Ecological Informatics 6 (2011) 364–370

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Ecological Informatics

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Predictive modeling of microhabitats for endemic birds in South Chilean temperateforests using Maximum entropy (Maxent)

Roberto Moreno a,⁎, Ricardo Zamora a, Juan Ramón Molina a, Angélica Vasquez b, Miguel Ángel Herrera a

a Department of Forest Engineering, University of Cordoba, 14071 Cordoba, Spainb ONG Ética en los Bosques, 4930000, Villarica, Chile

⁎ Corresponding author. Tel.: +34 957212210; fax: +E-mail address: z82mogar@uco.es (R. Moreno).

1574-9541/$ – see front matter © 2011 Elsevier B.V. Adoi:10.1016/j.ecoinf.2011.07.003

a b s t r a c t

a r t i c l e i n f o

Article history:Received 19 May 2011Received in revised form 12 July 2011Accepted 14 July 2011Available online 26 July 2011

Keywords:Microhabitat modelingMaximum entropyWildlife conservationChilean temperate forests

Temperate forests of Chile exhibit high biodiversity, which generates a wide range of habitats for wildlife.These valuable natural ecosystems have been affected by major natural and anthropogenic processes thathave reduced habitats, resulting in serious ecological problems, given both the high endemism of certainavian groups in these forests and the complexity of their habitat selection. Continued degradation andecosystem problems could lead to the extinction of such groups. In spite of this possibility, ecologicallyvaluable wildlife conservation is seldom integrated into forest management decision-making processes. Thisstudy aims to integrate wildlife into forest management, identifying potential habitats for two endemic birdsof high ecological value, the Black throated Huet-Huet (Pteroptochos tarnii), and the Ochre-flanked Tapaculo(Eugralla paradoxa). Both species inhabit an ecotonal area between evergreen and sclerophyllous forests,making them high-quality bio-indicator species for the degree of conservation of temperate forest. Theintegration of environmental information and a geostatistical model based on the criterion of maximumentropy (Maxent model) identifies the most important variables that explain the presence of each species.Pteroptochos tarnii is less restrictive in its choice of habitat than Eugralla paradoxa, requiring merely certaintopographical condition (elevation, ground slope and aspect). However Eugralla paradoxa requires not onlythe same topographical features, but also eco-geographical characteristics such as distance to trails,waterways and ecotones. Maxent analysis showed that for both species, the model most capable of predictingtheir choice of microhabitat was not random based, but rather one based on topographical and environmentalvariables. The integration of Maxent and Geographic Information Systems (GIS) tools could help to solveproblems of wildlife habitat conservation and forest planning.

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© 2011 Elsevier B.V. All rights reserved.

1. Introduction

The temperate forests of Chile enjoy high biodiversity, whichgenerates a multitude of habitats for wildlife and produces highspecies endemism, and are therefore regarded as a “biodiversityhotspot” (Myers et al., 2000). These valuable ecosystems have beenaffected by major natural and anthropogenic processes, which haveresulted in the reduction of wildlife habitat (Echeverría et al., 2007),resulting in turn in serious ecological problems, including degradationof environmental services, animal and plant biodiversity and carbonstorage potential.

High complexity in habitat selection puts species at the risk ofextinction. In spite of this, the conservation of fauna of high ecologicalvalue is rarely incorporated into forest planning and management inChile.

This study attempts to integrate wildlife conservation into forestmanagement. Since fauna are usually distributed neither homoge-neously nor randomly (Bustamante and Grez, 1995; Estades, 1997;Estades andTemple, 1999;Hall et al., 1997) itwould beuseful to identifyrelationships between the environment and the habitat of particularspecies. This would enable us to identify general criteria for thesustainable management of biodiversity in Chilean temperate forests.

Geographic Information Systems (GIS) permit climatic, ecologicaland topographical variables to be rapidly and directly associated atlandscape scale with points of presence of species (Anderson et al.,2002; Peterson et al., 2006), thus enabling us to predict speciesdistribution in the context of biodiversity analysis (Illoldi-Rangelet al., 2004).

Some studies have analyzed the selection of nesting habitat forbird species, such as the Black-chested Buzzard-Eagle (Geranoaetusmelanoleucus), the Bearded Vulture (Gypaetus barbatus) and theSpanish Imperial Eagle (Aquila adalberti) using habitat predictionmodels at landscape scale (Bustamante, 1996; Bustamante et al.,1997; Donázar et al., 1993). A species distribution can be generatedthrough either deterministic or geostatistical methods according to

Table 1Environmental and topographic variables.

Category Range

Distance toecotones(m)

Distance towaterways(m)

Distanceto trails(m)

Elevation(masl)

Groundslope(%)

Aspect

1 0–50 0–50 0–50 100–200 0–30 Shade2 50–100 50–100 50–100 200–300 30–60 Partial shade3 N100 N100 N100 300–400 N60 Sun

exposure

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the climatic, topographical and ecological attributes of the study area.Kriging (Knotters et al., 1995), Cokriging (Lesch et al., 1995), Garpanalysis (Peterson et al., 2002) and the Maxent model (Phillips et al.,2006; Yost et al., 2008) are some of the most common processes usedto model geographical species distribution. We chose Maxent becauseit tends to produce better models than other methods for habitatdistribution predictions (Baldwin, 2009; Costa et al., 2010).

Kriging and Cokriging are geostatitical methods that interpolate one(Kriging) or more variables (Cokriging) based on random field valuesand their spatial Distributions. However, they are not specific ecologicalniche-modeling algorithms, and are usually used to predict potentialspecies distributions. They do not uses values related to the presence orabsence of the species from field observations. On the other hand, GarpandMaxent models can test the species richness and composition of anunsampled area of conservation interest using ecological niche-modeling algorithms. Garp attempts to identify correlations betweenthe presence or absence of a species, and environmental parameters.The conjunction of ranges for all of the variables indicates geographicregions where conditions are suitable for the species. Maxent fits aprobability distribution for each species based on the principle that thebest explanation of unknown phenomenawill maximize the entropy ofthe probability distribution. The differences between the Garp andMaxentmodels can be seen in their potential distribution results.WhileMaxent producesmoredetailedfine-grainedpredictions and is additive,Garp tends to produced overpredictions (Baldwin, 2009; Costa et al.,2010; Phillips et al., 2006).

Our study modeled the potential microhabitats of two species ofbirds using GIS and the Maxent model of maximization of entropy.Both species, the Black-throated Huet-Huet (Pteroptochos tarnii), andthe Ochre-flanked Tapaculo (Eugralla paradoxa), are endemic to thetemperate forests of Chile, and may play a role as bioindicators of thestate of conservation of such forests.

2. Methods

2.1. Study area

The studywas performed in a 435 ha forest area, whichwe regard assufficiently large for microhabitat modeling. The area forms part of themountain range of the central depression of the IX Region of Araucanía(Fig. 1). The climate is cold and humid, with average annual rainfall of1311 mm (Ramírez et al., 1988), mostly between May and September,and has an average annual temperature of 11.6 °C. The mature andyoung forests are dominated by Nothofagus spp. The forest ecosystemsof this area are transitional between the evergreen and sclerophyllousforests of the central Chile Mediterranean region (Frank and Finck,1998). The area lies within the biodiversity hotspot that covers thetemperate forests of South America (Myers et al., 2000).

2.2. Bird sampling

We studied two species of the family Rhinocryptidae, the Black-throated Huet-Huet (P. tarnii) and the Ochre-flanked Tapaculo(Eugralla paradoxa), both endemic to the temperate forests of SouthAmerica (De Santo et al., 2002; Reid et al., 2004) and regarded asbioindicators of the degree of forest conservation (Amico et al., 2008).These small birds occupy the ground level and forest understory. Theirhome range has been estimated at between 1 and 4 ha per individual(Castellón and Sieving, 2007; Sieving et al., 2000), so our study area(435 ha) is enough to cover the requirements of the study. Littleinformation exists about their diets, and some authors claim that theyare insectivorous, while others regard them as omnivores (Armestoet al., 1996; Rozzí et al., 1996).

To monitor the birds we used a method based on Blondel et al.(1981), recording both species as they were seen and heard at fixedcensus stations (CS). Four repetitions were performed at each spot, to

calculate the frequency of presence of each species (Bustamante et al.,1997; Donázar et al., 1993).

In order to avoid recording the same individual in two consecutiveCS, twenty listening stations (systematically separated by 250 m)were placed along set paths (Bibby et al., 1992). Each registration wascarried out as follows: on arrival at a CS, the observer waited in silencefor fiveminutes before recording bird sounds or sightings for a furtherfive minutes (Blondel et al., 1981).

All locations at which either of the two species was present wereconsidered to be a microhabitat for that species.

2.3. Environmental and topographical variables

At all the sampling locations, the microhabitat was characterized interms of ecological and topographical variables such as ground slope,elevation and aspect. Some of these have been employed in previousstudies (Costa et al., 2010), but we also included new variables such asdistance to waterways, trails and ecotones (Table 1).

2.4. Maxent model

The Maxent model is a useful technique for predictive modeling ofgeographical species distribution on the basis of the most significantenvironmental conditions Phillips et al. (2004, 2006). Maxent is basedon a machine-learning response that makes predictions from incom-plete data. This approach estimates the most uniformly distributed“maximum entropy” of sampling points compared to backgroundlocations, taking into account the constraints derived from the data. Themaximum entropy algorithm is deterministic and converges to themaximum entropy probability distribution (Baldwin, 2009; Berger etal., 1996; Phillips et al., 2006). A given location may be allocated to an“absence” or “presence” set, depending on whether a given speciesmight or might not be present. The environmental variables mostclosely associated with the presence of a species can be extrapolated tosimilar biotopes to in order to identify the probable geographicaldistribution of the species. Multivariate data analysis is a crucial part ofthe methodology (Hair et al., 1999; Mota et al., 2002). For each species,themodel starts with a uniform distribution, and performs a number ofiterations based on the most significant environmental variables untilno further improvements in the prediction are made.

When using Maxent, a given space “x” represents the set of discretegrid cells covering the study area. Each grid cell of “x” (x1, x2, …xm) isprovided with the most significant environmental variables defined,such as topographical and geographical ones. The probability distribu-tion function in the study area, “P(x)”, was implemented by an efficientalgorithm (Dudík et al., 2004) basedon themaximumentropy approachto the environmental characteristics. This approach can be shown to beidentical to the Gibbs distribution that maximizes the product of theprobabilities of the sample locations (x1, x2, …xm), where the Gibbsdistribution takes the form:

P xð Þ = exp c1�f1 ×ð Þ + c2�f2 ×ð Þ + …� �

= Z ð1Þ

Fig. 1. Study area location.

366 R. Moreno et al. / Ecological Informatics 6 (2011) 364–370

where “P(x)” is the probability function, “c1, c2 …” are constants, “f1,f2,…” are the functions for each environmental variable and “Z” is ascaling constant that ensures that “P” sums to 1 across the study area.

The Maxent distribution is calculated for the set of grid cells thatcontains data on all of the environmental variables. We used 25% of thesample points to test whether Maxent predictions (training data) arebetter than random prediction. Maxent uses the area under the curve(AUC) to evaluate the model statistically, and is among the statisticsmost frequently used to assess ecological niche modeling and nest-siteselection (Baldwin, 2009; Barry and Elith, 2006; Peterson et al., 2007;Peterson andNakazawa, 2008; Yost et al., 2008). In the evaluation of themodels produced by the two species, we used a binomial test based onthe omission rate and predicted areas given by themodel. The omissionrate is the proportion of the sample units lying within grid cells that arepredicted to beunsuitable for the species,while the predicted area is theproportion of grid cells that are predicted to be suitable for the species.Although it may be not sufficient, a low omission rate is a necessarycondition for a good model (Anderson et al., 2003). Receiver operatingcharacteristic (ROC) analysis of Maxent provides an objective measure

of model performance, while two values, specificity (absence of error ofcommission) and sensitivity (absence of error of omission), are used totest the predictions (Costa et al., 2010).

3. Results

Multiple correspondence analyses enabled us to estimate the mostrelevant variables for the presence of both species. There were greaterrestrictions on or stricter conditions for the presence of Eugrallaparadoxa. Thus, while the Pteroptochos tarnii model only requiredtopographic variables (elevation, ground slope and aspect) to obtainsuitable statistical results, themodel for Eugralla paradoxa required bothtopographic and environmental variables such as elevation, aspect anddistance to trails to explain this species' distribution. A second filteringof variables was performed using the Maxent jackknife test

Fig. 2 shows the jackknife test results for Pteroptochos tarnii. Theenvironmental variable with the highest gain (most significant) inisolation was ground slope, which therefore appeared to contain mostuseful information by itself. The environmental variable that most

Fig. 2. Pteroptochos tarnii and Eugralla paradoxa Jacknife test.

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decreased the gain when it was omitted was ground slope, whichtherefore appeared to have themost information that was not presentin the other variables The topographical variables elevation and

Fig. 3. Potential microhabitat modeling for P

aspect and the geographical variables trail distance and ecotonaldistance between forest and treeless areas were used to model thedistribution of Eugralla paradoxa. Fig. 2 also shows the jackknife test

teroptochos tarnii and Eugralla paradoxa.

Fig. 4. Receiver Operating Characteristic curve (ROC) for Pteroptochus tarnii and Eugralla paradoxa showing different Area Under Curve (AUC) values.

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results for that species. In this case, the environmental variable withthe highest gain when used in isolation was elevation, whichtherefore appeared to contain the most useful information by itself.The environmental variable that most decreased the gain when it wasomitted was also elevation, which therefore appeared to have themost information that was not present in the other variables.

The output models for Pteroptochus tarnii and Eugralla paradoxaindicated probabilities of presence ranging from 0 and 1 (Fig. 3). Forexample, the grid cell predicted to have the best conditions for eachspecies, according to the model, will have the cumulative value 1,while cumulative values close to 0 indicate predictions of unsuitableconditions. Since Maxent gives prediction values ranging from 0 to 1,we divided these into three categories (b0.4 as “UnselectedMicrohabitat”, 0.4–0.6 as “General Microhabitat” and N0.6 as“Preferred Microhabitat”) for easier interpretation.

Fig. 4 describes the predicted area (x-axis) and sensitivity (y-axis)for training data (medium-dark line), test data (dark line) andrandom prediction (light line). The closer the sensitivity value is to 1,the better is the prediction. It can be seen that for both species theMaxent model is better than the random one, with areas under thecurves (AUC) higher than 0.5 (random distribution case): 0.868 forPteroptochus tarnii and 0.994 for Eugralla paradoxa.

4. Discussion

This study shows that the preferred microhabitat of P. tarnii isareas with slopes greater than 30%, more rugged areas and withpatches of both partial and full shade. Relating this information to theresults of Amico et al. (2008) and Moreno (2003), suggests a widerrange of conditions that provide a potential microhabitat for thisspecies. P. tarnii thus appears to depend on high humidity and shadeconditions. These conditions are usually accompanied by others likevegetation in high density, a large basal area and abundant forestunderstory cover; according to the authors mentioned above, thisspecies also prefers areas with plenty of fallen branches and woodyspecies. Although it is possible that differences in these variables maylead to different interactions of these species with other animals (i.e.predators) we think that the presence of these birds can be explainedby environmental variables in a microhabitat level since their homerange is small in both cases.

E. paradoxa appeared to select areas far from roads or trails (70 m)and distance from ecotones (over 50 m), preferring low-elevationareas (below 280 m) and clear areas close to waterways (less than100 m). In accordance with the findings of Díaz et al. (2005), Moreno(2003) and Reid et al. (2004), a shortage of adequate microhabitat forthis species can be inferred, and areas undisturbed by humanintervention, i.e., mature forest stands with high ground andunderstory cover, are needed for good habitat conservation.

In order to preserve the biodiversity of fauna in these locations, weneed to identify the use of natural areas by individual species, that is,to characterize their microhabitats. If these species are endemic, andthus play a role as bioindicators of high biodiversity (Amico et al.;,2008; Fink et al.;, 1995; Rozzí et al., 1996), such information isessential for the proper conservation of South Chilean temperateforest biodiversity, which is highly susceptible to degradation and/orfragmentation (Reid et al., 2002; Willson et al., 1994).

Maximum entropy is a rapidly-evolving method (Baldwin, 2009)that could become a very useful tool for wildlife researchers and forestmanagers in predicting fauna distribution. We realize that it is possiblefor amicrohabitat to be suitable for a species even if that species has notactually been detected, due to both the census method itself and otherfactors such as the microhabitat not having reached its carryingcapacity for that species. We therefore recommend continuouslymonitoring the presence of any faunal species by regular sampling ofits populations. Such monitoring would test the validity of the model.

5. Conclusions

Associating topographic and environmental variables with wildlifedistribution on a microhabitat analysis scale is an essential steptowards implementing forest plans adapted to the associations thathave been identified.

Since the fragmentation of autochthonous forests in SouthAmerica is affecting wildlife habitats, it is important to prevent suchecosystem degradation, particularly when, because of their unique-ness, they are regarded as hotspots of global biodiversity.

Maxent and GIS are useful habitat prediction techniques forecological and forest management, allowing several variables to beintegrated into habitat modeling. Studies such as ours can help todefine conservation plans for bioindicators species and the foreststhey inhabit, as essential aspect of any global biodiversity “hotspot”preservation strategy. Forest management strategies (logging, pro-tection, recreational use, etc.) then need to be adapted to meetmicrohabitat selection requirements, particularly for endemic species,as in the case described in this paper.

Acknowledgements

We wish to thank Professor Norman Moreno for his help with thefieldwork and for his continued support of this study.

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