Ornamental marine species culture in the coral triangle: seahorse demonstration project in the Spermonde Islands, Sulawesi, Indonesia.

Ornamental marine species ('OMS') provide valuable income for developing nations in the Indo-Pacific Coral Triangle, from which most of the specimens are exported. OMS culture can help diversify livelihoods in the region, in support of management and conservation efforts to reduce destructive fishing and collection practices that threaten coral reef and seagrass ecosystems. Adoption of OMS culture depends on demonstrating its success as a livelihood, yet few studies of OMS culture exist in the region. We present a case study of a land-based culture project for an endangered seahorse (Hippocampus barbouri) in the Spermonde Islands, Sulawesi, Indonesia. The business model demonstrated that culturing can increase family income by seven times. A Strengths Weaknesses Opportunities Threats (SWOT) analysis indicated good collaboration among diverse stakeholders and opportunities for culturing non-endangered species and for offshoot projects, but complicated permitting was an issue as were threats of market flooding and production declines. The OMS international market is strong, Indonesian exporters expressed great interest in cultured product, and Indonesia is the largest exporting country for H. barbouri. Yet, a comparison of Indonesia ornamental marine fish exports to fish abundance in a single local market indicated that OMS culture cannot replace fishing livelihoods. Nevertheless, seahorse and other OMS culture can play a role in management and conservation by supplementing and diversifying the fishing and collecting livelihoods in the developing nations that provide the majority of the global OMS

Spatial Patterns of Fish Communities in Lake Michigan Tributaries

Understanding spatial patterns in freshwater fish communities is critical for the successful management of natural resources as well as a vital component for understanding aquatic ecosystems. Spatial patterns of species similarity of freshwater fish assemblages can be affected by dispersal processes and environmental conditions. We hypothesized that as distance increased between study systems, species similarity would decrease. We sampled 15 drowned river mouths (DRMs) connected to Lake Michigan by conducting 10-minute electrofishing transects (n = 5-6 per DRM) parallel to the shoreline in each DRM to characterize littoral fish assemblages. At each transect, we also characterized environmental conditions (e.g., specific conductivity or number of houses/buildings along shoreline). We captured 3,080 individual fish representing 45 species across the 15 DRMs, with catch among DRMs ranging from 115 to 358 individuals per system and species richness ranging from 11 to 26 species per system. The most abundant species in the catch were yellow perch Perca flavescens (13.9%), pumpkinseed Lepomis gibbosus (10.9%), and bluegill Lepomis macrochirus (9.8%). We found a weak positive correlation between species similarity and distance between each pair of DRMs (R2 = 0.03), which did not support our hypothesis that species similarity would decrease with distance, even though we found evidence of spatial autocorrelation of environmental variables. A potential explanation for our findings is related to gear selectivity associated with boat electrofishing. We suggest that sampling fish with additional gear or approaches is necessary to more rigorously test for the spatial pattern of species similarity among DRMs

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

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

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

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

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

Environmental context and contaminant biotransport by Pacific salmon interact to mediate the bioaccumulation of contaminants by stream-resident fish

1. The extent to which environmental context mediates the uptake of biotransported contaminants by stream-resident organisms is not understood. For example, there is no clear understanding of the extent to which contaminant type, instream characteristics, or resident fish identity interact to influence the uptake of contaminants deposited by Pacific salmon (Oncorhynchus spp.) during their spawning runs. 2. To address this uncertainty, we sampled four stream-resident fish species from 13 watersheds of the Laurentian Great Lakes in locations with and without salmon across a gradient of instream and watershed characteristics. We determined the polychlorinated biphenyl (PCB) and mercury (Hg) concentration along with the stable isotope ratio of C and N for each stream-resident fish. 3. We found that stream-resident fish PCB concentrations were 24-fold higher in reaches with salmon and were positively related to δ15N. In contrast, stream-resident fish Hg concentrations were similar or lower in reaches with salmon and either exhibited a negative or no relationship with δ15N. 4. Based upon AICc, stream-resident fish exhibited species-specific PCB concentrations that were positively related to salmon PCB flux. Hg burdens exhibited an interaction between fish length and salmon Hg flux – as salmon Hg inputs increased, Hg levels decreased with increasing resident fish length. We found no support for models that included the mediating influence of instream or watershed factors. Salmon eggs are enriched in PCBs but have very low Hg concentrations, so our results may be driven by the consumption of salmon eggs by stream-resident fish. 5. Synthesis and applications. Our results highlight that contaminants bioaccumulate differently depending on contaminant type, species identity, and the trophic pathway to contamination. Consequently, consideration of the recipient food web and route of exposure is critical to understanding the fate of biotransported contaminants in ecosystems. The transfer of contaminants by migratory organisms represents an understudied stressor in ecology. Effective management of biotransported contaminants will require the delineation of “hot-spots” of biotransport and implementation of best management practices in those watersheds that receive contaminants from spawning salmon