Ibis (2026), 168, 520–540
doi: 10.1111/ibi.13459

Species characteristics predict the effectiveness of
avian survey methods in an Afromontane sky-island
VIKRAM MALHI,*1	GINO BRIGNOLI,1 JANE HALLAM,†,1 PETER NJOROGE,2 LUC LENS,3
MARIUS SOMVEILLE‡,1 & JULIA J. DAY1
1Department of Genetics, Evolution and Environment, Centre for Biodiversity and Environment Research, University
College London, Gower Street, London, WC1E 6BT, UK
2Ornithology Section, National Museums of Kenya, Museum Hill Road, PO Box 78 420–00500, Nairobi, Kenya
3Terrestrial Ecology Unit, Department of Biology, Ghent University, K.L. Ledeganckstraat 35, Ghent, 9000, Belgium
Biodiversity conservation requires effective monitoring of ecological communities in remote locations, where limited accessibility often restricts survey efforts. Passive acoustic monitoring (PAM) is becoming an established method for measuring biodiversity, facilitated by the increased accessibility of autonomous recording units. Comparing the performance of PAM and traditional survey methods can provide insights into how species characteristics such as foraging strata, vocal behaviour and taxonomy influence the detection ability of each method. Here, avian species occurrences were collected using PAM and point count surveys (PCS) across three forest fragments in the Taita Hills, an Afromontane sky-island in southeastern Kenya. These montane forests contain high species-richness and endemism, but they have been severely fragmented and degraded as a result of deforestation, making comprehensive monitoring critical for conservation. We grouped detected species into clusters based on their foraging strata, using partitioning around medoid cluster analysis. From 25 survey sites, PAM and PCS detected 60 and 57 species, respectively, indicating that PAM is as effective as PCS for surveying montane tropical birds. However, species that primarily foraged on the ground and secondarily used the understorey, and species that primarily used the understorey and secondarily used the canopy, were more likely to be recorded by PAM than by PCS. Species that only used the understorey were more likely to be recorded by PCS. Investigation of broad taxonomic groupings showed that PAM was 20% more likely to record non-passerines than PCS, while passerines were equally likely to be recorded by either method. This study highlights how species characteristics, such as foraging strata and taxonomic group, influence the performance of surveying methods. By identifying which species are best monitored by each method, this study can inform more targeted monitoring strategies, improving accuracy and supporting biodiversity management efforts to mitigate ongoing species loss.
Keywords: avian diversity, k-means clustering, passive acoustic monitoring, point count surveys.

© 2025 The Author(s). IBIS published by John Wiley & Sons Ltd on behalf of British Ornithologists’ Union. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

†Present address: Yale School of the Environment, Yale University, New Haven, Connecticut, USA
‡Present address: School of Environmental Sciences, University of East Anglia, Norwich, UK
*Corresponding author.
Email: vikram.malhi.23@ucl.ac.uk
Current estimates for global biodiversity loss may be underestimating the actual rate (Isbell et al. 2023), especially for remote biodiversity hotspots, highlighting the need for comprehensive biodiversity monitoring. This is especially pertinent to endemic and forest-dependent species that have

been shown to typically benefit the most from conservation protection (Cazalis et al. 2020). Cost-effective and long-term emerging monitoring technologies, such as remote sensing and bioacoustics, can greatly aid in monitoring biodiversity (Sugai & Llusia 2019). They can also help to inform which conservation practices are most effective (Ramesh et al. 2023), because ineffective interventions are not only environmentally costly, but also economically expensive owing to the lack of return. As a disproportionate amount of the planet’s biodiversity is found in low-income countries (Isbell et al. 2023), where there are few resources assigned towards conservation (Vanhove et al. 2017), such technologies can provide powerful tools for monitoring biodiversity hotspots.
Passive acoustic monitoring (PAM) using audio recordings is becoming an established method for measuring avian species diversity, and has been shown to be at least as effective, if not more so, as traditional point count surveys (PCS) (Darras et al. 2018a). However, less attention has been given to how avian assemblages detected by these methods differ in terms of functional traits and taxonomy. To date, studies have successfully employed PAMs to estimate avian diversity, primarily species-richness, across terrestrial habitats (Eldridge et al. 2018, Bradfer-Lawrence et al. 2020, Owen et al. 2020, Smith et al. 2020), as well as other animal groups (Gibb et al. 2019, Odom et al. 2021). Studying acoustically detectable avian diversity has also been found to be a reliable indicator of the wider biodiversity trends in certain areas, where acoustically detectable species are reliable estimators of overall avian species-richness (Smith et al. 2020). An advantage of autonomous recording units used in PAM is that they do not require humans at the recording site. This logistical benefit allows for the collection of larger datasets, which can be verified by multiple experts and revisited at later dates as new methodologies emerge. Additionally, autonomous recording units eliminate any potential influence on bird behaviour as a consequence of human presence (Darras et al. 2018a).
Previous studies investigating the comparative performance of PAM compared to PCS in accurately measuring species diversity have predominantly focused on methodological factors such as the height at which the autonomous recording unit is deployed, or the number of days it is recording (Darras et al. 2018a). Fewer studies have investigated how species characteristics, such as foraging habits, affect the likelihood of detection by either method, with the exception of habitat use (see Alquezar & Machado 2015, Wheeldon et al. 2019). Whilst some studies indicate that PAM is more effective than PCS in closed environments (Celis-Murillo et al. 2012), other studies suggest that habitat type does not significantly affect the species-richness reported by either method (Alque- zar & Machado 2015, Wheeldon et al. 2019, Joel et al. 2024). Indeed, vegetation structure is known to affect the attenuation and propagation of sound (Dabelsteen & Mathevon 2002). This is further evidenced in PCS conducted in dense forest where more than half of the species are solely detected by vocalization (Sedl^a^cek et al. 2015). Although the vocalizations of birds have been hypothesized to have evolved to minimize this degradation under the acoustic adaptation hypothesis (Boncoraglio & Saino 2007), vegetation structure may still affect detectability in PAM.
In birds, foraging stratum (the vertical stratification of foraging bird species) has been shown to affect the likelihood of detection (Franklin et al. 2020) both directly as a result of vegetation cover and indirectly as a result of sunlight affecting when species vocalize (Berg et al. 2006). Foraging stratum has been investigated between traditional methods such as mist-netting and PCS, where PCS outperformed mist-netting in providing a more comprehensive report of avian diversity (Mulvaney & Cherry 2020, Zamora-Mar^ın et al. 2021). Mulvaney and Cherry (2020) also showed understorey birds to be better detected by PCS even though this stratum was previously thought to be better monitored by mist-netting. Although a study that focused on birds from dry montane sclerophyll forests in Southeast Australia found that species from most strata were more likely to be detected by PAM than by PCS (Franklin et al. 2020), it is not clear if this finding is common to montane forest habitats. However, it is likely that strata with dense vegetation, such as the canopy and the shrub layer, may be better monitored through acoustics rather than sight (Darras et al. 2018a, Franklin et al. 2020).

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Furthermore, taxonomic group can also affect how likely birds are to vocalize, and therefore the likelihood of a species being detected by either PAM or PCS (Berg et al. 2006). Phylogenetic relatedness has been shown to affect which method is more likely to detect a species (Zamora-Mar^ın et al. 2021). This is particularly relevant to passerine vs non-passerines owing to
both major lineages of passerines (oscines and suboscines) having a large vocal repertoire that is vocalized regularly at dawn and, to a lesser extent, at dusk (Norambuena & Victoriano 2020, R^ıos- Chel^en et al. 2020, Arato & Fitch 2021). In contrast, non-passerines have a much simpler vocal repertoire, with daily vocal temporal patterns that are more varied (Maina et al. 2016, Barrero et al. 2020, Arato & Fitch 2021, Niekerk et al. 2021), making their vocal repertoire less regular and/or predictable than that of passerines. This may not, however, affect the performance of PAM in comparison to PCS, as birds are also identified acoustically from PCS (Sedl^ac^ek et al. 2015).
Another factor which can affect the detectability of a species is the frequency of its vocalizations. Larger birds produce lower-frequency calls, which can be heard from greater distances, potentially influencing both PCS and PAM, as both methods rely on acoustics. However, this is further complicated by the fact that larger birds may be more likely to be detected visually by PCS. Therefore, body size may influence which method is more effective at detecting a species, although the exact impact of size on survey methods is unclear.
A key difference between these methods is how species are analysed. In PAM, recordings of detection events may be reviewed and revised multiple times, depending on the experience of the observer. The observer’s skill level can also influence detection accuracy, potentially leading to missing detections. Therefore, species that rarely vocalize or have generally less distinctive vocalizations may be more likely to be detected in PAM than PCS (Celis-Murillo et al. 2009, Johnson et al. 2022, Liedtke et al. 2022, Lo^pez et al. 2022, Ramesh et al. 2023), given that they are still somewhat soniferous (Darras et al. 2018a). As such, there is a need for a more critical evaluation of biological and environmental factors affecting species detection to determine which species are better suited to a particular monitoring method.
Tropical montane forests are a unique habitat characterized by high levels of endemism resulting from their natural isolation, and are often diagnosed as biodiversity hotspots (Johnson et al. 2022, Liedtke et al. 2022, Lo^pez et al. 2022, Ramesh et al. 2023). Despite this, they are currently highly threatened habitats via forest degradation caused by deforestation, charcoal production, clearance for agriculture and the expansion of exotic plantations (Hamunyela et al. 2020, Ahrends et al. 2021). The vulnerability of montane forest biodiversity is further exacerbated by their sensitivity to climate change (Maina et al. 2016, Newman et al. 2020, Teucher et al. 2020, L^opez et al. 2022), making the ability to accurately monitor their biodiversity critical. To date, the performance of monitoring methods in tropical mountain forests have produced mixed results, with PAM outperforming PCS in some studies (Su^arez-Garc^ıa et al. 2017, Wheeldon et al. 2019), and PCS outperforming PAM in others (Sedl^a^cek et al. 2015, Maina et al. 2016). To further test the performance of PAMs vs PCS in capturing montane forest avian diversity, we focused on one of the partially protected East African sky-islands, the Taita Hills, southeast Kenya. This study site has been substantially impacted by forest loss due to the increase of exotic plantations and subsistence agriculture, while forest degradation has resulted from charcoal production and the collection of deadwood (Himberg et al. 2009, Hamunyela et al. 2020, Ahrends et al. 2021).
In this study, we applied PCS and PAM methods to montane forest avian assemblages to investigate: (1) if these methods differ regarding avian assemblage composition and species-richness; and (2) how species characteristics, such as use of foraging strata and broad taxonomic group, affect the likelihood of detection by either method.
MATERIALS AND METHODS
Study system
The Taita Hills is the northernmost sky-island of Africa’s Eastern Arc Mountains, which due to anthropogenic activities is currently composed of multiple forest fragments that vary in human impact (Ulrich et al. 2016). We selected three forest fragments from these: Yale, Mbololo and Mt Kasigau (Supporting Information Figs S1–S3; see also Appendix S1, p2, for site selection and habitat description). The Taita Hills is a useful system for our study as it has a highly diverse avian assemblage comprising 202 bird species (Norfolk et al. 2017) originating from both the Kenyan highlands as well as the Eastern Arc Mountains, that have been well-studied over a 30-year period (Brooks et al. 1998).
Survey methods

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This study seeks to compare the methodologies and workflows of PCS and PAM to maximize the efficiency at which species-richness at a given survey station is reached. These methods were therefore conducted independently from each other, allowing PAM to capture vocalizations of individuals not habituated to observers (see Supporting Information Tables S1 and S2). To account for stochastic variables such as weather and temperature, PCS were conducted on different days and at different times to capture daily fluctuations in these variables (see Supporting Information Tables S1 and S2). While the autonomous recording units were deployed after a PCS was conducted and left to record for 2 days, on the second day the autonomous recording unit was collected before another PCS survey was carried out, ensuring that observers did not disturb the PAM process. PCS were conducted along pre-existing forest trails (hereafter termed transects) specifically to avoid any damage to the forests. Each survey location (survey station) was 100 m apart to accommodate the limit length of the transects. Birds were only documented if seen and heard within a 30-m radius to minimize double counting, for 10 min by two observers (Gino Brignoli [GB] and Nathaniel Mkombola [NM]), following Bibby et al. (1998).
We used AudioMoths 1.1.0 (firmware v. < 1.2.0) (Hill et al. 2018), which are open-source and programmable autonomous recording units from Open Acoustic Devices (Hill et al. 2018). These devices were placed in containers (see Supporting Information Methods) and affixed to trees 1.5 m above ground following Darras et al. (2018a). The configuration of the AudioMoths varied between the different forest fragments in terms of clip duration of each audio file recorded, the number of days the AudioMoths were recording for and whether the wave files were time-stamped (Table 1). Clip duration was set to 1 min (Table 1), which is considered optimal for PAM to capture species-richness (Metcalf et al. 2021; Wood et al. 2021), in contrast to the 10-min standard for PCS (Fuller & Langslow 1984, Lynch 1995, Bibby et al. 1998). This set-up allows for a direct comparison of each method under its most effective conditions. Each forest fragment also differed in the number of AudioMoths deployed. All AudioMoths were configured to a sample rate of 32 kHz with the gain set at the default medium setting.
Selection of PAM and PCS stations
A total of 30 AudioMoths were deployed in which 27 were analysed for this study (excluded units
	Number of Unit length (min) clips analysed Date (day/ of each recording per AudioMoth month/year) at each fragment	26–28 Feb	1
2020
26–27 Feb	1
2020
14 Feb 2020	1
04 Feb 2020	1	
			
	Number of consecutive days each AudioMoth recorded		
			
	Number of PCS analysed for OSR, BD, FD and TD		
Table 1. Details of PAM and PCS deployment and analysis for each forest fragment.			
	Number of AudioMoths analysed for OSR, BD, FD and TD		

	Number of PCS analysed for ESR		

	Number of AudioMoths analysed for
ESR		
			
	Number of AudioMoths deployed		
			
	Forest fragment	Mbololo.1 Mbololo.2 Kasigau
Yale	
See Supporting Information Tables S1 and S2 for further information. BD, beta diversity; ESR, estimated species-richness; FD, foraging; OSR, observed species-richness;
TD, taxonomic diversity.

included those placed closer than 100 m from one another, and one unit that failed to time-stamp its files). However, the 100-m detection range of AudioMoths is unlikely to have been maintained given the topography of the Afromontane Forest they were situated in, and the terrain and density of the vegetation are likely to have attenuated any vocalizations before they may have travelled 100 m. Conversely, interspecific variation in the loudness of vocalizations may result in some species being over-represented and heard outside the 100-m radius (Darras et al. 2018b). Of these, 25 were used to obtain observed species-richness, beta diversity, foraging diversity and taxonomic diversity (Table 1). To calculate these metrics, each PAM station had a nearby paired PCS station (Supporting Information Figs S1–S3). Only two PAM stations were not within a 100-m proximity to the other PCS stations and therefore were only used for calculating estimated species-richness (see Supporting Information Table S1 and Fig. S1). Whilst habitat type varied between stations as either indigenous forest or forest edge, especially between forest fragments (see Supporting Information S1.1 and Figs S1–S3), each PAM station had the same habitat type as their paired PCS station. The PCS stations used for estimated species-richness were selected if they were on the same transect and had similar habitat type as the PAM stations (n = 56). To compare sampling effort (i.e. species number detected) between PAM and PCS across the stations (27 PAM vs 56 PCS stations), extrapolation and interpolation of Hill’s species number was used. Audio files were then sampled following the workflow illustrated in Figure 1 (see also Supporting Information Methods).
Analysis of recordings

Figure 1. Workflow of how audio files were sampled for PAM (see also Supporting Information Methods).

To identify species presence, each recording was listened to, and the corresponding spectrogram visually inspected using the Adobe Audition 2020 software (Build: 13.0.10.32). We selected this software as it accounts for the low signal-to-noise ratio of AudioMoths owing to its ability to remove sound based on a threshold of sound pressure (dB). This was only used for confirmation; calls were initially detected without any alterations. Only vocalizations lasting at least 3 s, either individually or in total within the 1-min clip, were identified. Therefore, a species would only be missed if it vocalized for less than 3 s in total and 
did not repeat within the same 1-min window. Since no such species are known to occur at the study site, this limitation is unlikely to bias species identification. The vocalizations were graded on a scale of 1–4 for certainty, with 4 as most certain, highlighting those requiring further assessment (conducted by VM, GB and JJD).
To identify vocalizations, our own knowledge, alongside the eGuide to Birds of East Africa mobile app (eGBEA), was used as a primary reference. The websites Xeno-Canto (https://www. xeno-canto.org/) and Macaulay library (https:// www.macaulaylibrary.org/) were used secondarily; these are both citizen science-based publicly accessible repositories (Searfoss et al. 2020). Xeno-Canto provided further verification for species with large intraspecific vocal variation, especially if those species contained several races within East Africa. Vocalizations that were not identified were removed from analysis (Yale, n = 7; Mbololo, n = 12; Kasigau, n = 3).
Statistical analysis
To compare the species-richness reported by either method, an ANOVA was conducted to assess the variance amongst species counts at each survey station (n = 25), whilst controlling for the number of days each station was surveyed by the different methods and the duration of surveys. Since comparing relative abundance between PAM and PCS is challenging, the number of stations in which a species was detected was used as a proxy for relative abundance in all analyses. As PAM surveys within a forest fragment were conducted on the same dates, detection from multiple survey stations is unlikely to result in double-counting. Using the same proxy for PCS ensures comparability between the two methods. To account for differences in detection ranges, species size was included as a control, since larger species are typically detected at greater distances (Darras et al. 2018b, Winiarska et al. 2024). To measure dissimilarity in the avian assemblage reported between PAM and their corresponding PCS stations, Sørensen’s index, Simpson dissimilarity and nestedness were calculated using the betapart package (Baselga & Orme 2012, Chao et al. 2023). The former metric indicates the amount of dissimilarity attributed to species turnover, and the latter is the remaining proportion of the Sørensen’s index, which indicates the difference caused by nestedness.
To account for the difference in sampling intensity between the two methods (27 PAM stations vs 56 PCS stations), incidence-based speciesrichness estimates were extrapolated or rarefied using the iNEXT package v.2.0.20 (Chao et al. 2014, Hsieh et al. 2016). Species-richness was estimated following the methodology of Chao et al. (2014), Chao and Jost (2012) and Littlefair et al. (2019) by calculating estimated species-richness using the smallest sample coverage of either method. The smallest sample coverage was obtained by estimating species-richness from double the number of sampling units of the method with the smallest number of sampling units. Here, we defined sampling units as the number of stations. PAM had the fewest number of stations and was therefore used to calculate the sample coverage from which species-richness was estimated. Sample coverage is the proportion of individuals in a community that belong to a species (Wheeldon et al. 2019). Comparing species-richness estimated by sample coverage by PAM and PCS, instead of by the number of sampling units, therefore provides a more meaningful comparison of species-richness (Chao et al. 2014, Chao & Jost 2012, Littlefair et al. 2019). Aerial foragers were excluded from the surveys as their low detection rate make them poor indicators of species-richness (Watson 2010, Smith et al. 2020). This excluded the African Black Swift Apus barbatus, Little Swift Apus affinis and the Alpine Swift Tachymarptis melba.
The complementarity analysis investigated the amount of overlap of species-richness from using the two different methods (Colwell & Coddington 1994, Boullhesen et al. 2021): (uPAM + uPCS)/(uPA- M + uPCS + both), where uPAM = number of species only recorded by PAM, uPCS = number of species only recorded by PCS and both = number of species recorded by both methods.

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To investigate the functional diversity measured by PAM and PCS, functional groups were ordinated based on the percentage-use of all the foraging strata for each species collected from EltonTraits 1.0 (Wil- man et al. 2014). Two strata, the ‘mid-high’ and the ‘understorey’, were amalgamated by their sum to form the ‘mid-understorey’ as these two categories lacked distinction in their description between each other (Wilman et al. 2014). For several species, foraging data based on closely related species were used for completeness following Ulrich et al. (2016). This included the Taita White-eye
Zosterops silvanus, which was previously considered a subspecies of Zosterops poliogaster (Martins et al. 2020), while data for the Tropical Boubou Laniarius major were based on the Ethiopian Bou- bou Laniarius aethiopicus as they are part of a cryptic species complex (Nguembock et al. 2008). Functional and taxonomic diversity was then inferred from species presence and absence in a forest fragment. Since some of the stations in Mbololo collected 42 clips and others 48 (Table 1), the stations were split into two groups on this basis.
The foraging groups were ordinated into 10 clusters using the partitioning around medoid cluster analysis, using the ‘cluster’ package, v.2.1.4 (Maechler et al. 2023). Ten clusters were selected as the optimal number, using the elbow, gap statistic and silhouette methods implemented in the ‘factoextra’ package, v.1.0.7 (Kassambara & Mundt 2020). These methods use different approaches to group data into clusters based on the number of groups provided. The elbow method aims to minimize intra-cluster variation, the silhouette method measures how well the data lie within the clustering, and the gap statistic method compares the total intra-cluster variation at a given number of clusters to the expected value from the null reference distribution (Kassam- bara & Mundt 2020). These three methods indicated that these data are best split up into 10 clusters (see Supporting Information Fig. S4). Partitioning around the medoid analysis was then applied to form the 10 clusters by searching for 10 representative centres (medoids) from which to assign all of the data points to their closest medoid (Maechler et al. 2023). From the 10 clusters, two clusters were amalgamated into one owing to a lack of distinction between them in their use of the canopy and mid-understorey strata (Table 3; Supporting Information Fig. S4). The reduction in clusters was performed after the analysis was conducted, because changing the number of clusters in the partition analysis from 10 to nine resulted in the removal of the only aerial dominant cluster. Of these nine foraging clusters, seven were represented in the study site.
To investigate whether the foraging strata of a species were likely to affect whether it was more likely to be detected at a station by PAM or PCS, a logistic regression model was applied where the response variable was the presence of a species at a survey station, and the predictor variable was the interaction between foraging cluster and method used. To remove other biases, the model incorporated control variables including the foraging cluster, whether the species was passerine or not, the logarithm of the species’ mass, the number of days on which surveys were conducted at each station for each method, the number of minutes each station was surveyed by each method and the forest fragment in which the station was located. By using the log of species mass as a control, the model could also account for differences in detection range amongst species.
To account for the species-specific vocal repertoire and vocal temporal patterns, we selected the two broad taxonomic groups: passerines and nonpasserines, which have previously been identified to have large differences in how soniferous they are (Berg et al. 2006, Barrero et al. 2020, Arato & Fitch 2021). A logistic regression analysis was then performed to determine whether the likelihood of a species being detected at a station differed between methods, based on whether the species was passerine or non-passerine. In this model the response variable was the presence of a species at a survey station and the predictor variable was the interaction between taxonomic group and method used. The analysis included control variables for taxonomic group, foraging cluster, the log of species mass, the number of survey days at each station and the duration of surveys conducted with each method at each station. This indicates both sampling intensity and strategy used, which is likely to detect different species depending on the temporal patterns of their vocalization (Berg et al. 2006). Adding these control variables was found to increase the parsimony of the model, as shown using the ‘AICcmodavg’ R package (Mazerolle 2017).
To investigate how size influences the likelihood of a species being detected by each method, a separate logistic regression model was applied. Using mass data from AVONET (Tobias et al. 2022), the response variable was species presence at a station, and the predictor variable was the interaction between the method and the log-transformed species mass. The model also controlled for species-mass variation within the study area, as well as additional variables, including the foraging cluster of the species, whether the species was passerine or non-passerine, the number of survey days for each method at each station and the forest fragment in which the station was located.

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To ensure that the foraging, size and taxonomic models accounted for all significant sources of variation, the Hosmer–Lemeshow test was applied to
test goodness-of-fit. To compare model performance, predictions from each of the models were used to evaluate how well they predicted the observed data. All statistical analyses were conducted using RStudio 4.0.3 (RStudio Team 2024).
RESULTS
Observed species-richness
Amongst all three forest fragments, we showed that most species from the total pool of 68 detected species were detected by both PAM and PCS methods (Fig. 2). Species complementarity was shown to be reflective of the forest fragment, where similar Venn diagrams reflect similarity in the quality of the habitat. Both Kasigau and Mbololo are described as much less anthropogenically disturbed (Henkin et al. 2015, Adhikari et al. 2020), and both showed similar amounts of complementarity. Furthermore, we can infer that Yale could be sufficiently surveyed using either method.

Figure 2. Venn diagram showing the number of species detected by each method at each forest fragment with complementarity scores in parentheses next to each fragment: (a) all of the forest fragments; (b) Mbololo; (c) Kasigau; and (d) Yale.

There was no significant difference in the number of species recorded at each station (F = 0.001, P = 0.97). Amongst the 25 stations, 20 had their Sørensen’s dissimilarity attributed to turnover dominating over nestedness (Fig. 3). Five of the six stations with high Sørensen’s dissimilarity scores (> 0.5)—MB_5, KA_1, KA_3, KA_4, KA_6 and YA_4—showed greater dissimilarity owing to species turnover rather than nestedness (Fig. 3). The exception, KA_4, had similar contributions from both components. At four of these stations (MB_5, KA_1, KA_6 and YA_4), more species were detected exclusively by PAM than by PCS, suggesting that the two methods identified different avian assemblages. Notably, four of the six stations with high dissimilarity are located in Kasigau (Fig. 3).
Estimated species-richness

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Regarding sampling effort, Yale and Mbololo fragments showed that PAM had a higher initial accumulation of Chao’s estimated species-richness (q0) than PCS (Fig. 4). PAM also reported higher q0 based on sample coverage than PCS in Kasigau and Mbololo with some overlap in estimates, whilst Yale showed a negligible difference between the two surveying methods. This was consistent with PCS showing higher q0 based on the number of samples from Yale. Additionally, the species accumulation curves of Mbololo and Kasigau showed PAM to have higher q0 than PCS (Fig. 4). With regard to sample coverage, Mbololo showed that PCS had a higher sample coverage than PAM based on the sample size (Table 2). In contrast, Yale consistently showed sampling coverage to be higher in PAM than in PCS.

Figure 3. Beta diversity between corresponding PAM and PCS stations including Simpson’s dissimilarity which indicates turnover (beta.sim) in red, and nestedness (beta.sne) in blue which together form Sørensen’s dissimilarity.

Figure 4. Estimated species richness as a function of the number of sampling units (defined as survey stations) is shown for the two methods: PAM (red) and PCS (blue), across the three forest fragments: (a) Kasigau; (b) Mbololo; and (c) Yale. Bold lines represent interpolated (observed) species richness, while dashed lines represent extrapolated species richness.

Functional and taxonomic diversity
Non-passerines were approximately 20% more likely to be recorded by PAM than by PCS (b = 1.327, z = 2.933, P = 0.003; see Fig. 5). Taxonomic group was shown to explain more of the variation in

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probability of being detected by either survey method at a station than foraging behaviour (foraging model: AIC = 962.49, residual deviance = 922.49 on 810 df; taxonomic model: AIC = 995.34, residual deviance = 965.34 on 815 df; size model: AIC = 996.06, residual

	Sample coverage at twice	Estimated species-richness (q0)
Survey	Number of	the sample unit of the	Rarefaction/	using smallest sample coverage	
Fragment	method	sampling units	smallest sample unit	extrapolation	(^95% CI)a	
Mbololo	PAM	13	0.961 (t = 26)	Extrapolation	26.104 ^ 16.383
PCS	20	0.987 (t = 26)	Rarefaction	17.586 ^ 3.344	
Yale	PAM	8	0.996	(t =	16)	Rarefaction	39.195 ^ 7.087
PCS	16	0.934	(t =	16)	Observed	45.758 ^ 27.223	
Kasigau	PAM	6	0.814	(t =	12)	Extrapolation	28.563 ^ 18.880
PCS	20	0.969	(t =	12)	Rarefaction	17.279 ^ 1.521	

Table 2. Chao’s estimated species-richness per site, based on data rarefied and extrapolated to the smallest sample coverage, calculated as twice the size of the smallest sampling unit size. Sampling units were defined as the number of survey stations.

aNo overlap in CI.

Cluster	Ground	Mid-understorey	Canopy	Aerial	Below water	Water surface	
1	100	0	0	0	0	0	
2	76	17	1	1	0	5	
3	46	51	2	1	0	0	
4	0	100	0	0	0	0	
5	5	66	26	2	0	0	
6	1	23	74	2	0	0	
7	2	10	5	82	0	0	
8	20	1	0	0	9	70	
9	1	0	0	0	92	7	

Table 3. Mean values (rounded to whole numbers) indicating the extent to which each cluster uses different foraging strata to consume food.

deviance = 968.06 on 816 df). Both survey methods were just as effective as each other at detecting the majority of the functional groups, with three exceptions. Amongst the foraging clusters, species that primarily used the ground and secondarily used the understorey (Cluster 2, b = 2.847, z = 4.349, P > 0.0001), species that primarily used the understorey and secondarily used the canopy (Cluster 5, b = 0.956, z = 2.228, P = 0.026), and species that only used the understorey (Cluster 4, b = 1.437, z = 2.373, P = 0.017) were the only clusters where survey method had a significant effect on their probability of detection. Species from Cluster 2 were approximately three times more likely to be recorded by PAM, while species from Cluster 5 were approximately 20% more likely to be recorded by PAM. In contrast, species from Cluster 4 were approximately 25% more likely to be recorded by PCS than by PAM (Fig. 6), based on median detection probabilities. The size model shows that PCS is less likely to detect larger bird species compared to PAM comparatively (Log(Mass): methodPCS, b = 0.183, z = 2.660, P = 0.00782; Fig. 7).

DISCUSSION
This study showed that PAM is as effective as PCS for surveying montane tropical avifauna speciesrichness, supporting previous studies (Sedla^^cek et al. 2015, Maina et al. 2016, Su^arez-Garc^ıa et al. 2017, Wheeldon et al. 2019). However, whilst most foraging clusters ordinated here were equally as likely to be detected across survey methods, we found species that primarily used the ground and secondarily the understorey, and species which primarily used the understorey and secondarily the canopy are more likely to be recorded by PAM than by PCS, supporting Franklin et al. (2020). Conversely, species that only used the understorey were shown to be more likely to be detected by PCS. A significant, albeit slight, difference between PAM and PCS was the taxonomic category detected, with PAM more likely to detect non-passerines, whilst passerines did not show a significant difference. Thus, whilst phylogenetic relatedness affects overall detectability of species, it appears to have a diminished effect on the type

Figure 5. Detection probabilities for the two taxonomic groups, predicted using the log-odds from the taxonomic logistic regression model. An asterisk (*) indicates if the difference is significant.

of surveying method used, as reported in a study comparing PCS to mist-netting (Zamora-Mar^ın et al. 2021). Even when controlling for size variation, larger species are less likely to be recorded by PCS than by PAM. This suggests that detection range of larger species may explain the variation in detection probabilities across species. Therefore, using size as a control variable could be an effective way to account for detection range.
How foraging strata affects species detection between PAM and PCS
Amongst the functional characteristics of species, foraging stratum has been viewed as the characteristic most likely to affect the comparative performance between PAM and PCS (Franklin et al. 2020). This was supported by foraging cluster having the most parsimonious model with the lowest AIC. Foraging stratum was also found to be the species-level characteristic to affect the comparison between traditional surveying methods (e.g. PCS and mist-netting) most substantially (Mulvaney & Cherry 2020). A possible explanation as to why these differences are observed could be because studies often assign species to one category of a functional variable, such as food type, size, or vertical foraging guild based on a threshold or their primary usage (Ulrich et al. 2016, Franklin et al. 2020), and in doing so, secondary but significant categories of a species functionality are ignored (Secco et al. 2018). Although the percentages of foraging stratum listed for each species from Wilman et al. (2014), used here, only indicate where the food is consumed, but not their general use or where they forage, the benefit of that study is that it assigns approximate percentages to the use of each stratum, allowing species to be grouped into more than one category of the functional characteristic. Therefore, by using partitioning around medoid analysis, functional groups can be ordinated based on multiple categories of

Figure 6. Detection probabilities for the three foraging clusters, each showing a statistically significant effect on species detection by a given survey method, predicted using the log-odds derived from the foraging logistic regression model.

the same functional characteristic along with other sub-guilds (Ulrich et al. 2016, Asefa et al. 2017, Otieno & Mutati 2021).
Several functional clusters were found to have a significant interaction with the comparative performance of PCS and PAM, including species that primarily use the ground and secondarily use the mid-understorey, species that only use the understorey, and those that primarily use the mid-understorey and secondarily use the canopy. This finding supports Franklin et al. (2020), who showed that species using the ground and understorey (G/U) are approximately 20% more likely to be recorded by PAM than by observers, based on their posterior distribution. In contrast, the present study reports these species to be 52% more

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likely to be recorded by PAM than by PCS, based on median detection probabilities. However, it is important to note that these detection probabilities should not be directly compared owing to differences in methodology. Our study used logistic regression models, while Franklin et al. (2020) employed occupancy modelling. Given that there is a similar species number at both sites (57 in Franklin et al. 2020, 68 used in the foraging logistic regression here), the observed difference is more likely to be a consequence of methodological variations rather than differences in abundance or diversity. This is quite counterintuitive, as occupancy modelling accounts for false negatives and thus might be expected to yield higher detection probabilities. However, in multispecies occupancy

Figure 7. The effect of body mass on the predicted probability of a species being detected by the two survey methods, PCS vs. PAM.

models, when species detections are infrequent, the model adjusts by estimating lower detection probabilities. In contrast, logistic regression assumes all non-detections are true absences, potentially inflating detection probabilities.
Our study also found that species primarily using the understorey and secondarily the canopy (Cluster 5) were only slightly more likely to be recorded by PAM than by PCS, while Franklin et al. (2020) found canopy users to be better detected by observers. However, because species that primarily used the canopy in Cluster 6 were not affected by the method used, this difference is unlikely to be a result of how the foraging groups were assigned. Franklin et al. (2020) suggest that call characteristics may explain why these species were less effectively detected by PAM. However, the finding that species using only the understorey were better detected by PCS than by PAM highlights the importance of considering variation in stratum use when classifying foraging behaviour. The largest discrepancy observed between the two methods in Franklin et al. (2020) was between species that use all the strata, and similarly, our study shows that species that use multiple strata are more likely to be detected by PAM than by PCS.
There is conflicting evidence as to whether habitat type affects the comparative performance of PAM and PCS. Some studies suggest that habitat type does not affect performance (Celis-Murillo et al. 2012), while others report that it does (Alquezar & Machado 2015, Wheeldon et al. 2019, Joel et al. 2024). Therefore, it is unlikely that the difference in vegetation density between the strata directly causes the two methods to be more effective in detecting birds from different strata. However, how vegetation density affects sound propagation, interspecific signalling and, consequently, avian song evolution (Luther 2009) may affect the avian assemblages these two methods detect (Darras et al. 2018b). The thicker vegetation density of the understorey in tropical montane forests causes species that use this stratum to vocalize more frequently than canopy-dwelling species, which rely more on visual signalling (Laverde-R & Cadena 2018). Therefore, our findings that Cluster 5 is more likely to be recorded by PAM, while species in Cluster 4, which primarily use the understorey, are more likely to be recorded by PCS, support the hypothesis that PAM is more likely to detect species that vocalize infrequently (Darras et al. 2018a).

Another possible reason why species, particularly passerines that primarily use the canopy and secondarily the understorey, were better represented in PAM than in PCS may be a consequence of the time of the day when the surveys were conducted. Because it was not possible to control for the time of the day across methods, PAM may have captured a broader temporal range, especially in Mbololo, where the dawn chorus (from 05:00 to 11:00 AM) was recorded (Fig. 1). Foraging height influences dawn song onset owing to light availability, with lower-strata species typically singing later (Berg et al. 2006). However, Mbololo recorded only four Cluster 5 passerines via PAM, compared to three in Yale and two in Kasigau (see Supporting Information Table S3), suggesting that time-of-day alone may not fully explain this pattern. Furthermore, because many of these species are flycatchers and sunbirds with indistinct, inconspicuous songs, a simpler explanation is that they are more consistently identified via the aid of spectrograms from passive acoustic recordings than through PCS. Thus, time-of-day differences are likely to have had limited impact on the assemblages detected by the two methods.
An alternative explanation to why PAM is more likely than PCS to detect both ground stratum as well as the canopy is that these strata are not in the resting eye line of an observer (the ground is at 0 m, the canopy starts at > 3 m from the ground) (Laverde-R & Cadena 2018, Darras et al. 2018a, Gogoleva 2020). However, other clusters that do use these strata are not shown to be as affected by either of the methods employed. This could result from the fact these clusters only use one stratum, when it has been shown that species that use a large extent of the vertical strata are more likely to be detected by PAM than by PCS (Franklin et al. 2020). This is further supported by Cluster 4, which consists of species that only use the understorey—species that occupy a single stratum within the resting eye line. The effectiveness of PCS at detecting understorey has also been demonstrated in comparison with mist-netting (Mulvaney & Cherry 2020). This is of interest as Cluster 5 also included the critically endangered Taita Apalis Apalis fuscigularis (Supporting Information Table S3). However, this does not explain why species of Cluster 6 did not show a significant difference in the likelihood of being detected by either method. The lack of significance from Cluster 6, where the canopy was the primary stratum, may be because there were only five species in the Taita Hills (Wilman et al. 2014), which only amounted to seven detection events from both PCS and PAM. Therefore, the logistic model may not have been able to test whether this cluster’s detection was affected by the method employed. To further verify the hypothesis that PAM is more adept at monitoring species that forage outside the normal line-of-sight than PCS, future studies should concentrate on study sites with more canopy specialists (Franklin et al. 2020).
As foraging stratum has been shown to affect species community, different studies report that analysing the biases in this functional characteristic between methods allows better interpretation of trends in the species-richness reported by PAM (Franklin et al. 2020, Mulvaney & Cherry 2020, Zamora-Mar^ın et al. 2021). Monitoring foraging strata is particularly applicable to the Taita Hills owing to the forest usage by the local human population. One large usage of the forest is the collection of firewood (Himberg et al. 2009, Teucher et al. 2020). Consistent with traditional practices, deadwood is mostly used, and village elders are elected to supervise logging activities to ensure that the correct trees are felled and saplings are planted to replace them (Himberg et al. 2009). However, illegal logging remains owing to lack of monitoring and confusion over regulations (Teu- cher et al. 2020). Nevertheless, the removal of deadwood from the forest floor and understorey is likely to affect the ecosystem function of the forests. Both these strata have been shown to be well-monitored by PAM. This could have both negative and positive effects on the avian diversity as, whilst many nest predators reside in woody debris (Van De Loock et al. 2020), leaf litter and shrub cover both positively influence insect abundance and diversity (Maseko et al. 2016, Seibold et al. 2021).
Vocal behaviour and the performance of PAM vs PCS

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This study shows that PAM is more likely than PCS to detect species that vocalize rarely, as supported by the logistic regression analysis. However, whether a species was passerine or non-passerine only influenced detection likelihood, with PAM being 20% more likely to detect the non-passerine species. This suggests that more specific categories may have a larger effect. This is further supported
as it is apparent that the two survey methods are detecting different avian assemblages as shown by the Sorensen’s indices. These metrics could include focusing on lower-level taxonomic groups, specific acoustic features such as entropy, frequency and call duration, or physical aspects such as plumage (Laverde-R & Cadena 2018).
Our study found that raptors were more consistently recorded by PAM (Supporting Information Table S3) than PCS, supporting a previous study that suggested PAM favoured species that vocalize rarely (Celis-Murillo et al. 2009), although this was not the case for non-raptors in our study. Raptors are known to not be very soniferous (Smith et al. 2020), yet of the six raptors detected in this study, three of them: the African Hawk Eagle Aquila spilogaster, Little Sparrowhawk Accipiter minullus and African Goshawk Accipiter tachiro were solely recorded by PAM, whilst the other three were recorded by both methods, but more consistently by PAM (Supporting Information Tables S3 and S4). The better detection of raptors by PAM is also supported by results from the Aberdare Mountains, Kenya (Wheeldon et al. 2019), another Afromontane forest system, where six of the seven raptor species were detected by PAM and three of those six species were solely detected by PAM. These findings contrast with those from a survey in the semi-arid bush, Northwest Australia, in which raptors were consistently not detected by PAM (Smith et al. 2020), which is likely to be because raptors are easily detected from PCS in open habitats. Thus, habitat type may influence which birds are identified regarding the comparative performance of PAM and PCS. We also found that bird species not adapted to forests, such as savannah species visiting the forest fragments, were not detected as well as endemic montane forest birds by PAM. In this case it is likely that their songs are maladapted to forest environments, whereas the forest-adapted birds are often characterized with songs that can transmit through complex habitats (Winiarska et al. 2024).
The fact that raptors were more likely to be detected by PAM than by PCS may be a result of their larger size. Size was hypothesized to increase detectability for both methods, albeit for different reasons. For PCS, larger birds are more visually conspicuous and so more likely to be seen during surveys. For both methods, larger birds may also be more conspicuous acoustically owing to their higher amplitude and lower frequency. However, because detection ranges cannot be directly determined from spectrograms, this would benefit PAM more (Winiarska et al. 2024). The results of our logistic regression model support this hypothesis, as it was found that PCS is less likely than PAM to detect larger species of birds (Fig. 7). This is further exemplified by how doves and coucals were better represented by PAM than by PCS. Because detection ranges cannot be directly inferred from spectrograms without having reference data for each species vocalizations at different distances in the same habitat (Darras et al. 2018b, Winiarska et al. 2024), using body size as a control variable in the logistic regressions for foraging cluster and taxonomy may have indirectly accounted for differences in detection ranges between the two methods. Given the statistical significance of size to detection probability, particularly in favour of PAM, this suggests that size could be an effective proxy for detecting range.

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Several passerine species were solely recorded by PAM (Supporting Information Table S3), and is likely to be because this method is better at detecting species with non-distinctive vocalizations, and may explain, for example, why the Collared Sunbird Hedydipna collaris, Usambara Double-collared Sunbird Cinnyris usambaricus and Amethyst Sunbird Chalcomitra amethystina, which have very similar vocalizations, were only identified by this method. The ability to better distinguish sunbirds using PAM may have been aided by spectrograms, which visualize the differences in vocal patterns, frequency range and spacing between calls to compare with online databases such as Xeno-Canto that cannot be carried out through in-field audio detection alone. The aid of spectrograms may also make vocalizations that are short in duration more apparent, especially if there are other species vocalizing simultaneously with overlapping frequencies (Darras et al. 2018a), as was evidenced by the Common Chiffchaff Phyllos- copus collybita only being detected by PAM. These species, specifically the female sunbirds as well as Common Chiffchaff and Ashy Flycatcher, may further be less likely to be visually detected during PCS as a consequence of their duller plumage (Laverde-R & Cadena 2018). Furthermore, PAM also provides the surveyor with an unlimited amount of time to identify these indistinct vocalizations (Celis-Murillo et al. 2012, Darras et al. 2018a), in contrast to PCS where the
surveyor has a fixed time to identify an individual both acoustically and visually (Bibby et al. 1998). Therefore, species that are both visually and audibly cryptic appear to be better monitored by PAM than PCS.
Species diversity differences recorded by PAM vs PCS between forest fragments
This study indicated that PAM detected a larger avian assemblage than PCS in more pristine forests of denser vegetation, as PAM had a higher Chao’s estimated species-richness (q0) than PCS in the two largest forest fragments Mbololo and Kasigau (Fig. 4), although we acknowledge the overlap in estimates of species-richness and limited number of comparisons. This finding contrasted with a previous study that showed habitat does not affect the comparative performance of PAM and PCS in tropical montane forests (Wheeldon et al. 2019). Furthermore, when comparing census-based studies from across different geographical locations, the proportion of species each method detected from their respective study site showed that PCS outperformed PAM in dense habitats such as in coniferous forests (northern California, USA; Furnas and Callas (2015); Ontario, Canada, Venier et al. (2012)); rainforest (North Australia, Leach et al. (2016)) and tropical montane forests (Mt Kenya, Kenya, Maina et al. (2016); Mt Cameroon, Cameroon, Sedla^^cek et al. (2015)). In contrast, PAM has been shown to outperform PCS in grasslands and other open habitats such as the Brazilian Cerrado (Alquezar & Machado 2015) and the wetlands of Ontario, Canada (La & Nudds 2016). An alternative explanation might be that the heavier use of Yale by the local communities (Henkin et al. 2015, Adhikari et al. 2020) may make the birds in this fragment more habituated to human presence, allowing PCS to perform as well as the less invasive PAM (Darras et al. 2018a, Biondi et al. 2020). Comparisons of additional Taita Hills forest fragments are needed to fully test our findings.
CONCLUSIONS AND FUTURE RESEARCH
From the evidence provided by this study, PAM appears to be a reliable and efficient method of surveying species diversity in tropical montane forests. Using the number of surveying stations as a proxy for species abundance we found species that primarily use the ground and secondarily use the understorey, and those primarily using the canopy and secondarily the understorey, were more likely to be detected by PAM than by PCS. However, there are multiple challenges to overcome when evaluting diversity using PAM, primarily the reliance on abunance proxies.
Recommendations for future PAM research should focus on the collection of additional recordings to build a database of site-specific vocalizations of species (Wheeldon et al. 2019). Such libraries can then be used to create machine-learning neural network-based algorithms to expedite the process of surveying an area (Kershenbaum et al. 2024) as opposed to using acoustics indices as a form of rapid biodiversity analysis owing to their inaccuracy and lack of transferability to different environments (see Chhaya et al. 2021, Sethi et al. 2023, Kotian et al. 2024). In terms of speed through automation and if the training data is large enough, machine-learning algorithms can supersede human ability to distinguish between species with a smaller error rate than that of an experienced observer (Miller et al. 2022). Indeed, previous studies have used machine-learning vocalization classifiers, such as BirdNET (Kahl et al. 2021), to accelerate the detection of bird species to investigate avian diversity (Cole et al. 2022), as well as increasing citizen science participation (Wood et al. 2022). However, BirdNET’s performance has not yet been thoroughly assessed in the tropics, where it is expected that the increased species-richness, increased biophony and smaller training dataset will make the software less effective in terms of both recall and precision (P^erez- Granados 2023). This further emphasizes the need to create larger libraries of manually annotated recordings to enhance the performance of machine-learning vocalization classifiers.
We are grateful to the Taita Research Station and the Dawida Biodiversity Centre for their support, and particularly thank Nathaniel Mkombola for his dedicated field assistance. We thank the Associate Editor Riddhika Kalle and anonymous reviewers for their helpful comments that greatly improved our manuscript.
AUTHOR CONTRIBUTIONS

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Vikram Malhi: Conceptualization; writing – original draft; writing – review and editing; formal analysis; methodology; visualization; data curation;
software. Gino Brignoli: Investigation; methodology; data curation; validation; software; funding acquisition. Jane Hallam: Methodology; supervision; software. Peter Njoroge: Writing – review and editing. Luc Lens: Writing – review and editing. Marius Somveille: Methodology; writing – review and editing. Julia J. Day: Conceptualization; investigation; supervision; methodology; writing – review and editing; project administration; funding acquisition.
ETHICAL NOTE
All fieldwork was conducted in accordance with Kenyan regulations and under the following permits: National Commission for Science, Technology and Innovation NACOSTI/P/19/82425/30479, Kenya Forest Service RESEA/1/KFS/VOL.IV(106), Kenya Wildlife Service KWS/BRP/5001.
FUNDING
This work was supported by Natural Environmental Research Council NE/L002485/1. For further fieldwork support, GB thanks the Society for Tropical Ecology (GTO Student Grant), and the Daisy Balogh Fund, ZSL. JJD thanks UCLs Global Engagement fund and the Percy Sladen Memorial Fund.
CONFLICT OF INTEREST
None.
DATA AVAILABILITY STATEMENT
Data supporting these findings are available on the UCL Data Research Repository DOI: 10.5522/04/c.
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Received 6 June 2024;
Revision 1 June 2025;
revision accepted 22 September 2025.
Associate Editor: Riddhika Kalle
SUPPORTING INFORMATION
Additional supporting information may be found online in the Supporting Information section at the end of the article.
Table S1. Details of AudioMoth deployment and their inclusion in specific analyses. ESR: estimated species richness, OSR: observed species richness, BD: beta diversity, FD: foraging, TD: taxonomic diversity.
Table S2. The date and time of point count surveys (PCS) conducted at each survey station.
Table S3. Species reported by only one method across all fragments from the 25 selected stations, along with their species characteristics.
Table S4. Avian species surveyed in each forest fragment. Selected PAM and selected PCS indicate the number of stations (out of 25 used to calculate observed species-richness) where each species was detected. All PAM and all PCS indicate the number of stations where a species was detected across all possible stations (27 PAM and 56 PCS, respectively).
Figure S1. The location of survey stations in the forest fragment Mbololo. Blue circles are PAM recording stations; red circles are their paired PCS stations. Opaque circles are the stations used to calculate estimated species-richness; bold circles denote stations used for the observed species-richness, Sørensen’s dissimilarity, complementarity analysis, and the logistic regressions of phylogenetic diversity and foraging diversity. Inset, Taita Hills, East Africa highlighted in red.
Figure S2. The location of all survey stations in the forest fragment Yale. Blue circles are PAM recording stations; red circles are their paired PCS stations. Opaque circles are the stations used to calculate estimated species-richness; bold circles denote stations used for the observed species-richness, Sørensen’s dissimilarity, complementarity analysis, and the logistic regressions of phylogenetic diversity and foraging diversity. Inset Taita- Taveta, Taita Hills, highlighted in red.
Figure S3.		The location of all survey stations in the forest fragment Kasigau. Blue circles are PAM recording stations; red circles are their paired PCS stations. Opaque circles are the stations used to calculate estimated species-richness; bold circles denote stations used for the observed species-richness, Sørensen’s dissimilarity, complementarity analysis, and the logistic regressions of phylogenetic diversity and foraging diversity. Inset Taita- Taveta, Tatia Hills county highlighted in red.
Figure S4.		The ordination of study species into clusters based on their usage of vertical strata from the EltonTraits 1.0 database (Wilman et al. 2014).
Figure S5.		
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Avian species-richness based on survey methods employed at each survey station. The number of species uniquely reported by PAM (red), PCS (green), both methods (blue).