scholarly journals The logic of ecological patchiness

2012 ◽  
Vol 2 (2) ◽  
pp. 150-155 ◽  
Author(s):  
Daniel Grünbaum
Keyword(s):  
Species Interactions ◽  
Large Scale ◽  
Mean Field ◽  
Theoretical Models ◽  
Ecological Indices ◽  
Ecological Interactions ◽  
Patchy Environments ◽  
Wide Range ◽  
Resource Interactions ◽  
Consumer Resource

Most ecological interactions occur in environments that are spatially and temporally heterogeneous—‘patchy’—across a wide range of scales. In contrast, most theoretical models of ecological interactions, especially large-scale models applied to societal issues such as climate change, resource management and human health, are based on ‘mean field’ approaches in which the underlying patchiness of interacting consumers and resources is intentionally averaged out. Mean field ecological models typically have the advantages of tractability, few parameters and clear interpretation; more technically complex spatially explicit models, which resolve ecological patchiness at some (or all relevant) scales, generally lack these advantages. This report presents a heuristic analysis that incorporates important elements of consumer–resource patchiness with minimal technical complexity. The analysis uses scaling arguments to establish conditions under which key mechanisms—movement, reproduction and consumption—strongly affect consumer–resource interactions in patchy environments. By very general arguments, the relative magnitudes of these three mechanisms are quantified by three non-dimensional ecological indices: the Frost, Strathmann and Lessard numbers. Qualitative analysis based on these ecological indices provides a basis for conjectures concerning the expected characteristics of organisms, species interactions and ecosystems in patchy environments.

scholarly journals Ecological Interactions and the Netflix Problem

2016 ◽  
Author(s):  
Philippe Desjardins-Proulx ◽  
Idaline Laigle ◽  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Random Forests ◽  
Species Interactions ◽  
Similarity Measures ◽  
Theoretical Models ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Ecological Interactions ◽  
Learning Techniques

0AbstractSpecies interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with other machine learning techniques. Recommenders are algorithms developed for companies like Netflix to predict if a customer would like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. We also explore how the K nearest neighbour approach can be used with both positive and negative information, in which case the goal of the algorithm is to fill missing entries from a matrix (imputation). By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species’ phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species’ interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species’ interactions. Further work should focus on developing custom similarity measures specialized to ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.

scholarly journals Ecological interactions and the Netflix problem

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3644 ◽  
Author(s):  
Philippe Desjardins-Proulx ◽  
Idaline Laigle ◽  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Random Forests ◽  
Species Interactions ◽  
Similarity Measures ◽  
Theoretical Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Ecological Interactions

Species interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with a supervised machine learning technique. Recommenders are algorithms developed for companies like Netflix to predict whether a customer will like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species’ phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species’ interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species’ interactions. Further work should focus on developing custom similarity measures specialized for ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.

scholarly journals Distinct Growth Strategies of Soil Bacteria as Revealed by Large-Scale Colony Tracking

2011 ◽  
Vol 78 (5) ◽  
pp. 1345-1352 ◽  
Author(s):  
Morten Ernebjerg ◽  
Roy Kishony
Keyword(s):  
Species Interactions ◽  
Soil Bacteria ◽  
Large Scale ◽  
Colony Growth ◽  
Automated Image Analysis ◽  
Growth Strategies ◽  
Phenotypic Data ◽  
Wide Range ◽  
Growth Properties

ABSTRACTOur understanding of microbial ecology has been significantly furthered in recent years by advances in sequencing techniques, but comprehensive surveys of the phenotypic characteristics of environmental bacteria remain rare. Such phenotypic data are crucial for understanding the microbial strategies for growth and the diversity of microbial ecosystems. Here, we describe a high-throughput measurement of the growth of thousands of bacterial colonies using an array of flat-bed scanners coupled with automated image analysis. We used this system to investigate the growth properties of members of a microbial community from untreated soil. The system provides high-quality measurements of the number of CFU, colony growth rates, and appearance times, allowing us to directly study the distribution of these properties in mixed environmental samples. We find that soil bacteria display a wide range of growth strategies which can be grouped into several clusters that cannot be reduced to any of the classical dichotomous divisions of soil bacteria, e.g., into copiotophs and oligotrophs. We also find that, at early times, cells are most likely to form colonies when other, nearby colonies are present but not too dense. This maximization of culturability at intermediate plating densities suggests that the previously observed tendency for high density to lead to fewer colonies is partly offset by the induction of colony formation caused by interactions between microbes. These results suggest new types of growth classification of soil bacteria and potential effects of species interactions on colony growth.

scholarly journals Turbulence in Large-Scale Two-Dimensional Balanced Hard Sphere Gas Flow

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1520
Author(s):  
Rafail V. Abramov
Keyword(s):  
Turbulent Flows ◽  
Hard Sphere ◽  
Large Scale ◽  
Gas Flow ◽  
Spatial Scales ◽  
Mean Field ◽  
Current Work ◽  
Two Dimensional ◽  
Dynamical Mechanism ◽  
Wide Range

In recent works, we developed a model of balanced gas flow, where the momentum equation possesses an additional mean field forcing term, which originates from the hard sphere interaction potential between the gas particles. We demonstrated that, in our model, a turbulent gas flow with a Kolmogorov kinetic energy spectrum develops from an otherwise laminar initial jet. In the current work, we investigate the possibility of a similar turbulent flow developing in a large-scale two-dimensional setting, where a strong external acceleration compresses the gas into a relatively thin slab along the third dimension. The main motivation behind the current work is the following. According to observations, horizontal turbulent motions in the Earth atmosphere manifest in a wide range of spatial scales, from hundreds of meters to thousands of kilometers. However, the air density rapidly decays with altitude, roughly by an order of magnitude each 15–20 km. This naturally raises the question as to whether or not there exists a dynamical mechanism which can produce large-scale turbulence within a purely two-dimensional gas flow. To our surprise, we discover that our model indeed produces turbulent flows and the corresponding Kolmogorov energy spectra in such a two-dimensional setting.

scholarly journals rescomp: An R package for defining, simulating and visualizing ODE models of consumer-resource interactions

2022 ◽  
Author(s):  
Andrew D Letten
Keyword(s):  
Resource Competition ◽  
R Package ◽  
Type I ◽  
Functional Responses ◽  
Ode Models ◽  
Wide Range ◽  
Resource Interactions ◽  
Barrier To Entry ◽  
Dependent Growth ◽  
Consumer Resource

Mechanistic models of resource competition underpin numerous foundational concepts and theories in ecology, and continue to be employed widely to address diverse research questions. Nevertheless, current software tools present a comparatively steep barrier to entry. I introduce the R package rescomp to support the specification, simulation and visualisaton of a broad spectrum of consumer-resource interactions. rescomp is compatible with diverse model specifications, including an unlimited number of consumers and resources, different consumer functional responses (type I, II and III), different resource types (essential or substitutable) and supply dynamics (chemostats, logistic and/or pulsed), delayed consumer introductions, time dependent growth and consumption parameters, and instantaneous changes to consumer and/or resource densities. Several examples on implementing rescomp are provided. In addition, a wide variety of additional examples can be found in the package vignettes, including using rescomp to reproduce the results of several well known studies from the literature. rescomp provides users with an accessible tool to reproduce classic models in ecology, to specify models resembling a wide range of experimental designs, and to explore diverse novel model formulations.

Use of Time in the Phenology of Vertical Species Interactions

2019 ◽  
pp. 133-166
Author(s):  
Eric Post
Keyword(s):  
Species Interactions ◽  
Trophic Levels ◽  
Plant Interactions ◽  
Predator Prey ◽  
Host Interactions ◽  
Use Of Time ◽  
Resource Interactions ◽  
Survival And Reproduction ◽  
Consumer Resource

This chapter examines the role of time in vertical species interactions. Vertically structured communities are those shaped primarily by interactions among organisms at different trophic levels. Hence, these comprise exploitation interactions typified by predator—prey interactions, pathogen—host interactions, herbivore—plant interactions, and consumer—resource interactions in general. In such interactions, consumer success—in terms of growth, survival, and reproduction—depends upon synchronization of consumer phenology with resource phenology. In contrast, the success of resource species may depend upon minimizing synchronization of their phenology with that of species by which they are consumed. In mutualistic interactions, however, in which both species function as a resource for one another, the success of both species depends upon phenological overlap. The chapter then explores some examples of the role of time in the phenology of all three types of players in vertical species interactions—resource species, consumer species, and mutualistic species.

A stochastic maximum principle for switching diffusions using conditional mean-fields with applications to control problems

2020 ◽  
Vol 26 ◽  
pp. 69 ◽  
Author(s):  
Son L. Nguyen ◽  
Dung T. Nguyen ◽  
George Yin
Keyword(s):  
Maximum Principle ◽  
Large Scale ◽  
Mean Field ◽  
Control Problems ◽  
Optimal Controls ◽  
Linear Quadratic ◽  
Conditional Mean ◽  
Switching Diffusions ◽  
Wide Range ◽  
Mean Fields

This paper obtains a maximum principle for switching diffusions with mean-field interactions. The motivation stems from a wide range of applications for networked control systems in which large-scale systems are encountered and mean-field interactions are involved. Because of the complexity due to the switching, little has been done for the associate control problems with mean-field interactions. The main ingredient of this work is the use of conditional mean-fields, which is distinct from the existing literature. Using the maximum principle, optimal controls of linear quadratic Gaussian controls with mean-field interactions for switching diffusions are carried out. Numerical examples are also provided for demonstration.

Solar Prominences

2021 ◽  
Author(s):  
Duncan H. Mackay
Keyword(s):  
Large Scale ◽  
Dense Plasma ◽  
Magnetic Energy ◽  
Solar Rotation ◽  
Energy Levels ◽  
Theoretical Models ◽  
The Sun ◽  
Solar Prominences ◽  
Wide Range ◽  
Free Magnetic Energy

Solar prominences (or filaments) are cool dense regions of plasma that exist within the solar corona. Their existence is due to magnetic fields that support the dense plasma against gravity and insulate it from the surrounding hot coronal plasma. They can be found across all latitudes on the Sun, where their physical dimensions span a wide range of sizes (length ~60–600 Mm, height ~10–100 Mm, and width ~4–10 Mm). Their lifetime can be as long as a solar rotation (27 days), at the end of which they often erupt to initiate coronal mass ejections. When viewed at the highest spatial resolution, solar prominences are found to be composed of many thin co-aligned threads or vertical sheets. Within these structures, both horizontal and vertical motions of up to 10–20 kms−1 are observed, along with a wide variety of oscillations. At the present time, a lack of detailed observations of filament formation gives rise to a wide variety of theoretical models of this process. These models aim to explain both the formation of the prominence’s strongly sheared and highly non-potential magnetic field along with the origin of the dense plasma. Prominences also exhibit a large-scale hemispheric pattern such that “dextral” prominences containing negative magnetic helicity dominate in the northern hemisphere, while “sinistral” prominences containing positive helicity dominate in the south. Understanding this pattern is essential to understanding the build-up and release of free magnetic energy and helicity on the Sun. Future theoretical studies will have to be tightly coordinated with observations conducted at multiple wavelengths (i.e., energy levels) in order to unravel the secrets of these objects.

Electron microscopy study of reactively sputter-deposited Ti/N films on silicon

1992 ◽  
Vol 50 (2) ◽  
pp. 1362-1363
Author(s):  
V. C. Kannan ◽  
A. K. Singh ◽  
R. B. Irwin ◽  
S. Chittipeddi ◽  
F. D. Nkansah ◽  
...  
Keyword(s):  
Integrated Circuits ◽  
Large Scale ◽  
Hexagonal Phase ◽  
Microscopy Study ◽  
Electron Microscopy Study ◽  
Process Conditions ◽  
Tin Films ◽  
Wide Range ◽  
Fcc Phase ◽  
Circuits Vlsi

Titanium nitride (TiN) films have historically been used as diffusion barrier between silicon and aluminum, as an adhesion layer for tungsten deposition and as an interconnect material etc. Recently, the role of TiN films as contact barriers in very large scale silicon integrated circuits (VLSI) has been extensively studied. TiN films have resistivities on the order of 20μ Ω-cm which is much lower than that of titanium (nearly 66μ Ω-cm). Deposited TiN films show resistivities which vary from 20 to 100μ Ω-cm depending upon the type of deposition and process conditions. TiNx is known to have a NaCl type crystal structure for a wide range of compositions. Change in color from metallic luster to gold reflects the stabilization of the TiNx (FCC) phase over the close packed Ti(N) hexagonal phase. It was found that TiN (1:1) ideal composition with the FCC (NaCl-type) structure gives the best electrical property.

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