Deforestation scenarios for the Bolivian lowlands

Tejada, Graciela; Dalla-Nora, Eloi L.; Córdoba, Diana; Lafortezza, Raffaele; Ovando, Alex; Assis, Talita Oliveira; Aguiar, Ana Paula Dutra

doi:10.1016/j.envres.2015.10.010

Cited by 38 publications

(40 citation statements)

References 31 publications

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“…This is consistent with economic and social trends reported for our study area Steininger et al 2001a;Pacheco 2002;Killeen et al 2008). Unfortunately, the trend has continued in the last decade with reported deforestation rates as high as 200,000 ha/year (Müller et al 2014;Tejada et al 2016) which is exacerbating poor habitat availability for wildlife in the lowland areas of Bolivia even more.…”

Section: Discussionsupporting

confidence: 76%

Impact of deforestation on habitat connectivity thresholds for large carnivores in tropical forests

Zemanova

Perotto‐Baldivieso

Dickins³

et al. 2017

Ecol Process

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Introduction: Deforestation significantly impacts large carnivores that depend on large tracts of interconnected forest habitat and that are sensitive to human activities. Understanding the relationship between habitat use and spatial distribution of such species across human modified landscapes is critical when planning effective conservation strategies. This study assessed the presence of potential landscape connectivity thresholds resulting from habitat fragmentation associated with different deforestation patterns using a scale-based approach that links species-specific home ranges with the extent of anthropogenic activities. The objectives were (1) to quantify the spatial and temporal distribution of natural vegetation for five common deforestation patterns and (2) to evaluate the connectivity associated with these patterns and the existence of potential thresholds affecting jaguar dispersal. The Bolivian lowlands, located within jaguar conservation units, were analysed with landscape metrics to capture the spatial and temporal changes within deforested areas and to determine potential impact on jaguar connectivity and connectivity thresholds for dispersal. Results: Over the period of 1976-2005, the amount of natural vegetation has decreased by more than 40% in all locations with the biggest changes occurring between 1991 and 2000. Landscape spatial structure around jaguar locations showed that jaguars used areas with mean proportion of natural areas = 83.14% (SE = 3.72%), mean patch density = 1.16 patches/100 ha (SE = 0.28 patches/100 ha), mean patch area = 616.95 ha (SE = 172.89 ha) and mean edge density = 705.27 m/ha (SE = 182.19 m/ha).We observed strong fragmentation processes in all study locations, which has resulted in the connectivity of jaguar habitat decreasing to <20% by 2005. A connectivity threshold zone was observed when the proportion of natural vegetation was less than 58.4% (SE = 1.3). Conclusions: Assessing fragmentation and connectivity for carnivores within the extent of human-modified landscapes proved to be an effective way to understand the changes caused by deforestation and their potential effects on large carnivore habitats. Our study highlights the importance of scale-based approaches for assessing current conservation challenges to protect large carnivores.

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Section: Discussionsupporting

confidence: 76%

Impact of deforestation on habitat connectivity thresholds for large carnivores in tropical forests

Zemanova

Perotto‐Baldivieso

Dickins³

et al. 2017

Ecol Process

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“…The present paper examines challenges to sustainable development in Brazilian Amazonia, but many of the challenges in this region are similar to those in other parts of the world, including Peru (Doleac, ; Erickson‐Davis, ), Bolivia (Bottazzi & Dao, ; Müller, Müller, Schierhorn, Gerold, & Pacheco, ; Müller, Pistorius, Rohde, Gerold, & Pacheco, ; Redo, Millington, & Hindery, ; Tejada et al, ), Ecuador (Mosandl, Günter, Stimm, & Weber, ; Tapia‐Armijos, Homeier, Espinosa, Leuschner, & de la Cruz, ), Indonesia (Busch et al, ; Fearnside, ; Purnomo et al, 2017), Malaysia (Bryan et al, ; Miettinen, Shi, & Liew, ) the Democratic Republic of the Congo (Ickowitz, Slayback, Asanzi, & Nasi, ; Wilkie, Shaw, Rotberg, Morelli, & Auzel, ) and Papua New Guinea (Filer, Keenan, Allen, & McAlpine, ; Shearman, Ash, Mackey, Bryan, & Lokes, ).…”

Section: Introductionmentioning

confidence: 96%

Challenges for sustainable development in Brazilian Amazonia

Fearnside

2018

Sustainable Development

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Most economic initiatives and infrastructure projects in Brazilian Amazonia have social benefits that are small and ephemeral, while their socioenvironmental impacts are severe. More sustainable forms of development are inhibited by barriers such as a decision-making system with heavy influence (including corruption) from actors with interests in nonsustainable activities. These interests have driven a recent surge of legislative threats to environmental licensing. Better alternatives exist for many destructive forms of "development" projects. Examples include transport using rivers (rather than building highways) and electricity generation from Brazil's vast solar and wind resources (rather than hydroelectric dams). Traditional rural populations could receive support from programs that tap the value of the Amazon forest's environmental services, but institutional mechanisms are in their infancy, among challenges that include differing political interests of countries providing environmental services and those that might pay for them, lack of data and a "theoretical battlefield" regarding accounting for benefits.

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“…As stated by Bax et al (2016), 'non-spatial variables as sociocultural and political drivers are often beyond the scope of the models'. In two recent studies, future deforestation scenarios were built following assumptions made based on a good knowledge of economic pressures or social and political tensions in Bolivia and Brazil (Aguiar et al, 2016;Tejada et al, 2016). When going through the modelling process, however, predicted deforestation trends in the different scenarios are simply expressed as a fraction of past observed deforestation trends or established without an explicit estimation based on socio-economic variables.…”

Section: Introductionmentioning

confidence: 99%

Moving forward socio‐economically focused models of deforestation

Dezécache

Salles

Vieilledent

et al. 2017

Global Change Biology

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Whilst high-resolution spatial variables contribute to a good fit of spatially explicit deforestation models, socio-economic processes are often beyond the scope of these models. Such a low level of interest in the socio-economic dimension of deforestation limits the relevancy of these models for decision-making and may be the cause of their failure to accurately predict observed deforestation trends in the medium term. This study aims to propose a flexible methodology for taking into account multiple drivers of deforestation in tropical forested areas, where the intensity of deforestation is explicitly predicted based on socio-economic variables. By coupling a model of deforestation location based on spatial environmental variables with several sub-models of deforestation intensity based on socio-economic variables, we were able to create a map of predicted deforestation over the period 2001-2014 in French Guiana. This map was compared to a reference map for accuracy assessment, not only at the pixel scale but also over cells ranging from 1 to approximately 600 sq. km. Highly significant relationships were explicitly established between deforestation intensity and several socio-economic variables: population growth, the amount of agricultural subsidies, gold and wood production. Such a precise characterization of socio-economic processes allows to avoid overestimation biases in high deforestation areas, suggesting a better integration of socio-economic processes in the models. Whilst considering deforestation as a purely geographical process contributes to the creation of conservative models unable to effectively assess changes in the socio-economic and political contexts influencing deforestation trends, this explicit characterization of the socio-economic dimension of deforestation is critical for the creation of deforestation scenarios in REDD+ projects.

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Deforestation scenarios for the Bolivian lowlands

Cited by 38 publications

References 31 publications

Impact of deforestation on habitat connectivity thresholds for large carnivores in tropical forests

Impact of deforestation on habitat connectivity thresholds for large carnivores in tropical forests

Challenges for sustainable development in Brazilian Amazonia

Moving forward socio‐economically focused models of deforestation

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