Improved female abundance and reproductive parameter estimates through subpopulation-scale genetic capture-recapture of loggerhead turtles

AbstractInferring interactions between populations of different species is a challenging statistical endeavour, which requires a large amount of data. There is therefore some incentive to combine all available sources of data into a single analysis to do so. In demography and single-population studies, Integrated Population Models combine population counts, capture-recapture and reproduction data to fit matrix population models. Here, we extend this approach to the community level in a stage-structured predator-prey context. We develop Integrated Community Models (ICMs), implemented in a Bayesian framework, to fit multispecies nonlinear matrix models to multiple data sources. We assessed the value of the different sources of data using simulations of ICMs under different scenarios contrasting data availability. We found that combining all data types (capture-recapture, counts, and reproduction) allows the estimation of both demographic and interaction parameters, unlike count-only data which typically generate high bias and low precision in interaction parameter estimates for short time series. Moreover, reproduction surveys informed the estimation of interactions particularly well when compared to capture-recapture programs, and have the advantage of being less costly. Overall, ICMs offer an accurate representation of stage structure in community dynamics, and foster the development of efficient observational study designs to monitor communities in the field.

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Consequences of ignoring variable and spatially-autocorrelated detection probability in spatial capture-recapture

10.1101/2020.12.24.424360 ◽

2020 ◽

Author(s):

Ehsan M. Moqanaki ◽

Cyril Milleret ◽

Mahdieh Tourani ◽

Pierre Dupont ◽

Richard Bischof

Keyword(s):

Spatial Heterogeneity ◽

Spatial Autocorrelation ◽

Detection Probability ◽

Model Misspecification ◽

Simulated Data ◽

Parameter Estimates ◽

Abundance Estimates ◽

Credible Intervals ◽

Capture Recapture ◽

The Impact

AbstractContextSpatial capture-recapture (SCR) models are increasingly popular for analyzing wildlife monitoring data. SCR can account for spatial heterogeneity in detection that arises from individual space use (detection kernel), variation in the sampling process, and the distribution of individuals (density). However, unexplained and unmodeled spatial heterogeneity in detectability may remain due to cryptic factors, intrinsic and extrinsic to the study system.ObjectivesWe identify how the magnitude and configuration of unmodeled, spatially variable detection probability influence SCR parameter estimates.MethodsWe simulated realistic SCR data with spatially variable and autocorrelated detection probability. We then fitted a single-session SCR model ignoring this variation to the simulated data and assessed the impact of model misspecification on inferences.ResultsHighly autocorrelated spatial heterogeneity in detection probability (Moran’s I = 0.85 - 0.96), modulated by the magnitude of that variation, can lead to pronounced negative bias (up to 75%), reduction in precision (249%), and decreasing coverage probability of the 95% credible intervals associated with abundance estimates to 0. Conversely, at low levels of spatial autocorrelation (median Moran’s I = 0), even severe unmodeled heterogeneity in detection probability did not lead to pronounced bias and only caused slight reductions in precision and coverage of abundance estimates.ConclusionsUnknown and unmodeled variation in detection probability is liable to be the norm, rather than the exception, in SCR studies. We encourage practitioners to consider the impact that spatial autocorrelation in detectability has on their inferences and urge the development of SCR methods that can take structured unknown or partially unknown spatial variability in detection probability into account.

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Where to spot: individual identification of leopard cats (Prionailurus bengalensis euptilurus) in South Korea

Journal of Ecology and Environment ◽

10.1186/s41610-019-0138-z ◽

2019 ◽

Vol 43 (1) ◽

Author(s):

Heebok Park ◽

Anya Lim ◽

Tae-Young Choi ◽

Seung-Yoon Baek ◽

Eui-Geun Song ◽

...

Keyword(s):

South Korea ◽

Wildlife Conservation ◽

Camera Traps ◽

Individual Identification ◽

Parameter Estimates ◽

Population Parameter ◽

Study Results ◽

Capture Recapture ◽

Leopard Cat ◽

Prionailurus Bengalensis

AbstractKnowledge of abundance, or population size, is fundamental in wildlife conservation and management. Camera-trapping, in combination with capture-recapture methods, has been extensively applied to estimate abundance and density of individually identifiable animals due to the advantages of being non-invasive, effective to survey wide-ranging, elusive, or nocturnal species, operating in inhospitable environment, and taking low labor. We assessed the possibility of using coat patterns from images to identify an individual leopard cat (Prionailurus bengalensis), a Class II endangered species in South Korea. We analyzed leopard cat images taken from Digital Single-Lense Relfex camera (high resolution, 18Mpxl) and camera traps (low resolution, 3.1Mpxl) using HotSpotter, an image matching algorithm. HotSpotter accurately top-ranked an image of the same individual leopard cat with the reference leopard cat image 100% by matching facial and ventral parts. This confirms that facial and ventral fur patterns of the Amur leopard cat are good matching points to be used reliably to identify an individual. We anticipate that the study results will be useful to researchers interested in studying behavior or population parameter estimates of Amur leopard cats based on capture-recapture models.

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

10.1101/357459 ◽

2018 ◽

Cited By ~ 1

Author(s):

Alec Wong ◽

Angela Fuller ◽

J. Andrew Royle

Keyword(s):

Adaptive Sampling ◽

A Priori ◽

Local Population ◽

Analytical Framework ◽

Simple Random Sample ◽

Parameter Estimates ◽

Probabilistic Sampling ◽

Standard Scr ◽

Capture Recapture ◽

Scr Model

AbstractRare 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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Consequences of ignoring variable and spatially autocorrelated detection probability in spatial capture-recapture

Landscape Ecology ◽

10.1007/s10980-021-01283-x ◽

2021 ◽

Author(s):

Ehsan M. Moqanaki ◽

Cyril Milleret ◽

Mahdieh Tourani ◽

Pierre Dupont ◽

Richard Bischof

Keyword(s):

Spatial Heterogeneity ◽

Spatial Autocorrelation ◽

Detection Probability ◽

Model Misspecification ◽

Simulated Data ◽

Parameter Estimates ◽

Reference Scenario ◽

Abundance Estimates ◽

Capture Recapture ◽

The Impact

Abstract Context Spatial capture-recapture (SCR) models are increasingly popular for analyzing wildlife monitoring data. SCR can account for spatial heterogeneity in detection that arises from individual space use (detection kernel), variation in the sampling process, and the distribution of individuals (density). However, unexplained and unmodeled spatial heterogeneity in detectability may remain due to cryptic factors, both intrinsic and extrinsic to the study system. This is the case, for example, when covariates coding for variable effort and detection probability in general are incomplete or entirely lacking. Objectives We identify how the magnitude and configuration of unmodeled, spatially variable detection probability influence SCR parameter estimates. Methods We simulated SCR data with spatially variable and autocorrelated detection probability. We then fitted an SCR model ignoring this variation to the simulated data and assessed the impact of model misspecification on inferences. Results Highly-autocorrelated spatial heterogeneity in detection probability (Moran’s I = 0.85–0.96), modulated by the magnitude of the unmodeled heterogeneity, can lead to pronounced negative bias (up to 65%, or about 44-fold decrease compared to the reference scenario), reduction in precision (249% or 2.5-fold) and coverage probability of the 95% credible intervals associated with abundance estimates to 0. Conversely, at low levels of spatial autocorrelation (median Moran’s I = 0), even severe unmodeled heterogeneity in detection probability did not lead to pronounced bias and only caused slight reductions in precision and coverage of abundance estimates. Conclusions Unknown and unmodeled variation in detection probability is liable to be the norm, rather than the exception, in SCR studies. We encourage practitioners to consider the impact that spatial autocorrelation in detectability has on their inferences and urge the development of SCR methods that can take structured, unknown or partially unknown spatial variability in detection probability into account.

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Evaluation of the completeness of the epidemiological surveillance systems for malaria by the Capture-recapture system in the French armies in 1994

Tropical Medicine & International Health ◽

10.1046/j.1365-3156.1997.d01-301.x ◽

1997 ◽

Vol 2 (5) ◽

pp. 433-439 ◽

Cited By ~ 2

Author(s):

X. Deparis ◽

B. Pascal ◽

D. Baudon

Keyword(s):

Epidemiological Surveillance ◽

Surveillance Systems ◽

Capture Recapture

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Benefits from Computerized Adaptive Testing as Seen in Simulation Studies

European Journal of Psychological Assessment ◽

10.1027//1015-5759.15.2.91 ◽

1999 ◽

Vol 15 (2) ◽

pp. 91-98 ◽

Cited By ~ 10

Author(s):

Lutz F. Hornke

Keyword(s):

Measurement Error ◽

Computerized Adaptive Testing ◽

Test Procedure ◽

Adaptive Testing ◽

Parameter Estimates ◽

Simulation Studies ◽

Computerized Adaptive Test ◽

Item Banks ◽

Item Parameters ◽

General Reliability

Summary: Item parameters for several hundreds of items were estimated based on empirical data from several thousands of subjects. The logistic one-parameter (1PL) and two-parameter (2PL) model estimates were evaluated. However, model fit showed that only a subset of items complied sufficiently, so that the remaining ones were assembled in well-fitting item banks. In several simulation studies 5000 simulated responses were generated in accordance with a computerized adaptive test procedure along with person parameters. A general reliability of .80 or a standard error of measurement of .44 was used as a stopping rule to end CAT testing. We also recorded how often each item was used by all simulees. Person-parameter estimates based on CAT correlated higher than .90 with true values simulated. For all 1PL fitting item banks most simulees used more than 20 items but less than 30 items to reach the pre-set level of measurement error. However, testing based on item banks that complied to the 2PL revealed that, on average, only 10 items were sufficient to end testing at the same measurement error level. Both clearly demonstrate the precision and economy of computerized adaptive testing. Empirical evaluations from everyday uses will show whether these trends will hold up in practice. If so, CAT will become possible and reasonable with some 150 well-calibrated 2PL items.

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