J. Field Ornithol. 82(4):355–365, 2011 DOI: 10.1111/j.1557-9263.2011.00339.x

Assessing the geographic range of Black-frontedGround-Tyrants (Muscisaxicola frontalis) using

extralimital and winter range occurrence recordsand ecological niche modeling

Richard E. Gibbons,1,3 Javier Barrio,2 Gustavo A. Bravo,1 and Luis Alza2

1Louisiana State University Department of Biological Sciences and Museum of Natural Science 119 Foster Hall, BatonRouge, Louisiana 70803, USA

2Centro de Ornitologıa y Biodiversidad (CORBIDI) Santa Rita 105, Dpto. 202, Urb. Huertos de San Antonio, Lima33, Peru

Received 22 November 2010; accepted 23 July 2011

ABSTRACT. Estimating the geographic range of a species can be complicated by insufficient occurrencedata and a lack of information about range limit determinants. Accurate estimates of species distributions areneeded to assess the impacts of anthropogenic actions and for exploring evolutionary and ecological processes thatmaintain biological diversity. After documenting several extralimital locations for Black-fronted Ground-Tyrants(Muscisaxicola frontalis; Tyrannidae), we questioned the accuracy of the current winter range estimate. We providespecimen and observation records from central and southern Peru that represent new information about the winterdistribution of Black-fronted Ground-Tyrants. We used ecological niche models generated from new extralimitalrecords and records from the winter range to assess the current range estimate. We also tested winter and extralimitalniche models for model equivalency using a resampling technique available through Maxent and ENM Tools. Nichemodels developed with locations from the winter range predicted with high probability (>90%) the area of theextralimital records. Reciprocally, niche models developed with the extralimital locations predicted the majority ofthe winter range locations, although the probability was lower for some locations and the most southerly points werenot included in the prediction. The test for model equivalency did not distinguish the two models, suggesting thepossibility that the extralimital records were from poorly sampled areas of the true winter range. Smaller scale habitatassociations of Black-fronted Ground-Tyrants, such as a preference for sparsely vegetated slopes, were documentedthat were more specific than published accounts. Finally, we present the first case of frugivory in Muscisaxicola withthe identification of Cumulopuntia boliviana ignescens (Cactaceae) seeds and pericarp in all five stomach samples ofBlack-fronted Ground-Tyrants collected in southern Peru.

RESUMEN. Evaluacion de la distribucion geografica de Muscisaxicola frontalis medianteel uso de registros dentro y fuera de los lımites conocidos en el invierno y modelos de nichoecologico

Estimar el rango geografico de una especie puede ser complicado debido a la insuficiencia de registros y eldesconocimiento de los mecanismos que limitan su distribucion. Una estimacion precisa es necesaria para evaluarlos impactos de las acciones antropogenicas y para explorar los mecanismos ecologicos y evolutivos responsables demantener la diversidad biologica. Ademas de documentar varios registros que representan una ampliacion en el rangode distribucion de la dormilona de frente negra (Muscisaxicola frontalis, Tyrannidae), evaluamos la precision delactual estimativo de su rango de distribucion. En este estudio, presentamos registros de observaciones y especımenesobtenidos en el centro y sur de Peru que representan nueva informacion sobre la distribucion de la especie duranteel invierno. Mediante el desarrollo de modelos de nicho ecologico generados a partir de nuevos registros dentro yfuera del rango de distribucion conocido generamos modelos de nicho ecologico (ENM) para estimar el rango dedistribucion de la especie durante el invierno. Ademas, evaluamos si los modelos de nicho basados en registros dentroy fuera del rango de distribucion son equivalentes usando una tecnica de remuestreo disponible a traves de Maxenty ENM Tools. Los modelos realizados usando los registros dentro del rango de distribucion conocido predijeroncon alta probabilidad (>90%) el area donde encontramos los nuevos registros de la especie. De igual forma, losmodelos basados en los nuevos registros fuera del rango de distribucion predijeron la mayorıa de las localidadesdentro del rango de distribucion, aunque con probabilidades bajas para algunas localidades,especialmente en elsur de la distribucion. El test de equivalencia de nicho no pudo distinguir los dos modelos, lo que sugiere quelos registros recientes fuera de rango de distribucion provienen de areas pobremente muestreadas del rango real

3Corresponding author. Email: rgibbo3@gmail.com

C©2011 The Authors. Journal of Field Ornithology C©2011 Association of Field Ornithologists

355

Journal of Field Ornithology

356 R. E. Gibbons et al. J. Field Ornithol.

de distribucion en el invierno. Finalmente, describimos la preferencia de la especie por pendientes con vegetacionescasa, y presentamos el primer caso de frugivorıa en el genero Muscisaxicola mediante la identificacion de semillas yel pericarpo de Cumulopuntia boliviana ignescens (Cactaceae) en los contenidos estomacales de los cinco individuoscolectados en el sur de Peru.

Key words: austral migrant, frugivory, Peru, microhabitat, niche equivalency test

Knowledge of the geographic ranges and basicecology of some species of birds is limited, andthis is especially true in South America. Difficul-ties in access and a shortage of researchers havelimited our ability to obtain ecological informa-tion about many Neotropical birds, includingmost austral migrants that breed in southernSouth America and migrate north to the Andesfor the austral winter (Stotz et al. 1996, Chesserand Levey 1998). Recently, investigators usingecological niche models (ENMs) have takenadvantage of museum locality data to developspecies distribution estimates (Peterson 2001)and to test ecological (Anciaes and Peterson2006, Cadena and Loiselle 2007) and evolution-ary (Peterson et al. 1999, Graham et al. 2004,Kozak et al. 2008) hypotheses. A promisingapplication of ENMs is identifying potentialareas of occupancy in unexplored areas (Engleret al. 2004, Overton et al. 2006, Kumar andStohlgren 2009) and determining if those areasrepresent an extension of a species’ niche breadth(Warren et al. 2008). These models can alsobe used to explore the relative influence ofbiotic and abiotic factors that shape distributions(Graham et al. 2010). This can be particularlyuseful for conservation planning for uncommonspecies in remote areas of the Neotropics (e.g.,Loiselle et al. 2003, Marini et al. 2010).

Black-fronted Ground-Tyrants (Muscisaxicolafrontalis), one of at least six austral migratoryspecies in the genus Muscisaxicola (Chesser andLevey 1998), breed in the Andes from Antofa-gasta, Chile, south to Rıo Negro, Argentina,and migrate north in the austral fall to Bolivia,western Argentina, and southwest Peru (Fjeldsaand Krabbe 1990, Jaramillo 2003, Narozkyand Yzurieta 2003, Schulenberg et al. 2007).Schulenberg et al. (2007) noted that someindividuals rarely stray farther north. Similarly,Ridgely and Tudor (1989) assigned vagrant sta-tus to a single northern record in his distributionmap. Schulenberg (pers. comm.) and presum-ably Ridgely and Tudor (1989) comments referto a specimen (LSUMZ 80625) collected on22 May 1975 by Ted Parker in dpto. Ancash

at 4267 m. The northernmost previously pub-lished record is from the Chuquibamba area ofdpto. Arequipa, Peru, at 4150 m (Fjeldsa 1987).

During fieldwork in central and southernPeru, we documented several new occurrencerecords of Black-fronted Ground-Tyrants be-yond their current estimated geographic range.These records suggest that the current rangeestimate based on few records may need revision.The new records provided an opportunity to ad-dress the question of whether the locations of theextralimital records were in niche space similarto those of existing records or, in other words,whether the extralimital records represent anextension of niche space or just geographic space.To address this question, we used a novel appli-cation of ENMs to test the equivalency of mod-els derived from extralimital and within-rangeoccurrence records. If the extralimital recordsrepresent a geographic range extension as well asan extension in niche space, then we should beable to distinguish statistically the climatic nichespaces of the known range and the extralimitalrange. Any difference would indicate that therealized niche of Black-fronted Ground-Tyrantsis broader than previously estimated. However,if the climatic envelope models are statisticallyindistinguishable, then the extralimital recordswould suggest a larger wintering range. In eithercase, this provides a large-scale starting pointfrom which additional range-limiting variablescan be explored (Fig. 1).

METHODS

New and historical records. We com-piled historical and new occurrence records us-ing both sight records and specimens (Table 1).Locations were vetted, with questionable dataomitted. We obtained six new records (Sup-plementary Table S1) during fieldwork in June2007, August 2009, and from 19 September to10 October 2008, 2–7 and 27–30 April 2009,16–20 August 2009, 31 January to 8 February2010, 9–15 March 2010, and 1–4 April 2010in the Peruvian departments of Ancash, Lima,

Vol. 82, No. 4 Muscisaxicola frontalis Winter Range 357

Fig. 1. The study area is shown with the occurrence records used to develop ecological niche models. Themodels were restricted to the area (shown in gray) above 3200 m. The current geographic wintering rangeestimated from Fjeldsa and Krabbe (1990) and Schulenberg et al. (2007) is circumscribed with a bold blackline, and dark-centered white circles represent winter range localities. Extralimital records are represented withwhite plus signs.

Junın, Huancavelica, Arequipa, Moquegua, andPuno. Fieldwork by REG included surveys alongline transects (N = 38; mean = 1.2 km,range = 0.5–1.75 km) for a study ofpuna bofedales and surrounding habitats lo-cated in the departments mentioned above.Transects were surveyed during the wet(December–February) and dry (June–August)seasons.

Additional records were obtained during op-portunistic collecting, most often within a fewkilometers of transects or when traveling be-tween transects. Specimen preparation includeda thorough necropsy, collection of tissue sam-ples, and preservation of stomachs and contents.Specimens collected by REG were deposited ineither the Centro de Ornitologıa y Biodiversidad(Lima, Peru) or the Louisiana State University

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Vol. 82, No. 4 Muscisaxicola frontalis Winter Range 359

Museum of Natural Science (Baton Rouge, LA).JB and LA also gathered Black-fronted Ground-Tyrant records opportunistically during surveysfor White-bellied Cinclodes (Cinclodes pallia-tus), a rare inhabitant of the central Peruvianpuna.

We used global positioning system units(Models Colorado 300 or 60Csx, Garmin,Olathe, KS; GPS 315, Magellan, Santa Clara,CA) to determine elevations and geographiccoordinates of transects and collecting localities.Coordinates and elevations were verified using1:100,000 topographic maps obtained fromPeru’s Instituto Geografico Nacional.

We supplemented our records with museumspecimen locations and observations of otherinvestigators (Table 1) to increase the numberof model development points. Locations rangedfrom 3900 to 5100 m elevation in puna habitats(sensu Fjeldsa and Krabbe 1990), i.e., seasonaldry grasslands ranging from central Peru tonorthern Argentina and Chile. We used 13records for the typical winter range model andsix records for the extralimital range model(Table 1 and Fig. 1).

Seasonal restriction. Developing dis-tribution predictions for migratory species iscomplicated by seasonal variation of climaticvariables (e.g., Marini et al. 2010). Using dataappropriate for the desired time frame is oneway to refine model signal. In addition, de-termining the true winter range of a speciescan be complicated by dispersal and migra-tion. For example, Marantz and Remsen (1991)and Remsen (2001) showed that winter rangescan be overestimated by inclusion of seasonallyinappropriate records. We selected occurrencerecords within a range of dates to refine modelsignal. We defined the winter period arbitrarilyas May through August.

Spatial restriction. To assess whetherextralimital winter records represented an exten-sion in niche space or geographic space, we de-fined extralimital winter records as those northof the wintering range described by Schulenberget al. (2007) and Fjeldsa and Krabbe (1990).Locations within those limits were consideredtypical winter records. To minimize spatialautocorrelation, we only used records >5 kmfrom the nearest known location.

Ecological niche modeling andenvironmental layers. To create winterdistribution models for the Black-fronted

Ground-Tyrant, we used a maximum entropyalgorithm implemented in the software Maxent3.3.3e. This program uses species’ presencerecords in combination with the distribution ofenvironmental variables over the study areato estimate a probability distribution for thespecies (see details in Phillips et al. 2006).Barve et al. (2011) provided suggestions forspatially restricting model analyses followingthe framework of Soberon and Peterson (2005)based on the earlier work of Hutchinson (1978).We limited our models to a long-term estimateof accessible area to include the last glacialmaximum (LGM), following the rationaleand recommendation of Barve et al. (2011),because these areas would have been available.To summarize the rationale, inclusion of thepotential historical range permits the modelingalgorithm to run within connected modelingspace in a biogeographically relevant context.This permits the inclusion of potential dispersalpathways that may have resulted in present-dayisolated populations. Considerable debatepersists regarding the puna’s precipitationregime during the LGM (between 20,000and 26,000 years ago), but there is agreementthat it was 2–9 ◦C cooler and this coolingwas accompanied by a downslope vegetationshift of 800–900 m (Flenley 1998). Ourenvironmental layers were clipped to includepixels above 3200 m, 800 m below the currentpuna boundary, to include the probablehistorical puna extent for the reasons givenabove.

ENMs were developed for both winter andextralimital winter ranges using climatic vari-ables related to temperature and precipitationat 1-km2 resolution obtained from WorldClim(Hijmans et al. 2005) and two topographicvariables (slope and aspect) calculated with theSpatial Analyst from ArcGIS v. 9.3. We mini-mized our set of variables by conducting analysisimplemented in the software ENM Tools 1.1(Warren et al. 2009). Using the correlationcoefficients, we created a pair-wise matrix in-cluding all environmental and topographicallayers. We identified clusters of variables thatwere highly correlated. Then, we chose sixdissimilar climatic variables from the correlatedgroups and two topographic variables (slope andaspect) with correlation coefficients lower than0.85. Climatic variables used were: (1) meandiurnal temperature range (mean of monthly

360 R. E. Gibbons et al. J. Field Ornithol.

[maximum temperature − minimum tempera-ture]), (2) isothermality (variable 1/variable 6)(×100), (3) temperature seasonality (standarddeviation × 100), (4) maximum temperature ofthe warmest month, (5) precipitation during thewettest quarter, and (6) precipitation during thedriest quarter (Hijmans et al. 2005).

Assessing the effects of sample sizeon ecological niche models. To deter-mine if a small sample size affected the validityof our models, we performed two separate tests.First, we evaluated the predictive ability ofour winter and extralimital datasets using thejackknifing (leave-one-out) technique presentedby Pearson et al. (2007). This allowed us todetermine the contribution of each location inour models and to know if our models wereprimarily driven by a subset of our locations. Toperform this test, we used the lowest presencethreshold (LPT) value provided by Maxent tothreshold our models. LPT provides a conserva-tive estimation of the potential distribution of aspecies, enhancing the results of our jackknifingand low-N tests. The commission rates, i.e., howmany occurrence points were in pixels predictedabove our threshold, were 85% and 67% forthe winter and extralimital models, respectively.Pearson’s pValueCompute program provided P-values (<0.0001) for these results, showing thatour dataset had a good predictive ability and thatno particular location biased the models.

To assess the potential impact of our smallsample size, we performed a slightly modifiedversion of the low-N test presented by Pearsonet al. (2007). This test examines changes inmodel performance as sample size is reducedby one for each subsequent model, making apower assessment of analyses. Changes in predic-tive performance were evaluated with additionalmodels developed with a random subsample ofall locations. By developing models with a step-wise reduction of one location, we were able todetermine where model performance collapsed,i.e., a rapid decrease in commission rate. Thepercentage of locations from the complete setthat were included in the models developed withthe subset of locations provided an estimateof model performance. We performed threedifferent random sequences of location removalsusing the LPT value and found that, for both theextralimital and winter range models, predictiveperformance was affected negatively as samplesize decreased (Fig. 2). Also, all three replicate

chains for both models approached asymptotesat high values of predictive abilities at smallersample sizes than the one we used, suggestingthat the predictive performance of our modelswere not affected by the sample size.

Niche equivalency test. After deter-mining that our models were performing welldespite the small sample size, we constructedfinal ENMs for winter and extralimital datasetsusing Maxent 3.3.3e (Phillips et al. 2006).Each model was run with 100 cross-validatedreplicates using all locations. Then, using theunthresholded logistic output of our models,we performed a niche equivalency test (Warrenet al. 2008) using the software ENM Tools1.1 (Warren et al. 2009). This test follows apermutation approach to estimate whether dif-ferences between the climatic envelopes of twospecies are statistically significant. A significantdifference suggests that the niches of the winterand extralimital locations are not equivalent. Onthe other hand, if differences are not significant,equivalency between models cannot be rejected.We performed our test using 50 replicates.

RESULTS

Both winter and extralimital models predictedthe presence of the remaining occurrence recordswith high probability (Fig. 3). The wintermodel predicted an area from the higher eleva-tions of dpto. Ancash, Peru, south to northernArgentina, and Chile. The model developedwith extralimital points was mostly restricted toupper elevations in Peru, with a few nearby areasin Bolivia. The model developed with all pointsincluded the upper elevations of dpto. Ancash,Peru, south through the Andes to central Bolivia,an area significantly reduced in size from thewinter model.

We failed to reject the null hypothesis thatthe two models were distinguishable. Observedvalues of niche overlap (I = 0.82, and D =0.71) fell within the 5–95 percentiles of a nulldistribution (I 5 0.755–I 95 0.93, D5 0.624–D95

0.899) estimated after 50 randomizations usingall locations. Although we did not detect a sig-nificant difference between the modeled nichespaces, contributions of the variables differed.The winter model was driven primarily by pre-cipitation during the wettest month (33%) andsecondarily by the maximum temperature of thewarmest month (30%). The extralimital model

Vol. 82, No. 4 Muscisaxicola frontalis Winter Range 361

Fig. 2. Models were developed with winter range and extralimital occurrence records. The percentage ofoccurrence points predicted by the model to have a presence probability higher than the lowest presencethreshold (LPT) is shown for each model. Three replicates were performed to account for random removaleffect. Both models performed moderately well (80–100%) with only six locations in the models.

primarily was driven by temperature seasonality(50%) followed by the maximum temperaturein the warmest month (35%).

DISCUSSION

The current wintering geographic range ofBlack-fronted Ground-Tyrants was developedwith few records, and Schulenberg et al.

Fig. 3. Occurrence records of Black-fronted Ground-Tyrants are shown as dark-centered white circles.Environmental niche models were developed using occurrence records within the (A) current estimatedwinter range, (B) extralimital occurrence records, and (C) all occurrence points. The thick-lined polygonapproximates the winter range using range maps from Fjeldsa and Krabbe (1990) and Schulenberg et al.(2007). Model prediction probability is shown in five colors corresponding to 10% probability intervalsranging from light gray (50%) to dark gray (90%).

(2007) and Fjeldsa and Krabbe (1990) wereunderstandably conservative with their esti-mates. Our ecological niche model developedusing the current winter range predicted withhigh probability all extralimital points, suggest-ing that the “extralimital” records may be withinthe true wintering range. Reciprocally, the modeldeveloped with the extralimital locations pre-dicted many of the winter range locations, but

362 R. E. Gibbons et al. J. Field Ornithol.

did not perform as well as the winter modeldevelopment. When all locations were used formodel development, the niche model predictedan area smaller than the two areas combined,with a southern contraction accounting for mostof the difference.

A benefit discovered in the resampling andjackknifing of model locations was our abil-ity to assess the influence of individual loca-tions on model performance. This is similarto a method Chapman (2005) discussed indescribing principles and methods for cleaningbioinformatics data. For example, the Chocayalocation in southern Bolivia was excluded fromseveral resampled model predictions, essentiallyidentifying it as an outlier. This was one of thetwo 1936 records from M. A. Carriker, and ourresampling may have detected the imprecisionof locations often associated with specimen datagathered prior to the late 20th century. Wevetted this particular record with S. Herzog,who investigated many historical records and theareas worked by Carriker. Whether this record isat the limit of the winter range, an outlier, or wasimprecisely recorded by Carriker is unknown,but the ability of our method to identify outliersthat could be caused by misidentified taxa ordata entry mistakes is nonetheless illustrated.

Winter and extralimital models were drivenprimarily by different variables, namely, precipi-tation during the wettest month (January, onsetof wet season) for the winter model and temper-ature seasonality for the extralimital model. Sec-ondarily, both models were driven by maximumtemperature of the warmest month (Octoberand November, depending on location). Thesethree variables are definitive characteristics ofthe puna, a highly seasonal grassland maintainedby freezing overnight temperatures that preventexpansion of freeze-susceptible vegetation. Thedifference in model contribution may reflectspatial variation of these variables. That themodels were driven by a variable associatedwith the wet season (maximum temperatureduring the warmest month) when Black-frontedGround-Tyrants have migrated from south totheir breeding range is perhaps counterintuitiveprima facie, but climatic features such as precip-itation or temperature associated with the wetseason with warmer overnight temperatures (i.e.,growing season) would be the expected driverof puna habitats that Black-fronted Ground-Tyrants may be selecting during the dry season.

We used the niche equivalency test in ENMTools to determine if the winter and extralimitalmodels could be statistically distinguished, i.e.,more dissimilar than expected by chance. Thistest creates a random distribution of modelsfrom all points and then compares the testmodels (winter and extralimital) to the distri-bution. If the test models were more dissimilarthan expected by chance, they would occur ateither end of the randomized distribution tails.Our results show that the extralimital winterrecords of Black-fronted Ground-Tyrants werein niche space no more different than expectedby chance. In fact, some differences are presentas shown by the different variable contributions.This result supports the hypothesis that theniche models are no more different from eachother than we would expect by chance. In otherwords, the extralimital model did not repre-sent an extension of the wintering range nichebreadth per se, but a geographical extension ofthe wintering range with similar niche space.Our approach represents a useful complemen-tary tool for better understanding distributionpatterns of species in remote areas. Pearson et al.(2007) emphasized the ramifications of thistechnique by showing how the ranges of manyadditional species in Mexico’s bird atlas couldbe modeled. Peterson et al. (1998) showedthat there are few location records for manyspecies in the Neotropics, and this correspondsdirectly with the difficulty in accessing theseareas. With our approach, a few records mightprovide enough information to identify addi-tional areas where a species might occur andto pursue relevant ecological and evolutionaryquestions.

Although relying heavily on modeling andcomputer work, our approach highlights theimportance of continuing fieldwork in poorlyknown areas. With more field-based informa-tion, models will be more robust and analysesbased on more robust datasets will be morereliable. In addition, our models generate infor-mation that must be corroborated in the field.Our approach for comparing the environmentalniche space of ranges could also be appliedto other types of studies. We believe that thistype of assessment could be applied in fieldssuch as evolutionary ecology, migration ecology,conservation biology, and systematics. The fieldof phylogeography, in particular, may benefitfrom niche-space comparisons (Peterson 2009).

Vol. 82, No. 4 Muscisaxicola frontalis Winter Range 363

Although we were unable to reject simi-larity of niche space using models based oneight environmental variables, the area northof the current winter range of Black-frontedGround-Tyrants could be unsuitable in finerecological dimensions not captured in ENMs.Field observations of Black-fronted Ground-Tyrants offer clues concerning factors that mightinfluence their winter range. Published accountsof their habitat use during the winter are generaland somewhat conflicting. Suitable habitat hasbeen described as open grassland near wetlands(Schulenberg et al. 2007), rocky slopes (Fjeldsaand Krabbe 1990), open habitats near water(Ridgely and Tudor 1989), and rocky habitatwith little vegetation (Jaramillo 2003). Our ob-servations in central and southern Peru suggesta preference for rocky slopes with Baccharisshrubs, Cumulopuntia cacti, and Festuca andParastrephia grasses. If Black-fronted Ground-Tyrants have a preference for xeric microhabi-tats, we predict they would occur in higher num-bers at the drier end of the precipitation gradientthat spans the puna from the wetter north to thedrier south. The necessary microhabitat spatialdata and survey effort in respective microhabi-tats needed to test this hypothesis are lacking,but recent progress using remotely sensed datain the high Andes offers hope for these types ofinquiries (Otto 2011).

Another potential range-limiting factor is adietary requirement. We found numerous seedsand pericarp of the cactus Cumulopuntia boli-viana ignescens in the stomachs of all five Black-fronted Ground-Tyrants collected in dpto. Are-quipa. This cactus occurs above 4400 m insouthern Peru in dptos. Arequipa, Moquegua,and Puno, whereas other species of Cactaceaewith palatable fruit occur in the northern puna(D. Montesinos, pers. comm.). Whether theyconsume the fruit of these northern species isunknown, but Black-fronted Ground-Tyrantsappear to be at least facultative frugivores. Al-though frugivory in the genus Muscisaxicola is, toour knowledge, undocumented, it is well knownfor many species in the family Tyrannidae(Fitzpatrick 1980). The diets of most birds inthe Neotropics are poorly known, and studyinginterdependencies across a geographic mosaic(sensu Thompson 2005) will likely provide newinsights to the study of range limits.

In conclusion, we failed to reject dissimilaritybetween our two models based on extralimital

and winter-range records, suggesting that thewinter range of Black-fronted Ground-Tyrantsmay be larger than previously thought and gapsin current range estimates are sampling arti-facts. Additional field observations are neededto determine the significance of potential rangelimit drivers, including demography, trophicinteractions, and interspecific interactions (Holtand Barfeidl 2009, Price and Kirkpatrick 2009).

ACKNOWLEDGMENTS

We thank the museums and institutions that providedspecimen and observation data. In particular, we thankthe Museum of Vertebrate Zoology at the University ofCalifornia, Berkeley, the Academy of Natural Sciences,the American Museum of Natural History, the FieldMuseum, and the Yale Peabody Museum. F. Hernandezwas especially helpful in the field with both JB and REG.We especially appreciate S. Cardiff and D. Dittmann ofthe LSU Museum of Natural Science for assisting REG inpreparing for and returning from expeditions. T. Valquipatiently dealt with numerous requests and was generouswith his time and expertise helping REG navigate thePeruvian culture and permitting process. We thank T.Jones, A. Lopez, and D. Montesinos for help identifyingthe fruit and seeds, J. McCormack, G. Ritchison, J. V.Remsen, and anonymous reviewers for helpful comments,L. F. Elliott for GIS guidance, and A. T. Peterson for athorough and constructive review.

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Supporting Information

The following supporting information isavailable for this article online:

Table S1. New occurrence records ofBlack-fronted Ground-Tyrants (Muscisaxicolafrontalis) for Peru.

Please note: Wiley-Blackwell Publishing is notresponsible for the content or functionalityof any supporting materials supplied by theauthors. Any queries (other than missing ma-terial) should be directed to the correspondingauthor for the article.