Biological regime shifts and changes in predictability

Dippner, Joachim W.; Junker, Karin; Kröncke, Ingrid

doi:10.1029/2010gl045696

Cited by 24 publications

(31 citation statements)

References 44 publications

(53 reference statements)

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“…NAO has long been considered as a very good predictor in forecasting benthic time series (Kröncke, 2011). However, after the regime shift in 2000/2001 the correlation between NAO and macrofauna biomass and abundance diminished, perhaps reflecting the disappearance of autocorrelation and thus predictability (Dippner et al, 2010;Junker et al, 2012;Kröncke et al, 2013). Presumably, this may be the reason that the DFM exhibited the highest AIC when we consider NAOWI as the only explanatory variable.…”

Section: Relative Contribution Of Explanatory Variablesmentioning

confidence: 90%

Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Shojaei

Gutow

Dannheim

et al. 2016

Journal of Sea Research

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We examined long-term variability in the abundance of German Bight soft bottom macro-zoobenthos together with major environmental factors (sea surface temperature, winter NAO index, salinity, phosphate, nitrate and silicate) using one of the most comprehensive ecological long-term data sets in the North Sea . Two techniques, Min/Max Autocorrelation Factor Analysis (MAFA) and Dynamic Factor Analysis (DFA) were used to identify underlying common trends in the macrofaunal time series and the relationships between this series and environmental variables. These methods are particularly suitable for relatively short (N15-25 years), non-stationary multivariate data series. Both MAFA and DFA identify a common trend in German Bight macrofaunal abundance i.e. a slight decrease (1981-mid-1990s) followed by a sharp trough in the late 1990s. Subsequently, scores increased again towards 2011. Our analysis indicates that winter temperature and North Atlantic Oscillation were the predominant environmental drivers of temporal variation in German Bight macrofaunal abundance. The techniques applied here are suitable tools to describe temporal fluctuations in complex and noisy multiple time series data and can detect distinct shifts and trends within such time series.

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Section: Relative Contribution Of Explanatory Variablesmentioning

confidence: 90%

Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Shojaei

Gutow

Dannheim

et al. 2016

Journal of Sea Research

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“…This might be perhaps the case in terrestrial ecosystems considered by Hallett et al (2004). But, our results indicated that this is surely not the case in marine areas such as the North Sea (Dippner et al 2010) and the Baltic Sea (Möllmann et al 2009). Our results indicate that the multivariate BSE index combined of large-and regional-scale indices has an excellent performance and a high versatility much better than each single large-scale index only.…”

Section: Discussionmentioning

confidence: 57%

“…copepodite and T. longicornis copepodite spring biomass anomaly observed and predicted using the BSE index and Chen index. The observation indicates a negative biomass anomaly from 1960 until the 1989/1990 regime shift (Dippner et al 2010). Amplitude and trends are well reproduced by the BSE index including the 1989/1990 regime shift, whereas the amplitudes reproduced by the Chen index are too small.…”

Section: Performance Test With Biological Datamentioning

confidence: 88%

“…However, there is evidence that the correlation of NAO and biological variables, e.g. macrobenthos communities in the southern North Sea fails after 2001/2002 (Dippner et al 2010) which might be addressed to a climate global regime shift (Swanson and Tsonis 2009). The same regime shift has been observed in the Baltic Sea (Möllmann et al 2009).…”

Section: Introductionmentioning

confidence: 89%

“…Recently, Dippner et al (2010) showed that since 2001/2002 the high correlation between climate predictors and environmental response variables failed due to a nonlinear regime shift. In such a situation, the response of the biological system to climate variability cannot be predicted any longer with linear methods or simple indices such as the NAO.…”

Section: Introductionmentioning

confidence: 99%

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Since 2001/2002, the correlation between North Atlantic Oscillation index and biological variables in the North Sea and Baltic Sea fails, which might be addressed to a global climate regime shift. To understand inter-annual and inter-decadal variability in environmental variables, a new multivariate index for the Baltic Sea is developed and presented here. The multivariate Baltic Sea Environmental (BSE) index is defined as the 1st principal component score of four z-transformed time series: the Arctic Oscillation index, the salinity between 120 and 200 m in the Gotland Sea, the integrated river runoff of all rivers draining into the Baltic Sea, and the relative vorticity of geostrophic wind over the Baltic Sea area. A statistical downscaling technique has been applied to project different climate indices to the sea surface temperature in the Gotland, to the Landsort gauge, and the sea ice extent. The new BSE index shows a better performance than all other climate indices and is equivalent to the Chen index for physical properties. An application of the new index to zooplankton time series from the central Baltic Sea (Latvian EEZ) shows an excellent skill in potential predictability of environmental time series.

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Benthic species as mud patrol ‐ modelled effects of bioturbators and biofilms on large‐scale estuarine mud and morphology

Brückner

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Coco

et al. 2021

Earth Surf Processes Landf

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Sediment-stabilizing and -destabilizing organisms, i.e. microphytobenthos (biofilms) and macrozoobenthos (bioturbators), affect the erodibility of muddy sediments, potentially altering large-scale estuarine morphology. Using a novel eco-morphodynamic model of an idealized estuary, we investigate eco-engineering effects of microphytobenthos and two macrozoobenthic bioturbators. Local mud erodibility is based on species pattern predicted through hydrodynamics, soil mud content, competition and grazing. Mud resuspension and export is enhanced under bioturbation and prevented under biostabilization through respective exposure and protection of the supra-and intertidal. Bioturbation decreases mud thickness and bed elevations, which increases net mud fluxes. Microphytobenthos reduces erosion, leading to a local mud increase of intertidal sediments.

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Biological regime shifts and changes in predictability

Cited by 24 publications

References 44 publications

Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

A Multivariate Baltic Sea Environmental Index

Benthic species as mud patrol ‐ modelled effects of bioturbators and biofilms on large‐scale estuarine mud and morphology

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