Ecology

Generic nameRavenala Adans.1 (1763: 67). (equiv) Urania Schreb.22 (1789: 212). –Ravenala Scop.23, nom. illeg. (1777: 96) as “Ravenalla Adans”.Type species Ravenala madagascariensis Sonn.24.Note: Dorr & Parkinson25 proposed to conserve the spelling Ravenala Scop. (and correct Scopoli’s original orthography “Ravenalla”) against Ravenala Adans. on the basis that Adanson’s generic names (using a uninominal nomenclature for species) were invalid. Brummitt26 rejected this proposal and considered that Adanson’s generic names were valid27 and thus that there was no need to use Scopoli’s Ravenala (Ravenalla). Moreover, the exact wording in Scopoli23 (1777: 96) is “Ravenalla Adans.”, citing Adanson explicitly, but with an incorrect spelling for the generic name (the double “l”).Typification and emended descriptionRavenala madagascariensis Sonn. (1782: 2[ed. qto.]: 223, tt. 124–126).(equiv) Ravenala madagascariensis J.F.Gmel.28 (1791: 567). (equiv) Urania madagascariensis (Sonn.) Schreb. ex Forsyth f.29 (1794: 212). (equiv) Heliconia ravenala Willemet30 (1796: 22). (equiv) Urania speciosa Willdenow31 (1799: 7). (equiv) Urania ravenalia (Willemet) A.Rich.32 (1831: 19). –Ravenala madagascariensis Adans.1 (1763: 597), nomen invalid., appearing on page 597, abbreviated in the final index of Adanson’s book as “Ravenala madag. 67”, which can also be construed as referring to Madagascar as a locality.Type Lectotype, here designated: The plate numbered 126, representing the typical lax mature infructescence, in Sonnerat24 (1782: plate 126). Epitype, here designated: MADAGASCAR (bullet) Fort-Dauphin, Forêt de Manantantely, [24°58′ 59.988″S, 46°55′0.012″E, calc. from label], 60–300 m elev., 15 September 1928, H. Humbert 5730 (Epitype: MNHN-P-P02234599!, Isoepitypes: MNHN-P-P02234602!, MNHN-P-P02234604!, MNHN-P-P02234605!).Additional specimen examined: MADAGASCAR (bullet) Toamasina: Foulpointe, Analalava Forest, plant growing close to the main forest station, 17°42.3′S, 49°27.38′E, 50 m elev., 20 March 2016, T.Haevermans, M. Vorontsova, S. Dransfield & J. Razanatsoa 821 (TAN!, P!, K !) (bullet) X. Aubriot et al. 45 (P00696168!, P00696167!, P00685124!, TAN!) (bullet) Along Route #5 from Fenerive to Maroantsetra, disturbed areas along road, 100 m elev., 28 February 1975, T. B. Croat 32540 (L-WAG.1111446!, L-WAG.1111447!, MO-358490!, MO-358491!, MO-358523!) (bullet) Toalagnaro, Ebakika, District de Fort-Dauphin, 12 July 1932, R. Decary 10107 (P02234596!) (bullet) Vondrozo (commune de Farafangana), 16 September 1926, R. Decary 5428 (P02234588!, P02234591!, P02234592!) (bullet) 2 km E of Ranomafana towards Brickaville, 18.965° S, 48.8564° E, 4 March 1992, J. Kress et al. 92-3412 (US00424302!, US00424299!, US00424300!, US00424301!, US00424303!) (bullet) 18 km E of Ranomafana, 25 km W of Brickaville, 18.9453° S, 48.9664° E, 4 March 1992, J. Kress et al. 92-3414 (US00424312!, US00424309!, US00424310!, US00424311!, US00424313!). MAURITIUS (bullet) Isle de France, s.dat., Commerson s.n. (P02234587!, P-JU!, P-LAM!).Identity of Ravenala madagascariensis Sonn. —Figs. 2d, 3d, 4d, 5d— In the absence of a specimen undoubtedly collected or seen by Sonnerat (Commerson’s specimens, collected in Mauritius and preserved in both Jussieu’s and Lamarck’s herbaria at the Paris herbarium (P-JU and P-LAM), might actually be part of original material), we decided to lectotypify from plates 124, 125 and 126 of the protologue in Sonnerat’s valid publication24 of the species. On page 225, Sonnerat24 mentions that the plant originated from Madagascar but was transported and established in Mauritius (known at the time as Isle de France) at the “Jardin des Pamplemousses”. We observed plants growing in this garden as well as naturalized plants occurring in the wild in Mauritius; all the plants we saw suckered and possessed the characteristic pointed conical fruits also observed in the Fort-Dauphin population. Sonnerat also specified that the original plant grew in marshy areas, which corresponds exactly to the coastal populations that can be found on the eastern coast of Madagascar (i.e. the “Horonorona” variant of Blanc et al.13). Plate 126 shows the typical mature infructescence of the species, with the space between bracts increasing before releasing the seeds (unlike other species of Ravenala). However, the “tree” pictured on plate 124 is a non-suckering plant, which in our opinion can be explained as artistic license on the part of the illustrator, as all the plants observed in Mauritius consistently sucker, like the plants growing in the south-eastern marshy areas. We also decided to designate an epitype with a documented locality in Madagascar (the material in P-JU and P-LAM does not bear a precise indication of locality) to fix the application of the name R. madagascariensis to the populations occurring in the marshy areas surrounding Fort-Dauphin, where only one morphotype is known.Figure 3Comparison of petiole bases. (a) R. agatheae. (b) R. blancii. (c) R. grandis. (d) R. madagascariensis. (e) R. menahirana. (f) R. hladikorum. Photographs Thomas Haevermans©.Full size imageFigure 4Comparison of inflorescences. (a) R. agatheae. (b) R. blancii. (c) R. grandis. (d) R. madagascariensis. (e) R. menahirana. (f) R. hladikorum. Photographs Thomas Haevermans©.Full size imageFigure 5Species of Ravenala in their natural habitat. (a) R. agatheae. (b) R. blancii. (c) R. grandis. (d) R. madagascariensis. (e) R. menahirana. (f) R. hladikorum. Photographs Thomas Haevermans©.Full size imageEmended description Plants suckering, 6–12 meters tall (adult), trunk circumference (d.b.h.) 20–30 cm, juvenile and adult laminae distributed in a perfect fan, 14–25 leaves simultaneously alive on the adult plant, 1–3 leaves between inflorescences. Leaves adult petiole 380–440 cm long, greenish-yellow, slightly waxy, sheath margin undeveloped to moderately developed (0–9 mm), entire, not drying, slightly splitting when aged (Fig. 3d), petiole/lamina ratio 1.9–(2.2)–2.3, adult lamina (200 times 100) cm, light green, juvenile lamina base non-decurrent. Inflorescences 4–6 live lateral inflorescences at a time, (100 times 100) cm (peduncle excluded), 8–16 bracts per inflorescence, bracts 200–(450 times 50)–100 mm, with some wax to very waxy, margin uniformly green (Fig. 4d), cincinnii of ca. 10 flowers per bract, flowering sequentially, bracteoles without a colored stripe. Flowers 240–280 mm long (ovary included), inferior ovary 40–50 mm long, perianth yellowish, sepals narrowly triangular 240–250 (times 10)–12 mm, sheathing (fused) petals narrowly triangular 220–230(times)ca. 10 mm, free petal acicular 180–190 (times 5) mm, slightly smaller than the remaining perianth with mean free petal/mean fused petal length ratio = 0.8, petal blotches absent, stamens (roughly) the same size as the perianth, 200–210 mm long, style 200–230 mm long, stigma 15–20 mm long, oblong ovoid with a basal constriction. Infructescences lax (bract bases not imbricate at maturity), stiff and coriaceous persisting bracts, old infructescences deciduous, 4–8 fruits per bract. Fruits 70–120 (times 30)–35 mm, trilocular septifragal capsule, apices conical (Fig. 2d), seeds 6–(8.5)–(11 times 5)–(6.4)–8 mm, shiny, dark brown, mostly globose, varying in shape according to their distribution in the capsule, ultramarine blue aril.Ecology Ravenala madagascariensis is a low-altitude species restricted to swampy areas of the eastern coast of Madagascar. Populations outside of Madagascar on nearby islands are reputedly non-indigenous24.Preliminary IUCN assessments We propose a Least Concern status for R. madagascariensis, having an E.O.O ( > 20,000) km2 and an A.O.O. ( > 2,000) km2 (criterion B)33.Note This emended description for R. madagascariensis was drawn up from our own observations and collections, and was made comparable point by point to the descriptions of the five new species presented below, along with a dichotomous identification key to all six species.New species descriptions
Ravenala agatheae Haev. & Razanats. sp. nov.—Figs. 2a, 3a, 4a, 5a, 6
Type MADAGASCAR (bullet) Antsiranana: Ambanja District, along R.N.6 road to Ankaramibe, 13°45′54.8″S, 48°21′27.7″E, 30 m elev., on degraded lateritic slopes, 28 October 2018, T. Haevermans, A. Haevermans & J. Razanatsoa 830 (Holotype: TAN!, Isotypes: K!, MO!, P!).Figure 6Ravenala agatheae. (a) young infructescence. (b) adult plant habit showing the suckers at the base and the persistent petioles and old infructescences. (c) fruit with a conical apex. (d) infructescence with remains of dried flowers and dried bracts. (e) style apex. (f) inflorescence with open flowers. (g) open flower. Ink drawings on (75 , upmu) polyester tracing paper by Agathe Haevermans© from specimen Haevermans et al. 830, and observations in-situ.Full size imageParatypes MADAGASCAR (bullet) Antsiranana: 57–58 km N of Ambanja, 13°22′59.9″S, 48°48′E, 22 May 1974, A.H. Gentry 11878 (L-WAG.1111448!, L-WAG.1111449!, MO-358489!, TAN) (bullet) Ampasindava, forêts d’Ambilanivy et Rangoty, 13°48′36″S, 48°10′48″E, 29 November 2007, L. Nusbaumer 2658 (G334213/1!, MO!, TAN) (bullet) Mahajanga: Morafenobe, Beravy, 15 km from Beravy, near the road from Orombato to Beravy, 18°3′50″S, 44°31′46″E, 09 June 2016, F. Rakotonasolo et al. 2772 (K, P00782931!, TAN).Diagnosis Similar to Ravenala madagascariensis but differs in its dark green narrower laminae, tricolor petioles with very developed dryish petiole sheath margins, very waxy petioles, the persistence of older infructescences for several years, a purple stripe on the bract margin, longer bracts, a whitish perianth, brown blotches on its mature fused petals, the bracteole apex tinged with pink, an ovoid pointed stigma, dense infructescences, smaller inflorescences, the free petal much shorter than the fused petals, and an end of year flowering period.Distribution Plants restricted to Madagascar, growing in the north-western part of the island. We observed it growing from the southern part of the Diego Suarez area (on the hills along the road leading to Tsingy Rouge and the city of Sadjoavato) in the north to the western part of the Mahajanga province down to the Melaky region, with most observations around Ambanja34. We also observed that the species was cultivated on Nosy Be.Preliminary IUCN assessments We propose a Least Concern status for R. agatheae, having an E.O.O ( > 20,000) km2 and an A.O.O. ( > 2,000) km2 (criterion B)33.Ecology This species is adapted to seasonally dry and warm coastal habitats, growing on slopes at low elevations in north-western coastal areas of Madagascar, from Antsiranana (Diego-Suarez) down to the Melaky region in the Mahajanga province.Etymology This species is named after to the first author’s wife, Agathe Haevermans, a botanical illustrator at the Muséum National d’Histoire Naturelle, who helped discover this species in the field with the collecting team and who contributes greatly to botany by producing illustrations of new taxa from biodiversity hotspots such as Madagascar.Description Plants suckering, 6–10 meters tall (adult), trunk circumference (d.b.h.) 20–30 cm, juvenile and adult laminae distributed like a regular fan, 9–22 leaves simultaneously alive on the adult plant, 1–3 leaves between inflorescences. Leaves adult petiole 300–460 cm long, tricolor (dark green with a waxy white strip and red petiole sheath margin subsequently drying out, Fig. 3a), very waxy, sheath margin very developed (10 mm and more), entire, dryish-papyraceous and protruding at 90 degrees, petiole/lamina ratio 1.7–(1.95)–2.2, adult lamina 174–(210 times 72)–86 cm, dark green, juvenile lamina base non-decurrent. Inflorescences 4–6 live lateral inflorescences at a time, 70–(90 times 90)–100 cm (peduncle excluded), 10–14 bracts per inflorescence, bracts 450–500 (times 80)– 90 mm, with some waxiness (Fig. 4a), margin bearing a purple stripe, cincinnii of 8–10 flowers per bract, flowering sequentially, some pink tinge at the apex of bracteoles. Flowers 260–310 mm long (ovary included), inferior ovary 40–60 mm long, perianth whitish, sepals narrowly triangular 220–250(times)ca. 10 mm, sheathing (fused) petals narrowly triangular 200–(220times)ca. 10 mm, free petal acicular 130–(140 times 5) mm, much smaller than the remaining perianth with a mean free petal / mean fused petal length ratio = 0.6, petal blotches present, stamens (roughly) the same size as the perianth, 210–220 mm long, style 220 mm long, stigma 15 mm long, ovoid-pointed with basal constriction. Infructescences compact (bracts bases imbricate at all stages of maturity), stiff and coriaceous persisting bracts on mature infructescence, persistence of old infructescences, 4–10 fruits per bract. Fruits 90–110 (times) 30–45 mm, trilocular septifragal capsule, apices conical (Fig. 2a), seeds shiny, dark brown, mostly globose, varying in shape according to their distribution in the capsule, ultramarine blue aril.
Ravenala blancii Haev., V. Jeannoda & A. Hladik sp. nov. —Figs. 2b, 3b, 4b, 5b, 7
Type MADAGASCAR (bullet) Andasibe; 18°56′00″S, 48°25′06″E; 940 m elev.; 01 December 2002; A. Hladik & C.-M. Hladik 6760 (Holotype: TAN!, Isotypes: K!, MO!, P!).Paratypes MADAGASCAR (bullet) Andasibe; 18°56′00″S, 48°25′06″E; 940 m elev., 23 Aug. 1998, A. Hladik & al. 6239 (P!, fruits) (bullet) June 2001, A. Hladik & al. 6650 (P!, leaves, fruits, bracts) (bullet) Andasibe-Mantadia area, Vakôna, Kalonora; 18°53′17.3″S, 48°25′51.3″E, 08 November 2018, 934 m elev., T. Haevermans & al. 832 (K!, MO!, P!, TAN!).Diagnosis Similar to Ravenala madagascariensis but differs in its non-suckering habit, decurrent juvenile lamina bases, toroidal distribution of juvenile laminae, smaller number of leaves simultaneously alive on the adult plant, dark green lamina and green non waxy petiole, smaller leaves, smaller number of live inflorescences, smaller number of bracts in an inflorescence, non-waxy bracts, sub-simultaneous flowering, smaller flowers, smaller inflorescences, non-persistence of entire bracts on dry infructescences, October/November flowering period.Distribution Andasibe-Mantadia, Ranomafana21. Restricted to Madagascar.Preliminary IUCN assessments We propose a Data Deficient status for R. blancii; further fieldwork is required to understand its precise distribution and the status of its populations33.Ecology High-elevation species found in eastern rainforests at elevations between 600 and 1,100 m. The species seems to favor cool tropical humid and shady conditions.Etymology This species is named after Dr. Patrick Blanc, world renowned botanist, plant ecologist and street artist, inventor of the planted vertical walls known as “Mur Végétal” and who first recognized the sheer originality of the juvenile phases of this peculiar taxon.Description Plants solitary (never suckering), 10–15 meters tall (adult), trunk circumference (d.b.h.) 20–30 cm, juvenile laminae distributed in a toroidal shape, adult laminae arranged in a regular fan, 9–16 leaves simultaneously alive on the adult plant, 2–4 leaves between inflorescences. Leaves adult petiole 240–310 cm long, green, not waxy, sheath margin undeveloped, entire, not drying, smooth with a worn-out irregular aspect (Fig. 3b), petiole/lamina ratio 1.8–(2.0)–2.2, adult lamina 120–160 (times) 90–104 cm, dark green, juvenile lamina base decurrent. Inflorescences 2–3 live lateral inflorescences at a time, (60 times 70) cm (peduncle excluded), 4–6 bracts per inflorescence, bracts 160–350 (times) 50–90 mm, no waxiness (Fig. 4b), margin color uniformly green, cincinnii of 5–14 flowers per bract, flowering sub-simultaneously, bracteoles sometimes pink colored. Flowers 165–280 mm long (ovary included), inferior ovary 40–50 mm long, perianth whitish-yellowish, sepals narrowly triangular 125–231 (times) 10–12 mm, sheathing (fused) petals narrowly triangular 105–190 (times 10) mm, free petal acicular 105–178 (times 3)–5 mm, free petal and fused petals of sub-equal size with a mean free petal / mean fused petal length ratio = 1.0, petal blotches absent or present, stamens (roughly) the same size as the perianth, 115–186 mm long, style 132–220 mm long, stigma 20-25 mm long, ovoid to ovoid-pointed with a basal constriction. Infructescences compact (bract bases imbricate at all stages of maturity), torn and degraded bracts on mature infructescence, old infructescences deciduous, 5–14 fruits per bract. Fruits 80–120 (times) 32–45 mm, trilocular septifragal capsule, apices conical (Fig. 2b), seeds 6–10 (times) 3.2–6 mm, shiny, dark brown, mostly globose, varying in shape according to their distribution in the capsule, ultramarine blue aril.Note The strong leaf dimorphism between juvenile and adult forms is characteristic of this species13, a phenomenon which is not present in the other taxa. The base of the juvenile plant usually grows buried in the leaf litter due to the action of traction roots13, its decurrent leaves (Fig. 7) giving it the aspect of a bird’s nest fern.Figure 7Ravenala blancii. (a) juvenile plant habit with roots. (b) juvenile plant showing the arrangement of laminae. (c) adult plant habit. (d) mature infructescence segment. (e) juvenile leaf showing the attenuate base of the lamina. (f) inflorescence with sub-simultaneous opening of the flowers. (g) young infructescence with already degraded bracts. (h) seeds with arilla. (i) open flower. (j) details of the stigma. (k) style. Ink drawings on (75 , upmu) polyester tracing paper by Agathe Haevermans© from specimens Hladik 6790, 6239, 6650, Haevermans et al. 832, and observations in-situ.Full size image
Ravenala grandis Haev., Razanats., A. Hladik & P. Blanc sp. nov.—Figs. 2c, 3c, 4c, 5cType. MADAGASCAR (bullet) Ampasimbe Commune, Maromaniry Fokontany, along Route Nationale, 18°57′41.8″S, 48°42′41.4″E, 258 m elev., 08 November 2018, T. Haevermans, A. Haevermans & J. Razanatsoa 831 (Holotype: TAN!, Isotypes: K!, MO!, P!).Paratypes MADAGASCAR (bullet) Varifoana, près d’Ambohimahasoa-sud, 15 May 1964, R. Capuron 26014SF (P02234597!) (bullet) Soanierana-Antasibe[Andasibe], 350 m elev., 10 December 1938, H.J. Lam & A.D.J. Meeuse 5867 (L-WAG.1111450!, L-WAG.1111451!, L-WAG.1111452!, L-WAG.1111453!, L-L.1477714!, L-L.1477715!).Diagnosis Similar to Ravenala madagascariensis but differs in its non-suckering habit, much larger dimensions, very thick leathery laminae, very waxy dark green-yellowish petioles, much larger bracts and overall dimensions, whitish/pure white perianth, strong reddish-pink stripes on its bracteoles, cylindrical stigma without basal constriction, stamens much shorter than perianth, and fruit with a truncated apex.Distribution Eastern rainforests at around 200–500 m elevation in Madagascar13,20.Preliminary IUCN assessments We propose a Data Deficient status for R. grandis; further fieldwork is required to understand its precise distribution and the status of its populations33.Ecology This species seems to favor growing in low discontinuous forests on inselbergs12 and thrives in secondary degraded vegetation on the slopes of eastern rain forests.Etymology The name of this species is in reference to its stature and habit, the most robust species of Ravenala known.Description Plants solitary (never suckering), 20–30 meters tall (adult), trunk circumference (d.b.h.) 30 cm, juvenile and adult laminae distributed in a perfect fan, 15–30 leaves simultaneously alive on the adult plant, usually 3 leaves between inflorescences. Leaves adult petiole 390–440 cm long, dark green/light green-yellowish, very waxy (Fig. 3c), sheath margin moderately developed to undeveloped (0–9 mm), entire on young leaves, splitting and dryish when old, petiole/lamina ratio 1.8–(2.2)–2.6, adult lamina 170–230 (times) 94–120 cm, light green, juvenile lamina base non-decurrent. Inflorescences 4–6 live lateral inflorescences at a time, 100–120 (times) 80–100 cm (peduncle excluded), 10–20 bracts per inflorescence, bracts 440–540 (times) 140–170 mm, some waxiness (Fig. 4c), margin color uniformly green, cincinnii of ca. 20 flowers per bract, flowering sequentially, bracteoles with a strong reddish-pink stripe. Flowers 300 mm long (ovary included), inferior ovary 50–70 mm long, perianth whitish/pure white, sepals narrowly triangular 220–240 (times) 10–15 mm, sheathing (fused) petals narrowly triangular 210–220 (times) 10–12 mm, free petal acicular 150–170 (times 3) mm, slightly smaller than the rest of the perianth with a mean free petal / mean fused petal length ratio = 0.8, petal blotches absent, stamens much shorter than the perianth, 180–200 mm long, style 180–210 mm long, stigma 14–16 mm long, oblong without basal constriction (almost indistinguishable from style). Infructescences lax (bract bases not imbricate at some stages of maturity), stiff and coriaceous persisting bracts on mature infructescence, old infructescences deciduous, 5–18 fruits per bract. Fruits 100–120 (times) 35–40 mm, trilocular septifragal capsule, apices truncate (Fig. 2c), seeds shiny, dark brown, mostly globose, varying in shape according to their distribution in the capsule, ultramarine blue aril.Note The leaves of this species are the most robust and tough of all Ravenala species, with a thick leathery texture, making it the material of choice for building roofs35.
Ravenala hladikorum Haev., Razanats., V. Jeannoda & P. Blanc sp. nov. — Figs. 2f, 3f, 4f, 5fType MADAGASCAR (bullet) Andasibe; 18°56′00″S, 48°25′06″E; 940 m elev.; 05 February 2004; A. Hladik & C.-M. Hladik 6842 (Holotype: TAN!, Isotype: P!). Paratypes. MADAGASCAR (bullet) Andasibe; 18°56′00″S, 48°25′06″E; 940 m elev.; 23 August 1998; A. Hladik & al. 6240 (fruit with seeds: P!). (bullet) Andasibe-Mantadia area, Vakôna, Kalonora; 18°53′17.3″S, 48°25′51.3″E; 934 m elev., 08 November 2018; T. Haevermans & al. 833 (TAN!, P!, K!, MO!).Diagnosis Similar to Ravenala madagascariensis but differs in its non-suckering habit, the alternate positioning of its adult laminae, its dark green leaves, non-waxy petioles with their very papyraceous petiole sheath margins, more than 1 cm long, smaller lamina dimensions, smaller number of simultaneously live inflorescences, purple stripe on bracts and on bracteoles, non-waxy inflorescences, smaller inflorescences, dense infructescences, truncated fruit apices, and short flowering period from November to December.Distribution Andasibe, Mantady, Ranomafana21. Restricted to Madagascar.Preliminary IUCN assessments We propose a Data Deficient status for R. hladikorum; further fieldwork is required to understand its precise distribution and the status of its populations33.Ecology High-elevation species found in eastern rainforests at elevations between 600 and 1100 m. The species seems to favor cool tropical humid and shady conditions.Etymology This species is named in honor of Annette and Claude-Marcel Hladik from the Muséum National d’Histoire Naturelle in Paris, who dedicated their lives to the study of Madagascan biodiversity and contributed greatly to the discovery of this species.Description Plants solitary (never suckering), 10–15 meters tall (adult), trunk circumference (d.b.h.) 20–30 cm, juvenile laminae distributed like a fan, adult laminae arranged in an irregular fan, 9–18 leaves simultaneously alive on the adult plant, 1–3 leaves between inflorescences. Leaves adult petiole 280–440 cm long, greenish-yellow, not waxy (Fig. 3f), sheath margin very developed (10 mm and more), split, very papyraceous with min. 1 cm brown dry expansions, petiole/lamina ratio 2.1–(2.42)–2.8, adult lamina 120–160 (times) 102–116 cm, dark green, juvenile lamina base non-decurrent. Inflorescences 2–3 live lateral inflorescences at a time, (60 times 90) cm (peduncle excluded), 4–7 bracts per inflorescence, bracts 150–510 (times) 64–100 mm, no waxiness (Fig. 4f), margin green with a purple stripe, cincinnii of 5–14 flowers per bract, sequentially flowering, bracteoles with a dark purple colored stripe. Flowers 240–320 mm long (ovary included), inferior ovary 40–60 mm long, perianth whitish, sepals narrowly triangular 210–265(times)ca. 10 mm, sheathing (fused) petals narrowly triangular 190–240(times)ca. 10 mm , free petal acicular 135–220 (times) 5 mm, almost the same size as the fused petals with a mean free petal / mean fused petal length ratio = 0.9, petal blotches unknown, stamens (roughly) the same size as the perianth, 170–230 mm long, style 187–250 mm long, stigma 20–25 mm long, ovoid with a basal constriction. Infructescences compact (bract bases imbricate at all stages of maturity), stiff and coriaceous persistent bracts on mature infructescences, old infructescences deciduous, 5–14 fruits per bract. Fruits 82–108 (times) 34–48 mm, trilocular septifragal capsule, apices truncate (Fig. 2f), seeds 4–9 (times) 3–6 mm, shiny, dark brown, mostly globose, varying in shape according to their distribution in the capsule, ultramarine blue aril.
Ravenala menahirana Haev. & Razanats. sp. nov.—Figs. 2e, 3e, 4e, 5eType MADAGASCAR (bullet) Foulpointe, Analalava Forest; 17°42.3′S, 49°27.38′E; 50 m elev.; 20 March 2016; T.Haevermans, M. Vorontsova, S. Dransfield & J. Razanatsoa 826 (Holotype: TAN!, Isotypes: P!, K !, MO!).Diagnosis Similar to Ravenala madagascariensis but differs in its non-suckering habit, the alternate dark green laminae tending not to form a perfect fan (Fig. 5e), dark red petioles with a zigzagging well developed dryish sheath margin, more strongly obovoid laminae, smaller number of simultaneously live inflorescences, smaller inflorescences tinged with red, pure white/whitish perianth, smaller flowers, dense infructescences, the fruit apices truncate with a mucro, and subequal free and fused petals.Distribution Appears to be restricted to the east coast in the area around Analalava-Foulpointe up to the Mananara-Avaratra area. Two human observations from Marojejy (North-East) and Tampolo (Masoala) seem also to be this species. Restricted to Madagascar.Preliminary IUCN assessments We propose a Data Deficient status for R. menahirana; further fieldwork is required to understand its precise distribution and the status of its populations33.Ecology This coastal forest-dwelling species favors low-elevation tropical humid conditions in the Analalava-Foulpointe area, extending north to Mananara-Avaratra area, and maybe up to Marojejy.Etymology The name of this species is in reference to one of its local names “menahirana”, given to the species in the Analalava-Foulpointe area and meaning “red ravenala”.Description Plants solitary (never suckering), 6–10 meters tall (adult), trunk circumference (d.b.h.) 20–30 cm, juvenile laminae distributed like a fan, adult laminae arranged in an irregular to regular fan, 12–18 leaves simultaneously alive on the adult plant, 3 leaves between inflorescences. Leaves adult petiole 200–230 cm long, dark red, slightly to very waxy, sheath margin very developed (10 mm and more), red, entire, forming a three dimensional zigzag pattern (Fig. 3e), then splitting and drying on old leaves, petiole/lamina ratio 1.4–(1.7)–1.9, adult lamina (350 times 120) cm, lamina color dark green, juvenile lamina base non-decurrent. Inflorescences 1–2 live lateral inflorescences at a time, (60 times 70) cm (peduncle excluded), 10–12 bracts per inflorescence, bracts 260–360 (times) 50–80 mm, very waxy (Fig. 4e), margin color uniformly reddish-green, cincinnii of 8–12 flowers per bract, flowering sequentially, no colored stripe on bracteoles (apices sometimes suffused with pink). Flowers 220–250 mm long (ovary included), inferior ovary 40–60 mm long, perianth pure white to whitish, sepals narrowly triangular 180–230 (times) 12–16 mm, sheathing (fused) petals narrowly triangular 160–180 (times) 5 mm, free petal acicular 160–170 (times) 5 mm, free petal the same size as the remaining perianth with a mean free petal / mean fused petal length ratio = 1.0, petal blotches absent, stamens the same size (roughly) as the perianth, stamen 150–160 mm long, style 150–200 mm long, stigma 10 mm long, oblong with a basal constriction. Infructescences compact (bract bases imbricate at all stages of maturity), stiff and coriaceous persisting bracts on mature infructescences, old infructescences deciduous, 8–12 fruits per bract. Fruits 80–100 (times) 30–35 mm, trilocular septifragal capsule, apices truncate with a mucro (Fig. 2e), seeds shiny, dark brown, mostly globose, varying in shape according to their distribution in the capsule, ultramarine blue aril.Note This species is similar to R. hladikorum but is easily distinguished by, in addition to its petioles and its ecology, its truncate mucronate fruit apices, the shape of the synflorescence bracts and the absence of a red stripe on the cyme bracteoles.Identification key to the species of genus Ravenala More

1.Gelfand, I. et al. Sustainable bioenergy production from marginal lands in the US Midwest. Nature 493, 514–517 (2013).CAS 
PubMed 
Article 

Google Scholar 
2.Sprunger, C. D. & Philip Robertson, G. Early accumulation of active fraction soil carbon in newly established cellulosic biofuel systems. Geoderma 318, 42–51 (2018).CAS 
Article 

Google Scholar 
3.DuPont, S. T. et al. Root traits and soil properties in harvested perennial grassland, annual wheat, and never-tilled annual wheat. Plant Soil 381, 405–420 (2014).CAS 
Article 

Google Scholar 
4.Robertson, G. P. et al. Cellulosic biofuel contributions to a sustainable energy future: Choices and outcomes. Science 356, 6375. https://doi.org/10.1126/science.aal2324 (2017).CAS 
Article 

Google Scholar 
5.Kravchenko, A. N. et al. Microbial spatial footprint as a driver of soil carbon stabilization. Nat. Commun. https://doi.org/10.1038/s41467-019-11057-4 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
6.Yang, Y., Tilman, D., Furey, G. & Lehman, C. Soil carbon sequestration accelerated by restoration of grassland biodiversity. Nat. Commun. 10, 1–7 (2019).Article 

Google Scholar 
7.Lange, M. et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 1–8 (2015).
Google Scholar 
8.Young, I. M. & Crawford, J. W. Interactions and self-organization in the soil-microbe complex. Science 304, 1634–1637 (2004).CAS 
PubMed 
Article 

Google Scholar 
9.Rabot, E., Wiesmeier, M., Schlüter, S. & Vogel, H. J. Soil structure as an indicator of soil functions: A review. Geoderma 314, 122–137 (2018).Article 

Google Scholar 
10.Pohl, M., Alig, D., Körner, C. & Rixen, C. Higher plant diversity enhances soil stability in disturbed alpine ecosystems. Plant Soil 324, 91–102 (2009).CAS 
Article 

Google Scholar 
11.Bodner, G., Leitner, D. & Kaul, H. P. Coarse and fine root plants affect pore size distributions differently. Plant Soil 380, 133–151 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Bacq-Labreuil, A., Crawford, J., Mooney, S. J., Neal, A. L. & Ritz, K. Cover crop species have contrasting influence upon soil structural genesis and microbial community phenotype. Sci. Rep. 9, 1–9 (2019).CAS 
Article 

Google Scholar 
13.Kravchenko, A. N. et al. X-ray computed tomography to predict soil N2O production via bacterial denitrification and N2O emission in contrasting bioenergy cropping systems. GCB Bioenergy 10, 894–909 (2018).CAS 
Article 

Google Scholar 
14.Cambardella, C. A. & Elliott, E. T. Particulate soil organic-matter changes across a grassland cultivation sequence. Soil Sci. Soc. Am. J. 56, 777–783 (1992).Article 

Google Scholar 
15.Gregorich, E. G., Beare, M. H., McKim, U. F. & Skjemstad, J. O. Chemical and biological characteristics of physically uncomplexed organic matter. Soil Sci. Soc. Am. J. 70, 975–985 (2006).CAS 
Article 

Google Scholar 
16.Besnard, E., Chenu, C., Balesdent, J., Puget, P. & Arrouays, D. Fate of particulate organic matter in soil aggregates during cultivation. Eur. J. Soil Sci. 47, 495–503 (1996).CAS 
Article 

Google Scholar 
17.Haddix, M. L. et al. Climate, carbon content, and soil texture control the independent formation and persistence of particulate and mineral-associated organic matter in soil. Geoderma 363, 114160 (2020).CAS 
Article 

Google Scholar 
18.Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: Concept & review. Soil Biol. Biochem. 83, 184–199 (2015).CAS 
Article 

Google Scholar 
19.Moeslund, J. E. et al. Topographically controlled soil moisture drives plant diversity patterns within grasslands. Biodivers. Conserv. 22, 2151–2166 (2013).Article 

Google Scholar 
20.Shi, P. et al. The effects of ecological construction and topography on soil organic carbon and total nitrogen in the Loess Plateau of China. Environ. Earth Sci. 78, 1–8 (2019).Article 

Google Scholar 
21.Cnudde, V. & Boone, M. N. High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications. Earth-Science Rev. 123, 1–17 (2013).Article 

Google Scholar 
22.Wang, W., Kravchenko, A. N., Smucker, A. J. M., Liang, W. & Rivers, M. L. Intra-aggregate pore characteristics: X-ray computed microtomography analysis. Soil Sci. Soc. Am. J. 76, 1159–1171 (2012).CAS 
Article 

Google Scholar 
23.Diel, J., Vogel, H. J. & Schlüter, S. Impact of wetting and drying cycles on soil structure dynamics. Geoderma 345, 63–71 (2019).CAS 
Article 

Google Scholar 
24.Pires, L. F., Auler, A. C., Roque, W. L. & Mooney, S. J. X-ray microtomography analysis of soil pore structure dynamics under wetting and drying cycles. Geoderma 362, 114103 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Negassa, W. C. et al. Properties of soil pore space regulate pathways of plant residue decomposition and community structure of associated bacteria. PLoS ONE 10, 1–22 (2015).Article 

Google Scholar 
26.Quigley, M. Y., Negassa, W. C., Guber, A. K., Rivers, M. L. & Kravchenko, A. N. Influence of pore characteristics on the fate and distribution of newly added carbon. Front. Environ. Sci. 6, 1–13 (2018).Article 

Google Scholar 
27.Juyal, A., Otten, W., Baveye, P. C. & Eickhorst, T. Influence of soil structure on the spread of Pseudomonas fluorescens in soil at microscale. Eur. J. Soil Sci. 72, 141–153 (2021).CAS 
Article 

Google Scholar 
28.Kravchenko, A. N., Negassa, W., Guber, A. K. & Schmidt, S. New approach to measure soil particulate organic matter in intact samples using X-ray computed microtomography. Soil Sci. Soc. Am. J. 78, 1177–1185 (2014).Article 

Google Scholar 
29.Peth, S. et al. Localization of soil organic matter in soil aggregates using synchrotron-based X-ray microtomography. Soil Biol. Biochem. 78, 189–194 (2014).CAS 
Article 

Google Scholar 
30.Gee, G. W. & Or, D. 2.4 Particle-Size Analysis (Soil Science Society of America, 2018).
Google Scholar 
31.Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).CAS 
PubMed 
Article 

Google Scholar 
32.Münch, B. & Holzer, L. Contradicting geometrical concepts in pore size analysis attained with electron microscopy and mercury intrusion. J. Am. Ceram. Soc. 91, 4059–4067 (2008).Article 

Google Scholar 
33.Houston, A. N., Otten, W., Baveye, P. C. & Hapca, S. Adaptive-window indicator kriging: A thresholding method for computed tomography images of porous media. Comput. Geosci. 54, 239–248 (2013).Article 

Google Scholar 
34.Doube, M. et al. BoneJ: Free and extensible bone image analysis in ImageJ. Bone 47, 1076–1079 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Houston, A. N. et al. Effect of scanning and image reconstruction settings in X-ray computed microtomography on quality and segmentation of 3D soil images. Geoderma 207–208, 154–165 (2013).Article 

Google Scholar 
36.Milliken, G. A. & Johnson, D. E. Analysis of Messy Data, Volume II: Nonreplicated experiments. Analysis of Messy Data, Volume II: Nonreplicated Experiments (Chaoman/CRC Press, 2017).Book 

Google Scholar 
37.Ladoni, M., Basir, A., Robertson, P. G. & Kravchenko, A. N. Scaling-up: Cover crops differentially influence soil carbon in agricultural fields with diverse topography. Agric. Ecosyst. Environ. 225, 93–103 (2016).Article 

Google Scholar 
38.Ontl, T. A., Hofmockel, K. S., Cambardella, C. A., Schulte, L. A. & Kolka, R. K. Topographic and soil influences on root productivity of three bioenergy cropping systems. New Phytol. 199, 727–737 (2013).CAS 
PubMed 
Article 

Google Scholar 
39.Zhu, M. et al. Effects of topography on soil organic carbon stocks in grasslands of a semiarid alpine region, northwestern China. J. Soils Sediments 19, 1640–1650 (2019).CAS 
Article 

Google Scholar 
40.Shi, P. et al. Land-use types and slope topography affect the soil labile carbon fractions in the Loess hilly-gully area of Shaanxi, China. Arch. Agron. Soil Sci. 66, 638–650 (2020).CAS 
Article 

Google Scholar 
41.Ontl, T. A., Cambardella, C. A., Schulte, L. A. & Kolka, R. K. Factors influencing soil aggregation and particulate organic matter responses to bioenergy crops across a topographic gradient. Geoderma 255–256, 1–11 (2015).Article 

Google Scholar 
42.Kravchenko, A. N. et al. Spatial patterns of extracellular enzymes: Combining X-ray computed micro-tomography and 2D zymography. Soil Biol. Biochem. 135, 411–419 (2019).CAS 
Article 

Google Scholar 
43.Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: Do labile plant inputs form stable soil organic matter?. Glob. Change Biol. 19, 988–995 (2013).Article 

Google Scholar 
44.Oades, J. M. The role of biology in the formation, stabilization and degradation of soil structure. Geoderma 56, 377–400 (1993).Article 

Google Scholar 
45.Kravchenko, A. N. & Guber, A. K. Soil pores and their contributions to soil carbon processes. Geoderma 287, 31–39 (2017).CAS 
Article 

Google Scholar 
46.Wickings, K., Grandy, A. S. & Kravchenko, A. N. Going with the flow: Landscape position drives differences in microbial biomass and activity in conventional, low input, and organic agricultural systems in the Midwestern U.S. Agric. Ecosyst. Environ. 218, 1–10 (2016).Article 

Google Scholar 
47.da Jesus, E. C. et al. Influence of corn, switchgrass, and prairie cropping systems on soil microbial communities in the upper Midwest of the United States. GCB Bioenergy 8, 481–494 (2016).CAS 
Article 

Google Scholar 
48.Poirier, V., Roumet, C. & Munson, A. D. The root of the matter: Linking root traits and soil organic matter stabilization processes. Soil Biol. Biochem. 120, 246–259 (2018).CAS 
Article 

Google Scholar 
49.Toosi, E. R., Kravchenko, A. N., Guber, A. K. & Rivers, M. L. Pore characteristics regulate priming and fate of carbon from plant residue. Soil Biol. Biochem. 113, 219–230 (2017).CAS 
Article 

Google Scholar  More

Eukaryote composition (V9_PR2)Using V9_PR2 we were able to assign a total of 15 668 (shotgun) and 90 689 reads for the shotgun and amplicon data, respectively. These reads represented 14%, 54%, 0 and 32% (shotgun), and 0%, 29%, 0 and 71% (amplicon) unassigned cellular organisms, Bacteria, Archaea and Eukaryota, respectively. Within the eukaryotes, we determined 51 and 64 taxa for shotgun and amplicon data, respectively. Abundant taxa (average abundance >0.1% across all samples; 31 and 27 taxa in shotgun and amplicon, respectively) are shown in Fig. 2. The latter includes 23 taxa (including assignments made on “Eukaryota” level) that were shared between shotgun and amplicon, and four taxa only detected in the amplicon data (Fig. 2C).Fig. 2: Eukaryote composition in five Santa Barbara Basin sediment samples post-alignment with V9_PR2 database.Composition is shown in relative abundances for (A) shotgun, and (B) amplicon data (phylum-level). The surface sample should be considered with caution in both (A) and (B) due to the possibility of contamination (see “Methods”). C Venn diagram showing eukaryote taxa richness (phylum level) in the shotgun and amplicon data after alignment with the V9_PR2 database (diagram areas are proportional to the total number of taxa included, for a list of shared/non-shared taxa see Supplementary Material Fig. 1). Only taxa abundant on average >0.1% are included, as they make up >99% of the eukaryote composition.Full size imageWithin shotgun, the most abundant eukaryotes were Ascomycota (53%), Telonemia (11%), Eukaryota (not further determined, 8%), Polycystinea (4%), Dinophyceae (3.8%), Streptophyta (3.2%), Amoebozoa (3%), Cercozoa (1.6%), Bacillariophyta (1.6%), Arthropoda (1%). In the amplicon data, the most abundant eukaryotes were Ascomycota (33%), Apicomplexa (30%), Dinophyceae (9.5%), Stramenopiles (6.3%), Eukaryota (4.9%), Polycystinea (3.5%), Foraminifera (3.2%), Cercozoa (1.1%) and Chordata (1%). Thus, a total of 10 and 9 taxa were abundant with >1% (average across all samples) in the shotgun and amplicon data, including only five taxa (Ascomycota, Eukaryota, Dinophyceae, Polycystinea, Cercozoa) that were picked up by both methods (i.e., are amongst the shared taxa in Fig. 2C, Supplementary Material Fig. 1). Taxa detected by one method or the other were slightly rarer species (between 0.1 and 1% average relative abundance across all samples; Supplementary Material Table 3).The shotgun EBC detected two taxonomic groups, one prokaryotic (Gammaproteobacteria) and one eukaryotic (Poacea). The amplicon EBC detected 46 taxa, of which 12 were prokaryotes and 34 were eukaryotes, including dinoflagellate taxa (Dinophysis and Alexandrium), Calanoida and Bacillariophyta (copepods and diatoms, respectively; Supplementary Material Table 1). While any reads assigned to EBC taxa were removed from samples, including reads assigned to the Bacillariophyta node, reads assigned to Bacillariophyta at lower taxonomic levels (e.g., Bacillariophycidae, Bacillariaceae, etc.) remain summarised under the phylum-level Bacillariophyta node (Fig. 2).Relationship between Eukaryota composition and V9_PR2 reference sequence lengthV9_PR2 reference sequence-lengths for the relatively abundant taxa ( >0.1% across all samples, including all taxa that were shared and assigned below eukaryote-level, i.e., 22 taxa, see Supplementary Material Table 3) were around the overall average sequence length of the V9_PR2 database (121 bp) (Fig. 3). However, considerable length variation was observed, with most of the abundant taxa being represented by shorter than average reference sequences in the V9_PR2 database, and a few taxa (e.g., Arthropoda, Opisthokonta and Amoebozoa) with a number of reference sequences longer than average (Fig. 3).Fig. 3: Average sequence lengths for individual eukaryote taxa as per in the V9_PR2 database (A) and read counts for these taxa in shotgun (SG) and amplicon (Ampl) data (B).Listed are all taxa that occurred on average >0.1% across all samples in either the shotgun or amplicon dataset, or both. Only taxa that were determined in both shotgun and amplicon data are included.Full size imageWe determined a negative correlation between the average V9_PR2 reference sequence length (V9PR2AL) and the A:SG read counts ratio per taxon for all samples (rV9PR2AL,A:SG_1.2 = −0.27269, rV9PR2AL,A:SG_4.3 = −0.33233, rV9PR2AL,A:SG_7.3 = −0.28064, rV9PR2AL,A:SG_11.8 = −0.32559, rV9PR2AL,A:SG_16.4 = −0.30078). This means that shorter V9_PR2 reference sequences for our abundant taxa were associated with an overamplification of these taxa in the amplicon data (for average V9_PR2 reference sequence length of the abundant taxa and A:SG ratios see Supplementary Material Table 4).Eukaryota and Bacillariophyta sequence length and coverage post-V9_PR2 alignmentSequences assigned to Eukaryota in shotgun were on average 112 bp and in amplicon data 161 bp, i.e., shotgun reads were around ~50 bp shorter than amplicon reads (Table 2). Bases covered in shotgun were ~40 bp shorter than in amplicon data (Table 2). Similarly, sequences assigned to Bacillariophyta were on average 124 and 167 bp in shotgun and amplicon data, respectively, so showed an ~40 bp difference. For Eukaryota, there was a difference of ~23 bp and 29 bp between sequence length and coverage in shotgun and amplicon data, respectively. For Bacillariophyta, we found a ~36 and ~37 bp difference between sequence length and coverage in shotgun and amplicon data, respectively.Table 2 Lengths and coverage of sequences assigned to Eukaryota and Bacillariophyta in shotgun and amplicon data.Full size tableBacillariophyta read lengths and coverage were similar to those of Eukaryota, for both shotgun and amplicon data (Table 2). Variation in sequence lengths and coverage was much higher in shotgun than in amplicon data. We found no trend towards shorter (i.e., more fragmented) sequences with increasing subseafloor depth for either Eukaryota or Bacillariophyta in the shotgun data. Eukaryota shotgun read lengths were on average ~9 bp shorter (112 bp) than the average reference sequences in the V9_PR2 database (121 bp).Diatom composition detected via diat-rbcL and read length characteristicsA total of 60 (shotgun) and 80 674 (amplicon) reads were assigned to diatoms (Fig. 4). In total, 27 taxa were determined in the shotgun, and 140 in the amplicon dataset. When considering the “abundant” taxa (on average >0.1%), 27 and 49 diatoms were determined in the shotgun and amplicon data, respectively (Fig. 4). A total of 10 taxa were shared between the two datasets Bacillariophyta, Bacillariophycidae, Chaetoceros, C. cf. pseudobrevis 2 SEH-2013, Pseudo-nitzschia, P. fryxelliana, Thalassiosiraceae, Thalassiosirales, Thalassiosira and T. oceanica (Fig. 4C, Supplementary Material Fig. 2). Sequences assigned to diatoms via diat-rbcL were shorter (by ~16 bp) in the shotgun than in the amplicon data, with amplicon read lengths and coverage all 76 + 1 bases (Table 3).Fig. 4: Diatom composition in the Santa Barbara Basin sediment samples post-alignment with diat-rbcL database.Diatom composition is shown as relative abundance for (A) shotgun and (B) amplicon data. The surface sample should be considered with caution in both (A) and (B) due to the possibility of contamination (see “Methods”). C Venn diagram showing diatom taxa richness (species level) in the shotgun and amplicon data after alignment with the diat-rbcL database (diagram areas are proportional to the total number of taxa included, for a list of shared/non-shared taxa see Supplementary Material Fig. 2). Only taxa abundant on average >0.1% are included (in A, B, C).Full size imageTable 3 Bacillariophyta sequence lengths in shotgun and amplicon datasets.Full size tableNo diatoms were detected in the shotgun EBC, however, 45 taxa were determined in the amplicon EBC with most reads assigned to Chaetoceros spp. (especially, Chaetoceros debilis, C. socialis and C. radicans), several Thalassiosira and Pseudo-nitzschia species, as well as others (Supplementary Material Table 2).Comparison of V9_PR2 vs. diat-rbcL derived diatom compositionIn the shotgun data, 79 and 60 sequences were assigned to diatoms using V9_PR2 and diat-rbcL as the reference database, respectively, and composition differed considerably (Fig. 5). Using V9_PR2, diatoms were mostly assigned on relatively high taxonomic levels (e.g., Bacillariophyta) with few taxa being differentiated sporadically in the different samples (Fig. 5A, Supplementary Material Fig. 3). Using diat-rbcL, Chaetoceros, Thalassiosira and Pseudo-nitzschia were more prominent (Fig. 5B).Fig. 5: Comparison of diatom composition in Santa Barbara Basin sediment samples determined in shotgun and amplicon data using the V9_PR2 and diat-rbcL databases.Relative abundance of diatoms (genus level) in the shotgun data after aligning to (A) V9_PR2 and (B) diat-rbcL. Relative abundance of diatoms (genus level) in the amplicon data after aligning to (C) V9_PR2 and (D) diat-rbcL. The surface sample should be considered with caution in (A–D) due to the possibility of contamination (see “Methods”). Venn diagrams of shared and non-shared diatom taxa after alignment to the V9_PR2 (18S-V9) and diat-rbcL databases for the shotgun (E) and amplicon (F) data (species level, diagram areas are proportional to the total number of species included). For a complete species list and their read counts per sample see Supplementary Material Fig. 3, Supplementary Material Table 5.Full size imageIn the amplicon data, 329 sequences were assigned to diatoms using V9_PR2, and 80 674 using diat-rbcL. Using V9_PR2, few taxa were detected in the two top samples (Leptocylindrus and Fragilariaceae at 1.2 mbsf, Bacillariophycidae and Bacillariaceae at 4.3 mbsf) while the lowermost samples were more diverse (Fig. 5C). Using diat-rbcL, most reads were assigned to Thalassiosira, Chaetoceros, and Pseudo-nitzschia, with other taxa sporadically occurring at different depths (Fig. 5D). For a complete species list and their read counts see Supplementary Material Fig. 3, and Supplementary Material Table 5.We found large differences in the number of shared vs. non-shared taxa between shotgun and amplicon data, and V9_PR2 and diat-rbcL alignments (Fig. 5E, F). Database inspections showed that all taxa detected via V9_PR2 were also represented in the diat-rbcL database, except Rhizosoleniaceae. However, out of the 22 taxa exclusively detected via diat-rbcL in shotgun (Fig. 5E, F), 10 are only represented in the diat-rbcL database (Pseudo-nitzschia caciantha, P. dolorosa, Chaetoceros cf. contortus 1 SEH-2013, C. cf. lorenzianus 2 SEH-2013, C. cf. pseudobrevis 2 SEH-2013, Thalassiosirales, Thalassiosiraceae, Coscinodiscus wailesii, Arcocellulus mammifer, Meuniera membranacea, Supplementary Material Fig. 3). Similarly, out of the 134 taxa exclusively detected via diat-rbcl in amplicon, 84 were in this database only, noticeably including several species and strains of Chaetoceros, Pseudo-nitzschia, Thalassiosira and Cylindrotheca (eg., additions SHE-2013, BOF in species names), amongst others (see Supplementary Material Fig. 3, Supplementary Material Table 5). More

The Wuikinuxv, Kitasoo/Xai’xais, Heiltsuk and Nuxalk First Nations hold Indigenous rights to their territories, where all data were collected. Scientific staff who are members of these Nations or who work directly for them had direct approvals from Indigenous rights holders and were exempt from other research permit requirements. Collaborating DFO scientists worked in partnership with the First Nations to collect data in their territories..Sampling targeted rocky reefs, the preferred habitat for most Sebastidae38, which we located through local Indigenous knowledge or a bathymetric model49. Data were collected by four fishery-independent methods—shallow diver transects, mid-depth video transects, deep video transects, and hook-and-line sampling—detailed in earlier publications32,33,34,35,50,51 and summarized in Table 1. Data had a spatial resolution of ≤ 130 m2 and each sampling location (N = 2936 for Sebastidae, 2654 for sponges, 2321 for corals) was ascribed to a 1-km2 planning unit within the standardized grid used to design the MPA network (N = 632 for Sebastidae, 525 for sponges, 529 for corals, 516 inclusive of surveys for all taxonomic groups).Table 1 Survey methods used for data collection.Full size tableAlthough sampling encompassed 11 years (2006–2007, 2013–2021: Table 1), 84% of 1-km2 planning units were sampled during only one year (Appendix S2). Analyses, therefore, focus on spatial variability in species distributions and do not address temporal variability within planning units. When all years and methods are combined, 1-km2 planning units had a median of 3 samples (range = 1 to 80, Q1 = 2, Q3 = 6) (i.e., sum of dive transects, video sub-transects, and hook-and-line sessions). Supplementary Data Set 1 reports sampling effort by 1-km2 planning unit, survey type, and year (see Data Availability for link to these data).For each 1-km2 planning unit, u, we calculated hotspot indices for Sebastidae (BSEB,u), structural corals (BCor,u), and large-bodied sponges (BSp,u). These indices did not consider cup corals, whip-like corals or encrusting corals or sponges.As detailed below (Eqs. 1–4), each species of Sebastidae or genera of corals contributed to BSEB,u or BCor,u, according to their abundance weighted by Wt: a conservation prioritization score based on taxon characteristics. For the 26 species of Sebastidae that we observed, Wt equaled the sum of scores for (1) fishery vulnerability, using intrinsic population growth rate, r, as a proxy variable52,53, (2) depletion level, using the ratio of recent biomass to unfished biomass as a proxy variable, (3) ecological role, with trophic level as proxy, and (4) evolutionary distinctiveness14 (Table 2; Appendix S3). Because several rockfishes are very long-lived (i.e., have low values for r) and depleted, maximum potential scores were twice as large for fishery vulnerability and depletion level than for ecological role and evolutionary distinctiveness. Data for depletion level and evolutionary distinctiveness were unavailable for some species, and score calculations (detailed in Table 2) account for missing values (Appendix S3).Table 2 Criteria and equations used to calculate the conservation prioritization score, Wt, for each species of Sebastidae and for each taxa of structural corals.Full size tableFor the 6 genera of structural corals analyzed (Appendix S4), Wt depended on mean height (estimated from video transect images: Table 1), which correlates positively with vulnerability to physical damage from bottom-contact fishing gear (including longer time to recovery)20,54,55 and with strength of ecological role (e.g., amount of biogenic habitat and carbon sequestration increases with height)44,56 (Table 2, Appendix S4). Wt for corals did not include depletion level due to lack of data.The hotspot index for large-bodied sponges, BSp,u did not differentiate between species characteristics (i.e., ({W}_{t}=1)) and we pooled the abundances of all observed species of Hexactinellidae (Aphrocallistes vastus, Farrea occa, Heterochone calyx, Rhabdocalyptus dawsoni, Staurocalyptus dowlingi) and Demospongiae (Mycale cf loveni). This approach is consistent with regional fishery bodies worldwide, which treat large-bodied sponges as a single functional group57.To derive hotspot indices for each taxonomic group (Sebastidae, structural corals, or large-bodied sponges), we first developed a set of candidate generalized linear mixed models (GLMM) to explain relative abundance data for rockfish, corals, and sponges. For each GLMM, we estimated ({lambda }_{t,i,l}), the expected counts (or expected percent cover) for taxa t obtained with survey method i at point location l. (Point locations are individual dive transects, video transect bins, or hook-and-line timed sessions: Table 1.) Specifically,$${lambda }_{t,i,l}=gleft(beta {X}_{t,i,l}right)$$
(1)
$${C}_{t,i,l}mathrm {, or ,} {D}_{t,i,l}sim fleft({lambda }_{t,i,l}right)$$
(2)
where g was the link function for the GLMM and f the distribution for the likelihood function modelling either the observed counts C (negative binomial) for Sebastidae and structural corals or a combination of counts (negative binomial) and percent cover D (beta distribution) for large-bodied sponges. We used multiple GLMMs to model large-bodied sponges because deep video transects recorded actual counts whereas dive or mid-depth video transects recorded percent cover categories (Table 1).For each taxonomic group, we estimated a set of coefficients (beta) for the vector of X covariates that best estimated counts or percent cover. Our hypothesized covariates included the 1-km2 planning unit (modelled as a random intercept to control for repeated measures within a given planning unit), survey method, depth (including both linear or a 2nd order polynomial), and taxa. Each GLMM controlled for sample effort as an offset—effort was measured either as area covered by dive transects or video bins, or the duration of hook-and-line sessions. We also tested for possible covariate’s effects on the dispersion parameter (for the negative binomial GLMMs) and zero-inflation terms (for both the negative binomial and beta GLMMs). The best set of covariates to predict counts or percent cover were then chosen based on AIC model selection criteria. All models were fitted using ‘glmmTMB’58 in R version 4.0.259, and simulated residuals and diagnostic tests performed for each best-fit model using the package ‘DHARMa’60. For example, our best model for Sebastidae counts predicted 2% fewer zero counts than were observed.We applied depth and survey method selectivity criteria to reduce excessive zeroes in the count data that may be biologically unjustified (Appendix S5). For all taxon, if i detected t, then the method was valid for that taxon. If i did not detect t and t is a Sebastidae, then the method was valid (i.e., count = 0) only if the overall 10th and 90th percentiles of depths sampled by that method encompassed the expected depth range of t (Appendix S5). If i did not detect t and t is a coral or sponge (which are rarer than Sebastidae), then the method is valid only if the depth of the sampling event exceeded or equaled the minimum expected depth of t. Also, hook-and-line gear cannot systematically sample sessile benthic organisms or planktivores and this method was valid only for non-planktivorous Sebastidae (Appendix S5).Using the best-fit models from above, we calculated the expected count (or percent cover) per unit of effort, (mu), for taxa t observed with method i at each planning unit u:$${mu }_{t,i,u}=frac{{sum }_{l=1}^{{n}_{i,u}}left({lambda }_{t,i,l}right)}{{sum }_{l=1}^{{n}_{i,u}}left({mathrm{E}}_{t,i,l}right)}$$
(3)
where ({n}_{i,u}) was the total number of point locations sampled by that method within the planning unit and effort was either the cumulative area covered by dive or video surveys or the cumulative duration of hook-and-line sampling sessions within the planning unit. Because survey methods differed in their maximum values and potential biases (e.g., field of view is greater for divers than for video cameras; hook-and-line gear samples one fish at a time while visual methods can observe multiple fish simultaneously),({mu }_{t,i,u}) was rescaled as a min–max normalization,({mu }_{t,i,u}^{^{prime}}) (i.e., difference between the observed value and the minimum value across all u, divided by the range of values across all u).The hotspot index for each of Sebastidae, structural corals, and large-bodied sponges (denoted as taxonomic group g) was then calculated for each planning unit as:$${B}_{g,u }={sum }_{t=1}^{{n}_{s,g}}{sum }_{i=1}^{{n}_{m,g}}{mu }_{t,i,u}^{^{prime}}{W}_{t}$$
(4)
where Wt was the taxon-specific weighing factor (Table 2, Appendices S3, S4), ({n}_{s,g}) was the number of species in taxonomic group g, and ({n}_{m,g}) was the number of valid methods to sample group g.For each 1-km2 planning unit where all taxonomic groups were surveyed (N = 518), we then calculated the overall hotspot index:$${B}_{o,u }=H{sum }_{g=1}^{G}{B}_{g,u}.$$
(5)
where H is Shannon’s evenness index, with proportional abundance of each taxonomic group represented by BSEB,u, BCor,u, and BSp,u.Hotspot index values were normalized as the proportion of the maximum value and converted to decile ranks. Relationships between decile ranks and index values were nonlinear (Appendix S6).For Sebastidae, large-bodied sponges, and the overall hotspot index, we defined hotspots as planning units containing decile ranks 9 or 10: criterion which we deemed appropriate for the small spatial scales of conservation planning being used for the central portion of the Northern Shelf Bioregion (16-km2 planning units in Fig. 2). We are aware that other studies define hotspots based on a narrower range of values (e.g., top 10%26; top 2.5%28) but their context is generally one in which conservation planning is done at a much greater scale (e.g., ≈50,000-km2 grid cells26;1° latitude × 1° longitude grid cells28). For structural corals, which had near-zero index values in all but the top-ranking planning units (Appendix S6), we defined hotspots as planning units containing decile rank 10.Maximum depths sampled within planning units were deepest in the Mainland Fjord and shallowest in the Aristazabal Banks Upwelling Upper Ocean Subregion (Appendix S7). Accordingly, we used multiple logistic regression implemented with the ‘glm’ function in R to estimate the probabilities hotspot occurrence within 1-km2 planning units in relation to maximum depth sampled (including a 2nd-order polynomial) and Upper Ocean Subregion. Competing models were compared with AIC model selection procedures.Following the directive of Central Coast First Nations, decile rank distributions were mapped as 16-km2 planning units, u16 (N = 283 for Sebastidae, 264 for sponges, 263 for corals, 260 inclusive of surveys for all taxonomic groups), thereby protecting sensitive locations that would be revealed at smaller scales. To do so, we took the average between the maximum index value and the mean of the remainder of index values among the 1-km2 planning units, u, contained within each u16, and converted these values into decile ranks. This approach balances conservation prioritization among u16 that may have good average index values for multiple u, and u16 with a single high-ranking u among multiple low-scoring u. Relationships between decile ranks and hotspot index values also were nonlinear at this scale (Appendix S6). The same hotspot definitions developed for u apply to u16.Eighty one percent of 16-km2 planning units were sampled during only one or two years (Appendix S2). When all years and methods are combined, 16-km2 planning units had a median of 6 samples (range = 1 to 110, Q1 = 3, Q3 = 13). Supplementary Data Set 2 reports sampling effort by 16-km2 planning unit, survey type, and year (see Data Availability for link to these data). More

1.BirdLife International. Neophron percnopterus, Egyptian vulture. http://www.iucnredlist.org/details/22695180/0 (2017) https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T22695180A118600142.en2.Gradev, G., Garcia, V., Ivanov, I., Zhelev, P. & Kmetova, E. Data from Egyptian vultures (Neophron percnopterus) tagged with GPS/GSM transmitters in Bulgaria. Acta Zool. Bulg. 64, 141–146 (2012).
Google Scholar 
3.Green, R. E. et al. Diclofenac poisoning as a cause of vulture population declines across the Indian subcontinent. J. Appl. Ecol. 41, 793–800 (2004).CAS 
Article 

Google Scholar 
4.Arkumarev, V., Dobrev, V., Abebe, Y. D., Popgeorgiev, G. & Nikolov, S. C. Congregations of wintering Egyptian Vultures Neophron percnopterus in Afar, Ethiopia: Present status and implications for conservation. Ostrich 85, 139–145 (2014).Article 

Google Scholar 
5.Grubač, B., Velevski, M. & Avukatov, V. Long-term population decrease and recent breeding performance of the Egyptian vulture Neophron percnopterus in Macedonia. North. West. J. Zool. 10, 25–35 (2014).
Google Scholar 
6.Angelov, I., Hashim, I. & Oppel, S. Persistent electrocution mortality of Egyptian vultures neophron percnopterus over 28 years in East Africa. Bird Conserv. Int. 23, 1–6 (2013).Article 

Google Scholar 
7.Zuberogoitia, I., Zabala, J., Martínez, J. A., Martínez, J. E. & Azkona, A. Effect of human activities on Egyptian vulture breeding success. Anim. Conserv. 11, 313–320 (2008).Article 

Google Scholar 
8.Sen, B., Avares, J. P. & Bilgin, C. C. Nest site selection patterns of a local Egyptian Vulture Neophron percnopterus population in Turkey. Bird Conserv. Int. 27, 568–581 (2017).Article 

Google Scholar 
9.Ceballos, O. & Donázar, J. A. Factors influencing the breeding density and nest-site selection of the Egyptian vulture (Neophron percnopterus). J. Ornithol. 130, 353–359 (1989).Article 

Google Scholar 
10.Sarà, M. & Vittorio, M. Factors influencing the distribution, abundance and nest-site selection of an endangered Egyptian vulture (Neophron percnopterus) population in Sicily. Anim. Conserv. 6, 317–328 (2003).Article 

Google Scholar 
11.KC, K. B. et al. Factors influencing the presence of the endangered Egyptian vulture Neophron percnopterus in Rukum, Nepal. Glob. Ecol. Conserv. 20, e00727 (2019).Article 

Google Scholar 
12.Mateo-Tomás, P. & Olea, P. P. Livestock-driven land use change to model species distributions: Egyptian vulture as a case study. Ecol. Indic. 57, 331–340 (2015).Article 

Google Scholar 
13.García-RIPOLLÉS, C., López-LÓPEZ, P. & Urios, V. First description of migration and wintering of adult Egyptian vultures neophron percnopterus tracked by GPS satellite telemetry. Bird Study 57, 261–265 (2010).Article 

Google Scholar 
14.Oppel, S. et al. Landscape factors affecting territory occupancy and breeding success of Egyptian vultures on the Balkan Peninsula. J. Ornithol. 158, 443–457 (2017).Article 

Google Scholar 
15.Bhusal, K. Population status and breeding success of Himalayan Griffon, Egyption vulture and Lammergeier in Gherabhir, Arghakhanchi, Nepal. (MSc thesis. Institute of Science and Technology, Tribuvan University, Kritipur, Nepal, 2011). https://doi.org/10.13140/RG.2.2.18494.69447.16.López-lópez, A. P. et al. Food predictability determines space use of endangered vultures: Implications for management of supplementary feeding. Ecol. Appl. 24, 938–949 (2014).PubMed 
Article 

Google Scholar 
17.Cortés-avizanda, A., Ceballos, O. & Donázar, J. Long-term trends in population size and breeding success in the Egyptian Vulture (Neophron percnopterus) in Northern Spain. J. Raptor Res. 43, 43–49 (2009).Article 

Google Scholar 
18.Rosenblatt, E. Neophron percnopterus Egyptian vulture. Animal Diversity Web https://animaldiversity.org/accounts/Neophron_percnopterus/ (2007).19.ESRI. ArcGIS Desktop: Release 10.5. Environmental systems research Redlands, California, USA https://www.arcgis.com/features/index.html (2017).20.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
21.USGS/EarthExplorer. Data Sets. United States Geological Survey https://earthexplorer.usgs.gov/ (2017).22.JAXA EORC. Global PALSAR-2/PALSAR/JERS-1 Mosaic and Forest/Non-forest Map. Earth Observation Research Center https://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/data/index.htm (2017).23.CIESIN. Gridded population of the world (GPW), v4. http://sedac.ciesin.columbia.edu/data/collection/gpw-v4 (2000).24.Robinson, T. P. et al. Mapping the global distribution of livestock. PLoS ONE 9, e96084 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.FAO/GeoNetwork. Global land cover share database. http://www.fao.org/geonetwork/srv/en/main.home (2014).26.Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography (Cop.) 29, 129–151 (2006).Article 

Google Scholar 
27.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modelling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).Article 

Google Scholar 
28.Lobo, J. M., Jiménez-valverde, A. & Real, R. AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17, 145–151 (2008).Article 

Google Scholar 
29.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models : Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
30.Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Modell. 133, 225–245 (2000).Article 

Google Scholar 
31.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
32.Liu, C., White, M. & Newell, G. Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr. 40, 778–789 (2013).Article 

Google Scholar 
33.Cortés-Avizanda, A., Martín-López, B., Ceballos, O. & Pereira, H. M. Stakeholders perceptions of the endangered Egyptian vulture: Insights for conservation. Biol. Conserv. 218, 173–180 (2018).Article 

Google Scholar 
34.Hernández, M. & Margalida, A. Poison-related mortality effects in the endangered Egyptian vulture (Neophron percnopterus) population in Spain. Eur. J. Wildl. Res. 55, 415–423 (2009).Article 

Google Scholar 
35.Mateo-Tomás, P., Olea, P. P. & Fombellida, I. Status of the Endangered Egyptian vulture Neophron percnopterus in the Cantabrian Mountains, Spain, and assessment of threats. Oryx 44, 434–440 (2010).Article 

Google Scholar 
36.Carrete, M. et al. Habitat, human pressure, and social behavior : Partialling out factors affecting large-scale territory extinction in an endangered vulture. Biol. Conserv. https://doi.org/10.1016/j.biocon.2006.11.025 (2007).Article 

Google Scholar 
37.Zuberogoitia, I., Zabala, J., Martínez, J. E., González-Oreja, J. A. & López-López, P. Effective conservation measures to mitigate the impact of human disturbances on the endangered Egyptian vulture. Anim. Conserv. 17, 410–418 (2014).Article 

Google Scholar 
38.Garcia-Ripolles, C. & Lopez-Lopez, P. Population size and breeding performance of Egyptian vultures (Neophron percnopterus) in eastern Iberian Peninsula. J. Raptor Res. 40, 217–221 (2006).Article 

Google Scholar 
39.Velevski, M., Grubac, B. & Tomovic, L. Population viability analysis of the Egyptian vulture Neophron percnopterus in Macedonia and Implications for Its Conservation. Acta Zool. Bulg. 66, 43–58 (2014).
Google Scholar 
40.Arkumarev, V. et al. Breeding performance and population trend of the Egyptian vulture Neophron percnopterus in Bulgaria conservation implications. Ornis Fenn. 95, 115–127 (2018).
Google Scholar 
41.Dobrev, V. et al. Habitat of the Egyptian vulture (Neophron percnopterus) in Bulgaria and Greece (2003–2014). (2016).42.Milchev, B., Spassov, N. & Popov, V. Diet of the Egyptian vulture (Neophron percnopterus) after livestock reduction in Eastern Bulgaria. N. West. J. Zool. 8, 315–323 (2012).
Google Scholar 
43.Milchev, B. & Georgiev, V. Extinction of the globally endangered Egyptian vulture Neophron percnopterus breeding in SE Bulgaria. N. West. J. Zool. 10, 266–272 (2014).
Google Scholar 
44.Poirazidis, K., Goutner, V., Skartsi, T. & Stamou, G. Modelling nesting habitat as a conservation tool for the Eurasian black vulture (Aegypius monachus) in Dadia Nature Reserve, northeastern Greece. Biol. Conserv. 118, 235–248 (2004).Article 

Google Scholar 
45.Sanchis Serra, A. et al. Towards the identification of a new taphonomic agent: An analysis of bone accumulations obtained from modern Egyptian vulture (Neophron percnopterus) nests. Quat. Int. 330, 136–149 (2014).Article 

Google Scholar 
46.Vittorio, M. D., Lopez-Lopez, P., Cortone, G. & Luiselli, L. The diet of the Egyptian vulture (Neophron percnopterus) in Sicily: Temporal variation and conservation implications. Vie Milieu Life Environ. 67, 1–8 (2017).
Google Scholar 
47.Di Vittorio, M. et al. Successful fostering of a captive-born Egyptian Vulture (Neophron Percnopterus) in Sicily. J. Raptor Res. 40, 247–248 (2006).Article 

Google Scholar 
48.Sarà, M., Grenci, S. & Vittorio, M. D. Status of Egyptian vulture (Neophron percnopterus) in Sicily. J. Raptor Res. 43, 66–69 (2009).Article 

Google Scholar 
49.Vittorio, M. D. et al. Dispersal of Egyptian vultures Neophron percnopterus: the first case of long-distance relocation of an individual from France to Sicily. Ringing Migr. 31, 111–114 (2016).Article 

Google Scholar 
50.García-Heras, M. S., Cortés-Avizanda, A. & Donázar, J. A. Who are we feeding? Asymmetric individual use of surplus food resources in an insular population of the endangered Egyptian vulture Neophron percnopterus. PLoS ONE 8, e80523 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
51.Gangoso, L. et al. Susceptibility to infection and immune response in insular and continental populations of Egyptian vulture: Implications for conservation. PLoS ONE 4, e6333 (2009).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
52.Donazar, J. A. et al. Conservation status and limiting factors in the endangered population of Egyptian vulture (Neophron percnopterus) in the Canary Islands Conservation status and limiting factors in the endangered population of Egyptian vulture ( Neophron percnopterus ) in. Biol. Conserv. 107, 89–97 (2002).Article 

Google Scholar 
53.Rodríguez, B., Rodríguez, A., Siverio, F. & Siverio, M. Factors affecting the spatial distribution and breeding habitat of an insular cliff-nesting raptor community. Curr. Zool. 64, 173–181 (2018).PubMed 
Article 

Google Scholar 
54.Kret, E. et al. First documented case of the killing of an egyptian vulture (Neophron Percnopterus) for belief-based practices in Western Africa. Life Environ. 68, 45–50 (2018).
Google Scholar 
55.Thouless, C. R., Fanshawe, J. H. & Bertram, B. C. R. Egyptian vultures Neophron percnopterus and Ostrich Struthio camelus eggs: the origins of stone-throwing behaviour. Ibis (Lond.) 131, 9–15 (1989).Article 

Google Scholar 
56.Cuthbert, R. et al. Rapid population declines of Egyptian vulture (Neophron percnopterus) and red-headed vulture (Sarcogyps calvus) in India. Anim. Conserv. 9, 349–354 (2006).Article 

Google Scholar 
57.Samson, A. & Ramakarishnan, B. Observation of a population of Egyptian Vultures Neophron percnopterus in Ramanagaram Hills, Karnataka, southern India. Vulture News 71, 36–49 (2016).Article 

Google Scholar 
58.Farashi, A. & Alizadeh-Noughani, M. Niche modelling of the potential distribution of the Egyptian Vulture Neophron percnopterus during summer and winter in Iran, to identify gaps in protected area coverage. Bird Conserv. Int. 29, 423–436 (2019).Article 

Google Scholar 
59.Tauler-Ametller, H., Hernández-Matías, A., Pretus, J. L. L. & Real, J. Landfills determine the distribution of an expanding breeding population of the endangered Egyptian vulture Neophron percnopterus. Ibis (Lond). 159, 757–768 (2017).Article 

Google Scholar 
60.Mateo-Tomás, P. & Olea, P. P. Diagnosing the causes of territory abandonment by the Endangered Egyptian vulture Neophron percnopterus: The importance of traditional pastoralism and regional conservation. Oryx 44, 424–433 (2010).Article 

Google Scholar 
61.Galligan, T. H. et al. Have population declines in Egyptian vulture and Red-headed vulture in India slowed since the 2006 ban on veterinary diclofenac?. Bird Conserv. Int. 24, 272–281 (2014).Article 

Google Scholar 
62.Lieury, N., Gallardo, M., Ponchon, C., Besnard, A. & Millon, A. Relative contribution of local demography and immigration in the recovery of a geographically-isolated population of the endangered Egyptian vulture. Biol. Conserv. 191, 349–356 (2015).Article 

Google Scholar 
63.Porter, R. F. & Suleiman, A. S. the Egyptian Vulture Neophron percnopterus on Socotra, Yemen: Population, ecology, conservation and ethno-ornithology. Sandgrouse 34, 44–62 (2012).
Google Scholar  More

1.Conradt, L. & Roper, T. J. Group decision-making in animals. Nature 421, 155–158 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.King, A. J. & Cowlishaw, G. Leaders, followers and group decision-making. Commun. Integr. Biol. 2, 147–150 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Couzin, I. D. & Franks, N. R. Self-organized lane formation and optimized traffic flow in army ants. Proc. R. Soc. B Biol. Sci. 270, 139–146 (2003).CAS 
Article 

Google Scholar 
4.Ballerini, M. et al. Empirical investigation of starling flocks: a benchmark study in collective animal behaviour. Anim. Behav. 76, 201–215 (2008).Article 

Google Scholar 
5.Couzin, I. D., Krause, J., James, R., Ruxton, G. D. & Franks, N. R. Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218, 1–11 (2002).ADS 
MathSciNet 
PubMed 
Article 

Google Scholar 
6.Dyer, J. R. G., Johansson, A., Helbing, D., Couzin, I. D. & Krause, J. Leadership, consensus decision making and collective behaviour in humans. Philos. Trans. R. Soc. B Biol. Sci. 364, 781–789 (2009).7.Brent, L. J. N. et al. Ecological knowledge, leadership, and the evolution of menopause in killer whales. Curr. Biol. 25, 746–750 (2015).CAS 
PubMed 
Article 

Google Scholar 
8.Lee, H. C. & Teichroeb, J. A. Partially shared consensus decision making and distributed leadership in vervet monkeys: older females lead the group to forage. Am. J. Phys. Anthropol. 161, 580–590 (2016).PubMed 
Article 

Google Scholar 
9.Smith, J. E. et al. Collective movements, leadership and consensus costs at reunions in spotted hyaenas. Anim. Behav. 105, 187–200 (2015).Article 

Google Scholar 
10.Fischhoff, I. R. et al. Social relationships and reproductive state influence leadership roles in movements of plains zebra Equus burchellii. Anim. Behav. 73, 825–831 (2007).Article 

Google Scholar 
11.Conradt, L. & Roper, T. J. Consensus decision making in animals. Trends Ecol. Evol. 20, 449–456 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Stueckle, S. & Zinner, D. To follow or not to follow: decision making and leadership during the morning departure in chacma baboons. Anim. Behav. 75, 1995–2004 (2008).Article 

Google Scholar 
13.Sueur, C. & Petit, O. Shared or unshared consensus decision in macaques?. Behav. Processes 78, 84–92 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Strandburg, P, Eshkin, A., Farine, D. R., Couzin, I. D. & Crofoot, M. C. Shared decision-making drives collective movement in wild baboons. Science 348, 1358–1361 (2015).15.Fischer, J. & Zinner, D. Communication and cognition in primate group movement. Int. J. Primatol. 32, 1279–1295 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
16.Pyritz, L. W., King, A. J., Sueur, C. & Fichtel, C. Reaching a consensus: terminology and concepts used in coordination and decision-making research. Int. J. Primatol. 32, 1268–1278 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Raveling, D. G. Preflight and flight behavior of Canada geese. Auk 86, 671–681 (1969).Article 

Google Scholar 
18.Byrne, R. W., Whiten, A. & Henzi, S. P. Social relationships of mountain baboons: leadership and affiliation in a non-female-bonded monkey. Am. J. Primatol. 20, 313–329 (1990).CAS 
PubMed 
Article 

Google Scholar 
19.Boinski, S. & Garber, P. A. On the move: how and why animals travel in groups: on the move: how and why animals travel in groups. Am. Anthropol. 104, 669–670 (2002).Article 

Google Scholar 
20.Ramseyer, A., Thierry, B., Boissy, A. & Dumont, B. Decision-making processes in group departures of cattle. Ethology 115, 948–957 (2009).Article 

Google Scholar 
21.Petit, O. & Bon, R. Decision-making processes: the case of collective movements. Behav. Processes 84, 635–647 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
22.King, A. J., Johnson, D. D. P. & Van Vugt, M. The origins and evolution of leadership. Curr. Biol. 19, R911–R916 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Krause, J., Hoare, D., Krause, S., Hemelrijk, C. K. & Rubenstein, D. I. Leadership in fish shoals. Fish Fish. 1, 82–89 (2000).Article 

Google Scholar 
24.Allen, C. R. B., Brent, L. J. N., Motsentwa, T., Weiss, M. N. & Croft, D. P. Importance of old bulls: leaders and followers in collective movements of all-male groups in African savannah elephants (Loxodonta africana). Sci. Rep. 10, 1–9 (2020).Article 
CAS 

Google Scholar 
25.Pettit, B., Ákos, Z., Vicsek, T. & Biro, D. Speed determines leadership and leadership determines learning during pigeon flocking. Curr. Biol. 25, 3132–3137 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Mutinda, H., Poole, J. H. & Moss, C. F. Decision making and leadership in using the ecosystem. in The Amboseli Elephants: A Long-Term Perspective on a Long-Lived Mammal (Chicago Scholarship, 2011).27.Kummer, H. Social Organization of Hamadryas Baboons: A Field Study. Bibliotheca Primatologica (University of Chicago Press, 1968).28.Holekamp, K. E., Boydston, E. E., & Smale, L. Group travel in social carnivores. in On the move: How and why animals travel in groups 587–627 (University of Chicago Press, 2000).29.Pyritz, L. W., Kappeler, P. M. & Fichtel, C. Coordination of group movements in wild red-fronted lemurs (Eulemur rufifrons): processes and influence of ecological and reproductive seasonality. Int. J. Primatol. 32, 1325–1347 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
30.Jacobs, A., Maumy, M. & Petit, O. The influence of social organisation on leadership in brown lemurs (Eulemur fulvus fulvus) in a controlled environment. Behav. Processes 79, 111–113 (2008).CAS 
PubMed 
Article 

Google Scholar 
31.Farine, D. R., Strandburg-Peshkin, A., Couzin, I. D., Berger-Wolf, T. Y. & Crofoot, M. C. Individual variation in local interaction rules can explain emergent patterns of spatial organization in wild baboons. Proc. R. Soc. B Biol. Sci. 284, 25–29 (2017).
Google Scholar 
32.Kappeler, P. M. A framework for studying social complexity. Behav. Ecol. Sociobiol. 73, 13 (2019).Article 

Google Scholar 
33.Papageorgiou, D. & Farine, D. R. Shared decision-making allows subordinates to lead when dominants monopolize resources. Sci. Adv. 6, 1–8 (2020).Article 

Google Scholar 
34.Conradt, L., Krause, J., Couzin, I. D. & Roper, T. J. ‘Leading according to need’ in self-organizing groups. Am. Nat. 173, 304–312 (2009).CAS 
PubMed 
Article 

Google Scholar 
35.Rodriguez-Santiago, M. et al. Behavioral traits that define social dominance are the same that reduce social influence in a consensus task. Proc. Natl. Acad. Sci. U. S. A. 117, 18566–18573 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Grueter, C. C. et al. Multilevel organisation of animal sociality. Trends Ecol. Evol. 35, 834–847 (2020).PubMed 
Article 

Google Scholar 
37.Kummer, H. In Quest of the Sacred Baboon: a Scientist’s Journey. (Princeton University Press, 1995).38.Fischer, J. et al. Charting the neglected West: The social system of Guinea baboons. Am. J. Phys. Anthropol. 162, 15–31 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Whitehead, H. et al. Multilevel societies of female sperm whales (Physeter macrocephalus) in the Atlantic and Pacific: Why Are they so different?. Int. J. Primatol. 33, 1142–1164 (2012).Article 

Google Scholar 
40.Kummer, H. Two variations in the social organization of baboons. in Primates: studies in adaptation and variability 293–312 (Holt, Rinehart & Winston, 1968).41.Fischer, J. et al. The Natural History of Model Organisms: Insights into the evolution of social systems and species from baboon studies. Elife 8, e50989 (2019).42.Swedell, L. African Papionins: Diversity of social organization and ecological flexibility. in Primates in perspective 241–277 (Oxford University Press, 2011).43.Anandam, M., Bennett, E. & Davenport, T. Species accounts of Cercopithecidae. in Handbook ofthe mammals of the world Vol. 3 primates 628–753 (Lynx Edicions, 2013).44.Barrett, L. & Henzi, S. P. Baboons. Curr. Biol. 18, 404–406 (2008).Article 
CAS 

Google Scholar 
45.Ransom, T. W. Beach troop of the Gombe. (Bucknell University Press, 1981).46.Norton, G. Leadership: decision processes of group movement in yellow baboons. in Primate ecology and conservation. 145–156 (Cambridge University Press, 1986).47.Stoltz, L. & Saayman, G. S. Ecology and behaviour of baboons in the northern transvaal. Nature 26, 99–142 (1970).
Google Scholar 
48.Buskirk, W. H., Buskirk, R. E. & Hamilton, W. J. Troop-mobilizing behavior of adult male chacma baboons. Folia Primatol. 22, 9–18 (1974).CAS 
Article 

Google Scholar 
49.Collins, D. A. Spatial pattern in a troop of yellow baboons (Papio cynocephalus) in Tanzania. Anim. Behav. 32, 536–553 (1984).Article 

Google Scholar 
50.Rhine, R. J., Hendy, H. M., Stillwell-Barnes, R., Westlund, B. J. & Westlund, H. D. Movement Patterns of YeIIow Baboons (Papio cynocephaius): Central Positioning of Walking Infants. Am. J. Phys. Anthropol. 53, 159–167 (1980).Article 

Google Scholar 
51.Rhine, R. J. & Owens, N. W. The order of movement of adult male and black infant baboons (Papio anubis) entering and leaving a potentially dangerous clearing. Folia Primatol. 18, 276–283 (1972).CAS 
Article 

Google Scholar 
52.Rhine, R. J. & Westlund, B. J. Adult Male positioning in baboon progressions: order and chaos revisited. Folia Primatol. 35, 77–116 (1981).CAS 
Article 

Google Scholar 
53.Rhine, R. J., Bioland, P. & Lodwick, L. Progressions of adult male chacma baboons (Papio ursinus) in the moremi wildlife reserve. Int. J. Primatol. 6, 115–122 (1985).Article 

Google Scholar 
54.Rowell, T. Long-term changes in a population of ugandan baboons. Folia Primatol. 11, 241–254 (1969).CAS 
Article 

Google Scholar 
55.Sigg, H. & Stolba, A. Home range and daily march in a Hamadryas baboon troop. Folia Primatol. (Basel) 36, 40–75 (1981).CAS 
Article 

Google Scholar 
56.Schweitzer, C., Gaillard, T., Guerbois, C., Fritz, H. & Petit, O. Participant profiling and pattern of crop-foraging in chacma baboons (Papio hamadryas ursinus) in Zimbabwe: Why Does Investigating Age-Sex Classes Matter?. Int. J. Primatol. 38, 207–223 (2017).Article 

Google Scholar 
57.Stolba, A. Entscheidungstindung in verbanden von papio hamadryas. (University of Zurich, 1979).58.Strandburg-Peshkin, A., Papageorgiou, D., Crofoot, M. C. & Farine, D. R. Inferring influence and leadership in moving animal groups. Philos. Trans. R. Soc. B Biol. Sci. 373, (2018).59.Harding, R. S. O. Patterns of movement in open country baboons. Am. J. Phys. Anthropol. 47, 349–353 (1977).Article 

Google Scholar 
60.DeVore, I. & Washburn, S. L. Baboon Ecology and Human Evolution. in African Ecology and Human Evolution 335–367 (Routledge, 2017).61.Altmann, S. A. Baboon progressions: Order or chaos? A study of one-dimensional group geometry. Anim. Behav. 27, 46–80 (1979).Article 

Google Scholar 
62.Goffe, A. S., Zinner, D. & Fischer, J. Sex and friendship in a multilevel society : behavioural patterns and associations between female and male Guinea baboons. Behav. Ecol. Sociobiol. 70, 323–336 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
63.Patzelt, A. et al. Male tolerance and male – male bonds in a multilevel primate society. PNAS 111, 14740–14745 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Pines, M., Saunders, J. & Swedell, L. Alternative routes to the leader male role in a multi-level society: Follower vs. solitary male strategies and outcomes in hamadryas baboons. Am. J. Primatol. 73, 679–691 (2011).65.Schreier, A. L. & Swedell, L. The fourth level of social structure in a multi-level society: Ecological and social functions of clans in Hamadryas Baboons. Am. J. Primatol. 71, 948–955 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Dal Pesco, F., Trede, F., Zinner, D. & Fischer, J. Kin bias and male pair-bond status shape male-male relationships in a multilevel primate society. Behav. Ecol. Sociobiol. 75, 1–14 (2021).Article 

Google Scholar 
67.Strandburg-peshkin, A., Farine, D. R., Couzin, I. D. & Crofoot, M. C. Shared decision-making drives collective movement in wild baboons. Science 348, 1358–1361 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Leca, J. B., Gunst, N., Thierry, B. & Petit, O. Distributed leadership in semifree-ranging white-faced capuchin monkeys. Anim. Behav. 66, 1045–1052 (2003).Article 

Google Scholar 
69.Rhine, R. J. The order of movement of yellow baboons. Folia Primatol 23, 72–104 (1975).CAS 
Article 

Google Scholar 
70.Rhine, R. J. & Tilson, R. Reactions to fear as a proximate factor in the sociospatial organization of baboon progressions. Am. J. Primatol. 13, 119–128 (1987).PubMed 
Article 

Google Scholar 
71.Bonnell, T. R., Clarke, P. M., Henzi, S. P. & Barrett, L. Individual-level movement bias leads to the formation of higher-order social structure in a mobile group of baboons. R. Soc. Open Sci. 4, (2017).72.Strandburg-Peshkin, A., Farine, D. R., Crofoot, M. C. & Couzin, I. D. Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement. Elife 6, (2017).73.King, A. J., Douglas, C. M. S., Huchard, E., Isaac, N. J. B. & Cowlishaw, G. Dominance and affiliation mediate despotism in a social primate. Curr. Biol. 18, 1833–1838 (2008).CAS 
PubMed 
Article 

Google Scholar 
74.King, A. J., Sueur, C., Huchard, E. & Cowlishaw, G. A rule-of-thumb based on social affiliation explains collective movements in desert baboons. Anim. Behav. 82, 1337–1345 (2011).Article 

Google Scholar 
75.Harel, R., Loftus, C. J. & Crofoot, M. C. Locomotor compromises maintain group cohesion in baboon troops on the move. bioRxiv (2020).76.Wang, C. et al. Decision-making process during collective movement initiation in golden snub-nosed monkeys (Rhinopithecus roxellana). Sci. Rep. 10, 1–10 (2020).Article 
CAS 

Google Scholar 
77.Whitehead, H. Consensus movements by groups of sperm whales. Mar. Mammal Sci. 32, 1402–1415 (2016).Article 

Google Scholar 
78.Crook, J. H. Gelada baboon herd structure and movement a comparative report. Symp. Zool. Soc. London 18, 237–258 (1966).
Google Scholar 
79.Grueter, C. C., Li, D., Ren, B., Wei, F. & Li, M. Deciphering the social organization and structure of wild yunnan snub-nosed monkeys (Rhinopithecus bieti). Folia Primatol. 88, 358–383 (2017).Article 

Google Scholar 
80.Zinner, D. et al. Comparative ecology of Guinea baboons (Papio papio). Primate Biol. 8, 19–35 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
81.Altmann, J. Observational study of behavior: sampling methods. Behaviour 49, 227–266 (1974).CAS 
PubMed 
Article 

Google Scholar 
82.Sueur, C. & Petit, O. Organization of group members at departure is driven by social structure in Macaca. Int. J. Primatol. 29, 1085–1098 (2008).Article 

Google Scholar 
83.Seltmann, A., Majolo, B., Schülke, O. & Ostner, J. The Organization of Collective Group Movements in Wild Barbary Macaques (Macaca sylvanus): Social Structure Drives Processes of Group Coordination in Macaques. PLoS One 8, (2013).84.Core Team, R. R: A Language and Environment for Statistical Computing. (2018).85.Baayen, R. H. Analyzing linguistic data: A practical introduction to statistics using R. Anal. Linguist. Data A Pract. Introd. to Stat. Using R 1–353 (2008).86.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Software 67, 1–48 (2015).Article 

Google Scholar 
87.Dobson, A. An introduction to generalized linear models. (CRC Press, 2002).88.Forstmeier, W. & Schielzeth, H. Cryptic multiple hypotheses testing in linear models: Overestimated effect sizes and the winner’s curse. Behav. Ecol. Sociobiol. 65, 47–55 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
89.Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
90.Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: Keep it maximal. J. Mem. Lang. 68, 255–278 (2013).Article 

Google Scholar 
91.Fahrmeir, L., Kneib, T., Lang, S. & Marx, B. Regression Modesl (Springer, 2013).MATH 
Book 

Google Scholar 
92.Hadfield, J. D. MCMCglmm: MCMC Methods for Multi-Response GLMMs in R. J. Stat. Softw. 33, 1–22 (2010).Article 

Google Scholar  More

Agent-resource affiliation networksWe consider games involving populations of agents that extract from multiple common-pool sources (which term we use for nodes representing resources in accord with previous related work15,16). Agents’ access to sources is defined by bipartite networks, wherein a link between an agent and a source indicates that the agent can access that source. This access is determined by some exogenous factors and remains fixed in time. The set of agents affiliated with a particular source (s) is denoted as ({mathbf{A}}_{s}), while the set of sources affiliated with a particular agent (a) is denoted as ({mathbf{S}}_{a}). The degree of an agent (a) is denoted by (m(a)), and the degree of a source (s) by (n(s)).To explore the effects of network topology upon extraction dynamics and wealth distributions, we generate ensembles of ({10}^{3}) networks, each having (50) agents and (50) sources and sharing mean agent degree (langle mrangle =5) and mean source degree (langle nrangle =5). All networks thus share the same total numbers of agents, sources, and links, but differ in how these links are distributed among agents and sources. We generate 9 network ensembles, each generated to represent a particular combination of one of three types of degree heterogeneity in its source degree distribution (U: uniform-degree, L: low-heterogeneity, or H: high-heterogeneity) with one of three similar distributions of agent degree (u, l, or h39) (Supplementary Information S1.1). Degree histograms, averaged over each ensemble, provide a representative source degree distribution ({P}_{mathbf{S}}(n)) and agent degree distribution ({P}_{mathbf{A}}(m)) for each network type (Fig. 2a and b). It is worth noting that the results of the simulations depend primarily on the degree distributions of agents and sources rather than on the overall size of the networks used (Supplementary Information S3.1).Figure 2(a) Source degree distributions and (b) Agent degree distributions for 9 network ensembles, each representing a combination of a Uniform-degree (U), Low-heterogeneity (L), or High-heterogeneity (H) source degree distribution with a uniform-degree (u), low-heterogeneity (l), or high-heterogeneity (h) agent degree distribution. Ensemble mean time-averaged quantities from pure free adaptation dynamics: (c) Agent payoffs (f(a)) as a function of agent degree (m(a)); (d) Collective extraction (overrightarrow{q}(s)) as a function of source degree (n(s)); (e) Source quality (b(s)) as a function of source degree (n(s)); and (f) Period of oscillation (T(s)) as a function of source degree (n(s)). Means are computed from simulations on ({10}^{3}) networks of each type.Full size imageNetworked CPR extraction gameOn these networks, we simulate iterative games in which agents vary the extraction effort that they apply to their affiliated sources, altering the quality of these sources; in turn, these changes in source quality then influence how agents adapt their extraction levels in subsequent rounds. The extraction effort exerted by agent (a) upon its affiliated source (s) is denoted as (q(a,s)). The total effort exerted by an agent (a), its individual extraction, is denoted by (overleftarrow{q}left(aright)=sum_{sin {mathbf{S}}_{a}}q(a,s)). The total effort exerted upon source (s), or its collective extraction, is denoted by (overrightarrow{q}left(sright)=sum_{ain {mathbf{A}}_{s}}q(a,s)). The quality of a source (s) is quantified by the benefit (b(s)) per unit extraction effort applied that the source provides. The cost associated with extraction is given by a convex (quadratic) function of (overleftarrow{q}left(aright)), such that marginal costs increase with individual extraction15,16. In addition to modelling the increasing costs (i.e., diminishing returns) associated with the physical act of extraction itself, this could also reflect escalating, informal social penalties that result from increasing extraction (i.e., “graduated sanctions”1,40). The net payoff accumulated by an agent (a) in a game iteration is thus$$fleft( a right) = left[ {mathop sum limits_{{s in {mathbf{S}}_{a} }} qleft( {a,s} right) cdot bleft( s right)} right] – frac{gamma }{2}{ }mathop{q}limits^{leftarrow} left( a right)^{2} ,$$
(1)

where (gamma) is a positive cost parameter.Bistable model of CPR depletion and remediationSources are bistable, meaning that at any time they can occupy one of two states: (1) a viable state, during which the source provides a benefit of magnitude (alpha) in return for each unit of extraction effort, and (2) a depleted state, during which this benefit is reduced by (beta) ((0 vec{q}_{{text{D}}} left( s right)} \ {0, } & {{text{if }} chi_{t – 1} left( s right) = 1{text{ and }}vec{q}_{t} left( s right) le vec{q}_{R} left( s right)} \ {chi_{t – 1} left( s right),} & {text{otherwise }} \ end{array} } right.$$
(3)
In the results that follow, we focus upon a uniform capacity scenario, wherein all sources share identical threshold values (vec{q}_{{text{D}}} left( s right) equiv vec{q}_{{text{D}}}) and (vec{q}_{{text{R}}} left( s right) equiv vec{q}_{{text{R}}} left( s right)). An alternative degree-proportional capacity scenario, in which threshold values increase with source degree, is discussed in the Supplementary Information (S3.4.2).Free adaptationUnder the free adaptation strategy, an agent updates its extraction levels independently at each of its affiliated sources depending on the state of each (Fig. 1b). As in the replicator rule often applied in networked evolutionary game models17,41,42, the rate at which an agent adapts its extraction levels within a time interval ({Delta }t) is proportional to the marginal payoff that the agent expects to attain thereby:$$frac{{{Delta }qleft( {a,s} right)}}{{{Delta }t}} = kfrac{partial fleft( a right)}{{partial qleft( {a,s} right)}},$$
(4)
where (k) is a rate constant. So, each extraction level (qleft( {a,s} right)) is updated according to$$q_{t + 1} left( {a,s} right) = q_{t} left( {a,s} right) + kleft[ {alpha – beta chi_{t} left( s right) – gamma {mathop{q}limits^{leftarrow}}_{t} left( a right)} right].$$
(5)
The higher an agent’s individual extraction (overleftarrow{q}(a)), the more slowly it will increase its extraction from viable sources, and the more rapidly it will reduce its extraction from depleted sources.Uniform adaptationWhen applying the uniform adaptation strategy, an agent adjusts each of its extraction levels by the same magnitude (Delta qleft(a,sright)equivDelta overleftarrow{q}(a)/mleft(aright)) (Fig. 1c). Assuming again that the rate at which an agent enacts this update is proportional to the associated marginal payoff, an agent adapts its extraction levels at all of its affiliated sources (s) by$$q_{t + 1} left( {a,s} right) = q_{t} left( {a,s} right) + kleft[ {alpha – beta overline{chi }left( a right) – gamma {mathop{q}limits^{leftarrow}}_{t} left( a right)} right],$$
(6)
where (overline{chi }left( a right) = left[ {mathop sum nolimits_{{s^{prime} in {mathbf{S}}_{a} }} chi left( {s^{prime}} right)} right]/mleft( a right)) is the mean state of the agent’s affiliated sources.ReallocationWhen practicing reallocation, an agent shifts an increment of extraction effort from a depleted source to a viable source such that its overall individual extraction (mathop{q}limits^{leftarrow} left( a right)) remains unchanged (Fig. 1d). The agent thus randomly selects one depleted source (s_{{text{D}}} in {mathbf{S}}_{a}) and one viable source (s_{{text{V}}} in {mathbf{S}}_{a}), if available. Since the marginal payoff per unit reallocated is (beta), updates its extraction levels such that$$q_{t + 1} left( {a,s} right) = left{ {begin{array}{*{20}c} {q_{t} left( {a,s} right) – kbeta , } & {{text{if}} s = s_{{text{D}}} } \ {q_{t} left( {a,s} right) + kbeta , } & {{text{if}} s = s_{{text{V}}} } \ {q_{t} left( {a,s} right),} & {text{otherwise }} \ end{array} } right.$$
(7)

When an agent’s affiliated sources all share the same quality value, no such reallocation is possible, and so the agent retains its present extraction levels: (q_{t + 1} left( {a,s} right) = q_{t} left( {a,s} right)) for all (s in {mathbf{S}}_{a}).Mixed strategiesAn agent’s adaptation strategy (({p}_{0},{p}_{updownarrow },{p}_{leftrightarrow })) comprises the probabilities that it will practice each of these update rules in any given round: its free adaptation propensity (({p}_{0})), its uniform adaptation propensity (({p}_{updownarrow })), and its reallocation propensity (({p}_{leftrightarrow })). An agent’s choice of a particular update rule is thus based only on its own innate inclinations, but the rate at which it enacts the selected rule is influenced by current resource conditions. We first simulate dynamics in which the same adaptation strategy is shared by all members of a population throughout the entire course of a simulation. We then consider games in which agents’ individual adaptation strategies are each allowed to independently evolve under generalized reinforcement learning38,43 (Supplementary Information S1.3.4). That is, after enacting a chosen update rule in an iteration (t), each agent (a) observes the payoff change (Delta {f}_{t}left(aright)={f}_{t}left(aright)-{f}_{t-1}(a)). If (Delta {f}_{t}left(aright) >0), then the agent’s relative propensity to practice this update rule in subsequent rounds is increased. If the agent’s payoffs decreased ((Delta {f}_{t}left(aright)0) or remediation threshold ({overrightarrow{q}}_{mathrm{R}}left(sright)). That is, all resource depletion events are assumed to be extreme enough to motivate agents to continuously “self-regulate” by remediating depleted sources (see Supplementary Information S5 for a more thorough discussion of these parameter settings).In simulations where reinforcement learning is applied, all agents are initialized with ({p}_{updownarrow }={p}_{leftrightarrow }=.333). For pure free adaptation simulations (({p}_{0}=1)), initial extraction levels were randomized (({q}_{t=0}left(a,sright)in [0,frac{{overrightarrow{q}}_{mathrm{D}}left(sright)}{nleft(sright)}])). All other simulations (({p}_{0} More

1.Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 6032(332), 970–974 (2011).ADS 
Article 
CAS 

Google Scholar 
2.McKenzie, V. J. et al. The effects of captivity on the mammalian gut microbiome. Integr. Comp. Biol. 57, 690–704 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Flint, H. J., Scott, K. P., Duncan, S. H., Louis, P. & Forano, E. Microbial degradation of complex carbohydrates in the gut. Gut Microbes 3, 289–306 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Chen, T. et al. Fiber-utilizing capacity varies in Prevotella- versus Bacteroides-dominated gut microbiota. Sci. Rep. 7, 2594 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
5.Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. M. & Relman, D. A. The application of ecological theory toward an understanding of the human microbiome. Science 6086(336), 1255–1262 (2012).ADS 
Article 
CAS 

Google Scholar 
6.Campbell, C. J., Fuentes, A., MacKinnon, K. C., Bearder, S. K. & Stumpf, R. Primates in Perspective (Oxford University Press, 2010).
Google Scholar 
7.Chivers, D. J. Functional anatomy of the gastrointestinal tract. Colobine Monkeys Ecol. Behav. Evol. 205–227 (1994).8.Davies, G. E. Colobine Monkeys: Their Ecology, Behaviour and Evolution (Cambridge University Press, 1994).
Google Scholar 
9.Neish, A. S. Microbes in gastrointestinal health and disease. Gastroenterology 136, 65–80 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Hanya, G. & Chapman, C. A. Linking feeding ecology and population abundance: A review of food resource limitation on primates. Ecol. Res. 28, 183–190 (2013).Article 

Google Scholar 
11.Fan, P., Ni, Q., Sun, G., Huang, B. & Jiang, X. Gibbons under seasonal stress: the diet of the black crested gibbon (Nomascus concolor) on Mt. Wuliang, Central Yunnan, China. Primates 50, 37 (2009).PubMed 
Article 

Google Scholar 
12.Burrows, A. M. & Nash, L. T. The Evolution of Exudativory in Primates (Springer Science & Business Media, 2010).Book 

Google Scholar 
13.Amato, K. R. et al. Gut microbiome, diet, and conservation of endangered langurs in Sri Lanka. Biotropica 52, 981–990 (2020).Article 

Google Scholar 
14.Amato, K. R. et al. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Microb. Ecol. 69, 434–443 (2015).CAS 
PubMed 
Article 

Google Scholar 
15.Amato, K. R. et al. Variable responses of human and non-human primate gut microbiomes to a Western diet. Microbiome 3, 53 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
16.Clayton, J. B. et al. Captivity humanizes the primate microbiome. Proc. Natl. Acad. Sci. U. S. A. 113, 10376–10381 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Frankel, J. S., Mallott, E. K., Hopper, L. M., Ross, S. R. & Amato, K. R. The effect of captivity on the primate gut microbiome varies with host dietary niche. Am. J. Primatol. 81, 1–9 (2019).Article 

Google Scholar 
18.Lee, W., Hayakawa, T., Kiyono, M., Yamabata, N. & Hanya, G. Gut microbiota composition of Japanese macaques associates with extent of human encroachment. Am. J. Primatol. 81, 1–14 (2019).Article 

Google Scholar 
19.Amato, K. R. et al. Habitat degradation impacts black howler monkey (Alouatta pigra) gastrointestinal microbiomes. ISME J. 7, 1344–1353 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Moeller, A. H. et al. Sympatric chimpanzees and gorillas harbor convergent gut microbial communities. Genome Res. 23, 1715–1720 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Suzuki, T. A. & Worobey, M. Geographical variation of human gut microbial composition. Biol. Lett. 10, (2014).22.Sinha, A., Datta, A., Madhusudan, M. D. & Mishra, C. Macaca munzala: a new species from western Arunachal Pradesh, northeastern India. Int. J. Primatol. 26, 977–989 (2005).Article 

Google Scholar 
23.Sinha, A., Kumar, R. S., Gama, N., Madhusudan, M. D. & Mishra, C. Distribution and conservation status of the Arunachal macaque, Macaca munzala, in western Arunachal Pradesh, northeastern India. Primate Conserv. 2006, 145–148 (2006).Article 

Google Scholar 
24.Mendiratta, U., Kumar, A., Mishra, C. & Sinha, A. Winter ecology of the Arunachal macaque Macaca munzala in Pangchen Valley, western Arunachal Pradesh, northeastern India. Am. J. Primatol. 71, 939–947 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Kumar, R. S., Mishra, C. & Sinha, A. Foraging ecology and time-activity budget of the Arunachal macaque Macaca munzala: A preliminary study. Curr. Sci. 93, 532–539 (2007).
Google Scholar 
26.Ghosh, A., Thakur, M., Singh, S. K., Sharma, L. K. & Chandra, K. Gut microbiota suggests dependency of Arunachal Macaque (Macaca munzala) on anthropogenic food in Western Arunachal Pradesh, Northeastern India: Preliminary findings. Glob. Ecol. Conserv. 22, e01030 (2020).Article 

Google Scholar 
27.Song, S. J., Amir, A., Metcalf, J. L. & Amato, K. R. Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems 1, 1–12 (2016).Article 

Google Scholar 
28.Li, Q. & Zhang, Y. A molecular phylogeny of macaca based on mtochondrial corntrol region sequeryces. Zool. Res. 25, 385–390 (2004).
Google Scholar 
29.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Kanthaswamy, S. et al. Microsatellite markers for standardized genetic management of captive colonies of rhesus macaques (Macaca mulatta). Am. J. Primatol. Off. J. Am. Soc. Primatol. 68, 73–95 (2006).CAS 

Google Scholar 
31.Peakall, R. O. D. & Smouse, P. E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

Google Scholar 
32.Kalinowski, S. T., Wagner, A. P. & Taper, M. L. ML-RELATE: A computer program for maximum likelihood estimation of relatedness and relationship. Mol. Ecol. Notes 6, 576–579 (2006).CAS 
Article 

Google Scholar 
33.Takai, K. & Horikoshi, K. Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Appl. Environ. Microbiol. 66, 5066–5072 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Herlemann, D. P. R. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Muyzer, G., De Waal, E. C. & Uitterlinden, A. G. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59, 695–700 (1993).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U. S. A. 108, 4516–4522 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
37.Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Schloss, P. D. Reintroducing mothur: 10 years later. Appl. Environ. Microbiol. 86, 1–13 (2020).
Google Scholar 
39.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
Article 

Google Scholar 
41.McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, (2013).42.Cao, Y. microbiomeMarker: microbiome biomarker analysis. R package version 0.0.1.9000 https://github.com/yiluheihei/microbiomeMarker (2020).43.Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: An effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).PubMed 
Article 

Google Scholar 
44.Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
45.Mao, S., Zhang, R., Wang, D. & Zhu, W. The diversity of the fecal bacterial community and its relationship with the concentration of volatile fatty acids in the feces during subacute rumen acidosis in dairy cows. BMC Vet. Res. 8, 1 (2012).CAS 
Article 

Google Scholar 
46.Wang, B., Yao, M., Lv, L., Ling, Z. & Li, L. The human microbiota in health and disease. Engineering 3, 71–82 (2017).Article 

Google Scholar 
47.Carding, S., Verbeke, K., Vipond, D. T., Corfe, B. M. & Owen, L. J. Dysbiosis of the gut microbiota in disease. Microb. Ecol. Health Dis. 26, 26191 (2015).PubMed 
PubMed Central 

Google Scholar 
48.Kumar, R. S., Gama, N., Raghunath, R., Sinha, A. & Mishra, C. In search of the munzala: distribution and conservation status of the newly-discovered Arunachal macaque Macaca munzala. Oryx 42, 360–366 (2008).Article 

Google Scholar 
49.Sarania, B., Devi, A., Kumar, A., Sarma, K. & Gupta, A. K. Predictive distribution modeling and population status of the endangered Macaca munzala in Arunachal Pradesh, India. Am. J. Primatol. 79, 2592 (2017).Article 

Google Scholar 
50.Aiyadurai, A., Singh, N. J. & Milner-Gulland, E. J. Wildlife hunting by indigenous tribes: A case study from Arunachal Pradesh, north-east India. Oryx 44, 564–572 (2010).Article 

Google Scholar 
51.Mishra, C., Madhusudan, M. D. & Datta, A. Mammals of the high altitudes of western Arunachal Pradesh, eastern Himalaya: an assessment of threats and conservation needs. Oryx 40, 29–35 (2006).Article 

Google Scholar  More