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Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
Large-scale long-term networks to monitor and understand the changing ecology of tropical forests
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Large-scale long-term networks to monitor and understand the changing ecology of tropical forests

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This presentation examines the changing ecology of tropical forests and the effect that this has on maintaining data quality when it comes to monitoring large-scale sites over time. Some lessons …

This presentation examines the changing ecology of tropical forests and the effect that this has on maintaining data quality when it comes to monitoring large-scale sites over time. Some lessons learned are also outlined.

This presentation formed part of the CRP6 Sentinel Landscape planning workshop held on 30 September – 1 October 2011 at CIFOR’s headquarters in Bogor, Indonesia. Further information on CRP6 and Sentinel Landscapes can be accessed from http://www.cifor.org/crp6/ and http://www.cifor.org/fileadmin/subsites/crp/CRP6-Sentinel-Landscape-workplan_2011-2014.pdf respectively.

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  • 1. Large-scale long-term networks to monitor and understand the changing ecology of tropical forests Simon L. LewisDepartment of Geography, School of Geography,University College London, UK University of Leeds, UK CIFOR, Bogor, Indonesia, 30 Sept 201
  • 2. Changing ecology of tropical forests• Hypothesis: – Large-scale and global environmental changes are consistently changing the structure, function, dynamics and composition of otherwise intact and undisturbed tropical forests.• Therefore need consistent long-term data over large areas. Lewis et al. 2009 Annual Reviews in Ecology, Evolution & Systematics
  • 3. Dja, Cameroon
  • 4. Tropical Forest networks• RAINFOR – PI, Oliver Phillips, U. Leeds – Latin America-focussed, c. 150 locations – Typical monitoring, 1 ha plot, >10 cm dbh monitored• AfriTRON – PI, Simon Lewis, UCL+U. Leeds – Africa-focussed, c. 130 locations – Typical monitoring, 1 ha plot, >10 cm dbh monitored• CTFS – PI, Stuart Davies, Smithsonian Institution, USA – Pan-tropical, c. 25 locations – Typical monitoring, 50 ha plot, >1 cm dbh monitored• TEAM – PI, Sandy Andelman, Conservation International, USA – Pan-tropical, ?? locations (aim is 50) – Typical monitoring, 1 ha plot, >10cm dbh monitored plus other taxa
  • 5. Each siteCollaboration/partnershipField data collectionData entryData quality checksAnalysisPublication of results
  • 6. Long-term monitoring plot locations, ~1 ha www.rainfor.org www.afritron.org www.forestplots.net
  • 7. www.forestplots.net Lopez-Gonzalez, Lewis, Burkitt, Phillips 2011. J. Veg. Sci.
  • 8. tblPlot Column Name Data Type PlotID int PlotName nvarchar(150)tblBiogeographicalRegion PlotCode nvarchar(10) BiogeographicalRegionID int tblForestEdaphic ClusterID int SiteID int CountryID int tblForestMoisture tblCluster Area nvarchar(50) State nvarchar(50) Altitude int tblForestElevation tblSite LatitudeDecimal float LongitudeDecimal float tblForestComposition PlotLocationSourceID int PlotArea float MinimumDimension float tblForestStatus MaximumDimension float tblContinent tblCountry TotalPlotEdge float ForestMoistureID int NearestAntrhopogenicEdgeStart int tblAveragePlotSlope FragmentSizeStart int webUser MinTreeDiameter int ForestElevationID int Liana tinyint LocalClimate tinyint tblShapeType LocalSoil tinyint ForestEdaphicID int ForestCompositionID int SubstrateGeologyID int ForestStatusID int IsSingle tinyint tblPlotMeasurement PartOfLargerPlot tinyint LargerPlotID int IsConfirmed tinyint IsOpenAccess tinyint IsMetaDataPublic tinyint ManagerID int ShapeTypeID int AreaTypeID int AveragePlotSlopeID int LianasDataLocation nvarchar(200) LocalClimateDataLocation nvarchar(200) LocalSoilDataLocation nvarchar(200) TreesUnder100MMSampled tinyint AllTreesOver100MMSampled tinyint
  • 9. 2+ census plots in intact closed canopy forestn = 135
  • 10. Basic dataset statistics• Total 135 plots• Total 69,593 stems ≥100 mm diameter at initial census• Total 167 ha monitored• Mean start monitoring year 1995• Mean end monitoring year 2005Mean plot is: 1.2 ha, 515 stems, 9.9 year monitoring period
  • 11. Aboveground Carbon Stock change, Africa Aboveground C stock change, 79 AfriTRON plots 25 Extrapolation to unmeasured tree 20 roots and smallNo. of plots 15 trees and scaled to the continent 10 implies a sink of 5 0.3 Pg C yr-1 0 -6 -4 -2 0 2 4 Carbon stock change (Mg C ha-1 yr-1) Lewis et al. 2009, Nature.
  • 12. Pan et al. incl. Lewis, 2011, Science
  • 13. New pan-tropical biomass map Saatchi et al. incl. Lewis, White, 2011 PNAS
  • 14. Drive DGVM’s with 1980-2000 climate data and CO2, gives a sink of similar magnitude Lewis et al. 2009 . Ann. Rev. Ecol. Sys.
  • 15. Monitoring in Gabon: LopéMitchard, et al. incl. Lewis, White, in review, Biogeosciences
  • 16. 2009, Nature), woody encroachment in some savanna areas, and post-logging recovery, partially offset by some degradation (logging) in the North and East of the park. 51 Tg C (1 Tg = 1 million tonnes)of the pan-tropical Tg C) The release in 1996 (±15 using JERS.analysis should ALOS mosaic in early 2010 allow this change to be 64 Tg C in 2007 (±16 Tg C) extended to the whole of Gabon. using ALOS Aboveground 1996 2007 Biomass (Mg ha-1)>1 million T C per yearcoupled uptake suggest that it is possible, using satellite-based Earth / yr Conclusion: While the maps are preliminary, they +2.4 tonnes C / ha observation instruments net with extensive direct on-the-ground measurements of trees, to
  • 17. Lessons• Need to invest time in collaborations• Constant vigilance required to maintain data quality – Simple field sheets – We use post-docs with a both the training and vested interest in getting it right (some use double-collection) – Field-team members work in more than one location (for spatial consistency) and over >1 census• Everything should be modular• Invest in a database and data management, but keep it simple!• Deal with IPR and partner expectations from the start• Needs integrated training/skills development program
  • 18. Valuing the Arc• Monitor and map and value the flow of ecosystem services over the watersheds of the Eastern Arc Mountains, Tanzania• Integration of several ecosystem services with policy recommendations• Addresses question of intervention...
  • 19. 2+ census plots in intact closed canopy forestn = 135
  • 20. Deciding focal services Compiling existing data Collecting new data Modelling production, flow, use and value Exploring scenarios of plausible change Non-timber forest Integrating across products Timber Nature-based services and costs Informing policy tourismCarbon storage +sequestration Pollination Biodiversity Water Policy messages Balmford et al. incl. Lewis, unpubl. data
  • 21. Land Tanzania land Cover in Tanzania cover types5 km resolution forillustration, and 30classes reduced to 9
  • 22. Land-cover in the futureScenarios for 2025:1. A Hopeful Future Vision (sustainable development) of Land use Change2. A Less Hopeful Future (Business as Usual) Vision of Land Use ChangeSeries of workshops in Tanzania developed a series of ‘rules’of land use change, e.g.,• Agriculture expands in areas with: – suitable soils, rainfall >800mm yr-1, <20 km from roads, expands from existing agricultural areas.• More charcoal and/or timber extraction in forested areas closer toroads• Some many transitions are not possible, e.g. grassland to forest within25 yrs Swetman, et al. incl. Lewis, 2011 J. Env. Man
  • 23. Hopeful vision Charcoal extraction steady Pole extraction decreases Timber extraction steady Encroachment of agriculture Improvements in existing agriculture Small decreases through degradation & logging, coupled with small scale Small expansion as expansion of plantation Steady expansion woodlands cleared forests. Swetman, et al. incl. Lewis, 2011 J. Env. Man Swetman, et al. incl. Lewis, in review
  • 24. Contrasting scenarios 2000 Sus. Development 2025 Business as usual 2025Land coverCarbon storage
  • 25. Carbon scenarios in 2025Present More sustainable Business as UsualLand Cover Scenario 2025 Scenario 2025 3.11 Pg C LOSS LOSS 0.02 Pg C 0.2 Pg C 0.5% of 2000 value 5.1% of 2000 value1 Pg = 1 x 1015 g = 1 billion metric tonnes
  • 26. Simon L. Lewis s.l.lewis@leeds.ac.ukForest plots:Lewis et al. 2009. Ann. Revs. Ecol. Evol. Syst. 40; 529-49.Afritron network:Lewis et al. 2009. Nature, 457, 1003-7.Data:www.forestplots.net

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