Ecological Archives A025-146-A2

Takashi Yamamoto, Yutaka Watanuki, Elliott L Hazen, Bungo Nishizawa, Hiroko Sasaki, and Akinori Takahashi. 2015. Statistical integration of tracking and vessel survey data to incorporate life history differences in habitat models. Ecological Applications 25:23962408. http://dx.doi.org/10.1890/15-0142.1

Appendix B. Comparisons of four different modeling techniques.

The distribution of streaked shearwaters tracked in 2007 were modeled and compared with four different modeling techniques: generalized linear models (GLM), generalized additive models (GAM), Random Forests (RF), and ensemble models (EM). Ensemble models are calculated as weighted averages of single-model predictions (Araújo and New 2007, Coetzee et al. 2009, Marmion et al. 2009, Jones-Farrand et al. 2011), with weights assigned to each modeling technique based on its discriminatory power as measured by the area under the receiver-operated characteristic curve (AUC). We used a cross-validation technique by randomly splitting our datasets into 'training' and 'test', consisting of 70% and 30% of the data, respectively. Then, we used the training data to develop the models and evaluated the models using the test data. We assumed a binomial distribution with the logit-link function as density values range from 0 to 1 (Dobson 2008). Receiver operating characteristic (ROC) curves that assess the rate of false positives against true positives were used to calculate AUC to evaluate the performance of the models. AUC ranges from 0 to 1; values of 0.5 indicate a performance no better than random, values between 0.7–0.9 indicate reasonable model performance, and values greater than 0.9 indicate strong predictive accuracy (Pearce and Ferrier 2000). In addition to AUC, which assesses the predictive accuracy of distribution models, calibration measures how well the frequency of observations in the test data agrees with predicted probabilities of occurrence (Piñeiro et al. 2008, Potts and Elith 2006, Oppel et al. 2012). To measure calibration, we calculated the Pearson correlation coefficient in addition to the slope and intercept of a major axis linear regression of observed versus predicted values to evaluate the bias and consistency of model predictions. The slope and the intercept of this regression indicate the calibration and the bias of the model, respectively (Phillips and Elith 2010). Models were ranked based on multiple selection criterion, including Akaike's Information Criterion (AIC), AIC weight, AUC, correlation, calibration, and bias (Gollcher et al. 2012, Lawson et al. 2014, Warren et al. 2014), and the best performing model across all of these metrics was used to develop our predictions (Table B1).

RFs had the highest AUC and correlation of all predictions followed by EMs, but both showed larger calibration difference and bias than GAMs (Table B1). GLMs had the lowest AUC and correlation. The machine-learning methods, including RFs, show very good performance on the training data, but when used on spatially independent test data these methods likely suffer proportionally more from over fitting than parametric models (i.e. GLMs and GAMs) (Hastie et al. 2001), resulting in larger biases. In the ensemble prediction, a risk with combining multiple methods is that the stronger models may be degraded by dilution with poorly performing models (in our case GLM) (Peterson et al. 2011). Furthermore, the responses of streaked shearwater to environment variables are likely non-linear (i.e., they appear to exhibit a preference for particular ranges of SST; Yamamoto et al. 2011), meaning that GLMs would not be a good tool for use in this study. Although GAMs showed relatively lower AUC than RFs and EMs, these predictions were less biased and the performance of models was still reasonable (all AUC >0.8), including some with very good accuracy (>0.9) (Table B1).

Table B1. Model evaluation and calibration statistics of four modeling techniques. AUC = area under the receiver-operated characteristic curve; COR = point bi-serial correlation coefficient between observed and predicted values; calibration = slope of regression of observed vs. predicted values; bias = intercept of regression of observed vs. predicted values. Bolded values represent the best-performing model for each criterion.

State

Method

AUC

COR

Calibration

Bias

Sangan Island

GLM

0.90

0.21

0.75336

0.02093

 

GAM

0.96

0.83

1.00360

-0.00072

 

RF

0.99

0.99

1.02509

-0.00225

 

EM

0.96

0.89

1.24682

-0.02360

Sangan male

GLM

0.69

0.09

0.76304

0.03381

 

GAM

0.87

0.70

1.02229

-0.00159

 

RF

0.98

0.98

1.04591

-0.00531

 

EM

0.92

0.91

1.30309

-0.04256

Sangan female

GLM

0.83

0.20

0.84183

0.02012

 

GAM

0.95

0.81

1.01100

-0.00189

 

RF

0.99

0.99

1.03352

-0.00396

 

EM

0.95

0.90

1.26663

-0.02970

Mikura Island

GLM

0.73

0.20

0.91643

0.00518

 

GAM

0.82

0.48

0.98046

-0.00079

 

RF

0.98

0.97

1.06250

-0.00804

 

EM

0.91

0.85

1.35647

-0.04591

Mikura male

GLM

0.72

0.23

0.98859

0.00656

 

GAM

0.83

0.53

1.02062

0.00037

 

RF

0.98

0.97

1.07000

-0.00805

 

EM

0.93

0.86

1.34494

-0.03898

Mikura female

GLM

0.70

0.14

1.04848

-0.00763

 

GAM

0.84

0.43

0.99870

0.00013

 

RF

0.98

0.97

1.06189

-0.01153

 

EM

0.96

0.87

1.43874

-0.08196

Non-breeder

GLM

0.83

0.15

0.70605

0.02513

 

GAM

0.92

0.78

1.01550

-0.00197

 

RF

0.99

0.99

1.03073

-0.00265

 

EM

0.94

0.89

1.28249

-0.02723

 

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