Resolving resource partitioning in parrotfishes (Scarini) using microhistology of feeding substrata

Coral Reefs, Journal Year: 2020, Volume and Issue: 39(5), P. 1313 - 1327

Published: June 9, 2020

Language: Английский

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A framework for identifying and characterising coral reef “oases” against a backdrop of degradation

Journal of Applied Ecology, Journal Year: 2018, Volume and Issue: 55(6), P. 2865 - 2875

Published: June 18, 2018

Abstract Human activities have led to widespread ecological decline; however, the severity of degradation is spatially heterogeneous due some locations resisting, escaping, or rebounding from disturbances. We developed a framework for identifying oases within coral reef regions using long‐term monitoring data. calculated standardised estimates cover ( z ‐scores) distinguish sites that deviated positively regional means. also used coefficient variation CV ) quantify how varied temporally, and among types oases. estimated “coral calcification capacity” CCC ), measure community's ability produce calcium carbonate structures tested an association between this metric ‐scores cover. illustrated our ‐score approach modelling by extracting s simulated data based on four generalized trajectories then applied time‐series programmes in focal Pacific (the main Hawaiian Islands Mo'orea, French Polynesia) western Atlantic Florida Keys St. John, US Virgin Islands). Among 123 analysed, 38 had positive median were categorised as Synthesis applications . Our provides ecosystem managers with valuable tool conservation “oases” degraded areas. By evaluating change state (e.g., cover) oases, may help mechanisms responsible spatial variability condition. Increased mechanistic understanding can guide whether management particular location should emphasise protection, mitigation restoration. Analysis empirical suggest majority originated either escaping resisting disturbances, although showed high capacity recovery, while others candidates Finally, condition (i.e., correlated suggesting identified are exceptional one critical component function.

Language: Английский

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A meta-analysis to assess long-term spatiotemporal changes of benthic coral and macroalgae cover in the Mexican Caribbean

Scientific Reports, Journal Year: 2020, Volume and Issue: 10(1)

Published: June 1, 2020

Abstract Coral reefs in the wider Caribbean declined hard coral cover by ~80% since 1970s, but spatiotemporal analyses for sub-regions are lacking. Here, we explored benthic change patterns Mexican through meta-analysis between 1978 and 2016 including 125 reef sites. Findings revealed that decreased from ~26% 1970s to 16% 2016, whereas macroalgae increased ~30% 2016. Both groups showed high variability. Hard total 12% 2004 again 5% 2005 indicating some recovery after mass bleaching event hurricane impacts. In more than 80% of studied were dominated macroalgae, while only 15% corals. This stands contrast when all sites surveyed study is among first within region reports local Caribbean, other have failed recover. Most now no longer order prevent further degradation, viable reliable conservation alternatives required.

Language: Английский

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Interactions between coral restoration and fish assemblages: implications for reef management

Journal of Fish Biology, Journal Year: 2020, Volume and Issue: 97(3), P. 633 - 655

Published: June 21, 2020

Abstract Corals create complex reef structures that provide both habitat and food for many fish species. Because of numerous natural anthropogenic threats, coral reefs are currently being degraded, endangering the assemblages they support. Coral restoration, an active ecological management tool, may help reverse some current trends in degradation through transplantation stony corals. Although restoration techniques have been extensively reviewed relation to survival, our understanding effects adding live cover complexity on fishes is its infancy with a lack scientifically validated research. This study reviews limited data assemblages, complements this more extensive interactions between how might inform efforts. It also discusses which key species or functional groups promote, facilitate inhibit efforts and, turn, can be optimised enhance assemblages. By highlighting critical knowledge gaps interactions, aims stimulate research into role projects. A greater roles would whether projects return their compositions alternative develop, over what timeframe. alleviation local global stressors remains priority, important tool; increased replanted corals support ensuring success people nature.

Language: Английский

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Coral Reef Mapping with Remote Sensing and Machine Learning: A Nurture and Nature Analysis in Marine Protected Areas

Remote Sensing, Journal Year: 2021, Volume and Issue: 13(15), P. 2907 - 2907

Published: July 24, 2021

Mapping habitats is essential to assist strategic decisions regarding the use and protection of coral reefs. Coupled with machine learning (ML) algorithms, remote sensing has allowed detailed mapping reefs at meaningful scales. Here we integrated WorldView-3 Landsat-8 imagery ML techniques produce a map suitable for occurrence model species, hydrocoral Millepora alcicornis, in located inside marine protected areas Northeast Brazil. Conservation management efforts region were also analyzed, integrating human layers ecological seascape. Three applied: two derive base layers, namely geographically weighted regressions bathymetry support vector classifier (SVM) habitat mapping, one build species distribution (MaxEnt) conspicuous important reef-building area. Additionally, was mapped based on presence tourists fishers. SVM yielded 15 benthic classes (e.g., seagrass, sand, coral), an overall accuracy 79%. Bathymetry its derivative depicted topographical complexity The alcicornis identified distance from shore depth as factors limiting settling growth colonies. most variables ecological, showing importance maintaining high biodiversity ecosystem. comparison suitability absence maps indicated impact direct activities potential inhibitors development. Results reinforce establishment no-take zones other protective measures local biodiversity.

Language: Английский

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