10.5061/DRYAD.75S9S
Cheruvelil, Kendra S.
Michigan State University
Soranno, Patricia A.
Michigan State University
Webster, Katherine E.
Michigan State University
Bremigan, Mary T.
Michigan State University
Data from: Multi-scaled drivers of ecosystem state: quantifying the
importance of the regional spatial scale
Dryad
dataset
2014
landscape limnology
ecoregions
hydrogeomorphic
2014-02-24T15:25:25Z
2014-02-24T15:25:25Z
en
https://doi.org/10.1890/12-1872.1
1271713 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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.