Philippa Kaur

1.
Götzenberger, L. et al. Ecological assembly rules in plant communities—Approaches, patterns and prospects. Biol. Rev. 87, 111–127. https://doi.org/10.1111/j.1469-185X.2011.00187.x (2012).
Article  PubMed  Google Scholar 
2.
Díaz, S. et al. Incorporating plant functional diversity effects in ecosystem service assessments. P. Natl. Axad. Sci. USA 104, 20684–20689. https://doi.org/10.1073/pnas.0704716104 (2007).
ADS  Article  Google Scholar 

3.
McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185. https://doi.org/10.1016/j.tree.2006.02.002 (2006).
Article  PubMed  Google Scholar 

4.
Nobel, I.R. & Slatyer, R.O. Post-fire succession of plants in Mediterranean ecosystems in: Proceedings of the Symposium on the Environmental Consequences of Fire and Fuel Management in Mediterranean Ecosystems (eds. Mooney, H.A. & Conrad, C.E.) 27–36 (California Palo Alto, 1977).

5.
Cornwell, W. K. & Ackerly, D. D. Community assembly and shifts in plant trait distributions across an environmental gradient in coastal California. Ecol. Monogr. 79, 109–126. https://doi.org/10.1890/07-1134.1 (2009).
Article  Google Scholar 

6.
Kraft, N. J. B., Valencia, R. & Ackerly, D. D. Functional traits and niche-based tree community assembly in an amazonian forest. Science 322, 580–582. https://doi.org/10.1126/science.1160662 (2008).
ADS  CAS  Article  PubMed  Google Scholar 

7.
MacArthur, R. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101, 377–385. https://doi.org/10.1086/282505 (1967).
Article  Google Scholar 

8.
Pásztor, L., Botta-Dukát, Z., Magyar, G., Czárán, T. & Meszéna, G. Theory-Based Ecology: A Darwinian Approach (Oxford University Press, Oxford, 2016).
Google Scholar 

9.
Diamond, J.M. Assembly of Species Communities in Ecology and Evolution of Communities (eds. Cody, M.L. & Diamond, J.M.) 342–444 (Belknap Press, 1975).

10.
Litchman, E., Klausmeier, C. A., Schofield, O. M. & Falkowski, P. G. The role of functional traits and trade-offs in structuring phytoplankton communities: Scaling from cellular to ecosystem level. Ecol. Lett. 10, 1170–1181. https://doi.org/10.1111/j.1461-0248.2007.01117.x (2007).
Article  PubMed  Google Scholar 

11.
Reynolds, C. S., Huszár, V., Kruk, C., Naselli-Flores, L. & Melo, S. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428. https://doi.org/10.1093/plankt/24.5.417 (2002).
Article  Google Scholar 

12.
Salmaso, N. & Padisák, J. Morpho-functional groups and phytoplankton development in two deep lakes (Lake Garda, Italy and Lake Stechlin, Germany). Hydrobiologia 578, 97–112. https://doi.org/10.1007/s10750-006-0437-0 (2007).
Article  Google Scholar 

13.
Salmaso, N., Naselli-Flores, L. & Padisák, J. Functional classifications and their application in phytoplankton ecology. Freshw. Biol. 60, 603–619. https://doi.org/10.1111/fwb.12520 (2015).
Article  Google Scholar 

14.
Borics, G., Tóthmérész, B., Lukács, B. A. & Várbíró, G. Functional groups of phytoplankton shaping diversity of shallow lake ecosystems. Hydrobiologia 698, 251–262. https://doi.org/10.1007/s10750-012-1129-6 (2012).
Article  Google Scholar 

15.
Vellend, M. Conceptual synthesis in community ecology. Q. Rev. Biol. 85, 183–206. https://doi.org/10.1086/652373 (2010).
Article  PubMed  PubMed Central  Google Scholar 

16.
Padisák, J., Vasas, G. & Borics, G. Phycogeography of freshwater phytoplankton: Traditional knowledge and new molecular tools. Hydrobiologia 764, 3–27. https://doi.org/10.1007/s10750-015-2259-4 (2016).
CAS  Article  Google Scholar 

17.
Padisák, J. Cylindrospermopsis raciborskii (Woloszynska) Seenayya et Subba Raju, an expanding, highly adaptive cyanobacterium: worldwide distribution and review of its ecology. Arch. Hydrobiol. 107, 563–593 (1997).
Google Scholar 

18.
Méndez, V., Assaf, M., Masó-Puigdellosas, A., Campos, D. & Horsthemke, W. Demographic stochasticity and extinction in populations with Allee effect. Phys. Rev. E. 99, 022101. https://doi.org/10.1103/PhysRevE.99.022101 (2019).
ADS  Article  Google Scholar 

19.
Parvinen, K., Dieckmann, U., Gyllenberg, M. & Metz, J. A. Evolution of dispersal in metapopulations with local density dependence and demographic stochasticity. J. Evolut. Biol. 16, 143–153. https://doi.org/10.1046/j.1420-9101.2003.00478.x (2003).
CAS  Article  Google Scholar 

20.
Borics, G., Abonyi, A., Salmaso, N. & Ptacnik, R. Freshwater phytoplankton diversity: Models, drivers and implications for ecosystem properties. Hydrobiologia https://doi.org/10.1007/s10750-020-04332-9 (2020).
Article  PubMed  PubMed Central  Google Scholar 

21.
Naselli-Flores, L., Padisák, J., Dokulil, M. T. & Chorus, I. Equilibrium/steady-state concept in phytoplankton ecology. Hydrobiologia 502, 395–403. https://doi.org/10.1023/B:HYDR.0000004297.52645.59 (2003).
Article  Google Scholar 

22.
Weiher, E. & Keddy, P.A. Assembly rules, null models, and trait dispersion: New questions from old patterns. Oikos 74, 159–164 (1995). https://www.jstor.org/stable/3545686.

23.
Coyle, J. R. et al. Using trait and phylogenetic diversity to evaluate the generality of the stress-dominance hypothesis in eastern North American tree communities. Ecography 37, 814–826. https://doi.org/10.1111/ecog.00473 (2014).
Article  Google Scholar 

24.
Baastrup-Spohr, L., Sand-Jensen, K., Nicolajsen, S. V. & Bruun, H. H. From soaking wet to bone dry: Predicting plant community composition along a steep hydrological gradient. J. Veg. Sci. 26, 619–630. https://doi.org/10.1111/jvs.12280 (2015).
Article  Google Scholar 

25.
Lhotsky, B. et al. Changes in assembly rules along a stress gradient from open dry grasslands to wetlands. J. Ecol. 104, 507–517. https://doi.org/10.1111/1365-2745.12532 (2016).
Article  Google Scholar 

26.
Butterfield, B. J., Bradford, J. B., Munson, S. M. & Gremer, J. R. Aridity increases below-ground niche breadth in grass communities. Plant Ecol. 218, 385–394. https://doi.org/10.1007/s11258-016-0696-4 (2017).
Article  Google Scholar 

27.
Gastauer, M., Saporetti-Junior, A. W., Valladares, F. & Meira-Neto, J. A. Phylogenetic community structure reveals differences in plant community assembly of an oligotrophic white-sand ecosystem from the Brazilian Atlantic Forest. Acta Bot. Bras. 31, 531–538. https://doi.org/10.1590/0102-33062016abb0442 (2017).
Article  Google Scholar 

28.
Chapman, J. & McEwan, R. The role of environmental filtering in structuring appalachian tree communities: Topographic influences on functional diversity are mediated through soil characteristics. Forests 9, 19. https://doi.org/10.3390/f9010019 (2018).
Article  Google Scholar 

29.
Lukács, B. A. et al. Carbon forms, nutrients and water velocity filter hydrophyte and riverbank species differently: A trait-based study. J. Veg. Sci. 30, 471–484 (2019).
Article  Google Scholar 

30.
Kuczynski, L. & Grenouillet, G. Community disassembly under global change: Evidence in favor of the stress-dominance hypothesis. Glob. Chang. Biol. 24, 4417–4427. https://doi.org/10.1111/gcb.14320 (2018).
ADS  Article  PubMed  Google Scholar 

31.
Patrick, L. E. & Stevens, R. D. Phylogenetic community structure of North American desert bats: Influence of environment at multiple spatial and taxonomic scales. J. Anim. Ecol. 85, 1118–1130. https://doi.org/10.1111/1365-2656.12529 (2016).
Article  PubMed  Google Scholar 

32.
Lopez, B. et al. A new framework for inferring community assembly processes using phylogenetic information, relevant traits and environmental gradients. One Ecosyst. 1, e9501. https://doi.org/10.3897/oneeco.1.e9501 (2016).
Article  Google Scholar 

33.
Ács, E. et al. Trait-based community assembly of epiphytic diatoms in saline astatic ponds: a test of the stress-dominance hypothesis. Sci. Rep. 9(1), 15749 (2019).
ADS  Article  PubMed  PubMed Central  Google Scholar 

34.
Downing, J. A. & McCauley, E. The nitrogen:phosphorus relationship in lakes. Limnol. Oceanogr. 37, 936–945 (1992).
ADS  CAS  Article  Google Scholar 

35.
Phillips, G. et al. A phytoplankton trophic index to assess the status of lakes for the Water Framework Directive. Hydrobiologia 704, 75–95. https://doi.org/10.1007/s10750-012-1390-8 (2013).
Article  Google Scholar 

36.
Padisák, J. et al. Dominant species, functional assemblages and frequency of equilibrium phases in late summer phytoplankton assemblages in Hungarian small shallow lakes. Hydrobiologia 502, 157–168. https://doi.org/10.1023/B:HYDR.0000004278.10887.40 (2003).
Article  Google Scholar 

37.
HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43, 227–248. https://doi.org/10.1146/annurev-ecolsys-110411-160411 (2012).
Article  Google Scholar 

38.
Huisman, J., van Oostveen. P. & Weissing, F.J. Species dynamics in phytoplankton blooms: incomplete mixing and competition for light. Am. Nat. 154, 46–68, https://doi.org/10.1086/303220 (1999).

39.
Bird, D. F. & Kalff, J. Bacterial grazing by planktonic lake algae. Science 231, 493–495. https://doi.org/10.1126/science.231.4737.493 (1986).
ADS  CAS  Article  Google Scholar 

40.
Stoecker, D. K. Mixotrophy among Dinoflagellates. J. Eukaryot. Microbiol. 46, 397–401. https://doi.org/10.1111/j.1550-7408.1999.tb04619.x (1999).
Article  Google Scholar 

41.
Grime, J.P. Plant Strategies, Vegetation Processes, and Ecosystem Properties. (John Wiley and Sons, 2001). ISBN 0-471-49601-4.

42.
Navas, M. & Violle, C. Plant traits related to competition: How do they shape the functional diversity of communities?. Commun. Ecol. 10, 131–137. https://doi.org/10.1556/ComEc.10.2009.1.15 (2009).
Article  Google Scholar 

43.
Reynolds, C. S. The Ecology of Phytoplankton (Cambridge University Press, Cambridge, 2006).
Google Scholar 

44.
Borics, G., Grigorszky, I., Szabó, S. & Padisák, J. Phytoplankton associations in a small hypertrophic fishpond in East Hungary during a change from bottom-up to top-down control. Hydrobiologia 424, 79–90. https://doi.org/10.1023/A:1003948827254 (2000).
Article  Google Scholar 

45.
Mason, N. W. H., de Bello, F., Doležal, J. & Lepš, J. Niche overlap reveals the effects of competition, disturbance and contrasting assembly processes in experimental grassland communities. J. Ecol. 99, 788–796. https://doi.org/10.1111/j.1365-2745.2011.01801.x (2011).
Article  Google Scholar 

46.
Dobosi, Z. & Felméry, L. Climatology, ELTE TTK, Nemzeti Tankönyvkiadó, p. 500 (in Hungarian).

47.
Mihevc, A., Prelovšek, M. & Hajna, N.Z. Introduction to the Dinaric Karst. Inštitut za raziskovanje krasa ZRC SAZU. (2010).

48.
Borics, G. et al. Phytoplankton-based shallow lake types in the Carpathian basin: Steps towards a bottom-up typology. Fund. Appl. Limnol. 184, 23–34. https://doi.org/10.1127/1863-9135/2014/0518 (2014).
Article  Google Scholar 

49.
Borics, G., Abonyi, A., Várbíró, G., Padisák, J. &T-Krasznai, E. Lake stratification in the Carpathian basin and its interesting biological consequences. Inland Waters 5, 173–186, https://doi.org/10.5268/IW-5.2.702 (2015).

50.
Utermöhl, H. Zur Vervollkommnung der quantitative Phytolankton-Methodik. Mitt. Int. Verein. Limnol. 9, 1–38. https://doi.org/10.1080/05384680.1958.11904091 (1958).
Article  Google Scholar 

51.
Török, P. et al. Functional diversity supports the biomass–Diversity humped-back relationship in phytoplankton assemblages. Funct. Ecol. 30, 1593–1602. https://doi.org/10.1111/1365-2435.12631 (2016).
Article  Google Scholar 

52.
Hillebrand, H., Dürselen, C. D., Kirschtel, D., Pollingher, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424. https://doi.org/10.1046/j.1529-8817.1999.3520403.x (1999).
Article  Google Scholar 

53.
MSZ ISO 10260:1993. Water Quality. Measurement of Biochemical Parameters. Spectrometric Determination of the Chlorophyll-a Concentration.

54.
MSZ EN ISO 6878:2004. Water Quality. Determination of Phosphorus. Ammonium Molybdate Spectrometric Method.

55.
ISO 11905-1:1997. Water Quality. Determination of Nitrogen. Part 1: Method Using Oxidative Digestion with Peroxodisulfate.

56.
MSZ ISO 6060:1991.Water Quality. Determination of the Chemical Oxygen Demand.

57.
Botta-Dukát, Z. Cautionary note on calculating standardized effect size (SES) in randomization test. Commun. Ecol. 19, 77–83, https://doi.org/10.1556/168.2018.19.1.8 (2018).

58.
Botta-Dukát, Z. Rao’s quadratic entropy as a measure of functional diversity based on multiple traits. J. Veg. Sci. 16, 533–540. https://doi.org/10.1111/j.1654-1103.2005.tb02393.x (2005).
Article  Google Scholar 

59.
Legendre, P. & Legendre, L.F. Numerical Ecology Vol. 24. (Elsevier, 2012).

60.
Götzenberger, L. et al. Which randomizations detect convergence and divergence in trait‐based community assembly? A test of commonly used null models. J. Veg. Sci. 27, https://doi.org/10.1111/jvs.12452, 1275–1287.

61.
Botta-Dukát, Z. & Czúcz, B. Testing the ability of functional diversity indices to detect trait convergence and divergence using individual-based simulation. Methods Ecol. Evol. 7, 114–126. https://doi.org/10.1111/2041-210X.12450 (2016).
Article  Google Scholar 

62.
Garnier E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, e2630–2637, https://doi.org/10.1890/03-0799.

63.
Dray, S. & Dufour, A.B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20, https://doi.org/10.18637/jss.v022.i04.

64.
Oksanen J. et al. Package ‘vegan’. Community Ecology Package, Version, 2(9) (2013).

65.
Team R. Core (2017) R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017).
Google Scholar  More

1.
Bohonak, A. J. & Jenkins, D. G. Ecological and evolutionary significance of dispersal by freshwater invertebrates. Ecol. Lett. 6, 783–796 (2003).
Article  Google Scholar 
2.
Clobert, J., Baguette, M., Benton, T. G. & Bullock, J. M. Dispersal Ecology and Evolution (Oxford Univ. Press, 2012).

3.
Heino, J. et al. Metacommunity organisation, spatial extent and dispersal in aquatic systems: Patterns, processes and prospects. Freshw. Biol. 60, 845–869 (2015).
Article  Google Scholar 

4.
Barton, P. S. et al. Guidelines for using movement science to inform biodiversity policy. Environ. Manage. 56, 791–801 (2015).
ADS  Article  Google Scholar 

5.
Heino, J. et al. Integrating dispersal proxies in ecological and environmental research in the freshwater realm. Environ. Rev. 25, 334–349 (2017).
Article  Google Scholar 

6.
Rundle, S. D., Bilton, D. T. & Foggo, A. in Body Size: The Structure and Function of Aquatic Ecosystems (eds. Hildrew, A. G., Raffaelli, D. G. & Edmonds-Brown, R.) 186–209 (Cambridge Univ. Press, 2007).

7.
Macneale, K. H., Peckarsky, B. L. & Likens, G. E. Stable isotopes identify dispersal patterns of stonefly populations living along stream corridors. Freshw. Biol. 50, 1117–1130 (2005).
Article  Google Scholar 

8.
Troast, D., Suhling, F., Jinguji, H., Sahlén, G. & Ware, J. A global population genetic study of Pantala flavescens. PLoS One 11, e0148949 (2016).
Article  CAS  Google Scholar 

9.
French, S. K. & McCauley, S. J. The movement responses of three libellulid dragonfly species to open and closed landscape cover. Insect Conserv. Divers. 12, 437–447 (2019).
Article  Google Scholar 

10.
Arribas, P. et al. Dispersal ability rather than ecological tolerance drives differences in range size between lentic and lotic water beetles (Coleoptera: Hydrophilidae). J. Biogeogr. 39, 984–994 (2012).
Article  Google Scholar 

11.
Lancaster, J. & Downes, B. J. Dispersal traits may reflect dispersal distances, but dispersers may not connect populations demographically. Oecologia 184, 171–182 (2017).
ADS  Article  Google Scholar 

12.
Lancaster, J. & Downes, B. J. Aquatic Entomology (Oxford Univ. Press, 2013).

13.
Stevens, V. M. et al. Dispersal syndromes and the use of life-histories to predict dispersal. Evol. Appl. 6, 630–642 (2013).
Article  Google Scholar 

14.
Outomuro, D. & Johansson, F. Wing morphology and migration status, but not body size, habitat or Rapoport’s rule predict range size in North-American dragonflies (Odonata: Libellulidae). Ecography 42, 309–320 (2019).
Article  Google Scholar 

15.
Tonkin, J. D. et al. The role of dispersal in river network metacommunities: patterns, processes, and pathways. Freshw. Biol. 63, 141–163 (2018).
Article  Google Scholar 

16.
Brown, B. L. & Swan, C. M. Dendritic network structure constrains metacommunity properties in riverine ecosystems. J. Anim. Ecol. 79, 571–580 (2010).
CAS  Article  Google Scholar 

17.
Wikelski, M. et al. Simple rules guide dragonfly migration. Biol. Lett. 2, 325–329 (2006).
Article  Google Scholar 

18.
Schmidt-Kloiber, A. & Hering, D. An online tool that unifies, standardises and codifies more than 20,000 European freshwater organisms and their ecological preferences. Ecol. Indic. 53, 271–282, www.freshwaterecology.info (2015).
Article  Google Scholar 

19.
Serra, S. R. Q., Cobo, F., Graça, M. A. S., Dolédec, S. & Feio, M. J. Synthesising the trait information of European Chironomidae (Insecta: Diptera): towards a new database. Ecol. Indic. 61, 282–292 (2016).
Article  Google Scholar 

20.
Tachet, H., Richoux, P., Bournaud, M. & Usseglio-Polatera, P. Invertébrés d’Eau Douce: Systématique, Biologie, Écologie (CNRS Éditions, 2010).

21.
Vieira, N. K. M. et al. A Database of Lotic Invertebrate Traits for North America (U.S. Geological Survey Data Series 187, 2006).

22.
Chevenet, F., Dolédec, S. & Chessel, D. A fuzzy coding approach for the analysis of long-term ecological data. Freshw. Biol. 31, 295–309 (1994).
Article  Google Scholar 

23.
Schmera, D., Podani, J., Heino, J., Erös, T. & Poff, N. L. R. A proposed unified terminology of species traits in stream ecology. Freshw. Sci. 34, 823–830 (2015).
Article  Google Scholar 

24.
Lancaster, J., Downes, B. J. & Arnold, A. Lasting effects of maternal behaviour on the distribution of a dispersive stream insect. J. Anim. Ecol. 80, 1061–1069 (2011).
Article  Google Scholar 

25.
Jenkins, D. G. et al. Does size matter for dispersal distance? Glob. Ecol. Biogeogr. 16, 415–425 (2007).
Article  Google Scholar 

26.
Harrison, R. G. Dispersal polymorphisms in insects. Annu. Rev. Ecol. Syst. 11, 95–118 (1980).
Article  Google Scholar 

27.
Graham, E. S., Storey, R. & Smith, B. Dispersal distances of aquatic insects: upstream crawling by benthic EPT larvae and flight of adult Trichoptera along valley floors. New Zeal. J. Mar. Freshw. Res. 51, 146–164 (2017).
Article  Google Scholar 

28.
Hoffsten, P. O. Site-occupancy in relation to flight-morphology in caddisflies. Freshw. Biol. 49, 810–817 (2004).
Article  Google Scholar 

29.
Bonada, N. & Dolédec, S. Does the Tachet trait database report voltinism variability of aquatic insects between Mediterranean and Scandinavian regions? Aquat. Sci. 80, 1–11 (2018).
Article  Google Scholar 

30.
Sarremejane, R. et al. DISPERSE, a trait database to assess the dispersal potential of aquatic macroinvertebrates. figshare https://doi.org/10.6084/m9.figshare.c.5000633 (2020).

31.
Lévêque, C., Balian, E. V. & Martens, K. An assessment of animal species diversity in continental waters. Hydrobiologia 542, 39–67 (2005).
Article  Google Scholar 

32.
Green, A. J. & Figuerola, J. Recent advances in the study of long-distance dispersal of aquatic invertebrates via birds. Divers. Distrib. 11, 149–156 (2005).
Article  Google Scholar 

33.
Maasri, A. A global and unified trait database for aquatic macroinvertebrates: the missing piece in a global approach. Front. Environ. Sci. 7, 1–3 (2019).
Article  Google Scholar 

34.
Cañedo-Argüelles, M. et al. Dispersal strength determines meta-community structure in a dendritic riverine network. J. Biogeogr. 42, 778–790 (2015).
Article  Google Scholar 

35.
Datry, T. et al. Metacommunity patterns across three Neotropical catchments with varying environmental harshness. Freshw. Biol. 61, 277–292 (2016).
Article  Google Scholar 

36.
Swan, C. M. & Brown, B. L. Metacommunity theory meets restoration: isolation may mediate how ecological communities respond to stream restoration. Ecol. Appl. 27, 2209–2219 (2017).
Article  Google Scholar 

37.
Sarremejane, R., Mykrä, H., Bonada, N., Aroviita, J. & Muotka, T. Habitat connectivity and dispersal ability drive the assembly mechanisms of macroinvertebrate communities in river networks. Freshw. Biol. 62, 1073–1082 (2017).
Article  Google Scholar 

38.
Jacobson, B. & Peres-Neto, P. R. Quantifying and disentangling dispersal in metacommunities: How close have we come? How far is there to go? Landsc. Ecol. 25, 495–507 (2010).
Article  Google Scholar 

39.
Sarremejane, R. et al. Do metacommunities vary through time? Intermittent rivers as model systems. J. Biogeogr. 44, 2752–2763 (2017).
Article  Google Scholar 

40.
Datry, T., Moya, N., Zubieta, J. & Oberdorff, T. Determinants of local and regional communities in intermittent and perennial headwaters of the Bolivian Amazon. Freshw. Biol. 61, 1335–1349 (2016).
Article  Google Scholar 

41.
Cid, N. et al. A metacommunity approach to improve biological assessments in highly dynamic freshwater ecosystems. Bioscience 70, 427–438 (2020).
Article  Google Scholar 

42.
Datry, T., Bonada, N. & Heino, J. Towards understanding the organisation of metacommunities in highly dynamic ecological systems. Oikos 125, 149–159 (2016).
Article  Google Scholar 

43.
Hermoso, V., Cattarino, L., Kennard, M. J., Watts, M. & Linke, S. Catchment zoning for freshwater conservation: refining plans to enhance action on the ground. J. Appl. Ecol. 52, 940–949 (2015).
Article  Google Scholar 

44.
Thuiller, W. et al. A road map for integrating eco-evolutionary processes into biodiversity models. Ecol. Lett. 16, 94–105 (2013).
Article  Google Scholar 

45.
Mendes, P., Velazco, S. J. E., de Andrade, A. F. A. & De Marco, P. Dealing with overprediction in species distribution models: How adding distance constraints can improve model accuracy. Ecol. Model. 431, 109180 (2020).
Article  Google Scholar 

46.
Willis, S. G. et al. Integrating climate change vulnerability assessments from species distribution models and trait-based approaches. Biol. Conserv. 190, 167–178 (2015).
Article  Google Scholar 

47.
Cooper, J. C. & Soberón, J. Creating individual accessible area hypotheses improves stacked species distribution model performance. Glob. Ecol. Biogeogr. 27, 156–165 (2018).
Article  Google Scholar 

48.
Markovic, D. et al. Europe’s freshwater biodiversity under climate change: distribution shifts and conservation needs. Divers. Distrib. 20, 1097–1107 (2014).
Article  Google Scholar 

49.
Bush, A. & Hoskins, A. J. Does dispersal capacity matter for freshwater biodiversity under climate change? Freshw. Biol. 62, 382–396 (2017).
Article  Google Scholar 

50.
Bohonak, A. J. Dispersal, gene flow, and population structure. Q. Rev. Biol. 74, 21–45 (1999).
CAS  Article  Google Scholar 

51.
Dijkstra, K.-D. B., Monaghan, M. T. & Pauls, S. U. Freshwater biodiversity and aquatic insect diversification. Annu. Rev. Entomol. 59, 143–163 (2014).
CAS  Article  Google Scholar 

52.
Múrria, C. et al. Local environment rather than past climate determines community composition of mountain stream macroinvertebrates across Europe. Mol. Ecol. 26, 6085–6099 (2017).
Article  CAS  Google Scholar 

53.
Statzner, B. & Bêche, L. A. Can biological invertebrate traits resolve effects of multiple stressors on running water ecosystems? Freshw. Biol. 55, 80–119 (2010).
Article  Google Scholar 

54.
Strayer, D. L. & Dudgeon, D. Freshwater biodiversity conservation: recent progress and future challenges. J. North Am. Benthol. Soc. 29, 344–358 (2010).
Article  Google Scholar 

55.
Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).
Article  Google Scholar 

56.
R Core Team. R: A language and environment for statistical computing. https://www.r-project.org/ (2020).

57.
Dray, S. & Dufour, A.-B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 1, 1–20 (2007).
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Detritivore and leaf litter collection
We collected six phylogenetically diverse species of detritivores in various areas of the Scottish Lowlands in May and June 2018, including three millipede species (Diplopoda), two woodlouse species (Crustacea) and one snail species (Gastropoda). Millipede species include the common pill millipede (Glomeris marginata (Villers, 1789)) collected near Peebles, UK (55°38′45.8″N, 3°07′55.4″W), the striped millipede (Ommatoiulus sabulosus (Linnaeus, 1758)) collected near Dunfermline, UK (56°02′23.7″N, 3°19′49.2″W) and the white-legged millipede (Tachypodoiulus niger (Leach, 1815)) collected near Dundee, UK (56°32′08.5″N, 3°01′51.9″W). Woodlouse species include the common pill woodlouse (Armadillidium vulgare (Latreille, 1804)) collected near Dunfermline, UK (56°01′35.3″N 3°23′14.1″W) and the common rough woodlouse (Porcellio scaber (Latreille, 1804) collected in Stirling, UK (56°07′26.7″N, 3°55′51.2″W). The snail species was the brown-lipped snail (Cepaea nemoralis (Linnaeus, 1758)) collected in Stirling, UK (56°08′07.3″N, 3°55′16.3″W). These species are common in diverse ecosystems across Mediterranean and temperate ecosystems in Europe, where they feed on decomposing litter and produce large amounts of faeces16,38,39,40. Detritivores were kept in plastic boxes and fed with moist litter from various tree species from their respective collection sites before the start of the experiment.
To obtain a gradient of leaf litter quality, we collected leaf litter from six deciduous broadleaf tree species in the Scottish Lowlands. These species include sycamore maple (Acer pseudoplatanus, L.), horse chestnut (Aesculus hippocastanum, L.), common hazel (Corylus avellana, L.), European beech (Fagus sylvatica, L.), English oak (Quercus robur, L.) from a woodland near Dundee, UK (56°32′08.5″N, 3°01′51.9″W) and lime (Tilia platyphyllos, L.) from a woodland in Stirling, UK (56°08′29.5″N, 3°55′14.2″W). Because detritivores are most active in spring and summer in these ecosystems, they feed on partially decomposed litter, which they prefer over freshly fallen litter (David and Gillon8). We thus collected leaf litter from the forest floor in May 2018, air-dried it and stored it in cardboard boxes until use.
Faeces production
To compare the quality and decomposability of leaf litter with faeces derived from the same litter and produced by diverse detritivore species, we set up two series of boxes for the production of the needed material. In the first of these series, we placed each detritivore species together with each litter species to produce the 36 different faeces types (Fig. 1; 6 litter species × 6 detritivore species = 36 faeces types). The second of these series contained the litter species only without any detritivores to produce intact litter from each tree species (6 litter species) under the same conditions for the same amount of time. In total, 42 different substrates were generated. To do so, we placed ca. 30 g of air-dry leaf litter from each species separately in plastic boxes (30 cm × 22 cm × 5.5 cm) to which we added ca. 50 individuals from each detritivore species separately or no detritivore for the intact litter treatment. We sprayed the litter with water to optimise litter moisture for detritivore consumption while avoiding water accumulation at the bottom of the boxes. We kept the boxes at room temperature (ca. 20 °C) for 4 weeks and collected the produced faeces/intact litter twice a week. For the faeces, we placed the content of each box in a large bucket and gently agitated to let detritivores and faeces fall to the bottom of the bucket. After collecting the faeces, we placed all the leaf litter and detritivores back into their boxes and sprayed the litter with water to keep moisture conditions constant. For the intact litter treatment, we followed the same procedure but collected just three random leaves out of the buckets. After each collection step, the combination-specific pools of leaf litter and faeces were dried at 30 °C. At the end of the faeces production period, we manually removed small leaf litter fragments from all combination-specific pools of faeces. Additionally, because detritivores feed on leaf lamina and leave leaf veins mostly uneaten6, we cut out the veins from the species-specific pools of intact leaf litter. This was done to ensure the comparability of quality and decomposability between faeces and intact litter.
Litter and faeces quality
To evaluate the effect of litter conversion into detritivore faeces on organic matter quality, we compared the quality of faeces to that of intact litter by measuring a series of physical and chemical quality parameters on all 42 substrates (6 litter species + 36 faeces types). Chemical characteristics included total carbon (C) and nitrogen (N) concentrations, DOC and TDN concentrations, total tannin concentrations, and 13C solid-state NMR spectra. Physical characteristics included WHC and specific area (surface area per unit of mass). Prior to these measurements, we drew three subsamples from each pool of substrate type. A part of each subsample was ground using a ball mill (TissueLyser II, Qiagen) to measure total C, N and tannin concentration and generate NMR spectra. The other part of each subsample was kept intact and used for all other measurements. All measurements were thus done on these three subsamples per substrate type, except for NMR spectra that were measured once per substrate type on a sample made by pooling all three ground subsamples. This pooling was necessary to obtain a sample large enough for the NMR analyses. Total C and N concentrations were measured with a flash CHN elemental analyser (Flash Smart, ThermoScientific). To measure DOC and TDN, we extracted leachates by placing ca. 30 mg of air-dried material with 25 ml of deionised water in 50 ml Falcon tubes and agitating the tubes horizontally on a reciprocal shaker for 1 h. Water extracts were then filtered through 0.45-μm cellulose nitrate filters to isolate the leachate fraction. Concentrations of DOC and TDN in leachates were measured with a TOC analyser (Shimadzu, Kyoto, Japan) equipped with a supplementary module for N. Tannin concentrations were measured with the protein-precipitable phenolics microplate assay, a microplate protocol adapted from Hagerman and Butler41. We obtained 13C-NMR spectra by applying 13C cross-polarisation magic angle spinning NMR spectroscopy using a 200 MHz spectrometer (Bruker, Billerica, USA). The samples were spun in 7 mm zirconium dioxide rotors at 6.8 kHz with an acquisition time of 0.01024 s. To avoid Hartmann–Hahn mismatches, a ramped 1H impulse was applied during a contact time of 1 ms. We applied a delay time of 2.0 s and the number of scans was set to 1500, yet some of the samples required longer measurements due to the low amount of sample material; in this case, we multiplied the number of scans to 3000, 6000 or 15000. As reference for the chemical shift, tetramethylsilane was used (0 ppm). We used the following chemical shift regions to integrate the spectra: −10–45 ppm alkyl C, 45–110 ppm O/N alkyl C, 110–160 ppm aromatic C, and 160–220 ppm carboxylic C. We measured the WHC by placing ca. 15 mg of air-dried intact material with 1.5 ml of deionised water in 2 ml Eppendorf tubes, agitating the tubes horizontally on a reciprocal shaker for 2 h, retrieving the material and placing it on a Whatman filter to remove excess water, weighing the wet material and reweighing it after drying at 65 °C for 48 h. We measured the specific area of leaf litter, faecal pellets and faeces particles from photographs using a stereomicroscope (ZEISS STEMI 508). For leaf litter and faecal pellets, we took photographs of ca. 20 mg of air-dried intact material. To visualise faeces particles, we weighed ca. 1 mg of air-dried faecal pellets and placed them in a beaker with 20 ml of deionised water for 2 h, allowing complete dissolution of the faecal pellets. We then filtered the faeces particles and photographed the filters under a stereomicroscope. Dimensions of each litter pieces and faecal pellets/faeces particles were measured using the image analysis software (ImageJ, version 1.46r). For all substrate types, we divided the calculated surface area by the dry mass of the sample to obtain the specific area.
Faeces and litter decomposition parameters
To evaluate the effect of litter conversion into detritivore faeces on C and N cycling, we compared the C and N loss of faeces to that of intact litter by incubating all 42 substrates in microcosms under controlled conditions for 6 months (180 days). Microcosms consisted of 250-ml plastic containers filled with 90 mg of air-dry soil collected from a temperate grassland (56°8′40.1″N, 3°54′50.9″W). We chose this soil to avoid any home-field advantage effect as this soil did not receive litter input from any of the studied tree species and none of the selected soil animals were present at this site. About 120 mg of each substrate were placed separately within a small polyvinyl chloride tube (30 mm diameter × 30 mm height) closed in the bottom with a 100-µm mesh and left open on the top. Each tube was then placed on top of the soil within the microcosm. Five replicates per substrate were prepared, resulting in a total of 210 microcosms (42 substrates × 5 replicates). Microcosms were watered by adding water directly over the tube containing faeces/litter so as to reach 70% of soil WHC and incubated at 22 °C and 70% relative humidity in a controlled environment chamber. To limit desiccation while ensuring gas exchange, we drilled four 3-mm holes in each microcosm cap. These microcosms were then weighed weekly and watered to their initial weight at 70% soil WHC. We placed replicates on separated shelves according to a randomised complete block design. Both block positions within the controlled environment chamber and microcosm positions within blocks were randomised weekly. After 180 days, remaining intact litter and faeces in microcosms were collected, dried at 30 °C for 48 h, weighed and ground with a ball mill (TissueLyser II, Qiagen). We measured C and N concentrations in all samples with a flash CHN Elemental Analyser (Flash Smart, ThermoScientific). The percentage of C and N lost after the incubation was calculated as:

$$frac{{M_{rm{i}} times {rm{CN}}_{rm{i}} – M_{rm{f}} times {rm{CN}}_{rm{f}}}}{{M_{rm{i}} times {rm{CN}}_{rm{i}}}} times 100,$$

where Mi and Mf are the initial and final 30 °C dry masses, respectively, and CNi and CNf are the initial and final C or N concentrations, respectively.
Statistics and reproducibility
To visualise how the 11 physicochemical characteristics were related and how their values differed between all substrates, we used a PCA, with all variables centred and standardised prior to ordination. Because NMR spectra were measured on a composite sample combining the three replicates of each substrate, a unique value was attributed to all replicates for each NMR region.
To test our first hypothesis, we tested the overall effect of substrate form (faeces vs. intact litter) on quality (scores on PC1 and PC2) and decomposition (C and N losses) of all substrates using Student’s t tests. To identify the faeces types with significantly different quality (scores on PC1 and PC2) and decomposition (C and N losses) compared to that of the intact litter from which the faeces were derived, we tested the effect of substrate identity (all 42 substrates included as individual levels) on quality (scores on PC1 and PC2) and decomposition (C and N losses) using one-way ANOVAs. We then used Tukey’s honestly significant difference tests to determine significant differences between each faeces type and the corresponding intact litter.
To test our second hypothesis, we expressed the changes in quality and decomposition following litter conversion into detritivore faeces as net differences in quality (scores on PC1 and PC2) and decomposition (C and N losses) between faeces and the litter from which faeces were derived. We then compared the hypothesised role of intact litter quality/decomposition (PC1 and PC2 scores, C and N losses) and the role of detritivore species on changes in quality/decomposition (net differences in PC1 and PC2 scores, C and N losses) by performing ANCOVAs with intact litter quality/decomposition as the continuous variable and detritivore species as categorical variable (all six detritivore species as individual levels). For all ANVOCAs, the variance associated with each term (intact litter quality/decomposition; detritivore species; interaction) was computed by dividing the sum of squares by the total sum of squares.
To evaluate the relation between quality parameters (PC1 and PC2 scores) and C and N losses from intact litter and faeces separately, we determined the relations between intact litter and faeces C and N losses and their scores on PC1 and PC2 with simple linear regressions and visualised these relations by fitting these variables as supplementary variables on the PCA.
For all statistical tests on C and N losses, block was included in the model as a random variable. All data were checked for normal distribution and homoscedasticity of residuals. All analyses were performed using the R software (version 3.5.3).
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

We conducted a laboratory study in the facilities provided by the Natural History Museum of Funchal (Mosquito Lab). Three species of mosquitoes were recorded to determine their dominant frequencies and spectral behaviors. The species used for this collection and study were A. Aegypti, C. Quinquefasciatus & Pipiens and Culiseta, which came from a lab colony established from captures collected in Funchal city in 2019.
The mosquitoes were kept in an environmental room simulating natural conditions, with 60 ± 10% relative humidity and temperature of 20–25 °C. Mosquitoes were housed individuals in boxes (25 × 25 × 25 cm) covered with a mesh cap. They were fed with 20% sucrose solution supplemented with 1 g aquarium fish food mixed daily from the brand “Sera Guppy Gran”. The duration of the study was approximately 48 days. All mosquitoes used in these experiments were 7–25 days old. For the recording process, sensors were incorporated into the boxes and the tests conducted on 12–18 specimens for Aedes Aegypti, 7–12 specimens for Culex and 4 specimens for Culiseta. The duration of the extracted sequences ranged from 0 to 300 ms. To generate samples closer to real-world acquisition conditions we added environmental noise in some mosquito samples.
Uncompressed audio of real sound waves was converted to digital format without any further processing. This means that recordings are exact copies of the source audio, recorded in WAV files.
The acoustic sensor uses a low-noise omnidirectional microphone capsule2. The microphone converts sound into electrical signals with a specific signal to noise ratio (80 dB), self-noise, and residual noise. All these parameters influence the quality of the acquired sound.
Noise can be a significant problem when acquiring physical signals as voltages. Signal smoothing attempts to capture the essential information in the signal while leaving out the noise. This is done by interpolating the raw signal to estimate the original one17.
To collect samples, we used three devices: one of them was our prototype comprising a Teensy 3.2 audio board, microphone and environmental sensor for 44.1 kHz sampling rate. The other two were general-purpose smartphones (Huawei P20 Lite and IPhone 4) used to record samples with a 8 and 48 kHz sampling rate, respectively.
To start a colony for our experience, we installed traps and buckets of water to catch eggs and adult mosquitoes. The female Aedes mosquitoes require a blood meal before each egg-laying18. The eggs are deposited individually on the inner walls of any container capable of storing water. This work was conducted jointly with the Natural History Museum of Madeira and IASaude (the regional health authority of Madeira islands) as part of a plan to control the spread of mosquitoes in the city of Funchal (Fig. 1).
Fig. 1

Location and number of traps in the city of Funchal, Madeira, Portugal.

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A. Aegypti mosquitoes, lay the most eggs in the velcro tape, while Culex and Culiseta prefer to lay directly in rafts on still water or in other substances19. Traps with a ventilation system were also used to capture adult mosquitoes, especially Culex and Culiseta.
Figure 2 shows the procedure from egg collection to mosquito germination, and also the boxes that are used for further acquisition of sound samples. It is noteworthy that after 25–30 days the mosquitoes die due to the conditions imposed in the study.
Fig. 2

Procedure for collecting audio samples for different species.

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Step A comprises the gathering of eggs and mosquitoes. The figures show a bucket inside which mosquitoes lay eggs on a velcro tape, and also a trap. These traditional methods allow a fine assessment of the distribution of mosquito populations over time and space (periodically summarized in epidemiological bulletins). In step B the collected eggs are germinated to create a colony. Then, (step C) mosquitoes are placed in boxes and fed with a sugar solution and fish food20. Finally, in step D, audio samples are collected by the devices: mobile phones and low-cost IoT. This procedure is repeated when the colony dies after 25 days, starting from step B.
Audio was recorded inside boxes (25 × 25 × 25 cm) where the mosquitoes were located at a maximum distance of 27 cm from the microphone placed in the center of the box. The signal amplitude fluctuates significantly over time as the mosquitoes in free flight approach the microphone or move away.
Continuous recordings were then split into 300 millisecond (ms) snippets. Since mosquitoes have a very short flight, it was necessary to apply a slight stimulus on the wall of the boxes (covered by a net) to force them to fly.
To analyze each mosquito recording, 34 features were extracted taking into account several parameters of the signal belonging to three different domains: time (1–3), frequency (4–8, 22–34) and cepstrum (9–21), analyzed below in the Technical Validation section21,22.
These features are often used for speech signal classification, but are useful when handling non-speech signals as well. They enable a comprehensive analysis of the mosquito sounds in terms of amplitude, energy, zero crossing rate, power, frequency variation in the audio file, tonality, loudness, etc. The features are included in the dataset23 and their computation is demonstrated in the Code Availability section. More

Database structure
The Coupled Human And Natural Systems Nitrogen Cycling Model Spatial Distribution (CHANS-SD) 1.0 database consists of three files (Fig. 1). The ‘data file’ provides N inputs of 6 land use types, including cropland, forest, grassland, water, built-up area and unused land. The ‘readme file’ explains the abbreviations used in the ‘data file’ and ‘source file’, and provides the units of all variables (Fig. 1). The ‘source file’ includes the full references and input data used in the database (Fig. 1).
Fig. 1

A general view of N inputs to 6 land-use types in China and the framework of The Coupled Human And Natural Systems Nitrogen Cycling Model Spatial Distribution (CHANS-SD) 1.0 database. N input to cropland comprises of N fertilizer, N deposition, irrigation, livestock manure, human excretion, cropland BNF (Biological N Fixation), and straw recycle; N input to forest comprises of N fertilizer, N deposition and forest BNF; N input to grassland comprises of N fertilizer, N deposition and grassland BNF; N input to water comprises of cropland runoff, livestock runoff, forest runoff, human wastewater discharged, industrial wastewater, WTP (Water Treatment Plant) effluent and N deposition; N input to built-up area and unused land is N deposition. The data of Taiwan is absent. The data consists of three files: the ‘data file’ (CHANS-SD 1.0 Data File) is the main file, includes N inputs in all 6 land-use types. The ‘readme file’ (CHANS-SD 1.0 Read Me) explains the abbreviations and units, and the ‘source file’ includes the full references used in the database. Base map is applied without endorsement from GADM data (https://gadm.org/).

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Data compilation
We applied the CHANS model to calculate N inputs by land use type. The CHANS model calculates all nitrogen (N) fluxes that can be identified, together with the linkages among subsystems, within a country, state (province), city or watershed, with a mass balance principle. The system is divided into 14 subsystems: cropland, grassland, forest, livestock, aquaculture, industry, human, pet, urban green land, wastewater treatment, garbage treatment, atmosphere, surface water, and groundwater. N cycling starts from the entry of reactive N (Nr) that activated from N2 into the system or from Nr direct input to the system from outside, and terminates when Nr is transformed to N2 or lost to outside the system.
N input to cropland subsystem is the largest component of N input to China, including N fertilizer, atmospheric N deposition, N from irrigation, N from livestock manure, N from human excretion, cropland biological N fixation (BNF) and straw recycle. N input to forest subsystem, including forest BNF and N deposition, is the second largest component of N input to China given the large area of forest. N input into surface water subsystem, including Nr runoff from cropland, livestock and forest, human wastewater discharged, industrial waste water, WTP (Water Treatment Plant) effluent and N deposition, is the third largest component of N input in China (Fig. 2). The details of the CHANS model including all the code and parameters of N cycling, and the protocols for the calculation of all related N fluxes can be found from https://person.zju.edu.cn/en/bjgu#930811.
Fig. 2

N input to China and proportion of its six components. (a) A spatial distribution map of N input to China, N input in east of China is much higher than that in west of China; North Plain, Sichuan Basin and the North East Plain currently receive the highest N loads (The data of Taiwan is absent). (b) Proportion of N input to cropland, forest, grassland, built-up area, unused land and water, respectively; proportion of N input to cropland is highest among all land-use types. Base map is applied without endorsement from GADM data (https://gadm.org/).

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The National Bureau of Statistics of China1 provides data of cropland N fertilizer use, planting area, livestock and population in the statistical yearbook of each province. Taiwan, Hong Kong, and Macao were not included owing to data limitations. Gridded datasets of land use and GDP is derived from the Resource and Environment Data Cloud Platform12, and data on China’s hydro-basin distribution is collected from the FAO website GeoNetwork13.
In the cropland subsystem, livestock manure input to cropland (MANUREan, kg/yr) was calculated based on animal population (POPan, head), excretion factor (EXCREan, kg N/head/yr) and rate of livestock manure applied to cropland (REan, %) according to Eq. (1) (Fig. 3a),

$${MANURE}_{an}={POP}_{an}times {EXCRE}_{an}times {RE}_{an}$$
(1)

Fig. 3

Important components of N inputs to cropland. (a), N input from livestock manure to cropland, high values ( >100 kg/ha) concentrate in the North Plain; (b), N input from N fertilizer to cropland, compared to other items, high values ( >100 kg/ha) are widely distributed across China; (c), N input from human excretion to cropland; (d), N input from cropland BNF (biological N fixation) to cropland. The data of Taiwan is absent. Base map is applied without endorsement from GADM data (https://gadm.org/).

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Amounts of chemical N fertilizer consumption at county scale were obtained from the statistical yearbook of counties (Fig. 3b). Human manure to cropland (MANUREhu, kg /yr) was calculated by urban population (POPur, person), rural population (POPru, person), excretion factor (EXCREhu, kg N/person/yr), rate of urban excretion return to cropland (REur, %) and rate of rural excretion return to cropland (REru, %) according to Eq. (2) (Fig. 3c).

$${MANURE}_{hu}=({POP}_{ur}times {RE}_{ur}+{POP}_{ru}times {RE}_{ru})times {EXCRE}_{hu}$$
(2)

Cropland BNF (CBNF, kg/yr) was calculated by planting area (area, ha) and N fixation rate (rfix, kg N/ha/yr) according to Eq. (3) (Fig. 3d),

$$CBNF=areatimes {r}_{fix}$$
(3)

Straw recycling and N input from irrigation are calculated by using nationally uniform values (Table 1).
Table 1 A summary of N inputs to six land-use types in 2017 in China.
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N input to the grassland subsystem include N fertilizer, manure recycle, N deposition, grassland BNF and irrigation for artificial grassland. N input in forest subsystem include N fertilizer to artificial forests, N deposition and forest BNF.
In the surface water system, N inputs to each watershed include runoff from cropland, livestock, and forest areas, human wastewater discharge, industrial wastewater, WTP effluent and atmospheric N deposition. Cropland runoff was calculated by spatial distribution of cropland input (Fig. 4a). Livestock runoff using the spatial distribution of livestock manure (Fig. 4b). Finally, forest runoff was calculated by applying the spatial distribution of forest areas. Human wastewater discharge was calculated based on the spatial distribution of the human population (including urban and rural populations) (Fig. 4c). Industrial wastewater was calculated by using the spatial distribution of GDP since industrial output is highly correlated with GDP on regional scale (Fig. 4d). Similarly, the distribution of WTP and its effluent Nr are highly correlated with urban population on regional scale, therefore, WTP effluent was calculated by spatial distribution of urban population.
Fig. 4

Important components of N inputs to water. (a) N input from cropland runoff to water, shows similar distribution with N input to cropland; (b) N input from livestock runoff to water, shows similar distribution with N input from livestock manure to cropland; (c) N input from human wastewater to water, shows similar distribution with population; (d) N input from industrial wastewater to water, shows similar distribution with GDP. The data of Taiwan is absent. Base map is applied without endorsement from GADM data (https://gadm.org/).

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For N deposition, the satellite observations on NO2 columns were derived from GOME-2. GOME-2 overpass times provided global coverage of NO2 with a variable ground spatial resolution of 80 km × 40 km (every day). We used the monthly TEMIS NO2 product at a spatial resolution of 0.25° latitude × 0.25° longitude downloaded from the website of Tropospheric Emission Monitoring Internet Service14. The satellite observations on NH3 columns were derived from IASI onboard the meteorological platforms MetOp-A and MetOp-B15 with an elliptical footprint of 12 × 12 km up to 20 × 39 km depending on the satellite-viewing angle. The daily NH3 columns were downloaded from IASI Portal16. We processed the daily data into the monthly NH3 columns averaged by daily observations at a horizontal resolution of 0.25° latitude × 0.25° longitude using the arithmetic mean method17,18. N deposition of every subsystem was obtained from the national N deposition spatial distribution map by ‘spatial join’ tool of ArcGIS 10.6. More

Nests
A summary of the nest results can be found in Tables 1 (physical description of nests) and 2 (ploidy). All adult females that were weighed were classed as workers or founder queens using the information found in Rome et al.8, which defined a limit of 593 mg wet weight or 250 mg dry weight to discriminate between workers and founder queens. The average wet weight of founder queens in September was 624 mg (N = 5). All nests recovered had fewer adults present than expected, this was presumed to be due to the loss of adult hornets during the destruction process and subsequent removal of the nest from its original location.
Table 1 Summary of observations from all nests discovered in the UK.
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Table 2 Ploidy of nests.
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Tetbury
The findings from the examination of the Tetbury nest have been described previously10 but are briefly summarised again here. This nest was discovered on 28th September 2016. In total, 70 adult hornets were found in the nest. The wet weight of 57 adult female hornets ranged from 202 to 322 mg with a mean of 256 mg (N = 19), whilst that of 13 adult male hornets ranged from 248 to 326 mg with a mean of 290 mg (N = 7). The nest diameter was 23 cm and the nest contained five combs, four of which contained all life stages (eggs, larvae, pupae, teneral adults, adults) of the Asian hornet. All life stages examined were diploid. The nest was likely derived from a single queen mated to a single drone.
Woolacombe
A nest was discovered in Woolacombe on 27 September 2017. In total, 166 adult hornets were found in the nest, all female. The wet weight of adult female hornets ranged from 172 to 508 mg with a mean of 333 ± 5 mg (N = 166). Based on the information in Rome et al.8, described above, none of the females found in the Woolacombe nest were founder queens. The nest was 27 cm in diameter and is the largest nest discovered in England to date. The nest contained seven combs and all life stages were present, although the larval samples were too degraded for DNA analysis. Haploid individuals were present at the egg and pupal stages, the remainder of the individuals examined were diploid. Based on life stages present and the ploidy, the queen began laying haploid eggs on approximately 2nd September. The genetic analysis (results from COLONY2, verified manually) showed that the offspring sampled were likely to be the product of a single queen and three drones.
Fowey nests 1 + 2
Two nests were discovered in Fowey, Cornwall in 2018 on 3rd and 20th September, 40 m apart. The first nest contained three combs and had a diameter of 15 cm. No eggs or early instar larvae were present but late instar larvae, teneral adults and three adults were present. All individuals sampled were diploid. From the absence of eggs and early instar larvae, it was concluded that the queen was absent/missing in the 2–3 weeks prior to nest discovery. The second nest had a diameter of 19 cm and contained four combs with brood in all stages. Seven adult males and eight females were found in the nest; the males were diploid. All eggs and six of the ten larvae genotyped were haploid, while the pupae, teneral adults and adults were diploid. No queen was found. From the genetic analysis of the two nests, it was shown that both were highly likely to be offspring of a single queen and drone, with the first nest discovered presumably a primary nest and the other nest the secondary nest. From the ploidy of the life-stages present, it was inferred that the queen began laying haploid eggs around the 30th August.
New Alresford nest
The first nest found in Hampshire was discovered in New Alresford on 24th September 2018. The nest was 18 cm diameter and contained four combs with all life stages present. Twenty-eight males and 94 females were found. All the adults, teneral adults and pupae sampled were diploid, while within the larvae, five out of 10 were haploid, and within the eggs, two out of seven were haploid. The queen began laying haploid eggs around the 3rd September. The individuals from this nest were highly likely the offspring of a single queen and two drones.
Brockenhurst nest
The second nest found in Hampshire was discovered in Brockenhurst (approximately 30 miles away from New Alresford) and was destroyed on 04th October 2018. The nest was 18.5 cm diameter and there were three combs present with brood from the larvae stage onwards; no eggs were present indicating a recent loss of the queen or cessation of laying. All larvae and pupae were haploid and adult males were also haploid. The only diploid individuals present were worker females. The queen ceased laying before any diploid (future gyne) eggs were laid. The nest was consistent with being the offspring of a single queen mated with two drones.
Drayton Bassett nest
The first nest of 2019 was discovered on September 2nd at Drayton Bassett, Staffordshire. On arrival at Fera Science Ltd, the nest was too damaged to determine its size. Five adult female hornets were found in the nest. The wet weight of adult female hornets ranged from 197 to 312 mg with a mean of 271.6 (n = 5). The average wet weight of founder queens in September in the study by Rome et al.8 was 624 mg (n = 5). Based on this, it would appear that none of the females found in the nest were founder queens. All life stages were present in the nest, and all individuals genotyped were diploid. The nest was consistent with being the offspring of a single queen mated to a single drone.
Christchurch nests 1 + 2
On 01st October, 2019, a nest 13 cm diameter was discovered in Christchurch, Dorset. Two combs were present in the nest. One adult female hornet was found in the nest. The wet weight of this adult female hornet was 545 mg and the mesoscutum width was 4.6 mm. In a study by Pérez de Heredia et al.16 individuals taken from nests with a unimodal population had one individual per nest that had a mesoscutum width above 4.5 mm; no other individuals in these nests reached a mesoscutum width of 4.5 mm. It is therefore likely that the individual found in this nest was the queen. The combs contained eggs and larvae and had genotypes consistent with being the offspring of the queen that was present. Two eggs were haploid, the remainder of the eggs and larvae genotyped were diploid. On October 10th, a second nest was discovered in Christchurch, 10 m from the first nest, but could not be measured as it was intertwined with vegetation and fragmented upon removal. No adult hornets were found in the nest. Two combs were present, with capped and uncapped cells. Larvae, pupae and teneral adults were found, all of which were diploid. No eggs were found. Both nests from Christchurch were consistent with being the offspring of the same queen, mated to a single drone. The first nest found was likely to be the secondary nest, the second nest found likely to be the primary nest.
Information on all nests is found in Table 1. Map locations for each nest are shown in Fig. 1 and images of each nest are shown in Fig. 2.
Figure 2

Images of UK nests: (a) Tetbury, (b) Woolacombe, (c) Fowey nest 1, (d) Fowey nest 2, (e) Brockenhurst, (f) New Alresford, (g) Drayton Bassett, (h) Christchurch nest 1 and (i) Christchurch nest 2. Where shown, scale bar represents 5 cm.

Full size image

Genetic relatedness
Overall, the genetic diversity in the UK is relatively low for all locations, for all three measures used (mean number of alleles per locus, observed and expected heterozygosity; Table 3). However, it should be taken into consideration that the data for each UK nest are from individuals that were all closely related to each other (full, half siblings). A single combined figure for the UK was not calculated as it seems unlikely there is a UK population. Compared to the Asian hornet diversity data from Arca et al. (2015)11, the UK diversity is lower than France, which itself is lower than the diversity found in Asia (Table 2). This trend reflects the likely colonisation history of the hornet, which colonised France from Asia, and the UK incursions are likely to derive from populations on the European mainland.
Table 3 Genetic diversity measure (average number of alleles per locus) and the observed and expected heterozygosity for Asian hornet populations sampled in the British Isles, and from France (data from Arca et al. 201511).
Full size table

The occurrence of microsatellite alleles in the UK nests and France and Asia (from Arca et al.11) are given in supplementary material 1. In comparison with the Asian and French data in Arca et al.11, the UK samples had a restricted subset of alleles (35 alleles in total) that were all found in the French populations (60 alleles). In turn, all French alleles were a subset of those found in Asia (178 alleles). Similarly, the majority of private alleles were found in Asia (114), a small number in France (n = 3) and none in the UK (supplementary material 1).
In all cases, individuals recovered near a nest (within 2 km) were offspring of the recovered nearby nest (or nests, where there were primary and secondary nests). The majority of these individuals were found within 500 m of the nest. Most individuals caught in isolated locations away from nests (over 15 km) were not offspring of the recovered nests, with the exception of the individual recovered in Liskeard, which had a genotype compatible with being the offspring of the Fowey nest, some 17 km distant.
To exclude the possibility that any of the founding queens that escaped the destruction of the nest went on to produce viable nests that gave rise to nests caught in the subsequent year, we considered whether the inferred parental genotypes from a nest in year one could be the parents of the inferred parental genotypes in year two. For example, whether the queen from the Tetbury nest in 2016 could have formed a second nest and the offspring from that nest been the parents to the Woolacombe nest in 2017. In no case were the inferred parental genotypes compatible with this scenario. Additionally, where more than one nest was found in a single year (i.e. New Alresford/ Brockenhurst/ Fowey in 2018, Christchurch / Drayton Bassett in 2019), we considered whether the foundress queens and founder drones were full siblings to each other. Again, in no case was this possible (although they could have been half siblings to each other). Genotype data are provided in Supplementary material 2. More

1.
Kurihara, K. & Beidler, L. M. Taste-modifying protein from miracle fruit. Science 161, 1241–1243 (1968).
ADS  CAS  Article  PubMed  Google Scholar 
2.
Achigan-Dako, E. G., Tchokponhoué, D. A., N’Danikou, S., Gebauer, J. & Vodouhè, R. S. Current knowledge and breeding perspectives for the miracle plant Synsepalum dulcificum (Schum. et Thonn.) Daniell. Genet. Resour. Crop. Evol. 62, 465–476 (2015).

3.
Fandohan, A. B. et al. Usages traditionnels et valeur économique de Synsepalum dulcificum au Sud-Bénin. Bois For. Trop. 332, 17–30 (2017).
Article  Google Scholar 

4.
Oumorou, M., Dah-Dovonon, J., Aboh, B., Hounsoukaka, M. & Sinsin, B. Contribution á la conservation de Synsepalum dulcificum: régénération et importance socio-économique dans le département de l’ouémé (Bénin). Ann. Sci. Agron. 14, 101–120 (2010).
Google Scholar 

5.
Rodrigues, J. F., da Silva Andrade, R., Bastos, S. C., Coelho, S. B. & Pinheiro, A. C. M. Miracle fruit: An alternative sugar substitute in sour beverages. Appetite 107, 645–653 (2016).
Article  PubMed  Google Scholar 

6.
Andrade, A. C. et al. Effect of different quantities of miracle fruit on sour and bitter beverages. LWT 99, 89–97 (2019).
CAS  Article  Google Scholar 

7.
Swamy, K. B., Hadi, S. A., Sekaran, M. & Pichika, M. R. The clinical effects of Synsepalum dulcificum: a review. J. Med. Food. 17, 1165–1169 (2014).
CAS  Article  PubMed  Google Scholar 

8.
Chen, C. C., Liu, I. M. & Cheng, J. T. Improvement of insulin resistance by miracle fruit (Synsepalum dulcificum) in fructose-rich chow-fed rats. Phytother. Res. 20, 987–992 (2006).
Article  PubMed  Google Scholar 

9.
Han, Y. C., Wu, J. Y. & Wang, C. K. Modulatory effects of miracle fruit ethanolic extracts on glucose uptake through the insulin signaling pathway in C2C12 mouse myotubes cells. Food Sci. Nutr. 7, 1035–1042 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Obafemi, T. O., Akinmoladun, A. C., Olaleye, M. T., Agboade, S. O. & Onasanya, A. A. Antidiabetic potential of methanolic and flavonoid-rich leaf extracts of Synsepalum dulcificum in type 2 diabetic rats. J. Ayurveda Integr. Med. 8, 238–246. https://doi.org/10.1016/j.jaim.2017.01.008 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

11.
Buckmire, R. & Francis, F. Pigments of miracle fruit, Synsepalum dulcificum, Schum, as potential food colorants. J. Food Sci. 43, 908–911 (1978).
CAS  Article  Google Scholar 

12.
Del Campo, R., Zhang, Y. & Wakeford, C. Effect of miracle fruit (Synsepalum dulcificum) seed oil (MFSO®) on the measurable improvement of hair breakage in women with damaged hair: a randomized, double-blind, placebo-controlled, eight-month trial. J. Clin. Aesthet. Dermat. 10, 39–48 (2017).
Google Scholar 

13.
Gorin, S. et al. Beneficial effects of an investigational wristband containing Synsepalum dulcificum (miracle fruit) seed oil on the performance of hand and finger motor skills in healthy subjects: a randomized controlled preliminary study. Phytother. Res. 32, 321–332 (2018).
CAS  Article  PubMed  Google Scholar 

14.
Adomou, A. Vegetation patterns and environmental gradients in Benin PhD thesis, University of Wageningen, (2005).

15.
Tchokponhoué, D. et al. Regeneration ability and seedling growth in the miracle plant Synsepalum dulcificum (Schumach. & Thonn.) Daniell. Fruits 73, 13–21 (2018).

16.
Tchokponhoué, D. A., N’Danikou, S., Houéto, J. S. & Achigan-Dako, E. G. Shade and nutrient-mediated phenotypic plasticity in the miracle plant Synsepalum dulcificum (Schumach. & Thonn.) Daniell. Sci. Rep. 9(5137), 1–11 (2019).

17.
Tchokponhoué, D. A., N’Danikou, S., Hale, I., Van Deynze, A. & Achigan-Dako, E. G. Early fruiting in Synsepalum dulcificum (Schumach. & Thonn.) Daniell juveniles induced by water and inorganic nutrient management. F1000Res. 6, 1–17 (2017).

18.
Choi, S. E. & Garza, J. Effects of different miracle fruit products on the sensory characteristics of different types of sour foods by descriptive analysis. J. Food Sci. 85, 36–49 (2020).
CAS  Article  PubMed  Google Scholar 

19.
Tafazoli, S. et al. Safety assessment of miraculin using in silico and in vitro digestibility analyses. Food Chem. Toxicol. 133(110762), 1–10 (2019).
Google Scholar 

20.
Chibuzor, I. A., Bukola, O., Adejoke, A. O. & Chidozie, O. P. Genetic assessment of the shrub Synsepalum dulcificum (Schumach & Thonn.) Daniell in Nigeria using the Randomly Amplified Polymorphic DNA (RAPD). Int. J. Genet. Genom. 4, 45–50 (2017).

21.
Sogbohossou, E. D. et al. Association between vitamin content, plant morphology and geographical origin in a worldwide collection of the orphan crop Gynandropsis gynandra (Cleomaceae). Planta 250, 933–947 (2019).
CAS  Article  PubMed  Google Scholar 

22.
Singh, K., Sharma, Y. P. & Gairola, S. Morphological characterization of wild Rosa L. germplasm from the Western Himalaya, India. Euphytica 216(41), 1–24. https://doi.org/https://doi.org/10.1007/s10681-020-2567-2 (2020).

23.
Sun, W. et al. Multivariate analysis reveals phenotypic diversity of Euscaphis japonica population. PLoS ONE 14, 1. https://doi.org/10.1371/journal.pone.0219046 (2019).
CAS  Article  Google Scholar 

24.
Yazdanpour, F., Khadivi, A. & Etemadi-Khah, A. Phenotypic characterization of black raspberry to select the promising genotypes. Sci. Hortic. Amsterdam 235, 95–105. https://doi.org/10.1016/j.scienta.2018.02.071 (2018).
CAS  Article  Google Scholar 

25.
Fereidoonfar, H., Salehi-Arjmand, H., Khadivi, A. & Akramian, M. Morphological variability of sumac (Rhus coriaria L.) germplasm using multivariate analysis. Ind. Crops Prod. 120, 162–170, https://doi.org/https://doi.org/10.1016/j.indcrop.2018.04.034 (2018).

26.
Norouzi, E., Erfani-Moghadam, J., Fazeli, A. & Khadivi, A. Morphological variability within and among three species of Ziziphus genus using multivariate analysis. Sci. Hortic. Amsterdam 222, 180–186. https://doi.org/10.1016/j.scienta.2017.05.016 (2017).
Article  Google Scholar 

27.
Khadivi-Khub, A. & Anjam, K. Morphological characterization of Prunus scoparia using multivariate analysis. Plant Syst. Evol. 300, 1361–1372 (2014).
Article  Google Scholar 

28.
Vihotogbé, R., van den Berg, R. G. & Sosef, M. S. Morphological characterization of African bush mango trees (Irvingia species) in West Africa. Genet. Resour. Crop. Evol. 60, 1597–1614 (2013).
Article  Google Scholar 

29.
Ouborg, N. J. Integrating population genetics and conservation biology in the era of genomics. Biol. Lett. 6, 3–6 (2010).
Article  PubMed  Google Scholar 

30.
Martínez-García, P. J. et al. Predicting breeding values and genetic components using generalized linear mixed models for categorical and continuous traits in walnut (Juglans regia). Tree Genet. Genomes 13, 109 (2017).
Article  Google Scholar 

31.
Falconer, D. S. Introduction to quantitative genetics. (Oliver And Boyd; Edinburgh; London, 1960).

32.
Fonseca, C. E. L. d., Morais, F. M. d., Gonçalves, H. M., Aquino, F. d. G. & Rocha, F. S. Repeatability of fruit traits from two Hancornia speciosa populations from the core region of the Brazilian Cerrado. Pesqui. Agropecu Bras. 53, 710–716 (2018).

33.
Zou, S., Yao, X., Zhong, C., Zhao, T. & Huang, H. Genetic analysis of fruit traits and selection of superior clonal lines in Akebia trifoliate (Lardizabalaceae). Euphytica 214(111), 1–9. https://doi.org/10.1007/s10681-018-2198-z (2018).
ADS  CAS  Article  Google Scholar 

34.
Sanou, H. et al. Phenotypic variation of agromorphological traits of the shea tree, Vitellaria paradoxa CF Gaertn., in Mali. Genet. Resour. Crop. Evol. 53, 145–161 (2006).

35.
Albuquerque, A. S., Bruckner, C. H., Cruz, C. D., Salomão, L. C. C. & Neves, J. C. L. Repeatability and correlations among peach physical traits. Crop Breed. Appl. Biot. 4, 441–445 (2004).
Article  Google Scholar 

36.
Belaj, A. et al. Developing a core collection of olive (Olea europaea L.) based on molecular markers (DArTs, SSRs, SNPs) and agronomic traits. Tree Genet. Genomes 8, 365–378 (2012).

37.
Le Cunff, L. et al. Construction of nested genetic core collections to optimize the exploitation of natural diversity in Vitis vinifera L. subsp. sativa. BMC Plt. Biol. 8:31, 1–12 (2008).

38.
Mahmoodi, R. et al. Development of a core collection in Iranian walnut (Juglans regia L.) germplasm using the phenotypic diversity. Sci. Hortic. Amsterdam 249, 439–448 (2019).

39.
Tchokponhoué, D. A., N’Danikou, S. & Achigan-Dako, E. G. A combination of approaches evidenced seed storage behaviour in the miracle berry Synsepalum dulcificum (Schumach. et Thonn.) Daniell. BMC Plt. Biol. 19:117, 1–13 (2019).

40.
Edesi, J., Tolonen, J., Ruotsalainen, A. L., Aspi, J. & Häggman, H. Cryopreservation enables long-term conservation of critically endangered species Rubus humulifolius. Biodivers. Conserv. 29, 303–314. https://doi.org/10.1007/s10531-019-01883-9 (2020).
Article  Google Scholar 

41.
Bharuth, V., Naidoo, C., Pammenter, N. W., Lamb, J. M. & Moodley, T. Responses to chilling of recalcitrant seeds of Ekebergia capensis from different provenances. S. Afr. J. Bot. 130, 8–24 (2020).
Article  Google Scholar 

42.
Leal, M. E. The African rain forest during the Last Glacial Maximum an archipelago of forests in a sea of grass. (2004).

43.
Swenson, U., Richardson, J. E. & Bartish, I. V. Multi-gene phylogeny of the pantropical subfamily Chrysophylloideae (Sapotaceae): evidence of generic polyphyly and extensive morphological homoplasy. Cladistics 24, 1006–1031 (2008).
Article  Google Scholar 

44.
Juhé-Beaulaton, D. “Fèves”, “pois” et “grains” dans le golfe de Guinée : problèmes d’identification des plantes dans les sources historiques. in Plantes et paysages d’Afrique, une histoire à explorer (ed Chastanet M) 45–68 (1998).

45.
Inglett, G. E. & May, J. F. Tropical plants with unusual taste properties. Econ. Bot. 22, 326–331. https://doi.org/10.1007/BF02908127 (1968).
Article  Google Scholar 

46.
Salzmann, U. & Hoelzmann, P. The Dahomey Gap: an abrupt climatically induced rain forest fragmentation in West Africa during the late Holocene. Holocene 15, 190–199. https://doi.org/10.1191/0959683605hl799rp (2005).
ADS  Article  Google Scholar 

47.
Booth, A. The Niger, the Volta and the Dahomey Gap as geographic barriers. Evolution 12, 48–62 (1958).
Article  Google Scholar 

48.
White, F. The vegetation of Africa: a descriptive memoir to accompany the UNESCO/AETFAT/UNSO vegetation map of Africa. (1983).

49.
Niñez, V. Household gardens: theoretical and policy considerations. Agr. Syst. 23, 167–186. https://doi.org/10.1016/0308-521X(87)90064-3 (1987).
Article  Google Scholar 

50.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing,Vienna, Austria. https://www.R-project.org/. (2019).

51.
Peterson, B. G. et al. ‘Performance analytics’: econometric tools for performance and risk analysis. R Team Cooperation. (2018).

52.
Warton, D. I., Duursma, R. A., Falster, D. S. & Taskinen, S. smatr 3–an R package for estimation and inference about allometric lines. Methods Ecol. Evol. 3, 257–259 (2012).
Article  Google Scholar 

53.
Moon, K.-W. Interactive plot. in Learn ggplot2 Using Shiny App (ed Keon-Woong Moon) 295–347 (Springer, 2016).

54.
Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).
Article  Google Scholar 

55.
YiLan, L. & RuTong, Z. clustertend: Check the Clustering tendency. R package version 1 (2015).

56.
Kassambara, A. & Mundt, F. Factoextra: extract and visualize the results of multivariate data analyses. R package version 1, 4 (2017).
Google Scholar 

57.
Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
Article  Google Scholar 

58.
De Beukelaer, H., Davenport, G. F. & Fack, V. Core Hunter 3: flexible core subset selection. BMC Bioinformatics 19(203), 1–12 (2018).
Google Scholar 

59.
Kim, K.-W. et al. PowerCore: a program applying the advanced M strategy with a heuristic search for establishing core sets. Bioinformatics 23, 2155–2162 (2007).
CAS  Article  PubMed  Google Scholar 

60.
Hu, J., Zhu, J. & Xu, H. Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops. Theor. Appl. Genet. 101, 264–268 (2000).
CAS  Article  Google Scholar 

61.
61Wickham, H. ggplot2: elegant graphics for data analysis. (Springer, 2016).

62.
Pebesma, E. Simple features for R: standardized support for spatial vector data. R J. 10, 439–446 (2018).
Article  Google Scholar 

63.
South, A. Rworldxtra: Country boundaries at high resolution. R package version 1 (2012).

64.
South, A. rnaturalearth: World map data from Natural Earth. R package version 0.1. 0 (2017).

65.
Salako, V. K. et al. Home gardens: an assessment of their biodiversity and potential contribution to conservation of threatened species and crop wild relatives in Benin. Genet. Resour. Crop. Evol. 61, 313 (2014).
Article  Google Scholar 

66.
van Rompaey, R. S. Forest gradients in West Africa: a spatial gradient analysis, Wageningen, (1993).

67.
Gwali, S. et al. Morphological variation among shea tree (Vitellaria paradoxa subsp. nilotica)‘ethnovarieties’ in Uganda. Genet. Resour. Crop. Evol. 59, 1883–1898 (2012).

68.
Metougui, M. L., Mokhtari, M., Maughan, P. J., Jellen, E. N. & Benlhabib, O. Morphological variability, heritability and correlation studies within an argan tree population (Argania spinosa (L.) Skeels) preserved in situ. Int. J. Agr. For. 7, 42–51 (2017).

69.
Tsobeng, A. et al. Tree-to-tree variation in fruit of three populations of Trichoscypha acuminata in Cameroon. Sci. Afr. 7, 1–12 (2020).
Google Scholar 

70.
Omondi, M. et al. Fruit morphological diversity and productivity of baobab (Adansonia digitata L.) in coastal and lower eastern Kenya. For. Trees Livelihoods 28, 266–280 (2019).
Article  Google Scholar 

71.
Abdulai, I., Krutovsky, K. V. & Finkeldey, R. Morphological and genetic diversity of shea trea (Vitellaria paradoxa) in the savannah regions of Ghana. Genet Res Crop Evol 64, 1253–1268 (2017).
Article  Google Scholar 

72.
Karambiri, M., Elias, M., Vinceti, B. & Grosse, A. Exploring local knowledge and preferences for shea (Vitellaria pradoxa) ethnovarieties in Soutwest Burkina-Faso through a gender and ethnic lens. For. Trees Livelihoods 26, 13–28 (2016).
Article  Google Scholar 

73.
Ayensu, E. S. Morphology and anatomy of Synsepalum dulcificum (Sapotaceae). Bot. J. Linn. Soc. 65, 179–187 (1972).
Article  Google Scholar 

74.
Lim, T. K. in Edible medicinal and non-medicinal plants Vol. 6 (ed T.K. Lim) 146–150 (Springer, Dordrecht, 2013).

75.
Huang, W., Chung, H. Y., Xuan, W., Wang, G. & Li, Y. The cholesterol-lowering activity of miracle fruit (Synsepalum dulcificum). J. Food Biochem. 1, e13185. https://doi.org/10.1111/jfbc.13185 (2020).
Article  Google Scholar 

76.
Ahmed, A. A. O. et al. Tree-to-tree variability in fruits and kernels of a Balanites aegyptiaca (L.) Del. population grown in Sudan. Trees 34, 111–119 (2020).

77.
Zou, S., Yao, X., Zhong, C., Zhao, T. & Huang, H. Effectiveness of recurrent selection in Akebia trifoliata (Lardizabalaceae) breeding. Sci. Hortic. Amsterdam 246, 79–85 (2019).
Article  Google Scholar 

78.
Houehanou, T. D. et al. Morphological trait variation and relationships of Afzelia africana Sm. caused by climatic conditions and anthropogenic disturbance in Benin (West Africa). Genet. Resour. Crop. Evol. 66, 1091–1105 (2019).

79.
Gouwakinnou, G. N., Assogbadjo, A. E., Lykke, A. M. & Sinsin, B. Phenotypic variations in fruits and selection potential in Sclerocarya birrea subsp. birrea. Sci. Hortic. Amsterdam 129, 777–783 (2011).

80.
Cotterill, P. P. & Dean, C. A. Successful tree breeding with index selection (CSIRO, Division of Forestry and Forest Products, 1990).
Google Scholar 

81.
Zobel, B. & Talbert, J. Applied forest tree improvement. (John Wiley & Sons, 1984).

82.
Atangana, A. R. et al. Tree-to-tree variation in stearic and oleic acid content in seed fat from Allanblackia floribunda from wild stands: potential for tree breeding. Food Chem. 126, 1579–1585 (2011).
CAS  Article  PubMed  Google Scholar 

83.
Shelbourne, C. Genetic gains from different kinds of breeding population and seed or plant production population. S. Afr. For. J. 160, 49–65 (1992).
Google Scholar 

84.
Leakey, R. & Page, T. The ‘ideotype concept’and its application to the selection of cultivars of trees providing agroforestry tree products. For. Trees Livelihoods 16, 5–16 (2006).
Article  Google Scholar 

85.
Bhattacharjee, R., Khairwal, I., Bramel, P. J. & Reddy, K. Establishment of a pearl millet [Pennisetum glaucum (L.) R. Br.] core collection based on geographical distribution and quantitative traits. Euphytica 155, 35–45 (2007).

86.
Escribano, P., Viruel, M. & Hormaza, J. in XII EUCARPIA Symposium on Fruit Breeding and Genetics 814. 67–70.

87.
Duan, H. et al. Genetic characterization of Chinese fir from six provinces in southern China and construction of a core collection. Sci. Rep. 7, 1–10 (2017).
Article  CAS  Google Scholar  More

1.
Carey, H. V., Andrews, M. T. & Martin, S. L. Mammalian hibernation: cellular and molecular responses to depressed metabolism and low temperature. Physiological Rev. 83, 1153–1181 (2003).
CAS  Article  Google Scholar 
2.
Geiser, F. Metabolic rate and body temperature reduction during hibernation and daily torpor. Annu. Rev. Physiol. 66, 239–274 (2004).
CAS  Article  Google Scholar 

3.
Lindell, S. L. et al. Natural resistance to liver cold ischemia-reperfusion injury associated with the hibernation phenotype. Am. J. Physiol.-Gastrointest. Liver Physiol. 288, G473–G480 (2005).
CAS  Article  Google Scholar 

4.
Dave, K. R., Christian, S. L., Perez-Pinzon, M. A. & Drew, K. L. Neuroprotection: lessons from hibernators. Comp. Biochem. Physiol. Part B: Biochem. Mol. Biol. 162, 1–9 (2012).
CAS  Article  Google Scholar 

5.
Hofmann, S., Cherkasova, V., Bankhead, P., Bukau, B. & Stoecklin, G. Translation suppression promotes stress granule formation and cell survival in response to cold shock. Mol. Biol. Cell 23, 3786–3800 (2012).
CAS  Article  Google Scholar 

6.
Pluquet, O., Pourtier, A. & Abbadie, C. The unfolded protein response and cellular senescence. A review in the theme: cellular mechanisms of endoplasmic reticulum stress signaling in health and disease. Am. J. Physiol.-Cell Physiol. 308, C415–C425 (2015).
CAS  Article  Google Scholar 

7.
Hetz, C. The unfolded protein response: controlling cell fate decisions under ER stress and beyond. Nat. Rev. Mol. Cell Biol. 13, 89 (2012).
CAS  Article  Google Scholar 

8.
Guo, F.-J. et al. XBP1S protects cells from ER stress-induced apoptosis through Erk1/2 signaling pathway involving CHOP. Histochemistry Cell Biol. 138, 447–460 (2012).
CAS  Article  Google Scholar 

9.
Walter, P. & Ron, D. The unfolded protein response: from stress pathway to homeostatic regulation. Science 334, 1081–1086 (2011).
CAS  Article  Google Scholar 

10.
Kramer, G. Two phosphorylation sites on eIF-2α. FEBS Lett. 267, 181–182 (1990).
CAS  Article  Google Scholar 

11.
Wek, R., Jiang, H.-Y. & Anthony, T. Coping with stress: eIF2 kinases and translational control. (Portland Press Limited, 2006).

12.
Palam, L. R., Baird, T. D. & Wek, R. C. Phosphorylation of eIF2 facilitates ribosomal bypass of an inhibitory upstream ORF to enhance CHOP translation. J. Biol. Chem. 286, 10939–10949 (2011).

13.
Roller, C. & Maddalo, D. The molecular chaperone GRP78/BiP in the development of chemoresistance: mechanism and possible treatment. Front. Pharmacol. 4, 10 (2013).
Article  CAS  Google Scholar 

14.
Lee, A. S. The ER chaperone and signaling regulator GRP78/BiP as a monitor of endoplasmic reticulum stress. Methods 35, 373–381 (2005).
CAS  Article  Google Scholar 

15.
Jiang, H.-Y. et al. Phosphorylation of the α subunit of eukaryotic initiation factor 2 is required for activation of NF-κB in response to diverse cellular stresses. Mol. Cell. Biol. 23, 5651–5663 (2003).
CAS  Article  Google Scholar 

16.
Prell, T. et al. Endoplasmic reticulum stress is accompanied by activation of NF-κB in amyotrophic lateral sclerosis. J. Neuroimmunol. 270, 29–36 (2014).
CAS  Article  Google Scholar 

17.
Rajesh, K. et al. Phosphorylation of the translation initiation factor eIF2α at serine 51 determines the cell fate decisions of Akt in response to oxidative stress. Cell Death Dis. 6, e1591 (2015).
CAS  Article  Google Scholar 

18.
Nivon, M. et al. NFκB is a central regulator of protein quality control in response to protein aggregation stresses via autophagy modulation. Mol. Biol. Cell 27, 1712–1727 (2016).
CAS  Article  Google Scholar 

19.
Lemasters, J. J. In Molecular Pathology (second edn) 1–24 (Elsevier, 2018).

20.
Karin, M. & Lin, A. NF-κB at the crossroads of life and death. Nat. Immunol. 3, 221 (2002).
CAS  Article  Google Scholar 

21.
Schmidlin, C. J., Dodson, M. B., Madhavan, L. & Zhang, D. D. Redox regulation by NRF2 in aging and disease. Free Rad. Biol. Med. 134, 702–707 (2019).

22.
Cullinan, S. B. & Diehl, J. A. Coordination of ER and oxidative stress signaling: the PERK/Nrf2 signaling pathway. Int. J. Biochem. Cell Biol. 38, 317–332 (2006).
CAS  Article  Google Scholar 

23.
Wiersma, M. et al. Torpor-arousal cycles in Syrian hamster heart are associated with transient activation of the protein quality control system. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 223, 23–28 (2018).

24.
Mamady, H. & Storey, K. B. Up-regulation of the endoplasmic reticulum molecular chaperone GRP78 during hibernation in thirteen-lined ground squirrels. Mol. Cell. Biochem. 292, 89–98 (2006).
CAS  Article  Google Scholar 

25.
Mamady, H. & Storey, K. B. Coping with the stress: expression of ATF4, ATF6, and downstream targets in organs of hibernating ground squirrels. Arch. Biochem. Biophys. 477, 77–85 (2008).
CAS  Article  Google Scholar 

26.
Zhang, J. et al. Prosurvival roles mediated by the PERK signaling pathway effectively prevent excessive endoplasmic reticulum stress-induced skeletal muscle loss during high-stress conditions of hibernation. J. Cell. Physiol. 234, 19728–19739 (2019).

27.
Carey, H., Frank, C. & Seifert, J. Hibernation induces oxidative stress and activation of NF-κB in ground squirrel intestine. J. Comp. Physiol. B 170, 551–559 (2000).
CAS  Article  Google Scholar 

28.
Fleck, C. C. & Carey, H. V. Modulation of apoptotic pathways in intestinal mucosa during hibernation. Am. J. Physiol.-Regulatory, Integr. Comp. Physiol. 289, R586–R595 (2005).
CAS  Article  Google Scholar 

29.
Zhang, Y. et al. Critical roles of mitochondria in brain activities of torpid Myotis ricketti bats revealed by a proteomic approach. J. Proteom. 105, 266–284 (2014).
CAS  Article  Google Scholar 

30.
Cui, X. A., Zhang, H. & Palazzo, A. F. p180 promotes the ribosome-independent localization of a subset of mRNA to the endoplasmic reticulum. PLoS Biol. 10, e1001336 (2012).
CAS  Article  Google Scholar 

31.
Wen, W.-L. et al. Vgl1, a multi-KH domain protein, is a novel component of the fission yeast stress granules required for cell survival under thermal stress. Nucleic Acids Res. 38, 6555–6566 (2010).
CAS  Article  Google Scholar 

32.
Tsuchiya, N. et al. SND1, a component of RNA-induced silencing complex, is up-regulated in human colon cancers and implicated in early stage colon carcinogenesis. Cancer Res. 67, 9568–9576 (2007).
CAS  Article  Google Scholar 

33.
Yoo, B. K. et al. Increased RNA-induced silencing complex (RISC) activity contributes to hepatocellular carcinoma. Hepatology 53, 1538–1548 (2011).
CAS  Article  Google Scholar 

34.
Halperin, L., Jung, J. & Michalak, M. The many functions of the endoplasmic reticulum chaperones and folding enzymes. IUBMB Life 66, 318–326 (2014).
CAS  Article  Google Scholar 

35.
Obchoei, S. et al. Cyclophilin A: potential functions and therapeutic target for human cancer. Med. Sci. Monit. 15, RA221–RA232 (2009).
CAS  Google Scholar 

36.
Wei, Y. et al. Antiapoptotic and proapoptotic signaling of cyclophilin A in endothelial cells. Inflammation 36, 567–572 (2013).
Article  CAS  Google Scholar 

37.
Kelleher, D. J. & Gilmore, R. DAD1, the defender against apoptotic cell death, is a subunit of the mammalian oligosaccharyltransferase. Proc. Natl Acad. Sci. USA 94, 4994–4999 (1997).
CAS  Article  Google Scholar 

38.
Zhou, L. et al. DsbA-L alleviates endoplasmic reticulum stress-induced adiponectin downregulation. Diabetes 59, 2809–2816 (2010).
CAS  Article  Google Scholar 

39.
Liu, M. et al. Endoplasmic reticulum (ER) localization is critical for DsbA-L protein to suppress ER stress and adiponectin down-regulation in adipocytes. J. Biol. Chem. 290, 10143–10148 (2015).
CAS  Article  Google Scholar 

40.
Santhekadur, P. K. et al. Multifunction protein staphylococcal nuclease domain containing 1 (SND1) promotes tumor angiogenesis in human hepatocellular carcinoma through novel pathway that involves nuclear factor κB and miR-221. J. Biol. Chem. 287, 13952–13958 (2012).
CAS  Article  Google Scholar 

41.
Pan, Y. H. et al. Adaptation of phenylalanine and tyrosine catabolic pathway to hibernation in bats. PLoS ONE 8, e62039 (2013).
CAS  Article  Google Scholar 

42.
Vattem, K. M. & Wek, R. C. Reinitiation involving upstream ORFs regulates ATF4 mRNA translation in mammalian cells. Proc. Natl Acad. Sci. USA 101, 11269–11274 (2004).
CAS  Article  Google Scholar 

43.
Lu, Z. & Xu, S. ERK1/2 MAP kinases in cell survival and apoptosis. IUBMB Life 58, 621–631 (2006).
CAS  Article  Google Scholar 

44.
Sun, F.-C. et al. Localization of GRP78 to mitochondria under the unfolded protein response. Biochem. J. 396, 31–39 (2006).
CAS  Article  Google Scholar 

45.
van Breukelen, F. & Martin, S. L. Translational initiation is uncoupled from elongation at 18 C during mammalian hibernation. Am. J. Physiol.-Regulatory, Integr. Comp. Physiol. 281, R1374–R1379 (2001).
Article  Google Scholar 

46.
van Breukelen, F., Sonenberg, N. & Martin, S. L. Seasonal and state-dependent changes of eIF4E and 4E-BP1 during mammalian hibernation: implications for the control of translation during torpor. Am. J. Physiol.-Regulatory, Integr. Comp. Physiol. 287, R349–R353 (2004).
Article  Google Scholar 

47.
Pan, P. & van Breukelen, F. Preference of IRES-mediated initiation of translation during hibernation in golden-mantled ground squirrels, Spermophilus lateralis. Am. J. Physiol. Regul. Integr. Comp. Physiol. 301, R370–R377 (2011).
CAS  Article  Google Scholar 

48.
Liu, L. et al. Hypoxia-induced energy stress regulates mRNA translation and cell growth. Mol. Cell 21, 521–531 (2006).
Article  CAS  Google Scholar 

49.
Harvey, R., Dezi, V., Pizzinga, M. & Willis, A. E. Post-transcriptional control of gene expression following stress: the role of RNA-binding proteins. Biochem. Soc. Trans. 45, 1007–1014 (2017).
CAS  Article  Google Scholar 

50.
Mobin, M. B. et al. The RNA-binding protein vigilin regulates VLDL secretion through modulation of Apob mRNA translation. Nat. Commun. 7, 12848 (2016).
CAS  Article  Google Scholar 

51.
Srere, H. K., Wang, L. & Martin, S. L. Central role for differential gene expression in mammalian hibernation. Proc. Natl Acad. Sci. USA 89, 7119–7123 (1992).
CAS  Article  Google Scholar 

52.
Han, Y. et al. Adaptation of peroxisome proliferator-activated receptor alpha to hibernation in bats. BMC Evolut. Biol. 15, 88 (2015).
Article  CAS  Google Scholar 

53.
Lee, M., Choi, I. & Park, K. Activation of stress signaling molecules in bat brain during arousal from hibernation. J. Neurochem. 82, 867–873 (2002).
CAS  Article  Google Scholar 

54.
Storey, K. B. Out cold: biochemical regulation of mammalian hibernation-a mini-review. Gerontology 56, 220–230 (2010).
Article  Google Scholar 

55.
Lei, M., Dong, D., Mu, S., Pan, Y.-H. & Zhang, S. Comparison of brain transcriptome of the greater horseshoe bats (Rhinolophus ferrumequinum) in active and torpid episodes. PLoS ONE 9, e107746 (2014).
Article  CAS  Google Scholar 

56.
Wortel, I. M., van der Meer, L. T., Kilberg, M. S. & van Leeuwen, F. N. Surviving stress: modulation of ATF4-mediated stress responses in normal and malignant cells. Trends Endocrinol. Metab. 28, 794–806 (2017).
CAS  Article  Google Scholar 

57.
Cullinan, S. B. et al. Nrf2 is a direct PERK substrate and effector of PERK-dependent cell survival. Mol. Cell. Biol. 23, 7198–7209 (2003).
CAS  Article  Google Scholar 

58.
Ni, M., Zhang, Y. & Lee, A. S. Beyond the endoplasmic reticulum: atypical GRP78 in cell viability, signalling and therapeutic targeting. Biochem. J. 434, 181–188 (2011).
CAS  Article  Google Scholar 

59.
Yin, Q. et al. Antioxidant defenses in the brains of bats during hibernation. PLoS ONE 11, e0152135 (2016).
Article  CAS  Google Scholar 

60.
Allan, M. E. & Storey, K. B. Expression of NF-κB and downstream antioxidant genes in skeletal muscle of hibernating ground squirrels, Spermophilus tridecemlineatus. Cell Biochem. Funct. 30, 166–174 (2012).
CAS  Article  Google Scholar 

61.
Ni, Z., McMullen, D. C. & Storey, K. B. Expression of Nrf2 and its downstream gene targets in hibernating 13-lined ground squirrels, Spermophilus tridecemlineatus. Mol. Cell. Biochem. 312, 121–129 (2008).
Article  CAS  Google Scholar 

62.
Németh, J. et al. S100A8 and S100A9 are novel nuclear factor kappa B target genes during malignant progression of murine and human liver carcinogenesis. Hepatology 50, 1251–1262 (2009).
Article  CAS  Google Scholar 

63.
Sun, S. et al. Cyclophilin A (CypA) interacts with NF-κB subunit, p65/RelA, and contributes to NF-κB activation signaling. PLoS ONE 9, e96211 (2014).
Article  CAS  Google Scholar 

64.
Bolignano, D. et al. Neutrophil gelatinase-associated lipocalin (NGAL) in human neoplasias: a new protein enters the scene. Cancer Lett. 288, 10–16 (2010).
CAS  Article  Google Scholar 

65.
Drew, K. L., Rice, M. E., Kuhn, T. B. & Smith, M. A. Neuroprotective adaptations in hibernation: therapeutic implications for ischemia-reperfusion, traumatic brain injury and neurodegenerative diseases. Free Radic. Biol. Med. 31, 563–573 (2001).
CAS  Article  Google Scholar 

66.
Bouma, H. R. et al. Induction of torpor: mimicking natural metabolic suppression for biomedical applications. J. Cell Physiol. 227, 1285–1290 (2012).
CAS  Article  Google Scholar 

67.
Cerri, M. et al. Hibernation for space travel: Impact on radioprotection. Life Sci. Space Res. 11, 1–9 (2016).
Article  Google Scholar 

68.
Uchida, Y., Tokizawa, K. & Nagashima, K. Characteristics of activated neurons in the suprachiasmatic nucleus when mice become hypothermic during fasting and cold exposure. Neurosci. Lett. 579, 177–182 (2014).
CAS  Article  Google Scholar 

69.
Sato, N., Marui, S., Ozaki, M. & Nagashima, K. Cold exposure and/or fasting modulate the relationship between sleep and body temperature rhythms in mice. Physiol. Behav. 149, 69–75 (2015).
CAS  Article  Google Scholar 

70.
Tokizawa, K., Uchida, Y. & Nagashima, K. Thermoregulation in the cold changes depending on the time of day and feeding condition: physiological and anatomical analyses of involved circadian mechanisms. Neuroscience 164, 1377–1386 (2009).
CAS  Article  Google Scholar 

71.
Van Breukelen, F. & Martin, S. L. Invited review: molecular adaptations in mammalian hibernators: unique adaptations or generalized responses? J. Appl. Physiol. 92, 2640–2647 (2002).
Article  Google Scholar 

72.
Piersma, S. R. et al. Workflow comparison for label-free, quantitative secretome proteomics for cancer biomarker discovery: method evaluation, differential analysis, and verification in serum. J. Proteome Res. 9, 1913–1922 (2010).
CAS  Article  Google Scholar 

73.
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2018).
Article  CAS  Google Scholar 

74.
Yu, D. et al. EGPS 1.0: Comprehensive software for multi-omic and evolutionary analyses. Natl Sci. Rev. 6, 867–869 (2019).

75.
Yin, Q. et al. Maintenance of neural activities in torpid Rhinolophus ferrumequinum bats revealed by 2D gel-based proteome analysis. Biochim. Biophys. Acta Proteins Proteom. 1865, 1004–1019 (2017).
CAS  Article  Google Scholar 

76.
Romero-Calvo, I. et al. Reversible Ponceau staining as a loading control alternative to actin in Western blots. Anal. Biochem. 401, 318–320 (2010).
CAS  Article  Google Scholar 

77.
Deutsch, E. W. et al. The ProteomeXchange consortium in 2020: enabling ‘big data’approaches in proteomics. Nucleic Acids Res. 48, D1145–D1152 (2020).
CAS  PubMed  Google Scholar  More