The Relationship between Genus Richness and Geographic Area in Late Cretaceous Marine Biotas: Epicontinental Sea versus Open-Ocean-Facing Settings

Abstract

For present-day biotas, close relationships have been documented between the number of species in a given region and the area of the region. To date, however, there have been only limited studies of these relationships in the geologic record, particularly for ancient marine biotas. The recent development of large-scale marine paleontological databases, in conjunction with enhanced geographical mapping tools, now allow for their investigation. At the same time, there has been renewed interest in comparing the environmental and paleobiological properties of two broad-scale marine settings: epicontinental seas, broad expanses of shallow water covering continental areas, and open-ocean-facing settings, shallow shelves and coastlines that rim ocean basins. Recent studies indicate that spatial distributions of taxa and the kinetics of taxon origination and extinction may have differed in these two settings. Against this backdrop, we analyze regional Genus-Area Relationships (GARs) of Late Cretaceous marine invertebrates in epicontinental sea and open-ocean settings using data from the Paleobiology Database. We present a new method for assessing GARs that is particularly appropriate for fossil data when the geographic distribution of these data is patchy and uneven. Results demonstrate clear relationships between genus richness and area for regions worldwide, but indicate that as area increases, genus richness increases more per unit area in epicontinental seas than in open-ocean settings. This difference implies a greater degree of compositional heterogeneity as a function of geographic area in epicontinental sea settings, a finding that is consistent with the emerging understanding of physical differences in the nature of water masses between the two marine settings.

Controlling for the species-area effect supports constrained long-term Mesozoic terrestrial vertebrate diversification

“…Our equal-spread subsampled diversity estimates demonstrate that correcting for spatial biases can yield flatter diversity trajectories relative to uncorrected data. However, we anticipate that studies of diversity in deep time will increasingly focus on quantifying species-area relationships 32 36 37 38 39 67 —which encode information about patterns of alpha, beta and gamma diversity—and how they vary through time and space. This approach will provide rich new insights about the history of biodiversity on our planet.…”

Spatial Bias in the Marine Fossil Record

“…So a common cause explanation [14], where the geographical distribution of marine fossiliferous rocks and marine diversity are both affected by the same driver, needs to be considered. Indeed, a recent study of genus richness and geographic area during the late Cretaceous [28] has clearly demonstrated a positive relationship between genus richness and geographic area in both epicontinental seas and ocean-facing coastlines. By looking at genus richness from fixed paleolatitudinal strips, our analysis draws data from multiple cratonic blocks, each with its own unique tectonic history, rock record [9], genus-area relationships [28], and idiosyncratic response to sea-level change [29].…”

“…This highlights that while a common cause explanation fits large-scale (100–200 myr cycle) patterns in the fossil record, the shorter (ca. 50–60 myr) cycles [31] are much more likely to be a reflection of region-specific changes in the original marine area [14], [28], [32] and the extent of any subsequent degradation of that rock record at outcrop [25].…”

The apparent exponential radiation of Phanerozoic land vertebrates is an artefact of spatial sampling biases

“…To control for the pervasive spatial sampling biases affecting the terrestrial fossil record, we estimated diversity and other key variables for approximately equally sized palaeogeographical regions, which we defined by drawing spatial subsamples of adjacent fossil localities (on a per-interval basis). To define these palaeogeographical regions, we used a spatial subsampling algorithm that identifies all nested sets of adjacent spatial points [28]. Spatial points were defined by binning the palaeocoordinates for all collections in our cleaned occurrence dataset into equal-size hexagonal/pentagonal grid cells with 100 km spacings (figure 3a,b), using the R package dggridR [29].…”