averaging. Other important extensions are discussed. Our Bayesian PCRD models are written in the BUGS-like JAGS language for easy dissemination and customization by the community of capture-mark-recapture practitioners.

Improved method of estimating the product quality after multiple inspections

A complex product is often inspected more than once in a sequential manner to ensure the product’s quality. Based on the number of defects discovered during each round of inspection process, we can estimate the number of defects still remaining in the product. For each defect, the probability that the defect will be detected during each inspection cycle is usually assumed to be a known ‘constant’. However, in many practical situations, some defects are easily detected, while others are much more difficult to identify. In this paper, we propose a ‘beta-geometric’ inspection model in which the heterogeneity in detection probability is described by a beta distribution. In a numerical study, we show that our more realistic inspection model clearly outperforms traditional estimation methods that are based on the assumption of a constant detection probability.

Spatiotemporal factors affecting detection of black bears during noninvasive capture–recapture surveys

Abstract Accounting for low and heterogeneous detection probabilities in large mammal capture–recapture sampling designs is a persistent challenge. Our objective was to improve understanding of ecological and biological factors driving detection using multiple data sources from an American black bear ( Ursus americanus ) DNA hair trap study in south-central Missouri. We used Global Positioning System telemetry and remote camera data to examine how a bear’s distance to traps, probability of space use, sex-specific behavior, and temporal sampling frame affect detection probability and number of hair samples collected at hair traps. Regression analysis suggested that bear distance to nearest hair trap was the best predictor of detection probability and indicated that detection probability at encounter was 0.15 and declined to < 0.05 at nearest distances > 330 m from hair traps. From remote camera data, number of hair samples increased with number of visits, but the proportion of hair samples from known visits declined 39% from early June to early August. Bears appeared attracted to lured hair traps from close distances and we recommend a hair trap density of 1 trap/2.6 km 2 with spatial coverage that encompasses potentially large male home ranges. We recommend sampling during the late spring and early summer molting period to increase hair deposition rates.

Accounting for Lack of Independence and Partial Overlap of Observation Zones in Line-Transect Mark-Recapture Distance Sampling

Line-transect mark-recapture distance sampling methods can be used to estimate abundance when at least two observers sight and record distances to detected groups of individuals within the survey area. However, a lack of independence between the observer’s detections will cause biased abundance estimates. Studies are also typically designed such that there is complete overlap of the regions searched by the two observers, but that may not always be possible. Here we detail an intuitive approach for line-transect distance sampling applications based upon logistic regression to account for a potential lack of independence by using the detections of one observer as a covariate in the detection function of the second observer. Partial overlap of the observer survey regions can be addressed by constraining detection probability to equal 0 for the respective observer outside of the overlap zone. We show via simulation that the method provides reliable estimates of abundance and is not affected by random unmodeled heterogeneity in detection probability. The method is illustrated by estimating abundance within the covered region of an aerial line-transect survey for New Zealand’s endemic Hector’s dolphin (Cephalorhynchus hectori hectori) conducted in the austral summer of 2013, the motivating application for this work. Supplementary materials accompanying this paper appear on-line.

Evaluating species richness: Biased ecological inference results from spatial heterogeneity in detection probabilities.

Accurate estimates of species richness are necessary to test predictions of ecological theory and evaluate biodiversity for conservation purposes. However, species richness is difficult to measure in the field because some species will almost always be overlooked due to their cryptic nature or the observer's failure to perceive their cues. Common measures of species richness that assume consistent observability across species are inviting because they may require only single counts of species at survey sites. Single-visit estimation methods ignore spatial and temporal variation in species detection probabilities related to survey or site conditions that may confound estimates of species richness. We used simulated and empirical data to evaluate the bias and precision of raw species counts, the limiting forms of jackknife and Chao estimators, and multispecies occupancy models when estimating species richness to evaluate whether the choice of estimator can affect inferences about the relationships between environmental conditions and community size under variable detection processes. Four simulated scenarios with realistic and variable detection processes were considered. Results of simulations indicated that (1) raw species counts were always biased low, (2) single-visit jackknife and Chao estimators were significantly biased regardless of detection process, (3) multispecies occupancy models were more precise and generally less biased than the jackknife and Chao estimators, and (4) spatial heterogeneity resulting from the effects of a site covariate on species detection probabilities had significant impacts on the inferred relationships between species richness and a spatially explicit environmental condition. For a real data set of bird observations in northwestern Alaska, USA, the four estimation methods produced different estimates of local species richness, which severely affected inferences about the effects of shrubs on local avian richness. Overall, our results indicate that neglecting the effects of site covariates on species detection probabilities may lead to significant bias in estimation of species richness, as well as the inferred relationships between community size and environmental covariates.

Response of non‐grassland avian guilds to adjacent herbaceous field buffers: testing the configuration of targeted conservation practices in agricultural landscapes

Summary A substantial part of the world's land base is dominated by agriculture, and forest habitat often consists of discrete patches of forest and linear woody corridors. These natural components provide habitat for some forest birds, but make conservation of these species difficult. In‐field practices applied outside forest patches, such as specific juxtapositions of herbaceous field buffers adjacent to forest habitat, could increase avian diversity contributions of existing forest without creation of additional forest habitat. Our prediction was that herbaceous field buffers would increase bird richness in adjacent forest, and we evaluated four potential mechanisms. We used bird count data from a conservation buffer monitoring programme and hierarchical community models to estimate species richness of forest generalist, forest interior and shrubland (edge) species near forest edges with and without adjacent herbaceous field buffers. We accounted for heterogeneity in detection probabilities and forest cover in surrounding landscapes when estimating species‐ and guild‐level responses. Consistent with the drift fence hypothesis, adjacent herbaceous buffers were associated with a modest increase in richness of forest interior birds in woody corridors, but not in forest blocks. Consistent with resource complementation, adjacent herbaceous buffers were associated with modest increases in richness of shrubland (edge) birds in both woody corridors and forest blocks. Across all species and guilds, adjacent buffers were generally associated with greater abundance (e.g. 28 of 39 species), but these increases were also relatively small and highly variable (i.e. overlapping 95% credible intervals). Corroborating existing research, effects of adjacent herbaceous buffers are likely real, but neither pervasive nor strong. Synthesis and applications. Conservation practices targeted to grassland species often produce measurable conservation benefits for target species. However, biodiversity return for investment would be further increased if targeted practices could be deployed in ways that also produce benefits for non‐target species in adjacent habitats. Our results suggest that additional benefits for non‐target species using adjacent forest habitat are likely to be modest, so conservation planning should focus on species targeted by the conservation practices and avoidance of potential negative impacts on those species when positive benefits to adjacent habitat are weak or lacking.

Distribution and habitat use by pine marten Martes martes in a riparian corridor crossing intensively cultivated lowlands

AbstractThe location of pine marten records in northern Italy suggests that main rivers may play the role of natural corridors favouring this species’ colonisation of cultivated lowlands. We assessed the distribution and habitat use by the pine marten on a 35 km long stretch of the River Ticino. Surveys were carried out between October 2011 and June 2012 along linear transects in a 2 × 2 km grid. Using the variation in marking intensity as an indicator of habitat use, habitat selection was assessed at two landscape levels—at transect‐scale by the χ2 test with Bonferroni's confidence intervals for the proportion of use, and at grid‐scale by multiple linear regression. By a polymerase chain reaction–restriction fragment length polymorphism method, 91 faecal samples were assigned to the pine marten. Faeces were mainly located in wooded areas, while fields were avoided. At the grid‐scale of analysis, marking intensity was positively related to the mean area of wooded patches and negatively to their mean perimeter‐area ratio. This suggests that pine marten relative abundance may partially depend on the degree of fragmentation and structure of residual woods. The survey protocol allowed to assess the probability of detection. Occupancy models outlined that heterogeneity in detection probability may arise as a result of variation in marking intensity, i.e. the number of marking individuals. Our results suggest that the availability of both woodland corridors and wood patches are major factors shaping pine marten distribution in intensively cultivated plains and that non‐invasive genetic surveys are a cost‐effective method for future studies at a broader scale.

Trap Configuration and Spacing Influences Parameter Estimates in Spatial Capture-Recapture Models

An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.

Combining data from 43 standardized surveys to estimate densities of female American black bears by spatially explicit capture–recapture

AbstractSpatially explicit capture–recapture (SECR) models are gaining popularity for estimating densities of mammalian carnivores. They use spatially explicit encounter histories of individual animals to estimate a detection probability function described by two parameters: magnitude (g0), and spatial scale (σ). Carnivores exhibit heterogeneous detection probabilities and home range sizes, and exist at low densities, so g0 and σ likely vary, but field surveys often yield inadequate data to detect and model the variation. We sampled American black bears (Ursus americanus) on 43 study areas in ON, Canada, 2006–2009. We detected 713 animals 1810 times; however, study area‐specific samples were sometimes small (6–34 individuals detected 13–93 times). We compared AICc values from SECR models fit to the complete data set to evaluate support for various forms of variation in g0 and σ, and to identify a parsimonious model for aggregating data among study areas to estimate detection parameters more precisely. Models that aggregated data within broad habitat classes and years were supported over those with study area‐specific g0 and σ (ΔAICc ≥ 30), and precision was enhanced. Several other forms of variation in g0 and σ, including individual heterogeneity, were also supported and affected density estimates. If study design cannot eliminate detection heterogeneity, it should ensure that samples are sufficient to detect and model it. Where this is not feasible, combing sparse data across multiple surveys could allow for improved inference.

Improving distance sampling: accounting for covariates and non‐independency between sampled sites

Summary There is currently much interest in replacing the design‐based component of conventional distance sampling methods by a modelling approach where animal densities are related to environmental covariates. These models allow identification of relationships between density and covariates. One of the uses of such models is to assess the effects of some intervention on numbers for species of conservation interest in designed distance sampling experiments. In this context, we use an integrated likelihood approach for modelling sample counts, adopting a Poisson model and allowing imperfect detectability on the sample plots. We use the method of Royle, Dawson & Bates (2004, Ecology, 85, 1591), extended to model heterogeneity in detection probabilities using either multiple covariate distance sampling methods or stratification. Moreover, we include a random effect for site in the plot abundance model to accommodate correlation in repeat counts at a single site. These developments were motivated by a large‐scale experimental study to assess the effects of establishing conservation buffers along field margins on indigo buntings in several US states. We analyse the data using an integrated likelihood and include model selection for both the Poisson rate of counts and detection probabilities. We assess model performance by comparing our results with those using a two‐stage approach (Buckland et al. 2009, Journal of Agricultural, Biological, and Environmental Statistics, 14, 432) which we extended by including a random effect for site in the plot abundance model. The two methods led to the same selected models and gave similar results for parameters, which revealed significant beneficial effects of buffers on indigo bunting densities. Densities on buffered fields were on average 35% higher than on unbuffered fields. Using a detection function stratified by state captured some of the heterogeneity in detection probabilities between the nine states included in the analyses. Synthesis and applications. We develop and compare two methods for analysing data from large‐scale distance sampling experiments with imbalanced repeat measures. By including a random site effect in the plot abundance model, we relax the assumption of independent sample counts which is generally made for distance sampling methods, and we allow inference to be drawn for the wider region that the sites represent.

Hierarchical distance‐sampling models to estimate population size and habitat‐specific abundance of an island endemic

Population size and habitat-specific abundance estimates are essential for conservation management. A major impediment to obtaining such estimates is that few statistical models are able to simultaneously account for both spatial variation in abundance and heterogeneity in detection probability, and still be amenable to large-scale applications. The hierarchical distance-sampling model of J. A. Royle, D. K. Dawson, and S. Bates provides a practical solution. Here, we extend this model to estimate habitat-specific abundance and rangewide population size of a bird species of management concern, the Island Scrub-Jay (Aphelocoma insularis), which occurs solely on Santa Cruz Island, California, USA. We surveyed 307 randomly selected, 300 m diameter, point locations throughout the 250-km2 island during October 2008 and April 2009. Population size was estimated to be 2267 (95% CI 1613-3007) and 1705 (1212-2369) during the fall and spring respectively, considerably lower than a previously published but statistically problematic estimate of 12 500. This large discrepancy emphasizes the importance of proper survey design and analysis for obtaining reliable information for management decisions. Jays were most abundant in low-elevation chaparral habitat; the detection function depended primarily on the percent cover of chaparral and forest within count circles. Vegetation change on the island has been dramatic in recent decades, due to release from herbivory following the eradication of feral sheep (Ovis aries) from the majority of the island in the mid-1980s. We applied best-fit fall and spring models of habitat-specific jay abundance to a vegetation map from 1985, and estimated the population size of A. insularis was 1400-1500 at that time. The 20-30% increase in the jay population suggests that the species has benefited from the recovery of native vegetation since sheep removal. Nevertheless, this jay's tiny range and small population size make it vulnerable to natural disasters and to habitat alteration related to climate change. Our results demonstrate that hierarchical distance-sampling models hold promise for estimating population size and spatial density variation at large scales. Our statistical methods have been incorporated into the R package unmarked to facilitate their use by animal ecologists, and we provide annotated code in the Supplement.

Estimating patterns and drivers of infection prevalence and intensity when detection is imperfect and sampling error occurs

Summary1. Epidemiological studies are crucial for understanding the distribution and dynamics of emerging infectious diseases. To accurately assess infection states in wild populations, researchers need to account for observational uncertainty. We focus on two sources of uncertainty when estimating epidemiological parameters: nondetection of infection in sampled individuals and sampling error when quantifying infection intensity for infected individuals.2. We developed new analytical methods to simultaneously estimate prevalence and the distribution of infection intensities based on repeated sampling of individuals in the wild. The methods are an extension of those used for occupancy estimation and address both sources of observation error. At the same time, we account for heterogeneity in detection probability that results from individual variation in infection intensity.3. We use two estimation approaches to account for detection. The first is to use the complete likelihood in a hierarchical Bayesian model and fit using Markov chain Monte Carlo sampling. The second is to estimate the detection relationship using a mark–recapture abundance estimator and use those results to calculate weighted estimates for prevalence and mean infection intensities.4. We use data from a field survey of Batrachochytrium dendrobatidis in Illinois amphibians to test these methods. We show that detection probability using quantitative PCR is strongly related to infection intensity, measured in zoospore equivalents. Sites in the study varied greatly in estimated prevalence and to a lesser extent in mean infection intensities of infected individuals. We did not find evidence of a relationship of snout‐vent‐length to infection intensity or prevalence. Naïve estimates of prevalence that do not account for detection were less than estimates for either of our methods, which yielded similar prevalence values for most sites.5. Uncertainty when assessing disease state is a characteristic of most diagnostic tests. The estimators presented here account for this uncertainty and thus can improve accuracy when assessing the relationship of ecological factors to prevalence and infection intensity.

Landscape‐level influences of terrestrial snake occupancy within the southeastern United States

Habitat loss and degradation are thought to be the primary drivers of species extirpations, but for many species we have little information regarding specific habitats that influence occupancy. Snakes are of conservation concern throughout North America, but effective management and conservation are hindered by a lack of basic natural history information and the small number of large-scale studies designed to assess general population trends. To address this information gap, we compiled detection/nondetection data for 13 large terrestrial species from 449 traps located across the southeastern United States, and we characterized the land cover surrounding each trap at multiple spatial scales (250-, 500-, and 1000-m buffers). We used occupancy modeling, while accounting for heterogeneity in detection probability, to identify habitat variables that were influential in determining the presence of a particular species. We evaluated 12 competing models for each species, representing various hypotheses pertaining to important habitat features for terrestrial snakes. Overall, considerable interspecific variation existed in important habitat variables and relevant spatial scales. For example, kingsnakes (Lampropeltis getula) were negatively associated with evergreen forests, whereas Louisiana pinesnake (Pituophis ruthveni) occupancy increased with increasing coverage of this forest type. Some species were positively associated with grassland and scrub/shrub (e.g., Slowinski's cornsnake, Elaphe slowinskii) whereas others, (e.g., copperhead, Agkistrodon contortrix, and eastern diamond-backed rattlesnake, Crotalus adamanteus) were positively associated with forested habitats. Although the species that we studied may persist in varied landscapes other than those we identified as important, our data were collected in relatively undeveloped areas. Thus, our findings may be relevant when generating conservation plans or restoration goals. Maintaining or restoring landscapes that are most consistent with the ancestral habitat preferences of terrestrial snake assemblages will require a diverse habitat matrix over large spatial scales.

Heterogeneous detection probabilities for imperiled Missouri River fishes: implications for large-river monitoring programs

Occupancy modeling was used to determine (1) if detection probabilities (p) for 7 regionally imperiled Missouri River fishes (Scaphirhynchus albus, Scaphirhynchus platorynchus, Cycleptus elongatus, Sander canadensis, Macrhybopsis aestivalis, Macrhybopsis gelida, and Macrhybopsis meeki) differed among gear types (i.e. stationary gill nets, drifted trammel nets, and otter trawls), and (2) how detection probabilities were affected by habitat (i.e. pool, bar, and open water), longitudinal position (five 189 to 367 rkm long segments), sampling year (2003 to 2006), and season (July 1 to October 30 and October 31 to June 30). Adult, large-bodied fishes were best detected with gill nets (p: 0.02-0.74), but most juvenile large-bodied and all small-bodied species were best detected with otter trawls (p: 0.02-0.58). Trammel nets may be a redundant sampling gear for imperiled fishes in the lower Missouri River because most species had greater detection probabilities with gill nets or otter trawls. Detection probabilities varied with river segment for S. platorynchus, C. elongatus, and all small-bodied fishes, suggesting that changes in habitat influ- enced gear efficiency or abundance changes among river segments. Detection probabilities var- ied by habitat for adult S. albus and S. canadensis, year for juvenile S. albus, C. elongatus, and S. canadensis, and season for adult S. albus. Concentrating sampling effort on gears with the great- est detection probabilities may increase species detections to better monitor a population's response to environmental change and the effects of management actions on large-river fishes.

Identifiability and inferential issues in capture-recapture experiments with heterogeneous detection probabilities

We focus on a capture-recapture model in which capture probabilities arise from an unspecified distribution $F$. We show that model parameters are identifiable based on the unconditional likelihood. This is not true with the conditional likelihood. We also clarify that consistency and asymptotic equivalence of maximum likelihood estimators based on conditional and unconditional likelihood do not hold. We show that estimates of the undetected fraction of population based on the unconditional likelihood converge to the so-called estimable sharpest lower bound and we derive a new asymptotic equivalence result. We finally provide theoretical and simulation arguments in favor of the use of the unconditional likelihood rather than the conditional likelihood especially when one is willing to infer on the sharpest lower bound.

Population Size Influences Amphibian Detection Probability: Implications for Biodiversity Monitoring Programs

Monitoring is an integral part of species conservation. Monitoring programs must take imperfect detection of species into account in order to be reliable. Theory suggests that detection probability may be determined by population size but this relationship has not yet been assessed empirically. Population size is particularly important because it may induce heterogeneity in detection probability and thereby cause bias in estimates of biodiversity. We used a site occupancy model to analyse data from a volunteer-based amphibian monitoring program to assess how well different variables explain variation in detection probability. An index to population size best explained detection probabilities for four out of six species (to avoid circular reasoning, we used the count of individuals at a previous site visit as an index to current population size). The relationship between the population index and detection probability was positive. Commonly used weather variables best explained detection probabilities for two out of six species. Estimates of site occupancy probabilities differed depending on whether the population index was or was not used to model detection probability. The relationship between the population index and detectability has implications for the design of monitoring and species conservation. Most importantly, because many small populations are likely to be overlooked, monitoring programs should be designed in such a way that small populations are not overlooked. The results also imply that methods cannot be standardized in such a way that detection probabilities are constant. As we have shown here, one can easily account for variation in population size in the analysis of data from long-term monitoring programs by using counts of individuals from surveys at the same site in previous years. Accounting for variation in population size is important because it can affect the results of long-term monitoring programs and ultimately the conservation of imperiled species.

Avian conservation value of pine plantation forests in northern Vietnam

SummaryDecline of native forest cover is a worldwide concern. Recently, overall forest cover in Vietnam has increased, but most of the increase has been attributed to plantations of non-native trees. The conservation value of these plantations for birds is unknown. We compared avian species richness in pine plantations to that in second-growth and mature native forests in Tam Dao National Park, Vietnam. Bird species were classified into two categories: forest specialists or forest generalists. To account for strong heterogeneity in detection probabilities, the number of species in each category was estimated using the Pledger-Huggins estimator. We estimated total species richness and number of forest specialist species to be highest in mature forest (191; 95% CI = 96, 287, and 88; 95% CI = 47, 129 respectively), lower in second-growth forest (158; 95% CI = 87, 245 and 58; 95% CI = 18, 98 respectively), and lowest in pine plantation (106; 95% CI = 52, 158 and 49; 95% CI = 2, 97 respectively). The estimated number of forest generalist species was similar between mature forest and second-growth forest (103; 95% CI = 17, 189 and 100; 95% CI = 42, 158, respectively) and least in pine plantation (57; 95% CI = 31, 82). The maintenance of native forest types should receive priority for conservation in Vietnam and pine plantations should be managed to provide additional structure in the hope of increasing species richness.

Hair tubes for estimating site occupancy and activity-density of Sorex minutus

Hair tubes are efficient for sampling shrews and potentially important for sampling Sorex minutus, which is poorly sampled with other methods such as live trapping. We tested the effect of aperture size, bait, and position and type of sticky strip in hair tubes on the detection of S. minutus hairs. The optimum S. minutus hair tube design has high specificity for S. minutus (hairs are 12 times more likely to be from S. minutus than from other species), easily constructed and suitable for use in large-scale surveys. Between the end of July and middle of September 2008 we repeatedly surveyed 46 sites twice with a transect of 20 S. minutus hair tubes. Occupancy of S. minutus was higher in hedgerow sites (0.92) than grassland (0.59) and woodland sites (0.42). Relative activity-density was estimated from models allowing abundance-induced heterogeneity in detection probability and was also higher in hedgerow sites (1.75) than grassland (1.23) and woodland sites (0.72). Relative activity-density is a function of abundance and movement and hence should be interpreted with caution but is a valuable parameter to estimate from data on presence or absence of a species at a site. From our data we recommend that surveys are carried before the end of August, in which case three surveys per site at up to 120 sites per habitat (depending on habitat and start date) are necessary to obtain precise estimates of site occupancy. Site occupancy should be quantified as a relevant parameter alongside abundance when conducting indirect surveys of mammals and relative activity-density should also be considered.