Data from: What is an elevational range?
Data files
Aug 09, 2025 version files 358.05 MB
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az_elevs.tif
2.91 MB
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ebd_yeju_zf.csv
17.60 MB
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GMBA_Inventory_v2.0_standard.CPG
5 B
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GMBA_Inventory_v2.0_standard.dbf
66.63 MB
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GMBA_Inventory_v2.0_standard.prj
145 B
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GMBA_Inventory_v2.0_standard.sbn
86.84 KB
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GMBA_Inventory_v2.0_standard.sbx
4.88 KB
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GMBA_Inventory_v2.0_standard.shp
270.75 MB
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GMBA_Inventory_v2.0_standard.shx
66.72 KB
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README.md
6.36 KB
Abstract
Elevational distributions have long fascinated scientists, an interest that has burgeoned with studies of predicted upslope range shifts under climate change. However, this body of work has yielded conflicting results, perhaps due to varied conceptual and statistical approaches. Here, I explore how ecological processes and researcher decisions shape the patterns characterized by elevational ranges. I use community science data to illustrate 1) that elevational ranges include variation in abundance; 2) that elevational ranges are usually estimated, not observed directly; 3) that elevational ranges are dynamic across short distances and time intervals; and 4) that how we describe elevational ranges has consequences for inference of range shifts. I present a conceptual framework for understanding elevational ranges across multiple spatial scales, and propose elevational distributions are governed by scale-dependent processes. This hypothesis implies accurately quantifying elevational ranges and learning how they are formed or maintained requires matching questions to an appropriate scale domain. I provide a list of best practices for studying elevational ranges, and highlight promising directions for future research into these complex phenomena.
Overview
These paired Dryad and Zenodo archives include all data and code necessary to reproduce results and figures for the following manuscript:
Linck, E.B. Accepted. What is an elevational range? The American Naturalist.
Abstract
Elevational distributions have long fascinated scientists, an interest that has burgeoned with studies of predicted upslope range shifts under climate change. However, this body of work has yielded conflicting results, perhaps due to varied conceptual and statistical approaches. In my manuscript "What is an elevational range?" (Accepted, The American Naturalist) I explore how ecological processes and researcher decisions shape the patterns characterized by elevational ranges. The following notebook analyzes community science data from eBird to illustrate 1) that elevational ranges include variation in abundance; 2) that elevational ranges are usually estimated, not observed directly; 3) that elevational ranges are dynamic across short distances and time intervals; and 4) that how we describe elevational ranges has consequences for inference of range shifts. These analyses and the associated figures support a conceptual framework I introduced for understanding elevational ranges across multiple spatial scales, and my hypothesis that elevational distributions are governed by scale-dependent processes. An impliciation of this is that accurately quantifying elevational ranges and learning how they are formed or maintained requires matching questions to an appropriate scale domain.
Authorship
The data used in this paper were downloaded as part of the April 2023 release of the eBird Basic Dataset (EBD) and are used under eBird's data license. All code was written by Ethan Linck (ethan.linck@montana.edu) unless otherwise noted.
Layout
Dryad contains the following four data files necessary for downstream analyses:
ebird_yeju_zf.csv: Zero-filled dataset of Junco phaeonotus detections and nondetections within temporal and spatial extent of the study. Contains the following columns:
checklist_id: unique eBird checklist identifier
observer_id: unique eBird observer identifier
sampling_event_identifier: synonym forchecklist_id; included in eBird data to maximize compatibility with data pipelines
scientific_name: Latin binomial of focal species, indicating its presence or absence in a zero-filled dataset of a particular region
observation_count: number of individuals of focal species observed. Empty cells should be interpreted as "n/a", meaning "not available"; they result from the fact that eBird allows users to mark the presence of a species without an estimate of the number of individuals using "X". When these data are then converted to a zero-filled dataset (i.e., turned into presence / absence data), they are left blank by default inauk.
species_observed: Boolean indicating presence or absence of focal species
state_code: eBird code for US state where checklist was made
locality_id: unique eBird locality identifier
latitude: latitude in decimal degrees
longitude: longitude in decimal degrees
protocol_type: eBird categorical variable indicating whether checklist was made while traveling, from a stationary position, or reflects an incidental observation.
all_species_reported: Boolean indicating whether or not all observed species were included in a checklist
observation_date: date checklist was made
year: year checklist was made
day_of_year: Julian date from start ofyearchecklist was made
time_observations_started: hour time in decimals checklist recording began
duration_minutes: duration of checklist observation period in minutes
effort_distance_km: distanced covered during checklist observation period (for "Traveling" protocol type)
number_of_observers: number of observers contributing to checklist.
GMBA_Inventory_v2.0*: Global Mountain Biodiversity Inventory Shapefile for mountain ranges in SE Arizona. Includes compressed .shp file storing feature geometry; compressed .shx file storing the index of the geometries in the .shp file; compressed .sbx file to improve efficiency of spatial queries; .dbf dBase database file to store attribute data; .cpg file to specify the character set used in the .dbf file; and .prj file containing projection information (in plain text).
01_figures.Rmd: Rmarkdown notebook to run analyses and produce figures. Written in R version 4.4.1 (2024-06-14), using the the following packages:
ggforcev.0.4.2,viridisv.0.6.5,flextablev.0.9.6,metRv.0.15.0,fGarchv.4033.92,rangerv.0.16.0,ggdistv.3.3.2,cowplotv.1.1.3,rasterv.3.6-26,spv.2.1-4,elevatrv.0.99.0,PresenceAbsencev.1.1.11,scamv.1.2-17,mgcvv.1.9-1,nlmev.3.1-166,dggridRv.3.1.0,mapsv.3.4.2,forcatsv.1.0.0,stringrv.1.5.1,dplyrv.1.1.4,purrrv.1.0.2,readrv.2.1.5,tidyrv.1.3.1,tibblev.3.2.1,ggplot2v.3.5.1,tidyversev.2.0.0,tidyterrav.0.6.1,terrav.1.7-78,starsv.0.6-6,abindv.1.4-5,sfv.1.0-17,lubridatev.1.9.3 andaukv. 0.7.0.
01_figures.html: knit .html file of the above notebook.
az_elevs.tif: Compress .tif raster file with a digital elevation model of Arizona at a ground resolution of 1034 square meters per tile pixel, as calculated for the latitude of Tucson.
Pipeline
All data processing, modeling, and figure generation can be performed by knitting the Rmarkdown notebooks hosted on Zenodo. After downloading the Zenodo archive, move it to new working directory (here called wier, though this is not necessary). In this directory, make a subdirectory called data; put all data files from Dryad within this subdirectory. Your file structure should then look like this:
wier/README.md
wier/01_figures.Rmd
wier/01_figures.html
wier/data/GMBA_Inventory_v2.0_standard.CPG
wier/data/GMBA_Inventory_v2.0_standard.dbf
wier/data/GMBA_Inventory_v2.0_standard.prj
wier/data/GMBA_Inventory_v2.0_standard.sbn
wier/data/GMBA_Inventory_v2.0_standard.sbx
wier/data/GMBA_Inventory_v2.0_standard.shp
wier/data/GMBA_Inventory_v2.0_standard.shx
wier/data/az_elevs.tif
wier/data/ebd_yeju_zf.csv
I described the U.S. range of Junco phaeonotus in May, June, and July using observations and sampling effort from the April 2023 release of eBird’s Basic Dataset (EBD). eBird is a community science platform where contributors submit ‘checklists’ noting the presence and/or abundance of bird species detected during an observation period of arbitrary duration and distance. eBird includes automated quality filters to initiate expert review of implausible or unusual records, removing them from its data products. I used the R package auk v.0.6.0 (Strimas-Mackey et al. 2018) to apply a series of filters and manipulations that were adapted from eBird’s best practices for species distribution modeling (Johnston et al. 2021; Strimas-Mackey et al. 2023) but more stringent in maximum checklist duration (<5 hours) and traveled distance (<2 km). After pairing checklists with elevation and slope aspect data and assigning each observation to a 50-meter elevational bin, I trained a random forest algorithm on detection / non-detection data using the R package ranger v.0.15.1 (Wright & Ziegler 2015), extracting the marginal effect (or “partial dependence”) of elevation on encounter rate. Additional details on data filtering and species distribution modeling are available in the Supporting Information.
To understand how different elevational range metrics influence our understanding of climate warming-driven distributional shifts, I used the empirical mean, maximum, and minimum elevation of all Junco phaeonotus observations to simulate hypothetical elevational ranges where relative abundance or encounter rate fit one of two different statistical distributions (normal and skew normal). In each instance, I used the corresponding density distribution function in base R v.4.3.0 or fGarch v.4031.90 to simulate the elevations of 1000 observations before and after a 150-meter upslope shift in the mean, removing all records falling above a hypothetical 3200-meter summit elevation, and again assigning each observation to a 50-meter elevational bin.
Additional details on data filtering and species distribution modeling are available in the paper's Supporting Information.
