Access the full text.
Sign up today, get DeepDyve free for 14 days.
W. Thuiller (2004)
Patterns and uncertainties of species' range shifts under climate changeGlobal Change Biology, 10
E. Ziegel (2002)
Generalized Linear ModelsTechnometrics, 44
Thuiller Thuiller, Araújo Araújo, Pearson Pearson, Whittaker Whittaker, Brotons Brotons, Lavorel Lavorel (2004)
Uncertainty in predictions of extinction riskNature, 430
P. Legendre (1993)
Spatial Autocorrelation: Trouble or New Paradigm?Ecology, 74
M. Araújo, R. Whittaker, R. Ladle, M. Erhard (2005)
Reducing uncertainty in projections of extinction risk from climate changeGlobal Ecology and Biogeography, 14
A. Peterson, M. Ortega-Huerta, J. Bartley, V. Sánchez‐Cordero, Jorge Soberón, Robert Buddemeier, David Stockwell (2002)
Future projections for Mexican faunas under global climate change scenariosNature, 416
M. Araújo, Paul Williams (2000)
Selecting areas for species persistence using occurrence dataBiological Conservation, 96
C. Brito, E. Crespo, O. Paulo (1999)
Modelling wildlife distributions: Logistic Multiple Regression vs Overlap AnalysisEcography, 22
P. Walker (1990)
Modelling wildlife distributions using a geographic information system: kangaroos in relation to climateJournal of Biogeography, 17
B. Rowlingson, P. Diggle (1993)
SPLANCS: spatial point pattern analysis code in S-PlusComputers & Geosciences, 19
S. Harrison (1997)
How natural habitat patchiness affects the distribution of diversity in Californian serpentine chaparralEcology, 78
C. Thomas, A. Cameron, R. Green, R. Green, M. Bakkenes, L. Beaumont, Yvonne Collingham, B. Erasmus, M. Siqueira, A. Grainger, L. Hannah, L. Hughes, B. Huntley, A. Jaarsveld, G. Midgley, L. Miles, L. Miles, M. Ortega-Huerta, A. Peterson, O. Phillips, S. Williams (2004)
Extinction risk from climate changeNature, 427
T. Keitt, O. Bjørnstad, P. Dixon, S. Citron-Pousty, T. Keitt, O. Bjørnstad, P. Dixon, S. Citron-Pousty, Accounting, P Dixon
Accounting for Spatial Pattern When Modeling Organism- Environment Interactions
A. Guisan, N. Zimmermann (2000)
Predictive habitat distribution models in ecologyEcological Modelling, 135
D. Borcard, P. Legendre, P. Drapeau (1992)
Partialling out the spatial component of ecological variationEcology, 73
W. Thuiller, S. Lavorel, M. Araújo, M. Sykes, I. Prentice (2005)
Climate change threats to plant diversity in Europe.Proceedings of the National Academy of Sciences of the United States of America, 102 23
W. Koenig, J. Knops (1998)
Testing for spatial autocorrelation in ecological studiesEcography, 21
A. Fielding, J. Bell (1997)
A review of methods for the assessment of prediction errors in conservation presence/absence modelsEnvironmental Conservation, 24
(2002)
Mauremys leprosa (Schweiger, 1812)
J. Diniz‐Filho, L. Bini, B. Hawkins (2003)
Spatial autocorrelation and red herrings in geographical ecologyGlobal Ecology and Biogeography, 12
P. Stephens, S. Buskirk, G. Hayward, C. Rio (2005)
Information theory and hypothesis testing: a call for pluralismJournal of Applied Ecology, 42
G. Austin, C. Thomas, D. Houston, Des Thompson (1996)
Predicting the spatial distribution of buzzard Buteo buteo nesting areas using a geographical information system and remote sensingJournal of Applied Ecology, 33
K. Burnham, David Anderson (2003)
Model selection and multimodel inference : a practical information-theoretic approachJournal of Wildlife Management, 67
M. Fortin, S. Payette (2002)
How to test the significance of the relation between spatially autocorrelated data at the landscape scale: A case study using fire and forest mapsÉcoscience, 9
M. Hulme, P. Jones (1998)
REPRESENTING TWENTIETH CENTURY SPACE-TIME CLIMATE VARIABILITY.
J. Lennon (2000)
Red-shifts and red herrings in geographical ecologyEcography, 23
J. Lichstein, T. Simons, S. Shriner, K. Franzreb (2002)
Spatial autocorrelation and autoregressive models in ecologyEcological Monographs, 72
N. Augustin, M. Mugglestone, S. Buckland (1996)
An autologistic model for the spatial distribution of wildlifeJournal of Applied Ecology, 33
W. Thuiller (2003)
BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global changeGlobal Change Biology, 9
P. Smith (1994)
Autocorrelation in the logistic regression modelling of species distributions, 4
J. Pereira, R. Itami (1991)
GIS-based habitat modeling using logistic multiple regression : a study of the Mt. Graham red squirrelPhotogrammetric Engineering and Remote Sensing, 57
M. Araújo, R. Pearson, W. Thuiller, M. Erhard (2005)
Validation of species–climate impact models under climate changeGlobal Change Biology, 11
P. Legendre, M. Dale, M. Fortin, P. Casgrain, J. Gurevitch (2004)
EFFECTS OF SPATIAL STRUCTURES ON THE RESULTS OF FIELD EXPERIMENTSEcology, 85
P. Segurado, M. Araújo (2004)
An evaluation of methods for modelling species distributionsJournal of Biogeography, 31
L. Clark, D. Pregibon (1992)
Tree-based models
G. Midgley, L. Hannah, D. Millar, M. Rutherford, Powrie Lw (2002)
Assessing the vulnerability of species richness to anthropogenic climate change in a biodiversity hotspotGlobal Ecology and Biogeography, 11
P. Dutilleul, P. Clifford, S. Richardson, D. Hémon (1993)
Modifying the t test for assessing the correlation between two spatial processesBiometrics, 49
M. New, M. Hulme, P. Jones (2000)
Representing Twentieth-Century Space-Time Climate Variability. Part II: Development of 1901-96 Monthly Grids of Terrestrial Surface ClimateJournal of Climate, 13
M. Palmer, E. Vandermaarel (1995)
VARIANCE IN SPECIES RICHNESS, SPECIES ASSOCIATION, AND NICHE LIMITATIONOikos, 73
Transactions of the American Fisheries Society 131:329–336, 2002 � Copyright by the American Fisheries Society 2002 Predictive Models of Fish Species Distributions: A Note on Proper Validation and Chance Predictions
D. Storch, M. Konvička, J. Benes, J. Martínková, K. Gaston (2003)
Distribution patterns in butterflies and birds of the Czech Republic: separating effects of habitat and geographical positionJournal of Biogeography, 30
A. Guisan, J. Theurillat (2000)
Assessing alpine plant vulnerability to climate change: a modeling perspectiveIntegrated Assessment, 1
H. Akaike (1974)
A new look at the statistical model identificationIEEE Transactions on Automatic Control, 19
Olden Olden, Jackson Jackson, Peres‐Neto Peres‐Neto (2002)
Predictive models of fish species distributions: a note on proper validation and chance predictionsTransactions of the American Fisheries Society, 131
A. Hampe (2004)
Bioclimate envelope models: what they detect and what they hideGlobal Ecology and Biogeography, 13
J. Franklin (1998)
Predicting the distribution of shrub species in southern California from climate and terrain‐derived variablesJournal of Vegetation Science, 9
J. Pearce, S. Ferrier (2000)
An evaluation of alternative algorithms for fitting species distribution models using logistic regressionEcological Modelling, 128
P. Legendre, M. Dale, Marie-Josée Fortin, J. Gurevitch, M. Hohn, Donald Legendre, P. Dale, M. Fortin, M.-J Gurevitch, J. Hohn, M. Myers, P. Legendre, Marie-Josée Fortin
The Consequences of Spatial Structure for the Design and Analysis of Ecological Field Surveys
Fortin Fortin, Jacquez Jacquez (2000)
Randomization tests and spatially autocorrelated dataBulletin of the Ecological Society of America, 81
M. Dale, M. Fortin (2002)
Spatial autocorrelation and statistical tests in ecologyÉcoscience, 9
D. Hosmer, S. Lemeshow (1991)
Applied Logistic Regression
Richard Pearson, Terence Dawson (2003)
Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?Global Ecology and Biogeography, 12
(1999)
Atlas of the continental Portuguese herpetofauna: an assemblage of published and new data
M. Araújo, W. Thuiller, R. Pearson (2006)
Climate warming and the decline of amphibians and reptiles in EuropeJournal of Biogeography, 33
S. Roxburgh, P. Chesson (1998)
A NEW METHOD FOR DETECTING SPECIES ASSOCIATIONS WITH SPATIALLY AUTOCORRELATED DATAEcology, 79
W. Thuiller, M. Araújo, R. Pearson, R. Whittaker, L. Brotóns, S. Lavorel (2004)
Biodiversity conservation: Uncertainty in predictions of extinction riskNature, 430
S. Manel, J. Dias, S. Buckton, S. Ormerod (1999)
Alternative methods for predicting species distribution: an illustration with Himalayan river birdsJournal of Applied Ecology, 36
S. Derksen, H. Keselman (1992)
Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variablesBritish Journal of Mathematical and Statistical Psychology, 45
(1994)
Declining farmland bird species : modelling geographical patterns of abundance in Britain
(2002)
Emys orbicularis. Atlas y Libro Rojo de Los Anfibios y Reptiles
C. Randin, T. Dirnböck, S. Dullinger, N. Zimmermann, M. Zappa, A. Guisan (2006)
Are niche‐based species distribution models transferable in space?Journal of Biogeography, 33
P. Catry, A. Campos, P. Segurado, Mónica Silva, I. Strange (2003)
Population census and nesting habitat selection of thin-billed prion Pachyptila belcheri on New Island, Falkland IslandsPolar Biology, 26
A. Watkins, J. Wilson (1992)
Fine-scale community structure of lawnsJournal of Ecology, 80
B. Maurer (1994)
Geographical Population Analysis: Tools for the Analysis of Biodiversity
Summary 1 Spatial autocorrelation is an important source of bias in most spatial analyses. We explored the bias introduced by spatial autocorrelation on the explanatory and predictive power of species’ distribution models, and make recommendations for dealing with the problem. 2 Analyses were based on the distribution of two species of freshwater turtle and two virtual species with simulated spatial structures within two equally sized areas located on the Iberian Peninsula. Sequential permutations of environmental variables were used to generate predictor variables that retained the spatial structure of the original variables. Univariate models of species’ distributions using generalized linear models (GLM), generalized additive models (GAM) and classification tree analysis (CTA) were fitted for each variable permutation. Variation of accuracy measures with spatial autocorrelation of the original predictor variables, as measured by Moran's I, was analysed and compared between models. The effects of systematic subsampling of the data set and the inclusion of a contagion term to deal with spatial autocorrelation in models were assessed with projections made with GLM, as it was with this method that estimates of significance based on randomizations were obtained. 3 Spatial autocorrelation was shown to represent a serious problem for niche‐based species’ distribution models. Significance values were found to be inflated up to 90‐fold. 4 In general, GAM and CTA performed better than GLM, although all three methods were vulnerable to the effects of spatial autocorrelation. 5 The procedures utilized to reduce the effects of spatial autocorrelation had varying degrees of success. Subsampling was partially effective in avoiding the inflation effect, whereas the inclusion of a contagion term fully eliminated or even overcompensated for this effect. Direct estimation of probability using variable simulations was effective, yet seemed to show some residual spatial autocorrelation effects. 6 Synthesis and applications. Given the expected inflation in the estimates of significance when analysing spatially autocorrelated variables, these need to be adjusted. The reliability and value of niche‐based distribution models for management and other applied ecology purposes can be improved if certain techniques and procedures, such as the null model approach recommended in this study, are implemented during the model‐building process.
Journal of Applied Ecology – Wiley
Published: Jun 1, 2006
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.