Global demand for gold is another threat for tropical forests

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Global demand for gold is another threat for tropical forests

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2015 Environ. Res. Lett. 10 014006

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Environ. Res. Lett. 10 (2015) 014006 doi:10.1088/1748-9326/10/1/014006

LETTER

Global demand for gold is another threat for tropical forests

Nora LAlvarez-Berros1 andTMitchell Aide2

1 Department of Environmental Sciences, University of Puerto Rico-Ro Piedras POBox 70377 San Juan, Puerto Rico, 00936-8377, USA2 Department of Biology, University of Puerto Rico-Ro Piedras POBox 23360 San Juan, Puerto Rico, 00931-3360, USA

E-mail: [email protected] and [email protected]

Keywords: goldmining, deforestation, global economic crisis, protected areas, subsoil, land use

Supplementarymaterial for this article is available online

AbstractThe current global gold rush, driven by increasing consumption in developing countries and uncer-tainty innancialmarkets, is an increasing threat for tropical ecosystems. Goldmining causes sig-nicant alteration to the environment, yetmining is often overlooked in deforestation analysesbecause it occupies relatively small areas. As a result, we lack a comprehensive assessment of the spatialextent of goldmining impacts on tropical forests. In this study, we provide a regional assessment ofgoldmining deforestation in the tropicalmoist forest biome of SouthAmerica. Specically, we ana-lyzed the patterns of forest change in goldmining sites between 2001 and 2013, and evaluated theproximity of goldmining deforestation to protected areas (PAs). The forest covermapswere pro-duced using the LandMapperweb application and images from theMODIS satelliteMOD13Q1 vege-tation indices 250mproduct. Annualmaps of forest cover were used tomodel the incremental changein forest in1600 potential goldmining sites between 20012006 and 20072013. Approximately1680 km2 of tropicalmoist forest was lost in thesemining sites between 2001 and 2013. Deforestationwas signicantly higher during the 20072013 period, and this was associatedwith the increase inglobal demand for gold after the international nancial crisis.More than 90%of the deforestationoccurred in fourmajor hotspots: Guiananmoist forest ecoregion (41%), Southwest Amazonmoistforest ecoregion (28%), TapajsXingmoist forest ecoregion (11%), andMagdalenaValleymon-tane forest andMagdalenaUrabmoist forest ecoregions (9%). In addition, some of themore activezones of goldmining deforestation occurred inside orwithin 10 kmof32 PAs. There is an urgentneed to understand the ecological and social impacts of goldmining because it is an important cause ofdeforestation in themost remote forests in SouthAmerica, and the impacts, particularly in aquaticsystems, spreadwell beyond the actualmining sites.

Introduction

The deforestation of high diversity tropical ecosystemshas been mainly due to agricultural expansion, cattleranching, timber extraction, and urban expansion;and these activities have important consequences forthe global carbon budget, biodiversity loss, anddegradation of ecosystem services (Lambin et al 2003).In the last 1020 years, much of tropical deforestationhas been attributed to the growing economies ofdeveloping countries, particularly China. The increas-ing wealth in these countries is partly reected byincreased global demand for meat, which has beendirectly correlated with the expansion of croplands for

soybean production (animal feed) and grasslands formeat production in South America (Aide et al 2013).The deforestation associated with these land changesin SouthAmerica is on the scale ofmillions of hectares,which makes it easy to detect, but the increase indisposable income in developing countries can alsostimulate other causes of tropical deforestation thatare much more difcult to detect, specically goldmining.

Global gold production has increased from ~2445metric tons in 2000 to 2770 metric tons in 2013(USGS 2014). This increase has been driven by perso-nal consumption (e.g. jewelry), particularly in Chinaand India (World Gold Council 2012, Cremers

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et al 2013), and uncertainty in global nancial markets(e.g. value of the dollar and euro) (Shaee andTopal 2010). This increase in demand over the last13 years has been paralleled by a dramatic increase inprice (Shaee and Topal 2010). Over the last thirteenyears, the price of gold has increased from $250/ouncein 2000 to $1300/ounce in 2013 (gure 1(a); WorldGold Council 2012). This rise in global demand andthe price of gold have stimulated new gold miningactivities by multinational companies and small-scalegold miners throughout the world (Bury 2004,Creek 2009).

The high price of gold has made it feasible toextract gold from areas that were not previously prot-able for mining, including low-grade deposits under-neath tropical forests (Swenson et al 2011). In manycases, the mining of these deposits is characterized byunorganized occupation of lands and uncontrolledmining operations, causing signicant forest loss andenvironmental impacts (Hentschel et al 2002, Villegaset al 2012). Specically, goldmining impacts forests byremoving vegetation for mining pits, transportationaccess (roads, railways), and settlements. Small-scalemining operations also remove gallery forest to extractalluvial deposits of gold by using high-pressure waterjets to remove and process the soil (Almeida-Filho and

Shimabukuro 2002). Moreover, although gold miningis usually temporary and occupies relatively smallareas, mining effects and impacts are persistent. Long-lasting environmental effects of gold mining includeair, soil andwater pollution from arsenic, cyanide, andmercury (Eisler andWiemeyer 2004, Veiga et al 2006).Pollution and sediments from gold mining activitiestravel long distances through rivers and tributariesnegatively affecting water quality and access forhumans, sh and other wildlife (Uryu et al 2001). Fur-thermore, forest recovery after mining activities is sig-nicantly slower when compared to regeneration afterother land uses (e.g. agriculture, pasture) (PetersonandHeemskerk 2002).

As mining sites often occur in remote locations,they frequently coincide with protected areas (PAs)(Durn et al 2013) or areas of high biodiversity (Ville-gas et al 2012). Deforestation due to gold mining hasbecome amajor threat to some of themost remote andbetter-conserved old-growth forests in tropical SouthAmerica (Peterson and Heemskerk 2002, Asneret al 2013). For example, the department of Madre deDios (Peru), one of the most biologically rich areas onEarth, lost 400 km2 of forest due to gold miningbetween 1999 and 2012 (Asner et al 2013). In Sur-iname, estimates indicate that gold miners clear

Figure 1. (a)Global price of gold per ounce (USD) from January 1980 toMay 2014 (WorldGoldCouncil 2012). (b)Gold production( x 103 ounces) in Latin American countries from1970 to 2010 (Brown et al 2010)

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Environ. Res. Lett. 10 (2015) 014006 NLAlvarez-Berros et al

between 48 km2 and 96 km2 of old-growth forest peryear (Peterson andHeemskerk 2002).

Although the environmental costs of gold miningare high, it is a major contributor to the economies ofindustrialized and developing countries, as well as aprincipal source of income for many people. In LatinAmerican, the gold mining sector is growing rapidly(Bebbington and Bury 2013), with productionincreasing from414 000 ounces to 542 000 ounces ofgold in the last decade (gure 1(b)). In Peru, large-scale mining contributed an average of 6% to the GDPbetween 2000 and 2010 (World Gold Council 2012).In Colombia, the gold mining sector generates morethan 140 000 permanent jobs and an unknown num-ber of informal employments in small-scale miningoperations (International Labour Organization 2008).In addition, artisanal and small-scale gold miningemployed200 000 people in the Brazilian Amazon in2008 (Sousa et al 2011). In Suriname, gold miningsupports the livelihood of more than 60 000 people(12%of the population) (Cremers et al 2013).

As the global demand and price for gold continuesto increase (Shaee and Topal 2010), gold miningactivities will likely continue to increase in the tropicalforests of South America. Given this current gold rush,the known impacts of goldmining, and the presence ofgold mining in remote areas of high biodiversity, weurgently need better information on the distributionand impacts of gold mines in tropical forests. Toaddress these challenges, we identied potential goldmining sites below 1000 m within the tropical moistforest biome (TMFB) of South America. We then esti-mated forest cover change between 2001 and 2013using maps derived fromMODISMOD13Q1 imagery(250 m resolution). Specically, we addressed the fol-lowing questions: (1) what was the extent of forestchange associated with gold mining between 2001 and2013 in tropical forests of South America? (2) Whatwere the trends of forest change (e.g. deforestation andreforestation) before and after the InternationalFinancial Crisis of 20072008? (3) Where are the hot-spots of gold mining deforestation? and (4) Is goldmining occurringwithin or around PAs?

Methods

Study areaOur study encompasses the tropical and subtropicalmoist broadleaf forest biome in South America below1000 m (hereafter, TMFB) (Olson et al 2001), whichincludes the Amazonian lowlands, and extends intoColombia, Venezuela, Guyana, Suriname, FrenchGuiana, Brazil, Ecuador, Peru, and Bolivia (gure 2).The TMFB of South America has one of the highestdeforestation rates in the world (Hansen et al 2008,Asner et al 2009), mostly due to logging and theexpansion of cattle ranching and modern agriculture(Aide et al 2013). This region is rich in alluvial gold

deposits, and as gold mining reemerges as an impor-tant economic sector in the tropics, it has becomeanother important cause of deforestation and degra-dation of ecosystem services (Hammond et al 2007).

Goldmine geodatabaseTo understand forest cover changes associated withgold mining, we rst created a geographical databasethat included active or potential areas of gold extrac-tion obtained from government and private miningGIS databases (e.g. mining concessions, industrialmine locations) and by digitizing polygons aroundmining locations reported in peer-reviewed articles,news articles, and reports between 2000 and 2013(supplementary table 1). We also systematicallyreviewed all high and medium-resolution imagesavailable in Google Earth (very high resolution ima-gery (VHR) from Digital Globe and Landsat; from2001 to 2013), to include mining sites that were notreported in other sources. In addition to the extractionarea, the mining polygon encompassed other mining-associated activities inside or in the vicinity of theextraction site (e.g. roads, installations, settlements,and minor crop and grassland plots), given that theseland uses often occur simultaneously with mining.Mining polygons obtained from government GISlayers were incorporated in the database, but tominimize including mining concessions that were notbeingmined, we only included polygons located in themunicipalities that were top producers of gold as anindicator of active gold-mining production (see sup-plementary table 1).

Once completed, the geodatabase included a totalof 1606 polygons encompassing all sites (gure 2). Thedatabase mainly corresponded to surface miningoperations (open-pit, placer or alluvial) of differentscales of extraction: large-scale (i.e. highlymechanizedgold mining, industrial), medium-scale, and small-scale or artisanal mining (i.e. labor-intensive miningusing simple or artisanal technology and limitedmechanization). We did not record the legal status ofthe mining site. The gold mining sites were located in373 municipalities in Colombia, Peru, Suriname,Guyana, French Guiana, Brazil, Venezuela, andEcuador.

Forest covermappingTo map gold mining-related forest cover change, wecreated annual land cover maps derived from satelliteimages from 2001 to 2013. We used MODISMOD13Q1 Vegetation Indices product with 250 mresolution, distributed at no cost by the NationalAeronautics and Space Administration (NASA 2014).This product is a 16 days composite of the highest-quality pixels from daily images and includes theenhanced vegetation index (EVI), NDVI, red, nearinfrared (NIR), and mid-infrared (MIR), and pixelreliability with 23 scenes per year available from 2001

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to 2013 (Huete et al 2002). For each MODIS pixel, wecalculated annual statistics (mean, minimum, max-imum, kurtosis, skewness, and standard deviation) forEVI, NDVI, red, NIR, MIR reectance values forcalendar years 2001 to 2013. The MOD13Q1 pixelreliability layer was used to remove all unreliablesamples (value = 3) prior to calculating statistics.

We collected reference samples (9147 pixels) forclassier training and the accuracy assessment in thecustom web-based application Land Mapper (LandMapper 2014). Using this application, we overlaidMODIS pixel-grid (250 250m) on VHR imagery

fromDigital Globe in Google Earth.We assigned sam-ples of at least 2 2 pixel-grids to forest or to a non-forest class and recorded the image acquisition date foreach sample. Forest was dened as natural tree cover>2 m in height. Each forest sample had 100% forestcover.

To conduct the image classication we used theRandom Forests (RF) tree-based classier (Brei-man 2001) implemented using R (v. 2.12.2; R 2011),and the RandomForest package (v. 4.6-2; Liaw andWiener 2002), included in Land Mapper (Land Map-per 2014). To train the RF model, reference samples

Figure 2.The study site encompasses the tropical and subtropicalmoist broadleaf forest biome (Olson et al 2001)with elevations

collected fromhigh-resolution images inGoogle Earthwere paired with MODIS time series variables for thesame year. The 12 months statistics variables con-stituted the predictor variables in the RF classication.We used the RF per pixel probabilities to assign landcover classes. A pixel was assigned forest class if themaximum class probability was more than 60%; lessthan 60% probability was assigned a non-forest class(mixed-forest class) in a post-classication analysis.The nal RF model had an overall accuracy of 89%,with forest producers accuracy of 95% and forestusers accuracy of 98%. This model was used to con-struct annualmaps for each year from2001 to 2013.

We conducted a post-classication accuracyassessment of the forest versus non-forest classica-tion by comparing classied pixels with the corre-sponding high-resolution image in Google Earth andassigning a forest or non-forest class. Sampling selec-tion consisted of a stratied sampling method to bal-ance the number of samples per class, and wasrestricted to areas of high-resolution images that con-tained mining activities (see supplementary table 2).The sampling resulted in 791 pixels referenced tohigh-resolution images from 2003, 2004, 2005, 2006,2011, 2012, and 2013. The forest class had producersaccuracy values ranging from 86% to 100%, usersaccuracy values ranging from 82% to 98%, and anoverall accuracy of 92% (see supplementary table 2).

Forest cover dynamicsTo evaluate the patterns of forest cover change (i.e.deforestation and reforestation) within each miningsite, we analyzed the trends of forest cover area usingordinary least squares (OLS) linear regression modelsfollowing Clark et al (2012). This involved calculatingthe area of forest cover class for each of the miningpolygons in each of the 13 years. We tted an OLSlinear model of area versus time for each miningpolygon for the periods of 20012006 (n= 6) and20072013 (n= 7). These time periods were chosen tocapture patterns before and after the global nancialcrisis of 2007/08. To determine the strength of thelinear relationships, we used the coefcient of deter-mination (R2). We considered the trends signicant atp< 0.05 and used the slope of the line to determine thedirection of the trend, where positive values of theslope represent an increase in forest cover and negativevalues represent a decrease in cover. We used thisapproach to standardize forest cover change throughtime due to outliers or missing data in any given year.We report forest change as the difference in forestedarea between the beginning and ending year of eachperiod (e.g. 20062001), and we only includedminingpolygons that had a statistically signicant linear trend.These mining polygons highlight areas of forestchanges caused by gold extraction and activitiesassociated with mining sites (e.g. roads, settlements,small-scale agricultural and grazing activities). To

assess the proportion of other land use activitiesincluded in our analysis, we compared sample pointswithin our areas of signicant deforestation with high-resolution images in Google Earth. The samplingselection consisted of a random sampling methodrestricted to areas of signicant deforestation and>500 m distance between points, which resulted in204 points. The analysis indicated that 82% of thedeforestation points sampled were due to mining,13% to pasture, and 5%due to shifting river banks.

Spatial proximity betweenPAs and goldminingdeforestationTo analyze the spatial proximity of gold miningdeforestation with PAs we overlapped the distributionof PAs (IUCN and UNEP 2009) with sites that had asignicant trend of forest loss. We calculated forestloss within and around each PA (10 km buffer). Weused a 10 km buffer to capture mining activities withimmediate and regional effects of on PAs, followingDuran et al (2013). PAs were classied as: (1) Interna-tional Designation: Ramsar sites, UNESCO, WorldHeritage Sites; (2) Strict Protection: IUCN categories Iand II (which refer to areas managed mainly forscience and for ecosystem protection and recreation);(3)Multiple Use: IUCN categories IVVI, and includ-ing indigenous land usually managed for sustainableuse of natural resources (but with no IUCN category),and (4) Other: no IUCN category assigned, but somelevel of national protection existing.

CaveatsWe acknowledge that there are potential caveats to ourstudy. First, MODIS images will not detect forestchanges due to small and isolated mining activities(e.g. mining site

relatively infrequent revisit time (e.g. 16 d for Land-sat). In contrast, MODIS has the advantage of having ahigh temporal resolution (i.e. near daily revisit time)of imagery, which can be composited to reduce theamount of pixels adversely affected by cloud coverage(i.e. annual statistics in this study) (Clark et al 2012).Furthermore, the wide scenes provided by MODISsatellites (1200 1200 km) facilitate mapping overlarge areas. For example, our study site required 38MODIS scenes instead of 447 Landsat scenes(185 185 km), which reduced data processing, sto-rage and time. MODIS high-temporal resolution,combined with ancillary data on mining locations,provides the opportunity to highlight and monitorareas of rapid mining expansion through frequentvisiting times (e.g. every month) and to map miningactivities occurring simultaneously around tropicalcloudy areas of the world. Once these areas of rapidmining expansion are identied, higher resolutionimagery can be used to rene the extent of miningexpansion and expand the analysis to determine whatland uses are being converted to mining (e.g. agricul-tural lands being lost to gold mining activities). Ourmethodology could be best replicated in areas wheregold mining occurs amidst a background of denseforest (i.e. the tropical lowlands). Gold mining alsooccurs in areas of higher altitude (e.g. Andeanecoregions), however it is difcult to map with thedescribed methods given the sparse vegetation.Furthermore, we recognize that there is commercialimagery with the potential to generate more accurateresults in mapping mining expansion (e.g. high-resolution SAR data), but we wanted to limit our

analysis to freely available images (i.e. MODISproducts).

Second, some factorsmay contribute to the under-estimation of gold mining deforestation. For example,emerging mining sites (including illegal mining activ-ities) may be too recent to be identied using high-resolution images in Google Earth (see supplementarytable 1). Third, our mining polygons derived fromgovernment mining concessions may include othersources of deforestation besides those associated withgold; therefore, increasing our estimates of deforesta-tion associated with gold mining (see Forest coverdynamics section). This may occur because miningconcessions can overlap with concessions for logging,agriculture, grazing lands, and conservation (Scullionet al 2014). We reduced this error by only includingconcessions in municipalities that are producing gold(see supplementary table 1) with the assumption thatthese are more likely to be under gold mining produc-tion rather than dedicated to other uses. Given thesechallenges, our area calculations of gold mining defor-estation are approximate, but we believe that ourmethodology is detecting the hotspots of deforestationdue to goldmining and associated activities.

Results

Forest change associatedwith goldminingBetween 2001 and 2013, approximately 1680 km2 offorest was cleared and 245 km2 of forest regeneratedwithin gold mining sites in the lowlands of the TMFB(gures 3, 4). Forest loss and gain varied greatly

Figure 3.Distribution of goldmining sites with signicant change in forest cover (km2) in periods 20012006 and 20072013. Greendots represent an increase in forest cover, red dots represent a decrease in forest cover, and gray areas indicate no signicant change incover.

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between the two time periods. Between 2001 and2006, there was a loss of 377 km2 of forest at 61 goldmining sites; whereas between 2007 and 2013, the areaof forest loss quadrupled to 1303 km2 and thenumber of goldmining sites with signicant forest lossdoubled to 116 sites (gures 3, 4). Forest regrowthdeclined between the two periods. Between 2001 and2006, there was regrowth of 178 km2 of forest at 20gold mining sites; whereas in 20072013, regrowthdecreased to 67 km2 and the number of gold miningsites dropped to 19 sites (gures 3, 4).

Gold mining sites with signicant deforestationwere distributed across the TMFB. Most forest loss(89%) occurred in four regions (described below), andthe remaining 11% of forest loss occurred at goldmining sites across 11 other ecoregions. (gure 3;table 1).

The Guianan moist forest ecoregion lost684 km2 of forest to gold mining activities (repre-senting 41% of the total gold mining deforestation inthe entire TMFB) (table 1). The majority of the defor-estation in this ecoregion was concentrated in the Sur-iname municipalities of Brokopondo and Sipaliwini(gure 5).

In the Southwest Amazon moist forest ecoregion,473 km2 of forest was cleared at gold mining sites(representing 28% of gold mining deforestation in theTMFB) (table 1). Most of this deforestation occurredin the municipalities of Inambari, Madre de Dios, andHuepetuhe in the Department of Madre de Dios(Peru) (gure 5).

The TapajsXing moist forest ecoregion lost183 km2 of forest at gold mining sites (representing11% of gold mining deforestation in the TMFB). Sev-eral patches of gold mining deforestation were foundwithin this ecoregion, with many concentrated in themunicipality of Itaituba (Brazil) (gure 5).

The Magdalena Valley-Urab region lost144 km2 of forest at gold mining sites (representing9% of gold mining deforestation in the TMFB)(table 1). The majority of the deforestation was

concentrated in the municipalities of Zaragoza, ElBagre, and Segovia in the Department of Antioquia(Colombia) (gure 5). Signicant sites of the refor-estation in this ecoregion occurred in the munici-palities ofNech andCaucasia (gure 3).

Gold-mining deforestation in and aroundPAsMany gold mining sites occurred in or around PAs.Gold-mining deforestation inside PAs occurred pre-dominantly in multiple use zones (94%), and was lesscommon inside strict protection areas (6%) (gure 6,supplementary table 3). Signicant forest loss due togoldminingwas found inside 13multiple use zones, in14 strict protection areas, and in 1 PA categorized asother. The Rio Novo National Park (Brazil) had thegreatest loss (12 km2) inside a strict protection area(supplementary table 3), and the Tapajs Environ-mental Protection Area (Brazil) had the greatest loss(142 km2) inside a multiple use zone (gure 5;supplementary table 3).

Although there was little deforestation inside strictprotection areas (15 km2), 31% of the total defor-estation occurred within their 10 km buffer zone(172 km2) (gure 6, supplementary table 3). The twoPAs with the most deforestation in their buffer zoneswere Rio Novo National Park (84 km2) in Brazil andthe Bahuaja Sonene National Park (27 km2) in Peru(supplementary table 3). Themajority (58%) of defor-estation within 10 km buffer zones occurred sur-rounding multiple use zones (332 km2) (gure 6).The two multiple use PAs with the most forest loss intheir buffer zones were the Communal Reserve Amar-akaeri (103 km2) and the Tambopata NationalReserve (66 km2) in Peru (gure 5; supplementarytable 3).

Discussion

In this study, remote sensing analyses combined withancillary information revealed widespread goldmining deforestation throughout the TMFB of South

Figure 4.Number of goldmining sites with signicant change in forest cover (p< 0.05) and area (km2) of forest change (loss/gain).Histogram values indicate corresponding number of goldmining sites.

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America. Between 2001 and 2013, gold miningresulted in the loss of approximately 1680 km2 offorest of gold mining sites showing a signicant trendof deforestation. Furthermore, our analysis showedthat deforestation due to gold mining increased in

extent after the international nancial crisis of20072008.

Given unprecedented high gold prices, miningactivities have increased throughout the tropics whereit has become protable to extract the gold that lies in

Figure 5. Forest loss associatedwith goldmining activities inmunicipalities and protected areas within the four hotspots of goldmining deforestation. Scatter plots show total forest area versus time (from2001 to 2013)within themining polygons (outlined ingray). Themining polygons encompass areas of goldmining activity including associated land uses (roads, installations, settlements,and crop and grassland plots).

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the subsoil of the forest, thus promoting deforestation(Swenson et al 2011). Although gold mining defor-estation is usually smaller in extent than other tropicalforest land-uses, gold mining is currently one of theleading causes of forest loss in some of the mostimportant tropical forests of South America. A largeextent of this deforestation occurred within andaround multiple use or strict protection areas. Belowwe discuss the land change dynamics of the four hot-spots of gold-mining related forest loss, and the envir-onmental implications of goldmining deforestation inproximity to PAs.

Guianan forestsSuriname,Guyana, FrenchGuiana andVenezuelaThe Guianan moist forest ecoregion had the largestproportion of deforestation (41%) of the four goldmining hotspots. This region is renowned for itsgeological formations rich in deposits of gold, dia-monds, iron and bauxite, andmining has been amajorland use and cause of deforestation (e.g. 68% of thetotal deforestation in Guyana between 2001 and 2010;(Guyana Forestry Commission 2011). Between 1990and 2004, gold mining activities expanded rapidly inthis region because of liberalization of the interna-tional gold market and the inux of Brazilian minersafter increased national enforcement of tribal landintegrity and land-use laws (Butler 2006, Hammondet al 2007). Small- and medium-scale operationsaccounted for the majority of this deforestation, but

large-scale operations, presumably operating understrict regulations, are also causing forest loss in theregion (Hammond et al 2007). Our results demon-strate that the expansion of gold mining in this regionis continuing at a rapid rate and often occurring inareas of high conservation priority (e.g. Tepuis inVenezuela, Brownsberg Nature Park in Suriname)(Peterson andHeemskerk 2002,Hammond et al 2007)(gure 5).

Southwest Amazon in PeruMost of the gold-mining deforestation in the South-west Amazon moist forest ecoregion is occurring inthe Department of Madre de Dios around theBahuaja-Sonene National Park (strict protection), theCommunal Reserve Amarakaeri (multiple use), andthe Tambopata National Reserve (multiple use)(gure 5; supplementary table 3). High-resolutionsatellite data ofMadre de Dios showed that the averageannual rate of forest loss related to gold mining tripledbetween 19992007 and 20082012 (from21.66 km2 yr1 to 61.56 km2 yr1, respectively) (Asneret al 2013). In this region, agricultural expansion wasthe major driver of forest changes from 2001 to 2006,but after 2007, artisanal and small-scale gold miningexpansion was the predominant land change (Scullionet al 2014). The shift from agriculture to goldmining isnot surprising given the sizeable increase in income($1518 USD daily as a farm laborer to $10230 USDdaily for a typical miner; see Keane (2009), Scullionet al (2014)).

TapajsXingmoist forest in BrazilThe TapajsXing moist forest ecoregion containsthe largest extractive reserve for artisanal and small-scale mining in Brazil, and it is the most importantgold producing region in this country (Gonalo deMiranda et al 1997, Sousa and Veiga 2009). Thedramatic rise in the price of gold has resulted in therecolonization of small-scale mining across the Brazi-lian Amazon because mining areas deemed exhaustedof gold are now protable. From the 1990s to 2010, thenumber of small-scaleminers in the Brazilian Amazonhas increased ten-fold (from 20 000 to 200 000)(Cremers et al 2013), and since 2008, the TapajsXing moist forest ecoregion has experienced aninux of thousands of new gold miners (e.g. up to5000 new garimpeiros in the municipality of Itaituba;see Carvalho (2013)) (gure 5). Although new PAshave been created in this ecoregion, the presence ofgold and the large inux of goldminers will likely havelarge impacts in and around these PAs. The TapajsEnvironmental Protection Area was the multiple useprotected area with the greatest loss of forest, butmining is ofcially permitted in this PA (Cremerset al 2013). Our visual observation of satellite imageryindicated that forest loss in this PA is often caused by

Figure 6.Percentage of forest loss due to goldminingoccurring inside protected areas (PAs) andwithin a 10 kmbuffer surrounding PAs between 2001 and 2013.Only goldmining sites with signicant trends of deforestationwereincluded in this analysis. Categories of PAswere dened asfollow: (1) International Designation: Ramsar sites, UNESCO,WorldHeritage Sites; (2) Strict Protection: IUCN categories Iand II (which refer to areasmanagedmainly for science andfor ecosystemprotection and recreation); (3)Multiple Use:IUCN categories IVVI, and including indigenous landusuallymanaged for sustainable use of natural resources (butwith no IUCN category), and (4)Other: no IUCNcategoryassigned, but some level of national protection existing.

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grazing activities occurring simultaneously with goldmining activities.

MagdalenaValley-UrabBetween 2001 and 2010, the Magdalena Valley-Urabregion (i.e. Magdalena Valley montane forest andMagdalenaUrab moist forest ecoregions) has been ahotspot of deforestation in Colombia due to oilexploration, cattle ranching, small-scale agriculture,and gold mining (Snchez-Cuervo et al 2012). Goldmining has been an important economic activity inthis region since 1990, but as in the other regions it hasexpanded rapidly in the last ten years (Mass andCamargo 2012). This region is unique due to thepresence of guerrilla and paramilitary groups who areusing mining as a new source of income (Mass andCamargo 2012). Up to 20% of the prots from illegalmining in Colombia goes to the guerilla and para-military groups (e.g. Revolutionary Armed Forces ofColombiaFARC and National Liberation ArmyELN), and 86% of gold production in Colombia isestimated to be illegal (Mass and Camargo 2012).Interestingly, the presence of paramilitary groups wascorrelated with reforestation in certain areas (Sn-chez-Cuervo and Aide 2013). Our analysis detectedreforestation in the municipalities of Nech andCaucasia, areas of ongoing conict, which can lead toforced human displacement and the subsequentabandonment of agricultural lands (Snchez-CuervoandAide 2013).

Goldmining coincideswith remote areas that areimportant for conservationAlthough therewas little deforestationwithin the strictPAs, themining operations in the buffer zones can stillhave serious consequences due to the far-reachingimpacts known to affect water, soil, and vegetation.For example, ecological and environmental effectscaused by industrial mining activities of several metalminerals (e.g. copper, zinc) have been reported up to50 km from mines (Durn et al 2013) and elevatedmercury concentrations have been found in humanshundreds of kilometers away fromgoldmining centers(Frry et al 2001, Ashe 2012). Furthermore, increasedsedimentation in water bodies (Mol and Oubo-ter 2004), heightened wildlife stress resulting frommercurial biomagnication (Eisler 2004), noise pollu-tion (Francis and Barber 2013), increased hunting(Villegas et al 2012), and the degradation of vegetationdue to various chemical pollutants (Eisler and Wie-meyer 2004) act to compound goldmining impacts onsurrounding ecosystems.

Conclusion

Tropical deforestation studies have traditionally ana-lyzed forest loss due to agricultural expansion, cattleranching, and urban growth, but less attention has

been given to deforestation related to extractiveactivities of the subsoil such as mining (Bebbingtonand Bury 2013, Sonter et al 2013). Our studycontributes to the understanding of gold-miningdeforestation in the tropical rain forest biome in SouthAmerica, and shows that deforestation has been animportant consequence of the global demand for gold.While deforestation due to other land changes hasdecelerated in this region (Nepstad et al 2014), gold-driven deforestation accelerated after the global eco-nomic crisis of 2007. Furthermore, most of the gold-mining deforestation has been concentrated in remoteareas, which have high conservation value.

Acknowledgements

NSF IGERT (Number 0801577) and NSF GRFPprovided nancial support to N. A-B. We thank SergeAucoin, Paul Furumo, Ricardo Grau, AnaM Snchez-Cuervo, and Sebastin Martinuzzi for their commentsto previous versions. We also thank Jordan Graesser,Carlos J Corrada-Bravo, Rafael lvarez-Berros, andGiovany Vega for their assistance with satellite imageprocessing. Valuable insights and improvements onthe manuscript were provided by anonymousreviewers. Finally, we would like to thank the Agenciade Regulacin y Control Minero in Ecuador forproviding access to mining information, and EddyMendoza and Arelis Arocho for their assistance withdata gathering.

References

Aide TM,ClarkML,GrauHR, Lpez-CarrD, LevyMA, RedoD,Bonilla-MohenoM,Riner G, Andrade-NezM J andMuizM2013Deforestation and reforestation of LatinAmerica and theCaribbean (20012010)Biotropica 4526271

Almeida-Filho R and Shimabukuro Y E 2002 Digital processing ofa Landsat-TM time series formapping andmonitoringdegraded areas caused by independent goldminers, Ror-aima State, Brazilian Amazon Remote Sens. Environ. 794250

AsheK2012 Elevatedmercury concentrations in humans ofMadredeDios, Peru PLoSOne 7 e33305

AsnerGP, LlactayoW, Tupayachi R and Luna ER 2013 Elevatedrates of goldmining in theAmazon revealed through high-resolutionmonitoring Proc. Natl Acad. Sci. USA 2 184549

AsnerGP, Rudel TK, Aide TM,DeFries R S and EmersonR 2009Acontemporary assessment of change in humid tropical forestsConserv. Biol. 23 138695

BebbingtonA J andBury J 2013 Subterranean Struggles: NewDynamics ofMining, Oil, andGas in Latin America (Austin,TX:University of Texas Press)

Breiman L 2001Random forestsMach. Learn. 45 532BrownT J,Walters S A, IdoineNE, ShawRA,WrightonCE and

Bide T 2010WorldMineral Production (Keyworth, Notting-ham: BritishGeological Survey)

Bury J 2004 Livelihoods in transition: transnational goldminingoperations and local change in Cajamarca, PeruGeogr. J. 1707891

Butler R 2006Mining inVenezuelanAmazon threatens biodiversity,indigenous groupsMongabay (http://news.mongabay.com/2006/1109-atbc.html)

10


CarvalhoC2013Trsmil garimpos clandestinosnoPar ameaamRioTapajsOGlobo (http://oglobo.globo.com/brasil/tres-mil-garimpos-clandestinos-no-para-ameacam-rio-tapajos-8710538)

ClarkML, Aide TMandRinerG 2012 Land change for allmunicipalities in Latin America and theCaribbean assessedfrom250 mMODIS imagery (20012010)Remote Sens.Environ. 126 84103

CreekS2009Moving frontiers in theAmazon:Brazilian small-scale goldminers in surinameEur.Rev. Lat.Am.Caribb. Stud.87525

Cremers L, Kolen J andTheijeMD2013 Small-scale goldmining intheAmazon ed LCremers et al (Amsterdam:Center for LatinAmerican Studies andDocumentation) (www.cedla.uva.nl/50_publications/pdf/cuadernos/cuad26.pdf#page=15)

DurnAP, Rauch J andGastonK J 2013Global spatial coincidencebetween protected areas andmetalmining activitiesBiol.Conserv. 160 2728

Eisler R 2004Mercury hazards fromgoldmining to humans, plants,and animalsRev. Environ. Contam. Toxicol. 181 13998

Eisler R andWiemeyer SN 2004Cyanide hazards to plants andanimals from goldmining and relatedwater issuesRev.Environ. Contam. Toxicol. 183 2154

Francis CD andBarber J R 2013A framework for understandingnoise impacts onwildlife: an urgent conservation priorityFront. Ecol. Environ. 11 30513

FrryN,Maillot E, DeheegerM,Mrona BD andBoudouA 2001Gold-mining activities andmercury contamination of nativeamerindian communities in FrenchGuiana: key role ofsh indietary uptakeEnviron. Health Perspect. 109 44956

Gonalo deMiranda J, CirpianiM,Mrtires RAC andGiaconiW J1997Atividades Garimpeiras no Brasil: Aspectos Tcnicos,Econmicos e Sociais ed R LCdos Santos (Rio de Janeiro,Brazil: Centro de TecnologiaMineral)

Guyana Forestry Commission 2011GuyanaREDD+MonitoringReporting andVerication System (MRVS); InterimMeasuresReport (Georgetown, Guyana: Guyana ForestryCommissionand PyryManagementConsulting)

HammondDS,GondV, de Thoisy B, Forget P-M andDeDijn B PE2007Causes and consequences of a tropical forest gold rushin theGuiana Shield, SouthAmericaAmbio 36 66170

HansenMC et al 2013High-resolution globalmaps of 21st-centuryforest cover change Science 342 8503

HansenMC et al 2008Humid tropical forest clearing from2000 to2005 quantied by usingmultitemporal andmultiresolutionremotely sensed dataProc. Natl Acad. Sci. USA 105 943944

Hentschel T,Hruschka F and PriesterM2002Global Report onArtisanal and Small ScaleMining (http://pubs.iied.org/pubs/pdfs/G00723.pdf)

Huete AR,DidanK,Miura T, Rodriguez E P,GaoX andFerreira LG 2002Overview of the radiometric and biophysi-cal performance of theMODIS vegetation indicesRemoteSens. Environ. 83 195213

International LabourOrganization 2008 Employment by economicactivity LABORSTALabour Stat. Database (http://laborsta.ilo.org/)

IUCNandUNEP 2009TheWorldDatabase on Protected Areas(WDPA) (Cambridge, UK)www.protectedplanet.net

Keane L 2009Rising prices spark a new gold rush in PeruvianAmazonWashingt. Post. (www.washingtonpost.com/wp-dyn/content/article/2009/12/18/AR2009121804139.html)

Lambin E F, GeistH J and Lepers E 2003Dynamics of land-use andland-cover change in tropical regionsAnnu. Rev. Environ.Resour. 28 20541

LandMapper 2014 LandMapperWeb-application (http://mapper.arbimon.com/)

LiawA andWienerM2002Classication and regression byrandomforestR.News 2 1822

Mass F andCamargo J 2012Actores Armados Ilegales y SectorExtractivo en Colombia (BogotD.C., Colombia: CITpaxColombia, Observatorio Internacional)

Mol JH andOuboter P E 2004Downstream effects of erosion fromsmall-scale goldmining on the instreamhabitat and shcommunity of a small neotropical rainforest streamConserv.Biol. 18 20114

National Aeronautics and Space Administration (NASA) 2014MODISMOD13Q1Veg. Indices Prod. (http://modis.gsfc.nasa.gov/data/dataprod/dataproducts.php?MOD_NUMBER=13)

NepstadD et al 2014 SlowingAmazon deforestation through publicpolicy and interventions in beef and soy supply chains Science344 111823

OlsonDM et al 2001Terrestrial ecoregions of theworld: a newmapof life on EarthBioscience 51 9338

PetersonGD andHeemskerkM2002Deforestation and forestregeneration following small-scale goldmining in theAma-zon: the case of surinameEnviron. Conserv. 28 11726

Snchez-CuervoAMandAide TM2013Consequences of thearmed conict, forced human displacement, and landabandonment on forest cover change inColombia: amulti-scaled analysisEcosystems 16 105270

Snchez-CuervoAM,Aide TM,ClarkML and Etter A 2012 Landcover change inColombia: surprising forest recovery trendsbetween 2001 and 2010PLoSOne 7 e43943

Scullion J J, Vogt KA, Sienkiewicz A,Gmur S J andTrujillo C 2014Assessing the inuence of land-cover change and conictingland-use authorizations on ecosystem conversion on theforest frontier ofMadre deDios, PeruBiol. Conserv. 17124758

Shaee S andTopal E 2010An overview of global goldmarket andgold price forecastingResour. Policy 35 17889

Sonter L J, Barrett D J,MoranC J and Soares-Filho B S 2013A landsystem sciencemeta-analysis suggests we underestimateintensive land uses in land use change dynamics J. LandUseSci. 00 114

Sousa RN andVeigaMM2009Using performance indicators toevaluate an environmental education program in artisanalgoldmining communities in the BrazilianAmazonAmbio 38406

Sousa RN,VeigaMM,Meech J, Jokinen J and Sousa A J 2011Asimpliedmatrix of environmental impacts to support anintervention program in a small-scalemining site J. Clean.Prod. 19 5807

Swenson J J, Carter CE,Domec J-C andDelgadoC I 2011Goldmining in the PeruvianAmazon: global prices, deforestation,andmercury importsPLoSOne 6 e18875

TropekR, SedlekO, Beck J, Keil P,Musilov Z, Smov I andStorchD2014Comment on high-resolution globalmaps of21st-century forest cover change Science 344 981

UryuY,MalmO, Thornton I, Payne I, ClearyD and Payne I AN2001Mercury contaminationwildlife of sh its implicationsfor otherwildlife of the Tapajs Basin, Brazilian AmazonConserv. Biol. 15 43846

USGS 2014Gold Statistics and Information (http://minerals.usgs.gov/minerals/pubs/commodity/g)

VeigaMM,MaxsonPA andHylander LD 2006Origin andconsumption ofmercury in small-scale goldmining J. Clean.Prod. 14 43647

Villegas BC,Weinberg R, Levin E andHundK 2012Artisanal andsmall-scalemining in protected areas and critical ecosystemsprogramme (ASM-PACE) (Cambridge, UK: Estelle LevinandWWF) (www.profor.info/sites/profor.info/les/docs/ASM_PACE-GlobalSolutions.pdf)

WorldGoldCouncil 2012WorldGoldCouncil (www.gold.org/)

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IntroductionMethodsStudy areaGold mine geodatabaseForest cover mappingForest cover dynamicsSpatial proximity between PAs and gold mining deforestationCaveats

ResultsForest change associated with gold miningGold-mining deforestation in and around PAs

DiscussionGuianan forestsSuriname, Guyana, French Guiana and VenezuelaSouthwest Amazon in PeruTapajs-Xing moist forest in BrazilMagdalena Valley-UrabGold mining coincides with remote areas that are important for conservation

ConclusionAcknowledgementsReferences

Documents

Global demand for gold is another threat for tropical forests