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We acknowledge the herbaria that contributed data to this work: HA, FCO, MFU, UNEX, VDB, ASDM, BPI, BRI, CLF, L, LPB, AD, TAES, FEN, FHO, A, ANSM, BCMEX, RB, TRH, AAH, ACOR, AJOU, UI, AK, ALCB, AKPM, EA, AAU, ALU, AMES, AMNH, AMO, ANA, GH, ARAN, ARM, AS, CICY, ASU, BAI, AUT, B, BA, BAA, BAB, BACP, BAF, BAL, COCA, BARC, BBS, BC, BCN, BCRU, BEREA, BG, BH, BIO, BISH, SEV, BLA, BM, MJG, BOL, CVRD, BOLV, BONN, BOUM, BR, BREM, BRLU, BSB, BUT, C, CAMU, CAN, CANB, CAS, CAY, CBG, CBM, CEN, CEPEC, CESJ, CHR, ENCB, CHRB, CIIDIR, CIMI, CLEMS, COA, COAH, COFC, CP, COL, COLO, CONC, CORD, CPAP, CPUN, CR, CRAI, FURB, CU, CRP, CS, CSU, CTES, CTESN, CUZ, DAO, HB, DAV, DLF, DNA, DS, DUKE, DUSS, E, HUA, EAC, ECU, EIF, EIU, GI, GLM, GMNHJ, K, GOET, GUA, EKY, EMMA, HUAZ, ERA, ESA, F, FAA, FAU, UVIC, FI, GZU, H, FLAS, FLOR, HCIB, FR, FTG, FUEL, G, GB, GDA, HPL, GENT, GEO, HUAA, HUJ, CGE, HAL, HAM, IAC, HAMAB, HAS, HAST, IB, HASU, HBG, IBUG, HBR, IEB, HGI, HIP, IBGE, ICEL, ICN, ILL, SF, NWOSU, HO, HRCB, HRP, HSS, HU, HUAL, HUEFS, HUEM, HUSA, HUT, IAA, HYO, IAN, ILLS, IPRN, FCQ, ABH, BAFC, BBB, INPA, IPA, BO, NAS, INB, INEGI, INM, MW, EAN, IZTA, ISKW, ISC, GAT, IBSC, UCSB, ISU, IZAC, JBAG, JE, SD, JUA, JYV, KIEL, ECON, TOYA, MPN, USF, TALL, RELC, CATA, AQP, KMN, KMNH, KOR, KPM, KSTC, LAGU, UESC, GRA, IBK, KTU, KU, PSU, KYO, LA, LOMA, SUU, UNITEC, NAC, IEA, LAE, LAF, GMDRC, LCR, LD, LE, LEB, LI, LIL, LINN, AV, HUCP, MBML, FAUC, CNH, MACF, CATIE, LTB, LISI, LISU, MEXU, LL, LOJA, LP, LPAG, MGC, LPD, LPS, IRVC, MICH, JOTR, LSU, LBG, WOLL, LTR, MNHN, CDBI, LYJB, LISC, MOL, DBG, AWH, NH, HSC, LMS, MELU, NZFRI, M, MA, UU, UBT, CSUSB, MAF, MAK, MB, KUN, MARY, MASS, MBK, MBM, UCSC, UCS, JBGP, OBI, BESA, LSUM, FULD, MCNS, ICESI, MEL, MEN, TUB, MERL, CGMS, FSU, MG, HIB, TRT, BABY, ETH, YAMA, SCFS, SACT, ER, JCT, JROH, SBBG, SAV, PDD, MIN, SJSU, MISS, PAMP, MNHM, SDSU, BOTU, MPU, MSB, MSC, CANU, SFV, RSA, CNS, JEPS, BKF, MSUN, CIB, VIT, MU, MUB, MVFA, SLPM, MVFQ, PGM, MVJB, MVM, MY, PASA, N, HGM, TAM, BOON, MHA, MARS, COI, CMM, NA, NCSC, ND, NU, NE, NHM, NHMC, NHT, UFMA, NLH, UFRJ, UFRN, UFS, ULS, UNL, US, NMNL, USP, NMR, NMSU, XAL, NSW, ZMT, BRIT, MO, NCU, NY, TEX, U, UNCC, NUM, O, OCLA, CHSC, LINC, CHAS, ODU, OKL, OKLA, CDA, OS, OSA, OSC, OSH, OULU, OXF, P, PACA, PAR, UPS, PE, PEL, SGO, PEUFR, PH, PKDC, SI, PMA, POM, PORT, PR, PRC, TRA, PRE, PY, QMEX, QCA, TROM, QCNE, QRS, UH, R, REG, RFA, RIOC, RM, RNG, RYU, S, SALA, SANT, SAPS, SASK, SBT, SEL, SING, SIU, SJRP, SMDB, SNM, SOM, SP, SRFA, SPF, STL, STU, SUVA, SVG, SZU, TAI, TAIF, TAMU, TAN, TEF, TENN, TEPB, TI, TKPM, TNS, TO, TU, TULS, UADY, UAM, UAS, UB, UC, UCR, UEC, UFG, UFMT, UFP, UGDA, UJAT, ULM, UME, UMO, UNA, UNM, UNR, UNSL, UPCB, UPNA, USAS, USJ, USM, USNC, USZ, UT, UTC, UTEP, UV, VAL, VEN, VMSL, VT, W, WAG, WII, WELT, WIS, WMNH, WS, WTU, WU, Z, ZSS, ZT, CUVC, AAS, AFS, BHCB, CHAM, FM, PERTH and SAN. X.F., D.S.P., E.A.N., A.L. and J.R.B. were supported by the University of Arizona Bridging Biodiversity and Conservation Science program. Z.L. was supported by NSFC (41922006) and K. C. Wong Education Foundation. The BIEN working group was supported by the National Center for Ecological Analysis and Synthesis, a centre funded by NSF EF-0553768 at the University of California, Santa Barbara, and the State of California. Additional support for the BIEN working group was provided by iPlant/Cyverse via NSF DBI-0735191. B.J.E., B.M. and C.M. were supported by NSF ABI-1565118. B.J.E. and C.M. were supported by NSF ABI-1565118 and NSF HDR-1934790. B.J.E., L.H. and P.R.R. were supported by the Global Environment Facility SPARC project grant (GEF-5810). D.D.B. was supported in part by NSF DEB-1824796 and NSF DEB-1550686. S.R.S. was supported by NSF DEB-1754803. X.F. and A.L. were partly supported by NSF DEB-1824796. B.J.E. and D.M.N. were supported by NSF DEB-1556651. M.M.P. is supported by the São Paulo Research Foundation (FAPESP), grant 2019/25478-7. D.M.N. was supported by Instituto Serrapilheira/Brazil (Serra-1912-32082). E.I.N. was supported by NSF HDR-1934712. We thank L. López-Hoffman and L. Baldwin for constructive comments. More

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Study lakesThe study was conducted in two water bodies created after aquatic restorations of mining pits, Lakes Milada (2.5 km2, high structural complexity; 50° 39′ N, 13° 58′ E) and Most (3.1 km2, low structural complexity lake; 50° 32′ N, 13° 38′ E), in the Czech Republic (Fig. 1). Aquatic restoration took place from 2001 to 2010 in Milada and from 2008 to 2014 in Most. Both lakes are medium-sized (surface area = 252 and 311 hectares, respectively), relatively deep (Zmean = 16 and 22 m, Zmax = 25 and 75 m), oligotrophic (mean summer total phosphorus  More

Because biological materials are often viscoelastic composites, with properties dependent on orientation as well as spatial and temporal scales, all tests were designed to mimic natural conditions of tool use. For example, because of possible anisotropies, indentations were made on contact surfaces instead of cross sections of the “tools”; abrasion resistance was measured in the contact direction. Fracture tests were designed to mimic the tension on the side of a tooth or sting subjected to lateral forces; the velocities in our impact tests were between 0.1 and 1 m/s, typical for tool interactions, though 1/10 as fast as for some organisms48,49.OrganismsAll organisms, except the salmon, were housed alive in our laboratory until just before testing. Samples were measured within 24 h of removal from the organism, and were maintained in high-humidity environments during the preparation period. This is particularly important because mechanical properties can depend strongly on water content and this dependence can differ between materials. For example, non-HEBs can harden more than HEBs with drying30,50,51.

1.

Leafcutter ants, Atta cephalotes, were obtained from colonies we collected in Flores, Guatemala and Arena Forest Reserve, Trinidad. They were maintained on Himalayan blackberry leaves, Rubus armeniacus, Portugese laurel, Prunus lusitanica, and Japanese spurge, Pachysandra terminalis.

2.

Nereid worms, Neanthes brandti (synonymous with Alitta brandti), were collected from trenches dug in bars near the mouth of the Coos River, Charleston, Oregon. They were kept in sea-water in a laboratory refrigerator at about 5 °C.

3.

Scorpions, Hadrurus arizonensis, from Arizona, were obtained from commercial suppliers (e.g. Bugs of America, http://bugsofamerica.com) and fed House crickets, Acheta domesticus, and mealworms, Tenebrio molitor larvae.

4.

Spiders, Araneus diadematus, were collected seasonally around the University of Oregon campus, and kept alive in a laboratory refrigerator at about 5 °C. Tarantulas, Aphonopelma hentzi were obtained from Carolina (https://www.carolina.com).

5.

Chitons, Katharina tunicata, and Cryptochiton stelleri were collected in rocky intertidal regions along the coast near Charleston, Oregon and kept alive in a laboratory refrigerator.

6.

Salmon heads, Oncorhynchus sp., were obtained fresh from seafood stores.

7.

Leaf cutter bees, Megachile rotundata, were obtained from Mason Bees for Sale (www.masonbeesforsale.com).

Hardness, modulus of elasticity and damping measurementsHardness, modulus of elasticity and dynamic mechanical property measurements were made by pressing a sharp diamond probe into specimens and measuring the resulting indentation as it changed in time. A higher modulus of elasticity indicates that a structure is stiffer and suffers less elastic (quickly recovered) deformation. A higher hardness value indicates that the material will undergo less plastic (non-recovered) deformation and thus will have a smaller pit left behind after the indentation. A material with a higher loss tangent will absorb more energy of vibration (higher damping), and is characterized by lagging surface deformation and recovery (viscoelasticity) as the indention force changes. Damping can reduce damage because the energy absorbed and converted to heat is not available for breaking bonds in the material.We used an Atomic Force Microsocpe (AFM; NanoScope IIIa, Digital Instruments, Santa Barbara, CA) with an add-on force/displacement transducer (TriboScope, Hysitron Inc., Minneapolis, Minnesota). The Hysitron transducer held a polished diamond probe in place with capacitors that were used to sense the position of the probe and to impart vertical forces for indenting and imaging the specimen. Measurement regions were selected for minimal slope and surface topography as evidenced by the depth variation in AFM scans and the symmetry of the residual indents.In order to make the most biologically relevant measurements, indentations were made on regions of the external surface of structures that directly contact the environment. However, we also made measurements on cross-sections of the arthropod “tools” as part of preliminary SEM and indentation investigations to ensure that the thickness of epicuticle or other surface material would not distort the results for HEBs. Figure 2A shows an indentation on an un-polished surface—the original surface topography is visible as linear scratches that are small compared to the pyramidal indentation. When we could not avoid indentation-scale topography, we hand-polished the surface with 2000–12,000 grit sandpaper (Micro-mesh sheets, http://micro-surface.com), to smooth the surface on the scale of the test indents.Figure 2Images of testing samples for each of the measured properties. (A) AFM image of a residual indentation on the natural surface of the Mn-HEB region of the sting of a scorpion, Hadrurus arizonensis. Indentations were used in measuring hardness, modulus of elasticity and damping properties. Natural “scratches” are visible on the original surface around the triangular indentation made by the cube-cornered indenter. (B) Mandible of an ant, Atta cephalotes, before and after an abrasion testing session. The tip of the zinc-rich distal tooth in the “before” image has been flattened during a preliminary abrasion session. (C) Images from before and after energy of fracture testing of a 12 μm thick Zn-HEB test piece made from the nearly-flat side-surface of the fang of a spider, Araneus diadematus. The original fang shape is evident with the tip of the fang towards the top and the proximal side to the left. (D) Images from before and after impact resistance testing of a 12 μm thick Zn-HEB test piece made from the fang of a spider, Araneus diadematus. The test piece covers a 50 μm diameter backing-pit milled (using FIB-SEM) into a (reflective) silicon chip. The piece has been shattered from the impact in the image on the right.Full size imageSpecimens were mounted on atomic force microscopy (AFM) specimen disks (TedPella Inc., Redding, CA) in a mound of epoxy composite. The composite was prepared by mixing approximately 0.45 g of 400-grit aluminum oxide powder (Buehler, Evanston, Ill., Ted Pella Inc., Redding CA, or Kramer Industries, Piscataway, NJ—the later preferred because it was less reflective) with 0.075 mL each of resin and hardener (Quick Set Epoxy; Loctite, Rocky Hill, CT, and 5 m Quik-Cure Epoxy, Bob Smith Industries, Atascadero CA). The composite was stiff enough that the unpolished specimen had to be pressed in and could be oriented before curing so that the desired indentation region would retro-reflect a light beam sent through the eyepiece of a dissecting microscope back into the microscope, ensuring that the desired region would be flat for AFM scanning and indentation. The mounted specimens were placed in an oven at 39 °C for at least 1 h to cure the epoxy composite. Samples that were not tested immediately were kept on moist paper in a container in a refrigerator and were tested within 24 h to avoid dehydration and other changes. Additionally, the epoxy composite served as a barrier to reduce loss of water through the cut surfaces.The epoxy composite mounting technique was tested to ensure that small (about the same size as the biological specimens) “floating” glass cover-slip pieces would yield the same hardness and modulus of elasticity values as large, flat-mounted cover-slip pieces, and re-checked when relevant products were changed.In order to test whether there were any rapid changes in hardness or modulus of elasticity of HEBs, we tested a H. arizonensis sting, that was mounted for AFM measurements while still attached to an anaesthetized scorpion. We did not find a significant trend with time for 14 measurements made between 10 or 20 min (Zn-HEB and Mn-HEB respectively) after separating the live scorpion from the sting, and 5 h (largest R2: 0.006). These fast results were also comparable to results made using the standard technique, indicating that the technique described above was sufficient to prevent significant changes from dehydration. We also made preliminary measurements on scorpion joint cuticle, armour teeth, and other non-HEB regions of the cuticle, and found none that were harder than the region at the base of the sting, used here to represent non-HEB cuticle.We used a pyramid-shaped diamond probe with cubic corner facets (90° between the three faces)14,31. The steeper angle of the cubic tip, relative to a more commonly used Berkovich tip, made it easier to avoid surface features such as hairs. The diamond probe was positioned on the specimen using a 30× extra short focus monocular (M1030, Specwell Corporation, Tokyo, Japan). The indentation sequence began with the force being ramped linearly from 0 to 2 milliNewtons (mN) in 0.1 s, maintained at 2 mN for 10 s. The force was then ramped down to 1.5 mN over 0.1 s and then the force was varied sinusoidally at 10 Hz (for 25 cycles) with a peak-to-peak amplitude of 1.0 mN, in order to measure dynamic properties. The force was then ramped to 0 in 0.1 s.Probe-extension (Oliver–Pharr) and image-based measurementsTwo methods of obtaining the modulus of elasticity and hardness were employed14. In the first method, values were obtained only from force–displacement curves using the Oliver–Pharr technique52,53. The modulus of elasticity was obtained from the slope of the force–displacement curve at the beginning of withdrawal of the indenter. Oliver–Pharr hardness values were calculated from the intercept of this sloped line with the line of zero force.In addition to the hardness value obtained from the force–displacement curves, we also obtained a hardness value based on measurements of the size of the residual indentation. These image-based hardness values were calculated as H = F/A, where “F” is the maximum force applied to the probe, and “A” is the projected area of the residual indentation, obtained from the perimeter of the indentation measured on an AFM image (e.g. Fig. 2A) made by scanning the indenting probe itself minutes after indenting the specimen.The Oliver–Pharr method is inaccurate if the indentation force causes the surface of the specimen to move, such as for improperly backed specimens, because it assumes probe extension is a measure of indentation depth. In contrast, the image-based method is nearly insensitive to global displacements of the specimen because it is based only on the applied force and measurements of the residual indentation. We found it useful to obtain both values to check each other: on several occasions differences between the two measured values indicated support problems. This is important for biological specimens with multiple layers, voids, etc. For example, if there is a lumen under the shaft but not the tip, the tip may appear harder because it displaces less. In addition, calibration problems, such as from fractured silica, were quickly identified by differences in the image and Oliver–Pharr values. Finally, the image method is not sensitive to other artifacts, such as “pile-up”, that are associated with estimating contact region from probe extension53,54,55,56.There is a potential difference between the Oliver–Pharr and image hardness values associated with the different time scales. The Oliver–Pharr hardness is measured during the probe withdrawal while the image of the residual indent is obtained a couple of minutes after indentation. If the indent partially recovers in the interim, the image technique would be based on a smaller residual indentation resulting in a higher hardness value. We prefer the image method not only because it is robust to imperfectly supported specimens, but also because we would like our hardness measurement to reflect the long-term indentation damage done to the tips and blades. To test that the indent size had stabilized by the time we measured it, we re-measured an indentation in the zinc-region of a scorpion sting after more than 6 months and found that the indentation diameter had decreased little, by about 15%.We also calculated an image-based modulus of elasticity value that has been suggested for materials that produce “pile-up” artifacts53. The area measured from the image of the residual indentation (used for the image-based hardness), was substituted for the contact area calculated from probe extension in Eq. (6) of Oliver & Farr, 2004 53.Notwithstanding the differences in image-based and probe-extension based (Oliver–Pharr) measurements, there was little practical difference in results, as shown in Fig. 3A,B. The metals and plastics that we measured for Fig. 3 tended to have slightly higher Oliver–Pharr hardness values than image-based values, possibly because of “pile-up” artifacts.Figure 3Comparison of probe-extension (Oliver–Pharr) and image-based techniques for hardness (A) and reduced modulus of elasticity (B) for our indentation data. Pile-up may account for higher (above the line) Oliver–Pharr values of some of the metals and plastics.Full size imageIn the “Results” section, we plot the values obtained using the images, but the Oliver–Pharr values are included along with image-based values in the results table, Table 1.Loss tangentDynamic mechanical properties were measured from the sinusoidal segments of the indentation sequence by comparing the amplitude and phase of the displacement to the applied sinusoidal force57. Phase lags associated with the transducer and electronics were determined assuming zero true-lag from a fused silica standard obtained using the same indentation sequence and force. The loss tangents obtained in this way were for the high-stress regimes associated with indentation, as compared to low-stress tests involving bending without plastic deformation.Calibration for Oliver–Pharr measurementsIn order to obtain the contact area from the indent depth, the shape of the indenting tip must be known. We characterized the indenting tip shape directly using scanning electron microscope images, so that we could make deep, micron-scale indents that would be evident on un-polished biological surfaces. We could not make indents in silica as deep as the indents desired for our biological specimens without fracturing the silica (fracture for the cubic cornered tip began at about 3 mN) so we could not use the usual technique of estimating the tip shape at depth by calibrating with fused silica. The tip shape was characterized by three measurements: first, the angle of the three-sided pyramidal tip (α), second, a measure of the bluntness of the tip (B), the distance between the apex of the tip if it were an ideal pyramid and the actual blunt tip, and third, a measure of the distance from the blunt tip beyond which the shape of the tip was not distinguishable from an ideal pyramid (I). The value “I” was used as a limit: only indents with greater depth than “I” were used to calculate mechanical properties. For these deeper indents, the following description of the projected area (A) of the contact region between the tip and the specimen was used:$$A= frac{(0.433)(4){(D+B)}^{2}}{frac{1}{{mathrm{tan}}^{2}left(frac{alpha }{2}right)}-0.3333},$$where D is the depth of the indent, determined by the extension of the indenting probe, 0.433 is the ratio of the area of an equilateral triangle to the square of the length of a side, and 0.3333 is tan2 (30°). As an example, the tip used for the majority of measurements was characterized by α = 89.9°, B = 115 nm, I = 100 nm. Thus, for indents with a depth greater than 100 nm, for our tip, A = 2.58 (D + 115 nm)2.Measurement of residual indentation areaThe area of the residual indent was measured using an AFM image, obtained minutes after the indentation, using the indenting tip as the imaging tip. To minimize inaccuracies in indent perimeter determination, caused by finite size of the imaging probe or other systematic errors, we calibrated our area measurements so that we obtained a median value of 70 GPa for measurements of the modulus of elasticity on fused silica. Because there is some subjectivity in measuring the size of the indentation, operator-specific calibrations, based on each operator’s measurements of fused silica, were used for most of the measurements.Test piece preparation for impact resistance and energy of fracture measurementsWe measured resistance to impact and fracture using custom miniature versions of testing devices that fracture or damage standardized “test pieces” of materials. We prepared test pieces as follows: the fresh (usually immediately after removal from the organism) specimens were adhered to one end of a glass slide using a marine epoxy (Loctite, Rocky Hill, CT) which required a curing time of 2 h at 39 °C, or with cyanoacrylate adhesive (Krazy Glue, all purpose, Elmer’s Products)14, which required no extra curing time. A flat region ( > 100 μm diameter, but not wide enough to reduce the thickness of the HEB region in the center to less than 12 μm) was polished on a specimen by grinding the slide with the specimen against a sequence of flat 2000, 6000 and 12,000 grit sandpaper (Micro-mesh sheets, http://micro-surface.com). The specimen was removed from the adhesive using a scalpel, inverted, and the polished-flat region was adhered to the glass with a thin film of water and surrounded by a small bead of marine epoxy. The water film kept the epoxy from getting pulled under the specimen by capillary action. The epoxy was cured and the specimen polished to a thickness of 12 ± 2 μm as determined with a digital micrometer, using the same sandpaper sequence. The resulting test piece was then freed by scraping the epoxy from around the edges using a scalpel blade. The area of the pieces varied according to the size of flat regions and was, typically, hundreds of microns on a side.Maintaining hydration was especially important for these test pieces because they were only 12 μm thick and so they could dry quickly. To reduce artifacts from drying or other changes in the tested material, all preparation and testing took place in a ~ 15 m3 enclosure maintained at greater than 90% relative humidity.Although we used FIB-SEM (Focused Ion Beam-Scanning Electron Microscope) to shape specimens of the materials for molecular fragment analysis, we did not use this technique for preparing our micron-scale test pieces for several reasons: potential material property changes caused by beam damage, subjection to vacuum, and because some of the test pieces needed to be large enough that they would be difficult to make with FIB-SEM.Impact resistance measurementsA custom testing device was built to compare the energy required for a swinging pendulum to shatter test pieces of the different materials (Fig. 2D). A 12 ± 2 μm thick test piece was adhered by the moisture in the high-humidity enclosure and held in place with an adhesive (spots of cyanoacrylate, Krazy Glue, all purpose, Elmer’s Products, www.elmers.com, or 5-min epoxy gel) over a 50 μm-diameter circular pit milled in a silicon wafer using a FIB-SEM apparatus. The pendulum, made of carbon fiber and aluminum (length: 0.2 m, moment of inertia: 4.25 x 10−6 kg m2) with a diamond impactor tip polished to a diameter of 20 μm, was held by miniature bearings and electronically released from increasing heights until the test piece fractured. The energy required to fracture the specimen was calculated from the release height from which the pendulum fractured the test piece. This energy was normalized by the measured thickness of the specimens to give joules required per meter of thickness. Nevertheless, we consider this test to be a relative test that is not expected to be generalizable to all impacts, as the energy to fracture is likely to depend not only on thickness but also on variables such as the diameter of the impactor tip and the diameter of the backing hole.This impact test differs from Charpy and Izod tests in that the energy required to fracture the specimen was measured by releasing the pendulum from increasing heights until the specimen fractured, rather than by releasing it from a height sufficient to fracture all specimens, and measuring the residual energy of the pendulum after impact. The advantage of our threshold technique is that the threshold of fracture is likely the biologically important quantity, and direct determination of the fracture threshold avoids the possibility that the energy deposited in a single highly-energetic impact might be partially expended in plastic deformation, leading to an overestimate of the threshold energy. A drawback of our technique is that impacts that do not break the specimen may produce damage that weakens the specimen for subsequent impacts. Nevertheless, all specimens were subjected to the same series of increasingly energetic impacts, until fracture, and were thus comparable.Energy of fracture measurementsWe measured the energy or work required to slowly break a test piece in two (Fig. 2c) using a custom fracture toughness measuring device14. The device drove apart two microscope cover slips bridged by the test piece until it split in two, while recording the required force and the displacement (work is the product of force and incremental distance). This work, divided by the area of the new post-fracture surfaces, is the energy of fracture, reported in Joules per meter squared. It is a measure of the energy required, per unit area, to break the bonds that originally held the two pieces together (as long as the kinetic energy is relatively small—the pieces do not fly away), and is one indication of the resistance of a material to fracture.The length of the fracture was measured using a microscope and multiplied by the thickness of the test piece to obtain the fracture area. This area was used to normalize force–displacement curves. The work of fracture per unit area of the fracture was obtained by numerically integrating these normalized force–displacement curves. The load cell was designed to be stiff in order to minimize storage of energy within the apparatus as the specimen underwent tension58. Fracture planes were perpendicular to the original surface and approximately perpendicular to the long axis of the “tool” (Fig. 1C).The test protocol was altered from that used previously14 because the test pieces used here were smaller. Test pieces were not notched in order to avoid fractures from the notching process, and the specimens were adhered to the test apparatus in place (Krazy Glue, all purpose, Elmer’s Products, www.elmers.com) to avoid premature fracture. To improve the bonding of the cyanoacrylate adhesive to the glass cover slips in the high humidity atmosphere, we treating the cover slips with a 10-s dip in a 2% (by volume) 3-aminopropyltriethoxysilane (Sigma Chemical Co., www.sigmaaldrich.com), 98% acetone solution, followed by rinses in deionized water and air drying. The test protocol for the ant mandibular teeth varied from the others in that whole teeth were fractured instead of 12μ polished test pieces.A consistency test with AFM data was developed to identify cases of imperfect bonding to the cover slips, when part of the measured energy was expended in partially pulling the test piece out of the cyanoacrylate adhesive. For samples subject to this problem (usually specimens with small adhesive contact areas, such as the fang specimen in Fig. 1C), we required that the force–displacement curve be consistent with the stiffness of the test piece, expected from a model based on the shape of the individual test piece and the slope of the force–displacement curve for the insertion portion of the nano-indentation sequence for that material. When a piece partially pulled out and failed this test, the apparent stiffness was much lower than the expected stiffness (from nano-indentation) and slight stretch marks were often visible in the adhesive on close inspection.This stiffness consistency test was also found to be useful in identifying cases where part of the fracture was pre-existing but had not been visible in the test piece. The pre-existing fracture would tend to reduce the effective width of the specimen and thus could be identified by a lower than expected stiffness under tension.Abrasion resistance measurementsWe measured the energy required to abrade away a volume of material from our specimens by holding them against a rotating abrasive disk. The energy used in eroding the material is given by the force of friction multiplied by the distance traveled over the abrasive paper (work is the product of the force and incremental displacement), with units of Joules per meter cubed of volume worn away.The “pin on disk” type testing device, developed for testing pieces of crab cuticle14, was used with modified procedures for the smaller specimens here. Instead of cylindrical core samples, whole, approximately conical tips of teeth, fangs or stings were used (Fig. 2B). The samples were affixed with cyanoacrylate gel adhesive (Maxi-Cure, Bob Smith Industries, Atascadero CA) to a steel pin held in the head of the wear tester. This head was mounted on a custom-made load cell that measured the horizontal force produced by friction between the specimen pin and the abrasive turntable. During the wear test, the specimen pin was held against the turntable with adjustable weights that, for the standard test, produced a downward force of 0.019 Newtons. The surface of the turntable was covered with 600 grit abrasive paper (#413Q, 3 M Corporation, www.3M.com). The turntable rotation period was usually set to about 4 s, resulting in an interaction velocity of 0.027 m/s.The volume worn away was calculated from “before” and “after” measurements of microscope images (image J software) taken from the side (e.g. Fig. 2B) to measure the height of the approximate cone of worn material, and from face-on in order to measure the area of the base and top of the frustum of worn-away material. The horizontal force was recorded continuously during the wear period. The wear rate (w), defined as the volume worn away per unit energy expended, was approximated as follows:$$w= frac{V}{Fd} ,$$where F is the average force of friction measured during the wear period by the load cell, d is the distance traveled by the pin over the abrasive paper and “V” is the worn volume, approximated as a frustum:$$V = 1{/}3,Delta L , (A1 + left( {A1*A2} right)^{1/2} + A2)$$where “A1” and “A2” are the areas of the worn surface before and after the wear sessions (the area of any voids or internal lumens was subtracted from the area of the cross sections) and ΔL, the change in the length of the specimen due to wear. We defined wear resistance as the inverse of the wear rate, 1/w. While we expect this test to be most useful for relative comparisons, and the value is expected to vary somewhat with abrasive properties and normal forces, we found no statistically distinguishable difference in values from 4 samples that were re-run using a ten-times greater force to press them against the abrasive paper14.Molecular composition and nanometer-scale structureIn order to better understand the composition and structure of the HEBs—down to an atomic scale—we examined a representative HEB using Atom Probe Tomography (APT). We checked the APT results and studied their generality using Time-of-Flight–Secondary Ion Mass Spectrometry (ToF–SIMS). Both of these techniques use a pulsed beam (laser and ion respectively) to break the specimen into molecular fragments that are accelerated to a detector; for a particular charge, heavier fragments travel more slowly and arrive later at the detector. The arrival time differences are used to identify the fragments by their mass, giving information about, for example, the atoms attached to zinc atoms in the specimen and, from APT, the spatial distribution of zinc atoms on a nanometer scale.Atom probe tomography (APT)APT is a 3D nanoscale characterization method in which field evaporated ions from a sharpened needle specimen are analyzed by a position-sensitive single-particle detector, in order to provide an isotopically resolved three-dimensional representation of the real-space specimen elemental distribution59. The field evaporation of non-conductive samples is achieved using a pulsed laser focused on the needle specimen apex.A FIB-SEM based lift-out procedure was used to prepare needle-shaped APT specimens using FEI Helios 600i at the University of Oregon CAMCOR facility, and a Helios Dual Beam Nanolab 600 FIB-SEM housed at Environmental Molecular Sciences Laboratory, PNNL.The APT analysis was carried out using a CAMECA LEAP (local electrode atom probe) 4000X HR system equipped with a 355 nm wavelength picosecond pulsed UV laser. A 30 K sample base temperature and a 100 or 200 kHz laser pulse repetition rate was used. Atom probe data reconstruction and analysis was performed using Cameca IVAS software.
Development of APT techniques for these organic materials.APT has not typically been used to examine organic materials, so we began by examining standards (such as zinc picolinate) and adjusting beam current densities and other parameters in both the FIB-SEM preparation of APT samples and in APT itself in order to minimize physical damage detected with SEM and to minimize differences between the chemical formulae and APT results for standards60. We found that current densities often employed in FIB-SEM milling were much too high for our organic specimens, resulting in beam damage visible in SEM.Based on the standards and SEM evidence of damage, we used FIB-SEM currents for producing the sample needles, and APT laser pulses for promoting evaporation, that were similar to or smaller than those used in other investigations of organic materials61,62,63,64,65,66,67,68. We used ≤ 21 pA for the electron beam, ≤ 80 pA for ion beam “trenching”, 7.7 pA for ion beam imaging and for cutting the cantilever “liftout” piece, and ≤ 24 pA for sharpening needles. The results reported here are based on samples analyzed using 10, 20 or 100 pJ laser pulses.Identification of molecular fragments from mass-to-charge ratios is particularly difficult for organic materials because of the many possible fragments of similar mass, and several techniques have been developed to aid in this analysis61,62,63,64,68,69,70,71,72,73.Our identification of zinc-containing fragments was simplified by the pattern of the 3 or 4 main zinc isotopes (Fig. 6A). In addition, we used resources with lists of fragments as a function of mass (e.g. https://webbook.nist.gov/chemistry/mw-ser/). We also cross-checked fragment identification using a Time-of-Flight–Secondary Ion Mass Spectrometry (ToF–SIMS) system.Time of flight–secondary ion mass spectrometry (ToF–SIMS)We used a ToF–SIMS system that had a higher mass-to-charge resolution than the APT system (although it had micron- instead of nanometer-scale spatial resolution) in order to check APT fragment identification. The higher mass sensitivity of the ToF–SIMS system provided additional evidence that, for example, the fragments identified as ZnCN were not actually ZnC2H2, which is only about 0.02% lighter. We also used ToF–SIMS to study the larger-scale spatial distribution of fragments, and similarities with HEBs from other species.We used an ION-TOF ToF–SIMS IV, manufactured by ION-TOF GmbH, Muenster, Germany. The primary ion beam was Bi3+ (25 kV, 10 kHz, 0.4 pA); the static limit (2 × 1012 ions/cm2) was not exceeded. The dimensions of the analysis area varied, but were between 100 × 100 μm and 300 × 300 μm. A low energy electron beam was used for charge neutralization. The spectra were analyzed using the vendor’s software. Chemical maps of peaks of interest were created from the total spectra and used as a basis for retrospective analysis—i.e., pixel-specific extraction of spectra in order to determine the chemical makeup of features of interest. More

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Evaluating indicatorsAmong the selected parameters the ratio of rural to the urban population directly relates to the per capita renewable water, whereas the population density, internet users, and education index exhibit an inverse relation with the per capita renewable water worldwide. It means the per capita renewable water decreases with decreasing rural to urban population and increasing population density, internet users, and education index. The urban population has increased in developing regions, which feature increasing population density. People’s health is threatened by poor urban sanitary infrastructure leading to disease and social decay. Increasing population density and a reduction in per capita renewable water inflict social harm and disrupt society’s economic growth58. Population density also is positively related to the relative number of elderly and social vulnerability because potential casualties increase with population size40. On the other hand, with the increase of Internet users and education index, the per capita renewable water has increased. As long as the knowledge and awareness of communities improved, the consumption algorithm decreased, leading to a reduction of renewable water per capita. Therefore, the level of literacy and knowledge for a community can be the basis for making the right decisions in agriculture, health, natural resource management, and other activities related to water resources for decision-makers. The latter situation calls for better communication among water users through social media and improved education to learn and develop optimal water management.Evaluating models and developing hydro-social equationsThree soft-computing approaches, namely ANN-LM, ANFIS-SC, and GEP, were applied to develop predictive equations with social indicators worldwide. The ANN-Levenberg–Marquardt (LM) backpropagation algorithm with one hidden layer was applied, and the hidden nodes’ number was determined by trial and error. A hybrid algorithm was combined with the ANFIS-SC models. There is no rule for determining the radii values of the ANFIS-SC models. The final radii values were determined by trial-and-error.The numbers of neurons in the ANN-LM models and the radii values of the ANFIS-SC models are listed in Table 4. The activation functions of the output nodes were linear for all the continents. The activation functions of the hidden nodes of the ANN-LM models for the P1 through P4 indicators were respectively the tangent sigmoid, tangent sigmoid, tangent sigmoid, and logarithm sigmoid for Africa; the activation functions of the proportion of rural to urban population was the tangent sigmoid for all the continents. Table 5 lists the results of the soft computing optimal models’ estimates of the proportion of rural to urban population (PRUP), population density (PD), internet users (IU), and education index (EI), denoted respectively by P1 through P4, during the test period in the world’s continents. Figures 4 and 5 display the characteristics of ANN (the number of neurons and activation functions of hidden and output layers) and ANFIS-SC (radii values) models, respectively. The values of R and RMSE for Africa corresponding to the ANN-LM models were respectively (0.921, 0.981, 0.858, 0.862) and (0.193, 0.058, 0.190, 0.172) associated with the PRUP, PD, IU, and EI parameters, respectively. The values of R and RMSE for Africa corresponding to the ANFIS-SC models equaled respectively (0.933, 0.991, 0.868, 0.891) and (0.130, 0.044, 0.186, 0.156) for the P1 through P4 parameters, respectively. Concerning the GEP models, the root relative squared error (RRSE) was selected as the pressure tree’s fitness function. The values of RMSE for GEP models equaled (0.084, 0.029, 0.178, 0.135), (0.197, 0.056, 0.152, 0.163), (0.151, 0.036, 0.123, 0.210), (0.182, 0.039, 0.148, 0.204) and (0.141, 0.030, 0.226, 0.082) for Africa, America, Asia, Europe, and Oceania, respectively. Table 5 results for the R, RMSE, and MAE values establish the GEP model estimates of PRUP, PD, IU, and EI indicators had the highest R values and the lowest RMSE values. The average R values of the best models (GEP) for all selected social parameters equaled 0.942, 0.909, 0.910, 0.889, and 0.947 for Africa, America, Asia, Europe, and Oceania, respectively. These results indicate the climatic characteristics of the continents influence the performance of the models. The models’ performances for Africa and Oceania associated with the type B dominant Koppen climate classification was the best. The models’ performances for Asia and America that have similar climatic classification were nearly equal. The average model performance for Europe in the type D climate classification was the poorest among the continents.Table 4 The characteristics of ANN (the number of neurons) and ANFIS (radii values) models corresponding to social indicators and continents.Full size tableTable 5 The results of soft computing optimal models corresponding to the testing period in the world’s continents.Full size tableFigure 4The characteristics of optimal ANN models; showing the number of neurons and activation functions of hidden and output layers.Full size imageFigure 5The characteristic of optimal ANFIS-SC model showing the radii values.Full size imageFigures 6, 7, 8, 9 and 10 show the observed and estimated social parameters obtained with the soft-computing models during the test period in Africa, America, Asia, Europe, and Oceania, respectively. Figure 11 compares the R, RMSE, and MAE values from the soft-computing models. The R values for soft-computing models are close to 1, with the quality relations being: RGEP  > RANFIS-SC  > RANN-LM for all social indicators. Figure 11 establishes that the ANFIS-SC model exceeded the ANN-LM models’ performance. Also, the GEP models had better performance than the ANFIS-SC and ANN-LM for estimating the proportion of rural to urban population (PRUP), population density (PD), internet users (IU), and education index (EI) parameters in Africa, America, Asia, Europe, and Oceania.Figure 6Observed and estimated social parameters during the testing period in Africa.Full size imageFigure 7Observed and estimated social parameters during the testing period in America.Full size imageFigure 8Observed and estimated social parameters during the testing period in Asia.Full size imageFigure 9Observed and estimated social parameters during the testing period in Europe.Full size imageFigure 10Observed and estimated social indicators during the testing period in Oceania.Full size imageFigure 11Comparison of R, RMSE and MAE values corresponding to the soft computing methods.Full size imageThe main advantage of the GEP over other soft computing methods (e.g., ANFIS and ANN) is in producing predictive equations. The equations obtained with the optimal models for the social indicators (i.e., the proportion of rural to urban population (PRUP), population density (PD), internet users (IU), and education index (EI) in Africa, America, Asia, Europe, and Oceania) are listed in Table 6. The equations that the GEP model discovers as a structure do not necessarily correspond to reality. The equations listed in Table 6 merely show the optimal equations extracted from the model after the evolution, for all indicators and in all basins (considering renewable water per capita as a decision variable).Table 6 Mathematical equations governing hydro-social indicators.Full size tableThe performance of the GEP models in estimating the social indicators in three ranges of values, namely, 20% of the maximum estimated values (20%max), 60% of median estimated values (60%mid or 20%min to 20%max), and 20% of minimum estimated values (20%min), during the test period for the proportion of rural to urban population (PRUP), population density (PD), internet users (IU) and the education index (EI) parameters of Africa, America, Asia, Europe, and Oceania are listed in Table 7. Table 7’s results indicate there is not a regular rule to determine the best-cited ranges performances. The education index and the population density have the lowest and highest R values among the other parameters in the three different ranges (20%max, 60%mid, and 20%min) in Africa, America, Asia, Europe, and Oceania. Therefore, the results indicate a strong pattern of association between the population density parameter and water resources status in all continents of the world.Table 7 The performance of GEP models with respect to selected ranges.Full size tableFigure 12 depicts the distribution of estimated data values of the social parameters (i = 1, 2, 3, 4) and their comparison through the continents. The box plots are a graphic display integrating multiple numerical relations. One approach to understanding the distribution or dispersion of data is through the box diagram, which is based on the “minimum,” “first quartile-Q1(0.25%)”, “median (0.50%)”, “third quartile-Q3(0.75%)” and “maximum” statistical indicators. Figure 12 shows Oceania and Africa exhibit the smallest and largest values of the rural to urban population, respectively. America has the lowest values of the first to the third quartile. The estimated population density value in Europe has the most values in the third quartile (0.75%). The median values of estimated internet users have the smallest and largest values in Africa and Europe, respectively. America has the lowest values of the first quartile, median, third quartile, and maximum values associated with the estimated education index values among the continents.Figure 12Distribution of estimated data values of social indicators (Pi, i = 1, 2, …, 4).Full size imageThe summary of hydro-social equations performance is listed in Table 8, where it is seen the best models’, performances are such that PD  > PRUP  > EI  > IU, PD  > IU  > EI  > PRUP, PD  > IU  > PRUP  > EI, PD  > PRUP  > IU  > EI and PD  > EI  > IU  > PRUP for Africa, America, Asia, Europe, and Oceania, respectively.Table 8 Summary of hydro-social equations performance.Full size tableThis paper’s results indicate the pattern of association between social parameters and water resources is complex. Renewable water per capita was estimated using social indicators PRUP, PD, IU, and EI based on gene expression programming. The results of GEP to estimate RWPC corresponding to the testing period in the world’s continents as listed in Table 9. The values of RMSE for optimal GEP models equaled 0.089, 0.058, 0.042, 0.049, and 0.036 for Africa, America, Asia, Europe, and Oceania, respectively. Figure 13 displays the observed and estimated RWPC parameter during the test period in the world’s continents. The equations obtained with the optimal models for the renewable water per capita in Africa, America, Asia, Europe, and Oceania are listed in Table 10. The fitted equations can be applied at variable spatial and temporal scales. The derived equations imply that water resources in Africa and Oceania are governed by the PRUP, PD, IU, and EI indicators. Also, the PRUP, PD, and IU indicators in Europe and PD and IU indicators in America and Asia have the most influence on their water resources status. The association between social parameters and water resources in all continents is variable. The linking of these social indicators with the per capita renewable water is a function of the countries’ cultural and economic conditions, thus bearing on the future management and policymaking across continents. This study’s results concerning hydro-social indicators are consistent with the findings by Forouzani et al.2, Carey et al.15, Lima et al.25, Pande et al.7, Diep et al.26, and Diaz et al.22.Table 9 The results of GEP estimating RWPC corresponding to the testing period in the world’s continents.Full size tableFigure 13Observed and estimated RWPC parameters during the test period in the world’s continents.Full size imageTable 10 Mathematical equations governing hydro-social indicators.Full size tableThis paper’s results establish the importance of examining the interactions between climate, the status of water resources, and social indicators. The state and social conditions of a country reflect the status of its water resources. Therefore, this study has shown how significant an impact the management and planning of a country can have on its water resources. Each successful water resources project rests on a successful social setting. More

EDITORIAL
31 August 2021

The world’s scientific panel on biodiversity needs a bigger role

IPBES, the international panel of leading biodiversity researchers, should be consulted on how best to measure species loss.

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A baby green sea turtle in Madagascar, one of the regions where the probability of widespread biodiversity loss is greatest.Credit: Alexis Rosenfeld/Getty

For more than 30 years, the international community has tried and failed to find a path to slow down — and eventually reverse — worldwide declines in the richness of plant and animal species. Next year, it will have another chance. The 15th Conference of the Parties (COP 15) to the United Nations Convention on Biological Diversity, recently delayed for the third time, is now slated to take place in person in Kunming, China, in April and May 2022.Biodiversity is fundamental to Earth’s life-support systems, and humans depend on the services that nature provides. In 2010, countries committed to slowing the overall rate of biodiversity loss by 2020. But just 6 out of the 20 targets that were agreed on that occasion — at COP 10 in Aichi, Japan — have been even partially met, notable among them a commitment to conserve 17% of the world’s land and inland waters.Ahead of the Kunming meeting, policymakers and scientists are discussing a new action plan, called the Global Biodiversity Framework, which they hope to agree next year. The latest draft (published in July; see go.nature.com/3kbvspd) includes a promise to conserve 30% of the world’s land and sea areas by 2030 and reiterates the need to meet earlier targets, including the provision of greater financial support to low-income countries to help them to protect their biodiversity.Missing linkResearchers around the world are advising on the plan, through the UN’s institutions and through universities and various scientific networks. But one piece of the puzzle is missing. In 2012, a host of governments established the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). It periodically reviews the literature and provides summaries of the latest knowledge. However, the countries organizing the COP are not involving IPBES in the action plan in the way that the UN Intergovernmental Panel on Climate Change has been consulted for advice ahead of climate COPs. It is important that IPBES be asked, because policymakers are being presented with a range of ideas that would benefit from the systematic evaluation that a global scientific advisory body would bring.
The world’s species are playing musical chairs: how will it end?
For example, biodiversity terminology is often unfamiliar, and therefore challenging, for most policymakers. The word itself — defined by the biodiversity convention as the variety and variability of life on Earth, at the level of genes, species and ecosystems — is not commonly used, nor well understood beyond the scientific community. The magnitude of biodiversity’s value to the planet and to people, as well as the risks of losing it, are also not widely appreciated.Over the years, various teams of scientists have researched and offered ideas on how to communicate the state of biodiversity both accurately and in a way that is accessible and engages the wider public. Some are advocating a biodiversity equivalent of the 1.5 °C warming target, or of net-zero emissions. One suggestion, published last year, is for the international community to adopt a target for limiting species extinctions. The goal would be to keep extinctions of known species to below 20 per year globally for the next 100 years — a single headline number to represent biodiversity (M. D. A. Rounsevell et al. Science 368, 1193–1195; 2020).A focus on species extinctions as a proxy for biodiversity is not a new idea, and is controversial. However, the authors say that their intention is not to replace biodiversity’s many facets with only one number, but to communicate biodiversity in a way that would resonate with more people.Another group is proposing a composite index — a single score made up of measures of some of biodiversity’s main components, including the health of species and ecosystems, as well as the services that biodiversity provides to people, such as pollination and clean water (C. A. Soto-Navarro et al. Nature Sustain. https://doi.org/gmjs2f; 2021). This would be biodiversity’s equivalent of the UN Human Development Index — first published in 1990 — which amalgamates information on health, education and income into a single number and has been adopted worldwide as a measure of prosperity and well-being.
Fewer than 20 extinctions a year: does the world need a single target for biodiversity?
A third idea, published by the leaders of some of the world’s most influential conservation and environmental science organizations, is called Nature Positive (see go.nature.com/2ydk89n). Its authors are proposing that the UN’s many global environmental agreements should include three common targets: no net loss of nature from 2020 (meaning that although nature might continue to be degraded in some areas, this would be offset by conservation gains elsewhere); some recovery by 2030; and full recovery by 2050. At present, the UN agreements on biodiversity, stopping climate change and combating desertification all have their own processes, occasionally acting together, but more often operating independently. The goal is to get them to sign up to one set of principles.All of these ideas have advantages and risks, which is why they need to be systematically evaluated by researchers. That’s where IPBES’s role is crucial. IPBES comprises a broad community of researchers, and, importantly, it represents voices from under-represented low- and middle-income countries, as well as the world’s Indigenous peoples. The governments involved in organizing the Kunming COP should ask IPBES to evaluate the ideas being put forward for the next biodiversity action plan, so they can be confident that what they decide has the support of a consensus of researchers, particularly in more-biodiverse regions of the world. Although preparations for the Kunming COP are well under way, this could also happen after the COP.Biodiversity loss could be as serious for the planet — and for humanity — as climate change. World leaders have become skilled at organizing complex international meetings and making promises that they then fail to keep. The upcoming biodiversity COP risks being one more such event, which is why researchers offering solutions are right to feel frustrated. They should work with IPBES to review their ideas. A unified voice is powerful, and if scientists can present a united front, policymakers will have fewer excuses to continue with business as usual.

Nature 597, 7-8 (2021)
doi: https://doi.org/10.1038/d41586-021-02339-3

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