Landscape approaches for understanding and managing lakes

Dr. Kendra Spence Cheruvelil Scottish Freshwater Group 24 October 2013 Making inferences from well‐studied ecosystems Variable Time

Lake (data) Making inferences from well‐studied ecosystems

Option 1

Lake (data) Making inferences from well‐studied ecosystems

Option 1Option 2

Data Making inferences from well‐studied ecosystems

Within‐region variability

Levy et al. under review Frontiers in Ecology & the Environment Making inferences from well‐studied ecosystems

Option 1Option 2

Data Making inferences from well‐studied ecosystems

Option 1OptionOption 2 3

Data

Data

Data Data

Data What do we need to make better inferences?

• Conceptual models of relationships across scales

• Large datasets ‐ Multi‐scaled, multi‐themed

• Robust modeling approaches for multi‐scaled data Conceptual Underpinning: Landscape limnology

The spatially explicit study of lakes, streams, and wetlands as they interact with freshwater, terrestrial, and human landscapes to determine the effects of pattern on ecosystem processes across temporal and spatial scales

www.fw.msu.edu/~LLRG

From left to right: Patricia Soranno, Kendra Spence Cheruvelil, Katherine Webster, Mary Tate Bremigan Soranno et al. 2010 BioScience Conceptual Underpinning: Landscape limnology

HydrologyPrecipitation Geomorph.Glacial history HumanPrecipitation Climate-Atm

Freshwater state or response across space and time (ecosystem variation) Conceptual Scale and hierarchy Hydrology Precipitation Groundwater Ecosystem Drainage network Runoff Runoff morph.

Underpinning: Freshwater stateor response Geomorph. across space andtime (ecosystem variation) Glacial history Catchment Geology Geology Riparian morph. char. Soils Human

Landscape Precipitation Groundwater Ecosystem Drainage network Runoff Runoff morph. Climate-Atm

limnology Conceptual Scale and hierarchy Hydrology Precipitation Groundwater Ecosystem Drainage network Runoff Runoff morph.

Underpinning: Freshwater stateor response Geomorph. across space andtime (ecosystem variation) Glacial history Catchment Geology Geology Riparian morph. char. Soils Human

Landscape Precipitation Groundwater Ecosystem Drainage network Runoff Runoff morph. Climate-Atm

limnology Conceptual Scale and hierarchy Patch connectivity Hydrology Precipitation Groundwater Ecosystem Drainage network Runoff Runoff morph.

Underpinning: Freshwater stateor response Geomorph. across space andtime (ecosystem variation) Glacial history Catchment Geology Geology Riparian morph. char. Soils Patch context Human

Landscape Precipitation Groundwater Ecosystem Drainage network Runoff Runoff morph. Climate-Atm

limnology Conceptual model

Regional‐scale Driver Driver drivers:

Local‐scale Driver Driver Driver drivers:

Lake state or response

Soranno et al. In press. Frontiers in Ecology & the Environment Conceptual model

Regional‐scale Driver Driver drivers:

Local‐scale Driver Driver Driver drivers:

Lake state or response

CSI = Process occurring at one scale interacting with process at another scale (Peters et al. 2007 Ecosystems)

Soranno et al. In press. Frontiers in Ecology & the Environment Conceptual model Cross‐scale interactions (CSIs) What do we need to make better inferences?

• Conceptual models of relationships across scales

• Large datasets ‐ Multi‐scaled, multi‐themed

• Robust modeling approaches for multi‐scaled data Case Study: BIG data across scales Local: Lake watershed

Watershed

Lake

Soranno et al. In press. Frontiers in Ecology & the Environment Case Study: BIG data across scales

Regional: Ecological Drainage Units

Cheruvelil et al. 2013 Ecological Applications; Soranno et al. In press. FEE Case study: Testing for CSIs

Q: Does regional agriculture affect local wetland relationships with lake phosphorus?

http://michpics.wordpress.com/2009/12/19/michigan‐farming‐and‐other‐success‐stories/ http://www.visitusa.com/maine Mid‐west US region Northeast US region Case study: Conceptual model

% Lake Drain. % % Local: wetlands GW depth ratio Agric Urban

Lake phosphorus (~2,000 lakes) Case study: Conceptual model

Cross‐scale interaction between regional agriculture and local wetlands

% Regional: Agric

% Lake Drain. % % Local: wetlands GW depth ratio Agric Urban

Lake phosphorus (~2,000 lakes) Case study: Study lakes & data

n = 1,790 lakes N = 23 regions *

* Ecological Drainage Units Case study: Multilevel mixed effect models

Fixed‐Effect Random‐ Random‐ Intercept Intercept & Slope

Grand mean

P slope P

P

Lake Lake Lake

Wetland % Wetland % Wetland %

Emi Fergus Case study: Multilevel mixed effect models

Random‐Intercept & Slope P

on

P

the

effect

of

Lake Slopes wetland

Wetland % Regional agriculture (%) Case study: Results

CSI: Regional agriculture modifies the relationship between local wetlands and lake P

0.9 LOW regional agric.: P Wetlands have POS effect slope

on 0.4 TP

‐ 0 the

effect

‐0.1 of wetland

differences ‐0.6 HIGH regional agric.: Wetlands have NEG effect Slopes wetland 2 Regional r = 0.62 ‐1.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 RegionalRegional Agriculture Agriculture (proportion) Case study: Results

Wetland and lake P relationships are affected by regional context

Low regional agriculture High regional agriculture Making inferences from well‐studied ecosystems

What about time? NSF‐MSB Project: The effects of cross‐scale interactions on freshwater ecosystem state www.CSI‐limnology.org

C. Filstrup, K. Webster, K. Spence Cheruvelil, E. Norton Henry, N. Lottig, E. Stanley, J. Downing, C.E. Fergus, P‐N. Tan, P. Soranno, C. Stow, E. Bissell, T. Kratz, S. Oliver, S. Yuan, L. Winslow, E. Erdman. (missing, M. Tate Bremigan, T. Wagner, P. Hanson) not in order of appearance! Lagos A geospatial database of all lakes >4 ha

~60,000 total lakes ~ 15,000 lakes with nutrient data Lagos ‐ Lake datasets with >20 yrs of data Summary: Landscape approaches for understanding and managing lakes

• Develop conceptual models – Landscape limnology

• Compile multi‐scaled, multi‐themed databases ‐ Lagos

• Test for CSIs – Hierarchical models (Bayesian)

Soranno et al. In Press. Frontiers in Ecology and the Environment Summary: Landscape approaches “across the pond”

• Understanding variation in biota among lakes at different scales (Scotland, UK, EU)

• Making better inferences  GLOBO Lakes, UK Lake Hydromorphology Typology (Rowan 2010)

• Building capacity and training  high‐ performance research teams Creating and maintaining high‐performing collaborative research teams: the importance of diversity and interpersonal skills

Lead

PA Soranno KC Weathers PC Hanson

KS Cheruvelil

SJ Goring CT Filstrup EK Read

Cheruvelil et al. In press. Frontiers in Ecology and the Environment How can you make great research teams?

Diversity Interpersonal skills

Team Team functioning communication

Research outcomes • Generation/publication of transformative knowledge • Creation of new high‐performing teams • Translation to sound management, conservation and policy • Innovative training and education • Effective public engagement

Cheruvelil et al. In press. Frontiers in Ecology and the Environment Acknowledgements

Award# EF‐1065786

STRIVE AWARD: EPA‐2011‐W‐FS‐7

More Qs? Email me! Kendra Spence Cheruvelil [email protected]