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Coral reefs are biodiverse marine ecosystems that are undergoing rapid changes, making monitoring vital as we seek to manage and mitigate stressors. Healthy reef soundscapes are rich with sounds, enabling passive acoustic recording and soundscape analyses to emerge as cost-effective, long-term methods for monitoring reef communities. Yet most biological reef sounds have not been identified or described, limiting the effectiveness of acoustic monitoring for diversity assessments. Machine learning offers a solution to scale such analyses but has yet to be successfully applied to characterize the diversity of reef fish sounds. Here we sought to characterize and categorize coral reef fish sounds using unsupervised machine learning methods. Pulsed fish and invertebrate sounds from 480 min of data sampled across 10 days over a 2-month period on a US Virgin Islands reef were manually identified and extracted, then grouped into acoustically similar clusters using unsupervised clustering based on acoustic features. The defining characteristics of these clusters were described and compared to determine the extent of acoustic diversity detected on these reefs. Approximately 55 distinct calls were identified, ranging in centroid frequency from 50 Hz to 1,300 Hz. Within this range, two main sub-bands containing multiple signal types were identified from 100 Hz to 400 Hz and 300 Hz–700 Hz, with a variety of signals outside these two main bands. These methods may be used to seek out acoustic diversity across additional marine habitats. The signals described here, though taken from a limited dataset, speak to the diversity of sounds produced on coral reefs and suggest that there might be more acoustic niche differentiation within soniferous fish communities than has been previously recognized. 

ABSTRACT Environment structure often shapes social interactions. Spatial attractors that draw multiple individuals may play a particularly important role in dispersed groups, where individuals must first encounter one another to interact. We use GPS data recorded simultaneously from five spotted hyenas (Crocuta crocuta) within a single clan to investigate how communal dens and daily ranging patterns shape fission-fusion dynamics (subgroup splits and merges). We introduce a species-general framework for identifying and characterizing dyadic fission-fusion events and describe a taxonomy of ten possible configurations of these events. Applying this framework to the hyena data illuminates the spatiotemporal structure of social interactions within hyenas’ daily routines. The most common types of fission-fusion events involve close approaches between individuals, do not involve co-travel together, and occur at the communal den. Comparison to permutation-based reference models suggests that den usage structures broad-scale patterns of social encounters, but that other factors influence how those encounters unfold. We discuss the dual role of communal dens in hyenas as physical and social resources, and suggest that dens are an example of a general “social piggybacking” process whereby environmental attractors take on social importance as reliable places to encounter conspecifics, causing social and spatial processes to become fundamentally intertwined. 

Abstract Fission–fusion dynamics describe the tendency for members of some animal societies to associate in subgroups that change size and structure fluidly over time. These dynamics shape social complexity and social structure, but are difficult to study because they unfold simultaneously over large spatial scales. Here we use simultaneous, fine-scale GPS data from spotted hyenas to examine fission–fusion dynamics through a dyadic analysis ofmerge-split eventsbetween pairs of individuals. We introduce a species-agnostic framework for identifying merge-split events and discretizing them into three phases (merging, together, and splitting), enabling analysis of each phase as well as the connections among phases. Applying this framework to the hyena data, we examine the temporal and spatial properties of merges and splits between dyads and test the extent to which social encounters are driven by key locations. Specifically, we focus on communal dens—shelters for juvenile hyenas where classical observational studies often report large aggregations of adults. We find that overall, 62% of merges occurred at communal dens, supporting the idea that dens facilitate meet-ups and subsequent social behavior. Social encounters most commonly involved close approaches within a few meters between hyenas, while co-travel together occurred in only 11% of events. Comparison to permutation-based reference models suggests that independent movement decisions structure broad-scale patterns of social encounters but do not explain the fine-scale dynamics of interactions that unfold during these encounters. We reflect on how physical features such as dens can become social hotspots, causing social and spatial processes to become fundamentally intertwined. 

Animal activity patterns are highly variable and influenced by internal and external factors, including social processes. Quantifying activity patterns in natural settings can be challenging, as it is difficult to monitor animals over long time periods. Here, we developed and validated a machine-learning-based classifier to identify behavioural states from accelerometer data of wild spotted hyenas(Crocuta crocuta), social carnivores that live in large fission–fusion societies. By combining this classifier with continuous collar-based accelerometer data from five hyenas, we generated a complete record of activity patterns over more than one month. We used these continuous behavioural sequences to investigate how past activity, individual idiosyncrasies, and social synchronization influence hyena activity patterns. We found that hyenas exhibit characteristic crepuscular-nocturnal daily activity patterns. Time spent active was independent of activity level on previous days, suggesting that hyenas do not show activity compensation. We also found limited evidence for an effect of individual identity on activity, and showed that pairs of hyenas who synchronized their activity patterns must have spent more time together. This study sheds light on the patterns and drivers of activity in spotted hyena societies, and also provides a useful tool for quantifying behavioural sequences from accelerometer data. 

In animal societies, identity signals are common, mediate interactions within groups, and allow individuals to discriminate group-mates from out-group competitors. However, individual recognition becomes increasingly challenging as group size increases and as signals must be transmitted over greater distances. Group vocal signatures may evolve when successful in-group/out-group distinctions are at the crux of fitness-relevant decisions, but group signatures alone are insufficient when differentiated within-group relationships are important for decision-making. Spotted hyenas are social carnivores that live in stable clans of less than 125 individuals composed of multiple unrelated matrilines. Clan members cooperate to defend resources and communal territories from neighbouring clans and other mega carnivores; this collective defence is mediated by long-range (up to 5 km range) recruitment vocalizations, called whoops. Here, we use machine learning to determine that spotted hyena whoops contain individual but not group signatures, and that fundamental frequency features which propagate well are critical for individual discrimination. For effective clan-level cooperation, hyenas face the cognitive challenge of remembering and recognizing individual voices at long range. We show that serial redundancy in whoop bouts increases individual classification accuracy and thus extended call bouts used by hyenas probably evolved to overcome the challenges of communicating individual identity at long distance.