Project description:Tropical deforestation is one of the most pressing threats to biodiversity, and substantially reduces ecosystem services at the global scale. Little is known however about the global spatial distribution of the actors behind tropical deforestation. Newly available maps of global cropland field size offer an opportunity to gain understanding towards the spatial distribution of tropical deforestation actors. Here we use a map of global cropland field size and combine it with maps of forest loss to study the spatial association between field size and deforestation while accounting for other anthropogenic and geographical drivers of deforestation. We then use linear mixed-effects models and bootstrapping to determine what factors affect field sizes within deforested areas across all countries in the global tropics and subtropics. We find that field size within deforested areas is largely determined by country-level effects indicating the importance of socio-economic, cultural and institutional factors on the distribution of field sizes. Typically, small field sizes appear more commonly in deforested areas in Africa and Asia while the association was with larger field sizes in Australia and the Americas. In general, we find that smaller field sizes are associated with deforestation in protected areas and large field sizes with areas with lower agricultural value, although these results have low explanatory power. Our results suggest that the spatial patterns of actors behind deforestation are aggregated geographically which could help target conservation and sustainable land-use strategies.
Project description:A better understanding of deforestation drivers across countries and spatial scales is a precondition for designing efficient international policies and coherent land use planning strategies such as REDD+. However, it is so far unclear if the well-studied drivers of tropical deforestation behave similarly across nested subnational jurisdictions, which is crucial for efficient policy implementation. We selected three countries in Africa, America and Asia, which present very different tropical contexts. Making use of spatial econometrics and a multi-level approach, we conducted a set of regressions comprising 3,035 administrative units from the three countries at micro-level, plus 361 and 49 at meso- and macro-level, respectively. We included forest cover as dependent variable and seven physio-geographic and socioeconomic indicators of well-known drivers of deforestation as explanatory variables. With this, we could provide a first set of highly significant econometric models of pantropical deforestation that consider subnational units. We identified recurrent drivers across countries and scales, namely population pressure and the natural condition of land suitability for crop production. The impacts of demography on forest cover were strikingly strong across contexts, suggesting clear limitations of sectoral policy. Our findings also revealed scale and context dependencies, such as an increased heterogeneity at local scopes, with a higher and more diverse number of significant determinants of forest cover. Additionally, we detected stronger spatial interactions at smaller levels, providing empirical evidence that certain deforestation forces occur independently of the existing de jure governance boundaries. We demonstrated that neglecting spatial dependencies in this type of studies can lead to several misinterpretations. We therefore advocate, that the design and enforcement of policy instruments-such as REDD+-should start from common international entry points that ensure for coherent agricultural and demographic policies. In order to achieve a long-term impact on the ground, these policies need to have enough flexibility to be modified and adapted to specific national, regional or local conditions.
Project description:Using a consistent, 20?year series of high- (30?m) resolution, satellite-based maps of forest cover, we estimate forest area and its changes from 1990 to 2010 in 34 tropical countries that account for the majority of the global area of humid tropical forests. Our estimates indicate a 62% acceleration in net deforestation in the humid tropics from the 1990s to the 2000s, contradicting a 25% reduction reported by the United Nations Food and Agriculture Organization Forest Resource Assessment. Net loss of forest cover peaked from 2000 to 2005. Gross gains accelerated slowly and uniformly between 1990-2000, 2000-2005, and 2005-2010. However, the gains were overwhelmed by gross losses, which peaked from 2000 to 2005 and decelerated afterward. The acceleration of humid tropical deforestation we report contradicts the assertion that losses decelerated from the 1990s to the 2000s.
Project description:The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case-control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.
Project description:It has been suggested that rainfall in the Amazon decreases if forest loss exceeds some threshold, but the specific value of this threshold remains uncertain. Here, we investigate the relationship between historical deforestation and rainfall at different geographical scales across the Southern Brazilian Amazon (SBA). We also assess impacts of deforestation policy scenarios on the region's agriculture. Forest loss of up to 55-60% within 28 km grid cells enhances rainfall, but further deforestation reduces rainfall precipitously. This threshold is lower at larger scales (45-50% at 56 km and 25-30% at 112 km grid cells), while rainfall decreases linearly within 224 km grid cells. Widespread deforestation results in a hydrological and economic negative-sum game, because lower rainfall and agricultural productivity at larger scales outdo local gains. Under a weak governance scenario, SBA may lose 56% of its forests by 2050. Reducing deforestation prevents agricultural losses in SBA up to US$ 1 billion annually.
Project description:Policies to effectively reduce deforestation are discussed within a land rent (von Thünen) framework. The first set of policies attempts to reduce the rent of extensive agriculture, either by neglecting extension, marketing, and infrastructure, generating alternative income opportunities, stimulating intensive agricultural production or by reforming land tenure. The second set aims to increase either extractive or protective forest rent and--more importantly--create institutions (community forest management) or markets (payment for environmental services) that enable land users to capture a larger share of the protective forest rent. The third set aims to limit forest conversion directly by establishing protected areas. Many of these policy options present local win-lose scenarios between forest conservation and agricultural production. Local yield increases tend to stimulate agricultural encroachment, contrary to the logic of the global food equation that suggests yield increases take pressure off forests. At national and global scales, however, policy makers are presented with a more pleasant scenario. Agricultural production in developing countries has increased by 3.3-3.4% annually over the last 2 decades, whereas gross deforestation has increased agricultural area by only 0.3%, suggesting a minor role of forest conversion in overall agricultural production. A spatial delinking of remaining forests and intensive production areas should also help reconcile conservation and production goals in the future.
Project description:Total internal reflection fluorescence microscopy (TIRFM) is becoming an increasingly common methodology to narrow the illumination excitation thickness to study cellular process such as exocytosis, endocytosis, and membrane dynamics. It is also frequently used as a method to improve signal/noise in other techniques such as in vitro single-molecule imaging, stochastic optical reconstruction microscopy/photoactivated localization microscopy imaging, and fluorescence resonance energy transfer imaging. The unique illumination geometry of TIRFM also enables a distinct method to create an excitation field for selectively exciting fluorophores that are aligned either parallel or perpendicular to the optical axis. This selectivity has been used to study orientation of cell membranes and cellular proteins. Unfortunately, the coherent nature of laser light, the typical excitation source in TIRFM, often creates spatial interference fringes across the illuminated area. These fringes are particularly problematic when imaging large cellular areas or when accurate quantification is necessary. Methods have been developed to minimize these fringes by modulating the TIRFM field during a frame capture period; however, these approaches eliminate the possibility to simultaneously excite with a specific polarization. A new, to our knowledge, technique is presented, which compensates for spatial fringes while simultaneously permitting rapid image acquisition of both parallel and perpendicular excitation directions in ~25 ms. In addition, a back reflection detection scheme was developed that enables quick and accurate alignment of the excitation laser. The detector also facilitates focus drift compensation, a common problem in TIRFM due to the narrow excitation depth, particularly when imaging over long time courses or when using a perfusion flow chamber. The capabilities of this instrument were demonstrated by imaging membrane orientation using DiO on live cells and on lipid bilayers that were supported on a glass slide (supported lipid bilayer). The use of the approach to biological problems was illustrated by examining the temporal and spatial dynamics of exocytic vesicles.
Project description:The recent adoption of the Global Compact on Refugees formally recognizes not only the importance of supporting the nearly 26 million people who have sought asylum from conflict and persecution but also of easing the pressures on receiving areas and host countries. However, few countries may enforce the Compact out of concern over the economic or environmental repercussions of hosting refugees. We examine whether narratives of refugee-driven landscape change are empirically generalizable to continental Africa, which fosters 34% of all refugees. Estimates of the causal effects of the number of refugees-located in 493 camps distributed across 49 African countries-on vegetation from 2000 to 2016 are provided. Using a quasi-experimental design, we find refugees bear a small increase in vegetation condition while contributing to increased deforestation. Such a combination is mainly explained not by land clearance and massive biomass extraction but by agricultural expansion in refugee-hosting areas. A one percent increase in the number of refugees amplifies the transition from dominant forested areas to cropland by 1.4 percentage points. These findings suggest that changes in vegetation condition may ensue with the elevation of population-based constraints on food security.
Project description:Key messageEstablished spatial models improve the analysis of agricultural field trials with or without genomic data and can be fitted with the open-source R package INLA. The objective of this paper was to fit different established spatial models for analysing agricultural field trials using the open-source R package INLA. Spatial variation is common in field trials, and accounting for it increases the accuracy of estimated genetic effects. However, this is still hindered by the lack of available software implementations. We compare some established spatial models and show possibilities for flexible modelling with respect to field trial design and joint modelling over multiple years and locations. We use a Bayesian framework and for statistical inference the integrated nested Laplace approximations (INLA) implemented in the R package INLA. The spatial models we use are the well-known independent row and column effects, separable first-order autoregressive ([Formula: see text]) models and a Gaussian random field (Matérn) model that is approximated via the stochastic partial differential equation approach. The Matérn model can accommodate flexible field trial designs and yields interpretable parameters. We test the models in a simulation study imitating a wheat breeding programme with different levels of spatial variation, with and without genome-wide markers and with combining data over two locations, modelling spatial and genetic effects jointly. The results show comparable predictive performance for both the [Formula: see text] and the Matérn models. We also present an example of fitting the models to a real wheat breeding data and simulated tree breeding data with the Nelder wheel design to show the flexibility of the Matérn model and the R package INLA.