Projecting the fate of fish populations using ecological-economic modeling

Four marine fish species are among the most important on the world market: cod, salmon, tuna, and sea bass. While the supply of North American and European markets for two of these species - Atlantic salmon and European sea bass - mainly comes from fish farming, Atlantic cod and tunas are mainly caught from wild stocks. We address the question what will be the status of these wild stocks in the midterm future, in the year 2048, to be specific. Whereas the effects of climate change and ecological driving forces on fish stocks have already gained much attention, our prime interest is in studying the effects of changing economic drivers, as well as the impact of variable management effectiveness. Using a process-based ecological-economic multispecies optimization model, we assess the future stock status under different scenarios of change. We simulate (i) technological progress in fishing, (ii) increasing demand for fish, and (iii) increasing supply of farmed fish, as well as the interplay of these driving forces under different sce- narios of (limited) fishery management effectiveness. We find that economic change has a substantial effect on fish populations. Increasing aquaculture production can dampen the fishing pressure on wild stocks, but this effect is likely to be overwhelmed by increasing demand and technological progress, both increasing fishing pressure. The only solution to avoid collapse of the majority of stocks is institutional change to improve management effectiveness significantly above the current state. We conclude that full recognition of economic drivers of change will be needed to successfully develop an integrated ecosystem management and to sustain the wild fish stocks until 2048 and beyond.

Seawater carbonate chemistry and growth rate during experiments with phylotypes of Symbiodinium (Dinophyceae), 2011

We investigated the effect of elevated partial pressure of CO2 (pCO2) on the photosynthesis and growth of four phylotypes (ITS2 types A1, A13, A2, and B1) from the genus Symbiodinium, a diverse dinoflagellate group that is important, both free-living and in symbiosis, for the viability of cnidarians and is thus a potentially important model dinoflagellate group. The response of Symbiodinium to an elevated pCO2 was phylotype-specific. Phylotypes A1 and B1 were largely unaffected by a doubling in pCO2 in contrast, the growth rate of A13 and the photosynthetic capacity of A2 both increased by ~ 60%. In no case was there an effect of ocean acidification (OA) upon respiration (dark- or light-dependent) for any of the phylotypes examined. Our observations suggest that OA might preferentially select among free-living populations of Symbiodinium, with implications for future symbioses that rely on algal acquisition from the environment (i.e., horizontal transmission). Furthermore, the carbon environment within the host could differentially affect the physiology of different Symbiodinium phylotypes. The range of responses we observed also highlights that the choice of species is an important consideration in OA research and that further investigation across phylogenetic diversity, for both the direction of effect and the underlying mechanism(s) involved, is warranted.

High-resolution dynamical downscaling products for the North Slope of Alaska and surrounding areas, links to model result files

Climatic changes are most pronounced in northern high latitude regions. Yet, there is a paucity of observational data, both spatially and temporally, such that regional-scale dynamics are not fully captured, limiting our ability to make reliable projections. In this study, a group of dynamical downscaling products were created for the period 1950 to 2100 to better understand climate change and its impacts on hydrology, permafrost, and ecosystems at a resolution suitable for northern Alaska. An ERA-interim reanalysis dataset and the Community Earth System Model (CESM) served as the forcing mechanisms in this dynamical downscaling framework, and the Weather Research & Forecast (WRF) model, embedded with an optimization for the Arctic (Polar WRF), served as the Regional Climate Model (RCM). This downscaled output consists of multiple climatic variables (precipitation, temperature, wind speed, dew point temperature, and surface air pressure) for a 10 km grid spacing at three-hour intervals. The modeling products were evaluated and calibrated using a bias-correction approach. The ERA-interim forced WRF (ERA-WRF) produced reasonable climatic variables as a result, yielding a more closely correlated temperature field than precipitation field when long-term monthly climatology was compared with its forcing and observational data. A linear scaling method then further corrected the bias, based on ERA-interim monthly climatology, and bias-corrected ERA-WRF fields were applied as a reference for calibration of both the historical and the projected CESM forced WRF (CESM-WRF) products. Biases, such as, a cold temperature bias during summer and a warm temperature bias during winter as well as a wet bias for annual precipitation that CESM holds over northern Alaska persisted in CESM-WRF runs. The linear scaling of CESM-WRF eventually produced high-resolution downscaling products for the Alaskan North Slope for hydrological and ecological research, together with the calibrated ERA-WRF run, and its capability extends far beyond that. Other climatic research has been proposed, including exploration of historical and projected climatic extreme events and their possible connections to low-frequency sea-atmospheric oscillations, as well as near-surface permafrost degradation and ice regime shifts of lakes. These dynamically downscaled, bias corrected climatic datasets provide improved spatial and temporal resolution data necessary for ongoing modeling efforts in northern Alaska focused on reconstructing and projecting hydrologic changes, ecosystem processes and responses, and permafrost thermal regimes. The dynamical downscaling methods presented in this study can also be used to create more suitable model input datasets for other sub-regions of the Arctic.