Ecology

1.Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).CAS 
Article 

Google Scholar 
2.Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).CAS 
Article 

Google Scholar 
3.Veldkamp, E., Schmidt, M., Powers, J. S. & Corre, M. D. Deforestation and reforestation impacts on soils in the tropics. Nat. Rev. Earth Environ. 1, 590–605 (2020).Article 

Google Scholar 
4.Ceccherini, G. et al. Abrupt increase in harvested forest area over Europe after 2015. Nature 583, 72–77 (2020).CAS 
Article 

Google Scholar 
5.Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).CAS 
Article 

Google Scholar 
6.Curran, L. M. et al. Lowland forest loss in protected areas of Indonesian Borneo. Science 303, 1000–1003 (2004).CAS 
Article 

Google Scholar 
7.Friedl, A. et al. MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets, 2001–2012 Collection 5.1 (Boston University, 2010).8.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
Article 

Google Scholar 
9.Margono, B. A., Potapov, P. V., Turubanova, S., Stolle, F. & Hansen, M. C. Primary forest cover loss in Indonesia over 2000–2012. Nat. Clim. Change 4, 730–735 (2014).Article 

Google Scholar 
10.Turubanova, S. et al. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 13, 074028 (2018).Article 

Google Scholar 
11.Searchinger, T. et al. Creating a Sustainable Food Future: A Menu of Solutions to Feed Nearly 10 Billion People by 2050 (World Resources Institute, 2019).12.Gibbs, H. K. et al. Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proc. Natl Acad. Sci. USA 107, 16732–16737 (2010).CAS 
Article 

Google Scholar 
13.Tyukavina, A. et al. Congo Basin forest loss dominated by increasing smallholder clearing. Sci. Adv. 4, eaat2993 (2018).Article 

Google Scholar 
14.Achard, F. et al. Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Glob. Change Biol. 20, 2540–2554 (2014).Article 

Google Scholar 
15.Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).CAS 
Article 

Google Scholar 
16.Aide, T. M. et al. Woody vegetation dynamics in the tropical and subtropical Andes from 2001 to 2014: satellite image interpretation and expert validation. Glob. Change Biol. 25, 2112–2126 (2019).Article 

Google Scholar 
17.Song, X. P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).CAS 
Article 

Google Scholar 
18.Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1–15 (2017).Article 

Google Scholar 
19.Zeng, Z. et al. Highland cropland expansion and forest loss in Southeast Asia in the twenty-first century. Nat. Geosci. 11, 556–562 (2018).CAS 
Article 

Google Scholar 
20.Zeng, Z., Gower, D. B. & Wood, E. F. Accelerating forest loss in Southeast Asian Massif in the 21st century: a case study in Nan Province, Thailand. Glob. Change Biol. 24, 4682–4695 (2018).Article 

Google Scholar 
21.Zarin, D. J. et al. Can carbon emissions from tropical deforestation drop by 50% in 5 years? Glob. Change Biol. 22, 1336–1347 (2016).Article 

Google Scholar 
22.Spracklen, D. & Righelato, R. Tropical montane forests are a larger than expected global carbon store. Biogeosciences 11, 2741–2754 (2014).CAS 
Article 

Google Scholar 
23.Miettinen, J., Shi, C. & Liew, S. C. Deforestation rates in insular Southeast Asia between 2000 and 2010. Glob. Change Biol. 17, 2261–2270 (2011).Article 

Google Scholar 
24.Austin, K. G. et al. What causes deforestation in Indonesia? Environ. Res. Lett. 14, 024007 (2019).Article 

Google Scholar 
25.Hansen, M. et al. Response to comment on ‘high-resolution global maps of 21st-century forest cover change’. Science 344, 981–981 (2014).CAS 
Article 

Google Scholar 
26.Chan, N., Xayvongsa, L. & Takeda, S. in Environmental Resources Use and Challenges in Contemporary Southeast Asia (eds Lopez, M. I. & Suryomenggolo, J.) 231–246 (Springer, 2018).27.Thompson, J. R., Carpenter, D. N., Cogbill, C. V. & Foster, D. R. Four centuries of change in northeastern United States forests. PLoS ONE 8, e72540 (2013).CAS 
Article 

Google Scholar 
28.Lawrence, D. & Vandecar, K. Effects of tropical deforestation on climate and agriculture. Nat. Clim. Change 5, 27–36 (2015).Article 

Google Scholar 
29.Zeng, Z. et al. Deforestation-induced warming over tropical mountain regions regulated by elevation. Nat. Geosci. 14, 23–29 (2021).CAS 
Article 

Google Scholar 
30.Senior, R. A., Hill, J. K., Benedick, S. & Edwards, D. P. Tropical forests are thermally buffered despite intensive selective logging. Glob. Change Biol. 24, 1267–1278 (2018).Article 

Google Scholar 
31.Senior, R. A., Hill, J. K., González del Pliego, P., Goode, L. K. & Edwards, D. P. A pantropical analysis of the impacts of forest degradation and conversion on local temperature. Ecol. Evol. 7, 7897–7908 (2017).Article 

Google Scholar 
32.Sodhi, N. S. et al. The state and conservation of Southeast Asian biodiversity. Biodivers. Conserv. 19, 317–328 (2010).Article 

Google Scholar 
33.Ahrends, A. et al. Current trends of rubber plantation expansion may threaten biodiversity and livelihoods. Glob. Environ. Change 34, 48–58 (2015).Article 

Google Scholar 
34.Edwards, D. P. et al. Degraded lands worth protecting: the biological importance of Southeast Asia’s repeatedly logged forests. Proc. R. Soc. B 278, 82–90 (2011).Article 

Google Scholar 
35.Srinivasan, U., Elsen, P. R. & Wilcove, D. S. Annual temperature variation influences the vulnerability of montane bird communities to land-use change. Ecography 42, 2084–2094 (2019).Article 

Google Scholar 
36.Rahbek, C. et al. Humboldt’s enigma: what causes global patterns of mountain biodiversity? Science 365, 1108–1113 (2019).CAS 
Article 

Google Scholar 
37.Guo, F., Lenoir, J. & Bonebrake, T. C. Land-use change interacts with climate to determine elevational species redistribution. Nat. Commun. 9, 1315 (2018).Article 
CAS 

Google Scholar 
38.Elsen, P. R., Monahan, W. B. & Merenlender, A. M. Topography and human pressure in mountain ranges alter expected species responses to climate change. Nat. Commun. 11, 1974 (2020).CAS 
Article 

Google Scholar 
39.Spracklen, D. V., Arnold, S. R. & Taylor, C. M. Observations of increased tropical rainfall preceded by air passage over forests. Nature 489, 282–285 (2012).CAS 
Article 

Google Scholar 
40.Spracklen, D. V. & Garcia-Carreras, L. The impact of Amazonian deforestation on Amazon Basin rainfall. Geophys. Res. Lett. 42, 9546–9552 (2015).Article 

Google Scholar 
41.Cheng, L. et al. Quantifying the impacts of vegetation changes on catchment storage–discharge dynamics using paired-catchment data. Water Resour. Res. 53, 5963–5979 (2017).Article 

Google Scholar 
42.Chappell, A., Baldock, J. & Sanderman, J. The global significance of omitting soil erosion from soil organic carbon cycling schemes. Nat. Clim. Change 6, 187–191 (2015).Article 
CAS 

Google Scholar 
43.Yue, Y. et al. Lateral transport of soil carbon and land–atmosphere CO2 flux induced by water erosion in China. Proc. Natl Acad. Sci. USA 113, 6617–6622 (2016).CAS 
Article 

Google Scholar 
44.Ziegler, A. D. et al. Carbon outcomes of major land-cover transitions in SE Asia: great uncertainties and REDD+ policy implications. Glob. Change Biol. 18, 3087–3099 (2012).Article 

Google Scholar 
45.Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 8, 14855 (2017).CAS 
Article 

Google Scholar 
46.Fox, J., Castella, J. C. & Ziegler, A. D. Swidden, rubber and carbon: can REDD+ work for people and the environment in montane mainland Southeast Asia? Glob. Environ. Change 29, 318–326 (2014).Article 

Google Scholar 
47.Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change https://doi.org/10.1038/s41558-020-00976-6 (2021).48.Tachikawa, T., Hato, M., Kaku, M. & Iwasaki, A. Characteristics of ASTER GDEM version 2. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 3657–3660 (IEEE, 2011).49.Burrough, P. A., McDonnell, R., McDonnell, R. A. & Lloyd, C. D. Principles of Geographical Information Systems (Oxford Univ. Press, 2015).50.Mokany, K., Raison, R. J. & Prokushkin, A. S. Critical analysis of root : shoot ratios in terrestrial biomes. Glob. Change Biol. 12, 84–96 (2006).Article 

Google Scholar 
51.Tyukavina, A. et al. Aboveground carbon loss in natural and managed tropical forests from 2000 to 2012. Environ. Res. Lett. 10, 074002 (2015).Article 
CAS 

Google Scholar 
52.Ryan, S. E. & Porth, L. S. A Tutorial on the Piecewise Regression Approach Applied to Bedload Transport Data (CreateSpace, 2015).53.Toms, J. D. & Lesperance, M. L. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84, 2034–2041 (2003).Article 

Google Scholar 
54.Zeng, Z. et al. A reversal in global terrestrial stilling and its implications for wind energy production. Nat. Clim. Change 9, 979–985 (2019).Article 

Google Scholar 
55.Zaiontz, C. Real Statistics Using Excel (accessed 16 June 2021); http://www.real-statistics.com/ More

A combination of airborne and satellite remote sensing data were used to quantify changes in mangrove forest structure and function from Hurricane Irma (Supplementary Fig. 1). Findings based on multi-sensor airborne data were scaled to the entire study area using estimates of forest structure and vegetation phenology derived from satellite data.G-LiHT Airborne campaignDuring April 2017, NASA Goddard’s Lidar, Hyperspectral, and Thermal (G-LiHT) airborne imager conducted an extensive airborne campaign in South Florida covering >130,000 ha. The same flight lines were resurveyed with G-LiHT eight months later, during November and December of 2017, to quantify structural changes in coastal forests of South Florida and Everglades National Park (ENP) following Hurricane Irma (Fig. 1). Lidar data was collected with two VQ-280i (Riegl USA) and synced during flight using RiACQUIRE version 2.3.7. The plane flew at a nominal height of 335 m above ground level at a pulse repetition frequency of 300 kHz to collect ~12 laser pulses per square meter. The analysis of pre- and post-hurricane conditions used 1-m resolution lidar data products (Supplementary Fig. 2) and 3-cm resolution stereo aerial and ground photos to estimate changes in vegetation structure, fractional cover, and terrain heights across the study domain. G-LiHT lidar canopy height models, digital terrain models, and estimates of fractional vegetation canopy cover (FVC) were produced using standard processing methodology21. All Level 1 through 3 lidar data products and fine-resolution imagery are openly shared through the G-LiHT webpage (https://gliht.gsfc.nasa.gov/).High resolution stereo maps of canopy heightStereo imagery from high-resolution commercial satellites can be used to estimate canopy and terrain surfaces42,43. Here, we derived digital surface models (DSMs) from DigitalGlobe’s WorldView 2 Level 1B imagery. DigitalGlobe provides these data to U.S. Government agencies and non-profit organizations that support U.S. interests via the NextView license agreement44. The spatial resolution of these data depends on the degree of off-nadir pointing for each acquisition. In this study, image resolution ranged from 0.5 to 0.7 m. We selected along-track stereopairs within the study domain to identify stereo image strips (each ~17 km × 110 km) that were nominally cloud-free over the forested domain of interest for years 2012–2013, the most recent cloud-free stereo data available for the study region prior to Hurricane Irma. The DSMs were produced using the Ames Stereo Pipeline (ASP) v. 2.5.1 on the NASA Center for Climate Simulation’s Advanced Data Analytics Platform at Goddard Space Flight Center (ADAPT, https://www.nccs.nasa.gov/services/adapt). The Worldview DSMs have been shown to accurately estimate mangrove canopy height when compared to airborne lidar and radar interferometry42,43. The processing workflow was adapted from ref. 45, and was implemented semi-global matching algorithms with a 5 × 5 correlation kernel, and a 3 × 3 median-filter applied to the output point cloud prior to producing a 1 m DSM using a weighted average gridding rule46. The ASP processing yielded five DSMs at 1-m resolution that were used to capture pre-storm canopy surface elevations.Each of the five Worldview DSMs were individually calibrated using overlapping pre-storm G-LiHT lidar data to estimate mangrove canopy heights across the study region (Supplementary Fig. 1). We sampled 1000 points within the mangrove forest cover (see mangrove classification, below) to develop a bias-correction equation between G-LiHT lidar-derived canopy heights and stereo DSM elevations (Supplementary Fig. 6). The bias-corrected canopy height models from high-resolution stereo imagery were mosaicked together to generate a 1-m resolution CHM for the entire study region (Supplementary Fig. 7). A pre-storm canopy volume was calculated by summing the 1 m × 1 m WorldView CHM for the entire region of interest. Similarly, a post-storm canopy volume was derived using the canopy damage model (see the section below), the relationship between the pre-storm CHM and the max wind speed. This analysis was conducted in ArcMap 10.7.1.Landsat mangrove forest classificationLandsat 8 Operational Land Imager (OLI) imagery was used to map mangrove cover for the southern Florida study region. The imagery was preprocessed to surface reflectance47 and clouds were masked following methods outlined in ref. 48. The Surface Reflectance Tier 1 product in Google Earth Engine was used to create a cloud-free image mosaic for 2016 based on the median values of all cloud-free images for the year for all bands (Supplementary Fig. 1).Training points were hand-selected using contemporary Google Earth imagery, field photos, and expert knowledge of the region. Twenty-four polygons covering a mangrove area of 1243 ha and 17 polygons covering a non-mangrove area of 2759 ha were identified for training regions. Within each of the two classes (i.e., mangrove and non-mangrove), 100,00 points were sampled and used for the training data in a Random Forest Classification implemented in Google Earth Engine49. The Random Forest model used 20 trees and a bag fraction of 0.5. The Landsat-based mangrove map was validated using the Region 3 species land cover map developed by the National Park Service for Everglades National Park50. The National Park species map was reclassified into mangrove and non-mangrove land cover, and 500 randomly generated points were sampled within each of the two land cover classes. The resulting error matrix indicated an overall accuracy of 90.6%.Post-storm canopy coverTime series of Landsat data were used to estimate hurricane damages of mangrove forest cover through December 31, 2017. We combined data from Landsat 7 ETM+ and Landsat 8 OLI to create a dense time series of cloud-free observations. All images were pre-processed to surface reflectance and masked for clouds using the same methods as the mangrove classification. Landsat 7 and Landsat 8 data were then harmonized to account for differences in the sensor specifications following51. We calculated the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for each image in the collection. We calculated two reference maps from the time series of Landsat imagery (Supplementary Fig. 1). A pre-storm reference was calculated as the median value for each reflectance and index band for all cloud-free imagery in the two years prior Hurricane Irma, August 31, 2015 through August 31, 2017. Similarly, a post-storm median mosaic image was made using Landsat data between October 1, 2017 and December 31, 2017.Pre- and post-storm wall-to-wall Fractional Vegetation Cover (FVC) maps were generated using a combination of lidar-based FVC metrics and Landsat imagery (Supplementary Fig. 1). First, lidar-based FVC was binned into five classes; 0–20%, 20–40%, 40–60%, 60–80%, and 80–100% (Supplementary Fig. 7). We then collected 1000 randomly generated points in each of the five FVC classes, a total of 5000 points, to be used as training data in the Landsat classification. Here, we implemented a Random Forest Classifier using 100 trees and a bag fraction of 0.5. These steps were applied to both the pre-storm and post-storm lidar-derived FVC and Landsat mosaic image metrics. Changes between the pre- and post-storm FVC were then calculated based on the five different FVC classes (Supplementary Fig. 7). For example, a pixel with pre-storm FVC of 80–100% and a post-storm FVC of 20–40%, a reduction of three FVC classes, was assigned a drop in FVC of 40–60% (Fig. 1).Recovery times and resilienceWe estimated the time to full recovery of pre-storm mangrove green canopy cover using the time series of Landsat NDVI during the first 15-months following Hurricane Irma. The pre-storm mean NDVI layer was used as a reference, as described in the previous section. Next we calculated the NDVI anomaly for each image during the post-storm period, September 17, 2017 through December 31, 2018 (Supplementary Fig. 1). We then summed the individual anomaly values from each Landsat image and normalized by the total number of valid pixels (i.e., pixels meeting quality control measures) to estimate the average change in NDVI within the 15 months after the storm. We used anomaly values to identify mangrove forests with large decreases in the 15 months after the storm using a threshold of 0.2 for the 15-month NDVI average anomaly19,52. These areas suffered large losses of canopy material and limited new growth during the post-storm period. We used the slope in NDVI values for each pixel during 2018 to estimate the time in years to full recovery to pre-storm NDVI values, excluding data from October to December 2017 to remove delayed browning of damaged vegetation and spurious NDVI values from surface water features following the storm. Areas with a negative NDVI slope were not assigned a recovery time.We used a combination of the NDVI slope, estimated time to full NDVI recovery, and the average change in NDVI between the pre- and post-storm periods to categorize mangrove forest resilience, the potential for mangroves to rebound to pre-disturbance conditions. The specific criteria for mangrove recovery rates and mangrove damage thresholds were adapted from field and remote sensing studies, respectively6,19,25. Regions of high resilience (a combination of high resistance and resilience) were identified based on rapid recovery and/or little to no immediate impact from the storm: (1) areas that were observed to recover to pre-disturbance conditions during 2018, (2) areas that were predicted to recover within 5 years regardless of the post-storm drop in NDVI6, and (3) regions with a post-storm change in NDVI 15 years or a negative NDVI slope that occurred in regions with the largest ( >0.2) post-storm drop in average NDVI25 (Supplementary Fig. 9). The resilience class map is available online for download53.Mangrove species and elevationWe used species level maps developed by the National Park Service for Everglades National Park50 to characterize the impact of Hurricane Irma on different mangrove species. For that study, dominant species were identified through photo-interpretation of stereoscopic, color-infrared aerial imagery. Grid cells of 50 m × 50 m covering an area (Region 3) of ~100,000 ha in southwest Florida were interpreted based on the majority cover type and validated using field observations. A total of 169 vegetation cover classes were identified in this region, however, only five mangrove cover classes were considered for these analyses: Avicennia germinans (Black Mangrove), Laguncularia racemosa (White Mangrove), Rhizophora mangle (Red Mangrove), Conocarpus erectus (Buttonwood), and a single mixed species mangrove class. Mangrove forest communities were defined as the dominant diagnostic species in the upper-most stratum50. The mangrove species data were reprojected to match the Landsat resolution and the resilience maps. We used the intersection of the resilience and species extent maps to estimate the proportion of each resilience class by dominant species.The USGS National Elevation Dataset (NED) was used to estimate the soil elevation across southwest Florida. The 1/9 arc second (~3 m × 3 m) products were acquired from NED, and reprojected to Landsat resolution to estimate the proportion of each resilience class by soil elevation.Additional data and analysisModeled maximum storm surge data for Hurricane Irma were acquired from Coastal Emergency Risks Assessment data portal. Storm surge is derived from the ADCIRC Prediction System that solves for time dependent, circulation, and transport in multiple dimensions54. Maximum sustained hurricane wind speed was modeled hourly at a 5 km × 5 km resolution for 2017 by NASA’s Global Modeling and Assimilation Office (GMAO)55. The storm maximum wind speed for each 5 km × 5 km grid cell was calculated and binned into six discrete classes of wind speeds at 5 m s−1 increments: 26–30, 31–35, 36–40, 41–45, 46–50, and >50.Statistical analysesCanopy height losses measured from NASA G-LiHT data were grouped by five pre-storm canopy height classes (0–5 m, 5–10 m, 10–15 m, 15–20 m, and >20 m). All valid pixels within the lidar footprint was used to calculate the mean, standard error, and area (sum of 1 m × 1 m pixels) for each class (Supplementary Table 1). These results were then tested for significant differences between canopy height losses and pre-storm canopy height classes between using a one-way ANOVA analysis with a post-hoc Tukey test in R (version 4.0.3). For testing the significance between environmental variables (i.e., pre-storm canopy height, canopy height loss, percent canopy height loss, surface elevation, and storm surge water level above ground) we employed a two-sided Kolmogorov–Smirnov test56 implemented in R (version 4.0.3). First, we created a multi-band stacked image which included each of the variable layers. Within each resilience class (i.e., Low, Intermediate, and High) with randomly selected 10,000–20,000 points using Google Earth Engine to sample from the environmental variables images. From that sample set we then randomly selected 500 samples within each of the resilience classes. Each class combination (1) Low-Intermediate, (2) Low-High, and (3) Intermediate-High were compared using the Kolmogorov–Smirnov test. We repeated this procedure using 5000 iterations in order to provide a robust estimate of the Kolmogorow–Smirnov statistic, including the mean and first and third quartiles, which were then compared to the critical value (Supplementary Table 2).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Arim, M. & Marquet, P. A. Intraguild predation: A widespread interaction related to species biology. Ecol. Lett. 7, 557–564. https://doi.org/10.1111/j.1461-0248.2004.00613.x (2004).Article 

Google Scholar 
2.Polis, G. A., Myers, C. A. & Holt, R. D. The ecology and evolution of intraguild predation: Potential competitors that eat each other. Annu. Rev. Ecol. Syst. 20, 297–330. https://doi.org/10.1146/annurev.es.20.110189.001501 (1989).Article 

Google Scholar 
3.Rosenheim, J. A., Kaya, H. K., Ehler, L. E., Marois, J. J. & Jaffee, B. A. Intraguild predation among biological-control agents: Theory and evidence. Biol. Control 5, 303–335. https://doi.org/10.1006/bcon.1995.1038 (1995).Article 

Google Scholar 
4.Rosenheim, J. A. & Harmon, J. P. The influence of intraguild predation on the suppression of a shared prey population: An empirical reassessment. In Trophic and Guild in Biological Interactions Control (eds Brodeur, J. & Boivin, G.) 1–20 (Springer, 2006) https://doi.org/10.1007/1-4020-4767-3_1.Chapter 

Google Scholar 
5.Fonseca, M. M. et al. How to evaluate the potential occurrence of intraguild predation. Exp. Appl. Acarol. 72, 103–114. https://doi.org/10.1007/s10493-017-0142-x (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
6.Ferguson, K. I. & Stiling, P. Non-additive effects of multiple natural enemies on aphid populations. Oecologia 108, 375–379 (1996).ADS 
Article 

Google Scholar 
7.Hindayana, D., Meyhöfer, R., Scholz, D. & Poehling, H.-M. Intraguild predation among the hoverfly Episyrphus balteatus de Geer (Diptera: Syrphidae) and other aphidophagous predators. Biol. Control 20, 236–246 (2001).Article 

Google Scholar 
8.Denoth, M., Frid, L. & Myers, J. H. Multiple agents in biological control: Improving the odds?. Biol. Control 24, 20–30. https://doi.org/10.1016/S1049-9644(02)00002-6 (2002).Article 

Google Scholar 
9.Muştu, M., Kilinçer, N., Ülgentürk, S. & Kaydan, M. B. Feeding behavior of Cryptolaemus montrouzieri on mealybugs parasitized by Anagyrus pseudococci. Phytoparasitica 36, 360–367 (2008).Article 

Google Scholar 
10.Lucas, E. Intraguild predation among aphidophagous predators. Eur. J. Entomol. 102, 351–364 (2005).
Google Scholar 
11.Muştu, M. & Kilinçer, N. Intraguild predation of Planococcus ficus parasitoids Anagyrus pseudococci and Leptomastix dactylopii by Nephus kreissli. Biocontrol Sci. Technol. 24, 257–269. https://doi.org/10.1080/09583157.2013.856866 (2014).Article 

Google Scholar 
12.Diehl, S. & Feißel, M. Effects of enrichment on three-level food chains with omnivory. Am. Nat. 155, 200–218 (2000).Article 

Google Scholar 
13.Holt, R. D. & Polis, G. A. A theoretical framework for intraguild predation. Am. Nat. 149, 745–764 (1997).Article 

Google Scholar 
14.Kuijper, L. D. J., Kooi, B. W., Zonneveld, C. & Kooijman, S. A. L. M. Omnivory and food web dynamics. Ecol. Modell. 163, 19–32 (2003).Article 

Google Scholar 
15.Morin, P. Productivity, intraguild predation, and population dynamics in experimental food webs. Ecology 80, 752–760 (1999).Article 

Google Scholar 
16.Mylius, S. D., Klumpers, K., de Roos, A. M. & Persson, L. Impact of intraguild predation and stage structure on simple communities along a productivity gradient. Am. Nat. 158, 259–276 (2001).CAS 
Article 

Google Scholar 
17.Polis, G. A. & Holt, R. D. Intraguild predation: The dynamics of complex trophic interactions. Trends Ecol. Evol. 7, 151–154 (1992).CAS 
Article 

Google Scholar 
18.Janssen, A. et al. Intraguild predation usually does not disrupt biological control. In Trophic and Guild in Biological Interactions Control (eds Brodeur, J. & Boivin, G.) 21–44 (Springer, 2006) https://doi.org/10.1007/1-4020-4767-3_2.Chapter 

Google Scholar 
19.Skalski, G. T. & Gilliam, J. F. Functional responses with predator interference: Viable alternatives to the Holling type II model. Ecology 82, 3083–3092 (2001).Article 

Google Scholar 
20.de Villemereuil, P. B. & López-Sepulcre, A. Consumer functional responses under intra- and inter-specific interference competition. Ecol. Modell. 222, 419–426. https://doi.org/10.1016/j.ecolmodel.2010.10.011 (2011).Article 

Google Scholar 
21.Sutherland, W. J. From Individual Behaviour to Population Ecology (Oxford Series in Ecology and Evolution, 1996).
Google Scholar 
22.Pedersen, B. S. & Mills, N. J. Single vs. multiple introduction in biological control: The roles of parasitoid efficiency, antagonism and niche overlap. J. Appl. Ecol. 41, 973–984 (2004).Article 

Google Scholar 
23.Godfray, H. C. J. & Godfray, H. C. J. Parasitoids: Behavioral and Evolutionary Ecology Vol. 67 (Princeton University Press, 1994).Book 

Google Scholar 
24.Harvey, J. A., Poelman, E. H. & Tanaka, T. Intrinsic inter-and intraspecific competition in parasitoid wasps. Annu. Rev. Entomol. 58, 333–351 (2013).CAS 
Article 

Google Scholar 
25.Peri, E., Cusumano, A., Amodeo, V., Wajnberg, E. & Colazza, S. Intraguild interactions between two egg parasitoids of a true bug in semi-field and field conditions. PLoS ONE 9(6), e99876. https://doi.org/10.1371/journal.pone.0099876 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Bruzzone, O. A., Logarzo, G. A., Aguirre, M. B. & Virla, E. G. Intra-host interspecific larval parasitoid competition solved using modelling and bayesian statistics. Ecol. Modell. 385, 114–123 (2018).Article 

Google Scholar 
27.Triapitsyn, S. V. et al. Complex of primary and secondary parasitoids (Hymenoptera: Encyrtidae and Signiphoridae) of Hypogeococcus spp. mealybugs (Hemiptera: Pseudococcidae) in the New World. Florida Entomol. 101, 411–434. https://doi.org/10.1653/024.101.0320 (2018).Article 

Google Scholar 
28.Aguirre, M. B. et al. Analysis of biological traits of Anagyrus cachamai and Anagyrus lapachosus to assess their potential as biological control candidate agents against Harrisia cactus mealybug pest in Puerto Rico. Biocontrol 64, 539–551. https://doi.org/10.1007/s10526-019-09956-y (2019).CAS 
Article 

Google Scholar 
29.Poveda-Martínez, D. et al. Species complex diversification by host plant use in an herbivorous insect: The source of Puerto Rican cactus mealybug pest and implications for biological control. Ecol. Evol. 10, 10463–10480. https://doi.org/10.1002/ece3.6702 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Poveda-Martínez, D. et al. Untangling the Hypogeococcus pungens species complex (Hemiptera: Pseudococcidae) for Argentina, Australia, and Puerto Rico based on host plant associations and genetic evidence. PLoS ONE 14(7), e0220366. https://doi.org/10.1371/journal.pone.0220366 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Thurstone, L. L. A law of comparative judgment. Psychol. Rev. 34, 273 (1927).Article 

Google Scholar 
32.Bradley, R. A. & Terry, M. E. Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika 39, 324–345. https://doi.org/10.2307/2334029 (1952).MathSciNet 
Article 
MATH 

Google Scholar 
33.Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. Bayesian data analysis. In Texts Stat. Sci. 2nd ed, 661 (CRC Press, 2003).34.Stevens, S. S. On the Theory of Scales of Measurement, vol. 103, 677–680 (1946).35.Patil, A., Huard, D. & Fonnesbeck, C. J. PyMC: Bayesian stochastic modelling in Python. J. Stat. Softw. 35, 1 (2010).Article 

Google Scholar 
36.Zwölfer, H. The structure and effect of parasite complexes attacking phytophagous host insects. In Proc. Adv. Study Inst. Dyn. Numbers Popul. 405–418 (1971).37.Zwölfer, H. Strategies and counterstrategies in insect population systems competing for space and food in flower headsand plant galls. Fortschr. Zool. 25(2/3), 331–353 (1979).
Google Scholar 
38.Vance, R. R. The stable coexistence of two competitors for one resource. Am. Nat. 126, 72–86 (1985).Article 

Google Scholar 
39.Fellers, J. H. Interference and exploitation in a guild of woodland ants. Ecology 68, 1466–1478 (1987).Article 

Google Scholar 
40.Cusumano, A., Peri, E., Vinson, S. B. & Colazza, S. Intraguild interactions between two egg parasitoids exploring host patches. Biocontrol 56, 173–184 (2011).Article 

Google Scholar 
41.Mizutani, N. Interspecific larval competition among three egg parasitoid species on the host, Riptortus clavatus (Thunberg) (Heteroptera: Alydidae). Proc. Assoc. Plant Prot. Kyushu 40, 106–110 (1994).Article 

Google Scholar 
42.Weber, C. A., Smilanick, J. M., Ehler, L. E. & Zalom, F. G. Ovipositional behavior and host discrimination in three scelionid egg parasitoids of stink bugs. Biol. Control 6, 245–252 (1996).Article 

Google Scholar 
43.Alim, M. A. & Lim, U. T. Interspecific larval competition between two egg parasitoids in refrigerated host eggs of Riptortus pedestris (Hemiptera: Alydidae). Biocontrol Sci. Technol. 21, 395–407 (2011).Article 

Google Scholar 
44.De Moraes, C. M. & Lewis, W. J. Analyses of two parasitoids with convergent foraging strategies. J. Insect Behav. 12, 571–583 (1999).Article 

Google Scholar 
45.Mackauer, M. Host discrimination and larval competition in solitary endoparasitoids. Crit. Issues Biol. Control, Intercept, Andover, Hants, UK. xvii + 330 pp (1990).46.Strand, M. R. & Godfray, H. C. J. Superparasitism and ovicide in parasitic Hymenoptera: Theory and a case study of the ectoparasitoid Bracon hebetor. Behav. Ecol. Sociobiol. 24, 421–432 (1989).Article 

Google Scholar 
47.Abram, P. K., Brodeur, J., Urbaneja, A. & Tena, A. Nonreproductive effects of insect parasitoids on their hosts. Annu. Rev. Entomol. 64, 259–276 (2019).CAS 
Article 

Google Scholar 
48.Abram, P. K., Brodeur, J., Burte, V. & Boivin, G. Parasitoid-induced host egg abortion: An underappreciated component of biological control services provided by egg parasitoids. Biol. Control 98, 52–60 (2016).Article 

Google Scholar 
49.Abram, P. K., Gariepy, T. D., Boivin, G. & Brodeur, J. An invasive stink bug as an evolutionary trap for an indigenous egg parasitoid. Biol. Invasions 16, 1387–1395 (2014).Article 

Google Scholar 
50.Steiner, A. L. Stinging behaviour of solitary wasps. In Venoms of the Hymenoptera. Biochemical, Pharmacological and Behavioural Aspects (ed Piek, T.) 63–148 (Academic Press, London, 1986) https://doi.org/10.1016/b978-0-12-554770-3.50008-5.51.Feng, Y., Wratten, S., Sandhu, H. & Keller, M. Interspecific competition between two generalist parasitoids that attack the leafroller Epiphyas postvittana (Lepidoptera: Tortricidae). Bull. Entomol. Res. 105, 426–433 (2015).CAS 
Article 

Google Scholar 
52.De Moraes, C. M. & Mescher, M. C. Intrinsic competition between larval parasitoids with different degrees of host specificity. Ecol. Entomol. 30, 564–570 (2005).Article 

Google Scholar 
53.Desneux, N., Barta, R. J., Hoelmer, K. A., Hopper, K. R. & Heimpel, G. E. Multifaceted determinants of host specificity in an aphid parasitoid. Oecologia 160, 387–398 (2009).ADS 
Article 

Google Scholar 
54.Brodeur, J. & Boivin, G. Functional ecology of immature parasitoids. Annu. Rev. Entomol. 49, 27–49 (2004).CAS 
Article 

Google Scholar 
55.Rogers, D. Random search and insect population models. J. Anim. Ecol. 41, 369–383 (1972).Article 

Google Scholar 
56.Holling, C. S. Some characteristics of simple types of predation and parasitism. Can. Entomol. 91, 385–398. https://doi.org/10.4039/Ent91385-7 (1959).Article 

Google Scholar  More

1.Zhao, F. et al. Diversity and species abundance patterns of the early Cambrian (Series 2, Stage 3) Chengjiang Biota from China. Paleobiology 40, 50–69 (2014).Article 

Google Scholar 
2.Zhu, M.-Y., Zhang, J.-M. & Li, G.-X. Sedimentary environments of the early Cambrian Chengjiang biota: sedimentology of the Yu’anshan Formation in Chengjiang County, eastern Yunnan. Acta Palaeontol. Sin. 40, 80–105 (2001).
Google Scholar 
3.Hu, S.-X. Taphonomy and palaeoecology of the early Cambrian Chengjiang Biota from eastern Yunnan, China. Berl. Palobiologische Abhandlungen 7 (2005).4.Hou, X. et al. The Cambrian Fossils of Chengjiang, China. The Flowering of Early Animal Life 2nd edn (John Wiley & Sons, 2017).5.Zhang, W.-T. & Hou, X.-G. Preliminary notes on the occurrence of the unusual trilobite Naraoia in Asia. Acta Palaeontol. Sin. 24, 591–595 (1985).
Google Scholar 
6.Luo, H.-L, Hu, S.-X, Chen, L.-Z, Zhang, S.-S & Tao, Y.-H. Early Cambrian Chengjiang Fauna from Kunming Region, China (Yunnan Science and Technology Press, 1999).7.Chen, J.-Y The Dawn of Animal World (Jiangsu Science and Technology Press, China, 2004).8.Duan, Y. et al. Reproductive strategy of the bradoriid arthropod Kunmingella douvillei from the lower Cambrian Chengjiang Lagerstätte, South China. Gondwana Res. 25, 983–990 (2014).Article 

Google Scholar 
9.Zhao, F.-C., Zhu, M.-Y. & Hu, S.-X. Community structure and composition of the Cambrian Chengjiang biota. Sci. China Earth Sci. 53, 1784–1799 (2010).Article 

Google Scholar 
10.Liu, Y. et al. Three-dimensionally preserved minute larva of a great-appendage arthropod from the early Cambrian Chengjiang biota. Proc. Natl Acad. Sci. USA 113, 5542–5546 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Ou, Q. et al. Evolutionary trade-off in reproduction of Cambrian arthropods. Sci. Adv. 6, 33–76 (2020).
Google Scholar 
12.Dornbos, S. Q. & Chen, J.-Y. Community palaeoecology of the Early Cambrian Maotianshan Shale biota: ecological dominance of priapulid worms. Palaeogeogr. Palaeoclimatol. Palaeoecol. 258, 200–212 (2008).Article 

Google Scholar 
13.Fu, D. et al. The Qingjiang biota—a Burgess Shale-type fossil Lagerstätte from the early Cambrian of South China. Science 363, 1338–1342 (2019).CAS 
PubMed 
Article 

Google Scholar 
14.Caron, J.-B. & Jackson, D. A. Paleoecology of the Greater Phyllopod Bed community, Burgess Shale. Palaeogeogr. Palaeoclimatol. Palaeoecol. 258, 222–256 (2008).15.Nanglu, K., Caron, J.-B. & Gaines, R. R. The Burgess Shale paleocommunity with new insights from Marble Canyon, British Columbia. Paleobiology 46, 58–81 (2020).Article 

Google Scholar 
16.Gaines, R. R. in Reading and Writing of the Fossil Record: Preservational Pathways to Exceptional Fossilization Vol. 20 (eds Laflamme, M. et al.) 123–146 (Paleontological Research Institution, 2014).17.Zhai, D. et al. Variation in appendages in early Cambrian bradoriids reveals a wide range of body plans in stem-euarthropods. Commun. Biol. 2, 329 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Isaevaa, V. V., Ozernyukc, N. D. & Rozhnov, S. V. Evidence for evolutionary changes in ontogeny: paleontological, comparative morphological, and molecular aspects. Biol. Bull. 40, 243–252 (2013).Article 

Google Scholar 
19.Liu, Y., Haug, J. T., Haug, C., Briggs, D. E. G. & Hou, X.-G. A 520 million-year-old chelicerate larva. Nat. Commun. 5, 4440 (2014).CAS 
PubMed 
Article 

Google Scholar 
20.Chipman, A. D. An embryological perspective on the early arthropod fossil record. BMC Evol. Biol. 15, 285 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Wolfe, J. M. Metamorphosis is ancestral for crown euarthropods, and evolved in the Cambrian or earlier. Integr. Comp. Biol. 57, 499–509 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Haug, T. J. Why the term “larva” is ambiguous, or what makes a larva? Acta Zool. 101, 167–188 (2018).Article 

Google Scholar 
23.Fu, D., Zhang, X., Budd, G. E., Liu, W. & Pan, X. Ontogeny and dimorphism of Isoxys auritus (Arthropoda) from the Early Cambrian Chengjiang biota, South China. Gondwana Res. 25, 975–982 (2014).Article 

Google Scholar 
24.Yang, X.-F., Kimmig, J., Lieberman, B. S. & Peng, S.-C. A new species of the deuterostome Herpetogaster from the early Cambrian Chengjiang biota of South China. Sci. Nat. 107, 37 (2020).CAS 
Article 

Google Scholar 
25.Zhai, D. Y. et al. Fine-scale appendage structure of the Cambrian trilobitomorph Naraoia spinosa and its ontogenetic and ecological implications. Proc. R. Soc. B 286, 20192371 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Hughes, N. C. et al. Articulated trilobite ontogeny: suggestions for a methodological standard. J. Paleont. 95, 298–304 (2021).Article 

Google Scholar 
27.Chen, J.-Y. & Zhou, G.-Q. Biology of the Chengjiang fauna. Bull. Natl Mus. Nat. Sci. 10, 11–106 (1997).
Google Scholar 
28.Haug, J. T., Caron, J.-B. & Haug, C. Demecology in the Cambrian: synchronized moulting in arthropods from the Burgess Shale. BMC Biol. 11, 64 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
29.Robison, R. A., Babcock, L. E. & Gunther, V. G. Exceptional Cambrian fossils from Utah: A Window into the Age of Trilobites (Utah Geological Survey, 2015).30.Kimmig, J., Strotz, L. C., Kimmig, S. R., Egenhoff, S. O. & Lieberman, B. S. The Spence Shale Lagerstätte: an important window into Cambrian biodiversity. J. Geol. Soc. Lond. 176, 609–619 (2019).Article 

Google Scholar 
31.Paterson, J. R. et al. The Emu Bay Shale Konservat-Lagerstätte: a view of Cambrian life from East Gondwana. J. Geol. Soc. Lond. 173, 3107 (2016).32.Du, K. et al. A new early Cambrian Konservat-Lagerstätte expands the occurrence of Burgess Shale-type deposits on the Yangtze Platform. Earth Sci. Rev. 211, 103409 (2020).Article 

Google Scholar 
33.Harper, D. A. T. et al. The Sirius Passet Lagerstätte of North Greenland: a remote window on the Cambrian explosion. J. Geol. Soc. Lond. 176, 1023–1037 (2019).Article 

Google Scholar 
34.Chen, L. Z et al. Early Cambrian Chengjiang Fauna in Eastern Yunnan, China (Yunnan Science and Technology Press, 2002).35.Zhao, F. C., Caron, J.-B., Hu, S. X. & Zhu, M. Y. Quantitative analysis of taphofacies and paleocommunities in the Early Cambrian Chengjiang Lagerstätte. PALAIOS 24, 826–839 (2009).CAS 
Article 

Google Scholar 
36.Beck, M. K. et al. The identification, conservation, and management of estuarine and marine nurseries for fish and invertebrates. BioScience 51, 633–641 (2001).Article 

Google Scholar 
37.Botton, M. L. & Loveland, R. E. Abundance and dispersal potential of horseshoe crab (Limulus polyphemus) larvae in the Delaware estuary. Estuar. Coasts 26, 1472–1479 (2003).Article 

Google Scholar 
38.Watson, W. H. & Chabot, C. C. High resolution tracking of adult horseshoe crabs Limulus polyphemus in a New Hampshire estuary using a fixed array ultrasonic telemetry. Curr. Zool. 56, 599–610 (2010).Article 

Google Scholar 
39.Perry, F. A. et al. Habitat partitioning in Antarctic krill: spawning hotspots and nursery areas. PLoS ONE 14, e0219325 (2019).40.Nagelkerken, I. in Ecological Connectivity among Tropical Coastal Ecosystems (ed. Nagelkerken, I.) 357–399 (Springer, 2009).41.Kanciruk, P. in The Biology and Management of Lobsters Vol. 2 (eds Cobb, J. S. & Phillips, B. F.) 59–96 (Academic Press, 1980).42.Sandt, V. J. & Stoner, A. W. Ontogenetic shift in habitat by early juvenile queen conch, Strombus gigas: patterns and potential mechanisms. Fish. Bull. 91, 516–525 (1993).
Google Scholar 
43.Pedrotti, M. L. & Fenaux, L. Dispersal of echinoderm larvae in a geographical area marked by upwelling (Ligurian Sea, NW Mediterranean). Mar. Ecol. Prog. Ser. 87, 217–227 (1992).Article 

Google Scholar 
44.Zhai, D. et al. Spatial heterogeneity of the population age structure of the ostracode Limnocythere inopinata in Hulun Lake, Inner Mongolia and its implications. Hydrobiologia 716, 29–46 (2013).CAS 
Article 

Google Scholar 
45.Baillon, S., Hamel, J. F., Wareham, V. E. & Mercier, A. Deep cold-water corals as nurseries for fish larvae. Front. Ecol. Environ. 10, 351–356 (2012).Article 

Google Scholar 
46.Treude, T., Kiel, S., Linke, P., Peckmann, J. & Goedert, J. Elasmobranch egg capsules associated with modern and ancient cold seeps: a nursery for marine deep-water predators. Mar. Ecol. Prog. Ser. 437, 175–181 (2011).Article 

Google Scholar 
47.Rooper, C. N., Boldt, J. L. & Zimmermann, M. An assessment of juvenile Pacific Ocean perch (Sebastes alutus) habitat use in a deepwater nursery. Estuar. Coast. Shelf Sci. 75, 371–380 (2007).Article 

Google Scholar 
48.Pimiento, C., Ehret, D. J., MacFadden, B. J. & Hubbell, G. Ancient nursery area for the extinct giant shark Megalodon from the Miocene of Panama. PLoS ONE 5, e10552 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
49.Villafaña, J. A. et al. First evidence of a palaeo-nursery area of the great white shark. Sci. Rep. 10, 8502 (2020).50.Paterson, J. R., Jago, J. B., Brock, G. A. & Gehling, J. G. Taphonomy and palaeoecology of the emuellid trilobite Balcoracania dailyi (early Cambrian, South Australia). Palaeogeogr. Palaeoclimatol. Palaeoecol. 249, 302–321 (2007).Article 

Google Scholar 
51.Hartnoll, R. G. in Physiology and Behaviour of Marine Organisms (eds McLusky, D. S. & Berry, A. J.) 349–358 (Pergamon Press, 1978).52.Hartnoll, R. G. & Bryant, A. D. Size-frequency distributions in decapod Crustacea—the quick, the dead and the cast-offs. J. Crust. Biol. 10, 14–19 (1990).Article 

Google Scholar 
53.Sheldon, P. R. Trilobite size-frequency distributions, recognition of instars, and phyletic size changes. Lethaia 21, 293–306 (1988).Article 

Google Scholar 
54.Herrnkind, W. F. in The Biology and Management of Lobsters Vol. 1 (eds Cobb, J. S. & Phillips B. F.) 349–407 (Academic Press, 1980)55.Linnane, A., Dimmlich, W. & Ward, T. Movement patterns of the southern rock lobster, Jasus edwardsii, of South Australia. NZ J. Mar. Freshw. Res. 39, 335–346 (2005).Article 

Google Scholar 
56.Blazejowski, B. et al. Ancient animal migration: a case study of eyeless, dimorphic Devonian trilobites from Poland. Palaeontology 59, 743–751 (2016).Article 

Google Scholar 
57.Hughes, N. C., Kříž, J., Macquaker, J. H. S. & Huff, W. D. The depositional environment and taphonomy of the Homerian “Aulacopleura shales” fossil assemblage near Loděnice, Czech Republic (Prague Basin, Perunican microcontinent). Bull. Geosci. 89, 219–238 (2014).Article 

Google Scholar 
58.Whitaker, A. F. & Kimmig, J. Anthropologically introduced biases in natural history collections, with a case study on the invertebrate paleontology collections from the middle Cambrian Spence Shale Lagerstätte. Palaeontol. Electron. 23, a58 (2020).
Google Scholar 
59.Conway Morris, S. The community structure of the Middle Cambrian phyllopod bed (Burgess Shale). Palaeontology 29, 423–467 (1986).
Google Scholar 
60.Caron, J.-B., Gaines, R. R., Aria, C., Mángano, M. G. & Streng, M. A new phyllopod bed-like assemblage on from the Burgess Shale of the Canadian Rockies. Nat. Commun. 5, 3210 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
61.Ihaka, R. R. & Gentleman, R. A language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314 (1996).
Google Scholar  More

1.Boudreau BP, Huettel M, Forster S, Jahnke RA, McLachlan A, Middelburg JJ, et al. Permeable marine sediments: overturning an old paradigm. Eos Trans AGU. 2001;82:133–6.
Google Scholar 
2.Huettel M, Berg P, Kostka JE. Benthic exchange and biogeochemical cycling in permeable sediments. Annu Rev Mar Sci. 2014;6:23–51.Article 

Google Scholar 
3.Huettel M, Ziebis W, Forster S. Flow-induced uptake of particulate matter in permeable sediments. Limnol Oceanogr. 1996;41:309–22.Article 

Google Scholar 
4.Huettel M, Rusch A. Transport and degradation of phytoplankton in permeable sediment. Limnol Oceanogr. 2000;45:534–49.CAS 
Article 

Google Scholar 
5.Rusch A, Forster S, Huettel M. Bacteria, diatoms and detritus in an intertidal sandflat subject to advective transport across the water-sediment interface. Biogeochemistry. 2001;55:1–27.CAS 
Article 

Google Scholar 
6.Ahmerkamp S, Winter C, Krämer K, de Beer D, Janssen F, Friedrich J, et al. Regulation of benthic oxygen fluxes in permeable sediments of the coastal ocean. Limnol Oceanogr. 2017;62:1935–54.CAS 
Article 

Google Scholar 
7.Jahnke RA Global Synthesis. In: Liu KK, Atkinson L, Quinones R, Talaue-McManus L, editors. Carbon and nutrient fluxes in continental margins. Ch. 16 Berlin: Springer; 2010.8.Joiris C, Billen G, Lancelot C, Daro MH, Mommaerts JP, Bertels A, et al. A budget of carbon cycling in the Belgian coastal zone: relative roles of zooplankton, bacterioplankton and benthos in the utilization of primary production. Neth. J. Sea Res. 1982;16:260–75.CAS 
Article 

Google Scholar 
9.Jørgensen BB, Bang M, Blackburn TH. Anaerobic mineralization in marine-sediments from the Baltic-Sea-North Sea transition. Mar Ecol Prog Ser. 1990;59:39–54.Article 

Google Scholar 
10.Middelburg JJ, Barranguet C, Boschker HTS, Herman PMJ, Moens T, Heip CHR. The fate of intertidal microphytobenthos carbon: an in situ 13C-labeling study. Limnol Oceanogr. 2000;45:1224–34.CAS 
Article 

Google Scholar 
11.Böer SI, Arnosti C, van Beusekom JEE, Boetius A. Temporal variations in microbial activities and carbon turnover in subtidal sandy sediments. Biogeosciences. 2009;6:1149–65.Article 

Google Scholar 
12.Goto N, Mitamura O, Terai H. Biodegradation of photosynthetically produced extracellular organic carbon from intertidal benthic algae. J Exp Mar Biol Ecol. 2001;257:73–86.CAS 
PubMed 
Article 

Google Scholar 
13.Rusch A, Huettel M, Reimers CE, Taghon GL, Fuller CM. Activity and distribution of bacterial populations in Middle Atlantic Bight shelf sands. FEMS Microb Ecol. 2003;44:89–100.CAS 
Article 

Google Scholar 
14.Hewson I, Vargo GA, Fuhrman JA. Bacterial diversity in shallow oligotrophic marine benthos and overlying waters: effects of virus infection, containment, and nutrient enrichment. Microb Ecol. 2003;46:322–36.CAS 
PubMed 
Article 

Google Scholar 
15.Teske A, Durbin A, Ziervogel K, Cox C, Arnosti C. Microbial community composition and function in permanently cold seawater and sediments from an Arctic fjord of Svalbard. Appl Environ Microbiol. 2011;77:2008–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Zinger L, Amaral-Zettler LA, Fuhrman JA, Horner-Devine MC, Huse SM, Welch DBM, et al. Global patterns of bacterial beta-diversity in seafloor and seawater ecosystems. PLoS ONE. 2011;6:e24570.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Cardman Z, Arnosti C, Durbin A, Ziervogel K, Cox C, Steen AD, et al. Verrucomicrobia are candidates for polysaccharide-degrading bacterioplankton in an Arctic fjord of Svalbard. Appl Environ Microbiol. 2014;80:3749–56.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.CAS 
PubMed 
Article 

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

Google Scholar 
20.Fuhrman JA, Hewson I, Schwalbach MS, Steele JA, Brown MV, Naeem S. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc Natl Acad Sci USA. 2006;103:13104–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Chafee M, Fernàndez-Guerra A, Buttigieg PL, Gerdts G, Eren AM, Teeling H, et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 2018;12:237–52.PubMed 
Article 

Google Scholar 
22.Mayer LM. Extracellular proteolytic enzyme activity in sediments of an intertidal mudflat. Limnol Oceanogr. 1989;34:973–81.CAS 
Article 

Google Scholar 
23.Middelburg J, Klaver G, Nieuwenhuize J, Wielemaker A, Haas W, Vlug T, et al. Organic matter mineralization in intertidal sediment along an estuarine gradient. Mar Ecol Prog Ser. 1996;132:157–68.24.Tabuchi K, Kojima H, Fukui M. Seasonal changes in organic matter mineralization in a sublittoral sediment and temperature-driven decoupling of key processes. Microb Ecol. 2010;60:551–60.PubMed 
Article 

Google Scholar 
25.Hoffmann K, Hassenrück C, Salman-Carvalho V, Holtappels M, Bienhold C. Response of bacterial communities to different detritus compositions in Arctic deep-sea sediments. Front Microbiol. 2017;8:266.PubMed 
PubMed Central 

Google Scholar 
26.Gobet A, Boer SI, Huse SM, van Beusekom JEE, Quince C, Sogin ML, et al. Diversity and dynamics of rare and of resident bacterial populations in coastal sands. ISME J. 2012;6:542–53.PubMed 
Article 

Google Scholar 
27.Mills HJ, Hunter E, Humphrys M, Kerkhof L, McGuinness L, Huettel M, et al. Characterization of nitrifying, denitrifying, and overall bacterial communities in permeable marine sediments of the northeastern Gulf of Mexico. Appl Environ Microbiol. 2008;74:4440–53.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Probandt D, Knittel K, Tegetmeyer HE, Ahmerkamp S, Holtappels M, Amann R. Permeability shapes bacterial communities in sublittoral surface sediments. Environ Microbiol. 2017;19:1584–99.CAS 
PubMed 
Article 

Google Scholar 
29.Tait K, Airs RL, Widdicombe CE, Tarran GA, Jones MR, Widdicombe S. Dynamic responses of the benthic bacterial community at the Western English Channel observatory site L4 are driven by deposition of fresh phytodetritus. Prog Oceanogr. 2015;137:546–58.Article 

Google Scholar 
30.Wiltshire K, Kraberg A, Bartsch I, Boersma M, Franke H-D, Freund J, et al. Helgoland Roads, North Sea: 45 years of change. Estuaries and Coasts. 2010;33:295–310.CAS 
Article 

Google Scholar 
31.Probandt D. Microbial ecology of subtidal sandy sediments [PhD thesis]. Bremen: University of Bremen; 2017.32.Berge J, Renaud PE, Darnis G, Cottier F, Last K, Gabrielsen TM, et al. In the dark: a review of ecosystem processes during the Arctic polar night. Prog Oceanogr. 2015;139:258–71.Article 

Google Scholar 
33.Boehnert S, Ruiz Soto S, Fox BRS, Yokoyama Y, Hebbeln D. Historic development of heavy metal contamination into the Firth of Thames, New Zealand. Geo-Mar Lett. 2020;40:149–65.CAS 
Article 

Google Scholar 
34.Lorenzen CJ. Determination of chlorophyll and pheo-pigments: spectrophotometric eqations. Limnol Oceanogr. 1967;12:343–6.CAS 
Article 

Google Scholar 
35.Zhou J, Bruns MA, Tiedje JM. DNA recovery from soils of diverse composition. Appl Environ Microbiol. 1996;62:316–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Herlemann DPR, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 2011;5:1571–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Bushnell B, Rood J, Singer E. BBMerge—accurate paired shotgun read merging via overlap. PLoS ONE. 2017;12:e0185056.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.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 
Article 

Google Scholar 
39.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. 2013;41:D590–D596.CAS 
PubMed 
Article 

Google Scholar 
40.Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43.PubMed 
PubMed Central 
Article 

Google Scholar 
41.Oksanen J, Blanchet F, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. R package version. 2019;2:5–6.
Google Scholar 
42.Team R.C. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.r-project.org/; 2019.43.Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4:1686.Article 

Google Scholar 
44.Chapman MG, Underwood AJ. Ecological patterns in multivariate assemblages: information and interpretation of negative values in ANOSIM tests. Mar Ecol Prog Ser. 1999;180:257–65.Article 

Google Scholar 
45.Pernthaler A, Pernthaler J, Amann R. Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl Environ Microbiol. 2002;68:3094–101.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Pernthaler J, Pernthaler A, Amann R. Automated enumeration of groups of marine picoplankton after fluorescence in situ hybridization. Appl Environ Microbiol. 2003;69:2631–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Bennke CM, Reintjes G, Schattenhofer M, Ellrott A, Wulf J, Zeder M, et al. Modification of a high-throughput automatic microbial cell enumeration system for shipboard analyses. Appl Environ Microbiol. 2016;82:3289–96.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Snaidr J, Amann R, Huber I, Ludwig W, Schleifer K, Snaidr J, et al. Phylogenetic analysis and in situ identification of bacteria in activated sludge. Appl Environ Microbiol. 1997;63:2884–96.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Bockelmann F-D, Puls W, Kleeberg U, Müller D, Emeis K-C. Mapping mud content and median grain-size of North Sea sediments—a geostatistical approach. Mar Geol. 2018;397:60–71.Article 

Google Scholar 
51.Hoshino T, Doi H, Uramoto G-I, Wörmer L, Adhikari RR, Xiao N, et al. Global diversity of microbial communities in marine sediment. Proc Natl Acad Sci USA. 2020;117:27587–97.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Probandt D, Eickhorst T, Ellrott A, Amann R, Knittel K. Microbial life on a sand grain: from bulk sediment to single grains. ISME J. 2017;12:623.PubMed 
PubMed Central 
Article 

Google Scholar 
53.Acosta-González A, Rosselló-Móra R, Marqués S. Characterization of the anaerobic microbial community in oil-polluted subtidal sediments: aromatic biodegradation potential after the Prestige oil spill. Environ Microbiol. 2013;15:77–92.PubMed 
Article 
CAS 

Google Scholar 
54.Tian F, Yu Y, Chen B, Li H, Yao Y-F, Guo X-K. Bacterial, archaeal and eukaryotic diversity in Arctic sediment as revealed by 16S rRNA and 18S rRNA gene clone libraries analysis. Polar Biol. 2009;32:93–103.Article 

Google Scholar 
55.Zeng Y, Zou Y, Grebmeier JM, He J, Zheng T. Culture-independent and culture-dependent methods to investigate the diversity of planktonic bacteria in the northern Bering Sea. Polar Biol. 2012;35:117–29.Article 

Google Scholar 
56.Santelli CM, Orcutt BN, Banning E, Bach W, Moyer CL, Sogin ML, et al. Abundance and diversity of microbial life in ocean crust. Nature. 2008;453:653–6.CAS 
PubMed 
Article 

Google Scholar 
57.Ravenschlag K, Sahm K, Pernthaler J, Amann R. High bacterial diversity in permanently cold marine sediments. Appl Environ Microbiol. 1999;65:3982–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Hunter EM, Mills HJ, Kostka JE. Microbial community diversity associated with carbon and nitrogen cycling in permeable shelf sediments. Appl Environ Microbiol. 2006;72:5689–701.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Dyksma S, Bischof K, Fuchs BM, Hoffmann K, Meier D, Meyerdierks A, et al. Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. ISME J. 2016;10:1939–53.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Allers E, Wright JJ, Konwar KM, Howes CG, Beneze E, Hallam SJ, et al. Diversity and population structure of Marine Group A bacteria in the Northeast subarctic Pacific Ocean. ISME J. 2013;7:256–68.CAS 
PubMed 
Article 

Google Scholar 
61.Hodal H, Falk-Petersen S, Hop H, Kristiansen S, Reigstad M. Spring bloom dynamics in Kongsfjorden, Svalbard: nutrients, phytoplankton, protozoans and primary production. Polar Biol. 2012;35:191–203.Article 

Google Scholar 
62.Jönsson BF, Salisbury JE, Mahadevan A. Large variability in continental shelf production of phytoplankton carbon revealed by satellite. Biogeosciences. 2011;8:1213–23.Article 
CAS 

Google Scholar 
63.Kuliński K, Kędra M, Legeżyńska J, Gluchowska M, Zaborska A. Particulate organic matter sinks and sources in high Arctic fjord. J Mar Syst. 2014;139:27–37.Article 

Google Scholar 
64.Bourgeois S, Kerhervé P, Calleja ML, Many G, Morata N. Glacier inputs influence organic matter composition and prokaryotic distribution in a high Arctic fjord (Kongsfjorden, Svalbard). J Mar Syst. 2016;164:112–27.Article 

Google Scholar 
65.Zaborska A, Włodarska-Kowalczuk M, Legeżyńska J, Jankowska E, Winogradow A, Deja K. Sedimentary organic matter sources, benthic consumption and burial in west Spitsbergen fjords—signs of maturing of Arctic fjordic systems? J Mar Syst. 2018;180:112–23.Article 

Google Scholar 
66.McGovern M, Pavlov AK, Deininger A, Granskog MA, Leu E, Søreide JE, et al. Terrestrial inputs drive seasonality in organic matter and nutrient biogeochemistry in a high Arctic fjord system (Isfjorden, Svalbard). Front Mar Sci. 2020;7:747.Article 

Google Scholar 
67.Avci B, Krüger K, Fuchs BM, Teeling H, Amann RI. Polysaccharide niche partitioning of distinct Polaribacter clades during North Sea spring algal blooms. ISME J. 2020;14:1369–83.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Braeckman U, Janssen F, Lavik G, Elvert M, Marchant H, Buckner C, et al. Carbon and nitrogen turnover in the Arctic deep sea: in situ benthic community response to diatom and coccolithophorid phytodetritus. Biogeosciences. 2018;15:6537–57.CAS 
Article 

Google Scholar 
69.Guilini K, Oevelen DV, Soetaert K, Middelburg JJ, Vanreusela A. Nutritional importance of benthic bacteria for deep-sea nematodes from the Arctic ice margin: results of an isotope tracer experi5ment. Limnol Oceanogr. 2010;55:1977–89.CAS 
Article 

Google Scholar 
70.van Oevelen D, Soetaert K, Middelburg J, Herman P, Moodley L, Hamels I, et al. Carbon flows through a benthic food web: Integrating biomass, isotope and tracer data. J Mar Res. 2006;64:453–82.Article 

Google Scholar 
71.Danovaro R, Dell’Anno A, Corinaldesi C, Magagnini M, Noble R, Tamburini C. et al. Major viral impact on the functioning of benthic deep-sea ecosystems. Nature. 2008;454:1084–7.CAS 
PubMed 
Article 

Google Scholar 
72.Miller DC. Abrasion effects on microbes in sandy sediments. Mar Ecol Prog Ser. 1989;55:73–82.Article 

Google Scholar 
73.Ahmerkamp S, Marchant HK, Peng C, Probandt D, Littmann S, Kuypers MM. et al. The effect of sediment grain properties and porewater flow on microbial abundance and respiration in permeable sediments. Sci. Rep. 2020;10:3573CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Barka EA, Vatsa P, Sanchez L, Gaveau-Vaillant N, Jacquard C, Klenk HP. et al. Taxonomy, physiology, and natural products of Actinobacteria. Microbiol Mol Biol Rev. 2016;80:1–43.PubMed 
Article 

Google Scholar 
75.Schrempf H. Actinobacteria within soils: capacities for mutualism, symbiosis and pathogenesis. FEMS Microbiol Lett. 2013;342:77–78.CAS 
PubMed 
Article 

Google Scholar 
76.Giovannoni SJ, Stingl U. Molecular diversity and ecology of microbial plankton. Nature. 2005;437:343–8.CAS 
PubMed 
Article 

Google Scholar 
77.Yilmaz P, Iversen MH, Hankeln W, Kottmann R, Quast C, Glöckner FO. Ecological structuring of bacterial and archaeal taxa in surface ocean waters. FEMS Microbiol Ecol. 2012;81:373–85.CAS 
PubMed 
Article 

Google Scholar 
78.Bienhold C, Zinger L, Boetius A, Ramette A. Diversity and biogeography of bathyal and abyssal seafloor bacteria. PLoS ONE. 2016;11:e0148016.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
79.Rappé MS, Kemp PF, Giovannoni SJ. Phylogenetic diversity of marine coastal picoplankton 16S rRNA genes cloned from the continental shelf off Cape Hatteras, North Carolina. Limnol Oceanogr. 1997;42:811–26.Article 

Google Scholar 
80.Zeng Y-X, Yu Y, Li H-R, Luo W. Prokaryotic community composition in Arctic Kongsfjorden and sub-arctic northern Bering Sea sediments as revealed by 454 pyrosequencing. Front Microbiol. 2017;8:2498.PubMed 
PubMed Central 
Article 

Google Scholar 
81.Fang X-M, Zhang T, Li J, Wang NF, Wang Z, Yu LY. Bacterial community pattern along the sediment seafloor of the Arctic fjorden (Kongsfjorden, Svalbard). Antonie Van Leeuwenhoek. 2019;112:1121–36.PubMed 
Article 

Google Scholar 
82.Ziemert N, Lechner A, Wietz M, Millán-Aguiñaga N, Chavarria KL, Jensen PR. et al. Diversity and evolution of secondary metabolism in the marine actinomycete genus salinispora. Proc Natl Acad Sci USA. 2014;111:e1130–1139.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Manivasagan P, Venkatesan J, Sivakumar K, Kim SK. Pharmaceutically active secondary metabolites of marine actinobacteria. Microbiol Res. 2014;169:262–78.CAS 
PubMed 
Article 

Google Scholar 
84.Kamjam M, Sivalingam P, Deng Z, Hong K. Deep sea Actinomycetes and their secondary metabolites. Front Microbiol. 2017;8:760.PubMed 
PubMed Central 
Article 

Google Scholar 
85.Lewin GR, Carlos C, Chevrette MG, Horn HA, McDonald BR, Stankey RJ. et al. Evolution and ecology of Actinobacteria and their bioenergy applications. Annu Rev Microbiol. 2016;70:235–54.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Matsumoto A, Kasai H, Matsuo Y, Ōmura S, Shizuri Y, Takahashi Y. Ilumatobacter fluminis gen. nov., sp. nov., a novel actinobacterium isolated from the sediment of an estuary. J Gen Appl Microbiol. 2009;55:201–5.CAS 
PubMed 
Article 

Google Scholar 
87.Ghai R, Mizuno CM, Picazo A, Camacho A, Rodriguez-Valera F. Metagenomics uncovers a new group of low GC and ultra-small marine Actinobacteria. Sci Rep. 2013;3:2471.PubMed 
PubMed Central 
Article 

Google Scholar 
88.El Kaoutari A, Armougom F, Gordon J, Raoult D, Henrissat B. The abundance and variety of carbohydrate-active enzymes in the human gut microbiota. Nat Rev Microbiol. 2013;11:497–504.89.Berlemont R, Martiny AC. Glycoside hydrolases across environmental microbial communities. PLoS Comp. Biol. 2016;12:e1005300.Article 
CAS 

Google Scholar 
90.Becker S, Tebben J, Coffinet S, Wiltshire K, Iversen MH, Harder T, et al. Laminarin is a major molecule in the marine carbon cycle. Proc Natl Acad Sci USA. 2020;117:6599–607.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
91.Coutinho MCL, Teixeira VL, Santos CSG. A review of “Polychaeta” chemicals and their possible ecological role. J Chem Ecol. 2018;44:72–94.CAS 
PubMed 
Article 

Google Scholar 
92.Arnosti C. Functional differences between Arctic seawater and sedimentary microbial communities: contrasts in microbial hydrolysis of complex substrates. FEMS Microbiol Ecol. 2008;66:343–51.CAS 
PubMed 
Article 

Google Scholar 
93.Krüger K, Chafee M, Francis TB, Del Rio TG, Becher D, Schweder T, et al. In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes. ISME J. 2019;13:2800–16.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
94.Reintjes G, Arnosti C, Fuchs BM, Amann R. An alternative polysaccharide uptake mechanism of marine bacteria. ISME J. 2017;11:1640–50.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
95.Arnosti C, Jørgensen BB. High activity and low temperature optima of extracellular enzymes in Arctic sediments: implications for carbon cycling by heterotrophic microbial communities. Mar Ecol Prog Ser. 2003;249:15–24.CAS 
Article 

Google Scholar 
96.Arnosti C, Jørgensen BB. Organic carbon degradation in Arctic marine sediments, Svalbard: a comparison of initial and terminal steps. Geomicrobiol J. 2006;23:551–63.CAS 
Article 

Google Scholar  More

Data sourcesNo single comprehensive dataset of planktonic foraminiferal distributional records currently exists. Instead, these data are available from a wide range of sources in many different structures. Some of these sources are compilations of existing data (e.g., Neptune14,15,16, ForCenS21), and others derive from individual sampling sites (e.g. ocean drilling expeditions). Triton combines these disparate sources (Fig. 1) to produce a single spatio-temporal dataset of Cenozoic planktonic foraminifera with updated and consistent taxonomy, age models, and paleo-coordinates.Neptune is currently the most comprehensive database of fossil plankton data, with records exclusively from the DSDP, ODP and IODP representing planktonic foraminifera, calcareous nannofossils, diatoms, radiolaria and dinoflagellates14,15,16. A subset of these sites is included in Neptune, representing those with the most continuous sampling through time. The raw data from Neptune form the core of our dataset. All foraminiferal occurrences for the Cenozoic (i.e. last 66 Ma) were downloaded using the GTS 2012 timescale. In the download options, all questionable identifications and invalid taxa were removed, as were records that had been identified as reworked.In addition to Neptune, three other compilation datasets were included in Triton: ForCenS21, which consists of global core-top samples; the Eocene data from Fenton, et al.8 created based on literature searches for planktonic foraminiferal data in the Eocene; and the land-based records from Lloyd, et al.22 that were created from literature searches. The marine records in Lloyd, et al.22 were not included, as they were obtained from Neptune.Following preliminary compilation of existing datasets, we identified all legacy DSDP, ODP and IODP cores missing from Triton. The online DESCLogik (http://web.iodp.tamu.edu/DESCReport/) and Pangaea17 databases were then mined for .csv files containing planktonic foraminiferal species count data for the missing cores, supplemented with data from AWI_Paleo (URI: http://www.awi.de/en/science/geosciences/marine-geology.html), GIK/IFG (URI: http://www.ifg.uni-kiel.de/), MARUM (URI: https://www.marum.de/index.html), and QUEEN (URI: http://ipt.vliz.be/eurobis/resource?r=pangaea_2747). All additional cores were assessed individually by inspecting the scientific drilling proceedings to determine whether sites were suitable to contribute to our dataset. The primary assessment criterion was identification of continuous sedimentary sections, wherein two or more confidently assigned consecutive chronostratigraphic tie points existed to allow for construction of age models.In addition to these longer cores, many sediment sampling projects have produced planktonic foraminiferal distribution data from shorter cores that tend to correspond in age to the last few million years. The website PANGAEA17 (www.pangaea.de) has been used as a repository for most of these occurrence data. This website was searched using the terms “plank* AND foram”, with resulting datasets downloaded using the R package ‘pangaear’23. These datasets were filtered to exclude records collected using multinets, sediment traps or box cores, as these methods produce samples not easily correlated to sediment cores. Column names allowed for further filtering to exclude records with no species-level data, records that had only isotopic data (rather than abundance data), or records with no age controls.Data processingThe data sources underpinning Triton serve their records in different formats. Therefore, processing was necessary to convert records into a unified framework, with one species per row for each sample and associated metadata (see below for details). Some metadata could be used without modification when available (e.g. water depth, data source), whereas other data needed processing to ensure consistency (e.g. abundance, paleo-coordinates, age). Without this processing, samples from different sources were not directly comparable. Where data were not available, they were set to NA. Those records with missing data in crucial columns (species name, abundance, age, and paleo-coordinates) were removed from the final dataset. All data processing was performed using R v. 3.6.124.Taxonomic consistency is essential to enable comparison of datasets created at different times. The species and synonymy lists used in Triton are based on the Paleogene Atlases20,25,26, with additional information from mikrotax27 (http://www.mikrotax.org/pforams/). These sources were supplemented, when necessary with more up to date literature including Poole and Wade28 and Lam and Leckie29. (A full list of the taxonomic sources can be found in the PFdata.xlsx file18.) A synonymy list was generated to convert species names to the senior synonym. At the same time, typographic errors were corrected. For example, Globototalia flexuosa should be Globorotalia flexuosa. Exclusively Mesozoic taxa were omitted, as were all instances when species names were unclear or imprecise (i.e. not at the species level). Junior synonyms were merged with their senior synonyms and their abundances summed, although the original names and abundances are also retained in the processed dataset. For presence/absence samples, these numerical merged abundances were set to one (i.e. present). The full species list and list of synonyms can be found in the accompanying data.Abundance data for planktonic foraminifera are provided in different formats: presence/absence, binned abundance, relative abundance, species counts, and number of specimens per gram. These metrics were converted into numeric relative abundance to make comparisons easier, although both the original abundance value and its numeric version are retained, as is a record of the abundance type. Presence/absence data were converted to a binary format (one for present; zero for absent). Species counts were converted to relative percent abundances based on the total number of specimens in the sample (this was calculated where it was not already recorded). When full counts were not performed, binned abundances were frequently used. These binned abundances were converted into numeric abundances based on the sequence. So, for example, the categorical labels of N, P, R, F, C, A, D (indicating none, present, rare, few, common, abundant, dominant) were converted to a numerical sequence of 0 to 6. As the meaning of letters can depend on the context (e.g. ‘A’ could be absent or abundant), conversion was done in a semi-automated fashion on a sample-by-sample basis. A value of 0.01 was assigned to records where an inconsistent abundance was recorded (e.g. samples with mostly numeric counts but a few species were designated ‘P’, indicating presence). Samples with zero abundance were retained in the full dataset to provide an indication of sampling.The age of samples were recorded in multiple ways. For some samples, age models provide precise numerical estimates of the age (e.g., those in Neptune). Other samples are dated relative to stratigraphic events such as biostratigraphic zones (including benthic and planktonic foraminifera, diatoms, radiolarians and nannofossils) or magnetic reversals. In this case, ages sometimes needed to be converted to reflect revised age estimates. The start and end dates of biostratigraphic zones are defined in relation to events in marker species, e.g. their speciation, extinction or acme events. All such marker events were updated to their most recent estimates and tuned to the GTS 2020 timescale19. The process of updating included correction of synonymies. Additional care was taken to ensure the correct interpretation of abbreviations (e.g. determining whether LO meant lowest occurrence or last occurrence) based on the entire list of events for a study. Where up-to-date ages were not available or events were ambiguous, they were removed from the age models.The marker events defining a zone can depend on the zonal scheme used. For example, Berggren30 defined the base of the planktonic foraminifera zone M8 as the first occurrence of Fohsella fohsi. Wade et al.31 used this same event to define the base of M9. Therefore, the zonal scheme was recorded when collecting age models, to accurately convert ages to the GTS 2020 time scale. Some marker events have different ages depending on the ocean basin or latitude, and these differences are not necessarily well studied31,32. Where these differences in marker events have been recorded, the coordinates of a site were used to determine whether sites were in the Atlantic or Indo-Pacific Ocean, and whether they were tropical or temperate (with the division at 23.5° latitude). However, this is an area where more research is needed to improve the accuracy of higher-latitude dating32. Magnetostratigraphic ages were also tuned to the GTS 2020 timescale.We constructed new age models for samples not already assigned a numeric age. Where the depths of biostratigraphic events were already recorded, these were converted directly to GTS 2020. Where samples were not given any ages, often the case for the cores collected in the early days of ocean drilling, ages were reconstructed from the shipboard and post-cruise biostratigraphic data available in DESCLogik, Pangaea, and drilling publications. For holes where no tie point data were retrievable, biostratigraphic count data were extracted directly from drilling publications, and biostratigraphic events were assigned via GTS 2020. The first and last occurrences in raw shipboard biostratigraphic data often do not represent true datums, and careful assessment of the shipboard, and post-cruise literature was a prerequisite to confidently assigning chronostratigraphic datums. Tie point depths were assigned as the midpoint depth between the core sample before and after an event. For example, for an extinction event, the recorded depth was the midway point between the last recorded occurrence of a species and the first sample from which the species is absent. All sites were assessed individually to determine the age of the seafloor. Where IODP reports or sample-based publications strictly stated that the sediment surface (i.e. 0.00 meters below seafloor (mbsf)) was deemed to be “Holocene”, “Recent” or “Modern” in age, an additional 0 Ma tie point was assigned appropriately. All samples present outside the maximum/minimum age tie points for that site were removed, as they could not be confidently assigned an age. During assessment, individual drilling reports were investigated for geological structures. Where features such as unconformities, reverse faults, stratigraphic inversions, décollements, and major slips and slumps were identified, separate age models were generated for individual intact stratal intervals to account for potential externally emplaced or repeated strata (see “Age models” and “Triton working” in the figshare data repository18). Similarly, age gaps of greater than 10% of the age range of the core were classified as hiatuses, leading to separate age models (see Fig. 4). Cores of denser sediments that have been sampled using rotary drilling will often have only ~50–60% recovery in a core (9.5 m)33. As it is not possible to determine where the recovered core material came from within this length, all intact core pieces are grouped together as a continuous section from the section top, regardless of where the pieces were sourced (e.g. 4.5 m of recovered material will be recorded as 0–4.5 m of cored interval even if some came from 9–9.5 m). Consequently, age estimates within cores where recovery was low, typically the samples collected longer ago, will necessarily be less certain.Fig. 4Different age model estimates applied to core material from IODP Site U1499A in the South China Sea. Mag – mean age based only on the magnetostratigraphic marker events. Zones – mean age based on all the marker events. Int Mag – interpolation of the points between the magnetostratigraphic marker events. Interp – interpolation between the full set of marker events. Model – the model of age as a function of depth. Note the hiatus between 50 and 100 m. For the shallower section of the dataset, with only three data points, a simple linear model was used. For the deeper section, a GAM smooth was fitted. For this site, the model predictions were chosen as the best fit.Full size imageUsing the updated marker event ages, we created age-depth plots and modelled the best fit to the data. There are different ways of creating these models, and multiple methods were applied to each core. The one that provided the best fit to the original data was chosen (the different age models are available in “Age models” in the figshare data repository18). These choices were confirmed manually (see Fig. 4). The simplest age model used interpolation of the marker events to create ‘zones’ and assign estimated ages assuming a continuous sedimentation rate between the start and end of each of these zones. Where the events do not provide a continuous sequence (e.g. gaps in the zonal markers), age estimates were assigned as the mean of that zone with error estimates of the width of the zone. Where magnetostratigraphic events were present they were given preference. This method leads to different estimates of sedimentation rate for each zone. The more complex age model estimates a smoother sedimentation rate. When there were fewer than 5 marker events, a linear model of age as a function of depth was fitted for the entire core. For larger datasets, generalised additive models (GAMs) for the same variables were used, to allow for variation in sedimentation rates through time. GAMs were run using the mgcv R library, with a gamma value of 1.134. The type of age model used in the analysis was recorded. Where appropriate, the number of points and the r2 of the model are recorded to give an indication of the accuracy of the age model.The latitude and longitude coordinates of samples were recorded in decimal degrees. For all samples except modern ones, plate tectonic reconstructions were necessary to determine the coordinates at which the sample was originally deposited. Reconstructions were performed using the Matthews, et al.35 plate motion model, which is an updated version of the Seton, et al.36 model used by Neptune. Comparisons of age models35,36,37,38,39 suggest this model is most appropriate for the deep sea environment where most of the samples occur, and is able to assign coordinates to significantly more sites than the Scotese39 GPlates model. This test was performed with a subset of the data (10633 unique sites); the Matthews, et al.35 model provided paleocoordinates for 95% of the data, whilst the GPlates model only provided coordinates for 17% of the data. The calculation of paleocoordinates was automated using an adaptation of https://github.com/macroecology/mapast.When sediment samples are derived from multiple sources, duplication will inevitably occur. All such duplicated records, identified based on the combination of species, abundance, sample depth, and coordinate values, were removed. Additionally, working on an individual record level, species that occurred significantly outside their known ranges were flagged (following updated age models) on the assumption these records were misidentifications, contamination or re-working. Records were classified as falling significantly outside their known range if they were more than 5 Ma outside the species’ range in the Palaeogene (66-23 Ma) and more than 2 Ma in the Neogene (23-0 Ma). These values were chosen based on the tradeoff between removing reworked specimens and allowing for some errors in the age estimates. Age estimates for older samples tend to be less precise. Ages were obtained from Lamyman et al. (in prep) and are available in “PFdata” in the figshare data repository18. In total, 10,990 suspect records were flagged (~2% of all records). More

Based on the present-day distribution of photosynthetic bacteria31, we assume a competitive advantage for anoxygenic photosynthetic bacteria in early environments where electron donors such as Fe2+, H2S, or H2 were present. We also assume the contemporaneous existence of environments where cyanobacterial populations could thrive, providing a seedbed for migration. Non-marine waters provide an example of the latter, supported by the branching of non-marine taxa from basal nodes in cyanobacterial phylogenies44,45 and also by the presence of stromatolites in Archean lacustrine successions46, despite the likelihood that many Archean lakes and rivers had low levels of potential electron donors such as Fe2+ and H2S47.Following Jones et al.40 and Ozaki et al.42, we use Fe (iron) and P (phosphorus) to represent the environment, which is similar to the H2 and P employed in other studies48,49. The logic of this choice is that in Archean oceans, Fe2+ is thought to have been the principal electron donor for anoxygenic photosynthesis50,51, whereas P governed total rates of photosynthesis. (Kasting14 argued that H2 was key to photosynthesis on the early Earth, a view supported by low iron concentrations in some early Archean stromatolites52.). In any event, under the conditions of low P availability thought to have characterized early oceans25,40,49,53,54,55, anoxygenic photosynthesis would have depleted limiting nutrients before alternative electron donors were exhausted. In consequence, rates of photosynthetic oxygen production would be low. As iron availability declined and/or P availability increased, the biosphere would inevitably reach a point where P would remain after Fe2+ had been depleted, expanding the range of environments where cyanobacteria are favored by natural selection42.Our model keeps track of the abundances of anoxygenic photosynthetic bacteria (APB), x1, cyanobacteria, x2, and three crucial chemicals: iron(II) (Fe2+), y1, phosphate (PO43−), y2, and dioxygen (O2), z. Both types of bacteria require phosphate for reproduction. APB needs iron(II) (or some other suitable reductant) as an electron donor in photosynthesis. The following five equations describe the reproduction and death of APB and of cyanobacteria as well as the dynamics of iron(II), phosphate, and dioxygen:$${rm{APB}}: {dot{x}}_{1} ={x}_{1}{y}_{1}{y}_{2}-{x}_{1}+{u}_{1}\ {rm{Cyano}}: {dot{x}}_{2} =c{x}_{2}{y}_{2}-{x}_{2}+{u}_{2}\ {{rm{Fe}}}^{2+}: {dot{y}}_{1} ={f}_{1}-{y}_{1}-{x}_{1}{y}_{1}{y}_{2}-{y}_{1}z\ {{rm{PO}}_{4}}^{3-}: {dot{y}}_{2} ={f}_{2}-{y}_{2}-{x}_{1}{y}_{1}{y}_{2}-{x}_{2}{y}_{2}\ {{rm{O}}}_{2}: dot{z} =a{x}_{2}{y}_{2}-bz-{y}_{1}z$$
(1)
Here, we have omitted to write symbols for those rate constants that, for understanding the GOE, can be set to one without loss of generality (Supplementary Note 1). Each remaining rate constant is a free parameter. Equations (1) thus satisfy redox balance by construction. We are left with a system that has five main parameters: c specifies the rate of reproduction of cyanobacteria; f1 and f2 denote the rates of supply of iron(II) and phosphate, respectively; a denotes biogenic production of oxygen; b denotes geochemical consumption of oxygen. Note that iron(II) and phosphate are also removed by geochemical processes at a rate proportional to their abundance. In addition, iron(II) is used up during anoxygenic photosynthesis, and iron(II) reacts with oxygen and is thereby removed from the system. Phosphate is used up during the growth of APB and cyanobacteria. (We investigate extensions of the model that incorporate bounded bacterial growth rates and organic carbon in Supplementary Note 2 and Supplementary Note 3, respectively.)We posit iron(II) as the primary electron donor for anoxygenic photosynthesis, and for simplicity of presentation, we refer to y1 and f1 in this context. However, as noted above, y1 and f1 can similarly represent the abundances and influxes of other alternative electron donors, especially dihydrogen (H2)56,57 and hydrogen sulfide (H2S)58. Our model, its analytical solution, and the conclusions that follow hold equally well by considering any of these electron donors or all together.We also include small migration rates, u1 and u2, which allow for the possibility that APB and cyanobacteria persist in privileged sites from which they can migrate into the main arena of competition. On the Archean Earth, these parameters could have been affected by the flow of water and by surface winds. For the mathematical analysis presented in the main text, we assume that these rates are negligibly small.The GOE represents the transition from a world dominated by APB (Equilibrium E1) to one that is dominated by cyanobacteria (Equilibrium E2) (Figs. S1, S2). On a slowly changing planet, the abundances of APB and cyanobacteria and of the three chemicals are approximately in steady state. Therefore, we consider the fixed points of Eqs. (1).Pure equilibriaIn the absence of APB and cyanobacteria, the abiotic equilibrium abundances of iron(II) and of phosphate are given by f1 and f2, respectively, and there is no oxygen in the system. If f1f2  > 1, then APB can emerge. Subsequently, the system settles to Equilibrium E1, where only APB are present and there is still no oxygen. E1 is stable against invasion of cyanobacteria if$${f}_{1}-{f}_{2}, > ,frac{(c+1)(c-1)}{c}.$$
(2)
This condition can be fulfilled if the influx of iron, f1, is large enough, or if the influx of phosphate, f2, is small enough. The term on the right-hand side of the inequality is an increasing function of the reproductive rate, c, of cyanobacteria.If cf2  > 1, then the system admits another equilibrium, E2, where only cyanobacteria are present and oxygen is abundant. Equilibrium E2 is stable against invasion of APB if$$a(c{f}_{2}-1), > ,(b+c)({f}_{1}-c).$$
(3)
The left-hand side of the inequality is positive. If the right-hand side is negative (that is, if f1  ,c(a-1).$$
(4)
Condition (4) is understood as follows. If b is sufficiently large, then there is not enough atmospheric oxygen for rusting to render E2 stable against invasion of APB before E1 loses stability; the result is stable coexistence. But if b is sufficiently small, then rusting causes E2 to become stable before E1 becomes unstable. The critical value of b therefore depends on the input of atmospheric oxygen for Equilibrium E2; it is an increasing function of the reproductive rate of cyanobacteria and of their rate of production of oxygen.If a  c(a − 1). Figure 3 shows gradual oxygenation due to decreasing f1. In this case, the transition occurs via the mixed equilibrium, (hat{E}), where both types of bacteria coexist (Fig. 4). A subsequent increase in f1 can cause APB to regain dominance (Fig. S3a).Fig. 3: The GOE can be triggered by a decline in the influx of iron(II) and is gradual if b  > c(a − 1).Equilibrium E1 (APB dominate) loses stability and Equilibrium E2 (cyanobacteria dominate) gains stability when f1 drops below ({f}_{1}^{* }) and (f_1^{prime}), respectively. We set f2 = 80, c = 10, a = 10, b = 100, and u1 = u2 = 10−3. a We simulate Eqs. (8) from Supplementary Note 1 with α1 = α2 = β1 = β2 = 1, and we set f1 = 100 − 40(t/105). t* denotes the time at which Equilibrium E1 loses stability. b There is stable coexistence of both types of bacteria for (f_1^{prime} , More

1.Walcott, C. D. Smithson. Misc. Collect. 57, 17–40 (1911).
Google Scholar 
2.Sepkoski, J. J. Jr. Paleobiology 10, 246–267 (1984).Article 

Google Scholar 
3.Hughes, N. C. Curr. Sci. 110, 774–775 (2016).
Google Scholar 
4.Kühl, G., Briggs, D. E. G. & Rust, J. Science 323, 771–773 (2009).Article 

Google Scholar 
5.Moysiuk, J., Smith, M. R. & Caron, J.-B. Nature 541, 394–397 (2017).CAS 
Article 

Google Scholar 
6.Yang, X. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01490-4 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Sánchez, M. Embryos in Deep Time (Univ. California Press, 2012).8.Fusco, G., Hong, P. S. & Hughes, N. C. Proc. R. Soc. Lond. B 281, 20133037 (2014).
Google Scholar 
9.Hughes, N. C., Hong, P. S., Hou, J. & Fusco, G. Front. Ecol. Evol. 5, 37 (2017).Article 

Google Scholar 
10.Hopkins, M. J. Pap. Palaeontol. 7, 985–1002 (2020).Article 

Google Scholar 
11.Moczek, A. P. et al. Evol. Dev. 17, 198–219 (2015).Article 

Google Scholar 
12.Walossek, D. & Müller, K. J. Lethaia 23, 409–427 (1990).Article 

Google Scholar 
13.Fu, D., Ortega-Hernández, J., Daley, A. C., Zhang, X. & Shu, D. BMC Evol. Biol. 18, 147 (2018).Article 

Google Scholar 
14.Hughes, N. C., Kříž, J., MacQuaker, J. H. S. & Huff, W. D. Bull. Geosci. 89, 219–238 (2014).Article 

Google Scholar 
15.Hartnoll, R. G. & Bryant, A. D. J. Crustac. Biol 10, 14–19 (1990).Article 

Google Scholar 
16.Minelli, A. & Fusco, G. Evolving Pathways–Key Themes in Evolutionary Developmental Biology (Cambridge Univ. Press, 2008). More