Barents Sea plankton production and controlling factors in a fluctuating climate

The Barents Sea and its marine ecosystem is exposed to many different processes related to the seasonal light variability, formation and melting of sea-ice, wind-induced mixing, and exchange of heat and nutrients with neighbouring ocean regions. A global model for the RCP4.5 scenario was downscaled, evaluated, and combined with a biophysical model to study how future variability and trends in temperature, sea-ice concentration, light, and wind-induced mixing potentially affect the lower trophic levels in the Barents Sea marine ecosystem. During the integration period (2010–2070), only a modest change in climate variables and biological production was found, compared to the inter-annual and decadal variability. The most prominent change was projected for the mid-2040s with a sudden decrease in biological production, largely controlled by covarying changes in heat inflow, wind, and sea-ice extent. The northernmost parts exhibited increased access to light during the productive season due to decreased sea-ice extent, leading to increased primary and secondary production in periods of low sea-ice concentrations. In the southern parts, variable access to nutrients as a function of wind-induced mixing and mixed layer depth were found to be the most dominating factors controlling variability in primary and secondary production.publishedVersio

Mesoscale Eddies Are Oases for Higher Trophic Marine Life

Mesoscale eddies stimulate biological production in the ocean, but knowledge of energy transfers to higher trophic levels within eddies remains fragmented and not quantified. Increasing the knowledge base is constrained by the inability of traditional sampling methods to adequately sample biological processes at the spatio-temporal scales at which they occur

Global and Polynomial-Time Convergence of an Infeasible-Interior-Point Algorithm Using Inexact Computation(Continuous and Discrete Mathematical Optimization)

Social learning can be fundamental to cohesive group living, and schooling fishes have proven ideal test subjects for recent work in this field. For many species, both demographic factors, and inter- (and intra-) generational information exchange are considered vital ingredients in how movement decisions are reached. Yet key information is often missing on the spatial outcomes of such decisions, and questions concerning how migratory traditions are influenced by collective memory, density-dependent and density-independent processes remain open. To explore these issues, we focused on Atlantic herring (Clupea harengus), a long-lived, dense-schooling species of high commercial importance, noted for its unpredictable shifts in winter distribution, and developed a series of Bayesian space-time occurrence models to investigate wintering dynamics over 23 years, using point-referenced fishery and survey records from Icelandic waters. We included covariates reflecting local-scale environmental factors, temporally-lagged prey biomass and recent fishing activity, and through an index capturing distributional persistence over time, derived two proxies for spatial memory of past wintering sites. The previous winter's occurrence pattern was a strong predictor of the present pattern, its influence increasing with adult population size. Although the mechanistic underpinnings of this result remain uncertain, we suggest that a ‘wisdom of the crowd’ dynamic may be at play, by which navigational accuracy towards traditional wintering sites improves in larger and/or denser, better synchronized schools. Wintering herring also preferred warmer, fresher, moderately stratified waters of lower velocity, close to hotspots of summer zooplankton biomass, our results indicative of heightened environmental sensitivity in younger cohorts. Incorporating spatiotemporal correlation structure and time-varying regression coefficients improved model performance, and validation tests on independent observations one-year ahead illustrate the potential of uniting demographic information and non-stationary models to quantify both the strength of collective memory in animal groups and its relevance for the spatial management of populations

Modeling Emergent Life Histories of Copepods

The distribution and population dynamics of zooplankton are affected by the interplay between currents, behavior, and selective growth, mortality, and reproduction. Here, we present an individual based model for a copepod where life-history and behavioral traits are adapted using a genetic algorithm approach. The objectives were to investigate the importance of spatial and inter-annual variability in biophysical forcing and different predator densities on the adaptation of emergent life history traits in a copepod. The results show that in simulations with adaptation, the populations remained viable (positive population growth) within the study area over 100-year simulation whereas without adaptation populations were unviable. In one dimensional simulations with fixed spatial position there were small differences between replicate simulations. Inter-annual variability in forcing resulted in increased difference in fitness between years. Simulations with spatial-, but without inter-annual variability in forcing produced large differences in the geographic distribution, fitness, and life history strategies between replicate simulations. In simulations with both spatial and inter-annual variability the replicates had rather small variability in traits. Increased predator density lead to increased day depth and avoidance of the lit upper waters. The model can be used for a range of different applications such as studying individual and population responses to environmental changes including climate change as well as to yield robust behavioral strategies for use in fully coupled end to end ecosystem models

Space time models

This folder contains R code and data to run all models described in the paper, in addition to model output for plotting Figures. 3-5, A7 and reproducing Tables 2, 3, A1-A3. See the README file for further information and file descriptions

Spatial similarity index

This folder contains R code and data to calculate the spatial similarity index (SSI), and to compute and map the ‘distrib(t)’ and ‘counts(t)’ variables, as described in Appendix 2 of the paper. See the README file for further information and file descriptions

Schematic overview of potentially interacting ecosystem processes.

<p>Bottom-up and top-down effects on all trophic levels may interact with climate on all trophic levels to determine the biomass and production of phytoplankton, zooplankton and fish.</p

Annual net primary production (NPP) in the Arctic region of the Barents Sea.

<p>(A) Annual NPP in relationship to the mean open water area. (B) Seasonal development of open water area for five different years.</p

Overview of main currents and geographical range of the study area in the Barents Sea.

<p>(A) Warmer Atlantic water (red) meets colder Arctic water (blue). The Barents Sea was divided into five regions: CSTW = Coastal west, CSTE = Coastal east, ATLW = Atlantic west, ATLE = Atlantic east, ARC = Arctic (see Materials and Methods for more details). Red lines indicate sections within the Barents Sea: FB = Fugløya-Bjørnøya section, K = Kola; (B) Standard stations on FB section (Chl <i>a</i> = filled red circles; zooplankton = open circles) and autumn survey (August to early October) shown as an example for 2010 (triangles).</p