DOI: 10.5061/dryad.75s9s
title: Data from: Multi-scaled drivers of ecosystem state: quantifying the importance of the regional spatial scale

description: The regional spatial scale is a vital linkage for the informed
 extrapolation of results from local to continental scales to address
 broad-scale environmental problems. Among-region variation in ecosystem
 state is commonly accounted for by using a regionalization framework, such
 as an ecoregion classification. Rarely have alternative regionalization
 frameworks been tested for variables measuring ecosystem state, nor have
 the underlying relationships with the variables that are used to define
 them been assessed. In this study, we asked two questions: (1) How much
 among-region variation is there for ecosystems and does it differ by
 regionalization framework? (2) What are the likely causes of this
 among-region variation? We present a case study using a large data set of
 lake water chemistry, uni- and multi-scaled hydrogeomorphic and
 anthropogenic driver variables that likely influence lake chemistry at the
 subcontinental scale, and seven existing regionalization frameworks. We
 used multilevel models to quantify and explain within- and among-region
 variation in lake water chemistry. Our models account for local driver
 variables of ecosystem variation within regions, differences in regional
 mean ecosystem state (i.e., random intercepts in multilevel models), and
 differences in relationships between local drivers and ecosystem state by
 region (i.e., random slopes in multilevel models). Using one of the best
 performing regionalization frameworks (Ecological Drainage Units), we
 found that for lake phosphorus and alkalinity: (1) a majority of all the
 variation in water chemistry among the studied lakes occurred among
 regions, (2) very few regional-scale landscape driver variables were
 required to explain among-region variation in lake water chemistry, (3) a
 much higher proportion of the total variation among lakes was explained at
 the regional scale than at the local scale, and (4) some relationships
 between local-scale driver variables and lake water chemistry varied by
 region. Our results demonstrate the importance of considering the regional
 spatial scale for broad-scale research and ecosystem management and
 conservation. Our quantitative approach can be easily applied to other
 response variables, ecosystem types, geographic areas, and spatial extents
 to inform ecosystem responses to global environmental stressors.
description: Cheruvelil EPA-NLAPP
 6-state lake-landscape databaseLake data were compiled
 for six U.S. states: Maine (N=~593), New Hampshire (N=~651), Ohio (N=~55),
 Iowa (N=~117), Michigan (N=~557), and Wisconsin (N=~347). Lake water
 chemistry were sampled from the epilimnion. Data were collected from
 databases maintained by state agencies responsible for monitoring lakes
 under the Federal Clean Water Act, which requires standard procedures and
 quality assurance and quality control protocols. Lakes with surface area ≥
 1 ha and maximum depth ≥ 2 m were included in the dataset. Each lake was
 assigned a unique identifier. We collected data on LAKES (chemistry,
 clarity and depth); NATURAL LANDSCAPE FEATURES (catchment area, lake
 elevation, and features obtained from GIS (hydrology, runoff, precip,
 baseflow); and HUMAN IMPACT FEATURES (Land use/cover (NLCD) in 500m buffer
 around lakes, roads in 500m buffer, human census data within the smallest
 unit available (county subdivision). We also quantified land cover, land
 use, groundwater hydrology, and several other geographic variables within
 the Ecological Drainage Unit (EDU) region. Lake data came from the
 following state agency sources: Maine Department of Environmental
 Protection, Maine Department of Inland Fisheries and Wildlife’s Lake
 Survey table (1/21/03); New Hampshire Department of Environmental
 Services; Michigan Department of Environmental Quality; Wisconsin
 Department of Natural Resources; Ohio Environmental Protection Agency; and
 Iowa State University Limnology Laboratory (Joint Iowa DNR / ISU Project).
 The purpose of the dataset was to investigate lake and landscape controls
 on lake water chemistry across broad geographic
 regions.