Environmental predictors of forest change: An analysis of natural predisposition to deforestation in the tropical Andes region, Peru
Introduction
Ongoing processes of forest destruction across tropical regions pose a major threat to biodiversity, climate stability and the functioning of biogeochemical and hydrological cycles (Bonan, 2008, Malhi et al., 2008). In spite of the worldwide recognition of this environmental problem and the implementation of manifold initiatives to halt further reduction of tropical forest areas, the rates of deforestation in the tropics have remained consistently at high levels (Achard et al., 2014, Sloan and Sayer, 2015). Much attention goes to the tropical forest ecosystems in Central and South America, which harbor some of the world's greatest amounts of species diversity (Poveda et al., 2011, Ribeiro et al., 2011, pp. 405–434) and constitute the largest portion of the global terrestrial carbon sink (Pan et al., 2011). Between 2000 and 2010, the extent of forest cover in Central and South America shrunk by approximately 55,000 km2 per year, with important deforestation hotspots located in northern Argentina, southeastern Bolivia, western Paraguay and the Brazilian Amazon (Aide et al., 2013). According to recent land-use change scenarios for the Neotropics (Soares-Filho et al., 2013), in particular the lowland Amazon basin and forests located at the Andean foothills will experience extensive forest losses in the future, whereas forest recovery could be expected in the highland forests of the Andean mountain range (Middendorp et al., 2016, Sanchez-Cuervo and Aide, 2013a). These dynamics of Neotropical forest cover change will largely shape the future well-being of people relying directly on forest ecosystem services.
In the last decades, substantial efforts have been made by researchers to determine why forest change happens and why the patterns and rates of forest change vary across the landscape (Rudel, 2007). An important contribution to the deforestation literature is the work by Geist and Lambin (2002), which conceptualizes the dynamics between fundamental social processes such as population changes, and human activities or actions at the local level with direct impacts on forest cover such as logging and agricultural expansion. Proximate causes and underlying driving forces typically relate to anthropogenic systems, however, environmental factors are also recognized to play a crucial role in the process of forest cover change. Geist and Lambin (2002) reviewed 152 subnational case studies from the tropical belt, out of which a third reported a link between deforestation and factors associated with the biophysical environment, including a range of landscape attributes and climate variables. The effect of these environmental factors is linked to human behavior at the local level, as they set the necessary conditions for land-use change processes to occur, and place physical thresholds on the types of land-use practices that are feasible in a region (Aide et al., 2013). While this emphasizes the importance of considering landscape elements and climate conditions at local scales, the mainstream deforestation literature is particularly oriented towards analyzing the political, economic, and social context in which forest change processes occur (Jusys, 2016, Lambin et al., 2001, Robinson et al., 2014, Rudel et al., 2009).
Nonetheless, a great variety of models have been developed throughout the years to describe the role of the natural environment in the deforestation process (Busch and Ferretti-Gallon, 2017, Kaimowitz and Angelsen, 1998). The environmental variables commonly included in models relate to land accessibility, land suitability and climate variability. Landscape characteristics that provide natural access routes to forests include rivers and lakes (Salonen, Toivonen, Cohalan, & Coomes, 2012), especially in areas where roads and other infrastructure is scarce (Armenteras, Rudas, Rodriguez, Sua, & Romero, 2006). Furthermore, forests located along the coastline that were better accessible were found to be subjected to more deforestation compared to mainland forests (Rudel & Roper, 1996). Elevation and slope gradients have been associated with forest accessibility and deforestation as well (Bax, Francesconi, & Quintero, 2016), although they particularly determine the suitability of the land for productive activities (Pope et al., 2015). Higher sloped terrain is less attractive for agriculture, given that harvests are generally lower (Barrowclough et al., 2016) and working the land requires greater efforts and resources (Grau, Kuemmerle, & Macchi, 2013). The relationship between deforestation and topography is likely to become weaker through time when low-lying lands become scarcer and exhausted, leaving people no other choice but to move to steeper areas. Deforestation induced by the suitability of the land is also determined by the quality of the soil, mainly within the context of agricultural production (Laurance et al., 2002), and by forest type (Chowdhury, 2006), given the potential preferences of loggers for tree species with high economic value (Asner et al., 2005). Climate also seems to affect deforestation through local variations in precipitation, temperature, and dry season severity. Precipitation and dry season severity can either have a hindering or an enabling effect: less rainfall results in dryer forests which are easier to burn (Aragao et al., 2008) while deficit or excessive rainfall tends to reduce crop yields (Grau, Gasparri, & Aide, 2005). On the other hand, areas characterized by moderate local temperatures provide desirable conditions for establishing human settlements, which transform the natural landscape (Armenteras, Rodríguez, Retana, & Morales, 2011).
Given that the tropical Andes region is characterized by a great variation in altitude, forest structure, temperature and rainfall patterns, the way in which land cover transformations are being undertaken could be related to these environmental attributes. A better understanding of nature's influence on deforestation decision making (here defined as the decision of land-managers to conserve or convert forest) is needed, as it is currently not adequately addressed in land-use regulation policies (Joppa and Pfaff, 2009, Miteva et al., 2012). However, studies specifically focusing on the environmental dimensions of deforestation are scarce. Generally more attention is given to human-related drivers and causes. To the best of our knowledge, the work by Rolett and Diamond (2004) may be the best known study that focuses on the effect of predisposing environmental factors on forest transitions. Hence, understanding the biophysical and climate context of forest cover change in the highly diverse landscape of the Andean mountains could advance our understanding of montane forest management. Furthermore, most studies on deforestation in the Neotropical region focus on lowland Amazon ecosystems, while information on Andean ecosystems remains limited (Armenteras et al., 2011, Zuluaga and Rodewald, 2015). In particular, very few studies have analyzed deforestation practices in the Peruvian Andes region. Current rates of forest cover change and hotspot locations in the Peruvian Andes are not provided in the scientific literature, and the drivers associated with these changes are not clearly understood (Robiglio, Armas, Silva Aguad, & White, 2014). Nonetheless, the tropical Andes have been identified as the most critical biodiversity hotspot on the planet in terms of plant and vertebrate species richness (Myers, Mittermeier, Mittermeier, Da Fonseca, & Kent, 2000), which emphasizes the importance of investigating deforestation dynamics in this region. More specifically, the objectives of this study were to 1) identify and examine the environmental factors that facilitate or mitigate deforestation in the tropical Peruvian Andes; 2) analyze the influence of these environmental factors on some of the known direct deforestation drivers in the region; and 3) map the areas where the natural landscape facilitates or mitigates deforestation.
Section snippets
Study area
Peru's tropical forest region can be disaggregated into the lowland Amazon consisting of humid forests at low elevations, the northern coast region consisting of dry forests, and the tropical Andes region consisting of sub-tropical forests located along the eastern slopes and valleys of the Andean mountain range. The Peruvian tropical Andes are located between coordinates 3˚5′10 South, 79˚1′15 West, 14˚29′24 South and 68˚49′37 West, with most of the forests located at elevations ranging from
Results
The merge of the MINAM land cover map with the Global Forest Watch and Terra-i datasets yielded a layer representing deforestation in the Peruvian tropical Andes until the year 2017 (Fig. 2). On the basis of this layer, the total extent of cleared forests equaled 44,200 km2, which corresponds to 23% of the studied area. Between 2011 and 2017, more than 1150 km2 of forests were converted into other land-uses, yielding an annual deforestation rate of 19,300 ha per year (0.1%). This implies that
Discussion
It is well known that the geophysical variation in topography, climate and natural access routes such as rivers significantly influences the spatial patterns of human population and settlement distribution (Lung et al., 2013, Small and Cohen, 2004). With respect to the Andes, archeological evidence points to a very long history of human occupation of the subtropical Andean forests, especially at elevations below 1500 m.a.s.l. where environmental conditions are more conducive to agricultural
Conclusions
Using the tropical Peruvian Andes as a case study, we provided an example of how spatially explicit models can be used to characterize localized deforestation processes at the landscape level. This study applied Random Forest to examine the spatial patterns of deforestation and explore which types of deforestation activities are feasible throughout the region. In contrast to prior deforestation modeling studies, we specifically examined to what degree the structure of the natural landscape
Conflicts of interest
None.
Acknowledgement
This work was supported by funding provided by Universidad de Ciencias y Humanidades.
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