Latest Articles

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  Section: Ecology ; Topics: Ecology, Population biology, Statistics

  Disentangling different sources of variation in functional responses: between-individual variability, measurement error and inherent stochasticity of the prey-predator interaction process

  Baey, Charlotte;   Billiard, Sylvain;   Delattre, Maud  

  10.24072/pcjournal.729 - Peer Community Journal, Volume 6 (2026), article no. e53

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  The consumption rate of prey by predators, or functional responses, are known to be highly variable even within a single population. Identifying and estimating the different sources of variation of functional responses is a long-standing challenge. We develop here a statistical framework derived from a mechanistic stochastic process model that explicitly accounts for different sources of variation. We apply it to disentangle and estimate in particular 1) residual variance due to measurement errors and model misspecification, 2) between-predator variability, and 3) the interaction stochasticity,  i.e. the intrinsic and mechanistic variability due to interactions processes between prey and predators. We show that it is possible to estimate these sources of variation under realistic experimental conditions. Our results also show that model fitting can compensate by overestimating residual source of variation, leading to biased parameter estimates when interaction stochasticity is misspecified. Applied to empirical data, the model reveals that standard assumptions, such as prey renewal and lack of spatial structure, fail to capture observed variability. We also show how experimental design affects parameter identifiability, highlighting the trade-off between the number of individuals and repeated observations.

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  Section: Ecology ; Topics: Ecology, Statistics

  Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area

  Lasgorceux, Florian;   Papaïx, Julien;   Bunz, Yoann;   Combrisson, Damien;   Opitz, Thomas  

  10.24072/pcjournal.731 - Peer Community Journal, Volume 6 (2026), article no. e52

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  Over the recent decades, Europe has experienced a significant decline in common bird species, particularly farmland species, due to anthropic pressures like agricultural intensification. Protected areas, such as the Écrins National Park (ENP) in France, can help mitigate these impacts. We evaluated whether an opportunistic presence-only dataset collected by trained ENP rangers contains biological signals strong enough to support robust statistical inference. Using a generalized additive Poisson model with spatial and spatio-temporal covariates, monthly latent spatio-temporal Gaussian random fields, and a non-spatial inter-annual effect, we estimated the relative abundance of 76 passerine species on a regular grid, with occurrences aggregated per spatio-temporal cell used as a proxy for sampling effort. The model showed good calibration for most species ($\text{AUC} > 0.8$) and reliably captured habitat preferences and migratory status. Relative-abundance trends in ENP were compared with relative abundance from three monitoring programs: STOM (ENP), STOC (France), and MHB (Switzerland). For most species with significant trends, model predictions aligned with survey-based trends. Forest specialists benefited most from the protected-area status, and farmland species declined more slowly in ENP than in France. High-elevation specialists generally decreased in both ENP and Switzerland. Discrepancies mostly arose for common species, likely reflecting uncorrected declines in ranger reporting rates. These results demonstrate that high-resolution opportunistic presence-only data can provide valuable insights into biological patterns and trends while reducing reliance on external data to estimate sampling effort.

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  Section: Ecology ; Topics: Ecology

  Tracking changes in birds' interaction milieu

  Rigal, Stanislas;   Devictor, Vincent; Dakos, Vasilis  

  10.24072/pcjournal.730 - Peer Community Journal, Volume 6 (2026), article no. e51

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  As biodiversity is declining, the dynamics of species interactions is a growing conservation concern. However, estimating and monitoring explicit species interactions across large spatial and temporal scales remain challenging. An alternative and yet under-explored approach is to track whether and how the interaction milieu, defined as the background of all realised interactions, is changing in space and time. Here, we assess changes in the interaction milieu of common bird communities in France. We estimate associated species pairs using spatial and temporal information for 109 species monitored across 1,969 sites during 17 years. We validate the ecological significance of associated species pairs by testing the relationship between the propensity to be associated and species functional proximity or shared habitat preference. We reconstruct association networks for these intra-guild bird communities and track temporal changes in network layout in terms of size, density of links, modularity and degree distribution. We show that, beyond changes usually documented based on species numbers and abundances, the interaction milieu is also changing non-randomly. Communities become smaller with a similar relative number of associations that becomes unevenly distributed through time. These structural changes vary among bird communities according to their habitat and may impact community functioning and how communities can cope with global change.

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  Section: Microbiology ; Topics: Microbiology, Ecology, Environmental sciences

  Consensus statement from the second RdRp Summit: towards a unified framework for RNA virus biology

  Lucaci, Alexander G;   Shaikh , Hisham;   Chong, Li Chong;   Tahzima, Rachid;   Forgia, Marco;   Ben Mansour, Karima;   Sakaguchi, Shoichi;   Nakagawa, So;   Hou, Xin;   Demina, Tatiana;   Raj Jayaraj Mallika, Fhilmar;   Kupczok, Anne;   Lytras, Spyros;   Debat, Humberto;   Charon, Justine;   Urzo, Michael;   Raco, Milica;   Kim, Rachel;   Rivero, Ricardo;   Karapliafis, Dimitris;   Sirkinti, Leyla;   Luebbert, Laura;   Nishimura, Luca;   Chikhi, Rayan;   De Coninck, Lander;   Charriat, Florian;   Soufir, Emma;   Gajdov, Vladimir;   Krannich, Thomas;   Dudas, Gytis;   Lood, Cédric;   Rodríguez-Ramos, Josue;   Pecman, Anja;   Neri, Uri;   Werner, Almut;   Le, Mia;   Osundahunsi, Bolaji;   Petersen, Nils;   Maclot, François;   Gutierrez, Serafin;   Paraskevopoulou, Sofia;   Hillary, Luke;   Olendraite, Ingrida  

  10.24072/pcjournal.727 - Peer Community Journal, Volume 6 (2026), article no. e50

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  RNA-dependent RNA polymerase, or RdRp, remains the central molecular hallmark of RNA viruses. It serves as both a universal anchor for virus detection and a critical target for understanding the functional and evolutionary properties of RNA viruses. Since the inaugural RdRp summit in 2023, there have been significant advances in sequencing, structural prediction and artificial intelligence, all of which have accelerated the pace of RNA virus discovery and taxonomic annotation, revealing unprecedented levels of viral diversity, including novel phyla and unique genome architectures. Recent advances include the discovery of novel viral phyla such as Ambiviricota and the application of AI-driven models like LucaProt, highlighting both the rapid expansion of viral diversity and the growing role of machine learning in RNA virus research. The second RdRp summit, which was held in Lisbon in May 2025, gathered a group of research scientists from diverse subfields of virology to address emerging challenges in RNA virus biology. These challenges ranged from standardising annotation and data sharing to harnessing structure-guided phylogenetics and petabyte-scale computational tools. Here, our consensus statement outlines key progress, current and future challenges and community-driven initiatives, including benchmarking, virus-host inference, and ongoing knowledge exchange efforts - all of which are designed to unify the field. Importantly, this statement reflects a clear community consensus and provides concrete recommendations to prioritize standardized benchmarking, structure-informed evolutionary analysis, and reproducible virus–host inference as foundational pillars for advancing RNA virus research. By fostering an environment of sustained collaboration, our efforts aim to build a coherent framework for modern RNA virus biology and to accelerate the exploration of the hidden RNA virosphere.