期刊
PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
卷 286, 期 1896, 页码 -出版社
ROYAL SOC
DOI: 10.1098/rspb.2018.2544
关键词
boosted regression trees; ecology; Hawai'i; interactions; management; regime shift
资金
- Erling-Persson Foundation
- Swedish Research Council Formas [2015-743]
- Gordon and Betty Moore Foundation [2897.01]
- NOAA Coral Reef Conservation Program [NA14NOS4820098]
- Mistra
Coral reefs worldwide face unprecedented cumulative anthropogenic effects of interacting local human pressures, global climate change and distal social processes. Reefs are also bound by the natural biophysical environment within which they exist. In this context, a key challenge for effective management is understanding how anthropogenic and biophysical conditions interact to drive distinct coral reef configurations. Here, we use machine learning to conduct explanatory predictions on reef ecosystems defined by both fish and benthic communities. Drawing on the most spatially extensive dataset available across the Hawaiian archipelago-20 anthropogenic and biophysical predictors over 620 survey sites-we model the occurrence of four distinct reef regimes and provide a novel approach to quantify the relative influence of human and environmental variables in shaping reef ecosystems. Our findings highlight the nuances of what underpins different coral reef regimes, the overwhelming importance of biophysical predictors and how a reef's natural setting may either expand or narrow the opportunity space for management interventions. The methods developed through this study can help inform reef practitioners and hold promises for replication across a broad range of ecosystems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据