Biology subjects

Hoey, A.

Publications and source records attributed to Hoey, A..

5 recordsLinked to original sources

bioRxiv2026

Estimates of habitat selection reveal distinct habitat associations across life-stages in three coral-reef damselfish.

Understanding habitat association of animals and how they change through ontogeny is critical to predict the likely effects of habitat change on populations. We investigated how fine scale habitat associations of three common coral reef damselfish species changed among life-stages on reefs surrounding Lizard Island, northern Great Barrier Reef. All three species showed distinct habitat selection at settlement, however the degree to which these initial associations changed through ontogeny were species specific. Pomacentrus amboinensis associated with sandy areas throughout all life-stages; Pomacentrus chrysurus settled to areas with high cover of sand and rubble, but displayed no clear habitat preferences as juveniles or adults. Pomacentrus moluccensis settled to areas with high cover of fine branching corals before shifting to areas with relatively high cover of soft corals as adults. We also compared two different approaches to estimate habitat selection; one that quantified the benthic composition within the approximate home range of individuals versus a more widely used approach of recording a single point underneath the focal individual when they were first observed. Although results were broadly similar, the benthic composition approach revealed details that was overlooked using the single point method. Decreases in the availability of any of these preferred benthic habitats may adversely affect future populations, therefore understanding habitat associations and their transitions among life stages will be crucial in predicting future reef fish communities under ongoing coral loss and habitat change. This will require to systematically study a broader range of species, integrating relevant spatial and temporal scales.

ecology↗

bioRxiv2024

Unlocking the soundscape of coral reefs with artificial intelligence

Passive acoustic monitoring can offer insights into the state of coral reef ecosystems at low-costs and over extended temporal periods. Comparison of whole soundscape properties can rapidly deliver broad insights from acoustic data, in contrast to the more detailed but time-consuming analysis of individual bioacoustic signals. However, a lack of effective automated analysis for whole soundscape data has impeded progress in this field. Here, we show that machine learning (ML) can be used to unlock greater insights from reef soundscapes. We showcase this on a diverse set of tasks using three biogeographically independent datasets, each containing fish community, coral cover or depth zone classes. We show supervised learning can be used to train models that can identify ecological classes and individual sites from whole soundscapes. However, we report unsupervised clustering achieves this whilst providing a more detailed understanding of ecological and site groupings within soundscape data. We also compare three different approaches for extracting feature embeddings from soundscape recordings for input into ML algorithms: acoustic indices commonly used by soundscape ecologists, a pretrained convolutional neural network (P-CNN) trained on 5.2m hrs of YouTube audio and a CNN trained on individual datasets (T-CNN). Although the T-CNN performs marginally better across the datasets, we reveal that the P-CNN is a powerful tool for identifying marine soundscape ecologists due to its strong performance, low computational cost and significantly improved performance over acoustic indices. Our findings have implications for soundscape ecology in any habitat. Author SummaryArtificial intelligence has the potential to revolutionise bioacoustic monitoring of coral reefs. So far, a limited set of work has used machine learning to train detectors for specific sounds such as individual fish species. However, building detectors is a time-consuming process that involves manually annotating large amounts of audio followed by complicated model training, this must then be repeated all over again for any new dataset. Instead, we explore machine learning techniques for whole soundscape analysis, which compares the acoustic properties of raw recordings from the entire habitat. We identify multiple machine learning methods for whole soundscape analysis and rigorously test these using datasets from Indonesia, Australia and French Polynesia. Our key findings show use of a neural network pretrained on 5.2m hours of unrelated YouTube audio offers a powerful tool to produce compressed representations of reef audio data, conserving the datas key properties whilst being executable on a standard personal laptop. These representations can then be used to explore patterns in reef soundscapes using "unsupervised machine learning", which is effective at grouping similar recordings periods together and dissimilar periods apart. We show these groupings hold relationships with ground truth ecological data, including coral coverage, the fish community and depth.

ecology↗

bioRxiv2022

Sustainable reference points for multispecies coral reef fisheries

Sustainably managing fisheries requires regular and reliable evaluation of stock status. However, most multispecies reef fisheries around the globe tend to be data-poor and lack research and monitoring capacity (e.g., long-term fishery data), preventing the estimation of sustainable reference points against which stocks can be assessed. Here, combining fish biomass data for more than 2000 coral reefs with catch estimates from 99 jurisdictions, we estimate site-specific sustainable reference points for coral reef fisheries and use these to assess the status of coral reef fish stocks. We reveal that more than half of jurisdictions with available information have stocks of conservation concern, having failed at least one fisheries sustainability benchmark. We quantify the trade-offs between biodiversity, mean fish length, and ecosystem functions relative to key benchmarks and highlight the ecological benefits of increasing sustainability. Our approach yields multispecies sustainable reference points for coral reef fisheries using environmental conditions, a promising means for enhancing the sustainability of the worlds coral reef fisheries. TeaserA global assessment of the sustainability of multispecies reef fisheries indicates that more than half of jurisdictions have failed at least one of two key sustainability benchmarks.

ecology↗

bioRxiv2021

Selecting species for restoration in foundational assemblages

O_LIHumans have long sought to restore species, but little attention has been directed at how to best select a subset of foundation species for maintaining rich assemblages that support ecosystems, like coral reefs and rainforests that are increasingly threatened by environmental change. C_LIO_LIWe propose a two-part hedging approach that selects optimized sets of species for restoration. The first part acknowledges that biodiversity supports ecosystem functions and services, and so it takes precaution against loss by ensuring an even spread of phenotypic traits. The second part maximizes species and ecosystem persistence by weighting species based on characteristics that are known to improve ecological persistence--e.g., abundance, species range and tolerance to environmental change. C_LIO_LIUsing existing phenotypic trait and ecological characteristic data for reef building corals, we identified sets of ecologically persistent species by examining marginal returns in occupancy of phenotypic trait space. We compared optimal sets of species with those from the worlds southern-most coral reef which naturally harbors low coral diversity to show these occupy much of the trait space. Comparison with an existing coral restoration program indicated that current corals used for restoration only cover part of the desired trait space and may be improved by including species with different traits. C_LIO_LISynthesis and applications. While there are many possible criteria for selecting species for restoration, the approach proposed here addresses the need to insure against unpredictable losses of ecosystem services by focusing on a wide range of phenotypic traits and ecological characteristics. Furthermore, the flexibility of the approach enables the functional goals of restoration to vary depending on environmental context, stakeholder values, and the spatial and temporal scales at which meaningful impacts can be achieved. C_LI

ecology↗

bioRxiv2017

Pair bond endurance promotes cooperative food defense and inhibits conflict in coral reef butterflyfishes

Pair bonding is generally linked to monogamous mating systems, where the reproductive benefits of extended mate guarding and/or of bi-parental care are considered key adaptive functions. However, in some species, including coral reef butterflyfishes (f. Chaetodonitidae), pair bonding occurs in sexually immature and homosexual partners, and in the absence of parental care, suggesting there must be non-reproductive adaptive benefits of pair bonding. Here, we examined whether pair bonding butterflyfishes cooperate in defense of food, conferring direct benefits to one or both partners. Pairs of Chaetodon lunulatus and C. baronessa use contrasting cooperative strategies. In C. lunulatus, both partners mutually defend their territory, while in C. baronessa, males prioritize territory defence; conferring improvements in feeding and energy reserves in both sexes relative to solitary counterparts. We further demonstrate that partner fidelity contributes to this function by showing that re-pairing invokes intra-pair conflict and inhibits cooperatively-derived feeding benefits, and that partner endurance is required for these costs to abate. Overall, our results suggest that in butterflyfishes, pair bonding enhances cooperative defense of prey resources, ultimately benefiting both partners by improving food resource acquisition and energy reserves.

ecology↗