title: Combining and aggregating environmental data for status and trend assessments: challenges and approaches.
abstract: Increasingly, natural resource management agencies and nongovernmental organizations are sharing monitoring data across geographic and jurisdictional boundaries. Doing so improves their abilities to assess local-, regional-, and landscape-level environmental conditions, particularly status and trends, and to improve their ability to make short- and long-term management decisions. Status monitoring assesses the current condition of a population or environmental condition across an area. Monitoring for trends aims at monitoring changes in populations or environmental condition through time. We wrote this paper to inform agency and nongovernmental organization managers, analysts, and consultants regarding the kinds of environmental data that can be combined with suitable techniques and statistically aggregated for new assessments. By doing so, they can increase the (1) use of available data and (2) the validity and reliability of the assessments. Increased awareness of the difficulties inherent in combining and aggregating data for local- and regional-level analyses can increase the likelihood that future monitoring efforts will be modified and/or planned to accommodate data from multiple sources.
authors.affiliation: Department of Fisheries and Wildlife, Oregon State University, Corvallis, OR, 97331, USA, kathleen.maashebner@oregonstate.edu.
authors.forename: Kathleen G
authors.initials: KG
authors.lastname: Maas-Hebner
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Harte
authors.forename: Nancy
authors.initials: N
authors.lastname: Molina
authors.forename: Robert M
authors.initials: RM
authors.lastname: Hughes
authors.forename: Carl
authors.initials: C
authors.lastname: Schreck
authors.forename: J Alan
authors.initials: JA
authors.lastname: Yeakley
mesh_terms: D003247:Conservation of Natural Resources / Q000379:methods; D003625:Data Collection; D004784:Environmental Monitoring / Q000379:methods*; D004787:Environmental Pollution / Q000706:statistics & numerical data; D015203:Reproducibility of Results