Armadillos

Data previously gathered comparing with acceleration data for animals during activity on a treadmill at Buenos Aires Zoo [12] were reanalyzed to supplement the work on humans. Species used were; coypu (Myocastor coypus) (4 individuals), larger hairy armadillo (Chaetophractus villosus) (1 individual), Muscovy duck (Cairina moschata) (1 individual), greylag goose (Anser anser) (2 individuals), Magellanic penguin (Spheniscus magellanicus) (2 individuals) and rockhopper penguin (Eudyptes chrysocome) (1 individual). Briefly, animals were equipped with acceleration data loggers, attached variously, before being exposed to a treadmill with the tread moving at a range of speeds between 0 and 2.52 km/h, the upper limit dependent on their capacities. The animals were given rests between the higher speeds where the predominant behavior was locomotion however at the lower speeds the animal typically exhibited a range of behaviors including searching, scratching and lying. An open circuitry respirometry system was used to measure . Full details of the protocol are given in Halsey et al. [12] .

Data from 47 structures in the PHENIX library of MAD, SAD and MIR data sets were used along with 246 MAD and SAD structures from the Joint Center for Structural Genomics (JCSG; http://www.jcs.org). The structures from the PHENIX library included 1029B (PDB code 1n0e; Chen et al., 2004 ▶ ), 1038B (1lql; Choi et al., 2003 ▶ ), 1063B (1lfp; Shin et al., 2002 ▶ ), 1071B (1nf2; Shin, Roberts et al., 2003 ▶ ), 1102B (1l2f; Shin, Nguyen et al., 2003 ▶ ), 1167B (1s12; Shin et al., 2005 ▶ ), aep-transaminase (1m32; Chen et al., 2002 ▶ ), armadillo (3bct; Huber et al., 1997 ▶ ), calmodulin (1exr; Wilson & Brunger, 2000 ▶ ), cobd (1kus; Cheong et al., 2002 ▶ ), cp-synthase (1l1e; Huang et al., 2002 ▶ ), cyanase (1dw9; Walsh et al., 2000 ▶ ), epsin (1edu; Hyman et al., 2000 ▶ ), flr (1bkj; Tanner et al., 1996 ▶ ), fusion-complex (1sfc; Sutton et al., 1998 ▶ ), gene-5 (1vqb; Skinner et al., 1994 ▶ ), gere (1fse; Ducros et al., 2001 ▶ ), gpatase (1ecf; Muchmore et al., 1998 ▶ ), granulocyte (2gmf; Rozwarski et al., 1996 ▶ ), groEL (1oel; Braig et al., 1995 ▶ ), group2-intron (1kxk; Zhang & Doudna, 2002 ▶ ), hn-rnp (1ha1; Shamoo et al., 1997 ▶ ), ic-lyase (1f61; Sharma et al., 2000 ▶ ), insulin (2bn3; Nanao et al., 2005 ▶ ), lysozyme (unpublished results; CSHL Macromolecular Crystallo­graphy Course), mbp (1ytt; Burling et al., 1996 ▶ ), mev-kinase (1kkh; Yang et al., 2002 ▶ ), myoglobin (A. Gonzales, personal communication), nsf-d2 (1nsf; Yu et al., 1998 ▶ ), nsf-n (1qcs; Yu et al., 1999 ▶ ), p32 (1p32; Jiang et al., 1999 ▶ ), p9 (1bkb; Peat et al., 1998 ▶ ), pdz (1kwa; Daniels et al., 1998 ▶ ), penicillopepsin (3app; James & Sielecki, 1983 ▶ ), psd-95 (1jxm; Tavares et al., 2001 ▶ ), qaprtase (1qpo; Sharma et al., 1998 ▶ ), rab3a (1zbd; Ostermeier & Brunger, 1999 ▶ ), rh-dehalogenase (1bn7; Newman et al., 1999 ▶ ), rnase-p (1nz0; Kazantsev et al., 2003 ▶ ), rnase-s (1rge; Sevcik et al., 1996 ▶ ), rop (1f4n; Willis et al., 2000 ▶ ), s-hydrolase (1a7a; Turner et al., 1998 ▶ ), sec17 (1qqe; Rice & Brunger, 1999 ▶ ), synapsin (1auv; Esser et al., 1998 ▶ ), synaptotagmin (1dqv; Sutton et al., 1999 ▶ ), tryparedoxin (1qk8; Alphey et al., 1999 ▶ ), ut-synthase (1e8c; Gordon et al., 2001 ▶ ) and vmp ( l8w; Eicken et al., 2002 ▶ ).
The structures from the JCSG included PDB (Bernstein et al., 1977 ▶ ; Berman et al., 2000 ▶ ) entries 1o1x (Xu et al., 2004 ▶ ), 1vjf, 1vjr, 1vk4, 1vk8, 1vk9, 1vkd, 1vkn, 1vl0, 1vl5, 1vli, 1vlo, 1vly, 1vm8, 1vmg, 1vmi, 1vp8, 1vpm, 1vpz (Rife et al., 2005 ▶ ), 1vqr (Xu, Schwarzenbacher, McMullan et al., 2006 ▶ ), 1vqs, 1vqy, 1vqz, 1vr0 (DiDonato et al., 2006 ▶ ), 1vr3 (Xu, Schwarzenbacher, Krishna et al., 2006 ▶ ), 1vr5, 1vr8 (Xu, Krishna et al., 2006 ▶ ), 1vrm (Han et al., 2006 ▶ ), 1z82, 1z85, 1zbt, 1zej, 1zh8, 1zko, 1ztc, 1zx8 (Jin et al., 2006 ▶ ), 1zy9, 1zyb, 2a3n, 2aam, 2aml, 2ax3, 2b8n (Schwarzenbacher et al., 2006 ▶ ), 2etd, 2ets, 2evr, 2f4i, 2f4l, 2fg0, 2fg9, 2fna, 2ftr, 2fup, 2fur, 2g0w, 2gb5, 2gc9, 2gf6, 2gfg, 2ghr (Zubieta et al., 2007 ▶ ), 2gno, 2go7, 2gpi, 2gpj, 2grj, 2gvh, 2h1q, 2h1t, 2h9f, 2hcf, 2hh6, 2hhz, 2hi0, 2hq7, 2hq9, 2hr2, 2hsz, 2huh, 2hx1, 2hx5, 2hxv, 2i02, 2i8d, 2i9w, 2ig6, 2ii1, 2ilb, 2isb, 2it9, 2itb, 2nuj, 2o08, 2o2g, 2o2x, 2o2z, 2o3l, 2o62, 2oa2, 2oaf, 2oc6, 2od5, 2ogi, 2oh1, 2oh3, 2oik, 2ooj, 2ook, 2op5, 2opl, 2oqm, 2ord, 2osd, 2otm, 2ou3, 2ou5, 2ou6, 2own, 2oyo, 2ozg, 2ozj, 2p10, 2p1a, 2p7i, 2p8j, 2pbl, 2peb, 2pfw, 2pg4, 2pgc, 2pke, 2pn1, 2pq7, 2pr7, 2prr, 2prv, 2pv4, 2pv7, 2pwn, 2py6, 2pyq, 2pyx, 2q02, 2q04, 2q0t, 2q14, 2q3l, 2q78, 2q7x, 2q9k, 2q9r, 2qe6, 2qe9, 2qez, 2qg3, 2qhp, 2qj8, 2ql8, 2qml, 2qpx, 2qr6, 2qtp, 2qtq, 2qw5, 2qww, 2qwz, 2qyv, 2r01, 2r0x, 2r1i, 2r3b, 2r44, 2r4i, 2r9v, 2ra9, 2ras, 2rcc, 2rcd, 2rd9, 2rdc, 2re3, 2re7, 2rfp, 2rgq, 2rha, 2rhm, 2rij, 2ril, 2rkh, 3b5e, 3b5o, 3b77, 3b7f, 3b81, 3b8l, 3bb5, 3bb9, 3bcw, 3bdd and 3bde.

Dissection, fixation and immunostaining were performed as described by Micchelli, 2014 (link). Dilutions of the various antibodies were: mouse anti-Armadillo N27A1 at 1:50 (DSHB), mouse anti-Connectin-C1-427 at 1/200 (DSHB), mouse anti-Prospero MR1A at 1:200 (DSHB), rabbit anti-PH3 at 1:1000 (Millipore, 06–570), Rabbit anti-Cleaved Caspase-3 at 1/600 (Cell Signalling Asp175 #9661), Goat anti-mouse AlexaFluor-647 at 1/500 (Molecular Probes Cat# A-21235), Goat anti-mouse AlexaFluor-546 at 1/500 (Molecular Probes Cat# A-11003), Goat anti-rabbit AlexaFluor-647 at 1/500 (Thermo Fisher Scientific Cat# A32733), Goat anti-rabbit AlexaFluor-546 at 1/500 (Thermo Fisher Scientific Cat# A-11010). For microscopy, guts were mounted in Fluoroshield DAPI medium (Sigma, # F6057) and immediately observed on a Zeiss Axioplan Z1 with Apotome 2 microscope. Images were analyzed using ZEN (Zeiss), ImageJ and Photoshop software. Image acquisition was performed at the microscopy platform of the Sophia Agrobiotech Institute (INRAE1355-UCA-CNRS7254 – Sophia Antipolis).

For all regressions and flexible discriminant analyses (FDAs), we chose to use both phylogenetic and nonphylogenetic methods because ecology and phylogeny are closely aligned in extant xenarthrans, likely making it difficult to cleanly isolate phylogenetic and ecological signals (Fig. 1) (Zack et al. 2021 ). Using both phylogenetic and nonphylogenetic methods will allow us to directly compare an analysis that assumes that phylogeny has no effect to an analysis that assumes the similarity of organisms is due to their relatedness rather than to convergence (Revell 2010 ). The phylogenetic regressions we used do not allow for more than one specimen from each species, so we had to use the average of each. Because of this, we also calculated regressions using the species means for each vertebral position. If there is no impact of using the species means, then we would expect the species mean regressions to be exactly the same as the nonphylogenetic specimen-based regressions. Comparing these three methods will allow us to better understand how the inclusion of phylogenetic information is affecting the slope estimation in these regressions.
To statistically analyze the impact of body size on each bone microstructure metric, we used GLS regressions on log-transformed metrics for the entire dataset (Table 2, Fig. 5). We additionally calculated the linear regressions for species averages (Table S3, Fig. S3). BV.TV and GC are ratios and are therefore unitless with an isometric slope of 0. Tb.N is measured in numbers/mm, so the isometric slope is −1. Tb.Th is a length value with an isometric slope of 1; CSA is a measure of area with an isometric slope of 2. Isometric slope for Conn.D is −3 because it is measured in numbers/mm³ (see Mielke et al. 2018 for further explanation of isometry for these metrics; Plasse et al. 2019 (link)). We also calculated confidence intervals (CIs) for all regressions using the confint function in R (Team RC 2022 ). A slope was considered allometric if the isometric slope fell outside the CI. We calculated the regressions for each of the three clades examined (Cingulata [armadillos], Vermillingua [anteaters], and Folivora [sloths]) and for each of the ecologies (arboreal, hook-and-pull digging, and scratch digging). We then compared the slopes using the standardized major axis estimation and testing function in the smatr package in R (Tables 3 and 4) (Warton et al. 2012 ; Team RC 2022 ). We also used PGLS regressions to determine the impact of phylogenetic covariance on the scaling of TBA metrics (Table 2, S2). We pruned the time-scaled tree of Gibb et al. (2015) (link) to include only the species in our dataset. We prepared the data by calculating the species means of each metric by vertebral position. We used the GLS function and corBrownian in the R package ape to calculate the PGLS of each metric (Table 2, S2) (Paradis and Schliep 2019 (link)). We calculated Blomberg's K using the R package phytools to further analyze the phylogenetic signal of each variable (Revell 2012 ). Because we analyzed each vertebral position separately, we used the average of the PGLS regressions of each metric along the vertebral column to compare to the individual linear regressions.
To further quantify the impact of size, ecology, and phylogeny on TBA, we used both pFDA and FDA. We determined ecology groups using the primary locomotor ecology of each genus (Rood 1970 ; Ramsey 1978 ; Navarrette and Ortega 2010 ; Hayssen 2011 ; Hayssen et al. 2012 ; Gaudin et al. 2018 ; Attias et al. 2020 ), and we determined size class based on the groups from GLS regressions (Table 2). Using the same data as the PGLS, we performed the pFDA using code from Motani and Schmitz 2011 (link) and Smith et al. 2018 ). This package can only use up to three metrics, so we could not undertake a fully multivariate analysis of our dataset. Therefore, we used three subsets of metrics to complete each analysis: the most size-correlated metrics (Tb.Th, CSA, and Conn.D), the least size-correlated metrics (BV.TV, GC, and DA), and the most phylogenetically-correlated metrics (DA, Tb.Th, and CSA). We chose to use the least size-correlated metrics in our analyses because this model most accurately resolved ecology (Table S4, Fig. S4). For the FDA, we used the mda and nnet R packages to complete the analysis and visualize the results (Venables and Ripley 2002 ; Leisch and Hornick 2022 ).