Non-random dispersal in the butterfly Maniola jurtina: implications for metapopulation models

The dispersal patterns of animals are important in metapopulation ecology because they affect the dynamics and survival of populations. Theoretical models assume random dispersal but little is known in practice about the dispersal behaviour of individual animals or the strategy by which dispersers locate distant habitat patches. In the present study, we released individual meadow brown butterflies (Maniola jurtina) in a non-habitat and investigated their ability to return to a suitable habitat. The results provided three reasons for supposing that meadow brown butterflies do not seek habitat by means of random flight. First, when released within the range of their normal dispersal distances, the butterflies orientated towards suitable habitat at a higher rate than expected at random. Second, when released at larger distances from their habitat, they used a non-random, systematic, search strategy in which they flew in loops around the release point and returned periodically to it. Third, butterflies returned to a familiar habitat patch rather than a non-familiar one when given a choice. If dispersers actively orientate towards or search systematically for distant habitat, this may be problematic for existing metapopulation models, including models of the evolution of dispersal rates in metapopulations

Automated Plankton Classification With a Dynamic Optimization and Adaptation Cycle

With recent advances in Machine Learning techniques based on Deep Neural Networks (DNNs), automated plankton image classification is becoming increasingly popular within the marine ecological sciences. Yet, while the most advanced methods can achieve human-level performance on the classification of everyday images, plankton image data possess properties that frequently require a final manual validation step. On the one hand, this is due to morphological properties manifesting in high intra-class and low inter-class variability, and, on the other hand is due to spatial-temporal changes in the composition and structure of the plankton community. Composition changes enforce a frequent updating of the classifier model via training with new user-generated training datasets. Here, we present a Dynamic Optimization Cycle (DOC), a processing pipeline that systematizes and streamlines the model adaptation process via an automatic updating of the training dataset based on manual-validation results. We find that frequent adaptation using the DOC pipeline yields strong maintenance of performance with respect to precision, recall and prediction of community composition, compared to more limited adaptation schemes. The DOC is therefore particularly useful when analyzing plankton at novel locations or time periods, where community differences are likely to occur. In order to enable an easy implementation of the DOC pipeline, we provide an end-to-end application with graphical user interface, as well as an initial dataset of training images. The DOC pipeline thus allows for high-throughput plankton classification and quick and systematized model adaptation, thus providing the means for highly-accelerated plankton analysis

Copycat dynamics in leaderless animal group navigation

Background: Many animals are known to have improved navigational efficiency when moving together as a social group. One potential mechanism for social group navigation is known as the 'many wrongs principle', where information from many inaccurate compasses is pooled across the group. In order to understand how animal groups may use the many wrongs principle to navigate, it is important to consider how directional information is transferred and shared within the group. Methods: Here we use an individual-based model to explore the information-sharing and copying dynamics of a leaderless animal group navigating towards a target in a virtual environment. We assume that communication and information-sharing is indirect and arises through individuals partially copying the movement direction of their neighbours and weighting this information relative to their individual navigational knowledge. Results: We find that the best group navigation performance occurs when individuals directly copy the direction of movement of a subset of their neighbours while only giving a small (6%) weighting to their individual navigational knowledge. Surprisingly, such a strategy is shown to be highly efficient regardless of the level of individual navigational error. We find there is little relative improvement in navigational efficiency when individuals copy from more than 7 influential neighbours. Conclusions: Our findings suggest that we would expect navigating group-living animals to predominantly copy the movement of others rather than relying on their own navigational knowledge. We discuss our results in the context of individual and group navigation behaviour in animals

NLRC5 promotes transcription of BTN3A1-3 genes and Vγ9Vδ2 T cell-mediated killing

BTN3A molecules-BTN3A1 in particular-emerged as important mediators of Vγ9Vδ2 T cell activation by phosphoantigens. These metabolites can originate from infections, e.g. with Mycobacterium tuberculosis, or by alterations in cellular metabolism. Despite the growing interest in the BTN3A genes and their high expression in immune cells and various cancers, little is known about their transcriptional regulation. Here we show that these genes are induced by NLRC5, a regulator of MHC class I gene transcription, through an atypical regulatory motif found in their promoters. Accordingly, a robust correlation between NLRC5 and BTN3A gene expression was found in healthy, in M. tuberculosis-infected donors' blood cells, and in primary tumors. Moreover, forcing NLRC5 expression promoted Vγ9Vδ2 T-cell-mediated killing of tumor cells in a BTN3A-dependent manner. Altogether, these findings indicate that NLRC5 regulates the expression of BTN3A genes and hence open opportunities to modulate antimicrobial and anticancer immunity

Scalable Rules for Coherent Group Motion in a Gregarious Vertebrate

Individuals of gregarious species that initiate collective movement require mechanisms of cohesion in order to maintain advantages of group living. One fundamental question in the study of collective movement is what individual rules are employed when making movement decisions. Previous studies have revealed that group movements often depend on social interactions among individual members and specifically that collective decisions to move often follow a quorum-like response. However, these studies either did not quantify the response function at the individual scale (but rather tested hypotheses based on group-level behaviours), or they used a single group size and did not demonstrate which social stimuli influence the individual decision-making process. One challenge in the study of collective movement has been to discriminate between a common response to an external stimulus and the synchronization of behaviours resulting from social interactions. Here we discriminate between these two mechanisms by triggering the departure of one trained Merino sheep (Ovis aries) from groups containing one, three, five and seven naïve individuals. Each individual was thus exposed to various combinations of already-departed and non-departed individuals, depending on its rank of departure. To investigate which individual mechanisms are involved in maintaining group cohesion under conditions of leadership, we quantified the temporal dynamic of response at the individual scale. We found that individuals' decisions to move do not follow a quorum response but rather follow a rule based on a double mimetic effect: attraction to already-departed individuals and attraction to non-departed individuals. This rule is shown to be in agreement with an adaptive strategy that is inherently scalable as a function of group size

Communication and Cognition in Primate Group Movement

We here review the communicative and cognitive processes underpinning collective group movement in animals. Generally, we identify 2 major axes to explain the dynamics of decision making in animal or human groups or aggregations: One describes whether the behavior is largely determined by simple rules such as keeping a specific distance from the neighbor, or whether global information is also factored in. The second axis describes whether or not the individual constituents of the group have overlapping or diverging interests. We then review the available evidence for baboons, which have been particularly well studied, but we also draw from further studies on other nonhuman primate species. Baboons and other nonhuman primates may produce specific signals in the group movement context, such as the notifying behavior of male hamadryas baboons at the departure from the sleeping site, or clear barks that are given by chacma baboons that have lost contact with the group or specific individuals. Such signals can be understood as expressions of specific motivational states of the individuals, but there is no evidence that the subjects intend to alter the knowledge state of the recipients. There is also no evidence for shared intentionality. The cognitive demands that are associated with decision making in the context of group coordination vary with the amount of information and possibly conflicting sources of information that need to be integrated. Thus, selective pressures should favor the use of signals that maintain group cohesion, while recipients should be selected to be able to make the decision that is in their own best interest in light of all the available information

Control of Apoptosis by Asymmetric Cell Division

Asymmetric cell division and apoptosis (programmed cell death) are two fundamental processes that are important for the development and function of multicellular organisms. We have found that the processes of asymmetric cell division and apoptosis can be functionally linked. Specifically, we show that asymmetric cell division in the nematode Caenorhabditis elegans is mediated by a pathway involving three genes, dnj-11 MIDA1, ces-2 HLF, and ces-1 Snail, that directly control the enzymatic machinery responsible for apoptosis. Interestingly, the MIDA1-like protein GlsA of the alga Volvox carteri, as well as the Snail-related proteins Snail, Escargot, and Worniu of Drosophila melanogaster, have previously been implicated in asymmetric cell division. Therefore, C. elegans dnj-11 MIDA1, ces-2 HLF, and ces-1 Snail may be components of a pathway involved in asymmetric cell division that is conserved throughout the plant and animal kingdoms. Furthermore, based on our results, we propose that this pathway directly controls the apoptotic fate in C. elegans, and possibly other animals as well

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda