scholarly journals Network Properties of Local Fungal Communities Reveal the Anthropogenic Disturbance Consequences of Farming Practices in Vineyard Soils

mSystems ◽  
2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Rüdiger Ortiz-Álvarez ◽  
Héctor Ortega-Arranz ◽  
Vicente J. Ontiveros ◽  
Miguel de Celis ◽  
Charles Ravarani ◽  
...  
Keyword(s):  
Soil Health ◽  
The United States ◽  
Community Level ◽  
Fungal Communities ◽  
Farming Practices ◽  
Local Networks ◽  
Niche Specialization ◽  
Network Properties ◽  
Vineyard Soils ◽  
The Impact

ABSTRACT Agroecosystems are human-managed ecosystems subject to generalized ecological rules. Understanding the ecology behind the assembly and dynamics of soil fungal communities is a fruitful way to improve management practices and plant productivity. Thus, monitoring soil health would benefit from the use of metrics that arise from ecological explanations that can also be informative for agricultural management. Beyond traditional biodiversity descriptors, community-level properties have the potential of informing about particular ecological situations. Here we assess the impact of different farming practices in a survey of 350 vineyard soils from the United States and Spain by estimating network properties based on spatial associations. Our observations using traditional approaches show results concurring with previous literature: the influence of geographic and climatic factors on sample distributions, or different operational taxonomic unit (OTU) compositions depending on agricultural managements. Furthermore, using network properties, we observe that fungal communities ranged from dense arrangements of associations to a sparser structure of associations, indicating differential levels of niche specialization. We detect fungal arrangements capable of thriving in wider or smaller ranges of temperature, revealing that niche specialization may be a critical soil process impacting soil health. Low-intervention practices (organic and biodynamic managements) promoted densely clustered networks, describing an equilibrium state based on mixed collaborative communities. In contrast, conventionally managed vineyards had highly modular sparser communities, supported by a higher coexclusion proportion. Thus, we hypothesize that network properties at the community level may help to understand how the environment and land use can affect community structure and ecological processes in agroecosystems. IMPORTANCE Soil fungal communities play a key role in agroecosystem sustainability. The complexity of fungal communities, at both taxonomic and functional levels, makes it difficult to find clear patterns connecting community composition with ecosystem function and to understand the impact of biotic (interspecies interactions) and abiotic (e.g., climate or anthropogenic disturbances) factors on it. Here we combine network analysis methods and properties, proposing a novel analytical approach: to infer ecological properties from local networks, which we apply to the study of fungal communities in vineyard soils. We conclude that different levels of farming intensification may lead to different ecological strategies in soil fungal communities settled by particular association arrangements.

scholarly journals Emergent properties in microbiome networks reveal the anthropogenic disturbance of farming practices in vineyard soil fungal communities

2020 ◽  
Author(s):  
Rüdiger Ortiz-Álvarez ◽  
Hector Ortega-Arranz ◽  
Vicente J. Ontiveros ◽  
Charles Ravarani ◽  
Alberto Acedo ◽  
...  
Keyword(s):  
Small World ◽  
Fungal Communities ◽  
Local Network ◽  
Emergent Properties ◽  
Farming Practices ◽  
Natural Systems ◽  
Vineyard Soil ◽  
Global Agriculture ◽  
Network Properties ◽  
The Impact

AbstractAgro-ecosystems are human-managed natural systems, and therefore are subject to generalized ecological rules. A deeper understanding of the factors impacting on the biotic component of ecosystem stability is needed for promoting the sustainability and productivity of global agriculture. Here we propose a method to determine ecological emergent properties through the inference of network properties in local microbial communities, and to use them as biomarkers of the anthropogenic impact of different farming practices on vineyard soil ecosystem functioning. In a dataset of 350 vineyard soil samples from USA and Spain we observed that fungal communities ranged from random to small-world network arrangements with differential levels of niche specialization. Some of the network properties studied were strongly correlated, defining patterns of ecological emergent properties that are influenced by the intensification level of the crop management. Low-intervention practices (from organic to biodynamic approaches) promoted densely clustered networks, describing an equilibrium state based on mixed (generalist-collaborative) communities. Contrary, in conventionally managed vineyards, we observed highly modular (niche-specialized) low clustered communities, supported by a higher degree of selection (more co-exclusion proportion). We also found that, although geographic factors can explain the different fungal community arrangements in both countries, the relationship between network properties in local fungal communities better capture the impact of farming practices regardless of the location. Thus, we hypothesize that local network properties can be globally used to evaluate the effect of ecosystem disturbances in crops, but also in when evaluating the effect of clinical interventions or to compare microbiomes of healthy vs. disturbed conditions.

Soil quality—The key to a sustainable agriculture

1992 ◽  
Vol 7 (1-2) ◽  
pp. 2-3 ◽  
Author(s):  
Robert I. Papendick ◽  
James F. Parr
Keyword(s):  
Soil Quality ◽  
Cover Crops ◽  
Crop Residues ◽  
The United States ◽  
Reduced Tillage ◽  
Farming Practices ◽  
Natural Processes ◽  
Vital Component ◽  
The Impact ◽  
Wind And Water Erosion

Soil is the world's most vital component for food and fiber production: preservation of this critical natural resource is paramount for protecting the environment, and ensuring that current and future populations are healthy and well-fed. In many countries of the world, including the United States, soils are being degraded at an alarming rate by wind and water erosion, desertification, and salinization resulting from misuse and improper farming practices. Often the topsoil is lost at a rate that far exceeds the capacity of natural processes to regenerate it. With soil degradation, there is a concomitant decline in soil quality, i.e., the soil's capacity to produce healthy and nutritious crops, resist erosion, and reduce the impact of environmental stresses on plants. Many decades of research have consistently shown that the best means of improving and restoring soil quality and productivity is by proper and regular additions of organic materials mainly through the use of crop rotations, cover crops, crop residues, animal manures, composts, and reduced tillage.

scholarly journals Prospective prediction of PTSD diagnosis in a nationally representative sample using machine learning

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Michelle A. Worthington ◽  
Amar Mandavia ◽  
Randall Richardson-Vejlgaard
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Adverse Event ◽  
Representative Sample ◽  
The United States ◽  
Community Level ◽  
Machine Learning Classification ◽  
Nationally Representative ◽  
The Impact ◽  
New Onset

Abstract Background Recent research has identified a number of pre-traumatic, peri-traumatic and post-traumatic psychological and ecological factors that put an individual at increased risk for developing PTSD following a life-threatening event. While these factors have been found to be associated with PTSD in univariate analyses, the complex interactions of these risk factors and how they contribute to individual trajectories of the illness are not yet well understood. In this study, we examine the impact of prior trauma, psychopathology, sociodemographic characteristics, community and environmental information, on PTSD onset in a nationally representative sample of adults in the United States, using machine learning methods to establish the relative contributions of each variable. Methods Individual risk factors identified in Waves 1 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were combined with community-level data for the years concurrent to the NESARC Wave 1 (n = 43,093) and 2 (n = 34,653) surveys. Machine learning feature selection and classification analyses were used at the national level to create models using individual- and community-level variables that would best predict the new onset of PTSD at Wave 2. Results Our classification algorithms yielded 89.7 to 95.6% accuracy for predicting new onset of PTSD at Wave 2. A prior diagnosis of DSM-IV-TR Borderline Personality Disorder, Major Depressive Disorder or Anxiety Disorder conferred the greatest relative influence in new diagnosis of PTSD. Distal risk factors such as prior psychiatric diagnosis accounted for significantly greater relative risk than proximal factors (such as adverse event exposure). Conclusions Our findings show that a machine learning classification approach can successfully integrate large numbers of known risk factors for PTSD into stronger models that account for high-dimensional interactions and collinearity between variables. We discuss the implications of these findings as pertaining to the targeted mobilization emergency mental health resources. These findings also inform the creation of a more comprehensive risk assessment profile to the likelihood of developing PTSD following an extremely adverse event.

scholarly journals Clostridium difficileAlters the Structure and Metabolism of Distinct Cecal Microbiomes during Initial Infection To Promote Sustained Colonization

mSphere ◽  
2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Matthew L. Jenior ◽  
Jhansi L. Leslie ◽  
Vincent B. Young ◽  
Patrick D. Schloss
Keyword(s):  
Clostridium Difficile ◽  
Mouse Model ◽  
Abundant Species ◽  
The United States ◽  
Community Level ◽  
Content Type ◽  
Risk Levels ◽  
Context Specific ◽  
Mouse Model Of Infection ◽  
The Impact

ABSTRACTSusceptibility toClostridium difficileinfection (CDI) is primarily associated with previous exposure to antibiotics, which compromise the structure and function of the gut bacterial community. Specific antibiotic classes correlate more strongly with recurrent or persistentC. difficileinfection. As such, we utilized a mouse model of infection to explore the effect of distinct antibiotic classes on the impact that infection has on community-level transcription and metabolic signatures shortly following pathogen colonization and how those changes may associate with persistence ofC. difficile. Untargeted metabolomic analysis revealed thatC. difficileinfection had significantly larger impacts on the metabolic environment across cefoperazone- and streptomycin-pretreated mice, which became persistently colonized compared to clindamycin-pretreated mice, where infection quickly became undetectable. Through metagenome-enabled metatranscriptomics, we observed that transcripts for genes associated with carbon and energy acquisition were greatly reduced in infected animals, suggesting that those niches were instead occupied byC. difficile. Furthermore, the largest changes in transcription were seen in the least abundant species, indicating thatC. difficilemay “attack the loser” in gut environments where sustained infection occurs more readily. Overall, our results suggest thatC. difficileis able to restructure the nutrient-niche landscape in the gut to promote persistent infection.IMPORTANCEClostridium difficilehas become the most common single cause of hospital-acquired infection over the last decade in the United States. Colonization resistance to the nosocomial pathogen is primarily provided by the gut microbiota, which is also involved in clearing the infection as the community recovers from perturbation. As distinct antibiotics are associated with different risk levels for CDI, we utilized a mouse model of infection with 3 separate antibiotic pretreatment regimens to generate alternative gut microbiomes that each allowed forC. difficilecolonization but varied in clearance rate. To assess community-level dynamics, we implemented an integrative multi-omics approach that revealed that infection significantly changed many aspects of the gut community. The degree to which the community changed was inversely correlated with clearance during the first 6 days of infection, suggesting thatC. difficiledifferentially modifies the gut environment to promote persistence. This is the first time that metagenome-enabled metatranscriptomics have been employed to study the behavior of a host-associated microbiota in response to an infection. Our results allow for a previously unseen understanding of the ecology associated withC. difficileinfection and provide the groundwork for identification of context-specific probiotic therapies.

Demonstrating Nutrient Cost Gradients: A Brooklyn Case Study

2014 ◽  
Vol 84 (5-6) ◽  
pp. 244-251 ◽  
Author(s):  
Robert J. Karp ◽  
Gary Wong ◽  
Marguerite Orsi
Keyword(s):  
Low Income ◽  
Energy Content ◽  
Food Selection ◽  
The United States ◽  
United States Department ◽  
Micronutrient Deficiencies ◽  
Caloric Density ◽  
Low Income Families ◽  
The Cost ◽  
The Impact

Abstract. Introduction: Foods dense in micronutrients are generally more expensive than those with higher energy content. These cost-differentials may put low-income families at risk of diminished micronutrient intake. Objectives: We sought to determine differences in the cost for iron, folate, and choline in foods available for purchase in a low-income community when assessed for energy content and serving size. Methods: Sixty-nine foods listed in the menu plans provided by the United States Department of Agriculture (USDA) for low-income families were considered, in 10 domains. The cost and micronutrient content for-energy and per-serving of these foods were determined for the three micronutrients. Exact Kruskal-Wallis tests were used for comparisons of energy costs; Spearman rho tests for comparisons of micronutrient content. Ninety families were interviewed in a pediatric clinic to assess the impact of food cost on food selection. Results: Significant differences between domains were shown for energy density with both cost-for-energy (p < 0.001) and cost-per-serving (p < 0.05) comparisons. All three micronutrient contents were significantly correlated with cost-for-energy (p < 0.01). Both iron and choline contents were significantly correlated with cost-per-serving (p < 0.05). Of the 90 families, 38 (42 %) worried about food costs; 40 (44 %) had chosen foods of high caloric density in response to that fear, and 29 of 40 families experiencing both worry and making such food selection. Conclusion: Adjustments to USDA meal plans using cost-for-energy analysis showed differentials for both energy and micronutrients. These differentials were reduced using cost-per-serving analysis, but were not eliminated. A substantial proportion of low-income families are vulnerable to micronutrient deficiencies.

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