Movement Ecology

O_LIStudying animal movement at the population scale requires a stable, modern software substrate. Within R, the legacy move package supplied that substrate for over a decade, but its sp/rgeos backbone has been retired. The successor package move2 deliberately confined its scope to the data class and core movebank API functions. C_LIO_LIThe analytical machinery of move, namely dynamic Brownian-bridge utilisation distributions, the directional bivariate-Gaussian variant, corridor segmentation, and along-track thinning, was left to port to the modern sf/terra stack. C_LIO_LIWe present move2utils, an R package that completes and complements that transition. move2utils provides move2-native ports of the move analytical functions, preserves the original C kernels where they exist, and replaces the deprecated spatial scaffolding around them. It additionally ports some of the legacy R-based code to faster C kernels to improve computational speed. move2utils also exposes novel outlier-detection methodology described in detail in a companion paper. C_LIO_LIThe package is open-source (GPL [≤] 3), is developed on the MPCDF GitLab and mirrored on GitHub for public installation, and ships with vignettes and a CI-tested check suite. We illustrate it with a worked example on real tracking data and synthetic datasets. C_LI

Hidden Markov models are standard for inferring behavioural states from animal movement data, but checking whether a fitted latent-state model predicts held-out movement well remains difficult. We develop sequential predictive e-diagnostics that evaluate a fitted movement HMM as a generator of validation trajectories. Each diagnostic specifies a predictable alternative density, and its ratio to the fitted models observable one-step predictive density defines an e-value increment. The denominator is obtained by filtering over latent states, not by conditioning on a decoded path. Under a fixed train/validation protocol, the cumulative product is an e-process, giving anytime-valid thresholds under optional stopping and predictable switching. The construction extends to weighted and state-localized evidence, feature-level circular-linear checks, and blockwise summaries. Controlled simulations show calibration under the fitted-generator null and sensitivity to targeted misspecifications. A leave-one-animal-out elk case study illustrates pooled, individual-specific and state-localized predictive model criticism in a standard movement-HMM workflow.

Despite the ecological importance of ungulate migrations, we lack a complete understanding of why some ungulates migrate and others do not. Though progress has been made towards understanding differences across species and between populations, migratory behavior varies even within populations: in many populations, some individuals remain behind as residents (partial migration). Theoretical population-level work has suggested that these different migratory tactics can coexist, but such approaches stop short of providing insights into how individuals make the decision to stay or go each year. Using long-term data from three ungulate populations, we find that individuals probabilities of migrating are highly variable across years, which points to a non-trivial context-dependent decision-making process, whose underlying mechanisms must be probed via individual-level modeling. Drawing on existing knowledge, we propose a decision-making model of ungulate migration onset wherein individuals probabilistically decide to start migrating based on the local intensity of environmental and/or social cues. Residents arise as a robust collective organization phenomenon in our model. At sufficiently large population sizes, the number of residents is invariant with total population size, consistent with empirical patterns. Instead, resident numbers are influenced by the severity of the bad season, by relevant character differences among individuals, and by how individuals contribute and respond to environmental and/or social cues; for instance, when social cues contribute to decision-making in addition to environmental ones, fewer residents result, and migration is more likely to be complete. Overall, our model provides a potential mechanistic explanation for how residents might emerge within migratory ungulate populations.

Various hypotheses have been proposed to explain why some species persist or even flourish in urban areas. Yet, despite its central role in determining when and where animals encounter resources, disturbance, and risk, movement behaviour remains an overlooked mechanism of urban success. In urban areas, human activities are strongly periodic, i.e. predictable in space and time. This may favour species able to adjust their behaviour to predictable cycles of resources and risks in space and time. Here, we tested this hypothesis and tracked movement behaviour along an urbanisation gradient in three mammal species with different urban success: red fox (Vulpes vulpes), an urban dweller; raccoon (Procyon lotor), an invasive urban dweller; and wild boar (Sus scrofa), an urban utiliser. We analysed periodicity in movement behaviour and investigated whether increasing urbanisation is associated with periodic reorganisation of activity timing, space use, and further analysed alterations in habitat selection along the urbanisation gradient. Our results show that foxes aligned their movement behaviour with human activity, having stronger day-night contrasts and more repeatable space use than their rural counterparts. Urban raccoons showed a contrasting strategy; they were more active during the day, without changes in their movement routines under increasing urbanisation, suggesting a flexible strategy that explains their urban success. In contrast, wild boars reduced routine movement behaviours with increasing urbanisation, consistent with their occurrence in less predictable suburban environments and avoidance of city centres. In summary, our results suggest that movement behaviour may be a key mechanism enabling animals to persist in cities, revealing distinct behavioural strategies for coping with urban environments.

Collective movement requires coordination between individuals, yet how this emerges during early interactions remains poorly understood. We investigated how partner familiarity influences coordination, leader-follower dynamics, and learning in homing pigeon pairs navigating from novel sites. Birds were released repeatedly with either familiar or unfamiliar partners, followed by solo releases to assess learning. By quantifying bidirectional information flow, we found familiarity influenced information-transfer dynamics during the first release: familiar pairs exhibited more asymmetric information transfer, likely reflecting established leader-follower relationships, whereas unfamiliar pairs showed more symmetric exchange. These differences disappeared after one release. Conversely, familiarity had little effect on cohesion or navigational performance. There was some evidence for an influence on learning: birds from familiar pairings had higher homing efficiency on a subsequent solo release. Finally, across partnerships, followership was more predictable than leadership with respect to individual identity and flight speed, indicating stable variation in individuals' tendency to follow rather than lead. This suggests that a shift in emphasis from leadership to followership might enhance our understanding of collective decision-making dynamics. Our results demonstrate how flight partners rapidly coordinate, producing limited downstream effects on navigation and learning, with implications for many animals that travel in fission-fusion transitory collectives.

Fledging is a critical period of a seabird life cycle. Using satellite telemetry, we compared movements and survival proxies (transmission duration) of chinstrap penguin fledglings tracked in 2017 (n=8) and 2025 (n=17) relative to krill fishing vessel activity. In 2017, fishing vessels operated intensively near colonies during summer, resulting in early, frequent encounters (median 1.3 days post-fledging) and short transmission durations (median 9.2 days). In 2025, reduced fishing delayed encounters (median 10.0 days) and tripled tracking duration (median 24.0 days). Hidden Markov Models revealed that vessel encounters reduced the probability of transitioning from foraging to transit behavior ({beta} = -0.76), an effect stronger than the productivity ({beta} = -0.11). While 87.5% of 2017 fledglings ceased transmission prematurely within weeks (half of those right after entering areas intensively used by fishing vessels), 65% of 2025 fledglings survived beyond March, with half of those five transmitting until May after dispersing eastward to the South Orkney Islands. These findings suggest that spatiotemporal overlap with krill fisheries during the critical post-fledging window affected foraging behavior and was associated with shorter transmission durations. Our results support further research of post-fledging penguin ecology to better understand the potential impact of fishery, and, following the precautionary principle, support fishing seasonal protection of important areas during critical periods of krill predators life cycle.

O_LIMonitoring and limiting the spread of invasive species on islands requires efficient detection and population estimation methods. However, elusive species can be difficult to monitor using traditional methods, making autonomous approaches such as camera trapping and acoustic monitoring increasingly valuable. C_LIO_LIOn the island of Okinawa, Japan, the small Indian mongoose ( Urva auropunctata) threatens many native species since its introduction in 1910. Listed among the worlds worst invasive species, effective monitoring of U. auropunctata in Okinawa is critical. The Okinawa Environmental Observation Network (OKEON) uses camera traps to detect U. auropunctata, but success depends on precise placement. Though OKEON also includes a high-resolution acoustic monitoring programme, no audio classification model currently exists for U. auropunctata. Developing such a model could improve substantially our capacity to detect and manage the species. C_LIO_LIUsing sparse U. auropunctata vocalisations collected from camera trap videos, we built a lightweight Convolutional Neural Network distilled from a more complex model for classifying contact calls and alarm calls of U. auropunctata. Our distilled model performed similarly to the full model at detecting vocalisations from training data, but was considerably faster. C_LIO_LIWe applied the distilled classifier to [~]486 hrs of audio collected over eight years from southern Okinawa, where we successfully detected U. auropunctata a handful of times in each year of recording. In spite of strong model performance on test data, our model did not transfer well to unseen data, perhaps owing to the rarity of U. auropunctata calls and consequent small training dataset size, limiting its utility for ecological monitoring. C_LIO_LIPractical implication. The use of sparse audio data from camera trap videos to train an acoustic classifier had limited utility to detect the rarely vocalising U. auropunctata from passive acoustic monitoring data. We provide several recommendations for enhancing classifier performance to provide robust actionable insights into the distribution and spread of U. auropunctata, and aid targeted conservation efforts for Okinawas threatened biodiversity. C_LI

High Pathogenicity Avian Influenza (HPAI) H5N1 clade 2.3.4.4b has spread beyond birds to affect seals across the Southern Ocean and sub-Antarctic region, with southern elephant seals (Mirounga leonina) particularly devastated. The virus, likely introduced via spillover from infected migratory birds, has killed tens of thousands of adult seals and pups throughout most of their range, though Macquarie Island remains unaffected so far. We used twenty years of elephant seal movement data from the southern Indian and Pacific oceans to assess whether seal-to-seal transmission could spread HPAI H5N1 between breeding colonies, despite the vast distances separating them (Marion Island, Iles Crozet, Iles Kerguelen, and Macquarie Island). There was substantial overlap in seals' at-sea distributions during their winter post-moult trips, when seals travel for weeks at average speeds of 3.5 km/h. Two transmission pathways were examined: (1) terrestrial "stepping stone" routes, where infected seals could pass the virus between colonies during short intervals to remain infectious were feasible from Marion Island to Kerguelen but not from Kerguelen to Macquarie Island; and (2) at-sea encounters between seals, which occurred frequently enough to enable transmission. The findings suggest that once established at Macquarie Island, the virus could potentially spread further to New Zealand's sub-Antarctic islands and mainland New Zealand. While seal-to-seal transmission appears possible, we conclude this is unlikely. Nonetheless, understanding at-sea contact rates enhances knowledge of H5N1 epidemiology and demonstrates the value of combining long-term population monitoring with movement data to understand wildlife disease dynamics.

Age-dependent survival is central to understanding population dynamics and life-history evolution. We analysed carcass weight and age-at-harvest data from 6022 red foxes (Vulpes vulpes) collected across Sweden between 1967 and 1971 to evaluate latitudinal effects on body mass and age-dependent survival. Carcass weights decreased from south to north in both adults and sub-adults, contrary to Bergmann's rule, with southern foxes weighing approximately 1.27 times more than northern foxes. The latitudinal weight gradient exceeded the sex difference in both age classes, and no sex x region interaction was detected. The decrease in weight with latitude is consistent with reduced prey availability and harsher winter conditions in the north, which limit growth and body size during development. Using a Bayesian age-at-harvest model with region-specific population growth rates (lambda), we estimated age-dependent survival probabilities for four latitudinal regions and both sexes. Despite the strong latitudinal gradient in weight, survival did not show a corresponding pattern - regional differences were uncertain, with all credible intervals spanning zero. Regional population growth rates were consistent with slight decline in the north and near-stability in the south-central region, which suggests that body condition and population dynamics are coupled at the regional scale despite no survival gradient. The decoupling of body condition and survival across regions suggests that mortality patterns are similar across the latitudinal gradient. We discuss these patterns in terms of latitudinal productivity gradients, prey availability, and life-history trade-offs in a widely distributed carnivore.

Antarctic benthic organisms are highly diverse and play a critical role in the Southern Ocean ecosystem. Despite decades of sampling, vast areas of the Antarctic continental shelf remain biologically unsurveyed due to logistical and financial constraints, limiting baseline knowledge essential for effective conservation planning. Species distribution models (SDMs) allow biodiversity to be inferred in the absence of biological data by linking benthic community patterns to environmental predictors. However, the resolution of the environmental predictors, particularly bathymetry, varies significantly between regions, casting doubt about how reliably SDMs can be used to predict into regions where only coarse-resolution data are available. Here, we show that SDMs trained on high-resolution data underestimate Antarctic benthic morphospecies richness by up to 18% when applied to aggregated coarse-resolution environmental data (and up to 50% when using satellite-derived ETOPO bathymetry). Using six systematically degraded versions of high-resolution multibeam bathymetry and annotated seafloor imagery across three Antarctic regions, we evaluate SDM performance both with and without additional environmental variables. High-resolution bathymetry captures terrain complexity most effectively, but we find that the spatial distribution of richness hotspots and the median richness per cell remain consistent, provided models are applied at the same resolution at which they were trained. Our results suggest that while high-resolution bathymetry may enhance local predictions, coarse-resolution data may be more robust for regional-scale predictions, such as those used for Antarctic shelf-wide spatial planning.

Collective movement in social organisms emerges from local interactions and can generate large-scale spatial patterns of ecological relevance. In termites, trail formation is a well-known collective phenomenon, yet reproducing and recording its emergence under controlled laboratory conditions using whole colonies remains challenging. Existing laboratory approaches often rely on confined arenas or manually assembled subgroups, which can restrict movement and limit observation of colony-level dynamics. Here, we present a semi-folded arena designed for whole-colony observation of termite movement under controlled conditions. We developed a circular semi-folded arena that remained continuously connected to an intact nest and allowed individuals to move across a central observation surface while recirculating through a folded peripheral section. Using whole colonies of the Neotropical termite Constrictotermes cyphergaster, we recorded exploratory activity under baseline conditions, in the absence of added food or water. High-resolution video recordings were analysed using automated movement extraction to recover trajectories and visualise collective trail structure. Within the first 6 min of activity, collective trail structure was observed in 15 of the 16 colonies analysed. Under these conditions, the semi-folded setup captured early collective trail structure, visible as convergence of cumulative trajectories along shared routes radiating from the arena entrance region. Automated movement extraction was compatible with dense whole-colony recordings and yielded large quantities of positional data during the initial observation interval. Descriptive trajectory-based outputs, including speed distributions for workers and soldiers, showed that the recordings were suitable for recovery of fine-scale movement information. Repeatedly used routes were also often marked by visible dark traces on the paper lining by the end of the observations, providing a qualitative record of cumulative route use. The semi-folded arena provides a practical method for recording whole-colony termite movement under laboratory conditions while maintaining continuous nest access and avoiding manual transfer of individuals during trials. Rather than replacing conventional arena designs, this approach offers an additional methodological option for studying emergent movement patterns in species for which whole-colony observation is feasible. More broadly, it expands the experimental toolkit available for investigating colony-scale spatial organisation under controlled conditions.

Littoral mysids facilitate benthic-pelagic coupling through horizontal migration, yet quantitative monitoring in structurally complex habitats remains methodologically challenged where traditional active gears fail. We evaluated the efficacy of standardized light traps for monitoring littoral mysids (Neomysis integer, Praunus flexuosus) and mesopredatory three-spined sticklebacks (Gasterosteus aculeatus) in the northern Baltic proper, Baltic Sea. Using a paired experimental design with predator-exclusion and unmodified traps, alongside concurrent passive benthic trapping, we assessed abiotic drivers affecting catchability, biotic interactions, and statistical power to monitor changes in population size over time. Results indicated significant biotic interference: unmodified traps attracted high densities of sticklebacks, which reduced mysid catches by approximately 85% through predation or behavioural avoidance. Consequently, physical predator exclusion is mandatory for accurate mysid sampling. Generalized Linear Mixed Models (GLMMs) confirmed that catch rates for all taxa were primarily driven by night duration rather than water temperature. While passive benthic trap catches tracked metabolic activity (peaking in warm summer months), light trap efficiency peaked in spring and collapsed during summer, confirming that sampling efficiency was strictly limited by the short duration of the night. Simulation-based power analysis revealed a stark contrast in monitoring utility based on spatial aggregation. For highly aggregated mysids, the method demonstrated low precision (Power < 0.25 to detect a 50% decline), rendering it suitable primarily for detecting substantial population collapses (>90%). In contrast, for less aggregated sticklebacks, the method achieved a more robust statistical power (>0.80 for a 60% decline), validating light traps as a precise tool for monitoring these abundant mesopredators. We conclude that light traps fill a critical methodological gap for winter and early spring monitoring when traditional passive gears underperform. Appropriate abundance indices should be based on statistical models accounting for night duration and strictly employ physical exclusion barriers when targeting mysids.

Aim: To forecast near-future arrivals of non-native terrestrial and freshwater vertebrates at the regional level. Location: Global (geopolitical regions worldwide, including countries and main administrative divisions). Methods: We compiled first regional record data and assembled functional and macroecological variables for 1,931 non-native vertebrate species. For each region, we identified recently arrived non-native species using retrospective windows of thirty and twenty years ending in 2015 (1986-2015; 1996-2015). We then fitted region-specific random-forest models classifying recently arrived species versus those not yet arrived using as predictors: (i) harmonised species traits (e.g., habitat, diet, body size and native-range attributes) and (ii) spread history, capturing time since first record elsewhere. Predictive performance was evaluated using leave-one-out cross-validation, comparing full models with trait-only and spread-only variants. We also assessed relationships between predictive accuracy, predictor importance, and the geographic positioning and trade connectedness of regions. Finally, we predicted region-specific probabilities of arrival for species not yet recorded. Results: Forecasting accuracy was consistently high across regions and taxa, with AUC values above 0.9 in more than half of the focal regions. Full models substantially outperformed models using either predictor set alone, and spread-history-only models typically exceeded trait-only models. Relative importance of spread-history predictors declined with geographic distance to the focal region, whereas predictability was lower in highly trade-connected regions. Predicted near-future high-risk arrivals were dominated by birds and freshwater fishes and showed strong regional structuring. A small set of species ranked highly across many regions (e.g., birds: Phasianus colchicus, Acridotheres tristis, Amandava amandava, Colinus virginianus, Corvus splendens and Lonchura malacca; fishes: Coregonus peled and Oreochromis mossambicus; mammal: Oryctolagus cuniculus), suggesting substantial unrealised spread potential. Main conclusions: Near-future regional arrivals of non-native vertebrates are predictable from spread history and species traits. This enables scalable, updateable regional watchlists to support prevention, early detection and horizon scanning.

Effective conservation depends on demographic metrics that reliably reflect species status, particularly population abundance. For elusive species occurring at low densities, however, such metrics remain difficult to obtain. Spatial capture-recapture (SCR) models are the standardized approach for estimating density in marked populations, but their data requirements, especially the need for multiple spatial recaptures across individuals, often limit applicability in small or data-poor populations. This constraint has resulted in knowledge gaps for some of the most vulnerable species, undermining evidence-based conservation planning and management. Using camera-trap data and SCR-derived density estimates from data-rich populations, we evaluated alternative, less data-demanding metrics and tested the hypothesis: Space to Event (STE), Mean Local Abundance (MLA), and Relative Abundance Index (RAI) exhibit predictable relationships with SCR-derived density; if supported, these metrics can reliably estimate density in populations where SCR models cannot be implemented. We applied this framework to the European wildcat (Felis silvestris), an elusive small felid with highly fragmented populations across Europe, for which density estimates are largely lacking despite growing conservation concern. Across 21 study areas spanning most of the species' range, our results indicate that European wildcats generally occur at lower densities than previously reported. SCR-derived estimates (n=10) averaged 10.32 {+/-} 11.56 inds/100km2, while STE enabled density estimation in five additional data-poor areas (mean 5.52 {+/-} 5.33 inds/100km2). STE showed a strong linear relationship with SCR-derived density (R2=0.98), supporting its use as a viable alternative when SCR is infeasible, although it tended to underestimate compared to SCR, especially at higher densities. In contrast, MLA and RAI showed weaker and non-linear relationships with SCR-derived density (R2=0.65), indicating substantially lower explanatory power and suggesting their estimates are more strongly influenced by confounding processes. By explicitly calibrating alternative metrics across a wide density gradient throughout most of the species' distribution, this study provides a transferable methodological framework for estimating density in low-density wildlife populations and the first continent-wide, standardized density assessment of a carnivore species. From a management perspective, our findings identify populations that may be most vulnerable, particularly those with the lowest densities, and highlight the need to prioritize absolute abundance monitoring.

Scientific bottom-trawl surveys provide essential fisheries-independent data for fisheries and ecosystem research. In the Northeast Atlantic, the ICES Database of Trawl Surveys (DATRAS) compiles haul-level information, species- and length-specific catch data, and individual biological observations across multiple long-term surveys. However, reproducible workflows for processing and integrating these relational datasets remain challenging. We present DATRASextra, an open-source R package that provides modular end-to-end workflows for accessing, cleaning, harmonising, quality-controlling, and analysing DATRAS survey data. The package supports derivation of standardised haul-level survey variables, integration of multiple surveys, and generation of analysis-ready datasets for downstream applications including stock assessment, biodiversity analyses, and large-scale synthesis efforts such as FishGlob.

Species distribution models (SDMs) increasingly inform conservation and biosecurity decisions in freshwater systems, where the reliability of its uncertainty estimates matters as much as its point predictions. Ensemble SDMs derive prediction intervals from across-replicate variance, but this variance captures systematic error only when replicates disagree about it, an assumption that fails when training data are contaminated with low-accuracy records, the norm in citizen-science datasets. Whether this failure is spatially uniform or concentrates in identifiable parts of a range is unknown. Using a panel of European freshwater crayfish spanning native headwater-associated species and invasive lowland colonizers, we show that contamination-induced calibration failure is strongly spatially structured: it concentrates at stream-network headwaters, the topological tops of the network, where upstream-aggregated predictors are structurally undefined, and scales with contamination severity, replicated across four species and both dominant ensemble protocols (replicate and consensus). The failure is driven by upward prediction bias, not by intervals failing to widen: contaminated ensembles overpredict suitability in headwaters, and because the bias is shared across ensemble members, the intervals do not flag it. This is a conservation-relevant blind spot, because headwaters are both refugia for threatened native crayfish and front lines for invasion; an SDM that silently overpredicts suitability there misdirects survey and management effort toward the segments where its predictions are least trustworthy. Standard leave-one-basin-out conformal calibration, the recommended panel-wide remedy, repairs marginal coverage but leaves headwaters undercovered, because a single calibration threshold is dominated by the abundant non-headwater segments. A group-conditional (Mondrian) variant, calibrating the two populations separately, restores reliable coverage in both at no extra cost and reallocates width where it is needed. We recommend network-position-stratified calibration as a default for ensemble SDMs in dendritic freshwater systems.

Mass mortality events (MMEs) create sudden pulses of carrion that can alter how vertebrate scavengers use carcass resources, including the frequency, duration, and timing of species-carcass contacts. These changes could have implications for pathogen transmission at the scavenger-carcass interface. We aimed to develop and apply a reproducible analytical framework for using camera-trap data to quantify transmission-relevant vertebrate activity at carcass sites under differing carrion biomass scenarios. We studied experimental carcass plots (single carcass ~43 kg; 'mass mortality' plots [10 carcasses, >350 kg total]; 6 of each) in Australia's alpine ecosystem. The framework integrated descriptive summaries (bipartite network analysis, Kaplan-Meier curves) and marked temporal point-process models to characterise structural and temporal dimensions of species-carcass activity. Mass mortality plots had greater overall visitation duration, occurring as sustained activity (50% of visitation event volume by day 17), compared with intense then rapidly declining activity at single carcasses (50% by day 8). Mass mortality plots also had higher predicted daily arrival probability and contact hours across most species, indicating an extended window for pathogen transmission. This framework provides empirically derived contact parameters for MME-related disease spread models using camera-trap data to identify potential transmission pathways at the scavenger-carcass interface.

Waterbirds serve as important indicators of both aquatic and terrestrial ecosystem health, making effective monitoring essential for tracking population health and identifying potential causes of decline. Drones have provided opportunities to overcome historic waterbird monitoring challenges, but the expertise and time required for manual image analysis creates a major bottleneck. Recent advances in deep learning-based object detection have enabled rapid, automatic detection of features in complex ecological imagery, though applications have largely been limited to single-species colonies, and practitioners lack quantitative comparisons of annotation time and accuracy across different levels of automation. We systematically compared four waterbird monitoring approaches using identical survey areas from Chester Island, a mixed-species colony in Matagorda Bay, Texas, in 2025: (1) traditional ground-based counts, (2) manual drone imagery-based counts, (3) computer-assisted counts using pre-annotations from an object detector with manual human verification (Human+ML), and (4) fully automated counts using object detector annotations (ML-only). We trained a YOLOv10 object detection model on manually annotated imagery of Chester Island in 2021 and applied it to the 2025 imagery. Manual drone annotation detected 6,530 birds in 40.5 hr and served as the primary reference standard. Human+ML detected 5,826 birds (89% of manual) in 7.7 hr, an 81% reduction in annotation time. ML-only detected 5,679 birds (87% of manual) in approximately 46 min, a 98% reduction. Ground counts recorded 5,868 birds (90% of manual). Detection generalized well across species while classification depended heavily on training data and morphological distinctiveness. The Human+ML workflow emerged as a practical middle ground, providing practitioners with empirical data to evaluate partial versus full automation strategies based on monitoring objectives.

Artificial intelligence (AI)-based image analysis can efficiently estimate fish length, but differences in devices, imaging conditions, operators, and AI models limit comparability among surveys. We propose a standardization framework that estimates a bin-specific error matrix from paired reference measurements and AI-derived lengths and applies it to standardize (correct) AI-derived length-frequency distributions. The Richardson-Lucy expectation-maximization algorithm was used, with the number of iterations selected via cross-validation. Simulations based on empirical length-frequency data from 110 species showed that standardization reduced relative bias and distributional discrepancy; median relative-bias and root mean square error ratios were below 1, and the performance was more affected by the amount of paired data than by the number of cross-validation folds. In real data from 957 Japanese jack mackerel, standardized AI-derived distributions approached human-observer histograms, although discrepancies remained in the range of 160-230 mm. The proposed framework provides a practical approach for improving the comparability of image-derived length-frequency data using paired calibration data, without retraining the underlying AI model.

Behaviour innovation plays a pivotal role in a species adaptability to dynamic environments. Investigating innovative behaviour and its underlying mechanism is therefore crucial for elucidating the development of cognitive flexibility across animals. Animal personality--which shapes how individuals perceive and engage with their surroundings--could offer insights into individual variation in this process. This study used a three-step foraging puzzle to evaluate the innovation capacity in 28 rosy-faced lovebirds (Agapornis roseicollis), specifically examining their capacity to recombine individually innovated component behaviours into integrated, more sophisticated techniques. We found that nearly half of the individuals spontaneously innovated multiple component behaviours to solve novel puzzles. Crucially, when challenged with a more sophisticated task, they recombined these behaviours into functionally dependent sequences without prior social demonstration. We further identified sex, persistence, and asocial learning capacity as key predictors of innovative problem-solving performance, with females, persistent individuals, and superior asocial learners excelled at problem-solving. Our findings demonstrate that behavioural innovation is not a static event, but a dynamic process--modulated by physical, cognitive, and personality variables--in which behaviours are flexibly transferred and recombined into increasingly complex forms to enable rapid individual adaptation.