Effects of imperfect detection on inferences from bird surveys

Studies on ecological communities often address patterns of species distribution and abundance, but few consider uncertainty in counts of both species and individuals when computing diversity measures.We evaluated the extent to which imperfect detection may influence patterns of taxonomic, functional, and phylogenetic diversity in ecological communities.We estimated the true abundance of fruit‐feeding butterflies sampled in canopy and understory strata in a subtropical forest. We compared the diversity values calculated by observed and estimated abundance data through the hidden diversity framework. This framework evaluates the deviation of observed diversity when compared with diversities derived from estimated true abundances and whether such deviation represents a bias or a noise in the observed diversity pattern.The hidden diversity values differed between strata for all diversity measures, except for functional richness. The taxonomic measure was the only one where we observed an inversion of the most diverse stratum when imperfect detection was included. Regarding phylogenetic and functional measures, the strata showed distinct responses to imperfect detection, despite the tendency to overestimate observed diversity. While the understory showed noise for the phylogenetic measure, since the observed pattern was maintained, the canopy had biased diversity for the functional metric. This bias occurred since no significant differences were found between strata for observed diversity, but rather for estimated diversity, with the canopy being more clustered.We demonstrate that ignore imperfect detection may lead to unrealistic estimates of diversity and hence to erroneous interpretations of patterns and processes that structure biological communities. For fruit‐feeding butterflies, according to their phylogenetic position or functional traits, the undetected individuals triggered different responses in the relationship of the diversity measures to the environmental factor. This highlights the importance to evaluate and include the uncertainty in species detectability before calculating biodiversity measures to describe communities.

Building useful models of species distributions requires attention to several important issues, one being imperfect detection of species. Data sets of species detections are likely to suffer from false absence records. Depending on the type of survey, false positive records can also be a problem. Disregarding these observation errors may lead to important biases in model estimation as well as overconfidence about precision. The severity of the problem depends on the intensity of these errors and how they correlate with environmental characteristics (e.g. where species detectability strongly depends on habitat features). A powerful modelling framework that accounts for imperfect detection in the modelling of species distributions has developed in the last 10–15 yr. Fundamental to this framework is that data must be collected in a way that is informative about the observation process. For instance, such data can be in the form of multiple detection/non‐detection records obtained from several visits/observers/detection methods at (at least) some of the sites, or from data on times to detection within a survey visit. The framework can extend to studying species’ range dynamics and the modelling of communities, as well as approaches for analysing data on abundance and multiple occupancy states (rather than binary presence/absence). This paper summarizes these modelling advances, discusses evidence about effects of imperfect detection and the difficulties of working with it, and concludes with the current outlook for future research and application of these methods.

Environmental DNA (eDNA) methods are used to detect DNA that is shed into the aquatic environment by cryptic or low density species. Applied in eDNA studies, occupancy models can be used to estimate occurrence and detection probabilities and thereby account for imperfect detection. However, occupancy terminology has been applied inconsistently in eDNA studies, and many have calculated occurrence probabilities while not considering the effects of imperfect detection. Low detection of invasive giant constrictors using visual surveys and traps has hampered the estimation of occupancy and detection estimates needed for population management in southern Florida, USA. Giant constrictor snakes pose a threat to native species and the ecological restoration of the Florida Everglades. To assist with detection, we developed species-specific eDNA assays using quantitative PCR (qPCR) for the Burmese python (Python molurus bivittatus), Northern African python (P. sebae), boa constrictor (Boa constrictor), and the green (Eunectes murinus) and yellow anaconda (E. notaeus). Burmese pythons, Northern African pythons, and boa constrictors are established and reproducing, while the green and yellow anaconda have the potential to become established. We validated the python and boa constrictor assays using laboratory trials and tested all species in 21 field locations distributed in eight southern Florida regions. Burmese python eDNA was detected in 37 of 63 field sampling events; however, the other species were not detected. Although eDNA was heterogeneously distributed in the environment, occupancy models were able to provide the first estimates of detection probabilities, which were greater than 91%. Burmese python eDNA was detected along the leading northern edge of the known population boundary. The development of informative detection tools and eDNA occupancy models can improve conservation efforts in southern Florida and support more extensive studies of invasive constrictors. Generic sampling design and terminology are proposed to standardize and clarify interpretations of eDNA-based occupancy models.

AimDuring the past decade ecologists have attempted to estimate the parameters of species distribution models by combining locations of species presence observed in opportunistic surveys with spatially referenced covariates of occurrence. Several statistical models have been proposed for the analysis of presence‐only data, but these models have largely ignored the effects of imperfect detection and survey bias. In this paper I describe a model‐based approach for the analysis of presence‐only data that accounts for errors in the detection of individuals and for biased selection of survey locations.InnovationI develop a hierarchical, statistical model that allows presence‐only data to be analysed in conjunction with data acquired independently in planned surveys. One component of the model specifies the spatial distribution of individuals within a bounded, geographic region as a realization of a spatial point process. A second component of the model specifies two kinds of observations, the detection of individuals encountered during opportunistic surveys and the detection of individuals encountered during planned surveys.Main conclusionsUsing mathematical proof and simulation‐based comparisons, I demonstrate that biases induced by errors in detection or biased selection of survey locations can be reduced or eliminated by using the hierarchical model to analyse presence‐only data in conjunction with counts observed in planned surveys. I show that a relatively small number of high‐quality data (from planned surveys) can be used to leverage the information in presence‐only observations, which usually have broad spatial coverage but may not be informative of both occurrence and detectability of individuals. Because a variety of sampling protocols can be used in planned surveys, this approach to the analysis of presence‐only data is widely applicable. In addition, since the point‐process model is formulated at the level of an individual, it can be extended to account for biological interactions between individuals and temporal changes in their spatial distributions.

Understanding species–habitat relationships for endangered species is critical for their conservation. However, many studies have limited value for conservation because they fail to account for habitat associations at multiple spatial scales, anthropogenic variables, and imperfect detection. We addressed these three limitations by developing models for an endangered wetland bird, Yuma Ridgway's rail (Rallus obsoletus yumanensis), that examined how the spatial scale of environmental variables, inclusion of anthropogenic disturbance variables, and accounting for imperfect detection in validation data influenced model performance. These models identified associations between environmental variables and occupancy. We used bird survey and spatial environmental data at 2473 locations throughout the species' U.S. range to create and validate occupancy models and produce predictive maps of occupancy. We compared habitat‐based models at three spatial scales (100, 224, and 500 m radii buffers) with and without anthropogenic disturbance variables using validation data adjusted for imperfect detection and an unadjusted validation dataset that ignored imperfect detection. The inclusion of anthropogenic disturbance variables improved the performance of habitat models at all three spatial scales, and the 224‐m‐scale model performed best. All models exhibited greater predictive ability when imperfect detection was incorporated into validation data. Yuma Ridgway's rail occupancy was negatively associated with ephemeral and slow‐moving riverine features and high‐intensity anthropogenic development, and positively associated with emergent vegetation, agriculture, and low‐intensity development. Our modeling approach accounts for common limitations in modeling species–habitat relationships and creating predictive maps of occupancy probability and, therefore, provides a useful framework for other species.

Many estimation and inference problems arising from large‐scale animal surveys are focused on developing an understanding of patterns in abundance or occurrence of a species based on spatially referenced count data. One fundamental challenge, then, is that it is generally not feasible to completely enumerate (“census”) all individuals present in each sample unit. This observation bias may consist of several components, including spatial coverage bias (not all individuals in the population are exposed to sampling) and detection bias (exposed individuals may go undetected). Thus, observations are biased for the state variable (abundance, occupancy) that is the object of inference. Moreover, data are often sparse for most observation locations, requiring consideration of methods for spatially aggregating or otherwise combining sparse data among sample units. The development of methods that unify spatial statistical models with models accommodating non‐detection is necessary to resolve important spatial inference problems based on animal survey data.In this paper, we develop a novel hierarchical spatial model for estimation of abundance and occurrence from survey data wherein detection is imperfect. Our application is focused on spatial inference problems in the Swiss Survey of Common Breeding Birds. The observation model for the survey data is specified conditional on the unknown quadrat population size,N(s). We augment the observation model with a spatial process model forN(s), describing the spatial variation in abundance of the species. The model includes explicit sources of variation in habitat structure (forest, elevation) and latent variation in the form of a correlated spatial process. This provides a model‐based framework for combining the spatially referenced samples while at the same time yielding a unified treatment of estimation problems involving both abundance and occurrence.We provide a Bayesian framework for analysis and prediction based on the integrated likelihood, and we use the model to obtain estimates of abundance and occurrence maps for the European Jay (Garrulus glandarius), a widespread, elusive, forest bird. The naive national abundance estimate ignoring imperfect detection and incomplete quadrat coverage was 77 766 territories. Accounting for imperfect detection added approximately 18 000 territories, and adjusting for coverage bias added another 131 000 territories to yield a fully corrected estimate of the national total of about 227 000 territories. This is approximately three times as high as previous estimates that assume every territory is detected in each quadrat.

Species identification and recording in breeding bird surveys vastly rely on the registration of avian calls and songs. Despite comprehensive expert knowledge on species-specific activity patterns, data-based analyses of vocal activity patterns are lacking. Recent advances in passive acoustic monitoring allow the direct measurement of bird vocal activity at very high temporal resolution. We conducted a comprehensive survey, recording 25,000 h of audio data at 256 forest sites in Lower Saxony, Germany, to investigate vocal activity patterns of the European forest bird community. Our results reveal a high degree of inter-specific variability in seasonal and diel vocal activity patterns, including strong circular patterns along the day–night cycle and a significant seasonal component. Comparing acoustic detectability to species-specific survey recommendations revealed critical temporal discrepancies for 64.2% of species, and standard protocols (sunrise to 4 h after sunrise) showed discrepancies for 41.5% of species. This highlights the potential for temporal survey optimization to reduce imperfect detection and increase accuracy and precision. Emphasis should be given to the hours before and after sunrise and also sunset for sampling less detectable species. Combining observer-based surveys with passive acoustic monitoring might leverage the strengths of both methods. Our results also emphasize the potential of continuous recording schedules in passive acoustic monitoring to capture diverse temporal patterns. This study provides a baseline for future research on vocal activity patterns across habitats, throughout the year, and regarding anthropogenic impacts. Our findings may raise awareness among ornithologists about the sources of variation in acoustic detectability and its implications for breeding bird surveys, highlighting potential for methodological adjustments in survey timing and consequences for carful interpretation of bird surveys.

Biologists now use a variety of survey platforms to assess the spatial distribution and abundance of marine birds, yet few attempts have been made to integrate data from multiple survey platforms to improve model accuracy or precision. We used density surface models (DSMs) to incorporate data from two survey platforms to predict the distribution and abundance of a diving marine bird, the Common Loon (Gavia immer). We conducted strip transect surveys from a multiengine, fixed-wing aircraft and line surveys from a 28 m ship during winter 2009–2010 in a 3,800 km2 study area off the coast of Rhode Island, USA. We accounted for imperfect detection and availability bias due to Common Loon diving behavior. We incorporated spatially explicit environmental covariates (water depth and latitude) to provide predictions of the spatial distribution and abundance of wintering Common Loons. The combined-platform DSM estimated the highest Common Loon densities (>20 individuals km−2) in nearshore waters <35 m deep...

BackgroundReconciling agriculture and biodiversity conservation is a challenge given the growing demand for agricultural products. In recent decades, Argentina has witnessed agricultural expansion and intensification affecting biodiversity and associated ecosystem services. Within agroecosystems, the level of habitat quality is critical for birds, and may depend on vegetation structure, availability of invertebrate prey, and the use of pesticides. Although the relationship between vegetation structure and avian occurrence has been widely studied, to our knowledge, there are no studies that also incorporate prey availability throughout the cycle of soybean crops in Argentina. We estimated and predicted the effects of land cover and temporal variation on the occurrence of avian foraging guilds in Entre Ríos, Argentina, in order to guide management related to potential ecosystem services provided by birds. We also estimated temporal effects of vegetation structure and insecticides on the main arthropod orders consumed by birds to evaluate prey availability.MethodsWe conducted bird and arthropod surveys for 2 years along transects located in 20 randomly selected soybean fields (N = 60) and their adjacent borders (N = 78) throughout the crop growing season, in four seasons. We estimated avian occupancy, accounting for imperfect detection, and arthropod counts fitting generalized linear mixed models.ResultsThe number of native trees in field borders positively influenced the occurrence of most bird species, mainly insectivores. Granivore foliage gleaners, also were positively affected by grass height. Salliers and aerial foragers were weakly affected by distance to forest and native trees. In general, the availability of invertebrates to birds was highest during the third season. Arthropod counts in borders were greater during the last three crop stages than during the pre-sowing period.ConclusionsWe found that with 10 to 15 native tree species in borders, coupled with a complex vegetation structure with shrubs and grasses, we could conserve a wide spectrum of insectivorous birds, and may contribute to the invertebrate pest control service. Vegetated field borders function as a refuge for arthropods, especially agriculturally beneficial taxa such as Hymenopterans. Finally, several groups of birds use the interior of the fields and could help control pests.

Monitoring wildlife abundance across space and time is an essential task to study their population dynamics and inform effective management. Acoustic recording units are a promising technology for efficiently monitoring bird populations and communities. While current acoustic data models provide information on the presence/absence of individual species, new approaches are needed to monitor population abundance, ideally across large spatio‐temporal regions. We present an integrated modelling framework that combines high‐quality but temporally sparse bird point count survey data with acoustic recordings. Our models account for imperfect detection in both data types and false positive errors in the acoustic data. Using simulations, we compare the accuracy and precision of abundance estimates using differing amounts of acoustic vocalizations obtained from a clustering algorithm, point count data, and a subset of manually validated acoustic vocalizations. We also use our modelling framework in a case study to estimate abundance of the Eastern Wood‐Pewee (Contopus virens) in Vermont, USA. The simulation study reveals that combining acoustic and point count data via an integrated model improves accuracy and precision of abundance estimates compared with models informed by either acoustic or point count data alone. Improved estimates are obtained across a wide range of scenarios, with the largest gains occurring when detection probability for the point count data is low. Combining acoustic data with only a small number of point count surveys yields estimates of abundance without the need for validating any of the identified vocalizations from the acoustic data. Within our case study, the integrated models provided moderate support for a decline of the Eastern Wood‐Pewee in this region. Our integrated modelling approach combines dense acoustic data with few point count surveys to deliver reliable estimates of species abundance without the need for manual identification of acoustic vocalizations or a prohibitively expensive large number of repeated point count surveys. Our proposed approach offers an efficient monitoring alternative for large spatio‐temporal regions when point count data are difficult to obtain or when monitoring is focused on rare species with low detection probability.

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass‐participation internet‐based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high‐quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state‐of‐the‐art analytical methods, and well‐supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.

A Gedankenexperiment is presented on the effect of imperfect detection of the electron path on the double slit interference pattern. Incorporating into our quantum system the detectors for the electron path and the observer who recognizes the response of the detectors, one can decompose the ensemble of electrons with their spots on the film into three subensembles according as the upper, lower or neither detector responds. Only the third ensemble shows the interference pattern. Negative result measurement is included as a special case. This Gedankenexperiment allows one to put the argument of Bohr's complementarity of the wave/particle duality on a quantitative basis. The realization of the experiment is well within the high technology of the present day.

The study of random access protocols has recently regained attention due to new cross-layer schemes such as multipacket reception (MPR) systems and network diversity multiple access protocols (NDMA). Despite their relevance, these two systems have only been simultaneously studied employing finite user population models and considering perfect detection of the active users, which are assumptions only useful in scenarios with low numbers of users and high values of the SNR. The purpose of this paper is to introduce an infinite user population model, valid for scenarios with large numbers of users and finite traffic loads, which allows us to extend the available results on ALOHA MPR protocols to systems that use retransmission diversity (RD). Unlike existing approaches our model includes both the effects of packet decoding errors and the effects of imperfect detection of the active users, which considerably affect the performance of conventional NDMA systems in finite SNR environments. Additionally, the proposed model provides a better approximation to the queuing delay of NDMA protocols than the conventional formula of an M/G/1 queue with vacations. Finally, the proposed algorithm also represents an extension and generalization of contention binary tree algorithms assisted by signal processing tools such as SICTA (successive interference cancellation tree algorithm) and other algorithms assisted by source separation. The benefits of the proposed model are assessed using simulation and analytic results.

This work presents an adaptive self-interference cancellation (SIC) method for in-band full-duplex underwater acoustic (IBFD-UWA) systems along with a model for the self-interference (SI) for shallow-water acoustic channels. The proposed system utilizes orthogonal frequency division multiplexing with quadrature phase shift keying modulation to exchange information between two nodes operating in IBFD mode. The proposed adaptive SIC scheme employs the normalized least-mean-square (NLMS) algorithm to suppress the SI signal and avoid saturating the local analog-to-digital (ADC) converter. Unlike existing research works, we investigate the effect of imperfect detection of the signal of interest on the ability of the SIC to diminish the SI signal. We provide experimental results to support the SI model developed and simulation results to demonstrate the ability of the proposed adaptive SIC scheme to mitigate the SI signal to approximately the level of the ambient noise.

ABSTRACTAim Which community metrics should be used to reflect community response to large‐scale habitat alterations is unclear. Here, we assess what and how community changes should be measured to accurately track community responses to large‐scale disturbance in space and/or time.Location France.Method We first developed a simulation model to examine temporal changes in the species composition of large‐scale metacommunities. Using this model, we assessed how species richness, Shannon index, trends of particular subset of species or community indices of habitat specialization were influenced by different disturbance scenarios, and whether these indices were biased by imperfect detectability. We further used more than 1000 empirical bird communities from the French Breeding Bird Survey recently exposed to disturbances of various intensities as a case study.Results Our simulation and empirical results both demonstrate that species richness and diversity measures can show confusing trends and even provide misleading messages of communities’ fate. In contrast, reflecting the composition of the community in terms of habitat specialist and generalist species was more robust and powerful to reflect disturbance effects.Main conclusions We highlight the weakness of using community metrics that fail to incorporate ecological difference among species when summarizing community‐level trends in disturbed landscapes.