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.

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.

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.

Clinical relevance of marginal factors on ultrafiltration in peritoneal dialysis.

BACKGROUND: Ultrafiltration (UF) in peritoneal dialysis (PD) is mainly driven by the osmotic gradient and peritoneal permeability, but other factors-such as intraperitoneal pressure (IPP)-also have an influence. METHODS: To assess the clinical relevance of these marginal factors, we studied 41 unselected PD patients undergoing two consecutive 2 h, 2.27% glucose exchanges, first with 2.5 L and then with 1.5 L. RESULTS: IPP, higher in the 2.5 L exchange, had a wide interpatient range, was higher in obese and polycystic patients and their increase with infusion volume was higher for women regardless of body size. UF with 2.5 L correlated inversely with IPP and was higher for patients with polycystosis or hernias, while for 1.5 L we found no significant correlations. The effluent had higher glucose and osmolarity in the 2.5 L exchange than in the 1.5 L one, similar for both sexes. In spite of this stronger osmotic gradient, only 21 patients had more UF in the 2.5 L exchange, with differences up to 240 mL. The other 20 patients had more UF in the 1.5 L exchange, with stronger differences (up to 800 mL, and more than 240 mL for 9 patients). The second group, with similar effluent osmolarity and peritoneal equilibration test (PET) parameters than the first, has higher IPP and preponderance of men. The sex influence is so intense that men decreased average UF with 2.5 L with respect to 1.5 L, while women increased it. CONCLUSIONS: With 2.27% glucose, sex and IPP-modulated by obesity, polycystosis, hernias, and intraperitoneal volume-significantly affect UF in clinical settings and might be useful for its management.

Intraperitoneal pressure: Stability over time and validation of Durand's measurement method.

Intraperitoneal pressure (IPP) is gaining consideration as a relevant parameter of peritoneal dialysis (PD) in adults, although many of its aspects are still pending clarification. We address here its stability over time and the validity of the usual method of clinical measurement, as proposed by Durand in 1992 but never specifically validated. We performed this validation by comparing Durand's method and direct measurements with a central venous pressure system. We performed a total of 250 measurement pairs in 50 patients with different intraperitoneal volumes plus in-vitro measurements with a simulated peritoneum. Absolute differences between the two systems in vivo were 0.87 ± 0.91 cmH2O (range 0-5 cmH2O); only 6.4% of them were ≥3 cmH2O. In vitro results for both methods were identical. We also compared IPP measurements in the same patient separated by 1-4 h (514 measurement pairs in 136 patients), 1 week (92 pairs in 92 patients), and 2 years (34 pairs in 17 patients). Net differences of measurements separated by hours or 1 week were close to 0 cmH2O, with oscillations of 1.5 cmH2O in hours and 2.3 cmH2O in 1 week. IPP measured 2 years apart presented a net decrease of 2.5 ± 4.9 cmH2O, without correlation with body mass index changes or any other usual parameter of PD. In hours, 7% of IPP differences were >3 cmH2O, 22% in 1 week, and 50% in 2 years. In conclusion, Durand's method is precise enough to measure IPP in peritoneal dialysis. This parameter is not stable over long timescales, so it is necessary to use recent measurements.

Correlation between radius and asphericity in surfaces fitted by conics.

The optical surfaces of the eye are often described in terms of their radius and asphericity. The variations caused by experimental noise in repeated measurements of radius and asphericity of the same surface are strongly correlated. We show this correlation in experimental corneal elevation data from videokeratoscopy and Scheimpflug topography, in non-contact profilometry data of artificial lenses, and in simulations. The effect is a characteristic of the fits to conic curves, and not restricted to any experimental device or fitting procedure. A separate analysis of radius and asphericity may estimate incorrectly the statistical significance of the changes in the ocular surfaces. We propose a MANOVA-based statistical analysis that increases sensitivity by a factor of 4.

Corneal light transmission and roughness after refractive surgery.

PURPOSE: To determine the relation between the corneal light transmission measurements and the epithelial surface properties in hen corneas after different refractive surgery techniques photorefractive keratectomy, laser in situ keratomileusis, and laser-assisted subepithelial keratomileusis, and a group with only epithelial corneal removal (deepithelialization). METHODS: Five groups of hen corneas with different treatments and a control group were analyzed at 30 days. Direct transmittance and corneal light scattering were measured by a scatterometer developed by our group. Quantitative and systematic measurements of external and internal roughness and epithelium thickness were assessed using standard techniques developed for quantitative analysis of microphotographs of the corneal epithelium. RESULTS: Data analysis revealed that the roughness in the epithelial surface was associated with the corneal light transmission. The direct transmittance of light showed a significant correlation with the epithelial roughness in the control (r = -0.99, p < 0.05) and photorefractive keratectomy (r = -0.99, p < 0.05) groups. However, there was no relation between the epithelial thickness and the corneal light transmission measurements. CONCLUSIONS: The experimental results suggested that the roughness of the epithelial surfaces is related to the light transmission in the cornea.

Asymmetric migration decreases stability but increases resilience in a heterogeneous metapopulation.

Many natural populations are spatially distributed, forming a network of subpopulations linked by migration. Migration patterns are often asymmetric and heterogeneous, with important consequences on the ecology and evolution of the species. Here we investigate experimentally how asymmetric migration and heterogeneous structure affect a simple metapopulation of budding yeast, formed by one strain that produces a public good and a non-producer strain that benefits from it. We study metapopulations with star topology and asymmetric migration, finding that all their subpopulations have a higher fraction of producers than isolated populations. Furthermore, the metapopulations have lower tolerance to challenging environments but higher resilience to transient perturbations. This apparent paradox occurs because tolerance to a constant challenge depends on the weakest subpopulations of the network, while resilience to a transient perturbation depends on the strongest ones.