This diagram of the fast carbon cycle shows the movement of carbon between land, atmosphere, soil and oceans in billions of tons of carbon per year. Yellow numbers are natural fluxes, red are human contributions in billions of tons of carbon per year. White numbers indicate stored carbon.
Title: 'Carbon Dioxide Sink Ability of Several Plants Species in Bogor. BAB IV Kondisi Umum Lokasi Penelitian.pdf, BAB IV, 286.35 kB, Adobe PDF, View/Open.
Air-sea exchange of CO2
A carbon sink is a natural reservoir that stores carbon-containing chemical compounds accumulated over an indefinite period of time. The process by which carbon sinks remove carbon dioxide (CO
2) from the atmosphere is known as carbon sequestration. Public awareness of the significance of CO2 sinks has grown since passage of the Kyoto Protocol, which promotes their use as a form of carbon offset. There are also different strategies used to enhance this process.[1]
2) from the atmosphere is known as carbon sequestration. Public awareness of the significance of CO2 sinks has grown since passage of the Kyoto Protocol, which promotes their use as a form of carbon offset. There are also different strategies used to enhance this process.[1]
- 3Storage in terrestrial and marine environments
- 4Enhancing natural sequestration
- 5Artificial sequestration
- 5.4Mineral sequestration
General[edit]
Increase in atmospheric carbon dioxide means increase in global temperature. The amount of carbon dioxide varies naturally. The natural sinks are:
- Trees serve as carbon sinks during growing seasons.
- Absorption of carbon dioxide by the oceans via physicochemical and biological processes
- Photosynthesis by terrestrial plants
Whilst the creation of artificial sinks has been discussed, no major artificial systems remove carbon from the atmosphere on a material scale.
Carbon sources include:
- Combustion of fossil fuels (coal, natural gas, and oil) by humans for energy and transportation [2]
- Farmland (by animal respiration); there are proposals for improvements in farming practices to reverse this.[3]
Kyoto Protocol[edit]
Because growing vegetation takes in carbon dioxide, the Kyoto Protocol allows Annex I countries with large areas of growing forests to issue Removal Units to recognize the sequestration of carbon. The additional units make it easier for them to achieve their target emission levels. It is estimated that forests absorb between 10 and 20 tons of carbon dioxide per hectare each year, through photosynthetic conversion into starch, cellulose, lignin, and wooden biomass. While this has been well documented for temperate forests and plantations, the fauna of the tropical forests place some limitations for such global estimates.[citation needed]
Some countries seek to trade emission rights in carbon emission markets, purchasing the unused carbon emission allowances of other countries. If overall limits on greenhouse gas emission are put into place, cap and trade market mechanisms are purported to find cost-effective ways to reduce emissions.[4] There is as yet no carbon audit regime for all such markets globally, and none is specified in the Kyoto Protocol. National carbon emissions are self-declared.
In the Clean Development Mechanism, only afforestation and reforestation are eligible to produce certified emission reductions (CERs) in the first commitment period of the Kyoto Protocol (2008–2012). Forest conservation activities or activities avoiding deforestation, which would result in emission reduction through the conservation of existing carbon stocks, are not eligible at this time.[5] Also, agricultural carbon sequestration is not possible yet.[6]
Storage in terrestrial and marine environments[edit]
Soils[edit]
Soils represent a short to long-term carbon storage medium, and contain more carbon than all terrestrial vegetation and the atmosphere combined.[7][8][9]Plant litter and other biomass including charcoal accumulates as organic matter in soils, and is degraded by chemical weathering and biological degradation. More recalcitrant organiccarbon polymers such as cellulose, hemi-cellulose, lignin, aliphatic compounds, waxes and terpenoids are collectively retained as humus.[10] Organic matter tends to accumulate in litter and soils of colder regions such as the boreal forests of North America and the Taiga of Russia. Leaf litter and humus are rapidly oxidized and poorly retained in sub-tropical and tropical climate conditions due to high temperatures and extensive leaching by rainfall. Areas where shifting cultivation or slash and burn agriculture are practiced are generally only fertile for two to three years before they are abandoned. These tropical jungles are similar to coral reefs in that they are highly efficient at conserving and circulating necessary nutrients, which explains their lushness in a nutrient desert.[citation needed] Much organic carbon retained in many agricultural areas worldwide has been severely depleted due to intensive farming practices.
Grasslands contribute to soil organic matter, stored mainly in their extensive fibrous root mats. Due in part to the climatic conditions of these regions (e.g. cooler temperatures and semi-arid to arid conditions), these soils can accumulate significant quantities of organic matter. This can vary based on rainfall, the length of the winter season, and the frequency of naturally occurring lightning-induced grass-fires. While these fires release carbon dioxide, they improve the quality of the grasslands overall, in turn increasing the amount of carbon retained in the humic material. They also deposit carbon directly to the soil in the form of char that does not significantly degrade back to carbon dioxide.
Forest fires release absorbed carbon back into the atmosphere,[11] as does deforestation due to rapidly increased oxidation of soil organic matter.[12]
Organic matter in peat bogs undergoes slow anaerobic decomposition below the surface. This process is slow enough that in many cases the bog grows rapidly and fixes more carbon from the atmosphere than is released. Over time, the peat grows deeper. Peat bogs hold approximately one-quarter of the carbon stored in land plants and soils.[13]
Under some conditions, forests and peat bogs may become sources of CO2, such as when a forest is flooded by the construction of a hydroelectric dam. Unless the forests and peat are harvested before flooding, the rotting vegetation is a source of CO2 and methane comparable in magnitude to the amount of carbon released by a fossil-fuel powered plant of equivalent power.[14]
Regenerative agriculture[edit]
Current agricultural practices lead to carbon loss from soils. It has been suggested that improved farming practices could return the soils to being a carbon sink. Present worldwide practises of overgrazing are substantially reducing many grasslands' performance as carbon sinks.[15]The Rodale Institute says that regenerative agriculture, if practiced on the planet’s 3.6 billion tillable acres, could sequester up to 40% of current CO2 emissions.[16] They claim that agricultural carbon sequestration has the potential to mitigate global warming. When using biologically-based regenerative practices, this dramatic benefit can be accomplished with no decrease in yields or farmer profits.[17] Organically managed soils can convert carbon dioxide from a greenhouse gas into a food-producing asset.
In 2006, U.S. carbon dioxide emissions, largely from fossil fuel combustion, were estimated at nearly 6.5 billion tons.[18] If a 2,000 (lb/ac)/year sequestration rate was achieved on all 434,000,000 acres (1,760,000 km2) of cropland in the United States, nearly 1.6 billion tons of carbon dioxide would be sequestered per year, mitigating close to one quarter of the country's total fossil fuel emissions.
Oceans[edit]
Presently, oceans are CO2 sinks, and represent the largest active carbon sink on Earth, absorbing more than a quarter of the carbon dioxide that humans put into the air.[19] The solubility pump is the primary mechanism responsible for the CO2 absorption by the oceans.
The biological pump plays a negligible role, because of the limitation to pump by ambient light and nutrients required by the phytoplankton that ultimately drive it. Total inorganic carbon is not believed to limit primary production in the oceans, so its increasing availability in the ocean does not directly affect production (the situation on land is different, since enhanced atmospheric levels of CO2 essentially 'fertilize' land plant growth to some threshold). However, ocean acidification by invading anthropogenic CO2 may affect the biological pump by negatively impacting calcifying organisms such as coccolithophores, foraminiferans and pteropods. Climate change may also affect the biological pump in the future by warming and stratifying the surface ocean, thus reducing the supply of limiting nutrients to surface waters.[20]
A 2008 study found that CO2 could potentially increase primary productivity, particularly in eel grasses in coastal and estuarine habitats.[21]
In January 2009, the Monterey Bay Aquarium Research Institute and the National Oceanic and Atmospheric Administration announced a joint study to determine whether the ocean off the California coast was serving as a carbon source or a carbon sink. Principal instrumentation for the study will be self-contained CO2 monitors placed on buoys in the ocean. They will measure the partial pressure of CO2 in the ocean and the atmosphere just above the water surface.[22]
In February 2009, Science Daily reported that the Southern Indian Ocean is becoming less effective at absorbing carbon dioxide due to changes to the region's climate which include higher wind speeds.[23]
On longer timescales, oceans may be both sources and sinks – during ice agesCO
2 levels decrease to ≈180 ppmv, and much of this is believed to be stored in the oceans. As ice ages end, CO
2 is released from the oceans and CO
2 levels during previous interglacials have been around ≈280 ppmv. This role as a sink for CO2 is driven by two processes, the solubility pump and the biological pump.[24] The former is primarily a function of differential CO2 solubility in seawater and the thermohaline circulation, while the latter is the sum of a series of biological processes that transport carbon (in organic and inorganic forms) from the surface euphotic zone to the ocean's interior. A small fraction of the organic carbon transported by the biological pump to the seafloor is buried in anoxic conditions under sediments and ultimately forms fossil fuels such as oil and natural gas.
2 levels decrease to ≈180 ppmv, and much of this is believed to be stored in the oceans. As ice ages end, CO
2 is released from the oceans and CO
2 levels during previous interglacials have been around ≈280 ppmv. This role as a sink for CO2 is driven by two processes, the solubility pump and the biological pump.[24] The former is primarily a function of differential CO2 solubility in seawater and the thermohaline circulation, while the latter is the sum of a series of biological processes that transport carbon (in organic and inorganic forms) from the surface euphotic zone to the ocean's interior. A small fraction of the organic carbon transported by the biological pump to the seafloor is buried in anoxic conditions under sediments and ultimately forms fossil fuels such as oil and natural gas.
At the end of glacials with sea level rapidly rising, corals tend to grow slower due to increased ocean temperature as seen on the Showtime series 'Years of Living Dangerously'. The calcium carbonate from which coral skeletons are made is just over 60% carbon dioxide. If we postulate that coral reefs were eroded down to the glacial sea level, then coral reefs have grown 120m upward since the end of the recent glacial.[citation needed]
Enhancing natural sequestration[edit]
Forests[edit]
Forests can be carbon stores,[25][26] and they are carbon dioxide sinks when they are increasing in density or area. In Canada's boreal forests as much as 80% of the total carbon is stored in the soils as dead organic matter.[27] A 40-year study of African, Asian, and South American tropical forests by the University of Leeds, shows tropical forests absorb about 18% of all carbon dioxide added by fossil fuels. Truly mature tropical forests, by definition, grow rapidly as each tree produces at least 10 new trees each year. Based on studies of the FAO and UNEP it has been estimated that Asian forests absorb about 5 tonnes of carbon dioxide per hectare each year. The global cooling effect of carbon sequestration by forests is partially counterbalanced in that reforestation can decrease the reflection of sunlight (albedo). Mid-to-high latitude forests have a much lower albedo during snow seasons than flat ground, thus contributing to warming. Modeling that compares the effects of albedo differences between forests and grasslands suggests that expanding the land area of forests in temperate zones offers only a temporary cooling benefit.[28][29][30][31]
In the United States in 2004 (the most recent year for which EPA statistics[32] are available), forests sequestered 10.6% (637 megatonnes)[33] of the carbon dioxide released in the United States by the combustion of fossil fuels (coal, oil and natural gas; 5,657 megatonnes[34]). Urban trees sequestered another 1.5% (88 megatonnes).[33] To further reduce U.S. carbon dioxide emissions by 7%, as stipulated by the Kyoto Protocol, would require the planting of 'an area the size of Texas [8% of the area of Brazil] every 30 years'.[35]Carbon offset programs are planting millions of fast-growing trees per year to reforest tropical lands, for as little as $0.10 per tree; over their typical 40-year lifetime, one million of these trees will fix 1 to 2 megatonnes of carbon dioxide.[citation needed] In Canada, reducing timber harvesting would have very little impact on carbon dioxide emissions because of the combination of harvest and stored carbon in manufactured wood products along with the regrowth of the harvested forests. Additionally, the amount of carbon released from harvesting is small compared to the amount of carbon lost each year to forest fires and other natural disturbances.[27]
The Intergovernmental Panel on Climate Change concluded that 'a sustainable forest management strategy aimed at maintaining or increasing forest carbon stocks, while producing an annual sustained yield of timber fibre or energy from the forest, will generate the largest sustained mitigation benefit'.[36] Sustainable management practices keep forests growing at a higher rate over a potentially longer period of time, thus providing net sequestration benefits in addition to those of unmanaged forests.[37]
Life expectancy of forests varies throughout the world, influenced by tree species, site conditions and natural disturbance patterns. In some forests carbon may be stored for centuries, while in other forests carbon is released with frequent stand replacing fires. Forests that are harvested prior to stand replacing events allow for the retention of carbon in manufactured forest products such as lumber.[38] However, only a portion of the carbon removed from logged forests ends up as durable goods and buildings. The remainder ends up as sawmill by-products such as pulp, paper and pallets, which often end with incineration (resulting in carbon release into the atmosphere) at the end of their lifecycle. For instance, of the 1,692 megatonnes of carbon harvested from forests in Oregon and Washington (U.S) from 1900 to 1992, only 23% is in long-term storage in forest products.[39]
Oceans[edit]
One way to increase the carbon sequestration efficiency of the oceans is to add micrometre-sized iron particles in the form of either hematite (iron oxide) or melanterite (iron sulfate) to certain regions of the ocean. This has the effect of stimulating growth of plankton. Iron is an important nutrient for phytoplankton, usually made available via upwelling along the continental shelves, inflows from rivers and streams, as well as deposition of dust suspended in the atmosphere. Natural sources of ocean iron have been declining in recent decades, contributing to an overall decline in ocean productivity (NASA, 2003).[citation needed] Yet in the presence of iron nutrients plankton populations quickly grow, or 'bloom', expanding the base of biomass productivity throughout the region and removing significant quantities of CO2 from the atmosphere via photosynthesis. A test in 2002 in the Southern Ocean around Antarctica suggests that between 10,000 and 100,000 carbon atoms are sunk for each iron atom added to the water.[citation needed] More recent work in Germany (2005)[citation needed] suggests that any biomass carbon in the oceans, whether exported to depth or recycled in the euphotic zone, represents long-term storage of carbon. This means that application of iron nutrients in select parts of the oceans, at appropriate scales, could have the combined effect of restoring ocean productivity while at the same time mitigating the effects of human caused emissions of carbon dioxide to the atmosphere.[citation needed]
Because the effect of periodic small scale phytoplankton blooms on ocean ecosystems is unclear, more studies would be helpful. Phytoplankton have a complex effect on cloud formation via the release of substances such as dimethyl sulfide (DMS) that are converted to sulfate aerosols in the atmosphere, providing cloud condensation nuclei, or CCN.[40] But the effect of small scale plankton blooms on overall DMS production is unknown.
Other nutrients such as nitrates, phosphates, and silica as well as iron may cause ocean fertilization. There has been some speculation that using pulses of fertilization (around 20 days in length) may be more effective at getting carbon to ocean floor than sustained fertilization.[41]
There is some controversy over seeding the oceans with iron however, due to the potential for increased toxic phytoplankton growth (e.g. 'red tide'), declining water quality due to overgrowth, and increasing anoxia in areas harming other sea-life such as zooplankton, fish, coral, etc.[42][43]
Soils[edit]
Since the 1850s, a large proportion of the world's grasslands have been tilled and converted to croplands, allowing the rapid oxidation of large quantities of soil organic carbon. However, in the United States in 2004 (the most recent year for which EPA statistics are available), agricultural soils including pasture land sequestered 0.8% (46 megatonne)[33] as much carbon as was released in the United States by the combustion of fossil fuels (5,988 megatonne).[34] The annual amount of this sequestration has been gradually increasing since 1998.[33]
Methods that significantly enhance carbon sequestration in soil include no-till farming, residue mulching, cover cropping, and crop rotation, all of which are more widely used in organic farming than in conventional farming.[44][45] Because only 5% of US farmland currently uses no-till and residue mulching, there is a large potential for carbon sequestration.[46] Conversion to pastureland, particularly with good management of grazing, can sequester even more carbon in the soil.
Terra preta, an anthropogenic, high-carbon soil, is also being investigated as a sequestration mechanism.By pyrolysing biomass, about half of its carbon can be reduced to charcoal, which can persist in the soil for centuries, and makes a useful soil amendment, especially in tropical soils (biochar or agrichar).[47][48]
Savanna[edit]
Controlled burns on far north Australian savannas can result in an overall carbon sink. One working example is the West Arnhem Fire Management Agreement, started to bring 'strategic fire management across 28,000 km² of Western Arnhem Land'. Deliberately starting controlled burns early in the dry season results in a mosaic of burnt and unburnt country which reduces the area of burning compared with stronger, late dry season fires. In the early dry season there are higher moisture levels, cooler temperatures, and lighter wind than later in the dry season; fires tend to go out overnight. Early controlled burns also results in a smaller proportion of the grass and tree biomass being burnt.[49] Emission reductions of 256,000 tonnes of CO2 have been made as of 2007.[50]
Artificial sequestration[edit]
For carbon to be sequestered artificially (i.e. not using the natural processes of the carbon cycle) it must first be captured, or it must be significantly delayed or prevented from being re-released into the atmosphere (by combustion, decay, etc.) from an existing carbon-rich material, by being incorporated into an enduring usage (such as in construction). Thereafter it can be passively stored or remain productively utilized over time in a variety of ways.
For instance, upon harvesting, wood (as a carbon-rich material) can be immediately burned or otherwise serve as a fuel, returning its carbon to the atmosphere, or it can be incorporated into construction or a range of other durable products, thus sequestering its carbon over years or even centuries.
Indeed, a very carefully designed and durable, energy-efficient and energy-capturing building has the potential to sequester (in its carbon-rich construction materials), as much as or more carbon than was released by the acquisition and incorporation of all its materials and than will be released by building-function 'energy-imports' during the structure's (potentially multi-century) existence. Such a structure might be termed 'carbon neutral' or even 'carbon negative'. Building construction and operation (electricity usage, heating, etc.) are estimated to contribute nearly half of the annual human-caused carbon additions to the atmosphere.[51]
Natural-gas purification plants often already have to remove carbon dioxide, either to avoid dry ice clogging gas tankers or to prevent carbon-dioxide concentrations exceeding the 3% maximum permitted on the natural-gas distribution grid.[52]
Beyond this, one of the most likely early applications of carbon capture is the capture of carbon dioxide from flue gases at power stations (in the case of coal, this coal pollution mitigation is sometimes known as 'clean coal'). A typical new 1000 MW coal-fired power station produces around 6 million tons of carbon dioxide annually. Adding carbon capture to existing plants can add significantly to the costs of energy production; scrubbing costs aside, a 1000 MW coal plant will require the storage of about 50 million barrels (7,900,000 m3) of carbon dioxide a year. However, scrubbing is relatively affordable when added to new plants based on coal gasification technology, where it is estimated to raise energy costs for households in the United States using only coal-fired electricity sources from 10 cents per kW·h to 12 cents.[53]
Carbon capture[edit]
Currently, capture of carbon dioxide is performed on a large scale by absorption of carbon dioxide onto various amine-based solvents. Other techniques are currently being investigated, such as pressure swing adsorption, temperature swing adsorption, gas separation membranes, cryogenics and flue capture.
In coal-fired power stations, the main alternatives to retrofitting amine-based absorbers to existing power stations are two new technologies: coal gasification combined-cycle and oxy-fuel combustion. Gasification first produces a 'syngas' primarily of hydrogen and carbon monoxide, which is burned, with carbon dioxide filtered from the flue gas. Oxy-fuel combustion burns the coal in oxygen instead of air, producing only carbon dioxide and water vapour, which are relatively easily separated. Some of the combustion products must be returned to the combustion chamber, either before or after separation, otherwise the temperatures would be too high for the turbine.
Another long-term option is carbon capture directly from the air using hydroxides. The air would literally be scrubbed of its CO2 content. This idea offers an alternative to non-carbon-based fuels for the transportation sector.
Examples of carbon sequestration at coal plants include converting carbon from smokestacks into baking soda,[54][55] and algae-based carbon capture, circumventing storage by converting algae into fuel or feed.[56]
Oceans[edit]
Another proposed form of carbon sequestration in the ocean is direct injection. In this method, carbon dioxide is pumped directly into the water at depth, and expected to form 'lakes' of liquid CO2 at the bottom. Experiments carried out in moderate to deep waters (350–3600 m) indicate that the liquid CO2 reacts to form solid CO2clathrate hydrates, which gradually dissolve in the surrounding waters.[citation needed]
This method, too, has potentially dangerous environmental consequences. The carbon dioxide does react with the water to form carbonic acid, H2CO3; however, most (as much as 99%) remains as dissolved molecular CO2. The equilibrium would no doubt be quite different under the high pressure conditions in the deep ocean. In addition, if deep-sea bacterial methanogens that reduce carbon dioxide were to encounter the carbon dioxide sinks, levels of methane gas may increase, leading to the generation of an even worse greenhouse gas.[57]The resulting environmental effects on benthic life forms of the bathypelagic, abyssopelagic and hadopelagic zones are unknown. Even though life appears to be rather sparse in the deep ocean basins, energy and chemical effects in these deep basins could have far-reaching implications. Much more work is needed here to define the extent of the potential problems.
Carbon storage in or under oceans may not be compatible with the Convention on the Prevention of Marine Pollution by Dumping of Wastes and Other Matter.[58]
An additional method of long-term ocean-based sequestration is to gather crop residue such as corn stalks or excess hay into large weighted bales of biomass and deposit it in the alluvial fan areas of the deep ocean basin. Dropping these residues in alluvial fans would cause the residues to be quickly buried in silt on the sea floor, sequestering the biomass for very long time spans. Alluvial fans exist in all of the world's oceans and seas where river deltas fall off the edge of the continental shelf such as the Mississippi alluvial fan in the gulf of Mexico and the Nile alluvial fan in the Mediterranean Sea. A downside, however, would be an increase in aerobic bacteria growth due to the introduction of biomass, leading to more competition for oxygen resources in the deep sea, similar to the oxygen minimum zone.[citation needed]
Geological sequestration[edit]
The method of geo-sequestration or geological storage involves injecting carbon dioxide directly into underground geological formations. Declining oil fields, saline aquifers, and unminable coal seams have been suggested as storage sites. Caverns and old mines that are commonly used to store natural gas are not considered, because of a lack of storage safety.
CO2 has been injected into declining oil fields for more than 40 years, to increase oil recovery. This option is attractive because the storage costs are offset by the sale of additional oil that is recovered. Typically, 10–15% additional recovery of the original oil in place is possible. Further benefits are the existing infrastructure and the geophysical and geological information about the oil field that is available from the oil exploration. Another benefit of injecting CO2 into Oil fields is that CO2 is soluble in oil. Dissolving CO2 in oil lowers the viscosity of the oil and reduces its interfacial tension which increases the oils mobility. All oil fields have a geological barrier preventing upward migration of oil. As most oil and gas has been in place for millions to tens of millions of years, depleted oil and gas reservoirs can contain carbon dioxide for millennia. Identified possible problems are the many 'leak' opportunities provided by old oil wells, the need for high injection pressures and acidification which can damage the geological barrier. Other disadvantages of old oil fields are their limited geographic distribution and depths, which require high injection pressures for sequestration. Below a depth of about 1000 m, carbon dioxide is injected as a supercritical fluid, a material with the density of a liquid, but the viscosity and diffusivity of a gas.Unminable coal seams can be used to store CO2, because CO2 absorbs to the coal surface, ensuring safe long-term storage. In the process it releases methane that was previously adsorbed to the coal surface and that may be recovered. Again the sale of the methane can be used to offset the cost of the CO2 storage. Release or burning of methane would of course at least partially offset the obtained sequestration result – except when the gas is allowed to escape into the atmosphere in significant quantities: methane has a higher global warming potential than CO2.
Saline aquifers contain highly mineralized brines and have so far been considered of no benefit to humans except in a few cases where they have been used for the storage of chemical waste. Their advantages include a large potential storage volume and relatively common occurrence reducing the distance over which CO2 has to be transported. The major disadvantage of saline aquifers is that relatively little is known about them compared to oil fields. Another disadvantage of saline aquifers is that as the salinity of the water increases, less CO2 can be dissolved into aqueous solution. To keep the cost of storage acceptable the geophysical exploration may be limited, resulting in larger uncertainty about the structure of a given aquifer. Unlike storage in oil fields or coal beds, no side product will offset the storage cost. Leakage of CO2 back into the atmosphere may be a problem in saline-aquifer storage. However, current research shows that several trapping mechanisms immobilize the CO2 underground, reducing the risk of leakage.[citation needed]
A major research project examining the geological sequestration of carbon dioxide is currently being performed at an oil field at Weyburn in south-eastern Saskatchewan. In the North Sea, Norway's Equinor natural-gas platform Sleipner strips carbon dioxide out of the natural gas with amine solvents and disposes of this carbon dioxide by geological sequestration. Sleipner reduces emissions of carbon dioxide by approximately one million tonnes a year. The cost of geological sequestration is minor relative to the overall running costs. As of April 2005, BP is considering a trial of large-scale sequestration of carbon dioxide stripped from power plant emissions in the Miller oilfield as its reserves are depleted.
In October 2007, the Bureau of Economic Geology at The University of Texas at Austin received a 10-year, $38 million subcontract to conduct the first intensively monitored, long-term project in the United States studying the feasibility of injecting a large volume of CO2 for underground storage.[59] The project is a research program of the Southeast Regional Carbon Sequestration Partnership (SECARB), funded by the National Energy Technology Laboratory of the U.S. Department of Energy (DOE). The SECARB partnership will demonstrate CO2 injection rate and storage capacity in the Tuscaloosa-Woodbine geologic system that stretches from Texas to Florida. Beginning in fall 2007, the project will inject CO2 at the rate of one million tons[vague] per year, for up to 1.5 years, into brine up to 10,000 feet (3,000 m) below the land surface near the Cranfield oil field about 15 miles (24 km) east of Natchez, Mississippi. Experimental equipment will measure the ability of the subsurface to accept and retain CO2.
Mineral sequestration[edit]
Mineral sequestration aims to trap carbon in the form of solid carbonate salts. This process occurs slowly in nature and is responsible for the deposition and accumulation of limestone over geologic time. Carbonic acid in groundwater slowly reacts with complex silicates to dissolve calcium, magnesium, alkalis and silica and leave a residue of clay minerals. The dissolved calcium and magnesium react with bicarbonate to precipitate calcium and magnesium carbonates, a process that organisms use to make shells. When the organisms die, their shells are deposited as sediment and eventually turn into limestone. Limestones have accumulated over billions of years of geologic time and contain much of Earth's carbon. Ongoing research aims to speed up similar reactions involving alkali carbonates.[60]
Several serpentinite deposits are being investigated as potentially large scale CO2 storage sinks such as those found in NSW, Australia, where the first mineral carbonation pilot plant project is underway.[61] Beneficial re-use of magnesium carbonate from this process could provide feedstock for new products developed for the built environment and agriculture without returning the carbon into the atmosphere and so acting as a carbon sink.
One proposed reaction is that of the olivine-rich rock dunite, or its hydrated equivalent serpentinite with carbon dioxide to form the carbonate mineral magnesite, plus silica and iron oxide (magnetite).
Serpentinite sequestration is favored because of the non-toxic and stable nature of magnesium carbonate. The ideal reactions involve the magnesium endmember components of the olivine (reaction 1) or serpentine (reaction 2), the latter derived from earlier olivine by hydration and silicification (reaction 3). The presence of iron in the olivine or serpentine reduces the efficiency of sequestration, since the iron components of these minerals break down to iron oxide and silica (reaction 4).
Serpentinite reactions[edit]
+ → + + | (Reaction 1) |
+ → + + | (Reaction 2) |
+ + → | (Reaction 3) |
+ → + + | (Reaction 4) |
Zeolitic imidazolate frameworks[edit]
Zeolitic imidazolate frameworks is a metal-organic framework carbon dioxide sink which could be used to keep industrial emissions of carbon dioxide out of the atmosphere.[62]
Trends in sink performance[edit]
One study in 2009 found that the fraction of fossil-fuel emissions absorbed by the oceans may have declined by up to 10% since 2000, indicating oceanic sequestration may be sublinear.[19] Another 2009 study found that the fraction of CO
2 absorbed by terrestrial ecosystems and the oceans has not changed since 1850, indicating undiminished capacity.[63]
2 absorbed by terrestrial ecosystems and the oceans has not changed since 1850, indicating undiminished capacity.[63]
See also[edit]
- Fluxnet-Canada Research Network, research initiative on post forest disturbance carbon sinking
References[edit]
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Published online 2018 Jun 27. doi: 10.1371/journal.pone.0198369
PMID: 29949588
Edward Webb, Editor
This article has been cited by other articles in PMC.
Associated Data
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S2 Fig: The response curves of the probability of Javan leopard presence as a function of environmental variables. The curves show the mean response of the 10 replicates (red) and associated one standard deviation (grey area, error bar for categorical variables).(DOCX)
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S1 Table: Contributors of Javan leopard records between 2008 and 2014. (DOCX)
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S2 Table: List of predicted suitable landscapes and land use characteristics in each landscape. Suitable landscapes were defined based on Maxent model outputs with logistic probabilities of 0.42 or greater.(PDF)
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Due to the sensitive nature and ownership of the study data, it has not been made publicly available. Data deposition could present some other threat, such as revealing the locations of Javan leopard that are listed as Critically Endangered by the IUCN Red Databook and protected by the Indonesian Law No. 5/1990 regarding the Biodiversity Conservation and Ecosystem, Government Regulation No. 7/1999 regarding the Preservation of Animals and Plants. Part of the dataset are owned by third parties who limit the use of their data for the purpose of this study. Others can access these datasets by directly contacting the individual contributors listed in S1 Table and we confirm that others would be able to access these data in the same manner as the authors. Additionally, the corresponding author may assist others in obtaining contacts of the individual contributors. We also confirm that we did not have any special access privileges that others would not have.
Abstract
With the extirpation of tigers from the Indonesian island of Java in the 1980s, the endemic and Critically Endangered Javan leopard is the island’s last remaining large carnivore. Yet despite this, it has received little conservation attention and its population status and distribution remains poorly known. Using Maxent modeling, we predicted the locations of suitable leopard landscapes throughout the island of Java based on 228 verified Javan leopard samples and as a function of seven environmental variables. The identified landscapes covered over 1 million hectares, representing less than 9% of the island. Direct evidence of Javan leopard was confirmed from 22 of the 29 identified landscapes and included all national parks, which our analysis revealed as the single most important land type. Our study also emphasized the importance of maintaining connectivity between protected areas and human-modified landscapes because adjacent production forests and secondary forests were found to provide vital extensions for several Javan leopard subpopulations. Our predictive map greatly improves those previously produced by the Government of Indonesia’s Javan Leopard Action Plan and the IUCN global leopard distribution assessment. It shares only a 32% overlap with the IUCN range predictions, adds six new priority landscapes, all with confirmed presence of Javan leopard, and reveals an island-wide leopard population that occurs in several highly fragmented landscapes, which are far more isolated than previously thought. Our study provides reliable information on where conservation efforts must be prioritized both inside and outside of the protected area network to safeguard Java’s last remaining large carnivore.
Introduction
Carnivores are one of the most threatened groups of terrestrial mammals on earth [,2]. Within this order, members of Felidae, have suffered the severest population declines and geographical range contractions [,]. The threats facing large felid species are widely shared. They include habitat loss and fragmentation caused by the expansion of small holder farmland, large-scale monoculture plantations and infrastructure, and also the killing of prey and the felid species itself, in retaliation to conflict or for trade [2,5].
The Endangered leopard (Panthera pardus) [6] has the widest geographic distribution of the wild felids [,7]. It occurs in a broad range of habitats and continents, such as the savannah plains in Africa [], temperate forests in Russia and China, and humid evergreen rainforests in Southeast Asia [,8–10]. The species’ behavioral plasticity allows it to survive in areas where other big cats have been extirpated or isolated, such as those close to major human settlements or with an unnaturally low prey base [11–14]. Their high adaptability, however, does not make them tolerant of all threats and a recent assessment revealed that the leopard has lost between 63% and 75% of its historical range, for which a disproportionately high loss (83–87%) has occurred in Asia [].
The Javan leopard P. p. melas (Cuvier 1809) is one of the most threatened subspecies of leopard. It is endemic to the Indonesian island of Java, which contains 141 million people and has one of the highest human population densities (1,115 people/km2) in the world, which greatly restricts the Javan leopard’s island-wide distribution [15]. From the nine recognized subspecies, the Javan leopard is among the three Critically Endangered, along with the Amur leopard and Arabian leopard [16].
Javan leopard is listed on CITES Appendix I [17] and a nationally protected species under Indonesian Government Regulation No. 7/1999. Yet to date, only a few scientific studies have been conducted on the Javan leopard [18–21] and there is a lack of reliable information on its population status across the island.
In 1990, a study recorded that Javan leopard occurred in 12 conservation areas, including national parks, nature reserves, game reserves and hunting reserves, and these supported an estimated population of 350–700 leopards [10]. Later studies, however, found that this subspecies also occurred outside of the conservation area network, such as in production forest and protected forest [22]. In 2013, this led a group of Indonesian scientists to estimated that there were 491–596 leopards in the remaining natural forests across Java [22]. The majority of these leopards existed in ten national parks, of which only two of them, Halimun Salak and Ujung Kulon, were considered to be able to support more than 100 individuals.
A recent global study on leopards found that the Javan leopard occurred in only 16% (20,600 km2) of its historical range that had once covered Java []. It also found that only 3% of the Javan leopard’s core area now remains and this yielded the lowest median patch size and core area index for any of the leopard subspecies; indicating that it is at a greater risk of extinction than any of the other leopard subspecies. Such spatial extent is much larger than that documented in the Javan Leopard National Action Plan (3% or 3,277 km2) [22]. This difference is most likely an artifact of the different spatial datasets used for deriving both estimates. The first estimate was based on 41 records, and restricted to protected areas, while the later on 34 localities with an unclear definition of whether a locality represented a distinct patch or not. This strongly suggests that a more comprehensive and finer-scale estimate is needed to produce a reliable baseline on Javan leopard distribution.
Species distribution modelling based on presence-only datasets is widely used to assist in the management of understudied and threatened species [23–25]. So, in this study we aim to: 1) collate all recent data from 2008–2014 on Javan leopard occurrence; 2) model suitable habitat of Javan leopard, 3) define the extent of suitable habitat inside and outside of the protected area network, 4) investigate land classes that have the potential to support Javan leopard outside of this network, and 5) identify priority actions for future Javan leopard conservation based on our key findings. The main purpose of this study is to provide decision makers and conservation planners alike with the first robust estimate of Javan leopard distribution using a species distribution modelling approach that is based on the most extensive Javan leopard occurrence dataset available.
Methods
Permits of several projects in this study were obtained from the national parks and the local natural resources conservation agencies, the Indonesian Ministry of Environment and Forestry.
Data preparation
We collated unpublished Javan leopard points of occurrences recorded by various field research projects and individuals between 2008 and 2014 (S1 Table). For security reasons, we present the approximate location of each Javan leopard locality within a 100 km2 rectangle (S1 Fig). We then selected points that had verifiable evidence, including camera trap videos and photographs, human-leopard conflict reports, pugmarks, scratches and feces. Given the absence of other large cats in Java, we believed that misidentification of these signs to be unlikely.
We used ten environmental variables that have been shown to be suitable predictors of large carnivore presence by other studies. These included distance from roads [23], distance from forest edges to the interior and exterior, and protected areas [], elevation [23,], distance from rivers [27], slope [23,28], land use classification, precipitation, and temperature [29]. The original spatial dataset included protected area land use classification layers [30], roads, rivers and Indonesia baseline maps [31], digital elevation map (30 m resolution) [32], and mean precipitation and temperature (1 km resolution) [33]. We converted the protected area layer into a binary raster containing '0' (non-protected area) and '1' (protected area).
The original land use classification layer contained 18 classes. For better interpretation, we reduced it to 15 classes based on similarities and converted them into raster layers. We generated the slope layer from the digital elevation map. We selected forest polygons from the land use classification and generated Euclidian distances to produce the distance from forest edge to the Javan leopard points recorded both inside and outside of the primary forest polygons. Thus, for distance from forest edge to the exterior, all Javan leopard points that fell within the forest were assigned a value of '0' and vice versa for the distance from forest edge to the interior. Similarly, we employed Euclidian distance to roads (vector) and rivers (vector) to generate the distance from roads and rivers (30 m resolution) layers, respectively.
The primary analysis tool used in this study required all datasets to have exactly overlapping cells and spatial extent [34], which in this study was confined to the island of Java. We generated a raster mask of 0.25 km2 cell size covering Java to provide a baseline environment setting for further resample processing of the background layers. We then re-sampled the Javan leopard points and all environmental variables with the application of a mask layer. This procedure removed any duplication of Javan leopard points within a cell for subsequent analysis. Because several environmental variables were re-sampled from finer to coarser resolution, we performed a bilinear interpolation technique to assign a new value to a cell by using a weighted distance average of four adjacent input cells. All spatial processes were performed using Raster Processing and Spatial Analyst tools in the software ArcGIS 10.2 (ESRI, Redlands).
The Javan leopard points were found to be biased toward the sampled areas, a situation common to presence-only datasets []. To control for this sampling bias, we converted the mask layer into a bias grid []. In the analysis, the value of a cell (c1) in the bias grid was used to assign greater weight to Javan leopard points (c2) with fewer spatial neighbors and vice versa. The value of c1 was a sum of the distances between c1 and c2 as calculated using the Gaussian Kernel function, w = exp(−d2/2s2), where w is the weight, d is the distance (in km) between c1 and c2, and s is the standard deviation. We used 4.2 km for s because it represents the known diameter of the largest Javan leopard home range size (13.6 km2) [18,19]. Following the Gaussian distribution, s, yielded points that are, for example, located 4.2 and 8.4 km away from a cell as having 60.6% and 13.5% as strong an influence, respectively. We used the Distance Among Points tool of the Geospatial Modeling Environment version 0.7.3.0 [37] and the Calculate Variable and Aggregate tools of the IBM SPSS Statistics software version 20 (IBM Corporation 2011) to calculate the distance between c1 and c2 and the Gaussian Kernel function, respectively.
Data analysis
We predicted suitable landscape of Javan leopard using Maxent version 3.4.1. Maxent has been widely used for presence only dataset over other techniques due to its robustness against autocorrelated environmental predictors [38,39], lower sensitivity to small sample sizes [40], and being less affected by spatial errors [41]. The final sample inputs consisted of 169 Javan leopard presence points. We performed a Pearson’s correlation analysis using the Hmsc package within R software (R Development Core Team 2010) to test for correlations between the ten environmental variables, from which a pair of variables was removed if the coefficient correlation was > 0.50 [42]. The final set of uncorrelated environmental variables used in the subsequent analysis included distance from forest edges to the interior and exterior, distance from roads, protected areas, elevation, distance from rivers, land classification and mean precipitation.
We set the protected areas and land classifications as categorical variables and the rest as continuous variables. We performed a Bootstrap procedure with 25% random tests, ten replicates, and 5,000 iterations, and kept the other settings at the default option. We also performed Jackknife tests to assess consistency in variable importance between the training and test gains [43]. The overall model performance was measured by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve [39]. We estimated the relative importance of each predictor to the Maxent model using the percent contribution and permutation importance, averaged over ten replicates. We investigated the response curves to explore how the environmental predictors effected the Maxent prediction.
Identifying landscapes and defining its characteristics
We used the model prediction to determine suitable Javan leopard landscapes based on a ten percentile training presence logistic threshold. Model pixels with logistic probabilities smaller than the threshold were omitted from the final predictive model [24,34,44]. We converted the predicted suitable patch raster into a polygon format and defined the minimum suitable patch size for Javan leopard as being large enough to contain at least five mature individuals [28]. Subsequently, we retained all predicted suitable patches of at least 68 km2 or equal to five times the largest known Javan leopard home range [18,19]. For a Critically Endangered subspecies like Javan leopard, smaller suitable patches with confirmed evidence of Javan leopard may still be important if it supports connectivity between patches. We therefore retained all smaller suitable patches with and/or close to Javan leopard localities up to 4.2 km away or equal to the diameter of their home range. Predicted suitable patches that did not meet these criteria were removed from further analyses.
We assumed all protected areas that fully or partially overlapped with the predicted suitable patches as being important for Javan leopard. We updated these patches with protected areas using the Update tool of the ArcGIS 10.2. This step removed all stand-alone protected areas and combined them with associated suitable patches into one unique polygon. We calculated the area of the identified suitable patches in each protected area as a surrogate indicator of the ecological importance of each protected area to support Javan leopard.
We included the protected area layer in the land use classification layer, so that it would be considered in the analysis. This step removed all land classes that fell within a protected area into one unique polygon coded as “protected area” and produced 16 land use classes. We defined suitable landscapes as patches that: 1) are predicted to be suitable for Javan leopard based on a ten percentile training presence logistic threshold, 2) are larger than 68 km2, and/or 3) have evidence of Javan leopard presence, and/or, 4) within 4.2 km of a Javan leopard locality. We then extracted the land use classes in each suitable landscape by intersecting the landscape layer with a land use classification layer using the ArcGIS 10.2 Intersect tool.
Results
We obtained 228 verified Javan leopard presence points, the majority of which came from a combined record of Javan leopard signs (n = 196) and conflict incidents (n = 32). The elevation range of these data points was 1 to 2,540 m asl, with a mean value of 714 m asl. Approximately half (50.9%) of the leopard records occurred inside the protected area network, with evidence of leopard from all nine national parks in Java. For those records occurring outside of the protected area network (51.6%), most (36.6%) points were located in secondary forest, followed by mixed agriculture (22.3%), production forest (20.5%) and other land use types (20.5%). For the conflict records, most (53.1%) leopard records were recorded from mixed agriculture areas (Table 1).
Table 1
Contribution of Javan leopard occurrence records in different land use types.Javan leopard occurrence was identified based on direct and indirect signs, and conflict incidents.
Land use | Conflict | Signs | Total | Percent |
---|---|---|---|---|
Protected area | 3 | 113 | 116 | 50.9% |
Secondary forest | 1 | 40 | 41 | 18.0% |
Production forest | 3 | 20 | 23 | 10.1% |
Mixed agriculture | 17 | 8 | 25 | 11.0% |
Plantation | 2 | 9 | 11 | 4.8% |
Rice field | 3 | 4 | 7 | 3.1% |
Settlement | 3 | 0 | 3 | 1.3% |
Shrub | 0 | 2 | 2 | 0.9% |
Total | 32 | 196 | 228 | 100% |
The Maxent analysis identified distance from forest edge and mean precipitation as providing the highest contribution to the predicted leopard distribution, respectively explaining 49.7% and 25.4% of the variation in the predicted suitable patches (Table 2). The Jackknife test for variable importance using the regularized training gain, test gain, and AUC on test data indicated that both distance from forest edge and mean precipitation were the two predictors with the highest gains when used in isolation, which decreased most when these variables were omitted from other candidate models.
Table 2
The relative contribution and permutation importance of each variable calculated by Maxent.Values are averaged over the 10 replicates and normalized to give percentages. The permutation importance was used to assess variable importance.
Variable | Percent contribution | Permutation importance |
---|---|---|
Distance from forest edge to the exterior | 49.7 | 60.1 |
Precipitation | 25.4 | 21.5 |
Protected Area | 9.8 | 3.6 |
Land use type | 5.9 | 4.5 |
Distance from river | 4.1 | 3.4 |
Distance from forest edge to the interior | 2.9 | 3.3 |
Elevation | 1.1 | 1.9 |
Distance from road | 1 | 1.7 |
The response curves indicated that Javan leopard was more likely to occur in forest and in landscapes with higher precipitation (S2 Fig). The species distribution model performed well, with a mean AUC±SE of 0.95±0.007. Applying a ten percentile threshold, only model pixels with a logistic probability of 0.42 or greater were classified as being suitable for Javan leopard. The area of suitable landscape identified was 1,159,864 ha and covered 8.9% of the island of Java [15]. Protected area (40.5%), production forest (17.2%), and secondary forest (13.6%) contributed the largest area (62%) of suitable landscape (S2 Table). Evidence of Javan leopard was recorded from 22 of the 29 suitable landscapes, meaning that seven landscapes would require further field checks to confirm leopard presence (Fig 1).
Predictive map identifying suitable Javan leopard landscapes on the Indonesian island of Java.The Maxent model outputs were defined to be suitable for Javan leopard if they had a logistic probability of 0.42 or greater. The numbers represent the 29 predicted suitable landscapes listed in S2 Table.
Discussion
The predictive map produced from our study not only refines maps produced from the Government of Indonesia’s Javan Leopard Action Plan [22] and the IUCN global leopard distribution assessment [,16], but represents the first to be developed from a spatially-explicit modelling process for this subspecies. Our dataset, which is based on 228 occurrence records, is a marked improvement on the 2017 IUCN Javan leopard assessment that was based on significantly fewer (34) data points [16]. Our map shared only a 32% overlap with the IUCN range prediction, but adds six new landscapes (ID: 2, 5, 11, 15, 19, 28), all with confirmed evidence of Javan leopard, and reveals an island-wide leopard population that occurs in several highly fragmented landscapes, indicating that it is far more isolated than previously thought (Fig 2).
Protected areas in 24 out of 29 suitable landscapes.
Study limitations
A limitation of this study was that it was unable to incorporate a data layer on prey availability due to the absence of such data from the island of Java [22]. It was not, therefore, possible to investigate the potential of the predicted suitable landscapes, especially those are outside the protected areas and forest habitats, to support viable populations of Javan leopard over the long-term. Future studies should therefore aim to address this question, especially in the priority landscapes. The Javan leopard occurrence data used in our analysis might have certain limitations that are associated with the use of a presence-only dataset. First, data were collated from multiple sources and not based on surveys that used the same sampling method. However, because such a dataset does not exist, we therefore aimed to collect as much data as possible and then filter these to remove redundancy whilst maintaining a comprehensive dataset. Second, although Maxent is less sensitive to small sample sizes [40] and less affected by spatial errors [41], it requires that the samples (occurrence data points) be unbiased and therefore independent of the distribution of the target species [43]. The high AUC value recorded in our model might be an artifact of the AUC statistic, which tends to be higher for species with small home range sizes relative to the study area [43]. However, the potential sampling bias here might have been overcome by our study using a relatively large dataset recorded from all possible habitat types across Java. Furthermore, we controlled for potential sampling bias by using bias grids as a function of their Gaussian Kernel distribution and overcame the dependency issue by resampling the original Javan leopard records into a density of one point per 0.25 km2.
Characterizing Javan leopard landscapes
Our study provides fresh thinking into the role of modified landscapes surrounding protected areas for improving the carrying capacity and, ultimately, long-term survival of the Javan leopard. The Maxent model shows higher association between leopards and more productive landscapes in western Java than the drier forests in eastern Java. This was revealed by the sharp increase in the probability of Javan leopard occurrence in landscapes with higher precipitation. Thus, higher precipitation should support higher plant productivity for the ungulate prey base [45]. However, with the absence of prey data, we were not able to further evaluate the relationship between precipitation and prey base availability. The low contribution of the protected area variable to the overall model output might be explained by the fact that most protected area boundaries were located within larger forest areas, therefore outperformed by the distance from the forest habitats variable.
We identified 70% of the protected area network in Java as providing suitable leopard habitat. However, this network contributed only 40% of the total area of the suitable landscapes. A population viability analysis on ten leopard populations in South Africa predicted that no population with fewer than 50 individuals would survive over 100 years without dispersal and connectivity to a neighboring population [46]. Thus, based on a maximum estimated Javan leopard density, 16 leopards/100 km2 [19], 12 suitable landscape patches (#1, 4, 6, 7, 9, 14, 16, 18, 23, 24, 25, 26) were considered large enough to support at least 50 individuals. Here, Javan leopard recovery efforts should focus on strengthening protected area management to reduce poaching and maintain landscape integrity, before moving on to options such as connecting landscapes and their respective leopard populations.
From the landscapes identified with good recovery potential, it is important to stress that, besides the national parks, all other protected area types were found to be too small on their own to hold viable populations of Javan leopard. This situation was also found in several studies in West Africa and North America where many wildlife reserves were to be too small or had inadequate natural resources for achieving large carnivore conservation objectives [47,]. For the Javan leopard, this highlights the importance of managing suitable landscapes that adjoin these protected areas so as to support viable populations. However, these buffer zone habitats may represent poorer quality habitat and have a lower prey base and therefore support a lower density and number of leopards. Determining leopard densities in these areas and, therefore, how large these areas should be is a topic for future research.
National Park is listed as a Category II protected area of the World Conservation Union [49] and is the strongest protected area type afforded by the Government of Indonesia, in terms of infrastructure, management, financial support and human resources allocated. Thus, strengthening national park management should be considered as a top priority for the long-term survival of the Javan leopard. We identified only three national parks with sufficient habitat to potentially support more than 50 individuals: Gunung Halimun Salak (4, 74,018 ha); Ujung Kulon (1, 42,910 ha); and, Meru Betiri (#26, 42,465 ha). Among these, only Gunung Halimun Salak could potentially support more than 100 individuals, which underlines its importance as a flagship protected area. Two other national parks, Gunung Gede Pangrango (#6) and Bromo Tengger Semeru (#24) could potentially support 50 leopards or more if suitable habitat in the adjacent areas is included.
Understanding the wider landscape characteristics is vital to increasing the population size of large carnivores that are threatened with extinction [50,51]. This not only includes the identification of suitable landscape adjacent to the protected areas, but the key stakeholder groups who should be engaged by conservation managers. Our study found that more than half of the suitable landscape outside of the protected areas is in production forest and secondary forest. Further, with less than 5% of primary forest occurring outside of protected areas, production and secondary forests should therefore play an important role in providing additional habitat for the remaining Javan leopard populations. Here, Government Regulation No. 72/2010 mandates the management of non-conservation state forest in Java to Perum Perhutani, a company under the Ministry of State-owned Enterprises, as the main investor, and the Ministry of Environment and Forestry, as an advisor for its technical and operational activities.
Beside state forest, the Government of Indonesia recognizes the legal right of local communities to manage private forest, which is commonly known as hutan rakyat (Community Forest). The role of local communities in managing hutan rakyat adjacent to state forest is legalized by Perum Perhutani through the establishment of a Community Village Forest Institution (Lembaga Masyarakat Desa Hutan). Under this scheme, local communities are permitted to develop agroforestry for commodities, such as teak, acacia, silk trees and mahogany. In 2009, 2.7 million ha of hutan rakyat in Java was managed by 690,895 households through agroforestry schemes [52,53]. Most hutan rakyat is situated adjacent to state forests and may therefore contribute significant Javan leopard habitat that is located around protected areas.
This study confirms the resilience of leopard outside of its main forest habitat type [54]. We found that nearly half of the Javan leopard data points were recorded outside of protected areas and primary forest, which concurs with other studies that found a high level of leopard adaptability in modified habitats [,7,11,24]. This indicates the ability of leopards to subsist in and move through modified habitat [13], as has been found in India [55], South Africa [56] and Russia []. These modified habitat may therefore serve as structural corridors that facilitate dispersal to enable source-sink connectivity between viable and otherwise non-viable leopard populations [46]. Balme et al [58] recorded leopards moving far beyond the productive natural habitats and into areas where they were then killed, either deliberately or accidentally, by people. A successful strategy for conserving a wide-ranging large carnivore will therefore rely on protecting the source population and providing dispersal opportunities with sink populations through maintaining connectivity [24,29,59,60]. This should be conducted with a reduction in leopard and prey offtake from hunting, retaliatory killing and problem animal removal [46]. Further studies on Javan leopard movement and home range size as a function of prey availability and potential threats in different habitat types, especially human-modified ones, will allow conservation managers to better understand the species’ response to available resources and different threats, and thus their survival and reproduction potential [61].
More than a quarter of our Javan leopard occurrences were outside of the protected area network and from conflicts with local communities. For a Critically Endangered subspecies, this is of great concern because of the risk posed by retaliatory killings to conflict incidents can have a disproportionately large impact on small population sizes. These types of attacks are well documented in causing negative local community perceptions and attitudes towards the conservation of large carnivores [62–64]. For Java, the history of the tiger’s extirpation from the island approximately 30 years’ ago provides a salutary lesson for the future of the Javan leopard because ultimately the loss of habitat and ensuing competition with people for space and resources, particularly its ungulate prey base, was to the tiger’s great detriment [65]. The Javan leopard, despite being more adaptable than the tiger, now occupies an island that whilst only accounting for 6.9% of Indonesia’s land mass is home to 60% of its human population. This restricts core forest habitat to volcanic peaks and coastal corners. Despite this, our study identifies that many of the occupied landscapes may still contain viable leopard populations, making their protection a top priority. It also identifies smaller landscapes that should be connected to achieve the same aim. Thus, our spatially-explicit modelling provides time-critical information for implementing the 2016–2026 Javan leopard action plan to better effect. This approach could also be adopted for other Critically Endangered species for which there is sparse data, such as the saola.
Supporting information
S1 Fig
The distribution of Javan leopard localities.For security reasons, Javan leopard localities were approximated in 100 km2 rectangles.
(TIF)
S2 Fig
The response curves of the probability of Javan leopard presence as a function of environmental variables.The curves show the mean response of the 10 replicates (red) and associated one standard deviation (grey area, error bar for categorical variables).
(DOCX)
S1 Table
Contributors of Javan leopard records between 2008 and 2014.(DOCX)
S2 Table
List of predicted suitable landscapes and land use characteristics in each landscape.Suitable landscapes were defined based on Maxent model outputs with logistic probabilities of 0.42 or greater.
(PDF)
Acknowledgments
We would like to thank the Indonesian Ministry of Environment and Forestry, civil society partners, and individual contributors for supporting the project as listed in the S1 Table.
Funding Statement
Projects in this study were partly funded by the Mohammed Bin Zayed Species Conservation Fund 13256220 (URL: https://www.speciesconservation.org/case-studies-projects/javan-leopard/6220) (EW), IdeaWild for a project titled 'Initiating the protection of the last big carnivore of Java at Mount Slamet, Central Java' (HAW), and PT Holchim Indonesia and Fauna & Flora International for a biodiversity conservation planning in Nusakambangan Island (IP, MP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data Availability
Due to the sensitive nature and ownership of the study data, it has not been made publicly available. Data deposition could present some other threat, such as revealing the locations of Javan leopard that are listed as Critically Endangered by the IUCN Red Databook and protected by the Indonesian Law No. 5/1990 regarding the Biodiversity Conservation and Ecosystem, Government Regulation No. 7/1999 regarding the Preservation of Animals and Plants. Part of the dataset are owned by third parties who limit the use of their data for the purpose of this study. Others can access these datasets by directly contacting the individual contributors listed in S1 Table and we confirm that others would be able to access these data in the same manner as the authors. Additionally, the corresponding author may assist others in obtaining contacts of the individual contributors. We also confirm that we did not have any special access privileges that others would not have.
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