Sudden shifts in marine plankton communities in response to environmental changes are of special concern because of their low predictability and high potential impacts on ocean ecosystems. We explored how anthropogenic climate change influences the spatial extent and frequency of changepoints in plankton populations by comparing the behavior of a plankton community in a coupled Earth system model under pre-industrial, historical 20th century, and projected 21st century forcing. The ocean areas where surface ocean temperature, nutrient concentrations, and different plankton types exhibited changepoints expanded over time. In contrast, regional hotspots where changepoints occur frequently largely disappeared. Heterotrophy and larger organism sizes were associated with more changepoints. In the pre-industrial and 20th century, plankton changepoints were associated with shifts in physical fronts, and more often with changepoints for iron and silicate than for nitrate and phosphate. In the 21st century, climate change disrupts these interannual-variability-driven changepoint patterns. Together, our results suggest anthropogenic climate change may drive less frequent but more widespread changepoints simultaneously affecting several components of pelagic food webs.
Taboada, Fernando G., Jong-Yeon Park, Barbara A Muhling, Desiree Tommasi, Kisei R Tanaka, Ryan R Rykaczewski, Charles A Stock, and Jorge L Sarmiento, in press: Anticipating fluctuations of bigeye tuna in the Pacific Ocean from three-dimensional ocean biogeochemistry. Journal of Applied Ecology. DOI:10.1111/1365-2664.14346. December 2022. Abstract
Subseasonal to decadal ocean forecasting can make significant contributions to achieving effective management of living marine resources in a changing ocean. Most applications rely on indirect proxies, however, often measured at the ocean surface and lacking a direct mechanistic link to the dynamics of marine populations.
Here, we take advantage of three-dimensional, dynamical reconstructions and forecasts of ocean biogeochemistry based on a global Earth system model to hindcast and assess the capacity to anticipate fluctuations in the dynamics of bigeye tuna (Thunnus obesus Lowe) in the Pacific Ocean during the last six decades. We reconstructed spatial patterns in catch per unit effort (CPUE) through the combination of physiological indices capturing both habitat preferences and physiological tolerance limits in bigeye tuna.
Our analyses revealed a sequence of four distinct regimes characterized by changes in the zonal distribution and average CPUE of bigeye tuna in the Pacific Ocean. Habitat models accounting for basin-wide fluctuations in the thermal structure and oxygen concentration throughout the water column captured interannual fluctuations in CPUE and regime switches that models based solely on surface information were unable to reproduce. Decade-long forecast experiments further suggested that forecasts of three-dimensional biogeochemical information might enable anticipation of fluctuations in bigeye tuna several years ahead.
Synthesis and applications. Together, our results reveal the impact of variability of biogeochemical conditions in the ocean interior on the dynamics of bigeye tuna on the Pacific Ocean, raising concerns about the future impact of ocean warming and deoxygenation. The results also lend support to incorporating subsurface biogeochemical information into ecological forecasts to implement efficient dynamic management strategies and promote the sustainable use of marine living resources.
Patterns of population renewal in marine fishes are often irregular and lead to volatile fluctuations in abundance that challenge management and conservation efforts. Here, we examine the relationship between life‐history strategies and recruitment variability in exploited marine fish species using a macroecological approach.
Barton, Andrew D., Fernando Gonzalez Taboada, Angus Atkinson, Claire E Widdicombe, and Charles A Stock, August 2020: Integration of temporal environmental variation by the marine plankton community. Marine Ecology Progress Series, 647, DOI:10.3354/meps13432. Abstract
Theory and observations suggest that low frequency variation in marine plankton populations, or red noise, may arise through cumulative integration of white noise atmospheric forcing by the ocean and its amplification within food webs. Here, we revisit evidence for the integration of stochastic atmospheric variations by comparing the power spectra of time series of atmospheric and oceanographic conditions to the population dynamics of 150 plankton taxa at Station L4 in the Western English Channel. The power spectra of oceanographic conditions (sea surface temperature, surface nitrate) are redder than those of atmospheric forcing (surface wind stress, net heat fluxes) at Station L4. However, plankton populations have power spectral slopes across trophic levels and body sizes that are redder than atmospheric forcing but whiter than oceanographic conditions. While zooplankton have redder spectral slopes than phytoplankton, there is no significant relationship between power spectral slope and body size or generation length. Using a predator-prey model, we show that the whitening of plankton time series relative to oceanographic conditions arises from noisy plankton bloom dynamics in this strongly seasonal system. The model indicates that, for typical predator-prey interactions, where the predator is on average 10 times longer than the prey, grazing leads to a modest reddening of phytoplankton variability by their larger and longer lived zooplankton consumers. Our findings suggest that, beyond extrinsic forcing by the environment, predator-prey interactions play a role in influencing the power spectra of time series of plankton populations.
Seasonal to interannual predictions of ecosystem dynamics have the potential to improve the management of living marine resources. Prediction of oceanic net primary production (NPP), the foundation of marine food webs and the biological carbon pump, is particularly promising, with recent analysis suggesting that ecosystem feedback processes may lead to higher predictability of NPP at interannual scales than for physical variables like sea surface temperature (SST). Here, we assessed the potential predictability of oceanic NPP and SST across seasonal to interannual lead times using reduced dimension, linear dynamical spatio-temporal models (rDSTM). This approach combines empirical orthogonal function (EOF) analysis with vector autoregressive (VAR) modeling to simplify the analysis of spatio-temporal data. The rDSTMs were fitted to monthly NPP and SST anomalies derived from 20 years of remote sensing data (1997-2017), considering two alternative algorithms commonly used to estimate NPP (VGPM and Eppley-VGPM) and optimally analyzed SST fields (AVHRR OISST). The local decay of anomalies provided high predictability up to three months, and subsequent interactions with remote forcing significantly extended predictability beyond the initial anomaly decay. Indeed, interactions among spatial modes associated with the propagation of major climate modes, particularly the El Niño-Southern Oscillation (ENSO), extended the predictability horizon above one year in some regions. Patterns of enhanced NPP predictability matched the location of oligotrophic gyres and transition regions between ocean biomes, where fluctuations in biome boundaries generate large biogeochemical perturbations that leave lasting imprints on NPP. In these areas, the predictability horizon for NPP was longer than for SST, although SST was more predictable over large areas of the equatorial and northeast Pacific. Our results support the potential for extending seasonal to interannual physical climate predictions to predict ocean productivity.
Ocean surface winds determine energy, material and momentum fluxes through the air-sea interface. Accounting for wind variability in time and space is thus essential to reliably analyze and simulate ocean circulation and the dynamics of marine ecosystems. Here, we present an assessment of surface winds from three widely used atmospheric reanalysis products (NCEP/NCAR, ERA-Interim and JRA-55) and their corresponding ocean forcing data sets (CORE v2.1, DFS v5.2 and JRA55-do), which include corrections for use in ocean simulations. We compared wind patterns most relevant to ocean circulation (surface wind stress, its curl and estimates of induced vertical upwelling velocity) across global and regional scales, with added emphasis on the main Eastern Boundary Upwelling Ecosystems (EBUEs). All products provided consistent large-scale patterns in surface winds and wind stress, although agreement was reduced for indices involving the calculation of spatial derivatives, like wind stress curl and Ekman pumping. Fidelity with respect to a reference reanalysis based on blended satellite and buoy observations (CCMP v2.0) improved in more recent, higher resolution products like JRA-55 and ERA-Interim. Adjustments applied when deriving ocean forcing data sets from atmospheric reanalysis robustly improved wind speed and wind stress vectors, but degraded wind stress curl (and implied Ekman upwelling) in two of the three ocean forcing products considered (DFS v5.2 and CORE v2.1).
At regional scales, we found significant inconsistencies in equatorial and polar regions, as well as in coastal areas. In EBUEs, upwelling favorable winds were weaker in atmospheric reanalysis products and ocean forcing data sets than estimates based on CCMP v2.0 and QuikSCAT. All reanalysis products featured lower amplitude seasonal cycles and contrasting patterns of low frequency variability within each EBUE, including the presence of sudden changes in mean upwelling only for some products.
Taken together, our results highlight the importance of incorporating uncertainties in wind forcing into ocean simulation experiments and retrospective analysis, and of correcting reanalysis products for ocean forcing data sets. Despite the continued improvement in the quality of wind data sets, prevailing limitations in reanalysis models demonstrate the need to confirm global products against regional measurements whenever possible and improve correction strategies across multiple ocean-relevant wind properties.
Gonzalez-Gil, R, and Fernando Gonzalez Taboada, et al., May 2018: Winter-mixing preconditioning of the spring phytoplankton bloom in the Bay of Biscay. Limnology and Oceanography, 63(3), DOI:10.1002/lno.10769. Abstract
The spring phytoplankton bloom plays a key role in the dynamics of temperate and polar seas. Nevertheless, the mechanisms and processes behind these blooms remain a subject of considerable debate. We analyzed the influence of deep mixing during winter on the spring phytoplankton bloom in the Cantabrian Sea (southern Bay of Biscay). To this end, we combined long-term physical and biogeochemical in situ data (1993–2012) and satellite observations (1997–2012). Deeper winter mixing led to higher nitrate and chlorophyll concentrations through the water column during the spring bloom. However, this effect was modified by short-term variability in near-surface stratification in spring. Winter-mixing preconditioning also influenced different spring bloom metrics: deeper and later mixing in winter was followed by later blooms with a larger peak. In these enhanced blooms, nitrate was taken up at faster rates, indicating higher rates of phytoplankton production. Winters with weaker mixing (that led to weaker spring blooms) were associated with warmer surface temperatures. This relationship suggests that the multi-decadal trend toward warmer surface temperatures in the Bay of Biscay may promote a decrease in the magnitude of the spring bloom, which could impact upper trophic levels and also deep carbon export in the future.
Martínez Cano, Isabel, and Fernando Gonzalez Taboada, et al., November 2016: Decline and recovery of a large carnivore: environmental change and long-term trends in an endangered brown bear population. Proceedings of the Royal Society B, 283(1843), DOI:10.1098/rspb.2016.1832. Abstract
Understanding what factors drive fluctuations in the abundance of endangered
species is a difficult ecological problem but a major requirement to
attain effective management and conservation success. The ecological traits
of large mammals make this task even more complicated, calling for integrative
approaches. We develop a framework combining individual-based
modelling and statistical inference to assess alternative hypotheses on
brown bear dynamics in the Cantabrian range (Iberian Peninsula). Models
including the effect of environmental factors on mortality rates were able to
reproduce three decades of variation in the number of females with cubs of
the year (Fcoy), including the decline that put the population close to extinction
in the mid-nineties, and the following increase in brown bear numbers.
This external effect prevailed over density-dependent mechanisms (sexually
selected infanticide and female reproductive suppression), with a major
impact of climate driven changes in resource availability and a secondary
role of changes in human pressure. Predicted changes in population structure
revealed a nonlinear relationship between total abundance and the number of
Fcoy, highlighting the risk of simple projections based on indirect abundance
indices. This study demonstrates the advantages of integrative, mechanistic
approaches and provides a widely applicable framework to improve our
understanding of wildlife dynamics.
