Modeling bee movement shows how a perceptual masking effect can influence flower discovery.

Understanding how pollinators move across space is key to understanding plant mating patterns. Bees are typically assumed to search for flowers randomly or using simple movement rules, so that the probability of discovering a flower should primarily depend on its distance to the nest. However, experimental work shows this is not always the case. Here, we explored the influence of flower size and density on their probability of being discovered by bees by developing a movement model of central place foraging bees, based on experimental data collected on bumblebees. Our model produces realistic bee trajectories by taking into account the autocorrelation of the bee's angular speed, the attraction to the nest (homing), and a gaussian noise. Simulations revealed a « masking effect ¼ that reduces the detection of flowers close to another, with potential far reaching consequences on plant-pollinator interactions. At the plant level, flowers distant to the nest were more often discovered by bees in low density environments. At the bee colony level, foragers found more flowers when they were small and at medium densities. Our results indicate that the processes of search and discovery of resources are potentially more complex than usually assumed, and question the importance of resource distribution and abundance on bee foraging success and plant pollination.

Photosynthetic performance under adaxial and abaxial illumination in three Mediterranean Quercus species differing in branch architecture and individual leaf area.

Light availability effects on canopy-level carbon balance constitute an especially difficult issue to address, owing to the strong spatial and temporal changes of the light environment within the canopy. One of the least explored aspects in relation to light environment is the interaction between leaf angle and leaf anatomy. The inclination of the leaf may affect the distribution of light between the adaxial and abaxial surface. The purpose of this study is determining the proportions of the leaf area receiving light from the abaxial side in branches of isolated trees in three Mediterranean oaks, as well as the photosynthetic responses to light under adaxial and abaxial illumination. The proportions of the leaf area illuminated from below were low for sun incidence angles near 0° with respect to the main axis of the branch. However, for sun incidence angles about 45°, the proportion of leaves receiving abaxial illumination was considerable. PPFD levels on the sunlit part of the abaxial surface were always lower than those in the upper leaf side, as a consequence of the lower projection efficiency for the leaves facing the sun by the lower side. Light absorptance was also lower on the abaxial side. The differences between both sides of the leaf tended to be stronger for thicker, longer-living leaves. We conclude that mean C assimilation of the canopy is significantly decreased by the presence of leaves facing the sun by the lower side and that this decrease is stronger in evergreen species with thicker leaves.

Branch architecture in relation to canopy positions in three Mediterranean oaks.

Branch architecture is a key determinant of plant performance owing to its role in a light interception by photosynthetic tissues. However, under stressed conditions, excess light may be harmful to the photosynthetic apparatus, and plants often present structural mechanisms to avoid photoinhibition. Three-dimensional models were constructed of the aerial parts in different locations within the crown of three co-occurring tree species (Quercus ilex, Q. suber and Q. faginea) growing in a Mediterranean environment. We hypothesized that the species with the shorter leaf life span would exhibit higher leaf display efficiency (silhouette to total leaf area, STAR), maximizing light interception and photosynthesis in the short term. In addition, more exposed positions within a canopy should develop more structural avoidance mechanisms to minimize excessive radiation. Significant differences were detected in architectural traits at both the intra- and interspecific level. Architectural traits promoting greater self-shading were more frequent in the species with longer leaf longevity and in the canopy locations experiencing higher temperatures at the times of maximum sunlight. However, these trends were in part counteracted by the changes in individual leaf area, which tended to be larger in the species with shorter leaf longevity and in the less exposed canopy locations. We conclude that the variation in architectural traits occurs mainly as a means to avoid the excessive self-shading of branches with the largest leaf size.

Deciduous and evergreen oaks show contrasting adaptive responses in leaf mass per area across environments.

Increases in leaf mass per area (LMA) are commonly observed in response to environmental stresses and are achieved through increases in leaf thickness and/or leaf density. Here, we investigated how the two underlying components of LMA differ in relation to species native climates and phylogeny, across deciduous and evergreen species. Using a phylogenetic approach, we quantified anatomical, compositional and climatic variables from 40 deciduous and 45 evergreen Quercus species from across the Northern Hemisphere growing in a common garden. Deciduous species from shorter growing seasons tended to have leaves with lower LMA and leaf thickness than those from longer growing seasons, while the opposite pattern was found for evergreens. For both habits, LMA and thickness increased in arid environments. However, this shift was associated with increased leaf density in evergreens but reduced density in deciduous species. Deciduous and evergreen oaks showed fundamental leaf morphological differences that revealed a diverse adaptive response. While LMA in deciduous species may have diversified in tight coordination with thickness mainly modulated by aridity, diversification of LMA within evergreens appears to be dependent on the infrageneric group, with diversification in leaf thickness modulated by both aridity and cold, while diversification in leaf density is only modulated by aridity.

Immobilization in Ionogel: A New Way to Improve the Activity and Stability of Candida antarctica Lipase B.

New Candida antarctica lipase B derivatives with higher activity than the free enzyme were obtained by occlusion in an organogel of an ionic liquid (ionogel) based on the ionic liquid [Omim][PF6] and polyvinyl chloride. The inclusion of glutaraldehyde as a crosslinker improved the properties of the ionogel, allowing the enzymatic derivative to reach 5-fold higher activity than the free enzyme and also allowing it to be reused at 70 °C. The new methodology allows enzymatic derivatives to be designed by changing the ionic liquid, thus providing a suitable microenvironment for the enzyme. The ionic liquid may act on substrates to increase their local concentration, while reducing water activity in the enzyme's microenvironment. All this allows the activity and selectivity of the enzyme to be improved and greener processes to be developed. The chemical composition and morphology of the ionogel were also studied by scanning electron microscopy-energy dispersive X-ray spectroscopy, finding that porosity, which was related with the chemical composition, was a key factor for the enzyme activity.

Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors.

The development of tracking methods for automatically quantifying individual behavior and social interactions in animal groups has open up new perspectives for building quantitative and predictive models of collective behavior. In this work, we combine extensive data analyses with a modeling approach to measure, disentangle, and reconstruct the actual functional form of interactions involved in the coordination of swimming in Rummy-nose tetra (Hemigrammus rhodostomus). This species of fish performs burst-and-coast swimming behavior that consists of sudden heading changes combined with brief accelerations followed by quasi-passive, straight decelerations. We quantify the spontaneous stochastic behavior of a fish and the interactions that govern wall avoidance and the reaction to a neighboring fish, the latter by exploiting general symmetry constraints for the interactions. In contrast with previous experimental works, we find that both attraction and alignment behaviors control the reaction of fish to a neighbor. We then exploit these results to build a model of spontaneous burst-and-coast swimming and interactions of fish, with all parameters being estimated or directly measured from experiments. This model quantitatively reproduces the key features of the motion and spatial distributions observed in experiments with a single fish and with two fish. This demonstrates the power of our method that exploits large amounts of data for disentangling and fully characterizing the interactions that govern collective behaviors in animals groups.

Preferential interactions promote blind cooperation and informed defection.

It is common sense that costs and benefits should be carefully weighed before deciding on a course of action. However, we often disapprove of people who do so, even when their actual decision benefits us. For example, we prefer people who directly agree to do us a favor over those who agree only after securing enough information to ensure that the favor will not be too costly. Why should we care about how people make their decisions, rather than just focus on the decisions themselves? Current models show that punishment of information gathering can be beneficial because it forces blind decisions, which under some circumstances enhances cooperation. Here we show that aversion to information gathering can be beneficial even in the absence of punishment, due to a different mechanism: preferential interactions with reliable partners. In a diverse population where different people have different-and unknown-preferences, those who seek additional information before agreeing to cooperate reveal that their preferences are close to the point where they would choose not to cooperate. Blind cooperators are therefore more likely to keep cooperating even if conditions change, and aversion to information gathering helps to interact preferentially with them. Conversely, blind defectors are more likely to keep defecting in the future, leading to a preference for informed defectors over blind ones. Both mechanisms-punishment to force blind decisions and preferential interactions-give qualitatively different predictions, which may enable experimental tests to disentangle them in real-world situations.

idTracker: tracking individuals in a group by automatic identification of unmarked animals.

Animals in groups touch each other, move in paths that cross, and interact in complex ways. Current video tracking methods sometimes switch identities of unmarked individuals during these interactions. These errors propagate and result in random assignments after a few minutes unless manually corrected. We present idTracker, a multitracking algorithm that extracts a characteristic fingerprint from each animal in a video recording of a group. It then uses these fingerprints to identify every individual throughout the video. Tracking by identification prevents propagation of errors, and the correct identities can be maintained indefinitely. idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. We tested it on fish (Danio rerio and Oryzias latipes), flies (Drosophila melanogaster), ants (Messor structor) and mice (Mus musculus).

Phenotypic variability in unicellular organisms: from calcium signalling to social behaviour.

Historically, research has focused on the mean and often neglected the variance. However, variability in nature is observable at all scales: among cells within an individual, among individuals within a population and among populations within a species. A fundamental quest in biology now is to find the mechanisms that underlie variability. Here, we investigated behavioural variability in a unique unicellular organism, Physarum polycephalum. We combined experiments and models to show that variability in cell signalling contributes to major differences in behaviour underpinning some aspects of social interactions. First, following thousands of cells under various contexts, we identified distinct behavioural phenotypes: 'slow-regular-social', 'fast-regular-social' and 'fast-irregular-asocial'. Second, coupling chemical analysis and behavioural assays we found that calcium signalling is responsible for these behavioural phenotypes. Finally, we show that differences in signalling and behaviour led to alternative social strategies. Our results have considerable implications for our understanding of the emergence of variability in living organisms.

A common rule for decision making in animal collectives across species.

A diversity of decision-making systems has been observed in animal collectives. In some species, choices depend on the differences of the numbers of animals that have chosen each of the available options, whereas in other species on the relative differences (a behavior known as Weber's law), or follow more complex rules. We here show that this diversity of decision systems corresponds to a single rule of decision making in collectives. We first obtained a decision rule based on Bayesian estimation that uses the information provided by the behaviors of the other individuals to improve the estimation of the structure of the world. We then tested this rule in decision experiments using zebrafish (Danio rerio), and in existing rich datasets of argentine ants (Linepithema humile) and sticklebacks (Gasterosteus aculeatus), showing that a unified model across species can quantitatively explain the diversity of decision systems. Further, these results show that the different counting systems used by animals, including humans, can emerge from the common principle of using social information to make good decisions.

Fungal infection alters collective nutritional intake of ant colonies.

In animals, parasitic infections impose significant fitness costs.1,2,3,4,5,6 Infected animals can alter their feeding behavior to resist infection,7,8,9,10,11,12 but parasites can manipulate animal foraging behavior to their own benefits.13,14,15,16 How nutrition influences host-parasite interactions is not well understood, as studies have mainly focused on the host and less on the parasite.9,12,17,18,19,20,21,22,23 We used the nutritional geometry framework24 to investigate the role of amino acids (AA) and carbohydrates (C) in a host-parasite system: the Argentine ant, Linepithema humile, and the entomopathogenic fungus, Metarhizium brunneum. First, using 18 diets varying in AA:C composition, we established that the fungus performed best on the high-amino-acid diet 1:4. Second, we found that the fungus reached this optimal diet when given various diet pairings, revealing its ability to cope with nutritional challenges. Third, we showed that the optimal fungal diet reduced the lifespan of healthy ants when compared with a high-carbohydrate diet but had no effect on infected ants. Fourth, we revealed that infected ant colonies, given a choice between the optimal fungal diet and a high-carbohydrate diet, chose the optimal fungal diet, whereas healthy colonies avoided it. Lastly, by disentangling fungal infection from host immune response, we demonstrated that infected ants foraged on the optimal fungal diet in response to immune activation and not as a result of parasite manipulation. Therefore, we revealed that infected ant colonies chose a diet that is costly for survival in the long term but beneficial in the short term-a form of collective self-medication.

Caenorhabditis elegans foraging patterns follow a simple rule of thumb.

Rules of thumb are behavioral algorithms that approximate optimal behavior while lowering cognitive and sensory costs. One way to reduce these costs is by simplifying the representation of the environment: While the theoretically optimal behavior may depend on many environmental variables, a rule of thumb may use a smaller set of variables that performs reasonably well. Experimental proof of this simplification requires an exhaustive mapping of all relevant combinations of several environmental parameters, which we performed for Caenorhabditis elegans foraging by covering systematically combinations of food density (across 4 orders of magnitude) and food type (across 12 bacterial strains). We found that worms' response is dominated by a single environmental variable: food density measured as number of bacteria per unit surface. They disregard other factors such as biomass content or bacterial strain. We also measured experimentally the impact on fitness of each type of food, determining that the rule is near-optimal and therefore constitutes a rule of thumb that leverages the most informative environmental variable. These results set the stage for further investigations into the underlying genetic and neural mechanisms governing this simplification process, and into its role in the evolution of decision-making strategies.

Collective animal behavior from Bayesian estimation and probability matching.

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is mainly based on empirical fits to observations, with less emphasis in obtaining first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching. In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability equal to the Bayesian-estimated probability that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior.

Structure of deviations from optimality in biological systems.

Optimization theory has been used to analyze evolutionary adaptation. This theory has explained many features of biological systems, from the genetic code to animal behavior. However, these systems show important deviations from optimality. Typically, these deviations are large in some particular components of the system, whereas others seem to be almost optimal. Deviations from optimality may be due to many factors in evolution, including stochastic effects and finite time, that may not allow the system to reach the ideal optimum. However, we still expect the system to have a higher probability of reaching a state with a higher value of the proposed indirect measure of fitness. In systems of many components, this implies that the largest deviations are expected in those components with less impact on the indirect measure of fitness. Here, we show that this simple probabilistic rule explains deviations from optimality in two very different biological systems. In Caenorhabditis elegans, this rule successfully explains the experimental deviations of the position of neurons from the configuration of minimal wiring cost. In Escherichia coli, the probabilistic rule correctly obtains the structure of the experimental deviations of metabolic fluxes from the configuration that maximizes biomass production. This approach is proposed to explain or predict more data than optimization theory while using no extra parameters. Thus, it can also be used to find and refine hypotheses about which constraints have shaped biological structures in evolution.

Patterns of leaf morphology and leaf N content in relation to winter temperatures in three evergreen tree species.

The competitive equilibrium between deciduous and perennial species in a new scenario of climate change may depend closely on the productivity of leaves along the different seasons of the year and on the morphological and chemical adaptations required for leaf survival during the different seasons. The aim of the present work was to analyze such adaptations in the leaves of three evergreen species (Quercus ilex, Q. suber and Pinus pinaster) and their responses to between-site differences in the intensity of winter harshness. We explore the hypothesis that the harshness of winter would contribute to enhancing the leaf traits that allow them to persist under conditions of stress. The results revealed that as winter harshness increases a decrease in leaf size occurs in all three species, together with an increase in the content of nitrogen per unit leaf area and a greater leaf mass per unit area, which seems to be achieved only through increased thickness, with no associated changes in density. P. pinaster was the species with the most intense response to the harshening of winter conditions, undergoing a more marked thickening of its needles than the two Quercus species. Our findings thus suggest that lower winter temperatures involve an increase in the cost of leaf production of evergreen species, which must be taken into account in the estimation of the final cost and benefit balance of evergreens. Such cost increases would be more pronounced for those species that, like P. pinaster, show a stronger response to the winter cold.

The motor Wisdom of the Crowd.

Wisdom of the Crowd is the aggregation of many individual estimates to obtain a better collective one. Because of its enormous social potential, this effect has been thoroughly investigated, but predominantly on tasks that involve rational thinking (such as estimating a number). Here we tested this effect in the context of drawing geometrical shapes, which still enacts cognitive processes but mainly involves visuomotor control. We asked more than 700 school students to trace five patterns shown on a touchscreen and then aggregated their individual trajectories to improve the match with the original pattern. Our results show the characteristics of the strongest examples of Wisdom of the Crowd. First, the aggregate trajectory can be up to 5 times more accurate than the individual ones. Second, this great improvement requires aggregating trajectories from different individuals (rather than trials from the same individual). Third, the aggregate trajectory outperforms more than 99% of individual trajectories. Fourth, while older individuals outperform younger ones, a crowd of young individuals outperforms the average older one. These results demonstrate for the first time Wisdom of the Crowd in the realm of motor control, opening the door to further studies of human and also animal behavioural trajectories and their mechanistic underpinnings.

Inversion of pheromone preference optimizes foraging in C. elegans.

Foraging animals have to locate food sources that are usually patchily distributed and subject to competition. Deciding when to leave a food patch is challenging and requires the animal to integrate information about food availability with cues signaling the presence of other individuals (e.g., pheromones). To study how social information transmitted via pheromones can aid foraging decisions, we investigated the behavioral responses of the model animal Caenorhabditis elegans to food depletion and pheromone accumulation in food patches. We experimentally show that animals consuming a food patch leave it at different times and that the leaving time affects the animal preference for its pheromones. In particular, worms leaving early are attracted to their pheromones, while worms leaving later are repelled by them. We further demonstrate that the inversion from attraction to repulsion depends on associative learning and, by implementing a simple model, we highlight that it is an adaptive solution to optimize food intake during foraging.