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F. Corsi, J. Leeuw, A. Skidmore (2000)
Modeling species distribution with GIS
Cristina Mourelle, E. Ezcurra (1996)
Species richness of Argentine cacti: A test of biogeographic hypothesesJournal of Vegetation Science, 7
(2003)
Habitat and corridor selection of an expanding red deer (Cevus elaphus) population
(2002)
Poisson regression : a better approach to modeling abundance data ? Predicting species occurrences : issues of accuracy and scale , Chapter 35 ( ed . by
J. Copas (1999)
The Effectiveness of Risk Scores: the Logit Rank PlotJournal of the Royal Statistical Society: Series C (Applied Statistics), 48
J. Elith, M. Burgman, H. Regan (2002)
Mapping epistemic uncertainties and vague concepts in predictions of species distributionEcological Modelling, 157
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
J. Franklin, P. McCullough, Curtis Gray (2000)
Terrain variables used for predictive mapping of vegetation communities in southern California
H. Leser (1977)
Feld- und Labormethoden der Geomorphologie
C. Jaberg, A. Guisan (2001)
Modelling the distribution of bats in relation to landscape structure in a temperate mountain environmentJournal of Applied Ecology, 38
P. Cheek, P. McCullagh, J. Nelder (1990)
Generalized Linear Models, 2nd Edn.Applied statistics, 39
(1986)
A biogeographic analysis of Australian elapid snakes. Atlas of elapid snakes of Australia (ed. by R. Longmore), pp. 4–15
N. Gotelli (2003)
Predicting Species Occurrences: Issues of Accuracy and Scale, 120
D. Mladenoff, Theodore Sickley, Adrian Wydeven (1999)
Predicting gray wolf landscape recolonization: logistic regression models vs. new field dataEcological Applications, 9
S. Mcdowell, R. Longmore (1987)
Atlas of elapid snakes of AustraliaCopeia, 1987
M. Austin (1980)
Searching for a model for use in vegetation analysisVegetatio, 42
A. Zaniewski, A. Lehmann, J. Overton (2002)
Predicting species spatial distributions using presence-only data: a case study of native New Zealand fernsEcological Modelling, 157
M. Austin (2002)
Spatial prediction of species distribution: an interface between ecological theory and statistical modellingEcological Modelling, 157
R. Aspinall (1992)
An inductive modelling procedure based on Bayes' theorem for analysis of pattern in spatial dataInt. J. Geogr. Inf. Sci., 6
H. Birks (1996)
Statistical approaches to interpreting diversity patterns in the Norwegian mountain floraEcography, 19
A. Guisan, T. Edwards, T. Hastie (2002)
Generalized linear and generalized additive models in studies of species distributions: setting the sceneEcological Modelling, 157
S. Manel, H. Williams, S. Ormerod (2001)
Evaluating presence-absence models in ecology: the need to account for prevalenceJournal of Applied Ecology, 38
A. Guisan, N. Zimmermann (2000)
Predictive habitat distribution models in ecologyEcological Modelling, 135
J. Owen (1989)
Patterns of Herpetofaunal Species Richness: Relation to Temperature, Precipitation, and Variance in ElevationJournal of Biogeography, 16
(2001)
Modelling the influence of landscape structure on bat species distribution and community composition in the Swiss Jura Mountains
R. Anderson, A. Peterson, Marcela Gómez‐Laverde, R. Anderson, A. Peterson, Gó Mez-Laverde (2002)
Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket miceOikos, 98
P. Vincent, J. Haworth (1983)
Poisson regression models of species abundanceJournal of Biogeography, 10
Guisan Guisan, Theurillat Theurillat (2000)
Equilibrium modeling of alpine plant distribution and climate change: how far can we goPhytocoenologia, 30
A. Guisan, S. Weiss, Andrew Weiss (1999)
GLM versus CCA spatial modeling of plant species distributionPlant Ecology, 143
S. Mastrorillo, S. Lek, F. Dauba, A. Belaud (1997)
The use of artificial neural networks to predict the presence of small‐bodied fish in a riverFreshwater Biology, 38
J. Leathwick (1998)
Are New Zealand's Nothofagus species in equilibrium with their environment?Journal of Vegetation Science, 9
U. Hofer, J. Monney, G. Dušej (2001)
Die Reptilien der Schweiz / Les reptiles de Suisse / I rettili della Svizzera
A. Fielding, J. Bell (1997)
A review of methods for the assessment of prediction errors in conservation presence/absence modelsEnvironmental Conservation, 24
(2001)
Les reptiles de Suisse
(2001)
Régions biogéographique de Suisse -Explications et divisions standards. Cahier de l'Environnement, Swiss Federal Office of the Environment
S. Manel, J. Dias, S. Ormerod (1999)
Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river birdEcological Modelling, 120
F. Corsi, E. Dupré, L. Boitani (1999)
A Large‐Scale Model of Wolf Distribution in Italy for Conservation PlanningConservation Biology, 13
J. Leathwick, M. Austin (2001)
COMPETITIVE INTERACTIONS BETWEEN TREE SPECIES IN NEW ZEALAND'S OLD‐GROWTH INDIGENOUS FORESTSEcology, 82
S. Barry, A. Welsh (2002)
Generalized additive modelling and zero inflated count dataEcological Modelling, 157
J. Oksanen, P. Minchin (2002)
Continuum theory revisited: what shape are species responses along ecological gradients?Ecological Modelling, 157
(1996)
Biologie comparée de Vipera aspis L. et de Vipera berus L. (Reptilia, Ophidia, Viperidae) dans une station des Préalpes bernoises
(2001)
Modéliser le domaine de distribution potentiel des espèces
J. Pearce, S. Ferrier (2000)
Evaluating the predictive performance of habitat models developed using logistic regressionEcological Modelling, 133
J. Teixeira, J. Arntzen (2002)
Potential impact of climate warming on the distribution of the Golden-striped salamander, Chioglossa lusitanica, on the Iberian PeninsulaBiodiversity & Conservation, 11
Jacob Cohen (1960)
A Coefficient of Agreement for Nominal ScalesEducational and Psychological Measurement, 20
A. Guisan, J. Theurillat (2000)
Equilibrium modeling of alpine plant distribution: how far can we go?Phytopathology, 30
N. Augustin, M. Mugglestone, S. Buckland (1996)
An autologistic model for the spatial distribution of wildlifeJournal of Applied Ecology, 33
Aim To explore the respective power of climate and topography to predict the distribution of reptiles in Switzerland, hence at a mesoscale level. A more detailed knowledge of these relationships, in combination with maps of the potential distribution derived from the models, is a valuable contribution to the design of conservation strategies. Location All of Switzerland. Methods Generalized linear models are used to derive predictive habitat distribution models from eco‐geographical predictors in a geographical information system, using species data from a field survey conducted between 1980 and 1999. Results The maximum amount of deviance explained by climatic models is 65%, and 50% by topographical models. Low values were obtained with both sets of predictors for three species that are widely distributed in all parts of the country (Anguis fragilis, Coronella austriaca, and Natrix natrix), a result that suggests that including other important predictors, such as resources, should improve the models in further studies. With respect to topographical predictors, low values were also obtained for two species where we anticipated a strong response to aspect and slope, Podarcis muralis and Vipera aspis. Main conclusions Overall, both models and maps derived from climatic predictors more closely match the actual reptile distributions than those based on topography. These results suggest that the distributional limits of reptile species with a restricted range in Switzerland are largely set by climatic, predominantly temperature‐related, factors.
Journal of Biogeography – Wiley
Published: Aug 1, 2003
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