Ecology

Each sedimentary sequence from the three excavated ponds (Tampolove [TAMP], Ankatoke [ANKA], and Andranobe [ANDR]) includes a layer of clay (defined as zone 2), which separates the surface soil formation (zone 1) from the underlying fossiliferous muddy sand and bedrock (zone 3, Figs. S4–S7 & S9). Details regarding the composition of this sediment and its microfossils are given in Appendix-Results-Excavation (Figs. S9–S12).Subfossils and chronologyCoastal survey recovered mostly zebu bones on exposed sandy surfaces, some pygmy hippo and giant tortoise bones on the margins of shallow ponds, and giant tortoise carapace under overhanging limestone outcrops (Appendix-Results-Survey, Fig. S3). A high proportion of surface bone failed 14C analysis (~ 55%, Table S1), yet the successfully analyzed specimens (n = 8) span up to 3390–3220 calibrated years before present (cal BP, PSUAMS 8681, 3150 ± 15 14C BP, a hippo molar). Pond deposits that are relatively deep include bones that cover a relatively long period of time (Figs. S14–S16, Dataset S6). This span ranges from ~ 6000 years at TAMP (~ 120 cm deep) to ~ 2500 years at ANDR (~ 100 cm deep), with the oldest bones present in the fossiliferous sedimentary zone 3 and scarce bones in the overlying clay (zone 2).Zone 3Most bones in this layer are relatively intact and include readily identifiable pygmy hippo long bones and cranial fragments (e.g., Fig. S13a,f), giant tortoise carapace and plastron fragments (Fig. S13d), ratite eggshell and long bones (Fig. S13c,m), and crocodile scutes, cranial fragments, and teeth (Fig. S13b). Scarce bones of a duck (genus Anas) were recovered at ANDR. Remains of subfossil lemurs were scarce or absent, but they may be represented by an unknown type of bone fragment identified through protein fingerprinting (ANDR-1-5-55, Dataset S3). The widespread success of collagen extraction from these bones attests to the excellent preservation of organics in this zone. ANKA also includes keratin (mostly in the form of crocodile claws, e.g., Fig. S13i), as well as two rounded agates found associated with ratite eggshell (Fig. S13m).Remains of a juvenile pygmy hippo were recovered from both TAMP and ANDR (a femur and tibia, respectively, Dataset S3). The epiphyses of some of the pygmy hippo long bones have gnaw marks (Fig. S13f), and none of the bones include chop marks. In association with these bones towards the top of this zone are some large ( > 1 cm diameter) charcoal fragments and scarce bones of bushpig (Fig. S13k) and zebu (Fig. S13e). Protein fingerprinting identified a screened fragment of a non-zebu bovid in ANKA zone 3 and confirmed that a tentatively identified bushpig canine fragment (ANKA 1-4-151) belonged to a hippo. This zone at TAMP and ANDR also includes occasional mangrove whelk (Terebralia palustris) shells (Fig. S13g). These whelks currently live at least ~ 500 m distant from these ponds, and whelk shells at ANDR each have an irregular hole above the operculum.The span of time represented by bones in zone 3 ranges up to ~ 4000 years (~ 6000–2000 cal BP at TAMP, Fig. S14). Confirmed introduced animal bones from zone 3 failed direct 14C analysis. There are multiple examples of directly 14C-dated bone in close stratigraphic association that nonetheless differ in age by  > 1000 years, and there are a couple of examples of bones from the same individual that are separated stratigraphically. For example, two giant tortoise carapace and plastron fragments from TAMP that have indistinguishable 14C ages are separated by 22 cm of sediment (PSUAMS 8670 comes from 112 cm depth, and PSUAMS 8668 comes from 90 cm depth).Although ANKA produced what is thus far the oldest directly 14C dated pygmy hippo bone from a coastal subfossil site (PSUAMS 9383, 4380 ± 25 BP, 5030–4840 cal BP), the mean calibrated age of hippos from the Tampolove excavations (n = 11, x̄ = 2858 cal BP, SD = 972 yr) is significantly less than that of the giant tortoises (n = 9, x̄ = 4582 cal BP, SD = 705 yr, t(18) = − 4.4, p  2000 years older than a closely associated charcoal sample (38 cm depth, PSUAMS 8849, 575 ± 30 14C BP, 630–510 cal BP), which makes this molar comparable in age to bone from zone 3. Consequently, the youngest directly 14C-dated ancient bone from the Tampolove excavations comes from the lowermost zone 3: a pygmy hippo’s vertebra recovered at 90 cm depth at TAMP (PSUAMS 8730, 1865 ± 15 14C BP, 1819–1705 cal BP). Though poorly constrained in time, the deposition of zone 2 sediment came sometime within the past two millennia, which witnessed marine regression and dry intervals recorded in both the δ18O record of a nearby speleothem27 and the salinization of a nearby pan36. Previously directly 14C-dated bone collected around Tampolove attests to the local persistence of at least pygmy hippos and giant tortoises until the start of the last millennium (n = 15), and an atlas from Lamboara/Lamboharana is in fact the most recent confidently dated pygmy hippo bone from the island (PSUAMS 5629, 1100 ± 15 14C BP, 980–930 cal BP).Figure 4Cutmarked pygmy hippo femur recovered from Tampolove during recent excavation at ~ 40 cm depth (TAMP-1-2-61, above), and previously-recovered and directly 14C-dated (~ 3500 and 1600 cal BP37) cutmarked pygmy hippo femora from the nearby site of Lamboara/Lamboharana that are currently housed in the National Museum of Natural History in Paris (MAD 1709 & MAD 1710, below). Four views highlight three locations of cutmarks on the broken shaft of TAMP-1-2-61, and the inset frames show 20 × magnification of these areas, with corresponding orientations given by red lines. Note that the false color insets of TAMP-1-2-61 are meant to highlight linear edges and crevices, and the overview photos of all three femur fragments are on the same scale.Full size imageZone 1A fragment of iron (from TAMP, 16 cm depth) and sparse ceramic fragments (from ANKA, 3 & 9 cm depth) are present only in zone 1, and three 14C dates from TAMP and ANKA suggest that these specimens span the past ~ 200 years (Figs. S14–S15).CharcoalThe directly 14C dated charcoal spans all three stratigraphic zones yet consistently dates to the past millennium (Figs. S14–16). Multiple charcoal samples from different excavated ponds have practically indistinguishable 14C ages (Table S2), and much of the charcoal from Tampolove formed during peaks in the deposition of macrocharcoal at nearby Namonte (17 km distant; Fig. 5A). The onset of directly 14C-dated charcoal deposition approximately coincides with a decrease in Asafora speleothem δ18O values and with multiple directly 14C-dated first and final local occurrences of large animals. While directly 14C dated charcoal is limited to the past millennium, microcharcoal particles were abundant in all TAMP sediment samples (x̄ ± SD = 2.0 × 106 ± 2.8 × 106 particles). Additionally, microcharcoal is relatively abundant near the bottom of TAMP and ANKA, which contains bones that span ~ 6000–2000 cal BP (Fig. 5B).Figure 5Records of fire, drought, and faunal turnover from the vicinity of Tampolove within the past 1200 years, with dashed horizontal lines for reference (5A), and macrocharcoal concentrations from the excavated ponds, with depth intervals containing directly 14C-dated charcoal that spans the past millennium marked in red (5B). The past 1200 years includes the entire summed calibrated distribution of the 10 directly dated prebomb charcoal fragments from the Tampolove excavations. The calibrated probability distributions associated with the latest dates from endemic megafauna bone (giant tortoises and pygmy hippos) and earliest dates from introduced animal bone (zebu cattle and bushpigs) are shown as black distributions, and 95% of each distribution is bracketed. Considering directly dated remains within the past 4 ka from hippos (n = 26), giant tortoises (n = 18), and zebu (n = 9) and the assumption that bones were deposited uniformly over time, the grey distributions and bracketed 95% credible intervals give estimates of extirpation and arrival times. As in Fig. 3, the red line on the Asafora record follows from BCPA.Full size image More

HFRS distribution in China, 2004–2018From January 1, 2004 to December 31, 2018, 190 203 cases of HFRS were reported nationwide in China, with an average annual incidence rate of 0.950 per 100,000 people, with the highest incidence in 2004 (1.926 per 100,000) and the lowest in 2018 (0.86 per 100,000) (Fig. 1A), and the cases showed obvious seasonal fluctuations (Fig. 1B). HFRS cases existed every month and showed an obvious dual-season mode every year, with a spring peak from May to June and a winter peak from November to December. The highest number of cases were in May and November, with the composition ratios accounting of 9.51% and 17.06%, respectively (Fig. 1B).Figure 1The incidence and number of HFRS cases reported in China, 2004–2018. (A) Number of cases and incidence by year. Trend of the incidence rate of HFRS between 2004 and 2018 shown by the joinpoint regression (upper right corner). The red squares represent the observed crude incidence of HFRS and the lines represent the slope of the annual percentage change (APC). (B) The pink line represents the monthly incidence of HFRS. The bar chart shows the number of cases at peak and trough.Full size imageThe incidence of HFRS in northern regions was higher than that in the south, especially in Heilongjiang, Liaoning, Jining, Shaanxi, Shandong and Hebei provinces. Relatively few cases existed in south China, which were mainly concentrated in Jiangxi, Zhejiang, Hunan and Fujian (Figs. S1 and S2). Spatial autocorrelation analysis indicated that HFRS cases were positively correlated (Moran’s I = 0.09, p  More

Hart, M. B. et al. The search for the origin of the planktic foraminifera. J. Geol. Soc. Lond. 160, 341–343 (2003).Article 

Google Scholar 
Aze, T. et al. A phylogeny of Cenozoic macroperforate planktonic foraminifera from fossil data. Biol. Rev. 86, 900–927 (2011).Article 
PubMed 

Google Scholar 
Gradstein, F., Waskowska, A. & Glinskikh, L. The first 40 million years of planktonic foraminifera. Geosci 11, 1–25 (2021).Article 

Google Scholar 
Ujiié, Y., Kimoto, K. & Pawlowski, J. Molecular evidence for an independent origin of modern triserial planktonic foraminifera from benthic ancestors. Mar. Micropaleontol. 69, 334–340 (2008).Article 
ADS 

Google Scholar 
Darling, K. F. et al. Surviving mass extinction by bridging the benthic/planktic divide. Proc. Natl Acad. Sci. USA 106, 12629–33 (2009).Article 
ADS 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Kucera, M. et al. Caught in the act: anatomy of an ongoing benthic–planktonic transition in a marine protist. J. Plankton Res. 39, 436–449 (2017).
Google Scholar 
Ezard, T. H. G., Aze, T., Pearson, P. N. & Purvis, A. Interplay between changing climate and species’ ecology drives macroevolutionary dynamics. Science 332, 349–352 (2011).Article 
ADS 
PubMed 
CAS 

Google Scholar 
Lowery, C. M., Bown, P. R., Fraass, A. J. & Hull, P. M. Ecological response of plankton to environmental change: thresholds for extinction. Annu. Rev. Earth Planet. Sci. 48, 403–429 (2020).Article 
ADS 
CAS 

Google Scholar 
Pawlowski, J., Holzmann, M. & Tyszka, J. New supraordinal classification of foraminifera: molecules meet morphology. Mar. Micropaleontol. 100, 1–10 (2013).Article 
ADS 

Google Scholar 
Lecroq, B. et al. Ultra-deep sequencing of foraminiferal microbarcodes unveils hidden richness of early monothalamous lineages in deep-sea sediments. Proc. Natl Acad. Sci. USA 108, 13177–13182 (2011).Article 
ADS 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Pawlowski, J. et al. The evolution of early foraminifera. Proc. Natl Acad. Sci. USA 100, 11494–8 (2003).Article 
ADS 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Vachard, D. Macroevolution and biostratigraphy of paleozoic foraminifers. in Stratigraphy and Timescales (Ed. Montenari, M.) Vol. 1, 257–323 (Academic Press, 2016).Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097.e21 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Guillou, L. et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597–D604 (2013).Article 
PubMed 
CAS 

Google Scholar 
Holzmann, M. & Pawlowski, J. An updated classification of rotaliid foraminifera based on ribosomal DNA phylogeny. Mar. Micropaleontol. 132, 18–34 (2017).Article 
ADS 

Google Scholar 
John, A. W. G. The regular occurrence of Reophax Scottie Chaster, a benthic foraminiferan, in plankton samples from the North Sea. J. Micropalaeontol. 6, 61–63 (1987).Article 

Google Scholar 
Kucera, M. et al. Caught in the act: anatomy of an ongoing benthic-planktonic transition in a marine protist. J. Plankton Res. 39, 436–449 (2017).Darling, K. F., Wade, C. M., Kroon, D. & Brown, A. J. L. Planktic foraminiferal molecular evolution and their polyphyletic origins from benthic taxa. Mar. Micropaleontol. 30, 251–266 (1997).Article 
ADS 

Google Scholar 
Church, S. H., Ryan, J. F. & Dunn, C. W. Automation and evaluation of the SOWH test with SOWHAT. Syst. Biol. 64, 1048–1058 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Shimodaira, H. An approximately unbiased test of phylogenetic tree selection. Syst. Biol. 51, 492–508 (2002).Article 
PubMed 

Google Scholar 
Pawlowski, J. et al. Extreme differences in rates of molecular evolution of foraminifera revealed by comparison of ribosomal DNA sequences and the fossil record. Mol. Biol. Evol. 14, 498–505 (1997).Article 
PubMed 
CAS 

Google Scholar 
Peijnenburg, K. T. C. A. et al. The origin and diversification of pteropods precede past perturbations in the Earth’s carbon cycle. Proc. Natl Acad. Sci. USA 117, 25609–25617 (2020).Article 
ADS 
PubMed 
PubMed Central 
CAS 

Google Scholar 
O’Brien, C. L. et al. Cretaceous sea-surface temperature evolution: constraints from TEX86 and planktonic foraminiferal oxygen isotopes. Earth-Sci. Rev. 172, 224–247 (2017).Article 
ADS 

Google Scholar 
Olsson, R. K., Berggren, W. A., Hemleben, C. & Huber, B. T. Atlas of Paleocene planktonic foraminifera. Smithson. Contrib. Paleobiol. 1–252 https://doi.org/10.5479/si.00810266.85.1 (1999).Arenillas, I. & Arz, J. A. Benthic origin and earliest evolution of the first planktonic foraminifera after the Cretaceous/Palaeogene boundary mass extinction. Hist. Biol. 29, 25–42 (2017).Article 

Google Scholar 
Huber, B. T., Petrizzo, M. R. & MacLeod, K. G. Planktonic foraminiferal endemism at southern high latitudes following the terminal cretaceous extinction. J. Foraminifer. Res. 50, 382–402 (2020).Article 

Google Scholar 
Arenillas, I., Arz, J. A. & Gilabert, V. An updated suprageneric classification of planktic foraminifera after growing evidence of multiple benthic-planktic transitions. Spanish J. Palaeontol. https://doi.org/10.7203/sjp.22189 (2022).Culver, S. J. Benthic foraminifera across the Cretaceous–Tertiary (K–T) boundary: a review. Mar. Micropaleontol. 47, 177–226 (2003).Article 
ADS 

Google Scholar 
Widmark, J. G. V. & Malmgren, B. A. Benthic foraminiferal changes across the Cretaceous/Tertiary boundary in the deep sea; DSDP sites 525, 527, and 465. J. Foraminifer. Res. 22, 81–113 (1992).Article 

Google Scholar 
Rigaud, S., Martini, R. & Vachard, D. Early evolution and new classification of the order Robertinida (foraminifera). J. Foraminifer. Res. 45, 3–28 (2015).Article 

Google Scholar 
Rigaud, S., Granier, B. & Masse, J. P. Aragonitic foraminifers: an unsuspected wall diversity. J. Syst. Palaeontol. 19, 461–488 (2021).Article 

Google Scholar 
Hull, P. M. et al. On impact and volcanism across the Cretaceous-Paleogene boundary. Science 367, 266–272 (2020).Article 
ADS 
PubMed 
CAS 

Google Scholar 
Morard, R. et al. PFR2: a curated database of planktonic foraminifera 18S ribosomal DNA as a resource for studies of plankton ecology, biogeography and evolution. Mol. Ecol. Resour. 15, 1472–1485 (2015).Article 
PubMed 
CAS 

Google Scholar 
Morard, R. et al. Genetic and morphological divergence in the warm-water planktonic foraminifera genus Globigerinoides. PLoS ONE 14, 1–30 (2019).Article 

Google Scholar 
Morard, R., Vollmar, N. M., Greco, M. & Kucera, M. Unassigned diversity of planktonic foraminifera from environmental sequencing revealed as known but neglected species. PLoS ONE 14, e0213936 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinforma. 10, 1–9 (2009).Article 

Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014).Liaw, A. & Wiener, M. Classification and Regression by randomForest. R. N. 2, 18–22 (2002).
Google Scholar 
Lang, M. et al. mlr3: a modern object-oriented machine learning framework in R. J. Open Source Softw. 4, 1903 (2019).Article 
ADS 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Darriba, D. et al. ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models. Mol. Biol. Evol. 37, 291–294 (2020).Article 
MathSciNet 
PubMed 
CAS 

Google Scholar 
Kozlov, A. M. et al. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Barbera, P. et al. EPA-ng: massively parallel evolutionary placement of genetic sequences. Syst. Biol. 68, 365–369 (2019).Article 
MathSciNet 
PubMed 

Google Scholar 
Letunic, I. & Bork, P. Interactive tree of life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, 293–296 (2021).Article 

Google Scholar 
Löytynoja, A. & Goldman, N. WebPRANK: a phylogeny-aware multiple sequence aligner with interactive alignment browser. BMC Bioinform. 11, 1–7 (2010).Ronquist, F. et al. MrBayes 3. 2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Dos Reis, M., Donoghue, P. C. J. & Yang, Z. Bayesian molecular clock dating of species divergences in the genomics era. Nat. Rev. Genet. 17, 71–80 (2016).Article 
PubMed 

Google Scholar 
Song, H., Tong, J. & Chen, Z. Q. Evolutionary dynamics of the Permian-Triassic foraminifer size: Evidence for Lilliput effect in the end-Permian mass extinction and its aftermath. Palaeogeogr. Palaeoclimatol. Palaeoecol. 308, 98–110 (2011).Article 

Google Scholar 
Copestake, P. & Johnson, B. Lower Jurassic Foraminifera from the Llanbedr (Mochras Farm) Borehole, North Wales, UK. Monogr. Palaeontogr. Soc. 167, 1–403 (2013).Article 

Google Scholar 
Rigaud, S. & Blau, J. New Robertinid Foraminifers from the Early Jurassic of Adnet, Austria and Their Evolutionary Importance. Acta Palaeontol. Pol. 61, 721–734 (2016).Article 

Google Scholar 
Boudagher-fadel, M. K. Evolution and Geological Significance of Larger Benthic Foraminifera. Evolution and Geological Significance of Larger Benthic Foraminifera (UCL Press, 2018).Piuz, A. & Meister, C. Cenomanian rotaliids (Foraminiferida) from Oman and Morocco. Swiss J. Palaeontol. 132, 81–97 (2013).Article 

Google Scholar 
Kucera, M. & Schönfeld, J. The origin of modern oceanic foraminiferal faunas and Neogene climate change. in Deep-Time Perspectives on Climate Change: Marrying the Signal from Computer Models and Biological Proxies. (ed. The Micropalaeontological Society, S. P.) 409–425 (The Geological Society, 2007).Drummond, A. J. & Suchard, M. A. Bayesian random local clocks, or one rate to rule them all. BMC Biol. 8, 114 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Rambaut, A. FigTree version 1.3.1. http://tree.bio.ed.ac.uk (2009).Groussin, M., Pawlowski, J. & Yang, Z. Bayesian relaxed clock estimation of divergence times in foraminifera. Mol. Phylogenet. Evol. 61, 157–166 (2011).Article 
PubMed 

Google Scholar 
Loeblich Jr, A. R. & Tappan, H. Foraminiferal Genera and Their Classification (Springer, 1988). More

Prosser JI, Hink L, Gubry-Rangin C, Nicol GW. Nitrous oxide production by ammonia oxidizers: Physiological diversity, niche differentiation and potential mitigation strategies. Glob Chang Biol. 2020;26:103–18.Article 
PubMed 

Google Scholar 
Huang L, Chakrabarti S, Cooper J, Perez A, John SM, Daroub SH, et al. Ammonia-oxidizing archaea are integral to nitrogen cycling in a highly fertile agricultural soil. ISME Commun. 2021;1:19.Article 

Google Scholar 
Hink L, Gubry-Rangin C, Nicol GW, Prosser JI. The consequences of niche and physiological differentiation of archaeal and bacterial ammonia oxidisers for nitrous oxide emissions. ISME J. 2018;12:1084–93.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Li Y, Chapman SJ, Nicol GW, Yao H. Nitrification and nitrifiers in acidic soils. Soil Biol Biochem. 2018;116:290–301.Article 
CAS 

Google Scholar 
Ahlgren NA, Fuchsman CA, Rocap G, Fuhrman JA. Discovery of several novel, widespread, and ecologically distinct marine Thaumarchaeota viruses that encode amoC nitrification genes. ISME J 2019;13:618–31.Article 
PubMed 
CAS 

Google Scholar 
Kim J-G, Kim S-J, Cvirkaite-Krupovic V, Yu W-J, Gwak J-H, López-Pérez M, et al. Spindle-shaped viruses infect marine ammonia-oxidizing thaumarchaea. Proc Natl Acad Sci USA. 2019;116:15645–50.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Emerson JB. Soil Viruses: A New Hope. mSystems 2019;4:e00120–19.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Santos-Medellín C, Estera-Molina K, Yuan M, Pett-Ridge J, Firestone MK, Emerson JB. Spatial turnover of soil viral populations and genotypes overlain by cohesive responses to moisture in grasslands. Proc Natl Acad Sci USA. 2022;119:e2209132119.Article 
PubMed 
PubMed Central 

Google Scholar 
Wu R, Davison MR, Gao Y, Nicora CD, Mcdermott JE, Burnum-Johnson KE, et al. Moisture modulates soil reservoirs of active DNA and RNA viruses. Commun Biol. 2021;4:992.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Braga LPP, Spor A, Kot W, Breuil M-C, Hansen LH, Setubal JC, et al. Impact of phages on soil bacterial communities and nitrogen availability under different assembly scenarios. Microbiome 2020;8:52.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Albright MBN, Gallegos-Graves LV, Feeser KL, Montoya K, Emerson JB, Shakya M, et al. Experimental evidence for the impact of soil viruses on carbon cycling during surface plant litter decomposition. ISME Commun. 2022;2:24.Article 

Google Scholar 
Starr EP, Shi S, Blazewicz SJ, Koch BJ, Probst AJ, Hungate BA, et al. Stable-isotope-informed, genome-resolved metagenomics uncovers potential cross-kingdom interactions in rhizosphere soil. mSphere 2021;6:e0008521.Article 
PubMed 

Google Scholar 
Trubl G, Kimbrel JA, Liquet-Gonzalez J, Nuccio EE, Weber PK, Pett-Ridge J, et al. Active virus-host interactions at sub-freezing temperatures in Arctic peat soil. Microbiome 2021;9:208.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Lee S, Sieradzki ET, Nicolas AM, Walker RL, Firestone MK, Hazard C, et al. Methane-derived carbon flows into host–virus networks at different trophic levels in soil. Proc Natl Acad Sci USA. 2021;118:e2105124118.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Nicol GW, Leininger S, Schleper C, Prosser JI. The influence of soil pH on the diversity, abundance and transcriptional activity of ammonia oxidizing archaea and bacteria. Environ Microbiol. 2008;10:2966–78.Article 
PubMed 
CAS 

Google Scholar 
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2020;36:1925–7.CAS 

Google Scholar 
Alves RJE, Minh BQ, Urich T, von Haeseler A, Schleper C. Unifying the global phylogeny and environmental distribution of ammonia-oxidising archaea based on amoA genes. Nat Commun. 2018;9:1517.Article 
PubMed 
PubMed Central 

Google Scholar 
Cardinale DJ, Duffy S. Single-stranded genomic architecture constrains optimal codon usage. Bacteriophage 2011;1:219–24.Article 
PubMed 
PubMed Central 

Google Scholar 
Lee S, Sorensen JW, Walker RL, Emerson JB, Nicol GW, Hazard C. Soil pH influences the structure of virus communities at local and global scales. Soil Biol Biochem. 2022;166:108569.Article 
CAS 

Google Scholar 
Jang HB, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol. 2019;37:632–9.Article 

Google Scholar 
Nishimura Y, Yoshida T, Kuronishi M, Uehara H, Ogata H, Goto S. ViPTree: the viral proteomic tree server. Bioinformatics 2017;33:2379–80.Article 
PubMed 
CAS 

Google Scholar 
Kerou M, Offre P, Valledor L, Abby SS, Melcher M, Nagler M, et al. Proteomics and comparative genomics of Nitrososphaera viennensis reveal the core genome and adaptations of archaeal ammonia oxidizers. Proc Natl Acad Sci Usa 2016;113:7937–46.Article 

Google Scholar 
Reyes C, Hodgskiss LH, Kerou M, Pribasnig T, Abby SS, Bayer B, et al. Genome wide transcriptomic analysis of the soil ammonia oxidizing archaeon Nitrososphaera viennensis upon exposure to copper limitation. ISME J 2020;14:2659–74.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Sieradzki ET, Greenlon A, Nicolas AM, Firestone MK, Pett-Ridge J, Blazewicz SJ, et al. Functional succession of actively growing soil microorganisms during rewetting is shaped by precipitation history. bioRxiv. 2022; 2022.06.28.498032. More

Data sourcesData for the trophic parameters and data categories listed in Tables 1 and 2 were gathered from peer-reviewed scientific publications, grey literature (e.g., agency reports, theses, and dissertations) and unpublished data by the authors of this paper. Data compilation on stomach contents, stable isotopes, FATM, and trophic positions, focussed on mesopelagic organisms, their potential prey and predators. For major and trace elements, energy density and estimates of diet proportions, our search concentrated on mesopelagic taxa. Nevertheless, we also gathered information from small or intermediate-sized epi-, bathy- or benthopelagic species found in the compiled data sources. These species were included because they play key roles in most marine ecosystems, both as important consumers of phytoplankton and zooplankton, and prey for many top predators, and can represent alternative energy pathways to mesopelagic organisms. However, we stress that the data coverage for these species in the current version of the database is very incomplete. Our main interest was on data from the central and eastern North Atlantic, and the Mediterranean, corresponding to the study regions of the SUMMER project. When we could not find suitable data within this region, we extended the geographic scope of our literature search to the western North Atlantic. We did not search for datasets in open access repositories since those data can be easily accessed and extracted. However, some of the data provided by the authors of this paper have been previously deposited in PANGAEA.DNA sequencing-based methods, such as metabarcoding and direct shotgun sequencing, are emerging as promising tools in dietary analyses due to the high resolution in taxonomic identification of many prey simultaneously, and the potential to provide quantitative diet estimates from relative read abundance29. However, recent studies have shown that various methodological and biological factors can break the correlation between the number and abundance of ingested prey and the prey DNA present in the sample, and lead to biased estimates of taxonomic diversity and composition of diet29,30. Given the uncertainties remaining in the interpretation of DNA sequencing-based diet data, we decided not to include these data in MesopTroph until additional research demonstrates that these techniques can be confidently applied for quantitative diet assessment.We identified available data sources in the literature through systematic searches on Web of Science, Google Scholar, ResearchGate, and the Google search engine. We used multiple combinations of terms related to specific data categories (Table 3), in conjunction with the common or scientific taxon names (from genus to order), and the ocean basin. For example, the search for stomach content data of fishes belonging to the family Myctophidae was undertaken using the following terms: “stomach content” OR “gut content” OR “prey composition” OR “diet composition”, AND “mesopelagic fish” OR “myctophid” OR “Myctophiformes” OR “Myctophidae”, AND “Atlantic” or “Mediterranean”. For the mesopelagic and predator species known to be numerically abundant in the SUMMER study regions, we performed a second literature search using the common or scientific name of the species, along with the terms “diet”, “feeding habits”, “trophic ecology”, “trophic markers”, or “food web”. We also examined the literature cited within each collected publication to locate additional data sources.Table 3 Terms used in the literature search for each data category.Full size tableWe next screened the full text of the compiled studies and retained data sources that: (1) were collected within the region of interest, (2) reported quantitative data for the trophic parameters of interest, (3) reported the number of samples for pooled or aggregated data, and (4) provided sufficient details on the methodology to enable a quality check. In the case of stable isotope data, we only included data sources reporting both δ13C and δ15N measurements.Data extraction, cleaning, and formattingWe created a template table for each data category in Microsoft Excel to assemble all datasets into a single file, and to facilitate cleaning and standardization of data records. We added a large number of metadata fields to the tables to annotate details about the sampling (e.g., location, date, methods), sampled specimen(s) (e.g., taxonomy, number and size of individuals, number of replicates, tissue analysed), and data source (e.g., full reference, DOI) for every record.Data contributors formatted and incorporated their datasets directly into the tables. For published sources, the data and associated metadata were extracted manually or digitized from the article text, tables, or supplementary material into the tables. Extraneous or hidden characters, and values such as “NA” (Not Available) or “ND” (Not Determined), were deleted from the parameter and metadata fields. Measurements of trophic parameters were standardized to the same units (see Tables 1 and 2). Parameter values that were clearly incorrect (e.g., δ15N  > 20, or the frequency of occurrence of a prey higher than the number of stomachs sampled) were corrected by searching for the value within the data source. When values could not be corrected, we deleted that data record.When available, we extracted information at the individual level. However, most studies reported data obtained from pooled samples of the same species. In some cases (e.g., small specimens such as planktonic organisms), a minimum and maximum number of individuals in the sample was provided instead of the actual number of individuals sampled. We added two columns to the tables presenting the minimum and maximum number of individuals in the sample. By filtering the column “Ind No (maximum per sample)” for values >1, users can easily identify records with aggregated data and differentiate them from records where information was drawn from a single individual (i.e., where “Ind No (maximum per sample)” =1). In addition, the tables Stomach contents and Estimates of diet proportions include a field “Sample ID” with a unique identifier of the sample. If data are reported at the individual level (i.e., “Ind No (maximum per sample)” =1) then Sample ID is the individual animal ID. If the data are from a group of individuals (i.e., “Ind No (maximum per sample)” >1), then Sample ID identifies that group.We standardized the taxonomic classification and nomenclature of fishes and elasmobranchs following the Eschmeyer’s Catalog of Fishes (http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp)31,32. For the remaining taxa, we used the World Register of Marine Species (http://www.marinespecies.org/)33. Unaccepted or alternate taxon names were replaced by the most up-to-date valid name. When the identification of a taxon was uncertain, the taxonomic level of identification was decreased to a satisfactory level. For example, prey reported as “Cephalopods” were changed to “Cephalopoda”, “Sepiolids” to “Sepiolidae”, and “Myctophum punctatum?” to the genus “Myctophum”.Stomach contentsStomach contents analysis is a standard dietary assessment method that potentially enables quantifying diet components with high taxonomic resolution34. Three parameters are typically used to describe diet composition from stomach contents: the number of individuals of a prey type as a proportion of the total number of prey items (%N), the proportion of a prey item by weight or volume (%W), and the proportion of stomachs containing a particular prey item (i.e., percent frequency of occurrence, %F)35. When available, we collected data on the three parameters, as well as on the absolute number, weight, and frequency of occurrence of each prey type in the stomachs of each sampled individual or group of individuals. If stated in the data source, we indicate if prey weights were directly measured or reconstructed from hard remains (fish otoliths and vertebrae, cephalopod beaks), and if they represent dry or wet weight. Some datasets contained records of prey items without corresponding weights or numbers. As a result, the cumulative percent of all prey items did not sum to 100%. This occurred in 11 data records for the cumulative %W, and nine for the cumulative %N. While we checked the accuracy of percentage values and adjusted rounding errors, we did not attempt to fill in missing values nor did we remove records with missing values. When prey values were reported by an upper bound (e.g., “ More

MaterialsTrunk wash was collected from one male (Tembo, born 1985) and five female (Tonga, 1984; Numbi, 1992; Mongu, 2003; Iqhwa, 2013; Kibali, 2019) African elephants at the Vienna Zoo during routine procedures. Briefly, 100 mL of a sterile 0.9% saline solution is injected in each nostril of the trunk, which is kept in a lifted position, so that the solution is running up to the base of the trunk. The mixture of the solution and trunk mucus is collected in sterile plastic bags by active blowing of the elephant. Chemicals were all from Merck, Austria, unless otherwise stated. Restriction enzymes and kits for DNA extraction and purification were from New England Biolabs, USA. Oligonucleotides and synthetic genes were custom synthesised at Eurofins Genomics, Germany.Ethics declarationWe confirm that the trunk wash performed to provide a sample of the mucus was carried out as a routine procedure to monitor the health of elephants at the Vienna Zoo and in accordance with relevant guidelines and regulations.Trunk wash fractionationTrunk wash was centrifuged for 1 h at 10,000 g, the supernatant was dialyzed against 50 mM Tris–HCl buffer, pH 7.4 and concentrated by ultrafiltration in the Amicon stirred cell, then fractionated by anion-exchange chromatography on HiPrep-Q 16/10 column, 20 mL (Bio-Rad), along with standard protocols.Protein alkylation and digestion, and mass spectrometry analysisSDS-PAGE gel portions of proteins from whole elephant trunk wash (for component identification), chromatographic fractions of the elephant trunk wash (for PTMs analysis) or SDS-PAGE gel bands of LafrOBP1 expressed in P. pastoris were in parallel triturated, washed with water, in gel-reduced, S-alkylated, and digested with trypsin (Sigma, sequencing grade). Resulting peptide mixtures were desalted by μZip-TipC18 (Millipore) using 50% (v/v) acetonitrile, 5% (v/v) formic acid as eluent, vacuum-dried by SpeedVac (Thermo Fisher Scientific, USA), and then dissolved in 20 μL of aqueous 0.1% (v/v) formic acid for subsequent MS analyses by means of a nanoLC-ESI-Q-Orbitrap-MS/MS system, comprising an UltiMate 3000 HPLC RSLC nano-chromatographer (Thermo Fisher Scientific) interfaced with a Q-ExactivePlus mass spectrometer (Thermo Fisher Scientific) mounting a nano-Spray ion source (Thermo Fisher Scientific). Chromatographic separations were obtained on an Acclaim PepMap RSLC C18 column (150 mm × 75 μm ID; 2 μm particle size; 100 Å pore size, Thermo Fisher Scientific), eluting the peptide mixtures with a gradient of solvent B (19.92/80/0.08 v/v/v water/acetonitrile/formic acid) in solvent A (99.9/0.1 v/v water/formic acid), at a flow rate of 300 nL/min. In particular, solvent B started at 3%, increased linearly to 40% in 45 min, then achieved 80% in 5 min, remaining at this percentage for 4 min, and finally returned to 3% in 1 min. The mass spectrometer operated in data-dependent mode in positive polarity, carrying out a full MS1 scan in the range m/z 345–1350, at a nominal resolution of 70,000, followed by MS/MS scans of the 10 most abundant ions in high energy collisional dissociation (HCD) mode. Tandem mass spectra were acquired in a dynamic m/z range, with a nominal resolution of 17,500, a normalized collision energy of 28%, an automatic gain control target of 50,000, a maximum ion injection time of 110 ms, and an isolation window of 1.2 m/z. Dynamic exclusion was set to 20 s36.Bioinformatics for peptide identification and post-translational modification assignmentRaw mass data files were searched by Proteome Discoverer v. 2.4 package (Thermo Fisher Scientific), running the search engine Mascot v. 2.6.1 (Matrix Science, UK), Byonic™ v. 2.6.46 (Protein Metrics, USA) and Peaks Studio 8.0 (BSI, Waterloo, Ontario, Canada) software, both for peptide assignment/protein identification and for post-translational modification analysis.In the first case, analyses were carried out against a customized database containing protein sequences downloaded from NCBI (https://www.ncbi.nlm.nih.gov/) for superorder Afrotheria (consisting of 192,838 protein sequences, December 2021) plus the most common protein contaminants and trypsin. Parameters for database searching were fixed carbamidomethylation at Cys, and variable oxidation at Met, deamidation at Asn/Gln, and pyroglutamate formation at Gln. Mass tolerance was set to ± 10 ppm for precursors and to ± 0.05 Da for MS/MS fragments. Proteolytic enzyme and maximum number of missed cleavages were set to trypsin and 3, respectively. All other parameters were kept at default values. In the latter case, raw mass data were analyzed against a customized database containing LafrOBP1 (XP_023395442.1) protein sequence plus the most common protein contaminants and trypsin, allowing to search Lys-acetylation (Δm =  + 42.01), Ser/Thr/Tyr-phosphorylation (Δm =  + 79.97), and the most common mammals N-linked glycans at Asn and O-linked glycans at Ser/Thr/Tyr, using the same parameters previously set. The max PTM sites per peptide was set to 2.Proteome Discoverer peptide candidates were considered confidently identified only when the following criteria were satisfied: (i) protein and peptide false discovery rate (FDR) confidence: high; (ii) peptide Mascot score:  > 30; (iii) peptide spectrum matches (PSMs): unambiguous; (iv) peptide rank (rank of the peptide match): 1; (v) Delta CN (normalized score difference between the selected PSM and the highest-scoring PSM for that spectrum): 0. Byonic peptide candidates were considered confidently identified only when the following criteria were satisfied: (i) PEP 2D and PEP 1D:  More

DNA sample collectionTo analyze the population structure of wild medaka populations, we selected samples from the DNA collection of Takehana et al.29, deposited in University of Shizuoka. The original DNA collection had been made throughout 1980s and 2000s. The selected samples covered the major mitotypes and contained more than three individuals of each population (Table S11, Fig. 3), which were collected from three collection sites for O. sakaizumii and 12 collection sites for O. latipes. We also examined several artificial strains: HNI and Hd-rR, which are inbred strains derived from O. sakaizumii and O. latipes, respectively, and four Himedaka individuals from commercial stock (Uruma city, Okinawa Prefecture, Japan).In addition, samples were newly collected at Kunigami Village, Okinawa Prefecture. Live fish were anesthetized with MS-222 (aminobenzene methanesulfonate, FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan) and then fixed in 99% ethanol. Genomic DNA was extracted using a DNeasy kit (Qiagen Inc., Hilden, Germany) from ethanol-fixed pectoral fin samples according to the manufacturer’s protocol. The DNA concentration was measured using a spectrophotometer (Nanodrop 1000, Thermo Fisher Scientific, Waltham, Massachusetts, USA), and the DNA was diluted with PCR-grade water to a concentration of c.a. 10 ng/µl (UltraPure™ DNase/RNase-Free Distilled Water, Thermo Fisher Scientific).Ethic statementAll methods were carried out in accordance with the Regulation for Animal Experiments at University of the Ryukyus for handling live fish. All experiments were approved by the Animal Care Ethics Committee of University of the Ryukyus (R2019035). All experimental methods are reported in accordance with ARRIVE guidelines.PCR primer designThe following steps were used to select primers for MAAS (Fig. 1). (1) All possible 10-mer sequence combinations (i.e., 410 = 1,048,576 sequences) were generated in silico. (2) The sequences containing simple sequence repeats, some of which had been used in the MIG-seq method17, were excluded. (3) Sequences containing a functional motif, such as a transcription factor-binding site, were also excluded because they may not be suitable for examining neutral genetic markers. We obtained a catalog of motifs from the JASPAR CORE40 (http://jaspar.genereg.net). (4) To avoid taxon-dependency in primer performance, we used information about the k-mer (k = 10) frequency of reference genomes from multiple phyla. Sequences that showed marked differences in frequency among taxa were excluded. The frequencies of each 10-mer sequence in the reference genomes of 17 species belonging to 12 phyla of metazoa were counted (Table S12) using the “oligonucleotideFrequency” function in the “Biostrings” package ver. 2.441. In each of these taxa, the frequencies of sequences were stratified into three grades ( 103). We then selected the sequences that showed the same grade in more than 80% (14/17) of the species. (5) To avoid synthesizing primer dimers, self-complementary sequences were excluded, taking Illumina adapter sequences (5′-CGCTCTTCCGATCT-3′ and 5′-TGCTCTTCCGATCT-3′) into account. Self-complementation of two bases at the 3′-end or every three continuous bases in primer sequences was then evaluated using a custom script in R ver. 3.5.0 (R Development Core Team, http://cran.r-project.org). Based on the selected 10-mer sequences (i.e., 129 sequences, Fig. 1), 7-mer primer sequences were designed by removing the 3 bases at the 3′ end. Finally, we selected 24 candidate sequences for both 10-mer and 7-mer primers for the subsequent step (Table S1).The primer sequence consisted of three parts17: partial sequence of the Illimina adapter, 7 N bases, and a short priming sequence, e.g., 5′-CGCTCTTCCGATCTNNNNNNNGTCGCCC-3′. PCR amplification was performed using the candidate primers using the first PCR protocol described below (Table S1). Banding patterns were observed by electrophoresis on 1% agarose gels (agarose S; TaKaRa, Japan). Of the candidate primers, we selected four 7-mer primers and four 10-mer primers that each gave a smeared banding pattern with amplification products ranging from 500 to 2000 bp, indicating uniform amplification of multiple target sequences (Table S1).Library construction and sequencingThe library was constructed by a two-step PCR approach using a modification of a MIG-seq protocol14. In the first PCR step, multiple regions of genomic DNA were amplified using a cocktail of primers with a Multiplex PCR Assay Kit Ver.2 (TaKaRa) (Table 1). The volume of the PCR reaction mixture was 7 μl, containing 1 μl of template DNA, 2 μM of each PCR primer, 3.5 μl of 2 × Multiplex PCR Buffer, and 0.035 μl of Multiplex PCR Enzyme Mix. PCR was performed under the following conditions: denaturation at 94 °C for 1 min; 25 cycles of 94 °C for 30 s, 38 °C for 1 min, and 72 °C for 1 min, followed by a final extension step at 72 °C for 10 min.The primers in the second PCR step contained the Illumina sequencing adapter and an index sequence to identify each sample. Following the Truseq indexes, we used the combinations of eight forward indexes (i5) and 12 reverse indexes (i7), which resulted in a total of 96 combinations. To be used as a template for the second PCR, the first PCR product from each sample was diluted 50 times with PCR-grade water. The second PCR was performed in a 15-μl reaction mixture containing, 3 μl of diluted first PCR product, 3 μl of 5 × PrimeSTAR GXL Buffer, 200 μM of each dNTP, 0.2 μM of forward index primer and reverse index primer, 0.375 U of PrimeSTAR GXL DNA Polymerase (TaKaRa). The PCR conditions were as follows: 12 cycles at 98 °C for 10 s, 54 °C for 15 s, and 68 °C for 30 s.The second PCR product of each sample was pooled by equal volume and size-selected from 600 to 1000 bp using solid phase reversible immobilization (SPRI) select beads (Beckman Coulter Inc, Brea, California, USA) according to the manufacturer’s protocol. The DNA concentration of the pooled library was measured using a Qubit fluorometer (Thermo Fisher Scientific). We sequenced the libraries using two NGS platforms, MiSeq (Illumina, MiSeq Reagent Kit v2 Micro, Paired-End (PE), 150 bp) and HiSeq X (Illumina, PE, 150 bp). Sequencing using the HiSeq X platform was performed by Macrogen Japan (Tokyo, Japan).To compare primer performance, the DNA libraries constructed using the 7-mer and 10-mer primers for one individual were sequenced using MiSeq. Then, a 7-mer primer cocktail containing four sets of mixed primers was used for the subsequent analyses (Table 1). We also constructed DNA libraries using 7-mer and MIG-seq primer cocktails for three individuals and sequenced them using the HiSeq X platform. Finally, we constructed DNA libraries using 7-mer primer cocktails for 67 wild individuals and six artificial strain individuals for population genetics analyses (Table S11, Fig. 3).Mapping and SNV callingGenotyping was conducted using the following BWA-GATK best-practices pipeline for each sample42. Primer sequences were removed using cutadapt with the –b option selected43. The Illumina adapter sequences were also removed and quality filtering was performed using fastp ver. 0.20.0 with the “–detect_adapter_for_pe, –cut_front” option selected44. The remaining reads were mapped on the reference genome of medaka, Hd-rR strain, GCA_002234675.1; ASM223467v127 using Burrows-Wheeler Alignment tool, BWA mem ver. 0.7.1745. After mapping, output files were converted to Binary Alignment/Map (BAM) format using SAMtools ver. 1.746. SNVs and InDels in the sample were determined following the best practice guidelines set out in the Genome Analysis Tool Kit (GATK ver. 3.8.0)42. We then filtered out SNVs and InDels based on the following criteria: “QD  60.0 || MQ  More

Howe, H. F. & Smallwood, J. Ecology of seed dispersal. Annu. Rev. Ecol. Syst. 13, 201–228 (1982).Article 

Google Scholar 
Wang, B. C. & Smith, T. B. Closing the seed dispersal loop. Trends Ecol. Evol. 17, 379–385 (2002).Article 

Google Scholar 
Fleming, T. H., Breitwish, R. & Whitesides, G. H. Patterns of tropical vertebrate frugivore diversity. Annu. Rev. Ecol. Syst. 18, 91–109 (1987).Article 

Google Scholar 
Schupp, E. W. Quantity, quality and the effectiveness of seed dispersal by animals. Vegetatio 107(108), 15–29 (1993).Article 

Google Scholar 
Garber, P. A. & Lambert, J. E. Primates as seed dispersers: Ecological processes and directions for future research. Am. J. Primatol. 45, 3–8 (1998).Article 
CAS 
PubMed 

Google Scholar 
Godínez-Alvarez, H. & Jordano, P. An empirical approach to analysing the demographic consequences of seed dispersal by frugivores. In Seed Dispersal: Theory and its Application in a Changing World (eds Dennis, A. J. et al.) 391–406 (CAB International, 2007).Chapter 

Google Scholar 
Schupp, E. W., Jordano, P. & Gomez, J. M. Seed dispersal effectiveness revisited: A conceptual review. New Phytol. 188, 333–353 (2010).Article 
PubMed 

Google Scholar 
McConkey, K. R., Brockelman, W. Y. & Saralamba, C. Mammalian frugivores with different foraging behavior can show similar seed dispersal effectiveness. Biotropica 46, 647–651 (2014).Article 

Google Scholar 
Culot, L., Huynen, M. C. & Heymann, E. W. Partitioning the relative contribution of one-phase and two-phase seed dispersal when evaluating seed dispersal effectiveness. Methods Ecol. Evol. 6, 178–186 (2015).Article 

Google Scholar 
McConkey, K. R., Brockelman, W. Y., Saralamba, C. & Nathalang, A. Effectiveness of primate seed dispersers for an “oversized’’ fruit, Garcinia benthamii. Ecology 96, 2737–2747 (2015).Article 
PubMed 

Google Scholar 
Camargo, P., Martins, M. M., Feitosa, R. M. & Christianini, A. V. Bird and ant synergy increases the seed dispersal effectiveness of an ornithochoric shrub. Oecologia 181, 507–518 (2016).Article 
ADS 
PubMed 

Google Scholar 
McConkey, K. R. et al. Different megafauna vary in their seed dispersal effectiveness of the megafaunal fruit Platymitra macrocarpa (Annonaceae). PLoS One 13, e0198960 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Balcomb, S. R. & Chapman, C. A. Bridging the gap: Influence of seed deposition on seedling recruitment in a primate-tree interaction. Ecol. Monogr. 73, 625–642 (2003).Article 

Google Scholar 
Russo, S. E. Responses of dispersal agents to tree and fruit traits in Virola calophylla (Myristicaceae): Implications for selection. Oecologia 136, 80–87 (2003).Article 
ADS 
PubMed 

Google Scholar 
Gross-Camp, N. D., Masozera, M. & Kaplin, B. A. Chimpanzee seed dispersal quantity in a tropical montane forest of Rwanda. Am. J. Primatol. 71, 901–911 (2009).Article 
PubMed 

Google Scholar 
Jordano, P. & Schupp, E. W. Seed disperser effectiveness: The quantity component and patterns of seed rain for Prunus mahaleb. Ecol. Monogr. 70, 591–615 (2000).Article 

Google Scholar 
Leighton, M. Modeling dietary selectivity by Bornean orangutans: evidence for integration of multiple criteria in fruit selection. Int. J. Primatol. 14, 257–313 (1993).Article 

Google Scholar 
Stevenson, P. R. Fruit choice by woolly monkeys in Tinigua National Park, Colombia. Int. J. Primatol. 25, 367–381 (2004).Article 

Google Scholar 
Palacio, F. X. & Ordano, M. The strength and drivers of bird-mediated selection on fruit crop size: A meta-analysis. Front. Ecol. Evol. 6, 18 (2018).Article 

Google Scholar 
Flörchinger, M., Braun, J., Böhning-Gaese, K. & Schaefer, H. M. Fruit size, crop mass, and plant height explain differential fruit choice of primates and birds. Oecologia 164, 151–161 (2010).Article 
ADS 
PubMed 

Google Scholar 
Wenny, D. G. Seed dispersal, seed predation, and seedling recruitment of a neotropical montane tree. Ecol. Monogr. 70, 331–351 (2000).Article 

Google Scholar 
Calviño-Cancela, M. Spatial patterns of seed dispersal and seedling recruitment in Corema album (Empetraceae): The importance of unspecialized dispersers for regeneration. J. Ecol. 90, 775–784 (2002).Article 

Google Scholar 
Masaki, T. & Nakashizuka, A. Seedling demography of Swida controversa: Effect of light and distance to conspecifics. Ecology 83, 3497–3507 (2002).Article 

Google Scholar 
Vulinec, K. Dung beetle communities and seed dispersal in primary forest and disturbed land in Amazonia. Biotropica 34, 297–309 (2002).Article 

Google Scholar 
Janzen, D. H. Herbivores and the number of tree species in tropical forests. Am. Nat. 104, 501–528 (1970).Article 

Google Scholar 
Connell, J. H. On the role of natural enemies in preventing competitive exclusion in some marine animals and in rain forest trees. In Dynamics of Populations (eds Den Boer, P. J. & Gradwell, G.) 298–312 (PUDOC, 1971).
Google Scholar 
Howe, H. F., Schupp, E. W. & Westley, L. C. Early consequences of seed dispersal for a Neotropical tree (Virola surinamensis). Ecology 66, 781–791 (1985).Article 

Google Scholar 
Valenta, K. & Fedigan, L. M. Spatial patterns of seed dispersal by white-faced capuchins in Costa Rica: Evaluating distant-dependent seed mortality. Biotropica 42, 223–228 (2010).Article 

Google Scholar 
Andresen, E. Seed dispersal by monkeys and the fate of dispersed seeds in a Peruvian rain forest. Biotropica 31, 145–158 (1999).
Google Scholar 
Dausmann, K. H., Glos, J., Linsenmair, K. E. & Ganzhorn, J. U. Improved recruitment of a lemur-dispersed tree in Malagasy dry forests after the demise of vertebrates in forest fragments. Oecologia 157, 307–316 (2008).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Jordano, P. & Herrera, C. M. Shuffling the offspring: uncoupling and spatial discordance of multiple stages in vertebrate seed dispersal. Ecoscience 2, 230–237 (1995).Article 

Google Scholar 
Rey, P. J. & Alcantara, J. M. Recruitment dynamics of a fleshy-fruited plant (Olea europaea): Connecting patterns of seed dispersal to seedling establishment. J. Ecol. 88, 622–633 (2000).Article 

Google Scholar 
Traveset, A., Gulias, J., Riera, N. & Mus, M. Transition probabilities from pollination to establishment in a rare dioecious shrub species (Rhamnus ludovici salvatoris) in two habitats. J. Ecol. 91, 427–437 (2003).Article 

Google Scholar 
Cordeiro, N. J. & Howe, H. F. Forest fragmentation severs mutualism between seed dispersers and an endemic African tree. Proc. Natl. Acad. Sci. USA 100, 14052–14056 (2003).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Blendinger, P. G., Blake, J. G. & Loiselle, B. A. Connecting fruit production to seedling establishment in two co-occurring Miconia species: consequences of seed dispersal by birds in upper Amazonia. Oecologia 167, 61–73 (2011).Article 
ADS 
PubMed 

Google Scholar 
Corlett, R. T. The impact of hunting on the mammalian fauna of tropical Asian forests. Biotropica 39, 292–303 (2007).Article 

Google Scholar 
Nuñez-Iturri, G., Olsson, O. & Howe, H. F. Hunting reduces recruitment of primate-dispersed trees in Amazonian Peru. Biol. Conserv. 141, 1536–1546 (2008).Article 

Google Scholar 
Effiom, E. O., Nuñez-Iturri, G., Smith, H. G., Ottosson, U. & Olsson, O. Bushmeat hunting changes regeneration of African rainforests. Proc. R. Soc. B-Biol. Sci. 280, 20130246 (2013).Article 

Google Scholar 
Harrison, R. D. et al. Consequences of defaunation for a tropical tree community. Ecol. Lett. 16, 687–694 (2013).Article 
PubMed 

Google Scholar 
Fuentes, E. R., Hoffmann, A. J., Poiani, A. & Alliende, M. C. Vegetation change in large clearings: patterns in the Chilean matorral. Oecologia 68, 358–366 (1986).Article 
ADS 
PubMed 

Google Scholar 
Holl, K. D. Do bird perching structures elevate seed rain and seedling establishment in abandoned tropical pasture?. Restor. Ecol. 6, 253–261 (1998).Article 

Google Scholar 
Beltran, L. C. & Howe, H. F. The frailty of tropical restoration plantings. Restor. Ecol. 28, 16–21 (2020).Article 

Google Scholar 
Bollen, A., Van Elsacker, L. & Ganzhorn, J. U. Relations between fruits and disperser assemblages in a Malagasy littoral forest: a community-level approach. J. Trop. Ecol. 20, 599–612 (2004).Article 

Google Scholar 
Ganzhorn, J. U. et al. Possible fruit protein effects of primate communities in Madagascar and the Neotropics. PLoS One 4, e8253 (2009).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Albert-Daviaud, A. et al. The ghost fruits of Madagascar: identifying dysfunctional seed dispersal in Madagascar’s endemic flora. Biol. Conserv. 242, 108438 (2020).Article 

Google Scholar 
Dew, J. L. & Wright, P. Frugivory and seed dispersal by four species of primates in Madagascar’s eastern rain forest. Biotropica 30, 425–437 (1998).Article 

Google Scholar 
Moses, K. L. & Semple, S. Primary seed dispersal by the black-and-white ruffed lemur (Varecia variegata) in the Manombo forest, south-east Madagascar. J. Trop. Ecol. 27, 529–538 (2011).Article 

Google Scholar 
Sato, H. Frugivory and seed dispersal by brown lemurs in a Malagasy tropical dry forest. Biotropica 44, 479–488 (2012).Article 

Google Scholar 
Razafindratsima, O. H., Jones, T. A. & Dunham, A. E. Patterns of movement and seed dispersal by three lemur species. Am. J. Primatol. 76, 84–96 (2014).Article 
PubMed 

Google Scholar 
Steffens, K. J., Sanamo, J. & Razafitsalama, J. The role of lemur seed dispersal in restoring degraded forest ecosystems in Madagascar. Folia Primatol. 93, 1–19 (2022).Article 

Google Scholar 
Ganzhorn, J. U., Fietz, J., Rakotovao, E., Schwab, D. & Zinner, D. Lemurs and the regeneration of dry deciduous forest in Madagascar. Conserv. Biol. 13, 794–804 (1999).Article 

Google Scholar 
Razafindratsima, O. H. et al. Consequences of lemur loss for above-ground carbon stocks in a Malagasy rainforest. Int. J. Primatol. 39, 415–426 (2018).Article 

Google Scholar 
Razafindratsima, O. H. Seed dispersal by vertebrates in Madagascar’s forests: Review and future directions. Madag. Conserv. Dev. 9, 90–97 (2014).Article 

Google Scholar 
Nathan, R. & Muller-Landau, H. C. Spatial patterns of seed dispersal, their determinants and consequences for recruitment. Trends Ecol. Evol. 15, 278–285 (2000).Article 
CAS 
PubMed 

Google Scholar 
Pérez-Ramos, I. M., Urbieta, I. R., Maranon, T., Zavala, M. A. & Kobe, R. K. Seed removal in two coexisting oak species: Ecological consequences of seed size, plant cover and seed-drop timing. Oikos 117, 1386–1396 (2008).Article 

Google Scholar 
Sato, H. Seasonal fruiting and seed dispersal by the brown lemur in a tropical dry forest, north-western Madagascar. J. Trop. Ecol. 29, 61–69 (2013).Article 

Google Scholar 
Chapman, C. A. & Chapman, L. J. Determinants of group size in primates: the importance of travel costs. In On the Move: How and Why Animals Travel in Groups (eds Boinski, S. & Garber, P. A.) 24–42 (The University of Chicago Press, 2000).
Google Scholar 
Sato, H. Diurnal resting in brown lemurs in a dry deciduous forest, northwestern Madagascar: Implications for seasonal thermoregulation. Primates 53, 255–263 (2012).Article 
PubMed 

Google Scholar 
Razanaparany, P. T. & Sato, H. Abiotic factors affecting the cathemeral activity of Eulemur fulvus in the dry deciduous forest of north-western Madagascar. Folia Primatol. 91, 463–480 (2020).Article 

Google Scholar 
Sato, H. Predictions of seed shadows generated by common brown lemurs (Eulemur fulvus) and their relationship to seasonal behavioral strategies. Int. J. Primatol. 39, 377–396 (2018).Article 

Google Scholar 
Agetsuma, N. & Nakagawa, N. Effects of habitat differences on feeding behaviors of Japanese monkeys: Comparison between Yakushima and Kinkazan. Primates 39, 275–289 (1998).Article 

Google Scholar 
Stevenson, P. R. Seed dispersal by woolly monkeys (Lagothrix lagothricha) at Tinigua National Park, Colombia: Dispersal distance, germination rates, and dispersal quantity. Am. J. Primatol. 50, 275–289 (2000).Article 
CAS 
PubMed 

Google Scholar 
Hladik, A. & Miquel, S. Seedling types and plant establishment in an African rain forest. In Reproductive Ecology of Tropical Forest Plants (eds Bawa, K. S. & Hadley, M.) 261–282 (UNESCO and The Parthenon Publishing Group, 1990).
Google Scholar 
Ibarra-Manriquez, G., Ramos, M. M. & Oyama, K. Seedling functional types in a lowland rain forest in Mexico. Am. J. Bot. 88, 1801–1812 (2001).Article 
CAS 
PubMed 

Google Scholar 
Blate, G. M., Peart, D. R. & Leighton, M. Post-dispersal predation on isolated seeds: A comparative study of 40 tree species in a Southeast Asian rainforest. Oikos 82, 522–538 (1998).Article 

Google Scholar 
Hosaka, T. et al. Responses of pre-dispersal seed predators to sequential flowering of Dipterocarps in Malaysia. Biotropica 49, 177–185 (2017).Article 

Google Scholar 
Iku, A., Itioka, T., Shimizu-Kaya, U., Kishimoto-Yamada, K. & Meleng, P. Differences in the fruit maturation stages at which oviposition occurs among insect seed predators feeding on the fruits of five dipterocarp tree species. Entomol. Sci. 21, 412–422 (2018).Article 

Google Scholar 
Kitajima, K. Impact of cotyledon and leaf removal on seedling survival in three tree species with contrasting cotyledon functions. Biotropica 35, 429–434 (2003).Article 

Google Scholar 
Marchand, P. et al. Seed-to-seedling transitions exhibit distance-dependent mortality but no strong spacing effects in a Neotropical forest. Ecology 101, e02926 (2020).Article 
PubMed 

Google Scholar 
Moles, A. T. & Westoby, M. Seedling survival and seed size: a synthesis of the literature. J. Ecol. 92, 372–383 (2004).Article 

Google Scholar 
Sonesson, L. K. Growth and survival after cotyledon removal in Quercus robur seedlings, grown in different natural soil types. Oikos 69, 65–70 (1994).Article 

Google Scholar 
Hubbell, S. P., Condit, R. & Foster, R. B. Presence and absence of density dependence in a neotropical tree community. Philos. Trans. R. Soc. Lond. Ser. B-Biol. Sci. 330, 269–281 (1990).Article 
ADS 

Google Scholar 
Terborgh, J. Enemies maintain hyperdiverse tropical forests. Am. Nat. 179, 303–314 (2012).Article 
PubMed 

Google Scholar 
Godínez-Alvarez, H., Valiente-Banuet, A. & Rojas-Martínez, A. The role of seed dispersers in the population dynamics of the columnar cactus Neobuxbaumia tetetzo. Ecology 83, 2617–2629 (2002).Article 

Google Scholar 
Carson, W. P., Anderson, J. T., Leigh, E. G. Jr. & Schnitzer, S. A. Challenges associated with testing and falsofying the Janzen-Connell hypothesis: a review and critique. In Tropical Forest Community Ecology (eds Carson, W. P. & Schnitzer, S. A.) 210–241 (Wiley-Blackwell, 2008).
Google Scholar 
Swamy, V. et al. Are all seeds equal? Spatially explicit comparisons of seed fall and sapling recruitment in a tropical forest. Ecol. Lett. 14, 195–201 (2011).Article 
PubMed 

Google Scholar 
Reid, J. L. et al. Multi-scale habitat selection of key frugivores predicts large-seeded tree recruitment in tropical forest restoration. Ecosphere 12, e03868 (2021).Article 

Google Scholar 
Steffens, K. J. E. Lemur food plants as options for forest restoration in Madagascar. Restor. Ecol. 28, 1517–1527 (2020).Article 

Google Scholar 
Chapman, C. A. & Dunham, A. E. Primate seed dispersal and forest restoration: an African perspective for a brighter future. Int. J. Primatol. 39, 427–442 (2018).Article 

Google Scholar 
Morris, P. & Hawkins, F. Birds of Madagascar: A Photographic Guide (Yale University Press, 1998).
Google Scholar 
Garbutt, N. Mammals of Madagascar: A Complete Guide (Yale University Press, 2007).
Google Scholar 
Mittermeier, R. A. et al. Lemurs of Madagascar 3rd edn. (Conservation International, 2010).
Google Scholar 
Sato, H., Ichino, S. & Hanya, G. Dietary modification by common brown lemurs (Eulemur fulvus) during seasonal drought conditions in western Madagascar. Primates 55, 219–230 (2014).Article 
PubMed 

Google Scholar 
Laman, T. G. Ficus seed shadows in a Bornean rain forest. Oecologia 107, 347–355 (1996).Article 
ADS 
PubMed 

Google Scholar 
Clark, C. J., Poulsen, J. R., Connor, E. F. & Parker, V. T. Fruiting trees as dispersal foci in a semi-deciduous tropical forest. Oecologia 139, 66–75 (2004).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Sato, H. Gut passage time and size of swallowed seeds in the common brown lemur and the mongoose lemur. Primate Res. 25, 45–54 (2009) (In Japanese with English summary).Article 

Google Scholar 
Menard, S. W. Applied Logistic Regression Analysis 2nd edn. (Sage Publication, 2002).Book 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn. (Springer, 2002).MATH 

Google Scholar 
Richards, S. A. Testing ecological theory using the information-theoretic approach: Examples and cautionary results. Ecology 86, 2805–2814 (2005).Article 

Google Scholar 
Symonds, M. R. E. & Moussalli, A. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav. Ecol. Sociobiol. 65, 13–21 (2011).Article 

Google Scholar  More