Section: Ecology
Topic: Ecology, Statistics
Conference: Euring 2023
Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models
10.24072/pcjournal.357 - Peer Community Journal, Volume 4 (2024), article no. e1.
Get full text PDF Peer reviewed and recommended by PCI
State variables such as abundance and occurrence of species are central to many questions in ecology and conservation, but our ability to detect and enumerate species is imperfect and often varies across space and time. Accounting for imperfect and variable detection is important for obtaining unbiased estimates of state variables. Here, I investigate whether closed spatial capture-recapture (SCR) and single season occupancy models are robust to ignoring temporal variation in detection probability. Ignoring temporal variation allows collapsing detection data across repeated sampling occasions, speeding up computations, which can be important when analyzing large datasets with complex models. I simulated data under different scenarios of temporal and spatio-temporal variation in detection, analyzed data with the data-generating model and an alternative model ignoring temporal variation in detection, and compared estimates between these two models with respect to relative bias, coefficient of variation (CV) and relative root mean squared error (RMSE). SCR model estimates of abundance, the density-covariate coefficient β and the movement-related scale parameter of the detection function σ were robust to ignoring temporal variation in detection, with relative bias, CV and RMSE of the two models generally being within 4% of each other. An SCR case study for brown tree snakes showed identical estimates of density and σ under models accounting for or ignoring temporal variation in detection. Occupancy model estimates of the occupancy-covariate coefficient β and average occupancy were also largely robust to ignoring temporal variation in detection, and differences in occupancy predictions were mostly <<0.1. But there was a slight tendency for bias in β under the alternative model to increase when detection varied more strongly over time. Thus, when temporal variation in detection is extreme, it may be necessary to model that variation to avoid bias in parameter estimates in occupancy models. An occupancy case study for ten bird species with a more complex model structure showed considerable differences in occupancy parameter estimates under models accounting for or ignoring temporal variation in detection; but estimates and predictions from the latter were always within 95% confidence intervals of the former. There are cases where we cannot or may not want to ignore temporal variation in detection: a behavioral response to detection and certain SCR observation models do not allow collapsing data across sampling occasions; and temporal variation in detection may be informative of species phenology/behavior or for future study planning. But this study shows that it can be safely ignored under a range of conditions when analyzing SCR or occupancy data.
Type: Research article
CC-BY 4.0
@article{10_24072_pcjournal_357,
author = {Sollmann, Rahel},
title = {M\protect\textsubscript{t} or not {M\protect\textsubscript{t}:} {Temporal} variation in detection probability in spatial capture-recapture and occupancy models},
journal = {Peer Community Journal},
eid = {e1},
publisher = {Peer Community In},
volume = {4},
year = {2024},
doi = {10.24072/pcjournal.357},
language = {en},
url = {https://peercommunityjournal.org/articles/10.24072/pcjournal.357/}
}
TY - JOUR AU - Sollmann, Rahel TI - Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models JO - Peer Community Journal PY - 2024 VL - 4 PB - Peer Community In UR - https://peercommunityjournal.org/articles/10.24072/pcjournal.357/ DO - 10.24072/pcjournal.357 LA - en ID - 10_24072_pcjournal_357 ER -
%0 Journal Article %A Sollmann, Rahel %T Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models %J Peer Community Journal %D 2024 %V 4 %I Peer Community In %U https://peercommunityjournal.org/articles/10.24072/pcjournal.357/ %R 10.24072/pcjournal.357 %G en %F 10_24072_pcjournal_357
Sollmann, Rahel. Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models. Peer Community Journal, Volume 4 (2024), article no. e1. doi : 10.24072/pcjournal.357. https://peercommunityjournal.org/articles/10.24072/pcjournal.357/
Peer reviewed and recommended by PCI : 10.24072/pci.ecology.100583
Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.
[1] Simple Capture-Recapture Models Permitting Unequal Catchability and Variable Sampling Effort, Biometrics, Volume 50 (1994) no. 2 | DOI
[2] Brown Treesnake detections on transects using potential attractants of live-mouse lures or fish-spray scent, Guam: U.S. Geological Survey data release., 2021 | DOI
[3] Tools for increasing visual encounter probabilities for invasive species removal: a case study of brown treesnakes, NeoBiota, Volume 70 (2021), pp. 107-122 | DOI
[4] Uninformative Parameters and Model Selection Using Akaike's Information Criterion, The Journal of Wildlife Management, Volume 74 (2010) no. 6, pp. 1175-1178 | DOI
[5] Effects of human land-use on Africa's only forest-dependent felid: The African golden cat Caracal aurata, Biological Conservation, Volume 199 (2016), pp. 1-9 | DOI
[6] Estimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring, Proceedings of the National Academy of Sciences, Volume 117 (2020) no. 48, pp. 30531-30538 | DOI
[7] Caught in the mesh: roads and their network‐scale impediment to animal movement, Ecography, Volume 40 (2017) no. 12, pp. 1369-1380 | DOI
[8] Useful clarity on the value of considering temporal variability in detection probability, Peer Community in Ecology (2023) | DOI
[9] Spatially Explicit Maximum Likelihood Methods for Capture–Recapture Studies, Biometrics, Volume 64 (2008) no. 2, pp. 377-385 | DOI
[10] Heterogeneity in detection probability along the breeding season in Black-legged Kittiwakes: implications for sampling design, Journal of Ornithology, Volume 152 (2010) no. S2, pp. 371-380 | DOI
[11] Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE, Journal of Computational and Graphical Statistics, Volume 26 (2017) no. 2, pp. 403-413 | DOI
[12] Does the punishment fit the crime? Consequences and diagnosis of misspecified detection functions in Bayesian spatial capture–recapture modeling, Ecology and Evolution, Volume 12 (2022) no. 2 | DOI
[13] Density estimation in live‐trapping studies, Oikos, Volume 106 (2004) no. 3, pp. 598-610 | DOI
[14] secr: Spatially explicit capture-recapture models. R package version 4.5.6., 2022 (https://CRAN.R-project.org/package=secr)
[15] Varying effort in capture–recapture studies, Methods in Ecology and Evolution, Volume 4 (2013) no. 7, pp. 629-636 | DOI
[16] unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance, Journal of Statistical Software, Volume 43 (2011) no. 10 | DOI
[17] Key seabird areas in southern New England identified using a community occupancy model, Marine Ecology Progress Series, Volume 533 (2015), pp. 277-290 | DOI
[18] Using occupancy modeling to monitor dates of peak vocal activity for passerines in California, The Condor, Volume 120 (2018) no. 1, pp. 188-200 | DOI
[19] The Risk of Flawed Inference in Evolutionary Studies When Detectability Is Less than One, The American Naturalist, Volume 172 (2008) no. 3, pp. 441-448 | DOI
[20] Integrating opportunistic and structured non-invasive surveys with spatial capture-recapture models to map connectivity of the Pyrenean brown bear population, Biological Conservation, Volume 278 (2023) | DOI
[21] AHMbook: Functions and Data for the Book 'Applied Hierarchical Modeling in Ecology' Vols 1 and 2. R package version 0.2.6, 2022 (https://CRAN.R-project.org/package=AHMbook)
[22] Modeling Demographic Processes in Marked Populations, Inference about species richness and community structure using species-specific occupancy models in the national Swiss breeding bird survey MHB (Environmental and Ecological Statistics, vol 3.), Springer, 2009, pp. 639-656
[23] Applied hierarchical modelling in ecology—Modeling distribution, abundance and species richness using R and BUGS, Academic Press, New York, NY, USA, 2015
[24] Calculating waterfowl abundance on the basis of banding returns, United States Department of Agriculture, Washintong D.C., USA, 1930, pp. 1-4 (https://ia801906.us.archive.org/9/items/calculatingwater118linc/calculatingwater118linc.pdf)
[25] Estimating site occupancy rates when detection probabilities are less than one, Ecology, Volume 83 (2002) no. 8, pp. 2248-2255 | DOI
[26] Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence, Academic Press, 2017
[27] Consequences of ignoring variable and spatially autocorrelated detection probability in spatial capture-recapture, Landscape Ecology, Volume 36 (2021) no. 10, pp. 2879-2895 | DOI
[28] Performance of several variable‐selection methods applied to real ecological data, Ecology Letters, Volume 12 (2009) no. 10, pp. 1061-1068 | DOI
[29] Statistical concepts: assessing spatial distributions, Monitoring tigers and their prey: A manual for wildlife researchers, managers and conservationists in tropical Asia, Centre for Wildlife Studies, 2002, pp. 29-38
[30] Statistical inference from capture data on closed animal populations, Wildlife Monographs, Volume 62 (1978), pp. 3-135 (https://www.jstor.org/stable/3830650)
[31] Statistical inference for capture-recapture experiments, Wildlife Monographs, Volume 107 (1990), pp. 3-97 (https://www.jstor.org/stable/3830560)
[32] Large scale wildlife monitoring studies: statistical methods for design and analysis, Environmetrics, Volume 13 (2002) no. 2, pp. 105-119 | DOI
[33] R: A language and environment for statistical computing, 2022 (https://www.R-project.org/)
[34] Two‐species occupancy models: a new parameterization applied to co‐occurrence of secretive rails, Ecological Applications, Volume 20 (2010) no. 7, pp. 2036-2046 | DOI
[35] Effects of Prior Detections on Estimates of Detection Probability, Abundance, and Occupancy, The Auk, Volume 127 (2010) no. 1, pp. 94-99 | DOI
[36] Spatial capture-recapture, Academic Press, Waltham, MA, USA., 2014
[37] Hierarchical modeling and inference in ecology: the analysis of data from populations, metapopulations and communities, Academic Press, London, UK, 2008
[38] Spatial capture–recapture models allowing Markovian transience or dispersal, Population Ecology, Volume 58 (2015) no. 1, pp. 53-62 | DOI
[39] Estimating abundance from repeated presence–absence data or point counts, Ecology, Volume 84 (2003) no. 3, pp. 777-790 | DOI
[40] A hierarchical model for spatial capture–recapture data, Ecology, Volume 89 (2008) no. 8, pp. 2281-2289 | DOI
[41] Überwachung der Bestandsentwicklung häufiger Brutvögel in der Schweiz, 2004
[42] Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models; Supplementary Information, Zenodo, 2023 | DOI
[43] rasrage/Sollmann_2023_PCJ: Revisions (v1.1), Zenodo, 2023 | DOI
[44] Improving density estimates for elusive carnivores: Accounting for sex-specific detection and movements using spatial capture–recapture models for jaguars in central Brazil, Biological Conservation, Volume 144 (2011) no. 3, pp. 1017-1024 | DOI
[45] Studying phenology by flexible modelling of seasonal detectability peaks, Methods in Ecology and Evolution, Volume 5 (2014) no. 5, pp. 483-490 | DOI
[46] Efficient estimation of large‐scale spatial capture–recapture models, Ecosphere, Volume 12 (2021) no. 2 | DOI
[47] Improving precision and reducing bias in biological surveys: estimating false‐negative error rates, Ecological Applications, Volume 13 (2003) no. 6, pp. 1790-1801 | DOI
[48] Temporal sampling frame selection in DNA-based capture–mark–recapture investigations, Ursus, Volume 23 (2012) no. 1, pp. 42-51 | DOI
[49] Survey frequency and timing affect occupancy and abundance estimates for salt marsh birds, The Journal of Wildlife Management, Volume 80 (2015) no. 1, pp. 48-56 | DOI
[50] Analysis and management of animal populations, Academic Press, San Diego, CA, US, 2002
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