Philippa Kaur

COMMENT
20 October 2021

Congo Basin rainforest — invest US$150 million in science

The world’s second-largest rainforest is key to limiting climate change — it needs urgent study and protection.

Lee J. T. White

0
,

Eve Bazaiba Masudi

1
,

Jules Doret Ndongo

2
,

Rosalie Matondo

3
,

Arlette Soudan-Nonault

4
,

Alfred Ngomanda

5
,

Ifo Suspense Averti

6
,

Corneille E. N. Ewango

7
,

Bonaventure Sonké

8
&

Simon L. Lewis

9

Lee J. T. White

Lee J. T. White is Minister of Water, Forests, Oceans, Environment, Climate Change and Land-use Planning, Gabonese Republic.

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Eve Bazaiba Masudi

Eve Bazaiba Masudi is Deputy Prime Minister and Minister of Environment and Sustainable Development, Democratic Republic of the Congo.

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Jules Doret Ndongo

Jules Doret Ndongo is Minister of Forestry and Wildlife, Republic of Cameroon.

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Rosalie Matondo

Rosalie Matondo is Minister of Forest Economy, Republic of the Congo.

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Arlette Soudan-Nonault

Arlette Soudan-Nonault is Minister of Environment, Sustainable Development and the Congo Basin, Republic of the Congo.

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Alfred Ngomanda

Alfred Ngomanda is director of the National Centre for Scientific Research and Technology (CENAREST), Gabonese Republic.

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Ifo Suspense Averti

Ifo Suspense Averti is an associate professor in tropical forest ecology at Marian Ngouabi University, Republic of the Congo.

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Corneille E. N. Ewango

Corneille E. N. Ewango is a professor of tropical forest ecology and management at the University of Kisangani, Democratic Republic of the Congo.

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Bonaventure Sonké

Bonaventure Sonké is a professor of plant systematics and ecology at the University of Yaounde I, Republic of Cameroon.

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Simon L. Lewis

Simon L. Lewis is professor of global change science at University College London and the University of Leeds, UK.

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A warden with an orphaned mountain gorilla in the Virunga National Park sanctuary in the Democratic Republic of the Congo.Credit: Phil Moore/AFP/Getty

Earth’s second-largest expanse of tropical forest lies in central Africa, in the Congo Basin. The region supports the livelihoods of 80 million people. The rainfall that the forest generates as far away as the Sahel and the Ethiopian highlands supports a further 300 million rural Africans. These forests are crucial to regulating Earth’s climate, and are home to forest elephants, gorillas and humans’ closest relatives, chimpanzees and bonobos.Such services to people and the planet are not guaranteed, given rapid climate change and ongoing development in the region. The forest’s ability to absorb carbon dioxide is slowing as temperatures rise1. Deforestation, although lower than elsewhere in the tropics over recent decades, has led to the loss of more than 500,000 hectares of forest in 2019 alone (see go.nature.com/3dnxm9e). Without new policies, this is expected to increase.Yet, too often, central Africa’s rainforests are ignored or downplayed. The Congo Basin forests receive much less academic and public attention than do those in the Amazon and southeast Asia. Between 2008 and 2017, the Congo Basin received just 11.5% of international financial flows for forest protection and sustainable management in tropical areas, compared with 55% for southeast Asia and 34% for the Amazon region2.The area is neglected even by comparison with the rest of Africa. For example, a key UK-funded programme of climate research, called Future Climate for Africa, invested £20 million (US$27 million) in modelling and four projects focused on eastern, western and southern Africa. None focused on the Congo Basin or central Africa.The result of this neglect is clear in high-level climate assessments. Central Africa was one of only two regions worldwide without enough data for the Intergovernmental Panel on Climate Change to assess past trends in extreme heat in its 2021 Working Group I report (the other was the southern tip of South America).
A collaborative look at the Congo Basin
We are a group of ministers who have responsibility for forests in the region, and scientists who work on the ground and advise governments. Together we call for a Congo Basin Climate Science Initiative. This should comprise a $100-million, decade-long programme of research, tied to a separate $50-million fund to train Congo Basin nationals to become PhD-level scientists. Such funding would transform our understanding of these majestic forests, providing crucial input for policymakers to help them enact policies to avoid the region’s looming environmental crises.There is precedent for such a transformation. In the mid-1990s, rainforest science in the Amazon region was limited and was largely conducted by overseas scientists. Formally beginning in 1998 and led by Brazilians, the Large-Scale Biosphere-Atmosphere Experiment in Amazonia programme, known as the LBA, was a 10-year, $100-million effort. It revolutionized understanding of the Amazon rainforest and its role in the Earth system.The LBA involved 6 years of intensive measurements and covered climatology, hydrology, ecology and biogeochemistry across an area of 550 million hectares. It comprised 120 projects and 1,700 participants, 990 of whom were Brazilians3. One of its greatest legacies was the creation of a new cadre of Brazilian researchers. Two decades on, Brazil is now widely acknowledged as the world’s leading nation for tropical forest monitoring, and is at the forefront of rainforest science.We should — we must — do the same for central Africa.Known unknownsThe Greater Congo Basin covers some 240 million hectares of contiguous forests, straddling 8 nations (see ‘Earth’s second green lung’). Merely sampling this vast area is daunting. Access often requires days of travel in dugout canoes and long treks through the humid jungle, punctuated by wading through swamps. There is also a pervasive prejudice: too many people think working in the Congo Basin region is perilous, whether the hazards are political instability, unfamiliar diseases or dangerous animals. In reality, for the vast majority of central Africa, the risks are similar to working in the Amazon rainforest or east African savannah ecosystems.

Source: Ref. 1

These various challenges can be surmounted. Papers from the past few years, co-authored by many of us, highlight how important and understudied the region is. In 2017, the world’s largest tropical peatland complex was mapped for the first time — an area spanning 14.6 million hectares in the heart of the Congo basin4. This work radically shifted our understanding of carbon stores in the region. In March 2020, an international consortium showed that Africa’s rainforests annually absorb the same amount of carbon1 as was emitted each year by fossil-fuel use across the entire African continent in the 2010s5.In December 2020, a striking 81% decline in fruit production over 3 decades in an area of forest in Gabon was shown to coincide with climate warming and an 11% decline in the body condition of forest elephants (they rely on fruit for part of their diet)6. And in April, the first region-wide assessment of tree community composition in central Africa was published7, mapping areas that are vulnerable to climate change and human pressures.
Biodiversity needs every tool in the box: use OECMs
Overall, the strikingly recent (although somewhat limited) data suggest that the tropical forests of the Congo Basin are more carbon-dense8, more efficient at slowing climate change1 and more resistant to our changing climate9 than are Amazon tropical forests. But we do not know how increasing droughts, higher temperatures, selective logging and deforestation might interact — including the possibility of reduced rainfall in the Sahel10 and Ethiopian highlands11. Some 2,500 years ago, vast swathes of the Congo Basin forests were lost during a period of climate stress, but researchers do not understand the historic context of that event, nor the likelihood of a repeat12.Little is known about the region because not enough science is done in central Africa. Remarkably, researchers still do not understand the basic principles of why different types of forest occur where they do in the Congo Basin. Climate models for this region are poor, both because of the complex interplay of Atlantic, Indian and Southern ocean influences and because of a lack of local climate data. Without more data and more specialists, it is impossible to make reliable predictions of these forests’ responses to changes in climate and land use.Next stepsInvestment in basic science is urgently needed to fill these gaps. A Congo Basin Climate Science Initiative should focus on three important overarching questions: how does the Congo Basin currently operate as an integrated system? How will changes in land use and climate affect its function? And how sustainable are different options for development?Within these broad topics are more specific questions that politicians will need answers to if nations are to achieve net-zero CO2 emissions by 2050. One such question is how much carbon is stored in vegetation and soils. These and other quantities must be reported as part of countries’ commitments to the 2015 Paris climate agreement. At present, most central African countries rely on default values, which could be way off the mark. A recent paper13 on African montane forests largely near the edges of the basin, for example, showed that measured carbon storage values were 67% higher than the default values.

A child on the Mongala River in the dense forest of the Democratic Republic of the Congo.Credit: Pascal Maitre/Panos

A science initiative will work only if there is enthusiasm and leadership from researchers and active support from key Congo Basin countries, alongside buy-in from funders. We envision three steps to achieve these aims.First, scientists from the Congo region should hold a workshop with the LBA architects and participants to assess lessons from the Amazon region. This south–south cooperation would build a scientist-led framework to address the crucial research questions.Second, a meeting of politicians and advisers from the region would facilitate discussions of the policy-relevant questions that scientists should investigate. This would be led by Cameroon, the Democratic Republic of the Congo, Gabon and the Republic of the Congo — the four nations conducting the most research in the region. The meeting will help to lock in political support across ministries responsible for forests, environment, water, climate, science and universities.
Nature-based solutions can help cool the planet — if we act now
Third, partners will need to develop an overarching science programme that is acceptable to funders. Such a programme will probably include scaling up many efforts that are already under way, but which are currently insufficient in scope or unreliably funded. This would speed up scientific progress.For example, a handful of established, long-term field sites already exist in the Greater Congo Basin, including in Lopé National Park in Gabon and in the Yangambi Biosphere Reserve in the Democratic Republic of the Congo. These ‘supersites’ are sophisticated field stations with full-time staff who collect reliable, long-term data sets on vegetation, animals and the physical environment, including greenhouse-gas fluxes at Yangambi. But the sites are too few in number, and they rely on the heroic efforts of local champions. There should be a dozen or so locations across the region, with consistent funding to support complex research projects.Similarly, the African Tropical Rainforest Observation Network (AfriTRON), established in 2009, tracks every tree in permanent sample plots to estimate the carbon balance of undisturbed forests. Although this observatory has ramped up from its original 40 sites in central Africa to more than 200 today, these cover just 250 hectares of the roughly 240-million-hectare total. That is very sparse sampling from which to draw regional conclusions.Meanwhile, the Forest Global Earth Observatory (ForestGEO), established in 1990 to understand how tropical forests maintain such a diverse number of tree species, has established just 4 sites in central Africa in 30 years, with none in the centre of the basin. There is an obvious need for expansion.

African forest elephants in Ivindo National Park, Gabon.Credit: Amaury Hauchard/AFP/Getty

Finally, the 2016 AfriSAR airborne field campaign, a collaboration between NASA, the European Space Agency and the Gabonese Agency for Space Studies and Observation, showed how to combine different data sets to carefully map forest types and their carbon stocks in Lopé National Park in Gabon. This model could be replicated elsewhere in the basin.All of this work will require linking theory, observations, experiments and modelling. It should attract a diversity of leading international experts to focus on Africa and provide training to Congo Basin nationals. A $100-million research programme would provide new opportunities and much-needed career options for African scientists. The tied investment of $50 million, focused on building talent, could produce approximately 200 PhDs awarded by leading universities worldwide. This would create a new generation of scientists, including future leaders, from central Africa. The training programme would ensure the necessary step-change in science capacity, and provide opportunities for young African researchers who currently find it hard to compete for international scholarships, which are often won by students from Asia or South America.Agreeing on open access for all the data collected, as in the LBA programme, will significantly boost the initiative’s science impact.Money well spentThis $150-million science programme over 10 years needs investors. One option would be to combine funds from governments that have made large forest- and science-related investments in the Congo Basin in the past, notably Belgium, France, Germany, Norway, the United Kingdom, the United States and the European Union. Alternatives include United Nations agencies, international climate funds and private philanthropy organizations. Such a programme should be high on funders’ agendas, given the UN Sustainable Development Goals (SDGs). These include raising capacity for effective climate-change-related planning and management (SDG13), increasing financial resources to conserve and sustainably use biodiversity and ecosystems (SDG15), boosting the number of researchers in lower-income countries, and increasing research and development (R&D) funding (SDG9), all before 2030.Global R&D funding was $2.2 trillion in 201914. Thus, investing $150 million over a decade to better understand and protect the world’s second-largest extent of tropical forest is modest. To put this sum in context, the US government’s total projected cost for the Human Genome Project was $2.7 billion, and the European Space Agency spends approximately $500 million on its larger, long-lasting scientific satellites. The $100 million that the LBA brought to the Amazon in the 1990s is equivalent to about $160 million in today’s terms.
Ethiopia, Somalia and Kenya face devastating drought
The investment in science will pay for itself many times over. Consider just the role of forests as reservoirs of zoonotic diseases. Better forest management lowers the risk of disease outbreaks, let alone a pandemic15.Critics might argue that direct interventions in development aid are more urgent than investing in climate and ecological science. However, these funds are usually independent and do not compete. Furthermore, the old division between ending poverty and protecting the environment no longer applies: Africans will suffer disproportionately if temperatures are not limited as per the Paris agreement. That must include protection of the forests of the Congo Basin.Further efforts could help to support the goals of the Congo Basin science programme. For example, there is a lack of economic models that show how standing forests can become more valuable than converted landscapes. Developing these would support policy decisions to maintain forest cover.There are also several efforts under way to improve forest management that aim to empower local people, increase income and protect the environment. These include the transfer of land-management decisions to local populations, such as through community forestry, and creating high-value end products from selective logging rather than relying on the export of raw, unprocessed timber. A new science initiative could assess various approaches to understand what works best.We know so little about the majestic forests of central Africa. A Congo Basin Climate Science Initiative would curb our collective ignorance. A lack of investment is the barrier to safeguarding these precious ecosystems. Surmount this, and the future of Earth’s second ‘great green lung’ will be brighter.

Nature 598, 411-414 (2021)
doi: https://doi.org/10.1038/d41586-021-02818-7

References1.Hubau, W. et al. Nature 579, 80–87 (2020).PubMed 
Article 

Google Scholar 
2.Atyi, R. E. et al. International Financial Flows to Support Nature Protection and Sustainable Forest Management in Central Africa (Central Africa Forest Observatory, 2019).
Google Scholar 
3.Lahsen, M. & Nobre, C. A. Environ. Sci. Policy 10, 62–74 (2007).Article 

Google Scholar 
4.Dargie, G. C. et al. Nature 542, 86–90 (2017).PubMed 
Article 

Google Scholar 
5.Ayompe, L. M., Davis, S. J. & Egoh, B. N. Environ. Res. Lett. 15, 124039 (2020).Article 

Google Scholar 
6.Bush, E. R. et al. Science 370, 1219–1222 (2020).PubMed 
Article 

Google Scholar 
7.Réjou-Méchain, M. et al. Nature 593, 90–94 (2021).PubMed 
Article 

Google Scholar 
8.Lewis, S. L. et al. Phil. Trans. R. Soc. B 368, 20120295 (2013).PubMed 
Article 

Google Scholar 
9.Bennett, A. C. et al. Proc. Natl Acad. Sci. USA 118, e2003169118 (2021).PubMed 
Article 

Google Scholar 
10.Salih, A. A. M., Zhang, Q. & Tjernström, M. J. Geophys. Res. Atmospheres 120, 6793–6808 (2015).Article 

Google Scholar 
11.Gebrehiwot, S. G. et al. WIREs Water 6, e1317 (2019).Article 

Google Scholar 
12.Malhi, Y. Proc. Natl Acad. Sci. USA 115, 3202–3204 (2018).PubMed 
Article 

Google Scholar 
13.Cuni-Zanchez, A. et al. Nature 596, 536–542 (2021).PubMed 
Article 

Google Scholar 
14.Sargent, J. F. Global Research and Development Expenditures: Fact Sheet R44283 (Congressional Research Service, 2021).15.Everard, M., Johnston, P., Santillo, D. & Staddon, C. Environ. Sci. Policy 111, 7–17 (2020).PubMed 
Article 

Google Scholar 
Download references

Competing Interests
L.J.T.W. (Gabon), E.B.M. (Democratic Republic of the Congo), J.D.N. (Cameroon), R.M. (Republic of the Congo) and A.S-N. (Republic of the Congo) are ministers of forests and/or the environment. Their countries stand to benefit if international donors take on board the recommendations in this Comment article.

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1.Pauly, D. et al. Towards sustainability in world fisheries. Nature 418, 689–695 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Butchart, S. H. M. et al. Global biodiversity: Indicators of recent declines. Science 328, 1164–1168 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Pinsky, M. L. & Palumbi, S. R. Meta-analysis reveals lower genetic diversity in overfished populations. Mol. Ecol. 23, 29–39 (2014).PubMed 
Article 

Google Scholar 
4.Neubauer, P., Jensen, O. P., Hutchings, J. A. & Baum, J. K. Resilience and recovery of overexploited marine populations. Science 340, 347–349 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Lotze, H. K., Hoffmann, R. & Erlandson, J. Lessons from historical ecology and management. In The Sea, Volume 19: Ecosystem-Based Management (Harvard University Press, 2014).6.Erlandson, J. M. & Rick, T. C. Archaeology meets marine ecology: The antiquity of maritime cultures and human impacts on marine fisheries and ecosystems. Ann. Rev. Mar. Sci. 2, 231–251 (2010).PubMed 
Article 

Google Scholar 
7.Schwerdtner Máñez, K. et al. The future of the oceans past: Towards a global marine historical research initiative. PLoS ONE 9, e101466 (2014).8.Palsbøll, P. J., Zachariah Peery, M., Olsen, M. T., Beissinger, S. R. & Bérubé, M. Inferring recent historic abundance from current genetic diversity. Mol. Ecol. 22, 22–40 (2013).9.Oosting, T. et al. Unlocking the potential of ancient fish DNA in the genomic era. Evol. Appl. 12, 1513–1522 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Heino, M., Pauli, B. D. & Dieckmann, U. Fisheries-induced evolution. Annu. Rev. Ecol. Evol. Syst. 46, 461–480 (2015).Article 

Google Scholar 
11.Riccioni, G. et al. Spatio-temporal population structuring and genetic diversity retention in depleted Atlantic Bluefin tuna of the Mediterranean Sea. Proc. Natl. Acad. Sci. 107, 2102–2107 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Cort, J. L. Age and growth of the bluefin tuna (Thunnus thynnus) of the Northeast Atlantic. In Domestication of the bluefin tuna Thunnus thynnus thynnus. Cahiers Options Méditerranéennes (CIHEAM) 45–49 (2003).13.Mather, F. J., Mason, J. M. & Jones, A. C. Historical document: life history and fisheries of Atlantic bluefin tuna. (1995). NOAA Technical Memorandum NMFS-SEFSC – 370.14.Puncher, G. N. et al. Spatial dynamics and mixing of bluefin tuna in the Atlantic Ocean and Mediterranean Sea revealed using next-generation sequencing. Mol. Ecol. Resour. 18, 620–638 (2018).CAS 
PubMed 
Article 

Google Scholar 
15.Rodríguez-Ezpeleta, N. et al. Determining natal origin for improved management of Atlantic bluefin tuna. Front. Ecol. Environ. 17, 439–444 (2019).Article 

Google Scholar 
16.Richardson, D. E. et al. Discovery of a spawning ground reveals diverse migration strategies in Atlantic bluefin tuna (Thunnus thynnus). Proc. Natl. Acad. Sci. USA 113, 3299–3304 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Piccinetti, C., Di Natale, A. & Arena, P. Eastern bluefin tuna (Thunnus thynnus, L.) reproduction and reproductive areas and season. Collect. Vol. Sci. Pap. ICCAT/Recl. Doc. Sci. CICTA/Colecc. Doc. Cient. CICAA 69, 891–912 (2013).18.Cort, J. L. & Abaunza, P. The present state of traps and fisheries research in the strait of Gibraltar. In The Bluefin Tuna Fishery in the Bay of Biscay : Its Relationship with the Crisis of Catches of Large Specimens in the East Atlantic Fisheries from the 1960s (eds. Cort, J. L. & Abaunza, P.) 37–78 (Springer International Publishing, 2019).19.Alemany, F., Tensek, S. & Pagà Garcia, A. ICCAT Atlantic-Wide Research programme for Bluefin Tuna (GBYP) activity report for the Phase 9 and the first part of Phase 10. Collect. Vol. Sci. Pap. ICCAT/Recl. Doc. Sci. CICTA/Colecc. Doc. Cient. CICAA 77, 666–700 (2020).20.MacKenzie, B. R. & Mariani, P. Spawning of bluefin tuna in the Black Sea: historical evidence, environmental constraints and population plasticity. PLoS ONE 7, e39998 (2012).21.Di Natale, A. The Eastern Atlantic bluefin tuna: Entangled in a big mess, possibly far from a conservation red alert. Some comments after the proposal to include bluefin tuna in CITES Appendix I. Collect. Vol. Sci. Pap. ICCAT/Recl. Doc. Sci. CICTA/Colecc. Doc. Cient. CICAA 65(3), 1004–1043 (2010).22.Worm, B. & Tittensor, D. P. Range contraction in large pelagic predators. Proc. Natl. Acad. Sci. USA 108, 11942–11947 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Fromentin, J.-M. Lessons from the past: Investigating historical data from bluefin tuna fisheries. Fish Fish. 10, 197–216 (2009).Article 

Google Scholar 
24.Siskey, M. R., Wilberg, M. J., Allman, R. J., Barnett, B. K. & Secor, D. H. Forty years of fishing: Changes in age structure and stock mixing in northwestern Atlantic bluefin tuna (Thunnus thynnus) associated with size-selective and long-term exploitation. ICES J. Mar. Sci. 73, 2518–2528 (2016).Article 

Google Scholar 
25.ICCAT. Report of the 2020 second ICCAT intersessional meeting of the bluefin tuna species group. Online, 20–28 July 2020. SECOND BFT INTERSESSIONAL MEETING – ONLINE 2020 (2020).26.Ravier, C. & Fromentin, J.-M. Long-term fluctuations in the eastern Atlantic and Mediterranean bluefin tuna population. ICES J. Mar. Sci. 58, 1299–1317 (2001).Article 

Google Scholar 
27.Garcia, A. P. et al. Report on revised trap data recovered by ICCAT GBYP from Phase 1 to Phase 6. Collect. Vol. Sci. Pap. ICCAT/Recl. Doc. Sci. CICTA/Colecc. Doc. Cient. CICAA 73, 2074–2098 (2017).28.Anderson, C. N. K. et al. Why fishing magnifies fluctuations in fish abundance. Nature 452, 835–839 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
29.Di Natale, A. & Idrissi, M. Factors to be taken into account for a correct reading of tuna trap catch series. Collect. Vol. Sci. Pap. ICCAT/Recl. Doc. Sci. CICTA/Colecc. Doc. Cient. CICAA 67, 242–261 (2012).30.Laconcha, U. et al. New nuclear SNP markers unravel the genetic structure and effective population size of Albacore Tuna (Thunnus alalunga). PLoS ONE 10, e0128247 (2015).31.Speller, C. F. et al. High potential for using DNA from ancient herring bones to inform modern fisheries management and conservation. PLoS ONE 7, e51122 (2012).32.Montes, I. et al. No loss of genetic diversity in the exploited and recently collapsed population of Bay of Biscay anchovy (Engraulis encrasicolus, L.). Mar. Biol. 163, 98 (2016).33.Chapman, D. D. et al. Genetic diversity despite population collapse in a critically endangered marine fish: The smalltooth sawfish (Pristis pectinata). J. Hered. 102, 643–652 (2011).PubMed 
Article 

Google Scholar 
34.Hutchinson, W. F., van Oosterhout, C., Rogers, S. I. & Carvalho, G. R. Temporal analysis of archived samples indicates marked genetic changes in declining North Sea cod (Gadus morhua). Proc. Biol. Sci. 270, 2125–2132 (2003).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Ólafsdóttir, G. Á., Westfall, K. M., Edvardsson, R. & Pálsson, S. Historical DNA reveals the demographic history of Atlantic cod (Gadus morhua) in medieval and early modern Iceland. Proc. Biol. Sci. 281, 20132976 (2014).PubMed 
PubMed Central 

Google Scholar 
36.Bonanomi, S. et al. Archived DNA reveals fisheries and climate induced collapse of a major fishery. Sci. Rep. 5, 15395 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Nielsen, E. E., Hansen, M. M. & Loeschcke, V. Analysis of microsatellite DNA from old scale samples of Atlantic salmon Salmo salar : A comparison of genetic composition over 60 years. Mol. Ecol. 6, 487–492 (1997).CAS 
Article 

Google Scholar 
38.Johnson, B. M., Kemp, B. M. & Thorgaard, G. H. Increased mitochondrial DNA diversity in ancient Columbia River basin Chinook salmon Oncorhynchus tshawytscha. PLoS ONE 13, e0190059 (2018).39.Bowles, E., Marin, K., Mogensen, S., MacLeod, P. & Fraser, D. J. Size reductions and genomic changes within two generations in wild walleye populations: associated with harvest?. Evol. Appl. 13, 1128–1144 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Royle, T. C. A. et al. Investigating the sex-selectivity of a middle Ontario Iroquoian Atlantic salmon (Salmo salar) and lake trout (Salvelinus namaycush) fishery through ancient DNA analysis. J. Archaeol. Sci. Rep. 31, 102301 (2020).41.Therkildsen, N. O. et al. Microevolution in time and space: SNP analysis of historical DNA reveals dynamic signatures of selection in Atlantic cod. Mol. Ecol. 22, 2424–2440 (2013).CAS 
PubMed 
Article 

Google Scholar 
42.Pinsky, M. L. et al. Genomic stability through time despite decades of exploitation in cod on both sides of the Atlantic. Proc. Natl. Acad. Sci. USA 118, (2021).43.Onar, V., Pazvant, G. & Armutak, A. Radiocarbon dating results of the animal remains uncovered at Yenikapi Excavations. In Istanbul Archaeological Museums, Proceedings of the 1st Symposium on Marmaray-Metro Salvage Excavations 249–256 (2008).44.Bernal-Casasola, D., Expósito, J. A. & Díaz, J. J. The Baelo Claudia paradigm: The exploitation of marine resources in Roman cetariae. J. Marit. Archaeol. 13, 329–351 (2018).ADS 
Article 

Google Scholar 
45.Bernal, D. & Monclova, A. Pescar con Arte. Fenicios y romanos en el origen de los aparejos andaluces. Monografías del Proyecto Sagena 3, (2011).46.Puncher, G. N. et al. Comparison and optimization of genetic tools used for the identification of ancient fish remains recovered from archaeological excavations and museum collections in the Mediterranean region. Int J Osteoarchaeol 29, 365–376 (2019).Article 

Google Scholar 
47.Kemp, B. M. & Smith, D. G. Use of bleach to eliminate contaminating DNA from the surface of bones and teeth. Forensic Sci. Int. 154, 53–61 (2005).CAS 
PubMed 
Article 

Google Scholar 
48.Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl. Acad. Sci. USA 110, 15758–15763 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Serventi, P. et al. Iron Age Italic population genetics: The Piceni from Novilara (8th–7th century BC). Ann. Hum. Biol. 45, 34–43 (2018).PubMed 
Article 

Google Scholar 
50.Star, B. et al. The genome sequence of Atlantic cod reveals a unique immune system. Nature 477, 207–210 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Tine, M. et al. European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation. Nat. Commun. 5, 5770 (2014).ADS 
CAS 
Article 

Google Scholar 
52.Chini, V. et al. Genes expressed in bluefin tuna (Thunnus thynnus) liver and gonads. Gene 410, 207–213 (2008).CAS 
PubMed 
Article 

Google Scholar 
53.Gardner, L. D., Jayasundara, N., Castilho, P. C. & Block, B. Microarray gene expression profiles from mature gonad tissues of Atlantic bluefin tuna, Thunnus thynnus in the Gulf of Mexico. BMC Genomics 13, 530 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281 (2014).55.Team, R. C. R development core team. RA Lang. Environ. Stat. Comput. 55, 275–286 (2013).56.Paradis, E. pegas: An R package for population genetics with an integrated–modular approach. Bioinformatics 26, 419–420 (2010).CAS 
Article 

Google Scholar 
57.Rousset, F. genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).Article 

Google Scholar 
58.Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180, 977–993 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Whitlock, M. C. & Lotterhos, K. E. Reliable detection of loci responsible for local adaptation: Inference of a null model through trimming the distribution of FST. Am. Nat. 186, S24–S36 (2015).PubMed 
Article 

Google Scholar 
60.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. (1995).61.Goudet, J. hierfstat, a package for r to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

Google Scholar 
62.Waples, R. S. A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conserv. Genet. 7, 167–184 (2006).Article 

Google Scholar 
63.Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne ) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).CAS 
PubMed 
Article 

Google Scholar 
64.Wang, J., Santiago, E. & Caballero, A. Prediction and estimation of effective population size. Heredity 117, 193–206 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Jenkins, T. L., Ellis, C. D., Triantafyllidis, A. & Stevens, J. R. Single nucleotide polymorphisms reveal a genetic cline across the north-east Atlantic and enable powerful population assignment in the European lobster. Evol. Appl. 12, 1881–1899 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Jombart, T. et al. Package ‘adegenet’. Bioinform. Appl. Note 24, 1403–1405 (2008).CAS 
Article 

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

Google Scholar 
68.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 
Article 

Google Scholar 
69.Earl, D. A. & vonHoldt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).70.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Nei, M. Molecular Evolutionary Genetics. (Columbia University Press, 1987). https://doi.org/10.7312/nei-92038.72.Frankham, R., Scientist Emeritus Jonathan, Briscoe, D. A. & Ballou, J. D. Introduction to Conservation Genetics. (Cambridge University Press, 2002).73.Di Natale, A. Due to the new scientific knowledge, is it time to reconsider the stock composition of the Atlantic bluefin tuna? Collect. Vol. Sci. Pap. ICCAT/Recl. Doc. Sci. CICTA/Colecc. Doc. Cient. CICAA 75, 1282–1292 (2019).74.Di Natale, A., Tensek, S. & Pagá García, A. ICCAT Atlantic-wide research programme for bluefin tuna (GBYP) activity report for the last part of phase and the first part of phase (2016–2017). https://www.iccat.int/Documents/CVSP/CV074_2017/n_6/CV074063100.pdf (2017).75.Leonard, J. A. Ancient DNA applications for wildlife conservation. Mol. Ecol. 17, 4186–4196 (2008).CAS 
PubMed 
Article 

Google Scholar 
76.Alter, S. E., Newsome, S. D. & Palumbi, S. R. Pre-whaling genetic diversity and population ecology in eastern Pacific gray whales: Insights from ancient DNA and stable isotopes. PLoS ONE 7, e35039 (2012).77.Cole, T. L. et al. Ancient DNA of crested penguins: Testing for temporal genetic shifts in the world’s most diverse penguin clade. Mol. Phylogenet. Evol. 131, 72–79 (2019).PubMed 
Article 

Google Scholar 
78.Dures, S. G. et al. A century of decline: Loss of genetic diversity in a southern African lion-conservation stronghold. Divers. Distrib. 25, 870–879 (2019).Article 

Google Scholar 
79.Thomas, J. E. et al. Demographic reconstruction from ancient DNA supports rapid extinction of the great auk. Elife 8, (2019).80.Colson, I. & Hughes, R. N. Rapid recovery of genetic diversity of dogwhelk (Nucella lapillus L.) populations after local extinction and recolonization contradicts predictions from life-history characteristics. Mol. Ecol. 13, 2223–2233 (2004).81.McEachern, M. B., Van Vuren, D. H., Floyd, C. H., May, B. & Eadie, J. M. Bottlenecks and rescue effects in a fluctuating population of golden-mantled ground squirrels (Spermophilus lateralis). Conserv. Genet. 12, 285–296 (2011).Article 

Google Scholar 
82.Jangjoo, M., Matter, S. F., Roland, J. & Keyghobadi, N. Connectivity rescues genetic diversity after a demographic bottleneck in a butterfly population network. Proc. Natl. Acad. Sci. USA 113, 10914–10919 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Porch, C. E., Bonhommeau, S., Diaz, G. A., Haritz, A. & Melvin, G. The journey from overfishing to sustainability for Atlantic bluefin tuna, Thunnus thynnus. In The Future of Bluefin Tunas: Ecology, Fisheries Management, and Conservation 3–44 (2019).84.Di Natale, A., Macias, D. & Cort, J. L. Atlantic bluefin tuna fisheries: temporal changes in the exploitation pattern, feasibility of sampling, factors that can influence our ability to understand spawning structure and dynamics. Collect. Vol. Sci. Pap. ICCAT/Recl. Doc. Sci. CICTA/Colecc. Doc. Cient. CICAA 76, 354–388 (2020).85.Viñas, J. & Tudela, S. A validated methodology for genetic identification of tuna species (genus Thunnus). PLoS ONE 4, e7606 (2009).86.MacKenzie, B. R., Mosegaard, H. & Rosenberg, A. A. Impending collapse of bluefin tuna in the northeast Atlantic and Mediterranean. Conserv. Lett. 2, 26–35 (2009).Article 

Google Scholar 
87.Collette, B. B. Bluefin tuna science remains vague. Science 358, 879–880 (2017).ADS 
CAS 
PubMed 

Google Scholar 
88.Nøttestad, L., Boge, E. & Ferter, K. The comeback of Atlantic bluefin tuna (Thunnus thynnus) to Norwegian waters. Fish. Res. 231, 105689 (2020).89.Lehodey, P. et al. Climate variability, fish, and fisheries. J. Clim. 19, 5009–5030 (2006).ADS 
Article 

Google Scholar 
90.Kuwae, M. et al. Sedimentary DNA tracks decadal-centennial changes in fish abundance. Commun Biol 3, 558 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
91.Domingues, R. et al. Variability of preferred environmental conditions for Atlantic bluefin tuna (Thunnus thynnus) larvae in the Gulf of Mexico during 1993–2011. Fish. Oceanogr. 25, 320–336 (2016).Article 

Google Scholar 
92.Reglero, P. et al. Pelagic habitat and offspring survival in the eastern stock of Atlantic bluefin tuna. ICES J. Mar. Sci. 76, 549–558 (2019).Article 

Google Scholar 
93.Faillettaz, R., Beaugrand, G., Goberville, E. & Kirby, R. R. Atlantic Multidecadal Oscillations drive the basin-scale distribution of Atlantic bluefin tuna. Sci. Adv. 5, eaar6993 (2019).94.Hanke, A. et al. Stock mixing rates of bluefin tuna from Canadian landings: 1975–2015. Collect. Vol. Sci. Pap. ICCAT/Recl. Doc. Sci. CICTA/Colecc. Doc. Cient. CICAA 74, 2622–2634 (2017).95.Fraser, D. J. et al. Comparative estimation of effective population sizes and temporal gene flow in two contrasting population systems. Mol. Ecol. 16, 3866–3889 (2007).PubMed 
Article 

Google Scholar 
96.Albrechtsen, A., Nielsen, F. C. & Nielsen, R. Ascertainment biases in SNP chips affect measures of population divergence. Mol. Biol. Evol. 27, 2534–2547 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
97.Clark, A. G., Hubisz, M. J., Bustamante, C. D., Williamson, S. H. & Nielsen, R. Ascertainment bias in studies of human genome-wide polymorphism. Genome Res. 15, 1496–1502 (2005).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
99.Hofreiter, M. et al. The future of ancient DNA: Technical advances and conceptual shifts. BioEssays 37, 284–293 (2015).PubMed 
Article 

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

Google Scholar 
101.Bradbury, I. R. et al. Evaluating SNP ascertainment bias and its impact on population assignment in Atlantic cod, Gadus morhua. Mol. Ecol. Resour. 11, 218–225 (2011).PubMed 
Article 

Google Scholar 
102.Lou, R. N., Jacobs, A., Wilder, A. & Therkildsen, N. O. A beginner’s guide to low-coverage whole genome sequencing for population genomics. Mol. Ecol. https://doi.org/10.1111/mec.16077 (2020).Article 

Google Scholar 
103.Schlötterer, C. Hitchhiking mapping–functional genomics from the population genetics perspective. Trends Genet. 19, 32–38 (2003).PubMed 
Article 

Google Scholar  More

1.Hughes, T. P. et al. Climate change, human impacts, and the resilience of coral reefs. Science 301, 929–933. https://doi.org/10.1126/science.1085046 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
2.Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737. https://doi.org/10.1126/science.1152509 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
3.Sokolow, S. Effects of a changing climate on the dynamics of coral infectious disease: A review of the evidence. Dis. Aquat. Org. 87, 5–18. https://doi.org/10.3354/dao02099 (2009).Article 

Google Scholar 
4.De’ath, G., Fabricius, K. E., Sweatman, H. & Puotinen, M. The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc. Natl. Acad. Sci. USA 109, 17995–17999. https://doi.org/10.1073/pnas.1208909109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
5.Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377. https://doi.org/10.1038/nature21707 (2017).ADS 
CAS 
Article 

Google Scholar 
6.May, L. A. et al. Effect of Louisiana sweet crude oil on a Pacific coral, Pocillopora damicornis. Aquat. Toxicol. 28, 105454. https://doi.org/10.1016/j.aquatox.2020.105454 (2020).CAS 
Article 

Google Scholar 
7.Bell, J. J., Davy, S. K., Jones, T., Taylor, M. W. & Webster, N. S. Could some coral reefs become sponge reefs as our climate changes?. Glob. Change. Biol. 19, 2613–2624. https://doi.org/10.1111/gcb.12212 (2013).ADS 
Article 

Google Scholar 
8.Bell, J. J. & Smith, D. Ecology of sponge assemblages (Porifera) in the Wakatobi region, south-east Sulawesi, Indonesia: Richness and abundance. J. Mar. Biol. Assoc UK 84, 581–591. https://doi.org/10.1017/S0025315404009580h (2004).Article 

Google Scholar 
9.Wulff, J. L. Ecological interactions of marine sponges. Can. J. Zool. 84, 146–166. https://doi.org/10.1139/z06-019 (2006).Article 

Google Scholar 
10.Wooster, M. K., Marty, M. J. & Pawlik, J. R. Defense by association: Sponge-eating fishes alter the small-scale distribution of Caribbean reef sponges. Mar. Ecol. 38, e12410. https://doi.org/10.1111/maec.12410 (2017).ADS 
Article 

Google Scholar 
11.Bryan, P. G. Growth rate, toxicity, and distribution of the encrusting sponge Terpios sp. (Hadromerida: Suberitidae) in Guam, Mariana Islands. Micronesica 9, 237–242 (1973).
Google Scholar 
12.Plucer-Rosario, G. The effect of substratum on the growth of Terpios, an encrusting sponge which kills corals. Coral Reefs 5, 197–200. https://doi.org/10.1007/BF00300963 (1987).ADS 
Article 

Google Scholar 
13.Rützler, K. & Muzik, K. Terpios hoshinota, a new cyanobacteriosponge threatening Pacific reefs. Sci. Mar. 57, 395-403.e0120853 (1993).
Google Scholar 
14.Reimer, J. D., Nozawa, Y. & Hirose, E. Domination and disappearance of the black sponge: A quarter century after the initial Terpios outbreak in Southern Japan. Zool. Stud. 50, 394 (2010).
Google Scholar 
15.Reimer, J. D., Mizuyama, M., Nakano, M., Fujii, T. & Hirose, E. Current status of the distribution of the coral-encrusting cyanobacteriosponge Terpios hoshinota in southern Japan. Galaxea J. Coral Reef Stud. 13, 35–44. https://doi.org/10.3755/galaxea.13.35 (2011).Article 

Google Scholar 
16.Yomogida, M., Mizuyama, M., Kubomura, T. & Reimer, J. D. Disappearance and return of an outbreak of the coral-killing cyanobacteriosponge Terpios hoshinota in Southern Japan. Zool. Stud. 56, 1–7. https://doi.org/10.6620/ZS.2017.56-07 (2017).Article 

Google Scholar 
17.Liao, M.-H. et al. The ‘“black disease”’ of reef-building corals at Green Island, Taiwan outbreak of a cyanobacteriosponge Terpios hoshinota (Suberitidae; Hadromerida). Zool. Stud. 46, 520 (2007).
Google Scholar 
18.Nozawa, Y., Huang, Y. S. & Hirose, E. Seasonality and lunar periodicity in the sexual reproduction of the coral-killing sponge, Terpios hoshinota. Coral Reefs 35, 1071–1081. https://doi.org/10.1007/s00338-016-1417-0 (2016).ADS 
Article 

Google Scholar 
19.Fujii, T. et al. Coral-killing cyanobacteriosponge (Terpios hoshinota) on the Great Barrier Reef. Coral Reefs 30, 483. https://doi.org/10.1007/s00338-011-0734-6 (2011).ADS 
Article 

Google Scholar 
20.Shi, Q., Liu, G. H., Yan, H. Q. & Zhang, H. L. Black disease (Terpios hoshinota): A probable cause for the rapid coral mortality at the northern reef of Yongxing Island in the South China Sea. Ambio 41, 446–455. https://doi.org/10.1007/s13280-011-0245-2 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
21.Hoeksema, B. W., Waheed, Z. & de Voogd, N. J. Partial mortality in corals overgrown by the sponge Terpios hoshinota at Tioman Island, Peninsular Malaysia (South China Sea). Bull. Mar. Sci. 90, 989–990. https://doi.org/10.5343/bms.2014.1047 (2014).Article 

Google Scholar 
22.Van der Ent, E., Hoeksema, B. W. & de Voogd, N. J. Abundance and genetic variation of the coral-killing cyanobacteriosponge Terpios hoshinota in the Spermonde Archipelago, SW Sulawesi, Indonesia. J. Mar. Biol. Assoc. UK 96, 453–463. https://doi.org/10.1017/S002531541500034X (2015).Article 

Google Scholar 
23.Madduppa, H., Schupp, P. J., Faisal, M. R., Sastria, M. Y. & Thoms, C. Persistent outbreaks of the “black disease” sponge Terpios hoshinota in Indonesian coral reefs. Mar. Biodivers. 47, 149–151. https://doi.org/10.1007/s12526-015-0426-5 (2017).Article 

Google Scholar 
24.Montano, S., Chou, W.-H., Chen, C. A., Galli, P. & Reimer, J. D. First record of the coral-killing sponge Terpios hoshinota in the Maldives and Indian Ocean. Bull. Mar. Sci. 91, 97–98. https://doi.org/10.5343/bms.2014.1054 (2015).Article 

Google Scholar 
25.Elliott, J. B., Patterson, M., Vitry, E., Summers, N. & Miternique, C. Morphological plasticity allows coral to actively overgrow the aggressive sponge Terpios hoshinota (Mauritius, Southwestern Indian Ocean). Mar. Biodivers. 46, 489–493. https://doi.org/10.1007/s12526-015-0370-4 (2016).Article 

Google Scholar 
26.Thinesh, T., Mathews, G., Raj, K. D. & Edward, J. K. P. Outbreaks of Acropora white syndrome and Terpios sponge overgrowth combined with coral mortality in Palk Bay, southeast coast of India. Dis. Aquat. Org. 126, 63–70. https://doi.org/10.3354/dao03155 (2017).CAS 
Article 

Google Scholar 
27.Birenheide, R., Amemiya, S. & Motokawa, T. Penetration and storage of sponge spicules in tissues and coelom of spongivorous echinoids. Mar. Biol. 115, 677–683. https://doi.org/10.1007/BF00349376 (1993).Article 

Google Scholar 
28.Vicente, J., Osberg, A., Marty, M. J., Rice, K. & Toonen, R. J. Influence of palatability on the feeding preferences of the endemic Hawaiian tiger cowrie for indigenous and introduced sponges. Mar. Ecol. Prog. Ser. 647, 109–122. https://doi.org/10.3354/meps13418 (2020).ADS 
Article 

Google Scholar 
29.Penney, B. K. How specialized are the diets of northeastern Pacific sponge-eating dorid nudibranchs?. J. Moll. Stud. 79, 64–73. https://doi.org/10.1093/mollus/eys038 (2013).Article 

Google Scholar 
30.Teruya, T. et al. Nakiterpiosin and nakiterpiosinone, novel cytotoxic C-nor-D-homosteroids from the Okinawan sponge Terpios hoshinota. Tetrahedron 60, 6989–6993. https://doi.org/10.1016/j.tet.2003.08.083 (2004).CAS 
Article 

Google Scholar 
31.Marshall, B. A. Cerithiopsidae (Mollusca: Gastropoda) of New Zealand, and a provisional classification of the family. New Zeal. J. Zool. 5, 47–120. https://doi.org/10.1080/03014223.1978.10423744 (1978).Article 

Google Scholar 
32.Collin, R. Development of Cerithiopsis gemmulosum (Gastropoda: Cerithiopsidae) from Bocas del Toro, Panama. Caribb. J. Sci. 40, 192–197 (2004).
Google Scholar 
33.Cecalupo, A. & Perugia, I. Cerithiopsidae and Newtoniellidae (Gastropoda: Triphoroidea) from New Caledonia, western Pacific. Visaya Suppl. 7, 1–175 (2016).
Google Scholar 
34.Cecalupo, A. & Perugia, I. Cerithiopsidae. In Philippine Marine Mollusks Vol. V (ed. Poppe, G.) 1352–1375 (Conchbooks, 2017).
Google Scholar 
35.Cecalupo, A. & Perugia, I. New species of Cerithiopsidae (Gastropoda: Triphoroidea) from Papua New Guinea (Pacific Ocean). Visaya Suppl. 11, 1–187 (2018).
Google Scholar 
36.Cecalupo, A. & Perugia, I. New species of Cerithiopsidae and Newtoniellidae from Okinawa (Japan-Pacific Ocean). Visaya Suppl. 12, 1–84 (2019).
Google Scholar 
37.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotech. 3, 294–299 (1994).CAS 

Google Scholar 
38.Kano, Y. & Fukumori, H. Predation on hardest molluscan eggs by confamilial snails (Neritidae) and its potential significance in egg-laying site selection. J. Moll. Stud. 76, 360–366. https://doi.org/10.1093/mollus/eyq018 (2010).Article 

Google Scholar 
39.Maddison, W. P. & Maddison, D. R. Mesquite: a modular system for evolutionary analysis. Version 3.61. http://www.mesquiteproject.org (2019).40.Modica, M. V., Mariottini, P., Prkić, J. & Oliverio, M. DNA-barcoding of sympatric species of ectoparasitic gastropods of the genus Cerithiopsis (Mollusca: Gastropoda: Cerithiopsidae) from Croatia. J. Mar. Biol. Assoc. UK 93, 1059–1065. https://doi.org/10.1017/S0025315412000926 (2012).CAS 
Article 

Google Scholar 
41.Takano, T. & Kano, Y. Molecular phylogenetic investigations of the relationships of the echinoderm-parasite family Eulimidae within Hypsogastropoda (Mollusca). Mol. Phylogenet. Evol. 79, 258–269. https://doi.org/10.1016/j.ympev.2014.06.021 (2014).Article 
PubMed 

Google Scholar 
42.Kimura, M. A. Simple method for estimating evolutionary rate of base substitutions through comparative studies of nucleotide sequence. J. Mol. Evol. 16, 111–120. https://doi.org/10.1007/BF01731581 (1980).ADS 
CAS 
Article 
PubMed 

Google Scholar 
43.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 
44.Stamatakis, A. RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22, 2688–2690. https://doi.org/10.1093/bioinformatics/btl446 (2006).CAS 
Article 
PubMed 

Google Scholar  More

Rhizosphere soil samplingA total of 20 rhizosphere soil samples (20 tomato plants) were collected at the flowering stage from a tomato field located in Qilin town, Jiangsu province, China, 118°57’ E, 32°03’ N, which had been infested by the pathogen Ralstonia solanacearum for more than 15 years [8]. After uprooting plants, excess soil was first gently shaken from the roots, and the remaining soil attached to roots was considered as rhizosphere soil. Each rhizosphere soil sample was then used for bacterial strain isolation.Isolation and identification of rhizobacteriaIsolationA total of 640 bacterial strains were isolated from the fresh rhizosphere soil samples, according to a previously established protocol [11]. Briefly, 1 g of each rhizosphere sample was mixed with 9 mL MS buffer solution (50 mM Tris-HCl [pH 7.5], 100 mM NaCl, 10 mM MgSO4, 0.01% gelatin) in a rotary shaker at 170 rpm min−1 for 30 min at 30 °C. After serial dilution in MS buffer solution, 100-μl volumes of the diluted soil suspensions were plated on 1/10 tryptone soy agar (1/10 TSA, 1.5 g L−1 tryptone, 0.5 g L−1 soytone, 0.5 g L−1 sodium chloride, and 15 g L−1 agar, pH 7.0). After a 48-h incubation at 30 °C in the dark, 32 isolates were randomly picked per rhizosphere soil sample. To avoid potential fungal contamination, only highly diluted samples were used for isolation. The isolates were then re-streaked on TSA plates for colony purification. Approximately 5.5% (35 isolates) of the bacterial isolates failed to grow on the TSA plates for unknown reasons when we re-streaked them and were therefore omitted from the dataset. The final collection thus consisted of 605 bacterial isolates derived from 20 rhizosphere soil samples. All purified isolates were cultured in 100 μl tryptone soy broth (TSB, liquid TSA) in 96-well microtiter plates at 30 °C with shaking (rotary shaker at 170 rpm) for 18 h before freezing and storing at −80 °C in 15% glycerol.Strain identificationWe sequenced the full 16 S rRNA gene to taxonomically identify all 605 rhizobacterial isolates. The 16 S rRNA gene was sequenced via Sanger sequencing of PCR products from glycerol stocks by Shaihai Songon Biotechnology Co., Ltd, Shaihai Station. The PCR system (25 µl) was composed of 1 µl of bacterial cells (overnight culture), 12.5 µl mixture, 1 µl of forward (27 F: 5-AGA GTT TGA TCA TGG CTC AG-3) and reverse primer (1492 R: 5-TAC GGT TAC CTT GTT ACG ACT T-3) each [17] and 9.5 µl of sterilized water. PCR was performed by initially denaturizing at 95 °C for 5 min, cycling 30 times with a 30-s denaturizing step at 94 °C, annealing at 58 °C for 30 s, extension at 72 °C for 1 min 30 s, and a final extension at 72 °C for 10 min. The 16 S rRNA gene sequences were identified using NCBI databases and homologous sequence similarity. A total of 90 bacterial isolates that were identified as Ralstonia solanacearum were removed from further analyses, resulting in 515 remaining isolates.Direct effect of rhizobacteria on pathogen growth in vitroWe used R. solanacearum strain QL-Rs1115 tagged with the pYC12-mCherry plasmid as a model bacterial pathogen [8, 18]. We first tested the direct effects of the 515 non-R. solanacearum bacterial strains on the growth of R. solanacearum in vitro by using supernatant assays. Briefly, after 48 h of growth in NB (nutrient broth) medium (glucose 10.0 g l−1, tryptone 5.0 g l−1, yeast extract 0.5 g l−1, beef extract 3.0 g l−1, pH 7.0) on a shaker at 170 rpm, 30 °C, all bacterial cultures were filter sterilized to remove living cells (0.22 µm filter). Subsequently, 20 µl of sterile supernatant from each strain’s culture and 2 µl overnight culture of the pathogen (adjusted to OD600 = 0.5 after 12 h growth at 30 °C with shaking) were added into 180 µl of fresh NB medium (5-times diluted, in order to better reflect the effect of the supernatant). Control treatments were inoculated with 20 µl of 5 X diluted NB media instead of the bacterial supernatant. Each treatment was conducted in triplicate. All bacterial cultures were grown for 48 h at 30 °C with shaking (170 rpm) before measuring pathogen density as red mCherry protein fluorescence intensity (excitation: 587 nm, emission: 610 nm) [9, 11] which was linearly related to the CFU of pathogen R. solanacearum (Fig. S1). To test for significance of growth promotion or inhibition, R. solanacearum densities were log10-transformed prior to analyses of variance (ANOVA) and Bonferroni t test to compare mean differences between each rhizobacterial supernatant treatment and the control treatment, with p values less than 0.05 considered statistically significant. The effect on pathogen growth was defined as the percentage of improvement or reduction in pathogen growth by the supernatant compared to the control treatment. When the effect on pathogen growth was positive, i.e., when the supernatants from strains significantly promoted the growth of the pathogen, they were considered as helpers of the pathogen. If the effect on pathogen growth was negative, i.e., when the supernatants from strains significantly inhibited the growth of the pathogen, they were considered as inhibitors of the pathogen.Assessing strain redundancy among the 515 non-Ralstonia solanacearum bacteriaWe assessed possible redundancy among the 515 strains of the non-Ralstonia solanacearum rhizobacteria. To encompass both taxonomic and functional redundancies, we considered the 16 S rRNA gene sequences as well as the direct effect of their supernatant on Ralstonia solanacearum. Self BLAST searches were performed on the full 515 sequence dataset using the makeblastdb and blastn commands from the BLAST command line tool [19]. Sequences showing >99% identity over >95% of the full length of the 16 S rRNA gene were considered as taxonomically redundant. We then compared the direct effects on pathogen growth of the taxonomically redundant strains, and removed those showing the same patterns of interactions (positive, negative or neutral). Accordingly, (see the dataset “Library of rhizobacterial strains” in the supplementary information), 355 of the 515 strains (68.9%) were removed from the original dataset for further analyses.Phylogenetic tree constructionThe 16 S rRNA gene sequences of the 160 non-redundant bacteria were aligned using MUSCLE [20]. Sequences in the alignment were trimmed at both ends to obtain maximum overlap using the MEGA X software, which was also used to construct taxonomic cladograms [21]. We constructed a maximum-likelihood (ML) tree, using a General Time Reversible (GTR) + G + I model, which yielded the best fit to our data set. Bootstrapping was carried out with 100 replicates retaining gaps. A taxonomic cladogram was created using the EVOLVIEW web tool (https://evolgenius.info//evolview-v2/). To show the relationship between phylogeny and the effects of rhizobacteria on pathogen growth, we added taxonomic status (phylum) of each rhizobacterial strain and its effect on pathogen growth as heatmap rings to the outer circle of the tree separately (Fig. 2B).Fig. 2: Taxonomic characterization of rhizobacterial isolates that inhibited or helped the growth of Ralstonia solanacearum.A Distribution of in vitro effects of 160 rhizobacterial supernatants on R. solanacearum growth. The red vertical line represents no effect on R. solanacearum growth. B Cladogram depicting the phylogenetic relationship among the 160 isolates based on their full-length 16 S rRNA gene sequences. The inner ring depicts the different effect of isolates supernatant on R. solanacearum growth: positive effect (blue), negative effect (red) and no significant effect (gray). The outer ring shows the four phyla to which the isolates belong. C The proportion of rhizobacterial isolates per phylum whose supernatant showed inhibitory, stimulatory or no effect on R. solanacearum growth. The size of the circles represents the number of rhizobacterial isolates in the given phylum. The thickness of lines represents the percentage of rhizobacterial isolates that have the indicated effect on R. solanacearum growth in each phylum.Full size imageEffects of rhizobacteria on pathogen helper strains growth in vitroWe then assessed the potential of different rhizosphere isolates to inhibit helper strains. We first selected two model helper strains (Phyllobacterium ifriqiyense LM1 (Pi) and Microbacterium paraoxydans LM2 (Mp)), which showed strong positive effects on pathogen growth both in co-culture and in supernatant assays (Fig. S2). We defined the effect of rhizobacterial strains on the growth of helpers as the indirect effect on R. solanacearum growth. To study these indirect effects, we first chose a subset of 46 rhizobacterial strains representing a gradient of positive, neutral or negative effect on pathogen growth based on supernatant assays (results in x axis of Figs. 3C and 4A, B, C). We then tested the effects of these 46 rhizobacterial strains on the growth of each of the two helper strains using supernatant assays. Briefly, after 48 h growth in NB media, each of the 46 bacterial monocultures was passed through a 0.22 µm filter to remove living cells. Then 20 µl of sterile supernatant from each strain’s culture and 2 µl overnight culture of Pi or Mp (adjusted to OD600 = 0.5 after 12 h growth at 30 °C with shaking) were added into 180 µl of fresh NB medium (5-times diluted, in order to better reflect the effect of the supernatant). Control treatments were inoculated with 20 µl of 5× diluted NB media instead of a bacterial supernatant. Each treatment was replicated four times. All bacterial cultures were grown for 24 h at 30 °C with shaking (170 rpm) before measuring helper density as optical density (OD600). To test for significance of growth promotion or inhibition, we used analyses of variance (ANOVA) and Bonferroni t test to compare mean differences of helper density between each rhizobacterial supernatant treatment and the control treatment, with p values lower than 0.05 being considered statistically significant. The effect of rhizobacteria on the helpers’ growth (results in y axis of Fig. 3C and x axis of Fig. 4D, E, F) was defined as the percentage of increase or reduction in helper growth by the supernatant compared to the control treatment.Fig. 3: Effect of helper strains on Ralstonia solanacearum growth and plant disease severity.Effects of the two helper strains Phyllobacterium ifriqiyense (Pi) and Microbacterium paraoxydans (Mp) on Ralstonia solanacearum (Rs) growth in vitro (A) and in vivo (B) and on plant disease severity (C). Different letters indicate significant differences based on Tukey post hoc test. Error bars show ±1 SE (n = 3 for in vitro, n = 4 for in vivo). D Effects of 46 rhizobacterial strains on the growth of R. solanacearum and the two model helper strains in vitro. The x-axis shows the direct effect of each rhizobacterial strain on R. solanacearum growth (data from the experiment in which R. solanacearum was grown in the presence of supernatant from each of the 46 rhizobacterial strains—the same data is presented on the x axis of Fig. 4A). The y-axis shows the effect of each rhizobacterial strain on each of the two helper strains (data from the experiment in which each helper was grown in the presence of supernatant from each of the 46 rhizobacterial strains—the same data is presented on the x axis of Fig. 4C). In (C), “−1”, “0” and “1” on the x-axis denote that R. solanacearum growth is completely inhibited, not influenced or increased 2× by supernatant from the rhizobacteria, respectively. Similarly, “−1”, “0” and “1” on the y-axis denote the same growth effects with reference to growth of the helper strains. Black dots indicate results involving interactions with Pi, and red dots indicate results involving interactions with Mp.Full size imageFig. 4: The importance of direct versus indirect effects on Ralstonia solanacearum density and disease severity in the presence of helper strains.In the presence of helper Phyllobacterium ifriqiyense (Pi) or Microbacterium paraoxydans (Mp), respectively, the importance of direct effects on the density of R. solanacearum both (A) in vitro and (B) in vivo, and (C) disease severity (the data on the x axis of (A) are the same data which was presented on the x axis of Fig. 3C, the data on x axis of (B) and (C) are part of the data on x axis of (A)); the importance of indirect effects on the density of R. solanacearum both (D) in vitro and (E) in vivo, and (F) disease severity (the data on the x axis of (D) are the same data which was presented on the y axis of Fig. 3C, the data on x axis of (E) and (F) are part of the data on x axis of (D)). In all panels, “−1”, “0” and “1” on the x-axis denote that R. solanacearum growth (A, B, and C) or helper growth (D, E, and F) is completely inhibited, not influenced or increased 2× by supernatant from the rhizobacteria, respectively.Full size imageIn vitro pathogen growth in the presence of a helper strain and supernatant from rhizobacterial isolatesTo disentangle the direct effects from the indirect effects of rhizobacteria on R. solanacearum growth, we compared their relative effects using in vitro triculture assays comprised of R. solanacearum, one of the two helper strains and supernatant of one of the 46 chosen rhizobacterial strains. Briefly, after 48 h of growth in NB media, each of the 46 bacterial monocultures was passed through a 0.22 µm filter to remove living cells. Then, 20 µl of sterile supernatant from each strain’s culture and 2 µl overnight culture of Pi or Mp (densities were adjusted to ~107 cells per ml) were added to 180 µl of fresh NB medium (5-times diluted). Each treatment was replicated four times. At the same time, 2 µl overnight culture of mCherry-tagged R. solanacearum (density was adjusted to ~106 cells per ml) was added to each treatment in 96-well plates at 30 °C with shaking (170 rpm). After 24-h growth, R. solanacearum density (results in y axis of Fig. 4A, D) was measured as the red mCherry protein fluorescence intensity (excitation: 587 nm, emission: 610 nm) with a SpectraMax M5 plate reader.In vivo pathogen growth and plant disease development in the presence of a helper strain and a rhizobacterial strainTo validate in vitro results, we set up greenhouse experiments where plants were inoculated with a bacterial consortium consisting of R. solanacearum, one of the two helper strains and a test rhizobacterial strain. Tomato seeds (Lycopersicon esculentum, cultivar “Ai hong sheng”) were surface-sterilized by soaking them in 3% NaClO for 5 min and in 70% ethyl alcohol for 1 min before being germinated on water-agar plates for 2 days. Seeds were then sown into seedling trays containing gamma irradiation-sterilized (to avoid potential effects of the resident community) seedling substrate (Huainong, Huaian Soil and Fertilizer Institute). At the three-leaf stage, tomato plants were transplanted to seedling trays containing 200 g of the same seedling substrate as describe above.To relate our results to practical application conditions, we selected a subset of 12 strains that displayed a range of inhibitions effects on pathogen and helpers (Table S1) out of the 46 rhizobacterial isolates used for the in vitro assays. Each rhizobacterial strain was used in combination with each of the two helper strains and R. solanacearum, resulting in a total of 28 treatments (Table S2), including a water control, R. solanacearum alone, and R. solanacearum with just each of the two helper strains (results in Fig. 3B, C). For each treatment, four replicate seedling trays were used, with each replicate seedling tray containing 4 tomato plants. Three days after transplantation, plants of each treatment were inoculated with one of the two helper strains, alone or in combination with one of the rhizobacterial strains, using the root drenching method at a final concentration of 108 CFU g−1 soil for each bacterial strain [22]. Seven days after inoculation of helper alone or together with rhizobacteria, R. solanacearum was introduced to the roots of all plants at a final concentration of 107 CFU g−1 soil. The positive control treatment with R. solanacearum alone was inoculated only with the pathogen, and the negative control treatment was not inoculated with any bacteria. Tomato plants were maintained under standard greenhouse conditions (i.e., at natural temperature variation ranging from 28 °C to 32 °C, 15/9 h day/night conditions) and watered regularly with sterile water. Seedling trays were rearranged randomly every two days. Forty days after transplantation, plants were destructively harvested. The disease index for each plant was recorded based on a scale ranging from 0 to 4 [23]. Disease severity for each replicate seedling plate was calculated as described by: Disease severity = [∑ (The number of diseased plants in the disease index category × disease index category)/ (Total number of plants used in the experiment × highest disease index category)] ×100% [23, 24]. Simultaneously, we collected rhizosphere soil samples following an established protocol [4]. Briefly, two plants were randomly chosen from each replicate seedling tray to collect rhizosphere soils and further combined to yield one sample, resulting in a total of 112 rhizosphere soil samples for which R. solanacearum population densities were determined.Quantification of R. solanacearum at the end of the in vivo experimentWe determined R. solanacearum densities using quantitative PCR (qPCR). DNA was extracted from rhizosphere soils using a Power Soil DNA isolation kit (Mo Bio Laboratories) following the manufacturer’s protocol. DNA concentrations were determined by using a NanoDrop 1000 spectrophotometer (Thermo Scientific) and extracted DNA was used for R. solanacearum density measurements using specific primers (forward, 5ʹ-GAA CGC CAA CGG TGC GAA CT-3ʹ; reverse, 5ʹ-GGC GGC CTT CAG GGA GGT C-3ʹ) targeting the fliC gene, which encodes the R. solanacearum flagellum subunit [25]. The qPCR analyses were carried out with a StepOnePlus Real-Time RCR Instrument using SYBR green fluorescent dye detection and three technical replicates as described previously [4].Statistical analysesTo meet assumptions of normality and homogeneity of variance, R. solanacearum densities measured in vitro and in vivo were log10-transformed. When comparing mean differences between treatments, we used analyses of variance (ANOVA) and the Tukey Test, where p values lower than 0.05 were considered statistically significant. R. solanacearum densities were explained by two quantitative indices, the direct effect of rhizobacteria on R. solanacearum growth (the effect of rhizobacteria on R. solanacearum growth) and the indirect effect of rhizobacteria on R. solanacearum growth (the effect of rhizobacteria on helper strains’ growth). Nonlinear regression analyses (Sigmoidal, Sigmoid, 3 Parameter) were used to analyze the relationship between the direct effect and pathogen density, as well as the relationship between indirect effects and pathogen density in the presence of helper strains in vitro. The relationships between them, and between direct/indirect effects and disease severity in the presence of helper strains in vivo, were analyzed using linear regressions. These analyses were carried out using the R 3.6.3 program (www.r-project.org) and Sigma Plot (V.12.5).To further consider the growth inhibition of R. solanacearum, and disease suppression, we fitted a linear model to estimate the relative importance of direct effects versus indirect effects on the density of R. solanacearum both in vitro and in vivo, and on disease severity. This model considered the interaction scenario where rhizobacterial strains inhibited both the pathogen and its helpers (see the R script “Model” in the supplementary information). These analyses were performed in R version 3.6.3 [26] in conjunction with the package car, readxl and dplyr, and tidyverse 1.2.1 [27]. Briefly, proportional effects were normalized using a folded cube root transformation as suggested in J.W. Tukey [28] and fitted using a linear model with direct effects, indirect effects, and an interaction between helper strains and indirect effects as fixed factors. Normality of residuals was tested using the Shapiro-Wilk normality test and visual inspection of QQ-plots with standardized residuals. Type-II sum of squares were calculated using the ANOVA function from car 3.0-2 [29]. Subsequent visualization of the model outcome (results in Fig. 5) showed the predicted R. solanacearum densities and disease severity for different values of the inhibition via pathogen (Direct) or helper (Indirect) as estimated from the statistical model. For the Direct effect line, the indirect effect is set to be zero, while for the Indirect effect line, the direct effect is set to be zero.Fig. 5: The relative importance of direct versus indirect effects on Ralstonia solanacearum density and disease severity in the presence of helper strains.Relative importance of direct versus indirect effects on Ralstonia solanacearum density both in vitro (A) and in vivo (B), and disease severity (C) in presence of helper strains on the interaction scenario where rhizobacterial strains inhibited both the pathogen and its helpers (quadrant “H−P−” in Fig. 3C). This shows the predicted R. solanacearum densities and disease incidence for different values of the inhibition via pathogen (Direct) or helper (Indirect) as estimated from the statistical model (Table 1) which with direct effects, indirect effects, and an interaction between helper strains and indirect effects as fixed factors. For the Direct line, the indirect effect was set to zero, while for the indirect line, the direct effect was set to zero.Full size image More

1.Cowen, R. K., Gawarkiewicz, G., Pineda, J., Thorrold, S. R. & Werner, F. E. Population connectivity in marine systems an overview. Oceanography 20, 14–21 (2007).Article 

Google Scholar 
2.Vendrami, D. L. et al. RAD sequencing sheds new light on the genetic structure and local adaptation of European scallops and resolves their demographic histories. Sci. Rep. UK 9, 1–13 (2019).CAS 

Google Scholar 
3.Holsinger, K. & Weir, B. Genetics in geographically structured populations: Defining, estimating and interpreting FST. Nat. Rev. Genet. 10, 639–650 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Smedbol, R. K., McPherson, A., Hansen, M. M. & Kenchington, E. Myths and moderation in marine metapopulations?. Fish Fish. 3, 20–35 (2002).Article 

Google Scholar 
5.Makinen, H. S., Cano, J. M. & Merila, J. Identifying footprints of directional and balancing selection in marine and freshwater three-spined stickleback (Gasterosteus aculeatus) populations. Mol. Ecol. 17, 3565–3582 (2008).CAS 
PubMed 
Article 

Google Scholar 
6.Tine, M. et al. European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation. Nat. Commun. 5, 5770 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Thompson, P. L. & Fronhofer, E. A. The conflict between adaptation and dispersal for maintaining biodiversity in changing environments. Proc. Natl. Acad. Sci. 116, 21061–21067 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Samuk, K. et al. Gene flow and selection interact to promote adaptive divergence in regions of low recombination. Mol. Ecol. 26, 4378–4390 (2017).PubMed 
Article 

Google Scholar 
9.van Tienderen, P. H., de Haan, A. A., van der Linden, C. G. & Vosman, B. Biodiversity assessment using markers for ecologically important traits. Trends Ecol. Evol. 17, 577–582 (2002).Article 

Google Scholar 
10.Cadrin, S. X., Kerr, L. A. & Mariani, S. Interdisciplinary evaluation of spatial population structure for definition of fishery management units. In Stock Identification Methods: Applications in Fishery Science (eds Cadrin, S. X. et al.) (Academic Press, 2014).Chapter 

Google Scholar 
11.Hoffmann, A. et al. A framework for incorporating evolutionary genomics into biodiversity conservation and management. Clim. Change Res. 2, 1–24 (2015).Article 

Google Scholar 
12.Narum, S. R., Buerkle, C. A., Davey, J. W., Miller, M. R. & Hohenlohe, P. A. Genotyping by sequencing in ecological and conservation genomics. Mol. Ecol. 22, 2841–2847 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Davey, J. W. & Blaxter, M. L. RADSeq: Next-generation population genetics. Brief Funct. Genom. 9, 416–423 (2010).CAS 
Article 

Google Scholar 
14.Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Valencia, L. M., Martins, A., Ortiz, E. M. & Di Fiore, A. A. RAD-sequencing approach to genome-wide marker discovery, genotyping, and phylogenetic inference in a diverse radiation of primates. PLoS ONE 13, e0201254 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Andrews, K. R., Good, J. M., Miller, M. R., Luikart, G. & Hohenlohe, P. A. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat. Rev. Genet. 17, 81 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Zalapa, J. E. et al. Using next-generation sequencing approaches to isolate simple sequence repeat (SSR) loci in the plant sciences. Am. J. Bot. 99, 193–208 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Hohenlohe, P. et al. Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. Plos Genet. 6, e1000862 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Emerson, K. J. et al. Resolving postglacial phylogeography using high-throughput sequencing. Proc. Natl. Acad. Sci. 107, 16196–16200 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.McCormack, J. E., Hird, S. M., Zellmer, A. J., Carstens, B. C. & Brumfield, R. T. Applications of next-generation sequencing to phylogeography and phylogenetics. Mol. Phylogenet. Evol. 62, 397–406 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Genner, M. J. & Turner, G. F. The mbuna cichlids of Lake Malawi: A model for rapid speciation and adaptive radiation. Fish Fish. 6, 1–34 (2005).Article 

Google Scholar 
22.Brawand, D. et al. The genomic substrate for adaptive radiation in African cichlid fish. Nature 513, 375–381 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.FAO. Fishery and Aquaculture Statistics Yearbook 2014 (Food and Agriculture Organization, 2016).
Google Scholar 
24.CMFRI. Marine Fish Landings in India 2019. Technical Report (ICAR-Central Marine Fisheries Research Institute, 2020).
Google Scholar 
25.Longhurst, A. R. & Wooster, W. S. Abundance of oil sardine (Sardinella longiceps) and upwelling in the southwest coast of India. Can. J. Fish Aquat. Sci. 47, 2407–2419 (1990).Article 

Google Scholar 
26.Krishnakumar, P. K. et al. How environmental parameters influenced fluctuations in oil sardine and mackerel fishery during 1926–2005 along the southwest coast of India. Mar. Fish. Inf. Service T & E Ser. No. 198, 1–5 (2008).
Google Scholar 
27.Xu, C. & Boyce, M. S. Oil sardine (Sardinella longiceps) off the Malabar coast: Density dependence and environmental effects. Fish. Oceanogr. 18, 359–370 (2009).Article 

Google Scholar 
28.Checkley, D. M. Jr., Asch, R. G. & Rykaczewski, R. R. Climate, anchovy and sardine. Annu. Rev. Mar. Sci. 9, 469–493 (2017).ADS 
Article 

Google Scholar 
29.Kripa, V. et al. Overfishing and climate drives changes in biology and recruitment of the Indian oil sardine Sardinella longiceps in southeastern Arabian Sea. Front. Mar. Sci. 5, 443 (2018).Article 

Google Scholar 
30.Kuthalingam, M. D. K. Observations on the life history and feeding habits of the Indian sardine, Sardinella longiceps (Cuv. & Val.). Treubia 25, 207–213 (1960).
Google Scholar 
31.Sebastian, W., Sukumaran, S., Zacharia, P. U. & Gopalakrishnan, A. Genetic population structure of Indian oil sardine, Sardinella longiceps assessed using microsatellite markers. Conserv. Genet. 18, 951–964 (2017).CAS 
Article 

Google Scholar 
32.Sebastian, W. et al. Signals of selection in the mitogenome provide insights into adaptation mechanisms in heterogeneous habitats in a widely distributed pelagic fish. Sci. Rep. UK 10, 1–14 (2020).Article 
CAS 

Google Scholar 
33.Sukumaran, S., Sebastian, W. & Gopalakrishnan, A. Population genetic structure of Indian oil sardine, Sardinella longiceps along Indian coast. Gene 576, 372–378 (2016).CAS 
PubMed 
Article 

Google Scholar 
34.Sukumaran, S. et al. Morphological divergence in Indian oil sardine, Sardinella longiceps Valenciennes, 1847 Does it imply adaptive variation?. J. Appl. Ichthyol. 32, 706–711 (2016).CAS 
Article 

Google Scholar 
35.Burgess, S. C., Treml, E. A. & Marshall, D. J. How do dispersal costs and habitat selection influence realized population connectivity?. Ecology 93, 1378–1387 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Pardoe, H. Spatial and temporal variation in life-history traits of Atlantic cod (Gadus morhua) in Icelandic waters, Reykjavik University of Iceland. PhD thesis https://doi.org/10.13140/RG.2.2.27158.70727 (2009).Article 

Google Scholar 
37.Devaraj, M. et al. Status, prospects and management of small pelagic fisheries in India. In Small Pelagic Resources and Their Fisheries in the Asia-Pacific Region: Proceedings of the APFIC Workshop (eds Devaraj, M. & Martosubroto, P.) 91–198 (Asia-Pacific Fishery Commission, Food and Agriculture Organization of the United Nations Regional Office for Asia and the Pacific, 1997).
Google Scholar 
38.Mohamed, K. S. et al. Minimum Legal Size (MLS) of capture to avoid growth overfishing of commercially exploited fish and shellfish species of Kerala. Mar. Fish. Inf. Service T & E Ser. No. 220, 3–7 (2014).
Google Scholar 
39.Hartl, D. L. & Clark, A. G. Principles of Population Genetics (Sinauer Associates, 2006).
Google Scholar 
40.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 
41.Chatterjee, A. et al. A new atlas of temperature and salinity for the North Indian Ocean. J. Earth. Syst. Sci. 121, 559–593 (2012).ADS 
Article 

Google Scholar 
42.Nair, A. K. K., Balan, K. & Prasannakumari, B. The fishery of the oil sardine (Sardinella longiceps) during the past 22 years. Indian J. Fish. 20, 223–227 (1973).
Google Scholar 
43.Krishnakumar, P. K. & Bhat, G. S. Seasonal and inter annual variations of oceanographic conditions off Mangalore coast (Karnataka, India) in the Malabar upwelling system during 1995–2004 and their influences on the pelagic fishery. Fish. Oceanogr. 17, 45–60 (2008).Article 

Google Scholar 
44.Hamza, F., Valsala, V., Mallissery, A. & George, G. Climate impacts on the landings of Indian oil sardine over the south-eastern Arabian Sea. Fish Fish. 22, 175–193 (2021).Article 

Google Scholar 
45.Shankar, D., Vinayachandran, P. N. & Unnikrishnan, A. S. The monsoon currents in the north Indian Ocean. Prog. Oceanogr. 52, 63–120 (2002).ADS 
Article 

Google Scholar 
46.Shetye, S. R. & Gouveia, A. D. Coastal Circulation in the North Indian Ocean: Coastal Segment (14, SW) (Wiley, 1998).
Google Scholar 
47.Kumar, S. P. et al. High biological productivity in the central Arabian Sea during the summer monsoon driven by Ekman pumping and lateral advection. Curr. Sci. India 1, 1633–1638 (2001).
Google Scholar 
48.Frichot, E. & Francois, O. LEA: An R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).Article 

Google Scholar 
49.Raja, A. B. T. The Indian Oil Sardine. Kochi. Central Mar. Fish. Res. Inst. Bull. No. 16, 151 (1969).
Google Scholar 
50.Nair, R. V. & Chidambaram, K. Review of the oil sardine fishery. Proc. Natl. Acad. Sci. India 17, 71–85 (1951).
Google Scholar 
51.Rijavec, L., Krishna Rao, K. & Edwin, D. G. P. Distribution and Abundance of Marine Fish Resources Off the Southwest Coast of India (Results of Acoustic Surveys, 1976–1978) (Food and Agriculture Organization of the United Nations, 1982).
Google Scholar 
52.Hauser, L. & Carvalho, G. R. Paradigm shifts in marine fisheries genetics: Ugly hypotheses slain by beautiful facts. Fish Fish. 9, 333–362 (2008).Article 

Google Scholar 
53.Catchen, J. et al. The population structure and recent colonisation history of Oregon threespine stickleback determined using restriction-site associated DNA-sequencing. Mol. Ecol. 22, 2864–2883 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Schott, F. A. & McCreary, J. P. Jr. The monsoon circulation of the Indian Ocean. Prog. Oceanogr. 51, 1–123 (2001).ADS 
Article 

Google Scholar 
55.Aykanat, T. et al. Low but significant genetic differentiation underlies biologically meaningful phenotypic divergence in a large Atlantic salmon population. Mol. Ecol. 24, 5158–5174 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
56.Xu, J. et al. Genomic basis of adaptive evolution: the survival of Amur ide (Leuciscus waleckii) in an extremely alkaline environment. Mol. Biol. Evol. 34, 145–149 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Pappas, F. & Palaiokostas, C. Genotyping strategies using ddRAD sequencing in farmed arctic charr (Salvelinus alpinus). Animals 11, 899 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Gleason, L. U. & Burton, R. S. Genomic evidence for ecological divergence against a background of population homogeneity in the marine snail Chlorostoma funebralis. Mol. Ecol. 25, 3557–3573 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
59.Bailey, D. A., Lynch, A. H. & Hedstrom, K. S. Impact of ocean circulation on regional polar climate simulations using the Arctic Region Climate System Model. Ann. Glaciol. 25, 203–207 (1997).ADS 
Article 

Google Scholar 
60.Oomen, R. A. & Hutchings, J. A. Variation in spawning time promotes genetic variability in population responses to environmental change in a marine fish. Conserv. Physiol. 3, p.cov027 (2015).Article 
CAS 

Google Scholar 
61.Cury, P. et al. Small pelagics in upwelling systems: Patterns of interaction and structural changes in “wasp-waist” ecosystems. ICES J. Mar. Sci. 57, 603–618 (2000).Article 

Google Scholar 
62.Marshall, D. J. & Morgan, S. G. Ecological and evolutionary consequences of linked life-history stages in the sea. Curr. Biol. 21, R718–R725 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Churchill, J. H., Runge, J. & Chen, C. Processes controlling retention of spring-spawned Atlantic cod (Gadus morhua) in the western Gulf of Maine and their relationship to an index of recruitment success. Fish Oceanogr. 20, 32–46 (2011).Article 

Google Scholar 
64.John, S., Muraleedharan, K. R., Azeez, S. A. & Cazenave, P. W. What controls the flushing efficiency and particle transport pathways in a tropical estuary? Cochin Estuary, Southwest Coast of India. Water 12, 908 (2020).Article 

Google Scholar 
65.Seena, G., Muraleedharan, K. R., Revichandran, C., Azeez, S. A. & John, S. Seasonal spreading and transport of buoyant plumes in the shelf off Kochi, South west coast of India A modeling approach. Sci. Rep. UK 9, 1–15 (2019).ADS 

Google Scholar 
66.Marshall, D. J., Monro, K., Bode, M., Keough, M. J. & Swearer, S. Phenotype environment mismatches reduce connectivity in the sea. Ecol. Lett. 13, 128–140 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Gruss, A. & Robinson, J. Fish populations forming transient spawning aggregations: Should spawners always be the targets of spatial protection efforts?. ICES J. Mar. Sci. 72, 480–497 (2015).Article 

Google Scholar 
68.Chollett, I., Priest, M., Fulton, S. & Heyman, W. D. Should we protect extirpated fish spawning aggregation sites?. Biol. Conserv. 241, 108395 (2020).Article 

Google Scholar 
69.Nielsen, E. E., Hemmer-Hansen, J. A. K. O. B., Larsen, P. F. & Bekkevold, D. Population genomics of marine fishes: Identifying adaptive variation in space and time. Mol. Ecol. 18, 3128–3150 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Johannesson, K., Smolarz, K., Grahn, M. & Andre, C. The future of Baltic Sea populations: Local extinction or evolutionary rescue?. Ambio 40, 179–190 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Wang, L. et al. Population genetic studies revealed local adaptation in a high gene-flow marine fish, the small yellow croaker (Larimichthys polyactis). PLoS ONE 8, e83493 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
72.Brennan, R. S., Hwang, R., Tse, M., Fangue, N. A. & Whitehead, A. Local adaptation to osmotic environment in killifish, Fundulus heteroclitus, is supported by divergence in swimming performance but not by differences in excess post-exercise oxygen consumption or aerobic scope. Comp. Biochem. Phys. B 196, 11–19 (2016).CAS 
Article 

Google Scholar 
73.Fan, S., Elmer, K. R. & Meyer, A. Genomics of adaptation and speciation in cichlid fishes: Recent advances and analyses in African and Neotropical lineages. Philos. T. R. Soc. B. 367, 385–394 (2012).Article 

Google Scholar 
74.Turner, T. L. & Hahn, M. W. Genomic islands of speciation or genomic islands and speciation?. Mol. Ecol. 19, 848–850 (2010).PubMed 
Article 

Google Scholar 
75.Seehausen, O. et al. Genomics and the origin of species. Nat. Rev. Genet. 15, 176 (2014).CAS 
PubMed 
Article 

Google Scholar 
76.Wolf, J. B. & Ellegren, H. Making sense of genomic islands of differentiation in light of speciation. Nat. Rev. Genet. 18, 87 (2017).CAS 
PubMed 
Article 

Google Scholar 
77.Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
78.Christensen, C., Jacobsen, M. W., Nygaard, R. & Hansen, M. M. Spatiotemporal genetic structure of anadromous Arctic char (Salvelinus alpinus) populations in a region experiencing pronounced climate change. Conserv. Genet. 19, 687–700 (2018).Article 

Google Scholar 
79.Nielsen, E. E. et al. Genomic signatures of local directional selection in a high gene flow marine organism; the Atlantic cod (Gadus morhua). BMC Evol. Biol. 9, 1–11 (2009).Article 
CAS 

Google Scholar 
80.Vivekanandan, E., Rajagopalan, M. & Pillai, N. G. K. Recent trends in sea surface temperature and its impact on oil sardine. In Global Climate Change and Indian Agriculture (eds Aggarwal, P. K. et al.) 89–92 (Indian Council of Agricultural Research, 2009).
Google Scholar 
81.DeTolla, L. J. et al. Guidelines for the care and use of fish in research. Ilar J. 1(37), 159–173 (1995).Article 

Google Scholar 
82.Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: An analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
83.Andrews, S. FASTQC. A Quality Control Tool for High Throughput Sequence Data (Babraham Institute, 2010).
Google Scholar 
84.Paris, J. R., Stevens, J. R. & Catchen, J. M. Lost in parameter space: A road map for stacks. Methods Ecol. Evol. 8, 1360–1373 (2017).Article 

Google Scholar 
85.Rousset, F. genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 
Article 

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

Google Scholar 
87.Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
Article 

Google Scholar 
88.Felsenstein, J. PHYLIP—Phylogeny inference package (Version 3.2). Cladistics 5, 164–166 (1989).
Google Scholar 
89.Andrew, R. Tree Figure Drawing Tool Version 1.4.2 2006–2014 (Institute of Evolutionary, Biology University of Edinburgh, 2014).
Google Scholar 
90.Bonnet, E. & Van de Peer, Y. zt: A sofware tool for simple and partial mantel tests. J. Stat. Softw. 7, 1 (2002).Article 

Google Scholar 
91.Rousset, F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219–1228 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
92.Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180, 977–993 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
93.Lischer, H. E. & Excoffier, L. PGDSpider: An automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28, 298–299 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Ellis, N., Smith, S. J. & Pitcher, C. R. Gradient forests: Calculating importance gradients on physical predictors. Ecology 93, 156–168 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
95.Chen, C., Liu, H. & Beardsley, R. C. An unstructured grid, finite-volume, three-dimensional, primitive equations ocean model: Application to coastal ocean and estuaries. J. Atmos. Ocean. Technol. 20, 159–186 (2003).ADS 
Article 

Google Scholar  More

1.Heron, S. F., Maynard, J. A., van Hooidonk, R. & Eakin, C. M. Warming trends and bleaching stress of the World’s coral reefs 1985–2012. Sci. Rep. 6, 38402 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).ADS 
Article 

Google Scholar 
3.Glynn, P. W. Widespread coral mortality and the 1982–83 El Nino warming events. Environ. Conserv. 11, 133–146 (1984).Article 

Google Scholar 
4.Spalding, M. D. & Brown, B. E. Warm-water coral reefs and climate change. Science 350, 769–771 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Eakin, C. M. et al. Global coral bleaching 2014–2017. Reef Curr. 31, 1 (2016).
Google Scholar 
6.Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Rossi, S., Bramanti, L., Gori, A. & Orejas, C. An overview of the animal forests of the world. In Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds Rossi, S. et al.) 1–26 (Springer, 2017).Chapter 

Google Scholar 
8.Chimienti, G. Vulnerable forests of the pink sea fan Eunicella verrucosa in the Mediterranean Sea. Diversity 12, 176 (2020).Article 

Google Scholar 
9.Chimienti, G., De Padova, D., Mossa, M. & Mastrototaro, F. A mesophotic black coral forest in the Adriatic Sea. Sci. Rep. 10, 8504 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.FAO, Food and Agricultural Organization. International Guidelines for the Management of Deep-Sea Fisheries in the High Seas (FAO, 2009).
Google Scholar 
11.Coll, M. et al. The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats. PLoS ONE 5(8), e11842 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
12.Lejeusne, C., Chevaldonne, P., Pergent-Martini, C., Boudouresque, C.-F. & Pérez, T. Climate change effects on a miniature ocean: The highly diverse, highly impacted Mediterranean Sea. Trends Ecol. Evol. 25(4), 250–260 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Marbà, N., Jordà, G., Agustí, S., Girard, C. & Duarte, C. M. Footprints of climate change on Mediterranean Sea biota. Front. Mar. Sci. 2, 56 (2015).Article 

Google Scholar 
14.Cramer, W. et al. Climate change and interconnected risks to sustainable development in the Mediterranean. Nat. Clim. Change 8, 972–980 (2018).ADS 
Article 

Google Scholar 
15.Albano, P. G. et al. Native biodiversity collapse in the eastern Mediterranean. Proc. R. Soc. B 288, 20202469 (2021).PubMed 
Article 

Google Scholar 
16.Harmelin, J. G. Biologie du corail rouge. Paramètres de populations, croissance et mortalité naturelle. Etat des connaissances en France. FAO Fish. Rep. 306, 99–103 (1984).
Google Scholar 
17.Bavestrello, G. & Boero, F. Necrosi e rigenerazione in Eunicella cavolinii (Anthozoa, Cnidaria) in Mar Ligure. Boll. Mus. Ist. Biol. Univ. Genova 52, 295–300 (1986).
Google Scholar 
18.Cerrano, C. et al. Catastrophic mass-mortality episode of gorgonians and other organisms in the Ligurian Sea (North-western Mediterranean), Summer 1999. Ecol. Lett. 3, 284–293 (2000).Article 

Google Scholar 
19.Linares, C. et al. Immediate and delayed effects of a mass mortality event on gorgonian population dynamics and benthic community structure in the NW Mediterranean Sea. Mar. Ecol. Prog. Ser. 305, 127–137 (2005).ADS 
Article 

Google Scholar 
20.Coma, R. et al. Consequences of a mass mortality in populations of Eunicella singularis (Cnidaria: Octocorallia) in Menorca (NW Mediterranean). Mar. Ecol. Prog. Ser. 327, 51–60 (2006).ADS 
Article 

Google Scholar 
21.Garrabou, J. et al. Mass mortality in Northwestern Mediterranean rocky benthic communities: Effects of the 2003 heat wave. Glob. Change Biol. 15, 1090–1103 (2009).ADS 
Article 

Google Scholar 
22.Huete-Stauffer, C. et al. Paramuricea clavata (Anthozoa, Octocorallia) loss in the Marine Protected Area of Tavolara (Sardinia, Italy) due to a mass mortality event. Mar. Ecol. 32, 107–116 (2011).ADS 
Article 

Google Scholar 
23.Rubio-Portillo, E. et al. Effects of the 2015 heat wave on benthic invertebrates in the Tabarca Marine Protected Area (southeast Spain). Mar. Environ. Res. 122, 135–142 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Crisci, C., Bensoussan, N., Romano, J. C. & Garrabou, J. Temperature anomalies and mortality events in marine communities: Insights on factors behind differential mortality impacts in the NW Mediterranean. PLoS ONE 6, e23814 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Turicchia, E., Abbiati, M., Sweet, M. & Ponti, M. Mass mortality hits gorgonian forests at Montecristo Island. Dis. Aquat. Org. 131, 79–85 (2018).Article 

Google Scholar 
26.von Schuckmann, K. et al. Copernicus Marine Service Ocean State Report, issue 3. J. Oper. Oceanogr. 12(1), S1–S123 (2019).
Google Scholar 
27.Garrabou, J. et al. Collaborative database to track mass mortality events in the Mediterranean Sea. Front. Mar. Sci. 6, 707 (2019).Article 

Google Scholar 
28.Linares, C., Doak, D. F., Coma, R., Díaz, D. & Zabala, M. Life history and viability of a long-lived marine invertebrate: The octocoral Paramuricea clavata. Ecology 88, 918–928 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Linares, C., Coma, R., Garrabou, J., Díaz, D. & Zabala, M. Size distribution, density and disturbance in two Mediterranean gorgonians: Paramuricea clavata and Eunicella singularis. J. Appl. Ecol. 45(2), 688–699 (2008).Article 

Google Scholar 
30.Ponti, M., Turicchia, E., Ferro, F., Cerrano, C. & Abbiati, M. The understorey of gorgonian forests in mesophotic temperate reefs. Aquat. Conserv. Mar. Freshw. Ecosyst. 28, 1153–1166 (2018).Article 

Google Scholar 
31.Otero, M. M. et al. Overview of the conservation status of Mediterranean anthozoans. IUCN, x + 73 p (2017).32.Pastor, F., Valiente, J. A. & Khodayar, S. A. Warming Mediterranean: 38 years of increasing sea surface temperature. Remote Sens. 12(17), 2687 (2020).ADS 
Article 

Google Scholar 
33.DHI. Mike 3 Flow Model: Hydrodynamic Module-Scientific Documentation (DHI Software 2016, 2016).
Google Scholar 
34.Moore, S. K. et al. Impacts of climate variability and future climate change on harmful algal blooms and human health. Environ. Health 7(2), S4 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Piazza, G. et al. Prime osservazioni sul bloom mucillaginoso dell’estate 2018 sui fondali a coralligeno delle Isole Tremiti. Biol. Mar. Mediterr. 26(1), 320–321 (2019).
Google Scholar 
36.van de Water, J. A. J. M., Allemand, D. & Ferrier-Pagès, C. Host-microbe interactions in octocoral holobionts—Recent advances and perspectives. Microbiome 6, 64 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
37.Bavestrello, G. et al. Mass mortality of Paramuricea clavata (Anthozoa: Cnidaria) on Portofino Promontory cliffs (Ligurian Sea). Mar. Life 4, 15–19 (1994).
Google Scholar 
38.Mistri, M. & Ceccherelli, V. U. Damage and partial mortality in the gorgonian Paramuricea clavata in the Strait of Messina (Tyrrhenian Sea). Mar. Life 5, 43–49 (1995).
Google Scholar 
39.Cerrano, C. & Bavestrello, G. Medium-term effects of dieoff of rocky benthos in the Ligurian Sea. What can we learn from gorgonians?. Chem. Ecol. 24, 73–82 (2008).Article 

Google Scholar 
40.Guiry, M. D. & Guiry, G. M. AlgaeBase (World-Wide Electronic Publication, National University of Ireland, 2021).
Google Scholar 
41.Cormaci, M., Furnari, G., Alongi, G., Catra, M. & Serio, D. The benthic algal flora on rocky substrata of the Tremiti Islands (Adriatic Sea). Plant Biosyst. 134(2), 133–152 (2000).Article 

Google Scholar 
42.Cebrian, E., Linares, C., Marschal, C. & Garrabou, J. Exploring the effects of invasive algae on the persistence of gorgonian populations. Biol. Invasions 14, 2647–2656 (2012).Article 

Google Scholar 
43.Verlaque, M., Ruitton, S., Mineur, F. & Boudouresque, C.-F. CIESM Atlas of Exotic Species of the Mediterranean: Macrophytes 1–362 (CIESM Publishers, 2015).
Google Scholar 
44.Ghabbourl, E. A. et al. Isolation of humic acid from the brown alga Pilayella littoralis. J. Appl. Phycol. 6, 459–468 (1994).Article 

Google Scholar 
45.Raberg, S., Jönsson, R. B., Björn, A., Granél, E. & Kautsky, L. Effects of Pilayella littoralis on Fucus vesiculosus recruitment: Implications for community composition. Mar. Ecol. Prog. Ser. 289, 131–139 (2005).ADS 
Article 

Google Scholar 
46.Adloff, F. et al. Mediterranean Sea response to climate change in an ensemble of twenty first century scenarios. Clim. Dyn. 45(9–10), 2775–2802 (2015).Article 

Google Scholar 
47.Darmaraki, S. et al. Future evolution of marine heatwaves in the Mediterranean Sea. Clim. Dyn. 53, 1371–1392 (2019).Article 

Google Scholar 
48.Bavestrello, G., Cerrano, C., Zanzi, D. & Cattaneo-Vietti, R. Damage by fishing activities in the gorgonian coral Paramuricea clavata in the Ligurian Sea. Aquat. Conserv. 7, 253–262 (1997).Article 

Google Scholar 
49.Linares, C. & Doak, D. F. Forecasting the combined effects of disparate disturbances on the persistence of long-lived gorgonians: A case study of Paramuricea clavata. Mar. Ecol. Prog. Ser. 402, 59–68 (2010).ADS 
Article 

Google Scholar 
50.Chimienti, G. et al. An explorative assessment of the importance of Mediterranean Coralligenous habitat to local economy: The case of recreational diving. J. Environ. Account. Manag. 5(4), 310–320 (2017).
Google Scholar 
51.Di Camillo, C. G., Ponti, M., Bavestrello, G., Krzelj, M. & Cerrano, C. Building a baseline for habitat-forming corals by a multi-source approach, including web ecological knowledge. Biodivers. Conserv. 27, 1257–1276 (2018).Article 

Google Scholar 
52.Ingrosso, G. et al. Mediterranean bioconstructions along the Italian coast. Adv. Mar. Biol. 79, 61–136 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
53.Chimienti, G., Angeletti, L., Rizzo, L., Tursi, A. & Mastrototaro, F. ROV vs trawling approaches in the study of benthic communities: The case of Pennatula rubra (Cnidaria: Pennatulacea). J. Mar. Biol. Assoc. U. K. 98(8), 1859–1869 (2018).Article 

Google Scholar 
54.Chimienti, G., Angeletti, L., Furfaro, G., Canese, S. & Taviani, M. Habitat, morphology and trophism of Tritonia callogorgiae sp. nov., a large nudibranch inhabiting Callogorgia verticillata forests in the Mediterranean Sea. Deep-Sea Res. Pt. I 165, 103364 (2020).Article 

Google Scholar 
55.Mastrototaro, F. et al. Mesophotic rocks dominated by Diazona violacea: A Mediterranean codified habitat. Eur. Zool. J. 87(1), 688–695 (2020).Article 

Google Scholar 
56.Walton, C. C., Pichel, W. G., Sapper, J. F. & May, D. A. The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites. J. Geophys. Res. 103(C12), 27999–28012 (1998).ADS 
Article 

Google Scholar 
57.Kilpatrick, K. A. et al. A decade of sea surface temperature from MODIS. Remote Sens. Environ. 165, 27–41 (2015).ADS 
Article 

Google Scholar 
58.Cleveland, R. B., Cleveland, W. S., McRae, J. E. & Terpenning, I. STL: A seasonal-trend decomposition procedure based on loess. J. Off. Stat. 6, 3–73 (1990).
Google Scholar 
59.Simoncelli, S. et al. Mediterranean Sea Physical Reanalysis (CMEMS MED-Physics) (Copernicus Monitoring Environment Marine Service (CMEMS), 2019). https://doi.org/10.25423/MEDSEA_REANALYSIS_PHYS_006_004.60.Copernicus Climate Change Service (C3S). ERA5: Fifth Generation of ECMWF Atmospheric Reanalyses of the Global Climate (Copernicus Climate Change Service Climate Data Store (CDS), 2017). https://cds.climate.copernicus.eu/cdsapp#!/home.61.Xie, P. & Arkin, P. A. Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Am. Meteor. Soc. 78, 2539–2558 (1997).ADS 
Article 

Google Scholar 
62.Rodi, W. Examples of calculation methods for flow and mixing in stratified fluids. J. Geophys. Res. Ocean 92(C5), 5305–5328 (1987).ADS 
Article 

Google Scholar 
63.Galperin, B. & Orszag, S. A. Large Eddy Simulation of Complex Engineering and Geophysical Flows 3–36 (Cambridge University Press, 1993).
Google Scholar 
64.De Padova, D., De Serio, F., Mossa, M. & Armenio, E. Investigation of the current circulation offshore Taranto by using field measurements and numerical model. In Proceedings of the IEEE International Instrumentation and Measurement Technology Conference 1–5 (IEEE, 2017).65.Armenio, E., De Padova, D., De Serio, F. & Mossa, M. Monitoring system for the sea: Analysis of meteo, wave and current data. In Workshop on Metrology for the Sea, MetroSea 2017: Learning to Measure Sea Health Parameters 143–148 (IMEKO TC19, 2017).66.Armenio, E., Ben Meftah, M., De Padova, D., De Serio, F. & Mossa, M. Monitoring systems and numerical models to study coastal sites. Sensors 19(7), 1552 (2019).ADS 
PubMed Central 
Article 

Google Scholar 
67.Chu, P. C. & Fan, C. Global ocean synoptic thermocline gradient, isothermal-layer depth, and other upper ocean parameters. Sci. Data 6, 119 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
68.Clementi, E. et al. Mediterranean Sea Analysis and Forecast (CMEMS MED-Currents, EAS5 System) (Copernicus Monitoring Environment Marine Service (CMEMS), 2019). https://doi.org/10.25423/CMCC/MEDSEA_ANALYSIS_FORECAST_PHY_006_013_EAS5. More

1.Cole, M., Lindeque, P., Halsband, C. & Galloway, T. S. Microplastics as contaminants in the marine environment: A review. Mar. Pollut. Bull. 62, 2588–2597. https://doi.org/10.1016/j.marpolbul.2011.09.025,22001295 (2011).CAS 
Article 
PubMed 

Google Scholar 
2.Cauwenberghe, V. L., Vanreusel, A., Mees, J. & Janssen, C. R. Microplastic pollution in deep-sea sediments. Environ. Pollut. 182, 495–499. https://doi.org/10.1016/j.envpol.2013.08.013,24035457 (2013).Article 
PubMed 

Google Scholar 
3.Cole, M. et al. Microplastic ingestion by zooplankton. Environ. Sci. Technol. 47, 6646–6655. https://doi.org/10.1021/es400663f,23692270 (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
4.Rodrigues, J. P., Duarte, A. C., Santos-Echeandía, J. & Rocha-Santos, T. Significance of interactions between microplastics and POPs in the marine environment: A critical overview. TrAC Trends Anal. Chem. 111, 252–260. https://doi.org/10.1016/j.trac.2018.11.038 (2019).CAS 
Article 

Google Scholar 
5.Brennecke, D., Duarte, B., Paiva, F., Caçador, I. & Canning-Clode, J. Microplastics as vector for heavy metal contamination from the marine environment. Estuar Coast Shelf Sci. 178, 189–195. https://doi.org/10.1016/j.ecss.2015.12.003 (2016).ADS 
CAS 
Article 

Google Scholar 
6.Martins, I., Rodríguez, Y. & Pham, C. K. Trace elements in microplastics stranded on beaches of remote islands in the NE Atlantic. Mar. Pollut. Bull. 156, 111270. https://doi.org/10.1016/j.marpolbul.2020.111270 (2020).CAS 
Article 
PubMed 

Google Scholar 
7.Woodall, L. C. et al. The deep sea is a major sink for microplastic debris. R. Soc. Open Sci. 1, 140317. https://doi.org/10.1098/rsos.140317 (2014).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Alomar, C., Estarellas, F. & Deudero, S. Microplastics in the Mediterranean Sea: Deposition in coastal shallow sediments, spatial variation and preferential grain size. Mar. Environ. Res. 115, 1–10. https://doi.org/10.1016/j.marenvres.2016.01.005,26803229 (2016).CAS 
Article 
PubMed 

Google Scholar 
9.Wright, S. L., Thompson, R. C. & Galloway, T. S. The physical impacts of microplastics on marine organisms: A review. Environ. Pollut. 178, 483–492. https://doi.org/10.1016/j.envpol.2013.02.031,23545014 (2013).CAS 
Article 
PubMed 

Google Scholar 
10.Fendall, L. S. & Sewell, M. A. Contributing to marine pollution by washing your face: Microplastics in facial cleansers. Mar. Pollut. Bull. 58, 1225–1228. https://doi.org/10.1016/j.marpolbul.2009.04.025,19481226 (2009).CAS 
Article 
PubMed 

Google Scholar 
11.Andrady, A. L. Microplastics in the marine environment. Mar. Pollut. Bull. 62, 1596–1605. https://doi.org/10.1016/j.marpolbul.2011.05.030 (2011). CAS 
Article 
PubMed 

Google Scholar 
12.Pakula, C. & Stamminger, R. Electricity and water consumption for laundry washing by washing machine worldwide. Energy Effic. 3, 365–382. https://doi.org/10.1007/s12053-009-9072-8 (2010).Article 

Google Scholar 
13.Belzagui, F., Crespi, M., Álvarez, A., Gutiérrez-Bouzán, C. & Vilaseca, M. Microplastics’ emissions: Microfibers’ detachment from textile garments. Environ. Pollut. 248, 1028–1035. https://doi.org/10.1016/j.envpol.2019.02.059,31091635 (2019).CAS 
Article 
PubMed 

Google Scholar 
14.Ziajahromi, S., Drapper, D., Hornbuckle, A., Rintoul, L. & Leusch, F. D. L. Microplastic pollution in a stormwater floating treatment wetland: Detection of tyre particles in sediment. Sci. Total Environ. 713, 136356. https://doi.org/10.1016/j.scitotenv.2019.136356 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
15.Fadare, O. O. & Okoffo, E. D. Covid-19 face masks: A potential source of microplastic fibers in the environment. Sci. Total Environ. 737, 140279. https://doi.org/10.1016/j.scitotenv.2020.140279 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Klein, S., Worch, E. & Knepper, T. P. Occurrence and spatial distribution of microplastics in river shore sediments of the rhine-main area in Germany. Environ. Sci. Technol. 49, 6070–6076. https://doi.org/10.1021/acs.est.5b00492,25901760 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
17.Rummel, C. D., Jahnke, A., Gorokhova, E., Kühnel, D. & Schmitt-Jansen, M. Impacts of biofilm formation on the fate and potential effects of microplastic in the aquatic environment. Environ. Sci. Technol. Lett. 4, 258–267. https://doi.org/10.1021/acs.estlett.7b00164 (2017).CAS 
Article 

Google Scholar 
18.Claessens, M., Meester, S., Landuyt, V. L., Clerck, K. & Janssen, C. R. Occurrence and distribution of microplastics in marine sediments along the Belgian coast. Mar. Pollut. Bull. 62, 2199–2204. https://doi.org/10.1016/j.marpolbul.2011.06.030,21802098 (2011).CAS 
Article 
PubMed 

Google Scholar 
19.Song, Y. K. et al. A comparison of microscopic and spectroscopic identification methods for analysis of microplastics in environmental samples. Mar. Pollut. Bull. 93, 202–209. https://doi.org/10.1016/j.marpolbul.2015.01.015,25682567 (2015).CAS 
Article 
PubMed 

Google Scholar 
20.Lenz, R., Enders, K., Stedmon, C. A., Mackenzie, D. M. A. & Nielsen, T. G. A critical assessment of visual identification of marine microplastic using Raman spectroscopy for analysis improvement. Mar. Pollut. Bull. 100, 82–91. https://doi.org/10.1016/j.marpolbul.2015.09.026,26455785 (2015).CAS 
Article 
PubMed 

Google Scholar 
21.Majewsky, M., Bitter, H., Eiche, E. & Horn, H. Determination of microplastic polyethylene (PE) and polypropylene (PP) in environmental samples using thermal analysis (TGA-DSC). Sci. Total Environ. 568, 507–511. https://doi.org/10.1016/j.scitotenv.2016.06.017,27333470 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
22.Shim, W. J., Song, Y. K., Hong, S. H. & Jang, M. Identification and quantification of microplastics using Nile red staining. Mar. Pollut. Bull. 113, 469–476. https://doi.org/10.1016/j.marpolbul.2016.10.049,28340965 (2016).CAS 
Article 
PubMed 

Google Scholar 
23.Lv, L. et al. A simple method for detecting and quantifying microplastics utilizing fluorescent dyes – safranine T, fluorescein isophosphate, Nile red based on thermal expansion and contraction property. Environ. Pollut. 255, 113283. https://doi.org/10.1016/j.envpol.2019.113283 (2019).CAS 
Article 
PubMed 

Google Scholar 
24.Cauwenberghe, V. L., Devriese, L., Galgani, F., Robbens, J. & Janssen, C. R. Microplastics in sediments: A review of techniques, occurrence and effects. Mar. Environ. Res. 111, 5–17. https://doi.org/10.1016/j.marenvres.2015.06.007 (2015).CAS 
Article 
PubMed 

Google Scholar 
25.Isobe, A. et al. An interlaboratory comparison exercise for the determination of microplastics in standard sample bottles. Mar. Pollut. Bull. 146, 831–837. https://doi.org/10.1016/j.marpolbul.2019.07.033,31426225 (2019).CAS 
Article 
PubMed 

Google Scholar 
26.Uddin, S., Fowler, S. W., Saeed, T., Naji, A. & Al-Jandal, N. Standardized protocols for microplastics determinations in environmental samples from the Gulf and marginal seas. Mar. Pollut. Bull. 158, 111374. https://doi.org/10.1016/j.marpolbul.2020.111374 (2020).CAS 
Article 
PubMed 

Google Scholar 
27.Vermeiren, P., Muñoz, C. & Ikejima, K. Microplastic identification and quantification from organic rich sediments: A validated laboratory protocol. Environ. Pollut. 262, 114298. https://doi.org/10.1016/j.envpol.2020.114298 (2020).CAS 
Article 
PubMed 

Google Scholar 
28.Coppock, R. L., Cole, M., Lindeque, P. K., Queirós, A. M. & Galloway, T. S. A small-scale, portable method for extracting microplastics from marine sediments. Environ. Pollut. 230, 829–837. https://doi.org/10.1016/j.envpol.2017.07.017 (2017).CAS 
Article 
PubMed 

Google Scholar 
29.Ankley, G. T., Di Toro, D. M., Hansen, D. J. & Berry, W. J. Technical basis and proposal for deriving sediment quality criteria for metals. Environ. Toxicol. Chem. 15, 2056–2066. https://doi.org/10.1002/etc.5620151202 (1996).CAS 
Article 

Google Scholar 
30.Japanese Geotechnical Society (JGS) 0131 (JIS A1204). Test method for particle size distribution of soils (2009).31.Japanese Geotechnical Society JGS 0121(JIS A1203). Test method for water content of soils (2009).32.BS ISO 11277: Soil quality. Determination of particle size distribution in mineral soil material. Method by sieving and sedimentation (2009).33.BS ISO 1377:Part 2: Clause 3.2: Determination of the moisture content of soils (1990).34.BS EN 933-2: Tests for geometrical properties of aggregates. Determination of particle size distribution. Test sieves, nominal size of apertures (2020).35.ASTM D6913/D6913M – 17: Standard Test Methods for Particle-Size Distribution (Gradation) of Soils Using Sieve Analysis (2004).36.ASTM D2216 – 19: Standard Test Methods for Laboratory Determination of Water (Moisture) Content of Soil and Rock by Mass (2019).37.Maxwell, S. H., Melinda, K. F. & Matthew, G. Counterstaining to separate Nile red-stained microplastic particles from terrestrial invertebrate biomass. Environ. Sci. Technol. 54, 5580–5588. https://doi.org/10.1021/acs.est.0c00711 (2020).ADS 
CAS 
Article 

Google Scholar 
38.Ehlers, S. M., Maxein, J. & Koop, J. H. E. Low-cost microplastic visualization in feeding experiments using an ultraviolet light-emitting flashlight. Ecol. Res. 35, 265–273. https://doi.org/10.1111/1440-1703.12080 (2020).Article 

Google Scholar 
39.Karakolis, E. G., Nguyen, B., You, J. B., Rochman, C. M. & Sinton, D. Fluorescent dyes for visualizing microplastic particles and fibers in laboratory-based studies. Environ. Sci. Technol. Lett. 6, 334–340. https://doi.org/10.1021/acs.estlett.9b00241 (2019).CAS 
Article 

Google Scholar 
40.Penthala, R. et al. Synthesis of azo and anthraquinone dyes and dyeing of nylon-6,6 in supercritical carbon dioxide. J. CO2 Util. 38, 49–58. https://doi.org/10.1016/j.jcou.2020.01.013 (2020).CAS 
Article 

Google Scholar 
41.Prata, J. C., Costa, J. P., Duarte, A. C. & Rocha-Santos, T. Methods for sampling and detection of microplastics in water and sediment: A critical review. TrAC Trends Anal. Chem. 110, 150–159. https://doi.org/10.1016/j.trac.2018.10.029 (2019).CAS 
Article 

Google Scholar 
42.Hengstmann, E. & Fischer, E. K. Nile red staining in microplastic analysis – proposal for a reliable and fast identification approach for large microplastics. Environ. Monit. Assess. 191, 612. https://doi.org/10.1007/s10661-019-7786-4,31489505 (2019).Article 
PubMed 

Google Scholar 
43.Jung, M. R. et al. Validation of ATR FT-IR to identify polymers of plastic marine debris, including those ingested by marine organisms. Mar. Pollut. Bull. 127, 704–716. https://doi.org/10.1016/j.marpolbul.2017.12.061 (2018).CAS 
Article 
PubMed 

Google Scholar  More

Study areaThe study area (78 000 km2) is located between 68–71°N and 20–26°E, with strong climatic gradients, ranging from wet maritime to relatively dry continental, over tens of kilometers. The landscape of this climatically sensitive high-latitude region has been affected by multiple glaciations in the past. It includes the Scandes Mountains near the Arctic Ocean and low-relief areas to the south and east. The majority of the region (52%) is underlain by sporadic permafrost. Continuous and discontinuous permafrost are limited to the highest mountains of the study area (2% and 7%, respectively)17,26. This large proportion of sporadic, typically warm and shallow permafrost in the study area indicates that ground thermal response to climate warming can be rapid27. Our data do not cover low-relief plateaus of continuous permafrost (similar to northern Siberia and Alaska), where the generally high ice content of soil may lead to different and enhanced LSP responses under climate warming (e.g., ice wedge degradation and surface ponding) with altered AGB feedbacks43,44.LSP observationsThe data consist of 2917 study sites (each 25 m × 25 m) and includes previously combined observations (both in-situ [n = 581] and remote-sensing [n = 2336]) of the active surface features of three cryogenic LSP common in the area: cryoturbation, solifluction, and nivation. These LSP are mainly associated with seasonal freeze–thaw processes. Cryoturbation (i.e., frost churning) is a general term for soil movement caused by differential heave, and it creates typical surface features such as patterned ground, frost boils and hummocky terrain5. Solifluction is the slow mass wasting of surficial deposits through frost creep and permafrost flow, where gravitation causes frost-heaved soil to settle downwards during the summer thaw, creating features of lobes and terraces50. In addition, solifluction also includes gelifluction which is a mass wasting process caused by high porewater pressure in unconsolidated surface debris creating similar lobes and terraces5,50. We use the term nivation to collectively designate various weathering and fluvial processes which are intensified and depicted by the presence of snowbeds (which in general are melting in mid-July – late-August) and nivation hollows28,51. We expect the presence of such a snowbed to be an indication of active nivation processes, since in these environments the year-to-year spatial snow patters are fairly consistent31.The rationale behind LSP sampling is described in previous geomorphic studies which served as a basis for the used protocol52,53. Due to the large study domain, study objectives (focus on distribution of active surface features, not on activity itself) and modeling resolution (50 m × 50 m), we used a visual method to estimate the presence/absence of the mapped LSP. We used high-resolution aerial photography (spatial resolution of 0.25 m−2) and targeted field surveys (GPS accuracy ~5 m; Garmin eTrex personal navigator) to construct the LSP dataset. A binary variable (1 = presence, 0 = absence) was assigned to each LSP to indicate their evident activity (or absence). The activity/absence of the LSP was visually estimated based on the evidence in ground surface, indicated by e.g., frost-heaving, cracking, microtopography (e.g., erosional and depositional forms), soil displacement indicative to a process form (e.g., solifluction lobes, patterned ground), changes in vegetation cover and late-lying snow. Such indicators average the LSP activity over several years. Even small areas with slight indication of activity were considered active processes. However, such a protocol based on a visual assessment is susceptible for incorrect activity classification; solifluction may be active despite having a complete vegetation cover19 and the presence of late-lying snowbed, although being a good indication28, does not necessarily mean that active nivation processes are present.Remotely sensed vegetation indexFor obtaining remotely sensed vegetation index for the study area, we employed a maximum-value compositing approach. We downloaded all available clear sky (less than 80% land cloud cover) Landsat OLI 8 images overlapping the study area from June to September between 2013 and 2017 (total of 1086 scenes) from the United States Geological Survey (USGS) database (http:\earthexplorer.usgs.gov). Images were USGS surface reflectance products, which were preprocessed (georeferencing, projection, and atmospheric corrections) by USGS54. Landsat-8 satellite is the latest addition to the Landsat mission that has provided repeated land surface information globally since the 1970’s and is the most commonly used fine-scale satellite system for vegetation mapping. The native resolution of the Landsat OLI sensor is 30 m for the spectral bands used in the image processing steps of this study.Normalized difference vegetation index (NDVI), a widely used spectral index to estimate the amount of green vegetation, was calculated as55:$$({{{{{rm{rho }}}}}}{{{{{rm{NIR}}}}}}-{{{{{rm{rho }}}}}}{{{{{rm{red}}}}}})/({{{{{rm{rho }}}}}}{{{{{rm{NIR}}}}}}+{{{{{rm{rho }}}}}}{{{{{rm{red}}}}}})$$
(1)
where ρNIR and ρred are the surface reflectance for their respective Landsat bands, 0.851–0.879 (mu)m and 0.636–0.673 (mu)m.USGS provides pixel-based quality assessment bands for all surface reflectance products. These bands were used to mask clouds, snow, water, and other low-quality pixels from the individual NDVI scenes. Additionally, if the NDVI images still had unphysical values over 1 or under -1, these pixels and their surroundings of 100 m radius were excluded. We determined maximum values for each 30 m resolution pixel of the study area individually. After masking cloud, snow, and water from the scenes, obvious scattered erroneous NDVI values remained in some scenes. Therefore, we excluded the values outside the pixel-based 95% percentile prior to maximum composite.The CFmask cloud detection algorithm that is used to generate the quality assessment band has clear difficulties in distinguishing small snow patches from clouds. As such, a large portion of late-lying snow beds were repeatedly and incorrectly classified as clouds. Moreover, the CFmask algorithm creates buffers around the cloud pixels54, hence much information was lost around the snow patches that were incorrectly identified as clouds. After these processing steps, some pixels around the extreme late-lying snow beds had still too low number of NDVI records to provide reliable NDVI values for the maximum composite. To fill these small and scattered gaps in the initial maximum NDVI composite, we selected 74 mostly cloud-free scenes between August and September. For these 74 scenes, we manually digitized cloud masks to exclude cloud-contaminated pixels with high certainty. Moreover, every pixel must have passed the following quality checks to be included in the gap-filling composite: not classified as water in the USGS quality assessment band; normalized difference snow index (NDSI) value less than 0.4, and blue band reflectance less than 0.1 (to exclude snow); reflectance of red band between 0.03 and 0.4 (second check for water and snow, and deepest shadows); NDVI between 0 and 0.4 (lower threshold to exclude snow and water contamination; higher threshold to exclude erroneous values, as very late snowbed habitats always have very limited vegetation cover). Additionally, if the NDVI images had unphysical values over 1 or under -1, these pixels and their surroundings (200 m radius) were excluded. Pixels in the 74 selected images which passed these checks, were then used to create a secondary maximum NDVI composite that was used to fill the gaps in the initial maximum NDVI composite. The secondary composite comprised 0.4% of the pixels in the final composite. Among all 2917 LSP observation sites, 2.9% were located within the gaps in the initial maximum NDVI composite, and thus received their maximum NDVI values from the secondary NDVI composite.In the used Landsat data, the nivation sites were not covered by snow, but instead were associated with generally lower AGB values as nival processes affect the vegetation’s structure and composition (Supplementary Table 1).Above-ground biomass dataAbove-ground biomass (AGB) reference data were collected from two regions, with a total of 433 sites that represent an area of > 4000 km2 (Supplementary Fig. 9). The first dataset (hereafter BM region 1; centering to ca. 69°N, 21°E) was collected between 2008 and 2011, and the second dataset (BM region 2; centering to ca. 70°N, 26.2°E) between 2015 and 2017. Both study regions are representative of an arctic and alpine treeline ecotone and include data from mountain birch forest to barren oroarctic tundra56,57.The BM region 1 dataset consists of 309 field sites (each 10 m × 10 m), which are located around eight different massifs covering a wide range of environmental conditions (Supplementary Figs. 9–10). Sampling was performed in transects to cover various aspects of the slope (i.e., topoclimatic conditions), starting from the foothill of the mountain, and ending at the summit. A plot was systematically established at every 20 m increase in elevation and recorded with a GPS device. Four clip-harvest biomass samples (20 cm × 20 cm) were taken 5 m from the plot center in every cardinal direction. Two samples were used in bare mountaintops (north, south). The clip-harvest samples were dried for 48 h at +65 °C, and dry weight was recorded. The sample biomass values were converted to g m-2 and the average sample value was calculated for each site (Supplementary Fig. 9). The original BM region 1 dataset contains forest and treeline plots, but these were excluded from the final analyses due to an incomparable tree sampling strategy with BM region 2, which could introduce uncertainty into biomass estimates.The BM region 2 data were collected from three different massifs having an elevation range from 120 m to 1064 m (Supplementary Fig. 9). The biomasses were sampled from 102 sites (each 24 m × 24 m in size) that were chosen using a stratified sampling to cover gradients of thermal radiation (potential incoming solar radiation), soil moisture (topographic wetness index, TWI) and vegetation zone (forest, treeline, and alpine zones). Radiation and TWI were calculated from a 10 m digital elevation model (DEM, provided by the National Land Surveys of Finland and Kartverket, the Norwegian mapping authority), and assigned to one of three classes based on observation percentiles (breaks at 20% and 80%) leading to total of 27 strata. Vegetation zones were digitized based on aerial imagery. After the first field survey, 22 sites were added to account for vegetation types that were not sufficiently represented by the GIS-based stratification. Thus, the total sample size of the BM region 2 dataset is 124 AGB sites.The same clip-harvest sample protocol was used as in BM region 1; additional samples were also taken from 12 m in every cardinal direction, thus each site had eight AGB samples (Supplementary Fig. 9). Trees with diameter at breast height (DBH) greater than 20 mm were measured from a 900 m2 circular plot, which corresponds to the size of the NDVI product resolution. Large stems (DBH  > 80 mm in the forest and 40 mm at the treeline) were measured from the whole plot, whereas smaller stems were measured from five subplots. Specifically, the center subplot was 100 m2, and the four subplots located at 8 m to every cardinal direction were each 12.5 m2. For the subplot observations, we used a plot expansion factor (900/150 = 6) to generalize the observations for the whole plot assuming a homogeneous forest structure i.e., each subplot stem represents six trees within the 900 m2 plot. A total of 98% of the measured stems were mountain birch (Betula pubescens ssp. czerepanovii), making it the most abundant species in the area. For predicting stem biomass, we used the average of three allometric equations58,59,60, in order to reduce the uncertainty related to the transferability of an individual allometric model. In addition, Populus tremula (1% of the observations) were found on low-altitude south-facing slopes, and Salix caprea (1%) in moist, nutrient-rich sites. Species-specific models61,62 were used to estimate their respective stem biomasses. Individual pines (Pinus sylvestris) were scattered in the area but were not present in any of the sampled plots.The plots of above-ground tree biomass were converted to g m−2 and added to the mean clip-harvest AGB to obtain the total vascular plant AGB for each site. The BM region 1 and BM region 2 datasets were combined, and the NDVI value was extracted from the site center coordinates.Spatial autocorrelation (SAC) is a common property of any spatial dataset and means that observations are related to one another by the geographical distance63. SAC in the model residuals violates the independence assumption commonly required by statistical models and can lead to inflated hypothesis testing and biased model estimates64. To investigate whether the plot-scale AGB data are spatially autocorrelated, we calculated semivariogram which describes the spatial dependency between the observations as a function of distance between the point pairs65. Semivariogram were calculated as:$${{{{{rm{gamma }}}}}}(h)=frac{1}{2N(h)}mathop{sum }limits_{i=1}^{{N}_{h}}{left(Zleft({s}_{i}right)-Zleft({s}_{i}+hright)right)}^{2}$$
(2)
where N(h) denotes the number of data pairs within distance h, and (Zleft({s}_{i}right)) is an observation (or model residual) in location i. For the calculation, we used R package gstat66 (version 2.0-0). A visual inspection of the semivariogram indicated spatial autocorrelation at short distances (“AGB” in Supplementary Fig. 11). Therefore, for the NDVI-AGB conversion, we used a generalized least squares modeling (GLS, as implemented in R package nlme67 [version 3.1-137]) that can explicitly account for SAC in the data. For the modeling, the AGB values were log(x+0.1) transformed. The GLS, where AGB was modeled as a function of NDVI, were fitted assuming an exponential spatial correlation structure:$$gamma left(hright)={c}_{0}+cleft(1-{e}^{-h/a}right)$$
(3)
where ({c}_{0}) is the difference between the intercept and origin (i.e., the “nugget” parameter in geostatistics), (c) is the amount of variance (i.e., the “sill”) and a represents the distance of spatial dependency (i.e., the “range”). The fitted GLS was as follows:$${{log }}left({AGB}right)=-1.038629+9.725572times {NDVI}$$
(4)
The estimated spatial correlation parameters were c0 = 0.516, c = 0.484 and a = 260.605, indicating that the distance of spatial autocorrelation extends to ca. 261 m. The semivariogram for the model residuals indicated a notable reduction in the amount of spatial autocorrelation compared to the AGB data (Supplementary Fig. 11). The fitted model explained 70.6% of the deviance in the data. When the predicted values were converted back to the response scale, the model explained 60.5% of the deviance. Therefore, for the subsequent analyses we use the above-ground biomass estimated by the model.Environmental predictorsIn addition to LSP, we used climate, topography, and soil predictors to model AGB. Gridded monthly average temperatures and precipitation data (1981–2010; spatial resolution 50 m × 50 m) based on a large collection ( > 950) of Fennoscandian meteorological stations were used in a spatial interpolation scheme17. Three climate predictors—growing degree days (GDD, °C, base temperature 5 °C), mean February air temperature (Tfeb, °C) and water balance (WAB, mm)—were calculated from the gridded climate data. WAB is the difference between total annual precipitation and potential evapotranspiration (PET), which was estimated from the monthly air temperature and precipitation data68:$${PET}=58.93times {T}_{{above}0^circ C}/12$$
(5)
These climatic predictors were selected to represent different aspects of climate that are critical for tundra vegetation: heat requirements, cold tolerance and moisture availability. In addition, two local scale topographic predictors were calculated from a DEM (spatial resolution of 50 m × 50 m, provided by the National Land Survey Institutes of Finland, Norway, and Sweden): topographic wetness index69 (TWI, a proxy for soil moisture) and potential annual direct solar radiation70 (MJ cm-2 a-1). Slope angle was initially considered as a potential predictor for AGB but was later omitted due to the strong correlation with TWI (-0.93, P ≤ 0.001). We also calculated peat cover (%) from a digital land cover classification71. Here, the native resolution of 100 m was resampled at 50 m to match the resolution of the climatic and topographic predictors, using nearest-neighbor interpolation. The binary peat cover variable was transformed to a continuous scale using a spatial mean filter of 3 × 3 pixels52. Finally, the topmost soil layer of a global gridded soil database72 was used to obtain pH data. Again, the original resolution of 250 m was also resampled to 50 m resolution using bilinear interpolation.Our fine-scale data revealed strong environmental gradients over the 78,000 km2 study area (Supplementary Table 1), most of which were only moderately inter-correlated (Spearman’s correlation coefficient  More