The robustness of ecosystems to the species loss of community

Will biomimetic robots be able to change a hivemind to guide honeybees' ecosystem services?

We study whether or not a group of biomimetic waggle dancing robots is able to significantly influence the swarm-intelligent decision making of a honeybee colony, e.g. to avoid foraging at dangerous food patches using a mathematical model. Our model was successfully validated against data from two empirical experiments: one examined the selection of foraging targets and the other cross inhibition between foraging targets. We found that such biomimetic robots have a significant effect on a honeybee colony's foraging decision. This effect correlates with the number of applied robots up to several dozens of robots and then saturates quickly with higher robot numbers. These robots can reallocate the bees' pollination service in a directed way towards desired locations or boost it at specific locations, without having a significant negative effect on the colony's nectar economy. Additionally, we found that such robots may be able to lower the influx of toxic substances from potentially harmful foraging sites by guiding the bees to alternative places. These effects also depend on the saturation level of the colony's nectar stores. The more nectar is already stored in the colony, the easier the bees are guided by the robots to alternative foraging targets. Our study shows that biomimetic and socially immersive biomimetic robots are a relevant future research target in order to support (a) the bees by guiding them to safe (pesticide free) places, (b) the ecosystem via boosted and directed pollination services and (c) human society by supporting agricultural crop pollination, thus increasing our food security this way.

Extinction of biotic interactions due to habitat loss could accelerate the current biodiversity crisis

Habitat loss disrupts species interactions through local extinctions, potentially orphaning species that depend on interacting partners, via mutualisms or commensalisms, and increasing secondary extinction risk. Orphaned species may become functionally or secondarily extinct, increasing the severity of the current biodiversity crisis. While habitat destruction is a major cause of biodiversity loss, the number of secondary extinctions is largely unknown. We investigate the relationship between habitat loss, orphaned species, and bipartite network properties. Using a real seed dispersal network, we simulate habitat loss to estimate the rate at which species are orphaned. To be able to draw general conclusions, we also simulate habitat loss in synthetic networks to quantify how changes in network properties affect orphan rates across broader parameter space. Both real and synthetic network simulations show that even small amounts of habitat loss can cause up to 10% of species to be orphaned. More area loss, less connected networks, and a greater disparity in the species richness of the network's trophic levels generally result in more orphaned species. As habitat is lost to land-use conversion and climate change, more orphaned species increase the loss of community-level and ecosystem functions. However, the potential severity of repercussions ranges from minimal (no species orphaned) to catastrophic (up to 60% of species within a network orphaned). Severity of repercussions also depends on how much the interaction richness and intactness of the community affects the degree of redundancy within networks. Orphaned species could add substantially to the loss of ecosystem function and secondary extinction worldwide.

Robustness: linking strain design to viable bioprocesses

Microbial cell factories are becoming increasingly popular for the sustainable production of various chemicals. Metabolic engineering has led to the design of advanced cell factories; however, their long-term yield, titer, and productivity falter when scaled up and subjected to industrial conditions. This limitation arises from a lack of robustness - the ability to maintain a constant phenotype despite the perturbations of such processes. This review describes predictable and stochastic industrial perturbations as well as state-of-the-art technologies to counter process variability. Moreover, we distinguish robustness from tolerance and discuss the potential of single-cell studies for improving system robustness. Finally, we highlight ways of achieving consistent and comparable quantification of robustness that can guide the selection of strains for industrial bioprocesses.

Extreme flood disturbance effects on multiple dimensions of river invertebrate community stability

Multidimensional analysis of community stability has recently emerged as an overarching approach to evaluating ecosystem response to disturbance. However, the approach has previously been applied only in experimental and modelling studies. We applied this concept to an 18-year time series (2000-2017) of macroinvertebrate community dynamics from a southeast Alaskan river to further develop and test the approach in relation to the effects of two extreme flood events occurring in 2005 (event 1) and 2014 (event 2). Five components of stability were calculated for pairs of pre- or post-event years. Individual components were tested for differences between pre- and post-event time periods. Stability components' pairwise correlations were assessed and ellipsoids of stability were developed for each time period and compared to a null model derived from the permuted dataset. Only one stability component demonstrated a significant difference between time periods. In contrast, 80% of moderate and significant correlations between stability components were degraded post-disturbance and significant changes to the form of stability ellipsoids were observed. Ellipsoids of stability for all periods after the initial disturbance (2005) were not different to the null model. Our results illustrate that the dimensionality of stability approach can be applied to natural ecosystem time-series data. The major increase in dimensionality of stability observed following disturbance potentially indicates significant shifts in the processes which drive stability following disturbance. This evidence improves our understanding of community response beyond what is possible through analysis of individual stability components.

Reinterpreting the relationship between number of species and number of links connects community structure and stability

For 50 years, ecologists have examined how the number of interactions (links) scales with the number of species in ecological networks. Here, we show that the way the number of links varies when species are sequentially removed from a community is fully defined by a single parameter identifiable from empirical data. We mathematically demonstrate that this parameter is network-specific and connects local stability and robustness, establishing a formal connection between community structure and two prime stability concepts. Importantly, this connection highlights a local stability-robustness trade-off, which is stronger in mutualistic than in trophic networks. Analysis of 435 empirical networks confirmed these results. We finally show how our network-specific approach relates to the classical across-network approach found in literature. Taken together, our results elucidate one of the intricate relationships between network structure and stability in community networks.