A New Method for Estimation of Resource Selection Probability Function

2009 ◽  
Vol 73 (1) ◽  
pp. 122-127 ◽  
Author(s):  
Subhash R. Lele
Keyword(s):  
Resource Selection ◽  
Probability Function ◽  
New Method ◽  
Selection Probability

scholarly journals Estimating a resource selection function with line transect sampling

2002 ◽  
Vol 6 (4) ◽  
pp. 213-228 ◽  
Author(s):  
Bryan F. J. Manly
Keyword(s):  
Resource Selection ◽  
Probability Function ◽  
Resource Selection Function ◽  
Line Transect ◽  
Selection Function ◽  
Line Transect Sampling ◽  
Selection Probability ◽  
Transect Sampling ◽  
Conditional Maximum ◽  
Habitat Variables

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 modified sampling scheme is examined, with the used units obtained by line transect sampling. A logistic regression type of model is proposed, with estimation by conditional maximum likelihood. A simulation study indicates that the proposed method should be useful in practice.

Dynamics of opinion formation with strengthen selection probability

2014 ◽  
Vol 25 (10) ◽  
pp. 1450050 ◽  
Author(s):  
Haifeng Zhang ◽  
Zhen Jin ◽  
Binghong Wang
Keyword(s):  
Complex Networks ◽  
Majority Rule ◽  
Probability Function ◽  
Adjustable Parameter ◽  
Opinion Formation ◽  
Selection Probability ◽  
Optimal Value ◽  
Linear Selection ◽  
Linear Probability ◽  
Regular Networks

The local majority rule is extensively accepted as a paradigmatic model to reflect the formation of opinion. In this paper, we study a model of opinion formation where opinion update rule is not based on the majority rule or linear selection probability but on a strengthen selection probability controlled by an adjustable parameter β. In particular, our proposed probability function can proximately fit the two extreme cases–linear probability function and majority rule or in between the two cases under different values of β. By studying such model on different kinds of networks, including different regular networks and complex networks, we find that there exists an optimal value of β giving the most efficient convergence to consensus regardless of the topology of networks. This work reveals that, compared with the majority rule and linear selection probability, the strengthen selection probability might be a more proper model in understanding the formation of opinions in society.

scholarly journals First records on habitat use of semi-feral cattle in southern forests: topography reveals more than vegetation

2015 ◽  
Author(s):  
Nicolás Seoane
Keyword(s):  
Habitat Selection ◽  
Habitat Use ◽  
Resource Selection ◽  
Beech Forest ◽  
Individual Variability ◽  
Geographical Information ◽  
Gps Tracking ◽  
Suitable Model ◽  
Selection Probability ◽  
Southern Beech

While the presence of cattle in forests is quite common, how they use this habitat is often overlooked. When this is examined, most studies focus on measurements of the vegetation variables influencing habitat selection. This current report provides a suitable model to study habitat use by livestock in forested areas by means of GPS tracking on selected individuals. The model was applied to data from semi-feral cattle in order to obtain the first description of their habitat use in southern forests. Furthermore, the model accounted for individual variability, and hinted at population patterns of habitat use. The positions of 15 individual cows with GPS collars were recorded covering twelve months in a Nothofagus (southern beech) forest in Patagonia (Argentina). By projecting these GPS location data into a geographical information system (GIS), a resource selection probability function (RSPF) that considers topographic and vegetation variables was built. The habitat selection by semi-feral cattle in southern beech forests showed a large interindividual variability, but also some similar characteristics which enable a proper description of habitat-use patterns. It was found that habitat selection by cattle was mainly affected by topographic variables such as altitude and the combination of slope and aspect. In both cases the variables were selected below average relative to availability, suggesting a preferred habitat range. Livestock also tended to avoid areas of closed shrublands and showed a slight preference for meadows. Cattle give significant importance to topographic variables to define their habitat selection in this type of mountainous forests. This might be because of an ecological adaptation to the major features of these types of forests due to ferality. Furthermore, these results are the basis for management applications such as predictive maps of use by semi-feral livestock in forested landscapes.

scholarly journals Seasonal resource selection of woodland caribou (Rangifer tarandus caribou) across a gradient of anthropogenic disturbance

2016 ◽  
Vol 94 (2) ◽  
pp. 79-93 ◽  
Author(s):  
Megan L. Hornseth ◽  
Robert S. Rempel
Keyword(s):  
Anthropogenic Disturbance ◽  
Resource Selection ◽  
Rangifer Tarandus ◽  
The Other ◽  
Hudson Bay ◽  
Rangifer Tarandus Caribou ◽  
Woodland Caribou ◽  
Selection Probability ◽  
Hudson Bay Lowlands ◽  
Selection Of

Resource selection functions are useful tools for land-use planning, especially for wide-ranging species with sensitivity to anthropogenic disturbance. We evaluated five a priori hypotheses describing seasonal habitat selection of woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)) across three regions in northern Ontario. Two regions were Boreal Shield dominated, one area with relatively high anthropogenic disturbance (due to commercial forestry) and the other with relatively low anthropogenic disturbance. The final region was located on the wetland-dominated Hudson Bay Lowlands. Each region encompassed two caribou management ranges: one was used for model development and the other for model evaluation. We developed seasonal resource selection probability functions using seasonal utilization distributions and isopleths derived from GPS collar data (from 212 caribou) to identify high- and low-use areas. We explored selection across five spatial scales; selection patterns were strongest at the 10 000 ha scale. We found temporal and spatial variations in all environmental predictors across ranges and seasons, especially in the Hudson Bay Lowlands. Our results consistently supported the integrated global model (with common variables but range-specific coefficients) where caribou habitat use is related to minimizing apparent competition with moose (Alces alces (L., 1758)) while avoiding disturbed areas, and utilizing areas with adequate forage.

scholarly journals Empirical Likelihood for Multidimensional Linear Model with Missing Responses

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Liping Zhu
Keyword(s):  
Linear Regression ◽  
Likelihood Ratio ◽  
Empirical Likelihood ◽  
Missing Values ◽  
Probability Function ◽  
Likelihood Ratio Statistic ◽  
Selection Probability ◽  
Missing Responses ◽  
Chi Squared ◽  
Multidimensional Linear

Imputation is a popular technique for handling missing data especially for plenty of missing values. Usually, the empirical log-likelihood ratio statistic under imputation is asymptotically scaled chi-squared because the imputing data are not i.i.d. Recently, a bias-corrected technique is used to study linear regression model with missing response data, and the resulting empirical likelihood ratio is asymptotically chi-squared. However, it may suffer from the “the curse of high dimension” in multidimensional linear regression models for the nonparametric estimator of selection probability function. In this paper, a parametric selection probability function is introduced to avoid the dimension problem. With the similar bias-corrected method, the proposed empirical likelihood statistic is asymptotically chi-squared when the selection probability is specified correctly and even asymptotically scaled chi-squared when specified incorrectly. In addition, our empirical likelihood estimator is always consistent whether the selection probability is specified correctly or not, and will achieve full efficiency when specified correctly. A simulation study indicates that the proposed method is comparable in terms of coverage probabilities.

scholarly journals First records on habitat use of semi-feral cattle in southern forests: topography reveals more than vegetation

2015 ◽  
Author(s):  
Nicolás Seoane
Keyword(s):  
Habitat Selection ◽  
Habitat Use ◽  
Resource Selection ◽  
Beech Forest ◽  
Individual Variability ◽  
Geographical Information ◽  
Gps Tracking ◽  
Suitable Model ◽  
Selection Probability ◽  
Southern Beech

While the presence of cattle in forests is quite common, how they use this habitat is often overlooked. When this is examined, most studies focus on measurements of the vegetation variables influencing habitat selection. This current report provides a suitable model to study habitat use by livestock in forested areas by means of GPS tracking on selected individuals. The model was applied to data from semi-feral cattle in order to obtain the first description of their habitat use in southern forests. Furthermore, the model accounted for individual variability, and hinted at population patterns of habitat use. The positions of 15 individual cows with GPS collars were recorded covering twelve months in a Nothofagus (southern beech) forest in Patagonia (Argentina). By projecting these GPS location data into a geographical information system (GIS), a resource selection probability function (RSPF) that considers topographic and vegetation variables was built. The habitat selection by semi-feral cattle in southern beech forests showed a large interindividual variability, but also some similar characteristics which enable a proper description of habitat-use patterns. It was found that habitat selection by cattle was mainly affected by topographic variables such as altitude and the combination of slope and aspect. In both cases the variables were selected below average relative to availability, suggesting a preferred habitat range. Livestock also tended to avoid areas of closed shrublands and showed a slight preference for meadows. Cattle give significant importance to topographic variables to define their habitat selection in this type of mountainous forests. This might be because of an ecological adaptation to the major features of these types of forests due to ferality. Furthermore, these results are the basis for management applications such as predictive maps of use by semi-feral livestock in forested landscapes.

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