Effects on zooplankton of a warmer ocean: Recent evidence from the Northeast Pacific

The consequences for pelagic communities of warming trends in mid and high latitude ocean regions could be substantial, but their magnitude and trajectory are not yet known. Environmental changes predicted by climate models (and beginning to be confirmed by observations) include warming and freshening of the upper ocean and reduction in the extent and duration of ice cover. One way to evaluate response scenarios is by comparing how "similar" zooplankton communities have differed among years and/or locations with differing temperature. The subarctic Pacific is a strong candidate for such comparisons, because the same mix of zooplankton species dominates over a wide range of temperature climatologies, and observations have spanned substantial temperature variability at interannual-to-decadal time scales. In this paper, we review and extend copepod abundance and phenology time series from net tow and Continuous Plankton Recorder surveys in the subarctic Northeast Pacific. The two strongest responses we have observed are latitudinal shifts in centers of abundance of many species (poleward under warm conditions), and changes in the life cycle timing of Neocalanus plumchrus in both oceanic and coastal regions (earlier by several weeks in warm years and at warmer locations). These zooplankton data, plus indices of higher trophic level responses such as reproduction, growth and survival of pelagic fish and seabirds, are all moderately-to-strongly intercorrelated (vertical bar r vertical bar = 0.25-0.8) with indices of local and basin-scale temperature anomalies. A principal components analysis of the normalized anomaly time series from 1979 to 2004 shows that a single "warm-and-low-productivity" vs. "cool-and-high-productivity" component axis accounts for over half of the variance/covariance. Prior to 1990, the scores for this component were negative ("cool" and "productive") or near zero except positive in the El Nino years 1983 and 1987. The scores were strongly and increasingly positive ("warm" and "low productivity") from 1992 to 1998; negative from 1999 to 2002; and again increasingly positive from 2003-present. We suggest that, in strongly seasonal environments, anomalously high temperature may provide misleading environmental cues that contribute to timing mismatch between life history events and the more-nearly-fixed seasonality of insolation, stratification, and food supply. Crown Copyright (c) 2007 Published by Elsevier Ltd. All rights reserved.

Region-wide changes in marine ecosystem dynamics: state-space models to distinguish trends from step changes

Regime shifts are sudden changes in ecosystem structure that can be detected across several ecosystem components. The concept that regime shifts are common in marine ecosystems has gained popularity in recent years. Many studies have searched for the step-like changes in ecosystem state expected under a simple interpretation of this idea. However, other kinds of change, such as pervasive trends, have often been ignored. We assembled over 300 ecological time series from seven UK marine regions, covering two to three decades. We developed state-space models for the first principal component of the time series in each region, a common measure of ecosystem state. Our models allowed both trends and step changes, possibly in combination. We found trends in three of seven regions and step changes in two of seven regions. Gradual and sudden changes are therefore important trajectories to consider in marine ecosystems.

A method for semi-automated objective quantification of linear bedforms from multi-scale Digital Elevation Models

The increasing availability of large, detailed digital representations of the Earth’s surface demands the application of objective and quantitative analyses. Given recent advances in the understanding of the mechanisms of formation of linear bedform features from a range of environments, objective measurement of their wavelength, orientation, crest and trough positions, height and asymmetry is highly desirable. These parameters are also of use when determining observation-based parameters for use in many applications such as numerical modelling, surface classification and sediment transport pathway analysis. Here, we (i) adapt and extend extant techniques to provide a suite of semi-automatic tools which calculate crest orientation, wavelength, height, asymmetry direction and asymmetry ratios of bedforms, and then (ii) undertake sensitivity tests on synthetic data, increasingly complex seabeds and a very large-scale (39 000km2) aeolian dune system. The automated results are compared with traditional, manually derived,measurements at each stage. This new approach successfully analyses different types of topographic data (from aeolian and marine environments) from a range of sources, with tens of millions of data points being processed in a semi-automated and objective manner within minutes rather than hours or days. The results from these analyses show there is significant variability in all measurable parameters in what might otherwise be considered uniform bedform fields. For example, the dunes of the Rub’ al Khali on the Arabian peninsula are shown to exhibit deviations in dimensions from global trends. Morphological and dune asymmetry analysis of the Rub’ al Khali suggests parts of the sand sea may be adjusting to a changed wind regime from that during their formation 100 to 10 ka BP.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.