DOI: 10.5061/dryad.w3r2280xs
title: Citizen science data reveal altitudinal movement and seasonal ecosystem use by hummingbirds in the Andes Mountains

description: Ensuring connectivity is crucial to protect landscapes but it requires
 knowledge about how animals use ecosystems throughout the year. However,
 animal movements remain largely unknown in biodiversity hotspots, even for
 species that fulfill key ecological roles, as is the case of hummingbirds
 in the Andes. In the complex topography of mountain slopes, movement of
 these avian pollinators may occur either between habitat patches with
 asynchronous plant blooms or across ecosystems that are located within
 same elevation bands or along altitudinal gradients. Here, we used two
 decades (2000-2020) of records from citizen science data and boosted
 regression trees to predict monthly distributions for 55 hummingbird
 species in the Andes. We identified shifts in altitudinal distribution
 between contiguous months and calculated changes in the proportion of
 predicted distributions occupied by ecosystem types. Our findings reveal
 substantial altitudinal movement and differences in the proportion of
 ecosystem types utilized throughout the year that had not been previously
 reported for several species. Yet the magnitude of altitudinal and
 ecosystem shifts varies between hummingbird clades, and in some cases
 changes in the proportion of ecosystem types within estimated
 distributions occurs with little variation in altitude. All ecosystems
 across the Andes show temporal changes in hummingbird occurrence, but
 these are higher in natural landscapes compared to croplands or urban
 areas. Finally, we used phylogenetic logistic regression to test whether
 altitudinal and ecosystem shifts affect population trends. We found that
 higher ecosystem seasonality is more strongly associated with decreasing
 populations in comparison to altitudinal shifts. Altogether, our study
 reveals complex patterns of movement in hummingbirds and highlights the
 importance of ecological connectivity across different ecosystem types.
 More generally, it demonstrates the opportunity of using citizen science
 data to increase understanding about species’ seasonal occurrences so that
 landscapes can be better managed to protect animal movement.
description: # Citizen science data reveal altitudinal movement and seasonal ecosystem
 use by hummingbirds in the Andes Mountains
 [https://doi.org/10.5061/dryad.w3r2280xs](https://doi.org/10.5061/dryad.w3r2280xs) The zip file includes R code and dataset that were used to generate results for this publication. Note that the dataset does not include eBird Basic Data (EBD) and Sampling Event Dataset (SED), which were downloaded from eBird ([ebird.org](https://ebird.org/home)) on 2021-09-01 with this date as last edition date. Download of other data available from its original source (see manuscript methods): ground elevation layer (Amatulli et al., 2018), climatic covariates retrieved from the ERA5 monthly averaged data in the Copernicus Climate Data Store (0.25ºx0.25º grid, Hersbach et al., 2018), transformed land covers from the Copernicus Climate Change Service (ESA CCI Land cover, 2019), South America Ecosystems layer (The Nature Conservancy, 2008), GMBA Mountain inventory (https://ilias.unibe.ch/goto.php?target=file 1047353), and hummingbird phylogenetic tree generated by Leimberger et al. (2021) [https://doi.org/10.5281/zenodo.5787072](https://doi.org/10.5281/zenodo.5787072) ## Description of the data and file structure **spp\_list.csv** List of hummingbird species from the Andes region included in SDMs. Variable names of tabular data indicate: clade = major hummingbird clade, genus = genus, scientific\_name = scientific name of hummingbird species, common\_name = common name of hummingbird species in English, spp\_code = species identifier code. **summary.csv** Information of IUCN threat status, population trends and endemism for species with model outputs used in downstream analyses. Variable names of tabular data indicate: clade = major hummingbird clade, Scientific\_name = scientific name of hummingbird species, spp = species identifier code, IUCN\_threat\_status = threat status according to IUCN redlist, population\_trend = population trend according to IUCN, endemic = country endemics. **prev\_record\_sum.csv** Previous records of altitudinal migration or movement for studied hummingbird species. Variable names of tabular data indicate: Scientific\_name = scientific name of hummingbird species, Common\_name = common name of hummingbird species, Barcante\_2017 = reported altitudinal movement in Barçante et al. 2017, BOW = reported altitudinal movement in Birds of the World species accounts (birdsoftheworld.org), Scopus = reported altitudinal movement according to literature search in Scopus database, Web of Science = reported altitudinal movement according to literature search in Web of Science database, report\_sum = summary of any reports found checked across sources. ## Code/Software All data cleaning and organization, as well as analyses and graphs were developed in R. **ebird\_filtering.R** joins the eBird Basic Data (EBD) and Sampling Event Dataset (SED), cleans eBird data with the filters applied in this study, and spatially subsamples checklists. **prediction\_surfaces.R** creates covariate prediction surfaces to generate sdm model output. **sdm\_monthly.R** will run boosted regression trees SDMs to calculate probability of occurrence for all the months of the study period 2000-2020, join to environmental covariates and produce monthly distribution maps. **sdm\_output.R** uses the calculated probability of occurrence to calculate elevation and ecosystem use, as well as run all statistical tests included in the study.