Methods in Ecology and Evolution

O_LIWith small-bodied species, it is difficult to directly track individual movements, leaving mark-recapture as the most feasible method for collecting movement data. Mark-recapture data are challenging to analyse because they are indirect: many individuals are never seen after release, and for recaptured individuals there is no information on the movements between release and recapture locations. This makes it difficult to apply many statistical approaches that have been developed for continuous movement data. Among the statistical methods targeted specifically to mark-recapture data, most are focused on the estimation of population sizes or vital parameters rather than the estimation of movement behaviours. C_LIO_LIWe present the R-package Jsmm that expands and implements the earlier published Joint Species Movement Modelling (JSMM) framework with Bayesian inference. Jsmm estimates parameters related to habitat selection (behaviour at edges between habitat types), diffusion (random component of movement), advection (directional component of movement) and reaction (mortality rate), and their dependence on spatial, temporal or spatiotemporal covariates. Jsmm implements both instantaneous capture process and cumulative capture process, enabling its applications to a broad range of studies. If applying Jsmm to data on multiple species, it can estimate how species-specific parameters depend on species traits and/or phylogenetic relationships. C_LIO_LIWe use real and simulated case studies to demonstrate the workflow of Jsmm: (1) defining the model through importing the spatial domain, the spatiotemporal covariates, and the capture-recapture data; (2) fitting the model with Bayesian inference and evaluating model fit through posterior predictive checks; and (3) using the fitted model for inference and/or prediction. The simulated example validates the technical implementation by showing that the estimated parameters match with the assumed values. The real data example on moth light-trapping illustrates the practical utility of the package. C_LIO_LIThe R-package Jsmm offers a flexible resource for analysing capture-recapture data in a model-based framework that explicitly accounts for the spatiotemporal study design of where and when captures are attempted. By analysing data jointly on multiple species, the approach facilitates analyses of sparse datasets where the low number of recaptures would not allow fitting species-specific models separately for each species. C_LI

O_LIAlthough the use of accelerometer-based behavioural classification to quantify animal activity budgets is gaining widespread traction, the interactions between key preprocessing decisions and modern classification algorithms remain poorly understood. Moreover, classification pipelines are commonly assessed using global performance metrics, despite increasing evidence that such metrics poorly reflect behaviour-specific patterns and ecological reliability. C_LIO_LIUsing a free-ranging primate (Chlorocebus pygerythrus) as a case study, we benchmarked how temporal segmentation (burst length), collar orientation correction, and model architecture jointly shape behavioural inference. We compared nine supervised algorithms spanning classical machine learning, feature-based deep learning including a tabular foundation model (TabPFN), and state of the art time-series architectures (HydraMultiROCKET). Beyond conventional metrics, performance was further evaluated using ecological validation against independent focal observations to assess model stability and biological plausibility. C_LIO_LIModel architecture exerted the strongest influence on classification outcomes. Modern deep-learning approaches substantially outperformed classical models, doubling recall for rare behaviours (e.g., grooming, self-scratching) without compromising precision. In contrast, burst length and collar orientation correction had little effect on global metrics but produced substantial, behaviour-specific trade-offs. Shorter bursts improved the detection of rare events by increasing training instances, while orientation correction suppressed dataset-specific artifacts at the cost of degrading common behaviours. Crucially, models with similar global and behaviour-level validation metrics produced divergent predictions when applied outside the annotated context. C_LIO_LIOur findings reveal that global metrics are insufficient for optimizing behavioural inference in complex wild systems. We demonstrate that modern deep-learning architectures, such as the ROCKET family, provide a robust, accessible baseline that handles class imbalance more effectively than traditional methods. We propose that reliable inference requires behaviour-aware evaluation frameworks that integrate ecological validation, and advocate for ensemble or hierarchical strategies to leverage the complementary strengths of different preprocessing and modelling configurations. C_LI

Accurately estimating predator diets is crucial for understanding and predicting ecosystem changes. The desire for non-lethal but accurate diet estimation techniques has led to new approaches such as quantitative fatty acid signature analysis (QFASA), a now widely accepted method for estimating the diets of marine predators. We propose a novel alternative to QFASA, namely maximum unified fatty acid signature analysis (MUFASA), to estimate dietary proportions using fatty acid signatures. MUFASA is based on maximum likelihood estimation principles and consequently offers several theoretical advantages over QFASA, including estimates that possess desirable properties when model assumptions hold. In addition, the availability of a likelihood function enables the use of a broad range of existing methodologies that may have the capacity to address current well-known challenges associated with diet estimation via fatty acids. MUFASA and QFASA are compared using simulations based on wide-ranging diets, as well as real-life data from a captive study of harbour seals, for which diets are known. Estimates derived from QFASA and MUFASA are similar, suggesting that diet estimation in this context can potentially be viewed through an MLE framework. While not the primary focus of this work, bootstrap confidence intervals are also developed and preliminary results yield high coverage probabilities when the diet proportions are not near 0 or 1.

Applications of machine learning in ecology are rapidly expanding. Symbolic regression is gaining particular attention for its success in reverse-engineering human-readable explanatory population models, including the logistic growth and Lotka-Volterra equations, from simulated and laboratory-based population time series. However, field-based populations often lack the characteristics of the idealized time series used in prior assessments. We evaluated the utility of symbolic regression for such time series by quantifying its success for synthetic data varying in sampling density, population cycle asymmetry, process noise, and the erroneous consideration of spurious variables. We further compared two data preprocessing options for estimating population growth rates, and four evaluation workflows for selecting equations. Results indicate that a trade-off between sampling density and process noise primarily drives equation and variable recovery. Symbolic regression failed to recover the underlying equation at sampling densities below 10 points per cycle; however, at higher sampling densities, process noise did increase equation recovery rates. Importantly, although the true model was frequently recovered at sampling densities of 25 or more points per cycle, it was not consistently selected by the evaluation workflows. This discrepancy highlights a need for more robust post-algorithm selection criteria to identify the focal equation among competing candidates.

O_LIStatistical methods related to causal inference are fundamental in ecological research as ecologists often deal with causal research questions. Consequently, recent years have seen an increase in articles discussing causal inference in ecological context. However, generalizing causal findings across ecological systems that differ in environmental context still remains a challenge. While we may assess causal relationships in one location or population from experimental or observational data, replicating these findings in different settings can be impractical, expensive, or sometimes impossible. C_LIO_LIWe introduce causal effect transportability to ecological research - a formal framework for transferring causal effects assessed in one domain (the source) to estimate outcomes in different domain (the target), where broader data collection may be infeasible. Using structural causal models, this framework provides formal criteria for determining when causal effects can be validly transferred between populations and derives appropriate statistical adjustment formulas when the transportation is possible. Recent algorithmic developments, implemented in accessible R software packages, automate the mathematical derivations and make transportability analysis more practical for ecologists. C_LIO_LIWe demonstrate the framework through a case study examining the effect of tree canopy cover on dissolved oxygen concentrations across different watersheds. We succeed to show that transported estimates outperform naive applications of source population models. C_LIO_LICausal effect transportability offers critical tools for predicting ecological responses across heterogeneous settings, with particular relevance when experimental replication is constrained by cost, ethics, or urgency, and when management decisions require extrapolating findings to novel environmental contexts. C_LI

AO_SCPLOWBSTRACTC_SCPLOWO_LIHigh-resolution ultrasonic recording is central to modern bioacoustics, behavioural ecology, and passive acoustic monitoring. Yet, implementing flexible, multichannel acquisition systems with real-time playback, monitoring and experimental control remains technically demanding. Existing solutions often require bespoke code development or expensive proprietary systems, limiting experimental accessibility and reproducibility. C_LIO_LII present RUBAT Studio (v4.0), an integrated software platform for multichannel ultrasonic recording, real-time heterodyne monitoring for experimental control and data acquisition in laboratory and field environments. The system supports high sample rates, configurable channel routing, automated triggering, retrospective ring-buffer capture, calibration-aware visualisation, and modular signal-processing integration. Its architecture is designed for extensibility, enabling synchronised acquisition across multiple microphones and compatibility with custom hardware configurations. C_LIO_LIField and laboratory testing with devices ranging from USB microphones to analogue microphones via professional audio interfaces confirmed stable multichannel acquisition at sampling rates up to 384 kHz over multi-hour sessions without crashes or artefacts. Real-time heterodyne monitoring, dual-channel live spectrograms, and calibrated sound-pressure-level display provided immediate acoustic and visual feedback throughout data collection. The platform enables complex experimental paradigms, including spatial localisation studies, closed-loop playback experiments, active-sensing investigations, and array-based behavioural assays, without requiring specialised knowledge of audio systems. C_LIO_LIBy lowering the technical barrier to high-performance audio recording and experimental control, RUBAT Studio expands the methodological toolkit available to behavioural ecologists and bioacousticians. The platform facilitates rigorous, scalable and reproducible acoustic research designs, enabling experiments that were previously technically prohibitive and thereby advancing the study of animal communication, spatial hearing and active sensing. C_LI

O_LIEcology has entered the big data era. This influx of data has now enabled ecologists to address questions about life history and demographic patterns across the tree of life. Supporting this momentum, the COMADRE and COMPADRE databases represent a boon to comparative demography. However, these initiatives also present challenges due to the complexities of the life cycles that they describe. Matrix population models can vary in sampling frequency, life cycle stage width, life cycle complexity, and census type (e.g. pre- or post-reproduction). Complicating this picture is the fact that there are myriad matrix model representations of the same population, and model representation influences key demographic parameters. Thus, a key challenge in exploiting large demographic datasets is fair comparison of models constructed with different projection intervals and complexities. C_LIO_LIOne way to compare models of different complexity is to reduce ( aggregate) the larger models to the dimensionality of the smaller models. The commonly used aggregator (i.e. the standard aggregator), although it yields stable growth rate and stable stage distribution consistent with the original demographic model, does not provide consistent reproductive values and elasticities. To address this limitation, we extend and validate an existing elasticity-consistent aggregator, overcoming several of its methodological and biological limitations. Specifically, we derive the aggregator using balancing and interstage flows, which removes the requirement of matrix primitivity, and we restrict aggregation to Leslie-to-Leslie models, thereby preventing biologically infeasible survival probabilities exceeding one. We further introduce explicit metrics of aggregation effectiveness and apply the approach to 12 Leslie matrix population models representing animal populations from diverse taxonomic classes. C_LIO_LIThe elasticity-consistent aggregator returns a Leslie matrix that preserves key properties of the original matrix, including irreducibility and primitivity, and yields consistent estimates of population growth rate, stable age distribution, and reproductive values. Moreover, across all aggregated models from the 12 examined animal populations, the elasticity-consistent aggregator produced more accurate estimates than the standard aggregator for 86% of generation times, 60% of Demetrius entropies, and 76% of net reproductive rates. C_LIO_LIBy preserving key properties of the original model, the elasticity-consistent aggregator provides a useful framework for comparing matrix population models of varying complexity in comparative demography. Such a method helps seize the promise of big data in ecology and discover principles across the tree of life, from microbes to fungi, plants, and animals. C_LI

Much ecological research focuses on phenomena where a given variable can affect another either directly or indirectly through the effect of one or more intervening variables. While various methods to quantify the magnitude of these effects are available, they can be difficult to interpret in a meaningful way, especially for indirect effects. Studies thus often quantity direct effects and infer indirect ones more or less formally. We propose a method for visualizing indirect effects by means of plotting model predictions of an outcome in the presence of both exposure and mediator variables. We demonstrate the method through simulations and apply them to a real-world example involving Tree Swallows (Tachycineta bicolor) and their obligatory hematophagous ectoparasites, Protocalliphora bird blowflies (Diptera: Calliphoridae). Our procedure, which can be seamlessly integrated into an analysts workflow using commonplace software, should prove instrumental to disentangle and interpret relationships among variables involved in ecological mechanisms.

The influence of environmental factors on the dynamics of living organisms can imply non-linear relationships. Some of them exhibit threshold effects. Hyperbolic functions effectively represent ecological processes that display threshold behaviours, such as those described by the law of the minimum, or law of the limiting factor. However, the mathematical formulation of the hyperbola is complex, which makes its use challenging and its parameters difficult to interpret. In this article, we propose an efficient mathematical formulation for the hyperbola, one in which all the parameters are independent and easily interpretable. We also provide an R script and a Python script to facilitate the implementation of this hyperbolic formulation in modelling studies. We then used this new hyperbolic function to model the influence of edaphic and climatic factors on the growth of 18 forest tree species widely distributed across Europe based on a dataset of 8,330 plots from the French National Forest Inventory. Our hyperbolic function allowed us to identify the threshold effects of summer climatic constraints on forest growth for several species. In particular, we found negative effects for soil water deficit and maximum summer temperature, although for several species these effects only appear beyond a certain level of constraint. Accounting for such threshold effects is crucial to improve our ability to understand and predict forest ecosystem responses in the context of climate change.

Accurately localizing vocalizing animals from passive acoustic recordings remains challenging in complex outdoor soundscapes. Although automated detection and classification of bird calls have advanced rapidly, reliable spatial localization has lagged, particularly for small arrays of autonomous recorders operating without manual intervention. Here we describe a fully automated pipeline for three-dimensional localization of bird vocalizations using distributed networks of 4 to 6 GPS-synchronized recorders deployed in heterogeneous forest environments. Our framework integrates automated multi-recorder event matching, frequency-selective FFT-based cross-correlation for time-difference-of-arrival (TDOA) estimation, geometric cycle-consistency filtering to resolve ambiguous cross-correlation peaks, and nonlinear optimization of source location and effective sound speed. Applied to multi-year datasets from three field sites, the localizations exhibit strong concordance of localizations with independently known landscape features and species-specific habitat associations. These analyses indicate that small, practical arrays can recover ecologically meaningful spatial structure in complex soundscapes without manual curation. This preprint documents the current state of the system and its performance under realistic field conditions.

O_LIRoot colonisation by endomycorrhizal fungi can indicate habitat condition. However, due to the significant time required to assess colonisation using traditional microscope techniques, studies of colonisation at large scales are impractical. AI-powered approaches may increase output and facilitate ecosystem assessments. C_LIO_LIWe trained our AI-powered tool MycorrhizaFinder (MFKew) on field roots from diverse ecosystems. It was trained to recognise a range of arbuscular and ericoid mycorrhizal fungal structures, and to differentiate dark septate endophytes common in field-sourced roots. C_LIO_LIHere we describe the semi-automated workflow from root processing and microscope slide scanning to model training and performance evaluation, proposing Macro F1 as the appropriate metric to be optimised. Without human supervision, Macro F1 currently stands at 66% for arbuscular and at 57% for ericoid mycorrhizal colonisation assessment. C_LIO_LIMFKew is user friendly, requires no programming skills and offers flexibility for advanced users who wish to further train the tool using their own labelled mycorrhizal root datasets, including images acquired from different devices or staining protocols. This adaptability allows users to customize the model for specific needs, making it optimal for ecologists and agronomists. Additionally, MFKew supports large-scale, repeatable, medium-throughput monitoring across ecosystems, enabling the assessment of mycorrhizal status and tracking changes over time. C_LI

O_LIDemographic processes in populations are inherently heterogeneous across both space and time. Many ecological models explicitly account for temporal heterogeneity in the demographic rates that govern these processes, but assume spatial homogeneity. Ignoring spatial heterogeneity can bias inference, limit predictive performance, and obscure key spatial structure in demographic rates. Integrated population models (IPMs) offer a powerful framework to estimate spatio-temporal demographic rates by combining diverse ecological data sources collected from multiple sampling locations. However, to accomplish this, IPMs face significant statistical and computational hurdles, including misalignment between different data sources and the need to efficiently account for residual spatial autocorrelation. C_LIO_LIWe present a novel Bayesian spatially explicit integrated population model (sIPM) which integrates population count and capture-recapture data from multiple sampling locations to estimate and predict continuous spatio-temporal demographic rates, such as survival, recruitment and population growth rate, across large geographic domains. This framework employs a joint likelihood approach with change of support to flexibly accommodate spatial and spatio-temporal data misalignment, and incorporates a nearest-neighbor Gaussian process to efficiently model residual spatial autocorrelation and generate spatial predictions. C_LIO_LIWe assess the performance of our sIPM through an extensive simulation study. Results show that our approach provides unbiased and precise estimates and predictions of spatio-temporal demographic rates, even in the presence of significant data misalignment and residual spatial autocorrelation. We demonstrate the utility of our method by analyzing data on Gray Catbirds (Dumetella carolinensis) from the North American Breeding Bird Survey and the Monitoring Avian Productivity and Survivorship program across the eastern coast of the United States from 2004-2014. This analysis results in maps of apparent survival, recruitment and population growth rate, thereby revealing important spatio-temporal variations in demographic rates that would have been obscured by traditional, spatially homogeneous IPMs. C_LIO_LIOur sIPM offers a robust and computationally efficient method for studying spatio-temporal variation in demographic processes across large areas, even in the presence of data misalignment and residual spatial autocorrelation. Ultimately, this framework, applicable to many ecological monitoring programs, facilitates the development of spatially targeted strategies necessary for effective conservation and management. C_LI

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.

O_LIMovement paths reflect temporal shifts in behavioural states, typically driven by internal and external drivers. However, the inherently multiphasic nature of these trajectories is frequently overlooked in empirical studies, an oversight that can hinder progress in our understanding of movement ecology. While Hidden Markov Models (HMMs) can successfully identify latent states--such as foraging or travelling--they face significant challenges, particularly in determining the appropriate number of states and in interpreting their ecological relevance in the context of both movement patterns and environmental covariates. C_LIO_LIWe present a framework based on Hidden Markov Models with Step Selection Functions (HMM-SSFs) that identifies behavioural states, represented by ecologically meaningful labels linked to explicit hypotheses about animal movement, that best explain observed movement patterns. The framework imposes interpretable conditions and diagnostic criteria on the post-identified behavioural states to ensure ecological coherence. It is grounded in the evaluation of biologically motivated scenarios rather than purely data-driven partitioning. The framework proceeds in two main steps: first, movement-based states are identified using movement-derived covariates only; second, these states are refined by incorporating environmental predictors, such as habitat structure or species interactions (e.g., predator-prey dynamics). This sequential integration enables the detection of ecological responses that are conditional on behavioural context. C_LIO_LISimulations show that the framework effectively recovers behavioural states across most conditions. State decoding accuracy was notably higher when control locations were drawn from an exponential-family distribution, compared to a uniform one. The exponential-family approach improved state separation and reduced mislabelling, especially when few control locations are generated. However, low state persistence--particularly in Encamped behaviours--resulted in an overestimation of the number of states. These findings underscore the influence of transition probabilities on behavioural labelling. Finally, we applied our framework to zebra (Equus quagga) movement data by combining movement predictors with changes in direction toward the nearest preferred habitat. This enabled us to distinguish between habitat-dependent and habitat-independent travelling behaviours, as well as to identify spatially finer-scale such as encamped state. C_LIO_LIThe proposed framework balances complexity and biological interpretability by using basic movement metrics to identify the behavioural states and their sequence that best explain multiphasic movement paths, together with environmental factors directing movement in each state. Unlike traditional methods that predefine the number of states, the framework estimates both state number and labels, offering a flexible and ecologically meaningful approach for behavioural inference. C_LI

Vascular plants are a major component of terrestrial diversity, yet they are overrepresented among the worlds threatened species. To effectively manage this biodiversity crisis, data with high spatiotemporal resolution are crucial, yet often lacking for plants. Environmental DNA analysis (eDNA) is capable of rapid detection of biodiversity by metabarcoding the collection of DNA molecules retrieved from environmental samples such as soil cores. The technology may soon support the generation of broad-scale longitudinal plant community data, yet much methodological work on sampling strategies and analytical choices remains if soil-based eDNA is to become a reliable tool for monitoring terrestrial plant communities. Therefore, this dual purpose study in seven Swedish semi-natural grasslands investigated if and when eDNA-generated community data can be used as a stand-alone information source 1) to inform on the presence of a rare, small-statured grassland specialist (Gentianella campestris) and 2) to simultaneously infer community compositional change. We demonstrate eDNA to be an effective means of finding a rare species in a highly taxonomically diverse habitat, uncovering G. campestris DNA in 31% of the core samples. Evidence suggests the eDNA signal reflects recent spatio-temporal population dynamics at fine spatial scales. Although the entire plant community was not uncovered, molecular community data proved a representative subset, effectively capturing changes in community diversity and composition at plot sizes commonly used for plant surveys. Choices surrounding typical RRA-filtering had significant bearing on eDNAs discriminating power: filtering may overly conservatively remove true observations of a rare species, while filtering highly localized plot noise led to more robust patterns emerging in species richness and plant composition turn-over. Given careful alignment of study goals and sampling strategies, soil-based eDNA may already provide a stand-alone tool for generating reliable, scalable and observer-independent longitudinal data for unveiling and monitoring changes in plant diversity in terrestrial habitats.

O_LIStandardising temperature data across heterogeneous study sites is essential for ecological meta-analyses, yet elevation-driven lapse rates often confound direct comparisons of coarse-grid climate data. Ecological studies frequently document only site altitude - particularly historical datasets - limiting analysis of thermal influences on spatial organism distribution. C_LIO_LIA dual-approach protocol was developed to derive regional correction factors ({Delta}H) from altitude-temperature regressions (Lapse Rate Method: SW Germany/Italian Alps, n=33 stations) and cross-regional station pairs (TAV Matching Method, n=27) with closely aligned long-term mean temperatures ({Delta}TAV [≤] 1.2{degrees}C). Applied to 109 Ixodes ricinus study sites across nine European regions, correction factors were calculated only for regions with consistent altitude shifts ({Delta}H > 100m) relative to Southwest German reference stations. C_LIO_LIRegional correction factors ({Delta}H) from both methods included +1300 m (Finland, TAV Matching), +370 m (Netherlands/NE Germany, TAV Matching), and -220 m (Italian Alps, Lapse Rate Method) across five regions. In total, 27 cross-regional TAV matched pairs demonstrated high matching precision (median {Delta}TAV = 0.05{degrees}C, 89 % [≤] 0.2{degrees}C). These factors standardised site altitudes to a common SW German thermal reference frame, enabling cross-site comparability. C_LIO_LIThe dual-method protocol requires no automation and is applicable to any taxa with documented site altitudes. The complete methodological workflow - including station data, lapse rate regressions, matching decisions, and correction calculations is publicly available at Zenodo [DOI 10.5281/zenodo.18835116], providing ecologists with a pragmatic, fully reproducible template for elevation-standardised temperature estimation in meta-analyses. C_LI

Integral projection models (IPMs) are a powerful tool for predicting structured forest dynamics under global change. Inverse calibration approaches allow IPMs to be fit using widely available forest inventory data increasing potential applications. Yet inventory data may not provide sufficient information for IPMs to accurately identify underlying demographic rates leading to poor predictions. We construct a Bayesian dynamical IPM framework to integrate forest inventory (population density) with tree ring (individual growth) data to better predict size-structured forest dynamics. The framework pairs an IPM process model with data models that control for individual growth variability and a mismatch in the scale of forest inventory and tree ring data. The model is applied to a combination of experimental forest and simulated data to assess its ability to predict size-structured population density and estimate underlying demographic rates. We focus on the ability of the model to make inference about high-frequency variables associated with weather extremes and disturbance given their importance for predicting forest dynamics under global change. Predictions of size-structured population density were similar regardless of whether the dynamical IPM was provided both forest inventory and tree ring data (integrated model) or forest inventory data alone (population model). The population model, however, did not identify annual growth effects driven by high-frequency weather variables leading to poor estimates of population growth rate. Simulation trials under which the integrated model was provided varying numbers of tree ring records indicated that 10 records were sufficient for the model to estimate annual growth effects with near equivalent inference when 30 or more records were applied. Results highlight the potential for inversely calibrated IPMs to correctly predict structured population dynamics while incorrectly estimating underlying demographic rates. Integrating individual demographic data resolves this issue allowing for inference on growth responses to high-frequency weather and disturbance variables, thereby improving the ability of IPMs to predict structured forest dynamics under global change. While individual demographic rate data is often limited, simulation results indicate that only a small number of individual records are needed for valid inference.

O_LISpecies occurrence data are fundamental to understanding, predicting, and conserving global biodiversity. However, biodiversity datasets remain affected by substantial data-quality issues, particularly erroneous or imprecise geographic coordinates. Most available tools for identifying problematic records rely primarily on automated spatial or metadata-based checks and rarely integrate expert-curated species range information, which can reveal introductions or geographic errors that often escape standard validation procedures. C_LIO_LIHere, we introduce RuHere, an R package designed to manage species occurrence data, flag potential errors, and support the iterative exploration of problematic records. RuHere streamlines the data-cleaning process by integrating six main steps: (1) obtaining species occurrence records; (2) merging datasets and standardizing spatial information; (3) flagging records based on metadata; (4) flagging records using expert-derived distribution data; (5) visualizing, investigating, and summarizing flagged issues in the final datasets; and (6) exploring and reducing sampling bias. C_LIO_LIWe demonstrate the applicability of RuHere using occurrence data for a plant species (Araucaria angustifolia) and an animal species (Cyanocorax caeruleus). Nearly 75% of records were flagged as potentially problematic, including records identified exclusively by functions relying on specialist range information. C_LIO_LIThe main strengths of RuHere lie in its integrated and computationally efficient workflow, its tools for exploring and evaluating flagged records, and its ability to incorporate expert-derived distribution data to identify occurrences outside a species known natural range. By combining metadata-based checks, coordinate validation, and specialist knowledge, RuHere provides a robust and reproducible framework for improving the quality of species occurrence datasets. C_LI

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