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Table 1 Algorithms used to predict the occurrence of Canadian census sub-divisions containing resident I. scapularis populations.

From: Risk maps for range expansion of the Lyme disease vector, Ixodes scapularis, in Canada now and with climate change

  Algorithm AUC SE 95% CI AIC
1 No. of ticks at model equilibrium (T) 0.921 0.030 0.863 – 0.979 214
2 No. of ticks at model equilibrium categorised (Tc) 0.780 0.052 0.678 – 0.881 206
3 Percent forest area (F) 0.387 0.038 0.313 – 0.461 232
4 Index of larval tick immigration (range 255 km: I L ) 0.816 0.052 0.713 – 0.919 211
5 Index of nymphal tick immigration (range 425 km: I N ) 0.896 0.025 0.848 – 0.949 216
6 T * I N 0.926 0.029 0.869 – 0.983 180
7 Tc * I N 0.807 0.055 0.699 – 0.914 183
8 T * I L 0.845 0.052 0.743 – 0.947 207
9 Tc * I L 0.723 0.056 0.614 – 0.832 207
10 T * I N * (0.05* I L ) 0.926 0.029 0.869 – 0.983 1851
11 Tc * I N * (0.05* I L ) 0.807 0.055 0.699 – 0.914 1861
12 T * I N * F 0.821 0.050 0.723 – 0.919 1852
13 Tc * I N * F 0.752 0.054 0.646 – 0.858 1862
14 T * I N * Log10 F 0.832 0.051 0.732 – 0.933 1852
15 Tc * I N * Log10 F 0.762 0.056 0.652 – 0.871 1872
  1. Larva-to-nymph survival of I. scapularis is approximately one twentieth of nymph-to-adult survival (Ogden et al., 2005). 1 In all logistic regression models containing variables relating to forest cover, these variables were not significant. 2 In neither of these models was the variable (0.05* I L ) significant.
  2. The performance of different risk algorithms in ROC analysis is shown: AUC = area under the ROC curve, SE = standard error, 95% CI = 95% confidence interval for AUC. AIC = Aikeke's Information criterion of a logistic regression model for each algorithm in which the outcome was the occurrence of a known I. scapularis population, and the explanatory variables were the algorithm component(s).