Citizen science data reveal altitudinal movement and seasonal ecosystem use by hummingbirds in the Andes Mountains
Data files
Oct 31, 2023 version files 33.99 KB
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andes_hummers.zip
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README.md
Nov 17, 2023 version files 33.99 KB
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andes_hummers.zip
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README.md
Abstract
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.
README: Citizen science data reveal altitudinal movement and seasonal ecosystem use by hummingbirds in the Andes Mountains
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) 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
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.