Erratum

White et al. (2011) stated that equal prior probabilities were used to compute overall correct classification rates for logistic regression models (page 539, column 1, lines 22–24), and that 62.5% was used as the criterion to judge whether overall correct classification rates were higher than chance classification (page 539, column 1, lines 28–31). K.J.G. realized recently, however, that at the time of the analyses, he misinterpreted information in SAS Proc Logistic software (SAS Institute 1999) about how to code for equal prior probabilities and information in Hair et al. (1998) about when the 62.5% criterion is applicable. Regarding the first issue, we drew inferences about an area that we were not able to sample completely. When sample sites may not be representative of the conditions about which inferences are being drawn, one should use a conservative approach and use equal prior probabilities (0.5 for each presence-absence group in our case) to compute correct classification rates instead of using sample proportions for these prior probabilities (Hair et al. 1998). Hair et al. (2010) and Hahs-Vaughn (2017) recommend this same approach. Although we stated this information in the article (page 539, column 1, lines 17–24), by mistake sample proportions (default setting in SAS Proc Logistic) of site types (species present or species absent), not equal probabilities, were used as prior probabilities to compute overall correct classification rates. Regarding the second issue, the 62.5% rule-of-thumb criterion is appropriate for 2-group analyses only when the sample sizes of the 2 groups are equal (Hair et al. 1998, 2010). In the context of our paper, that would mean that the number of sites where a species was present would need to be the same as the number of sites where that species was absent. This was not the case for the species involved. An alternative approach was identified for assessing the significance of an overall correct classification rate for logistic regression (Press’s Q statistic) (Hair et al. 1998, 2010; Hahs-Vaughn 2017) that enables a probabilistic inference and that does not require equal sample sizes for the 2 groups. We reported (page 541, column 1, lines 6–9) that the overall correct classification rate was greater than chance classification accuracy for all 9 models (3 annual models each for Scaled Quail [Callipepla squamata], Loggerhead Shrike [Lanius ludovicianus], and Rock Wren [Salpinctes obsoletus]). Using equal prior probabilities and Press’s Q in reanalyses of the data for the 9 models, we found that 5 of the models had an overall correct classification rate that was significantly higher (p < 0.05) than chance accuracy: Scaled Quail (1999–2000) 69%, Scaled Quail (2000–2001) 75%, Loggerhead Shrike (2000–2001) 69%, Scaled Quail (2001–2002) 70%, and Rock Wren (2001–2002) 69%. Thus, the general conclusions about the predictive ability of these 5 models did not differ from our original conclusions. For the remaining 4 models, the overall correct classification rate was not significantly higher than the rate expected by chance: Loggerhead Shrike (1999–2000) 56%, Rock Wren (1999–2000) 53%, Rock Wren (2000–2001) 53%, and Loggerhead Shrike (2001–2002) 52%. Hence, the conclusions about the predictive ability of these 4 models differed from our original conclusions. All the original models themselves were appropriate because all statistical assumptions were met and the models accounted for modest but important amounts of variation in the relative probability of species’ occurrences. The updated results indicate, however, that the 5 models that had overall correct classification rates that were significantly higher than the chance rate were likely to be applicable to the 70 sites and to a larger population of sites with similar conditions; whereas, for the 4 models that had overall correct classification rates that were not significantly higher than the chance rate, there was insufficient evidence to conclude that their results apply to a larger population of sites. The emphasis in White et al. (2011) was on how to combine mechanistic and statistical models to project how climate change and fire management may affect species’ occurrences. The article’s methodological information on combining mechanistic and statistical models was not affected by correction of the 2 errors, but the projected relative probabilities of occurrence for Loggerhead Shrike (1999–2000, 2001–2002) and Rock Wren (1999–2000, 2000–2001) were probably not as accurate as we believed. This lower predictive ability may have added some uncertainty to the projected probabilities of occurrence for these species and years. We thank M. Burgman, D. Johnson, and an anonymous reviewer for comments about statistical issues. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.