DOI: 10.5061/dryad.2ngf1vj33
title: Data from: Patterns of understorey bird diversity across Amazonian forests: survey effort and range maps predict local species richness

description: Species diversity typically increases from higher to lower latitudes, but
 the regional-scale variation along this geographic gradient remains
 unclear. It has been suggested that species diversity throughout Amazonia
 generally increases westward toward the Andes, but this pattern and its
 environmental determinants require further investigation for most taxa.
 Using mist-net data on understorey birds, we evaluated patterns of species
 richness using two approaches by addressing methodological issues that
 influence local species richness and the determinants of species richness
 across Pan-Amazonia. Specifically, we examined (i) the disparity between
 observed and expected species richness obtained from geographic range
 maps; (ii) how species' eco-morphological traits influence their
 detection and relative abundance; (iii) the spatial variation in estimated
 local species richness after controlling for sampling effort; and (iv) the
 environmental determinants of estimated richness. We found no evidence for
 a longitudinal westward increase in estimated species richness, but there
 was a marked difference between the northern and southern banks of the
 Amazon river. Species detection and abundance were modestly explained by
 species traits, and estimated richness was weakly associated with
 latitude, aboveground biomass and climatic aridity. We found that observed
 variation in the local species richness was primarily driven by
 differences in sampling effort, while estimated species richness showed
 modest variation across large spatial scales and was poorly explained by
 environmental and spatial gradients. Despite wide variation in local
 species richness, we conclude that at broader scales, species richness of
 understorey bird assemblages was surprisingly stable across Pan-Amazonia,
 suggesting that evolutionary processes may be important in determining
 these patterns at larger scales.
description: # Data from: Patterns of understorey bird diversity across Amazonian
 forests: survey effort and range maps predict local species richness
 Dataset DOI:
 [10.5061/dryad.2ngf1vj33](https://doi.org/10.5061/dryad.2ngf1vj33) ##
 Description of the data and file structure We compiled data on understorey
 bird captures using mist-nets at 67 Amazonian forest sites, including 716
 plots (86,278 individuals captured representing 646 species) incorporating
 information from published articles, MSc and PhD theses, unpublished data
 from collaborating researchers, environmental licensing data generated by
 major infrastructure projects, and the avian ecology sampling conducted by
 the Amazon Biodiversity and Carbon (ABC) Expeditions Project. Filtering
 our data excluded 23 sites, 484 plots, 415 species, and 51,556
 individuals, which amounted to a total of 232 plots retained in our
 analyses of eco-morphological traits (including non-*terra firme* plots).
 We excluded 24 sites, 505 plots, 456 species, and 56,984 individuals,
 which amounted to a total of 211 plots retained in our analyses of
 environmental and spatial predictors of species richness (only *terra
 firme* plots). **Table_S1.csv.** Bird sampling plots compiled for this
 study (n = 211). The number of individuals refers to the total used (only
 understorey birds) after the filtering process described in the main text.
 **Table_S2.csv.** Species list and eco-morphological traits used in our
 analyses, including those extracted from range maps and observed data (n =
 284). Taxonomy follows HBW & BirdLife International (January 2024).
 Body mass = species traits extracted from Wilman et al. (2014) Trophic
 level = From the proportional use of food items available in Wilman et al
 (2014), we converted categorical dietary data into a continuous measure of
 trophic level (see Bueno et al. 2018), where the sum of the percentages of
 each food item consumed by each species was weighted by an energy level:
 (1) foliage and other vegetative plant materials, (2) fruits and nectar,
 (3) seeds, (4) invertebrate prey, and (5) vertebrate prey. For example, an
 insectivorous species relying entirely on invertebrates was assigned a
 value 4 (1.0 × 4), whereas a species relying equally on fruits (50%) and
 invertebrates (50%) was assigned a value 3 [(0.5 × 2) + (0.5 × 4)]. Number
 of habitats = number of distinct habitat types used by each species,
 extracted from Stotz et al. (1986). Geographic range size: information
 extracted for each species from Tobias et al. (2022). **Table_S3.csv.**
 Table used in Species richness analyses. We distinguish two spatial scales
 of sampling: (i) *plots* refer to individual sampling units (typically, a
 net-line or the smaller sample unit in each study, which may be a location
 containing several net-lines), and (ii) *site* pertains to an Amazonian
 landscape, protected area, municipality or other spatial designation
 adopted by individual authors to identify their study area. For instance,
 one of our sites consists of the 10,000-ha Adolpho Ducke Forest Reserve,
 in which 87 plots of 250 m each were sampled. Relative_abundance =
 calculated for each plot. Observed richness = numbers of species observed
 in each plot RM richness = number of species extracted from range maps GSI
 richness = estimated species richness using Geometric Series Index All
 environmental variables are described bellow: **Aboveground biomass
 (AGB)** can be used as a measure of forest structure and complexity (Zhang
 et al. 2013) and is related to the spectrum of resources available for
 aboveground consumers (MacArthur and MacArthur, 1961; Zelaya et al. 2022).
 We extracted AGB estimates derived from a combination of Earth observation
 data, the Copernicus Sentinel-1 mission, and the Envisat ASAR instrument
 (see Santoro and Cartus, 2023) for the years 2010, 2017, 2018, 2019, and
 2020, depending on the time of bird sampling.  **Canopy height (CH)** data
 was extracted from the Global Land Analysis and Discovery (GLAD) dataset.
 The maximum forest canopy height map has a 30-m spatial resolution and was
 developed by integrating NASA's spaceborne LiDAR measurements
 available from the Global Ecosystem Dynamics Investigation (GEDI; dates
 April – October 2019; Dubayah et al. 2020) and the Landsat temporal data
 series (see Potapov et al. 2021).  **Global aridity index (GAI)** provides
 a measure of water availability for potential vegetation growth (Zomer and
 Trabucco, 2022) and is an excellent large-scale predictor of plant
 diversity (O’Brien, 1993; 1998). The GAI is a high-resolution (30-sec)
 dataset spanning the 1970–2000 period and describes the precipitation
 deficit for potential vegetative growth. This index represents the ratio
 of precipitation to evapotranspiration, with values decreasing with more
 arid conditions and increasing with more humid conditions (see Trabucco
 and Zomer, 2019).  **Temperature (TEMP)** is a measure of energy
 availability in an ecosystem, and is related to metabolic and speciation
 rates, with most species worldwide adapted to warm places (Brown et al.
 2004). According to the species-energy theory (Wright, 1983), energy
 availability, such as temperature, drives species diversity. Thus, for
 each plot, we extracted the mean annual TEMP data (bio1) from WorldClim
 (Fick and Hijmans, 2017), with a 10-m resolution. **Soil fertility
 (SOIL).** Soil base cation concentrationdata were extracted from a
 Pan-Amazonian map of the sum of exchangeable base cation concentration
 (Zuquim et al. 2023). High soil heterogeneity sites are expected to
 support more plant species (Tuomisto et al. 2014), and spatial
 heterogeneity in plant species composition is expected to influence
 species diversity of other taxonomic groups (Pomara et al. 2012). More
 nutrient-rich sites in Amazonian forests also support higher arboreal
 vertebrate diversity and biomass (Peres, 2008).  **Table_S4.csv.** List of
 species extracted from range maps that were not recorded in the compiled
 studies. List of species per plot. **Table_S5_Complete_dataset.csv -**
 Complete species list per plot. ## Code/software All of our analyses were
 run in R software, and the scripts are available for replication
 (_____R_code_-_Ecography_ECOG-07625..txt).