the importance of improving collection and access to environmental data in the americas

The Importance of Improving Collection and Access to Environmental Data in the Americas Gilberto CmaraDirector for Earth ObservationNational Institute for Space Research

With thanks to...Carlos Nobre, CPTEC/INPE Antonio Nobre, INPAEduardo Assad, EMBRAPAJoo Vianei Soares, Miguel Monteiro, INPEDaniel Hogan, UNICAMPIma Vieira, Peter Toledo, Mike Hopkins, MPEGLeandro Ferreira, Ana Albernaz, MPEGLuiz Bevilacqua, AEB/Brazilian Academy of Sciencesand the whole INPE team....

What is Environmental Data?Environment == catch-all word

Enviromental Data Earth Sciences dataAthmosphere, oceans, biosphere

General feature Collected on a geographical locationEither in situ or by remote sensingIn many cases, in someone elses backyard

LBA Flux Towers on AmazoniaSource: Carlos Nobre (INPE)

Biodiversity...Source: Carlos Nobre (INPE)

CBERS Image

Challenges of Sustainable DevelopmentUnlike other factors of production (such as capital and labor), natural resources are inflexible in their location. The Amazonian Forest is where it is; the water resources for our cities cannot be very far away from them. The challenge posed by sustainable development is that we can no longer consider natural resources as indefinitely replaceable, and move people and capital to new areas when existing resources become scarce or exhausted: there are no new frontiers in a globalized world.(Daniel Hogan)

Sustainability Science Core QuestionsHow can the dynamic interactions between nature and society be better incorporated in emerging models and conceptualizations that integrate the earth system, human development and sustainability? How are long-term trends in environment and development, including consumption and population, reshaping nature-society interactions in ways relevant to sustainability? What determines vulnerability/resilience of nature-society interactions for particular places and for particular types of ecosystems and human livelihoods? Source: Sustainability Science Workshop, Friibergh, SE, 2000

Sustainability Science Core QuestionsCan scientifically meaningful limits or boundaries be defined that would provide effective warning of conditions beyond which the nature-society systems incur a significantly increased risk of serious degradation? How can todays relatively independent activities of research planning, monitoring, assessment and decision support be better integrated into systems for adaptative management and societal learning?Source: Sustainability Science Workshop, Friibergh, SE, 2000

Public Policy IssuesWhat are the acceptable limits to land cover change activities in the tropical regions in the Americas?What are the future scenarios of land use?How can food production be made more efficient and productive?How can our biodiversity be known and the benefits arising from its use be shared fairly? How can we manage our water resources to sustain our expected growth in urban population?

The Importance of Environmental DataOur knowledge of earth system science is very incompleteSupport for earth science modellingUnderstanding of processes Supporting conjectures and refutationsHelps address sustainability science questionsFrom scientific questions to public policy issuesData collection brings new questions and helps formulate new onesBreaking the five orders of ignorance

The Five Orders of Ignorance0th Order Ignorance (0OI): Lack of IgnoranceI (provably) know something

1st Order Ignorance (1OI): Lack of KnowledgeI do not know something

2nd Order Ignorance (2OI): Lack of AwarenessI do not know that I do not know something

3rd Order Ignorance (3OI): Lack of ProcessI do not know a suitably effective way to find out that I dont know that I dont know something

4th Order Ignorance (4OI): Meta-IgnoranceI do not know about the Five Orders of IgnoranceThe five orders of ignorance, Phillip G. Armour, CACM, 43(10), Oct 2000

Why is Environmental Data Different?Cannot be re-created or synthesized in a laboratoryUnlike data in Physical, Chemical and Biological Sciences

Requirement of access to a data collection sizeGranted by mutual consent Implicitly conceded by international conventions Remote Sensing is ruled by COPUOS Biodiversity collection is guided by Biodiversity conventionExtremely sensitive topicMany governments and politicians think of data collection as stealing our valuable resources

Amazonia (LBA - GEOMA): Scientific Questions that need Good DataWhat is the age of the trees in Amazonia?

What is the extension of the Amazonian wetlands?

What is the environmental impact of the forest fires?

What is the CO2 balance of the rain forest?

What are the driving factors of deforestation?

What are the true extent of biodiversity in Amazonia?

The ChallengesData Collection over large regions is tough work...

ConsequencesSparse data In many cases, limited by reachability of field campaignsFast degradation of infra-structureCan indirect data help? How can improvements in Remote Sensing help us?There is a need for much more in situ data collection

What do you do with bad or incomplete data?

Operational sitePlanned siteUp to 5 years of dataUp to 3 years of data1 to 2 years of dataLBA Sites

Dados com boa taxonomia e bons dados de distribuio.........Flora Neotropica etc: Mimosoideae: Inga; Lauraceae: Nectandra; Sapotaceae, Chrysobalanaceae, algumas Annonaceae, Marantaceae: Montagma, etc, 1425 spp geo-referenciadas at grau de longitude/latitude e mapeadas em Arcview.

Data from Floras..................Reserva Ducke:Best kinown area in Amazonia in 1993 (ca. 1100 spp.)By 1999, it had 2175 species, including between 50 100 undescribed ones........Tambm:Sal (Guiana Francesa Mori et al.) 1997 & 2002.Iquitos (Vsquez et al.) - 1997Flora of Ecuador (Renner et al.) em progresso

Sapotaceae densidade das espciesSalBelmTabatingaRio de JaneiroSantarmAlto Rio Negro

Isso realmente a distribuio da diversidade de espcies neotropicais???1425 espciesDe jeito nenhum!!!!!

What are we doing?INPEs roleProduction of basic dataCBERS, LANDSAT, NOAA imageryLBA dataIntegration of Remote Sensing, GIS, Meteorology, Climatology, Earth Sciences in Environmental ModelsSome Programmes we are participatingMonitoring Forest FiresMonitoring and Modelling DeforestationLBA Experiment in AmazoniaLand management and zoning for Brazil

Land Management: Dealing with Old Data

Land Management: RADAM x SRTM

Land Management: RADAM x LANDSAT/NASA

Landsat/CBERS ReceptionNOAA ReceptionCPTECImagem TMNOAA ImageWeather ForecastCartographic BaseInternetDecision MakingProductsFire Monitoring in Brazil

Risque soil moisture model (Woods Hole) integrated with INPE/CPTEC data/modelsD. NepstadC. NobreA SetzerJ. TomasellaU. LopesP. Lefebvre

Plinio Alval1, C. von Randow2, A. O. Manzi2, A. de Souza3, L. S1, R. Alval1

CO2 FLUXES OVER PANTANAL REGION UNDER DRY AND FLOOD CONDITIONSPOSTER Start of floodingwater layer height20 cm20 cm10 cm55 cm14 cm

Deforestation...

What Drives Tropical Deforestation?Underlying Factorsdriving proximate causesCausative interlinkages atproximate/underlying levelsInternal drivers*If less than 5%of cases,not depicted here.source:Geist &Lambin 5% 10% 50%% of the cases

1 9 7 3

1 9 9 1Courtesy: INPE/OBT

1 9 9 9Courtesy: INPE/OBT

Deforestation in Amazonia PRODES (Total 1997) = 532.086 km2PRODES (Total 2001) = 607.957 km2

Desmatamentos Ocorridos em reas Prioritrias Conservao-2002Desmat. em rea PrioritriaDesmat. em Outras reas

MTAMPATOFonte: MMA/SBF

Modelling Tropical DeforestationFine: 25 km x 25 km gridCoarse: 100 km x 100 km grid

Factors Affecting Deforestation

Plan1

CategoryVariablesUnit

DemographicPopulation Densityhab/ha

Proportion of urban population%

Proportion of migrant population (before 1991, from 1991 to 1996)%

TechnologyNumber of tractors per number of farms

Percentage of farms with technical assistance%

Agrarian strutucturePercentage of small, medium and large properties in terms of area%

Percentage of small, medium and large properties in terms of number%

Infra-structureDistance to paved and non-paved roads10km

Distance to urban centers10km

Distance to ports

EconomyDistance to wood extraction poles10km

Distance to mining activities in operation (*)

Connection index to national markets-

PoliticalPercentage cover of protected areas (National Forests, Reserves, Indigenous lands, etc.)%

Presence of INCRA settlementsbool

Number of families settled (*)

EnvironmentalSoils (classes of fertility, texture, slope)

Climatic (avarage precipitation, temperature*, relative umidity*)

Plan2

Plan3

Coarse resolution: candidate models

Modelo7

MODELO 7

Predicted & Residual ValuesStandard Residual: LOG_USOS

Dependent variable: LOG_USOSOutliers - Modelo 7

ObservedPredictdStandardStandardStd.Err.Mahalns.DeletedCook'sObservedPredictdStandardStandardStd.Err.

ValueValueResidualPred. v.ResidualPred.ValDistanceResidualDistanceValueValueResidualPred. v.ResidualPred.Val

1-1.7695510387-1.7813627720.0118117332-2.22664880750.03155807410.082099720816.90131950380.01240877620.000007103 . . . *. . . .-1.287350297-0.4643215239-0.8230288029-0.7680549622-2.19893264770.0358571336

3-1.8728952408-1.577590704-0.2953045368-2.0009756088-0.78898179530.05662709477.5176920891-0.30222234130.001866115 . . . * . . . .-1.2740883827-0.3242131174-0.9498752356-0.6128880382-2.53783535960.044862248

40.0949949026-0.41614672540.5111416578-0.71470242741.36564600470.04056146373.37149882320.51721590760.002803136 . . . * . . .-1.946921587-1.175152421-0.771769166-1.5552842617-2.06197929380.0966818631

6-0.8917733431-0.6613464952-0.2304268479-0.9862557054-0.61564439540.0466951024.7926917076-0.23407004770.000761144 . . . *. . . .-1.5917600393-0.7436103821-0.8481496572-1.0773611069-2.26604938510.042028714

7-1.8761483431-1.4453427792-0.4308055639-1.854514122-1.15100753310.07816939815.2286338806-0.45045351980.007897169 . . . . * . . .1.57876062390.61724072690.9615198970.42975103862.56894707680.0577637032

9-1.6757175922-1.5214169025-0.1543006897-1.9387644529-0.41225385670.05740508817.7532734871-0.15801775460.000524177 . . . * . . . .-0.24116377530.6763167977-0.9174805880.4951764643-2.4512846470.0458744131

10-1.673664093-1.4675071239-0.2061569691-1.879060626-0.55080121760.073438942413.324213028-0.21441154180.001579199 . . . * . . .-0.18815699220.5987419486-0.78689897060.4092640877-2.10240244870.0638862476

120.36769130830.6789228916-0.31123158340.4980627-0.83153498170.03789926692.8168354034-0.31445574760.000905204 . . . *. . . .0.49356958271.3193056583-0.82573604581.2072722912-2.20616555210.0783860758

130.34184983370.5278497934-0.18599995970.3307526112-0.49694660310.03902058683.0458717346-0.18804377320.000343225 . . .* . . . .-1.05158698560.05670904-1.1082960367-0.1910252571-2.9610970020.0482701957

15-1.7144427299-1.6079370975-0.1065056324-2.0345835686-0.28455710410.05851066118.0935764313-0.10917360340.000260226 . * . . . . . .-0.31659269331.4628682137-1.7794609071.3662645817-4.75428628920.0758473426

16-1.673664093-1.5813550949-0.0923089981-2.0051445961-0.24662716690.069130130111.6929664612-0.09556921570.000278316 . . . .* . . .1.23801350590.39180648330.84620702270.18008765582.26085901260.042704314

18-0.4614262581-0.3855518997-0.0758743584-0.6808193326-0.20271781090.04112870253.494546175-0.07680173220.000064348 . . . .* . . .1.0744653940.27107265590.80339276790.04637764392.14646983150.0446085148

190.31983447070.6548758149-0.33504134420.4714310765-0.89514887330.03170908241.6726367474-0.33746340870.000729349 . . . *. . . .-1.6497520208-0.8014944196-0.8482576013-1.1414663792-2.26633763310.057369072

21-0.02682594770.0202814173-0.047107365-0.2313680351-0.12585940960.04437040914.230535984-0.04777881880.000029417 . . . .* . . .0.99100887780.18549412490.8055147529-0.048398642.15213918690.050730519

22-1.939302206-1.9828678370.0435656309-2.44981098180.1163967550.093693919522.31360626220.04647812620.000121480 . . . .* . . .1.35809290410.55205261710.8060402870.35755670072.15354347230.045774579

24-0.9132843614-1.23844337460.3251590133-1.6253776550.86874568460.068767666811.56024074550.33651882410.003411Minimum . * . . . . . .-1.946921587-1.175152421-1.779460907-1.5552842617-4.75428628920.0358571336

25-0.6174426675-0.69438040260.0769377351-1.0228400230.20555889610.05354641386.61641168590.07854532450.000113Maximum . . . . * . . .1.57876062391.46286821370.9615198971.36626458172.56894707680.0966818631

270.38412034510.8011580706-0.41703772540.6334354877-1.11422324180.03455436972.1732895374-0.4206227660.001346Mean . . . .* . . .-0.18756411970.1748544276-0.3624185622-0.0601818785-0.96829408410.0553763285

280.04688519240.5485984087-0.50171321630.353731215-1.34045553210.03196031231.7151119709-0.50539833310.001662Median . . . *. . . .-0.24116377530.2710726559-0.82302880290.0463776439-2.19893264770.0482701957

30-0.7246886492-0.4964245856-0.2282640636-0.8036083579-0.60986596350.04244803263.7873494625-0.23123826090.000614

31-1.4089354277-1.77520525460.3662698269-2.21982932090.97858375310.065605007110.43174552920.37787953020.003915

33-1.5951663256-1.5898426771-0.0053236485-2.014544487-0.01422349180.05528700727.1194438934-0.00544239720.000001

34-1.9871628284-1.682613492-0.3045493364-2.1172862053-0.81368166210.06341696539.6820983887-0.31355077030.002518Analysis of Variance; DV: LOG_USOS

36-0.9875846505-1.65470910070.6671244502-2.08638262751.78239405160.086326852418.79189300540.70460730790.023566Sums ofMean

37-0.4647058845-0.91210913660.4474032521-1.2639697791.19535255430.093415714822.17537689210.4771243930.012653SquaresdfSquaresFp-level

39-0.8520146608-0.9980183840.1460037231-1.35911238190.39008638260.066693864810.81427383420.15079160030.000644Regress.303.3004075114743.3286296445309.29199218750

40-0.7552282214-0.4449571669-0.3102710545-0.7466093302-0.82896870370.03576783092.3998868465-0.31313064690.000799Residual51.13274687483650.1400897175

420.25614023210.6038682461-0.3477280140.4149413109-0.92904454470.03342986111.9702841043-0.35052430630.000875Total354.4331543862

43-0.05041484910.2485633641-0.29897820950.0214491263-0.79879695180.04675050824.8064398766-0.30371665950.001284

450.14245204630.4682074785-0.32575541730.2647000849-0.87033915520.05767142777.8346614838-0.33367756010.002359

46-1.1580152512-1.57344853880.4154332876-1.99638819691.1099365950.072136476612.8207254410.43145999310.006170

48-1.2652001381-1.45956051350.1943603754-1.8702598810.51928359270.070895381312.34933948520.20159316060.001301

49-1.9430952072-1.3263881207-0.6167070866-1.7227745056-1.64769113060.04217505833.7260081768-0.62463819980.004420

51-0.620331943-1.01202571390.3916937709-1.37462508681.04651033880.067471653211.09137344360.40484997630.004753

52-0.7522721887-1.0489819050.2967097163-1.41555333140.79273611310.083594597917.5590534210.31228744980.004341

54-1.0710923672-0.8615072966-0.2095850706-1.2079293728-0.55996024610.05887043488.2057180405-0.21490159630.001019

55-0.836838603-0.2623170614-0.5745215416-0.5443395376-1.53498160840.04585242644.5856003761-0.58327525850.004556

570.00212284620.3487690687-0.34664621950.1324246973-0.92615425590.02951679381.3162103891-0.34881556030.000675

580.27627792950.6217130423-0.34543511270.4347040653-0.92291849850.03787339112.8116295338-0.34900864960.001113

600.83539247510.83120101690.00419145820.66670727730.01119855610.0519016726.15586423870.00427363580.000000

61-1.6478174925-1.76192569730.1141082048-2.20512270930.3048693240.069469764811.81796646120.11817944790.000429

63-1.1301817894-1.43878471850.3086029291-1.84725105760.82451188560.080086998616.03448677060.3234100640.004273

64-1.1456940174-1.41964232920.2739483118-1.82605147360.73192316290.079444982115.7625093460.28687289360.003308

66-1.3736596107-1.1479636431-0.2256959677-1.5251733065-0.60300463440.045478754.4949746132-0.22907812890.000691

67-0.5131446719-0.70620757340.1930629015-1.03593838210.51581704620.0575010597.78255653380.1977296770.000823

69-0.8013429046-0.90575098990.1044080853-1.25692820550.2789529860.080599650716.25323486330.10948518660.000496

70-0.6607473493-0.6029995084-0.0577478409-0.9216377735-0.15428817270.06375082589.7948379517-0.05947323140.000092

72-0.9476909041-0.3850628734-0.5626280308-0.6802777648-1.5032050610.03356927631.9950875044-0.56719058750.002309

730.25321680310.5510895252-0.29787272210.3564900756-0.79584336280.02737581920.992762804-0.29947483540.000428

751.0809689761.4516897202-0.37072074411.353884697-0.99047553540.04938403895.4787268639-0.3772888780.002211

760.71726262570.40471911430.31254351140.19438810650.83504015210.05128610515.98719358440.31852397320.001700

78-1.3036435843-1.3962895870.0926460028-1.80018877980.24752755460.071050785512.40791797640.09610936050.000297

79-1.1301817894-1.23058235650.1004005671-1.61667168140.26824587580.066371336610.70030784610.10366018120.000301

81-1.5734887123-1.5041553974-0.0693333149-1.9196478128-0.1852417290.05694322667.6130309105-0.07097613810.000104

82-0.5199930668-0.84814739230.3281543255-1.19313359260.87674844260.03804200522.84561991690.3315797150.001013

84-1.0614802837-0.8869338036-0.1745464802-1.2360886335-0.46634569760.079421751215.7527103424-0.18277634680.001342

85-0.5883802772-0.59216010570.0037798285-0.90963333850.01009878140.070230901212.10032558440.00391776810.000000

87-1.0472075939-0.5076995492-0.5395080447-0.8160951734-1.4414341450.04265679423.8345274925-0.5466078520.003463

88-1.040958643-0.55060637-0.490352273-0.8636135459-1.31010186670.03371030462.0202832222-0.49436247350.001769

900.34648990630.7953134179-0.44882351160.6269626617-1.19914710520.0326313191.8302030563-0.45226109030.001387

911.13425040250.95723575350.17701464890.80628794430.47294002770.04857313635.26779508590.18004694580.000487

93-1.9746941328-2.0451986790.0705045462-2.51884102820.18837097290.106152966628.92539024350.07667182390.000422

94-1.4962092638-1.57699859140.0807893276-2.00031995770.21584941450.080933645416.39649772640.0847521350.000300

96-1.5361070633-1.3338652849-0.2022417784-1.7310552597-0.54034078120.04554570094.5111570358-0.20528154080.000557

97-0.3736596406-0.75816524030.3845055997-1.09348034861.02730536460.04496455574.37148141860.39013615250.001960

99-0.6607473493-0.67118549350.0104381442-0.99715220930.02788817880.077358253314.89363574980.01090393490.000005

100-0.4910664856-0.3670054078-0.1240610778-0.660279572-0.33146098260.06145172949.0304632187-0.12749795620.000391

102-1.4685211182-0.8095001578-0.6590209603-1.1503325701-1.76074349880.03729027512.6952438354-0.66562813520.003924

103-1.287350297-0.4643215239-0.8230288029-0.7680549622-2.19893264770.03585713362.4168717861-0.83065241580.005651

105-0.06489598010.6205933094-0.68548929690.4334639907-1.83146047590.03208994871.7371606827-0.69056546690.003128

106-0.1013845056-0.14462956790.0432450622-0.41400328280.11554027350.04383849354.1059432030.043846570.000024

108-1.7594507933-1.6442893744-0.1151614189-2.0748429298-0.30768325920.06093939398.8639535904-0.11829733850.000331

109-0.6556077003-0.88994055990.2343328595-1.23941850660.62608033420.04877896235.32100391390.23838171360.000861

111-0.2421486527-0.49136853220.2492198795-0.79800891880.66585481170.071138821512.44115829470.25856035950.002155

112-0.3525192142-0.55076998470.1982507706-0.8637947440.52967780830.0516398816.08388566970.20209780340.000694

114-1.2924298048-0.7561476827-0.5362821221-1.0912458897-1.43281519410.04237598933.7711219788-0.54324567320.003375

115-1.2740883827-0.3242131174-0.9498752356-0.6128880382-2.53783535960.0448622484.3470783234-0.96372073890.011906

1170.13605435190.857983768-0.7219294310.6963685751-1.92881965640.03268774971.8399907351-0.72747802730.003602

1180.27487337590.5601034164-0.28523004060.3664727807-0.76206523180.05046088255.7642321587-0.2905104160.001369

120-0.9605858922-0.2327341288-0.7278517485-0.5115770698-1.9446426630.06303274639.5530853271-0.74909716840.014201

1210.2841599286-0.06307322530.3472331464-0.32368144390.92772239450.06209808229.2425184250.35706180330.003131

123-1.1079053879-1.34381186960.2359064817-1.74207091330.6302846670.04984431345.60000705720.24016577010.000913

124-0.8880656958-0.98838913440.1003234386-1.34844815730.26803979280.05943275998.38237094880.10291844610.000238

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561-0.8607507944-0.2588359416-0.6019148827-0.5404842496-1.60817003250.085653021918.4841651917-0.63517892360.018853

5620.1814719886-0.02579975690.2072717398-0.28240191940.55377960210.075379170514.09094905850.21603406970.001689

5640.77787613870.62469786410.15317827460.43800970910.40925505760.06166883199.10144329070.15745267270.000601

Minimum-1.9871628284-2.045198679-1.779460907-2.5188410282-4.75428628920.02737581920.992762804-1.85566425320.000000

Maximum1.7852764132.20577335360.9615198972.18901538852.56894707680.118312045936.17285919190.98498004670.126176

Mean0.2350965440.23447817560.00061836440.00585009390.00165211970.05298027776.9671688080.00053661370.002983

Median0.33148804310.40471911430.04125794770.19438810650.11023119090.05010728175.6698017120.04190326110.001011

graficos

MODELO 4MODELO 7: parece que se comporta melhor para valores pequenos? Ver ultimo grafico

Erros distribuidos de maneira mais uniforme?

Modelo4

MODELO 6(mais outliers)

Predicted & Residual ValuesStandard Residual: LOG_USOSStandard Residual: LOG_USOS

Dependent variable: LOG_USOSOutliersOutliers - Modelo 7

ObservedPredictdStandardStandardStd.Err.Mahalns.DeletedCook'sObservedPredictdStandardStandardStd.Err.ObservedPredictdStandardStandardStd.Err.

ValueValueResidualPred. v.ResidualPred.ValDistanceResidualDistanceValueValueResidualPred. v.ResidualPred.ValValueValueResidualPred. v.ResidualPred.Val

1-1.7695510387-1.6883254051-0.08123-2.160131216-0.19847185910.096917830419.8649730682-0.08605152370.0003582 . . . .* . . .-0.5199930668-1.34907889370.8290858269-1.77796208862.02584099770.06425751103 . . . *. . . .-1.287350297-0.4643215239-0.8230288029-0.7680549622-2.19893264770.0358571336

3-1.8728952408-1.135868907-0.73703-1.537776351-1.8008968830.06108514597.290225029-0.75382024050.0108097 . . . .* . . .-0.3736596406-1.21222901340.8385694027-1.62379777432.04901361470.0597555637115 . . . * . . . .-1.2740883827-0.3242131174-0.9498752356-0.6128880382-2.53783535960.044862248

40.0949949026-0.35163161160.44663-0.65431523321.09131550790.03329293431.464515090.44960191850.00114103 . . . * . . . .-1.287350297-0.2626713812-1.0246789455-0.5540995002-2.5037655830.0386080965136 . . . * . . .-1.946921587-1.175152421-0.771769166-1.5552842617-2.06197929380.0966818631

6-0.8917733431-0.6981729269-0.19360-1.0447019339-0.47305554150.06030802067.0806980133-0.19789779190.00073115 . . .* . . . .-1.2740883827-0.1303294152-1.1437590122-0.4050133228-2.79473328590.0401904173144 . . . *. . . .-1.5917600393-0.7436103821-0.8481496572-1.0773611069-2.26604938510.042028714

7-1.8761483431-1.8223580122-0.05379-2.3111219406-0.13143470880.097790747920.2424678802-0.05704752730.00016159 . . . * . . .-1.671620369-0.8387511373-0.8328692317-1.2030665874-2.03508543970.0611013062169 . . . . * . . .1.57876062390.61724072690.9615198970.42975103862.56894707680.0577637032

9-1.6757175922-1.2812157869-0.39450-1.7015129328-0.96395075320.0577537386.4109168053-0.40251779560.00275169 . . . .* . . .1.57876062390.67872643470.90003418920.50640642642.19920063020.0613101013177 . . . * . . . .-0.24116377530.6763167977-0.9174805880.4951764643-2.4512846470.0458744131

10-1.673664093-1.2596940994-0.41397-1.6772682667-1.0115205050.05882766846.6889910698-0.42270395160.00315202 . . . * . . . .-0.7736579180.2719897628-1.04564762120.0482083559-2.55500173570.0411608182199 . . . * . . .-0.18815699220.5987419486-0.78689897060.4092640877-2.10240244870.0638862476

120.36769130830.01229767040.35539-0.24434067310.86839139460.04915052654.36819076540.360594660.00160204 . . . * . . . .0.49356958271.4648766518-0.97130703931.3920224905-2.37335324290.0806133896204 . . . *. . . .0.49356958271.3193056583-0.82573604581.2072722912-2.20616555210.0783860758

130.3418498337-0.02468032580.36653-0.28599724170.89560300110.04880841824.29375791550.37181866170.00168225 . . * . . . . .-1.05158698560.408929795-1.46051681040.2024744004-3.56871938710.0425308421225 . . .* . . . .-1.05158698560.05670904-1.1082960367-0.1910252571-2.9610970020.0482701957

15-1.7144427299-1.2430822849-0.47136-1.6585546732-1.15175199510.05526660385.7865924835-0.48011600970.00359226 * . . . . . .-0.31659269331.8412872553-2.15787982941.8160575628-5.27270030980.0773108825226 . * . . . . . .-0.31659269331.4628682137-1.7794609071.3662645817-4.75428628920.0758473426

16-1.673664093-1.2845826149-0.38908-1.7053055763-0.95070636270.05801205346.4773344994-0.39705964920.00270264 . . .* . . . .-1.5346171856-0.3463089168-1.188308239-0.6483191252-2.90358781810.0374153741316 . . . .* . . .1.23801350590.39180648330.84620702270.18008765582.26085901260.042704314

18-0.4614262581-0.3190222085-0.14240-0.6175799966-0.34795910120.03791541982.1955883503-0.14363689720.00015349 . . . * . . .-1.6497520208-0.817222476-0.8325295448-1.1788139343-2.03425550460.0585544631348 . . . .* . . .1.0744653940.27107265590.80339276790.04637764392.14646983150.0446085148

190.31983447070.21433560550.10550-0.01674037050.25778260830.03718844062.07432246210.10637722910.00008498 . . . * . . . .1.25635445122.3447930813-1.08843863012.3832683563-2.65955996510.1249284819349 . . . *. . . .-1.6497520208-0.8014944196-0.8482576013-1.1414663792-2.26633763310.057369072

21-0.02682594770.2769404054-0.303770.0537853688-0.7422419190.03581610691.8518049717-0.30611082910.00061Minimum * . . . . . .-1.671620369-1.3490788937-2.1578798294-1.7779620886-5.27270030980.0374153741417 . . . .* . . .0.99100887780.18549412490.8055147529-0.048398642.15213918690.050730519

22-1.939302206-1.330004096-0.60930-1.7564740181-1.48879766460.06898700459.5730257034-0.62711757420.00953Maximum . . . .* . . .1.57876062392.34479308130.90003418922.38326835632.19920063020.1249284819480 . . . .* . . .1.35809290410.55205261710.8060402870.35755670072.15354347230.045774579

24-0.9132843614-0.91553992030.00226-1.28957080840.00551137610.04735640063.98362874980.00228616990.00000Mean . . . .* . . .-0.54801797870.1580009013-0.7060188651-0.0802026689-1.72513127330.0605951734Minimum . * . . . . . .-1.946921587-1.175152421-1.779460907-1.5552842617-4.75428628920.0358571336

25-0.6174426675-0.4978480637-0.11959-0.8190314174-0.2922250330.03829697152.2601742744-0.1206511110.00011Median . . . * . . . .-0.773657918-0.1303294152-1.0246789455-0.4050133228-2.5037655830.0597555637Maximum . . . . * . . .1.57876062391.46286821370.9615198971.36626458172.56894707680.0966818631

270.38412034510.462600857-0.078480.2629360557-0.19176426530.03079527241.1089930534-0.07892741260.00003Mean . . . .* . . .-0.18756411970.1748544276-0.3624185622-0.0601818785-0.96829408410.0553763285

280.04688519240.3962064981-0.349320.1881413162-0.85355383160.03750676292.1271324158-0.35228013990.00089Median . . . *. . . .-0.24116377530.2710726559-0.82302880290.0463776439-2.19893264770.0482701957

30-0.7246886492-0.4536039829-0.27108-0.769189477-0.66238546370.06782212119.2190694809-0.278739810.00182

31-1.4089354277-1.61893260480.21000-2.08195853230.51312041280.080328531613.33426666260.21841165420.00157Analysis of Variance; DV: LOG_USOS

33-1.5951663256-1.5474728346-0.04769-2.0014574528-0.11653730270.06909202049.6052331924-0.04909270630.00006Sums ofMean

34-1.9871628284-1.4147577286-0.57241-1.8519508839-1.39865088460.06764467819.1656799316-0.58848237990.00807SquaresdfSquaresFp-level

36-0.9875846505-1.510378480.52279-1.95966982841.27742755410.06691772498.94841766360.53715515140.00658Regress.293.131894723648.8553157872291.69131469730

37-0.4647058845-0.325715512-0.13899-0.625120163-0.33961787820.04764794934.0451483727-0.14090026920.00023Residual61.30125966323660.1674897805

39-0.8520146608-0.94317072630.09116-1.32069742680.22273650770.05418230595.52301168440.09278233350.00013Total354.4331543862

40-0.7552282214-0.4679425955-0.28729-0.7853422165-0.70197194810.03838277612.2747871876-0.2898350060.00063

420.25614023210.24094767870.015190.01323874020.0371224470.02891014520.85901081560.01526874680.00000

43-0.0504148491-0.15073023740.10032-0.42799532410.24511699380.04238847642.99339151380.1014032140.00009

450.14245204630.00380212280.13865-0.25391110780.33878603580.05442708365.5820574760.14114630220.00030

46-1.1580152512-1.85813915730.70012-2.35143041611.71072721480.084406435514.82629871370.73122787480.01940

48-1.2652001381-1.85696661470.59177-2.35010910031.44595968720.081262618313.66950988770.61605566740.01276

49-1.9430952072-1.8780925274-0.06500-2.3739080429-0.15883167090.078761905412.780708313-0.06750283390.00014

51-0.620331943-1.22691655160.60658-1.64034354691.48216736320.05639297896.06593275070.61832487580.00619

52-0.7522721887-1.24087846280.48861-1.65607202051.19389152530.05542623625.82583856580.49773564930.00388

54-1.0710923672-1.0129566193-0.05814-1.3993128538-0.14205257590.0589446096.7195792198-0.05936728050.00006

55-0.836838603-0.1403697282-0.69647-0.4163239598-1.70179629330.03803139552.2151517868-0.70253574850.00364

570.0021228462-0.07617194950.07829-0.34400370720.19131048020.02800956180.74515885110.07866325970.00002

580.27627792950.19770531360.07857-0.0354747660.19198931750.03803662962.21603560450.07925724240.00005

600.83539247510.61095869540.224430.43006452920.54839575290.03786915912.18780231480.22637200360.00037

61-1.6478174925-1.62771523-0.02010-2.0918524265-0.04911914470.084622949414.907579422-0.02100012640.00002

63-1.1301817894-1.90544867520.77527-2.40472531321.89433622360.086095519415.46593570710.81116586920.02484

64-1.1456940174-1.89474976060.74906-2.39267277721.83029031750.085199527415.12505531310.78299033640.02266

66-1.3736596107-1.73542666440.36177-2.21319174770.88396453860.074154011911.21572494510.37404730920.00392

67-0.5131446719-0.87731069330.36417-1.24650478360.88982635740.05091482034.760301590.36989098790.00181

69-0.8013429046-0.85441237690.05307-1.22070908550.12967331710.06326804317.89312458040.05436883490.00006

70-0.6607473493-0.6502584219-0.01049-0.9907251596-0.02562930990.06250054397.678735733-0.01073939910.00000

72-0.9476909041-0.3941770196-0.55351-0.7022436261-1.35249090190.03437545521.6272109747-0.55744677780.00187

730.25321680310.3200011551-0.066780.1022942588-0.16318511960.03005269911.0086377859-0.06714642790.00002

751.0809689761.2197893858-0.138821.1159260273-0.33920258280.06170352187.4588665962-0.14204943180.00039

760.71726262570.34962666030.367640.1356680840.89830505850.0335362741.50063371660.37012130020.00078

78-1.3036435843-1.68323111530.37959-2.15439224240.9275082350.080245219213.30455493930.39476460220.00511

79-1.1301817894-1.90544867520.77527-2.40472531321.89433622360.086095519415.46593570710.81116586920.02484

81-1.5734887123-1.5088378191-0.06465-1.9579342604-0.15797209740.071570277210.3794794083-0.06669047470.00012

82-0.5199930668-1.34907889370.82909-1.77796208862.02584099770.064257518.17337989810.85004138950.01519

84-1.0614802837-1.21823155880.15675-1.63055980210.38301602010.06739242379.09002399440.16112029550.00060

85-0.5883802772-0.67338287830.08500-1.01677536960.20770074430.0751613911.54980754850.08796972040.00022

87-1.0472075939-0.3551126719-0.69209-0.6582368016-1.69110858440.04125089572.7820680141-0.6991985440.00424

88-1.040958643-0.5382400751-0.50272-0.8645339608-1.22837448120.0367063881.9952071905-0.50679546590.00176

900.34648990630.5345105529-0.188020.3439439833-0.45942157510.02912326530.8864808083-0.1889776140.00015

911.13425040250.75740832090.376840.59504336120.92079985140.04857787494.2438917160.3822273910.00176

93-1.9746941328-2.21558094020.24089-2.7540965080.58859813210.101795516922.01773071290.25677293540.00348

94-1.4962092638-1.59863221650.10242-2.05908966060.25026673080.08143347513.73125076290.10664535310.00038

96-1.5361070633-1.2768040895-0.25930-1.6965430975-0.63359731440.05642599246.0742058754-0.26432770490.00113

97-0.3736596406-1.21222901340.83857-1.62379777432.04901361470.05975556376.93337726590.8568363190.01335

99-0.6607473493-0.6713483930.01060-1.01448345180.02590326220.06089164327.23780250550.01084103620.00000

100-0.4910664856-0.4468652308-0.04420-0.7615980506-0.10800415280.05961138016.8951516151-0.04515936970.00004

102-1.4685211182-0.7716062069-0.69691-1.1274261475-1.70288610460.05909515176.7590475082-0.71175533530.00901

103-1.287350297-0.2626713812-1.02468-0.5540995002-2.5037655830.03860809652.3133168221-1.03387999530.00811

105-0.06489598010.2776954472-0.342590.0546359383-0.83710962530.04415413363.332775116-0.34662616250.00119

106-0.1013845056-0.0587857515-0.04260-0.3244177699-0.10408850010.05469140415.6461172104-0.04337334630.00003

108-1.7594507933-1.733335495-0.02612-2.2108359337-0.0638117790.087419211915.9760637283-0.02736383860.00003

109-0.6556077003-1.20722007750.55161-1.61815512181.34784471990.05171295634.94222831730.56056261060.00428

111-0.2421486527-0.50354737040.26140-0.82545185090.63871824740.05760875726.37376928330.26668298240.00120

112-0.3525192142-0.37492650750.02241-0.68055748940.05475139990.04272615163.0572261810.02265420930.00000

114-1.2924298048-0.7730943561-0.51934-1.1291025877-1.26897716520.06208739437.5644106865-0.53156971930.00555

115-1.2740883827-0.1303294152-1.14376-0.4050133228-2.79473328590.04019041732.5902447701-1.15489673610.01097

1170.13605435190.5068833828-0.370830.3128213584-0.90610718730.04647013173.7989375591-0.37567263840.00155

1180.27487337590.2758029401-0.000930.0525039844-0.00227135630.04602867743.7082445621-0.00094147330.00000

120-0.9605858922-0.5441191792-0.41647-0.8711568117-1.01762115960.0626127727.7099204063-0.42644840480.00363

1210.28415992860.11795921620.16620-0.12531055510.40610536930.0459045883.68290686610.1683183610.00030

123-1.1079053879-1.0469619036-0.06094-1.4376206398-0.14891317490.06846465179.4135608673-0.06269817050.00009

124-0.8880656958-0.8152851462-0.07278-1.1766314507-0.17783661190.0475933034.0335884094-0.07377832380.00006

126-0.3856301606-0.41552245620.02989-0.72628974910.07304073130.04555141553.61116838460.03026725910.00001

127-0.3213905692-0.36596366760.04457-0.67046064140.10891273620.03899824252.38056468960.04498154670.00002

129-0.4323855639-0.2983744442-0.13401-0.5943198204-0.32745125890.044905863.4814703465-0.13564424220.00019

130-0.5550189018-0.0932626501-0.46176-0.3632567823-1.12828457360.04096583282.7300140858-0.46642974020.00186

1320.49573853611.0970087051-0.601270.9776108861-1.4691817760.05728306626.2906599045-0.61328524350.00628

1330.66132986550.4996564090.161670.304679960.39504316450.04665866123.83793401720.16380256410.00030

1350.23819670080.5198321939-0.281640.3274084926-0.68816602230.03784966842.1845245361-0.2840651870.00059

136-1.946921587-1.8730567694-0.07386-2.3682351112-0.18048597870.108887471325.3362941742-0.07949199530.00038

1380.30713218450.3297136724-0.022580.1132356226-0.05517704040.0429208393.0942606926-0.02283262090.00000

139-0.16165472570.0527538806-0.21441-0.1987658441-0.52389961480.03676392142.0045950413-0.21615287660.00032

141-0.6416843534-0.4190842807-0.22260-0.7303022146-0.54391515250.04324541613.1563777924-0.22511366010.00048

142-0.3195738196-0.36472424860.04515-0.66906440260.11032342170.03884272282.35367751120.04556084420.00002

144-1.5917600393-0.8388311267-0.75293-1.2031565905-1.83975422380.06466733668.2907323837-0.77220934630.01270

145-0.7680209875-0.5033246875-0.26470-0.8252009749-0.64677572250.05506578095.7373809814-0.26957672830.00112

1470.48597681521.1029415131-0.616960.9842943549-1.50753068920.05058755354.6865229607-0.62653768060.00512

1480.95097303391.1111295223-0.160160.9935182929-0.39133653040.05782435466.4290447235-0.16341887410.00045

1501.09883403780.56234431270.536490.37529930471.31089305880.04317700123.14324545860.54252839090.00279

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the importance of improving collection and access to environmental data in the americas

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natural resources

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