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Can I view this online? Ask a librarian. Aboriginal, Torres Strait Islander and other First Nations people are advised that this catalogue contains names, recordings and images of deceased people and other content that may be culturally sensitive. Second, we use the approach to identify and analyze key predictors that are structurally associated with observed long-term vegetation growth trajectories in a SES in the Indian Himalaya.
This second objective entails ranking predictors associated with different levels of vegetation growth to explore heterogeneity in long-term outcome trajectories and the underlying predictors. Our approach for analyzing key factors predictive of long-term trends in ecological outcomes in SESs includes four steps, as described in Fig 1.
To develop a representative set of structural counterfactuals necessary to identify key factors shaping observed long-term vegetation growth trajectories in the study area Step 3 in Fig 1 we drew a random sample of 30 out of the FMRs in Kangra. We chart the observed outcome trajectory for vegetation using a mean annual normalized difference vegetation index NDVI for the studied FMRs from to [ 27 ] and then use a nested optimization procedure developed for synthetic control matching SCM to create a structural counterfactual to observed vegetation outcome trajectories for selected FMRs based on indicators we identified Fig 1 ; see below for a detailed description of the method.
As the fourth and final step in our approach, we calculate and rank critically important factors associated with observed long-term ecological growth based on synthetic weights. This procedure enables testing of theories from common property literature to draw conclusions regarding the constellation of relevant variables that explain observed ecological outcome trajectories over the long term.
We selected Kangra District in the Indian Himalaya to explore the factors and processes that explain greater potential for social—ecological sustainability. The area has been well studied, is relatively data-rich, and has experienced increased vegetation growth over the past fifteen years. Common property scholars have previously conducted intensive research on the social—ecological determinants of forest condition in Kangra District [ 13 , 28 ].
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We examine the dynamics of vegetation growth since vegetation, including forest area, provides crucial socioeconomic and ecological benefits in the region. For example, vegetation constitutes an important livelihood base by supplying green fodder, construction timber, and fuelwood for most of the population, carbon mitigation, and species habitat [ 13 , 28 ].
Forest and wetland areas also provide key regulating and other ecosystem services that include maintenance of soil fertility, retention of soil moisture, and prevention of soil erosion [ 29 ]. We expect that a range of often interlinked causal factors and mechanisms will shape long-term trends in vegetation growth Figure A and Table C in S1 File.
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Higher rates of literacy, for instance, may have expanded off-farm job opportunities leading to abandonment of agricultural fields and increasing vegetation growth [ 30 ]. The methodology we develop and apply to identify critical determinants of long-term trajectories in the Himalayan study context is broadly relevant and can be employed in other SESs around the world to build knowledge of factors leading to sustainable natural resource governance.
NDVI was used as a proxy for estimating long-term vegetation growth. Consistent, long-term NDVI data for the region were drawn from [ 27 , 31 ]. We applied quality mosaic in Google Earth Engine to reduce the set of NDVI layers during a specific season to a composite and to replace pixels represented as clouds with an accurately estimated pixel value by choosing the greenest pixel in the composite of multiple NDVI layers.
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NDVI is commonly used by ecologists as a proxy for vegetation productivity especially in contexts where empirical vegetation data collection is limited due to high costs, time restrictions, and short climatic windows [ 27 , 31 , 33 — 35 ]. The index has shown consistent correlation with vegetation growth and biomass in various ecosystems worldwide [ 36 , 37 ]. The NDVI algorithm takes advantage of the fact that green vegetation reflects less visible light and more NIR, whereas sparse or less green vegetation reflects a greater portion of the visible and less NIR.
NDVI combines these reflectance characteristics in a ratio so it is an index related to photosynthetic capacity. Only positive values correspond to vegetated zones; the higher the index, the greater the chlorophyll content of the target species. Relevant social and ecological explanatory variables that relate to long-term vegetation growth trajectories were selected based on previous studies using a SES framework [ 13 , 28 ] with their measurable indicators constructed from available secondary social and spatial datasets Tables A, B and C in S1 File. The choice of these variables reflects previous knowledge of the relative importance of such drivers in influencing the ecological outcomes as suggested by common property scholars studying Himachal Pradesh in the past [ 13 , 28 , 39 ].
We identified 24 indicators for the nine variables and quantified them based on local secondary social and spatial data. In addition, we identified three more variables based on secondary data: i Interactions I , the reciprocal interactions between social and ecological subsystems based on forest fires; ii Related Ecosystems ECO , attributes of related ecosystems, especially climatic factors of temperature, precipitation, and land surface temperature; and iii Outcome O , the ecological performance of the SES in terms of average NDVI values.
Synthetic control matching uses a nested optimization process and identifies a set of predictive weights for potential SES factors such that matching those weighted factors results in the closest possible match to NDVI outcomes over the full study period [ 23 , 25 ]. A plausible synthetic counterfactual provides the ability to assess the factors that structurally relate to the observed long-term trends in vegetation growth by re-creating those trends in the study FMR. To determine whether a particular synthetic counterfactual trajectory is plausible, it must closely match observed trends with minimum mean square prediction error, LOSS V predictor weight matrix , and should be parallel [ 23 — 25 ].
We used nested optimization procedure developed for SCM and conducted our analysis in the Synth R package [ 40 ]. We constructed synthetic controls for the randomly selected set of 30 FMRs to get a set of critical factors that explain the observed long-term — outcome trajectories in our study area. Each of the 30 FMRs was assigned to a hypothetical policy intervention in treated group , and the remaining FMRs constituted a control untreated group for each treated FMR. Using nested optimization for SCM, explained below, we constructed counterfactual trajectories using a weighted combination of synthetically matched control FMRs.
The analytical approach and the standard notation for the method is adapted from Abadie and Gardeazabal [ 25 ] and Abadie et al.
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The Synth package provides a data-driven procedure to create synthetic control units based on a weighted combination of control units that approximates the characteristics of the treated unit to allow for effect estimation of a policy intervention treatment. The reason for restricting W to nonnegative weights that sum to one is to restrict extrapolation within the support convex hull of the vegetation growth predictors for the control FMRs.
The value of V could also be subjective and rely on prior knowledge of the relative importance of each vegetation growth predictor to the observed vegetation growth trajectory. The construction of the counterfactual trajectories using weighted combinations of synthetically matched control FMRs provides information on the different factors associated with the observed long-term vegetation trajectories.
The list of selected counterfactual outcome trajectories and their mean square prediction errors and loss W weights across control units are given in Table E in S1 File. This matrix of V predictor weights permits the assessment of the relative importance of different factors in explaining the observed long-term vegetation growth trajectories for each FMR. We also checked the Synth R package—derived covariate balance for treated and control groups to select our final set of matched structural counterfactuals [ 40 ].
Higher covariate balance suggests well-matched treated and control groups, which further validates our approach. FMRs are aggregate entities comprised of several villages. In such situations, a combination of comparison units or synthetic control; a weighted average of all potential comparison FMRs is usually a better option for reproducing the characteristics of units under study than any single comparison unit[ 23 ].
One important advantage of the SCM approach in tracing long-term outcome trajectories is that we expect FMRs that are similar in both observed and unobserved determinants of long-term vegetation growth, as well as their effect on this growth, should produce similar outcome trajectories over longer terms. Moreover, well-matched outcome trajectories of the unit of interest and their comparison units over of longer period helps control for unobserved factors as well as for the heterogeneity of the effects of the observed and unobserved factors on the outcome vegetation growth under study[ 23 ].
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This study used an SES framework to identify reciprocal interactions between forest users Actors and the planted or regenerated trees Resource Units inside the FMRs, and the institutional Governance System and ecological Resource System settings within which users and trees are embedded Figure A in S1 File.
Establishment of planted or regenerated trees and resultant ecological outcomes depends on interactions among forest users and managers within the larger social, economic, and ecological context. For example, specific Forest Department regulations affect these interactions, including rules promoting broadleaf species over conifers, an emphasis on afforestation of degraded landscapes to provide fodder, fuelwood, and other minor forest products, and expanding plantations via tree nurseries. However, successful tree establishment or regeneration in an FMR also depends on the socioeconomic and demographic attributes of resource users and the importance of the resource to the users.
Other key factors affecting long-term vegetation growth include the number of mobile grazing animals, size of the resource system, system productivity, geographic location, and climatic variables. In this Himalayan context, the occurrence of forest fires comprises a crucial reciprocal interaction between forest users and planted trees that influences long-term vegetation cover in the region[ 30 , 43 ].
Forest users usually burn forests to obtain better grass for their livestock, but burnt areas might influence ecological outcomes due to the direct loss of plants, which can influence long-term ecological outcome trajectories by promoting grass over trees Figure A in S1 File. At the same time, the Forest Department seeks to control fires to support the vegetation improvement efforts it promotes. Successful management of this tree—grass tradeoff is critical for long-term vegetation growth in the region. Forest cover Table D in S1 File and vegetation more generally Fig 3 exhibited an overall upward trend over the past two decades in Kangra District and the broader Himalayan region of India Construction of a synthetic counterfactual trajectory matched to the observed vegetation NDVI trajectory suggests similarity between the factors in the two trajectories.
Strong matches provide high confidence in the factors identified as determining long-term vegetation growth trajectories. The theoretically-informed predictors used in our analysis successfully reproduced the observed vegetation growth trajectories in nearly all the studied FMRs 28 of 30; The average V predictor weights for the 28 FMRs with plausible synthetic outcome trajectories are listed individually in Table 1. In the Actors subsystem of the SES framework, the number of households and number of literates were the two indicators with the highest predictive weights 0.
Interaction strategies between governments and hybrid coercive organisations
The proportion of broadleaf species planted together with number of nurseries were the two Governance System GS indicators with the highest predictive weight. These two indicators relate to the strength of the Forest Department, a central governance actor in the region. The Department has long supported the implementation of afforestation programs, which is likely to influence long-term vegetation growth in interaction with local community participation in forest management see Discussion below.
Predictive weights ranged from 0. Baseline vegetation, total carbon, and total organic carbon had the largest predictive weights among these subsystems in explaining observed vegetation growth.
The occurrence of forest fires in an FMR had a predictive weight of 0. Finally, climatic factors temperature, precipitation, land surface temperature also had higher relative importance in explaining observed vegetation trajectories with predictive weights of 0. Several predictors increased in importance as levels of change in NDVI growth increased.
These include: number of households, number of marginal people, number of literates, forest area planted, soil quality carbon and organic carbon , temperature and baseline vegetation. By contrast, nearly all of the indicators with greater predictive importance i. In this case, number of unemployed people, crop acreage, economic activity and number of farmers emerged as more important predictors in shaping the long-term vegetation growth Table G in S1 File.
This finding held in analyses of those FMRs that experienced higher vegetation growth levels i.