Biodiversity and Constrained Information Dynamics in Ecosystems: A Framework for Living Systems

The increase in ecosystem biodiversity can be perceived as one of the universal processes converting energy into information across a wide range of living systems. This study delves into the dynamics of living systems, highlighting the distinction between ex post adaptation, typically associated with natural selection, and its proactive counterpart, ex ante adaptability. Through coalescence experiments using synthetic ecosystems, we (i) quantified ecosystem stability, (ii) identified correlations between some biodiversity indexes and the stability, (iii) proposed a mechanism for increasing biodiversity through moderate inter-ecosystem interactions, and (iv) inferred that the information carrier of ecosystems is species composition, or merged genomic information. Additionally, it was suggested that (v) changes in ecosystems are constrained to a low-dimensional state space, with three distinct alteration trajectories-fluctuations, rapid environmental responses, and long-term changes-converging into this state space in common. These findings suggest that daily fluctuations may predict broader ecosystem changes. Our experimental insights, coupled with an exploration of living systems' information dynamics from an ecosystem perspective, enhance our predictive capabilities for natural ecosystem behavior, providing a universal framework for understanding a broad spectrum of living systems.

Predicting collapse of complex ecological systems: quantifying the stability-complexity continuum

Dynamical shifts between the extremes of stability and collapse are hallmarks of ecological systems. These shifts are limited by and change with biodiversity, complexity, and the topology and hierarchy of interactions. Most ecological research has focused on identifying conditions for a system to shift from stability to any degree of instability-species abundances do not return to exact same values after perturbation. Real ecosystems likely have a continuum of shifting between stability and collapse that depends on the specifics of how the interactions are structured, as well as the type and degree of disturbance due to environmental change. Here we map boundaries for the extremes of strict stability and collapse. In between these boundaries, we find an intermediate regime that consists of single-species extinctions, which we call the extinction continuum. We also develop a metric that locates the position of the system within the extinction continuum-thus quantifying proximity to stability or collapse-in terms of ecologically measurable quantities such as growth rates and interaction strengths. Furthermore, we provide analytical and numerical techniques for estimating our new metric. We show that our metric does an excellent job of capturing the system's behaviour in comparison with other existing methods-such as May's stability criteria or critical slowdown. Our metric should thus enable deeper insights about how to classify real systems in terms of their overall dynamics and their limits of stability and collapse.

Stability and Resiliency of Biological Control of the Twospotted Spider Mite (Acari: Tetranychidae) in Hop

The twospotted spider mite (Tetranychus urticae Koch) is a common pest in agricultural and ornamental crops. This pest can be controlled by resident predatory arthropods in certain situations. This research quantified the stability and resiliency of established conservation biological control of the twospotted spider mite in hop over a 5-yr period associated with nitrogen fertilization rate and use of a broad-spectrum insecticide. Biological control generally was stable and resilient over a sixfold range of nitrogen fertilization rates, and in only 1 of 5 yr did elevated nitrogen rates significantly affect populations of spider mites. In contrast, one application of the insecticide bifenthrin was associated with disruption of biological control and a severe outbreak of spider mites. The complex of natural enemies suppressed the outbreak during the same year in which bifenthrin was applied, but only after populations of spider mites exceeded levels associated with economic damage. However, in the following year the system returned to an equilibrium state where spider mites were suppressed below economically damaging levels. Therefore, conservation biological control in hop appears stable and robust to factors such as nitrogen fertilization that increase reproductive rates of spider mites but may be sensitive to factors such as nonselective insecticides that are lethal to natural enemies. Conservation biological control can be considered resilient to a single use of a nonselective insecticide in the year following the application, but not within the year of application.

Network Analysis: A Systems Framework to Address Grand Challenges in Plant Pathology

Plant pathology must address a number of challenges, most of which are characterized by complexity. Network analysis offers useful tools for addressing complex systems and an opportunity for synthesis within plant pathology and between it and relevant disciplines such as in the social sciences. We discuss applications of network analysis, which ultimately may be integrated together into more synthetic analyses of how to optimize plant disease management systems. The analysis of microbiome networks and tripartite phytobiome networks of host-vector-pathogen interactions offers promise for identifying biocontrol strategies and anticipating disease emergence. Linking epidemic network analysis with social network analysis will support strategies for sustainable agricultural development and for scaling up solutions for disease management. Statistical tools for evaluating networks, such as Bayesian network analysis and exponential random graph models, have been underused in plant pathology and are promising for informing strategies. We conclude with research priorities for network analysis applications in plant pathology.

Network connectivity during mergers and growth: optimizing the addition of a module

The principal eigenvalue λ of a network's adjacency matrix often determines dynamics on the network (e.g., in synchronization and spreading processes) and some of its structural properties (e.g., robustness against failure or attack) and is therefore a good indicator for how "strongly" a network is connected. We study how λ is modified by the addition of a module, or community, which has broad applications, ranging from those involving a single modification (e.g., introduction of a drug into a biological process) to those involving repeated additions (e.g., power-grid and transit development). We describe how to optimally connect the module to the network to either maximize or minimize the shift in λ, noting several applications of directing dynamics on networks.

Consequences of adaptive behaviour for the structure and dynamics of food webs

Species coexistence within ecosystems and the stability of patterns of temporal changes in population sizes are central topics in ecological theory. In the last decade, adaptive behaviour has been proposed as a mechanism of population stabilization. In particular, widely distributed adaptive trophic behaviour (ATB), the fitness-enhancing changes in individuals' feeding-related traits due to variation in their trophic environment, may play a key role in modulating the dynamics of feeding relationships within natural communities. In this article, we review and synthesize models and results from theoretical research dealing with the consequences of ATB on the structure and dynamics of complex food webs. We discuss current approaches, point out limitations, and consider questions ripe for future research. In spite of some differences in the modelling and analytic approaches, there are points of convergence: (1) ATB promotes the complex structure of ecological networks, (2) ATB increases the stability of their dynamics, (3) ATB reverses May's negative complexity-stability relationship, and (4) ATB provides resilience and resistance of networks against perturbations. Current knowledge supports ATB as an essential ingredient for models of community dynamics, and future research that incorporates ATB will be well positioned to address questions important for basic ecological research and its applications.

Understanding ecosystem robustness

Understanding the properties of ecosystems that make them either vulnerable or resistant to species extinctions is vital in the context of rapidly increasing habitat modification and climatic warming. New research by Sinha et al. incorporating 'small-world' topologies and population dynamics into ecosystem network models provides new explanations for species link distributions, extinction events and the maintenance of biodiversity. Their work reveals that previously unconnected ecosystems that come into contact with each other by anthropogenic or natural means are vulnerable to large losses in species.

Evolutionary ecology in silico: Does mathematical modelling help in understanding 'generic' trends?

Motivated by the results of recent laboratory experiments, as well as many earlier field observations, that evolutionary changes can take place in ecosystems over relatively short ecological time scales, several 'unified' mathematical models of evolutionary ecology have been developed over the last few years with the aim of describing the statistical properties of data related to the evolution of ecosystems. Moreover, because of the availability of sufficiently fast computers, it has become possible to carry out detailed computer simulations of these models. For the sake of completeness and to put these recent developments in perspective, we begin with a brief summary of some older models of ecological phenomena and evolutionary processes. However, the main aim of this article is to review critically these 'unified' models, particularly those published in the physics literature, in simple language that makes the new theories accessible to a wider audience.

Experimental evidence rejects pairwise modelling approach to coexistence in plant communities

Competition is often invoked as the cause of plant species loss with increasing system productivity. Experimental results for multispecies assemblages are virtually absent and mathematical models are thus used to explore the relationship between competition and coexistence. Modelling approaches to coexistence and diversity in competitive communities commonly employ Lotka-Volterra-type (LV) models with additive pairwise competitive effects. Using pairwise plant competition experiments, we calibrate the LV system and use it to predict plant biomass and coexistence in six three-species and one seven-species experimental mixture. Our results show that five out of the six three-species sets and the seven-species set deviate significantly from LV model predictions. Fitting an additional non-additive competition coefficient resulted in predictions that more closely matched the experimental results, with stable coexistence suggested in all but one case. These results are discussed with particular reference to the possible underlying mechanisms of coexistence in our experimental community. Modelling the effect of competition intensity on stability indicates that if non-additive effects occur, they will be relevant over a wide range of community sizes. Our findings caution against relying on coexistence predictions based on LV models.

Gray wolves as climate change buffers in Yellowstone

Understanding the mechanisms by which climate and predation patterns by top predators co-vary to affect community structure accrues added importance as humans exert growing influence over both climate and regional predator assemblages. In Yellowstone National Park, winter conditions and reintroduced gray wolves (Canis lupus) together determine the availability of winter carrion on which numerous scavenger species depend for survival and reproduction. As climate changes in Yellowstone, therefore, scavenger species may experience a dramatic reshuffling of food resources. As such, we analyzed 55 y of weather data from Yellowstone in order to determine trends in winter conditions. We found that winters are getting shorter, as measured by the number of days with snow on the ground, due to decreased snowfall and increased number of days with temperatures above freezing. To investigate synergistic effects of human and climatic alterations of species interactions, we used an empirically derived model to show that in the absence of wolves, early snow thaw leads to a substantial reduction in late-winter carrion, causing potential food bottlenecks for scavengers. In addition, by narrowing the window of time over which carrion is available and thereby creating a resource pulse, climate change likely favors scavengers that can quickly track food sources over great distances. Wolves, however, largely mitigate late-winter reduction in carrion due to earlier snow thaws. By buffering the effects of climate change on carrion availability, wolves allow scavengers to adapt to a changing environment over a longer time scale more commensurate with natural processes. This study illustrates the importance of restoring and maintaining intact food chains in the face of large-scale environmental perturbations such as climate change.

Reintroducing wolves can help ameliorate the negative effects of warmer winters on other species and reveals the importance of maintaining intact food chains in the face of climate change

Evidence of universality for the May-Wigner stability theorem for random networks with local dynamics

We consider a random network of nonlinear maps exhibiting a wide range of local dynamics, with the links having normally distributed interaction strengths. The stability of such a system is examined in terms of the asymptotic fraction of nodes that persist in a nonzero state. Scaling results show that the probability of survival in the steady state agrees remarkably well with the May-Wigner stability criterion derived from linear stability arguments. This suggests universality of the complexity-stability relation for random networks with respect to arbitrary global dynamics of the system.

Emergence of non-random structure in local food webs generated from randomly structured regional webs

Previous studies have shown that high-resolution, empirical food webs possess a non-random network structure, typically characterized by uniform or exponential degree distributions. However, the empirical food webs that have been investigated for their structural properties represent local communities that are only a subset of a larger pool of regionally coexisting species. Here, we use a simple model to investigate the effects of regional food web structure on local food webs that are assembled by two simple processes: random immigration of species from a source web (regional food web), and random extinction of species within the local web. The model shows that local webs with non-random degree distributions can arise from randomly structured source webs. A comparison of local webs assembled from randomly structured source webs with local webs assembled from source webs generated by the niche model shows that the former have higher species richness at equilibrium, but have a nonlinear response to changing extinction rates. These results imply that the network structure of regional food webs can play a significant role in the assembly and dynamics of local webs in natural ecosystems. With natural landscapes becoming increasingly fragmented, understanding such structure may be a necessary key to understanding the maintenance and stability of local species diversity.

Punctuated equilibria and 1/f noise in a biological coevolution model with individual-based dynamics

We present a study by linear stability analysis and large-scale Monte Carlo simulations of a simple model of biological coevolution. Selection is provided through a reproduction probability that contains quenched, random interspecies interactions, while genetic variation is provided through a low mutation rate. Both selection and mutation act on individual organisms. Consistent with some current theories of macroevolutionary dynamics, the model displays intermittent, statistically self-similar behavior with punctuated equilibria. The probability density for the lifetimes of ecological communities is well approximated by a power law with exponent near -2, and the corresponding power spectral densities show 1/f noise (flicker noise) over several decades. The long-lived communities (quasisteady states) consist of a relatively small number of mutualistically interacting species, and they are surrounded by a "protection zone" of closely related genotypes that have a very low probability of invading the resident community. The extent of the protection zone affects the stability of the community in a way analogous to the height of the free-energy barrier surrounding a metastable state in a physical system. Measures of biological diversity are on average stationary with no discernible trends, even over our very long simulation runs of approximately 3.4 x 10(7) generations.