Abstract The increasing use of 3D imaging technologies in biological sciences is generating vast repositories of anatomical data, yet significant barriers prevent these data from reaching its full potential in educational and collaborative contexts. While sharing raw computed tomography and magnetic resonance imaging scans has become routine, distributing value‐added segmented datasets—where anatomical structures are precisely labelled and delineated—remains difficult and rare. Current repositories function primarily as static archives, lacking mechanisms for iterative refinement, community‐driven curation and the controlled terminology essential for downstream computational applications, including artificial intelligence (AI). We propose that segmented 3D morphological datasets should be reconceptualized as collaborative, version‐controlled projects rather than static archival products. We introduce MorphoDepot, a framework that adapts the ‘fork‐and‐contribute’ model—a cornerstone of modern open‐source software development—for collaborative management of 3D morphological data. By integrating git version control and GitHub's collaborative infrastructure with 3D Slicer and its SlicerMorph extension, MorphoDepot transforms segmented anatomical datasets into dynamic, community‐curated projects. By treating each anatomical dataset as an individual repository that can be forked, modified, reviewed and merged, MorphoDepot enables communities of researchers, educators and students to co‐create high‐quality, standardized anatomical atlases through a transparent, auditable process. This approach directly addresses the challenges of distributed collaboration, enforces transparent provenance tracking and creates high‐quality, standardized training data for AI model development. The result is a system that embodies FAIR (Findable, Accessible, Interoperable and Reusable) data principles while creating powerful new opportunities for remote learning and collaborative science. We argue that format accessibility and collaborative curation are inseparable, genuine community‐driven data refinement requires open formats and open tools as prerequisites for participation.

Abstract Predation is a critical ecosystem process that shapes the structure and functioning of biological communities. However, due to its intermittent nature, fast pace and general unpredictability, predation is difficult to observe and quantify. Therefore, we commonly rely on indirect metrics or proxies of predation, which reflect the outcome of predation events but do not allow for inference about the predator's decision‐making process or predation rates. In terrestrial ecosystems, lifelike prey replicas have allowed ecologists to gain a broad understanding of predator choice, predation intensity and their drivers. Yet in aquatic ecosystems, few scalable, interactive predation assays have been developed. We introduce Goby Gummies , a customizable, edible prey model developed for aquatic ecology. Gummies are constructed using an inert, edible medium that can be cast into any desired shape (in our case, a goby fish), dyed various colours and supplemented with edible material to introduce variation in nutritional profiles. As such, goby gummies are a cheap, sustainable, high‐throughput assay that can be tailored to a variety of aquatic ecosystems. We performed two pilot studies to test goby gummies in a natural setting on coral reefs in Belize during which gummies were reliably consumed by a range of predatory fishes. First, we show that gummies supplemented with fishmeal were preferred by predators over agar‐only gummies, but the strength of this preference was dependent on their coloration, suggesting an intriguing interplay between external appearance and internal composition. Second, we compared fish‐supplemented gummies to squidpops, a previously developed predation assay for marine systems. Goby gummies were consumed first more frequently and eaten at quicker rates than squidpops and consistently attracted carnivorous predators, whereas squidpops were frequently consumed by herbivorous parrotfishes. Our results highlight that goby gummies provide a new predation assay tool in aquatic ecosystems that permits the exploration of many exciting questions surrounding prey and predator traits and their interplay. We envision goby gummies to kindle a diverse range of impactful studies across disciplines that mirror those conducted in terrestrial ecosystems.

Abstract Step‐selection functions (SSFs), typically fitted using step‐selection analysis (SSA) or integrated step‐selection analysis (iSSA) are widely used to infer habitat selection and movement kernels from high‐frequency telemetry data, but most standard validation tools focus on one‐step‐ahead prediction and do not guarantee that fitted models generate realistic trajectories or emergent space‐use patterns. We propose a multi‐criteria generative validation framework for SSF‐based movement models (typically fitted via SSA/iSSA), built around four pillars that target emergent utilization distributions, mean squared displacement, path sinuosity and barrier crossing. For each pillar, we define an ecologically interpretable trajectory‐level summary (Wasserstein distance between utilization distributions, mean squared displacement, straightness index and barrier‐crossing counts) and embed it in a Monte Carlo rank‐testing scheme that propagates parameter uncertainty. Applied to six synthetic ‘stress tests’ (sedentary home range, hard barrier, corridor follower, multi‐state movement, orbiter and return‐conditioned sinuosity), the framework reveals distinct failure modes that may not be detected by conventional stepwise validation. An empirical application to a GPS‐tracked red deer illustrates partial generative realism at a 6‐h sampling interval: the fitted iSSA reproduces long‐horizon space‐use and path sinuosity but shows a detectable mismatch in displacement dynamics (mean squared displacement).

Abstract Metagenome sequencing not only plays a pivotal role in unravelling the genetic diversity and functional potential of microbial communities but also facilitates the discovery of genome context for microbial dark matter. This study presents a comparative analysis of metagenome sequencing strategies, focusing on the impact of read length on the assembly quality of metagenome binning. We employed metaSPAdes assembly with varying k ‐mer lists and the read lengths on 19 Illumina datasets, revealing that longer reads significantly improve the number of contigs and their length, despite a trade‐off in N50. Specially, longer reads also contribute to better performance of gene fragment reconstruction from contigs. Next, the substantial potential of Nanopore sequencing was further evaluated by comparing the short‐read assembly by Illumina, long‐read assembly by Nanopore and hybrid assembly strategies on samples from extreme environments, including both cold seep and hot spring. The binning of assembled contigs and subsequent metagenome‐assembled genome quality assessment highlighted the superiority of long‐read data in reconstructing medium‐ and high‐quality drafted genomes, specifically, increasing medium‐quality species‐level representative genomes by 1.32‐fold. These findings advocate for the integration of extended read lengths and Nanopore sequencing in metagenome analysis, which can lead to a more nuanced comprehension of the environmental microbiome.

Abstract Phylogenetic comparative methods are widely used to study trait coevolution across biological and cultural domains. The most common methods are phylogenetic generalized linear (mixed) models, phylogenetic path analysis, Pagel's ‘discrete’ method and Ornstein–Uhlenbeck models. While some frameworks like generalized linear mixed models are quite flexible in terms of the data structure, they are ill‐suited for inferring causal effects; others, like Pagel's ‘discrete’ can more explicitly infer causal sequences, but are limited in the number and types of traits that can be modelled. Here, we develop a novel class of generalized dynamic phylogenetic models (GDPMs) that overcomes these limitations and synthesizes the strengths of existing methods into a flexible framework for dynamic inference. Treating the phylogeny as an implicit time series, GDPMs model trait coevolution for any number of traits undergoing both deterministic adaptation and stochastic drift, capable of inferring directed evolution ( vs. ), feedback (), and contingencies (e.g. first , then ). We introduce the coevolve R package, a user‐friendly interface for fitting GDPMs in a Bayesian framework using Stan. To demonstrate the GDPM framework, we first work through a biologically motivated synthetic example of predation and mating system among cichlid fish. We also perform simulation‐based calibration as a computational validation of our models. Additionally, we present some empirical applications of GDPMs, including analyses of brain size in non‐human primates and societal complexity across human populations. These examples highlight the flexibility and potential of the GDPM framework, which allows researchers to model latent variables, multilevel structures and repeated measures, measurement error, missing data and other complexities inherent in comparative data.

Abstract Molecular identification of insect food webs can accurately reveal complex trophic interactions and serve as a foundation for understanding ecosystem functioning and advancing biodiversity conservation. This process typically involves DNA library construction, high‐throughput sequencing and subsequent steps, including data assembly, annotation, denoising and taxonomic classification. However, the use of multiple long genetic markers substantially increases both the cost of high‐throughput sequencing and the complexity of downstream data analysis in insect diet studies. In this study, we developed a low‐cost, time‐efficient and highly accurate method for analysing the dietary habits of herbivorous insects and constructed an automated tool, ‘NGSdiet’, that processes high‐throughput sequencing data related to insect food webs to enable rapid identification of insect diets. We utilized four long‐fragment DNA barcodes (insect COI and plant rbcL , ITS and trnL ) in a multiplex PCR approach, which significantly reduced the cost of DNA library preparation. The resulting high‐throughput sequencing data can be automatically processed through a one‐click command using the NGSdiet tool, greatly simplifying the otherwise complex workflow. NGSdiet incorporates functions, such as BWA alignment, Trinity de novo assembly, sequencing depth calculation, adjacent‐base depth ratio (‘base_ratio’) filter, sequence annotation and barcode marker sorting. By setting thresholds for average barcode sequencing depth, length, base_ratio, and applying a sliding window based on base_ratio values, the tool effectively filters out errors caused by PCR chimeras, contamination or misassemblies. The workflow begins by aligning reads against a local reference database using BWA, followed by filtering and output of mapped results. If the initial alignment fails, the pipeline automatically initiates de novo assembly with Trinity and filtering. It also enables automatic separation of different genetic markers across samples. To validate the robustness of the method, three batches of Lepidoptera larval samples were subjected to sequencing analysis. The identification accuracies of the COI gene were 98.26%, 92.13% and 100%, respectively, while the combined plant barcodes achieved identification accuracies of 92.17%, 68.53% and 92.06%. Results demonstrate that NGSdiet is a fast, cost‐effective and highly sensitive tool for high‐throughput analysis. It also shows considerable potential for scalability, making it applicable to animal diet identification across various ecosystems.

Abstract We present a new acoustic telemetry tag capable of detecting whether its carrier stops moving for long enough to presume the organism has died, and of reporting the time elapsed since movement ceased. The tag uses existing environmental sensor technology together with an algorithm with user‐specifiable thresholds, and importantly, can separate predation events from non‐predation mortality. The objective of developing the tag was to provide a means by which mobile telemetry surveys outside of telemetry receiver grids can better determine when and how tagged animals die. Using a field test in a freshwater lake in New Brunswick, Canada, in which 18 tags were implanted into two species of salmonid fish, we demonstrate that the new tag achieves its objective as long as users select species‐ and system‐appropriate inactivity sensor thresholds during tag programming. The tag will be a valuable addition to the acoustic telemetry toolbox, and especially when mobile surveys are an important source of detection data.

Abstract A central aim in biology is understanding the heritability of traits and how trait interactions contribute to success in diverse environments. Experiments that record multiple traits from individuals of known pedigree or genetic relatedness in distinct environments are key to addressing this aim. Mixed modelling approaches have been proposed for analysing such multivariate trait data. The parameter space of these mixed models grows quadratically with the number of traits and environments considered, which increases computational demand and the risk of overfitting. Existing approaches can also be challenging to implement for datasets in which different traits were measured in different environments. We developed a latent variable model that incorporates genetic marker data for estimating heritability and improving predictions of traits. Our approach promotes model parsimony by estimating environmental associations and genetic variances for a reduced number of latent traits. The model can accommodate variation in genetic correlations across environments and can be applied in settings where only a subset of traits is observed in each environment, maximizing use of the data. We show that existing model‐based ordination methods can be viewed as simplifications of our approach. In a simulation study, we found that our approach improves sampling efficiency by an order of magnitude relative to standard multivariate mixed modelling approaches. Compared with existing ordination methods, our approach also improved inference for environmental associations and predictive performance. We applied our model to reconcile partially overlapping datasets collected from growth chamber and common garden experiments of Bromus tectorum , an annual grass invasive to the United States. Fitting mixed models independently to the data sources resulted in biologically unreasonable estimates of narrow‐sense heritability, and a joint analysis with our latent variable model improved inference. Drawing from the joint analysis, we present a holistic explanation for the strength of several clines in Bromus tectorum and discuss their relevance for invasion in the Intermountain West. The flexibility, tractability and performance of our approach make it appealing for joint inference and prediction in experiments of multiple traits. More broadly, we demonstrate the value of incorporating genetic marker data into latent variable models.

Abstract Measuring directional root placement is critical for understanding plant responses to their below‐ground environment, and particularly their competing neighbours. Directional root placement is commonly measured using image analysis of roots growing in transparent pots (rhizoboxes), where the length of the root system of the target plants is tracked. However, tracking roots with a soil background can be highly challenging, particularly in competition studies, where two or more root systems are intertwined within the same experimental setup. In this study, we propose a new approach for measuring directional root placement in competitive set‐ups, with two methods that calculate the centroid of the root system without measuring overall root length. In the first method, the centroid is calculated by marking all the intersection points of the target plant roots along a fixed number of equally spaced horizontal lines superimposed on the image. In the second method, the centroid is calculated from a contour line (polygon) created by marking only the peripheral intersection points. We developed an open‐access, interactive Python algorithm that automates and standardizes the centroid calculation for both methods. We validated these methods by comparing them to the centroid calculated from the traditional root length measurements using results from two rhizobox competition experiments, with either uniform or patchy soil nutrient distribution. While the two methods offer a more rapid and standardized calculation of the root system centroid, they differ in their investment time vs. accuracy levels, particularly when root density is heterogeneous. By focussing solely on a few sample points from the root system or its contour line rather than tracking the entire root system, this approach offers a potentially faster way for measuring directional root placement. The centroid approach could therefore facilitate the study of plant responses to below‐ground competition, enabling more efficient tracking over time and across multiple samples.