Estimating Site Occupancy Rates When Detection Probabilities Are Less Than One

MacKenzie, Darryl I.; Nichols, James D.; Lachman, Gideon B.; Droege, Sam; Royle, J. Andrew; Langtimm, Catherine A.

doi:10.2307/3072056

Cited by 999 publications

(1,787 citation statements)

References 16 publications

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“…Diagnosing the leading edge in the spatial spread of a cryptic disease is notoriously difficult (Mertens and LowBeer, 1996;Filipe et al, 2012). Most methods for accommodating the potential for false absences require repeated surveys to control for detection when modeling species occurrence (MacKenzie et al, 2002;Tyre et al, 2003;Royle et al 2005). Unfortunately, multiple visits to hibernacula during winter are recommended against, according to the guidelines for ''Census Taking'' in the 1983 Indiana bat recovery plan (Appendix VI in USFWS, 1983;Brack et al, 1983).…”

Section: Discussionmentioning

confidence: 99%

Space-Time Models for a Panzootic in Bats, With a Focus on the Endangered Indiana Bat

Thogmartin

King

Szymanski

et al. 2012

Journal of Wildlife Diseases

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ABSTRACT:Knowledge of current trends of quickly spreading infectious wildlife diseases is vital to efficient and effective management. We developed space-time mixed-effects logistic regressions to characterize a disease, white-nose syndrome (WNS), quickly spreading among endangered Indiana bats (Myotis sodalis) in eastern North America. Our goal was to calculate and map the risk probability faced by uninfected colonies of hibernating Indiana bats. Model covariates included annual distance from and direction to nearest sources of infection, geolocational information, size of the Indiana bat populations within each wintering population, and total annual size of populations known or suspected to be affected by WNS. We considered temporal, spatial, and spatiotemporal formulae through the use of random effects for year, complex (a collection of interacting hibernacula), and year3complex. Since first documented in 2006, WNS has spread across much of the range of the Indiana bat. No sizeable wintering population now occurs outside of the migrational distance of an infected source. Annual rates of newly affected wintering Indiana bat populations between winter 2007 to 2008 and 2010 to 2011 were 4, 6, 8, and 12%; this rate increased each year at a rate of 3%. If this increasing rate of newly affected populations continues, all wintering populations may be affected by 2016. Our models indicated the probability of a wintering population exhibiting infection was a linear function of proximity to affected Indiana bat populations and size of the at-risk population. Geographic location was also important, suggesting broad-scale influences. For every 50-km increase in distance from a WNS-affected population, risk of disease declined by 6% (95% CI55.2-5.7%); for every increase of 1,000 Indiana bats, there was an 8% (95% CI51-21%) increase in disease risk. The increasing rate of infection seems to be associated with the movement of this disease into the core of the Indiana bat range. Our spatially explicit estimates of disease risk may aid managers in prioritizing surveillance and management for wintering populations of Indiana bats and help understand the risk faced by other hibernating bat species.

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Section: Discussionmentioning

confidence: 99%

Space-Time Models for a Panzootic in Bats, With a Focus on the Endangered Indiana Bat

Thogmartin

King

Szymanski

et al. 2012

Journal of Wildlife Diseases

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“…The MSOM is an extension of the single-species, single-season occupancy model [86] that combines and analyzes the history of detections and non-detections (denoted by 1s and 0s, respectively) of all species encountered during replicated surveys at a set of sites [14]. The hierarchical model includes three levels, one each for species (i), site (j), and replicate (k).…”

Section: Box 1 Multispecies Models For Estimating Occupancy and Abunmentioning

confidence: 99%

Detecting diversity: emerging methods to estimate species diversity

Iknayan

Tingley

Furnas

et al. 2014

Trends in Ecology & Evolution

298

393

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“…In order to improve the knowledge of species use of buffer zones, occupancy modeling based on camera trap data can be used to compare the occurrence of a given species among different sites such as the interior and the buffer zone of a PA (Mackenzie et al 2002, O'Connell 2011. This approach has the differential of dealing with imperfect detection, because individuals may not be detected due to sampling limitations or low population density, which is common issue for several species of medium and large-sized tropical mammals (Mackenzie et al 2002, O'Connell et al 2006.…”

Section: Introductionmentioning

confidence: 99%

Buffer zone use by mammals in a Cerrado protected area

et al. 2016

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Habitat loss and degradation is threatening mammals worldwide. Therefore, Protected Areas (PA) are of utmost importance to preserve biodiversity. Their effectiveness, however, depends on some management strategies such as buffer zones, which prevent/mitigate the impact of external threats and might increase the amount of available habitat for wildlife existing within reserves. Nevertheless, how intensively terrestrial mammals use buffer zones remains little studied, particularly in the Neotropical region. Aiming to analyse the use of a buffer zone (5 km wide) by medium and large-sized mammals, we modelled the occupancy probabilities of five species of conservation concern including local (interior and buffer zone) as a site covariate, simultaneously controlling for imperfect detection. Data collection was made with camera traps from April to September 2013 in a 9000 ha Cerrado PA (“interior”) and in its surrounding area (39721.41 ha; “buffer zone”). This PA (Jataí Ecological Station) is immersed in a landscape where sugarcane plantations predominate in the northeastern of the state of São Paulo. We also conducted an inventory to compare the number and composition of species between interior and buffer zone. A total of 31 mammal species (26 natives) was recorded via camera traps and active search for sightings, vocalizations, tracks and signs. Occupancy estimates for Myrmecophaga tridactyla, Leopardus pardalis and Pecari tajacu were numerically higher in interior. On the other hand, Chrysocyon brachyurus had the highest occupancy in buffer zone, while the largest predator, Puma concolor, used both areas similarly. However, as the confidence intervals (95%) overlapped, the differences in occupancy probabilities between interior and buffer were weak for all these species. Additionally, regarding only the species recorded by cameras, the observed and estimated richness were similar between interior and buffer zone of the PA. Our data demonstrated that the buffer zone is indeed used by medium and large-sized mammals, including conservation-dependent ones. The lack of enforcement of current legislation regarding buffer zones is therefore a real threat for mammals, even when protection is guaranteed in the interior of protected areas.

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Estimating Site Occupancy Rates When Detection Probabilities Are Less Than One

Cited by 999 publications

References 16 publications

Space-Time Models for a Panzootic in Bats, With a Focus on the Endangered Indiana Bat

Space-Time Models for a Panzootic in Bats, With a Focus on the Endangered Indiana Bat

Detecting diversity: emerging methods to estimate species diversity

Buffer zone use by mammals in a Cerrado protected area

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