Effective monitoring of seagrass is essential for the conservation of this critical marine ecosystem. The choice of monitoring method depends on balancing accuracy, efficiency, cost and accessibility, especially in contexts requiring community engagement and ownership. This study evaluates and compares three seagrass monitoring methods, including video, quadrat and point count, at a field site in Kenya to assess their suitability for community-led monitoring in resource-limited settings. The video method, used as a benchmark, demonstrated high precision with moderate resource demands, requiring 18 samples to detect a 20% change in cover with 80% statistical power. However, its technical complexity and initial setup costs could limit community adoption. Quadrat sampling, while labour-intensive, provided reliable estimates of species richness and total cover, although it required a larger sample size (36) than the video method to achieve the same power. Point count, the least resource-intensive method, was time-efficient but consistently overestimated seagrass cover. These results support the widely used SeagrassNet and Seagrass-Watch sampling methods. These accessible methods can both supply accurate data and help to foster local ownership and capacity. The findings highlight trade-offs between the methods, suggesting that combining technical training with simplified tools such as automated analyses and accessible data interpretation frameworks could enhance the applicability of advanced techniques like the video method in community settings. While accessible technological advancements might improve accuracy and efficiency, such benefits need to be balanced against the wider community engagement in monitoring and conservation. Nevertheless, quadrat sampling emerges as the most suitable method for immediate community use, given its reliability and accessibility, while the integration of video methods could be pursued where resources and training permit.

Abstract Underwater receiver networks (passive acoustic telemetry systems) are deployed to track animals in aquatic habitats all over the world, but remarkably limited attention has been given over to how we can strengthen the value of these networks through statistical and computational advances. Here, we upscale state-of-the-art methods of Bayesian inference to big animal-tracking datasets from acoustic telemetry, with the largest geolocation analysis in a sparse passive acoustic telemetry system (with non-overlapping receivers) to date. Using four years of data from 93 lake trout ( Salvelinus namaycush ) in North America’s Lake Champlain (657,360 timesteps per individual), we formulate and fit state-space models to reconstruct animal movements through time. Uniquely, we directly embed biological expertise and detailed complementary datasets from fine-scale positioning systems, accelerometry, swim-tunnel experiments and field range tests in our analysis. Using simulated and real-world datasets, we map movement patterns and estimate residency in distinct management zones. We quantify array precision and deliver maps and residency estimates with a median error and precision (standard error) below 1 %. These results strengthen the evidence base for management. This work takes us a step towards robust inference of movement patterns at scale in acoustic telemetry systems across the world. We can, and should, build on prior scientific progress and extend the value of hard-earned data beyond individual studies to refine inferences for ecology and management. Significance statement Acoustic receivers are deployed across the globe to track aquatic animals, but reconstructing detailed movement patterns from detections at receivers remains a considerable challenge. Here, we upscale state-of-the-art methods of Bayesian inference by two orders of magnitude to analyse big, real-world datasets, using an extensive case study of lake trout ( Salvelinus namaycush ) in Lake Champlain. By directly integrating diverse complementary datasets from animal-borne tags, swim-tunnel experiments, field studies and close-kin mark-recapture in our analysis, we resolve detailed movement patterns over a four-year period, with broad implications for ecology and management. This work provides a powerful framework for acoustic telemetry studies that strives to meet the challenges of big, real-world datasets from telemetry networks across the world.

ABSTRACT Stable isotope analysis is widely used in ecology to trace spatial origins and trophic interactions for many species. Salmonid fish, including brown trout (Salmo trutta), are often a focal species for stable isotope analysis due to the high ecological importance and anadromous life cycles. Variations in δ13C values of trout have been linked to catchment characteristics, such as land use. These variations reflect carbon processes at the base of the food web, with chemosynthetic processes such as methane oxidation producing lower values than those of the photosynthetic carbon processing. We examined δ13C values in brown trout fry across 51 sites in two tributaries of the River Tweed, Scotland to investigate catchment drivers of trout δ13C variation. Soil drainage and pasture cover were identified as the strongest predictors of fry δ13C values, with lower drainage and higher pasture correlating with lower δ13C. A subset (26) of the sites was sampled for mayflies (Baetis spp.), finding a strong correlation between the δ13C values of mayflies and trout fry, suggesting trout δ13C is indicative of broader carbon processes at the site. Increases in cover of pasture and low drainage soils are known to result in elevated CH4 concentrations in streams, while low δ13C values are an indicator of methane‐derived carbon in the food web. The observed link between δ13C variation and catchment features associated with methane production, such as poorly drained soils and pasture, points to a potential role for methane‐derived carbon in structuring upland stream food webs.

Recent advances in machine learning have accelerated automated species detection across diverse ecological domains, enabling large-scale, non-invasive monitoring of biodiversity. In ornithological research, the combination of passive acoustic monitoring (PAM) and rapidly-developing novel identification tools such as BirdNET—a deep learning–based sound recognition algorithm—offers new opportunities for surveying vocally active bird communities. Here, we present the first worldwide evaluation of BirdNET using 4224 one-minute recordings from 67 sites across all continents annotated by local experts. More specifically, we assessed the capacity of BirdNET to accurately identify individual vocalizations and characterize bird communities based on the automated analysis of passively collected soundscapes. We further analyzed how its performance varies across continents, biomes, species, and minimum confidence thresholds. The proportion of correct BirdNET predictions (precision) was generally high and consistent across continents (range: 0.57–0.71) and biomes (range: 0.55–0.76). In contrast, the proportion of vocalizations successfully detected (recall) was generally lower and more heterogeneous across continents (range: 0.24–0.52) and biomes (range: 0.34–0.72), reflecting differences in species coverage and local ecological context. BirdNET predictive power, as measured by the Precision-Recall Area Under the Curve (PR AUC; higher values indicating better performance), was highest in North America, Oceania, and Europe (range: 0.16–0.23), moderate in Central/South America (0.13), and lowest in Africa and Asia (range: 0.03–0.04). Species-specific analyses revealed substantial heterogeneity in detection accuracy, with optimal confidence thresholds varying widely by species and analytical goal. Our results establish a global reference point for BirdNET reliability and highlight where algorithmic refinement and expanded acoustic sampling are most needed.

Exploration of new wildlife surveying methodologies that leverage advances in sensor technology and machine learning has led to tentative research into the application of seismology techniques. This, most commonly, involves the deployment of a footfall trap - a seismic sensor and data logger customised for wildlife footfall. While well-established in fields such as border security and human-intruder detection, the use of seismology in wildlife surveying remains nascent. This systematic review, conducted in accordance with PRISMA reporting guidelines, for the first time, compiles existing research in this developing area and offers a synthesis and discussion to support its progression. Our comprehensive search and screening process identified 34 studies that recorded or analysed wildlife footfall using ground-based sensors. Analysis of publication dates shows a clear upward trajectory in wildlife seismology research: the earliest included study was published in 2000, with 45% of studies appearing in the last five years. The primary driver behind most research was human-wildlife conflict (70%), followed by behavioural studies (15%) and wildlife monitoring (15%). Geophones were used in the majority of studies (70%), valued for their accessibility and affordability, though other sensors, such as pressure plates, vibration sensors, and seismometers, were also represented. Performance synthesis was challenging due to varied parameters. Binary classification tasks reported a median accuracy of 90%, while two recent studies involving four-class classification achieved accuracies of 91.3 and 91.7% using a Mahalanobis distance classifier and a convolutional neural network, respectively.We examine common trends and biases across the studies and conduct a horizon scan to identify key research questions that must be addressed to advance the methodology. High-priority areas include distinguishing closely related species, benchmarking against existing sensor technologies, evaluating generalisation across diverse sites, and investigating how footfall detectability might inform abundance estimates.

Under the current global biodiversity crisis, there is a need for automated and noninvasive monitoring techniques that can gather large amounts of data cost-effectively at various ecological scales, from local to large spatial scales. These data can then be analyzed to inform stakeholders and decision-makers. One such technique is passive acoustic monitoring, which is commonly coupled with automatic identification of animal species based on their sound. Automated sound analyses usually require the training of sound detection and identification algorithms. These algorithms are based on annotated acoustic datasets which mark the occurrence of sounds of species inside sound recordings. However, compiling large annotated acoustic datasets is time-consuming and requires experts, and therefore, they normally cover reduced spatial, temporal, and taxonomic scales. This data paper presents WABAD, the World Annotated Bird Acoustic Dataset for passive acoustic monitoring. WABAD is designed to provide the public, the research community, and conservation managers with a novel and globally representative annotated acoustic dataset. This database includes 5047 min of audio files annotated to species-level by local experts with the start and end time and the upper and lower frequencies of each identified bird vocalization in the recordings. The database has a wide taxonomic and spatial coverage, including information on 91,931 vocalizations from 1192 bird species recorded at 72 recording sites in 29 recording locations (mainly countries) and distributed across 13 biomes. WABAD can be used, for example, for developing and/or validating automatic species detection algorithms, answering ecological questions, such as assessing geographical variations on bird vocalizations, or comparing acoustic diversity indices with species-based diversity indices. The dataset is published under a Creative Commons Attribution 4.0 International license that permits redistribution and reuse on the condition that the original work is properly credited.

Under the current global biodiversity crisis, there is a need for automated and non-invasive monitoring techniques that are able to gather large amounts of information cost-effectively at large scales. One such technique is passive acoustic monitoring, which is commonly coupled with automatic identification of animal species based on their sound. Automated sound analyses usually require the training of sound detection and identification algorithms. These algorithms are based on annotated acoustic datasets which mark the occurrence of sounds of particular species. However, compiling large annotated acoustic datasets is time consuming and requires experts, and therefore they normally cover a reduced spatial and taxonomic scale. This data paper presents WABAD, the World Annotated Bird Acoustic Dataset for passive acoustic monitoring. WABAD is designed to provide the public, the research community, and conservation managers with a novel and globally representative annotated acoustic dataset. This database includes 5,044 minutes of audio files annotated to species-level by local experts with the start and end time, and the upper and lower frequencies of each identified bird vocalisation in the recordings. The database has a wide taxonomic and spatial coverage, including information on 90,662 vocalisations from 1,147 bird species recorded at 70 recording sites in 27 countries and distributed across 13 biomes. WABAD can be used, for example, for developing and/or validating automatic species detection algorithms, answering ecological questions, such as assessing geographical variations on bird vocalisations, or comparing acoustic diversity indices with species-based diversity indices. The dataset is published under a Creative Commons Attribution Non Commercial 4.0 International copyright.

Freshwater ecosystems are full of underwater sounds produced by amphibians, aquatic arthropods, reptiles, plants, fishes, and methane bubbles escaping from the sediment. Although much headway has been made in recent years investigating the overall soundscapes of various freshwater ecosystems around the world, there remains a significant knowledge gap in our collective inability to accurately and reliably link recorded sounds with the species that produced them. Here, we present The Freshwater Sounds Archive, a new global initiative, which seeks to address this knowledge gap by collating species-specific freshwater sound recordings into a publicly available database. By means of metadata collection, we also present a snapshot of the species studied, the recording equipment, and recording parameters used by freshwater ecoacousticians globally. In total, 61 entries were submitted to the archive between the 4th of March 2023 and the 30th of April 2025, representing 16 countries and 6 continents. The most numerous taxonomic group was arthropods (29 entries), followed by fishes (14 entries), amphibians (10 entries), macrophytes (7 entries), and a freshwater mollusk (1 entry). The majority of the submissions were from European countries (27 entries), of which the United Kingdom was the most represented with 14 entries. The next most represented region was North America (11 entries), followed by South America (8 entries), Oceania and Asia (5 entries each), Africa (3 entries), and the Middle East and Central America with 1 entry each. The global south, polar regions, and areas with an elevation >500 m (asl) were underrepresented. The field of freshwater ecoacoustics to date has largely focused on the analysis of ‘sound types’ due to a current lack of knowledge of species-specific sounds. The Freshwater Sounds Archive presents an opportunity to move beyond the ‘sound type’ approach, and towards an approach with higher taxonomic resolution, ultimately resulting in species-specific descriptions. Furthermore, The Freshwater Sounds Archive will provide freshwater ecoacousticians with one of the main tools required to start creating annotated training datasets for machine learning models from soundscape recordings by referring to known species sounds present in the archive. In the long-term, this will result in the automatic detection and classification of species-specific freshwater sounds from soundscape recordings, such as indicator, invasive, and endangered species. ### Competing Interest Statement The authors have declared no competing interest. UK Acoustics Network (UKAN+) via EPSRC, EP/V007866/1 Banting Research Foundation, https://ror.org/05xe9t676 Stoney Lake community, Ontario, Canada National Science Foundation Research Experience for Undergraduates program: Sensors in Earth, Oceans, and Space Science IIHS University of Liverpool, https://ror.org/04xs57h96 Kieckhefer Adirondack Fellowship

BirdNET is a popular machine learning tool for automated recognition of bird sounds. However, evidence on how to optimize its settings for accurate bird monitoring remains limited. Here, we evaluate how BirdNET settings influence model performance in identifying bird vocalizations and characterizing bird communities, using 4224 1-min recordings from 67 recording locations worldwide. Giving equal importance to recall and precision, a low confidence score threshold (0.1-0.3) appears optimal for detecting bird vocalizations, whereas higher thresholds (around 0.5) are more suitable for characterizing bird communities. Based on our findings, we recommend increasing the Overlap parameter from its default value of 0 to 2 s, as this consistently improves BirdNET performance in detecting both bird vocalizations and species presence. The effect of the Sensitivity parameter varied across regions. However, a value of 0.5 maximizes global performance for community-level analyses across all confidence thresholds, and a value of 1.5 generally yields better results for vocalization-level studies, particularly at low confidence thresholds. Our findings offer practical guidance for selecting BirdNET settings in passive acoustic bird surveys, enhancing both the identification of bird vocalizations and the characterization of bird communities.

Aim: The urgency for remote, reliable and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To address this gap, we created the Worldwide Soundscapes project, a collaborative network and growing database comprising metadata from 416 datasets across all realms (terrestrial, marine, freshwater and subterranean). Location: Worldwide, 12,343 sites, all ecosystem types. Time Period: 1991 to present. Major Taxa Studied: All soniferous taxa. Methods: We synthesise sampling coverage across spatial, temporal and ecological scales using metadata describing sampling locations, deployment schedules, focal taxa and audio recording parameters. We explore global trends in biological, anthropogenic and geophysical sounds based on 168 selected recordings from 12 ecosystems across all realms. Results: Terrestrial sampling is spatially denser (46 sites per million square kilometre-Mkm(2)) than aquatic sampling (0.3 and 1.8 sites/Mkm(2) in oceans and fresh water) with only two subterranean datasets. Although diel and lunar cycles are well sampled across realms, only marine datasets (55%) comprehensively sample all seasons. Across the 12 ecosystems selected for exploring global acoustic trends, biological sounds showed contrasting diel patterns across ecosystems, declined with distance from the Equator, and were negatively correlated with anthropogenic sounds. Main Conclusions: PAM can inform macroecological studies as well as global conservation and phenology syntheses, but representation can be improved by expanding terrestrial taxonomic scope, sampling coverage in the high seas and subterranean ecosystems, and spatio-temporal replication in freshwater habitats. Overall, this worldwide PAM network holds promise to support cross-realm biodiversity research and monitoring efforts.

1. Seagrass ecosystems are crucial for supporting biodiversity and serve as vital fishing grounds. Unfortunately, their cover is declining globally. In Kenya, seagrass cover is falling by similar to 1.6% annually but the causes are unknown. This study investigated the possible anthropogenic drivers of seagrass decline along the Kenyan coastline. 2. Satellite and large-scale geographic data on population growth, chlorophyll alpha trends, housing, and road density were used to explore their effects on seagrass cover loss along the whole coastline. Direct investigations were conducted into the effects of seine netting and basket trapping within seagrasses. 3. There was an average loss of 1.9 km(2) per 25 km(2) seagrass cover between 2000 and 2016 and a weak but significant relationship between population growth and seagrass decline, with losses concentrated in areas with the highest population density. In contrast with studies elsewhere, there was no evidence implicating eutrophication, supporting the suggestion that declines are linked to direct anthropogenic impacts such as fishing. A field experiment showed that a single instance of seine netting caused a significant loss of seagrass cover of 8.3% within the area fished, while no significant changes were observed with basket traps. 4. These findings support the evidence that declines in seagrass in Kenya and in other African countries are anthropogenic and are linked with fishing pressure and endorse existing efforts to restrict use of seine netting within seagrasses. 5. Understanding the status, changes, and drivers of change in seagrass ecosystems in Africa is crucial for developing effective national and local seagrass conservation plans, and for compliance with international commitments on seagrass conservation.

The urgent need for remote, reliable, and scalable biodiversity monitoring amidst mounting human pressures on ecosystems and changing climate has sparked interest in Passive Acoustic Monitoring (PAM) worldwide. PAM holds potential for supporting global sustainability goals by aiding conservation efforts, but so far, there is no comprehensive overview of its coverage. Here we present metadata from 293 PAM datasets recorded since 1991 in the first global synthesis of ecoacoustic sampling coverage across spatial, temporal, and ecological scales. We report data on sampling sites, deployment schedules, focal taxa, and recording parameters, and quantify biological, anthropogenic, and geophysical soundscape components across nine terrestrial and aquatic ecosystems. We found that terrestrial sampling is spatially denser (33 sites/Mkm2) compared to aquatic realms, with significant data gaps in subterranean realms. While diel and lunar cycles are well-sampled, seasonal coverage is lacking in freshwater and terrestrial ecosystems, while 57% of marine datasets cover all seasons. Opportunities for improvement include broader taxonomic sampling on land, expanding coverage in the high seas, and increasing spatial replication in freshwater environments. Additionally, we highlight nine case studies showcasing how PAM-based soundscape ecology can contribute to macroecology, conservation, and phenology studies, illustrating its potential to support global sustainability efforts both on land and underwater.### Competing Interest StatementThe authors have declared no competing interest.

The recognition of the benefits that seagrasses contribute has enhanced the research interest in these marine ecosystems. Seagrasses provide critical goods and services and support the livelihoods of millions of people. Despite this, they are declining around the globe. To conserve these ecosystems, it is necessary to understand their extent and the drivers leading to their loss. However, global seagrass cover estimates are highly uncertain and there are large regional data gaps, especially in the African continent. This work reviewed all available data on the extent of seagrass cover, evidence of changes in cover and drivers of this change in Africa, to inform management and conservation approaches across the continent and identify gaps in knowledge. Using a systematic review and expert consultation, 43 relevant articles were identified. Of the 41 African countries with a coastline, 27% had no data on seagrass cover. For 44%, data were available for some parts of their coastline, while 29% had data for their entire coastline. Quantitative information on trends in seagrass cover change was only available from three countries. The study identified 32 suggested drivers of seagrass cover loss, with impacts from fishing mentioned most frequently. Direct anthropogenic drivers accounted for 66.7% of the mentions, while climate and biologically induced drivers accounted for 22.7% and 10.6%, respectively. This study demonstrates the need for better estimates of seagrass extent, in at least 70% of relevant African nations, and major gaps in our understanding of the drivers of seagrass decline in Africa.

In response to the combined impacts of the climate and biodiversity crises, as well as for timber security and increased recreational access to green spaces, there is a global drive to increase tree cover. In the UK, an estimated 1.5 million ha of afforestation are required to meet its carbon net-zero emissions targets (Committee on Climate Change, 2018). Despite the potential benefits, careful consideration must be given to the impacts of woodland creation on species adapted to open habitats. To investigate potential risks and mitigation for the IUCN Near Threatened Eurasian curlew Numenius arquata, a national spatially extensive field-scale dataset was used to investigate the relationships between curlew presence during the breeding season and a range of forest and landscape variables at two different spatial scales (0.5 km and 1 km). Variables included forest extent and configuration, and interaction between forests and extent of semi-natural open habitats, moorland management and topography. At both spatial scales, a negative relationship existed between extent of forest and the probability of curlew presence, and at 1 km, between probability of presence and the number of forest patches. However, these negative patterns depended on landscape context and were reduced where there was a greater quantity of semi-natural open habitat, such as moorland or rough grassland, and moorland management present. Overall, the findings emphasise the need to consider the impacts of woodland creation projects on species adapted to open habitats. However, the results suggest that these impacts can be influenced by landscape. These results could help inform decisions regarding the appropriateness of woodland creation in different landscapes and possible mitigation measures that could be applied against the risks created by afforestation.

Conservation of elusive species affected by habitat degradation, population fragmentation and poaching is challenging. The remaining wild populations of a desert-adapted ungulate, Nubian ibex (Capra nubiana), within Oman are small and fragmented. The appropriateness of captive insurance populations for reinforcing existing, or establishing new, wild populations remains uncertain for Oman due to ambiguity regarding their genetic provenance. For effective management of this threatened species, it is essential to assess the genetic relationships between the wild and captive populations, and to investigate hybridisation with domestic goats (Capra hircus). We identified 5,775 high-quality SNPs using double digest restriction-site associated DNA (ddRAD), to assess genetic structure, gene flow and divergence between Oman’s wild populations of Nubian ibex and in captivity, which are likely of North African provenance. We detected hybridisation with goats in captivity and recommend that genetic assessments of captive individuals are routinely used to evaluate their suitability for conservation programs. Building on previous mitochondrial evidence, substantial nuclear divergence (FST = 0.540) was found between wild Oman and captive populations, providing further evidence that Nubian ibex may be composed of multiple species and urgently needs a taxonomic review. Additionally, an appropriate insurance population should be established for Oman’s threatened wild population. The data provided here will be invaluable for developing marker systems to assess wild populations using low-quality DNA from non-invasive sampling. Consequently, it will support further research into Nubian ibex throughout their range and highlights the need to integrate genetic information for effective conservation management of Nubian ibex.

Mammals' resting sites (dens) are important features of their ecology. Eurasian otterLutra lutraresting sites are strictly protected by UK and European legislation and are ostensibly identified from associated field‐signs. This legislation is difficult to apply given the poor understanding of resting sites coupled with the lack of evidence supporting a field‐sign signature. We aimed to use camera‐trap data to identify resting sites, investigate whether field‐signs differed between resting and non‐resting sites and describe behaviours recorded on camera‐traps that are associated with resting. An evidence‐based approach to identifying resting sites of Eurasian otterLutra lutrafrom camera‐trap and field‐sign data camera‐trap data showed that otters frequently visited potential resting sites, characterised by a very short time within the structure (often < 4 min). Resting sites were characterised by longer durations (often hours) during the daytime and night‐time. Based on these data, six of our 26 sites were identified as resting sites. Modelling suggested that no single field‐sign had a clear association with resting sites. However, we found a hitherto unrecognised distinction between otter latrines (defecation sites) and spraint (scent‐marking) sites, and that camera‐trap observations of latrine behaviour and bedding collection were exclusive to resting sites. As bedding and latrines are not always visible, presence of either indicates a resting site but no interpretation can be drawn from their absence, so camera‐trapping would be recommended to identify resting site status. Data simulations found that camera‐trapping for 38 d in winter, followed by 38 d in spring, was the optimal approach for a 95% chance of detecting a rest across all resting sites. Ours is the first study to identify standards and expectations for surveys using camera‐trap and field‐signs at Eurasian otter resting sites. Our novel account of their resting activity facilitates better interpretation of legislation.

Plastic pollution is threatening aquatic ecosystems and wildlife. Understanding the characteristics and extent of plastic pollution is the first step towards improving management and therefore the environmental impacts. Pre-production pellets are used in the manufacture of a range of consumer items. The Avon–Heathcote Estuary/Ihutai in Aotearoa–New Zealand, an important wildlife habitat, was assessed for the presence and characteristics of pre-production pellets. Following a visual survey of the estuary’s perimeter to establish overall levels, seven accumulation hotspots were identified, and surveyed in more detail. The enumeration and characterisation of pellet colour, size, morphology, degree of weathering and polymer type was undertaken. A total of 3819 pellets were identified, with pellets present at all sites. The pellets were predominantly clear (86%), 3 mm in size (54%), cylindrical in shape (62%), showed moderate weathering (41%) and were made of low-density polyethylene (LDPE) (53%). Pellet abundance and characteristics varied between sites. Accumulation and abundance may be influenced by river inflows along which plastic manufacturers are located, weather conditions, locality to stormwater outlets and pellet characteristics. Pellet pollution is a notable problem in the Avon–Heathcote Estuary/Ihutai and it highlights the need to better understand the sources and improve best management practices.

There is an urgent need to understand how organisms respond to multiple, potentially interacting drivers in today's world. The effects of the pollutants anthropogenic sound (pile driving sound playbacks) and waterborne cadmium were investigated across multiple levels of biology in larval and juvenile Norway lobster, Nephrops norvegicus under controlled laboratory conditions. The combination of pile driving playbacks (170 dB(pk-pk) re 1 mu Pa) and cadmium combined synergistically at concentrations > 9.62 mu g([Cd]) L-1 resulting in increased larval mortality, with sound playbacks otherwise being antagonistic to cadmium toxicity. Exposure to 63.52 mu g([Cd]) L-1 caused significant delays in larval development, dropping to 6.48 mu g([Cd]) L-1 in the presence of piling playbacks. Pre-exposure to the combination of piling playbacks and 6.48 mu g([Cd]) L-1 led to significant differences in the swimming behaviour of the first juvenile stage. Biomarker analysis suggested oxidative stress as the mechanism resultant deleterious effects, with cellular metallothionein (MT) being the predominant protective mechanism.