Changes in biodiversity are occurring at an unprecedented rate world-wide in response to global climate change. As a result, the need for understanding the drivers of biodiversity change, especially on long-term scales, has increased significantly in the past few decades. For this reason, the Marine Environmental Program (MEP) was developed at the Bermuda Institute of Ocean Sciences (BIOS, formally the Bermuda Biological Station for Research) in 2004. This program was designed to monitor key physiographic reef zones of Bermuda in response to a large scale coral bleaching event in 2003 and overall trends in global climate change.
Here, we present data from the benthic diversity, coral disease, and water temperature sub-programs of the MEP from 2004 – 2010. Whittaker’s diversity indices (alpha, beta, and gamma) in addition to a new diversity index known as zeta diversity were used to quantify diversity across temporal and spatial scales. Sea water temperature, coral disease prevalence, and reef depth data were used to explain changes in benthic diversity across both temporal and spatial scales using Redundancy Analysis (RDA), variance partitioning, and Principle Component of Neighboring Matrices (PCNM) analyzes.
The diversity indices calculated across a total of four spatial scales indicate that Bermuda’s coral reefs are relatively stable. Several study locations experienced significant declines in benthic diversity in both 2008 and 2010 with as many as five species being absent from the yearly survey records.
RDA analyzes indicate that increasing seawater temperatures and decreasing coral disease prevalence are associated with these study years. Using variance partitioning to examine the relative influence of these variables on benthic diversity on each of the spatial scales, a dichotomy between local and regional scale processes was observed. On local scales, reef depth explained the most variance, 10.6% of the total variance in the benthic community across time. However, on regional scales, significant temperature variables explained the greatest proportion of variance, 46.5%. These findings were corroborated by PCNM analysis with 30% of variance explained by regional processes compared to 6.1% explained by local processes. This indicates that different processes are driving biodiversity change at different spatial scales. These results highlight the importance of including multiple scales in long-term biodiversity studies such as the MEP at BIOS.