The effect of temporal variability on the stability of species interactions

Violeta Calleja-Solanas 

Functional team selection as a framework for local adaptation in plants and their belowground microbiomes 

Nancy Collins and César Marín Johnson

The paper presents functional team selection (FTS) as a major conceptual advance in plant–microbiome ecology. FTS explains how limiting resources and/or stress selects cooperative microbial teams that promote plant adaptation, integrating ecological feedback and evolutionary selection to predict when and where resilient plant–microbiome partnerships will arise.

Abstract

Multicellular organisms are hosts to diverse communities of smaller organisms known as microbiomes. Plants have distinctive microbiomes that can provide important functions related to nutrition, defense, and stress tolerance. Empirical studies provide convincing evidence that in some—but not all—circumstances, belowground microbiomes help plants adapt to their local environment. The purpose of this review is to develop functional team selection (FTS) as a framework to help predict the conditions necessary for root microbiomes to generate local adaptation for their plant hosts. FTS envisions plants and their microbiomes as complex adaptive systems, and plant adaptations as emergent properties of these systems. If plants have the capacity to recognize and cultivate beneficial microbes and suppress pathogens, then it is possible for plants to evolve the capacity to gain adaptations by curating their microbiome. In resource-limited and stressful environments, the emergent functions of complex microbial systems may contribute to positive feedback linked to plant vigor, and ultimately, local adaptation. The key factors in this process are: (i) selective force, (ii) host constitution, (iii) microbial diversity, and (iv) time. There is increasing interest in harnessing beneficial microbial interactions in agriculture and many microbial growth-promoting products are commercially available, but their use is controversial because a large proportion of these products fail to consistently enhance plant growth. The FTS framework may help direct the development of durable plant-microbiome systems that enhance crop production and diminish pathogens. It may also provide valuable insights for understanding and managing other kinds of host-microbe systems.

https://academic.oup.com/ismej/article/19/1/wraf137/8182121?login=false

Landscape and crop diversity contributes to greater yield stability

Tobajas et al., 2025.

1. Maintaining stable agricultural production is a critical challenge for food security. Stable yields depend not only on climatic variables, but also on agricultural landscape management. While agricultural intensification can increase productivity in the short term, it often reduces long-term yield stability due to reduced crop diversity and the loss of semi-natural habitats.
2. This study investigates the relationships between landscape heterogeneity, climatic variables and temporal stability of crop yields across Spain. Using an extensive national dataset of productivity for 31 crops from 2013 to 2019, we analysed how landscape composition (crop richness, semi-natural habitat cover) and configuration (field size, edge density), along with climatic factors (precipitation, temperature, water deficit), influence yield stability.
3. Our results show that yield stability is influenced by climatic factors and landscape characteristics. Greater land-use heterogeneity and stable precipitation favour yield stability. Furthermore, moderate within-season precipitation concentrations also improved yield stability. We also detected interactive effects between crop pollinator dependence and landscape-level crop diversity and climate. Pollinator-dependent crops showed greater stability with increasing crop diversity and variable temperatures, while non-pollinator-dependent crops benefited from simpler crop areas and stable temperatures.
4. Synthesis and applications. These findings underscore the importance of promoting crop diversity and maintaining heterogeneous agricultural landscapes, particularly in pollinator-dependent crops. Promoting diverse agricultural landscapes with balanced heterogeneity can enhance the resilience of agricultural systems to climate change and contribute to long-term food security.

https://review.frontiersin.org/review/1569843/14/894232

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