A Two-Species Occupancy Model with a Continuous-Time Detection Process Reveals Spatial and Temporal Interactions

2022 ◽  
Author(s):  
Kenneth F. Kellner ◽  
Arielle W. Parsons ◽  
Roland Kays ◽  
Joshua J. Millspaugh ◽  
Christopher T. Rota
Keyword(s):  
Continuous Time ◽  
Occupancy Model ◽  
Detection Process ◽  
Species Occupancy ◽  
Temporal Interactions

scholarly journals A two-species occupancy model accommodating simultaneous spatial and interspecific dependence

Ecology ◽  
2016 ◽  
Vol 97 (1) ◽  
pp. 48-53 ◽  
Author(s):  
Christopher T. Rota ◽  
Christopher K. Wikle ◽  
Roland W. Kays ◽  
Tavis D. Forrester ◽  
William J. McShea ◽  
...  
Keyword(s):  
Occupancy Model ◽  
Species Occupancy

scholarly journals Occupancy and abundance of large macaws in the Beni savannahs, Bolivia

Oryx ◽  
2014 ◽  
Vol 50 (1) ◽  
pp. 113-120 ◽  
Author(s):  
Igor Berkunsky ◽  
Rosana E. Cepeda ◽  
Claudia Marinelli ◽  
M. Verónica Simoy ◽  
Gonzalo Daniele ◽  
...  
Keyword(s):  
Species Conservation ◽  
Probability Of Detection ◽  
The Other ◽  
Wild Populations ◽  
Efficient Tool ◽  
Occupancy Model ◽  
Priority Areas ◽  
Cavity Nesting ◽  
Species Occupancy ◽  
Landscape Level

AbstractMonitoring of wild populations is central to species conservation and can pose a number of challenges. To identify trends in populations of parrots, monitoring programmes that explicitly take detectability into account are needed. We assessed an occupancy model that explicitly accounted for detectability as a tool for monitoring the large macaws of Bolivia's Beni savannahs: the blue-throated Ara glaucogularis, blue-and-yellow Ara ararauna and red-and-green macaws Ara chloropterus. We also evaluated the joint presence of the three macaw species and estimated their abundance in occupied areas. We modelled occupancy and detection for the three macaw species by combining several site and visit covariates and we described their conditional occupancy. Macaws occupied two thirds of the surveyed area and at least two species occurred together in one third of this area. Probability of detection was 0.48–0.86. For each macaw species, occupancy was affected by the abundance of the other two species, the richness of cavity-nesting species, and the distance to the nearest village. We identified key priority areas for the conservation of these macaws. The flexibility of occupancy methods provides an efficient tool for monitoring macaw occupancy at the landscape level, facilitating prediction of the range of macaw species at a large number of sites, with relatively little effort. This technique could be used in other regions in which the monitoring of threatened parrot populations requires innovative approaches.

Collision Detection of 3D Complex Objects

1994 ◽  
Author(s):  
Rong Mo ◽  
Horst Nowacki
Keyword(s):  
Discrete Time ◽  
Collision Detection ◽  
Continuous Time ◽  
Surface Modeling ◽  
Second Step ◽  
Time Interval ◽  
Parametric Surface ◽  
Complex Objects ◽  
Practical Applications ◽  
Detection Process

Abstract Collision detection of 3D complex objects is often needed for practical applications. In this paper, a new algorithm for testing collision is proposed. The algorithm combines the sweeping technique and a parametric surface modeling method. A collision detection process is carried out firstly in continuous time using sweeping, in order to determine a time interval for collision as early as possible. In the second step the collision detection is performed at discrete time in this time interval, so that exact collision positions and times are found.

Utilization of a species occupancy model for management and conservation

2012 ◽  
Vol 36 (3) ◽  
pp. 432-439 ◽  
Author(s):  
Tiffany M. McFarland ◽  
Heather A. Mathewson ◽  
Julie E. Groce ◽  
Michael L. Morrison ◽  
J. Cal Newnam ◽  
...  
Keyword(s):  
Occupancy Model ◽  
Species Occupancy ◽  
Management And Conservation

scholarly journals TRAMPOline: a Temporal Relative Abundance-focused multi-sPecies Occupancy model, illustrated using fossils

2021 ◽  
Author(s):  
Trond Reitan ◽  
Torbjørn Ergon ◽  
Lee Hsiang Liow
Keyword(s):  
Relative Abundance ◽  
Biased Sampling ◽  
Geological Time ◽  
Modeling Framework ◽  
Time Intervals ◽  
Occupancy Model ◽  
Focal Species ◽  
Additional Information ◽  
Wide Range ◽  
Species Occupancy

The occupancy and relative abundance of species are temporally varying. Estimating these, given incomplete and biased sampling is challenging, not least for fossilized organisms, where preservation is an additional issue. Here, we describe a relative abundance-focused multi-species occupancy model (TRAMPOline) in a hierarchical Bayesian framework. We designed our model on the basis of the need to understand the dynamics of several focal species over 2.3 million years, by drawing on additional information provided by non-focal species observed in the same fossilized community. We expanded our model by adding random effects of species and time intervals (geological formations) and explored potential explanatory factors (paleoenvironmental proxies) and temporal autocorrelation that could provide extra information on unsampled geological time intervals. Our new model, set in an occupancy modeling framework widely used in ecology but little applied in paleoecology, is applicable across a wide range of questions on species-level dynamics in contemporary and palaeoecological community settings.

Heavy Tails of Discounted Aggregate Claims in the Continuous-Time Renewal Model

2007 ◽  
Vol 44 (02) ◽  
pp. 285-294 ◽  
Author(s):  
Qihe Tang
Keyword(s):  
Asymptotic Formula ◽  
Continuous Time ◽  
Heavy Tails ◽  
Tail Behavior ◽  
Renewal Model ◽  
Discounted Aggregate Claims ◽  
Aggregate Claims ◽  
Tail Asymptotic

We study the tail behavior of discounted aggregate claims in a continuous-time renewal model. For the case of Pareto-type claims, we establish a tail asymptotic formula, which holds uniformly in time.

Hierarchical Bayesian continuous time dynamic modeling.

2018 ◽  
Vol 23 (4) ◽  
pp. 774-799 ◽  
Author(s):  
Charles C. Driver ◽  
Manuel C. Voelkle
Keyword(s):  
Dynamic Modeling ◽  
Continuous Time ◽  
Hierarchical Bayesian ◽  
Time Dynamic
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