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.

View author publications

You can also search for this author in PubMed
 Google Scholar

Eve Bazaiba Masudi

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

View author publications

You can also search for this author in PubMed
 Google Scholar

Jules Doret Ndongo

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

View author publications

You can also search for this author in PubMed
 Google Scholar

Rosalie Matondo

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

View author publications

You can also search for this author in PubMed
 Google Scholar

Arlette Soudan-Nonault

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

View author publications

You can also search for this author in PubMed
 Google Scholar

Alfred Ngomanda

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

View author publications

You can also search for this author in PubMed
 Google Scholar

Ifo Suspense Averti

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

View author publications

You can also search for this author in PubMed
 Google Scholar

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.

View author publications

You can also search for this author in PubMed
 Google Scholar

Bonaventure Sonké

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

View author publications

You can also search for this author in PubMed
 Google Scholar

Simon L. Lewis

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

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Download PDF

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.

Related Articles

Nature-based solutions can help cool the planet — if we act now

Biodiversity needs every tool in the box: use OECMs

Ethiopia, Somalia and Kenya face devastating drought

A collaborative look at the Congo Basin

Subjects

Conservation biology

Climate change

Ecology

Latest on:

Climate change

Before making a mammoth, ask the public
World View 20 OCT 21

Why fossil fuel subsidies are so hard to kill
News Feature 20 OCT 21

The broken $100-billion promise of climate finance — and how to fix it
News Feature 20 OCT 21

Ecology

Rhinoceros genomes uncover family secrets
News & Views 19 OCT 21

A toxic ‘tide’ is creeping over bountiful Arctic waters
Research Highlight 06 OCT 21

Spatiotemporal origin of soil water taken up by vegetation
Article 06 OCT 21

Jobs

Community Practice Pathologist (AP/CP)

University of Vermont (UVM)
Plattsburgh, NY, United States

Postdoctoral Fellowship in Vascular Biology at Boston Children’s Hospital and Harvard Medical School

Boston Children’s Hospital (BCH)
Boston, MA, United States

Assistant Professor/Associate Professor of Physiology and Cellular Biophysics (Tenure-Track)

Columbia University in the City of New York (CU)
New York, United States

Associate or Senior Editor, Nature Biomedical Engineering

Springer Nature
New York, NY, United States More

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

Trends in global biomass C stocksThe CRAFT model reliably reproduces the observed trends in primary and managed forest biomass C stocks (including both above-ground and belowground biomass) in 1990–2020 with a relative root mean square error (RMSE) of 0.57% between simulated and observed biomass C stocks by the FRA2 at the global level. These low divergences between stock estimates result, however, in global C emissions c. 2 times lower according to the CRAFT simulations than the estimates derived from the FRA (Supplementary Table 2). Still, the CRAFT simulations corroborated the FRA observations while adding information on annual estimations of forest C stocks, rather than the 5-years interval data provided by the FRA (Fig. 1a), and dynamic annual net C emissions (Fig. 1c, d) from managed and primary forests. The five sensitivity analyses carried out on the most uncertain model inputs and assumptions (see ‘Methods’ descriptions and Supplementary Figs. 5–10) confirmed the results presented in Fig.1: The largest deviation derived from the sensitivity analysis considering forest gross instead of net area changes results in a relative RMSE of 1.94% with global C emissions c.2.5 times higher than the FRA estimates (Supplementary Table 2). The simulations from the reference model assumptions yield the best RMSE and closest agreement with the C budgets derived from the FRA, indicating that they are the most optimal.Fig. 1: Global trends in total, primary, and managed forests.a Forest biomass C stocks (GtC); b cumulated change in forest area (Mha; negative values indicate area loss); c cumulated C net emissions (GtC; positive values indicate a C source while negative values indicate a C sink); and d cumulated net change in C-stock densities (tC/ha). See Supplementary Fig. 1 for annual fluxes.Full size imageIn line with the FRA data, we find here that the main trend is a loss of total biomass C stocks following three phases: increase in annual emissions, stagnation and slight recovery of C stocks, resulting in net C emissions from forest biomass (Fig. 1c) by 0.74 GtC or 0.03 GtC/yr between 1990 and 2020, contrasted by an opposite trend of increasing biomass density from 70 to 73 tC/ha in total forest (Fig. 1b, d). These figures are within the range of the estimated sink in forest soil and biomass of 0.1 ± 7.3 GtC/yr in 2001–2019 found by Harris et al.17. Our estimation is also consistent with that of Tubiello et al.3 of 0.11 GtC/yr net C emissions from forest ecosystems. A comparison3 of FRA-derived global forest C emissions with other independent estimates reported in 1990–2015 by National Greenhouse Gas Inventories (NGHGIs)—including the Russian Federation, the USA, China, Indonesia, and India—and by the United Nations Framework Convention on Climate Change for other countries (UNFCCC, 202018) yields a slight difference of c. 18%, although the UNFCCC and NGHGI’s account, by definition, only for emissions from managed land3. Further independent comparisons at the national and macro-regional levels are compiled in Supplementary Table 1 and reveal that C emissions estimated in the present study are in good agreement with other research.Here we find that the net C emissions mostly arise from primary forests, which undergo area loss, but also biomass thickening (Fig. 1b, d). By contrast, in spite of area loss, managed forests act as C-sinks following biomass thickening (Fig. 1b, d). Increasing biomass density is therefore key to counteract net C emissions from forest biomass in 1990–2020. While both harvest rate and burnt area increase globally over the period of observation, the increased forest growth rate that we calculate with CRAFT for both primary and managed forests over 1990–2020 emerges here as the only factor explaining increased biomass density at the global level. This is in line with other research pointing to the relevance of biomass thickening for forest C sequestration19. In addition, our finding that the forest growth rate increased annually by 0.19%, 0.21%, and 0.21% from 1990 to 2020, respectively, for primary, managed and total forests of the world is consistent with Kolby Smith et al.20 who find that also net primary production (NPP) increased annually between 0.10 and 0.25% in the period 1982–2011, as well as with other modeling and remote-sensing studies documenting a global greening trend, i.e., vegetation thickening following increased vegetation growth rate21,22. Note that estimates of annual growth rate increase in 1990–2020 by the sensitivity analyses provide narrow ranges of 0.17–0.19, 0.21–0.23, and 0.20–0.22%, respectively, for primary, managed, and total forests of the world (Supplementary Table 2).Proximate drivers of net C emissionsWe develop six counterfactual scenarios23,24,25 in order to investigate how forest biomass density and forest biomass C stocks would evolve in the hypothetical absence of (i) changes in harvest (CF1); (ii) changes in forest growth rates (CF2); (iii) change in burnt area (CF3); (iv) change in forest area (CF4); (v) harvest (CF5); (vi) burnt area (CF6) (see “Methods” section). The comparison of observed and simulated counterfactual trends allows us to isolate and quantify the influence of these four main drivers on global forest C-stock changes at national resolution (CF1 to 4) as well as to quantify the overall effects of total wood extraction and burnt area (CF5 and 6).At the global level, we find that loss of forest area (CF4) is the main driver of the net C emissions from forest biomass (Fig. 2a). In the absence of changes in area, global forest biomass would act as a cumulative net C sink of c. 26.9 GtC in the study period, creating a difference of 27.6 GtC between the actual and the CF4 C budget. This effect in the absence of area change, however, is a composite of an additional C sink of 30.7 in deforesting countries and an additional C source of 3.8 GtC in reforesting countries. Changes in harvest and burnt area from 1990 to 2020 also drove net C emissions from global forest biomass as emissions drop by c. 5.7 and 1.4 GtC in the respective counterfactual scenarios, thus generating net C-sinks of c. 4.9 and 0.63 GtC (Fig. 2a). These figures are in stark contrast with the estimated total sink of c. 49.1 and 5.4 GtC that would emerge in the hypothetical absence of harvest (CF5) and burnt area (CF6; Fig. 2a), respectively. Only changes in forest growth rates counteract the net C emissions from global forest biomass (CF2; Fig. 2a). In the absence of changes in forest growth rates, global forests would act as net C source of c. 7.4 GtC in 1990–2020, i.e., c. 10 times the actually observed source. This net effect in the absence of growth rate change results from an additional C source of 30.4 in countries experiencing growth rate increase and an additional C sink of 23.0 GtC in countries experiencing growth rate decline.Fig. 2: Counterfactual scenarios (1990–2020) assessing the cumulative impact of: changes in harvest (CF1); changes in forest growth rate (CF2); changes in burnt area (CF3); changes in forest area (CF4); total harvest (CF5); and total fire (CF6) on C-dynamics.Panels (a) and (b) show the global country-level gross and net CF C budgets (GtC) and changes in biomass density (tC/ha), respectively, with negative (red) and positive values (blue) indicating net emissions and sinks, respectively, error bars indicate the range of C budgets estimated across the five sensitivity analyses performed to test the model robustness (see Supplementary Fig. 5 for additional figures showing the net difference between CF and actual C budgets and changes in biomass density, Supplementary Table 3 and Supplementary Fig. 5 for results from sensitivity analyses). Maps show the effects of c CF1; d CF2; e CF3; f CF4; g CF5; h CF6, and are represented as the % of actual biomass C stocks that would be reached in each CF in 2020. Values above 100% (red) indicate that actual change result in net C emissions while values below 100% (blue) indicate that actual change result in a net C sink.Full size imageA sensitivity analysis on the potential underestimation of C-dynamics resulting from the use of net area change data at country level (see “Methods” section and Supplementary Fig. 5) reveals that accounting for gross area changes26 instead of net area change would result in higher global C emissions estimates (4.19 GtC in the sensitivity test versus 0.74 GtC in the reference simulation) but would reveal the same patterns of forest C-dynamic drivers (Supplementary Fig. 5). However, the magnitude of the main drivers would be slightly changed with a lower effect of changes in area (C sink in the hypothetical absence of area changes reaching 20.8 GtC in the sensitivity tests versus 26.9 GtC in the reference assessment) and a higher effect of growth rate changes (C source in the hypothetical absence of growth rate changes reaching 13.1 GtC in the sensitivity tests versus 7.4 GtC in the reference assessment). Generally, the range of results derived from the five sensitivity analyses does not change the relative importance of the individual drivers in any of the scenarios (Fig. 2a, Supplementary Table 3, and Supplementary Fig. 5). However, the sensitivity analyses highlight that the uncertainty is large enough to reverse the cumulated C signal in the absence of changes in harvest (CF1), changes in burnt area (CF3), and the complete absence of burnt areas (CF6). By contrast, the signals of CF2 (no growth rate change), CF4 (no area change), and CF5 (no harvest) are larger than the uncertainty across sensitivity analyses, signaling that our findings on these drivers are most robust.The global trends displayed in Fig. 2a, b are the combined results of diverging national forest dynamics (Fig. 2c, h). In particular, shifts in forest area (CF4) contribute to global net C emissions only in the Global South, excluding Vietnam, India, and Chile (Fig. 2f). The impacts of changes in burnt area and harvest are similarly heterogenous, with considerable effects only in some regions (e.g., Vietnam, Mozambique, Fig. 2c, e). In contrast, changes in forest growth rates are more ubiquitous, mainly positive (leading to C-sinks) for most countries, with a few notable exceptions, mainly in arid or boreal regions (e.g., India, Spain, Argentina, Canada; Fig. 2d). Possible reasons explaining the negative effect of change in forest growth rate are forest degradation, increasing drought, cloudiness, or insect outbreaks15,16,17,18,19. Over the period 1990–2020, the strongest harvest impacts are observed in countries with large area of managed forest and high harvest pressure, mostly located in temperate and subtropical areas (CF5; Fig. 2g), while fire impacts are strong in only a few countries (CF6; Fig. 2h).The fact that we use here country-level data comes both with limitations and advantages. The main limitation associated with national data is that it conceals gross C fluxes in forest biomass dynamics and blurs heterogeneity in growth conditions and anthropogenic management within countries. The country-level resolution aggregates the effects of manifold, partly counteracting processes at the local level—including photosynthesis, maintenance respiration, growth respiration, as well as forest area loss and expansion—on the annual dynamic of primary and managed forest biomass. As a consequence, our optimization of the growth function actually reflects apparent national growth rates resulting from the aggregate of these processes. However, this simplification of forest ecosystem functioning is also an advantage. Our approach reproduces forest biomass dynamics very accurately, which is complementary to most process-based models aimed at depicting biological processes and their abiotic controls27 but providing a wide range of C flux estimations1 and hardly reproducing observation from inventory data1,28,29. By contrast, the strength of the modeling approach implemented here is that it can be run with parsimonious data availability and allows to disentangle the major drivers behind forest C-stock and flux trajectories.Typology of forest biomass changeIn order to identify spatial and temporal patterns of drivers in forest biomass trends, we establish a typology of the main drivers over the period 1990–2020 (Fig. 3b). The typology we established is based on the positive versus negative shift in biomass C stocks, and highlights the most important driver of this shift as assessed through the counterfactual assessment, irrespective of the relative importance of the other drivers shown in Fig. 2. However, as the early separation between increasing and decreasing biomass C stocks in the decision tree (Fig. 3b) may conceal the effect of a major driver counteracting the observed C dynamic, the typology also accounts for possible antagonistic effects by identifying cases in which the main driver of observed C-dynamics is not, in absolute terms, the most important driver (e.g., C stocks increase but the driver with the strongest absolute effect counteracts this positive budget, see also Supplementary Fig. 3). By pinpointing the major drivers of forest change at national levels, such an approach enables to identify major levers for forest conservation.Fig. 3: Main drivers of the net C emissions from forest biomass.a Applied at the national level to the 1990–2020 period; b established according to a Boolean typology using the results from the counterfactual scenario assessment as criteria; c enabling to calculate the sum of net C-sinks and net C sources in each type of forest C-dynamics trajectory identified through the typology, error bars indicating the range of C-sinks and sources by main driver estimated across the five sensitivity analyses, with black and gray bars standing, respectively, for solid and hatched countries (see Supplementary Figs. 6–7 for results from sensitivity analyses). The hatches on the countries (a), typology (b), and bar chart (c) stand for cases in which the driver with the strongest effect actually counteracts the observed carbon budget. The color of the hatches corresponds to the main factor identified by the decision tree algorithm. Abbreviation on the typology: E: C sink driven by forest area Expansion; LH: C sink driven by Lower Harvest; FR: C sink driven by Fire Reduction; EG: C sink driven by Enhanced Growth rate; DG: C source driven by Declining Growth rate; FI: C source driven by Fire Increase; HH: C source driven by Higher Harvest; D: C source driven by Deforestation; NS: non-significant change.Full size imageDeforestation was the dominant driver of net C emissions from forest biomass in most countries of South America and Sub-Saharan Africa, corroborating findings from the literature11,30,31 (Fig. 3a, c). The net C emissions by countries where deforestation is the most significant driver reach c. 21.3 GtC, with only 0.3 GtC of these emissions being counteracted by another major driver (either increased growth rate or lower harvest pressure). These emissions represent c. 92.7% of the 21.9 GtC net emissions arising from all countries acting as net C sources (Fig. 3c). Changes in forest growth rates act as the primary drivers in most countries experiencing a net C sink over the period (Fig. 3a, c). The net C-sinks by countries where changes in forest growth rates are the main driver reach c. 16.4 GtC, with 0.9 GtC of these sinks being counteracted by another major driver (increased harvest pressure in all cases except for Sudan where area loss was the major driver counteracting the C sink). These C-sinks mainly driven by increased growth rate represented c. 77.5% of the 21.1 GtC net sink created by all countries acting as net C-sinks (Fig. 3c).Forest area expansion from 1990 to 2020 is the main driver of forest biomass net C sink in only a few Northern countries but also some Southern countries, namely Vietnam, India, and Chile, in line with findings reported for these countries32,33,34, all together accounting for a net C sink of 3.9 GtC. However, more than half of the C-sinks mainly driven by reforestation are counteracted by another major driver (either declining forest biomass growth rate or increased harvest pressure). Similarly, changes in harvest as well as changes in burnt areas are the main drivers of net C sink or source for a handful of countries in 1990–2020 (Fig. 3a). Finally, declining forest biomass growth rate is the primary driver of net C emissions only in Mongolia and Canada, which is consistent with other studies highlighting slower growth, higher mortality, and insect outbreak events in Canadian forests35,36,37.These highlights derived from the typology remained the same in all sensitivity analyses (Supplementary Figs. 6–7), despite some possible changes in country type identification (Fig. 3a and Supplementary Fig. S6) and amplitude shifts in the attribution of main drivers globally (Fig. 3c and Supplementary Fig. 7). The ranges of values in the attribution of main drivers result from the previously reported differences between the counterfactual and actual C budget estimates across sensitivity analyses (see also Supplementary Tables 2–3) combined with some changes in the type of forest C-dynamics trajectory identified through the typology in countries with large forest biomass stocks: China, India, and Australia (Supplementary Note 1 and Supplementary Fig. 6). However, these shifts do not affect the main conclusions derived from Fig. 3c: in all sensitivity analyses, growth rate changes remain the main driver of global forest biomass C sink with total net C-sinks in countries where increasing growth rate is the main driver (including both solid and hatched countries) ranging from 12.1 to 21.1 GtC, while afforestation always holds the second place of global C sink driver (total net C-sinks in countries where afforestation is the main driver ranging from 2.4 to 7.7 GtC). Similarly, total net C sources by countries where deforestation is the main driver range from −21.9 to −14.0 GtC, thus highlighting that deforestation would by far remain the main driver of forest biomass C emissions across all sensitivity analyses.Implications for forest-based solutionsOur results allow to identify major mechanisms behind observed forest biomass C changes that are immediately relevant for forest-based climate-change-mitigation strategies. We show that deforestation, increasing harvest, and burnt area have driven the net C emissions from forest biomass over the last three decades. Deforestation is the dominant driver, corroborating that protection from deforestation is indispensable1,11,38. On the other hand, forest growth rate is identified as the major driver counteracting net C emissions (Fig. 2a, d). In fact, most of the temperate and boreal countries, with the noteworthy exception of Canada, fall under a type in which enhanced forest growth rate is the major driver of a net C sink (Fig. 3b). Besides, even countries dominated by deforestation in the tropics show significant increases in growth rate (Figs. 2d and 4). These results highlight that enhanced growth rate, rather than reforestation, is the main driver counteracting biomass C emissions in 1990–2020.Fig. 4: Change in forest growth rate and its effects on global carbon stocks.The diagrams show national forest growth rate changes (y-axis) scaled along the cumulated size of the carbon stock in 1990 (x-axis). The area between the graph and the x-axis indicates the C-stock change due to growth rate for total (a), primary (b), and managed forests (c) (see Supplementary Figs. 8–10 for results from sensitivity analyses).Full size imageThese increases in forest growth rate may arise from diverse processes, including climatic and land-use drivers. On the one hand, several studies highlight the effects of environmental drivers—such as warming, atmospheric carbon dioxide (CO2), and nitrogen (N) fertilization1,6,8,11,21,39—on the terrestrial C sink. On the other hand, changes in forest growth rate can also be driven by shifts in forest management practices, such as tree species selection, forest recovery from past degradation and lesser litter grazing12,40,41. Advancing the understanding of the underlying processes of forest growth rate change is key for forging climate-change-mitigation strategies, but it is not straightforward to isolate climatic (e.g., altered CO2 concentration or temperature) from land-use drivers (e.g., non-timber forest uses such as grazing)42. Still, a comparison of trajectories in primary and managed forest growth rate change based on our results allows to derive insights into the interplay of these different drivers (Fig. 4 and Supplementary Fig. 3). From the fact that only 11% of primary forest carbon stocks show declining growth rate trends (Fig. 4c) while a relatively larger carbon stock in managed forest (22%) is affected by declining growth rate trends (Fig. 4b), we can infer that in overall terms—and assuming primary and managed forests of a given country to be similarly affected by climatic drivers —land use is likely to exert a degrading effect on growth rate dynamics. Nevertheless, some countries reveal declining growth rate in primary forest but increasing growth rate in managed forest, thus suggesting that forest management may have an improving effect on forest growth rate in those countries (e.g., USA, Fig. 4b, c, see also Supplementary Fig. 4). In overall terms, this result suggests that globally a reduction of forest use may have the potential to enhance growth rate, thus corroborating previous findings by Quesada et al.14. However, these interpretations warrant a caveat that primary versus managed forest growth rate changes are derived from the FRA data and a state-of-the-art of the literature on changes in primary forest density (see “Methods” section and Supplementary Note 2), the latter being associated with higher uncertainties although the corresponding sensitivity analysis testing suggests these uncertainties to have little impact on the figures displayed here (see Supplementary Tables 2-3 and Supplementary Figs. 5–10).Independent of their origin (management or climate driven), the future trajectories of this driver, forest growth rate, is subject to large uncertainties43,44,45. Research suggests that increasing forest growth rate is a transient phenomenon and might be discontinued in the future46. For instance, several recent studies have pointed toward the saturating effect of CO2 fertilization, which is suspected to be a key process underlying vegetation greening and ensuing thickening21, the risk of increasing mortality and slower growth rate following increasing drought6,47,48, temperature49, and natural disturbances such as insect outbreaks50,51. Even more recently, Duffy et al.52 showed that, in the near-future, temperature increases from business-as-usual trajectories of climate change shall result in a severe reduction, and possibly a reversal, of the terrestrial C sink, despite the remaining unknowns.Therefore, we conclude that, while increasing forest growth rate is the dominant driver counteracting the global net C emissions from forest biomass in the past three decades, it is against a precautionary principle to forge climate strategies that rely on a continuous net C sink effect from the same processes in the future. By contrast, our results suggest that reducing wood harvest (Fig. 2g) and halting deforestation (Fig. 2c) are key strategies to address the challenge of climate-change mitigation. In this context, increasing forest harvest volumes—a strategy often promoted in the course of climate-change-mitigation efforts embraced as the “bioeconomy”—appears to have critical unintended side-effects, despite the potential of wood for substituting some emissions-intensive products and processes53,54,55: by not only reducing the carbon sink function in forests, but also accelerating the overall C turnover rates through rejuvenation of forests and transfer to harvested wood products of lifetimes shorter than those of old-growth forests56,57,58, such strategies result in a critical loss of C sink capacity. Overall, our results plead for a double strategy to enable future forest-based solutions for climate-change mitigation: in the Global South, ending deforestation is the main priority to reverse the net C source toward a net C sink, while in the Global North, lowering wood harvest has the strongest potential to immediately enhance the C sink in forest biomass. More

1.BirdLife International. State of the World’s Birds: Taking the Pulse of the Planet (BirdLife International, 2018).
Google Scholar 
2.Dias, M. P. et al. Threats to seabirds: A global assessment. Biol. Conserv. 237, 525–537 (2019).Article 

Google Scholar 
3.Gales, R. in Albatross Biology and Conservation (eds Robertson, G. & Gales, R.) 20–45 (Surrey Beatty and Sons, Chipping Norton, 1998).4.Gales, R., Brothers, N. & Reid, T. Seabird mortality in the Japanese tuna longline fishery around Australia, 1988–1995. Biol. Conserv. 86, 37–56 (1998).Article 

Google Scholar 
5.Anderson, O. R. et al. Global seabird bycatch in longline fisheries. Endanger. Species Res. 14, 91–106 (2011).Article 

Google Scholar 
6.Warham, J. The Petrels: Their Ecology and Breeding Systems (Academic Press, 1990).
Google Scholar 
7.Warham, J. The Behaviour, Population Biology and Physiology of the Petrels (Academic Press, 1996).
Google Scholar 
8.Dietrich, K. S., Parrish, J. K. & Melvin, E. F. Understanding and addressing seabird bycatch in Alaska demersal longline fisheries. Biol. Conserv. 142, 2642–2656 (2009).Article 

Google Scholar 
9.Zhou, C., Jiao, Y. & Browder, J. A. Seabird bycatch vulnerability to pelagic longline fisheries: Ecological traits matter. Aquat. Conserv.: Mar. Freshw. Ecosyst. https://doi.org/10.1002/aqc.3066 (2019).Article 

Google Scholar 
10.Brothers, N. The incidental catch of seabirds by longline fisheries: Worldwide review and technical guidelines for mitigation. FAO Fish. Circ. 937, 1–100 (1999).ADS 

Google Scholar 
11.Gilman, E. Integrated management to address the incidental mortality of seabirds in longline fisheries. Aquat. Conserv.: Mar. Freshw. Ecosyst. 11, 391–414 (2001).Article 

Google Scholar 
12.Li, Y. & Jiao, Y. Modeling spatial patterns of rare species using eigenfunction-based spatial filters: An example of modified delta model for zero-inflated data. Ecol. Model. 299, 51–63 (2015).Article 

Google Scholar 
13.Li, Y., Jiao, Y. & Browder, J. A. Assessment of seabird bycatch in the US Atlantic pelagic longline fishery, with an extra exploration on modeling spatial variation. ICES J. Mar. Sci. 73, 2687–2694 (2016).Article 

Google Scholar 
14.Brothers, N. Albatross mortality and associated bait loss in the Japanese longline fishery in the Southern Ocean. Biol. Conserv. 55, 255–268 (1991).Article 

Google Scholar 
15.Croxall, J. P. & Nicol, S. Management of Southern Ocean fisheries: Global forces and future sustainability. Antarct. Sci. 16, 569–584 (2004).ADS 
Article 

Google Scholar 
16.Løkkeborg, S. Best practices to mitigate seabird bycatch in longline, trawl and gillnet fisheries – efficiency and practical applicability. Mar. Ecol. Prog. Ser. 435, 285–303 (2011).ADS 
Article 

Google Scholar 
17.Beerkircher, L. R., Brown, C. J., Abercrombie, D. L. & Lee, D. W. Overview of the SEFSC pelagic observer program in the Northwest Atlantic from 1992–2002. ICCAT CVSP 58, 1729–1748 (2005).
Google Scholar 
18.Diaz, G. A., Beerkircher, L. R. & Restrepo, V. R. Description of the US pelagic observer program (POP). ICCAT CVSP 64, 2415–2426 (2009).
Google Scholar 
19.Lee, D. W. & Brown, C. J. SEFSC pelagic observer program data summary for 1992–1996. US Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southeast Fisheries Science Center (1998).20.Lo, N. C., Jacobson, L. D. & Squire, J. L. Indices of relative abundance from fish spotter data based on delta-lognornial models. Can. J. Fish. Aquat. Sci. 49, 2515–2526 (1992).Article 

Google Scholar 
21.Martin, T. G. et al. Zero tolerance ecology: improving ecological inference by modelling the source of zero observations. Ecol. Lett. 8, 1235–1246 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Winter, A., Jiao, Y. & Browder, J. A. Modeling low rates of seabird bycatch in the US Atlantic longline fishery. Waterbirds 34, 289–303 (2011).Article 

Google Scholar 
23.Cortés, V., Arcos, J. M. & González-Solís, J. Seabirds and demersal longliners in the northwestern Mediterranean: Factors driving their interactions and bycatch rates. Mar. Ecol. Prog. Ser. 565, 1–16 (2017).ADS 
Article 

Google Scholar 
24.Bi, R., Jiao, Y., Zhou, C. & Hallerman, E. M. A Bayesian spatiotemporal approach to inform management unit appropriateness. Can. J. Fish. Aquat. Sci. 76, 217–237 (2018).Article 

Google Scholar 
25.Wikle, C. K. Hierarchical models in environmental science. Int. Stat. Rev. 71, 181–199 (2003).MATH 
Article 

Google Scholar 
26.Cressie, N., Calder, C. A., Clark, J. S., Hoef, J. M. V. & Wikle, C. K. Accounting for uncertainty in ecological analysis: The strengths and limitations of hierarchical statistical modeling. Ecol. Appl. 19, 553–570 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Banerjee, S., Carlin, B. P. & Gelfand, A. E. Hierarchical Modeling and Analysis for Spatial Data (CRC Press, 2014).MATH 
Book 

Google Scholar 
28.Besag, J., York, J. & Mollié, A. Bayesian image restoration, with two applications in spatial statistics. Ann. Inst. Stat. Math. 43, 1–20 (1991).MathSciNet 
MATH 
Article 

Google Scholar 
29.Rue, H. & Held, L. Gaussian Markov Random Fields: Theory and Applications (CRC Press, 2005).MATH 
Book 

Google Scholar 
30.Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Ser. B Stat. Methodol. 71, 319–392 (2009).MathSciNet 
MATH 
Article 

Google Scholar 
31.Held, L., Schrödle, B. & Rue, H. in Statistical Modelling and Regression Structures (eds Kneib, T. & Tutz, G.) 91–110 (Springer, Berlin Heidelberg, 2010).32.Lindgren, F., Rue, H. & Lindström, J. An explicit link between Gaussian fields and Gaussian Markov random fields: The stochastic partial differential equation approach. J. R. Stat. Soc. Ser. B Stat. Methodol. 73, 423–498 (2011).MathSciNet 
MATH 
Article 

Google Scholar 
33.Bakka, H., Vanhatalo, J., Illian, J. B., Simpson, D. & Rue, H. Non-stationary Gaussian models with physical barriers. Spat. Stat. 29, 268–288 (2019).MathSciNet 
Article 

Google Scholar 
34.NCAR (National Center for Atmospheric Research). The Climate Data Guide: Hurrell North Atlantic Oscillation (NAO) Index (PC-based). Available from: https://climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-pc-based. Retrieved: March 1, 2019.35.ESRL (Earth Science Research Laboratory, NOAA). Climate timeseries: AMO (Atlantic Multidecadal Oscillation) Index. Available from: http://www.esrl.noaa.gov/psd/data/timeseries/AMO/. Retrieved: March 1, 2019.36.Spiegelhalter, D. J., Best, N. G., Carlin, B. P. & Van Der Linde, A. Bayesian measures of model complexity and fit. J. R. Stat. Soc. Ser. B Stat. Methodol. 64, 583–639 (2002).MathSciNet 
MATH 
Article 

Google Scholar 
37.Watanabe, S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594 (2010).MathSciNet 
MATH 

Google Scholar 
38.Shumway, R. H. & Stoffer, D. S. Time Series Analysis and Its Applications (Springer, 2011).MATH 
Book 

Google Scholar 
39.Bi, R., Jiao, Y., Bakka, H. & Browder, J. A. Long-term climate ocean oscillations inform seabird bycatch from pelagic longline fishery. ICES J. Mar. Sci. 77, 668–679 (2020).Article 

Google Scholar 
40.Lear, W. H. History of fisheries in the Northwest Atlantic: The 500 year perspective. J. Northwest Atl. Fish. Sci. 23, 41–73 (1998).Article 

Google Scholar 
41.Veit, R. R., Goyert, H. F., White, T. P., Martin, M. C., Manne, L. L. & Gilbert, A. Pelagic Seabirds off the East Coast of the United States 2008–2013. US Dept. of the Interior, Bureau of Ocean Energy Management, Office of Renewable Energy Programs, Sterling, VA. OCS Study BOEM, 24, 186 (2015).42.Harrison, P. Seabirds, an identification guide (Houghton Mifflin, 1983).
Google Scholar 
43.Onley, D. & Scofield, P. Albatrosses, petrels and shearwaters of the world (Princeton University Press, 2013).
Google Scholar 
44.Gladics, A. J. et al. Fishery-specific solutions to seabird bycatch in the U.S. West Coast sablefish fishery. Fish. Res. 196, 85–95 (2017).Article 

Google Scholar 
45.Grieve, B. D., Hare, J. A. & Saba, V. S. Projecting the effects of climate change on Calanus finmarchicus distribution within the U.S. Northeast Continental Shelf. Sci. Rep. 7, 6264 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Petersen, S. L., Honig, M. B., Ryan, P. G. & Underhill, L. G. Seabird bycatch in the pelagic longline fishery off southern Africa. Afr. J. Mar. Sci. 31, 191–204 (2009).Article 

Google Scholar 
47.Arcos, J. M. & Oro, D. Significance of fisheries discards for a threatened Mediterranean seabird, the Balearic shearwater Puffinus mauretanicus. Mar. Ecol. Prog. Ser. 239, 209–220 (2002).ADS 
Article 

Google Scholar 
48.Furness, R., Edwards, A. & Oro, D. Influence of management practices and of scavenging seabirds on availability of fisheries discards to benthic scavengers. Mar. Ecol. Prog. Ser. 350, 235–244 (2007).ADS 
Article 

Google Scholar 
49.Grémillet, D. et al. A junk-food hypothesis for gannets feeding on fishery waste. Proc. Biol. Sci. 275, 1149–1156 (2008).PubMed 
PubMed Central 

Google Scholar 
50.Skov, H. & Durinck, J. Seabird attraction to fishing vessels is a local process. Mar. Ecol. Prog. Ser. 214, 289–298 (2001).ADS 
Article 

Google Scholar 
51.Chapman, D. C., Barth, J. A., Beardsley, R. C. & Fairbanks, R. G. On the continuity of mean flow between the Scotian Shelf and the Middle Atlantic Bight. J. Phys. Oceanogr. 16, 758–772 (1986).ADS 
Article 

Google Scholar 
52.Steimle, F. W. & Zetlin, C. Reef habitats in the middle Atlantic bight: Abundance, distribution, associated biological communities, and fishery resource use. Mar. Fish. Rev. 62, 24–42 (2000).
Google Scholar 
53.Lee, D. S. Pelagic seabirds and the proposed exploration for fossil fuels off North Carolina: A test for conservation efforts of a vulnerable international resource. J. Elisha Mitchell Sci. Soc. 115, 294–315 (1999).
Google Scholar 
54.Kai, E. T. et al. Top marine predators track Lagrangian coherent structures. Proc. Natl. Acad. Sci. 106, 8245–8250 (2009).ADS 
CAS 
Article 

Google Scholar 
55.Li, Y., Browder, J. A. & Jiao, Y. Hook effects on seabird bycatch in the United States Atlantic pelagic longline fishery. Bull. Mar. Sci. 88, 559–569 (2012).Article 

Google Scholar 
56.Taylor, A. H. & Stephens, J. A. The North Atlantic Oscillation and the latitude of the Gulf Stream. Tellus 50, 134–142 (1998).Article 

Google Scholar 
57.Hobday, A. J., Hartog, J. R., Spillman, C. M. & Alves, O. Seasonal forecasting of tuna habitat for dynamic spatial management. Can. J. Fish. Aquat. Sci. 68, 898–911 (2011).Article 

Google Scholar 
58.FAO (Food and Agriculture Organization of the United Nations). Guidelines to reduce sea turtle mortality in fishing operations. FAO Technical Guidelines for Responsible Fisheries Prepared by Gilman, E., Bianchi, G. FAO: Rome. ISBN 978-92-106226-5 (2009).59.Bethoney, N. D., Schondelmeier, B. P., Kneebone, J. & Hoffman, W. S. Bridges to best management: Effects of a voluntary bycatch avoidance program in a mid-water trawl fishery. Mar. Policy 83, 172–178 (2017).Article 

Google Scholar 
60.Lindgren, F. & Rue, H. Bayesian spatial modelling with R-INLA. J. Stat. Softw. 63, 19 (2015).Article 

Google Scholar 
61.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019).62.Rue, H., Martino, S. & Lindgren, F. INLA: Functions which allow to perform a full Bayesian analysis of structured (geo-)additive models using integrated nested Laplace approximation. R package version 0.0., GNU General Public License, version 3 (2009).63.Simpson, D., Rue, H., Riebler, A., Martins, T. G. & Sørbye, S. H. Penalising model component complexity: A principled, practical approach to constructing priors (with discussion). Stat. Sci. 32, 1–28 (2017).MATH 

Google Scholar 
64.Fuglstad, G.-A., Simpson, D., Lindgren, F. & Rue, H. Constructing priors that penalize the complexity of Gaussian random fields. J. Am. Stat. Assoc. 114, 445–452 (2018).MathSciNet 
MATH 
Article 
CAS 

Google Scholar 
65.Plummer, M. Penalized loss functions for Bayesian model comparison. Biostat. 9, 523–539 (2008).MATH 
Article 

Google Scholar 
66.Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014).MathSciNet 
MATH 
Article 

Google Scholar  More

1.Krebs, C. J. Two paradigms of population regulation. Wildl. Res. 22, 1–10 (1995).MathSciNet 
Article 

Google Scholar 
2.McLaren, I. A. Natural Regulation of Animal Populations (Routledge, 2017).Book 

Google Scholar 
3.Deffner, D. & McElreath, R. The importance of life history and population regulation for the evolution of social learning. Philos. Trans. R. Soc. B 375, 20190492 (2020).Article 

Google Scholar 
4.Leão, S. M., Pianka, E. R. & Pelegrin, N. Is there evidence for population regulation in amphibians and reptiles? J. Herpetol. 52, 28–33 (2018).Article 

Google Scholar 
5.Hanski, I. A. Density dependence, regulation and variability in animal populations. Philos. Trans. R. Soc. B 330, 141–150 (1990).ADS 
Article 

Google Scholar 
6.Reznick, D., Bryant, M. J. & Bashey, F. r-and K-selection revisited: The role of population regulation in life-history evolution. Ecology 83, 1509–1520 (2002).Article 

Google Scholar 
7.Stenseth, N. C., Falck, W., Bjørnstad, O. N. & Krebs, C. J. Population regulation in snowshoe hare and Canadian lynx: Asymmetric food web configurations between hare and lynx. Proc. Natl Acad. Sci. USA 94, 5147–5152 (1997).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Lande, R. et al. Estimating density dependence from population time series using demographic theory and life-history data. Am. Nat. 159, 321–337 (2002).CAS 
PubMed 
Article 

Google Scholar 
9.Ferguson, G. W., Bohlen, C. H. & Woolley, H. P. Sceloporus undulatus: Comparative life history and regulation of a Kansas population. Ecology 61, 313–322 (1980).Article 

Google Scholar 
10.Andrews, R. M. Population stability of a tropical lizard. Ecology 72, 1204–1217 (1991).Article 

Google Scholar 
11.Tinkle, D. W., Dunham, A. E. & Congdon, J. D. Life history and demographic variation in the lizard Sceloporus graciosus: A long-term study. Ecology 74, 2413–2429 (1993).Article 

Google Scholar 
12.Altwegg, R., Dummermuth, S., Anholt, B. R. & Flatt, T. Winter weather affects asp viper Vipera aspis population dynamics through susceptible juveniles. Oikos 110, 55–66 (2005).Article 

Google Scholar 
13.Madsen, T. & Shine, R. Rain, fish and snakes: Climatically driven population dynamics of Arafura filesnakes in tropical Australia. Oecologia 124, 208–215 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
14.Madsen, T., Ujvari, B., Shine, R. & Olsson, M. Rain, rats and pythons: Climate-driven population dynamics of predators and prey in tropical Australia. Austral Ecol. 31, 30–37 (2006).Article 

Google Scholar 
15.Brown, G. P. & Shine, R. Rain, prey and predators: Climatically driven shifts in frog abundance modify reproductive allometry in a tropical snake. Oecologia 154, 361–368 (2007).ADS 
PubMed 
Article 

Google Scholar 
16.Brown, G. P., Ujvari, B., Madsen, T. & Shine, R. Invader impact clarifies the roles of top-down and bottom-up effects on tropical snake populations. Funct. Ecol. 27, 351–361 (2013).Article 

Google Scholar 
17.Massot, M., Clobert, J., Pilorge, T., Lecomte, J. & Barbault, R. Density dependence in the common lizard: Demographic consequences of a density manipulation. Ecology 73, 1742–1756 (1992).Article 

Google Scholar 
18.Fordham, D. A., Georges, A. & Brook, B. W. Experimental evidence for density-dependent responses to mortality of snake-necked turtles. Oecologia 159, 271–281 (2009).ADS 
PubMed 
Article 

Google Scholar 
19.Burns, G. & Heatwole, H. Home range and habitat use of the olive sea snake, Aipysurus laevis, on the Great Barrier Reef, Australia. J. Herpetol. 32, 350–358 (1998).Article 

Google Scholar 
20.Ward, T. M. Age structures and reproductive patterns of two species of sea snake, Lapemis hardwickii Grey (1836) and Hydrophis elegans (Grey 1842), incidentally captured by prawn trawlers in northern Australia. Mar. Freshw. Res. 52, 193–203 (2001).Article 

Google Scholar 
21.Dennis, B. & Ponciano, J. M. Density-dependent state-space model for population-abundance data with unequal time intervals. Ecology 95, 2069–2076 (2014).PubMed 
Article 

Google Scholar 
22.Bonnet, X., Naulleau, G. & Shine, R. The dangers of leaving home: Dispersal and mortality in snakes. Biol. Conserv. 89, 39–50 (1999).Article 

Google Scholar 
23.Yacelga, M., Cayot, L. J. & Jaramillo, A. Dispersal of neonatal Galápagos marine iguanas Amblyrhynchus cristatus from their nesting zone: Natural history and conservation implications. Herpetol. Conserv. Biol. 7, 470–480 (2012).
Google Scholar 
24.Lukoschek, V. & Shine, R. Sea snakes rarely venture far from home. Ecol. Evol. 2, 1113–1121 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Shine, R., Cogger, H. G., Reed, R. R., Shetty, S. & Bonnet, X. Aquatic and terrestrial locomotor speeds of amphibious sea-snakes (Serpentes, Laticaudidae). J. Zool. 259, 261–268 (2003).Article 

Google Scholar 
26.Forsman, A. Body size and net energy gain in gape-limited predators: A model. J. Herpetol. 30, 307–319 (1996).Article 

Google Scholar 
27.Shine, R., LeMaster, M. P., Moore, I. T., Olsson, M. M. & Mason, R. T. Bumpus in the snake den: Effects of sex, size and body condition on mortality in red-sided garter snakes. Evolution 55, 598–604 (2001).CAS 
PubMed 
Article 

Google Scholar 
28.Sherratt, E., Rasmussen, A. R. & Sanders, K. L. Trophic specialization drives morphological evolution in sea snakes. R. Soc. Open Sci. 5, 172141 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Lemen, C. A. & Voris, H. K. A comparison of reproductive strategies among marine snakes. J. Anim. Ecol. 50, 89–101 (1981).Article 

Google Scholar 
30.Shine, R., Shine, T. & Shine, B. Intraspecific habitat partitioning by the sea snake Emydocephalus annulatus (Serpentes, Hydrophiidae): The effects of sex, body size, and colour pattern. Biol. J. Linn. Soc. 80, 1–10 (2003).Article 

Google Scholar 
31.Heatwole, H., Grech, A., Monahan, J. F., King, S. & Marsh, H. Thermal biology of sea snakes and sea kraits. Integr. Comp. Biol. 52, 257–273 (2012).PubMed 
Article 

Google Scholar 
32.Van Dyke, J. U., Beaupre, S. J. & Kreider, D. L. Snakes allocate amino acids acquired during vitellogenesis to offspring: Are capital and income breeding consequences of variable foraging success? Biol. J. Linn. Soc. 106, 390–404 (2012).Article 

Google Scholar 
33.Masunaga, G., Matsuura, R., Yoshino, T. & Ota, H. Reproductive biology of the viviparous sea snake Emydocephalus ijimae (Reptilia: Elapidae: Hydrophiinae) under a seasonal environment in the Northern Hemisphere. Herpetol. J. 13, 113–119 (2003).
Google Scholar 
34.Goiran, C., Dubey, S. & Shine, R. Effects of season, sex and body size on the feeding ecology of turtle-headed sea snakes (Emydocephalus annulatus) on IndoPacific inshore coral reefs. Coral Reefs 32, 527–538 (2013).ADS 
Article 

Google Scholar 
35.Phillips, B. L. The evolution of growth rates on an expanding range edge. Biol. Lett. 5, 802–804 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Shine, R., Shine, T. G., Brown, G. P. & Goiran, C. Life history traits of the sea snake Emydocephalus annulatus, based on a 17-yr study. Coral Reefs 39, 1407–1414 (2020).Article 

Google Scholar 
37.Goiran, C. & Shine, R. Decline in sea snake abundance on a protected coral reef system in the New Caledonian Lagoon. Coral Reefs 32, 281–284 (2013).ADS 
Article 

Google Scholar 
38.Somaweera, R. et al. Pinpointing drivers of extirpation in sea snakes: A synthesis of evidence from Ashmore Reef. Front. Mar. Sci. 8, 658756 (2021).Article 

Google Scholar 
39.Udyawer, V. et al. Future directions in the research and management of marine snakes. Front. Mar. Sci. 5, 399 (2018).Article 

Google Scholar 
40.Udyawer, V., Cappo, M., Simpfendorfer, C. A., Heupel, M. R. & Lukoschek, V. Distribution of sea snakes in the Great Barrier Reef Marine Park: Observations from 10 yrs of baited remote underwater video station (BRUVS) sampling. Coral Reefs 33, 777–791 (2014).ADS 
Article 

Google Scholar 
41.Udyawer, V., Goiran, C. & Shine, R. Peaceful coexistence between people and deadly wildlife: Why are recreational users of the ocean so rarely bitten by sea snakes? People Nat. 3, 335–346 (2021).Article 

Google Scholar 
42.Shine, R., Goiran, C., Shine, T., Fauvel, T. & Brischoux, F. Phenotypic divergence between seasnake (Emydocephalus annulatus) populations from adjacent bays of the New Caledonian Lagoon. Biol. J. Linn. Soc. 107, 824–832 (2012).Article 

Google Scholar 
43.Goiran, C., Brown, G. P. & Shine, R. Niche partitioning within a population of sea snakes is constrained by ambient thermal homogeneity and small prey size. Biol. J. Linn. Soc. 129, 644–651 (2020).Article 

Google Scholar 
44.Li, M., Fry, B. G. & Kini, R. M. Eggs-only diet: Its implications for the toxin profile changes and ecology of the marbled sea snake (Aipysurus eydouxii). J. Mol. Evol. 60, 81–89 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
45.Heatwole, H. Predation on sea snakes. In The Biology of Sea Snakes (ed. Dunson, W. A.) 233–250 (University Park Press, 1975).
Google Scholar 
46.Rancurel, P. & Intes, A. L. requin tigre, Galeocerdo cuvieri Lacepede, des eaux neocaledoniennes examen des contenus stomacaux. Tethys 10, 195–199 (1982).
Google Scholar 
47.Ineich, I. & Laboute, P. Les Serpents Marins de Nouvelle-Calédonie (IRD éditions, 2002).
Google Scholar 
48.Masunaga, G., Kosuge, T., Asai, N. & Ota, H. Shark predation of sea snakes (Reptilia: Elapidae) in the shallow waters around the Yaeyama Islands of the southern Ryukyus, Japan. Mar. Biodivers. Rec. 1, e96 (2008).Article 

Google Scholar 
49.Wirsing, A. J. & Heithaus, M. R. Olive-headed sea snakes Disteria major shift seagrass microhabitats to avoid shark predation. Mar. Ecol. Progr. Ser. 387, 287–293 (2009).ADS 
Article 

Google Scholar 
50.White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).Article 

Google Scholar 
51.Brischoux, F., Rolland, V., Bonnet, X., Caillaud, M. & Shine, R. Effects of oceanic salinity on body condition in sea snakes. Integr. Comp. Biol. 52, 235–244 (2012).PubMed 
Article 

Google Scholar 
52.Lovich, J. E. & Gibbons, J. W. A review of techniques for quantifying sexual size dimorphism. Growth Dev. Aging 56, 269–269 (1992).CAS 
PubMed 

Google Scholar 
53.Dennis, B., Ponciano, J. M., Lele, S. R., Taper, M. L. & Staples, D. F. Estimating density dependence, process noise, and observation error. Ecol. Monogr. 76, 323–341 (2006).Article 

Google Scholar  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