Data

The research estimated the causal impact on avoided deforestation of PAs established after the SNASPE creation in 1984. Until the 1970s, several government agencies were in charge of PAs creation and management. However, unified legislation on PAs was not available until 1984. With the creation of SNASPE, the government tried to promote the definition and legalization of PAs boundaries and the assignment of management objectives for each unit in the system, none of which previously had been clear for a large proportion of PAs [13]. For that reason, the SNASPE creation in 1984 can be considered the true beginning of PAs in Chile.

The PAs located in the study area and selected for this study cover 561,920 ha. We used Geographic Information Systems (GIS) to build a geospatial data set of relevant biophysical and socioeconomic conditions. We first established the forest cover conditions using a mosaic of Landsat Multi-Spectral Scanner (MSS) satellite images between 1974 and 1976, and from 1986 and 2011 a mosaic of Landsat Thematic Mapper (TM) satellite images (Landscape Ecology Laboratory, Universidad de Concepción, Chile) (see Fig 2). MSS images consist of four spectral bands with 60 meter spatial resolution and TM images consist of seven spectral bands with a spatial resolution of 30-meter pixels. The MSS pixels were resampled to make them comparable to TM pixels. A random sample of 2,549 and 36,417 points (pixels) was obtained to characterize protected and unprotected land respectively. These treated and control points were selected to well represent the study area and to include approximately one pixel per 1 km2 of land within the study area. After removing points that were not usable because of the land use change obtained from the satellite images classification (e.g. a forested point in 1986 without data in the 2011 satellite image), the final dataset comprised 1,978 treated points covering all protected areas included in the analysis and 23,181 control points. To determine if a land pixel is considered protected for the analyses, a layer containing all PAs was overlaid with a general map of the study area. The sampling excluded indigenous land and private PAs because they are subject to different legal and land use regimes. For the purpose of this analysis, a private PA is a piece of land of any size that: (i) is managed with the purpose of conserving biodiversity; (ii) is protected with or without formal recognition from the government; and (iii) is managed directly or indirectly by individuals, communities, corporations or non-governmental organizations.

Source: Landscape Ecology Lab, Universidad de Concepcion, Chile. FONDECYT grant N°11110271.

To check the accuracy of the random sampling process, we confirmed that there were no significant differences between our sample of land pixels and the entire land area shown in Fig 1 in terms of important characteristics (i.e., protected status, type of protection, proportion under each land capacity and land suitability classes).

Deforestation between 1986 and 2011 was calculated based on a forest cover variable, defined as the presence or absence of forest at the pixel level (i.e., a binary variable indicating if a pixel is either forested or deforested in each year). We took land that was forested in 1986 and compared deforestation in protected and unprotected forests. As a result, the outcome variable measured the change in forest as the difference between the change in forest cover on protected pixels (Y = 1 if conserved) and the change in forest cover on matched unprotected pixels in the same period (1986–2011). Thus, a positive sign indicates that protection resulted in avoided deforestation. Table 1 shows, through a simple comparison between protected and unprotected areas, a higher probability of conservation on unprotected land for the period 1975–1986, and a higher probability of conservation on protected land for the period 1986–2011. These results do not allow for a conclusion about PAs’ impacts in terms of avoided deforestation, but they show statistically significant differences in terms of the outcome variable between protected and unprotected areas in the study region.

^a These outcomes show the difference between the change in forest cover on protected plots (Y = 1 if not deforested) and the change in forest cover on matched unprotected plots in the same period. Thus, a positive sign indicates that protection resulted in higher probability of conservation or avoided deforestation.

^b Normalized difference = X¯T−X¯CST2+SC22 where T = protected and C = unprotected [27].

To control for differences among protected and unprotected areas in terms of characteristics that affect both deforestation and protection decisions, the forest cover data was combined with spatially explicit data on covariates believed to affect both PA location and LULCC. The biophysical, geographical and socioeconomic characterization of Chilean PAs is oriented to reveal the drivers of LULCC and conservation status in Chile when compared with non-protected areas. In the scientific literature, the main drivers of PAs establishment are related to land use [9,10], soil characteristics [28,29] and transportation costs [9,28–31]. Other drivers may include ecological characteristics like slope and distance to rivers [9,29,31]. We also draw on previous impact evaluation of PAs [9,10,13,32]. For the purpose of this paper, variables describing terrain, climate, and remoteness were used to compare protected land with unprotected land as shown in Table 2, and Table 3 presents the summary statistics for confounders used in our analysis.

* The distance to roads was calculated from an adaptation of a data set from the Ministry of Public Works (2012). The adaptation process involved the use of a road cadastral map from 1969. This map allowed us to identify the road network that existed in Chile in 1969 which was the road network used as a covariate during the matching process.

^a N treated = 1978; N available controls = 23181.

^b Weighted means for matched controls.

^c Mean (for categorical covariate) or median (for continuous covariate) difference in the empirical quantile-quantile plot of treatment and control groups on the scale in which the covariate is measured.

^d Mean eCDF = mean differences in empirical cumulative distribution function.

^e Normalized difference = X¯T−X¯CST2+SC22 where T = protected and C = unprotected [27].

^f According to FAO, the erodibility of a soil as a material with a greater or lesser degree of coherence is defined by its resistance to two energy sources: the impact of raindrops on the soil surface, and the shearing action of runoff between clods in grooves or rills (see http://www.fao.org/docrep/t1765e/t1765e0f.htm).