Ecology

Changepoint detection methodAlthough most of the reflectance time series used in the BinSeg–Normal–Mean and BinSeg–Normal–MeanVar algorithms had a normal distribution, several lagoons had distributions that were skewed or did not follow a normal distribution (Fig. S1). However, results suggested that the accuracy of the detected changepoints were not sensitive to the normality assumption or distributional characteristics.The BinSeg-Normal-Mean algorithm had the highest performance (81% of the 340 validation sites) in detecting the correct year of swine waste lagoon construction, followed by BinSeg-Normal-MeanVar (77%). The two algorithms did not detect the same year of construction for 19 waste lagoons; of these 19, the BinSeg-Normal-Mean detected the correct year for 84% of them, while the BinSeg-Normal-MeanVar detected the correct year for only 16%. Therefore, the BinSeg-Normal-MeanVar algorithm was abandoned given it did not provide additional useful information relative to the BinSeg-Normal-Mean algorithm.Despite good performance, the BinSeg-Normal-Mean algorithm consistently detected a changepoint during the period of record for all sites included in the 10% validation set (n = 340 swine waste lagoons). However, 58 of the 340 swine waste lagoons were constructed prior to 1986, before the period of record suitable for detecting an accurate changepoint. Changepoints before 1986 either (1) detected the correct construction year, or (2) incorrectly detected a changepoint due to artifact signals identified on the images taken in 1984, probably associated with the initial satellite commissioning. In the latter circumstance, if the algorithm detected a changepoint due to this signal, it meant that no land-use change was detected after 1986. Therefore, these waste lagoons were estimated as having been constructed before 1986. In some conditions, when a large number of images was available for the year 1985 and 1986, the algorithm was able to detect the changepoint occurring for the years 1985 or 1986. Further, the BinSeg-Normal-Mean algorithm detected a false year of construction for swine waste lagoons for which the mean of the segment after the changepoint (S2) had a greater average than the segment before the changepoint (S1).To increase algorithm performance, we developed a workflow to address some of the aforementioned caveats (Fig. 4). In this workflow, the BinSeg-Normal-Mean algorithm is applied to a B4 reflectance time series at location j. If the BinSeg-Normal-Mean changepoint is identified for a time in or prior to 1986 (Fig. 4a,i,b,i) we assume that the lagoon was constructed in or prior to 1986. Similarly, a lagoon is assumed to be constructed in or prior to 1986 if a BinSeg-Normal-Mean changepoint is identified after 1986 and the mean of S2 is greater than the mean of S1 (Fig. 4a,ii,b,ii). If a changepoint occurred after 1986 and the mean of S1 was greater than S2, then the changepoint was estimated as having occurred between 1987 and 2010 (Fig. 4a,iii,b,iii).Figure 4Changepoint detection algorithm for determining the year of construction of swine waste lagoons. Panel (a) summarizes the algorithm workflow, while panel (b) illustrates specific examples corresponding to each step (i–iii) in the workflow.Full size imageThe performance of the workflow was evaluated using the validation set composed of 10% of the total number of swine waste lagoons (n = 340). With the new approach, 94% of the swine waste lagoon construction years (+ /- one year) were accurately retrieved. A tolerance of + /− 1 year was chosen to account for a lack of images in some years due to issues with image quality (e.g. high cloud cover) (e.g., Fig. 5a), or because construction spanned at least a year (e.g., Fig. 5b). The changepoint detection workflow incorrectly estimated the construction years for 19 of the 340 swine waste lagoons in the validation set; the differences between the observed and predicted years of construction of these lagoons ranged from 2 to 26 years with a median of 8 years.Figure 5Examples of limitations to the changepoint detection algorithm. In some cases, an insufficient number of high-quality Landsat 5 images were available to capture the year of construction of an individual swine waste lagoon (a), resulting in errors of + /− 1 year. In other cases, the changepoint algorithms detected the start of the construction of the swine waste lagoon but the swine waste lagoon was not fully operational until later years due to prolonged construction timelines (b).Full size imageBy visually inspecting historical Google Earth images for each of the lagoon sites for which the model incorrectly estimated construction year, we identified that model errors were associated with swine waste lagoon expansion, pixel transitions to land-use classes other than swine waste lagoons, or issues with pixels being partly covered by clouds or incompletely covered by the lagoon (i.e., narrow and small waste lagoons that do not entirely cover a pixel).Estimating swine waste lagoon construction yearsUsing the newly developed algorithm (Fig. 4), construction years were estimated for each swine waste lagoon in the NC Coastal Plain (Fig. 6); the years of construction for each swine waste lagoon are included in the supplementary material. Most swine waste lagoons were built in the early 90s and prior to the moratorium of 1997. More specifically, 80% of the swine waste lagoons (n = 2,736) were built between 1987 and 1997. Sixteen percent of the swine waste lagoons were constructed in or prior to 1986. A large decrease in the construction of swine waste lagoons occurred after the moratorium of 1997, with only 3.7% of swine waste lagoons being constructed after the moratorium. These results suggest that the 1997 moratorium did not completely halt the construction of lagoons, but dramatically slowed the rate of expansion.Figure 6Spatiotemporal distribution of swine waste lagoon construction (+/- 1 year) across the HUC6 watersheds. This figure was produced using QGIS version QGIS 3.18.3 (https://www.qgis.org/).Full size imageWith regards to hydrological boundaries (Fig. 7a–h), the Cape Fear River watershed had the highest number of swine waste lagoons (i.e., 56%; Fig. 7b), followed by the Neuse River (i.e., 23%; Fig. 7d), the Lower Pee Dee River (i.e., 9%; Fig. 7c) watersheds. The Albemarle-Chowan (Fig. 7a), Onslow Bay (Fig. 7e), Pamlico (Fig. 7f), Roanoke (Fig. 7g), and Upper Pee Dee (Fig. 7h) watersheds all had less than 9% of the total lagoons within the study area.Figure 7Year of construction of the swine waste lagoons (+ /− 1 year) for the HUC6 watersheds. The y-axis scales are unequal between the plots to improve readability. The dashed red lines correspond to the establishment of the moratorium in 1997.Full size imageResults suggested that the Cape Fear River watershed was the center of the historical growth of the swine industry, where over 300 swine waste lagoons were built prior to 1987. The Cape Fear River watershed experienced a steady increase in the number of swine waste lagoons from 1987 to 1990, with an average of 46 swine waste lagoons being built annually. However, after 1991, the pace of swine waste lagoon construction increased dramatically with an average of 192 swine waste lagoons built annually between 1991 and 1997. The highest construction rate occurred in 1994, with 242 swine waste lagoons built. However, after the 1997 moratorium, the construction rate decreased dramatically; in 1997, 153 swine waste lagoons were constructed, and this number dropped to 23 in 1998. After 1998, the annual average number of swine waste lagoons constructed plunged to 5. Although the swine waste lagoon construction rate fell considerably after the 1997 moratorium, the decrease had already started in 1995. The same pattern was observed for the Neuse, Pamlico, Albemarle-Pamlico, and Onslow Bay watersheds.The spatiotemporal distribution of swine waste lagoons at the HUC12 watershed scale emphasized the historical clustering of the swine industry in the NC Coastal Plain. After the moratorium, swine waste lagoons were present within 436 HUC12 watersheds. However, before 1986, they were spread across only 197 HUC12 watersheds (Fig. 8). Before 1986, the density of waste lagoons was relatively low with an average of 3.38 swine waste lagoons per 100 km2 and a maximum of 15.13 swine waste lagoons per 100 km2 (i.e., Clayroot Swamp-Swift Creek watershed) (Fig. 8). In the 90s, swine waste lagoon construction expanded and continued to intensify in the region. After the moratorium of 1997, the average density of waste lagoons per HUC12 watersheds was 10 per 100 km2 with a maximum of 78 waste lagoons per 100 km2 identified in the Maxwell Creek-Stocking Head Creek basin. After 1997, 16 of 436 HUC12 watersheds had a swine waste lagoon density greater than 40 per 100 km2 (Fig. 8).Figure 8Cumulative swine waste lagoon density per 100 km2 reported at the HUC12 watershed scale; HUC6 watersheds shown in gray for reference. This figure was produced using QGIS version QGIS 3.18.3 (https://www.qgis.org/).Full size imageSpatiotemporal distribution of swine waste lagoons in relation to water resourcesDistance of swine waste lagoon sites to the nearest water feature (i.e., reservoir, canal/ditch, lake/pond, stream/river, estuary) were assessed using the NHD. The analysis revealed that over 150 swine waste lagoons were misclassified by the NHD and were documented in the NHD as lake/pond (n = 102) or swamp/marsh (n = 46). Further, we observed that some NHD water features were misclassified as other non-water features (e.g., forest, pasture), and most of these misclassifications were for polygons with an area less than 0.05 km2. Therefore, NHD water features with areas less than 0.05 km2 were removed from subsequent analyses. Distances between swine waste lagoons and waterways were computed from the NHD without features with areas less than 0.05 km2. The new analysis revealed that 3 swine waste lagoons remained misclassified as lake/pond (n = 1) and swamp/marsh (n = 2). Canal/Ditch, lake/pond, stream/river, and swamp/marsh were identified as the NHD features that were most commonly near swine waste lagoons (Fig. 9). Two swine waste lagoons were near a reservoir in which one was identified as a treatment-sewage pond by the NHD.Figure 9Nearest water features distance to swine waste lagoons.Full size imageThe average and median distance of all swine waste lagoons (including those built early and late in the period of record) to the nearest water features were 234 and 177 m, respectively. Further, 92% of the swine waste lagoons were less than 500 m from the nearest waterways. The Mann–Kendall results revealed a significant upward trend over time of swine waste lagoon distances to the nearest water features (alpha = 0.05, p-value = 0.01). A slight increase over time of swine waste lagoon distances to the nearest water feature is also documented in Table 1.Table 1 Temporal average and median of nearest distance (m) of swine waste lagoons to water features. NA indicated that the water feature was not the closest waterway to any of the studied swine waste lagoons for the time period.Full size table More

1.MacLachlan, N. J. & Guthrie, A. J. Re-emergence of bluetongue, African horse sickness, and other Orbivirus diseases. Vet. Res. 41, 35 (2010).Article 

Google Scholar 
2.Zientara, S., Weyer, C. T. & Lecollinet, S. African horse sickness. OIE Revue Sci. Tech. 34, 315–327 (2015).CAS 
Article 

Google Scholar 
3.Ayelet, G. et al. Outbreak investigation and molecular characterization of African horse sickness virus circulating in selected areas of Ethiopia. Acta Trop. 127, 91–96 (2013).Article 

Google Scholar 
4.Diarra, M. et al. Spatial distribution modelling of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in Senegal. Parasit. Vectors 11, 1–15 (2018).Article 

Google Scholar 
5.Karamalla, S. T. et al. Sero-epidemioloical survey on African horse sickness virus among horses in Khartoum State, Central Sudan. BMC Vet. Res. 14, 1–6 (2018).Article 

Google Scholar 
6.Escobar, L. E. Ecological Niche modeling: An introduction for veterinarians and epidemiologists. Front. Vet. Sci. 7, 519059. https://doi.org/10.3389/fvets.2020.519059 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Okely, M., Anan, R., Gad-Allah, S. & Samy, A. M. Mapping the environmental suitability of etiological agent and tick vectors of Crimean-Congo hemorrhagic fever. Acta Trop. 203, 105319 (2020).CAS 
Article 

Google Scholar 
8.Chavy, A. et al. Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome. PLoS Negl. Trop. Diseases 13, e0007629 (2019).Article 

Google Scholar 
9.Sloyer, K. E. et al. Ecological niche modeling the potential geographic distribution of four Culicoides species of veterinary significance in Florida, USA. PLoS ONE 14, e0206648 (2019).CAS 
Article 

Google Scholar 
10.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
11.Cao, Z., Jin, Y., Shen, T., Xu, F. & Li, Y. Risk factors and distribution for peste des petits ruminants (PPR) in Mainland China. Small Rumin. Res. 162, 12–16 (2018).Article 

Google Scholar 
12.Naimi, B. & Araújo, M. B. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375 (2016).Article 

Google Scholar 
13.Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling. undefined 37, 191–203 (2014).
Google Scholar 
14.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, (2020).15.Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD—a platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).Article 

Google Scholar 
16.Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).Article 

Google Scholar 
17.Uusitalo, R. et al. Predicting spatial patterns of sindbis virus (Sinv) infection risk in finland using vector, host and environmental data. Int. J. Environ. Res. Public Health 18, 7064 (2021).Article 

Google Scholar 
18.Raffini, F. et al. From nucleotides to satellite imagery: Approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability (Switzerland) 12, 4508 (2020).CAS 
Article 

Google Scholar 
19.Phillips, S. B., Aneja, V. P., Kang, D. & Arya, S. P. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
20.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
21.Hernández-Urcera, J., Murillo, F. J., Regueira, M., Cabanellas-Reboredo, M. & Planas, M. Preferential habitats prediction in syngnathids using species distribution models. Marine Environ. Res. 172, 105488 (2021).Article 

Google Scholar 
22.Smeraldo, S. et al. Generalists yet different: distributional responses to climate change may vary in opportunistic bat species sharing similar ecological traits. Mammal Rev. 51, 571–584 (2021).Article 

Google Scholar 
23.Thomson, A. M. et al. RCP4.5: A pathway for stabilization of radiative forcing by 2100. Clim. Change 109, 77–94 (2011).ADS 
CAS 
Article 

Google Scholar 
24.QGIS Development Team. QGIS Geographic Information System. Open-Source Geospatial Foundation Project. (2020).25.Ramirez-Reyes, C. et al. Embracing ensemble species distribution models to inform at-risk species status assessments. J. Fish Wildl. Manag. 12, 98–111 (2021).Article 

Google Scholar 
26.Stephenson, F. et al. Presence-only habitat suitability models for vulnerable marine ecosystem indicator taxa in the South Pacific have reached their predictive limit. ICES J. Mar. Sci. 78, 2830–2843 (2021).Article 

Google Scholar 
27.Zhu, G., Fan, J. & Peterson, A. T. Cautions in weighting individual ecological niche models in ensemble forecasting. Ecol. Modelling 448, 109502 (2021).Article 

Google Scholar 
28.Leta, S. et al. Modeling the global distribution of Culicoides imicola: an Ensemble approach. Sci. Rep. 9, 1–9 (2019).ADS 
CAS 
Article 

Google Scholar 
29.Onyango, M. G. et al. Delineation of the population genetic structure of Culicoides imicola in East and South Africa. Parasit. Vectors 8, 660 (2015).Article 

Google Scholar 
30.Carpenter, S., Mellor, P. S., Fall, A. G., Garros, C. & Venter, G. J. African horse sickness virus: history. Transm. Curr. Status. 62, 343–358. https://doi.org/10.1146/annurev-ento-031616-035010 (2017).CAS 
Article 

Google Scholar 
31.Carpenter, S., Mellor, P. S., Fall, A. G., Garros, C. & Venter, G. J. African Horse Sickness Virus: History, Transmission, and Current Status. Annu. Rev. Entomol. 62, 343–358 (2017).CAS 
Article 

Google Scholar 
32.Fall, M. et al. Culicoides (Diptera: Ceratopogonidae) midges, the vectors of African horse sickness virus—a host/vector contact study in the Niayes area of Senegal. Parasit. Vectors 8, 1–13 (2015).Article 

Google Scholar 
33.Mellor, P. S. Epizootiology and vectors of African horse sickness virus. Comp. Immunol. Microbiol. Infect. Dis. 17, 287–296 (1994).CAS 
Article 

Google Scholar 
34.Wu, X., Lu, Y., Zhou, S., Chen, L. & Xu, B. Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environ. Int. 86, 14–23 (2016).Article 

Google Scholar 
35.Nosrat, C. et al. Impact of recent climate extremes on mosquito-borne disease transmission in Kenya. PLOS Negl. Trop. Diseases 15, e0009182 (2021).CAS 
Article 

Google Scholar 
36.Abiodun, G. J., Maharaj, R., Witbooi, P. & Okosun, K. O. Modelling the influence of temperature and rainfall on the population dynamics of Anopheles arabiensis. Malar. J. 15, 1–15 (2016).Article 

Google Scholar  More

1.Holway, D. A. & Suarez, A. V. Animal behavior: An essential component of invasion biology. TREE 14, 328–330 (1999).CAS 
PubMed 

Google Scholar 
2.Chapple, D. G., Simmonds, S. M. & Wong, B. B. M. Can behavioral and personality traits influence the success of unintentional species introductions? Trends Ecol. Evol. 27, 57–64 (2012).PubMed 

Google Scholar 
3.Weis, J. & Sol, D. Behaviour and the Invasion Process. in Biological Invasions and Animal Behaviour 5–116 (Cambridge University Press, 2016).4.Cote, J., Fogarty, S., Weinersmith, K., Brodin, T. & Sih, A. Personality traits and dispersal tendency in the invasive mosquitofish (Gambusia affinis). Proc. R. Soc. B Biol. Sci. 277, 1571–1579 (2010).
Google Scholar 
5.Myles-Gonzalez, E., Burness, G., Yavno, S., Rooke, A. & Fox, M. G. To boldly go where no goby has gone before: Boldness, dispersal tendency, and metabolism at the invasion front. Behav. Ecol. 26, 1083–1090 (2015).
Google Scholar 
6.Mutascio, H. E., Pittman, S. E. & Zollner, P. A. Investigating movement behavior of invasive Burmese pythons on a shy–bold continuum using individual-based modeling. Perspect. Ecol. Conserv. 15, 25–31 (2017).
Google Scholar 
7.Chuang, A. Living Life on the Edge: The Role of Invasion Processes in Shaping Personalities in a Non-Native Spider Species (The University of Tennessee, Knoxville, 2019). https://doi.org/10.1017/CBO9781107415324.004.Book 

Google Scholar 
8.Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).PubMed 

Google Scholar 
9.Pintor, L. M., Sih, A. & Kerby, J. L. Behavioral correlations provide a mechanism for explaining high invader densities and increased impacts on native prey. Ecology 90, 581–587 (2009).PubMed 

Google Scholar 
10.Petren, K. & Case, T. J. An experimental demonstration of exploitation competition in an ongoing invasion. Ecology 77, 118–132 (1996).
Google Scholar 
11.Wright, T. F., Eberhard, J. R., Hobson, E. A., Avery, M. L. & Russello, M. A. Behavioral flexibility and species invasions: The adaptive flexibility hypothesis. Ethol. Ecol. Evol. 22, 393–404 (2010).
Google Scholar 
12.Dick, J. T. A. Role of behaviour in biological invasions and species distributions; lessons from interactions between the invasive Gammarus pulex and the native G. duebeni (Crustacea: Amphipoda). Contrib. Zool. 77, 91–98 (2008).
Google Scholar 
13.Dick, J. T. A. et al. Invader Relative Impact Potential: A new metric to understand and predict the ecological impacts of existing, emerging and future invasive alien species. J. Appl. Ecol. 54, 1259–1267 (2017).
Google Scholar 
14.Dick, J. T. A., Elwood, R. W. & Montgomery, W. I. The behavioural basis of a species replacement: differential aggresssion and predation between the introduced Gammarus pulex and the native G. duebeni celticus (Amphipoda). Behav. Ecol. Sociobiol. 37, 393–398 (1995).
Google Scholar 
15.Dick, J. T. A. et al. Ecological impacts of an invasive predator explained and predicted by comparative functional responses. Biol. Invasions 15, 837–846 (2013).
Google Scholar 
16.Dick, J. T. A. et al. Advancing impact prediction and hypothesis testing in invasion ecology using a comparative functional response approach. Biol. Invasions 16, 735–753 (2014).
Google Scholar 
17.Iacarella, J. C., Dick, J. T. A. & Ricciardi, A. A spatio-temporal contrast of the predatory impact of an invasive freshwater crustacean. Divers. Distrib. 21, 803–812 (2015).
Google Scholar 
18.Toscano, B. J. & Griffen, B. D. Trait-mediated functional responses: Predator behavioural type mediates prey consumption. J. Anim. Ecol. 83, 1469–1477 (2014).PubMed 

Google Scholar 
19.MacCrimmon, H. R. World distribution of rainbow trout (Salmo gairdneri): further observations. J. Fish. Res. Board Canada 28, 663–704 (1971).
Google Scholar 
20.MacCrimmon, H. R., Marshall, T. L. & Gots, B. L. World distribution of brown trout, Salmo trutta: further observations. J. Fish. Res. Board Canada 27, 811–818 (1970).
Google Scholar 
21.Crawford, S. S. & Muir, A. M. Global introductions of salmon and trout in the genus Oncorhynchus: 1870–2007. Rev. Fish Biol. Fish. 18, 313–344 (2008).
Google Scholar 
22.Crowl, T. A., Townsend, C. R. & Mcintosh, A. R. The impact of introduced brown and rainbow trout on native fish: The case of Australasia. Rev. Fish Biol. Fish. 241, 217–241 (1992).
Google Scholar 
23.Hasegawa, K. Invasions of rainbow trout and brown trout in Japan: A comparison of invasiveness and impact on native species. Ecol. Freshw. Fish 29, 419–428 (2020).
Google Scholar 
24.Cambray, J. A. The global impact of alien trout species—A review; with reference to their impact in South Africa. African J. Aquat. Sci. 28, 61–67 (2003).
Google Scholar 
25.Dunham, J. B., Wheeler, A. & Rosenberger, A. Assessing the consequences of nonnative trout in headwater ecosystems in western North America. Fisheries 29, 37–41 (2004).
Google Scholar 
26.Fausch, K. D., Taniguchi, Y., Nakano, S., Grossman, G. D. & Townsend, C. R. Flood disturbance regimes influence rainbow trout invasion success among five holarctic regions. Ecol. Appl. 11, 1438–1455 (2001).
Google Scholar 
27.Anderson, R. M. & Nehring, R. B. Effects of a catch-and-release regulation on a wild trout population in Colorado and its acceptance by Anglers. North Am. J. Fish. Manag. 4, 257–265 (1984).
Google Scholar 
28.Young, K. A. et al. A trial of two trouts: Comparing the impacts of rainbow and brown trout on a native galaxiid. Anim. Conserv. 13, 399–410 (2010).
Google Scholar 
29.Conrad, J. L., Weinersmith, K. L., Brodin, T., Saltz, J. B. & Sih, A. Behavioural syndromes in fishes: A review with implications for ecology and fisheries management. J. Fish Biol. 78, 395–435 (2011).CAS 
PubMed 

Google Scholar 
30.Mowles, S. L., Cotton, P. A. & Briffa, M. Consistent crustaceans: The identification of stable behavioural syndromes in hermit crabs. Behav. Ecol. Sociobiol. 66, 1087–1094 (2012).
Google Scholar 
31.Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 

Google Scholar 
32.Bell, A. M. Behavioural differences between individuals and two populations of stickleback (Gasterosteus aculeatus). J. Evol. Biol. 18, 464–473 (2005).CAS 
PubMed 

Google Scholar 
33.Bourne, G. R. & Sammons, A. J. Boldness, aggression and exploration: evidence for a behavioural syndrome in male pentamorphic livebearing fish, Poecilia parae. AACL Bioflux 1, 39–50 (2008).
Google Scholar 
34.Lukas, J. et al. Consistent behavioral syndrome across seasons in an invasive freshwater fish. Front. Ecol. Evol. 8, 466 (2021).ADS 

Google Scholar 
35.Gjedrem, T., Gjøen, H. M. & Gjerde, B. Genetic origin of Norwegian farmed Atlantic salmon. Aquaculture 98, 41–50 (1991).
Google Scholar 
36.Huntingford, F. & Adams, C. Behavioural syndromes in farmed fish: Implications for production and welfare. Behaviour 142, 1207–1221 (2005).
Google Scholar 
37.Alvarez, D. & Nicieza, A. G. Predator avoidance behaviour in wild and hatchery-reared brown trout : The role of experience and domestication. J. Fish Biol. 63, 1565–1577. https://doi.org/10.1046/j.1095-8649.2003.00267.x (2003).Article 

Google Scholar 
38.Geffroy, B. et al. Evolutionary dynamics in the anthropocene: Life history and intensity of human contact shape antipredator responses. PLoS Biol. 18, 1–17 (2020).
Google Scholar 
39.Lincoln, R. F. & Scott, A. P. Production of all-female triploid rainbow trout. Aquaculture 30, 375–380 (1983).
Google Scholar 
40.Maxime, V. The physiology of triploid fish: Current knowledge and comparisons with diploid fish. Fish Fish. 9, 67–78 (2008).
Google Scholar 
41.Chatterji, R., Longley, D., Sandford, D., Roberts, D. & Stubbing, D. Performance of stocked triploid and diploid brown trout and their effects on wild brown trout in UK rivers. (2008).42.Benfey, T. J. The physiology and behavior of triploid fishes. Rev. Fish. Sci. 7, 39–67 (1999).
Google Scholar 
43.Carter, C. G. et al. Food consumption, feeding behaviour, and growth of triploid and diploid Atlantic salmon, Salmo salar L., parr.. Can. J. Zool. 72, 609–617 (1994).
Google Scholar 
44.Weber, G. M., Hostuttler, M. A., Cleveland, B. M. & Leeds, T. D. Growth performance comparison of intercross-triploid, induced triploid, and diploid rainbow trout. Aquaculture 433, 85–93 (2014).
Google Scholar 
45.Øverli, Ø., Pottinger, T. G., Carrick, T. R., Øverli, E. & Winberg, S. Differences in behaviour between rainbow trout selected for high- and low-stress responsiveness. J. Exp. Biol. 205, 391–395 (2002).PubMed 

Google Scholar 
46.Sadoul, B., Leguen, I., Colson, V., Friggens, N. C. & Prunet, P. A multivariate analysis using physiology and behavior to characterize robustness in two isogenic lines of rainbow trout exposed to a confinement stress. Physiol. Behav. 140, 139–147 (2015).CAS 
PubMed 

Google Scholar 
47.Adriaenssens, B. & Johnsson, J. I. Learning and context-specific exploration behaviour in hatchery and wild brown trout. Appl. Anim. Behav. Sci. 132, 90–99 (2011).
Google Scholar 
48.Näslund, J. & Johnsson, J. I. State-dependent behavior and alternative behavioral strategies in brown trout (Salmo trutta L.) fry. Behav. Ecol. Sociobiol. 70, 2111–2125 (2016).PubMed 
PubMed Central 

Google Scholar 
49.Mortensen, E. Density-dependent mortality of trout fry (Salmo trutta L.) and its relationship to the management of small streams. J. Fish Biol. 11, 613–617 (1977).
Google Scholar 
50.Armstrong, J. D. & Nislow, K. H. Critical habitat during the transition from maternal provisioning in freshwater fish, with emphasis on Atlantic salmon (Salmo salar) and brown trout (Salmo trutta). J. Zool. 269, 403–413 (2006).
Google Scholar 
51.Walsh, R. N. & Cummins, R. A. The open-field test: A critical review. Psychol. Bull. 83, 482–504 (1976).CAS 
PubMed 

Google Scholar 
52.Adriaenssens, B. & Johnsson, J. I. Shy trout grow faster: Exploring links between personality and fitness-related traits in the wild. Behav. Ecol. 22, 135–143 (2010).
Google Scholar 
53.Sneddon, L. U. The bold and the shy: Individual differences in rainbow trout. J. Fish Biol. 62, 971–975 (2003).
Google Scholar 
54.Adriaenssens, B. Individual variation in behaviour: personality and performance of brown trout in the wild (University of Gothenburg, 2010).55.Elias, A., Thrower, F. & Nichols, K. M. Rainbow trout personality: Individual behavioural variation in juvenile Oncorhynchus mykiss. Behaviour 155, 205–230 (2018).
Google Scholar 
56.Dick, J. T. A. et al. Functional responses can unify invasion ecology. Biol. Invasions 19, 1667–1672 (2017).
Google Scholar 
57.Sloman, K. A., Metcalfe, N. B., Taylor, A. C. & Gilmour, K. M. Plasma cortisol concentrations before and after social stress in rainbow trout and brown trout. Physiol. Biochem. Zool. 74, 383–389 (2001).CAS 
PubMed 

Google Scholar 
58.Sadoul, B., Blumstein, D. T., Alfonso, S. & Geffroy, B. Human protection drives the emergence of a new coping style in animals. PLoS Biol. 19, 1–11 (2021).
Google Scholar 
59.Campbell, J. M., Carter, P. A., Wheeler, P. A. & Thorgaard, G. H. Aggressive behavior, brain size and domestication in clonal rainbow trout lines. Behav. Genet. 45, 245–254 (2015).PubMed 

Google Scholar 
60.Berejikian, B. A., Mathews, S. B. & Quinn, T. P. Effects of hatchery and wild ancestry and rearing environments on the development of agonistic behavior in steelhead trout (Oncorhynchus mykiss) fry. Can. J. Fish. Aquat. Sci. 53, 2004–2014 (1996).
Google Scholar 
61.Laverty, C. et al. Assessing the ecological impacts of invasive species based on their functional responses and abundances. Biol. Invasions 19, 1653–1665 (2017).
Google Scholar 
62.Alexander, M. E., Dick, J. T. A., Weyl, O. L. F., Robinson, T. B. & Richardson, D. M. Existing and emerging high impact invasive species are characterized by higher functional responses than natives. Biol. Lett. 10, 20130946 (2014).PubMed 
PubMed Central 

Google Scholar 
63.Dickey, J. W. E., Cuthbert, R. N., Steffen, G. T., Dick, J. T. A. & Briski, E. Sea freshening may drive the ecological impacts of emerging and existing invasive non-native species. Divers. Distrib. 27, 144–156 (2021).
Google Scholar 
64.Sadler, J., Pankhurst, P. M. & King, H. R. High prevalence of skeletal deformity and reduced gill surface area in triploid Atlantic salmon (Salmo salar L.). Aquaculture 198, 369–386 (2001).
Google Scholar 
65.Benfey, T. J. & Biron, M. Acute stress response in triploid rainbow trout (Oncorhynchus mykiss) and brook trout (Salvelinus fontinalis). Aquaculture 184, 167–176 (2000).CAS 

Google Scholar 
66.Sadler, J., Pankhurst, N. W., Pankhurst, P. M. & King, H. Physiological stress responses to confinement in diploid and triploid Atlantic salmon. J. Fish Biol. 56, 506–518 (2000).
Google Scholar 
67.Berrebi, P., Splendiani, A., Palm, S. & Berna, R. Genetic diversity of domestic brown trout stocks in Europe. Aquaculture 544, 737043 (2021).CAS 

Google Scholar 
68.Gross, R., Lulla, P. & Paaver, T. Genetic variability and differentiation of rainbow trout (Oncorhynchus mykiss) strains in northern and Eastern Europe. Aquaculture 272, 139–146 (2007).
Google Scholar 
69.Whelan, K. Assessing and mitigating the impact of a major rainbow trout escape on the wild salmon and trout populations of the Mourne river system, Northern Ireland. (2017).70.Shelton, J. et al. Temperature mediates the impact of non-native rainbow trout on native freshwater fishes in South Africa’s Cape Fold Ecoregion. Biol. Invasions 20, 2927–2944 (2018).
Google Scholar 
71.Michelangeli, M. et al. Sex-dependent personality in two invasive species of mosquitofish. Biol. Invasions 22, 1353–1364 (2020).
Google Scholar 
72.Friard, O. & Gamba, M. BORIS: A free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).
Google Scholar 
73.R Core Team. R: A language and environment for statistical computing. (2018).74.RStudio Team. RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/. 2019 (2020).75.Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. Springer https://doi.org/10.1086/648138 (2008).Article 
MATH 

Google Scholar 
76.Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 18637 (2015).
Google Scholar 
77.Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version. Media https://doi.org/10.1007/978-0-387-98141-3 (2019).Article 

Google Scholar 
78.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 

Google Scholar 
79.Barton, K. MuMIn: Multi-Model Inference. 2020 (2020).80.Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. emmeans: estimated marginal means, aka least-squares means. R package version 1.5.2-1 (2020).81.Pritchard, D. frair: tools for functional response analysis. R package version 0.0.100 (2017).82.Juliano, S. A. Predation and functional response curves. in Design and Analysis of Ecological Experiments (eds. Scheiner, S. & Gurevitch, J.) Chapter 10 (2001).83.Rogers, D. Random search and insect population models. J. Anim. Ecol. 41, 369–383 (1972).
Google Scholar 
84.Bolker, B. M. Rogers random predator equation: extensions and estimation by numerical integration. 1–20 (2012). More

1.Angel, S. et al. The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Prog. Plan. 75, 53–107 (2011).
Google Scholar 
2.Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).ADS 
CAS 
PubMed 

Google Scholar 
3.McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).
Google Scholar 
4.Groffman, P. M. et al. Ecological homogenization of urban USA. Front. Ecol. Environ. 12, 74–81 (2014).
Google Scholar 
5.Bolnick, D. I. et al. (Non)Parallel evolution. Annu. Rev. Ecol. Evol. Syst. 49, 303–330 (2018).
Google Scholar 
6.Donihue, C. M. & Lambert, M. R. Adaptive evolution in urban ecosystems. Ambio 44, 194–203 (2015).PubMed 

Google Scholar 
7.Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).
Google Scholar 
8.Rivkin, L. R. et al. A roadmap for urban evolutionary ecology. Evol. Appl. 12, 384–398 (2019).PubMed 

Google Scholar 
9.Santangelo, J. S. et al. Urban environments as a framework to study parallel evolution. In Urban Evolutionary Biology (eds Szulkin, M. et al.) (Oxford University Press, 2020).
Google Scholar 
10.Cosentino, B. J., Moore, J.-D., Karraker, N. E., Ouellet, M. & Gibbs, J. P. Evolutionary response to global change: Climate and land use interact to shape color polymorphism in a woodland salamander. Ecol. Evol. 7, 5426–5434 (2017).PubMed 
PubMed Central 

Google Scholar 
11.Koprowski, J. L., Munroe, K. E. & Edelman, A. J. Gray not grey: Ecology of Sciurus carolinensis in their native range in North America. In Grey Squirrels: Ecology and Management of an Invasive Species in Europe (eds Shuttleworth, C. M. et al.) (European Squirrel Initiative, 2016).
Google Scholar 
12.McRobie, H., Thomas, A. & Kelly, J. The genetic basis of melanism in the gray squirrel (Sciurus carolinensis). J. Hered. 100, 709–714 (2009).CAS 
PubMed 

Google Scholar 
13.Gibbs, J. P., Buff, M. F. & Cosentino, B. J. The biological system: Urban wildlife, adaptation and evolution: Urbanization as a driver of contemporary evolution in gray squirrels (Sciurus carolinensis). In Understanding Urban Ecology (eds Hall, M. A. & Balogh, S.) (Springer, 2019).
Google Scholar 
14.Lehtinen, R. M. et al. Dispatches form the neighborhood watch: Using citizen science and field survey data to document color morph frequency in space and time. Ecol. Evol. 10, 1526–1538 (2020).PubMed 
PubMed Central 

Google Scholar 
15.Perlut, N. G. Long-distance dispersal by eastern gray squirrels in suburban habitats. Northeast. Nat. 27, 195–200 (2020).
Google Scholar 
16.Goheen, J. R., Swihart, R. K., Gehring, T. M. & Miller, M. S. Forces structuring tree squirrel communities in landscapes fragmented by agriculture: Species differences in perceptions of forest connectivity and carrying capacity. Oikos 102, 95–103 (2003).
Google Scholar 
17.Ducharme, M. B., Larochelle, J. & Richard, D. Thermogenic capacity in gray and black morphs of the gray squirrel, Sciurus carolinensis. Physiol. Zool. 62, 1273–1292 (1989).
Google Scholar 
18.Linnen, C. R. & Hoekstra, H. E. Measuring natural selection on genotypes and phenotypes in the wild. Cold Spring Harb. Symp. Quant. Biol. 74, 155–168 (2010).PubMed Central 

Google Scholar 
19.Campbell-Staton, S. C. et al. Parallel selection on thermal physiology facilitates repeated adaptation of city lizards to urban heat islands. Nat. Ecol. Evol. 4, 652–658 (2020).PubMed 

Google Scholar 
20.Reid, N. M. et al. The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish. Science 354, 1305–1308 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Bowers, M. A. & Breland, B. Foraging of gray squirrels on an urban-rural gradient: Use of the GUD to assess anthropogenic impact. Ecol. Appl. 6, 1135–1142 (1996).
Google Scholar 
22.McCleery, R. A., Lopez, R. R., Silvy, N. J. & Gallant, D. L. Fox squirrel survival in urban and rural environments. J. Wildl. Manage. 72, 133–137 (2008).
Google Scholar 
23.Benson, E. The urbanization of the eastern gray squirrel in the United States. J. Am. Hist. 100, 691–710 (2013).
Google Scholar 
24.Leveau, L. United colours of the city: A review about urbanization impact on animal colours. Austral Ecol. 46, 670–679 (2021).
Google Scholar 
25.Ducrest, A.-L., Keller, L. & Roulin, A. Pleiotropy in the melanocortin system, coloration, and behavioural syndromes. Trends Ecol. Evol. 23, 502–510 (2008).PubMed 

Google Scholar 
26.Stothart, M. R. & Newman, A. E. M. Shades of grey: Host phenotype dependent effect of urbanization on the bacterial microbiome of a wild mammal. Anim. Microbiome. 3, 46 (2021).PubMed 
PubMed Central 

Google Scholar 
27.Vasemägi, A. The adaptive hypothesis of clinal variation revisited: Single-locus clines as a result of spatially restricted gene flow. Genetics 173, 2411–2414 (2006).PubMed 
PubMed Central 

Google Scholar 
28.Merrick, M. J., Evans, K. L. & Bertolino, S. Urban grey squirrel ecology, associated impacts, and management challenges. In Grey Squirrels: Ecology and Management of an Invasive Species in Europe (eds Shuttleworth, C. M. et al.) (European Squirrel Initiative, 2016).
Google Scholar 
29.Chipman, R., Slate, D., Rupprecht, C. & Mendoza, M. Downside risk of wildlife translocation. In Towards the Elimination of Rabies in Eurasia (eds Dodet, B. et al.) (Dev. Biol Basel, Karger, 2008).
Google Scholar 
30.Allen, D. L. Michigan Fox Squirrel Management (Michigan Department of Conservation, 1943).
Google Scholar 
31.Schorger, A. W. Squirrels in early Wisconsin. Trans. Wis. Acad. Sci. Arts Lett. 39, 195–247 (1949).
Google Scholar 
32.Robertson, G. I. Distribution of Color Morphs of Sciurus carolinensis in Eastern North America (University of Western Ontario, 1973).
Google Scholar 
33.MacCleery, D. W. American Forests: A History of Resiliency and Recovery (Forest History Society, 2011).
Google Scholar 
34.Foster, D. R. et al. Wildlands and Woodlands: A Vision for the New England Landscape (Harvard University Press, 2010).
Google Scholar 
35.Thompson, R. T., Carpenter, D. N., Cogbill, C. V. & Foster, D. R. Four centuries of change in northeastern United States forests. PLoS ONE 8(9), e72540 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Lambert, M. R. et al. Adaptive evolution in cities: Progress and misconceptions. Trends Ecol. Evol. 36, 239–257 (2021).PubMed 

Google Scholar 
37.Farquhar, D. N. Some Aspects of Thermoregulation as Related to the Geographic Distribution of the Northern Melanic Phase of the Grey Squirrel (York University, 1974).
Google Scholar 
38.Innes, S. & Lavigne, D. M. Comparative energetics of coat colour polymorphs in the eastern gray squirrel Sciurus carolinensis. Can. J. Zool. 57, 585–592 (1979).
Google Scholar 
39.Santangelo, J. S. et al. Predicting the strength of urban-rural clines in a Mendelian polymorphism along a latitudinal gradient. Evol. Lett. 4, 212–225 (2020).PubMed 
PubMed Central 

Google Scholar 
40.Fidino, M. et al. Landscape-scale differences among cities alter common species’ responses to urbanization. Ecol. Appl. 31, e02253 (2021).PubMed 

Google Scholar 
41.Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).
Google Scholar 
42.Alberti, M. Global urban signatures of phenotypic change in animal and plant populations. Proc. Natl. Acad. Sci. U.S.A. 114, 8951–8956 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.United States Census Bureau. 2019 TIGER/Line Shapefiles (machine-readable data files) https://www2.census.gov/geo/tiger/TIGER2019/UAC/ (2019).44.XX. Statistics Canada. Population Centre Boundary File, Census year 2016 https://www150.statcan.gc.ca/n1/en/catalogue/92-166-X (2017).45.Aiello-Lammens, M. E. et al. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).
Google Scholar 
46.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2020).47.Brown de Colstoun, E. C. et al. Documentation for the Global Man-made Impervious Surface (GMIS) Dataset from Landsat (NASA Socioeconomic Data and Applications Center, 2017).
Google Scholar 
48.Steele, M. A. & Koprowski, J. L. North American Tree Squirrels (Smithsonian Books, 2001).
Google Scholar 
49.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).ADS 
CAS 
PubMed 

Google Scholar 
50.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
51.Hijmans, R. L. raster: Geographic data analysis and modeling. R package version 3.3–13. https://CRAN.R-project.org/package=raster (2020).52.Baston, D. exactextractr: Fast extraction from raster datasets using polygons. R package version 0.5.1. https://CRAN.R-project.org/package=exactextractr (2020).53.Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).PubMed 
PubMed Central 

Google Scholar 
54.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
55.Gelman, A. & Su, Y. arm: Data analysis using regression and multilevel/hierarchical models. R package version 1.11–2. https://CRAN.R-project.org/package=arm (2020).56.Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press, 2007).
Google Scholar 
57.Crase, B., Liedloff, A. C. & Wintle, B. A. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 35, 879–888 (2012).
Google Scholar 
58.Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).MathSciNet 
MATH 

Google Scholar 
59.Bardos, D. C., Guillera-Arroita, G. & Wintle, B. A. Valid auto-models for spatially autocorrelated occupancy and abundance data. Methods Ecol. Evol. 6, 1137–1149 (2015).
Google Scholar  More

1.Tomlinson, P. B. The Botany of Mangroves. (Cambridge University Press, 1994).2.Carrasquilla-Henao, M. & Juanes, F. Mangroves enhance local fisheries catches: A global meta-analysis. Fish Fish. 18, 79–93 (2017).
Google Scholar 
3.del Valle, A., Eriksson, M., Ishizawa, O. A. & Miranda, J. J. Mangroves protect coastal economic activity from hurricanes. Proc. Natl. Acad. Sci. U.S.A. 117, 265–270 (2020).PubMed 

Google Scholar 
4.Zhang, K. et al. The role of mangroves in attenuating storm surges. Estuar. Coast. Shelf Sci. 102–103, 11–23 (2012).ADS 

Google Scholar 
5.Menendez, P., Losada, I. J., Torres-Ortega, S., Narayan, S. & Beck, M. W. The global flood protection benefits of mangroves. Sci. Rep. 10, 1–11 (2020).
Google Scholar 
6.Macreadie, P. I. et al. The future of Blue Carbon science. Nat. Commun. 10, 1–13 (2019).
Google Scholar 
7.Valiela, I., Bowen, J. L. & York, J. K. Mangrove forests: One of the world’s threatened major tropical environments. Bioscience 51, 807–815 (2001).
Google Scholar 
8.Bryan-Brown, D. N. et al. Global trends in mangrove forest fragmentation. Sci. Rep. https://doi.org/10.1038/s41598-020-63880-1 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
9.Duke, N. C. et al. A world without mangroves ?. Science 317, 41–43 (2007).CAS 

Google Scholar 
10.Friess, D. A. et al. The state of the world’s mangrove forests: Past, present, and future. Annu. Rev. Environ. Resour. 44, 89–115 (2019).
Google Scholar 
11.Friess, D. A. et al. Mangroves give cause for conservation optimism, for now. Curr. Biol. 30, R153–R154 (2020).CAS 
PubMed 

Google Scholar 
12.Reynolds, L. K., McGlathery, K. J. & Waycott, M. Genetic diversity enhances restoration success by augmenting ecosystem services. PLoS ONE 7, 1–7 (2012).
Google Scholar 
13.Lowenfeld, R. & Klekowski, E. J. Mangrove genetics. I. Mating system and mutation rates of rhizophora mangle in Florida and San Salvador Island, Bahamas. Int. J. Plant Sci. 153, 394–399 (1992).14.Kennedy, J. P., Sammy, J. M., Rowntree, J. K. & Preziosi, R. F. Mating system variation in neotropical black mangrove, Avicennia germinans, at three spatial scales towards an expanding northern distributional limit. Estuarine Coastal Shelf Sci. https://doi.org/10.1016/j.ecss.2020.106754 (2020).Article 

Google Scholar 
15.Van Der Stocken, T. et al. Impact of landscape structure on propagule dispersal in mangrove forests. Mar. Ecol. Prog. Ser. 524, 95–106 (2015).ADS 

Google Scholar 
16.Hamilton, J. F., Osman, R. W. & Feller, I. C. Modeling local effects on propagule movement and the potential expansion of mangroves and associated fauna: Testing in a sub-tropical lagoon. Hydrobiologia 803, 173–187 (2017).
Google Scholar 
17.Binks, R. M. et al. Habitat discontinuities form strong barriers to gene flow among mangrove populations, despite the capacity for long-distance dispersal. Divers. Distrib. 25, 298–309 (2019).
Google Scholar 
18.Ngeve, M. N., Van der Stocken, T., Sierens, T., Koedam, N. & Triest, L. Bidirectional gene flow on a mangrove river landscape and between-catchment dispersal of Rhizophora racemosa (Rhizophoraceae). Hydrobiologia 790, 93–108 (2017).
Google Scholar 
19.Cisneros-de la Cruz, D. J. et al. Short-distance barriers affect genetic variability of Rhizophora mangle in the Yucatan Peninsula. Ecol. Evolut. https://doi.org/10.1002/ece3.4575 (2018).Article 

Google Scholar 
20.Kennedy, J. P. et al. Postglacial expansion pathways of red mangrove Rhizophora mangle, in the Caribbean Basin and Florida. Am. J. Bot. 103, 260–276 (2016).PubMed 

Google Scholar 
21.Wee, A. K. S. et al. Vicariance and oceanic barriers drive contemporary genetic structure of widespread mangrove species Sonneratia alba. J. Sm Indo-West Pac. For. 8, 1–21 (2017).
Google Scholar 
22.Iuit, L. R. C. et al. Genetic structure and connectivity of the red mangrove at different geographic scales through a complex transverse hydrological system from freshwater to marine ecosystems. Diversity 12, 113 (2020).
Google Scholar 
23.Ngeve, M. N., Van der Stocken, T., Menemenlis, D., Koedam, N. & Triest, L. Hidden founders? Strong bottlenecks and fine-scale genetic structure in mangrove populations of the Cameroon Estuary complex. Hydrobiologia 803, 189–207 (2017).
Google Scholar 
24.Triest, L. et al. Channel network structure determines genetic connectivity of landward–seaward Avicennia marina populations in a tropical bay. Ecol. Evol. 10, 12059–12075 (2020).PubMed 
PubMed Central 

Google Scholar 
25.Canty, S. W. J., Fox, G., Rowntree, J. K. & Preziosi, R. F. Genetic structure of a remnant Acropora cervicornis population. Sci. Rep. 11, 1–9 (2021).
Google Scholar 
26.Kettenring, K. M., Mossman, B. N., Downard, R. & Mock, K. E. Fine-scale genetic diversity and landscape-scale genetic structuring in three foundational bulrush species: Implications for wetland revegetation. Restor. Ecol. 27, 408–420 (2019).
Google Scholar 
27.Mijangos, J. L., Pacioni, C., Spencer, P. B. S. & Craig, M. D. Contribution of genetics to ecological restoration. Mol. Ecol. 24, 22–37 (2015).PubMed 

Google Scholar 
28.Ross, M. S. et al. Early post-hurricane stand development in Fringe mangrove forests of contrasting productivity. Plant Ecol. 185, 283–297 (2006).
Google Scholar 
29.Kennedy, J. P. et al. Hurricanes overcome migration lag and shape intraspecific genetic variation beyond a poleward mangrove range limit. Mol. Ecol. https://doi.org/10.1111/mec.15513 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
30.NOAA. Historical Hurricane Tracks. https://coast.noaa.gov/hurricanes/ (National Hurricane Center | National Oceanic and Atmospheric Administration).31.Cahoon, D. R. et al. Mass tree mortality leads to mangrove peat collapse at Bay Islands, Honduras after Hurricane Mitch. J. Ecol. 91, 1093–1105 (2003).
Google Scholar 
32.Cannicci, S. et al. Faunal impact on vegetation structure and ecosystem function in mangrove forests: A review. Aquat. Bot. 89, 186–200 (2008).
Google Scholar 
33.Krauss, K. W. et al. Environmental drivers in mangrove establishment and early development: A review. Aquat. Bot. 89, 105–127 (2008).
Google Scholar 
34.Clarke, P. J. Effects of experimental canopy gaps on mangrove recruitment: Lack of habitat partitioning may explain stand dominance. J. Ecol. 92, 203–213 (2004).
Google Scholar 
35.Sandoval-Castro, E. et al. Post-glacial expansion and population genetic divergence of Mangrove species Avicennia germinans (L.) stearn and Rhizophora mangle L. along the Mexican coast. PLoS ONE 9, 113 (2014).
Google Scholar 
36.Rabinowitz, D. Dispersal properties of Mangrove propagules. Biotropica 10, 47–57 (1978).
Google Scholar 
37.Chollett, I. et al. A case for redefining the boundaries of the Mesoamerican reef ecoregion. Coral Reefs https://doi.org/10.1007/s00338-017-1595-4 (2017).Article 

Google Scholar 
38.Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, 1–10 (2015).
Google Scholar 
39.Jump, A. S. & Peñuelas, J. Genetic effects of chronic habitat fragmentation in a wind-pollinated tree. Proc. Natl. Acad. Sci. U.S.A. 103, 8096–8100 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
40.Jalonen, R., Hong, L. T., Lee, S. L., Loo, J. & Snook, L. Integrating genetic factors into management of tropical Asian production forests: A review of current knowledge. For. Ecol. Manag. 315, 191–201 (2014).
Google Scholar 
41.Pacioni, C., Trocini, S., Wayne, A. F., Rafferty, C. & Page, M. Integrating population genetics in an adaptive management framework to inform management strategies. Biodivers. Conserv. 29, 947–966 (2020).
Google Scholar 
42.Van der Stocken, T. et al. A general framework for propagule dispersal in mangroves. Biol. Rev. 94, 1547–1575 (2019).PubMed 

Google Scholar 
43.Bologna, P. A. X. et al. Lingering impacts of Hurricane Hugo on Rhizophora mangle (Red Mangrove) population genetics on St. John, USVI. Diversity 11, 1–14 (2019).
Google Scholar 
44.Cerón-Souza, I., Bermingham, E., McMillan, W. O. & Jones, F. A. Comparative genetic structure of two mangrove species in Caribbean and Pacific estuaries of Panama. BMC Evol. Biol. 12, 205 (2012).PubMed 
PubMed Central 

Google Scholar 
45.Núñez-Farfán, J. et al. Genetic divergence among Mexican populations of red mangrove (Rhizophora mangle): Geographic and historic effects. Evol. Ecol. Res. 4, 1049–1064 (2002).
Google Scholar 
46.Coleman, M. A. et al. Restore or redefine: Future trajectories for restoration. Front. Mar. Sci. 7, 1–12 (2020).
Google Scholar 
47.Breed, M. F. et al. Priority actions to improve provenance decision-making. Bioscience 68, 510–516 (2018).
Google Scholar 
48.Breed, M. F. et al. The potential of genomics for restoring ecosystems and biodiversity. Nat. Rev. Genet. 20, 615–628 (2019).CAS 
PubMed 

Google Scholar 
49.Kandil, F. E., Grace, M. H., Seigler, D. S. & Cheeseman, J. M. Polyphenolics in Rhizophora mangle L. leaves and their changes during leaf development and senescence. Trees 18, 518–528 (2004).CAS 

Google Scholar 
50.Sahu, S. K., Thangaraj, M. & Kathiresan, K. DNA extraction protocol for plants with high levels of secondary metabolites and polysaccharides without using liquid nitrogen and phenol. ISRN Mol. Biol. 2012, 1–6 (2012).
Google Scholar 
51.Wang, S., Meyer, E., Mckay, J. K. & Matz, M. V. 2b-RAD: A simple and flexible method for genome-wide genotyping. Nat. Methods 9, 808–810 (2012).CAS 
PubMed 

Google Scholar 
52.Guo, Y. et al. An improved 2b-RAD approach (I2b-RAD) offering genotyping tested by a rice (Oryza sativa L.) F2 population. BMC Genomics 15, 1–13 (2014).CAS 

Google Scholar 
53.Eaton, D. A. R. & Overcast, I. ipyrad: Interactive assembly and analysis of RADseq datasets. Bioinformatics 36, 2592–2594 (2020).CAS 
PubMed 

Google Scholar 
54.Xu, S. et al. The origin, diversification and adaptation of a major mangrove clade (Rhizophoreae) revealed by whole-genome sequencing. Natl. Sci. Rev. 4, 721–734 (2017).CAS 
PubMed 

Google Scholar 
55.Marandel, F. et al. Estimating effective population size using RADseq: Effects of SNP selection and sample size. Ecol. Evol. 10, 1929–1937 (2019).
Google Scholar 
56.Team, R. C. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021).57.Jombart, T. & Ahmed, I. adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).58.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 
59.Rousset, F. GENEPOP’007: A complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 

Google Scholar 
60.Garnier-Géré, P. & Chikhi, L. Population subdivision, Hardy-Weinberg equilibrium and the Wahlund effect. Els. https://doi.org/10.1002/9780470015902.a0005446.pub3 (2013).Article 

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

Google Scholar 
62.Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332 (2009).PubMed 
PubMed Central 

Google Scholar 
63.Earl, D. A. & vonHoldt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).64.Pritchard, J. K. & Wen, W. Documentation for Structure Software: Version 2.2. http://pritch.bsd.uchicago.edu (2002).65.Vähä, J. P., Erkinaro, J., Niemelä, E. & Primmer, C. R. Life-history and habitat features influence the within-river genetic structure of Atlantic salmon. Mol. Ecol. 16, 2638–2654 (2007).PubMed 

Google Scholar 
66.Caliński, T. & Harabasz, J. A dendrite method for cluster analysis. Commun. Stat. 3, 1–27 (1974).MathSciNet 
MATH 

Google Scholar 
67.Meirmans, P. G. genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids. Mol. Ecol. Resour. 20, 1126–1131 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research–An update. Bioinformatics 28, 2537–2539 (2012).69.Smouse, P. E. & Peakall, R. Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity 82, 561–573 (1999).PubMed 

Google Scholar 
70.Peakall, R., Ruibal, M. & Lindenmayer, D. B. Spatial autocorrelation analysis offers new insights into gene flow in the Australian bush rat, Rattus fuscipes. Evolution 57, 1182–1195 (2003).PubMed 

Google Scholar  More

1.Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).CAS 

Google Scholar 
2.Arrhenius, S. On the influence of carbonic acid in the air upon the temperature of the ground. London, Edinburgh, Dublin Phil. Mag. J. Sci 41, 237–276 (1896).CAS 

Google Scholar 
3.Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G. & Nemani, R. R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).CAS 

Google Scholar 
4.Bhatt, U. S. et al. Circumpolar Arctic tundra vegetation change is linked to sea ice decline. Earth Intract 14, 1–20 (2010).
Google Scholar 
5.Francis, J. A. & Vavrus, S. J. Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophys. Res. Lett. 39, L06801 (2012).
Google Scholar 
6.Cohen, J. et al. Recent Arctic amplification and extreme mid-latitude weather. Nat. Geosci. 7, 627–637 (2014).CAS 

Google Scholar 
7.Kim, J.-S. et al. Reduced North American terrestrial primary productivity linked to anomalous Arctic warming. Nat. Geosci. 10, 572–576 (2017).CAS 

Google Scholar 
8.Kug, J. S. et al. Two distinct influences of Arctic warming on cold winters over North America and East Asia. Nat. Geosci. 8, 759–762 (2015).CAS 

Google Scholar 
9.Jeong, S. J., Medvigy, D., Shevliakova, E. & Malyshev, S. Uncertainties in terrestrial carbon budgets related to spring phenology. J. Geophys. Res. 117, G01030 (2012).
Google Scholar 
10.Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).
Google Scholar 
11.Piao, S. L. et al. The carbon budget of terrestrial ecosystems in East Asia over the last two decades. Biogeosciences 9, 3571–3586 (2012).CAS 

Google Scholar 
12.Mori, M., Watanabe, M., Shiogama, H., Inoue, J. & Kimoto, M. Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades. Nat. Geosci. 7, 869–873 (2014).CAS 

Google Scholar 
13.Takaya, K. & Nakamura, H. Mechanisms of intraseasonal amplification of the cold Siberian high. J. Atmos. Sci 62, 4423–4440 (2005).
Google Scholar 
14.Honda, M., Inoue, J. & Yamane, S. Influence of low Arctic sea-ice minima on anomalously cold Eurasian winters. Geophys. Res. Lett. 36, L08707 (2009).
Google Scholar 
15.Piao, S. L. et al. Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. J. Geophys. Res. 108, D144401 (2003).
Google Scholar 
16.Hua, W. et al. Observational quantification of climatic and human influences on vegetation greening in China. Remote Sens 9, 425 (2017).
Google Scholar 
17.Zhou, B., Gu, L., Ding, Y. & Shao, L. The great 2008 Chinese ice storm: its socioeconomic–ecological impact and sustainability lessons learned. Bull. Am. Meteorol. Soc. 92, 47–60 (2011).
Google Scholar 
18.Shao, Q., Huang, L., Liu, J., Kuang, W. & Li, J. Analysis of forest damage caused by the snow and ice chaos along a transect across southern China in spring 2008. J. Geogr. Sci. 21, 219–234 (2011).
Google Scholar 
19.Wang, X., Huang, S., Li, J., Zhou, G. & Shi, L. Sprouting response of an evergreen broad‐leaved forest to a 2008 winter storm in Nanling Mountains, southern China. Ecosphere 7, e01395 (2016).
Google Scholar 
20.Woodward, F. I. & Williams, B. G. Climate and plant distribution at global and local scales. Vegetatio 69, 189–197 (1987).
Google Scholar 
21.Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol 12, 1969–1976 (2006).
Google Scholar 
22.Piao, S. L., Fang, J. Y., Zhou, L. M., Ciais, P. & Zhu, B. Variations in satellite-derived phenology in China’s temperate vegetation. Glob. Change Biol 12, 672–685 (2006).
Google Scholar 
23.Cook, B. I., Wolkovich, E. M. & Parmesan, C. Divergent responses to spring and winter warming drive community level flowering trends. Proc. Natl Acad. Sci. USA 109, 9000–9005 (2012).CAS 

Google Scholar 
24.Smith, B., Prentice, I. C. & Sykes, M. T. Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob. Ecol. Biogeogr 10, 621–637 (2001).
Google Scholar 
25.Zhu, D. et al. Improving the dynamics of Northern Hemisphere high-latitude vegetation in the ORCHIDEE ecosystem model. Geosci. Model Dev. 8, 2263–2283 (2015).
Google Scholar 
26.Peano, D. et al. Plant phenology evaluation of CRESCENDO land surface models – Part 1: Start and end of the growing season. Biogeosciences 18, 2405–2428 (2021).
Google Scholar 
27.Zeng, N., Mariotti, A. & Wetzel, P. Terrestrial mechanisms of interannual CO2 variability. Glob. Biogeochem. Cycles 19, GB1016 (2005).
Google Scholar 
28.Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst 11, 4245–4287 (2019).
Google Scholar 
29.White, M. A., Thornton, P. E. & Running, S. W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob. Biogeochem. Cycles 11, 217–234 (1997).CAS 

Google Scholar 
30.Chen, X. Q., Wang, L. X. & Inouye, D. Delayed response of spring phenology to global warming in subtropics and tropics. Agric. For. Meteorol. 234–235, 222–235 (2017).
Google Scholar 
31.Aono, Y. & Kazui, K. Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century. Int. J. Climatol. 914, 905–914 (2008).
Google Scholar 
32.Pearse, W. D., Davis, C. C., Inouye, D. W., Primack, R. B. & Davies, T. J. A statistical estimator for determining the limits of contemporary and historic phenology. Nat. Ecol. Evol 1, 1876–1882 (2017).
Google Scholar 
33.Jang, Y. S., Kug, J. S. & Kim, B. M. How well do current climate models simulate the linkage between Arctic warming and extratropical cold winters? Clim. Dyn. 53, 4005–4018 (2019).
Google Scholar 
34.Park, H. & Jeong, S. J. Leaf area index in Earth system models: how the key variable of vegetation seasonality works in climate projections. Environ. Res. Lett. 16, 034027 (2021).
Google Scholar 
35.Alexeev, V. A., Esau, I. N., Polyakov, I. V., Byam, S. J. & Sorokina, S. Vertical structure of recent Arctic warming from observed data and reanalysis products. Climatic Change 111, 215–239 (2011).
Google Scholar 
36.Hänninen, H. Climate warming and the risk of frost damage to boreal forest trees: identification of critical ecophysiological traits. Tree Physiol 26, 889–898 (2006).
Google Scholar 
37.Augspurger, C. K. Spring 2007 warmth and frost: phenology, damage and refoliation in a temperate deciduous forest. Funct. Ecol. 23, 1031–1039 (2009).
Google Scholar 
38.Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 426 (2018).
Google Scholar 
39.Ichii, K. et al. New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression. J. Geophys. Res. Biogeosci. 122, 767–795 (2017).CAS 

Google Scholar 
40.Li, X. & Xiao, J. A global, 0.05‐degree product of solar‐induced chlorophyll fluorescence derived from OCO‐2, MODIS, and reanalysis data. Remote Sens 11, 517 (2019).
Google Scholar 
41.Cowtan, K. & Way, R. G. Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Q. J. R. Meteorol. Soc. 140, 1935–1944 (2014).
Google Scholar 
42.Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
Google Scholar 
43.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
Google Scholar 
44.Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI) 3g and fraction of photosynthetically active radiation (FPAR) 3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5, 927–948 (2013).
Google Scholar 
45.Bontemps, S. et al. Consistent global land cover maps for climate modeling communities: current achievements of the ESA’s land cover CCI. In ESA Living Planet Symp. 2013 CCI-4 (ESA, 2013).46.Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
Google Scholar 
47.O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).
Google Scholar 
48.Kim, H. Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1) [Data set]. Data Integration and Analysis System (DIAS), https://doi.org/10.20783/DIAS.501 (2017).49.Zheng, F., Li, J., Ding, R. & Feng, J. Cross-Seasonal Influence of the SAM on Southern Hemisphere Extratropical SST and its Relationship with Meridional Circulation in CMIP5 models. Int. J. Climatol. 38, 1499–1519 (2018).
Google Scholar 
50.Livezey, R. E. & Chen, W. Y. Statistical field significance and its determination by Monte Carlo techniques. Month. Weath. Rev 111, 46–59 (1983).
Google Scholar 
51.Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).CAS 

Google Scholar 
52.Kim, J. S., Kug, J. S. & Jeong, S. J. Intensification of terrestrial carbon cycle related to El Nino-Southern Oscillation under greenhouse warming. Nat. Commun. 8, 1674 (2017).
Google Scholar  More

1.Armbrust EV. The life of diatoms in the world’s oceans. Nature. 2009;459:185–92.CAS 
PubMed 

Google Scholar 
2.Bowler C, Allen AE, Badger JH, Grimwood J, Jabbari K, Kuo A, et al. The Phaeo- dactylum genome reveals the evolutionary history of diatom genomes. Nature. 2008;456:239–44.CAS 
PubMed 

Google Scholar 
3.Amin SA, Parker MS, Armbrust EF. Interactions between Diatoms and Bacteria. Microbiol Mol Biol Rev. 2012;76:667–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Cirri E, Pohnert G. Algae- bacteria interactions that balance the planktonic microbiome. New Phytologist. 2019;223:100–6.
Google Scholar 
5.Mühlenbruch M, Grossart H, Eigemann F, Voss M. Mini‐review: Phytoplankton‐ derived polysaccharides in the marine environment and their interactions with heterotrophic bacteria. Environ Microbiol. 2018;20:2671–85.PubMed 

Google Scholar 
6.Koedooder C, Stock W, Willems A, Mangelinckx S, de Troch M, Vyverman W, et al. Diatom-bacteria interactions modulate the composition and productivity of benthic diatom biofilms. Front Microbiol. 2019;10:1255.PubMed 
PubMed Central 

Google Scholar 
7.Teeling H, Fuchs BM, Bennke CM, Krüger K, Chafee M, Kappelmann L, et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. eLife. 2016;5:e11888.PubMed 
PubMed Central 

Google Scholar 
8.von Scheibner M, Sommer U, Jürgens K. Tight coupling of glaciecola spp. and diatoms during cold-water phytoplankton spring blooms. Front Microbiol. 2017;8:27.
Google Scholar 
9.Zhang H, Hou F, Xie W, Wang K, Zhou X, Zhang D, et al. Interaction and assembly processes of abundant and rare microbial communities during a diatom bloom process. Environ Microbiol. 2020;22:1707–19.CAS 
PubMed 

Google Scholar 
10.Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature. 2015;522:98–101.CAS 
PubMed 

Google Scholar 
11.Bigalke A, Pohnert G. Algicidal bacteria trigger contrasting responses in model diatom communities of different composition. MicrobiologyOpen. 2019;8:e00818.PubMed 
PubMed Central 

Google Scholar 
12.Meyer N, Pohnert G. Isolate-specific resistance to the algicidal bacterium Kordia algicida in the diatom Chaetoceros genus. Botanica Marina. 2019;62:527–35.CAS 

Google Scholar 
13.Sison-Mangus MP, Jiang S, Tran KN, Kudela RM. Host-specific adaptation governs the interaction of the marine diatom, Pseudo-nitzschia and their microbiota. ISME J. 2014;8:63–76. https://doi.org/10.1038/ismej.2013.138CAS 
Article 
PubMed 

Google Scholar 
14.Stock W, Blommaert L, de Troch M, Mangelinckx S, Willems A, Vyverman W, et al. Host specificity in diatom–bacteria interactions alleviates antagonistic effects. FEMS Microbiol Ecol. 2019;95:fiz171.CAS 
PubMed 

Google Scholar 
15.Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev. 2013;77:342–56.PubMed 
PubMed Central 

Google Scholar 
16.Ahern OM, Whittaker KA, Williams TC, Hunt DE, Rynearson TA. Host genotype structures the microbiome of a globally dispersed marine phytoplankton. Proc Natl Acad Sci. 2021;118:e2105207118.CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Vega NM, Gore J. Stochastic assembly produces heterogeneous communities in the Caenorhabditis elegans intestine. PLoS Biol. 2017;15:e2000633.PubMed 
PubMed Central 

Google Scholar 
18.Lazzaro BP, Fox GM. Host–microbe interactions: winning the colonization lottery. Curr Biol. 2017;27:R642–R644.CAS 
PubMed 

Google Scholar 
19.Burke C, Steinberg P, Rusch D, Kjelleberg S, Thomas T. Bacterial community assembly based on functional genes rather than species. Proc Natl Acad Sci. 2011;108:14288–93.CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Foster KR, Schluter J, Coyte KZ, Rakoff-nahoum S. The evolution of the host microbiome as an ecosystem on a leash. Nature. 2017;548:43–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Shibl AA, Isaac A, Ochsenkühn MA, Cárdenas A, Fei C, Behringer G, et al. Diatom modulation of select bacteria through use of two unique secondary metabolites. Proc Natl Acad Sci. 2020;117:27445–55.CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Zhou J, Ning D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:e00002–17.PubMed 
PubMed Central 

Google Scholar 
23.Behringer G, Ochsenkühn MA, Fei C, Fanning J, Koester JA, Amin SA. Bacterial communities of diatoms display strong conservation across strains and time. Front Microbiol. 2018;9:1–15.
Google Scholar 
24.Crenn K, Duffieux D, Jeanthon C. Bacterial Epibiotic Communities of Ubiquitous and Abundant Marine Diatoms Are Distinct in Short- and Long-Term Associa- tions. Front Microbiol. 2018;9:1–12.
Google Scholar 
25.Grossart HP, Levold F, Allgaier M, Simon M, Brinkhoff T. Marine diatom species harbour distinct bacterial communities. Environ Microbiol. 2005;7:860–73.CAS 
PubMed 

Google Scholar 
26.Guannel ML, Horner-Devine MC, Rocap G. Bacterial community composition differs with species and toxigenicity of the diatom Pseudo-nitzschia. Aquatic Microbial Ecol. 2011;64:117–33.
Google Scholar 
27.Ajani PA, Kahlke T, Siboni N, Carney R, Murray SA, Seymour JR. The microbiome of the cosmopolitan diatom Leptocylindrus reveals significant spatial and temporal variability. Front Microbiol. 2018;9:1–12.
Google Scholar 
28.Kaczmarska I, Ehrman JM, Bates SS, Green DH, Léger C, Harris J. Diversity and distribution of epibiotic bacteria on Pseudo-nitzschia multiseries (Bacillar- iophyceae) in culture, and comparison with those on diatoms in native seawater. Harmful Algae. 2005;4:725–41.
Google Scholar 
29.Sapp M, Wichels A, Gerdts G. Impacts of cultivation of marine diatoms on the associated bacterial community. Appl Environ Microbiol. 2007;73:3117–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Mönnich J, Tebben J, Bergemann J, Case R, Wohlrab S, Harder T. Niche-based assembly of bacterial consortia on the diatom Thalassiosira rotula is stable and reproducible. ISME J. 2020;14:1614–25.PubMed 
PubMed Central 

Google Scholar 
31.Baker LJ, Kemp PF. Exploring bacteria-diatom associations using single-cell whole genome amplification. Aquatic Microbial Ecol. 2014;72:73–88.
Google Scholar 
32.Candela M, Biagi E, Maccaferri S, Turroni S, Brigidi P. Intestinal microbiota is a plastic factor responding to environmental changes. Trends Microbiol. 2012;20:385–91.CAS 
PubMed 

Google Scholar 
33.Goh C, Vallejos DFV, Nicotra AB, Mathesius U. The Impact of Beneficial Plant-Associated Microbes on Plant Phenotypic Plasticity. J Chem Ecol. 2013;826–39.34.Vanormelingen P, Vanelslander B, Sato S, Gillard J, Trobajo R, Sabbe K, et al. Heterothallic sexual reproduction in the model diatom Cylindrotheca. Eur J Phycol. 2013;48:93–105.
Google Scholar 
35.Li H, Yang G, Sun Y, Wu S, Zhang X. Cylindrotheca closterium is a species complex as was evidenced by the variations of rbcL gene and SSU rDNA. J Ocean Univer China. 2007;6:167–74.
Google Scholar 
36.Stock W, Vanelslander B, Rüdiger F, Sabbe K, Vyverman W, Karsten U. Thermal niche differentiation in the benthic diatom Cylindrotheca closterium (Bacillar- iophyceae) complex. Front Microbiol. 2019;10:1395.PubMed 
PubMed Central 

Google Scholar 
37.de Brouwer JFC, Wolfstein K, Ruddy GK, Jones TER, Stal LJ. Biogenic stabilization of intertidal sediments: the importance of extracellular polymeric substances produced by benthic diatoms. Micro Ecol. 2005;49:501–12.CAS 

Google Scholar 
38.Najdek M, Blažina M, Djakovac T, Kraus R. The role of the diatom Cylindrotheca closterium in a mucilage event in the northern Adriatic Sea: Coupling with high salinity water intrusions. J Plankton Res. 2005;27:851–62.
Google Scholar 
39.Eaton Jw, Moss B. The estimation of numbers and pigment content in epipelic algal populations. Limnol Oceanogr. 1966;11:584–95.
Google Scholar 
40.Anderson RA. (editor). Algal Culturing Techniques. Elsevier Academic Press; 2005.41.Muyzer G, de Waal EC, Uitterlinden AG. Profiling of complex microbial popula- tions by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol. 1993;59:695–700.CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Tytgat B, Verleyen E, Sweetlove M, D’hondt S, Clercx P, van Ranst E, et al. Bacterial community composition in relation to bedrock type and macrobiota in soils from the Sør Rondane Mountains, East Antarctica. FEMS Microbiol Ecol. 2016;92.43.D’Hondt AS, Stock W, Blommaert L, Moens T, Sabbe K. Nematodes stimulate biomass accumulation in a multispecies diatom biofilm. Marine Environ Res. 2018;140:78–89.
Google Scholar 
44.Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: A fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30:614–20.CAS 
PubMed 

Google Scholar 
45.Edgar RC. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
PubMed 

Google Scholar 
46.Murali A, Bhargava A, Wright ES. IDTAXA: a novel approach for accurate taxo- nomic classification of microbiome sequences. Microbiome. 2018;6:1–14.
Google Scholar 
47.Wright ES. Using DECIPHER v2. 0 to analyze big biological sequence data in R. R Journal. 2016;8.48.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–D596.PubMed 
PubMed Central 

Google Scholar 
49.Nawrocki EP. Structural RNA Homology Search and Alignment using Covariance Models. Washington University in St. Louis; 2009.50.Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35:518–22.CAS 
PubMed 

Google Scholar 
51.Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.CAS 
PubMed 

Google Scholar 
52.Vanelslander B, Créach V, Vanormelingen P, Ernst A, Chepurnov VA, Sahan E, et al. Ecological differentiation between sympatric pseudocryptic species in the estuarine benthic diatom Navicula phyllepta (Bacillariophyceae). J Phycol. 2009;45:1278–89.CAS 
PubMed 

Google Scholar 
53.Borcard D, Legendre P. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol Modelling. 2002;153:51–68.
Google Scholar 
54.Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.CAS 
PubMed 

Google Scholar 
55.Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.CAS 
PubMed 
PubMed Central 

Google Scholar 
56.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Green- genes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.CAS 
PubMed 
PubMed Central 

Google Scholar 
57.McDonald D, Price MN, Goodrich J, Nawrocki EP, Desantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolu- tionary analyses of bacteria and archaea. ISME J. 2012;6:610–8.CAS 
PubMed 

Google Scholar 
58.Stone L, Roberts A. The checkerboard score and species distributions. Oecologia. 1990;85:74–9.PubMed 

Google Scholar 
59.Sloan WT, Lunn M, Woodcock S, Head IM, Nee S, Curtis TP. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ Microbiol. 2006;8:732–40.PubMed 

Google Scholar 
60.Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, et al. Con- tribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 2016;10:655–64.CAS 
PubMed 

Google Scholar 
61.Hill MO. Diversity and evenness: a unifying notation and its consequences. Ecology. 1973;54:427–32.
Google Scholar 
62.Chiarello M, Auguet JC, Bettarel Y, Bouvier C, Claverie T, Graham NAJ, et al. Skin microbiome of coral reef fish is highly variable and driven by host phylogeny and diet. Microbiome. 2018;6:1–14.
Google Scholar 
63.Easson CG, Thacker RW. Phylogenetic signal in the community structure of host- specific microbiomes of tropical marine sponges. Front Microbiol. 2014;5:1–11.
Google Scholar 
64.Swierts T, Cleary DFR, de Voogd NJ. Prokaryotic communities of Indo-Pacific giant barrel sponges are more strongly influenced by geography than host phylogeny. FEMS Microbiol Ecol. 2018;94:1–12.
Google Scholar 
65.Mazel F, Davis KM, Loudon A, Kwong WK. Is Host Filtering the Main Driver of Phylosymbiosis across the Tree of Life. Msystems. 2018;3:1–15.
Google Scholar 
66.Fu H, Uchimiya M, Gore J, Moran MA. Ecological drivers of bacterial community assembly in synthetic phycospheres. Proc Natl Acad Sci. 2020;117.7:3656–62.
Google Scholar 
67.Taylor JD, Cunliffe M. Coastal bacterioplankton community response to diatom- derived polysaccharide microgels. Environ Microbiol Rep. 2017;9:151–7.CAS 
PubMed 

Google Scholar 
68.Becker JW, Berube PM, Follett CL, Waterbury JB, Chisholm SW, Delong EF, et al. Closely related phytoplankton species produce similar suites of dissolved organic matter. Front Microbiol. 2014;5:1–14.CAS 

Google Scholar 
69.Jackrel SL, Yang JW, Schmidt KC, Denef VJ. Host specificity of microbiome assembly and its fitness effects in phytoplankton. ISME J. 2021;15:774–88.PubMed 

Google Scholar 
70.Eigemann F, Hilt S, Salka I, Grossart HP. Bacterial community composition asso- ciated with freshwater algae: Species specificity vs. dependency on environ- mental conditions and source community. FEMS Microbiol Ecol. 2013;83:650–63.CAS 
PubMed 

Google Scholar 
71.Barreto Filho MM, Walker M, Ashworth MP, Morris JJ. Structure and Long-Term Stability of the Microbiome in Diverse Diatom Cultures. Microbiol Spectr. 2021;9:e00269–21.PubMed Central 

Google Scholar 
72.Horner-Devine MC, Bohannan BJM. Phylogenetic clustering and overdispersion in bacterial communities. Ecology. 2006;87:S100–8.PubMed 

Google Scholar 
73.Zelezniak A, Andrejev S, Ponomarova O, Mende DR, Bork P, Patil KR. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc Natl Acad Sci. 2015;112:6449–54.CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Goldford JE, Lu N, Bajić D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361:469–74.CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Li Y, Shipley B, Price JN, Dantas V, de L, Tamme R, et al. Habitat filtering deter- mines the functional niche occupancy of plant communities worldwide. J Ecol. 2018;106:1001–9.
Google Scholar 
76.Louca S, Jacques SMS, Pires APF, Leal JS, Srivastava DS, Parfrey LW, et al. High taxonomic variability despite stable functional structure across microbial com- munities. Nat Ecol Evol. 2016;1:0015.
Google Scholar 
77.Seymour JR, Amin SA, Raina JB, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships. Nat Microbiol. 2017;2:1–12.
Google Scholar 
78.Geng H, Belas R. Molecular mechanisms underlying Roseobacter–phytoplankton symbioses. Curr Opinion Biotechnol. 2010;21:332–8.CAS 

Google Scholar 
79.Christie-Oleza JA, Sousoni D, Lloyd M, Armengaud J, Scanlan DJ. Nutrient recy- cling facilitates long-term stability of marine microbial phototroph-heterotroph interactions. Nat Microbiol. 2017;2:1–10.
Google Scholar 
80.Edmundson SJ, Huesemann MH. The dark side of algae cultivation: characterizing night biomass loss in three photosynthetic algae, Chlorella sorokiniana, Nanno- chloropsis salina and Picochlorum sp. Algal Res. 2015;12:470–6.
Google Scholar 
81.Grossart H-P. Interactions between marine bacteria and axenic various conditions in the lab. Aquatic Microbial Ecol. 1999;19:1–11.
Google Scholar  More

Improving our understanding of “viral tagging” flow cytometric signalsVT is a deceptively simple idea whereby a mixture of natural viruses are labeled with a DNA-binding fluorescent dye and ‘bait’ hosts infected by these stained viruses can be detected with flow cytometry via the fluorescent shift of “viral-tagged cells” [38, 39] (Fig. 1A, B). These viral-tagged cells can then be sorted, and the viral DNA separated using isotopic fractionation (the DNA of the cultured host is pre-labeled with “heavy” DNA) to access the metagenomes of the viruses that were experimentally determined to have infected these cell types. However, in practice, VT has been only minimally adopted by the community [43], presumably because it requires costly equipment (a high-performance flow sorter) and diverse technical expertise (flow cytometry, phage biology, and bioinformatics), while lacking sufficient benchmarking. To the latter, we sought to use a cultured phage-host model system (Pseudoalteromonas strain H71, hereafter H71, and its specific myophage PSA-HM1, hereafter HM1) to systematically assess the impact of various multiplicities of infection (MOIs; the ratio of the number of virus particles to the number of target cells, [48]) on the resultant VT signals. Further, we sought to augment VT to add an “and grow” capability whereby scalable single-virus cultivation, characterization, and sequencing could be enabled (Fig. 1C).Fig. 1: Overview of viral tagging, and the variant developed here—viral tag and grow.A Viruses are labeled with a green fluorescent dye and then mixed with potential host bacteria. B Fluorescence detection of individual cells with fluorescently-labeled viruses (FLVs) by flow cytometer. The flow cytometry plot (side scatter or forward scatter versus green fluorescence) shows the expected locations of FLV-tagged (VTs) and nontagged cells (NTs), which are flow-cytometrically green positive and negative, respectively. C Single-cell sorting of VTs is followed by subsequent amplification of infectious viruses. Single VTs are sorted into a 96-well plate that contains host culture. Culture growth is monitored by measuring optical density (OD) over time. A decrease in the OD curve from VT-containing wells (relative to the phage-negative control) indicates cell lysis by progeny viruses produced from a single isolated VT cell.Full size imageTo gain a better understanding of the biology behind VT signatures, we examined how H71 interacts with HM1, a phage specific for this host, and HS8, a phage that does not adsorb to this host – both assayed via flow cytometry and microscopy (for details, see Methods and online protocol, https://www.protocols.io/view/viral-tagging-and-grow-a-scalable-approach-to-captbwutpewn?form=MY01SV&OCID=MY01SV). Briefly, phages were stained with SybrGold (fluoresces green upon blue-light excitation) and for microscopy, H71 cells were stained with DAPI (fluoresces blue upon blue-light excitation, 4′,6-diamidino-2-phenylindole), as previously described [39, 49]. Replicate cultures of stained cells were then mixed with fluorescently-labeled phages (either HM1 or HS8 in each treatment) at infective MOIs = 1, 2, and 4, then these infections were incubated for 10 min, and processed (centrifuged and resuspended; see Methods for details) three times to remove free phages (see Methods for details). For microscopy, the relative fraction of virus-tagged (VTs) and nontagged cells (NTs) was measured from the available cells up to ~500 cells for each sample. For flow cytometry, cell detection was optimized to minimize background noise [50], and negative controls consisted of stained and washed sheath buffer and filtered Q water samples, as previously described [39].Overall, the resulting VT experiments were robust and informative. First, our cell-only optimizations resulted in controls that were impeccably clean (see representative cytograms and gating counts in Fig. 2A–C and  Supplementary Fig. S1). Second, in “virus addition” treatments, the resultant VT signal was distinct for specific (HM1) versus nonspecific (HS8) phages. Specifically, adding HM1 at MOIs = 1, 2, and 4 corresponded to VT population shifts of an average of 25%, 50%, and 80%, respectively, while NT populations proportionally decreased (Fig. 2D, E, linear regression r2 = 0.98). In contrast, for all tested MOIs of the nonspecific HS8 phage, the shifted populations were negligible (range: ~1.0–1.9%) and uncorrelated (Supplementary Fig. S2A, B; r2 = 0.14).Fig. 2: Flow cytometric and microscopic analyses of Pseudoalteromonas-phage associations.A Hierarchical gating for detection of Pseudoalteromonas strain H71 (hereafter, H71) and its subpopulations of viral tagged (VTs) and nontagged cells (NTs). A parent gate was drawn on H71 cells using FSC vs. SSC (Fig. S1) and represented in two types of contour and dot plots (left and right in the top of the gray box, respectively). From this gate, green-positive (VT) and -negative (NT) populations were sub-gated in the green fluorescence vs. SSC (right, dot plot) and quantified as percentage fractions of a parent population (bar charts in the gray box). B, C Flow cytometric plots of sheath buffer only (B) and stained/washed sheath buffer without phages (C) (see Methods and Fig. S1). D Flow cytometric detections for H71 cells (~106/ml) that were incubated with fluorescently-labeled specific phage HM1 at MOIs of 1, 2, and 4, respectively (from left to right). E Linear regression relationships between the MOIs (x-axis) and the percentages (Y-axis) of flow cytometric VT (green) and NT (black) populations for phage HM1 at MOIs of 1, 2, and 4, respectively. R-square values are represented. F DAPI (4′,6-diamidino-2-phenylindole, blue)-stained H71 cells were mixed with fluorescent phages HM1 (SybrGold, green) at MOIs of 1, 2, and 4, respectively (Methods for details). Above, the merged images of phage-host mixtures (Additional images are shown in Figs. S4–7). Below, an enlarged view of four regions selected from the above images. Interpretations of virus-tagged cells, nontagged cells, and “free” viruses are represented in the results and discussion and methods, respectively. Arrows point to phages found on the margin of bacterial cells. Scale bar, 2 µm. Microscopic observations for nonspecific phage HS8-H71 are shown in Fig. S8. G Correlation between the MOI (x-axis) and the microscopic fractions (y-axis) of VTs (green) and NTs (black) for phage HM1 at MOIs of 1, 2, and 4, respectively. R-square value is shown. H Impact of cell physiology on viral tagging signals. H71 cells (~106/ml) in the early log, late log, and stationary phase were infected by phage HM1 at MOIs of 1 (Left) and 4 (Right), respectively. Percentages of tagged populations were measured at the time point after fluorescently-labeled HM1 were inoculated for 20 min at various MOIs followed by centrifugation and resuspension to remove free viruses (see Methods for details). Each test was done in duplicate (error bars show standard deviations).Full size imageDespite observing a strong linear correlation between MOI and %VT for HM1, it was surprising that even at high MOIs = 1, 2, and 4, the resultant population shifts were 1.2- to 2.5-fold less than expected from theory alone based on Poisson distribution (see Supplementary Fig. S3). To investigate this, we used microscopy to inspect for virus clumping, positioning relative to cell surfaces, and background noise. These results revealed spot-like green signals of various sizes outside of host cells, which we interpreted as free viruses, and this was true even (a) at these higher MOIs, and (b) despite centrifugation to remove free viruses following incubation (see Methods; Fig. 2F and  Supplementary Figs. S4–7). We suspect these unincorporated SYBR-stained particles are viral aggregates, possibly due to host cell parts and/or debris in the lysate [51,52,53] or tangling of phage tails [54]. Prior work has shown that these and other mechanisms that decrease the accessibility of viral particles to host receptors could reduce observed infectious particles [48].Our third key observation in these experiments rested with an improved understanding of the ‘signal shift’ between VT and NT populations in the flow cytogram across varied MOIs. Again, comfortably, increasing the MOI pushed VT signals toward higher fluorescence, with NTs decreasing proportionally (Fig. 2F). We posited that such increased “VT” signal could result from multiple phages adsorbing per cell. Indeed, microscopy visualization of ~500 single cells per treatment revealed that the number of detectable phages per infected cell increased proportionally to the MOI (Fig. 2F, G and  Supplementary Figs. S4–6). For example, of the tagged cells, few (~14%) cells exhibited multiple phages adsorbed at an MOI = 1, whereas those numbers increased drastically at MOIs = 2 and 4, where most (~55% and 67%) tagged cells exhibited multiple adsorbed phages per cell. As a negative control, we examined VT signals for a nonspecific phage, and this revealed that virtually all of the 545 single cells that were examined were nontagged (99.3%) even at an MOI = 10 (Supplementary Fig. S7). Presumably, the remaining ~0.7% of cells that appeared to have a phage adsorbed represent promiscuous, reversible binding to nonhost cells as is known to occur in other phage model systems [39]. Mechanistically, multiple phages can bind to a single host cell. For example, under very high-titer infection conditions (e.g., MOI = 100) phages can distribute over an entire cell surface [55], presumably accessing broadly distributed receptors [56]. Prior VT work has demonstrated strong VT signals under very high MOI (e.g., MOI = 1000) conditions [43], though no optimization experiments were presented to understand these patterns and the false positives that would result from free phages coincidently sorted (see further discussion later).Finally, we re-evaluated the impact of cell physiology (e.g., early, middle, and late log phase host growth) and adsorption time (e.g., 20 min intervals from 0 to 120 min) on Pseudoalteromonas VT signals—and did so at two MOIs = 1 and 4, respectively (Fig. 2H). At both MOIs tested, growth phase was seen to impact the VT signals, with late log phase cells showing the highest fluorescent shift for VT cells in contrast to signals that were reduced in early log phase cells and nearly absent from stationary phase cells (Fig. 2H). This finding is consistent with our prior optimizations with Pseudoalteromonas phage-host model systems [39]. However, we observed that VT signals were optimal at 20 min after adsorption (see Methods) and, rather than stay high as we had previously observed, these experiments revealed that the VT signals were reduced by nearly half at subsequent time points. Though conflicting with our prior work [39], these current experiments employ hierarchical gating (Supplementary Fig. S1; see Methods), which we feel more appropriately quantify these patterns. This is because we interpret the signal reduction to be due to the lysis of first-adsorbed tagged cells and/or the injection of fluorescent DNA of the adsorbed virus(es) into cells as the latent period of phage HM1 for H71 cells under these conditions dictates [24]. Indeed, it has been reported that for phage lambda—E.coli system, the injection of fluorescent phage DNA followed by signal diffusion inside the cells decreased ~40% of the overall signal intensities of individual virus–host pairs [57].Together, though an extensive set of experiments, these findings are largely confirmatory with our prior work characterizing Pseudoalteromonas phages [39]. However, and critically, our prior work failed to rigorously investigate these phenomena with respect to their (i) flow cytogram population signatures, (ii) single-cell microscopy imaging, and (iii) hierarchically gated tagged-cell timing estimates. We hope that these additional clarifications here provide a better mechanistic understanding of VT signals, and encourage wider adoption of this promising high-throughput method to identify viruses that infect a particular host.Introducing VT and grow: VT coupled to plate-based cultivation assaysGiven this improved understanding of the VT signal, we next sought to expand VT to include an “and grow” capability to scalably capture and characterize viruses linked to hosts (conceptually presented in Fig. 1C). Pragmatically, this should also help resolve long-standing questions of (i) what fraction of VT cells lead to productive infections (i.e., does adsorption equal infection?, [45]), and (ii) whether sample processing (e.g., laser detection, sheath fluid growth inhibition [37, 58]) or cell density effects resulting from single-cell sorts [59, 60] would prohibit downstream growth assays.To this end, we used the Pseudoalteromonas-virus HM1 model system to optimize sorting and growth conditions. Specifically, we wondered how many cells from sorted populations would be required to observe lysis (both dynamically, and terminally) under various MOI conditions. To test this, viral-tagged cells (the “VT” treatment) or nontagged cells (the “NT” treatment) were sorted into individual wells of a 96-well plate containing growth medium; fresh host cells were added, and growth-lysis curves were established by measuring optical density (OD) over time (see Methods). Treatment variables included the number of cells sorted (n = 1, 3, or 9) and infection conditions (MOI = 1 or 4), while controls included (i) NT cells to control for false-positive culture lyses by free viruses coincidently sorted with target cells, and (ii) sorting process controls against host cell lysis and growth in plates consisting of wells containing cultures with and without phage HM1, respectively. For all experiments, cells were infected during late-exponential phase for 10 min, followed by dilution to halt further infection, and centrifugation to remove free viruses (see Methods, [41]).We first analyzed the reduced-titer MOI = 1 infection. When only single cells were sorted, the growth curves from those wells as compared to those of phage-free controls, showed that more than half (56%; 20/36) of the VT wells with detectably reduced OD, whereas only a single NT well (8%; 1/12) showed such a decrease (Fig. 3A). This low rate of false-positive culture lysis in NT wells suggests that in most of the VT wells, progeny phages produced from an isolated parent VT—not free viruses―infect and lyse the host culture (For more details, see the burst size distribution of sorted single VTs below). Presumably, the 16 VT wells that did not lyse were due to one of the following: (i) reduced viability of isolated VTs through multiple steps of sample preparation or sorting with high sheath pressure [37, 58], (ii) possible reversible virus adsorption from the VT cell prior to well capture, and/or (iii) mis-diagnoses due to the weak fluorescent shift of singly-VT cells as is a known challenge in fluorescence-based cell sorting [58, 61].Fig. 3: Evaluation of viral growth assay under various infection conditions.Two liquid cultures of Pseudoalteromonas strain H71 (105/ml) in the late-logarithmic growth phase were infected by specific phage HM1 at MOIs of 1 and 4, respectively. From each infected culture, varying numbers of tagged (VT) and nontagged (NT) cells were sorted into individual wells of a 96-well plate containing growth medium followed by the addition of fresh host cells (104 cells per well). Positive and negative controls (host culture with HM1 at an MOI of 0.1 and without HM1, respectively) were included in each plate (see Methods for details). From top to bottom, left to right in panels (A) MOI = 1 and (B) MOI = 4, respectively, pie charts depict the percentages of lysed (yellow) and nonlysed (gray) wells from the total wells containing the given numbers (n = 1, 3, and 9) of isolated VTs and NTs. Culture lysis for VT- and NT-containing wells was determined by comparing their growth curves (next to each pie chart, black lines) to those of negative (red) and positive controls (blue). The X-axis indicates the OD590nm and the Y-axis, the time in hours.Full size imageTo assess the MOI = 1 infections further, we evaluated the data for wells containing more than 1 cell sorted to each well. This revealed that sorting 3 or 9 cells improved the fraction of wells lysed in the VT treatments to 88 and 100%, respectively, but this came at the cost of increased false positives in the NT treatment (pie charts in Fig. 3A). The latter is likely due to the same challenges described above of differentiating the NT from VT populations when signal intensity was relatively low. Given the 96-well plate format, these experiments demonstrate the ability to follow growth kinetics for each well (time course OD figures in Fig. 3A). This revealed that single VT cell sorts had delayed lysis relative to the multiple-cell sorts and hints at the power such kinetics data could provide for scalably characterizing new en masse captured phage isolates from field samples. Stepping back, however, it is promising that the number of sorted cells per well, for both VT and NT wells, was linearly proportional to the percentages of lysed wells (r2 = 0.73 and 0.99), respectively (Supplementary Fig. S8). This suggests a robustness and repeatability for these experiments.Beyond the fraction of the VT and NT wells displaying clear lysis, the kinetics of lysis—particularly for single-cell sorts—can be a valuable first read-out for variability in virus infection dynamics. To assess this in our dataset, we examined the kinetics of OD readings through 20 h (growth-lysis curves in Fig. 3A). Focusing on the 36 wells containing a single VT cell, 20 lysed (reported above), but their lysis kinetics drastically differed—some wells showed stepwise decreases after early increases in OD and the others a very low or no increase followed by the curve recovery. Similar lysis patterns have been observed in other phage-host systems, where host culture growth depended on phage concentration, with suppression of host cells increasing with higher phage titers and vice versa [62, 63]. Our observation of the well-to-well variation in culture lysis is likely due to different progeny production from isolated VT per well, relating to the stochasticity of viral infection [37, 64,65,66,67]. However, the stochastic infection alone cannot explain such diverse lysis patterns, given the random nature of diffusion and contact of progeny particles from infected cells to neighboring susceptible cells in the fluid (i.e., the host culture) [68, 69]. Either biological or physical infection process, or both, could impact varied lysis pattern. Further experiments are required to test this hypothesis (e.g., single-cell burst size assay, [37]; see below).Finally, given that flow cytometric population separation was critical for optimizing lysis success and that simply sorting more cells comes at the cost of increased false-positive lysis, we next explored the impact of increasing the per-cell fluorescent VT signal with MOI = 4 infections. Indeed, sorting from these better-resolved populations improved our per-well lysis results as all of the VT wells lysed, and this was the case whether sorting 1, 3, or 9 cells per well (pie charts in Fig. 3B). For the NT wells, false positives were less problematic, but they did remain a minor problem as some wells (4–8%) lysed, and this increased in the multiple-cell sorted wells. Though VT and NT populations are likely better resolved, thereby reducing false-positive lysis in the NT wells from the MOI = 1 infections, presumably the higher MOI infections lead to free viruses being coincidently co-sorted in the sort droplets. Notably, the kinetic read-outs (growth-lysis curves in Fig. 3B) were relatively invariable, possibly suggesting that the much higher number of viruses-per-cell in these infections obscured virus-to-virus variability in life history traits [66, 67, 70].Together, these experiments provide strong baseline data for assessing the impact of VT signal quality, MOIs, and growth data and hint that the approach may also open up new windows into variation in trait space across virus isolates.New biology enabled by viral tag and grow: a window into “viral individuality”?A major challenge in viral ecology is scaling from the handful of viruses that might be well characterized to the millions of virus types in an average seawater or field sample. While diversity surveys have come a long way (e.g., hundreds of thousands of viruses in a single study [23]), the pragmatic challenges of taking physiological measurements across many viral isolates leaves modeling efforts with very little empirical data on virus life history traits, severely bottlenecking the viruses brought into predictive models [71]. Further, microbiologists have revealed that even among “clonal” isolates, there can be remarkable phenotypic heterogeneity, or “microbial individuality” [72,73,74]; does the same exist for viruses? Hints that there is such “virus individuality” among DNA viruses, including phages, are emerging with data demonstrating variability in single-cell burst size (progeny per infected cell), with up to ~100-fold differences and these differences attributed to stochastic events such as variation in starting points in cell size, growth stage, and resources [37, 64,65,66].Of particular interest in understanding ‘virus individuality’ are recent single-cell analyses developed for a Synechococcus phage-host model system that revealed a wide range of burst sizes (from 2 to 200 infective viruses/cell) within a laboratory clonal isolate [37]. Methodologically, this approach sorts cells—infected or not—into wells (e.g., of a 96-well plate) and follows their infection dynamics. This has the benefit of assessing a single cell’s growth-lysis curve in each well. However, a drawback is that experiments are more conveniently done at high MOI conditions (e.g., an MOI = 3 was used) to get larger numbers of wells lysing among the randomly sorted cells (see Methods). Increasing MOI will lead to more virus-containing and, therefore, lysing wells, subsequently greatly increasing the number of cells with multiple viruses attached such that it will confound measurements of lysis dynamics since they will be a function of both virus-to-virus ‘individuality’ and an unknown, but variable per-cell MOI [70, 75].Inspired by this latter work, we sought to improve such single-cell growth-lysis assays in ways that might leverage the scalability of VT + Grow. For these experiments, we wanted to reduce the MOI (to MOI = 0.5) since theory predicts that most (77%) of the infected cells would be singly infected (Poisson distribution), but keep it high enough to have a reasonably separated VT cell population (see Methods). After cells and viruses were mixed, individual VT cells were sorted into different wells containing growth medium, plates were incubated to allow lysis of the single sorted VT cell, and the number of plaques per well were determined by pour plate plaque assays (Fig. 4A; see Methods for details). This operationally single-cell burst size assay showed a wide range of infective viruses per cell (2 to 397, X-axis) from a total of 72 individual cells assessed (Y-axis) (on average = 100; Fig. 4B), with similar average population burst sizes of 110 ± 15 [24]. Though a clonal virus isolate, these findings suggest, just as seen for cyanophages [37], that stochastic events must dictate the specific burst size for any given interaction. However, unlike the prior work, it is unlikely that cells with multiple viruses adsorbed any of this signal since such events should be much rarer at an MOI = 0.5 instead of MOI = 3. This suggests that these stochastic events are of a biological nature, which we posit might mechanistically result from the timing of initial virus–host interactions and/or cell-to-cell or virus-to-virus variation in nonheritable traits such as per-cell nutrient stores. If we interpret such infected cell variability as ecologically relevant variation in “virocells” (sensu [13, 76, 77]), then these findings open a window into “virus individuality” via a more scalable and controllable characterization approach than previously available.Fig. 4: Distribution of virus burst sizes per single viral-tagged cell.A Schematic overview of single-cell assay for viral burst size determination by viral tagging and grow. In the latent period of infection, single viral-tagged cells (VTs) were sorted by flow cytometer from Pseudoalteromonas sp. H71 cells infected by phage HM1 at an MOI of 0.5 (see Methods for details). Following sorting single VTs into different wells of the 96-well plate containing growth medium (MSM), the plate was incubated to allow for viral progenies to release from infected cells. The number of viruses produced per VT was then determined by the number of plaques per poured plate using the traditional plaque assay. B Distribution of viral burst size from individual tagged cells. The number of progeny viruses (X-axis) per cell (Y-axis) are represented in bins of 20, with the exception of the first bin excluding single plaques. The number (n) of individual tagged cells assessed is represented at the top right corner.Full size imageLimitations and future development opportunities for VT and GrowThough these efforts provide a more robust foundation for broadening the use of VT related methods, there remain challenges. First, researchers must be aware that VT is not a simple method, and its success depends on instrument calibration and ultraclean sample processing to establish maximally separated VT and NT populations (see the link below for details on flow cytometric setup and optimization). Second, sorting purity, particularly in field applications, will be challenged by suboptimal VT flow cytometric signatures, e.g., mis-identification of NT cells. Though this can be overcome with very high MOI infections (e.g., 1000 viruses per cell, [43]), two issues remain: (i) the effective MOIs cannot be measured in field samples (and thus, unknown), and (ii) at such high MOIs, the experiments will suffer from coincident sorting of free viruses that will increase false positives. Another factor that could affect sorting purity is nonviral DNA in the environmental sample, whether it is associated with bacterial cells or not, which could be coincidently sorted. It is thus necessary to ensure that prior to any VT work, environmental samples are properly processed or treated for the removal of nonviral genes and other materials (e.g., filtration and/or centrifugation). Fortunately, the “and grow” approach added to VT provides an additional screening step whereby false-negatives and false positives can be discerned via growth-lysis monitoring. Further, the “and grow” component, a plate-based assay, enables faster and more scalable lysis screening (e.g., 96-well format) than the time- and labor-intensive traditional plaque assay [62, 63]. Third, viral aggregates that alter the effective MOI infection conditions could lead to confounding results when comparing results across laboratories. Here, we invite efforts to find and optimize approaches to reduce viral aggregates (e.g., detergents, sonication, syringe pumping), and until viral aggregates are eliminated, to microscopically examine the state of free viruses in new sample types, particularly for outlier results. Fourth, the methods remain dependent upon a cultivable host, and though VT has been applied to multiple heterotroph and cyanobacterial phage-host pairs [39], two big unknowns remain: (i) how will the “and grow” processing impact growth of these strains, and (ii) will non-marine model systems be amenable to these approaches. The in-depth optimizations presented here for a Pseudoalteromonas phage-host model system serve a foundation for understanding other target virus–host pairs. To this end, we suggest deep investigation for any new model systems being studied, and as information becomes more broadly available, invite a community-standards and benchmarking approach to determine ideal setups for infectious conditions (e.g., growth curve, MOIs) and instrumental parameters. To facilitate this, we have established a VT forum on the Viral Ecology VERVE Net living protocols at protocols.io (below) as a way to empower and broadly engage researchers interested in these new methods and the many variants that could blossom from this base. Specifically, the details for viral and bacterial sample processing can be found at https://www.protocols.io/view/viral-tagging-and-grow-a-scalable-approach-to-capt-bwutpewn?form=MY01SV&OCID=MY01SV and for flow cytometric optimization at https://www.protocols.io/view/bd-influx-cell-sorter-start-up-and-shut-427down-for-v-bv8cn9sw. Both protocols provide additional notes for critical steps to improve methodological reproducibility and/or sensitivity, and particularly for the latter, it will be updated regularly to better optimize, calibrate, and standardize a flow cytometer. More