Relevant bibliographies by topics / Resource selection probability function (RSPF)

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  2. Dissertations / Theses
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Academic literature on the topic 'Resource selection probability function (RSPF)'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Resource selection probability function (RSPF)"

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O'Neil, Shawn T., Dean E. Beyer, and Joseph K. Bump. "Territorial landscapes: incorporating density-dependence into wolf habitat selection studies." Royal Society Open Science 6, no. 11 (2019): 190282. http://dx.doi.org/10.1098/rsos.190282.

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Habitat selection is a process that spans space, time and individual life histories. Ecological analyses of animal distributions and preferences are most accurate when they account for inherent dynamics of the habitat selection process. Strong territoriality can constrain perception of habitat availability by individual animals or groups attempting to colonize or establish new territory. Because habitat selection is a function of habitat availability, broad-scale changes in habitat availability or occupancy can drive density-dependent habitat functional responses. We investigated density-depen
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Hagani, Jason S., Sara M. Kross, Michael Clark, Rae Wynn-Grant, and Mary Blair. "Mapping and modeling human-black bear interactions in the Catskills region of New York using resource selection probability functions." PLOS ONE 16, no. 9 (2021): e0257716. http://dx.doi.org/10.1371/journal.pone.0257716.

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Black bears (Ursus americanus) are an iconic and common species throughout much of the United States and people regularly interact with these large predators without conflict. However, negative interactions between people and bears can manifest in conflicts that can hinder conservation efforts. Black bears are highly attracted to anthropogenic sources of food, and negative interactions with people are primarily a product of trash mismanagement. In the Catskills region of New York State, home to a large population of black bears, over 400 such conflicts are reported each year. While the New Yor
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Ya, Mao, and Zuo Bing Chen. "Simulation on NOx Generation of RSP Calciner for Cement Production." Advanced Materials Research 535-537 (June 2012): 1647–51. http://dx.doi.org/10.4028/www.scientific.net/amr.535-537.1647.

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At present, cement industry will be the third major pollution source of nitrogen oxide pollution following electric power and automobile industry. According to Chinese 12th Five-Year Plan, nitrogen oxide emission must be decreased 10%. In this paper, the resources of NOx emission from cement production were analyzed. On the basis of forming mechanism of thermal NOx and fuel NOx, a simulation model was constructed to predict NOx generation process in RSP calciner. For the turbulent flow in calciner, a probability density function (PDF) describing time variation is used in model to predict NOx g
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Lele, Subhash R. "A New Method for Estimation of Resource Selection Probability Function." Journal of Wildlife Management 73, no. 1 (2009): 122–27. http://dx.doi.org/10.2193/2007-535.

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Manly, Bryan F. J. "Estimating a resource selection function with line transect sampling." Journal of Applied Mathematics and Decision Sciences 6, no. 4 (2002): 213–28. http://dx.doi.org/10.1155/s1173912602000159.

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A resource selection probability function is a function that gives the prob- ability that a resource unit (e.g., a plot of land) that is described by a set of habitat variables X1 to Xp will be used by an animal or group of animals in a certain period of time. The estimation of a resource selection function is usually based on the comparison of a sample of resource units used by an animal with a sample of the resource units that were available for use, with both samples being assumed to be effectively randomly selected from the relevant populations. In this paper the possibility of using a mod
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Lemaître, Jérôme, and Marc-André Villard. "Foraging patterns of pileated woodpeckers in a managed Acadian forest: a resource selection function." Canadian Journal of Forest Research 35, no. 10 (2005): 2387–93. http://dx.doi.org/10.1139/x05-148.

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We analyzed the relative influence of foraging substrate characteristics as predictors of the probability of use by the pileated woodpecker (Dryocopus pileatus L.) and determined threshold values for significant predictors. We sampled used and available substrates around 126 stations distributed in an intensively managed forest in northwestern New Brunswick, Canada. We developed a resource selection function (RSF), validated by a resampling procedure, and compared selection ratios for significant predictors. Diameter at breast height (DBH) of trees and snags was the most significant predictor,
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Mosloff, Alisha R., Mitch D. Weegman, Frank R. Thompson, and Thomas R. Thompson. "Northern bobwhite select for shrubby thickets interspersed in grasslands during fall and winter." PLOS ONE 16, no. 8 (2021): e0255298. http://dx.doi.org/10.1371/journal.pone.0255298.

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Resource selection is a key component in understanding the ecological processes underlying population dynamics, particularly for species such as northern bobwhite (Colinus virginianus), which are declining across their range in North America. There is a growing body of literature quantifying breeding season resource selection in bobwhite; however, winter information is particularly sparse despite it being a season of substantial mortality. Information regarding winter resource selection is necessary to quantify the extent to which resource requirements are driving population change. We modeled
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Liu, Dingrong, Zhigang Yao, and Liukui Chen. "Emergency Scheduling Optimization Simulation of Cloud Computing Platform Network Public Resources." Complexity 2021 (April 24, 2021): 1–11. http://dx.doi.org/10.1155/2021/9950198.

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Emergency scheduling of public resources on the cloud computing platform network can effectively improve the network emergency rescue capability of the cloud computing platform. To schedule the network common resources, it is necessary to generate the initial population through the Hamming distance constraint and improve the objective function as the fitness function to complete the emergency scheduling of the network common resources. The traditional method, from the perspective of public resource fairness and priority mapping, uses incremental optimization algorithm to realize emergency sche
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Qi, Jie, Bo Zhang, Bin Lyu, and Kurt Marfurt. "Seismic attribute selection for machine-learning-based facies analysis." GEOPHYSICS 85, no. 2 (2020): O17—O35. http://dx.doi.org/10.1190/geo2019-0223.1.

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Interpreters face two main challenges in seismic facies analysis. The first challenge is to define, or “label,” the facies of interest. The second challenge is to select a suite of attributes that can differentiate a target facies from the background reflectivity. Our key objective is to determine which seismic attributes can best differentiate one class of chaotic seismic facies from another using modern machine-learning technology. Although simple 1D histograms provide a list of candidate attributes, they do not provide insight into the optimum number or combination of attributes. To address
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Benício, Ronildo Alves, Daniel Cunha Passos, Abraham Mencía, and Zaida Ortega. "Microhabitat selection of the poorly known lizard Tropidurus lagunablanca (Squamata: Tropiduridae) in the Pantanal, Brazil." Papéis Avulsos de Zoologia 61 (February 18, 2021): e20216118. http://dx.doi.org/10.11606/1807-0205/2021.61.18.

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Understanding how different environmental factors influence species occurrence is a key issue to address the study of natural populations. However, there is a lack of knowledge on how local traits influence the microhabitat use of tropical arboreal lizards. Here, we investigated the microhabitat selection of the poorly known lizard Tropidurus lagunablanca (Squamata: Tropiduridae) and evaluated how environmental microhabitat features influence animal’s presence. We used a Resource Selection Function approach, in a case/control design where we analyzed the effect of substrate temperature and tre
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Dissertations / Theses on the topic "Resource selection probability function (RSPF)"

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Cardozo, Sandra Vergara. "Função da probabilidade da seleção do recurso (RSPF) na seleção de habitat usando modelos de escolha discreta." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-11032009-143806/.

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Em ecologia, o comportamento dos animais é freqüentemente estudado para entender melhor suas preferências por diferentes tipos de alimento e habitat. O presente trabalho esta relacionado a este tópico, dividindo-se em três capítulos. O primeiro capitulo refere-se à estimação da função da probabilidade da seleção de recurso (RSPF) comparado com um modelo de escolha discreta (DCM) com uma escolha, usando as estatísticas qui-quadrado para obter as estimativas. As melhores estimativas foram obtidas pelo método DCM com uma escolha. No entanto, os animais não fazem a sua seleção baseados apenas em u
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Book chapters on the topic "Resource selection probability function (RSPF)"

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Manly, Bryan F. J., Lyman L. McDonaldd, and Dana L. Thomas. "Estimating a resource selection probability function from a census of resource units using logistic regression." In Resource Selection by Animals. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1558-2_5.

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Manly, Bryan F. J., Lyman L. McDonaldd, and Dana L. Thomas. "Estimating a resource selection probability function from a census of resource units at several points in time using the proportional hazards model." In Resource Selection by Animals. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1558-2_6.

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