Data for conditional logistic regression for moose

Data used to model the relative probability of used an inter-patch movement for moose in winter as a function of network connectivity and landscape features (Tables 2 and 3). Covariates are provided at observed inter-patch movement and at available inter-patch movement.

Bibliographic Details
Main Authors: Courbin, Nicolas, Fortin, Daniel, Dussault, Christian, Courtois, R.
Format: Dataset
Language:unknown
Published: Dryad Digital Repository 2014
Subjects:
Predator-prey interaction
Graph theory
Connectivity
Spatial game
Côte Nord
Québec
Canada
Boreal forest
Alces alces
Caribou rangifer tarandus
Canis lupus
Rangifer tarandus
Online Access:https://dx.doi.org/10.5061/dryad.h65f4/5
http://datadryad.org/resource/doi:10.5061/dryad.h65f4/5
id ftdatacite:10.5061/dryad.h65f4/5
record_format openpolar
spelling ftdatacite:10.5061/dryad.h65f4/5 2023-05-15T13:12:48+02:00 Data for conditional logistic regression for moose Courbin, Nicolas Fortin, Daniel Dussault, Christian Courtois, R. 2014 https://dx.doi.org/10.5061/dryad.h65f4/5 http://datadryad.org/resource/doi:10.5061/dryad.h65f4/5 unknown Dryad Digital Repository https://dx.doi.org/10.5061/dryad.h65f4 http://creativecommons.org/publicdomain/zero/1.0 CC0 Predator-prey interaction Graph theory Connectivity Spatial game Côte Nord Québec Canada Boreal forest Alces alces Caribou rangifer tarandus Canis lupus dataset Dataset DataFile 2014 ftdatacite https://doi.org/10.5061/dryad.h65f4/5 https://doi.org/10.5061/dryad.h65f4 2021-11-05T12:55:41Z Data used to model the relative probability of used an inter-patch movement for moose in winter as a function of network connectivity and landscape features (Tables 2 and 3). Covariates are provided at observed inter-patch movement and at available inter-patch movement. Dataset Alces alces Canis lupus Rangifer tarandus DataCite Metadata Store (German National Library of Science and Technology) Canada
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Predator-prey interaction
Graph theory
Connectivity
Spatial game
Côte Nord
Québec
Canada
Boreal forest
Alces alces
Caribou rangifer tarandus
Canis lupus
spellingShingle Predator-prey interaction
Graph theory
Connectivity
Spatial game
Côte Nord
Québec
Canada
Boreal forest
Alces alces
Caribou rangifer tarandus
Canis lupus
Courbin, Nicolas
Fortin, Daniel
Dussault, Christian
Courtois, R.
Data for conditional logistic regression for moose
topic_facet Predator-prey interaction
Graph theory
Connectivity
Spatial game
Côte Nord
Québec
Canada
Boreal forest
Alces alces
Caribou rangifer tarandus
Canis lupus
description Data used to model the relative probability of used an inter-patch movement for moose in winter as a function of network connectivity and landscape features (Tables 2 and 3). Covariates are provided at observed inter-patch movement and at available inter-patch movement.
format Dataset
author Courbin, Nicolas
Fortin, Daniel
Dussault, Christian
Courtois, R.
author_facet Courbin, Nicolas
Fortin, Daniel
Dussault, Christian
Courtois, R.
author_sort Courbin, Nicolas
title Data for conditional logistic regression for moose
title_short Data for conditional logistic regression for moose
title_full Data for conditional logistic regression for moose
title_fullStr Data for conditional logistic regression for moose
title_full_unstemmed Data for conditional logistic regression for moose
title_sort data for conditional logistic regression for moose
publisher Dryad Digital Repository
publishDate 2014
url https://dx.doi.org/10.5061/dryad.h65f4/5
http://datadryad.org/resource/doi:10.5061/dryad.h65f4/5
geographic Canada
geographic_facet Canada
genre Alces alces
Canis lupus
Rangifer tarandus
genre_facet Alces alces
Canis lupus
Rangifer tarandus
op_relation https://dx.doi.org/10.5061/dryad.h65f4
op_rights http://creativecommons.org/publicdomain/zero/1.0
op_rightsnorm CC0
op_doi https://doi.org/10.5061/dryad.h65f4/5
https://doi.org/10.5061/dryad.h65f4
_version_ 1766254147921772544

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