Sampling bias overestimates climate change impacts on forest growth in the southwestern United States

Stefan Klesse (), R. Justin DeRose, Christopher H. Guiterman, Ann M. Lynch, Christopher D. O’Connor, John D. Shaw and Margaret E. K. Evans
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Stefan Klesse: University of Arizona
R. Justin DeRose: U.S. Forest Service, Rocky Mountain Research Station, Forest Inventory and Analysis
Christopher H. Guiterman: University of Arizona
Ann M. Lynch: University of Arizona
Christopher D. O’Connor: U.S. Forest Service, Rocky Mountain Research Station
John D. Shaw: U.S. Forest Service, Rocky Mountain Research Station, Forest Inventory and Analysis
Margaret E. K. Evans: University of Arizona

Nature Communications, 2018, vol. 9, issue 1, 1-9

Abstract: Abstract Climate−tree growth relationships recorded in annual growth rings have recently been the basis for projecting climate change impacts on forests. However, most trees and sample sites represented in the International Tree-Ring Data Bank (ITRDB) were chosen to maximize climate signal and are characterized by marginal growing conditions not representative of the larger forest ecosystem. We evaluate the magnitude of this potential bias using a spatially unbiased tree-ring network collected by the USFS Forest Inventory and Analysis (FIA) program. We show that U.S. Southwest ITRDB samples overestimate regional forest climate sensitivity by 41–59%, because ITRDB trees were sampled at warmer and drier locations, both at the macro- and micro-site scale, and are systematically older compared to the FIA collection. Although there are uncertainties associated with our statistical approach, projection based on representative FIA samples suggests 29% less of a climate change-induced growth decrease compared to projection based on climate-sensitive ITRDB samples.

Date: 2018
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DOI: 10.1038/s41467-018-07800-y

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