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The bigmouth buffalo Ictiobus cyprinellus is a long‐lived, migratory freshwater fish native to North America whose numbers are declining amidst increasing conservation concerns. Recent studies have uncovered long lifespans, delayed maturation, and episodic recruitment of bigmouth buffalo. Building from previous work in the Qu'Appelle watershed of Saskatchewan, here we quantify otolith‐derived population demographics of bigmouth buffalo and invasive common carp Cyprinus carpio across multiple sites in the drainage. The common carp ( n = 125) and bigmouth buffalo ( n = 173) collected from 2018 to 2024 reveal that common carp reach asymptotic size two times faster, live three times shorter lives, and invest significantly more into reproduction while also exhibiting recruitment stability during the water control era (post‐1958). Indeed, invasive common carp now outnumber native bigmouth buffalo in this watershed by at least an order of magnitude. In contrast, only a single year class (1997) was evident for bigmouth buffalo after 1949. Therefore, only one recruitment year was evident for this species since common carp were first detected in the system in 1955. Remarkably, we find that as of 2024 more than 90% of bigmouth buffalo in this system are greater than 75 years old with a known maximum age of 148 years. We now know that the bigmouth buffalo is the 11th longest‐lived vertebrate out of more than 66,000 species, and across diverse systems can have recruitment gaps longer than any other animal. Bigmouth buffalo require immediate conservation reassessment amidst ongoing population declines.

Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous‐time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement. The machine learning formulation of TGPs enables modeling to be nearly automated, while their Bayesian formulation allows for the derivation of movement descriptors with associated uncertainty measures. We demonstrate the use of an existing R package to implement TGPs using the familiar Markov chain Monte Carlo algorithm. We then use estimated movement trajectories to derive movement descriptors that can be compared across individuals and populations. We applied the TGP model to a case study of lesser prairie‐chickens (Tympanuchus pallidicinctus) to demonstrate the benefits of TGP modeling and compared distance traveled and residence times across lesser prairie‐chicken individuals and populations. For broad usability, we outline all steps necessary for practitioners to specify relevant movement descriptors (e.g., turn angles, speed, contact points) and apply TGP modeling and trajectory comparison to their own telemetry datasets. Combining the predictive power of machine learning and the statistical inference of Bayesian methods to model movement trajectories allows for the estimation of statistically comparable movement descriptors from telemetry studies. Our use of an accessible R package allows practitioners to model trajectories and estimate movement descriptors, facilitating the use of telemetry data to answer applied management questions. We apply a recently developed Bayesian machine learning model to both increase the accessibility of complex animal movement models and simultaneously achieve advanced modeling of highly varied telemetry data. By nesting this model within a widely applicable inferential framework and utilizing an accessible R package for modeling, we facilitate application by practitioners to model trajectories, estimate movement descriptors, and answer applied management questions.

The carpsuckers (Carpiodes spp.) are a little‐studied genus of three medium‐sized (0.5–3 kg at maturity) freshwater fishes within Catostomidae. These three species are widespread and common in some larger rivers in central North America, as well as some larger lakes and reservoirs. The lapillus otoliths of 189 carpsuckers (117 quillbacks [Carpiodes cyprinus], 44 highfin carpsuckers [Carpiodes velifer], 23 river carpsuckers [Carpiodes carpio] and 5 intergrades) were aged, all collected from the same Lower Wisconsin River community in 2023 and 2024 during Wisconsin Department of Natural Resources fish surveys. Age scores were precise among readers (mean coefficient of variation = 4.7%). Growth in size of each species was best modelled by von Bertalanffy growth functions accounting for sexual dimorphism, and the first comparative assessment of all three species’ growth profiles was provided. These results showed that each species exhibited highly variable recruitment across time, with most individuals from year classes produced in 2005 or earlier. Maximum ages greater than 50 years for each of the three species were observed: highfin carpsucker (56 years), quillback (51 years) and river carpsucker (56 years), as well as a maximum age of 44 years among the small sample of intergrades, making Carpiodes the second genus of freshwater fish for which three or more species have been shown to live more than 50 years. A maximum age of 56 years for the highfin carpsucker is more than six times greater than previously reported and, based on available knowledge, represents the longest lived, smallest bodied (L∞ < 40 cm total length [TL]) freshwater fish by more than 25 years. Carpsuckers are long‐lived periodic strategists living in increasingly human‐altered ecosystems, as is the case for many catostomids.

To identify causation, model-free inference methods, such as Granger Causality, have been widely used due to their flexibility. However, they have difficulty distinguishing synchrony and indirect effects from direct causation, leading to false predictions. To overcome this, model-based inference methods that test the reproducibility of data with a specific mechanistic model to infer causality were developed. However, they can only be applied to systems described by a specific model, greatly limiting their applicability. Here, we address this limitation by deriving an easily testable condition for a general monotonic ODE model to reproduce time-series data. We built a user-friendly computational package, General ODE-Based Inference (GOBI), which is applicable to nearly any monotonic system with positive and negative regulations described by ODE. GOBI successfully inferred positive and negative regulations in various networks at both the molecular and population levels, unlike existing model-free methods. Thus, this accurate and broadly applicable inference method is a powerful tool for understanding complex dynamical systems. Traditional causal inference methods struggle to distinguish direct causation from synchrony and indirect effects. Here, authors present GOBI that overcomes this by testing a general model’s ability to reproduce data, providing accurate and broadly applicable causality inference for complex systems.

The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials, typically using simple estimands of presumptively homogeneous populations. Yet the manifest complexity of the brain’s functional, connective, and vascular architectures introduces heterogeneities that violate the underlying statistical premisses, potentially leading to substantial errors at both individual and population levels. The counterfactual nature of interventional inference renders quantifying the impact of this defect difficult. Here we conduct a comprehensive series of semi-synthetic, biologically plausible, virtual interventional trials across 100M+ distinct simulations. We generate empirically grounded virtual trial data from large-scale meta-analytic connective, functional, genetic expression, and receptor distribution data, with high-resolution maps of 4K+ acute ischaemic lesions. Within each trial, we estimate treatment effects using models varying in complexity, in the presence of increasingly confounded outcomes and noisy treatment responses. Individualized prescriptions inferred from simple models, fitted to unconfounded data, are less accurate than those from complex models, even when fitted to confounded data. Our results indicate that complex modelling with richly represented lesion data may substantively enhance individualized prescriptive inference in ischaemic stroke. Stroke affects the brain in complex, highly individual ways. Here, the authors show that applying generative and causal AI methods to routinely collected brain scans may enable more closely personalized treatment recommendations.

Many animal paths have an intricate statistical pattern that manifests itself as a power law-like tail in the distribution of movement lengths. Such distributions occur if individuals move according to a Lévy flight (a mode of dispersal in which the distance moved follows a power law), or if there is variation between individuals such that some individuals move much farther than others. Distinguishing between these two mechanisms requires large quantities of data, which are not available for most species studied. Here, we analyze paths of black bean aphids (Aphis fabae Scopoli) and show that individual animals move in a predominantly diffusive manner, but that, because of variation at population level, they collectively appear to display superdiffusive characteristics, often interpreted as being characteristic for a Lévy flight.

The insect mitogenome is typically a compact circular molecule with highly conserved gene contents. Nonetheless, mitogenome structural variations have been reported in specific taxa, and gene rearrangements, usually the tRNAs, occur in different lineages. Because synapomorphies of mitogenome organizations can provide information for phylogenetic inferences, comparative analyses of mitogenomes have been given increasing attention. However, most studies use a very few species to represent the whole genus, tribe, family, or even order, overlooking potential variations at lower taxonomic levels, which might lead to some incorrect inferences. To provide new insights into mitogenome organizations and their implications for phylogenetic inference, this study conducted comparative analyses for mitogenomes of three social bee tribes (Meliponini, Bombini, and Apini) based on the phylogenetic framework with denser taxonomic sampling at the species and population levels. Comparative analyses revealed that mitogenomes of Apini and Bombini are the typical type, while those of Meliponini show diverse variations in mitogenome sizes and organizations. Large inverted repeats (IRs) cause significant gene rearrangements of protein coding genes (PCGs) and rRNAs in Indo-Malay/Australian stingless bee species. Molecular evolution analyses showed that the lineage with IRs have lower dN/dS ratios for PCGs than lineages without IRs, indicating potential effects of IRs on the evolution of mitochondrial genes. The finding of IRs and different patterns of gene rearrangements suggested that Meliponini is a hotspot in mitogenome evolution. Unlike conserved PCGs and rRNAs whose rearrangements were found only in the mentioned lineages within Meliponini, tRNA rearrangements are common across all three tribes of social bees, and are significant even at the species level, indicating that comprehensive sampling is needed to fully understand the patterns of tRNA rearrangements, and their implications for phylogenetic inference.

Characterising context‐dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large‐scale genetic perturbation screens is based on ad hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge‐ and Context‐driven Machine Learning (KCML), a framework that systematically predicts multiple context‐specific functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As a proof of concept, we test KCML on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, KCML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and TGFβ and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcomes. These results highlight KCML as a systematic framework for discovering novel scale‐crossing and context‐dependent gene functions. KCML is highly generalisable and applicable to various large‐scale genetic perturbation screens. Synopsis KCML is a Knowledge‐ and Context‐driven Machine Learning framework that systematically analyses large‐scale genetic screens. KCML utilises gene ontology to identify phenotypes associated with a particular gene function in a given cellular context. KCML is a novel approach for inferring context‐specific gene functions. KCML predicts multiple functions per gene based on phenotypic similarity along the confidence in these predictions. KCML allows generating a data‐driven map of functions represented in a certain dataset. Measurements of nuclear morphology and multicellular organisation can be predictive of many biological functions. KCML revealed a novel role for olfactory receptors in multicellular organisation of colorectal cancer cells. Graphical Abstract KCML is a Knowledge‐ and Context‐driven Machine Learning framework that systematically analyses large‐scale genetic screens. KCML utilises gene ontology to identify phenotypes associated with a particular gene function in a given cellular context.

In the present study, we investigated the genetic polymorphisms of 39 ancestry informative marker-insertion/deletion (AIM-InDel) loci in the Chinese Hui group using a previously self-developed panel, further clarified the genetic relationships between the Hui group and other reference populations, and assessed the ancestry inference efficiency of the AIM-InDel panel based on the worldwide population data from 1000 Genomes Phase 3. The results of the locus-specific informativeness (In) and pairwise fixation index (Fst) values, multidimensional scaling analysis, and success ratio of estimation with cross-validation showed that the novel panel could well reveal the genetic structural differentiations of the East Asian, European, African, and South Asian populations. Besides, the biogeographical ancestry origin inference both at the individual and population levels was conducted on the Chinese Hui group by principal component analysis and STRUCTURE analysis, and the results revealed that the Hui group had the East Asian origin, and the East Asian component ratio of Hui group was approximately 88.87%. Furthermore, the population genetic analyses among the Hui group and reference populations were performed based on the insertion allele frequency heat map, population pairwise Fst values and phylogenetic tree, and the results indicated that the Hui group was genetically closer to East Asian populations, especially two Chinese Han populations (CHS and CHB populations).

The Caribbean archipelago is a region with an extremely complex geological history and an outstanding plant diversity with high levels of endemism. The aim of this study was to better understand the historical assembly and evolution of endemic seed plant genera in the Caribbean, by first determining divergence times of endemic genera to test whether the hypothesized Greater Antilles and Aves Ridge (GAARlandia) land bridge played a role in the archipelago colonization and second by testing South America as the main colonization source as expected by the position of landmasses and recent evidence of an asymmetrical biotic interchange. We reconstructed a dated molecular phylogenetic tree for 625 seed plants including 32 Caribbean endemic genera using Bayesian inference and ten calibrations. To estimate the geographic range of the ancestors of endemic genera, we performed a model selection between a null and two complex biogeographic models that included timeframes based on geological information, dispersal probabilities, and directionality among regions. Crown ages for endemic genera ranged from Early Eocene (53.1 Ma) to Late Pliocene (3.4 Ma). Confidence intervals for divergence times (crown and/or stem ages) of 22 endemic genera occurred within the GAARlandia time frame. Contrary to expectations, the Antilles appears as the main ancestral area for endemic seed plant genera and only five genera had a South American origin. In contrast to patterns shown for vertebrates and other organisms and based on our sampling, we conclude that GAARlandia did not act as a colonization route for plants between South America and the Antilles. Further studies on Caribbean plant dispersal at the species and population levels will be required to reveal finer‐scale biogeographic patterns and mechanisms. This research looks at the origin of endemic Caribbean plant genera. The role of the GAARlandia land bridge and the ancestral areas of endemic plant genera are explored.