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Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis
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Responding to evolving threats using innovative tools, technologies and datasets - Kathy Willis

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  • Shift away from traditional protection recommendations to one that attempts to incorporate people, value the ecosystem services and create sustainable system
  • BIOCLIM/WORLDCLIM Annual mean temperature (BIO1) Temperature seasonality (BIO4) Annual precipitation (BIO12) Precipitation seasonality (BIO15)Global Lakes and Wetlands Database Distance to lakes, rivers, wetlands, etc. FAO Soil data % nitrogen % water in soil (soil/water holding capacity)
  • Field data on distributions of globally threatened vertebrates were collected from the two case study sites. The data were used to make and validate distribution models and hence map relative numbers of threatened species. The results were compared with LEFT results from queries on the same study areas.The key point to make when you show these slides is that LEFT does quite a good job of predicting the set of threatened species present and their general distribution in the landscape. Commision errors seem to be more prevalent than omission errors for species and land (at least in these sites), but this makes LEFT err on the side of caution which is what we and responsible resource extraction companies would want. The migratory species also exhibit this pattern of more commision than ommision errors in the species set, although I haven't made comparison maps for these yet.
  • The IUCN method is very straightforward: they asked experts to draw polygons on maps representing the ranges of each globally threatened terrestrial vertebrates species. At broad scale, this works very well, but has limitations at very fine spatial scale for species with patchy areas of occupancy within their range. To generate the maps of relative numbers of globally threatened terrestrial vertebrates in Cusuco and Mahamavo we used a spatial sampling framework stratified with respect to land cover types and elevation to collect large numbers of spatially unique records of threatened species presences. We then generated equal numbers of pseudo absences for each species with the same sampling framework. I )then made and validated with ROC plots GLM distribution models for each species as a function of a common set of environemental covariates: tasseled cap (TC) brightness, TC greenness, TC moistness, elevation, slope, sin(aspect), cos(aspect), topographic wetness, distance to roads and distance to villages. The habitat suitability maps for each species were thresholded by the Kappa-maximising threshold, then the thresholded maps were added to make a map of estimated number of threatened species and then normalised before comaprison to the LEFT vulenerability map (to account for the fact that both analyses used a different set of species).
  • Studies indicate that, as systems approach critical thresholds, their sensitivity to environmental changes increases and they experience an increase in magnitude in the amplitude of response to an environmental change (Scheffer et al. 2009). Our tool incorporates this theoretical framework in order to identify regions which are more resilient to environmental variability by assessing the variance of productivity in relation to two parameters of climatic data (temperature and precipitation). The procedure is as follows:1. Obtain NDVI time-series at 5km resolution.Time-series of monthly time-slice satellite data of MODIS Normalised Difference Vegetation Index (NDVI) from April 2000-present is collected. NDVI is used as a proxy for productivity. The data are detrended for seasonality, and then standardised z-score anomalies for temporal NDVI are calculated in order to provide a robust and valid estimate of variability in each pixel. A z-score is a dimensionless value is derived by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation (Snedecor and Cochran, 1980; Hammond and McCullagh 1982).  Here, the population is comprised of all the monthly values in the 2000-2011 time series within each pixel. This standardization procedure converts data from different magnitudes to the same scale, and provides an insight into how “typical” this observation is to the population. This method has been successfully used to assess global desertification trends in arid regions (Helldén and Eklund 1988; Helldén and Tottrup 2008). The final product of this stage will be a map of temporal variance in NDVI at 240 m resolution.
  • Note pattern by biomes: boreal has low resilience, deserts have high resilience, India agriculture high resilienceIs this really the final map?
  • Transcript

    • 1. Responding to evolving threats using innovative tools, technologies and datasets Professor Kathy Willis, Biodiversity Institute, University of Oxford
    • 2. Evolving threats
    • 3. • Global population Population projection (Lutz & Samir 2010) most likely to peak ~9B 95% 12B 60% • People will be richer 20% 8B and demand higher quality diet 4BLivestock consumption (FAO 2009) 2000 2050 2100 Livestock consumption Developed nations China India Increasing demand Africa on land 1970 1980 1990 2000
    • 4. Protected (12%)Not protected (88%) Hwange National Park, Zimbabwe
    • 5. Biodiversity declinesStokard 2010. Despite progress, biodiversitydeclines. Science. 329: 1272-1273.
    • 6. Is all lost for biodiversity?
    • 7. Convention of Biological Diversity targets (2011)Target 5By 2020, the rate of loss of all natural habitats, including forests, is at least halved andwhere feasible brought close to zero, and degradation and fragmentation issignificantly reduced.Target 14By 2020, ecosystems that provide essential services, including services related towater, and contribute to health, livelihoods and well-being, are restored andsafeguardedTarget 15By 2020, ecosystem resilience and the contribution of biodiversity to carbon stockshas been enhanced, through conservation and restoration
    • 8. Talk outlineWhat innovative tools, technologies and datasets do we need to:1. Identify and reduce loss of natural habitats?2. Enhance ecosystem resilience?3. Conserve ecosystems that provide essential services related to human well-being?
    • 9. What tools are available to Identify and reduce loss of natural habitats?Case study:Determining the ecological value of landscapesbeyond protected areas Willis, K.J. et al., 2012, Biological Conservation, 147, 3-12
    • 10. “ Where can we damage? ”? ? ? ? ?
    • 11. Points arising from workshops with Statoil1. Need a tool that provides estimation of ecological value of land outside of protected areas2. To produce landscape information at a spatial scale less than 500m;3. Use existing available web-based databases;4. Produce simplified displays – preferably maps;5. Simple user input;6. Able to assess any region in world;
    • 12. What is the finest spatial resolution (pixel size)? Global vegetation cover at 300m pixel size resolution (GLOBCOVER (Bicheron et al. 2009)
    • 13. What data are needed to provide an spatialdistribution of ecological value on a landscape?Need data on:1. Key ecological properties of the landscape (e.g. biodiversity, threatened species)2. Key features for supporting ecosystem functions (e.g. connectivity (migration routes, wetlands) habitat integrity, resilience)3. Their spatial configuration on the landscape.
    • 14. Biodiversity data• For most regions in the world will rarely be enough detailed species data to obtain clear picture• Necessary to model predictive diversity across landscape (generalised dissimilarity modelling)• Can then use combination of point species occurrences + environmental variables to predict diversity (spatial heterogeneity) across landscape
    • 15. Biodiversity speciesoccurrence dataGlobal Biodiversity (GBIF):Data Portal (http://data.gbif.org)that provides access to more than330 million records of speciesoccurrence worldwide
    • 16. GBIF network Data Coverage>330 million occurrence records from >8,500 datasets from>360 publishers and spanning a wide range of geospatial,temporal and taxonomic coverages being shared throughdistributed network Last updated: 2
    • 17. Data sources for environmental variables
    • 18. Beta-diversity for Canadian site measured using Generalised Dissimilarity modelling Value provided for every 300m pixel
    • 19. Threatened species data sources• 2010 IUCN Red List of Threatened Species• Assessments for ~56,000 species, of which about 28,000 have spatial data.• Consider all categories in concession area except ‘least concerned’ and ‘extinct’• More threatened species in pixel, higher its value
    • 20. Threatened species distribution in Canadian concession area
    • 21. Fragmentation data• Spatial continuity of natural vegetation based on the size (ha) of each continuous patch• Computer programme FRAGSTATS (McGarigal and Marks, 1995) defines individual patches and calculates patch size• Apply FRAGSTATS to vegetation cover• Greater the patch size, higher the ecological value
    • 22. Fragmentation map Canadian concession areas
    • 23. Connectivity (1) Migratory routes Global Register of Migratory Species • Contains list of 2,880 migratory vertebrate species in digital format • Also their threat status according to the International Red List 2000, • Digital maps for 545 species • Sum the number of migratory ranges occurring in each per pixel www.groms.de
    • 24. Connectivity (2) – Migration processes• Prioritize pixels that support migratory processes: – Rivers, wetlands and lakes (at 300m resolution) – Adjacent pixels to rivers (so as to allow migratory corridors) Data source: HYDROSHEDS (USGS), Global lakes & wetlands database (WWF)
    • 25. Water bodies and drainage networks for Canadian concession area Global Lakes and Wetlands Database, HYDROSHEDS; 30m pixel resolution
    • 26. Resilience– Areas of landscape that are particularly resistant to climate change/disturbance– Areas of landscape that are able to recover from disturbance quicker than others
    • 27. Resilience: measured through ability of vegetation tomaintain relatively high levels of productivity despite low levels of rainfall Rainfall (mm) in driest month Scoring Rule: 1, if highest quartile of productivity & lowest Annualized NPP quartile of rainfall 0.5, if highest quartile of productivity & next lowest quartile of rainfall 0, otherwise Assessed per vegetation Vegetation Type type
    • 28. Resilience, Canadian concession area
    • 29. To summariseFactors and data sources used in LEFT Willis, K.J. et al., 2012, Biological Conservation, 147, 3-12
    • 30. Final index: Local ecological footprint valuation Species richness + Vulnerability + Connectivity + Fragmentation + Resilience Final index
    • 31. Automation
    • 32. How accurate in comparison to field data?Cusuco, Honduras• Montane tropical moist forest• Surveyed 2004-2010• Extensive datasets e.g >50,000 records of terrestrial vertebrates in database
    • 33. Cusuco national park, HondurasCan LEFT correctly identify which globally threatened terrestrialvertebrates are present in a study site? All threatened terrestrial vertebrates Threatened birds Field data Web data 3 4 10 5 26 17 Threatened mammals LEFT 1 2 6 correct LEFT LEFT omission error commission error Threatened reptiles (detected by (not detected by fieldwork, but fieldwork, yet 0 1 0 missed by LEFT) included in LEFT) Threatened amphibians 1 19 1
    • 34. Cusuco – normalised number of threatened species Can LEFT correctly identify which locations in a study site are most important for threatened species? Difference map White = agreement. Red = LEFT predicts relatively more threatened species than field data (commission error) Blue = LEFT predicts relatively fewer threatened species than field data (omission error)
    • 35. Cusuco – beta-diversity using GBIFBeta-diversitycalculated usingspecies occurrencedata (birds) in GBIF Cusuco, Aves Beta-diversity based on GBIF data n = 405 (67 sites)
    • 36. Cusuco – beta-diversity using field dataBeta-diversitycalculated usingspecies occurrencedata (birds) fromfield data Cusuco, Aves Beta-diversity based on field data n = 3297 (116 sites)
    • 37. Summary• Tool will work anywhere in the world at local- scale resolution (~ 300m pixel)• Provides report, maps, files on all values used to calculated ecological value in ~10 minutes• Preliminary studies to compare tool output with high resolution field data indicates that general ecological trends well represented• Consistent and quick approach for obtaining most up-to-date biodiversity information
    • 38. Talk outlineWhat innovative tools, technologies and datasets do we need to:1. Identify and reduce loss of natural habitats?2. Enhance ecosystem resilience?3. Conserve ecosystems that provide essential services related to human well-being?
    • 39. Target 15“By 2020, ecosystem resilienceand the contribution ofbiodiversity to carbon stockshas been enhanced, throughconservation and restoration
    • 40. ”Resilience is the capacity of a system to absorbdisturbance and still retain its basic function andstructure” (Holling, 1973) Alternative definition: ‘Resilience is speed of return to an equilibrium state following a perturbation from that state’ (Nystrom et al. 2000)
    • 41. What is scientific information is needed to determine and plan for resilient landscapes?1. How resilient is the landscape to environmental perturbations? – e.g. climate change/land-use change2. What is the spatial arrangement of resilient ecosystems across the landscape?
    • 42. How resilient is the landscape to environmental disturbance?Recovery rates of tropical forests to disturbance events L. Cole, S. Bhagwat & K.J Willis, in prep
    • 43. • Data from 40 individual fossil sedimentary pollen sequences• Contain records of vegetation dynamics spanning last 10,000 years• Document a total of 140 disturbance events across 3 continents
    • 44. Classification of disturbance typeDisturbance Disturbance type ProxysourceNATURAL Climate (C) Oxygen isotopes, fire (low levels, not linked to human presence), magnetic susceptibility, lithology Precipitation (CP) Rainfall, monsoon strength variation, climate drying Sea-level rise (CS) (CD) Sea level Large infrequent (LI) Hurricane (LI-H), landslide (LI-L), fire (LI-F), volcano (volcanic ash) (LI-V)HUMAN Burning (B) Micro- & macro-charcoal Forest clearing Temporary, predominantly resulting from shifting (FC) cultivation (SC), or more permanent, generally selective clearing, or not described (FC) signified by e.g. fruit trees, Poaceae, & disturbance indicators/secondary forest taxa, e.g. Arenga and Macaranga, or magnetic susceptibility Agriculture (Ag) Agricultural indicators, e.g. fruit trees - Ficus, crops - PoaceaeUnclear U Disturbance indicators but type undefined
    • 45. Calculation of resilienceMetric Description CalculationRecovery Rate (RR) Rate of forest recovery relative to degree of RR = ((Fmax - Fmin)/(Fpre - Fmin))*100/ Trec disturbance-induced percentage changeForest % decline (FD) Forest percentage decline relative to baseline Rel.D = ((Fpre - Fmin)/ Fpre)*100 forest cover percentageResilience (RS) Change in RR through time (RR1 represents (RS) = RR2 – RR1 oldest sample in study)
    • 46. How quickly have tropical forests recoveredfrom disturbances in the past? L. Cole, S. Bhagwat & K.J Willis, in prep
    • 47. Does geographical location affect recovery rates?Fastestrecoveryrates inCentral SlowestAmerica recovery rates in S. America
    • 48. Type of disturbance also indicated significant impact on recovery ratesForest clearancethrough burningetc. resulted inslowest recordedrecovery rates(and greatest L. Cole, S. Bhagwat & K.J Willis, in prepvariation)
    • 49. • Using long-term datasets it is possible to start to determine relative recovery rates• But this still doesn’t give a clear indication of which areas across a landscape are more resilient to climatic perturbations• To do this we need to examine shorter- term/finer resolution datasets
    • 50. Resilience: measured through ability of vegetation tomaintain relatively high levels of productivity despite low levels of rainfall Rainfall (mm) in driest month Scoring Rule: 1, if highest quartile of productivity & lowest quartile of rainfall Annualized NPP 0.5, if highest quartile of productivity & next lowest quartile of rainfall 0, otherwise Assessed per vegetation type Vegetation Type K.J. Willis et al., 2012 Biological Conservation, in press
    • 51. Devising A Global Map of Ecological Resilience: Step 1- NDVI (photosynthetic ‘health’) • 12 year monthly time-slice of NDVI (MODIS) (144 layers in total) • 5km resolution • Masked for sea-areas/ large terrestrial water bodies • Red = high, green = low• Data are detrended for seasonality and transformed to Z-scores in each pixel.• Provides an estimate of amount of variability away from the mean over the 10 years. A.W.R. Seddon, P. Long and K.J. Willis in prepRed = high; Green = low
    • 52. Devising A Global Map of Ecological Resilience: Step 1- NDVI • Variance of these Z scores provides a global map of the variance in productivity for each pixel • Red = high variance, green = low variance A.W.R. Seddon, P. Long and K.J. Willis in prep
    • 53. Towards A Global Map of Ecological Resilience: Step 2- Temperature variance •12 year monthly time-slices of mean monthly surface temperature (MOD-7 profiles) •5km resolution • Converted to z scores to provide a global map of the variance in temperature for each pixel at 5 km resolution • Red = high variance, green = low variance
    • 54. Towards a Global Map of Ecological Resilience: Step 3 Sensitivity (γ) = Temporal Variance in Productivity Temporal Variance in Climate Resilience = 1/γ (of NDVI (productivity) to climate variability over a 10 year period)
    • 55. Global 12 year Resilience of NDVI to Climate Variability• red = low and green = high
    • 56. Talk outlineWhat innovative tools, technologies and datasets do we need to:1. Identify and reduce loss of natural habitats?2. Enhance and identify ecosystem resilience?3. Conserve ecosystems that provide essential services related to human well-being?
    • 57. Target 14“By 2020, ecosystems that provideessential services, includingservices related to water, andcontribute to health, livelihoodsand well-being, are restored andsafeguarded.”
    • 58. What knowledge do we need? R.S. de Groot et al. 2010 Ecological Complexity 7 (2010) 260–272
    • 59. Current landscape planning, management and decision making toolsARIES (ARtificial Intelligence InVESTfor Ecosystem Services) (Integrated Valuation of Ecosystem Services and Tradeoffs) ESValue
    • 60. ARIES (ARtificial Intelligence for Ecosystem Services)End-user needs to work with the ARIES team; developed for specific area; one siteoutput requires 200-300 hours of Senior GIS technician timeInVEST (Integrated Valuation of Ecosystem Services and Tradeoffs)Time varies depending on the site and the technician’s expertise; one site outputrequires 160-280 hours of Senior GIS technician timeESValue~ 200 hours for one site; requires GIS expertise, expert knowledge of ecologicalrelationships plus data from stakeholdersEcoAIM (Ecological Asset Inventory and Management)>25 hours; involves reviewing, downloading, converting and uploading data bystakeholderCurrent Ecosystem Service Tools:(http://www.bsr.org/reports/BSR_ESTM_WG_Comp_ES_Tools_Synthesis3.pdf)
    • 61. "a gap in biodiversity market infrastructurethat persists is lack of landscape-scaleecological monitoring. While site-levelecological monitoring is not uncommon, thedata is not easily available, much lesscomplied in a comprehensive way". Madsen, B., Caroll, N., Kandy, D., Bennett, G (2011) Update: State of Biodiversity Markets. Washington, DC: Forest Trends, 2011. http://www. ecosystemmarketplace.com/reports/2011_update_sbdm.
    • 62. landowner What data do we need to provide a tool to quickly and remotely determine ecosystem service provision?
    • 63. What information is required to map pollination services? GBIF species Land cover occurrence data Environmental co-variables DISTRIBUTIONS OF Crops Nesting habitat for P. POLLINATORS Pollinator foraging distance Pollination Availability ofDEPENDENT pollinators CROP Pollination service delivery P.= pollinator
    • 64. Steps to followDistribution Model +Landscape + Foraging distancefeaturese.g. nesting Landscapehabitat containing pollinators x Crop dependency Final pollination service delivery
    • 65. Preliminary pollination service delivery for Tenerife Tenerife foraging Tenerife nesting habitat Tenerife tree crops distance More service delivered Less service delivered Tenerife actual pollination service delivery Important areas for pollination services for tree crops More service deliveredNogues, Long & Willis, Less service deliveredin prep 0.5 km
    • 66. Responding to evolving threats usinginnovative tools, technologies and datasets• Large scientific biodiversity resource becoming available through databases, modelling and ecological knowledge• Creation of tools to link this information together requires highly interdisciplinary research community• … but must also have good knowledge of requirements of end-user• The challenge is to bring together these tools, technologies and datasets but in a framework that is relevant to both science and stakeholder communities• This requires pragmatism and a different approach to funding such work…

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