Effects of sagebrush treatments on multi-scale resource selection by pygmy rabbits

2011 ◽  
Vol 75 (2) ◽  
pp. 393-398 ◽  
Author(s):  
Tammy L. Wilson ◽  
Frank P. Howe ◽  
Thomas C. Edwards
Keyword(s):  
Resource Selection ◽  
Multi Scale ◽  
Pygmy Rabbits

scholarly journals Predicting queue wait time probabilities for multi-scale computing

2019 ◽  
Vol 377 (2142) ◽  
pp. 20180151 ◽  
Author(s):  
Vytautas Jancauskas ◽  
Tomasz Piontek ◽  
Piotr Kopta ◽  
Bartosz Bosak
Keyword(s):  
Resource Selection ◽  
High Performance ◽  
Wait Time ◽  
Regression Methods ◽  
Multi Scale ◽  
Time To Completion ◽  
Probability Estimates ◽  
Single Number ◽  
Rest Api ◽  
Performance Computing

We describe a method for queue wait time prediction in supercomputing clusters. It was designed for use as a part of multi-criteria brokering mechanisms for resource selection in a multi-site High Performance Computing environment. The aim is to incorporate the time jobs stay queued in the scheduling system into the selection criteria. Our method can also be used by the end users to estimate the time to completion of their computing jobs. It uses historical data about the particular system to make predictions. It returns a list of probability estimates of the form ( t i ,  p i ), where p i is the probability that the job will start before time t i . Times t i can be chosen more or less freely when deploying the system. Compared to regression methods that only return a single number as a queue wait time estimate (usually without error bars) our prediction system provides more useful information. The probability estimates are calculated using the Bayes theorem with the naive assumption that the attributes describing the jobs are independent. They are further calibrated to make sure they are as accurate as possible, given available data. We describe our service and its REST API and the underlying methods in detail and provide empirical evidence in support of the method's efficacy. This article is part of the theme issue ‘Multiscale modelling, simulation and computing: from the desktop to the exascale’.

Modeling multi-scale resource selection for bear rubs in northwestern Montana

Ursus ◽  
2015 ◽  
Vol 26 (1) ◽  
pp. 28-39 ◽  
Author(s):  
Matthew J. Morgan Henderson ◽  
Mark Hebblewhite ◽  
Michael S. Mitchell ◽  
Jeff B. Stetz ◽  
Katherine C. Kendall ◽  
...  
Keyword(s):  
Resource Selection ◽  
Multi Scale ◽  
Northwestern Montana ◽  
Selection For

scholarly journals Multi-scale foraging decisions made by woodland caribou (Rangifer tarandus caribou) in summer

2020 ◽  
Vol 98 (5) ◽  
pp. 331-341
Author(s):  
E.P. McNeill ◽  
I.D. Thompson ◽  
P.A. Wiebe ◽  
G.M. Street ◽  
J. Shuter ◽  
...  
Keyword(s):  
Resource Selection ◽  
Rangifer Tarandus ◽  
Scaling Up ◽  
Rangifer Tarandus Caribou ◽  
Fine Scale ◽  
Patch Selection ◽  
Woodland Caribou ◽  
Movement Trajectories ◽  
Multi Scale ◽  
Selection For

Multi-scale selection patterns can be understood from two perspectives: coarse-scale patterns as the summation of fine-scale patterns (scaling-up), or as a hierarchy produced from multiple contributory factors with differential effects on organismal fitness (hierarchical). We examined woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)) selection of foraging locations across two spatiotemporal scales to test whether selection patterns between them were consistent (scaling-up) or different (hierarchical) to determine which framework most accurately describes their foraging behaviour. Seven adult female woodland caribou were equipped with GPS telemetry radio collars outfitted with high-definition video cameras that recorded woodland caribou foraging choices throughout the summer. Fine-scale data from videos combined with direct measurements in the field along movement trajectories obtained from GPS fixes were used to estimate (i) feeding station selection and (ii) food patch selection. We estimated resource selection functions for each scale following a use–availability structure. Woodland caribou exhibited resource selection at both scales. Apart from selection for species of the lichen Cladina (Nyl.) Nyl. and patches associated with high abundance of Cladina, few patterns were consistent across both scales. Our study suggests that even at very fine scales, woodland caribou selection for foraging locations is hierarchical in nature.

scholarly journals Multi-scale Cover Selection by White-tailed Deer, Odocoileus virginianus, in an Agro-forested Landscape

2009 ◽  
Vol 123 (1) ◽  
pp. 32
Author(s):  
Tim L. Hiller ◽  
Henry Campa ◽  
Scott R. Winterstein
Keyword(s):  
Spatial Scale ◽  
Odocoileus Virginianus ◽  
Resource Selection ◽  
Selection Process ◽  
Spatial Scales ◽  
Relative Importance ◽  
Multi Scale ◽  
South Central ◽  
Hierarchical Selection ◽  
And Behavior

Resource selection studies are commonly conducted at a single spatial scale, but this likely does not fully or accurately assess the hierarchical selection process used by animals. We used a multi-spatial scale approach to quantify White-tailed Deer (Odocoileus virginianus) cover selection in south-central Michigan during 2004–2006 by varying definitions of use and availability and ranking the relative importance of cover types under each study design. The number of cover types assigned as selected (proportional use > proportional availability) decreased from coarse (landscape level) to fine (within home range) scales, although at finer scales, selection seemed to be more consistent. Although the relative importance changed substantially across spatial scales, two cover types (conifers, upland deciduous forests) were consistently ranked as the two most important, providing strong evidence of their value to deer in the study area. Testing for resource selection patterns using a multi-spatial scale approach would provide additional insight into the ecology and behavior of a particular species.

scholarly journals Integrating telemetry data into spatial capture-recapture modifies inferences on multi-scale resource selection

2017 ◽  
Author(s):  
Daniel W. Linden ◽  
Alexej P. K. Sirén ◽  
Peter J. Pekins
Keyword(s):  
Resource Selection ◽  
Wildlife Conservation ◽  
Coniferous Forest ◽  
Space Use ◽  
Camera Traps ◽  
Telemetry Data ◽  
Multi Scale ◽  
Management Objectives ◽  
Capture Recapture ◽  
Conservation And Management

AbstractEstimating population size and resource selection functions (RSFs) are common approaches in applied ecology for addressing wildlife conservation and management objectives. Traditionally such approaches have been undertaken separately with different sources of data. Spatial capture-recapture (SCR) provides a framework for jointly estimating density and multi-scale resource selection, and data integration techniques provide opportunities for improving inferences from SCR models. Here we illustrate an application of integrated SCR-RSF modeling to a population of American marten (Martes americana) in alpine forests of northern New England. Spatial encounter data from camera traps were combined with telemetry locations from radio-collared individuals to examine how density and space use varied with spatial environmental features. We compared multi-model inferences between the integrated SCR-RSF model with telemetry and a standard SCR model with no telemetry. The integrated SCR-RSF model supported more complex relationships with spatial variation in third-order resource selection (i.e., individual space use), including selection for areas with shorter distances to mixed coniferous forest and rugged terrain. Both models indicated increased second-order selection (i.e., density) for areas close to mixed coniferous forest, while the integrated SCR-RSF model had a lower effect size due to modulation from spatial variability in space use. Our application of the integrated SCR-RSF model illustrates the improved inferences from spatial encounter data that can be achieved from integrating auxiliary telemetry data. Integrated modeling allows ecologists to join empirical data to ecological theory using a robust quantitative framework to better address conservation and management objectives.

scholarly journals Multi-level, multi-scale resource selection functions and resistance surfaces for conservation planning: Pumas as a case study

PLoS ONE ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. e0179570 ◽  
Author(s):  
Katherine A. Zeller ◽  
T. Winston Vickers ◽  
Holly B. Ernest ◽  
Walter M. Boyce
Keyword(s):  
Conservation Planning ◽  
Resource Selection ◽  
Resource Selection Functions ◽  
Multi Scale ◽  
Multi Level
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