Distance models as a tool for modelling detection probability and density of native bumblebees

Effective monitoring of native bee populations requires accurate estimates of population size and relative abundance among habitats. Current bee survey methods, such as netting or pan trapping, may be adequate for a variety of study objectives but are limited by a failure to account for imperfect detection. Biases due to imperfect detection could result in inaccurate abundance estimates or erroneous insights about the response of bees to different environments. To gauge the potential biases of currently employed survey methods, we compared abundance estimates of bumblebees (Bombus spp.) derived from hierarchical distance sampling models (HDS) to bumblebee counts collected from fixed‐area net surveys (“net counts”) and fixed‐width transect counts (“transect counts”) at 47 early‐successional forest patches in Pennsylvania. Our HDS models indicated that detection probabilities of Bombus spp. were imperfect and varied with survey‐ and site‐covariates. Despite being conspicuous, Bombus spp. were not reliably detected beyond 5 m. Habitat associations of Bombus spp. density were similar across methods, but the strength of association with shrub cover differed between HDS and net counts. Additionally, net counts suggested sites with more grass hosted higher Bombus spp. densities whereas HDS suggested that grass cover was associated with higher detection probability but not Bombus spp. density. Density estimates generated from net counts and transect counts were 80%–89% lower than estimates generated from distance sampling. Our findings suggest that distance modelling provides a reliable method to assess Bombus spp. density and habitat associations, while accounting for imperfect detection caused by distance from observer, vegetation structure, and survey covariates. However, detection/non‐detection data collected via point‐counts, line‐transects and distance sampling for Bombus spp. are unlikely to yield species‐specific density estimates unless individuals can be identified by sight, without capture. Our results will be useful for informing the design of monitoring programs for Bombus spp. and other pollinators.     

Use of hidden Markov capture–recapture models to estimate abundance in the presence of uncertainty: Application to the estimation of prevalence of hybrids in animal populations

Estimating the relative abundance (prevalence) of different population segments is a key step in addressing fundamental research questions in ecology, evolution, and conservation. The raw percentage of individuals in the sample (naive prevalence) is generally used for this purpose, but it is likely to be subject to two main sources of bias. First, the detectability of individuals is ignored; second, classification errors may occur due to some inherent limits of the diagnostic methods. We developed a hidden Markov (also known as multievent) capture–recapture model to estimate prevalence in free‐ranging populations accounting for imperfect detectability and uncertainty in individual's classification. We carried out a simulation study to compare naive and model‐based estimates of prevalence and assess the performance of our model under different sampling scenarios. We then illustrate our method with a real‐world case study of estimating the prevalence of wolf (Canis lupus) and dog (Canis lupus familiaris) hybrids in a wolf population in northern Italy. We showed that the prevalence of hybrids could be estimated while accounting for both detectability and classification uncertainty. Model‐based prevalence consistently had better performance than naive prevalence in the presence of differential detectability and assignment probability and was unbiased for sampling scenarios with high detectability. We also showed that ignoring detectability and uncertainty in the wolf case study would lead to underestimating the prevalence of hybrids. Our results underline the importance of a model‐based approach to obtain unbiased estimates of prevalence of different population segments. Our model can be adapted to any taxa, and it can be used to estimate absolute abundance and prevalence in a variety of cases involving imperfect detection and uncertainty in classification of individuals (e.g., sex ratio, proportion of breeders, and prevalence of infected individuals).     

Predicting species distributions from checklist data using site‐occupancy models

Aim (1) To increase awareness of the challenges induced by imperfect detection, which is a fundamental issue in species distribution modelling; (2) to emphasize the value of replicate observations for species distribution modelling; and (3) to show how ‘cheap’ checklist data in faunal/floral databases may be used for the rigorous modelling of distributions by site‐occupancy models. Location Switzerland. Methods We used checklist data collected by volunteers during 1999 and 2000 to analyse the distribution of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly in Switzerland. We used data from repeated visits to 1‐ha pixels to derive ‘detection histories’ and apply site‐occupancy models to estimate the ‘true’ species distribution, i.e. corrected for imperfect detection. We modelled blue hawker distribution as a function of elevation and year and its detection probability of elevation, year and season. Results The best model contained cubic polynomial elevation effects for distribution and quadratic effects of elevation and season for detectability. We compared the site‐occupancy model with a conventional distribution model based on a generalized linear model, which assumes perfect detectability (p = 1). The conventional distribution map looked very different from the distribution map obtained using site‐occupancy models that accounted for the imperfect detection. The conventional model underestimated the species distribution by 60%, and the slope parameters of the occurrence–elevation relationship were also underestimated when assuming p = 1. Elevation was not only an important predictor of blue hawker occurrence, but also of the detection probability, with a bell‐shaped relationship. Furthermore, detectability increased over the season. The average detection probability was estimated at only 0.19 per survey. Main conclusions Conventional species distribution models do not model species distributions per se but rather the apparent distribution, i.e. an unknown proportion of species distributions. That unknown proportion is equivalent to detectability. Imperfect detection in conventional species distribution models yields underestimates of the extent of distributions and covariate effects that are biased towards zero. In addition, patterns in detectability will erroneously be ascribed to species distributions. In contrast, site‐occupancy models applied to replicated detection/non‐detection data offer a powerful framework for making inferences about species distributions corrected for imperfect detection. The use of ‘cheap’ checklist data greatly enhances the scope of applications of this useful class of models.     

Double sampling to assess the accuracy of ground‐based surveys of tree hollows in eucalypt woodlands

Ground‐based surveys of tree hollows often give poor estimates of hollow abundance in forests. Woodlands have shorter trees and a more open structure than forests, which may make hollows easier to detect. Therefore, one would expect ground‐based surveys of tree hollows to be more accurate in woodlands than in forests. We compared hollow counts from ground‐based and climbing surveys (double sampling) for four species of Eucalyptus trees in woodlands of central‐western New South Wales, Australia: E. camaldulensis Dehnh, E. melliodora A. Cunn. ex Schauer, E. microcarpa Maiden and E. populnea F. Muell. ssp. bimbil L.A.S. Johnson & K.D. Hill and E. melliodora A. Cunn. ex Schauer. Overall, 83% of hollow‐bearing trees and 93% of trees without hollows were correctly classified by ground‐based surveys. Mean difference in hollow counts of ground‐based surveys to climbed surveys was 1.7 hollows ± 0.2 SE (all species combined) with 91% of ground‐based hollow counts being within five of the actual number of hollows. The error in ground‐based counts of hollows in E. microcarpa was larger than for the other three species. Errors in all species resulted from both overestimation and underestimation of hollow abundance by ground‐based surveys. A larger error was associated with the detection of hollows located in branches compared with hollows located in the main trunk(s). Total number of hollows in the tree (based on climbing surveys), crown area or maximum trunk diameter were significant predictors of ground‐based survey accuracy. Overall, the accuracy associated with ground surveys was relatively high and generally error rates were lower than those published for forests.     

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.     

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.     

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.     

Detection Probability and Sources of Variation in White-Tailed Deer Spotlight Surveys

Abstract: As a first step in understanding structure and dynamics of white-tailed deer (Odocoileus virginianus) populations, managers require knowledge of population size. Spotlight counts are widely used to index deer abundance; however, detection probabilities using spotlights have not been formally estimated. Using a closed mark—recapture design, we explored the efficiency of spotlights for detecting deer by operating thermal imagers and spotlights simultaneously. Spotlights detected only 50.6% of the deer detected by thermal imagers. Relative to the thermal imager, spotlights failed to detect 44.2% of deer groups (≥1 deer). Detection probabilities for spotlight observers varied between and within observers, ranging from 0.30 (SE = 0.053) to 0.66 (SE = 0.058). Managers commonly assume that although road counts based on convenience sampling designs are imperfect, observers can gather population-trend information from repeated counts along the same survey route. Our results indicate detection rate varied between and within observers and surveyed transects. If detection probabilities are substantially affected by many variables, and if transect selection is not based on appropriate sampling designs, it may be impractical to correct road spotlight counts for detection probabilities to garner unbiased estimates of population size.     

Disentangling mite predator‐prey relationships by multiplex PCR

Gut content analysis using molecular techniques can help elucidate predator‐prey relationships in situations in which other methodologies are not feasible, such as in the case of trophic interactions between minute species such as mites. We designed species‐specific primers for a mite community occurring in Spanish citrus orchards comprising two herbivores, the Tetranychidae Tetranychus urticae and Panonychus citri, and six predatory mites belonging to the Phytoseiidae family; these predatory mites are considered to be these herbivores’ main biological control agents. These primers were successfully multiplexed in a single PCR to test the range of predators feeding on each of the two prey species. We estimated prey DNA detectability success over time (DS₅₀), which depended on the predator‐prey combination and ranged from 0.2 to 18 h. These values were further used to weight prey detection in field samples to disentangle the predatory role played by the most abundant predators (i.e. Euseius stipulatus and Phytoseiulus persimilis). The corrected predation value for E. stipulatus was significantly higher than for P. persimilis. However, because this 1.5‐fold difference was less than that observed regarding their sevenfold difference in abundance, we conclude that P. persimilis is the most effective predator in the system; it preyed on tetranychids almost five times more frequently than E. stipulatus did. The present results demonstrate that molecular tools are appropriate to unravel predator‐prey interactions in tiny species such as mites, which include important agricultural pests and their predators.     

Estimating the abundance of Long‐billed Curlews in Nebraska

ABSTRACT Long‐billed Curlews (Numenius americanus) are shorebirds of high conservation concern in North America. Populations have declined in the last 150 yr primarily due to habitat loss and conversion. We conducted a 2‐yr study to estimate the density and statewide abundance of breeding Long‐billed Curlews in Nebraska during 2008 and 2009. Surveys were conducted during the prenesting period in April when Long‐billed Curlews were likely to be detected. We used a simple random sample of roadside survey routes (N= 39), each consisting of 40 5‐min point‐counts at 800‐m intervals. We modeled detection probability and found that wind speed negatively affected detectability, but found no evidence of either year effects or observer bias. We estimated there were 0.0038 Long‐billed Curlews per hectare (0.38 Long‐billed Curlews per km²) along survey routes and, by extrapolation, estimated there were 23,909 (SE = 1685; 95% CI: 20,810—27,471) Long‐billed Curlews in Nebraska. Our population estimate suggests that ～15 to 22% of the United States population of Long‐billed Curlews is found in Nebraska. Curlews were not evenly distributed within our survey area, with the highest densities in the central Sandhills, an area dominated by grass‐stabilized sand dunes and shallow wetlands, and the lowest densities in predominantly agricultural areas. Because Long‐billed Curlews in Nebraska face many potential threats resulting from land‐use changes, monitoring of the breeding population should be continued. Our survey method was efficient and yielded interpretable results; other states or regions should consider adopting this approach for estimating curlew abundance.     

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.     

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.     

Trap‐shyness subsidence is a threshold function of mark–recapture interval in brown mudfish Neochanna apoda populations

The influence of capture interval on trap shyness, and temperature, rainfall and drought on capture probability (p) in 827 brown mudfish Neochanna apoda was quantified using mark–recapture models. In particular, it was hypothesized that the loss of trapping memory in marked N. apoda would lead to a capture‐interval threshold required to minimize trap shyness. Neochanna apoda trap shyness approximated a threshold response to capture interval, declining rapidly with increasing capture intervals up to 16·5 days, after which p remained constant. Tests for detecting trap‐dependent capture probability in Cormack–Jolly–Seber models failed to detect trap shyness in N. apoda capture histories with capture intervals averaging 16 days. This confirmed the applicability of the 16 day capture‐interval threshold for mark–recapture studies. Instead, N. apoda p was positively influenced by water temperature and rainfall during capture. These results imply that a threshold capture interval is required to minimize the trade‐off between the competing assumptions of population closure and p homogeneity between capture occasions in closed mark–recapture models. Moreover, environmental factors that influence behaviour could potentially confound abundance indices, and consequently abundance trends should be interpreted with caution in the face of long‐term climate change, such as with global warming.     

Optimising the capacity of field trials to detect the effect of genetically modified maize on non‐target organisms through longitudinal sampling

To assess risks of cultivation of genetically modified crops (GMCs) on non‐target arthropods (NTAs), field tests are necessary to verify laboratory results and in situations where exposure pathways are very complex and cannot be reproduced in the laboratory. A central concern in the design of field trials for this purpose is whether the tests are capable of detecting differences in the abundance or activity of NTAs in a treated crop in comparison with a non‐treated comparator plot. The detection capacity of a trial depends on the abundance and variability of the taxon, the values assumed for type I (α) and II (β) errors, and the characteristics of the trial and statistical design. To determine the optimal trial layout and statistical analysis, 20 field trials carried out in Spain from 2000 to 2009 to assess risks of GMCs on NTAs were examined with α and β set at 0.05 and 0.20, respectively. In this article we aim to determine the optimal number of sampling dates during a season, or longitudinal samples, in the design of field trials for assessing effects of GM maize on NTAs, and the ones that contribute most to achieving detectable treatment effects (d_c) less than 50% of the mean of the control. Detection capacities are a function of the number of individual samples taken during the season but a high number of samples is rarely justified because gains of repeated sampling can be relatively low. These gains depend primarily on field tests relative experimental variability in individual samplings (i.e. experimental variability relative to the mean of the control in each sampling date) which in turn depends on the sampling method (visual counts, pitfall traps or yellow sticky traps) and the density (or abundance) of the taxon in question. Taxa showing more density (or abundance) have less relative experimental variability. The smaller the experimental variability, the lower the profit of increasing the number of sampling dates. Sticky traps have a good effect detection capacity and need very few sampling dates, whereas visual counts and pitfall traps have a poorer effect detection capacity and need more individual samples to achieve d_c values lower than 50%. In maize field trials, it is recommended to concentrate sampling efforts in certain growth stages; the optimal ones for achieving an acceptable detection capacity are variable but, in general, samples in the first half of the season render better detection capacity than samples in the second half.     

Improved methods for estimating abundance and related demographic parameters from mark‐resight data

Over the past decade, there has been much methodological development for the estimation of abundance and related demographic parameters using mark‐resight data. Often viewed as a less‐invasive and less‐expensive alternative to conventional mark recapture, mark‐resight methods jointly model marked individual encounters and counts of unmarked individuals, and recent extensions accommodate common challenges associated with imperfect detection. When these challenges include both individual detection heterogeneity and an unknown marked sample size, we demonstrate several deficiencies associated with the most widely used mark‐resight models currently implemented in the popular capture‐recapture freeware Program MARK. We propose a composite likelihood solution based on a zero‐inflated Poisson log‐normal model and find the performance of this new estimator to be superior in terms of bias and confidence interval coverage. Under Pollock's robust design, we also extend the models to accommodate individual‐level random effects across sampling occasions as a potentially more realistic alternative to models that assume independence. As a motivating example, we revisit a previous analysis of mark‐resight data for the New Zealand Robin (Petroica australis) and compare inferences from the proposed estimators. For the all‐too‐common situation where encounter rates are low, individual detection heterogeneity is non‐negligible, and the number of marked individuals is unknown, we recommend practitioners use the zero‐inflated Poisson log‐normal mark‐resight estimator as now implemented in Program MARK.     

Effects of experimental playbacks on availability for detection during point counts

Point counts are the most commonly used technique for surveying passerines during the breeding season. Several methods for estimating probabilities of detection during point count surveys have been developed. These methods have focused primarily on accounting for the influence of environmental factors (e.g., weather and noise) on detectability, however, the probability that birds are available for detection (e.g., sings or moves) during point counts has received less attention. We used sequential point counts to determine the effect of playback of the mobbing calls of Black‐capped Chickadees (Poecile atricapillus) and the flight calls of Red‐tailed Hawks (Buteo jamaicensis) on availability for detection (e.g., singing or moving) during point‐count surveys. We conducted 180 point counts over a 2‐yr period in central – east central Minnesota to evaluate the possible effect of playbacks on observed density, overall species richness, minute of first detection, and distance of first detection. We also used removal models to quantify the magnitude of changes in detectability and direction of response to playbacks for 10 focal species. Playback of the mobbing calls of Black‐capped Chickadees increased observed density and decreased the average distance of detection and time of first detection, whereas playback of the flight calls of a Red‐tailed Hawk resulted in a decrease in observed density and species richness, and an increased time of first detection. Playback treatment was a covariate in all best performing models for the 10 species analyzed, but the magnitude and direction of response to playbacks were species specific. The importance of playback type in detectability models indicates that the calls of heterospecifics can influence species availability for detection. As such, researchers using playback methods should seek to quantify species‐specific responses in detection probability and consider how component detection probabilities could influence survey outcomes.     

Seasonal temperature and precipitation regulate brook trout young‐of‐the‐year abundance and population dynamics

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Sources of Variation in a Two-Step Monitoring Protocol for Species Clustered in Conspicuous Points: Dolichotis patagonum as a Case Study

In species showing distributions attached to particular features of the landscape or conspicuous signs, counts are commonly made by making focal observations where animals concentrate. However, to obtain density estimates for a given area, independent searching for signs and occupancy rates of suitable sites is needed. In both cases, it is important to estimate detection probability and other possible sources of variation to avoid confounding effects on measurements of abundance variation. Our objective was to assess possible bias and sources of variation in a two-step protocol in which random designs were applied to search for signs while continuously recording video cameras were used to perform abundance counts where animals are concentrated, using mara (Dolichotis patagonum) as a case study. The protocol was successfully applied to maras within the Península Valdés protected area, given that the protocol was logistically suitable, allowed warrens to be found, the associated adults to be counted, and the detection probability to be estimated. Variability was documented in both components of the two-step protocol. These sources of variation should be taken into account when applying this protocol. Warren detectability was approximately 80% with little variation. Factors related to false positive detection were more important than imperfect detection. The detectability for individuals was approximately 90% using the entire day of observations. The shortest sampling period with a similar detection capacity than a day was approximately 10 hours, and during this period, the visiting dynamic did not show trends. For individual mara, the detection capacity of the camera was not significantly different from the observer during fieldwork. The presence of the camera did not affect the visiting behavior of adults to the warren. Application of this protocol will allow monitoring of the near-threatened mara providing a minimum local population size and a baseline for measuring long-term trends.     

Design‐ and model‐based recommendations for detecting and quantifying an amphibian pathogen in environmental samples

Accurate pathogen detection is essential for developing management strategies to address emerging infectious diseases, an increasingly prominent threat to wildlife. Sampling for free‐living pathogens outside of their hosts has benefits for inference and study efficiency, but is still uncommon. We used a laboratory experiment to evaluate the influences of pathogen concentration, water type, and qPCR inhibitors on the detection and quantification of Batrachochytrium dendrobatidis (Bd) using water filtration. We compared results pre‐ and post‐inhibitor removal, and assessed inferential differences when single versus multiple samples were collected across space or time. We found that qPCR inhibition influenced both Bd detection and quantification in natural water samples, resulting in biased inferences about Bd occurrence and abundance. Biases in occurrence could be mitigated by collecting multiple samples in space or time, but biases in Bd quantification were persistent. Differences in Bd concentration resulted in variation in detection probability, indicating that occupancy modeling could be used to explore factors influencing heterogeneity in Bd abundance among samples, sites, or over time. Our work will influence the design of studies involving amphibian disease dynamics and studies utilizing environmental DNA (eDNA) to understand species distributions.     

Comparison of a culture‐based and a PCR‐based methods for estimating bacterial abundance on eggshells,with comments on statistical analyses

Field ornithologists have used traditional culture‐based techniques to determine the presence and abundance of microbes on surfaces such as eggshells, but culture‐independent PCR‐based methods have recently been introduced. We compared the traditional culture‐based and the real‐time PCR‐based methods for detecting and quantifying Escherichia coli on the eggshells of Eurasian Magpies (Pica pica). PCR estimates of bacterial abundance were ～10 times higher than culture‐based estimates, and the culture‐based technique failed to detect bacteria at lower densities. When both methods detected bacteria, bacterial densities determined by the two methods were positively correlated, indicating that both methods can be used to study factors affecting bacterial densities. The difference between the two methods is consistent with generally acknowledged higher sensitivity of the PCR method, but the extent of the difference in our study (10×) may have been influenced by both a PCR‐based overestimation and culture‐based underestimation of bacterial densities. Our results also illustrate that bacterial counts may sometimes produce left‐censored data (i.e., we did not detect E. coli in 62% of our samples using the culture‐based method). Specific statistical methods have been developed for analyzed left‐censored data, but, to our knowledge, have not been used by ornithologists. In future studies, investigators studying bacterial loads should provide information about the possible degree of left censoring and should justify their choice of statistical methods from the broad set of available methods, including those explicitly designed for censored data.