DOI: 10.5061/dryad.ht76hdrr1
title: Data from: What is an elevational range?

description: 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.
description: I described the U.S. range of <em>Junco phaeonotus
 </em>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 (&lt;5
 hours) and traveled distance (&lt;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 <em>Junco phaeonotus</em> 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.
description: ## Overview These paired Dryad and Zenodo archives include all data and
 code necessary to reproduce results and figures for the following
 manuscript: &gt; 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](mailto: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](https://ebird.github.io/ebird-best-practices/ebird.html#sec-ebird-zf) dataset of *Junco phaeonotus* detections and nondetections within temporal and spatial extent of the study. Contains the following columns: &gt; `checklist_id`: unique eBird checklist identifier &gt; `observer_id`: unique eBird observer identifier &gt; `sampling_event_identifier`: synonym for `checklist_id`; included in eBird data to maximize compatibility with data pipelines &gt; `scientific_name`: Latin binomial of focal species, indicating its presence or absence in a zero-filled dataset of a particular region &gt; `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 in `auk`.  &gt; `species_observed`: Boolean indicating presence or absence of focal species &gt; `state_code`: eBird code for US state where checklist was made &gt; `locality_id`: unique eBird locality identifier &gt; `latitude`: latitude in decimal degrees &gt; `longitude`: longitude in decimal degrees &gt; `protocol_type`: eBird categorical variable indicating whether checklist was made while traveling, from a stationary position, or reflects an incidental observation. &gt; `all_species_reported`: Boolean indicating whether or not all observed species were included in a checklist &gt; `observation_date`: date checklist was made &gt; `year`: year checklist was made &gt; `day_of_year`: Julian date from start of `year` checklist was made &gt; `time_observations_started`: hour time in decimals checklist recording began &gt; `duration_minutes`: duration of checklist observation period in minutes &gt; `effort_distance_km`: distanced covered during checklist observation period (for "Traveling" protocol type) &gt; `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: &gt; `ggforce` v.0.4.2, `viridis` v.0.6.5, `flextable` v.0.9.6, `metR` v.0.15.0, `fGarch` v.4033.92, `ranger` v.0.16.0, `ggdist` v.3.3.2, `cowplot` v.1.1.3, `raster` v.3.6-26, `sp` v.2.1-4, `elevatr` v.0.99.0, `PresenceAbsence` v.1.1.11, `scam` v.1.2-17, `mgcv` v.1.9-1, `nlme` v.3.1-166, `dggridR` v.3.1.0, `maps` v.3.4.2, `forcats` v.1.0.0, `stringr` v.1.5.1, `dplyr` v.1.1.4, `purrr` v.1.0.2, `readr` v.2.1.5, `tidyr` v.1.3.1, `tibble` v.3.2.1, `ggplot2` v.3.5.1, `tidyverse` v.2.0.0, `tidyterra` v.0.6.1, `terra` v.1.7-78, `stars` v.0.6-6, `abind` v.1.4-5, `sf` v.1.0-17, `lubridate` v.1.9.3 and `auk` v. 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 ```