Literature DB >> 30601582

Non-circular home ranges and the estimation of population density.

M G Efford1.   

Abstract

Spatially explicit capture-recapture (SECR) models have emerged as one solution to the problem of estimating the population density of mobile and cryptic animals. Spatial models embody assumptions regarding the spatial distribution of individuals and the spatial detection process. The detection process is modeled in SECR as a radial decline in detection probability with distance from the activity center of each individual. This would seem to require that home ranges are circular. The robustness of SECR when home ranges are not circular has been the subject of conflicting statements. Ivan et al. previously compared the SECR density estimator to a telemetry-scaled non-spatial estimator. I suggest that the apparent non-robustness of SECR in their study was a simulation artefact. New simulations of elliptical home ranges establish that the SECR density estimator is largely robust to non-circularity when detectors are spread in two dimensions, but may be very biased if the detector array is linear and home ranges align with the array. Transformation to isotropy reduces bias from designs of intermediate dimension, such as hollow square arrays. Possible alignment of home ranges should be considered when designing detector arrays.
© 2019 by the Ecological Society of America.

Keywords:  anisotropic detection function; density estimation; home range; non-circularity; radiotelemetry; spatially explicit capture-recapture; study design; telemetry-scaled non-spatial estimator

Mesh:

Year:  2019        PMID: 30601582     DOI: 10.1002/ecy.2580

Source DB:  PubMed          Journal:  Ecology        ISSN: 0012-9658            Impact factor:   5.499


  6 in total

1.  Precision and bias of spatial capture-recapture estimates: A multi-site, multi-year Utah black bear case study.

Authors:  Greta M Schmidt; Tabitha A Graves; Jordan C Pederson; Sarah L Carroll
Journal:  Ecol Appl       Date:  2022-05-17       Impact factor: 6.105

2.  Dingo Density Estimates and Movements in Equatorial Australia: Spatially Explicit Mark-Resight Models.

Authors:  Vanessa Gabriele-Rivet; Julie Arsenault; Victoria J Brookes; Peter J S Fleming; Charlotte Nury; Michael P Ward
Journal:  Animals (Basel)       Date:  2020-05-17       Impact factor: 2.752

3.  Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models.

Authors:  Shannon M Gaukler; Sean M Murphy; Jesse T Berryhill; Brent E Thompson; Benjamin J Sutter; Charles D Hathcock
Journal:  PLoS One       Date:  2020-09-17       Impact factor: 3.240

4.  Leopard and spotted hyena densities in the Lake Mburo National Park, southwestern Uganda.

Authors:  Aleksander Braczkowski; Ralph Schenk; Dinal Samarasinghe; Duan Biggs; Allie Richardson; Nicholas Swanson; Merlin Swanson; Arjun Dheer; Julien Fattebert
Journal:  PeerJ       Date:  2022-01-27       Impact factor: 2.984

5.  Does the punishment fit the crime? Consequences and diagnosis of misspecified detection functions in Bayesian spatial capture-recapture modeling.

Authors:  Soumen Dey; Richard Bischof; Pierre P A Dupont; Cyril Milleret
Journal:  Ecol Evol       Date:  2022-02-15       Impact factor: 2.912

6.  Leopard density and interspecific spatiotemporal interactions in a hyena-dominated landscape.

Authors:  Sander Vissia; Julien Fattebert; Frank van Langevelde
Journal:  Ecol Evol       Date:  2022-10-05       Impact factor: 3.167

  6 in total

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