The Use of Models in Ecology and Natural Resources Scott Ollinger Assistant Professor of Natural Resources Institute for the Study of Earth, Oceans and.

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Presentation transcript:

The Use of Models in Ecology and Natural Resources Scott Ollinger Assistant Professor of Natural Resources Institute for the Study of Earth, Oceans and Space University of New Hampshire 449 Morse Hall

What is a Model? The American Heritage® Dictionary of the English Language, Fourth Edition * mod·el (n). 1. A small object, usually built to scale, that represents in detail another, often larger object. 2. A preliminary work or construction that serves as a plan from which a final product is to be made. 3. A schematic description of a system or phenomenon that accounts for its known or inferred properties and may be used for further study of its characteristics. 4. One that serves as the subject for an artist, especially a person employed to pose for a painter, sculptor, or photographer. 5. A person employed to display merchandise, such as clothing or cosmetics.

Better Working Definitions Models are tools and concepts that help us understand, explain, and predict systems that are too complex or difficult to observe for us to comprehend on our own. -- Models are simplifications of reality. -- “The most useless scale for a road map is 1:1”

Why Use a Model? Synthesize existing knowledge in ways not possible using human CPU (Cranial Processing Unit).Synthesize existing knowledge in ways not possible using human CPU (Cranial Processing Unit). Forecast future conditions, often with policy- relevant goals.Forecast future conditions, often with policy- relevant goals. Examine the fundamental behavior of a system.Examine the fundamental behavior of a system. Identify gaps in current knowledge and to guide future research.Identify gaps in current knowledge and to guide future research. Generate hypotheses (as opposed to predictions).Generate hypotheses (as opposed to predictions). “The purpose of models is not to fit the data but to sharpen the questions”. -S. Karlin

Models Don’t Need to be Complex to be Useful (Pool) STANDING STOCK (Pool) Forest Biomass INPUTS (Wood Growth) OUTPUTS (Mortality + Woody Litter) A “Box and Arrow” or “Pool and Flux” model Flux Flux ~400 g/m2 * yr~2% / year

Models Don’t Need to be Complex to be Useful F OREST B IOMASS Growth Mortality + Woody Litter OUTPUT =  (?)  2% per year (Biomass * 0.02) Turnover Rate = Outputs  Standing Stock

Global Carbon Cycle Model

Foliar N—Amax Building a slightly more sophisticated Ecosystem Model Responses are interactive and nonlinear

1) Gross Psn 2) Foliar resp., 3) Transfer to mobile pools, 4) Growth and maint. Resp., 5) Allocation to buds, 6) Root Allocation 7) Wood Allocation, 8) Foliar production, 9) Wood production, 10) Soil resp., 11) Precip. & N Deposition, 12) Canopy interception 13&14) Snowfall & melt, 15) Macro-pore flow, 16) Plant uptake, 17) Transpiration, 18) Drainage, 19) Woody litter, 20) Root litter decay, 21) Foliar litter, 22) Wood decay, 23) N Mineralization & Nitrification, 24) Plant N uptake, 25) N transfer to soil solution. CarbonWater Nitrogen P n E T - C N M o d e l S t r u c t u r e

Multiple Stress Effects on Northeastern Forests

CO 2 effects on Plant Growth (Free Air Carbon Enrichment)

Synthesis of Results from F.A.C.E. Sites (Nowak et al. 2004) Photosynthesis ANPP (elevated / ambient) BNPP (elevated / ambient) Large and sustained rise in photosynthesis Basic structure of A-Ci curves maintained Ci/Ca ratios are unchanged Increased N limitation, decrease in leaf N Photosynthetic enhancement is larger than growth enhancement Evidence for fertilization of native ecosystems is weak

Summary of Plant-Level Ozone Effects Ozone injury on white ash Ozone Formation: f (NOx & VOCs) Ozone is a Strong Oxidant Enters Foliage Through Stomates Oxidizes Cell Membranes and other reduced bio-molecules Linear Decline in Net Photosynthesis with increasing O 3 uptake Typical N.E. summers have days in violation of EPA Ambient Air Quality Standards > 120 ppb P.B. Reich

Ambient Nitrogen Deposition and Regional Nitrate Leaching N Deposition (kg ha -1 yr -1 ) Stream Nitrate Summer (n=354) NO 3 - (mmol/L) # S # S #S # S Stream NO 3 - (  mol/L) Ollinger et al 1993, Aber et al. 2003

Analysis #1: Historical Effects on Present-Day NPP. Multiple stress simulations from IMPLICATION: Enhancement effects of CO 2 and N deposition are at least partially offset by historical disturbance and tropospheric ozone pollution. Pre-Industrial Atmosphere Rising CO 2 CO 2 + O 3 CO 2 + N dep. CO 2 + O 3 + N dep. No Land Use Net Primary Production (g/m 2 *yr) Timber Harvest Agriculture Year 2000 NPP (g m -2 yr -2 )

Analysis #2: Climate Change Effects on Northeastern Forests Hayhoe et al. High resolution climate change modeling ( ) Using GCMs, Statistical Downscaling and Mesoscale Meteorology Models Ollinger, Goodale et al. Ecosystem response to predicted changes in climate and CO 2 using PnET-CN Huntington Forest, NY Hubbard Brook, NH Harvard Forest, MA Howland Forest, ME Biscuit Brook, NY

Predicted changes in temperature and precipitation from 2 GCMs (HADCM3, PCM), with 2 scenarios of CO2 (IPCC A1, B1). Hubbard Brook

Predicted end-of-century ( ) changes in temperature and precipitation at five sites across the northeast.

Predicted Net Primary Production (With Full CO2 growth enhancement) PCM B1HAD A1

Predicted Net Primary Production (With NO CO2 growth enhancement) PCM B1HAD A1

Predicted Effects of CO2 versus CO2 + Climate NPP (g m -2 yr -1 )

HAD A1 PCM B1 Predicted Runoff (With full CO2 growth enhancement)

HAD A1 PCM B1 Predicted Runoff (With NO CO2 growth enhancement)

HAD A1PCM B1 Figure 12. Predicted annual nitrate leaching (gN m-2 yr-1) for the five study sites under four climate scenarios and the full growth enhancement effects of elevated CO2. Nitrate Leaching (With CO2 growth enhancement)

HAD A1 PCM B1 Nitrate Leaching (With NO CO2 growth enhancement)

With the complexities of atmospheric change and disturbance history, how can we characterize present-day C and N cycles across real forested landscapes?

Guiding Principle: Models should be transparent and decipherable 1. Two common pitfalls: 2. And then we fiddled around with the parameters until the predictions came out right Interaction between data and models: Validation vs. Calibration

Mechanism VS. Empiricism 1 st principles: f = ma p = mv K = ½ mv 2 e = mc 2 The Pursuit of a Mechanistic Ecological Model: An inconvenient truth: Ecology is still very much an empirical science! Quantitative application parameterized for ecological systems