Understanding ecosystem robustness

Understanding phycosomal dynamics to improve industrial microalgae cultivation

Algal-bacterial interactions are ubiquitous in both natural and industrial systems, and the characterization of these interactions has been reinvigorated by potential applications in biosystem productivity. Different growth conditions can be used for operational functions, such as the use of low-quality water or high pH/alkalinity, and the altered operating conditions likely constrain microbial community structure and function in unique ways. However, research is necessary to better understand whether consortia can be designed to improve the productivity, processing, and sustainability of industrial-scale cultivations through different controls that can constrain microbial interactions for maximal light-driven outputs. The review highlights current knowledge and gaps for relevant operating conditions, as well as suggestions for near-term and longer-term improvements for large-scale cultivation and polyculture engineering.

Estimating co-extinction threats in terrestrial ecosystems

The biosphere is changing rapidly due to human endeavour. Because ecological communities underlie networks of interacting species, changes that directly affect some species can have indirect effects on others. Accurate tools to predict these direct and indirect effects are therefore required to guide conservation strategies. However, most extinction-risk studies only consider the direct effects of global change-such as predicting which species will breach their thermal limits under different warming scenarios-with predictions of trophic cascades and co-extinction risks remaining mostly speculative. To predict the potential indirect effects of primary extinctions, data describing community interactions and network modelling can estimate how extinctions cascade through communities. While theoretical studies have demonstrated the usefulness of models in predicting how communities react to threats like climate change, few have applied such methods to real-world communities. This gap partly reflects challenges in constructing trophic network models of real-world food webs, highlighting the need to develop approaches for quantifying co-extinction risk more accurately. We propose a framework for constructing ecological network models representing real-world food webs in terrestrial ecosystems and subjecting these models to co-extinction scenarios triggered by probable future environmental perturbations. Adopting our framework will improve estimates of how environmental perturbations affect whole ecological communities. Identifying species at risk of co-extinction (or those that might trigger co-extinctions) will also guide conservation interventions aiming to reduce the probability of co-extinction cascades and additional species losses.

Hide or die when the winds bring wings: predator avoidance by activity shift in a mountain snake

BACKGROUND

Understanding predator-prey relationships is fundamental in many areas of ecology and conservation. In reptiles, basking time often increases the risk of predation and one way to minimise this risk is to reduce activity time and to stay within a refuge. However, this implies costs of lost opportunities for foraging, reproduction, and thermoregulation. We aimed to determine the main potential and observed predators of Vipera graeca, to infer predation pressure by estimating the incidence and the body length and sex distribution of predation events based on body injuries, and to assess whether and how the activity of V. graeca individuals is modified by predation pressure.

RESULTS

We observed n = 12 raptor bird species foraging at the study sites, of which Circaetus gallicus, Falco tinnunculus and Corvus cornix were directly observed as predators of V. graeca. We found injuries and wounds on 12.5% of the studied individuals (n = 319). The occurrence of injuries was significantly positively influenced by the body length of vipers, and was more frequent on females than on males, while the interaction of length and sex showed a significant negative effect. The temporal overlap between predator and viper activity was much greater for the vipers' potential activity than their realised activity. Vipers showed a temporal shift in their bimodal daily activity pattern as they were active earlier in the morning and later in the afternoon than could be expected based on the thermal conditions.

CONCLUSION

The time spent being active on the surface has costs to snakes: predation-related injuries increased in frequency with length, were more frequent in females than in males and occurred in shorter length for males than for females. Our results suggest that vipers do not fully exploit the thermally optimal time window available to them, likely because they shift their activity to periods with fewer avian predators.

Extreme drought disrupts plant phenology: Insights from 35 years of cloud forest data in Venezuela

The potential effects of climate change on plant reproductive phenology include asynchronies with pollinators and reductions in plant fitness, leading to extinction and loss of ecosystem function. In particular, plant phenology is sensitive to extreme weather events, which are occurring with increasing severity and frequency in recent decades and are linked to anthropogenic climate change and shifts in atmospheric circulation. For 15 plant species in a Venezuelan cloud forest, we documented dramatic changes in monthly flower and fruit community composition over a 35-year time series, from 1983 to 2017, and these changes were linked directly to higher temperatures, lower precipitation, and decreased soil water availability. The patterns documented here do not mirror trends in temperate zones but corroborate results from the Asian tropics. More intense droughts are predicted to occur in the region, which will cause dramatic changes in flower and fruit availability.

Non-target effects of agri-environmental schemes on solitary bees and fungi in the United Kingdom

Agri-environmental schemes (AES) are used to enhance pollinator diversity on agricultural farms within the UK. Though the impacts of these schemes on archetypal pollinator species such as the bumblebee (Bombus) and honeybee (Apis) are well-studied, the effects on non-target bee species like solitary bees, in the same environment, are generally lacking. One goal of AES is to alter floral provision and taxonomic composition of plant communities to provide better forage for pollinators, however, this may potentially impact other ecological communities such as fungal diversity associated with plant-bee communities. Fungi are integral in these bee communities as they can impact bee species both beneficially and detrimentally. We test the hypothesis that alteration of the environment through provision of novel plant communities has non-target effects on the fungi associated with solitary bee communities. We analyse fungal diversity and ecological networks formed between fungi and solitary bees present on 15 agricultural farms in the UK using samples from brood cells. The farms were allocated to two categories, low and high management, which differ in the number of agri-environmental measures implemented. Using internal transcribed spacer metabarcoding, we identified 456 fungal taxa that interact with solitary bees. Of these, 202 (approximately 44%) could be assigned to functional groups, the majority being pathotrophic and saprotrophic species. A large proportion was Ascosphaeraceae, a family of bee-specialist fungi. We considered the connectance, nestedness, modularity, nestedness overlap and decreasing fill, linkage density and fungal generality of the farms' bee-fungi ecological networks. We found no difference in the structure of bee-fungi ecological networks between low and high management farms, suggesting floral provision by AES has no significant impact on interactions between these two taxonomic groups. However, bee emergence was lower on the low management farms compared to high management, suggesting some limited non-target effects of AES. This study characterizes the fungal community associated with solitary bees and provides evidence that floral provision through AES does not impact fungal interactions.

First Steps into Ruminal Microbiota Robustness

Despite its central role in ruminant nutrition, little is known about ruminal microbiota robustness, which is understood as the ability of the microbiota to cope with disturbances. The aim of the present review is to offer a comprehensive description of microbial robustness, as well as its potential drivers, with special focus on ruminal microbiota. First, we provide a briefing on the current knowledge about ruminal microbiota. Second, we define the concept of disturbance (any discrete event that disrupts the structure of a community and changes either the resource availability or the physical environment). Third, we discuss community resistance (the ability to remain unchanged in the face of a disturbance), resilience (the ability to return to the initial structure following a disturbance) and functional redundancy (the ability to maintain or recover initial function despite compositional changes), all of which are considered to be key properties of robust microbial communities. Then, we provide an overview of the currently available methodologies to assess community robustness, as well as its drivers (microbial diversity and network complexity) and its potential modulation through diet. Finally, we propose future lines of research on ruminal microbiota robustness.

Evolution of plasticity: metabolic compensation for fluctuating energy demands at the origin of life

Phenotypic plasticity of physiological functions enables rapid responses to changing environments and may thereby increase the resilience of organisms to environmental change. Here, we argue that the principal hallmarks of life itself, self-replication and maintenance, are contingent on the plasticity of metabolic processes ('metabolic plasticity'). It is likely that the Last Universal Common Ancestor (LUCA), 4 billion years ago, already possessed energy-sensing molecules that could adjust energy (ATP) production to meet demand. The earliest manifestation of metabolic plasticity, switching cells from growth and storage (anabolism) to breakdown and ATP production (catabolism), coincides with the advent of Darwinian evolution. Darwinian evolution depends on reliable translation of information from information-carrying molecules, and on cell genealogy where information is accurately passed between cell generations. Both of these processes create fluctuating energy demands that necessitate metabolic plasticity to facilitate replication of genetic material and (proto)cell division. We propose that LUCA possessed rudimentary forms of these capabilities. Since LUCA, metabolic networks have increased in complexity. Generalist founder enzymes formed the basis of many derived networks, and complexity arose partly by recruiting novel pathways from the untapped pool of reactions that are present in cells but do not have current physiological functions (the so-called 'underground metabolism'). Complexity may thereby be specific to environmental contexts and phylogenetic lineages. We suggest that a Boolean network analysis could be useful to model the transition of metabolic networks over evolutionary time. Network analyses can be effective in modelling phenotypic plasticity in metabolic functions for different phylogenetic groups because they incorporate actual biochemical regulators that can be updated as new empirical insights are gained.

Re-thinking our understanding of immunity: Robustness in the tissue reconstruction system

Robustness, understood as the maintenance of specific functionalities of a given system against internal and external perturbations, is pervasive in today's biology. Yet precise applications of this notion to the immune system have been scarce. Here we show that the concept of robustness sheds light on tissue repair, and particularly on the crucial role the immune system plays in this process. We describe the specific mechanisms, including plasticity and redundancy, by which robustness is achieved in the tissue reconstruction system (TRS). In turn, tissue repair offers a very important test case for assessing the usefulness of the concept of robustness, and identifying different varieties of robustness.

Optimal interdependence enhances the dynamical robustness of complex systems

Although interdependent systems have usually been associated with increased fragility, we show that strengthening the interdependence between dynamical processes on different networks can make them more likely to survive over long times. By coupling the dynamics of networks that in isolation exhibit catastrophic collapse with extinction of nodal activity, we demonstrate system-wide persistence of activity for an optimal range of interdependence between the networks. This is related to the appearance of attractors of the global dynamics comprising disjoint sets ("islands") of stable activity.

Scaling nitrogen and carbon interactions: what are the consequences of biological buffering?

Understanding the consequences of elevated CO₂ (eCO₂; 800 ppm) on terrestrial ecosystems is a central theme in global change biology, but relatively little is known about how altered plant C and N metabolism influences higher levels of biological organization. Here, we investigate the consequences of C and N interactions by genetically modifying the N-assimilation pathway in Arabidopsis and initiating growth chamber and mesocosm competition studies at current CO₂ (cCO₂; 400 ppm) and eCO₂ over multiple generations. Using a suite of ecological, physiological, and molecular genomic tools, we show that a single-gene mutant of a key enzyme (nia2) elicited a highly orchestrated buffering response starting with a fivefold increase in the expression of a gene paralog (nia1) and a 63% increase in the expression of gene network module enriched for N-assimilation genes. The genetic perturbation reduced amino acids, protein, and TCA-cycle intermediate concentrations in the nia2 mutant compared to the wild-type, while eCO₂ mainly increased carbohydrate concentrations. The mutant had reduced net photosynthetic rates due to a 27% decrease in carboxylation capacity and an 18% decrease in electron transport rates. The expression of these buffering mechanisms resulted in a penalty that negatively correlated with fitness and population dynamics yet showed only minor alterations in our estimates of population function, including total per unit area biomass, ground cover, and leaf area index. This study provides insight into the consequences of buffering mechanisms that occur post-genetic perturbations in the N pathway and the associated outcomes these buffering systems have on plant populations relative to eCO₂.

Breaking down complex Saproxylic communities: understanding sub-networks structure and implications to network robustness

Saproxylic insect communities inhabiting tree hollow microhabitats correspond with large food webs which simultaneously are constituted by multiple types of plant-animal and animal-animal interactions, according to the use of trophic resources (wood- and insect-dependent sub-networks), or to trophic habits or interaction types (xylophagous, saprophagous, xylomycetophagous, predators and commensals). We quantitatively assessed which properties of specialised networks were present in a complex networks involving different interacting types such as saproxylic community, and how they can be organised in trophic food webs. The architecture, interacting patterns and food web composition were evaluated along sub-networks, analysing their implications to network robustness from random and directed extinction simulations. A structure of large and cohesive modules with weakly connected nodes was observed throughout saproxylic sub-networks, composing the main food webs constituting this community. Insect-dependent sub-networks were more modular than wood-dependent sub-networks. Wood-dependent sub-networks presented higher species degree, connectance, links, linkage density, interaction strength, and were less specialised and more aggregated than insect-dependent sub-networks. These attributes defined high network robustness in wood-dependent sub-networks. Finally, our results emphasise the relevance of modularity, differences among interacting types and interrelations among them in modelling the structure of saproxylic communities and in determining their stability.

Global adaptation in networks of selfish components: emergent associative memory at the system scale

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organize into structures that enhance global adaptation, efficiency, or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology, and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalization, and optimization are well understood. Such global functions within a single agent or organism are not wholly surprising, since the mechanisms (e.g., Hebbian learning) that create these neural organizations may be selected for this purpose; but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviors when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g., when they can influence which other agents they interact with), then, in adapting these inter-agent relationships to maximize their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviors as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalize by idealizing stored patterns and/or creating new combinations of subpatterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviors in the same sense, and by the same mechanism, as with the organizational principles familiar in connectionist models of organismic learning.