Eric J. Gustafson, Northern Research Station, Rhinelander WI, USA
Because LANDIS-II is being used to make projections under novel environmental conditions that have no empirically observed analog (e.g., climate change), it is becoming critical to strengthen the links between the fundamental drivers of tree cohort growth and competition (temperature, precipitation, CO2 concentration) within the process extensions of LANDIS-II. A more mechanistic approach (PnET-Succession) to simulating growth within LANDIS-II was recently developed by De Bruijn et al. (2014) by embedding algorithms of the PnET-II stand-level ecophysiology model (Aber et al. 1995) within the Biomass Succession extension to more mechanistically simulate growth as a competition for light and water to support photosynthesis. Where the Biomass Succession extension simulates growth and competition among tree species cohorts using an average maximum aboveground net primary productivity that is not linked to weather extremes within a time step, PnET-Succession mechanistically simulates photosynthesis monthly using physiological attributes such as light and water use efficiency and drought tolerance. PnET-Succession models soil water monthly as a function of soil texture, precipitation, interception, evaporation and consumption by trees (similar to DGVMs), allowing response to extreme drought events rather than being limited to the mean weather values (typically decadal) used by less mechanistic approaches. Accordingly, photosynthetic rates (and respiration rates) vary monthly by species and cohorts as a function of precipitation and temperature (among other factors, including CO2 concentration), which directly affect competition and ultimately successional outcomes.
The opportunities afforded by this approach for simulating climate change effects on forests are significant. First, because there are several parameters related to drought-tolerance (including cohort establishment and CO2 effects on conductance) and temperature effects (on photosynthesis and respiration), the differential effects of climate on the ability of species to compete can result in altered successional outcomes through time. These outcomes are an emergent property of the mechanistic simulation of growth that accounts for the monthly interaction of precipitation, temperature, light (including seasonal cloudiness), CO2 concentration and species’ physiological attributes, rather than phenomenological estimates of their combined effects based on behavior seen under past conditions. Second, because the extension tracks carbon reserves, drought and competition induced growth reductions can cause carbon reserves to become depleted by respiration, which can result in direct mortality (McDowell et al. 2013), or the level of carbon reserves can be used by disturbance extensions to realistically target disturbance-induced mortality to stressed cohorts (complete or partial mortality of cohorts). Physiological water stress may be dependent on either the intensity or duration of water limitations (or both) depending on the ability of a species to extract water from the soil and maintenance respiration rates, including interactions with all the other factors that affect growth (e.g., light, temperature, CO2). Physiological light or temperature stress is similarly dependent on the intensity and duration of shading and heat waves. For studies of the effects of climate change on forest successional dynamics, a weather stream of temperature, precipitation and radiation from downscaled global circulation models can allow growth and establishment rates to vary monthly, rather than using longer-term averages that make it difficult to simulate extreme events. Mortality is simulated when moisture, heat or light stress depresses growth rates below respiration levels long enough to reduce carbon reserves below survival thresholds, or when a disturbance targets stressed cohorts.
This more mechanistic approach to modeling drought effects at landscape scales is conceptually appealing because of its reliance on first principles and ecological theory, but it does require more input parameters and therefore increases parameter uncertainty. However, less mechanistic approaches result in uncertainty when extrapolating phenomenological relationships beyond the domain in which they were developed to the novel conditions of the future (Keane et al. in press). Thus, both approaches result in uncertainty, but the extrapolation uncertainty of phenomenological approaches has increasingly been deemed to exceed the parameter uncertainty of mechanistic approaches (Cuddington et al. 2013, Gustafson 2013). The more mechanistic approach also increases run times, but because the mechanistic approach causes many cohorts to die before their longevity age, fewer cohorts must be simulated, resulting in only modest performance declines.
Some tests of PnET-Succession have been conducted. Gustafson et al. (2015) used it to predict the outcome of a precipitation manipulation experiment in a piñon-juniper ecosystem in New Mexico (USA), with considerable success. Importantly, they discovered that the amount of non-structural carbon reserves (NSC) predicted by PnET-Succession was negatively related to the incidence of mortality on experimental plots. Another test of the ability of the extension to predict cohort mortality as a function of drought in more diverse forest of the upper Midwest was recently completed (Gustafson et al. in review). A more comprehensive evaluation of the response of Midwestern species assemblages to variation in temperature, precipitation and cloudiness is underway (Gustafson et al. in prep). PnET-Succssion is also being used for climate change research by Jonathan Thompson and Matthew Duveneck (Harvard University) and Renaud Colmant (FAO, Rome).
PnET-Succession was officially released on the LANDIS-II website in August 2015.
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Gustafson, E.J., A.M.G. De Bruijn, B.R. Miranda, B.R. Sturtevant. in review. Mechanistic modeling of drought effects on landscape forest succession. Ecosphere.
Gustafson, E.J., A.M.G. De Bruijn, B.R. Sturtevant, B.R. Miranda. in prep. Using first principles to increase the robustness of forest landscape models for projecting climate change impacts. Outlet TBD.
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