DOI: 10.5061/dryad.jwstqjqb0
title: Monitoring resistance and resilience using carbon trajectories: analysis of forest management-disturbance interactions

description: 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·m2·y-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.
description: <strong>Remote sensing procedures</strong>
 We used multitemporal NPP trajectories to develop our resilience
 and resistance metrics. The Numerical Terradynamic Simulation Group at the
 University of Montana has developed a vegetation productivity product that
 estimates yearly vegetation productivity at 30m resolution for the
 contiguous US from 1986 to 2018 (Robinson et al. 2018). We downloaded the
 annual Landsat NPP time series data from 1986 to 2018 across the landscape
 of interest that experienced a spruce beetle outbreak from 2008-2015. The
 product includes annual NPP and 16-daily gross primary productivity (GPP)
 estimates. GPP (gC/m<sup>2</sup>/y<sup>-1</sup>)
 is derived using a light use efficiency model based on meteorological,
 Landsat data, and biome parameter lookup tables, following this
 equation: GPP =
 LUE<sub>max</sub> × f<sub>Tmin</sub> ×
 f<sub>vpd</sub> × 0.45 × SW<sub>rad</sub> ×
 FPAR                         (Eq. 1)  LUE is the biome
 specific maximum light use efficiency (in gC/MJ),
 f<sub>Tmin</sub> and f<sub>vpd</sub> are
 biome-specific temperature and vapor pressure deficit down regulators.
 SW<sub>rad</sub> is the incoming shortwave radiation (45% of
 that is available for photosynthesis) and FPAR is the fraction of
 photosynthetically active radiation, which is derived from the Landsat
 imagery. This GPP product is calculated for every 16-day period throughout
 the year. Annual NPP is calculated as the total sum of the 16-day GPP
 estimates within a given year minus the autotrophic respiration (Robinson
 et al. 2018). As the Landsat data record is inherently noisy due to
 atmospheric effects, sensor differences, clouds, and retrieval errors, we
 adopted the methods of Robinson et al. (2018) to perform a gap-filling
 method, employing climatology and smoothing approaches to harmonize the
 satellite data record (for greater detail see Robinson et al.
 2017). To assess the resistance and resilience
 functions of the plot locations following spruce beetle disturbance, we
 developed NPP anomalies from an undisturbed reference mean. First, we
 randomly selected a set of 60 locations in the vicinity of our plot
 locations (i.e., southcentral Colorado) that did not show any mortality.
 These undisturbed locations were selected using aerial survey data (i.e.,
 selection of locations that were outside areas identified as affected by
 the spruce beetle) and placed outside known management areas, but within
 Engelmann spruce dominated forest as determined by the Individual Tree
 Species Parameter Maps (U. S. Forest Service 2020). Next, we plotted these
 reference sites over time and removed plots (n=35) that showed a deviation
 from a temporal mean at the end of the time series; such locations likely
 experienced other unaccounted disturbances (such as harvest) or
 experienced spruce beetle mortality but might have been omitted by the
 aerial surveys.   To compare sites and calculate our
 resilience and resistance metrics (Figure 1), we calculated an NPP anomaly
 at the pixel-level, i.e., the difference of NPP from a temporal mean. The
 NPP anomaly (dNPP) for each plot was then calculated as follows:         
                                                 dNPP<sub>plot,
 year</sub> = NPP<sub>plot, year</sub> -
 Mean(NPP_undisturbed<sub>year</sub>)      
                         (Eq.2)  Where
 NPP<sub>plot, year</sub> was the NPP
 (gC/m<sup>2</sup>/y<sup>-1</sup>) for a given year
 (1986 - 2018) for a given plot and
 Mean(NPP_undisturbed<sub>plot,</sub>
 <sub>year</sub>) was the mean NPP for the undisturbed plots
 for a given year. We corrected for differences in productivity between
 disturbed and undisturbed plots by adjusting the anomalies with the mean
 difference of NPP over the five years before the outbreak event. We
 compared the maximum NPP reduction (dNPP<sub>max</sub>) with
 the reduction of the percent biomass from field observations, and this
 comparison verified that the NPP data product was sensitive to the effects
 of the spruce beetle outbreak and reflected changes in carbon fixation due
 to loss of photosynthetic biomass (R<sup>2</sup>=0.804, RMSE=
 1195 gC/m<sup>2</sup>/y<sup>-1</sup>; Appendix S1:
 Figure S1). Following the establishment of the
 anomalies, we calculated the maximum decrease in dNPP in a single year
 after the initial outbreak; we used this metric as the resistance metric,
 i.e., the maximum reduction of productivity (dNPPmax). For most stands
 this occurred between 2011-2012. Therefore, dNPPmax reflects the degree to
 which the ecosystem departs from equilibrium due to a disturbance (in this
 case, a spruce beetle outbreak). Subsequently, we determined the year at
 which the maximum reduction of NPP occurred and fitted a linear model of
 dNPP across the years from the maximum reduction of NPP to the final year
 in the time series. The slope of that fitted line was analyzed as the
 resilience metric (Figure 1). In other words, we treat resilience as the
 rate (gC/m2/y2) at which ecosystem processes recover from disturbance
 towards the prior steady state. These metrics where then used to assess
 ecosystem resistance and resilience for the plot locations and we explored
 how different management, environmental, and topographical characteristics
 affected the resilience and resistance metrics.