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provinces is significantly higher than the 20% losses seen for genera found in eight or more provinces [of a global total of 16; following Jablonski and Raup, 1995)]. That said, even 20% represents a major, and highly unusual, drawdown of diversity in this most extinction-resistant part of the biota, equivalent to losing 20% of the most widespread genera in the sea today, such as the mussels (Mytilus, Modiolus) and the scallops (Pecten, Chlamys). [Although not ideal in some respects, analyses were conducted at the provincial scale rather than based on occurrences at individual localities, because clades are distributed not along simple linear coastlines, thereby undermining the use of linear distances or simple latitude/longitude extremes. Binning by province also damps some aspects of sampling and taxonomic uncertainty at the genus level, the range-endpoints of present-day molluscan genera tend to cluster at province boundaries (Campbell and Valentine, 1977; Roy et al., 1996), and the results are robust to different approaches to quantifying province-based range sizes.]

Multifactorial analyses corroborate the importance of clade-level distribution in determining survival during mass extinctions and show the value of testing for interaction among factors. For example, if variables are treated independently in the updated K-T dataset, geographic range remains the most important factor in clade survivorship, but species richness also appears to play a significant role (and body size is insignificant as a survivorship predictor). However, multiple logistic regression models taking the three variables simultaneously into account, using Akaike’s Information Criterion (AIC) as a basis for model selection (Burnham and Anderson, 2002), shows species richness to covary with range such that when range is factored out, richness has an insignificant effect on survivorship (P = 0.85, as opposed to P = 0.002 for clade range, in the multiple-factor model). Body size also enters into the multiple-factor model as a weak, but significant, variable, but the multiple-factor model does not have significantly more explanatory power than the geographic range model alone, according both to the similar AIC weights (Table 10.1) and a likelihood ratio test [P = 0.09; see Hosmer and Lemeshow (2000)]. Multivariate approaches will help clarify patterns of extinction selectivity, even if, as here, they show that survivorship virtually collapses to the single variable of geographic range for K-T bivalves. The overlapping variation in range size among the victims and survivors suggests, however, that additional factors, or strong stochastic elements, enter into the fates of individual clades.

Widespread clades are probably always extinction-resistant compared with narrow-ranging relatives. However, during times of low extinction intensity, range is evidently just one significant feature among many, becoming increasingly important as the crowd of factors influencing taxon duration falls away as intensity mounts. How the relation between range

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