Biology subjects

Ogino, M.

Publications and source records attributed to Ogino, M..

5 recordsLinked to original sources

bioRxiv2025

Space use fidelity of non-territorial vulturine guineafowl groups is shaped by both environmental and social processes

Animals often use consistent areas. Some are territorial, restricting their space use within territorial boundaries, whereas others at not territorial animals but still restrict their space use despite not being constrained by surrounding conspecifics. Staying within a familiar area can provide a range of benefits, such as using previous knowledge (i.e. memory) to efficiently exploit resources or because they can consistently return to key locations (such as a nest or sleeping site). In group-living animals, consistent space use could reduce the complexity of decision-making time (e.g. by choosing among known foraging sites), facilitating group cohesion. However, to date, little research has explicitly asked what factors determine whether groups use consistent areas. Here we used repeated movements by groups of vulturine guineafowl (Acryllium vulturinum)--leaving and returning back to the same areas in response to seasonal conditions--to examine and disentangle social processes from spatial and ecological factors that might shape the distribution of animals over space. Specifically, we quantified (i) how groups distribute themselves over the landscape, (ii) if their space use is consistent across seasons with similar environmental conditions, (iii) how different social and spatial factors shape the consistency of space use by groups over time, and (iv) how social and spatial factors affect home range overlap between groups. We found that groups were highly consistent in their space use over time and that home ranges were distinct across groups. Fidelity to the core home range area was higher when group composition was more stable, while overall home range fidelity was higher when groups recently experienced milder ecological conditions. Overlap in core areas and the overall home ranges among groups were greater among groups that shared roosts and groups that were fused in the previous season. Home range overlap was also lowest during long intermediate seasons (i.e. a sampling period that immediately follows intermediate season conditions, as opposed to sampling periods that followed dry or wet conditions), suggesting that extended intermediate conditions allow groups to increasingly partition their overall space use. These results provide insights into how the movement decisions by groups, the distribution of animals, and group-level space use emerge, and the role of social and ecological conditions as potential precursors to territoriality.

ecology↗

bioRxiv2025

Group size influences behavioral plasticity in responses to thermoregulation-foraging trade-offs by a socially cohesive bird

Behavioral plasticity, such as changes in habitat use and activity, can be a critical modulator of thermal pressures on endotherms. However, shifts in behaviors can meet diversified costs such as missed feeding opportunities. Individual decision-making should therefore capture the trade-off between the costs and the benefits of thermoregulation. In the case of social species, the decision process could also be facilitated or slowed down by the modulation of costs arising through the social environment. In this study, we tested how vulturine guineafowl (Acryllium vulturinum) change their use of open areas (where they predominately forage) according to heat using GPS data from 105 birds collected every 5 minutes for 6 months. Because animals vary in their sensitivity to risk according to group size--e.g. due to the dilution effect--we compared the responses of individuals depending on the size of the group they belong to. We also analyzed if behavioral responses translate into less precise thermoregulation by recording the body temperature of a subset of birds in two groups. We foundd that birds avoid heat by selectively using open areas and moving to cover, as well as reducing activity when temperature increases, birds use open areas less and move less. Individuals from intermediate-sized groups seemed to be able to use open areas during warmer conditions compared to individuals from small and large groups (10% higher probability of use). However, active birds in the open did not present hyperthermia, suggesting that behavioral changes are efficient or that individuals have other efficient strategies such as physiological cooling. Together, these results indicated that responses to high temperatures are complex, as they included not only a range of environmental constraints but that responses can also vary according to social context.

ecology↗

bioRxiv2025

Designing optimal perturbation inputs for system identification in neuroscience

AO_SCPLOWBSTRACTC_SCPLOWInvestigating the dynamics of neural networks, which are governed by connectivity between neurons, is a fundamental challenge in neuroscience. Because passive (spontaneous) activity provides only limited information for estimating connectivity, perturbation-based approaches are widely applied in neuroscience, as they can evoke underlying hidden dynamics. However, the characteristics of such perturbations have typically been designed based on empirical or biological intuition. To enable more accurate estimation of connectivity, we propose a data-driven and theoretically grounded framework for optimally designing perturbation inputs, based on formulating the neural model as a control system. The core theoretical insight underlying our approach is that neural signals observed in the passive state lack sufficient latent information, which leads to failures in the system identification. Perturbations reveal these hidden dynamics and lead to improved estimation. Guided by these insights, we derive a theoretical basis for optimizing perturbation inputs that minimize estimation errors in neural system identification. Building upon this, we further explore the relationship of this theory with stimulation patterns commonly used in neuroscience, such as frequency, impulse, and step inputs. We demonstrate the effectiveness of this framework for neuroscience through simulations grounded in experimental paradigms such as neural state classification and optimal control of neural states. Our theoretical analysis, together with multiple simulations, consistently shows that perturbations designed according to our framework achieve substantially more accurate system identification compared to the conventional, intuition-based inputs. This study provides a theoretical foundation for designing perturbation inputs to achieve accurate estimation of neural dynamics. This, in turn, enables reliable discrimination of neural states such as levels of consciousness and pathological conditions, and facilitates precise control of their transitions toward recovery from abnormal states.

neuroscience↗

bioRxiv2024

Moving towards more holistic validation of machine learning-based approaches in ecology and evolution

Machine-learning (ML) is revolutionizing field and laboratory studies of animals. However, a challenge when deploying ML for classification tasks is ensuring the models are reliable. Currently, we evaluate models using performance metrics (e.g., precision, recall, F1), but these can overlook the ultimate aim, which is not the outputs themselves (e.g. detected species or individual identities, or behaviour) but their incorporation for hypothesis testing. As improving performance metrics has diminishing returns, particularly when data are inherently noisy (as human-labelled, animal-based data often are), researchers are faced with the conundrum of investing more time in maximising metrics versus doing the actual research. This raises the question: how much noise can we accept in ML models? Here, we start by describing an under-reported factor that can cause metrics to underestimate model performance. Specifically, ambiguity between categories or mistakes in labelling validation data produces hard ceilings that limit performance metrics. This likely widespread issue means that many models could be performing better than their metrics suggest. Next, we argue and show that imperfect models (e.g. low F1 scores) can still be useable. Using a case study on ML-identified behaviour from vulturine guineafowl accelerometer data, we first propose a simulation framework to evaluate robustness of hypothesis testing using models that make classification errors. Second, we show how to determine the utility of a model by supplementing existing performance metrics with biological validations. This involves applying ML models to unlabelled data and using the models outputs to test hypotheses for which we can anticipate the outcome. Together, we show that effects sizes and expected biological patterns can be detected even when performance metrics are relatively low (e.g., F1: 60-70%). In doing so, we provide a roadmap for validation approaches of ML classification models tailored to research in animal behaviour, and other fields with noisy, biological data. HighlightsEvaluating machine learning (ML) models must go beyond performance metrics Mislabels in validation data leads to underestimation of models performance Underestimated metrics can cause research delays despite models being useful We propose simulations and biological validations to evaluate model performance Models with low standard metrics can still be powerful for hypothesis testing

ecology↗

bioRxiv2024

Collective intelligence facilitates emergent resource partitioning through frequency dependent learning

Deciding where to forage must not only account for variation in habitat quality, but also where others might forage. Recent studies have suggested that when individuals remember recent foraging outcomes, negative frequency-dependent learning can allow them to avoid resources exploited by others (indirect competition). This process can drive the emergence of consistent differences in resource use (resource partitioning) at the population level. However, indirect cues of competition can be difficult for individuals to sense. Here, we propose that information pooling through collective decision-making--i.e. collective intelligence--can allow populations of group-living animals to more effectively partition resources relative to populations of solitary animals. We test this hypothesis by simulating (i) individuals preferring to forage where they were recently successful, and (ii) cohesive groups that choose one resource using a majority rule. While solitary animals can partially avoid indirect competition through negative frequency-dependent learning, resource partitioning is more likely to emerge in populations of group-living animals. Populations of larger groups also better partition resources than populations of smaller groups, especially in environments with more choices. Our results give insight into the value of long- vs. short-term memory, home range sizes, and the evolution of specialisation, optimal group sizes, and territoriality.

ecology↗