Biology subjects

Fahimipour, A. K.

Publications and source records attributed to Fahimipour, A. K..

11 recordsLinked to original sources

bioRxiv2026

Collective behavior drives diversification across the tree of ray-finned fishes

Trait evolution and lineage diversification are thought to be driven by selection on the phenotypes of individuals. However, many important phenotypes are only manifested when individuals interact. Collective behavior is one such phenotype. During collective behavior, interactions among individuals lead to emergent group-level phenotypes that can govern individual fitness. Yet, whether collective behaviors exhibit similar patterns of trait evolution to individual traits and whether these group phenotypes have influenced macroevolutionary diversification is unknown. In ray-finned fishes, the most species-rich of all vertebrate classes, many species exhibit striking displays of collective behavior in the form of collective movement. Here, we use state-of-the-art machine learning methods for text and image analysis to quantify collective movement phenotypes for 2,839 Actinopterygian fishes, comprising nearly 10% of all extant species. We find that collective movement is highly evolutionarily stable in some clades and extremely labile in others, exhibiting over 300 evolutionary transitions. Consistent with theoretical predictions, collective movement is associated with more patchy diets and higher predation pressure. Finally, we find that the evolution of collective movement coincides with an increase in net macroevolutionary diversification rate. Our analyses reveal that collective behavior is deeply intertwined with ecological and evolutionary dynamics across a vast phylogenetic scale, unlocking new questions about the role of collective phenotypes in shaping the diversity of life on Earth. SignificanceCollective behavior is thought to contribute to the fitness of individuals that live in groups, but we still do not understand why some species exhibit collective behavior and others do not. We developed machine learning methods to quantify collective phenotypes for thousands of species spanning the phylogeny of ray-finned fishes and found collective behavior to be widespread and consistently correlated with experiencing higher predation pressure and foraging for grouping prey. Lineages that evolve collective movement show a higher net diversification rate. Our results illustrate that collective behavior is foundational to the broad-scale patterns of evolution and diversification in the largest vertebrate class.

evolutionary biology↗

bioRxiv2026

Collective learning and manifold behaviors in predator groups

Collective foraging in animal groups often relies on behavioral diversity, with individuals adopting different, sometimes complementary roles during shared tasks. However, most theoretical models predict that individuals responding to similar information cues in a shared environment should converge on a single optimal behavioral strategy. Using a spatially explicit multi-agent deep reinforcement learning model embedded in a three-species food chain, we show that stable behavioral diversity can emerge spontaneously among initially naive agents. Rather than converging on a single optimum, agents differentiate along a low-dimensional manifold of sensorimotor control, reflecting tradeoffs in speed regulation, spatial exploration, and deterministic turning rules. While multiple strategies yield comparable individual energetic returns, they are not interchangeable; group performance depends on how specific strategies combine to produce spatial resource partitioning and distributed directional influence. Replacing co-learned individuals with similarly competent agents trained in other groups disrupts these interaction structures and strongly reduces total energy acquisition. These results demonstrate that coordinated collective behavior and diverse, compatible strategies can arise endogenously from shared learning histories, but that this form of collective performance is path dependent and may be fragile to changes in group composition.

ecology↗

bioRxiv2025

Generalized dynamics of cooperating bacteria

Bacterial cooperation involves the exchange of metabolites, which can range from costless byproducts of metabolism to intentionally produced and costly molecules. These interactions occur across spatial scales, from direct cell contact to the diffusion of metabolites in the environment. Due in part to this variety of interaction modes, the impacts of mutualism on bacterial community dynamics remain unclear. Using generalized models, we derive conditions for the onset of different dynamical behaviors in communities of bacterial cooperators across these scenarios. These include exchanges of low-cost metabolites, costly cross-feeding, and cases where bacteria produce either most or only a small fraction of available metabolites. Stability depends strongly on metabolic production costs and the balance between metabolite uptake and production. We further show that perturbations to bacteria have larger impacts than to metabolites. Finally, we demonstrate that spatial metabolite diffusion drives pattern formation, emphasizing the links between local stability and spatial structures in bacterial cooperation.

ecology↗

bioRxiv2024

Behavioral plasticity and the valence of indirect interactions

Behavioral plasticity in animals influences direct species interactions, but its effects can also spread unpredictably through ecological networks, creating indirect interactions that are difficult to anticipate. We use coarse-grained models to investigate how changes in species behavior shape these indirect interactions and influence the broader dynamics of ecological networks. As an illustrative example, we examine predators that feed on two types of prey, each of which temporarily reduces activity after evading an attack, thereby lowering vulnerability at the expense of growth. We demonstrate that this routine behavior can shift the indirect interaction between prey species from apparent competition to mutualism or parasitism. These shifts occur when predator capture efficiency drops below a critical threshold, causing frequent hunting failures. As a result, one prey species indirectly promotes the growth of the other by relaxing its density dependence through a cascade of network effects, paradoxically increasing predator biomass despite decreased hunting success. Empirical capture probabilities often fall within the range where such dynamics are predicted. We characterize these shifts in the qualitative nature of species interactions as changes in interaction valence, highlighting how routine animal behaviors reshape community structure through cascading changes within ecological networks.

ecology↗

bioRxiv2022

Demystifying image-based machine learning: A practical guide to automated analysis of field imagery using modern machine learning tools

Image-based machine learning methods are quickly becoming among the most widely-used forms of data analysis across science, technology, and engineering. These methods are powerful because they can rapidly and automatically extract rich contextual and spatial information from images, a process that has historically required a large amount of manual labor. The potential of image-based machine learning methods to change how researchers study the ocean has been demonstrated through a diverse range of recent applications. However, despite their promise, machine learning tools are still under-exploited in many domains including species and environmental monitoring, biodiversity surveys, fisheries abundance and size estimation, rare event and species detection, the study of wild animal behavior, and citizen science. Our objective in this article is to provide an approachable, application-oriented guide to help researchers apply image-based machine learning methods effectively to their own research problems. Using a case study, we describe how to prepare data, train and deploy models, and avoid common pitfalls that can cause models to underperform. Importantly, we discuss how to diagnose problems that can cause poor model performance on new imagery to build robust tools that can vastly accelerate data acquisition in the marine realm. Code to perform our analyses is provided at https://github.com/heinsense2/AIO_CaseStudy

ecology↗

bioRxiv2022

Wild animals suppress the spread of socially-transmitted misinformation

Understanding the mechanisms by which information and misinformation spread through groups of individual actors is essential to the prediction of phenomena ranging from coordinated group behaviours [1-3] to global misinformation epidemics [4-7]. Transmission of information through groups depends on the decision-making strategies individuals use to transform the perceived actions of others into their own behavioural actions [8-10]. Because it is often not possible to directly infer these strategies in situ, most studies of behavioural spread in groups assume individuals make decisions by pooling [7, 8, 10, 11] or averaging [8, 9] the actions or behavioural states of neighbours. Whether individuals adopt more sophisticated strategies that exploit socially-transmitted information, while remaining robust to misinformation exposure, is unknown. Here we uncover the impacts of individual decision-making on misinformation spread in natural groups of wild coral reef fish, where misinformation occurs in the form of false alarms that can spread contagiously. Using automated tracking and visual field reconstruction, we infer the precise sequences of socially-transmitted stimuli perceived by each individual during decision-making. Our analysis reveals a novel feature of decision-making essential for controlling misinformation spread: dynamic adjustments in sensitivity to socially-transmitted cues. We find that this property can be achieved by a simple and biologically widespread decision-making circuit. This form of dynamic gain control makes individual behaviour robust to natural fluctuations in misinformation exposure, and radically alters misinformation spread relative to predictions of widely-used models of social contagion.

ecology↗

bioRxiv2022

Quantification of metabolic niche occupancy dynamics in a Baltic Sea bacterial community

Progress in molecular methods has enabled the monitoring of bacterial populations in time. Nevertheless, understanding community dynamics and its links with ecosystem functioning remains challenging due to the tremendous diversity of microorganisms. Conceptual frameworks that make sense of time-series of taxonomically-rich bacterial communities, regarding their potential ecological function, are needed. A key concept for organizing ecological functions is the niche, the set of strategies that enable a population to persist and define its impacts on the surroundings. Here we present a framework based on manifold learning, to organize genomic information into potentially occupied bacterial metabolic niches over time. We apply the method to re-construct the dynamics of putatively occupied metabolic niches using a long-term bacterial time-series from the Baltic Sea, the Linnaeus Microbial Observatory (LMO). The results reveal a relatively low-dimensional space of occupied metabolic niches comprising groups of taxa with similar functional capabilities. Time patterns of occupied niches were strongly driven by seasonality. Some metabolic niches were dominated by one bacterial taxon whereas others were occupied by multiple taxa, and this depended on season. These results illustrate the power of manifold learning approaches to advance our understanding of the links between community composition and functioning in microbial systems. ImportanceThe increase in data availability of bacterial communities highlights the need for conceptual frameworks to advance our understanding of these complex and diverse communities alongside the production of such data. To understand the dynamics of these tremendously diverse communities, we need tools to identify overarching strategies and describe their role and function in the ecosystem in a comprehensive way. Here, we show that a manifold learning approach can coarse grain bacterial communities in terms of their metabolic strategies and that we can thereby quantitatively organize genomic information in terms of potentially occupied niches over time. This approach therefore advances our understanding of how fluctuations in bacterial abundances and species composition can relate to ecosystem functions and it can facilitate the analysis, monitoring and future predictions of the development of microbial communities.

microbiology↗

bioRxiv2021

Body size dependent dispersal influences stability in heterogeneous metacommunities

Body size affects key biological processes across the tree of life, with particular importance for food web dynamics and stability. Traits influencing movement capabilities depend strongly on body size, yet the effects of allometrically-structured dispersal on food web stability are less well understood than other demographic processes. Here we study the stability properties of spatially-arranged model food webs in which larger bodied species occupy higher trophic positions, while species body sizes also determine the rates at which they traverse spatial networks of heterogeneous habitat patches. Our analysis shows an apparent stabilizing effect of positive dispersal rate scaling with body size compared to negative scaling relationships or uniform dispersal. However, as the global coupling strength among patches increases, the benefits of positive body size-dispersal scaling disappear. A permutational analysis shows that breaking allometric dispersal hierarchies while preserving dispersal rate distributions rarely alters qualitative aspects of metacommunity stability. Taken together, these results suggest that the oft-predicted stabilizing effects of large mobile predators may, for some dimensions of ecological stability, be attributed to increased patch coupling per se, and not necessarily coupling by top trophic levels in particular.

ecology↗

bioRxiv2019

Moving microbes: the dynamics of transient microbial residence on human skin

The human skin microbiome interacts intimately with human health, yet the drivers of skin microbiome composition and diversity are not well-understood. The composition of the skin microbiome has been characterized as both highly variable and relatively stable, depending on the time scale under consideration, and it is not clear what role contact with environmental sources of microbes plays in this variability. We experimentally mimicked human skin contact with two common environmental sources of microorganisms -- soils and plant leaves -- and characterized the dynamics of microbial acquisition and persistence on skin on very short time scales. Repeatable changes in skin community composition following encounters with environmental sources were observed, and these trajectories largely depend on donor community biomass distributions. Changes in composition persisted for at least 24 hours and through a soap and water wash following exposures to relatively high biomass soil communities. In contrast, exposures to lower biomass leaf communities were undetectable after a 24 hour period. Absolute abundances of bacterial taxa in source communities predicted transmission probabilities and residence times, independent of phylogenetic considerations. Our results suggest that variability in the composition of the skin microbiome can be driven by transient encounters with common environmental sources, and that these relatively transient effects can persist when the source is of sufficient biomass.\n\nImportanceHumans come into contact with environmental sources of microbes, such as soil or plants, constantly. Those microbial exposures have been linked to health through training and modulation of the immune system. While much is known about the human skin microbiome, the short term dynamics after a contact event, such as touching soil, have not been well characterized. In this study, we examine what happens after such a contact event, describing trends in microbial transmission to and persistence on the skin. Additionally, we use computational sampling model simulations to interrogate null expectations for these kinds of experiments. This work has broad implications for infection control strategies and therapeutic techniques that rely on modification of the microbiome, such as probiotics and faecal transplantation.

microbiology↗

bioRxiv2018

Omnivory does not preclude strong trophic cascades

Omnivory has been cited as an explanation for why trophic cascades are weak in many ecosystems, but empirical support for this prediction is equivocal. Compared to predators that feed only on herbivores, top omnivores -- species that feed on both herbivores and primary producers -- have been observed generating cascades ranging from strong, to moderate, null, and negative. To gain intuition about the sensitivity of cascades to omnivory, we analyzed models describing systems with top omnivores that display either fixed or flexible diets, two foraging strategies that are supported by empirical observations. We identified regions of parameter space wherein omnivores following a fixed foraging strategy, with herbivores and producers comprising a constant proportion of the diet, non-intuitively generate stronger cascades than predators that are otherwise demographically identical: (i) high productivity relative to herbivore mortality, and (ii) small discrepancies in producer versus herbivore reward create conditions in which cascades are stronger with moderate omnivory. In contrast, flexible omnivores that attempt to optimize per capita growth rates during search never induce cascades that are stronger than the case of predators. Although we focus on simple models, the consistency of these general patterns together with prior empirical evidence suggests that omnivores should not be uniformly ruled out as agents of strong trophic cascades.

ecology↗

bioRxiv2016

Global-scale structure of the eelgrass microbiome

Plant-associated microorganisms are essential for their hosts' survival and performance. Yet, most plant microbiome studies to date have focused on terrestrial plant species sampled across relatively small spatial scales. Here we report results of a global-scale analysis of microbial communities associated with leaf and root surfaces of the marine eelgrass Zostera marina throughout its range in the Northern Hemisphere. By contrasting host microbiomes with those of their surrounding seawater and sediment communities, we uncovered the structure, composition and variability of microbial communities associated with Z. marina. We also investigated hypotheses about the mechanisms driving assembly of the eelgrass microbiome using a whole-genomic metabolic modeling approach. Our results reveal aboveground leaf communities displaying high variability and spatial turnover, that strongly mirror their adjacent coastal seawater microbiomes. In contrast, roots showed relatively low spatial turnover and were compositionally distinct from surrounding sediment communities -- a result largely driven by the enrichment of predicted sulfur-oxidizing bacterial taxa on root surfaces. Metabolic modeling of enriched taxa was consistent with an assembly process whereby similarity in resource use drives taxonomic co-occurrence patterns on belowground, but not aboveground, host tissues. Our work provides evidence for a core Z. marina root microbiome with putative functional roles and highlights potentially disparate processes influencing microbiome assembly on different plant compartments.

microbiology↗