Wild animals suppress the spread of socially transmitted misinformation.

Understanding the mechanisms by which information and misinformation spread through groups of individual actors is essential to the prediction of phenomena ranging from coordinated group behaviors to misinformation epidemics. Transmission of information through groups depends on the rules that individuals use to transform the perceived actions of others into their own behaviors. Because it is often not possible to directly infer decision-making strategies in situ, most studies of behavioral spread assume that individuals make decisions by pooling or averaging the actions or behavioral states of neighbors. However, whether individuals may instead adopt more sophisticated strategies that exploit socially transmitted information, while remaining robust to misinformation, is unknown. Here, we study the relationship between individual decision-making and misinformation spread in groups of wild coral reef fish, where misinformation occurs in the form of false alarms that can spread contagiously through groups. Using automated visual field reconstruction of wild animals, we infer the precise sequences of socially transmitted visual stimuli perceived by individuals during decision-making. Our analysis reveals a feature of decision-making essential for controlling misinformation spread: dynamic adjustments in sensitivity to socially transmitted cues. This form of dynamic gain control can be achieved by a simple and biologically widespread decision-making circuit, and it renders individual behavior robust to natural fluctuations in misinformation exposure.

The effect of temperature on specific dynamic action of juvenile fall-run Chinook salmon, Oncorhynchus tshawytscha.

Juvenile fall-run Chinook salmon (Oncorhynchus tshawytscha) in the Sacramento-San Joaquin River Basin experience temporally and spatially heterogenous temperature regimes, between cool upper tributaries and the warm channelized Delta, during freshwater rearing and outmigration. Limited water resources necessitate human management of dam releases, allowing temperature modifications. The objective of this study was to examine the effect of temperature on specific dynamic action (SDA), or the metabolic cost associated with feeding and digestion, which is thought to represent a substantial portion of fish energy budgets. Measuring SDA with respect to absolute aerobic scope (AAS), estimated by the difference between maximum metabolic rate (MMR) and standard metabolic rate (SMR), provides a snapshot of its respective energy allocation. Fish were acclimated to 16°C, raised or lowered to each acute temperature (13°C, 16°C, 19°C, 22°C or 24°C), then fed a meal of commercial pellets weighing 2% of their wet mass. We detected a significant positive effect of temperature on SMR and MMR, but not on AAS. As expected, there was no significant effect of temperature on the total O2 cost of digestion, but unlike other studies, we did not see a significant difference in duration, peak metabolic rate standardized to SMR, time to peak, percent of meal energy utilized, nor the ratio of peak O2 consumption to SMR. Peak O2 consumption represented 10.4-14.5% of AAS leaving a large amount of aerobic capacity available for other activities, and meal energy utilized for digestion ranged from 5.7% to 7.2%, leaving substantial remaining energy to potentially assimilate for growth. Our juvenile fall-run Chinook salmon exhibited thermal stability in their SDA response, which may play a role in maintaining homeostasis of digestive capability in a highly heterogeneous thermal environment where rapid growth is important for successful competition with conspecifics and for avoiding predation.

Quantification of thermal impacts across freshwater life stages to improve temperature management for anadromous salmonids.

Water temperature is the major controlling factor that shapes the physiology, behaviour and, ultimately, survival of aquatic ectotherms. Here we examine temperature effects on the survival of Chinook salmon (Oncorhynchus tshawytscha), a species of high economic and conservation importance. We implement a framework to assess how incremental changes in temperature impact survival across populations that is based on thermal performance models for three freshwater life stages of Chinook salmon. These temperature-dependent models were combined with local spatial distribution and phenology data to translate spatial-temporal stream temperature data into maps of life stage-specific physiological performance in space and time. Specifically, we converted temperature-dependent performance (i.e. energy used by pre-spawned adults, mortality of incubating embryos and juvenile growth rate) into a common currency that measures survival in order to compare thermal effects across life stages. Based on temperature data from two abnormally warm and dry years for three managed rivers in the Central Valley, California, temperature-dependent mortality during pre-spawning holding was higher than embryonic mortality or juvenile mortality prior to smolting. However, we found that local phenology and spatial distribution helped to mitigate negative thermal impacts. In a theoretical application, we showed that high temperatures may inhibit successful reintroduction of threatened Central Valley spring-run Chinook salmon to two rivers where they have been extirpated. To increase Chinook salmon population sizes, especially for the threatened and declining spring-run, our results indicate that adults may need more cold-water holding habitat than currently available in order to reduce pre-spawning mortality stemming from high temperatures. To conclude, our framework is an effective way to calculate thermal impacts on multiple salmonid populations and life stages within a river over time, providing local managers the information to minimize negative thermal impacts on salmonid populations, particularly important during years when cold-water resources are scarce.

Differential sensitivity to warming and hypoxia during development and long-term effects of developmental exposure in early life stage Chinook salmon.

Warming and hypoxia are two stressors commonly found within natural salmon redds that are likely to co-occur. Warming and hypoxia can interact physiologically, but their combined effects during fish development remain poorly studied, particularly stage-specific effects and potential carry-over effects. To test the impacts of warm water temperature and hypoxia as individual and combined developmental stressors, late fall-run Chinook salmon embryos were reared in 10 treatments from fertilization through hatching with two temperatures [10°C (ambient) and 14°C (warm)], two dissolved oxygen saturation levels [normoxia (100% air saturation, 10.4-11.4 mg O2/l) and hypoxia (50% saturation, 5.5 mg O2/l)] and three exposure times (early [eyed stage], late [silver-eyed stage] and chronic [fertilization through hatching]). After hatching, all treatments were transferred to control conditions (10°C and 100% air saturation) through the fry stage. To study stage-specific effects of stressor exposure we measured routine metabolic rate (RMR) at two embryonic stages, hatching success and growth. To evaluate carry-over effects, where conditions during one life stage influence performance in a later stage, RMR of all treatments was measured in control conditions at two post-hatch stages and acute stress tolerance was measured at the fry stage. We found evidence of stage-specific effects of both stressors during exposure and carry-over effects on physiological performance. Both individual stressors affected RMR, growth and developmental rate while multiple stressors late in development reduced hatching success. RMR post-hatch showed persistent effects of embryonic stressor exposure that may underlie differences observed in developmental timing and acute stress tolerance. The responses to stressors that varied by stage during development suggest that stage-specific management efforts could support salmon embryo survival. The persistent carry-over effects also indicate that considering sub-lethal effects of developmental stressor exposure may be important to understanding how climate change influences the performance of salmon across life stages.

Quantifying thermal exposure for migratory riverine species: Phenology of Chinook salmon populations predicts thermal stress.

Migratory species are particularly vulnerable to climate change because habitat throughout their entire migration cycle must be suitable for the species to persist. For migratory species in rivers, predicting climate change impacts is especially difficult because there is a lack of spatially continuous and seasonally varying stream temperature data, habitat conditions can vary for an individual throughout its life cycle, and vulnerability can vary by life stage and season. To predict thermal impacts on migratory riverine populations, we first expanded a spatial stream network model to predict mean monthly temperature for 465,775 river km in the western U.S., and then applied simple yet plausible future stream temperature change scenarios. We then joined stream temperature predictions to 44,396 spatial observations and life-stage-specific phenology (timing) for 26 ecotypes (i.e., geographically distinct population groups expressing one of the four distinct seasonal migration patterns) of Chinook salmon (Oncorhynchus tshawytscha), a phenotypically diverse anadromous salmonid that is ecologically and economically important but declining throughout its range. Thermal stress, assessed for each life stage and ecotype based on federal criteria, was influenced by migration timing rather than latitude, elevation, or migration distance such that sympatric ecotypes often showed differential thermal exposure. Early-migration phenotypes were especially vulnerable due to prolonged residency in inland streams during the summer. We evaluated the thermal suitability of 31,699 stream km which are currently blocked by dams to explore reintroduction above dams as an option to mitigate the negative effects of our warmer stream temperature scenarios. Our results showed that negative impacts of stream temperature warming can be offset for almost all ecotypes if formerly occupied habitat above dams is made available. Our approach of combining spatial distribution and phenology data with spatially explicit and temporally explicit temperature predictions enables researchers to examine thermal exposure of migrating populations that use seasonally varying habitats.

The biophysical basis of thermal tolerance in fish eggs.

A warming climate poses a fundamental problem for embryos that develop within eggs because their demand for oxygen (O2) increases much more rapidly with temperature than their capacity for supply, which is constrained by diffusion across the egg surface. Thus, as temperatures rise, eggs may experience O2 limitation due to an imbalance between O2 supply and demand. Here, we formulate a mathematical model of O2 limitation and experimentally test whether this mechanism underlies the upper thermal tolerance in large aquatic eggs. Using Chinook salmon (Oncorhynchus tshawytscha) as a model system, we show that the thermal tolerance of eggs varies systematically with features of the organism and environment. Importantly, this variation can be precisely predicted by the degree to which these features shift the balance between O2 supply and demand. Equipped with this mechanistic understanding, we predict and experimentally confirm that the thermal tolerance of these embryos in their natural habitat is substantially lower than expected from laboratory experiments performed under normoxia. More broadly, our biophysical model of O2 limitation provides a mechanistic explanation for the elevated thermal sensitivity of fish embryos relative to other life stages, global patterns in egg size and the extreme fecundity of large teleosts.

An Algorithmic Approach to Natural Behavior.

Uncovering the mechanisms and implications of natural behavior is a goal that unites many fields of biology. Yet, the diversity, flexibility, and multi-scale nature of these behaviors often make understanding elusive. Here, we review studies of animal pursuit and evasion - two special classes of behavior where theory-driven experiments and new modeling techniques are beginning to uncover the general control principles underlying natural behavior. A key finding of these studies is that intricate sequences of pursuit and evasion behavior can often be constructed through simple, repeatable rules that link sensory input to motor output: we refer to these rules as behavioral algorithms. Identifying and mathematically characterizing these algorithms has led to important insights, including the discovery of guidance rules that attacking predators use to intercept mobile prey, and coordinated neural and biomechanical mechanisms that animals use to avoid impending collisions. Here, we argue that algorithms provide a good starting point for studies of natural behavior more generally. Rather than beginning at the neural or ecological levels of organization, we advocate starting in the middle, where the algorithms that link sensory input to behavioral output can provide a solid foundation from which to explore both the implementation and the ecological outcomes of behavior. We review insights that have been gained through such an algorithmic approach to pursuit and evasion behaviors. From these, we synthesize theoretical principles and lay out key modeling tools needed to apply an algorithmic approach to the study of other complex natural behaviors.

Information limitation and the dynamics of coupled ecological systems.

The dynamics of large ecological systems result from vast numbers of interactions between individual organisms. Here, we develop mathematical theory to show that the rate of such interactions is inherently limited by the ability of organisms to gain information about one another. This phenomenon, which we call 'information limitation', is likely to be widespread in real ecological systems and can dictate both the rates of ecological interactions and long-run dynamics of interacting populations. We show how information limitation leads to sigmoid interaction rate functions that can stabilize antagonistic interactions and destabilize mutualistic ones; as a species or type becomes rare, information on its whereabouts also becomes rare, weakening coupling with consumers, pathogens and mutualists. This can facilitate persistence of consumer-resource systems, alter the course of pathogen infections within a host and enhance the rates of oceanic productivity and carbon export. Our findings may shed light on phenomena in many living systems where information drives interactions.

Reverse-engineering ecological theory from data.

Ecologists have long sought to understand the dynamics of populations and communities by deriving mathematical theory from first principles. Theoretical models often take the form of dynamical equations that comprise the ecological processes (e.g. competition, predation) believed to govern system dynamics. The inverse of this approach-inferring which processes and ecological interactions drive observed dynamics-remains an open problem in ecology. Here, we propose a way to attack this problem using a machine learning method known as symbolic regression, which seeks to discover relationships in time-series data and to express those relationships using dynamical equations. We found that this method could rapidly discover models that explained most of the variance in three classic demographic time series. More importantly, it reverse-engineered the models previously proposed by theoretical ecologists to describe these time series, capturing the core ecological processes these models describe and their functional forms. Our findings suggest a potentially powerful new way to merge theory development and data analysis.

Integrating lipid storage into general representations of fish energetics.

Fish, even of the same species, can exhibit substantial variation in energy density (energy per unit wet weight). Most of this variation is due to differences in the amount of storage lipids. In addition to their importance as energy reserves for reproduction and for survival during unfavourable conditions, the accumulation of lipids represents a large energetic flux for many species, so figuring out how this energy flux is integrated with other major energy fluxes (growth, reproduction) is critical for any general theory of organismal energetics. Here, we synthesize data from a wide range of fish species and identify patterns of intraspecific variation in energy storage, and use these patterns to formulate a general model of energy allocation between growth, lipid storage and reproduction in fishes. From the compiled data we identified two patterns: (1) energy density increases with body size during the juvenile period, but is invariant with body size within the adult size range for most species, and (2) energy density changes across seasons, with depletion over winter, but increases fastest in periods of transition between favourable and unfavourable conditions for growth (i.e. fall). Based on these patterns we propose DEBlipid, a simple, general model of energy allocation that is closely related to a simplified version of Dynamic Energy Budget theory, DEBkiss. The crux of the model is that assimilated energy is partitioned, with κ fraction of energy allocated to pay maintenance costs first, and the surplus allocated to growth, and 1 - κ fraction of assimilated energy is allocated to accumulating storage lipids during the juvenile phase, and later to reproduction as adults. This mechanism, in addition to capturing the two patterns that motivated the model, was able to predict lipid dynamics in a novel context, the migration of anadromous fish from low-food freshwater to high-food marine environments. Furthermore, the model was used to explain intra and interspecific variation in reproductive output based on patterns of lipid accumulation as juveniles. Our results suggest that many seemingly complex, adaptive energy allocation strategies in response to ontogeny, seasonality and habitat quality can emerge from a simple physiological heuristic.

Phenomenological vs. biophysical models of thermal stress in aquatic eggs.

Predicting species responses to climate change is a central challenge in ecology. These predictions are often based on lab-derived phenomenological relationships between temperature and fitness metrics. We tested one of these relationships using the embryonic stage of a Chinook salmon population. We parameterised the model with laboratory data, applied it to predict survival in the field, and found that it significantly underestimated field-derived estimates of thermal mortality. We used a biophysical model based on mass transfer theory to show that the discrepancy was due to the differences in water flow velocities between the lab and the field. This mechanistic approach provides testable predictions for how the thermal tolerance of embryos depends on egg size and flow velocity of the surrounding water. We found support for these predictions across more than 180 fish species, suggesting that flow and temperature mediated oxygen limitation is a general mechanism underlying the thermal tolerance of embryos.

Mechanistic effect modeling for ecological risk assessment: where to go from here?

Mechanistic effect models (MEMs) consider the mechanisms of how chemicals affect individuals and ecological systems such as populations and communities. There is an increasing awareness that MEMs have high potential to make risk assessment of chemicals more ecologically relevant than current standard practice. Here we discuss what kinds of MEMs are needed to improve scientific and regulatory aspects of risk assessment. To make valid predictions for a wide range of environmental conditions, MEMs need to include a sufficient amount of emergence, for example, population dynamics emerging from what individual organisms do. We present 1 example where the life cycle of individuals is described using Dynamic Energy Budget theory. The resulting individual-based population model is thus parameterized at the individual level but correctly predicts multiple patterns at the population level. This is the case for both control and treated populations. We conclude that the state-of-the-art in mechanistic effect modeling has reached a level where MEMs are robust and predictive enough to be used in regulatory risk assessment. Mechanistic effect models will thus be used to advance the scientific basis of current standard practice and will, if their development follows Good Modeling Practice, be included in a standardized way in future regulatory risk assessments.

DEBkiss or the quest for the simplest generic model of animal life history.

Understanding the life cycle of individual animals, and how it responds to stress, requires a model that causally links life-history traits (feeding, growth, development and reproduction). Dynamic Energy Budget (DEB) theory offers a powerful and formalised framework for building process-based models for organism life cycles. However, it takes some serious investment to understand the resulting equations and to implement them into software, and a substantial amount of data to parameterise. For many practical applications, there is therefore a need for further simplification. Here, we present a simple and transparent model that fully specifies the life cycle of an (invertebrate) animal, applies a strict mass balance, and has direct access to the primary parameters that determine the metabolic processes. We derive our 'DEBkiss' in a formalised manner, starting from an explicit formulation of the simplifying assumptions. The presented model can serve as a teaching tool and a smooth introduction into the much richer world of DEB theory. Furthermore, the model may prove useful as a building block for individual-based population modelling (where simplicity of the blocks is essential), and for the analysis of toxicity data (where ease of model verification and parameterisation is crucial). The model is illustrated using a fit on growth and reproduction data for the pond snail (Lymnaea stagnalis) at three food levels, and subsequent predictions for embryonic growth and respiration (oxygen use), and weight loss on starvation, for the same species.

Predicting population dynamics from the properties of individuals: a cross-level test of dynamic energy budget theory.

Individual-based models (IBMs) are increasingly used to link the dynamics of individuals to higher levels of biological organization. Still, many IBMs are data hungry, species specific, and time-consuming to develop and analyze. Many of these issues would be resolved by using general theories of individual dynamics as the basis for IBMs. While such theories have frequently been examined at the individual level, few cross-level tests exist that also try to predict population dynamics. Here we performed a cross-level test of dynamic energy budget (DEB) theory by parameterizing an individual-based model using individual-level data of the water flea, Daphnia magna, and comparing the emerging population dynamics to independent data from population experiments. We found that DEB theory successfully predicted population growth rates and peak densities but failed to capture the decline phase. Further assumptions on food-dependent mortality of juveniles were needed to capture the population dynamics after the initial population peak. The resulting model then predicted, without further calibration, characteristic switches between small- and large-amplitude cycles, which have been observed for Daphnia. We conclude that cross-level tests help detect gaps in current individual-level theories and ultimately will lead to theory development and the establishment of a generic basis for individual-based models and ecology.

Extrapolating ecotoxicological effects from individuals to populations: a generic approach based on Dynamic Energy Budget theory and individual-based modeling.

Individual-based models (IBMs) predict how dynamics at higher levels of biological organization emerge from individual-level processes. This makes them a particularly useful tool for ecotoxicology, where the effects of toxicants are measured at the individual level but protection goals are often aimed at the population level or higher. However, one drawback of IBMs is that they require significant effort and data to design for each species. A solution would be to develop IBMs for chemical risk assessment that are based on generic individual-level models and theory. Here we show how one generic theory, Dynamic Energy Budget (DEB) theory, can be used to extrapolate the effect of toxicants measured at the individual level to effects on population dynamics. DEB is based on first principles in bioenergetics and uses a common model structure to model all species. Parameterization for a certain species is done at the individual level and allows to predict population-level effects of toxicants for a wide range of environmental conditions and toxicant concentrations. We present the general approach, which in principle can be used for all animal species, and give an example using Daphnia magna exposed to 3,4-dichloroaniline. We conclude that our generic approach holds great potential for standardized ecological risk assessment based on ecological models. Currently, available data from standard tests can directly be used for parameterization under certain circumstances, but with limited extra effort standard tests at the individual would deliver data that could considerably improve the applicability and precision of extrapolation to the population level. Specifically, the measurement of a toxicant's effect on growth in addition to reproduction, and presenting data over time as opposed to reporting a single EC50 or dose response curve at one time point.