Estimating the tolerance of species to the effects of global environmental change

Global environmental change is affecting species distribution and their interactions with other species. In particular, the main drivers of environmental change strongly affect the strength of interspecific interactions with considerable consequences to biodiversity. However, extrapolating the effects observed on pair-wise interactions to entire ecological networks is challenging. Here we propose a framework to estimate the tolerance to changes in the strength of mutualistic interaction that species in mutualistic networks can sustain before becoming extinct. We identify the scenarios where generalist species can be the least tolerant. We show that the least tolerant species across different scenarios do not appear to have uniquely common characteristics. Species tolerance is extremely sensitive to the direction of change in the strength of mutualistic interaction, as well as to the observed mutualistic trade-offs between the number of partners and the strength of the interactions.Comment: Nature Communications 4, Article number: 2350, (2013

Components of phylogenetic signal in antagonistic and mutualistic networks.

Recent studies have shown a phylogenetic signal in the structure of ecological networks, making the point that evolutionary history is important in explaining network architecture. However, this previous work has focused on either antagonistic (i.e., predator-prey) or mutualistic networks and has used different methodologies. Thus, a comparative assessment of both the frequency and the strength of phylogenetic signal across network types and components of network structure has been precluded. Here, we address this issue using a data set comprising 60 antagonistic and mutualistic networks. By quantifying simultaneously the matching and centrality components of network architecture—capturing the modular and nested structure, respectively—we test the presence and quantify the strength of phylogenetic signal across network types, species sets, and components of network structure. We find contrasting differences across such groups. First, phylogenetic signal is stronger in antagonistic webs than in mutualistic webs. Second, resources are more strongly constrained than consumers in food webs, while animals show more constraints than plants in mutualistic networks. Third, phylogenetic constraints are stronger for the matching component than for the centrality component of network structure. These results can shed light on the contrasting evolutionary constraints shaping network structure across interaction types and species sets

How structurally stable are global socioeconomic systems?

The stability analysis of socioeconomic systems has been centered on answering whether small perturbations when a system is in a given quantitative state will push the system permanently to a different quantitative state. However, typically the quantitative state of socioeconomic systems is subject to constant change. Therefore, a key stability question that has been under-investigated is how strong the conditions of a system itself can change before the system moves to a qualitatively different behavior, i.e., how structurally stable the systems is. Here, we introduce a framework to investigate the structural stability of socioeconomic systems formed by the network of interactions among agents competing for resources. We measure the structural stability of the system as the range of conditions in the distribution and availability of resources compatible with the qualitative behavior in which all the constituent agents can be self-sustained across time. To illustrate our framework, we study an empirical representation of the global socioeconomic system formed by countries sharing and competing for multinational companies used as proxy for resources. We demonstrate that the structural stability of the system is inversely associated with the level of competition and the level of heterogeneity in the distribution of resources. Importantly, we show that the qualitative behavior of the observed global socioeconomic system is highly sensitive to changes in the distribution of resources. We believe this work provides a methodological basis to develop sustainable strategies for socioeconomic systems subject to constantly changing conditions

Nestedness in mutualistic networks

James et al. (2012) presented simulations that apparently falsify the analytical result by Bastolla et al. (2009), who showed that nested mutualistic interactions decrease interspecific competition and increase biodiversity in model ecosystems. This contradiction, however, mainly stems from the incorrect application of formulas derived for fully connected networks to empirical, sparse networks.Comment: 2 pages, 1 figur

Biological waste gas treatment with a modified rotating biological contactor. Ι. Control of biofilm growth and long-term performance

In this work, we introduce a modified rotating biological contactor (RBC) system and demonstrate its feasibility by applying the newly devised process to the biological treatment of artificial waste gas. In the proposed system, the waste gas is introduced to the bioreactor in the spacings between the rotating discs through a hollow shaft, thus allowing for intimate gas-liquid contact. A 91-l modified RBC containing 20 biofilm support discs 40cm in diameter was used in the experiments. Toluene was used as the model pollutant, and the system was operated under standard operating conditions for more than one year in order to investigate its long-term performance and assess its ability to control the growth of the biofilm. It was demonstrated that the proposed system allows to efficiently control the growth of the biofilm, thus overcoming the clogging problem inherent in most conventional methods for the biological treatment of waste gas. Moreover, the system was shown to exhibit stationary long-term performance for a period of more than one year, hence indicating its feasibility for industrial applicatio

On the structural stability of mutualistic systems

In theoretical ecology, traditional studies based on dynamical stability and numerical simulations have not found a unified answer to the effect of network architecture on community persistence. Here, we introduce a mathematical framework based on the concept of structural stability to explain such a disparity of results. We investigated the range of conditions necessary for the stable coexistence of all species in mutualistic systems. We show that the apparently contradictory conclusions reached by previous studies arise as a consequence of overseeing either the necessary conditions for persistence or its dependence on model parameterization. We show that observed network architectures maximize the range of conditions for species coexistence. We discuss the applicability of structural stability to study other types of interspecific interactions

Towards the integration of niche and network theories

The quest for understanding how species interactions modulate diversity has progressed by theoretical and empirical advances following niche and network theories. Yet, niche studies have been limited to describe coexistence within tropic levels despite incorporating information about multi-trophic interactions. Network approaches could address this limitation, but they have ignored the structure of species interactions within trophic levels. Here we call for the integration of niche and network theories to reach new frontiers of knowledge exploring how interactions within and across trophic levels promote species coexistence. This integration is possible due to the strong parallelisms in the historical development, ecological concepts, and associated mathematical tools of both theories. We provide a guideline to integrate this framework with observational and experimental studies

How structurally stable are global socioeconomic systems?

The stability analysis of socioeconomic systems has been centred on answering whether small perturbations when a system is in a given quantitative state will push the system permanently to a different quantitative state. However, typically the quantitative state of socioeconomic systems is subject to constant change. Therefore, a key stability question that has been under-investigated is how strongly the conditions of a system itself can change before the system moves to a qualitatively different behaviour, i.e. how structurally stable the systems is. Here, we introduce a framework to investigate the structural stability of socioeconomic systems formed by a network of interactions among agents competing for resources. We measure the structural stability of the system as the range of conditions in the distribution and availability of resources compatible with the qualitative behaviour in which all the constituent agents can be self-sustained across time. To illustrate our framework, we study an empirical representation of the global socioeconomic system formed by countries sharing and competing for multinational companies used as proxy for resources. We demonstrate that the structural stability of the system is inversely associated with the level of competition and the level of heterogeneity in the distribution of resources. Importantly, we show that the qualitative behaviour of the observed global socioeconomic system is highly sensitive to changes in the distribution of resources. We believe that this work provides a methodological basis to develop sustainable strategies for socioeconomic systems subject to constantly changing conditions

Matching–centrality decomposition and the forecasting of new links in networks

Networks play a prominent role in the study of complex systems of interacting entities in biology, sociology, and economics. Despite this diversity, we demonstrate here that a statistical model decomposing networks into matching and centrality components provides a comprehensive and unifying quantification of their architecture. The matching term quantifies the assortative structure in which node makes links with which other node, whereas the centrality term quantifies the number of links that nodes make. We show, for a diverse set of networks, that this decomposition can provide a tight fit to observed networks. Then we provide three applications. First, we show that the model allows very accurate prediction of missing links in partially known networks. Second, when node characteristics are known, we show how the matching–centrality decomposition can be related to this external information. Consequently, it offers us a simple and versatile tool to explore how node characteristics explain network architecture. Finally, we demonstrate the efficiency and flexibility of the model to forecast the links that a novel node would create if it were to join an existing network