Ecology

FAO. Global Forest Resource Assessment 2020—Key Findings (FAO, 2020).
Google Scholar 
Rasmussen, L. V. et al. A combination of methods needed to assess the actual use of provisioning ecosystem services. Ecosyst. Serv. 17, 75–86 (2016).Article 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the World’s Forests. Science 333, 988 (2011).ADS 
CAS 
Article 

Google Scholar 
Gao, J., Tang, X. G., Lin, S. Q. & Bian, H. Y. The influence of land use change on key ecosystem services and their relationships in a mountain region from past to future (1995–2050). Forests 12, 616 (2021).Article 

Google Scholar 
Rodríguez-Echeverry, J., Echeverría, C., Oyarzún, C. & Morales, L. Impact of land-use change on biodiversity and ecosystem services in the Chilean temperate forests. Landsc. Ecol. 33(3), 439–453 (2018).Article 

Google Scholar 
Hoque, M. Z., Islam, I., Ahmed, M., Hasan, S. S. & Prodhan, F. A. Spatio-temporal changes of land use land cover and ecosystem service values in coastal Bangladesh. Egypt. J. Remote Sens. Space Sci. 25(1), 173–180 (2022).
Google Scholar 
Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change 26, 152–158 (2014).Article 

Google Scholar 
Sil, Â. et al. Analysing carbon sequestration and storage dynamics in a changing mountain landscape in Portugal: Insights for management and planning. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 13(2), 82–104 (2017).Article 

Google Scholar 
Xu, Y., Tang, H., Wang, B. & Chen, J. Effects of land-use intensity on ecosystem services and human well-being: A case study in Huailai County, China. Environ. Earth. Sci. 75(5), 416 (2016).Article 

Google Scholar 
Liang, Y., Liu, L. & Huang, J. Integrating the SD-CLUE-S and InVEST models into assessment of oasis carbon storage in northwestern China. PLoS ONE 12(2), e0172494 (2017).Article 

Google Scholar 
Zhao, M. et al. Assessing the effects of ecological engineering on carbon storage by linking the CA-Markov and InVEST models. Ecol. Indic. 98, 29–38 (2019).Article 

Google Scholar 
Leh, M. D., Matlock, M. D., Cummings, E. C. & Nalley, L. L. Quantifying and mapping multiple ecosystem services change in West Africa. Agric. Ecosyst. Environ. 165, 6–18 (2013).Article 

Google Scholar 
Zhao, Z. et al. Assessment of carbon storage and its influencing factors in Qinghai-Tibet Plateau. Sustainability 10(6), 1864 (2018).Article 

Google Scholar 
Fu, Q. et al. Scenario analysis of ecosystem service changes and interactions in a mountain-oasis-desert system: A case study in Altay Prefecture, China. Sci. Rep. 8(1), 1–13 (2018).ADS 

Google Scholar 
Li, Z., Cheng, X. & Han, H. Future impacts of land use change on ecosystem services under different scenarios in the ecological conservation area, Beijing, China. Forests 11(5), 584 (2020).CAS 
Article 

Google Scholar 
Liu, H., Xiao, W., Li, Q., Tian, Y. & Zhu, J. Spatio-temporal change of multiple ecosystem services and their driving factors: A case study in Beijing, China. Forests 13(2), 260 (2022).CAS 
Article 

Google Scholar 
Nizami, S. M. The inventory of the carbon stocks in sub tropical forests of Pakistan for reporting under Kyoto Protocol. J. For. Res. 23(3), 377–384 (2012).CAS 
Article 

Google Scholar 
Ghafoor, G. Z., Sharif, F., Khan, A. U., Shahzad, L. & Hayyat, M. U. Assessment of tree biomass carbon stock of subtropical scrub forest, Soan valley Pakistan. App. Ecol. Environ. Res. 18(2), 2231–2245 (2020).Article 

Google Scholar 
Siddiq, Z. et al. Models to estimate the above and below ground carbon stocks from a subtropical scrub forest of Pakistan. Glob. Ecol. Conserv. 27, e01539 (2021).Article 

Google Scholar 
Ali, A., Ashraf, M. I., Gulzar, S. & Akmal, M. Estimation of forest carbon stocks in temperate and subtropical mountain systems of Pakistan: Implications for REDD+ and climate change mitigation. Environ. Monit. Assess. 192(3), 1–13 (2020).Article 

Google Scholar 
Mannan, A. et al. Application of land-use/land cover changes in monitoring and projecting forest biomass carbon loss in Pakistan. Glob. Ecol. Conserv. 17, e00535 (2019).Article 

Google Scholar 
Khan, A. U. et al. Piloting restoration initiatives in subtropical scrub forest: Specifying areas asserting adaptive management. Environ. Monit. Assess. 191(11), 675 (2019).Article 

Google Scholar 
Mohajane, M. et al. Land use/land cover (LULC) using Landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments 5(12), 131 (2018).Article 

Google Scholar 
Brown, J. NDVI, the foundation for remote sensing phenology. In USGS Remote Sensing Phenology: Vegetation Indices (2015).Liping, C., Yujun, S. & Saeed, S. Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PLoS ONE 13(7), e0200493 (2018).Article 

Google Scholar 
Ricke, K., Drouet, L., Caldeira, K. & Tavoni, M. Country-level social cost of carbon. Nat. Clim. Change 8(10), 895–900 (2018).ADS 
CAS 
Article 

Google Scholar 
Salehi, M. H., Beni, O. H., Harchegani, H. B., Borujeni, I. E. & Motaghian, H. R. Refining soil organic matter determination by loss-on-ignition. Pedosphere 21(4), 473–482 (2011).Article 

Google Scholar 
Tivet, F. et al. Soil carbon inventory by wet oxidation and dry combustion methods: Effects of land use, soil texture gradients, and sampling depth on the linear model of C-equivalent correction factor. Soil Sci. Soc. Am. J. 76(3), 1048–1059 (2012).ADS 
CAS 
Article 

Google Scholar 
Government of Punjab. The Punjab Forest (Amendment) Act, 2010 (Government of the Punjab, 2010).
Google Scholar 
Kamwi, J. M., Kaetsch, C., Graz, F. P., Chirwa, P. & Manda, S. Trends in land use and land cover change in the protected and communal areas of the Zambezi Region, Namibia. Environ. Monit. Assess. 189(5), 242 (2017).Article 

Google Scholar 
Negassa, M. D., Mallie, D. T. & Gemeda, D. O. Forest cover change detection using Geographic Information Systems and remote sensing techniques: A spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia. Environ. Syst. Res. 9(1), 1 (2020).Article 

Google Scholar 
Government of Punjab. Punjab Development Statistics 2007. Burreau of Statistics (Government of the Punjab, 2007).
Google Scholar 
Government of Punjab. Punjab Development Statistics 2013. Burreau of Statistics (Government of the Punjab, 2013).
Google Scholar 
Government of Punjab. Punjab Development Statistics 2019. Burreau of Statistics (Government of the Punjab, 2019).
Google Scholar 
Dunn, R. J. H., Stanitski, D. M., Gobron, N. & Willett, K. M. State of the climate in 2019: Global climate. Special online supplement to the B. Am. Meteorol. Soc. 101(8), S9. https://doi.org/10.1175/BAMS-D-20-0104.1 (2020).Article 

Google Scholar 
Gray, S. B. & Brady, S. M. Plant developmental responses to climate change. Dev. Biol. 419(1), 64–77 (2016).CAS 
Article 

Google Scholar 
Ghafoor, G. Z. et al. Effect of climate warming on seedling growth and biomass accumulation of Acacia modesta and Olea ferruginea in a subtropical scrub forest of Pakistan. Écoscience 29, 1–14 (2021).
Google Scholar 
Bibi, S., Sultana, J., Sultana, H. & Malik, R. N. Ethnobotanical uses of medicinal plants in the highlands of Soan valley, salt range, Pakistan. J. Ethnopharmacol. 155(1), 352–361 (2014).Article 

Google Scholar 
Chaudhry, Q. U. Z. Climate Change Profile of Pakistan (Asian Development Bank, 2017).
Google Scholar 
Shaheen, H. et al. Carbon stocks assessment in subtropical forest types of Kashmir Himalayas. Pak. J. Bot. 48, 2351–2357 (2016).CAS 

Google Scholar 
Arunyawat, S. & Shrestha, R. P. Assessing land use change and its impact on ecosystem services in Northern Thailand. Sustainability 8(8), 768 (2016).Article 

Google Scholar 
Sing, L., Metzger, M. J., Paterson, J. S. & Ray, D. A review of the effects of forest management intensity on ecosystem services for northern European temperate forests with a focus on the UK. For. Int. J. For. Res. 91(2), 151–164 (2018).
Google Scholar  More

Suding, K. Understanding successes and failures in restoration ecology. Annu. Rev. Ecol. Evol. Syst. 42, (2011).Brudvig, L. A. et al. Interpreting variation to advance predictive restoration science. J. Appl. Ecol. 54, 1018–1027 (2017).Article 

Google Scholar 
Germino, M. J. et al. Thresholds and hotspots for shrub restoration following a heterogeneous megafire. Landsc. Ecol. 33, 1177–1194 (2018).Article 

Google Scholar 
Shriver, R. K. et al. Transient population dynamics impede restoration and may promote ecosystem transformation after disturbance. Ecol. Lett. 22, 1357–1366 (2019).PubMed 
Article 

Google Scholar 
Chambers, J. C. et al. Resilience and resistance of sagebrush ecosystems: implications for state and transition models and management treatments. Rangel. Ecol. Manag. 67, 440–454 (2014).Article 

Google Scholar 
Pilliod, D. S., Welty, J. L. & Toevs, G. R. Seventy-five years of vegetation treatments on public rangelands in the great basin of North America. Rangelands 39, 1–9 (2017).Article 

Google Scholar 
Applestein, C., Germino, M. J., Pilliod, D. S., Fisk, M. R. & Arkle, R. S. Appropriate sample sizes for monitoring burned pastures in sagebrush steppe: how many plots are enough, and can one size fit all? Rangel. Ecol. Manag. 71, 721–726 (2018).Article 

Google Scholar 
Homer, C. et al. Completion of the 2011 National Land Cover Database for the Conterminous United States-Representing a Decade of Land Cover Change Information Landsat-based mapping project. Photogramm. Eng. Remote Sens. 81, 345–354 (2015).
Google Scholar 
Homer, C. G., Aldridge, C. L., Meyer, D. K. & Schell, S. J. Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: Laying a foundation for monitoring. Int. J. Appl. Earth Obs. Geoinf. 14, 233–244 (2012).ADS 

Google Scholar 
Tredennick, A. T. et al. Forecasting climate change impacts on plant populations over large spatial extents. Ecosphere 7, 1–16 (2016).Article 

Google Scholar 
Rigge, M. et al. Quantifying western U.S. rangelands as fractional components with multi-resolution remote sensing and in situ data. Remote Sens. 12, 1–26 (2020).Article 

Google Scholar 
Shi, H., Homer, C., Rigge, M., Postma, K. & Xian, G. Analyzing vegetation change in a sagebrush ecosystem using long-term field observations and Landsat imagery in Wyoming. Ecosphere 11, 1–20 (2020).Article 

Google Scholar 
Williamson, M. A., Schwartz, M. W. & Lubell, M. N. Spatially explicit analytical models for social–ecological systems. Bioscience 68, 885–895 (2018).
Google Scholar 
Reid, J. L., Fagan, M. E. & Zahawi, R. A. Positive site selection bias in meta-analyses comparing natural regeneration to active forest restoration. Sci. Adv. 4, 1–4 (2018).Article 

Google Scholar 
Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS One 4, 1–6 (2009).Article 
CAS 

Google Scholar 
Prach, K., Šebelíková, L., Řehounková, K. & del Moral, R. Possibilities and limitations of passive restoration of heavily disturbed sites. Landsc. Res. 45, 247–253 (2020).Article 

Google Scholar 
Andam, K. S., Ferraro, P. J., Pfaff, A., Sanchez-Azofeifa, G. A. & Robalino, J. A. Measuring the effectiveness of protected area networks in reducing deforestation. Proc. Natl Acad. Sci. USA 105, 16089–16094 (2008).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Jones, K. W. & Lewis, D. J. Estimating the counterfactual impact of conservation programs on land cover outcomes: The role of matching and panel regression techniques. PLoS One 10, 1–22 (2015).
Google Scholar 
Christie, A. P. et al. Simple study designs in ecology produce inaccurate estimates of biodiversity responses. J. Appl. Ecol. 56, 2742–2754 (2019).Article 

Google Scholar 
Larsen, A. E., Meng, K. & Kendall, B. E. Causal analysis in control–impact ecological studies with observational data. Methods Ecol. Evol. 10, 924–934 (2019).Article 

Google Scholar 
Parkhurst, T., Prober, S. M., Hobbs, R. J. & Standish, R. J. Global meta-analysis reveals incomplete recovery of soil conditions and invertebrate assemblages after ecological restoration in agricultural landscapes. J. Appl. Ecol. 1–15. https://doi.org/10.1111/1365-2664.13852. (2021)Crouzeilles, R. et al. A global meta-Analysis on the ecological drivers of forest restoration success. Nat. Commun. 7, 1–8 (2016).Article 
CAS 

Google Scholar 
Kettenring, K. M. & Adams, C. R. Lessons learned from invasive plant control experiments: a systematic review and meta-analysis. J. Appl. Ecol. 48, 970–979 (2011).Article 

Google Scholar 
Atkinson, J. & Bonser, S. P. “Active” and “passive” ecological restoration strategies in meta-analysis. Restor. Ecol. 28, 1032–1035 (2020).Article 

Google Scholar 
Rosenbaum, P. R. & Rubin, D. B. The central role of the propensity score in observational studies for causal effects. Biometrika 170–184. https://doi.org/10.1017/CBO9780511810725.016. (1983)Angrist, J. D., & Pischke, J. S. Mostly harmless econometrics. (Princeton University Press, 2009).Bernes, C. et al. How are biodiversity and dispersal of species affected by the management of roadsides? A systematic map. Environ. Evid. 6, 1–16 (2017).Article 

Google Scholar 
França, F. et al. Do space-for-time assessments underestimate the impacts of logging on tropical biodiversity? An Amazonian case study using dung beetles. J. Appl. Ecol. 53, 1098–1105 (2016).Article 

Google Scholar 
Davies, K. W. et al. Saving the sagebrush sea: an ecosystem conservation plan for big sagebrush plant communities. Biol. Conserv. 144, 2573–2584 (2011).Article 

Google Scholar 
Miller, R. F. et al. Characteristics of Sagebrush Habitats and Limitations to Long-term Conservation. Greater sage-grouse: ecology and conservation of a landscape species and its habitats. USGS Adm. Rep. (2011).Pierson, F. B. et al. Hydrologic and erosion responses of sagebrush steppe following juniper encroachment, wildfire, and tree cutting. Rangel. Ecol. Manag. 66, 274–289 (2013).Article 

Google Scholar 
Wijayratne, U. C. & Pyke, D. A. Burial increases seed longevity of two Artemisia tridentata (Asteraceae) subspecies. Am. J. Bot. 99, 438–447 (2012).PubMed 
Article 

Google Scholar 
Pyke, D. A., Wirth, T. A. & Beyers, J. L. Does seeding after wildfires in rangelands reduce erosion or invasive species? Restor. Ecol. 21, 415–421 (2013).Article 

Google Scholar 
Knutson, K. C. et al. Long-term effects of seeding after wildfire on vegetation in Great Basin shrubland ecosystems. J. Appl. Ecol. 51, 1414–1424 (2014).Article 

Google Scholar 
Shriver, R. K. et al. Adapting management to a changing world: Warm temperatures, dry soil, and interannual variability limit restoration success of a dominant woody shrub in temperate drylands. Glob. Chang. Biol. 24, 4972–4982 (2018).PubMed 
Article 
ADS 

Google Scholar 
Eiswerth, M. E., Krauter, K., Swanson, S. R. & Zielinski, M. Post-fire seeding on Wyoming big sagebrush ecological sites: Regression analyses of seeded nonnative and native species densities. J. Environ. Manag. 90, 1320–1325 (2009).Article 

Google Scholar 
Arkle, R. S. et al. Quantifying restoration effectiveness using multi-scale habitat models: Implications for sage-grouse in the Great Basin. Ecosphere 5, 1–32 (2014).Article 

Google Scholar 
Davies, K. W. & Bates, J. D. Restoring big sagebrush after controlling encroaching western juniper with fire: aspect and subspecies effects. Restor. Ecol. 25, 33–41 (2017).Article 

Google Scholar 
Davies, K. W., Bates, J. D. & Boyd, C. S. Postwildfire seeding to restore native vegetation and limit exotic annuals: an evaluation in juniper-dominated sagebrush steppe. Restor. Ecol. 27, 120–127 (2019).Article 

Google Scholar 
Davies, K. W., Boyd, C. S., Madsen, M. D., Kerby, J. & Hulet, A. Evaluating a seed technology for Sagebrush restoration across an elevation gradient: support for Bet Hedging. Rangel. Ecol. Manag. 71, 19–24 (2018).Article 

Google Scholar 
Rinella, M. J. et al. High precipitation and seeded species competition reduce seeded shrub establishment during dryland restoration. Ecol. Appl. 25, 1044–1053 (2015).Davies, K. W., Boyd, C. S. & Nafus, A. M. Restoring the sagebrush component in crested wheatgrass-dominated communities. Rangel. Ecol. Manag. 66, 472–478 (2013).Article 

Google Scholar 
United States General Accounting. WILDLAND FIRES: Better Information Needed on Effectiveness of Emergency Stabilization and Rehabilitation Treatments. Report to Congressional Requesters. https://doi.org/10.1089/blr.2006.9996. (2003)Requena-Mullor, J. M., Maguire, K. C., Shinneman, D. J. & Caughlin, T. T. Integrating anthropogenic factors into regional-scale species distribution models—A novel application in the imperiled sagebrush biome. Glob. Chang. Biol. 00, 1–15 (2019).
Google Scholar 
Pyke, D. A. et al. Restoration handbook for sagebrush steppe ecosystems with emphasis on greater sage-grouse habitat—Part 3. Site level restoration decisions. U.S. Geological Survey Circular 1426 (2017).Chambers, J. C. et al. Science framework for conservation and restoration of the sagebrush biome: Linking the department of the interior’s integrated rangeland fire management strategy to long-term strategic conservation actions. USDA . Serv. – Gen. Tech. Rep. RMRS-GTR 2017, 1–217 (2017).
Google Scholar 
US-BLM. Burned Area Emergency Stabilization and Rehabilitation – BLM Handbook H-1742-1. 2, (2007).Pilliod, D. S. & Welty, J. L. Land Treatment Digital Library. Data Series. https://doi.org/10.3133/ds806. (2013)Bradley, B. A. et al. Cheatgrass (Bromus tectorum) distribution in the intermountain Western United States and its relationship to fire frequency, seasonality, and ignitions. Biol. Invasions 20, 1493–1506 (2018).Article 

Google Scholar 
Fusco, E. J., Finn, J. T., Balch, J. K., Chelsea Nagy, R. & Bradley, B. A. Invasive grasses increase fire occurrence and frequency across US ecoregions. Proc. Natl Acad. Sci. USA 116, 23594–23599 (2019).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
O’Connor, R. C. et al. Small-scale water deficits after wildfires create long-lasting ecological impacts. Environ. Res. Lett. 15, 044001 (2020).Applestein, C., Caughlin, T. T. & Germino, M. J. Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites. Ecosphere 12, (2021).Cameron, A. C. & Miller, D. L. A. Practitioner’ s Guide to Cluster-Robust Inference. J. Human Resources. 50, 317–372 (2015).Oshchepkov, A. & Shirokanova, A. Bridging the gap between multilevel modeling and economic methods. Soc. Sci. Res. in press, (2022).Aldridge, C. L. & Boyce, M. S. Linking occurrence and fitness to persistence: habitat-based approach for endangered Greater Sage-Grouse. Ecol. Appl. 17, 508–526 (2007).PubMed 
Article 

Google Scholar 
Allen-Diaz, B. & Bartolome, J. W. Sagebrush-grass vegetation dynamics: Comparing Classical and State-Transition models. Ecol. Appl. 8, 795–804 (1998).
Google Scholar 
Schlaepfer, D. R., Lauenroth, W. K. & Bradford, J. B. Natural regeneration processes in big sagebrush (Artemisia tridentata). Rangel. Ecol. Manag. 67, 344–357 (2014).Article 

Google Scholar 
Melgoza, G., Nowak, R. S. & Tausch, R. J. Soil water exploitation after fire: competition between Bromus tectorum (cheatgrass) and two native species. Oecologia 83, 7–13 (1990).PubMed 
Article 
ADS 

Google Scholar 
Williamson, M. A. et al. Fire, livestock grazing, topography, and precipitation affect occurrence and prevalence of cheatgrass (Bromus tectorum) in the central Great Basin, USA. Biol. Invasions 22, 663–680 (2020).Article 

Google Scholar 
Groves, A. M., Bauer, J. T. & Brudvig, L. A. Lasting signature of planting year weather on restored grasslands. Sci. Rep. 10, 1–10 (2020).Article 
CAS 

Google Scholar 
Groves, A. M. & Brudvig, L. A. Interannual variation in precipitation and other planting conditions impacts seedling establishment in sown plant communities. Restor. Ecol. 27, 128–137 (2019).Article 

Google Scholar 
Werner, C. M., Stuble, K. L., Groves, A. M. & Young, T. P. Year effects: Interannual variation as a driver of community assembly dynamics. Ecology 0, 1–8 (2020).
Google Scholar 
Stuble, K. L., Fick, S. E. & Young, T. P. Every restoration is unique: testing year effects and site effects as drivers of initial restoration trajectories. J. Appl. Ecol. 54, 1051–1057 (2017).Article 

Google Scholar 
Stuble, K. L., Zefferman, E. P., Wolf, K. M., Vaughn, K. J. & Young, T. P. Outside the envelope: rare events disrupt the relationshipbetween climate factors and species interactions. Ecology 98, 1623–1630 (2017).PubMed 
Article 

Google Scholar 
Hardegree, S. P. et al. Weather-Centric Rangeland Revegetation Planning. Rangel. Ecol. Manag. 71, 1–11 (2018).Article 

Google Scholar 
Allison, B., Cara, S-W. & Applestein, M. J., Germino Interannual variation in climate contributes to contingency in post‐fire restoration outcomes in seeded sagebrush steppe. Conservation Science and Practice https://doi.org/10.1111/csp2.12737.Callaway, B. & Sant’Anna, P. H. C. Difference-in-Differences with multiple time periods. J. Econom. 225, 200–230 (2021).MathSciNet 
MATH 
Article 

Google Scholar 
Goodman-Bacon, A. Difference-in-differences with variation in treatment timing. J. Econom. 225, 254–277 (2021).MathSciNet 
MATH 
Article 

Google Scholar 
Starrs, C. F., Butsic, V., Stephens, C. & Stewart, W. The impact of land ownership, firefighting, and reserve status on fire probability in California. Environ. Res. Lett. 13, (2018).Ferraro, P. J. & Miranda, J. J. Panel data designs and estimators as substitutes for randomized controlled trials in the evaluation of public programs. J. Assoc. Environ. Resour. Econ. 4, 281–317 (2017).
Google Scholar 
Schlaepfer, D. R., Lauenroth, W. K. & Bradford, J. B. Modeling regeneration responses of big sagebrush (Artemisia tridentata) to abiotic conditions. Ecol. Modell. 286, 66–77 (2014).Article 

Google Scholar 
Kleinhesselink, A. R. & Adler, P. B. The response of big sagebrush (Artemisia tridentata) to interannual climate variation changes across its range. Ecology 99, 1139–1149 (2018).PubMed 
Article 

Google Scholar 
Brabec, M. M., Germino, M. J. & Richardson, B. A. Climate adaption and post-fire restoration of a foundational perennial in cold desert: insights from intraspecific variation in response to weather. J. Appl. Ecol. 54, 293–302 (2017).Article 

Google Scholar 
Eidenshink, J. C. et al. A project for monitoring trends in burn severity. Fire Ecol. 3, 3–21 (2007).Article 

Google Scholar 
Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5. http://cran.r-project.org/doc/Rnews/ (2005).Applestein, C. & Germino, M. J. Detecting shrub recovery in sagebrush steppe: comparing Landsat-derived maps with field data on historical wildfires. Fire Ecol. 17, (2021).Rigge, M. et al. Rangeland fractional components across the western United States from 1985 to 2018. Remote Sens. 13, 1–26 (2021).Article 

Google Scholar 
Hijmans, R. J. & van Etten, J. raster: Geographic analysis and modeling with raster data. (2012).U.S. Geological, S. 1/3rd arc-second Digital Elevation Models (DEMs)–USGS National Map 3DEP Downloadable Data Collection. (2017).Walkinshaw, Mike, A. T. O’Geen, D. E. B. Soil Properties. California Soil Resource Lab,McCune, B. & Keon, D. Equations for potential annual direct incident radiation and heat load. J. Veg. Sci. 13, 603–606 (2002).Article 

Google Scholar 
Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33, 121–131 (2013).Article 

Google Scholar 
Ferraro, P. J. & Hanauer, M. M. Advances in measuring the environmental and social impacts of environmental programs. Annu. Rev. Environ. Resour. 39, 495–517 (2014).Article 

Google Scholar 
Butsic, V., Lewis, D. J., Radeloff, V. C., Baumann, M. & Kuemmerle, T. Quasi-experimental methods enable stronger inferences from observational data in ecology. Basic Appl. Ecol. 19, 1–10 (2017).Article 

Google Scholar 
Ho, D., Imai, K., King, G. & Stuart, E. MatchIt: nonparametric preprocessing for parametric causal inference. J. Stat. Softw. 42, 1–28, https://www.jstatsoft.org/v42/i08/ (2011).Article 

Google Scholar 
Guo, S. & Fraser, M. Propensity score analysis: statistical methods and applications. (Sage Publications, 2010).Puhani, P. A. The treatment effect, the cross difference, and the interaction term in nonlinear “difference-in-differences” models. Econ. Lett. 115, 85–87 (2012).MathSciNet 
MATH 
Article 

Google Scholar 
Schlaepfer, D. R., Lauenroth, W. K. & Bradford, J. B. Effects of ecohydrological variables on current and future ranges, local suitability patterns, and model accuracy in big sagebrush. Ecography (Cop.). 35, 374–384 (2012).Article 

Google Scholar 
Stan Development Team. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org (2020).Bürkner, P. C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, (2017).Mahr, T. & Gabry, J. bayesplot: Plotting for Bayesian Models. https://mc-stan.org/bayesplot/ R package version (2021).Kay, M. tidybayes: Tidy Data and Geoms for Bayesian Models. https://doi.org/10.5281/zenodo.1308151 R package version 3.0.1. (2021).Simler-Williamson, A. & Germino, M. J. Data associated with “Statistical consideration of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action”. https://doi.org/10.25338/B8W63R (2022).Simler‐Williamson, A. B. R code associated with “Statistical consideration of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action”. https://doi.org/10.5281/zenodo.6565074 (2022). More

The biotic induction of OAE-2The rapid proliferation of select microbial communities at 427.54 mcd likely represents a pre-OAE biotic perturbation (pre-OAE BP) presaging the protracted period of widespread marine deoxygenation during OAE-2, and progressive deoxygenation predating the +CIE7 (Fig. 4). At the beginning of the pre-OAE BP (427.54 mcd), abruptly elevated tetrapyrroles and crenarchaeol concentrations signify an abrupt increase in primary production by photoautotrophs and chemoautotrophs residing above the chemocline. Increased volumes of precipitating biogenic snow concordantly consumed oxygen, expanding the preexisting OMZ as anaerobic bacteria thrived based on accelerated obGDGTs synthesis. Euxinia did not penetrate the photic zone at the outset of the productivity bloom as isorenieratane was not detected and heightened rates of microbial sulfate reduction were seemingly transient, inferred from the DAGEs profile, and limited to pre-OAE BP initiation. The lack of a well-stratified water column, evinced by absent to low concentrations of halophilic archaeal lipids (i.e., extended archaeols), relatively low rates of microbial sulfate reduction, and a dense oxygenic microbial plate likely precluded the development of PZE initially.Establishing a definitive causal mechanism for the pre-OAE BP is difficult, but the concomitance of LIP activity with the productivity spike is intriguing. Application of a linear sedimentation rate from OAE-2 to the pre-OAE BP interval following previous works6,7 approximated the pre-OAE BP occurring 220 ± 4 kyr before OAE-2, lasting for ~100 kyr (427.54–426.88 mcd; see Estimating the duration of the pre-OAE BP in Supplementary Information for rationale and calculation). Significantly, this was roughly coincident with the onset of LIP activity (~200–300 kyr before OAE-2) inferred from marine osmium isotope stratigraphy27. Similarities in the modern planktonic community response, such as elevated productivity and compositional changes, between the 2018 Kilauea eruption28 and the pre-OAE BP reinforce inference of a potential magmatic trigger for this event (see Evidence for LIP trigger of the pre-OAE biotic perturbation in Supplementary Information for additional details).A constant, yet overall lower, nutrient and trace metal inventory6 (Fig. S4) combined with a redox-driven shift in fixed N species (from NO3− to NH4+)15, potentially leading to a fixed N shortage29 via intensified denitrification and annamox reactions30, were probable culprits in the failure to sustain prolific rates of primary production beyond 100 kyr at the Demerara Rise. The gradual decline in biomass production, indicated by decreasing tetrapyrrole and crenarchaeol profiles (Fig. 4), was accompanied by a notable shift in deep water communities. Sulfate-reducing bacteria exerted increasing predominance over methanogenic archaea, a trend coeval with the primary productivity spike and extending well into the OAE (Fig. 3). A collapse of autotrophic communities to pre-perturbation levels was concordant with the progressive shoaling of H2S-laden waters. Continued vertical migration of the chemocline intruded the photic zone, producing PZE that enabled anoxygenic photosynthesis by Chlorobiaceae (Fig. 4). Unlike the overall oscillatory character of PZE throughout the studied section, this protracted phase of PZE was sustained until the onset of OAE-2 (426.43–426.00 mcd, Figs. 3 and 4) and is approximately contemporaneous with a thallium (Tl) isotope excursion7 (426.40–426.30 mcd).The positive Tl isotope excursion represents the progressive expansion of bottom water anoxia predating OAE-2 by 43 ± 11 kyr6,7. However, evidence for a causal mechanism of pre-OAE deoxygenation remains indeterminate. Our comprehensive biomarker inventory provides an interpreted sequence of events culminating in the regional to global expansion of anoxia predating OAE-2. A protracted phase of enhanced primary productivity began ~220 ± 4 kyr prior to OAE-2, increasing localized production and export of organic carbon at Demerara Rise. Similar productivity spikes likely occurred in settings of comparable paleogeographic configuration (e.g., equatorial, continental margins/shelves), seeding the oceans with fixed carbon. Continued scavenging of marine oxygen via organic carbon remineralization resulted in OMZ expansion locally, and likely initiated oxygen drawdown in much of the proto-North Atlantic Ocean. Stratigraphic records of sulfur isotopes of pyrite (δ34Spyrite) from the proto-North Atlantic and Tethys Oceans11 validate the areal extrapolation of our interpretations. A gradual decline in δ34Spyrite values at Demerara Rise begins at 427.50 mcd, nearly identical to the onset of the pre-OAE BP (427.54 mcd, Fig. 4). Correlation of δ34Spyrite in a global transect (Western Interior Seaway, proto-North Atlantic, Tethys) revealed consistent behavior in δ34Spyrite prior to the +CIE, indicating increasingly expansive marine deoxygenation on a global scale11. Over ~100 kyr, increased regional biomass production induced pervasive marine anoxia, inhibiting Mn-oxide formation, producing the observed positive Tl isotope excursion, and ultimately, the globally observed +CIE reflecting enhanced organic carbon burial signaling the onset of OAE-2. Thus, the local biotic signal recorded at ODP Site 1258 underlines the crucial role the Demerara Rise, and similar undocumented settings, served in initiating deoxygenation of the global ocean.Microbial ecological dynamics during and after OAE-2Changes in microbial community compositions during OAE-2 were apparent, signified by a shift in the normalized total biomarker pool (Fig. 3) and variations in the absolute concentrations of individual biomarkers (Fig. 4). In general, OAE-2 was defined by an expansion and diversification of intermediate and deep water communities (426.00–423.07 mcd), followed by a period of instability leading to the termination of the OAE (423.07–422.00 mcd). Photo- and chemoautotrophs residing above the chemocline were adversely affected, evinced by relatively low, invariant tetrapyrrole and crenarchaeol profiles (Fig. 4). Based on these observations, we divided OAE-2 into two periods defined by contrasting paleoenvironmental conditions modulating the microbial inhabitants of Demerara Rise.The first period of OAE-2 (426.00–423.07 mcd, Fig. 4) was marked by the intrusion of a euxinic OMZ into the photic zone. Elevated, yet fluctuating isorenieratane concentrations suggest relatively persistent PZE of varying vertical extent, in agreement with previous investigations using biomarkers and nitrogen isotopes at nearby sites12,13,31. During this interval, microbial sulfate reduction was likely active as DAGEs continually increased, aligning with estimates of expanded seafloor euxinia32. The co-occurrence of abundant extended archaeols and isorenieratane intimates the role that density stratification served in maintaining the protracted PZE of OAE-2, substantiating concurrent findings based on neodymium33 and oxygen isotopes34. Vertical nutrient advection via upwelling35 led to preferential exposure to expanding intermediate water communities tolerant to sulfidic conditions in the OMZ. Scavenging of a potentially limited fixed N inventory30, depleted in NO3− and predominated by NH4+[ 15,29, and inhibition of efficient nutrient transfer by pronounced density stratification likely induced severe N deficiency in surface water communities, explaining the relatively muted productivity of oxygenic photoautotrophs (i.e., tetrapyrroles) and chemoautotrophs (i.e., crenarchaeol) observed (Fig. 4). The concentration and predominant utilization of fixed N in the OMZ led to the proliferation and diversification of intermediate and deep water microbial taxa, while a shoaling chemocline led to increased nutrient (i.e., fixed N) competition between photoautotrophs and retreating Thaumarchaeota as highlighted by our biomarker inventory and the nitrogen isotopic record31. These findings challenge previous interpretations of highly productive, predominantly eukaryotic primary producers reliant on the upwelling of isotopically depleted NH4+ during OAE-215. Instead, the decline of C30-17-nor-DPEP (Fig. S5; Supplementary Data 3), a source-specific tetrapyrrole diagenetically derived from algal chlorophyll-c36, and reconstructed water column conditions during OAE-2 indirectly support a rise in cyanobacteria, diazotrophs able to fix N2, in oxygenated, nutrient-depleted shallow waters. Increased cyanobacterial contribution is further supported by C and N stable isotopes16,37, as well as the prominence of potentially phylum-specific biomarkers across OAE-2 (e.g., 2-methylhopanoids6,14).Fig. 5: Contrasting biogeochemical conditions between the pre-OAE BP and OAE-2.a, b Microbial ecology and water column conditions during the pre-OAE BP, reflecting high primary production of organic carbon (a) and OAE-2, characterized by relatively lower organic carbon production, but substantially enhanced biomass preservation (b). c, d Averaged fractional abundances of individual biomarkers throughout the pre-OAE BP (c) and OAE-2 (d). Biomarker source organisms are abbreviated as follows: phytoplankton (P), ammonia oxidizing archaea (AOA), sulfur oxidizing bacteria (SOB), unknown anaerobic bacteria (UAB), sulfate reducing bacteria (SRB), halophilic archaea (HA), methanogenic archaea (MA).Full size imageA reversal from the formerly outlined conditions typified the second period of OAE-2 (423.07–421.99 mcd, Fig. 4). Destabilization of the stratified water column and reduced production of H2S led to deepening and contraction of the euxinic OMZ. The observed decline in halophilic archaea, coincident with an overall decline in Chlorobiaceae populations, is roughly coeval with positive neodymium isotopic excursions observed across the proto-North Atlantic33 attributed to the enhanced latitudinal commingling of proto-North Atlantic water masses38. Although detrimental to sustained PZE, the persistence of a well-developed anaerobic bacterial community (i.e., obGDGTs) suggests the lasting presence of a non-euxinic OMZ despite improved bottom water circulation. A premature recovery of the chemoautotrophic Thaumarchaeota, inhabiting the base of the photic zone, relative to the shallower dwelling obligately oxygenic phototrophs (Fig. 3) likely reflects reduced toxicity associated with retreating euxinic waters, lessened resource competition with [primarily] Chlorobiaceae, and a competitive advantage tied to preferential exposure to upwelled nutrients and tolerance to low O2 conditions.The termination of OAE-2 was marked by the temporary re-establishment of microbial community compositions mirroring those observed prior to the pre-OAE BP (Figs. 3 and 4). Contraction of the OMZ led to a deep chemocline, with PZE restricted to the basal photic zone as the production of reduced sulfide species diminished. The Thaumarchaeota continued the recovery initiated towards the latter half of OAE-2, accompanied by the rebounding oxygenic photoautotrophs. However, the recovery of shallow autotrophic communities was halted by an episode of PZE (421.19–421.04 mcd) based on abrupt increases in isorenieratane concentrations (Fig. 4). Temporary development of pronounced density stratification likely facilitated the accumulation of H2S in the lower to intermediate photic zone, producing the short-lived PZE episode. Interestingly, covariant responses observed in additional biomarker profiles (e.g., obGDGTs) to PZE during OAE-2 were not evident across this post-OAE interval, possibly due to the transient nature of PZE at this time. For example, the initial increase in isorenieratane concentrations at the onset of OAE-2 was not immediately accompanied by shifts in other biomarker classes (e.g., obGDGTs; Fig. 4), suggesting frequent recurrences of PZE may be required to illicit a major microbial ecological response as observed later during the OAE. Still, this brief episode of post-OAE PZE (421.19–421.04 mcd) coincides with a positive organic carbon isotope excursion9 (Fig. S5), trace metal drawdown6 (Fig. S4), and minor positive Tl isotope excursion7 at the Demerara Rise. Prior study7 tentatively attributed this interval to enhanced carbon burial during a post-OAE deoxygenation event of smaller magnitude, with subsequent work revealing continued pyrite burial post-OAE 211. Our biomarker inventory revealed some environmental consistencies (e.g., PZE) between this interval and OAE-2, but the overall biotic response to this post-OAE geochemical perturbation was relatively subdued and requires additional sampling and investigation to properly constrain.Broader implicationsThe recognition of the pre-OAE BP and evolving water column conditions at Demerara Rise highlights additional complexities of a dynamic ocean relevant to interpretations of OAE-2 and the +CIE. Enhanced, sustained, and widespread carbon burial is required to produce the +CIE used to define OAE-28,10. Still, the principal forcing, productivity or preservation, remains enigmatic as evidence for the former mounts12,39.Based on the tetrapyrrole profiles (Fig. 4) primary production was greatest during the pre-OAE BP and relatively muted throughout OAE-2 at Demerara Rise, assuming minimal alteration to the genetic tetrapyrrole stratigraphic signal. Biomass preservation was presumedly enhanced during OAE-2 through sulfurization11, as the OMZ transitioned from anoxic to euxinic and penetrated the photic zone, yet low tetrapyrrole concentrations persist. Previous work noted a similar discrepancy between preservation potential and porphyrin abundance, postulating a paucity of trace metals to chelate with the free-base porphyrins induced poor preservation as desulfurization did not reveal additional porphyrin content16. However, both the pre-OAE BP and OAE-2 were characterized by relatively depleted trace metal inventories6 (Fig. S4), yet exhibit contrasting tetrapyrrole profiles, suggesting relative changes in primary production were the predominate control on the stratigraphic distribution of tetrapyrroles across the studied interval at the Demerara Rise. The strong covariance between tetrapyrrole and crenarchaeol concentrations reinforces the interpretation tetrapyrroles faithfully reflect primary production (Fig. S6). Crenarchaeol, a biosynthetic product of chemoautotrophic archaea (Thaumarchaeota) comprising up to 20% of all archaea and bacteria in the modern ocean40, is structurally distinct from the tetrapyrroles making it likely that diagenetic alteration of the two biomarkers is not consistent in rate or form. Thus, the positive correlation between key proxies for major contributors to primary production, the photoautotrophs and chemoautotrophs, minimizes concern for the integrity of the biotic signal at Demerara Rise (see Tetrapyrroles as a record of primary production in Supplementary Information for additional details).These findings provide direct evidence for a causal mechanism resulting in both the Tl isotope excursion and +CIE as previously described. It is highly probable the pre-OAE BP was not exclusive to the Demerara Rise based on the immense and presently unconstrained organic carbon burial required to produce the +CIE10. Further characterization of comparable localities to Demerara Rise may reveal similar high productivity events, as primed, highly productive settings likely capitalized on exogenous nutrient delivery via efficient upwelling to the photic zone prior to stratification during OAE-2. Hence, OAE-2 and the +CIE were not coincident with heightened surface water productivity relative to the pre-OAE BP at the Demerara Rise. Rather, antecedent increases in primary production locally facilitated the initiation of the OAE as a mechanism to consume marine oxygen and subsequently enhance organic carbon preservation globally. This highlights how OAE-2, and perhaps other OAEs in the geologic record, were not instantaneously induced but rather a gradual transition stemming from sustained forcing(s). In addition, the occurrence of the pre-OAE BP well before the established onset of OAE-2 reveals how fluctuations in primary production can be linked to marine deoxygenation but may not necessarily be concurrent. As shown here, OAE-2 at the Demerara Rise was preceded by elevated primary production that progressively attenuated towards event onset. While the hallmark features of an OAE are well-established, further identification and refinement of trends preceding widespread anoxia in the past will improve our understanding of how marine deoxygenation develops, as well as our ability to assess planetary health today.A shift from a productivity- to preservation-dominant system during OAE-2 at Demerara Rise, and possibly similar paleogeographic settings experiencing the pre-OAE BP, facilitated substantial organic carbon burial producing the +CIE. Distinct shifts in water column chemistry and structure from the pre-OAE BP to OAE-2 imparted considerable changes on microbial life, which altered the primary driver governing biomass sequestration (Fig. 5). Yet, both intervals reveal relatively comparable carbonate-corrected total organic carbon values6 (Fig. S5), signifying enhanced preservation as a critical component of organic carbon burial during OAE-2 at Demerara Rise. Consequently, this work suggests that sustained increases in primary production prior to OAE-2 initiated and regulated pre-OAE deoxygenation, resulting in a progressive shift to preservation as the primary control on organic carbon accumulation in sediments. Expanding euxinia and attendant changes to biogeochemical cycling adversely affected primary producers while simultaneously enhancing organic matter preservation via sulfurization11. Flourishment of Thaumarchaeota in oligotrophic settings in the modern open ocean41, and lack thereof during OAE-2 based on diminished crenarchaeol concentrations, underscores the scarcity of bioessential elements (e.g., fixed N) caused by microbial utilization of electron acceptors further down the redox ladder due to intensified marine anoxia, ultimately limiting primary production. The switch from a productivity to preservation model, reconstructed using biomarkers (Fig. 5) and initially suggested based on drawdown of the trace metal inventory6, was also concomitant with relative warming4. Simulated projections of the marine microbial response to continued global warming in the future revealed similar biotic trends (e.g., decreased primary productivity) to warming-induced oceanographic changes42 (e.g., intensified stratification) observed during OAE-2. Thus, an abundance of proxy- and model-based results paired with conceptual evidence suggest relatively low production and enhanced preservation of organic carbon throughout OAE-2 at the equatorial Demerara Rise.The pre-OAE BP may foreshadow greater regional trends observed during OAE-2. Equatorial upwelling centers, like Demerara Rise, are spatially restricted and represent regions of already high primary production before OAE-2. Climatic shifts concurrent with OAE-2 may have produced favorable conditions for elevated primary productivity in regions unable to capitalize on or exposed to allochthonous nutrient delivery prior to the +CIE. While the pre-OAE BP offers a causal mechanism for the Tl isotope excursion and +CIE initiation, areal expansion of organic carbon preservation and production is necessary to sustain enhanced organic carbon burial for the duration of the +CIE.Continued development of preexisting proxies is critical to extract and clarify current understandings of major climatic events in Earth history. Although reliant on excellent preservation of the microbial signal, the analytical and interpretative approach used here enables simultaneous examination of a wide array of biomarkers, producing a more holistic reconstruction of oceanographic changes inferred from microbial ecological variations spanning the surface to the sediment. This is timely, as investigations of the sedimentary archives become increasingly valuable analogs to understand the response of modern oceans to natural and anthropogenic forcings. Similarities between the pre-OAE BP and modern, climate-driven marine deoxygenation are concerning, while particular attention to preexisting highly productive settings may hold the key to forecasting the geologically rapid transition to a global OAE. Even though natural processes are currently beyond our control, stifling anthropogenic catalysts of climate change may decelerate the unfortunate, progressive suitability of OAEs as climate analogs in the future. More

Experimental siteAll procedures for the experiment involving animals were carried out in accordance with relevant guidelines and regulations and they were approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Florida (protocol #201509019). The experiment was conducted at the University of Florida North Florida Research and Education Center (NFREC) located in Marianna, FL (30° 52ʹ N, 85° 11ʹ W, 35 m asl) during 2018 and 2019.The study site was an existing mixed RP-bahiagrass grazing study where ‘Ecoturf’ RP was strip-planted into ‘Argentine’ bahiagrass on 12 June 2014. Rhizoma peanut strips were approximately 2-m wide, making it possible to harvest RP forage, roots, and rhizomes free of bahiagrass contamination3,4. The RP was collected from a nursery at the University of Florida—NFREC, whereas the bahiagrass seeds were bought from a seed company. All plants were collected, purchased, managed, and the research was conducted in compliance with relevant institutional, the corresponding national, and international guidelines and legislation.Soils at the experimental site were classified as Orangeburg loamy sand (fine-loamy, kaolinitic, thermic Typic Kandiudults24. At the beginning of the study, soil pH was 5.7 and soil OM was 15.4 g kg−1. Additionally, Mehlich-I extractable soil P, K, Mg, and Ca concentrations at the beginning of the experiment were 26, 99, 43, and 224 mg kg−1, respectively. Total annual rainfall and average annual temperature at the experimental site were 1889 and 602 mm, and 19 and 21 °C, for 2018 and 2019, respectively, and their monthly averages are shown in Fig. 5.Figure 5Monthly weather conditions at North Florida Research and Education Center (NFREC) Marianna, FL, during the experimental years.Full size imageTreatments and experimental designTreatments were two defoliation regimes applied to RP, continuously stocking and 56-days interval between clipping harvests. At the continuous stocking, stocking rates were variable to maintain similar herbage allowance among pastures, which was assessed every 14 days as described by Sollenberger et al.25. Two tester Angus crossbred steers (Bos spp.) remained on each pasture throughout the experimental period. Put-and-take cattle were allocated as needed to maintain a target herbage allowance of 1.5 kg DM kg−1 bodyweight3. Treatments were situated adjacent to each other (i.e., paired sites) in monoculture strips of RP within each of three 0.85-ha pastures. Each pasture was considered a block, thus the experiment consisted of three replicates of each treatment in a randomized complete block design. Within each replicate, treatments had three repetitions (pseudo replicates). To prohibit animal access to the non-grazed treatment, three 2 × 2-m exclusion cages were placed on RP strips in each pasture. Rhizoma peanut herbage mass was determined at both the grazed and non-grazed sites three times each year, at days 56, 112, and 168 of the experimental period by using a 0.25-m−2 quadrat. Two quadrats were collected in each repetition by clipping all the biomass within each quadrat at 2-cm stubble height. After each herbage mass sampling, the non-grazed residual dry matter inside the cages was clipped to a 2-cm stubble height using a weed eater and the herbage removed by raking. On average, across sampling dates and years, herbage mass at the grazed and non-grazed sites was 1050 and 1810 kg of organic matter (OM) ha−1, respectively.Long-term and short-term decomposition studiesThere were two types of root-rhizome decomposition trials. The first is referred to as the long-term decomposition study, and the second is the short-term decomposition study. The long-term study had an incubation period of 168 days, with a single in-situ incubation per year starting in May. The short-term study had in-situ incubation periods of 56 days and there were three incubations per year, occurring in May, June, and August. In all cases, only roots and rhizomes attached to the plant were used in both trials.Long-term studyOn 26 Apr. 2018 and on 23 Apr. 2019, right after RP emergence after breaking dormancy, RP roots and rhizomes were collected from an existing mixed RP-bahiagrass grazing study where RP had been planted in strips into bahiagrass (Paspalum notatum Flüggé) in 2014. Rhizoma peanut strips were approximately 2.75-m wide, alternating with similar wide bahiagrass strips. A pure stand of RP had been maintained in the strips during previous years using herbicides3, making it possible to harvest RP roots and rhizomes free of bahiagrass contamination. Roots and rhizomes were collected at 24 different points in each of three blocks of the original experiment. Roots and rhizomes were collected at 20-cm depth using shovels. As defoliation treatments had not being applied at this time of the year, the same material was used to perform the incubation inside and outside the exclusion cages. After harvesting, excess soil was removed by shaking from the root-rhizome mat using a 1.4-cm diameter sieve. Thereafter, the existing aboveground material was clipped, and the roots and rhizomes were then washed over the same sieve to remove the remaining soil. After washing, roots and rhizomes were dried to constant weight in a forced-air drying oven at 55 °C.To perform the decomposition study, approximately 12 g of dry roots and rhizomes were placed in Ankom bags (10 by 20 cm, 50 µm porosity; ANKOM Technology) and sealed17. Roots and rhizomes were aimed to be placed intact into Ankom bags, nonetheless, when they could not fit inside the bags, they were cut in the middle before being placed. On 2 May 2018 and 1 May 2019, the incubation period began. For each treatment, bags were incubated in situ in the field at 10-cm depth in the same blocks from which they were collected. Bags were removed from the field after 0, 3, 7, 14, 28, 56, 112, and 168 days. For each treatment within each block, three bags were incubated for each incubation time. Additionally, empty bags (one bag per treatment per time per block) were placed in the field. After removal of the in-situ bags from the field, samples and empty bags were dried at 55 °C for 72 h, cleaned with a brush, and weighed. Thereafter, samples were ground to pass a 2-mm screen using a Wiley Mill (Model 4, Thomas-Wiley Laboratory Mill, Thomas Scientific) and analyzed for DM and OM. Subsamples of the 2-mm ground samples were ball milled in a Mixer Mill (MM 400, Retsch) at 25 Hz for 9 min. Ball-milled samples were analyzed for C and N by dry combustion using an elemental analyzer (Vario Micro cube, Elementar). Additionally, samples ground at 2-mm were used to determine ADF in aboveground samples26. The N concentration in the ADF was determined using the above protocol to obtain the ADIN.Short-term studiesThe short-term studies were performed following the same procedures as the long-term study, except that the incubation period was only 56 days, and these studies were repeated three times each year. Roots and rhizomes were incubated in situ on 2 May, 27 June, and 23 Aug. 2018 and on 1 May, 26 June, and 21 Aug. 2019, following the same protocol as described above, except that bags were removed from the field after 0, 3, 7, 14, 28, and 56 days of incubation. The incubations occurring in May, June, and August will be referred as early, middle, and late season, respectively.The early-season incubation period uses the data from the first 56 days of the long-term study described above. For the middle- and late-season incubations each year, roots and rhizomes were harvested approximately 7 days days prior to incubation. Approximately six points in each repetition were collected at 20-cm depth using shovels. For the grazed treatment, roots and rhizomes were collected in the grazed area nearby the exclusion cages, whereas for the non-grazed treatment, the material was collected inside the exclusion cages. After removal of the bags from the field, they were processed and analyzed for DM, OM, C, and N following the protocol described above.Statistical analysesLong-term studyRemaining biomass, remaining N, C:N ratio, ADF, and ADIN were analyzed using the PROC GLIMMIX from SAS27, with treatment and days of incubation as fixed effects, and years and blocks as random effects. Days of incubation were considered repeated measures. Means were compared using the PDIFF procedure at the 5% significance level. When treatment or the interaction of treatment × day of incubation were statistically significant in the ANOVA, nonlinear models were tested to fit the data for each variable and treatment. Nonlinear models were selected for a given response based on data distribution and type of response. If only days of incubation was significant, the same model was applied for all treatments.Remaining biomass (OM basis), remaining N, and C:N ratio were explained by the single exponential decay model14,17,28. The equation describing this process is:$$X=B0, {exp}^{-kt},$$
(1)
where X is the remaining biomass, remaining N, or C:N ratio at day t, B0 is the disappearance coefficient, and k is the relative decay rate (g g−1 day−1). The model used to describe ADF and ADIN was the two-stage model “linear plateau”15,29. The equation describing this process is:$$begin{gathered} Xt = A + b1 times t, {text{if t }} le {text{ T}}, hfill \ {text{and}},{ } Xt = A + b1 times T, {text{if t }} > {text{ T}}, hfill \ end{gathered}$$
(2)
where X is the concentration of ADIN, t is the day of incubation, A is the initial concentration, b1 is the rate of increase in concentration from the beginning of incubation until plateau is reached; and T is the day in which concentration reaches the plateau.Short-term studiesThe single exponential model was applied in the remaining OM and remaining N, for each experimental unit, to obtain individual values for B0 and k. The data for initial N concentration, initial C:N ratio, and B0 and k for remaining OM and remaining N were analyzed using the PROC GLIMMIX from SAS27, with treatment and period as fixed effects, and years and blocks as random effects. Means were compared using the PDIFF procedure at the 5% significance level.Arrive guidelinesThis is study is reported in accordance to ARRIVE guidelines. More

Marcogliese, D. J. & Cone, D. K. Food webs: A plea for parasites. Trends Ecol. Evol. 12, 320–325. https://doi.org/10.1016/S0169-5347(97)01080-X (1997).CAS 
Article 
PubMed 

Google Scholar 
Marcogliese, D. J. Food webs and the transmission of parasites to marine fish. Parasitology 124(7), 83–99. https://doi.org/10.1017/s003118200200149x (2002).Article 

Google Scholar 
Marcogliese, D. J. Parasites: Small players with crucial roles in the ecological theater. EcoHealth 1, 151–164. https://doi.org/10.1007/s10393-004-0028-3 (2004).Article 

Google Scholar 
Marcogliese, D. J. Parasites of the superorganism: Are they indicators of ecosystem health?. Int. J. Parasitol. 35(7), 705–716. https://doi.org/10.1016/j.ijpara.2005.01.015 (2005).Article 
PubMed 

Google Scholar 
Rasmussen, T. K. & Randhawa, H. S. Host diet influences parasite diversity: A case study looking at tapeworm diversity among sharks. Mar. Ecol. Prog. Ser. 605, 1–16. https://doi.org/10.3354/meps12751 (2018).ADS 
Article 

Google Scholar 
Vidal-Martínez, V. M., Pech, D., Sures, B., Purucker, S. T. & Poulin, R. Can parasites really reveal environmental impact?. Trends Parasitol. 26(1), 44–51. https://doi.org/10.1016/j.pt.2009.11.001 (2010).Article 
PubMed 

Google Scholar 
Derbel, H., Châari, M. & Neifar, L. Digenean species diversity in teleost fishes from the Gulf of Gabes, Tunisia (Western Mediterranean). Parasite 19(2), 129–135. https://doi.org/10.1051/parasite/2012192129 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mattiucci, S. et al. Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica). Int. J. Parasitol. Parasites Wildl. 4(3), 356–367. https://doi.org/10.1016/j.ijppaw.2015.10.004 (2015).Sures, B., Nachev, M., Selbach, C. & Marcogliese, D. J. Parasite responses to pollution: what we know and where we go in ‘Environmental Parasitology’. Parasit. Vectors 10, 65. https://doi.org/10.1186/s13071-017-2001-3 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Santoro, M., Iaccarino, D. & Bellisario, B. Host biological factors and geographic locality influence predictors of parasite communities in sympatric sparid fishes off the southern Italian coast. Sci. Rep. 10(1), 13283. https://doi.org/10.1038/s41598-020-69628-1 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Thomas, F., Poulin, R., de Meeüs, T., Guégan, J. F. & Renaud, F. Parasites and ecosystem engineering: What roles could they play?. Oikos 84, 167–171. https://doi.org/10.2307/3546879 (1999).Article 

Google Scholar 
Timi, J. T. & Poulin, R. Why ignoring parasites in fish ecology is a mistake. Int. J. Parasitol. 50(10–11), 755–761. https://doi.org/10.1016/j.ijpara.2020.04.007 (2020).Article 
PubMed 

Google Scholar 
Lafferty, et al. Parasites in food webs: The ultimate missing links. Ecol. Lett. 11, 533–546. https://doi.org/10.1111/j.1461-0248.2008.01174.x (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Heithaus, M. R., Frid, A., Wirsing, A. J. & Worm, B. Predicting ecological consequences of marine top predator declines. Trends Ecol. Evol. 23(4), 202–210. https://doi.org/10.1016/j.tree.2008.01.003 (2008).Article 
PubMed 

Google Scholar 
Stevens, J. D., Bonfil, R., Dulvy, N. K. & Walker, P. A. The effects of fishing on sharks, rays, and chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES J. Mar. Sci. 57(3), 476–494. https://doi.org/10.1006/jmsc.2000.0724 (2000).Article 

Google Scholar 
Myers, R. A., Baum, J. K., Shepherd, T. D., Powers, S. P. & Peterson, C. H. Cascading effects of the loss of apex predatory sharks from a coastal ocean. Science 315(5820), 1846–1850. https://doi.org/10.1126/science.1138657 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Palm, H. W. Fish parasites as biological indicators in a changing world: can we monitor environmental impact and climate change? In: Progress in Parasitology (ed. Melhorn, H.) 223–250 (Berlin, 2011).Dallarés, S. Twenty thousand parasites under the sea: A multidisciplinary approach to parasite communities of deep-dwelling fishes from the slopes of the Balearic Sea (NW Mediterranean). PhD Thesis. Universitat Autònoma de Barcelona. (2016).Dallarés, S., Pérez-del-Olmo, A., Montero, F. E. & Carrassón, M. Composition and seasonal dynamics of the parasite communities of Scyliorhinus canicula (L., 1758) and Galeus melastomus Rafinesque. (Elasmobranchii) from the NW Mediterranean Sea in relation to host biology and ecological features. Hydrobiologia 799(275–291). https://doi.org/10.1007/s10750-017-3226-z (2017).Article 

Google Scholar 
Dallarés, S., Padrós, F., Cartes, J. E., Solé, M. & Carrassón, M. The parasite community of the sharks Galeus melastomus, Etmopterus spinax and Centroscymnus coelolepis from the NW Mediterranean deep-sea in relation to feeding ecology and health condition of the host and environmental gradients and variables. Deep Sea Res. Part I Oceanogr. Res. Pap. 129, 41–58. https://doi.org/10.1016/j.dsr.2017.09.007 (2017).Ebert, D. A. & Dando, M. Field Guide to Sharks, Rays & Chimaeras of Europe and the Mediterranean. 385 (Princeton University Press, 2021).Santoro, M., Bellisario, B., Crocetta, F., Degli Uberti, B. & Palomba, M. A molecular and ecological study of Grillotia (Cestoda: Trypanorhyncha) larval infection in small to mid-sized benthonic sharks in the Gulf of Naples, Mediterranean Sea. Ecol. Evol. 11(20), 13744–13755. https://doi.org/10.1002/ece3.7933 (2021).Crocetta, F. et al. Bottom-trawl catch composition in a highly polluted coastal area reveals multifaceted native biodiversity and complex communities of fouling organisms on litter discharge. Mar. Environ. Res. 155, 104875. https://doi.org/10.1016/j.marenvres.2020.104875 (2020).CAS 
Article 
PubMed 

Google Scholar 
Hay Mele, B. et al. Ecological assessment of anthropogenic impact in marine ecosystems: the case of Bagnoli Bay. Mar. Environ. Res. 158, 104953. https://doi.org/10.1016/j.marenvres.2020.104953 (2020).CAS 
Article 
PubMed 

Google Scholar 
Rizzo, L., Musco, L. & Crocetta, F. Cohabiting with litter: Fish and benthic assemblages in coastal habitats of a heavily urbanized area. Mar. Poll. Bull. 164, 112077. https://doi.org/10.1016/j.marpolbul.2021.112077 (2021).CAS 
Article 

Google Scholar 
Tanduo, V., Osca, D. & Crocetta, F. A bycatch surprise: Scyllarus subarctus Crosnier, 1970 (Decapoda: Achelata: Scyllaridae) in the Mediterranean Sea. J. Crust. Biol. 41(2), ruab010. https://doi.org/10.1093/jcbiol/ruab010 (2021).Follesa, M. C. & Carbonara, P. Atlas of the maturity stages of Mediterranean fishery resources in General Fisheries Commission for the Mediterranean.Studies and Reviews (ed. FAO) 259 (FAO, 2019).Le Cren, E. D. The length-weight relationship and seasonal cycle in gonad weight and condition in the perch (Perca fuviatilis). J. Anim. Ecol. 20, 201–219 (1951).Article 

Google Scholar 
Mouine, N., Francour, P., Ktari, M. H. & Chakroun-Marzouk, N. The reproductive biology of Diplodus sargus sargus in the Gulf of Tunis (Central Mediterranean). Sci. Mar. 71(3), 461–469. https://doi.org/10.3989/scimar.2007.71n3461 (2007).Article 

Google Scholar 
Santoro, M., Palomba, M., Mattiucci, S., Osca, D. & Crocetta, F. New parasite records for the sunfish Mola mola in the Mediterranean Sea and their potential use as biological tags for long-distance host migration. Front. Vet. Sci. 7, 579728. https://doi.org/10.3389/fvets.2020.579728 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Kabata, Z. Parasitic Copepoda of British Fishes. 468 (The Ray Society, British Museum, 1979).Bray, R. A. & Moore, A. B. M. The first record of the elasmobranch parasite Diphterostomum betencourti (Monticelli, 1893) (Digenea, Zoogonidae) in the coastal waters of southern England. Acta Parasitol. 45(4), 299–302 (2000).
Google Scholar 
Gibson, D. I. Superfamily Azygioidea Lühe, 1909 in Keys to the Trematoda. Vol. 1(eds. Gibson, D. I., Jones, A. & Bray, R. A.) 19–24 (CAB International, 2002).Anderson, R. C., Chabaud, A. G. & Willmott, S. CIH keys to the nematode parasites of vertebrates: Archival volume (eds. Anderson, R. C., Chabaud, A. G. & Willmott, S.) 463 (CAB International, 2009).Palm, H. W. The Trypanorhyncha Diesing, 1863 (IPB-PKSPL Press, 2004).
Google Scholar 
Dallarés, S., Pérez-del-Olmo, A., Carrassón, M. & Kuchta, R. Morphological and molecular characterisation of Ditrachybothridium macrocephalum Rees, 1959 (Cestoda: Diphyllidea) from Galeus melastomus Rafinesque in the Western Mediterranean. Syst. Parasitol. 92, 45–55. https://doi.org/10.1007/s11230-015-9586-8 (2015).Article 
PubMed 

Google Scholar 
Zhu, X., Gasser, R. B., Podolska, M. & Chilton, N. B. Characterisation of anisakid nematodes with zoonotic potential by nuclear ribosomal DNA sequences. Int. J. Parasitol. 28(12), 1911–1921. https://doi.org/10.1016/s0020-7519(98)00150-7 (1998).CAS 
Article 
PubMed 

Google Scholar 
Van der Auwera, G., Chapelle, S. & De Wachter, R. Structure of the large ribosomal subunit RNA of Phytophthora megasperma, and phylogeny of the oomycetes. FEBS Lett. 338(2), 133–136. https://doi.org/10.1016/0014-5793(94)80350-1 (1994).Article 
PubMed 

Google Scholar 
Palm, H. W., Waeschenbach, A., Olson, P. D. & Littlewood, D. T. Molecular phylogeny and evolution of the Trypanorhyncha Diesing, 1863 (Platyhelminthes: Cestoda). Mol. Phyl. Evol. 52(2), 351–367. https://doi.org/10.1016/j.ympev.2009.01.019 (2009).CAS 
Article 

Google Scholar 
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Morgulis, A. et al. Database indexing for production MegaBLAST searches. Bioinformatics 24(16), 1757–1764. https://doi.org/10.1093/bioinformatics/btn322 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al. revisited. J. Parasitol. 83, 575–583. https://doi.org/10.2307/3284227 (1997).CAS 
Article 
PubMed 

Google Scholar 
Euzet, L. & Combes, C. Les problèmes de l’espèces chez les animaux parasites. Bull. Soc. Zool. Fr. 40, 239–285 (1980).Article 

Google Scholar 
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26(1), 32–46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x (2001).Article 

Google Scholar 
McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data: .a comment on distance-based redundancy analysis. Ecology 82(1), 290–297. https://doi.org/10.1890/0012-9658(2001)082[0290:FMMTCD]2.0.CO;2 (2001).Article 

Google Scholar 
Locke, S. A., McLaughlin, J. D. & Marcogliese, D. J. Predicting the similarity of parasite communities in freshwater fishes using the phylogeny, ecology and proximity of hosts. Oikos 122(1), 73–83. https://doi.org/10.1111/j.1600-0706.2012.20211.x (2013).Article 

Google Scholar 
McArtor, D. B., Lubke, G. H. & Bergeman, C. S. Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic. Psychometrika 82, 1052–1077. https://doi.org/10.1007/s11336-016-9527-8 (2018).MathSciNet 
Article 
MATH 

Google Scholar 
Core Team. R: a language and environment for statistical computing. https://www.R-project.org/ (2017).Caira, J. N., Bueno, V. & Jensen, K. Emerging global novelty in phyllobothriidean tapeworms (Cestoda: Phyllobothriidea) from sharks and skates (Elasmobranchii). Zool. J. Linnean Soc. 193, 1336–1363. https://doi.org/10.1093/zoolinnean/zlaa185 (2021).Article 

Google Scholar 
Fanelli, E., Rey, J., Torres, P. & Gil de Sola, L. Feeding habits of blackmouth catshark Galeus melastomus Rafinesque, 1810 and velvet belly lantern shark Etmopterus spinax (Linnaeus, 1758) in the western Mediterranean. J. Appl. Ichthyol. 25(1), 83–93. https://doi.org/10.1111/j.1439-0426.2008.01112.x (2009).Article 

Google Scholar 
Anastasopoulou, A. et al. Diet and feeding strategy of blackmouth catshark Galeus melastomus. J. Fish Biol. 83(6), 1637–1655. https://doi.org/10.1111/jfb.12269 (2013).CAS 
Article 
PubMed 

Google Scholar 
D’Iglio, C. et al. Biological and ecological aspects of the blackmouth catshark (Galeus melastomus Rafinesque, 1810) in the Southern Tyrrhenian Sea. J. Mar. Sci. Eng. 9(9), 967. https://doi.org/10.3390/jmse9090967 (2021).Article 

Google Scholar 
Saldanha, L., Almeida, A. J., Andrade, F. & Guerreiro, J. Observations on the diet of some slope dwelling fishes of Southern Portugal. Int. Rev. der Gesamten Hydrobiol. 80(2), 217–234. https://doi.org/10.1002/iroh.19950800210 (1995).Article 

Google Scholar 
Mnasri, N., El Kamel, O., Boumaïza, M., Reynaud, C. & Capapé, C. Food and feeding habits of the small-spotted catshark, Scyliorhinus canicula (Chondrichthyes: Scyliorhinidae) from the northern coast of Tunisia (central Mediterranean). Cah. Biol. Mar. 53(1), 139–150 (2012).
Google Scholar 
Šantić, M., Rađa, B. & Pallaoro, A. Feeding habits of small-spotted catshark (Scyliorhinus canicula Linnaeus, 1758) from the eastern central Adriatic Sea. Mar. Biol. Res. 8(10), 1003–1011. https://doi.org/10.1080/17451000.2012.702912 (2012).Article 

Google Scholar 
Mattiucci, S., Cipriani, P., Levsen, A., Paoletti, M. & Nascetti, G. Molecular epidemiology of Anisakis and Anisakiasis: an ecological and evolutionary road map. Adv. Parasitol. 99, 93–263. https://doi.org/10.1016/bs.apar.2017.12.001 (2018).Article 
PubMed 

Google Scholar 
Santoro, M. et al. Helminth parasites of the dwarf sperm whale Kogia sima (Cetacea: Kogiidae) from the Mediterranean Sea, with implications on host ecology. Dis. Aquat. Org. 129(3), 175–182. https://doi.org/10.3354/dao03251 (2018).CAS 
Article 

Google Scholar 
Cipriani, P. et al. The Mediterranean European hake, Merluccius merluccius: detecting drivers influencing the Anisakis spp. larvae distribution. Fish. Res. 202, 79–89. https://doi.org/10.1016/j.fishres.2017.07.010 (2018).Article 

Google Scholar 
Levsen, A. et al. Anisakis species composition and infection characteristics in Atlantic mackerel, Scomber scombrus, from major European fishing grounds—Reflecting changing fish host distribution and migration pattern. Fish. Res. 202, 112–121. https://doi.org/10.1016/j.fishres.2017.07.030 (2018).Article 

Google Scholar 
Palomba, M., Mattiucci, S., Crocetta, F., Osca, D. & Santoro, M. Insights into the role of deep-sea squids of the genus Histioteuthis (Histioteuthidae) in the life cycle of ascaridoid parasites in the Central Mediterranean Sea waters. Sci. Rep. 11, 7135. https://doi.org/10.1038/s41598-021-86248-5 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Palombi, A. Il ciclo biologico di Diphterostomum brusinae Stossich (Trematode digenetico: fam. Zoogonidae Odhner). Considerazioni sui cicli evolutivi delle specie affini e dei trematodi in generale. Pubbl. Stn. Zool. 10, 111–149 (1930).
Google Scholar 
Gilardoni, C. et al. Cryptic speciation of the zoogonid digenean Diphterostomum flavum n. sp. demonstrated by morphological and molecular data. Parasite 27(44). https://doi.org/10.1051/parasite/2020040 (2020).Campbell, R. A., Haedrich, R. L. & Munroe, T. A. Parasitism and ecological relationships among deep-sea benthic fishes. Mar. Biol. 57, 301–313. https://doi.org/10.1007/BF00387573 (1980).Article 

Google Scholar 
Campbell R. A. Parasitism in the deep-sea in Deep-sea Biology, The Sea (ed. Rowe, G. T.) 473–552 (Wiley, 1983).Isbert, W. et al. Metazoan parasite communities and diet of the velvet belly lantern shark Etmopterus spinax (Squaliformes: Etmopteridae): a comparison of two deep-sea ecosystems. J. Fish Biol. 86(2), 687–706. https://doi.org/10.1111/jfb.12591 (2015).CAS 
Article 
PubMed 

Google Scholar 
Klimpel, S., Palm, H. W. & Seehagen, A. Metazoan parasites and food composition of juvenile Etmopterus spinax (L., 1758) (Dalatiidae, Squaliformes) from the Norwegian Deep. Parasitol. Res. 89, 245–251. https://doi.org/10.1007/s00436-002-0741-1 (2003).Article 
PubMed 

Google Scholar 
Moore, A. B. M. Metazoan parasites of the lesser-spotted dogfish Scyliorhinus canicula and their potential as stock discrimination tools. J. Mar. Biol. Assoc. UK 81(6), 1009–1013. https://doi.org/10.1017/S0025315401004982 (2001).Article 

Google Scholar 
Silva, C., Veríssimo, A., Cardoso, P., Cable, J. & Xavier, R. Infection of the lesser spotted dogfish with Proleptus obtusus Dujardin, 1845 (Nematoda: Spirurida) reflects ontogenetic feeding behaviour and seasonal differences in prey availability. Acta Parasit. 62(2), 471–476. https://doi.org/10.1515/ap-2017-0055 (2017).CAS 
Article 

Google Scholar 
Bakopoulos, V. et al. Parasites of Scyliorhinus canicula (Linnaeus, 1758) in the north-eastern Aegean Sea. J. Mar. Biol. Assoc. UK 98(8), 2133–2143. https://doi.org/10.1017/S0025315417001552 (2018).MathSciNet 
Article 

Google Scholar 
Moravec, F., Van As, J. G. & Dyková, I. Proleptus obtusus Dujardin, 1845 (Nematoda: Physalopteridae) from the puffadder shyshark Haploblepharus edwardsii (Scyliorhinidae) from off South Africa. Syst. Parasitol. 53, 169–173. https://doi.org/10.1023/A:1021130825469 (2002).Article 
PubMed 

Google Scholar 
Morris, T., Avenant-Oldewage, A., Lamberth, S. & Reed, C. Shark parasites as bio-indicators of metals in two South African embayments. Mar. Pollut. Bull. 104(1–2), 221–228. https://doi.org/10.1016/j.marpolbul.2016.01.027 (2016).CAS 
Article 
PubMed 

Google Scholar 
Kennedy, C. R., Bush, A. O. & Aho, J. M. Patterns in helminth communities: why are birds and fish different?. Parasitology 93(1), 205–215. https://doi.org/10.1017/S0031182000049945 (1986).Article 
PubMed 

Google Scholar 
Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al. revisited. J. Parasitol. 83(4), 575–583. https://doi.org/10.2307/3284227 (1997).CAS 
Article 
PubMed 

Google Scholar 
Poulin, R. Species richness of parasite assemblages: Evolution and patterns. Annu. Rev. Ecol. Syst. 28, 341–358 (1997).Article 

Google Scholar 
Rizzo, E. & Bazzoli, N. Reproduction and embryogenesis in Biology and physiology of freshwater neotropical fish (eds. Baldisserotto, B., Criscuolo Urbinati, E. & Cyrino J. E. P.) 287–313 (Academic Press, 2020).Poulin, R. Variation in the intraspecific relationship between fish length and intensity of parasitic infection: Biological and statistical causes. J. Fish Biol. 56(1), 123–137. https://doi.org/10.1111/j.1095-8649.2000.tb02090.x (2005).Article 

Google Scholar 
Poulin, R. & Morand, S. Parasite Biodiversity (eds.) 216 Smithsonian Institution Books, 2004.Capapé, C. et al. Production, maturity, reproductive cycle and fecundity of small-spotted catshark, Scyliorhinus canicula (Chondrichthyes: Scyliorhinidae) from the northern coast of Tunisia (Central Mediterranean). J. Ichthyol. 54, 111–126. https://doi.org/10.1134/S0032945214010020 (2014).Article 

Google Scholar 
Kennedy, C.R Helminth communities in freshwater fish: structured communities or stochastic assemblages? in Parasite Communities: Patterns and Processes (eds. Esch G. W., Bush A. O., Aho J. M.) 156 (Chapman and Hall, 1990).Compagno, L. J. V. Sharks of the world in An annotated and illustrated catalogue of shark species known to date. Part 1 – Hexanchiformes to Lamniformes (ed. FAO) 249 (FAO, 1984).Compagno, L. J. V. Sharks of the world in An annotated and illustrated catalogue of shark species known to date. Part 2 – Carcharhiniformes (ed. FAO) 486 (FAO, 1984). More

Fluorescent markingDispersal of SLF adults was tracked using a fluorescent marking system (FMS), which has been demonstrated to be applicable for multiple insect species including SLF nymphs21,22,24. To mark the SLF, either red, yellow, or blue fluorescent paint (#1166R, #1166Y, #1166B, BioQuip Products, USA) was diluted with distilled water (1:4). The mixture was then gently sprayed three times (ca. 20 mg each time) on each SLF individual using a mist sprayer from a distance of 30–50 cm (SI 2). Throughout the field survey, a handheld ultraviolet (UV) laser (PX 600 mW, class IIIB purple laser, 405 nm, Big Lasers, USA) was used to detect fluorescent-marked SLF individuals25.Effect of fluorescent marking on SLFPrior to field survey, the potential effects of fluorescent marking on the survivorship and flight behavior of SLF adults (sex ratio 1:1) were evaluated. SLF adults were collected using sweeping nets (BioQuip Products, USA) from Gyeonggi-do, South Korea (37°47′85.95″ N, 127°11′64.58″E) in September 2020. Two hours after fluorescent marking of SLF, both fluorescent-marked and unmarked SLF were subjected to survivorship and flight behavior assessment.Survivorship of insects was measured on two A. altissima trees (ca. 2 m in height) located in Gachon University, South Korea (37°45′38.50″N, 127°13′37.75″E). Two fluorescent-marked and two unmarked insects were placed in a cylindrical mesh cage [25 × 30 cm (radius × height)] enclosing a tree branch; a total of 20 groups were tested (n = 40). Then, survivorship of SLF was determined once every two days until no individuals were alive. Survivorship was compared between fluorescent-marked and unmarked SLF using Kaplan-Meir survivorship analysis (JMP 12, SAS Institute Inc., USA).The effects of fluorescent marking on flight behavior were evaluated in an open space (986 m2) in Gachon University, South Korea (37°45′08.37″N, 127°12′79.69″E) at 26 ± 1 °C and a relative humidity of 30 ± 5%. To induce flight of SLF adults, a wooden square rod [3 × 3 × 100 cm (width × length × height)] was established upright at the center of the arena. The SLF adult was placed individually 10 cm away from the top on the wooden square rod. To minimize any unnecessary stimuli from experimenter, SLF flight was induced by following the same sequence: once the insect climbed up the rod and oriented itself staying still to a random direction, then an experimenter carefully positioned at the back of the insect and gently pecked the forewings using tweezers to initiate its flight33,34. Pecking was intended to mimic predatory behavior of birds. Once the insect jumped away, an operator followed the individual until it landed on the ground (n = 30). The experiment was conducted for 2 h between 13:00–15:00 and marked and unmarked SLF were randomly tested during the evaluation. The number of pecks to initiate the flight, flight duration, and flight distance of SLF were compared using t-test (JMP 12, SAS Institute Inc., USA).Field study sitesDispersal patterns of SLF adults in A. altissima patches and their oviposition patterns were investigated in multiple A. altissima patches located along two streams in Gyeonggi-do, South Korea: Tan stream in Seongnam-si (37°48′01.80″N, 127°11′56.03″E) and Gyeongan stream in Gwangju-si (37°41′54.21″N, 127°27′12.37″E). Both Tan and Gyeongan streams run along suburban residential areas in their respective cities, with pedestrian lanes built along the streams. We selected seven A. altissima patches as study patches when more than 10 SLF adults were found per patch (Fig. 3). In the study patch, all SLF individuals or ca. up to 30 adults were florescent-marked. In addition, when the number of SLF adults was less than 10 from an A. altissima patch, those patches were designated as neighboring patches (Fig. 3). Dispersal and oviposition of SLF adults were monitored from both study and neighboring patches during the study.In Tan stream, four study patches (patches A–D) and one neighboring patch, which were distributed over ca. 1760 m, were selected (Fig. 3a). Areas around the patches were generally covered with grass and shrubs, and the areas were occasionally managed by local administration. Deciduous trees were regularly planted along the pedestrian lanes. There were a total of four, four, 61, and 47 A. altissima trees in patches A to D, respectively (Table 2). Compared with Tan stream, A. altissima patches were located closely to each other in Gyeongan stream: three study patches (patches E–G) and three neighboring patches were spread over only ca. 90 m (Fig. 3b). Vegetation surrounding A. altissima patches consisted of grasses and small shrubs as well as deciduous trees planted along the border of residential area nearby. There were a total of 69, nine, and 53 A. altissima trees in patches E to G, respectively (Table 2). Unlike Tan stream, 45% of A. altissima trees had trunks having cut off by local administration in Gyeongan stream (Table 2; Fig. 5).Dispersal pattern of SLF on A. altissima
Three fluorescent paint colors were used to mark SLF individuals in the study patches (Fig. 3; SI 2). Insects that took off during marking were captured and excluded from the experiment. Among the selected study patches, SLF adults were generally distributed throughout each patch, while SLF adults were observed only from one out of 61 A. altissima trees in patch C. As a result, in Tan stream, 15 (color of paint used to fluorescent-marking; red), 31 (yellow), 11 (blue), and 32 (red) adults were marked from patches A to D, respectively, whereas in Gyeongan stream, 30 (red), 30 (blue), and 33 (yellow) adults were marked from patches E to G, respectively. Starting from September 14th, 2020 in Tan stream and September 18th in Gyeongan stream, fluorescent-marked SLF adults on A. altissima trees in both study and neighboring patches were counted with a UV laser twice a week (Fig. 3). Survey continued until no individuals were observed from the study patches.Oviposition pattern of SLF on A. altissima
Oviposition pattern of SLF was surveyed on all A. altissima trees in the study patches in December in both streams (Table 2). For the survey, SLF egg masses were categorized into three types as follows: egg mass with waxy layer, egg mass without waxy layer, and scattered eggs (SI 3). Eggs that were not covered with waxy layer and did not form aggregates were categorized as scattered (SI 3). In the field, A. altissima trees were visually inspected to identify SLF egg mass, and the number of egg masses and their distances from the ground were recorded. In addition, the number of eggs per egg mass was recorded for egg masses located  5 generally indicates collinearity35,36. VIF between height and DRC was 1.56, and therefore the two variables were included together in the GLMM model.Policy statementExperiments involving Ailanthus altissima were conducted in compliance with relevant institutional, national, and international guidelines and legislation. More

We took as a starting point the hake – sole – Norway lobster Bay of Biscay ISIS-Fish database used for COSELMAR project16,20 (see http://isis-fish.org/download.html section “Bay of Biscay scenario dataset”, Database V0 in Fig. 1). This database was built using 2010 data, and was not calibrated, as it was designed for a geo-foresight study. Since our aim was to describe the system over a decade and simulate realistic dynamics close to available observations to assess management measures, we needed to update the parametrisation and calibrate the database. We took 2010–2012 as the calibration period, and 2013–2020 as the simulation period (grey arrow Fig. 1). The database has a monthly temporal resolution (constrained by the ISIS-Fish framework) and the spatial scale was set to match ICES statistical rectangles (0.5° latitude by 1° longitude rectangles, defined by the International Council for the Exploration of the Sea (ICES) https://www.ices.dk/data/maps/Pages/ICES-statistical-rectangles.aspx), consistent with available knowledge and data.In this section, we firstly describe all the data sources used to update and calibrate the database. Then, for each main component of an ISIS-Fish database – i.e. populations, exploitation and management – we describe this paper’s database parameters and assumptions. We finally describe the calibration procedure (inspired by previous work21,22), of which some results are shown in the Technical Validation section. We summarized this workflow in Fig. 1.Data sourcesData sources, estimates, and literature (including grey literature) were needed to update and calibrate the model. They are marked in Fig. 1 with salmon (data sources and estimates) and mustard (literature) blocks:

SACROIS23: French landings and effort logbook declarations for 2010 were made available at the log-event*commercial category*ICES statistical rectangle*population scale. It was used to design exploitation features of the database, as well as populations spatial structure.

LANGOLF survey: 2006–2010 LANGOLF surveys observations for 2006–2010 were made available for Norway lobster. They were used to work on Norway lobster abundance per length class and sex.

Intercatch: catch observations for 2010–2020 in the Bay of Biscay for hake, at the quarter-métier group scale, and catch observations per class for sole on 2010–2012, and 2010 Norway lobster catch observations per sex and length class24, used to describe the inter-annual effort dynamics, to calibrate and validate the model.

Estimates of hake abundance per size class in 2010, and hake quarterly estimates of recruitment on 2010–2012 from a northern hake spatial stock assessment model21, used to inform hake biology assumptions (named Other 1 in Fig. 1).

ICES WGBIE24 2010 estimates of abundance per class (sole and Norway lobster), to inform their abundance at the initial time step; 2010–2012 yearly fishing mortality estimates per age class (sole) to calibrate the database (named Other 2 in Fig. 1).

Other population, exploitation and management assumptions were informed with scientific literature25 and grey literature26,27 (Literature block in Fig. 1).

Management assumptions were informed with legal texts2,4,28,29,30,31,32,33,34 and reported quota values in working group reports24.

About populationsThis section describes for each species the assumptions and parameters values, except for accessibility, which has been calibrated, as described in section Calibration procedure. For all assumptions and values, more details are provided in Supplementary Information’s section 2.2.HakeThe stock size structure was defined with 1 cm size bins for [1;40[cm individuals, 2 cm for [40;100[cm individuals, and 10 cm for [100;130+] cm individuals35. Areas of presence were defined based on 2010 SACROIS French landings data per commercial category and statistical rectangle23, leading to the definition of a presence, a recruitment, an interim recruitment and a spawning area25 (see Supplementary Information’s section 2.2 and Figure S1). These areas allow for the description of intra-Bay of Biscay migrations related to spawning and recruitment processes: mature individuals aggregate at the beginning of the year on the shelf break to spawn, and then disperse on the shelf36,37,38,39,40 (at the beginning of April and July in the model). Also, from age 1 (around 20 cm), individuals in recruitment zone spread in interim recruitment zone, to model a diffusion towards areas neighbouring the nursery area, at the beginning of each time step (see Supplementary Information’s section 2.2 and Table S11). Maturity-at-size and weight-at-length relationships were the same functions as used by ICES working group35,41. Natural mortality was fixed at 0.5, basing on preliminar runs, instead of the commonly used 0.442. Recruitment values were defined prior to the simulation for 2010–2020 using available estimates on the 2010–2015 time series21,27. Deterministic estimates from these sources were allocated to the recruitment area in the Bay of Biscay and the beginning of each month in January-September on the whole time series, of which values are provided in the Supplementary Information’s section 2.2 and Table S3. Growth is modelled through monthly growth increments5,25. However, given the different widths of size bins in the implemented size structure, a correction was provided to values in the transition matrix to eliminate artifacts when growing to a size bin wider than the size bin of origin, as detailed in Supplementary Information’s section 2.2. Abundance at the initial step in each zone was estimated from Bay of Biscay abundance estimates for 201021. Mature individuals over 20 cm were allocated to the spawning area, all individuals strictly shorter than 20 cm were allocated to the recruitment area (as they were assumed to be less than 1 year old), and remaining individuals were allocated to the interim recruitment area. None were allocated to the presence area, in which individuals will go later in the time series, after disaggregating from the spawning area25 (Table S13).SoleThe stock is age structured, with 7 classes going from ages 2 to 7+43 (Table S2). No seasonal variations were implemented. Only a single presence zone was defined (see Supplementary Information’s section 2.2 and Figure S1), as in preliminary runs defining more presence areas for sole did not yield more knowledge in this study. We implemented ICES working group values for natural mortality, weight-at-age (Table S1) and maturity-at-age43. Recruitment occurs at the beginning of each year, individuals being recruited at age 2 (ages 0–1 were not modelled; Table S4). We implemented ICES working group estimates27 for abundance at initial time step (Table S14).Norway lobsterThe stock has a sex-size structure, with 1 mixed recruitment class at 0 cm; 33 length classes for males at 2 carapace length mm intervals, from [10;12[to [72;74[carapace length mm; 23 length classes for females at 2 carapace length mm intervals, from [10;12[to [52;54[carapace length mm. A single presence area was defined: the Great Mudbank21 (see Supplementary Information’s section 2.2 and Figure S1). Several seasonal processes occur for this stock, impacting recruitment, accessibility and growth: 1/ January, begins with the annual recruitment. Females are inside their burrows, less accessible; 2/ February-March females are inside their burrows, less accessible; 3/ April: Spring moulting, females are more accessible; 4–5/ May-August females are more accessible; 6/ September, females are inside their burrows, less accessible; 7/ October: Autumn moulting only for immature females and all males, females are inside their burrows, less accessible; 8/ November-December, females are inside their burrows, less accessible44. We implemented ICES working group values for natural mortality, weight-at-class and maturity-at-class45,46,47. Growth occurs twice a year, when moulting in April and October, and is modelled with growth increments. Recruitment occurs at the beginning of each year, modelled with a Beverton-Holt relationship26, and was assumed to have the same spatial distribution as spawning stock biomass. Abundance at initial step was derived from LANGOLF survey observations and ICES WGBIE estimates25,26 (Table S16).About exploitationThe fishing exploitation structure (fleets, strategies, métiers and gears) were derived following a classification method on SACROIS 2010 landings and effort data13,23 from French fleets, and taken from a TECTAC project (https://cordis.europa.eu/project/id/Q5RS-2002-01291) database for Spanish trawlers. More details on their definition are provided in Supplementary Information’s section 2.3, Tables S5–S9 and S20–S21 and Figure S3. Spanish longliners and gillnetters fleets exploitation was described based on catch (observations from Intercatch48) rather than effort.Hake selectivity and discarding functions (one for each gear) were taken from estimates of a spatial hake stock assessment model21. Parameters values and formulæ are provided in Supplementary Information’s section 2.3 and Tables S6-S7. On top of this, inter-annual fleet dynamics factors were included in equation (21) of ISIS-Fish documentation8 in order to account for observed catch temporal variations. These factors are therefore multiplicative parameters of the target factor of each species for each métier. They are computed using observed catch27 and differ according to the period and targeted species:

over 2010–2016, it is a ratio of observed catch in weight per year over catch observations for 2010: for hake, one per métier *season*year (left(frac{ObservedCatc{h}_{metier,season,year}}{ObservedCatc{h}_{metier,season,2010}}right)), for sole, one per métier *year (left(frac{ObservedCatc{h}_{metier,year}}{ObservedCatc{h}_{metier,2010}}right)), and for Norway lobster, one per year (identical for each métier catching Norway lobster) (left(frac{ObservedCatc{h}_{year}}{ObservedCatc{h}_{2010}}right));

over 2017–2020: at the time of writing these assumptions, more recent data was not available, and ratios were deduced from trends on 2014–2016. A linear model was fitted on ratios deduced earlier on 2014–2016. If a significant trend was identified (hake: whitefish trawlers quarters 2 and 4, longliners and gillnetters seasons 2–3; sole and Norway lobster: all métiers), the slope was used to deduce 2017–2020 ratios (the slope was halved for hake whitefish trawlers and sole and Norway lobster values to avoid unrealistic high values of effort). Otherwise, 2016 ratios were used.

All values are provided in Supplementary Information’s section A.2 Tables S22–S24, and the final values of target factors are derived from the Calibration procedure.About managementWe implemented a set of management rules close to what is currently implemented in the Bay of Biscay.All stocks are managed by TALs (Total Allowable Landings) until 2015 and then by TACs (Total Allowable Catch), except for Norway lobster, managed by TALs on the whole time series, not being under the landings obligation. To favour a better parametrisation, allowing for more reliable dynamics on the following years of the time series, no TALs were implemented during the calibration period (2010–2012; Fig. 1). These regulations were implemented from 2013 using historically TALs and TACs values24.Landings of the three stocks are also constrained by a Minimum Conservation Reference Size regulation that was implemented for all stocks using values currently enforced in the studied fishery28. Likewise, from 2016, the Landings Obligation was implemented, with de minimis exemptions for hake and sole, depending on the year and the gear used to fish them2,31,32,33,34. See Supplementary Information’s sections 2.4 and A.3, Figure S2 and Table S10 for further details on these restrictions.In response to the above management rules, a fishers’ behaviour algorithm has been developed to describe fishermen adaptation. Some métiers may be forbidden, depending on some conditions – the catch quota has been reached, the landings obligation is enforced – but also some values – the proportion of discarded catch, and also catch on previous years. Therefore fishermen change métiers within their strategy métiers set through a re-allocation of fishing effort to the latter set. This re-allocation aims to avoid quota overshooting. Further details about this algorithm are provided in the Supplementary Information’s sections 2.4 and A.3 and Figure S2.Calibration procedureThe model has been calibrated using two parameters (population accessibility and fishing target factor) involved in the catchability process (equation (21) in ISIS-Fish documentation8). The objective of the calibration is to reproduce the dynamics of catch over 2010–2012 at the species*métiers group scale, for each year or quarter depending on available data’s granularity. Calibration is sequentially performed: accessibility parameters for each population were estimated first followed by the target factors. The estimation of each parameter set (parameter type * population) combination was separated, and values were estimated jointly within each parameter set. To account for the specificity of each population model dynamics (global age-based for sole, spatial and size-based for hake, spatial, sex and size-based for Norway lobster), an objective function is defined for each population to calibrate their accessibility. More details on objective functions and procedures are provided in Supplementary Information’s section 2.5, as well as estimated values in Tables S17–S19.Hake accessibilityThe calibration for hake accessibility is based on a procedure developed for a former version of the database25. One parameter was estimated per quarter, all values being equal across length classes. The model outputs were fitted to hake catch observations in weight in the Bay of Biscay in 2010–2012 per length class.Sole accessibilityOne parameter was estimated per age class. The model outputs were fitted to WGBIE fishing mortality per age class for sole27 in 2010–2012.Norway lobster accessibilityOne parameter was calibrated per sex and length class. The model outputs were fitted to catch in numbers per length class and sex in 2010 per quarter provided by WGBIE.About target factorsTarget factors drive how the effort is distributed between populations, métiers and season*year combinations. They were split in 3 components: a fixed component derived from the SACROIS effort dataset analysis (Tables S25–S27), another fixed component driving inter-annual variations of fishing effort (Tables S22–S24), derived from catch observations, and finally an estimated component (Tables S28–S30), allowing to tune the model’s dynamics to observed catch. This section focuses on the estimation of the latter.Hake target factors20 parameters were defined, for each combination of the 5 groups of métiers (longliners, gillnetters, whitefish trawler (coastal), whitefish trawler (not coastal), Norway lobster trawler, see definition Table S8) and 4 quarters. We fitted the model’s outputs to the same data and with the same objective function as for hake’s accessibilities estimation.Sole target factors1 estimated component per group of métiers (gillnetters, Norway lobster trawlers and whitefish trawlers) and quarter. We fitted the model’s outputs to sole catch in weight on 2010–2012 for each métier and quarter.Norway lobster target factors1 estimated component per group of métiers (Norway lobster trawlers and whitefish trawlers). We fitted the model’s outputs to monthly Norway lobster landings data per length and sex class for 2010.Base simulationThe base simulation ran from January 2010 to December 2020 inclusive, with a monthly time step, using the database and parameters values described in this document. Several outputs of interest may be explored after a run: catch (discards and landings), as done in several figures in this paper, but also biomass (total biomass or mature biomass), fishing mortality values, or effort, all at a fine spatio-temporal scale. More

Clavero, M. & García-Berthou, E. Invasive species are a leading cause of animal extinctions. Trends Ecol. Evol. 20(3), 5451. https://doi.org/10.1016/j.tree.2005.01.003 (2005).Article 

Google Scholar 
Duenas, M. A., Hemming, D. J., Roberts, A. & Diaz-Soltero, H. The threat of invasive species to IUCN-listed critically endangered species: a systematic review. Glob. Ecol. Conserv. p. e01476 (2021).Diagne, C. et al. InvaCost, a public database of the economic costs of biological invasions worldwide. Sci. Data 7(1), 1–12 (2020).Article 

Google Scholar 
Cuthbert, R. N. et al. Global economic costs of aquatic invasive alien species. Sci. Total Environ. 775, 145238 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Diagne, C. et al. High and rising economic costs of biological invasions worldwide. Nature 592(7855), 571–576 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Gregory, R. & Long, G. Using structured decision making to help implement a precautionary approach to endangered species management. Risk Anal. 29(4), 518–532. https://doi.org/10.1111/j.1539-6924.2008.01182.x (2009).Article 
PubMed 

Google Scholar 
Berroneau, M., Detaint, M. & Coi, C. Bilan du programme de mise en place d’une stratégie d’éradication de la grenouille taureau Lithobates catesbeianus (Shaw 1802) en Aquitaine (2003–2007) et perspectives. Bull. Soc. Herpétol. France 127, 35–45 (2008).
Google Scholar 
Orchard, S. A. Removal of the American bullfrog, Rana (Lithobates) catesbeiana, from a pond and a lake on Vancouver Island, British Columbia, Canada. Island invasives: eradication and management. IUCN (Gland, Switzerland), 1–542 (2011).Robertson, B. C. & Gemmell, N. J. Defining eradication units to control invasive pests. J. Appl. Ecol. 41(6), 1042–1048 (2004).Article 

Google Scholar 
Shaw, G. General Zoology or Systematic Natural History Vol. 3, 106–108 (Society for the study of Amphibians and Reptiles, 1802).
Google Scholar 
Howard, R. D. Sexual dimorphism in bullfrogs. Ecology 62(2), 303–310 (1981).Article 

Google Scholar 
Kaefer, Í. L., Boelter, R. A. & Cechin, S. Z. Reproductive biology of the invasive bullfrog Lithobates catesbeianus in southern Brazil. In Annales Zoologici Fennici 435–444 (2007).Bissattini, A. M. & Vignoli, L. Let’s eat out, there’s crayfish for dinner: American bullfrog niche shifts inside and outside native ranges and the effect of introduced crayfish. Biol. Invasions 19(9), 2633–2646 (2017).Article 

Google Scholar 
Boelter, R. A. & Cechin, S. Z. Impacto da dieta de rã-touro (Lithobates catesbeianus – Anura, Ranidae) sobre a fauna nativa: estudo de caso na região de Agudo – RS – Brasil 1. Nat. Conserv. 5(2), 45–53 (2007).
Google Scholar 
Govindarajulu, P., Price, W. S. & Anholt, B. R. Introduced bullfrogs (Rana catesbeiana) in western Canada: has their ecology diverged?. J. Herpetol. 40(2), 249–261 (2006).Article 

Google Scholar 
McCoy, C. J. Diet of bullfrogs (Rana catesbeiana) in Central Oklahoma farm ponds. In Proceedings of the Oklahoma Academy of Sciences 44–45 (1967).Teixeira, E., Silva, D., Pinto, O., Filho, R. & Feio, R. N. Predation of native anurans by invasive bullfrogs in Southeastern Brazil: spatial variation and effect of microhabitat use by prey. S. Am. J. Herpetol. 6(1), 1–11. https://doi.org/10.2994/057.006.0101 (2011).Article 

Google Scholar 
Wu, Z., Li, Y., Wang, Y. & Adams, M. J. Diet of introduced Bullfrogs (Rana catesbeiana): predation on and diet overlap with native frogs on Daishan Island China. J. Herpetol. 39(4), 668–675 (2005).Article 

Google Scholar 
Howard, R. D. The influence of male-defended oviposition sites on early embryo mortality in bullfrogs. Ecol. Soc. Am. 59(4), 789–798 (1978).
Google Scholar 
Van Wilgen, N. J., Gillespie, M. S., Richardson, D. M. & Measey, J. A taxonomically and geographically constrained information base limits non-native reptile and amphibian risk assessment: a systematic review. PeerJ 6, 5850 (2018).Article 

Google Scholar 
Sales, L., Rebouças, R. & Toledo, L. F. Native range climate is insufficient to predict anuran invasive potential. Biol. Invasions 23, 2635–2647 (2021).Article 

Google Scholar 
Kumschick, S. et al. How repeatable is the Environmental Impact Classification of Alien Taxa (EICAT)? Comparing independent global impact assessments of amphibians. Ecol. Evol. 7(8), 2661–2670 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Kupferberg, S. J. Bullfrog (Rana catesbeiana) invasion of a California river: the role of larval competition. Ecology 78(6), 1736–1751 (1997).Article 

Google Scholar 
Toledo, L. F., Ribeiro, R. S. & Haddad, C. F. Anurans as prey: an exploratory analysis and size relationships between predators and their prey. J. Zool. 271(2), 170–177 (2007).Article 

Google Scholar 
Daszak, P. et al. Experimental evidence that the bullfrog (Rana catesbeiana) is a potential carrier of chytridiomycosis, an emerging fungal disease of amphibians. Herpetol. J. 14, 201–208 (2004).
Google Scholar 
Gervasi, S. S. et al. Experimental evidence for American bullfrog (Lithobates catesbeianus) susceptibility to chytrid fungus (Batrachochytrium dendrobatidis). EcoHealth 10(2), 166–171 (2013).PubMed 
Article 

Google Scholar 
Urbina, J., Bredeweg, E. M., Garcia, T. S. & Blaustein, A. R. Host–pathogen dynamics among the invasive American bullfrog (Lithobates catesbeianus) and chytrid fungus (Batrachochytrium dendrobatidis). Hydrobiologia 817(1), 267–277 (2018).CAS 
Article 

Google Scholar 
Schloegel, L. M. et al. The North American bullfrog as a reservoir for the spread of Batrachochytrium dendrobatidis in Brazil. Anim. Conserv. 13, 53–61. https://doi.org/10.1111/j.1469-1795.2009.00307.x (2010).Article 

Google Scholar 
Ohanlon, S. J. et al. Recent Asian origin of chytrid fungi causing global amphibian declines. Science 360(6389), 621–627 (2018).ADS 
CAS 
Article 

Google Scholar 
Adams, A. J. et al. Extreme drought, host density, sex, and bullfrogs influence fungal pathogen infection in a declining lotic amphibian. Ecosphere 8(3), 01740 (2017).Article 

Google Scholar 
Santos, R. C. et al. High prevalence and low intensity of infection by Batrachochytrium dendrobatidis in rainforest bullfrog populations in southern Brazil. Herpetol. Conserv. Biol. 15(1), 118–130 (2020).
Google Scholar 
Ribeiro, L. P. et al. Bullfrog farms release virulent zoospores of the frog-killing fungus into the natural environment. Sci. Rep. 9, 13422 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Both, C. & Grant, T. Biological invasions and the acoustic niche: the effect of bullfrog calls on the acoustic signals of white-banded tree frogs. Biol. Let. 8(5), 1–3. https://doi.org/10.1098/rsbl.2012.0412 (2012).Article 

Google Scholar 
Medeiros, C. I., Both, C., Grant, T. & Hartz, S. M. Invasion of the acoustic niche: variable responses by native species to invasive American bullfrog calls. Biol. Invasions 19(2), 675–690 (2017).Article 

Google Scholar 
Ferrante, L., Kaefer, I. L. & Baccaro, F. B. Aliens in the backyard: Did the American bullfrog conquer the habitat of native frogs in the semi-deciduous Atlantic Forest?. Herpetol. J. 30, 93–98 (2020).Article 

Google Scholar 
da Silva Silveira, S. & Guimarães, M. The enemy within: consequences of the invasive bullfrog on native anuran populations. Biol. Invasions 23(2), 373–378 (2021).Article 

Google Scholar 
Kraus, F. Impacts from invasive reptiles and amphibians. Annu. Rev. Ecol. Evol. Syst. 46, 75–97 (2015).Article 

Google Scholar 
Ribeiro, L. P. & Toledo, L. F. An overview of the Brazilian frog farming. Aquaculture 548, 737623 (2022).Article 

Google Scholar 
Cunha, E. R. & Delariva, R. L. Introdução da rã-touro, Lithobates catesbeianus (SHAW, 1802): uma revisão. Saúde e Biologia 4(2), 34–46 (2009).
Google Scholar 
Ferreira, C. M., Pimenta, A. G. C. & Neto, J. S. P. Introdução à ranicultura. Boletim Técnico Do Instituto de Pesca 33, 15 (2002).
Google Scholar 
Fontanello, D. & Ferreira, C. M. Histórico da ranicultura nacional. Instituto de Pesca de São Paulo (2007).Both, C. et al. Widespread occurrence of the American bullfrog, Lithobates catesbeianus (Shaw, 1802) (Anura: Ranidae), in Brazil. S. Am. J. Herpetol. 6(2), 127–135 (2011).Article 

Google Scholar 
Bai, C., Ke, Z., Consuegra, S., Liu, X. & Yiming, L. The role of founder effects on the genetic structure of the invasive bullfrog (Lithobates catesbeianaus) in China. Biol. Invasions 14, 1785–1796. https://doi.org/10.1007/s10530-012-0189-x (2012).Article 

Google Scholar 
Liu, X. & Li, Y. Aquaculture enclosures relate to the establishment of feral populations of introduced species. PLoS ONE https://doi.org/10.1371/journal.pone.0006199 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Santos-pereira, M. & Rocha, C. F. D. Invasive bullfrog Lithobates catesbeianus (Anura: Ranidae) in the Paraná state, Southern Brazil : a summary of the species spread. Revista Brasileira De Zoociências 16, 141–147 (2015).
Google Scholar 
Moreira, C. R., Henriques, M. B. & Ferreira, C. M. Frog farms as proposed in agribusiness aquaculture: economic viability based in feed conversion. Pesca Inst. Bull. 39(4), 389–399 (2018).
Google Scholar 
Ficetola, G. F., Thuiller, W. & Miaud, C. Prediction and validation of the potential global distribution of a problematic alien invasive species – The American bullfrog. Divers. Distrib. 13(4), 476–485. https://doi.org/10.1111/j.1472-4642.2007.00377.x (2007).Article 

Google Scholar 
Funk, W. C., Garcia, T. S., Cortina, G. A. & Hill, R. H. Population genetics of introduced bullfrogs, Rana (Lithobates) catesbeianus, in the Willamette Valley, Oregon, USA. Biol. Invasions 13, 651–658. https://doi.org/10.1007/s10530-010-9855-z (2011).Article 

Google Scholar 
Rollins, L. A., Woolnough, A. P., Wilton, A. N., Sinclair, R. & Sherwin, W. B. Invasive species can’t cover their tracks: using microsatellites to assist management of starling (Sturnus vulgaris) populations in Western Australia. Mol. Ecol. 18, 1560–1573. https://doi.org/10.1111/j.1365-294X.2009.04132.x (2009).Article 
PubMed 

Google Scholar 
Schwartz, M. K., Luikart, G. & Waples, R. S. Genetic monitoring as a promising tool for conservation and management. Trends Ecol. Evol. 22(1), 25–33. https://doi.org/10.1016/j.tree.2006.08.009 (2007).Article 
PubMed 

Google Scholar 
Ficetola, G. F., Bonin, A. & Miaud, C. Population genetics reveals origin and number of founders in a biological invasion. Mol. Ecol. 17, 773–782. https://doi.org/10.1111/j.1365-294X.2007.03622.x (2008).CAS 
Article 
PubMed 

Google Scholar 
Kamath, P. L., Sepulveda, A. J. & Layhee, M. Genetic reconstruction of a bullfrog invasion to elucidate vectors of introduction and secondary spread. Ecol. Evol. 6(15), 5221–5233. https://doi.org/10.1002/ece3.2278 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Du Sert, N. P. et al. Reporting animal research: explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18(7), e3000411 (2020).Article 
CAS 

Google Scholar 
Austin, J. D. Genetic evidence for female-biased dispersal in the bullfrog, Rana catesbeiana (Ranidae). Mol. Ecol. 12(11), 3165–3172. https://doi.org/10.1046/j.1365-294X.2003.01948.x (2003).Article 
PubMed 

Google Scholar 
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4(3), 535–538. https://doi.org/10.1111/j.1471-8286.2004.00684.x (2004).CAS 
Article 

Google Scholar 
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11(1), 94. https://doi.org/10.1186/1471-2156-11-94 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Jombart, T. Adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24(11), 1403–1405. https://doi.org/10.1093/bioinformatics/btn129 (2008).CAS 
Article 
PubMed 

Google Scholar 
Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17(18), 4015–4026. https://doi.org/10.1111/j.1365-294X.2008.03887.x (2008).Article 
PubMed 

Google Scholar 
Winter, D. J. MMOD: An R library for the calculation of population differentiation statistics. Mol. Ecol. Resour. 12(6), 1158–1160. https://doi.org/10.1111/j.1755-0998.2012.03174.x (2012).CAS 
Article 
PubMed 

Google Scholar 
Gerlach, G. Calculations of population differentiation based on GST and D: forget GST but not all of statistics!. Mol. Ecol. 19(18), 3845–3852 (2010).PubMed 
Article 

Google Scholar 
Hochberg, Y. & Benjamini, Y. More powerful procedures for multiple statistical significance testing. Stat. Med. 9, 811–818 (1990).CAS 
PubMed 
Article 

Google Scholar 
Hauser, S., Wakeland, K. & Leberg, P. Inconsistent use of multiple comparison corrections in studies of population genetic structure: Are some type I errors more tolerable than others?. Mol. Ecol. Resour. 19(1), 144–148 (2019).CAS 
PubMed 
Article 

Google Scholar 
Team R Core. R: A language and environment for statistical computing. R Foundation for Statistical Computing URL. Vienna, Austria. Retrieved from https://www.r-project.org/. (2017).Dyer, R. J. gstudio: Analyses and functions related to the spatial analysis of genetic marker data. R Package Version (2014).Rousset, F. GENEPOP’007: A complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8(1), 103–106. https://doi.org/10.1111/j.1471-8286.2007.01931.x (2008).Article 
PubMed 

Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155(2), 945–959 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
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(2), 359–361. https://doi.org/10.1007/s12686-011-9548-7 (2012).Article 

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

Google Scholar 
Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10(3), 564–567 (2010).PubMed 
Article 

Google Scholar 
Moritz, C., Schneider, C. J. & Wake, D. B. Evolutionary relationships within the Ensatina eschscholtzii complex confirm the ring species interpretation. Syst. Biol. 41(3), 273–291 (1992).Article 

Google Scholar 
Goebel, A. M., Donnelly, J. M. & Atz, M. E. PCR primers and amplification methods for 12S ribosomal DNA, the control region, cytochrome oxidase I, and cytochromebin bufonids and other frogs, and an overview of PCR primers which have amplified DNA in amphibians successfully. Mol. Phylogenet. Evol. 11(1), 163–199 (1999).CAS 
PubMed 
Article 

Google Scholar 
Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28(12), 1647–1649 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30(4), 772–780 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Labonne, J. et al. From the bare minimum: genetics and selection in populations founded by only a few parents. Evol. Ecol. Res. 17(1), 21–34 (2016).
Google Scholar 
Chapuis, M. P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24(3), 621–631 (2006).PubMed 
Article 
CAS 

Google Scholar 
Carlsson, J. Effects of microsatellite null alleles on assignment testing. J. Hered. 99(6), 616–623 (2008).CAS 
PubMed 
Article 

Google Scholar 
Consuegra, S., Phillips, N., Gajardo, G. & Leaniz, C. G. Winning the invasion roulette: escapes from fish farms increase admixture and facilitate establishment of non-native rainbow trout. Evol. Appl. 4, 660–671. https://doi.org/10.1111/j.1752-4571.2011.00189.x (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Peacock, M. M., Beard, K. H., O’Neill, E. M., Kirchoff, V. S. & Peters, M. B. Strong founder effects and low genetic diversity in introduced populations of Coqui frogs. Mol. Ecol. 18(17), 3603–3615. https://doi.org/10.1111/j.1365-294X.2009.04308.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Austin, J. D., Lougheed, S. C. & Boag, P. T. Discordant temporal and geographic patterns in maternal lineages of eastern north American frogs, Rana catesbeiana (Ranidae) and Pseudacris crucifer (Hylidae). Mol. Phylogenet. Evol. 32, 799–816. https://doi.org/10.1016/j.ympev.2004.03.006 (2004).Article 
PubMed 

Google Scholar 
Selechnik, D. et al. Increased adaptive variation despite reduced overall genetic diversity in a rapidly adapting invader. Front. Genet. 10, 1221 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More