In:
Biogeosciences, Copernicus GmbH, Vol. 18, No. 14 ( 2021-07-30), p. 4473-4490
Kurzfassung:
Abstract. Plant community composition influences carbon, water, and
energy fluxes at regional to global scales. Vegetation demographic models
(VDMs) allow investigation of the effects of changing climate and
disturbance regimes on vegetation composition and fluxes. Such investigation
requires that the models can accurately resolve these feedbacks to simulate
realistic composition. Vegetation in VDMs is composed of plant functional
types (PFTs), which are specified according to plant traits. Defining PFTs
is challenging due to large variability in trait observations within and
between plant types and a lack of understanding of model sensitivity to
these traits. Here we present an approach for developing PFT
parameterizations that are connected to the underlying ecological processes
determining forest composition in the mixed-conifer forest of the Sierra
Nevada of California, USA. We constrain multiple relative trait
values between PFTs, as opposed to randomly sampling within the range of
observations. An ensemble of PFT parameterizations are then filtered based
on emergent forest properties meeting observation-based ecological criteria
under alternate disturbance scenarios. A small ensemble of alternate PFT
parameterizations is identified that produces plausible forest composition
and demonstrates variability in response to disturbance frequency and
regional environmental variation. Retaining multiple PFT parameterizations
allows us to quantify the uncertainty in forest responses due to variability
in trait observations. Vegetation composition is a key emergent outcome from
VDMs and our methodology provides a foundation for robust PFT
parameterization across ecosystems.
Materialart:
Online-Ressource
ISSN:
1726-4189
DOI:
10.5194/bg-18-4473-2021
DOI:
10.5194/bg-18-4473-2021-supplement
Sprache:
Englisch
Verlag:
Copernicus GmbH
Publikationsdatum:
2021
ZDB Id:
2158181-2
