algorithm to compile two datasets for analysis. The ﬁrst dataset included one location per duty cycle, based on the highest-accuracy location class. Quality of signals was judged ﬁrst on the basis of location class (G > 3 > 2 > 1 > 0 > A > B), and then by indices of residual fre- quency error. Our primary interest for compiling a dataset with one location per duty cycle was to evaluate broad-scale migration routes and movement chronology. For ﬁner scale spatial analyses, we constructed a comprehensive dataset that included all GPS locations and Argos readings of class 1 or better. Analysis We used an autologistic multiple regression model to analyze the relationship between HPAI H5N1 presence and the sets of predictors (Gilbert et al., 2008). This method was chosen so that our results could be compared with previous work carried out in Southeast Asia (Gilbert et al., 2008). The autologistic approach adds an autoregressive term to the standard multiple logistic regression to account for spatial autocorrelation in the response variable (Augustin et al., 1996). In addition, low prevalence values for the response variable (<10%) tend to bias multiple logistic regression performance metrics (McPherson et al., 2004). For each model, we selected all HPAI H5N1 outbreak present pixels in addition to nine randomly selected neg- ative pixels, and bootstrapped this operation 250 times so that the obtained model parameters and performance metrics would not depend on a particular set of negatives. For each model, and each set of negatives, we estimated the coefﬁcient of each variable and their corresponding odd- ratio, the v2 resulting from log-likelihood ratio tests be- tween the full model and a model with each variable re- moved. As indicators of goodness-of-ﬁt, we estimated the area under the curve (AUC) of the receiver-operating characteristic (ROC) plots, Cohen’s Kappa index (Cohen, 1960) with a threshold probability of 0.9 to be classiﬁed as positive, and the correlation coefﬁcient between observed presence/absence and ﬁtted values (Elith et al., 2006). Although the use of AUC as a single measure of goodness- of-ﬁt in distribution modeling has been recently questioned (Lobo et al., 2008), it was used in this study because we needed to be able to compare the accuracy of models based on very different prevalence in the training set. It should, however, be interpreted cautiously, as we compare it for analyses over different extents. We then averaged these estimates over the 250 bootstraps. The autologistic Flying Over an HPAI H5N1 Infected Landscape 451 approach allows obtaining unbiased estimates of each model variable coefﬁcient and signiﬁcance, but it tends to artiﬁcially inﬂate the autologistic model performance metrics index because it incorporates information on the neighboring sites in the same data set. Therefore, the individual variable coefﬁcient and log-likelihood ratio tests were estimated based on an autologistic model, while the AUC, Kappa, and correlation coefﬁcient were estimated based on the same model without the autoregressive term. RESULTS The ﬁrst occurrence of HPAI H5N1 outbreak in South Asia was reported in India in early 2006 (Dubey et al., 2009) in the western part of the country and was quickly contained (Fig. 1a). In 2007, H5N1 outbreaks were reported in Ban- gladesh (n outbreaks = 69) and in surrounding India states (n = 4), and the virus reoccurred in those two regions in 2008 (n = 288) and in May 2009 (n = 38). Our previous studies of HPAI H5N1 risk in Southeast Asia showed that domestic duck density, cropping intensity, human popu- lation, and chicken density were important risk factors (Gilbert et al., 2008). When applied to South Asia, the previous risk model correctly predicted high risk in Ban- gladesh but tended to over-predict areas at risk in India (see Fig. S1B). In Southeast Asia, cropping intensity was heavily weighted in the model because it is a good indicator of domestic duck distribution. A strong association between duck, paddy rice, and double cropping occurs in Southeast Asia (Gilbert et al., 2007), and an identical association is apparent in Bangladesh and surrounding regions of northeastern India (Hossain et al., 2005). However, crop- ping intensity does not closely relate to duck distribution in other parts of India where vegetarian diets dominate (see Fig. S1), and hence, cropping intensity has a comparatively lower importance as a risk indicator. What remained was that the distribution of domestic ducks, one of the most important risk factors in the Southeast Asia Model, ap- peared to match the distribution of HPAI H5N1 outbreak cases in Bangladesh and India (Fig. 1a,b). Exploratory analysis showed that incremental increases in duck density were consistently associated with incremental HPAI H5N1 risk in Southeast Asia, but HPAI H5N1 risk quickly reaches a plateau as duck density increases in South Asia (Fig. S2). This prompted the development of a new model where the effect of risk factors was considered separately for regions where duck farming was present at relatively high densities