DOI: 10.5061/dryad.2z34tmpkw
title: Community characteristics of forest understory birds along an elevational gradient in the Horn of Africa: A multi-year baseline of Afromontane birds

description: Tropical mountains are global hotspots for birdlife. However, there is a
 dearth of baseline avifaunal data along eleva-tional gradients,
 particularly in Africa, limiting our ability to observe and assess changes
 over time in tropical montane avian communities. In this study, we
 undertook a multi-year assessment of understory birds along a 1,750 m
 elevational gradient (1,430-3,186 m) in an Afrotropical moist evergreen
 montane forest within Ethiopia's Bale Mountains. Analyzing 6 years of
 systematic bird-banding data from 5 sites, we describe the patterns of
 species richness, abundance, community composition, and demographic rates
 over space and time. We found bimodal patterns in observed and estimated
 species richness across the elevational gradient (peaking at 1,430 and
 2,388 m), although no sites reached asymptotic species richness throughout
 the study. Species turnover was high across the gradient, though forested
 sites at mid-elevations resembled each other in species composition. We
 found significant variation across sites in bird abundance in some of the
 dietary and habitat guilds. However, we did not find any significant
 trends in species richness or guild abundances over time. For the majority
 of analyzed species, capture rates did not change over time and there were
 no changes in species' mean elevations. Population growth rates,
 recruitment rates, and apparent survival rates averaged 1.02, 0.52, and
 0.51 respectively, and there were no elevational patterns in demographic
 rates. This study establishes a multi-year baseline for Afrotropical birds
 along an elevational gradient in an under-studied international
 biodiversity hotspot. These data will be critical in assessing the
 long-term responses of tropical montane birdlife to climate change and
 habitat degradation.
description: Statistical
 Analyses <i>Community-level
 Analyses</i> To test whether our survey effort
 had adequately surveyed the local bird community, we calculated rarified
 species accumulation curves across sampling days for each site, based on
 observed and expected (sample-based rarefaction) species richness (Colwell
 et al. 2012) using the “exact” method of the specaccum function from the R
 package <em>VEGAN </em>(Oksanen et al. 2019). Since our
 species accumulation curves did not reach asymptotes for species richness,
 observed species richness likely does not capture true species richness.
 We, therefore, used sample-size-based rarefaction and extrapolation (R/E)
 of Hill numbers (the effective number of species, which integrates species
 richness and relative abundances; Chao et al. 2014). Sample-size-based
 rarefaction and extrapolation of Hill numbers is an emerging approach used
 to standardize and compare estimates of diversity between samples (see Cox
 et al. 2017, Fair et al. 2018, Baumel et al. 2018, Chao et al. 2019,
 Debela et al. 2020). Specifically, we used this framework to estimate two
 values of Hill number 0 (i.e. estimated species richness). First, we
 calculated standardized species richness. We used the function
 <em>iNEXT </em>from the R package <em>iNEXT
 </em>(Hsieh et al. 2016) to calculate R/E curves, standardizing our
 curve parameters to a maximum of 1,000 individual bird captures (endpoint
 = 1,000), knots = 500, and a bootstrap replication of 1,000 (nboot =
 1,000). From these curves, we provide standardized estimates of
 species richness based on the sampling of 1,000 individuals at each site.
 We also estimated asymptotic species richness using the function
 <em>ChaoRichness </em>from the package
 <em>iNEXT </em>(Hsieh et al. 2016). Although the asymptotic
 species richness<br> is an estimate of true species richness, in
 practice, reaching an asymptote can take a long time and a lot
 of sampling. We then plotted the R/E curves of standardized species
 richness (i.e. over 1,000 individuals) for each site as a function of
 sample size using the function <em>ggiNEXT </em>(Hsieh et al.
 2016). We also visualized asymptotic species richness by setting the
 endpoint of the <em>iNEXT </em>function to 10,000
 individuals.  Next, we assessed the spatial and
 temporal patterns in observed species richness and guild-specific
 captures. For guild-specific captures, we identified the primary diet
 and habitat association of each species using a global dataset of avian
 ecological traits (Table 1; see Şekercioğlu et al. 2004, 2019 for a
 description of the dataset) and summed captures for each separate guild
 based on either primary diet or habitat. We restricted our analyses to
 guilds that had ≥40 captures and ≥5 species over the study period and
 modeled each guild independently. We chose a ≥40 capture threshold as our
 cutoff between infrequently and frequently encountered species. Most
 species above this threshold were recorded each year and more than once
 or twice in each year (the few species that were not recorded each year
 were recorded multiple times in the other years), whereas individuals
 under this threshold tended to have few captures across more than one
 year. We chose a ≥5 species threshold for the guild models to ensure that
 results for these metrics represented more than a few species.
 We constructed models comparing
 each response variable (observed species richness, dietary, and
 habitat guild-specific captures) as a function of the site, and<br>
 included the number of survey days per site and year (Table 2) as a
 covariate to control for the variation in the sampling effort. We used
 generalized linear models (GLMs) for species richness and guild-specific
 captures, as these represent count data. Within the GLMs, we used a
 Poisson error structure for species richness, and for guild-specific
 captures, we used a quasi-Poisson error structure to account for
 over-dispersion in the count data. To assess changes in the bird
 community over time, we ran an additional model for each response variable
 that contained year and site, with a year * site interaction (error
 structures were applied as above). We tested the significance of the
 explanatory variables in the GLMs with an analysis of deviance.
 We assessed species dissimilarity
 between sites along the elevational gradient by calculating the Sørenson dissimilarity
 index (<i>S</i><sub>8</sub>) for pairs of sites
 adjacent to each other along the elevational gradient, as well as for
 Chiri-1430 and Dinsho-3186 at either end of the gradient.
 <i>S</i><sub>8</sub> can range from complete
 dissimilarity (<i>S</i><sub>8</sub> = 1) to
 complete similarity (<i>S</i><sub>8</sub> = 0).
 This dissimilarity can be further decomposed into turnover and nestedness,
 which we calculated using the function <i>beta.pair</i> in the
 package betapart (Baselga et al. 2020). Finally, to compare community
 composition (captures of different species, weighted by abundance), we ran
 a Principal Coordinate Analysis (PCoA) based on a Bray-Curtis
 dissimilarity matrix (Legendre and Legendre 2012). A PCoA extracts the
 greatest orthogonal axes of variation in community composition, plotting
 them in multidimensional space such that more similar communities are
 closer to each other in Euclidean space. We extracted the first two axes
 from the PCoA that represent the greatest variation in community
 composition. <i>Species-level
 Analyses</i> As a proxy for species abundance
 (Dulle et al. 2016), we calculated species-specific captures (the number
 of captured and recaptured individuals of a particular species) per site
 and year for the most frequently-captured species (≥ 40 captures over the
 study period). To assess the variation in species’ elevational
 distributions, we calculated the mean elevation at which each species was
 detected each year (hereafter “mean elevation”) for frequently-captured
 species that were detected at least once in every year of the study.
 Smaller range shifts in tropical birds are more detectable when analyzing
 mean elevational occurrence rather than the changes in upper or lower
 range boundaries, as the position of range boundaries is strongly
 dependent on the sampling effort (Shoo et al. 2006). We
 regressed both species-specific captures (in a GLM with a quasi-Poisson
 error structure) and mean elevation (in a simple linear model) against
 year. Since the Dinsho-3186 site was located far from the other sites, we
 decided to re-run the species-level analyses with Dinsho-3186 data
 removed. The results remained similar with Dinsho-3186 excluded
 (Supplemental Material Tables S2 and S3) and, therefore, we
 retained Dinsho-3186 data in the analyses to increase our
 statistical power. Additionally, we compared our elevational records for
 banded birds with those reported in the literature for Ethiopia and the
 Horn of Africa (Ash and Atkins 2009, Dowsett and Dowsett-Lemaire
 2015, Rannestad 2016) in order to assess whether any species were detected
 outside of their recorded elevational distributions. We used an
 elevational difference of at least 150 m to indicate whether a species had
 clearly been recorded in our study higher or lower than
 previously reported in Ethiopia, a distance previously used to
 signify extralimital records of birds in Ethiopia (Dowsett and
 Dowsett-Lemaire 2015). A difference of &lt;150 m could result from
 chance, whereas a difference &gt;150 m is more likely to result from a
 systematic change in the elevational range. At
 the population level, we used Pradel models (Pradel 1996) implemented with
 the package RMark (Laake and Rexstad 2012) to estimate the rates of
 apparent survival (<i>φ</i>), recruitment
 (<i>F</i>), and realized population growth
 (<i>λ</i>) while controlling for encounter probabilities
 (<i>p</i>). <i>φ</i> is the rate at which
 individuals remain in the population; <i>F</i> is the rate at
 which new individuals join the population via birth or immigration; and
 <i>λ</i> is the combined effect of survival and recruitment. A
 population does not change in size when <i>λ</i> = 1, declines
 when <i>λ </i>&lt;1, and grows when <i>λ
 </i>&gt;1. These mark-recapture models cannot distinguish
 movement in and out of a study area (immigration/emigration) from true
 birth and survival. However, birds living in tropical mountains are known
 to have small range sizes (Orme et al. 2006), and tropical understory
 birds of interior forests have limited dispersal ability (Janzen 1967,
 Moore et al. 2008, Lees and Peres 2009, Visco et al. 2015, Polato et al.
 2018, Sheard et al. 2020), suggesting that the differences in recruitment
 or apparent survival rates of understory birds at our study sites are
 unlikely to be highly affected by immigration and emigration.
 We initially
 considered all species with &gt;50 captures. For every species we
 estimated all 4 demographic parameters in constant (time-invariant) Pradel
 models, extracting the parameter estimates and their 95% confidence
 intervals (CI). We then censored any species where the models failed to
 estimate parameters, or where the 95% CI on the parameter estimates were
 exceedingly large. Then, to assess whether the population growth rates
 depended on elevation, we modeled λ as a function of the site for
 each species. Additionally, we ran models where both λ and p were
 constrained by site, but these models involved many parameters and they
 were, therefore, suitable only for those species with the largest sample
 size. All statistical
 analyses and graphing were conducted in R (R Core Team 2020; version
 4.0.2, 2020-06-22).
description: For the individual pages of data pertaining to each individual
 site, the name of the site includes the elevation in m asl for this
 location. Otherwise, there is no additional information that one needs to
 know to use this dataset. There are no missing values that would impede
 someone from re-running our analyses. Please note that there are multiple
 tabs of data in this file for the dataset. Please see our manuscript for
 additional information about the study site and the bird
 surveys.