Biology subjects

Reina, A.

Publications and source records attributed to Reina, A..

6 recordsLinked to original sources

bioRxiv2026

Forest fragmentation, anthropogenic land use, and climate shape sand fly community composition in central Panama.

Background: American cutaneous leishmaniasis (ACL) is endemic in central Panama, where rapid land use change is transforming tropical landscapes and potentially altering the ecology of sand fly vectors. Although researchers have widely studied environmental determinants of individual vector species, community-level responses of sand fly assemblages to landscape modification remain poorly understood. We evaluated how landscape composition, fragmentation, climate, and topography influence sand fly community structure in a rural landscape of central Panama. Methodology/Principal Findings: We combined metacommunity analyses with joint species distribution models (JSDMs) to quantify species-specific and community-level responses to climatic variables, land-cover composition, and forest configuration. We collected 1,692 individuals representing 30 species, including nine anthropportunist taxa of epidemiological importance. Sand fly assemblages had strong nestedness, low species turnover, and predominantly positive residual associations. Several anthropportunist sand fly species occurred across heterogeneous landscapes, although their responses to environmental conditions differed among taxa. Land-cover composition, forest fragmentation metrics, and elevation together explained about half of the variance in species occurrence and abundance, with the remainder attributed to climate and spatial, temporal, and community-level random effects. Grassland cover contributed most strongly to sand fly occurrence and abundance, whereas artificial surfaces consistently reduced both metrics. Fragmentation effects varied by species: edge density often increased occurrence and abundance, whereas more forest patches generally suppressed populations. Increased precipitation broadly enhanced occurrence and abundance, whereas higher temperatures negatively affected several taxa. Conclusions/significance: Landscape heterogeneity predictably structures sand fly communities, and different forms of anthropogenic disturbance exert non-uniform effects on vector assemblages. The nested organization of these assemblages and the co-occurrence of several anthropophilic species suggest that multiple potential vector species may be present across heterogeneous landscapes. These findings highlight the need for landscape-informed surveillance and control strategies that prioritize grassland-forest interfaces and other disturbed ecotones where sand fly species of epidemiological importance frequently co-occur.

ecology↗

bioRxiv2025

Energetic benefits of social information for movement in patchy landscapes

Movement is costly, and animals are under strong selective pressure to move efficiently, yet, in patchy, dynamic landscapes, decision-making is inherently uncertain. We quantify the energetic savings achieved by using up-to-date information presented within social cues for reducing movement costs. We use an agent-based model, founded on realistic aeronautical rules and parametrised on the Andean condor (Vultur gryphus), to study movement in patchy landscapes. By explicitly considering altitude, flight results in a sequence of soaring and gliding in the 3D space. We investigate how the cost of movement to an overall goal varies when birds use social information from others that are either fixed in space or moving collectively to the common goal, and under different risk-taking speed strategies, from slow and cautious to fast and risky. The value of social information is operationalised as energetic savings in units of basal metabolic rate. Under low predictability, agents with intermediate risk and high social-information use exhibit lowest movement costs, with up to 41% energy savings over asocial movement. By extending classical aeronautical theory to social and variable environments we demonstrate the adaptive value of social information for efficient movement in patchy, unpredictable landscapes.

animal behavior and cognition↗

bioRxiv2022

Asynchrony rescues statistically-optimal group decisions from information cascades through emergent leaders

It is usually assumed that information cascades are most likely to occur when an early but incorrect opinion spreads through the group. Here we analyse models of confidence-sharing in groups and reveal the opposite result: simple but plausible models of naive Bayesian decision-making exhibit information cascades when group decisions are synchronous; however, when group decisions are asynchronous, the early decisions reached by Bayesian decision makers tend to be correct, and dominate the group consensus dynamics. Thus early decisions actually rescue the group from making errors, rather than contribute to it. We explore the likely realism of our assumed decision-making rule with reference to the evolution of mechanisms for aggregating social information, and known psychological and neuroscientific mechanisms.

animal behavior and cognition↗

bioRxiv2021

Magnitude-sensitive reaction times reveal non-linear time costs in multi-alternative decision-making

Optimality analysis of value-based decisions in binary and multi-alternative choice settings predicts that reaction times should be sensitive only to differences in stimulus magnitudes, but not to overall absolute stimulus magnitude. Yet experimental work in the binary case has shown magnitude sensitive reaction times, and theory shows that this can be explained by switching from linear to geometric time costs, but also by nonlinear subjective utility. Thus disentangling explanations for observed magnitude sensitive reaction times is difficult. Here for the first time we extend the theoretical analysis of geometric time-discounting to ternary choices, and present novel experimental evidence for magnitude-sensitivity in such decisions, in both humans and slime moulds. We consider the optimal policies for all possible combinations of linear and geometric time costs, and linear and nonlinear utility; interestingly, geometric discounting emerges as the predominant explanation for magnitude sensitivity.

neuroscience↗

bioRxiv2020

Negative feedback may suppress variation to improve collective foraging performance

Social insect colonies use negative as well as positive feedback signals to regulate foraging behaviour. In ants and bees individual foragers have been observed to use negative pheromones or mechano-auditory signals to indicate that forage sources are not ideal, for example being unrewarded, crowded, or dangerous. Here we propose an additional function for negative feedback signals during foraging, variance reduction. We show that while on average populations will converge to desired distributions over forage patches both with and without negative feedback signals, in small populations negative feedback reduces variation around the target distribution compared to the use of positive feedback alone. Our results are independent of the nature of the target distribution, providing it can be achieved by foragers collecting only local information. Since robustness is a key aim for biological systems, and deviation from target foraging distributions may be costly, we argue that this could be a further important and hitherto overlooked reason that negative feedback signals are used by foraging social insects.

ecology↗

bioRxiv2018

Inhibition and excitation shape activity selection: effect of oscillations in a decision-making circuit

Decision-making is a complex task and requires adaptive mechanisms that facilitate efficient behaviour. Here, we consider a neural circuit that guides the behaviour of an animal in ongoing binary choice tasks. We adopt an inhibition motif from neural network theory and propose a dynamical system characterized by nonlinear feedback, which links mechanism (the implementation of the neural circuit) and function (increasing reproductive value). A central inhibitory unit influences evidence-integrating excitatory units, which in our terms correspond to motivations competing for selection. We determine the parameter regime where the animal exhibits improved decision-making behaviour, and explain different behavioural outcomes by making the link between bifurcation analysis of the nonlinear neural circuit model and decision-making performance. We find that the animal performs best if it tunes internal parameters of the neural circuit in accordance with the underlying bifurcation structure. In particular, variation of inhibition strength and excitation-over-inhibition ratio have a crucial effect on the decision outcome, by allowing the animal to break decision deadlock and to enter an oscillatory phase that describes its internal motivational state. Our findings indicate that this oscillatory phase may improve the overall performance of the animal in an ongoing foraging task. Our results underpin the importance of an integrated functional and mechanistic study of animal activity selection.\n\nAuthor summaryOrganisms frequently select activities, which relate to economic, social and perceptual decision-making problems. The choices made may have substantial impact on their lives. In foraging decisions, for example, animals aim at reaching a target intake of nutrients; it is generally believed that a balanced diet improves reproductive success, yet little is known about the underlying mechanisms that integrate nutritional needs within the brain. In our study, we address this coupling between physiological states and a decision-making circuit in the context of foraging decisions. We consider a model animal that has the drive to eat or drink. The motivation to select and perform one of these activities (i.e. eating or drinking), is processed in artificial neuronal units that have access to information on how hungry and thirsty the animal is at the point it makes the decision. We show that inhibitory and excitatory mechanisms in the neural circuit shape ongoing binary decisions, and we reveal under which conditions oscillating motivations may improve the overall performance of the animal. Our results indicate that inefficient or pathological decision-making may originate from suboptimal modulation of excitation and inhibition in the neurobiological network.

neuroscience↗