arXiv subjects

Hannah Worthington

Publications and source records attributed to Hannah Worthington.

5 recordsLinked to original sources

arXiv2026

Incorporating Animal Movement into Continuous-Time Spatial Capture-Recapture Models

Estimation of wildlife population size and spatial dynamics is central to ecology and conservation. Spatial capture-recapture (SCR) models estimate abundance, using detections at sensors such as camera traps, by linking detection probability to the distance between detectors and latent individual activity centres. However, standard SCR formulations assume detections are conditionally independent over time given activity centres without explicitly modelling movement between detection events. This assumption can be problematic when individuals exhibit movement-driven dependence in detections, potentially leading to biased inference on population size. We address this important issue by developing a continuous-time framework that integrates movement into spatial capture-recapture. Individual movement is modelled as a continuous-time Markov chain over a discretised landscape, and detections arise as state-dependent Poisson events. This yields a Markov-modulated marked Poisson process representation, in which detections provide information about an individual's latent location at the time of observation and allow likelihood-based inference in continuous time. We show through simulation studies that ignoring movement-driven dependence can lead to positively biased estimates of population size, whereas the proposed model recovers unbiased estimates and provides additional inference on space use. An application to camera-trap data of American martens illustrates how the framework yields new insights into movement and density. These results demonstrate that explicitly modelling movement is critical for reliable inference in spatial capture-recapture studies.

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arXiv2026

Discovering Causal Relationships Between Time Series With Spatial Structure

Causal discovery is the subfield of causal inference concerned with estimating the structure of cause-and-effect relationships in a system of interrelated variables, as opposed to quantifying the strength or describing the form of causal effects. As interest in causal discovery builds in fields such as ecology, public health, and environmental sciences where data are regularly collected with spatial and temporal structures, approaches must evolve to manage autocorrelation and complex confounding. As it stands, the few proposed causal discovery algorithms for spatiotemporal data require summarizing across locations, ignore spatial autocorrelation, and/or scale poorly to high dimensions. Here, we introduce our developing framework that extends time-series causal discovery to systems with spatial structure, building upon work on causal discovery across contexts and methods for handling spatial confounding in causal effect estimation. We close by outlining remaining gaps in the literature and directions for future research.

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arXiv2025

Incorporating Memory into Continuous-Time Spatial Capture-Recapture Models

Obtaining reliable and precise estimates of wildlife species abundance and distribution is essential for the conservation and management of animal populations and natural reserves. Spatial capture-recapture (SCR) models provide estimates of population size and spatial density from data collected from remote sensors such as camera traps. Such data contain spatial correlation between observations of the same individual, which SCR models partly account for through a latent individual-specific activity centre, a location near which the individual is more likely detected. However, SCR models assume that the observations of an individual are independent over time and space, conditional on its activity centre, so that observed sightings at a given time and location do not influence the probability of being seen at future times and/or locations. This assumption is ecologically unrealistic given the smooth movement of animals over space through time. We propose a new continuous-time modelling framework that incorporates both an individual's (latent) activity centre and its (known) previous location and time of detection. By formulating the detections of an individual as an inhomogeneous temporal Poisson process, we develop a model drawing inspiration from the Ornstein-Uhlenbeck process, which is commonly used to model animal movement. Applying our model to a camera-trap survey of American martens, we observe a substantial improvement in model fit and notable differences in the estimated spatial distribution of activity centres. A simulation study shows that standard SCR models can produce substantially biased population estimates when spatio-temporal dependence is ignored, while the memory-based model remains robust. These findings highlight the importance of accounting for memory of previous detections in SCR models to improve ecological interpretation and inference.

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arXiv2018

Estimating abundance from multiple sampling capture-recapture data via a multi-state multi-period stopover model

The collection of capture-recapture data often involves collecting data on numerous capture occasions over a relatively short period of time. For many study species this process is repeated, for example annually, resulting in capture information spanning multiple sampling periods. The robust design class of models provide a convenient framework in which to analyse all of the available capture data in a single likelihood expression. However, these models typically rely either upon the assumption of closure within a sampling period (the closed robust design) or condition on the number of individuals captured within a sampling period (the open robust design). The models we develop in this paper require neither assumption by explicitly modelling the movement of individuals into the population both within and between the sampling periods, which in turn permits the estimation of abundance. These models are further extended to allow parameters to depend not only on capture occasion but also the amount of time since joining the population and to the case of multi-state data where there is individual time-varying discrete covariate information. We derive an efficient likelihood expression for the new multi-state multi-period stopover model using the hidden Markov model framework. We demonstrate the new model through a simulation study before considering a dataset on great crested newts, Triturus cristatus.

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arXiv2017

Estimation of population size when capture probability depends on individual states

We develop a multi-state model to estimate the size of a closed population from ecological capture-recapture studies. We consider the case where capture-recapture data are not of a simple binary form, but where the state of an individual is also recorded upon every capture as a discrete variable. The proposed multi-state model can be regarded as a generalisation of the commonly applied set of closed population models to a multi-state form. The model permits individuals to move between the different discrete states, whilst allowing heterogeneity within the capture probabilities. A closed-form expression for the likelihood is presented in terms of a set of sufficient statistics. The link between existing models for capture heterogeneity are established, and simulation is used to show that the estimate of population size can be biased when movement between states is not accounted for. The proposed unconditional approach is also compared to a conditional approach to assess estimation bias. The model derived in this paper is motivated by a real ecological data set on great crested newts, Triturus cristatus.

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