Factors affecting aural detections of songbirds.

Many factors affect the number of birds detected on point count surveys of breeding songbirds. The magnitude and importance of these factors are not well understood. We used a bird song simulation system to quantify the effects of detection distance, singing rate, species differences, and observer differences on detection probabilities of birds detected by ear. We simulated 40 point counts consisting of 10 birds per count for five primary species (Black-and-white Warbler Mniotilta varia, Black-throated Blue Warbler Dendroica caerulescens, Black-throated Green Warbler Dendroica virens, Hooded Warbler Wilsonia citrina, and Ovenbird Seiurus aurocapillus) over a range of 15 distances (34-143 m). Songs were played at low (two songs per count) and high (13-21 songs per count) singing rates. Detection probabilities averaged across observers ranged from 0.60 (Black-and-white Warbler) to 0.83 (Hooded Warbler) at the high singing rate and 0.41 (Black-and-white Warbler) to 0.67 (Hooded Warbler) at the low singing rate. Logistic regression analyses indicated that species, singing rate, distance, and observer were all significant factors affecting detection probabilities. Singing rate x species and singing rate X distance interactions were also significant. Simulations of expected counts, based on the best logistic model, indicated that observers detected between 19% (for the worst observer, lowest singing rate, and least detectable species) and 65% (for the best observer, highest singing rate, and most detectable species) of the true population. Detection probabilities on actual point count surveys are likely to vary even more because many sources of variability were controlled in our experiments. These findings strongly support the importance of adjusting measures of avian diversity or abundance from auditory point counts with direct estimates of detection probability.     

Generalized site occupancy models allowing for false positive and false negative errors

Site occupancy models have been developed that allow for imperfect species detection or "false negative" observations. Such models have become widely adopted in surveys of many taxa. The most fundamental assumption underlying these models is that "false positive" errors are not possible. That is, one cannot detect a species where it does not occur. However, such errors are possible in many sampling situations for a number of reasons, and even low false positive error rates can induce extreme bias in estimates of site occupancy when they are not accounted for. In this paper, we develop a model for site occupancy that allows for both false negative and false positive error rates. This model can be represented as a two-component finite mixture model and can be easily fitted using freely available software. We provide an analysis of avian survey data using the proposed model and present results of a brief simulation study evaluating the performance of the maximum-likelihood estimator and the naive estimator in the presence of false positive errors.     

Improving occupancy estimation when two types of observational error occur: non-detection and species misidentification

Efforts to draw inferences about species occurrence frequently account for false negatives, the common situation when individuals of a species are not detected even when a site is occupied. However, recent studies suggest the need to also deal with false positives, which occur when species are misidentified so that a species is recorded as detected when a site is unoccupied. Bias in estimators of occupancy, colonization, and extinction can be severe when false positives occur. Accordingly, we propose models that simultaneously account for both types of error. Our approach can be used to improve estimates of occupancy for study designs where a subset of detections is of a type or method for which false positives can be assumed to not occur. We illustrate properties of the estimators with simulations and data for three species of frogs. We show that models that account for possible misidentification have greater support (lower AIC for two species) and can yield substantially different occupancy estimates than those that do not. When the potential for misidentification exists, researchers should consider analytical techniques that can account for this source of error, such as those presented here.     

Use of ambiguous detections to improve estimates from species distribution models

As large carnivores recover throughout Europe, their distribution needs to be studied to determine their conservation status and assess the potential for human-carnivore conflicts. However, efficient monitoring of many large carnivore species is challenging due to their rarity, elusive behavior, and large home ranges. Their monitoring can include opportunistic sightings from citizens in addition to designed surveys. Two types of detection errors may occur in such monitoring schemes: false negatives and false positives. False-negative detections can be accounted for in species distribution models (SDMs) that deal with imperfect detection. False-positive detections, due to species misidentification, have rarely been accounted for in SDMs. Generally, researchers use ad hoc data-filtering methods to discard ambiguous observations prior to analysis. These practices may discard valuable ecological information on the distribution of a species. We investigated the costs and benefits of including data types that may include false positives rather than discarding them for SDMs of large carnivores. We used a dynamic occupancy model that simultaneously accounts for false negatives and positives to jointly analyze data that included both unambiguous detections and ambiguous detections. We used simulations to compare the performances of our model with a model fitted on unambiguous data only. We tested the 2 models in 4 scenarios in which parameters that control false-positive detections and true detections varied. We applied our model to data from the monitoring of the Eurasian lynx (Lynx lynx) in the European Alps. The addition of ambiguous detections increased the precision of parameter estimates. For the Eurasian lynx, incorporating ambiguous detections produced more precise estimates of the ecological parameters and revealed additional occupied sites in areas where the species is likely expanding. Overall, we found that ambiguous data should be considered when studying the distribution of large carnivores through the use of dynamic occupancy models that account for misidentification.     

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes toward conservation. Approaches used to assess attitudes rarely account for bias arising from reporting error, which can lead to falsely reporting a positive attitude toward conservation (false-positive error) or not reporting a positive attitude when the respondent has a positive attitude toward conservation (false-negative error). Borrowing from developments in applied conservation science, we used a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude toward wildlife notionally (or in abstract terms) and at localized scales while accounting for reporting error. We compared estimates from our model, Likert scores, and naïve estimates (i.e., proportion of respondents reporting a positive attitude in at least 1 question that was only susceptible to false-negative error) with true stakeholder attitudes through simulations. We then applied the model in a survey of tea estate staff on their attitudes toward Asian elephants (Elephas maximus) in the Kaziranga–Karbi Anglong landscape of northeast India. In simulations, Bayesian model estimates of stakeholder attitudes toward wildlife were less biased than naïve estimates or Likert scores. After accounting for reporting errors, we estimated the probability of having a positive attitude toward elephants notionally as 0.85 in the Kaziranga landscape, whereas the proportion of respondents who had positive attitudes toward elephants at a localized scale was 0.50. In comparison, without accounting for reporting errors, naïve estimates of proportions of respondents with positive attitudes toward elephants were 0.69 and 0.23 notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently not 0 (0.22–0.68). Regular and reliable assessment of stakeholder attitudes–combined with inference on drivers of positive attitudes–can help assess the success of initiatives aimed at facilitating human behavioral change and inform conservation decision making.     

False feedings at the nests of carrion crows<Emphasis Type="Italic"> Corvus corone corone</Emphasis>

False feedings, when individuals visit the nest but refrain from feeding the chicks, occur in some cooperative species and have been interpreted in the white-winged chough (Corcorax melanorhamphos) as active deception by helpers towards the rest of the group. In a cooperatively breeding population of carrion crows (Corvus corone corone) 81.5% of the individuals that provided nestling care showed various kinds of false feedings: arriving at the nest with no food, consuming part or all the food brought to the nest, or taking back from a chicks gape the food that had just been delivered. False feedings occurred on average during 16.3% of nest visits, with some individuals performing them at very high rates (up to 64% of nest visits). False feedings occurred at similar rates in unassisted pairs and groups with helpers, and breeding females showed false feeding at significantly higher rates than other group members. Furthermore, individuals showed false feedings regardless of whether they were alone on the nest or in the presence of other group members, and false feedings did not provoke aggression by the rest of the group. False feedings are not likely to represent deceptive help in the carrion crow. We suggest that crows evaluate the chicks condition during nest visits and that false feedings occur as result of a trade-off between their own hunger and the chicks needs.Communicated by W.A. Searcy     

Transient dynamics of invasive competition: barred owls, spotted owls, habitat, and the demons of competition present

The recent range expansion of Barred Owls (Strix varia) into the Pacific Northwest, where the species now co-occurs with the endemic Northern Spotted Owl (Strix occidentalis caurina), resulted in a unique opportunity to investigate potential competition between two congeneric, previously allopatric species. The primary criticism of early competition research was the use of current species' distribution patterns to infer past processes; however, the recent expansion of the Barred Owl and the ability to model the processes that result in site occupancy (i.e., colonization and extinction) allowed us to address the competitive process directly rather than inferring past processes through current patterns. The purpose of our study was to determine whether Barred Owls had any negative effects on occupancy dynamics of nesting territories by Northern Spotted Owls and how these effects were influenced by habitat characteristics of Spotted Owl territories. We used single-species, multi-season occupancy models and covariates quantifying Barred Owl detections and habitat characteristics to model extinction and colonization rates of Spotted Owl pairs in southern Oregon, USA. We observed a strong, negative association between Barred Owl detections and colonization rates and a strong positive effect of Barred Owl detections on extinction rates of Spotted Owls. We observed increased extinction rates in response to decreased amounts of old forest at the territory core and higher colonization rates when old-forest habitat was less fragmented. Annual site occupancy for pairs reflected the strong effects of Barred Owls on occupancy dynamics with much lower occupancy rates predicted for territories where Barred Owls were detected. The strong Barred Owl and habitat effects on occupancy dynamics of Spotted Owls provided evidence of interference competition between the species. These effects increase the importance of conserving large amounts of contiguous, old-forest habitat to maintain Northern Spotted Owls in the landscape.     

Utilization of energy substrates during calling activity in tropical frogs

Calling activity in frogs is energetically demanding to males because they usually perform at or near their physiological capacities. Metabolic fuel for muscle contractions during bouts of aerobic calling activity comes from carbohydrates and lipids that are stored in the trunk muscles. I monitored nightly calling performance in males of seven tropical frog species from two families, Hylidae and Leptodactylidae, and compared levels of glycogen and lipid in the trunk muscles from males collected before and after a three-hour period of calling activity. Trunk muscles from late-evening males in five species had up to 63% less glycogen than the trunk muscles from early-evening males; relatively little depletion was observed in two other species. Overall, glycogen reserves and rates of depletion were highest in species with very high calling rates. It was not possible to measure changes in the relatively large stores of lipid in the trunk muscles after only 3 h of calling. Nevertheless, intramuscular lipid stores probably provide a greater percentage of the energy needed for sound production than glycogen stores, and are largest in species with high calling rates. Received: 7 January 1997 / Accepted after revision: 20 July 1997     

Dose-response relationships in mutagenicity assays including an appropriate positive control group: a multiple testing approach

The objective of mutagenicity assays in regulatory toxicology is the decision on non-mutagenicity or mutagenicity. An inherent problem of statistical tests is the possibility of false decisions, i.e., a mutagenic substance will be falsely labeled as non-mutagenic or a non-mutagenic substance will be falsely labeled as mutagenic. These probabilities of false negative (consumer's risk=type II error) and/or false positive decision (producer's risk=type I error) can be limited by using suitable testing procedures as well as a design including an appropriate positive control. Using the proof of hazard concept the well-known many-to-one procedures with total order restriction for increasing effect differences are used, while using the proof of safety concept procedures on equivalence with total order restriction are discussed. Both approaches are demonstrated on a real data example.     

Analyzing Variability and the Rate of Decline of Migratory Shorebirds in Moreton Bay,Australia

Abstract: Estimating the abundance of migratory species is difficult because sources of variability differ substantially among species and populations. Recently developed state‐space models address this variability issue by directly modeling both environmental and measurement error, although their efficacy in detecting declines is relatively untested for empirical data. We applied state‐space modeling, generalized least squares (with autoregression error structure), and standard linear regression to data on abundance of wetland birds (shorebirds and terns) at Moreton Bay in southeast Queensland, Australia. There are internationally significant numbers of 8 species of waterbirds in the bay, and it is a major terminus of the large East Asian‐Australasian Flyway. In our analyses, we considered 22 migrant and 8 resident species. State‐space models identified abundances of 7 species of migrants as significantly declining and abundance of one species as significantly increasing. Declines in migrant abundance over 15 years were 43–79%. Generalized least squares with an autoregressive error structure showed abundance changes in 11 species, and standard linear regression showed abundance changes in 15 species. The higher power of the regression models meant they detected more declines, but they also were associated with a higher rate of false detections. If the declines in Moreton Bay are consistent with trends from other sites across the flyway as a whole, then a large number of species are in significant decline.     

A critical analysis of ‘false-feeding’ behavior in a cooperatively breeding bird: disturbance effects,satiated nestlings or deception?

‘False feeding,’ where helpers arrive at nests with food but fail to provision the young, has been reported in several cooperative species. This and other potentially ‘deceptive’ behavior has been interpreted as indicating that helping may operate as a signal within such social groups. We critically examine these phenomena in the provisioning behavior of the bell miner Manorina melanophrys. Excessively close observation distances can artificially elevate the rate of false feeding in this (and other) species, but once this had been accounted for, there was little evidence for any ‘deceptive’ behavior by helpers or breeders. Natural and experimentally induced variation in the presence of a potential conspecific audience at the nest did not have any consistent influence upon the rate of false feeds, which was low at 7.94% of 6,880 nest visits. Instead, encountering unexpectedly low levels of brood demand provided a more parsimonious explanation for those visits where helpers failed to feed nestlings or ate the food themselves. Failure to completely transfer a load to nestlings was more likely when the load contained a high proportion of sticky lerp, indicating a simple prey-transfer problem. Finally, individuals that arrived at nests without prey were often members of neighboring breeding pairs, suggesting that these few non-feeding visits may instead involve an information-gathering function. We, therefore, suggest that future studies explicitly exclude the possibility of observer disturbance and all aspects of normal provisioning behavior before applying the terms ‘false feeding’ or ‘deceptive’ and inferring anything more than straightforward helping at the nest.     

Variability in Population Abundance and the Classification of Extinction Risk

Abstract: Classifying species according to their risk of extinction is a common practice and underpins much conservation activity. The reliability of such classifications rests on the accuracy of threat categorizations, but very little is known about the magnitude and types of errors that might be expected. The process of risk classification involves combining information from many sources, and understanding the quality of each source is critical to evaluating the overall status of the species. One common criterion used to classify extinction risk is a decline in abundance. Because abundance is a direct measure of conservation status, counts of individuals are generally the preferred method of evaluating whether populations are declining. Using the thresholds from criterion A of the International Union for Conservation of Nature (IUCN) Red List (critically endangered, decline in abundance of >80% over 10 years or 3 generations; endangered, decline in abundance of 50–80%; vulnerable, decline in abundance of 30–50%; least concern or near threatened, decline in abundance of 0–30%), we assessed 3 methods used to detect declines solely from estimates of abundance: use of just 2 estimates of abundance; use of linear regression on a time series of abundance; and use of state‐space models on a time series of abundance. We generated simulation data from empirical estimates of the typical variability in abundance and assessed the 3 methods for classification errors. The estimates of the proportion of falsely detected declines for linear regression and the state‐space models were low (maximum 3–14%), but 33–75% of small declines (30–50% over 15 years) were not detected. Ignoring uncertainty in estimates of abundance (with just 2 estimates of abundance) allowed more power to detect small declines (95%), but there was a high percentage (50%) of false detections. For all 3 methods, the proportion of declines estimated to be >80% was higher than the true proportion. Use of abundance data to detect species at risk of extinction may either fail to detect initial declines in abundance or have a high error rate.     

Experimental and environmental factors affect spurious detection of ecological thresholds

Threshold detection methods are increasingly popular for assessing nonlinear responses to environmental change, but their statistical performance remains poorly understood. We simulated linear change in stream benthic macroinvertebrate communities and evaluated the performance of commonly used threshold detection methods based on model fitting (piecewise quantile regression [PQR]), data partitioning (nonparametric change point analysis [NCPA]), and a hybrid approach (significant zero crossings [SiZer]). We demonstrated that false detection of ecological thresholds (type I errors) and inferences on threshold locations are influenced by sample size, rate of linear change, and frequency of observations across the environmental gradient (i.e., sample-environment distribution, SED). However, the relative importance of these factors varied among statistical methods and between inference types. False detection rates were influenced primarily by user-selected parameters for PQR (tau) and SiZer (bandwidth) and secondarily by sample size (for PQR) and SED (for SiZer). In contrast, the location of reported thresholds was influenced primarily by SED. Bootstrapped confidence intervals for NCPA threshold locations revealed strong correspondence to SED. We conclude that the choice of statistical methods for threshold detection should be matched to experimental and environmental constraints to minimize false detection rates and avoid spurious inferences regarding threshold location.     

Determining Decision Thresholds and Evaluating Indicators when Conservation Status is Measured as a Continuum

Categorization of the status of populations, species, and ecosystems underpins most conservation activities. Status is often based on how a system's current indicator value (e.g., change in abundance) relates to some threshold of conservation concern. Receiver operating characteristic (ROC) curves can be used to quantify the statistical reliability of indicators of conservation status and evaluate trade‐offs between correct (true positive) and incorrect (false positive) classifications across a range of decision thresholds. However, ROC curves assume a discrete, binary relationship between an indicator and the conservation status it is meant to track, which is a simplification of the more realistic continuum of conservation status, and may limit the applicability of ROC curves in conservation science. We describe a modified ROC curve that treats conservation status as a continuum rather than a discrete state. We explored the influence of this continuum and typical sources of variation in abundance that can lead to classification errors (i.e., random variation and measurement error) on the true and false positive rates corresponding to varying decision thresholds and the reliability of change in abundance as an indicator of conservation status, respectively. We applied our modified ROC approach to an indicator of endangerment in Pacific salmon (Oncorhynchus nerka) (i.e., percent decline in geometric mean abundance) and an indicator of marine ecosystem structure and function (i.e., detritivore biomass). Failure to treat conservation status as a continuum when choosing thresholds for indicators resulted in the misidentification of trade‐offs between true and false positive rates and the overestimation of an indicator's reliability. We argue for treating conservation status as a continuum when ROC curves are used to evaluate decision thresholds in indicators for the assessment of conservation status. Determinación de Umbrales de Decisiones y Evaluación delos Indicadores cuando se Mide el Estado de de Conservación como un Continuo     

Socially facilitated antipredator behavior by ringed salamanders (<Emphasis Type="Italic">Ambystoma annulatum</Emphasis>)

Many aspects of animal behavior can be socially facilitated, including foraging behavior, exploration behavior, and antipredator behavior. Although larvae of the ringed salamander (Ambystoma annulatum) are not gregarious, they can live in high densities and face intense predation pressure during a short period following hatching. In a predator-recognition experiment, we found that these salamanders responded to chemical cues from dragonfly nymphs (Family: Libellulidae) with appropriate antipredator behavior (decreased activity), and this response was absent when salamanders were exposed to chemical cues from nonpredatory mayfly nymphs (Family: Heptageniidae). In a second experiment, we tested whether antipredator behavior in response to chemical cues of dragonflies could be socially facilitated by larval ringed salamanders. We placed an “observer” salamander into a central arena with four “demonstrator” salamanders behind clear barriers around an arena. The barriers ensured that chemical cues would not be detected by the observer. When demonstrators were exposed to chemical cues from dragonflies, the data were consistent with the hypothesis that both demonstrators and observers decreased activity relative to a blank control. Our results provide evidence that social facilitation can occur in larval ringed salamanders, a nonsocial species.     

Reducing over-reporting of deterministic co-occurrence patterns in biotic communities

Null models of species co-occurrence are widely used to infer the existence of various ecological processes. Here we investigate the susceptibility of the most commonly used of these models (the C-score in conjunction with the sequential swap algorithm) to type 1 and type 2 errors. To do this we use simulated datasets with a range of numbers of sites, species and coefficients of variation (CV) in species abundance. We find that this model is particularly susceptible to type 1 errors when applied to large matrices and those with low CV in species abundance. As expected, type 2 error rates decrease with increasing numbers of sites and species, although they increase with increasing CV in species abundance. Despite this, power remains acceptable over a wide range of parameter combinations. The susceptibility of this analytical method to type 1 errors indicates that many previous studies may have incorrectly reported the existence of deterministic patterns of species co-occurrence. We demonstrate that in order to overcome the problem of high type 1 error rates, the number of swaps used to generate null distributions for smaller matrices needs to be increased to over 50,000 swaps (well beyond the 5000 commonly used in published analyses and the 30,000 suggested by Lehsten and Harmand, 2006). We also show that this approach reduces type 1 error rates in real datasets. However, even using this solution, larger datasets still suffer from high type 1 error rates. Such datasets therefore require the use of very large numbers of swaps, which calls for improvements in the most commonly used software. In general, users of this powerful analytical method must be aware that they need surprisingly large numbers of swaps to obtain unbiased estimates of structuring in biotic communities.     

Will Observation Error and Biases Ruin the Use of Simple Extinction Models?

Abstract: Estimating the risk of extinction for populations of endangered species is an important component of conservation biology. These estimates must be made from data that contain both environmental noise in the year-to-year transitions in population size (so-called "process error"), random errors in sampling, and possible biases in sampling ( both forms of observation errors). To determine how much faith to place in estimated extinction rates, it is important to know how sensitive they are to observation error. We used three simple, commonly employed models of population dynamics to generate simulated population time series. We then combined random observation error or systematic biases with those data, fit models to the time series data, and observed how close the extinction dynamics of the fitted models compared with the dynamics of the underlying models. We found that systematic biases in sampling rarely affected estimates of extinction risk. We also found that even moderate levels of random observation error do not significantly affect extinction estimates except over a small range of process errors, corresponding to the region where extinction risk is most uncertain. With more substantial sampling error, estimates of extinction risk degraded rapidly. Field census techniques for a variety of taxa often involve observation errors within ±32% of actual population sizes. For typical time series used in conservation, therefore, we often may not need to be overly concerned about observation errors as an extra source of imperfection in our estimated extinction rates.     

Growth rate and the cost of calling activity in male carpenter frogs,Rana virgatipes

Summary Energy stress during the breeding season and relationships between calling activity and growth were investigated in male carpenter frogs, Rana virgatipes. This species has a prolonged breeding season of up to three months in Southern New Jersey. Monthly collections made in 1985 revealed that both dry mass and percent body lipid decreased throughout the breeding season but sharply increased at the end of the breeding season. Observations of free-living males showed that small males were more likely to gain mass than large males during the breeding season. All males gained mass at higher rates after the breeding season. A simultaneous record of calling activity and mass change was obtained for 42 males. Males called on 95% of nights, indicating that they rarely ceased their reproductive activities. Small males tended to have low calling efforts and high growth rates. When the effect of initial mass was removed, growth rate was negatively correlated with calling effort among small males. This is the first demonstration of a direct tradeoff between a reproductive activity and growth in an anuran.     

Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery

The spatial behavior of numerous fishing fleets is nowadays well documented thanks to satellite Vessel Monitoring Systems (VMS). Vessel positions are recorded on a frequent and regular basis which opens promising perspectives for improving fishing effort estimation and management. However, no specific information is provided on whether the vessel is fishing or not. To answer that question, existing works on VMS data usually apply simple criteria (e.g. threshold on speed). Those simple criteria generally focus in detecting true positives (a true fishing set detected as a fishing set); conversely, estimation errors are given no attention. For our case study, the Peruvian anchovy fishery, those criteria overestimate the total number of fishing sets by 182%. To overcome this problem an artificial neural network (ANN) approach is presented here. In order to set both the optimal parameterization and use “rules” for this ANN, we perform an extensive sensitivity analysis on the optimization of (1) the internal structure and training algorithm of the ANN and (2) the “rules” used for choosing both the relative size and the composition of the databases (DBs) used for training and inferring with the ANN. The “optimized” ANN greatly improves the estimates of the number and location of fishing events. For our case study, ANN reduces the total estimation error on the number of fishing sets to 1% (in average) and obtains 76% of true positives. This spatially explicit information on effort, provided with error estimation, should greatly reduce misleading interpretations of catch per unit effort and thus significantly improve the adaptive management of fisheries. While fitted on Peruvian anchovy fishery data, this type of neural network approach has wider potential and could be implemented in any fishery relying on both VMS and at-sea observer data. In order to increase the accuracy of the ANN results, we also suggest some criteria for improving sampling design by at-sea observers and VMS data.