False balance

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/False_balance

Among climate scientists in 2013, 97% of peer-reviewed papers that took a position on the cause of global warming said that humans are responsible, while 3% said they were not. Meanwhile, 69% of Fox News guests on Intergovernmental Panel on Climate Change stories in late 2013 were "climate contrarians".

False balance, known colloquially as bothsidesism, is a media bias in which journalists present an issue as being more balanced between opposing viewpoints than the evidence supports. Journalists may present evidence and arguments out of proportion to the actual evidence for each side, or may omit information that would establish one side's claims as baseless. False balance has been cited as a cause of misinformation.

False balance is a bias which often stems from an attempt to avoid bias and gives unsupported or dubious positions an illusion of respectability. It creates a public perception that some issues are scientifically contentious, though in reality they are not, therefore creating doubt about the scientific state of research. This can be exploited by interest groups such as corporations like the fossil fuel industry or the tobacco industry, or ideologically motivated activists such as vaccination opponents or creationists.

Examples of false balance in reporting on science issues include the topics of human-caused climate change versus natural climate variability, the health effects of tobacco, the disproven relation between thiomersal and autism, alleged negative side effects of the HPV vaccine, and evolution versus intelligent design.

Description and origin

False balance emerges from the ideal of journalistic objectivity, where factual news is presented in a way that allows the reader to make determinations about how to interpret the facts, and interpretations or arguments around those facts are left to the opinion pages. Because many newsworthy events have two or more opposing camps making competing claims, news media are responsible for reporting all (credible or reasonable) opposing positions, along with verified facts that may support one or the other side of an issue. At one time, when false balance was prevalent, news media sometimes reported all positions as though they were equally credible, even though the facts clearly contradicted a position, or there was a substantial consensus on one side of an issue, and only a fringe or nascent theory supporting the other side.

More recently, in contrast to prior decades, most media are willing to advocate for a particular viewpoint which they regarded as better evidenced. For instance, claims that the Earth is not warming are regularly referred to in news (vs only editorials) as "denial", "misleading", or "debunked". Prior to this shift, media would sometimes list all positions without clarifying that one position is known or generally agreed to be false.

Unlike most other media biases, false balance may result from an attempt to avoid bias; producers and editors may consider treating competing viewpoints fairly—i.e., in proportion to their actual merits and significance—as equivalent to treating them equally, giving them equal time to present their views, even though one of the viewpoints may be overwhelmingly dominant. Media would then present two opposing viewpoints on an issue as equally credible, or present a major issue on one side of a debate as having the same weight as a minor one on the other. False balance can also originate from other motives such as sensationalism, where producers and editors may feel that a story portrayed as a contentious debate will be more commercially successful than a more accurate (or widely-agreed) account of the issue.

Science journalist Dirk Steffens mocked the practice as comparable to inviting a flat Earther to debate with an astrophysicist over the shape of the Earth, as if the truth could be found somewhere in the middle. Liz Spayd of The New York Times wrote: "The problem with false balance doctrine is that it masquerades as rational thinking."

Examples

Climate change

Main article: Media coverage of climate change

 

A 2022 study found that the public in many countries substantially underestimates the degree of scientific consensus that humans are causing climate change. Studies from 2019–2021 found scientific consensus to range from 98.7–100%.

 

Research found that 80–90% of Americans underestimate the prevalence of support for major climate change mitigation policies and climate concern. While 66–80% Americans support these policies, Americans estimate the prevalence to be 37–43%. Researchers have called this misperception a false social reality, a form of pluralistic ignorance.

Although the scientific community almost unanimously attributes a majority of the global warming since 1950 to the effects of the Industrial Revolution, there are a very small number – a few dozen scientists out of tens of thousands – who dispute the conclusion. Giving equal voice to scientists on both sides makes it seem like there is serious disagreement within the scientific community, when in fact there is an overwhelming scientific consensus on climate change that anthropogenic global warming exists.

MMR vaccine controversy

Main article: MMR vaccine controversy

Observers have criticized the involvement of mass media in the MMR vaccine controversy, what is known as "science by press conference", alleging that the media provided Andrew Wakefield's study with more credibility than it deserved. A March 2007 paper in BMC Public Health by Shona Hilton, Mark Petticrew, and Kate Hunt postulated that media reports on Wakefield's study had "created the misleading impression that the evidence for the link with autism was as substantial as the evidence against". Earlier papers in Communication in Medicine and the British Medical Journal concluded that media reports provided a misleading picture of the level of support for Wakefield's hypothesis.

Microgeneration

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Microgeneration

A group of small-scale wind turbines providing electricity to a community in Dali, Yunnan, China

Microgeneration is the small-scale production of heat or electric power from a "low carbon source," as an alternative or supplement to traditional centralized grid-connected power.

Microgeneration technologies include small-scale wind turbines, micro hydro, solar PV systems, microbial fuel cells, ground source heat pumps, and micro combined heat and power installations. These technologies are often combined to form a hybrid power solution that can offer superior performance and lower cost than a system based on one generator.

History

In the United States, Microgeneration had its roots in the 1973 oil crisis and the Yom Kippur War which prompted innovation.

On June 20, 1979, 32 solar panels were installed at the White House. The solar cells were dismantled 7 years later during the Reagan administration.

The use of Solar water heating dates back before 1900 with "the first practical solar cell being developed by Bell Labs in 1954." The "University of Delaware is credited with creating one of the first solar buildings, “Solar One,” in 1973. The construction ran on a combination of solar thermal and solar photovoltaic power. The building didn't use solar panels; instead, solar was integrated into the rooftop."

Technologies and set-up

Power plant

In addition to the electricity production plant (e.g. wind turbine and solar panel), infrastructure for energy storage and power conversion and a hook-up to the regular electricity grid is usually needed and/or foreseen. Although a hookup to the regular electricity grid is not essential, it helps to decrease costs by allowing financial recompensation schemes. In the developing world however, the start-up cost for this equipment is generally too high, thus leaving no choice but to opt for alternative set-ups.

Extra equipment needed besides the power plant

A complete PV-solar system

The whole of the equipment required to set up a working system and for an off-the-grid generation and/or a hook up to the electricity grid herefore is termed a balance of system and is composed of the following parts with PV-systems:

Energy storage apparatus

A major issue with off-grid solar and wind systems is that the power is often needed when the sun is not shining or when the wind is calm, this is generally not required for purely grid-connected systems:

• a series of deep cycle, stationary or sealed maintenance free batteries (the most common solution)

or other means of energy storage (e.g. hydrogen fuel cells, Flywheel energy storage, pumped-storage hydroelectricity, compressed air tanks, ...)

• a charge controller for charging the batteries or other energy storage

For converting DC battery power into AC as required for many appliances, or for feeding excess power into a commercial power grid:

• an inverter or grid-interactive inverter. The whole is also sometimes referred to as "power conditioning equipment"

Safety equipment

• Groundings, transfer switches or isolator switches and surge protectors. The whole is also sometimes referred to as "safety equipment"

Usually, in microgeneration for homes in the developing world, prefabricated house-wiring systems (as wiring harnesses or prefabricated distribution units) are used instead. Simplified house-wiring boxes and cables, known as wiring harnesses, can simply be bought and mounted into the building without requiring much knowledge about the wiring itself. As such, even people without technical expertise are able to install them. They are also comparatively cheap and offer safety advantages.

• battery meters (for charging rate and voltage), and meters for power consumption and electricity provision to the regular power grid

Small-scale (DIY) generation system

Wind turbine specific

Main article: Small wind turbine

With wind turbines, hydroelectric plants, ... the extra equipment needed is more or less the same as with PV-systems (depending on the type of wind turbine used), yet also include:

• a manual disconnect switch
• foundation for the tower
• grounding system
• shutoff and/or dummy-load devices for use in high wind when power generated exceeds current needs and storage system capacity.

Vibro-wind power

A new wind energy technology is being developed that converts energy from wind energy vibrations to electricity. This energy, called Vibro-Wind technology, can use winds of less strength than normal wind turbines, and can be placed in almost any location.

A prototype consisted of a panel mounted with oscillators made out of pieces of foam. The conversion from mechanical to electrical energy is done using a piezoelectric transducer, a device made of a ceramic or polymer that emits electrons when stressed. The building of this prototype was led by Francis Moon, professor of mechanical and aerospace engineering at Cornell University. Moon's work in Vibro-Wind Technology was funded by the Atkinson Center for a Sustainable Future at Cornell. Vibro-wind power is not yet commercially viable and in early development stages. Significant progress will be needed to commercialize this early stage venture.

Possible set-ups

Several microgeneration set-ups are possible. These are:

• Off-the-grid set-ups which include:
  • Off-the grid set-ups without energy storage (e.g., battery, ...)
  • Off-the grid set-ups with energy storage (e.g., battery, ...)
  • Battery charging stations
• Grid-connected set-ups which include:
  • Grid connected with backup to power critical loads
  • Grid-connected set-ups without financial recompensation scheme
  • Grid-connected set-ups with net metering
  • Grid connected set-ups with net purchase and sale

All set-ups mentioned can work either on a single power plant or a combination of power plants (in which case it is called a hybrid power system). For safety, grid-connected set-ups must automatically switch off or enter an "anti-islanding mode" when there is a failure of the mains power supply. For more about this, see the article on the condition of islanding.

Costs

Depending on the set-up chosen (financial recompensation scheme, power plant, extra equipment), prices may vary. According to Practical Action, microgeneration at home which uses the latest in cost saving-technology (wiring harnesses, ready boards, cheap DIY-power plants, e.g. DIY wind turbines) the household expenditure can be extremely low-cost. In fact, Practical Action mentions that many households in farming communities in the developing world spend less than $1 on electricity per month. However, if matters are handled less economically (using more commercial systems/approaches), costs will be dramatically higher. In most cases however, financial advantage will still be done using microgeneration on renewable power plants; often in the range of 50-90% as local production has no electricity transportation losses on long distance power lines or energy losses from the Joule effect in transformers where in general 8-15% of the energy is lost.

In the UK, the government offers both grants and feedback payments to help businesses, communities and private homes to install these technologies. Businesses can write the full cost of installation off against taxable profits whilst homeowners receive a flat-rate grant or payments per kW h of electricity generated and paid back into the national grid. Community organizations can also receive up to £200,000 in grant funding.

In the UK, the Microgeneration Certification Scheme provides approval for Microgeneration Installers and Products which is a mandatory requirement of funding schemes such as the Feed in Tariffs and Renewable Heat Incentive.

Grid parity

Grid parity (or socket parity) occurs when an alternative energy source can generate electricity at a levelized cost of energy (LCOE) that is less than or equal to the price of purchasing power from the electricity grid. Reaching grid parity is considered to be the point at which an energy source becomes a contender for widespread development without subsidies or government support. It is widely believed that a wholesale shift in a generation to these forms of energy will take place when they reach grid parity.

Grid parity has been reached in some locations with on-shore wind power around 2000, and with solar power it was achieved for the first time in Spain in 2013.

Comparison with large-scale generation

	microgeneration	large-scale generation	Notes
Other names	Distributed generation	Centralized generation
Economy of scale	Necessitates mass production of generators which will create an associated environmental impact. Systems are less expensive when produced in quantity.	Depends on power source - generally more economical given the larger scale of the generators. Photovoltaics, similar panels are used in all applications are affected less by this whilst wind power, where power scales approximately as the square of size is affected greatly.
Ability to meet needs	supply within the limits of the installed generation or storage For wind and solar energy, the actual production is only a fraction of nameplate capacity. Fuel based systems are fully dispatchable Solar panels are simple and reliable, they can provide a little electricity at a reasonable cost.	generally more flexible supply within the limits of local transmission as long as the grid is effectively maintained
Environmental impact	larger number of smaller devices may lead to greater impact from device production especially with the wind.	larger generators can have more local impact, transmission equipment can also disrupt areas, however, the overall impact is likely reduced due to economies of scale.	Commentators claim  that householders who buy their electricity with green energy tariffs can reduce their carbon usage further than with microgeneration and at a lower cost.
Transmission losses	Proximity to end user typically closer resulting in potentially fewer losses. (Potentially, because the lack of scale at each individual installation may lead to use of less efficient transmission technologies.)	A significant proportion of electrical power is lost during transmission (approximately 8% in the United Kingdom according to BBC Radio 4 Today programme in March 2006).
Changes to Grid	reduces the transmission load, and thus reduces the need for grid upgrades	increases the power transmitted, and thus increases the need for grid upgrades
Grid failure event	Electricity may still be available to local area in many circumstances	Electricity may be not available due to grid
Generator failure event	Electricity will not be available except in hybrid scenario	Electricity is very likely to be available due to grid redundancy
Consumer choices	May choose to purchase any legal system	May choose to purchase offerings of the power companies depending on market
Reliability and Maintenance requirements	photovoltaics, Stirling engines, and certain other systems, are usually extremely reliable [citation needed], and can generate electric power continuously for many thousands of hours with little or no maintenance. However, unreliable systems will incur additional maintenance labor and costs.	Managed by power company. Grid reliability varies with location.
Waste Heat by-product	Can be used for heating purposes in cold climates, thus greatly increasing efficiency and offsetting energy total costs. This method is known as micro combined heat and power (microCHP).	Used in some privately owned industrial combined heat and power (CHP) installations. It is also used in large-scale applications where it's called district heating and uses the heat that is normally exhausted by inefficient powerplants.

Most forms of microgeneration can dynamically balance the supply and demand for electric power, by producing more power during periods of high demand and high grid prices, and less power during periods of low demand and low grid prices. This "hybridized grid" allows both microgeneration systems and large power plants to operate with greater energy efficiency and cost effectiveness than either could alone.

Domestic self-sufficiency

Further information: Autonomous building

See also: Earthship and Off-the-grid

Horizontal Axis Micro-Windmill in Lahore, 1000Watt Rated Output

Microgeneration can be integrated as part of a self-sufficient house and is typically complemented with other technologies such as domestic food production systems (permaculture and agroecosystem), rainwater harvesting, composting toilets or even complete greywater treatment systems. Domestic microgeneration technologies include: photovoltaic solar systems, small-scale wind turbines, micro combined heat and power installations, biodiesel and biogas.

A small Quietrevolution QR5 Gorlov type vertical axis wind turbine in Bristol, England. Measuring 3 m in diameter and 5 m high, it has a nameplate rating of 6.5 kW to the grid.

Private generation decentralizes the generation of electricity and may also centralize the pooling of surplus energy. While they have to be purchased, solar shingles and panels are both available. Capital cost is high, but saves in the long run. With appropriate power conversion, solar PV panels can run the same electric appliances as electricity from other sources.

Passive solar water heating is another effective method of utilizing solar power. The simplest method is the solar (or a black plastic) bag. Set between 5 and 20 litres (1 and 5 US gal) out in the sun and allow to heat. Perfect for a quick warm shower.

The ‘breadbox’ heater can be constructed easily with recycled materials and basic building experience. Consisting of a single or array of black tanks mounted inside a sturdy box insulated on the bottom and sides. The lid, either horizontal or angled to catch the most sun, should be well sealed and of a transparent glazing material (glass, fiberglass, or high temp resistant molded plastic). Cold water enters the tank near the bottom, heats and rises to the top where it is piped back into the home.

Ground source heat pumps exploit stable ground temperatures by benefiting from the thermal energy storage capacity of the ground. Typically ground source heat pumps have a high initial cost and are difficult to install by the average homeowner. They use electric motors to transfer heat from the ground with a high level of efficiency. The electricity may come from renewable sources or from external non-renewable sources.

Fuel

Biodiesel is an alternative fuel that can power diesel engines and can be used for domestic heating. Numerous forms of biomass, including soybeans, peanuts, and algae (which has the highest yield), can be used to make biodiesel. Recycled vegetable oil (from restaurants) can also be converted into biodiesel.

Biogas is another alternative fuel, created from the waste product of animals. Though less practical for most homes, a farm environment provides a perfect place to implement the process. By mixing the waste and water in a tank with space left for air, methane produces naturally in the airspace. This methane can be piped out and burned, and used for a cookfire.

Government policy

Policymakers were accustomed to an energy system based on big, centralised projects like nuclear or gas-fired power stations. A change of mindsets and incentives are bringing microgeneration into the mainstream. Planning regulations may also require streamlining to facilitate the retrofitting of microgenerating facilities onto homes and buildings.

Most of developed countries, including Canada (Alberta), the United Kingdom, Germany, Poland, Israel and USA have laws allowing microgenerated electricity to be sold into the national grid.

Alberta, Canada

In January 2009, the Government of Alberta's Micro-Generation Regulation came into effect, setting rules that allow Albertans to generate their own environmentally friendly electricity and receive credit for any power they send into the electricity grid.

Poland

In December 2014, the Polish government will vote on a bill which calls for microgeneration, as well as large scale wind farms in the Baltic Sea as a solution to cut back on CO₂ emissions from the country's coal plants as well as to reduce Polish dependence on Russian gas. Under the terms of the new bill, individuals and small businesses which generate up to 40 kW of 'green' energy will receive 100% of market price for any electricity they feed back into the grid, and businesses who set up large-scale offshore wind farms in the Baltic will be eligible for subsidization by the state. Costs of implementing these new policies will be offset by the creation of a new tax on non-sustainable energy use.

United States

The United States has inconsistent energy generation policies across its 50 states. State energy policies and laws may vary significantly with location. Some states have imposed requirements on utilities that a certain percentage of total power generation be from renewable sources. For this purpose, renewable sources include wind, hydroelectric, and solar power whether from large or microgeneration projects. Further, in some areas transferable "renewable source energy" credits are needed by power companies to meet these mandates. As a result, in some portions of the United States, power companies will pay a portion of the cost of renewable source microgeneration projects in their service areas. These rebates are in addition to any Federal or State renewable-energy income-tax credits that may be applicable. In other areas, such rebates may differ or may not be available.

United Kingdom

The UK Government published its Microgeneration Strategy in March 2006, although it was seen as a disappointment by many commentators. In contrast, the Climate Change and Sustainable Energy Act 2006 has been viewed as a positive step. To replace earlier schemes, the Department of Trade and Industry (DTI) launched the Low Carbon Buildings Programme in April 2006, which provided grants to individuals, communities and businesses wishing to invest in microgenerating technologies. These schemes have been replaced in turn by new proposals from the Department for Energy and Climate Change (DECC) for clean energy cashback via Feed-In Tariffs for generating electricity from April 2010 and the Renewable Heat Incentive for generating renewable heat from 28 November 2011.

Feed-In Tariffs are intended to incentivise small-scale (less than 5MW), low-carbon electricity generation. These feed-in tariffs work alongside the Renewables Obligation (RO), which will remain the primary mechanism to incentivise deployment of large-scale renewable electricity generation. The Renewable Heat Incentive (RHI) in intended to incentivise the generation of heat from renewable sources. They also currently offer up to 21p per kWh from December 2011 in the Tariff for photovoltaics plus another 3p for the Export Tariff - an overall figure which could see a household earning back double what they currently pay for their electricity.

On 31 October 2011, the government announced a sudden cut in the feed-in tariff from 43.3p/kWh to 21p/kWh with the new tariff to apply to all new solar PV installations with an eligibility date on or after 12 December 2011.

Prominent British politicians who have announced they are fitting microgenerating facilities to their homes include the Conservative party leader, David Cameron, and the Labour Science Minister, Malcolm Wicks. These plans included small domestic sized wind turbines. Cameron, before becoming Prime Minister in the 2010 general elections, had been asked during an interview on BBC One's The Politics Show on October 29, 2006, if he would do the same should he get to 10 Downing Street. “If they’d let me, yes,” he replied.

In the December 2006 Pre-Budget Report the government announced that the sale of surplus electricity from installations designed for personal use, would not be subject to Income Tax. Legislation to this effect has been included in the Finance Bill 2007.

In popular culture

Several movies and TV shows such as The Mosquito Coast, Jericho, The Time Machine and Beverly Hills Family Robinson have done a great deal in raising interest in microgeneration among the general public. Websites such as Instructables and Practical Action propose DIY solutions that can lower the cost of microgeneration, thus increasing its popularity. Specialised magazines such as OtherPower and Home Power also provide practical advice and guidance.

Soil ecology

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Soil_ecology 

Soil ecology studies interactions among soil organisms, and their environment. It is particularly concerned with the cycling of nutrients, soil aggregate formation and soil biodiversity.

Overview

Soil is made up of a multitude of physical, chemical, and biological entities, with many interactions occurring among them. It is a heterogenous mixture of minerals and organic matter with variations in moisture, temperature and nutrients. Soil supports a wide range of living organisms and is an essential component of terrestrial ecology.

Features of the ecosystem

• Moisture is a significant limiting factor in terrestrial ecosystems and majorly in the soil. Soil organisms are constantly confronted with the problem of dehydration. Soil microbial communities experience shifts in the diversity and composition during dehydration and rehydration cycles. Soil moisture affects carbon cycling a phenomenon known as Birch effect.
• Temperature variations in soil are influenced by factors such as seasonality, environmental conditions, vegetation, and soil composition. Soil temperature also varies with depth; upper soil layers are majorly influence by air temperature, while soil temperature fluctuations decrease with depth. Soil temperature influences biological and biochemical processes in soil, playing an important role in microbial and enzymatic activities, mineralization and organic matter decomposition.
• Air is vital for respiration in soil organisms and in plant growth. Both wind and atmospheric pressure play critical roles in soil aeration. In addition, convection and diffusion also influence the rates of soil aeration
• Soil structure refers to the size, shape and arrangement of solid particles in soil. Factors such as climate, vegetation and organisms influence the complex arrangement of particles in the soil  Structural features of the soil include microporosity and pore size which are also affected by minerals and soil organic matter.
• Land, unlike the ocean, is not continuous; there are important geographical barriers to free movement.
• The nature of the substrate, although important in water is especially vital in terrestrial environment. Soil, not air, is the source of highly variable nutrients; it is a highly developed ecological subsystem.

Soil fauna

Soil fauna is crucial to soil formation, litter decomposition, nutrient cycling, biotic regulation, and for promoting plant growth. Yet soil organisms remain underrepresented in studies on soil processes and in existing modeling exercises. This is a consequence of assuming that much below ground diversity is ecologically redundant and that soil food webs exhibit a higher degree of omnivory. However, evidence is accumulating on the strong influence of abiotic filters, such as temperature, moisture and soil pH, as well as soil habitat characteristics in controlling their spatial and temporal patterns.

Soils are complex systems and their complexity resides in their heterogeneous nature: a mixture of air, water, minerals, organic compounds, and living organisms. The spatial variation, both horizontal and vertical, of all these constituents is related to soil forming agents varying from micro to macro scales. Consequently, the horizontal patchy distribution of soil properties (soil temperature, moisture, pH, litter/nutrient availability, etc.) also drives the patchiness of the soil organisms across the landscape, and has been one of the main arguments for explaining the great diversity observed in soil communities. Because soils also show vertical stratification of their elemental constituents along the soil profile as result of microclimate, soil texture, and resource quantity and quality differing between soil horizons, soil communities also change in abundance and structure with soil depth.

The majority of these organisms are aerobic, so the amount of porous space, pore-size distribution, surface area, and oxygen levels are crucial to their life cycles and activities. The smallest creatures (microbes) use the micropores filled with air to grow, whereas other bigger animals require bigger spaces, macropores, or the water film surrounding the soil particles to move in search for food. Therefore, soil textural properties together with the depth of the water table are also important factors regulating their diversity, population sizes, and their vertical stratification. Ultimately, the structure of the soil communities strongly depends not only on the natural soil forming factors but also on human activities (agriculture, forestry, urbanization) and determines the shape of landscapes in terms of healthy or contaminated, pristine or degraded soils.

Macrofauna

Soil macrofauna, climatic gradients and soil heterogeneity 

 

Historical factors, such as climate and soil parent materials, shape landscapes above and below ground, but the regional/local abiotic conditions constraint biological activities. These operate at different spatial and temporal scales and can switch on and off different organisms at different microsites resulting in a hot moment in a particular hotspot. As a result, trophic cascades can occur up and down the food web.
Soil invertebrates are shown. Ellipses indicate hot (red) or cold spots (blue), with the curved arrows giving some examples of the factors that could switch on/off a hot moment and the straight black arrows (continuous black line = on, dashed = off) showing the implications for soil processes along the soil profile. In the boxes, the main ecosystem characteristics are listed.

Since all these drivers of biodiversity changes also operate above ground, it is thought that there must be some concordance of mechanisms regulating the spatial patterns and structure of both above and below ground communities. In support of this, a small-scale field study revealed that the relationships between environmental heterogeneity and species richness might be a general property of ecological communities. In contrast, the molecular examination of 17,516 environmental 18S rRNA gene sequences representing 20 phyla of soil animals covering a range of biomes and latitudes around the world indicated otherwise, and the main conclusion from this study was that below-ground animal diversity may be inversely related to above-ground biodiversity.

The lack of distinct latitudinal gradients in soil biodiversity contrasts with those clear global patterns observed for plants above ground and has led to the assumption that they are indeed controlled by different factors. For example, in 2007 Lozupone and Knight found salinity was the major environmental determinant of bacterial diversity composition across the globe, rather than extremes of temperature, pH, or other physical and chemical factors. In another global scale study in 2014, Tedersoo et al. concluded fungal richness is causally unrelated to plant diversity and is better explained by climatic factors, followed by edaphic and spatial patterns. Global patterns of the distribution of macroscopic organisms are far poorer documented. However, the little evidence available appears to indicate that, at large scales, soil metazoans respond to altitudinal, latitudinal or area gradients in the same way as those described for above-ground organisms. In contrast, at local scales, the great diversity of microhabitats commonly found in soils provides the required niche portioning to create hot spots of diversity in just a gram of soil.

Spatial patterns of soil biodiversity are difficult to explain, and its potential linkages to many soil processes and the overall ecosystem functioning are debated. For example, while some studies have found that reductions in the abundance and presence of soil organisms results in the decline of multiple ecosystem functions, others concluded that above-ground plant diversity alone is a better predictor of ecosystem multi-functionality than soil biodiversity. Soil organisms exhibit a wide array of feeding preferences, life-cycles and survival strategies and they interact within complex food webs. Consequently, species richness per se has very little influence on soil processes and functional dissimilarity can have stronger impacts on ecosystem functioning. Therefore, besides the difficulties in linking above and below ground diversities at different spatial scales, gaining a better understanding of the biotic effects on ecosystem processes might require incorporating a great number of components together with several multi-trophic levels  as well as the much less considered non-trophic interactions such as phoresy, passive consumption.) In addition, if soil systems are indeed self-organized, and soil organisms concentrate their activities within a selected set of discrete scales with some form of overall coordination, there is no need for looking for external factors controlling the assemblages of soil constituents. Instead we might just need to recognize the unexpected and that the linkages between above and below ground diversity and soil processes are difficult to predict.

Microfauna

Recent advances are emerging from studying sub-organism level responses using environmental DNA  and various omics approaches, such as metagenomics, metatranscriptomics, proteomics and proteogenomics, are rapidly advancing, at least for the microbial world. Metaphenomics has been proposed recently as a better way to encompass the omics and the environmental constraints.

Soil microbes

Main article: Soil microbiology

Soil harbors many microbes: bacteria, archaea, protist, fungi and viruses. A majority of these microbes have not been cultured and remain undescribed. Development of next generation sequencing technologies open up the avenue to investigate microbial diversity in soil.  One feature of soil microbes is spatial separation which influences microbe to microbe interactions and ecosystem functioning in the soil habitat.  Microorganisms in soil are found to be concentrated in specific sites called 'hot spots' which is characterized by an abundance of resources such as moisture or nutrients.  An example is the rhizosphere, and areas with accumulated organic matter such as the detritusphere. These areas are characterized by the presence of decaying root litter and exudates released from plant roots which regulates the availability of carbon and nitrogen and in consequence modulate microbial processes.  Apart from labile organic carbon, spatial separation of microbes in soil may be influenced by other environmental factors such as temperature and moisture.  Other abiotic factors like pH and mineral nutrient composition may also influence the distribution of microorganisms in soil. Variability of these factors make soil a dynamic system.  Interactions between members of the soil microhabitat takes place via chemical signaling which is mediated by soluble metabolites and volatile organic compounds, in addition to extracellular polysaccharides.  Chemical signals enable microbes to interact, for example bacterial peptidoglycans stimulate growth of Candida albicans. Reciprocally, C. albicans production of farnesol modulates the expression of virulence genes and influences bacterial quorum sensing.  Trophic interactions by microbes in the same environment is driven by molecular communication. Microbes may also exchange metabolites to support each other's growth, e.g., the release of extracellular enzymes by ectomycorrhiza decomposes organic matter and releases nutrients which then benefits other members of the population, in exchange organic acids from bacteria stimulate fungal growth  These examples of trophic interactions especially metabolite dependencies drive species interactions and are important in the assembly of soil microbial communities.

Soil food web

Main article: soil food web

Diverse organisms make up the soil food web. They range in size from one-celled bacteria, algae, fungi, and protozoa, to more complex nematodes and micro-arthropods, to the visible earthworms, insects, small vertebrates, and plants. As these organisms eat, grow, and move through the soil, they make it possible to have clean water, clean air, healthy plants, and moderated water flow.

There are many ways that the soil food web is an integral part of landscape processes. Soil organisms decompose organic compounds, including manure, plant residues, and pesticides, preventing them from entering water and becoming pollutants. They sequester nitrogen and other nutrients that might otherwise enter groundwater, and they fix nitrogen from the atmosphere, making it available to plants. Many organisms enhance soil aggregation and porosity, thus increasing infiltration and reducing surface runoff. Soil organisms prey on crop pests and are food for above-ground animals.

Research

Research interests span many aspects of soil ecology and microbiology. Fundamentally, researchers are interested in understanding the interplay among microorganisms, fauna, and plants, the biogeochemical processes they carry out, and the physical environment in which their activities take place, and applying this knowledge to address environmental problems.

Example research projects are to examine the biogeochemistry and microbial ecology of septic drain field soils used to treat domestic wastewater, the role of anecic earthworms in controlling the movement of water and nitrogen cycle in agricultural soils, and the assessment of soil quality in turf production.

Of particular interest as of 2006 is to understand the roles and functions of arbuscular mycorrhizal fungi in natural ecosystems. The effect of anthropic soil conditions on arbuscular mycorrhizal fungi and the production of glomalin by arbuscular mycorrhizal fungi are both of interest due to their roles in sequestering atmospheric carbon dioxide.

Quantum relative entropy

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Quantum_relative_entropy 

In quantum information theory, quantum relative entropy is a measure of distinguishability between two quantum states. It is the quantum mechanical analog of relative entropy.

Motivation

For simplicity, it will be assumed that all objects in the article are finite-dimensional.

We first discuss the classical case. Suppose the probabilities of a finite sequence of events is given by the probability distribution P = {p₁...p_n}, but somehow we mistakenly assumed it to be Q = {q₁...q_n}. For instance, we can mistake an unfair coin for a fair one. According to this erroneous assumption, our uncertainty about the j-th event, or equivalently, the amount of information provided after observing the j-th event, is

${\displaystyle -\log q_j.}$

The (assumed) average uncertainty of all possible events is then

${\displaystyle -\sum _j{p}_j\log q_j.}$

On the other hand, the Shannon entropy of the probability distribution p, defined by

${\displaystyle -\sum _j{p}_j\log p_j,}$

is the real amount of uncertainty before observation. Therefore the difference between these two quantities

${\displaystyle -\sum _j{p}_j\log q_j-\left(-\sum _j{p}_j\log p_j\right)=\sum _j{p}_j\log p_j-\sum _j{p}_j\log q_j}$

is a measure of the distinguishability of the two probability distributions p and q. This is precisely the classical relative entropy, or Kullback–Leibler divergence:

${\displaystyle D_{\mathrm{K}\mathrm{L}}(P\Vert Q)=\sum _j{p}_j\log \frac{p_j}{q_j}.}$

Note

1. In the definitions above, the convention that 0·log 0 = 0 is assumed, since ${\displaystyle \underset{x\searrow 0}{lim}x\log (x)=0}$. Intuitively, one would expect that an event of zero probability to contribute nothing towards entropy.
2. The relative entropy is not a metric. For example, it is not symmetric. The uncertainty discrepancy in mistaking a fair coin to be unfair is not the same as the opposite situation.

Definition

As with many other objects in quantum information theory, quantum relative entropy is defined by extending the classical definition from probability distributions to density matrices. Let ρ be a density matrix. The von Neumann entropy of ρ, which is the quantum mechanical analog of the Shannon entropy, is given by

${\displaystyle S(\rho )=-\mathrm{Tr}\rho \log \rho .}$

For two density matrices ρ and σ, the quantum relative entropy of ρ with respect to σ is defined by

${\displaystyle S(\rho \Vert \sigma )=-\mathrm{Tr}\rho \log \sigma -S(\rho )=\mathrm{Tr}\rho \log \rho -\mathrm{Tr}\rho \log \sigma =\mathrm{Tr}\rho (\log \rho -\log \sigma ).}$

We see that, when the states are classically related, i.e. ρσ = σρ, the definition coincides with the classical case, in the sense that if ${\displaystyle \rho =S{D}_1{S}^{\mathsf{T}}}$ and ${\displaystyle \sigma =S{D}_2{S}^{\mathsf{T}}}$ with ${\displaystyle D_1=\text{diag}({\lambda }_1,\dots ,{\lambda }_n)}$ and ${\displaystyle D_2=\text{diag}({\mu }_1,\dots ,{\mu }_n)}$ (because ${\displaystyle \rho }$ and ${\displaystyle \sigma }$ commute, they are simultaneously diagonalizable), then ${\displaystyle S(\rho \Vert \sigma )=\sum _{j=1}^n{\lambda }_j\ln \left(\frac{{\lambda }_j}{{\mu }_j}\right)}$ is just the ordinary Kullback–Leibler divergence of the probability vector ${\displaystyle ({\lambda }_1,\dots ,{\lambda }_n)}$ with respect to the probability vector ${\displaystyle ({\mu }_1,\dots ,{\mu }_n)}$.

Non-finite (divergent) relative entropy

In general, the support of a matrix M is the orthogonal complement of its kernel, i.e. ${\displaystyle \text{supp}(M)=\text{ker}(M{)}^{\perp }}$. When considering the quantum relative entropy, we assume the convention that −s · log 0 = ∞ for any s > 0. This leads to the definition that

${\displaystyle S(\rho \Vert \sigma )=\mathrm{\infty }}$

when

${\displaystyle \text{supp}(\rho )\cap \text{ker}(\sigma )\ne \{0\}.}$

This can be interpreted in the following way. Informally, the quantum relative entropy is a measure of our ability to distinguish two quantum states where larger values indicate states that are more different. Being orthogonal represents the most different quantum states can be. This is reflected by non-finite quantum relative entropy for orthogonal quantum states. Following the argument given in the Motivation section, if we erroneously assume the state ${\displaystyle \rho }$ has support in ${\displaystyle \text{ker}(\sigma )}$, this is an error impossible to recover from.

However, one should be careful not to conclude that the divergence of the quantum relative entropy ${\displaystyle S(\rho \Vert \sigma )}$ implies that the states ${\displaystyle \rho }$ and ${\displaystyle \sigma }$ are orthogonal or even very different by other measures. Specifically, ${\displaystyle S(\rho \Vert \sigma )}$ can diverge when ${\displaystyle \rho }$ and ${\displaystyle \sigma }$ differ by a vanishingly small amount as measured by some norm. For example, let ${\displaystyle \sigma }$ have the diagonal representation

${\displaystyle \sigma =\sum _n{\lambda }_n|f_n⟩⟨f_n|}$

with ${\displaystyle {\lambda }_n>0}$ for ${\displaystyle n=0,1,2,\dots }$ and ${\displaystyle {\lambda }_n=0}$ for ${\displaystyle n=-1,-2,\dots }$ where ${\displaystyle \{|f_n⟩,n\in \mathbb{Z}\}}$ is an orthonormal set. The kernel of ${\displaystyle \sigma }$ is the space spanned by the set ${\displaystyle \{|f_n⟩,n=-1,-2,\dots \}}$. Next let

${\displaystyle \rho =\sigma +ϵ|f_{-1}⟩⟨f_{-1}|-ϵ|f_1⟩⟨f_1|}$

for a small positive number ${\displaystyle ϵ}$. As ${\displaystyle \rho }$ has support (namely the state ${\displaystyle |f_{-1}⟩}$) in the kernel of ${\displaystyle \sigma }$, ${\displaystyle S(\rho \Vert \sigma )}$ is divergent even though the trace norm of the difference ${\displaystyle (\rho -\sigma )}$ is ${\displaystyle 2ϵ}$ . This means that difference between ${\displaystyle \rho }$ and ${\displaystyle \sigma }$ as measured by the trace norm is vanishingly small as ${\displaystyle ϵ\to 0}$ even though ${\displaystyle S(\rho \Vert \sigma )}$ is divergent (i.e. infinite). This property of the quantum relative entropy represents a serious shortcoming if not treated with care.

Non-negativity of relative entropy

Corresponding classical statement

For the classical Kullback–Leibler divergence, it can be shown that

${\displaystyle D_{\mathrm{K}\mathrm{L}}(P\Vert Q)=\sum _j{p}_j\log \frac{p_j}{q_j}\ge 0,}$

and the equality holds if and only if P = Q. Colloquially, this means that the uncertainty calculated using erroneous assumptions is always greater than the real amount of uncertainty.

To show the inequality, we rewrite

${\displaystyle D_{\mathrm{K}\mathrm{L}}(P\Vert Q)=\sum _j{p}_j\log \frac{p_j}{q_j}=\sum _j(-\log \frac{q_j}{p_j})(p_j).}$

Notice that log is a concave function. Therefore -log is convex. Applying Jensen's inequality, we obtain

${\displaystyle D_{\mathrm{K}\mathrm{L}}(P\Vert Q)=\sum _j(-\log \frac{q_j}{p_j})(p_j)\ge -\log (\sum _j\frac{q_j}{p_j}{p}_j)=0.}$

Jensen's inequality also states that equality holds if and only if, for all i, q_i = (Σq_j) p_i, i.e. p = q.

The result

Klein's inequality states that the quantum relative entropy

${\displaystyle S(\rho \Vert \sigma )=\mathrm{Tr}\rho (\log \rho -\log \sigma ).}$

is non-negative in general. It is zero if and only if ρ = σ.

Proof

Let ρ and σ have spectral decompositions

${\displaystyle \rho =\sum _i{p}_i{v}_i{v}_i^{\ast },\sigma =\sum _i{q}_i{w}_i{w}_i^{\ast }.}$

So

${\displaystyle \log \rho =\sum _i(\log p_i)v_i{v}_i^{\ast },\log \sigma =\sum _i(\log q_i)w_i{w}_i^{\ast }.}$

Direct calculation gives

${\displaystyle S(\rho \Vert \sigma )=\sum _k{p}_k\log p_k-\sum _{i,j}(p_i\log q_j)|v_i^{\ast }{w}_j{|}^2}$

${\displaystyle =\sum _i{p}_i(\log p_i-\sum _j\log q_j|v_i^{\ast }{w}_j{|}^2)}$

${\displaystyle =\sum _i{p}_i(\log p_i-\sum _j(\log q_j)P_{ij}),}$

where P_{i j} = |v_i*w_j|².

Since the matrix (P_{i j})_{i j} is a doubly stochastic matrix and -log is a convex function, the above expression is

${\displaystyle \ge \sum _i{p}_i(\log p_i-\log (\sum _j{q}_j{P}_{ij})).}$

Define r_i = Σ_jq_j P_{i j}. Then {r_i} is a probability distribution. From the non-negativity of classical relative entropy, we have

${\displaystyle S(\rho \Vert \sigma )\ge \sum _i{p}_i\log \frac{p_i}{r_i}\ge 0.}$

The second part of the claim follows from the fact that, since -log is strictly convex, equality is achieved in

${\displaystyle \sum _i{p}_i(\log p_i-\sum _j(\log q_j)P_{ij})\ge \sum _i{p}_i(\log p_i-\log (\sum _j{q}_j{P}_{ij}))}$

if and only if (P_{i j}) is a permutation matrix, which implies ρ = σ, after a suitable labeling of the eigenvectors {v_i} and {w_i}.

Joint convexity of relative entropy

The relative entropy is jointly convex. For ${\displaystyle 0\le \lambda \le 1}$ and states ${\displaystyle {\rho }_{1(2)},{\sigma }_{1(2)}}$ we have

${\displaystyle D(\lambda {\rho }_1+(1-\lambda ){\rho }_2\Vert \lambda {\sigma }_1+(1-\lambda ){\sigma }_2)\le \lambda D({\rho }_1\Vert {\sigma }_1)+(1-\lambda )D({\rho }_2\Vert {\sigma }_2)}$

Monotonicity of relative entropy

The relative entropy decreases monotonically under completely positive trace preserving (CPTP) operations ${\displaystyle \mathcal{N}}$ on density matrices,

${\displaystyle S(\mathcal{N}(\rho )\Vert \mathcal{N}(\sigma ))\le S(\rho \Vert \sigma )}$.

This inequality is called monotonicity of quantum relative entropy and was first proved by Göran Lindblad.

An entanglement measure

Let a composite quantum system have state space

${\displaystyle H={\otimes }_k{H}_k}$

and ρ be a density matrix acting on H.

The relative entropy of entanglement of ρ is defined by

${\displaystyle D_{\mathrm{R}\mathrm{E}\mathrm{E}}(\rho )=\underset{\sigma }{min}S(\rho \Vert \sigma )}$

where the minimum is taken over the family of separable states. A physical interpretation of the quantity is the optimal distinguishability of the state ρ from separable states.

Clearly, when ρ is not entangled

${\displaystyle D_{\mathrm{R}\mathrm{E}\mathrm{E}}(\rho )=0}$

by Klein's inequality.

Relation to other quantum information quantities

One reason the quantum relative entropy is useful is that several other important quantum information quantities are special cases of it. Often, theorems are stated in terms of the quantum relative entropy, which lead to immediate corollaries concerning the other quantities. Below, we list some of these relations.

Let ρ_AB be the joint state of a bipartite system with subsystem A of dimension n_A and B of dimension n_B. Let ρ_A, ρ_B be the respective reduced states, and I_A, I_B the respective identities. The maximally mixed states are I_A/n_A and I_B/n_B. Then it is possible to show with direct computation that

${\displaystyle S({\rho }_A||I_A/n_A)=\mathrm{l}\mathrm{o}\mathrm{g}(n_A)-S({\rho }_A),}$

${\displaystyle S({\rho }_{AB}||{\rho }_A\otimes {\rho }_B)=S({\rho }_A)+S({\rho }_B)-S({\rho }_{AB})=I(A:B),}$

${\displaystyle S({\rho }_{AB}||{\rho }_A\otimes I_B/n_B)=\mathrm{l}\mathrm{o}\mathrm{g}(n_B)+S({\rho }_A)-S({\rho }_{AB})=\mathrm{l}\mathrm{o}\mathrm{g}(n_B)-S(B|A),}$

where I(A:B) is the quantum mutual information and S(B|A) is the quantum conditional entropy.