90 research outputs found

    Slower recovery in space before collapse of connected populations

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    Slower recovery from perturbations near a tipping point and its indirect signatures in fluctuation patterns have been suggested to foreshadow catastrophes in a wide variety of systems. Recent studies of populations in the field and in the laboratory have used time-series data to confirm some of the theoretically predicted early warning indicators, such as an increase in recovery time or in the size and timescale of fluctuations. However, the predictive power of temporal warning signals is limited by the demand for long-term observations. Large-scale spatial data are more accessible, but the performance of warning signals in spatially extended systems needs to be examined empirically. Here we use spatially extended yeast populations, an experimental system with a fold bifurcation (tipping point), to evaluate early warning signals based on spatio-temporal fluctuations and to identify a novel spatial warning indicator. We found that two leading indicators based on fluctuations increased before collapse of connected populations; however, the magnitudes of the increases were smaller than those observed in isolated populations, possibly because local variation is reduced by dispersal. Furthermore, we propose a generic indicator based on deterministic spatial patterns, which we call ‘recovery length’. As the spatial counterpart of recovery time, recovery length is the distance necessary for connected populations to recover from spatial perturbations. In our experiments, recovery length increased substantially before population collapse, suggesting that the spatial scale of recovery can provide a superior warning signal before tipping points in spatially extended systems.United States. National Institutes of Health (NIH R00 GM085279-02)United States. National Institutes of Health (NIH DP2)Alfred P. Sloan FoundationNational Science Foundation (U.S.

    Early warning signals of simulated Amazon rainforest dieback

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    Copyright © The Author(s) 2013. This article is published with open access at Springerlink.comWe test proposed generic tipping point early warning signals in a complex climate model (HadCM3) which simulates future dieback of the Amazon rainforest. The equation governing tree cover in the model suggests that zero and non-zero stable states of tree cover co-exist, and a transcritical bifurcation is approached as productivity declines. Forest dieback is a non-linear change in the non-zero tree cover state, as productivity declines, which should exhibit critical slowing down. We use an ensemble of versions of HadCM3 to test for the corresponding early warning signals. However, on approaching simulated Amazon dieback, expected early warning signals of critical slowing down are not seen in tree cover, vegetation carbon or net primary productivity. The lack of a convincing trend in autocorrelation appears to be a result of the system being forced rapidly and non-linearly. There is a robust rise in variance with time, but this can be explained by increases in inter-annual temperature and precipitation variability that force the forest. This failure of generic early warning indicators led us to seek more system-specific, observable indicators of changing forest stability in the model. The sensitivity of net ecosystem productivity to temperature anomalies (a negative correlation) generally increases as dieback approaches, which is attributable to a non-linear sensitivity of ecosystem respiration to temperature. As a result, the sensitivity of atmospheric CO2 anomalies to temperature anomalies (a positive correlation) increases as dieback approaches. This stability indicator has the benefit of being readily observable in the real world.NERCJoint DECC/Defra Met Office Hadley Centre Climate ProgrammeUniversity of Exete

    Long-Term Vegetation Change in Central Africa: The Need for an Integrated Management Framework for Forests and Savannas

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    peer reviewedTropical forests and savannas are the main biomes in sub-Saharan Africa, covering most of the continent. Collectively they offer important habitat for biodiversity and provide multiple ecosystem services. Considering their global importance and the multiple sustainability challenges they face in the era of the Anthropocene, this chapter undertakes a comprehensive analysis of the past, present, and future vegetation patterns in central African forests and savannas. Past changes in climate, vegetation, land use, and human activity have affected the distribution of forests and savannas across central Africa. Currently, forests form a continuous block across the wet and moist areas of central Africa, and are characterized by high tree cover (>90% tree cover). Savannas and woodlands have lower tree cover (<40% tree cover), are found in drier sites in the north and south of the region, and are maintained by frequent fires. Recent tree cover loss (2000–2015) has been more important for forests than for savannas, which, however, reportedly experienced woody encroachment. Future cropland expansion is expected to have a strong impact on savannas, while the extent of climatic impacts depends on the actual scenario. We finally identify some of the policy implications for restoring ecosystems, expanding protected areas, and designing sustainable ecosystem management approaches in the region

    The Effect of Carbon Credits on Savanna Land Management and Priorities for Biodiversity Conservation

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    Carbon finance offers the potential to change land management and conservation planning priorities. We develop a novel approach to planning for improved land management to conserve biodiversity while utilizing potential revenue from carbon biosequestration. We apply our approach in northern Australia's tropical savanna, a region of global significance for biodiversity and carbon storage, both of which are threatened by current fire and grazing regimes. Our approach aims to identify priority locations for protecting species and vegetation communities by retaining existing vegetation and managing fire and grazing regimes at a minimum cost. We explore the impact of accounting for potential carbon revenue (using a carbon price of US14pertonneofcarbondioxideequivalent)onpriorityareasforconservationandtheimpactofexplicitlyprotectingcarbonstocksinadditiontobiodiversity.OurresultsshowthatimprovedmanagementcanpotentiallyraiseapproximatelyUS14 per tonne of carbon dioxide equivalent) on priority areas for conservation and the impact of explicitly protecting carbon stocks in addition to biodiversity. Our results show that improved management can potentially raise approximately US5 per hectare per year in carbon revenue and prevent the release of 1–2 billion tonnes of carbon dioxide equivalent over approximately 90 years. This revenue could be used to reduce the costs of improved land management by three quarters or double the number of biodiversity targets achieved and meet carbon storage targets for the same cost. These results are based on generalised cost and carbon data; more comprehensive applications will rely on fine scale, site-specific data and a supportive policy environment. Our research illustrates that the duel objective of conserving biodiversity and reducing the release of greenhouse gases offers important opportunities for cost-effective land management investments

    Carbon losses from deforestation and widespread degradation offset by extensive growth in African woodlands

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    Degradation—the loss of carbon stored in intact woodland—is very difficult to measure over large areas. Here, the authors show that carbon emissions from degradation in African woodlands greatly exceed those from deforestation, but are happening alongside widespread increases in biomass in remote areas
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