Fernando González Taboada

Seasonality of North Atlantic phytoplankton from space: impact of environmental forcing on a changing phenology (1998-2012).

Seasonal pulses of phytoplankton drive seasonal cycles of carbon fixation and particle sedimentation, and might condition recruitment success in many exploited species. Taking advantage of long-term series of remotely sensed chlorophyll a (1998-2012), we analyzed changes in phytoplankton seasonality in the North Atlantic Ocean. Phytoplankton phenology was analyzed based on a probabilistic characterization of bloom incidence. This approach allowed us to detect changes in the prevalence of different seasonal cycles and, at the same time, to estimate bloom timing and magnitude taking into account uncertainty in bloom detection. Deviations between different sensors stressed the importance of a prolonged overlap between successive missions to ensure a correct assessment of phenological changes, as well as the advantage of semi-analytical chlorophyll algorithms over empirical ones to reduce biases. Earlier and more intense blooms were detected in the subpolar Atlantic, while advanced blooms of less magnitude were common in the Subtropical gyre. In the temperate North Atlantic, spring blooms advanced their timing and decreased in magnitude, whereas fall blooms delayed and increased their intensity. At the same time, the prevalence of locations with a single autumn/winter bloom or with a bimodal seasonal cycle increased, in consonance with a poleward expansion of subtropical conditions. Changes in bloom timing and magnitude presented a clear signature of environmental factors, especially wind forcing, although changes on incident photosynthetically active radiation and sea surface temperature were also important depending on latitude. Trends in bloom magnitude matched changes in mean chlorophyll a during the study period, suggesting that seasonal peaks drive long-term trends in chlorophyll a concentration. Our results link changes in North Atlantic climate with recent trends in the phenology of phytoplankton, suggesting an intensification of these impacts in the near future.

Patterns of change in sea surface temperature in the North Atlantic during the last three decades: beyond mean trends

Sea surface temperature (SST) is an important indicator of changes in the climate system and a key driver of marine ecosystems. Here we studied the strength and spatial patterns of changes in North Atlantic SST during the last three decades (1982–2010). Regional and local patterns of change were studied using data derived from the Advanced Very High Resolution Radiometer (AVHRR) sensors. Apart from changes in mean SST, we studied changes in the seasonal cycle, in the spatial patterning of temperature anomalies and in the location of selected isotherms. We quantified the degree of nonlinearity in mean SST as an indicator of the rate at which SST trends changed during the study period. Changes in the timing and intensity of seasonal extremes were explored, and a heuristic method was used to derive the length of the period of stratification and to estimate its variation. Our results were in general coherent with the main impacts predicted by climate change projections, with greatest changes located at northern latitudes and near land. Marked variation in the spatial patterns was also found for different variables, strengthening the view that physical changes could be promoting the arrangement of novel marine biological communities. The observed changes in ocean SST highlighted the need of a more local and regional focus in future climate change studies.

Phytoplankton Growth and Microzooplankton Grazing in the Subtropical Northeast Atlantic

Dilution experiments were performed to estimate phytoplankton growth and microzooplankton grazing rates during two Lagrangian surveys in inner and eastern locations of the Eastern North Atlantic Subtropical Gyre province (NAST-E). Our design included two phytoplankton size fractions (0.2–5 µm and >5 µm) and five depths, allowing us to characterize differences in growth and grazing rates between size fractions and depths, as well as to estimate vertically integrated measurements. Phytoplankton growth rates were high (0.11–1.60 d−1), especially in the case of the large fraction. Grazing rates were also high (0.15–1.29 d−1), suggesting high turnover rates within the phytoplankton community. The integrated balances between phytoplankton growth and grazing losses were close to zero, although deviations were detected at several depths. Also, O2 supersaturation was observed up to 110 m depth during both Lagrangian surveys. These results add up to increased evidence indicating an autotrophic metabolic balance in oceanic subtropical gyres.

Critique of the methods used to project global sea-level rise from global temperature

Based on previous work (1), Vermeer and Rahmstorf (2) proposed a model to link global surface temperature (T) and sea level (H) (Eq. 1):where a, T0, and b are parameters that were estimated for both synthetic and real data. The model was fitted to global sea-level (3) and surface-temperature time series (4), combining an iterative procedure with simple linear regression, and it was used to project future sea-level rise.

The allometry of the smallest: superlinear scaling of microbial metabolic rates in the Atlantic Ocean

Prokaryotic planktonic organisms are small in size but largely relevant in marine biogeochemical cycles. Due to their reduced size range (0.2 to 1 μm in diameter), the effects of cell size on their metabolism have been hardly considered and are usually not examined in field studies. Here, we show the results of size-fractionated experiments of marine microbial respiration rate along a latitudinal transect in the Atlantic Ocean. The scaling exponents obtained from the power relationship between respiration rate and size were significantly higher than one. This superlinearity was ubiquitous across the latitudinal transect but its value was not universal revealing a strong albeit heterogeneous effect of cell size on microbial metabolism. Our results suggest that the latitudinal differences observed are the combined result of changes in cell size and composition between functional groups within prokaryotes. Communities where the largest size fraction was dominated by prokaryotic cyanobacteria, especially Prochlorococcus, have lower allometric exponents. We hypothesize that these larger, more complex prokaryotes fall close to the evolutionary transition between prokaryotes and protists, in a range where surface area starts to constrain metabolism and, hence, are expected to follow a scaling closer to linearity.

Determining the causes behind the collapse of a small pelagic fishery using Bayesian population modeling

Small pelagic fish species present complex dynamics that challenge population biologists and prevent effective management. Huge fluctuations in abundance have traditionally been associated with external environmental forcing on recruitment, exempting other processes from contributing to fisheries collapse. On the other hand, theory predicts that density dependence and overexploitation can increase the likelihood of population oscillations. Here, we combined nonlinear population modeling with Bayesian analysis to examine the importance of different regulatory mechanisms on the collapse of European anchovy (Engraulis encrasicolus) in the Bay of Biscay. The approach relied on detailed population data and in a careful characterization of changes in the environment experienced by anchovy early stages based mainly on satellite remote sensing. Alternative hypotheses about external forcing on recruitment determined prediction skill and provided alternative interpretations of the causes behind the collapse. Density dependence was weak and unable to generate huge oscillations. Instead, models considering changes in phytoplankton phenology or in larval drift presented the best prediction skill. Nevertheless, an extensive surrogate analysis showed that environmental fluctuations alone barely explain anchovy collapse without considering the impact of fishing. Our results highlight the effectiveness of a Bayesian approach to analyze the dynamics and collapse of managed populations.

Decline and recovery of a large carnivore: environmental change and long-term trends in an endangered brown bear population

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.

Contrasting the ability of data to make inferences regarding dispersal: case study of the Red-cockaded woodpecker (Picoides borealis)

Dispersal is a critical biological process that contributes to the persistence of species in complex and dynamic landscapes. However, little is known about the ability of different types of data to reveal how species interact with landscape patterns during dispersal. Further, application of process-based, landscape-scale models able to capture the influence of land use and climate change are limited by this lack of dispersal knowledge. Here we highlight a method for building such models when dispersal parameters are unknown, but information on the mating system and survival are available. We applied a common statistical framework, rooted in information theory, to contrast the ability of abundance, movement, and genetic data to estimate dispersal parameters for endangered Red-cockaded woodpecker (RCW), using an individual-based, spatially-explicit population model. Dispersal was modeled as a multifaceted process in which foray distance, long-distance dispersal, competition for mates, and landscape permeability were treated as uncertain. We found that movement data are three-times more powerful than abundance data collected at the same spatial and temporal scales. However, habitat occupancy data collected over much a shorter time scale but at regional spatial scales were very effective for estimating dispersal. We also found that one-year of abundance data provided a similar reduction in uncertainty as genetic differences among breeding groups estimated using a 24-year pedigree. Substituting population genetic data for movement and abundance data often led to the same parameter values, but not always. Our study highlights important differences in the information content of data commonly collected in the field.