ALBERT

Description: Rising atmospheric CO2 reduces seawater pH causing ocean acidification (OA). Understanding how resilient marine organisms respond to OA may help predict how community dynamics will shift as CO2 continues rising. The common slipper shell snail Crepidula fornicata is a marine gastropod native to eastern North America that has been a successful invader along the western European coastline and elsewhere. It has also been previously shown to be resilient to global change stressors. To examine the mechanisms underlying C. fornicata's resilience to OA, we conducted two controlled laboratory experiments. First, we examined several phenotypes and genome-wide gene expression of C. fornicata in response to pH treatments (7.5, 7.6, and 8.0) throughout the larval stage and then tested how conditions experienced as larvae influenced juvenile stages (i.e., carry-over effects). Second, we examined genome-wide gene expression patterns of C. fornicata larvae in response to acute (4, 10, 24, and 48 h) pH treatment (7.5 and 8.0). Both C. fornicata larvae and juveniles exhibited resilience to OA and their gene expression responses highlight the role of transcriptome plasticity in this resilience. Larvae did not exhibit reduced growth under OA until they were at least 8 days old. These phenotypic effects were preceded by broad transcriptomic changes, which likely served as an acclimation mechanism for combating reduced pH conditions frequently experienced in littoral zones. Larvae reared in reduced pH conditions also took longer to become competent to metamorphose. In addition, while juvenile sizes at metamorphosis reflected larval rearing pH conditions, no carry-over effects on juvenile growth rates were observed. Transcriptomic analyses suggest increased metabolism under OA, which may indicate compensation in reduced pH environments. Transcriptomic analyses through time suggest that these energetic burdens experienced under OA eventually dissipate, allowing C. fornicata to reduce metabolic demands and acclimate to reduced pH. Carry-over effects from larval OA conditions were observed in juveniles; however, these effects were larger for more severe OA conditions and larvae reared in those conditions also demonstrated less transcriptome elasticity. This study highlights the importance of assessing the effects of OA across life history stages and demonstrates how transcriptomic plasticity may allow highly resilient organisms, like C. fornicata, to acclimate to reduced pH environments.

Description: Phytoplankton biomass exhibits significant year-to-year changes, and understanding these changes is crucial for fisheries management and projecting future climate. These annual changes are usually attributed to low-frequency climate modes that also lead to variations in sea surface temperature (SST). We evaluate the contribution of small scales to annual fluctuations based on a global analysis of satellite observations of sea surface chlorophyll (SChl), an indicator of phytoplankton biomass, and of SST from 1999 to 2018. To quantitatively disentangle the spatio-temporal scales of variability, we utilize a timeseries decomposition method that isolates distinct frequency bands. We show that besides the prominent seasonal cycle, SChl is dominated by high-frequency fluctuations (〈3 months) at small spatial scales (〈50 km)—which accumulate over annual scales, in contrast to SST. This implies that slow variations in the environment linked to climate modes can’t fully explain the annual variations in phytoplankton biomass. Instead, the cumulative effect of fine-scale variations drives the year-to-year changes. This result is further examined over the Southern Ocean, where large annual variations are evident. We find that the Southern Annular Mode (SAM), the dominant low-frequency climate signal in the region, can explain only 10% of the annual variations in SChl. Rather, most of the annual variations are associated with small spatial-scale, high-frequency fluctuations, which are not correlated with the SAM. Our results suggest that observations and models with high spatio-temporal resolutions are necessary to understand annual variations in phytoplankton biomass and to detect climate change driven trends.