Knepp Estate

Natural History Museum London

Sensitivity analysis for time varying ecological networks

Gonzalo Robledo

Exploring the importance of aromatic plants' extrafloral volatiles for pollinator attraction

Kantsa et al., 2025

Aromatic plants occur in many plant lineages and have widespread ethnobiological significance. Yet, the ecological significance and evolutionary origins of aromatic volatile emissions remain uncertain. Aromatic emissions have been implicated in defensive interactions but may also have other important functions. In this Viewpoint article, we propose an ecologically relevant definition for the aromatic phenotype and evaluate available evidence relating to the ecological role of aromatic emissions, focusing specifically on their role in pollinator attraction. We synthesize available literature addressing the use of extrafloral volatiles by pollinators, including evidence that aromatic plant emissions are primary foraging cues for some species, and present new behavioral findings documenting bee attraction to the aromatic lemon thyme in the absence of flowers. We highlight recent ecological research showing that aromatic species are highly influential in Mediterranean plant–pollinator communities and their emissions predict key interactions, particularly with bees. Based on the available evidence, we hypothesize that aromatic plants represent a form of chemical aposematism, wherein high levels of constitutive defense enable signaling phenotypes that convey information to both potential antagonists and mutualists. Finally, we outline future research priorities to clarify the role of aromatic emissions in information ecology and explore their application in agricultural systems.

https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.70496

Threats to conservation from artificial-intelligence-generated wildlife images and videos 

Guerrero-Casado et al., 2025

Cada vez son más frecuentes los videos de animales generados por IA ¿Cuáles son las consecuencias de esto? Este es precisamente en tema que se trabaja en el artículo. 

Resumen generado por IA del artículo:

Vínculo al articulo: 

https://conbio.onlinelibrary.wiley.com/doi/full/10.1111/cobi.70138

Vínculos a algunos videos de comportamientos animales generados por IA. Algunos claramente falsos, otros más engañosos:

https://www.instagram.com/reel/DQ8rZoGiLSo/?igsh=MXgwbWpjMTJzaHFxcg==

https://www.instagram.com/reel/DPo3oIIDsOn/?igsh=MWhvaXgyNms2N2dzbQ==

https://www.instagram.com/reel/DRIMz7xFhE0/?igsh=MTV1MGFnaTdueno4eA==

https://www.instagram.com/reel/DRHs-ulDRbD/?igsh=MXV3Mmw1d3JlanpqdA==

https://www.instagram.com/reel/DRC430-kQFH/?igsh=Z24yZ3kyMHl3bmd1

https://www.instagram.com/reel/DQjlez9inh0/?igsh=MWU4NHBlMGFhOGV3

https://www.instagram.com/reel/DPG8vQ1EpOx/?igsh=MXV6b2Y3OW1qeDR3bg==

https://www.instagram.com/reel/DP1lkC4DRiP/?igsh=MXg0NDVnNGRvcXNzdA==

https://www.instagram.com/reel/DQjv_pYE-lm/?igsh=MWFydnZ4cHBhYjQ0dg==

https://www.instagram.com/reel/DQgZ5V9CWMv/?igsh=MWNha2Q4cnhjd3pwNA==

https://www.instagram.com/reel/DQ6T20Wijpf/?igsh=MXZvZnU0bTkxejg0ZQ==

https://www.instagram.com/reel/DQGTBBhjE-J/?igsh=b2ZobjJzcHpxNmZ6

https://www.instagram.com/reel/DP-AxJICC6m/?igsh=Y2g5eTI2cGYxb2Jm

https://www.instagram.com/reel/DQw05CViMQE/?igsh=MTI1dGd6NmpsNDRqZg==

https://www.instagram.com/reel/DPyRrFCjMNH/?igsh=ZjFib3RjbHVma2Vh

https://www.instagram.com/reel/DQ94EFEDlPe/?igsh=MTgyMWY2dWo5ZWt4bA==

https://www.instagram.com/reel/DRChYI0lVrG/?igsh=MXZ4MHlmOTQwZjFjdQ==

https://www.instagram.com/reel/DP1UHr4k4aO/?igsh=MTlkN3B5bXp0NDR3dA==

https://www.instagram.com/reel/DQ7V2JTgSbi/?igsh=azU2aGpiN3k0aWJz

https://www.instagram.com/reel/DRF07uajRzI/?igsh=MTllZXVvdXh0eXFhaw==

https://www.instagram.com/reel/DRARE25EjIj/?igsh=bG0wd3B6bGVvZDd3

https://www.instagram.com/reel/DPbkwKoDJhx/?igsh=NTl0NXBwNWo4ajVi

Entanglements of art and ecology 

Ros Gray

 A diverse and distinct microbiome inside living trees

Arnold et al., preprint

Despite significant advances in microbiome research across various environments, the microbiome of Earth’s largest biomass reservoir– the wood of living trees– remains largely unexplored. This oversight neglects a critical aspect of global biodiversity and potentially key players in tree health and forest ecosystem functions. Here we illuminate the microbiome inhabiting and adapted to wood, and further specialized to individual host species. We demonstrate that a single tree can host approximately a trillion microbes in its aboveground internal tissues, with microbial communities partitioned between heartwood and sapwood, each maintaining a distinct microbiome with minimal similarity to other plant tissues or nearby ecosystem components. Notably, the heartwood microbiome emerges as a unique ecological niche, distinguished in part by endemic archaea and anaerobic bacteria that drive consequential biogeochemical processes. Our research supports the emerging idea of a plant as a “holobiont”—a single ecological unit comprising host and associated microorganisms—and parallels human microbiome research in its implications for host health, disease, and functionality. By mapping the structure, composition, and potential sources and functions of the tree internal microbiome, our findings pave the way for novel insights into tree physiology and forest ecology, and establish a new frontier in environmental microbiology.

Overview of the black oak (Quercus velutina) prokaryotic microbiome. a.) Relative abundance of the top 9 prokaryotic classes (all other classes grouped in beige) in the a) bark, b) sapwood, c) heartwood, d) fine roots, e) coarse roots, f) mineral soil, g) organic soil, h) leaf litter, i) heart-rot, j) branches, and k) leaves. Source-tracking percent estimations (out of 1 or 100%) for microbial contribution from neighboring sites to the b.) heartwood and c.) sapwood microbiomes, based on FEAST analyses (taxa agglomerated at the species level). Mean value represented by the colored dot; SE represented by the bar. d.) Principal coordinate analysis for black oak tissues and surrounding environments, based on weighted UniFrac distance, with dashed lines converging on the centroid for each sample type.

https://www.biorxiv.org/content/10.1101/2024.05.30.596553v1

Discover network dynamics with neural symbolic regression

Yu et al., 2025

Discovering network dynamics—automatically

Complex systems are everywhere: from ecosystems and gene networks to epidemics and social behavior. Each consists of many interacting components whose collective dynamics are notoriously difficult to model. Traditionally, scientists have relied on intuition and simplified equations—but for most real systems, the governing laws remain unknown.

Zihan Yu, Jingtao Ding & Yong Li  introduce ND2, a neural symbolic regression framework that discovers network dynamics directly from data. The key insight is to reduce the overwhelming search space of possible equations—normally exploding with the number of nodes—to an equivalent one-dimensional symbolic problem. ND2 combines graph neural networks and transformers (via a pretrained model called NDformer) with Monte Carlo tree search to automatically uncover concise, interpretable formulas that explain how networks evolve.

Applied to ten benchmark systems, ND2 correctly recovered the governing equations of classics such as Kuramoto oscillators, FitzHugh–Nagumo neurons, and Lotka–Volterra populations. More impressively, when tested on real data, it corrected existing biological models—reducing prediction errors by 60% in gene regulation and 56% in microbial communities—by revealing hidden higher-order interactions. In epidemic networks, ND2 uncovered transmission laws that explain cross-country differences in intervention effects and even captured the same power-law patterns across scales.

This study demonstrates how machine-driven discovery can bridge observational data and theoretical understanding, advancing complexity science from describing what we observe to deriving why it happens—directly from the data itself.

Abstract:

Network dynamics are fundamental to analyzing the properties of high-dimensional complex systems and understanding their behavior. Despite the accumulation of observational data across many domains, mathematical models exist in only a few areas with clear underlying principles. Here we show that a neural symbolic regression approach can bridge this gap by automatically deriving formulas from data. Our method reduces searches on high-dimensional networks to equivalent one-dimensional systems and uses pretrained neural networks to guide accurate formula discovery. Applied to ten benchmark systems, it recovers the correct forms and parameters of underlying dynamics. In two empirical natural systems, it corrects existing models of gene regulation and microbial communities, reducing prediction error by 59.98% and 55.94%, respectively. In epidemic transmission across human mobility networks of various scales, it discovers dynamics that exhibit the same power-law distribution of node correlations across scales and reveal country-level differences in intervention effects. These results demonstrate that machine-driven discovery of network dynamics can enhance understandings of complex systems and advance the development of complexity science.

https://www.nature.com/articles/s43588-025-00893-8

Rafael Araujo

GEOMETRY/Flight

http://rafael-araujo.com

AI is helping to decode animals’ speech. Will it also let us talk with them?

Rachel Fieldhouse

Deep in the rainforests of the Democratic Republic of the Congo, Mélissa Berthet found bonobos doing something thought to be uniquely human.

During the six months that Berthet observed the primates, they combined calls in several ways to make complex phrases1. In one example, bonobos (Pan paniscus) that were building nests together added a yelp, meaning ‘let’s do this’, to a grunt that says ‘look at me’. “It’s really a way to say: ‘Look at what I’m doing, and let’s do this all together’,” says Berthet, who studies primates and linguistics at the University of Rennes, France.

In another case, a peep that means ‘I would like to do this’ was followed by a whistle signalling ‘let’s stay together’. The bonobos combine the two calls in sensitive social contexts, says Berthet. “I think it’s to bring peace.”

The study, reported in April, is one of several examples from the past few years that highlight just how sophisticated vocal communication in non-human animals can be. In some species of primate, whale and bird, researchers have identified features and patterns of vocalization that have long been considered defining characteristics of human language. These results challenge ideas about what makes human language special — and even how ‘language’ should be defined.

Perhaps unsurprisingly, many scientists turn to artificial intelligence (AI) tools to speed up the detection and interpretation of animal sounds, and to probe aspects of communication that human listeners might miss. “It’s doing something that just wasn’t possible through traditional means,” says David Robinson, an AI researcher at the Earth Species Project, a non-profit organization based in Berkeley, California, that is developing AI systems to decode communication across the animal kingdom.

As the research advances, there is increasing interest in using AI tools not only to listen in on animal speech, but also to potentially talk back.

Continue reading:

https://www.nature.com/articles/d41586-025-02917-9

Common mycorrhizal networks facilitate plant disease resistance by altering rhizosphere microbiome assemblyAuthor links open overlay panel

Zhang et al., 2025

Arbuscular mycorrhizal fungi can interconnect the roots of individual plants by forming common mycorrhizal networks (CMNs). These symbiotic structures can act as conduits for interplant communication. Despite their importance, the mechanisms of signal transfer via CMNs and their implications for plant community performance remain unknown. Here, we demonstrate that CMNs act as a pathway to elicit defense responses in healthy receiver plants connected to pathogen-infected donors. Specifically, we show that donor plants infected by the phytopathogen Botrytis cinerea transfer jasmonic acid via CMNs, which then act as a chemical signal in receiver plants. This signal transfer to receiver plants induces shifts in root exudates, promoting the recruitment of specific microbial taxa (Streptomyces and Actinoplanes) that are directly linked to the suppression of B. cinerea infection. Collectively, our study reveals that CMNs act as interplant chemical communication conduits, transferring signals that contribute to plant disease resistance via modulation of the rhizosphere microbiota.

https://www.sciencedirect.com/science/article/pii/S1931312825003427