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dc.contributor.author
Gorosito, Irene Laura
dc.contributor.author
Marziali Bermudez, Mariano
dc.contributor.author
Douglass, Richard J.
dc.contributor.author
Busch, Maria
dc.date.available
2018-06-19T16:16:01Z
dc.date.issued
2016-11
dc.identifier.citation
Gorosito, Irene Laura; Marziali Bermudez, Mariano; Douglass, Richard J.; Busch, Maria; Evaluation of statistical methods and sampling designs for the assessment of microhabitat selection based on point data; British Ecological Society; Methods in Ecology and Evolution; 7; 11; 11-2016; 1316-1324
dc.identifier.issn
2041-210X
dc.identifier.uri
http://hdl.handle.net/11336/49281
dc.description.abstract
Information on resource selection by a species is essential for understanding the species’ ecology, distribution and requirements for survival. Research on habitat selection frequently relies on animal detection at point locations to determine which resource units are used. A variety of approaches and statistical tools can be employed for assessing selection based on habitat variables measured in those units. The aim of this work was to evaluate the reliability of common sampling designs and statistical methods in detecting habitat selection at fine scales based on point data We reviewed literature on microhabitat selection to determine characteristics of typical studies and analysed simulated small-mammal live-trapping data as a case study. We considered various scenarios differing in the number of sampled units and sampling duration. For each scenario, a set of simulated surveys was analysed through two univariate tests (Welch's t- and Mann–Whitney U-test), generalized linear models (GLMs), mixed-effect models (GLMMs) and occupancy models (OMs). Analysis of simulated data revealed that overall performance of all statistical methods improved with increased trapping effort. Univariate tests were especially sensitive to the number of sampling units, while modelling methods took also advantage of longer trapping sessions. Univariate tests and GLMs provided partially correct information in most cases, whereas GLMMs and OMs offered higher probabilities of fully describing simulated habitat preferences. With typical sampling efforts, appropriate statistical analysis of point data is able to provide a moderately accurate description of habitat selection at small scales, in spite of the violation of closure and independence assumptions of applied models. Modelling approaches are proliferating; we encourage using models that can deal with multiple sources of variability, such as GLMMs and OMs, when data are hierarchically structured. There is no a priori best survey design; it should be chosen according to the scope and goals of the study, environment heterogeneity, species characteristics and practical constraints. Researchers should realize that sampling design and statistical methods likely affect conclusions regarding habitat selection.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
British Ecological Society
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Generalized Linear Model
dc.subject
Live Trapping
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Mixed-Effect Model
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Occupancy Model
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Trapping Effort
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Univariate Test
dc.subject.classification
Otras Ciencias Biológicas
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Ciencias Biológicas
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CIENCIAS NATURALES Y EXACTAS
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Matemática Pura
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Matemáticas
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CIENCIAS NATURALES Y EXACTAS
dc.title
Evaluation of statistical methods and sampling designs for the assessment of microhabitat selection based on point data
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-06-12T16:07:04Z
dc.journal.volume
7
dc.journal.number
11
dc.journal.pagination
1316-1324
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
Fil: Gorosito, Irene Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
dc.description.fil
Fil: Marziali Bermudez, Mariano. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Douglass, Richard J.. University Of Montana, Montana Tech; Estados Unidos
dc.description.fil
Fil: Busch, Maria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
dc.journal.title
Methods in Ecology and Evolution
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1111/2041-210X.12605
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