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Applied Geography 40 (2013) 199e211

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Applied Geography

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High rates of forest loss and turnover obscured by classical landscapemeasures

Adison Altamirano a,*, Paul Aplin b, Alejandro Miranda a, Luis Cayuela c, Adam C. Algar b,Richard Field b

a Laboratorio de Análisis Cuantitativo de Recursos Naturales, Departamento de Ciencias Forestales, Universidad de La Frontera, P.O. Box 54-D, Temuco, Chileb School of Geography, University of Nottingham, Nottingham NG7 2RD, UKcÁrea de Biodiversidad y Conservación, Universidad Rey Juan Carlos, Departamental 1 DI. 231 c/ Tulipán s/n. E-28933 Móstoles, Madrid, Spain

Keywords:ConservationData constraintsDeforestationFragmentationHabitat turnoverLandscape indicesRemote sensing

* Corresponding author: Tel.: þ56 45 325658; fax:E-mail addresses: adison.altamirano@ufrontera.cl

nottingham.ac.uk (P. Aplin), alejandro.miranda@luis.cayuela@urjc.es (L. Cayuela), adam.algar@notrichard.field@nottingham.ac.uk (R. Field).

0143-6228/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.apgeog.2013.03.003

a b s t r a c t

Loss of native forest is a key conservation concern globally, for reasons of biodiversity, climate changeand ecosystem services. Landscape measures are used widely to characterize forest loss and associatedlandscape structure, but often without regard for structure imposed by the data used, and associatedassumptions. Notably, forest loss is often expressed simply as net change in forest cover over time, butthis approach does not account for turnover (i.e. the gross losses and gains of forest). It also ignores forestage (e.g. young regrowth forest or mature forest), which is significant in conservation terms. Weinvestigate the effects of removing common data constraints on landscape characterization, as typicallyused in landscape dynamic analyses. We produced fine-resolution (0.0225 ha) classified maps fromsatellite imagery of the temperate Araucanía Region of Chile, for 1986, 1999 and 2008. We calculatedareas of land-use classes and associated landscape indices. Landscape measures and trends through timevaried markedly around the region, with forest loss and fragmentation confined to areas not designatedas protected. Net (‘headline’) figures masked very large turnover through time, with about 30% of un-protected land switching land use each decade. Accounting for this, in unprotected areas the loss ofestablished native forest was 2.4% and 3.5% per year in the two time periods, much higher thanequivalent ‘standard’ figures. Using finer-resolution data increased estimates of native forest loss andreversed temporal trends in patch density and mean patch size, compared with the commonly-usedNational Vegetation Classification (6.25 and 4.5 ha resolution). Interestingly, mean patch size of nativeforest actually switched, from a decreasing trend to an increasing one, with continued deforestation. Weconclude that landscape characterization can lead to effective conservation practices, but it is necessaryto use appropriate data resolution, define the data domain carefully and examine change through time,including the degree of dynamism (turnover) within the landscape: our results suggest a strong need toconsider continuity of forest cover as well as overall totals.

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1. Introduction

Habitat loss is a serious threat to ecosystems (Foley et al., 2005),and forest loss in particular is considered a main driver of globalbiodiversity loss (Fahrig, 2003), carbon emissions and climatechange (IPCC, 2007). Estimates of forest loss are typically based onclassified remotely sensed imagery andmeasured as the net change

þ56 45 325634.(A. Altamirano), paul.aplin@ufrontera.cl (A. Miranda),tingham.ac.uk (A.C. Algar),

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in forest cover over time. This approach does not quantify turnover(i.e. the gross losses and gains of forest). Nor does it distinguishbetween young regrowth forest and mature forest, beyond the landcover class definition being useddthat is, once regrowth reachesthe stage of being classified as ‘forest’, it is usually not distinguishedfrom older forest. This is particularly important when new habitat,such as young secondary forest forming by ecological successionfrom abandoned farmland, is of lower conservation value thanolder habitat in the same class, such as primary forest.

Habitat loss also affects landscape pattern, with significantecological consequences that typically include decreased habitatarea, increased physical separation of individuals of each speciesand degraded habitat quality, for example through increased edgeeffects (Fischer & Lindenmayer, 2007). Various landscape indices

A. Altamirano et al. / Applied Geography 40 (2013) 199e211200

are in widespread use to detect change in spatial pattern of habitatpatches at a range of different levels (e.g. landscape- and class-level). Most of the measures of spatial pattern are related in someway to the amount of habitat. For example, both mean patch sizeand mean patch isolation are affected by the amount of habitat(Fahrig, 2003), which usually implies that continued habitat lossleads to smaller andmore isolated patches. Another typical effect ofhabitat loss is an increase in the number of patches until athreshold is reached, followed by a decrease (Trani & Giles, 1999).

Most conservation strategies are informed by overall measures ofhabitat loss and, for fragmented landscapes, are typically based onmanipulation of landscape pattern. For example, conservation stra-tegies oftenaim to increase the connectivityof remnant small patchesby restoring, creating or expanding protected areas, and to maintainand monitor large patches to conserve species populations (e.g.Anand, Krishnaswamy, Kumar, & Bali, 2010; Etter, McAlpine, Phinn,Pullar, & Possingham, 2006; Smith-Ramírez, 2004). Therefore,many studies have used landscape indices as measures of habitatfragmentation, to feed into conservationplanning tools and strategies(Johst et al., 2011; Lindenmayer, Cunningham, Donnelly, & Lesslie,2002; Sundell-Turner & Rodewald, 2008; Theobald et al., 2000).Within these, it is typically assumed that mean patch size decreases,and mean patch isolation increases, as habitat is lost.

In most cases, application of such conservation tools or strate-gies involves empirical data that are constrained in at least fivepotentially significant ways. First, the domain of the dataset (extentof the study area) may be defined by availability or convenience, aswhen the dataset derives from a satellite sensor image or existingdata organized by political units, such as administrative regions.Often the available dataset is used without much consideration ofwhether its domain is appropriate for the use to which it is beingput. Second, the data typically represent a snapshot in time, anexample again being when the dataset is derived from a satelliteimage, or a single period of change, such as when two satelliteimages are compared. Third, usually only one spatial resolution (or‘grain size’ or ‘focus’; Scheiner, 2011; Whittaker, Willis, & Field,2001) of data is used. The ‘modifiable areal unit problem’ is wellknown: that the impression we get of an area depends on how weview it. For example, the landscape pattern can vary considerablywith the resolution of the data (Openshaw, 1984). Typically theresolution is determined by the provenance of the data andmay notbe appropriate for the analysis (Harris & Jarvis, 2011). Specifically,the resolution used is often quite coarse, limiting the ability toinclude small habitat patches. Fourth, the thematic detail providedby the classification scheme employed may be relatively general,such as when the land use or land cover classes do not distinguishbetween different forest types (old growth, newgrowth, plantation,etc.). Fifth, reliance on ‘headline’ metrics (common metrics used inlandscape characterization) such as overall habitat loss, or meanpatch size and isolation, may mask important information aboutdynamic change within the landscape. In particular, net changesmay mask fluctuation through time and space; many landscapesmay be much more dynamic than is commonly appreciated. Allthese constraints bring with them assumptions that are often notstated, or even considered, and may vary according to the tool orstrategy being used.

Here we test the influence of four of the constraints outlinedabove, asking whether land cover/use patterns and spatial structureanalyses, as typically used, are able to detect the nature of change atthe local level. Specifically, we investigate the effects on land cover/use patterns and spatial structure of (1) using an image-defineddomain of the data (determined by the entire image area withingeneral political boundaries) compared with a conservation-baseddomain (delimited by national park boundaries); (2) using snapshotdata compared with examining a trajectory through time; (3) using

typical (coarser) spatial resolution compared with the relatively fineresolutionof ourdata; and (4) usingonly ‘headline’metrics comparedwith supplementing the metrics with examination of continuity ofhabitat through time. Given that the main conservation issue is theloss of native forest to exotic forest plantations, we do not investigatethe constraint of coarse thematic resolution, except in testing theability of our own classification to accurately distinguish betweenthese different forest types. Our primary focus is on the turnover ofhabitat within the landscape (i.e. changes in land cover/use at aconstant location through time). Influences of the other constraintshave been tested to some extent (e.g. Lechner, Langford, Bekessy, &Jones, 2012; Shao & Wu, 2008; Zhao, Bergen, Brown, & Shugart,2009). Nonetheless, when examining turnover, it is useful to do soin relation to the effects of the other constraints in the context of ourdata.We also consider the issue of temporal changes inmetrics to beunder-researched; this forms our secondary focus.

We use fine spatial resolution data derived from a series ofsatellite images in the temperate Araucanía Region of Chile,including part of the Chilean Coastal Range. We focus on nativeforests, which form the primary conservation issue. This region hasbeen promoted as a primary target for new conservation effortsbecause of the high levels of both species endemism and extinctionthreat (Smith-Ramírez, 2004). Both rapid deforestation and frag-mentation have been reported in the threatened temperate forestsof south-central Chile (Echeverría et al., 2006), which are includedamong the 25 global hotspots for biological diversity identified byMyers, Mittermeler, Mittermeler, Da Fonseca, and Kent (2000).Recent research on forest loss effects in the temperate ecosystemsof Chile has led researchers and practitioners to propose theadoption of conservation strategies involving expansion of pro-tected areas, restoration of native forests and the connection ofremnant forest patches using corridors (Smith-Ramírez, 2004).

2. Methods

2.1. Study area

The study area is in the Araucanía Region of Chile (Fig.1), coveringmuch of the municipality of Angol and including part of the centralvalley and the coastal range (Nahuelbuta Range). The study area isapproximately 120,000 ha, with elevations ranging between 60 and1200m.a.s.l. Nahuelbuta National Park, covering about 6800 ha, is inthe west (Fig. 1). The climate is warm temperate, with mean annualprecipitation 1500e2000mmandmean annual temperature around12 �C (DirecciónMeteorológica de Chile, 2011). The native forests aredominated byNothofagus tree species, principallyNothofagus obliquaand N. dombeyi (Luebert & Pliscoff, 2006). Many endangered species,such as Araucaria araucana, Lycopodium paniculatum, Lophosoriaquadripinnata, Citronella mucronata and Ribes integrifolium, alsooccur in the study area (Hechenleitner et al., 2005).

The main economic activities in the Angol municipality areforestry, agriculture and tourism. Forestry is characterized by com-mercial timber plantations of exotic species such as Pinus radiata andEucalyptus spp. In the temperate forests of Chile, conversion to forestplantation is considered the main driver of deforestation and frag-mentation (Altamirano & Lara 2010; Echeverría et al., 2006). In thelast 30 years, forestry has increased sharply through the establish-ment of commercial plantations of exotic species to supply rawmaterial to the pulp and timber industry, facilitated through affor-estation incentives (Lara et al., 2006).

2.2. Data acquisition and classification

We used images from Landsat Thematic Mapper (TM, 1986),EnhancedThematicMapper Plus (ETMþ,1999)andASTER (Advanced

Fig. 1. Map of study area in the Nahuelbuta Range in south-central Chile.

Table 1Description of land use/cover classes defined in the study area.

Land use/cover class Description

Native forest Vegetation with native tree species >2 mheight, >25% canopy cover, including oldgrowth forests with species as Nothofagusdombeyi, Araucaria araucana, Cryptocarya alba,Persea lingue, Gevuina avellana, and secondaryforest with mainly Nothofagus obliqua asdominant species.

Shrubland Vegetation with native species <2 m height,tree cover <25%, and shrubland cover of 10e75%.

Forest plantation Vegetation with planted exotic species likePinus radiata and Eucalyptus sp., including youngand harvested plantations.

Agriculture Crops of wheat, maize and vegetables. Alsoincluding annual and semi-annual pastures.

Urban Land occupied by cities, industry and otheranthropogenic surfaces.

Bare land Cleared land, rocks and river beds.Water Land occupied by water bodies such as small

lakes and ponds.

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Spaceborne Thermal Emission and Reflection Radiometer, 2008) tocharacterize change over 22 years, divided into two periods: 1986e1999 and 1999e2008. To minimize any differences in species’phenology, all images were acquired in the austral summer season(December to February).

We homogenized the spatial resolution of the images toenable direct comparability, our central goal being to use me-dium spatial resolution satellite sensor imagery for investigatingforest change at a minimum mapping unit (MMU) of 0.5 ha. Wefirst tested the effect of re-sampling to both 15 m and 30 m (i.e.the 15 m ASTER image was re-sampled to 30 m and the 30 mLandsat images were re-sampled to 15 m), performing land coverclassification, change detection and landscape metrics at bothscales of observation, but the comparative results (i.e. between15 m and 30 m resolution) exhibited no noticeable differences.This is unsurprising given that different medium spatial resolu-tion satellite sensors such as ASTER and Landsat share broadlysimilar mapping capabilities and because the MMU of 0.5 haused for subsequent analysis is considerably larger than eitherimage spatial resolution. Finally, therefore, we re-sampled theLandsat images to match the 15 m spatial resolution of the ASTERimages, adopting the approach used by Cayuela, Rey Benayas,and Echeverría (2006), Echeverria et al. (2006) and Staus,Strittholt, Dellasala, and Robinson (2002). We performed geo-metric, atmospheric and topographic corrections on each image.The geometric registration error obtained was smaller than onepixel. We performed atmospheric correction using the dark ob-ject image-based correction method, where the surface radianceof dark objects (minimum values) was set to zero surface radi-ance (Mather, 1999). We performed topographic correction usingthe approach developed by Mariotto & Gutschick (2010), using a30 m spatial resolution digital elevation model derived from theASTER data.

We performed supervised classification using the maximumlikelihood algorithm on each image, to generate land cover/use

maps for 1986, 1999 and 2008. We used seven classes, representingthe dominant and environmentally significant land cover and landuse categories present in the study area (Table 1). We consultedindependent reference data to help identify training classes (be-tween 200 and 1000 pixels per class), including aerial photographsfrom 1978 (sourced from the Aerial Photogrammetric Service ofChile), the Native Vegetation Survey of Chile from the 1990s(CONAF et al., 1999) and its 2007 update (CONAF, 2011), and fieldmeasurements conducted in 2010 (land cover surveys at 120 sites).We supplemented this information through interpretation of finespatial resolution images accessible through online virtual globes(e.g., Google Earth), especially in areas with limited accessibility.Subsequently, to improve accuracy and minimize classification er-rors, we applied expert classification, consisting of a decisionmodel

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based on rules defined by the user and assuming a priori knowledgeof the landscape under analysis (Cayuela, Golicher, Salas, & ReyBenayas, 2006; Kahya, Bayram, & Reis, 2010; Manandhar, Odeh, &Ancev, 2009). The main source of expert knowledge was theNative Vegetation Survey of Chile (CONAF et al., 1999) and its laterrevision, which have been used previously in differentiating mis-classified forest plantations from other vegetation cover (Schulz,Cayuela, Echeverria, Salas, & Rey Benayas, 2010). Expert classifica-tion thus provided refinement of the original classification,increasing classification accuracy of three target classes e nativeforest, forest plantation and shrubland.We focused on these classesbecause they were of greatest significance to our work, but alsosince they exhibited the highest commission errors in the originalclassification.

We used the reference data (described above) to guide classifi-cation accuracy assessment, whereby points were selectedrandomly and, at each point, the classified image pixel wascompared with the reference data land cover class. Importantly,different sample points were used for accuracy assessment andclass training. That is, the data used for class training and classifi-cation accuracy assessment were wholly independent. Whilepoints were generated randomly, any problematic points (e.g. thoselocated on the edges of patches) or repeat points (e.g. multiplepoints selected from individual patches) were omitted (Lillesand,Kiefer, & Chipman, 2008). We used samples of 287, 282 and 265points, respectively, for the 1986, 1999 and 2008 images. We pre-sent the accuracy assessment results using confusion matrices(Table 2), showing the level of agreement between classified andreference points (Congalton & Green, 1999).

Table 2Confusion matrices for the three images.

Classified data Reference data

Native Forest Shrubland Forest plantation

A. 1986 imageNative forest 49 3 4Shrubland 0 37 0Forest plantation 1 3 45Agriculture 0 6 1Bare land 0 1 0Urban 0 0 0Water 0 0 0Producer’s accuracy (%) 98 74 90Total 50 50 50Overall classification accuracy: 85.7%

B. 1999 imageNative forest 47 7 2Shrubland 0 25 0Forest plantation 3 0 54Agriculture 0 8 0Bare land 0 0 0Urban 0 0 0Water 0 0 0Producer’s accuracy (%) 94 63 96Total 50 40 56Overall classification accuracy: 86.2%

C. 2008 imageNative forest 47 0 1Shrubland 0 25 0Forest plantation 3 7 50Agriculture 0 7 0Bare land 0 1 0Urban 0 0 0Water 0 0 0Producer’s accuracy (%) 94 63 98Total 50 40 51Overall classification accuracy: 86.8%

Weused the resulting land cover/usemaps to calculate net gainsand losses per class, for each time-period. To estimate the annualdeforestation rate of native forest, we used the compound interestformula described in Puyravaud (2003):

Rate of change in native forest cover

¼ lnðA2=A1Þ � ð100=ðt2 � t1ÞÞ

where A1 and A2 and are the areas of native forest at the beginning(t1) and end (t2) of the time-period, respectively.

2.3. Landscape pattern indices

The classification process described above generated a pixel-based classification. However, to calculate landscape indices werequired land cover/use patches (Aplin & Smith, 2011). We definedpatches simply and directly by interrogating groups of pixels of thesame class. We then applied an image filtering technique to elim-inate land cover/use patches smaller than 0.5 ha, based on the FAOdefinition of a forest patch (FAO, 2001).

We calculated commonly used indices for assessing landscapespatial structure (McGarigal, Cushman, Neel, & Ene, 2002). Acrossall classes we calculated the Simpson diversity index (SDI) and theaggregation index (AI-L; these are often referred to as ‘landscape-level indices’). Within classes we calculated patch density (PD),mean patch size (MPS) and mean proximity index (MPI). Thesewere calculated separately for each class and are often referred toas ‘class-level indices’. When the landscape consists of a singlecover type, SDI is zero. It approaches a maximum of one as the

Consumer’saccuracy (%)

Agriculture Bare land Urban Water

1 0 0 0 863 0 0 0 930 0 0 0 9241 9 0 0 725 38 4 0 790 0 26 0 1000 0 0 10 10082 81 87 10050 47 30 10

0 0 0 0 842 2 1 0 832 0 0 0 9240 4 0 0 776 40 2 0 830 0 27 0 1000 0 0 10 10080 87 90 10050 46 30 10

2 0 0 0 942 0 0 0 932 0 0 0 8141 5 1 0 763 29 1 0 850 0 28 0 1000 0 0 10 10082 85 93 10050 34 30 10

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number of different cover types increases, and as the proportions ofthe different patch types become more even (McGarigal et al.,2002). The AI-L is 0% when patch types are completely dis-aggregated and increases as pixels become more aggregated,reaching 100%when the landscape consists of a single type of cover(McGarigal et al., 2002).

Within classes, the indices we selected are typically employed tomeasure the effects of forest loss on spatial patterns (Fahrig, 2003;Trani & Giles, 1999). The PD is the number of patches in a classdivided by class area; MPS is the total area of all patches in a classdivided by the number of patches in that class; MPI is the ratio ofthe size to the proximity of all patches whose edges are within agiven search radius of the focal patch (we used a 500 m searchradius). We calculated these within-class indices for the mostrepresentative and dynamic land covers/uses in the study area:native forest, forest plantation and agricultural land.

2.4. Evaluation of landscape turnover and the influences of commondata constraints

We compared each 0.0225 ha pixel between maps, allowing usto evaluate the turnover of each class through each time period. Foreach class, we calculated the total area lost, gained and experi-encing no change. For estimating turnover, this assumes nocryptoturnoverdthat is, no loss of habitat followed by gain of thatsame habitat, within the same time period. Given that our mainfocus is on native forest in time periods of no more than 13 years,we consider such cryptoturnover to be minimal. We compared theresults obtained with the overall net changes in cover of each class.

We compared results between our three images (1986, 1999,2008), and measures of change between the two time periods, toquantify non-stationarity through time and to examine how muchof the overall information about the regionwould be missed if onlysnapshot data were useddour secondary focus.

In addition, in order to compare the landscape patterns betweenthe protected area and the rest of the study region, we performedall relevant calculations for each of three domains: the whole re-gion (defined by political boundaries), Nahuelbuta National Parkonly and the region excluding the national park. Finally, to compareresults for our data with those for the standard dataset used forequivalent analysis and information-gathering in the study region,we repeated calculations on coarser-resolution data from the Na-tional Vegetation Survey of Chile (CONAF, 2011; CONAF et al., 1999).The National Survey data have a minimum unit size of 6.25 and4.5 ha for the 1997 and 2007 datasets respectively, much coarserthan both the 0.0225 ha pixel size of our classified maps and ourminimum patch size of 0.5 ha. There were some compatibility is-sues, however. The National Survey data are for 1997 and 2007,both of which dates differ slightly from our satellite imagery (1999and 2008). There is also an imperfect match between the maps (a1.6% difference in area) resulting from differences in data type(vector vs. grid). Further, the National Survey data are land use data,rather than land cover data, with bare land usually being labelled asagriculture, rather than having its own separate class as in ourclassification. However, our main focus is on native forest andexotic plantations, so this mismatch is of little importance here.Taken together, these issues will have caused some minor artefac-tual differences in results, but these appear wholly inconsequentialcompared with the very large differences in our main researchfindings (see Results).

3. Results

The overall classification accuracy was 86%, 86% and 87% for the1986, 1999 and 2008 images respectively (Table 2). In all images,

much of the overall inaccuracy was related to confusion betweenthe agriculture class and the bare land and shrubland classes,which is unsurprising given their shared spectral properties (e.g.soil understory), but also these classes are of only subsidiaryconcern to this study. A small number of exotic forest plantationpixels were misclassified as native forest and vice-versa. Further,some shrubland was wrongly classified as either native forest orforest plantation. These misclassifications indicate that someclassification-based error is present in our main analyses, but thisappears relatively minor and is unlikely to impact our mainfindings. In relation to forests our classificationmeets the criterionof 90% accuracy or better identified by Shao and Wu (2008) astending to produce stable landscape metrics. On the contrary,these levels of accuracy give us confidence in the accuracy of ourpatch indices, even though we are distinguishing between twotypes of forest cover (native forest and forest plantation). Theturnover estimates are likely to have been inflated to some extent,more than counteracting any cryptoturnover for the forest classesin particular. However, close visual assessment of our land use/cover and turnover maps reveals minimal evidence of patternscaused by significant artefactual turnover (e.g. very small patchesof plantation appearing in the midst of native forest, or gains ofplantation within the national park), suggesting that its impact onour results is negligible.

3.1. Overall land cover/use and landscape pattern in protected andunprotected areas

Outside the national park, the main changes were loss of nativeforest and gain in exotic forest plantations (Fig. 2). In 1986, thelargest single class was native forest (Table 3), representing 38% ofthe unprotected area, but declining to 26.5% by 2008 (Fig. 2F). Thus,in 2008 native forest area was 70% of its area in 1986, equivalent toan annual deforestation rate of 1.6%. The main increase was inexotic forest plantation, rising from 12% of the unprotected area in1986 to 39% in 2008, by which time it was the largest single coverclass; this is an increase of 239% over the 22 years, the gain in areabeing 31,762 ha. The other notable features of the data are therelatively stable proportion of agricultural land (about a quarter ofthe region) and a decrease in bare land from 15.5% of the area in1986 to 4% by 2008. This is a predominantly rural landscape, sourban areas represent a small proportion of the study areathroughout the whole period. Nevertheless, urban areas increasedby 419 ha, equivalent to 108%, over the whole study period.

Nahuelbuta National Park is mostly native forest. In starkcontrast to the rest of the study area, it remained this waythroughout the study period, with the proportion of the park thatwas native forest actually increasing, from 94.4% in 1986 to 97.5% in2008, which is 0.14% per year. Shrubland comprised 5% of the parkin 1986, all but disappearing by 1999. There was only a very smallpresence of agricultural and bare land. The patterns in space andtime in the national park are thus very different from those in therest of the study area (Fig. 2E). Nahuelbuta National Park is only5.2% of the overall study area, so the overall amounts and trends inland area for the whole map were similar to those for the areaexcluding the national park (Table 3 and compare D and F in Fig. 2),except for the smaller proportion of native forest in the unprotectedarea. Nonetheless, the annual deforestation rate (1986e2008)when the national park is included was 1.35%, which is notablysmaller than the 1.60% for the unprotected area.

The landscape pattern indices (Table 4; Fig. 3) also showNahuelbuta National Park behaving very differently from the rest ofthe study area. It had much lower values of Simpson’s diversityindex and patch density and higher values of the landscape ag-gregation index andmean patch sizedreflecting the predominance

Fig. 2. Maps of land cover/use and changes in their extents in the study area for: A) 1986, B) 1999, C) 2008. Map resolution: 0.0225 ha. The overall proportions of each mapcontributed by the main classes are shown in the graphs: D) whole study area, E) national park only, F) study area excluding the national park.

A. Altamirano et al. / Applied Geography 40 (2013) 199e211204

of large native forest patches in the national park. The trends inthese values through time (see next section) were also verydifferent: for example, SDI decreased through time in the nationalpark, but both showed little trend in the unprotected area (Table 4).These findings reflect a decrease in fragmentation in the nationalpark, as small, scattered areas of shrubland, bare land and agri-culture reverted to native forest, while elsewhere most classes

tended to fragment, except for the exotic forest plantation, whichtended to consolidate and counteract the fragmentation of theother classes. In the national park, the landscape pattern indicesreflect the strong, and increasing, dominance of a single land cover/use class. Outside the national park there is high landscape di-versity, with many small patches, and the AI-L values indicateconsiderable aggregation of patches within classes.

Table 3Gains and losses of land, by class, in the two time-periods. All figures are in hectares.

Class Area in 1986 1986e1999 1999e2008 Area in 2008

Gains Losses Gains Losses

Whole study areaNative forest 49,445 8176 11,586 1667 10,951 36,751Shrubland 7170 8136 6660 4247 7279 5614Forest plantation 13,299 20,333 185 12,034 414 45,067Agriculture 33,039 11,484 18,759 12,753 9,753 28,764Bare land 17,888 4227 15,094 3312 5875 4458Urban areas 388 156 48 334 23 807Water 245 40 219 4 54 16

Nahuelbuta national parkNative forest 6019 211 129 187 75 6211Shrubland 308 15 298 27 21 31Forest plantation 0 3 0 3 0 6Agriculture 39 157 17 78 152 89Bare land 8 58 0 10 56 13Urban areas 0 0 0 0 0 0Water 0 0 0 0 0 0

Excluding national parkNative forest 43,426 7965 11,457 1480 10,876 30,538Shrubland 6862 8121 6362 4220 7258 5583Forest plantation 13,299 20,330 185 12,031 414 45,061Agriculture 33,000 11,327 18,742 12,675 9601 28,659Bare land 17,880 4169 15,094 3302 5819 4438Urban areas 388 156 48 334 23 807Water 245 40 219 4 54 16

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3.2. Changes in rates and indices through time

Overall changes between 1986 and 2008 often mask consider-able variation between the two time periods (Table 3, Figs. 2e4). Insuch cases, very different results are obtained, and different con-clusions would be drawn, for all three of the time-periods consid-ered (1986e1999, 1999e2008, 1986e2008). Particularly notable isthe switch from a sharp decrease in mean native forest patch sizeoutside the national park in 1986e1999 to an increase in this metricfor 1999e2008 (Fig. 3). This unusual switch, and the increase innative forest MPS since 1999, are missed when only the overallchange (a decrease) is calculated. Similarly, native forest patchdensity first increased markedly (indicating fragmentation) andthen decreased markedly (loss of native forest patches more thancompensating for continued fragmentation), but over the full 22years there was little change. Within the national park, the pro-portion of agricultural land increased from 0.6% in 1986 to 2.8% in1999, before declining to 1.6% by 2008da trend that could be a littlestronger than suggested by Table 3, if some of the bare land was infact agriculture with minimal crop biomass when the imagery was

Table 4Across-class patch indices: Simpson’s diversity index (SDI) and the landscape ag-gregation index (AI-L).

Class 1986 1999 2008

Whole study areaSDI 0.72 0.73 0.71AI-L 92% 92% 91%

Nahuelbuta national parkSDI 0.09 0.07 0.04AI-L 98% 98% 99%

Excluding national parkSDI 0.73 0.74 0.71AI-L 92% 91% 91%

captured. Outside the national park, in contrast, agricultural landfirst decreased then increased in overall area, the decline over thefull 22 years masking the recent increase.

Some changes, while in the same direction in all time-periods,varied in their rates of change. In particular, the net rate of loss ofnative forest outside the national park accelerated from 0.64% peryear in 1986e1999 to 2.98% per year in 1999e2008, almost a five-fold increase (four-fold when measured as annual absolute, ratherthan proportional, net loss: 3492 ha lost in the first 13 years and9396 in the next 9 years [Fig. 4B]). The proportional rate of increasein exotic forest plantation, however, was much more rapid in thefirst period (7.1% per year) than the second (3.3% per year); theabsolute rate of increase also slowed.

A few cases showed consistent rates of change over the twoconsecutive time-periods,most notably the constant decline inmeanproximity index for native forest patches outside the national park(Fig. 3C), indicating steadily increasingpatch isolationdmirroredbyanear-constant increase in MPI (decrease in patch isolation) for exoticforest plantation.

3.3. Comparison with the standard dataset

Our fine spatial resolution data suggest much greater loss ofnative forest (Table 5) than do the standard data for the region (thecoarser-resolution National Vegetation Survey data). This probablyreflects the large number of small patches of native forest lostduring this perioddtoo small to be detected by the National Surveydata. The similarity between the datasets in percentage increase offorest plantation may reflect the fact that such plantations aretypically large enough to be detected at the coarser resolution.

The values of all three within-class patch indices were whollydifferent for the two datasets (Table 5; only shown for native for-est). In fact, for both patch density and mean patch size, the di-rection of change through time was reversed when swapping onedataset for another. According to the National Survey data, patchdensity was low but increased greatly over the time period. Notsurprisingly, our fine-resolution data produced much higher PDvalues. Much more interesting is the marked decrease over the 9years. Similarly, while the National Survey data showed the typicalsharp decrease of mean patch size with deforestation (approxi-mately halving in a decade), our data showed MPS actuallyincreasing, despite 9 years of deforestation. This is an unusualpattern, usually only noted as a theoretical possibility (Fahrig,2003). The direction and proportion of change in mean proximityindex were the same for both datasets, but the values were morethan two orders of magnitude higher for the finer-resolution data.In all these cases, the differences reflect the great losses of nativeforest patches that were too small to be detected in the NationalSurvey data.

3.4. Turnover of land cover/use through time

Even when broken into shorter time periods, measures such asnet change in forest cover (as in Table 5) can mask considerablespatial turnover within the time period. The change in area of eachland cover/use class in any given time period is the net result ofboth gains and losses within the period, with this turnover beingremarkably high outside the national park (Table 3). Several fea-tures of our results are particularly notable, as follows.

First, there was little change of land cover/use in NahuelbutaNational Park: 7.0% of pixels (444 out of 6374 ha) changed theirclass between the 1986 and 1999 maps, and 4.8% (304 ha) between1999 and 2008. About half of this turnover was shrublandbecoming native forest in the first time period (by ecological suc-cession, presumably), and reversion of agricultural (or bare) land to

Fig. 3. Changes over time of within-class patch indices applied to the main land cover/use classes in the study area: patch density (top row), mean patch size (middle row) andmean proximity index (bottom row), shown for each domain (columns): A) whole study area, B) national park only, C) study area excluding the national park. Note the differentscales on the vertical axes. For mean proximity index graphs with two vertical scales, those with the higher numbers are for native forest.

A. Altamirano et al. / Applied Geography 40 (2013) 199e211206

native forest in the second time period (Table 6). About 134 ha ofnative forest appear to have been cleared for agriculture in theNational Park during the whole study perioddnot much, butconsiderably more than suggested by the net changes and headlinefigures.

Second, there was very little loss of exotic forest plantation; thelarge net increases in forest plantation reported above were nearlythe full extent of plantation establishment. New plantations weredistributed widely around the study area outside the national park(Fig. 5C). Thus, the headline figures for this land cover/use class donot mask much cancelling out of gains and losses. Similarly, urbanareas were gained but not lost. Pixels classed as water were mostlylost (drained), rarely gained.

Third, the other land cover/use classes were all very dynamicoutside the national park, with headline figures masking substantial

gains and losses. According to our data, 45% of all pixels (52,108 outof 115,100 ha) changed their class between the 1986 and 1999 maps,and 30% (34,046 ha) between 1999 and 2008dvery similar annualrates. For agriculture, bare land and shrubland, part of this may beartefactual because there was some inaccuracy in distinguishingbetween these classes (Table 2 and see start of Results section).However, our main focus is on native forest and the extent ofmisclassification for this class should be limited; certainly it cannotaccount for the great turnover found (Table 3). In 1986e1999, the netchange in native forest cover outside the national park was 3492 ha(3% of the total unprotected area), but this involved 17% of the areaturning over: 7695 ha (7% of the map) converted to this class fromothers and 11,457 ha (10%) lost to other classes. Most of this turnoverwas concentrated in the northern and western parts of the studyarea (Fig. 5A). In terms of area under native forest in 1986 that was

Fig. 4. Net changes for land use/cover classes for the periods 1986e1999 and 1999e2008 in (A) Nahuelbuta National Park (total domain area ¼ 6349 ha) and (B) the rest of the studyarea (total domain area ¼ 115,100 ha).

Table 6

A. Altamirano et al. / Applied Geography 40 (2013) 199e211 207

not under native forest in 1999 (i.e. the native forest ‘losses’), thisrepresents a deforestation rate of 2.36% per yearda very differentfigure from the net 0.64% per year reported in Section 3.2 above. Thegains of native forest that make the difference between these twofigures were presumably early-successional secondary forest. In thetime period 1999e2008, the dynamics were very different, withlittle gain in native forest to balance the losses. The adjusted defor-estation rate for this period is 3.53% per year in the unprotected area,which is only a little higher than the headline figure of 2.98% peryear. Put together, the data for native forest losses represent muchless of an acceleration of forest loss between the two time periodsthan those for net loss of native forest.

Table 5Comparison of class areas and within-class patch indices (‘metrics’) between thestandard National Vegetation Survey of Chile dataset (resolution 6.25 ha for 1997,4.5 ha for 2007) and our dataset (0.0225 ha resolution for area calculations; 0.5 haminimum patch size). All figures are for the entire study area.

National survey data (coarse) Data from this study (fine)

1997 2007 Change 1999 2008 Change

Total areaNative forest (ha) 45,369 41,100 �9.4% 46,035 36,751 �20.2%Forest plantation

(ha)37,219 49,606 33.3% 33,447 45,067 34.7%

Metrics for native forestPatch density

(N/100 ha)0.09 0.15 66.7% 0.68 0.47 �29.9%

Mean patch size (ha) 416 225 �46.0% 23.4 26.7 14.1%Mean proximity index 186 74 �60.1% 37,229 15,849 �57.4%

Finally, about half the losses of native forest were conversion toexotic forest plantation, primarily in the northern part of the studyarea (Fig. 5), withmost of the remainder representing conversion toagriculture (Table 6B). There was some loss to bare land, and someto shrubland; these may mostly represent clear-cuts and subse-quent succession. Gains of native forest were mostly from agri-cultural land (Table 6A), with sizeable contributions from bare landand shrubland (first period only). Apart from native forest, newexotic forest plantations in 1986e1999 mostly replaced agricultural

Area of each class of land cover/use (A) replaced by and (B) replacing native forest.

National park Rest of study area

1986e1999 1999e2008 1986e1999 1999e2008

A. Native forest gainsShrubland 196 0 1279 0Forest plantation 0 0 50 81Agriculture 14 148 5665 956Bare land 1 39 902 430Urban 0 0 0 0Water 0 0 69 13Total 211 187 7965 1480

B. Native forest lossesShrubland 15 26 978 592Forest plantation 3 2 5479 5431Agriculture 94 40 4405 4272Bare land 17 7 584 580Urban 0 0 3 2Water 0 0 8 0Total 129 75 11,457 10,876

Fig. 5. Spatial distribution of gains and losses for: A) native forest in the period 1986e1999; B) native forest in the period 1999e2008; (C) forest plantation in the period 1986e2008.Map resolution: 0.0225 ha.

A. Altamirano et al. / Applied Geography 40 (2013) 199e211208

land (6490 ha) and bare land (4965 ha), and in 1999e2008 mostlyagricultural land (3837 ha); these changes were generally in thesouth-eastern part of the study area.

4. Discussion

Our most remarkable finding is probably the very high turnoverof land cover/use over relatively short time periods: about a third ofthe study area changed class each decade. We suggest that land-scapes in general are much more dynamic than is often appreci-ated, not least in Chile. Focussing on native forest, we argue thatsuch turnover is extremely important, yet typically ignored. Lossand fragmentation of native forest are key conservation issues inour study area (Altamirano, Echeverría, & Lara, 2007; Echeverríaet al., 2006; Hechenleitner et al., 2005; Pauchard & Alaback,2004; Smith-Ramírez, 2004), as elsewhere. The net deforestationand its acceleration are worrying enough: 0.64% loss per year1986e1999, rising to 2.98% per year 1999e2008. However, thisdoes not take into account continuity of forest habitat. Instead, thefigures for native forest losses may be more appropriate: 2.36% and3.53% per year, respectively. Losses and gains are treated as equalwhen calculating net change, but the loss of established native

forest is qualitatively different from the gain of early-successionalregrowth forest. For example, early-successional forest typicallylacks forest specialists and species of high conservation value(Gardner, Barlow, Parry, & Peres, 2007). Thus, although it is of greatconservation concern that the net loss of native forest in the un-protected area in 1986e1999 was 8.0% of its original extent (3.0% ofthe entire unprotected area), these figures are unduly optimisticfrom a biodiversity and conservation perspective. More importantis that 26.4% of the native forest in 1986 was lost by 1999 (10.0% ofthe entire unprotected area). The gain of new native forest bysuccession from agricultural land (mainly), representing 6.9% of theunprotected area, represents limited compensation for the loss.

These considerations may be no less important in the regionbeyond our study area. The headline deforestation rates we foundare actually lower than other parts of south-central Chile (Aguayo,Pauchard, Azocar, & Parra, 2009; Altamirano & Lara, 2010;Echeverría et al., 2006), though higher in the second period thanregions further south (Echeverría, Newton, Nahuelhual, Coomes, &Rey-Benayas, 2012). Reasons for lower net forest loss may relate tocharacteristics of the study area, particularly the fact that in 1986,ca. 70% of the native forest in the area was above 600 m altitude,where conditions restrict the establishment of forest plantations

A. Altamirano et al. / Applied Geography 40 (2013) 199e211 209

and farm crops, resulting in less replacement of native forest.Native forest is principally lost at lower elevations, where it covers asmaller area and access by land developers is easier.

Our findings suggest a great amount of very small-scale defor-estation (Fig. 5A and B): losses of a few hundred square metres ofnative forest at a time, both from larger forest patches and from thevery high losses of small patches (of the order of 0.5e3 ha in size).The preponderance of pixel-scale losses on high-resolution mapssuggests deforestation processes that are difficult to detect usingstandard approaches, and demonstrates the importance of scale.Certainly, much information was lost when we used the NationalVegetation Survey datasetdthe standard data source for forestconservation and landscape decision-making in Chile. Deforesta-tion overall was under-estimated with the standard data, a ten-dency that we suspect is quite general and shows that fine-scalelandscape analysis can reveal landscape dynamisms that areobscured in coarse-scale analysis (Plieninger, 2012). We suggest,therefore, that forest loss has been, and remains, much moreextensive than previously recognized. However, it is possible thatfine-scale analysis may be more sensitive to falsely detectingchange when none actually occurs. The trade-off between missingimportant turnover and possible increase in false change positivesrequires further empirical evaluation.

In conservation terms, small patches of native forest may havecertain types of value (compared to large patches) (Smith-Ramírez,2004), such as acting as ‘stepping stones’ for organisms that cancross the non-forest matrix to a limited extent. They may alsofunction as important providers of ecosystem services such aspollination and seed dispersal (Bodin, Tengo, Norman, Lundberg, &Elmqvist, 2006). This is especially important in the context of alandscape such as our study region, in which area, aggregation andmean patch size of exotic forest plantation have all rapidlyincreased over the last 25 years. Small patches of native forest oftenrepresent important features of landscapes for species diversity(Arroyo-Rodríguez, Pineda, Escobar, & Benítez-Malvido, 2009;Benedick et al., 2006; Bodin et al., 2006; Fischer & Lindenmayer,2002). They can retain their tree species for a considerable periodafter fragmentation (dos Santos, Kinoshita, & dos Santos, 2007) andthe losses threatened by extinction debt may be reversed if long-term restoration initiatives are implemented (Cayuela, Golicher,Rey-Benayas, González-Espinosa, & Ramírez-Marcial, 2006).

About half of the overall loss of native forest was to exotic forestplantations (Table 6), which is typical of the south-central region ofChile (Aguayo et al., 2009; Altamirano & Lara, 2010; Echeverríaet al., 2006), and supports increasing concerns about invasions byalien trees into remaining native forest (Bustamante & Simonetti,2005). Since 1974, forest policy throughout Chile has providedeconomic incentives for establishing forest plantations (Lara,Echeverría, & Reyes, 2002). This policy stimulated the establish-ment of fast-growing exotic forest species such as Pinus radiata andEucalyptus globulus in large parts of the country. This, along withthe liberalization of exports and privatization of state-ownedplantations and pulp mills, explains the fast growth of the forestplantation industry, often regarded as an economically successfulmodel in other Latin American countries and elsewhere (Lara,Reyes, & Urrutia, 2006; Lara & Veblen, 1993). However, thereplacement of native forest by forest plantation is not permitted byexisting Chilean legislation. Although, like early-successional nativeforest regrowth, exotic plantations do absorb carbon (depending onthe end use of the product), they tend to be poor substitutes fornative forest in terms of biodiversity, pollinator provision and cleanwater supply (Lara et al., 2009). We argue that externalities,particularly those related to ecosystem service provision (Costanzaet al., 1997), should be factored into the accounting and incentivesystem to a much greater extent. Permanent, effective monitoring

may also be required; current technologies allow effective moni-toring not only of native forest loss, but also its degradation (Asner,Knapp, Balaji, & Paez-Acosta, 2009; Echeverría et al., 2012; Panta,Kim, & Joshi, 2008).

We found the study area to be highly dynamic temporally (aswell as spatially). Some trends were in the opposite direction in twotime-periods, for example, even though the time-periods were onlyabout a decade long, and adjacent. Again we suggest that this levelof temporal dynamism is not unique, and indeed under-appreciated, with at least two important implications for plan-ning and conservation. First, trends may be masked when usingchange calculated over relatively long time-periods. In our data,several indices (e.g. native forest MPS and PD in the unprotectedarea and MPI in the protected area) showed little overall changebetween 1986 and 2008, masking considerable (but opposite)change within each shorter time-period. Second, the high level oftemporal dynamism suggests that recent data are needed in orderto make well-informed decisions.

In the temperate forests of Chile, conversion to forest plantationis considered the main driver of deforestation and fragmentation(Bustamante & Castor, 1998; Echeverría et al., 2006). While ourfindings do support a key role of forestry (as discussed above),conversion to agriculture accounted for almost as much nativeforest loss (Table 6B). This role of agriculture is largelymissed whenusing the National Survey data (results not shown), probablybecause much of the agriculture is very small in scale, in most casesnot changing the classification of 4.5- or 6.25 ha pixels (NationalSurvey data), but changing our 0.0225 ha ones. Forestry planta-tions, on the other hand, tend to be large enough to be reflected bythe coarser-resolution data. It may therefore be that agriculture is amuch more significant contributor to native forest loss than iscommonly appreciated in the region, and probably beyond. Thisrequires further investigation.

From a theoretical perspective this great impact, on conclusionsdrawn, of the change in data resolution is an example of the well-known modifiable areal unit problem. More broadly, we obtainedvery different signals about landscape change and trends from thesame input data when we varied spatial resolution and when wechanged the domain (i.e. separated protected and unprotectedareas)din addition to the more novel outcomes discussed above.Thus we reinforce the already-known fact that the outcomesdepend crucially on these variables. Even so, we are struck by themagnitude of the effects. In particular, the impacts of data resolu-tion on the landscape patternmetrics were stark, not only changingthe values of these indices up to 200-fold, but actually reversing thetrends through time for both patch density and mean patch size(Table 5). Thus, the interpretation of the nature of forest loss andfragmentation fundamentally changes. Using the standard dataset,the signal is the typical one (e.g. Chai, Tanner, & McLare, 2009;Cayuela, Rey Benayas, et al., 2006; Echeverría et al., 2006; Fahrig,2003; Miller, 2012; Trani & Giles, 1999) of continued deforesta-tion being associated with continued fragmentation: smaller, moreisolated forest patches remaining, and, as a consequence, increasedpatch density per unit area. Using our fine-resolution dataset, thecontinuing deforestation was instead associated with reducedfragmentation in that mean patch size actually increased in 1999e2008, but with patch density declining and patch isolationincreasing. This pattern of landscape change has rarely been re-ported and may usefully form a focus for further research.

We also note that the widely used landscape-level patch indicesSDI and AI-L were rather insensitive to landscape changes (Table 4),supporting the findings of Pôças, Cunha, and Pereira (2011) andRescia, Willaarts, Schmitz, and Aguilera (2010). Overall, for con-servation planning and landscape management, we stronglycaution against uncritical or dogmatic use of available datasets,

A. Altamirano et al. / Applied Geography 40 (2013) 199e211210

landscape indices and headline figures. Some are clearly verysensitive to the exact form of the input data, while others are tooinsensitive to change. We argue for greater focus on continuity offorest cover (and habitat stability more generally) which is a keyfactor for maintaining a sustainable ecosystem (Levin et al., 2007).For example, net changes may mask fluctuation through time andspace; stable mean patch size, isolation and habitat area are likelyto be interpreted as reflecting continuity of habitat, when in factthere may be much dynamic gain and loss of habitat throughoutthe landscape. Correct characterization of landscape fluctuations isadditionally important because evaluation of landscape continuityhas been identified as a way to identify and quantify cultural andhistorical value of landscapes (Skalos & Kasparova, 2012).

Finally, our results showed that Nahuelbuta National Park iscompletely different from the rest of the study area, with verydifferent land cover and trends through time: near total cover ofnative forest, increasing through time and with minimal turnover.(Some might even argue that the disturbance levels are too low inthe park!) Control on illegal developments such as logging is strongin Chilean National Parks; it may also be that the park helps inmonitoring forest management activities in adjacent areas (Wright,1996). In the study area at least, it is reassuring that this form ofprotection appears to be so effective, implying that extension ofnative forest protection is a viable conservation measure in the re-gion. For example, scattered remnants of native forests along creekbeds, owned by forestry companies operating the surrounding exoticforest plantations, could easily and inexpensively be used to expandthe current protected area. This may aid biodiversity conservationunder climate change (Altamirano et al., 2010) and could be part of alow-cost, integrated management plan (Birch et al., 2010; Frêne &Nuñez-Ávila, 2010; Lindenmayer & Franklin, 2002).

5. Conclusion

Native forests in our study area, as in many parts of the world,are of major conservation importance. Landscape indices representwidely used tools for characterizing landscapes undergoing defor-estation and associated fragmentation of remaining native forest,and for conservation and land use planning.We have demonstratedthat routine use of these indices in dynamic analysis can bemisleading, with results influenced strongly by data characteristicsand constraints rather than underlying patterns of forest and forestchange. Most significant, here, net (‘headline’) figures of landscapechange masked very large turnover through time, with at least 30%of unprotected land switching land use each decade. While netnative forest change between 1986 and 1999 was only 3%, turnoverwas very much higher at 17% (comprising 7% conversion to forestand 10% loss of forest). This discrepancy is very significant whenforest age and conservation value are considered, since youngregrowth forest (the 7% gain) is of markedly lower conservationvalue than mature forest (which is likely to form much of the 10%loss).

We also found that examining only a single time period, ratherthan two sequential periods (1986e1999, 1999e2008), gave amisleading representation of forest loss and fragmentation. Simi-larly, only using the relatively coarse spatial resolution NationalVegetation Survey data (as is typically the case) not only under-estimated deforestation but also reversed directions of change oflandscape metrics such as mean patch size. It also biased data onthe causes for forest loss away from agriculture and towards exoticplantations. In this study we accurately characterized the nature offorest change in a globally important ecoregion. The results,though, have wider significance for conservation studies aroundthe world, providing cautionary notes and recommendations forappropriate use of measures of forest and landscape change.

Acknowledgements

This research was supported by funding from the CONICYTproject 74110043, FONDEF project D08I1056, and Dirección deInvestigación of Universidad de La Frontera. Also, the authorswould like to thank to Corporación Nacional Forestal (CONAF) fortheir support to this study by providing data of Native VegetationSurvey of Chile. Thanks to Cristian Delpiano for assistance with thefield work.

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