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dc.contributor.authorStober, Jonathan M.
dc.contributor.authorPrieto-Gonzalez, Rocio
dc.contributor.authorSmith, Lora L.
dc.contributor.authorMarques, Tiago A.
dc.contributor.authorThomas, Len
dc.date.accessioned2018-11-12T12:30:06Z
dc.date.available2018-11-12T12:30:06Z
dc.date.issued2017-12-01
dc.identifier255606712
dc.identifier19e23785-eb05-40be-8c15-78791ebff7cc
dc.identifier85038865330
dc.identifier000418056300004
dc.identifier.citationStober , J M , Prieto-Gonzalez , R , Smith , L L , Marques , T A & Thomas , L 2017 , ' Techniques for estimating the size of low-density gopher tortoise populations ' , Journal of Fish and Wildlife Management , vol. 8 , no. 2 , pp. 377-386 . https://doi.org/10.3996/012017-JFWM-005en
dc.identifier.issn1944-687X
dc.identifier.otherORCID: /0000-0002-7436-067X/work/54818842
dc.identifier.otherORCID: /0000-0002-2581-1972/work/56861247
dc.identifier.urihttps://hdl.handle.net/10023/16434
dc.descriptionPartial support by CEAUL (funded by Fundação para a Ciência e a Tecnologia, Portugal, through the project UID/MAT/00006/2013) (TAM).en
dc.description.abstractGopher tortoises (Gopherus polyphemus) are candidates for range-wide listing as threatened under the U.S. Endangered Species Act. Reliable population estimates are important to inform policy and management for recovery of the species. Line transect distance sampling has been adopted as the preferred method to estimate population size. However, when tortoise density is low, it can be challenging to obtain enough tortoise observations to reliably estimate the probability of detection, a vital component of the method. We suggest a modification to the method based on counting usable tortoise burrows (more abundant than tortoises) and separately accounting for the proportion of burrows occupied by tortoises. The increased sample size of burrows can outweigh the additional uncertainty induced by the need to account for the proportion of burrows occupied. We demonstrate the method using surveys conducted within a 13,118-ha portion of the Gopher Tortoise Habitat Management Unit at Fort Gordon Army Installation, Georgia. We used a systematic random design to obtain more precise estimates, using a newly developed systematic variance estimator. Individual transects had a spatially efficient design (pseudocircuits), which greatly improved sampling efficiency on this large site. Estimated burrow density was 0.091 ± 0.011 burrows/ha (CV = 12.6%, 95% CI = 0.071–0.116), with 25% of burrows occupied by a tortoise (CV = 14.4%), yielding a tortoise density of 0.023 ± 0.004 tortoise/ha (CV = 19.0%, 95% CI = 0.016–0.033) and a population estimate of 297 tortoises (95% CI = 210–433). These techniques are applicable to other studies and species. Surveying burrows or nests, rather than animals, can produce more reliable estimates when it leads to a significantly larger sample of detections and when the occupancy status can reliably be ascertained. Systematic line transect survey designs give better precision and are practical to implement and analyze.
dc.format.extent10
dc.format.extent2990155
dc.language.isoeng
dc.relation.ispartofJournal of Fish and Wildlife Managementen
dc.subjectAbundanceen
dc.subjectBurrowsen
dc.subjectCluster size analysisen
dc.subjectGopher tortoiseen
dc.subjectLine transect distance samplingen
dc.subjectPopulation densityen
dc.subjectSystematic samplingen
dc.subjectQH301 Biologyen
dc.subjectQL Zoologyen
dc.subjectAnimal Science and Zoologyen
dc.subjectEcology, Evolution, Behavior and Systematicsen
dc.subjectEcologyen
dc.subjectNature and Landscape Conservationen
dc.subjectNDASen
dc.subject.lccQH301en
dc.subject.lccQLen
dc.titleTechniques for estimating the size of low-density gopher tortoise populationsen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.identifier.doi10.3996/012017-JFWM-005
dc.description.statusPeer revieweden


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