Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper.
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Competition for fluctuating resources reproduces statistics of species abundance over time across wide-ranging microbiotas
Across diverse microbiotas, species abundances vary in time with distinctive statistical behaviors that appear to generalize across hosts, but the origins and implications of these patterns remain unclear. Here, we show that many of these macroecological patterns can be quantitatively recapitulated by a simple class of consumer-resource models, in which the metabolic capabilities of different species are randomly drawn from a common statistical distribution. Our model parametrizes the consumer-resource properties of a community using only a small number of global parameters, including the total number of resources, typical resource fluctuations over time, and the average overlap in resource-consumption profiles across species. We show that variation in these macroscopic parameters strongly affects the time series statistics generated by the model, and we identify specific sets of global parameters that can recapitulate macroecological patterns across wide-ranging microbiotas, including the human gut, saliva, and vagina, as well as mouse gut and rice, without needing to specify microscopic details of resource consumption. These findings suggest that resource competition may be a dominant driver of community dynamics. Our work unifies numerous time series patterns under a simple model, and provides an accessible framework to infer macroscopic parameters of effective resource competition from longitudinal studies of microbial communities.
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- Award ID(s):
- 2125383
- NSF-PAR ID:
- 10376263
- Date Published:
- Journal Name:
- eLife
- Volume:
- 11
- ISSN:
- 2050-084X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Global biodiversity is declining at rates faster than at any other point in human history. Experimental manipulations at small spatial scales have demonstrated that communities with fewer species consistently produce less biomass than higher diversity communities. Understanding the consequences of the global extinction crisis for ecosystem functioning requires understanding how local experimental results are likely to change with increasing spatial and temporal scales and from experiments to naturally assembled systems.
Scaling across time and space in a changing world requires baseline predictions. Here, we provide a graphical null model for area scaling of biodiversity–ecosystem functioning relationships using observed macroecological patterns: the species–area curve and the biomass–area curve. We use species–area and biomass–area curves to predict how species richness–biomass relationships are likely to change with increasing sampling extent. We then validate these predictions with data from two naturally assembled ecosystems: a Minnesota savanna and a Panamanian tropical dry forest.
Our graphical null model predicts that biodiversity–ecosystem functioning relationships are scale‐dependent. However, we note two important caveats. First, our results indicate an apparent contradiction between predictions based on measurements in biodiversity–ecosystem functioning experiments and from scaling theory. When ecosystem functioning is measured as per unit area (e.g. biomass per m2), as is common in biodiversity–ecosystem functioning experiments, the slope of the biodiversity ecosystem functioning relationship should decrease with increasing scale. Alternatively, when ecosystem functioning is not measured per unit area (e.g. summed total biomass), as is common in scaling studies, the slope of the biodiversity–ecosystem functioning relationship should increase with increasing spatial scale. Second, the underlying macroecological patterns of biodiversity experiments are predictably different from some naturally assembled systems. These differences between the underlying patterns of experiments and naturally assembled systems may enable us to better understand when patterns from biodiversity–ecosystem functioning experiments will be valid in naturally assembled systems.
Synthesis . This paper provides a simple graphical null model that can be extended to any relationship between biodiversity and any ecosystem functioning across space or time. Furthermore, these predictions provide crucial insights into how and when we may be able to extend results from small‐scale biodiversity experiments to naturally assembled regional and global ecosystems where biodiversity is changing. -
Assisted restoration interventions drive functional recovery of tropical wet forest tree communitiesChoosing appropriate forest restoration interventions is challenging. Natural regeneration can rapidly facilitate forest recovery in many situations. However, barriers such as dispersal limitation and competition with non-native species can require assisted restoration approaches to facilitate plant community recovery. We used a study that has directly compared the outcomes of tropical wet forest restoration interventions across 11 replicate sites in southern Costa Rica. Within this framework, we examined the functional recovery trajectories of recruiting tree sapling communities across a gradient of restoration interventions including low (natural regeneration), intermediate (applied nucleation), and high (plantation) initial resource-investment, which we compared to remnant reference forest. We collated leaf and stem functional traits for tree species that comprised the bulk of recruiting saplings, then determined how community-weighted trait means and functional diversity metrics changed over a decade across treatments. Results show that assisted restoration approaches (applied nucleation, plantation) sped the development of more functionally diverse tree communities, more than tripling the functional richness (FRic) of recruiting communities when compared to natural regeneration. However, functional dispersion (i.e., the trait range of dominant species) was equivalent across interventions, and between 28 and 44% lower than remnant forest, indicating that increases in FRic under assisted restoration were driven by species recruiting in low abundances (<10 individuals across treatments). Recruits in assisted restoration treatments also had 10–15% tougher, less-palatable leaves, and leaves were even tougher in reference forest, which could be driven by increasing herbivory pressure along the gradient of interventions. Results show that tracking simple metrics such as species richness can mask a more mechanistic understanding of ecosystem recovery that is elucidated by taking a functional trait-driven approach toward evaluating outcomes. For example, our work identified a paucity of dense-wooded species recruiting across restoration interventions, wood density was 11–13% lower in restoration treatments than reference forests, underscoring such species as prime targets for enrichment planting. Overall, findings suggest that assisted restoration can catalyze the functional recovery of naturally recruiting tree communities in landscapes that are slow to recover naturally and highlight the importance of evaluating how different components of functional diversity shift over time to fully understand restoration outcomes.more » « less
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Abstract Understanding the drivers and impacts of spatiotemporal variation in species abundance on community trajectories is key to understanding the factors contributing to ecosystem resilience. Temporal variation in species trajectories across patches can provide compensation for species loss and can influence successional patterns. However, little is known about the underlying mechanisms that lead to patterns of species or spatial compensation and how those patterns may be mediated by consumer–resource relationships. Here we describe an experiment testing whether habitat attributes (e.g., structural complexity and spatial heterogeneity) mediate the effects of herbivory on tropical marine macroalgal communities by reducing accessibility and detectability, respectively, leading to variable trajectories among algal species at community (within patch) and metacommunity (i.e., among patch) scales. Reduced accessibility (greater habitat complexity) decreased the effects of herbivory (i.e., depressed consumption rate, increased algal species richness), and both accessibility and detectability (spatial heterogeneity) influenced algal community structure. Moreover, decreased accessibility at the community scale and a mosaic of accessibility at the metacommunity scale led to variation in community assembly. We suggest that habitat attributes can be important influencers of consumer–resource interactions on coral reefs, which in turn can increase species diversity, promote species succession, and enhance stability in algal metacommunities.
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Abstract Understanding the distribution of biodiversity across the Earth is one of the most challenging questions in biology. Much research has been directed at explaining the species latitudinal pattern showing that communities are richer in tropical areas; however, despite decades of research, a general consensus has not yet emerged. In addition, global biodiversity patterns are being rapidly altered by human activities. Here, we aim to describe large‐scale patterns of species richness and diversity in terrestrial vertebrate scavenger (carrion‐consuming) assemblages, which provide key ecosystem functions and services. We used a worldwide dataset comprising 43 sites, where vertebrate scavenger assemblages were identified using 2,485 carcasses monitored between 1991 and 2018. First, we evaluated how scavenger richness (number of species) and diversity (Shannon diversity index) varied among seasons (cold vs. warm, wet vs. dry). Then, we studied the potential effects of human impact and a set of macroecological variables related to climatic conditions on the scavenger assemblages. Vertebrate scavenger richness ranged from species‐poor to species rich assemblages (4–30 species). Both scavenger richness and diversity also showed some seasonal variation. However, in general, climatic variables did not drive latitudinal patterns, as scavenger richness and diversity were not affected by temperature or rainfall. Rainfall seasonality slightly increased the number of species in the community, but its effect was weak. Instead, the human impact index included in our study was the main predictor of scavenger richness. Scavenger assemblages in highly human‐impacted areas sustained the smallest number of scavenger species, suggesting human activity may be overriding other macroecological processes in shaping scavenger communities. Our results highlight the effect of human impact at a global scale. As species‐rich assemblages tend to be more functional, we warn about possible reductions in ecosystem functions and the services provided by scavengers in human‐dominated landscapes in the Anthropocene.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7554/eLife.75168
