Functional-structural plant models simulate plant responses to environmental conditions, but their development and evaluation are often limited by the lack of datasets combining detailed architectural and physiological measurements. Here, we present a comprehensive dataset acquired from four oil palm plants ( Elaeis guinnensis) grown under controlled and contrasting climate scenarios. The dataset includes (i) three-dimensional reconstructions of plant architecture derived from terrestrial lidar point clouds, (ii) leaf-level gas exchange measurements used to parameterize photosynthesis and stomatal conductance models, and (iii) continuous plant-scale measurements of CO 2 and H 2 O fluxes obtained in a microcosm under precisely monitored and manipulated environmental conditions (light, temperature, humidity, and CO 2 concentration) across height climate scenarios. By combining detailed structural data with physiological measurements at both leaf and whole-plant scales, this database has been designed to build and evaluate digital twins (or shadows) of plants functioning under controlled conditions. It provides a valuable resource for calibrating biophysical models (light interception and photosynthesis), benchmarking model predictions across scales, and investigating the consistency between leaf-level parameterization and plant-level fluxes. All data and processing workflows are openly available, facilitating reuse for model development, evaluation, and intercomparison in plant and crop modelling communities.
Understanding bacterial communities responsible for the slow biodeterioration of waterlogged archaeological wood is critical for reconstructing decay processes, predicting preservation trajectories, and informing conservation strategies. The extraction of sufficient and amplifiable bacterial DNA from waterlogged archaeological wood is essential for robust sequencing and accurate bacterial community profiling. However, DNA yield and inferred community composition can vary depending on the extraction protocol used. In this study, we evaluated the performance of three commercially available DNA extraction kits - Bead-Beat Micro AX Gravity, DNeasy PowerSoil Pro and MagAttract PowerSoil Pro DNA – on samples from a single waterlogged Neolithic wooden pole recovered from a submerged archaeological site in Denmark and preserved under anoxic conditions. Quantitative analysis showed that the Bead-beat Micro AX Gravity kit yielded the highest DNA concentrations by Qubit fluorometry and lowest PCR cycle number in quantitative PCR. Elevated DNA signals were observed in some extraction blanks during Qubit quantification; however, these blanks did not produce sequencing reads, indicating that the signal likely represented non-amplifiable material. Comparative analysis of bacterial community composition, based on 16S rRNA gene amplicon sequencing (V4 region, primers 515F/806R), revealed minimal variation between extraction methods, but distinct taxonomic profiles were observed across different sampling areas within a single archaeological wooden pole. These results indicate that while extraction protocol has limited influence on bacterial community composition in this context, spatial heterogeneity within samples can affect results. Protocols yielding higher DNA concentrations may therefore be advantageous when working with degraded archaeological materials or DNA-intensive downstream applications. However, extraction methods optimized for the recovery of highly fragmented DNA may offer additional advantages for ancient heavily degraded substrates.
The extent to which phenotypic evolution can be constrained by genetic correlations is an important question in evolutionary biology. To address this question, biological invasions are opportune models where derived, invasive populations can be compared to their extant ancestors, allowing to track the evolution of genetic correlations from the ancestor, throughout the invasion process. In this paper, we focused on the worldwide invasion of Drosophila suzukii (Matsumara, 1931) , and investigated the evolution of the genetic covariance matrix G of wing shape between ancestral native, and derived invasive populations. Leveraging demographic history resolved by population genetics approaches, we tested whether G remained stable during the invasion. Using a multivariate Q ST -F ST approach, we further tested whether or not the observed phenotypic divergence in wing shape aligned with a neutral scenario of evolution. Our results show moderate yet significant quantitative genetic differentiation of wing shape among D. suzukii populations and a relative stability in the structure of G , presenting a roughly spherical shape but slightly different volumes. These characteristics likely reflect the demographic history of populations and suggest a low level of genetic constraint on wing shape evolution. The divergence between populations was greater than expected under a purely neutral model of evolution, compatible with an effect of divergent selection among them. Overall, our study suggests that selection and drift, but not ancestral genetic constraints, affected the early stages of wing shape evolution during D. suzukii invasion.
Objectives : In a global context of increasing telework, this study explores its health effects, to determine if there is an optimal teleworking frequency during an epidemic. We aim to quantify the relationship between teleworking frequency and both infectious disease (ID) transmission and non-communicable disease (NCD) risk. Methods : We developed a mathematical model simulating ID transmission and NCD acquisition in a medium-sized company, analysing how different teleworking levels impact workers health. We conducted a rapid literature review to identify potential exposure-response relationships between teleworking and NCD risk and inform this model. We then simulated infection dynamics over a three-month epidemic wave to contrast ID and NCD risks in relation to the extent of telework engagement. Results : Evidence from the literature showed varying patterns of NCD risk across different teleworking frequencies. Depending on these relationships, we observed that risk may peak at low, intermediate, or high teleworking levels. We demonstrated the existence of a benefit-risk balance between reducing ID transmission and potentially increasing NCD burden. Conclusions :. Based on current scientific knowledge, no single optimal teleworking frequency can be identified, as the optimum varies depending on the NCD outcome considered. Our study highlights the need for stronger evidence to estimate robust exposure-response functions linking teleworking frequency and NCDs, and ultimately to inform prevention strategies for both infectious and NCD risks in an epidemic context.
Reconstructing the rules of ancient games remains one of archaeology's most intricate interpretive challenges. While surviving boards and pieces provide tangible evidence, the logic of play, rules, roles, and goals have largely vanished. Traditional reconstructions rely on text and comparisons to better known games, yet these methods seldom capture the experiential and social dimensions of ancient gameplay. This paper presents an experimental framework designed to explore how such systems might emerge through play itself. Using Ludus Latrunculorum ("The Game of Little Soldiers") as a case study, participants engaged in iterative, co-creative sessions where they invented, refined, and transmitted rules across successive lineages of play. The resulting games revealed reWhile the outcomes do not claim historical accuracy, they demonstrate how collective experimentation can illuminate the cognitive and social processes underlying the devela complementary archaeological tool for examining how rulesets evolve, stabilize, and reflect the societies that produce them.
The yellow mealworm, Tenebrio molitor ( T. molitor ), is increasingly considered a promising protein and lipid source for circular bioeconomy strategies in food and feed. Interest is driven by the need to diversify protein supplies and reduce environmental footprints, but feasibility depends on safety, regulation, and scalable operating conditions. Alongside industrial systems, low-input models adapted to arid conditions have been proposed, yet evidence remains heterogeneous and context-dependent. This review covers developments between 2020 and 2025, a period that coincides with accelerated EU novel food assessments and a rapid expansion of applied research on processing, safety, and valorization, with a focus on scientific progress and regulatory approvals such as those issued by EFSA in Europe. Several new applications have emerged, including enzymatic hydrolysates, lipid recovery, and the extraction of chitosan from exuviae. Uses now span animal nutrition, biodegradable materials, and bioactive food ingredients. Life-cycle assessments often report lower greenhouse gas emissions and land use than conventional livestock, but outcomes are sensitive to energy inputs, feed substrates, and system boundaries. Key constraints include variable frass composition, allergenicity and cross-reactivity risks, regulatory and compliance constraints, and mixed consumer acceptance. For research, priority needs include longer-term safety datasets and field-relevant validation of bioactive claims beyond in vitro assays. For policy and industry, priorities include harmonised criteria for substrate safety and traceability, and transparent supply-chain controls that enable reproducible quality at scale.
Movement analogies are often employed by ecologists to describe how ecological dynamics relate to one another. For instance, two communities whose similarity increases in time may be said to converge. Here we argue that the movement analogies used by ecologists to compare ecological dynamics could be enriched with other notions such as “pursuit” or “parallel” movements, if accompanied by appropriate statistical testing. By building on the framework of Ecological Trajectory Analysis, we present here Relative Trajectory Movement Assessment (RTMA), a framework to detect and qualify relative movements in ecological dynamics defined as trajectories in multivariate space. Using synthetic trajectory data, we illustrate how RTMA can reveal a diverse range of relative movements beyond the convergence and divergence patterns already recognized in ecology. We exemplify the use of RTMA on real ecological datasets describing 1) old field successional dynamics in eastern North America and 2) temporal patterns in tree size structure of a New Zealand forest. RTMA provides ecologists with a new way of describing and comparing ecological dynamics that could be widely applied, from plot-scale dynamics to the effects of global change.
Transparent and reproducible research in large interdisciplinary projects, such as the Cluster of Excellence ROOTS, depends on combining two perspectives that are usually considered separately: the stewardship concerns of Research Data Management (RDM) and the analytical concerns of Data Science. This paper introduces Modular Research Processes (MRP), a framework representing a research endeavour as distinct, referable products and procedures described at several layers of granularity, expressing resolution rather than a hierarchy of authority. MRP provides a shared representation in which RDM and Data Science converge, focusing on the point where (materialised) products are FAIR-ready, while their procedural counterparts are not (yet) – a discrepancy that motivates the development of systematic descriptions and, where applicable, recommendations. We organise the framework’s value into three application fields: illustration and communication; documentation and organisation; as well as comparison and recommendation. Our examinations and activities are based on the Data Management and Data Science Platform (DMDSP), which is an organisational component of the ROOTS project. We present the DMDSP as an example of how such a bridge can be shaped, aligned with an existing ecosystem, filled with collaborative method development, and sustained through training. Beyond the platform, it is MRP that is the transferable contribution, with a claim to cross-disciplinary and cross-project application. We report on two archaeological analyses that have been reconstructed as first proofs of concept, techniques for early empirical feedback on the conditions for adoption of an MRP-related tool, and a research roadmap anchored in this beginning.
The purpose of the current research was to examine the extent to which beliefs that the Confederate flag stands for remembrance, rebellion, or racism were associated with discrimination in a helping situation. Similar methods were used in a Pilot Study and a Registered Report: nationwide samples of White adults in the United States were recruited and randomly assigned to read arguments stating that the Confederate flag stands for remembrance, rebellion, or racism. Next, they were asked to report their emotional reactions toward the argument and the extent to which they wished to help a White or Black person. The Pilot Study found that participants who read, but disagreed with, arguments that the flag stands for racism were less willing to help Black than White persons. Conversely, those who disagreed with arguments that the flag stands for remembrance or rebellion were more willing to help Black than White persons. Study 1 (the Registered Report) found that participants were more willing to help a Black person but did not replicate the Pilot Study findings as the greater likelihood of helping was independent of whether they agreed with an argument about the Confederate flag and the race of the person asking for help. Concerning emotional responses to arguments, Study 1 found that disagreement with the remembrance and rebellion arguments were associated with greater discomfort, negative feelings toward others, reactance, and White guilt. Agreement with the racism argument was related to greater negative feelings toward others and White guilt. Findings contribute to understanding the significance of beliefs about Confederate symbols in the United States.
Per-and polyluoroalkylated substances (PFAS) are a group of man-made chemical substances used in everyday products and industry processes since the 1950s. They contain carbon-fluorine bonds, among the strongest in chemistry, resulting in intrinsic or indirect extreme environmental persistence and earning them the nickname "forever chemicals". In a context of growing awareness of PFAS toxicity and widespread pollution, the Forever Pollution Project (FPP), a cross-border journalistic investigation, compiled data on measured and estimated PFAS contamination across Europe, published as an interactive map. In this data paper we present the PFAS Data Hub (PDH), a project building upon the FPP dataset and reprocessing it using a more robust and transparent methodology. We incorporated several additional data sources, most of which are automatically updated on a monthly basis. To our knowledge, this constitutes the only compilation of PFAS contamination data at the European scale. It is intended to support research projects across a wide range of different disciplines, and to be used as a source of information by journalists, citizens and civil society organisations. The data, as well as a geovisualisation tool with filtering and export options, is available on the PDH website: https://pdh.cnrs.fr .
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 & Eacute;crins 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 (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.
5’ and 3’ RNA-end sequencing protocols have unlocked new opportunities to study aspects of RNA metabolism such as synthesis, maturation and degradation, by enabling the quantification of exact ends of RNA molecules in vivo . From RNA-Seq data that have been generated with one of the specialized protocols, it is possible to identify transcription start sites (TSS) and/or endoribonucleolytic cleavage sites, and even, in some cases, co-translational 5’ to 3’ degradation dynamics. Furthermore, post-transcriptional addition of ribonucleotides at the 3’ end of RNA can be studied at the nucleotide resolution. While different RNA-end sequencing library protocols exist that have been adapted to a specific organism (prokaryote or eukaryote) or specific biological question, the generated RNA-Seq data are very similar and share common processing steps. Most importantly, the major aspect of RNA-end sequencing is that only the 5’ or 3’ end mapped location is of interest, contrary to conventional RNA sequencing that considers genomic ranges for gene expression analysis. This translates to a simple representation of the quantitative data as a count matrix of RNA-end location on the reference sequences. This representation seems under-exploited and is, to our knowledge, not available in a generic package focused on the analyses on the exact transcriptome ends. Here, we present the rnaends R package which is dedicated to RNA-end sequencing analysis. It offers functions for raw read pre-processing, RNA-end mapping and quantification, RNA-end count matrix post-processing, and further downstream count matrix analyses such as TSS identification, fast Fourier transform for signal periodic pattern analysis, or differential proportion of RNA-end analysis. The use of rnaends is illustrated here with applications in RNA metabolism studies through selected rnaends workflows on published RNA-end datasets: (i) TSS identification, (ii) ribosome translation speed and co-translational degradation, (iii) post-transcriptional modification analysis and differential proportion analysis.
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.
Habitat selection is a key mechanism that enables animals to optimize their fitness in response to varying environmental conditions. Differences in habitat selection between populations in different geographical areas may indicate behavioral adaptations to local environmental conditions. Understanding the adaptive potential of species across broad geographical ranges is of primary interest to anticipate possible changes in species behavior or distribution in the context of climate change. In this study, we investigated the habitat selection and the daily movement patterns of the Eurasian Woodcock, Scolopax rusticola , a bird species that winters across widely varying climatic zones. We tracked 84 individuals wintering in the Mediterranean regions with GPS-VHF transmitters, where climate and habitat conditions differ significantly from the regions with Atlantic climate influence, which have formed the main background for the ecology and behavior of this species in winter. To assess how woodcocks responded to varying habitat and environmental conditions, we collected data across three geographical regions spanning a gradient of Mediterranean climatic influence — ranging from northern subareas with denser forest and deeper soil to southern subareas characterized by less productive forests, garrigues, and rocky soil. In the northern Mediterranean region, woodcocks visited open habitats at night less than 53% of the time and less than 40% of the time in the two other regions with a stronger Mediterranean climate influence. This behavior was much less frequent than reported in studies conducted in areas with Atlantic climate influence (>80%). Woodcocks also changed their day/night activity patterns, as illustrated by their daily movements. They increased their daytime movements (11 to 29% higher) and reduced their nocturnal movements (12 to 18% lower) in the two regions with the strongest Mediterranean climate influence. During the day, when birds used only forested areas, denser forests were preferred in all studied Mediterranean subareas. Birds used different forested habitats between subareas, especially at night. For example, denser but shorter vegetation and higher rock cover were more strongly used at night in southern subareas. These forested habitats contrasted sharply with those in areas with Atlantic climate influence, the latter being plots rich in humus and deep soils. Our findings highlight that basic ecological knowledge of species can be biased towards those known in certain types of habitats. They also underscore the remarkable behavioral flexibility of woodcocks, highlighting their potential to adapt to global change. However, the occurrence of escape movements under the driest conditions suggest that this change in behavior and habitat selection may be an early warning sign of the effects of climate change on the wintering areas. Overall, our study emphasizes the need to study the ecology of species across diverse environmental conditions to better understand their habitat requirements and adaptive capacity.
Simulation-based methods such as approximate Bayesian computation (ABC) are widely used to infer the evolutionary history of populations from molecular genetic data. We describe and evaluate a new iterative method of statistical inference about model parameters, which revisits the idea of inferring a likelihood surface using simulation when the likelihood function cannot be evaluated. It is based on combining the random forest machine learning method, and multivariate Gaussian mixture (MGM) models, in an effective inference workflow, here used to fit models with up to 15 variable parameters. In addition to the traditional assessment of precision in terms of bias and mean square error, we also evaluate the coverage of confidence intervals. The method is compared with approximate Bayesian computation using random forests (ABC-RF), a non-iterative method sharing some technical features with the proposed approach, across scenarios of historical demographic inference from population genetic data. It is also compared to another iterative method, sequential neural likelihood estimation (SNLE). These comparisons highlight the importance of an iterative workflow for exploring the parameter space efficiently. For equivalent simulation effort of the data-generating process, the new summary-likelihood method provides intervals whose coverage is better controlled than the marginal coverage of intervals provided by ABC with random forests, and than generally reported for ABC methods. The iterative workflow can also yield greater improvements in estimator precision when larger datasets are used.
Hyalomma marginatum tick establishment has been recorded in southern mainland France for around ten years. This two-host species is one of the main vectors of the Crimean-Congo haemorrhagic fever (CCHF) virus. Knowing the phenology of the tick is a prerequisite for a better understanding of its spread and of CCHF virus dispersal. A longitudinal study was carried out between 2016 and 2022 in a horse herd to determine the seasonal variations of H. marginatum infestation. In the study site, adult ticks started infesting their hosts from the beginning of March. Infestation peaked during the first weeks of May. At that period, on average, up to 4 or 5 ticks attached daily to each horse. Then, the infestation steadily decreased until the end of August. In this site, birds are parasitised by H. marginatum immatures (larvae and nymphs) in summer, from mid/late June to the end of September. The larvae are therefore not yet active when the migratory birds pass through in spring. Blackbird, Turdus merula , was the most heavily infested bird species: nearly two-thirds of the blackbirds examined in summer were parasitised by H. marginatum . It is suggested that this bird species could be a good sentinel host for monitoring the establishment of the tick in currently H. marginatum -free areas. To complete these surveys, an experimental study was carried out in quasi-natural conditions with engorged ticks placed in cages in the garrigue . It was shown that the nymphs moulted into adults within 3-4 weeks in July-August. However, they remain completely immobile in the litter, undergoing a behavioural diapause, until the following spring. On the other hand, almost all the nymphs released later, in September or October, died during the cold and rainy season. It was also observed that engorged females were able to survive the cold conditions up to 6-8 months before laying eggs. The study also suggested that there were predators of H. marginatum , likely ants and spiders, in the study site. The hosts monitored over all these years were not only infested by H. marginatum . The phenology of other tick species parasitizing horses ( Dermacentor marginatus , Haemaphysalis punctata , Rhipicephalus bursa ) and birds ( Ixodes frontalis , I. ricinus , H. punctata ) was also highlighted for this study site.
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.
Mosquito control remains the cornerstone of the prevention and control of diseases caused by Aedes-borne pathogens, such as dengue, chikungunya and Zika viruses. An innovative vector control method adapted to Aedes albopictus mosquitoes is the Sterile Insect Technique (SIT), which consists of the mass-release of sterilized male mosquitoes. The impact of SIT and the optimization of release strategies can be studied through modelling. The objective of this study was to evaluate the ability of a mathematical model to simulate the impact of SIT releases by comparing the simulation outputs with entomological data collected during and after SIT trials in Mauritius. We modified a model of Ae. albopictus population dynamics (ARBOCARTO) that incorporates variations in temperature and rainfall, as well as the availability of breeding sites to introduce SIT. We then simulated SIT releases under the same conditions as the field trials and assessed the model's ability to realistically reproduce the impact of SIT releases by comparing the simulation outputs with entomological data observed in a trial site (where SIT releases were performed between May 2017 and February 2018) and a control site (without SIT releases). Four simulation scenarios were considered: without SIT, and with SIT applied on 50%, 75% and 100% of the trial area. Results showed that the ARBOCARTO model reproduced the major trends in the intra-annual Ae. albopictus population variations: simulated abundances of eggs, based on weather conditions, were highly and significantly correlated with the egg abundances observed at the SIT control site. The model also matched the trial site data for both the predicted number of newly produced eggs and the percentage of fertile eggs. The simulation results also revealed the importance of the percentage of the area covered by SIT releases as a key parameter for SIT impact, both for the reduction rate and for the resilience time, defined as the time required after the end of releases for the mosquito population to return to its initial state. Thanks to its user-friendly interface, the ARBOCARTO model can be used by vector control services and health stakeholders to simulate the impact of SIT releases and optimize release strategies, taking into account the operational capacity of sterile mosquito rearing facilities and the environmental conditions of the releases.
The rapid development of technology and its integration into archaeological practices have greatly benefited archaeologists. Despite the many advantages of using technological tools, archaeologists face a major risk related to their limitations and technological dependence on specific software platforms. This article presents two case studies in which archaeological teams transitioned to open-source software that natively supports GIS technologies, replacing previously closed or limited digital frameworks. The first case concerns the transition from the iDig recording system to the QField ecosystem for excavation documentation. The second case presents a similar technological transition where QField replaced an ODK-based data collection framework in the context of surface survey. Each case includes a comparison of the software solutions used, focusing on their features and performance in archaeological workflows. The purpose of the article is to highlight that the long-term improvement of performance in archaeological field documentation is not tied to any single software, but depends mainly on the team's ability to transition and adapt to new digital environments. Archaeological teams can benefit far more from embracing openness, interoperability, and long-term sustainability through the use of open-source software.
Although non-invasive sampling is increasingly used in capture-recapture (CR) monitoring, it carries a risk of misidentification that, if ignored, causes an overestimation of population size. Models that deal with misidentification have been proposed. However, these models assume that only one sample can be collected per individual at one occasion. This is not true for several monitoring programs based on DNA, for example for those that extract the DNA from faecal samples. The models do not take repeated observations into account, leading to biased estimates. In this paper, we develop an approach that extends the latent multinomial model (LMM) of Link et al. (2010) using a Poisson distribution to model the number of samplings of the same individual on a given occasion. We then conduct simulations to test how our new model performs. As an illustration, we applied the new Poisson model to a collection of Eurasian otter faeces (Lampa et al., 2015). Our model yields unbiased estimates of population size when the expected number of samples per individual (lambda) is sufficiently high: simulations with lambda >= 0.36 and five capture occasions or with lambda >= 0.23 and seven or more occasions. In contrast, when lambda = 0.11 (corresponding to about 42%, 53% and 62% of the individuals being detected with respectively 5, 7 and 9 occasions), the population size is consistently underestimated. Applying the model to the otter dataset confirms the presence of misidentifications, consistent with the authors' expectations. Our findings indicate that repeated observations can be modelled without bias. The application on otters shows that our model is necessary to accurately estimate population size in presence of misidentification and repeated observations.
