Monitoring resistance and resilience using carbon trajectories: Analysis of forest management-disturbance interactions

Abstract

A changing climate is altering ecosystem carbon dynamics with consequences for natural systems and human economies, but there are few tools available for land managers to meaningfully incorporate carbon trajectories into planning efforts. To address uncertainties wrought by rapidly changing conditions, many practitioners adopt resistance and resilience as ecosystem management goals, but these concepts have proven difficult to monitor across landscapes. Here, we address the growing need to understand and plan for ecosystem carbon with concepts of resistance and resilience. Using time series of carbon fixation (n = 103), we evaluate forest management treatments and their relative impacts on resistance and resilience in the context of an expansive and severe natural disturbance. Using subalpine spruce-fir forest with a known management history as a study system, we match metrics of ecosystem productivity (net primary production, g C m^-2 year^-1 ) with site-level forest structural measurements to evaluate (1) whether past management efforts impacted forest resistance and resilience during a spruce beetle (Dendroctonus rufipennis) outbreak, and (2) how forest structure and physiography contribute to anomalies in carbon trajectories. Our analyses have several important implications. First, we show that the framework we applied was robust for detecting forest treatment impacts on carbon trajectories, closely tracked changes in site-level biomass, and was supported by multiple evaluation methods converging on similar management effects on resistance and resilience. Second, we found that stand species composition, site productivity, and elevation predicted resistance, but resilience was only related to elevation and aspect. Our analyses demonstrate application of a practical approach for comparing forest treatments and isolating specific site and physiographic factors associated with resistance and resilience to biotic disturbance in a forest system, which can be used by managers to monitor and plan for both outcomes. More broadly, the approach we take here can be applied to many scenarios, which can facilitate integrated management and monitoring efforts.

Keywords: ecosystem function; forest carbon; forest disturbance; forest management; spruce beetle.

FIGURE 1

Conceptual model of ecosystem resistance and resilience to perturbation, shown as a function of carbon fixation trajectory (net primary productivity, NPP). Here, we represent the change in pre‐ and post‐disturbance NPP (dNPP_max) as the relative “resistance” to disturbance, where a greater departure of dNPP_max from zero is consistent with lower resistance to change. The y = mx + b slope of post‐disturbance NPP (αNPP) represents the “resilience” to disturbance; a steeper slope is consistent with a more rapid rate of return to pre‐disturbance productivity.

FIGURE 2

Map of the study region within the state of Colorado, USA. The study landscape was located within the Grand Mesa‐Uncompaghre‐Gunnison National Forest; green shading indicates the density of Picea engelmannii and black polygons denote aerially mapped observations of spruce beetle mortality. Gray lines show major regional roadways.

FIGURE 3

(a) Time series of mean net primary productivity (NPP) anomaly (±SE) aggregated across forest management treatments. A sharp decline in NPP is observable following initial forest mortality from spruce beetle outbreak (ca. 2011–2015), with clear differences in the recovery rate of carbon trajectories across treatments. The open box denotes the period over which baseline anomalies in NPP were computed, and the gray shading shows the period where the outbreak occurred. Analysis of variance also showed no clear differences in (b) resistance, but there were significant differences in resilience metrics (c) due to different silvicultural treatments.

FIGURE 4

Empirical cumulative density functions of (a) resistance (dNPP_max) and (b) resilience metrics (αNPP), showing variation in the distribution of resilience (but not resistance) due to effects of forest management. NPP, net primary productivity.

FIGURE 5

Effects plots showing linear relationships between residual variance in resistance (dNPP_max) and (a) elevation, (b) percent spruce basal area (BA), and (c) pre‐disturbance mean NPP. Effects are also shown for residual variance in resilience (αNPP) for (d) aspect and (e) elevation. Shading represents the 95% confidence interval of modeled effects. Rug plots show distributions of respective independent variables along the x‐axis. NPP, net primary productivity.

FIGURE 6

Raster products projecting metrics of (a) resistance (dNPP_max) and (b) resilience (αNPP) across the study landscape, located in the Grand Mesa‐Uncompaghre‐Gunnison National Forest. Black dots denote plots where data were collected on stand structure and composition. NPP, net primary productivity.

FIGURE 7

(a) Comparison of stand composition associated with two categories: “resistant” stands that were below average in dNPP_max (i.e., stands that did not have large drops in productivity, n = 49), and “susceptible” stands that were above average in dNPP_max (i.e., had a larger negative departure from the long‐term NPP average following spruce beetle outbreak, n = 54). PIEN, Engelmann spruce (Picea engelmannii); POTR, aspen (Populus tremuloides); ABLA, fir (Abies lasiocarpa). (b) A comparison of the relationship between basal area mortality and dNPP_max; green‐shaded points represent the “resistant” category shown in (a). NPP, net primary productivity.