great environmental indicators (glei) lakes

Great

EnvironmentalIndicators

(GLEI)

Lakes

http://glei.nrri.umn.edu/default/Reports.htm

Objectives

Quantify stressor-response relationships for novel and existing indicators;

Develop predictive models to infer ecological status;

Develop integrative metrics among sub-components

SOLEC Indicator Classes

SOLEC differentiates between indicator types:

• Pressure (= stressor) indicators (e.g. contaminants)

• State (= response) indicators (e.g. fish populations)

Multimetric&

MultivariateApproach

LANDSCAPEsegment-sheds

& watersheds

LOCAL-Habitat quality,-Habitat quantity

STATE INDICATORSGeology ElevationHydrography ClimateLand Use/Cover

Habitat patch HydrologyFood web SubstrateNutrient dynamics

DiatomsVegetationMacrobenthosFishAmphibiansBirdsWater QualityContaminants

INTEGRATEDINDICATORS

-Habitat-Chemical-Biotic-Physical-Hydrologic

REFERENCEDEGRADED

PRESSURE INDICATORS

Shoreline UnitsHigh energy shore

EmbaymentCoastal marsh

River-influenced wetlandProtected wetland

Spatial Scale

Temporal Scale

meters 10km

days

year

10yr

month

100yr

vegetation

Contaminants, diatoms(cores)

fish

Spatial & Temporal Scales

WQ

invertebrates

birds

amphibians

3 types of wetlands

Protected wetland(Barrier beach)open shorelineriverine influenced wetland

2 types of shoreline

High energylow energy

(embayment)

Stratified Random Sample

REFERENCEDEGRADED REFERENCEDEGRADED

Site Selection vs Site Characterization

• Need to move quickly into the field.

• No complete inventory of geomorphic types or anthropogenic stressors.

• Data for site selection can be coarse, but across the Great Lakes Basin.

• Site characterization data should be high resolution, but only needed for sampled sites.

Site Selecton -

• Segment Sheds as Summary units– Watersheds for lengths of shoreline beginning and

ending ½ way between 2nd order and higher streams (n = 762).

– Data summarized across US side of Great Lakes

2nd orderSegments

Lake Ontario 90Lake Erie 102Lake Huron 148Lake St. Clair 12Lake Michigan 157Lake Superior 236Connecting Channels 17

TOTAL 762

Stressor Gradient (cont.)

DATA SOURCE

Agriculture fertilizer and herbicide use (NRCS)

Ag Runoff (erosion, pesticides, and nitrogen; NRCS) 

Distance to nearest AOC (Areas Of Concern; EPA) 

National Atmospheric Deposition Program (NADP)

Population density (US Census Bureau)

Land use by cropland type (NRCS)

Erosion from agricultural land (NRCS)

Fertilizer use on agricultural land (NRCS)

Confined animal facility waste treatment (NRCS)

DATA SOURCE

Shoreline alteration (MRV; ACOE)

Land use, general (USGS-NLCD)

N, P runoff potential (USGS-NAWQA SPARROW)

NPDES categories (EPA)

Urbanization amount/rate (NRCS)

Wetland amount (total; NRCS)

Wetland types, hydric soils, and erosion (NRCS-NRI)

Road area, 4 types (US CENSUS TIGER)

Soil properties (NRCS STATSGO)

Toxic Release Inventory points (EPA from BASINS)

Stressor Gradient (cont.)

Classify Stressors

Purpose: to reduce overlap in the types of information from different stressor source

Seven ‘natural’ categories• Agricultural / Ag-chemical (n = 21)• Atmospheric Deposition (n = 11)• Human Population / Development (n = 14)• Landcover (n = 23)• Point-source / Pollution (n = 79)• Shoreline (n = 6)• Soils (n = 53)

Define Segment-sheds

Compile Stressor Data

Evaluate & Categorize Stressors

Data organization

Select Segment-sheds

Select Sampling Locations within Segment-sheds

Site selection

Cluster Analysis

Second-round PCAs

Ordering sites in stressor-space (multivariate statistics)

PCAs of Individual Stressor Cat.

1 2 3 4 5 6 7

•Anthropogenic Stressor GradientAnthropogenic Stressor GradientSummarized 217 variables from 19 Summarized 217 variables from 19 different sources to identify a multi-different sources to identify a multi-deminsional stressor gradient deminsional stressor gradient (Represented using PC’s of 7 natural (Represented using PC’s of 7 natural categories)categories)

Site Selecton (cont.)Site Selecton (cont.)

Site Characterization

• Identified specific watershed for sampled sites– GPS from the field– Locale polygons created

for each sub-component team.

– Complex polygons for each sampled area

– Watersheds delineated for complexes

– Stressors summarized.

GPS points from fieldLocale Polygons from GPSComplex polygons from locale polygonsWatersheds for Complexes

High Energy Site

Scalable Watersheds – Arc Hydro

• ArcHydro data model – developed to “pre-process” elevation data to more efficiently delineate watersheds.– AGREE drainage enforcement using NHD

line- work– Fill sinks, flow direction, flow accumulation,

stream identification, sub-catchment delineation.

ArcHydro – Cont.

• Catchments are delineated for each stream confluence and river mouth along coast. – Catchments for river

systems are dissolved together.

Streams

Catchments

Catchment

Grand River Watersheds

Extending Watersheds to the Coast

ArcHydro – Cont.

• Along the coast, areas between river mouths, but outside of watersheds remain. We refer to these mostly small coastal watersheds draining directly to the coast but without significant streams as “Coastal Interfluves”.

Extending Watersheds to the Coast

Coastal Interfluves

ArcHydro – Cont.

• Both stream and interfluve sheds are then ordered and numbered along the coast from west to east.

• This provides a framework for scaling stressor summaries up and down the coast.

Additional Efforts

• Lake Erie Integrated Habitat Map– Both US and Canadian sides of Lake Erie basin.

• Canadian Great Lakes Anthropogenic Stressor Gradient.– Currently summarizing stressors in the same way as

we have done for GLEI for Canadian side of Great Lakes Basin

• Full ArcHydro implementation for Saint Louis River (Lake Superior), Maumee and Grand River watersheds (Lake Erie).– Provides for “accumulated stress” or landscape

characterization down the drainage network.

LAND COVER – Great Lakes

N = 9,860

Accumulated SumRel stressor score for the Saint Louis River Watershed