Institute for Sea Fisheries

Hamburg, Germany

Institute for Sea Fisheries

Hamburg, Germany
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Appeltans W.,Flanders Marine Institute | Appeltans W.,Intergovernmental Oceanographic Commission of UNESCO | Ahyong S.T.,South Australian Museum | Ahyong S.T.,University of New South Wales | And 124 more authors.
Current Biology | Year: 2012

Background: The question of how many marine species exist is important because it provides a metric for how much we do and do not know about life in the oceans. We have compiled the first register of the marine species of the world and used this baseline to estimate how many more species, partitioned among all major eukaryotic groups, may be discovered. Results: There are ∼226,000 eukaryotic marine species described. More species were described in the past decade (∼20,000) than in any previous one. The number of authors describing new species has been increasing at a faster rate than the number of new species described in the past six decades. We report that there are ∼170,000 synonyms, that 58,000-72,000 species are collected but not yet described, and that 482,000-741,000 more species have yet to be sampled. Molecular methods may add tens of thousands of cryptic species. Thus, there may be 0.7-1.0 million marine species. Past rates of description of new species indicate there may be 0.5 ± 0.2 million marine species. On average 37% (median 31%) of species in over 100 recent field studies around the world might be new to science. Conclusions: Currently, between one-third and two-thirds of marine species may be undescribed, and previous estimates of there being well over one million marine species appear highly unlikely. More species than ever before are being described annually by an increasing number of authors. If the current trend continues, most species will be discovered this century. © 2012 Elsevier Ltd.

Schuckel U.,Senckenberg Institute | Ehrich S.,Institute for Sea Fisheries | Kroncke I.,Senckenberg Institute
Estuarine, Coastal and Shelf Science | Year: 2010

In 1999, 2003 and 2007 macrofauna communities were sampled in three different areas (" Boxes" ) of 10 × 10 nautical miles in the northern North Sea in order to study the temporal changes in community structure in relation to changes in temperature or changes in hydrography.Box D which was influenced by the Fair Isle Current revealed an increase in abundance of sand-licking species between 1999, 2003 and 2007. Significant positive correlations between SST and abundance of characteristic species as well as different feeding types in this mixed water column area seemed to be related to enhanced primary production and SST resulting in higher food supply.Within the East Shetland Basin (Boxes L and M) temporal changes in the macrofaunal communities between 1999 and 2003 were caused by a strong decrease in abundance of surface deposit and suspension feeders which indicated a lower food supply. An increase in abundance of species known for a rapid response to sudden food supply was found in 2007.There is evidence that hydrographic conditions such as stratification and different water masses in this area influence the variability of food supply for the macrofauna and caused changes in community structure. © 2010 Elsevier Ltd.

Lindegren M.,University of California at San Diego | Lindegren M.,Technical University of Denmark | Dakos V.,Wageningen University | Groger J.P.,Institute for Sea Fisheries | And 5 more authors.
PLoS ONE | Year: 2012

Critical transitions between alternative stable states have been shown to occur across an array of complex systems. While our ability to identify abrupt regime shifts in natural ecosystems has improved, detection of potential early-warning signals previous to such shifts is still very limited. Using real monitoring data of a key ecosystem component, we here apply multiple early-warning indicators in order to assess their ability to forewarn a major ecosystem regime shift in the Central Baltic Sea. We show that some indicators and methods can result in clear early-warning signals, while other methods may have limited utility in ecosystem-based management as they show no or weak potential for early-warning. We therefore propose a multiple method approach for early detection of ecosystem regime shifts in monitoring data that may be useful in informing timely management actions in the face of ecosystem change. © 2012 Lindegren et al.

Groger J.P.,Institute for Sea Fisheries | Groger J.P.,University of Rostock | Missong M.,University of Bremen | Rountree R.A.,Technology Applications, Inc.
Ecological Indicators | Year: 2011

Regime shifts in ecosystems whose patterns and properties may be very complex and thus manifold have profound implications for sustainability. Detecting structural breaks in natural processes, however, turns out to be an ambitious task because the lack of well defined target values and reference periods renders application of standard statistical (process or quality) control methods all but impossible. We develop an iterative procedure combining econometric, time series and quantile methods that produce a graphic display referred to as a "shiftogram," which indicates potential shifts within univariate components of an ecosystem of interest by characterizing their specific and often fairly complex properties. The shiftogram approach can be routinely applied as a scanning device to any (univariate) time series. We provide a search algorithm that iteratively looks for the best value of some quality-of-fit criterion for a time series where the break point is not known beforehand. The approach is demonstrated by the application to univariate examples of fish recruitment, a climate change phenomenon and a canonical variable bundling the effect of different biodiversity indices. Analysis of ecosystem level shifts (i.e. regime shifts) can then be conducted by applying the shiftogram method to multiple component variables and examining correspondence among their resulting shift point and shift types. Alternatively we illustrate how regime shifts can be examined directly by applying the shiftogram approach to multivariate time series data after reduction to a univariate case through canonical data reduction techniques. © 2010 Elsevier Ltd.

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