Complex positive connections between functional groups are revealed by neural network analysis of ecological time series

The distribution and role of functional abundance in cross-scale resilience

The cross-scale resilience model suggests that system-level ecological resilience emerges from the distribution of species' functions within and across the spatial and temporal scales of a system. It has provided a quantitative method for calculating the resilience of a given system and so has been a valuable contribution to a largely qualitative field. As it is currently laid out, the model accounts for the spatial and temporal scales at which environmental resources and species are present and the functional roles species play but does not inform us about how much resource is present or how much function is provided. In short, it does not account for abundance in the distribution of species and their functional roles within and across the scales of a system. We detail the ways in which we would expect species' abundance to be relevant to the cross-scale resilience model based on the extensive abundance literature in ecology. We also put forward a series of testable hypotheses that would improve our ability to anticipate and quantify how resilience is generated, and how ecosystems will (or will not) buffer recent rapid global changes. This stream of research may provide an improved foundation for the quantitative evaluation of ecological resilience.

Multivariate methods and artificial neural networks in the assessment of the response of infaunal assemblages to sediment metal contamination and organic enrichment

A 4-year annual sediment survey was conducted in an organically enriched tidal channel to compare the performance of univariate community descriptors, traditional multivariate techniques (TM) and artificial neural networks (AANs), in the assessment of infaunal responses to moderate levels of sediment metal contamination. Both TM approaches and the SOM ANN revealed spatiotemporal patterns of environmental and biological variables, suggesting a causal relationship between them and further highlighting subsets of taxa and sediment variables as potential main drivers of those patterns. Namely, high values of non-natural metals and organic content prompted high abundances of opportunists, while high values of natural metals yielded typical tolerant assemblages of organically enriched areas. The two approaches yielded identical final results but ANNs showed the following advantages over TM: ability to generalise results, powerful visualization tools and the ability to account simultaneously for sediment and faunal variables in the same analysis. Therefore, the SOM ANN, combined with the K-means clustering algorithm, is suggested as a promising tool for the assessment of the ecological quality of estuarine infaunal communities, although further work is needed to ensure the accuracy of the method.

Bioinformatics tools in predictive ecology: applications to fisheries

There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their 'crossover potential' with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse.

Forecasting the limits of resilience: integrating empirical research with theory

Despite the increasing evidence of drastic and profound changes in many ecosystems, often referred to as regime shifts, we have little ability to understand the processes that provide insurance against such change (resilience). Modelling studies have suggested that increased variance may foreshadow a regime shift, but this requires long-term data and knowledge of the functional links between key processes. Field-based research and ground-truthing is an essential part of the heuristic that marries theoretical and empirical research, but experimental studies of resilience are lagging behind theory, management and policy requirements. Empirically, ecological resilience must be understood in terms of community dynamics and the potential for small shifts in environmental forcing to break the feedbacks that support resilience. Here, we integrate recent theory and empirical data to identify ways we might define and understand potential thresholds in the resilience of nature, and thus the potential for regime shifts, by focusing on the roles of strong and weak interactions, linkages in meta-communities, and positive feedbacks between these and environmental drivers. The challenge to theoretical and field ecologists is to make the shift from hindsight to a more predictive science that is able to assist in the implementation of ecosystem-based management.