Abstract
Monitoring and predicting the potential impacts of outer continental shelf (OCS) energy production on nearshore ecosystems requires an ability to distinguish between changes caused by natural processes and those caused by human activities. The ability to distinguish such changes in turn requires long-term, spatially extensive data to describe natural patterns of temporal and spatial variation in species abundances and the environmental factors that influence them. This is particularly true for giant kelp forests, which are highly productive and diverse ecosystems in temperate regions that fluctuate greatly in space and time. These systems are highly valued for the milieu of goods and services they provide to society and there is general interest in minimizing anthropogenic activities that adversely affect them. The purpose of this project was to partner with agencies in the Department of the Interior (DOI) to document, integrate and analyze data produced from long-term kelp forest monitoring programs to improve our understanding of the causes and consequences of change in these iconic ecosystems.
The primary objectives in this collaborative partnership were fourfold: (1) Work with two Department of the Interior agencies to assimilate, document and published their long-term (30 + years) data sets pertaining to kelp forest community structure at the northern Channel Islands and San Nicolas Island; (2) Expand the spatial scope of these data sets by integrating them with other long-term kelp forest monitoring programs in the region and with appropriately temporally and spatially scaled environmental data to produce a data resource with unrivaled temporal, spatial and taxonomic scope; (3) Analyze integrated data sets across multiple spatial and temporal scales to ascertain patterns of variation in population and community dynamics and to identify key environmental and anthropogenic factors that drive them; (4) Use the fully integrated data sets to collaborate with BOEM partners and other BOEMfunded programs on issues relevant to BOEM’s mission.
The value of this project to BOEM lies in its ability to assist managers in detecting and evaluating possible impacts from offshore energy activities, and in developing options to mitigate these impacts. In addition, identification of patterns in these data sets will aid in predicting potential ecosystem impacts due to climate change and advancing adaptive management, both of which are goals central to DOI stewardship responsibilities and trust resources.
One lesson of this project is that the time and expense necessary to make data visible, easy to use and easy to combine with other data should not be underestimated, but that such efforts can have very positive results on how widely that data is accessed and used. When long term ecological monitoring continues for decades, there is a danger either that too little metadata is assembled, preventing interpretation of the data, or that so much metadata is assembled that the volume of information becomes a barrier to understanding what was done. The process of publishing data sets forces metadata to be constructed in a standard format and ensures that an appropriate level of detail is maintained. When combining data sets, we note that decisions about taxonomic resolution can present obstacles to data synthesis, especially when organisms are separated into life stages or size classes. If a new monitoring project is proposed, it is worth investing time up front to ensure that the newly collected taxa groups and size classes will be compatible with existing data sets.
We show that the combined data sets we produce have considerable potential for detecting impacts in this region. Although there are statistical challenges to detecting localized impacts, Before-After-ControlImpact analyses or similar approaches can reliably detect impacts of moderate size and severity particularly if several species are similarly impacted and thus included in the analysis. However, we also reveal that the spatial and temporal dynamics of this marine system can result in extremely high false positive rates if temporal autocorrelation is not properly accounted for. Because adjusting for such temporal effects requires long-term monitoring data, this emphasizes the continued value of DOI monitoring for impact analysis in the region.
Our analyses of the drivers of spatial variation within the region indicate that moderate scale variation (65 km+) is relatively predictable based on available environmental covariates. This suggests that detection of moderate or large sized impacts such as oil spills could be further improved by considering such covariates in analyses. Unfortunately, local scale impacts such as cable burial occur on a background of high small-scale natural variability. This small-scale variability was not well predicted by any of the covariates we examined, and would rarely be co-located with existing monitoring sites. These results emphasize the challenge of reliably estimating small impacts without direct collection of ecological data before and after an anthropogenic disturbance.