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

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

Monitoring butterfly abundance: beyond pollard walks

Most butterfly monitoring protocols rely on counts along transects (Pollard walks) to generate species abundance indices and track population trends. It is still too often ignored that a population count results from two processes: the biological process (true abundance) and the statistical process (our ability to properly quantify abundance). Because individual detectability tends to vary in space (e.g., among sites) and time (e.g., among years), it remains unclear whether index counts truly reflect population sizes and trends. This study compares capture-mark-recapture (absolute abundance) and count-index (relative abundance) monitoring methods in three species (Maculinea nausithous and Iolana iolas: Lycaenidae; Minois dryas: Satyridae) in contrasted habitat types. We demonstrate that intraspecific variability in individual detectability under standard monitoring conditions is probably the rule rather than the exception, which questions the reliability of count-based indices to estimate and compare specific population abundance. Our results suggest that the accuracy of count-based methods depends heavily on the ecology and behavior of the target species, as well as on the type of habitat in which surveys take place. Monitoring programs designed to assess the abundance and trends in butterfly populations should incorporate a measure of detectability. We discuss the relative advantages and inconveniences of current monitoring methods and analytical approaches with respect to the characteristics of the species under scrutiny and resources availability.     

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

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

Estimation and Correction of Visibility Bias in Aerial Surveys of Wintering Ducks

Abstract: Incomplete detection of all individuals leading to negative bias in abundance estimates is a pervasive source of error in aerial surveys of wildlife, and correcting that bias is a critical step in improving surveys. We conducted experiments using duck decoys as surrogates for live ducks to estimate bias associated with surveys of wintering ducks in Mississippi, USA. We found detection of decoy groups was related to wetland cover type (open vs. forested), group size (1–100 decoys), and interaction of these variables. Observers who detected decoy groups reported counts that averaged 78% of the decoys actually present, and this counting bias was not influenced by either covariate cited above. We integrated this sightability model into estimation procedures for our sample surveys with weight adjustments derived from probabilities of group detection (estimated by logistic regression) and count bias. To estimate variances of abundance estimates, we used bootstrap resampling of transects included in aerial surveys and data from the bias-correction experiment. When we implemented bias correction procedures on data from a field survey conducted in January 2004, we found bias-corrected estimates of abundance increased 36–42%, and associated standard errors increased 38–55%, depending on species or group estimated. We deemed our method successful for integrating correction of visibility bias in an existing sample survey design for wintering ducks in Mississippi, and we believe this procedure could be implemented in a variety of sampling problems for other locations and species. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):808–813; 2008)     

Dealing with varying detection probability, unequal sample sizes and clumped distributions in count data

Temporal variation in the detectability of a species can bias estimates of relative abundance if not handled correctly. For example, when effort varies in space and/or time it becomes necessary to take variation in detectability into account when data are analyzed. We demonstrate the importance of incorporating seasonality into the analysis of data with unequal sample sizes due to lost traps at a particular density of a species. A case study of count data was simulated using a spring-active carabid beetle. Traps were 'lost' randomly during high beetle activity in high abundance sites and during low beetle activity in low abundance sites. Five different models were fitted to datasets with different levels of loss. If sample sizes were unequal and a seasonality variable was not included in models that assumed the number of individuals was log-normally distributed, the models severely under- or overestimated the true effect size. Results did not improve when seasonality and number of trapping days were included in these models as offset terms, but only performed well when the response variable was specified as following a negative binomial distribution. Finally, if seasonal variation of a species is unknown, which is often the case, seasonality can be added as a free factor, resulting in well-performing negative binomial models. Based on these results we recommend (a) add sampling effort (number of trapping days in our example) to the models as an offset term, (b) if precise information is available on seasonal variation in detectability of a study object, add seasonality to the models as an offset term; (c) if information on seasonal variation in detectability is inadequate, add seasonality as a free factor; and (d) specify the response variable of count data as following a negative binomial or over-dispersed Poisson distribution.     

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.     

A novel method for quantifying overdispersion in count data and its application to farmland birds

The statistical modelling of count data permeates the discipline of ecology. Such data often exhibit overdispersion compared with a standard Poisson distribution, so that the variance of the counts is greater than that of the mean. Whereas modelling to reveal the effects of explanatory variables on the mean is commonplace, overdispersion is generally regarded as a nuisance parameter to be accounted for and subsequently ignored. Instead, we propose a method that models the overdispersion as a biologically interesting property of a data set and show how novel inference is provided as a result. We adapted the double hierarchical generalized linear model approach to create an easily extendible model structure that quantifies the influence of explanatory variables on the overdispersion of count data, and apply it to farmland birds. These data were from a study within Irish agricultural ecosystems, in which total bird species abundance and the abundance of farmland indicator species were compared on dairy and non‐dairy farms in the winter and breeding seasons. In general, overdispersion in bird counts was greater on dairy farms than on non‐dairy farms, and for total bird numbers, overdispersion was greatest on dairy farms in winter. Our code is fitted using the Bayesian package Rstan, and we make all code and data available in a GitHub repository. Within a Bayesian framework, this approach facilitates a meaningful quantification of the effects of categorical explanatory variables on any response variable with a tendency to overdispersion that has a meaningful biological or ecological explanation.     

Reptile responses to fire and the risk of post-disturbance sampling bias

Altered fire regimes are a driver of biodiversity decline. To plan effective management, we need to know how species are influenced by fire and to develop theory describing fire responses. Animal responses to fire are usually measured using methods that rely on animal activity, but animal activity may vary with time since fire, potentially biasing results. Using a novel approach for detecting bias in the pit-fall trap method, we found that leaf-litter dependent reptiles were more active up to 6 weeks after fire, giving a misleading impression of abundance. This effect was not discovered when modelling detectability with zero-inflated binomial models. Two species without detection bias showed early-successional responses to time since fire, consistent with a habitat-accommodation succession model. However, a habitat specialist did not have the predicted low abundance after fire due to increased post-fire movement and non-linear recovery of a key habitat component. Interactions between fire and other processes therefore must be better understood to predict reptile responses to changing fire-regimes. We conclude that there is substantial bias when trapping reptiles after fire, with species that are otherwise hard to detect appearing to be abundant. Studies that use a survey method based on animal activity such as bird calls or animal movements, likely face a similar risk of bias when comparing recently-disturbed with control sites.     

A Bayesian approach for multiscale modeling of the influence of seasonal and annual habitat variation on relative abundance of ring-necked pheasant roosters

Finding ecologically relevant relationships between environmental covariates and response variables requires determining appropriate scales of effect. While considering multiple spatial scales of effect in hierarchical models has been the focus of recent studies, the effect of spatiotemporal scales, and temporal resolution of data on habitat suitability and species abundance has received less attention. We investigated relationships between ring-necked pheasant rooster abundance and environmental covariates with the goal of identifying important variables and their scales of effect in South Dakota, U.S.A. Using a suite of remote sensing data, we examined whether seasonal environmental conditions influence pheasant relative abundance and how survey conditions might affect detectability of roosters. To select optimal scales of effect and the best subset of covariates simultaneously, we employed a Reversible-Jump Monte Carlo Markov Chain (RJMCMC) approach in a Bayesian framework. We explored sources of uncertainty in data and controlled them through consideration of random effects. The use of seasonal covariates in addition to annual covariates revealed differential effects on species abundance. The proportion of grasslands on the landscape was an important covariate in models in all years, with rooster abundance generally being highest at intermediate levels of grassland density at local scales of effect. Pheasant abundance was also positively related to the proportion of small grain crop cover on the landscape at >2 km scales. Spring gross primary productivity and percentage of herbaceous wetlands on the landscape, both at a large scale (8 km), were the most important covariates in the wet years of 2018 and 2019 and were positively related to pheasant abundance. Grasslands at intermediate levels of density explained variability of pheasant abundance. However, other variables important to pheasant relative abundance varied among years depending on prevailing weather and climate conditions. Our workflow to model relationships between relative abundance and habitat components for pheasants can also be employed to model count data for other species to inform management decisions.     

Modelling occurrence and abundance of species when detection is imperfect

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

Modelling variation in calling rates to develop a reliable monitoring method for the Australasian Bittern Botaurus poiciloptilus

Monitoring the abundance of cryptic species inevitably relies on the use of index methods. Unfortunately, detectability is often confounded by unidentified covariates. One such species is the critically endangered Australasian Bittern Botaurus poiciloptilus. Current monitoring relies upon the ability to count males based on the conspicuous breeding calls of males. However, as in many vocal species, calling rates vary spatially and temporally, making it necessary to account for this when using call counts to index abundance. We undertook 461 15‐min call counts of Australasian Bitterns, in a range of conditions, during two breeding seasons at Whangamarino wetland, New Zealand. We fitted a range of generalized linear mixed models to these data to determine which factors were the best predictors of calling rate per individual Bittern (CRPI), allowing us to make recommendations regarding the optimum time and conditions for monitoring. Bittern CRPI was predictable in terms of time of day, month, cloud cover, rainfall and certain moon parameters, but some spatial and temporal variation remained unexplained. Results showed that the best time to detect Australasian Bitterns was 1 h before sunrise, in September (austral spring), on a moonlit night with no cloud or rain. Such models are useful for identifying times and conditions when counts are the highest and least variable, and could be applied to any species or cue count monitoring method where detection depends on counting calling individuals. Results can be used to standardize index counts, or sensibly to adjust and compare counts from different times. Standardizing monitoring in this way can lead to the development of monitoring methods that have a greater power to show population changes across shorter time periods. Moreover, the use of modelling processes to estimate effect sizes creates potential for such methods to be applied in circumstances where monitoring conditions are rarely optimum and standardization creates logistical trade‐offs, something that is particularly common in studies of cryptic species.     

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.     

Detectability of the global weed Hypochaeris radicata is influenced by species,environment and observer characteristics

Aims To determine the detectability of a global weedy perennial weed Hypochaeris radicata and its relationship with five common observer, species and environmental variables.Methods Trained independent observers conducted time-limited repeat surveys of H. radicata during autumn in an endangered grassy box-gum woodland ecosystem in south-east Australia. Single-species single-season site-occupancy modelling was used to determine if detectability of H. radicata was altered by five covariates, observer, litter height, grazing, maximum plant height and flowering state.Important findings Detectability for H. radicata varied significantly with observer, litter height, plant maximum height and flowering state, but not with grazing. Despite significant observer-specific variation, there was a consistent increase in detectability with plant height and when plants are in flower for all observers. Detectability generally decreased as litter height increases. Perfect or constant detection rates cannot be assumed in plant surveys, even for easily recognizable plants in simple survey conditions. Understanding how detectability is influenced by common survey variables can help improve the efficacy of plant monitoring programs by quantifying the extent of uncertainty in inferences made from survey data, or by determining optimal survey conditions to increase the reliability of collected data. For plants with traits similar to H. radicata, surveying when most plants are at maximum height or in flower, increasing search intensity when litter levels are high and minimizing observer-related heterogeneity are potentially simple and effective ways to reduce detection errors. We speculate that detection rates may be lower, more variable and involve additional covariates when surveying during the peak flowering spring season with the presence of more warm season and taller annual species.     

Re-Constructing Historical Adélie Penguin Abundance Estimates by Retrospectively Accounting for Detection Bias

Seabirds and other land-breeding marine predators are considered to be useful and practical indicators of the state of marine ecosystems because of their dependence on marine prey and the accessibility of their populations at breeding colonies. Historical counts of breeding populations of these higher-order marine predators are one of few data sources available for inferring past change in marine ecosystems. However, historical abundance estimates derived from these population counts may be subject to unrecognised bias and uncertainty because of variable attendance of birds at breeding colonies and variable timing of past population surveys. We retrospectively accounted for detection bias in historical abundance estimates of the colonial, land-breeding Adélie penguin through an analysis of 222 historical abundance estimates from 81 breeding sites in east Antarctica. The published abundance estimates were de-constructed to retrieve the raw count data and then re-constructed by applying contemporary adjustment factors obtained from remotely operating time-lapse cameras. The re-construction process incorporated spatial and temporal variation in phenology and attendance by using data from cameras deployed at multiple sites over multiple years and propagating this uncertainty through to the final revised abundance estimates. Our re-constructed abundance estimates were consistently higher and more uncertain than published estimates. The re-constructed estimates alter the conclusions reached for some sites in east Antarctica in recent assessments of long-term Adélie penguin population change. Our approach is applicable to abundance data for a wide range of colonial, land-breeding marine species including other penguin species, flying seabirds and marine mammals.     

An integrated modeling approach to estimating Gunnison sage‐grouse population dynamics: combining index and demographic data

Evaluation of population dynamics for rare and declining species is often limited to data that are sparse and/or of poor quality. Frequently, the best data available for rare bird species are based on large‐scale, population count data. These data are commonly based on sampling methods that lack consistent sampling effort, do not account for detectability, and are complicated by observer bias. For some species, short‐term studies of demographic rates have been conducted as well, but the data from such studies are typically analyzed separately. To utilize the strengths and minimize the weaknesses of these two data types, we developed a novel Bayesian integrated model that links population count data and population demographic data through population growth rate (λ) for Gunnison sage‐grouse (Centrocercus minimus). The long‐term population index data available for Gunnison sage‐grouse are annual (years 1953–2012) male lek counts. An intensive demographic study was also conducted from years 2005 to 2010. We were able to reduce the variability in expected population growth rates across time, while correcting for potential small sample size bias in the demographic data. We found the population of Gunnison sage‐grouse to be variable and slightly declining over the past 16 years.     

Local and regional weather patterns influencing post‐breeding migration counts of soaring birds at the Strait of Gibraltar,Spain

Migration is a significant event in the annual cycle of many avian species. During migration birds face many challenges, including unfamiliar foraging and refuge habitats, resulting in a much higher rate of mortality during migration than during other seasons of the year. Weather may significantly affect a bird's decision to initiate migration, the course and pace of migration, and its survival during migration. Each of these influences may impact the counts of migrating birds at geographical convergence zones or bottlenecks. It is important to quantify the effect of short‐term weather on these counts to appropriately interpret and use such counts in other analyses. To this end, we aim to assess the effects of local and regional weather conditions on the migration counts of soaring birds at the Strait of Gibraltar during post‐breeding migration. We used information‐theoretic approaches to analyse the influence of local weather and weather in northern Spain on the migration counts of five soaring bird species from two count sites near the Strait of Gibraltar. Migration counts were higher on days with local northerly and westerly winds, often following a day of easterly winds, on days with local high pressure systems, and often following a day of lower pressure. Weather conditions in northern Spain influenced migration counts at the Strait of Gibraltar, but the effects were much weaker than local weather conditions. We confirm that short‐term weather conditions, locally and regionally, can influence migration counts and should thus be considered when these counts are used in other analyses.     

Quantifying observer heterogeneity in bird counts

An essential pilot study was designed to quantify observer heterogeneity and to compare observation methods for the detectability of forest birds in stands of Eucalyptus and Pinus radiata forest as a basis for a major research project on habitat fragmentation near Tumut, southern New South Wales. Twelve experienced observers participated in the investigation. Point interval counts, zig-zag walks and strip transects were used to count birds in both eucalypt and pine forests. The 65 species of birds recorded in the study were assigned to one of nine groups classified by a set of attributes that characterized bird detection by field observers (e.g. body size, colour and calling patterns). Observer heterogeneity varied between groups of birds and was most apparent for small birds foraging in low shrubs (species such as the white-browed scrub wren, assigned to group 2), frequent calling, active birds (species such as the golden whistler, assigned to group 7), and midstorey, undercanopy foragers with distinctive behaviour (species such as the grey fantail assigned to group 4). For bird groups 2, 4 and 7, additional variability due to observer differences resulted in an average increase of ~ 40% in the width of a 95% confidence interval for the logarithm of bird abundance generated from a 20 minute count. Our analysis shows that taking the average of counts obtained by two or more observers would negate the increase in variance of counts due to observer heterogeneity. Few differences between methods of field observation were found. However, for frequent calling, active birds (group 7) there was evidence that more birds were heard using the point interval count method. Our study clearly demonstrated a need to either control for observer differences or to assign at least two observers to individual sites when designing bird surveys for comparative studies. Failure to do so will result in a decrease in precision of bird counts.     

Modeling Systematic Change in Stopover Duration Does Not Improve Bias in Trends Estimated from Migration Counts

The use of counts of unmarked migrating animals to monitor long term population trends assumes independence of daily counts and a constant rate of detection. However, migratory stopovers often last days or weeks, violating the assumption of count independence. Further, a systematic change in stopover duration will result in a change in the probability of detecting individuals once, but also in the probability of detecting individuals on more than one sampling occasion. We tested how variation in stopover duration influenced accuracy and precision of population trends by simulating migration count data with known constant rate of population change and by allowing daily probability of survival (an index of stopover duration) to remain constant, or to vary randomly, cyclically, or increase linearly over time by various levels. Using simulated datasets with a systematic increase in stopover duration, we also tested whether any resulting bias in population trend could be reduced by modeling the underlying source of variation in detection, or by subsampling data to every three or five days to reduce the incidence of recounting. Mean bias in population trend did not differ significantly from zero when stopover duration remained constant or varied randomly over time, but bias and the detection of false trends increased significantly with a systematic increase in stopover duration. Importantly, an increase in stopover duration over time resulted in a compounding effect on counts due to the increased probability of detection and of recounting on subsequent sampling occasions. Under this scenario, bias in population trend could not be modeled using a covariate for stopover duration alone. Rather, to improve inference drawn about long term population change using counts of unmarked migrants, analyses must include a covariate for stopover duration, as well as incorporate sampling modifications (e.g., subsampling) to reduce the probability that individuals will be detected on more than one occasion.     

Ignoring Imperfect Detection in Biological Surveys Is Dangerous: A Response to ‘Fitting and Interpreting Occupancy Models'

In a recent paper, Welsh, Lindenmayer and Donnelly (WLD) question the usefulness of models that estimate species occupancy while accounting for detectability. WLD claim that these models are difficult to fit and argue that disregarding detectability can be better than trying to adjust for it. We think that this conclusion and subsequent recommendations are not well founded and may negatively impact the quality of statistical inference in ecology and related management decisions. Here we respond to WLD''s claims, evaluating in detail their arguments, using simulations and/or theory to support our points. In particular, WLD argue that both disregarding and accounting for imperfect detection lead to the same estimator performance regardless of sample size when detectability is a function of abundance. We show that this, the key result of their paper, only holds for cases of extreme heterogeneity like the single scenario they considered. Our results illustrate the dangers of disregarding imperfect detection. When ignored, occupancy and detection are confounded: the same naïve occupancy estimates can be obtained for very different true levels of occupancy so the size of the bias is unknowable. Hierarchical occupancy models separate occupancy and detection, and imprecise estimates simply indicate that more data are required for robust inference about the system in question. As for any statistical method, when underlying assumptions of simple hierarchical models are violated, their reliability is reduced. Resorting in those instances where hierarchical occupancy models do no perform well to the naïve occupancy estimator does not provide a satisfactory solution. The aim should instead be to achieve better estimation, by minimizing the effect of these issues during design, data collection and analysis, ensuring that the right amount of data is collected and model assumptions are met, considering model extensions where appropriate.     

On the efficiency of using song playback during call count surveys of Red-legged partridges (<Emphasis Type="BoldItalic">Alectoris rufa</Emphasis>)

Successful management practices for declining bird species depend often on long-term surveys acquired by point counts. Despite high standardization of field protocols, uncertain detection probability remains an important source of variability and bias in point-count data. This effect is of main importance in low-responsive species as the Red-legged partridge (Alectoris rufa), but it can be counterbalanced, increasing detection probability. In this 2-year study, we sampled using traditional point-count methods, followed by playback sessions for each repetition. We measured detection probability and the efficiency of playback for detectability in the context of a feasibility study on long-term point-count surveys for a harvested game bird, the Red-legged partridge. The results for both study years show a distinct increase in detection probability (23% and 45%, respectively) when using playback vs. the traditional point-count method. We also tested our results for heterogeneity, trap dependence, and time dependence, and no effect was detected. Thus, we suggest that the future design of long-term surveys on Red-legged partridges should consider abundance indices using playback sessions.