More stories

  • in

    Continuous presence of proto-cereals in Anatolia since 2.3 Ma, and their possible co-evolution with large herbivores and hominins

    Vegetation history of the Acıgöl areaOur palynological analyses of 72 regularly spaced samples show a diversified vegetal landscape alternately wooded and open, in response to orbitally driven climatic cyclicity. However, arboreal pollen values remain almost constantly below 50% of the Pollen Sum (PS) (average 27.5%, median 22.8%), which corresponds to an overall open landscape (Fig. 3). Among herbaceous plants, the dominant taxa are steppics such as Artemisia, heliophilous and halophilous taxa including Calystegia, several Compositae, Convolvulus, Linum, Plantago ssp., Poaceae and Chenopodiaceae that could develop on the saline shores of Acıgöl lake during evaporitic periods. Forests are composed of a mixture of conifers, Mediterranean Pinus, Abies, Cedrus, Cupressaceae and Picea, associated with broadleaved trees dominated by Mediterranean oaks, i.e. deciduous and evergreen Quercus, with some Olea. Riverine trees such as Alnus, Salix, Populus, Tamarix, Juglans and Platanus have also been identified. Few Tertiary or megathermic relictual taxa (Carya, Liquidambar, Parrotia, Pterocarya fraxinifolia, Taxodiaceae, Tsuga, Zelkova) were identified so far in the pollen assemblages, mostly before 2.2 Ma, due to climatic cooling17,18 since the end of Tertiary which led to a decline in global biodiversity19,20.Figure 3Simplified pollen and NPP diagram in percentages of Acıgöl, core 3, based on the age model of Demory et al. [1]. Equidistant scale. Values are in percentages calculated on a pollen sum without Non-Pollen Palynomorphs (NPP), Ferns, Bryophytes and Algae. The beige rectangle corresponds to the date of the presence of Homo erectus at Kocabaş (Lebatard et al. [4]).Full size imageThe vast freshwater stretch of Acıgöl, located in a predominantly arid limestone hills environment, seems to have been a crucial resource for the mammalian fauna, which probably concentrated around the site in search of water and pastures. Indeed, low percentages of arboreal pollen imply that the landscape remained open throughout the sequence and suggest a marked grazing pressure by herbivores in addition to climatic factors21,22,23.Coprophilous fungi spores, cereals and other ancestors of cultivated plantsCoprophilous fungi spores are excellent indicators of herbivorous mega-mammal herds since they grow exclusively on dung deposited by these animals24. At Acıgöl, a wide variety of coprophilous fungi spores has been identified throughout the pollen record including: Sporormiella sp., Podospora sp., Delitschia sp., Sordaria sp. and Valsaria variospora (Figs. 3, 4). They provide evidence for a continuous presence of large herbivorous mammals around the lake throughout Quaternary.Figure 4Coprophilous fungi spores of Acıgöl, core 3. Equidistant scale. Age model is from Demory et al. [1]. In red: coprophilous fungi taxa..Full size imagePollens of Poaceae, such as Secale (rye) and Cerealia-type, have been identified throughout the sequence (Figs. 3, 5). Unexpectedly, they present the same morphological characteristics as that of modern cereal grains25,26, namely an average size of ≥ 40 µm and a large pore + annulus (≥ 8 µm). As by definition cereals are cultivated plants, we will call the corresponding plants “proto-cereals” to highlight that their pollen are identical to those of cereals. This resemblance can be seen clearly in Fig. 5, where we have brought together fossil cereals from Acιgöl (Fig. 5, photos 1–7), from Roman time (Fig. 5, photo 8), not modified by modern agricultural practices, and from the current wheat field of the Lauragais agricultural plain, Gardouch, France (Fig. 5, photo 9). Cerealia-type frequencies reach a maximum of 9% of the PS around 2.2 Ma and can be as abundant as wild Poaceae pollen (Fig. 3). The Cerealia/Poaceae ratio shows that 24.66% of all Poaceae are proto-cereals from 2.0 to 2.3 Ma (Supplementary Table 1). Such high proto-cereal rates are almost never reached in pollen records, even in recent periods and in the presence of agriculture, because of the very low pollen dispersal capacity of cereals27. A lowering of frequencies down to 2–4% range is recorded in younger periods (Fig. 3), as well as a step like decrease of the Cerealia/Poaceae ratio (Fig. 6). This change may be related to the Middle-Pleistocene Transition (MPT) cooling and to the mega-mammal fauna change from a Villafranchian to a Galerian type28. MPT and faunal changes occurred around 0.9–1.0 Ma, while a decrease in our proto-cereal starts around 1.5 Ma, however signs of cooling and amplified climatic cycles predate the MPT28.Figure 5Pollen grain of Cerealia and Triticum sp. from Acıgöl (ACI), core 3 (photos 1–7), the Roman site of La Verrerie, Arles, France (photo 8) and Gardouch, France, current wheat field (photo 9). Photographies with a photonic (photo 1 – 4 and 8) and a confocal microscope (photos 5-7 and 9). 1) sample ACI 239 m, age: 0.871 Ma. 2) sample ACI 435.50 m, age: 1.709 Ma. 3) sample ACI 532.44 m, age: 2.122 Ma. 4) sample ACI 509.50 m, age: 2.026 Ma. 5) sample ACI 552.57 m, age 2.206. 6) sample ACI 552.57 m, age: 2.206 Ma. 7) sample ACI 429.50 m, age: 1.681 Ma. 8) sample La Verrerie 1455, age: 50-70 BC (Roman). 9) current pollen of Triticum sp., age: 2000 AD. L: maximal length (µm).Full size imageFigure 6Cerealia/Poaceae ratio in %, % cultivated tree ancestors and % Olea of Acıgöl, core 3.Full size imageThe histogram of wild Poaceae and proto-cereal pollen size (Fig. 7a) shows that there are a number of pollen populations modes around 30, 37.5, 45–50, supporting the idea that the larger grain sizes cannot be interpreted as a tail of ‘anomalous’ wild Poaceae pollen. Moreover, comparison with the present-day pollen rain recorded in moss pollsters, sampled around the lake of Acıgöl (Fig. 7b and Supplementary Table 2), show that the large pollen size mode (≥ 40 µm) is nowadays nearly absent (0–0.97% of the PS, Cerealia/Poaceae ratio of 4.52%, Supplementary Tables 3 and 4), even in biotopes with wild Poaceae considered to be ancestors of cereals (Aegilops, sample 2a, cereal rate: 0.97% of the PS) or with cereals such as Hordeum (sample 3a, b and 4, cereal rate 0.31, 0.00, 0.33 of the PS respectively, Supplementary Tables 2 and 3).Figure 7a) Pollen size of wild Poaceae and proto-cereal of Acıgöl, core 3. The measurements were made on the 10 samples with the highest cereal pollen content. A total of 991 grains of pollen were measured. b) Current pollen rain at the Acıgöl lake and surroundings. 8 moss samples were collected and 354 measurements of the longest axis of the wild Poaceae and cereal pollen grain were made.Full size imageOur interpretation is that proto-cereals recorded throughout the Acıgöl sequence derive from wild Poaceae. Their emergence and predominance may have been favoured by the impact of large herbivore herds attracted to Acıgöl lake shores, and through genetic drift. Through the process of trampling, nitrogen enrichment of soils and browsing, large mammal herds could have altered the genotype of proto-cereals naturally present in Acıgöl and thus, favoured the emergence of modern cereals. For genetic reasons, the descendants of these proto-cereals are not represented today among cultivated Poaceae because domestication bottlenecks eliminate genetic variation29.Is there a relationship between the size of proto-cereal pollen and climate? To our knowledge, the genetic literature does not show any relationship between the increase in pollen size and temperature. However, there does seem to be a relationship with atmospheric drought30,31 which is said to have favoured the appearance of polyploidy in certain species of Poaceae. It cannot be excluded that climate has had an influence on the proto-cereal genome, but only the interaction between herds of large herbivores and proto-cereal steppes can explain why proto-cereal pollen has never been found in such abundance elsewhere in Pleistocene pollen records.The ancestors of cultivated trees (Olea sp., Juglans sp., Castanea sp., Corylus sp., Prunus t.), typical of the modern Mediterranean agriculture, are also present in the Acıgöl sequence (Fig. 3 and Supplementary Table 5). Their amount increases after 1.5 Ma, mainly due to Olea (Fig. 6). Other potentially edible plants such as Ephedra, Hippophae, all the Compositae and the Fagaceae have been identified in the pollen assemblages. They correspond to 54.4% of plants identified in the pollen assemblages. Among these plants, there are 72% grasses and 28% trees and, among edible organs, 51% are vegetables and 20% are seeds (Supplementary Fig. 1a,b). These results testify to the potential wealth of accessible food resources that human and animal populations could feed on. Interestingly, studies carried out in Spain on the present-day consumption of wild plants lead to results close to those obtained at Acıgöl, with 87% grasses and 13% trees32.In recent years, new biological and archaeological data obtained from sites with human occupation have improved our knowledge of the beginnings of agriculture and the modalities of its implementation. In the Levant, the Ohalo II site highlights the presence of proto-cereal seeds, and flint tools to harvest, as early as 23,000 years before the present33. Further north, on the archaeological site of Gesher Benot Ya’aqov, proto-cereal seeds (oats, Avena) as well as pollen from cereals and trees currently cultivated, were identified over a period ranging from 750,000 to 820,000 years34,35. Moreover, recent genetic data indicate that the emergence of agriculture did not occur at a single location at the onset of the Neolithic (e.g. the “Fertile Crescent” hypothesis) but is, on the contrary, an evolutionary and multi-regional long-term phenomenon36,37,38. Alternatively, or simultaneously, are the hominins also partly responsible by having developed episodes of a form of transitory “proto-agriculture”? We already know that this domestication process was discontinuous with shutdown and restart phases37,39. Acheulean lithic tools, characterised by symmetrically shaped bifaces, testify to the rather advanced cognitive capacities of early Pleistocene populations that may have visited the lakeshore of Acıgöl5. Hominin populations may also have benefited from this opportunity to diversify their food regime with easily harvested and nutrient-rich wild plants (Supplementary Table 5), as it is the case today for hunter-gatherer populations in Africa and elsewhere in the world. More

  • in

    Reef foraminifera as bioindicators of coral reef health in southern South China Sea

    1.Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshwater Res. 50(8), 839–866. https://doi.org/10.1071/MF99078 (1999).Article 

    Google Scholar 
    2.Moberg, F. & Folke, C. Ecological goods and services of coral reef ecosystems. Ecol. Econ. 29(2), 215–233. https://doi.org/10.1016/S0921-8009(99)00009-9 (1999).Article 

    Google Scholar 
    3.Shahbudin, S., Fikri Akmal, K. F., Faris, S., Normawaty, M. N. & Mukai, Y. Current status of coral reefs in Tioman Island Peninsular Malaysia. Turk. J. Zool. 41(2), 294–305. https://doi.org/10.3906/zoo-1511-42 (2017).Article 

    Google Scholar 
    4.Anthony, K. R. N. et al. Operationalizing resilience for adaptive coral reef management under global environmental change. Glob. Change Biol. 21(1), 48–61. https://doi.org/10.1111/gcb.12700,Pubmed:25196132 (2015).ADS 
    Article 

    Google Scholar 
    5.Bruno, J. F. & Selig, E. R. Regional decline of coral cover in the Indo-Pacific: timing, extent, and subregional comparisons. PLoS ONE 2(8), e711. https://doi.org/10.1371/journal.pone.0000711,Pubmed:17684557 (2007).ADS 
    Article 
    PubMed Central 
    PubMed 

    Google Scholar 
    6.Cowburn, B., Samoilys, M. A. & Obura, D. The current status of coral reefs and their vulnerability to climate change and multiple human stresses in the Comoros Archipelago Western Indian Ocean. Mar. Pollut. Bull. 133, 956–969. https://doi.org/10.1016/j.marpolbul.2018.04.065,Pubmed:29778407 (2018).CAS 
    Article 

    Google Scholar 
    7.Schueth, J. D. & Frank, T. D. Reef foraminifera as bioindicators of coral reef health: Low Isles Reef, northern Great Barrier Reef Australia. J. Foram. Res. 38(1), 11–22. https://doi.org/10.2113/gsjfr.38.1.11 (2008).Article 

    Google Scholar 
    8.Uthicke, S., Thompson, A. & Schaffelke, B. Effectiveness of benthic foraminiferal and coral assemblages as water quality indicators on inshore reefs of the Great Barrier Reef Australia. Coral Reefs 29(1), 209–225. https://doi.org/10.1007/s00338-009-0574-9 (2010).ADS 
    Article 

    Google Scholar 
    9.Natsir, S. M. & Subkhan, M. The distribution of benthic foraminifera in coral reefs community and seagrass bad of Belitung Islands based on FORAM Index. J. Coast. Dev. 15(1), 51–58 (2012).
    Google Scholar 
    10.Alve, E. Benthic foraminiferal responses to estuarine pollution: a review. J. Foram. Res. 25(3), 190–203. https://doi.org/10.2113/gsjfr.25.3.190 (1995).Article 

    Google Scholar 
    11.Hallock, P., Lidz, B. H., Cockey-Burkhard, E. M. & Donnelly, K. B. Foraminifera as bioindicators in coral reef assessment and monitoring: the FORAM index. Foraminifera in reef assessment and monitoring. Environ. Monit. Assess. 81(1–3), 221–238 (2003).Article 

    Google Scholar 
    12.Sen Gupta, B. K. Systematics of modern Foraminifera. In Sen Gupta, B.K. (ed.) Modern Foraminifera (Springer, 2003) 7–36. https://doi.org/10.1007/0-306-48104-9.13.Carnahan, E. A. Foraminiferal Assemblages as Bioindicators of Potentially Toxic Elements in Biscayne Bay, Florida. M.Sc. thesis (U.S.A.: University of South Florida, 2005)14.Barbosa, C. F., Prazeres, M. D. F., Ferreira, B. P. & Seoane, J. C. S. Foraminiferal assemblage and Reef Check census in coral reef health monitoring of East Brazilian margin. Mar. Micropaleontol. 73(1–2), 62–69. https://doi.org/10.1016/j.marmicro.2009.07.002 (2009).ADS 
    Article 

    Google Scholar 
    15.Dimiza, M. D., Koukousioura, O., Triantaphyllou, M. V. & Dermitzakis, M. D. Live and dead benthic foraminiferal assemblages from coastal environments of the Aegean Sea (Greece): distribution and diversity. Rev. Micropaleontol. 59(1), 19–32. https://doi.org/10.1016/j.revmic.2015.10.002 (2016).Article 

    Google Scholar 
    16.Uthicke, S. & Nobes, K. Benthic foraminifera as ecological indicators for water quality on the Great Barrier Reef. Estuarine Coast. Shelf Sci. 78(4), 763–773. https://doi.org/10.1016/j.ecss.2008.02.014 (2008).ADS 
    Article 

    Google Scholar 
    17.Renema, W. Terrestrial influence as a key driver of spatial variability in large benthic foraminiferal assemblage composition in the Central Indo-Pacific. Earth Sci. Rev. 177, 514–544. https://doi.org/10.1016/j.earscirev.2017.12.013 (2018).ADS 
    Article 

    Google Scholar 
    18.Förderer, M. & Langer, M. R. Exceptionally species-rich assemblages of modern larger benthic foraminifera from nearshore reefs in northern Palawan (Philippines). Rev. Micropaleontol. 100, 65. https://doi.org/10.1016/j.revmic.2019.100387 (2019).Article 

    Google Scholar 
    19.Eichler, P. P. B. & de Moura, D. S. Symbiont-bearing foraminifera as health proxy in coral reefs in the equatorial margin of Brazil. Environ. Sci. Pollut. Res. 27(12), 13637–13661. https://doi.org/10.1007/s11356-019-07483-y,Pubmed:32034594 (2020).CAS 
    Article 

    Google Scholar 
    20.Renema, W. Is increased calcarinid (foraminifera) abundance indicating a larger role for macro-algae in Indonesian Plio-Pleistocene coral reefs?. Coral Reefs 29(1), 165–173. https://doi.org/10.1007/s00338-009-0568-7 (2010).ADS 
    Article 

    Google Scholar 
    21.Chen, C. & Lin, H. L. Applying benthic Foraminiferal assemblage to evaluate the coral reef condition in Dongsha Atoll lagoon. Zool. Stud. 56, e20. https://doi.org/10.6620/ZS.2017.56-20,Pubmed:31966219 (2017).Article 
    PubMed Central 
    PubMed 

    Google Scholar 
    22.Hallock, P. Interoceanic differences in foraminifera with symbiotic algae: a result of nutrient supplies. Mar. Sci. Faculty Publication 1228 (1988). https://scholarcommons.usf.edu/msc_facpub/122823.Langer, M. R., Weinmann, A. E., Lötters, S., Bernhard, J. M. & Rödder, D. Climate-driven range extension of Amphistegina (protista, foraminiferida): Models of current and predicted future ranges [Protista, Foraminiferida]. PLoS ONE 8(2), e54443. https://doi.org/10.1371/journal.pone.0054443,Pubmed:23405081 (2013).ADS 
    CAS 
    Article 
    PubMed Central 
    PubMed 

    Google Scholar 
    24.Culver, S. J. et al. Distribution of foraminifera of the Poverty continental margin, New Zealand: implications for sediment transport. J. Foram. Res. 42(4), 305–326. https://doi.org/10.2113/gsjfr.42.4.305 (2012).Article 

    Google Scholar 
    25.Szarek, R. Biodiversity and Biogeography of Recent Benthic Foraminiferal Assemblages in the South Western South China Sea (Sunda Shelf). Doctoral dissertation (Kiel, Kiel, Germany: Christian-Albrechts Universität, 2001).26.Prazeres, M., Martínez-Colón, M. & Hallock, P. Foraminifera as bioindicators of water quality: the FoRAM Index revisited. Environ. Pollut. 257, 113612. https://doi.org/10.1016/j.envpol.2019.113612,Pubmed:31784269 (2020).CAS 
    Article 

    Google Scholar 
    27.Toda, T. et al. Community structures of coral reefs around Peninsular Malaysia. J. Oceanogr. 63(1), 113–123. https://doi.org/10.1007/s10872-007-0009-6 (2007).Article 

    Google Scholar 
    28.Zakai, D. & Chadwick-Furman, N. E. Impacts of intensive recreational diving on reef corals at Eilat, northern Red Sea. Biol. Conserv. 105(2), 179–187. https://doi.org/10.1016/S0006-3207(01)00181-1 (2002).Article 

    Google Scholar 
    29.Carnahan, E. A., Hoare, A. M., Hallock, P., Lidz, B. H. & Reich, C. D. Foraminiferal assemblages in Biscayne Bay, Florida, USA: responses to urban and agricultural influence in a subtropical estuary. Mar. Pollut. Bull. 59(8–12), 221–233. https://doi.org/10.1016/j.marpolbul.2009.08.008 (2009).CAS 
    Article 

    Google Scholar 
    30.Oliver, L. M. et al. Contrasting responses of coral reef fauna and foraminiferal assemblages to human influence in la Parguera Puerto Rico. Mar. Environ. Res. 99, 95–105. https://doi.org/10.1016/j.marenvres.2014.04.005 (2014).CAS 
    Article 

    Google Scholar 
    31.Unsworth, R. K., Clifton, J. & Smith, D. J. Marine Research and Conservation in the Coral Triangle: The Wakatobi National Park (Nova Science Publishers, 2010).
    Google Scholar 
    32.Praveena, S. M., Siraj, S. S. & Aris, A. Z. Coral reefs studies and threats in Malaysia: a mini review. Rev. Environ. Sci. Bio Technol. 11(1), 27–39. https://doi.org/10.1007/s11157-011-9261-8 (2012).Article 

    Google Scholar 
    33.Akmal, K. F., Shahbudin, S., Faiz, M. H. M. & Hamizan, Y. M. Diversity and abundance of scleractinian corals in the East Coast of peninsular Malaysia: a case study of Redang and Tioman Islands. Ocean Sci. J. 54(3), 435–456. https://doi.org/10.1007/s12601-019-0018-6 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    34.Oron, S., Abramovich, S., Almogi-Labin, A., Woeger, J. & Erez, J. Depth related adaptations in symbiont bearing benthic foraminifera: new insights from a field experiment on Operculina ammonoides. Sci. Rep. 8(1), 9560. https://doi.org/10.1038/s41598-018-27838-8,Pubmed:29934603 (2018).ADS 
    Article 
    PubMed Central 
    PubMed 

    Google Scholar 
    35.Oliver, J. K., Berkelmans, R. & Eakin, C. M. Coral bleaching in space and time in (eds van Oppen, M. J. H. & Lough, J. M.) Coral Bleaching. Ecological Studies. 205 (Springer, 2009) 21–39.36.Harborne, A., Fenner, D., Barnes, A., Beger, M., Harding, S. & Roxburgh, T. Status Report on the Coral Reefs of the East Coast of Peninsular Malaysia. Report Prepared to Department of Fisheries Malaysia, Kuala Lumpur, Malaysia, 361–369 (2000)37.Akhir, M., Fadzil, M., Zakaria, N. Z. & Tangang, F. Intermonsoon variation of physical characteristics and current circulation along the east coast of Peninsular Malaysia. Int. J. Oceanogr., 1–9 (2014)38.Chu, P. C., Qi, Y., Chen, Y., Shi, P. & Mao, Q. South China sea wind-wave characteristics. Part I: validation of WAVEWATCH-III using TOPEX/Poseidon data. J. Atmos. Ocean. Technol. 21(11), 1718–1733. https://doi.org/10.1175/JTECH1661.1 (2004).ADS 
    Article 

    Google Scholar 
    39.Marghany, M. Velocity bunching model for modelling wave spectra along east coast of Malaysia. J. Indian Soc. Remote Sens. 32(2), 185–198. https://doi.org/10.1007/BF03030875 (2004).Article 

    Google Scholar 
    40.Department of Marine Park, Malaysia. Laporan Tahunan Jabatan Taman Laut Malaysia. Annual report (2012)41.Chia, K. W., Ramachandran, S., Ho, J. A. & Ng, S. S. I. Conflicts to consensus: Stakeholder perspectives of Tioman Island tourism sustainability. Int. J. Bus. Soc. 19, 159 (2018).
    Google Scholar 
    42.Game, E. T., Meijaard, E., Sheil, D. & McDonald-Madden, E. Conservation in a wicked complex world; challenges and solutions. Conserv. Lett. 7(3), 271–277. https://doi.org/10.1111/conl.12050 (2014).Article 

    Google Scholar 
    43.Murray, J. W. Ecology and applications of benthic foraminifera. Cambridge University Press (2006)44.Loeblich, A. R. & Tappan, H. Foraminiferal Genera and Their Classification (Van Nostrand Reinhold, 1987).
    Google Scholar 
    45.Szarek, R., Kuhnt, W., Kawamura, H. & Kitazato, H. Distribution of recent benthic foraminifera on the Sunda Shelf (South China Sea). Mar. Micropaleontol. 61(4), 171–195. https://doi.org/10.1016/j.marmicro.2006.06.005 (2006).ADS 
    Article 

    Google Scholar 
    46.Martin, S. Q. et al. Announcements. J. Foram. Res. 48(4), 388–389. https://doi.org/10.2113/gsjfr.48.4.388 (2018).Article 

    Google Scholar 
    47.Folk, R. L. Petrology of Sedimentary Rocks (Hemphill Publishing Company, 1980)48.Dean, W. E. Determination of carbonate and organic matter in calcareous sediments and sedimentary rocks by loss on ignition; comparison with other methods. J. Sediment. Res. 44(1), 242–248 (1974).CAS 

    Google Scholar 
    49.Heiri, O., Lotter, A. F. & Lemcke, G. Loss on ignition as a method for estimating organic and carbonate content in sediments: reproducibility and comparability of results. J. Paleolimnol. 25(1), 101–110. https://doi.org/10.1023/A:1008119611481 (2001).ADS 
    Article 

    Google Scholar 
    50.Romesburg, C. Cluster Analysis for Researchers (Lulu Press, 2004).
    Google Scholar 
    51.Milker, Y. et al. Distribution of recent benthic foraminifera in shelf carbonate environments of the western Mediterranean Sea. Mar. Micropaleontol. 73(3–4), 207–225. https://doi.org/10.1016/j.marmicro.2009.10.003 (2009).ADS 
    Article 

    Google Scholar  More

  • in

    Pupal cannibalism by worker honey bees contributes to the spread of deformed wing virus

    1.Grassly, N. C. & Fraser, C. Mathematical models of infectious disease transmission. Nat. Rev. Microbiol. 6, 477–487 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Cressler, C. E., McLeod, D. V., Rozins, C., Van Den Hoogen, J. & Day, T. The adaptive evolution of virulence: A review of theoretical predictions and empirical tests. Parasitology 143, 915–930 (2016).PubMed 
    Article 

    Google Scholar 
    3.Lanzi, G. et al. Molecular and biological characterization of deformed wing virus of honeybees (Apismellifera L.). J. Virol. 80, 4998–5009 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Dainat, B., Evans, J. D., Chen, Y. P., Gauthier, L. & Neumann, P. Dead or alive: Deformed wing virus and Varroa destructor reduce the life span of winter honeybees. Appl. Environ. Microbiol. 78, 981–987 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Highfield, A. C. et al. Deformed wing virus implicated in overwintering honeybee colony losses. Appl. Environ. Microbiol. 75, 7212–7220 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Le Conte, Y., Ellis, M. & Ritter, W. Varroa mites and honey bee health: Can Varroa explain part of the colony losses?. Apidologie 41, 353–363 (2010).Article 

    Google Scholar 
    7.De Miranda, J. R. & Genersch, E. Deformed wing virus. J. Invertebr. Pathol. 103, S48–S61 (2010).PubMed 
    Article 
    CAS 

    Google Scholar 
    8.Martin, S. J. & Brettell, L. E. Deformed wing virus in honeybees and other insects. Annu. Rev. Virol. 6, 49–69 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    9.Sumpter, D. J. & Martin, S. J. The dynamics of virus epidemics in Varroa-infested honey bee colonies. J. Anim. Ecol. 73, 51–63 (2004).Article 

    Google Scholar 
    10.Ramsey, S. D. et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc. Natl. Acad. Sci. 116, 1792–1801 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Yang, X. & Cox-Foster, D. L. Impact of an ectoparasite on the immunity and pathology of an invertebrate: Evidence for host immunosuppression and viral amplification. Proc. Natl. Acad. Sci. 102, 7470–7475 (2005).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Rosenkranz, P., Aumeier, P. & Ziegelmann, B. Biology and control of Varroa destructor. J. Invertebr. Pathol. 103, S96–S119 (2010).PubMed 
    Article 

    Google Scholar 
    13.Wilfert, L. et al. Deformed wing virus is a recent global epidemic in honeybees driven by Varroa mites. Science 351, 594–597 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Dalmon, A. et al. Evidence for positive selection and recombination hotspots in deformed wing virus (DWV). Sci. Rep. 7, 1–12 (2017).Article 
    CAS 

    Google Scholar 
    15.Martin, S. J. et al. Global honey bee viral landscape altered by a parasitic mite. Science 336, 1304–1306 (2012).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    16.Moore, J. et al. Recombinants between deformed wing virus and Varroa destructor virus-1 may prevail in Varroa destructor-infested honeybee colonies. J. Gen. Virol. 92, 156–161 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    17.Ryabov, E. V. et al. A virulent strain of deformed wing virus (DWV) of honeybees (Apis mellifera) prevails after Varroa destructor-mediated, or in vitro, transmission. PLoS Pathog. 10, e1004230 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    18.Ryabov, E. V. et al. Dynamic evolution in the key honey bee pathogen deformed wing virus: Novel insights into virulence and competition using reverse genetics. PLoS Biol. 17, e3000502 (2019).MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Mondet, F. et al. Specific cues associated with honey bee social defence against Varroa destructor infested brood. Sci. Rep. 6, 25444 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Spivak, M. & Danka, R. G. Perspectives on hygienic behavior in Apismellifera and other social insects. Apidologie https://doi.org/10.1007/s13592-020-00784-z (2020).Article 

    Google Scholar 
    21.Spivak, M. & Gilliam, M. Facultative expression of hygienic behaviour of honey bees in relation to disease resistance. J. Apic. Res. 32, 147–157 (1993).Article 

    Google Scholar 
    22.Baracchi, D., Fadda, A. & Turillazzi, S. Evidence for antiseptic behaviour towards sick adult bees in honey bee colonies. J. Insect Physiol. 58, 1589–1596 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Traynor, K. S. et al. Varroa destructor: A complex parasite, crippling honey bees worldwide. Trends Parasitol. 36, 592–606 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    24.Sun, Q. & Zhou, X. Corpse management in social insects. Int. J:. Biol. Sci. 9, 313 (2013).
    Google Scholar 
    25.Van Allen, B. G. et al. Cannibalism and infectious disease: Friends or foes?. Am. Nat. 190, 299–312 (2017).PubMed 
    Article 

    Google Scholar 
    26.Bourke, A. F. Queen behaviour, reproduction and egg cannibalism in multiple-queen colonies of the ant Leptothorax acervorum. Anim. Behav. 42, 295–310 (1991).Article 

    Google Scholar 
    27.Pulliainen, U., Helanterä, H., Sundström, L. & Schultner, E. The possible role of ant larvae in the defence against social parasites. Proc. R. Soc. B 286, 20182867 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Evans, H. & West-Eberhard, M. The Wasps (Univ. Michigan, 1970).
    Google Scholar 
    29.Schmickl, T. & Crailsheim, K. Cannibalism and early capping: Strategy of honeybee colonies in times of experimental pollen shortages. J. Comp. Physiol. A 187, 541–547 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    30.Webster, T. C., Peng, Y. S. & Duffey, S. S. Conservation of nutrients in larval tissue by cannibalizing honey bees. Physiol. Entomol. 12, 225–231 (1987).CAS 
    Article 

    Google Scholar 
    31.Woyke, J. Cannibalism and brood-rearing efficiency in the honeybee. J. Apic. Res. 16, 84–94 (1977).Article 

    Google Scholar 
    32.Chouvenc, T. Limited survival strategy in starving subterranean termite colonies. Insectes Soc. 67, 71–82 (2020).Article 

    Google Scholar 
    33.Raina, A. K., Park, Y. I. & Lax, A. Defaunation leads to cannibalism in primary reproductives of the Formosan subterranean termite, Coptotermes formosanus (Isoptera: Rhinotermitidae). Ann. Entomol. Soc. Am. 97, 753–756 (2004).Article 

    Google Scholar 
    34.Schmickl, T. & Crailsheim, K. Inner nest homeostasis in a changing environment with special emphasis on honey bee brood nursing and pollen supply. Apidologie 35, 249–263 (2004).Article 

    Google Scholar 
    35.Meunier, J. Social immunity and the evolution of group living in insects. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140102 (2015).Article 

    Google Scholar 
    36.Rueppell, O., Hayworth, M. K. & Ross, N. Altruistic self-removal of health-compromised honey bee workers from their hive. J. Evol. Biol. 23, 1538–1546 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Halling, L. & Oldroyd, B. P. Do policing honeybee (Apis mellifera) workers target eggs in drone comb?. Insectes Soc. 50, 59–61 (2003).Article 

    Google Scholar 
    38.Santomauro, G., Oldham, N. J., Boland, W. & Engels, W. Cannibalism of diploid drone larvae in the honey bee (Apis mellifera) is released by odd pattern of cuticular substances. J. Apic. Res. 43, 69–74 (2004).Article 

    Google Scholar 
    39.Imdorf, A., Rickli, M., Kilchenmann, V., Bogdanov, S. & Wille, H. Nitrogen and mineral constituents of honey bee worker brood during pollen shortage. Apidologie 29, 315–325 (1998).Article 

    Google Scholar 
    40.Rudolf, V. H. & Antonovics, J. Disease transmission by cannibalism: Rare event or common occurrence?. Proc. R. Soc. B Biol. Sci. 274, 1205–1210 (2007).Article 

    Google Scholar 
    41.Chapman, J. W. et al. Age-related cannibalism and horizontal transmission of a nuclear polyhedrosis virus in larval Spodoptera frugiperda. Ecol. Entomol. 24, 268–275 (1999).Article 

    Google Scholar 
    42.Hamano, K. et al. Waterborne and cannibalism-mediated transmission of the Yellow head virus in Penaeus monodon. Aquaculture 437, 161–166 (2015).Article 

    Google Scholar 
    43.Möckel, N., Gisder, S. & Genersch, E. Horizontal transmission of deformed wing virus: Pathological consequences in adult bees (Apis mellifera) depend on the transmission route. J. Gen. Virol. 92, 370–377 (2011).PubMed 
    Article 
    CAS 

    Google Scholar 
    44.Ryabov, E. V. et al. Development of a honey bee RNA virus vector based on the genome of a deformed wing virus. Viruses 12, 374 (2020).CAS 
    PubMed Central 
    Article 
    PubMed 

    Google Scholar 
    45.Posada-Florez, F. et al. Deformed wing virus type A, a major honey bee pathogen, is vectored by the mite Varroa destructor in a non-propagative manner. Sci. Rep. 9, 1–10 (2019).CAS 
    Article 

    Google Scholar 
    46.Bull, J. C. et al. A strong immune response in young adult honeybees masks their increased susceptibility to infection compared to older bees. PLoS Pathog. 8, e1003083 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Shi, M. et al. Redefining the invertebrate RNA virosphere. Nature 540, 539–543 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Masterman, R., Ross, R., Mesce, K. & Spivak, M. Olfactory and behavioral response thresholds to odors of diseased brood differ between hygienic and non-hygienic honey bees (Apis mellifera L.). J. Comp. Physiol. A 187, 441–452 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    49.Crailsheim, K. Trophallactic interactions in the adult honeybee (Apis mellifera L.). Apidologie 29, 97–112 (1998).Article 

    Google Scholar 
    50.Nixon, H. & Ribbands, C. R. Food transmission within the honeybee community. Proc. R. Soc. Lond. Ser. B Biol. Sci. 140, 43–50 (1952).ADS 
    CAS 
    Article 

    Google Scholar 
    51.Arathi, H. & Spivak, M. Influence of colony genotypic composition on the performance of hygienic behaviour in the honeybee, Apis mellifera L. Anim. Behav. 62, 57–66 (2001).Article 

    Google Scholar 
    52.Knecht, D. & Kaatz, H. Patterns of larval food production by hypopharyngeal glands in adult worker honey bees. Apidologie 21, 457–468 (1990).Article 

    Google Scholar 
    53.Li, Z. et al. Transcriptional and physiological responses of hypopharyngeal glands in honeybees (Apis mellifera L.) infected by Nosema ceranae. Apidologie 50, 51–62 (2019).CAS 
    Article 

    Google Scholar 
    54.Lass, A. & Crailsheim, K. Influence of age and caging upon protein metabolism, hypopharyngeal glands and trophallactic behavior in the honey bee (Apis mellifera L.). Insectes Soc. 43, 347–358 (1996).Article 

    Google Scholar 
    55.Chiou, S.-S. & Chen, W.-J. Mutations in the NS3 gene and 3′-NCR of Japanese encephalitis virus isolated from an unconventional ecosystem and implications for natural attenuation of the virus. Virology 289, 129–136 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    56.Steel, A., Gubler, D. J. & Bennett, S. N. Natural attenuation of dengue virus type-2 after a series of island outbreaks: A retrospective phylogenetic study of events in the South Pacific three decades ago. Virology 405, 505–512 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    57.de Souza, F. S., Allsopp, M. H. & Martin, S. J. Deformed wing virus prevalence and load in honeybees in South Africa. Arch. Virol. 166, 237–241 (2020).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    58.Martin, S. J. et al. Varroa destructor reproduction and cell re-capping in mite-resistant Apis mellifera populations. Apidologie 51, 369–381 (2020).CAS 
    Article 

    Google Scholar 
    59.Kulhanek, K. et al. Survey-derived best management practices for backyard beekeepers improve colony health and reduce mortality. PLoS ONE 16, e0245490 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Peck, D. T. & Seeley, T. D. Mite bombs or robber lures? The roles of drifting and robbing in Varroa destructor transmission from collapsing honey bee colonies to their neighbors. PLoS ONE 14, e0218392 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Ryabov, E. V. et al. Recent spread of Varroa destructor virus-1, a honey bee pathogen, in the United States. Sci. Rep. 7, 1–10 (2017).CAS 
    Article 

    Google Scholar 
    62.Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophoton. Int. 11, 36–42 (2004).
    Google Scholar  More

  • in

    Progeny fitness determines the performance of the parasitoid Therophilus javanus, a prospective biocontrol agent against the legume pod borer

    1.Dung, D. T., Thi, L., Phuong, H. & Long, K. D. Insect parasitoid composition on soybean, some eco-biological characteristics of the parasitoid, xanthopimpla punctata fabricius on soybean leaffolder omiodes indicata ( Fabricius ) in Hanoi Vietnam. ISSAAS J. 17, 58–69 (2011).
    Google Scholar 
    2.Srinivasan, R., Yule, S., Lin, M. Y. & Khumsuwan, C. Recent developments in the biological control of legume pod borer (Maruca vitrata) on yard-long bean. Acta Hort. 1102, 143–149 (2015).Article 

    Google Scholar 
    3.Van Lenteren, J. C. et al. Environmental risk assessment of exotic natural enemies used in inundative biological control. Biocontrol 48, 3–38 (2003).Article 

    Google Scholar 
    4.Aboubakar Souna, D. et al. An insight in the reproductive biology of Therophilus javanus (hymenoptera, braconidae, and agathidinae), a potential biological control agent against the legume pod borer (Lepidoptera, Crambidae). Psyche (London) 2017, 1–8 (2017).Article 

    Google Scholar 
    5.Aboubakar Souna, D. et al. Volatiles from Maruca vitrata (Lepidoptera, Crambidae) host plants influence olfactory responses of the parasitoid Therophilus javanus (Hymenoptera, Braconidae, Agathidinae). Biol. Control 130, 104–109 (2019).CAS 
    Article 

    Google Scholar 
    6.Bellows, T. S. & Van Driesche, R. G. Life Table Construction and Analysis for Evaluating Biological Control Agents. in Handbook of Biological Control 199–223 (Elsevier, 1999). https://doi.org/10.1016/b978-012257305-7/50055-2.7.Maia, A. D. H., Luiz, A. J. & Campanhola, C. Statistical inference on associated fertility life table parameters using jackknife technique: computational aspects. J. Econ. Entomol. 93(2), 511–518 (2000).Article 

    Google Scholar 
    8.Roy, M., Brodeur, J. & Cloutier, C. Effect of temperature on intrinsic rates of natural increase (rm) of a coccinellid and its spider mite prey. Biocontrol 48, 57–72 (2003).Article 

    Google Scholar 
    9.Harvey, J. A. Factors affecting the evolution of development strategies in parasitoid wasps: The importance of functional constraints and incorporating complexity. Entomol. Exp. Appl. 117, 1–13 (2005).Article 

    Google Scholar 
    10.Henderson, R. E., Kuriachan, I. & Vinson, S. B. Postegression feeding enhances growth, survival, and nutrient acquisition in the endoparasitoid Toxoneuron nigriceps (Hymenoptera: Braconidae). J. Insect Sci. 15, 51 (2015).Article 

    Google Scholar 
    11.Kuriachan, I., Henderson, R., Laca, R. & Vinson, S. B. Post-egression host tissue feeding is another strategy of host regulation by the koinobiont wasp toxoneuron nigriceps. J. Insect Sci. 11, 1–11 (2011).Article 

    Google Scholar 
    12.Benelli, G. et al. The impact of adult diet on parasitoid reproductive performance. J. Pest. Sci. 90, 807–823 (2017).Article 

    Google Scholar 
    13.Harvey, J. A. & Strand, M. R. The developmental strategies of endoparasitoid wasps vary with host feeding ecology. Ecology 83, 2439–2451 (2002).Article 

    Google Scholar 
    14.Harvey, J. A., Bezemer, T. M., Gols, R., Nakamatsu, Y. & Tanaka, T. Comparing the physiological effects and function of larval feeding in closely-related endoparasitoids (Braconidae: Microgastrinae). Physiol. Entomol. 33, 217–225 (2008).Article 

    Google Scholar 
    15.Harvey, J. A. et al. Development of a solitary koinobiont hyperparasitoid in different instars of its primary and secondary hosts. J. Insect Physiol. 90, 36–42 (2016).CAS 
    Article 

    Google Scholar 
    16.Harvey, J. A. & Malcicka, M. Nutritional integration between insect hosts and koinobiont parasitoids in an evolutionary framework. Entomol. Exp. Appl. 159, 181–188 (2016).Article 

    Google Scholar 
    17.Gols, R., Ros, V. I. D., Ode, P. J., Vyas, D. & Harvey, J. A. Varying degree of physiological integration among host instars and their endoparasitoid affects stress-induced mortality. Entomol. Exp. Appl. 167, 424–432 (2019).Article 

    Google Scholar 
    18.Vieira, L. J. P., Franco, G. M. & Sampaio, M. V. Host preference and fitness of Lysiphlebus testaceipes (Hymenoptera: Braconidae) in different instars of the aphid Schizaphis graminum. Neotrop. Entomol. 48, 391–398 (2019).CAS 
    Article 

    Google Scholar 
    19.Harvey, J. A. Dynamic effects of parasitism by an endoparasitoid wasp on the development of two host species: Implications for host quality and parasitoid fitness. Ecol. Entomol. 25, 267–278 (2000).Article 

    Google Scholar 
    20.Harvey, J. A., Kadash, K. & Strand, M. R. Differences in larval feeding behavior correlate with altered developmental strategies in two parasitic wasps: Implications for the size-fitness hypothesis. Oikos 88, 621–629 (2000).Article 

    Google Scholar 
    21.van Achterberg, C. & Long, K. D. Revision of the Agathidinae (Hymenoptera, Braconidae) of Vietnam, with the description of forty-two new species and three new genera. Zookeys https://doi.org/10.3897/zookeys.54.475 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Kuriachan, I., Consoli, F. L. & Vinson, S. B. In vitro rearing of Toxoneuron nigriceps (Hymenoptera: Braconidae), a larval endoparasitoid of Heliothis virescens (Lepidoptera: Noctuidae) from early second instar to third instar larvae. J. Insect Physiol. 52, 881–887 (2006).CAS 
    Article 

    Google Scholar 
    23.Pennacchio, F., Vinson, S. B. & Tremblay, E. Growth and development of Cardiochiles nigriceps viereck (hymenoptera, braconidae) larvae and their synchronization with some changes of the hemolymph composition of their host, Heliothis virescens (F.) (Lepidoptera, Noctuidae). Arch. Insect Biochem. Physiol. 24, 65–77 (1993).CAS 
    Article 

    Google Scholar 
    24.Dannon, E. A., Tamò, M., van Huis, A. & Dicke, M. Functional response and life history parameters of Apanteles taragamae, a larval parasitoid of Maruca vitrata. Biocontrol 55, 363–378 (2010).Article 

    Google Scholar 
    25.Henry, L. M., Gillespie, D. R. & Roitberg, B. D. Does mother really know best? Oviposition preference reduces reproductive performance in the generalist parasitoid Aphidius ervi. Entomol. Exp. Appl. 116, 167–174 (2005).Article 

    Google Scholar 
    26.Moreau, S. J. M. & Asgari, S. Venom proteins from parasitoid wasps and their biological functions. Toxins (Basel). 7, 2385–2412 (2015).CAS 
    Article 

    Google Scholar 
    27.Aboubakar Souna, D. Assessing the potential of Therophilus javanus, a biological control candidate against the cowpea pod borer Maruca vitrata in West Africa. (University of Montpellier (France); University of Abomey-Calavi (Benin), 2018).28.Beckage, N. E. & Gelman, D. B. Wasp parasitoid disruption of host development: Implications for new biologically based strategies for insect control. Annu. Rev. Entomol. 49, 299–330 (2004).CAS 
    Article 

    Google Scholar 
    29.Qiu, B., Zhou, Z. & Xu, Z. Age Preference and Fitness of Microplitis manilae (Hymenoptera: Braconidae) Reared on Spodoptera exigua (Lepidoptera: Noctuidae). Florida Entomol. 96, 602–609 (2013).Article 

    Google Scholar 
    30.Mayhew, P. J. Comparing parasitoid life histories. Entomol. Exp. Appl. 159, 147–162 (2016).Article 

    Google Scholar 
    31.Sithole, R., Chinwada, P. & Lohr, B. L. Effects of host larval stage preferences and diet on life history traits of Diadegma mollipla, an African parasitoid of the Diamondback Moth. Biocontrol Sci. Technol. 28, 172–184 (2018).Article 

    Google Scholar 
    32.Boulton, R. A., Collins, L. A. & Shuker, D. M. Beyond sex allocation: The role of mating systems in sexual selection in parasitoid wasps. Biol. Rev. https://doi.org/10.1111/brv.12126 (2015).Article 
    PubMed 

    Google Scholar 
    33.Beukeboom, L. W., Ellers, J. & Van Alphen, J. J. M. Absence of single-locus complementary sex determination in the braconid wasps Asobara tabida and Alysia manducator. Heredity (Edinb). 84, 29–36 (2000).Article 

    Google Scholar 
    34.Zhou, Y., Gu, H. & Dorn, S. Single-locus sex determination in the parasitoid wasp Cotesia glomerata (Hymenoptera: Braconidae). Heredity (Edinb). https://doi.org/10.1038/sj.hdy.6800829 (2006).Article 
    PubMed 

    Google Scholar 
    35.Van Nieuwenhove, G. A. & Ovruski, S. M. Influence of Anastrepha fraterculus (Diptera: Tephritidae) larval instars on the production of Diachasmimorpha longicaudata (Hymneoptera: Braconidae) progeny and their sex ratio. Florida Entomol. 94, 863–868 (2011).Article 

    Google Scholar 
    36.Huang, Y. B. & Chi, H. Life tables of Bactrocera cucurbitae (Diptera: Tephritidae): With an invalidation of the jackknife technique. J. Appl. Entomol. 137, 327–339 (2013).Article 

    Google Scholar 
    37.Shapiro, A. S. S. & Wilk, M. B. Biometrika trust an analysis of variance test for normality (Complete Samples ) Published by : Oxford University Press on behalf of Biometrika Trust Stable. Biometrika 52, 591–611 (1965).38.Bretz, F., Hothorn, T. & Westfall, P. H. Multiple comparisons using R. 187 (2011). https://doi.org/10.1128/AAC.03728-14.39.Mangiafico, S. S. rcompanion: Functions to Support Extension Education Program Evaluation. R package version 2.0.0. Cran R (2016). https://doi.org/10.1016/J.AMJSURG.2007.06.026.40.R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria (2019).41.Chi, H. & Su, H.-Y. Age-stage, two-sex life tables of Aphidius gifuensis (Ashmead) (Hymenoptera: Braconidae) and its host Myzus persicae (Sulzer) (Homoptera: Aphididae) with mathematical proof of the relationship between female fecundity and the net reproductive rate. Environ. Entomol. 35, 10–21 (2006).Article 

    Google Scholar 
    42.Ning, S., Zhang, W., Sun, Y. & Feng, J. Development of insect life tables: Comparison of two demographic methods of Delia antiqua (Diptera: Anthomyiidae) on different hosts. Sci. Rep. 7, 4821 (2017).ADS 
    Article 

    Google Scholar 
    43.Chi, H. TWOSEX-MSChart: a computer program for the age stage, two-sex life table analysis. National Chung Hsing University, Taichung, Taiwan. 2015b Available: http://140.120.197.173/Ecology/ (access (2015).44.Akca, I., Ayvaz, T., Yazici, E., Smith, C. L. & Chi, H. Demography and population projection of Aphis fabae (Hemiptera: Aphididae): with additional comments on life table research criteria. J. Econ. Entomol. 108, 1466–1478 (2015).Article 

    Google Scholar  More

  • in

    Patch selection by bumble bees navigating discontinuous landscapes

    1.Marden, J. H. & Waddington, K. D. Floral choices by honeybees in relation to the relative distances to flowers. Physiol. Entomol. 6, 431–435 (1981).Article 

    Google Scholar 
    2.Waddington, K. D., Allen, T. & Heinrich, B. Floral preferences of bumblebees (Bombus edwardsii) in relation to intermittent versus continuous rewards. Anim. Behav. 29, 779–784 (1981).Article 

    Google Scholar 
    3.Bauer, A. A., Clayton, M. K. & Brunet, J. Floral traits influencing plant attractiveness to three bee species: consequences for plant reproductive success. Am. J. Bot. 104, 772–781 (2017).PubMed 
    Article 

    Google Scholar 
    4.Bradshaw, H. D. & Schemske, D. W. Allele substitution at a flower colour locus produces a pollinator shift in monkeyflowers. Nature 426, 176–178 (2003).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Klahre, U. et al. Pollinator choice in petunia depends on two major genetic loci for floral scent production. Curr. Biol. 21, 730–739 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Muth, F., Papaj, D. R. & Leonard, A. S. Bees remember flowers for more than one reason: pollen mediates associative learning. Anim. Behav. 111, 93–100 (2016).Article 

    Google Scholar 
    7.Brunet, J., Thairu, M. W., Henss, J. M., Link, R. I. & Kluever, J. A. The effects of flower, floral display, and reward sizes on bumblebee foraging behavior when pollen is the reward and plants are dichogamous. Int. J. Plant Sci. 176, 811–819 (2015).Article 

    Google Scholar 
    8.Nicholls, E. & De Ibarra, N. H. Bees associate colour cues with differences in pollen rewards. J. Exp. Biol. 217, 2783–2788 (2014).PubMed 
    Article 

    Google Scholar 
    9.Thairu, M. W. & Brunet, J. The role of pollinators in maintaining variation in flower colour in the Rocky Mountain columbine, Aquilegia coerulea. Ann. Bot. 115, 971–979 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Ishii, H. S. Floral display size influences subsequent plant choice by bumble bees. Funct. Ecol. 20, 233–238 (2006).Article 

    Google Scholar 
    11.Mitchell, R. J., Karron, J. D., Holmquist, K. G. & Bell, J. M. The influence of Mimulus ringens floral display size on pollinator visitation patterns. Funct. Ecol. 18, 116–124 (2004).Article 

    Google Scholar 
    12.Makino, T. T. & Sakai, S. Experience changes pollinator responses to floral display size: from size-based to reward-based foraging. Funct. Ecol. 21, 854–863 (2007).Article 

    Google Scholar 
    13.Osborne, J. L. et al. A landscape-scale study of bumble bee foraging range and constancy, using harmonic radar. J. Appl. Ecol. 36, 519–533 (1999).Article 

    Google Scholar 
    14.Osborne, J. L. & Williams, I. H. Site constancy of bumble bees in an experimentally patchy habitat. Agric. Ecosyst. Environ. 83, 129–141 (2001).Article 

    Google Scholar 
    15.Saville, N. M., Dramstad, W. E., Fry, G. L. A. & Corbet, S. A. Bumblebee movement in a fragmented agricultural landscape. Agric. Ecosyst. Environ. 61, 145–154 (1997).Article 

    Google Scholar 
    16.Ogilvie, J. E. & Thomson, J. D. Site fidelity by bees drives pollination facilitation in sequentially blooming plant species. Ecology 97, 1442–1451 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Cresswell, J. E. & Osborne, J. L. The effect of patch size and separation on bumblebbe foraging in oilseed rape: implications for gene flow. J. Appl. Ecol. 41, 539–546 (2004).Article 

    Google Scholar 
    18.Ohashi, K. & Thomson, J. D. Trapline foraging by pollinators: its ontogeny, economics and possible consequences for plants. Ann. Bot. 103, 1365–1378 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Saleh, N. & Chittka, L. Traplining in bumblebees (Bombus impatiens): a foraging strategy’s ontogeny and the importance of spatial reference memory in short-range foraging. Oecologia 151, 719–730 (2007).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Woodgate, J. L., Makinson, J. C., Lim, K. S., Reynolds, A. M. & Chittka, L. Continuous radar tracking illustrates the development of multi-destination routes of bumblebees. Sci. Rep. 7, 1–15 (2017).CAS 
    Article 

    Google Scholar 
    21.Lihoreau, M., Chittka, L. & Raine, N. E. Trade-off between travel distance and prioritization of high-reward sites in traplining bumblebees. Funct. Ecol. 25, 1284–1292 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Lihoreau, M., Chittka, L. & Raine, N. E. Travel optimization by foraging bumblebees through readjustments of traplines after discovery of new feeding locations. Am. Nat. 176, 744–757 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Lihoreau, M., Chittka, L., Le Comber, S. C. & Raine, N. E. Bees do not use nearest-neighbour rules for optimization of multi-location routes. Biol. Lett. 8, 13–16 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Minahan, D. F. & Brunet, J. Strong interspecific differences in foraging activity observed between honey bees and bumble bees using miniaturized radio frequency identification (RFID). Front. Ecol. Evol. 6, 156 (2018).Article 

    Google Scholar 
    25.Brunet, J., Zhao, Y. & Clayton, M. K. Linking the foraging behavior of three bee species to pollen dispersal and gene flow. PLoS ONE 14, e0212561 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Reynolds, A. M., Lihoreau, M. & Chittka, L. A simple iterative model accurately captures complex trapline formation by bumblebees across spatial scales and flower arrangements. PLoS Comput. Biol. 9, e1002938 (2013).ADS 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Marschall, E. A., Chesson, P. L. & Stein, R. A. Foraging in a patchy environment: prey-encounter rate and residence time distributions. Anim. Behav. 37, 444–454 (1989).Article 

    Google Scholar 
    28.Pyke, G. H. Optimal foraging theory : a critical review. Ann. Rev. Ecol. Syst. 15, 523–575 (1984).Article 

    Google Scholar 
    29.Rands, S. A. Landscape fragmentation and pollinator movement within agricultural environments: a modelling framework for exploring foraging and movement ecology. PeerJ 2, e269 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Lima, S. L. & Zollner, P. A. Towards a behavioral ecology of ecological landscapes. Trends Ecol. Evol. 11, 131–135 (1996).CAS 
    PubMed 
    Article 

    Google Scholar 
    31.Brunet, J. A conceptual framework that links pollinator foraging behavior to gene flow. In Proceedings for the 2018 Winter Seed Conference 63–67 (2018).32.Macarthur, R. H. & Pianka, E. R. On optimal use of a patchy environment. Am. Nat. 100, 603–609 (1966).Article 

    Google Scholar 
    33.Pyke, G. H. Understanding movements of organisms: it’s time to abandon the Lévy foraging hypothesis. Methods Ecol. Evol. 6, 1–16 (2015).Article 

    Google Scholar 
    34.Heinrich, B. ‘Majoring’ and ‘minoring’ by foraging bumblebees, Bombus vagans: an experimental analysis. Ecology 60, 245–255 (1979).Article 

    Google Scholar 
    35.Somme, L. et al. Pollen and nectar quality drive the major and minor floral choices of bumble bees. Apidologie 46, 92–106 (2015).ADS 
    Article 

    Google Scholar 
    36.Levey, D. J., Bolker, B. M., Tewksbury, J. J., Sargent, S. & Haddad, N. M. Effects of landscape corridors on seed dispersal by birds. Science 309, 146–148 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Levey, D. J., Tewksbury, J. J. & Bolker, B. M. Modelling long-distance seed dispersal in heterogeneous landscapes. J. Ecol. 96, 599–608 (2008).Article 

    Google Scholar 
    38.Pasquet, R. S. et al. Long-distance pollen flow assessment through evaluation of pollinator foraging range suggests transgene escape distances. Proc. Natl. Acad. Sci. U. S. A. 105, 13456–13461 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Smith, K. & Spangenberg, G. Considerations for managing agricultural co-existence between transgenic and non-transgenic cultivars of outcrossing perennial forage plants in dairy pastures. Agronomy 6, 59–68 (2016).Article 

    Google Scholar 
    40.Ellstrand, N. C. et al. Introgression of crop alleles into wild or weedy populations. Annu. Rev. Ecol. Evol. Syst. 44, 325–345 (2013).Article 

    Google Scholar 
    41.Gupta, R. M. & Musunuru, K. Expanding the genetic editing tool kit: ZFNs, TALENs, and CRISPR-Cas9. J. Clin. Invest. 124, 4154–4161 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Esch, H. E. & Burns, J. E. Distance estimation by foraging honeybees. J. Exp. Biol. 199, 155–162 (1996).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Srinivasan, M. V., Zhang, S., Altwein, M. & Tautz, J. Honeybee navigation: nature and calibration of the ‘odometer’. Science (80-.) 287, 851–853 (2000).ADS 
    CAS 
    Article 

    Google Scholar 
    44.Collett, M. & Collett, T. S. How do insects use path integration for their navigation?. Biol. Cybern. 83, 245–259 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Collett, M., Chittka, L. & Collett, T. S. Spatial memory in insect navigation. Curr. Biol. 23, R789–R800 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Chittka, L., Geiger, K. & Kunze, J. The influences of landmarks on distance estimation of honey bees. Anim. Behav. 50, 23–31 (1995).Article 

    Google Scholar 
    47.Srinivasan, M. V., Lehrer, M. & Horridge, G. A. Visual figure-ground discrimination in the honeybee: the role of motion parallax at boundaries. Proc. R. Soc. B Biol. Sci. 238, 331–350 (1990).ADS 

    Google Scholar 
    48.Lehrer, M. Looking all around: honeybees use different cues in different eye regions. J. Exp. Biol. 201, 3275–3292 (1998).PubMed 
    PubMed Central 

    Google Scholar 
    49.Goulson, D. Foraging strategies of insects for gathering nectar and pollen, and implications for plant ecology and evolution. Perspect. Plant Ecol. Evol. Syst. 2, 185–209 (1999).Article 

    Google Scholar 
    50.Ohashi, K., Thomson, J. D. & D’Souza, D. Trapline foraging by bumble bees: IV. Optimization of route geometry in the absence of competition. Behav. Ecol. 18, 1–11 (2007).Article 

    Google Scholar 
    51.Comba, L. Patch use by bumblebees (hymenoptera apidae): temperature, wind, flower density and traplining. Ethol. Ecol. Evol. 11, 243–264 (1999).Article 

    Google Scholar 
    52.Ohashi, K., Leslie, A. & Thomson, J. D. Trapline foraging by bumble bees: V. Effects of experience and priority on competitive performance. Behav. Ecol. 19, 936–948 (2008).Article 

    Google Scholar 
    53.Klein, S., Pasquaretta, C., Barron, A. B., Devaud, J. M. & Lihoreau, M. Inter-individual variability in the foraging behaviour of traplining bumblebees. Sci. Rep. 7, 1–12 (2017).Article 
    CAS 

    Google Scholar 
    54.Chittka, L. Bee cognition. Curr. Biol. 27, R1049–R1053 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Ohashi, K. & Yahara, T. Visit larger displays but probe proportionally fewer flowers: counterintuitive behaviour of nectar-collecting bumble bees achieves an ideal free distribution. Funct. Ecol. 16, 492–503 (2002).Article 

    Google Scholar 
    56.Brunet, J. & Stewart, C. M. Impact of bee species and plant density on alfalfa pollination and potential for gene flow. Psyche A J. Entomol. https://doi.org/10.1155/2010/201858 (2010).Article 

    Google Scholar 
    57.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
    Google Scholar 
    58.Weisberg, S. Applied Linear Regression (Wiley, 2013). https://doi.org/10.2307/3150981.Book 
    MATH 

    Google Scholar  More

  • in

    Serum correlation, demographic differentiation, and seasonality of blubber testosterone in common bottlenose dolphins, Tursiops truncatus, in Sarasota Bay, FL

    1.Kellar, N. M. et al. Blubber testosterone: a potential marker of male reproductive status in short-beaked common dolphins. Mar. Mamm. Sci. 25(3), 507–522 (2009).CAS 
    Article 

    Google Scholar 
    2.Atkinson, S. & Yoshioka, M. Endocrinology of reproduction. In Reproductive biology and phylogeny of Cetacea. Whales, dolphins and porpoises (ed. Miller, D. L.) 171–192 (Science Publishers, 2007).
    Google Scholar 
    3.Cates, K. A. et al. Testosterone trends within and across seasons in male humpback whales (Megaptera novaeangliae) from Hawaii and Alaska. Gen. Comp. Endocrinol. 279, 164–173 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    4.McKenna, T. J. et al. 2 A critical review of the origin and control of adrenal androgens. Baillieres Clin. Obstet. Gynaecol. 11(2), 229–248 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Sharpe, R. et al. Testosterone and Spermatogenesis identification of stage-specific, androgen-regulated proteins secreted by adult rat seminiferous tubules. J. Androl. 13, 172–184 (1992).CAS 
    PubMed 

    Google Scholar 
    6.Kita, S., Yoshioka, M. & Kashiwagi, M. Relationship between sexual maturity and serum and testis testosterone concentrations in short-finned pilot whales Globicephala macrorhynchus. Fish. Sci. 65(6), 878–883 (1999).CAS 
    Article 

    Google Scholar 
    7.Schroeder, J. P. & Keller, K. V. Seasonality of serum testosterone levels and sperm density in Tursiops truncatus. J. Exp. Zool. 249(3), 316–321 (1989).CAS 
    PubMed 
    Article 

    Google Scholar 
    8.Robeck, T. R. et al. Reproduction, growth and development in captive beluga (Delphinapterus leucas). Zoo Biol. 24(1), 29–49 (2005).Article 

    Google Scholar 
    9.Wells, R. Reproductive behavior and hormonal correlates in Hawaiian spinner dolphins, Stenella longirostris. In Reproduction in Whales, Dolphins, and Porpoises (eds Perrin, W. F. et al.) 465–472 (Reports of the International Whaling Commission, 1984).
    Google Scholar 
    10.Mogoe, T. et al. Functional reduction of the southern minke whale (Balaenoptera acutorostrata) testis during the feeding season. Mar. Mamm. Sci. 16(3), 559–569 (2000).Article 

    Google Scholar 
    11.Kjeld, M. et al. Changes in blood testosterone and progesterone concentrations of the North Atlantic minke whale (Balaenoptera acutorostrata) during the feeding season. Can. J. Fish. Aquat. Sci. 61(2), 230–237 (2004).CAS 
    Article 

    Google Scholar 
    12.Temte, J. L. Use of serum progesterone and testosterone to estimate sexual maturity in Dall’s porpoise Phocoenoides dalli. Fish. Bull. 89(1), 161–166 (1991).
    Google Scholar 
    13.Robeck, T. R. et al. Reproduction, growth and development in captive beluga (Delphinapterus leucas). Zoo Biol. Publ. Affil. Am. Zoo Aquar. Assoc. 24(1), 29–49 (2005).
    Google Scholar 
    14.Kirby, V. L. Endocrinology of marine mammals. In Handbook of Marine Mammal Medicine: Health (ed. Dierauf, L. A.) 303–351 (Disease and Rehabilitation CRC Press Inc, 1990).
    Google Scholar 
    15.Desportes, G., Saboureau, M. & Lacroix, A. Growth-related changes in testicular mass and plasma testosterone concentrations in long-finned pilot whales, Globicephala melas. J. Reprod. Fertil. 102(1), 237–244 (1994).CAS 
    PubMed 
    Article 

    Google Scholar 
    16.Kjeld, J., Sigurjonsson, J. & Arnason, A. Sex hormone concentrations in blood serum from the North Atlantic fin whale (Balaenoptera physalus). J. Endocrinol. 134(3), 405–413 (1992).CAS 
    PubMed 
    Article 

    Google Scholar 
    17.Boggs, A. S. et al. Remote blubber sampling paired with liquid chromatography tandem mass spectrometry for steroidal endocrinology in free-ranging bottlenose dolphins (Tursiops truncatus). Gen. Comp. Endocrinol. 281, 164–172 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Weller, D. W. et al. Behavioral responses of bottlenose dolphins to remote biopsy sampling and observations of surgical biopsy wound healing. Aquat. Mamm. 23(1), 49–58 (1997).19.Krahn, M. M. et al. Stratification of lipids, fatty acids and organochlorine contaminants in blubber of white whales and killer whales. J. Cetac. Res. Manage. 6(2), 175–189 (2004).
    Google Scholar 
    20.Marsili, L. et al. Skin biopsies for cell cultures from Mediterranean free-ranging cetaceans. Mar. Environ. Res. 50(1–5), 523–526 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Hobbs, K. E. et al. PCBs and organochlorine pesticides in blubber biopsies from free-ranging St. Lawrence River Estuary beluga whales (Delphinapterus leucas), 1994–1998. Environ. Pollut. 122(2), 291–302 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Kellar, N. M. et al. Determining pregnancy from blubber in three species of delphinids. Mar. Mamm. Sci. 22(1), 1–16 (2006).Article 

    Google Scholar 
    23.Mingramm, F. et al. Evaluation of respiratory vapour and blubber samples for use in endocrine assessments of bottlenose dolphins (Tursiops spp.). Gen. Comp. Endocrinol. 274, 37–49 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Galligan, T. M. et al. Blubber steroid hormone profiles as indicators of physiological state in free-ranging common bottlenose dolphins (Tursiops truncatus). Comp. Biochem. Physiol. A Mol. Integr. Physiol. 239, 110583 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Dierauf, L. & Gulland, F. M. CRC Handbook of Marine Mammal Medicine: Health, Disease, and Rehabilitation (CRC Press, 2001).Book 

    Google Scholar 
    26.Champagne, C. D. et al. Comprehensive endocrine response to acute stress in the bottlenose dolphin from serum, blubber, and feces. Gen. Comp. Endocrinol. 266, 178–193 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Kellar, N. M. et al. Variation of bowhead whale progesterone concentrations across demographic groups and sample matrices. Endang. Species Res. 22(1), 61–72 (2013).Article 

    Google Scholar 
    28.Richard, J. T. et al. Testosterone and progesterone concentrations in blow samples are biologically relevant in belugas (Delphinapterus leucas). Gen. Comp. Endocrinol. 246, 183–193 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Hunt, K. E. et al. Multi-year patterns in testosterone, cortisol and corticosterone in baleen from adult males of three whale species. Conserv. Physiol. 6(1), coy049 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Wells, R. S. Dolphin social complexity: lessons from long-term study and life history. In Animal Social Complexity: Intelligence, Culture, and Individualized Societies (eds de Waal, F. B. M. & Tyack, P. L.) 32–56 (Harvard University Press, 2003).
    Google Scholar 
    31.Brook, F. et al. Ultrasonographic imaging of the testis and epididymis of the bottlenose dolphin, Tursiops truncatus aduncas. J. Reprod. Fertil. 119(2), 233–240 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Wells, R. S. Social structure and life history of bottlenose dolphins near Sarasota Bay, Florida: Insights from four decades and five generations. In Primates and Cetaceans: Field Research and Conservation of Complex Mammalian Societies, Primatology Monographs (eds Yamagiwa, J. & Karczmarski, L.) 149–172 (Springer, 2014).
    Google Scholar 
    33.Barratclough, A. et al. Health assessments of common bottlenose dolphins (Tursiops truncatus): past, present, and potential conservation applications. Front. Vet. Sci. 6, 444 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Champagne, C. D. et al. Blubber cortisol qualitatively reflects circulating cortisol concentrations in bottlenose dolphins. Mar. Mamm. Sci. 33(1), 134–153 (2017).CAS 
    Article 

    Google Scholar 
    35.Norman, A. W. & Litwack, G. Hormones (Academic Press, 1997).
    Google Scholar 
    36.Urian, K. et al. Seasonality of reproduction in bottlenose dolphins, Tursiops truncatus. J. Mammal. 77(2), 394–403 (1996).Article 

    Google Scholar 
    37.Wells, R. Reproduction in wild bottlenose dolphins: overview of patterns observed during a long-term study. in Bottlenose Dolphins Reproduction Workshop. AZA marine mammal taxon advisory group. 2000. Silver Springs, MD.38.Read, A. et al. Patterns of growth in wild bottlenose dolphins, Tursiops truncatus. J. Zool. 231(1), 107–123 (1993).Article 

    Google Scholar 
    39.Trego, M. L. et al. Comprehensive screening links halogenated organic compounds with testosterone levels in male Delphinus delphis from the Southern California bight. Environ. Sci. Technol. 52(5), 3101–3109 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Kannan, K. et al. Toxicity reference values for the toxic effects of polychlorinated biphenyls to aquatic mammals. Hum. Ecol. Risk Assess. 6(1), 181–201 (2000).CAS 
    Article 

    Google Scholar 
    41.Jepson, P. D. et al. Relationships between polychlorinated biphenyls and health status in harbor porpoises (Phocoena phocoena) stranded in the United Kingdom. Environ. Toxicol. Chem. Int. J. 24(1), 238–248 (2005).CAS 
    Article 

    Google Scholar 
    42.Minter, L. & DeLiberto, T. Seasonal variation in serum testosterone, testicular volume, and semen characteristics in the coyote (Canis latrans). Theriogenology 69(8), 946–952 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Preston, B. T. et al. Testes size, testosterone production and reproductive behaviour in a natural mammalian mating system. J. Anim. Ecol. 81(1), 296–305 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Desportes, G., Saboureau, M. & Lacroix, A. Growth-related changes in testicular mass and plasma testosterone concentrations in long-finned pilot whales Globicephala melas. Reproduction 102(1), 237–244 (1994).CAS 
    Article 

    Google Scholar 
    45.Ryan, C. et al. Lipid content of blubber biopsies is not representative of blubber in situ for fin whales (Balaenoptera physalus). Mar. Mamm. Sci. 29(3), 542–547 (2013).CAS 
    Article 

    Google Scholar 
    46.Wells, R. S. et al. Bottlenose dolphins as marine ecosystem sentinels: developing a health monitoring system. EcoHealth 1(3), 246–254 (2004).Article 

    Google Scholar 
    47.Wells, R. S. et al. Integrating life-history and reproductive success data to examine potential relationships with organochlorine compounds for bottlenose dolphins (Tursiops truncatus) in Sarasota Bay Florida. Sci. Total Environ. 349(1–3), 106–119 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Yordy, J. E. et al. Partitioning of persistent organic pollutants between blubber and blood of wild bottlenose dolphins: implications for biomonitoring and health. Environ. Sci. Technol. 44(12), 4789–4795 (2010).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Kellar, N. M. et al. Low reproductive success rates of common bottlenose dolphins Tursiops truncatus in the northern Gulf of Mexico following the Deepwater Horizon disaster (2010–2015). Endang. Species Res. 33, 143–158 (2017).Article 

    Google Scholar 
    50.Kellar, N. M. et al. Blubber cortisol: a potential tool for assessing stress response in free-ranging dolphins without effects due to sampling. PLoS ONE 10(2), e0115257 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    51.Harrison, R. & Ridgway, S. Gonadal activity in some bottlenose dolphins (Tursiops truncatus). J. Zool. 165(3), 355–366 (1971).Article 

    Google Scholar 
    52.Wells, R. S. & Scott, M. D. Bottlenose Dolphin: common bottlenose dolphin: Tursiops truncates. In Encyclopedia of Marine Mammals 3rd edn (eds Würsig, B. et al.) 118–125 (Academic Press/Elsevier, 2009).
    Google Scholar 
    53.R Core Team. R: A Language and Environment for Statistical Computing. 2020, R Foundation for Statistical Computing: 2020, Vienna, Austria. URL https://www.R-project.org/.54.Wells, R. S. & Scott, M. D. Common Bottlenose Dolphin: Tursiops truncates, in Encyclopedia of Marine Mammals 252 (Elsevier, 2009).
    Google Scholar  More

  • in

    Correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity

    Let us first describe the setting and introduce notation. The main object of analysis is a matrix A (a contingency table with (n_r) rows and (n_c) columns) that contains the counts of two variables. A common example from ecology is that (A_{ij}) contains some measure of abundance of species i (rows) in sampling site j (columns). The matrix A can also be a binary incidence matrix, containing either the presence (1) or absence (0) of species in sites. The matrix A can be interpreted as the bi-adjacency matrix of a bipartite network that connects species to sites. The network contains (n_r) nodes on one side (the species, given by the rows of A, indexed by i), and (n_c) nodes on the other side (the sites, given by the columns of A, indexed by j). In general, we will refer to the two sets of nodes as row nodes and column nodes, respectively. The degree of a row node i is defined by the row sum (r_i = sum _j A_{ij}), which gives the total abundance of species i in all sites. Likewise, the degree of a column node j is defined as the column sum (c_j = sum _i A_{ij}), which gives the total abundance of species in a site j. The degrees of the row and column nodes are given by the vectors (mathbf {r}= (r_1, r_2, dots , r_{n_r})^T) and (mathbf {c}= (c_1,c_2,dots ,c_{n_c})^T). We further define two square matrices, (D_r) ((n_r times n_r)) and (D_c) ((n_c times n_c)) as the diagonal matrices that have (mathbf {r}) and (mathbf {c}) on the diagonal, respectively. The sum (n = sum _{ij} A_{ij}) gives the total number of occurrences in the table (in the case of a species-site example, the total abundance of species).CA as canonical correlation analysisOne of the first derivations of CA was obtained by applying canonical correlation analysis to categorical variables12,19,22. Here we follow the derivation in Ref.1 (Chapter 9), where CA is derived as an application of canonical correlation analysis applied to a bipartite network, and to which we refer for further details. For ease of explanation, we will assume the network is defined by a binary presence-absence matrix (i.e. the network is unweighted), but the result generalizes to any contingency table (i.e. weighted bipartite networks).The aim is to assign a ‘score’ to each node in the network, under the assumption that row and column nodes with similar scores connect to each other. Hence, connected nodes get assigned similar scores, and the scores can be thought of as a latent variable that drive the formation of links in the network. In ecology, such latent variables are referred to as gradients2,3. Considering a bipartite network describing the occurrence of species in a set of sites, for example, the resulting scores may reflect some variable determining why species locate in specific sites, such as the temperature preference of a species and the temperature at a site. In practice, the interpretation of a gradient resulting from application of CA can be verified by correlating it with known environmental variables (e.g. data on the temperature of each site).Mathematically, such gradients can be inferred from the edges of the bipartite network. Recall that for a presence-absence matrix, the total number of edges in the bipartite network is given by (n = sum _{ij} A_{ij}). Let us construct a vector (mathbf {y}_r) of length n that contains, for each edge, the scores of the row node it connects to, and a vector (mathbf {y}_c) of length n that contains, again for each edge, the score of the column node it connects to. Given the assumption that edges connect row nodes and column nodes with similar scores, the node scores can be found by maximizing the correlation between (mathbf {y}_r) and (mathbf {y}_c), so that the row- and column scores for each edge are as similar as possible. Denoting the vector of length (n_r) containing the row scores by (mathbf {v}) and the vector of length (n_c) containing the column scores by (mathbf {u}), this leads to the optimization problem$$begin{aligned} max _{mathbf {v}, mathbf {u}} mathrm {corr}(mathbf {y}_r,mathbf {y}_c). end{aligned}$$
    (1)
    In order to obtain standardized scores, the constraints that (mathbf {y}_r) and (mathbf {y}_c) have zero mean and unit variance need to be added. Solving this problem using Lagrangian optimization, the solution is given by$$begin{aligned} D_r^{-1}A D_c^{-1} A^T mathbf {v}&= lambda mathbf {v}nonumber \ D_c^{-1}A^T D_r^{-1} A mathbf {u}&= lambda mathbf {u}. end{aligned}$$
    (2)
    The score vectors (mathbf {v}) and (mathbf {u}) can thus be found by solving an eigenvector problem. Following Ref.1 , they are subject to the constraint that (|mathbf {v}|=|mathbf {u}|=1). The general interpretation of the elements of (mathbf {v}) and (mathbf {u}) is as follows. Each row node (of A) is represented in (mathbf {v}), and each column node is represented in (mathbf {u}). The smaller the difference between the values of two row (column) nodes in (mathbf {v}) ((mathbf {u})), the more similar these nodes are. The similarity among row nodes that is reflected in (mathbf {v}) vectors arises because they are connected to a similar set of column nodes in the original bipartite network (vice versa for similarities in (mathbf {u})). In literature, this is referred to as row nodes being similar because of their similar ‘profile’11,23, and reciprocal averaging defines exactly how the scores are calculated in terms of the profiles and reduces to the same set of equations15. Both matrices on the left-hand side of Eq. (2) are row-stochastic and positive definite, and have identical eigenvalues that are real and take values between 0 and 1. Assuming that we have a connected network, sorting the eigenvalues in decreasing order leads to (1=lambda _1 > lambda _2 dots ge 0).It can be shown that the correlation between (mathbf {y}_r) and (mathbf {y}_c) for a given set of eigenvectors (mathbf {v}) and (mathbf {u}) is given by their corresponding eigenvalue, so that (lambda = mathrm {corr}^2(mathbf {y}_{r},mathbf {y}_{c})). Note that the correlations between the row and column vectors can be negative, meaning that merely the absolute value of the correlation between (y_r) and (y_c) is related to the (square root) of the eigenvalues. Iterative approaches to extract potential negative correlations exist in literature24. The node scores leading to the highest correlation are thus given by the eigenvectors associated with the largest eigenvalue. However, the eigenvectors corresponding to (lambda _1) have all constant values and represent the trivial solution in which all row nodes and all column nodes have equal scores (leading to a perfect correlation). This trivial solution does not satisfy the condition that the scores have to be centered, and thus it must be rejected. The solution to Eq. (1) is thus given by the eigenvectors (mathbf {v}_2) and (mathbf {u}_2), corresponding to the second largest eigenvalue (lambda _2), which corresponds to the square root of the (maximized) correlation. We notice here that this derivation leads to analogous results than observed in classical derivations of CA, where the matrix A is centered both with respect to the rows and to the columns.The second eigenvectors (mathbf {v}_2) and (mathbf {u}_2) hold the unique scores such that row- and column nodes with similar scores connect to each other. The second eigenvalue (lambda _2) indicates to what extent the row- and column scores can be ‘matched’, where high values (close to 1) indicate a high association between the inferred scores (the gradient) and the structure of the network.The higher order eigenvectors in Eq. (2) and their eigenvalues are solutions to Eq. (1) with the additional constraint that (mathbf {y}_r) and (mathbf {y}_c) are orthogonal to the other solutions. The vectors (mathbf {v}_3) and (mathbf {u}_3), for example, may represent other variables that drive the formation of links (e.g. precipitation, primary productivity, etc.) on top of the gradients described by (mathbf {v}_2) and (mathbf {u}_2). We note that, differently from notation in some CA literature, we here denote the k-th non-trivial eigenvector with the subscript k+1.CA as a clustering algorithmA completely different approach shows that the eigenvectors (mathbf {v}_2) and (mathbf {u}_2) (i.e. the second eigenvectors in Eq. (2)) can also be interpreted as cluster labels, obtained when identifying clusters in the network of similarities that is derived from the bipartite network.A similarity network can be constructed from a bipartite network by ‘projecting’ the bipartite network onto one of its layers (either the row nodes or the column nodes) through stochastic complementation18. Projecting the bipartite network defined by A onto its row layer leads to the (n_r times n_r) similarity matrix (S_r = A D_c^{-1} A^T). The entries of (S_r) represent pairwise similarities between row nodes of A, based on how many links they share with the same column node, weighted for the degree of each column node. Similarly, the (n_c times n_c) similarity matrix (S_c = A^T D_r^{-1} A) defines the pairwise similarities between the column nodes of A.Identifying clusters in the similarity network can be done by minimizing the so-called ‘normalized cut’20. The normalized cut assigns, for a given partition of a network into K clusters, a score that represents the strength of the connections between the clusters for that partition. A partition can be described by assigning a discrete cluster label to each node. Hence, minimizing the normalized cut is equivalent to assigning a cluster label to each node in the network in such a way that the clusters are minimally connected. Finding the discrete cluster labels that minimize the normalized cut in large networks is in general not possible20. However, a solution of a related problem can be obtained when the cluster labels are allowed to take continuous values as opposed to discrete values. Solutions of this ‘relaxed’ problem can be interpreted as continuous approximations of the discrete cluster labels.Minimizing the normalized cut in (S_r) leads to the generalized eigensystem20$$begin{aligned} (D_r – S_r) mathbf {v}= tilde{lambda } D_r mathbf {v}, end{aligned}$$
    (3)
    where the entries of the generalized eigenvector (mathbf {v}_2) corresponding to the second smallest eigenvalue (tilde{lambda }_2) of Eq. (3) hold the approximate cluster labels of the optimal partition into two clusters. It is easily shown that generalized eigenvectors in Eq. (3) are exactly the eigenvectors of Eq. (2), where the eigenvalues are related by (tilde{lambda }_k = 1 – lambda _k), where (k=1,2,dots ,n_r) (see “Suppl. Material A”).The matrix (L_r = D_r-S_r) is known as the Laplacian matrix of the similarity network defined by (S_r), and is well known in spectral graph theory25. The number of eigenvalues of (L_r) for which (tilde{lambda } = 0) (or equivalently (lambda = 1) in Eq. (2)) denotes the number of disconnected clusters in the network. Each of these ’trivial’ eigenvalues has a corresponding generalized eigenvector that has constant values for the nodes in a particular cluster, indicating cluster membership.The situation changes when the clusters are weakly connected. The optimal solution for partitioning the similarity network into two clusters is given by the eigenvector (mathbf {v}_2) associated to eigenvalue (lambda _2). Although continuous, the entries of (mathbf {v}_2) can be interpreted as approximations to cluster labels, which indicate for each row node to which cluster it belongs. In other words, nodes with high values in this eigenvector (i.e., high scores) belong to one cluster, and nodes with low scores to the other. A discrete partition can then be obtained from the approximate (continuous) cluster labels by discretizing them, for example by assigning all negative values to one cluster and all positive values to the other26. The corresponding eigenvalue (lambda _2) represents the quality of the partitioning, as determined by the normalized cut criterion. High values are indicative of a network that can be well partitioned into two clusters (two totally disconnected clusters would yield eigenvalues (lambda _1 = lambda _2=1)), whereas lower values correspond to a network that is less easily grouped into two clusters (i.e. the resulting clusters are more interconnected).Finding a partitioning into multiple, say K, clusters is more involved, where (Kle n_r) (or (Kle n_c) if working with column variables). Minimizing the normalized cut for K clusters yields a trace minimization problem of which the relaxed solution is given by the first K eigenvectors in Eq. (2)27. Because the first eigenvector in Eq. (2) is trivial, in practice we only require (K-1) eigenvectors (i.e., the 2nd, 3rd, … up to the Kth). The discrete cluster labels can then be obtained, for example, by running a k-Means algorithm on the matrix consisting of those (K-1) eigenvectors, a technique that is also known as spectral clustering28,29. How well the network can be partitioned into K clusters is given by the average value of the first K eigenvalues, i.e. (frac{1}{K} sum _{k=1}^K lambda _k)27.The clustering approach thus brings an alternative interpretation to CA results. A key observation is that the eigenvalues and eigenvectors in Eq. (2) are directly related to the generalized eigenvectors of the Laplacian of the similarity matrix (S_r), and thus hold information on the structure of the similarity network. The entries of the second eigenvector (mathbf {v}_2) can be interpreted as the approximate cluster labels of a two-way partitioning of the similarity network defined by (S_r). Although at first sight the interpretation of CA scores as cluster labels may seem different from the interpretation as a latent variable described above in “CA as canonical correlation analysis”, note that cluster labels can be seen as latent variables, albeit discrete rather than continuous.CA as a graph embedding techniqueA third interpretation of the eigenvectors and eigenvalues in Eq. 2 arises from a so-called graph embedding of the similarity matrix (S_r) (or (S_c)). Graph embeddings provide a way to obtain a low-dimensional representation of a high-dimensional network, that are used for example for graph drawing. A graph embedding represents the nodes of a graph as node vectors in a space, such that nodes that are ‘close’ in the network are also close in terms of their distance in the embedding. A key feature of these embeddings is that their dimensionality can be reduced in order to obtain a low-dimensional representation of the data, while retaining its most important structural properties (see Ref.1, chapter 10 for an overview of graph embedding techniques).As noted by several authors, CA is equivalent to graph embedding in the case of a similarity matrix obtained through stochastic complementation. For example, computing a 1-step diffusion map of the similarity matrix (S_r) leads exactly to the eigenvectors of Eq. (2)18,30. Also, the graph embedding using the Laplacian eigenmap has been shown to be equivalent to graph partitioning using the normalized cut, which is in turn equivalent to CA31. CA-specific type of embedding is based on the chi-square statistics and it is thus Euclidean.Embedding the similarity network (S_r) in a ((K-1))-dimensional space yields an ‘embedding matrix’ (X_r in mathbb {R}^{n_r times K-1}) (known in CA-related literature as ’principal coordinates’). Each row of (X_r) represents a node of (S_r) as a ‘node vector’ in the embedding. The rows of (X_r) can be seen as components of ((K-1))-dimensional basis vectors that span the embedding, and are identical to what is referred to as the ‘axes’ in CA. Every entry (X_{i,k}) represents the coordinate of row node i on the k’th basis vector, and can be seen as the ‘score’ of i on the k’th CA axis. An embedding matrix of (S_r) can defined as (X_r = [sqrt{lambda _2} mathbf {v}_2, dots , sqrt{lambda _K} mathbf {v}_{K}]), where the vectors (mathbf {v}_k) are the eigenvectors defined in (2), and each of them is weighted by the square root of their corresponding eigenvalue. We will refer to columns of the embedding matrix as ‘CA-axes’, given by (mathbf {x}_k = sqrt{lambda _k}mathbf {v}_k) (with (mathbf {x}_2) being the ’first CA axis’, and so on).The axes are constructed in such a way that they capture the largest amount of ‘variation’ or ‘inertia’ in the data, which is given by their corresponding eigenvalue11. The sum of all the eigenvalues gives the total variation in the data (in CA, this is referred to as the total inertia). CA decomposes the total variation in such a way that the first axis captures a maximal part of the variation, the second a maximal part of the remaining variation, and so on. A low-dimensional embedding that preserves the maximal amount of variation can thus be obtained by discarding the eigenvectors corresponding to smaller eigenvalues. The ‘quality’ of the embedding can then be expressed as the share of the total variation that is preserved in the embedding.A typical way of presenting CA results is by showing the first two coordinates of each row (or column) node, i.e. plotting (mathbf {x}_2) against (mathbf {x}_3), which is usually referred to as a ’correspondence plot’. Since the first two axes capture a maximal amount of inertia, such a plot is in a way the optimal two-dimensional representation of the data that captures the relations between the rows (or columns) of A. The distances between points in the correspondence plot approximate the similarities between nodes. How well the correspondence plot represents the similarities is given by the percentage of variation explained by the first two axes.Each axis can be interpreted as a latent variable that account for part of the total variation in the data. Since the axes in the embedding are given by a scaled version of the eigenvectors discussed above in “CA as canonical correlation analysis”, the interpretation of the eigenvalues as the amount of variation explained is complementary to the interpretation as the correlation between row and column scores which we also introduced above in “CA as canonical correlation analysis”. Furthermore, the axes spanning the K-dimensional embedding are exactly the generalized eigenvectors that follow from minimizing the normalized cut for K clusters31. Indeed, when there are clear clusters in the similarity network, they will show up in the embedding space as separate groups of points.Summarizing, we find three interpretations of CA axes and their corresponding eigenvalues: as latent variables that drive the formation of links in the bipartite network, as approximate clusters labels of a bi-partition of the similarity network, and as coordinates of an embedding of the similarity network. The different derivations of CA and their interpretations are summarized in Table 1.Table 1 Different interpretations of the eigenvectors and eigenvalues resulting from CA.Full size table More

  • in

    Decay stages of wood and associated fungal communities characterise diversity–decomposition relationships

    1.Bradford, M. A. et al. Climate fails to predict wood decomposition at regional scales. Nat. Clim. Chan. 4, 625–630 (2014).CAS 
    Article 
    ADS 

    Google Scholar 
    2.Crowther, T. W. et al. The global soil community and its influence on biogeochemistry. Science 365, eaav0550 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Lustenhouwer, N. et al. A trait-based understanding of wood decomposition by fungi. Proc. Nat. Acad. Sci. USA 117, 11551–11558 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Tilman, D. et al. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 (2001).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    5.Ammer, C. Diversity and forest productivity in a changing climate. New Phytol. 221, 50–66 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Dickie, I. A., Fukami, T., Wilkie, J. P., Allen, R. B. & Buchanan, P. K. Do assembly history effects attenuate from species to ecosystem properties? A field test with wood-inhabiting fungi. Ecol. Lett. 15, 133–141 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.van der Wal, A., Ottosson, E. & de Boer, W. Neglected role of fungal community composition in explaining variation in wood decay rates. Ecology 96, 124–133 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Hoppe, B. et al. Linking molecular deadwood-inhabiting fungal diversity and community dynamics to ecosystem functions and processes in Central European forests. Fung. Div. 77, 367–379 (2016).Article 

    Google Scholar 
    9.Purahong, W. et al. Determinants of deadwood-inhabiting fungal communities in temperate forests: Molecular evidence from a large scale deadwood decomposition experiment. Front. Microbiol. 9, Article 2120 (2018).10.Skelton, J. et al. Relationships among wood-boring beetles, fungi, and the decomposition of forest biomass. Mol. Ecol. 28, 4971–4986 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Toljander, Y. K., Lindahl, B. D., Holmer, L. & Hogberg, N. O. S. Environmental fluctuations facilitate species co-existence and increase decomposition in communities of wood decay fungi. Oecologia 148, 625–631 (2006).PubMed 
    Article 
    ADS 

    Google Scholar 
    12.Fukami, T. et al. Assembly history dictates ecosystem functioning: evidence from wood decomposer communities. Ecol. Lett. 13, 675–684 (2010).PubMed 
    Article 

    Google Scholar 
    13.Boddy, L. Fungal community ecology and wood decomposition process in angiosperms: From standing tree to complete decay of coarse woody debris. Ecol. Bull. 49, 43–56 (2001).
    Google Scholar 
    14.Boddy, L. & Heilmann-Clausen, J. Basidiomycete community development in temperate angiosperm wood. In Ecology of saprotrpophic basidiomycetes. (Eds. Boddy, L., Frankland, J.C., & van West, P.) 211–237 (Academic Press, 2008).15.Parfitt, D., Hunt, J., Dockrell, D., Rogers, H. J. & Boddy, L. Do all trees carry the seed of their own destruction? PCR reveals numerous wood decay fungi latently present in sapwood of a wide range of angiosperm trees. Fung. Ecol. 3, 338–346 (2010).Article 

    Google Scholar 
    16.Song, Z., Kennedy, P. G., Liew, F. J. & Schilling, J. S. Fungal endophytes as priority colonizers initiating wood decomposition. Func. Ecol. 31, 407–418 (2017).Article 

    Google Scholar 
    17.Cline, L. C., Schilling, J. S., Menke, J., Groenhof, E. & Kennedy, P. G. Ecological and functional effects of fungal endophytes on wood decomposition. Func. Ecol. 32, 181–191 (2018).Article 

    Google Scholar 
    18.Coates, D. & Rayner, A. D. M. Fungal population and community development in cut beech logs I. Establishment via the aerial cut surface. New Phytol. 101, 153–171 (1985).Article 

    Google Scholar 
    19.Fukasawa, Y., Osono, T. & Takeda, H. Beech log decomposition by wood-inhabiting fungi in a cool temperate forest floor: A quantitative analysis focused on the decay activity of a dominant basidiomycetes Omphalotus guepiniformis. Ecol. Res. 25, 959–966 (2010).Article 

    Google Scholar 
    20.Boddy, L. & Hiscox, J. Fungal ecology: principles and mechanisms of colonization and competition by saprotrophic fungi. Microbiol. Spec. 4, FUNK-0019-2016 (2016).
    Google Scholar 
    21.Rajala, T., Peltoniemi, M., Pennanen, T. & Makipaa, R. Fungal community dynamics in relation to substrate quality of decaying Norway spruce (Picea abies [L.] Karst.) logs in boreal forests. FEMS Microbiol. Ecol. 81, 494–505 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    22.Rajala, T., Tuomivirta, T., Pennanen, T. & Mäkipää, R. Habitat models of wood-inhabiting fungi along a decay gradient of Norway spruce logs. Fung. Ecol. 18, 48–55 (2015).Article 

    Google Scholar 
    23.Rayner, A.D.M., & Boddy, L. Fungal decomposition of wood: Its biology and ecology. (Willey, 1988).24.Bunnell, F. L. & Houde, I. Down wood and biodiversity—Implications to forest practices. Environ. Rev. 18, 397–421 (2010).Article 

    Google Scholar 
    25.Wells, J. M. & Boddy, L. Interspecific carbon exchange and cost of interactions between basidiomycete mycelia in soil and wood. Func. Ecol. 16, 153–161 (2002).Article 

    Google Scholar 
    26.Hiscox, J. et al. Effects of pre-colonisation and temperature on interspecific fungal interactions in wood. Fung. Ecol. 21, 32–42 (2016).Article 

    Google Scholar 
    27.Fukasawa, Y., Osono, T. & Takeda, H. Wood decomposition abilities of diverse lignicolous fungi on nondecayed and decayed beech wood. Mycologia 103, 474–482 (2011).PubMed 
    Article 

    Google Scholar 
    28.Valentin, L. et al. Loss of diversity in wood-inhabiting fungal communities affects decomposition activity in Norway spruce wood. Front. Microbiol. 5, Article 230 (2014).29.Maynard, D., Crowther, T. W. & Bradford, M. A. Fungal interactions reduce carbon use efficiency. Ecol. Lett. 20, 1034–1042 (2017).PubMed 
    Article 

    Google Scholar 
    30.Woodward, S., & Boddy, L. Interactions between saprotrophic fungi. In Ecology of saprotrophic basidiomycetes (eds Boddy, L., Frankland, J.C., van West, P.) 125–141 (Academic Press, 2008).31.Gessner, M. O. et al. Diversity meets decomposition. Trends Ecol. Evol. 25, 372–380 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Fukasawa, Y., Gilmartin, E. C., Savoury, M. & Boddy, L. Inoculum volume effects on competitive outcome and wood decay rate of brown- and white-rot basidiomycetes. Fung. Ecol. 45, 100938 (2020).Article 

    Google Scholar 
    33.O’Leary, J. et al. The whiff of decay: Linking volatile production and extracellular enzymes to outcomes of fungal interactions at different temperatures. Fung. Ecol. 39, 336–348 (2019).Article 

    Google Scholar 
    34.Boddy, L., Owens, E. M. & Chapela, I. H. Small scale variation in decay rate within logs one year after felling: effect of fungal community structure and moisture content. FEMS Microbiol. Ecol. 62, 173–184 (1989).Article 

    Google Scholar 
    35.Setälä, H. & McLean, M. A. Decomposition rate of organic substrates in relation to the species diversity of soil saprophytic fungi. Oecologia 139, 98–107 (2004).PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    36.Yang, C. et al. Higher fungal diversity is correlated with lower CO2 emissions from dead wood in a natural forest. Sci. Rep. 6, 31066 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    37.Berg, B., & McClaugherty, C. Plant litter: Decomposition, humus formation, carbon sequestration (Springer, 2003).38.Fukasawa, Y., Takahashi, K., Arikawa, T., Hattori, T. & Maekawa, N. Fungal wood decomposer activities influence community structure of myxomycetes and bryophytes on coarse woody debris. Fung. Ecol. 14, 44–52 (2015).Article 

    Google Scholar 
    39.Fukasawa, Y., Hyodo, F. & Kawakami, S. Foraging association between myxomycetes and fungal communities on coarse woody debris. Soil Biol. Biochem. 121, 95–102 (2018).CAS 
    Article 

    Google Scholar 
    40.Fukasawa, Y. Fungal succession and decomposition of Pinus densiflora snags. Ecol. Res. 33, 435–444 (2018).Article 

    Google Scholar 
    41.Fukasawa, Y., Osono, T. & Takeda, H. Effects of attack of saprobic fungi on twig litter decomposition by endophytic fungi. Ecol. Res. 24, 1067–1073 (2009).Article 

    Google Scholar 
    42.Hiscox, J. & Boddy, L. Armed and dangerous—Chemical warfare in wood decay communities. Fung. Biol. Rev. 31, 169–184 (2017).Article 

    Google Scholar 
    43.Presley, G.N., Zhang, J., Purvine, S.O., & Schilling, J.S. Functional genomics, transcriptomics, and proteomics reveal distinct combat strategies between lineages of wood-degrading fungi with redundant wood decay mechanisms. Front. Microbiol. 11, article 1646 (2020).44.Hiscox, J., Savoury, M., Vaughan, I. P., Muller, C. T. & Boddy, L. Antagonistic fungal interactions influence carbon dioxide evolution from decomposing wood. Fung. Ecol. 14, 24–32 (2015).Article 

    Google Scholar 
    45.Zhang, X., Xu, C. & Wang, H. Pretreatment of bamboo residues with Coriolus versicolor for enzymatic hydrolysis. J. Biosci. Bioengineer. 104, 149–151 (2007).CAS 
    Article 

    Google Scholar 
    46.Horisawa, S., Inoue, A. & Yamanaka, Y. Direct ethanol production from lignocellulosic materials by mixed culture of wood rot fungi Schizophyllum commune, Bjerkandera adusta, and Fomitopsis palustris. Fermentation 5, 21 (2019).CAS 
    Article 

    Google Scholar 
    47.Schilling, J. S., Kaffenberger, J. T., Held, B. W., Ortiz, R. & Blanchette, R. A. Using wood rot phenotypes to illuminate the “Gray” among decomposer fungi. Front. Microbiol 11, 1288 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Crawford, R. H., Carpenter, S. E. & Harmon, M. E. Communities of filamentous fungi and yeast in decomposing logs of Pseudotsuga menziesii. Mycologia 82, 759–765 (1990).Article 

    Google Scholar 
    49.Lumley, T. C., Gignac, L. D. & Currah, R. S. Microfungus communities of white spruce and trembling aspen logs and different stages of decay in disturbed and undisturbed sites in the boreal mixedwood region of Alberta. Can. J. Bot. 79, 76–92 (2001).
    Google Scholar 
    50.Fukasawa, Y., Osono, T. & Takeda, H. Microfungus communities of Japanese beech logs at different stages of decay in a cool temperate deciduous forest. Can. J. For. Res. 39, 1606–1614 (2009).CAS 
    Article 

    Google Scholar 
    51.Fukasawa, Y., Osono, T. & Takeda, H. Dynamics of physicochemical properties and occurrence of fungal fruit bodies during decomposition of coarse woody debris of Fagus crenata. J. For. Res. 14, 20–29 (2009).CAS 
    Article 

    Google Scholar 
    52.Maynard, D., Crowther, T. W. & Bradford, M. A. Competitive network determines the direction of the diversity-function relationship. Proc. Natl. Acad. Sci. USA 114, 11464–11469 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Kubart, A., Vasaitis, R., Stenlid, J. & Dahlberg, A. Fungal communities in Norway spruce stumps along a latitudinal gradient in Sweden. For. Ecol. Manag. 371, 50–58 (2016).Article 

    Google Scholar 
    54.MacArthur, R.H., & Wilson, E.O. The Theory of Island Biogeography. (Princeton University Press, 2001).55.Yachi, S. & Loreau, M. Biodiversity and ecosystem functioning productivity in a fluctuating environment: The insurance hypothesis. Proc. Natl. Acad. Sci. USA 96, 1463–1468 (1999).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    56.Maynard, D. et al. Consistent trade-offs in fungal trait expression across broad spatial scales. Nat. Microbiol. 4, 846–853 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    57.Talbot, J. M. et al. Endemism and functional convergence across the North American soil mycobiome. Proc. Natl. Acad. Sci. USA 111, 6341–6346 (2014).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    58.Pyle, C. & Brown, M. M. Heterogeneity of wood decay classes within hardwood logs. For. Ecol. Manag. 114, 253–259 (1999).Article 

    Google Scholar 
    59.Carini, P. et al. Effects of spatial variability and relic DNA removal on the detection of temporal dynamics in soil microbial communities. mBio 11, e02776-19 (2020).60.Fukasawa, Y. & Matsuoka, S. Communities of wood-inhabiting fungi in dead pine logs along a geographical gradient in Japan. Fung. Ecol. 18, 75–82 (2015).Article 

    Google Scholar 
    61.Worrall, J. J., Anagnost, S. E. & Zabel, R. A. Comparison of wood decay among diverse lignicolous fungi. Mycologia 89, 199–219 (1997).Article 

    Google Scholar 
    62.Deacon, J. W. Decomposition of filter paper cellulose by thermophilic fungi acting singly, in combination, and in sequence. Tr. Br. Mycol. Soc. 85, 663–669 (1985).CAS 
    Article 

    Google Scholar 
    63.Fukasawa, Y. Effects of wood decomposer fungi on tree seedling establishment on coarse woody debris. For. Ecol. Manag. 266, 232–238 (2012).Article 

    Google Scholar 
    64.Toju, H., Tanabe, A. S., Yamamoto, S. & Sato, H. High-coverage ITS primers for the DNA-based identification of ascomycetes and basidiomycetes in environmental samples. PLoS ONE 7, e40863 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    65.Tanabe, A. S. & Toju, H. Two new computational methods for universal DNA barcoding: A benchmark using barcode sequences of bacteria, archaea, animals, fungi, and land plants. PLoS ONE 8, e76910 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    66.Osono, T. Metagenomic approach yields insights into fungal diversity and functioning. In Species diversity and community structure (eds Sota, T., Kagata, H., Ando, Y., Utsumi, S., & Osono, T.) 1–23 (Springer, 2014).67.Ohtsubo, Y., Ikeda-Ohtsubo, W., Nagata, Y. & Tsuda, M. GenomeMatcher: a graphical user interface for DNA sequence comparison. BMC Bioinform. 9, 376. https://doi.org/10.1186/1471-2105-9-376 (2008).CAS 
    Article 

    Google Scholar 
    68.Ovaskainen, O., & Abrego, N. Joint Species Distribution Modelling: With Application in R (Cambridge University Press, 2020).69.R Core Team. R: A language and environment for statistical computing. The R Foundation for Statistical Computing, Vienna, Austria. www.R-project.org (2019). More