DOI: 10.5061/dryad.bzkh189g3
title: Ecological and Morphological Correlates of Acuity in Birds

description: Birds use their visual systems for a variety of important tasks, such as
 foraging and predator detection, that require them to resolve an image.
 However, visual acuity (the ability to perceive spatial detail) varies by
 two orders of magnitude across birds. Prior studies indicate that eye size
 and aspects of a species’ ecology may drive variation in acuity, but these
 studies have been restricted to small numbers of species. We used a
 literature review to gather data on acuity measured either behaviorally or
 anatomically for 94 species from 38 families. We then examined how acuity
 varies in relation to (1) eye size, (2) habitat spatial complexity, (3)
 habitat light level, (4) diet composition, (5) prey mobility, and (6)
 foraging mode. A phylogenetically-controlled model including all of the
 above factors as predictors indicated that eye size, habitat spatial
 complexity, light level, and diet composition are significant predictors
 of acuity. Examining each ecological variable in turn revealed that acuity
 is lower in species that inhabit spatially complex, vegetative habitats,
 and higher in species whose diet comprises vertebrates or scavenged food.
 Together, our results suggest that the need to detect important objects
 from far away—such as predators for species that live in open habitats,
 and food items for species that forage on vertebrate and scavenged
 prey—has likely been a key driver of higher acuity in some species,
 helping to elucidate how visual capabilities may be adapted to an animal’s
 visual needs.
description: We provide a brief description of methods here; refer to
 the published manuscript for more details. <em>Comparative database of
 acuity</em> We
 assembled a database of visual acuity in birds using published data; for
 each species, we recorded the highest reported acuity value. We then
 restricted the database to include only data measured using behavioral
 assays or anatomical methods (specifically the
 density of retinal ganglion cells, RGCs).
 To determine whether it would be appropriate to combine
 acuity data derived from RGCs and behavioral assays for analyses, we
 performed two analyses. First, we compiled measures of acuity in 28
 vertebrate species with camera eyes (the type of eye found in birds) in
 which acuity has been measured using both methods (Table S2). We found
 that behaviorally-derived and RGC-derived acuity measures from the same
 species are highly correlated (p&lt;0.0001; Figure S1) in a
 phylogenetically-corrected model using a tree from
 timetree.org.
 However, this analysis included only five species of birds; thus, to
 address this issue in a larger dataset of bird acuities, we created a PGLS
 regression in which acuity was the response variable and eye size and
 method of acuity measurement, and their interaction, were predictors. A
 phylogenetic ANCOVA (see below for details on the phylogenetic tree used)
 showed that, because the interaction term between eye size and method of
 measurement was not significant (p=0.39), the slope of the regression line
 between acuity and eye size is similar for both methods of measurement.
 Thus, we concluded that it was appropriate to include both RGC-derived and
 behaviorally-derived measures of acuity in our database for
 analyses. For five species, the database included acuity estimates
 from both behavioral assays and RGC density (with the average difference
 between the behaviorally- and RGC-derived estimates being only 0.84 cpd).
 Given the very small differences in acuity from the two methods, and the
 analyses above regarding combining behavioral and RGC data together, we
 preferentially used estimates from RGC density in analyses if both
 behavioral and RGC-based measures of acuity existed. If multiple studies
 had used the same method to measure acuity in a given species, we used the
 acuity estimate from the most recent study. Species were only included in
 the database for analysis if we could locate both eye axial diameter and
 body mass data for that species (see below), resulting in a sample size of
 93 species for analysis (Table S1). Here, we refer to acuity throughout in units of cycles per
 degree (cpd), which is the number of pairs of black and white stripes an
 organism can discriminate within a single degree of visual angle. Higher
 values in cpd indicate ability to resolve finer spatial details, and thus
 higher acuity. In some of the original literature we surveyed, acuity was
 reported in alternative units (such as minutes of arc or degrees); prior
 to inclusion in our database, we translated these values to cpd.
   <em>Phylogenetic relatedness and Phylogenetic
 Signal (</em><em>λ)</em> To account for phylogenetic relatedness
 between species, we used a published phylogeny.
 The published tree was trimmed to include only the 93 species in our
 acuity database, maintaining branch length information in our sub-tree.
 The degree of phylogenetic signal in acuity was estimated by calculating
 Pagel’s λ. <em> </em> <em>Eye size and body
 mass</em> Where possible, we recorded eye axial length (hereafter
 ‘eye size’) in our database as reported in the original citation; this
 yielded data on eye size in 73 species. For the remainder, we located
 published eye size values from a variety of sources (see Table S1), to
 maximize the number of species for which we had analyzable data. To obtain
 comparable body mass data for all of the species in our database, we used
 values from the CRC Handbook of Avian Body Masses.
   <em>Classifying Species According to
 Ecology</em> We examined the relationship between visual acuity and
 several aspects of a species’ ecology. Given that many bird species can
 inhabit a wide array of habitat types, or make use of a diversity of food
 sources, it can be complex to categorize birds by factors like diet and
 habitat; thus, our categories were relatively broad.
   Habitat
 We examined how acuity relates to two aspects of habitat:
 spatial complexity and light level.    Diet and
 Foraging To
 understand how acuity relates to diet, species were
 also classified by whether their primary prey type was mobile or immobile
 prey. Mobile prey included vertebrates and invertebrates (outside of
 scavenging); immobile prey included plant matter of all kinds, such as
 fruits, seeds, nectar, flowers, and fruits, as well as scavenged
 prey.  Lastly, we classified species by
 foraging mode.
 Specifically, species were classified as using foraging modes that involve
 resolving and targeting prey from a distance (far-sighted) versus from
 close up (near-sighted) foraging
 maneuvers. <br>
description: R
description: ## READ ME GUIDE TO SUPPLEMENTAL FILES Caves, Fernandez-Juricic and
 Kelley. Ecological and Morphological Correlates of Acuity in Birds.
 Journal of Experimental Biology. `Bird Acuity Analyses.R`: * An R code
 file which runs all of the analyses and creates all of the figures in the
 manuscript and extended data. Originally created and run using R Version
 4.0.3 * Several files are necessary to run the complete R file: 1.
 `Bird_Acuity_Data.csv`: this file contains data regarding acuity, eye
 size, body mass, and ecological categorizations in birds. Columns are: *
 Rowname: identical to Binomial_BirdTree, exists for purposes of assigning
 row names upon upload into R * Taxonomic_Order: a numeric variable that
 can be used to sort species into taxonomic order as specified by the Birds
 of the World Database () *
 Order: Taxonomic Order * Family: Taxonomic Family * Binomial_BirdTree:
 Latin name used in the phylogenetic tree * Binomial_IOC: The Latin name
 provided by the International Ornithological Congress, in the Birds of the
 World Database * Common name: Species common name, as provided by the
 Birds of the World Database * Acuity_CPD: a measure of the average acuity
 across the whole eye, in cycles per degree * Method.Overview: How acuity
 was measured, either RGC Density (Retinal Ganglion Cell Density) or
 Behavior * Eye_axial_Diameter: the axial diameter of the eye, in
 millimeters * BodyMass.Dunning: Body Mass in grams, from the CRC Handbook
 of Avian Body Masses (Dunning Jr. 2007) * Foraging_Technique: A list of
 foraging terms provided on the Cornell Birds of the World Database, used
 to place species in a '93Foraging_Classification'94 *
 Foraging_Classification: whether species forage visually close up or
 visually from a distance * Prey_Mobility: A binary measure of whether
 species forage primarily for mobile or immobile prey * Diet_Category: from
 the Elton Traits database variable '93Diet.5Cat,'94 an indicator
 of the primary diet type of a species * Hab_Light_Level: an indicator of
 whether habitat is open, semi-open, or closed * Spatial_Complexity:
 Habitat spatial complexity: vegetative=complex,
 ground_surface=horizon-dominated, Aerial_Pelagic=aerial or open water, or
 generalist * Nocturnal: A binary variable indicating if a species is (1)
 or is not (0) nocturnal, or operating in nocturnal conditions (i.e. for
 deep-diving birds) 2. `Behave_and_RGC_Data.csv`: this file contains data
 for 28 species with camera eyes in which acuity has been measured using
 both RGC density and behavioral methods. Columns are: * Class: Taxonomic
 class * Binomial: Latin name * Common.Name: species common name *
 Behavior: Acuity measured behaviorally, in cycles per degree *
 RGC_density: Acuity measured using Retinal Ganglion cell density, in
 cycles per degree 3. `pruned_acuity_tree.tre`: phylogenetic tree files for
 species in the acuity analyses. See main text for details of how the tree
 was trimmed from a larger published phylogeny (Burleigh et al. 2015). 4.
 `Image1_original.bmp`, `Image2_original.bmp`, and `Image3_original.bmp`:
 Original image files necessary for running the AcuityView R package to
 create Figure 4, which portrays visual scenes with spatial information
 below the acuity of a given viewer removed. 5. `nocturnal_birds.csv`: this
 file contains data for nocturnal species in the database, along with
 diurnal species with similarly-sized eyes. Column names are the same as in
 'Bird_Acuity_Data.csv' 6. `Timetree_list.nwk`: a Newick-format
 phylogenetic tree, generated from TimeTree.org, containing the 28 species
 listed in '93Behavioral and RGC Data.csv'94 for performing
 phylogenetically-controlled analyses on this set of species Note: .tre and
 .nwk files can be opened in several ways. First, they can be opened with a
 text editor to see the underlying textual structure of the phylogeny.
 Additionally, they can be opened as phylogenetic trees using software such
 as FigTree (