Ibis (2025)

doi: 10.1111/ibi.70015

How much sampling is enough? Four decades of
understorey bird mist-netting across Amazonia define
the minimum effort to uncover species assemblage
structure
PILAR L. MAIA-BRAGA,*1	ANDERSON S. BUENO,2 RICHARD G. DAVIES,3
MARINA F. A. MAXIMIANO,1 TORBJØRN HAUGAASEN,4	MARINA ANCIA~ES,1,5	JOHN G. BLAKE,6,7
BETTE A. LOISELLE,7,8 SERGIO H. BORGES,9	JULIANA MENGER,10,11	SIDNEI DANTAS,12
RAMIRO D. MELINSKI,1,13 FERNANDO H. T. DE ABREU,14 ROBERTA L. BOSS15 &
CARLOS A. PERES1,16,17
1Programa de Po^s-Graduac^ a~o em Ecologia, Instituto Nacional de Pesquisas da Amazo^nia, Manaus, AM,
69060-000, Brazil
2Instituto Federal de Educac^ a~o, Cie^ncia e Tecnologia Farroupilha, Ju^ lio de Castilhos, RS, 98130-000, Brazil
3School of Biological Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK
4Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences,
As, Norway
5Laborato^ rio de Biologia Evolutiva e Comportamento Animal (LABECA), Coordenac^ a~o de Biodiversidade (COBIO),
Instituto Nacional de Pesquisas da Amazo^nia, Manaus, AM, 69067-375, Brazil
6Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, 32611, USA
7Estacio^n de Biodiversidad Tiputini, Colegio de Ciencias Biolo^ gicas y Ambientales, Universidad San Francisco de
Quito, Quito, Ecuador
8Department of Wildlife Ecology and Conservation and Center for Latin American Studies, University of Florida,
Gainesville, FL, 32611, USA
9Departamento de Biologia, Universidade Federal do Amazonas, Manaus, AM, 69080-900, Brazil
10Department Biogeochemical Processes, Max Planck Institute for Biogeochemistry, 07745, Jena, Germany
11Department of Conservation Biology and Social-Ecological Systems, Helmholtz Centre for Environmental Research,
04318, Leipzig, Germany
12Instituto Tecnolo^ gico Vale, Bele^ m, PA, 66055-090, Brazil
13Centro de Estudos Integrados da Biodiversidade Amazo^nica (INCT-CENBAM), Manaus, AM, 69060-062, Brazil
14Instituto Federal de Educac^a~o, Cie^ncia e Tecnologia do Amazonas, Tefe^, AM, 69470-000, Brazil
15Sociedade de Pesquisa em Vida Selvagem e Educac^ a~o Ambiental (SPVS) Centro C^ıvico, Curitiba, PR,
80530-000, Brazil
16School of Environmental Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK
17Instituto Jurua^, Manaus, AM, 69083-020, Brazil
Mist-net sampling comprises a key methodological component of assemblage-wide avi- faunal studies, particularly in the understorey of closed-canopy tropical forests. To investigate mist-net bird captures and species assemblage structure, we compiled data from 312 sites across the Pan-Amazon. Using coverage-based rarefaction and extrapolation curves, we propose a minimum sampling effort, standardizing samples by completeness, to obtain a robust representation of local understorey bird assemblages in Amazonian forests. Furthermore, we aimed to assess the variation in capture rates across sampling days and hours, as well as to characterize species assemblages based on their ecological and morphological attributes. The sample coverage for 312 sites varied from 0.46 to 1.0. Sample size (represented by numbers of individuals) varied according to the standardized sample coverage. For sample coverage of 0.9, the sample size ranged from 30 to 271, with an average of 128.7 individuals. Species-rich sites (>60 understorey bird species)
© 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.

*Corresponding author. Pilar L. Maia-Braga
Email: pilarmaia12@gmail.com

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required a larger sample size than more depauperate sites to achieve a sample coverage of 0.9. Capture success was greatest on the first day of sampling (44.8% of all individuals and 83.7% of species), and in the morning (70.8% and 96.4%). The archetypal bird sampled by understorey mist-netting in Amazonian forests is a fairly common small-bodied insectivore that occupies only one habitat type and spends most of its time perched above ground on shrubs or saplings. We suggest that a representative minimum sample of understorey birds should include, on average, 129 individuals per sampling unit. However, this threshold can vary according to local species richness, and we propose different sample sizes for different levels of species richness. For ecological studies where abundance data are essential to derive robust diversity estimates, we recommend operating mist-nets from dawn to dusk. Our findings can guide future mist-net surveys of understorey birds, optimizing resource allocation in sampling effort.
A amostragem com redes de neblina constitui um componente metodolo^gico fundamental em estudos da avifauna, particularmente no sub-bosque de florestas tropicais. Para investigar os padro~es de capturas de aves com redes de neblina e a estrutura das assem- bleias de esp^ecies, compilamos dados de 312 locais ao longo da Pan-Amazo^nia. Utilizando curvas de rarefac^~ao e extrapolac^~ao baseadas em cobertura amostral (sample coverage – SC), propomos um esforc^o m^ınimo de amostragem para obter uma representac^~ao robusta das assembleias locais de aves de sub-bosque em florestas amazo^nicas. Al^em disso, objetivamos avaliar a variac^~ao nas taxas de captura entre dias e horas de amostragem, bem como caracterizar as assembleias de esp^ecies com base em seus atributos ecolo^gicos e morfolo^gicos. A cobertura amostral (SC) para 312 locais var- iou de 0,46 a 1,0. O tamanho da amostra (representado pelo nu^ mero de indiv^ıduos) var- iou de acordo com a cobertura amostral padronizada. Para SC = 0.9, o tamanho da amostra variou de 30 a 271, com uma m^edia de 128.7 indiv^ıduos. Locais ricos em esp^e cies (> 60 espe^cies de aves de sub-bosque) exigiram um tamanho de amostra maior do que locais com menos espe^cies para atingir um SC de 0.9. O sucesso de captura foi maior no primeiro dia de amostragem (44,8% de todos os indiv^ıduos e 83,7% das esp^e cies) e pela manh~a (70,8% e 96,4%). A ave t^ıpica amostrada por redes de neblina no sub-bosque amazo^nico e^ um inset^ıvoro de corpo pequeno, bastante comum, que ocupa um u^ nico tipo de habitat e passa a maior parte do tempo empoleirado acima do solo em arbustos. Sugerimos que uma amostra representativa de aves do sub-bosque deve incluir, em me^dia, 129 indiv^ıduos por local. No entanto, esse limite pode variar de acordo com a riqueza de esp^ecies local, e propusemos diferentes tamanhos amostrais para diferentes n^ıveis de riqueza de esp^ecies. Para estudos ecolo^gicos nos quais dados de abund^ancia s~ao essenciais para derivar estimativas robustas de diversidade, recomendamos operar redes de neblina do amanhecer ao anoitecer. Nossas descobertas podem orientar futuras pes- quisas sobre aves de sub-bosque usando redes de neblina, otimizando a alocac^a~o de recursos no esforc^o de amostragem.
Keywords: avian surveys, mist-net, Pan-Amazonia, rarity, sampling effort, sampling protocol, species traits.
INTRODUCTION
Amazonia hosts Earth’s highest bird diversity (IUCN 2025). Despite its vast size and ecological complexity, the basin-wide Amazonian avifauna is

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relatively well known, especially compared with other vertebrate taxa. Amazonian birds have been a central focus of several biogeographical and ecological studies (e.g. Blake & Loiselle 2024, Capurucho et al. 2024), with comprehensive datasets available on their distribution, taxonomy, morphology, diet and habitat use (Bierregaard Jr 1988, Stotz et al. 1996, Wilman et al. 2014, Tobias et al. 2022). This wide data availability enables us to analyse key characteristics of bird assemblages and provide critical insights into assemblage structure, thereby advancing our understanding of avian community ecology, ecosystem functioning and conservation (Petchey et al. 2008, Tobias et al. 2022).
Local Amazonian bird assemblages have been sampled with different objectives, techniques and sampling efforts, and under varying temporal and spatial scales. Among these methods, understorey mist-netting is one of the most commonly used approaches for sampling tropical forest birds and has been widely used in ecological studies (e.g. PPBio protocol; see Prestes et al. 2025). Mist-netting provides a highly standardised approach for sampling understorey birds, minimizing observer bias while ensuring reliable species identification and allowing comparisons between species assemblages that are difficult to sample through visual or auditory cues (MacArthur & MacArthur 1974, Karr 1981). Although ground-level mist-nets typically sample only birds moving up to 3 m above ground, they are generally highly effective in understorey bird diversity studies (Remsen Jr & Good 1996, Ralph et al. 2004, Martin et al. 2010).
Mist-nets have been widely adopted in long-term studies, such as those carried out at Tiputini Biodiversity Station, Ecuador (Blake & Loiselle 2009, 2024) and the Biological Dynamics of Forest Fragments Project in the central Amazon, Brazil (Bierregaard & Lovejoy 1989, Cohn-Haft et al. 1997, Stouffer 2020), and have been important for understanding changes in bird assemblages over time, for example in relation to population sizes (Blake & Loiselle 2024) and community dynamics (Stouffer 2020). Short-term studies, meanwhile, often employ mist-nets to assess environmental drivers that influence variation in local bird assemblages (Bueno et al. 2012, Menger et al. 2017), to assess the effects of forest disturbance on birds (Barlow & Peres 2004), or to produce a comprehensive inventory of local bird species in conjunction with other methods (Del-Rio et al. 2021). Both short- and long-term mist-net studies at the local scale are essential for uncovering broader biogeographical patterns and understanding assemblage structure (e.g. trophic organization). However, in many mist-net studies, the amount of sampling allocated may be insufficient to characterize understorey bird richness – an issue that remains understudied.
A critical gap in mist-net studies is the lack of consensus on the minimum effort required to adequately characterize understorey assemblages, hindering species diversity comparisons across studies. Karr (1981) proposed 100 captures as a practical threshold that balances sampling completeness with diminishing returns in capture rates. However, this recommendation stems from fieldwork in Panama, a region hosting distinct avian diversity patterns compared with Amazonia. Given the exceptional species richness of Amazonian birds, tailored assessments are needed to determine an appropriate effort. Furthermore, sampling effort should be assessed not only relative to a target number of captures, but also in terms of daily and hourly variation in capture rates. The variation in capture rates over time can be influenced by several factors, such as weather, species behaviour and net avoidance, affecting the efficiency of mist-nets and the reliability of diversity estimates (Remsen Jr & Good 1996). Therefore, a robust assessment of capture data must consider both local variation in species richness and capture rates over time.
Beyond sampling protocols, understanding assemblage structure, including functional traits such as body size, trophic guilds and habitat breadth, is essential for unravelling assemblage dynamics. Trait-based analyses can reveal species co-occurrence patterns (e.g. Barnagaud et al. 2014), or how environmental conditions shape diversity and functional composition (e.g. Cook et al. 2020), in addition to providing baselines for assessing variation in species assemblage structure across biogeographical regions (Braga et al. 2022). However, no Amazon-wide synthesis has yet integrated mist-net data to explore these patterns, leaving a gap in our knowledge of understorey bird ecology.
To assess understorey bird samples based on live captures, we compiled mist-netting data from 312 sites sampled across Amazonia. We aimed to evaluate sampling effort requirements and the temporal variation in capture rates, and to characterize assemblage structure through ecological and morphological attributes (hereafter, assemblage
structure). Specifically, we addressed the following questions. (1) What sampling effort would be necessary to achieve a representative characterization of understorey bird species richness in Amazonian forests? (2) How does the number of captures vary with time of day and consecutive sampling days? (3) How are understorey bird assemblages structured in terms of species trait profiles?
METHODS
Data compilation
We compiled data on bird captures using understorey mist-nets from 54 studies, representing 732 sites and 87 438 individuals (excluding recaptures) across the entire Amazon, incorporating information from published articles and theses, contributions from collaborating researchers, environmental licensing and compliance data, and avian ecology sampling conducted by the Amazon Biodiversity and Carbon (ABC) Expeditions project (see Table S1). After the data filtering described below, we considered capture data from only 312 sites (Fig. 1). The term ‘sites’ used here refers to individual sampling units (i.e. a netline for most, but not all, studies) within each study locality. For instance, in the approximately 10 000-ha Adolpho Ducke Forest Reserve, 87 sites were sampled.
Taxonomic harmonization

Figure 1. Understorey bird sampling sites (n = 312) throughout the Pan-Amazon across five countries considered in this study. Triangles indicate data extracted from previously published articles and theses, circles indicate data provided by collaborating researchers, diamonds indicate environmental licensing data and squares indicate sites sampled by the Amazon Biodiversity and Carbon (ABC) Expeditions project. Different colours in both the main and inset maps represent the nine main areas of endemism proposed for Pan-Amazonia (Cracraft 1985, Borges & Da Silva 2012). See site details in Table S1.

As a result of the heterogeneity of the database, with sampling conducted between 1985 and 2022, we standardized and updated species names following the taxonomy adopted by the Brazilian
Ornithological Records Committee (the CBRO; Pacheco et al. 2021). For example, the Guianan Warbling-Antbird Hypocnemis cantator, previously considered as a single species throughout Amazonia, has been split into seven congeners: Hypocnemis cantator, Hypocnemis peruviana, Hypocnemis flavescens, Hypocnemis ochrogyna, Hypocnemis subflava, Hypocnemis rondoni and Hypocnemis striata (Isler et al. 2007, Whitney et al. 2013). All splits accepted by CBRO were reflected in our database, adjusted according to their distribution and sampling locality. For species that do not occur in Brazil, we adopted the nomenclature from the South American Classification Committee (Remsen et al. 2024).
Data filtering
The different studies compiled here were conducted in light of different objectives. We therefore used a filtering process aiming to make the data as homogeneous as possible by excluding areas outside Amazonia sensu lato (e.g. the Andean highlands) as well as those sampled in either anthropogenic habitats (e.g. pastures, croplands) or degraded forests (e.g. burned and/or selectively logged forests). All forest fragments or forest islands smaller than 50 ha were also excluded. Species yielding inaccurate identifications, not in line with their expected distributions (BirdLife International 2024b), were also excluded. We retained data from studies carried out in different forest types across Amazonia, such as campinarana white-sand forests, and flooded and non-flooded forests, but data from 92% of all sites are derived from unflooded (terra firme) forest samples.
We selected only species that typically use the forest understorey based on the combined knowledge of expert researchers and the categorization of vertical stratification of habitat use proposed by Wilman et al. (2014) and Tobias et al. (2022). We selected all species for which the proportional sum of occurrences in the three lower forest strata (ground, understorey and midstorey) was greater than or equal to 50% according to Wilman’s classification (Wilman et al. 2014). We also selected only species classified as affiliates of forest (Tobias et al. 2022) and dense habitats (thereby excluding species from open and semi-open habitats). Species weighing more than 200 g were also excluded, as 36-mm-mesh mist-nets often fail to retain these large-bodied species even if they are occasionally caught. The inclusion of such species may also distort analyses and skew true patterns of species diversity (Blake & Loiselle 2009). Some families, such as Caprimulgidae, Cathartidae, Cracidae, Hir- undinidae, Psittacidae, Ramphastidae, Strigidae, Threskiornithidae and Tinamidae, were therefore removed from our database. The Wedge-billed Woodcreeper Glyphorynchus spirurus, despite Wil- man’s classification that considered it a mid- to high-canopy species, was added to the list, as it is one of the most common understorey birds across Amazonia (Braga et al. 2022). Filtering and cleaning our data therefore excluded species that are typically undersampled by mist-nets. Finally, because of the lack of standardization of sampling effort in our database, we selected only sites yielding 50 or more captures, because our goal was to evaluate minimally well-sampled assemblages. We selected this threshold based on the recommendations of Karr (1981), who suggests that 100 captures strike the optimum balance between capture yield and field time efficiency. This threshold was selected to align with our extrapolation method, which projects species richness up to double the observed sample size (Colwell et al. 2012). By setting a minimum of 50 captures, we ensure that all extrapolations are based on a robust foundation and reach a minimum standardized effort of 100 individuals. This entire process of locality and species filtering led to the exclusion of 420 (57.4%) sites and 38 680 (44.2%) individuals, thereby retaining in our subsequent analyses 312 sites, 48 758 individuals and 348 species.
Species traits

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All sampled species were classified according to their trophic guilds, body mass (Wilman et al. 2014), body size, primary lifestyle (Tobias et al. 2022), local rarity based on relative abundance and habitat breadth (Stotz et al. 1996; Table S2). Trophic guilds were represented by the dominant dietary mode among five categories, based on the summed scores of constituent individual diets (Wilman et al. 2014): FruiNect (fruits and nectar), invertebrates, omnivore (score ≤ 50 in all four categories), PlantSeed (fruits and seeds) and VertFishScav (carcasses, garbage and general vertebrates). We used body mass (Wilman et al. 2014) to categorize species into three body size classes: small (≤17 g), medium (>17 g and <40 g) and large (≥40 g). Primary lifestyle refers to the dominant way in which any given species
moves while foraging (Tobias et al. 2022): aerial, terrestrial, generalist (species has no primary lifestyle because it spreads time across different lifestyle classes) or insessorial (spend most of their time perching on saplings and shrubs immediately above ground). Based on relative abundance, we adapted the rarity classification proposed by Stotz et al. (1996), using only four classes: fairly common, common, uncommon and rare. Finally, habitat breadth varied from 1 to 6 and refers to the number of different habitats that each species uses, such as tropical lowland evergreen forests, secondary forests, gallery forests and seasonally flooded tropical forests (Stotz et al. 1996).
Data analysis
Sample coverage is used to measure the completeness of a sample and can be interpreted as the proportion of the total number of individuals in an assemblage that belong to the species represented in the sample (Chao & Jost 2012, Chiu 2023). For example, a sample coverage of 0.62 indicates that 62% of individuals belong to species that were detected by the sample. Conversely, the remaining 0.38 represents the coverage deficit, meaning that 38% of the individuals belong to undetected species missing from the sample (Chao & Jost 2012). According to Chao and Jost (2012), samples standardized by sample size are not the best way to compare species diversity, because they usually present different degrees of completeness, depending on the species abundance distributions of the compared communities. For example, a sample of a given size may be adequate to fully characterize a lower diversity community, but insufficient to characterize a species-rich community (Chao & Jost 2012). Therefore, the solution is to compare samples of equal completeness, not of equal size, satisfying the principle of replication, an essential property for characterizing diversity (see Chao et al. 2014). We assessed the sampling completeness of each site (n = 312) using the iNEXT function and package (Hsieh et al. 2022) in the R environment (R Core Team 2025).
Subsequently, we used the estimateD function, from the same package, to estimate the sample size (i.e. number of individuals) required to achieve different levels of sample coverage: 0.80, 0.85, 0.90 and 0.95. We established a minimum sampling effort based on a sampling coverage threshold of 0.9, which provides an optimal balance between sampling efficiency and reliable estimation of species richness. Although higher coverage (e.g. 0.95) further reduces the omission of rare species, the required sampling effort can increase substantially (but see Chao & Jost 2012, Chao et al. 2014).
The function estimateD uses rarefactions and extrapolations to estimate species diversity by standardizing samples to the same level of sample coverage. Colwell et al. (2012) suggested that extrapolation gives reliable estimates only up to approximately double or triple the reference sample. Therefore, for each level of sample coverage tested, we excluded all sites for which extrapolations exceeded twice the reference sample size. The number of individuals was used as our measure of sampling effort, because we do not have enough precise information on sampling effort (e.g. number of net-hours) for most studies compiled. Throughout the text, we refer to the raw number of species recorded in the samples as ‘observed species richness’ (Sobs), while ‘estimated species richness’ corresponds to the estimates derived from Hill numbers (q = 0).

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To assess the variation in numbers of captures with sampling day and time of sampling, we used a subset of data from 44 sites obtained by the ABC Expeditions project (https://www.abc- expeditions.com). We opted to use only this fraction of the data because the authors standardized sampling effort according to sampling period, length of netlines (number of end-to-end nets) and size of mist-nets. Each site was represented by a line comprised of 20 mist-nets (12 9 2.5 m), operated for 3 days (6:00 am to 5:00 pm on Days 1 and 2, and 6:00 am to 12:00 pm on Day 3) in undisturbed terra firme forest environments. We used linear models (using Gaussian family) and estimated marginal means (EMMs) to assess differences between the number of captures on each day (1st, 2nd or 3rd) and time of day in net-line operation (from 6:00 am to 5:00 pm). EMMs summarize linear models that include factors and are especially useful because they ensure fair comparisons between groups (see Searle et al. 1980). As mist-nets were only operated until noon on Day 3, we calculated capture rates by dividing the number of captures by the number of hours per day (to assess differences between sampling days) or by the number of days (to assess differences between time of day). Hence, relative abundance was our response variable, calculated as above.

Finally, to understand differences in the number of species per site among different classes of species traits (body size, rarity, trophic guilds, primary lifestyle and habitat breadth), we used a generalized linear mixed model (using Poisson family) and EMMs. The site was used as a random factor in the models. We used the glmmTMB package (Mollie et al. 2017) and, subsequently, post-hoc pairwise comparisons using the Tukey method. Model diagnostics for all analyses were performed using the DHARMa package (Hartig 2022), which indicated no significant deviations from the expected distribution.
RESULTS
Mist-net captures and sampling effort
We compiled a database containing information on 48 758 individual bird captures with mist-nets, distributed across nine orders, 35 families and 348 species. The total observed species richness (Sobs) sampled by mist-nets varied from 11 to 106 per site (mean 37.4 ^ 15.7 standard deviation (sd); n = 312). Coverage-based sampling curves for 312 sites showed that sample coverage varied from 0.46 to 1.0 (mean 0.85 ^ 0.08 sd; n = 312; Fig. 2a). Two sites showed a sample coverage of 1.0, both of which were within the Tapirap^e Biological Reserve (Sobs = 16, n = 111; Sobs = 22, n = 66). Considering a standardized sample coverage of 0.90 (excluding sites where extrapolation exceeded twice the reference sample), the estimated species richness (q = 0) varied from 9 to 89 species (mean 37.7 ^ 12.6 sd; n = 268). The sample size (represented by the number of individuals) varied according to the defined sample coverage (Fig. 2b). For sample coverage of 0.80, sample sizes ranged from 16 to 181 individuals (mean 70.2 ^ 24.7 sd; n = 299), also considering only sites for which extrapolations did not exceed twice the original sample size. Higher sample coverage values required larger samples: sample coverage = 0.85 (22 to 230 individuals; mean 91.6 ^ 32.4 sd; n = 288), sample coverage = 0.90 (30 to 271 individuals; mean 128.7 ^ 50.3 sd; n = 268) and sample coverage = 0.95 (45 to 540 individuals; mean 219.2 ^ 117.6 sd; n = 182).
The sample size varied according to the estimated species richness for each site, considering the same value of sample coverage. Sites with lower estimated species richness (i.e. fewer than 15 species) needed, on average, 41 individual captures to reach 0.9 of sample coverage. Conversely, sites with high estimated species richness (i.e. more than 60 species) needed, on average, 234 individual captures to reach the same level of sample coverage (Table 1). The complete results of the estimated sample size for each site and sample coverage are shown in Table S3.
Capture success relative to sampling time
Based on standardized data obtained at 44 sites surveyed by the ABC Expeditions (n = 3909 captures), we found that the first day of sampling (adjusted for number of individuals divided by daily net-hours) yielded the highest number of individuals. There were significant differences in capture numbers between the first day and either the second or the third day, but there were no significant differences between the second and the third days (Table S4; Fig. 3a). A total of 83.4% of all species were recorded on the first day of sampling, with an increase of 8.7% in newly captured species on the second day and an additional 7.6% on the third day. The highest number of captures was recorded early in the morning between 6:00 am and 8:00 am (Fig. 3b), with significant differences between almost all hourly checking times after 12:00 pm (Table S5). Overall, the morning period recorded 70.8% of the individuals captured, whereas 29.2% were captured in the afternoon. Most species (62.7%) were recorded during both periods of the day, 33.6% were recorded only in the morning (between 6:00 am and 12:00 pm) and 3.6% were exclusively recorded in the afternoon (after 1:00 pm).
Species traits

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The most frequently recorded species per site were small-bodied, followed by medium-sized and large-bodied species, although many of these often escaped before net inspection following capture (Fig. 4a,b; Table 2). Based on the local population rarity classes proposed by Stotz et al. (1996), we recorded 54 ‘common’ species, 174 ‘fairly common’, 100 ‘uncommon’ and only eight ‘rare’. The most frequently recorded rare species was the Reddish-winged Bare-eye Phlegopsis erythroptera, with 145 captures. Twelve recorded species remained unclassified by Stotz et al. (1996) in

Figure 2. (a) Coverage-based sampling curves for each sampling site considered in this study (n = 312) based on iNEXT estimates. Dashed lines indicate extrapolated curves extended to double the original sample size, whereas solid lines indicate interpolated curves. (b) The range of sample sizes required to achieve distinct levels of sample coverage.

Figure 3. Variation in the number of captures across understorey netline sites for (a) three consecutive days of sampling, and (b) 11 h of daytime netline operation per day (mist-nets were opened at 6:00 am). These data are based on 44 standardized sites sampled during the Amazon Biodiversity and Carbon (ABC) Expeditions, each of which represents a netline of 20 nets (12 m each) operated for 28 h (2½ days). Each circle represents a site per sampling day or hour. Significant differences between groups (P < 0.05) were determined by Tukey’s test. Groups not sharing a common letter (above the boxplot) are significantly different.

terms of local population abundance (Fig. 4c). Approximately 77% of species foraged on invertebrates and were primarily insectivorous, followed by frugivores and/or nectarivores, which feed on fruits and/or floral nectar, and omnivores. Three species were more strictly granivorous, and two were hypercarnivores, preying on vertebrates (Fig. 4d). In terms of primary lifestyle (Fig. 4e),
Table 1. Minimum, maximum, and mean sample size (number of individuals with standard deviation (sd)) required to achieve the estimated species richness (Hill numbers, q = 0), categorized by species richness classes (1–15 species, 16–30 species, etc.). The sample size and species richness estimates were calculated under the 0.9 sample coverage, considering only sites where extrapolation did not exceed twice the original sample size.
Species richness classes	Minimum	Maximum	Mean	sd	
1–15	30.04	50.85	40.51	7.87	
16–30	49.74	113.75	84.32	13.56	
31–45	77.36	189.41	127.32	23.84	
46–60	129.91	271.02	186.68	27.25	
>60	204.93	269.03	234.43	14.88	

most species were classified as insessorial, followed by terrestrial, aerial foragers and generalists. Finally, most species used only one type of habitat (Fig. 4f). Among them, the majority (168 species; 48%) inhabited tropical lowland evergreen forests, nine species inhabited only flooded forests, two were restricted to river-edge forests and two inhabited gallery forests. Only two species (0.6%) used six distinct habitat types: the Amazonian Motmot Momotus momota and the Yellow-olive Flatbill Tol- momyias sulphurescens. Details on the number of species per site for each species trait are presented in Table 2, statistical differences between categories are presented in Table S6 and species frequently captured in Amazonian forests are shown in Figure 5.
DISCUSSION
We present a synthesis of a comprehensive database of avian ecology and biogeography studies, comprising 48 758 individual bird captures across 312 sites, based on understorey mist-net samples conducted across the entire Pan-Amazon region over nearly four decades. Mist-netting remains a highly standardized sampling technique that ensures individual birds can be captured and then handled and processed while incurring minimum mortality (Spotswood et al. 2012). This ensures critical data acquisition, including morphometric, molecular, body physiology condition and epidemiological data collection that would not be possible through observational sampling based on visual and/or acoustic cues. Although mist-nets are commonly employed in ecological studies and some protocols have been recommended for sampling understorey birds (Ralph et al. 2004, Prestes et al. 2025), no previous study has proposed a minimum sampling effort specific to a major tropical forest realm, hindering comparisons of species diversity across different studies. Furthermore, there is a lack of studies that summarize the structure of understorey bird assemblages across the Amazon, providing an ecological framework for understanding community dynamics and establishing an important baseline to guide future studies using mist-nets.
Mist-net captures and sampling effort
The coverage-based sampling curves quantify diversity estimates as a function of sample coverage, enabling meaningful comparisons across equally complete samples (Chao et al. 2020). This method can also estimate the number of individuals required in a sample to achieve satisfactory coverage in areas with differing species richness. As observed species richness derived from sampling data is strongly influenced by sample size (i.e. the number of individuals in abundance-based studies; Colwell & Coddington 1994), more diverse assemblages require larger sample sizes or greater sampling effort to achieve equivalent levels of sample coverage (Chao & Jost 2012). Where the species richness of an area is known a priori, researchers can design a sampling protocol to ensure the minimum sample size needed for a robust representation of understorey avifauna. For example, if an area is expected to host an understorey bird diversity of approximately 60 species, an average of 187 individuals per sampling unit would need to be captured to achieve 0.9 sampling coverage. These sample size thresholds vary depending on the expected species richness of a given location (see Table 1). Our proposed sampling effort framework can guide future studies in optimizing sampling design, ensuring that surveys effectively capture representative understorey bird assemblages.
Our estimates from Amazonian mist-net samples, based on a 0.9 sample coverage threshold, reveal an average species richness of 38 (mean 37.7 ^ 12.6 sd), requiring, on average, 129 individuals (mean 128.7 ^ 50.3 sd) to be captured to achieve this target. Species richness can vary considerably across the Amazon, influenced by factors such as size of habitat area and forest type.

Figure 4. (a) Variation in the proportion of Amazonian forest understorey bird individuals captured in mist-nets in relation to log body mass (n = 48 758 individuals); and number of species per site in each category of (b) body size, (c) rarity (Comm = common, Fair comm = fairly common, Unco = uncommon), (d) trophic guilds (Inve = invertebrates, FruiNect = fruits and nectar, Omni = omnivores, PlanSeed = plants and seeds, Vert = vertebrates), (e) primary lifestyle and (f) habitat breadth (number of habitats that each species uses). Significant differences between groups (P < 0.05) were determined by Tukey’s method. Groups not sharing a common letter (above the boxplot) are significantly different.

Nevertheless, in the absence of previous data for a study area, we recommend capturing at least 129 individuals per netline and ideally 179 individuals (the upper sd limit) to ensure a robust representation of the local understorey avifauna. We therefore propose these thresholds as a standard reference sample size for future ecological studies employing mist-nets in Amazonian and other Neotropical forests.
Karr (1981) proposed that 100 captures in a netline sample from Mesoamerican forests represented the optimum balance between capture yield and field time efficiency, as capture rates tend to decline over time because of net
Table 2. Average number of species, standard deviation (sd), minimum (Smin) and maximum (Smax) number of species observed per site and the total observed (Sobs) for each category of species traits.
Species traits	Categories	Mean	sd	Smin	max	Sobs	
Body size	Small	6.91	0.39	5	43	137	
	Medium	6.09	0.34	1	39	126	
	Large	4.06	0.23	1	25	85	
Rarity	Common	8.15	0.21	1	22	54	
	Fairly	20.20	0.46	6	50	174	
	common						
	Uncommon	8.50	0.28	1	32	100	
	Rare	1.14	0.05	1	3	8	
Trophic	Invertebrate	27.40	0.68	2	80	269	
guilds	FruiNect	6.93	0.18	1	18	49	
	Omnivore	2.81	0.10	1	8	25	
	PlanSeed	1.04	0.02	1	2	3	
	VertFishScav	1.05	0.03	1	2	2	
Primary	Aerial	3.86	0.13	1	10	21	
lifestyle	Generalist	1.85	0.06	1	5	18	
	Insessorial	29.30	0.69	5	80	283	
	Terrestrial	3.24	0.12	1	12	26	
Habitat	One habitat	19.40	0.47	2	56	181	
breadth	Two habitats	9.12	0.25	2	31	83	
	Three	7.04	0.27	1	21	51	
	habitats						
	Four habitats	2.38	0.14	1	9	19	
	Five habitats	1.24	0.06	1	3	7	
	Six habitats	1.14	0.04	1	2	2	

avoidance. However, for richer areas the sample size can exceed 200 individuals. The discrepancy in the minimum required sample size in this review compared with the threshold suggested by Karr (1981) probably reflects the higher understorey bird species packing in Amazonian forests, compared with Mesoamerica. For areas that require larger sample sizes, we suggest the use of two, and ideally three, different netlines (as pseudo-replicates) installed at other locations within a forest landscape to inhibit net avoidance, which frequently occurs on subsequent days of sampling (Marques et al. 2013). As our data largely encompass undisturbed Amazonian terra firme forests, our suggestions apply mainly to these environments, and the sampling effort should be further investigated in other habitat types.
Insufficient sampling effort in terms of time and personnel often leads to high rates of false absences, severe underestimates of species richness, and distorted estimates of biodiversity and species distribution (Moore et al. 2011). Furthermore, the general lack of standardization in sampling effort hinders comparative analyses of species diversity between studies. Of the 312 sites investigated, in 68% fewer than 129 individuals were captured. Considering only our data from the ABC Expeditions project, in which sampling was standardized across 44 sites across the Brazilian Amazon, one netline (with a total sampling effort of 560 net-hours per site) was required to accumulate from 50 to 136 captures (mean 87.1 ^ 19.3 sd; n = 3833) in undisturbed terra firme forests. To obtain a cumulative total number of 129 captures, we therefore suggest a sampling effort of approximately 830 net-hours (number of mist-nets 9 total number of hours) per sampling unit.
A sampling effort of 830 net-hours can be achieved in different ways, adjusting the number of nets and sampling duration according to logistical constraints and local settings. Other factors not assessed here are also important for enhancing the efficiency of mist-nets in capturing understorey birds, such as net length, height above ground, mesh size and weather conditions. Success in capturing birds can be improved by modifying the length and height of the nets to suit specific habitats and the behaviour of the target bird species (Keyes & Grue 1982). Piratelli (2003) showed that a mesh size of 36 mm captured a greater number of species and individuals compared with a 61-mm mesh size in cerrado woody savanna areas of central-western Brazil. Therefore, to enhance the efficiency of mist-nets in representing the overall species diversity in tropical environments, we recommend using mist-nets that are at least 2.5 m high and 12 m long to optimize the survey area, with a mesh size of 36 mm, which typically yields successful captures across a broad size spectrum, from hummingbirds to doves and even forest hawks.
Capture success relative to sampling time
Considering data from the sites surveyed by the ABC Expeditions, which operated 44 netlines over three consecutive days (28 h per netline), we showed that the first day of sampling recorded the highest capture rates and species number, while both declined steeply during the second and third days. It is well known that birds that have already been captured tend to subsequently avoid mist-nets placed at the same location (MacArthur

Figure 5. Typical insectivorous understorey birds frequently sampled with mist-nets across Amazonian forests. (a) Willisornis poecili- notus, (b) Myrmoderus ferrugineus, (c) Isleria hauxwelli, (d) Xenops minutus, (e) Pithys albifrons and (f) Glyphorynchus spirurus. Photos: Pilar Maia-Braga and Marina Maximiano/ABC Expeditions Project.

& MacArthur 1974, Marques et al. 2013). Added to which, mist-nets operated over several days tend to gradually saturate the finite local pool of resident birds whose territories are intersected by any netline transect (Karr 1980). Both net avoidance and local saturation therefore contribute to widely observed declines in capture success. However, capture rates on the second and third days combined represented 56% of all individuals captured, indicating that a netline should not be operated for a single day if sampling efficiency is to be maximized, particularly in studies that go beyond a qualitative checklist (e.g. when abundance data are a key objective).
As expected, the largest number of captures occurred early in the morning, as peak bird activity is unimodal and occurs at that time of day (Ralph et al. 2004). However, even given lower levels of bird activity and a lower number of captures per sampling hour, 29% of all individuals were also recorded between 1:00 pm and 5:00 pm, but only 3.6% of all species were only recorded in the afternoon. Many studies using mist-nets restrict the operation of the nets from sunrise to noon or 1:00 pm (Blake & Loiselle 2009, Borges 2013, Del-Rio et al. 2021), although captures continue to occur throughout the day, even with less frequency. Increasing the daily sampling time may favour the capture of low-activity species and/or species that are poorly sampled by mist-nets. During the ABC fieldwork, we observed that some canopy species tend to be captured in the
afternoon, such as tanagers. Although we did not investigate this pattern in detail, we suppose that these species tend to use the understorey and midstorey when canopy temperatures increase. Capturing these species may be useful for studies with objectives that go beyond estimating the local diversity of understorey birds. Furthermore, species richness estimates become more robust with greater sampling effort, that is, when more individuals are sampled. For instance, rarefaction curves, commonly used in ecological studies, only stabilize when newly added individuals no longer represent previously unrecorded species. Richness estimates are strongly influenced by the number of individuals sampled, so we recommend that mist-netting protocols could adopt a standard dawn-to-dusk sampling period to maximize sampling effort, particularly if data from recaptures are important. If the overall objective is primarily to quantify community structure, however, then investigators could opt to operate netlines until 12:00 pm.
Species traits
Trophic guilds, body size and primary lifestyle
An important measure of community structure can be obtained by examining the guild structure of an avifauna, because different functional groups can be influenced by the environment in different ways (Pearman 2002). The archetypal bird sampled by understorey mist-netting in Amazonian forests is a small-bodied insectivore holding a stable year-round territory and spending most of its time perched on saplings and shrubs immediately above ground (82% of species were insessorials). Insectivorous species comprised the most dominant trophic guild across all studies, unifying any given understorey bird assemblage across Amazonia (Braga et al. 2022). Within this trophic guild, some insectivores are obligatory army-ant followers (Willis & Oniki 1978), such as White-plumed Antbird Pithys albifrons and Black-spotted Bare-eye Phlegopsis nigromaculata. These species do not forage on ants but rather assiduously attend mixed-species understorey flocks that take advantage of arthropod prey flushed by large ant swarms (Willis & Oniki 1978). Professional army-ant followers are mainly restricted to the Neotropics and Afrotropics, whereas avian foraging associated with ant swarms is either facultative or non-existent elsewhere (Karr 1980, Craig 2022). The dominance of insectivores in the understorey probably reflects the relative availability of food resources (Thiollay 1994), and the behaviour of insectivorous species to actively forage on the ground and in the understorey probably facilitates captures with mist-nets. Of all 348 species captured, 67% were insessorials that prey on invertebrates, suggesting that this foraging behaviour predisposes captures in understorey mist-nets.
After excluding species larger than 200 g because they are only occasionally sampled by mist-nets, our data still included a wide spectrum of body mass between 2 g and 177 g. Our results showed that three-quarters of all species were small to mid-sized. Large-bodied species may be infrequently detected in mist-net samples because they tend to be less abundant due to high per-capita energy consumption (Peters 1986); can frequently exploit a wider range of vertical strata within the forest; and/or are too large to be effectively enmeshed and restrained within a mist-net pocket.
Rarity and habitat breadth
Understanding how ecological communities are structured is crucial to questions about species declines, environmental change, trophic relationships and ecosystem functioning (Petchey et al. 2008). Rare species can be characterized by low local abundance, restricted distribution, low occupancy across their distribution range and/or high levels of habitat specificity (Rabinowitz 1981, Crisfield et al. 2024). Several authors suggest that tropical forests are typically represented by a few numerically dominant species and many rare species (Thiollay 1994, Siqueira et al. 2012, Markham 2014). However, the most frequently recorded species in this study were common and fairly common, with only 31% of all species being considered uncommon or rare.

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The most frequently recorded rare species was a behaviourally dominant member of army-antfollowing flocks, Reddish-winged bare-eye Phlegop- sis erythroptera. This species is widely distributed in Western Amazonia and accumulated 145 captures. Compared with its close congener, P. nigro- maculata, classified as fairly common and with a more extensive geographical distribution (BirdLife 2024a), P. erythroptera was eight times less frequent, since 1374 captures were recorded for P. nigromaculata. Rare species do not necessarily share a deterministic set of traits that unify them, such as specialization in terms of diet,
habitat use, foraging technique or social system (Thiollay 1994). For example, the two bare-eyed antbirds mentioned above are almost identical relative to diet, flocking behaviour, foraging substrate use and primary lifestyle (Willson 2004). Hence, rare species may not differ in these aspects from species that could be described as common. Nonetheless, although local populations of rare species can be stable over long periods of time (Mart^ınez et al. 2023), rare species are inherently at greater risk of rapid population decline and extinction due to anthropogenic pressures (Crisfield et al. 2024).
Some species exhibit high ecological specialization, persisting only in areas with a narrow range of environmental conditions and relying on a single habitat type. In contrast, generalist species can tolerate broader environmental conditions and use multiple habitat types (Ducatez et al. 2014). Habitat breadth is a key indicator of a species’ resilience to environmental change and is strongly linked to extinction risk (Gregory et al. 2005). Specialist species, with few exceptions (Attum et al. 2006), tend to be more vulnerable to environmental disturbances than generalists (Evans et al. 2005). In this study, over half of all species (52%) were found to inhabit just one macrohabitat type (mainly tropical lowland forests), indicating a high degree of specialization in core forest habitats. Species classified as rare or restricted to specific habitats can serve as focal points for conservation efforts, ensuring the protection of populations that are particularly susceptible to habitat degradation.
CONCLUSIONS
We sought to propose a minimal sampling effort and protocol to sample what could be defined as a representative assemblage of understorey tropical forest birds. For an average richness of 38 understorey bird species across the Amazon, we suggest capturing at least 129 individuals per site (and ideally as many as 179) and deploying at least 830 net-hours of sampling effort to accumulate this minimum sample size. For areas known to be more species-rich (>60 species), the minimum sample size should be at least 234 individuals. In ecological studies in which abundance data are required, we also suggest that mist-nets should be operated continuously from dawn to dusk for at least 2 days. However, if the objective is just a checklist, the sampling effort can be concentrated in the morning period (until noon). Additionally, we provide an overview of how understorey bird assemblages are structured, which can be used as a baseline for future ecological studies. The archetypal bird sampled by understorey mist-netting in Amazonian forests is a fairly common small-bodied insectivore that occupies one or two habitat types and spends most of its time perched on shrubs above ground. Our findings emphasize the importance of standardizing sampling protocols to enhance comparability across studies, optimizing time, personnel, resource allocation and logistics in future field studies on understorey birds in tropical forest environments. Furthermore, a comparable protocol would ensure that future community ecology studies at different scales and motivated by different objectives can be integrated for assessments of large-scale species diversity.
We thank Brazil’s Ministry of Education (CAPES), the Ecology Postgraduate Program at INPA and the State of Amazonas Funding Council (FAPEAM) for a scholarship that supported the early stages of this study. The Research Council of Norway funded the ABC Expeditions project. Affonso Souza and Phillip Stouffer provided unpublished data. We thank the Norte Energia electricity company for making data available from the Belo Monte Hydroelectric Dam. We are grateful to ICMBio and state environmental agencies for providing research permits to access most study sites and to the Research Council of Norway, which funded the ABC Expeditions project.
FUNDING
Research Council of Norway and Coordenac^a~o de Aperfeic^oamento de Pessoal de N^ıvel Superior.
CONFLICT OF INTEREST
We declare that there are no conflicts of interest.
ETHICAL NOTE
The majority of the data used in this study comprised information previously gathered by various researchers. All data collected by the ABC Expeditions project were obtained with the necessary state and federal collection permits.
AUTHOR CONTRIBUTIONS

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Pilar L. Maia-Braga: Conceptualization; methodology; data curation; investigation; validation; formal
analysis; writing – original draft; writing – review and editing; visualization; project administration. Anderson S. Bueno: Conceptualization; methodology; investigation; supervision; visualization; writing – original draft; writing – review and editing; validation; project administration. Richard G. Davies: Investigation; writing – review and editing. Marina F. A. Maximiano: Investigation; writing – review and editing. Torbjørn Haugaasen: Investigation; funding acquisition; writing – review and editing; resources. Marina Anci~aes: Investigation; writing – review and editing. John G. Blake: Investigation; writing – review and editing. Bette A. Loiselle: Investigation; writing – review and editing. Sergio H. Borges: Investigation; writing – review and editing. Juliana Menger: Investigation; writing – review and editing. Sidnei Dantas: Investigation; writing – review and editing. Ramiro D. Melinski: Investigation; writing – review and editing. Fernando H. T. de Abreu: Investigation; writing – review and editing. Roberta L. Boss: Investigation; writing – review and editing. Carlos A. Peres: Conceptualization; methodology; investigation; supervision; funding acquisition; project administration; writing – review and editing; writing – original draft; resources; validation; visualization.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Received 29 June 2025;
Revision 11 September 2025;
revision accepted 10 November 2025.
Associate Editor: Zhijun Ma.
SUPPORTING INFORMATION
Additional supporting information may be found online in the Supporting Information section at the end of the article.
Table S1. Bird sampling sites compiled for this study (N = 312). The number of individuals refers to the total used in the study (only understorey birds), after the filtering process described in the main text.
Table S2. Species list (N = 348) and traits (body mass, body size, rarity, trophic guilds, primary lifestyle and habitat breadth) used in this study.
Table S3. Estimates of sample size (i.e. number of individuals) and species richness (q = 0) for each site (N = 312), using rarefaction or extrapolation methods for different standardized levels of sample coverage (0.80, 0.85, 0.90, 0.95). The reference sample size refers to the original number of captures obtained in each study.
Table S4. Pairwise comparisons between sampling days using estimated marginal means (EMMs) from a Linear Model with a Gaussian distribution. Values include the estimated difference (estimate), standard error (SE), degrees of freedom (df), t-ratio and adjusted P-value. Significant values (P < 0.05) are in bold.
Table S5. Pairwise comparisons between times of the day using estimated marginal means (EMMs) from a Linear Model with a Gaussian distribution. Values include the estimated difference (estimate), standard error (SE), degrees of freedom (df), t-ratio and adjusted P-value. Significant values (P < 0.05) are in bold.

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Table S6. Pairwise comparisons between species traits using estimated marginal means (EMMs) from a Generalized Linear Mixed Model with a Poisson distribution. Values include the estimated difference (estimate), standard error (SE), t-ratio and adjusted P-value. Significant values (P < 0.05) are in bold.