Ecology

1.
Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).
PubMed  PubMed Central  Google Scholar 
2.
Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
Google Scholar 

3.
Robinson, N. M., Nelson, W. A., Costello, M. J., Sutherland, J. E. & Lundquist, C. J. A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice. Front. Mar. Sci. 4, 421 (2017).
Google Scholar 

4.
Sofaer, H. R. et al. Development and delivery of species distribution models to inform decision-making. Bioscience 69, 480–480 (2019).
Google Scholar 

5.
Guisan, A. & Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).
Google Scholar 

6.
Hao, T., Elith, J., Guillera-Arroita, G. & Lahoz-Monfort, J. J. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Divers. Distrib. https://doi.org/10.1111/DDI.12892 (2019).
Article  Google Scholar 

7.
Gogol-Prokurat, M. Predicting habitat suitability for rare plants at local spatial scales using a species distribution model. Ecol. Appl. 21, 33–47 (2011).
PubMed  Google Scholar 

8.
Lomba, A. et al. Overcoming the rare species modelling paradox: A novel hierarchical framework applied to an Iberian endemic plant. Biol. Conserv. 143, 2647–2657 (2010).
Google Scholar 

9.
Breiner, F. T., Guisan, A., Bergamini, A. & Nobis, M. P. Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol. Evol. 6, 1210–1218 (2015).
Google Scholar 

10.
Magurran, A. E. & Henderson, P. A. Explaining the excess of rare species in natural species abundance distributions. Nature 422, 714–716 (2003).
ADS  CAS  PubMed  Google Scholar 

11.
Cao, Y., Larsen, D. P. & Thorne, R.S.-J.J. Rare species in multivariate analysis for bioassessment: Some considerations. J. N. Am. Benthol. Soc. 20, 144–153 (2001).
Google Scholar 

12.
Foden, W. B. et al. Climate change vulnerability assessment of species. Wiley Interdiscip. Rev. Clim. Chang. 10, e551 (2019).
Google Scholar 

13.
Guisan, A. et al. Using niche-based models to improve the sampling of rare species. Conserv. Biol. 20, 501–511 (2006).
PubMed  Google Scholar 

14.
Ancillotto, L. et al. An African bat in Europe, Plecotus gaisleri: Biogeographic and ecological insights from molecular taxonomy and Species Distribution Models. Ecol. Evol. https://doi.org/10.1002/ece3.6317 (2020).
Article  PubMed  PubMed Central  Google Scholar 

15.
Della Rocca, F., Bogliani, G., Breiner, F. T. & Milanesi, P. Identifying hotspots for rare species under climate change scenarios: Improving saproxylic beetle conservation in Italy. Biodivers. Conserv. 28, 433–449 (2019).
Google Scholar 

16.
Cunningham, R. B. & Lindenmayer, D. B. Modeling count data of rare species: Some statistical issues. Ecology 86, 1135–1142 (2005).
Google Scholar 

17.
Vaughan, I. P. & Ormerod, S. J. The continuing challenges of testing species distribution models. J. Appl. Ecol. 42, 720–730 (2005).
Google Scholar 

18.
Franklin, J., Wejnert, K. E., Hathaway, S. A., Rochester, C. J. & Fisher, R. N. Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California. Divers. Distrib. 15, 167–177 (2009).
Google Scholar 

19.
Engler, R., Guisan, A. & Rechsteiner, L. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J. Appl. Ecol. 41, 263–274 (2004).
Google Scholar 

20.
Chefaoui, R. M. & Lobo, J. M. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecol. Modell. 210, 478–486 (2008).
Google Scholar 

21.
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).
PubMed  Google Scholar 

22.
Meynard, C. N. & Quinn, J. F. Predicting species distributions: A critical comparison of the most common statistical models using artificial species. J. Biogeogr. 34, 1455–1469 (2007).
Google Scholar 

23.
Royle, J. A., Nichols, J. D. & Kéry, M. Modelling occurrence and abundance of species when detection is imperfect. Oikos 110, 353–359 (2005).
Google Scholar 

24.
Welsh, A. H., Cunningham, R. B., Donnelly, C. F. & Lindenmayer, D. B. Modelling the abundance of rare species: Statistical models for counts with extra zeros. Ecol. Modell. 88, 297–308 (1996).
Google Scholar 

25.
Yates, K. L. et al. Outstanding challenges in the transferability of ecological models. Trends Ecol. Evol. 33, 790–802 (2018).
PubMed  Google Scholar 

26.
Williams, J. N. et al. Using species distribution models to predict new occurrences for rare plants. Divers. Distrib. 15, 565–576 (2009).
Google Scholar 

27.
Rufener, M.-C., Kinas, P. G., Nóbrega, M. F. & Lins Oliveira, J. E. Bayesian spatial predictive models for data-poor fisheries. Ecol. Modell. 348, 125–134 (2017).
Google Scholar 

28.
Blangiardo, M. & Cameletti, M. Spatial and spatial-temporal bayesian models with R-INLA. Spat Spat. Epidemiol. 4, 33–49 (2013).
MATH  Google Scholar 

29.
Nieto-Lugilde, D., Maguire, K. C., Blois, J. L., Williams, J. W. & Fitzpatrick, M. C. Multiresponse algorithms for community-level modelling: Review of theory, applications, and comparison to species distribution models. Methods Ecol. Evol. 9, 834–848 (2018).
Google Scholar 

30.
Warton, D. I. et al. So many variables: Joint modeling in community ecology. Trends Ecol. Evol. 30, 766–779 (2015).
PubMed  Google Scholar 

31.
Thorson, J. T., Pinsky, M. L. & Ward, E. J. Model-based inference for estimating shifts in species distribution, area occupied and centre of gravity. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.12567 (2016).
Article  Google Scholar 

32.
Ovaskainen, O. et al. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett. 20, 561–576 (2017).
PubMed  Google Scholar 

33.
Hui, F. K. C. Boral-Bayesian ordination and regression analysis of multivariate abundance data in R. Methods Ecol. Evol. 7, 744–750 (2016).
Google Scholar 

34.
Warton, D. I., Foster, S. D., De’ath, G., Stoklosa, J. & Dunstan, P. K. Model-based thinking for community ecology. Plant Ecol. 216, 669–682 (2015).
Google Scholar 

35.
Ovaskainen, O. & Soininen, J. Making more out of sparse data: Hierarchical modeling of species communities. Ecology 92, 289–295 (2011).
PubMed  Google Scholar 

36.
Hui, F. K. C., Warton, D. I., Foster, S. D. & Dunstan, P. K. To mix or not to mix: Comparing the predictive performance of mixture models vs separate species distribution models. Ecology 94, 1913–1919 (2013).
PubMed  Google Scholar 

37.
Leach, K., Montgomery, W. I. & Reid, N. Modelling the influence of biotic factors on species distribution patterns. Ecol. Modell. 337, 96–106 (2016).
Google Scholar 

38.
Anderson, R. P. When and how should biotic interactions be considered in models of species niches and distributions?. J. Biogeogr. 44, 8–17 (2017).
Google Scholar 

39.
D’Amen, M., Rahbek, C., Zimmermann, N. E. & Guisan, A. Spatial predictions at the community level: From current approaches to future frameworks. Biol. Rev. 92, 169–187 (2017).
PubMed  Google Scholar 

40.
Kindsvater, H. K. et al. Overcoming the data crisis in biodiversity conservation. Trends Ecol. Evol. 33, 676–688 (2018).
PubMed  Google Scholar 

41.
Thorson, J. T., Kell, L. T., De Oliveira, J. A. A., Sampson, D. B. & Punt, A. E. Introduction to data-poor stock assessment. Fish. Res. 171, 1–3 (2015).
Google Scholar 

42.
Schliep, E. M. et al. Joint species distribution modelling for spatio-temporal occurrence and ordinal abundance data. Glob. Ecol. Biogeogr. 27, 142–155 (2018).
MathSciNet  Google Scholar 

43.
Maguire, K. C. et al. Controlled comparison of species- and community-level models across novel climates and communities. Proc. R. Soc. B Biol. Sci. 283, 20152817 (2016).
Google Scholar 

44.
Zhang, C., Chen, Y., Xu, B., Xue, Y. & Ren, Y. Comparing the prediction of joint species distribution models with respect to characteristics of sampling data. Ecography (Cop.) 41, 1876–1887 (2018).
Google Scholar 

45.
Wilkinson, D. P., Golding, N., Guillera-Arroita, G., Tingley, R. & McCarthy, M. A. A comparison of joint species distribution models for presence–absence data. Methods Ecol. Evol. 10, 198–211 (2019).
Google Scholar 

46.
Ehrlén, J. & Morris, W. F. Predicting changes in the distribution and abundance of species under environmental change. Ecol. Lett. 18, 303–314 (2015).
PubMed  PubMed Central  Google Scholar 

47.
Smeraldo, S. et al. Modelling risks posed by wind turbines and power lines to soaring birds: The black stork (Ciconia nigra) in Italy as a case study. Biodivers. Conserv. 29, 1959–1976 (2020).
Google Scholar 

48.
Rizvanovic, M., Kennedy, J. D., Nogués-Bravo, D. & Marske, K. A. Persistence of genetic diversity and phylogeographic structure of three New Zealand forest beetles under climate change. Divers. Distrib. 25, 142–153 (2019).
Google Scholar 

49.
Guillera-Arroita, G. et al. Is my species distribution model fit for purpose? Matching data and models to applications. Glob. Ecol. Biogeogr. 24, 276–292 (2015).
Google Scholar 

50.
Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785 (2006).
Google Scholar 

51.
Thibaud, E., Petitpierre, B., Broennimann, O., Davison, A. C. & Guisan, A. Measuring the relative effect of factors affecting species distribution model predictions. Methods Ecol. Evol. 5, 947–955 (2014).
Google Scholar 

52.
Rabinowitz, D., Cairns, S. & Dillon, T. Seven forms of rarity and their frequency in the flora of the British Isles. In Conservation Biology: The Science of Scarcity and Diversity 182–204 (Sinauer, 1986).

53.
Gaston, K. J. What is Rarity? In The Biology of Rarity: Causes and Consequences of Rare-Common Differences 30–47 (Chapman and Hall, New York, 1997).
Google Scholar 

54.
Boulesteix, A.-L., Janitza, S., Kruppa, J. & König, I. R. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2, 493–507 (2012).
Google Scholar 

55.
Özesmi, S. L., Tan, C. O. & Özesmi, U. Methodological issues in building, training, and testing artificial neural networks in ecological applications. Ecol. Modell. 195, 83–93 (2006).
Google Scholar 

56.
Norberg, A. et al. A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecol. Monogr. 89, e01370 (2019).
Google Scholar 

57.
Harris, D. J. Generating realistic assemblages with a joint species distribution model. Methods Ecol. Evol. 6, 465–473 (2015).
Google Scholar 

58.
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Google Scholar 

59.
Clark, J. S., Nemergut, D., Seyednasrollah, B., Turner, P. J. & Zhang, S. Generalized joint attribute modeling for biodiversity analysis: Median-zero, multivariate, multifarious data. Ecol. Monogr. 87, 34–56 (2017).
Google Scholar 

60.
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
MATH  Google Scholar 

61.
Suryanarayana, I. et al. Neural networks in fisheries research. Fish. Res. 92, 115–139 (2008).
Google Scholar 

62.
Brun, P., Kiørboe, T., Licandro, P. & Payne, M. R. The predictive skill of species distribution models for plankton in a changing climate. Glob. Chang. Biol. 22, 3170–3181 (2016).
ADS  PubMed  Google Scholar 

63.
Smoliński, S. & Radtke, K. Spatial prediction of demersal fish diversity in the Baltic Sea: Comparison of machine learning and regression-based techniques. ICES J. Mar. Sci. J. Cons. 74, 102–111 (2017).
Google Scholar 

64.
Segal, M. & Xiao, Y. Multivariate random forests. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 1, 80–87 (2011).
Google Scholar 

65.
Rahman, R., Otridge, J. & Pal, R. IntegratedMRF: Random forest-based framework for integrating prediction from different data types. Bioinformatics 33, 1407–1410 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

66.
Olden, J. D. A species-specific approach to modeling biological communities and its potential for conservation. Conserv. Biol. 17, 854–863 (2003).
Google Scholar 

67.
Ovaskainen, O., Roy, D. B., Fox, R. & Anderson, B. J. Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods Ecol. Evol. 7, 428–436 (2016).
Google Scholar 

68.
Clark, J. S. Why species tell more about traits than traits about species: Predictive analysis. Ecology 97, 1979–1993 (2016).
PubMed  Google Scholar 

69.
Araújo, M. B. & Luoto, M. The importance of biotic interactions for modelling species distributions under climate change. Glob. Ecol. Biogeogr. 16, 743–753 (2007).
Google Scholar 

70.
Peres-Neto, P. R., Jackson, D. A. & Somers, K. M. How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Comput. Stat. Data Anal. 49, 974–997 (2005).
MathSciNet  MATH  Google Scholar 

71.
Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).
Google Scholar 

72.
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
Google Scholar 

73.
Basheer, I. & Hajmeer, M. Artificial neural networks: Fundamentals, computing, design, and application. J. Microbiol. Methods 43, 3–31 (2000).
CAS  PubMed  Google Scholar  More

1.
Lieth, H. & Whittaker, R. H. Primary Productivity of the Biosphere (Springer, New York, 1975).
Google Scholar 
2.
Heimann, M. & Reichstein, M. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289–292. https://doi.org/10.1038/nature06591 (2008).
ADS  CAS  Article  PubMed  Google Scholar 

3.
Duarte, C. M., Losada, I. J., Hendriks, I. E., Mazarrasa, I. & Marba, N. The role of coastal plant communities for climate change mitigation and adaptation. Nat. Clim. Change 3, 961–968. https://doi.org/10.1038/nclimate1970 (2013).
ADS  CAS  Article  Google Scholar 

4.
Duarte, C. M. Reviews and syntheses: hidden forests, the role of vegetated coastal habitats in the ocean carbon budget. Biogeosciences 14, 301–310. https://doi.org/10.5194/bg-14-301-2017 (2017).
ADS  CAS  Article  Google Scholar 

5.
Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240. https://doi.org/10.1126/science.281.5374.237 (1998).
ADS  CAS  Article  PubMed  Google Scholar 

6.
Charpy-Roubaud, C. & Sournia, A. The comparative estimation of phytoplanktonic, microphytobenthic and macrophytobenthic primary production in the oceans. Mar. Microb. Food Webs 4, 31–57 (1990).
Google Scholar 

7.
Duarte, C. M. & Cebrián, J. The fate of marine autotrophic production. Limnol. Oceanogr. 41, 1758–1766. https://doi.org/10.4319/lo.1996.41.8.1758 (1996).
ADS  CAS  Article  Google Scholar 

8.
Macreadie, P. I. et al. The future of Blue Carbon science. Nat. Commun. 10, 3998. https://doi.org/10.1038/s41467-019-11693-w (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
Krause-Jensen, D. & Duarte, C. M. Substantial role of macroalgae in marine carbon sequestration. Nat. Geosci. 9, 737–742. https://doi.org/10.1038/ngeo2790 (2016).
ADS  CAS  Article  Google Scholar 

10.
Mann, K. H. Seaweeds: their productivity and strategy for growth. Science 182, 975–981 (1973).
ADS  CAS  Article  Google Scholar 

11.
Renaud, P. E., Løkken, T. S., Jørgensen, L. L., Berge, J. & Johnson, B. J. Macroalgal detritus and food-web subsidies along an Arctic fjord depth-gradient. Front. Mar. Sci. 2, 1–7. https://doi.org/10.3389/fmars.2015.00031 (2015).
Article  Google Scholar 

12.
Vanderklift, M. A. & Wernberg, T. Detached kelps from distant sources are a food subsidy for sea urchins. Oecologia 157, 327–335 (2008).
ADS  Article  Google Scholar 

13.
Queirós, A. M. et al. Connected macroalgal-sediment systems: blue carbon and food webs in the deep coastal ocean. Ecol. Monogr. 89, e01366. https://doi.org/10.1002/ecm.1366 (2019).
Article  Google Scholar 

14.
Ortega, A. et al. Metagenomes reveal prevalence of exported macroalgae across the global ocean. Nat. Geosci. (in press).

15.
Smale, D. A., Burrows, M. T., Moore, P. J., O’ Connor, N. & Hawkins, S. J. Threats and knowledge gaps for ecosystem services provided by kelp forests: a northeast Atlantic perspective. Ecol. Evol. 3, 4016–4403 (2013).
Article  Google Scholar 

16.
Assis, J., Lucas, A. V., Bárbara, I. & Serrão, E. Á. Future climate change is predicted to shift long-term persistence zones in the cold-temperate kelp Laminaria hyperborea. Mar. Environ. Res. 113, 174–182. https://doi.org/10.1016/j.marenvres.2015.11.005 (2016).
CAS  Article  PubMed  Google Scholar 

17.
Smale, D. A., Wernberg, T., Yunnie, A. L. E. & Vance, T. The rise of Laminaria ochroleuca in the Western English Channel (UK) and preliminary comparisons with its competitor and assemblage dominant Laminaria hyperborea. Mar. Ecol. 36, 1033–1044 (2015).
ADS  Article  Google Scholar 

18.
Smale, D. A. & Moore, P. J. Variability in kelp forest structure along a latitudinal gradient in ocean temperature. J. Exp. Mar. Biol. Ecol. 486, 255–264. https://doi.org/10.1016/j.jembe.2016.10.023 (2017).
Article  Google Scholar 

19.
Bekkby, T., Rinde, E., Erikstad, L. & Bakkestuen, V. Spatial predictive distribution modelling of the kelp species Laminaria hyperborea. ICES J. Mar. Sci. 66, 2106–2115. https://doi.org/10.1093/icesjms/fsp195 (2009).
Article  Google Scholar 

20.
Teagle, H., Moore, P. J., Jenkins, H. & Smale, D. A. Spatial variability in the diversity and structure of faunal assemblages associated with kelp holdfasts (Laminaria hyperborea) in the northeast Atlantic. PLoS ONE 13, e0200411. https://doi.org/10.1371/journal.pone.0200411 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

21.
Pessarrodona, A., Moore, P. J., Sayer, M. D. J. & Smale, D. A. Carbon assimilation and transfer through kelp forests in the NE Atlantic is diminished under a warmer ocean climate. Glob. Change Biol. 24, 4386–4398. https://doi.org/10.1111/gcb.14303 (2018).
ADS  Article  Google Scholar 

22.
Smale, D. A. et al. Linking environmental variables with regional-scale variability in ecological structure and standing stock of carbon within kelp forests in the United Kingdom. Mar. Ecol. Prog. Ser. 542, 79–95 (2016).
ADS  CAS  Article  Google Scholar 

23.
Kain, J. M. A view of the genus Laminaria. Oceanogr. Mar. Biol. Annu. Rev. 17, 101–161 (1979).
Google Scholar 

24.
Jupp, B. P. & Drew, E. A. Studies on the growth of Laminaria hyperborea (Gunn.) Fosl. I .Biomass and productivity. J. Exp. Mar. Biol. Ecol. 15, 185–196. https://doi.org/10.1016/0022-0981(74)90044-6 (1974).
Article  Google Scholar 

25.
Sheppard, C. R. C., Jupp, B. P., Sheppard, A. L. S. & Bellamy, D. J. Studies on the growth of Laminaria hyperborea (Gunn.) Fosl. and Laminaria ochroleuca De La Pylaie on the French Channel Coast. Bot. Mar. 11, 109–116 (1978).
Google Scholar 

26.
Pessarrodona, A., Foggo, A. & Smale, D. A. Can ecosystem functioning be maintained despite climate-driven shifts in species composition? Insights from novel marine forests. J. Ecol. 107, 91–104 (2019).
Article  Google Scholar 

27.
Kain, J. M. The biology of Laminaria hyperborea. X The effect of depth on some populations. J. Mar. Biol. Assoc. UK 57, 587–607 (1977).
Article  Google Scholar 

28.
Reed, D. C., Rassweiler, A. & Arkema, K. K. Biomass rather than growth rate determines variation in net primary production by giant kelp. Ecology 89, 2493–2505. https://doi.org/10.2307/27650788 (2008).
Article  PubMed  Google Scholar 

29.
Bearham, D., Vanderklift, M. A. & Gunson, J. R. Temperature and light explain spatial variation in growth and productivity of the kelp Ecklonia radiata. Mar. Ecol. Prog. Ser. 476, 59–70. https://doi.org/10.3354/meps10148 (2013).
ADS  Article  Google Scholar 

30.
Boden, G. T. The effect of depth on summer growth of Laminaria saccharina (Phaeophyta, Laminariales). Phycologia 18, 405–408. https://doi.org/10.2216/i0031-8884-18-4-405.1 (1979).
Article  Google Scholar 

31.
Bonsell, C. & Dunton, K. H. Long-term patterns of benthic irradiance and kelp production in the central Beaufort sea reveal implications of warming for Arctic inner shelves. Prog. Oceanogr. 162, 160–170. https://doi.org/10.1016/j.pocean.2018.02.016 (2018).
ADS  Article  Google Scholar 

32.
Long, M. H., Rheuban, J. E., Berg, P. & Zieman, J. C. A comparison and correction of light intensity loggers to photosynthetically active radiation sensors. Limnol. Oceanog.: Methods 10, 416–424. https://doi.org/10.4319/lom.2012.10.416 (2012).
Article  Google Scholar 

33.
Devlin, M. J. et al. Relationships between suspended particulate material, light attenuation and Secchi depth in UK marine waters. Estuar. Coast. Shelf Sci. 79, 429–439. https://doi.org/10.1016/j.ecss.2008.04.024 (2008).
ADS  Article  Google Scholar 

34.
Kratzer, S., Buchan, S. & Bowers, D. G. Testing long-term trends in turbidity in the Menai Strait, North Wales. Estuar. Coast. Shelf Sci. 56, 221–226. https://doi.org/10.1016/S0272-7714(02)00159-2 (2003).
ADS  CAS  Article  Google Scholar 

35.
Foden, J., Sivyer, D. B., Mills, D. K. & Devlin, M. J. Spatial and temporal distribution of chromophoric dissolved organic matter (CDOM) fluorescence and its contribution to light attenuation in UK waterbodies. Estuar. Coast. Shelf Sci. 79, 707–717. https://doi.org/10.1016/j.ecss.2008.06.015 (2008).
ADS  Article  Google Scholar 

36.
White, M., Gaffney, S., Bowers, D. G. & Bowyer, P. Interannual variability in irish sea turbidity and relation to wind strength. Biol. Environ.: Proc. R. Irish Acad. 103B, 83–90 (2003).
Google Scholar 

37.
Lüning, K. Critical levels of light and temperature regulating the gametogenesis of three Laminaria species (Phaeophyceae). J. Phycol. 16, 1–15. https://doi.org/10.1111/j.1529-8817.1980.tb02992.x (1980).
Article  Google Scholar 

38.
Lüning, K. Temperature tolerance and biogeography of seaweeds: The marine algal flora of Helgoland (North Sea) as an example. Helgoländer Meeresun 38, 305–317. https://doi.org/10.1007/bf01997486 (1984).
Article  Google Scholar 

39.
Bolton, J. J. & Lüning, K. Optimal growth and maximal survival temperatures of Atlantic Laminaria species (Phaeophyta) in culture. Mar. Biol. 66, 89–94. https://doi.org/10.1007/bf00397259 (1982).
Article  Google Scholar 

40.
King, N. G., McKeown, N. J., Smale, D. A. & Moore, P. J. The importance of phenotypic plasticity and local adaptation in driving intraspecific variability in thermal niches of marine macrophytes. Ecography 41, 1469–1484. https://doi.org/10.1111/ecog.03186 (2018).
Article  Google Scholar 

41.
Stevens, C. L. & Hurd, C. L. Boundary-layers around bladed aquatic macrophytes. Hydrobiologia 346, 119–128. https://doi.org/10.1023/a:1002914015683 (1997).
Article  Google Scholar 

42.
Pedersen, M. F., Nejrup, L. B., Fredriksen, S., Christie, H. & Norderhaug, K. M. Effects of wave exposure on population structure, demography, biomass and productivity of the kelp Laminaria hyperborea. Mar. Ecol. Prog. Ser. 451, 45–60. https://doi.org/10.3354/meps09594 (2012).
ADS  Article  Google Scholar 

43.
Graham, M. H. Factors determining the upper limit of giant kelp, Macrocystis pyrifera Agardh, along the Monterey Peninsula, central California, USA. J. Exp. Mar. Biol. Ecol. 218, 127–149. https://doi.org/10.1016/S0022-0981(97)00072-5 (1997).
Article  Google Scholar 

44.
Zimmerman, R. C. & Robertson, D. L. Effects of El Niño on local hydrography and growth of the giant kelp, Macrocystis pyrifera, at Santa Catalina Island, California1. Limnol. Oceanogr. 30, 1298–1302. https://doi.org/10.4319/lo.1985.30.6.1298 (1985).
ADS  Article  Google Scholar 

45.
Dean, T. A. & Jacobsen, F. R. Nutrient-limited growth of juvenile kelp, Macrocystis pyrifera, during the 1982–1984 “El Niño” in southern California. Mar. Biol. 90, 597–601. https://doi.org/10.1007/bf00409280 (1986).
Article  Google Scholar 

46.
Krumhansl, K. A., Lauzon-Guay, J.-S. & Scheibling, R. E. Modeling effects of climate change and phase shifts on detrital production of a kelp bed. Ecology https://doi.org/10.1890/13-0228.1 (2013).
Article  Google Scholar 

47.
Wilmers, C. C., Estes, J. A., Edwards, M., Laidre, K. L. & Konar, B. Do trophic cascades affect the storage and flux of atmospheric carbon? An analysis of sea otters and kelp forests. Front. Ecol. Environ. 10, 409–415. https://doi.org/10.1890/110176 (2012).
Article  Google Scholar 

48.
Smyth, T. J. et al. A broad spatio-temporal view of the Western English Channel observatory. J. Plankton Res. 32, 585–601. https://doi.org/10.1093/plankt/fbp128 (2010).
ADS  Article  Google Scholar 

49.
Leclerc, J. C. et al. Trophic significance of kelps in kelp communities in Brittany (France) inferred from isotopic comparisons. Mar. Biol. 160, 3249–3258. https://doi.org/10.1007/s00227-013-2306-5 (2013).
Article  Google Scholar 

50.
Norderhaug, K. M. & Christie, H. C. Sea urchin grazing and kelp re-vegetation in the NE Atlantic. Mar. Biol. Res. 5, 515–528. https://doi.org/10.1080/17451000902932985 (2009).
Article  Google Scholar 

51.
Hagen, N. T. Destructive grazing of kelp beds by sea urchins in Vestfjorden, northern Norway. Sarsia 68, 177–190. https://doi.org/10.1080/00364827.1983.10420570 (1983).
Article  Google Scholar 

52.
Stephens, R. E. Studies on the development of the sea urchin Strongylocentrotus droebachiensus. I. Ecology and normal development. Biol. Bull. 142, 132–144. https://doi.org/10.2307/1540251 (1972).
CAS  Article  PubMed  Google Scholar 

53.
Edwards, M. S. & Connell, S. D. Competition, a major factor structuring seaweed communities. In Seaweed Biology: Novel Insights into Ecophysiology, Ecology and Utilization (eds Wiencke, C. & Bischof, K.) 135–156 (Springer, Berlin, 2012).
Google Scholar 

54.
Sjøtun, K. & Fredriksen, S. Growth allocation in Laminaria hyperborea (Laminariales, Phaeophyceae) in relation to age and wave exposure. Mar. Ecol. Prog. Ser. 126, 213–222 (1995).
ADS  Article  Google Scholar 

55.
Krumhansl, K. & Scheibling, R. E. Production and fate of kelp detritus. Mar. Ecol. Prog. Ser. 467, 281–302 (2012).
ADS  Article  Google Scholar 

56.
Pedersen, M. F., Nejrup, L. B., Pedersen, T. M. & Fredriksen, S. Sub-canopy light conditions only allow low annual net productivity of epiphytic algae on kelp Laminaria hyperborea. Mar. Ecol. Prog. Ser. 516, 163–176 (2014).
ADS  Article  Google Scholar 

57.
Abdullah, M. I. & Fredriksen, S. Production, respiration and exudation of dissolved organic matter by the kelp Laminaria hyperborea along the west coast of Norway. J. Mar. Biol. Assoc. UK 84, 887–894. https://doi.org/10.1017/S002531540401015Xh (2004).
Article  Google Scholar 

58.
de Bettignies, T., Wernberg, T., Lavery, P. S., Vanderklift, M. A. & Mohring, M. B. Contrasting mechanisms of dislodgement and erosion contribute to production of kelp detritus. Limnol. Oceanogr. 58, 1680–1688 (2013).
ADS  Article  Google Scholar 

59.
Smale, D. A., Moore, P. J., Queirós, A. M., Higgs, N. D. & Burrows, M. T. Appreciating interconnectivity between habitats is key to blue carbon management. Front. Ecol. Environ. 16, 71–73. https://doi.org/10.1002/fee.1765 (2018).
Article  Google Scholar 

60.
Bustamante, R. H. & Branch, G. M. The dependence of intertidal consumers on kelp-derived organic matter on the west coast of South Africa. J. Exp. Mar. Biol. Ecol. 196, 1–28. https://doi.org/10.1016/0022-0981(95)00093-3 (1996).
Article  Google Scholar 

61.
Miller, R. J., Reed, D. C. & Brzezinski, M. A. Partitioning of primary production among giant kelp (Macrocystis pyrifera), understory macroalgae, and phytoplankton on a temperate reef. Limnol. Oceanogr. 56, 119–132. https://doi.org/10.4319/lo.2011.56.1.0119 (2011).
ADS  Article  Google Scholar 

62.
Burrows, M. T., Harvey, R. & Robb, L. Wave exposure indices from digital coastlines and the prediction of rocky shore community structure. Mar. Ecol. Prog. Ser. 353, 1–12 (2008).
ADS  Article  Google Scholar 

63.
Steen, H., Moy, F. E., Bodvin, T. & Husa, V. Regrowth after kelp harvesting in Nord-Trøndelag, Norway. ICES J. Mar. Sci. 73, 2708–2720. https://doi.org/10.1093/icesjms/fsw130 (2016).
Article  Google Scholar 

64.
Krumhansl, K. & Scheibling, R. E. Detrital production in Nova Scotian kelp beds: patterns and processes. Mar. Ecol. Prog. Ser. 421, 67–82 (2011).
ADS  Article  Google Scholar 

65.
Fairhead, V. A. & Cheshire, A. C. Rates of primary productivity and growth in Ecklonia radiata measured at different depths, over an annual cycle, at West Island, South Australia. Mar. Biol. 145, 41–50 (2004).
Google Scholar 

66.
Mann, K. H. & Kirkman, H. A biomass method for measuring productivity of Ecklonia radiata with the potental for adaptation to other large brown algae. Austral. J. Mar. Freshw. Res. 32, 297–304 (1981).
Article  Google Scholar 

67.
Anderson-Teixeira, K. J., Wang, M. M. H., McGarvey, J. C. & LeBauer, D. S. Carbon dynamics of mature and regrowth tropical forests derived from a pantropical database (TropForC-db). Glob. Change Biol. 22, 1690–1709. https://doi.org/10.1111/gcb.13226 (2016).
ADS  Article  Google Scholar 

68.
Holl, K. D. & Zahawi, R. A. Factors explaining variability in woody above-ground biomass accumulation in restored tropical forest. For. Ecol. Manag. 319, 36–43. https://doi.org/10.1016/j.foreco.2014.01.024 (2014).
Article  Google Scholar 

69.
Dayton, P. K., Tegner, M. J., Parnell, P. E. & Edwards, P. B. Temporal and spatial patterns of disturbance and recovery in a kelp forest community. Ecol. Monogr. 62, 421–445 (1992).
Article  Google Scholar 

70.
Christie, H., Fredriksen, S. & Rinde, E. Regrowth of kelp and colonization of epiphyte and fauna community after kelp trawling at the coast of Norway. Hydrobiologia 375–376, 49–58. https://doi.org/10.1023/a:1017021325189 (1998).
Article  Google Scholar 

71.
Evans, S. N. & Abdo, D. A. A cost-effective technique for measuring relative water movement for studies of benthic organisms. Mar. Freshw. Res. 61, 1327–1335. https://doi.org/10.1071/MF10007 (2010).
CAS  Article  Google Scholar 

72.
Edwards, K. P., Barciela, R. & Butenschön, M. Validation of the NEMO-ERSEM operational ecosystem model for the North West European Continental Shelf. Ocean Sci. 8, 983–1000. https://doi.org/10.5194/os-8-983-2012 (2012).
ADS  Article  Google Scholar 

73.
Lüning, K. Standing crop and leaf area index of the sublittoral Laminaria species near Helgoland. Mar. Biol. 3, 282–286. https://doi.org/10.1007/bf00360961 (1969).
Article  Google Scholar 

74.
Sjøtun, K., Fredriksen, S., Rueness, J. & Lein, T. Ecological studies of the kelp Laminaria hyperborea (Gunnerus) Foslie in Norway. In Ecology of Fjords and Coastal Waters (eds Skjoldal, H. et al.) 525–536 (Elsevier, Amsterdam, 1995).
Google Scholar 

75.
Gunnarsson, K. Population de Laminaria hyperborea et Laminaria digitata dans le Baie de Breidafjördur, Island. J. Mar. Res. Inst. Reykjavik 12, 148 (1991).
Google Scholar  More

In this study, we showed that simple modification of leaves, that is, leaf rolling, caused marked changes in the fate of immature attelabid weevils related to natural enemies. In particular, a decrease in the parasitism rate by Eulophidae and the egg predation rate contributed to the increase in the survival rate. The fact that parasitism rate by Eulophidae in experimentally rolled leaves was 0% compared to that in unrolled leaves (36.6%) suggests that leaf roll acted as an “insuperable barrier” against Eulophidae. This result is consistent with our previous study revealing that leaf rolling species in Attelabidae were less parasitized by eulophid wasps16. The reason why eulophid wasps do not parasitize eggs or larvae in experimentally rolled leaves may be explained by two different hypotheses: failure-in-access and failure-in-finding. The failure-in-access hypothesis is that eulophid wasps can find hosts and attempt to parasitize, but the leaf roll acts as a structural barrier and eulophid wasps cannot reach weevil eggs or larvae. Considering that leaf rolls in this experiment were loosely rolled and oviposition sites would be easy to access, the plausibility of this hypothesis seems relatively low. On the other hand, the failure-in-finding hypothesis is that eulophid wasps cannot find hosts in leaf rolls because they cannot recognize a “rolled leaf” as a target structure containing hosts. Eulophidae are known as one of the dominant parasitoids of various leaf miners such as leaf mining moths, flies, sawflies, or beetles20,21. Some parasitoids of leaf miners have been reported to have evolved specific visual searching traits for leaf miners during flight22,23,24,25. For example, parasitoids were more attracted to the leaves with many leaf mines using visual cues25. In addition to visual cues, parasitoids of leaf miners also use chemical and vibrational cues for host searching, similar to other parasitoids26,27,28. Considering that in the present study, chemical and vibrational cues would not differ between experimentally rolled and unrolled treatments, changes in the visual cues likely affected the search success of eulophid wasps. In our study, of the 36.6% of eggs and larvae from unrolled leaves parasitized by Eulophidae, 25.6% was attributable to egg parasitism. This means that mine shape would not be an important visual cue in this case because no mine existed on leaves in the egg stage. Thus, eulophid wasps might have a host searching image of “cut-off leaf on the ground”, which was basically flat and thin, and leaf rolls were not recognized as a host because the shape differed from the searching image. The protective barrier effect of leaf roll for inner insects has been reported previously1,5,6,7,8,9,10,11,12,13,14,15. However, this study suggests that the leaf roll effect is not only a structural barrier but also a “visual modification” itself. In order to confirm this visually protective effect of leaf rolls, further experiments controlling leaf shapes in various patterns and comparing parasitism rates among treatments are needed. In this study, important information on the timing of parasitoid attack was also indicated: Mymarid wasps and Ophioneurus sp. were suggested to attack hosts on the tree shortly after weevil oviposit into the leaf and before the leaf was completely cut from the tree. The time from oviposition to leaf cutting is reported to be approximately 30 min in a rhynchitin non-leaf-roller species, Deporaus septemtrionalis29. It is surprising that parasitoids can finish their host finding and oviposit in such a brief time and probably represents a product of the arms race between parasitoids and weevils16. In addition, the weevil behavior of cutting leaves from the host tree might contribute to avoidance of heavier parasitism rates. If leaves including eggs remain suspended from the tree, the success rate of parasitism would most likely increase due to prolonged opportunity for parasitoids to attack.
The other factor related to the increase in survival rate of immature weevils was the decrease in mortality due to egg predation. Our results indicate the presence of predators on the ground that feed primarily on eggs in leaf tissue instead of on eggs in leaf tissue within leaf rolls. Few studies have revealed predators of leaf miners in the egg stage, and most studies of leaf miner mortality focus on larvae30. Digweed31 reported potential egg predators of a leaf mining sawfly as spiders, staphylinid beetles, coccinellid beetles, Hemiptera, and thrips. However, in this study, we should consider potential egg predators not on the tree but on the ground. From the soil meso-organisms and macro-organisms lists, predators and opportunistic predators in the litter would be mites, Opiliones, Isopoda, centipedes, millipedes, ground beetles, and spiders32. Furthermore, ants and dipteran larvae could also be considered as potential predators or opportunistic predators33,34. Eggs in unrolled and rolled leaves were protected in the leaf tissue, but leaf decomposition or egg dislodgement by soil organisms might occur more easily in unrolled leaves than in rolled leaves. Such decomposition or dislodgement causes exposure of eggs and a higher risk of predation. Thus, eggs in rolled leaves might show lower predation rates than those in unrolled leaves.
In contrast to predation, weevil mortality due to herbivory, especially by moth larvae, increased in experimentally rolled leaves compared to unrolled leaves. Thus, leaf rolls are not only protective refuges but also potentially risky hiding places for immature weevils. Our observations could be attributed to the fact that leaf rolls were preferred by herbivorous moth larvae as shelters; leaf shelters, that is, leaf rolls, leaf galls, leaf folds, or leaf ties, are often preferred and secondarily used by several insect species, sometimes providing them with a protective effect13,35,36,37,38. The reason why previous studies on the effect of leaf rolls did not detect the negative effect of herbivory could be that the observed leaf rolls were constructed by lepidopteran larvae that could escape herbivory and construct new leaf rolls. In the litter on the forest floor in Japan, lepidopteran larvae, such as those belonging to Blastobasidae or Tineidae, crawl while searching for fallen leaves to feed on. In an attelabid species, Cycnotrachelus roelofsi (Attelabinae), Neoblastobasis spiniharpella (Blastobasidae) larvae were found to infest the inside of leaf rolls; as a result, weevil larvae sometimes died of direct infestation or the lack of food34. In such cases, leaf roll construction had a negative effect on immature survival. However, in the species of Attelabinae, leaf roll construction is crucial to avoiding egg parasitoids, while mortality by herbivory is not so high34. Thus, the protective effect of leaf rolling against egg parasitoids exceeds the negative effects of herbivory. Further, leaf roll construction using excessive leaves by some Byctiscini species (Attelabidae, Rhynchitinae) may be a counter evolution to avoid mortality by herbivory. Various lepidopteran species and dipteran species emerge from leaf rolls of some Byctiscini species consuming leaf rolls (Kobayashi C, unpublished data), and competition for leaf roll resources sometimes causes larval death because weevil larvae cannot exit leaf rolls and search for new leaves. Thus, excessive leaves in the leaf roll may save weevil larvae from dying from food loss or infestation because of herbivory.
Regarding environmental stress, we did not detect any effect of leaf rolls. This may be because immature weevils in unrolled leaves were not directly exposed to environmental stresses due to their leaf mining habit. Therefore, both rolled and unrolled treatments experienced the same environmental conditions.
In summary, the survival rate of the attelabid weevil in this study was significantly increased by leaf rolling. Thus, this study suggests that selection pressure to evolve leaf rolling behavior still exists in the natural population, at least in Attelabidae. However, whether the leaf rolling behavior evolves will depend on the balance of positive and negative effects of leaf roll, that is, the degree of pressure exerted by parasitoids, predators and herbivores. Furthermore, constructing leaf rolls incurs energy costs and time for oviposition. Considering that most Deporaini species are less than 5 mm in length, the behavioral costs for rolling leaves would be high. In Deporaini, few species evolved leaf rolling traits independently, while the others were leaf miners in cut leaves and did not roll leaves17. Although the total survival rate was higher in rolled leaves than in unrolled leaves, contradictory effects of leaf rolls added to construction costs may explain this sporadic evolutionary pattern in leaf rolls of Deporaini. If leaf rolling traits have a mostly positive effect on egg and larval survival, leaf rolling behavior may have evolved more frequently or further diversification of leaf rolling species may have occurred. More

Addison JA, Hart MW (2005) Spawning, copulation and inbreeding coefficients in marine invertebrates. Biol Lett 1:450–453
CAS  PubMed  PubMed Central  Google Scholar 

Aguillon SM, Fitzpatrick JW, Bowman R, Schoech SJ, Clark AG, Coop G et al. (2017) Deconstructing isolation-by-distance: the genomic consequences of limited dispersal. PLoS Genet 13:1–27
Google Scholar 

Almany GR, Connolly SR, Heath DD, Hogan JD, Jones GP, McCook LJ et al. (2009) Connectivity, biodiversity conservation and the design of marine reserve networks for coral reefs. Coral Reefs 28:339–351
Google Scholar 

Bates D, Mächler M, Bolke B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48
Google Scholar 

Baums IB (2008) A restoration genetics guide for coral reef conservation. Mol Ecol 17:2796–2811
PubMed  Google Scholar 

Bell JJ, Smith D, Hannan D, Haris A, Jompa J, Thomas L (2014) Resilience to disturbance despite limited dispersal and self-recruitment in tropical barrel sponges: implications for conservation and management. PLoS ONE 9:e91635
PubMed  PubMed Central  Google Scholar 

Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29:1165–1188
Google Scholar 

Bertelsen RD, Butler MJ, Herrnkind WF, Hunt JH (2009) Regional characterisation of hard‐bottom nursery habitat for juvenile Caribbean spiny lobster (Panulirus argus) using rapid assessment techniques. N. Zeal J Mar Freshw Res 43:299–312
Google Scholar 

Blanquer A, Uriz M, Caujapé-Castells J (2009) Small-scale spatial genetic structure in Scopalina lophyropoda, an encrusting sponge with philopatric larval dispersal and frequent fission and fusion events. Mar Ecol Prog Ser 380:95–102
Google Scholar 

Butler MJ, Hunt JH, Herrnkind WF, Childress MJ, Bertelsen R, Sharp W et al. (1995) Cascading disturbances in Florida Bay, USA: cyanobacteria blooms, sponge mortality, and implications for juvenile spiny lobsters Panulirus argus. Mar Ecol Prog Ser 129:119–125
Google Scholar 

Butler J, Stanley JA, Butler MJ (2016) Underwater soundscapes in near-shore tropical habitats and the effects of environmental degradation and habitat restoration. J Exp Mar Bio Ecol 479:89–96
Google Scholar 

Butler IV MJ, Behringer DC, Valentine MM (2017) Commercial sponge fishery impacts on the population dynamics of sponges in the Florida Keys, FL (USA). Fish Res 190:113–121
Google Scholar 

Calderón I, Ortega N, Duran S, Becerro M, Pascual M, Turon X (2007) Finding the relevant scale: clonality and genetic structure in a marine invertebrate (Crambe crambe, Porifera). Mol Ecol 16:1799–1810
PubMed  Google Scholar 

Carrillo L, Johns EM, Smith RH, Lamkin JT, Largier JL (2015) Pathways and hydrography in the Mesoamerican Barrier Reef System Part 1: Circulation. Cont Shelf Res 109:164–176
Google Scholar 

Castorani MCN, Reed DC, Raimondi PT, Alberto F, Bell TW, Cavanaugh KC et al. (2017) Fluctuations in population fecundity drive variation in demographic connectivity and metapopulation dynamics. Proc R Soc B Biol Sci 284:20162086.

Chapuis M-P, Estoup A (2007) Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol 24:621–31
CAS  PubMed  Google Scholar 

Chaves-Fonnegra A, Feldheim KA, Secord J, Lopez JV (2015) Population structure and dispersal of the coral-excavating sponge Cliona delitrix. Mol Ecol 24:1447–66
PubMed  Google Scholar 

Chybicki IJ, Burczyk J (2009) Simultaneous estimation of null alleles and inbreeding coefficients. J Hered 100:106–13
CAS  PubMed  Google Scholar 

D’Aloia CC, Bogdanowicz SM, Harrison RG, Buston PM (2017) Cryptic genetic diversity and spatial patterns of admixture within Belizean marine reserves. Conserv Genet 18:211–223
Google Scholar 

Dailianis T, Tsigenopoulos CS, Dounas C, Voultsiadou E (2011) Genetic diversity of the imperilled bath sponge Spongia officinalis Linnaeus, 1759 across the Mediterranean Sea: patterns of population differentiation and implications for taxonomy and conservation. Mol Ecol 20:3757–72
CAS  PubMed  Google Scholar 

de Bakker DM, Meesters EHWG, van Bleijswijk JDL, Luttikhuizen PC, Breeuwer HJAJ, Becking LE (2016) Population genetic structure, abundance, and health status of two dominant benthic species in the Saba Bank National Park, Caribbean Netherlands: Montastraea cavernosa and Xestospongia muta. PLoS ONE 11:e0155969
PubMed  PubMed Central  Google Scholar 

DeBiasse MB, Richards VP, Shivji MS (2010) Genetic assessment of connectivity in the common reef sponge, Callyspongia vaginalis (Demospongiae: Haplosclerida) reveals high population structure along the Florida reef tract. Coral Reefs 29:47–55
Google Scholar 

DeBiasse MB, Richards VP, Shivji MS, Hellberg ME (2016) Shared phylogeographical breaks in a Caribbean coral reef sponge and its invertebrate commensals. J Biogeogr 43:2136–2146
Google Scholar 

Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39:1–38
Google Scholar 

Diaz M, Rutzler K (2001) Sponges: an essential component of Caribbean coral reefs. Bull Mar Sci 69:535–546
Google Scholar 

Drury C, Paris CB, Kourafalou VH, Lirman D (2018) Dispersal capacity and genetic relatedness in Acropora cervicornis on the Florida Reef Tract. Coral Reefs 37:585–596
Google Scholar 

Eldon B, Riquet F, Yearsley J, Jollivet D, Broquet T (2016) Current hypotheses to explain genetic chaos under the sea. Curr Zool 62:551–566
PubMed  PubMed Central  Google Scholar 

Ereskovsky AV, Tokina DB (2004) Morphology and fine structure of the swimming larvae of Ircinia oros (Porifera, Demospongiae, Dictyoceratida). Invertebr Reprod Dev 45:137–150
Google Scholar 

Estoup A, Jarne P, Cornuet J-M (2002) Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Mol Ecol 11:1591–1604
CAS  PubMed  Google Scholar 

Ezer T, Thattai DV, Kjerfve B, Heyman WD (2005) On the variability of the flow along the Meso-American Barrier Reef system: A numerical model study of the influence of the Caribbean current and eddies. Ocean Dyn 55:458–475
Google Scholar 

Foster NL, Paris CB, Kool JT, Baums IB, Stevens JR, Sanchez JA et al. (2012) Connectivity of Caribbean coral populations: complementary insights from empirical and modelled gene flow. Mol Ecol 21:1143–1157
PubMed  Google Scholar 

Garza JC, Williamson EG (2001) Detection of reduction in population size using data from microsatellite loci. Mol Ecol 10:305–18
CAS  PubMed  Google Scholar 

Giles EC, Saenz-Agudelo P, Hussey NE, Ravasi T, Berumen ML (2015) Exploring seascape genetics and kinship in the reef sponge Stylissa carteri in the Red Sea. Ecol Evol 5:2487–502
PubMed  PubMed Central  Google Scholar 

Griffiths SM, Antwis RE, Lenzi L, Lucaci A, Behringer DC, Butler MJ et al. (2019) Host genetics and geography influence microbiome composition in the sponge Ircinia campana. J Anim Ecol 88:1984–1695
Google Scholar 

Griffiths SM, Taylor-Cox ED, Behringer DC, Butler MJ, Preziosi RF (2020) Using genetics to inform restoration and predict resilience in declining populations of a keystone marine sponge. Biodivers Conserv 29:1383–1410
Google Scholar 

Guardiola M, Frotscher J, Uriz M-J (2016) High genetic diversity, phenotypic plasticity, and invasive potential of a recently introduced calcareous sponge, fast spreading across the Atlanto-Mediterranean basin. Mar Biol 163:123
PubMed  PubMed Central  Google Scholar 

Guillot G, Estoup A, Mortier F, Cosson JF (2005) A spatial statistical model for landscape genetics. Genetics 170:1261–1280
CAS  PubMed  PubMed Central  Google Scholar 

Guillot G, Santos F, Estoup A (2008) Analysing georeferenced population genetics data with Geneland: a new algorithm to deal with null alleles and a friendly graphical user interface. Bioinformatics 24:1406–1407
CAS  PubMed  Google Scholar 

Hedgecock D, Pudovkin AI (2011) Sweepstakes reproductive success in highly fecund marine fish and shellfish: a review and commentary. Bull Mar Sci 87:971–1002
Google Scholar 

Herrnkind WF, Butler IV MJ, Hunt JH, Childress M (1997) Role of physical refugia: implications from a mass sponge die-off in a lobster nursery in Florida. Mar Freshw Res 48:759
Google Scholar 

Hoffman JI, Peck LS, Linse K, Clarke A (2011) Strong population genetic structure in a broadcast-spawning Antarctic marine invertebrate. J Hered 102:55–66
CAS  PubMed  Google Scholar 

Hothorn T, Bretz F, Westfall P (2008) Simultaneous inference in general parametric models. Biometrical J 50:346–363
Google Scholar 

Janes JK, Miller JM, Dupuis JR, Malenfant RM, Gorrell JC, Cullingham CI et al. (2017) The K = 2 conundrum. Mol Ecol 26:3594–3602
PubMed  Google Scholar 

Japaud A, Bouchon C, Magalon H, Fauvelot C (2019) Geographic distances and ocean currents influence Caribbean Acropora palmata population connectivity in the Lesser Antilles. Conserv Genet 20:447–466
Google Scholar 

Johnson MS, Black R (1982) Chaotic genetic patchiness in an intertidal limpet, Siphonaria sp. Mar Biol 70:157–164
Google Scholar 

Jolly MT, Thiébaut E, Guyard P, Gentil F, Jollivet D (2014) Meso-scale hydrodynamic and reproductive asynchrony affects the source-sink metapopulation structure of the coastal polychaete Pectinaria koreni. Mar Biol 161:367–382
Google Scholar 

Jombart T (2008) Adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405
CAS  PubMed  Google Scholar 

Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11:94
PubMed  PubMed Central  Google Scholar 

Jost L (2008) G ST and its relatives do not measure differentiation. Mol Ecol 17:4015–4026
PubMed  Google Scholar 

Knutsen H, Jorde PE, Andre C, Stenseth NC (2003) Fine-scaled geographical population structuring in a highly mobile marine species: the Atlantic cod. Mol Ecol 12:385–394
CAS  PubMed  Google Scholar 

Lazure P, Salomon JC, Breton M (1996) Subtidal circulation in Fort-de-France Bay. In: Maul GA (ed) Small Islands: Marine Science and Sustainable Development, Volume 51. American Geophysical Union, Washington DC
Google Scholar 

López-Legentil S, Pawlik JR (2009) Genetic structure of the Caribbean giant barrel sponge Xestospongia muta using the I3-M11 partition of COI. Coral Reefs 28:157–165
Google Scholar 

Maebe K, Golsteyn L, Nunes-Silva P, Blochtein B, Smagghe G (2018) Temporal changes in genetic variability in three bumblebee species from Rio Grande do Sul, South Brazil. Apidologie 49:415–429
CAS  Google Scholar 

Maldonado M, Riesgo A (2008) Reproduction in Porifera: a synoptic overview. Treb la SCB 59:29–49
Google Scholar 

Maldonado M, Sánchez-Tocino L, Navarro C (2010) Recurrent disease outbreaks in corneous demosponges of the genus Ircinia: Epidemic incidence and defense mechanisms. Mar Biol 157:1577–1590
Google Scholar 

Maldonado M, Uriz M (1999) Sexual propagation by sponge fragments. Nature 398:1999
Google Scholar 

Mariani S, Uriz MJ, Turon X, Alcoverro T (2006) Dispersal strategies in sponge larvae: Integrating the life history of larvae and the hydrologic component. Oecologia 149:174–184
PubMed  Google Scholar 

Martínez S, Carrillo L, Marinone SG (2019) Potential connectivity between marine protected areas in the Mesoamerican Reef for two species of virtual fish larvae: Lutjanus analis and Epinephelus striatus. Ecol Indic 102:10–20
Google Scholar 

Meirmans PG, Van Tienderen PH (2004) Genotype and Genodive: two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes 4:792–794
Google Scholar 

Morin PA, Leduc RG, Archer FI, Martien KK, Huebinger R, Bickham JW et al. (2009) Significant deviations from Hardy-Weinberg equilibrium caused by low levels of microsatellite genotyping errors. Mol Ecol Resour 9:498–504
PubMed  Google Scholar 

Muhling BA, Smith RH, Vásquez-Yeomans L, Lamkin JT, Johns EM, Carrillo L et al. (2013) Larval fish assemblages and mesoscale oceanographic structure along the Mesoamerican Barrier Reef System. Fish Oceanogr 22:409–428
Google Scholar 

Norderhaug KM, Anglès d’Auriac MB, Fagerli CW, Gundersen H, Christie H, Dahl K et al. (2016) Genetic diversity of the NE Atlantic sea urchin Strongylocentrotus droebachiensis unveils chaotic genetic patchiness possibly linked to local selective pressure. Mar Biol 163:1–13
Google Scholar 

Pante E, Simon-Bouhet B (2013) marmap: a package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS One 8:e73051
CAS  PubMed  PubMed Central  Google Scholar 

Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research- an update. Bioinformatics 28:2537–2539
CAS  PubMed  PubMed Central  Google Scholar 

Peery MZ, Kirby R, Reid BN, Stoelting R, Douchet-Beer E, Robinson S et al. (2012) Reliability of genetic bottleneck tests for detecting recent population declines. Mol Ecol 21:3403–3418
PubMed  Google Scholar 

Pérez-Portela R, Noyer C, Becerro MA (2015) Genetic structure and diversity of the endangered bath sponge Spongia lamella. Aquat Conserv Mar Freshw Ecosyst 25:365–379
Google Scholar 

Perez T, Garrabou J, Sartoretto S, Harmelin JG, Francour P, Vacelet J (2000) Massive mortality of marine invertebrates: an unprecedented event in northwestern Mediterranean. C R Acad Sci III 323:853–65
CAS  PubMed  Google Scholar 

Piry S, Luikart G, Cornuet J-M (1999) Computer note. BOTTLENECK: a computer program for detecting recent reductions in the effective size using allele frequency data. J Hered 90:502–503
Google Scholar 

R Core Team (2017) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/

Raymond M, Rousset F (1995) GENEPOP (Version 1.2): population genetics software for exact tests and ecumenicism. J Hered 86:248–249
Google Scholar 

Richards VP, Bernard AM, Feldheim KA, Shivji MS (2016) Patterns of population structure and dispersal in the long-lived “redwood” of the coral reef, the giant barrel sponge (Xestospongia muta). Coral Reefs 35:1097–1107
Google Scholar 

Riesgo A, Pérez-Portela R, Pita L, Blasco G, Erwin PM, López-Legentil S (2016) Population structure and connectivity in the Mediterranean sponge Ircinia fasciculata are affected by mass mortalities and hybridization. Heredity 117:427–439
CAS  PubMed  PubMed Central  Google Scholar 

Riesgo A, Taboada S, Pérez-Portela R, Melis P, Xavier JR, Blasco G et al. (2019) Genetic diversity, connectivity and gene flow along the distribution of the emblematic Atlanto-Mediterranean sponge Petrosia ficiformis (Haplosclerida, Demospongiae). BMC Evol Biol 19:1–18
Google Scholar 

Rippe JP, Matz MV, Green EA, Medina M, Khawaja NZ, Pongwarin T et al. (2017) Population structure and connectivity of the mountainous star coral, Orbicella faveolata, throughout the wider Caribbean region. Ecol Evol 7:9234–9246
PubMed  PubMed Central  Google Scholar 

Rosenberg NA (2004) DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes 4:137–138
Google Scholar 

Schunter C, Pascual M, Raventos N, Garriga J, Garza JC, Bartumeus F et al. (2019) A novel integrative approach elucidates fine-scale dispersal patchiness in marine populations. Sci Rep 9:1–10
CAS  Google Scholar 

Schwartz MK, Luikart G, Waples RS (2007) Genetic monitoring as a promising tool for conservation and management. Trends Ecol Evol 22:25–33
PubMed  Google Scholar 

Shanks AL (2009) Pelagic larval duration and dispersal distance revisited. Biol Bull 216:373–85
PubMed  Google Scholar 

Siegel DA, Mitarai S, Costello CJ, Gaines SD, Kendall BE, Warner RR et al. (2008) The stochastic nature of larval connectivity among nearshore marine populations. Proc Natl Acad Sci 105:8974–9
CAS  PubMed  Google Scholar 

Sole-Cava AM, Klautau M, Boury-Esnault N, Borojecic R, Thorpe JP (1991) Genetic evidence for cryptic speciation in allopatric populations of two cosmopolitan species of the calcareous sponge genus Clathrina. Mar Biol 111:381–386
Google Scholar 

Soro A, Quezada-Euan JJG, Theodorou P, Moritz RFA, Paxton RJ (2017) The population genetics of two orchid bees suggests high dispersal, low diploid male production and only an effect of island isolation in lowering genetic diversity. Conserv Genet 18:607–619
Google Scholar 

Soto I, Andréfouët S, Hu C, Muller-Karger FE, Wall CC, Sheng J et al. (2009) Physical connectivity in the Mesoamerican Barrier Reef System inferred from 9 years of ocean color observations. Coral Reefs 28:415–425
Google Scholar 

Stabili L, Cardone F, Alifano P, Tredici SM, Piraino S, Corriero G et al. (2012) Epidemic mortality of the sponge Ircinia variabilis (Schmidt, 1862) associated to proliferation of a Vibrio bacterium. Micro Ecol 64:802–813
Google Scholar 

Stevely JM, Sweat DE, Bert TM, Sim-Smith C, Kelly M (2010) Sponge mortality at Marathon and Long Key, Florida: patterns of species response and population recovery. Proc 63rd Gulf Caribb Fish Inst 63:384–400
Google Scholar 

Szpiech ZA, Jakobsson M, Rosenberg NA (2008) ADZE: a rarefaction approach for counting alleles private to combinations of populations. Bioinformatics 24:2498–2504
CAS  PubMed  PubMed Central  Google Scholar 

Taboada S, Riesgo A, Wiklund H, Paterson GLJ, Koutsouveli V, Santodomingo N et al. (2018) Implications of population connectivity studies for the design of marine protected areas in the deep sea: an example of a demosponge from the Clarion-Clipperton Zone. Mol Ecol 27:4657–4679
PubMed  Google Scholar 

Tesson SVM, Montresor M, Procaccini G, Kooistra WHCF (2014) Temporal changes in population structure of a marine planktonic diatom. PLoS One 9:e114984
PubMed  PubMed Central  Google Scholar 

Truelove NK, Griffiths S, Ley-Cooper K, Azueta J, Majil I, Box SJ et al. (2014) Genetic evidence from the spiny lobster fishery supports international cooperation among Central American marine protected areas. Conserv Genet 16:347–358
Google Scholar 

Uriz MJ, Turon X, Mariani S (2008) Ultrastructure and dispersal potential of sponge larvae: tufted versus evenly ciliated parenchymellae. Mar Ecol 29:280–297
Google Scholar 

Vacelet J (1999) Planktonic armoured propagules of the excavating sponge Alectona (Porifera: Demospongiae) are larvae: evidence from Alectona wallichii and A. mesatlantica sp. nov. Mem Queensl Mus 44:627–642
Google Scholar 

Vaha J-P, Erkinaro J, Niemela E, Primmer CR (2007) Life-history and habitat features influence the within-river genetic structure of Atlantic salmon. Mol Ecol 16:2638–2654
PubMed  Google Scholar 

Valentine MM, Butler MJ IV (2019) Sponges structure water-column characteristics in shallow tropical coastal ecosystems. Mar Ecol Prog Ser 608:133–147
CAS  Google Scholar 

Wernberg T, Coleman MA, Bennett S, Thomsen MS, Tuya F, Kelaher BP (2018) Genetic diversity and kelp forest vulnerability to climatic stress. Sci Rep 8:1851
PubMed  PubMed Central  Google Scholar 

Wright S (1943) Isolation by distance. Genetics 28:114–38
CAS  PubMed  PubMed Central  Google Scholar 

Wulff JL (1991) Asexual fragmentation, genotype success, and population dynamics of erect branching sponges. J Exp Mar Bio Ecol 149:227–247
Google Scholar 

Wulff J (2006) Rapid diversity and abundance decline in a Caribbean coral reef sponge community. Biol Conserv 127:167–176
Google Scholar 

Xavier JR, Rachello-Dolmen PG, Parra-Velandia F, Schönberg CHL, Breeuwer JAJ, van Soest RWM (2010) Molecular evidence of cryptic speciation in the ‘cosmopolitan’ excavating sponge Cliona celata (Porifera, Clionaidae). Mol Phylogenet Evol 56:13–20
CAS  PubMed  Google Scholar  More

Cereal crop yield is determined by three yield components, namely, the number of grains per unit of land area, grain weight, and the ratio of filled grains. In rice, single grain weight is genetically constant, irrespective of growth environments5. This character of rice is largely different from that of other cereal crops. For example, in wheat, single grain weight varies depending on growth conditions23,24, and a negative correlation is frequently observed between grain weight and number23. Therefore, in rice, an important target for achieving a high yield is to increase the number of grains with a high ratio of ripened grains. At the same time, this means that genetic enlargement of grain size has another great impact on increase in yields in rice. However, the relationship between grain size and yield has remained uncertain. Meanwhile, we found that a large-grain cultivar, Akita 63, exhibited a high yield (983 g m−2 of brown rice yield = 1,230 g m−2 of rough rice)10. The single grain weight of Akita 63 was 35% larger and the yield was 20–60% higher than that of the reference cultivars. According to our analysis, in spite of the large grain, the number of grains of Akita 63 did not differ from the common japonica cultivars at any crop-N content10,25,26. Therefore, a large grain size without reduction of the number of grains directly enhances the sink capacity, leading to high yield potential. However, although Oochikara, the maternal cultivar of Akita 63, has 80% larger grains, the yield was not necessarily high (560 g m−2 of brown rice yield9,27). The results in Fig. 2 clearly show that a large grain in Oochikara is associated with reduction of the number of grains and consequently, Oochikara has the same yield as that of reference cultivars with normal grains.
Among the fine-mapped major genes determining grain size, we examined the large-grain alleles of GS3, GW2, TGW6 and qSW5 in the present work. The results show that Akita 63 and Oochikara have the large-grain alleles of GS3 and qSW5 (Fig. 3; Supplementary Fig. S3). Lu et al.28 surveyed natural variation and artificial selection in major genes determining grain size among 127 varieties of rice cultivars, and reported that GS3 and qSW5 are major genes controlling grain size and that japonica cultivars with a nonfunctional GS3 and qSW5 genotype combination show the largest grain weight. Regarding this point, our results clearly coincide with their conclusion. However, as qSW5 with a 1,212-bp deletion was also found for Akita 39 with normal grains, the effects of this allele on single grain weight may be limited. Actually, functional qSW5 mainly originated from indica cultivars and leads to enlarged grain-length. The qSW5 with a 1,212-bp deletion mainly originated from japonica cultivars and has an effect on the enlargement of grain width14,29.
The single grain weight of Oochikara is appreciably greater than that of Akita 63 (Table 1; Fig. 2C). Nevertheless, we did not find a difference in the large-grain alleles between them in our investigation. This indicates that Oochikara has other genes/alleles contributing to large grain size. At the same time, our results also indicate the possibility that Oochikara has other genes/alleles which function as a negative regulator(s) of the number of grains or no genes/alleles which function as a positive regulator(s). Although it is not known whether a trade-off between single grain weight and the number of grains in Oochikara is determined by the same gene(s), the breeding from Oochikara to Akita 63 overcomes such a trade-off trait. This means that the large-grain allele of GS3 and qSW5 combination does not affect the number of grains and can be one of major determinants for a further increase in yield. These two genes are widely observed in Oryza sativa species14,21,28, and GS3 has stronger effects on grain weight in japonica cultivars28. Thus, although there still remains a possibility that other unidentified genes also come into play, it is suggested that the nonfunctional GS3 and qSW5 combination mainly contributes to the large grain size of Akita 63.
The 4-year average yields of Akita 63 were about 20% higher than those of the parents Oochikara and Akita 39, and 40–60% higher than those of the reference cultivar, Akitakomachi (Table 1). These results indicate that when Akita 63 was compared with Akitakomachi, the large grain of Akita 63 is not the sole determinant for high yield. Another factor was N uptake capacity. For the same N application, total crop-N content at the harvest stage tended to be higher in Akita 63 than in Akitakomachi (Table 1). Actually a significant difference in crop N content between them was found at an application of 0 g and 6 g N m−2 (Supplementary Table S2). This indicates that Akita 63 has superior N uptake capacity. This trait may have been inherited from the parental lines, Oochikara and Akita 39.
As already discussed above, to achieve a high yield, it is important to enhance the number of grains with a high ratio of ripened grains. In many cases, however, a negative correlation between the number of grains and the ratio of filled grains is frequently observed, especially when rice is cultivated with heavy N application30. This trend was clearly found for our data in Table 1. The ratio of filled grains to total grains decreased with increasing N application in all cultivars. Among them, the ratio of filled grains of Akita 63 was the lowest at an application of 13 g N m−2 for 3 years (Supplementary Table S2). As we previously pointed out, we think that this is caused by source limitation relative to yield potential26. Although the number of grains was linearly correlated with crop-N content passing through the origin, total biomass was curvilinealy correlated (Fig. 2A,B). Of course, the curvilinear correlation between biomass and crop N content was simply caused by a decline in canopy photosynthesis due to an excessive leaf area that may cause mutual shading at high N application. Grain mass (rough rice) in Akita 63 reached 60% of the total aboveground biomass at harvest, while that of other varieties reached about 45% (Table 1; Fig. 2B,D). This is the highest level of all cereal crops31,32. Thus, yield potential of high-yielding cultivars such as Akita 63 may surpass source capacity, leading to a decline in the ratio of filled grain. This indicates that a further increase in sink capacity is no longer effective and that improvements in source capacity will be essential for maintenance of high ratio of filled grains.
Many recent trials conducted at free-air CO2 enrichment (FACE) facilities have shown a highly positive correlation between enhanced photosynthesis, biomass and yield32,33. These results indicate that enhancement of photosynthesis by elevated [CO2] directly leads to an increase in yield when genetic factors besides photosynthesis are not altered32. Therefore, to examine the effects of source enhancement on yield, we conducted FACE experiments on several rice cultivars, including Akita 6334. The results showed that Akita 63 had the greatest enhancement of yield by CO2 enrichment among all rice cultivars grown at FACE facilities. Furthermore, the absolute yield of Akita 63 was also highest and the ratio of filled grains remained at higher level. These results indicate that enhancement of photosynthesis is of the greatest importance for a further increase in the yield of high-yielding cultivars with a large sink size. While there has been a dispute as to whether photosynthesis improvement leads to an increase in cereal crop yields35, we have actually shown that an increase in photosynthesis by overproducing Rubisco results in increased rice yields under field conditions36,37 Thus, improving photosynthesis is a possible target for realizing a further increase in yield of today’s high-yielding cultivars. More

1.
Meltzer, D. J. The Great Paleolithic War: How Science Forged an Understanding of America’s Ice Age Past (Univ. Chicago Press, 2015).
2.
Mix, A. C., Bard, E. & Schneider, R. Environmental processes of the Ice Age: land, oceans, glaciers (EPILOG). Quat. Sci. Rev. 20, 627–657 (2001).
ADS  Google Scholar 

3.
Clark, P. U. et al. The Last Glacial Maximum. Science 325, 710–714 (2009).
ADS  CAS  PubMed  Google Scholar 

4.
Rasmussen, S. O. et al. A stratigraphic framework for abrupt climatic changes during the Last Glacial period based on three synchronized Greenland ice-core records: refining and extending the INTIMATE event stratigraphy. Quat. Sci. Rev. 106, 14–28 (2014).
ADS  Google Scholar 

5.
Waters, M. R. & Stafford, T. W. Jr. Redefining the age of Clovis: implications for the peopling of the Americas. Science 315, 1122–1126 (2007).
ADS  CAS  PubMed  Google Scholar 

6.
Dillehay, T. D. Monte Verde, a Late Pleistocene Settlement in Chile: The Archaeological Context and Interpretation (Smithsonian Institution Press, 1997).

7.
Williams, T. J. et al. Evidence of an early projectile point technology in North America at the Gault site, Texas, USA. Sci. Adv. 4, eaar5954 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

8.
Waters, M. R. et al. Pre-Clovis projectile points at the Debra L. Friedkin site, Texas–implications for the Late Pleistocene peopling of the Americas. Sci. Adv. 4, eaat4505 (2018).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

9.
Haynes, G. The millennium before Clovis. PaleoAmerica 1, 134–162 (2015).
Google Scholar 

10.
Sandweiss, D. H. et al. Quebrada Jaguay: early South American maritime adaptations. Science 281, 1830–1832 (1998).
ADS  CAS  PubMed  Google Scholar 

11.
Goebel, T. & Keene, J. L. in Archaeology in the Great Basin and Southwest: Papers in Honor of Don D. Fowler (eds Parezo, N. J. & Janetski, J. C.) 35–60 (Univ. Utah Press, 2014).

12.
Méndez, C., Jackson, D., Seguel, R. & Delaunay, A. N. Early high-quality lithic procurement in the semiarid north of Chile. Curr. Res. Pleistocene 27, 19–21 (2010).
Google Scholar 

13.
Méndez, C. & Jackson, D. Terminal Pleistocene lithic technology and use of space in Central Chile. Chungara (Arica) 47, 53–65 (2015).
Google Scholar 

14.
Jones, K. B., Hodgins, G. W. L. & Sandweiss, D. H. Radiocarbon chronometry of site QJ-280, Quebrada Jaguay, a Terminal Pleistocene to Early Holocene fishing site in southern Peru. J. Island Coast. Archaeol. 14, 82–100 (2017).
Google Scholar 

15.
Davis, L. G. et al. Late Upper Paleolithic occupation at Cooper’s Ferry, Idaho, USA shows Americas settled before ~16,000 years ago. Science 365, 891–897 (2019).
ADS  CAS  Google Scholar 

16.
Braje, T. J., Dillehay, T. D., Erlandson, J. M., Klein, R. G. & Rick, T. C. Finding the first Americans. Science 358, 592–594 (2017).
ADS  CAS  PubMed  Google Scholar 

17.
Potter, B. A. et al. Current evidence allows multiple models for the peopling of the Americas. Sci. Adv. 4, eaat5473 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

18.
Bronk Ramsey, C. Development of the radiocarbon program OxCal. Radiocarbon 43, 355–363 (2001).
Google Scholar 

19.
Bronk Ramsey, C. Bayesian analysis of radiocarbon dates. Radiocarbon 51, 337–360 (2009).
Google Scholar 

20.
Rasmussen, S. O. et al. A new Greenland ice core chronology for the last glacial termination. J. Geophys. Res. 111, D06102 (2006).
ADS  Google Scholar 

21.
Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55, 1869–1887 (2013).
CAS  Google Scholar 

22.
Adolphi, F. et al. Connecting the Greenland ice-core and U/Th timescales via cosmogenic radionuclides: testing the synchroneity of Dansgaard–Oeschger events. Clim. Past 14, 1755–1781 (2018).
Google Scholar 

23.
Ray, N. & Adams, J. A GIS-based vegetation map of the world at the last glacial maximum (25,000–15,000 BP). Internet Archaeol. 11, https://doi.org/10.11141/ia.11.2 (2001).

24.
Jackson, S. T. et al. Vegetation and environment in Eastern North America during the Last Glacial Maximum. Quat. Sci. Rev. 19, 489–508 (2000).
ADS  Google Scholar 

25.
Williams, J. W. Variations in tree cover in North America since the Last Glacial Maximum. Global Planet. Change 35, 1–23 (2003).
ADS  MathSciNet  Google Scholar 

26.
Lyle, M. et al. Out of the tropics: the Pacific, Great Basin lakes, and late Pleistocene water cycle in the western United States. Science 337, 1629–1633 (2012).
ADS  CAS  PubMed  Google Scholar 

27.
Goebel, T., Hockett, B., Adams, K. D., Rhode, D. & Graf, K. Climate, environment, and humans in North America’s Great Basin during the Younger Dryas, 12,900–11,600 calendar years ago. Quat. Int. 242, 479–501 (2011).
Google Scholar 

28.
Menking, K. M., Anderson, R. Y., Shafike, N. G., Syed, K. H. & Allen, B. D. Wetter or colder during the Last Glacial Maximum? Revisiting the pluvial lake question in southwestern North America. Quat. Res. 62, 280–288 (2004).
Google Scholar 

29.
Kirby, M. E. et al. A late Wisconsin (32–10k cal a BP) history of pluvials, droughts and vegetation in the Pacific south-west United States (Lake Elsinore, CA). J. Quat. Sci. 33, 238–254 (2018).
Google Scholar 

30.
Ibarra, D. E. et al. Warm and cold wet states in the western United States during the Pliocene–Pleistocene. Geology 46, 355–358 (2018).
ADS  Google Scholar 

31.
Feakins, S. J., Wu, M. S., Ponton, C. & Tierney, J. E. Biomarkers reveal abrupt switches in hydroclimate during the last glacial in southern California. Earth Planet. Sci. Lett. 515, 164–172 (2019).
ADS  CAS  Google Scholar 

32.
Stanford, D. J. & Bradley, B. A. Across Atlantic Ice: The Origin of America’s Clovis Culture (Univ of California Press, 2013).

33.
Aubry, T. & Almeida, M. Analyse critique des bases chronostratigraphiques de la structuration du Solutréen. Le Solutréen 40, 37e52 (2013).
Google Scholar 

34.
Eren, M. I., Patten, R. J., O’Brien, M. J. & Meltzer, D. J. Refuting the technological cornerstone of the Ice-Age Atlantic crossing hypothesis. J. Archaeol. Sci. 40, 2934–2941 (2013).
Google Scholar 

35.
Raff, J. A. & Bolnick, D. A. Does mitochondrial haplogroup X indicate ancient trans-Atlantic migration to the Americas? A critical re-evaluation. PaleoAmerica 1, 297–304 (2015).
Google Scholar 

36.
Lisiecki, L. E. & Raymo, M. E. A Pliocene–Pleistocene stack of 57 globally distributed benthic δ18O records. Paleoceanography 20, PA1003 (2005).
ADS  Google Scholar 

37.
Spratt, R. M. & Lisiecki, L. E. A Late Pleistocene sea level stack. Clim. Past 12, 1079–1092 (2016).
Google Scholar 

38.
Pico, T., Mitrovica, J. X., Ferrier, K. L. & Braun, J. Global ice volume during MIS 3 inferred from a sea-level analysis of sedimentary core records in the Yellow River Delta. Quat. Sci. Rev. 152, 72–79 (2016).
ADS  Google Scholar 

39.
Batchelor, C. L. et al. The configuration of Northern Hemisphere ice sheets through the Quaternary. Nat. Commun. 10, 3713 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

40.
Dalton, A. S., Finkelstein, S. A., Barnett, P. J. & Forman, S. L. Constraining the Late Pleistocene history of the Laurentide Ice Sheet by dating the Missinaibi Formation, Hudson Bay Lowlands, Canada. Quat. Sci. Rev. 146, 288–299 (2016).
ADS  Google Scholar 

41.
Pico, T., Mitrovica, J. X. & Mix, A. C. Sea level fingerprinting of the Bering Strait flooding history detects the source of the Younger Dryas climate event. Sci. Adv. 6, eaay2935 (2020).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

42.
Dyke, A. S. An outline of North American deglaciation with emphasis on central and northern Canada. Develop. Quat. Sci. 2, 373–424 (2004).
Google Scholar 

43.
Lesnek, A. J., Briner, J. P., Lindqvist, C., Baichtal, J. F. & Heaton, T. H. Deglaciation of the Pacific coastal corridor directly preceded the human colonization of the Americas. Sci. Adv. 4, eaar5040 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

44.
Tamm, E. et al. Beringian standstill and spread of Native American founders. PLoS ONE 2, e829 (2007).
ADS  PubMed  PubMed Central  Google Scholar 

45.
Moreno-Mayar, J. V. et al. Early human dispersals within the Americas. Science 362, eaav2621 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

46.
Moreno-Mayar, J. V. et al. Terminal Pleistocene Alaskan genome reveals first founding population of Native Americans. Nature 553, 203–207 (2018).
ADS  CAS  PubMed  Google Scholar 

47.
Ardelean, C. F. et al. Evidence of human occupation in Mexico around the Last Glacial Maximum. Nature https://doi.org/10.1038/s41586-020-2509-0 (2020).

48.
Goodyear, A. C. in Paleoamerican Origins: Beyond Clovis (eds. Bonnichesen, R. et al.) 103–112 (Centre for the Study of the First Americans, 2005).

49.
Llamas, B. et al. Ancient mitochondrial DNA provides high-resolution time scale of the peopling of the Americas. Sci. Adv. 2, e1501385 (2016).
ADS  PubMed  PubMed Central  Google Scholar 

50.
Pinotti, T. et al. Y chromosome sequences reveal a short Beringian standstill, rapid expansion, and early population structure of Native American founders. Curr. Biol. 29, 149–157.e3 (2019).
CAS  PubMed  Google Scholar 

51.
Bergström, A. et al. Insights into human genetic variation and population history from 929 diverse genomes. Science 367, eaay5012 (2020).
PubMed  Google Scholar 

52.
Raghavan, M. et al. Genomic evidence for the Pleistocene and recent population history of Native Americans. Science 349, aab3884 (2015).
PubMed  PubMed Central  Google Scholar 

53.
Rasmussen, M. et al. The genome of a Late Pleistocene human from a Clovis burial site in western Montana. Nature 506, 225–229 (2014).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

54.
Reich, D. et al. Reconstructing Native American population history. Nature 488, 370–374 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

55.
Scheib, C. L. et al. Ancient human parallel lineages within North America contributed to a coastal expansion. Science 360, 1024–1027 (2018).
ADS  CAS  PubMed  Google Scholar 

56.
Erlandson, J. M. & Braje, T. J. From Asia to the Americas by boat? Paleogeography, paleoecology, and stemmed points of the northwest Pacific. Quat. Int. 239, 28–37 (2011).
Google Scholar 

57.
Williams, T. J. & Madsen, D. B. The Upper Paleolithic of the Americas. PaleoAmerica 6, 4–22 (2019).
Google Scholar 

58.
Gilbert, M. T. P. et al. DNA from pre-Clovis human coprolites in Oregon, North America. Science 320, 786–789 (2008).
ADS  CAS  PubMed  Google Scholar 

59.
Pedersen, M. W. et al. Postglacial viability and colonization in North America’s ice-free corridor. Nature 537, 45–49 (2016).
ADS  CAS  Google Scholar 

60.
Heintzman, P. D. et al. Bison phylogeography constrains dispersal and viability of the ice free corridor in western Canada. Proc. Natl Acad. Sci. USA 113, 8057–8063 (2016).
CAS  PubMed  Google Scholar 

61.
Darvill, C. M., Menounos, B., Goehring, B. M., Lian, O. B. & Caffee, M. W. Retreat of the western Cordilleran Ice Sheet margin during the last deglaciation. Geophys. Res. Lett. 45, 9710–9720 (2018).
ADS  Google Scholar 

62.
Taylor, M. A., Hendy, I. L. & Pak, D. K. Deglacial ocean warming and marine margin retreat of the Cordilleran Ice Sheet in the North Pacific Ocean. Earth Planet. Sci. Lett. 403, 89–98 (2014).
ADS  CAS  Google Scholar 

63.
Martin, P. S. The discovery of America. Science 179, 969–974 (1973).
ADS  CAS  PubMed  Google Scholar 

64.
Surovell, T. A., Pelton, S. R., Anderson-Sprecher, R. & Myers, A. D. Test of Martin’s overkill hypothesis using radiocarbon dates on extinct megafauna. Proc. Natl Acad. Sci. USA 113, 886–891 (2016).
ADS  CAS  PubMed  Google Scholar 

65.
Robinson, G. S., Pigott Burney, L. & Burney, D. A. Landscape paleoecology and megafaunal extinction in southwestern New York state. Ecol. Monogr. 75, 295–315 (2005).
Google Scholar 

66.
Guthrie, R. D. New carbon dates link climatic change with human colonization and Pleistocene extinctions. Nature 441, 207–209 (2006).
ADS  CAS  PubMed  Google Scholar 

67.
Lorenzen, E. D. et al. Species-specific responses of Late Quaternary megafauna to climate and humans. Nature 479, 359–364 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

68.
Prescott, G. W., Williams, D. R., Balmford, A., Green, R. E. & Manica, A. Quantitative global analysis of the role of climate and people in explaining Late Quaternary megafaunal extinctions. Proc. Natl Acad. Sci. USA 109, 4527–4531 (2012).
ADS  CAS  PubMed  Google Scholar 

69.
Cooper, A. et al. Abrupt warming events drove Late Pleistocene Holarctic megafaunal turnover. Science 349, 602–606 (2015).
ADS  CAS  PubMed  Google Scholar 

70.
Araujo, B. B. A., Oliveira-Santos, L. G. R., Lima-Ribeiro, M. S., Diniz-Filho, J. A. F. & Fernandez, F. A. S. Bigger kill than chill: the uneven roles of humans and climate on Late Quaternary megafaunal extinctions. Quat. Int. 431, 216–222 (2017).
Google Scholar 

71.
Firestone, R. B. et al. Evidence for an extraterrestrial impact 12,900 years ago that contributed to the megafaunal extinctions and the Younger Dryas cooling. Proc. Natl Acad. Sci. USA 104, 16016–16021 (2007).
ADS  CAS  PubMed  Google Scholar 

72.
Broughton, J. M. & Weitzel, E. M. Population reconstructions for humans and megafauna suggest mixed causes for North American Pleistocene extinctions. Nat. Commun. 9, 5441 (2018).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

73.
Grayson, D. K. & Meltzer, D. J. Revisiting Paleoindian exploitation of extinct North American mammals. J. Archaeol. Sci. 56, 177–193 (2015).
Google Scholar 

74.
Buck, C. E. & Bard, E. A calendar chronology for Pleistocene mammoth and horse extinction in North America based on Bayesian radiocarbon calibration. Quat. Sci. Rev. 26, 2031–2035 (2007).
ADS  Google Scholar 

75.
Slon, V. et al. Neandertal and Denisovan DNA from Pleistocene sediments. Science 356, 605–608 (2017).
ADS  CAS  Google Scholar 

76.
Bueno, L., Politis, G., Prates, L. & Steele, J. A Late Pleistocene/early Holocene archaeological 14C database for Central and South America: palaeoenvironmental contexts and demographic interpretations. Quat. Int. 301, 1–158 (2013).
Google Scholar 

77.
Bond, J. D. Paleodrainage map of Beringia, Yukon Geological Survey, open file 2019-2. http://data.geology.gov.yk.ca/Reference/81642#InfoTab (2019).

78.
Clark, G. A. in The Settlement of the American Continents (ed. Barton, C. M. et al.) 103–112 (Univ. Arizona Press, 2004).

79.
Harris, E. C. Principles of Archaeological Stratigraphy (Elsevier, 2014).

80.
Gelfand, A. E. & Smith, A. F. M. Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85, 398–409 (1990).
MathSciNet  MATH  Google Scholar 

81.
Gilks, W. R., Richardson, S. & Spiegelhalter, D. Markov Chain Monte Carlo in Practice (Chapman and Hall/CRC, 1995).

82.
Bronk Ramsey, C. Radiocarbon calibration and analysis of stratigraphy: the OxCal program. Radiocarbon 37, 425–430 (1995).
CAS  Google Scholar 

83.
Bronk Ramsey, C. Dealing with outliers and offsets in radiocarbon dating. Radiocarbon 51, 1023–1045 (2009).
Google Scholar 

84.
Rosenblatt, M. Remarks on some nonparametric estimates of a density function. Ann. Math. Stat. 27, 832–837 (1956).
MathSciNet  MATH  Google Scholar 

85.
Parzen, E. On estimation of a probability density function and mode. Ann. Math. Stat. 33, 1065–1076 (1962).
MathSciNet  MATH  Google Scholar 

86.
Bronk Ramsey, C. Methods for summarizing radiocarbon datasets. Radiocarbon 59, 1809–1833 (2017).
Google Scholar 

87.
Silverman, B. W. Density Estimation for Statistics and Data Analysis (Chapman & Hall, 1986).

88.
Bronk Ramsey, C. OxCal 4.3 Manual. https://c14.arch.ox.ac.uk/oxcalhelp/hlp_contents.html (2020).

89.
Stafford, T. W. Jr, Duhamel, R. C., Haynes, C. V. Jr & Brendel, K. Isolation of proline and hydroxyproline from fossil bone. Life Sci. 31, 931–938 (1982).
CAS  PubMed  Google Scholar 

90.
Stafford, T. W., Brendel, K. & Duhamel, R. C. Radiocarbon, 13C and 15N analysis of fossil bone: removal of humates with XAD-2 resin. Geochim. Cosmochim. Acta 52, 2257–2267 (1988).
ADS  CAS  Google Scholar 

91.
Deviese, T., Comeskey, D., McCullagh, J., Bronk Ramsey, C. & Higham, T. New protocol for compound-specific radiocarbon analysis of archaeological bones. Rapid Commun. Mass Spectrom. 32, 373–379 (2018).
ADS  CAS  PubMed  Google Scholar 

92.
Devièse, T. et al. Increasing accuracy for the radiocarbon dating of sites occupied by the first Americans. Quat. Sci. Res. 198, 171–180 (2018).
ADS  Google Scholar 

93.
Becerra-Valdivia, L. et al. Reassessing the chronology of the archaeological site of Anzick. Proc. Natl Acad. Sci. USA 115, 7000–7003 (2018).
CAS  PubMed  Google Scholar 

94.
Waters, M. R., Stafford, T. W. Jr, Kooyman, B. & Hills, L. V. Late Pleistocene horse and camel hunting at the southern margin of the ice-free corridor: reassessing the age of Wally’s Beach, Canada. Proc. Natl Acad. Sci. USA 112, 4263–4267 (2015).
ADS  CAS  PubMed  Google Scholar 

95.
Bronk Ramsey, C., Housley, R. A., Lane, C. S., Smith, V. C. & Pollard, A. M. The RESET tephra database and associated analytical tools. Quat. Sci. Rev. 118, 33–47 (2015).
ADS  Google Scholar 

96.
Derek Hamilton, W. & Krus, A. M. The myths and realities of Bayesian chronological modeling revealed. Am. Antiq. 83, 187–203 (2018).
Google Scholar  More

A number of studies have assessed meat sourced from wild deer originating from various countries (i.e.11,12 from Poland or13 from South Africa) or from farmed deer (i.e.14 from Czech Republic11,15, from Poland16, from Italy and17from SP). Studies have also examined the main marketed deer meats: meat from wild SP deer1,2,10 and farmed NZ ones6,7,8,18. Despite the number of studies cited below which assess meat quality from different countries or different types of breeding/killing (wild or farmed, stressful or sudden death), they have been undertaken by different research teams, using different scientific equipment and protocols. None of these studies have assessed meat samples from different countries with the same scientific equipment, reagents, and staff. It is therefore likely that some of the anomalies found through comparison of the available literature may actually be caused by methodological rather than regional differences per se.
The quality of wild game meat depends on the hunting method19 and on the hunting season3. Wild deer shot with a projectile (e.g. a bullet) are not exsanguinated immediately after their death and often several hours elapse between death and dressing. Consequently, carcasses are often processed once rigor mortis has set in, which affects meat characteristics. In principle, a low stress death by stalking should result in a better quality of meat and, in fact, meat-processing companies pay better prices for meat from stalked animals than for stress hunted meat, as evidenced by game estate owners and personal interviews. Studies have assessed several types of hunted and farmed venison, but none included stressful pursuit by dogs. Our results show for the first time that the mixed effects of hunting type-season resulted in some differences in meat quality (pH, cooking losses), but surprisingly not in tenderness (shear force).
Recently, Stanisz et al.3 have not observed differences for body weight with hunting season of hunt-harvested does. However, the authors observed a higher technological quality for venison obtained in winter showing compared to that harvested in summer. In fact, it was observed lower purge loss in vacuum, drip loss, free water, free water share in total water, and water loss during roasting. In addition, venison obtained in winter showed a greater brightness and a reduced redness comparing to venison obtained in summer. Colour traits and water retention capacity determine the meat shelf life and its suitability for storage in vacuum packaging.
Values obtained in the current study for pH of meat were similar to those observed previously for meat from NZ farmed deer7,9 and for SP stressed, hunted red deer2,10. However, no data has been found to compare the average values of pH obtained in SP from stalked deer. Our results show that pH values were similar for wild deer in SP (pooled values) and farmed deer in NZ, but meat from stalked deer had the lowest values. This is not surprising as transporting deer to the abattoir also involves stress, and furthermore, recently, Gentsch et al.20 have observed that cortisol levels for stalked deer were much lower than those for deer hunted with dogs in driven hunts (21.8 vs. 66.1 nmol/L, respectively). With regard to the effect of seasons, meat hunted in winter had a higher pH than meat hunted in summer. However, recently, Stanisz et al.3 observed that the pH of the muscles Longissimus lumborum measured 24 h post-mortem was 0.22 units higher in the summer season, compared to the winter season. This is consistent with current results regarding colour and literature reports that pre-harvest stress affects the degree of bleed, leading to an increase in the level of oxymyoglobin21 and confirming the influence of hunting type on meat colour4. According to results obtained by Stanisz et al.3, venison sourced from does shot in summer was redder and had a greater chroma compared to venison obtained in winter. In our study, values obtained for colour traits were similar to those obtained for NZ farmed6,9 and for SP stress hunted10 deer meat.
The IMF content was similar to the values previously reported for NZ farmed deer8,9 and for autumn–winter hunted deer from the same region of SP1,10. However, the most interesting information came from slight differences in IMF between seasons/type of hunting in wild SP: meat from summer stalking had an average IMF content of 0.90%, a value significantly higher than that for winter chased meat (0.11%). This may be only a seasonal difference (unlikely to be attributable to stress at death) because of increased grazing available during spring and summer for the deer. Thus, it is well established that body condition of deer improves, mainly in gaining fat and body weight, during spring and summer. In contrast, loss condition, involving loss of body fat, is higher in autumn and winter22. In addition, deer lose weight in autumn because feed intake decreases considerably during the rut23. Confirming this hypothesis, Serrano et al.2 have found higher contents of IMF and cholesterol in the loin of deer hunted in driven hunts in autumn compared to the loin from deer hunted in driven hunts in winter. The cooking losses of meat were similar to values reported previously for NZ farmed deer7 and for stressed deer hunted in driven hunts in SP10, although there was an effect of stress/season with values higher for stalking-summer. The effect of season on cooking losses has been previously described3. However, the cause of the differences observed in current study is likely to be due to the level of stress at slaughter, corroborated by Cifuni et al.4, who found that meat from culled (selective hunting) deer produced a greater degree of water loss during cooking than meat from deer slaughtered in driven hunts.
In general, values reported for shear force present a high variability2,12,13. Values in the current trial were higher than those reported previously for deer meat1,8,9,10. Differences among authors might be due to a range of interrelated factors, including pH, amount of connective tissue, IMF content, proteolytic enzyme activity and age of the animal13. Differences observed for the shear force between countries of origin (58.7% higher for meat from SP than for NZ meat) are not caused by stress at death: no differences were observed regarding the shear force of SP meat by hunting type/season. In fact, Stanisz et al.3 concluded that a greater impact of season could be evidenced using biting measures (Volodkevich Bite Jaws, test speed 2 mm/s; strain 100%; force 5 g at 24 h and 14 days post-mortem) compared to Warner–Bratzler measures. However, biting measures were not included in the current study. Further studies may be needed to conclude whether there is an effect of season compensating for the apparent effect of stress that yields non-significant results.
In general, the country of origin did not influence the total content of SFA or PUFA and a trend was only detected in the increase of the MUFA content of NZ meat. However, meat from NZ farms had higher content of n-3 FA and long chain n-3 PUFA, less content of n-6 FA and, in consequence, a lower n-6/n-3 ratio than SP wild meat. Values obtained for the n-6/n-3 ratio (ranging from 1.22 to 3.71) correspond with those reported by other authors for deer meat8,14. In any case, the average n-6/n-3 ratio for meat from both countries of origin was lower than 4, as recommended by WHO/FAO24.
The FA profile differed for meat samples obtained by different hunting types/season. The differences observed in the current study between hunting types for the FA profile are likely to be caused by the effects of the diet FA profiles seasonal changes on ruminant products25. Thus, the main FA in winter driven hunt meat were PUFA, followed by SFA and MUFA. In summer stalked deer and farmed venison, the main FA were SFA followed by MUFA and PUFA. No differences were observed for the contents of n-3 FA. Consequently, the fat of animals sourced from driven hunts in winter showed a higher PUFA/SFA ratio, n-6 FA content and n-6/n-3 ratio than the fat of summer stalked deer.
In general, the AA profile obtained in this study corresponds with that reported for meat from red deer, as previously indicated by Lorenzo et al.1. Interesting effects of country of origin and hunting type on AA profile of deer meat were observed in the current study. The SP wild meat had higher contents of total, essential and non-essential AA than NZ farm meat. Moreover, meat collected from summer-stalked deer presented a higher ratio for the essential/non-essential AA than meat collected from deer slaughtered in winter driven hunts. However, authors have not found previous studies comparing the AA profile of meat from SP and NZ deer slaughtered by different methods to compare with current results. It is postulated, therefore, that AA differences are attributable to seasonal effect, not the level of stress at death.
Because mineral composition presented in deer meat is closely related to the natural environment as they graze and browse26, differences found in mineral content in our study seem to depend on season and diet composition rather than on level of stress at death. Therefore, the differences in fodder available in spring and summer as compared to winter, as well as the growth of leaves in deciduous trees and shrubs, may explain some of the differences in the mineral profile observed between meats sourced from winter driven hunts and summer stalking analysed in the current study. Actually, Estévez et al.27 found seasonal differences in the mineral content of plants consumed by deer in SP. It is highly unlikely that mineral differences between both meats are due to the level of stress at death: the only difference that could be expected (Na content) due to increased sweating resulting from the chase and stress, was actually the reverse of what was expected (greater in animals killed in winter driven hunts). These results suggest that the difference was due to a lower level of Na in plants in summer.
One of the most significant effects in the seasonal differences found in the current study may not be related to mineral content of the diet, but to another very interesting and unique physiological characteristic of male deer (the sex examined in this study): cyclic physiologic osteoporosis. This effect is caused by rapid growth of the antlers (more than 1 cm/day), causing a depletion of mineral stores in certain bones in order to transfer the material to antlers28. Because minerals are blood borne, it is not surprising that they may also affect the mineral composition of muscles. This could explain why meat from osteoporotic deer (summer) had less than half the content of Zn, which forms part of alkaline phosphatase, the enzyme needed to deposit Ca in bone tissue29. For the same reason, it may explain the differences found for P and Ca contents (despite Ca is more stable in blood), and even for Mg (which can substitute Ca in the hydroxyapatite forming the antlers and bones). More

1.
Irwin, H. S. & Barneby, R. C. Tribe Cassieae Bronn. In Recent Advances in Legume Systematics, pt 1 (eds Polhill, R. M. & Raven, P. H.) 97–106 (Royal Botanic Gardens, London, 1981).
Google Scholar 
2.
Irwin, H. S. & Barneby, R. C. The American Cassiinae, a synoptical revision of Leguminosae tribe Cassiae subtribe Cassiinae in the New World. Mem. N. Y. Bot. Gard. 35, 1–918 (1982).
Google Scholar 

3.
Irwin, H. S. & Turner, B. L. Chromosome relationships and taxonomic considerations in the genus Cassia. Am. J. Bot. 47, 309–318 (1960).
Google Scholar 

4.
Randell, B. R. Revision of the Cassiinae in Australia. 1. Senna Miller sect. Chamaefistula (Colladon) Irwin and Barneby. J. Adelaide Bot. Gard. 11, 19–49 (1988).
Google Scholar 

5.
Lewis, G. P. Tribe Cassieae. In Legumes of the World (eds Lewis, G. P. et al.) 111–126 (Royal Botanic Gardens, Kew, 2005).
Google Scholar 

6.
Janzen, D. H. & Martin, P. S. Neotropical anachronisms: The fruits the Gomphotheres ate. Science 215, 19–27 (1982).
ADS  CAS  PubMed  Google Scholar 

7.
Baskin, J. M. & Baskin, C. C. A classification system for seed dormancy. Seed Sci. Res. 14, 1–16 (2004).
ADS  Google Scholar 

8.
Baskin, C. C. & Baskin, J. M. Seed dormancy in trees of climax tropical vegetation types. Trop. Ecol. 46, 17–28 (2005).
Google Scholar 

9.
Baskin, C. C. & Baskin, J. M. Seeds: Ecology, biogeography and evolution of dormancy and germination 2nd edn. (Elsevier, Amsterdam, 2014).
Google Scholar 

10.
De Paula, A. S., Delgado, C. M. L., Paulilo, M. T. S. & Santos, M. Breaking physical dormancy of Cassia leptophylla and Senna macranthera (Fabaceae: Caesalpinioideae) seeds: Water absorption and alternating temperatures. Seed Sci. Res. 22, 259–267 (2012).
Google Scholar 

11.
Jayasuriya, K. M. G. G., Wijetunga, A. S. T. B., Baskin, J. M. & Baskin, C. C. Seed dormancy and storage behaviour in tropical Fabaceae: A study of 100 species from Sri Lanka. Seed Sci. Res. 23, 257–269 (2013).
Google Scholar 

12.
Rodrigues-Junior, A. G., Faria, J. M. R., Vaz, T. A. A., Nakamura, A. T. & José, A. C. Physical dormancy in Senna multijuga (Fabaceae: Caesalpinioideae) seeds: The role of seed structures in water uptake. Seed Sci. Res. 24, 147–157 (2014).
Google Scholar 

13.
Rodrigues-Junior, A. G. et al. A function for the pleurogram in physically dormant seeds. Ann. Bot. 123, 867–876 (2019).
PubMed  Google Scholar 

14.
Erickson, T. E., Merritt, D. J. & Turner, S. R. Overcoming physical seed dormancy in priority native species for use in arid-zone restoration programs. Aust. J. Bot. 64, 401–416 (2016).
Google Scholar 

15.
Baskin, J. M., Baskin, C. C. & Li, X. Taxonomy, anatomy and evolution of physical dormancy in seeds. Plant Species Biol. 15, 139–152 (2000).
Google Scholar 

16.
Gill, L. S., Hasaini, S. W. H. & Musa, A. H. Germination biology of some Cassia L. species (Leguminosae). Legume Res. 5, 97–104 (1982).
Google Scholar 

17.
Grice, A. C. & Westoby, M. Aspects of the dynamics of the seed-banks and seedling populations of Acacia victoriae and Cassia spp. in arid western New South Wales. Aust. J. Ecol. 12, 209–215 (1987).
Google Scholar 

18.
Jurado, E. & Westoby, M. Germination biology of selected central Australian plants. Aust. J. Ecol. 17, 341–348 (1992).
Google Scholar 

19.
Todaria, N. P. & Negi, A. K. Pretreatment of some Indian Cassia seeds to improve their germination. Seed Sci. Technol. 20, 583–588 (1992).
Google Scholar 

20.
Sahai, K. Structural diversity in the lens of the seed of some Cassia L. (Caesalpinioideae) species and its taxonomic significance. Phytomorphology 49, 203–208 (1999).
Google Scholar 

21.
Jayasuriya, K. M. G. G., Baskin, J. M. & Baskin, C. C. Sensitivity cycling and its ecological role in seeds with physical dormancy. Seed Sci. Res. 19, 3–13 (2009).
Google Scholar 

22.
Gama-Arachchige, N. S., Baskin, J. M., Geneve, R. L. & Baskin, C. C. Identification and characterization of ten new water gaps in seeds and fruits with physical dormancy and classification of water-gap complexes. Ann. Bot. 112, 69–84 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

23.
Geneve, R. L., Baskin, C. C., Baskin, J. M., Jayasuriya, K. M. G. G. & Gama-Arachchige, N. S. Functional morpho-anatomy of water-gap complexes in physically dormant seed. Seed Sci. Res. 28, 186–191 (2018).
Google Scholar 

24.
Rodrigues-Junior, A. G., Baskin, C. C., Baskin, J. M. & Garcia, Q. S. Sensitivity cycling in physically dormant seeds of the Neotropical tree Senna multijuga (Fabaceae). Plant Biol. 20, 698–706 (2018).
CAS  PubMed  Google Scholar 

25.
Rodrigues-Junior, A. G. et al. Why large seeds with physical dormancy become nondormant earlier than small ones. PLoS ONE 13, e0202038 (2018).
PubMed  PubMed Central  Google Scholar 

26.
Cherubin, M. R., Moraes, M. T., Weirich, S. W., Fabbris, C. & Rocha, E. M. T. Avaliação de métodos de superação de dormência tegumentar em sementes de Cassia leptophylla Vog. Encicl. Biosf. 7, 1–7 (2011).
Google Scholar 

27.
Reddy, V. Presowing treatments to improve Cassia and Calliandra seed germination. Acta Hortic. 226, 537–540 (1988).
Google Scholar 

28.
Lopes, J. C., Capucho, M. T., Krohling, B. & Zanotti, P. Germinação de sementes de espécies florestais de Caesalpinea ferrea Mart. ex Tul. Var. leiostachya Benth., Cassia grandis L. e Samanea saman Merrill, após tratamentos para superar a dormência. Rev. Bras. Sem. 20, 80–86 (1998).
Google Scholar 

29.
Faruqi, M. A., Kahan, M. K. & Uddin, M. G. Comparative studies on the effects of ultrasonics, red light and gibberellic acid, on the germination of Cassia holosericea Fres. seeds. Pak. J. Sci. Ind. Res. 81, 102–104 (1974).
Google Scholar 

30.
Martin, R. E., Miller, R. L. & Cushwa, C. T. Germination response of legume seeds subjected to moist and dry heat. Ecology 56, 1441–1445 (1975).
Google Scholar 

31.
Rizzini, C. T. Influência da temperatura sobre a germinação de diásporos do Cerrado. Rodriguésia 28, 341–381 (1976).
Google Scholar 

32.
Bhatia, R. K., Chawan, D. D. & Sen, D. N. Environment controlled seed dormancy in Cassia spp.. Geobios 4, 208–210 (1977).
Google Scholar 

33.
Zegers, C. D. & Lechuga, A. C. Antecedentes fenológicos y de germinación de espécies lenhosas chilenas. Rev. Cienc. For. 1, 31–37 (1978).
Google Scholar 

34.
Daiya, K. S., Sharma, H. K., Chawan, D. D. & Sen, D. N. Effect of salt solutions of different osmotic potential on seed germination and seedling growth in some Cassia species. Folia Geobot. 15, 149–153 (1980).
Google Scholar 

35.
Teem, D. H., Hoveland, C. S. & Buchanan, G. A. Sicklepod (Cassia obtusifolia) and Cofffe Senna (Cassia occidentalis): Geographic distribution, germination, and emergence. Weed Sci. 28, 68–71 (1980).
Google Scholar 

36.
Felippe, G. M. & Polo, M. Germinação de ervas invasoras: efeito de luz e escarificação. Rev. Bras. Bot. 6, 55–60 (1983).
Google Scholar 

37.
Kay, B. L., Pergler, C. C. & Graves, W. L. Storage of seed of Mojave desert shrubs. J. Seed Technol. 9, 20–28 (1984).
Google Scholar 

38.
Khan, D., Shaukat, S. S. & Faheemuddin, M. Germination studies of certain desert plants. Pak. J. Bot. 16, 231–254 (1984).
Google Scholar 

39.
Cissé, A.M. Dynamique de la strate herbacée des pâturages de la zone sud-sahélienne. Thesis. Wageningen, NL (1986).

40.
Al-Helal, A. A., Al-Farraj, M. M., El-Desoki, R. A. & Al-Habashi, I. Germination response of Cassia senna L. seeds to sodium salts and temperature. J Univ. Kuwait 16, 281–286 (1989).
CAS  Google Scholar 

41.
Elberse, W. T. & Breman, H. Germination and establishment of Sahelian rangeland species. I. Seed properties. Oecologia 80, 477–484 (1989).
ADS  PubMed  Google Scholar 

42.
Bhattacharya, A. & Saha, P. Ultrastructure of seed coat and water uptake pattern of seeds during germination in Cassia sp.. Seed Sci. Technol. 18, 97–100 (1990).
Google Scholar 

43.
Rodrigues, E. H. A., Aguiar, I. B. & Sader, R. Quebra de dormência de sementes de três espécies do gênero Cassia. Rev. Bras. Sem. 12, 17–27 (1990).
Google Scholar 

44.
Capelanes, T.M.C. Tecnologia de sementes de espécies florestais na Companhia Energética de São Paulo. II Simpósio Brasileiro sobre tecnologia de sementes florestais. 49–57 (1991).

45.
Francis, J.K. & Rodriguez, A. Seeds of Puerto Rican trees and shrubs: second installment. USDA. Forest Service, Southern Forest Experiment Station. Note SO-374: 1–5 (1993).

46.
Lezama, C. P., Polito, J. C. & Morfín, F. C. Germinación de semillas de espécies de vegetación primaria y secundaria (estúdios comparativos). Cienc. For. Méx. 18, 3–19 (1993).
Google Scholar 

47.
Agboola, D. A. Dormancy and seed germination in some weeds of tropical waste lands. Niger. J. Bot. 11, 79–87 (1998).
Google Scholar 

48.
Baskin, J. M., Nan, X. & Baskin, C. C. A comparative study of seed dormancy and germination in an annual and a perennial species of Senna (Fabaceae). Seed Sci. Res. 8, 501–512 (1998).
Google Scholar 

49.
Jeller, H. & Perez, S. C. J. G. A. Estudo da superação da dormência e da temperatura em sementes de Cassia excelsa Schrad. Rev. Bras. Sem. 21, 32–40 (1999).
Google Scholar 

50.
Fowler, J.A.P. & Bianchetti, A. Dormência em sementes florestais. Embrapa Florestas (2000).

51.
Bargali, K. & Singh, S. P. Germination behaviour of some leguminous and actinorhizal plants of Himalaya: Effect of temperature and medium. Trop. Ecol. 48, 99–105 (2007).
Google Scholar 

52.
Dutra, A. S., Medeiros Filho, S., Teófilo, E. M. & Diniz, F. O. Seed germination of Senna siamea (Lam.) H.S. Irwin & Barneby—Caesalpinoideae. Rev. Bras. Sem. 29, 160–164 (2007).
Google Scholar 

53.
Ellis, R. H. et al. Comparative analysis by protocol and key of seed storage behaviour of sixty Vietnamese tree species. Seed Sci. Technol. 35, 460–476 (2007).
Google Scholar 

54.
Mishra, D. K. & Bohra, N. K. Effect of seed pre-treatment and time of sowing on germination and biomass of Cassia angustifolia Vahl. in arid regions. Ann. Arid Zone 55, 29–34 (2016).
Google Scholar 

55.
Rolston, M. P. Water impermeable seed dormancy. Bot. Rev. 44, 365–396 (1978).
CAS  Google Scholar 

56.
Baskin, C. C. Breaking physical dormancy in seeds—focusing on the lens. New Phytol. 158, 229–232 (2003).
Google Scholar 

57.
Burrows, G. E., Alden, R. & Robinson, W. A. The lens in focus—lens structure in seeds of 51 Australian Acacia species and its implications for imbibition and germination. Aust. J. Bot. 66, 398–413 (2018).
Google Scholar 

58.
Deng, W., Jeng, D.-S., Toorop, P. E., Squire, G. R. & Iannetta, P. P. M. A mathematical model of mucilage expansion in myxospermous seeds of Capsella bursa-pastoris (shepherd’s purse). Ann. Bot. 109, 419–427 (2012).
CAS  PubMed  Google Scholar 

59.
Yang, X., Baskin, J. M., Baskin, C. C. & Huang, Z. More than just a coating: Ecological importance, taxonomic occurrence and phylogenetic relationships of seed coat mucilage. Perspect. Plant Ecol. Evol. Syst. 14, 434–442 (2012).
Google Scholar 

60.
Van Klinken, R. D. & Flack, L. Wet heat as a mechanism for dormancy release and germination of seeds with physical dormancy. Weed Sci. 53, 663–669 (2005).
Google Scholar 

61.
Van Klinken, R. D., Flack, L. K. & Pettit, W. Wet-season dormancy release in seed banks of a tropical leguminous shrub is determined by wet heat. Ann. Bot. 98, 875–883 (2006).
PubMed  Google Scholar 

62.
Jayasuriya, K. M. G. G., Baskin, J. M. & Baskin, C. C. Cycling of sensitivity to physical dormancy-break in seeds of Ipomoea lacunosa (Convolvulaceae) and ecological significance. Ann. Bot. 101, 341–352 (2008).
CAS  PubMed  Google Scholar 

63.
Jayasuriya, K. M. G. G., Baskin, J. M., Geneve, R. L. & Baskin, C. C. A proposed mechanism for physical dormancy break in seeds of Ipomoea lacunosa. Ann. Bot. 103, 433–445 (2009).
PubMed  Google Scholar 

64.
Western, T. L. The sticky tale of seed coat mucilages: Production, genetics, and role in seed germination and dispersal. Seed Sci. Res. 22, 1–25 (2012).
CAS  Google Scholar 

65.
Gutterman, Y. & Shem-Tov, S. Structure and function of the mucilaginous seed coats of Plantago coronopus inhabiting the Negev desert of Israel. Isr. J. Plant Sci. 44, 125–133 (1996).
Google Scholar 

66.
Gutterman, Y. & Shem-Tov, S. The efficiency of the strategy of mucilaginous seeds of some common annuals of the Negev adhering to the soil crust to delay collection by ants. Isr. J. Plant Sci. 45, 317–327 (1997).
Google Scholar 

67.
Yang, X., Dong, M. & Huang, Z. Role of mucilage in the germination of Artemisia sphaerocephala (Asteraceae) achenes exposed to osmotic stress and salinity. Plant Physiol. Biochem. 48, 131–135 (2010).
CAS  PubMed  Google Scholar 

68.
Moreno-Casasola, P., Grime, J. P. & Martínez, M. L. A comparative study of the effects of fluctuations in temperature and moisture supply on hard coat dormancy in seeds of coastal tropical legumes in Mexico. J. Trop. Ecol. 10, 67–86 (1994).
Google Scholar 

69.
Williams, P. R., Congdon, R. A., Grice, A. C. & Clarke, P. J. Fire-related cues break seed dormancy of six legumes of tropical eucalypt savannas in north-eastern Australia. Aust. Ecol. 28, 507–514 (2003).
Google Scholar 

70.
Maia, F. V., Messias, M. C. T. B. & Moraes, M. G. Efeitos de tratamentos pré-germinativos na germinação de Chamaecrista dentata (Vogel) H.S. Irwin & Barneby. Floram 17, 44–50 (2010).
Google Scholar 

71.
Fichino, B. S., Dombroski, J. R. G., Pivello, V. R. & Fidelis, A. Does fire trigger seed germination in the Neotropical savanas? Experimental tests with six Cerrado species. Biotropica 48, 181–187 (2016).
Google Scholar 

72.
Daibes, F. L. et al. Fire and legume germination in a tropical savanna: Ecological and historical factors. Ann. Bot. 123, 1219–1229 (2019).
PubMed  PubMed Central  Google Scholar 

73.
Finch-Savage, W. E. & Leubner-Metzger, G. Seed dormancy and the control of germination. New Phytol. 171, 501–523 (2006).
CAS  PubMed  Google Scholar 

74.
Willis, C. G. et al. The evolution of seed dormancy: Environmental cues, evolutionary hubs, and diversification of the seed plants. New Phytol. 203, 300–309 (2014).
PubMed  Google Scholar 

75.
Marazzi, B. & Sanderson, M. Large-scale patterns of diversification in the widespread legume genus Senna and the evolutionary role of extrafloral nectaries. Evolution 64, 3570–3592 (2010).
PubMed  Google Scholar 

76.
Acharya, L., Mukherjee, A. K. & Panda, P. C. Separation of the genera in the subtribe Cassiinae (Leguminosae: Caesalpinioideae). Acta Bot. Bras. 25, 223–233 (2011).
Google Scholar 

77.
Scheidegger, N.M.B. & Rando, J.G. Cassia in Flora do Brasil 2020. Jardim Botânico do Rio de Janeiro. https://floradobrasil.jbrj.gov.br/reflora/floradobrasil/FB22858 (2019).

78.
Klink, C. A. & Machado, R. B. Conservation of the Brazilian Cerrado. Conserv. Biol. 19, 707–713 (2005).
Google Scholar 

79.
McDowell, E. M. & Trump, B. F. Histologic fixatives suitable for diagnostic light and electron microscopy. Arch. Pathol. Lab. Med. 100, 405–414 (1976).
CAS  PubMed  Google Scholar 

80.
Robards, A. W. An introduction to techniques for scanning electron microscopy of plant cells. In Electron Microscopy and Cytochemistry of Plant Cells (ed. Hall, J. L.) 343–403 (Elsevier, Amsterdam, 1978).
Google Scholar 

81.
R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org (2011).

82.
Tietema, T., Merkesdal, E. & Schroten, J. Seed Germination of Indigenous Trees in Botswana (ACTS Press, Anaheim, 1992).
Google Scholar 

83.
Watkins, G. Trees and Shrubs for Planting in Tanganyika. (Government Printer, 1960).

84.
Martins, C. C., Machado, C. G., Martinelli-Seneme, A. & Zucareli, C. Methods for harvesting and breaking the dormancy of Cassia ferruginea seeds. Semina 33, 491–498 (2012).
Google Scholar 

85.
Moreira, F. M. S. & Moreira, F. W. Características da germinação de sementes de 64 espécies de leguminosas florestais nativas da Amazônia, em condições de viveiro. Acta Amaz. 26, 3–16 (1996).
Google Scholar 

86.
Souza, L. A. G. & Silva, M. F. Tratamentos escarificadores em sementes duras de sete leguminosas nativas da ilha de Maracá, Roraima, Brasil. Bol. Mus. Para. Emílio Goeldi Série botânica 14, 11–32 (1998).
MathSciNet  Google Scholar 

87.
Jaganathan, G. K. Physical dormancy alleviation and soil seed bank establishment in Cassia roxburghii is determined by soil microsite characteristics. Flora 244, 19–23 (2018).
Google Scholar 

88.
Knowles, O. H. & Parrotta, J. A. Amazonian forest restoration: An innovative system for native species selection based on phenological data and field performance indices. Commonw. For. Rev. 74, 230–243 (1995).
Google Scholar  More