Estimating population size in the presence of temporary migration using a joint analysis of telemetry and capture–recapture data

Bird, Tomas J.; Lyon, Jarod; Nicol, Simon; McCarthy, Michael A.; Barker, Richard J.

doi:10.1111/2041-210x.12202

Cited by 30 publications

(20 citation statements)

References 42 publications

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“…Estimated ages were then used as a covariate to help account for how individual capture and survival probabilities varied over time. Once we had accounted for these variables, we were then able to use the estimated capture rates to calculate what fraction of the true population had been sampled and therefore, the true size of the total population in each year, as per Bird et al (2014). Full details of the model structure and data are available in the supplementary methods section (Appendix S1: Note S2).…”

Section: Bayesian State-space Modelmentioning

confidence: 99%

Increased population size of fish in a lowland river following restoration of structural habitat

Lyon

Bird

Kearns

et al. 2019

Ecological Applications

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Most assessments of the effectiveness of river restoration are done at small spatial scales (<10 km) over short time frames (less than three years), potentially failing to capture large‐scale mechanisms such as completion of life‐history processes, changes to system productivity, or time lags of ecosystem responses. To test the hypothesis that populations of two species of large‐bodied, piscivorous, native fishes would increase in response to large‐scale structural habitat restoration (reintroduction of 4,450 pieces of coarse woody habitat into a 110‐km reach of the Murray River, southeastern Australia), we collected annual catch, effort, length, and tagging data over seven years for Murray cod (Maccullochella peelii) and golden perch (Macquaria ambigua) in a restored “intervention” reach and three neighboring “control” reaches. We supplemented mark–recapture data with telemetry and angler phone‐in data to assess the potentially confounding influences of movement among sampled populations, heterogeneous detection rates, and population vital rates. We applied a Bayesian hierarchical model to estimate changes in population parameters including immigration, emigration, and mortality rates. For Murray cod, we observed a threefold increase in abundance in the population within the intervention reach, while populations declined or fluctuated within the control reaches. Golden perch densities also increased twofold in the intervention reach. Our results indicate that restoring habitat heterogeneity by adding coarse woody habitats can increase the abundance of fish at a population scale in a large, lowland river. Successful restoration of poor‐quality “sink” habitats for target species relies on connectivity with high‐quality “source” habitats. We recommend that the analysis of restoration success across appropriately large spatial and temporal scales can help identify mechanisms and success rates of other restoration strategies such as restoring fish passage or delivering water for environmental outcomes.

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Section: Bayesian State-space Modelmentioning

confidence: 99%

Increased population size of fish in a lowland river following restoration of structural habitat

Lyon

Bird

Kearns

et al. 2019

Ecological Applications

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“…Researchers should attempt to deal with such problems at the study-design stage, such as increasing detection probability, increasing the number of primary periods or integrating auxiliary data into the analysis, especially observations of animals being alive or outside the study area when they may otherwise be classed as being in an unobservable state. For example, Bird et al (2014) augmented CMR data with observations based on a telemetry study which included precise observations about when animals left the CMR study area. A significant disadvantage of using ML estimation in Program MARK is its tendency for parameters to get stuck at boundary values, especiallyφ = 1 andγ ′ = 0, such that CIs are undefined.…”

Section: Bayesian Pcrd Vs Mlementioning

confidence: 99%

“…Fortunately, the Bayesian framework and the flexible BUGS syntax opens the possibility to easily integrate other datasets, and one priority should be to remove some of the uncertainty of the unseen state. For example, the use of other opportunistic sightings or telemetry data outside the study area (Bird et al, 2014), can partially clarify the unseen "offsite" state, and thereby help reduce the correlation in temporary emigration and survival. Please see the online tutorial at https://github.com/ faraway1nspace/PCRD_JAGS_demo, including access to the bottlenose dolphin Photo-ID data used in this study, as well as JAGS code for running select models.…”

Section: Future Workmentioning

confidence: 99%

A Full-Capture Hierarchical Bayesian Model of Pollock's Closed Robust Design and Application to Dolphins

et al. 2016

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We present a Hierarchical Bayesian version of Pollock's Closed Robust Design for studying the survival, temporary migration, and abundance of marked animals. Through simulations and analyses of a bottlenose dolphin photo-identification dataset, we compare several estimation frameworks, including Maximum Likelihood estimation (ML), model-averaging by AICc, as well as Bayesian and Hierarchical Bayesian (HB) procedures. Our results demonstrate a number of advantages of the Bayesian framework over other popular methods. First, for simple fixed-effect models, we show the near-equivalence of Bayesian and ML point-estimates and confidence/credibility intervals. Second, we demonstrate how there is an inherent correlation among temporary migration and survival parameter estimates in the PCRD, and while this can lead to serious convergence issues and singularities among MLEs, we show that the Bayesian estimates were more reliable. Third, we demonstrate that a Hierarchical Bayesian model with carefully thought-out hyperpriors, can lead to similar parameter estimates and conclusions as multi-model inference by AICc model-averaging. This latter point is especially interesting for mark-recapture practitioners, for whom model-uncertainty and multi-model inference have become a major preoccupation. Lastly, we extend the Hierarchical Bayesian PCRD to include full-capture histories (i.e., by modeling a recruitment process) and individual-level heterogeneity in detection probabilities, which can have important consequences for the range of phenomena studied by the PCRD, as well as lead to large differences in abundance estimates. For example, we estimate 8-24% more bottlenose dolphins in the western gulf of Shark Bay than previously estimated by ML and AICc-based model-averaging. Other important extensions are discussed. Our Bayesian PCRD models are written in the BUGS-like JAGS language for easy dissemination and customization by the community of capture-mark-recapture (CMR) practitioners.

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“…, Bird et al. ). Spatially explicit applications have generally focused on the contribution of telemetry data to informing the scale of individual movement in SCR models for the purposes of improving the precision and/or identifiability of parameter estimates (Sollmann et al.…”

Section: Discussionmentioning

confidence: 99%

Integrating telemetry data into spatial capture–recapture modifies inferences on multi‐scale resource selection

2018

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Citation: Linden, D. W., A. P. K. Sir en, and P. J. Pekins. 2018. Integrating telemetry data into spatial capture-recapture modifies inferences on multi-scale resource selection. Ecosphere 9(4):Abstract. Estimating population size and resource selection functions (RSFs) are common approaches in applied ecology for addressing wildlife conservation and management objectives. Traditionally such approaches have been undertaken separately with different sources of data. Spatial capture-recapture (SCR) provides a hierarchical framework for jointly estimating density and multi-scale resource selection, and data integration techniques provide opportunities for improving inferences from SCR models. Despite the added benefits, there have been few applications of SCR-RSF integration, potentially due to complexities of specifying and fitting such models. Here, we extend a previous integrated SCR-RSF model to accommodate overlap of individuals between data sources and add the functionality to an open-source R package (oSCR). We illustrate an application of the integrated model to a population of American marten (Martes americana) in alpine forests of northern New England. Spatial encounter data from camera traps were combined with telemetry locations from radio-collared individuals to examine how density and space use varied with spatial environmental features. We compared multi-model inferences between the integrated SCR-RSF model with telemetry and a standard SCR model with no telemetry. The integrated SCR-RSF model supported more complex relationships with spatial variation in third-order resource selection (i.e., individual space use), including selection for areas with shorter distances to mixed conifer forest and rugged terrain. Both models indicated increased second-order selection (i.e., density) for areas close to mixed conifer forest, while the integrated SCR-RSF model had a lower effect size due to modulation from spatial variability in space use. Our application of the integrated SCR-RSF model illustrates the improved inferences from spatial encounter data that can be achieved from integrating auxiliary telemetry data. Integrated modeling allows ecologists to join empirical data to ecological theory using a robust quantitative framework to better address conservation and management objectives.

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Estimating population size in the presence of temporary migration using a joint analysis of telemetry and capture–recapture data

Cited by 30 publications

References 42 publications

Increased population size of fish in a lowland river following restoration of structural habitat

Increased population size of fish in a lowland river following restoration of structural habitat

A Full-Capture Hierarchical Bayesian Model of Pollock's Closed Robust Design and Application to Dolphins

Integrating telemetry data into spatial capture–recapture modifies inferences on multi‐scale resource selection

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