The sound of recovery: coral reef restoration success is detectable in the soundscape (article)

This is the final version. Available on open access from Wiley via the DOI in this recordThe dataset associated with this article is available in ORE at https://doi.org/10.24378/exe.37031. Pantropical degradation of coral reefs is prompting considerable investment in their active restoration. However, current measures of restoration success are based largely on coral cover, which does not fully reflect ecosystem function or reef health. 2. Soundscapes are an important aspect of reef health; loud and diverse soundscapes guide the recruitment of reef organisms, but this process is compromised when degradation denudes soundscapes. As such, acoustic recovery is a functionally important component of ecosystem recovery. 3. Here, we use acoustic recordings taken at one of the world’s largest coral reef restoration projects to test whether successful restoration of benthic and fish communities is accompanied by a restored soundscape. We analyse recordings taken simultaneously on healthy, degraded (extensive historic blast fishing) and restored reefs (restoration carried out for 1–3 years on previously-degraded reefs). We compare soundscapes using manual counts of biotic sounds (phonic richness), and two commonly used computational analyses (acoustic complexity index [ACI] and sound-pressure level [SPL]). 4. Healthy and restored reef soundscapes exhibited a similar diversity of biotic sounds (phonic richness), which was significantly higher than degraded reef soundscapes. This pattern was replicated in some automated analyses but not others; the ACI exhibited the same qualitative result as phonic richness in a low-frequency, but not a high-frequency bandwidth, and there was no significant difference between SPL values in either frequency bandwidth. Further, the low-frequency ACI and phonic richness scores were only weakly correlated despite showing a qualitatively equivalent overall result, suggesting that these metrics are likely to be driven by different aspects of the reef soundscape. 5. Synthesis and applications: These data show that coral restoration can lead to soundscape recovery, demonstrating the return of an important ecosystem function. They also suggest that passive acoustic monitoring (PAM) might provide functionally important measures of ecosystemlevel recovery – but only some PAM metrics reflect ecological status, and those that did are likely to be driven by different communities of soniferous animals. Recording soundscapes represents a potentially valuable tool for evaluating restoration success across ecosystems, but caution must be exercised when choosing metrics and interpreting results.Natural Environment Research Council (NERC)Swiss National Science FoundationUniversity of ExeterMARS Sustainable Solution

Enhancing automated analysis of marine soundscapes using ecoacoustic indices and machine learning

This is the final version. Available on open access from Elsevier via the DOI in this record. Historically, ecological monitoring of marine habitats has primarily relied on labour-intensive, non-automated survey methods. The field of passive acoustic monitoring (PAM) has demonstrated the potential of this practice to automate surveying in marine habitats. This has primarily been through the use of ‘ecoacoustic indices’ to quantify attributes from natural soundscapes. However, investigations using individual indices have had mixed success. Using PAM recordings collected at one of the world’s largest coral reef restoration programmes, we instead apply a machine-learning approach across a suite of ecoacoustic indices to improve predictive power of ecosystem health. Healthy and degraded reef sites were identified through live coral cover surveys, with 90–95% and 0–20% cover respectively. A library of one-minute recordings were extracted from each. Twelve ecoacoustic indices were calculated for each recording, in up to three different frequency bandwidths (low: 0.05–0.8 kHz, medium: 2–7 kHz and broad: 0.05–20 kHz). Twelve of these 33 index-frequency combinations differed significantly between healthy and degraded habitats. However, the best performing single index could only correctly classify 47% of recordings, requiring extensive sampling from each site to be useful. We therefore trained a regularised discriminant analysis machine-learning algorithm to discriminate between healthy and degraded sites using an optimised combination of ecoacoustic indices. This multi-index approach discriminated between these two habitat classes with improved accuracy compared to any single index in isolation. The pooled classification rate of 1000 cross-validated iterations of the model had a 91.7% 0.8, mean SE) success rate at correctly classifying individual recordings. The model was subsequently used to classify recordings from two actively restored sites, established >24 months prior to recordings, with coral cover values of 79.1% (±3.9) and 66.5% (±3.8). Of these recordings, 37/38 and 33/39 received a classification as healthy respectively. The model was also used to classify recordings from a newly restored site established <12 months prior with a coral cover of 25.6% (±2.6), from which 27/33 recordings were classified as degraded. This investigation highlights the value of combining PAM recordings with machine-learning analysis for ecological monitoring and demonstrates the potential of PAM to monitor reef recovery over time, reducing the reliance on labour-intensive in-water surveys by experts. As access to PAM recorders continues to rapidly advance, effective automated analysis will be needed to keep pace with these expanding acoustic datasets.Natural Environment Research CouncilSwiss National Science FoundationNatural Environment Research Council (NERC)University of ExeterMars Sustainable Solution

Global patterns and drivers of ecosystem functioning in rivers and riparian zones

River ecosystems receive and process vast quantities of terrestrial organic carbon, the fate of which depends strongly on microbial activity. Variation in and controls of processing rates, however, are poorly characterized at the global scale. In response, we used a peer-sourced research network and a highly standardized carbon processing assay to conduct a global-scale field experiment in greater than 1000 river and riparian sites. We found that Earth's biomes have distinct carbon processing signatures. Slow processing is evident across latitudes, whereas rapid rates are restricted to lower latitudes. Both the mean rate and variability decline with latitude, suggesting temperature constraints toward the poles and greater roles for other environmental drivers (e.g., nutrient loading) toward the equator. These results and data set the stage for unprecedented "next-generation biomonitoring" by establishing baselines to help quantify environmental impacts to the functioning of ecosystems at a global scale.peerReviewe

The sound of recovery: coral reef restoration success is detectable in the soundscape (dataset)

The article associated with this dataset is available in ORE at: http://hdl.handle.net/10871/127915This is the dataset used for the Lamont et al. (2021) article "The sound of recovery: coral reef restoration success is detectable in the soundscape" published in the Journal of Applied Ecology.Natural Environment Research Council (NERC)Swiss National Science FoundationAustralian Institute of Marine ScienceUniversity of ExeterMARS Sustainable Solution

The sound of recovery:Coral reef restoration success is detectable in the soundscape

Pantropical degradation of coral reefs is prompting considerable investment in their active restoration. However, current measures of restoration success are based largely on coral cover, which does not fully reflect ecosystem function or reef health. Soundscapes are an important aspect of reef health; loud and diverse soundscapes guide the recruitment of reef organisms, but this process is compromised when degradation denudes soundscapes. As such, acoustic recovery is a functionally important component of ecosystem recovery. Here, we use acoustic recordings taken at one of the world's largest coral reef restoration projects to test whether successful restoration of benthic and fish communities is accompanied by a restored soundscape. We analyse recordings taken simultaneously on healthy, degraded (extensive historic blast fishing) and restored reefs (restoration carried out for 1–3 years on previously degraded reefs). We compare soundscapes using manual counts of biotic sounds (phonic richness), and two commonly used computational analyses (acoustic complexity index [ACI] and sound-pressure level [SPL]). Healthy and restored reef soundscapes exhibited a similar diversity of biotic sounds (phonic richness), which was significantly higher than degraded reef soundscapes. This pattern was replicated in some automated analyses but not others; the ACI exhibited the same qualitative result as phonic richness in a low-frequency, but not a high-frequency bandwidth, and there was no significant difference between SPL values in either frequency bandwidth. Furthermore, the low-frequency ACI and phonic richness scores were only weakly correlated despite showing a qualitatively equivalent overall result, suggesting that these metrics are likely to be driven by different aspects of the reef soundscape. Synthesis and applications. These data show that coral restoration can lead to soundscape recovery, demonstrating the return of an important ecosystem function. They also suggest that passive acoustic monitoring (PAM) might provide functionally important measures of ecosystem-level recovery—but only some PAM metrics reflect ecological status, and those that did are likely to be driven by different communities of soniferous animals. Recording soundscapes represents a potentially valuable tool for evaluating restoration success across ecosystems, but caution must be exercised when choosing metrics and interpreting results