In Latin America, the rate of land and forest degradation inside protected areas more than doubled from 2004 to 2009, increasing from 0.04% to 0.10% per year. This is a small fraction but of a large number. Thus, in 2004 there were 81,975 hectares of land and forest degradation inside protected areas in Latin America, while in 2009, there were 247,056 hectares—an increase of approximately 165,000 hectares. Assuming each land and forest degradation event was unique (i.e., no change, regrowth and change again during the six years) and considering only the negative changes in land cover, the 2004–2009 land and forest degradation in our protected area dataset was 1,097,618 hectares—an area the size of Jamaica.
3.1. Differences by Country
The mean annual rate of land and forest degradation 2004–2009 shows large differences by country, with French Guiana and Guatemala at more than double the Latin American average and Nicaragua and Mexico at less than one-fifth the average (
Table 2).
Guyana, Suriname and French Guiana are the countries where the percentage of protected areas with observed land and forest degradation is highest, but all three have small sample sizes (
Figure 1). Guatemala and Brazil have relatively high rates of land and forest degradation (
Table 2), yet fall to the middle in
Figure 1, indicating that the high rates of land and forest degradation are caused by relatively large changes in relatively few protected areas. On the other hand, Peru’s protected areas have experienced low average change but that change occurred in over 60% of its protected areas. Overall, 45% of all protected areas experienced land and forest degradation inside their administrative boundaries from 2004 to 2009.
Table 2.
2004–2009 mean annual change in land and forest degradation inside protected areas by country (standard deviations in parentheses).
Table 2.
2004–2009 mean annual change in land and forest degradation inside protected areas by country (standard deviations in parentheses).
Country | Number of protected areas | Mean annual change in % | Mean annual change in hectares |
---|
French Guiana | 10 | 0.231 | (0.465) | 51 | (54) |
Guatemala | 99 | 0.206 | (0.390) | 228 | (1003) |
Paraguay | 27 | 0.183 | (0.391) | 71 | (285) |
El Salvador | 29 | 0.132 | (0.288) | 3 | (8) |
Brazil | 563 | 0.127 | (0.353) | 156 | (717) |
Bolivia | 74 | 0.104 | (0.214) | 443 | (1010) |
Colombia | 38 | 0.086 | (0.118) | 214 | (357) |
Ecuador | 25 | 0.076 | (0.132) | 82 | (202) |
Honduras | 74 | 0.067 | (0.163) | 72 | (289) |
Belize | 43 | 0.054 | (0.101) | 9 | (21) |
Suriname | 12 | 0.046 | (0.099) | 17 | (23) |
Argentina | 166 | 0.044 | (0.184) | 13 | (110) |
Chile | 68 | 0.039 | (0.095) | 15 | (34) |
Venezuela | 108 | 0.028 | (0.072) | 107 | (325) |
Costa Rica | 15 | 0.016 | (0.043) | 1 | (5) |
Guyana | 1 | 0.015 | (n/a) | 69 | (n/a) |
Peru | 56 | 0.014 | (0.023) | 79 | (297) |
Nicaragua | 39 | 0.013 | (0.041) | 5 | (23) |
Mexico | 341 | 0.013 | (0.071) | 4 | (22) |
All protected areas | 1788 | 0.080 | (0.253) | 102 | (536) |
Figure 1.
2004–2009 proportion of protected areas with land and forest degradation by country.
Figure 1.
2004–2009 proportion of protected areas with land and forest degradation by country.
Other authors have suggested that income level and rural population density could be explanatory factors for variations in protected area land and forest degradation within a country [
13,
44]. We expand this and look at whether GDP per capita (as a proxy for income level), GDP growth (as a proxy for economic expansion), and average rural population density for a country (as a proxy for population densities around protected areas) can explain the differences between countries in land and forest degradation within protected areas. We acknowledge that GDP is an imprecise measure of income and economic expansion due to underlying issues with national accounts data [
45] and that there can be large variations in rural population densities within a country [
37], but we hypothesize that these may be explanatory factors for the observed country-level variation in protected area land and forest degradation.
Another possible explanatory factor is differing levels of protected area system funding. To test this hypothesis, we use Bovarnick
et al.’s data on 2007–2008 protected area system spending from all known sources for 12 Latin American countries (as a proxy for protected area system funding) [
35].
Using a panel-data tobit regression model with random effects, we tested for an association between the average annual rate of land and forest degradation between 2004–2009 inside protected areas and the independent variables of GDP per capita, GDP growth, rural population density, and protected area system funding. Data on protected area system funding were unavailable for Belize, Costa Rica, El Salvador, French Guiana, Guyana, Mexico, and Suriname, and thus they were excluded from the regression model.
Before running the analysis, we tested for collinearity among the independent variables. There are a number of statistically significant correlations between the independent variables, but all correlation coefficients are below 0.7 [
46]. The model thus includes all four independent variables. Tobit model coefficients are not interpretable as effect sizes [
47], and interpretation of coefficients should focus on the positive or negative sign of the coefficient and whether or not it is statistically significant. Our results show that among the four independent variables, only protected area funding has a statistically significant relationship with land and forest degradation inside protected areas (
Table 3). Our finding, however, of an association between protected area funding and land and forest degradation is tenuous and depends on the observations in just one country. Argentina’s protected area system funding level was more than three times the average for the countries in the dataset (US$8.60
versus US$2.50 per hectare), while its average rate of land and forest degradation inside protected areas was roughly half the Latin America average (0.044%
versus 0.080% per annum). If the observations from Argentina are excluded, the coefficient for protected area funding loses its significance (beta −0.047; SE: 0.048;
p = 0.33;
n = 1,171).
Table 3.
Regression results for country-level independent variables.
Table 3.
Regression results for country-level independent variables.
Dependent variable: Mean land and forest degradation in % | Coefficient | Std. Error | p value |
---|
GDP per capita (2004) | −2.39 × 10−6 | 1.64 × 10−5 | 0.884 |
Average GDP growth (2004–2009) | −7.69 × 10−3 | 2.04 × 10−2 | 0.706 |
Average rural population density (2004–2009) | 0.0026 | 0.0018 | 0.158 |
Funding per hectare (2007–2008) | −0.034 * | 0.015 | 0.023 |
Constant | −0.027 | 0.18 | 0.817 |
Our finding of a non-significant relationship between GDP per capita and protected area land and forest degradation echoes Nagendra [
13] who found that protected area land-cover clearing did not differ significantly among low, medium and high GDP per capita countries. This suggests that in the near term, regional growth in GDP per capita is unlikely to drive a regional change in land and forest degradation inside protected areas in Latin America.
The absence of a significant relationship between GDP growth and protected area land and forest degradation suggests that economic expansion may not be correlated with land and forest degradation inside protected areas in our dataset.
For rural population density and land and forest degradation, other authors have found that land and forest degradation in a country or region may be driven more by economic opportunities or an area’s suitability for agriculture than rural population densities [
48,
49], and we found no statistically significant coefficient for the variable.
The tenuous association between protected area funding and land and forest degradation in our dataset could be explained by several factors. First, total spending does not necessarily reflect the spending on protection activities likely to reduce land and forest degradation such as the number of guards per square kilometers [
50]. Second, spending may be concentrated in a few protected areas within a country [
35].
3.2. Differences by Protected Area
As in the country-level section above, in the protected-area section below we present the descriptive statistics first and then the aggregate regression results.
3.2.1. Major Habitat Type
With protected areas categorized by major habitat type, we found that flooded grasslands and savannas had the highest mean annual change, but a single protected area in Brazil (RPPN Rosana Jubran) skews the mean annual change in percentage, and a single protected area in Bolivia (2.9-million ha San Matias) skews the mean annual change in hectares. Remove these two protected area from the analysis, and the flooded grasslands and savannas habitat type falls to third among the habitat types and tropical and subtropical moist broadleaf forest rises to the top. The latter habitat type comprises 54% of the protected areas in our dataset, and a large share of these (43%) are located in Brazil (
Table 4).
The habitat type results show a split between those with large average annual changes and those with small average annual changes. There is no habitat type close to the overall average. This suggests a conservation focus in Latin America on the three habitat types with the highest rates of annual change: flooded grasslands and savannas; tropical and subtropical moist broadleaf forests; and tropical and subtropical grasslands, savannas and shrublands.
Table 4.
2004–2009 mean annual change in land and forest degradation inside protected areas by major habitat type (standard deviations in parentheses).
Table 4.
2004–2009 mean annual change in land and forest degradation inside protected areas by major habitat type (standard deviations in parentheses).
Major habitat type | Number of protected areas | Mean annual change in % | Mean annual change in hectares |
---|
Flooded grasslands and savannas | 15 | 0.146 | (0.296) | 367 | (1208) |
Tropical and subtropical moist broadleaf forests | 971 | 0.113 | (0.312) | 167 | (698) |
Tropical and subtropical grasslands, savannas and shrublands | 149 | 0.108 | (0.270) | 61 | (219) |
Temperate broadleaf and mixed forests | 62 | 0.043 | (0.099) | 15 | (33) |
Mangroves | 19 | 0.030 | (0.098) | 2 | (6) |
Tropical and subtropical dry broadleaf forests | 203 | 0.029 | (0.098) | 13 | (79) |
Deserts and xeric shrublands | 164 | 0.019 | (0.085) | 6 | (31) |
Temperate grasslands, savannas and shrublands | 57 | 0.016 | (0.098) | 3 | (15) |
Montane grasslands and shrublands | 51 | 0.015 | (0.083) | 22 | (138) |
Tropical and subtropical coniferous forests | 77 | 0.011 | (0.057) | 0 | (2) |
Mediterranean forests, woodlands and scrub | 19 | 0 | (0) | 0 | (0) |
Indeterminate habitat type | 1 | n/a | | n/a | |
All protected areas | 1788 | 0.080 | (0.253) | 102 | (536) |
3.2.2. Management Categories
The WDPA dataset divides protected areas into IUCN management categories, ranging from Category I strictly protected nature reserves and wilderness areas to Category VI protected areas with sustainable use of natural resources. The management objectives of Category I-IV protected areas are more restricted than multi-use Categories V and VI protected areas.
A number of the protected areas in our dataset lack an IUCN category designation, and thus we excluded 562 protected areas with no IUCN data, including all of Mexico and El Salvador’s protected areas. Category VI protected areas had the highest mean annual rate of change and Category IV had the lowest (
Table 5).
Table 5.
2004–2009 mean annual change in land and forest degradation inside protected areas by IUCN category (standard deviations in parentheses).
Table 5.
2004–2009 mean annual change in land and forest degradation inside protected areas by IUCN category (standard deviations in parentheses).
IUCN management category | Number of protected areas | Mean annual change in % | Mean annual change in hectares |
---|
Category VI | 363 | 0.125 | (0.286) | 179 | (681) |
Category III | 60 | 0.091 | (0.263) | 84 | (311) |
Category I | 148 | 0.084 | (0.249) | 174 | (692) |
Category V | 83 | 0.075 | (0.217) | 31 | (143) |
Category II | 403 | 0.074 | (0.260) | 86 | (301) |
Category IV | 169 | 0.052 | (0.180) | 43 | (365) |
No specified category | 562 | n/a | | n/a | |
All protected areas | 1788 | 0.080 | (0.253) | 102 | (536) |
IUCN management categories reflect differing management objectives rather than inherently different levels of protection against land and forest degradation, and the ambiguous results above are no surprise given that a well-managed Category V or VI protected area may be more effective in preventing land and forest degradation than a poorly managed Category I or II protected area.
3.2.3. Size
Other authors have shown that smaller protected areas with a high perimeter-to-interior-area ratio may be more prone to anthropogenic-induced changes, e.g., [
51,
52]. Larger protected areas may also have lower proportional land and forest degradation because they have more area that is farther away from human settlements. To facilitate the presentation of the size analysis, we split size into 10 equal groupings of
n = 178 ranging from the smallest (size group 1) to the largest (size group 10) [
53]. The larger protected areas (size groups 6–10) have a higher rate of land and forest degradation on average than the smaller protected areas (size groups 1–5) (
Figure 2).
Figure 2.
Mean annual rate of change by protected area group size showing slightly greater average change in the five larger size groups than the five smaller size groups.
Figure 2.
Mean annual rate of change by protected area group size showing slightly greater average change in the five larger size groups than the five smaller size groups.
Size groups 2–4 are the lowest suggesting that small size may not be a substantial risk factor for greater land and forest degradation. There may, however, be a threshold close to 500 hectares where the rate of land and forest degradation increases, given the relatively high rate of change in the smallest group. There also appears to be a benefit to being in the largest size, but this may be due to many of the largest protected areas in Latin America being located in remote areas such as Brazil’s 3.9 million hectares Tumucumaque National Park.
3.3. Differences Inside and Outside Protected Areas
Protected area locations are often biased towards higher elevations, steeper slopes, and greater distances to roads and cities [
37]. Moreover, a protected area’s habitat type can differ substantially from its adjacent geographic areas [
36]. In a global estimate of land and forest degradation inside protected areas compared to adjacent control sites matched for elevation, slope, ecoregion, distances to roads and to cities, and agricultural suitability, Joppa and Pfaff [
55] found that these land characteristics were different for approximately half of the protected areas and their adjacent areas
We compared the average annual land and forest degradation inside protected areas to the 5-km and 20-km wide zones adjacent to the protected areas. Both adjacent zones experienced a higher average land and forest degradation between 2004 and 2009: 0.12% and 0.13% for the 5-km and 20-km zones, respectively, versus 0.08% for the protected areas. Compared to the 5-km zone, land and forest degradation inside the protected areas was lower in 45% of the cases, higher in 19% of the cases, and there was no change in either area in 35% of the cases. Compared to the 20-km zone, land and forest degradation in the protected areas was lower in 66% of the cases, higher in 17% of the cases, and there was no change in either area in 17% of the cases. To test whether the differences in land and forest degradation between the protected areas and the adjacent zones are statistically significant, we used the Wilcoxon matched-pair signed-rank test, which is the non-parametric version of the paired samples t-test used for normally distributed variables. We found statistically significant differences for both the 5-km zone (Z = 12.46; p < 0.001; n = 1787) and the 20-km zone (Z = 18.20; p < 0.001; n = 1787).
This, however, should not be construed as evidence of protected areas effectively reducing land and forest degradation compared to having no protected areas. In order to be accurate, such a comparison would require matching of protected area characteristics with adjacent areas to provide the counterfactual as per Joppa and Pfaff [
55] and Ferraro
et al. [
54].
Perhaps more importantly, the rate of change for all three variables increased from 2004 to 2009 (
Figure 3). Using Friedman’s two-way ANOVA by ranks test, which is the non-parametric version of the repeated-measures ANOVA for normally distributed data, we find that the change over the six years are significantly different from each other at the 5% level (protected areas: Chi2 = 319.57;
p < 0.001; df = 5; n = 1788; 5-km zone: Chi2 = 265.63;
p < 0.001; df = 5; n = 1787; 20-km zone: Chi2 = 365.15;
p < 0.001; df = 5; n = 1787).
Figure 3.
Mean land and forest degradation 2004–2009 for protected areas and adjacent areas showing lower change inside protected areas than in the 5-km and 20-km zones around each protected area and a general trend towards increasing land and forest degradation.
Figure 3.
Mean land and forest degradation 2004–2009 for protected areas and adjacent areas showing lower change inside protected areas than in the 5-km and 20-km zones around each protected area and a general trend towards increasing land and forest degradation.