Sweating the small stuff: Microclimatic exposure and species habitat associations inform climate vulnerability in a grassland songbird community
Data files
Dec 19, 2024 version files 1.84 MB
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exposure_script.R
11.11 KB
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Figure_3_script.R
2.48 KB
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microclimate_layer.tif
1.23 MB
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nests_spatial.dbf
253.94 KB
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nests_spatial.prj
402 B
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nests_spatial.shp
5.92 KB
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nests_spatial.shx
1.76 KB
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README.md
7.92 KB
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renv.lock
78.46 KB
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selection_data.csv
135.42 KB
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selection_script.R
13.98 KB
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site_bound.dbf
172 B
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site_bound.prj
402 B
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site_bound.shp
236 B
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site_bound.shx
108 B
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success_data.csv
72.13 KB
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success_script.R
10.10 KB
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vegetation_data.csv
12.07 KB
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vegetation_script.R
3.58 KB
Abstract
Assessment of species vulnerability to climate change has been limited by the mismatch between coarse macroclimate data and the fine scales at which species select habitat. Habitat mediates climate conditions, and fine-scale habitat features may permit species to exploit favorable microclimates, but habitat preferences can also constrain their ability to do so. We leveraged fine-resolution models of near-surface temperature and humidity in grasslands to understand how microclimates affect climatic exposure and demographics in a grassland bird community. We asked: 1) Do species select favorable nest-site microclimates? 2) Do habitat preferences limit the ability of species to access microclimates? 3) What are the demographic consequences of microclimatic exposure? We found limited evidence that grassland birds select cooler microclimates, which may buffer eggs and nestlings from extreme heat. Instead, many species appeared constrained by vegetation preferences. While facultative generalists displayed flexibility to use denser vegetation that provided buffering from high temperatures (>39°C), most obligate species nested in more exposed microclimates. Nesting success in facultative species was not well explained by microclimate, but success in specialized grassland obligates declined with increasing microclimate temperatures. These findings illustrate how microclimate and habitat use can interact to influence the potential vulnerability of species to future climate change.
README: Sweating the small stuff: Microclimatic exposure and species habitat associations inform climate vulnerability in a grassland songbird community
https://doi.org/10.5061/dryad.mpg4f4r6x
The following repository includes processed data and code necessary to repeat analyses and create primary figures for the manuscript:
Bernath-Plaisted, J.S., C.A. Ribic, and B. Zuckerberg. Sweating the small stuff: microclimatic exposure and habitat associations inform climate vulnerability in a grassland songbird community
Description of the data and file structure
This repository includes the following files:
Scripts
renv (LOCK file): this file contains records of all packages and versions used in this project. This file can be used with the package renv (https://rstudio.github.io/renv/) to restore the correct package versions to a project working directory using the function renv: : restore().
selection_script (R file): this script performs conditional logistic regression to assess the influence of microclimates on nest-site selection and produces a manuscript in Figure 2. It also produces Figure S2 containing results for additional microclimate variables and Figure S3 showing the distribution of microclimate variables at random points for each species.
Figure_3_script (R file): this script visualizes nest locations and microclimate spatial layers to produce manuscript figure 3.
vegetation_script (R file): this script performs an ANOVA to compare nest-site vegetation densities among species and
produces manuscript figure 4a.
exposure_script (R file): this script performs Beta regression to analyze the relationship between vegetation and microclimate exposure and produces manuscript Figure 4b-c.
success_script (R file): this script performs logistic regression to model grassland bird nesting success in response to microclimate and produces manuscript Figure 4d, as well as figure S6 which shows raw binary data plotted over DSR.
Data
selection_data.csv contains grassland bird nest locations and 5 associated random points within 50 m of the nest location for each nest strata for use in conditional logistic regression.
columns:
nest_id: a unique ID for each nest
point_id: designation as a nest site or a random point associated with a given nest
status: point status as nest (1) or random (0)
species: a 4-letter alpha code denoting the nest species
study_year: a factor indicating the year of study the nest was found, 2020-22
site: a 4-letter abbreviation for one of four grassland study sites
imputed: a yes or no column indicating if the nest initiation date was imputed
species_group: a factor indicating if the nesting species is considered a grassland obligate (obl) or facultative (fac) species
max, min, avg _temp: numeric columns containing the modeled microclimate value for maximum, minimum, and average daily air temperature at a 60cm resolution and 5 cm height associated with each nest and random point
max, min, avg _vapor: numeric columns containing the modeled microclimate values for maximum, minimum, and average daily air vapor pressure at a 60cm resolution and 5 cm height associated with each nest and random point
nest_spatial.shp is a vector file containing GIS data describing the locations of all nests in the study and associated metadata. These data are used to create the map of nests overlaid with microclimate data found in manuscript figure 3. It is not necessary to use the metadata found in this file to create the figure, so additional columns are not described. The geometry column contains UTM coordinates in a WGS 84 UTM Zone 16N projection. The species and site columns can be used to filter desired nest locations.
site_bound.shp is a vector file containing the plot boundary of a single study site used in combination with nest locations and microclimate data to produce the map in manuscript Figure 3. These data are projected in WGS 84 UTM Zone 16N.
microclimate_layer.tif is a single-layer raster GeoTIFF file containing 60 cm resolution modeled estimates of daily maximum air temperature at 5 cm above the surface at a cool season grassland study site. This layer was produced by averaging layers from each day of the study season for all three years of the study to present a typical microclimate day in the creation of manuscript Figure 3. These data are projected in WGS 84 UTM Zone 16N.
vegetation_data.csv contains grassland bird nest records and associated nest vegetation densities used for ANOVA comparison of species vegetation nesting preferences as well as beta regression of vegetation conditions with microclimatic exposure at the nest site
columns:
nest_id: a unique ID for each nest
species: a 4-letter alpha code denoting the nest species
study_year: a factor indicating the year of study the nest was found, 2020-22
site: a 4-letter abbreviation for one of four grassland study sites
species_group: a factor indicating if the nesting species is considered a grassland obligate (obl) or facultative (fac) species
init_day: estimated initiation date for each nest used to model microclimatic exposure during the activity period of the nest. The date is in an ordinal format of days from April 1st
vor: a numeric column containing Visual Obstruction Reading measurements for each nest. VOR is a hybrid measure of vegetation height and density measured in cm
heat_exp: cumulative heat exposure for the entire estimated period for which a nest was actively calculated as a proportion of days that microclimate temperatures at the nest site exceeded 39 degrees C
dry_exp: cumulative dry exposure for the entire estimated period for which a nest was actively calculated as a proportion of days that microclimate vapor pressure at the nest site dropped below 0.915 kPA
success_data.csv contains grassland bird nest visit records used to model nesting success in relation to microclimate conditions. Each nest has multiple visit dates associated with it and the intervals between these visits are used to calculate potential exposure to failure risk.
columns:
nest_id: a unique ID for each nest
species: a 4-letter alpha code denoting the nest species
site: a 4-letter abbreviation for one of four grassland study site
study_year: a factor indicating the year of study the nest was found, 2020-22
visit_date: a continuous variable indicating the date of each visit. The date is in an ordinal format of days from April 1st
expos: length of exposure interval from the previous visit. This variable is used by the custom link function to estimate exposure
status: nest status at the time of visit, either active (1) or failed (0)
species_group: a factor indicating if the nesting species is considered a grassland obligate (obl) or facultative (fac) species
max_temp: numeric column containing the modeled microclimate value for the maximum daily air temperature at a 60cm resolution and 5 cm height associated with each nest and random point
min_vapor: numeric column containing the modeled microclimate value for minimum daily air vapor pressure at a 60cm resolution and 5 cm height associated with each nest and random point
heat_exp: cumulative heat exposure for the entire estimated period for which a nest was actively calculated as a proportion of days that microclimate temperatures at the nest site exceeded 39 degrees C
dry_exp: cumulative dry exposure for the entire estimated period for which a nest was actively calculated as a proportion of days that microclimate vapor pressure at the nest site dropped below 0.915 kPA
Code/Software
All code and data in this repository are intended for use with Program R:
R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for
Statistical Computing, Vienna, Austria. https://www.R-project.org/.