Management and policy implications of cross-and within-site, long-term studies

TitleManagement and policy implications of cross-and within-site, long-term studies
Publication TypeBook Chapter
Year of Publication2013
AuthorsHavstad K, Brown JR
Book TitleLong-Term Trends in Ecological Systems: A Basis for Understanding Responses to Global Change
PublisherNational Technical Information Services
CitySpringfield, Virginia
ARIS Log Number256723
Keywordsatmospheric chemistry, climate change, cross-site comparisons, disturbance, ecological response, ecology, ecosystem, EcoTrends, experimental forests, global change, human demography, human population growth, Long Term Ecological Research (LTER), long-term datasets, precipitation, rangeland, rangeland research stations, surface water chemistry

This chapter explains the implications long-term data bring to natural resource management and policy-making.  It first explains the two perspectives of long-term data, then explains the values brought by the two perspectives to natural resource management.  The two perspectives are retrospective and predictive:  long-term data enable us to examine data retrospectively to identify temporal and spatial sensitivities, and to build these historical perspectives into predictive models where we can objectively evaluate potential future scenarios. The values are as follows.  First, with the understandings gained by retrospectively examining the long-term data, the managers can develop data-based guidelines to direct the appropriate timing and application of management practices.  Second, long term data provide the opportunity to evaluate policies and programs that have been implemented for resource conservation.  The ability to evaluate environmental responses following policy implementation provides the data necessary to validate policies or may lead to their subsequent revision.  Third, long term data collection provides the opportunity for clients, partners, and stakeholders to be engaged in scientific processes. These interactions create opportunities, not only for technology and information transfers, but for users to inform the science and its research directions.  Fourth, the predictive models built based on long-term data can be used to estimate the effects of a variety of climatic and management scenarios and are critical to informed decision-making and effective communication between natural resource managers and policy makers.