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Table 1 Distribution models for ten amphibians and two reptiles by logistic regression analysis of presence-absence data for Portugal. Model conditions are with 13 variables (condition 1) or with nine variables (condition 2; details see text). Environmental data are standardized, except for LITH. Paramers with large effect (values >1) are shown in boldface type. The fit of the descriptive models is expressed by Cohen's kappa and the 'Area Under the Curve in Receiver Operating Characteristic' plots (AUC) and asymptotic standard error (SE). Distribution models derived from the equations are shown as 'probability of occurrence' maps for Portugal in figures 1 and 2 and as range maps for the Iberian Peninsula in figures 3 and 4.

From: From descriptive to predictive distribution models: a working example with Iberian amphibians and reptiles

 

Observed

Modelling

Model equation †

Model fit

Species

presences

condition

ACID

ALTI

FROD

FROM

HARD

HUMI

INSO

LITH ‡

NDVI

PRET

RELI

TEMP

TJUL

Constant

kappa

AUC ± SE

Amphibians

                   

a) Chioglossa lusitanica

202

1

-1.120

-1.635

        

3.405

1.919

 

-1.226

-2.511

0.8847

0.989 ± 0.002

  

2

 

-1.864

        

3.900

1.743

-1.170

 

-1.857

0.8547

0.987 ± 0.003

b) Pleurodeles waltl

144

1, 2

           

-0.803

0.848

 

-0.559

0.3885

0.745 ± 0.018

c) Triturus marmoratus

226

1

    

-1.406

  

-1.057

-0.316

  

0.671

  

-0.368

0.6305

0.853 ± 0.013

  

2

     

-0.522

-0.611

  

-0.780

0.607

 

-0.565

 

-0.649

0.596

0.854 ± 0.013

d) T. pygmaeus

130

1, 2

  

-0.512

   

-0.652

  

0.253

-0.943

 

0.984

0.444

-0.796

0.4072

0.751 ± 0.018

e) Alytes cisternasii

223

1

  

-0.623

  

-0.411

1.066

-1.376

0.759

 

-1.210

   

-0.915

0.5967

0.871 ± 0.012

f) A. obstetricans

169

1

 

0.872

  

-0.617

 

-0.416

   

0.535

   

-0.836

0.5737

0.879 ± 0.013

  

2

 

1.028

    

-0.554

   

0.610

   

-0.674

0.5581

0.879 ± 0.013

g) Hyla arborea

132

1

-0.458

 

-0.523

0.336

-0.404

-0.367

 

0.789

-0.538

0.486

-0.849

  

-0.631

-0.337

0.3533

0.742 ± 0.021

  

2

  

-0.463

  

-0.335

    

-0.526

-0.480

-0.615

-0.386

-0.167

0.2575

0.682 ± 0.023

h) H. meridionalis

95

1, 2

 

0.972

-0.577

   

0.408

    

-0.707

1.115

 

-0.709

0.4301

0.777 ± 0.02

i) Pelodytes ibericus

91

1

  

-0.371

-0.604

0.785

       

1.753

-0.717

-1.451

0.5909

0.863 ± 0.016

  

2

  

-0.738

         

2.533

-0.639

-1.446

0.5052

0.843 ± 0.018

j) Rana iberica

229

1

 

0.635

  

-0.902

     

1.988

   

-0.934

0.7448

0.936 ± 0.008

  

2

 

0.753

    

-0.605

   

1.890

   

-0.746

0.7494

0.935 ± 0.008

Reptiles

                   

k) Anguis fragilis

83

1, 2

     

-0.360

-0.605

   

0.512

  

-1.046

-1.141

0.5866

0.876 ± 0.016

l) Lacerta schreiberi

272

1

   

-0.273

  

-1.080

-1.239

-0.501

    

-1.327

-0.103

0.6441

0.899 ± 0.010

  

2

      

-0.858

   

0.479

  

-1.104

-0.594

0.6353

0.897 ± 0.010

  1. †) Model equation for e.g. Chioglossa lusitanica under model conditions of type 1 is: probability of occurrence = (1/(1+exp(1.120*ACID+1.35*ALTI-3.405*PRET-1.919*RELI+1.226*TJUL+2.511))).
  2. ‡) The categorical variable 'LITH' Is represented by two binary variables.