Predictive species distribution modelling in butterflies
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B. Schröder | J. Settele | T. Shreeve | M. Konvička | H. Dyck | Barbara Strauss | R. Biedermann | Birgit Binzenhöfer
[1] K. Jordan,et al. The palearctic Butterflies , 1909 .
[2] P. Moran. Notes on continuous stochastic phenomena. , 1950, Biometrika.
[3] Jacob Cohen. A Coefficient of Agreement for Nominal Scales , 1960 .
[4] H. Akaike. A new look at the statistical model identification , 1974 .
[5] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[6] P. Vincent,et al. Poisson regression models of species abundance , 1983 .
[7] Mike P. Austin,et al. Continuum Concept, Ordination Methods, and Niche Theory , 1985 .
[8] J A Swets,et al. Measuring the accuracy of diagnostic systems. , 1988, Science.
[9] G. Auble. Wildlife 2000—modeling habitat relationships of terrestrial vertebrates , 1988 .
[10] A. O. Nicholls. How to make biological surveys go further with generalised linear models , 1989 .
[11] David W. Hosmer,et al. Applied Logistic Regression , 1991 .
[12] David L. Verbyla,et al. Resampling methods for evaluating classification accuracy of wildlife habitat models , 1989 .
[13] N. Nagelkerke,et al. A note on a general definition of the coefficient of determination , 1991 .
[14] T. Yee,et al. Generalized additive models in plant ecology , 1991 .
[15] B Efron,et al. Statistical Data Analysis in the Computer Age , 1991, Science.
[16] Paul Opdam,et al. European Nuthatch Metapopulations in a Fragmented Agricultural Landscape , 1991 .
[17] A. H. Murphy,et al. Diagnostic verification of probability forecasts , 1992 .
[18] R. Leemans,et al. Comparing global vegetation maps with the Kappa statistic , 1992 .
[19] S. Cessie,et al. Ridge Estimators in Logistic Regression , 1992 .
[20] P. Legendre,et al. Partialling out the spatial component of ecological variation , 1992 .
[21] Ken D. Bovee,et al. Application and testing of a procedure to evaluate transferability of habitat suitability criteria , 1993 .
[22] P. Legendre. Spatial Autocorrelation: Trouble or New Paradigm? , 1993 .
[23] Joel C. Trexler,et al. Nontraditional Regression Analyses , 1993 .
[24] H. Olff,et al. A hierarchical set of models for species response analysis , 1993 .
[25] B. Manly,et al. Resource selection by animals: statistical design and analysis for field studies. , 1994 .
[26] I. Hanski. A Practical Model of Metapopulation Dynamics , 1994 .
[27] A. Fielding,et al. Testing the Generality of Bird‐Habitat Models , 1995 .
[28] Yoshua Bengio,et al. Pattern Recognition and Neural Networks , 1995 .
[29] O. Kindvall. The impact of extreme weather on habitat preference and survival in a metapopulation of the bush cricket Metrioptera bicolor in Sweden , 1995 .
[30] C. Chatfield. Model uncertainty, data mining and statistical inference , 1995 .
[31] Erich Barke,et al. Hierarchical partitioning , 1996, Proceedings of International Conference on Computer Aided Design.
[32] R. Mac Nally,et al. Hierarchical partitioning as an interpretative tool in multivariate inference , 1996 .
[33] F. Harrell,et al. Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .
[34] Edward J. Rykiel,et al. Testing ecological models: the meaning of validation , 1996 .
[35] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[36] Atte Moilanen,et al. Predicting the Occurrence of Endangered Species in Fragmented Landscapes , 1996, Science.
[37] Ilkka Hanski,et al. An experimental study of migration in the Glanville fritillary butterfly Melitaea cinxia , 1996 .
[38] K. Burnham,et al. Model selection: An integral part of inference , 1997 .
[39] Jonathan M. Graham,et al. Autologistic Model of Spatial Pattern of Phytophthora Epidemic in Bell Pepper: Effects of Soil Variables on Disease Presence , 1997 .
[40] Christian Wissel,et al. Modelling persistence in dynamic landscapes : lessons from a metapopulation of the grasshopper Bryodema tuberculata , 1997 .
[41] N. LeRoy Poff,et al. Landscape Filters and Species Traits: Towards Mechanistic Understanding and Prediction in Stream Ecology , 1997, Journal of the North American Benthological Society.
[42] John Bell,et al. A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.
[43] Katherine J. LaJeunesse Connette,et al. Conservation Biology , 2009, The Quarterly review of biology.
[44] F. Kienast,et al. Predicting the potential distribution of plant species in an alpine environment , 1998 .
[45] David L. Hawksworth,et al. Biodiversity and Conservation , 2007, Biodiversity & Conservation.
[46] J. Franklin. Predicting the distribution of shrub species in southern California from climate and terrain‐derived variables , 1998 .
[47] A. McQuarrie,et al. Regression and Time Series Model Selection , 1998 .
[48] R. Freckleton,et al. The Ecological Detective: Confronting Models with Data , 1999 .
[49] David R. Anderson,et al. Model Selection and Inference: A Practical Information-Theoretic Approach , 2001 .
[50] D. Lindenmayer,et al. The conservation of arboreal marsupials in the montane ash forests of the central highlands of Victoria, south-eastern Australia. VIII. Landscape analysis of the occurrence of arboreal marsupials , 1999 .
[51] David R. B. Stockwell,et al. The GARP modelling system: problems and solutions to automated spatial prediction , 1999, Int. J. Geogr. Inf. Sci..
[52] S. Manel,et al. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird , 1999 .
[53] Roger L. H. Dennis,et al. Probability of site occupancy in the large heath butterfly Coenonympha tullia determined from geographical and ecological data , 1999 .
[54] Sovan Lek,et al. Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .
[55] I. Hanski,et al. LOCAL SPECIALIZATION AND LANDSCAPE-LEVEL INFLUENCE ON HOST USE IN AN HERBIVOROUS INSECT , 2000 .
[56] Simon Ferrier,et al. Evaluating the predictive performance of habitat models developed using logistic regression , 2000 .
[57] Antoine Guisan,et al. Predictive habitat distribution models in ecology , 2000 .
[58] David Gutiérrez,et al. Habitat‐based statistical models for predicting the spatial distribution of butterflies and day‐flying moths in a fragmented landscape , 2000 .
[59] Jorge X Velasco-Hernández,et al. Extinction Thresholds and Metapopulation Persistence in Dynamic Landscapes , 2000, The American Naturalist.
[60] T. Tscharntke,et al. Butterfly community structure in fragmented habitats , 2000 .
[61] R. Briers,et al. Population turnover and habitat dynamics in Notonecta (Hemiptera: Notonectidae) metapopulations , 2000, Oecologia.
[62] J. Habbema,et al. Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. , 2000, Statistics in medicine.
[63] Boris Schröder,et al. Are habitat models transferable in space and time , 2000 .
[64] G. De’ath,et al. CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .
[65] W. Talloen,et al. Does the presence of ant nests matter for oviposition to a specialized myrmecophilous Maculinea butterfly? , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[66] Boris Schröder,et al. Zwischen Naturschutz und Theoretischer Ökologie : Modelle zur Habitateignung und räumlichen Populationsdynamik für Heuschrecken im Niedermoor , 2000 .
[67] H. Rusterholz,et al. Short-term responses of plants and invertebrates to experimental small-scale grassland fragmentation , 2000, Oecologia.
[68] J. Kerr,et al. Remotely sensed habitat diversity predicts butterfly species richness and community similarity in Canada , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[69] J. Thomas,et al. Food–plant niche selection rather than the presence of ant nests explains oviposition patterns in the myrmecophilous butterfly genus Maculinea , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[70] S. Manel,et al. Evaluating presence-absence models in ecology: the need to account for prevalence , 2001 .
[71] R. Céréghino,et al. Spatial analysis of stream invertebrates distribution in the Adour-Garonne drainage basin (France), using Kohonen self organizing maps , 2001 .
[72] D. Lindenmayer,et al. Towards a hierarchical framework for modelling the spatial distribution of animals , 2001 .
[73] A. Hirzel,et al. Assessing habitat-suitability models with a virtual species , 2001 .
[74] J. Habbema,et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. , 2001, Journal of clinical epidemiology.
[75] Leslie Ries,et al. Butterfly responses to habitat edges in the highly fragmented prairies of Central Iowa , 2001 .
[76] T. Ricketts. The Matrix Matters: Effective Isolation in Fragmented Landscapes , 2001, The American Naturalist.
[77] B. Goodger,et al. The quality and isolation of habitat patches both determine where butterflies persist in fragmented landscapes , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[78] E. Fleishman,et al. Modeling and Predicting Species Occurrence Using Broad‐Scale Environmental Variables: an Example with Butterflies of the Great Basin , 2001 .
[79] Dr Robert Bryant,et al. Modelling landscape-scale habitat use using GIS and remote sensing : a case study with great bustards , 2001 .
[80] Boris Schröder,et al. Habitat models and their transfer for single and multi species groups: a case study of carabids in an alluvial forest , 2001 .
[81] Ilkka Hanski,et al. Dynamic populations in a dynamic landscape: the metapopulation structure of the marsh fritillary butterfly , 2002 .
[82] T. Dawson,et al. Modelling potential impacts of climate change on the bioclimatic envelope of species in Britain and Ireland , 2002 .
[83] L. Beaumont,et al. Potential changes in the distributions of latitudinally restricted Australian butterfly species in response to climate change , 2002 .
[84] J. Oksanen,et al. Continuum theory revisited: what shape are species responses along ecological gradients? , 2002 .
[85] Trevor Hastie,et al. Generalized linear and generalized additive models in studies of species distributions: setting the scene , 2002 .
[86] Anthony Lehmann,et al. GRASP: generalized regression analysis and spatial prediction , 2002 .
[87] Eric R. Ziegel,et al. Generalized Linear Models , 2002, Technometrics.
[88] M. Boyce,et al. Evaluating resource selection functions , 2002 .
[89] David R. B. Stockwell,et al. Effects of sample size on accuracy of species distribution models , 2002 .
[90] Tuuli Toivonen,et al. Modelling butterfly distribution based on remote sensing data , 2002 .
[91] G. Quinn,et al. Experimental Design and Data Analysis for Biologists , 2002 .
[92] M. Brändle,et al. Range sizes in butterflies: correlation across scales , 2002 .
[93] Teja Tscharntke,et al. SCALE‐DEPENDENT EFFECTS OF LANDSCAPE CONTEXT ON THREE POLLINATOR GUILDS , 2002 .
[94] J. Friedman. Stochastic gradient boosting , 2002 .
[95] P. Dixon,et al. Accounting for Spatial Pattern When Modeling Organism- Environment Interactions , 2022 .
[96] Julian D. Olden,et al. A comparison of statistical approaches for modelling fish species distributions , 2002 .
[97] M. Austin. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling , 2002 .
[98] A. Hirzel,et al. Which is the optimal sampling strategy for habitat suitability modelling , 2002 .
[99] T. Simons,et al. Spatial autocorrelation and autoregressive models in ecology , 2002 .
[100] Gretchen G. Moisen,et al. Comparing five modelling techniques for predicting forest characteristics , 2002 .
[101] Roger L. H. Dennis,et al. A comparison of geographical and neighbourhood models for improving atlas databases. The case of the French butterfly atlas , 2002 .
[102] J. K. Hill,et al. Responses of butterflies to twentieth century climate warming: implications for future ranges , 2002, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[103] Z. Fric,et al. Uphill shifts in distribution of butterflies in the Czech Republic: effects of changing climate detected on a regional scale , 2003 .
[104] Boris Schr. Computer-intensive methods in the analysis of species-habitat relationships , 2003 .
[105] E. Fleishman,et al. Modelling butterfly species richness using mesoscale environmental variables: model construction and validation for mountain ranges in the Great Basin of western North America , 2003 .
[106] Ian Phillip Vaughan,et al. Improving the Quality of Distribution Models for Conservation by Addressing Shortcomings in the Field Collection of Training Data , 2003 .
[107] A. Hirzel,et al. Modeling Habitat Suitability for Complex Species Distributions by Environmental-Distance Geometric Mean , 2003, Environmental management.
[108] Sunil J Rao,et al. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2003 .
[109] Pieter Reitsma,et al. Educational and Psychological Measurement , 2003 .
[110] C. Thomas,et al. Ecological dynamics of extinct species in empty habitat networks. 2. The role of host plant dynamics , 2003 .
[111] J. Pausas,et al. Coarse-scale plant species richness in relation to environmental heterogeneity , 2003 .
[112] Douglas C. Montgomery,et al. Resampling methods for variable selection in robust regression , 2003, Comput. Stat. Data Anal..
[113] David A. Bohan,et al. Invertebrate responses to the management of genetically modified herbicide-tolerant and conventional spring crops. II. Within-field epigeal and aerial arthropods. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[114] J. Curry,et al. CONFRONTING MODELS WITH DATA , 2003 .
[115] M. Gilbert,et al. Prediction of butterfly diversity hotspots in Belgium: a comparison of statistically focused and land use‐focused models , 2003 .
[116] Hans Van Dyck,et al. Towards a functional resource-based concept for habitat: a butterfly biology viewpoint , 2003 .
[117] B. A. Hawkins,et al. Water–energy balance and the geographic pattern of species richness of western Palearctic butterflies , 2003 .
[118] Brendan A. Wintle,et al. The Use of Bayesian Model Averaging to Better Represent Uncertainty in Ecological Models , 2003 .
[119] Mikaela Huntzinger,et al. Effects of fire management practices on butterfly diversity in the forested western United States , 2003 .
[120] Ralph Mac Nally,et al. Validation Tests of Predictive Models of Butterfly Occurrence Based on Environmental Variables , 2003 .
[121] M. Graham. CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION , 2003 .
[122] Robert H. Kushler,et al. Statistical Computing: An Introduction to Data Analysis Using S-PLUS , 2003, Technometrics.
[123] P. Välimäki,et al. Migration of the clouded Apollo butterfly Parnassius mnemosyne in a network of suitable habitats – effects of patch characteristics , 2003 .
[124] L. Crozier. Winter warming facilitates range expansion: cold tolerance of the butterfly Atalopedes campestris , 2003, Oecologia.
[125] R. Dennis,et al. Gains and losses of French butterflies: tests of predictions, under-recording and regional extinction from data in a new atlas , 2003 .
[126] Mikko Kuussaari,et al. RESTORATION OF BUTTERFLY AND MOTH COMMUNITIES IN SEMI-NATURAL GRASSLANDS BY CATTLE GRAZING , 2004 .
[127] M. Franzén,et al. Occurrence patterns of butterflies (Rhopalocera) in semi-natural pastures in southeastern Sweden , 2004 .
[128] Atte Moilanen,et al. Combining probabilities of occurrence with spatial reserve design , 2004 .
[129] Ralph Mac Nally,et al. Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables , 2002, Biodiversity & Conservation.
[130] Martin Wassen,et al. Prediction of plant species distribution in lowland river valleys in Belgium: modelling species response to site conditions , 2002, Biodiversity & Conservation.
[131] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[132] D. Roy,et al. Host plants and butterfly biology. Do host‐plant strategies drive butterfly status? , 2004 .
[133] T. Tscharntke,et al. Landscape occupancy and local population size depends on host plant distribution in the butterfly Cupido minimus , 2004 .
[134] D. Richardson,et al. Assessing biological invasions in protected areas after 30 years: Revisiting nature reserves targeted by the 1980s SCOPE programme , 2020, Biological Conservation.
[135] Robert Biedermann,et al. Modelling the spatial dynamics and persistence of the leaf beetle Gonioctena olivacea in dynamic habitats , 2004 .
[136] E. Matthysen,et al. Incorporating landscape elements into a connectivity measure: a case study for the Speckled wood butterfly (Pararge aegeria L.) , 2003, Landscape Ecology.
[137] M. Araújo,et al. Presence-absence versus presence-only modelling methods for predicting bird habitat suitability , 2004 .
[138] Boris Schröder,et al. Habitat selection by the pale-headed brush-finch (Atlapetes pallidiceps) in southern Ecuador: implications for conservation , 2004 .
[139] M. WallisDeVries. A Quantitative Conservation Approach for the Endangered Butterfly Maculinea alcon , 2004 .
[140] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[141] M. Baguette,et al. Resource and habitat patches, landscape ecology and metapopulation biology: a consensual viewpoint , 2004 .
[142] M. Araújo,et al. An evaluation of methods for modelling species distributions , 2004 .
[143] S. Weiss,et al. GLM versus CCA spatial modeling of plant species distribution , 1999, Plant Ecology.
[144] R. Dennis,et al. Patch occupancy in Coenonympha tullia (Muller, 1764) (Lepidoptera: Satyrinae): habitat quality matters as much as patch size and isolation , 1997, Journal of Insect Conservation.
[145] R. M. Nally. Regression and model-building in conservation biology, biogeography and ecology: The distinction between – and reconciliation of – ‘predictive’ and ‘explanatory’ models , 2000, Biodiversity & Conservation.
[146] S. Herrando,et al. Butterfly species richness in the north‐west Mediterranean Basin: the role of natural and human‐induced factors , 2004 .
[147] A. Lehmann,et al. Regression models for spatial prediction: their role for biodiversity and conservation , 2002, Biodiversity & Conservation.
[148] M. Gilbert,et al. Species richness coincidence: conservation strategies based on predictive modelling , 2005, Biodiversity & Conservation.
[149] Marianne S. Fred,et al. Influence of Habitat Quality and Patch Size on Occupancy and Persistence in two Populations of the Apollo Butterfly (Parnassius apollo) , 2003, Journal of Insect Conservation.
[150] Nicolas Schtickzelle,et al. Metapopulation viability analysis of the bog fritillary butterfly using RAMAS/GIS , 2004 .
[151] S. Aviron,et al. Complementation/supplementation of resources for butterflies in agricultural landscapes , 2004 .
[152] W. Cramer,et al. The performance of models relating species geographical distributions to climate is independent of trophic level , 2004 .
[153] L. P. Koh,et al. IMPORTANCE OF RESERVES, FRAGMENTS, AND PARKS FOR BUTTERFLY CONSERVATION IN A TROPICAL URBAN LANDSCAPE , 2004 .
[154] K. Walker,et al. Assessing habitat quality for butterflies on intensively managed arable farmland , 2004 .
[155] R. Death,et al. Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks , 2004 .
[156] L. Fahrig,et al. Determining the Spatial Scale of Species' Response to Habitat , 2004 .
[157] J. Askling,et al. Landscape effects on butterfly assemblages in an agricultural region , 2004 .
[158] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[159] K. J. Wessels,et al. An evaluation of the gradsect biological survey method , 1998, Biodiversity & Conservation.
[160] J. Hortal,et al. Butterfly species richness in mainland Portugal: predictive models of geographic distribution patterns , 2004 .
[161] Eric Bauer,et al. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.
[162] Sonia G. Rabasa,et al. Egg laying by a butterfly on a fragmented host plant: a multi-level approach , 2005 .
[163] Boris Schröder,et al. Habitat models and habitat connectivity analysis for butterflies and burnet moths - The example of Zygaena carniolica and Coenonympha arcania , 2005 .
[164] L. Beaumont,et al. Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions , 2005 .
[165] S. Mastrorillo,et al. Using self-organizing maps to investigate spatial patterns of non-native species , 2005 .
[166] Mick Eyre,et al. Distribution of selected macroinvertebrates in a mosaic of temporary and permanent freshwater ponds as explained by autologistic models , 2005 .
[167] T. Hastie,et al. Using multivariate adaptive regression splines to predict the distributions of New Zealand ’ s freshwater diadromous fish , 2005 .
[168] A. Prasad,et al. Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.
[169] M. Luoto,et al. Uncertainty of bioclimate envelope models based on the geographical distribution of species , 2005 .
[170] M. WallisDeVries,et al. Using surrogate data in population viability analysis: the case of the critically endangered cranberry fritillary butterfly , 2005 .
[171] M. Luoto,et al. New insights into butterfly–environment relationships using partitioning methods , 2005, Proceedings of the Royal Society B: Biological Sciences.
[172] Dennis D. Murphy,et al. Using Indicator Species to Predict Species Richness of Multiple Taxonomic Groups , 2005 .
[173] W. Thuiller,et al. Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.
[174] J. Bayliss,et al. The use of probabilistic habitat suitability models for biodiversity action planning , 2005 .
[175] C. Wiklund,et al. Butterfly life history and temperature adaptations; dry open habitats select for increased fecundity and longevity , 2005 .
[176] T. Dawson,et al. Selecting thresholds of occurrence in the prediction of species distributions , 2005 .
[177] S. Matter,et al. Predicting Immigration of Two Species in Contrasting Landscapes: Effects of Scale, Patch Size and Isolation , 2005 .
[178] Barbara Strauss,et al. The Use of Habitat Models in Conservation of Rare and Endangered Leafhopper Species (Hemiptera, Auchenorrhyncha) , 2005, Journal of Insect Conservation.
[179] Ian Phillip Vaughan,et al. The continuing challenges of testing species distribution models , 2005 .
[180] D. Gutiérrez,et al. Changes to the elevational limits and extent of species ranges associated with climate change. , 2005, Ecology letters.
[181] David R. B. Stockwell,et al. Improving ecological niche models by data mining large environmental datasets for surrogate models , 2005, ArXiv.
[182] A. Brenning. Spatial prediction models for landslide hazards: review, comparison and evaluation , 2005 .
[183] S. Lavorel,et al. Niche properties and geographical extent as predictors of species sensitivity to climate change , 2005 .
[184] T. Tscharntke,et al. Relative importance of resource quantity, isolation and habitat quality for landscape distribution of a monophagous butterfly , 2005 .
[185] Boris Schr,et al. Constrain to perform: Regularization of habitat models , 2006 .
[186] Boris Schröder,et al. Analysis of pattern–process interactions based on landscape models—Overview, general concepts, and methodological issues , 2006 .
[187] R. Cowling,et al. Predicting patterns of plant species richness in megadiverse South Africa , 2006 .
[188] Jane Elith,et al. Comparing species abundance models , 2006 .
[189] R. Pearson,et al. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar , 2006 .
[190] Shanshan Wu,et al. Building statistical models to analyze species distributions. , 2006, Ecological applications : a publication of the Ecological Society of America.
[191] Mark S. Boyce,et al. Modelling distribution and abundance with presence‐only data , 2006 .
[192] A. Townsend Peterson,et al. Novel methods improve prediction of species' distributions from occurrence data , 2006 .
[193] M. Araújo,et al. Consequences of spatial autocorrelation for niche‐based models , 2006 .
[194] Nicolai Meinshausen,et al. Quantile Regression Forests , 2006, J. Mach. Learn. Res..
[195] H. Poethke,et al. Habitat suitability models for the conservation of thermophilic grasshoppers and bush crickets—simple or complex? , 2007, Journal of Insect Conservation.
[196] Ingolf Kühn,et al. Incorporating spatial autocorrelation may invert observed patterns , 2006 .
[197] J. Settele,et al. No Experimental Evidence for Host Ant Related Oviposition in a Parasitic Butterfly , 2006, Journal of Insect Behavior.
[198] Hon Keung Tony Ng,et al. Statistics: An Introduction Using R , 2006, Technometrics.
[199] A. Shapiro,et al. Building phenological models from presence/absence data for a butterfly fauna. , 2006, Ecological applications : a publication of the Ecological Society of America.
[200] Robert P. Anderson,et al. Maximum entropy modeling of species geographic distributions , 2006 .
[201] J. Kerr,et al. Contrasting spatial and temporal global change impacts on butterfly species richness during the 20th century , 2006 .
[202] Trevor Hastie,et al. Making better biogeographical predictions of species’ distributions , 2006 .
[203] F. Kienast,et al. The ghost of past species occurrence: improving species distribution models for presence-only data , 2006 .
[204] Niklaus E. Zimmermann,et al. Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods , 2006 .
[205] Antoine Guisan,et al. Are niche-based species distribution models transferable in space? , 2006 .
[206] Robert P Freckleton,et al. Why do we still use stepwise modelling in ecology and behaviour? , 2006, The Journal of animal ecology.
[207] B. Schröder,et al. Connectivity compensates for low habitat quality and small patch size in the butterfly Cupido minimus , 2008, Ecological Research.
[208] Barbara Strauss,et al. Evaluating temporal and spatial generality: How valid are species–habitat relationship models? , 2007 .
[209] H. Dyck,et al. When functional habitat does not match vegetation types: A resource-based approach to map butterfly habitat , 2007 .
[210] M. Kenward,et al. An Introduction to the Bootstrap , 2007 .
[211] H. Van Dyck,et al. Transferability of Species Distribution Models: a Functional Habitat Approach for Two Regionally Threatened Butterflies , 2007, Conservation biology : the journal of the Society for Conservation Biology.
[212] Jennifer A. Miller,et al. Incorporating spatial dependence in predictive vegetation models , 2007 .
[213] B. Schröder,et al. The generality of habitat suitability models: A practical test with two insect groups , 2007 .
[214] M. Kleyer,et al. Integrated Grid Based Ecological and Economic (INGRID) landscape model - A tool to support landscape management decisions , 2007, Environ. Model. Softw..
[215] R. G. Davies,et al. Methods to account for spatial autocorrelation in the analysis of species distributional data : a review , 2007 .
[216] M. Austin. Species distribution models and ecological theory: A critical assessment and some possible new approaches , 2007 .
[217] J Elith,et al. A working guide to boosted regression trees. , 2008, The Journal of animal ecology.
[218] C. Dormann,et al. Static species distribution models in dynamically changing systems: how good can predictions really be? , 2009 .
[219] E. Steyerberg,et al. [Regression modeling strategies]. , 2011, Revista espanola de cardiologia.
[220] W. Pan. Bootstrap Model Selection in Generalized Linear Models , 2011 .