Biology subjects

Royle, J. A.

Publications and source records attributed to Royle, J. A..

11 recordsLinked to original sources

bioRxiv2026

The Hidden Prior: Variance Constraints Under Data Augmentation

Data augmentation is now a standard device across capture--recapture and occupancy analysis: adding a fixed number M of all-zero encounter histories replaces a model of unknown dimension with one of fixed dimension. Although M is often treated as a computational tuning choice, it also specifies a finite superpopulation and hence a binomial support constraint on the number of undetected individuals. In a Bayesian implementation that constraint appears as an induced prior; in a likelihood implementation it is the same finite-support assumption reached by another route. That the Bernoulli specification for the inclusion indicators induces a binomial prior on abundance is established (Schofield & Barker 2014); our concern is what that choice costs in estimated uncertainty. We develop the argument using a simple closed-population abundance estimation problem. We show that augmented occupancy and Huggins conditional-likelihood analyses give numerically identical point estimates of N once M is sufficiently large. Their uncertainty estimates, however, need not agree. We distinguish two sources of discrepancy. First, when M is small relative to the number of undetected individuals, the finite binomial ceiling truncates the likelihood or posterior and suppresses uncertainty. Second, once that ceiling no longer binds, Taylor-series (Delta-method) approximations still understate variance, because the quantity of interest is a strongly non-linear function of the estimated parameters and local linearization does not reproduce its curvature. Gauss-Hermite quadrature on the unconstrained logit scale recovers much of the shortfall and approaches the MCMC posterior benchmark, though a small residual remains that does not close as M grows, reflecting the distinction between asymptotic likelihood theory and finite-sample Bayesian inference. Neither mechanism is peculiar to abundance estimation: the first follows from the augmented representation itself, the second from any derived quantity that is a non-linear function of estimated parameters. We close with framework-specific guidance for choosing M.

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bioRxiv2024

Estimating Species-specific U.S. Waterfowl Harvest

The U.S. Fish and Wildlife Service monitors species-specific waterfowl (ducks, seaducks, geese, and brant) harvest through two hunter surveys, one that estimates the total harvest for each waterfowl group, and a second that estimates the species composition of each waterfowl group. Point estimates for species-specific harvest can be computed by multiplying the estimated total harvest by the estimated proportion of the total harvest of each species. However, to date, no uncertainty estimates have been available. Here, we combine these two data sources to provide species-specific harvest estimates at the state and flyway level while characterizing the uncertainty via Bayesian estimation. We take a similar approach to Smith et al. (2022), providing both estimates that treat yearly data as independent and estimates that share information across years via a random walk process. We then discuss the advantages and disadvantages of each approach.

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bioRxiv2023

Towards estimating marine wildlife abundance using aerial surveys and deep learning with hierarchical classifications subject to error

Aerial count surveys of wildlife populations are a prominent monitoring method for many wildlife species. Traditionally, these surveys utilize human observers to detect, count, and classify observations to species. However, given recent technological advances, many research groups are exploring the combined use of remote sensing and deep learning methods to replace human observers in order to improve data quality and reproducibility, reduce disturbance to wildlife, and increase aircrew safety. Given that deep learning detection and classification are not perfect and that statistical inference from ecological models is generally very sensitive to misclassification, we require study designs and statistical models to accommodate these observation errors. As part of an ongoing effort by the U.S. Fish and Wildlife Service, Bureau of Ocean Energy Management, and U.S. Geological Survey to survey marine birds and other marine wildlife using digital aerial imagery and deep learning object detection and classification, we developed a general hierarchical model for estimating species-specific abundance that accommodates object-level errors in classification. We consider hierarchical deep learning classification at multiple taxonomic levels subject to misclassification, hierarchically-structured human validation data subject to partial and erroneous misclassification, and an image censoring process leading to preferential sampling. We demonstrate that this model can estimate species-specific abundance and habitat relationships without bias when the assumptions are met, and we discuss the plausibility of these assumptions in practice for this study and others like it. Finally, we use this model to demonstrate the relevance of the features of the ecological systems under study to the classification task itself. In models that couple the ecological and classification processes into a single, hierarchical model, the true classes are treated as latent variables to be estimated and are informed by both the classification probability parameters and the ecological parameters that determine the expected frequencies of each class at the level the data are being modeled (e.g., site or site by occasion). We show that ignoring the expected frequencies of each class (when they are imbalanced) can cause correction for misclassification to produce biased parameter estimates, but coupling the ecological and classification models allows for the variability in relative class frequency across space and time due to ecological and sampling conditions to be accommodated with spatial or temporal covariates. As a result, bias is removed, classification is more accurate, and uncertainty is propagated between the ecological and classification models. We therefore argue that ability of deep learning classifiers, and classifiers more generally, to produce reliable ecological inference depends, in part, on the ecological system under study.

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bioRxiv2021

Estimating occupancy dynamics and encounter rates with species misclassification: a semi-supervised individual-level approach

O_LILarge-scale, long-term biodiversity monitoring is essential to meeting conservation and land management goals and identifying threats to biodiversity. However, multispecies surveys are prone to various types of observation error, including false positive/negative detection, and misclassification, where a species is encountered but its species identity is not correctly identified. Previous methods assume an imperfect classifier produces species-level classifications, but in practice, particularly with human observers, we may end up with extraspecific classifications including "unknown", morphospecies designations, and taxonomic identifications coarser than species. Disregarding these types of species misclassification in biodiversity monitoring datasets can bias estimates of ecologically important quantities such as demographic rates, occurrence, and species richness. C_LIO_LIHere we develop an occupancy model that accounts for species non-detection and misclassification. Our framework accommodates extinction and colonization dynamics, allows for additional uncertain morphospecies designations in the imperfect species classifications, and makes use of individual specimen with known species identities in a semi-supervised setting. We compare the performance of our joint classification-occupancy model to a reduced classification model that discards information about occupancy and encounter rate on a withheld test set. We illustrate our model with an empirical case study of the carabid beetle (Carabidae) community at the National Ecological Observatory Network Niwot Ridge Mountain Research Station, west of Boulder, CO, USA, and quantify taxonomist identification error by accounting for classification probabilities. C_LIO_LISpecies occupancy varied through time and across sites and species. The model yielded high probabilities (30 to 92% medians) of classification where the imperfect classifier matched the true species. The classification model informed by occupancy and encounter rates outperformed the classification that was not, and these differences were most pronounced for abundant species. C_LIO_LIOur probabilistic framework can be applied to datasets with imperfect species detection and classification. This model can identify commonly misclassified species, helping biodiversity monitoring organizations systematically prioritize which samples need validation by an expert. Our Bayesian approach propagates classification uncertainty to offer an alternative to making conservation decisions based on point estimates C_LI

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bioRxiv2019

Movement-assisted localization from acoustic telemetry data

BackgroundAcoustic telemetry technologies are being rapidly deployed to study a variety of aquatic taxa including fishes, reptiles, and marine mammals. Large cooperative telemetry networks produce vast quantities of data useful in the study of movement, resource selection and species distribution. Efficient use of acoustic telemetry data requires estimation of acoustic source locations from detections at sensors (i.e. localization). Multiple processes provide information for localization estimation including detection/non-detection data at sensors, information on signal rate, and an underlying movement model describing how individuals move and utilize space. Frequently, however, localization methods only integrate a subset of these processes and do not utilize the full spatial encounter history information available from sensor arrays. MethodsIn this paper we draw analogies between the challenges of acoustic telemetry localization and newly developed methods of spatial capture-recapture (SCR). We develop a framework for localization that integrates explicit sub-models for movement, signal (or cue) rate, and detection probability, based on acoustic telemetry spatial encounter history data. This method, which we call movement-assisted localization, makes efficient use of the full encounter history data available from acoustic sensor arrays, provides localizations with fewer than three detections, and even allows for predictions to be made of the position of an individual when it was not detected at all. We demonstrate these concepts by developing generalizable Bayesian formulations of the SCR movement-assisted localization model to address study-specific challenges common in acoustic telemetry studies. ResultsSimulation studies show that movement-assisted localization models improve point-wise RMSE of localization estimates by > 50% and greatly increased the precision of estimated trajectories compared to localization using only the detection history of a given signal. Additionally, integrating a signal rate sub-model reduced biases in the estimation of movement, signal rate, and detection parameters observed in independent localization models. ConclusionsMovement-assisted localization provides a flexible framework to maximize the use of acoustic telemetry data. Conceptualizing localization within an SCR framework allows extensions to a variety of data collection protocols, improves the efficiency of studies interested in movement, resource selection, and space-use, and provides a unifying framework for modeling acoustic data.

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bioRxiv2019

Estimating spatially and temporally complex range dynamics when detection is imperfect

Species distributions are determined by the interaction of multiple biotic and abiotic factors, which produces complex spatial and temporal patterns of occurrence. As habitats and climate change due to anthropogenic activities, there is a need to develop species distribution models that can quantify these complex range dynamics. In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates. The model is flexible and can accommodate data from a range of sampling designs that provide information about both occupancy and detection probability. Output from the model can be used to create distribution maps and to estimate indices of temporal range dynamics. We demonstrate the utility of this approach by modeling long-term range dynamics of 10 eastern North American birds using data from the North American Breeding Bird Survey. We anticipate this framework will be particularly useful for modeling species distributions over large spatial scales and for quantifying range dynamics over long temporal scales.

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bioRxiv2018

Adaptive Sampling for Spatial Capture-Recapture: An efficient sampling scheme for rare or patchily distributed species

Rare species present challenges to data collection, particularly when the species is spatially clustered over large areas, such that the encounter frequency of the organism is low. Sampling where the organism is absent consumes resources, and offers relatively low-quality information which are often difficult to model using standard statistical methods. In adaptive sampling, a probabilistic sampling method is employed first, and additional effort is allocated in the vicinity of sites where some measured index variable - assumed to be proportional to local population size - exceeds an a priori threshold. We applied this principle to the spatial capture-recapture (SCR) analytical framework in a Bayesian hierarchical model incorporating capture-recapture (CR) and index information from unsampled sites to estimate density. We assessed the adaptively sampled SCR model (AS-SCR) by simulating CR data and compared performance with a standard SCR baseline (F-SCR), adaptive SCR discarding index information (AS-SCR-), and standard SCR applied at a simple random sample of sites. Under AS-SCR, we observed minimal bias and comparable variance with respect to parameter estimates provided by the standard F-SCR model and sampling implementation, but with substantially reduced effort and significant cost saving potential. This represents the first application of adaptive sampling to SCR.

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bioRxiv2018

Spatial Mark-Resight for Categorically MarkedPopulations with an Application to GeneticCapture-Recapture

The estimation of animal population density is a fundamental goal in wildlife ecology and management, commonly met using mark recapture or spatial mark recapture (SCR) study designs and statistical methods. Mark-recapture methods require the identification of individuals; however, for many species and sampling methods, particularly noninvasive methods, no individuals or only a subset of individuals are individually identifiable. The unmarked SCR model, theoretically, can estimate the density of unmarked populations; however, it produces biased and imprecise density estimates in many sampling scenarios typically encountered. Spatial mark-resight (SMR) models extend the unmarked SCR model in three ways: 1) by introducing a subset of individuals that are marked and individually identifiable, 2) introducing the possibility of individual-linked telemetry data, and 3) introducing the possibility that the capture-recapture data from the survey used to deploy the marks can be used in a joint model, all improving the reliability of density estimates. The categorical spatial partial identity model (SPIM) improves the reliability of density estimates over unmarked SCR along another dimension, by adding categorical identity covariates that improve the probabilistic association of the latent identity samples. Here, we combine these two models into a \"categorical SMR\" model to exploit the benefits of both models simultaneously. We demonstrate using simulations that SMR alone can produce biased and imprecise density estimates with sparse data and/or when few individuals are marked. Then, using a fisher (Pekania pennanti) genetic capture-recapture data set, we show how categorical identity covariates, marked individuals, telemetry data, and jointly modeling the capture survey used to deploy marks with the resighting survey all combine to improve inference over the unmarked SCR model. As previously seen in an application of the categorical SPIM to a real-world data set, the fisher data set demonstrates that individual heterogeneity in detection function parameters, especially the spatial scale parameter{sigma} , introduces positive bias into latent identity SCR models (e.g., unmarked SCR, SMR), but the categorical SMR model provides more tools to reduce this positive bias than SMR or the categorical SPIM alone. We introduce the possibility of detection functions that vary by identity category level, which will remove individual heterogeneity in detection function parameters than is explained by categorical covariates, such as individual sex. Finally, we provide efficient SMR algorithms that accommodate all SMR sample types, interspersed marking and sighting periods, and any number of identity covariates using the 2-dimensional individual by trap data in conjunction with precomputed constraint matrices, rather than the 3-dimensional individual by trap by occasion data used in SMR algorithms to date.

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bioRxiv2018

Spatial Capture-Recapture for Categorically Marked Populations with An Application to Genetic Capture-Recapture

Recently introduced unmarked spatial capture-recapture (SCR), spatial mark-resight (SMR), and 2-flank spatial partial identity models (SPIM) extend the domain of SCR to populations or observation systems that do not always allow for individual identity to be determined with certainty. For example, some species do not have natural marks that can reliably produce individual identities from photographs, and some methods of observation produce partial identity samples as is the case with remote cameras that sometimes produce single flank photographs. These models share the feature that they probabilistically resolve the uncertainty in individual identity using the spatial location where samples were collected. Spatial location is informative of individual identity in spatially structured populations with home range sizes smaller than the extent of the trapping array because a latent identity sample is more likely to have been produced by an individual living near the trap where it was recorded than an individual living further away from the trap. Further, the level of information about individual identity that a spatial location contains is determined by two key ecological concepts, population density and home range size. The number of individuals that could have produced a latent or partial identity sample increases as density and home range size increase because more individual home ranges will overlap any given trap. We show this uncertainty can be quantified using a metric describing the expected magnitude of uncertainty in individual identity for any given population density and home range size, the Identity Diversity Index (IDI). We then show that the performance of latent and partial identity SCR models varies as a function of this index and produces imprecise and biased estimates in many high IDI scenarios when data are sparse. We then extend the unmarked SCR model to incorporate partially identifying covariates which reduce the level of uncertainty in individual identity, increasing the reliability and precision of density estimates, and allowing reliable density estimation in scenarios with higher IDI values and with more sparse data. We illustrate the performance of this \"categorical SPIM\" via simulations and by applying it to a black bear data set using microsatellite loci as categorical covariates, where we reproduce the full data set estimates with only slightly less precision using fewer loci than necessary for confident individual identification. The categorical SPIM offers an alternative to using probability of identity criteria for classifying genotypes as unique, shifting the \"shadow effect\", where more than one individual in the population has the same genotype, from a source of bias to a source of uncertainty. We discuss the difficulties that real world data sets pose for latent identity SCR methods, most importantly, individual heterogeneity in detection function parameters, and argue that the addition of partial identity information reduces these concerns. We then discuss how the categorical SPIM can be applied to other wildlife sampling scenarios such as remote camera surveys, where natural or researcher-applied partial marks can be observed in photographs. Finally, we discuss how the categorical SPIM can be added to SMR, 2-flank SPIM, or other future latent identity SCR models.

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bioRxiv2018

Modeling sound attenuation in heterogeneous environments for improved bioacoustic sampling of wildlife populations

Acoustic sampling methods are becoming increasingly important in biological monitoring. Sound attenuation is one of the most important dynamics affecting the utility of bioacoustic data as it directly affects the probability of detection of individuals from bioacoustic arrays and especially the localization of acoustic signals necessary in telemetry studies. Therefore, models of sound attenuation are necessary to make efficient use of bioacoustic data in ecological monitoring and assessment applications. Models of attenuation in widespread use are based on Euclidean distance between source and sensor, which is justified under spherical attenuation of sound waves in homogeneous environments. In some applications there are efforts to evaluate the detection range of sensors in response to local environmental characteristics at the sensor or at sentinel source locations with known environmental characteristics. However, attenuation is a function of the total environment between source and sensor, not just their locations. In this paper I develop a model of signal attenuation based on a non-Euclidean cost-weighted distance metric which contains resistance parameters that relate to environmental heterogeneity in the vicinity of an array. Importantly, these parameters can be estimated by maximum likelihood using experimental data from an array of fixed sources, thus allowing investigators who use bioacoustic methods to devise explicit models of sound attenuation in situ. In addition, drawing on analogy with classes of models known as spatial capture-recapture, I show that parameters of the non-Euclidean model of attenuation can be estimated when source locations are unknown. Thus, the models can be applied to real field studies which require localization of signals in heterogeneous environments.

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bioRxiv2017

Unifying Population and Landscape Ecology with Spatial Capture-recapture

Spatial heterogeneity in the environment induces variation in population demographic rates and dispersal patterns, which result in spatio-temporal variation in density and gene flow. Unfortunately, applying theory to learn about the role of spatial structure on populations has been hindered by the lack of mechanistic spatial models and inability to make precise observations of population structure. Spatial capture-recapture (SCR) represents an individual-based analytic framework for overcoming this fundamental obstacle that has limited the utility of ecological theory. SCR methods make explicit use of spatial encounter information on individuals in order to model density and other spatial aspects of animal population structure, and have been widely adopted in the last decade. We review the historical context and emerging developments in SCR models that enable the integration of explicit ecological hypotheses about landscape connectivity, movement, resource selection, and spatial variation in density, directly with individual encounter history data obtained by new technologies (e.g., camera trapping, non-invasive DNA sampling). We describe ways in which SCR methods stand to revolutionize the study of animal population ecology.

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