Population-specific vulnerability to ocean change in a multistressor environment

Abstract

Variation in environmental conditions across a species’ range can alter their responses to environmental change through local adaptation and acclimation. Evolutionary responses, however, may be challenged in ecosystems with tightly coupled environmental conditions, where changes in the covariance of environmental factors may make it more difficult for species to adapt to global change. Here, we conduct a 3-month-long mesocosm experiment and find evidence for local adaptation/acclimation in populations of red sea urchins, Mesocentrotus franciscanus , to multiple environmental drivers. Moreover, populations differ in their response to projected concurrent changes in pH, temperature, and dissolved oxygen. Our results highlight the potential for local adaptation/acclimation to multivariate environmental regimes but suggest that thresholds in responses to a single environmental variable, such as temperature, may be more important than changes to environmental covariance. Therefore, identifying physiological thresholds in key environmental drivers may be particularly useful for preserving biodiversity and ecosystem functioning.

A high-resolution synthesis dataset for multistressor analyses along the US West Coast

“…We discarded any dissolved oxygen and carbonate system datasets that lacked accompanying temperature data. While we preferred carbonate system observations that also included salinity measurements, we retained pH and pCO 2 data without concurrent salinity measurements if they passed all other QC checks (e.g., Chan et al, 2017;Donham et al, 2023). Data collection methods are available for all parameters except temperature and salinity and have been simplified into four groups: (1) "discrete", for bottle-collected samples analyzed in a laboratory; (2) "CTD" (conductivity, temperature, and depth), for observations from ship-side profiles with autonomous sensor arrays; (3) "autonomous sensors", for stationary instruments collecting data at pre-programmed intervals; and (4) "handheld sensors", for observations collected in the field via a glass-electrode probe.…”

“…During upwelling, extreme values of seasonal dissolved oxygen (DO) and carbonate chemistry parameters such as pH are naturally close to biologically significant thresholds, making organisms in the CCS particularly vulnerable to ocean acidification and hypoxia (OAH) events (e.g., Chan et al, 2008;Connolly et al, 2010;Feely et al, 2008;Gruber et al, 2012;Low et al, 2021;. Local adaptation to high environmental variability may provide some ecological resilience (e.g., Sanford and Kelly, 2011;Kelly and Hofmann, 2013;Donham et al, 2023), but widespread die-offs are already a feature of some OAH events (e.g., Grantham et al, 2004;Barton et al, 2015). The CCS is also vulnerable to warming and heatwaves (Cavole et al, 2016;Frölicher and Laufkötter, 2018;Rogers-Bennett and Catton, 2019;Sanford et al, 2019;Fumo et al, 2020;Cheung and Frölicher, 2020;Free et al, 2023).…”

A demographic approach for predicting population responses to multifactorial stressors

“…Meanwhile, investigating a large number of factors results in the ‘combinatorial explosion problem’ ( Katzir et al . 2019 ) wherein manipulating a larger number of factors, while more biologically realistic, becomes increasingly difficult due to the rapid increase in the possible number of combinations (although dimension reduction techniques such as Principal Components Analysis can facilitate analysis; Donham et al . 2023 ).…”

Unveiling Microbial Nitrogen Metabolism in Rivers using a Machine Learning Approach

“…The complex effects (e.g., cumulative and synergistic effects) of environmental factors on microbial nitrogen metabolism have challenged traditional linear methods. , Machine learning is a data-driven method that can learn complex and potential relationships among variables from real-world data . In contrast to physical models, machine learning is not limited by people’s empirical perceptions, thus enabling the discovery of potential relationships that were previously overlooked .…”