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Faculty Research & Creative Activity Biological Sciences
January 2012 Effectiveness and utility of acoustic recordings for surveying tropical birds Antonio Celis-Murillo University of Illinois at Urbana-Champaign
Jill L. Deppe Eastern Illinois University, [email protected]
Michael P. Ward University of Illinois at Urbana-Champaign
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Recommended Citation Celis-Murillo, Antonio; Deppe, Jill L.; and Ward, Michael P., "Effectiveness and utility of acoustic recordings for surveying tropical birds" (2012). Faculty Research & Creative Activity. 153. http://thekeep.eiu.edu/bio_fac/153
This Article is brought to you for free and open access by the Biological Sciences at The Keep. It has been accepted for inclusion in Faculty Research & Creative Activity by an authorized administrator of The Keep. For more information, please contact [email protected]. Journal of Field Ornithology
J. Field Ornithol. 83(2):166–179, 2012 DOI: 10.1111/j.1557-9263.2012.00366.x
Effectiveness and utility of acoustic recordings for surveying tropical birds Antonio Celis-Murillo1,2,3,5 Jill L. Deppe4 and Michael P. Ward2,3 1School of Integrative Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois 61821, USA 2Illinois Natural History Survey, Champaign, Illinois 61821, USA 3Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Champaign, Illinois 61821, USA 4Eastern Illinois University, Department of Biological Sciences, Charleston, Illinois 61920, USA Received 21 September 2011; accepted 20 February 2012
ABSTRACT. Although acoustic recordings have recently gained popularity as an alternative to point counts for surveying birds, little is known about the relative performance of the two methods for detecting tropical bird species across multiple vegetation types. During June and July 2008, we collected species detection/nondetection data to compare the performance of a quadraphonic acoustic recording system and point counts for estimating species richness and composition and detection probabilities of 15 rare, moderately common, and common tropical bird species across six structurally distinct vegetation types (coastal dune scrub, mangrove, low-stature deciduous thorn forest, early and late successional medium-stature semievergreen forest, and grazed pastures) in the northern Yucatan Peninsula. We selected five rare species endemic to the Yucatan Peninsula and 10 moderately common and common species that also occur in other tropical regions. Species richness and composition did not differ between survey methods in any of the vegetation types. At the population level, however, we found support for an effect of method on detection probability for most species. For 13 species, regardless of their abundance, acoustic recordings yielded detection probabilities as high as or higher than those for point counts across all vegetation types. The remaining two species were better detected by point counts in pastures and coastal scrub, where greater visibility likely improved sightings of these species. However, these species were detected as well as or better by acoustic recordings in forests and mangroves where detections were primarily auditory. In tropical regions where experienced field observers may not be available and funding for field surveys may be limited, acoustic recordings offer a practical solution for determining species richness and composition and the occupancy patterns of most species. However, for some species, a combination of methods will provide the most reliable data. Regardless of the method selected, analyses that account for variation in detection probability among vegetation types will be necessary because most species in our study demonstrated vegetation-dependent detection probabilities.
RESUMEN. Eficacia y utilidad de las grabaciones acusticas´ para el muestreo de aves tropicales El uso de grabaciones acusticas´ recientemente ha ganado popularidad como una alternativa a los conteos por punto para el muestreo de aves. Sin embargo, poco se sabe sobre el desempeno˜ de ambos metodos´ en la deteccion´ de especies de aves tropicales en multiples´ tipos de vegetacion.´ En Junio y Julio del 2008, se colectaron datos de deteccion/no-detecci´ on´ para comparar el desempeno˜ de un sistema portatil´ de grabacion´ cuadrafonico´ y los conteos por punto en la estimacion´ de riqueza de especies, composicion´ y probabilidades de deteccion´ de 15 especies tropicales raras, moderadamente comunes y comunes a traves´ de seis diferentes tipos de vegetacion´ tropicales (duna costera, manglar, selva baja caducifolia espinosa, selva mediana subcaducifolia primaria y secundaria y pastizales) enelnortedelaPen´ınsula de Yucatan,´ Mexico.´ De las 15 especies seleccionadas para estimar probabilidades de deteccion,´ las 5 especies raras son endemicas´ de la Pen´ınsula de Yucatan´ y las 10 especies moderadamente comunes y comunes, se distribuyen en otras regiones tropicales. Las estimaciones de la riqueza de especies no fueron significativamente diferentes entre ambos metodos´ y entre cualquiera de los tipos de vegetacion.´ A nivel poblacional, encontramos efectos de metodolog´ıa en la probabilidad de deteccion´ para la mayor´ıa de las especies. Para 13 especies, independientemente de su estatus de abundancia, el metodo´ de grabaciones acusticas´ resulto´ en altas o mayores probabilidades de deteccion´ que los puntos de conteo en todos los tipos de vegetacion.´ Las otras dos especies fueron mejor detectadas en conteos por puntos en pastizales y en dunas costeras, donde la visibilidad posiblemente mejoro´ su deteccion.´ Sin embargo, estas especies, se detectaron tan bien o mejor por las grabaciones acusticas´ en selvas y manglares, donde las detecciones fueron primeramente auditivas. En regiones tropicales donde existe limitacion´ de observadores de campo bien capacitados y financiamiento para estudios de campo, las grabaciones acusticas´ ofrecen una solucion´ practica para describir la riqueza de especies, composicion´ y patrones de ocupacion´ para la mayor´ıa de las especies. Sin embargo, paras algunas especies, la combinacion´ de metodos´ ofreceradatosm´ as´ confiables.
5Corresponding author. Email: [email protected]
C 2012 The Authors. Journal of Field Ornithology C 2012 Association of Field Ornithologists 166 Vol. 83, No. 2 Acoustic Recordings for Surveying Tropical Birds 167
Independientemente del metodo´ seleccionado, seran´ necesarios analisis´ que tomen en cuenta la variacion´ en la probabilidad de deteccion´ entre tipos de vegetacion´ ya que la mayor´ıa de las especies demostraron probabilidades de deteccion´ dependientes de la vegetacion.´ Key words: acoustic monitoring, acoustic recording systems, detection probability, multimethod model, point counts, tropical birds, Yucatan Peninsula
Point counts have long been used to survey (Haselmayer and Quinn 2000, Hobson et al. tropical birds (Blake 1992, Lynch 1995) because 2002, Acevedo and Villanueva-Rivera 2006, they are easy to implement, require minimal Celis-Murillo et al. 2009). However, Hutto equipment, and allow sampling of birds across and Stutzman (2009) found that significantly multiple vegetation types during all seasons more bird species were detected using point (Lynch 1995). More recently, researchers have counts than acoustic recordings, a pattern they used acoustic recordings to collect data on bird suggested was due to the inability of their populations and communities (Hobson et al. recording system to record distant sounds or 2002, Celis-Murillo et al. 2009, Hutto and sounds embedded in other noise. Stutzman 2009, Dawson and Efford 2009). Haselmayer and Quinn (2000) and Acevedo Acoustic recordings have several potential ad- and Villanueva-Rivera (2006) compared species vantages, e.g., recordings create a permanent richness estimates determined using acoustic record of surveys that can be replayed to resolve recordings and point counts in tropical forests, ambiguities, can be listened to by multiple ob- where recordings may be advantageous because servers to verify species identifications (Rempel of the high species diversity and low visi- et al 2005), and can be re-analyzed using song bility. Haselmayer and Quinn (2000) found identification programs (Brandes 2008, Goyette that more species were detected using acoustic et al. 2011). recordings than point counts at locations with Point counts and acoustic recordings also have a greater number of species, but reported no limitations, with neither method performing difference when data were averaged over all equally well at sampling all species in all environ- survey locations. Additionally, rare species were ments at all times (Gregory et al. 2004). Thus, detected more frequently with point counts than when sampling birds to produce a species inven- recordings, possibly due to the limited detection tory (composition) or estimate species richness, range of the highly directional recording system. occupancy, or abundance, determining which Haselmayer and Quinn (2000) hypothesized survey method is most effective is critical. In that use of an omni-directional system could some cases, such as when an entire community improve detection rates of rare species. Acevedo is of interest, a combination of methods may and Villanueva-Rivera (2006) used an omni- be required. Because many, if not most, studies directional microphone to estimate species rich- examining population trends and responses to ness and documented significantly higher rich- habitat alteration involve sampling birds across ness using an acoustic recording system, but vegetation types, the method used must either they did not evaluate the relative effectiveness perform similarly across vegetation types or of the two methods for surveying rare species. investigators must know and account for biases Furthermore, their acoustic recording data in- associated with the method used in the various cluded birds sampled over 24 h, whereas point vegetation types. count data were restricted to mornings, therefore Several investigators have examined the ca- biasing their evaluation of the acoustic recording pabilities and biases of acoustic recording sur- system. veys by comparing data collected with record- The results of studies comparing the effec- ings (and subsequently reviewed in the lab) to tiveness of point counts and acoustic surveys in data collected by observers performing point detecting rare species in temperate regions have counts (Haselmayer and Quinn 2000, Hob- also been inconclusive. For example, Hutto and son et al. 2002, Celis-Murillo et al. 2009, Stutzman (2009) found that point counts were Hutto and Stutzman 2009). Acoustic record- better for detecting rare species than recordings ings have generally produced similar or higher made using a stereo microphone system. In estimates of species richness than point counts contrast, Celis-Murillo et al. (2009) detected 168 A. Celis-Murillo et al. J. Field Ornithol. more rare species using a quadraphonic record- structured vegetation to tall, dense, structurally ing system than when conducting point counts. diverse vegetation (Miranda 1958, Rzedowski The ability of acoustic recordings to effectively 1978). Most of these vegetation types occur in sample rare species is a key issue in the tropics other tropical regions, e.g., mangroves, coastal because bird communities are comprised mostly scrub, and grazed pastures (Moreno-Cassasola of rare species (Ricklefs and Schulter 1994), and and Espejel 1986, Britton and Morton 1989, J. underdetection of such species can bias estimates L. Deppe and A. Celis-Murillo, pers. obs.) of species occurrence and richness. During the same months as the surveys Tropical habitats vary widely in vegetation (June and July 2008), we characterized the structure, ranging from open pastures to tall structure of vegetation at each survey location dense evergreen forests. Although such differ- using a modified circular plot technique (5-m ences may affect the performance of acoustic radius; James and Shugart 1970, Deppe and recordings relative to point counts (Haselmayer Rotenberry 2008). For each survey plot, we and Quinn 2000, Acevedo and Villanueva- visually estimated the percent cover of the tree Rivera 2006), the effectiveness of these two canopy, shrubs and saplings, and herbaceous methods in different tropical vegetation types vegetation in the 5-m radius circular plots. In has not been compared. Thus, our objective was each plot, we also counted the number of trees to compare the relative effectiveness of a portable with a dbh (diameter at breast height) ≥ 5cm, quadraphonic acoustic recording system and measured vegetation height along the circum- point counts for estimating bird species rich- ference of the plot at each of the four cardinal ness, composition, and detection probabilities of directions, and measured maximum vegetation rare, moderately common, and common species height using a meter tape or clinometer. We across six structurally distinct tropical vegetation calculated the average vegetation height by aver- types in the Yucatan Peninsula. We compared aging over the four height measurements taken bird community and population parameters at the four cardinal directions. based on species detection/nondetection data, Coastal dune scrub was comprised of dense accounting for imperfect detection probability woody shrubs, cacti, herbaceous plants, scat- through use of repeated visits to survey locations. tered introduced coconut palms (Cocos nucifera), Our specific objectives were to determine (1) and high local abundances of several native palm if the two survey methods generated similar species. Coastal scrub was relatively short, with estimates of species richness and composition a mean height of 2.2 m. Dominant species in- across vegetation types, and (2) how detection cluded Pseudophoenix sargentii, Thrinax radiata, probabilities of rare and common resident bird Pithecellobium keyense, Bravaisia tubiflora,and species differed between the two methods across Caesalpinia vesicaria (see Table 1 for summary a range of vegetation types. of all vegetation measurements). Areas of man- grove sampled in our study were characterized METHODS by halophytic, wetland species, including red (Rhizophora mangle) and black (Avicennia ger- Our study was conducted at three locations in minans) mangroves, buttonwoods (Conocarpus the northeastern region of the Yucatan Peninsula erectus), Batis maritima,andSalicornia bigelovii. of Mexico, including the Ria Lagartos Bio- They were relatively short (mean height = 3.0 sphere Reserve and the Santa Isabel Ejidos in m) and best described as mangrove scrub. Low- Yucatan, and the El Eden Ecological Reserve stature thorn forests were also primarily shrubby in Quintana Roo. Study sites included natural in nature with few trees with a dbh > 5.0 cm and human-modified vegetation types, includ- and a dense herbaceous layer. Thorn forests were ing coastal dune scrub, mangrove scrub, mature dominated by Fabaceae spp. and cactus species. low-stature deciduous thorn forest, early and With the exception of the open grassy areas of late successional medium-stature semievergreen grazed pastures (see below), low-stature thorn forest, and grazed pastures. These six vegetation forests were the shortest vegetation type. Early- types represent the dominant vegetation types and late successional semievergreen forests had in the northeastern Yucatan Peninsula, and span similar plant species composition, and the dom- the spectrum of variation in vegetation structure inant tree species included Manilkara zapota, in the region, ranging from short, open, simply Sideroxylong foetidissium, Metopium brownii, Vol. 83, No. 2 Acoustic Recordings for Surveying Tropical Birds 169 Table 1. Mean (± SD) vegetation measurements for each of the six vegetation types where bird surveys were conducted in the northeastern Yucatan Peninsula. Percent Percent Percent Maximum Average tree shrub/sapling herbaceous vegetation vegetation Number Vegetation type cover cover cover height (m) height (m) of trees Coastal dune scrub 41.9 ± 17.9 89 ± 9.5 33.3 ± 16.3 4.4 ± 1.1 2.2 ± 0.3 8.9 ± 7.4 Mangrove 42.4 ± 37.3 52.3 ± 31.9 67.2 ± 36.6 7.1 ± 3.0 3 ± 2.3 13.7 ± 17.5 Thorn forest 23.6 ± 25.3 58.6 ± 20.4 76.4 ± 14.1 4.3 ± 1.8 1.7 ± 1.1 3.7 ± 5.4 Early successional 78.7 ± 11.8 80 ± 4.7 46.8 ± 15.9 10 ± 2.2 6.3 ± 1.7 14.1 ± 9.1 semievergreen forest Late successional 89.1 ± 5.9 75.4 ± 17.7 59.4 ± 26.4 14.7 ± 5.8 8.4 ± 2.0 42.8 ± 19.3 semievergreen forest Pasturea 42.1 ± 26.6 36.3 ± 18.7 68.5 ± 23.7 8.6 ± 2.4 3 ± 1.2 14.7 ± 9.7 aVegetation measurements in pasture included living fences. Average vegetation height in the grassy areas of the pasture was 1.2 m at the time of our surveys and overhead canopy cover was absent.
Ceiba aesculifolia,andBursera simaruba.Early contiguous vegetation. Living fences were an in- successional forests (<15 yr old) at our sites tegral component of the pasture vegetation type were shorter than late successional forests (> 40 for birds in the northeastern Yucatan Peninsula; yr old) and had less tree and herbaceous cover, grassland birds observed in our study frequently greater shrub/sapling cover, and fewer trees with flew between open grassy areas of the pasture adbh> 5.0 cm. Pastures ranged in size from ∼5 where they foraged and trees where they sought to 200 ha, and were surrounded by living fences cover (A. Celis-Murillo and J. L. Deppe, pers. (i.e., narrow fence rows consist of remnant trees observ.). or established plantings of cuttings harvested At each survey location, we simultaneously from nearby trees; Zahawi 2005) and some had surveyed birds using acoustic recordings and scattered remnant trees in their interior. Pastures point counts on either two (25 locations) or were dominated by Panicum spp., and remnant three (28 locations) consecutive days in June trees were those found in semievergreen forests. and July 2008. Sampling on consecutive days Livestock grazing at survey locations occurred at reduced issues caused by possible temporal vari- a low intensity during the time of sampling; ation in weather and vocalization rates and by the herbaceous strata was the best developed birds entering or leaving the population (Suther- strata in pastures, and vegetation height in the land et al. 2004). Upon arriving at each point, open grassy areas of the pasture averaged 1.2 m. the observer (ACM) waited for 5 min then began Overhead canopy cover was absent in the grassy a 10-min point count and acoustic recording. portion of the pastures. Deppe and Rotenberry Only the presence of species was recorded at each (2008) and Carabias Lillo et al. (1999) provided location, not numbers of individuals. During detailed descriptions and photographs of the point counts, we included both aural and visual vegetation types in our study. detections because visual detection represents Point counts and acoustic recordings. the main advantage of field surveys over record- We established 53 survey points distributed ings, and are included in most analyses of count among the six vegetation types, including eight data (Celis-Murillo et al. 2009). All surveys were in coastal dune scrub, eight in mangrove, seven conducted within 4 h after local sunrise, with the in thorn forest, 10 in early successional semiev- earliest surveys conducted at 06:00. ergreen forest, 12 in late successional semiev- We used a portable quadraphonic acoustic ergreen forest, and eight in pasture. Points were recording system to record the soundscape in placed along available paths and dirt roads. Each 360◦ by having each of the four microphones point was 250 to 500 m from adjacent points pointed in one of the four cardinal directions. and at least 250 m from the nearest edge of This microphone array was originally designed 170 A. Celis-Murillo et al. J. Field Ornithol. to record audio in four channels and to be able Evaluation of alternative methods for estimating to listen to the recordings using a quadraphonic richness demonstrated that Chao2 provides a playback system to interpret acoustic recordings robust estimate (Walther and Martin 2001) even and estimate the abundance of bird species with relatively small sample sizes (Colwell and (Soundscape Recording System, SRS; Celis- Coddington 1994). We compared species rich- Murillo et al. 2009). However, for this study, ness between the two survey methods for each we were only interested in species detections of the six vegetation types and for all vegetation so we converted the four-channel recordings to types combined by examining the overlap of stereo recordings and, in the lab, listened to 95% log-linear confidence intervals (CI) for the them using headphones (Sennheiser HD-280). estimates. To produce a two-channel (stereo) recording, We used the Chao-Jaccard multiple we merged the north and east channels into incidence-based similarity index (Chao-Jaccard one channel and the south and west channels MIB) to estimate overlap in species composition into a second channel; each of the resulting two between the acoustic recordings and point channels provided the listener with information counts in each of the six vegetation types from 180◦ of the soundscape. Four-channel (Chao et al. 2005). This index ranges from 0 microphone recording arrays were used instead to 1, where 0 indicates no overlap in species of stereo arrays because they sample 360◦ more composition and 1 denotes complete overlap. evenly. The Chao-Jaccard MIB similarity index is Analysis of acoustic recordings. The slightly sensitive to rare species and small same person (ACM) conducted all point counts sample sizes (Chao et al. 2005). Additionally, and reviewed all recordings, listing all species it uses information about the frequencies detected during each 10-min survey. To ensure and identities of rare species to adjust for that prior knowledge from point counts did the probability of undetected species. We not influence data collected from recordings, a used SPADE to calculate Chao-Jaccard MIB second person copied the master set of record- similarity indices using data from repeated visits ings and removed all identifying information to the 53 survey locations as the replicated (e.g., location and date). Each recording was incidence data. We compared the overlap of then assigned a random number and reviewed 95% CI to assess differences in species similarity randomly in the lab. ACM reviewed 8 to 11 between the two survey methods in each of the recordings each day. Recordings were reviewed six vegetation types and for all vegetation types multiple times in full or in part until ACM felt combined. confident that all species in the recordings were We used an occupancy modeling approach detected, taking on average ∼20 min to listen to estimate detection probabilities for 15 year- to each recording. Additionally, ACM compared round resident bird species for the two survey vocalizations to an independent reference col- methods and six vegetation types using data lection (Celis-Murillo et al. 2008), evaluated from repeat visits to survey locations. This set spectrograms, and, in a few instances, requested of 15 species included widespread and common verification from regional experts. Recordings species, species with restricted distributions, but were not reviewed until almost 1 yr after surveys common in the vegetation types where they were conducted to avoid confounding experi- are found, and rarer species that have restricted ence and method (Celis-Murillo et al. 2009). distributions or are uncommon where they are Statistical analyses. We used the pro- found. We ranked species based on the propor- gram Species Prediction And Diversity Esti- tion of visits during which they were detected mation (SPADE, Chao and Shen 2003) to by at least one survey method. Only five species estimate species richness using the Chao2 es- were detected on >20% of visits (the most timator. Chao2 is a nonparametric estima- common species was detected on 34% of visits); tor (Colwell and Coddington 1994) that ac- we analyzed all five species. Most species (87 counts for the relationship between sampling of 132, 65.9%) were rare (detected on <5% of effort and species richness indices as well as visits). We selected five rare species based on their imperfect species detection (i.e., total species endemic status; six endemic species fell into the richness is usually unknown because not all rare category, but one species was not included species, particularly rare ones, are detected). because it was detected in only one vegetation Vol. 83, No. 2 Acoustic Recordings for Surveying Tropical Birds 171 type. We also selected five moderately common regarding the factors affecting bird species detec- species (10–15% of visits). tion probabilities. We considered the following: Occupancy modeling uses species detec- (1) a constant model estimated a single detection tion/nondetection data collected over a se- probability for both methods and all vegetation ries of visits to each survey location (detec- types and represented the hypothesis of no effect tion histories) to estimate species detection of method or vegetation, (2) a method model probabilities and occupancy rates (MacKenzie estimated separate detection probabilities for et al. 2002). Occupancy models assume that each survey method and tested the hypothesis (1) the population is closed with no emigration that detection probability was different between or immigration occurring during the sampling acoustic recordings and point counts, (3) a period, (2) species are correctly identified, and vegetation model estimated separate detection (3) the probability of detecting a species at probabilities for each vegetation type and rep- one survey location is independent of detect- resented the hypothesis that vegetation alone ing it at another location (MacKenzie et al. influences detection probability, and (4) the 2002). Covariates, such as vegetation attributes interaction model estimated detection probabil- or meteorological variables, may be included ities for each method by vegetation type com- in the models to reduce variance in parameter bination, evaluating the hypothesis that method estimates (Mackenzie et al. 2006) and to assess and vegetation interact to influence a species relationships between detection probability or detection probability. We used Akaike’s Infor- occupancy rates and covariates (Bailey et al. mation Criterion (AIC) to compare models and 2004, Ball et al. 2005, Watson et al. 2008). estimate relative model fit (Burnham and Ander- Nichols et al. (2008) developed an extension son 2002). We calculated second order Akaike’s of the single-season, single-species occupancy Information Criterion (AICc) values, AICc model to deal with data from multiple survey values, and model weights for each model in methods conducted at the same survey locations. the candidate set; all models with AICc val- This “multimethod” approach accounts for the ues ≤ 2.0 were considered to have substantial lack of independence of detections inherent with support (Burnham and Anderson 2002). We simultaneous survey methods while allowing used multimodel averaging to estimate model one to make inferences about method-specific parameters. detection probabilities (Nichols et al. 2008). We used the multimethod occupancy modeling approach to compare species’ detection proba- RESULTS bilities between acoustic recordings and point counts in the six vegetation types. Species richness. During acoustic record- We created detection histories for each of the ing and point count surveys, 132 species belong- 15 species using data from consecutive visits to ing to 22 families were detected; 120 species the 53 survey locations; detection histories for were detected in the acoustic recordings and the acoustic recordings and point count surveys 123 during point counts (Table S1). Based were incorporated into a single data set. Vegeta- on Chao2, we found no significant differences tion type was included as a categorical covariate between acoustic recordings and point counts in our models. We only included data for vege- in estimated species richness when vegetation tation types where a species was detected during types were combined or considered separately at least one visit. Because the goal of our study (Fig. 1). was to compare detection probabilities between Species composition. Seven species were the two survey methods in each vegetation type detected only by acoustic recordings, 12 only by rather than to examine vegetation occupancy point counts, and the remaining 113 species by patterns, removing the vegetation types where both methods (Table S1). When we accounted the species was not detected does not influence for imperfect species detection and considered the results. We ran multimethod occupancy all six vegetation types collectively, overlap in models using the program PRESENCE (Hines species composition estimates was very high 2006). (0.98 similarity). When vegetation types were For each species, we used acoustic recording considered separately, estimates of species over- and point count data to evaluate four hypotheses lap between the two survey methods varied 172 A. Celis-Murillo et al. J. Field Ornithol.
Fig. 1. Estimates of species richness based on the Chao2 estimator and 95% confidence intervals for six tropical vegetation types analyzed collectively and separately for acoustic recordings and point counts. Confidence intervals are log-linear and asymmetrical. CD = coastal dune scrub, MN = mangrove, TF = mature low-stature deciduous thorn forest, SF = early-successional or secondary semievergreen forest, MF = late- successional or mature medium-stature semievergreen forest, and PA = grazed pastures.
Fig. 2. Estimate of similarity in species composition based on Chao-Jaccard multiple incidence-based (MIB) and 95% confidence intervals between acoustic recording surveys and point counts surveys in six vegetation types. CD = coastal dune scrub, MN = mangrove, TF = mature low-stature deciduous thorn forest, SF = early-successional or secondary semievergreen forest, MF = late-successional or mature medium-stature semievergreen forest, and PA = grazed pastures. nonsignificantly from 0.92 to 1.00 (Fig. 2); xantholora; and Black-headed Trogon, Trogon species composition was most similar between melanocephalus). Based on detection probabil- survey methods in areas with tall, dense vegeta- ities averaged across all models, detection prob- tion (semievergreen forests) and least similar in abilities were higher for acoustic recordings than coastal dune scrub. point counts (Fig. 3). However, there was con- Species detection probabilities. Based on siderable uncertainty in selecting the best model the multimethod occupancy models and AICc for two species (i.e., multiple models had AICc model selection criteria for the 15 bird species, values ≤ 2.0; Table 2); the vegetation model the method model was the best-supported had strong empirical support for Black-headed model for four species (Golden-fronted Wood- Trogons, and the constant model had substantial pecker, Melanerpes aurifrons; Orange Oriole, support for both Yellow-lored Parrots and Black- Icterus auratus; Yellow-lored Parrot, Amazona headed Trogons. Vol. 83, No. 2 Acoustic Recordings for Surveying Tropical Birds 173 c 3 3 3 K AIC i 06 3 16 3 30 3 00 8 00 4 00 3 11 3 00 3 00 3 00 3 00 3 02 3 60 59 41 ) etation ...... w st y between c 36 0 69 0 77 0 64 0 28 0 3307 0 0 82 0 80 0 59 0 93 0 85 0 00 0 00 0 00 0 ...... AIC 0 0 0 constant model c ) for models examining 76 5 51 1 85 0 17 14 15 60 2927 25 4 58 16 74 19 40 31 63 12 57 6 98 19 57 K ...... AIC )p( K 8 171 14 244 12 230 i 01 10 129 12 12 251 08 6 57 02 14 319 00 14 304 02 1401 235 12 149 30 12 209 30 10 199 95 75 71 02 8 146 00 13 160 01 12 128 ...... w c Rare 50 0 35 0 26 0 31 0 69 0 8148 0 0 74 0 72 0 00 0 00 0 00 0 77 0 17 0 74 0 = ...... AIC 0 0 0 R ) and number of parameters ( c i 90 9 17 2 34 3 84 8 56 12 7768 7 8 50 1 66 1 81 70 72 75 6 36 15 31 8 w ...... interaction method-vegetation AIC K 8 256 8 217 7 153 7 194 6 181 i 01 6 133 33 7 252 15 4 60 00 3 312 00 98 84 70 70 05 8 212 24 7 217 16 5 164 10 5 153 14 8 175 41 7 137 ...... w )p( c ), model weights ( c Moderately common, and 19 0 08 0 16 0 78 0 00 1 0000 0 0 00 0 00 0 75 0 29 0 02 0 54 0 81 0 01 0 = ...... AIC AIC 0 0 0 0 0 M vegetation c 31 12 59 9 10 0 24 2 87 96 20 76 94 56 5 99 2 74 3 52 3 00 2 58 0 ...... AIC Common, = K 4 317 4 133 4 250 459 C i 98 92 38 00 3 243 00 404 209 4 145 00 4 192 00 4 179 00 4 218 00 4 219 10 4 167 28 4 150 27 4 163 17 4 128 45 ...... w relative differences ( )p( c c 00 0 00 0 00 0 32 0 1996 0 0 75 0 14 0 36 0 49 0 84 0 50 0 60 0 80 0 00 0 ...... ), AIC AIC method c 0 0 0 0 p( c 53 40 82 19 58 1516 24 5 51 18 08 19 17 28 19 10 56 3 48 1 79 1 37 1 08 ...... AIC 57 ) are shown in bold. Common names and scientific names follow the American Ornithologists’ Union Checklist of North i 304 124 249 302 234 151 211 199 241 228 168 148 161 130 w R C ) ) C ) R C ) M ) M Trogon ) ) Turdus Amazona Myiarchus Tyrannus Crypturellus M Leptotila Vireo pallens ) M C Icterus auratus ) ) R R ) C ) R ) Cyanocorax ) M ) a Melanerpes aurifrons Colinus nigrogularis Thryothorus maculipectus Cyclarhis gujanensis Melanerpes pygmaeus xantholora ( jamaicensis yucatanicus ( ( melancholicus ( melanocephalus cinnamomeus grayi ( yucatanensis Superscripts after species names indicate relative abundance: detection probability of birds,methods), including vegetation a constant model modelinteraction (different influencing (no detection detection effects probability). probabilities Occupancy of was among vegetation kept constant or vegetation in method), all types), models method because and model it interaction was (differences not in method-vegetation a detection focus model of probabilit our (method study. Models and with the veg lowe American birds, 7th edition (2010). Species are arranged in order of increasing model weight for the top-ranked model. Table 2. Akaike’s Information Criterion (AIC value and highest model weight ( a Yellow-lored Parrot ( Caribbean Dove ( Species Golden-fronted Woodpecker Mangrove Vireo ( Black-headed Trogon ( Orange Oriole ( Yucatan Jay ( Black-throated Bobwhite Spot-breasted Wren Tropical Kingbird ( Rufous-browed Peppershrike Thicket Tinamou ( Clay-colored Robin ( Yucatan Woodpecker Yucatan Flycatcher ( 174 A. Celis-Murillo et al. J. Field Ornithol.
Fig. 3. Model-averaged detection probability estimates and standard error for acoustic recordings and point counts for 15 species in the study area and the vegetation types where they were detected on at least one visit. Species in the left column were considered rare, species in the central column were considered moderately common, and species in the right column were considered common. CD = coastal dune scrub, MN = mangrove, TF = mature low-stature deciduous thorn forest, SF = secondary semievergreen forest, MF = mature medium-stature semievergreen forest, and PA = grazed pastures.
The vegetation model was the best model for Vireo pallens; and Yucatan Jay, Cyanocorax five species whose detection probabilities varied yucatanicus), the vegetation model was the only among the vegetation types where they were model with substantial support. Detection prob- found (Table 2). For three species (Caribbean ability was lowest in vegetation types with the Dove, Leptotila jamaicensis;MangroveVireo, highest vegetation density for some species, but, Vol. 83, No. 2 Acoustic Recordings for Surveying Tropical Birds 175 for other species, was lowest in structurally open detection probabilities for point counts. How- vegetation types (e.g., pastures and thorn forest; ever, only common and moderately common Fig. 3). For two species (Black-throated Bob- species demonstrated interactions among vege- white, Colinus nigrogularis; and Spot-breasted tation type and method, and only two species ex- Wren, Thryothorus maculipectus), the interac- hibited interactions showing patterns of higher ≤ tion model had a AICc value 2.0 and a detection rates for acoustic recordings in some high model weight, indicating that a method vegetation types and for point counts in others. by vegetation interaction could not be ruled out. Support for the interaction model in these DISCUSSION species was due to higher detection probabilities for acoustic recordings in some vegetation types We found no difference in the performance (forests) and for point counts in others (pastures of acoustic recordings and point counts at and coastal dunes; Fig. 3). the community level in the northern Yucatan The interaction model was the top-ranked Peninsula. The two methods provided compa- model for three species (Tropical Kingbird, rable estimates of richness and composition, Tyrannus melancholicus; Rufous-browed Pep- and vegetation type did not affect the rela- pershrike, Cyclarhis gujanensis; and Thicket tive performance of the methods. However, Tinamou, Crypturellus cinnamomeus), indicat- at the population level, we found support for ing that both method and vegetation type influ- a method effect on the detection probabil- enced detection probability (Table 2). In all veg- ity of 12 species (i.e., the method or inter- ∗ etation types, detection probabilities for acoustic action method vegetation models had AICc recordings were the same as or higher than those values ≤ 2) and for no method effect for three for point counts, although the magnitude of the species (i.e., those where only the vegetation ≤ advantage of acoustic recordings varied among model had a AICc value 2). Only two vegetation types (Fig. 3). species showed an interaction where detection Finally, the constant model was the top- probabilities for point counts were higher in ranked model for three species (Clay-colored some vegetation types and those for acoustic Robin, Turdus grayi; Yucatan Woodpecker, recordings were higher in others, indicating Melanerpes pygmaeus; and Yucatan Flycatcher, that selecting a single method for those species Myiarchus yucatanensis), suggesting little or no may not be wise. For the remaining 13 species, influence of method or vegetation on detection regardless of whether they were rare, moderately probability (Table 2). For all species, however, common, or common, detection probabilities the method model also had low AICc values were either similar for the two methods or higher and high model weights, suggesting that the for acoustic recordings in the vegetation types type of survey method may influence the proba- where they occurred. bility of detecting these species, and detection Estimates of species richness did not differ probabilities were slightly higher for acoustic significantly between acoustic recordings and recordings. point counts in any vegetation type. Similar Based on AICc values, model weights, and results have been reported in previous studies detection probability estimates for the two (Haselmayer and Quinn 2000, Celis-Murillo methods in each of the six vegetation types, et al. 2009). Hobson et al. (2002) documented the relative performance of the two methods higher species richness using acoustic recordings, generally was not influenced by the status of a but differences between methods were small species as rare, common, or moderately com- (≤ 5 species). In contrast, Hutto and Stutzman mon (Table 2, Fig. 3). No rare species showed (2009) reported lower species richness using evidence of an interaction and, for the four rare acoustic recordings. A larger detection radius species where the method model had substan- for point counts and the inclusion of flyovers ≤ tial empirical support ( AICc value 2.0), in their point count data may have contributed detection probability estimates were higher for to the differences between survey methods in acoustic recordings than point counts (Fig. 3). their study. However, like Hutto and Stutzman Similarly, for most common and moderately (2009), we found that the performance of common species, detection probabilities for acoustic recordings and point counts for esti- acoustic recordings were as high or higher than mating richness was unaffected by vegetation 176 A. Celis-Murillo et al. J. Field Ornithol. type. Although few investigators have compared We found that detection probabilities for acoustic recordings and point counts, the results some species were influenced by survey method, of most studies suggest that acoustic recordings either independently of or interactively with can perform as well as or better than point counts vegetation type. Detection probabilities were for enumerating species richness in landbird- higher using acoustic recordings for seven species dominated systems. Furthermore, we found that in all six vegetation types (Golden-fronted acoustic recordings performed well in vegetation Woodpecker, Orange Oriole, Yellow-lored ranging from forests to pastures. Parrot,Black-headed Trogon, Yucatan Fly- The similarity between methods in determin- catcher, Yucatan Woodpecker, and Clay-colored ing species composition in our study (92–100% Robin). For three additional species (Rufous- across vegetation types) was comparable to that browed Peppershrike, Thicket Tinamou, and in temperate mixed coniferous-deciduous forests Tropical Kingbird), there was an interaction in Canada (83–97%, Hobson et al. 2002). In between method and vegetation; detection prob- contrast, Acevedo and Villanueva-Rivera (2006) abilities for acoustic recordings were the same detected 74% of species using both acoustic as those for point counts in some vegetation recordings and point counts in Puerto Rico types, slightly higher in others, and higher still in (based on data presented in Table 1 of their others. Some species detected more often using paper), whereas Celis-Murillo et al. (2009; 59%) acoustic recordings sang infrequently during the and Hutto and Stutzman (2009; 54%) reported 10-min surveys, including the two species of lower similarity estimates in temperate vegeta- woodpeckers, Orange Orioles, Yellow-lored Par- tion types. Differences among studies may be rots, and Thicket Tinamous. These species were due to variation in specific vegetative features detected better using recordings because record- (e.g., vertical and horizontal distribution of ings could be replayed multiple times; in the vegetation), bird behavior (e.g., proportion of field, especially in tropical regions characterized individuals present that vocalize), characteris- by high species diversity, they may go undetected tics of species’ vocalizations (e.g., frequency or or recorded. Furthermore, some of these species intensity), species richness, sample size, study are difficult to see (e.g., Thicket Tinamou) and, design, or the type of recording/playback system thus, do not provide field observers with any (e.g., number and arrangement of microphones, visual advantage. On the other hand, other number of channels recorded and played back species, such as Mangrove Vireos, Caribbean during review of recordings, and use of head- Doves, and Yucatan Jays, sang frequently during phones vs. speakers during review). This last surveys and were detected equally well by both factor may influence key aspects of the detection survey methods, although the overall probability process, such as relative differences in detection of detection varied among vegetation types. range or area between the two methods. In our Variation in detection probability among the study, similarity in composition was high even in vegetation types was likely due to a low number open vegetation types where, despite the greater of detections in some vegetation types, reflected potential to visually detect species, most are as very high CI, and often resulting in very low detected by their vocalizations (A. Celis-Murillo detection probabilities. and J. L. Deppe, pers. observ.). Of particular interest were Black-throated If the goal of a study is to perform a species Bobwhites and Spot-breasted Wrens that were inventory, investigators should consider the ef- detected better by point counts in pastures fectiveness and utility of acoustic recordings and coastal dunes, but as well as or better by and point counts in their study area prior to acoustic recordings in forests and mangrove. selecting a method and perform a preliminary These species vocalize frequently, although in comparison, especially if surveys are limited coastal dune and pasture they are seen al- to a single method. An important part of the most as often as they are heard (A. Celis- evaluation process will be to identify the cause Murillo and J. L. Deppe, per. observ.), likely of any discrepancies in composition (Hutto and because vegetation density is generally low in Stutzman 2009) and determine which method the shrub/sampling or herbaceous strata where yields the most reliable data, keeping in mind these birds are most active, thereby enhancing that no single method works equally well for all detection during point counts. For example, in species. combination with the low shrub/sapling cover Vol. 83, No. 2 Acoustic Recordings for Surveying Tropical Birds 177 of pastures, the characteristic display behavior detection distances vary among species, among of Spot-breasted Wrens in the open grassy areas human observers, among particular survey lo- of the pasture makes them especially obvious to cations within each vegetation type, and with an observer in the field (A. Celis-Murillo and environmental conditions. However, previous J. L. Deppe, per. observ.). For these two bird assessments of the recording system used in species, a combination of methods would likely our study have demonstrated that its average provide the most reliable data. The multimethod detection range across species is comparable to occupancy modeling approach provides a way that of a human observer in the field (Celis- to incorporate data from both techniques into Murillo et al. 2009). Thus, although the choice a single analysis to estimate occupancy rates of recording system specifications may be less more accurately than using data from a single important for estimating species richness at the method (Nichols et al. 2008). From a practical community level, the technical specifications of standpoint, acoustic recordings were effective for the recording array likely impact its performance surveying 13 of 15 tropical species in our study, at detecting a given bird species at the population and provide a practical method for surveying level. birds in the tropics where experienced field In sum, we found that acoustic recordings observers and funding for field surveys may performed as well or better than point counts for be limited. Regardless of the method selected, detecting most species across all vegetation types. however, analyses that account for variation in Furthermore, the two methods produced similar detection probability among vegetation types are estimates of species richness and composition in needed. all vegetation types, demonstrating that acoustic Our results indicate that acoustic recordings recordings can be used effectively for surveying were equally effective at detecting rare, common, both rare and common species across a variety and moderately common species. However, of tropical vegetation types. One advantage of Celis-Murillo et al. (2009) suggested that acous- using acoustic recordings in the tropics is that tic recordings were better than point counts they can be used to survey remote areas without for surveying rare species in riparian vegetation the need for trained field surveyors; acoustic in southern California. In contrast, Hutto and recordings can be made by personnel on-site and Stutzman (2009) noted that rare species were later reviewed in the lab by a skilled surveyor detected more frequently using point counts (Celis-Murillo et al. 2009, Hutto and Stutzman because of visual cues and the smaller apparent 2009). Because of the cost and effort associated detection radius of their microphone relative to with finding skilled observers and the time point counts. Haselmayer and Quinn (2000) needed to review recordings and examine spec- also detected rare species more frequently using trograms, conducting acoustic recording surveys point counts than recordings in Peru, but at- may not be cost effective (Hutto and Stutzman tributed this to the limited detection area of their 2009). However, automated sound recognition single, directional microphone system that was software could reduce the cost and time needed rotated during their surveys. They hypothesized to review recordings (Brandes 2008, Blumstein that an omni-directional microphone system et al. 2011). Software designed to detect and would enhance detection of rare species. Our re- recognize species vocalizations autonomously sults, obtained using a quadraphonic recording have been used successfully in other studies system that provided an omni-directional pat- (Figueroa and Robbins 2008, Trifa et al. 2008, tern, support Haselmayer and Quinn’s (2000) Kasten et al. 2010, Goyette et al. 2011), and such hypothesis. Our recording system may have results suggest that, in the near future, acoustic detected rare species better than the system survey methods could potentially be as or even used by Hutto and Stutzman (2009) because more effective than traditional point counts the technical specifications of our microphone for surveys of species richness, composition, or array allowed us to sample in all directions with occupancy in tropical regions. an average detection range comparable to that of a field observer. We did not calibrate our recording system to detect birds to a distance ACKNOWLEDGMENTS comparable to the effective hearing distance of a We thank D. Enstrom for insightful discussions re- human observer, a challenging exercise because garding acoustic monitoring and for comments on earlier 178 A. Celis-Murillo et al. J. Field Ornithol. drafts of this paper. T. J. Benson provided valuable CELIS-MURILLO,A.,F.GONZALES-GARCIA, AND D. comments and suggestions regarding occupancy model- MELTZER. 2008. Bird songs of Mexico: Yucatan ing, and J. Hines provided assistance in implementing Peninsula, vol. 1. Terrapin Records. Baltimore, MD. multimethod models in program PRESENCE. We thank CHAO,A.,R.L.CHAZLON,R.K.COLWELL, AND T. J. P. Stouffer, M. Murphy, and anonymous reviewers for SHEN. 2005. A new statistical approach for assessing comments on earlier versions of this manuscript. Funding similarity of species composition with incidence and for this research was provided by the Association of Field abundance data. Ecology Letters 8: 148–159. Ornithologists (Alexander Bergstrom Award), Cleveland CHAO,A.,AND T. J. S HEN [online]. 2003. Program Metroparks Zoo and Cleveland Zoological Society (Scott SPADE (Species Prediction And Diversity Esti- Neotropical Fund), Idea Wild, and the Illinois Natural mation).
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