Drivers of mixed native woodland creation in Scotland: Compatibility with land use practices and environmental benefits influence decision making

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Despite this, our theory-based understanding of what drives landholders’ decisions to create woodland types important to achieve ecosystem restoration at scale, such as mixed native woodlands which have the highest potential for environmental benefits, remains limited. Building on Diffusion of Innovations Theory, we survey landholders in and around the Cairngorms National Park in Scotland to quantify the effects of woodland creation initiatives, individual landholders’, and contextual characteristics on decisions to create new mixed native woodland using a Bayesian Bernoulli distributed logistic regression. We find that landholders are more likely to have engaged in mixed native woodland creation if they believe it is compatible with their current land use practices; or if they anticipate increases to wildlife habitat or soil quality. Landholders that create mixed native woodland are more likely to perceive interventions to control herbivore damage to new woodland as difficult to understand. Our results suggest that, in contexts with established targets and economic incentives for woodland creation, governments could more effectively promote the uptake of mixed native woodland creation by: emphasising woodlands’ compatibility with other land uses such as agriculture (e.g. agroforestry) or some types of hunting; maintaining or improving the flexibility of woodland creation actions; facilitating access to easy-to-understand evidence-based comparisons of the potential benefits of different woodland types; and supporting landholders’ knowledge needs, especially for herbivore control tasks like fencing. Overall, we show that to achieve ecosystem restoration and its multiple benefits at scale, programme designs need to be more sensitive to landholders’ perspectives whilst incorporating safeguards against negative outcomes. Environmental Policy Adoption Bayesian analysis Behaviour change Diffusion of Innovations Ecosystem restoration Mixed native woodland creation Figures Figure 1 Figure 2 Figure 3 1 INTRODUCTION Ecosystem restoration has emerged as a global priority for mitigating the climate and biodiversity crises (Aronson & Alexander, 2013; Cook-Patton et al., 2021; Griscom et al., 2017), as highlighted by the recent declaration of the United Nations (UN) Decade on Ecosystem Restoration (UNEP & FAO, 2021) and the restoration targets within the Kunming-Montreal Global Biodiversity Framework (CBD, 2022). The practice of ecosystem restoration encompasses a range of activities — in terms of their target ecosystems, restoration approaches (active vs. passive), objectives, and scales — that ultimately aim to halt ecosystem degradation and promote recovery (Gann et al., 2019; Nelson et al., 2024). Out of all ecosystems, forests have captured the imagination of publics, politicians, and scientists (Temperton et al., 2019), triggering a flurry of forest-focused high-level international restoration campaigns such as the Bonn Challenge (IUCN & Government of Germany, 2011) or the One Trillion Trees Initiative (World Economic Forum, 2020). Forest restoration is a process occurring in complex social-ecological systems, with short and long-term, biophysical, social, economic, and political implications, particularly at a local scale and for different groups of local people (Elias et al., 2021; Löfqvist et al., 2023; Tedesco et al., 2023). The restoration of forests can provide a host of environmental (climate change mitigation, biodiversity, water quality, etc.) and social (health, recreation, etc.) benefits (Sing et al., 2017). However, forest restoration outcomes can be varied and involve trade-offs (e.g. Löfqvist et al., 2023). The environmental outcomes of forest restoration depend on factors such as the type, location, and size of forests restored; the time elapsed since restoration; or how restored forests are managed or their products used (Di Sacco et al., 2021; Fleischman et al., 2020; Oliver et al., 2014). Trade-offs resulting from this include less timber production in more biodiverse forests (Hua et al., 2022), or reduced carbon sequestration from forests created on carbon rich soils (Friggens et al., 2020). The social outcomes of forest restoration, in turn, hinge on its impacts on people’s livelihoods, land tenure, governance, and well-being through effects on equity, health, power, poverty, and food security (Adams et al., 2016). For example, the Trees for Global Benefit Program in Uganda improved livelihoods through restoration, but increased inequalities (Fisher et al., 2018). Overall, the pressure to scale forest restoration and the range of potential outcomes have prompted calls to carefully weigh the net social and environmental consequences of restoration (Di Sacco et al., 2021; Elias et al., 2021; Fleischman et al., 2020; Sandbrook et al., 2023). The delivery (passive or active) and success of forest restoration, ultimately depends on the decisions and behaviours of people, notably landholders. The incentive mechanisms for such behavioural change are generally social or economic in nature and vary significantly across a diverse community of stakeholders (Tedesco, Brancalion, et al., 2023). Understanding how these incentives are perceived by individuals, particularly landholders, and the factors which drive their uptake, spread, and abandonment will be crucial to design restoration initiatives that scale successfully (Ambrose-Oji et al., 2018; Lawrence & Dandy, 2014; Mills et al., 2019). Individuals’ motivations to participate in restoration and other environmentally minded behaviours are always diverse and their relative importance changes over time with, for example, changes to demographics, knowledge, or politics (Aradóttir et al., 2013; Hagger et al., 2017). Several studies show that individuals’ perceptions of an initiative, together with their objectives and intentions are, amongst others, key determinants for the adoption and scaling of environmentally focused initiatives such as sustainable agricultural practices (Swart et al., 2023) or different conservation and resource management initiatives (Jagadish et al., 2024; Mascia & Mills, 2018; Mills et al., 2019; Romero-de-Diego et al., 2021; Sorice et al., 2011). This in turn suggests that programmes focused only on economic incentives to promote the uptake of initiatives may be insufficient (Sorice & Donlan, 2015; Swart et al., 2023), requiring a shift towards more holistic programmes that gather and incorporate stakeholders’ inputs throughout (Sorice & Donlan, 2015). Decisions to engage in woodland creation, which includes forest restoration, are influenced by an array of factors including: the availability of land, support or funding for woodland creation; bureaucratic burdens; or landholder’s motivations, perceptions, and attitudes towards woodland creation (e.g. Hemery et al., 2018, 2020; Kaine et al., 2023; Lawrence & Dandy, 2014; Powlen & Jones, 2019; Ross-Davis et al., 2005; Tran et al., 2019). The role of these factors as drivers or barriers to woodland creation is circumstantial, depending on individual landholders and their contexts at a particular point in time. For example, a landholder’s ability to create woodland will change according to their economic situation or knowledge, and the political forces influencing them. Given the diverse, context-specific, and ever-changing nature of both landholders’ motivations and the potential outcomes of creating woodland, broader theories of human decision making able to capture its dynamism and two-way interplay with its context, may help us better understand woodland creation decisions (Schill et al., 2019). One such theory is Diffusion of Innovations Theory (DOIT) (Rogers, 2003), which suggests that decisions to engage in new behaviours are driven by characteristics of the new behaviour itself, the person adopting it and their context. Here, we undertake one of the first (Garbach & Long, 2017; Pienkowski et al., 2024) applications of DOIT to forest restoration and ecosystem restoration more broadly. We focus on landholders’ decisions to create mixed native woodlands — woodlands comprising a combination of different native tree species — in and around the Cairngorms National Park in Scotland. We do this given mixed native woodlands’ superior environmental benefits when compared to other woodland types (e.g. Allek et al., 2022; Hua et al., 2022; Wang et al., 2021; Waring et al., 2020) and their wide consideration as the woodlands with the highest potential for ecosystem restoration, when created in the right places (Di Sacco et al., 2021; Gann et al., 2019). Moreover, by focusing on a single woodland type and a relatively small and socially homogeneous area, we limit the influence of contextual noise while testing theory. Given recent shifts in woodland creation narratives towards ecosystem restoration goals (biodiversity, carbon, etc.) (Raum, 2020; Raum & Potter, 2015), we assume that engagement with initiatives to create mixed native woodland is a new behaviour for most landholders. Consequently, we investigate what drives landholders’ decisions to adopt mixed native woodland creation initiatives in and around the Cairngorms National Park in Scotland, comparing the influence of financial and non-financial aspects of these initiatives in that context, using a survey based on DOIT. Our theory-based approach enhances the generalisability of our case study by contributing to a robust understanding of the factors driving ecosystem restoration adoption among landholders, through the accumulation of comparable studies (Muthukrishna & Henrich, 2019). It also minimises the risk of spurious conclusions (Smaldino & McElreath, 2016). 2 METHODS 2.1 Study area The Cairngorms National Park is the largest in the United Kingdom (UK). Located in Northeast Scotland, it straddles the Perth and Kinross, Angus, Aberdeenshire, Moray, and Highland councils. The national park is home to over 18,000 people and encompasses more than 150 independently managed landholdings (mainly privately owned) with land uses including hunting, farming, conservation, and forestry (Cairngorms National Park Authority, 2023). We focused our study on the area in and around the Cairngorms National Park as it has high levels of both woodland creation and management funding uptake relative to surrounding areas (Scottish Forestry, 2019c, 2019b, 2019a). 2.2 Survey design 2.2.1 Theoretical framework We conceptualised the behaviour change process leading to the adoption or rejection of mixed native woodland creation using DOIT (Rogers, 2003). DOIT describes a general process through which innovations — novel ideas, practices, or technologies — can diffuse over time through the communication channels of a social system leading to their adoption or rejection by system members. According to DOIT, there are three main components that influence the adoption of innovations: (i) the innovation itself, (ii) the potential adopter, and (iii) the context where the innovation and its potential adopters interact (Rogers, 2003; Wejnert, 2002). Each of the three components have a set of attributes that influence the adoption process. We adapt two frameworks (Jagadish et al., 2021; Mahajan et al., 2021) that build on previous studies of DOIT (e.g. Rogers, 2003; Wejnert, 2002). These frameworks outline the important attributes that influence adoption in the context of nature/biodiversity conservation across the theory’s three main components (Table 1). TABLE 1. Components of the Diffusion of Innovations Theory (DOIT) and their attributes. Adapted from Jagadish et al., 2021 and Mahajan et al., 2021. Component Attribute Definition Innovation Relative advantage The expected net benefits (financial, social, environmental, or other) of adopting an innovation compared to the status quo. Compatibility The degree to which an innovation is perceived as consistent with the existing values, existing actions, past experiences, and needs of potential adopters. Complexity The degree to which the innovation is perceived as relatively difficult to understand and use. Trialability The degree to which the innovation may be experimented with on a limited basis. Observability The degree to which the innovation and its results are visible or communicable to others. Flexibility The ability to transform the innovation to something that aligns with the adopter's desires and constraints. Adopter Social-economics The social and economic characteristics of an individual that influence its ability to implement or learn about an innovation. These include education, skills, relative wealth, organisational size, and financial resources. Personality Personality traits that influence an adopter's willingness to learn and implement new practices, such as risk orientation and competitiveness. Knowledge The degree to which the adopter is familiar with the innovation and its potential consequences through its existing or newly acquired knowledge and skills. Decision making Decision making arrangements specify the rights of individuals or groups to make choices regarding various aspects of the innovation design and management. Context Geographical setting Physical features of the landscape/seascape, as well as spatial proximities to other adopters, markets, etc. that affect adoption by influencing the applicability of the innovation. Culture Shared behaviours and ideas - belief systems, traditionalism, and socialisation of adopters - that influence the adoption of innovations. Political conditions Character of political systems, along with the regulations and norms inherent in the legal systems that influence the potential adopters’ behaviours. Extension support Organisations’ and individuals’ (public or private) activities relating to technology transfer, education, human resource development, and information sharing that influence the adoption and implementation of the innovation. Global discourse The extent to which the adopters’ context influences and is influenced by globally circulating ideas, norms, and practices related to the innovation. 2.2.2 Survey design Our analysis explores how different characteristics of woodland creation interventions (innovations), landholders (potential adopters) and their physical, environmental, and social context (context) influence the adoption of mixed native woodland creation interventions in our study area. To achieve this, we designed two survey versions, one targeted at landholders who had created woodland and another at those who had not. The main difference between survey versions was the time frame they asked respondents to consider when answering questions targeting DOIT constructs, as well as some background questions. The phrasing of these questions was the same in both surveys. However, the survey version for landholders that had created woodland asked respondents to think back to the time when they were still deciding to create new woodland on the landholding they represent, while the survey version for those that hadn’t created woodland asked them to respond in the present. Responses about the past are prone to recall bias (Catalogue of Bias Collaboration, Spencer, et al., 2017), since when answering these questions, landholders that had created woodland relied on memories, which may be inaccurate and/or incomplete. Nonetheless, we were limited to this data as no data was collected at the time landholders first adopted woodland creation. The survey was divided into three sets of questions relevant to this study: (i) participant screening questions, (ii) background questions, and (iii) questions targeting DOIT constructs. The participant screening questions ensured that respondents were involved in both the management of the land they referred to in their response, and decision making processes that lead to changes in the use of that land, such as the decisions to create a new woodland on it or not. If respondents did not meet both criteria, their responses were not recorded. Background questions focused on the landholding, their woodland creation activity, and the respondent’s organisation and personal data. For the set of questions targeting DOIT constructs we used the framework outlined in Table 1 as a guide. Overall, we considered 44 sub-attributes stemming from the 15 attributes and three main components in our DOIT framework (Table 1). We operationalised the framework to suit the UK woodland creation context, deeming seven context and two woodland creation initiative sub-attributes irrelevant and excluding them from our survey. We designed questions (n=61) targeting the remaining 35 DOIT sub-attributes to explore their influence on decisions to create woodland. Most of these questions (n=45) were Likert items with five response levels (e.g. Strongly disagree to Strongly agree). The remaining questions (n=16) were categorical (n=15) and binary (n=1). Our surveys and full DOIT framework are available in supporting information one to three. 2.3 Sampling and data collection We collected data through both in-person (n=59) and online (n=25) surveys. We targeted landowners or managers within the study area involved in land use decisions. As no unified registry of this population exists, we used a combination of non-probability methods to obtain a sample of respondents, namely convenience and snowball sampling (Stratton, 2021). Our final sample included 84 landholders. Only 77 responses (59 in-person, 18 online) were within our study area, thus included in the analysis. We chose to split our data collection across in-person and online surveys to provide flexibility to our respondents and maximise our sampling reach given our limited field team. For our in-person survey, one researcher drove around the study area visiting landholdings, inviting them to take part in the study (convenience sampling (Stratton, 2021)) and asking them for references of other local landholders that might be interested in participating in the study (snowball sampling (Stratton, 2021)). We distributed the survey online via organisation newsletters and mailouts to publicly listed emails for estates, farms, and other rural businesses. If landholders were unavailable during/for in-person visits, we invited them to complete the survey online. We used convenience sampling when collecting data online, advertising the survey and letting recipients decide whether to participate. We ensured that our sample for in-person and online interviews did not overlap by collecting and screening, respondents’ personal data and landholdings’ locations. We piloted the survey in our study area in April 2023 and collected data between July and September 2023. Pilot responses were included in analysis since the survey only needed minor changes post-pilot. We obtained informed written consent from all respondents before starting the survey. This study was granted ethics approval by Imperial College London’s Science Engineering Technology Research Ethics Committee (SETREC reference number 22IC7888). 2.4 Analysis Our analysis focuses on decisions to create mixed native woodland as a proxy for the adoption of forest restoration initiatives. Other types of woodland creation can be considered restorative and are possible in our study area (i.e. via other types of woodland creation schemes or voluntarily without subsidy). Despite potential similarities in landholders’ motivations when creating different woodland types, we believe decisions to create mixed native woodland creation, given their superior potential for ecosystem restoration (Di Sacco et al., 2021; Gann et al., 2019), can provide relevant theoretical insights for all forest restoration initiatives. Accordingly, we differentiate between landholders who declared that they or their organisations had (adopters (n=31)) or had not (non-adopters (n=46)) created at least one mixed native woodland on the landholding they represented since their management and/or occupancy started. Of the 48 respondents that had created new woodland, many had done so repeatedly. However, 17 (35%) were non-adopters since they had created woodland but not mixed native one yet. Our analysis focuses on the relative advantage, compatibility, complexity, observability, and trialability attributes of our theoretical framework and the variables within them (focus variables) since these explain most of the variability of adoption decisions according to DOIT (Rogers, 2003). The aim of our analysis is inference (Tredennick et al., 2021), meaning we aim to find focus variables with credible, non-zero coefficients, and thus a meaningful association with the adoption of mixed native woodland creation initiatives, given the data. We also aim to determine the direction (positive or negative) of credible associations and to assess whether they align with our DOIT based a priori hypotheses (Table 2). We developed two models, one causal and another statistical to answer these questions. 2.4.1 Causal model Following best practice guidance for causal inference (McElreath, 2020), we outlined our causal model using a Directed Acyclic Graph (DAG) — also known as a Causal Diagram or Causal Bayesian Network — using the ‘dagitty’ R package (Textor et al., 2017). Our causal model’s DAG describes the hypothesised causal pathways between our outcome of interest (adoption of mixed native woodland creation initiatives) and its predictors (DOIT framework attributes and the variables within them), as well as causal links between the predictors themselves (Fig. 1). We based our DAG on DOIT and the knowledge of Scottish woodland creation we gained through research and fieldwork. For example, we link extension support to knowledge in our DAG, since DOIT suggests promotional efforts influence the discovery and understanding of innovations among potential adopters (Rogers, 2003). Full justifications for our causal pathways are available in supporting information four. The causal model allows us to identify which variables we must control for in our statistical model to minimise bias in estimating the causal effects of interest. Given our DAG and our focus variables, we identified the variables associated to the 'Decision making' attribute as the minimum adjustment needed to control our estimates bias. Thus, included them in our statistical model (Pearl, 2009). Moreover, using a theory-based causal model and variable selection, we aimed to limit the likelihood of spurious findings (Smaldino & McElreath, 2016) and enhance the replicability of our study (Muthukrishna & Henrich, 2019). 2.4.2 Statistical model Given that our outcome of interest, mixed native woodland creation, is binary, we developed a Bernoulli distributed logistic regression implemented within a Bayesian framework (Equation 1). Our focus variables (i.e. those associated to the relative advantage, compatibility, complexity, observability, and trialability attributes of our theoretical framework) enter the model as predictors. Whilst variables associated to the “Decision making” attribute enter the model as controls to account for their biasing impact on our focus variable effect estimates according to our causal model (Table 2). We measured most statistical model variables as Likert items. We modelled these as ordered factors with a monotonic effect — consistent positive or negative effect that varies in size across adjacent variable levels — on the outcome (Bürkner & Charpentier, 2020). Only the “Innovation decision” variable enters the model as a non-ordered factor with three independent levels: "By a single individual"; "Collectively, by all members of the organisation"; and "By a select group of individuals within the organisation". We estimated the Highest Density Interval (HDI) for each estimand. The HDI encompasses all values in the posterior distribution with the highest probability density (i.e. credibility). In other words, values inside the HDI are more credible than those outside it (Kruschke, 2014). When interpreting model outputs, we considered any parameter estimate whose 90% HDI did not overlap with zero to have a credible effect on the outcome. This equates to at least a 0.95 probability of a true effect given our model and data (McElreath, 2020). Our model estimates and diagnostic tests are available in supporting information six. TABLE 2. List of variables entering our model. Variable (DOIT Element) DOIT Component DOIT Attribute Data type Effect type A priori effect direction* Outcome Adoption of mixed native woodland creation NA NA Binary NA NA Predictors- Estimands Initial cost - Money Innovation Relative advantage Ordinal Monotonic Negative Initial cost - Time Innovation Relative advantage Ordinal Monotonic Negative Perceived risk Innovation Relative advantage Ordinal Monotonic Negative Environmental benefit - Carbon Innovation Relative advantage Ordinal Monotonic Positive Environmental benefit – Habitat for wildlife Innovation Relative advantage Ordinal Monotonic Positive Environmental benefit – Diversity of species Innovation Relative advantage Ordinal Monotonic Positive Environmental benefit – Water quality Innovation Relative advantage Ordinal Monotonic Positive Environmental benefit – Flooding prevention Innovation Relative advantage Ordinal Monotonic Positive Environmental benefit – Soil quality Innovation Relative advantage Ordinal Monotonic Positive Economic benefit Innovation Relative advantage Ordinal Monotonic Positive Social benefit – Community well-being Innovation Relative advantage Ordinal Monotonic Positive Social benefit – Jobs created by landholding Innovation Relative advantage Ordinal Monotonic Positive Social prestige Innovation Relative advantage Ordinal Monotonic Positive Compatibility with values and beliefs Innovation Compatibility Ordinal Monotonic Positive Compatibility with needs Innovation Compatibility Ordinal Monotonic Positive Compatibility with current practices Innovation Compatibility Ordinal Monotonic Positive Ease of understanding - Ground preparations Innovation Complexity Ordinal Monotonic Positive Ease of understanding - Control herbivore damage Innovation Complexity Ordinal Monotonic Positive Ease of understanding - Planting Innovation Complexity Ordinal Monotonic Positive Ease of doing - Ground preparations Innovation Complexity Ordinal Monotonic Positive Ease of doing - Control herbivore damage Innovation Complexity Ordinal Monotonic Positive Ease of doing - Planting Innovation Complexity Ordinal Monotonic Positive Visibility of results Innovation Observability Ordinal Monotonic Positive Visibility of practice Innovation Observability Ordinal Monotonic Positive Reversibility Innovation Trialability Ordinal Monotonic Positive Predictors - Controls Innovation decision Adopter Decision making Nominal Non-monotonic NA Formalisation Adopter Decision making Ordinal Monotonic Negative *A priori effect direction hypotheses based on Diffusion of Innovations Theory (Rogers, 2003). 2.4.3 Conditional effects estimation For each predictor with a credible effect on the outcome, we calculated the conditional effect of a single unit increase in that predictor on the probability of having adopted mixed native woodland creation initiatives. Conditional effects quantify these probabilities maintaining the parameter of focus’ full range of values, whilst holding all other predictors constant. For example, when estimating the conditional effect of “compatibility with current land use practices” (Fig. 3A), said predictor could take any value in its range (“Strongly disagree” to “Strongly agree”), whilst all other predictors remained constant. We held predictors measured with Likert items constant at their neutral value (e.g. Neither agree nor disagree) and “Innovation decision” constant at “Collectively, by all members of the organisation”, the most common response. 2.5 Qualitative analysis Our survey includes an open-ended question designed to examine why adopters decided to create new woodland on their land. We analysed responses to this question using inductive coding (Thomas, 2006), developing codes for the justifications given by respondents to create woodlands as we read their responses. For example, we developed the codes “Grant income” and “Using poor land” after reading a response listing grant aid and using poor pasture as the main reasons to create woodland. We reused these codes whenever similar reasons were mentioned in other responses. We analysed the final set of codes by counting their mentions. 3 RESULTS 3.1 Sample 3.1.1 Landholding types, tenure, sizes and uses Our sample is dominated by landholdings of less than 1,250 acres (~ 505 hectares) (70%), managed for profit (83%), for ten years or more (in some case much longer) (88%), by family organisations (83%) of less than five members (70%). Most respondents (92%) were landowners directly managing the landholding they referred to in their responses. Some landowners (24%) also acted as landlords (21%) and/or licensors (3%) to others, whilst some tenanted (13%) or crofted (1%) other land. Only 7% of all respondents were tenants (3%) and/or crofters (4%) with no ownership, but powers to decide how land was used. Generally, decisions to create woodland had been or would be made as a group (62%), either collectively by all member of the organisation (45%) or by a select group of individuals (17%). For a third of respondents (33%) these decisions depended on a single individual. Most of the 77 landholdings within our sample are farms (47%) or estates (30%), accompanied by crofts (11%), nature reserves (2%), or other landholding types (9%) such as private homes, and mixed farms and crofts. The land use most often reported by respondents was farming (86%), followed by forestry (40%) and private enjoyment (38%). Nonetheless, the land uses reported were varied including wildlife conservation (32%), public recreation (27%), game/hunting (26%), horse grazing (3%), holiday lets (3%), fishing (1%), wind farms (1%), land-based crafts (1%), or therapy and meditation (1%). Most landholdings (56%) reported a mix of land uses, with some respondents (13%) reporting six or more distinct uses. In terms of holding size or extent, although most landholding (70%) were under 1,250 acres, some were much larger with one surpassing 50,000 acres. Likewise, although small organisations (9 or less members) dominated our sample (84%), some respondents (4%) represented large organisations with more than 50 members. 3.1.2 New woodland creation The extent of new woodland (mixed native and others) created was usually under 125 acres (73%), but most commonly under 12 acres (39%). Generally new woodlands were planted (87%), with only a few created through natural regeneration (35%). In some cases, the same landholding used both planting and natural regeneration (33%). Only six landholders (13%) created woodland without using external funding. The rest funded their woodland creation projects using government funds (73%), non-governmental funds (6%), bank loans (2%) or other sources (4%). Two landholdings (4%) used a mix of funding sources. Our qualitative analysis revealed that the main reasons for woodland creation reported by adopters and non-adopters who had created woodland of any type were similar. Wildlife, using poor land, shelter, and amenity were amongst the top five reasons to create woodland for both groups. A higher proportion of adopters (A) than non-adopters (NA) mentioned: wildlife (A 39%, NA 24%); shelter (A 26%, NA 18%); diversifying habitats (A 16%, NA 12%) and finances (A 13%, NA 6%) as reasons to create woodland. Whilst the opposite was true for: amenity (A 13%, NA 29%); using poor land (A 26%, NA 29%); business diversification (A 3%, NA 18%); aesthetics (A 13%, NA 18%); grant income (A 13%, NA 18%); environment (A 13%, NA 18%); firewood (A 3%, NA 6%); and legacy (A 3%, NA 6%). 3.1.3 Existing woodland Seventy percent (n = 54) of the 77 landholdings in our sample contained existing woodland, either before new woodland (mixed native and others) was created (61%) or at present if no new woodlands had been created (39%). Most of those landholdings (59%) had less than 125 acres of existing woodland. Mixed native and predominantly native woodland were the most common types of existing woodland, being present in 50% and 37% of landholdings with existing woodland respectively. Thirty-one percent (n = 17) of landholdings with existing woodland contained more than one type of existing woodland (e.g. native and non-native). Most landholdings with existing woodland (mixed native and others) managed it for timber production, mainly through even aged clear-fells (41%), but also through uneven aged regular or irregular fells (28%), and coppicing (4%). Hunting (11%), as well as wildlife conservation through either active (26%) or passive (26%) management, were also common existing woodland management objectives. Thirty-three percent of landholdings with existing woodland did not manage it at all, whilst 4% managed some patches but not others. Thirty-nine percent of landholdings with existing woodland had a mix of management objectives. Full sample descriptive statistics are available in supporting information five. 3.2 Statistical model results Our analysis provides strong evidence that decisions to adopt mixed native woodland creation initiatives are positively influenced by landholder’s expectations of changes to soil quality and habitat for wildlife, and perceived compatibility with current land use practices. Conversely, landholders’ perceived ease of understanding interventions to protect new woodland from herbivore damage (herbivore controls) is negatively associated with decisions to adopt mixed native woodland creation initiatives (Fig. 2). Ten thousand draws from the posterior distribution indicate a greater than 0.95 probability of those four associations, as highlighted by the lack of overlap between their 90% HDIs and the zero line in Fig. 2. The probability of having adopted mixed native woodland creation initiatives increases with the perceived compatibility with land use practices. All else held constant, respondents who agreed or strongly agreed with the statement “Creating woodland matches our land use practices” have a 12% (90% HDI 0–40%) and a 23% (90% HDI 0–73%) mean probability of having adopted respectively (Fig. 3A, Table 3 ). The likelihood of adopting mixed native woodland creation initiatives also rises with expectations of changes in wildlife habitat. Holding all other factors constant, the mean probability of having adopted mixed native woodland creation initiatives increases by 34%, from 2% (90% HDI 0–4%) for landholders expecting great declines in wildlife habitat due to woodland creation, to 36% (90% HDI 0–90%) for those expecting great increases (Fig. 3C, Table 3 ). Respondents expecting larger improvements in soil quality were also more likely to have adopted, with the mean probability of having adopted mixed native woodland creation initiatives increasing from 2% (90% HDI 0–4%) for landholders expecting great declines, to 18% (90% HDI 0–62%) for those expecting great increases in soil quality (Fig. 3D, Table 3 ). Finally, the probability of having adopted mixed native woodland creation interventions decreases as the perceived ease of understanding herbivore controls increases. In this case, and perhaps counterintuitively, respondents who saw herbivore controls as “Very difficult” or “Difficult” to understand were the most likely to have adopted, with a 28% (90% HDI 0–87%) and a 15% (90% HDI 0–57%) probability of having adopted respectively. Whilst those who thought controlling herbivores would be easy or very easy have a 1.2% (90% HDI 0–3%) and 0.6% (90% HDI 0–1%) mean probability of having adopted (Fig. 3B, Table 3 ). Table 3 Conditional effect estimates for credible predictors according to our model and data. Probability of having adopted mixed native creation initiatives Likert response level Mean Median 50% HDI [Lower, Upper] 90% HDI [Lower, Upper] Compatibility with land use practices Strongly disagree 0.00793 0.000393 [0.00, 0.00] [0.00, 0.01] Disagree 0.0137 0.001 [0.00, 0.00] [0.00, 0.03] Neutral 0.0572 0.00888 [0.00, 0.01] [0.00, 0.17] Agree 0.121 0.0297 [0.00, 0.03] [0.00, 0.40] Strongly agree 0.227 0.0883 [0.00, 0.09] [0.00, 0.73] Expected changes to habitat for wildlife Decrease greatly 0.0204 0.00139 [0.00, 0.00] [0.00, 0.04] Decrease slightly 0.032 0.00332 [0.00, 0.00] [0.00, 0.08] No change 0.0572 0.00888 [0.00, 0.01] [0.00, 0.17] Increase slightly 0.191 0.0667 [0.00, 0.07] [0.00, 0.62] Increase greatly 0.356 0.239 [0.00, 0.24] [0.00, 0.90] Expected changes to soil quality Decrease greatly 0.0183 0.00121 [0.00, 0.00] [0.00, 0.04] Decrease slightly 0.028 0.00282 [0.00, 0.00] [0.00, 0.07] No change 0.0572 0.00888 [0.00, 0.01] [0.00, 0.17] Increase slightly 0.113 0.0231 [0.00, 0.02] [0.00, 0.38] Increase greatly 0.176 0.0467 [0.00, 0.05] [0.00, 0.62] Ease of understanding intervention to protect new woodland from herbivore damage Very difficult 0.277 0.112 [0.00, 0.11] [0.00, 0.87] Difficult 0.159 0.0363 [0.00, 0.04] [0.00, 0.57] Neutral 0.0572 0.00888 [0.00, 0.01] [0.00, 0.17] Easy 0.0123 0.00117 [0.00, 0.00] [0.00, 0.03] Very easy 0.00603 0.000401 [0.00, 0.00] [0.00, 0.01] Model and conditional effect estimates are available in supporting information six. DISCUSSION There is now wide agreement concerning the need to increase rates and extents of ecosystem restoration worldwide. Success in delivering restoration schemes depends heavily on the uptake of ecosystem restoration initiatives by landholders. In this research, we focused on forest restoration and undertook a DOIT based exploration of the factors driving the uptake of mixed native woodland creation in and around the Cairngorms National Park in Scotland. We found that landholders were more likely to have created mixed native woodland if they believed doing so was compatible with their land use practices or would increase habitat for wildlife or soil quality. Landholders that had created mixed native woodland were more likely to find herbivore controls difficult to understand. These findings corroborate effects hypothesised by DOIT for these attributes, except for the ease of understanding herbivore controls, which according to DOIT should have a positive effect on adoption (Table 2). We attribute this discrepancy between our results and theory to the influence of landholders’ previous woodland creation experiences, since many (71%) adopters had created other woodlands, potentially learning about the challenges of herbivore controls as part of this process. Our results exemplify how DOIT could be used to understand forest restoration decisions and suggest UK mixed native woodland creation could be boosted by modifying current woodland creation schemes to (i) emphasise woodland’s compatibility with other land uses; (ii) foster flexibility within woodland creation actions, so landholders can more easily adapt them to their circumstances and needs; (iii) mainstream easy-to-understand evidence-based comparisons of the potential benefits of different woodland types, so landholders can make better informed choices; and (iv) support landholders’ knowledge and management needs, particularly with respect to herbivore control tasks. We found compatibility with landholders’ land use practices was positively associated to decisions to create mixed native woodland. This is congruent with DOIT, previous woodland creation studies in the UK (Crabtree et al., 1998 ; Wynne-Jones, 2013 ) and studies on the diffusion of biodiversity conservation initiatives around the world (e.g. Jagadish et al., 2024 ; Romero-de-Diego et al., 2021 ). Some land use types are likely to be more compatible with mixed native woodland creation than others. For instance, the dominance of silvopastural systems — integrating trees and livestock — in UK agroforestry (~ 88% of UK agroforestry (Chanarin et al., 2022 )), suggests woodland is more compatible with livestock rather than arable farming. Similarly, landholdings with a game management and/or hunting interest might find woodland more or less compatible with their current land use based on their focal species (e.g. Duckworth et al., 2003 ; Ewald & Gibbs, 2019 ; Oldfield et al., 2003 ). For example, pheasant shoots when compared to grouse moors should perceive mixed native woodland as more compatible, since pheasants mainly live on woodland edges (Sage et al., 2020 ), whilst red grouse is a heathland and moorland specialist (Tharme et al., 2001 ). Specific characteristics of landholdings (e.g. higher prevalence of poorer soils) might also influence landholders’ perceptions of the compatibility of mixed native woodland with land use practices. For example, in the UK, cereal, general cropping, cattle and sheep farms in Less Favoured Areas — areas of poorer land where agricultural production is more challenging (Scottish Government, 1997 ) — were the most likely to participate in the Farm Premium Woodland Creation Scheme (Crabtree et al., 1998 ). This suggests that perceptions of compatibility might be driven by the lower opportunity costs of woodland creation on less productive land. Specific practices within specific land use types, might also determine the compatibility of mixed native woodland with land use practices. For instance, current trends towards the use of larger farm machinery in arable settings (Keller et al., 2022 ) might result in more “idle” or “odd bits” (e.g. corners where harvesters turn) open for woodland or other land uses. Expectations that woodland will increase habitat for wildlife or soil quality, were also positively associated with decisions to create mixed native woodland as hypothesised by DOIT (Table 2). Intuitively, the chance to utilise less productive land on their holdings and/or promoting wildlife conservation were the two main reasons for woodland creation reported by our respondents based on our qualitative questions. A majority of these responses (75% and 62% respectively) came from landholders who had created mixed native woodland. These results agree with past reviews of woodland creation in the UK, which found prevailing motives for woodland creation and management to be consistently related to natural and environmental enhancement, as well as personal enjoyment, whilst productive and economic motivations tended to be lower (Hemery et al., 2018 , 2020 ; Lawrence et al., 2010 ; Lawrence & Dandy, 2014 ). Overall, these results suggest that landholders have clear woodland creation objectives and create the type of woodland they think is best to meet them. Counter to DOIT, which suggests easier to understand innovations are more likely to be adopted (Rogers, 2003 ), landholders who had created mixed native woodland were more likely to find herbivore controls difficult to understand. Further research is needed to detail the mechanisms leading to this result, but our data suggest our landholders’ previous woodland creation experiences (71% of adopters had created other woodland/s), may have demonstrated the complexity of herbivore controls to them. High levels of browsing by herbivores can slow or stop the natural or assisted regeneration and expansion of woodlands, and reduce the survival rates of new plantings (Côté et al., 2004 ). This is a major constraint to woodland creation in the UK (Hemery et al., 2018 , 2020 ), with sheep and deer impacts being particularly significant in our study area (Gullett et al., 2023 ; Hare et al., 2021 ; Hobbs, 2009 ; Newton et al., 2001 ). Herbivore controls such as culling, fencing, tree-guards or repellent chemicals are diverse, and their success depends on several aspects (Hodge & Pepper, 1998 ). For example, effective fencing requires considering factors such as a site’s topography, climate, and herbivore species, as well as potential impacts on other wildlife (e.g. capercaillie, or black grouse collisions), and adapting the fence's design (layout, type, height, etc.) to them (Trout & Pepper, 2006 ). Our results suggest, landholders often misjudge the difficulty of understanding such nuances of herbivore control until they experience them first-hand. However, learning about these challenges does not stop them from creating woodland again, supporting DOIT’s notion that knowledge lessens uncertainty facilitating adoption (Rogers, 2003 ). Generally, mixed native woodlands created by our landholders were small (< 12 acres), created using government grant schemes and planted rather than naturally regenerated. This highlights the key role of government woodland creation grant funding in the UK (Raum, 2020 ; Raum & Potter, 2015 ), and our landholders’ preference for active instead of passive woodland creation methods. While the small size of newly created woodlands hints to prevalent barriers to woodland creation such as: lack of land, insufficient funding (Hemery et al., 2018 , 2020 ) or perceptions of woodland as an inferior, aesthetically untidy, and irreversible land use, that contravenes traditional practices and takes too long to yield benefits (Lawrence et al., 2010 ; Lawrence & Dandy, 2014 ). 4.1 Limitations Our study’s main limitation is that we used non-probabilistic sampling methods, hence our results only apply to our sample and have limited generalisability to broader populations (Stratton, 2021 ). This is mainly due to selection bias (Catalogue of Bias Collaboration, Nunan, et al., 2017 ), since our respondent were self-selected. Future studies could avoid this by using probabilistic sampling if robust unified registries of land ownership for their focus area exist and are accessible. Survey studies can incur in a broad range of biases because of poor question and/or questionnaire design, and/or administration of the survey (Choi et al., 2005 ). We mitigated design flaws through careful crafting of our questions and questionnaire, and by piloting them. Biases linked to the administration of our survey were harder to mitigate. Recall bias (Catalogue of Bias Collaboration, Spencer, et al., 2017 ) is a particular risk in our study, since when respondents had created woodland, we relied on their memories — which may be inaccurate — for some questions. To mitigate recall bias, we piloted our surveys to ensure their layout and phrasing was effective and used mainly close ended questions (Bernard et al., 1984 ). Respondents answering questions in a way they perceived as socially desirable, taking shortcuts (i.e. satisficing), and tending to select the ends (e.g. “Strongly disagree” or “Strongly agree”) or centres (e.g. “Neither agree nor disagree”) of our Likert items (Choi et al., 2005 ) are also risks in our study. We mitigated these risks through piloting, ensuring anonymity, and the use of simple neutral questions and a short questionnaire to avoid respondents’ fatigue. 4.2 Implications for policy and practice Our results reaffirm the importance of considering landholders perspectives when designing scalable ecosystem restoration programs (Ambrose-Oji et al., 2018 ; Lawrence & Dandy, 2014 ; Mascia & Mills, 2018 ; Mills et al., 2019 ; Tedesco, Brancalion, et al., 2023 ). Through our theory-based approach, we aim for our findings to be easily scrutinised by future studies and to contribute to a DOIT based understanding of forest and ecosystem restoration adoption through the accumulation of comparable studies (Muthukrishna & Henrich, 2019 ). Pending said scrutiny to confirm their generalisability to other areas and samples, our findings already point to a series of improvements to the design and rollout of current and future woodland creation schemes. First, our findings suggest governments should improve access to easy-to-understand evidence-based comparisons of the environmental, social, and economic benefits of different woodland types (i.e. single and multi-species native, single and multi-species non-native, or mixed non-native and native woodland). Some frameworks that do this have already been proposed (e.g. Baral et al., 2016 ). Nonetheless, UK focused research on woodland expansion benefits seems to be skewed towards biodiversity and regulating ecosystem services (carbon sequestration, flood control, etc.) and limited for other services (Burton et al., 2018 ). Expanding this evidence-base seems essential for better policy design and to facilitate more robust decision making by landholders, who according to our findings have clear woodland creation motivations that directly inform the type of woodland they create. We stress that comparisons of benefits of different woodlands should always be caveated by at least the woodland’s location, extent, and the habitat/land use it replaces (e.g. Bradfer-Lawrence et al., 2021 ; Friggens et al., 2020 ; Matthews et al., 2020 ; Monger et al., 2024 ; Stephens & Wagner, 2007 ). Ultimately those and the woodland type created are the main factors that can be controlled at a woodland’s creation stage, with woodland benefits contingent on them and on how the woodland is managed after creation. This is particularly important given our landholder’s apparent focus on using poor land to create woodland. Poor land might be less productive (e.g. lower soil quality) or harder to work/access (field corners, stony soils, steep slopes, etc.) for agriculture, but can host important ecosystems like grasslands or peatlands. Conversion of these ecosystems to woodland will negatively affect biodiversity and climate change mitigation (e.g. Bradfer-Lawrence et al., 2021 ; Di Sacco et al., 2021 ; Friggens et al., 2020 ; Matthews et al., 2020 ). Second, woodland expansion schemes should increase their emphasis on the compatibility of woodlands (mixed native and others) with other land uses such as agriculture, hunting, or recreation. Such efforts have been ongoing and continue in all UK nations, with a vast array of online and in-person support from governmental and non-governmental sources highlighting the compatibility of trees with other land uses, particularly farming (e.g. Forestry Commission, n.d.; Forestry Commission Wales, 2012 ; Scottish Forestry, n.d.; Woodland Trust, 2022 ). However, official sources showing persistently low rates of woodland creation (e.g. Forestry Research, 2023 ), together with our findings, suggest that better targeting of these efforts could improve rates of adoption. Third, the flexibility of a scheme’s woodland creation options is also key to enhance the compatibility of woodland with other land uses. Currently, although bound by the UK Forestry Standard (Forestry Commission, 2017 ), landholders have considerable leeway in matters such as the size, content, or method of creating their woodland (DAERA, 2024 ; Scottish Government, 2021 ; UK Government, 2021 ; Welsh government, 2024 ). This level of flexibility should be maintained, subject to appropriate benchmarks, since it most likely helps landholders adapt woodland to their circumstances and needs increasing its compatibility. Finally, woodland creation schemes need to consider how to better support landholders’ knowledge needs, particularly regarding herbivore control tasks. In Scotland, most woodland creation projects require some herbivore control measures, such as fencing or culling (Gullett et al., 2023 ; Hobbs, 2009 ). Our results imply landholders struggle to understand how to implement these controls. Suggesting that woodland creation rates could improve if better support for herbivore controls was available. Ecosystem restoration is a process defined by change. Ultimately change to the use of a piece of land, but first and foremost to the perspectives and practices of those managing it and their contexts. Targets and economic incentives are common and essential strategies to foster that change and achieve ecosystem restoration at scale (Tedesco, López-Cubillos, et al., 2023 ). But our research and that of others (e.g. Tedesco et al., 2023 ) suggests a narrow focus on them will almost certainly lead to failure. Instead, a more holistic approach that considers landholders’ goals, their context, and their perceptions of ecosystem restoration initiatives is needed. Grounding this holistic approach on suitably broad theories of human decision making able to capture its dynamism and two-way interplay with its context — such as DOIT — would deepen our understanding of restoration decisions (Schill et al., 2019 ), and help build a robust knowledge base through the accumulation of comparable studies (Muthukrishna & Henrich, 2019 ). The restoration strategies resulting from this approach will be different for different places. With our results suggesting that in contexts like the UK’s, where targets and economic incentives are established, the focus should be on: (i) emphasising and facilitating restoration’s compatibility with other land-uses; (ii) mainstreaming evidence-based comparisons of the benefits and trade-offs of different restoration actions; and (iii) supporting landholders’ knowledge needs. Overall, this more holistic approach should allow for more robust design and re-design of ecosystem restoration programs. Thus, increasing their adoption and, provided that safeguards against potential negative outcomes (environmental, social or economic) are observed, contributing to the realisation of ecosystem restoration’s multiple benefits at scale. Declarations this study was granted ethics approval by Imperial College London's Science Engineering Technology Research Ethics Committee (SETREC reference number 22IC7888). Additionally, I confirm all study participants provided informed consent before taking part in our study. AUTHOR CONTRIBUTIONS Alvaro Roel Bellot: Conceptualisation, Data curation, Formal Analysis, Investigation, Methodology, Project administration, Validation, Visualisation, Writing – original draft, Writing – review & editing. Matthew Clark: Formal Analysis, Methodology, Validation, Visualisation, Writing – review & editing. Arundhati Jagadish: Conceptualisation, Methodology, Validation, Writing – review & editing. Clive Potter: Validation, Writing – review & editing. Morena Mills: Conceptualisation, Funding acquisition, Methodology, Supervision, Validation, Writing – review & editing. ACKNOWLEDGEMENTS First and foremost a heartfelt thank you to all our study participants, without their kind contribution this research would have not been possible. Thanks to Angela Dean and Ans Vercammen who helped refine our questions design; Jeffrey Andrews who helped refine our statistical model; the administrative staff at Imperial College London’s Centre for Environmental Policy who helped with project logistics; and the Imperial College London’s Research Governance and Integrity Team who reviewed our ethics application. A final and special thanks to our first host during our second visit to our study area, who shall remain anonymous, but went above and beyond hosting and supporting us. FUNDING INFORMATION This research was part of a PhD studentship funded by the UK’s Research and Innovation (UKRI) Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnerships (UKRI Project reference number: 2521695). Morena Mills and Matthew Clark thank the Leverhulme Trust for the research grant: RPG-2021-440. CONFLICT OF INTEREST STATEMENT The authors declare no conflict of interest. DATA AVAILABILITY STATEMENT The data and R code used in this study are available at: https://github.com/Alvaro-RoelBellot/DriversOfMixedNativeWoodlandCreationScotland References Adams, C., Rodrigues, S. T., Calmon, M., & Kumar, C. (2016). Impacts of large‐scale forest restoration on socioeconomic status and local livelihoods: what we know and do not know. Biotropica , 48 (6), 731–744. https://doi.org/10.1111/btp.12385 Allek, A., Viany Prieto, P., Korys, K. A., Rodrigues, A. F., Latawiec, A. E., & Crouzeilles, R. (2022). How does forest restoration affect the recovery of soil quality? A global meta-analysis for tropical and temperate regions. In Restoration Ecology . John Wiley and Sons Inc. https://doi.org/10.1111/rec.13747 Ambrose-Oji, B., Robinson, J. S., & O’Brien, L. (2018). Influencing behaviour for resilient treescapes: Rapid Evidence Assessment . https://cdn.forestresearch.gov.uk/2018/11/rea_treescapes.pdf Aradóttir, Á. L., Petursdottir, T., Halldorsson, G., Svavarsdottir, K., & Arnalds, O. (2013). Drivers of ecological restoration: Lessons from a century of restoration in Iceland. Ecology and Society , 18 (4). https://doi.org/10.5751/ES-05946-180433 Aronson, J., & Alexander, S. (2013). Ecosystem restoration is now a global priority: Time to roll up our sleeves. Restoration Ecology , 21 (3), 293–296. https://doi.org/10.1111/rec.12011 Baral, H., Guariguata, M. R., & Keenan, R. J. (2016). A proposed framework for assessing ecosystem goods and services from planted forests. Ecosystem Services , 22 , 260–268. https://doi.org/10.1016/j.ecoser.2016.10.002 Bernard, H. R., Killworth, P., Kronenfeld, D., & Sailer, L. (1984). The Problem of Informant Accuracy: The Validity of Retrospective Data. In Source: Annual Review of Anthropology (Vol. 13). https://www.jstor.org/stable/2155679 Bradfer-Lawrence, T., Finch, T., Bradbury, R. B., Buchanan, G. M., Midgley, A., & Field, R. H. (2021). The potential contribution of terrestrial nature-based solutions to a national ‘net zero’ climate target. Journal of Applied Ecology , 58 (11), 2349–2360. https://doi.org/10.1111/1365-2664.14003 Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software , 80 . https://doi.org/10.18637/jss.v080.i01 Bürkner, P. C., & Charpentier, E. (2020). Modelling monotonic effects of ordinal predictors in Bayesian regression models. British Journal of Mathematical and Statistical Psychology , 73 (3), 420–451. https://doi.org/10.1111/bmsp.12195 Burton, V., Moseley, D., Brown, C., Metzger, M. J., & Bellamy, P. (2018). Reviewing the evidence base for the effects of woodland expansion on biodiversity and ecosystem services in the United Kingdom. Forest Ecology and Management , 430 (April), 366–379. https://doi.org/10.1016/j.foreco.2018.08.003 Cairngorms National Park Authority. (2023). Cairngorms National Park Authority . https://cairngorms.co.uk/ Catalogue of Bias Collaboration, Nunan, D., Bankhead, C., & Aronson, J. (2017). Selection bias . Catalogue of Bias. http://www.catalogofbias.org/biases/selection-bias/ Catalogue of Bias Collaboration, Spencer, E. A., Brassey, J., & Mahtani, K. (2017). Recall bias . Catalogue of Bias. https://www.catalogueofbiases.org/biases/recall-bias CBD. (2022). Kunming-Montreal Global Biodiversity Framework . https://www.cbd.int/conferences/post20202CBD/WG8J/11/7,CBD/SBSTTA/23/9,CBD/SBSTTA/24/12andCBD/SBI/3/21,respectively. Chanarin, G., Lewis, D., Silcock, P., & Thomas, C. (2022). Woodland and trees in the farmed landscape: Towards a diverse, resilient and vibrant agroforestry and farm woodland economy for the UK. https://www.soilassociation.org/media/24798/woodland-and-trees-in-farmed-landscapes-report.pdf Choi, B. C., Pak, A. W., & Cdc, for. (2005). A Catalog of Biases in Questionnaires . http://www.cdc.gov/pcd/issues/2005/jan/ 04_0050.htm Cook-Patton, S. C., Drever, C. R., Griscom, B. W., Hamrick, K., Hardman, H., Kroeger, T., Pacheco, P., Raghav, S., Stevenson, M., Webb, C., Yeo, S., & Ellis, P. W. (2021). Protect, manage and then restore lands for climate mitigation. Nature Climate Change , 11 (12), 1027–1034. https://doi.org/10.1038/s41558-021-01198-0 Côté, S. D., Rooney, T. P., Tremblay, J. P., Dussault, C., & Waller, D. M. (2004). Ecological impacts of deer overabundance. In Annual Review of Ecology, Evolution, and Systematics (Vol. 35, pp. 113–147). https://doi.org/10.1146/annurev.ecolsys.35.021103.105725 Crabtree, B., Chalmers, N., & Barron, N.-J. (1998). Information for Policy Design: Modelling Participation in a Farm Woodland Incentive Scheme. Journal of Agricultural Economics , 49 (3), 306–320. https://doi.org/10.1111/j.1477-9552.1998.tb01274.x DAERA. (2024). DAERA Forestry Grants . https://www.daera-ni.gov.uk/articles/daera-forestry-grants Di Sacco, A., Hardwick, K. A., Blakesley, D., Brancalion, P. H. S., Breman, E., Cecilio Rebola, L., Chomba, S., Dixon, K., Elliott, S., Ruyonga, G., Shaw, K., Smith, P., Smith, R. J., & Antonelli, A. (2021). Ten golden rules for reforestation to optimize carbon sequestration, biodiversity recovery and livelihood benefits. Global Change Biology , 27 (7), 1328–1348. https://doi.org/10.1111/gcb.15498 Duckworth, J. C., Firbank, L. G., Stuart, R. C., & Yamamoto, S. (2003). Changes in land cover and parcel size of British lowland woodlands over the last century in relation to game management. Landscape Research , 28 (2), 171–182. https://doi.org/10.1080/0142639032000070184 Elias, M., Kandel, M., Mansourian, S., Meinzen-Dick, R., Crossland, M., Joshi, D., Kariuki, J., Lee, L. C., McElwee, P., Sen, A., Sigman, E., Singh, R., Adamczyk, E. M., Addoah, T., Agaba, G., Alare, R. S., Anderson, W., Arulingam, I., Bellis, S. Ḵung V., … Winowiecki, L. (2021). Ten people-centered rules for socially sustainable ecosystem restoration. Restoration Ecology . https://doi.org/10.1111/rec.13574 Ewald, J., & Gibbs, S. (2019). Gamekeepers: conservation and wildlife 2019 survey . https://www.gwct.org.uk/media/1095291/NGOGWCT-Survey2019-final.pdf Fisher, J. A., Cavanagh, C. J., Sikor, T., & Mwayafu, D. M. (2018). Linking notions of justice and project outcomes in carbon offset forestry projects: Insights from a comparative study in Uganda. Land Use Policy , 73 , 259–268. https://doi.org/10.1016/j.landusepol.2017.12.055 Fleischman, F., Basant, S., Chhatre, A., Coleman, E. A., Fischer, H. W., Gupta, D., Güneralp, B., Kashwan, P., Khatri, D., Muscarella, R., Powers, J. S., Ramprasad, V., Rana, P., Solorzano, C. R., & Veldman, J. W. (2020). Pitfalls of Tree Planting Show Why We Need People-Centered Natural Climate Solutions. In BioScience (Vol. 70, Issue 11, pp. 947–950). Oxford University Press. https://doi.org/10.1093/biosci/biaa094 Forestry Commission. (n.d.). It’s time to branch out How woodland creation benefits your farm . Retrieved March 29, 2024, from https://assets.publishing.service.gov.uk/media/65a6793996a5ec000d731a38/CFT_its_time_to_branch_out_how_woodland_creation_benefits_your_farm_Jan_24.pdf Forestry Commission. (2017). The UK Forestry Standard . https://cdn.forestresearch.gov.uk/2023/10/The-UK-Forestry-Standard.pdf Forestry Commission Wales. (2012). New farm woodlands . https://cdn.naturalresources.wales/media/689799/new-farm-woodlands.pdf?mode=pad&rnd=132098199310000000 Forestry Research. (2023). Forestry Statistics 2023 . https://www.forestresearch.gov.uk/tools-and-resources/statistics/forestry-statistics/ Friggens, N. L., Hester, A. J., Mitchell, R. J., Parker, T. C., Subke, J. A., & Wookey, P. A. (2020). Tree planting in organic soils does not result in net carbon sequestration on decadal timescales. Global Change Biology , 26 (9), 5178–5188. https://doi.org/10.1111/gcb.15229 Gann, G. D., Mcdonald, T., Walder, B., Aronson, J., Nelson, C. R., Jonson, J., Hallett, J. G., Eisenberg, C., Guariguata, M. R., Liu, J., Hua, F., Echeverría, C., Gonzales, E., Shaw, N., Decleer, K., & Dixon, K. W. (2019). International principles and standards for the practice of ecological restoration. Second edition: November 2019 . Garbach, K., & Long, R. F. (2017). Determinants of field edge habitat restoration on farms in California’s Sacramento Valley. Journal of Environmental Management , 189 , 134–141. https://doi.org/10.1016/j.jenvman.2016.12.036 Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., Schlesinger, W. H., Shoch, D., Siikamäki, J. V., Smith, P., Woodbury, P., Zganjar, C., Blackman, A., Campari, J., Conant, R. T., Delgado, C., Elias, P., Gopalakrishna, T., Hamsik, M. R., … Fargione, J. (2017). Natural climate solutions. Proceedings of the National Academy of Sciences of the United States of America , 114 (44), 11645–11650. https://doi.org/10.1073/pnas.1710465114 Gullett, P. R., Leslie, C., Mason, R., Ratcliffe, P., Sargent, I., Beck, A., Cameron, T., Cowie, N. R., Hetherington, D., MacDonell, T., Moat, T., Moore, P., Teuten, E., & Hancock, M. H. (2023). Woodland expansion in the presence of deer: 30 years of evidence from the Cairngorms Connect landscape restoration partnership. Journal of Applied Ecology . https://doi.org/10.1111/1365-2664.14501 Hagger, V., Dwyer, J., & Wilson, K. (2017). What motivates ecological restoration? Restoration Ecology , 25 (5), 832–843. https://doi.org/10.1111/rec.12503 Hare, D., Daniels, M., & Blossey, B. (2021). Public Perceptions of Deer Management in Scotland. Frontiers in Conservation Science , 2 . https://doi.org/10.3389/fcosc.2021.781546 Hemery, G., Petrokofsky, G., Ambrose-Oji, B., Edwards, D., O’Brien, L., Tansey, C., & Townsend, M. (2018). Shaping the Future of Forestry: Report of the British Woodlands Survey 2017 . https://sylva.org.uk/downloads/BWS2017report.pdf Hemery, G., Petrokofsky, G., Ambrose-Oji, B., Forster, J., Hemery, T., & O’Brien, L. (2020). Awareness, action, and aspirations in the forestry sector in responding to environmental change: Report of the British Woodlands Survey 2020 . https://sylva.org.uk/downloads/BWS2020-report.pdf Hobbs, R. (2009). Woodland restoration in Scotland: Ecology, history, culture, economics, politics and change. Journal of Environmental Management , 90 (9), 2857–2865. https://doi.org/10.1016/J.JENVMAN.2007.10.014 Hodge, S., & Pepper, H. (1998). The prevention of mammal damage to trees in woodland . http://www.forestry.gov.uk Hua, F., Bruijnzeel, L. A., Meli, P., Martin, P. A., Zhang, J., Nakagawa, S., Miao, X., Wang, W., McEvoy, C., Peña-Arancibia, J. L., Brancalion, P. H. S., Smith, P., Edwards, D. P., & Balmford, A. (2022). The biodiversity and ecosystem service contributions and trade-offs of forest restoration approaches. Science , 376 (6595), 839–844. https://doi.org/10.1126/science.abl4649 IUCN, & Government of Germany. (2011, September 2). Bonn Challenge . http://www.bonnchallenge.org/ Jagadish, A., Freni-Sterrantino, A., He, Y., O’ Garra, T., Gecchele, L., Mangubhai, S., Govan, H., Tawake, A., Tabunakawai Vakalalabure, M., Mascia, M. B., & Mills, M. (2024). Scaling Indigenous-led natural resource management. Global Environmental Change , 84 , 102799. https://doi.org/10.1016/J.GLOENVCHA.2024.102799 Jagadish, A., Mills, M., & Mascia, M. B. (2021). Catalyzing Conservation at Scale: A Practitioner’s handbook (version 0.1) . https://doi.org/10.5281/zenodo.4894933 Kaine, G., Edwards, P., Polyakov, M., & Stahlmann-Brown, P. (2023). Who knew afforestation was such a challenge? Motivations and impediments to afforestation policy in New Zealand. Forest Policy and Economics , 154 . https://doi.org/10.1016/j.forpol.2023.103031 Keller, T., Or, D., by Rattan Lal, E., & Firestone, M. K. (2022). Farm vehicles approaching weights of sauropods exceed safe mechanical limits for soil functioning . https://doi.org/10.1073/pnas Kruschke, J. K. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan, second edition. In Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition . Elsevier Science. https://doi.org/10.1016/B978-0-12-405888-0.09999-2 Kruschke, J. K. (2021). Bayesian Analysis Reporting Guidelines. In Nature Human Behaviour (Vol. 5, Issue 10, pp. 1282–1291). Nature Research. https://doi.org/10.1038/s41562-021-01177-7 Lawrence, A., & Dandy, N. (2014). Private landowners’ approaches to planting and managing forests in the UK: What’s the evidence? Land Use Policy , 36 , 351–360. https://doi.org/10.1016/j.landusepol.2013.09.002 Lawrence, A., Dandy, N., & Urquhart, J. (2010). Landowners’ attitudes to woodland creation and management in the UK . www.forestry.gov.uk/fr/ownerattitudes Löfqvist, S., Kleinschroth, F., Bey, A., De Bremond, A., Defries, R., Fleischman, F., Lele, S., Martin, D. A., Messerli, P., Meyfroidt, P., Pfeifer, M., Rakotonarivo, S. O., Ramankutty, N., Ramprasad, V., Rana, P., Rhemtulla, J. M., Ryan, C. M., Vieira, I. C. G., Wells, G. J., & Garrett, R. D. (2023). How Social Considerations Improve the Equity and Effectiveness of Ecosystem Restoration. BioScience , 73 (2), 134–148. https://doi.org/10.1093/biosci/biac099 Mahajan, S. L., Jagadish, A., Glew, L., Ahmadia, G., Becker, H., Fidler, R. Y., Jeha, L., Mills, M., Cox, C., DeMello, N., Harborne, A. R., Masuda, Y. J., McKinnon, M. C., Painter, M., Wilkie, D., & Mascia, M. B. (2021). A theory‐based framework for understanding the establishment, persistence, and diffusion of community‐based conservation. Conservation Science and Practice , 3 (1). https://doi.org/10.1111/csp2.299 Mascia, M. B., & Mills, M. (2018). When conservation goes viral: The diffusion of innovative biodiversity conservation policies and practices. Conservation Letters , 11 (3), 1–9. https://doi.org/10.1111/conl.12442 Matthews, K. B., Wardell-Johnson, D., Miller, D., Fitton, N., Jones, E., Bathgate, S., Randle, T., Matthews, R., Smith, P., & Perks, M. (2020). Not seeing the carbon for the trees? Why area-based targets for establishing new woodlands can limit or underplay their climate change mitigation benefits. Land Use Policy , 97 (June), 104690. https://doi.org/10.1016/j.landusepol.2020.104690 McElreath, R. (2020). Statistical Rethinking . Chapman and Hall/CRC. https://doi.org/10.1201/9780429029608 Mills, M., Bode, M., Mascia, M. B., Weeks, R., Gelcich, S., Dudley, N., Govan, H., Archibald, C. L., Romero-de-Diego, C., Holden, M., Biggs, D., Glew, L., Naidoo, R., & Possingham, H. P. (2019). How conservation initiatives go to scale. Nature Sustainability , 2 (10), 935–940. https://doi.org/10.1038/s41893-019-0384-1 Monger, F., Spracklen, D. V., Kirkby, M. J., & Willis, T. (2024). Investigating the impact of woodland placement and percentage cover on flood peaks in an upland catchment using spatially distributed TOPMODEL. Journal of Flood Risk Management . https://doi.org/10.1111/jfr3.12977 Muthukrishna, M., & Henrich, J. (2019). A problem in theory. Nature Human Behaviour , 3 (3), 221–229. https://doi.org/10.1038/s41562-018-0522-1 Nelson, C. R., Hallet, J. G., Romero Montoya, A. E., Andrade, A., Besacier, C., Boerger, V., Bouazza, K., Chazdon, R., Cohen-Shacham, E., Danano, D., Diederichsen, A., Fernandez, Y., Gann, G. D., Gonzales, E. K., Gruca, M., Guariguata, M. R., Gutierrez, V., Hancock, B., Innecken, P., … Weidlich, E. W. A. (2024). Standards of practice to guide ecosystem restoration . FAO; SER; IUCN; https://doi.org/10.4060/cc9106en Newton, A. C., Stirling, M., & Crowell, M. (2001). Current approaches to native woodland restoration in Scotland. Botanical Journal of Scotland , 53 (2), 169–195. https://doi.org/10.1080/03746600108685021 Oldfield, T. E. E., Smith, R. J., Harrop, S. R., & Leader-Williams, N. (2003). Field sports and conservation in the United Kingdom. Nature , 423 (6939), 531–533. https://doi.org/10.1038/nature01678 Oliver, C. D., Nassar, N. T., Lippke, B. R., & McCarter, J. B. (2014). Carbon, Fossil Fuel, and Biodiversity Mitigation With Wood and Forests. Journal of Sustainable Forestry , 33 (3), 248–275. https://doi.org/10.1080/10549811.2013.839386 Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys , 3 , 96–146. https://doi.org/10.1214/09-SS057 Pienkowski, T., Freni Sterrantino, A., Tedesco, A. M., Clark, M., Brancalion, P. H. S., Jagadish, A., Mendes, A., Pugliese de Siqueira, L., & Mills, M. (2024). Spatial predictors of landowners’ engagement in the restoration of the Brazilian Atlantic Forest. People and Nature . https://doi.org/10.1002/pan3.10765 Powlen, K. A., & Jones, K. W. (2019). Identifying the determinants of and barriers to landowner participation in reforestation in Costa Rica. Land Use Policy , 84 , 216–225. https://doi.org/10.1016/j.landusepol.2019.02.021 Raum, S. (2020). Land-use legacies of twentieth-century forestry in the UK: a perspective. Landscape Ecology , 35 (12), 2713–2722. https://doi.org/10.1007/s10980-020-01126-1 Raum, S., & Potter, C. (2015). Forestry paradigms and policy change: The evolution of forestry policy in Britain in relation to the ecosystem approach. Land Use Policy , 49 , 462–470. https://doi.org/10.1016/j.landusepol.2015.08.021 Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press. Romero-de-Diego, C., Dean, A., Jagadish, A., Witt, B., Mascia, M. B., & Mills, M. (2021). Drivers of adoption and spread of wildlife management initiatives in Mexico. Conservation Science and Practice , February , 1–12. https://doi.org/10.1111/csp2.438 Ross-Davis, A. L., Broussard, S. R., Jacobs, D. F., & Davis, A. S. (2005). Afforestation Motivations of Private Landowners: An Examination of Hardwood Tree Plantings in Indiana . https://academic.oup.com/njaf/article/22/3/149/4779955 Sage, R. B., Hoodless, A. N., Woodburn, M. I. A., Draycott, R. A. H., Madden, J. R., & Sotherton, N. W. (2020). Summary review and synthesis: Effects on habitats and wildlife of the release and management of pheasants and red-legged partridges on UK lowland shoots. In Wildlife Biology (Vol. 2020, Issue 4). Nordic Council for Wildlife Research. https://doi.org/10.2981/wlb.00766 Sandbrook, C., Albury-Smith, S., Allan, J. R., Bhola, N., Bingham, H. C., Brockington, D., Byaruhanga, A. B., Fajardo, J., Fitzsimons, J., Franks, P., Fleischman, F., Frechette, A., Kakuyo, K., Kaptoyo, E., Kuemmerle, T., Kalunda, P. N., Nuvunga, M., O’Donnell, B., Onyai, F., … Zaehringer, J. G. (2023). Social considerations are crucial to success in implementing the 30×30 global conservation target. In Nature Ecology and Evolution . Nature Research. https://doi.org/10.1038/s41559-023-02048-2 Schill, C., Anderies, J. M., Lindahl, T., Folke, C., Polasky, S., Cárdenas, J. C., Crépin, A. S., Janssen, M. A., Norberg, J., & Schlüter, M. (2019). A more dynamic understanding of human behaviour for the Anthropocene. In Nature Sustainability (Vol. 2, Issue 12, pp. 1075–1082). Nature Research. https://doi.org/10.1038/s41893-019-0419-7 Scottish Forestry. (n.d.). Farm woodland . Retrieved March 29, 2024, from https://forestry.gov.scot/support-regulations/farm-woodlands Scottish Forestry. (2019a, November 30). Woodland Grant Scheme 1 1988-1991 (Scotland) . https://open-data-scottishforestry.hub.arcgis.com/datasets/d27213124aa94056a5f4689966cabcad_0/about Scottish Forestry. (2019b, November 30). Woodland Grant Scheme 2 1991-1994 (Scotland) . https://open-data-scottishforestry.hub.arcgis.com/datasets/0611e771fd014c809b4d02de194400fc_0/about Scottish Forestry. (2019c, November 30). Woodland Grant Scheme 3 1994-2003 (Scotland) . https://open-data-scottishforestry.hub.arcgis.com/datasets/cc1f51699439430c968a507eaf9acca7_0/about Scottish Government. (1997, January 1). Less Favoured Areas . https://spatialdata.gov.scot/geonetwork/srv/api/records/f4e358c1-df06-4107-bfd2-03f7581ecb07 Scottish Government. (2021). Forestry Grant Scheme . https://www.ruralpayments.org/publicsite/futures/topics/all-schemes/forestry-grant-scheme/ Sing, L., Metzger, M. J., Paterson, J. S., & Ray, D. (2017). A review of the effects of forest management intensity on ecosystem services for northern European temperate forests with a focus on the UK. Forestry , 91 (2), 151–164. https://doi.org/10.1093/forestry/cpx042 Smaldino, P. E., & McElreath, R. (2016). The natural selection of bad science. Royal Society Open Science , 3 (9). https://doi.org/10.1098/rsos.160384 Sorice, M. G., & Donlan, C. J. (2015). A human-centered framework for innovation in conservation incentive programs. Ambio , 44 (8), 788–792. https://doi.org/10.1007/s13280-015-0650-z Sorice, M. G., Haider, W., Conner, J. R., & Ditton, R. B. (2011). Incentive Structure of and Private Landowner Participation in an Endangered Species Conservation Program. Conservation Biology , 25 (3), 587–596. https://doi.org/10.1111/j.1523-1739.2011.01673.x Stan Development Team. (2024). RStan: the R interface to Stan (2.32.5). https://mc-stan.org/ Stephens, S. S., & Wagner, M. R. (2007). Forest Plantations and Biodiversity: A Fresh Perspective . https://academic.oup.com/jof/article/105/6/307/4599271 Stratton, S. J. (2021). Population Research: Convenience Sampling Strategies. In Prehospital and Disaster Medicine (Vol. 36, Issue 4, pp. 373–374). Cambridge University Press. https://doi.org/10.1017/S1049023X21000649 Swart, R., Levers, C., Davis, J. T. M., & Verburg, P. H. (2023). Meta-analyses reveal the importance of socio-psychological factors for farmers’ adoption of sustainable agricultural practices. One Earth , 6 (12), 1771–1783. https://doi.org/10.1016/j.oneear.2023.10.028 Tedesco, A. M., Brancalion, P. H. S., Hepburn, M. L. H., Walji, K., Wilson, K. A., Possingham, H. P., Dean, A. J., Nugent, N., Elias-Trostmann, K., Perez-Hammerle, K. V., & Rhodes, J. R. (2023). The role of incentive mechanisms in promoting forest restoration. In Philosophical Transactions of the Royal Society B: Biological Sciences (Vol. 378, Issue 1867). Royal Society Publishing. https://doi.org/10.1098/rstb.2021.0088 Tedesco, A. M., López-Cubillos, S., Chazdon, R., Rhodes, J. R., Archibald, C. L., Pérez-Hämmerle, K. V., Brancalion, P. H. S., Wilson, K. A., Oliveira, M., Correa, D. F., Ota, L., Morrison, T. H., Possingham, H. P., Mills, M., Santos, F. C., & Dean, A. J. (2023). Beyond ecology: ecosystem restoration as a process for social-ecological transformation. In Trends in Ecology and Evolution (Vol. 38, Issue 7, pp. 643–653). Elsevier Ltd. https://doi.org/10.1016/j.tree.2023.02.007 Temperton, V. M., Buchmann, N., Buisson, E., Durigan, G., Kazmierczak, Ł., Perring, M. P., de Sá Dechoum, M., Veldman, J. W., & Overbeck, G. E. (2019). Step back from the forest and step up to the Bonn Challenge: how a broad ecological perspective can promote successful landscape restoration. Restoration Ecology , 27 (4), 705–719. https://doi.org/10.1111/rec.12989 Textor, J., van der Zander, B., Gilthorpe, M. S., Liśkiewicz, M., & Ellison, G. T. H. (2017). Robust causal inference using directed acyclic graphs: the R package ‘dagitty.’ International Journal of Epidemiology , 45 (6), 1887–1894. https://doi.org/10.1093/ije/dyw341 Tharme, A. P., Green, R. E., Baines, D., Bainbridge, I. P., & O’Brien, M. (2001). The effect of management for red grouse shooting on the population density of breeding birds on heather-dominated moorland. Journal of Applied Ecology , 38 (2), 439–457. https://doi.org/10.1046/j.1365-2664.2001.00597.x Thomas, D. R. (2006). A General Inductive Approach for Analyzing Qualitative Evaluation Data. American Journal of Evaluation , 27 (2), 237–246. https://doi.org/10.1177/1098214005283748 Tran, T. M. A., Ko, D. W., Park, C. R., & Le, H. D. (2019). A bayesian network analysis of reforestation decisions by rural mountain communities in Vietnam. Forest Science and Technology , 15 (2), 51–57. https://doi.org/10.1080/21580103.2019.1581665 Tredennick, A. T., Hooker, G., Ellner, S. P., & Adler, P. B. (2021). A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology , 102 (6). https://doi.org/10.1002/ecy.3336 Trout, R., & Pepper, H. (2006). Forest Fencing - Forestry Commission Technical Guide . https://cdn.forestresearch.gov.uk/2006/03/fctg002.pdf UK Government. (2021, May 18). England Woodland Creation Offer . https://www.gov.uk/guidance/england-woodland-creation-offer UNEP, U. E. P., & FAO, F. and A. O. of the U. N. (2021). UN Decade on Restoration . https://www.decadeonrestoration.org/ Wang, C., Zhang, W., Li, X., & Wu, J. (2021). A global meta‐analysis of the impacts of tree plantations on biodiversity. Global Ecology and Biogeography . https://doi.org/10.1111/geb.13440 Waring, B., Neumann, M., Prentice, I. C., Adams, M., Smith, P., & Siegert, M. (2020). Forests and Decarbonization – Roles of Natural and Planted Forests. Frontiers in Forests and Global Change , 3 . https://doi.org/10.3389/ffgc.2020.00058 Wejnert, B. (2002). Integrating models of diffusion of innovations: A conceptual framework. In Annual Review of Sociology (Vol. 28, pp. 297–326). https://doi.org/10.1146/annurev.soc.28.110601.141051 Welsh government. (2024). Rural grants and payments . https://www.gov.wales/rural-grants-payments Woodland Trust. (2022). Farming for the future: how agroforestry can deliver for nature and climate . https://www.woodlandtrust.org.uk/publications/2022/11/farming-for-the-future/ World Economic Forum. (2020). One Trillion Trees Initiative . https://www.1t.org/ Wynne-Jones, S. (2013). Carbon blinkers and policy blindness: The difficulties of ‘Growing Our Woodland in Wales.’ Land Use Policy , 32 , 250–260. https://doi.org/10.1016/j.landusepol.2012.10.012 Additional Declarations The authors declare no competing interests. Supplementary Files 01SuppInfo1SurveyAdopter.pdf Supporting information 1 - Adopters' survey 02SuppInfo2SurveyNonAdopter.pdf Supporting information 2 - Non-adopters' survey 03SuppInfo3FullFramework.pdf Supporting information 3 - Full Diffusion of Innovations Theory framework 04SuppInfo4DAGJustifications.pdf Supporting information 4 - Directed Acyclic Graph's (DAG) justifications 05SuppInfo5DescriptiveStats.pdf Supporting information 5 - Full sample descriptive statistics 06SuppInfo6ModelEstimatesDiagnostics.pdf Supporting information 6 - Model estimates and diagnostic tests Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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07:24:23","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":264303,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7697572/v1/553da951df2b7b5a9da6ae5f.html"},{"id":92236223,"identity":"9c3cf2d7-1dff-40b6-bd58-58e5a8319fb8","added_by":"auto","created_at":"2025-09-26 07:24:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141405,"visible":true,"origin":"","legend":"\u003cp\u003eDirected Acyclic Graph (DAG) displaying the hypothesised causal connections underpinning our model. Arrows signal the direction of causal effect. I, A or C in brackets indicate innovation, adopter, and context attributes respectively. Attributes assumed irrelevant for the UK woodland creation context were not included in our DAG.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7697572/v1/3dda42fa8b170421466672ea.png"},{"id":92236202,"identity":"b8caf7e0-e9e8-4a5f-b6c1-3030a638e1b8","added_by":"auto","created_at":"2025-09-26 07:24:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150325,"visible":true,"origin":"","legend":"\u003cp\u003eStandardised posterior estimates for our model (Bernoulli distributed logistic regression implemented within a Bayesian framework) to estimate drivers of decisions to adopt mixed native woodland creation initiatives. Black dots indicate the median posterior coefficient estimates. The thick and thin black bars show the 50% and 90% Highest Density Interval (HDI) of the posterior distribution respectively. Grey density curves show the full posterior distribution. No overlap between the 90% HDI and the zero line indicates a higher than 0.95 probability of a true effect on the outcome given our model and data. Asterisks mark DOIT adopter sub-attributes, the rest are innovation sub-attributes. To enhance figure readability, predictors with a credible effect on the outcome are listed at the top separated by a space from predictors with non-credible effects.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7697572/v1/4c9e5cdead6b22752fafd3c1.png"},{"id":92236231,"identity":"c13b90c8-ba16-459f-a041-0e1ea536e496","added_by":"auto","created_at":"2025-09-26 07:24:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122687,"visible":true,"origin":"","legend":"\u003cp\u003eConditional effect of the participants’: (A) perceptions that creating woodland matches their land use practices; (B) perceptions of how difficult or easy it is/was to understand interventions to protect new woodland from herbivore damage; and expectations of change to (C) habitat for wildlife and (D) soil quality after woodland creation; on the probability of having adopted mixed native woodland creation initiatives, given that all other predictors are held constant. Likert item predictors are held constant at their neutral response level, whilst innovation decision is held constant at “Collectively, by all members of the organisation”, the most common response. Black dots and line show mean conditional effect estimates and their trend. Shading shows the 50% and 90% Highest Density Interval (HDI).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7697572/v1/e225cc72a074b01cf567cfdd.png"},{"id":92236415,"identity":"0bb24030-3495-4130-89d3-6ea8e7f93077","added_by":"auto","created_at":"2025-09-26 07:32:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1535813,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7697572/v1/a0f44c4b-1a74-44a8-96fc-f78d38f4e8a9.pdf"},{"id":92236222,"identity":"c5fd2ee6-2154-47f9-a7c7-33d96e06eed8","added_by":"auto","created_at":"2025-09-26 07:24:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":269825,"visible":true,"origin":"","legend":"\u003cp\u003eSupporting information 1 - Adopters' survey\u003c/p\u003e","description":"","filename":"01SuppInfo1SurveyAdopter.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7697572/v1/3ee631b96ddf7cb09726175d.pdf"},{"id":92236408,"identity":"70619690-0175-4ad2-a72e-a842b5841761","added_by":"auto","created_at":"2025-09-26 07:32:18","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":273804,"visible":true,"origin":"","legend":"\u003cp\u003eSupporting information 2 - Non-adopters' survey\u003c/p\u003e","description":"","filename":"02SuppInfo2SurveyNonAdopter.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7697572/v1/af3336e9a687e606244e0da6.pdf"},{"id":92236218,"identity":"8de275ea-9bcd-4266-a0f9-e9c01f01c6a8","added_by":"auto","created_at":"2025-09-26 07:24:18","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":151645,"visible":true,"origin":"","legend":"\u003cp\u003eSupporting information 3 - Full Diffusion of Innovations Theory framework\u003c/p\u003e","description":"","filename":"03SuppInfo3FullFramework.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7697572/v1/f02e9ed969cdd07a3ebabbc5.pdf"},{"id":92236235,"identity":"0c061fa2-46a8-4850-b93c-7bfcc8aff724","added_by":"auto","created_at":"2025-09-26 07:24:22","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":313703,"visible":true,"origin":"","legend":"\u003cp\u003eSupporting information 4 \u0026nbsp;- Directed Acyclic Graph's (DAG) justifications\u003c/p\u003e","description":"","filename":"04SuppInfo4DAGJustifications.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7697572/v1/8eab0956fda7bef1979dddab.pdf"},{"id":92236409,"identity":"ec89274a-3ae9-4c0e-9b8c-7b6de4aeb947","added_by":"auto","created_at":"2025-09-26 07:32:20","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":171753,"visible":true,"origin":"","legend":"\u003cp\u003eSupporting information 5 - Full sample descriptive statistics\u003c/p\u003e","description":"","filename":"05SuppInfo5DescriptiveStats.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7697572/v1/6fe3ad854b6bcafd8ce7a426.pdf"},{"id":92236226,"identity":"0fcb67ac-0861-48ec-b4df-c755a0e00829","added_by":"auto","created_at":"2025-09-26 07:24:20","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":180916,"visible":true,"origin":"","legend":"\u003cp\u003eSupporting information 6 - Model estimates and diagnostic tests\u003c/p\u003e","description":"","filename":"06SuppInfo6ModelEstimatesDiagnostics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7697572/v1/7d98d676d7d9e7e4abc785cc.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDrivers of mixed native woodland creation in Scotland: Compatibility with land use practices and environmental benefits influence decision making\u003c/p\u003e","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eEcosystem restoration has emerged as a global priority for mitigating the climate and biodiversity crises (Aronson \u0026amp; Alexander, 2013; Cook-Patton et al., 2021; Griscom et al., 2017), as highlighted by the recent declaration of the United Nations (UN) Decade on Ecosystem Restoration (UNEP \u0026amp; FAO, 2021) and the restoration targets within the Kunming-Montreal Global Biodiversity Framework (CBD, 2022). The practice of ecosystem restoration encompasses a range of activities — in terms of their target ecosystems, restoration approaches (active vs. passive), objectives, and scales — that ultimately aim to halt ecosystem degradation and promote recovery (Gann et al., 2019; Nelson et al., 2024). Out of all ecosystems, forests have captured the imagination of publics, politicians, and scientists (Temperton et al., 2019), triggering a flurry of forest-focused high-level international restoration campaigns such as the Bonn Challenge (IUCN \u0026amp; Government of Germany, 2011) or the One Trillion Trees Initiative (World Economic Forum, 2020).\u003c/p\u003e\n\u003cp\u003eForest restoration is a process occurring in complex social-ecological systems, with short and long-term, biophysical, social, economic, and political implications, particularly at a local scale and for different groups of local people (Elias et al., 2021; Löfqvist et al., 2023; Tedesco et al., 2023). The restoration of forests can provide a host of environmental (climate change mitigation, biodiversity, water quality, etc.) and social (health, recreation, etc.) benefits (Sing et al., 2017). However, forest restoration outcomes can be varied and involve trade-offs (e.g. Löfqvist et al., 2023). The environmental outcomes of forest restoration depend on factors such as the type, location, and size of forests restored; the time elapsed since restoration; or how restored forests are managed or their products used (Di Sacco et al., 2021; Fleischman et al., 2020; Oliver et al., 2014). Trade-offs resulting from this include less timber production in more biodiverse forests (Hua et al., 2022), or reduced carbon sequestration from forests created on carbon rich soils (Friggens et al., 2020). The social outcomes of forest restoration, in turn, hinge on its impacts on people’s livelihoods, land tenure, governance, and well-being through effects on equity, health, power, poverty, and food security (Adams et al., 2016). For example, the Trees for Global Benefit Program in Uganda improved livelihoods through restoration, but increased inequalities (Fisher et al., 2018). Overall, the pressure to scale forest restoration and the range of potential outcomes have prompted calls to carefully weigh the net social and environmental consequences of restoration (Di Sacco et al., 2021; Elias et al., 2021; Fleischman et al., 2020; Sandbrook et al., 2023).\u003c/p\u003e\n\u003cp\u003eThe delivery (passive or active) and success of forest restoration, ultimately depends on the decisions and behaviours of people, notably landholders. The incentive mechanisms for such behavioural change are generally social or economic in nature and vary significantly across a diverse community of stakeholders (Tedesco, Brancalion, et al., 2023). Understanding how these incentives are perceived by individuals, particularly landholders, and the factors which drive their uptake, spread, and abandonment will be crucial to design restoration initiatives that scale successfully (Ambrose-Oji et al., 2018; Lawrence \u0026amp; Dandy, 2014; Mills et al., 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndividuals’ motivations to participate in restoration and other environmentally minded behaviours are always diverse and their relative importance changes over time with, for example, changes to demographics, knowledge, or politics (Aradóttir et al., 2013; Hagger et al., 2017). Several studies show that individuals’ perceptions of an initiative, together with their objectives and intentions are, amongst others, key determinants for the adoption and scaling of environmentally focused initiatives such as sustainable agricultural practices (Swart et al., 2023) or different conservation and resource management initiatives (Jagadish et al., 2024; Mascia \u0026amp; Mills, 2018; Mills et al., 2019; Romero-de-Diego et al., 2021; Sorice et al., 2011). This in turn suggests that programmes focused only on economic incentives to promote the uptake of initiatives may be insufficient (Sorice \u0026amp; Donlan, 2015; Swart et al., 2023), requiring a shift towards more holistic programmes that gather and incorporate stakeholders’ inputs throughout (Sorice \u0026amp; Donlan, 2015).\u003c/p\u003e\n\u003cp\u003eDecisions to engage in woodland creation, which includes forest restoration, are influenced by an array of factors including: the availability of land, support or funding for woodland creation; bureaucratic burdens; or landholder’s motivations, perceptions, and attitudes towards woodland creation (e.g. Hemery et al., 2018, 2020; Kaine et al., 2023; Lawrence \u0026amp; Dandy, 2014; Powlen \u0026amp; Jones, 2019; Ross-Davis et al., 2005; Tran et al., 2019). The role of these factors as drivers or barriers to woodland creation is circumstantial, depending on individual landholders and their contexts at a particular point in time. For example, a landholder’s ability to create woodland will change according to their economic situation or knowledge, and the political forces influencing them.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the diverse, context-specific, and ever-changing nature of both landholders’ motivations and the potential outcomes of creating woodland, broader theories of human decision making able to capture its dynamism and two-way interplay with its context, may help us better understand woodland creation decisions (Schill et al., 2019). One such theory is Diffusion of Innovations Theory (DOIT) (Rogers, 2003), which suggests that decisions to engage in new behaviours are driven by\u0026nbsp;characteristics of the new behaviour itself, the person adopting it and their context.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we undertake one of the first (Garbach \u0026amp; Long, 2017; Pienkowski et al., 2024) applications of DOIT to forest restoration and ecosystem restoration more broadly. We focus on landholders’ decisions to create mixed native woodlands — woodlands comprising a combination of different native tree species — in and around the Cairngorms National Park in Scotland. \u0026nbsp;We do this given mixed native woodlands’ superior environmental benefits when compared to other woodland types (e.g. Allek et al., 2022; Hua et al., 2022; Wang et al., 2021; Waring et al., 2020) and their wide consideration as the woodlands with the highest potential for ecosystem restoration, when created in the right places (Di Sacco et al., 2021; Gann et al., 2019). Moreover, by focusing on a single woodland type and a relatively small and socially homogeneous area, we limit the influence of contextual noise while testing theory.\u0026nbsp;Given recent shifts in woodland creation narratives towards ecosystem restoration goals (biodiversity, carbon, etc.) (Raum, 2020; Raum \u0026amp; Potter, 2015), we assume that engagement with initiatives to create mixed native woodland is a new behaviour for most landholders. Consequently, we investigate what drives landholders’ decisions to adopt mixed native woodland creation initiatives in and around the Cairngorms National Park in Scotland, comparing the influence of financial and non-financial aspects of these initiatives in that context, using a survey based on DOIT. Our theory-based approach enhances the generalisability of our case study by contributing to a robust understanding of the factors driving ecosystem restoration adoption among landholders, through the accumulation of comparable studies\u0026nbsp;(Muthukrishna \u0026amp; Henrich, 2019). It also minimises the risk of spurious conclusions (Smaldino \u0026amp; McElreath, 2016).\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"2 METHODS","content":"\u003ch2\u003e2.1 Study area\u003c/h2\u003e\n\u003cp\u003eThe Cairngorms National Park is the largest in the United Kingdom (UK). Located in Northeast Scotland, it straddles the Perth and Kinross, Angus, Aberdeenshire, Moray, and Highland councils. The national park is home to over 18,000 people and encompasses more than 150 independently managed landholdings (mainly privately owned) with land uses including hunting, farming, conservation, and forestry (Cairngorms National Park Authority, 2023). We focused our study on the area in and around the Cairngorms National Park as it has high levels of both woodland creation and management funding uptake relative to surrounding areas (Scottish Forestry, 2019c, 2019b, 2019a).\u003c/p\u003e\n\u003ch2\u003e2.2 Survey design\u003c/h2\u003e\n\u003ch3\u003e2.2.1 Theoretical framework\u003c/h3\u003e\n\u003cp\u003eWe conceptualised the behaviour change process leading to the adoption or rejection of mixed native woodland creation using DOIT (Rogers, 2003). DOIT describes a general process through which innovations \u0026mdash; novel ideas, practices, or technologies \u0026mdash; can diffuse over time through the communication channels of a social system leading to their adoption or rejection by system members.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to DOIT, there are three main components that influence the adoption of innovations: (i) the innovation itself, (ii) the potential adopter, and (iii) the context where the innovation and its potential adopters interact (Rogers, 2003; Wejnert, 2002). Each of the three components have a set of attributes that influence the adoption process. We adapt two frameworks (Jagadish et al., 2021; Mahajan et al., 2021) that build on previous studies of DOIT (e.g. Rogers, 2003; Wejnert, 2002). These frameworks outline the important attributes that influence adoption in the context of nature/biodiversity conservation across the theory\u0026rsquo;s three main components (Table 1).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTABLE 1.\u003c/strong\u003e Components of the Diffusion of Innovations Theory (DOIT) and their attributes. Adapted from Jagadish et al., 2021 and Mahajan et al., 2021.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttribute\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinition\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eThe expected net benefits (financial, social, environmental, or other) of adopting an innovation compared to the status quo.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eCompatibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eThe degree to which an innovation is perceived as consistent with the existing values, existing actions, past experiences, and needs of potential adopters.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eThe degree to which the innovation is perceived as relatively difficult to understand and use.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eThe degree to which the innovation may be experimented with on a limited basis.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eThe degree to which the innovation and its results are visible or communicable to others.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eFlexibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eThe ability to transform the innovation to something that aligns with the adopter\u0026apos;s desires and constraints.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAdopter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSocial-economics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eThe social and economic characteristics of an individual that influence its ability to implement or learn about an innovation. These include education, skills, relative wealth, organisational size, and financial resources.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePersonality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003ePersonality traits that influence an adopter\u0026apos;s willingness to learn and implement new practices, such as risk orientation and competitiveness.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eThe degree to which the adopter is familiar with the innovation and its potential consequences through its existing or newly acquired knowledge and skills.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDecision making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eDecision making arrangements specify the rights of individuals or groups to make choices regarding various aspects of the innovation design and management.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eContext\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eGeographical setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003ePhysical features of the landscape/seascape, as well as spatial proximities to other adopters, markets, etc. that affect adoption by influencing the applicability of the innovation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eCulture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eShared behaviours and ideas - belief systems, traditionalism, and socialisation of adopters - that influence the adoption of innovations.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePolitical conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eCharacter of political systems, along with the regulations and norms inherent in the legal systems that influence the potential adopters\u0026rsquo; behaviours.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eExtension support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eOrganisations\u0026rsquo; and individuals\u0026rsquo; (public or private) activities relating to technology transfer, education, human resource development, and information sharing that influence the adoption and implementation of the innovation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eGlobal discourse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eThe extent to which the adopters\u0026rsquo; context influences and is influenced by globally circulating ideas, norms, and practices related to the innovation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u003cbr\u003e\u003c/h3\u003e\n\u003ch3\u003e2.2.2 Survey design\u003c/h3\u003e\n\u003cp\u003eOur analysis explores how different characteristics of woodland creation interventions (innovations), landholders (potential adopters) and their physical, environmental, and social context (context) influence the adoption of mixed native woodland creation interventions in our study area. To achieve this, we designed two survey versions, one targeted at landholders who had created woodland and another at those who had not.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe main difference between survey versions was the time frame they asked respondents to consider when answering questions targeting DOIT constructs, as well as some background questions. The phrasing of these questions was the same in both surveys. However, the survey version for landholders that had created woodland asked respondents to think back to the time when they were still deciding to create new woodland on the landholding they represent, while the survey version for those that hadn\u0026rsquo;t created woodland asked them to respond in the present. Responses about the past are prone to recall bias (Catalogue of Bias Collaboration, Spencer, et al., 2017), since when answering these questions, landholders that had created woodland relied on memories, which may be inaccurate and/or incomplete. Nonetheless, we were limited to this data as no data was collected at the time landholders first adopted woodland creation.\u003c/p\u003e\n\u003cp\u003eThe survey was divided into three sets of questions relevant to this study: (i) participant screening questions, (ii) background questions, and (iii) questions targeting DOIT constructs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe participant screening questions ensured that respondents were involved in both the\u0026nbsp;management of the land they referred to in their response, and decision making processes that lead to changes in the use of that land, such as the decisions to create a new woodland on it or not. If respondents did not meet both criteria, their responses were not recorded.\u003c/p\u003e\n\u003cp\u003eBackground questions focused on the landholding, their woodland creation activity, and the respondent\u0026rsquo;s organisation and personal data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the set of questions targeting DOIT constructs we used the framework outlined in Table 1 as a guide. Overall, we considered 44 sub-attributes stemming from the 15 attributes and three main components in our DOIT framework (Table 1). We operationalised the framework to suit the UK woodland creation context, deeming seven context and two woodland creation initiative sub-attributes irrelevant and excluding them from our survey.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe designed questions (n=61) targeting the remaining 35 DOIT sub-attributes to explore their influence on decisions to create woodland. Most of these questions (n=45) were Likert items with five response levels (e.g. Strongly disagree to Strongly agree). The remaining questions (n=16) were categorical (n=15) and binary (n=1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur surveys and full DOIT framework are available in supporting information one to three.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.3 Sampling and data collection\u003c/h2\u003e\n\u003cp\u003eWe collected data through both in-person (n=59) and online (n=25) surveys. We targeted landowners or managers within the study area involved in land use decisions. As no unified registry of this population exists, we used a combination of non-probability methods to obtain a sample of respondents, namely convenience and snowball sampling (Stratton, 2021). Our final sample included 84 landholders. Only 77 responses (59 in-person, 18 online) were within our study area, thus included in the analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe chose to split our data collection across in-person and online surveys to provide flexibility to our respondents and maximise our sampling reach given our limited field team.\u0026nbsp;For our in-person survey, one researcher drove around the study area visiting landholdings, inviting them to take part in the study (convenience sampling (Stratton, 2021)) and asking them for references of other local landholders that might be interested in participating in the study (snowball sampling (Stratton, 2021)).\u0026nbsp;We distributed the survey online via organisation newsletters and mailouts to publicly listed emails for estates, farms, and other rural businesses. If landholders were unavailable during/for in-person visits, we invited them to complete the survey online. We used convenience sampling when collecting data online, advertising the survey and letting recipients decide whether to participate. We ensured that our sample for in-person and online interviews did not overlap by collecting and screening, respondents\u0026rsquo; personal data and landholdings\u0026rsquo; locations.\u003c/p\u003e\n\u003cp\u003eWe piloted the survey in our study area in April 2023 and collected data between July and September 2023. Pilot responses were included in analysis since the survey only needed minor changes post-pilot. We obtained informed written consent from all respondents before starting the survey. This study was granted ethics approval by Imperial College London\u0026rsquo;s Science Engineering Technology Research Ethics Committee (SETREC reference number 22IC7888).\u003c/p\u003e\n\u003ch2\u003e2.4 Analysis\u003c/h2\u003e\n\u003cp\u003eOur analysis focuses on decisions to create mixed native woodland as a proxy for the adoption of forest restoration initiatives. Other types of woodland creation can be considered restorative and are possible in our study area (i.e. via other types of woodland creation schemes or voluntarily without subsidy). Despite potential similarities in landholders\u0026rsquo; motivations when creating different woodland types, we believe decisions to create mixed native woodland creation, given their superior potential for ecosystem restoration (Di Sacco et al., 2021; Gann et al., 2019), can provide relevant theoretical insights for all forest restoration initiatives.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccordingly, we differentiate between landholders who declared that they or their organisations had (adopters (n=31)) or had not (non-adopters (n=46)) created at least one mixed native woodland on the landholding they represented since their management and/or occupancy started. Of the 48 respondents that had created new woodland, many had done so repeatedly. However, 17 (35%) were non-adopters since they had created woodland but not mixed native one yet.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur analysis focuses on the relative advantage, compatibility, complexity, observability, and trialability attributes of our theoretical framework and the variables within them (focus variables) since these explain most of the variability of adoption decisions according to DOIT (Rogers, 2003).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe aim of our analysis is inference (Tredennick et al., 2021), meaning we aim to find focus variables with credible, non-zero coefficients, and thus a meaningful association with the adoption of mixed native woodland creation initiatives, given the data. We also aim to determine the direction (positive or negative) of credible associations and to assess whether they align with our DOIT based a priori hypotheses (Table 2).\u003c/p\u003e\n\u003cp\u003eWe developed two models, one causal and another statistical to answer these questions.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.4.1 Causal model\u003c/h3\u003e\n\u003cp\u003eFollowing best practice guidance for causal inference (McElreath, 2020), we outlined our causal model using a Directed Acyclic Graph (DAG)\u0026nbsp;\u0026mdash;\u0026nbsp;also known as a Causal Diagram or Causal Bayesian Network\u0026nbsp;\u0026mdash;\u0026nbsp;using the \u0026lsquo;dagitty\u0026rsquo; R package (Textor et al., 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur causal model\u0026rsquo;s DAG describes the hypothesised causal pathways between our outcome of interest (adoption of mixed native woodland creation initiatives) and its predictors (DOIT framework attributes and the variables within them), as well as causal links between the predictors themselves (Fig. 1). We based our DAG on DOIT and the knowledge of Scottish woodland creation we gained through research and fieldwork. For example, we link extension support to knowledge in our DAG, since DOIT suggests promotional efforts influence the discovery and understanding of innovations among potential adopters (Rogers, 2003).\u003c/p\u003e\n\u003cp\u003eFull justifications for our causal pathways are available in supporting information four.\u003c/p\u003e\n\u003cp\u003eThe causal model allows us to identify which variables we must control for in our statistical model to minimise bias in estimating the causal effects of interest. Given our DAG and our focus variables, we identified the variables associated to the \u0026apos;Decision making\u0026apos; attribute as the minimum adjustment needed to control our estimates bias. Thus, included them in our statistical model \u0026nbsp;(Pearl, 2009). Moreover, using a theory-based causal model and variable selection, we aimed to limit the likelihood of spurious findings (Smaldino \u0026amp; McElreath, 2016) and enhance the replicability of our study (Muthukrishna \u0026amp; Henrich, 2019).\u003c/p\u003e\n\u003ch3\u003e2.4.2 Statistical model\u003c/h3\u003e\n\u003cp\u003eGiven that our outcome of interest, mixed native woodland creation, is binary, we developed a Bernoulli distributed logistic regression implemented within a Bayesian framework (Equation 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur focus variables (i.e. those associated to the relative advantage, compatibility, complexity, observability, and trialability attributes of our theoretical framework) enter the model as predictors. Whilst variables associated to the \u0026ldquo;Decision making\u0026rdquo; attribute enter the model as controls to account for their biasing impact on our focus variable effect estimates according to our causal model (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe measured most statistical model variables as Likert items. We modelled these as ordered factors with a monotonic effect\u0026nbsp;\u0026mdash;\u0026nbsp;consistent positive or negative effect that varies in size across adjacent variable levels\u0026nbsp;\u0026mdash;\u0026nbsp;on the outcome (B\u0026uuml;rkner \u0026amp; Charpentier, 2020).\u0026nbsp;Only the \u0026ldquo;Innovation decision\u0026rdquo; variable enters the model as a non-ordered factor with three independent levels: \u0026quot;By a single individual\u0026quot;; \u0026quot;Collectively, by all members of the organisation\u0026quot;; and \u0026quot;By a select group of individuals within the organisation\u0026quot;.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eWe estimated the Highest Density Interval (HDI) for each estimand. The HDI encompasses all values in the posterior distribution with the highest probability density (i.e. credibility). In other words, values inside the HDI are more credible than those outside it (Kruschke, 2014). When interpreting model outputs, we considered any parameter estimate whose 90% HDI did not overlap with zero to have a credible effect on the outcome. This equates to at least a 0.95 probability of a true effect given our model and data (McElreath, 2020).\u003c/p\u003e\n\u003cp\u003eOur model estimates and diagnostic tests are available in supporting information six.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2.\u003c/strong\u003e List of variables entering our model.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"678\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable (DOIT Element)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDOIT Component\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDOIT Attribute\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA priori effect direction*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eAdoption of mixed native woodland creation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eBinary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors- Estimands\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eInitial cost - Money\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eInitial cost - Time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePerceived risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEnvironmental benefit - Carbon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEnvironmental benefit \u0026ndash; Habitat for wildlife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEnvironmental benefit \u0026ndash; Diversity of species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEnvironmental benefit \u0026ndash; Water quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEnvironmental benefit \u0026ndash; Flooding prevention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEnvironmental benefit \u0026ndash; Soil quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEconomic benefit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eSocial benefit \u0026ndash; Community well-being\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eSocial benefit \u0026ndash; Jobs created by landholding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eSocial prestige\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRelative advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eCompatibility with values and beliefs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCompatibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eCompatibility with needs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCompatibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eCompatibility with current practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCompatibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEase of understanding - Ground preparations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEase of understanding - Control herbivore damage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEase of understanding - Planting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEase of doing - Ground preparations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEase of doing - Control herbivore damage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eEase of doing - Planting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eVisibility of results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eVisibility of practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eReversibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInnovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors - Controls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eInnovation decision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAdopter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDecision making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eNominal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNon-monotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eFormalisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAdopter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDecision making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOrdinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMonotonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;*A priori effect direction hypotheses based on Diffusion of Innovations Theory (Rogers, 2003).\u003c/p\u003e\n\u003ch3\u003e2.4.3 Conditional effects estimation\u003c/h3\u003e\n\u003cp\u003eFor each predictor with a credible effect on the outcome, we calculated the conditional effect of a single unit increase in that predictor on the probability of having adopted mixed native woodland creation initiatives. Conditional effects quantify these probabilities maintaining the parameter of focus\u0026rsquo; full range of values, whilst holding all other predictors constant. For example, when estimating the conditional effect of \u0026ldquo;compatibility with current land use practices\u0026rdquo; (Fig. 3A), said predictor could take any value in its range (\u0026ldquo;Strongly disagree\u0026rdquo; to \u0026ldquo;Strongly agree\u0026rdquo;), whilst all other predictors remained constant. We held predictors measured with Likert items constant at their neutral value (e.g. Neither agree nor disagree) and \u0026ldquo;Innovation decision\u0026rdquo; constant at \u0026ldquo;Collectively, by all members of the organisation\u0026rdquo;, the most common response.\u003c/p\u003e\n\u003ch2\u003e2.5 Qualitative analysis\u003c/h2\u003e\n\u003cp\u003eOur survey includes an open-ended question designed to examine why adopters decided to create new woodland on their land. We analysed responses to this question using inductive coding (Thomas, 2006), developing codes for the justifications given by respondents to create woodlands as we read their responses. For example, we developed the codes \u0026ldquo;Grant income\u0026rdquo; and \u0026ldquo;Using poor land\u0026rdquo; after reading a response listing grant aid and using poor pasture as the main reasons to create woodland. We reused these codes whenever similar reasons were mentioned in other responses. We analysed the final set of codes by counting their mentions.\u003c/p\u003e"},{"header":"3 RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Sample\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Landholding types, tenure, sizes and uses\u003c/h2\u003e\n \u003cp\u003eOur sample is dominated by landholdings of less than 1,250 acres (~\u0026thinsp;505 hectares) (70%), managed for profit (83%), for ten years or more (in some case much longer) (88%), by family organisations (83%) of less than five members (70%).\u003c/p\u003e\n \u003cp\u003eMost respondents (92%) were landowners directly managing the landholding they referred to in their responses. Some landowners (24%) also acted as landlords (21%) and/or licensors (3%) to others, whilst some tenanted (13%) or crofted (1%) other land. Only 7% of all respondents were tenants (3%) and/or crofters (4%) with no ownership, but powers to decide how land was used.\u003c/p\u003e\n \u003cp\u003eGenerally, decisions to create woodland had been or would be made as a group (62%), either collectively by all member of the organisation (45%) or by a select group of individuals (17%). For a third of respondents (33%) these decisions depended on a single individual.\u003c/p\u003e\n \u003cp\u003eMost of the 77 landholdings within our sample are farms (47%) or estates (30%), accompanied by crofts (11%), nature reserves (2%), or other landholding types (9%) such as private homes, and mixed farms and crofts.\u003c/p\u003e\n \u003cp\u003eThe land use most often reported by respondents was farming (86%), followed by forestry (40%) and private enjoyment (38%). Nonetheless, the land uses reported were varied including wildlife conservation (32%), public recreation (27%), game/hunting (26%), horse grazing (3%), holiday lets (3%), fishing (1%), wind farms (1%), land-based crafts (1%), or therapy and meditation (1%). Most landholdings (56%) reported a mix of land uses, with some respondents (13%) reporting six or more distinct uses.\u003c/p\u003e\n \u003cp\u003eIn terms of holding size or extent, although most landholding (70%) were under 1,250 acres, some were much larger with one surpassing 50,000 acres. Likewise, although small organisations (9 or less members) dominated our sample (84%), some respondents (4%) represented large organisations with more than 50 members.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 New woodland creation\u003c/h2\u003e\n \u003cp\u003eThe extent of new woodland (mixed native and others) created was usually under 125 acres (73%), but most commonly under 12 acres (39%). Generally new woodlands were planted (87%), with only a few created through natural regeneration (35%). In some cases, the same landholding used both planting and natural regeneration (33%).\u003c/p\u003e\n \u003cp\u003eOnly six landholders (13%) created woodland without using external funding. The rest funded their woodland creation projects using government funds (73%), non-governmental funds (6%), bank loans (2%) or other sources (4%). Two landholdings (4%) used a mix of funding sources.\u003c/p\u003e\n \u003cp\u003eOur qualitative analysis revealed that the main reasons for woodland creation reported by adopters and non-adopters who had created woodland of any type were similar. Wildlife, using poor land, shelter, and amenity were amongst the top five reasons to create woodland for both groups. A higher proportion of adopters (A) than non-adopters (NA) mentioned: wildlife (A 39%, NA 24%); shelter (A 26%, NA 18%); diversifying habitats (A 16%, NA 12%) and finances (A 13%, NA 6%) as reasons to create woodland. Whilst the opposite was true for: amenity (A 13%, NA 29%); using poor land (A 26%, NA 29%); business diversification (A 3%, NA 18%); aesthetics (A 13%, NA 18%); grant income (A 13%, NA 18%); environment (A 13%, NA 18%); firewood (A 3%, NA 6%); and legacy (A 3%, NA 6%).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3 Existing woodland\u003c/h2\u003e\n \u003cp\u003eSeventy percent (n\u0026thinsp;=\u0026thinsp;54) of the 77 landholdings in our sample contained existing woodland, either before new woodland (mixed native and others) was created (61%) or at present if no new woodlands had been created (39%). Most of those landholdings (59%) had less than 125 acres of existing woodland.\u003c/p\u003e\n \u003cp\u003eMixed native and predominantly native woodland were the most common types of existing woodland, being present in 50% and 37% of landholdings with existing woodland respectively. Thirty-one percent (n\u0026thinsp;=\u0026thinsp;17) of landholdings with existing woodland contained more than one type of existing woodland (e.g. native and non-native).\u003c/p\u003e\n \u003cp\u003eMost landholdings with existing woodland (mixed native and others) managed it for timber production, mainly through even aged clear-fells (41%), but also through uneven aged regular or irregular fells (28%), and coppicing (4%). Hunting (11%), as well as wildlife conservation through either active (26%) or passive (26%) management, were also common existing woodland management objectives. Thirty-three percent of landholdings with existing woodland did not manage it at all, whilst 4% managed some patches but not others. Thirty-nine percent of landholdings with existing woodland had a mix of management objectives.\u003c/p\u003e\n \u003cp\u003eFull sample descriptive statistics are available in supporting information five.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Statistical model results\u003c/h2\u003e\n \u003cp\u003eOur analysis provides strong evidence that decisions to adopt mixed native woodland creation initiatives are positively influenced by landholder\u0026rsquo;s expectations of changes to soil quality and habitat for wildlife, and perceived compatibility with current land use practices. Conversely, landholders\u0026rsquo; perceived ease of understanding interventions to protect new woodland from herbivore damage (herbivore controls) is negatively associated with decisions to adopt mixed native woodland creation initiatives (Fig. 2). Ten thousand draws from the posterior distribution indicate a greater than 0.95 probability of those four associations, as highlighted by the lack of overlap between their 90% HDIs and the zero line in Fig. 2.\u003c/p\u003e\n \u003cp\u003eThe probability of having adopted mixed native woodland creation initiatives increases with the perceived compatibility with land use practices. All else held constant, respondents who agreed or strongly agreed with the statement \u0026ldquo;Creating woodland matches our land use practices\u0026rdquo; have a 12% (90% HDI 0\u0026ndash;40%) and a 23% (90% HDI 0\u0026ndash;73%) mean probability of having adopted respectively (Fig. 3A, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe likelihood of adopting mixed native woodland creation initiatives also rises with expectations of changes in wildlife habitat. Holding all other factors constant, the mean probability of having adopted mixed native woodland creation initiatives increases by 34%, from 2% (90% HDI 0\u0026ndash;4%) for landholders expecting great declines in wildlife habitat due to woodland creation, to 36% (90% HDI 0\u0026ndash;90%) for those expecting great increases (Fig. 3C, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRespondents expecting larger improvements in soil quality were also more likely to have adopted, with the mean probability of having adopted mixed native woodland creation initiatives increasing from 2% (90% HDI 0\u0026ndash;4%) for landholders expecting great declines, to 18% (90% HDI 0\u0026ndash;62%) for those expecting great increases in soil quality (Fig. 3D, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFinally, the probability of having adopted mixed native woodland creation interventions decreases as the perceived ease of understanding herbivore controls increases. In this case, and perhaps counterintuitively, respondents who saw herbivore controls as \u003cem\u003e\u0026ldquo;Very difficult\u0026rdquo;\u003c/em\u003e or \u003cem\u003e\u0026ldquo;Difficult\u0026rdquo;\u003c/em\u003e to understand were the most likely to have adopted, with a 28% (90% HDI 0\u0026ndash;87%) and a 15% (90% HDI 0\u0026ndash;57%) probability of having adopted respectively. Whilst those who thought controlling herbivores would be easy or very easy have a 1.2% (90% HDI 0\u0026ndash;3%) and 0.6% (90% HDI 0\u0026ndash;1%) mean probability of having adopted (Fig. 3B, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eConditional effect estimates for credible predictors according to our model and data.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eProbability of having adopted\u003c/p\u003e\n \u003cp\u003emixed native creation initiatives\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLikert response level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e50% HDI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[Lower, Upper]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e90% HDI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[Lower, Upper]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eCompatibility with land use practices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly agree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.73]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eExpected changes to habitat for wildlife\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecrease greatly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecrease slightly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncrease slightly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.62]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncrease greatly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eExpected changes to soil quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecrease greatly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecrease slightly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncrease slightly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.38]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncrease greatly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.62]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eEase of understanding intervention to protect new woodland from herbivore damage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery difficult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.87]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifficult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEasy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery easy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.00, 0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eModel and conditional effect estimates are available in supporting information six.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThere is now wide agreement concerning the need to increase rates and extents of ecosystem restoration worldwide. Success in delivering restoration schemes depends heavily on the uptake of ecosystem restoration initiatives by landholders. In this research, we focused on forest restoration and undertook a DOIT based exploration of the factors driving the uptake of mixed native woodland creation in and around the Cairngorms National Park in Scotland. We found that landholders were more likely to have created mixed native woodland if they believed doing so was compatible with their land use practices or would increase habitat for wildlife or soil quality. Landholders that had created mixed native woodland were more likely to find herbivore controls difficult to understand. These findings corroborate effects hypothesised by DOIT for these attributes, except for the ease of understanding herbivore controls, which according to DOIT should have a positive effect on adoption (Table\u0026nbsp;2). We attribute this discrepancy between our results and theory to the influence of landholders\u0026rsquo; previous woodland creation experiences, since many (71%) adopters had created other woodlands, potentially learning about the challenges of herbivore controls as part of this process. Our results exemplify how DOIT could be used to understand forest restoration decisions and suggest UK mixed native woodland creation could be boosted by modifying current woodland creation schemes to (i) emphasise woodland\u0026rsquo;s compatibility with other land uses; (ii) foster flexibility within woodland creation actions, so landholders can more easily adapt them to their circumstances and needs; (iii) mainstream easy-to-understand evidence-based comparisons of the potential benefits of different woodland types, so landholders can make better informed choices; and (iv) support landholders\u0026rsquo; knowledge and management needs, particularly with respect to herbivore control tasks.\u003c/p\u003e\u003cp\u003eWe found compatibility with landholders\u0026rsquo; land use practices was positively associated to decisions to create mixed native woodland. This is congruent with DOIT, previous woodland creation studies in the UK (Crabtree et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Wynne-Jones, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and studies on the diffusion of biodiversity conservation initiatives around the world (e.g. Jagadish et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Romero-de-Diego et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSome land use types are likely to be more compatible with mixed native woodland creation than others. For instance, the dominance of silvopastural systems \u0026mdash; integrating trees and livestock \u0026mdash; in UK agroforestry (~\u0026thinsp;88% of UK agroforestry (Chanarin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)), suggests woodland is more compatible with livestock rather than arable farming.\u003c/p\u003e\u003cp\u003eSimilarly, landholdings with a game management and/or hunting interest might find woodland more or less compatible with their current land use based on their focal species (e.g. Duckworth et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ewald \u0026amp; Gibbs, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Oldfield et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). For example, pheasant shoots when compared to grouse moors should perceive mixed native woodland as more compatible, since pheasants mainly live on woodland edges (Sage et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), whilst red grouse is a heathland and moorland specialist (Tharme et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSpecific characteristics of landholdings (e.g. higher prevalence of poorer soils) might also influence landholders\u0026rsquo; perceptions of the compatibility of mixed native woodland with land use practices. For example, in the UK, cereal, general cropping, cattle and sheep farms in Less Favoured Areas \u0026mdash; areas of poorer land where agricultural production is more challenging (Scottish Government, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) \u0026mdash; were the most likely to participate in the Farm Premium Woodland Creation Scheme (Crabtree et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This suggests that perceptions of compatibility might be driven by the lower opportunity costs of woodland creation on less productive land.\u003c/p\u003e\u003cp\u003eSpecific practices within specific land use types, might also determine the compatibility of mixed native woodland with land use practices. For instance, current trends towards the use of larger farm machinery in arable settings (Keller et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) might result in more \u003cem\u003e\u0026ldquo;idle\u0026rdquo;\u003c/em\u003e or \u003cem\u003e\u0026ldquo;odd bits\u0026rdquo;\u003c/em\u003e (e.g. corners where harvesters turn) open for woodland or other land uses.\u003c/p\u003e\u003cp\u003eExpectations that woodland will increase habitat for wildlife or soil quality, were also positively associated with decisions to create mixed native woodland as hypothesised by DOIT (Table\u0026nbsp;2). Intuitively, the chance to utilise less productive land on their holdings and/or promoting wildlife conservation were the two main reasons for woodland creation reported by our respondents based on our qualitative questions. A majority of these responses (75% and 62% respectively) came from landholders who had created mixed native woodland. These results agree with past reviews of woodland creation in the UK, which found prevailing motives for woodland creation and management to be consistently related to natural and environmental enhancement, as well as personal enjoyment, whilst productive and economic motivations tended to be lower (Hemery et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lawrence et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lawrence \u0026amp; Dandy, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Overall, these results suggest that landholders have clear woodland creation objectives and create the type of woodland they think is best to meet them.\u003c/p\u003e\u003cp\u003eCounter to DOIT, which suggests easier to understand innovations are more likely to be adopted (Rogers, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), landholders who had created mixed native woodland were more likely to find herbivore controls difficult to understand. Further research is needed to detail the mechanisms leading to this result, but our data suggest our landholders\u0026rsquo; previous woodland creation experiences (71% of adopters had created other woodland/s), may have demonstrated the complexity of herbivore controls to them. High levels of browsing by herbivores can slow or stop the natural or assisted regeneration and expansion of woodlands, and reduce the survival rates of new plantings (C\u0026ocirc;t\u0026eacute; et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This is a major constraint to woodland creation in the UK (Hemery et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with sheep and deer impacts being particularly significant in our study area (Gullett et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hare et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hobbs, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Newton et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Herbivore controls such as culling, fencing, tree-guards or repellent chemicals are diverse, and their success depends on several aspects (Hodge \u0026amp; Pepper, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). For example, effective fencing requires considering factors such as a site\u0026rsquo;s topography, climate, and herbivore species, as well as potential impacts on other wildlife (e.g. capercaillie, or black grouse collisions), and adapting the fence's design (layout, type, height, etc.) to them (Trout \u0026amp; Pepper, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Our results suggest, landholders often misjudge the difficulty of understanding such nuances of herbivore control until they experience them first-hand. However, learning about these challenges does not stop them from creating woodland again, supporting DOIT\u0026rsquo;s notion that knowledge lessens uncertainty facilitating adoption (Rogers, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGenerally, mixed native woodlands created by our landholders were small (\u0026lt;\u0026thinsp;12 acres), created using government grant schemes and planted rather than naturally regenerated. This highlights the key role of government woodland creation grant funding in the UK (Raum, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Raum \u0026amp; Potter, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and our landholders\u0026rsquo; preference for active instead of passive woodland creation methods. While the small size of newly created woodlands hints to prevalent barriers to woodland creation such as: lack of land, insufficient funding (Hemery et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) or perceptions of woodland as an inferior, aesthetically untidy, and irreversible land use, that contravenes traditional practices and takes too long to yield benefits (Lawrence et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lawrence \u0026amp; Dandy, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Limitations\u003c/h2\u003e\u003cp\u003eOur study\u0026rsquo;s main limitation is that we used non-probabilistic sampling methods, hence our results only apply to our sample and have limited generalisability to broader populations (Stratton, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This is mainly due to selection bias (Catalogue of Bias Collaboration, Nunan, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), since our respondent were self-selected. Future studies could avoid this by using probabilistic sampling if robust unified registries of land ownership for their focus area exist and are accessible.\u003c/p\u003e\u003cp\u003eSurvey studies can incur in a broad range of biases because of poor question and/or questionnaire design, and/or administration of the survey (Choi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). We mitigated design flaws through careful crafting of our questions and questionnaire, and by piloting them.\u003c/p\u003e\u003cp\u003eBiases linked to the administration of our survey were harder to mitigate.\u003c/p\u003e\u003cp\u003eRecall bias (Catalogue of Bias Collaboration, Spencer, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) is a particular risk in our study, since when respondents had created woodland, we relied on their memories \u0026mdash; which may be inaccurate \u0026mdash; for some questions. To mitigate recall bias, we piloted our surveys to ensure their layout and phrasing was effective and used mainly close ended questions (Bernard et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRespondents answering questions in a way they perceived as socially desirable, taking shortcuts (i.e. satisficing), and tending to select the ends (e.g. \u0026ldquo;Strongly disagree\u0026rdquo; or \u0026ldquo;Strongly agree\u0026rdquo;) or centres (e.g. \u0026ldquo;Neither agree nor disagree\u0026rdquo;) of our Likert items (Choi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) are also risks in our study. We mitigated these risks through piloting, ensuring anonymity, and the use of simple neutral questions and a short questionnaire to avoid respondents\u0026rsquo; fatigue.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Implications for policy and practice\u003c/h2\u003e\u003cp\u003eOur results reaffirm the importance of considering landholders perspectives when designing scalable ecosystem restoration programs (Ambrose-Oji et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lawrence \u0026amp; Dandy, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mascia \u0026amp; Mills, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mills et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tedesco, Brancalion, et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Through our theory-based approach, we aim for our findings to be easily scrutinised by future studies and to contribute to a DOIT based understanding of forest and ecosystem restoration adoption through the accumulation of comparable studies (Muthukrishna \u0026amp; Henrich, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Pending said scrutiny to confirm their generalisability to other areas and samples, our findings already point to a series of improvements to the design and rollout of current and future woodland creation schemes.\u003c/p\u003e\u003cp\u003eFirst, our findings suggest governments should improve access to easy-to-understand evidence-based comparisons of the environmental, social, and economic benefits of different woodland types (i.e. single and multi-species native, single and multi-species non-native, or mixed non-native and native woodland). Some frameworks that do this have already been proposed (e.g. Baral et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Nonetheless, UK focused research on woodland expansion benefits seems to be skewed towards biodiversity and regulating ecosystem services (carbon sequestration, flood control, etc.) and limited for other services (Burton et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Expanding this evidence-base seems essential for better policy design and to facilitate more robust decision making by landholders, who according to our findings have clear woodland creation motivations that directly inform the type of woodland they create.\u003c/p\u003e\u003cp\u003eWe stress that comparisons of benefits of different woodlands should always be caveated by at least the woodland\u0026rsquo;s location, extent, and the habitat/land use it replaces (e.g. Bradfer-Lawrence et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Friggens et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Matthews et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Monger et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Stephens \u0026amp; Wagner, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Ultimately those and the woodland type created are the main factors that can be controlled at a woodland\u0026rsquo;s creation stage, with woodland benefits contingent on them and on how the woodland is managed after creation. This is particularly important given our landholder\u0026rsquo;s apparent focus on using poor land to create woodland. Poor land might be less productive (e.g. lower soil quality) or harder to work/access (field corners, stony soils, steep slopes, etc.) for agriculture, but can host important ecosystems like grasslands or peatlands. Conversion of these ecosystems to woodland will negatively affect biodiversity and climate change mitigation (e.g. Bradfer-Lawrence et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Di Sacco et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Friggens et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Matthews et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, woodland expansion schemes should increase their emphasis on the compatibility of woodlands (mixed native and others) with other land uses such as agriculture, hunting, or recreation. Such efforts have been ongoing and continue in all UK nations, with a vast array of online and in-person support from governmental and non-governmental sources highlighting the compatibility of trees with other land uses, particularly farming (e.g. Forestry Commission, n.d.; Forestry Commission Wales, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Scottish Forestry, n.d.; Woodland Trust, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, official sources showing persistently low rates of woodland creation (e.g. Forestry Research, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), together with our findings, suggest that better targeting of these efforts could improve rates of adoption.\u003c/p\u003e\u003cp\u003eThird, the flexibility of a scheme\u0026rsquo;s woodland creation options is also key to enhance the compatibility of woodland with other land uses. Currently, although bound by the UK Forestry Standard (Forestry Commission, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), landholders have considerable leeway in matters such as the size, content, or method of creating their woodland (DAERA, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Scottish Government, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; UK Government, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Welsh government, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This level of flexibility should be maintained, subject to appropriate benchmarks, since it most likely helps landholders adapt woodland to their circumstances and needs increasing its compatibility.\u003c/p\u003e\u003cp\u003eFinally, woodland creation schemes need to consider how to better support landholders\u0026rsquo; knowledge needs, particularly regarding herbivore control tasks. In Scotland, most woodland creation projects require some herbivore control measures, such as fencing or culling (Gullett et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hobbs, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Our results imply landholders struggle to understand how to implement these controls. Suggesting that woodland creation rates could improve if better support for herbivore controls was available.\u003c/p\u003e\u003cp\u003eEcosystem restoration is a process defined by change. Ultimately change to the use of a piece of land, but first and foremost to the perspectives and practices of those managing it and their contexts. Targets and economic incentives are common and essential strategies to foster that change and achieve ecosystem restoration at scale (Tedesco, L\u0026oacute;pez-Cubillos, et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). But our research and that of others (e.g. Tedesco et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) suggests a narrow focus on them will almost certainly lead to failure. Instead, a more holistic approach that considers landholders\u0026rsquo; goals, their context, and their perceptions of ecosystem restoration initiatives is needed. Grounding this holistic approach on suitably broad theories of human decision making able to capture its dynamism and two-way interplay with its context \u0026mdash; such as DOIT \u0026mdash; would deepen our understanding of restoration decisions (Schill et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and help build a robust knowledge base through the accumulation of comparable studies (Muthukrishna \u0026amp; Henrich, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The restoration strategies resulting from this approach will be different for different places. With our results suggesting that in contexts like the UK\u0026rsquo;s, where targets and economic incentives are established, the focus should be on: (i) emphasising and facilitating restoration\u0026rsquo;s compatibility with other land-uses; (ii) mainstreaming evidence-based comparisons of the benefits and trade-offs of different restoration actions; and (iii) supporting landholders\u0026rsquo; knowledge needs. Overall, this more holistic approach should allow for more robust design and re-design of ecosystem restoration programs. Thus, increasing their adoption and, provided that safeguards against potential negative outcomes (environmental, social or economic) are observed, contributing to the realisation of ecosystem restoration\u0026rsquo;s multiple benefits at scale.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003ethis study was granted ethics approval by Imperial College London\u0026apos;s Science Engineering Technology Research Ethics Committee (SETREC reference number 22IC7888).\u003c/p\u003e\n\u003cp\u003eAdditionally, I confirm all study participants provided informed consent before taking part in our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlvaro Roel Bellot:\u003c/strong\u003e Conceptualisation, Data curation, Formal Analysis, Investigation, Methodology, Project administration, Validation, Visualisation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eMatthew Clark:\u003c/strong\u003e Formal Analysis, Methodology, Validation, Visualisation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eArundhati Jagadish:\u003c/strong\u003e Conceptualisation, Methodology, Validation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eClive Potter:\u003c/strong\u003e Validation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eMorena Mills:\u003c/strong\u003e Conceptualisation, Funding acquisition, Methodology, Supervision, Validation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst and foremost a heartfelt thank you to all our study participants, without their kind contribution this research would have not been possible. Thanks to Angela Dean and Ans Vercammen who helped refine our questions design; Jeffrey Andrews who helped refine our statistical model; the administrative staff at Imperial College London\u0026rsquo;s Centre for Environmental Policy who helped with project logistics; and the Imperial College London\u0026rsquo;s Research Governance and Integrity Team who reviewed our ethics application. A final and special thanks to our first host during our second visit to our study area, who shall remain anonymous, but went above and beyond hosting and supporting us.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was part of a PhD studentship funded by the UK\u0026rsquo;s Research and Innovation (UKRI) Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnerships (UKRI Project reference number: 2521695). Morena Mills and Matthew Clark thank the Leverhulme Trust for the research grant: RPG-2021-440.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and R code used in this study are available at: https://github.com/Alvaro-RoelBellot/DriversOfMixedNativeWoodlandCreationScotland\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdams, C., Rodrigues, S. T., Calmon, M., \u0026amp; Kumar, C. (2016). Impacts of large‐scale forest restoration on socioeconomic status and local livelihoods: what we know and do not know. \u003cem\u003eBiotropica\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(6), 731\u0026ndash;744. https://doi.org/10.1111/btp.12385\u003c/li\u003e\n \u003cli\u003eAllek, A., Viany Prieto, P., Korys, K. A., Rodrigues, A. F., Latawiec, A. E., \u0026amp; Crouzeilles, R. (2022).\u0026nbsp;How does forest restoration affect the recovery of soil quality? A global meta-analysis for tropical and temperate regions. In \u003cem\u003eRestoration Ecology\u003c/em\u003e. John Wiley and Sons Inc. https://doi.org/10.1111/rec.13747\u003c/li\u003e\n \u003cli\u003eAmbrose-Oji, B., Robinson, J. S., \u0026amp; O\u0026rsquo;Brien, L. (2018). \u003cem\u003eInfluencing behaviour for resilient treescapes: Rapid Evidence Assessment\u003c/em\u003e. https://cdn.forestresearch.gov.uk/2018/11/rea_treescapes.pdf\u003c/li\u003e\n \u003cli\u003eArad\u0026oacute;ttir, \u0026Aacute;. L., Petursdottir, T., Halldorsson, G., Svavarsdottir, K., \u0026amp; Arnalds, O. (2013). Drivers of ecological restoration: Lessons from a century of restoration in Iceland. \u003cem\u003eEcology and Society\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(4). https://doi.org/10.5751/ES-05946-180433\u003c/li\u003e\n \u003cli\u003eAronson, J., \u0026amp; Alexander, S. (2013). Ecosystem restoration is now a global priority: Time to roll up our sleeves. \u003cem\u003eRestoration Ecology\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(3), 293\u0026ndash;296. https://doi.org/10.1111/rec.12011\u003c/li\u003e\n \u003cli\u003eBaral, H., Guariguata, M. R., \u0026amp; Keenan, R. J. (2016). A proposed framework for assessing ecosystem goods and services from planted forests. \u003cem\u003eEcosystem Services\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 260\u0026ndash;268. https://doi.org/10.1016/j.ecoser.2016.10.002\u003c/li\u003e\n \u003cli\u003eBernard, H. R., Killworth, P., Kronenfeld, D., \u0026amp; Sailer, L. (1984). The Problem of Informant Accuracy: The Validity of Retrospective Data. In \u003cem\u003eSource: Annual Review of Anthropology\u003c/em\u003e (Vol. 13). https://www.jstor.org/stable/2155679\u003c/li\u003e\n \u003cli\u003eBradfer-Lawrence, T., Finch, T., Bradbury, R. B., Buchanan, G. M., Midgley, A., \u0026amp; Field, R. H. (2021). The potential contribution of terrestrial nature-based solutions to a national \u0026lsquo;net zero\u0026rsquo; climate target. \u003cem\u003eJournal of Applied Ecology\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(11), 2349\u0026ndash;2360. https://doi.org/10.1111/1365-2664.14003\u003c/li\u003e\n \u003cli\u003eB\u0026uuml;rkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. \u003cem\u003eJournal of Statistical Software\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e. https://doi.org/10.18637/jss.v080.i01\u003c/li\u003e\n \u003cli\u003eB\u0026uuml;rkner, P. C., \u0026amp; Charpentier, E. (2020). Modelling monotonic effects of ordinal predictors in Bayesian regression models. \u003cem\u003eBritish Journal of Mathematical and Statistical Psychology\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(3), 420\u0026ndash;451. https://doi.org/10.1111/bmsp.12195\u003c/li\u003e\n \u003cli\u003eBurton, V., Moseley, D., Brown, C., Metzger, M. J., \u0026amp; Bellamy, P. (2018). Reviewing the evidence base for the effects of woodland expansion on biodiversity and ecosystem services in the United Kingdom. \u003cem\u003eForest Ecology and Management\u003c/em\u003e, \u003cem\u003e430\u003c/em\u003e(April), 366\u0026ndash;379. https://doi.org/10.1016/j.foreco.2018.08.003\u003c/li\u003e\n \u003cli\u003eCairngorms National Park Authority. (2023). \u003cem\u003eCairngorms National Park Authority\u003c/em\u003e. https://cairngorms.co.uk/\u003c/li\u003e\n \u003cli\u003eCatalogue of Bias Collaboration, Nunan, D., Bankhead, C., \u0026amp; Aronson, J. (2017). \u003cem\u003eSelection bias\u003c/em\u003e. Catalogue of Bias. http://www.catalogofbias.org/biases/selection-bias/\u003c/li\u003e\n \u003cli\u003eCatalogue of Bias Collaboration, Spencer, E. A., Brassey, J., \u0026amp; Mahtani, K. (2017). \u003cem\u003eRecall bias\u003c/em\u003e. Catalogue of Bias. https://www.catalogueofbiases.org/biases/recall-bias\u003c/li\u003e\n \u003cli\u003eCBD. (2022). \u003cem\u003eKunming-Montreal Global Biodiversity Framework\u003c/em\u003e. https://www.cbd.int/conferences/post20202CBD/WG8J/11/7,CBD/SBSTTA/23/9,CBD/SBSTTA/24/12andCBD/SBI/3/21,respectively.\u003c/li\u003e\n \u003cli\u003eChanarin, G., Lewis, D., Silcock, P., \u0026amp; Thomas, C. (2022). \u003cem\u003eWoodland and trees in the farmed landscape: Towards a diverse, resilient and vibrant agroforestry and farm woodland economy for the UK.\u003c/em\u003e https://www.soilassociation.org/media/24798/woodland-and-trees-in-farmed-landscapes-report.pdf\u003c/li\u003e\n \u003cli\u003eChoi, B. C., Pak, A. W., \u0026amp; Cdc, for. (2005). \u003cem\u003eA Catalog of Biases in Questionnaires\u003c/em\u003e. http://www.cdc.gov/pcd/issues/2005/jan/ 04_0050.htm\u003c/li\u003e\n \u003cli\u003eCook-Patton, S. C., Drever, C. R., Griscom, B. W., Hamrick, K., Hardman, H., Kroeger, T., Pacheco, P., Raghav, S., Stevenson, M., Webb, C., Yeo, S., \u0026amp; Ellis, P. W. (2021). Protect, manage and then restore lands for climate mitigation. \u003cem\u003eNature Climate Change\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(12), 1027\u0026ndash;1034. https://doi.org/10.1038/s41558-021-01198-0\u003c/li\u003e\n \u003cli\u003eC\u0026ocirc;t\u0026eacute;, S. D., Rooney, T. P., Tremblay, J. P., Dussault, C., \u0026amp; Waller, D. M. (2004). Ecological impacts of deer overabundance. In \u003cem\u003eAnnual Review of Ecology, Evolution, and Systematics\u003c/em\u003e (Vol. 35, pp. 113\u0026ndash;147). https://doi.org/10.1146/annurev.ecolsys.35.021103.105725\u003c/li\u003e\n \u003cli\u003eCrabtree, B., Chalmers, N., \u0026amp; Barron, N.-J. (1998). Information for Policy Design: Modelling Participation in a Farm Woodland Incentive Scheme. \u003cem\u003eJournal of Agricultural Economics\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(3), 306\u0026ndash;320. https://doi.org/10.1111/j.1477-9552.1998.tb01274.x\u003c/li\u003e\n \u003cli\u003eDAERA. (2024). \u003cem\u003eDAERA Forestry Grants\u003c/em\u003e. https://www.daera-ni.gov.uk/articles/daera-forestry-grants\u003c/li\u003e\n \u003cli\u003eDi Sacco, A., Hardwick, K. A., Blakesley, D., Brancalion, P. H. S., Breman, E., Cecilio Rebola, L., Chomba, S., Dixon, K., Elliott, S., Ruyonga, G., Shaw, K., Smith, P., Smith, R. J., \u0026amp; Antonelli, A. (2021). Ten golden rules for reforestation to optimize carbon sequestration, biodiversity recovery and livelihood benefits. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(7), 1328\u0026ndash;1348. https://doi.org/10.1111/gcb.15498\u003c/li\u003e\n \u003cli\u003eDuckworth, J. C., Firbank, L. G., Stuart, R. C., \u0026amp; Yamamoto, S. (2003). Changes in land cover and parcel size of British lowland woodlands over the last century in relation to game management. \u003cem\u003eLandscape Research\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(2), 171\u0026ndash;182. https://doi.org/10.1080/0142639032000070184\u003c/li\u003e\n \u003cli\u003eElias, M., Kandel, M., Mansourian, S., Meinzen-Dick, R., Crossland, M., Joshi, D., Kariuki, J., Lee, L. C., McElwee, P., Sen, A., Sigman, E., Singh, R., Adamczyk, E. M., Addoah, T., Agaba, G., Alare, R. S., Anderson, W., Arulingam, I., Bellis, S.\u0026nbsp;Ḵung V., \u0026hellip; Winowiecki, L. (2021). Ten people-centered rules for socially sustainable ecosystem restoration. \u003cem\u003eRestoration Ecology\u003c/em\u003e. https://doi.org/10.1111/rec.13574\u003c/li\u003e\n \u003cli\u003eEwald, J., \u0026amp; Gibbs, S. (2019). \u003cem\u003eGamekeepers: conservation and wildlife 2019 survey\u003c/em\u003e. https://www.gwct.org.uk/media/1095291/NGOGWCT-Survey2019-final.pdf\u003c/li\u003e\n \u003cli\u003eFisher, J. A., Cavanagh, C. J., Sikor, T., \u0026amp; Mwayafu, D. M. (2018). Linking notions of justice and project outcomes in carbon offset forestry projects: Insights from a comparative study in Uganda. \u003cem\u003eLand Use Policy\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e, 259\u0026ndash;268. https://doi.org/10.1016/j.landusepol.2017.12.055\u003c/li\u003e\n \u003cli\u003eFleischman, F., Basant, S., Chhatre, A., Coleman, E. A., Fischer, H. W., Gupta, D., G\u0026uuml;neralp, B., Kashwan, P., Khatri, D., Muscarella, R., Powers, J. S., Ramprasad, V., Rana, P., Solorzano, C. R., \u0026amp; Veldman, J. W. (2020). Pitfalls of Tree Planting Show Why We Need People-Centered Natural Climate Solutions. In \u003cem\u003eBioScience\u003c/em\u003e (Vol. 70, Issue 11, pp. 947\u0026ndash;950). Oxford University Press. https://doi.org/10.1093/biosci/biaa094\u003c/li\u003e\n \u003cli\u003eForestry Commission. (n.d.). \u003cem\u003eIt\u0026rsquo;s time to branch out How woodland creation benefits your farm\u003c/em\u003e. Retrieved March 29, 2024, from https://assets.publishing.service.gov.uk/media/65a6793996a5ec000d731a38/CFT_its_time_to_branch_out_how_woodland_creation_benefits_your_farm_Jan_24.pdf\u003c/li\u003e\n \u003cli\u003eForestry Commission. (2017). \u003cem\u003eThe UK Forestry Standard\u003c/em\u003e. https://cdn.forestresearch.gov.uk/2023/10/The-UK-Forestry-Standard.pdf\u003c/li\u003e\n \u003cli\u003eForestry Commission Wales. (2012). \u003cem\u003eNew farm woodlands\u003c/em\u003e. https://cdn.naturalresources.wales/media/689799/new-farm-woodlands.pdf?mode=pad\u0026amp;rnd=132098199310000000\u003c/li\u003e\n \u003cli\u003eForestry Research. (2023). \u003cem\u003eForestry Statistics 2023\u003c/em\u003e. https://www.forestresearch.gov.uk/tools-and-resources/statistics/forestry-statistics/\u003c/li\u003e\n \u003cli\u003eFriggens, N. L., Hester, A. J., Mitchell, R. J., Parker, T. C., Subke, J. A., \u0026amp; Wookey, P. A. (2020). Tree planting in organic soils does not result in net carbon sequestration on decadal timescales. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(9), 5178\u0026ndash;5188. https://doi.org/10.1111/gcb.15229\u003c/li\u003e\n \u003cli\u003eGann, G. D., Mcdonald, T., Walder, B., Aronson, J., Nelson, C. R., Jonson, J., Hallett, J. G., Eisenberg, C., Guariguata, M. R., Liu, J., Hua, F., Echeverr\u0026iacute;a, C., Gonzales, E., Shaw, N., Decleer, K., \u0026amp; Dixon, K. W. (2019). \u003cem\u003eInternational principles and standards for the practice of ecological restoration. Second edition: November 2019\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eGarbach, K., \u0026amp; Long, R. F. (2017). Determinants of field edge habitat restoration on farms in California\u0026rsquo;s Sacramento Valley. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, \u003cem\u003e189\u003c/em\u003e, 134\u0026ndash;141. https://doi.org/10.1016/j.jenvman.2016.12.036\u003c/li\u003e\n \u003cli\u003eGriscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., Schlesinger, W. H., Shoch, D., Siikam\u0026auml;ki, J. V., Smith, P., Woodbury, P., Zganjar, C., Blackman, A., Campari, J., Conant, R. T., Delgado, C., Elias, P., Gopalakrishna, T., Hamsik, M. R., \u0026hellip; Fargione, J. (2017). Natural climate solutions. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e, \u003cem\u003e114\u003c/em\u003e(44), 11645\u0026ndash;11650. https://doi.org/10.1073/pnas.1710465114\u003c/li\u003e\n \u003cli\u003eGullett, P. R., Leslie, C., Mason, R., Ratcliffe, P., Sargent, I., Beck, A., Cameron, T., Cowie, N. R., Hetherington, D., MacDonell, T., Moat, T., Moore, P., Teuten, E., \u0026amp; Hancock, M. H. (2023). Woodland expansion in the presence of deer: 30 years of evidence from the Cairngorms Connect landscape restoration partnership. \u003cem\u003eJournal of Applied Ecology\u003c/em\u003e. https://doi.org/10.1111/1365-2664.14501\u003c/li\u003e\n \u003cli\u003eHagger, V., Dwyer, J., \u0026amp; Wilson, K. (2017). What motivates ecological restoration? \u003cem\u003eRestoration Ecology\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(5), 832\u0026ndash;843. https://doi.org/10.1111/rec.12503\u003c/li\u003e\n \u003cli\u003eHare, D., Daniels, M., \u0026amp; Blossey, B. (2021). Public Perceptions of Deer Management in Scotland. \u003cem\u003eFrontiers in Conservation Science\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e. https://doi.org/10.3389/fcosc.2021.781546\u003c/li\u003e\n \u003cli\u003eHemery, G., Petrokofsky, G., Ambrose-Oji, B., Edwards, D., O\u0026rsquo;Brien, L., Tansey, C., \u0026amp; Townsend, M. (2018). \u003cem\u003eShaping the Future of Forestry: Report of the British Woodlands Survey 2017\u003c/em\u003e. https://sylva.org.uk/downloads/BWS2017report.pdf\u003c/li\u003e\n \u003cli\u003eHemery, G., Petrokofsky, G., Ambrose-Oji, B., Forster, J., Hemery, T., \u0026amp; O\u0026rsquo;Brien, L. (2020). \u003cem\u003eAwareness, action, and aspirations in the forestry sector in responding to environmental change: Report of the British Woodlands Survey 2020\u003c/em\u003e. https://sylva.org.uk/downloads/BWS2020-report.pdf\u003c/li\u003e\n \u003cli\u003eHobbs, R. (2009). Woodland restoration in Scotland: Ecology, history, culture, economics, politics and change. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(9), 2857\u0026ndash;2865. https://doi.org/10.1016/J.JENVMAN.2007.10.014\u003c/li\u003e\n \u003cli\u003eHodge, S., \u0026amp; Pepper, H. (1998). \u003cem\u003eThe prevention of mammal damage to trees in woodland\u003c/em\u003e. http://www.forestry.gov.uk\u003c/li\u003e\n \u003cli\u003eHua, F., Bruijnzeel, L. A., Meli, P., Martin, P. A., Zhang, J., Nakagawa, S., Miao, X., Wang, W., McEvoy, C., Pe\u0026ntilde;a-Arancibia, J. L., Brancalion, P. H. S., Smith, P., Edwards, D. P., \u0026amp; Balmford, A. (2022). The biodiversity and ecosystem service contributions and trade-offs of forest restoration approaches. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e376\u003c/em\u003e(6595), 839\u0026ndash;844. https://doi.org/10.1126/science.abl4649\u003c/li\u003e\n \u003cli\u003eIUCN, \u0026amp; Government of Germany. (2011, September 2). \u003cem\u003eBonn Challenge\u003c/em\u003e. http://www.bonnchallenge.org/\u003c/li\u003e\n \u003cli\u003eJagadish, A., Freni-Sterrantino, A., He, Y., O\u0026rsquo; Garra, T., Gecchele, L., Mangubhai, S., Govan, H., Tawake, A., Tabunakawai Vakalalabure, M., Mascia, M. B., \u0026amp; Mills, M. (2024). Scaling Indigenous-led natural resource management. \u003cem\u003eGlobal Environmental Change\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e, 102799. https://doi.org/10.1016/J.GLOENVCHA.2024.102799\u003c/li\u003e\n \u003cli\u003eJagadish, A., Mills, M., \u0026amp; Mascia, M. B. (2021). \u003cem\u003eCatalyzing Conservation at Scale: A Practitioner\u0026rsquo;s handbook (version 0.1)\u003c/em\u003e. https://doi.org/10.5281/zenodo.4894933\u003c/li\u003e\n \u003cli\u003eKaine, G., Edwards, P., Polyakov, M., \u0026amp; Stahlmann-Brown, P. (2023). Who knew afforestation was such a challenge? Motivations and impediments to afforestation policy in New Zealand. \u003cem\u003eForest Policy and Economics\u003c/em\u003e, \u003cem\u003e154\u003c/em\u003e. https://doi.org/10.1016/j.forpol.2023.103031\u003c/li\u003e\n \u003cli\u003eKeller, T., Or, D., by Rattan Lal, E., \u0026amp; Firestone, M. K. (2022). \u003cem\u003eFarm vehicles approaching weights of sauropods exceed safe mechanical limits for soil functioning\u003c/em\u003e. https://doi.org/10.1073/pnas\u003c/li\u003e\n \u003cli\u003eKruschke, J. K. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan, second edition. In \u003cem\u003eDoing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition\u003c/em\u003e. Elsevier Science. https://doi.org/10.1016/B978-0-12-405888-0.09999-2\u003c/li\u003e\n \u003cli\u003eKruschke, J. K. (2021). Bayesian Analysis Reporting Guidelines. In \u003cem\u003eNature Human Behaviour\u003c/em\u003e (Vol. 5, Issue 10, pp. 1282\u0026ndash;1291). Nature Research. https://doi.org/10.1038/s41562-021-01177-7\u003c/li\u003e\n \u003cli\u003eLawrence, A., \u0026amp; Dandy, N. (2014). Private landowners\u0026rsquo; approaches to planting and managing forests in the UK: What\u0026rsquo;s the evidence? \u003cem\u003eLand Use Policy\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e, 351\u0026ndash;360. https://doi.org/10.1016/j.landusepol.2013.09.002\u003c/li\u003e\n \u003cli\u003eLawrence, A., Dandy, N., \u0026amp; Urquhart, J. (2010). \u003cem\u003eLandowners\u0026rsquo; attitudes to woodland creation and management in the UK\u003c/em\u003e. www.forestry.gov.uk/fr/ownerattitudes\u003c/li\u003e\n \u003cli\u003eL\u0026ouml;fqvist, S., Kleinschroth, F., Bey, A., De Bremond, A., Defries, R., Fleischman, F., Lele, S., Martin, D. A., Messerli, P., Meyfroidt, P., Pfeifer, M., Rakotonarivo, S. O., Ramankutty, N., Ramprasad, V., Rana, P., Rhemtulla, J. M., Ryan, C. M., Vieira, I. C. G., Wells, G. J., \u0026amp; Garrett, R. D. (2023). How Social Considerations Improve the Equity and Effectiveness of Ecosystem Restoration. \u003cem\u003eBioScience\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(2), 134\u0026ndash;148. https://doi.org/10.1093/biosci/biac099\u003c/li\u003e\n \u003cli\u003eMahajan, S. L., Jagadish, A., Glew, L., Ahmadia, G., Becker, H., Fidler, R. Y., Jeha, L., Mills, M., Cox, C., DeMello, N., Harborne, A. R., Masuda, Y. J., McKinnon, M. C., Painter, M., Wilkie, D., \u0026amp; Mascia, M. B. (2021). A theory‐based framework for understanding the establishment, persistence, and diffusion of community‐based conservation. \u003cem\u003eConservation Science and Practice\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1). https://doi.org/10.1111/csp2.299\u003c/li\u003e\n \u003cli\u003eMascia, M. B., \u0026amp; Mills, M. (2018). When conservation goes viral: The diffusion of innovative biodiversity conservation policies and practices. \u003cem\u003eConservation Letters\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(3), 1\u0026ndash;9. https://doi.org/10.1111/conl.12442\u003c/li\u003e\n \u003cli\u003eMatthews, K. B., Wardell-Johnson, D., Miller, D., Fitton, N., Jones, E., Bathgate, S., Randle, T., Matthews, R., Smith, P., \u0026amp; Perks, M. (2020). Not seeing the carbon for the trees? Why area-based targets for establishing new woodlands can limit or underplay their climate change mitigation benefits. \u003cem\u003eLand Use Policy\u003c/em\u003e, \u003cem\u003e97\u003c/em\u003e(June), 104690. https://doi.org/10.1016/j.landusepol.2020.104690\u003c/li\u003e\n \u003cli\u003eMcElreath, R. (2020). \u003cem\u003eStatistical Rethinking\u003c/em\u003e. Chapman and Hall/CRC. https://doi.org/10.1201/9780429029608\u003c/li\u003e\n \u003cli\u003eMills, M., Bode, M., Mascia, M. B., Weeks, R., Gelcich, S., Dudley, N., Govan, H., Archibald, C. L., Romero-de-Diego, C., Holden, M., Biggs, D., Glew, L., Naidoo, R., \u0026amp; Possingham, H. P. (2019). How conservation initiatives go to scale. \u003cem\u003eNature Sustainability\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(10), 935\u0026ndash;940. https://doi.org/10.1038/s41893-019-0384-1\u003c/li\u003e\n \u003cli\u003eMonger, F., Spracklen, D. V., Kirkby, M. J., \u0026amp; Willis, T. (2024). Investigating the impact of woodland placement and percentage cover on flood peaks in an upland catchment using spatially distributed \u0026lt;scp\u0026gt;TOPMODEL\u0026lt;/scp\u0026gt;. \u003cem\u003eJournal of Flood Risk Management\u003c/em\u003e. https://doi.org/10.1111/jfr3.12977\u003c/li\u003e\n \u003cli\u003eMuthukrishna, M., \u0026amp; Henrich, J. (2019). A problem in theory. \u003cem\u003eNature Human Behaviour\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(3), 221\u0026ndash;229. https://doi.org/10.1038/s41562-018-0522-1\u003c/li\u003e\n \u003cli\u003eNelson, C. R., Hallet, J. G., Romero Montoya, A. E., Andrade, A., Besacier, C., Boerger, V., Bouazza, K., Chazdon, R., Cohen-Shacham, E., Danano, D., Diederichsen, A., Fernandez, Y., Gann, G. D., Gonzales, E. K., Gruca, M., Guariguata, M. R., Gutierrez, V., Hancock, B., Innecken, P., \u0026hellip; Weidlich, E. W. A. (2024). \u003cem\u003eStandards of practice to guide ecosystem restoration\u003c/em\u003e. FAO; SER; IUCN; https://doi.org/10.4060/cc9106en\u003c/li\u003e\n \u003cli\u003eNewton, A. C., Stirling, M., \u0026amp; Crowell, M. (2001). Current approaches to native woodland restoration in Scotland. \u003cem\u003eBotanical Journal of Scotland\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(2), 169\u0026ndash;195. https://doi.org/10.1080/03746600108685021\u003c/li\u003e\n \u003cli\u003eOldfield, T. E. E., Smith, R. J., Harrop, S. R., \u0026amp; Leader-Williams, N. (2003). Field sports and conservation in the United Kingdom. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e423\u003c/em\u003e(6939), 531\u0026ndash;533. https://doi.org/10.1038/nature01678\u003c/li\u003e\n \u003cli\u003eOliver, C. D., Nassar, N. T., Lippke, B. R., \u0026amp; McCarter, J. B. (2014). Carbon, Fossil Fuel, and Biodiversity Mitigation With Wood and Forests. \u003cem\u003eJournal of Sustainable Forestry\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(3), 248\u0026ndash;275. https://doi.org/10.1080/10549811.2013.839386\u003c/li\u003e\n \u003cli\u003ePearl, J. (2009). Causal inference in statistics: An overview. \u003cem\u003eStatistics Surveys\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 96\u0026ndash;146. https://doi.org/10.1214/09-SS057\u003c/li\u003e\n \u003cli\u003ePienkowski, T., Freni Sterrantino, A., Tedesco, A. M., Clark, M., Brancalion, P. H. S., Jagadish, A., Mendes, A., Pugliese de Siqueira, L., \u0026amp; Mills, M. (2024).\u0026nbsp;Spatial predictors of landowners\u0026rsquo; engagement in the restoration of the Brazilian Atlantic Forest. \u003cem\u003ePeople and Nature\u003c/em\u003e. https://doi.org/10.1002/pan3.10765\u003c/li\u003e\n \u003cli\u003ePowlen, K. A., \u0026amp; Jones, K. W. (2019). Identifying the determinants of and barriers to landowner participation in reforestation in Costa Rica. \u003cem\u003eLand Use Policy\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e, 216\u0026ndash;225. https://doi.org/10.1016/j.landusepol.2019.02.021\u003c/li\u003e\n \u003cli\u003eRaum, S. (2020). Land-use legacies of twentieth-century forestry in the UK: a perspective. \u003cem\u003eLandscape Ecology\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(12), 2713\u0026ndash;2722. https://doi.org/10.1007/s10980-020-01126-1\u003c/li\u003e\n \u003cli\u003eRaum, S., \u0026amp; Potter, C. (2015). Forestry paradigms and policy change: The evolution of forestry policy in Britain in relation to the ecosystem approach. \u003cem\u003eLand Use Policy\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e, 462\u0026ndash;470. https://doi.org/10.1016/j.landusepol.2015.08.021\u003c/li\u003e\n \u003cli\u003eRogers, E. M. (2003). \u003cem\u003eDiffusion of innovations\u003c/em\u003e (5th ed.). Free Press.\u003c/li\u003e\n \u003cli\u003eRomero-de-Diego, C., Dean, A., Jagadish, A., Witt, B., Mascia, M. B., \u0026amp; Mills, M. (2021).\u0026nbsp;Drivers of adoption and spread of wildlife management initiatives in Mexico. \u003cem\u003eConservation Science and Practice\u003c/em\u003e, \u003cem\u003eFebruary\u003c/em\u003e, 1\u0026ndash;12. https://doi.org/10.1111/csp2.438\u003c/li\u003e\n \u003cli\u003eRoss-Davis, A. L., Broussard, S. R., Jacobs, D. F., \u0026amp; Davis, A. S. (2005). \u003cem\u003eAfforestation Motivations of Private Landowners: An Examination of Hardwood Tree Plantings in Indiana\u003c/em\u003e. https://academic.oup.com/njaf/article/22/3/149/4779955\u003c/li\u003e\n \u003cli\u003eSage, R. B., Hoodless, A. N., Woodburn, M. I. A., Draycott, R. A. H., Madden, J. R., \u0026amp; Sotherton, N. W. (2020). Summary review and synthesis: Effects on habitats and wildlife of the release and management of pheasants and red-legged partridges on UK lowland shoots. In \u003cem\u003eWildlife Biology\u003c/em\u003e (Vol. 2020, Issue 4). Nordic Council for Wildlife Research. https://doi.org/10.2981/wlb.00766\u003c/li\u003e\n \u003cli\u003eSandbrook, C., Albury-Smith, S., Allan, J. R., Bhola, N., Bingham, H. C., Brockington, D., Byaruhanga, A. B., Fajardo, J., Fitzsimons, J., Franks, P., Fleischman, F., Frechette, A., Kakuyo, K., Kaptoyo, E., Kuemmerle, T., Kalunda, P. N., Nuvunga, M., O\u0026rsquo;Donnell, B., Onyai, F., \u0026hellip; Zaehringer, J. G. (2023). Social considerations are crucial to success in implementing the 30\u0026times;30 global conservation target. In \u003cem\u003eNature Ecology and Evolution\u003c/em\u003e. Nature Research. https://doi.org/10.1038/s41559-023-02048-2\u003c/li\u003e\n \u003cli\u003eSchill, C., Anderies, J. M., Lindahl, T., Folke, C., Polasky, S., C\u0026aacute;rdenas, J. C., Cr\u0026eacute;pin, A. S., Janssen, M. A., Norberg, J., \u0026amp; Schl\u0026uuml;ter, M. (2019). A more dynamic understanding of human behaviour for the Anthropocene. In \u003cem\u003eNature Sustainability\u003c/em\u003e (Vol. 2, Issue 12, pp. 1075\u0026ndash;1082). Nature Research. https://doi.org/10.1038/s41893-019-0419-7\u003c/li\u003e\n \u003cli\u003eScottish Forestry. (n.d.). \u003cem\u003eFarm woodland\u003c/em\u003e. Retrieved March 29, 2024, from https://forestry.gov.scot/support-regulations/farm-woodlands\u003c/li\u003e\n \u003cli\u003eScottish Forestry. (2019a, November 30). \u003cem\u003eWoodland Grant Scheme 1 1988-1991 (Scotland)\u003c/em\u003e. https://open-data-scottishforestry.hub.arcgis.com/datasets/d27213124aa94056a5f4689966cabcad_0/about\u003c/li\u003e\n \u003cli\u003eScottish Forestry. (2019b, November 30). \u003cem\u003eWoodland Grant Scheme 2 1991-1994 (Scotland)\u003c/em\u003e. https://open-data-scottishforestry.hub.arcgis.com/datasets/0611e771fd014c809b4d02de194400fc_0/about\u003c/li\u003e\n \u003cli\u003eScottish Forestry. (2019c, November 30). \u003cem\u003eWoodland Grant Scheme 3 1994-2003 (Scotland)\u003c/em\u003e. https://open-data-scottishforestry.hub.arcgis.com/datasets/cc1f51699439430c968a507eaf9acca7_0/about\u003c/li\u003e\n \u003cli\u003eScottish Government. (1997, January 1). \u003cem\u003eLess Favoured Areas\u003c/em\u003e. https://spatialdata.gov.scot/geonetwork/srv/api/records/f4e358c1-df06-4107-bfd2-03f7581ecb07\u003c/li\u003e\n \u003cli\u003eScottish Government. (2021). \u003cem\u003eForestry Grant Scheme\u003c/em\u003e. https://www.ruralpayments.org/publicsite/futures/topics/all-schemes/forestry-grant-scheme/\u003c/li\u003e\n \u003cli\u003eSing, L., Metzger, M. J., Paterson, J. S., \u0026amp; Ray, D. (2017). A review of the effects of forest management intensity on ecosystem services for northern European temperate forests with a focus on the UK. \u003cem\u003eForestry\u003c/em\u003e, \u003cem\u003e91\u003c/em\u003e(2), 151\u0026ndash;164. https://doi.org/10.1093/forestry/cpx042\u003c/li\u003e\n \u003cli\u003eSmaldino, P. E., \u0026amp; McElreath, R. (2016). The natural selection of bad science. \u003cem\u003eRoyal Society Open Science\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(9). https://doi.org/10.1098/rsos.160384\u003c/li\u003e\n \u003cli\u003eSorice, M. G., \u0026amp; Donlan, C. J. (2015). A human-centered framework for innovation in conservation incentive programs. \u003cem\u003eAmbio\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(8), 788\u0026ndash;792. https://doi.org/10.1007/s13280-015-0650-z\u003c/li\u003e\n \u003cli\u003eSorice, M. G., Haider, W., Conner, J. R., \u0026amp; Ditton, R. B. (2011). Incentive Structure of and Private Landowner Participation in an Endangered Species Conservation Program. \u003cem\u003eConservation Biology\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(3), 587\u0026ndash;596. https://doi.org/10.1111/j.1523-1739.2011.01673.x\u003c/li\u003e\n \u003cli\u003eStan Development Team. (2024). \u003cem\u003eRStan: the R interface to Stan\u003c/em\u003e (2.32.5). https://mc-stan.org/\u003c/li\u003e\n \u003cli\u003eStephens, S. S., \u0026amp; Wagner, M. R. (2007). \u003cem\u003eForest Plantations and Biodiversity: A Fresh Perspective\u003c/em\u003e. https://academic.oup.com/jof/article/105/6/307/4599271\u003c/li\u003e\n \u003cli\u003eStratton, S. J. (2021). Population Research: Convenience Sampling Strategies. In \u003cem\u003ePrehospital and Disaster Medicine\u003c/em\u003e (Vol. 36, Issue 4, pp. 373\u0026ndash;374). Cambridge University Press. https://doi.org/10.1017/S1049023X21000649\u003c/li\u003e\n \u003cli\u003eSwart, R., Levers, C., Davis, J. T. M., \u0026amp; Verburg, P. H. (2023). Meta-analyses reveal the importance of socio-psychological factors for farmers\u0026rsquo; adoption of sustainable agricultural practices. \u003cem\u003eOne Earth\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(12), 1771\u0026ndash;1783. https://doi.org/10.1016/j.oneear.2023.10.028\u003c/li\u003e\n \u003cli\u003eTedesco, A. M., Brancalion, P. H. S., Hepburn, M. L. H., Walji, K., Wilson, K. A., Possingham, H. P., Dean, A. J., Nugent, N., Elias-Trostmann, K., Perez-Hammerle, K. V., \u0026amp; Rhodes, J. R. (2023). The role of incentive mechanisms in promoting forest restoration. In \u003cem\u003ePhilosophical Transactions of the Royal Society B: Biological Sciences\u003c/em\u003e (Vol. 378, Issue 1867). Royal Society Publishing. https://doi.org/10.1098/rstb.2021.0088\u003c/li\u003e\n \u003cli\u003eTedesco, A. M., L\u0026oacute;pez-Cubillos, S., Chazdon, R., Rhodes, J. R., Archibald, C. L., P\u0026eacute;rez-H\u0026auml;mmerle, K. V., Brancalion, P. H. S., Wilson, K. A., Oliveira, M., Correa, D. F., Ota, L., Morrison, T. H., Possingham, H. P., Mills, M., Santos, F. C., \u0026amp; Dean, A. J. (2023). Beyond ecology: ecosystem restoration as a process for social-ecological transformation. In \u003cem\u003eTrends in Ecology and Evolution\u003c/em\u003e (Vol. 38, Issue 7, pp. 643\u0026ndash;653). Elsevier Ltd. https://doi.org/10.1016/j.tree.2023.02.007\u003c/li\u003e\n \u003cli\u003eTemperton, V. M., Buchmann, N., Buisson, E., Durigan, G., Kazmierczak, Ł., Perring, M. P., de S\u0026aacute; Dechoum, M., Veldman, J. W., \u0026amp; Overbeck, G. E. (2019). Step back from the forest and step up to the Bonn Challenge: how a broad ecological perspective can promote successful landscape restoration. \u003cem\u003eRestoration Ecology\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(4), 705\u0026ndash;719. https://doi.org/10.1111/rec.12989\u003c/li\u003e\n \u003cli\u003eTextor, J., van der Zander, B., Gilthorpe, M. S., Liśkiewicz, M., \u0026amp; Ellison, G. T. H. (2017). Robust causal inference using directed acyclic graphs: the R package \u0026lsquo;dagitty.\u0026rsquo; \u003cem\u003eInternational Journal of Epidemiology\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(6), 1887\u0026ndash;1894. https://doi.org/10.1093/ije/dyw341\u003c/li\u003e\n \u003cli\u003eTharme, A. P., Green, R. E., Baines, D., Bainbridge, I. P., \u0026amp; O\u0026rsquo;Brien, M. (2001). The effect of management for red grouse shooting on the population density of breeding birds on heather-dominated moorland. \u003cem\u003eJournal of Applied Ecology\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(2), 439\u0026ndash;457. https://doi.org/10.1046/j.1365-2664.2001.00597.x\u003c/li\u003e\n \u003cli\u003eThomas, D. R. (2006). A General Inductive Approach for Analyzing Qualitative Evaluation Data. \u003cem\u003eAmerican Journal of Evaluation\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(2), 237\u0026ndash;246. https://doi.org/10.1177/1098214005283748\u003c/li\u003e\n \u003cli\u003eTran, T. M. A., Ko, D. W., Park, C. R., \u0026amp; Le, H. D. (2019). A bayesian network analysis of reforestation decisions by rural mountain communities in Vietnam. \u003cem\u003eForest Science and Technology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 51\u0026ndash;57. https://doi.org/10.1080/21580103.2019.1581665\u003c/li\u003e\n \u003cli\u003eTredennick, A. T., Hooker, G., Ellner, S. P., \u0026amp; Adler, P. B. (2021). A practical guide to selecting models for exploration, inference, and prediction in ecology. \u003cem\u003eEcology\u003c/em\u003e, \u003cem\u003e102\u003c/em\u003e(6). https://doi.org/10.1002/ecy.3336\u003c/li\u003e\n \u003cli\u003eTrout, R., \u0026amp; Pepper, H. (2006). \u003cem\u003eForest Fencing - Forestry Commission Technical Guide\u003c/em\u003e. https://cdn.forestresearch.gov.uk/2006/03/fctg002.pdf\u003c/li\u003e\n \u003cli\u003eUK Government. (2021, May 18). \u003cem\u003eEngland Woodland Creation Offer\u003c/em\u003e. https://www.gov.uk/guidance/england-woodland-creation-offer\u003c/li\u003e\n \u003cli\u003eUNEP, U. E. P., \u0026amp; FAO, F. and A. O. of the U. N. (2021). \u003cem\u003eUN Decade on Restoration\u003c/em\u003e. https://www.decadeonrestoration.org/\u003c/li\u003e\n \u003cli\u003eWang, C., Zhang, W., Li, X., \u0026amp; Wu, J. (2021). A global meta‐analysis of the impacts of tree plantations on biodiversity. \u003cem\u003eGlobal Ecology and Biogeography\u003c/em\u003e. https://doi.org/10.1111/geb.13440\u003c/li\u003e\n \u003cli\u003eWaring, B., Neumann, M., Prentice, I. C., Adams, M., Smith, P., \u0026amp; Siegert, M. (2020). Forests and Decarbonization \u0026ndash; Roles of Natural and Planted Forests. \u003cem\u003eFrontiers in Forests and Global Change\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e. https://doi.org/10.3389/ffgc.2020.00058\u003c/li\u003e\n \u003cli\u003eWejnert, B. (2002). Integrating models of diffusion of innovations: A conceptual framework. In \u003cem\u003eAnnual Review of Sociology\u003c/em\u003e (Vol. 28, pp. 297\u0026ndash;326). https://doi.org/10.1146/annurev.soc.28.110601.141051\u003c/li\u003e\n \u003cli\u003eWelsh government. (2024). \u003cem\u003eRural grants and payments\u003c/em\u003e. https://www.gov.wales/rural-grants-payments\u003c/li\u003e\n \u003cli\u003eWoodland Trust. (2022). \u003cem\u003eFarming for the future: how agroforestry can deliver for nature and climate\u003c/em\u003e. https://www.woodlandtrust.org.uk/publications/2022/11/farming-for-the-future/\u003c/li\u003e\n \u003cli\u003eWorld Economic Forum. (2020). \u003cem\u003eOne Trillion Trees Initiative\u003c/em\u003e. https://www.1t.org/\u003c/li\u003e\n \u003cli\u003eWynne-Jones, S. (2013). Carbon blinkers and policy blindness: The difficulties of \u0026lsquo;Growing Our Woodland in Wales.\u0026rsquo; \u003cem\u003eLand Use Policy\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e, 250\u0026ndash;260. https://doi.org/10.1016/j.landusepol.2012.10.012\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"52fd4fc8-6357-4c50-96bb-1907d7b1ec4c","identifier":"10.13039/501100000266","name":"Engineering and Physical Sciences Research Council","awardNumber":"2521695","order_by":0},{"identity":"984cec49-fad0-4796-9c7a-b17cf3432701","identifier":"10.13039/501100000275","name":"Leverhulme Trust","awardNumber":"RPG-2021-440","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Imperial College London","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adoption, Bayesian analysis, Behaviour change, Diffusion of Innovations, Ecosystem restoration, Mixed native woodland creation","lastPublishedDoi":"10.21203/rs.3.rs-7697572/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7697572/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eForest restoration has become a global priority due to its potential benefits (carbon storage, biodiversity, etc.), but its successful delivery depends on decisions made by people, especially landholders. Despite this, our theory-based understanding of what drives landholders’ decisions to create woodland types important to achieve ecosystem restoration at scale, such as mixed native woodlands which have the highest potential for environmental benefits, remains limited.\u003c/p\u003e\n\u003cp\u003eBuilding on Diffusion of Innovations Theory, we survey landholders in and around the Cairngorms National Park in Scotland to quantify the effects of woodland creation initiatives, individual landholders’, and contextual characteristics on decisions to create new mixed native woodland using a Bayesian Bernoulli distributed logistic regression.\u003c/p\u003e\n\u003cp\u003eWe find that landholders are more likely to have engaged in mixed native woodland creation if they believe it is compatible with their current land use practices; or if they anticipate increases to wildlife habitat or soil quality. Landholders that create mixed native woodland are more likely to perceive interventions to control herbivore damage to new woodland as difficult to understand.\u003c/p\u003e\n\u003cp\u003eOur results suggest that, in contexts with established targets and economic incentives for woodland creation, governments could more effectively promote the uptake of mixed native woodland creation by: emphasising woodlands’ compatibility with other land uses such as agriculture (e.g. agroforestry) or some types of hunting; maintaining or improving the flexibility of woodland creation actions; facilitating access to easy-to-understand evidence-based comparisons of the potential benefits of different woodland types; and supporting landholders’ knowledge needs, especially for herbivore control tasks like fencing. Overall, we show that to achieve ecosystem restoration and its multiple benefits at scale, programme designs need to be more sensitive to landholders’ perspectives whilst incorporating safeguards against negative outcomes.\u003c/p\u003e","manuscriptTitle":"Drivers of mixed native woodland creation in Scotland: Compatibility with land use practices and environmental benefits influence decision making","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-26 07:23:58","doi":"10.21203/rs.3.rs-7697572/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b6d87bcd-8002-48d9-870a-aa7eaec0fee8","owner":[],"postedDate":"September 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55373220,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2025-09-26T07:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-26 07:23:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7697572","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7697572","identity":"rs-7697572","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}