DOI: 10.5061/dryad.tqjq2bvxf
title: Estimating social-ecological resilience: fire management futures in the Sonoran Desert

description: Resilience quantifies the ability of a system to remain in or return to
 its current state following disturbance. Due to inconsistent terminology
 and usage of resilience frameworks, quantitative resilience studies are
 challenging, and resilience is often treated as an abstract concept rather
 than a measurable system characteristic. We used a novel,
 spatially-explicit stakeholder engagement process to quantify
 social-ecological resilience to fire, in light of modeled
 social-ecological fire risk, across the non-fire-adapted Sonoran Desert
 Ecosystem in Arizona, USA. Depending on its severity and the
 characteristics of the ecosystem, fire as a disturbance has the potential
 to drive ecological state change. As a result, fire regime change is of
 increasing concern as global change and management legacies alter the
 distribution and flammability of fuels. Because management and use
 decisions impact resources and ecological processes, social and ecological
 factors must be evaluated together to predict resilience to fire. We found
 highest fire risk in the central and eastern portions of the study area,
 where flammable fuels occur with greater density and frequency and
 managers reported fewer management resources than in other locations. We
 found lowest fire resilience in the southeastern portion of the study
 area, where combined ecological and social factors, including abundant
 fuels, few management resources, and little evidence of past institutional
 adaptability, indicated that sites were least likely to retain their
 current characteristics and permit achievement of current management
 objectives. Analyzing ecological and social characteristics together
 permits regional managers to predict the effects of changing fire regimes
 across large, multi-jurisdictional landscapes and to consider where to
 direct resources. This study brought social and ecological factors
 together into a common spatial framework to produce vulnerability maps;
 our methods may inform researchers and managers in other systems facing
 novel disturbance and spatially-variable resilience.
description: In this study, we used existing ecological layers and a
 stakeholder mapping approach to develop quantitative maps of
 social-ecological resilience to fire across the EPA Level III Sonoran
 Desert Ecoregion of Arizona. We used an overlay analysis to map coupled
 social-ecological resilience across the region. This analysis enabled us
 to identify areas of likely low and high resilience. Since resilience is
 meaningless unless examined in light of disturbance risk, we also modeled
 across the landscape both ecological and social aspects of fire risk. We
 then plotted resilience against modeled ecological and social fire risk to
 assess vulnerability across the study region.
 <i>Study system</i> The Sonoran
 Desert is the most biologically diverse desert in the US and also one of
 the regions of fastest human population growth, with more than 6 million
 residents according to the 2010 US Census (Dimmitt et al. 2015). Due to
 lack of fuels continuity in native plant communities, the Sonoran Desert
 is considered non-fire-adapted; occurrence of fire reduces both plant and
 associated animal diversity at local scales (McCaffrey 2015). However,
 fire risk has been increasing across the region in recent decades (Gray et
 al. 2014). Emergence of a novel, high-frequency fire regime in the Sonoran
 Desert is driven by increasingly variable precipitation in combination
 with the spread of invasive, fine fuels (Seager et al. 2007, Abatzoglou
 and Kolden 2011, McDonald and McPherson 2013, Moloney et al. 2019). These
 changes impact native plant communities with limited inherent resilience
 to disturbance, placing desert systems at risk of fundamental ecosystem
 state change (Abella 2009, Brooks and Chambers 2011). That is, a feedback
 loop promoting fire-adapted non-native species threatens to turn burned
 Sonoran Desert ecological communities into grasslands dominated by
 non-natives (McDonald & McPherson 2013). Resource managers and
 restoration professionals in the Sonoran confront biological invasions and
 aim to reduce fire risk in order to effectively retain functioning
 ecosystems and native Sonoran Desert biodiversity on the landscape
 (McCarty 2001). In the US, the Sonoran Desert Ecoregion consists largely
 of government-managed land, but is a patchwork of federal, state, and
 local jurisdictions, military bases, and tribal reservations. Management
 responses to fire across the region are varied and span a range of
 jurisdictional mandates as well as constraints and resources (Aslan et al.
 In revision). <i>Modeling ecological aspects of
 resilience</i> We gathered ecological spatial
 layers to (a) predict ecological large fire risk over the next 20 and 40
 years across the study area, so that we could present risk maps to
 managers and discuss their responses, and (b) include ecological
 components of system resilience in our coupled systems GIS. We defined
 fire risk throughout the study as the probability of large fire
 (&gt;1000 acres) if an ignition were to occur, and used the current
 and prior year maximum NDVI as predictors of annual fire risk (after Gray
 et al. 2014). We also included as dynamic model variables climate
 variables (total winter precipitation, winter mean daily minimum
 temperature, and fire season mean daily wind speed, maximum temperature,
 and humidity) to predict past and future fire risk (after Gray et al.
 2014). Fixed variables in the model included road density, distance to
 urban development, surface heat load index, topographic roughness,
 elevation, aspect, and slope (Gray et al. 2014). To forecast fire risk
 into two future time periods, it was first necessary to forecast the
 dynamic predictor variables. We used data at 4-km resolution, downscaled
 from 12 Global Climate Models (GCMs), to forecast the meteorological
 predictors. To forecast NDVI into the future, we used 32 years of
 historical precipitation and NDVI data to statistically relate cool season
 precipitation to the subsequent maximum annual NDVI. Predicted NDVI values
 were plotted against observed NDVI values and averaged over the whole
 study area. With the results of that statistical relationship and using
 available forecasted precipitation data, we were able to forecast annual
 maximum NDVI into the future. To build models of fire
 risk, we used points that either burned historically in a large fire or
 points that burned but did not become a large fire and related these to
 the predictor variables. We included the entire U.S. Sonoran Desert
 ecoregion, so that we had many historical fires to draw from, and from
 these burned/unburned points created 10 of these independent, random
 datasets, which were combined with the accompanying predictor variables
 described above to build 10 independent models that predict the
 probability of a large fire. The annualized estimate of fire risk was then
 averaged over these 10 datasets and over 12 GCMs. This approach thus
 incorporated the variability resulting from independent fire risk models
 as well as variability resulting from future climate projections, which
 heightened the robustness of final estimates. This effort focused on the
 maximum fire risk (i.e., an extreme rather than the mean) in each of three
 time periods. This decision was based on the concern that even a single,
 extreme climate and fire year over a 20-year period can cause changes in
 land cover (Gray et al. 2014), and this worst-case scenario can help focus
 adaptation planning. While some areas may not show significant change in
 maximum risk from the past to the future, even a slight increase would
 indicate a meaningful and challenging change over the conditions a given
 manager has experienced in their tenure. To model
 ecological aspects of resilience, we mapped variables indicative of a
 system’s likelihood of retaining its characteristics and species in the
 face of disturbance, as indicated in ecological resilience literature. We
 thus created layers for topographic diversity, geophysical diversity,
 vegetation diversity, water availability, habitat connectivity (sensu
 Theobald et al. 2012), species richness, and human modification (Table 1).
 To create ecological rasters that could later be combined with social
 rasters, we summed these indicators to obtain a single value per location
 for ecological aspects of resilience across the Sonoran Desert.
 <i>Modeling social aspects of
 resilience</i> To collect data on social aspects
 of resilience, we designed a mapping exercise for land managers, who were
 the stakeholders for this study. We reached out to managers of all
 governmental jurisdictions in the study area (county, state, federal, and
 tribal) and invited them to participate in the study. To identify
 invitees, we examined land ownership maps of the region and used internet
 searches and our existing contacts to match management units to individual
 managers. In all, we obtained participation from managers of 25
 jurisdictions, accounting for 79% of the study area (Table 2). We included
 only governmental jurisdictions, since private landowners represent an
 extremely small proportion of the study area (&lt;8 percent).
 Throughout the below, we use the term “jurisdiction” to refer to a
 contiguous land area managed by a given government agency.
 To all participating land managers, we presented background
 information about fire in the Sonoran Desert, drivers and consequences of
 fire regime change, and social-ecological resilience. We provided managers
 with maps of their jurisdictions displaying the projected large fire risk
 we had developed; maps included 10 randomly generated points established
 within the jurisdiction of each manager. We asked managers to identify on
 the map the locations of their various management objectives and
 activities, as well as the likelihood that these objectives and activities
 would be effective under projected future fire risk (Supplementary
 information). Managers provided this information for all of the random
 points and also for polygons they hand-drew on printed maps to indicate
 areas of particular concern (e.g., locations of cultural resources or
 endangered species management) in light of fire regime change. Following
 interviews, we transferred all management activity and objective
 information into a GIS. To do this, we transcribed into the digital
 platform the polygons of areas identified by participants as important. We
 attributed both random points and polygons with the objectives and
 activities relevant to those locations, as well as the manager-reported
 likelihood (see below) that activities could continue to achieve
 objectives, new activities could be employed, or new objectives could be
 adopted (Supplementary information). For analysis, we classified
 participant-reported management objectives and current management
 activities in the GIS into 9 objective categories and 12 activity
 categories (Table 3). We then imported rasters of each category, along
 with levels of likelihood as described above, as a grid of points into R
 version 2.14.1 (R Core Team, 2012). After completing
 the mapping exercise, participants were asked to complete a paper survey
 reporting their experience with past environmental change, fire, and
 adaptive management. Responses to survey statements were recorded on
 Likert scales as 1-7 (strongly disagree to strongly agree) (Supplementary
 information). Survey questions were designed to assess jurisdictions’ past
 adaptability and constraints to change, and Likert-scale values were
 mapped onto the GIS at the scale of the jurisdiction. For institutional
 reasons or due to resource constraints, some agencies or individuals have
 difficulty changing their management methods, while others are more
 adaptive (LeQuire 2013, Haase 2013). Quantifying the flexibility of each
 jurisdiction can be critical to assessing the implications of changing
 conditions for management. We defined the “state” of
 the social system in this study as the set of management objectives at a
 location, and the social element of resilience as the likelihood that
 current management objectives would continue to be met, either using
 current management practices or via the adoption of new practices. To
 quantify this, we used spatially-explicit data from both the interviews
 and the surveys. From the interviews, we used the answers (very unlikely
 to very likely) to the questions “[Given predicted future fire regimes]
 How likely are you to continue to meet current management objectives?” and
 “[Given predicted future fire regimes] How likely are you to change
 current management practices in order to be able to meet current
 management objectives?” Participants responded to these questions with
 respect to specific polygons or points using a Likert scale from 1-5
 (where 1=very unlikely, 5=very likely), and those Likert values were
 mapped into the GIS as point and polygon attributes. From the survey, our
 estimate of social resilience was informed by the responses (Likert scale
 1-7) to the statements “In my organization, we expect and try to prepare
 for ‘surprises’ in ecosystem behaviors.” and “I have seen my organization
 successfully change management strategies when necessary” (Supplementary
 information). We defined social fire risk as a
 combination of four factors influencing whether an ignition becomes a
 large fire. From the interviews, we included whether or not a given
 location (as defined at the point or polygon level) reported 1) fire
 suppression as a management objective and 2) fire management as a current
 management practice. From the surveys, we included the responses to two
 statements about organizational resources: 3) “My organization has
 sufficient resources & personnel to manage fire and fuels on a
 day-to-day basis” and 4) “My organization has sufficient resources
 & personnel to manage fire and fuels during fire incidents.” The
 responses to each of these questions were scaled from 0 to 1 and summed
 for use in the overlay analysis (below). Although the
 indicators of ecological resilience that we included were measured
 objectively, these indicators of social resilience are clearly subjective,
 reflecting the experiences and opinions of the interviewed land managers.
 This is an important distinction to acknowledge, but we felt that our
 interview approach allowed us to capture the lived experiences of the
 managers and therefore incorporated the subtleties of fire response
 options that are difficult to adequately equate to more objective
 indicators of social management context such as parcel size, funding,
 staffing, etc. <i>Overlay analysis: coupled
 ecological and social estimate of resilience</i>
 We developed spatial layers of social-ecological fire risk as
 well as social-ecological resilience (Supplementary information). We
 scaled all ecological and social measures on a 0-1 scale, with 0
 indicating lowest concern for managers and 1 indicating highest concern
 (Dressel et al. 2018). We used a simple additive model (i.e., a linear
 relationship) between ecological and social factors because, since
 research on quantitative social-ecological resilience is so limited, we
 are unable to defensibly select any nonlinear relationship that would
 mathematically inflate the contribution of either the social or ecological
 factors to the overall estimate of integrated resilience. To calculate
 total risk across the Sonoran Desert, then, we summed the ecological and
 social risk layers, and rescaled them to 1, giving equal weight to each.
 We did the same for total resilience. When rasters were at different
 resolutions, we resampled them to make all rasters equivalent in
 resolution to the social rasters. We performed a coarse check of
 sensitivity to the social component within the total resilience assessment
 by up-weighting or down-weighting the social resilience raster and
 comparing the resultant, scaled total resilience values for the socially
 up-weighted, socially down-weighted, and equal weights rasters.
 Lastly, we plotted total risk against total resilience to
 determine vulnerability of locations to fire across the Sonoran Desert
 (sensu Comer et al. 2012). Vulnerability can be considered the
 intersection of system sensitivity (which in our study we equate to risk)
 and resilience, with the most vulnerable areas being those where high
 sensitivity and low resilience intersect (Comer et al. 2012). We
 translated these categorical vulnerability scores into a map to identify
 areas of low, medium, high, and very high vulnerability within the Sonoran
 Desert.