More stories

  • in

    Climate drives global functional trait variation in lizards

    Higham, T. E. et al. Linking ecomechanical models and functional traits to understand phenotypic diversity. Trends Ecol. Evol. 36, 860–873 (2021).Article 
    PubMed 

    Google Scholar 
    Kearney, M. R., Jusup, M., McGeoch, M. A., Kooijman, S. A. & Chown, S. L. Where do functional traits come from? The role of theory and models. Funct. Ecol. 35, 1385–1396 (2021).Article 
    CAS 

    Google Scholar 
    Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108 (1956).Article 

    Google Scholar 
    Gaston, K. J., Chown, S. L. & Evans, K. L. Ecogeographical rules: elements of a synthesis. J. Biogeogr. 35, 483–500 (2008).Article 

    Google Scholar 
    Chown, S. L. & Gaston, K. J. Macrophysiology for a changing world. Proc. R. Soc. B 275, 1469–1478 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rubalcaba, J. G. & Jimeno, B. Biophysical models unravel associations between glucocorticoids and thermoregulatory costs across avian species. Funct. Ecol. 36, 64–72 (2022).Article 
    CAS 

    Google Scholar 
    Anderson, R. O., White, C. R., Chapple, D. G. & Kearney, M. R. A hierarchical approach to understanding physiological associations with climate. Glob. Ecol. Biogeogr. 31, 332–346 (2022).Article 

    Google Scholar 
    Angilletta, M. J. Jr, Niewiarowski, P. H. & Navas, C. A. The evolution of thermal physiology in ectotherms. J. Therm. Biol. 27, 249–268 (2002).Article 

    Google Scholar 
    Olalla‐Tárraga, M. Á., Rodríguez, M. Á. & Hawkins, B. A. Broad‐scale patterns of body size in squamate reptiles of Europe and North America. J. Biogeogr. 33, 781–793 (2006).Article 

    Google Scholar 
    Amado, T., Moreno Pinto, M. G. & Olalla‐Tárraga, M. Á. Anuran 3D models reveal the relationship between surface area‐to‐volume ratio and climate. J. Biogeogr. 46, 1429–1437 (2019).
    Google Scholar 
    Castro, K. M. S. A. et al. Water constraints drive allometric patterns in the body shape of tree frogs. Sci. Rep. 11, 1218 (2021).Clusella-Trullas, S., Terblanche, J. S., Blackburn, T. M. & Chown, S. L. Testing the thermal melanism hypothesis: a macrophysiological approach. Funct. Ecol. 22, 232–238 (2008).Ghalambor, C. K., Huey, R. B., Martin, P. R., Tewksbury, J. J. & Wang, G. Are mountain passes higher in the tropics? Janzen’s hypothesis revisited. Integr. Comp. Biol. 46, 5–17 (2006).Article 
    PubMed 

    Google Scholar 
    Bennett, J. M. et al. The evolution of critical thermal limits of life on Earth. Nat. Commun. 12, 1198 (2021).Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Muñoz, M. M. The Bogert effect, a factor in evolution. Evolution 76, 49–66 (2021).Article 
    PubMed 

    Google Scholar 
    Bogert, C. M. Thermoregulation in reptiles, a factor in evolution. Evolution 3, 195–211 (1949).Article 
    CAS 
    PubMed 

    Google Scholar 
    Huey, R. B., Hertz, P. E. & Sinervo, B. Behavioral drive versus behavioral inertia in evolution: a null model approach. Am. Nat. 161, 357–366 (2003).Article 
    PubMed 

    Google Scholar 
    Kearney, M. R. & Porter, W. P. NicheMapR—an R package for biophysical modelling: the microclimate model. Ecography 40, 664–674 (2017).Article 

    Google Scholar 
    Messier, J., McGill, B. J., Enquist, B. J. & Lechowicz, M. J. Trait variation and integration across scales: is the leaf economic spectrum present at local scales? Ecography 40, 685–697 (2017).Article 

    Google Scholar 
    Ricklefs, R. E. & Schluter, D. (eds) Species Diversity in Ecological Communities: Historical and Geographical Perspectives (Univ. Chicago Press, 1993).Angilletta, M. J. Jr, Steury, T. D. & Sears, M. W. Temperature, growth rate, and body size in ectotherms: fitting pieces of a life-history puzzle. Integr. Comp. Biol. 44, 498–509 (2004).Article 
    PubMed 

    Google Scholar 
    Pincheira-Donoso, D. The balance between predictions and evidence and the search for universal macroecological patterns: taking Bergmann’s rule back to its endothermic origin. Theory Biosci. 129, 247–253 (2010).Article 
    PubMed 

    Google Scholar 
    Slavenko, A. et al. Global patterns of body size evolution in squamate reptiles are not driven by climate. Glob. Ecol. Biogeogr. 28, 471–483 (2019).Article 

    Google Scholar 
    Stevenson, R. D. Body size and limits to the daily range of body temperature in terrestrial ectotherms. Am. Nat. 125, 102–117 (1985).Article 

    Google Scholar 
    Rubalcaba, J. G., Gouveia, S. F. & Olalla‐Tárraga, M. A. A mechanistic model to scale up biophysical processes into geographical size gradients in ectotherms. Glob. Ecol. Biogeogr. 28, 793–803 (2019).Article 

    Google Scholar 
    Rubalcaba, J. G. & Olalla‐Tárraga, M. Á. The biogeography of thermal risk for terrestrial ectotherms: scaling of thermal tolerance with body size and latitude. J. Anim. Ecol. 89, 1277–1285 (2020).Article 
    PubMed 

    Google Scholar 
    Pincheira-Donoso, D., Hodgson, D. J. & Tregenza, T. The evolution of body size under environmental gradients in ectotherms: why should Bergmann’s rule apply to lizards? BMC Evol. Biol. 8, 68 (2008).Jablonski, D. Biotic interactions and macroevolution: extensions and mismatches across scales and levels. Evolution 62, 715–739 (2008).Article 
    PubMed 

    Google Scholar 
    Kearney, M. R., Porter, W. P. & Huey, R. B. Modelling the joint effects of body size and microclimate on heat budgets and foraging opportunities of ectotherms. Methods Ecol. Evol. 12, 458–467 (2021).Article 

    Google Scholar 
    Campbell-Staton, S. C., Bare, A., Losos, J. B., Edwards, S. V. & Cheviron, Z. A. Physiological and regulatory underpinnings of geographic variation in reptilian cold tolerance across a latitudinal cline. Mol. Ecol. 27, 2243–2255 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Boretto, J. M., Fernández, J. B., Cabezas-Cartes, F., Medina, M. S. & Ibargüengoytía, N. R. in Lizards of Patagonia (eds Morando, M. & Avila, L. J.) 335–371 (Springer, 2020).Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).Article 
    PubMed 

    Google Scholar 
    Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B 278, 1823–1830 (2011).Article 
    PubMed 

    Google Scholar 
    Hoffmann, A. A., Chown, S. L. & Clusella‐Trullas, S. Upper thermal limits in terrestrial ectotherms: how constrained are they? Funct. Ecol. 27, 934–949 (2013).Article 

    Google Scholar 
    Sunday, J. et al. Thermal tolerance patterns across latitude and elevation. Philos. Trans. R. Soc. B 374, 20190036 (2019).Article 

    Google Scholar 
    Huey, R. B. & Slatkin, M. Cost and benefits of lizard thermoregulation. Q. Rev. Biol. 51, 363–384 (1976).Article 
    CAS 
    PubMed 

    Google Scholar 
    Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B 281, 20132612 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Porter, W. P., Mitchell, J. W., Beckman, W. A. & DeWitt, C. B. Behavioral implications of mechanistic ecology. Oecologia 13, 1–54 (1973).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hertz, P. E., Huey, R. B. & Stevenson, R. D. Evaluating temperature regulation by field-active ectotherms: the fallacy of the inappropriate question. Am. Nat. 142, 796–818 (1993).Article 
    CAS 
    PubMed 

    Google Scholar 
    Fey, S. B. et al. Opportunities for behavioral rescue under rapid environmental change. Glob. Change Biol. 25, 3110–3120 (2019).Article 

    Google Scholar 
    Martin, T. L. & Huey, R. B. Why ‘suboptimal’ is optimal: Jensen’s inequality and ectotherm thermal preferences. Am. Nat. 171, E102–E118 (2008).Article 
    PubMed 

    Google Scholar 
    R Core Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).Campbell, G. S. & Norman, J. M. An Introduction to Environmental Biophysics 2nd edn (Springer-Verlag, 1998).Mao, J. & Yan, B. Global Monthly Mean Leaf Area Index Climatology, 1981–2015 (ORNL DAAC, 2019).Meiri, S. et al. Are lizards feeling the heat? A tale of ecology and evolution under two temperatures. Glob. Ecol. Biogeogr. 22, 834–845 (2013).Article 

    Google Scholar 
    Marino, S., Hogue, I. B., Ray, C. J. & Kirschner, D. E. A methodology for performing global uncertainty and sensitivity analysis in systems biology. J. Theor. Biol. 254, 178–196 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Renardy, M., Hult, C., Evans, S., Linderman, J. J. & Kirschner, D. E. Global sensitivity analysis of biological multiscale models. Curr. Opin. Biomed. Eng. 11, 109–116 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Carnell, R. lhs: Latin hypercube samples. R package version 1.1.1 (2020).Meiri, S. Traits of lizards of the world: variation around a successful evolutionary design. Glob. Ecol. Biogeogr. 27, 1168–1172 (2018).Article 

    Google Scholar 
    Clusella-Trullas, S., Blackburn, T. M. & Chown, S. L. Climatic predictors of temperature performance curve parameters in ectotherms imply complex responses to climate change. Am. Nat. 177, 738–751 (2011).Article 
    PubMed 

    Google Scholar 
    Bennett, J. M. et al. GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms. Sci. Data 5, 180022 (2018).Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677–1682 (2017).Article 
    PubMed 

    Google Scholar 
    Tonini, J. F. R., Beard, K. H., Ferreira, R. B., Jetz, W. & Pyron, R. A. Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biol. Conserv. 204, 23–31 (2016).Article 

    Google Scholar 
    Ives, A. R. R2s for correlated data: phylogenetic models, LMMs, and GLLMs. Syst. Biol. 68, 234–251 (2019).Article 
    PubMed 

    Google Scholar 
    Johnson, T. F., Isaac, N. J. B., Paviolo, A. & González-Suárez, M. Handling missing values in trait data. Glob. Ecol. Biogeogr. 30, 51–62 (2020).Article 

    Google Scholar 
    Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within‐species variation. Methods Ecol. Evol. 8, 22–27 (2017).Article 

    Google Scholar 
    Koenker, R. et al. Package ‘quantreg’ (R-CRAN, 2018); https://cran.r-project.org/web/packages/quantreg/quantreg.pdfGriffith, D. A. & Peres-Neto, P. R. Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. Ecology 87, 2603–2613 (2006).Article 
    PubMed 

    Google Scholar 
    Bivand, R. R packages for analyzing spatial data: a comparative case study with areal data. Geogr. Anal. 54, 488–518 (2022).Article 

    Google Scholar 
    Rubalcaba, J. G. et al. Data: ‘Climate drives global functional trait variation in lizards’. figshare https://doi.org/10.6084/m9.figshare.19949315 (2022). More

  • in

    Astrobiologists train an AI to find life on Mars

    Artificial intelligence (AI) and machine learning could revolutionize the search for life on other planets. But before these tools can tackle distant locales such as Mars, they need to be tested here on Earth.A team of researchers have successfully trained an AI to map biosignatures — any feature which provides evidence of past or present life — in a three-square-kilometre area of Chile’s Atacama Desert. The AI substantially reduced the area the team needed to search and boosted the likelihood of finding living organisms in one of the driest places on the planet. The results were reported on 6 March in Nature Astronomy1.Kimberley Warren-Rhodes, a senior research scientist at the SETI Institute in Mountain View, California, and lead author on the paper, has been chasing biosignatures since the early 2000s, when she realized how few tools existed to study the biology of other planets. She wanted to combine her background in statistical ecology with emerging technologies such as AI to help mission scientists, “who are under a lot of pressure to find biosignatures” but tightly constrained in how they do so. Rovers that are controlled remotely from Earth, for example, can travel only limited distances and collect relatively few specimens, placing a premium on sampling locations that are the most likely to yield life. Mission scientists base these predictions in part on Mars analogues on Earth, where scientists scour extreme habitats to determine how and where living organisms thrive.Searching for lifeBeginning in 2016, Warren-Rhodes’ group travelled to the high, parched plateau of the Atacama Desert — a proposed Mars analogue at an elevation of around 3,500 metres in the Chilean Andes — to search for rock-dwelling, photosynthetic organisms called endoliths. To fully characterize the environment, the researchers collected everything from drone footage to geochemical analyses to DNA sequences. Together, this data set mimics the types of information researchers are collecting on Mars with orbital satellites, drones and rovers.Warren-Rhodes’ team fed its data into an AI-based convolutional neural network (CNN) and a machine-learning algorithm that in turn predicted where life was most likely to be found in the Atacama.

    Aerial view (left) and ground view from a rover of a biosignature probability map of the same area.Credit: M. Phillips, K. A. Warren-Rhodes & F. Kalaitzis

    By targeting their sample collection on the basis of AI feedback, the researchers were able to reduce their search area by up to 97% and increase their likelihood of finding life by up to 88%. “At the end, you could plop us down, and instead of wandering around for a long time, it would take us a minute to find life,” Warren-Rhodes says. Specifically, the team found that endoliths in the Atacama were most often found in a mineral called alabaster — which is porous and retains water — and tended to aggregate in transitional areas between various microhabitats, such as where sand and alabaster crystals abut one another.“I’m very impressed and very happy to see this suite of work,” says Kennda Lynch, an astrobiologist at the Lunar and Planetary Institute in Houston, Texas, who studies biosignatures. “It’s really cool that they can show some success with an AI to help predict where to go and look.”Graham Lau, an astrobiologist at the Blue Marble Space Institute of Science who is based in Boulder, Colorado, worked on another Mars analogue in the Canadian Arctic as a graduate student, to study how biology influences the formation of rare minerals that can serve as biosignatures on other planets. “Ever since I first read Frank Herbert’s Dune as a young child, I was struck by this idea of applying ecology to planets,” he says. But up until the last decade or so, the tools and data weren’t available to address such questions with scientific rigour. “The place where we have almost unlimited data possibilities is through these orbital observations and drone imaging,” he says, “and I do see this paper as being one of many pieces along the pathway to doing these larger analyses.”Deceptively simpleThe new method will need to be verified across multiple ecosystems, Lau and Lynch say, including those with more complex geology and greater biodiversity. The Atacama, Lau notes, is relatively simple in terms of the habitats and the types of life that are likely to be found there. And on Mars, the high level of ultraviolet radiation striking the planet’s surface means that scientists might need to detect clues that hint at life below ground.

    NASA’s Perseverance rover collected its first rock sample from an area in Mars’ Jezero Crater.Credit: NASA/JPL-Caltech/ASU/MSSS

    Ultimately, Warren-Rhodes says she would like to see a comprehensive database of different Mars analogues that could feed valuable information to mission scientists planning their next sampling run. Her team’s advance, she adds, might appear “deceptively simple” to anyone who grew up watching Star Trek explorers scanning alien worlds with a tricorder. But, it represents an important advance in extraterrestrial research, in which biology has often lagged behind chemistry and geology. Imagine, for instance, virtual-reality headsets that feed mission scientists real-time data as they scan a surface, using a rover’s ‘eyes’ to direct their activities. “To have our team make one of these first steps towards reliably detecting biosignatures using AI is exciting,” she says. “It’s really a momentous time.” More

  • in

    No impact of nitrogen fertilization on carbon sequestration in a temperate Pinus densiflora forest

    SettingThis study was conducted in approximately 40-year-old naturally regenerated P. densiflora stands in Wola National Experimental Forest in Gyeongnam province in South Korea (35°12′ N, 128°10′ E; Table 1). The productivity of this forest is low, with a dominant tree height of 10 m at 20 years of age. Over the last 10 years, the mean annual precipitation was 1490 mm, of which one third fell during summer (July–August), and the mean temperature was 13.1 °C. The vegetation growing season generally lasts for approximately 200 days, extending from early April to October. The soil texture is a silt loam originating from sandstone and shale (clay 13.0 ± 0.8%, silt 44.1 ± 1.3%, sand 42.9 ± 1.6%; n = 18). The given texture results in volumetric water contents at 13.4 ± 0.7% (m3 m−3) at permanent wilting point (1500 kPa) and 40.7 ± 1.2% at field capacity (10 kPa)55. The understory is covered with Lespedeza spp., Quercus variabilis, Q. serrata, Smilax china, and Lindera glauca.In 2010, we selected two adjacent P. densiflora stands approximately 100 m apart from each other (180 m and 195 m above sea level, on slopes of 15° and 33°, both stands face south). Following a completely randomized design, we established nine plots (10 × 10 m2 with a 5 m untreated buffer) within each stand, of which three were randomly assigned to annual NPK fertilization, three to PK fertilization, and the rest to a control treatment without fertilization. The fertilizer, composed of urea, fused superphosphate and potassium chloride (N3P4K1) or P4K1 was added manually by deposition on the forest floor for 3 years in April 2011, April 2012, and March 2013. Over these 3 years, the NPK plots received 33.9 g N, 45 g P, and 11.1 g K m−2, while the PK plots received 45 g P and 11.1 g K m−2.Tree and stand measurementsThe standing biomass of trees was estimated using a combination of site-specific allometric equations based on destructive harvesting56 and repeated measurements of the dimensions of all trees in each plot (5–18 trees plot−1). The stem diameter at 1.2 m (D) was measured for all trees in each plot for which D was ≥ 6 cm. Selecting a representative tree in size for each plot within the 4 × 4 m2 center of the plot, we measured the tree height (H) and crown base for the representative trees. Measurements were performed in April and September 2011, September 2012–2014, and October 2021. We observed no effect of fertilization on the relationship between D and H or between D and crown base, so we assumed no effect on the allometric functions for foliage or branch biomass. A dendrometer band (Series 5 Manual Band, Forestry Suppliers Inc., Jackson, MS, USA) was installed on 18 representative trees (one per plot) to monitor radial growth monthly.Three 0.25 m2 circular litter traps were installed 60 cm above the forest floor in each plot in April 2011. Litter was collected at 3-month intervals between June 2011 and March 2015. The litter from each trap was transported to the laboratory and then oven-dried at 65 °C for 48 h. All dried samples were separated into needles, bark, cones, branches, and miscellaneous components, and weighed separately.In September 2014, we estimated the biomass of understory vegetation, separately for woody plants and herbaceous plants. All woody plants  More

  • in

    Assessment of the variability of the morphological traits and differentiation of Cucurbita moschata in Cote d’Ivoire

    Description of the phenological, vegetative and yield traits of the accessions per habitatThe process of data management included the computation of mean squares for the assessed phenological, vegetative and yield traits of the accessions with the sampling habitats considered as the treatment factor. The error mean squares served in the multiple comparison of means reported in Table 1.Table 1 Means of the measured phonological, vegetative and flowering and yield traits of Cucurbita moschata genotypes sampled from seven habitats.Full size tableRegarding the phenological traits, the accessions from the habitat of Zh have the longest period from seeding to first male (102.39 d) and first female (108.14 d) flower appearances, and the longest period from seeding to physiological maturity (153.95 d). For those traits, the accessions from Tiassale and Soubre are not significantly different from those of Zh. And, accessions from Tiassale and Zh have the longest periods from seeding to 50% flowering. On the other hand, accessions from Korho, Ferke, Bondu and Burki develop their first male and female flowers and attain 50% flowering in a very short period. They also reach physiological maturity faster. Accessions from Korho, however, have the longest period from seeding to 50% emergence (6.07 d) and accessions from Bondu have the longest period from first female flower appearance to physiological maturity (53.04 d).
    For the vegetative traits, accessions from Tiassale and Soubre have the largest girth size (4.43 cm and 4.63 cm, respectively). Accessions from Tiassale have the longest (24.98 cm) and widest (19.94 cm) leaves, the longest male (16.2 cm) and female (4.03 cm) peduncles and the longest petioles (34.94 cm). The measures for those organs on accessions from Soubre rank second to those of Tiassale. On the other hand, accessions from Korho, Ferke, Bondu and Burki are characterized by smaller girth size, smaller leaves, smaller petioles and smaller peduncles of male and female flowers. But the accessions from Bondu are the tallest (586.91 cm) followed by the accessions from Ferke (489.20 cm). And the accessions from Zh are the shortest (417.38 cm).For the flowering and yield traits, accessions from Tiassale and Soubre show the largest numbers of male (27.33 units and 22.58 units, respectively) and female (5.22 units and 6.05 units, respectively) flowers per plant, largest numbers of fruits per plant (2.78 units and 2.53 units, respectively) and largest measures of all fruit-related traits. Their seeds are very large, but in small numbers. In contrast, accessions from Korho, Ferke, Bondu and Burki have the smallest numbers of male and female flowers per plant, the smallest numbers of fruits per plant and the smallest measures of fruit-related traits. They have large numbers of seeds, but their seeds are smaller, except the seeds of the accessions from Burki. Refer to Table 1 for more detailed information.
    Variability of the phenological, vegetative and yield traitsTable 2 shows the spread of the phenological and morphological traits of the assessed accessions of C. moschata. All the evaluated traits showed very wide ranges of distribution of the observations. Some conspicuously wide ranges of traits include number of days to 50% flowering (DTF) that goes from 52 to 152 d, plant height with a minimum of 48 cm and a maximum of 1510 cm, diameter of the fruit that is between 5.8 cm and 35 cm, weight of the fruit that varies between 150 g and 10,930 g and number of seeds per fruit that spreads in the interval from 32 units per fruit to 729 units per fruit. Excluding the number of days to 50% emergence (DTE), all the other assessed traits have remarkably wide ranges of phenotypic expressions (Table 2). All the traits but DTE, DTF, days from first female flower appearance to fruit maturity, fruit length and length of the dry seed, had outliers. The number of outliers ranged from 1 to 67. Except the outliers observed with the width of the dry seed, all the outliers were above 1.5*IQR + Q3 where IQR is the inter-quartile range and Q3 is the third quartile. The presence of outliers is indicative of the richness and large variability of the population of accessions. The outliers are exceptional performances that fall outside the normal distribution of the observations. They are a stock of unusual traits that can be used in a crop improvement program when beneficial. For example, the observed outliers for diameter of the fruit, weight of the fruit or thickness of the pulp can be used in a breeding program for the improvement of fruit yield. Similarly, outliers for beneficial traits related to the seed can be used to improve C. moschata crop for seed yield. Besides, the computed mean squares (data not reported) showed highly significant variations between accessions for the assessed traits. They all yielded p-values less than 0.01, providing additional support to the evidence of large variability among the accessions of C. moschata of Cote d’Ivoire. The computed standard deviation, and median absolute deviation for each trait are additional evidence. We should note that in most cases, the mean squares associated to year (data not reported) were not significant, indicating the relative stability of the assessed traits.Table 2 Minimum (Min), first quartile (Q1), median, third quartile (Q3), maximum (Max), standard deviation (SD), median absolute deviation (MAD) and outliers obtained from the phenological, vegetative and flowering and yield traits of 663 accessions of C. moschata.Full size tableThe components of variance, the quantitative genetic differentiation, the overall mean, and the coefficients of variation are reported in Table 3. The lme4 package37 used in the determination of the components of variance, does not provide p-values in the analysis of mixed or random models. The reported quantities in Table 3 are not accompanied with tests of significance. It is worth mentioning that the respective units of measure of the assessed traits are squared for the variances and the evaluated estimates will be reported without the units of measure. The phenotypic variance ((sigma_{p}^{2})) is partitioned into variance between morphotypes or genotypic variance ((sigma_{g}^{2})), and within morphotypes or residual variance ((sigma_{e}^{2})). For the class of phenological traits, considerable genotypic variances were observed with days to 50% flowering (266.21) and days to first male flower appearance (254.40), compared with their respective residual variances (148.13 and 199.50). Regarding the class of vegetative traits, only the peduncle length of male flowers had a genotypic variance (9.22) greater than its residual variance (8.86). In the class of flowering and yield traits, 8 of the 15 traits assessed showed large genotypic variances in comparison with their respective residual variances. They are number of female flowers per plant ((sigma_{g}^{2}) = 3.02 versus (sigma_{e}^{2}) = 2.36), length of the fruit ((sigma_{g}^{2}) = 53.96 versus (sigma_{e}^{2}) = 48.97), diameter of the fruit ((sigma_{g}^{2}) = 37.17 versus (sigma_{e}^{2}) = 16.76), volume of the fruit ((sigma_{g}^{2}) = 10,713,468 versus (sigma_{e}^{2}) = 3,904,590), weight of the fruit ((sigma_{g}^{2}) = 5,413,819 versus (sigma_{e}^{2}) = 1,420,187), diameter of the cavity enclosing the seed ((sigma_{g}^{2}) = 19.12 versus (sigma_{e}^{2}) = 7.75), thickness of the fruit pulp ((sigma_{g}^{2}) = 1.11 versus (sigma_{e}^{2}) = 0.94) and weight of the fruit pulp ((sigma_{g}^{2}) = 5,979,212 versus (sigma_{e}^{2}) = 1,088,750). For a trait to have a lager genotypic variance than the residual variance is synonymous to a relative ease of improvement of the crop for that trait through a breeding program.Table 3 Components of variances ((sigma_{p}^{2}), (sigma_{g}^{2}), (sigma_{e}^{2}), (sigma_{a}^{2})), quantitative genetic differentiation ((Q_{ST})), overall mean ((mu)), and coefficients of variation (%) ((CV_{p}),(CV_{g}),(CV_{e})), of the measured phenological, vegetative and yield traits of the accessions of C. moschata of Cote d’Ivoire.Full size tableThe coefficient of variation (CV) is another statistic that measures variation. It is actually the dispersion of a trait per unit measure of its mean, which can be used to compare variations of traits with different measurement units or different scales. As a rule-of-thumb, a coefficient of variation greater than 20% is indicative of large variation for the trait. The phenotypic coefficient of variation is considerably high for 25 of the 28 assessed traits. Only the number of days from seeding to physiological maturity, the first and second longest axes of the dry seed show coefficients of variation less than 20%. Traits with very large phenotypic coefficients of variation include the peduncle length of female flowers ((CV_{p}) = 93.98%), weight of the pulp ((CV_{p}) = 92.96%), volume of the fruit ((CV_{p}) = 89.17%), weight of the fruit ((CV_{p}) = 78.30%) and number of female flowers per plant ((CV_{p}) = 65.81%). With respect to the residual coefficients of variation, only the number of days from seeding to 50% emergence and number of days from first female flower appearance to physiological maturity have residual coefficients of variation greater than 20%, among the phenological traits. All the vegetative traits have residual coefficients of variation greater than 20%, and show a near-perfect linear relation (r = 0.98; p  More

  • in

    Extinction drives a discontinuous temporal pattern of species–area relationships in a microbial microcosm system

    Preparation of the pao cai soupFirst, 35 kg of white radish (Raphanus sativus), 35 kg of cabbage (Brassica oleracea), 2 kg of chili pepper (Capsicum frutescens), 1 kg of ginger (Zingiber officinale), 1 kg of peppercorns (Zanthoxylum bungeanum), 2.5 kg of rock sugar, and 210 kg of cold boiled water (containing 6% salt) were divided into six ceramic jars. After 7 days of natural fermentation at room temperature, the pao cai was filtered out with sterile gauze to obtain 200 kg of pao cai soup. To ensure an even distribution of microorganisms in the soup, the soup was mixed well and then left to rest for 12 h, the supernatant was taken, and the soup was left to rest for 12 h again.The plants used in this study were cultivated vegetables which purchased from the vegetable market at the study site. All local, national or international guidelines and legislation were adhered to in the production of this study.Establishment of the microcosm systemSeventy-eight for each size of 10 ml, 20 ml, 50 ml, 100 ml, 250 ml, 500 ml, and 1000 ml sterile glass culture flasks were filled with pao cai soup, the bottle mouth was sealed with sterile sealing film, and the bottle was capped without leaving any air (Fig. 1). Each flask became a microcosm and was cultured in a 25 °C incubator.Figure 1Schematic diagram of the establishment of the microcosmic system.Full size imageSample collectionBefore the microcosm system was established, a sample of well-mixed pao cai soup was taken as a reference to establish background biodiversity. The microbial community dynamics should change the fastest at the beginning of the microcosm system establishment and gradually become slower over time. Considering the workload and cost, this study collected samples daily for 1–10 day after the establishment of the microcosm and then collected every 2 days for 10–30 day and every 5 days for 30–60 day. Three different microcosms of the same volume were established. Monitoring was carried out for 60 days, and a total of 546 samples of 7 volumetric gradients were obtained at 26 time points. At the time of sampling, the pao cai soup in the microcosm was mixed, and 50 mL of sample (10 mL of sample was collected for microcosm systems with a volume of less than 50 mL) was collected. The sample was centrifuged at 8000 rpm for 10 min, the supernatant was collected for pH determination, and the pellet was stored in a − 80 °C freezer.Microbial analysesMicrobial DNA was extracted from pao cai samples using the E.Z.N.A.® Soil DNA Kit (Omega Biotek, Norcross, GA, U.S.) according to the manufacturer’s protocols. For bacteria, we targeted the V3-V4 region of the 16S ribosomal RNA (rRNA) gene, using the 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) primer pairs. For fungi, we targeted the ITS1-1F region of the nuclear ribosomal internal transcribed spacer region (ITS rDNA) gene, using ITS1-1F-F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS-1F-R (5′-GCTGCGTTCTTCATCGATGC-3′). PCRs were performed in triplicate in a 20 μL mixture containing 4 μL of 5 × FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu Polymerase and 10 ng of template DNA. The PCR program for the 16S rRNA gene was as follows: 3 min of denaturation at 95 °C; 27 cycles of 30 s at 95 °C, 30 s of annealing at 55 °C, and 45 s of elongation at 72 °C; and a final extension at 72 °C for 10 min. For the ITS1-1F region, the PCR program was as follows: samples were initially denatured at 98 °C for 1 min, followed by 30 cycles of denaturation at 98 °C for 10 s, primer annealing at 50 °C for 30 s, and extension at 72 °C for 30 s. A final extension step of 5 min at 72 °C was added to ensure complete amplification of the target region. The resulting PCR products were extracted from a 2% agarose gel, further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using QuantiFluor™-ST (Promega, Madison, WI, USA).Purified amplicons were pooled in equimolar amounts and paired-end sequenced (2 × 300) on an Illumina NovaSeq platform (Illumina, San Diego, CA, USA) according to standard protocols. The analysis was conducted by following the “Atacama soil microbiome tutorial” of QIIME2 docs along with customized program scripts (https://docs.qiime2.org/2019.1/). Briefly, raw data FASTQ files were imported in the QIIME2 system using the qiime tools import program. Demultiplexed sequences from each sample were quality filtered, trimmed, denoised, and merged, and then the chimeric sequences were identified and removed using the QIIME2 DADA2 plugin to obtain the feature table of amplicon sequence variants (ASVs)24. Compared with traditional OTU that clusters at 97% similarity, ASV has higher accuracy, equivalent to 99% similarity clustering. The QIIME2 feature-classifier plugin was then used to align ASV sequences to the pretrained GREENGENES 13_8 99% database (trimmed to the V3-V4 region bound by the 338F/806R primer pair for bacteria) and UNITE database (for fungi) to generate the taxonomy table25. Any contaminating mitochondrial and chloroplast sequences were filtered using the QIIME2 feature-table plugin. Based on the sequence number of the lowest sample, perform the resampling to make the sequence number equal for each sample. Due to the random nature of sequencing, ASVs specific to each sample in this study were present. To reduce the uncertainty introduced by the sequencing process, we filtered out rare ASVs with less than 0.001% of the total sequence volume.Data analysisIn this study, the data of fungi and bacteria were integrated and analyzed, and all microbial diversity appearing in the text represent the sum of all fungi and bacteria. Species richness is equal to the number of taxa, which is equal to the total number of all bacterial and fungal ASVs. The vegan package in R 4.2.1 was used to calculate the species richness of each sample based on the ASV feature table26. Using flask volume instead of area, SAR fitting was performed using a semi-logarithmic model, and its significance was tested. The semi-logarithmic model is the function S = c + b*logA, where S is species richness, A is area (in this case, volume is used instead), and b and c are fit parameters27.The microcosmic system in this study is hermetically sealed, and all microorganisms originate from a single portion of well-mixed paocai soup (ie species pool). The speciation process in the 60-day experimental system should be negligible due to the short experimental period. The extinction rate of a microcosm system is equal to the number of ASVs lost in the microcosm system compared to the species pool divided by the total number of ASVs in the species pool. The extinction rate is the number of extinct ASVs in each system compared to the species pool. Pearson correlation analysis was performed with volume as the independent variable and extinction rate as the dependent variable to determine the correlation between volume and extinction rate at each time point. When microorganisms of a microcosmic system disappear entirely or cannot be detected, the microcosm is recorded as an annihilated microcosm. The annihilation rate at a time point is equal to the number of microcosms annihilated at that time, divided by the total number of microcosms. The difference between the extinction rate and annihilation rate defined in this paper is that the extinction rate is for ASVs within each sample, and the annihilation rate is for microcosmic system at each sampling time point. The two indicators jointly characterize the local extinction of microorganisms from different perspectives. Non-linear regression with a bell-shaped form was performed with time as an independent variable and pH and annihilation rate as dependent variables, and regression lines were plotted based on R 4.2.1.According to the taxonomy table, bacterial ASVs were divided into acid-producing and non-acid-producing categories, and their extinction rates were calculated separately. The agricola, ggplot2, vegan and ggpubr packages were used to draw alpha diversity box plots and perform the Wilcoxon rank sum test for differences between groups26,28,29,30. Non-metric multidimensional scaling (NMDS) analysis was performed with the vegan package based on Bray–Curtis dissimilarity. In addition, the potential Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologue (KO) functional profiles of microbial communities were predicted with PICRUSt31. Resistance-related genes were screened using the gene function predictions. The relationship between the relative abundance of resistance-related genes and the volume of the microcosm was analysed by Pearson correlation, and a forest map was plotted to present the results. More

  • in

    Applying an ecosystem services framework on nature and mental health to recreational blue space visits across 18 countries

    We investigated the complex relationships between the environmental characteristics of blue spaces and visit-related mental well-being in a multi-country study including 17 bluespace types and four facets of subjective well-being. Our aim was to operationalise, and consider the utility of, the Bratman et al.9 conceptual model that links ecosystem services (ESS) with mental health.Consistent with the proposed conceptual model, mental well-being outcomes relied on a complex interplay of individual, environmental, and visit characteristics.Summary of findingsOverall, bluespace visits were associated with better subjective mental well-being outcomes if the visits took place in nearby coastal areas or rural rivers, were perceived as safe and to have good water quality, and had a long duration. They could involve a range of activities such as playing with children, socialising, or walking. The degree to which the perceived presence of wildlife predicted visit satisfaction varied depending on the bluespace type, suggesting that the importance of ecosystem features such as biodiversity may vary by the setting.We can also identify the combination of environmental and visit characteristics associated with particularly high levels of well-being for a specific outcome. For example, an optimal visit in terms of happiness might be to sandy beaches where there are high levels of perceived safety and excellent water quality; with a visit lasting at least three hours; and possibly involving playing with children, socialising, sunbathing/paddling and/or walking with a dog; and has short travel times that do not involve public transport.RQ1—natural and environmental featuresResearch question 1a—Which bluespace type(s) were associated with the highest levels of recalled visit mental well-being?Four of the five bluespace types associated with the highest levels of visit satisfaction were coastal (sea cliffs, rocky shore, sandy beaches, rural river and seaside promenade), indicating that these environments may be particularly beneficial for well-being. Visits to these environments were also associated with the lowest levels of visit anxiety, with the exception of seaside promenade and sea cliffs, which were not significantly different to the grand mean. Seaside promenade was the only urban environment in the top five.In addition, only coastal sites were associated with significantly higher levels of visit happiness (compared to the grand mean), further highlighting the potential importance of these environments. Although not explored here, coastal scenes tend to be associated with particularly high aesthetic and scenic value25,26 which may also be positively related to subjective well-being.These findings are broadly consistent with other studies from the UK17,27, but are extended here to our international sample. White et al.28 also used data from the BlueHealth International Survey (BIS) and explored visit frequency to different environments and associations with general mental health and well-being outcomes, including the World Health Organisation five-item Well-being index referring to the two weeks prior to the survey. Consistent with the results here, they found that visit frequency to “coastal blue” environments was more strongly associated with psychological well-being in general than visit frequency to “inland blue” environments. Our study adds to these more general findings by showing that these associations may come as a direct result of the recalled well-being experienced on specific visits to these locations.Confidence in our results was strengthened as we included general mental well-being in our analysis to adjust for whether happier people tend to visit sandy beaches, for example. The results for visit anxiety were not always the inverse of the trends observed in the positive measures of well-being, supporting the need to look at multiple aspects of mental well-being when considering the effects of nature contact.Research question 1b—Which bluespace qualities were associated with the highest levels of recalled visit mental well-being?Of the range of qualities that we investigated as predictors, perceived safety and ‘excellent’ water quality (vs. ‘sufficient’) consistently exhibited the strongest relationships with subjective mental well-being. Perceived safety has been found to be important when visiting blue spaces in several qualitative studies29,30,31, as well as a quantitative study with older adults in Hong Kong14. Blue spaces have particular safety issues with respect to drowning32,33, but fear of crime29,30,33 or pedestrian safety34 may also be relevant.Water quality has also been found to be important in previous economic valuation studies of recreational use and enjoyment of lakes and estuaries in the USA and Australia35,36 as well as a contingent behaviour experiment carried out as part of the BlueHealth International Survey (in European countries only)37. We recognise that here we used a metric of perceived water quality, rather than measures based on biological or toxicological sampling. Nevertheless, perceptions have been reported to positively correlate with sampled water quality parameters38, although assessments can vary systematically such as by bluespace type39. Highly visible harmful algal blooms, for instance, have also been found to affect experiences of blue spaces40.Further, and again consistent with earlier work15,41,42, the presence of facilities and wildlife, and absence of litter, were generally associated with better subjective mental well-being. Both perceived presence of wildlife and facilities were also associated with higher levels of anxiety however, indicating complexities between environmental qualities and well-being. Some wildlife may be deemed unpleasant or an ecosystem disservice, for example. The presence of good facilities may indicate the presence of more people; and visitor density in natural environments can be related to preference43. These results highlight the importance of environmental quality and not just type, consistent with other frameworks12,37.Research question 2—How is exposure, as operationalised by visit duration, related to recalled visit mental well-being?Broadly consistent with research in the green and bluespace literature14,17,44, we found that mental well-being outcomes were generally higher with greater exposure as indicated by visit duration. For decreasing visit anxiety, this was only significant when visits were longer than an hour and a half. As we did not measure pre-visit anxiety levels, we are cautious about identifying this as a potential temporal threshold for reducing anxiety at this stage.Similarly, also using the BlueHealth International Survey, White et al.28 found that well-being outcomes were higher with greater visit exposure to green and blue spaces using a metric of visit frequency. However, in contrast to this and other research which looked at overall weekly aggregated time in nature (e.g.28,45), we have no evidence of diminishing marginal returns as the effect sizes associated with specific visit duration continued to increase with increasing duration.Research question 3—What experiences in blue spaces, in terms of activities (3a) and companions (3b), are associated with the most positive recalled visit mental well-being outcomes?Although walking was the most popular activity, the activity with the highest mental well-being ratings was playing with children, especially in certain locations such as beaches (Fig. 4). However, we also find that anxiety tended to be higher when children were present. We speculate that the purpose of the visit may be important. For example, many who go to the beach with children do so in order to play. However, if children are present on more adult-oriented activities such as hiking, this may increase adult anxiety during the visit. From a representative sample of English adults, White et al.17 found that recent nature visits with children were associated with the lowest levels of well-being. Therefore, visits with children may be associated with a more complex set of emotions, being both slightly more stressful, but also potentially more rewarding and ‘meaningful’46. Ecosystem features of beaches may be particularly supportive of high well-being activities. A qualitative study in the UK, for instance, highlighted the particular opportunities for adults and children to play together at the beach, including rock-pooling and making sandcastles as well as water-based activities47.Visits with other adults were associated with higher levels of both visit satisfaction and worthwhile-ness, and socialising as an activity was associated with better visit well-being for all outcomes compared to the grand mean. This is consistent with studies using the day reconstruction method, which link activities with experiential well-being, in the USA48 and Germany49 where socialising was associated with the highest, or second highest, levels of well-being for all the activities assessed. Further, social interactions have been recognised as an important benefit by many of those visiting freshwater blue spaces in a previous study18.Research question 4—Does the relationship between wildlife presence and recalled visit well-being vary by bluespace settings?The relationship between the presence of wildlife and visit satisfaction varied with bluespace type. The strongest positive association was found for fen, marsh and bog areas, which may also be related to the purpose of visit. For instance, those who visit places such as fens, marshes and bogs, may do so for the explicit purpose of observing wildlife (often birds) and the presence of wildlife would therefore be important for satisfaction with the visit.Perceptions towards wildlife have been found to vary by location in other studies. For example, in Sweden, greater prior experience with geese at beaches was associated with a negative attitude towards geese50. Further, the species present are likely to vary across different environments. In three urban areas in the UK, green spaces correlated with the abundance and species richness of birds considered to provide cultural services (songbirds and woodpeckers), while an abundance of birds considered to provide disservices (e.g. some gull species, feral pigeons) was independent of green spaces51. Preferences for some species over others may explain some of the negative or null relationships between the presence of wildlife at different blue spaces. These examples from the literature, alongside our own results, indicate the potential for benefits from the management of wildlife for psychological ecosystem services differentially across environments, although these should be considered alongside other conservation and ESS goals.MechanismsSeveral mechanisms potentially explain the beneficial effects of visiting blue spaces on mental health and well-being12, including the provision of opportunities for physical activity52,53; social interaction18; cognitive restoration and stress reduction17,54; emotion regulation55 and connecting with nature12. Consistent with these mechanisms, we found that respondents were using blue spaces for both physical activity and social interaction; and that playing with children and socialising were associated with particularly high levels of well-being.In addition to the positive association we find between some ESS and well-being, including presence of wildlife and water quality, additional bluespace ESS not considered here, may also affect mental health and well-being12. For example, the provision of a cooling effect56 and air pollution mitigation57.Strengths and limitationsA key strength is our operationalisation of the Bratman et al.9 conceptual model for mental health using data from a large, 18 country survey that included 17 different bluespace types, five quality metrics and four subjective mental well-being outcomes. The relatively high explanatory power of our models suggests all the variables we explored were important for subjective well-being.Despite the strengths, however, there were also several limitations. The survey was cross-sectional and causality cannot be inferred. For example, happier people may choose to visit a beach rather than another location, although we also controlled for general levels of subjective mental well-being in an attempt to control for this possibility (See Supplemental Materials). Further, although the majority of respondents (53%) recalled a visit within the last 7 days, some were recalling visits up to a month ago, with potential memory biases increasing in line with length of recall.Although our data were collected by an international market research company to be representative of age, gender and region within country, our online sample may not be fully representative across more characteristics and any country-level conclusions need to be treated with caution. We also acknowledge that there were no results from Africa, the Middle East or South America; and Hong Kong was the only representative from Asia. This suggests far more research is needed in other regions to better understand how bluespace ecosystems interact with subjective well-being globally.There may also be socioeconomic confounds that we did not include in our models which may account for some of the effects. Not everyone visits nature for recreation58, including about 4000 people here who did not visit a bluespace in the four weeks prior to responding to the survey. Some groups may therefore have been under-represented; and we should be careful in assuming that our findings generalise to all sub-population groups.Nevertheless, our visit sub-sample distributions were generally similar to that of the weighted percentages in the full sample, with the exception of age where those aged over 60 were under-represented (Table S2); therefore, we suspect these issues were not too influential for the overall results, although care needs to be extended to inferences with respect to older adults.A further limitation was that we only considered the qualities of places where people reported making recreational visits, with respondents presumably less likely to visit places where they feel really unsafe or lacking in facilities29. Further research may want to study responses to a broader range of bluespace settings, including those that are less visited, to determine the generalisability of the generally positive results found here. Such studies could use pre-existing tools to objectively assess the quality of blue spaces59.ImplicationsOur finding that coastal environments are particularly beneficial adds to the body of evidence linking coastal environments with health and well-being and suggests this is consistent across many countries. Previous research has found that greater proximity to blue spaces, especially coastal settings, predicts visit frequency14,60,61 as well as other health outcomes—e.g. reduced risk of mortality and better general health, well-being and physical activity53,62. Here, we found that shorter travel times also predict visit well-being, highlighting the importance of having equitable access to good quality natural environments near to people’s homes.We also identified that different types of coastal and inland blue spaces (e.g. seaside promande, rural rivers), with different qualities (e.g. wildlife present), involving particular types of activities in specific social configurations (e.g. playing with children), were especially good at promoting well-being. This moves beyond a simple location-based assessment of benefit to one that recognises the complex interplay between location, behavioural and social processes. Numerous commentators63 (including Bratman et al.9 on which we have based this paper) have argued that we need to go beyond the determinate effects of green and blue spaces and develop a far richer, more nuanced understanding. The approach we have taken here is intended as a step in this direction.In terms of policy applications, these results provide support for the potential health benefits of efforts to improve equitable access to high quality environments, such as the English Coast Path (https://englandcoastpath.co.uk/) and the creation of beaches in Barcelona with the Olympic project in 199264. Our results also hint at the importance of high-level legislation, such as the EU’s Bathing Waters Directive65 for mental well-being37. If conducted on a more fine-grained geographical level, results could have the potential to leverage public support for more localised conservation initiatives. Furthermore, such results could be used as a basis for integration into more systematic conservation planning66.Further researchAlthough we incorporate a range of variables in our analysis, and our pseudo-R2 values are relatively high for a social research context, considerable variation remains unexplained. Although other individual characteristics may be important, such as nature connectedness67 and memories68, further research could explore the specific ecosystem features and social contexts associated with the particular positive results from coastal spaces, which would be of interest to policy makers and environmental managers. We also speculated that purpose of visit may explain some of our findings. Further research could explore the interactions between motivations and location, experience, and well-being outcomes.The presence of wildlife was differentially important across bluespace types and further research could unpack this. Exploring similar possibilities for the other quality metrics, as well as considering additional ecosystem characteristics, would also be informative. For example, identifying which factors are important in perceptions of safety in blue spaces. Bratman et al.9 also considered effect modification by visitor characteristics and further research could include interactions, or sub-group analysis, by socio-demographic factors.Further research could also explore longer-term benefits of these features over repeated visits; the potential for ecosystem disservices, such as the relationships we find between an interaction of wildlife and ice rinks and well-being; the potential for negative outcomes associated with ecosystem degradation69; and the potential for positive mental health outcomes from ecological restoration70.We have demonstrated some of the complexities involved in the human-nature relationship and that many factors are related to the outcome from a visit. The conceptual model applied allows the investigation of a wide range of variables including natural features and other environmental qualities, and characteristics of the exposure and experience, as well as individual parameters. We suggest that other researchers can apply this conceptual model and design data collection accordingly to target specific research questions and hypotheses (as opposed to where we have fitted already collected data). More

  • in

    Ontogenetic changes in the body structure of the Arctic fish Leptoclinus maculatus

    Meyer Ottesen, C. A. et al. Early life history of the daubed shanny (Teleostei: Leptoclinus maculatus) in Svalbard waters. Mar. Biodivers. 41(3), 383–394 (2011).Article 

    Google Scholar 
    Murzina, S.A. Role of Lipids and Their Fatty Acid Components in Ecological and Biochemical Adaptations of Fish of the Northern Seas. Dr. Sci. Thesis (IPEE RAS, 2019).Murzina, S. A. et al. Tiny but fatty: Lipids and fatty acids in the Daubed Shanny (Leptoclinus maculatus), a small fish in Svalbard waters. Biomolecules 10, 368 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Falk-Petersen, S., Falk-Petersen, I. B. & Sargent, J. R. Structure and function of an unusal lipid storage organ in the Arctic fish Lumpenus maculatus Fries. Sarsia 71(1), 1–6 (1986).Article 
    CAS 

    Google Scholar 
    Murzina S.A. The Role of Lipids and Their Fatty Acid Components in the Biochemical Adaptations of the Daubed Shanny Leptoclinus maculatus F. of the Northwestern Coast of Svalbard. PhD Thesis 184 (IB KarRC RAS, 2010)Pekkoeva, S. N. et al. Ecological role of lipids and fatty acids in the early postembryonic development of daubed shanny, Leptoclinus maculatus (Fries, 1838) from Kongsfjorden, West Spitsbergen in winter. Rus. J. Ecol. 48(3), 240–244 (2017).Article 
    CAS 

    Google Scholar 
    Hovde, S. C., Albert, O. T. & Nilssen, E. M. Spatial, seasonal and ontogenetic variation in diet of Northeast Arctic Greenland halibut (Reinhardtius hippoglossoides). ICES J. Mar. Sci. 59, 421–437 (2002).Article 

    Google Scholar 
    Labansen, A. L., Lydersen, C., Haug, T. & Kovacs, K. M. Spring diet of ringed seals (Phoca hispida) from northwestern Spitsbergen. Norway. ICES J. Mar. Sci. 64, 1246–1256 (2007).Article 

    Google Scholar 
    Moser, H. G. Morphological and functional aspect of marine fish larvae. in Marine Fish Larvae—Morphology, Ecology, and Relation to Fisheries (ed. Lasker, R.). 89–131. (University of Washington Press, 1981).Moser, H. G. et al. Ontogeny and systematics of fishes. in American Society Ichthyologists Herpetologists Special Publication. Vol. 760 (Allen Press, 1984).Webb, J. F. Larvae in fish development and evolution in The Origin and Evolution of Larval Forms. 109–158 (Academic Press, 1999).Govoni, J. J., Olney, J. E., Markle, D. F. & Curtsinger, W. R. Observations on structure and evaluation of possible functions of the vexillum in larval Carapidae (Ophidiiformes). Bull. Mar. Sci. 34, 60–70 (1984).
    Google Scholar 
    Pekkoeva, S. N. et al. Fatty acid composition of the postlarval daubed shanny (Leptoclinus maculatus) during the polar night. Polar Biol. 43, 657–664 (2020).Article 

    Google Scholar 
    Pekkoeva, S. N. et al. Ecological groups of the Daubed Shanny Leptoclinus maculatus (Fries, 1838), an Arcto-boreal species, regarding growth and early development. Rus. J. Ecol. 49(3), 253–259 (2018).Article 

    Google Scholar 
    Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9(7), 676–682 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Version 12/2021. (R Foundation for Statistical Computing, 2020.)Kabakoff, R. R in Action: Data Analysis and Graphics with R 588 (DMK Press, 2014).
    Google Scholar 
    Murzina, S. A. et al. Oogenesis and lipids in gonad and liver of daubed shanny (Leptoclinus maculatus) females from Svalbard waters. Fish Physiol. Biochem. 38(5), 1393–1407 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kondakova, E. A., Efremov, V. I. & Nazarov, V. A. Structure of the yolk syncytial layer in Teleostei and analogous structures in animals of the meroblastic type of development. Biol. Bull. 43(3), 208–215 (2016).Article 

    Google Scholar 
    Webster, M., Witkin, K. L. & Cohen-Fix, O. Sizing up the nucleus: Nuclear shape, size and nuclear-envelope assembly. J. Cell Sci. 122(10), 1477–1486 (2009).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jevtić, P., Edens, L. J., Vuković, L. D. & Levy, D. L. Sizing and shaping the nucleus: mechanisms and significance. Curr. Opin. Cell Biol. 28, 16–27 (2014).Article 
    PubMed 

    Google Scholar 
    Kondakova, E. A., Efremov, V. I. & Kozin, V. V. Common and specific features of organization of the yolk syncytial layer of teleostei as exemplified in Gasterosteus aculeatus L. Biol. Bull. 46(1), 26–32 (2019).Article 

    Google Scholar 
    Enders, A. C. Reasons for diversity of placental structure. Placenta 30, 15–18 (2009).Article 

    Google Scholar 
    Carvalho, L. & Heisenberg, C. P. The yolk syncytial layer in early zebrafish development. Trends Cell Biol. 20(10), 586–592 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Jaroszewska, M. & Dabrowski, K. Utilization of yolk: transition from endogenous to exogenous nutrition in fish. in Larval Fish Nutrition. 183–218 (2011).Kondakova, E. A., Efremov, V. I. & Bogdanova, V. A. Structure of the yolk syncytial layer in the larvae of whitefishes: A histological study. Russ. J. Dev. Biol. 48(3), 176–184 (2017).Article 
    CAS 

    Google Scholar 
    Kondakova, E. A. & Bogdanova, V. A. The fate of the yolk syncytial layer during postembryonic development of Stenodus leucichthys nelma. Ann. Zool. Fenn. 58(4–6), 155–160 (2021).
    Google Scholar 
    Chanet, B. & Meunier, F. J. The anatomy of the thyroid gland among “fishes”: phylogenetic implications for the Vertebrata. Cybium 38(2), 89–116 (2014).
    Google Scholar 
    Zenzerov, V.S. Features of the Structure and Functioning of the Thyroid Gland of Fish in the Barents Sea. Doctor of Science Thesis. Vol. 42 (PetrGU, 2007).Chalde, T. & Miranda, L. A. Pituitary–thyroid axis development during the larval–juvenile transition in the pejerrey Odontesthes bonariensis. J. Fish Biol. 91(3), 818–834 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Otero, A. P., Rodrigues, R. V., Sampaio, L. A., Romano, L. A. & Tesser, M. B. Thyroid gland development in Rachycentron canadum during early life stages. An. Acad. Bras. Ciênc. 86(3), 1507–1516 (2014).Article 
    PubMed 

    Google Scholar 
    Nilsson, M. & Fagman, H. Development of the thyroid gland. Development 144(12), 2123–2140 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Eales, J. G. & Brown, S. B. Measurement and regulation of thyroidal status in teleost fish. Rev. Fish Biol. Fish. 3(4), 299–347 (1993).Article 

    Google Scholar 
    Raine, J. C. & Leatherland, J. F. Morphological and functional development of the thyroid tissue in rainbow trout (Oncorhynchus mykiss) embryos. Cell Tissue Res. 301(2), 235–244 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    de Jesus, E. G., Inui, Y. & Hirano, T. Cortisol enhances the stimulating action of thyroid hormones on dorsal fin-ray resorption of flounder larvae in vitro. Gen. Comp. Endocrinol. 79(2), 167–173 (1990).Article 
    PubMed 

    Google Scholar 
    Inui, Y. & Miwa, S. Metamorphosis of flatfish (Pleuronectiformes). in Metamorphosis in Fish. 107–153 (Taylor & Francis, 2012)Nemova, N. N., Rendakov, N. L., Pekkoeva, S. N., Nikerova, K. M. & Murzina, S. A. Dynamics of estradiol level during metamorphosis in the Daubed Shanny (Leptoclinus maculatus, Fries, 1838) from Spitsbergen Island. Dokl. Biol. Sci. 482, 188–190 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Icardo, J. M. The teleost heart: A morphological approach in Ontogeny and Phylogeny of the Vertebrate Heart. 35–53 (Springer, 2012).Icardo, J. M. Heart morphology and anatomy. in Fish Physiology. 1–54 (Academic Press, 2017).Hu, N., Yost, H. J. & Clark, E. B. Cardiac morphology and blood pressure in the adult zebrafish. Anatomic. Rec. 264(1), 1–12 (2001).Article 
    CAS 

    Google Scholar 
    Icardo, J. M., Colvee, E., Cerra, M. C. & Tota, B. The bulbus arteriosus of stenothermal and temperate teleosts: A morphological approach. J. Fish Biol. 57, 121–135 (2000).Article 

    Google Scholar 
    Benjamin, M., Norman, D., Santer, R. M. & Scarborough, D. Histological, histochemical and ultrastructural studies on the bulbus arteriosus of the sticklebacks, Gasterosteus aculeatus and Pungitius pungitius (Pisces: Teleostei). J. Zool. 200(3), 325–346 (1983).Article 

    Google Scholar 
    Braun, M. H., Brill, R. W., Gosline, J. M. & Jones, D. R. Form and function of the bulbus arteriosus in yellowfin tuna (Thunnus albacares), bigeye tuna (Thunnus obesus) and blue marlin (Makaira nigricans): static properties. J. Exp. Biol. 206(19), 3311–3326 (2003).Article 
    PubMed 

    Google Scholar 
    Icardo, J. M. Conus arteriosus of the teleost heart: Dismissed, but not missed. Anat. Rec. Part A Discov. Mol. Cell. Evolut. Biol. 288(8), 900–908 (2006).Article 

    Google Scholar 
    Tota, B. Vascular and metabolic zonation in the ventricular myocardium of mammals and fishes. Comp. Biochem. Physiol. A Physiol. 76(3), 423–437 (1983).Article 
    CAS 

    Google Scholar 
    Gardinal, M. V. B. et al. Myocardium arrangement and coronary vessel distribution in the ventricle of three neotropical freshwater teleosts. Zool. Sci. 35(4), 360–367 (2018).Article 

    Google Scholar 
    BuzeteGardinal, M. V. et al. Heart structure in the Amazonian teleost Arapaima gigas (Osteoglossiformes, Arapaimidae). J. Anat. 234(3), 327–337 (2019).Article 
    CAS 

    Google Scholar 
    Icardo, J. M. & Colvee, E. The atrioventricular region of the teleost heart. A distinct heart segment. Anatomic. Rec. Adv. Integr. Anat. Evolut. Biol. 294(2), 236–242 (2011).Article 

    Google Scholar 
    Kock, K. H. Antarctic icefishes (Channichthyidae): A unique family of fishes. A review, Part I. Polar Biol. 28, 862–895 (2005).Article 

    Google Scholar 
    Cocca, E. et al. Genomic remnants of alpha-globin genes in the hemoglobinless antarctic icefishes. Proc. Natl. Acad. Sci. 92(6), 1817–1821 (1995).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    di Prisco, G., Cocca, E., Parker, S. K. & Detrich, H. W. III. Tracking the evolutionary loss of hemoglobin expression by the white-blooded Antarctic icefishes. Gene 295(2), 185–191 (2002).Article 
    PubMed 

    Google Scholar 
    Sidell, B. D. & O’Brien, K. M. When bad things happen to good fish: The loss of hemoglobin and myoglobin expression in Antarctic icefishes. J. Exp. Biol. 209(10), 1791–1802 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kaufman, Z. S. Adaptation of aquatic organisms to existence in high latitudes. Proc. Karelian Sci. Center Russ. Acad. Sci. 1, 3–19 (2015).
    Google Scholar 
    Jakubowski, M. Dimensions of respiratory surfaces of the gills and skin in the Antarctic white-blooded fish, Chaenocephalus aceratus Lönnberg (Chaenichthyidae). Z. Mikrosk.-Anat. Forschung. 96(1), 145–156 (1982).CAS 

    Google Scholar 
    Graham, J. B. Air-breathing fishes: The biology, diversity, and natural history of air-breathing fishes. in Encyclopedia of Fish Physiology. 1861–1874 (Elsevier, 2011).Maniatis, G. M. & Ingram, V. M. Erythropoiesis during amphibian metamorphosis: I. Site of maturation of erythrocytes in Rana catesbeiana. J. Cell Biol. 49(2), 372–379 (1971).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maruyama, K., Yasumasu, S. & Iuchi, I. Characterization and expression of embryonic and adult globins of the teleost Oryzias latipes (medaka). J. Biochem. 132(4), 581–589 (2002).Article 
    CAS 
    PubMed 

    Google Scholar 
    Brownlie, A. et al. Characterization of embryonic globin genes of the zebrafish. Dev. Biol. 255(1), 48–61 (2003).Article 
    CAS 
    PubMed 

    Google Scholar 
    Feng, J. et al. Channel catfish hemoglobin genes: Identification, phylogenetic and syntenic analysis, and specific induction in response to heat stress. Comp. Biochem. Physiol. D Genom. Proteom. 9, 11–22 (2014).CAS 

    Google Scholar 
    Miwa, S. & Inui, Y. Thyroid hormone stimulates the shift of erythrocyte populations during metamorphosis of the flounder. J. Exp. Zool. 259(2), 222–228 (1991).Article 
    CAS 

    Google Scholar 
    Hansen, A., Reutter, K. & Zeiske, E. Taste bud development in the zebrafish, Danio rerio. Dev. Dyn. 223(4), 483–496 (2002).Article 
    PubMed 

    Google Scholar 
    Wang, C. A. et al. The development of pharyngeal taste buds in Hucho taimen (Pallas, 1773) larvae. Iran. J. Fish. Sci. 15(1), 426–435 (2016).ADS 

    Google Scholar 
    Fraser, G. J., Graham, A. & Smith, M. M. Conserved deployment of genes during odontogenesis across osteichthyans. Proc. R. Soc. Lond. Ser. B Biol. Sci. 271(1555), 2311–2317 (2004).Article 

    Google Scholar 
    Zambonino-Infante, J. L. et al. Ontogeny and physiology of the digestive system of marine fish larvae. in Feeding and Digestive Functions of Fishes. 281–348 (Science Publishers, 2008)Rønnestad, I. et al. Feeding behaviour and digestive physiology in larval fish: Current knowledge, and gaps and bottlenecks in research. Rev. Aquac. 5, S59–S98 (2013).Article 

    Google Scholar 
    Wallace, R. A. & Selman, K. Physiological aspects of oogenesis in two species of stickelebacks, Gasterosteus aculeatus (L.) and Apeltes quadracus (Mitchill). J. Fish Biol. 14, 551–564 (1979).Article 

    Google Scholar  More

  • in

    Genetic structuring and invasion status of the perennial Ambrosia psilostachya (Asteraceae) in Europe

    Van Kleunen, M. et al. Global exchange and accumulation of non-native plants. Nature 525, 100–101 (2015).Article 
    ADS 
    PubMed 

    Google Scholar 
    Simberloff, D. et al. Impacts of biological invasions: What’s what and the way forward. Trends Ecol. Evol. 28, 58–66 (2013).Article 
    PubMed 

    Google Scholar 
    Fried, G., Chauvel, B., Reynaud, P. & Sache, I. Decreases in crop production by non-native weeds, pests, and pathogens. In Impact of Biological Invasions on Ecosystem Services (ed. Vilà, M.) 83–101 (Springer, 2017).Chapter 

    Google Scholar 
    Nentwig, W., Mebs, D. & Vilà, M. Impact of non-native animals and plants on human health. In Impact of Biological Invasions on Ecosystem Services (ed. Vilà, M.) 277–293 (Springer, 2017).Chapter 

    Google Scholar 
    Smith, M., Cecchi, L., Skjøth, C. A., Karrer, G. & Šikoparija, B. Common ragweed: A threat to environmental health in Europe. Environ. Int. 61, 115–126 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Strother, J. L. Ambrosia L. in Flora of North America, Vol. 21 efloras.org. http://www.efloras.org/florataxon.aspx?flora_id=1&taxon_id=101325 (2007). Accessed 10 August 2022.Oswalt, M. L. & Marshall, G. D. Ragweed as an example of worldwide allergen expansion. All. Asth. Clin. Immun. 4, 130–135 (2008).Article 

    Google Scholar 
    Payne, W. W. Biosystematic studies of four widespread weedy species of ragweeds, Ambrosia: Compositae. PhD Thesis, University of Michigan (1962).Burbach, G. J. et al. Ragweed sensitization in Europe—GA(2)LEN study suggests increasing prevalence. Allergy 64, 664–665 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ghosh, B. et al. Immunological and molecular characterization of Amb P V allergens from Ambrosia psilostachya (western ragweed) pollen. J. Immunol. 152, 2882–2889 (1994).Article 
    CAS 
    PubMed 

    Google Scholar 
    Karrer, G. et al. Ambrosia in Europe. Habitus, Leaves, Seeds, 6 European Ragweed Species. Comparison of traits. EU-COST-Action FA-1203 ‘Sustainable management of Ambrosia artemisiifolia in Europe’. http://internationalragweedsociety.org/smarter/wp-content/uploads/6AmbrosiaSpecies.pdf (2016). Accessed 10 August 2022.Essl, F. et al. Biological flora of the British Isles: Ambrosia artemisiifolia L.. J. Ecol. 103, 1069–1098 (2015).Article 

    Google Scholar 
    Payne, W. W. A re-evaluation of the genus Ambrosia (Compositae). J. Arnold Arbor. 45, 401–438 (1964).Article 

    Google Scholar 
    Müller-Schärer, H. et al. Cross-fertilizing weed science and plant invasion science. Basic Appl. Ecol. 33, 1–13 (2018).Article 

    Google Scholar 
    Chapman, D. S. et al. Modelling the introduction and spread of non-native species: International trade and climate change drive ragweed invasion. Glob. Change Biol. 22, 3067–3079 (2016).Article 
    ADS 

    Google Scholar 
    Mang, T., Essl, F., Moser, D. & Dullinger, S. Climate warming drives invasion history of Ambrosia artemisiifolia in central Europe. Preslia 90, 59–81 (2018).Article 

    Google Scholar 
    Liu, X.-L. et al. The current and future potential geographical distribution of common ragweed, Ambrosia artemisiifolia in China. Pak. J. Bot. 53, 167–172 (2021).ADS 

    Google Scholar 
    Allard, H. A. The North American ragweeds and their occurrence in other parts of the world. Science 98, 292–293 (1943).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Greuter, W. Compositae (pro parte majore) in Compositae. Euro+Med Plantbase – the information resource for Euro-Mediterranean plant diversity (ed. Greuter, W. & Raab-Straube, E. von) https://europlusmed.org/cdm_dataportal/taxon/76610e67-b2d4-4aef-a785-c4555af5b150 (Accessed 22 August 2022).Abramova, L. M. Expansion of invasive alien plant species in the Republic of Bashkortostan, the Southern Urals: Analysis of causes and ecological consequences. Russ. J. Ecol. 43, 352–357 (2012).Article 

    Google Scholar 
    Montagnani, C., Gentili, R., Smith, M., Guarino, M. F. & Citterio, S. The worldwide spread, success, and impact of ragweed (Ambrosia spp.). Crit. Rev. Plant. Sci. 36, 139–178 (2017).Article 

    Google Scholar 
    Vermeire, L. T. & Gillen, R. L. Western ragweed effects on herbaceous standing crop in Great Plains grasslands. J. Range Manag. 53, 335–341 (2000).Article 

    Google Scholar 
    Reece, P. E., Brummer, J. E., Northup, B. K., Koehler, A. E. & Moser, L. E. Interactions among western ragweed and other sandhills species after drought. J. Range Manag. 57, 583–589 (2000).Article 

    Google Scholar 
    Wagner, W. H. & Beals, T. F. Perennial ragweeds (Ambrosia) in Michigan, with description of a new, intermediate Taxon. Rhodora 60, 177–204 (1958).
    Google Scholar 
    Hansen, A. Ambrosia L. In Flora Europaea Vol. 4 (eds Tutin, T. G. et al.) (Cambridge University Press, 1976).
    Google Scholar 
    Sell, P. & Murrell, G. Flora of Great Britain and Ireland, Campanulaceae–Asteraceae Vol. 4, 513–514 (Cambridge University Press, 2006). Book 

    Google Scholar 
    Pignatti, S. Flora d’Italia Vol. 3 (Edagricola, 1982).
    Google Scholar 
    Amor Morales, À., Navarro Andrés, F. & Sánchez Anta, M. Datos corológicos y morfológicos de las especies del género Ambrosia L. (Compositae) presentes en la Península Ibérica. Bot. Complut. 36, 85–96 (2012).Article 

    Google Scholar 
    Karrer, G. Ambrosia. In Flora d’Italia 2nd edn, Vol. 3 (eds Guarino, R. & La Rosa, M.) 808–810 (Edagricola, 2018).
    Google Scholar 
    Rich, T. C. G. Ragweeds (Ambrosia L.) in Britain. Grana 33, 38–43 (1994).Article 

    Google Scholar 
    Chauvel, B., Fried, G., Monty, A., Rossi, J. P. & Le Bourgeois, T. Analyse de Risques Relative à L’ambroisie à Épis Lisses (Ambrosia Psilostachya DC.) et Élaboration de Recommandation De gestion (ANSES, 2017).
    Google Scholar 
    Lawalreé, A. Les Ambrosia adventices en Europe occidentale. Bull. Jard. Botan. l’Etat Bruxelles 18, 305–315 (1947).Article 

    Google Scholar 
    Karrer, G. Interessante Gefäßpflanzenfunde aus Österreich, 1. Neilreichia 12, 183–187 (2021).
    Google Scholar 
    Bassett, I. J. & Crompton, C. W. The biology of Canadian weeds. 11. Ambrosia artemisiifolia L. and A. psilostachya DC. Can. J. Plant Sci. 55, 463–476 (1975).Article 

    Google Scholar 
    Djemaa, S. Caractérisation de la banque de graines de l’Ambroisie à épis lisses Ambrosia psilostachya DC (Asteraceae) et moyens de contrôle de cette espèce envahissante et allergène (Rapport de stage de Master 1 – Université de Montpellier 2 – Master IEGB, 2014).Chun, Y. J., Le Corre, V. & Bretagnolle, F. Adaptive divergence for a fitness-related trait among invasive Ambrosia artemisiifolia populations in France. Mol. Ecol. 20, 1378–1388 (2011).Article 
    PubMed 

    Google Scholar 
    Genton, B. J. et al. Isolation of five polymorphic microsatellite loci in the invasive weed Ambrosia artemisiifolia (Asteraceae) using an enrichment protocol. Mol. Ecol. Notes 5, 381–383. https://doi.org/10.1111/j.1365-294X.2005.02750.x (2005).Article 
    CAS 

    Google Scholar 
    Genton, B. J., Shykoff, J. A. & Giraud, T. High genetic diversity in French invasive populations of common ragweed, Ambrosia artemisiifolia, as a result of multiple sources of introduction. Mol. Ecol. 14, 4275–4285 (2005).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gaudeul, M., Giraud, T., Kiss, L. & Shykoff, J. A. Nuclear and chloroplast microsatellites show multiple introductions in the worldwide invasion history of common Ragweed Ambrosia artemisiifolia. PLoS One 6, e17658. https://doi.org/10.1371/journal.pone.0017658 (2011).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chun, Y. J., Fumanal, B., Laitung, B. & Bretagnolle, F. Gene flow and population admixture as the primary post-invasion processes in common ragweed (Ambrosia artemisiifolia) populations in France. New Phytol. 185, 1100–1107 (2010).Article 
    PubMed 

    Google Scholar 
    Gladieux, P. et al. Distinct invasion sources of common ragweed (Ambrosia artemisiifolia) in Eastern and Western Europe. Biol. Invasions 13, 933–944 (2010).Article 

    Google Scholar 
    Li, X.-M., Liao, W.-J., Wolfe, L. M. & Zhang, D.-Y. No evolutionary shift in the mating system of North American Ambrosia artemisiifolia (Asteraceae) following its introduction to China. PLoS One 7(2), e31935. https://doi.org/10.1371/journal.pone.0031935 (2012).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kočiš Tubić, N., Djan, M., Veličković, N., Anačkov, G. & Obreht, D. Microsatellite DNA variation within and among invasive populations of Ambrosia artemisiifolia from the southern Pannonian Plain. Weed Res. 55, 268–277 (2015).Article 

    Google Scholar 
    Ciappetta, S. et al. Invasion of Ambrosia artemisiifolia in Italy: Assessment via analysis of genetic variability and herbarium data. Flora 223, 106–113 (2016).Article 

    Google Scholar 
    Meyer, L. et al. New gSSr and EST-SSR markers reveal high genetic diversity in the invasive plant Ambrosia artemisiifolia L. and can be transferred to other invasive Ambrosia species. PLoS One 12(5), e0176197. https://doi.org/10.1371/journal.pone.0176197 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Van Boheemen, L. A. et al. Multiple introductions, admixture and bridgehead invasion characterize the introduction history of Ambrosia artemisiifolia in Europe and Australia. Mol. Ecol. 26, 5421–5434 (2017).Article 
    PubMed 

    Google Scholar 
    Kropf, M., Huppenberger, A. S. & Karrer, G. Genetic structuring and diversity patterns along rivers—Local invasion history of Ambrosia artemisiifolia (Asteraceae) along the Danube River in Vienna (Austria) shows non-linear pattern. Weed Res. 58, 131–140 (2018).Article 
    CAS 

    Google Scholar 
    Sun, Y. & Roderick, G. K. Rapid evolution of invasive traits facilitates the invasion of common ragweed Ambrosia artemisiifolia. J. Ecol. 107, 2673–2687 (2019).Article 

    Google Scholar 
    Li, F. et al. Patterns of genetic variation reflect multiple introductions and pre-admixture sources of common ragweed (Ambrosia artemisiifolia) in China. Biol. Invasions 21, 2191–2209 (2019).Article 

    Google Scholar 
    Payne, W. W., Raven, P. H. & Kyhos, D. W. Chromosome numbers in Compositae. IV. Ambrosieae. Am. J. Bot. 51, 419–424 (1964).Article 

    Google Scholar 
    Miller, H. E., Mabry, T. J., Turner, B. L. & Payne, W. W. Infraspecific variation of sesquiterpene lactones in Ambrosia psilostachya (Compositae). Am. J. Bot. 55, 316–324 (1968).Article 
    CAS 

    Google Scholar 
    Del Amo Rodriguez, S. & Gomez-Pompa, A. Variability in Ambrosia cumanensis (Compositae). Syst. Bot. 1, 363–372 (1976).Article 

    Google Scholar 
    Grünwald, N. J., Everhart, S. E., Knaus, B. J. & Kamvar, Z. N. Best practices for population genetic analyses. Phytopathology 107, 1000–1010 (2017).Article 
    PubMed 

    Google Scholar 
    Arnaud-Haond, S., Stoeckel, S. & Bailleul, D. New insights into the population genetics of partially clonal organisms: When seagrass data meet theoretical expectations. Mol. Ecol. 29, 3248–3260 (2020).Article 
    PubMed 

    Google Scholar 
    Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Watkinson, A. & Powell, J. Seedling recruitment and the maintenance of clonal diversity in plant populations—A computer simulation of Ranunculus repens. J. Ecol. 81, 707–717 (1993).Article 

    Google Scholar 
    Balloux, F., Lehmann, L. & de Meeus, T. The population genetics of clonal and partially clonal diploids. Genetics 164, 1635–1644 (2003).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: A r package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281. https://doi.org/10.7717/peerj.281 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bonin, A. et al. How to track and assess genotyping errors in population genetics studies. Mol. Ecol. 13, 3261–3273 (2004).Article 
    CAS 
    PubMed 

    Google Scholar 
    Guretzky, J., Anderson, A. & Fehmi, J. Grazing and military vehicle effects on grassland soils and vegetation. Great Plains Res. 16, 51–61 (2006).
    Google Scholar 
    Vitalos, M. & Karrer, G. Dispersal of Ambrosia artemisiifolia seeds along roads: the contribution of traffic and mowing machines. NeoBiota 8, 53–60 (2009).
    Google Scholar 
    Karrer, G. Das österreichische Ragweed Projekt—übertragbare Erfahrungen. The Austrian Ragweed Project—Experiences and Generalisations. Julius-Kühn-Archiv 445, 27–33 (2014).
    Google Scholar 
    Lemke, A., Buchholz, S., Kowarik, I., Starfinger, U. & von der Lippe, M. Interaction of traffic intensity and habitat features shape invasion dynamics of an invasive alien species (Ambrosia artemisiifolia) in a regional road network. NeoBiota 64, 155–175 (2021).Article 

    Google Scholar 
    Orlić, M., Gačić, M. & La Violette, P. E. The currents and circulation of the Adriatic Sea. Oceanol. Acta 15, 109–124 (1992).
    Google Scholar 
    Fumanal, B., Chauvel, B., Sabatier, A. & Bretagnolle, F. Variability and cryptic heteromorphism of Ambrosia artemisiifolia seeds: What consequences for its invasion in France?. Ann. Bot. 100, 305–313 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    González, L. et al. An Atlantic Odissey: The fate of invading propagules across the coastline of the Iberian Peninsula. In 15th Ecology and Management of Alien Plant Invasions (EMAPi) Book of Abstracts: Integrating Research, Management and Policy (eds Pyšek, P. et al.) 24 (Institute of Botany, Czech Academy of Sciences, 2019).
    Google Scholar 
    Ward, S. Genetic analysis of invasive plant populations at different spatial scales. Biol. Invasions 8, 541–552 (2006).Article 

    Google Scholar 
    Halkett, F., Simon, J.-C. & Balloux, F. Tackling the population genetics of clonal and partially clonal organisms. Trends Ecol. Evol. 20, 194–201 (2005).Article 
    PubMed 

    Google Scholar 
    Kočiš Tubić, N., Djan, M., Veličković, N., Anačkov, G. & Obreht, D. Gradual loss of genetic diversity of Ambrosia artemisiifolia L. populations in the invaded range of central Serbia. Genetika 46, 255–268 (2014).Article 

    Google Scholar 
    Suehs, C. M., Affre, L. & Médail, F. Invasion dynamics of two alien Carpobrotus (Aizoaceae) taxa on a Mediterranean island: I. Genetic diversity and introgression. Heredity 92, 31–40 (2004).Article 
    CAS 
    PubMed 

    Google Scholar 
    Stoeckel, S. et al. Heterozygote excess in a self-incompatible and partially clonal forest tree species—Prunus avium L. Mol. Ecol. 15, 2109–2118 (2005).Article 

    Google Scholar 
    Balloux, F. Heterozygote excess in small populations and the heterozygote-excess effective population size. Evolution 58, 1891–1900 (2004).PubMed 

    Google Scholar 
    Hansson, B. & Westerberg, L. On the correlation between heterozygosity and fitness in natural populations. Mol. Ecol. 11, 2467–2474 (2002).Article 
    PubMed 

    Google Scholar 
    Hewitt, A., Rymer, P., Holford, P., Morris, E. C. & Renshaw, A. Evidence for clonality, breeding system, genetic diversity and genetic structure in large and small populations of Melaleuca deanei (Myrtaceae). Aust. J. Bot. 67, 36–45 (2019).Article 

    Google Scholar 
    Dlugosch, K. M. & Parker, I. M. Founding events in species invasions: Genetic variation, adaptive evolution, and the role of multiple introductions. Mol. Ecol. 17, 431–449 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Novak, S. J. & Mack, R. N. Genetic bottlenecks in alien plant species: influences of mating systems and introduction dynamics. In Species Invasions: Insights into Ecology, Evolution, and Biogeography (eds Sax, D. F. et al.) 201–228 (Sinauer Associates, 2005).
    Google Scholar 
    Karnkowski, W. Pest Risk Analysis and Pest Risk Assessment for the territory of the Republic of Poland (as PRA area) on Ambrosia spp., updated version. (Torun, 2001).Karrer, G. et al. Ausbreitungsbiologie und Management einer extrem allergenen, eingeschleppten Pflanze – Wege und Ursachen der Ausbreitung von Ragweed (Ambrosia artemisiifolia) sowie Möglichkeiten seiner Bekämpfung. (Final Report, BMLFUW, Vienna, Austria). https://dafne.at/projekte/ragweed (2011). Accessed 10 August 2022.Honnay, O. & Jacquemyn, H. A meta-analysis of the relation between mating system, growth form and genotypic diversity in clonal plant species. Evol. Ecol. 22, 299–312 (2008).Article 

    Google Scholar 
    Vallejo-Marín, M., Dorken, M. E. & Barrett, S. C. H. The ecological and evolutionary consequences of clonality for plants mating. Annu. Rev. Ecol. Syst. 41, 193–213 (2010).Article 

    Google Scholar 
    McKey, D., Elias, M., Pujol, B. & Duputiè, A. The evolutionary ecology of clonally propagated domesticated plants. New Phytol. 186, 318–332 (2010).Article 
    PubMed 

    Google Scholar 
    WFO Ambrosia psilostachya DC. http://www.worldfloraonline.org/taxon/wfo-0000137200 (accessed 21 July 2022).Tomasello, S., Stuessy, T. F., Oberprieler, C. & Heubl, G. Ragweeds and relatives: Molecular phylogenetics of Ambrosiinae (Asteraceae). Mol. Phylogenet. Evol. 130, 104–114 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Délye, C., Matéjicek, A. & Gasquez, J. PCR-based detection of resistance to Acetyl-CoA carboxylase-inhibiting herbicides in black-grass (Alopecurus myosuroides Huds) and ryegrass (Lolium rigidum Gaud). Pest Manag. Sci. 58, 474–478 (2002).Article 
    PubMed 

    Google Scholar 
    Adamack, A. T. & Gruber, B. PopGenReport: Simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5, 384–387 (2014).Article 

    Google Scholar 
    Brookfield, J. F. Y. A simple new method for estimating null allele frequency from heterozygote deficiency. Mol. Ecol. 5, 453–455 (1996).Article 
    CAS 
    PubMed 

    Google Scholar 
    Harper, J. L. Population Biology of Plants (Academic Press, 1977).
    Google Scholar 
    Lambertini, C. et al. Genetic diversity in three invasive clonal aquatic species in New Zealand. BMC Genet. 11(52), 1–18. https://doi.org/10.1186/1471-2156-11-52 (2010).Article 
    CAS 

    Google Scholar 
    Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in excel. Population genetic software for teaching and research—An update. Bioinformatics 28, 2537–2539 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brown, A. H. D., Feldman, M. W. & Nevo, E. Multilocus structure of natural populations of Hordeum spontaneum. Genetics 96, 523–536 (1980).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Goudet, J. Hierfstat, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

    Google Scholar 
    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kropf, M., Comes, H. P. & Kadereit, J. W. An AFLP clock for the absolute dating of shallow-time evolutionary history based on the intraspecific divergence of southwestern European alpine plant species. Mol. Ecol. 18, 697–708 (2009).Article 
    PubMed 

    Google Scholar 
    Nei, M. Genetic distance between populations. Am. Nat. 106, 283–292 (1972).Article 

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

    Google Scholar 
    Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn. (Springer, 2002).Book 
    MATH 

    Google Scholar 
    Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. diversity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).Article 

    Google Scholar 
    Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More