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Data from: Ecological drivers of avian community assembly along a tropical elevation gradient

Citation

Montaño-Centellas, Flavia A.; Loiselle, Bette A.; Tingley, Morgan W. (2021), Data from: Ecological drivers of avian community assembly along a tropical elevation gradient, Dryad, Dataset, https://doi.org/10.5061/dryad.fxpnvx0qj

Abstract

Community assembly theory hypothesizes that two main niche-based processes act to shape composition and organization of biological assemblages: abiotic filtering and biological interactions. Here, we conducted repeated surveys of bird abundance along an undisturbed elevational gradient in the tropical Andes to investigate (1) signals of deterministic processes driving community assembly and (2) potential mechanisms by which these forces operate (temperature, habitat complexity, fruit and insect availability), while correcting for imperfect detection and modeling species abundances with N-mixture models. We observed strong signals of abiotic filtering driving functionally and phylogenetically clustered assemblages towards higher elevations, and a weaker signal of limiting similarity resulting in few overdispersed assemblages at lower elevations. Whereas the decay in species richness with increasing elevation was explained by temperature, trait and phylogenetic dispersion were explained by both temperature and vegetation structure, implying that an interplay of abiotic and biotic mechanisms determines abundance-based community structure in our montane assemblages. Interestingly, trait and phylogenetic dispersion consistently decreased until ~3000 m but increased above this elevation, highlighting a potential role of competition in resource-scarce habitats. Combined, our findings suggest abiotic filters are still the main process shaping montane biotas across elevations, whereas resource availability might act locally upon assemblages further modifying them. Our study challenges recent studies in tropical mountains that suggest that biotic filters are a stronger force than abiotic filters in shaping tropical montane assemblages, and exemplifies how accounting for imperfect detection might overcome potential biases in detecting environmental filtering signals in community assembly studies.

Methods

Bird surveys were conducted between May and July 2014 and May and October 2015 and 2016  along an extensive gradient (1350 – 3650 m asl) in Cotapata National Park, a protected area in the Andes of western Bolivia (67°43′−68°03′ W/16°05′−16°20′ S, c. 80 km NE from La Paz city). For the purpose of analysis, we defined a ‘site’ as a band with 50-m elevational change along the gradient.

We used a multi-season community N-mixture model implemented in a Bayesian framework to estimate abundance-based community diversity and structure while accounting explicitly for unobserved species and individuals. Detectability was modeled as logit-linear combination of site- or survey-specific covariates (i.e., date, start time, survey direction, and duration). Then, we calculated taxonomic, functional and phylogenetic diversity for each 50-m elevational band using the posterior samples of the N-matrix. For each posterior sample of the N-matrix (n=500), we calculated taxonomic diversity simply as species richness. Abundance-weighted functional and phylogenetic diversity were calculated with two dispersion metrics: mean pairwise dissimilarity (MPD) and mean nearest taxon distance (MNTD). Finally, we regressed taxonomic, functional and phylogeneetic diversity as a function of temperature, habitat complexity and the diversity of food resources (fruits and insects) across elevations.

Usage Notes

R files included as Supplementary materials

AbundData.Rdata

Contains all data required to perform the MSAMs. List of 7 elements:

y = array of observed counts of 300 species of birds surveyed in up to 20 visits to 45 50-m of elevation bands across an elevational gradient in the Bolivian Andes over 5 seasons

jday = array of the normalized day of survey of each visit

tod = array of the normalized time of the day each survey started, for each visit

effort = array of the normalized time in minutes, spent at each site in each visit

direction = array of the direction of survey during each visit. 0 = upslope, 1 = downslope

elev = vector of the normalized elevation in m of each site

SiteCov = dataframe with environmental variables for each of the 45 elevational bands

  • temp = mean daily temperature in centigrade degrees (see text for details)
  • fruits = fruit availability (see text for details)
  • insects = arthropod diversity (see text for details)
  • complexity = habitat complexity (see text for details)
  • elevation = raw elevation, in meters above the sea level

MSAM.R

R Code to implement a multi-season N-mixture model implemented in JAGS within R environment. Code produces the N-matrix used in subsequent analyses, as described in text.

traits.csv

Database with nine functional traits used in the analyses, for 300 bird species. See main text and Supporting information for details.

Code = species number

Diet = Categorical, based on the main items included in bird diets.

ForagStrata = Foraging strata, categorical. Based on the main vegetation strata used by birds for foraging.

BodyMass.value = Log10 of body mass values, extracted from Wilman et al. 2014.

billen = Average of Log10 of individual measurements of bill length (culmen).

billhei = Average of Log10 of individual measurements of bill height.

billwid = Average of Log10 of individual measurements of bill width (gape).

wing = Average of Log10 of individual measurements of wing length.

tail = Average of Log10 of individual measurements of tail length

kipp = Average of Log10 of individual measurements of Kipp’s distance

Funding

Tropical Conservation Development Program (TCD-UF)

Idea Wild

American Ornithologists' Union

American Ornithologists' Union

Neotropical Bird Club

American Philosophical Society

Tropical Conservation Development Program (TCD-UF)