Tropical dry forests: The state of global knowledge and recommendations for future research

Blackie, R.; Baldauf, Cristina; Gautier, Denis; Gumbo, Davison; Kassa, Habtemariam; Parthasarathy, Narayanaswamy; Paumgarten, Fiona; Sola, Phosiso; Pulla, Sandeep; Waeber, Patrick O.; Sunderland, T.C.H.

doi:10.17528/cifor/004408

Cited by 15 publications

(3 citation statements)

References 85 publications

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“…High deforestation and degradation rates were observed for TDFs in the Americas (Miles et al, 2006;Portillo-Quintero and Sánchez-Azofeifa, 2010;Grinand et al, 2013;Rudel, 2017;Dupin et al, 2018), a situation that is worsened in Brazil by the neglection of these ecosystems in terms of research and conservation efforts, especially when compared to tropical wet forests (Sánchez-Azofeifa et al, 2005;Santos et al, 2011;Blackie et al, 2014). The present study aimed to estimate the extent of LCC (with focus on forest recovery, deforestation and net forest cover change) in Brazilian TDFs during 10 years (from 2007 to 2016).…”

Section: Introductionmentioning

confidence: 99%

Biophysical and Socioeconomic Factors Associated to Deforestation and Forest Recovery in Brazilian Tropical Dry Forests

Espírito-Santo

Rocha

Leite

et al. 2020

Front. For. Glob. Change

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The determination of land cover changes (LCCs) and their association to biophysical and socioeconomic factors is vital to support government policies toward the sustainable use of natural resources. The present study aimed to quantify deforestation, forest recovery and net cover change in tropical dry forests (TDFs) in Brazil from 2007 to 2016, and investigate how they are associated to biophysical and socioeconomic factors. We also assessed the effects of LCC variables in human welfare indicators. For this purpose, we used MODIS imagery to calculate TDF gross loss (deforestation), gross gain (forest recovery) and net cover change (the balance between deforestation and forest recovery) for 294 counties in three Brazilian states (Minas Gerais, Bahia, and Piauí). We obtained seven factors potentially associated to LCC at the county level: total county area, road density, humidity index, slope, elevation, and % change in human population and in cattle density. From 2007 to 2016, TDF cover increased from 76,693 to 80,964 km2 (+5.6%). This positive net change resulted from a remarkable forest recovery of 19,018 km2 (24.8%), offsetting a large deforested area (14,748 km2; 19.2%). Practically all these cover changes were a consequence of transitions from TDF to pastures and vice-versa, highlighting the importance of developing sustainable policies for cattle raising in TDF regions. Each LCC variable was associated to different set of factors, but two biophysical variables were significantly associated both to TDF area gained and lost per county: county area (positively) and slope (negatively), indicating that large and flat counties have very dynamic LCCs. The TDF net area change was only associated (negatively) to the humidity index, reflecting an increase in TDF cover in more arid counties. The net increase in Brazilian TDF area is likely a result from an interplay of biophysical and socioeconomic factors that reduced deforestation and caused pasture abandonment. Although the ecological integrity and permanence of secondary TDFs need further investigation, the recovery of this semi-arid ecosystem must be valued and accounted for in the national forest restoration programs, as it would significantly help achieving the goals established in the Bonn agreement and the Atlantic Rain Forest pact.

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Section: Introductionmentioning

confidence: 99%

Biophysical and Socioeconomic Factors Associated to Deforestation and Forest Recovery in Brazilian Tropical Dry Forests

Espírito-Santo

Rocha

Leite

et al. 2020

Front. For. Glob. Change

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“…Certain populations and sectors are likely to be harder hit, particularly where natural-resource-based systems support livelihoods (Kok et al 2016). Dryland areas, including much of South and southern Africa, already face challenges that create vulnerable socioecological systems (Safriel and Adeel 2008;Blackie et al 2014). These include marginal ecosystems with low agroecological potential under pressure from multiple users; low, seasonal, and unreliable rainfall; poor infrastructure and public services; poor governance; a marginalized population; limited opportunities for alternative Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WCAS-D-19-0134.s1.…”

Section: Introductionmentioning

confidence: 99%

Archetypes of Climate-Risk Profiles among Rural Households in Limpopo, South Africa

Paumgarten

Locatelli

Witkowski

2020

Weather, Climate, and Society

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More frequent and intense climate hazards, a predicted outcome of climate change, are likely to threaten existing livelihoods in rural communities, undermining households’ adaptive capacity. To support households’ efforts to manage and reduce this risk, there is a need to better understand the heterogeneity of risk within and between communities. The Intergovernmental Panel on Climate Change revised their climate vulnerability framework to incorporate the concept of risk. This study contributes toward the operationalization of this updated framework by applying a recognized methodology to the analysis of the climate-related risk of rural households. Using a mixed-method approach, including a cluster analysis, it determined and assessed archetypical patterns of household risk. The approach was applied to 170 households in two villages, in different agroecological zones, in the Vhembe District Municipality of South Africa’s Limpopo Province. Six archetypical climate-risk profiles were identified based on differences in the core components of risk, namely, the experience of climate hazards, the degree of exposure and vulnerability, and the associated impacts. The method’s application is illustrated by interpreting the six profiles, with possible adaptation pathways suggested for each. The archetypes show how climate-related risk varies according to households’ livelihood strategies and capital endowments. There are clear site-related distinctions between the risk profiles; however, the age of the household and the gender of the household head also differentiate the profiles. These different profiles suggest the need for adaptation responses that account for these site-related differences, while still recognizing the heterogeneity of risk at the village level.

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“…Globally, tropical dry forests are the most threatened terrestrial biome by intensive land use (Miles et al 2006a;Blackie et al 2014b). They are being rapidly converted into livestock pastures and agricultural land altering biodiversity, vegetation-climate feedbacks, and biogeochemical cycles (Andren 1994;Harrison & Bruna 1999b;MEA 2005).…”

Section: Introductionmentioning

confidence: 99%

Resilience of dry tropical rangelands : How native palms and trees mediate the effects of seasonal droughts

Salmerón¹

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Socio-ecological networks in cattle rangelands are severely threatened by global warming. Higher temperatures and erratic rains resulting from climate change accentuate existing problems in livestock systems, and are expected to compromise pasture and cattle productivity worldwide (Lobell & Gourdji 2012;Field 2014). At the same time, livestock farming is expanding to respond to increases in human population, Trees can ameliorate abiotic stress through several mechanisms above and belowground that facilitate grass growth and improve grass quality (

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Tropical dry forests: The state of global knowledge and recommendations for future research

Cited by 15 publications

References 85 publications

Biophysical and Socioeconomic Factors Associated to Deforestation and Forest Recovery in Brazilian Tropical Dry Forests

Biophysical and Socioeconomic Factors Associated to Deforestation and Forest Recovery in Brazilian Tropical Dry Forests

Archetypes of Climate-Risk Profiles among Rural Households in Limpopo, South Africa

Resilience of dry tropical rangelands : How native palms and trees mediate the effects of seasonal droughts

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