Simulating Growth and Competition on Wet and Waterlogged Soils in a Forest Landscape Model

Abstract

Changes in CO2 concentration and climate are likely to alter disturbance regimes and competitive outcomes among tree species, which ultimately can result in shifts of species and biome boundaries. Such changes are already evident in high latitude forests, where waterlogged soils produced by topography, surficial geology, and permafrost are an important driver of forest dynamics. Predicting such effects under the novel conditions of the future requires models with direct and mechanistic links of abiotic drivers to growth and competition. We enhanced such a forest landscape model (PnET-Succession in LANDIS-II) to allow simulation of waterlogged soils and their effects on tree growth and competition. We formally tested how these modifications alter water balance on wetland and permafrost sites, and their effect on tree growth and competition. We applied the model to evaluate its promise for mechanistically simulating species range expansion and contraction under climate change across a latitudinal gradient in Siberian Russia. We found that higher emissions scenarios permitted range expansions that were quicker and allowed a greater diversity of invading species, especially at the highest latitudes, and that disturbance hastened range shifts by overcoming the natural inertia of established ecological communities. The primary driver of range advances to the north was altered hydrology related to thawing permafrost, followed by temperature effects on growth. Range contractions from the south (extirpations) were slower and less tied to emissions or latitude, and were driven by inability to compete with invaders, or disturbance. An important non-intuitive result was that some extant species were killed off by extreme cold events projected under climate change as greater weather extremes occurred over the next 30 years, and this had important effects on subsequent successional trajectories. The mechanistic linkages between climate and soil water dynamics in this forest landscape model produced tight links between climate inputs, physiology of vegetation, and soils at a monthly time step. The updated modeling system can produce high quality projections of climate impacts on forest species range shifts by accounting for the interacting effects of CO2 concentration, climate (including longer growing seasons), seed dispersal, disturbance, and soil hydrologic properties.

Beyond blight: Phytophthora root rot under climate change limits populations of reintroduced American chestnut

“…The user specifies this threshold (LethalTemp) for computation of the probability that the pathogen is present (pPresence): where ExtremeTmin is the lowest minimum monthly air temperature observed across years in the time step, and p(Presence) is constrained to be between 0 and 1 (Figure 2). ExtremeTmin is estimated within the succession extension as the monthly average temperature minus three times the winter standard deviation as described in Gustafson et al (2020). Presence is computed as a binary stochastic variable, being 1 if a uniform random number is greater than pPresence, or 0 otherwise.…”

“…A minimum temperature parameter (MinSoilTemp) defines a soil temperature below which pathogen populations drop and do not cause infection. Soil temperature at depth of SoilTempDepth ( T soil ) is estimated for each month of the growing season with the methods presented in Gustafson et al (2020) using: where T ave is the average air temperature for month m , A is the amplitude of air temperature over the previous 12 months, d is the damping depth (meters), and Ω is the angular frequency of oscillation (radians per month) (Sitch et al, 2003). The damping depth ( d ) and angular frequency of oscillation (Ω) are calculated as follows: where k is the thermal diffusivity (in square millimeters per month) of the soil given its water content.…”

The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0

“…However, permafrost and the legacies that affect its dynamics are rarely considered in forest models. In fact, just a handful of models explicitly simulate permafrost (Foster et al, 2019;Gustafson et al, 2020;Kruse et al, 2022), and those that do often operate at relatively coarse spatial (≥ 25 ha grid cells) and/or temporal (≥ monthly) resolutions (but see Kruse et al, 2022, who describe a permafrost module that runs with a 5 min temporal resolution). This makes it difficult to capture the fine-scale spatial heterogeneity of permafrost distributions and the effects of daily temperature variability on plant water availability during short but critical shoulder seasons.…”

The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0

“…However, permafrost and the legacies that affect its dynamics, are rarely considered in forest models. In fact, just a handful of models explicitly simulate permafrost (Foster et al, 2019;Gustafson et al, 2020;Kruse et al, 2022), and those that do often operate at relatively coarse spatial (≥ 25 ha grid cells) and/or temporal (≥ monthly) resolutions (but see Kruse et al 2022). This makes it difficult to capture the fine-scale spatial heterogeneity of permafrost distributions and the effects of daily temperature variability on plant water availability during short, but critical shoulder seasons.…”