ORI GIN AL PA PER

Comparing alternative systematic conservation planningstrategies against a politically driven conservation plan

Christine N. Meynard Æ Christine A. Howell Æ James F. Quinn

Received: 12 November 2008 / Accepted: 16 March 2009� Springer Science+Business Media B.V. 2009

Abstract Decisions regarding the level of detail included in conservation planning andthe importance given to feasibility considerations can greatly influence management in

terms of total area required, achievement of conservation targets and costs. This work had

two main objectives: (1) to compare priority sites proposed by the Chilean commission for

the environment in a politically driven process to the results of alternative systematic

conservation planning scenarios; and (2) to compare the efficacy of systematic conserva-

tion planning based on different types of conservation targets (forest types and bird

species) and minimum area thresholds. To address these issues, we used vegetation cover

as well as field data on forest birds in central Chile. Bird species distributions were

modeled using a variety of climatic and environmental layers, allowing for the integration

of environmental heterogeneity into the planning process. We then ran several conserva-

tion planning scenarios considering conservation targets based on vegetation types alone,

birds alone, or a combination of vegetation and birds. Collectively these results show that

conservation planning results differ significantly when considering birds or vegetation

types, and that minimum area requirements for each conservation feature has a great

influence on the final results. Moreover, important conservation sites are not represented in

the current government plan, and these sites are related to the small representation of rare

C. N. Meynard (&)Institut des Sciences de l’Evolution, UMR 5554, Université de Montpellier 2, Place Eugène Bataillon,CC 065, 34095 Montpellier Cedex 5, Francee-mail: cnmeynard@gmail.com

C. N. MeynardGrupo Cientı́fico Milenio FORECOS, Instituto de Silvicultura, 48 Piso Facultad de Ciencias Forestales,Universidad Austral, Casilla 567, Valdivia, Chile

C. A. HowellPRBO Conservation Science, 3820 Cypress Dr., Petaluma, CA 94954, USA

J. F. QuinnDepartment of Environmental Science and Policy, University of California, 1 Shields Ave., Davis,CA 95616, USA

123

Biodivers ConservDOI 10.1007/s10531-009-9625-3

vegetation types. This study suggests that using appropriate minimum area requirements

can greatly affect the results of a conservation planning exercise and therefore represents a

key knowledge gap in the region.

Keywords Conservation GIS � Species distribution modeling � MARXAN �Systematic conservation planning � Chilean birds

Introduction

The design of science-based conservation strategies has become a major area of research in

ecology (Knight et al. 2007; Pressey et al. 2007). Some of the challenges that often render

this a difficult task include the lack of biodiversity inventories over large geographic areas,

the lack of knowledge of ecological processes within the units of interest, and the geo-

graphic coincidence of biodiversity hotspots with human populations and major economic

activities (Fjeldsa 2007b; Knight and Cowling 2007; Pressey et al. 2007).

The challenges of biodiversity conservation have led to varying methodologies for

prioritizing conservation areas and establishing reserve networks. These prioritization

strategies are not mutually exclusive, and often require varying degrees of biological

information (Pressey 2004). In increasing order of information needs, these strategies range

from expert opinion, identification of hotspots, GAP analysis, and representation of veg-

etation heterogeneity or of individual species distributions (Feria and Peterson 2002;

Wilson et al. 2005b; Solano and Feria 2007). Strategies based on broad filter information,

such as those based on land classifications, are often criticized by their limited represen-

tation of endangered or threatened species requirements (Pressey 2004; Hess et al. 2006).

On the other hand, strategies based on individual species distributions are limited by the

quality and quantity of occurrence data, and are often criticized for the biases related to

sampling more common taxa and the many uncertainties related to distribution maps

(Wilson et al. 2005a; Fjeldsa 2007a; Wisz et al. 2008).

Conservation planning in Chile has been confronted with many of these challenges.

While Chile has signed a series of political commitments to promote sustainable devel-

opment and biological conservation, biological information related to individual species

distributions and biodiversity inventories needed to inform conservation actions remains

rather limited. The National Commission for the Environment (CONAMA) has released a

strategy for biodiversity conservation which sets the national conservation goal at 10% of

the area of every ecosystem (CONAMA 2005). Although ecosystems were not defined, it is

likely that any national conservation strategy will be based on vegetation types which were

recently mapped through the first national vegetation inventory (CONAF-CONAMA-BIRF

1999). CONAMA also chose a set of priority areas (CONAMA 2005), herein called

‘‘CONAMA sites’’, which were delimited based on expert opinion within a politically

driven framework. This was a hierarchical process in which the existence of habitats with

minimal human intervention (usually the lack of agricultural or forestry activities) was the

first factor considered. However, feasibility factors, such as the willingness of the current

owners to conserve, were the next most important criterion (Sandra Miethke, personal

communication). As a result, large areas which are not attractive for agriculture and other

important economic activities were classified as CONAMA sites (e.g., Andean foothills),

whereas some of the most economically productive areas were poorly represented (e.g.,

coastal range and central valley).

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Despite the many flaws that such a politically driven strategy may have from a purely

scientific point of view, several authors have recently emphasized the need to consider such

feasibility-based strategies as opportunities to merge scientific and social needs into the

conservation planning process (Knight and Cowling 2007; Knight et al. 2007; Wilson et al.

2007). Here we tested the efficacy of different systematic conservation planning strategies

against the politically driven approach proposed by CONAMA. This work had two main

objectives: (1) to compare priority sites selected in a politically driven process to the

results of systematic conservation planning efforts; and (2) to compare the efficacy of

systematic conservation planning based on different types of conservation targets (forest

types vs. bird species) and minimum area thresholds.

To address our objectives, we collected field data on bird communities and used

distribution modeling to generate maps of individual species distributions for forest birds

in central Chile. We also used the national vegetation survey to extract the area of

remaining forests and a broad filter vegetation classification within the study region that

contains eight forest types. We ran several conservation planning scenarios based on

vegetation types alone, birds alone, or a combination of vegetation and birds. In all

scenarios the final result was compared in terms of total area required by each strategy

and level of representation of different bird species and forest types. While this paper

compares the potential effectiveness of a politically driven process and the recom-

mendations of systematic conservation planning specifically in Chile, the issues raised—

rapid land conversion, limited distribution information for most taxa, and the need to

develop a reserve system—are widespread, and our approach can be implemented in

other locations.

Methods

Study area and bird surveys

The South American temperate forests are separated from other forest ecosystems by over

2,000 km of dry environments (Armesto et al. 1998) and are considered as an important

threatened biome at the global scale (Brooks et al. 2006). Two-thirds of the South

American temperate forest bird species are endemic and about half of them are forest

specialists (Vuilleumier 1985; Kelt 2001). There is strong economic pressure to replace

native forests with pine and eucalyptus plantations (Lara et al. 1996); particularly in the

northern portion of the temperate forest region, where 78% of Chileans live but only 1.3%

of the land is protected (Pauchard and Villarroel 2002). Most studies on South American

temperate forests have concentrated on ecosystems outside central Chile, such as the

evergreen forest habitat type distributed mainly south of 38�S (Rozzi et al. 1996b), onislands (Rozzi et al. 1996a), relict forest fragments (Reid et al. 2002), or small habitat

fragments (Kelt 2001). Very few studies have examined vegetation gradients in central

Chile (Estades 1997; Diaz et al. 2002; Meynard and Quinn 2008).

The bird field surveys were conducted in the temperate forests of central Chile between

34 and 398S during the reproductive seasons (October through December) of 2003 and2004 at nine study sites (Fig. 1). Sites were chosen to maximize the representation of

environmental gradients using a GRADSECT technique based on representation of altitude

and vegetation types (Wessels et al. 1998). However, accessibility to some sites was

limited due to road access or to inability to contact private owners. As a consequence, one

large park in the region was left out of the sampling strategy, and only one large private

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property could be visited. This resulted in a good representation of the climatic ranges, but

a general bias of the sampling sites towards higher altitude and low disturbance sites. The

vast majority of areas that were not sampled during this study are young forests that are

held by private landowners, and that are usually allocated to exotic pine plantations after a

few years of regeneration (Lara et al. 1996; Echeverria et al. 2006). Further details about

site characteristics and representation can be found in Appendix 1 and in Meynard and

Quinn (2008).

Within each site, we surveyed birds at 6–23 points along forested trails, yielding a total

of 147 survey points. Survey points were at least 250 m apart, within 50 m of an existing

trail, and at least 50 m from any forest border. Trails were generally narrow and we rarely

encountered visitors. Forest bird surveys were carried out during three consecutive days

between dawn and 11 a.m. An experienced observer recorded all birds heard or seen at

each point during 8 min within a 50 m radius. This is a standard bird point count protocol

in the region, since it has been shown to allow detection of more than 90% of bird species

present at a site (Jiménez 2000). Each site was surveyed during two consecutive breeding

seasons, except for Los Cipreses (2003) and Villa Baviera (2004), which were surveyed

during one breeding season.

Fig. 1 Map of the study area. Country borders and main administrative subdivisions are shown forreference. Surveys sites: 1, Reserva Nacional (R.N.) Rı́o Los Cipreses; 2, R.N. Radal Siete Tazas; 3, R.N.Altos del Lircay; 4, R.N. Los Ruiles; 5, Villa Baviera; 6, Parque Nacional (P.N.) Nahuelbuta; 7, R.N. Ralco;8, P.N. Conguillio Los Paraguas; 9, P.N. Huerquehue

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Environmental data

Climatic data related to rainfall, temperature, and altitude were obtained from the

Worldclim database (Hijmans et al. 2005). The data were available in a regular grid of 30

degree seconds, and were then re-sampled to an equal area grid of 0.72 km2. Only grid

cells that had some proportion of native forests were considered. The different climatic

variables were highly correlated. They were therefore reduced using a principal component

analysis (PCA), and the three main axes were used as predictors in the following regression

analyses. Vegetation characteristics were obtained from the national vegetation inventory

available as vector data (CONAF-CONAMA-BIRF 1999). Since we were mainly inter-

ested in the conservation of forests, all vegetation types that were not native forests,

including native scrublands, exotic pine plantations, and mixtures of native forests with

significant proportions of exotic vegetation as identified in the national vegetation

inventory, were excluded from the analysis. There were eight native forest types in our

study area, but one type (Evergreen) was only marginally present since its main distri-

bution is centered further south. For this reason we excluded the Evergreen forest type

from the conservation planning targets.

Values for canopy height given in the national vegetation survey were categorized as

\20 or [20 m. Values for forest stage were regrouped as young-intermediate forest andmature forest based on the classification provided in the vegetation survey. For each

categorical variable, we calculated the percentage of each type in a grid cell, rather than

using the categorical value per se.

Distribution models

We eliminated from the analysis all bird species that were scrubland specialists sensuVuilleumier (1985) and that appeared occasionally in our surveys (in \10 survey points),which left 26 species to be modeled. All species remaining in the analysis are therefore

either strict forest specialists or regularly use the forests, even though they may also be

seen in the surrounding scrubland. We also divided these species into ‘‘common’’ and

‘‘rare’’ species, according to their frequency of occurrence. However, our frequency

classification applies to a group that is already highly dependent on the forests, and

therefore rare in this sense.

Distribution models for each species were built using the statistical package R-2.4.1.

(R-Development-Core-Team 2005). We used Generalized Additive Models (GAM)

(Hastie and Tibshirani 1990) within the R package mgcv. The variables included aspredictors were the three PCA axes from climate data, percent of each grid cell covered

by forest, percent covered by forests of \20 m canopy height, and percent covered byyoung and intermediate forest. Residuals of all regressions were checked for spatial

autocorrelation using a Moran’s I test (Haining 2003). Latitude and longitude were

included in the model if spatial autocorrelation in the residuals was significant. We used

the Akaike Information Criterion (AIC) to select a subset of variables for each species

(Burnham and Anderson 2002).

If a species was present in C50% of the surveyed points, we classified it as a

‘‘common species’’ and relative abundance was used as the response variable. Abun-

dance data were log-transformed to improve normality using the function f(x) = log(x ? 1) (Steel et al. 1997). Model predictions over the entire study area were dividedinto quartiles. Only the upper 50% abundance predictions were considered in the con-

servation planning scenarios.

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If the species was present in \50% of the surveyed points, we classified it as a ‘‘rarespecies’’ and presence or absence was used as the response variable in a GAM with a

binomial distribution and a logit link (Hastie and Tibshirani 1990).

To assess model predictive ability for common and rare bird species, the data were

divided into a training set (90% of the data) and a testing set (the remaining 10%; Latimer

et al. 2006). We calculated a measure of predictive ability by comparing predicted versus

observed values in the testing set and repeating this procedure 100 times. For common

species, we calculated the Spearman correlation value between predicted and observed

abundance. For rare species, we calculated the area under the receiver operator curve or

AUC, as well as the kappa statistic, sensitivity and specificity (Fielding and Bell 1997). All

these statistics have been widely used to evaluate species distribution model performance

(Fielding and Bell 1997; Latimer et al. 2006; Wisz et al. 2008). To predict species presence

and absence, we divided the predicted probability of occurrence into habitat and no-habitat

for rare species by taking the maximum Kappa probability value as the cut-off probability

threshold (Fielding and Bell 1997).

We used these models to predict the species distribution over the entire study area.

Based on the prediction maps, we calculated the area available for each species within

currently protected areas and CONAMA sites, and the total area of distribution for each

species within forested regions. We used models for common species with an adjusted

R2 [ 0.45, and for rare species models with AUC [ 0.8 as inputs in subsequent conser-vation planning scenarios. The same exercises were carried out using all species, using

only the best modeled species or using only presence/absence models, yielding qualita-

tively similar results (results not shown).

We also considered using range maps found in NatureServe (http://www.nature

serve.org/) to compare the results of the conservation planning exercises. However, these

maps would predict the presence of all the bird species concerned in all forested areas,

because forest specialization as well as altitudinal or latitudinal range were the main

criteria used by naturalists to draw the maps (for example see Fjeldsa and Krabbe 1990) but

environmental variability within those boundaries is usually not considered. Using these

maps would render the conservation planning exercises completely uninformative, and

therefore this approach was discarded.

Conservation planning scenarios

We used MARXAN v1.8.10 (Ball and Possingham 2000) to select sites of potential interest

for different conservation planning scenarios. This program was designed to implement a

systematic reserve selection in which a set of conservation targets are achieved while costs

are minimized, a strategy that is commonly known as systematic conservation planning or

systematic reserve selection (Ball and Possingham 2000; Balmford 2003). The conserva-

tion targets were birds (common and rare) and forest types, depending on the conservation

scenario examined (detailed below). Notice that this strategy will reduce redundancy

between sites as long as conservation targets (minimum area for each species or forest

type) are met.

Systematic conservation planning was implemented using simulated annealing (Poss-

ingham et al. 2000). We carried out 1,000 runs, with 1 million iterations per run, where the

planning units were defined as the grid cells of 0.72 km2 used in the previous modeling

process. The currently protected areas were locked into the reserve system. This means that

the existing reserves remain in the reserve network throughout the analysis and in the final

solution. CONAMA sites were given preference over the rest of the non-protected planning

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http://www.natureserve.org/http://www.natureserve.org/

units when selecting new sites by including them in the initial seed reserve. However if a

CONAMA site did not contribute significantly to meeting the conservation objectives it

was not retained in the reserve network. All other sites within the forest areas were

considered as available for conservation.

Eight conservation planning scenarios were carried out using these settings (Table 1). In

scenario 1, birds were not considered in the planning process and the conservation target

was 10% of the current area for each forest type. In scenario 2, the conservation target was

10% of the current area of each forest type or a minimum area of 10,000 ha per forest type

(whichever was higher). This constrained the protection of the rare schlerophyllous type to

100% of its current area, which is only 6,500 ha (see Appendix 6). In scenario 3 the

conservation target was 10% of the current area or a minimum area of 50,000 ha for each

forest type (whichever was higher). Here a second rare forest type, Austrocedrus chilensis,is protected to 100% (Appendix 6). In scenario 4 the conservation target was set as both

10% of area for each forest type and 10% of the distribution of each common and rare bird

species distributions. Scenario 5 considered only 10% of the area of distribution for rare

bird species, while scenario 6 considered only 10% of area of distribution of common

species. Finally, scenarios 7 and 8 considered forest types with a minimum area as in

scenarios 2 and 3 respectively, plus 10% of area of distribution for all birds. Notice that

conservation scenarios 1 to 3 do not include any requirements for bird species, whereas the

following scenarios do.

Results

The first three axes on the PCA of climatic variables explained a large proportion of the

variance (99.6%) in environmental factors (Appendix 2) and were used as predictors in the

GAM models. Of the 42 species detected, 61% were endemic to the Patagonian temperate

forest, and another 19% were endemic to southern South America (Vuilleumier 1985). Of

these, twenty-six species were considered as forest specialists or forest-scrubland gener-

alists, while the rest are mainly scrubland specialists that occasionally visit nearby forests

Table 1 Summary of thedifferent conservation scenarios

Scenario Conservation target expressed in terms of areaof distribution

Proposed byCONAMA

10% of the distribution of all existing ecosystems.In practice, sum of currently protected areas andCONAMA sites

Scenario 1 10% of each forest type

Scenario 2 10% of each forest type with minimum area of10,000 ha per type

Scenario 3 10% of each forest type with minimum area of50,000 ha per type

Scenario 4 10% of each forest type ? 10% of each birdspecies

Scenario 5 10% of each rare bird species

Scenario 6 10% of each common bird species

Scenario 7 10% of each forest type with minimum area of10,000 ha per type ? 10% of all bird species

Scenario 8 10% of each forest type with minimum area of50,000 ha per type ? 10% of all bird species

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(Vuilleumier 1985; Cornelius et al. 2000). These were the twenty-six species considered

for modeling.

The percentage of variance in abundance explained for the ten common bird species

modeled ranged from 18% for the Red-Eye Diucon (Xolmis pyrope) to 63% for the ThornTailed Rayadito (Aphrastura spinicauda). Five out of ten modeled species had anR2 [ 0.45, which translated into correlation values between predicted and observedabundance in the testing set [0.65 for those five species retained in the conservationplanning scenarios (Table 2). The AUC value for the 16 rare species ranged from 0.660 to

0.906, with 11 species having an AUC [ 0.8 (Table 2). Additional performance measuresfor these models can be found in Appendix 3. Maps for individual species are in general

agreement with previous regional scale descriptions of the species distributional ranges

(Fjeldsa and Krabbe 1990), and are available from the authors upon request.

Conservation planning scenarios

Although the conservation planning scenarios were run using all bird species as well as

taking only the best modeled species, results were similar in both cases. We present here

results using only the best modeled species. Considering model performance, 15 bird

species were included in these conservation planning scenarios. However, area of pro-

tection was calculated for all species modeled at the end of each run. Results for each

species and vegetation type are presented in the Appendices 4–6.

Among the bird species with less total distribution area in the region were some species

that can be found in non-forest habitat types such as the Tufted-Tit Tyrant (Anairetesparulus) and some strict forest specialists, such as the Chesnut Throated Huet-Huet(Pteroptochos castaneus), with only 2.5% of its distribution within the currently protectedareas (Appendix 5). The forest types Austrocedrus chilensis, Nothofagus obliqua–N. gla-uca and the Schlerophyllous types were underrepresented in the currently protected areas.Although they were better represented in CONAMA sites, their total area within priority

sites would remain low considering their current remaining area (often\25,000 ha; Fig. 2;Appendix 6).

The conservation scenario that required the least total additional area is scenario 6

(protecting 10% of common bird area of distribution) which represents 15.4% of the

currently forested areas within the study region (Fig. 2). However, this strategy leaves

eight out of 26 bird species (31%) with \10% of their distribution protected, and four ofthe forest types under-represented (Fig. 2).

The scenario requiring the most area is the one proposed by CONAMA, where the sum

of currently protected areas and CONAMA sites is 588,000 ha representing 43.1% of the

remaining forested area in the region (Fig. 2; Appendix 4). Under this scenario, all bird

species and forest types are represented at the 10% level of their current area (Fig. 2;

Appendices 5, 6). However, the total area protected under the proposed scenario for the

two most restricted forest types, the schlerophyllous and the Austrocedrus chilensis types,would be\1,500 and\15,000 ha respectively, while a very large area would be added tothe most common forest types (Appendix 6).

Scenario 2 (protecting 10% of each forest type with a minimum area of 10,000 ha) is an

intermediate strategy in terms of the amount of area required. This would protect 100% of

the schlerophyllous type and only one bird species would have \10% of its distributionprotected (even though this scenario does not include any bird distribution requirement). At

the same time, under this scenario all the forest types would be protected with a minimum

of 10,000 ha, and the total area required would be 250,000 ha less than currently protected

Biodivers Conserv

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Biodivers Conserv

123

areas ? CONAMA sites (Fig. 2; Appendices 4–6). This strategy also contrasts with the

proposed CONAMA plan in that the additional area for the most common forest types is

greatly reduced, while the area for the rarest forest types is increased.

The conservation scenarios differed significantly in their geographic distributions

when no minimum area was set for each forest type, in contrast to scenarios specifying a

minimum area of either 10,000 or 50,000 ha per forest type (Fig. 3). When a minimum

area is not required (scenarios 1, 4–6, see Table 1), the algorithm does not select any one

site more than 5% of the time (other than the currently protected sites which are locked

into the reserve system; Fig. 3). Therefore, individual sites are highly substitutable under

those conservation scenarios, and planners have flexibility to consider factors not in the

analysis, such as land prices and willing sellers to successfully achieve conservation

goals.

Adding a minimum required total area to each forest type (scenarios 2, 3, 7, 8) dra-

matically changes this flexibility. In particular, some sites in the central valley and costal

range are selected repeatedly (e.g., [90% of the time) and are therefore highly irre-placeable (Fig. 3c, d, h, i). Although CONAMA sites north of 36�S are a valuable additionto currently protected areas, there is a clear lack of representation of sites in the central

valley and coastal range under both the 10,000 or 50,000 ha minimum area scenarios

(Fig. 3).

Adding the conservation target of protecting 10% of the distributional area occupied by

each bird species leads to similar results (Scenarios 5 and 6, Fig. 3f, g). Most conservation

units, within or outside the priority sites, are selected \5% of the time, revealing highsubstitutability of sites, suggesting that birds could be protected in a variety of areas, and

that many particular configurations of a conservation network could be similarly effective.

0

10

20

30

40

50

60

C C+P 1 2 3 4 5 6 7 8

% Area Required% Unprotected Bird Species% Unprotected Forest Types

**********

Fig. 2 Summary of results from the different conservation scenarios. Open bars represent the percent areaof remaining forest required under each conservation scenario; full bars represent the percent of bird speciesthat remain unprotected at the 10% level; striped bars represent the percent of forest types that remainunprotected at the 10% level. Asterisks indicate values of 0%. Details of each strategy, with thecorresponding area for each bird species or forest type for each conservation strategy can be found in theappendices 5 and 6. C, currently protected areas. C ? P, currently protected areas ? CONAMA sites.Numbers indicate conservation scenarios one through eight (see Table 1)

Biodivers Conserv

123

StatusProtected

CONAMA Site

Other

Percent Selected0 %

0.1 - 10 %

10.1 - 25 %

25.1 - 90 %

90.1 - 100 %

(a) (b) (c)

(d) (e) (f)

(g) (h) (i)

Fig. 3 Results from the simulated annealing using the following conservation targets: a distribution ofremaining forests in the study area and their current status; b scenario 1: 10% of each forest type; c scenario2: 10% of each forest type with a minimum area of 10,000 ha; d scenario 3: 10% of each forest type with aminimum area of 50,000 ha; e scenario 4: 10% of each forest type and 10% of bird habitat area; f scenario 5:10% of habitat for rare bird species; g scenario 6: 10% of habitat for common bird species; h scenario 7:10% of each forest type with a minimum area of 10,000 ha and 10% of bird habitat; i scenario 8: 10% ofeach forest type with a minimum area of 50,000 ha and 10% of bird habitat

Biodivers Conserv

123

This is corroborated by the fact that scenarios that considered only forest types (scenarios

1–3) tend to show a good representation of bird species (Fig. 2; Appendices 4–6). On the

contrary, scenarios that considered only bird distributions (scenarios 5 and 6) generally fail

to protect 10% of each forest type (Fig. 2; Appendices 4–6).

Discussion

This study reveals at least two interesting results that we want to emphasize. First, sites

selected as conservation priorities vary greatly, depending on the minimum area assumed

to be needed to protect each conservation target and the methodology used. In particular,

requiring minimum areas for each forest type dramatically changes the perceived substi-

tutability of selected sites. This is a natural result, as adding a minimum area of 10,000 ha

forces the algorithm to select all available sites where there is some schlerophyllous forests

left. Adding a more stringent requirement of 50,000 ha will also force the selection of all

sites with the forest type Austrocedrus chilensis. Second, the protection of forest typesserves as an effective umbrella to achieve conservation goals related to individual forest

bird species.

Regarding the first result, there is no general rule dictating the minimum area required

to protect a particular species or forest type. In a global analysis of conservation pri-

orities for vertebrates, for example, Rodrigues et al. (2004) set arbitrary targets

depending on total species distribution area. Species that had a distribution range

\1,000 km2 (100,000 ha) had a conservation target of 100% of their area, those having adistribution range [150,000 km2 (15,000,000 ha) had a target of 10% of their area, andspecies with intermediate distribution ranges had intermediate conservation targets. The

rationale for this choice was that species with smaller distribution ranges tend to be more

vulnerable to extinction and that the total area for a given percent of protection has to

have a minimum to sustain a viable population (Rodrigues et al. 2004). However, their

study was carried out at a very coarse resolution (one-half-degree cells), which was

necessary due to the large extent of the study, but can lead to an overestimation of the

area required to protect individual species, as well as a mismatch between coarse and

fine scale conservation priorities (Shriner et al. 2006). Previous large scale species dis-

tribution mapping efforts in Southern South America have been largely based on

literature reviews and envelope maps drawn by experts at a coarse scale (Meynard et al.

2004; Tognelli et al. 2005; Soutullo and Gudynas 2006; Tognelli et al. 2008). These

efforts usually over-estimate the area of occupancy of species (Barry and Elith 2006;

Solano and Feria 2007; Loiselle et al. 2008), but they can guide the site identification at

national or continental scales (Knight et al. 2007). In Chile, Tognelli et al. (2008) draw

distribution maps of vertebrate species, including birds, reptiles, amphibians, and

mammals, using literature reviews and expert opinion, and carried out a conservation

planning exercise using 100 km2 planning units. These authors also found inadequacies

with CONAMA sites in capturing the biodiversity of the taxa they considered. However,

the total area required to achieve their conservation target was more than 50% of the

area of the country. Our results demonstrate that considering vegetation can greatly

change these results, and whether or not CONAMA sites meet their conservation goal

depends greatly on whether or not the 10% area criterion fits the minimum population

size for all vegetation types.

Shriner et al. (2006) recently showed that total area required under each conservation

scenario increases with the cell size of the species distribution data used. Therefore, the

Biodivers Conserv

123

large area required to protect vertebrates in Tognelli et al. (2008) could have been an

artifact of the resolution used for the analysis. Higher resolution studies such as that

presented here complement coarser scale efforts, such as that of Tognelli et al. (2008), by

providing the fine detail needed to carry out reserve implementation. We therefore agree

with Shriner et al. (2006) that generating finer resolution distribution maps should be

a priority for conservation practitioners.

In contrast to Rodrigues et al. (2004) and Tognelli et al. (2008), our study was carried

out using smaller planning units (\1 km2) which have a better correspondence with thescale at which conservation management is likely to take place and at which birds respond

to habitat quality. Therefore, setting a minimum area of 1,000 km2 (100,000 ha) would

seem inappropriate. On the other hand, to our knowledge, there are no available population

data that would allow us to determine objectively which is the minimum population size

and density, and therefore the minimum area required to sustain viable populations for all

species or vegetation types concerned. This minimum total area is likely to depend on the

degree of fragmentation of the remaining forests as well as the dispersal ability of the

different species. In this case, the forest types with the smallest total remaining area are

also the most fragmented (Echeverria et al. 2006), which is likely to increase the area

required for an effective conservation of the most threatened types. Filling the gap

regarding these highly vulnerable vegetation types is therefore critical to designing sound

conservation plans in the near future.

Regarding the second important result, our study also suggests that combining broad

scale considerations and species-specific requirements leads to a more robust evaluation

and planning tool than considering only one or the other. This strategy has been

proposed before to reduce uncertainties related to individual species distributions and to

take advantage of coarse filter data (e.g., vegetation maps) at the same time (Lombard

et al. 2003; Hess et al. 2006; Fjeldsa 2007a). However, this combination can only be

achieved if enough coarse and fine filter elements are available to carry out the

analysis. Birds have been advocated as effective biodiversity indicators due to the wide

availability of information regarding their distributions and the relative ease of sampling

(Scott et al. 1993). However, the correlation between bird diversity patterns and other

taxa may be dependent both the taxon and the particular biogeographic region studied

(Prendergast et al. 1993; Larsen et al. 2007). In our study, protecting bird species alone

did not adequately protect all forest types, suggesting that some taxa may be better

represented by following a broad-filter approach based on vegetation types, rapid survey

methods, or mapping of more complex ecological surrogates for other target taxa

(Raphael and Molina 2007). This result is very important from the conservation

planning and monitoring perspectives. It suggests that putting more effort into large-

scale monitoring and inventorying of vegetation could be effective to protect at least

some groups of vertebrates. This is economically and logistically attractive, since

satellite analysis can be used over long periods of times and large spatial scales to

monitor vegetation, while monitoring vertebrate populations is much more demanding

and expensive.

The generalization of these results may be limited by the particular biogeographic

histories of each region, as well as by the limitations of the sampling and modeling

strategies used here. Several authors have noted the lower performance of different

distribution model strategies when sample sizes are small or limited in their geographic

extent (Feria and Peterson 2002; Kadmon et al. 2004; Wisz et al. 2008). Our bird

distribution models suffer from some of these flaws due to the inaccessibility of private

lands and the clustering of national parks and reserves in coastal and Andean regions.

Biodivers Conserv

123

However, it is important to note that our analysis reveals some important inconsistencies

of the CONAMA strategy, even in the absence of the bird distribution data. Even when

comparing CONAMA sites to the distribution of the different vegetation types without

considering bird species distributions, CONAMA sites are less efficient, both in terms of

total area required as well as in terms of the effective protection of each one of those

forest types (Appendix 6). Moreover, previous research has shown that distribution

models are generally robust to sampling bias, but are more sensitive to the representation

of range values for the predictor variables (Kadmon et al. 2004; Loiselle et al. 2008). In

addition general broad distribution maps, such as those available in NatureServe, do not

allow assessment among different quality habitats in this region (almost all birds are

predicted to be present in every forest of the region). Therefore, using these data instead

of our distribution models would have just exacerbated the finding that bird protection is

more flexible than vegetation. Furthermore, our conservation planning analysis was

carried out using all bird species, only the best models, only rare species or only

common species, and in all cases we came to the same conclusion: birds can be pro-

tected in a variety of sites with much more flexibility than vegetation types can. This

could be a particularity of the birds of Patagonian forests. Vuilleumier (1985) argued, in

discussing the origin of this avifauna, that birds of the Patagonian temperate forests are

not highly specialized: although there are forest specialists, those forest specialists tend

to be found in all types of forests. Some of those species have been later found to be

more vulnerable to habitat isolation and other disturbances (Cornelius et al. 2000; Cofre

et al. 2007), which are not necessarily correlated to forest type. All this leads us to

believe that our analyses regarding bird distributions and their implications in conser-

vation planning are realistic.

While this paper compared the potential effectiveness of a politically driven process and

the recommendations of systematic conservation planning specifically in Chile, the issues

raised—rapid land conversion, limited distribution information for most taxa, and the need

to develop a reserve system—are widespread in developing countries (Feria and Peterson

2002; Balmford 2003; Solano and Feria 2007). As elsewhere, the degree to which sys-

tematic conservation planning directed toward birds and bird habitat represent effective

surrogates for very different taxa, such as annual plants or most invertebrate groups, is

highly variable (Prendergast et al. 1993). Our study further suggests that basing conser-

vation planning solely on bird distributions will not be adequate for conserving major

vegetation types in the region, and that conservation in Chile would be better served by

incorporating broad vegetation filters into the conservation planning process.

Acknowledgments This research was supported by a Rufford Small Grant, a Jastro Shield fellowship, theUS National Biological Information Infrastructure program, a Fulbright fellowship to C. N. M. and theIniciativa Cientı́fica Milenio (P04-065-F) of the government of Chile. We thank the CONAF for providingall the necessary permits, and the hospitality of all the park rangers. Field assistance was provided by AnaMarı́a Venegas, Marı́a Angélica Vukasovic, Erik Inestroza and Erin Espeland. Art Shapiro, Steve Greco andan anonymous reviewer provided useful comments in early versions of this manuscript, Pablo A. Marquetprovided useful discussions regarding conservation in Chile. To all we are grateful. This is PRBO contri-bution #1663 and ISEM contribution #2009-012.

Appendix 1

See Table 3.

Biodivers Conserv

123

Appendix 2

See Table 4.

Appendix 3

See Table 5.

Table 3 Summary of general sampling sites characteristics with respect to the variables used as predictorsin the species distribution models

Variable name Sampling sites Study area

Range Mean ± SD Range Mean ± SD

PC1 -598.2–1373 179.9 ± 571.5 -1348.3–1,373.2 0 ± 612.1

PC2 -845.0–1,011.9 -167.7 ± 376.7 -999.8–1,419.9 0 ± 437.7

PC3 -570.0–1,102.1 195.4 ± 411.9 -981.1–1,140.4 0 ± 333.6

% Forest 0–100 87.1 ± 25.0 0–100 74.1 ± 25.1

% Forest \20 m 0–100 69.5 ± 34.2 0–100 65.4 ± 31.3% Young and intermediate forest 0–100 49.4 ± 42.7 0–100 57.5 ± 34.8

PC1, PC2, and PC3 represent the first three axes of a principal component analysis (see Appendix 2)

Table 4 Factor loadings for each environmental principal component axis used in the modeling process

PC1 PC2 PC3

Altitude 0.4068 -0.7677 0.4831

Annual mean temperature -0.0038 0.0339 -0.0288

Mean diurnal range 0.0139 -0.0136 -0.0140

Isothermality -0.0010 -0.0024 0.0016

Temperature seasonality 0.6684 -0.1106 -0.7293

Max temperature of warmest month 0.0109 0.0183 -0.0477

Min temperature of coldest month -0.0187 0.0321 -0.0153

Temperature annual range 0.0296 -0.0138 -0.0324

Mean temperature of wettest quarter -0.0120 0.0362 -0.0199

Mean temperature of driest quarter 0.0050 0.0318 -0.0368

Mean temperature of wettest quarter 0.0050 0.0328 -0.0392

Mean temperature of coldest quarter -0.0128 0.0352 -0.0193

Annual precipitation -0.5306 -0.5221 -0.3765

Precipitation of wettest month -0.0698 -0.0739 -0.0710

Precipitation of driest month -0.0164 -0.0131 -0.0059

Precipitation seasonality 0.0109 0.0093 -0.0045

Precipitation of wettest quarter -0.2186 -0.2402 -0.2170

Precipitation of driest quarter -0.0635 -0.0564 -0.0229

Precipitation of warmest quarter -0.0655 -0.0558 -0.0270

Precipitation of coldest quarter -0.2071 -0.2205 -0.1770

The first three axes explain 99.6% of the variance observed in the climatic variables. Variables are describedin detail in Hijmans et al. (2005)

Biodivers Conserv

123

Appendix 4

See Table 6.

Appendix 5

See Table 7.

Table 5 Additional performance measures for occurrence models

Species name Kappa Sensitivity Specificity

Anairetes parulus 0.757 0.914 0.902

Campephilus magellanicus 0.561 0.737 0.853

Colaptes pitius 0.550 0.773 0.880

Colorhamphus parvirostris 0.440 0.588 0.908

Columba araucana 0.313 0.333 0.956

Curaeus curaeus 0.483 0.522 0.935

Enicognatus ferrugineus 0.683 0.688 0.957

Eugralla paradoxa 0.699 0.941 0.858

Leptasthenura aegithaloides 0.575 1 0.854

Picoides lignarius 0.319 0.429 0.897

Pteroptochos castaneus 0.577 0.600 0.953

Pteroptochos tarnii 0.683 0.889 0.892

Scelorchilus rubecula 0.729 0.692 0.989

Scytalopus magellanicus 0.603 0.786 0.848

Sylviorthorhynchus desmursii 0.706 0.795 0.917

Zonotrichia capensis 0.696 0.784 0.918

While kappa is an overall measure of prediction success that takes into account both presence and absencepredictions, sensitivity deals with absence prediction success, and specificity deals with presence predictionsuccess. All indices range from 0 to 1, 1 representing perfect predictions

Table 6 Area required for eachconservation strategy

Total area here is the areacovered by the grids in theanalysis; in other words, it is onlythe fraction of the study area thathas a forest cover [0%

Conservation strategy Total area(thousands of ha)

% Total areaconsidered

SNASPE 183.4 13.5

SNASPE ? CONAMA sites 588.0 43.1

Experiment 1 260.7 19.1

Experiment 2 335.0 24.6

Experiment 3 451.7 33.1

Experiment 4 268.5 19.7

Experiment 5 241.8 17.7

Experiment 6 210.1 15.4

Experiment 7 334.9 24.6

Experiment 8 451.6 33.1

Biodivers Conserv

123

Tab

le7

Per

centa

ge

of

area

of

each

spec

ies

hab

itat

(acc

ord

ing

toth

esp

ecie

sdis

trib

uti

on

model

s)w

ithin

each

pro

tect

ion

cate

gory

(SN

AS

PE

or

CO

NA

MA

site

s)an

din

the

bes

tso

luti

on

for

each

of

the

eight

conse

rvat

ion

scen

ario

s

Spec

ies

Curr

ent

area

(th

ousa

nd

so

fh

a)%

of

curr

ent

area

SN

AS

PE

CO

NA

MA

Sce

n.

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cen

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Sce

n.

3S

cen

.4

Sce

n.

5S

cen

.6

Sce

n.

7S

cen

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An

air

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pa

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58

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cast

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17

2.6

2.5

32

.19

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32

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10

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33

.8

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26

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9.0

12

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2.3

Eu

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79

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3.6

13

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0.5

14

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0.0

10

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.31

0.4

14

.3

Pte

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toch

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tarn

ii5

04

.85

.87

.78

.81

0.1

12

.31

0.3

10

.07

.71

0.2

12

.6

Cur

aeu

scu

raeu

sa6

83

.96

.81

4.7

10

.41

3.3

19

.01

0.5

9.3

8.3

13

.81

9.8

Zono

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capen

sis

51

3.6

7.0

28

.01

0.6

20

.72

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11

.41

0.2

8.5

20

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8.2

Syl

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us

des

mu

rsii

49

2.9

7.1

10

.49

.81

1.7

13

.71

1.0

11

.29

.21

1.7

14

.2

Sep

han

oid

esse

ph

anio

des

97

4.8

7.5

21

.51

1.3

17

.42

3.6

12

.01

0.7

10

.01

7.3

23

.7

Tro

glo

dyt

esa

edo

n9

74

.87

.91

5.9

11

.01

5.0

19

.01

1.8

11

.31

0.0

14

.91

8.9

Car

du

elis

ba

rba

taa

97

3.8

8.4

15

.81

2.2

14

.71

8.6

12

.71

1.4

10

.11

4.6

18

.5

En

ico

gn

atu

sfe

rrug

ineu

s5

69

.78

.58

.71

2.0

12

.91

5.5

12

.41

1.5

10

.21

2.9

15

.4

Pyg

arr

hic

ha

sa

lbo

gul

ari

sa9

75

.09

.22

4.9

13

.41

8.5

24

.11

3.3

12

.01

0.8

18

.52

4.3

Tu

rdu

sfa

lkla

nd

iia

97

4.7

9.6

29

.31

4.4

19

.72

8.1

14

.61

2.4

10

.81

9.7

28

.3

Col

ap

tes

pit

iusa

68

8.1

9.7

21

.01

4.5

17

.72

3.3

14

.21

2.3

11

.11

7.7

23

.3

Pic

oid

esli

gn

ari

usa

39

0.5

10

.73

4.4

15

.51

7.0

25

.71

4.2

11

.41

1.9

16

.42

5.0

Ela

enia

alb

icep

s9

69

.31

0.7

16

.41

4.3

16

.32

0.5

14

.71

3.9

12

.11

6.3

20

.6

Lep

tast

hen

ura

aeg

ith

alo

ides

a3

39

.21

1.9

25

.51

6.0

17

.22

2.9

14

.81

2.9

13

.11

7.4

22

.4

Sce

lorc

hil

us

rub

ecu

la3

32

.01

2.8

20

.61

7.9

18

.52

4.6

17

.31

4.8

13

.91

8.6

24

.6

Ta

chyc

inet

am

eyen

ia9

74

.71

3.1

23

.21

7.3

18

.92

6.3

17

.51

5.7

14

.31

8.8

25

.8

Ap

hra

stu

rasp

inic

au

da

97

4.8

13

.91

7.4

16

.72

0.0

24

.91

7.6

16

.71

5.0

19

.92

5.0

Xo

lmis

pyr

ope

a9

73

.11

5.3

29

.92

0.3

21

.73

0.4

19

.41

6.9

16

.22

1.7

30

.2

Ph

rygi

lus

pa

tag

on

icu

s9

73

.41

5.4

18

.01

8.8

20

.22

5.4

18

.71

7.4

16

.52

0.0

25

.4

Biodivers Conserv

123

Tab

le7

con

tin

ued

Spec

ies

Curr

ent

area

(th

ou

san

ds

of

ha)

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Comparing alternative systematic conservation planning strategies against a politically driven conservation planAbstractIntroductionMethodsStudy area and bird surveysEnvironmental dataDistribution modelsConservation planning scenarios

ResultsConservation planning scenarios

DiscussionAcknowledgmentsAppendix 1Appendix 2Appendix 3Appendix 4Appendix 5Appendix 6References

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