DOI: 10.5061/dryad.59zw3r2cg
title: Opening the museum’s vault: Historical field records preserve reliable ecological data

description: Museum specimens have long served as foundational data sources for
 ecological, evolutionary, and environmental research. Continued
 reimagining of museum collections is now also generating new types of data
 associated with, but beyond physical specimens, a concept known as
 “extended specimens”. Field notes penned by generations of naturalists
 contain first-hand ecological observations associated with museum
 collections and comprise a form of extended specimens with the potential
 to provide novel ecological data spanning broad geographic and temporal
 scales. Despite their data-yielding potential, however, field notes remain
 underutilized in research due to their heterogeneous, unstandardized, and
 qualitative nature. We introduce an approach for transforming descriptive
 ecological notes into quantitative data suitable for statistical analysis.
 Tests with simulated and real-world published data show that field notes
 and our transformation approach retain reliable quantitative ecological
 information under a range of sample sizes and evolutionary scenarios.
 Unlocking the wealth of data contained within field records could
 facilitate investigations into the ecology of clades whose diversity,
 distribution, or other demographic features present challenges to
 traditional ecological studies, improve our understanding of long-term
 environmental and evolutionary change, and enhance predictions of future
 change.
description: Simulations are performed using the R code provided.
 Real-world data is provided as excel documents. This data was
 downloaded from previously pubished articles and reduced to retain only
 the variables used in our study. See article and codes for citations for
 these articles.
description: # Overview of Publication and Associated Data/Scripts **Citation to
 Associated Publication:** Astudillo-Clavijo et al. 2023. Opening the
 museum’s vault: historical field records preserve reliable ecological
 data. American Naturalist. **Citation to Associated Data and Scripts:**
 Astudillo-Clavijo, et al. 2023. Opening the museum’s vault: historical
 field records preserve reliable ecological data [Dataset]. Dryad.
 https://doi.org/10.5061/dryad.59zw3r2cg. **Authors:** Viviana
 Astudillo-Clavijo[1,\*], Tobias Mankis[2], Hernán López-Fernández[1,3] [1]
 Ecology and Evolutionary Biology, University of Michigan, Ann Arbor,
 48109, USA [2] Ecology and Evolutionary Biology, University of Toronto,
 Toronto, M5S 3B2, Canada [3] Museum of Zoology, University of Michigan,
 Ann Arbor, 48108, USA [\*] Corresponding Author: vivianaa@umich.edu
 **Abstract:** Museum specimens have long served as foundational data
 sources for ecological, evolutionary, and environmental research.
 Continued reimagining of museum collections is now also generating new
 types of data associated with, but beyond physical specimens, a concept
 known as “extended specimens”. Field notes penned by generations of
 naturalists contain first-hand ecological observations associated with
 museum collections and comprise a form of extended specimens with the
 potential to provide novel ecological data spanning broad geographic and
 temporal scales. Despite their data-yielding potential, however, field
 notes remain underutilized in research due to their heterogeneous,
 unstandardized, and qualitative nature. We introduce an approach for
 transforming descriptive ecological notes into quantitative data suitable
 for statistical analysis. Tests with simulated and real-world published
 data show that field notes and our transformation approach retain reliable
 quantitative ecological information under a range of sample sizes and
 evolutionary scenarios. Unlocking the wealth of data contained within
 field records could facilitate investigations into the ecology of clades
 whose diversity, distribution, or other demographic features present
 challenges to traditional ecological studies, improve our understanding of
 long-term environmental and evolutionary change, and enhance predictions
 of future change. Data collection and analyses were performed by
 corresponding author, Viviana Astudillo-Clav ijo Repository Description:
 The Dryad repository associated with the cited publication (see
 publication and data citations above) contains scripts and subdirectories
 with all data used in the study. # Software Requirements Data are
 available as .csv files, which can be opened with software such as
 Microsoft ExcelData, most text editors, and imported into most coding
 programs, including R. Data analyses were performed in R version 4.0.2.
 The following R packages were used in analyses factoextra 1.0.7 FactoMineR
 2.4 corrplot 0.92 vegan 2.5-6 fmsb 0.7.2 ape 5.4 TreeSim 2.4 phytools
 0.7-47 mvMORPH 1.1.3 ggplot2 3.3.3 # Repository Layout and Contents
 **BAUMBERGER\_RAW.csv:** Real-world habitat data, California, USA. Percent
 composition of different soil and vegetation types across spadefoot
 burrows. **Data Link:**
 https://datadryad.org/stash/dataset/doi:10.5061/dryad.8359820
 **Column\_Name Description** AnimalNumber Individual spadefoot identity
 clay Percent of burrow sediment classified as clay silt Percent of burrow
 sediment classified as silt sand Percent of burrow sediment classified as
 sand gravel Percent of burrow sediment classified as gravel pebble Percent
 of burrow sediment classified as pebble cobble Percent of burrow sediment
 classified as cobble bolder\_bedrock Percent of burrow sediment classified
 as boulders or bedrock grass Percent cover of grass within 1m^2 of burrow
 opening forbs Percent cover of forbs within 1m^2 of burrow opening shrubs
 Percent cover of shrubs within 1m^2 of burrow opening trees Percent cover
 of trees within 1m^2 of burrow opening open\_ground Percent of forbs open
 ground 1m^2 of burrow opening leaf\_litter Percent cover of recently
 fallen leaves within 1m^2 of burrow opening duff Percent cover of dead and
 decomposing vegetation within 1m^2 of burrow opening NA cells correspond
 to samples with missing information in the original dataset and are
 excluded from analysis in the associated R code. **CLOYED2017\_RAW.csv:**
 Real-world diet data, Kentucky, USA. Composition of different invertebrate
 groups across anuran stomach contents. **Data Link:**
 https://datadryad.org/stash/dataset/doi:10.5061%2Fdryad.jh843
 **Column\_Name Description** Species Species sampled Site Habitat in which
 anuran was sampled Sp\_Site Combined denotation of sampled species and
 sampling site A.Orthoptera Preference for orthopteran prey, based on
 Chesson's alpha computed using stomach contents A.Beetle Preference
 for beetle prey, based on Chesson's alpha computed using stomach
 contents A.Ant Preference for ant prey, based on Chesson's alpha
 computed using stomach contents A.Flying Preference for miscellaneous
 flying prey (e.g., flying hymenopterans, dipterans, flying hemipterans,
 adult lepidopterans, adult odonates), based on Chesson's alpha
 computed using stomach contents A.Non-Flying Preference for miscellaneous
 non-flying prey (e.g., spiders, flightless Pentatomidae, Reduviidae,
 Membracidae, non-flying hemipterans, larval lepidopterans), based on
 Chesson's alpha computed using stomach contents
 **MELISetal\_RAW.csv**: Read-world habitat data, Northern Sweden.
 Proportion of different natural and artificial habitat elements across
 habitats from which racoon dogs were sampled. **Data link:**
 https://datadryad.org/stash/dataset/doi:10.5061/dryad.q2k7k **Column\_Name
 Description** Trajectory Identity of racoon dog Prop.Artificial Proportion
 artificial structures (e.g., cities, airports, roads) in sampled habitat
 Prop.Broadleaved.forest Proportion broad leaved forest in sampled habitat
 Prop.Conifer.forest Proportion coniferous forest in sampled habitat
 Prop.Mixed.forest Proportion mixed forest in sampled habitat
 Prop.Agricultural.area Proportion agricultural area in sampled habitat
 Prop.Open.natural.area Proportion open natural area (e.g., meadows) in
 sampled habitat Prop.Wetland Proportion wetland in sampled habitat
 Prop.Water Proportion open water in sampled habitat **ROFFetal\_RAW.csv:**
 Real-world habitat data, Palau Archipelago. Percent composition of
 different benthic habitat elements across coral-reef sites. **Data link:**
 https://datadryad.org/resource/doi:10.5061/dryad.0145sn6/1 **Column\_Name
 Description** Site Identity of sampled reef site Hard coral Percent cover
 of hard coral groups Soft coral / gorgonian / sponge Percent cover of soft
 coral, gorgonian, or sponge groups Crustose corallines Percent cover of
 crustose corallines Turf algae Percent cover of multi-species assemblages
 of tiny, primarily filamentous, algae Macroalgae Percent cover of
 macroalgae Lobophora Percent cover of Lobophora spp. Non crustose
 corallines Percent cover of non-crustose corallines Articulated corallines
 Percent cover of articulate corallines **SIEVERS2021\_RAW.csv:**
 Real-world habitat data, Philippines. Percent composition of different
 substrate and benthic cover elements across coral-reef sites. **Data
 Link:** https://datadryad.org/stash/dataset/doi:10.5061%2Fdryad.0rxwdbrxs
 **Column\_Name Description** Location\_Status Name of sampled location and
 status as a fished or marine protected area (MPA) P\_Rub Percent rubble
 substrate P\_Sand Percent sand substrate P\_HC Percent benthic cover
 comprising hard coral P\_MA Percent benthic cover comprising macroalgae
 P\_EAM Percent benthic cover comprising epithelial algal matrix P\_SC
 Percent benthic cover comprising soft coral Frag\_C Percent benthic cover
 comprising fragile coral Robust\_C Percent benthic cover of robust coral
 **WOOD2019\_RAW.csv:** Real-World diet data, Michigan, USA. Percent
 composition of different pollen species in honeybees diets. **Data link:**
 https://datadryad.org/stash/dataset/doi:10.5061%2Fdryad.913qd93
 **Column\_Name Description** Site Sampling year and identity of hive
 sample site Ambrosia Percent of honeybee pollen load from the Ambrosia
 genus Brassicaceae Percent of honeybee pollen load from the Brassicaceae
 family Centaurea Percent of honeybee pollen load from the Centaurea genus
 Chenopodium Percent of honeybee pollen load from the Chenopodium genus
 Cichorium/Sonchus Percent of honeybee pollen load from the Cichorium or
 Sonchus genera Daucus Percent of honeybee pollen load from the Daucus
 genus Plantago Percent of honeybee pollen load from the Plantago genus
 Solidago Percent of honeybee pollen load from the Solidago genus Trifolium
 pratense Percent of honeybee pollen load from the species Trifolium
 pratense Trifolium repens Percent of honeybee pollen load from the species
 Trifolium repens Zea mays Percent of honeybee pollen load from the species
 Zea mays Rhus Percent of honeybee pollen load from the Rhus genus #
 Scripts **DistributionComps.R:** Code for comparing distribution of values
 in real-world and simulated datasets (Fig. 5 in main manuscript). This
 script imports R Objects produced by the PubTests.R and SimTests.R
 scripts, so those two scripts must be run prior to the DistributionComps.R
 script. **PubTests.R:** Code for performing tests of our transformation
 approach on six real-world published datasets. **SimTests.R:** Code for
 simulating habitat data, and then performing tests of our transformation
 approach on simulated datasets. # How to Use Prior to running scripts do
 the following: (1) Download the repository directory from Dryad to your
 local computer (2) Unpack the repository directory and all subdirectories
 on your local computer (3) Open R and install/load required software for
 the script you are working with (see the "Prepare the workspace"
 section of the script you are working with for a list of required
 software) (4) All scripts should be run in R from within the downloaded
 repository directory. Navigate to the correct working directory in R with
 the setwd() function. For example, if the downloaded repository directory
 is in your local Downloads folder and is called
 "doi\_10.5061\_dryad.59zw3r2cg\_\_v3", then in R set the working
 directory with the following command:
 setwd("\~/Downloads/doi\_10.5061\_dryad.59zw3r2cg\_\_v2"
 SimTests.R and PubTests.R can be run in any order. DistributionComps.R can
 only be run once SimTests.R and PubTests.R have been completed, as it uses
 files output by these latter two scripts.