Philippa Kaur

Materials
BioSim2 version 2 (Information Technology Department of Norwich University, Las Vegas, NE, USA), Maxent version 3.3.3 (AT&T Labs–Research, Florham Park, NJ, USA; https://www.cs.princeton.edu/~schapire/maxent/), and ArcGIS version 10.5 (ESRI Inc., California, USA; https://www.esri.com/) software were respectively used for screening the environmental variables, predicting the A. membranaceus var. mongholicus habitat, and mapping the suitable distribution area of A. membranaceus var. mongholicus in Inner Mongolia. The geographic coordinate information of A. membranaceus var. mongholicus samples was obtained by Handheld GPS (Jarmin Rino530HCx, Nanjing Tiandi Precision Drawing Instrument Equipment Co. Ltd., Nanjing).
The extraction of active components (Astragaloside IV and calycosin-7-glucoside) from A. membranaceus var. mongholicus samples was carried out by experimental equipment, namely pulverizer (FLBP-200, Zhejiang Yili Industry and Trade Co., Ltd., Zhejiang), electronic analytical balance (ME204, Mettle Toledo, Shanghai), temperature-controlled electric heating jacket (KDM, Heze Jingke Instrument Co., Ltd., Heze), rotary evaporator (IKA RV 10, Shanghai Hanpei Electromechanical Equipment Co. Ltd., Shanghai), ultrasonic cleaner (KQ-500DE, Sonxi Ultrasonic, Kunshan), the Soxhlet extractor, reflux extractor, volumetric bottle, and other glass instruments produced by Shuniu Glass Instrument Co., Ltd. (Sichuan). High-performance liquid chromatography-ultraviolet (Ultimate3000, Thermo Fisher Scientific, Waltham, MA, USA), evaporative light scattering detector (ELSD 2000ES, Alltech (Shanghai in China) Co., Ltd.), air generator (XWK-III, Tianjin Jinmin Analytical Instrument Manufacturing Co., Ltd., Tianjin), and Astragaloside IV (CAS: 148321) and calycosin-7-glucoside (CAS: 150326) reference substances of  > 95% purity purchased from Chengdu Pufeide Biotech Co., Ltd (Chengdu) were used for the quantitative analyses of the samples. A laboratory water purifier (XGB-40-B, Shenyang Xinjie Technology Co., Ltd., Shenyang), chromatographic methanol, n-butanol, and phosphoric acid were purchased from Tianjin Fuchen Chemical Reagent Factory, chromatographic acetonitrile was purchased from Tianjin Comeo Chemical Reagent Co., Ltd., and ammonia water was purchased from Tianjin Fengchuan Chemical Reagent Technology Co., Ltd (Tianjin) for the entire High-performance liquid chromatography-ultraviolet analysis.
Acquisition of ecological factor data
In this study, we considered the influence of 74 ecological factors, including climate, soil, topography, vegetation type, and meteorological factors, on the distribution of A. membranaceus var. mongholicus. Climatic data included 59 ecological factors, e.g., monthly mean precipitation (mm), temperature (°C × 10), and sunshine duration (h × 10) from January to December (mm), mean precipitation (mm), temperature (Tmean4-10, °C × 10), and sunshine duration (Smean4-10, h × 10) in the growing season, mean annual sunshine duration (SunshineAnnu, h × 10), and 19 comprehensive climatic factors. These data were based on spatial interpolation of meteorological observation data from 752 surface and automatic meteorological stations in China, collected from 1951 to 2000, with a resolution of 1 km.
The soil data comprised eight ecological factors, which were determined according to a 1:100,000 soil map of the People’s Republic of China (compiled in 1995) provided by the Second National Land Survey. These factors were soil pH, cation exchange capacity (cmol kg−1), sand content (SoilSand, %), clay content (%), soil type (SoilType from FAO-90), soil available water content level (SoilWater), soil texture (SoilTexture, USDA), and organic carbon content (SoilCarbon, %).
Topographic data included three ecological factors, namely altitude (m), slope (°), and aspect with a resolution of 1 km. Vegetation type data included an ecological factor based on vegetation subtype data from a vegetation map of the People’s Republic of China (1:100,000) published by the Institute of Botany, Chinese Academy of Sciences. The comprehensive meteorological data comprised three ecological factors, namely the warmth and coldness indexes (°C) derived from Kira’s thermal index and the humidity index (mm∙°C−1) derived from Xu’s modified version of Kira’s humidity index42,43.
The abovementioned data of ecological factors for studying the ecological suitability and quality regionalization of A. membranaceus var. mongholicus were taken from the “Traditional Chinese Medicine Resources Spatial Information Grid Database” (https://www.tcm-resources.com/) provided by the National Resource Center for Chinese Materia Medica of the China Academy of Chinese Medical Sciences (Beijing, China). The relevant information for each ecological factor, including the category, name and type, is presented in Appendix 1 in Supplementary Information 1.
Collection of A. membranaceus var. mongholicus samples
The cultivation area of A. membranaceus var. mongholicus was approximately 6,666.67 ha (66.67 km2) in Inner Mongolia in 2016 and mainly covered Urad Front Banner, Guyang County, Tumd Right Banner, Wuchuan County, and Harqin Banner44. Based on a full understanding of the regional characteristics of Inner Mongolia, we adopted traditional route and quadrat surveys, taking the village or gacha (level with administrative village) as the smallest sampling unit, to conduct a field survey of A. membranaceus var. mongholicus in the eastern, central, and western regions of Inner Mongolia in 2016. To ensure the uniformity and representativeness of the sample data, we sampled different production areas with significant differences in ecological factors, such as terrain, soil, and vegetation type, according to these two routes. Three to five sampling points were set in each sampling area with a distance of 1 km, and a total of 63 samples of A. membranaceus var. mongholicus were collected in Inner Mongolia. The geographic coordinates of the sampling points were recorded using the handheld GPS. To diminish the influence of different harvesting periods or growth years on the contents of active components in A. membranaceus var. mongholicus, the samples were biennial medicinal materials and were collected in October 2016. Figure 10 shows the survey route and location of the sampling points. The geographical coordinates (latitude and longitude data) of each sampling point are listed in Appendix 4 in Supplementary Information 1.
Figure 10

Survey routes of A. membranaceus var. mongholicus and geographical location of its sampling points. Generated using the ArcMap version 10.5 software (ESRI Inc., California, USA. https://www.esri.com/).

Full size image

Preliminary screening of ecological factors
To diminish the influence of high correlations between ecological factors at the time of MaxEnt modeling, we used Biosim2 to calculate the correlation coefficients between all the ecological factor values extracted from the longitude and latitude of 63 sampling points. According to the correlation coefficient tree diagram calculated using Biosim2, we excluded any ecological factor that had a low correlation with A. membranaceus var. mongholicus growth, based on their potential biological relevance to this species, for each set of highly correlated ecological factors with correlation coefficients  > 0.845. We loaded the retained ecological factors into Biosim2 again and repeated the above-mentioned operation until the correlation coefficient between all the retained ecological factors was ≤ 0.8. The ecological factors finally screened are depicted in Fig. 3.
Calculation and accuracy testing of MaxEnt model
The point locality data of A. membranaceus var. mongholicus and the retained ecological factor data were imported into the MaxEnt model for the calculation. The model parameters were set as follows: The model was run 10 times, the maximum number of iterations was 1,000,000, the convergence threshold was 0.0005, the random test percentage was set to 10, namely, 90% of the point locality data were randomly selected as training data, and the remaining 10% of data points were the test data. Cross validation (the data set was divided into ten parts, of which 9 were used as training data and 1 as test data in turn for the experiment) was used as the replicated run type, and the max number of background points and the remaining parameters were set as default.
In this study, the receiver operating characteristic curve analysis of the distribution of A. membranaceus var. mongholicus in the model was used to evaluate the accuracy of MaxEnt. The area under the receiver operating characteristic (AUC) was not affected by the threshold, and its value ranged from 0 to 1. The larger the value, the higher was the accuracy of the model. When the AUC was in the range 0.5–0.8, the accuracy of the prediction made by the model was inferior; however, the prediction accuracy was reasonable when the AUC was in the range 0.8–0.9. Finally, when the AUC was greater than 0.9, the model produced reliable and accurate prediction results, and the potential distribution of the species could be accurately predicted46,47. The results of 10 training and test sample data repeatedly calculated using MaxEnt are presented in Table 1, and the mean AUC and standard deviation value of the test samples are depicted in Fig. 4. The average growth suitability image of A. membranaceus var. mongholicus obtained through the model was used as the probability layer file of potential A. membranaceus var. mongholicus distribution for studying the ecological suitability regionalization of A. membranaceus var. mongholicus.
Ecological suitability regionalization of A. membranaceus var. mongholicus in Inner Mongolia
The point locality data of A. membranaceus var. mongholicus and its average growth suitability image (distribution probability layer of this species) were simultaneously loaded into ArcGIS. The distribution data of the A. membranaceus var. mongholicus sampling points were used to extract the ecological suitability values in the distribution probability layer, which was rasterized according to the maximum and minimum values of the suitability value. This was done to remove the data outside the range of the sampling points and obtain the region suitable for the cultivation of A. membranaceus var. mongholicus. The natural breaks method in ArcGIS was used to divide the ecological suitability distribution area of the species into four levels: unsuitable (0.00–0.02), secondarily suitable (0.02–0.18), suitable (0.18–0.42), and optimum (0.42–0.90); we used an appropriate color ramp in ArcGIS to indicate the aforementioned levels. Finally, a legend, north arrow, and scale bar were added to complete the map of the ecological suitability of A. membranaceus var. mongholicus at the city level in Inner Mongolia (Fig. 5). To accurately obtain the potential distribution area of A. membranaceus var. mongholicus, based on the ecological suitability of this species in Inner Mongolia, we added county-level administrative data to ArcGIS. Next, we extracted the distribution probability layer of this species by mask to obtain a map of the ecological suitability of A. membranaceus var. mongholicus at the county level. Subsequently, the areas of suitable habitat in the Leagues or Cities in Inner Mongolia were statistically analyzed (Table 2).
Main ecological factors affecting A. membranaceus var. mongholicus growth
The ecological factors screened by Biosim2 were inputted as environmental variables into the Maxent model for model calculation, and the contribution rate of each factor to the growth of A. membranaceus var. mongholicus was determined. To determine the first estimate, in each iteration of the training algorithm the increase in regularized gain was added to the contribution of the corresponding variable, or subtracted from it if the change to the absolute value of λ was negative. For the second estimate, for each environmental variable, the values of that variable on training presence and background data were randomly permuted. Finally, the model was reevaluated on the permuted data and the contribution of each factor was obtained (Fig. 6). Ecological factors with a contribution rate of  > 0% were selected as the main factors to analyze the response curves (Fig. 8A–Q). Those contributing to the growth of the species were used as the main ecological factors for studying the suitability regionalization of high-quality A. membranaceus var. mongholicus in Inner Mongolia.
Content of index components and relationships with main ecological factors
Saponins and flavonoids are the primary active components of Radix Astragali, and they are valuable indicators for evaluating the quality of Radix Astragali in the Chinese, British, and European pharmacopoeia3,48,49. The contents of the saponin astragaloside IV and flavonoid calycosin-7-glucoside in 63 Radix Astragali samples were determined via high performance liquid chromatography according to the Chinese Pharmacopoeia (2015 Edition) (Appendix 3 in Supplementary Information 1). In addition, we used SPSS17.0 statistical analysis software to analyze differences in astragaloside IV and calycosin-7-glucoside content in A. membranaceus var. mongholicus from different production areas in Inner Mongolia. The relationships between astragaloside IV, calycosin-7-glucoside, and the main ecological factors were determined using the correlation matrix (Tables 3, 4). The relationship equations between these index components and the main ecological factors were obtained by stepwise linear regression analysis.
Suitability regionalization of high-quality A. membranaceus var. mongholicus in Inner Mongolia
The relationship equations were respectively inputted into ArcGIS’s grid calculator to obtain the quantitative distribution layers of astragaloside IV and calycosin-7-glucoside in A. membranaceus var. mongholicus. Using the spatial calculation function of ArcGIS, the two abovementioned layers were overlain on the ecological suitability distribution layer of A. membranaceus var. mongholicus, and the spatial suitability distribution regions of astragaloside IV and calycosin-7-glucoside in A. membranaceus var. mongholicus in Inner Mongolia were finally obtained. According to the content limits of these index components in 63 A. membranaceus var. mongholicus samples, the spatial suitability distribution regions of the index components were divided into five grades in ArcGIS, represented by a color ramp from blue to red. A map of the spatial distribution of astragaloside IV and calycosin-7-glucoside in A. membranaceus var. mongholicus in the study area was plotted in ArcGIS (Fig. 9A,B). To determine the regions in Inner Mongolia that were suitable for cultivating high-quality A. membranaceus var. mongholicus, we overlaid the spatial distribution layer of the two active ingredients and the administrative distribution data at the county level to find out where these contents are both maximized (Fig. 9C). The administrative areas under various suitability levels of astragaloside IV and calycosin-7-glucoside distribution are presented in the Table 5. More

1.
Altshuler, D. et al. An SNP map of the human genome generated by reduced representation shotgun sequencing. Nature 407, 513–516. https://doi.org/10.1038/35035083 (2000).
ADS  CAS  Article  PubMed  Google Scholar 
2.
Ali, O. A. et al. RAD capture (Rapture): flexible and efficient sequence-based genotyping. Genetics 202, 389 (2016).
CAS  PubMed  Google Scholar 

3.
Hoffberg, S. L. et al. RADcap: sequence capture of dual-digest RADseq libraries with identifiable duplicates and reduced missing data. Mol. Ecol. Resour. 16, 1264–1278. https://doi.org/10.1111/1755-0998.12566 (2016).
CAS  Article  PubMed  Google Scholar 

4.
Franchini, P., Monné Parera, D., Kautt, A. F. & Meyer, A. quaddRAD: a new high-multiplexing and PCR duplicate removal ddRAD protocol produces novel evolutionary insights in a nonradiating cichlid lineage. Mol. Ecol. 26, 2783–2795 (2017).
CAS  PubMed  Google Scholar 

5.
Darrier, B. et al. A comparison of mainstream genotyping platforms for the evaluation and use of barley genetic resources. Front. Plant Sci. 10, 544 (2019).
PubMed  PubMed Central  Google Scholar 

6.
Palti, Y. et al. The development and characterization of a 57 K single nucleotide polymorphism array for rainbow trout. Mol. Ecol. Resour. 15, 662–672 (2015).
CAS  PubMed  Google Scholar 

7.
Moragues, M. et al. Effects of ascertainment bias and marker number on estimations of barley diversity from high-throughput SNP genotype data. Theor. Appl. Genet. 120, 1525–1534 (2010).
CAS  PubMed  Google Scholar 

8.
Malomane, D. K. et al. Efficiency of different strategies to mitigate ascertainment bias when using SNP panels in diversity studies. BMC Genomics 19, 22 (2018).
PubMed  PubMed Central  Google Scholar 

9.
Lachance, J. & Tishkoff, S. A. SNP ascertainment bias in population genetic analyses: why it is important, and how to correct it. BioEssays 35, 780–786 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

10.
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 46, W537–W544 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

11.
Whitaker, K. Genetic evidence for mixed modes of reproduction in the coral Pocillopora damicornis and its effect on population structure. Mar. Ecol. Prog. Ser. 306, 115–124 (2006).
ADS  Google Scholar 

12.
Miller, K. J. & Ayre, D. J. The role of sexual and asexual reproduction in structuring high latitude populations of the reef coral Pocillopora damicornis. Heredity 92, 557–568 (2004).
CAS  PubMed  Google Scholar 

13.
Stoddart, J. A. Asexual production of planulae in the coral Pocillopora damicornis. Mar. Biol. 76, 279–284 (1983).
Google Scholar 

14.
Ayre, D. J. & Hughes, T. P. Genotypic diversity and gene flow in brooding and spawning corals along the Great Barrier Reef, Australia. Evolution 54, 1590–1605 (2000).
CAS  PubMed  Google Scholar 

15.
Adjeroud, M. & Tsuchiya, M. Genetic variation and clonal structure in the scleractinian coral Pocillopora damicornis in the Ryukyu Archipelago, southern Japan. Mar. Biol. 134, 753–760 (1999).
Google Scholar 

16.
Foster, N. L., Baums, I. B. & Mumby, P. J. Sexual vs. asexual reproduction in an ecosystem engineer: the massive coral Montastraea annularis. J. Anim. Ecol. 76, 384–391. https://doi.org/10.1111/j.1365-2656.2006.01207.x (2007).
Article  PubMed  Google Scholar 

17.
Neigel, J. E. & Avise, J. C. Clonal diversity and population structure in a reef-building coral, Acropora cervicornis: self-recognition analysis and demographic interpretation. Evolution 37, 437–453. https://doi.org/10.1111/j.1558-5646.1983.tb05561.x (1983).
Article  PubMed  Google Scholar 

18.
Baums, I. B., Miller, M. W. & Hellberg, M. E. Geographic variation in clonal structure in a reef building Caribbean coral, Acropora palmata. Ecol. Monogr. 76, 503–519. https://doi.org/10.1890/0012-9615 (2006).
Article  Google Scholar 

19.
Pinzón, J., Reyes-Bonilla, H., Baums, I. & LaJeunesse, T. Contrasting clonal structure among Pocillopora (Scleractinia) communities at two environmentally distinct sites in the Gulf of California. Coral Reefs 3, 765–777. https://doi.org/10.1007/s00338-012-0887-y (2012).
ADS  Article  Google Scholar 

20.
Parkinson, J. E. & Baums, I. B. The extended phenotypes of marine symbioses: ecological and evolutionary consequences of intraspecific genetic diversity in coral-algal associations. Front. Microbiol. https://doi.org/10.3389/fmicb.2014.00445 (2014).
Article  PubMed  PubMed Central  Google Scholar 

21.
Polato, N. R., Altman, N. S. & Baums, I. B. Variation in the transcriptional response of threatened coral larvae to elevated temperatures. Mol. Ecol. 22, 1366–1382 (2013).
CAS  PubMed  Google Scholar 

22.
Baums, I. et al. Genotypic variation influences reproductive success and thermal stress tolerance in the reef building coral, Acropora palmata. Coral Reefs 32, 703–717 (2013).
ADS  Google Scholar 

23.
Randall, C. J. & Szmant, A. M. Elevated temperature affects development, survivorship, and settlement of the Elkhorn coral, Acropora palmata (Lamarck 1816). Biol. Bull. 217, 269–282 (2009).
PubMed  Google Scholar 

24.
Meyer, E. et al. Genetic variation in responses to a settlement cue and elevated temperature in the reef-building coral Acropora millepora. Mar. Ecol. Prog. Ser. 392, 81–92 (2009).
ADS  CAS  Google Scholar 

25.
Baums, I. B., Hughes, C. R. & Hellberg, M. H. Mendelian microsatellite loci for the Caribbean coral Acropora palmata. Mar. Ecol. Prog. Ser. 288, 115–127. https://doi.org/10.3354/meps288115 (2005).
ADS  CAS  Article  Google Scholar 

26.
Fogarty, N. D., Vollmer, S. V. & Levitan, D. R. Weak Prezygotic isolating mechanisms in threatened Caribbean Acropora corals. PLoS ONE 7, e30486. https://doi.org/10.1371/journal.pone.0030486 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Baums, I. B. et al. Considerations for maximizing the adaptive potential of restored coral populations in the western Atlantic. Ecol. Appl. 19, e01978 (2019).
Google Scholar 

28.
Muscatine, L. & Cernichiari, E. Assimilation of photosynthetic products of zooxanthellae by a reef coral. Biol. Bull. 137, 506–523 (1969).
CAS  PubMed  Google Scholar 

29.
Davies, P. S. Effect of daylight variations on the energy budgets of shallow-water corals. Mar. Biol. 108, 137–144 (1991).
Google Scholar 

30.
Santos, S. R. & Coffroth, M. A. Molecular genetic evidence that dinoflagellates belonging to the genus Symbiodinium Freudenthal are haploid. Biol. Bull. 204, 10–20 (2003).
CAS  PubMed  Google Scholar 

31.
Pettay, D. T. LaJeunesse TC (2007) Microsatellites from clade B Symbiodinium spp. specialized for Caribbean corals in the genus Madracis. Mol. Ecol. Notes 7, 1271–1274. https://doi.org/10.1111/j.1471-8286.2007.01852.x (2007).
CAS  Article  Google Scholar 

32.
Pettay, D. T. & LaJeunesse, T. C. Microsatellite loci for assessing genetic diversity, dispersal and clonality of coral symbionts in ‘stress-tolerant’ clade D Symbiodinium. Mol. Ecol. Resour. 9, 1022–1025. https://doi.org/10.1111/j.1755-0998.2009.02561.x (2009).
CAS  Article  PubMed  Google Scholar 

33.
Pinzón, J. H., Devlin-Durante, M. K., Weber, M. X., Baums, I. B. & LaJeunesse, T. C. Microsatellite loci for Symbiodinium A3 (S. fitti) a common algal symbiont among Caribbean Acropora (stony corals) and Indo-Pacific giant clams (Tridacna). Conserv. Genet. Resour. 3, 45–47. https://doi.org/10.1007/s12686-010-9283-5 (2011).
Article  Google Scholar 

34.
Baums, I. B., Devlin-Durante, M. K. & LaJeunesse, T. C. New insights into the dynamics between reef corals and their associated dinoflagellate endosymbionts from population genetic studies. Mol. Ecol. 23, 4203–4215. https://doi.org/10.1111/mec.12788 (2014).
Article  PubMed  Google Scholar 

35.
Wham, D. C., Pettay, D. T. & LaJeunesse, T. C. Microsatellite loci for the host-generalist “zooxanthella” Symbiodinium trenchi and other Clade D Symbiodinium. Conserv. Genet. Resour. 3, 541–544. https://doi.org/10.1007/s12686-011-9399-2 (2011).
Article  Google Scholar 

36.
Grupstra, C. G. et al. Evidence for coral range expansion accompanied by reduced diversity of Symbiodinium genotypes. Coral Reefs 36, 981–985 (2017).
ADS  Google Scholar 

37.
Chan, A. N., Lewis, C. L., Neely, K. L. & Baums, I. B. Fallen pillars: the past, present, and future population dynamics of a rare, specialist coral-algal symbiosis. Front. Mar. Sci. 6, 218 (2019).
ADS  Google Scholar 

38.
Andras, J. P., Kirk, N. L., Coffroth, M. A. & Harvell, C. D. Isolation and characterization of microsatellite loci in Symbiodinium B1/B184, the dinoflagellate symbiont of the Caribbean sea fan coral, Gorgonia ventalina. Mol. Ecol. Resour. 9, 989–993 (2009).
CAS  PubMed  Google Scholar 

39.
Veron, J. E. N. Corals of the World (Australian Institute of Marine Science, Townsville, 2000).
Google Scholar 

40.
Wallace, C. C. Staghorn Corals of the World: A Revision of the Coral Genus Acropora (Scleractinia; Astrocoeniina; Acroporidae) Worldwide, with Emphasis on Morphology, Phylogeny and Biogeography (CSIRO publishing, Clayton, 1999).
Google Scholar 

41.
van Oppen, M. J. H., Willis, B. L., van Vugt, J. A. & Miller, D. J. Examination of species boundaries in the Acropora cervicornis group (Scleractinia, Cnidaria) using nuclear DNA sequence analyses. Mol. Ecol. 9, 1363–1373 (2000).
CAS  Google Scholar 

42.
Vollmer, S. V. & Palumbi, S. R. Hybridization and the evolution of reef coral diversity. Science 296, 2023–2025 (2002).
ADS  CAS  PubMed  Google Scholar 

43.
de Lamarck, J. B. P. A. Histoire Naturelle des Animaux sans Vertebres Vol. 2 (Verdiere, Paris, 1816).
Google Scholar 

44.
Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

45.
45Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. arXiv:1207.3907 (2012).

46.
Kitchen, S. A. et al. Genomic variants among threatened Acropora corals. G3: Genes Genomes Genet. https://doi.org/10.1534/g3.119.400125 (2019).
Article  Google Scholar 

47.
Liew, Y. J., Aranda, M. & Voolstra, C. R. Reefgenomics.org—a repository for marine genomics data. Database https://doi.org/10.1093/database/baw152 (2016).
Article  PubMed  PubMed Central  Google Scholar 

48.
Fuller, Z. L. et al. Population genetics of the coral Acropora millepora: towards a genomic predictor of bleaching. bioRxiv https://doi.org/10.1101/2020.02.10.943092 (2019).
Article  Google Scholar 

49.
49Hong, H. et al. in BMC Bioinformatics. (BioMed Central).

50.
Hong, H. et al. Technical reproducibility of genotyping SNP arrays used in genome-wide association studies. PLoS ONE 7, e44483 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

51.
Lee, Y. G. et al. Development, validation and genetic analysis of a large soybean SNP genotyping array. Plant J. 81, 625–636 (2015).
CAS  PubMed  Google Scholar 

52.
Marrano, A. et al. A new genomic tool for walnut (Juglans regia L.): development and validation of the high-density Axiom™ J. regia 700K SNP genotyping array. Plant Biotechnol. J. 17, 1027–1036 (2019).
CAS  PubMed  Google Scholar 

53.
Baums, I. B., Johnson, M. E., Devlin-Durante, M. K. & Miller, M. W. Host population genetic structure and zooxanthellae diversity of two reef-building coral species along the Florida Reef Tract and wider Caribbean. Coral Reefs 29, 835–842. https://doi.org/10.1007/s00338-010-0645-y (2010).
ADS  Article  Google Scholar 

54.
Hemond, E. M. & Vollmer, S. V. Genetic diversity and connectivity in the threatened Staghorn coral (Acropora cervicornis) in Florida. PLoS ONE 5, e8652 (2010).
ADS  PubMed  PubMed Central  Google Scholar 

55.
Vollmer, S. V. & Palumbi, S. R. Restricted gene flow in the Caribbean staghorn coral Acropora cervicomis: Implications for the recovery of endangered reefs. J. Hered. 98, 40–50 (2007).
CAS  PubMed  Google Scholar 

56.
Drury, C. et al. Genomic variation among populations of threatened coral: Acropora cervicornis. BMC Genomics 17, 286. https://doi.org/10.1186/s12864-016-2583-8 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

57.
Porto-Hannes, I. et al. Population structure of the corals Orbicella faveolata and Acropora palmata in the Mesoamerican Barrier Reef System with comparisons over Caribbean basin-wide spatial scale. Mar. Biol. https://doi.org/10.1007/s00227-014-2560-1 (2014).
Article  Google Scholar 

58.
Baums, I. B., Miller, M. W. & Hellberg, M. E. Regionally isolated populations of an imperiled Caribbean coral, Acropora palmata. Mol. Ecol. 14, 1377–1390 (2005).
CAS  PubMed  Google Scholar 

59.
Devlin-Durante, M. K. & Baums, I. B. Genome-wide survey of single-nucleotide polymorphisms reveals fine-scale population structure and signs of selection in the threatened Caribbean elkhorn coral, Acropora palmata. PeerJ 5, e4077 (2017).
PubMed  PubMed Central  Google Scholar 

60.
Palumbi, S. R., Vollmer, S., Romano, S., Oliver, T. & Ladner, J. The role of genes in understanding the evolutionary ecology of reef building corals. Evol. Ecol. 26, 317–335. https://doi.org/10.1007/s10682-011-9517-3 (2012).
Article  Google Scholar 

61.
Miller, D. J. & Van Oppen, M. J. H. A “fair go” for coral hybridization. Mol. Ecol. 12, 805–807 (2003).
CAS  PubMed  Google Scholar 

62.
Japaud, A., Bouchon, C., Magalon, H. & Fauvelot, C. Geographic distances and ocean currents influence Caribbean Acropora palmata population connectivity in the Lesser Antilles. Conserv. Genet. 20, 447–466 (2019).
Google Scholar 

63.
Liu, H. et al. Symbiodinium genomes reveal adaptive evolution of functions related to coral-dinoflagellate symbiosis. Commun. Biol. 1, 95. https://doi.org/10.1038/s42003-018-0098-3 (2018).
Article  PubMed  PubMed Central  Google Scholar 

64.
Thornhill, D. J., LaJeunesse, T. C., Kemp, D. W., Fitt, W. K. & Schmidt, G. W. Multi-year, seasonal genotypic surveys of coral-algal symbioses reveal prevalent stability or post-bleaching reversion. Mar. Biol. 148, 711–722 (2006).
Google Scholar 

65.
Hoadley, K. D. et al. Host–symbiont combinations dictate the photo-physiological response of reef-building corals to thermal stress. Sci. Rep. 9, 1–15 (2019).
CAS  Google Scholar 

66.
Rosser, N. L. et al. Phylogenomics provides new insight into evolutionary relationships and genealogical discordance in the reef-building coral genus Acropora. Proc. Roy. Soc. B: Biol. Sci. 284, 20162182 (2017).
Google Scholar 

67.
Hatta, M. et al. Reproductive and genetic evidence for a reticulate evolutionary history of mass-spawning corals. Mol. Biol. Evol. 16, 1607–1613 (1999).
CAS  PubMed  Google Scholar 

68.
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

69.
Shinzato, C. et al. Using the Acropora digitifera genome to understand coral responses to environmental change. Nature 476, 320–323. https://doi.org/10.1038/nature10249 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

70.
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

71.
Shoguchi, E. et al. Two divergent Symbiodinium genomes reveal conservation of a gene cluster for sunscreen biosynthesis and recently lost genes. BMC Genomics 19, 458 (2018).
PubMed  PubMed Central  Google Scholar 

72.
Takishita, K., Ishikura, M., Koike, K. & Maruyama, T. Comparison of phylogenies based on nuclear-encoded SSU rDNA and plastid-encoded psbA in the symbiotic dinoflagellate genus Symbiodinium. Phycologia 42, 285–291 (2003).
Google Scholar 

73.
Pochon, X., Putnam, H. M., Burki, F. & Gates, R. D. Identifying and characterizing alternative molecular markers for the symbiotic and free-living dinoflagellate genus Symbiodinium. PLoS ONE 7, e29816 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

74.
Arif, C. et al. Assessing Symbiodinium diversity in scleractinian corals via next-generation sequencing-based genotyping of the ITS2 rDNA region. Mol. Ecol. 23, 4418–4433. https://doi.org/10.1111/mec.12869 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

75.
LaJeunesse, T. C. Diversity and community structure of symbiotic dinoflagellates from Caribbean coral reefs. Mar. Biol. 141, 387–400 (2002).
Google Scholar 

76.
LaJeunesse, T. C. Investigating the biodiversity, ecology, and phylogeny of endosymbiotic dinoflagellates in the genus Symbiodinium using the ITS region: in search of a “species” level marker. J. Phycol. 37, 866–880 (2001).
CAS  Google Scholar 

77.
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

78.
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

79.
Affymetrix. (Affymetrix, 2007).

80.
R: a language and environment for statistical computing [Online] (R Foundation for Statistical Computing, Vienna, 2017).

81.
Knaus, B. J. & Grünwald, N. J. vcfr: a package to manipulate and visualize variant call format data in R. Mol. Ecol. Resour. 17, 44–53 (2017).
CAS  PubMed  Google Scholar 

82.
Kamvar, Z. N., Brooks, J. C. & Grünwald, N. J. Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Front. Genet. 6, 208 (2015).
PubMed  PubMed Central  Google Scholar 

83.
Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281 (2014).
PubMed  PubMed Central  Google Scholar 

84.
Prevosti, A., Ocana, J. & Alonso, G. Distances between populations of Drosophila subobscura, based on chromosome arrangement frequencies. Theor. Appl. Genet. 45, 231–241 (1975).
CAS  PubMed  Google Scholar 

85.
Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

86.
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).
CAS  PubMed  PubMed Central  Google Scholar 

87.
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
PubMed  PubMed Central  Google Scholar 

88.
Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

89.
Venables, W. & Ripley, B. Modern Applied Statistics with S, 4th edn (Springer, New York, 2002).
Google Scholar 

90.
Therneau, T. & Atkinson, B. rpart: Recursive Partitioning and Regression Trees. R package version 4.1-15 (2019). https://CRAN.R-project.org/package=rpart.

91.
Milborrow, S. rpart.plot: Plot ‘rpart’ Models: An Enhanced Version of ‘plot.rpart’. R package version 3.0.8 (2019). https://CRAN.R-project.org/package=rpart.plot.

92.
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
Google Scholar 

93.
González, M., Rosado-Falcón, O. & Rodríguez, J. D. ssc: Semi-Supervised Classification Methods. R package version 2.1-0 (2019). https://CRAN.R-project.org/package=ssc.

94.
Lirman, D. et al. Growth dynamics of the threatened Caribbean staghorn coral Acropora cervicornis: influence of host genotype, symbiont identity, colony size, and environmental setting. PLoS ONE 9, e107253 (2014).
ADS  PubMed  PubMed Central  Google Scholar  More

1.
Myers, S. S. et al. Annu. Rev. Public Health 38, 259–277 (2017).
Article  Google Scholar 
2.
Zhao, C. et al. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).
CAS  Article  Google Scholar 

3.
Sloat, L. L. et al. Nat. Commun. 11, 1243 (2020).
CAS  Article  Google Scholar 

4.
Janssens, C. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-020-0847-4 (2020).

5.
Dellink, R., Hwang, H., Lanzi, E. & Chateau, J. International Trade Consequences of Climate Change (OECD Publishing, 2017).

6.
Climate Change and Trade Agreements: Friends or Foes? (The Economist Intelligence Unit, 2019).

7.
OECD. Regional Trade Agreements and Agriculture OECD Food, Agriculture and Fisheries Papers No. 79 (OECD Publishing, 2015).

8.
Foster, V. & Briceño-Garmendia, C. M. Africa’s Infrastructure: a Time for Transformation (The World Bank, 2009).

9.
Cagé, J. & Gadenne, L. Explor. Econ. Hist. 70, 1–24 (2018).
Article  Google Scholar 

10.
Hallegatte, S. & Rozenberg, J. Nat. Clim. Change 7, 250–256 (2017).
Article  Google Scholar  More

1.
Turner, M. G. Landscape ecology: the effect of pattern on process. Ann. Rev. Ecol. Syst. 20, 171–197 (1989).
Google Scholar 
2.
Wiens, J. A. Spatial scaling in ecology. Func. Ecol. 3, 385–397 (1989).
Google Scholar 

3.
Levin, S. A. The problem of pattern and scale in ecology. Ecology 73, 1943–1967 (1992).
Google Scholar 

4.
Dunning, J. B. Jr., Danielson, B. J. & Pulliam, H. R. Ecological processes that affect populations in complex landscapes. Oikos 65, 169–175 (1992).
Google Scholar 

5.
Boström, C., Pittman, S. J., Simenstad, C. & Kneib, R. T. Seascape ecology of coastal biogenic habitats: advances, gaps, and challenges. Mar. Ecol. Prog. Ser. 427, 191–217 (2011).
ADS  Google Scholar 

6.
Kremen, C., Williams, N. M. & Thorp, R. W. Crop pollination from native bees at risk from agricultural intensification. Proc. Natl. Acad. Sci. USA 99, 16812–16816 (2002).
ADS  CAS  PubMed  Google Scholar 

7.
Robinson, N. M. et al. Refuges for fauna in fire prone landscapes: their ecological function and importance. J. Appl. Ecol. 50, 1321–1329 (2013).
Google Scholar 

8.
Chapin, F. S. III. et al. Consequences of changing biodiversity. Nature 405, 234–242 (2000).
CAS  PubMed  Google Scholar 

9.
Michel, N., Burel, F. & Butet, A. How does landscape use influence small mammal diversity, abundance and biomass in hedgerow networks of farming landscapes?. Acta Oecol. 30, 11–20 (2006).
ADS  Google Scholar 

10.
Kirk, D. A., Lindsay, K. E. & Brook, R. W. Risk of agricultural practices and habitat change to farmland birds. Avi. Conserv. Ecol. 6(1), 5 (2011).
Google Scholar 

11.
Connell, S. D. & Glasby, T. M. Do urban structures influence local abundance and diversity of subtidal epibiota? A case study from Sydney Harbour, Australia. Mar. Environ. Res. 47, 373–387 (1999).
CAS  Google Scholar 

12.
Tilman, D. et al. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 (2001).
ADS  CAS  PubMed  Google Scholar 

13.
Tylianakis, J. M. et al. Resource heterogeneity moderates the biodiversity-function relationship in real world ecosystems. PLoS Biol. 6(5), e122. https://doi.org/10.1371/journal.pbio.0060122 (2008).
CAS  Article  PubMed Central  Google Scholar 

14.
O’Connor, R. J. & Shrubb, M. Farming and Birds (Cambridge University Press, Cambridge, 1986).
Google Scholar 

15.
Galbraith, H. Effects of agriculture on the breeding ecology of lapwings Vanellus vanellus. J. Appl. Ecol. 25, 487–503 (1988).
Google Scholar 

16.
Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: is habitat heterogeneity the key?. Trends Ecol. Evol. 18, 182–188 (2003).
Google Scholar 

17.
Lubchenco, J. et al. The sustainable biosphere initiative: an ecological research agenda. Ecology 72, 371–412 (1991).
Google Scholar 

18.
Andrén, H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71, 355–366 (1994).
Google Scholar 

19.
McHugh, D. J. Worldwide distribution of commercial resources of seaweeds including Gelidium. Hydrobiologia 221, 19–29 (1991).
Google Scholar 

20.
Jensen, A. Present and future needs for algae and algal products. Hydrobiologia 260, 15–23 (1993).
Google Scholar 

21.
Ask, E. I., Batibasaga, A., Zertuche-Gonzalez, J. A. & de San, M. Three decades of Kappaphycus alvarezii (Rhodophyta) introduction to non-endemic locations. In 17th International Seaweed Symposium (eds Chapman, A. R. O. et al.) 49–57 (Oxford Univ Press, Cape Town, 2001).
Google Scholar 

22.
Rönnbäck, P., Bryceson, I. & Kautsky, N. Coastal aquaculture development in eastern Africa and the Western Indian Ocean: prospects and problems for food security and local economies. Ambio 31, 537–542 (2002).
PubMed  Google Scholar 

23.
Food and Agriculture Organization (FAO). The state of world fisheries and aquaculture. Rome, Italy pp. 243 (2014).

24.
Abhilash, K. R. et al. Impact of long-term seaweed farming on water quality: a case study from Palk Bay, India. J. Coast. Conserv. 23, 485–499 (2019).
Google Scholar 

25.
Eggertsen, M. & Halling, C. Knowledge gaps and management recommendations for future paths of sustainable seaweed farming in the Western Indian Ocean. Ambio https://doi.org/10.1007/s13280-020-01319-7 (2020).
Article  PubMed  Google Scholar 

26.
Hehre, E. J. & Meeuwig, J. J. A Global analysis of the relationship between farmed seaweed production and herbivorous fish catch. PLoS ONE 11(2), e148250. https://doi.org/10.1371/journal.pone.0148250 (2016).
CAS  Article  Google Scholar 

27.
Hedberg, N. et al. Habitat preference for seaweed farming—A case study from Zanzibar, Tanzania. Ocean Coast. Manag. 154, 186–195. https://doi.org/10.1016/j.ocecoaman.2018.01.016 (2018).
Article  Google Scholar 

28.
de la Torre-Castro, M. & Rönnbäck, P. Links between humans and seagrasses—an example from tropical east Africa. Ocean Coast. Manag. 47, 361–387 (2004).
Google Scholar 

29.
Halling, C., Wikström, S. A., Lilliesköld-Sjöö Mörk, E., Lundør, E. & Zuccarello, G. C. Introduction of Asian strains and low genetic variation in farmed seaweeds: indications for new management practices. J. Appl. Phycol. 25, 89–95 (2013).
Google Scholar 

30.
Tano, S. A., Halling, C., Eggertsen, L., Buriyo, A. & Wikström, S. A. Extensive spread of farmed seaweeds causes a shift from native to non-native haplotypes in natural seaweed beds. Mar. Biol. 162, 1983–1992 (2015).
Google Scholar 

31.
Conklin, E. J. & Smith, J. E. Abundance and spread of the invasive red algae, Kappaphycus spp., in Kane’ohe Bay, Hawai’i and an experimental assessment of management options. Biol. Invat. 7, 1029–1039 (2005).
Google Scholar 

32.
Keats, D. W., Steele, D. H. & South, G. R. The role of fleshy macroalgae in the ecology of juvenile cod (Gadus morhua L,) in inshore waters off eastern Newfoundland. Can. J. Fish. Aquat. Sci. 65, 49–53. https://doi.org/10.1139/Z87-008 (1987).
Article  Google Scholar 

33.
Carr, M. H. Effects of macroalgal dynamics on recruitment of a temperate reef fish. Ecol. Soc. Am. 75, 1320–1333 (1994).
Google Scholar 

34.
Levin, P. & Hay, M. Responses of temperate reef fishes to alterations in algal structure and species composition. Mar. Ecol. Prog. Ser. 134, 37–47 (1996).
ADS  Google Scholar 

35.
Bertocci, I., Araújo, R., Oliveira, P. & Sousa-Pinto, I. Potential effects of kelp species on local fisheries. J. Appl. Ecol. 52, 1216–1226 (2015).
Google Scholar 

36.
Wilson, S. K. et al. Seasonal changes in habitat structure underpin shifts in macroalgae-associated tropical fish communities. Mar. Biol. 161, 2597–2607 (2014).
Google Scholar 

37.
Tano, S. et al. Tropical seaweed beds are important habitats for mobile invertebrate epifauna. Estuar. Coast. Shelf Sci. 183, 1–12 (2016).
ADS  Google Scholar 

38.
Tano, S. A. et al. Tropical seaweed beds as important habitats for juvenile fish. Mar. Freshw. Res. 68, 1921–1934 (2017).
Google Scholar 

39.
Eggertsen, L. et al. Seaweed beds support more juvenile reef fish than seagrass beds: carrying capacity in a south-western Atlantic tropical seascape. Estuar. Coast. Shelf Sci. 196, 97–108. https://doi.org/10.1016/j.ecss.2017.06.041 (2017).
ADS  Article  Google Scholar 

40.
Fulton, C. J. et al. Form and function of tropical macroalgal reefs in the Anthropocene. Funct. Ecol. 33, 989–999 (2019).
Google Scholar 

41.
Garrigue, C. Macrophyte associations on the soft bottoms of the south-west lagoon of New Caledonia: description, structure and biomass. Bot. Mar. 38, 481–492 (1995).
Google Scholar 

42.
Kobryn, H. T., Wouters, K., Beckley, L. E. & Heege, T. Ningaloo Reef: shallow marine habitats mapped using a hyperspectral sensor. PLoS ONE 8, e70105 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

43.
Rossier, O. & Kulbicki, M. A comparison of fish assemblages from two types of algal beds and coral reefs in the south-west lagoon of New Caledonia. Cybium 24, 3–26 (2000).
Google Scholar 

44.
Chaves, L. T. C., Pereira, P. H. C. & Feitosa, J. L. L. Coral reef fish association with macroalgal beds on a tropical reef system in North-eastern Brazil. Mar. Freshw. Res. 64, 1101–1111 (2013).
Google Scholar 

45.
Evans, R. D., Wilson, S. K., Field, S. N. & Moore, J. A. Y. Importance of macroalgal fields as coral reef fish nursery habitat in north-west Australia. Mar. Biol. 161, 599–607 (2014).
Google Scholar 

46.
van Lier, J. R., Wilson, S. K., Depczynski, M., Wenger, L. N. & Fulton, C. J. Habitat connectivity and complexity underpin fish community structure across a seascape of tropical macroalgae meadows. Landsc. Ecol. 33, 1287–1300 (2018).
Google Scholar 

47.
Eggertsen, M., Chacin, D. H., Åkerlund, C., Halling, C. & Berkström, C. Contrasting distribution and foraging patterns of herbivorous and detritivorous fishes across multiple habitats in a tropical seascape. Mar. Biol. 166, 51. https://doi.org/10.1007/s00227-019-3498-0 (2019).
Article  Google Scholar 

48.
Johnstone, R. W. & Ólafsson, E. Some environmental aspects of open water algal cultivation, Zanzibar, Tanzania. Ambio 24, 465–469 (1995).
Google Scholar 

49.
Ólafsson, E., Johnstone, R. W. & Ndaro, S. G. M. Effects of intensive seaweed farming on the meiobenthos in a tropical lagoon. J. Exp. Mar. Biol. Ecol. 191, 101–117 (1995).
Google Scholar 

50.
Eklöf, J. S., de la Torre-Castro, M., Adelsköld, L., Jiddawi, N. S. & Kautsky, N. Differences in macrofaunal and seagrass assemblages in seagrass beds with and without seaweed farms. Estuar. Coast. Shelf Sci. 63, 385–396 (2005).
ADS  Google Scholar 

51.
Bergman, K. C., Svensson, S. & Öhman, M. C. Influence of algal farming on fish assemblages. Mar. Pollu. Bull. 42, 1379–1389 (2001).
CAS  Google Scholar 

52.
Russell, D. Ecology of the imported red seaweed Euchema striatum Schmitz on Coconut Island, Oahu, Hawaii. Pac. Sci. 37, 87–107 (1983).
Google Scholar 

53.
Eklöf, J. S., Henriksson, R. & Kautsky, N. Effects of tropical open-water seaweed farming on seagrass ecosystem structure and function. Mar. Ecol. Prog. Ser. 325, 73–84 (2006).
ADS  Google Scholar 

54.
Eklöf, J. S., de la Torre-Castro, M., Nilsson, C. & Rönnbäck, P. How do seaweed farms influence local fishery catches in a seagrass-dominated setting in Chwaka Bay, Zanzibar?. Aquat. Liv. Resour. 19, 137–147 (2006).
Google Scholar 

55.
Garpe, K. C. & Öhman, M. C. Coral and fish distribution patterns in Mafia Island Marine Park, Tanzania: fish−habitat interactions. Hydrobiologia 498, 191–211 (2003).
Google Scholar 

56.
McClanahan, T. R. Seasonality in East Africa’s coastal waters. Mar. Ecol. Prog. Ser. 44, 191–199 (1988).
ADS  Google Scholar 

57.
Msuya, F. E. Cultivation and utilisation of red seaweeds in the Western Indian Ocean (WIO) Region. J. Appl. Phycol. 26, 699–705 (2014).
CAS  Google Scholar 

58.
Msuya, F. E. The impact of seaweed farming on the social and economic structure of seaweed farming communities in Zanzibar, Tanzania. In World Seaweed Resources: An Authoritative Reference System (eds Critchley, A. T. et al.) (ETI BioInformatics, Amsterdam, 2006).
Google Scholar 

59.
Msuya, F. E. Social and economic dimensions of carrageenan seaweed farming in the United Republic of Tanzania. In Social and Economic Dimensions of Carrageenan Seaweed Farming Fisheries and Aquaculture Technical Paper No. 580 (eds Valderrama, D. et al.) 115–146 (FAO, Rome, 2013).
Google Scholar 

60.
Eklöf, J.S., Msuya, F.E., Lyimo, T.J. & Buriyo, A.S. Seaweed Farming in Chwaka Bay: A Sustainable Alternative in Aquaculture? – In: eds. de la Torre-Castro, M. and T. J. Lyimo, People, Nature and Research in Chwaka Bay, Zanzibar, Tanzania. ISBN: 978-9987-9559-1-6. Zanzibar Town: WIOMSA, 213–233 (2012).

61.
Valderrama, D. et al. The economics of Kappaphycus seaweed cultivation in developing countries: a comparative analysis of farming systems. Aquacul. Econ. Manag. 19, 251–277. https://doi.org/10.1080/13657305.2015.1024348 (2015).
Article  Google Scholar 

62.
Berkström, C., Jörgensen, T. L. & Hellström, M. Ecological connectivity and niche differentiation between two closely related fish species in the mangrove-seagrass-coral reef continuum. Mar. Ecol. Prog. Ser. 477, 01–215 (2013).
Google Scholar 

63.
Horrill, J. C., Darwall, W. R. T. & Ngoile, M. Development of a marine protected area: Mafia Island, Tanzania. Ambio 25, 50–57 (1996).
Google Scholar 

64.
Ogden, J. C. & Lobel, P. S. The role of herbivorous fishes and urchins in coral reef communities. Environ. Biol. Fish. 3, 49–63. https://doi.org/10.1007/BF00006308 (1978).
Article  Google Scholar 

65.
Lawrence, J. M. & Agatsuma, Y. Chapter 32: Tripneustes. In Sea urchins: Biology and Ecology (ed. Lawrence, J. M.) 491–507 (Elsevier BV, Amsterdam, 2013).
Google Scholar 

66.
Wall, K. R. & Stallings, C. D. Subtropical epibenthos varies with location, reef type, and grazing intensity. J. Exp. Mar. Biol. Ecol. 509, 54–65 (2018).
Google Scholar 

67.
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Aust. Ecol. 26, 32–46 (2001).
Google Scholar 

68.
McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82, 290–297 (2001).
Google Scholar 

69.
Dufrene, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monog. 67, 345–366 (1997).
Google Scholar 

70.
Anderson, M. J. & Willis, T. J. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology 84, 511–525 (2003).
Google Scholar 

71.
Legendre, P. & Legendre, L. Numerical Ecology. Vol 24 3rd Edition (2012).

72.
Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).
MathSciNet  PubMed  MATH  Google Scholar 

73.
Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9, 683–693 (2006).
PubMed  Google Scholar 

74.
Jones, D.L. Fathom Toolbox for Matlab: Software for Multivariate Ecological and Oceanographic Data Analysis. College of Marine Science, University of South Florida, St. Petersburg, FL, USA (2017) (Available from: https://www.marine.usf.edu/research/matlab-resources/fathom-toolbox-for-matlab/).

75.
Rojas-Sepulveda, J. Seaweeds, seagrasses, or both: feeding preferences of an important herbivore within a tropical seascape. Master Thesis. Stockholm University, Sweden (2017).

76.
Anyango, J. O., Mlewa, C. M. & Mwaluma, J. Abundance, diversity and trophic status of wild fish around seaweed farms in Kibuyuni, South Coast Kenya. Int. J. Fish. Aqua. Stud. 5, 440–446 (2017).
Google Scholar 

77.
Savino, J. F. & Stein, R. A. Predator–prey interaction between largemouth bass and bluegills as influenced by simulated submersed vegetation. Trans. Am. Fish. Soc. 111, 255–266 (1982).
Google Scholar 

78.
Anderson, T. W. Role of macroalgal structure in the distribution and abundance of a temperate reef fish. Mar. Ecol. Prog. Ser. 113, 279–290 (1994).
ADS  Google Scholar 

79.
Lim, I. E., Wilson, S. K., Holmes, T. H., Noble, M. M. & Fulton, C. Specialization within a shifting habitat mosaic underpins the seasonal abundance of a tropical fish. Ecosphere 7(2), e01212. https://doi.org/10.1002/ecs2.1212 (2016).
Article  Google Scholar 

80.
Wenger, L. N., van Lier, J. R. & Fulton, C. J. Microhabitat selectivity shapes the seascape ecology of a carnivorous macroalgae-associated tropical fish. Mar. Ecol. Prog. Ser. 590, 187–200 (2018).
ADS  Google Scholar 

81.
Tang, S., Graba-Landra, A. & Hoey, A. S. Density and height of Sargassum influence rabbit (F. siganidae) settlement on inshore reef flats of the Great Barrier reef. Coral Reefs 39, 467–473 (2020).
Google Scholar 

82.
Horinouchi, M. Review of the effects of within-patch scale structural complexity on seagrass fishes. J. Exp. Mar. Biol. Ecol. 350, 111–129 (2007).
Google Scholar 

83.
Chacin, D. H. & Stallings, C. D. Disentangling fine- and broad- scale effects of habitat on predator-prey interactions. J. Exp. Mar. Biol. Ecol. 483, 10–19 (2016).
Google Scholar 

84.
Orth, R. J., Heck, K. L. & Vanmontfrans, J. Faunal communities in seagrass beds: a review of the influence of plant structure and prey characteristics on predator prey relationships. Estuaries 7, 339–350 (1984).
Google Scholar 

85.
Heck, K. L. & Crowder, L. B. Habitat structure and predator–prey interactions in vegetated aquatic systems. In Habitat Complexity: The Physical Arrangement of Objects in Space (eds Bell, S. S. et al.) 280–299 (Chapman and Hall, New York, 1991).
Google Scholar 

86.
Johnson, D. W. Predation, habitat complexity, and variation in density-dependent mortality of temperate reef fishes. Ecology 87, 1179–1188 (2006).
PubMed  Google Scholar 

87.
Gregor, C. A. & Anderson, T. W. Relative importance of habitat attributes to predation risk in a temperate reef fish. Environ. Biol. Fish. 99, 539–556 (2016).
Google Scholar 

88.
Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).
ADS  CAS  PubMed  Google Scholar 

89.
Hortal, J., Triantis, K. A., Meiri, S., Thebault, E. & Sfenthourakis, S. Island species richness increases with habitat diversity. Am. Nat. 174, 205–217 (2009).
Google Scholar 

90.
Genner, M. J., Turner, G. F. & Hawkins, S. J. Foraging of rocky habitat cichlid fishes in Lake Malawi: co-existence through niche partitioning?. Oecologia 121, 283–292 (1999).
ADS  PubMed  Google Scholar 

91.
Arrizabalaga-Escudero, A. et al. Assessing niche partitioning of co-occurring sibling bat species by DNA metabarcoding. Mol. Ecol. 27, 1273–1283 (2018).
PubMed  Google Scholar 

92.
Wilson, S. & Bellwood, D. R. Cryptic dietary components of territorial damselfishes (Pomacentridae, Labroidei). Mar. Ecol. Prog. Ser. 153, 299–310 (1997).
ADS  CAS  Google Scholar 

93.
Horn, M. H. Biology of marine herbivorous fishes. Oceanog. Mar. Biol. Ann. Rev. 27, 167–272 (1989).
Google Scholar 

94.
Arnold, G. W., Maller, R. A. & Litchfield, R. Comparison of bird populations in remnants of Wandoo woodland and in adjacent farmland. Aust. Wildl. Res. 14, 331–341. https://doi.org/10.1071/WR9870331 (1987).
Article  Google Scholar 

95.
Bretagnolle, V. et al. Towards sustainable and multifunctional agriculture in farmland landscapes: lessons from the integrative approach of a French LTSER platform. Sci. Total Environ. 627, 822–834 (2018).
ADS  CAS  PubMed  Google Scholar 

96.
Carcamo, H. A., Niemala, J. K. & Spence, J. R. Farming and ground beetles – effects of agronomic practice on populations and community structure. Can. Entomol. 127, 123–140 (1995).
Google Scholar 

97.
Locham, A. G., Kaunda-Arara, B., Wakibia, J. G. & Muya, S. Diet and niche breadth variation in the marbled parrotfish, Leptoscarus vaigiensis, among coral reef sites in Kenya. Afr. J. Ecol. 53, 560–571 (2015).
Google Scholar 

98.
Fox, R. J. & Bellwood, D. R. Remote video bioassays reveal the potential feeding impact of the rabbitfish Siganus canaliculatus (f:Siganidae) on an inner-shelf reef of the Great Barrier Reef. Coral Reefs 27, 605–615 (2008).
ADS  Google Scholar 

99.
Hoey, A. S. & Bellwood, D. R. Limited functional redundancy in a high diversity system: single species dominates key ecological process on coral reefs. Ecosystems 12, 1316–1328 (2009).
Google Scholar 

100.
Öhman, M. C. & Rajasuriya, A. Relationships between habitat structure and fish assemblages on coral and sandstone reefs. Environ. Biol. Fish. 53, 19–31 (1998).
Google Scholar 

101.
Gratwicke, B. & Speight, M. R. Effects of habitat complexity on Caribbean marine fish assemblages. Mar. Ecol. Prog. Ser. 292, 301–310 (2005).
ADS  Google Scholar 

102.
Humphries, P., Potter, I. C. & Loneragan, N. R. The fish community in the shallows of a temperate Australian estuary: relationships with the aquatic marcophyte Ruppia megacarpa and environmental variables. Estuar. Coast. Shelf Sci. 34, 32–346 (1992).
Google Scholar 

103.
Nelson, W. G. Development of an epiphyte indicator of nutrient enrichment: a critical evaluation of observational and experimental studies. Ecol. Indic. 79, 207–227 (2017).
PubMed  PubMed Central  Google Scholar 

104.
Gullström, M., Berkström, C., Öhman, M., Bodin, M. & Dahlberg, M. Scale-dependent patterns of variability of a grazing parrotfish (Leptoscarus vaigiensis) in a tropical seagrass-dominated seascape. Mar. Biol. 158, 1483–1495 (2011).
Google Scholar 

105.
Vonk, J. A., Marjolijin, J. A. & Stapel, J. Redefining the trophic importance of seagrasses for fauna in tropical Indo-Pacific meadows. Estuar. Coast. Shelf. Sci. 79, 653–660 (2008).
ADS  Google Scholar 

106.
Wilson, J. D., Morris, A. J., Arroyo, B. E., Clark, S. C. & Bradbury, R. B. A review of the abundance and diversity of invertebrate and plant foods of granivorous birds in northern Europe in relation to agricultural change. Agric. Eco. Envir. 75, 13–30 (1999).
Google Scholar 

107.
Hoey, A. S. & Bellwood, D. R. Cross-shelf variation in browsing intensity on the Great Barrier Reef. Coral Reefs 29, 499–508 (2010).
ADS  Google Scholar 

108.
Chong-Seng, K. M., Nash, K. L., Bellwood, D. R. & Graham, N. A. J. Macroalgal herbivory on recovering versus degrading coral reefs. Coral Reefs 33, 409–419 (2014).
ADS  Google Scholar 

109.
Hoey, A. S. & Bellwood, D. R. Suppression of herbivory by macroalgal density: a critical feedback on coral reefs. Ecol. Lett. 14, 267–273 (2011).
PubMed  Google Scholar 

110.
Bauman, A. G. et al. Fear effects associated with predator presence and habitat structure interact to alter herbivory on coral reefs. Biol. Lett. https://doi.org/10.1098/rsbl.2019.0409 (2019).
Article  PubMed  Google Scholar 

111.
Menge, B. A. Organization of the New England rocky intertidal community: role of predation, competition, and environmental heterogeneity. Ecol. Monog. 46, 355–393 (1976).
Google Scholar 

112.
Siriwardena, G. M. Trends in the abundance of farmland birds: a quantitative comparison of smoothed Common Birds Census indices. J. Appl. Ecol. 35, 24–43 (1998).
Google Scholar 

113.
Krebs, J. R., Wilson, J. D., Bradbury, R. B. & Siriwardena, G. M. The second Silent Spring. Nature 400, 611–612 (1999).
ADS  CAS  Google Scholar 

114.
Heikkinen, R. K., Luoto, M., Virkkala, R. & Rainio, K. Effects of habitat cover, landscape structure and spatial variables on the abundance of birds in an agricultural-forest mosaic. J. Appl. Ecol. 41, 824–835 (2004).
Google Scholar 

115.
Dauber, J. et al. Local vs. landscape controls on diversity: a test using surface-dwelling soil macroinvertebrates of differing mobility. Glob. Ecol. Biogeol. 14, 213–221 (2005).
Google Scholar 

116.
Hendrickx, F. et al. How landscape structure, land-use intensity and habitat diversity affect components of total arthropod diversity in agricultural landscapes. J. Appl. Ecol. 44, 340–351 (2007).
Google Scholar 

117.
Froehlich, H. E., Afflerbach, J. C., Frazier, M. & Halpern, B. S. Blue growth potential to mitigate climate change through seaweed offsetting. Curr. Biol. 18, 3087–3093. https://doi.org/10.1016/j.cub.2019.07.041 (2019).
CAS  Article  Google Scholar  More

1.
Redding, D. W., Moses, L. M., Cunningham, A. A., Wood, J. & Jones, K. E. Environmental-mechanistic modelling of the impact of global change on human zoonotic disease emergence: a case study of Lassa fever. Methods Ecol. Evol. 7, 646–655 (2016).
Google Scholar 
2.
McCormick, J. B. & Fisher-Hoch, S. P. in Arenaviruses I: The Epidemiology, Molecular and Cell Biology of Arenaviruses — Current Topics in Microbiology and Immunology Vol. 262 (ed. Oldstone, M. B. A.) 75–109 (Springer, 2002).

3.
Jonsson, C. B., Figueiredo, L. T. M. & Vapalahti, O. A global perspective on hantavirus ecology, epidemiology, and disease. Clin. Microbiol. Rev. 23, 412–441 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

4.
Edson, D. et al. Routes of Hendra virus excretion in naturally-infected flying-foxes: implications for viral transmission and spillover risk. PLoS ONE 10, e0140670 (2015).
PubMed  PubMed Central  Google Scholar 

5.
Luby, S. P., Gurley, E. S. & Jahangir Hossain, M. Transmission of human infection with Nipah virus. Clin. Infect. Dis. 49, 1743–1748 (2009).
PubMed  PubMed Central  Google Scholar 

6.
Georgiou, G. et al. Display of heterologous proteins on the surface of microorganisms: from the screening of combinatorial libraries to live recombinant vaccines. Nat. Biotechnol. 15, 29–34 (1997).
CAS  PubMed  Google Scholar 

7.
Leitner, W. W., Ying, H. & Restifo, N. P. DNA and RNA-based vaccines: principles, progress and prospects. Vaccine 18, 765–777 (1999).
CAS  PubMed  PubMed Central  Google Scholar 

8.
Pardi, N., Hogan, M. J., Porter, F. W. & Weissman, D. mRNA vaccines — a new era in vaccinology. Nat. Rev. Drug Discov. 17, 261–279 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

9.
Rollier, C. S., Reyes-Sandoval, A., Cottingham, M. G., Ewer, K. & Hill, A. V. S. Viral vectors as vaccine platforms: deployment in sight. Curr. Opin. Immunol. 23, 377–382 (2011).
CAS  PubMed  Google Scholar 

10.
Ferretti, L. et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science 368, eabb6936 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

11.
Morse, S. S. et al. Prediction and prevention of the next pandemic zoonosis. Lancet 380, 1956–1965 (2012).
PubMed  PubMed Central  Google Scholar 

12.
Rupprecht, C. E., Hanlon, C. A. & Slate, D. in Control of Infectious Animal Diseases by Vaccination — Developments in Biologicals Vol. 119 (eds Schudel, A. & Lombard, M.) 173–184 (Karger, 2004).

13.
Bull, J. J., Smithson, M. W. & Nuismer, S. L. Transmissible viral vaccines. Trends Microbiol. 26, 6–15 (2018).
CAS  PubMed  Google Scholar 

14.
Murphy, A. A., Redwood, A. J. & Jarvis, M. A. Self-disseminating vaccines for emerging infectious diseases. Expert Rev. Vaccines 15, 31–39 (2016).
CAS  PubMed  Google Scholar 

15.
Shellam, G. R. The potential of murine cytomegalovirus as a viral vector for immunocontraception. Reprod. Fertil. Dev. 6, 401–409 (1994).
CAS  PubMed  Google Scholar 

16.
Tyndale-Biscoe, C. H. Virus-vectored immunocontraception of feral mammals. Reprod. Fertil. Dev. 6, 281–287 (1994).
CAS  PubMed  Google Scholar 

17.
Barcena, J. et al. Horizontal transmissible protection against myxomatosis and rabbit hemorrhagic disease by using a recombinant myxoma virus. J. Virol. 74, 1114–1123 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

18.
Torres, J. M. et al. First field trial of a transmissible recombinant vaccine against myxomatosis and rabbit hemorrhagic disease. Vaccine 19, 4536–4543 (2001).
CAS  PubMed  Google Scholar 

19.
Angulo, E. & Barcena, J. Towards a unique and transmissible vaccine against myxomatosis and rabbit haemorrhagic disease for rabbit populations. Wildl. Res. 34, 567–577 (2007).
CAS  Google Scholar 

20.
Nuismer, S. L. et al. Eradicating infectious disease using weakly transmissible vaccines. Proc. R. Soc. B 283, 20161903 (2016).
PubMed  Google Scholar 

21.
Basinski, A. J., Nuismer, S. L. & Remien, C. H. A little goes a long way: weak vaccine transmission facilitates oral vaccination campaigns against zoonotic pathogens. PLoS Negl. Trop. Dis. 13, e0007251 (2019).
PubMed  PubMed Central  Google Scholar 

22.
Basinski, A. J. et al. Evaluating the promise of recombinant transmissible vaccines. Vaccine 36, 675–682 (2018).
CAS  PubMed  Google Scholar 

23.
Smithson, M. W., Basinki, A. J., Nuismer, S. L. & Bull, J. J. Transmissible vaccines whose dissemination rates vary through time, with applications to wildlife. Vaccine 37, 1153–1159 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

24.
Lecompte, E. et al. Mastomys natalensis and Lassa fever, West Africa. Emerg. Infect. Dis. 12, 1971–1974 (2006).
PubMed  PubMed Central  Google Scholar 

25.
Olayemi, A. et al. New hosts of the Lassa virus. Sci. Rep. 6, 25280 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

26.
Douglass, R. J. et al. Longitudinal studies of Sin Nombre virus in deer mouse-dominated ecosystems of Montana. Am. J. Trop. Med. Hyg. 65, 33–41 (2001).
CAS  PubMed  Google Scholar 

27.
Luis, A. D., Douglass, R. J., Mills, J. N. & Bjornstad, O. N. The effect of seasonality, density and climate on the population dynamics of Montana deer mice, important reservoir hosts for Sin Nombre hantavirus. J. Anim. Ecol. 79, 462–470 (2010).
PubMed  Google Scholar 

28.
Viana, M. et al. Assembling evidence for identifying reservoirs of infection. Trends Ecol. Evol. 29, 270–279 (2014).
PubMed  PubMed Central  Google Scholar 

29.
Fenton, A., Streicker, D. G., Petchey, O. L. & Pedersen, A. B. Are all hosts created equal? Partitioning host species contributions to parasite persistence in multihost communities. Am. Nat. 186, 610–622 (2015).
PubMed  PubMed Central  Google Scholar 

30.
Fichet-Calvet, E. et al. Fluctuation of abundance and Lassa virus prevalence in Mastomys natalensis in Guinea, West Africa. Vector-Borne Zoonotic Dis. 7, 119–128 (2007).
PubMed  Google Scholar 

31.
Marien, J. et al. Evaluation of rodent control to fight Lassa fever based on field data and mathematical modelling. Emerg. Microbes Infect. 8, 640–649 (2019).
PubMed  PubMed Central  Google Scholar 

32.
Towner, J. S. et al. Marburg virus infection detected in a common african bat. PLoS ONE 2, e764 (2007).
PubMed  PubMed Central  Google Scholar 

33.
Nziza, J. et al. Coronaviruses detected in bats in close contact with humans in Rwanda. EcoHealth 17, 152–159 (2020).
PubMed  Google Scholar 

34.
Anthony, S. J. et al. Further evidence for bats as the evolutionary source of Middle East respiratory syndrome coronavirus. Mbio 8, e00373–17 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

35.
Ge, X.-Y. et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature 503, 535–538 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

36.
Bird, B. H. & Mazet, J. A. K. Detection of emerging zoonotic pathogens: an integrated one health approach. Annu. Rev. Anim. Biosci. 6, 121–139 (2018).
CAS  PubMed  Google Scholar 

37.
Goldstein, T. et al. The discovery of Bombali virus adds further support for bats as hosts of ebolaviruses. Nat. Microbiol. 3, 1084–1089 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

38.
Pernet, O. et al. Evidence for henipavirus spillover into human populations in Africa. Nat. Commun. 5, 5342 (2014).
PubMed  PubMed Central  Google Scholar 

39.
Grard, G. et al. A novel rhabdovirus associated with acute hemorrhagic fever in Central Africa. PLoS Pathog. 8, e1002924 (2012).
PubMed  PubMed Central  Google Scholar 

40.
Han, B. A. & Drake, J. M. Future directions in analytics for infectious disease intelligence. EMBO Rep. 17, 785–789 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

41.
Han, B. A., Schmidt, J. P., Bowden, S. E. & Drake, J. M. Rodent reservoirs of future zoonotic diseases. Proc. Natl Acad. Sci. USA 112, 7039–7044 (2015).
CAS  PubMed  Google Scholar 

42.
Han, B. A. et al. Undiscovered bat hosts of filoviruses. PLoS Negl. Trop. Dis. 10, e0004815 (2016).
PubMed  PubMed Central  Google Scholar 

43.
Guth, S., Visher, E., Boots, M. & Brook, C. E. Host phylogenetic distance drives trends in virus virulence and transmissibility across the animal-human interface. Philos. Trans. R. Soc. B 374, 20190296 (2019).
Google Scholar 

44.
Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

45.
Pepin, K. M., Lass, S., Pulliam, J. R. C., Read, A. F. & Lloyd-Smith, J. O. Identifying genetic markers of adaptation for surveillance of viral host jumps. Nat. Rev. Microbiol. 8, 802–813 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

46.
Babayan, S. A., Orton, R. J. & Streicker, D. G. Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes. Science 362, 577–580 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

47.
Bakker, K. M. et al. Fluorescent biomarkers demonstrate prospects for spreadable vaccines to control disease transmission in wild bats. Nat. Ecol. Evol. 3, 1697–1704 (2019).
PubMed  PubMed Central  Google Scholar 

48.
Garnier, R., Gandon, S., Chaval, Y., Charbonnel, N. & Boulinier, T. Evidence of cross-transfer of maternal antibodies through allosuckling in a mammal: potential importance for behavioral ecology. Mamm. Biol. 78, 361–364 (2013).
Google Scholar 

49.
Stading, B. et al. Protection of bats (Eptesicus fuscus) against rabies following topical or oronasal exposure to a recombinant raccoon poxvirus vaccine. PLoS Negl. Trop. Dis. 11, e0005958 (2017).
PubMed  PubMed Central  Google Scholar 

50.
Schreiner, C. L., Nuismer, S. L. & Basinski, A. J. When to vaccinate a fluctuating wildlife population: is timing everything? J. Appl. Ecol. 57, 307–319 (2020).
PubMed  Google Scholar 

51.
Varrelman, T. J., Basinski, A. J., Remien, C. H. & Nuismer, S. L. Transmissible vaccines in heterogeneous populations: implications for vaccine design. One Health 7, 100084 (2019).
PubMed  PubMed Central  Google Scholar 

52.
Alizon, S., Hurford, A., Mideo, N. & Van Baalen, M. Virulence evolution and the trade-off hypothesis: history, current state of affairs and the future. J. Evol. Biol. 22, 245–259 (2009).
CAS  PubMed  Google Scholar 

53.
Kew, O. M., Sutter, R. W., de Gourville, E. M., Dowdle, W. R. & Pallansch, M. A. Vaccine-derived polioviruses and the endgame strategy for global polio eradication. Annu. Rev. Microbiol. 59, 587–635 (2005).
CAS  PubMed  Google Scholar 

54.
Bull, J. J. Evolutionary reversion of live viral vaccines: can genetic engineering subdue it? Virus Evol. 1, vev005 (2015).
PubMed  PubMed Central  Google Scholar 

55.
Lauring, A. S., Jones, J. O. & Andino, R. Rationalizing the development of live attenuated virus vaccines. Nat. Biotechnol. 28, 573–579 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

56.
Nuismer, S. L., Basinski, A. & Bull, J. J. Evolution and containment of transmissible recombinant vector vaccines. Evol. Appl. 12, 1595–1609 (2019).
PubMed  PubMed Central  Google Scholar 

57.
Kew, O. M. et al. Circulating vaccine-derived polioviruses: current state of knowledge. Bull. World Health Organ. 82, 16–23 (2004).
PubMed  PubMed Central  Google Scholar 

58.
Hampson, K. et al. Estimating the global burden of endemic canine rabies. PLoS Negl. Trop. Dis. 9, e0003709 (2015).
PubMed  PubMed Central  Google Scholar 

59.
Cost of the Ebola Epidemic (US Centers for Disease Control and Prevention, 2020); https://go.nature.com/38iF7cg

60.
Forum on Microbial Threats Learning from SARS: Preparing for the Next Disease Outbreak: Workshop Summary (National Academies Press, 2004). More

1.
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [eds. Pachauri, R.K & L.A. Meyer) (IPCC, 2014).
2.
AMAP (Arctic Monitoring and Assessment Programme). Snow, Water (Ice and Permafrost in the Arctic, AMAP, Tromsø, 2017).
Google Scholar 

3.
Wik, M., Varner, R. K., Walter Anthony, K., MacIntyre, S. & Bastviken, D. Climate-sensitive northern lakes and ponds are critical components of methane release. Nat. Geosci. 9, 99–105 (2016).
ADS  CAS  Article  Google Scholar 

4.
Holgerson, M. A. & Raymond, P. A. Large contribution to inland water CO2 and CH4 emissions from very small ponds. Nat. Geosci. 9, 222–226 (2016).
ADS  CAS  Article  Google Scholar 

5.
Bastviken, D., Cole, J., Pace, M. & Tranvik, L. Methane emissions from lakes: Dependence of lake characteristics, two regional assessments, and a global estimate. Global Biogeochem. Cyc. 18, 1–12 (2004).
Article  Google Scholar 

6.
Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M. & Enrich-Prast, A. Freshwater methane emissions offset the continental carbon sink. Science 331, 50–51 (2011).
ADS  CAS  Article  Google Scholar 

7.
Walter, K. M., Smith, L. C. & Chapin, F. S. Methane bubbling from northern lakes: present and future contributions to the global methane budget. Philos. Trans. R. Soc. A 365, 1657–1676 (2007).
ADS  CAS  Article  Google Scholar 

8.
Walter Anthony, K. M. & Anthony, P. Constraining spatial variability of methane ebullition seeps in thermokarst lakes using point process models. J. Geophys. Res. Biogeosci. 118, 1015–1034. https://doi.org/10.1002/jgrg.20087 (2013).
Article  Google Scholar 

9.
Tan, Z. & Zhuang, Q. Arctic lakes are continuous methane sources to the atmosphere under warming conditions. Environ. Res. Lett. 10, 1–9 (2015).
Article  Google Scholar 

10.
Tan, Z. & Zhuang, Q. Methane emissions from pan-Arctic lakes during the 21st century: An analysis with process-based models of lake evolution and biogeochemistry. J. Geophys. Res. Biogeosci. 120, 2641 (2015).
CAS  Article  Google Scholar 

11.
Lehner, B. & Doell, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).
ADS  Article  Google Scholar 

12.
Verpoorter, C., Kutser, T., Seekell, D. A. & Tranvik, L. J. A global inventory of lakes based on high-resolution satellite imagery. Geophys. Res. Lett. 41, 6396–6402 (2014).
ADS  Article  Google Scholar 

13.
Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).
ADS  CAS  Article  Google Scholar 

14.
Downing, J. A. & Duarte, C. M. Abundance and size distribution of lakes, ponds, and impoundments. In Encyclopedia of Inland Water, 1 (ed. Likens, G. E.) 469–478 (Elsevier, Amsterdam, 2009).
Google Scholar 

15.
McGinnis, D. F., Greinert, J., Artemov, Y., Beaubien, S. E. & Wüest, A. Fate of rising methane bubbles in stratified waters: how much methane reaches the atmosphere?. J. Geophys. Res. 111, C09007 (2006).
ADS  Article  Google Scholar 

16.
Lamarche, C. et al. Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community. Rem. Sens. 9, 36 (2017).
ADS  Article  Google Scholar 

17.
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).
ADS  CAS  Article  Google Scholar 

18.
Sanches, L. F., Guenet, B., Marinho, C. C., Barros, N. & de Assis Esteves, F. Global regulation of methane emission from natural lakes. Sci. Rep. 9, 255 (2019).
ADS  Article  Google Scholar 

19.
Bruhwiler, L. et al. CarbonTracker-CH4: an assimilation system for estimating emissions of atmospheric methane. Atmos. Chem. Phys. 14, 8269–8293 (2014).
ADS  Article  Google Scholar 

20.
Du, J., Kimball, J. S., Duguay, C., Kim, Y. & Watts, J. D. Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015. Cryosphere 11, 47–63 (2017).
ADS  Article  Google Scholar 

21.
Kim, Y., Kimball, J. S., McDonald, K. C. & Glassy, J. Developing a global data record of daily landscape freeze/thaw status using satellite microwave remote sensing, Version 4. IEEE Trans. Geosci. Rem. Sens. 49, 949–960 (2016).
ADS  Article  Google Scholar 

22.
Brown, J., Ferrians, O.J., Heginbottom, J.A. & Melnikov, E.S. Circum-Arctic map of permafrost and ground-ice conditions, Version 2. Boulder, CO, National Snow and Ice Data Center/World Data Center for Glaciology. https://doi.org/10.3133/cp45 (2002).

23.
Obu, J. et al. Northern hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale. Earth Sci. Rev. 193, 299–316 (2019).
ADS  Article  Google Scholar 

24.
Harmonized World Soil Database (HWSD) https://daac.ornl.gov/SOILS/guides/HWSD.html.

25.
Allen, G. H. & Pavelsky, T. M. Global extent of rivers and streams. Science 361, 585–588 (2018).
MathSciNet  CAS  Article  Google Scholar 

26.
Lehner, B. et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Front. Ecol. Environ. 9(9), 494–502 (2011).
Article  Google Scholar 

27.
Mulligan, M., Saenz-Cruz, L., van Soesbergen, A., Smith, V.T. & Zurita, L. The Global georeferenced Database of Dams (GOOD2), Version 1. Global dams database and geowiki. https://geodata.policysupport.org/dams (2009).

28.
Chau, Y. K., Snodgrass, W. J. & Wong, P. T. S. A sampler for collecting evolved gases from sediment. Water Res. 11, 807–809 (1977).
CAS  Article  Google Scholar 

29.
Howard, D.L., Frea, J.I. & Pfister, R.M. The potential for methane carbon cycling in Lake Erie. In Paper Presented at 14th Conference on Great Lakes Research (Int. Assoc. of Great Lakes Res., Ann Arbor, Mich. 1971).

30.
Townsend-Small, A. et al. Quantifying emissions of methane derived from anaerobic organic matter respiration and natural gas extraction in Lake Erie. Limnol. Oceanogr. 61, S356–S366 (2016).
CAS  Article  Google Scholar 

31.
Joung, D., Leonte, M. & Kessler, J. D. Methane sources in the waters of Lake Michigan and Lake Superior as revealed by natural radiocarbon measurements. Geophys. Res. Lett. 46, 5436–5444 (2019).
ADS  CAS  Article  Google Scholar 

32.
Shimoda, Y. et al. Our current understanding of lake ecosystem response to climate change: what have we really learned from the north temperate deep lakes?. J. Great Lakes Res. 37, 173–193 (2011).
CAS  Article  Google Scholar 

33.
Blenckner, T. R. et al. Large-scale climatic signatures in lakes across Europe: a meta-analysis. Glob. Change Biol. 13, 1314–1326 (2007).
ADS  Article  Google Scholar 

34.
van Huissteden, J. et al. Methane emissions from permafrost thaw lakes limited by lake drainage. Nat. Clim. Change 1, 119–123 (2011).
ADS  Article  Google Scholar 

35.
Kalff, J. Limnology, Inland Water Ecosystems (Prentice Hall, Upper Saddle River, 2002).
Google Scholar 

36.
Kourzeneva, E., Asensio, H., Martin, E. & Faroux, S. Global gridded dataset of lake coverage and lake depth for use in numerical weather prediction and climate modelling. Tellus A 64, 1–14 (2012).
Google Scholar  More

Experimental design
Following five focus groups with SERNAPESCA’s head of enforcement and other personnel, we designed and implemented an online survey that targeted fisheries enforcement officers who are responsible for monitoring IUU activities in Chile. The survey was structured to capture expert knowledge on various aspects of illegal activities, as well as the relative experience of the officers. The survey defined illegal fishing as a fishing activity carried out in national jurisdiction waters by national or international boats that is in violation of the national fishing law, conducted without a legal permit, or activities that involve unreported or misreported captures to the authorities. The Director of SERNAPESCA delivered the survey via email to all SERNAPESCA enforcement officers. The list of officers was constructed by the Director (n = 86). The survey was anonymous in that the officers were not asked to report their name nor any information that could be used for identification (e.g., email). Answers to questions were not mandatory; that is, respondents could opt-out of answering particular questions and continue with the survey. The survey was available online for ten weeks, over which five reminder emails were sent to officers requesting them to complete the survey.
The survey, in Spanish, consisted of two sections. First, we asked respondents to rank the magnitude of illegal activity for twenty fisheries on a nominal scale (1–5), along with their relative experience with each fishery (nominal scale, 1–5). The twenty fisheries were selected a priori based on our focus groups and known information about illegal activity. All fisheries were single species, with the exception of four that included multiple species: skates (2 species, Zearaja chilensis and Bathyraja macloviana), kelp (4 species: Lessonia spicate, L. berteroana, L. traberculata, Macrocystis pyrifera), red algae (3 species: Sarcothalia crispate, Gigartina skottsbergii, Mazzaella laminarioides), and crabs (10 species excluding southern king crab: Cancer edwardsi, C. porter, C. setosus, C. coronatus, Homalaspis plana, Ovalipes trimaculatus, Taliepus dentatus, T. marginatus, Mursia gaudichaudi, Hemigrapsus crenulatus). In the second part of the survey, we asked respondents additional questions for four focal fisheries: South Pacific hake (Merluccius gayi gayi), southern hake (M. australis), loco or Chilean abalone (Concholepas concholepas), and kelp. For each fishery, we asked respondents to score on a nominal scale (1–5),

The frequency of six specific illegal activities in the industrial sector: size, gear, season, area, transshipment, and port.

The frequency of six specific illegal activities in the small-scale sector: size, gear, season, area, transshipment, and port.

The participation of illegal activity for six different stakeholders along the supply chain: fisher, purchaser, processor, wholesaler, exporter, and restaurateur.

The utilization of seven infrastructure types in illegal activities: fishing boats, refrigeration trucks, processing plants, markets, transshipment boats, export vehicles, and restaurants.

This study was approved by the Advanced Conservation Strategies and Pontificia Universidad Católica ethics institutional review boards and followed guidelines established by their ethics committees, which complies with national and international standards. The surveys included a written informed consent approved by all interviewees, which acknowledged research objectives and established that the survey was anonymous and that interviewees were free to choose to not answer questions. While all species have common names in Chile (which were used in the survey), we use Fishbase and Sealifebase as the taxonomic authority and for the common names reported here to facilitate comparisions34,35.
Statistical analysis
For both sections of the survey, we used a Bayesian cumulative multinomial logit model to predict illegal estimates. First, we fitted a model for illegal estimates for each of the twenty fisheries jointly. Second, we fitted models for illegal estimates for various aspects of the four focal fisheries (i.e., activities, stakeholders, and infrastructure) in a single analysis for each aspect. In both models, we included a random intercept term for respondent, along with a fixed effect for fishery. We evaluated the role of experience, as self-reported by the respondents, by comparing the difference between the illegal score by a respondent for a fishery and the model prediction for that fishery across respondents. If higher levels of expertise increased or decreased the value of a respondent’s scoring, there would be a relationship between the size of the differences and the level of experience reported for a fishery. Experience may also affect the difference in mean responses (i.e., bias), potentially due to more personal experience over a longer period of time, which would lead to a correlation between expertise and mean illegality scores. Depending on the patterns observed in the data, there are several ways to control for a respondent’s experience in illegality estimates. In our case, we used experience scores as a covariate in the model.
For the twenty fisheries, we used the following model,

$$Prleft{{S}_{ij}=kright}=phi left({tau }_{k}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{i}}}right)right)-phi left({tau }_{k-1}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{j}}}right)right)$$
(1)

in which the probability that the score for the level of illegal landings ({S}_{ij}) for the ith species by the jth respondent is equal to category k, can be represented as a latent continuous variable which is divided into K categories, by K − 1 thresholds at ({tau }_{k}). This latent continuous variable is represented by the cumulative normal distribution, (phi). For a given observation, the regression equation is composed of coefficients multiplied times predictor variables ({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}) plus a design matrix for the random effect, multiplied times the error term for the jth respondent, ({{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{i}}}) . The probability of that observation falling in category k, (Prleft{{S}_{ij}=kright}), is thus the probability of it being in a category equal to or smaller than k, (phi left({tau }_{k}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{i}}}right)right)), less the probability of the observation being in a category smaller than k, (phi left({tau }_{k-1}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{j}}}right)right)). Implemented in the R statistical language, using the brms package36, the call to fit this model looks like the following:

$${text{Score}}; , sim ;{text{Species}} + {text{Experience }} + left( {{1}|{text{Respondent}}} right),;{text{ data}} = {text{SurveyData}},;{text{family}} = {text{cumulative}}),$$

where Score is ({S}_{ij}) in (1) above, the fixed effects, ({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}) are the experience of the respondent and the species that was scored, and (1|Respondent) denotes a random intercept model, where each has a different intercept term, drawn from a shared error distribution. For more information on the application of this model to ordinal response data, see Burkner and Vuorre37.
For the estimates for the various aspects of the four focal fisheries, we used the following model,

$${text{Response}}; sim ;{text{Species}} + {text{Experience}} + left( {{1}|{text{Respondent}}} right),;{text{data}} = {text{SurveyData}},;{text{family}} = {text{cumulative}}),$$

which is structured as per (1) above, but with the responses to the various focal species questions (i.e., activities per sector, stakeholders, and infrastructure) substituted for the species scores as in (1).
We compared both models with simpler models, including a single-term null model using leave-one-out cross-validation. We did so in the R statistical language using the loo packages36,38,39. Prior distributions for all regression terms were improper flat priors over the real numbers, the default in the brms package for population parameters. The priors on the intercept and the random effects were student t3,0,10 distributions, as per the default for uninformative priors in the brms package.
We carried out a Principal Components Analysis (PCA) with the four focal fisheries as categorical variables and the illegal activity, stakeholder, and infrastructure estimates from the Bayesian cumulative multinomial logit model. For each fishery, we used 10,000 estimates from the model, along with a qualitative variable that represented the different factors (e.g., restaurateur). The latter has no influence on the principal components of the analysis but helps to interpret the dimensions of variability. Principal Components Analysis is especially powerful as an approach to visualize patterns, such as clusters, clines, and outliers in a dataset40. In our case, we sought to visualize whether there were common illegal factors with similar set of scores and whether there was any association between high or low scores of illegal factors and the focal fisheries. We used the FactoMineR package in the R statistical language41. More

1.
Bowe, L. M., Coat, G. & DePamphilis, C. W. Phylogeny of seed plants based on all three genomic compartments: Extant gymnosperms are monophyletic and Gnetales’ closest relatives are conifers. Proc. Natl. Acad. Sci. USA 97, 4092–4097 (2000).
ADS  Article  CAS  Google Scholar 
2.
Bohlmann, J. & Keeling, C. I. Terpenoid biomaterials. Plant J. 54, 656–669 (2008).
Article  CAS  Google Scholar 

3.
Celedon, J. M. & Bohlmann, J. Oleoresin defenses in conifers: Chemical diversity, terpene synthases, and limitations of oleoresin defense under climate change. New Phytol. 224, 1444–1463 (2019).
Article  CAS  Google Scholar 

4.
Whitehill, J. G. A. et al. Functions of stone cells and oleoresin terpenes in the conifer defense syndrome. New Phytol. 221, 1503–1517 (2019).
Article  CAS  Google Scholar 

5.
Hilker, M., Kobs, C., Varama, M. & Schrank, K. Insect egg deposition induces Pinus sylvestris to attract egg parasitoids. J. Exp. Biol. 205, 455–461 (2002).
PubMed  Google Scholar 

6.
Mumm, R., Schrank, K., Wegener, R., Schulz, S. & Hilker, M. Chemical analysis of volatiles emitted by Pinus sylvestris after induction by insect oviposition. J. Chem. Ecol. 29, 1235–1252 (2003).
Article  CAS  Google Scholar 

7.
Martin, D. M., Gershenzon, J. & Bohlmann, J. Induction of volatile terpene biosynthesis and diurnal emission by methyl jasmonate in foliage of Norway spruce. Plant Physiol. 132, 1586–1599 (2003).
Article  CAS  Google Scholar 

8.
Miller, B., Madilao, L. L., Ralph, S. & Bohlmann, J. Insect-induced conifer defense. White pine weevil and methyl jasmonate induce traumatic resinosis, de novo formed volatile emissions, and accumulation of terpenoid synthase and putative octadecanoid pathway transcripts in Sitka spruce. Plant Physiol. 137, 369–382 (2005).

9.
Niinemets, U., Reichstein, M., Staudt, M., Seufert, G. & Tenhunen, J. D. Stomatal constraints may affect emission of oxygenated monoterpenoids from the foliage of Pinus pinea. Plant Physiol. 130, 1371–1385 (2002).
Article  CAS  Google Scholar 

10.
Harley, P., Eller, A., Guenther, A. & Monson, R. K. Observations and models of emissions of volatile terpenoid compounds from needles of ponderosa pine trees growing in situ: Control by light, temperature and stomatal conductance. Oecologia 176, 35–55 (2014).
ADS  Article  Google Scholar 

11.
Tissier, A. Plant secretory structures: More than just reaction bags. Curr. Opin. Biotechnol. 49, 73–79 (2018).
Article  CAS  Google Scholar 

12.
Schilmiller, A. L., Last, R. L. & Pichersky, E. Harnessing plant trichome biochemistry for the production of useful compounds. Plant J. 54, 702–711 (2008).
Article  CAS  Google Scholar 

13.
Wagner, G. J., Wang, E. & Shepherd, R. W. New approaches for studying and exploiting an old protuberance, the plant trichome. Ann. Bot. 93, 3–11 (2004).
Article  CAS  Google Scholar 

14.
Lange, B. M. The evolution of plant secretory structures and the emergence of terpenoid chemical diversity. Annu. Rev. Plant Biol. 66, 139–159 (2015).
Article  CAS  Google Scholar 

15.
Lange, B. M. et al. Probing essential oil biosynthesis and secretion by functional evaluation of expressed sequence tags from mint glandular trichomes. Proc. Natl. Acad. Sci. USA 97, 2934–2939 (2000).
ADS  Article  CAS  Google Scholar 

16.
Wang, G. et al. Terpene biosynthesis in glandular trichomes of Hop. Plant Physiol. 148, 1254–1266 (2008).
ADS  Article  CAS  Google Scholar 

17.
Nagel, J. et al. EST analysis of hop glandular trichomes identifies an o-methyltransferase that catalyzes the biosynthesis of xanthohumol. Plant Cell 20, 186–200 (2008).
Article  CAS  Google Scholar 

18.
Czechowski, T. et al. Artemisia annua mutant impaired in Artemisinin synthesis demonstrates importance of nonenzymatic conversion in terpenoid metabolism. Proc. Natl. Acad. Sci. USA. 113, 15150–15155 (2016).
Article  CAS  Google Scholar 

19.
Johnson, H. B. Plant pubescence: An ecological perspective. Bot. Rev. 41, 233–258 (1975).
Article  Google Scholar 

20.
Fernald, M. L. Gray’s Manual of Botany. (American Book Company, 1950).

21.
Hernandez-Castillo, G. R., Stockey, R. A., Rothwell, G. W. & Mapes, G. Reconstructing Emporia lockardii (Voltziales: Emporiaceae) and initial thoughts on paleozoic conifer ecology. Int. J. Plant Sci. 170, 1056–1074 (2009).
Article  Google Scholar 

22.
Rothwell, G. W., Mapes, G. & Hernandez-Castillo, G. R. Hanskerpia gen. nov. and phylogenetic relationships among the most ancient conifers (Voltziales). Taxon 54, 733–750 (2005).

23.
Heinrich, M. Das Harz der Nadelhölzer, seine Entstehung, Vertheilung, Bedeutung und Gewinnung (Springer, Berlin, 1894).
Google Scholar 

24.
Tomlin, E. S., Antonejevic, E., Alfaro, R. I. & Borden, J. H. Changes in volatile terpene and diterpene resin acid composition of resistant and susceptible white spruce leaders exposed to simulated white pine weevil damage. Tree Physiol. 20, 1087–1095 (2000).
Article  CAS  Google Scholar 

25.
Birol, I. et al. Assembling the 20 Gb white spruce (Picea glauca) genome from whole-genome shotgun sequencing data. Bioinformatics 29, 1492–1497 (2013).
Article  CAS  Google Scholar 

26.
Warren, R. L. et al. Improved white spruce (Picea glauca) genome assemblies and annotation of large gene families of conifer terpenoid and phenolic defense metabolism. Plant J. 83, 189–212 (2015).
Article  CAS  Google Scholar 

27.
Zulak, K. G., Dullat, H. K., Keeling, C. I., Lippert, D. & Bohlmann, J. Immunofluorescence localization of levopimaradiene/abietadiene synthase in methyl jasmonate treated stems of Sitka spruce (Picea sitchensis) shows activation of diterpenoid biosynthesis in cortical and developing traumatic resin ducts. Phytochemistry 71, 1695–1699 (2010).
Article  CAS  Google Scholar 

28.
Whitehill, J. G. A., Henderson, H., Strong, W., Jaquish, B. & Bohlmann, J. Function of Sitka spruce stone cells as a physical defense against white pine weevil. Plant. Cell Environ. 39, 2545–2556 (2016).
Article  CAS  Google Scholar 

29.
Franceschi, V. R., Krokene, P., Krekling, T. & Christiansen, E. Phloem parenchyma cells are involved in local and distant defense responses to fungal inoculation or bark-beetle attack in Norway spruce (Pinaceae). Am. J. Bot. 87, 314–326 (2000).
Article  CAS  Google Scholar 

30.
Parent, G. J., Giguère, I., Mageroy, M., Bohlmann, J. & MacKay, J. J. Evolution of the biosynthesis of two hydroxyacetophenones in plants. Plant Cell Environ. 41, 620–629 (2018).
Article  CAS  Google Scholar 

31.
Mageroy, M. H. et al. A conifer UDP-sugar dependent glycosyltransferase contributes to acetophenone metabolism and defense against insects. Plant Physiol. 175, 00611.2017 (2017).

32.
Tissier, A. Glandular trichomes: What comes after expressed sequence tags?. Plant J. 70, 51–68 (2012).
Article  CAS  Google Scholar 

33.
Sacher, J. A. Structure and seasonal activity of the shoot apices of Pinus lambertiana and Pinus ponderosa. Am. J. Bot. 41, 749–759 (1954).
Article  Google Scholar 

34.
De Simón, B. F., Vallejo, M. C. G., Cadahía, E., Miguel, C. A. & Martinez, M. C. Analysis of lipophilic compounds in needles of Pinus pinea L. Ann. For. Sci. 58, 449–454 (2001).
Article  Google Scholar 

35.
Lange, W. & Weissman, G. Untersuchungen der Harzbalsame von Pinus resinosa Ait. und Pinus pinea L. Holz als Roh- und Werkst. 49, 476–480 (1991).

36.
Geisler, K., Jensen, N. B., Yuen, M. M. S., Madilao, L. & Bohlmann, J. Modularity of conifer diterpene resin acid biosynthesis: P450 enzymes of different CYP720B clades use alternative substrates and converge on the same products. Plant Physiol. 171, 152–164 (2016).
Article  CAS  Google Scholar 

37.
Hamberger, B., Ohnishi, T., Hamberger, B., Séguin, A. & Bohlmann, J. Evolution of diterpene metabolism: Sitka spruce CYP720B4 catalyzes multiple oxidations in resin acid biosynthesis of conifer defense against insects. Plant Physiol. 157, 1677–1695 (2011).
Article  CAS  Google Scholar 

38.
Ro, D., Arimura, G., Lau, S. Y. W., Piers, E. & Bohlmann, J. Loblolly pine abietadienol/abietadienal oxidase PtAO (CYP720B1) is a multifunctional, multisubstrate cytochrome P450 monooxygenase. Proc. Natl. Acad. Sci. USA 102, 8060–8065 (2005).
ADS  Article  CAS  Google Scholar 

39.
Hilker, M., Stein, C., Schröder, R., Varama, M. & Mumm, R. Insect egg deposition induces defence responses in Pinus sylvestris: characterisation of the elicitor. J. Exp. Biol. 208, 1849–1854 (2005).
Article  Google Scholar 

40.
Schuurink, R. & Tissier, A. Glandular trichomes: Micro-organs with model status?. New Phytol. https://doi.org/10.1111/nph.16283 (2019).
Article  PubMed  Google Scholar 

41.
Huchelmann, A., Boutry, M. & Hachez, C. Plant glandular trichomes: Natural cell factories of high biotechnological interest. Plant Physiol. 175, 00727.2017 (2017).

42.
Sallaud, C. et al. Characterization of two genes for the biosynthesis of the labdane diterpene Z-abienol in tobacco (Nicotiana tabacum) glandular trichomes. Plant J. 72, 1–17 (2012).
Article  CAS  Google Scholar 

43.
Liu, Y. et al. A geranylfarnesyl diphosphate synthase provides the precursor for sesterterpenoid (C25) formation in the glandular trichomes of the mint species Leucosceptrum canum. Plant Cell 28, 804–822 (2016).
Article  CAS  Google Scholar 

44.
Reynolds, E. S. The use of lead citrate at high pH as an electron-opaque stain in electron microscopy. J. Cell Biol. 17, 208–212 (1963).
Article  CAS  Google Scholar  More