Abstract
The global tree cover has kept reducing in the last two decades, mainly in tropical forests, despite the global efforts to conserve biodiversity and the ecosystem services that provides. Considering South America is the region that had the highest rate of net forest loss (2000–2020), it is crucial to understand the deforestation dynamics of each country and their regions for the evaluation of effective national conservation actions. Here, we carried out a spatiotemporal analysis of the deforestation rates from 2000 to 2020 on a national and regional scale, including the forest loss inside protected areas, in Peru. It was found that Peru lost 3.4 million ha of forest between 2000 and 2020, mainly in the Not Flooded Rainforest region. National deforestation rates accelerated, while within protected areas rates showed a very small increase. Regional deforestation rates followed the national pattern with one exception—the Coast region. Our results evidence the success of the national conservation strategy based on protected areas to avoid deforestation in all regions, except in the Andean. Moreover, the increment in deforestation rates is aligned with the growth in road infrastructure; increments in anthropic activities; and the more frequent and intense natural extreme events that vary according to the region. Therefore, it is urgent to differentiate the drivers of deforestation that operate at the national versus the regional scale, consider the inclusion of all forest types in the monitoring system, and the strengthening of policies related to land use change at all scales.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Recent advances reveal that global forest extent decreased by 1 million km2 (2.4% of the forest area since 2000) during the last two decades (Potapov et al. 2022). The highest deforestation occurred in tropical rainforests (32%) (Hansen et al. 2013), primarily in the Amazon, but also in the Congo basin, Indonesia, and other countries (Keenan et al. 2015; Potapov et al. 2022). Deforestation of tropical forests is an important global issue because these forests harbor a high percentage of land biodiversity and play a critical role in the global carbon and water cycles (Foley et al. 2007; Nobre et al. 2016; Vargas Zeppetello et al. 2020; Xu et al. 2022). Moreover, the conversion of tropical forests into other land uses contributes approximately to 20% of the world’s greenhouse gas emissions (GHG), and thus, accelerating deforestation rates have dramatic impacts on global climate change (Achard et al. 2007; Vargas Zeppetello et al. 2020). South America is the region that had the highest rate of 2000–2020 net forest loss (5% of the year 2000 forest area or 0.44 million km2) (Potapov et al. 2022). Concerning the different forest types, around 17% of the South American tropical humid forests have been lost in the last 50 years (Lovejoy and Nobre 2019). On the other hand, more than 50% of tropical dry forests deforestation occurs in South America (Miles et al. 2006), and 60% of South America’s dry forest has already been converted to other land uses (Portillo-Quintero and Sánchez-Azofeifa 2010). Forests of the Andes mountains have been occupied for millennia and historically they have been greatly reduced in the past, but currently the deforestation process continues (Aide et al. 2019; Armenteras et al. 2011; Bax and Francesconi 2018).
Tropical deforestation is the result of many processes driven by multiple causes. Despite this complexity, many efforts have been made to explain the dynamics of tropical forest loss, its patterns, and variation across landscapes. Environmental factors play a crucial role in the dynamics of tropical forest cover loss (Aide et al. 2019; Bax and Francesconi 2018; Bax et al. 2016; Geist and Lambin 2002). Tropical deforestation is also associated to social, political, and economic changes that operate at multiple scales, differ among regions and forest types, and change over time (Armenteras et al. 2011, 2017; Geist and Lambin 2002; Rudel 2007; Rudel et al. 2009). In the effort to minimize the impact of tropical forest deforestation, the extent of protected areas has been expanding in the last 20 years (Baldi et al. 2019; FAO 2020b). Although some studies on the topic suggest that protected areas often have lower deforestation rates than unprotected areas (Blankespoor et al. 2017; Ford et al. 2020; Wade et al. 2020; Walker et al. 2020), there is still little information on whether the effect of protected areas may halt deforestation. Another mechanism to prevent deforestation through the conservation of forest carbon stocks, linked or not with protected areas, is the Reduced Emissions from Deforestation and Forest Degradation (REDD+) that have been developing since 2005 (UNFCCC 2016). However, the lack of historical national forest inventory data, the multiple forest classifications (overall and in each country), and the different methods for monitoring and ways for reporting deforestation (e.g., total deforestation annual rate, total rate, gross, net loss in hectares, among others) makes monitoring, comparison, and understanding of the dynamics of forest loss still more difficult (Armenteras et al. 2017; Puyravaud 2003).
Peru has the ninth largest forest area in the world (72 million ha in 2020; FAO 2020a). In the last 20 years, Peru has improved the performance of its economic and social indicators (e.g., total GDP has doubled since 1998, with an average annual growth rate of 4.5%; or extreme poverty has dropped from 16.8 to 3.8%) with the development of mining, agriculture, and other extractive activities, maintaining emissions per capita lower than the average of Latin America and the world (De La Torre Ugarte et al. 2021). However, despite the efforts of the Peruvian government to decrease deforestation rates toward net zero deforestation by 2021 (Che Piu and Menton 2014), including the increase in surface of protected areas (Aguirre et al. 2021), the rate of forest loss grew from 115,000 ha/year in 1990–2000 to 125,000 ha/year in 2000–2010, and to 172,000 ha/year in the last decade (FAO 2020b). However, forest loss is considered the primary source of carbon emissions in the country (MINAM 2016). Its evaluation has been focused on the Amazon area as a whole or locally (Asner and Tupayachi 2017; Bax and Francesconi 2018; Bax et al. 2016; Potapov et al. 2014; Sánchez et al. 2021). Moreover, Peruvian forest loss has also been evaluated as part of cross-national studies (Armenteras et al. 2017; Dávalos et al. 2016; Furumo and Aide 2017; Smith et al. 2021). In this study, we have carried out a spatiotemporal analysis of the deforestation rates in Peru from 2000 to 2020 on a national and regional scale, with particular attention to the deforestation rates of Peruvian protected areas during these decades. More specifically, we ask (1) Do national deforestation rates change over time between 2000 and 2020? Are there any differences between protected areas and unprotected areas? (2) Do these patterns change in the different regions of the country?
Materials and methods
Study area
The study was carried out in Peru (central-western South America), a tropical country located at 0°02′00″North, 18°21′03″South, 68°39′00″East, and 81°19′35″West. The total continental area of Peru is approximately 1.28 million km2, within which 60% are tropical forests (FAO 2020a; IGN 2021). Furthermore, it is the fourth largest country in global tropical forest and second in Amazon rainforest after Brazil (FAO 2020b). Peru has highly heterogeneous climate, geomorphology, physiography, and edaphology, which results in a wide variety of vegetal covers, distributed in landscapes ranging from desert and semi-desert plains, as well as alluvial plains with rain forests, to hilly and mountainous landscapes (MINAM 2015). Here, we grouped the Peruvian forests in five regions according to MINAM (2019): coast (10% of whole area); Andean (30%); high rainforest (14%); and low rainforest or Amazon rainforest, further disaggregated into flooded (13%) and not flooded rainforest (33%) (Fig. 1a). The five regions considered here differ in their ecology and the spatial patterns of land use and human population distribution. The coast region is hot and dry, and extends from the sea level to approximately 1500 or 2000 m. It is the region that harbor more than half of the Peruvian population (58.0%; INEI 2018). The Andean region ranges from semi-warm arid to cold wet or cold dry, being wider and higher in the center and south. The Andean landscapes have highly transformed forest relicts with an estimated reduction of ten times its potential vegetation biomass (Miyamoto et al. 2018; Sylvester et al. 2017). The high rainforest region is on the eastern flank of the Andes Mountains, from 600 to approximately 3600 m. It is humid or subhumid, with permanent fog and strong slopes. This region is sparsely populated due to the highly sloped and arboreal landscape (INEI 2018). The flooded and not flooded rainforest regions are humid and rainy and they are in the Amazon basin. These regions contain the largest extent of forest in Peru and are sparsely populated, mainly near watercourses (13.9%; INEI 2018). The flooded rainforest region is mainly composed by floodplain alluvial forests (flooded several meters during the river crescent) and palm wetlands (permanently or nearly permanently water saturated). The not flooded rainforest region is mainly composed by hill and terrace forests. In this study, the terrestrial protected areas (Supplementary Table 1; SERNANP-MINAM 2021) have been considered as areas of interest (Fig. 1b).
Forest cover data
We obtained the forest cover maps (baseline: 2000; forest cover loss: 2001–2020) from the Global Forest Change (GFC) datasets of 30-m resolution- version 1.8 update to 2020 (). This allowed the analysis of the whole area of study with the longest time series available, in contrast to the official Peruvian national data, that only reports deforestation for the rainforest. We followed the forest definition of Hansen et al. (2013) that estimates the GFC using a decision tree approach based on the multitemporal profile of spectral metrics derived from Landsat satellite images. Our study area spanned five GFC tiles, which were clipped to match with the regional and country boundaries. In the GFC dataset, forest loss or deforestation was defined as the complete removal of pixel tree cover. Furthermore, forest gain areas were not considered, due to the annual exclusion of pixels after a deforestation event. Forest loss data are encoded as either 0 (no loss) or a value in the range 1–20, representing loss detected in the year 2001–2020, respectively (i.e., a pixel with value of 4 indicates that a forest loss event occurred in 2004) (GFC 2022).
Data analysis
We calculated the rate of deforestation (r; in %·yr−1) of the unprotected areas and protected areas for the whole country and each region separately every 5 years from 2000 to 2020 (i.e., four periods: r1 = 2001–2005; r2 = 2006–2010; r3 = 2011–2015; r4 = 2016–2020), based on the cover forest maps (baseline and forest loss; GFC 2022) and protected areas surface (SERNANP-MINAM 2021) using ArcGIS 10.5 (ESRI 2016a) and R 4.1.0 software (R CoreTeam 2021). We used the standardized deforestation rate proposed by Puyravaud (2003), in order to ease comparisons of forest change:
where A1 and A2 are the forest areas (in ha) in the years t1 and t2, respectively. In addition, we aggregated the new protected areas established in each period since terrestrial protected areas have increased from 37 protected areas (6.1% of national surface) in 2000 to 241 protected areas (17.7%) in 2020 (Fig. 1b; see Supplementary Table 2). Forest areas were calculated by converting the pixels (30 × 30 m) of forest cover maps into areas (unit: hectares; ha) within region or protected areas geometries using ArcGIS 10.5 (ESRI 2016a).
For the analysis, since the dependent variable was dichotomous (i.e., forest-1/no forest-0) and the independent variables were categorical, we elaborated a multiple contingency table for deforestation (dependent variable), period (1 = 2001–2005, 2 = 2006–2010, 3 = 2011–2015, and 4 = 2016–2020), status of protection (protected, not protected), and region (the five regions described above). Then, we evaluated the association between variables through log-lineal models using the log-likelihood ratio statistic (G2). We carried out two different analyses: one for Peru as a whole (variables: deforestation, period, and protection), and another one adding regions (variables: deforestation, period, protection, and region). Statistical analyses were carried out using R 4.1.0 software (R CoreTeam 2021).
To detect the areas with the highest deforestation rate (deforestation hotspot), a Kernel density mapping with a radius of 15,000 m (ESRI 2016b; Silverman 1986) was performed using ArcGIS 10.5 (ESRI 2016a) for each period.
Results
Spatiotemporal changes in national deforestation rates
According to the forest cover maps, overall forest loss in Peru between 2001 and 2020 was 3,464,829 ha (4.4% of Peruvian forest cover at 2000) for the whole country and 114,220 ha in protected areas. The only acceptable model that fitted the data of deforestation in Peru (log linear model, G2 = 53,140, p < 0.05) included the highest three-order interaction. Differences in deforestation increased progressively from the first to the last period considered, with a different pattern in unprotected areas and protected areas (Fig. 2; see Supplementary Table 3). Deforestation was higher in unprotected areas than in protected areas in all periods, but while deforestation rates increased in unprotected areas from 2001–2005 to 2016–2020, they showed a much smaller increase in protected areas (Fig. 2; see Supplementary Table 3).
Regional patterns of deforestation
Forest losses by regions from 2000 to 2020 were very different: 9299 ha in the coast region (2.2% of the region), 83,134 ha in the Andean region (4.4% of the region), 817,834 ha in the high rainforest region (4.9% of the region), 649,889 ha in the flooded rainforest region (4% of the region), and 1,904,673 ha in the not flooded region (4.5% of the region) (Fig. 3). The highest density of deforestation occurred in the unprotected areas of high, not flooded, and flooded rainforest regions, intensifying through periods (Fig. 4). The only acceptable model that fitted the regional data of deforestation (log linear model, G2 = 17,352, p < 0.05) was again the full model including the highest four-order interaction. Differences in deforestation increased progressively from the first to the last period considered in all regions, except the Coast and Andean regions (Fig. 5; see Supplementary Table 3). In the coast region, the highest deforestation occurred in the third period while in the Andean region, there was a significant fluctuation from the first to the third period. Deforestation was higher in unprotected areas than in protected areas in all periods and regions (Fig. 5; see Supplementary Table 3). Moreover, regional deforestation rates in protected areas were considerably lower than in unprotected areas in three regions, namely coast, high rainforest, and flooded rainforest, while deforestation rates in protected areas increased slightly in the not flooded rainforest and were particularly high in the Andean region in all periods, especially in the last one.
Discussion
Spatiotemporal deforestation rates trends
Deforestation in Peru from 2000 to 2020 (3.4 million ha; 4.4% of Peruvian forest cover at 2000) was lower in absolute and relative values than in other South American countries (see Supplementary Fig. 1). South America reduced to about half the annual rate of net forest loss in the decade 2010–2020 (2.6 million ha/year) than in the previous decade (4 million ha/year in 2000–2010) (FAO 2020b). However, deforestation in 2010–2020 was higher than in 2000–2010 in some countries, including Peru (FAO 2020b). Our analysis shows that deforestation in Peru increased over time (Fig. 2). We noticed that this increase in deforestation rate was aligned with the development and intensification of legal, informal, and illegal economic activities, such as agriculture or mining, depending on the region, that have transformed the landscape in the last twenty years. The development of these activities was exacerbated in the last two periods, which could be related to the almost doubling of the national road network during the second period of study (Aguirre et al. 2021). This is the case of the Interoceanic Highway (south national transversal axis; see Supplementary Fig. 2), that promoted deforestation of forests by expanding agricultural intensification, illegal gold mining, illegal crops, and illegal logging with an elevated social impact (Dourojeanni 2019; Gallice et al. 2019; Honorio Coronado et al. 2020).
Deforestation in Peru was considerably higher outside protected areas than inside. This pattern persisted over time (from about 8 times in the first period to 12 in the last; Fig. 2). This result is also consistent with other studies in tropical protected areas (Blankespoor et al. 2017; Ford et al. 2020; Wade et al. 2020; Walker et al. 2020) and other studies in Peru (Aguirre et al. 2021; Dourojeanni 2014; Miranda et al. 2015). Moreover, we noticed a very small increase in deforestation rates in protected areas over time (Fig. 2). This could be explained because most protected areas are still located in remote areas or areas of difficult access (see Supplementary Fig. 2). Thus, remoteness has a protective effect of protected areas at a national scale. Furthermore, there is evidence that awarding formal land titles to indigenous communities contributes to the reduction of deforestation in Peru (Blackman et al., 2017). However, indigenous people’s land and resource rights in Peru are legally recognized but only partially, which fosters insecurity in a context of strong external pressure (Rodríguez et al., 2022; Lasheras et al., 2023). Moreover, studies alert that different anthropogenic activities such as the expansion of agricultural lands, agro-industrial plantations, cattle ranching, mining, and illicit coca cultivation have surpassed buffer zones, causing deforestation inside protected areas in the last decades (Asner and Tupayachi 2017; Dourojeanni 2018; Sánchez et al. 2021; UNDOC and DEVIDA 2018). This is again a result of the chaotic growth of road infrastructure that in some cases have dissected protected areas at various points (Aguirre et al. 2021). Moreover, although the legislation prohibits production activities inside intangible protected areas, the application of these laws is quite elastic (Dourojeanni 2015, 2018).
Understanding the changes in deforestation rates by regions
This study is probably the first spatiotemporal analysis of deforestation that includes a regional scale and protected areas from 2000 to 2020. Differences in deforestation by region (Fig. 3) are likely related to the environmental and climatic conditions that determine the extent of forest and land aptitude, as well as the process of occupation and land use of each region. In absolute terms, the not flooded region is the region with the most deforestation and the coast region with the lowest. In the not flooded rainforest region, the region with the most forest cover and with a recent accelerated proliferation of intensive and agro-industrial crops, occurred the 55% of deforestation of Peru (Ravikumar et al. 2017). High deforestation rates are also found in the high rainforest and flooded rainforest regions, where the increased access by the national road network or navigable rivers respectively has allowed disorderly rainforest penetration, disorganized migrations and informal occupation of the land motivated by national policies from several decades ago (Dourojeanni 2019). Therefore, the deforestation focus is located along the national rainforest road network and navigable rainforest rivers (see Supplementary Fig. 2). In contrast, the coast and Andean regions had a lower deforestation rate (Fig. 3). These regions have less forest cover because of their long history of occupation and land use changes such as agriculture, livestock, and urban development (Portillo-Quintero and Sánchez-Azofeifa 2010; Sylvester et al. 2017). In the coast region, 95% of the potential Peruvian tropical dry forest has been already converted into other land uses, the highest percentage in Latin America, only remaining highly fragmented forest tracts (Portillo-Quintero and Sánchez-Azofeifa 2010). Likewise, in the Andean region, 90% of forest cover has been lost and only relict forests remain (Sylvester et al. 2017). Despite these differences in absolute values, our study shows that all regions lost between 2 and 5% of forest cover from 2000 to 2020 (Fig. 3). Moreover, the high rainforest and Andean regions had the highest forest cover loss relative to their cover in 2000. This result could be due to the presence of many roads established since 1940s, such as the “Carretera Marginal de la Selva” (1960s and 1970s) in the high rainforest region, which is strongly related to the deforestation process in the rainforest (Dourojeanni 2019; see Supplementary Fig. 2).
All regional deforestation rates had an accelerated growth following the national pattern with the coast region being the exception (Figs. 2 and 5). In this region, we observe a severe increment in the deforestation rate during the third period, followed by a considerable decrease in the last period. We relate this pattern to the influence of “El Niño” Southern Oscillation (ENSO) on the dynamics of regional forest cover. The Coastal “El Niño” Index (see Supplementary Fig. 3) maintained low during the periods one and two, increasing the fire risk from agricultural burning, which could have been increased due to national prohibition policies on the use of fire (Cuentas Romero and Salazar Toledo 2017; dos Reis et al. 2021). On the other hand, the reduction of the deforestation rate in the fourth period could be explained by the extreme “El Niño costero” (coastal) of 2017 (localized ENSO in the north of Peru, last documented in 1925; Ramírez and Briones 2017), which promoted a substantial increase of forest cover, reported by Rodríguez et al. (2018), altering the deforestation rates in the last period. The transition process between “El Niño” and “La Niña” in 2005, 2010, and 2016 in the central Pacific is related to the severe increase in the occurrence of forest fires in the Andean and High Rainforest regions (Zubieta et al. 2019). Therefore, in these regions the increase in deforestation rates (Fig. 5) could also be explained by the significant increase in fires resulting from agricultural burning practices that went out of control because of changes in precipitation and drought, and the national prohibition policies on the use of fire (Armenteras et al. 2020; Bax and Francesconi 2018; Zubieta et al. 2019, 2021).
For decades, the Government attributed the majority of rainforest forest cover loss (90%) to clearing for a small-scale, migratory, or subsistence agriculture (Ravikumar et al. 2017). However, there is an accelerated proliferation of agro-industrial crops and agroforestry, such as oil palm, mainly in not flooded rainforest; cocoa and coffee, mainly in high and not flooded rainforest; and fruits and other crops, in all rainforest regions, directly promoted by the rapid increase in global food and fuel demand (Castro-Nunez et al. 2021; Furumo and Aide 2017; Noble 2017; Ravikumar et al. 2017). Oil palm production has grown exponentially since the second period, reducing primary forests (Glinskis and Gutiérrez-Vélez 2019; Gutiérrez-Vélez et al. 2011; Vijay et al. 2018). In the same way, the increase in the deforestation rates of the last three decades, also in primary forests, was related to cocoa production increases (Finer and Mamani 2020; Noble 2017). Although there are more coffee cultivated areas than cocoa or oil palm in Peru (Castro-Nunez et al. 2021), there is surprisingly little information on the extent to which coffee is driving deforestation (Harvey et al. 2021). Moreover, in high and not flooded rainforest, oil palm, cocoa, and coffee production has been adopted by coca substitution programs to end its illicit cultivation (Castro-Nunez et al. 2021; Glinskis and Gutiérrez-Vélez 2019; Harvey et al. 2021). Although there is little evidence that coca increases deforestation rates (Dávalos et al. 2016), coca surface had a progressive increase during the first and second periods, decreased during the third period and increased again during the fourth period (see Supplementary Fig. 4).
The development of extractive activities, in particular mining and gas and oil extraction, in high, flooded, and not flooded rainforest regions also could explain the progressive increment in deforestation rates along the study periods (Fig. 5). After 2000, deforestation rates for gold mining grew every year, with an alarming increase at the end of the second period and remaining consistently high during the third and fourth periods (Espejo et al. 2018). These high deforestation rates have been related to the Interoceanic highway, the overcoming of the global economic recession (2008–2009), the large increase of gold price during 2005–2012, and national policies on mining legalization since 2012 (Dourojeanni 2015; Espejo et al. 2018). On the other hand, Peru had a new exploration boom in oil and gas blocks during the second period of study, as a consequence of national investment promoted policies (Finer and Orta-Martínez 2010). These blocks cover more than 70% of the Peruvian rainforest and their exploration and extraction could promote deforestation by facilitating access to previously remote primary forests. Although these activities are harmful, there is little information about them (Dourojeanni 2019).
Protected areas of all regions were outside the main deforestation focus through time (Fig. 4). Deforestation rates in the coast, high rainforest, and flooded rainforest protected areas had a very small increase along periods following the national pattern (Figs. 2 and 5). However, the protection effect of protected areas was not enough in the not flooded rainforest and, mainly, in the Andean region. In the not flooded rainforest, there are reports of anthropogenic activities, such as the expansion of agricultural lands, agro-industrial plantations; mining and the illicit coca cultivation, that had increased deforestation in protected areas (Asner and Tupayachi 2017; Dourojeanni 2018; Sánchez et al. 2021; UNDOC and DEVIDA 2018). On the other hand, the high deforestation rates in the Andean protected areas could be associated with the severe increase in fires due to droughts (Zubieta et al. 2021). The considerable increment in deforestation rate in the last period in the Andean region (Fig. 5) could be a consequence of the intense fire season at the end of 2016, before the 2017 “El Niño costero” that seriously affected protected areas in the coast and Andean regions in northern Peru (Novoa and Finer 2017). In 2017, 86.5% of the Andean deforestation was in the north of the country (departments of Piura, Lambayeque, Cajamarca, and La Libertad) and 93.6% was within seven Andean protected areas (see Supplementary Fig. 5).
Final remarks
Although our analysis shows that deforestation in Peru from 2000 to 2020 was lower than in other neighboring countries, national and regional deforestation rates were increasing, especially for 2011–2020, except for the coast region. However, the national conservation strategy based on protected areas succeeded in avoiding deforestation in all regions, except in the Andean region. It is important to note (1) the reports of anthropogenic activities that surpassed buffer zones causing deforestation inside protected areas have been more frequent in the last decade; (2) the episodes of drought and ENSO have been more frequent and intense in recent decades because of climate change; (3) forest fires are increasing in last years, but Peru is not prepared to face new and more intense fire risks (Dourojeanni 2016); (4) the development of road infrastructures near the protected areas increases the rates of deforestation and partially cancels the protective effects (Aguirre et al. 2021); and (5) we noticed that the increment in deforestation rates could be related to variations in environmental and climatic conditions, intensification of climatic events that influenced disturbed forest cover, increase in accessibility of forests. It also could be aligned with the increment in anthropic activities, which vary according to the region, and were promoted by subjacent causes (although we did not analyze these factors; supplementary Table 4). Therefore, it is urgent to differentiate the drivers of deforestation that operate at the national versus the regional scale and that all forest types are to be considered in the monitoring system and to strengthen the policies related to land use to change at all scales.
Data availability
As we indicated in forest cover data, the data utilized in our analysis originates from the publicly available Global Forest Change (GFC) version 1.8 dataset.
References
Achard F, Defries R, Eva H, Hansen M, Mayaux P et al (2007) Pan-tropical monitoring of deforestation. Environ Res Lett 2(4). https://doi.org/10.1088/1748-9326/2/4/045022
Aguirre J, Guerrero E, Campana Y (2021) How effective are protected natural areas when roads are present? An analysis of the Peruvian case. Environ Econ Policy Stud 23(4):831–859. https://doi.org/10.1007/s10018-021-00304-y
Aide T, Mitchell HR, Grau JG, Andrade-Nuñez MJ, Aráoz E et al (2019) Woody vegetation dynamics in the tropical and subtropical Andes from 2001 to 2014: satellite image interpretation and expert validation. Glob Change Biol 25(6):2112–2126. https://doi.org/10.1111/gcb.14618
Armenteras D, Rodríguez N, Retana J, Morales M (2011) Understanding deforestation in Montane and lowland forests of the Colombian Andes. Reg Environ Change 11(3):693–705. https://doi.org/10.1007/s10113-010-0200-y
Armenteras D, Espelta JM, Rodríguez N, Retana J (2017) Deforestation dynamics and drivers in different forest types in Latin America: three decades of studies (1980–2010). Glob Environ Chang 46:139–147. https://doi.org/10.1016/j.gloenvcha.2017.09.002
Armenteras D, González TM, Ríos OV, Elizalde MCM, Oliveras I (2020) Fire in the ecosystems of Northern South America: advances in the ecology of tropical fires in Colombia, Ecuador and Peru. Caldasia 42(1):1–16. https://doi.org/10.15446/caldasia.v42n1.77353
Asner GP, Tupayachi R (2017) Accelerated losses of protected forests from gold mining in the Peruvian Amazon. Environ Res Lett 12(094004):1–8. https://doi.org/10.1088/1748-9326/aa7dab
Baldi G, Schauman S, Texeira M, Marinaro S, Martin OA, Gandini P, Jobbágy EG (2019) Nature representation in South American protected areas : country contrasts and conservation priorities. PeerJ 1–23. https://doi.org/10.7717/peerj.7155
Bax V, Francesconi W (2018) Environmental predictors of forest change : an analysis of natural predisposition to deforestation in the tropical Andes region, Peru. Appl Geogr 91:99–110. https://doi.org/10.1016/j.apgeog.2018.01.002
Bax V, Francesconi W, Quintero M (2016) Spatial modeling of deforestation processes in the central Peruvian Amazon. J Nat Conserv 29:79–88. https://doi.org/10.1016/j.jnc.2015.12.002
Blankespoor B, Dasgupta S, Wheeler D (2017) Protected areas and deforestation: new results from high-resolution panel data. Nat Res Forum 41(1):55–68. https://doi.org/10.1111/1477-8947.12118
Castro-Nunez AC, Ma EJ, Villarino VB, Ganzenmüller R, Francesconi W (2021) Broadening the perspective of zero-deforestation interventions in peru by incorporating concepts from the global value chain literature. Sustainability 13(12138):1–17. https://doi.org/10.3390/su132112138
Che Piu H, Menton M (2014) The context of REDD+ in Peru: drivers, agents and institutions. Center for International Forestry Research (CIFOR). https://doi.org/10.17528/cifor/004438
Cuentas Romero MA, Salazar Toledo AÍ (2017) De La Especie Al Ecosistema; Del Ecosistema a La Sociedad: Revalorizando El Algarrobo (ProsoPis Pallida) y El Reto de Su Conservación En Lambayeque y En La Costa Norte Del Perú. Espacio y Desarrollo (30):129–59. https://doi.org/10.18800/espacioydesarrollo.201702.006
Dávalos LM, Sanchez KM, Armenteras D (2016) Deforestation and coca cultivation rooted in twentieth-century development projects. Bioscience 66(11):974–982. https://doi.org/10.1093/biosci/biw118
De La Torre Ugarte D, Collado M, Requejo F, Gomez X, Heros C (2021) A deep decarbonization pathway for Peru’s rainforest. Energy Strategy Rev 36(100675):1–13. https://doi.org/10.1016/j.esr.2021.10067
dos Reis M, Lima de Alencastro Graça PM, Yanai AM, Pacheco Ramos CJ, Fearnside PM (2021) Forest fires and deforestation in the Central Amazon: effects of landscape and climate on spatial and temporal dynamics. J Environ Manag 288:112310. https://doi.org/10.1016/j.jenvman.2021.112310
Dourojeanni MJ (2014) Ocupación Humana Y Áreas Protegidas De La Amazonia Del Perú. Ecología Aplicada 13(2):225–232. https://doi.org/10.21704/rea.v13i1-2.473
Dourojeanni M (2015) Oro de Madre de Dios... Ya Es Legal?. SPDA Actualidad Ambiental. https://www.actualidadambiental.pe/oro-de-madre-de-dios-ya-es-legal-escribe-marc-dourojeanni/. Accessed 26 Jan 2024
Dourojeanni M (2016) Incendios forestales y los bomberos que necesita el Perú. SPDA Actualidad Ambiental. https://www.actualidadambiental.pe/incendios-forestales-y-los-bomberos-que-necesita-el-peru-escribe-marc-dourojeanni/. Accessed 26 Jan 2024
Dourojeanni M (2018) ¿Producción de Cacao y Café En Áreas Naturales Protegidas? SPDA Actualidad Ambiental. https://www.actualidadambiental.pe/opinion-produccion-de-cacao-y-cafe-en-areas-naturales-protegidas/. Accessed 26 Jan 2024
Dourojeanni MJ (2019) Amazonía Peruana ¿Qué Futuro?. Grijley, Lima
Espejo JC, Messinger M, Rom F, Ascorra C, Fernandez LE et al (2018) Deforestation and forest degradation due to gold mining in the Peruvian Amazon : a 34-year perspective. 1–17. https://doi.org/10.3390/rs10121903
ESRI (2016a) ArcGIS Desktop: Release 10.5 Redlands, CA: Environmental Systems Research Institute
ESRI (2016b) How Kernel Density works. Retrieved https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-kernel-density-works.htm
FAO (Food and Agriculture Organization Organization of the United Nations) (2020a) Evaluación de Los Recursos Forestales Mundiales 2020 Informe- Perú. https://www.fao.org/3/cb0110es/cb0110es.pdf. Accessed 26 Jan 2024
FAO (Food and Agriculture Organization Organization of the United Nations) (2020b) Main Report. https://www.fao.org/3/ca9825en/ca9825en.pdf. Accessed 26 Jan 2024
Finer M, Mamani N (2020) United Cacao Case - 7 Years After Massive Deforestation in the Peruvian Amazon. MAAP # 128. https://www.maaproject.org/2020/cacao-tamshiyacu/. Accessed 26 Jan 2024
Finer M, Orta-Martínez M (2010) A second hydrocarbon boom threatens the Peruvian Amazon: trends, projections, and policy implications. Environ Res Lett 5(1). https://doi.org/10.1088/1748-9326/5/1/014012
Foley JA, Asner GP, Costa MH, Coe MT, Defries R et al (2007) Amazonia revealed: forest degradation and loss of ecosystem goods and services in the Amazon Basin In a Nutshell. Front Ecol Environ 5(1):155. https://doi.org/10.1890/1540-9295(2007)5[25:ARFDAL]2.0.CO;2
Ford SA, Jepsen MR, Kingston N, Lewis E, Brooks TM et al (2020) Deforestation leakage undermines conservation value of tropical and subtropical forest protected areas. Glob Ecol Biogeogr 29(11):2014–2024. https://doi.org/10.1111/geb.13172
Furumo PR, Aide TM (2017) Characterizing commercial oil palm expansion in Latin America : land use change and trade characterizing commercial oil palm expansion in Latin America : Land Use Change and Trade. 12(024008):1–12. https://doi.org/10.1088/1748-9326/aa5892
Gallice GR, Larrea-Gallegos G, Vázquez-Rowe I (2019) The threat of road expansion in the Peruvian Amazon. Oryx 53(2):284–292. https://doi.org/10.1017/S0030605317000412
Geist HJ, Lambin EF (2002) Proximate causes and underlying driving forces of tropical deforestation. Bioscience 52(2):143–150. https://doi.org/10.1641/0006-3568(2002)052[0143:PCAUDF]2.0.CO;2
GFC (Global Forest Change) (2022) Global Forest Change 2000–2020 Data Download. Retrieved https://storage.googleapis.com/earthenginepartners-hansen/GFC-2020-v1.8/download.html
Glinskis EA, Gutiérrez-Vélez VH (2019) Quantifying and understanding land cover changes by large and small oil palm expansion regimes in the Peruvian Amazon. Land Use Policy 80(September 2018):95–106. https://doi.org/10.1016/j.landusepol.2018.09.032
Gutiérrez-Vélez VH, DeFries R, Pinedo-Vásquez M, Uriarte M, Padoch C et al (2011) High-yield oil palm expansion spares land at the expense of forests in the Peruvian Amazon. Environ Res Lett 6(4):044029. https://doi.org/10.1088/1748-9326/6/4/044029
Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA et al (2013) High-resolution global maps of 21st-century forest cover change. 342:850–54. https://doi.org/10.1126/science.1244693
Harvey CA, Pritts AA, Zwetsloot MJ, Jansen K, Pulleman MM et al (2021) Transformation of coffee-growing landscapes across Latin America. A review. Agron Sustain Dev 41(5). https://doi.org/10.1007/s13593-021-00712-0
Honorio Coronado E, Mercado Torres A, Del Castillo Torres D, Dávila Cardoso N, Martín Brañas M et al (2020) Impacto De La Construcción De La Carretera Iquitos-Saramiriza Sobre Los Bosques Y Turberas Del Río Tigre, Loreto, Perú. Folia Amazónica 29(1):65–87. https://doi.org/10.24841/fa.v28i2.493
IGN, Instituto Geográfico Nacional (2021) Atlas Digital Del Perú. https://www.gob.pe/qu/institucion/ign/pages/11084-consultar-el-atlas-digital-del-peru. Accessed 26 Jan 2024
Instituto Nacional de Estadística e Informática (INEI) (2018) Perfil Sociodemográfico Del Perú. https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1539/. Accessed 26 Jan 2024
Keenan RJ, Reams GA, Achard F, De Freitas JV, Grainger A et al (2015) Forest ecology and management dynamics of global forest area : results from the FAO Global Forest Resources Assessment 2015 Q. For Ecol Manage 352:9–20. https://doi.org/10.1016/j.foreco.2015.06.014
Lovejoy TE, Nobre C (2019) Amazon tipping point: last chance for action. Sci Adv 5(12):4–6. https://doi.org/10.1126/sciadv.aba2949
Miles L, Newton AC, DeFries RS, Ravilious C, May I et al (2006) A global overview of the conservation status of tropical dry forests. J Biogeogr 33(3):491–505. https://doi.org/10.1111/j.1365-2699.2005.01424.x
MINAM (Ministerio del Ambiente Perú) (2019) Mapa Nacional de Ecosistemas Memoria Descriptiva. https://repositoriodigital.minam.gob.pe/handle/123456789/925?show=full. Accessed 26 Jan 2024
MINAM (2015) Mapa Nacional de Cobertura Vegetal. Memoria Descriptiva. https://www.minam.gob.pe/patrimonio-natural/wp-content/uploads/sites/6/2013/10/MAPA-NACIONAL-DE-COBERTURA-VEGETAL-FINAL.compressed.pdf. Accessed 26 Jan 2024
MINAM (2016) La Conservación de Bosques En El Perú (2011–2016). https://repositoriodigital.minam.gob.pe/handle/123456789/112. Accessed 26 Jan 2024
Miranda JJ, Corral L, Blackman A, Asner G, Lima E (2015) Effects of protected areas on forest cover change and local communities : evidence from the Peruvian Amazon. 78:288–307. https://doi.org/10.1016/j.worlddev.2015.10.026
Miyamoto K, Sato T, Olivos EAA, Orellana GC, Stornaiuolo CMR (2018) Variation in tree community composition and carbon stock under natural and human disturbances in Andean Forests, Peru. Forests 9(7):1–15. https://doi.org/10.3390/f9070390
Noble MD (2017) Chocolate and the consumption of forests: a cross-national examination of ecologically unequal exchange in cocoa exports. 23(2). https://doi.org/10.5195/JWSR.1
Nobre CA, Sampaio G, Borma LS, Castilla-rubio JC, Silva JS et al (2016) Land-use and climate change risks in the amazon and the need of a novel sustainable development paradigm. 113(39):10759–68. https://doi.org/10.1073/pnas.1605516113
Novoa S, Finer M (2017) Update – Fires Degrade 11 Protected Areas in Northern Peru. MAAP: 52. https://www.maaproject.org/2017/northern-fires-update/. Accessed 26 Jan 2024
Portillo-Quintero CA, Sánchez-Azofeifa GA (2010) Extent and conservation of tropical dry forests in the Americas. Biol Cons 143(1):144–155. https://doi.org/10.1016/j.biocon.2009.09.020
Potapov PV, Dempewolf J, Talero Y, Hansen MC, Stehman SV, et al. (2014) National satellite-based humid tropical forest change assessment in peru in support of REDD + implementation. Environ Res Lett 9(12):124012. https://doi.org/10.1088/1748-9326/9/12/124012
Potapov P, Hansen MC, Pickens A, Hernandez-serna A, Tyukavina A et al (2022) The global 2000–2020 land cover and land use change dataset derived from the Landsat Archive : first results. Front Remote Sensing 3(856903):1–22. https://doi.org/10.3389/frsen.2022.856903
Puyravaud J-P (2003) Standardizing the calculation of the annual rate of deforestation. For Ecol Abd Manag 177:593–596. https://doi.org/10.1016/S0378-1127(02)00335-3
RStudio Team (2021). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/
Ramírez IJ, Briones F (2017) Understanding the El Niño Costero of 2017: the definition problem and challenges of climate forecasting and disaster responses. Int J Disaster Risk Sci 8:489–492. https://doi.org/10.1007/s13753-017-0151-8
Ravikumar A, Sears RR, Cronkleton P, Menton M, Sills E (2017) Is small-scale agriculture really the main driver of deforestation in the Peruvian Amazon ? Moving beyond the prevailing narrative. Conserv Lett 10(2):170–177. https://doi.org/10.1111/conl.12264
Rodríguez R, Mabres A, Palacios E, Salazar P (2018) El Niño Registrado En Especies Arbóreas Del Bosque Seco de La Costa Norte Del Perú. Boletín Técnico: Generación de Modelos Climáticos Para El Pronóstico de La Ocurrencia Del Fenómeno El Niño, Instituto Geofísico Del Perú 5(4):5–9
Rudel TK (2007) Changing agents of deforestation : from state-initiated to enterprise driven processes, 1970–2000. Land Use Policy 24:35–41. https://doi.org/10.1016/j.landusepol.2005.11.004
Rudel TK, Defries R, Asner GP, Laurance WF (2009) Changing drivers of deforestation and new opportunities for conservation. Conserv Biol 23(6):1396–1405. https://doi.org/10.1111/j.1523-1739.2009.01332.x
Sánchez AC, Bandopadhyay S, Briceño NBR, Banerjee P, Guzmán CT et al (2021) Peruvian Amazon disappearing : transformation of protected areas during the last two decades ( 2001 – 2019 ) and potential future deforestation modelling using cloud computing and MaxEnt approach. J Nat Conserv 64(126081 Contents):1–15. https://doi.org/10.1016/j.jnc.2021.126081
SERNANP-MINAM (Servicio Nacional de Áreas Naturales Protegidas- Ministerio del Ambiente Perú) (2021) Sistema de Áreas Naturales Protegidas Del Perú. https://www.gob.pe/institucion/sernanp/campa%C3%B1as/4340-sistema-nacional-de-areas-naturales-protegidas-por-el-estado. Accessed 26 Jan 2024
Silverman BW (1986) Density estimation for statistics and data analysis. New York
Smith CC, Healey JR, Berenguer E, Young PJ, Taylor B (2021) Old-growth forest loss and secondary forest recovery across Amazonian countries OPEN ACCESS old-growth forest loss and secondary forest recovery across Amazonian countries. Environ Res Lett 16(085009):1–14. https://doi.org/10.1088/1748-9326/ac1701
Sylvester SP, Heitkamp F, Sylvester MDPV, Jungkunst HF, Sipman HJM et al (2017) Relict high-Andean ecosystems challenge our concepts of naturalness and human impact. Sci Rep 7(1):1–13. https://doi.org/10.1038/s41598-017-03500-7
UNDOC (Oficina de las Naciones Unidadas contra la Droga y el Delito), and DEVIDA (Comisión Nacional para el desarrollo y Vida sin Drogas) (2018) Monitoreo de Cultivos de Coca 2017. https://www.unodc.org/documents/crop-monitoring/Peru/Peru_Monitoreo_de_Cultivos_de_Coca_2017_web.pdf. Accessed 26 Jan 2024
UNFCCC (United Nations Climate Change) (2016) Key Decisions relevant for reducing emissions from deforestation and forest degradation in developing countries decision booklet REDD + ( Includes the Warsaw Framework for REDD + ). https://unfccc.int/files/land_use_and_climate_change/redd/application/pdf/compilation_redd_decision_booklet_v1.1.pdf. Accessed 26 Jan 2024
Vijay V, Reid CD, Finer M, Jenkins CN, Pimm SL (2018) Deforestation risks posed by oil palm expansion in the Peruvian Amazon. Environ Res Lett 13(114010):2–10. https://doi.org/10.1088/1748-9326/aae540
Wade CM, Austin KG, Cajka J, Lapidus D, Everett KH et al (2020) What is threatening forests in protected areas ? A Global assessment of deforestation in protected areas, 2001–2018. Forests 11(539):1–14. https://doi.org/10.3390/f11050539
Walker WS, Gorelik SR, Baccini A, Aragon-osejo JL, Josse C et al (2020) The role of forest conversion, degradation, and disturbance in the carbon dynamics of Amazon Indigenous territories and protected areas. Proc Natl Acad Sci (PNAS) 117(6):3015–3025. https://doi.org/10.1073/pnas.1913321117
Xu X, Zhang X, Riley WJ, Xue Y, Nobre CA et al (2022) Deforestation triggering irreversible transition in Amazon hydrological cycle OPEN ACCESS deforestation triggering irreversible transition in Amazon hydrological cycle. Environ Res Lett 17(034037):1–10. https://doi.org/10.1088/1748-9326/ac4c1d
Zeppetello V, Lucas R, Parsons LA, Spector JT, Naylor RL et al (2020) Large scale tropical deforestation drives extreme warming Lucas. Environ Res Lett 15(084012):1–7. https://doi.org/10.1088/1748-9326/ab96d2
Zubieta R, Prudencio F, Alarco G, Reupo J (2019) Ocurrencia de Incendios Forestales En El Perú Durante Eventos El Niño. Boletín Técnico: Generación De Información y Monitoreo Del Fenómeno El Niño, Instituto Geofísico Del Perú 6(5):5–9
Zubieta R, Fernando Prudencio A, Yerson Ccanchi A, Jorge Reupo A, Glory Alarco C et al (2021) Potential conditions for fire occurrence in vegetation in the Peruvian Andes. Int J Wildland Fire 30:836–849. https://doi.org/10.1071/WF21029
Acknowledgements
We are very grateful to Roberto Molowny for his statistical support and helping us with data processing. We also thank Gregory Asner for his useful advice about Global Forest Change (GFC) data. Maricel Móstiga received a predoctoral fellowship, Generación Bicentenario, funded by Peruvian Ministry of Education (Programa Nacional de Becas y Créditos Educativos- PRONABEC).
Funding
Open Access Funding provided by Universitat Autonoma de Barcelona.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by Xiangzheng Deng
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Móstiga, M., Armenteras, D., Vayreda, J. et al. Two decades of accelerated deforestation in Peruvian forests: a national and regional analysis (2000–2020). Reg Environ Change 24, 42 (2024). https://doi.org/10.1007/s10113-024-02189-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10113-024-02189-5