Ivan Ramler

Spatial patterns of agricultural expansion determine impacts on biodiversity and carbon storage

Significance Deforestation is a major threat to biodiversity and many ecosystem services and is closely linked to agricultural expansion. Sustainability assessment of different agricultural products and policies requires an understanding of the impacts of land conversion resulting from shifts in demand or incentives for production. The prevailing approaches to estimating such impacts do not account for the spatial context of the transformation. This study shows how different patterns of agricultural expansion into forested landscapes can vastly reduce or exacerbate the total impact, suggesting that methods to measure sustainability should consider not only the total area but also where and how the landscape is converted. The agricultural expansion and intensification required to meet growing food and agri-based product demand present important challenges to future levels and management of biodiversity and ecosystem services. Influential actors such as corporations, governments, and multilateral organizations have made commitments to meeting future agricultural demand sustainably and preserving critical ecosystems. Current approaches to predicting the impacts of agricultural expansion involve calculation of total land conversion and assessment of the impacts on biodiversity or ecosystem services on a per-area basis, generally assuming a linear relationship between impact and land area. However, the impacts of continuing land development are often not linear and can vary considerably with spatial configuration. We demonstrate what could be gained by spatially explicit analysis of agricultural expansion at a large scale compared with the simple measure of total area converted, with a focus on the impacts on biodiversity and carbon storage. Using simple modeling approaches for two regions of Brazil, we find that for the same amount of land conversion, the declines in biodiversity and carbon storage can vary two- to fourfold depending on the spatial pattern of conversion. Impacts increase most rapidly in the earliest stages of agricultural expansion and are more pronounced in scenarios where conversion occurs in forest interiors compared with expansion into forests from their edges. This study reveals the importance of spatially explicit information in the assessment of land-use change impacts and for future land management and conservation.

Degradation in carbon stocks near tropical forest edges.

Carbon stock estimates based on land cover type are critical for informing climate change assessment and landscape management, but field and theoretical evidence indicates that forest fragmentation reduces the amount of carbon stored at forest edges. Here, using remotely sensed pantropical biomass and land cover data sets, we estimate that biomass within the first 500 m of the forest edge is on average 25% lower than in forest interiors and that reductions of 10% extend to 1.5 km from the forest edge. These findings suggest that IPCC Tier 1 methods overestimate carbon stocks in tropical forests by nearly 10%. Proper accounting for degradation at forest edges will inform better landscape and forest management and policies, as well as the assessment of carbon stocks at landscape and national levels.

A k-mean-directions Algorithm for Fast Clustering of Data on the Sphere

A k-means-type algorithm is proposed for efficiently clustering data constrained to lie on the surface of a p-dimensional unit sphere, or data that are mean-zero-unit-variance standardized observations such as those that occur when using Euclidean distance to cluster time series gene expression data using a correlation metric. We also provide methodology to initialize the algorithm and to estimate the number of clusters in the dataset. Results from a detailed series of experiments show excellent performance, even with very large datasets. The methodology is applied to the analysis of the mitotic cell division cycle of budding yeast dataset of Cho et al. [Molecular Cell (1998), 2, 65–73]. The entire dataset has not been analyzed previously, so our analysis provides an understanding for the complete set of genes acting in concert and differentially. We also use our methodology on the submitted abstracts of oral presentations made at the 2008 Joint Statistical Meetings (JSM) to identify similar topics. Our identified groups are both interpretable and distinct and the methodology provides a possible automated tool for efficient parallel scheduling of presentations at professional meetings. The supplemental materials described in the article are available in the online supplements.

Clustering in the Presence of Scatter

SUMMARY A new methodology is proposed for clustering datasets in the presence of scattered observations. Scattered observations are defined as unlike any other, so traditional approaches that force them into groups can lead to erroneous conclusions. Our suggested approach is a scheme which, under assumption of homogeneous spherical clusters, iteratively builds cores around their centers and groups points within each core while identifying points outside as scatter. In the absence of scatter, the algorithm reduces to k-means. We also provide methodology to initialize the algorithm and to estimate the number of clusters in the dataset. Results in experimental situations show excellent performance, especially when clusters are elliptically symmetric. The methodology is applied to the analysis of the United States Environmental Protection Agencys Toxic Release Inventory reports on industrial releases of mercury for the year 2000.

Emission and Dispersion of Bioaerosols from Dairy Manure Application Sites: Human Health Risk Assessment

In this study, we report the human health risk of gastrointestinal infection associated with inhalation exposure to airborne zoonotic pathogens emitted following application of dairy cattle manure to land. Inverse dispersion modeling with the USEPAs AERMOD dispersion model was used to determine bioaerosol emission rates based on edge-of-field bioaerosol and source material samples analyzed by real-time quantitative polymerase chain reaction (qPCR). Bioaerosol emissions and transport simulated with AERMOD, previously reported viable manure pathogen contents, relevant exposure pathways, and pathogen-specific dose-response relationships were then used to estimate potential downwind risks with a quantitative microbial risk assessment (QMRA) approach. Median 8-h infection risks decreased exponentially with distance from a median of 1:2700 at edge-of-field to 1:13 000 at 100 m and 1:200 000 at 1000 m; peak risks were considerably greater (1:33, 1:170, and 1:2500, respectively). These results indicate that bioaerosols emitted from manure application sites following manure application may present significant public health risks to downwind receptors. Manure management practices should consider improved controls for bioaerosols in order to reduce the risk of disease transmission.

Bioaerosol Deposition to Food Crops near Manure Application: Quantitative Microbial Risk Assessment

Production of both livestock and food crops are central priorities of agriculture; however, food safety concerns arise where these practices intersect. In this study, we investigated the public health risks associated with potential bioaerosol deposition to crops grown in the vicinity of manure application sites. A field sampling campaign at dairy manure application sites supported the emission, transport, and deposition modeling of bioaerosols emitted from these lands following application activities. Results were coupled with a quantitative microbial risk assessment model to estimate the infection risk due to consumption of leafy green vegetable crops grown at various distances downwind from the application area. Inactivation of pathogens ( spp., spp., and O157:H7) on both the manure-amended field and on crops was considered to determine the maximum loading of pathogens to plants with time following application. Overall median one-time infection risks at the time of maximum loading decreased from 1:1300 at 0 m directly downwind from the field to 1:6700 at 100 m and 1:92,000 at 1000 m; peak risks (95th percentiles) were considerably greater (1:18, 1:89, and 1:1200, respectively). Median risk was below 1:10,000 at >160 m downwind. As such, it is recommended that a 160-m setback distance is provided between manure application and nearby leafy green crop production. Additional distance or delay before harvest will provide further protection of public health.

Rise of the bots: Bot prevalence and its impact on match outcomes in league of Legends

League of Legends is a multiplayer online battle arena game where features are unlocked as players level up their accounts. Because it takes a significant amount of time to reach the max level, there exist accounts that are leveled automatically by illicit “bots” and then sold on the market at the max level. These bots participate in matches like human players but are incapable of either playing intelligently or cooperatively with teammates. This paper presents an investigation into the prevalence of bots in player-versus-player match types and their impact on match outcomes on the North America and Europe West servers, using the data gathered through the Riot Games official application program interface. We demonstrate that bots are present in all major match modes at various levels and that they negatively influence the balance of matches on both servers.