Lithification, the transition of unconsolidated sediments to fully indurated rocks, can potentially bias estimates of species richness, evenness, and body size distribution derived from fossil assemblages. Fossil collections made from well-indurated rocks consistently exhibit lower species richness, lower evenness, and larger average specimen size relative to collections made from unconsolidated sediments, even when collections are drawn from the same assemblage. This phenomenon is known as “lithification bias.” While the bias itself has been demonstrated empirically, much less attention has been paid to its causes. Proposed causes include taphonomic processes (e.g., destruction of small specimens during early diagenesis) and methodological differences (e.g., sieving vs. counting specimens on outcrops, bedding surfaces, or mechanically split surfaces). Here we investigate the potential effects of preferential intersection that could also result in a methodologically related bias: the preferential sampling of larger specimens relative to smaller ones when fossils are counted on rock surfaces. We used an analogue model to simulate preferential intersection (fossil collection via splitting fossiliferous rocks) and compare the results with a random-draw model that approximates the effects of sieving. The model was parameterized using nine different combinations of species abundance and species size distributions. The results show that, with rare exceptions, species richness is 5–23% lower, evenness 5–25% lower, and average specimen size 24–150% larger in preferential-intersection than in random-draw simulations. We conclude that preferential intersection can impose a significant bias independent of other mechanisms (e.g., preferential destruction of smaller specimens during diagenetic or sampling processes), that the magnitude of this bias is partially dependent on the species abundance and size distributions, and that this bias alone does not fully account for empirically observed lithification bias on species richness (i.e., other sources of bias are also at work).

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