Marine diversity patterns in Australia are filtered through biogeography

Latitudinal diversity gradients are among the most striking patterns in nature. Despite a large body of work investigating both geographic and environmental drivers, biogeographical provinces have not been included in statistical models of diversity patterns. Instead, spatial studies tend to focus on species-area and local-regional relationships. Here, we investigate correlates of a latitudinal diversity pattern in Australian coastal molluscs. We use an online database of greater than 300 000 specimens and quantify diversity using four methods to account for sampling variation. Additionally, we present a biogeographic scheme using factor analysis that allows for both gradients and sharp boundaries between clusters. The factors are defined on the basis of species composition and are independent of diversity. Regardless of the measure used, diversity is not directly explained by combinations of abiotic variables. Instead, transitions between regions better explain the observed patterns. Biogeographic gradients can in turn be explained by environmental variables, suggesting that environmental controls on diversity may be indirect. Faunas within provinces are homogeneous regardless of environmental variability. Thus, transitions between provinces explain most of the variation in diversity because small-scale factors are dampened. This explanation contrasts with the species-energy hypothesis. Future work should more carefully consider biogeographic gradients when investigating diversity patterns.

Are all species necessary to reveal ecologically important patterns?

While studying ecological patterns at large scales, ecologists are often unable to identify all collections, forcing them to either omit these unidentified records entirely, without knowing the effect of this, or pursue very costly and time-consuming efforts for identifying them. These “indets” may be of critical importance, but as yet, their impact on the reliability of ecological analyses is poorly known. We investigated the consequence of omitting the unidentified records and provide an explanation for the results. We used three large-scale independent datasets, (Guyana/ Suriname, French Guiana, Ecuador) each consisting of records having been identified to a valid species name (identified morpho-species – IMS) and a number of unidentified records (unidentified morpho-species – UMS). A subset was created for each dataset containing only the IMS, which was compared with the complete dataset containing all morpho-species (AMS: = IMS + UMS) for the following analyses: species diversity (Fisher's alpha), similarity of species composition, Mantel test and ordination (NMDS). In addition, we also simulated an even larger number of unidentified records for all three datasets and analyzed the agreement between similarities again with these simulated datasets. For all analyses, results were extremely similar when using the complete datasets or the truncated subsets. IMS predicted ≥91% of the variation in AMS in all tests/analyses. Even when simulating a larger fraction of UMS, IMS predicted the results for AMS rather well. Using only IMS also out-performed using higher taxon data (genus-level identification) for similarity analyses. Finding a high congruence for all analyses when using IMS rather than AMS suggests that patterns of similarity and composition are very robust. In other words, having a large number of unidentified species in a dataset may not affect our conclusions as much as is often thought.