Research

INTRODUCTION

Grasslands, savannas, shrublands, and woodlands, collectively “rangelands,” constitute more than two-thirds of the Earth’s land surface (Lund 2007). Rangelands contribute 30–35% of terrestrial net primary production (NPP; Field et al. 1998), support 30% of the world’s population and a majority of global livestock production, provide critical wildlife habitat, and are integral in global carbon, water, and nitrogen cycles (Campbell and Stafford Smith 2000, Reid et al. 2005, Briske 2017). Livestock grazing has been the predominant, traditional use of rangelands, but these ecosystems provide a much broader array of products and services, giving them multidimensional value to social-ecological systems (Brown and MacLeod 2018). Collectively termed “ecosystem services” (hereafter, ES), i.e., the benefits that society receives from ecosystems (Daily 1997), these include the provisioning of food and fiber, carbon sequestration, maintenance of biodiversity (conservation), recreation opportunities, erosion control, water regulation, and other nonmaterial benefits (Sala and Paruelo 1997, Reid et al. 2005, Brown and MacLeod 2011).

Over the last 150 years, many rangelands have experienced a proliferation of unpalatable woody plants at the expense of perennial grasses (Archer et al. 2017). This phenomenon, termed “shrub encroachment,” has been characterized as a form of ecosystem degradation (Knapp et al. 2008) and desertification (Schlesinger et al. 1990). Decreases in forage production (Scholes and Archer 1997), increases in wind and water erosion (Wainwright et al. 2000, Breshears et al. 2009), and adverse impacts on ecohydrological processes (Huxman et al. 2005), have been widely documented when shrubs displace grasses in arid regions. Indeed, shrub encroachment and the management actions taken to retard or reverse it, impact a variety of ecosystem processes (Eldridge et al. 2011, Ding and Eldridge 2019), presenting challenging trade-offs in the delivery of ES (Archer and Predick 2014).

A growing body of work has documented the impacts of shrub encroachment on the ecosystem processes (Archer 2010, Barger et al. 2011, Eldridge et al. 2011). However, there is little documentation of the extent to which socio-cultural values and perceptions might influence decisions to manage shrub proliferation. Understanding stakeholder values is important because prioritization of ES depends on the ecosystem’s capacity to supply ES as well as the value society places on them (Tallis and Polansky 2011). Research to date has emphasized supply with little consideration of value (Yahdjian et al. 2015). This disconnect makes it difficult to evaluate trade-offs objectively or comprehensively, wherein managing for one particular ES or a suite of ES may adversely affect others (Archer and Predick 2014). The diminishment of a particular ES may, for example, be of modest concern if it has little perceived societal value. Conversely, a similar diminution of a highly valued ES may be of great concern.

Differences in stakeholder values for specific ES influences their delivery (Lamarque et al. 2011). Such influences can result in ES trade-offs or synergies as landscapes are modified in accordance with stakeholders’ perceptions, interests, and values (Martín-López et al. 2012, Wolff et al. 2015). Accordingly, an understanding of stakeholders’ motivations to engage in or implement recommended management actions that preserve or promote the delivery of desired ES is an important, but poorly understood component of sustainable management. Documentation of these values is a key first step in improving our understanding of how decisions are made, and priorities are set.

A variety of techniques (collectively “brush management”) have been employed to counteract the real and perceived threats of shrub proliferation (Hamilton et al. 2004). Historically, the primary motivation for brush management was to maintain or increase forage production for livestock (Brown and MacLeod 2011). However, this single-issue interpretation obscures other important considerations (Kreuter et al. 2005, Olenick et al. 2005). Increased shrub cover may, for example, make it more difficult to monitor livestock and gather them for health care or for grazing rotation. Open viewsheds in rangelands also enhance wildlife habitat and wildlife-related income and increase property values (Toledo et al. 2012). Brush management for water conservation may be a motivating factor in other cases (Kreuter et al. 2005). Such factors would be added justification for brush management, which is seldom economically viable when aimed solely at increasing forage production (Lee et al. 2001, Tanaka et al. 2011). At present, we know little of the values and perceptions that form the basis for landowner decisions regarding when, where, how, and under what circumstances to engage in brush management. Knowledge of such values and perceptions would be vital for the development of guidelines for determining when brush management might be used to promote sustainable rangeland management and facilitate the delivery of a diverse portfolio of ES of importance to numerous stakeholder groups.

The objective of our study was to address this knowledge gap by assessing the relative importance of rangeland ES among different stakeholder groups in the context of shrub encroachment and brush management. We hypothesized that the value placed on ES would vary among stakeholder groups. Specifically, we predicted that stakeholders who identified as ranchers would place the highest value on the provisioning ES of livestock forage, whereas other stakeholders (i.e., managers of public lands) would place higher value on ES related to biodiversity and erosion control. We also hypothesized that although stakeholders may value ES differently, they would similarly perceive them to decrease in abundance as shrub cover increases. To achieve this objective and test our hypotheses, we conducted a regional online survey with stakeholders in Southern Arizona and New Mexico, USA. The online survey utilized two approaches: an image-based assessment and a best-worst scaling (BWS) assessment.

METHODS

Study site

We targeted relevant stakeholders who either work, manage, research, or recreate on rangelands within the Natural Resource Conservation Service (NRCS) Major Land Resource Area (MLRA) #41 (Madrean Archipelago formally called Southeastern Arizona Basin and Range; USDA-NRCS 2022). This area covers 40,765 km² in Arizona (89%) and New Mexico (11%) and supports pine-oak woodlands at higher elevations, evergreen woodland savannas at intermediate elevations, and grassland and desert scrub vegetation assemblages at lower elevations. Land cover across the MLRA #41 consists of cropland: 2% private ownership; grassland: 59% private, 34% state/federal ownership; urban development: 2% private; other: 1% private, 2% state/federal. Livestock grazing (cattle) is the primary land use on wildlands in the region (USDA-NRCS 2006). An overview of county-level socioeconomic attributes of the study region can be found in Duval et al. (2020). Some 920 ranches are in operation across MLRA #41, 31% of which list annual sales > US$100,000 and 11% of which list annual sales > US$500,000 (Duval et al. 2020). Most ranches in MLRA #41 are a mosaic of private and leased public land, which is common in the Southwestern USA. Information about the individual allotments that constitute a ranch may therefore be dispersed among various agencies (e.g., U.S. Forest Service, U.S. Bureau of Land Management, Arizona Department of Lands). None of these agencies, or any other state or federal agency, collects socioeconomic data on ranching households making this type of information challenging to acquire (Sheridan 2001). Shrub encroachment has been well documented within MLRA #41 (Browning et al. 2008, Huang et al. 2018) and there has been a long history of brush management projects on both private and federally owned lands (Cox et al. 1983, Sayre 2007, Collins et al. 2015).

Survey development and administration

We created our survey using Qualtrics online platform (Qualtrics 2020) following Dillman et al. (2014). The initial survey instrument was developed using published and gray literature along with semi-structured consultations with ranchers, governmental employees, non-governmental land managers, and academic researchers (n = 16). The survey was then revised based on pre-testing feedback from eight subject matter experts with respect to length, appropriateness of language, terminology, images, choice task complexity, and framing of ES (see Table 1 for list of ES used in this study). The final version of the survey was approved by the University of Arizona (UA) Institutional Review Board. The survey instrument consisted of three sections: one section focusing on image-based questions (Survey Part 1: image-based analysis); another focusing on ES motivation rankings using BWS (Survey Part 2: best-worst scaling [BWS]); and a final section requesting demographics information (Appendix 1). The survey was sent through email to individuals within MLRA #41 in November 2020, with follow-up emails sent through February 2021.

Our survey focused on entities having a vested interest in the management and conservation of lands in MLRA #41: ranchers, state/federal governmental and non-governmental land managers, academicians, and recreationists and/or individuals who live in MLRA #41. We used purposeful sampling to achieve representativeness (Maxwell 2013) from each stakeholder group recruited using a mixed-mode approach. We generated an extensive list of potential participants, relying on input from a local conservation partnership, two private conservation organizations, co-authors’ professional rangeland research networks, and publicly available addresses of staff of government institutions and non-governmental organizations (NGOs) whose focus included rangeland management. The list was reviewed for accuracy and balance in terms of its representativeness of these stakeholder groupings, and personalized email invitations were sent to addresses provided by the above entities (Dillman et al. 2014). Before distributing the survey electronically, we reached out to members of the aforementioned conservation groups and partnerships to introduce the study. This ensured that most potential participants would have an a priori understanding of the study’s aims and goals that would translate into improved response rates. Recruitment efforts also included a snowball strategy, wherein participants were asked to assist us in identifying and distributing the survey to others whom they felt would be potential subjects.

Survey Part 1: image-based analysis

Humans experience the landscape as a socially constructed phenomenon (Tuan 1990), engaging with the environment through their experiences. The “quality” of a landscape is derived from its biophysical features in conjunction with the perceptual/judgmental perspectives of the viewer (Daniel 2001). Visual perception is a key process connecting people with ecological phenomena (Gobster et al. 2007, Fry et al. 2009). Understanding visual perceptions of a landscape can reveal patterns, preferences, and priorities related to the human-ecosystem interface, contributing to better management and decision making in environmental and ecological contexts. Because visual landscape perception can be used as a socially shared communication channel, it is a powerful tool for investigating the human-ecosystem interface (López-Santiago et al. 2014). Using methodology similar to Rodríguez-Lozano et al. (2020), our survey included an image-based component aimed at qualitatively assessing stakeholders’ perceptions regarding how differing levels of shrub cover impact the delivery of ES. We presented seven photos with shrub cover gradually increasing from virtually none (e.g., open grassland) to high (Fig. 1). Photographs were taken by the lead author in November of 2019 when grasses were senescent and the dominant shrub (Prosopis velutina, velvet mesquite) was still green. Besides differences in shrub size and cover, photos were otherwise similar (i.e., seasonality, direction, panoramic view, absence of human impacts, similar ground cover). Geographic location of each photograph was recorded via global positioning system (GPS; Appendix 2: Fig. 1).

Our aim with image selection was to present a suite of photographs representing the transition of an open grassland to a near-maximum level of shrub cover. The National Land Cover Database (NLCD) has classified landscapes with shrub cover > 20% as “shrubland” (Wickham et al. 2021). With this in mind, we presented three images with shrub cover < 20%, (grasslands at early stages of shrub encroachment), and three with levels of cover > 20%, representing advanced stages of shrub encroachment. We also selected one image with ~15% shrub cover that we considered transitional from grassland to shrubland. To ensure shrub cover values were aligned with our selection criteria, we selected photograph locations using an aerial cover map where shrub cover percent was known. Images and shrub cover values and descriptions of each image are summarized in Fig. 1.

Participants were asked to score landscape photos with respect to the ES categories in Table 1 and on the perceived need for brush management (see Appendix 1: Section 1: Image analysis for an example). Respondents rated each metric on a Visual Analog Scale (VAS) using a slider along a line segment with two endpoints ranging from “low” to “high” (Roster et al. 2015). A 101-point VAS was used with numerical feedback hidden allowing for freedom of response and avoiding biases like response rounding (Couper et al. 2006, Liu and Conrad 2016). We also included an optional section that allowed respondents the opportunity to provide text to elaborate their ratings. The order in which the images were presented was randomized to avoid potential positioning bias.

We performed a post-hoc Tukey pairwise comparison on the results of the VAS scoring to test for differences for each ES across each image. Spearman’s rank correlation coefficients were computed from participant scores for the aforementioned ES to measure the strength of the associations between them. A linear regression was used to ascertain how increases in shrub cover altered perceptions toward the delivery of the ES in Table 1 (post-hoc differences between groups analyzed using a Tukey pairwise test). We analyzed results using the R statistical software environment (version 4.0.5; R Core Team 2021) using the “emmeans” package to calculate post-hoc differences between images (Lenth 2021) and “lsmeans” to analyze comparisons between linear regression slopes (Lenth 2016).

Survey Part 2: best-worst scaling (BWS)

We used a “Case 1” BWS format (see Louviere et al. 2015) to assess the relative importance of ES among respondents, as reasons to restore and conserve rangelands. This technique has commonly been used in marketing and healthcare fields (e.g., Flynn et al. 2007, Lusk and Briggeman 2009), and more recently in economics, environmental science, and natural resource management applications (e.g., Soto et al. 2016, Kabaya et al. 2020, Tyner and Boyer 2020). Rooted in random utility theory, BWS is a rankings method that explores individual preferences by repeatedly asking respondents to select a “best” and a “worst” option from a set of items (Louviere et al. 2015). Essentially, this method ranks a set of items by presenting subsets of these to respondents, each containing at least three items, and asking participants to only consider the extremes (i.e., the best and worst choices within each subset). Subsets are constructed using experimental designs to permit uncorrelated estimations (e.g., main-effects orthogonal design). BWS improves upon other traditional rankings/ratings approaches (Lusk and Briggeman 2009). Namely, it advantages ratings-based methods by forcing participants to make trade-offs between the relative importance of issues (a limitation on the former), while also avoiding the known problems associated with subjectivity of scales (on a ratings scale of 1 to 10, one person’s “3” could be another’s “5”). By forcing participants to focus only on the “extremes,” BWS also avoids commonly occurring pitfalls from traditional rankings methods that are known to suffer from “middle” rankings ambiguity (Flynn et al. 2007).

To allocate ES to different choice sets (i.e., subsets), we used a balanced incomplete block design (BIBD; Auger et al. 2007). This method ensured that each of the ES in Table 1 occurred the same number of times across all choice-sets and that each ES was compared to all others the same number of times (see Appendix 2: Table 1). Following Loose and Lockshin (2013), we presented participants with seven choice-sets, each including four ES, and asked respondents to choose both the “most important (best)” and “least important (worst)” for each (see Appendix 1: Section 2: Best-worst Scaling for an example). The order of each choice-set and sequencing of ES within each choice-set was randomized to control for possible position-effects biases (Campbell and Erdem 2015). Our BIBD was created using the R “support.BWS” package (Aizaki and Aizaki 2017).

We calculated stakeholder relative preferences for ES (BWS descriptive statistics) using (i) a count-based approach (Louviere et al. 2015) and (ii) a conditional logit model (BWS Regression based analysis). The former was used to rank preferences for each ES relative to other preferences on a ratio-scale and to identify potential heterogeneity in ranking among stakeholder groups; the latter provides a more sophisticated regression-based analysis of the relative importance of each ES (Rankin et al. 2019, Tyner and Boyer 2020). Both approaches were analyzed using the R “support.BWS” (Aizaki and Aizaki 2017) package. We examined results at both the stakeholder group level and across all groups (hereafter, pooled).

BWS descriptive statistics

The BWS descriptive statistics were calculated using best-minus-worst scores (Louviere et al. 2015). For BWS questions, we used frequency to count the number of times that each respondent chose the same ES as the “most important” and subtracted the number of times that the same ES was listed as “least important” across all the questions (e.g., Soto et al. 2018). Specifically, we used Equation #1 to estimate the BWS Standard Score:

(1)

where Count_most is the number of times the ES was selected as the most important, Count_least is the number of times the ES was selected as the least important, F is the number of times the ES appears on all choice sets (frequency), and n is the number of participants. We analyzed the data from each stakeholder group to determine the differences.

BWS regression based analysis

Best-worst scaling models can be evaluated using marginal or paired methods (Flynn et al. 2007, Louviere et al. 2015). Paired methods (i.e., “maxdiff”) consider each best-worst pair to be a choice outcome, while marginal models aggregate best-worst pairs to derive choice frequencies (Louviere et al. 2015). In traditional resource economics, these methods are analyzed using a logit framework (Lusk and Briggeman 2009, Soto et al. 2016).

The Flynn et al. (2007) paired method assumes that individuals use a “maxdiff” process model (see Appendix 1: Section 2: Best-worst Scaling) and assumes respondents evaluate all available pairs before choosing the one that maximizes the difference in preference. Given that each BWS choice set contains J number of items (J = 4 in our study), this then translates to a total of J(J-1) possible combinations of “best” and “worst” alternative choice pairs. That is, for each of the 7 BWS choice sets, there are 12 possible best-worst pairs available to each respondent. As such, the pair that is chosen, at each BWS choice set, is assumed to have the maximum difference in preference (or importance in the case of this study), when compared to the other 11 available alternatives (i.e., identifying extremes). More formally, following Soto et al. (2016), if item j was selected as “best” and k as “worst,” then λ_j represents the location of j, for individual i, on an underlying scale of importance. The true level of unobserved importance, I_ij, for respondent i (for item j) is given by I_ij = λ_j + ε_ij, where ε_ij is a random error term. The same logic applies for item k. Accordingly, the probability that the i^th respondent chooses items j and k as their “best” and “worst,” respectively, is characterized by the condition of having the difference I_ij and I_ik being greater than all other J (J-1) - 1 alternatives pair-choices available in a given BWS choice set; namely, Probability [I_ij - I_ik > I_il - I_im], where l and m index said other alternative choice pairs of items not chosen as “best” or “worst.” Assuming that the error term is independent and identically distributed (IID) Type I extreme value (Louviere et al. 2015), the following multinomial logit (MNL) Equation #2 can be applied, where:

(2)

Given the above, each best-worst choice set was analyzed using the conditional logic command from statistical software R (i.e., “support.BWS”; Aizaki and Aizaki 2017). Following BWS convention, the items ranked least important in the previously mentioned BWS descriptive statistics section was omitted from that model to avoid the “dummy variable trap” and to also serve as the reference point (normalized to zero for identification purposes) for the underlying latent scale of importance (e.g., Lusk and Briggeman 2009, Soto et al. 2018).

Last, for a more intuitive interpretation of relative importance of BWS estimated coefficients (avoiding also possible confounding factors), we follow Lusk and Briggeman (2009) to calculate the “share of preference” for each of the 7 ES items of Table 1. These estimates denote the forecasted probability that each item would be chosen as most important (we keep the language of “preference” here for convention purposes):

(3)

The equation above reports importance of item j on a ratio scale; namely, the shares of preference must sum-up to 1 and their interpretation is that of proportionality (e.g., if the share of preference of item j is twice in magnitude as item k, then j is twice as important as the latter).

Post-hoc interviews

To better understand the rationale underlying responses provided by RANCH participants we conducted a virtual, open-ended follow-up discussion (September 2021) with eight cattle ranchers who operate in Southern Arizona. During the discussion, we summarized BWS survey results and asked (i) their thoughts on the rankings of ES and (ii) how they defined or perceived certain terminology such as “biodiversity.” Responses from this interview were recorded and transcribed. Excerpts from relevant comments and insights are included below when interpreting survey results.

RESULTS

Participant demographics

We administered the survey to 467 participants electronically and received a total of 119 responses for a ~26% response rate, consistent with the 20–51% range of response rates for similar studies (Markowski-Lindsay et al. 2011, Dickinson et al. 2012, Miller et al. 2012, Soto et al. 2016). It should be noted that the response rate may be slightly higher or lower because of encouraging participants to distribute the survey to others they felt appropriate, and we could not quantify this snowball effect. Of the 119 responses, 103 were used for analysis because some respondents did not complete the survey. Accepted respondents included academics or researchers (hereafter, EDU; n = 24; 23%), state/federal government employees (hereafter, GOV; n = 29; 28%), non-profit or non-governmental employees (hereafter, NGO; n = 19; 18%), landowners/ranchers (hereafter, RANCH; n = 16; 16%), and residents or recreationalists (hereafter, R&R; n = 15; 15%; Table 2). Forty-two percent of respondents reported owning or managing rangeland of which 46% was privately owned, 12% was publicly owned, and 42% had mixed ownership. Land use was evenly split between livestock grazing (44%) and recreation (42%) with 14% supporting both endeavors. Beef cattle was the breed of choice for livestock production for 84% of respondents. The rank-order of most common recreational activities reported included hiking, camping, bird/fauna watching, exercise, and relaxation/rest.

Image-based analysis of social perceptions regarding shrub cover impacts on ES

Correlations between benefit categories associated with images showing different levels of shrub cover (Fig. 1) ranged from -0.40 (Spearman’s rho; ranching potential and need of restoration) to 0.73 (aesthetics and cultural heritage; Table 3). Human well-being was best correlated with cultural heritage (0.72) and aesthetics (0.72). Habitat for biodiversity (hereafter, biodiversity) was most strongly correlated with aesthetics (0.69) and least correlated with ranching potential (0.54). Recreational opportunities’ strongest correlation was with biodiversity (0.64), while ranching potential most strongly correlated with aesthetics (0.64). The perceived need to implement restoration practices to improve rangelands was inversely correlated with each of the other benefit categories, ranging from -0.25 (with biodiversity and human well-being) to -0.40 (with ranching potential). Our findings suggest that from a visual standpoint, the perceived impact of shrubs on ranching potential may be a primary basis for undertaking brush management. Interestingly, the RANCH group rated the impact of shrub proliferation on ranching potential lower than all other stakeholder groups (Appendix 2: Table 2). Several RANCH respondents commented that shrub mesquite cover can actually benefit livestock grazing operations:

RANCH respondent on Fig. 1: Image 6: In times of drought the mesquite beans provide much needed sustenance for animals.

RANCH respondent on Fig. 1: Image 7: There’s good cover for wildlife in this photo, and mesquite beans can be beneficial to livestock and wildlife.

RANCH stakeholders also noted wildlife-livestock trade-offs with shrub proliferation and the potential need for intervention:

RANCH comments on Fig. 1: Image 6: (i) Decent ranch land but needs maintenance; and (ii) Cattle could still graze here but their grass is being reduced.

RANCH comments on Fig. 1: Image 7: (i) Really good for a variety of wildlife and okay for ranching, but I would be concerned about the trend of woody plants; and (ii) Current condition is good for a variety of wildlife and could still cattle ranch. I would like to see less brush and would be concerned that the trend is toward more brush, so intervention would be needed.

Post-hoc Tukey tests indicated that respondents visually perceived rangelands with lower shrub cover more positively than those with high shrub cover for six of the variables assessed and that landscapes with > 20% shrub cover would be best candidates for brush management (Fig. 2). Specifically, Fig. 1: Images 1 and 2 were rated highest with respect to providing the listed ES:

RANCH respondent on Fig. 1: Image 1: This photo is a great grassland. A few mature mesquite trees are very important to the wildlife.

EDU respondent on Fig. 1: Image 2: Large shrubs for shade and dense pockets available. Mix of woody and open grass to support a higher biodiversity. Could hike or hunt here. Wouldn’t think to spend money on restoration.

Respondents also perceived open grasslands with some shrubs (Fig. 1: Image 2) as being healthy, aesthetically pleasing, and capable of supporting both economic and recreational endeavors:

NGO respondent: With good grass cover, low shrub cover, and minimal bare/eroded soil, this photo implies a healthy landscape, so it is aesthetically pleasing and would be expected to be able to support ranching operations.

RANCH respondent: I like the combo of mesquite areas then open country. This site looks close to its grassland potential. I’d love to ranch, hunt, birdwatch, etc. here.

GOV respondent: There’s more of a discernable pattern to the woody cover which is healthier.

R&R respondent: I prefer grasslands with scattered shrubs, like in this picture.

Conversely, the cover levels depicted in Fig. 1: Image 7 were rated as the least likely to provide the listed ES. Post-hoc tests indicated that the shrub cover levels in Fig. 1: Images 5, 6, and 7 were statistically comparable with respect to “human well-being” ratings. Shrub cover levels depicted in Fig. 1: Images 3, 5, 6, and 7 were statistically comparable with respect to “biodiversity” ratings and Fig. 1: Images 5, 6, and 7 were statistically comparable in their “cultural heritage” ratings. Photos depicting lowest levels of shrub cover (Fig. 1: Images 1 and 2) were perceived as least needing brush management. Landscapes with the shrub cover levels in Fig. 1: Image 5 (shrub cover ~20%, mean = 69.9), 1.6 (shrub cover ~25%, mean = 68.5), and 1.7 (shrub cover ~35%, mean = 75.1) were identified as needing brush management. Perceptions toward biodiversity were found to have the least variance of means across images: Fig. 1: Image 2 scored highest (mean = 77.1) and Fig. 1: Image 7 the lowest (mean = 59.5) but there were few statistical differences (Fig. 2).

Stakeholder affiliation had a modest effect on perceptions of shrub cover impacts on ES (Appendix 2: Table 2). The exception to similarities in perceptions was for biodiversity. Both EDU and NGO identified no statistical difference in biodiversity across all the images. Similarly, for recreational opportunities, NGO and RANCH respondents observed no statistical difference across images:

NGO respondent on Fig. 1: Image 7: I consider the recreational value in landscapes to remain high regardless of biotic integrity; the landscape is still open and not eroding, so trails and other infrastructure would be fine here.

Whereas all other groups perceived a statistically significant negative correlation with increasing shrub cover and these ES (Appendix 2: Table 2).

Best-worst scaling (BWS) quantification of stakeholder preferences for rangeland ES

BWS descriptive statistics

The frequencies counts of participants’ most and least preferred (i.e., important) ES obtained from the BWS portion of the survey are summarized in Table 4. Among the ES evaluated, the pooled group scored habitat for biodiversity highest (0.68), followed by erosion control (0.43), and water quality/quantity (0.25). Unexpectedly, forage production was negatively scored (-0.28), implying that this ES was of generally low importance for the pooled group. Both recreation/tourism and cultural heritage scored comparably (-0.35 for each).

Priority rankings among stakeholder groups varied primarily with respect to forage for livestock. These findings contradict our a priori assumption that the RANCH stakeholder group would value livestock forage highest. Instead, RANCH respondents ranked forage as the third most important ES while all other groups scored water quality/quantity in the third position (Table 4). EDU respondents ranked forage production fourth in ES priorities and R&R, GOV, and NGO groups ranked it among the least important ES.

BWS regression based analysis

Estimates generated by the conditional logit model are presented in Table 5. Estimates reveal that the highest proportion (2.737) of the pooled respondents were expected to select habitat for biodiversity as the most important ES followed by erosion control (2.066) and water quality (1.591). Forage production (0.336), recreation and tourism (0.043), and cultural heritage (0.042) had lower estimates, respectively. It would be important to note that estimates for recreation and tourism and cultural heritage were not statistically significant. Aesthetics had the lowest estimate and therefore was omitted when constructing the conditional logic regression and serves as the “zero” for baseline comparisons.

All stakeholder groups ranked habitat for biodiversity as the most important reason to conserve or restore rangelands, followed by erosion control (Tables 4 and 5). Although the RANCH group scored erosion control and biodiversity comparably in the descriptive statistics (score = 0.48 for each), erosion control registered as having a slightly higher relative importance with this group (4.339) than did biodiversity (4.338). This again contradicted our a priori assumptions but as one RANCH participant succinctly stated:

RANCH respondent: Erosion control trumps everything. Stop erosion and you will improve the water quality.

The shares of preferences for ES relative importance by stakeholder group are summarized in Table 5 and shown graphically in Appendix 2: Fig. 2. Habitat for biodiversity was highest rated by NGO stakeholders (78.9%) followed by GOV (52.5%), R&R (44.3%), EDU (40.2%), and RANCH (31.5%) stakeholders (Table 5). The relative importance of habitat for biodiversity by members of the RANCH group was significantly smaller than that of respondents in the NGO subgroup (Table 5). Erosion control ranked highest in relative importance with EDU respondents (32.4%) but seem to capture a smaller share of preference among R&R respondents (15.2%). Perceptions of the forage production ES differed significantly amongst stakeholder groups with RANCH and EDU groups (relative importance = 18.5 and 7.2%, respectively) encompassing a higher share of preference than R&R (5.9%), GOV (1.1%), and NGO (0.4%) groups (Table 5).

DISCUSSION

Visual perceptions of shrub cover impacts on ES

Management recommendations are more likely to generate public support and acceptance when commonalities and differences present in a diverse community are addressed (McFarlane and Boxall 2000, Vouligny et al. 2009). Accordingly, one of our objectives for this study was to ascertain how different stakeholder groups perceived rangeland shrub encroachment and its impact on ES. Our results demonstrate that people familiar with rangelands and their management generally perceive rangelands with low shrub cover as providing a wider array of valued ES than rangelands with high shrub cover. Relative to photographs of rangelands with low shrub cover, images depicting landscapes with high shrub cover were rated less desirable with respect to aesthetics, human well-being, biodiversity, and recreational opportunities. Brush management was deemed necessary on rangelands with > 20% shrub cover.

The need for restoration through brush management was correlated highest with “ability to support ranching operations.” This is consistent with anecdotal generalizations regarding the primary objective and economic rationale for engaging in brush management (Hyder and Sneva 1956, Lee et al. 2001, Tanaka et al. 2011). Although other reasons for engaging in brush management have been promoted (USDA-NRCS 2003), our findings suggest that from a visual standpoint, impact on ranching potential may be the best indicator of a rangeland necessitating brush management. Interestingly, the RANCH group rated the adverse impact of shrub proliferation on ranching potential lower than all other stakeholder groups (Appendix 2: Table 2). Because RANCH stakeholders often have little choice but to graze their livestock on shrub-encroached rangelands, they have a direct knowledge of how shrubs fit into the broader context of livestock-wildlife management. Consequently, this could explain why they rated shrub encroachment as less detrimental to ranching potential than did other stakeholder groups.

Images depicting open grassland with scattered large shrubs or patches of shrubs (Fig. 1: Image 1 and 2) were consistently rated highest for all ES (Fig. 2). Respondents commented that a mixture of grasses and shrub was desirable. Respondents also perceived open grasslands with some shrubs (Fig. 1: Image 2) as being healthy, aesthetically pleasing, and capable of supporting both economic and recreational endeavors.

Perceptions of ES differ according to socio-demographic factors and individual backgrounds (Lamarque et al. 2011, Martín-López et al. 2012, Plieninger et al. 2013). However, we found few differences among stakeholder groups regarding the delivery of ES in the context of shrub encroachment. That being said, some statistical differences were found, most notably in views of recreational opportunities and biodiversity (Appendix 2: Table 2). NGO and RANCH respondents did not feel that the increases in shrub cover shown across Fig. 1 significantly impacted recreational opportunities. In contrast, all other groups felt that recreational opportunities would diminish as shrub cover increased. A potential reason NGO stakeholders might not perceive a decline in recreational opportunities with increased shrub cover could be their self-identified association with bird-watching and wildlife-viewing as recreational activities (see Data Availability section for base survey response data). Although shifts in avian community composition occur with shrub encroachment, species richness may be maintained or increased (Fulbright et al. 2018, Andersen and Steidl 2019). Likewise, RANCH participants, who primarily identified as sport-hunters, may perceive potential benefits for desired trophy species in areas with increased shrub cover (Archer 2010).

Last, EDU and NGO groups did not feel that the increases in shrub cover shown across the images significantly impacted biodiversity. Shrub encroachment impacts on biodiversity may be positive, neutral, or negative (Eldridge et al. 2011). Although shrub encroachment may adversely affect grassland-obligate species (Andersen and Steidl 2019), the colonization of grasslands by shrubs involves new species that would augment numerical biodiversity. Furthermore, shrub modification of soil properties, vegetation structure, and microclimate may alter habitats to facilitate establishment of plant and animal species not occurring in open grassland. Maximum diversity in savanna-like configurations occurs often where woody and herbaceous plants are both well-represented or where gains in new woody and herbaceous species outweigh losses of grassland-obligate species (Archer et al. 2017). It is noteworthy that EDU and NGO stakeholders perceived no statistical differences in biodiversity across images. Many of the respondents from these two groups have worked on projects and published in academic journals regarding this topic. Their familiarity with scientific studies showing that shrub encroachment impacts on biodiversity may be positive, neutral, or negative may have played a role in these stakeholder groups perceiving shrub proliferation to have little influence on biodiversity.

Stakeholder preferences for rangeland ES

Analysis of stakeholder perceptions of and preferences for ES is vital in identifying potential areas of controversy and developing management and conservation plans with compromises that make them palatable to the local/regional community (Anton et al. 2010). Successful implementation of such plans requires the participation of a variety of social groups whose views and priorities are represented in the decision-making process (Castro et al. 2011). We used the BWS rankings approach to ascertain the relative importance of rangeland ES among stakeholder groups who would be active participants in the development and implementation of local/regional landscape management plans.

Rankings of ES in the context of shrub encroachment and brush management were uniform across stakeholder groups. Biodiversity emerged as the most important ES among all stakeholder groups. This was followed by erosion control and water quality/quantity in all groups but RANCH, who scored forage production higher (Table 4). All stakeholder groups ranked cultural ES (aesthetics, cultural heritage, and recreation/tourism) lowest in relative importance. This fits with the general pattern of provisioning, regulating, and supporting ES being higher priority than cultural ES (Rodríguez et al. 2006, Hartter 2010). In contrast, urban residents may place highest values on cultural ES such as tourism, aesthetics, environmental education, or on “existence” value: the satisfaction derived from the knowledge that nature exists (Martín-López et al. 2012). Generalizations regarding rural vs. urban perspectives on ES are, however, context dependent. For example, aesthetics was the most valued ES among pastoral herders in Mongolia (Zhen et al. 2010). This differs markedly with our findings, wherein cultural ES ranked lowest among all stakeholder groups. This variation in ES valuation may be, in part, because of different research methods. Quintas-Soriano et al. (2018) found that when respondents were able to self-list ES, they typically identified specific cultural ES over other service types. In our study, we provided a list of ES (Table 1) and asked respondents to rank them. However, our pre-testing protocol ensured that the categories of ES in Table 1 were those that had been identified by stakeholders themselves.

The largest discrepancies among stakeholder groups pertained to the ranking of forage production. All groups, except RANCH, ranked forage low, with GOV and NGO ranking it as having the lowest relative importance (Table 4). Contrary to expectations, RANCH ranked biodiversity and erosion control higher than forage production. Nearly all (94%) of the RANCH respondents identified as living on and managing commercial ranching operations. Accelerated water erosion dating back to the 1880s in Southern Arizona (Cooke and Reeves 1976) necessitated extensive erosion control projects (Nichols et al. 2018) that include the construction of thousands of check dams, water spreaders, and contour berms (Nichols et al. 2021). Many of these projects were implemented in cost-share arrangements with ranchers (Bailey et al. 1996). Therefore, the scoring of erosion control as one of the most preferred ES is perhaps not surprising, particularly when considering (i) the multigenerational dynamic of numerous Southern Arizona ranches and (ii) that state and federal agencies in the region have had outreach and cost-sharing programs aimed at erosion management dating back to the mid-1900s (Sayre 2007).

The high ranking of biodiversity is harder to account for. Biodiversity, whether expressed in terms of richness of species, plant functional groups, or animal assemblages, is complex and open to interpretation. When Lamarque et al. (2011) asked, “What does biodiversity mean for you?” they found four different themes: scale, type of organism (plant and animal), species variety, and number. Although we provided a definition of biodiversity (Table 1), we suspect participants may have viewed this ES from different perspectives. Did RANCH respondents equate biodiversity to floral richness, specifically of grasses and forbs, but not fauna? Did GOV respondents tasked with managing or restoring rangelands across MLRA #41, narrowly view biodiversity as flora and fauna associated with grasslands while EDU respondents associated biodiversity in terms of species richness regardless of community structure (e.g., grassland, savanna, shrubland)? Or did EDU view biodiversity from a historical standpoint of what occurred on these landscapes when they were first settled by Anglo-Europeans? These nuances may explain why EDU respondents did not perceive shrub encroachment (Appendix 2: Table 2) as impacting biodiversity, yet rated it highest in the BWS exercise owing to the loss of grassland obligate species in concert with shrub proliferation. Future studies could address this by requiring respondents to articulate what biodiversity means to them (e.g., Lamarque et al. 2011).

Our post-survey discussion with eight cattle ranchers operating in Southern Arizona was conducted, in part, to better understand their perspectives on biodiversity. Two major themes emerged: Biodiversity was viewed (i) holistically as species richness and habitat diversity, and (ii) as being a connection between ES. A number of these respondents felt that maintaining habitat for grassland obligate species would promote floral richness while also enhancing forage quality. Furthermore, they viewed plant functional group diversity such as grasses, forbs, and shrubs as being beneficial, with the latter providing shade during hot, sunny summer months and fodder (mesquite pods) during droughts when grass production is low. Many ranchers within the MLRA #41 are descended from families who have worked the land for multiple generations and therefore have a deep connection to it. Our results indicate that the widespread assumption that ranchers undertake brush management solely to increase forage production may be too narrow, as landowners feel they are not only tasked with generating a livelihood from the land but are also stewards of it. The basis for many of the decisions being made go beyond livestock production. Rather, decisions are tied to protecting the land and the way of life connected to it. As one rancher in the focus group put it:

RANCH respondent: I don’t want to speak for all ranchers, but I believe many of them feel the same. We are not always rational with the choices we make on our land and choices we make are not just for our livelihoods. We do them for multiple reasons like to maintain our surroundings and native species.

Implications

Although the use of brush management to reverse shrub encroachment is widespread, its long-term efficacy is questionable. The economic cost of such projects is burdensome, with price tags reaching into the millions of dollars (Briske et al. 2011) and interventions aimed at improving forage production, herbaceous biomass and diversity, and stream flow often being sub-par or short-lived (typically 5–15 years; Wilcox et al. 2005, Archer et al. 2011, Archer and Predick 2014). Yet, without such interventions, the integrity of these and other ES could be compromised, posing a risk to the persistence of grassland and savanna ecosystem types and the plants and animals endemic to them. Indeed, promoting or re-establishing native grassland fauna, often referred to as “rewilding,” may help retard or reverse shrub expansion. For example, reintroduction of extirpated megafaunal herbivores has been found to constrain shrub encroachment in East Africa (Guyton et al. 2020), and reintroduction of prairie dogs has the potential to prolong the effectiveness of brush management treatments while simultaneously reviving ecosystem processes that promote biodiversity (Hale et al. 2020). Such rewilding efforts, conducted in conjunction with targeted brush management, have the potential to restore grassland ecosystem structure, function, and health in a more cost-effective and sustainable manner.

Rangelands of North America and Africa have largely been shaped and maintained by pyritic herbivory (herbivory shaped by fire; Fuhlendorf et al. 2009). Shrub proliferation in grasslands has been attributed, in part, to reductions in historic fire frequencies and intensities caused by livestock consumption of fine fuels (Bahre and Shelton 1993, Archer et al. 1995). Rewilding that promotes native herbivore functional diversity (i.e., granivores-browsers-grazers) could help reestablish a fire regime approximating evolutionarily relevant whole-system disturbance patterns. Focusing restoration actions on increasing native grassland biodiversity is not only popular from a societal perspective, as found in this study, but contributes to a more robust conservation strategy than has heretofore been the case.

Study limitations

Our use of visual stimuli in tandem with BWS represents a novel approach for ascertaining stakeholder perceptions of rangeland ES and the relative value placed on them by diverse stakeholder groups. We recognize, however, that our sample size was not necessarily representative of all stakeholder groups and that our results may not be generalizable to other bioclimatic regions. For example, Luvuno et al. (2022) explored perceived impacts of shrub encroachment on ES in South Africa and found a divergence in stakeholder perceptions: community members viewed shrub proliferation negatively owing to associated declines in livestock grazing capacity and increased risk of attack by wild animals and criminals, whereas the main negatives for conservation managers was loss of habitat for grazing wildlife and reductions in visibility for game viewing. Neither personal safety nor ecotourism emerged as important to the stakeholders in our study. This highlights how both geographic location and sociocultural systems can influence the valuation of ES. The land-tenure system in MLRA #41 is a complex intermingling of private, governmental (county, state, and federal lands), and non-governmental entities, with RANCH stakeholders often leasing governmental lands tasked with meeting “multiple use” mandates (e.g., BLM 2003). Stakeholders in regions with different land-tenure systems may value ES differently. Future studies could use our methodology to assess stakeholder ES preferences in other regions. Furthermore, socioeconomic status (Daw et al. 2011, Caballero-Serrano et al. 2017), gender (Daw et al. 2011, Ethan-Yang et al. 2018), or other socio-cultural attributes (Thiemann et al. 2022) have been shown to influence ES perceptions and values. Our analysis did not consider these factors within or across our stakeholder groups and should be a focus of future studies.

CONCLUSION

There is a long-standing need to advance our understanding of the valuation of ES, identifying trade-offs associated with considerations of different interests in ecosystem use, and how prioritization of ES might change with different land uses (Carpenter et al. 2009). This study ascertained stakeholder perceptions of rangeland ES in the context of shrub encroachment and brush management in grasslands using visual stimuli in conjunction with BWS methodology. Our results provide evidence that people familiar with rangelands and their management generally perceived landscapes with low shrub cover (e.g., < 15%) as providing a wider range of ES than those with higher shrub cover (e.g., > 20%). This perception held across all stakeholder groups surveyed. Respondents also rated open grasslands with some shrubs (i.e., Fig. 1: Image 2) highest and as being healthy, aesthetically pleasing, and capable of supporting both economic and recreational endeavors. This is consistent with that of stakeholders in New South Wales, Australia (Sharp et al. 2012) and with other research on human landscape preferences (Falk and Balling 2010, Herzog et al. 2000, Hill and Daniel 2007). Accordingly, brush management should aim to mimic this “patchy/open” mosaic of shrubs. It is evident from our findings that rangelands with lower shrub cover are perceived to provide more benefits to society. Consequently, prioritizing the restoration and maintenance of rangelands to pre-encroached shrub densities is a laudable conservation goal and objective. Emphasis on aspects of rewilding that center on the re-establishment of native granivores and browsers that utilize shrubs, conducted in conjunction with re-establishing historical fire regimes and patterns, may represent a cost-effective and sustainable solution.

Results presented here also provide a ranking of stakeholders’ preferences for conserving or restoring rangelands within the Southeastern Arizona Basin and Range region. The hypothesis that RANCH stakeholders would rank forage production highest among the ES surveyed was not supported. Instead, biodiversity and erosion control were clearly ranked as the top ES in the context of shrub encroachment into rangelands. Accordingly, the widespread perception/generalization that ranchers in this area are undertaking brush management to enhance livestock production for economic gain is seen as too narrow. Instead, they see themselves as stewards of the land balancing livelihoods with concerns for conservation. Our findings align with other studies indicating that rural populations have elevated levels of human-nature connectedness (HNC). This is believed to stem from living in close proximity to nature, leading to increased time interacting with the natural environment (Elwell et al. 2020, Pérez-Ramírez et al. 2021, Otamendi-Urroz et al. 2023). Such proximity has ostensibly aided in nurturing an emotional and experiential connection to the landscape among RANCH stakeholders, which Ives et al. (2018) argue is fundamental for establishing a robust HNC. Generational ties to the landscape would further reinforce this connectedness and shape one’s philosophical relationship with the environment and one’s role within it.

ES trade-offs arise from management choices, which can change the type, magnitude, and relative mix of ES provided. Trade-offs occur when the provision of one ES is increased at the expense of others (Rodríguez et al. 2006). We had initially assumed our study would identify potential trade-offs of rangeland ES associated with differences in stakeholders’ preferences. This was generally not the case; instead, our results indicated that preferences were mostly aligned between stakeholder groups. This presents an opportunity for brush management to serve as a potential win-win management action that can generate ecological benefits (i.e., enhance grassland biodiversity and decrease soil erosion) while also providing economic gains for landowners. Framing future brush management projects within a biodiversity context would ostensibly garnish the most support across stakeholder groups while also addressing their preferences for ES. Several brush management projects within our study site, such as the Bonita Grassland Project (Knuffman 2016) and the South Legunita Grassland Restoration Project (Altar Valley Conservation Alliance 2022) have successfully used this approach of framing to gain wider support of all stakeholders indicating its potential viability. Whether this sentiment holds true across other grasslands undergoing shrub encroachment or if it is unique to our study region (MLRA #41) is unknown and should be further investigated.

We believe our methodology and findings could provide a foundation for future studies on other rangelands that have experienced shrub proliferation as a way to compare results across different regions. The rankings presented here should also be useful for county, state, and federal agencies within the Southeastern Arizona Basin and Range contemplating brush management projects. Promoting such projects to increase grassland obligate wildlife and floral diversity and limit soil erosion rather than for the sole purpose of increased livestock production might make land management decisions more palatable to urban dwellers, where both tax dollars and votes are concentrated. By assessing both visual preferences for shrub cover across MLRA #41 and enumerating which ES are valued highest (i.e., most important reason to conserve rangelands), we believe our study adds important insights to the literature examining support or motivations for costly bush management projects within this region.

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