Modeling the impact of bioturbation and species abundance upon discrete-depth individual foraminifera analysis used in ENSO-type climate reconstructions.

We use a single foraminifera enabled, holistic hydroclimate-to-sediment transient modelling approach to fundamentally evaluate the efficacy of discrete-depth individual foraminifera analysis (IFA) for reconstructing past sea surface temperature (SST) distribution, a method that has been used for reconstructing El Nino Southern Oscillation (ENSO). The computer model environment allows us to control for variables such as sea surface temperature (SST), foraminifera species abundance response to SST, as well as depositional processes such as sediment accumulation rate (SAR) and bioturbation depth (BD), and subsequent laboratory processes such as sample size and machine error. Examining a number of best-case scenarios, we find that IFA-derived reconstructions of past SST distribution are sensitive to all of the aforementioned variables. Running 100 ensembles for each scenario, we find that the influence of bioturbation upon IFA-derived SST reconstructions, combined with typical samples sizes employed in the field, produces noisy SST reconstructions with poor correlation to the true SST distribution in the water. This noise is especially apparent for values near the edge of the SST distribution, which also happens to be the distribution region of particular interest for ENSO. The noise is further increased in the case of machine error and decreasing SAR. We also find poor agreement between ensembles, underscoring the need for replication studies in the field to confirm findings at particular sites and time periods. Furthermore, we show that a species’ abundance response to SST could bias IFA-derived SST reconstructions, which can have consequences when comparing IFA-derived SST from markedly different mean climate states.