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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. © 2016 The Authors. Methods in Ecology and Evolution © 2016 British Ecological Society

Registro:

Documento: Artículo
Título:Evaluation of statistical methods and sampling designs for the assessment of microhabitat selection based on point data
Autor:Gorosito, I.L.; Marziali Bermúdez, M.; Douglass, R.J.; Busch, M.
Filiación:Departamento de Ecología, Genética y Evolución, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
Instituto de Ecología, Genética y Evolución de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas, Intendente Güiraldes 2160 - Ciudad Universitaria, Buenos Aires, C1428EGA, Argentina
Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
Instituto de Física de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas, Intendente Güiraldes 2160 - Ciudad Universitaria, Buenos Aires, C1428EGA, Argentina
Montana Tech of the University of Montana, Butte, MT 59701, United States
Palabras clave:generalized linear model; live trapping; mixed-effect model; occupancy model; trapping effort; univariate test
Año:2016
Volumen:7
Número:11
Página de inicio:1316
Página de fin:1324
DOI: http://dx.doi.org/10.1111/2041-210X.12605
Título revista:Methods in Ecology and Evolution
Título revista abreviado:Methods Ecol. Evol.
ISSN:2041210X
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_2041210X_v7_n11_p1316_Gorosito

Referencias:

  • Abramson, G., Giuggioli, L., Kenkre, V., Dragoo, J., Parmenter, R., Parmenter, C., Yates, T., Diffusion and home range parameters for rodents: Peromyscus maniculatus in New Mexico (2006) Ecological Complexity, 3, pp. 64-70
  • Alldredge, J.R., Ratti, J.T., Comparison of some techniques for analysis of resource selection (1986) The Journal of Wildlife Management, 50, pp. 157-165
  • Banks-Leite, C., Pardini, R., Boscolo, D., Cassano, C.R., Puttker, T., Barros, C.S., Barlow, J., Assessing the utility of statistical adjustements for imperfect detection in tropical conservation science (2014) Journal of Applied Ecology, 51, pp. 849-859
  • Bartoń, K., (2013) MuMIn: Model selection and model averaging based on information criteria, , http://CRAN.R-project.org/package=MuMIn, R package version 1.9.5
  • Bennett, A., Habitat corridors and the conservation of small mammals in fragmented forest environment (1990) Landscape Ecology, 4, pp. 109-122
  • Bilenca, D., Kravetz, F., Seasonal variations in microhabitat use and feeding habits of the pampas mouse Akodon azarae in agroecosystems of central Argentina (1998) Acta Theriologica, 43, pp. 195-203
  • Bolker, B.M., (2013) lme4: Linear mixed-effects models using Eigen and S4, , http://CRAN.R-project.org/package=lme4, R package version 1.0-4
  • Boyce, M.S., Vernier, P.R., Nielsen, S.E., Schmiegelow, F.K.A., Evaluating resource selection functions (2002) Ecological Modelling, 157, pp. 281-300
  • Burnham, K.P., Anderson, D.R., (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, , &, 2nd edn, Springer-Verlag, New York, New York, USA
  • Dalmagro, A.D., Vieira, E.M., Patterns of habitat utilization of small rodents in an area of Araucaria forest in Southern Brazil (2005) Austral Ecology, 30, pp. 353-362
  • Dénes, F.V., Silveira, F.L., Beissinger, S.R., Estimating abundance of unmarked animal populations: accounting for imperfect detection and other sources of zero inflation (2015) Methods in Ecology and Evolution, 6, pp. 543-556
  • Douglass, R.J., The use of radio-telemetry to evaluate microhabitat selection by deer mice (1989) Journal of Mammalogy, 70, pp. 648-652
  • Efford, M., Density estimation in live-trapping studies (2004) Oikos, 106, pp. 598-610
  • Ellis, B.A., Mills, J.N., Childs, J.E., Muzzini, M.C., McKee, K.T., Jr., Enria, D.A., Glass, G.E., Structure and floristics of habitats associated with five rodent species in an agroecosystem in Central Argentina (1997) Journal of Zoology, 243, pp. 437-460
  • Garamszegi, L.Z., Calhim, S., Dochtermann, N., Hegyi, G., Hurd, P.L., Jørgensen, C., Changing philosophies and tools for statistical inferences in behavioral ecology (2009) Behavioral Ecology, 20, pp. 1363-1376
  • Garshelis, D.L., Delusions in habitat evaluation: measuring use, selection, and importance (2000) Research Techniques in Animal Ecology: Controversies and Consequences, pp. 111-164. , (ed., M. Pearl, L. Boitani, &, Fuller T, Columbia University Press, New York
  • Gomez, D., Sommaro, L., Steinmann, A., Chiappero, M., Priotto, J., Movement distances of two species of sympatric rodents in linear habitats of Central Argentine agro-ecosystems (2011) Mammalian Biology, 76, pp. 58-63
  • Goodin, D.G., Paige, R., Owen, R.D., Ghimire, K., Koch, D.E., Chu, Y.K., Jonsson, C.B., Microhabitat characteristics of Akodon montensis, a reservoir for hantavirus, and hantaviral seroprevalence in an Atlantic forest site in eastern Paraguay (2009) Journal of Vector Ecology, 34, pp. 104-113
  • Heithaus, M.R., Hamilton, I.M., Wirsing, A.J., Dill, L.M., Validation of a randomization procedure to assess animal habitat preferences: microhabitat use of tiger sharks in a seagrass ecosystem (2006) Journal of Animal Ecology, 75, pp. 666-676
  • Hodara, K., Busch, M., Kittlein, M.J., Kravetz, F.O., Density-dependent habitat selection between maize cropfields and their borders in two rodent species (Akodon azarae and Calomys laucha) of Pampean agroecosystems (2001) Evolutionary Ecology, 14, pp. 571-593
  • Ives, A.R., For testing the significance of regression coefficients, go ahead and log-transform count data (2015) Methods in Ecology and Evolution, 6, pp. 828-835
  • Jorgensen, E.E., Small mammals: consequences of stochastic data variation for modelling indicators of habitat suitability for a well-studied resource (2002) Ecological Indicators, 1, pp. 313-321
  • Jorgensen, E.E., Small mammal use of microhabitat reviewed (2004) Journal of Mammalogy, 85, pp. 531-539
  • Kajin, M., Grelle, C.E.V., Microhabitat selection when detection is imperfect: the case of an endemic Atlantic Forest mammal (2012) Ecological Research, 27, pp. 1005-1013
  • Lunn, D.J., Thomas, A., Best, N., Spiegelhalter, D., WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility (2000) Statistics and Computing, 10, pp. 325-337
  • MacKenzie, D.I., Modeling the probability of resource use: the effect of, and dealing with, detecting a species imperfectly (2006) The Journal of Wildlife Management, 70, pp. 367-374
  • MacKenzie, D.I., Nichols, J.D., Royle, J.A., Pollock, K.H., Bailey, L.L., Hines, J.E., (2006) Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, , &, Academic Press, Burlington
  • Manly, B.F., McDonald, L.L., Thomas, D., McDonald, T.L., Erickson, W.P., (2002) Resource Selection by Animals: Statistical Design and Analysis for Field Studies, , &, 2nd edn, Springer, London
  • Melo, G.L., Miotto, B., Peres, B., Cáceres, N.C., Microhabitat of small mammals at ground and understorey levels in a deciduous, southern Atlantic Forest (2013) Anais de Academia Brasileira de Ciências, 85, pp. 727-736
  • Mendel, S.M., Vieira, M.V., Movement distances and density estimation of small mammals using the spool-and-line technique (2003) Acta Theriologica, 48, pp. 289-300
  • Morris, D.W., Ecological scale and habitat use (1987) Ecology, 68, pp. 362-369
  • Morris, D.W., Optimally foraging deer mice in prairie mosaics: a test of habitat theory and absence of landscape effects (1997) Oikos, 80, pp. 31-42
  • (2012) GNU/Octave, , www.gnu.org/software/octave
  • Perea, R., González, R., San Miguel, A., Gil, L., Moonlight and shelter cause differential seed removal and selection by rodents (2011) Animal Behaviour, 84, pp. 717-723
  • Prevedello, J.A., Garcia Rodrigues, R., Leite de Araujo Monteiro-Filho, E., Habitat selection by two species of small mammals in the Atlantic Forest, Brazil: Comparing results from live-trapping and spool-and-line tracking (2010) Mammalian Biology, 75, pp. 106-114
  • Priotto, J.W., Steinmann, A.R., Factors affecting home range size and overlap in Akodon azarae (Muridae: Sigmodontinae) in natural pasture of Argentina (1999) Acta Theriologica, 44, pp. 37-44
  • Püttker, T., Pardini, R., Meyer-Lucht, Y., Sommer, S., Responses of five small mammal species to micro-scale variations in vegetation structure in secondary Atlantic Forest remnants, Brazil (2008) BMC Ecology, 8, p. 9
  • (2013) R: A Language and Environment for Statistical Computing, , http://www.R-project.org, R Foundation for Statistical Computing, Vienna
  • Royle, J.A., Nichols, J.D., Estimating abundance from repeated presence-absence data or point counts (2003) Ecology, 84, pp. 777-790
  • Royle, J.A., Chandler, R.B., Sun, C.C., Fuller, A.K., Integrating resource selection information with spatial capture-recapture (2013) Methods in Ecology and Evolution, 4, pp. 520-530
  • Ruxton, G.D., The unequal variance t-test is an underused alternative to Student's t-test and the Mann-Whitney U test (2006) Behavioral Ecology, 17, pp. 688-690
  • Sturtz, S., Ligges, U., Gelman, A., R2WinBUGS: a package for running WinBUGS from R (2005) Journal of Statistical Software, 12, pp. 1-16
  • Webb, W.L., Small mammal populations on islands (1965) Ecology, 44, pp. 479-488
  • Ylönen, H., Jacob, J., Davies, M.H., Singleton, G.R., Predation risk and habitat selection of Australian house mice Mus domesticus during an incipient plague: desperate behaviour due to food depletion (2002) Oikos, 99, pp. 284-289
  • Zar, J.H., (2010) Biostatistical Analysis, , 5th edn, Prentice Hall, Upper Saddle River, New Jersey, USA
  • Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A., Smith, G.M., (2009) Mixed Effects Models and Extensions in Ecology With R, , &, Springer Science+Business Media, New York

Citas:

---------- APA ----------
Gorosito, I.L., Marziali Bermúdez, M., Douglass, R.J. & Busch, M. (2016) . Evaluation of statistical methods and sampling designs for the assessment of microhabitat selection based on point data. Methods in Ecology and Evolution, 7(11), 1316-1324.
http://dx.doi.org/10.1111/2041-210X.12605
---------- CHICAGO ----------
Gorosito, I.L., Marziali Bermúdez, M., Douglass, R.J., Busch, M. "Evaluation of statistical methods and sampling designs for the assessment of microhabitat selection based on point data" . Methods in Ecology and Evolution 7, no. 11 (2016) : 1316-1324.
http://dx.doi.org/10.1111/2041-210X.12605
---------- MLA ----------
Gorosito, I.L., Marziali Bermúdez, M., Douglass, R.J., Busch, M. "Evaluation of statistical methods and sampling designs for the assessment of microhabitat selection based on point data" . Methods in Ecology and Evolution, vol. 7, no. 11, 2016, pp. 1316-1324.
http://dx.doi.org/10.1111/2041-210X.12605
---------- VANCOUVER ----------
Gorosito, I.L., Marziali Bermúdez, M., Douglass, R.J., Busch, M. Evaluation of statistical methods and sampling designs for the assessment of microhabitat selection based on point data. Methods Ecol. Evol. 2016;7(11):1316-1324.
http://dx.doi.org/10.1111/2041-210X.12605