Philippa Kaur

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

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

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

Day, J. W. et al. Pattern and process of land loss in the Mississippi Delta: a spatial and temporal analysis of wetland habitat change. Estuaries 23, 425–438 (2000).Article 

Google Scholar 
Syvitski, J. P. M. et al. Sinking deltas due to human activities. Nat. Geosci. 2, 681–686 (2009).Article 
CAS 

Google Scholar 
Higgins, S., Overeem, I., Tanaka, A. & Syvitski, J. P. Land subsidence at aquaculture facilities in the Yellow River delta, China. Geophys. Res. Lett. 40, 3898–3902 (2013).Article 

Google Scholar 
Giosan, L., Syvitski, J., Constantinescu, S. & Day, J. Climate change: protect the world’s deltas. Nature 516, 31–33 (2014).Couvillion, B. R., Beck, H., Schoolmaster, D. & Fischer, M. Land Area Change in Coastal Louisiana (1932 to 2016) (USGS, 2017); https://doi.org/10.3133/sim3381Corthell, E. L. The delta of the Mississippi River. Natl Geogr. Mag. 12, 351–354 (1897).
Google Scholar 
Blum, M. D. & Roberts, H. H. Drowning of the Mississippi Delta due to insufficient sediment supply and global sea-level rise. Nat. Geosci. 2, 488–491 (2009).Article 
CAS 

Google Scholar 
Gagliano, S. M., Meyer-Arendt, K. J. & Wicker, K. M. Land Loss in the Mississippi River Deltaic Plain. Gulf Coast Assoc. Geol. Soc. Trans. 31, 295–300 (1981).Day, J. W., Clark, H. C., Chang, C., Hunter, R. & Norman, C. R. Life cycle of oil and gas fields in the Mississippi River Delta: a review. Water 12, 1492 (2020).Article 
CAS 

Google Scholar 
Morton, R., Bernier, J., Barras, J. & Ferina, N. Rapid Subsidence and Historical Wetland Loss in the Mississippi Delta Plain: Likely Causes and Future Implications (USGS, 2005).Kolker, A. S., Allison, M. A. & Hameed, S. An evaluation of subsidence rates and sea‐level variability in the northern Gulf of Mexico. Geophys. Res. Lett. 38, L21404 (2011).Roy, S., Robeson, S. M., Ortiz, A. C. & Edmonds, D. A. Spatial and temporal patterns of land loss in the Lower Mississippi River Delta from 1983 to 2016. Remote Sens. Environ. 250, 112046 (2020).Sanks, K. M., Shaw, J. B. & Naithani, K. Field-based estimate of the sediment deficit in coastal Louisiana. J. Geophys. Res. Earth Surf. 125, e2019JF005389 (2020).Article 

Google Scholar 
Jankowski, K. L., Törnqvist, T. E. & Fernandes, A. M. Vulnerability of Louisiana’s coastal wetlands to present-day rates of relative sea-level rise. Nat. Commun. 8, 14792 (2017).Article 
CAS 

Google Scholar 
Turner, R. E. & McClenachan, G. Reversing wetland death from 35,000 cuts: opportunities to restore Louisiana’s dredged canals. PLoS ONE 13, e0207717 (2018).Article 

Google Scholar 
Falcini, F. et al. Linking the historic 2011 Mississippi River flood to coastal wetland sedimentation. Nat. Geosci. 5, 803–807 (2012).Chamberlain, E. L., Törnqvist, T. E., Shen, Z., Mauz, B. & Wallinga, J. Anatomy of Mississippi Delta growth and its implications for coastal restoration. Sci. Adv. 4, eaar4740 (2018).Article 

Google Scholar 
Roberts, H. H. Dynamic changes of the Holocene Mississippi River delta plain: the delta cycle. J. Coast. Res. 13, 605–627 (1997).
Google Scholar 
Siverd, C. G. et al. Coastal Louisiana landscape and storm surge evolution: 1850–2110. Clim. Change 157, 445–468 (2019).Article 

Google Scholar 
Tweel, A. W. & Turner, R. E. Watershed land use and river engineering drive wetland formation and loss in the Mississippi River birdfoot delta. Limnol. Oceanogr. 57, 18–28 (2012).Article 

Google Scholar 
Shen, Z. et al. Episodic overbank deposition as a dominant mechanism of floodplain and delta-plain aggradation. Geology 43, 875–878 (2015).Article 

Google Scholar 
Frederikse, T. et al. The causes of sea-level rise since 1900. Nature 584, 393–397 (2020).Article 
CAS 

Google Scholar 
Meade, R. H. & Moody, J. A. Causes for the decline of suspended‐sediment discharge in the Mississippi River system, 1940–2007. Hydrol. Process. 24, 35–49 (2010).
Google Scholar 
Xu, K., Bentley, S. J., Day, J. W. & Freeman, A. M. A review of sediment diversion in the Mississippi River Deltaic Plain. Estuar. Coast. Shelf Sci. 225, 106241 (2019).Article 

Google Scholar 
Vogel, H. D. Report on control of floods of the Lower Mississippi River, Annex no. 5, Basic data Mississippi River. House Doc. 798, 61–137 (1930).Craig, N. J., Turner, R. E. & Day, J. W. Land loss in coastal Louisiana (U.S.A.). Environ. Manage. 3, 133–144 (1979).Article 

Google Scholar 
Ko, J.-Y. & Day, J. W. A review of ecological impacts of oil and gas development on coastal ecosystems in the Mississippi Delta. Ocean Coast. Manage. 47, 597–623 (2004).Article 

Google Scholar 
Penland, S., Beall, A. D., Britsch, L. D. & Jeffress, W. S. Geologic classification of coastal land loss between 1932 and 1990 in the Mississippi River Delta Plain, Southeastern Louisiana. Gulf Coast Assoc. Geol. Soc. Trans. 52, 799–807 (2002).
Google Scholar 
Turner, R. Coastal wetland subsidence arising from local hydrologic manipulations. Estuaries 27, 265–272 (2004).Article 

Google Scholar 
Nienhuis, J. H., Törnqvist, T. E. & Erkens, G. Altered surface hydrology as a potential mechanism for subsidence in coastal Louisiana. Proc. IAHS 382, 333–337 (2020).Morton, R. A., Bernier, J. C. & Barras, J. A. Evidence of regional subsidence and associated interior wetland loss induced by hydrocarbon production, Gulf Coast region, USA. Environ. Geol. 50, 261–274 (2006).Karegar, M. A., Dixon, T. H. & Malservisi, R. A three-dimensional surface velocity field for the Mississippi Delta: implications for coastal restoration and flood potential. Geology 43, 519–522 (2015).Article 

Google Scholar 
Gambolati, G. & Teatini, P. Geomechanics of subsurface water withdrawal and injection. Water Resour. Res. 51, 3922–3955 (2015).Article 

Google Scholar 
Chang, C., Mallman, E. & Zoback, M. Time-dependent subsidence associated with drainage-induced compaction in Gulf of Mexico shales bounding a severely depleted gas reservoir. AAPG Bull. 98, 1145–1159 (2014).Article 

Google Scholar 
Guzy, A. & Malinowska, A. A. State of the art and recent advancements in the modelling of land subsidence induced by groundwater withdrawal. Water 12, 2051 (2020).Article 

Google Scholar 
Ortiz, A. C., Roy, S. & Edmonds, D. A. Land loss by pond expansion on the Mississippi River Delta Plain. Geophys. Res. Lett. 44, 3635–3642 (2017).Article 

Google Scholar 
Mariotti, G. Revisiting salt marsh resilience to sea level rise: are ponds responsible for permanent land loss? J. Geophys. Res. Earth Surf. 121, 1391–1407 (2016).Article 

Google Scholar 
Louisiana’s Comprehensive Master Plan for a Sustainable Coast. Coastal Protection and Restoration Authority https://coastal.la.gov/wp-content/uploads/2023/01/230105_CPRA_MP-Draft_Final-for-web_spreads-main.pdf (2023).Siverd, C. G. et al. Hydrodynamic storm surge model simplification via application of land to water isopleths in coastal Louisiana. Coast. Eng. 137, 28–42 (2018).Article 

Google Scholar 
Twilley, R. R. et al. Co-evolution of wetland landscapes, flooding, and human settlement in the Mississippi River Delta Plain. Sustain. Sci. https://doi.org/10.1007/s11625-016-0374-4 (2016).Edmonds, D. A. et al. in Treatise on Geomorphology 2nd edn (ed. Shroder, J. F.) 110–140 (Academic Press, 2022).Xu, K., Harris, C. K., Hetland, R. D. & Kaihatu, J. M. Dispersal of Mississippi and Atchafalaya sediment on the Texas–Louisiana shelf: model estimates for the year 1993. Cont. Shelf Res. 31, 1558–1575 (2011).Article 

Google Scholar 
Baptist, M. et al. On inducing equations for vegetation resistance. J. Hydraul. Res. 45, 435–450 (2007).Article 

Google Scholar 
Hopkinson, C. S., Morris, J. T., Fagherazzi, S., Wollheim, W. M. & Raymond, P. A. Lateral marsh edge erosion as a source of sediments for vertical marsh accretion. J. Geophys. Res. Biogeosci. 123, 2444–2465 (2018).Article 
CAS 

Google Scholar 
Danielson, J. et al. Topobathymetric Model of the Northern Gulf of Mexico, 1885 to 2021 (USGS, 2022); https://doi.org/10.5066/P99JULDNBomer, E. J. et al. Deltaic morphodynamics and stratigraphic evolution of Middle Barataria Bay and Middle Breton Sound regions, Louisiana, USA: implications for river-sediment diversions. Estuar. Coast. Shelf Sci. 224, 20–33 (2019).Article 
CAS 

Google Scholar 
Wolinsky, M., Edmonds, D. A., Martin, J. M. & Paola, C. Delta allometry: growth laws for river deltas. Geophys. Res. Lett. 37, L21403 (2010).Article 

Google Scholar 
Mariotti, G., Elsey-Quirk, T., Bruno, G. & Valentine, K. Mud-associated organic matter and its direct and indirect role in marsh organic matter accumulation and vertical accretion. Limnol. Oceanogr. 65, 2627–2641 (2020).Article 
CAS 

Google Scholar  More

Forbes, B. C. & Kumpula, T. The ecological role and geography of reindeer (Rangifer tarandus) in Northern Eurasia. Geogr. Compass 3, 1356–1380 (2009).Article 

Google Scholar 
Post, E. & Pedersen, C. Opposing plant community responses to warming with and without herbivores. Proc. Natl Acad. Sci. USA 105, 12353–12358 (2008).Article 
CAS 

Google Scholar 
Berkes, F., Colding, J. & Folke, C. Navigating Social-Ecological Systems: Building Resilience for Complexity and Change (Cambridge Univ. Press, 2003).Tremblay, R., Landry-Cuerrier, M. & Humphries, M. M. Culture and the social-ecology of local food use by Indigenous communities in northern North America. Ecol. Soc. 25, 8 (2020).Kenny, T.-A., Fillion, M., Simpkin, S., Wesche, S. & Chan, L. Caribou (Rangifer tarandus) and Inuit nutrition security in Canada. Ecohealth 15, 590–607 (2018).Article 

Google Scholar 
Benson, K. Gwich’in Knowledge of Porcupine Caribou: State of Current Knowledge and Gaps Assessment (Department of Cultural Heritage, Gwich’in Tribal Council, 2019); https://thelastgreatherd.com/wp-content/uploads/2020/06/GTC-current-knowledge-and-gaps-assessment.pdfParlee, B. & Caine, K. When the Caribou Do Not Come: Indigenous Knowledge and Adaptive Management in the Western Arctic (UBC Press, 2018).Herds: Status of Herds (CircumArctic Rangifer Monitoring and Assessment Network, accessed 3 November 2021); https://carma.caff.is/herdsFesta-Bianchet, M., Ray, J. C., Boutin, S., Côté, S. D. & Gunn, A. Conservation of caribou (Rangifer tarandus) in Canada: an uncertain future. Can. J. Zool. 89, 419–434 (2011).Article 

Google Scholar 
Gunn, A. Voles, lemmings and caribou: population cycles revisited? Rangifer 23, 105–111 (2003).Article 

Google Scholar 
Ferguson, M. A. D., Williamson, R. G. & Messier, F. Inuit knowledge of long-term changes in a population of Arctic tundra caribou. Arctic 51, 201–219 (1998).Article 

Google Scholar 
Beaulieu, D. Dene traditional knowledge about caribou cycles in the Northwest Territories. Rangifer 32, 59–67 (2012).Article 

Google Scholar 
Mallory, C. D. & Boyce, M. S. Observed and predicted effects of climate change on Arctic caribou and reindeer. Environ. Rev. 26, 13–25 (2018).Article 

Google Scholar 
Uboni, A. et al. Long-term trends and role of climate in the population dynamics of Eurasian reindeer. PLoS ONE 11, e0158359 (2016).Article 

Google Scholar 
Chapin, F. S. III et al. Directional changes in ecological communities and social-ecological systems: a framework for prediction based on Alaskan examples. Am. Nat. 168, S36–S49 (2006).Article 

Google Scholar 
Tengö, M. et al. Weaving knowledge systems in IPBES, CBD and beyond – lessons learned for sustainability. Curr. Opin. Environ. Sustain. 26, 17–25 (2017).Article 

Google Scholar 
Berkes, F. Sacred Ecology 4th edn (Routledge, 2018).Stuart Chapin, F. III et al. Earth stewardship: science for action to sustain the human-earth system. Ecosphere 2, 89 (2011).Parlee, B. L., Sandlos, J. & Natcher, D. C. Undermining subsistence: barren-ground caribou in a ‘tragedy of open access’. Sci. Adv. 4, e1701611 (2018).Article 

Google Scholar 
Johnson, J. T. et al. Weaving Indigenous and sustainability sciences to diversify our methods. Sustain. Sci. 11, 1–11 (2016).Article 
CAS 

Google Scholar 
Reid, A. J. et al. ‘Two-eyed seeing’: an Indigenous framework to transform fisheries research and management. Fish Fish. 22, 243–261 (2021).Article 

Google Scholar 
Tengö, M., Brondizio, E. S., Elmqvist, T., Malmer, P. & Spierenburg, M. Connecting diverse knowledge systems for enhanced ecosystem governance: the multiple evidence base approach. AMBIO 43, 579–591 (2014).Article 

Google Scholar 
Aminpour, P. et al. The diversity bonus in pooling local knowledge about complex problems. Proc. Natl Acad. Sci. USA 118, e2016887118 (2021).Article 
CAS 

Google Scholar 
Henri, D. A. et al. Weaving Indigenous knowledge systems and Western sciences in terrestrial research, monitoring and management in Canada: a protocol for a systematic map. Ecol. Solut. Evid. 2, e12057 (2021).Article 

Google Scholar 
Ljubicic, G. J., Mearns, R., Okpakok, S. & Robertson, S. Nunami iliharniq (learning from the land): reflecting on relational accountability in land-based learning and cross-cultural research in Uqšuqtuuq (Gjoa Haven, Nunavut). Arct. Sci. 8, 252–291 (2022).Article 

Google Scholar 
Stern, E. R. & Humphries, M. M. Interweaving local, expert, and Indigenous knowledge into quantitative wildlife analyses: a systematic review. Biol. Conserv. 266, 109444 (2022).Article 

Google Scholar 
Bourgeon, L., Burke, A. & Higham, T. Earliest human presence in North America dated to the last glacial maximum: new radiocarbon dates from Bluefish Caves, Canada. PLoS ONE 12, e0169486 (2017).Article 

Google Scholar 
Kuhnlein, H. V., McDonald, M., Spigelski, D., Vittrekwa, E. & Erasmus, B. in Indigenous Peoples’ Food Systems: the Many Dimensions of Culture, Diversity and Environment for Nutrition and Health (eds Kuhnlein, H. V. et al.) Ch. 3 (FAO, Centre for Indigenous Peoples’ Nutrition and Environment, 2009).Porcupine Caribou Technical Committee. Porcupine Caribou Annual Summary Report 2018–2019 (Porcupine Caribou Management Board, Whitehorse, Yukon, 2019); https://pcmb.ca/wp-content/uploads/2020/06/PCH_annual_summ_report_Nov29_2019_FINAL.pdfIPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).Zhang, X. et al. in Canada’s Changing Climate Report (eds Bush, E. & Lemmen, D. S.) Ch. 4 (Government of Canada, 2019).Griffith, B. et al. in Arctic Refuge Coastal Plain Terrestrial Wildlife Research Summaries Biological Science Report USGS/BRD BSR-2002-0001 (eds Douglas, D. C. et al.) 8–37 (US Geological Survey, 2002).Russell, D. & Gunn, A. Vulnerability Analysis of the Porcupine Caribou Herd to Potential Development of the 1002 lands in the Arctic National Wildlife Refuge, Alaska (Environment Yukon, Canadian Wildlife Service and GNWT Department of Environment and Natural Resources, 2019); https://pcmb.ca/wp-content/uploads/2021/10/Russell-and-Gunn-PCH-vulnerability-analysis-2019.pdfKruse, J. A. et al. Modeling sustainability of Arctic communities: an interdisciplinary collaboration of researchers and local knowledge holders. Ecosystems 7, 815–828 (2004).Berman, M., Nicolson, C., Fofinas, G., Tetlichi, J. & Martin, S. Adaptation and sustainability in a small Arctic community: results of an agent-based simulation model. Arctic 57, 401–414 (2004).Article 

Google Scholar 
Kofinas, G., Aklavik, Arctic Village, Old Crow & Fort McPherson. in The Earth is Faster Now: Indigenous Observations of Arctic Environmental Change (eds Krupnik, I. & Jolly, D.) 55–91 (Arctic Research Consortium of the United States, 2002).Eamer, J. in Bridging Scales and Knowledge Systems: Concepts and Applications in Ecosystem Assessment (eds Reid, W. V. et al.) 185–206 (Island Press, 2006).Shipley, B. Cause and Correlation in Biology: a User’s Guide to Path Analysis, Structural Equations and Causal Inference with R 2nd edn (Cambridge Univ. Press, 2016).Parlee, B. & Furgal, C. Well-being and environmental change in the Arctic: a synthesis of selected research from Canada’s International Polar Year program. Clim. Change 115, 13–34 (2012).Article 

Google Scholar 
Kofinas, G. P. The Costs of Power Sharing: Community Involvment in Canadian Porcuine Caribou Co-management. PhD thesis, Univ. of British Columbia (1998).Ford, J. D. et al. Including indigenous knowledge and experience in IPCC assessment reports. Nat. Clim. Change 6, 349–353 (2016).Article 

Google Scholar 
Brinkman, T. J. et al. Arctic communities perceive climate impacts on access as a critical challenge to availability of subsistence resources. Clim. Change 139, 413–427 (2016).Article 

Google Scholar 
McNeil, P., Russell, D. E., Griffith, B., Gunn, A. & Kofinas, G. Where the wild things are: seasonal variation in caribou distribution in relation to climate change. Rangifer 25, 51–63 (2005).Berman, M. & Kofinas, G. Hunting for models: grounded and rational choice approaches to analyzing climate effects on subsistence hunting in an Arctic community. Ecol. Econ. 49, 31–46 (2004).Article 

Google Scholar 
Hansen, B. B. et al. Climate events synchronize the dynamics of a resident vertebrate community in the High Arctic. Science 339, 313–315 (2013).Article 
CAS 

Google Scholar 
Collings, P., Marten, M. G., Pearce, T. & Young, A. G. Country food sharing networks, household structure, and implications for understanding food insecurity in Arctic Canada. Ecol. Food Nutr. 55, 30–49 (2016).Article 

Google Scholar 
BurnSilver, S., Magdanz, J., Stotts, R., Berman, M. & Kofinas, G. Are mixed economies persistent or transitional? Evidence using social networks from Arctic Alaska. Am. Anthropol. 118, 121–129 (2016).Article 

Google Scholar 
Baggio, J. A. et al. Multiplex social ecological network analysis reveals how social changes affect community robustness more than resource depletion. Proc. Natl Acad. Sci. USA 113, 13708–13713 (2016).Article 
CAS 

Google Scholar 
Gagnon, C. A. et al. Merging Indigenous and scientific knowledge links climate with the growth of a large migratory caribou population. J. Appl. Ecol. 57, 1644–1655 (2020).Article 

Google Scholar 
Houde, N. The six faces of traditional ecological knowledge: challenges and opportunities for Canadian co-management arrangements. Ecol. Soc. 12, 34 (2007).Article 

Google Scholar 
Fancy, S. G., Pank, L. F., Whitten, K. R. & Regelin, W. L. Seasonal movements of caribou in Arctic Alaska as determined by satellite. Can. J. Zool. 67, 644–650 (1989).Article 

Google Scholar 
Porcupine Caribou Technical Committee. Porcupine Caribou Annual Summary Report 2014 (Porcupine Caribou Management Board, Whitehorse, Yukon, 2014); https://pcmb.ca/wp-content/uploads/2021/07/PCH_annual_summ_report_2014_2015_NOV19_FINAL.pdfEastland, W. G. Influence of Weather on Movements and Migrations of Caribou. PhD thesis, Univ. of Alaska (1991).Tyler, N. J. C. Climate, snow, ice, crashes, and declines in populations of reindeer and caribou (Rangifer tarandus L.). Ecol. Monogr. 80, 197–219 (2010).Article 

Google Scholar 
Hansen, B. B., Aanes, R. & Saether, B. E. Feeding-crater selection by high-arctic reindeer facing ice-blocked pastures. Can. J. Zool. 88, 170–177 (2010).Article 

Google Scholar 
Solberg, E. J. et al. Effects of density-dependence and climate on the dynamics of a Svalbard reindeer population. Ecography 24, 441–451 (2001).Article 

Google Scholar 
Hansen, B. B., Aanes, R., Herfindal, I., Kohler, J. & Sæther, B.-E. Climate, icing, and wild arctic reindeer: past relationships and future prospects. Ecology 92, 1917–1923 (2011).Article 

Google Scholar 
Langlois, A. et al. Detection of rain-on-snow (ROS) events and ice layer formation using passive microwave radiometry: a context for Peary caribou habitat in the Canadian Arctic. Remote Sens. Environ. 189, 84–95 (2017).Article 

Google Scholar 
Russell, D. E., Gunn, A. & White, R. G. CircumArctic collaboration to monitor caribou and wild reindeer. Arctic 68, 6–10 (2015).Article 

Google Scholar 
Russell, D. E. et al. CARMA’s MERRA-based caribou range climate database. Rangifer 33, 145–152 (2013).Article 

Google Scholar 
ArcGIS version 10 (Environmental Systems Resource Institute, 2010).Cai, J., Russell, D. & Whitfield, P. Methodology and Algorithms for Constructing CARMA Bio-climate Tables (Simon Fraser Univ., 2011).Stenseth, N. C. & Mysterud, A. Weather packages: finding the right scale and composition of climate in ecology. J. Anim. Ecol. 74, 1195–1198 (2005).Article 

Google Scholar 
Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5, 9–13 (2005).Bivand, R. S., Pebesma, E. J. & Gomez-Rubio, V. Applied Spatial Data Analysis with R 2nd edn (Springer, 2013).Bivand, R. S., Keitt, T. & Rowlingson, B. Rgdal: Bindings for the Geospatial Data Abstraction Library. R package version 0.8-16 (R Foundation for Statistical Computing, 2014); https://cran.r-project.org/web/packages/rgdal/index.htmlBivand, R. S. & Rundel, C. Rgeos: Interface to Geometry Engine – Open Source (GEOS). R package version 0.3-4 (R Foundation for Statistical Computing, 2014); https://cran.r-project.org/web/packages/rgeos/index.htmlLefcheck, J. S. piecewise SEM: piecewise structural equation modelling in R for ecology, evolution and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368 (2009).Article 

Google Scholar 
Thomas, D. W. et al. Common paths link food abundance and ectoparasite loads to physiological performance and recruitment in nestling blue tits. Funct. Ecol. 21, 947–955 (2007).Article 

Google Scholar 
Shipley, B. The AIC model selection method applied to path analytic models compared using a d-separation test. Ecology 94, 560–564 (2013).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: a Practical Information-Theoretic Approach (Springer, 2002).Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 593, 103–113 (2010).Article 

Google Scholar  More

Des Marais, D. J. The biogeochemistry of hypersaline microbial mats. Adv. Microb. Ecol. 14, 251–274 (1995).Article 

Google Scholar 
Belnap, J., Welter, J., Grimm, N., Barger, N. & Ludwig, J. Linkages between microbial and hydrologic processes in arid and semiarid watersheds. Ecology 86, 298–307 (2005).Article 

Google Scholar 
Houghton, J. et al. Spatial variability in photosynthetic and heterotrophic activity drives locale δ13Corg fluctuations and carbonate precipitation in hypersaline microbial mats. Geobiology 12, 557–574 (2014).Article 

Google Scholar 
Allwood, A., Walter, M., Burch, I. & Kamber, B. 3.43 billion-year-old stromatolite reef from the Pilbara Craton of Western Australia: ecosystem-scale insights to early life on Earth. Precambrian Res. 158, 198–227 (2007).Article 
ADS 

Google Scholar 
Al-Najjar, M. et al. Spatial patterns and links between microbial community composition and function in cyanobacterial mats. Front. Microbiol. 5, 406 (2014).Article 

Google Scholar 
Warren-Rhodes, K., Dungan, J., Piatek, J. & McKay, C. Ecology and spatial pattern of cyanobacterial island patches in the Atacama Desert. J. Geophys. Res. Biogeosciences 112, G04S15 (2007).Article 

Google Scholar 
Allwood, A., Walter, M., Kamber, B., Marshall, C. & Burch, I. Stromatolite reef from the early Archaean era of Australia. Nature 441, 714–718 (2006).Article 
ADS 

Google Scholar 
Meslier, V. et al. Fundamental drivers for endolithic microbial community assemblies in the hyperarid Atacama Desert. Environ. Microbiol. 20, 1765–1781 (2018).Article 

Google Scholar 
Finstad, K. et al. Microbial community structure and the persistence of cyanobacterial populations in salt crusts of the hyperarid Atacama Desert from genome-resolved metagenomics. Front. Microbiol. 8, 1435 (2017).Article 

Google Scholar 
Wilhelm, M. et al. Constraints on the metabolic activity of microorganisms in Atacama surface soils inferred from refractory biomarkers: Implications for Martian habitability and biomarker detection. Astrobiology 18, 955–966 (2018).Dillon, J. et al. Spatial and temporal variability in a stratified microbial mat community. FEMS Microbiol. Ecol. 68, 46–58 (2009).Article 

Google Scholar 
Rillig, M. & Antonovics, J. Microbial biospherics: the experimental study of ecosystem function and evolution. Proc. Natl Acad. Sci. USA 116, 11093–11098 (2019).Article 
ADS 

Google Scholar 
Sephton, M. & Carter, J. The chances of detecting life on Mars. Planet. Space Sci. 112, 15–22 (2015).Article 
ADS 

Google Scholar 
Naveh, Z., & Lieberman, A. S. Landscape Ecology: Theory and Application (Springer, 2013).Mony, C., Vandenkoornhuyse, P., Bohannan, B. J. M., Peay, K. & Leibold, M. A. A landscape of opportunities for microbial ecology research. Front. Microbiol. 11, 2964 (2020).Article 

Google Scholar 
Summons, R. et al. Preservation of Martian organic and environmental records: final report of the Mars Biosignature Working Group. Astrobiology 11, 157–181 (2011).Article 
ADS 

Google Scholar 
Farmer, J. & Des Marais, D. J. Exploring for a record of ancient Martian life. J. Geophys. Res. 104, 26,977–26,995 (1999).Article 
ADS 

Google Scholar 
Stoker et al. We should search for extant life on Mars in this decade. Bull. AAS 53 (2021); https://doi.org/10.3847/25c2cfeb.36ef5e33Jakowsky, B. et al. Mars, the nearest habitable world—a comprehensive program for future Mars exploration. Bull. AAS 53 (2021); https://doi.org/10.3847/25c2cfeb.e5222017Hinman, N. et al. Surface morphologies in a Mars analog Ca sulfate salar, High Andes, Northern Chile. Front. Astron. Space Sci. 8, 797591 (2022).Article 

Google Scholar 
Cabrol, N. et al. Record solar UV irradiance in the tropical Andes. Front. Environ. Sci. 2, 19 (2014).Article 

Google Scholar 
Phillips, M.S. et al. Planetary mapping using Deep Learning: a method to evaluate feature identification confidence applied to habitats in Mars-analogue terrain. Astrobiology 23 (2023).Wierzchos, J. et al. Adaptation strategies of endolithic chlorophototrophs to survive the hyperarid and extreme solar radiation environment of the Atacama Desert. Front. Microbiol. 6, 934 (2015).Article 

Google Scholar 
Lynch, K. et al. Near-infrared spectroscopy of lacustrine sediments in the Great Salt Lake Desert: an analog study for Martian paleolake basins. J. Geophys. Res. Planets 120, 599–623 (2015).Article 
ADS 

Google Scholar 
El-Maarry, M., Pommerol, A. & Thomas, N. Analysis of polygonal cracking patterns in chloride-bearing terrains of Mars: indicators of ancient playa settings. J. Geophys. Res. 113, 2263–2278 (2013).Article 

Google Scholar 
Onstott, T. et al. Paleo-rock-hosted life on Earth and the search on Mars: a review and strategy for exploration. Astrobiology 19, 1230–1262 (2019).Article 
ADS 

Google Scholar 
Davila, A. & Schulze-Makuch, D. The last possible outposts for life on Mars. Astrobiology 16, 159–168 (2016).Article 
ADS 

Google Scholar 
Osterloo, M. M. et al. Geologic context of proposed chloride-bearing materials on Mars. J. Geophys. Res. 115, E10012 (2010).Article 
ADS 

Google Scholar 
Flauhaut, J., Martinot, M., Bishop, J.L., Davies, G.R. & Potts, N.J. Remote sensing and in situ mineralogic survey of the Chilean salars: an analog to Mars evaporate deposits? Icarus 282, 152–173 (2017).Bosak, T., Moore, K., Gong, J. & Grotzinger, J. Searching for biosignatures in sedimentary rocks from early Earth and Mars. Nat. Rev. Earth Environ. 2, 490–506 (2021).Article 
ADS 

Google Scholar 
Balci, N. et al. Biotic and abiotic imprints on Mg-rich stromatolites: lessons from Lake Salda, SW Turkey. Geomicrobiol. J. 37, 401–425 (2020).Article 

Google Scholar 
Williams, A., Buck, B., Soukup, D. & Merkler, D. Geomorphic controls on biological soil crust distribution: a conceptual model from the Mojave Desert (USA). Geomorphology 195, 99–109 (2013).Article 
ADS 

Google Scholar 
Warren, J. Evaporites: a Geological Compendium 2nd edn (Springer, 2016).Wierzchos, J. et al. Microbial colonization of Ca sulfate crusts in the hyperarid core of the Atacama Desert: implications for the search for life on Mars. Geobiology 9, 44–60 (2010).Article 

Google Scholar 
Robinson, C. K. et al. Microbial diversity and the presence of algae in halite endolithic communities are correlated to atmospheric moisture in the hyper-arid zone of the Atacama Desert. Environ. Microbiol. 17, 299–315 (2013).Article 

Google Scholar 
Jørgesen, B. & Des Marais, D. Optical properties of benthic photosynthetic communities: fiber-optic studies of cyanobacterial mats. Limnol. Oceanogr. 33, 99–113 (1988).Article 
ADS 

Google Scholar 
Szynkiewicz, A., Moore, C., Glamoclija, M., Bustos, D. & Pratt, L. Origin of coarsely crystalline gypsum domes in a saline playa environment at the White Sands National Monument, New Mexico. J. Geophys. Res. 115, F02021 (2010).Article 
ADS 

Google Scholar 
Walker, J., Spear, J. & Pace, N. Geobiology of a microbial endolithic community in the Yellowstone geothermal environment. Nature 434, 1011–1013 (2005).Article 
ADS 

Google Scholar 
Rasuk, M. et al. Microbial characterization of microbial ecosystems associated to evaporites domes of gypsum in Salar de Llamara in Atacama Desert. Microb. Ecol. 68, 483–494 (2014).Article 

Google Scholar 
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K. & Yuille, A. Semantic image segmentation with deep convolutional nets and fully connected CRFs. Preprint at arXiv https://arxiv.org/abs/1412.7062 (2014).Chan, M. et al. Exploring, mapping and data management integration of habitable environments in astrobiology. Frontiers in Microbiology 10, 147 (2019).Farmer, J. in From Habitability to Life on Mars 1–12 (Elsevier, 2018).Hays, L. et al. Biosignature preservation and detection in Mars analog environments. Astrobiology 17, 363–400 (2017).Article 
ADS 

Google Scholar 
Fairen, A. et al. Astrobiology through the ages of Mars: the study of terrestrial analogues to understand the habitability of Mars. Astrobiology 10, 821 (2010).Article 
ADS 

Google Scholar 
Green, J. et al. Call for a framework for reporting evidence for life beyond Earth. Nature 598, 575–579 (2021).Article 
ADS 

Google Scholar 
He, K., Xiangyu, Z., Shaoqing, R. & Jian, S. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In 2015 IEEE International Conference on Computer Vision (ICCV) 1026–1034 (IEEE, 2015).Adams, J. B. & Filice, A. L. Spectral reflectance 0.4 to 2.0 microns of silicate rock powders. J. Geophys. Res. 72, 5705–5715 (1967).National Academies of Sciences, Engineering & Medicine. Origins, Worlds and Life: a Decadal Strategy for Planetary Science and Astrobiology 2023–2032 (National Academies Press, 2022).Rodríguez Albornoz, C. Geology and Controls on Microbiota of the Salar de Pajonales (7.209.000–7.226.500 N.–510.000–530.000 E), Antofagasta, Northern Chile. Master’s thesis, Univ. Católica del Norte Antofagasta (2018).Naranjo, J., Villa, V. & Venegas, C. Geology of the Salar de Pajonales Area and Cerro Moño. Antofagasta and Atacama Regions (Geological Maps of Chile Basic Geology Series No. 153 (1: 100.000), National Geological Service, Geology and Mining Subsection, 2013).Schween, J., Hoffmeister, D. & Löhnert, U. Filling the observational gap in the Atacama Desert with a new network of climate stations. Glob. Planet. Chang. 184, 103034 (2020).Gutiérrez, F. & Cooper, A. Surface morphology of gypsum karst. Treatise Geomorphol. 6, 425–437 (2013).Article 

Google Scholar 
Bishop, J. L. et al. Spectral properties of Ca-sulfates: gypsum, bassanite and anhydrite. Am. Mineral. 99, 2105–2115 (2014).Article 
ADS 

Google Scholar 
Green, A., Berman, M., Switzer, P. & Craig, M. D. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Remote Sens. 26, 65–74 (1988).Article 
ADS 

Google Scholar 
Davis, W., Pater, I. & McKay, C. P. Rain infiltration and crust formation in the extreme arid zone of the Atacama Desert, Chile. Planet. Space Sci. 58, 616–622 (2010).Article 
ADS 

Google Scholar 
McKay, C. P. et al. Temperature and moisture conditions for life in the extreme arid region of the Atacama Desert: four years of observation including the El Niño of 1997–1998. Astrobiology 3, 393–406 (2003).Article 
ADS 

Google Scholar 
Warren-Rhodes, K., Rhodes, K., Liu, S., Zhou, P. & McKay, C. Nanoclimate environment of cyanobacterial communities in China’s hot and cold hyperarid deserts. J. Geophys. Res. 112, G01016 (2007).Article 
ADS 

Google Scholar 
Warren-Rhodes, K. et al. Physical ecology of hypolithic communities in the central Namib Desert: the role of fog, rain, rock habitat and light. J. Geophys. Res. 118, 1451–1460 (2013).Article 

Google Scholar 
Lange, O., Kilian, E. & Ziegler, H. Water vapor uptake and photosynthesis of lichens: performance differences in species with green and blue–green algae as phycobionts. Oecologia 71, 104–110 (1986).Article 
ADS 

Google Scholar 
Lange, O. L., Meyer, A. & Büdel, B. Net photosynthesis activation of a desiccated cyanobacterium without liquid water in high air humidity alone. Experiments with a Microcoleus sociatus isolated from a desert soil crust. Funct. Ecol. 8, 52–57 (1994).Article 

Google Scholar 
Palmer, R. & Friedmann, E. I. Water relations and photosynthesis in the cryptoendolithic microbial habitat of hot and cold deserts. Microb. Ecol. 18, 111–118 (1990).Article 

Google Scholar 
Potts, M. & Friedmann, E. Effects of water stress on cryptoendolithic cyanobacteria from hot desert rocks. Arch. Microbiol. 130, 267–271 (1981).Article 

Google Scholar 
Tracy, C. et al. Microclimate and limits to photosynthesis in a diverse community of hypolithic cyanobacteria in northern Australia. Environ. Microbiol. 12, 592–607 (2010).Article 

Google Scholar 
Azúa-Bustos, A. et al. Hypolithic cyanobacteria supported mainly by fog in the coastal range of the Atacama Desert. Microb. Ecol. 51, 568–581 (2011).Article 

Google Scholar 
Rull, F. et al. ExoMars Raman Laser Spectrometer for ExoMars. Proc. SPIE 8152, 81520J (2011).Kontoyannis, C. G., Orkoula, M. & Koutsoukos, P. Quantitative analysis of sulphated calcium carbonates using Raman spectrometry and X-ray powder diffraction. Analyst 122, 33–38 (1997).Lopez-Reyes, G. et al. Analysis of the scientific capabilities of the ExoMars Raman Laser Spectrometer Instrument. Eur. J. Mineral. 25, 721–733 (2013).Hunt, G. Spectral signatures of particulate minerals in the visible and near infrared. Geophysics 42, 501–513 (1977).Article 
ADS 

Google Scholar 
Bishop, J. L. in Remote Compositional Analysis: Techniques for Understanding Spectroscopy, Mineralogy, and Geochemistry of Planetary Surfaces (eds Bishop, J. L. et al.) 68–101 (Cambridge Univ. Press, 2019).Morris, R. V. et al. Evidence for pigmentary hematite on Mars based on optical, magnetic and Mössbauer studies of superparamagnetic (nanocrystalline) hematite. J. Geophys. Res. 94, 2760–2778 (1989).Article 
ADS 

Google Scholar 
Bishop, J. L., Pieters, C. M. & Burns, R. G. Reflectance and Mössbauer spectroscopy of ferrihydrite–montmorillonite assemblages as Mars soil analog materials. Geochim. Cosmochim. Acta 57, 4583–4595 (1993).Article 
ADS 

Google Scholar 
Levin, S. A. The problem of pattern and scale in ecology. Ecology 73, 1943–1967 (1992).Article 

Google Scholar 
Underwood, A. J., Chapman, M. G. & Connell, S. D. Observations in ecology: you can’t make progress on processes without understanding the patterns. J. Exp. Mar. Biol. Ecol. 250, 97–115 (2000).Article 

Google Scholar 
Turner, M. G. Landscape ecology: the effect of pattern on process. Annu. Rev. Ecol. Syst. 20, 171–197 (1989).Article 

Google Scholar 
Turner, M. G., Gardner, R. H. & O’Neill, R. V. Landscape Ecology in Theory and Practice (Springer, 2001).Wiens, J. A., Chr, N., Van Horne, B. & Ims, R. A. Ecological mechanisms and landscape ecology. Oikos 66, 369–380 (1993).Article 

Google Scholar 
Urban, D., O’Neill, R. & Shugart, H. Landscape ecology. BioScience 37, 119–127 (1987).Article 

Google Scholar 
Underwood, A. J. et al. Experiments in Ecology: their Logical Design and Interpretation using Analysis of Variance (Cambridge Univ. Press, 1997).Quinn, G. P., & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge Univ. Press, 2002).Neyman, J. & Pearson, E. S. On the problem of the most efficient tests of statistical hypotheses. Philos. Trans. R. Soc. Lond. A 231, 289–337 (1933).Article 
ADS 
MATH 

Google Scholar 
Zar, J. H. Biostatistical Analysis 5th edn (Prentice-Hall/Pearson, 2010).Ripley, B. D. Journal of the Royal Statistical Society Series B (Methodological) 39, 172-212 (1977).Royle, J. A. & Nichols, J. D. Estimating abundance from repeated presence–absence data or point counts. Ecology 84, 777–790 (2003).Article 

Google Scholar 
Krebs, C. Ecological Methodology 2nd edn (Addison-Wesley, 1999).Warren-Rhodes, K., Dungan, J., Piatek, J. & McKay, C. Ecology and spatial pattern of cyanobacterial community island patches in the Atacama Desert. J. Geophys. Res. 112, G04S15 (2007).Article 

Google Scholar 
Belnap, J., Phillips, S., Witwicki, D. & Miller, M. Visually assessing the level of development and soil surface stability of cyanobacterially dominated biological soil crusts. J. Arid Environ. 72, 1257–1264 (2008).Article 
ADS 

Google Scholar 
Warren-Rhodes, K. et al. Hypolithic cyanobacteria, dry limit of photosynthesis, and microbial ecology in the hyperarid Atacama Desert. Microb. Ecol. 52, 389–398 (2006).Article 

Google Scholar 
Yingst, R. et al. Is a linear or a walkabout protocol more efficient when using a rover to choose biologically relevant samples in a small region of interest? Astrobiology 20, 327–347 (2020).Article 
ADS 

Google Scholar 
Shen, J., Wyness, A., Claire, M. & Zerkle, A. Spatial variability of microbial communities and salt distributions across a latitudinal gradient in the Atacama Desert. Microb. Ecol. 82, 442–458 (2021).Article 

Google Scholar 
Barrett, J. et al. Variation in biogeochemistry and soil biodiversity across spatial scales in a polar desert ecosystem. Ecology 85, 3105–3118 (2004).Article 

Google Scholar 
Pointing, S. B. et al. Highly specialized microbial diversity in hyper-arid polar desert. Proc. Natl Acad. Sci. USA 106, 19964–19969 (2009).Article 
ADS 

Google Scholar 
Chiodini, R. et al. Microbial population differentials between mucosal and submucosal intestinal tissues in advanced Crohn’s disease of the ileum. PloS ONE 10, e0134382 (2015).Article 

Google Scholar 
Rivas, L. A. et al. A 200-antibody microarray biochip for environmental monitoring: searching for universal microbial biomarkers through immunoprofiling. Anal. Chem. 80, 7970–7979 (2008).Article 

Google Scholar 
Sanchez-Garcia, L. et al. Microbial biomarker transition in high-altitude sinter mounds from El Tatio (Chile) through different stages of hydrothermal activity. Front. Microbiol. 9, 3350 (2019).Article 

Google Scholar 
Parro, V. et al. SOLID3, a multiplex antibody microarray-based optical sensor instrument for in situ life detection in planetary exploration. Astrobiology 11, 15–28 (2011).Article 
ADS 

Google Scholar 
Parro, V. et al. A microbial oasis in the hypersaline Atacama subsurface discovered by a life detector chip: implications for the search for life on Mars. Astrobiology 11, 969–996 (2011).Article 
ADS 

Google Scholar 
Blanco, Y., Moreno-Paz, M., Aguirre, J. & Parro, V. in Hydrocarbon and Lipid Microbiology Protocols (eds McGenity, T. J. et al.) Ch. 159 (Springer, 2017).Moreno-Paz, M. et al. Detecting nonvolatile life and nonlife-derived organics in a carbonaceous chrondrite analogue with a new multiplex immunoassay and its relevance for planetary exploration. Astrobiology 18, 1041–1056 (2018).Article 
ADS 

Google Scholar 
Ekwealor, J. & Fisher, K. Life under quartz: hypolithic mosses in the Mojave Desert. PLoS ONE 15, e0235928 (2020).Article 

Google Scholar 
Williams, A., Buck, B. & Beyene, M. Biological soil crusts in the Mojave Desert, USA: micromorphology and pedogenesis. Soil Sci. Soc. Am. 76, 1685–1695 (2012).Article 

Google Scholar 
Archer, S. et al. Endolithic microbial diversity in sandstone and granite from the McMurdo Dry Valleys, Antarctica. Polar Biol. 40, 997–1006 (2017).Article 

Google Scholar 
Noffke, N., Gerdes, G., Klenke, T. & Krumbein, W. Microbially induced sedimentary structures—a new category within the classification of primary sedimentary structures. J. Sediment. Res. 71, 649–656 (2001).Article 
ADS 

Google Scholar 
Fierer, N. & Jackson, R. The diversity and biogeography of soil bacterial communities. Proc. Natl Acad. Sci. USA 103, 626–631 (2006).Article 
ADS 

Google Scholar 
Caruso, T. et al. Stochastic and deterministic processes interact in the assembly of desert microbial communities on a global scale. ISME J. 5, 1406–1413 (2011).Article 

Google Scholar 
Valverde et al. Prokaryotic community structure and metabolisms in shallow subsurface of Atacama Desert playas and alluvial fans after heavy rains: repairing and preparing for next dry period. Front. Microbiol. 10, 1641 (2019).Article 

Google Scholar 
Sun, H. Endolithic microbial life in extreme cold climate: snow is required, but perhaps less is more. Biology 2, 693–701 (2013).Maier, S. et al. Photoautotrophic organisms control microbial abundance, diversity and physiology in different types of biological soil crusts. ISME J. 12, 1032–1046 (2018).Article 

Google Scholar 
Roldan, M., Ascaso, C. & Weirzchos, J. Fluorescent fingerprint of endolithic phototrophic cyanobacteria living within halite rocks in the Atacama Desert. Appl. Environ. Microbiol. 80, 2998–3006 (2014).Article 
ADS 

Google Scholar 
Cockell, C. et al. 0.25 Ga salt deposits preserve geological signatures of habitable conditions and ancient lipids. Astrobiology 20, 864–877 (2019).Article 
ADS 

Google Scholar 
Ripley, B. D. Spatial Statistics (Wiley, 1981).Gelfand, A. E., Diggle, P., Guttorp, P., & Fuentes, M. (eds) Handbook of Spatial Statistics (CRC Press, 2010).Dixon, P. M. in Encyclopedia of Environmetrics, 1796-1803 (Wiley, 2006).Baddeley, A., Rubak, E. & Turner, R. Spatial point patterns: methodology and applications with R. J. Stat. Softw. 75, 2 (2016).Wood, S. Generalized Additive Models: an Introduction with R Ch 3–5 (Chapman and Hall/CRC, 2006).Simon, R. & Wood, N. GAMS in practice: mgcv. In Generalized Additive Models: an Introduction with R 2nd ed (eds Blitzstein, J., Faraway, J., Tanner, M. & Zidek, J.) Ch 7 (Chapman and Hall/CRC, 2017).Fang, X. & Chan, K.-S. Generalized Additive Models with Spatio-temporal Data (Univ. Iowa); https://stat.uiowa.edu/sites/stat.uiowa.edu/files/techrep/tr396.pdfLeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989).Article 

Google Scholar 
Roberts, M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 3, 199–217 (2021).Article 

Google Scholar 
Shelhamer, E., Long J. & Darrell, T. Fully convolutional networks for semantic segmentation. Preprint at arXiv https://arxiv.org/abs/1605.06211 (2016).Gal, Y. & Ghahramani, Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. Preprint at arXiv https://arxiv.org/abs/1506.02142 (2016).Bishop, J. L. & Murad, E. in Volcano–Ice Interactions on Earth and Mars (eds Smellie, J. L. & Chapman, M. G.) 357–370 (Special Publication No. 202, Geological Society, 2002).Buzgar, N., Buzatu, A. & Sanislav, I. V. The Raman study of certain sulfates. An. Stiintificie Univ. Al. I. Cuza IASI Geol. 55, 5–23 (2009).Jehlicka, J., Edwards, H. & Oren, A. Raman spectroscopy of microbial pigments. Appl. Environ. Microbiol. 80, 3286–3295 (2013). More

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

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