scholarly journals Biodiversity baseline for large marine ecosystems: an example from the Barents Sea

2015 ◽  
Vol 72 (6) ◽  
pp. 1756-1768 ◽  
Author(s):  
Grégoire Certain ◽  
Benjamin Planque
Keyword(s):  
Fish Community ◽  
Biological Diversity ◽  
Barents Sea ◽  
Marine Ecosystem ◽  
Marine Ecosystems ◽  
Spatial And Temporal Scales ◽  
Diversity Measures ◽  
The Barents Sea ◽  
True Diversity ◽  
Biodiversity Changes

Abstract Biodiversity is an increasingly important issue for the management of marine ecosystems. However, the proliferation of biodiversity indices and difficulties associated with their interpretation have resulted in a lack of clearly defined framework for quantifying biodiversity and biodiversity changes in marine ecosystems for assessment purpose. Recent theoretical and numerical developments in biodiversity statistics have established clear algebraic relationships between most of the diversity measures commonly used, and have highlighted those that most directly relates to the concept of biological diversity, terming them “true” diversity measures. In this study, we implement the calculation of these “true” diversity measures at the scale of a large-marine ecosystem, the Barents Sea. We applied hierarchical partitioning of biodiversity to an extensive dataset encompassing 10 years of trawl-surveys for both pelagic and demersal fish community. We quantify biodiversity and biodiversity changes for these two communities across the whole continental shelf of the Barents Sea at various spatial and temporal scales, explicitly identifying areas where fish communities are stable and variable. The method is used to disentangle areas where community composition is subject to random fluctuations from areas where the fish community is drifting over time. We discuss how our results can serve as a spatio-temporal biodiversity baseline against which new biodiversity estimates, derived from sea surveys, can be evaluated.

scholarly journals Change in Fish Community Structure in the Barents Sea

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e62748 ◽  
Author(s):  
Michaela Aschan ◽  
Maria Fossheim ◽  
Michael Greenacre ◽  
Raul Primicerio
Keyword(s):  
Community Structure ◽  
Fish Community ◽  
Barents Sea ◽  
Fish Community Structure ◽  
The Barents Sea

scholarly journals The importance of within-system spatial variation in drivers of marine ecosystem regime shifts

2015 ◽  
Vol 370 (1659) ◽  
pp. 20130271 ◽  
Author(s):  
J. A. D. Fisher ◽  
M. Casini ◽  
K. T. Frank ◽  
C. Möllmann ◽  
W. C. Leggett ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
North Sea ◽  
Barents Sea ◽  
Marine Ecosystem ◽  
Spatial Dynamics ◽  
Marine Ecosystems ◽  
Regime Shifts ◽  
Large Marine Ecosystems ◽  
The North ◽  
The Baltic

Comparative analyses of the dynamics of exploited marine ecosystems have led to differing hypotheses regarding the primary causes of observed regime shifts, while many ecosystems have apparently not undergone regime shifts. These varied responses may be partly explained by the decade-old recognition that within-system spatial heterogeneity in key climate and anthropogenic drivers may be important, as recent theoretical examinations have concluded that spatial heterogeneity in environmental characteristics may diminish the tendency for regime shifts. Here, we synthesize recent, empirical within-system spatio-temporal analyses of some temperate and subarctic large marine ecosystems in which regime shifts have (and have not) occurred. Examples from the Baltic Sea, Black Sea, Bengula Current, North Sea, Barents Sea and Eastern Scotian Shelf reveal the largely neglected importance of considering spatial variability in key biotic and abiotic influences and species movements in the context of evaluating and predicting regime shifts. We highlight both the importance of understanding the scale-dependent spatial dynamics of climate influences and key predator–prey interactions to unravel the dynamics of regime shifts, and the utility of spatial downscaling of proposed mechanisms (as evident in the North Sea and Barents Sea) as a means of evaluating hypotheses originally derived from among-system comparisons.

scholarly journals Integrated mathematical model of the Barents and White seas large marine ecosystem - a tool for assessing natural risks and efficient use of biological resources

2019 ◽  
Vol 487 (5) ◽  
pp. 566-572
Author(s):  
S. V. Berdnikov ◽  
V. V. Kulygin ◽  
V. V. Sorokina ◽  
L. V. Dashkevich ◽  
I. V. Sheverdyaev
Keyword(s):  
Mathematical Model ◽  
Fishery Management ◽  
Barents Sea ◽  
Climatic Factors ◽  
Gross Primary Production ◽  
Marine Ecosystem ◽  
Primary Data ◽  
Biological Resources ◽  
The Barents Sea ◽  
Primary Data Analysis

Integrated mathematical model of the Barents and White seas LME is proposed as a tool for assessing natural risks and rational use of biological resources. The model includes the following main blocks (modules): a) oceanographic conditions and biological productivity; b) trophodynamics and fishery management; c) environmental and biota pollution; d) socio-economic development; e) assessment of environmental risks from marine activities. Integrated model was used for assessing: the hydrological variability, long-term dynamics of ecosystem productivity and fishing load on the most important commercial species of the Barents Sea. A new zoning of the Barents Sea taking into account the geomorphological and hydrological factors was performed under the guidance of academician G.G. Matishov. Maps of the simulated gross primary production in the Barents Sea for the second half of the 20th and first decade of the 21st centuries are presented. The energy balance in the Barents Sea ecosystem at the end of the 20th and the beginning of the 21st century was calculated by trophodynamic model. It is concluded that determination of the fishing load on populations should base on using ecosystem mathematical models instead of single-species models. To estimate the fishing mortality, it is necessary to take into account not only the spatial effects associated with the characteristics of the fishes' life cycle and the distribution of fishing load, but also the influence of climatic factors and inner-ecosystem interactions. The use of modern information technologies, both in the field of primary data analysis, and in the area of their generalization to diagnose past changes, makes it possible to better understand the consequences for the Barents and White seas LME of existing natural resource use plans, taking into account the experience (sometimes negative) of past years and the expected climatic changes.

scholarly journals Satellite Remote Sensing and the Marine Biodiversity Observation Network: Current Science and Future Steps

Oceanography ◽  
2021 ◽  
Vol 34 (2) ◽  
Author(s):  
Maria Kavanaugh ◽  
◽  
Tom Bell ◽  
Dylan Catlett ◽  
Megan Cimino ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Marine Ecosystem ◽  
Marine Biodiversity ◽  
Human Populations ◽  
Coastal Zones ◽  
Distribution Models ◽  
Spatial And Temporal Scales ◽  
Biological Communities ◽  
Observation Network ◽  
Biodiversity Changes

Coastal ecosystems are rapidly changing due to human-caused global warming, rising sea level, changing circulation patterns, sea ice loss, and acidification that in turn alter the productivity and composition of marine biological communities. In addition, regional pressures associated with growing human populations and economies result in changes in infrastructure, land use, and other development; greater extraction of fisheries and other natural resources; alteration of benthic seascapes; increased pollution; and eutrophication. Understanding biodiversity is fundamental to assessing and managing human activities that sustain ecosystem health and services and mitigate humankind’s indiscretions. Remote-sensing observations provide rapid and synoptic data for assessing biophysical interactions at multiple spatial and temporal scales and thus are useful for monitoring biodiversity in critical coastal zones. However, many challenges remain because of complex bio-optical signals, poor signal retrieval, and suboptimal algorithms. Here, we highlight four approaches in remote sensing that complement the Marine Biodiversity Observation Network (MBON). MBON observations help quantify plankton functional types, foundation species, and unique species habitat relationships, as well as inform species distribution models. In concert with in situ observations across multiple platforms, these efforts contribute to monitoring biodiversity changes in complex coastal regions by providing oceanographic context, contributing to algorithm and indicator development, and creating linkages between long-term ecological studies, the next generations of satellite sensors, and marine ecosystem management.

scholarly journals Functional diversity of the Barents Sea fish community

2014 ◽  
Vol 495 ◽  
pp. 205-218 ◽  
Author(s):  
MA Wiedmann ◽  
M Aschan ◽  
G Certain ◽  
A Dolgov ◽  
M Greenacre ◽  
...  
Keyword(s):  
Functional Diversity ◽  
Fish Community ◽  
Barents Sea ◽  
The Barents Sea ◽  
Sea Fish

Method application of optimal multi-parameter analysis to assess the distribution of water masses as an example of CTD data of the Barents Sea in summer 2017

2019 ◽  
pp. 38-51
Author(s):  
Valeriy G. Yakubenko ◽  
Anna L. Chultsova
Keyword(s):  
Barents Sea ◽  
Field Measurements ◽  
Structural Similarity ◽  
Water Masses ◽  
Parameter Analysis ◽  
Water Dynamics ◽  
Phosphorus Concentration ◽  
Nitrogen And Phosphorus ◽  
The Barents Sea ◽  
Expert Assessments

Identification of water masses in areas with complex water dynamics is a complex task, which is usually solved by the method of expert assessments. In this paper, it is proposed to use a formal procedure based on the application of the method of optimal multiparametric analysis (OMP analysis). The data of field measurements obtained in the 68th cruise of the R/V “Academician Mstislav Keldysh” in the summer of 2017 in the Barents Sea on the distribution of temperature, salinity, oxygen, silicates, nitrogen, and phosphorus concentration are used as a data for research. A comparison of the results with data on the distribution of water masses in literature based on expert assessments (Oziel et al., 2017), allows us to conclude about their close structural similarity. Some differences are related to spatial and temporal shifts of measurements. This indicates the feasibility of using the OMP analysis technique in oceanological studies to obtain quantitative data on the spatial distribution of different water masses.

Sign in / Sign up

Export Citation Format

Share Document