A Replicated Network Approach to 'Big Data' in Ecology

International audienceGlobal environmental change is a pressing issue as evidenced by the rise of extreme weather conditions in many parts of the world, threatening the survival of vulnerable species and habitats. Effective monitoring of climatic and anthropogenic impacts is therefore critical to safeguarding ecosystems, and it would allow us to better understand their response to stressors and predict long-term impacts. Ecological networks provide a biomonitoring framework for examining the system-level response and functioning of an ecosystem, but have been, until recently, constrained by limited empirical data due to the laborious nature of their construction. Hence, most experimental designs have been confined to a single network or a small number of replicate networks, resulting in statistical uncertainty, low resolution, limited spatiotemporal scale and oversimplified assumptions. Advances in data sampling and curation methodologies, such as next-generation sequencing (NGS) and the Internet 'Cloud', have facilitated the emergence of the 'Big Data' phenomenon in Ecology, enabling the construction of ecological networks to be carried out effectively and efficiently. This provides to ecologists an excellent opportunity to expand the way they study ecological networks. In particular, highly replicated networks are now within our grasp if new NGS technologies are combined with machine learning to develop network building methods. A replicated network approach will allow temporal and spatial variations embedded in the data to be taken into consideration, overcoming the limitations in the current 'single network' approach. We are still at the embryonic stage in exploring replicated networks, and with these new opportunities we also face new challenges. In this chapter, we discuss some of these challenges and highlight potential approaches that will help us build and analyse replicated networks to better understand how complex ecosystems operate, and the services and functioning they provide, paving the way for deciphering ecological big data reliably in the future

Marginal native at work : anthropologiste in the field

x, 325 p.; 23 cm

“It’s Not in the Course Guide!” Reflections from a Dutch Field School on How Students Learn to Do Fieldwork

In this reflection we unpack students’ first fieldwork experiences and how this parallels a rite de passage. We do so in two domains: (1) students' first fieldwork with a focus on entering the field, staying in the field, and researcher identity; and (2) the impact of fieldwork experiences on students’ professional skills. Two struggles are prominent: letting go of the idea of “objectivity” and learning to deal with the whimsicalities of doing fieldwork

Bacteria dialog with Santa Rosalia: Are aggregations of cosmopolitan bacteria mainly explained by habitat filtering or by ecological interactions?

[Background] Since the landmark Santa Rosalia paper by Hutchinson, niche theory addresses the determinants of biodiversity in terms of both environmental and biological aspects. Disentangling the role of habitat filtering and interactions with other species is critical for understanding microbial ecology. Macroscopic biogeography explores hypothetical ecological interactions through the analysis of species associations. These methods have started to be incorporated into microbial ecology relatively recently, due to the inherent experimental difficulties and the coarse grained nature of the data.[Results] Here we investigate the influence of environmental preferences and ecological interactions in the tendency of bacterial taxa to either aggregate or segregate, using a comprehensive dataset of bacterial taxa observed in a wide variety of environments. We assess significance of taxa associations through a null model that takes into account habitat preferences and the global distribution of taxa across samples. The analysis of these associations reveals a surprisingly large number of significant aggregations between taxa, with a marked community structure and a strong propensity to aggregate for cosmopolitan taxa. Due to the coarse grained nature of our data we cannot conclusively reject the hypothesis that many of these aggregations are due to environmental preferences that the null model fails to reproduce. Nevertheless, some observations are better explained by ecological interactions than by habitat filtering. In particular, most pairs of aggregating taxa co-occur in very different environments, which makes it unlikely that these associations are due to habitat preferences, and many are formed by cosmopolitan taxa without well defined habitat preferences. Moreover, known cooperative interactions are retrieved as aggregating pairs of taxa. As observed in similar studies, we also found that phylogenetically related taxa are much more prone to aggregate than to segregate, an observation that may play a role in bacterial speciation.[Conclusions] We hope that these results stimulate experimental verification of the putative cooperative interactions between cosmopolitan bacteria, and we suggest several groups of aggregated cosmopolitan bacteria that are interesting candidates for such an investigation.This work was supported by the Spanish Ministry of Economy and Competitiveness (FPI grant BES-2009-013072 to APG and grants BFU2011-24595 and BFU2012-40020 to UB) and by the Comunidad de Madrid (Amarauto program to UB). Research at the CBMSO is facilitated by the Fundación Ramón Areces. We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI)