Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology.

A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical ecology is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. It also illustrates the types of outputs that can be generated, and the data inputs required for different implementations. Examples range from simple calculations of body temperature at a particular site and time, to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models relating to data availability, training, and the lack of common software ecosystems. We also discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. Finally, we highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shifting constraints, and novel abiotic or biotic environments.

Freedom of choice adds value to public goods.

Public goods, ranging from judiciary to sanitation to parkland, permeate daily life. They have been a subject of intense interdisciplinary study, with a traditional focus being on participation levels in isolated public goods games (PGGs) as opposed to a more recent focus on participation in PGGs embedded into complex social networks. We merged the two perspectives by arranging voluntary participants into one of three network configurations, upon which volunteers played a number of iterated PGGs within their network neighborhood. The purpose was to test whether the topology of social networks or a freedom to express preferences for some local public goods over others affect participation. The results show that changes in social networks are of little consequence, yet volunteers significantly increase participation when they freely express preferences. Surprisingly, the increase in participation happens from the very beginning of the game experiment, before any information about how others play can be gathered. Such information does get used later in the game as volunteers seek to correlate contributions with higher returns, thus adding significant value to public goods overall. These results are ascribable to a small number of behavioral phenotypes, and suggest that societies may be better off with bottom-up schemes for public goods provision.

Communicating sentiment and outlook reverses inaction against collective risks.

Collective risks permeate society, triggering social dilemmas in which working toward a common goal is impeded by selfish interests. One such dilemma is mitigating runaway climate change. To study the social aspects of climate-change mitigation, we organized an experimental game and asked volunteer groups of three different sizes to invest toward a common mitigation goal. If investments reached a preset target, volunteers would avoid all consequences and convert their remaining capital into monetary payouts. In the opposite case, however, volunteers would lose all their capital with 50% probability. The dilemma was, therefore, whether to invest one's own capital or wait for others to step in. We find that communicating sentiment and outlook helps to resolve the dilemma by a fundamental shift in investment patterns. Groups in which communication is allowed invest persistently and hardly ever give up, even when their current investment deficits are substantial. The improved investment patterns are robust to group size, although larger groups are harder to coordinate, as evidenced by their overall lower success frequencies. A clustering algorithm reveals three behavioral types and shows that communication reduces the abundance of the free-riding type. Climate-change mitigation, however, is achieved mainly by cooperator and altruist types stepping up and increasing contributions as the failure looms. Meanwhile, contributions from free riders remain flat throughout the game. This reveals that the mechanisms behind avoiding collective risks depend on an interaction between behavioral type, communication, and timing.

Autopoietic Influence Hierarchies in Pancreatic ß Cells.

ß cells are biologically essential for humans and other vertebrates. Because their functionality arises from cell-cell interactions, they are also a model system for collective organization among cells. There are currently two contradictory pictures of this organization: the hub-cell idea pointing at leaders who coordinate the others, and the electrophysiological theory describing all cells as equal. We use new data and computational modeling to reconcile these pictures. We find via a network representation of interacting ß cells that leaders emerge naturally (confirming the hub-cell idea), yet all cells can take the hub role following a perturbation (in line with electrophysiology).

Punishment diminishes the benefits of network reciprocity in social dilemma experiments.

Network reciprocity has been widely advertised in theoretical studies as one of the basic cooperation-promoting mechanisms, but experimental evidence favoring this type of reciprocity was published only recently. When organized in an unchanging network of social contacts, human subjects cooperate provided the following strict condition is satisfied: The benefit of cooperation must outweigh the total cost of cooperating with all neighbors. In an attempt to relax this condition, we perform social dilemma experiments wherein network reciprocity is aided with another theoretically hypothesized cooperation-promoting mechanism-costly punishment. The results reveal how networks promote and stabilize cooperation. This stabilizing effect is stronger in a smaller-size neighborhood, as expected from theory and experiments. Contrary to expectations, punishment diminishes the benefits of network reciprocity by lowering assortment, payoff per round, and award for cooperative behavior. This diminishing effect is stronger in a larger-size neighborhood. An immediate implication is that the psychological effects of enduring punishment override the rational response anticipated in quantitative models of cooperation in networks.

ß Cells Operate Collectively to Help Maintain Glucose Homeostasis.

Residing in the islets of Langerhans in the pancreas, ß cells contribute to glucose homeostasis by managing the body's insulin supply. Although it has been acknowledged that healthy ß cells engage in heavy cell-to-cell communication to perform their homeostatic function, the exact role and effects of such communication remain partly understood. We offer a novel, to our knowledge, perspective on the subject in the form of 1) a dynamical network model that faithfully mimics fast calcium oscillations in response to above-threshold glucose stimulation and 2) empirical data analysis that reveals a qualitative shift in the cross-correlation structure of measured signals below and above the threshold glucose concentration. Combined together, these results point to a glucose-induced transition in ß-cell activity thanks to increasing coordination through gap-junctional signaling and paracrine interactions. Our data and the model further suggest how the conservation of entire cell-cell conductance, observed in coupled but not uncoupled ß cells, emerges as a collective phenomenon. An overall implication is that improving the ability to monitor ß-cell signaling should offer means to better understand the pathogenesis of diabetes mellitus.

Quantifying impacts of plastic debris on marine wildlife identifies ecological breakpoints.

Quantifying sublethal effects of plastics ingestion on marine wildlife is difficult, but key to understanding the ontogeny and population dynamics of affected species. We developed a method that overcomes the difficulties by modelling individual ontogeny under reduced energy intake and expenditure caused by debris ingestion. The predicted ontogeny is combined with a population dynamics model to identify ecological breakpoints: cessation of reproduction or negative population growth. Exemplifying this approach on loggerhead turtles, we find that between 3% and 25% of plastics in digestive contents causes a 2.5-20% reduction in perceived food abundance and total available energy, resulting in a 10-15% lower condition index and 10% to 88% lower total seasonal reproductive output compared to unaffected turtles. The reported plastics ingestion is insufficient to impede sexual maturation, but population declines are possible. The method is readily applicable to other species impacted by debris ingestion.

Testing scalar diffraction theory: Gaussian beam on a slit.

We recorded diffraction patterns using a commercially available slit and sensor over a wide range of experimental circumstances, including near- and far-field regimes and oblique incidence at large angles. We then compared the measured patterns with theoretical intensity curves calculated via the numerical integration of formulas derived within the framework of scalar diffraction theory. Experiment and theory show particularly good agreement when the first Rayleigh-Sommerfeld (R-S) formula is used. The Kirchhoff formula, though problematic in the context of mathematical consistency, agrees with the first R-S formula, even for large incidence angles, whereas the second R-S formula differs visibly. To obtain such a good agreement, we replaced the assumption of an incident plane wave with that of a Gaussian beam and implemented geometric corrections to account for slit imperfections. These results reveal how the scope of scalar diffraction theory can be extended with a small set of auxiliary assumptions.

Biological conservation law as an emerging functionality in dynamical neuronal networks.

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law-the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective.

To vaccinate or not to vaccinate: A comprehensive study of vaccination-subsidizing policies with multi-agent simulations and mean-field modeling.

We combined the elements of evolutionary game theory and mathematical epidemiology to comprehensively evaluate the performance of vaccination-subsidizing policies in the face of a seasonal epidemic. We conducted multi-agent simulations to, among others, find out how the topology of the underlying social networks affects the results. We also devised a mean-field approximation to confirm the simulation results and to better understand the influences of an imperfect vaccine. The main measure of a subsidy' performance was the total social payoff as a sum of vaccination costs, infection costs, and tax burdens due to the subsidy. We find two types of situations in which vaccination-subsidizing policies act counterproductively. The first type arises when the subsidy attempts to increase vaccination among past non-vaccinators, which inadvertently creates a negative incentive for voluntary vaccinators to abstain from vaccination in hope of getting subsidized. The second type is a consequence of overspending at which point the marginal cost of further increasing vaccination coverage is higher than the corresponding marginal cost of infections avoided by this increased coverage. The topology of the underlying social networks considerably worsens the subsidy's performance if connections become random and heterogeneous, as is often the case in human social networks. An imperfect vaccine also worsens the subsidy's performance, thus narrowing or completely closing the window for vaccination-subsidizing policies to beat the no-subsidy policy. These results imply that subsidies should be aimed at voluntary vaccinators while avoiding overspending. Once this is achieved, it makes little difference whether the subsidy fully or partly offsets the vaccination cost.

Games of corruption in preventing the overuse of common-pool resources.

Maintaining human cooperation in the context of common-pool resource management is extremely important because otherwise we risk overuse and corruption. To analyse the interplay between economic and ecological factors leading to corruption, we couple the resource dynamics and the evolutionary dynamics of strategic decision making into a powerful analytical framework. The traits of this framework are: (i) an arbitrary number of harvesters share the responsibility to sustainably exploit a specific part of an ecosystem, (ii) harvesters face three strategic choices for exploiting the resource, (iii) a delegated enforcement system is available if called upon, (iv) enforcers are either honest or corrupt, and (v) the resource abundance reflects the choice of harvesting strategies. The resulting dynamical system is bistable; depending on the initial conditions, it evolves either to cooperative (sustainable exploitation) or defecting (overexploitation) equilibria. Using the domain of attraction to cooperative equilibria as an indicator of successful management, we find that the more resilient the resource (as implied by a high growth rate), the more likely the dominance of corruption which, in turn, suppresses the cooperative outcome. A qualitatively similar result arises when slow resource dynamics relative to the dynamics of decision making mask the benefit of cooperation. We discuss the implications of these results in the context of managing common-pool resources.

A biologically inspired immunization strategy for network epidemiology.

Well-known immunization strategies, based on degree centrality, betweenness centrality, or closeness centrality, either neglect the structural significance of a node or require global information about the network. We propose a biologically inspired immunization strategy that circumvents both of these problems by considering the number of links of a focal node and the way the neighbors are connected among themselves. The strategy thus measures the dependence of the neighbors on the focal node, identifying the ability of this node to spread the disease. Nodes with the highest ability in the network are the first to be immunized. To test the performance of our method, we conduct numerical simulations on several computer-generated and empirical networks, using the susceptible-infected-recovered (SIR) model. The results show that the proposed strategy largely outperforms the existing well-known strategies.

Barriers to cooperation aid ideological rigidity and threaten societal collapse.

Understanding the factors that promote, disrupt, or shape the nature of cooperation is one of the main tasks of evolutionary biology. Here, we focus on attitudes and beliefs supportive of in-group favoritism and strict adherence to moral consensus, collectively known as ideological rigidity, that have been linked with both ends of the political spectrum. The presence among the political right and the left is likely to make ideological rigidity a major determinant of the political discourse with an important social function. To better understand this function, we equip the indirect reciprocity framework--widely used to explain evaluation-mediated social cooperation--with multiple stylized value systems, each corresponding to the different degree of ideological rigidity. By running game theoretical simulations, we observe the competitive evolution of these systems, map conditions that lead to more ideologically rigid societies, and identify potentially disastrous outcomes. In particular, we uncover that barriers to cooperation aid ideological rigidity. The society may even polarize to the extent where social parasites overrun the population and cause the complete collapse of the social structure. These results have implications for lawmakers globally, warning against restrictive or protectionist policies.

The conflict of social norms may cause the collapse of cooperation: indirect reciprocity with opposing attitudes towards in-group favoritism.

Indirect reciprocity is a cooperation maintaining mechanism based on the social evaluation of players. Here, we consider the case of a group in which two social norms with opposing attitudes towards in-group favoritism are mixed. One norm, called Bushido (the way of warriors), regards cooperation with outsiders as betrayal, whereas the second norm, called Shonindo (the way of merchants), regards cooperation with outsiders as desirable. Each member of the group, irrespective of being a Bushido or a Shonindo player, is evaluated in two different ways and assigned two different labels: "ally" or "enemy" according to the Bushido evaluation; "good" or "bad" according to the Shonindo evaluation. These labels change in response to the action taken (cooperation or defection) when acting as a donor, as well as the label attached to the recipient. In addition to Bushido players, who cooperate with an ally and defect from an enemy, and Shonindo players, who cooperate with a good recipient and defect from a bad recipient, the group contains a third kind of players--unconditional defectors. The fractions of the three types of players follow the replicator dynamics. If the probability of interacting with outsiders is small, and if the cost-to-benefit ratio of cooperation is low, we observe several important patterns. Each social norm is able to maintain a high level of cooperation when dominant. Bushido and Shonindo players evaluate each other unfavorably and engage in a severe conflict. In the end, only one norm permeates the whole group driving the other to the extinction. When both social norms are equally effective, a rare occurrence of unconditional defectors may lead to a successful invasion.

Comment on "Metabolic scaling is the product of life-history optimization".

The model used by White et al. (1) to explore life-history optimization of metabolic scaling has limited ability to capture observed combinations of growth and reproduction, including those of the domestic chicken. The analyses and interpretations may change substantially with realistic parameters. The model's biological and thermodynamic realism needs further exploration and justification before being applied to life-history optimization studies.

Inferring the ecology of north-Pacific albacore tuna from catch-and-effort data.

Catch-and-effort data are among the primary sources of information for assessing the status of terrestrial wildlife and fish. In fishery science, elaborate stock-assessment models are fitted to such data in order to estimate fish-population sizes and guide management decisions. Given the importance of catch-and-effort data, we scoured a comprehensive dataset pertaining to albacore tuna (Thunnus alalunga) in the north Pacific Ocean for novel ecological information content about this commercially valuable species. Specifically, we used unsupervised learning based on finite mixture modelling to reveal that the north Pacific albacore-tuna stock can be divided into four pseudo-cohorts. We discovered that smaller body mass pseudo-cohorts inhabit relatively high-subtropical to temperate-latitudes, with hotspots off the coast of Japan. Larger body mass pseudo-cohorts inhabit lower-tropical to subtropical-latitudes, with hotspots in the western and central north Pacific. These results offer evidence that albacore tuna prefer different habitats depending on their body mass, and point to long-term migratory routes for the species that the current tagging technology is unlikely to capture in full. We discuss the implications of the results for data-driven modelling of albacore tuna in the north Pacific, as well as the management of the north Pacific albacore-tuna fishery.

Integrating dynamic energy budget (DEB) theory with traditional bioenergetic models.

Dynamic energy budget (DEB) theory offers a systematic, though abstract, way to describe how an organism acquires and uses energy and essential elements for physiological processes, in addition to how physiological performance is influenced by environmental variables such as food density and temperature. A 'standard' DEB model describes the performance (growth, development, reproduction, respiration, etc.) of all life stages of an animal (embryo to adult), and predicts both intraspecific and interspecific variation in physiological rates. This approach contrasts with a long tradition of more phenomenological and parameter-rich bioenergetic models that are used to make predictions from species-specific rate measurements. These less abstract models are widely used in fisheries studies; they are more readily interpretable than DEB models, but lack the generality of DEB models. We review the interconnections between the two approaches and present formulae relating the state variables and fluxes in the standard DEB model to measured bioenergetic rate processes. We illustrate this synthesis for two large fishes: Pacific bluefin tuna (Thunnus orientalis) and Pacific salmon (Oncorhynchus spp.). For each, we have a parameter-sparse, full-life-cycle DEB model that requires adding only a few species-specific features to the standard model. Both models allow powerful integration of knowledge derived from data restricted to certain life stages, processes and environments.

The microdynamics shaping the relationship between democracy and corruption.

Physics has a long tradition of laying rigorous quantitative foundations for social phenomena. Here, we up the ante for physics' forays into the territory of social sciences by (i) empirically documenting a tipping point in the relationship between democratic norms and corruption suppression, and then (ii) demonstrating how such a tipping point emerges from a micro-scale mechanistic model of spin dynamics in a complex network. Specifically, the tipping point in the relationship between democratic norms and corruption suppression is such that democratization has little effect on suppressing corruption below a critical threshold, but a large effect above the threshold. The micro-scale model of spin dynamics underpins this phenomenon by reinterpreting spins in terms of unbiased (i.e. altruistic) and biased (i.e. parochial) other-regarding behaviour, as well as the corresponding voting preferences. Under weak democratic norms, dense social connections of parochialists enable coercing enough opportunist voters to vote in favour of perpetuating parochial in-group bias. Society may, however, strengthen democratic norms in a rapid turn of events during which opportunists adopt altruism and vote to subdue bias. The emerging model outcome at the societal scale thus mirrors the data, implying that democracy either perpetuates or suppresses corruption depending on the prevailing democratic norms.

Optimal control of networked reaction-diffusion systems.

Patterns in nature are fascinating both aesthetically and scientifically. Alan Turing's celebrated reaction-diffusion model of pattern formation from the 1950s has been extended to an astounding diversity of applications: from cancer medicine, via nanoparticle fabrication, to computer architecture. Recently, several authors have studied pattern formation in underlying networks, but thus far, controlling a reaction-diffusion system in a network to obtain a particular pattern has remained elusive. We present a solution to this problem in the form of an analytical framework and numerical algorithm for optimal control of Turing patterns in networks. We demonstrate our method's effectiveness and discuss factors that affect its performance. We also pave the way for multidisciplinary applications of our framework beyond reaction-diffusion models.

Flexible imitation suppresses epidemics through better vaccination.

The decision of whether or not to vaccinate is a complex one. It involves the contribution both to a social good-herd immunity-and to one's own well-being. It is informed by social influence, personal experience, education, and mass media. In our work, we investigate a situation in which individuals make their choice based on how social neighbourhood responded to previous epidemics. We do this by proposing a minimalistic model using components from game theory, network theory and the modelling of epidemic spreading, and opinion dynamics. Individuals can use the information about the neighbourhood in two ways-either they follow the majority or the best-performing neighbour. Furthermore, we let individuals learn which of these two decision-making strategies to follow from their experience. Our results show that the flexibility of individuals to choose how to integrate information from the neighbourhood increases the vaccine uptake and decreases the epidemic severity if the following conditions are fulfilled. First, the initial fraction of individuals who imitate the neighbourhood majority should be limited, and second, the memory of previous outbreaks should be sufficiently long. These results have implications for the acceptance of novel vaccines and raising awareness about vaccination, while also pointing to promising future research directions.