Methods in Ecology and Evolution

Passive acoustic monitoring (PAM) enables non-invasive sampling of wildlife across broad spatial, temporal and taxonomic scales. Its ongoing and widespread use has generated unprecedented volumes of acoustic data, shifting the primary bottleneck from data collection to the storage, processing, integration, and interpretation of PAM outputs. Although many software tools exist to address these challenges, differences in their design, scope, and usability often create fragmented and complex analytical workflows. To identify the key barriers and opportunities shaping the implementation of PAM surveys, we conducted a structured expert solicitation involving 30 international practitioners working across terrestrial and aquatic ecosystems. Experts identified and ranked their most critical pain points in current PAM workflows, spanning data storage, processing, and interpretation. The top challenge identified related to accurate species identification using deep learning and artificial intelligence (AI) models, especially in noisy soundscapes or for underrepresented taxa. Eight additional priority challenges included workflow fragmentation, limited availability of user-friendly analytical and visualisation tools, uneven access to software, manual validation bottlenecks, computational constraints, and difficulties in data handling, standardisation, and sharing. Participants also proposed practical mitigation strategies for these priority challenges, supported by step-by-step guidance to help overcome key barriers. Together, these insights provide a roadmap toward more scalable, open-access, and collaborative software systems, which are increasingly essential to realise the full potential of PAM in global biodiversity monitoring.

Many animals communicate using sequences of signals, but identifying recurrent, non-random signal combinations remains methodologically challenging. Collocation analyses are increasingly popular approaches for detecting which signals animals combine at rates greater than expected by chance. However, existing methods for animal collocation analysis face several limitations that reduce their statistical rigour: they lack uncertainty estimates, fail to control for non-independence in sampled data, and do not account for inflated family-wise error rates when identifying attraction among many different signal types. These limitations restrict the broader applicability of animal collocation analysis, including preventing robust comparisons of signal combination strength between cohorts (e.g. populations, sexes or age classes). We adapt a novel form of Multiple Distinctive Collocation Analysis using Pearson residuals (MDCA-Pr) that addresses these statistical limitations, and validate its use in animal communication research in three ways: first, using numerous simulated datasets of different sizes and levels of signal recombination; second, using simulated data to evaluate the performance of MDCA-Pr in intercohort comparisons, and third, by demonstrating how MDCA-Pr can be applied to compare the vocal sequences produced by male and female captive-living common marmosets (Callithrix jacchus). MDCA-Pr shows high sensitivity, including at small sample sizes, and generally low false-positive rates, which we further reduce by applying additional criteria for identifying attraction between signals. During intercohort comparisons, MDCA-Pr is conservative, with low false-positive rates, and statistical power increases with sample size. MDCA-Pr is a robust method for evaluating signal attraction in animal communication and enables accurate intercohort comparison of animal signal combinations. Significance StatementBy assessing the performance of MDCA-Pr on simulated animal-like data, we demonstrate that this method reliably detects signal combinations within and across animal cohorts, while overcoming statistical limitations of previous collocation analyses. We present an analytical pipeline for applying MDCA-Pr to animal signal data, including for intercohort comparisons, enabling identification and comparison of combinatorial strategies across entire signal repertoires. We illustrate this approach by comparing call combination strategies of male and female common marmosets when presented with food under experimental conditions, finding similar combinatorial strategies between sexes. MDCA-Pr therefore permits rigorous characterization of animal signal combinatoriality and opens avenues for investigating how demographic, social, and group-level factors influence combinatorial patterns.

Peatlands are an important terrestrial carbon sink which, when drained, can produce substantial CO2 efflux. Low productivity forestry planted on drained peatlands can become a net carbon source if losses from drained soils exceed sequestration by the trees. Decision support tools which assist resource allocation and intervention planning in forest-to-bog restoration are needed to mediate this substantial environmental harm. Predicting carbon mitigation benefits associated with forest-to-bog restoration is a major challenge, however, due to the lack of long-term monitoring programs and the fact that mitigation times depend on processes distant from the intervention. Here we introduce the PEATREST life cycle assessment (LCA) which predicts carbon fluxes associated with forest-to-bog restoration, including due to processes far from restored sites. The LCA estimates mitigation timescales defined as the time following intervention at which the restored peatland is predicted to sequester or store more carbon than the forestry would have if retained. HighlightsO_LIHere we develop a novel forest-to-bog Life cycle assessment (LCA) tool C_LIO_LIThe LCA predicts carbon mitigation times following peatland restoration C_LIO_LIThe model combines a variety of process-based and empirical sub-models C_LIO_LIExample implementations for two different restoration scenarios are explored C_LIO_LISensitivity analysis highlights the model inputs that most impact outcomes C_LI Graphical abstract(A single, concise figure that serves as a visual summary of the main research findings described in your manuscript.) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/715261v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@f243f5org.highwire.dtl.DTLVardef@14bc4c7org.highwire.dtl.DTLVardef@164261borg.highwire.dtl.DTLVardef@1db3b_HPS_FORMAT_FIGEXP M_FIG The PEATREST Life cycle assessment (LCA) generates compound time series of carbon sequestration and carbon storage for two scenarios: the forest-to-bog peatland restoration (PR) and a counterfactual (CF) of forestry retention. By comparing the two scenarios, the LCA predicts the carbon mitigation timescales (vertical dashed lines). These are defined as the time following harvesting at which the peatland is predicted to sequester more (emit less), or to have stored more (lost less) carbon, than the forestry would have if retained. C_FIG

Phylogenetic trees represent the evolutionary histories of taxa and support tasks such as clustering and Tree of Life reconstruction. Many established comparison methods, including the Robinson-Foulds (RF) distance, assume identical taxon sets. A methodological gap remains for trees with distinct but overlapping taxa. Existing approaches either prune non-common leaves, which can discard information, or complete both trees such that they share the same taxa. Completion is more comprehensive, but current methods typically ignore branch lengths, which are essential for identifying evolutionary patterns. This paper introduces k-Nearest Common Leaves (k-NCL), an algorithm for completing rooted phylogenetic trees defined on different but overlapping taxa. The method uses branch lengths and topological characteristics and does not rely on a specific distance measure. The k-NCL algorithm is designed to preserve evolutionary relationships in the trees under comparison. The running time is O(n2), where n is the size of the union of the two leaf sets. Additional properties include preservation of original distances and topology, symmetry, and uniqueness of the completion. Implemented in Python, k-NCL is evaluated on biological datasets of amphibians, birds, mammals, and sharks. Experimental results show that RF combined with k-NCL improves phylogenetic tree clustering performance compared to the RF(+) tree completion approach. Availability and implementationAn open-source implementation of k-NCL in Python and the datasets used in this study are available at https://github.com/tahiri-lab/KNCL.

Radar aeroecology is dedicated to making ecological inference about aerial wildlife from radar-derived information. While producing unique, large-scale datasets describing biological activity in the sky, radar methodologies are largely incapable of relating these to specific species and are thus taxonomically limited. I describe a computational method to increase taxonomic resolution in vertical looking radar data by dividing detected organisms into morphology and movement-based aerial morphotypes. Using the Birdscan MR1 radar target classifier, wing flapping frequency calculation and target size estimation, I demonstrate a nearly 8 fold increase in classification resolution of bird radar data from the Hula Valley Research station, Israel. Furthermore, by relating each species in the regions species pool to its relevant morphotype, I show that most of these newly separated classes are related to small numbers of species (1-10), providing realistic opurtunities to bridge the taxonomy gap in radar data. By using the morphotype approach, radar aeroecologists can start observing and discussing the concept of "Aerodiversity", analogues to widely used biodiversity, a fundamental measure in ecology and conservation sciences. By analitically adressing taxonomy in radar-aeroecology, practitioners will increase the impact and dissemintation of their work and contribute to a better, more complete understanding of the aerial habitat.

BackgroundIn hibernating mammals, the timing of den entry and exit reflects complex interactions among environment, physiology, and energetic constraints, with consequences for fitness. Consequently, shifts in denning phenology can affect population dynamics, particularly under climate change. Reliable estimation of denning timing is therefore critical, yet current methods often rely on GPS-derived movement data, limited by coarse sampling intervals, detection issues, and the inability to distinguish true inactivity from active presence at the den site. In this study, we developed and apply a method to estimate denning phenology in a brown bear population in south-central Sweden using accelerometer-derived activity data. Our approach employs adaptive, individual-specific thresholds to account for variation in baseline activity across bears, focusing on day-to-day changes to identify the start and end of inactivity periods. This method allows flexible and reproducible detection of den entry and exit dates, overcoming limitations associated with fixed thresholds and small sample sizes. ResultsWe compared activity-based estimates with GPS-derived den occupancy and examined variation in denning behavior across demographic groups. Analyzing 388 bear-winters, the method successfully identified inactivity periods in 360 cases. The method failed to identify clear start and end dates of hibernation for 28 (7%) bear-winters, which were characterized by unusually high or low daily activity levels at the boundaries of the inactivity period. Den site occupancy ranged from September 5 to June 2, with durations of 112-260 days, whereas inactivity periods detected from activity data extended from September 6 to May 13, lasting 83-217 days. Our comparison of activity-based and GPS-based methods indicates that bears may arrive at the den site several weeks before the onset of inactivity, with timing varying among demographic groups. ConclusionWe show that activity-based analysis provides a robust framework for estimating denning phenology, distinguishing actual inactivity from site presence, and improving understanding of the timing and variability of bear denning behavior. Applying an individual-level activity-based method improves accuracy in assessing ecological mechanisms underlying hibernation in bears and other hibernators, while also enhancing interpretation of environmental drivers and providing a reliable tool to monitor phenological shifts in response to climate change.

Quantitative histological analysis of skeletal muscle morphometry provides critical insights into muscle physiology but remains labor-intensive and technically demanding. While recent developments in machine-learning-based image segmentation techniques have facilitated large-scale tissue analysis, existing tools that automate muscle morphometry analysis are largely tailored to mammalian models, with limited applicability to teleosts. Moreover, there is a lack of effective tools for visualizing spatial organization and morphometric variability of teleost muscle fibers, a feature that is important for understanding hyperplastic muscle growth dynamics in teleosts. In this study, we show that cytoplasmic staining combined with deep learning-based cell segmentation offers a robust and accurate approach for automated muscle morphometry analysis in developing zebrafish. We also introduce a FIJI2 plugin, implemented in Jython, that streamlines both morphometric analysis and visualization. This tool accommodates shallow and deep learning-based segmentation techniques and incorporates novel quantification and visualization methods suited to teleost-specific muscle features, including mosaic hyperplasia dynamics. The plugin features an intuitive graphical user interface and is designed for flexibility, with minimal constraints regarding species, image quality, or staining protocol. Its modular architecture allows it to be used as a baseline for automated muscle morphometry analysis, while permitting integration with other tools and workflows.

Integrating taxonomic data from various sources presents a significant challenge in the study of biodiversity research, due to non-standardized nomenclature and evolving species classifications. Discrepancies between major repositories like the Global Biodiversity Information Facility (GBIF) and the National Center for Biotechnology Information (NCBI), as well as citizen science platforms such as iNaturalist, lead to fragmented and sometimes inaccurate biological data. We present TaxonMatch, a tool designed to address these challenges. TaxonMatch aligns taxonomic names, resolves synonymy, and corrects typographical and structural inconsistencies across databases. We show how it can be used to build a common backbone arthropod taxonomy over NCBI, GBIF and iNaturalist, to find the closest molecular data to a given fossil, and to identify IUCN endangered species with molecular data. TaxonMatch provides a cohesive taxonomic framework and a consistent taxonomic backbone, and can be applied to any taxonomic source. The tool is available at https://github.com/MoultDB/TaxonMatch.

Field-based monitoring of tree species in forests is often sparse due to logistical constraints. Remote sensing enables repeated, spatially contiguous collection of reflectance data across large areas. Tree species classification accuracy using such data is variable, likely because most studies use observational datasets where species occurrence correlates with environmental variation. We used two sites of a tree biodiversity experiment in Germany (BIOTREE: Kaltenborn and Bechstedt), where different species have been planted with high replication under controlled diversity levels, to assess how well tree species could be classified using reflectance data from airborne imaging spectroscopy and different classification methods (linear discriminant analysis, LDA, and a non-linear support vector machine, SVM). Reflectance data for 589 wavelengths between 400-2400 nm were acquired at 1 m spatial resolution during peak growing season. Reflectance spectra showed large and significant variation between taxonomic classes, orders, and species, and weak, but still significant, interactions between classes or orders and diversity levels. Classification accuracy reached 100% in training datasets, 77%-83% for the four species in Kaltenborn prediction datasets, and 31%-49% for the 16 species in Bechstedt prediction datasets. LDA provided more accurate predictions than SVM; and using similarly-spaced original wavelengths with LDA was as efficient as using principal components derived from the original data. While airborne imaging spectroscopy effectively distinguished up to four tree species in our datasets, classification accuracy was lower in more species-rich plots. In these cases, the methodology may be more useful for functional diversity monitoring than for tree species classification.

Reliable models of species niches and distributions depend on accurately matching occurrences to environments via spatial and temporal coordinates. For fossil occurrences, time-averaging and age uncertainty can create mismatches between fossils and their associated environments, distorting inferred niches and distributions. Using a virtual ecology approach, we assessed how temporal uncertainty ({+/-}200 years to the full late Quaternary) influences niche and distribution estimates for four virtual species centered on three periods: Holocene (6,000 y.b.p), deglacial (13,500 y.b.p.), and Last Glacial Maximum (18,000 k.y.b.p.). We compared uncertain estimates, derived by matching occurrences with environmental layers drawn from different times within each uncertainty window, against true niches and distributions. We found that during environmentally stable intervals, niches and distributions were robust to temporal uncertainty until it reached {+/-}2500 years. Higher environmental variability reduced accuracy, with the greatest mismatch occurring during the deglacial. These results demonstrate both the promise and limitations of paleodistribution reconstruction.

AO_SCPLOWBSTRACTC_SCPLOWA new Bayesian measure of phylogenetic information content is introduced based on geodesic distances in treespace. The measure is based on the relative variance of phylogenetic trees sampled from the posterior distribution compared to the prior distribution. This ratio is expected to equal 1 if there is no information in the data about phylogeny and 0 if there is complete information. Trees can be scaled to have the same mean tree length to avoid dominance by edge length information and focus on topological information. The method scales well, requiring only that a valid sample can be obtained from both prior and posterior distributions. We show how dissonance (information conflict) among data sets can also be estimated. Both simulated and empirical examples are provided to illustrate that the new approach produces sensible and intuitive results.

AO_SCPLOWBSTRACTC_SCPLOWAs modern phylogenomics datasets become increasingly large, it is useful to develop recommendations for how to subsample datasets for best species tree inference. Here we apply a new measure of phylogenetic information content that estimates the reduction in tree space occupied by a posterior sample of inferred trees relative to a prior sample in order to assess the effects of gene tree parameters on species tree estimation. We find that, consistent with earlier studies, when data are informative, more data result in better species tree inference. However, when data are uninformative, subsampling a dataset to include only the most informative loci may produce a better species tree sample. We perform analyses on a variety of simulated and empirical datasets.

Environmental DNA metabarcoding is a powerful monitoring tool for assessing aquatic biodiversity, as well as the sustainability and impacts of fisheries and aquaculture. However, conventional laboratory workflows remain time-consuming and dependent on dedicated infrastructures. Here, we present a field trial of a fully portable, off-grid eDNA metabarcoding pipeline that enables end-to-end analysis within a few days using compact equipment, including a BentoLab workstation and an Oxford Nanopore Technologies (ONT) MinION sequencer. The workflow was implemented during two international training courses in Norway and Brazil, where students and early career researchers collected environmental samples, extracted and amplified DNA, prepared DNA libraries, and sequenced on-site before performing bioinformatics and statistical analyses. In the case study detailed here, seven eDNA samples collected and analysed on-site in the Oslofjord allowed detection of 16 fish and elasmobranch species. Although overall diversity was lower than in earlier studies using Illumina-based sequencing, our protocol reliably detected key species and demonstrates that portable eDNA metabarcoding is feasible for rapid ecological assessment, surveillance of high-risk regions and/or deployment in remote or resourcelZllimited settings.

O_LISpecies diversity potentially has a dual effect on communities: a generally positive effect on overall community biomass, reflecting the expression of species response and interaction traits, and a poorly characterised effect on mass-specific species contribution to ecosystem functions, reflecting the expression of their effect traits. Disentangling the effects of biodiversity on total biomass from those on effect trait expression would help settle a long-standing debate by clarifying how biodiversity relates to both facets of species effects on ecosystem functioning. C_LIO_LIFollowing the classical BEF approach, we calculate expected ecosystem function based on observed functioning in monoculture. We then derive a net biodiversity effect (NBE) and decompose it into four components: the classical complementarity and selection effects on total community biomass, and complementarity and selection effects on effect trait expression. The latter two reflect, respectively, a complementarity or facilitation in how effect traits influence the function, and how species with the highest potential for increasing the function become dominant in the community. C_LIO_LIWe illustrate this NBE decomposition with three ecosystem functions (nitrogen retention capacity, soil hydraulic conductivity improvement, and forage digestibility) measured in assembled communities under controlled experimental conditions of perennial grassland plants. Regarding nitrogen retention, we find a positive complementary effect via total biomass, but a negative biodiversity effect via effect trait expression. For hydraulic conductivity improvement, biodiversity effects are mostly mediated by total biomass. As for forage digestibility, we found a positive complementarity effect on trait expression, outweighed however by a negative selection effect. This analysis reveals how biodiversity may have contrasting effects on ecosystem functions via its impact on biomass and effect trait expression. C_LI SynthesisSeparating between the effect of biodiversity on plant community biomass and on effect trait expression at the community level is one important step towards understanding the pathways by which diverse plant communities drive ecosystem functioning.

O_LIUnderstanding functional community structure and the niche-based mechanisms that enable coexistence among sympatric species is essential for explaining how biodiversity is maintained in natural systems, and for anticipating how ecological communities will respond to ongoing environmental change. Stable isotope analysis provides a process-oriented perspective on resource use by integrating information across time and space, thereby allowing reconstruction of realised isotopic niches that reflect multiple dimensions of ecological differentiation. C_LIO_LIWe applied this framework to a community of ungulates in the Central-Eastern Italian Alps, including red deer (Cervus elaphus), roe deer (Capreolus capreolus), and Alpine chamois (Rupicapra rupicapra). Using stable isotope ratios in summer-grown hair segments ({delta}13C, {delta}15N, {delta}34S, {delta}18O, {delta}2H), we quantified species-specific n-dimensional niche hypervolumes within a Bayesian framework and estimated niche regions, overlap probabilities, univariate differentiation and multivariate structure. C_LIO_LIDespite broad dietary overlap typically observed among these ungulates, we found clear isotopic niche segregation, with mean pairwise overlap consistently remaining below 40%. Three dimensions emerged as primary drivers of differentiation: water sourcing ({delta}18O), diet quality ({delta}15N), and habitat openness ({delta}13C). Specifically, chamois appeared to derive more water from plants in their diet rather than from drinking, and to consume a higher-quality diet compared to Cervids. Red deer relied more heavily on forested habitats for resource use compared to roe deer and chamois, and additional isotopic differences between red deer and roe deer may stem from fine-scale abiotic conditions like microclimate and topography. We found no isotopic evidence for differential niche breadth among the three ungulate species. C_LIO_LITogether, these patterns highlight functional differentiation across multiple ecological axes, offering mechanistic insight into how these ungulates segregate realised niche space despite substantial potential for resource overlap. This multi-element isotope perspective underscores the value of integrative, process-based approaches for understanding current coexistence as well as improving predictions of how mammal communities may reorganise under accelerating environmental change. C_LI

Ancestral state reconstruction is a classical problem of broad relevance in phylogenetics. Likelihood-based methods for reconstructing ancestral states under discrete character models, such as Markov models, have proven extremely useful, but only work so long as the assumed model yields a tractable likelihood function. Unfortunately, extending a simple but tractable phylogenetic model to possess new, but biologically realistic, properties often results in an intractable likelihood, preventing its use in standard modeling tasks, including ancestral state reconstruction. The rapid advancement of deep learning offers a potential alternative to likelihood-based inference of ancestral states, particularly for models with intractable likelihoods. In this study, we modify the phylogenetic deep learning software O_SCPLOWPHYDDLEC_SCPLOW to conduct ancestral state reconstruction. We evaluate O_SCPLOWPHYDDLEC_SCPLOWs performance under various methodological and modeling conditions, while comparing to Bayesian inference when possible. For simple models and small trees, its performance resembles the performance of Bayesian inference, but worsens as tree size increases. While O_SCPLOWPHYDDLEC_SCPLOW still performs adequately for more complex models, such as speciation and extinction models, the estimates differ more from Bayesian inference in comparison with simpler models. Lastly, we use O_SCPLOWPHYDDLEC_SCPLOW to infer ancestral states for two empirical datasets, one of the ancestral ranges of a subclade of the genus Liolaemus and ancestral locations for sequences from the 2014 Sierra Leone Ebola virus disease outbreak.

Body orientation is a key variable in the analysis of insect flight behavior, yet it remains difficult to measure across the full extent of a trajectory in most experimental settings. Although modern tracking systems reliably capture the position and velocity of the center of mass, resolving body yaw orientation typically requires dedicated hardware confined to a small, purpose-built volume, and is impractical for large-scale or long-duration studies. Here, we develop a data-driven estimator that predicts body yaw orientation directly from translational flight trajectory data. We trained a fully connected feed-forward artificial neural network (ANN) on a dataset in which both flight trajectory and body orientation were recorded simultaneously in freely flying Drosophila, using a time-delay embedding of ground velocity, air velocity, and inferred thrust vectors as input features. To improve generalization across arbitrary coordinate frames, we augmented the training data with random rotational transformations. Evaluated on a withheld test set of 3,313 trajectories (101,576 frames), the rotation-augmented model achieved a median mean absolute angular error of 10.51{degrees}, with accurate heading recovery across the full [-{pi}, {pi}) range. The estimator provides a practical tool for recovering body orientation information from existing trajectory datasets in which only center- of-mass motion was recorded, extending the behavioral and computational analysis of insect navigation to previously inaccessible data.

Recent progress in mathematical kinship modelling has allowed one to predict the probable numbers of kin for a typical population member. In the models, kin may be structured by age and sex, both in static or time-variant demographies. Knowing the probable numbers of kin in different stages - such as parity, health status, or geographic location - however, remains an open challenge in Kinship Demography. Knowing how population structure delimits kin to distinct stages is an advance - for instance, the probability of having one sister at home and one sister away has different social implications from the probability of having two sisters. We present a novel analytical framework, grounded in branching process theory, that provides kin-number distributions jointly structured by age and stage. Using recursive compositions of probability generating functions (PGFs), we derive the joint age, stage, and age x stage kin-number distributions. All marginal distributions over either dimension naturally emerge. Simple extensions of the PGF approach additionally yield: the joint distribution of an individuals own stage and their kins stage; the probable numbers of kin deaths, both in total and by generation number; and the probabilities of being kinless and/or orphaned. We demonstrate the framework through novel results in an application using UK parity-specific fertility and mortality data. HighlightsO_LIA new method calculates probability generating functions for the number of kin structured by age and stage C_LIO_LIThe model allows predicting the probable numbers of kin organised by age and stage C_LIO_LIRecursive nesting of probability generating functions in branching processes is used C_LIO_LIAn application is presented highlighting the novel results C_LI

Computational bioacoustics has seen significant advances in recent decades. However, the rate of insights from automated analysis of bioacoustic audio lags behind our rate of collecting the data - due to key capacity constraints in data annotation and bioacoustic algorithm development. Gaps in analysis methodology persist: not because they are intractable, but because of resource limitations in the bioacoustics community. To bridge these gaps, we advocate the open science method of data challenges, structured as public contests. We conducted a bioacoustics data challenge named BioDCASE, within the format of an existing event (DCASE). In this work we report on the procedures needed to select and then conduct useful bioacoustics data challenges. We consider aspects of task design such as dataset curation, annotation, and evaluation metrics. We report the three tasks included in BioDCASE 2025 and the resulting progress made. Based on this we make recommendations for open community initiatives in computational bioacoustics.

Hybridization between invasive and native species poses a hidden but critical threat to biodiversity. While environmental DNA (eDNA) has revolutionized species monitoring, it has lacked the resolution to detect hybrid individuals. Here, we present the first experimental demonstration of hybrid identification using eDNA. Our method isolates a single cell in the environment (hereafter, eCell) and enables cellular-level analysis using multiplex digital PCR targeting nuclear markers from both parental species. Validation with controlled tank experiments using Oncorhynchus masou masou x Salvelinus leucomaenis leucomaenis hybrid individuals confirmed the methods ability to separately detect hybrid individuals from co-habiting purebred parent individuals. This eCell analysis overcomes the limitations of traditional eDNA methods and offers a scalable, non-invasive tool for detecting cryptic hybridization. By enabling early and accurate detection of hybrid individuals, it supports timely conservation decisions, including management prioritization and the protection of purebred populations. This novel technique bridges a critical gap in conservation genetics and enhances eDNAs utility for biodiversity management in the face of global change.