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Climatic and biophysical controls on conifer species distributions in mountains of Washington State, USA D. McKenzie, D. W. Peterson, D.L. Peterson USDA Forest Service, Pacific Northwest Research Station P. E. Thornton National Center for Atmospheric Research

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slide1

Climatic and biophysical controls on conifer species distributionsin mountains of Washington State, USA

D. McKenzie, D. W. Peterson, D.L. Peterson

USDA Forest Service, Pacific Northwest Research Station

P. E. Thornton

National Center for Atmospheric Research

slide2
What are the impacts of spatial and temporal climatic variability on critical plant resources and species distribution?
    • What is the range of variability in biophysical environments of mountain landscapes across a marine-to-continental climatic gradient? 
    • How would climate-driven changes in the biophysical environment affect species distribution?
slide3

Why model species distributions?

  • Plant community composition affects ecosystem properties and processes.
  • Forest management practices are often based on forest cover types.
  • Species have responded individually to past climatic changes.
slide4

Objectives

  • Define empirical environmental niches for major tree species in the Pacific Northwest.
  • Model changes in spatial distribution of environmental niches under future climate.
  • Identify core habitat areas, stress zones, and potential for speciesmigrations.
slide5

Forested Bioregions of the PNW

Sitka spruce

Western hemlock, Douglas-fir, and Western red cedar

Pacific silver fir, subalpine fir and mountain hemlock

Douglas-fir, grand fir

Ponderosa pine, Douglas-fir

Franklin and Dyrness 1973

slide8

Br. A. Brousseau – St. Mary’s College

C. Webber – California Academy of Sciences

methods 1
Methods (1)
  • Tree species data from Area Ecology Program.
  • 1-km climate coverages from DAYMET.
  • Biophysical variables computed from VIC and MT-CLIM.
slide12

Relative abundance of 6 key

species along a geographic gradient

methods 2
Methods (2)
  • Generalized linear models (GLMs) to predict probability of occurrence.
  • From proxy sets (correlated predictor variables), select no more than one.
  • Quadratic terms identify unimodal responses.
  • Models at multiple scales for each species.
  • Bootstrapped receiver operating characteristic (ROC) curves estimate accuracy and robustness.
slide18

Variables used to predict species distributions

Summer = June, July, August, September. Winter = December, January, February.

aSoil water indices were computed at three depths in the soil layer: 0-10 cm, 10-40 cm, and 40-100 cm.

slide19

PDE ~ steeper

environmental

gradients

slide20

Area under ROC curve represents the ability of the model to

discriminate between presence and absence at all cut levels

Grizzly bear – ponderosa pine

All 4 forests – Engelmann spruce

Area under ROC curve = 0.941

Area under ROC curve = 0.723

slide22

Probability of occurrence

Probability of occurrence

Ponderosa pine – Wenatchee NF

Douglas-fir – Wenatchee/Grizzly Bear combined

applications to modeling the effects of climatic change
Applications to modeling the effects of climatic change?
  • Is there a disconnect between scales?
    • 17 or 50 years of current climate predicts distributions in 75-300 yr-old forests.
  • Are equilibrium models useful?
    • Climate annual means and species presence/absence are snapshots.
    • What about “process-based” modeling?
other dimensions of the problem
Other dimensions of the problem
  • Competitive effects – species composition
  • Distribution (presence/absence) vs. abundance
  • Mature niche vs. regeneration niche
slide30

What forest types are most sensitive to climatic variability?

Western red cedar

Douglas-fir/grand fir

Pacific silver fir

Mean Productivity

Douglas-fir mixed conifer

Pacific silver fir/mountain hemlock

Pacific silver fir/western hemlock

Subalpine fir

Annual Variability in growth

Hessl et al.

slide31

Constraint lines representing limiting factors (Grizzly bear data)

Ponderosa pine

Douglas-fir

Mountain hemlock

Subalpine fir

slide32

Mature niche

Regeneration niche

Species 3

Species 1

Climate variable 2

Species 2

Climate variable 1

Species that currently coexist may not have

equal capacity to regenerate under changing climate

conclusions
Conclusions
  • Focus on climate and biophysical variables allows predictions under changing climate.
  • Models are consistent across scales – robust estimation of environmental niches.
  • More complete picture will emerge from complementary studies of abundance, composition, and regeneration.
thanks

Institutions

Thanks!

People

  • Amy Hessl
  • Dan Fagre
  • Bud Kovalchik
  • Robert Norheim