Scaling Contagious Disturbance: A Spatially-Implicit Dynamic Model

Abstract

Spatial processes often drive ecosystem processes, biogeochemical cycles, and land-atmosphere feedbacks at the landscape-scale. Climate-sensitive disturbances, such as fire, land-use change, pests, and pathogens, often spread contagiously across the landscape. While the climate-change implications of these factors are often discussed, none of these processes are incorporated into earth system models as contagious disturbances because they occur at a spatial scale well below model resolution. Here we present a novel second-order spatially-implicit scheme for representing the size distribution of spatially contagious disturbances. We demonstrate a means for dynamically evolving spatial adjacency through time in response to disturbance. Our scheme shows that contagious disturbance types can be characterized as a function of their size and edge-to-interior ratio. This emergent disturbance characterization allows for description of disturbance across scales. This scheme lays the ground for a more realistic global-scale exploration of how spatiallycomplex disturbances interact with climate-change drivers, and forwards theoretical understanding of spatial and temporal evolution of disturbance.

Forest responses to last‐millennium hydroclimate variability are governed by spatial variations in ecosystem sensitivity

“…The centennially resolved temporal grain of our analyses limits detection of annual‐scale growth variations, the effects of stochastic or short‐lived extreme events such as sub‐decadal to decadal drought (Breshears et al ., 2005; Allen et al ., 2010; Seidl et al ., 2011), or disturbance events such as fire and pest outbreaks, unless these are large enough to cause stand‐replacing mortality events. Disturbance processes are often unrepresented in ecosystem models or treated as purely stochastic and with implicit assumptions of landscape‐scale equilibria (Seidl et al ., 2011; Fisher et al ., 2018; McCabe and Dietze, 2019). Of the ecosystem models used here, LPJ‐WSL and LPJ‐GUESS included fire in their simulations as a semi‐mechanistic process following GLOBFIRM (Thonicke et al ., 2001), which estimates burned area as a function of daily fire probabilities that are a function of fuel moisture and fuel load threshold.…”

Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems

“…McCabe and Dietze (2019) further argue that the inclusion of the concept of adjacency (and its dynamics) would in principle allow for a myriad of additional ecological phenomena to be captured, including edge effects on forest microclimate (of particular importance for the spread of fires), the dependence of dispersal limitation on spatial arrangement of forests, simulation of invasive species dynamics, and also as above the flow of matter and energy between patches. Thus, the extraction and use of both tiling units and their bulk spatial relationships might also be elements of the “grand challenge” of representing the heterogeneity of the land surface and the living systems that exist within and upon it.…”

“…Relatedly, some phenomena (fire, insects) intrinsically “spread” through the landscape via contagion, a process which is difficult to model explicitly at the level of LSM grid cells. McCabe and Dietze (2019) propose a method for estimating the size distribution of contagious disturbance events based on their disturbance, initiation and spread probabilities as well as retaining through the simulation a metric of the “adjacency” of tiled elements within grid cells. Their method evolves the spatial adjacency of disturbed patches through time, and therefore could be generally applicable to the problem of retaining length‐scale information for time‐varying quantities.…”

Advancing global change biology through experimental manipulations: Where have we been and where might we go?

“…Figure shows a trend in disturbance‐related experiments reported in GCB publications, from zero in the initial years of GCB to close to 30 in the most recent period. The rise in disturbance‐related experiments is encouraging as disturbance regimes are shifting with global change (IPCC, ) and new modeling methods are coming online to simulate disturbance and its consequences with more realism (e.g., Fisher et al, ; McCabe & Dietze, ). Changing disturbance regimes can reduce the resilience of ecosystems (e.g., Turner, Braziunas, Hansen, & Harvey, ).…”