Simple shade plots aid better long-term choices of data pre-treatment in multivariate assemblage studies

Shade plots, simple visual representations of abundance matrices from multivariate species assemblage studies, are shown to be an effective aid in choosing an overall transformation (or other pre-treatment) of quantitative data for long-term use, striking an appropriate balance between dominant and less abundant taxa in ensuing resemblance-based multivariate analyses. Though the exposition is entirely general and applicable to all community studies, detailed illustrations of the comparative power and interpretative possibilities of shade plots are given in the case of two estuarine assemblage studies in south-western Australia: (a) macrobenthos in the upper Swan Estuary over a two-year period covering a highly significant precipitation event for the Perth area; and (b) a wide-scale spatial study of the nearshore fish fauna from five divergent estuaries. The utility of transformations of intermediate severity is again demonstrated and, with greater novelty, the potential importance seen of further mild transformation of all data after differential down-weighting (dispersion weighting) of spatially clumped' or schooled' species. Among the new techniques utilized is a two-way form of the RELATE test, which demonstrates linking of assemblage structure (fish) to continuous environmental variables (water quality), having removed a categorical factor (estuary differences). Re-orderings of sample and species axes in the associated shade plots are seen to provide transparent explanations at the species level for such continuous multivariate patterns.

The Importance of Regional, System-Wide and Local Spatial Scales in Structuring Temperate Estuarine Fish Communities

An extensive literature base worldwide demonstrates how spatial differences in estuarine fish assemblages are related to those in the environment at (bio)regional, estuary-wide or local (within-estuary) scales. Few studies, however, have examined all three scales, and those including more than one have often focused at the level of individual environmental variables rather than scales as a whole. This study has identified those spatial scales of environmental differences, across regional, estuary-wide and local levels, that are most important in structuring ichthyofaunal composition throughout south-western Australian estuaries. It is the first to adopt this approach for temperate microtidal waters. To achieve this, we have employed a novel approach to the BIOENV routine in PRIMER v6 and a modified global BEST test in an alpha version of PRIMER v7. A combination of all three scales best matched the pattern of ichthyofaunal differences across the study area (rho = 0.59; P = 0.001), with estuary-wide and regional scales accounting for about twice the variability of local scales. A shade plot analysis showed these broader-scale ichthyofaunal differences were driven by a greater diversity of marine and estuarine species in the permanently-open west coast estuaries and higher numbers of several small estuarine species in the periodically-open south coast estuaries. When interaction effects were explored, strong but contrasting influences of local environmental scales were revealed within each region and estuary type. A quantitative decision tree for predicting the fish fauna at any nearshore estuarine site in south-western Australia has also been produced. The estuarine management implications of the above findings are highlighted.

Modelling the future biogeography of North Atlantic zooplankton communities in response to climate change

Advances in habitat and climate modelling allow us to reduce uncertainties of climate change impacts on species distribution. We evaluated the impacts of future climate change on community structure, diversity, distribution and phenology of 14 copepod species in the North Atlantic. We developed and validated habitat models for key zooplankton species using continuous plankton recorder (CPR) survey data collected at mid latitudes of the North Atlantic. Generalized additive models (GAMs) were applied to relate the occurrence of species to environmental variables. Models were projected to future (2080–2099) environmental conditions using coupled hydroclimatix–biogeochemical models under the Intergovernmental Panel on Climate Change (IPCC) A1B climate scenario, and compared to present (2001–2020) conditions. Our projections indicated that the copepod community is expected to respond substantially to climate change: a mean poleward latitudinal shift of 8.7 km per decade for the overall community with an important species range variation (–15 to 18 km per decade); the species seasonal peak is expected to occur 12–13 d earlier for Calanus finmarchicus and C. hyperboreus; and important changes in community structure are also expected (high species turnover of 43–79% south of the Oceanic Polar Front). The impacts of the change expected by the end of the century under IPCC global warming scenarios on copepods highlight poleward shifts, earlier seasonal peak and changes in biodiversity spatial patterns that might lead to alterations of the future North Atlantic pelagic ecosystem. Our model and projections are supported by a temporal validation undertaken using the North Atlantic climate regime shift that occurred in the 1980s: the habitat model built in the cold period (1970–1986) has been validated in the warm period (1987–2004).

Effects of environmental conditions on the seasonal distribution of phytoplankton biomass in the North Sea

We study the spatial and seasonal variability of phytoplankton biomass (as phytoplankton color) in relation to the environmental conditions in the North Sea using data from the Continuous Plankton Recorder survey. By using only environmental fields and location as predictor variables we developed a nonparametric model (generalized additive model) to empirically explore how key environmental factors modulate the spatio-temporal patterns of the seasonal cycle of algal biomass as well as how these relate to the ,1988 North Sea regime shift. Solar radiation, as manifest through changes of sea surface temperature (SST), was a key factor not only in the seasonal cycle but also as a driver of the shift. The pronounced increase in SST and in wind speed after the 1980s resulted in an extension of the season favorable for phytoplankton growth. Nutrients appeared to be unimportant as explanatory variables for the observed spatio-temporal pattern, implying that they were not generally limiting factors. Under the new climatic regime the carrying capacity of the whole system has been increased and the southern North Sea, where the environmental changes have been more pronounced, reached a new maximum.

The influence of phytoplankton productivity, temperature and environmental stability on the control of copepod diversity in the North East Atlantic

The patterns of copepod species richness (S) and their relationship with phytoplankton productivity, temperature and environmental stability were investigated at climatological, seasonal and year-to-year time scales as well as scales along latitudinal and oceanic–neritic gradients using monthly time series of the Continuous Plankton Recorder (CPR) Survey collected in the North East Atlantic between 1958 and 2006. Time series analyses confirmed previously described geographic patterns. Equatorward and towards neritic environments, the climatological average of S increases and the variance explained by the seasonal cycle decreases. The bi-modal character of seasonality increases equatorward and the timing of the seasonal cycle takes place progressive earlier equatorward and towards neritic environments. In the long-term, the climatological average of S decreased significantly (p < 0.001) between 1958 and 2006 in the Bay of Biscay and North Iberian shelf at a rate of ca. 0.04 year−1, and increased at the same rate between 1991 and 2006 in the northernmost oceanic location. The climatological averages of S correlate positively with those of the index of seasonality of phytoplankton productivity (ratio between the minimum and maximum monthly values of surface chlorophyll) and sea surface temperature, and negatively with those of the proxy for environmental stability (monthly frequency of occurrence of daily averaged wind speed exceeding 10 m s−1). The seasonal cycles of S and phytoplankton productivity (surface chlorophyll as proxy) exhibit similar features in terms of shape, timing and explained variance, but the relationship between the climatological averages of both variables is non-significant. From year-to-year, the annual averages of S correlate negatively with those of phytoplankton productivity and positively with those of sea surface temperature along the latitudinal gradient, and negatively with those of environmental stability along the oceanic–neritic gradient. The annual anomalies of S (i.e. factoring out geographic variation) show a unimodal relationship with those of sea surface temperature and environmental stability, with S peaking at intermediate values of the anomalies of these variables. The results evidence the role of seasonality of phytoplankton productivity on the control of copepod species richness at seasonal and climatological scales, giving support to the species richness–productivity hypothesis. Although sea surface temperature (SST) is indeed a good predictor of richness along the latitudinal gradient, it is unable to predict the increase of richness form oceanic to neritic environments, thus lessening the generality of the species richness–energy hypothesis. Meteo-hydrographic disturbances (i.e. SST and wind speed anomalies as proxies), presumably through its role on mixed layer depth dynamics and turbulence and hence productivity, maximise local diversity when occurring at intermediate frequency and or intensity, thus providing support to the intermediate disturbance hypothesis on the control of copepod diversity.

Black-legged kittiwakes as indicators of environmental change in the North Sea; evidence from long-term studies

Top predators, particularly seabirds, have repeatedly been suggested as indicators of marine ecosystem status. One region currently under pressure from human fisheries and climate change is the North Sea. Standardized seabird monitoring data have been collected on the Isle of May, an important seabird colony in the northwestern North Sea, over the last 10–20 years. Over this period oceanographic conditions have varied markedly, and between 1990 and 1999 a major industrial fishery for sandlance (Ammodytes marinus), the main prey of most seabird species, was prosecuted nearby. Sandlance fishing grounds close to seabird colonies down the east coast of the UK were closed in 2000 in an attempt to improve foraging opportunities for breeding seabirds, particularly black-legged kittiwakes (Rissa tridactyla). Initially this closure seemed to be beneficial for kittiwakes with breeding success recovering to pre-fishery levels. However, despite the ban continuing, kittiwakes and many other seabird species in the North Sea suffered severe breeding failures in 2004. In this paper, we test the predictive power of four previously established correlations between kittiwake breeding success and climatic/trophic variables to explain the observed breeding success at the Isle of May in 2004. During the breeding season, kittiwakes at this colony switch from feeding on 1+ group to 0 group sandlance, and results up until 2003 indicated that availability of both age classes had a positive effect on kittiwake breeding success. The low breeding success of kittiwakes in 2004 was consistent with the late appearance and small body size of 0 group sandlance, but at odds with the two variables likely to operate via 1 group availability (lagged winter sea surface temperature and larval sandlance cohort strength in 2003). The reason for the discrepancy is currently unknown, but analysis of 1 group sandlance body composition indicated that lipid content in 2004 was extremely low, and thus fish eaten by kittiwakes during pre-breeding and early incubation were likely to be of poor quality. Monitoring of reproductive success of kittiwakes, although useful, was clearly not sufficient to tease apart the complex causation underlying the 2004 event. Monitoring programs such as this, therefore, need to be complemented by detailed research to identify the mechanisms involved, and to attribute and predict the effects of natural and human-induced environmental change.

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.