Philippa Kaur

1.Chatterjee, S., Goswami, A. & Scotese, C. R. The longest voyage: Tectonic, magmatic, and paleoclimatic evolution of the Indian plate during its northward flight from Gondwana to Asia. Gondwana Res. 23, 238–267 (2013).ADS 

Google Scholar 
2.Roxy, M. K. et al. Drying of Indian subcontinent by rapid Indian Ocean warming and a weakening land-sea thermal gradient. Nat. Commun. 6, 7423 (2015).ADS 
PubMed 

Google Scholar 
3.Ashwal, L. D., Wiedenbeck, M. & Torsvik, T. H. Archaean zircons in Miocene oceanic hotspot rocks establish ancient continental crust beneath Mauritius. Nat. Commun. 8, 14086 (2017).ADS 
PubMed 
PubMed Central 
CAS 

Google Scholar 
4.Agnarsson, I. & Kuntner, M. The Generation of a biodiversity hotspot: Biogeography and phylogeography of the Western Indian Ocean islands. In Current Topics in Phylogenetics and Phylogeography of Terrestrial and Aquatic Systems (ed. Anamthawat-Jónsson, K.) 33–82 (InTech, 2012).
Google Scholar 
5.Hall, R. Late Jurassic-Cenozoic reconstructions of the Indonesian region and the Indian Ocean. Tectonophysics 570–571, 1–41 (2012).ADS 

Google Scholar 
6.Metcalfe, I. Gondwana dispersion and Asian accretion: Tectonic and palaeogeographic evolution of eastern Tethys. J. Asian Earth Sci. 66, 1–33 (2013).ADS 

Google Scholar 
7.Aitchison, J. C., Ali, J. R. & Davis, A. M. When and where did India and Asia collide?. JGR https://doi.org/10.1029/2006JB004706 (2007).Article 

Google Scholar 
8.Chatterjee, S. & Scotese, C. R. The wandering Indian plate and its changing biogeography during the Late Cretaceous-Early Tertiary period. In New Aspects of Mesozoic Biodiversity (ed. Bandyopadhyay, S.) (Springer-Verlag, 2010).
Google Scholar 
9.Gourlan, A. T., Meynadier, L. & Allègre, C. J. Tectonically driven changes in the Indian Ocean circulation over the last 25 Ma: Neodymium isotope evidence. Earth Planet. Sci. Lett. 267, 353–364 (2008).ADS 
CAS 

Google Scholar 
10.Hall, R. Cenozoic geological and plate tectonic evolution of SE Asia and the SW Pacific: Computer-based reconstructions, model and animations. J. Asian Earth Sci. 20, 353–431 (2002).ADS 

Google Scholar 
11.Collier, J. S. et al. Age of Seychelles-India break-up. Earth Planet. Sci. Lett. 272, 264–277 (2008).ADS 
CAS 

Google Scholar 
12.Plummer, Ph. S. & Belle, E. R. Mesozoic tectono-stratigraphic evolution of the Seychelles microcontinent. Sediment. Geol. 96, 73–91 (1995).ADS 

Google Scholar 
13.Ashalatha, B., Subrahmanyam, C. & Singh, R. N. Origin and compensation of Chagos-Laccadive ridge, Indian ocean, from admittance analysis of gravity and bathymetry data. Earth Planet. Sci. Lett. 105, 47–54 (1991).ADS 

Google Scholar 
14.de Queiroz, A. The resurrection of oceanic dispersal in historical biogeography. Trends Ecol. Evol. 20, 68–73 (2005).PubMed 

Google Scholar 
15.Vences, M., Wollenberg, K. C., Vieites, D. R. & Lees, D. C. Madagascar as a model region of species diversification. Trends Ecol. Evol. 24, 456–465 (2009).PubMed 

Google Scholar 
16.Verma, O., Khosla, A., Goin, F. J. & Kaur, J. Historical biogeography of the late cretaceous vertebrates of India: Comparison of geophysical and paleontological data. New Mex. Mus. Nat. Hist. Sci. Bull. 71, 317–330 (2016).
Google Scholar 
17.Krause, D. W. Washed up in Madagascar. Nature 463, 613 (2010).ADS 
PubMed 
CAS 

Google Scholar 
18.Reeves, C. & De Wit, M. Making ends meet in Gondwana: Retracing the transforms of the Indian Ocean and reconnecting continental shear zones. Terra Nova 12, 272–280 (2000).ADS 

Google Scholar 
19.Pillon, Y. & Buerki, S. How old are island endemics?. Biol. J. Linn. Soc. 121, 469–474 (2017).
Google Scholar 
20.Thornton, I. W. B. et al. How important were stepping stones in the colonization of Krakatau?. Biol. J. Linn. Soc. 77, 275–317 (2002).
Google Scholar 
21.Crisp, M. D., Trewick, S. A. & Cook, L. G. Hypothesis testing in biogeography. Trends Ecol. Evol. 26, 66–72 (2011).PubMed 

Google Scholar 
22.Bouckaert, R. et al. BEAST 2: A software platform for Bayesian evolutionary analysis. PLOS Comput. Biol. 10, e1003537 (2014).PubMed 
PubMed Central 

Google Scholar 
23.Huelsenbeck, J. P., Larget, B. & Alfaro, M. E. Bayesian phylogenetic model selection using reversible jump Markov Chain Monte Carlo. Mol. Biol. Evol. 21, 1123–1133 (2004).PubMed 
CAS 

Google Scholar 
24.Bouckaert, R., Alvarado-Mora, M. V. & Pinho, J. R. R. Evolutionary rates and HBV: Issues of rate estimation with Bayesian molecular methods. Antivir. Ther. 18, 497–503 (2013).PubMed 

Google Scholar 
25.Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Meth. 9, 772–772 (2012).CAS 

Google Scholar 
26.Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).PubMed 
PubMed Central 
CAS 

Google Scholar 
27.Maddison, W. P. Gene trees in species trees. Syst. Biol. 46, 523–536 (1997).
Google Scholar 
28.Baele, G., Li, W. L. S., Drummond, A. J., Suchard, M. A. & Lemey, P. Accurate model selection of relaxed molecular clocks in Bayesian phylogenetics. Mol. Biol. Evol. 30, 239–243 (2012).PubMed 
PubMed Central 

Google Scholar 
29.Raftery, A. et al. Estimating the integrated likelihood via posterior simulation using the harmonic mean identity. Bayesian Stat. 8, 1–45 (2007).
Google Scholar 
30.Yang, Z. & Rannala, B. Bayesian estimation of species divergence times under a molecular clock using multiple fossil calibrations with soft bounds. Mol. Biol. Evol. 23, 212–226 (2006).CAS 

Google Scholar 
31.Katoh, K. & Standley, D. M. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol. Biol. Evol. 30, 772–780 (2013).PubMed 
PubMed Central 
CAS 

Google Scholar 
32.Erpenbeck, D. et al. Phylogenetic analyses under secondary structure-specific substitution models outperform traditional approaches: Case studies with diploblast LSU. J. Mol. Evol. 64, 543–557 (2007).ADS 
PubMed 
CAS 

Google Scholar 
33.Miller, M. A., Pfeiffer, W. & Schwartz, T. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. In Gateway Computing Environments Workshop (GCE), 2010 1–8 (2010).34.Yu, Y., Harris, A. J., Blair, C. & He, X. RASP (Reconstruct Ancestral State in Phylogenies): A tool for historical biogeography. Mol. Phylogenet. Evol. 87, 46–49 (2015).PubMed 

Google Scholar 
35.Matzke, N. J. Probabilistic historical biogeography: New models for founder-event speciation, imperfect detection, and fossils allow improved accuracy and model-testing. Front. Biogeogr. 5, 19694 (2013).
Google Scholar 
36.Ree, R. H. & Smith, S. A. Maximum likelihood inference of geographic range evolution by dispersal, local extinction, and cladogenesis. Syst. Biol. 57, 4–14 (2008).PubMed 

Google Scholar 
37.Landis, M. J., Matzke, N. J., Moore, B. R. & Huelsenbeck, J. P. Bayesian analysis of biogeography when the number of areas is large. Syst. Biol. 62, 789–804 (2013).PubMed 
PubMed Central 

Google Scholar 
38.Warren, B. H., Strasberg, D., Bruggemann, J. H., Prys-Jones, R. P. & Thébaud, C. Why does the biota of the Madagascar region have such a strong Asiatic flavour?. Cladistics 26, 526–538 (2010).
Google Scholar 
39.Huber, B. T., Hodell, D. A. & Hamilton, C. P. Middle-Late Cretaceous climate of the southern high latitudes: Stable isotopic evidence for minimal equator-to-pole thermal gradients. GSA Bull. 107, 1164–1191 (1995).
Google Scholar 
40.Yoder, A. D. & Nowak, M. D. Has vicariance or dispersal been the predominant biogeographic force in Madagascar? Only time will tell. Annu. Rev. Ecol. Evol. Syst. 37, 405–431 (2006).
Google Scholar 
41.Crottini, A. et al. Vertebrate time-tree elucidates the biogeographic pattern of a major biotic change around the K-T boundary in Madagascar. PNAS 109, 5358–5363 (2012).ADS 
PubMed 
PubMed Central 
CAS 

Google Scholar 
42.Ali, J. R. & Krause, D. W. Late Cretaceous bioconnections between Indo-Madagascar and Antarctica: Refutation of the Gunnerus Ridge causeway hypothesis. J. Biogeogr. 38, 1855–1872 (2011).
Google Scholar 
43.Kocsis, Á. T. & Scotese, C. R. Mapping paleocoastlines and continental flooding during the Phanerozoic. Earth-Sci. Rev. 213, 103463 (2021).
Google Scholar 
44.Hay, W. W. Cretaceous oceans and ocean modeling. In Cretaceous Oceanic Red Beds: Stratigraphy, Composition, Origins and Paleoceanographic and Paleoclimatic Significance (eds Hu, X. et al.) 244–271 (Sepm Society for Sedimentary, 2009).
Google Scholar 
45.Sereno, P. C., Wilson, J. A. & Conrad, J. L. New dinosaurs link southern landmasses in the Mid-Cretaceous. Proc. R. Soc. Lond. B 271, 1325–1330 (2004).
Google Scholar 
46.Morley, R. J. Assembly and division of the South and South-East Asian flora in relation to tectonics and climate change. J. Trop. Ecol. 34, 209–234 (2018).
Google Scholar 
47.Speijer, R. P. & Morsi, A.-M.M. Ostracode turnover and sea-level changes associated with the Paleocene-Eocene thermal maximum. Geology 30, 23–26 (2002).ADS 

Google Scholar 
48.McInerney, F. A. & Wing, S. L. The paleocene-eocene thermal maximum: A perturbation of carbon cycle, climate, and biosphere with implications for the future. Annu. Rev. Earth Planet. Sci. 39, 489–516 (2011).ADS 
CAS 

Google Scholar 
49.Henehan, M. J. et al. Revisiting the middle eocene climatic optimum “Carbon Cycle Conundrum” with new estimates of atmospheric pCO2 from boron isotopes. Palaeogeogr. Palaeoclimatol. 35, e2019PA003713 (2020).
Google Scholar 
50.Legendre, S. Les communautés de mammifères du Paléogène (Eocène supérieur et Oligocène) d’Europe occidentale: Structures, milieux et évolution. Münchner Geowiss. Abh. 16, 1–110 (1989).
Google Scholar 
51.Hartenberger, J.-L. Palaeontology: An Asian Grande Coupure. Nature 394, 321 (1998).ADS 
CAS 

Google Scholar 
52.Zachos, J., Pagani, M., Sloan, L., Thomas, E. & Billups, K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).ADS 
PubMed 
CAS 

Google Scholar 
53.Lohman, D. J. et al. Biogeography of the Indo-Australian Archipelago. Annu. Rev. Ecol. Evol. Syst. 42, 205–226 (2011).
Google Scholar 
54.Fernández, D. A., Palazzesi, L., González Estebenet, M. S., Tellería, M. C. & Barreda, V. D. Impact of mid Eocene greenhouse warming on America’s southernmost floras. Commun. Biol. 4, 1–9 (2021).
Google Scholar 
55.Ivany, L. C., Patterson, W. P. & Lohmann, K. C. Cooler winters as a possible cause of mass extinctions at the Eocene/Oligocene boundary. Nature 407, 887–890 (2000).ADS 
PubMed 
CAS 

Google Scholar 
56.Masters, J. C. et al. Biogeographic mechanisms involved in the colonization of Madagascar by African vertebrates: Rifting, rafting and runways. J. Biogeogr. 48, 492–510 (2021).
Google Scholar 
57.Ali, J. R. & Huber, M. Mammalian biodiversity on Madagascar controlled by ocean currents. Nature 463, 653–656 (2010).ADS 
PubMed 
CAS 

Google Scholar 
58.Ohba, M., Samonds, K. E., LaFleur, M., Ali, J. R. & Godfrey, L. R. Madagascar’s climate at the K/P boundary and its impact on the island’s biotic suite. Palaeogeogr. Palaeoclimatol. Palaeoecol. 441, 688–695 (2016).
Google Scholar 
59.Godfrey, L. R. et al. Mid-Cenozoic climate change, extinction, and faunal turnover in Madagascar, and their bearing on the evolution of lemurs. BMC Evol. Biol. 20, 97 (2020).PubMed 
PubMed Central 

Google Scholar 
60.Behrensmeyer, A. K. et al. Terrestrial Ecosystems Through Time: Evolutionary Paleoecology of Terrestrial Plants and Animals (University of Chicago Press, 1992).
Google Scholar 
61.Ali, J. R. & Aitchison, J. C. Gondwana to Asia: Plate tectonics, paleogeography and the biological connectivity of the Indian sub-continent from the Middle Jurassic through latest Eocene (166–35 Ma). Earth-Sci. Rev. 88, 145–166 (2008).ADS 

Google Scholar 
62.Klaus, S., Morley, R. J., Plath, M., Zhang, Y.-P. & Li, J.-T. Biotic interchange between the Indian subcontinent and mainland Asia through time. Nat. Commun. 7, 12132 (2016).ADS 
PubMed 
PubMed Central 
CAS 

Google Scholar 
63.Le Houedec, S., Meynadier, L., Cogné, J.-P., Allègre, C. J. & Gourlan, A. T. Oceanwide imprint of large tectonic and oceanic events on seawater Nd isotope composition in the Indian Ocean from 90 to 40 Ma. Geochem. Geophys. 13, 6. https://doi.org/10.1029/2011GC003963 (2012).Article 
CAS 

Google Scholar 
64.Datta-Roy, A. & Karanth, K. P. The Out-of-India hypothesis: What do molecules suggest?. J. Biosci. 34, 687–697 (2009).PubMed 

Google Scholar 
65.Kayaalp, P., Stevens, M. I. & Schwarz, M. P. ‘Back to Africa’: Increased taxon sampling confirms a problematic Australia-to-Africa bee dispersal event in the Eocene. Syst. Entomol. 42, 724–733 (2017).
Google Scholar 
66.Gillespie, R. G. et al. Long-distance dispersal: A framework for hypothesis testing. Trends Ecol. Evol. 27, 47–56 (2012).PubMed 

Google Scholar  More

Much remains to be learned across disciplines about the neopelagic community and ecosystem. That coastal species can survive for years in the open ocean environment has changed our prior understanding of the availability of trophic resources and of a conducive physiochemical environment to support coastal species in open ocean environments, which were previously considered inhospitable for long-term survival of coastal biota.Colonization and persistenceAt present, we have limited understanding of the ecology of neopelagic communities. Basic questions remain unanswered, such as what is the extent of the biodiversity of coastal species persisting at sea and how often do coastal species co-occur with neustonic species on plastic rafts? Raft characteristics are known to affect neopelagic community structure, with species diversity increasing with plastic raft surface area9,10, but research is needed to investigate how raft characteristics shape the ecological interactions between coastal and pelagic species. Perhaps most fundamentally, we need to know to what extent neopelagic communities self-sustain or require continued input of rafts, propagules, and gene flow from coastlines. For these communities to self-sustain, coastal species traits and life histories, the physical environment, and trophic resources must align for survival, successful reproduction, and population persistence. Understanding what trophic resources coastal species utilize in the open ocean as well as the ecological roles that they play in neopelagic communities and oceanic ecosystems is crucial to understanding the impact of permanent communities of coastal species on the open ocean.BiogeographyThe motion of floating plastic rafts is integral to future research on dynamics of coastal biota at sea since the physical oceanic environment shapes neopelagic communities. Origin might constrain neopelagic community development and composition. For example, a plastic buoy that comes loose from an offshore aquaculture facility, which is heavily fouled with coastal species upon departure, might undergo very different community succession dynamics than a plastic water bottle that falls overboard mid-ocean and is newly colonized by both neustonic and coastal species. How these objects are transported on ocean currents through space and time and the abiotic conditions encountered will further affect the neopelagic community associated with them.In addition to transport, aggregation of floating plastic rafts in the open ocean, and specifically in gyres where plastics can remain for years, might have important implications for recruitment and gene flow of coastal species. Differences in physical oceanic features and sources of plastics among ocean regions might further contribute to a complex biogeography of neopelagic communities. Many factors could influence the biogeography of these novel communities, including the scale of plastic input and their residence times, spatial and temporal patterns of productivity, temperature, and other environmental variables. An important early step is to determine whether neopelagic communities like those found in the North Pacific form in other oceans, and if so, to what extent these communities differ among ocean basins.Biological invasionsUnderstanding the ecology and biogeography of the neopelagic communities on floating plastics will provide essential insights about the role of plastics as vectors of non-native species. The persistence of coastal species on plastic debris might increase the potential for successful transoceanic dispersal of coastal species to new continents by increasing the duration and distance of dispersal than would be possible otherwise. Additionally, colonization of plastic debris at sea by coastal species suggests that the continued expansion of the plastisphere creates a novel source pool of non-native species on the high seas. Thus, the increase of plastic inputs to the global ocean, when combined with discovery of the neopelagic community, points to an underestimation of floating plastics as vectors of transoceanic invasive species dispersal and introductions. More

Nitrite oxidation rates in the ETNPWe sampled six stations in the ETNP OMZ with DO concentrations 1 µM at 100 m (Fig. 2A). Chlorophyll concentrations were also high in the upper water column (up to 5 mg m−3 at 20 m), with an SCM spanning 70–125 m (Fig. 2B). Nitrite oxidation displayed a local maximum at the base of the EZ at Station 1 (20–30 m), and then increased to higher levels ( >100 nmol L−1 day−1; Fig. 2C). This increase at 100 and 125 m corresponded with the overlap between the bottom of the SCM and the top of the SNM. Nitrite oxidation rates then reached higher values at 150 m within the SNM at Station 1. Stations 2 and 3 displayed similar nitrite oxidation rate profiles to each other, including elevated rates in the SCM (Fig. 2G, K). Nitrite oxidation rates were similar in magnitude, and peak values at the base of the EZ and in the OMZ were also similar (69–96 nmol L−1 day−1). Depth patterns tracked oceanographic differences across the three AMZ stations, as the depth of all features increased moving offshore from Stations 1 to 2 to 3. For example, the SCM extended from 105 to 155 m at Station 2, while nitrite concentrations began to increase below 100 m; nitrite oxidation rates were elevated at 140 m and declined slightly with increasing depth (Fig. 2E–G). At Station 3, the SCM (120–180 m) and SNM ( >140 m) depths were deeper, and nitrite oxidation rates increased from 100 to 200 m (Fig. 2I–K).Fig. 2: Biogeochemical depth profiles.Profiles of A, E, I dissolved oxygen (solid lines) and nitrite (data points connected by dashed lines), B, F, J chlorophyll a, C, G, K nitrite oxidation rates, and D, H, L oxygen consumption rates (OCR; data presented as mean values of five independent replicates ±1 SD) show consistent variation across A–D Station 1, E–H Station 2, and I–L Station 3 (denoted by different colors). Black horizontal lines denote the depth of the oxygen minimum zone (OMZ), and shaded areas show the secondary chlorophyll maximum (SCM) at each station. Rates measured below the SCM should be considered potential rates (see main text). Maximum chlorophyll values at Station 1 plot off-axis.Full size imageIn contrast to these three AMZ stations (Stations 1–3), rate profiles at Stations 4–6 showed peaks at the base of the EZ followed by decreases with depth and lacked a pronounced rate increase within the OMZ (Supplementary Fig. 1). Parallel measurements of ammonia oxidation rates also showed this type of pattern at all stations (Supplementary Fig. 1). Subsurface maxima in ammonia oxidation tracked variations in the EZ across all six stations, but rates were not elevated in OMZ/AMZ waters—again contrasting with nitrite oxidation rate profiles at the AMZ stations. These data accord with earlier work in OMZs showing contrasting ammonia and nitrite oxidation rate profiles, and particularly high rates of nitrite oxidation in OMZ waters6,7,8,29,30,31.Initial DO concentrations for these measurements closely matched in situ values above the SCM (where DO concentrations are higher), and starting DO ranged from 260–1500 nM for measurements in and below the SCM. These DO concentrations are generally lower than those used for previous nitrite oxidation rate measurements in OMZs6,9, but similar to work examining the oxygen affinity of nitrite oxidation22 and overall oxygen consumption16,19. Elevated nitrite oxidation in the limited number of samples (n = 5) collected below the SCM ( >125 m at Station 1, >155 m at Station 2, and >180 m at Station 3)—where little to no DO is typically available—should be considered potential rates and could have a number of possible explanations discussed below. Within the SCM, our data support the idea that nitrite oxidation contributes to ‘cryptic’ oxygen cycling15—i.e., that DO produced via oxygenic photosynthesis is rapidly consumed.Oxygen consumption via nitrite oxidationWe determined the contribution of nitrite oxidation to overall oxygen consumption via parallel measurements of OCRs using in situ optical sensor spots—which are noninvasive, provide nearly identical results as other low-level measurement approaches32, are the only effective means of achieving substantial replication, and for which sensitivity increases as DO decreases32,33. Decreases in DO were measured in both nitrite and ammonia oxidation rate sample bottles, as well as in three additional replicates, to leverage statistical power for increased sensitivity to low-level DO consumption (see “Methods”). Water column OCR profiles at all stations showed exponential declines with depth and decreasing DO concentrations (Fig. 2D, H, L and Supplementary Fig. 1). Rates were highest in the upper water column and declined sharply within the upper portion of the OMZ above the SCM. The majority of measurements within the SCM—where DO may be produced via photosynthesis—were 100 s of nmol L−1 day−1, with an overall range of 160–1380 nmol L−1 day−1. Below the SCM, DO would be available more rarely (e.g., ref. 16), and OCR measurements represent potential rates should oxygen be supplied; OCR ranged from 120 to 390 nmol L−1 day−1. OCR also tracked variations in DO across stations, with progressively steeper declines in OCR with depth from Station 6 to Station 1.These OCR results are similar to the limited previous measurements that have been conducted in OMZ regions, with some key differences. In particular, they are consistent with previous measurements of rapid DO consumption in the SCM, with OCR rates ranging from 482 to 1520 nmol-O2 L−1 day−1 in the ETSP, and from 55 to 418 nmol-O2  L−1 day−1 in the ETNP15. Earlier OCR measurements conducted in the ETNP near Stations 1 and 3 (across a wide range of DO values) likewise ranged from 420 to 828 nmol L−1 day−1 in the SCM near Station 1, and from 101 to 269 nmol L−1 day−1 in the SCM near Station 3 (ref. 16). Above the SCM, previous OCR measurements in the ETNP spanned 2260 to 662 nmol L−1 day−1 from the EZ to the edge of the OMZ; these values are lower than our measurements at 44 and 67 m depth at Station 2, but in line with our remaining measurements above the SCM. OCR reached 1610 nmol L−1 day−1 in the SCM in Namibian shelf waters and 200–400 nmol L−1 day−1 in the SCM off Peru18. Kalvelage et al.18 furthermore observed sharp decreases with depth in the ETSP, with rates declining from >1000 nmol L−1 day−1 above the SCM.This pattern of declining OCR with increasing depth and decreasing DO was also evident in our dataset and contrasted with that of nitrite oxidation rates, which were notably elevated in the SCM at the AMZ stations (Fig. 2). We directly compared nitrite oxidation rates with OCR, assuming that each mole of nitrite is oxidized using ½ mole of O2 (ref. 5). We found that nitrite oxidation systematically increased as a proportion of overall OCR at lower DO levels (Fig. 3A, B). Nitrite oxidation was responsible for up to 69% of OCR at Station 1, although most values were closer to 10–40% at Stations 2 and 3 (Fig. 3A, B). In contrast, ammonia oxidation contributed 100 s of nM represent potential rates. For OMZ edge samples, OCR values in the µM range were higher than observed in profiles—most likely due to the effects of bubbling19, which could physically break down the organic matter present in higher concentrations at these depths (Table 1). Throughout all experiments, rate magnitudes in the 100 s of nM DO concentration range (11–820 nmol L−1 day−1) were similar to profile measurements (Fig. 2), as well as to previous measurements in OMZs15,16,18,19 (see above).DO concentrations were also continuously monitored in a subset of experimental bottles, and DO consumption was consistently linear (see “Methods”). The few exceptions occurred in several experiments conducted at DO concentrations More

1.Sibomana, M. S., Workneh, T. S. & Audain, K. A review of postharvest handling and losses in the fresh tomato supply chain: A focus on Sub-Saharan Africa. Food Secur. 8, 389–404 (2016).
Google Scholar 
2.Ochilo, W. N. et al. Characteristics and production constraints of smallholder tomato production in Kenya. Sci. Afr. 2, e00014 (2019).
Google Scholar 
3.Pratt, C. F., Constantine, K. L. & Murphy, S. T. Economic impacts of invasive alien species on African smallholder livelihoods. Glob. Food Sec. 14, 31–37 (2017).
Google Scholar 
4.Aigbedion-Atalor, P. O. et al. The South America tomato leafminer, Tuta absoluta (Lepidoptera: Gelechiidae), spreads its wings in Eastern Africa: distribution and socioeconomic impacts. J. Econ. Entomol. 112, 2797–2807 (2019).PubMed 

Google Scholar 
5.Brévault, T., Sylla, S., Diatte, M., Bernadas, G. & Diarra, K. Tuta absoluta Meyrick (Lepidoptera: Gelechiidae): A new threat to tomato production in sub-Saharan Africa. African Entomol. 22, 441–444 (2014).
Google Scholar 
6.Guedes, R. N. C. & Picanço, M. C. The tomato borer Tuta absoluta in South America: Pest status, management and insecticide resistance. EPPO Bull. 42, 211–216 (2012).
Google Scholar 
7.Desneux, N. et al. Biological invasion of European tomato crops by Tuta absoluta: ecology, geographic expansion and prospects for biological control. J. Pest Sci. 83, 197–215 (2010).
Google Scholar 
8.Abbes, K., Harbi, A. & Chermiti, B. The tomato leafminer Tuta absoluta (Meyrick) in Tunisia: Current status and management strategies. EPPO Bull. 42, 226–233 (2012).
Google Scholar 
9.Biondi, A., Guedes, R. N. C., Wan, F.-H. & Desneux, N. Ecology, worldwide spread, and management of the invasive South American tomato pinworm, Tuta absoluta: past, present, and future. Annu. Rev. Entomol. 63, 239–258 (2018).PubMed 
CAS 

Google Scholar 
10.Niassy, S., Ekesi, S., Migiro, L. & Otieno, W. Sustainable management of invasive pests in Africa. (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-41083-4.11.Mansour, R. et al. Occurrence, biology, natural enemies and management of Tuta absoluta in Africa. Entomol. Gen. 38, 83–112 (2018).
Google Scholar 
12.Lietti, M. M. M., Botto, E. & Alzogaray, R. A. Insecticide resistance in Argentine populations of Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae). Neotrop. Entomol. 34, 113–119 (2005).
Google Scholar 
13.Guedes, R. N. C. C. et al. Insecticide resistance in the tomato pinworm Tuta absoluta: patterns, spread, mechanisms, management and outlook. J. Pest Sci. 92, 1329–1342 (2019).
Google Scholar 
14.APRD. Arthropod Pesticide Resistance Database, Michigan State University. https://www.pesticideresistance.org/display.php?pa.15.Lichtenberg, E. & Zimmerman, R. Adverse health experiences, environmental attitudes, and pesticide usage behavior of farm operators. Risk Anal. 19, 283–294 (1999).PubMed 
CAS 

Google Scholar 
16.Soares, M. A. et al. Botanical insecticide and natural enemies: a potential combination for pest management against Tuta absoluta. J. Pest Sci. 92, 1433–1443 (2019).
Google Scholar 
17.Aigbedion-Atalor, P. O. et al. Host stage preference and performance of Dolichogenidea gelechiidivoris (Hymenoptera: Braconidae), a candidate for classical biological control of Tuta absoluta in Africa. Biol. Control 144, 1–8 (2020).
Google Scholar 
18.Akutse, K. S., Subramanian, S., Khamis, F. M., Ekesi, S. & Mohamed, S. A. Entomopathogenic fungus isolates for adult Tuta absoluta (Lepidoptera: Gelechiidae) management and their compatibility with Tuta pheromone. J. Appl. Entomol. 1–11 (2020). https://doi.org/10.1111/jen.12812.19.Agbessenou, A. et al. Endophytic fungi protect tomato and nightshade plants against Tuta absoluta (Lepidoptera: Gelechiidae) through a hidden friendship and cryptic battle. Sci. Rep. 10, 22195 (2020).ADS 
PubMed 
PubMed Central 
CAS 

Google Scholar 
20.Lacey, L. A. et al. Insect pathogens as biological control agents: Back to the future. J. Invertebr. Pathol. 132, 1–41 (2015).PubMed 
CAS 

Google Scholar 
21.Chandler, D. et al. The development, regulation and use of biopesticides for integrated pest management. Philos. Trans. R. Soc. B Biol. Sci. 366, 1987–1998 (2011).22.Shang, Y., Feng, P. & Wang, C. Fungi that infect insects: Altering host behavior and beyond. PLoS Pathog. 11, 1–6 (2015).
Google Scholar 
23.Bayissa, W. et al. Selection of fungal isolates for virulence against three aphid pest species of crucifers and okra. J. Pest Sci. 90, 355–368 (2017).
Google Scholar 
24.Jackson, M. A., Dunlap, C. A. & Jaronski, S. T. Ecological considerations in producing and formulating fungal entomopathogens for use in insect biocontrol. Biocontrol 55, 129–145 (2010).
Google Scholar 
25.Fang, W., Azimzadeh, P. & St. Leger, R. J. Strain improvement of fungal insecticides for controlling insect pests and vector-borne diseases. Curr. Opin. Microbiol. 15, 232–238 (2012).26.Tumuhaise, V. et al. Temperature-dependent growth and virulence, and mass production potential of two candidate isolates of Metarhizium anisopliae (Metschnikoff) Sorokin for managing Maruca vitrata Fabricius (Lepidoptera: Crambidae) on cowpea. African Entomol. 26, 73–83 (2018).
Google Scholar 
27.Onsongo, S. K., Gichimu, B. M., Akutse, K. S., Dubois, T. & Mohamed, S. A. Performance of three isolates of Metarhizium anisopliae and their virulence against Zeugodacus cucurbitae under different temperature regimes, with global extrapolation of their efficiency. Insects 10, 1–13 (2019).
Google Scholar 
28.Dimbi, S., Maniania, N. K., Lux, S. A. & Mueke, J. M. Effect of constant temperatures on germination, radial growth and virulence of Metarhizium anisopliae to three species of African tephritid fruit flies. Biocontrol 49, 83–94 (2004).
Google Scholar 
29.Ekesi, S., Maniania, N. K. & Lux, S. A. Effect of soil temperature and moisture on survival and infectivity of Metarhizium anisopliae to four tephritid fruit fly puparia. J. Invertebr. Pathol. 83, 157–167 (2003).PubMed 
CAS 

Google Scholar 
30.Jaronski, S. T. Ecological factors in the inundative use of fungal entomopathogens. Biocontrol 55, 159–185 (2010).
Google Scholar 
31.Klass, J. I., Blanford, S. & Thomas, M. B. Development of a model for evaluating the effects of environmental temperature and thermal behaviour on biological control of locusts and grasshoppers using pathogens. Agric. For. Entomol. 9, 189–199 (2007).
Google Scholar 
32.Klass, J. I., Blanford, S. & Thomas, M. B. Use of a geographic information system to explore spatial variation in pathogen virulence and the implications for biological control of locusts and grasshoppers. Agric. For. Entomol. 9, 201–208 (2007).
Google Scholar 
33.Allen, C. & Mehler, D. M. A. Open science challenges, benefits and tips in early career and beyond. Plos Biol. 17, 1–14 (2019).
Google Scholar 
34.McCammon, S. A. & Rath, A. C. Separation of Metarhizium anisopliae strains by temperature dependent germination rates. Mycol. Res. 98, 1253–1257 (1994).
Google Scholar 
35.Ekesi, S., Maniania, N. K. & Ampong-Nyarko, K. Effect of temperature on germination, radial growth and virulence of Metarhizium anisopliae and Beauveria bassiana on Megalurothrips sjostedti. Biocontrol Sci. Technol. 9, 177–185 (1999).
Google Scholar 
36.De Croos, J. N. A. & Bidochka, M. J. Effects of low temperature on growth parameters in the entomopathogenic fungus Metarhizium anisopliae. Can. J. Microbiol. 45, 1055–1061 (1999).
Google Scholar 
37.Dahlberg, K. R. & Etten, J. L. V. Physiology and biochemistry of fungal sporulation. Annu. Rev. Phytopathol. 20, 281–301 (1982).CAS 

Google Scholar 
38.Hywel-Jones, N. L. & Gillespie, A. T. Effect of temperature on spore germination in Metarhizium anisopliae and Beauveria bassiana. Mycol. Res. 94, 389–392 (1990).
Google Scholar 
39.Acheampong, M. A., Coombes, C. A., Moore, S. D. & Hill, M. P. Temperature tolerance and humidity requirements of select entomopathogenic fungal isolates for future use in citrus IPM programmes. J. Invertebr. Pathol. 174, 107436 (2020).40.Allen, P. J. Metabolic aspects of spores germination in fungi. Annu. Rev. Phytopathol. 3, 313–342 (1965).CAS 

Google Scholar 
41.de Campos, M. R. et al. Thermal biology of Tuta absoluta: demographic parameters and facultative diapause. J. Pest Sci. (2004). (2020) doi:https://doi.org/10.1007/s10340-020-01286-8.42.Vidal, C., Fargues, J. & Lacey, L. A. Intraspecific variability of Paecilomyces fumosoroseus: Effect of temperature on vegetative growth. J. Invertebr. Pathol. 70, 18–26 (1997).
Google Scholar 
43.Smits, N., Brière, J.-F. & Fargues, J. Comparison of non-linear temperature-dependent development rate models applied to in vitro growth of entomopathogenic fungi. Mycol. Res. 107, 1476–1484 (2003).PubMed 

Google Scholar 
44.Cabanillas, H. E. & Jones, W. A. Effects of temperature and culture media on vegetative growth of an entomopathogenic fungus Isaria sp. (Hypocreales: Clavicipitaceae) naturally affecting the whitefly, Bemisia tabaci in Texas. Mycopathologia 167, 263–271 (2009).45.Guimapi, R. A. et al. Decision support system for fitting and mapping nonlinear functions with application to insect pest management in the biological control context. Algorithms 13, 1–21 (2020).
Google Scholar 
46.Smith, J. D. et al. Host range of the invasive tomato pest Tuta absoluta Meyrick (Lepidoptera: Gelechiidae) on solanaceous crops and Weeds in Tanzania. Florida Entomol. 101, 573–579 (2018).
Google Scholar 
47.Tumuhaise, V., Khamis, F. M., Agona, A., Sseruwu, G. & Mohamed, S. A. First record of Tuta absoluta (Lepidoptera: Gelechiidae) in Uganda. Int. J. Trop. Insect Sci. 36, 135–139 (2016).
Google Scholar 
48.Kassa, A., Brownbridge, M., Parker, B. L. & Skinner, M. Whey for mass production of Beauveria bassiana and Metarhizium anisopliae. Mycol. Res. 112, 583–591 (2008).PubMed 

Google Scholar 
49.Jenkins, N. E., Heviefo, G., Langewald, J., Cherry, A. J. & Lomer, C. J. Development of mass production technology for aerial conidia for use as mycopesticides. Biocontrol News Inf. 19, 21–32 (1998).
Google Scholar 
50.Barra, P., Barros, G., Etcheverry, M. & Nesci, A. Mass production studies in solid substrates with the entomopathogenic fungus, Purpureocillium lilacinum. Int. J. Adv. Agric. Res. 6, 78–84 (2018).
Google Scholar 
51.Jackson, M. A. Optimizing nutritional conditions for the liquid culture production of effective fungal biological control agents. J. Ind. Microbiol. Biotechnol. 19, 180–187 (1997).CAS 

Google Scholar 
52.Goettel, M. S. & Inglis, D. G. Fungi: Hyphomycetes. Manual of Techniques in Insect Pathology https://doi.org/10.1016/B978-012432555-5/50013-0 (1997).Article 

Google Scholar 
53.Fargues, J., Maniania, N., Delmas, J. & Smits, N. Influence de la température sur la croissance in vitro d’hyphomycètes entomopathogènes. Agronomie 12, 557–564 (1992).
Google Scholar 
54.Santana, P. A., Kumar, L., Da Silva, R. S. & Picanço, M. C. Global geographic distribution of Tuta absoluta as affected by climate change. J. Pest Sci. 92, 1373–1385 (2018).
Google Scholar 
55.Migiro, L. N., Maniania, N. K., Chabi-Olaye, A. & Vandenberg, J. Pathogenicity of entomopathogenic fungi Metarhizium anisopliae and Beauveria bassiana (Hypocreales: Clavicipitaceae) isolates to the adult pea leafminer (Diptera: Agromyzidae) and prospects of an autoinoculation device for infection in the field. Environ. Entomol. 39, 468–475 (2010).PubMed 
CAS 

Google Scholar 
56.Campbell, A., Frazer, B. D., Gilbert, N., Gutierrez, A. P. & Mackauer, M. Temperature requirements of some aphids and their parasites. J. Appl. Ecol. 11, 431–438 (1974).
Google Scholar 
57.Brière, J.-F., Pracros, P., Le Roux, A.-Y. & Pierre, J.-S. A novel rate model of temperature-dependent development for arthropods. Environ. Entomol. 28, 22–29 (1999).
Google Scholar 
58.Archontoulis, S. V. & Miguez, F. E. Nonlinear regression models and applications in agricultural research. Agron. J. 107, 786–798 (2015).
Google Scholar 
59.Logan, J. A., Wollkind, D. J., Hoyt, S. C. & Tanigoshi, L. K. An analytic model for description of temperature dependent rate phenomena in arthropods. Environ. Entomol. 5, 1133–1140 (1976).
Google Scholar 
60.Steiniger, S. & Hunter, A. J. S. Free and open source GIS software for building a spatial data infrastructure. in Geospatial Free and Open Source Software in the 21st Century 247–261 (2012).61.Abbott, W. S. A method of computing the effectiveness of an insecticide. J. Econ. Entomol. 18, 265–267 (1925).CAS 

Google Scholar 
62.R Core Team. R: A language and environment for statistical computing R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/. (2019). More

Model speciesThe American lobster Homarus americanus, is a crustacean found in temperate regions across the Northwest Atlantic Ocean. It is a highly valuable fishery species, caught off the coast of Eastern Canada and Northeast USA. For the past decade, they have been the most valuable single-species fishery in all of Canada and the US30,48. In Canada, catch was estimated at 104,000 tonnes and 14% of all of Canada’s total marine fisheries catch in 2019. However, their landed value was almost $1.6 billion and 44% of the Canada’s total marine fisheries landed value, and over 49% of the landed value from Canada’s Atlantic coast fisheries30,49. Canada’s Atlantic marine fisheries provide employment to over 40,000 people in primary harvesting and processing sectors, and supports many rural populations30.Dynamic bioclimate envelope modelWe used a dynamic bioclimate envelope model (DBEM)31,50 to assess spatial and temporal changes in the abundance and fisheries catch under different scenarios of climate change and fishing pressure. The model infers the environmental preference of the modelled species51, and simulates future changes in biomass and maximum catch potential. Notably, the DBEM integrates growth models52, ecophysiological models53, advection–diffusion models54, and surplus production population dynamics models55 to determine ocean change effects on species distribution, abundance, and catch. Since H. americanus is primarily a benthic invertebrate, we used values at sea floor for environmental variables in our model. However, they also have a pelagic larval phase and we used surface environmental variables to model this life stage.Initial distribution and abundanceThe DBEM uses an initial species distribution determined using a rule-based algorithm56,57,58. This algorithm determines species’ distribution range base on a series of geographic constraints, including latitudinal range, depth range, occurring ocean basins, and published or expert provided ‘bounding box’. It assumes that the relative abundance of the species distributes along gradients within these geographic limits with the centroid of the range having the highest relative abundance (Palomares et al. 2016). Species distributions are mapped on a global grid with a resolution of 0.5° longitude by 0.5° latitude cells with values representing relative abundance. Historical reconstructed catch data (http://www.seaaroundus.org)59 was used to estimate the global abundance and distributed accordingly with relative abundance values60. These catch data are based on various government and non-government reports, primary and grey literature, and also mapped on a 0.5° longitude by 0.5° latitude grid.The initial species distribution is then overlaid on climatologies of historical environmental conditions (e.g. temperature, salinity, oxygen, pH) simulated from outputs of Earth system models (e.g. Bopp et al. 2013; Dunne et al. 2013). The DBEM assumes that species distribution is in equilibrium with the average historical environmental condition (1971–2000 average) and abundance in cells are assumed to be at carrying capacity.Modelling individual growthGrowth is modelled using a derived von Bertalanffy growth model to incorporate how environmental stressors affects body size of lobsters31,32,61. We model the following important life history parameters as a function of relative changes in temperature, oxygen content [O2], and pH [H+]. Growth rate (dB/dt) is dependent on weight-specific anabolic and catabolic rates:$$frac{dB}{{dt}} = H_{i,t} W^{d} – k_{i,t} W$$
(1)
where H and k represent the coefficients for oxygen supply (anabolism) and oxygen demand for maintenance metabolism (catabolism), respectively, for cell i at time t. Body weight is scaled to anabolism with the exponent d  40 ppt), mixoeuhaline ( > 29 ppt), polyhaline ( > 18 ppt), mesophaline ( > 5 ppt), oligohaline ( > 0 ppt)—and SAssoc is the association of the species with each salinity class; and Icei is the sea ice % area coverage in a cell and IceP is the ice-dependency of the species. For H. americanus, they are not specifically associated with any habitat and thus only restricted by depth parameters. However, they are limited to mixoeuhaline and polyhaline salinities62.The TPP was estimated using the initial predicted relative abundance (described above) overlaid with the inputs of earth system models of initial environmental conditions. The relative weight for each temperature class z of the temperature preference profile was calculated as (TPP_{z} = R_{z} /sum R_{z}), where Rz is the relative abundance in each temperature class.A fuzzy logic model was used to model the movement between neighbouring cells based on differences in habitat suitability50. Emigration into a cell is favoured if habitat suitability is higher than surrounding cells, and immigration out of a cell is favoured if habitat suitability is lower than surrounding cells.We estimated larval production as 30% of spawning population biomass for each cell i, while larval mortality was 0.85 day−1 and settlement rate was 0.15 day−1—these values were chosen based on the sensitivity testing of these parameters50.Larval dispersal is modelled using an advection–diffusion54 and a larval duration model based on temperature66, such that abundance Ai,t in each cell is numerically solved for using the equation:$$frac{{partial A_{i,t} }}{partial t} = frac{partial }{partial x}left( {D_{i,t} frac{{partial A_{i,t} }}{partial x}} right) + frac{partial }{partial y}left( {D_{i,t} frac{{partial A_{i,t} }}{partial y}} right) – frac{partial }{partial y}left( {u cdot A_{i,t} } right) – frac{partial }{partial y}left( {v cdot A_{i,t} } right) – lambda cdot A_{i,t}$$
(16)
while adult dispersal is similarly modelled,$$frac{{partial A_{i,t} }}{partial t} = frac{partial }{partial x}left( {D_{i,t} frac{{partial A_{i,t} }}{partial x}} right) + frac{partial }{partial y}left( {D_{i,t} frac{{partial A_{i,t} }}{partial y}} right)$$
(17)
Advection was modelled for larval dispersal using parameters u and v for horizontal (east–west) and vertical (north–south) directions for surface current velocity (m2 s−1), respectively, between neighbouring cells x and y in the east–west and north–south direction, respectively. Instantaneous rate of larval mortality, ML, and settlement, SL was integrated into Eq. (16), where (lambda = 1 – e^{{ – left( {M_{L} + S_{L} } right)}}). The coefficient Di,t is the diffusion parameter:$$D_{i,t} = frac{{D_{i,0} cdot m}}{{1 + e^{{(tau cdot P_{i,t} cdot rho_{i,t} )}} }}$$
(18)
and$$rho_{i,t} = 1 – frac{{emptyset_{i,t} }}{{left( {C_{i,t} /overline{W}_{i,t} } right)}}$$
(19)
where Di,0 is the initial diffusion coefficient and a function of the spatial grid size (GR): (D_{i,0} = left( {1.1 times 10^{4} } right) cdot GR cdot 1.33). Parameters m and (tau)—both set at 2 in the model—determine the curvature of the functional relationship between D, P, and (rho)50. Parameter (rho_{i,t}) represents density-dependent factors and a function of population density (number of individuals), (emptyset_{i,t}), carrying capacity ((C_{i,t})), and mean body weight ((overline{W}_{i,t})) in each cell i.Models of ocean acidification effectsThe DBEM operates to model larval dispersal using advection–diffusion models. Survival is calculated at each time step (biweekly) based on a static annual survival rate. We recently tested a simple linear relationship between survival rate and pH, represented by percent changes in the survival rate given a change in pH32. We used parameters derived from previous experimental studies, where they observed a ~ 15% increase in mortality in larval and juvenile stages37 from a doubling of hydrogen ion concentration.We explore the OA effects by modelling variations in life stage-specific sensitivities to OA. Larvae, juveniles, and adults are modelled based on size classes, and the weight at maturity determines the size at which juveniles transition into adults65. Therefore, impacts of OA on survival can be modelled for various size classes. We model the effects of OA on the three major life history stages—larval, juvenile, and adult—and use a correlative approach to link changes in ocean acidity with changes in survival.The length transition matrix (Eqs. (8) and (9)) is split up into 40 length size classes, divided evenly from 0 to (l_{infty ,i,t}). We assume larvae transition from the pelagic phase to the growth transition matrix, and enter as the ‘larval’ stage for only the first size class. Next, juvenile size classes comprise all size classes below the length at maturity, lmat, as determined in Eq. (12), and lobster in any size classes greater are considered adults. While our models do not incorporate lobster-specific life cycle traits (i.e. transitioning between larval stages then to juvenile stages), we use more general models that can be broadly applied to many species.Modelling effects on survivalOA effects can be modelled as relative changes in survival rate for all life stages in Table 2. In other words, percent changes in acidity (i.e. hydrogen ion concentration) from baseline initial conditions results in a percent change in baseline survival rate. We use a model structure similar to that of previous work we have done32:$$Surv_{t} = Surv_{init} *left[ {1 + left( {p*left( {frac{{left[ {H^{ + } } right]_{t} }}{{left[ {H^{ + } } right]_{init} }} – 1} right)^{w} } right)} right]$$
(20)
Table 2 Scenario settings explored with model projections.Full size tableSurv is the survival rate per year and used here as an example but can be applied to other life histories affected by OA (e.g. growth, reproduction). Survival rate in year t is derived from the initial (init) survival rate and the relative change in [H+] between year t and initial [H+] conditions. Note that in our previous model, p represents the point value of the percent change effect size with a doubling of [H+]. This model utilizes single point effect size estimates that have no underlying assumed relationship between acidity and survival. In our model, we used an exponent value, w, equal to 1, which assumes a linear relationship32.Fishing pressureFishing mortality was assumed to be at maximum sustainable yield (MSY), which is the theoretical maximum biomass that can be sustainably removed from the population indefinitely. MSY is calculated using a Gordon Schaefer population growth model67:$$MSY = frac{{B_{infty } cdot r}}{4}$$where B∞ is the population carrying capacity and r is the intrinsic population growth rate. We use this measure of MSY as a proxy for the maximum catch potential (MCP) into the future, thus we assume that fisheries management are optimized and operate at MSY.Furthermore, the fishing mortality rate, Fi,t—i.e. the annual proportion at which biomass is taken from the current population biomass—in each cell i at time t at MSY can be calculated as:$$F_{MSY,i,t} = frac{r}{2}$$The fishing mortality rate was adjusted to explore scenarios of reduced fishing pressure and interactions with climate change and OA on the population dynamics of lobster. Any reductions in fishing pressure began in 2010 to represent how changes in fishing implemented now could change the state of lobster populations with the added stressors of climate change.Fishing size limitsFishing size limits were set to represent management scenarios and to observe their effects on lobster populations and size distribution of the population. We set four scenarios of minimum body size restrictions of lobster catch: no limit, canner small ( > 0.5 lb, 220 g), canner large ( > 0.75 lb, 320), and market ( > 1 lb, 430 g). Canner lobsters are smaller lobsters that are often sold at a cheaper price. They range from 0.5 to 1 lb, and are largely caught in Northumberland Strait where size limits are currently set lower due to warmer waters and smaller size at maturity44. For these scenarios of fishing size limits, we continue to assume catch is at maximum sustainable yield. Therefore, the same catch biomass (calculated using fishing mortality rate, F) will be the same for the various size limit restrictions, and more biomass will be taken from upper size classes where size limits are implemented. The no size limit results in fishing mortality to all size classes (including undersized lobsters).Climate change scenariosWe use outputs from three Earth system models for projections of various future climate change outcomes: NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL-ESM), Institute Pierre Simon Laplace Climate Modelling Centre (IPSL-ESM), and Max Planck Institute for Meteorology (MPI-ESM)68. These models are included in the Coupled Model Intercomparisons Projection Phase 5 (CMIP5). We used two Representative Concentration Pathways (RCPs)69 which are greenhouse gas (GHG) concentration trajectories derived to reflect possible combinations of various socioeconomic assumptions, RCP 2.6 and RCP 8.5. The number associated with RCPs represent the radiative forcing values by the year 2100 based on greenhouse gas concentrations. RCP 2.6 corresponds with a low climate change scenario and assumes immediate mitigation of GHG emissions where annual emissions peak by mid-decade (year 2025) but is reduced considerably. This scenario is more in line with the 2015 Paris Agreement to limit warming to + 1.5 °C relative to other emissions scenarios applied in most CMIP5 Earth system models. Conversely, RCP 8.5 corresponds to a high climate change scenario and our current trajectory where we continue to use fossil fuels, have little to no change to switch to renewable energy sources, and GHG emissions continue to increase with no implementation of any mitigation action. We chose these three Earth system models as they provide sea surface and bottom layers and the full range of environmental variables required by the DBEM (i.e. sea temperature, dissolved oxygen, primary production, pH, current advection, salinity, sea ice extent) for both RCP scenarios2.All statistical analyses and figures were generated using the programming software R v4.0.370. More

Our global database was derived from studies measuring soil seed bank diversity and density of natural plant communities across all continents, albeit with a strong data availability bias towards North America, Europe, eastern Asia and Oceania as compared to elsewhere (Fig. 1). The database contains 15,698 records for soil seed banks worldwide, including 6,480 for diversity (represented here by species richness) and 9,218 for density (number of seeds per soil surface area). The database represents more than a century of research with the oldest publication dating back to 191815. This most exhaustive and comprehensive set of research data on soil seed bank to date allowed us to identify the determinants and patterns of soil seed bank at the global scale.Fig. 1: Locations of the soil seed bank studies included in our database.a Diversity; b Density.Full size imageTo make data among studies comparable, we standardized them using a three-step process. First, we identified soil seed banks that showed seasonal patterns in both diversity and density, all of which peaked slightly in winter (Supplementary Fig. 1a, b). Thus, we standardized all data (from non-(sub-)tropical regions) for other seasons to winter. Second, sampling area for soil seed bank diversity varied among studies, with 0.01 m2 being the most commonly reported (Supplementary Fig. 2), to which we standardized all data using a species-area curve (Supplementary Table 1). Third, sampling depth also varied among studies, with 0–5 cm being the most frequently reported soil depth (Supplementary Fig. 3). Therefore, 0–5 cm was chosen as the soil depth for standardization of data in various soil depths. Such standardization is needed to find the relationships of seed bank data between different soil depths. We used the upper and lower limits of soil depths (e.g., for 0–5 cm, the upper limit was 0 cm and the lower 5 cm). The log-scale regressions showed that both soil seed bank diversity and density decreased significantly with lowering upper boundaries of soil depths but increased with lower ones (Supplementary Table 2), and thus we standardized all data to 0–5 cm depth using these relationships. To account for possible variation among biomes, the second and third standardization procedures were conducted for each biome separately. The analyses during standardization confirmed the need to standardize empirical findings when comparing seed bank patterns across studies, as previously stressed in a study on grassland soil seed banks10. Our standardization procedures made all data comparable in terms of season, sampling area and soil depth.Non-parametric Kruskal–Wallis tests showed that soil seed banks differed significantly among ecosystem types. Mangroves, tundra and tropical & subtropical dry broadleaf forests had a lower diversity of soil seed banks, whereas Mediterranean forests, woodlands & scrub, tropical & subtropical moist broadleaf forests and tropical & subtropical coniferous forests had a higher diversity (Supplementary Fig. 4a). For density, mangroves and flooded grasslands & savanna had the lowest value, while temperate broadleaf & mixed forests and temperate conifer forests had the highest value (Supplementary Fig. 4b).Prior to spatial analyses, we computed semivariograms to determine whether spatial autocorrelation could affect our models. We found that there was no obvious spatial autocorrelation in the data of soil seed bank diversity or density (Supplementary Fig. 5), indicating no spatial dependence in our data. We then used the random-forest algorithm (see Methods for details) to determine the importance (as increase in node purity) of the influence of 31 variables related to climate, soil, human disturbance and spatial coordinates (Supplementary Table 3) on diversity and density of soil seed banks. These variables previously were reported to affect plant performance at the global scale16,17,18, and thus they could affect soil seed banks via their effects on seed production. Moreover, we expected that potentially these variables could affect seed longevity in the soil. Full models using all 31 predictors showed that climate and soil were important in predicting soil seed banks (Fig. 2a and Supplementary Fig. 6). Moreover, spatial coordinates (absolute latitude) were the most important predictor for diversity, i.e., diversity of soil seed banks exhibit clear spatial patterns at the global scale. Net primary productivity (NPP) and soil characteristics were important in predicting the density of soil seed banks (Fig. 2b and Supplementary Fig. 6).Fig. 2: Variable importance (increase in node purity) of random forests run with all 31 predictors.a Soil seed bank diversity; b Density. abs.latit, absolute latitude; AMT, annual mean temperature; AP, annual precipitation; ATR, annual temperature range; AVWAC, available water capacity (%); BULK, bulk density; CEC, cation exchange capacity; CLAYC, clay (mass %); diversity, plant diversity; HFP, human footprint; Isoth, isothermality; npp, plant productivity (net primary production); ORGNC, organic carbon content; PCQ, precipitation of coldest quarter; PDM, precipitation of driest month; PDQ, precipitation of driest quarter; pH, pH measured in water; Pseason, precipitation seasonality (coefficient of variation); PWeQ, precipitation of wettest quarter; PWM, precipitation of wettest month; PWQ, precipitation of warmest quarter; SANDC, sand (mass %); SILTC, silt (mass %); TCM, min temperature of coldest month; TCQ, mean temperature of coldest quarter; TDQ, mean temperature of driest quarter; TDR, mean diurnal range (mean of monthly (max temp–min temp)); Tseason, temperature seasonality (standard deviation *100); TWeQ, mean temperature of wettest quarter; TWM, max temperature of warmest month; TWQ, mean temperature of warmest quarter.Full size imageWe then built final random-forest models using the most important predictors of seed banks selected from full models: nine variables for diversity and five for density (Supplementary Fig. 7). Final models explained more of the total variance than did full models (Supplementary Table 4), and they were robust to K-fold cross-validation (Supplementary Fig. 8), indicating that a small number of variables predicted soil seed bank diversity and density. Absolute latitude (abs.latit) was the most important predictor for diversity, which varied between 0–55° and then decreased beyond this range (Fig. 3a). Five climatic variables were important for diversity. Diversity peaked at intermediate annual temperature ranges (ATR), while it was the lowest at intermediate mean temperature of driest quarter of the year (TDQ), precipitation of the coldest quarter (PCQ) and precipitation of the driest quarter (PDQ). Diversity increased with increasing annual precipitation (AP). In addition, three soil variables were important for diversity. Diversity showed a humped relationship with soil pH, with pH 6–7 having the highest diversity. Diversity increased with soil cation exchange capacity (CEC) and soil silt content (SILT). These results indicate that diversity exhibits strong spatial patterns at the global scale. However, our spatial patterns differ from those found for a specific ecosystem worldwide (e.g., grasslands), where there were only weak latitudinal gradients in seed bank diversity10. In addition, climate emerged as an important predictor for seed bank diversity, which is consistent with the report that climate acts as environmental filters affecting soil seed bank of grasslands around the world13. Our results agree with a continental study in Europe, where ATR was more important than mean annual temperature for determining seed bank richness and warmer temperatures were associated with lower seed bank richness11. Possible mechanisms by which temperature affects soil seed banks are that it (1) influences seed bank inputs via its effects on seed production; (2) cues dormancy-breaking and germination1, thus determining germinable seed output from seed banks; and (3) affects seed metabolic activity and soil fungal activity19, thereby determining seed viability and persistence in the soil. Finally, our findings of a significant effect of soil pH are supported by some regional and local studies. For instance, seed bank composition is significantly associated with soil pH at high elevations on the Tibetan Plateau20. A negative effect of low pH also has been reported in a large-scale study of acidic and calcareous grasslands in England21. Two possible mechanisms for the effects of soil pH are that (1) low pH may cause loss of seed viability due to the toxicity from aluminum or other metals that become more readily available in soils with low pH22; and (2) high pH may accelerate decomposition and promote growth of pathogens that negatively affect seed persistence23. In our study, the two mechanism may operate synchronously, thereby resulting in the highest diversity of soil seed banks at intermediate pH at the global scale. Further, our results show that soil CEC and SILT affect seed bank diversity, which agrees with a study on the Tibetan Plateau20. The physical and chemical properties of soils can affect seed bank directly by affecting seed germination and aging via regulating soil water-holding capacity24, or indirectly by affecting seed viability via controlling the activity of soil pathogens21,22,25.Fig. 3: Partial feature contributions (the marginal effect of a variable on response) of the most important variables for soil seed banks.a diversity; b density. Variable importance (inc. node) is the decrease in the residual sum of squares that results from splitting regression trees using the variable. The percentage increase in mean squared error (% inc. MSE) is the increase in model error as a result of randomly shuffling the order of values in the vector. abs.latit, absolute latitude; AP, annual precipitation; ATR, annual temperature range; BULK, bulk density; CEC, cation exchange capacity; npp, plant productivity (net primary production); PCQ, precipitation of coldest quarter; PDM, precipitation of driest month; PDQ, precipitation of driest quarter; pH, pH measured in water; SILTC, silt (mass %); TDQ, mean temperature of driest quarter; TWM, max temperature of warmest month.Full size imageFor soil seed bank density, soil bulk density (BULK) was the most important predictor; density increased below 750 g/cm3 BULK but remained stable when BULK was higher than 800 g/cm3 (Fig. 3b). Density peaked when temperature of the warmest month (TWM) was 34 °C. Density showed similar variation with NPP, precipitation of the driest quarter of the year (PDQ) and of the driest month (PDM), i.e., it peaked at intermediate values of these variables. Precipitation influences the success of sexual reproduction of plants and the size of the seed bank through seed input26, and it also affects soil pathogenic fungi, which cause seed mortality27. Therefore, precipitation has a strong effect on seed bank density, as reported for 27 alpine meadows on the Tibetan Plateau28. Our results further illustrate that PDQ and PDM are the key factors determining seed bank density worldwide, suggesting that moisture fluctuation in soils triggered by precipitation of the driest time of the year can affect seed bank density. If soil moisture fluctuations are high, seed germination will be primed by increasing moisture24.At the global scale, we mapped soil seed bank diversity and density using the final random-forest models. Mapping soil seed bank values onto global maps revealed considerable geospatial variation, the pattern of which varied between diversity and density (Fig. 4). For diversity, western North America, central South America, central Africa, central Europe, southern and eastern Asia and eastern Oceania had high values. In contrast, eastern and central North America, northern Africa and central Asia had low values (Fig. 4a). For density, northern North America, northern Europe and northern Asia had higher values than elsewhere (Fig. 4a). Our results are consistent with the reports that larger seed banks are more common in cooler temperate climates19,29. The latitudinal pattern of higher density in colder regions in the Northern Hemisphere may be driven by lower seed mortality in colder soils6, resulting in stable seed bank densities of long-lived seeds that counteract low seed production in some years at cold northern latitudes, as shown in a study of temperate forests along a 1900 km latitudinal gradient in northwestern Europe29. The latitudinal pattern highlights that particularly species rich low-latitude biomes such as tropical rainforests generally have very low seed bank densities, while their seed bank diversity does not exceed that in higher latitudes biomes. However, our global assessment should be interpreted with caution since some studies in azonal vegetation or in rare habitats in our database did not fully reflect soil seed banks in that region, and thus these data shortcomings may have induced bias in our global predictions. Moreover, data gaps in our database are also likely to have had an effect on the global predictions, i.e., fewer data available from some continents (e.g., northern Asia and Africa) could lead to less confidence for prediction in these regions. For example, Russia has very few soil seed bank data, which may have led to an inaccurate prediction for this country. Nevertheless, based on our global patterns of soil seed bank diversity and density, the latitudinal pattern strongly suggests that the biodiversity of (sub-)tropical forests is particularly vulnerable to large-scale climatic or land-use disturbances. However, in-depth investigation is needed to quantify the extent to which temporal integration of seed bank effects for long-lived trees and seed masting events may buffer the effects of low seed bank diversity and density at any given time of sampling. In contrast, the higher-latitude plant diversity, while currently low compared to that in tropical rainforest, may rely on high soil seed bank densities to boost its resilience to large-scale climate- or land-use induced disturbances. Further, our analyses suggest that the least vulnerable ecosystems in terms of hidden diversity should be those that combine high seed-bank diversity with high density; and therefore the relationships between the two variables across the global map certainly would be an interesting topic worthy of further study.Fig. 4: Extrapolated global maps of soil seed banks.a diversity in terms of number of species per 0.01 m2; b density as number of seeds per m2. In b, values are log10-transformed to facilitate viewing. The spatial resolution of grid cells is 5 arcmin-by-5 arcmin.Full size imageOur global assessment reveals that both diversity and density exhibit clear spatial patterns of soil seed banks but differ in their environmental determinants. These findings alone do not necessarily mean that this biodiversity reservoir has strong buffering capacity under climate change, because both climate and soil conditions influence seed bank diversity and density. Based on a large number and long history of studies globally, we provide quantitative evidence of how environmental conditions shape soil seed bank distributions and spatially explicit maps of this biodiversity reservoir in plant communities worldwide. Our quantification of environmental determinants and global mapping can be readily applied to dynamic global vegetation and plant diversity models to enable a more complete and accurate prediction of the impact of ongoing environmental changes on plant diversity (both above- and belowground) at the global scale. The next research challenge will be to plot current (visible) aboveground plant diversity (ideally using the available data in the studies themselves) against soil seed bank diversity under global change scenarios in order to pinpoint even more accurately which plant communities, ecosystems and biomes (and their turn-over) are most at risk of losing their diversity due to global changes. More

1.Csiki-Sava, Z., Buffetaut, E., Ősi, A., Pereda-Suberbiola, X. & Brusatte, S. L. Island life in the Cretaceous – faunal composition, biogeography, evolution, and extinction of land-living vertebrates on the Late Cretaceous European archipelago. ZooKeys. https://doi.org/10.3897/zookeys.469.8439 (2015).2.Benton, M. J. et al. Dinosaurs and the island rule: The dwarfed dinosaurs from Haţeg Island. Palaeogeogr. Palaeoclimatol. Palaeoecol. 293, 438–454 (2010).
Google Scholar 
3.Scotese, C. R. An Atlas of phanerozoic Paleogeographic maps: The seas come in and the seas go out. Annu. Rev. Earth Planet. Sci. 49, 679–728 (2021).CAS 

Google Scholar 
4.Randazzo, V. et al. The migration path of Gondwanian dinosaurs toward Adria: New insights from the Cretaceous of NW Sicily (Italy). Cretac. Res. 126, 104919 (2021).5.Dal Sasso, C., Pierangelini, G., Famiani, F., Cau, A. & Nicosia, U. First sauropod bones from Italy offer new insights on the radiation of Titanosauria between Africa and Europe. Cretac. Res. 64, 88–109 (2016).
Google Scholar 
6.Vlahović, I., Tišljar, J., Velić, I. & Matičec, D. Evolution of the Adriatic Carbonate Platform: Palaeogeography, main events and depositional dynamics. Palaeogeogr. Palaeoclimatol. Palaeoecol. 220, 333–360 (2005).
Google Scholar 
7.Dalla Vecchia, F. M. Tethyshadros insularis, a new hadrosauroid dinosaur (Ornithischia) from the Upper Cretaceous of Italy. J. Vertebr. Paleontol. 29, 1100–1116 (2009).
Google Scholar 
8.Tarlao, A., Tentor, M., Tunis, G. & Venturini, S. Evidence of a tectonic phase in the Lower Senonian of the Villaggio del Pescatore area. Gortania Atti Museo Friulano Storia Naturale 135–142 (1993).9.Tarlao, A., Tentor, M., Tunis, G., Venturini, S. Stop 4: Villaggio del Pescatore. Atti Museo Geologico Paleontologico Monfalcone 135–142 (1995).10.Alessandro Palci. Ricostruzione Paleoambientale del Sito Fossilifero Senoniano del Villaggio del Pescatore (Trieste). (Università degli Studi di Trieste, 2003).11.Arbulla, D., Caffa, M., Cotza, F., Cucchi, F., Flora, O., Masetti, D., Pittau, P., Pugliese, N., Stenni, B., Tarlao, A., Tunisè, G. & Zini, L. The Santonina-Campanian succession of the Villaggio del Pescatore (Trieste karst) yielding the hadrosaur: palaeoecology, stratigraphy, sedimentology and geochemistry. in Sixth European workshop on vertebrate palaeontology abstracts (2001).12.Arbulla, D., Cotza, F., Cucchi, F., Dalla Vecchia, F. M., De Giusto, A., Flora, O., Masetti, D., Palci, A., Pittau, P., Pugliese, N., Stenni, B., Tarlao, A., Tunis, G. & Zini, L. La successione Santoniano–Campaniana del Villaggio del Pescatore (Carso Triestino) nel quale sono stati rinvenuti i resti di dinosauro. in Guida alle escursioni/excursions guide, Società Paleontologica Italiana 20–27 (EUT Edizioni Università di Trieste, 2006).13.Dal Sasso, C. Dinosaurs of Italy. Comptes Rendus Palevol. 2, 45–66 (2003).
Google Scholar 
14.Dal Sasso, C. & Brillante, G. Dinosaurs of Italy. (Indiana University Press, 2005).15.Dalla Vecchia, F., Tunis, G., Venturini, S. & Tarlao, A. Dinosaur track sites in the upper Cenomanian (Late Cretaceous) of Istrian Peninsula (Croatia). Boll. Della Soc. Paleontol. Ital. 40, 25–54 (2001).16.Dalla Vecchia, F. M. Observations on the presence of plant-eating dinosaurs in an oceanic carbonate platform. Nat. Nascosta 27 (2003).17.Nicosia, U., Avanzini, M., Barbera, C., Conti, M. A., Dalla Vecchia, F. M. e altri 15 coautori in ordine alfabetico. I vertebrati continentali del Paleozoico e Mesozoico. in Paleontologia dei Vertebrati in Italia vol. Memorie Museo Civico di Storia Naturale Verona 41–66 (Bonfiglio L., 2005).18.Dalla Vecchia, F. M. The impact of dinosaur palaeoichnology in palaeoenvironmental and palaeogeographic reconstructions: The case of the Periadriatic carbonate platforms. Oryctos 8, 19 (2008).
Google Scholar 
19.Delfino, M., Martin, J. E. & Buffetaut, E. A new species of Acynodon (Crocodylia) from the upper cretaceous (Santonian–Campanian) of Villaggio del Pescatore, Italy. Palaeontology 51, 1091–1106 (2008).
Google Scholar 
20.Dalla Vecchia, F. M. The unusual tail of Tethyshadros insularis (Dinosauria, Hadrosauroidea) from the Adriatic Island of the European Archipelago. Rivista Italiana di Paleontologia e Stratigrafia 126, 46 (2020).
Google Scholar 
21.Benson, R. B. J., Hunt, G., Carrano, M. T. & Campione, N. Cope’s rule and the adaptive landscape of dinosaur body size evolution. Palaeontology 61, 13–48 (2018).
Google Scholar 
22.Dalla Vecchia, F. Telmatosaurus and the other hadrosaurids of the Cretaceous European Archipelago. An update. Nat. Nascosta 39, 1–18 (2009).
Google Scholar 
23.Soul, L. & Wright, D. Phylogenetic Comparative Methods: A User’s Guide for Paleontologists. (2020) https://doi.org/10.32942/osf.io/ytm5x.24.Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).
Google Scholar 
25.Chiocchini, M., Farinacci, A., Mancinelli, A., Molinari, V. & Potetti, M. Biostratigrafia a foraminiferi, dasicladali e calpionelle delle successioni carbonatiche mesozoiche dell’Appennino centrale (Italia). in Biostratigrafia dell’Italia centrale. Studi Geologici Camerti vol. Biostratigrafia dell’Italia centrale 129 (Mancinelli, A, 1994).26.Chiocchini, M., Pampaloni, M. L., Pichezzi, R. M. Microfacies and microfossils of the Mesozoic carbonate successions of Latium and Abruzzi (Central Italy), Vol. 269 (Memorie per Servire alla Descrizione della Carta Geologica D’Italia, ISPRA, Dipartimento Difesa del Suolo, Roma, 2012).27.Frijia, G., Parente, M., Di Lucia, M. & Mutti, M. Carbon and strontium isotope stratigraphy of the Upper Cretaceous (Cenomanian-Campanian) shallow-water carbonates of southern Italy: Chronostratigraphic calibration of larger foraminifera biostratigraphy. Cretac. Res. 53, 110–139 (2015).
Google Scholar 
28.Steuber, T., Korbar, T., Jelaska, V. & Gušić, I. Strontium-isotope stratigraphy of Upper Cretaceous platform carbonates of the island of Brač (Adriatic Sea, Croatia): Implications for global correlation of platform evolution and biostratigraphy. Cretac. Res. 26, 741–756 (2005).
Google Scholar 
29.Schlüter, M., Steuber, T. & Parente, M. Chronostratigraphy of Campanian-Maastrichtian platform carbonates and rudist associations of Salento (Apulia, Italy). Cretac. Res. 29, 100–114 (2008).
Google Scholar 
30.Consorti, L., Frijia, G. & Caus, E. Rotaloidean foraminifera from the Upper Cretaceous carbonates of Central and Southern Italy and their chronostratigraphic age. Cretac. Res. 70, 226–243 (2017).
Google Scholar 
31.Fourcade, E. Murciella cuvillieri n. gen. n. sp. nouveau foraminifère du Sénonien supérieur du sud-est de l’Espagne. Rev. Micropaléontol. 147–155 (1966).32.Fleury, J.-J. Rhapydioninidés du Campanien-Maastrichtien en région méditerranéenne : Les genres Murciella, Sigalveolina n. gen. et Cyclopseudedomia. Carnets Géologie Noteb. Geol. 18, 233–280 (2018).33.Dercourt et al. Atlas of Peri-Tethys Palaeogeographical Maps-digital-CCGM-CGMW. https://ccgm.org/en/atlases/189-atlas-of-peri-tethys-palaeogeographical-maps-.html (2000).34.Bosellini, A. Dinosaurs, “re-write” the geodynamics of the eastern Mediterranean and the paleogeography of the Apulia Platform. Earth-Sci. Rev. 59, 211–234 (2002).ADS 

Google Scholar 
35.Zarcone, G. et al. A possible bridge between Adria and Africa: New palaeobiogeographic and stratigraphic constraints on the Mesozoic palaeogeography of the Central Mediterranean area. Earth-Sci. Rev. 103, 154–162 (2010).ADS 

Google Scholar 
36.Ustaszewski, K. et al. Late Cretaceous intra-oceanic magmatism in the internal Dinarides (northern Bosnia and Herzegovina): Implications for the collision of the Adriatic and European plates. Lithos 108, 106–125 (2009).ADS 
CAS 

Google Scholar 
37.Dragičević, I. & Velić, I. The Northeastern Margin of the Adriatic Carbonate Platform. Geol. Croat. 55, 185–232 (2002).
Google Scholar 
38.Petti, F. et al. Cretaceous tetrapod tracks from Italy: A treasure trove of exceptional biodiversity. J. Mediterr. Earth Sci. 12, 167–191 (2020).
Google Scholar 
39.Blatnik, M. et al. Late Cretaceous and Paleogene Paleokarsts of the Northern Sector of the Adriatic Carbonate Platform. in Karstology in the classical Karst (eds. Knez, M., Otoničar, B., Petrič, M., Pipan, T. & Slabe, T.) 11–31 (Springer, 2020). https://doi.org/10.1007/978-3-030-26827-5_2.40.Boscarolli, D. & Dalla Vecchia, F. The Upper Hauterivian-Lower Barremian dinosaur site of Bale/valle (SW Istria, Croatia). (1999).41.Dalla Vecchia, F. M. Theropod footprints in the Cretaceous Adriatic-Dinaric Carbonate Platform (Italy and Croatia). Gaia 15, 355–367 (1998).
Google Scholar 
42.Dalla Vecchia, F. Cretaceous dinosaurs in the Adriatic-Dinaric Carbonate Platform (Italy and Croatia): Paleoenvironmental implications and paleogeographical hypotheses. Mem. Della Soc. Geol. Ital. 57, 89–100 (2002).
Google Scholar 
43.Dalla Vecchia, F., Vlahović, I., Posocco, L., Tarlao, A. & Tentor, M. Late Barremian and late Albian (Early Cretaceous) dinosaur tracksites in the main Brioni/Brijun island (SW Istria, Croatia). Nat. Nascosta 25, 1–36 (2002).44.Dalla Vecchia, F. M. et al. New dinosaur track sites in the Albian (Early Cretaceous) of the Istrian peninsula (Croatia) part I—Stratigraphy and sedimentology part II—paleontology. 69 (2000).45.Debeljak, I., Košir, A. & Otoničar, B. A preliminary note on dinosaurs and non-dinosaurian reptiles from the Upper Cretaceous carbonate platform succession at Kozina (SW Slovenia). Razpr. Slov. Akad. Znan. Umet. Razred Za Naravosl. Vede Diss. Acad. Sci. Artium Slov. Cl. IV Hist. Nat. 3–25 (1999).46.Debeljak, I., Košir, A., Buffetaut, E. & Otoničar, B. The Late Cretaceous dinosaurs and crocodiles of Kozina (SW Slovenia): A preliminary study. Mem. Della Soc. Geol. Ital. 57, 193–201 (2002).
Google Scholar 
47.Mezga, A. et al. A new dinosaur tracksite in the Cenomanian of Istria, Croatia. Riv. Ital. Paleontol. E Stratigr. 112, 435–445 (2006).
Google Scholar 
48.Mezga, A., Meyer, C. A., Tešović, B. C., Bajraktarević, Z. & Gušić, I. The first record of dinosaurs in the Dalmatian part (Croatia) of the Adriatic-Dinaric carbonate platform (ADCP). Cretac. Res. 27, 735–742 (2006).
Google Scholar 
49.Weishampel, D., Norman, D. & Grigorescu, D. Telmatosaurus transsylvanicus from the Late Cretaceous of Romania: the most basal hadrosaurid dinosaur. Palaeontology 36, 361–385 (1993).
Google Scholar 
50.Richard Owen. Report on British fossil reptiles, Part 2. in vol. 11 60–204 (1842).51.Seeley, H. G. On the classification of the fossil animals commonly named Dinosauria. Proc. R. Soc. Lond. 43, 165–171 (1887).
Google Scholar 
52.Marsh, O. C. Principal characters of American Jurassic dinosaurs, IV. Am. J. Sci. s3–21, 167–170 (1881).53.Sereno, P. C. The Origin and Evolution of Dinosaurs. Annu. Rev. Earth Planet. Sci. 25, 435–489 (1997).ADS 
CAS 

Google Scholar 
54.Cope, E. D. Synopsis of the extinct Batrachia, Reptilia, and Aves of North America. Trans. Am. Philos. Soc. 1–252 (1870).55.Madzia, D., Jagt, J. W. M. & Mulder, E. W. A. Osteology, phylogenetic affinities and taxonomic status of the enigmatic late Maastrichtian ornithopod taxon Orthomerus dolloi (Dinosauria, Ornithischia). Cretac. Res. 108, 104334 (2020).56.Norman, D. On the history, osteology, and systematic position of the Wealden (Hastings Group) dinosaur Hypselospinus fittoni (Iguanodontia: Styracosterna). Zool. J. Linn. Soc. 173, 92 (2014).57.Xing, H., Mallon, J. C. & Currie, M. L. Supplementary cranial description of the types of Edmontosaurus regalis (Ornithischia: Hadrosauridae), with comments on the phylogenetics and biogeography of Hadrosaurinae. PLOS ONE 12, e0175253 (2017).58.Sues, H.-D. & Averianov, A. A new basal hadrosauroid dinosaur from the Late Cretaceous of Uzbekistan and the early radiation of duck-billed dinosaurs. Proc. R. Soc. B Biol. Sci. 276, 2549–2555 (2009).
Google Scholar 
59.Shibata, M., Jintasakul, P., Azuma, Y. & You, H.-L. A New Basal Hadrosauroid Dinosaur from the Lower Cretaceous Khok Kruat Formation in Nakhon Ratchasima Province, Northeastern Thailand. PLOS ONE 10, e0145904 (2015).60.McDonald, A. T., Bird, J., Kirkland, J. I. & Dodson, P. Osteology of the Basal Hadrosauroid Eolambia caroljonesa (Dinosauria: Ornithopoda) from the Cedar Mountain Formation of Utah. PLoS ONE 7, e45712 (2012).61.Gates, T., Horner, J., Hanna, R. & Nelson, R. New Unadorned Hadrosaurine Hadrosaurid (Dinosauria, Ornithopoda) from the Campanian of North America. J. Vertebr. Paleontol. 31, 798–811 (2011).
Google Scholar 
62.Prieto-Marquez, A. New information on the cranium of Brachylophosaurus canadensis (Dinosauria, Hadrosauridae), with a revision of its phylogenetic position. J. Vertebr. Paleontol. 25, 144–156 (2005).
Google Scholar 
63.Gates, T. A. & Lamb, J. Redescription of Lophorhothon atopus (Ornithopoda: Dinosauria) from the Late Cretaceous of Alabama based on new material. Can. J. Earth Sci. https://doi.org/10.1139/cjes-2020-0173 (2021).Article 

Google Scholar 
64.Prieto-Márquez, A., Erickson, G. M. & Ebersole, J. A. Anatomy and osteohistology of the basal hadrosaurid dinosaur Eotrachodon from the uppermost Santonian (Cretaceous) of southern Appalachia. PeerJ 4, e1872 (2016).65.You, H.-L. & Li, D.-Q. A new basal hadrosauriform dinosaur (Ornithischia: Iguanodontia) from the Early Cretaceous of northwestern China. Can. J. Earth Sci. 46, 949–957 (2009).ADS 

Google Scholar 
66.Fowler, E. A. F. & Horner, J. R. A New Brachylophosaurin Hadrosaur (Dinosauria: Ornithischia) with an Intermediate Nasal Crest from the Campanian Judith River Formation of Northcentral Montana. PLOS ONE 10, e0141304 (2015).67.Gates, T. A., Hall, B. & Lamb, J. P. A redescription of Lophorhothon atopus (Ornithopoda: Dinosauria) from the Cretaceous of Alabama based on new material. Can. J. Earth Sci. 58, 918–935 (2021).
Google Scholar 
68.Gates, T. A., Evans, D. C. & Sertich, J. J. W. Description and rediagnosis of the crested hadrosaurid (Ornithopoda) dinosaur Parasaurolophus cyrtocristatus on the basis of new cranial remains. PeerJ 9, e10669 (2021).69.Griffin, C. T. et al. Assessing ontogenetic maturity in extinct saurian reptiles. Biol. Rev. 96, 470–525. https://doi.org/10.1111/brv.12666?af=R (2021).
Google Scholar 
70.Bailleul, A. M., Scannella, J. B., Horner, J. R. & Evans, D. C. Fusion patterns in the skulls of modern archosaurs reveal that sutures are ambiguous maturity indicators for the Dinosauria. PLOS ONE 11, e0147687 (2016).71.Francillon-Vieillot, H. et al. Microstructure and mineralization of vertebrate skeletal tissues. in Skeletal biomineralization: Patterns, processes and evolutionary trends 175–234 (American Geophysical Union (AGU), 1989). https://doi.org/10.1029/SC005p0175.72.Woodward Ballard, H., Horner, J. & Farlow, J. Osteohistological evidence for determinate growth in the American Alligator. J. Herpetol. 45, 339–342 (2011).73.Horner, J. R., Ricqlès, A. D. & Padian, K. Long bone histology of the hadrosaurid dinosaur Maiasaura peeblesorum: Growth dynamics and physiology based on an ontogenetic series of skeletal elements. J. Vertebr. Paleontol. 20, 115–129 (2000).
Google Scholar 
74.Erickson, G. M. Assessing dinosaur growth patterns: A microscopic revolution. Trends Ecol. Evol. 20, 677–684 (2005).PubMed 

Google Scholar 
75.Hone, D. W. E., Farke, A. A. & Wedel, M. J. Ontogeny and the fossil record: What, if anything, is an adult dinosaur?. Biol. Lett. 12, 20150947 (2016).PubMed 
PubMed Central 

Google Scholar 
76.Sharma, P. P., Clouse, R. M. & Wheeler, W. C. Hennig’s semaphoront concept and the use of ontogenetic stages in phylogenetic reconstruction. Cladistics 33, 93–108 (2017).PubMed 

Google Scholar 
77.Campione, N. E., Brink, K. S., Freedman, E. A., McGarrity, C. T. & Evans, D. C. ‘Glishades ericksoni’, an indeterminate juvenile hadrosaurid from the Two Medicine Formation of Montana: implications for hadrosauroid diversity in the latest Cretaceous (Campanian-Maastrichtian) of western North America. Palaeobiodivers. Palaeoenviron. 93, 65–75 (2013).
Google Scholar 
78.Kobayashi, Y., Takasaki, R., Kubota, K. & Fiorillo, A. R. A new basal hadrosaurid (Dinosauria: Ornithischia) from the latest Cretaceous Kita-ama Formation in Japan implies the origin of hadrosaurids. Sci. Rep. 11(1), 1–15. https://www.nature.com/articles/s41598-021-87719-5 (2021).79.A new brachylophosaurin (Dinosauria: Hadrosauridae) from the Upper Cretaceous Menefee Formation of New Mexico [PeerJ]. https://peerj.com/articles/11084/.80.Prieto-Marquez, A. & Carrera Farias, M. The late-surviving early diverging Ibero-Armorican ‘duck-billed’ dinosaur Fylax and the role of the Late Cretaceous European Archipelago in hadrosauroid biogeography. Acta Palaeontol. Pol. 66, 425–435 (2021).81.Prieto-Márquez, A., Dalla Vecchia, F. M., Gaete, R. & Galobart, À. Diversity, Relationships, and biogeography of the Lambeosaurine Dinosaurs from the European Archipelago, with Description of the New Aralosaurin Canardia garonnensis. PLoS ONE 8, e69835 (2013).82.Longrich, N. R., Suberbiola, X. P., Pyron, R. A. & Jalil, N.-E. The first duckbill dinosaur (Hadrosauridae: Lambeosaurinae) from Africa and the role of oceanic dispersal in dinosaur biogeography. Cretac. Res. 120, 104678 (2021).83.Brown, C. M., Evans, D. C., Campione, N. E., O’Brien, L. J. & Eberth, D. A. Evidence for taphonomic size bias in the Dinosaur Park Formation (Campanian, Alberta), a model Mesozoic terrestrial alluvial-paralic system. Palaeogeogr. Palaeoclimatol. Palaeoecol. 372, 108–122 (2013).
Google Scholar 
84.Mallon, J. C., Evans, D. C., Ryan, M. J. & Anderson, J. S. Feeding height stratification among the herbivorous dinosaurs from the Dinosaur Park Formation (upper Campanian) of Alberta, Canada. BMC Ecol. 13, 14 (2013).PubMed 
PubMed Central 

Google Scholar 
85.Mallon, J. C. Competition structured a Late Cretaceous megaherbivorous dinosaur assemblage. Sci. Rep. 9, 15447 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
86.Campione, N. E. & Evans, D. C. The accuracy and precision of body mass estimation in non-avian dinosaurs. Biol. Rev. 95, 1759–1797 (2020).PubMed 

Google Scholar 
87.Benítez-López, A. et al. The island rule explains consistent patterns of body size evolution in terrestrial vertebrates. Nat. Ecol. Evol. 5, 768–786 (2021).PubMed 

Google Scholar 
88.Fabbri M. et al. A shift in ontogenetic timing produced the unique sauropod skull. Evolution 75, 819–831 (2021).PubMed 

Google Scholar 
89.Chinsamy, A. & Raath, M. A. Preparation of fossil bone for histological examination. (1992).90.Fabbri, M., Wiemann, J., Manucci, F. & Briggs, D. E. G. Three-dimensional soft tissue preservation revealed in the skin of a non-avian dinosaur. Palaeontology 63, 185–193 (2020).
Google Scholar 
91.Takasaki R., et al. Re-examination of the cranial osteology of the Arctic Alaskan hadrosaurine with implications for its taxonomic status. https://doi.org/10.1371/journal.pone.0232410 (2020).92.Raven, T. J. & Maidment, S. C. R. The systematic position of the enigmatic thyreophoran dinosaur Paranthodon africanus, and the use of basal exemplifiers in phylogenetic analysis. PeerJ 6, e4529 (2018).93.Goloboff, P. A. Extended implied weighting. Cladistics 30, 260–272 (2014).PubMed 

Google Scholar 
94.Goloboff, P. A., Farris, J. S. & Nixon, K. C. TNT, a free program for phylogenetic analysis. Cladistics 24, 774–786 (2008).
Google Scholar 
95.Herne, M. C., Nair, J. P., Evans, A. R. & Tait, A. M. New small-bodied ornithopods (Dinosauria, Neornithischia) from the Early Cretaceous Wonthaggi Formation (Strzelecki Group) of the Australian-Antarctic rift system, with revision of Qantassaurus intrepidus Rich and Vickers-Rich, 1999. J. Paleontol. 93, 543–584 (2019).
Google Scholar 
96.Madzia, D. & Cau, A. Inferring ‘weak spots’ in phylogenetic trees: application to mosasauroid nomenclature. PeerJ 5, e3782 (2017).97.Madzia, D. & Cau, A. Estimating the evolutionary rates in mosasauroids and plesiosaurs: discussion of niche occupation in Late Cretaceous seas. PeerJ 8, e8941 (2020).98.Nicholl, C. S. C., Rio, J. P., Mannion, P. D. & Delfino, M. A re-examination of the anatomy and systematics of the tomistomine crocodylians from the Miocene of Italy and Malta. J. Syst. Palaeontol. 18, 1853–1889 (2020).
Google Scholar 
99.Rio, J. P., Mannion, P. D., Tschopp, E., Martin, J. E. & Delfino, M. Reappraisal of the morphology and phylogenetic relationships of the alligatoroid crocodylian Diplocynodon hantoniensis from the late Eocene of the United Kingdom. Zool. J. Linn. Soc. 188, 579–629 (2020).
Google Scholar 
100.Groh, S. S., Upchurch, P., Barrett, P. M. & Day, J. J. The phylogenetic relationships of neosuchian crocodiles and their implications for the convergent evolution of the longirostrine condition. Zool. J. Linn. Soc. 188, 473–506 (2020).
Google Scholar 
101.Bell, M. A. & Lloyd, G. T. Strap: An R package for plotting phylogenies against stratigraphy and assessing their stratigraphic congruence. Palaeontology 58, 379–389 (2015).
Google Scholar 
102.Hansen, T. F. Stabilizing selection and the comparative analysis of adaptation. Evolution 51, 1341–1351 (1997).PubMed 

Google Scholar 
103.Butler, M. A. & King, A. A. Phylogenetic comparative analysis: A modeling approach for adaptive evolution. Am. Nat. 164, 683–695 (2004).PubMed 

Google Scholar 
104.Beaulieu, J. M., Jhwueng, D.-C., Boettiger, C. & O’Meara, B. C. Modeling stabilizing selection: Expanding the Ornstein–Uhlenbeck model of adaptive evolution. Evolution 66, 2369–2383 (2012).PubMed 

Google Scholar 
105.Hunt, G. Measuring rates of phenotypic evolution and the inseparability of tempo and mode. Paleobiology 38, 351–373 (2012).
Google Scholar 
106.Grosheny, D. et al. The Cenomanian-Turonian Boundary Event (CTBE) in northern Lebanon as compared to regional data—Another set of evidences supporting a short-lived tectonic pulse coincidental with the event?. Palaeogeogr. Palaeoclimatol. Palaeoecol. 487, 447–461 (2017).
Google Scholar 
107.Prieto-Márquez, A. Global historical biogeography of hadrosaurid dinosaurs. Zool. J. Linn. Soc. 159, 503–525 (2010).
Google Scholar 
108.Nariaki Sugiura. Further analysts of the data by akaike’ s information criterion and the finite corrections: Further analysts of the data by Akaike’s: Communications in Statistics—Theory and Methods: Vol 7, No 1. https://doi.org/10.1080/03610927808827599 (1978).109.Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: A practical information-theoretic approach. (Springer, Berlin, 2002). https://doi.org/10.1007/b97636.110.Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
111.Gates, T. A., Organ, C. & Zanno, L. E. Bony cranial ornamentation linked to rapid evolution of gigantic theropod dinosaurs. Nat. Commun. 7, 12931 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
112.Revell, L. J. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol. Evol. 4, 754–759 (2013).
Google Scholar 
113.Pennell, M. W. et al. geiger v2.0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees. Bioinformatics 30, 2216–2218 (2014). More

Alcala N, Goldberg A, Ramakrishnan U, Rosenberg NA (2019) Coalescent theory of migration network motifs. Mol Biol Evol 36:2358–2374CAS 
PubMed 
PubMed Central 

Google Scholar 
Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664CAS 
PubMed 
PubMed Central 

Google Scholar 
Anjana S, Sammeta SP, Das R (2020) Developing ancestry informative marker panel for Nigeria-Cameroonian chimpanzees. J Genet 99:1–7
Google Scholar 
Armstrong E, Khan A, Taylor RW, Gouy A, Greenbaum G, Thiéry A et al. (2021) Recent evolutionary history of tigers highlights contrasting roles of genetic drift and selection. Mol Biol Evol 38:2366–2379CAS 
PubMed 
PubMed Central 

Google Scholar 
Ballou JD (1992) Genetic and demographic considerations in endangered species captive breeding and reintroduction programs. In: Wildlife 2001: populations, Springer: Dordrecht, pp. 262–275Balloux F, Lugon‐Moulin N (2002) The estimation of population differentiation with microsatellite markers. Mol Ecol 11:155–165PubMed 

Google Scholar 
Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120CAS 
PubMed 
PubMed Central 

Google Scholar 
Brandies P, Peel E, Hogg CJ, Belov K (2019) The value of reference genomes in the conservation of threatened species. Genes (Basel) 10:846CAS 

Google Scholar 
Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) Stacks: an analysis tool set for population genomics. Mol Ecol 22:3124–3140PubMed 
PubMed Central 

Google Scholar 
Chapman JR, Nakagawa S, Coltman DW, Slate J, Sheldon BC (2009) A quantitative review of heterozygosity–fitness correlations in animal populations. Mol Ecol 18:2746–2765CAS 
PubMed 

Google Scholar 
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al. (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158CAS 
PubMed 
PubMed Central 

Google Scholar 
Das R, Roy R, Venkatesh N (2019) Using ancestry informative markers (AIMs) to detect fine structure within gorilla populations. Front Genet 10:43CAS 
PubMed 
PubMed Central 

Google Scholar 
Das R, Upadhyai P (2018) An ancestry informative marker set which recapitulates the known fine structure of populations in South Asia. Genome Biol Evol 10:2408–2416PubMed 
PubMed Central 

Google Scholar 
Das R, Upadhyai P (2019) Application of the geographic population genetic structure (GPS) algorithm for biogeographical analyses of wild and captive gorillas. BMC Bioinforma 20:35
Google Scholar 
Dudash MR, Fenster CB (2000) Inbreeding and outbreeding depression in fragmented populations. Conserv Biol Ser 35–54Esposito U, Das R, Syed S, Pirooznia M, Elhaik E (2018) Ancient ancestry informative markers for identifying fine-scale ancient population genetic structure in Eurasians. Genes (Basel) 9:625
Google Scholar 
Feng S, Stiller J, Deng Y, Armstrong J, Fang Q, Reeve AH et al. (2020) Dense sampling of bird diversity increases power of comparative genomics. Nature 587:252–257CAS 
PubMed 
PubMed Central 

Google Scholar 
Frantz AC, Pourtois JT, Heuertz M, Schley L, Flamand MC, Krier A et al. (2006) Genetic structure and assignment tests demonstrate illegal translocation of red deer (Cervus elaphus) into a continuous population. Mol Ecol 15:3191–3203CAS 
PubMed 

Google Scholar 
Friar EA, Boose DL, LaDoux T, Roalson EH, Robichaux RH (2001) Population genetic structure in the endangered Mauna Loa silversword, Argyroxiphium kauense (Asteraceae), and its bearing on reintroduction. Mol Ecol 10:1657–1663CAS 
PubMed 

Google Scholar 
Fuentes‐Pardo AP, Ruzzante DE (2017) Whole‐genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations. Mol Ecol 26:5369–5406PubMed 

Google Scholar 
Goodrich J, Lynam A, Miquelle D, Wibisono H, Kawanishi K, Pattanavibool A et al. (2015) Panthera tigris. In: The IUCN Red List of Threatened Species 2015, p. 15955 50659951Ivy JA, Lacy RC (2010) Using molecular methods to improve the genetic management of captive breeding programs for threatened species. In: Molecular approaches in natural resource conservation and management, pp. 267–295Jangtarwan K, Koomgun T, Prasongmaneerut T, Thongchum R, Singchat W, Tawichasri P et al. (2019) Take one step backward to move forward: Assessment of genetic diversity and population structure of captive Asian woolly-necked storks (Ciconia episcopus). PLoS One 14:e0223726CAS 
PubMed 
PubMed Central 

Google Scholar 
Jiménez‐Mena B, Schad K, Hanna N, Lacy RC (2016) Pedigree analysis for the genetic management of group‐living species. Ecol Evol 6:3067–3078PubMed 
PubMed Central 

Google Scholar 
Kanthaswamy S, Johnson Z, Trask JS, Smith DG, Ramakrishnan R, Bahk J et al. (2014) Development and validation of a SNP‐based assay for inferring the genetic ancestry of rhesus macaques (Macaca mulatta). Am J Primatol 76:1105–1113CAS 
PubMed 
PubMed Central 

Google Scholar 
Khan A, Patel K, Bhattacharjee S, Sharma S, Chugani AN, Sivaraman K et al. (2020) Are shed hair genomes the most effective noninvasive resource for estimating relationships in the wild? Ecol Evol 10:4583–4594PubMed 
PubMed Central 

Google Scholar 
Khan A, Patel K, Shukla H, Viswanathan A, van der Valk T, Borthakur U, et al. (2021). Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers. bioRxivKhan A, Tyagi A (2021) Considerations for initiating a wildlife genomics research project in South and South-East Asia. J Indian Inst Sci 101:243–256. https://doi.org/10.1007/s41745-021-00243-3Article 

Google Scholar 
Kirk H, Freeland JR (2011) Applications and implications of neutral versus non-neutral markers in molecular ecology. Int J Mol Sci 12:3966–3988PubMed 
PubMed Central 

Google Scholar 
Kolipakam V, Singh S, Pant B, Qureshi Q, Jhala YV (2019) Genetic structure of tigers (Panthera tigris tigris) in India and its implications for conservation. Glob. Ecol Conserv 20:710
Google Scholar 
Kunde MN, Martins RF, Premier J, Fickel J, Förster DW (2020) Population and landscape genetic analysis of the Malayan sun bear Helarctos malayanus. Conserv Genet 21:123–135CAS 

Google Scholar 
Laikre L, Palm S, Ryman N (2005) Genetic population genetic structure of fishes: implications for coastal zone management. AMBIO A J Hum Environ 34:111–119
Google Scholar 
Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357CAS 
PubMed 
PubMed Central 

Google Scholar 
Liang Z, Bu L, Qin Y, Peng Y, Yang R, Zhao Y (2019) Selection of optimal ancestry informative markersfor classification and ancestry proportion estimation in pigs. Front genet 10:183PubMed 
PubMed Central 

Google Scholar 
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al. (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079PubMed 
PubMed Central 

Google Scholar 
Lott MJ, Wright BR, Kemp LF, Johnson RN, Hogg CJ (2020) Genetic Management of Captive and Reintroduced Bilby Populations. J Wildl Manag 84:20–32
Google Scholar 
Luo SJ, Johnson WE, Martenson J, Antunes A, Martelli P, Uphyrkina O et al. (2008) Subspecies genetic assignments of worldwide captive tigers increase conservation value of captive populations. Curr Biol 18:592–596CAS 
PubMed 

Google Scholar 
Miller W, Hayes VM, Ratan A, Petersen DC, Wittekindt NE, Miller J et al. (2011) Genetic diversity and population structure of the endangered marsupial Sarcophilus harrisii (Tasmanian devil) Proc Natl Acad Sci 108:12348–12353CAS 
PubMed 
PubMed Central 

Google Scholar 
Mondol S, Bruford MW, Ramakrishnan U (2013) Demographic loss, genetic structure and the conservation implications for Indian tigers. Proc R Soc B Biol Sci 280:20130496
Google Scholar 
Muñoz I, Henriques D, Johnston JS, Chávez-Galarza J, Kryger P, Pinto MA (2015) Reduced SNP Panels for Genetic Identification and Introgression Analysis in the Dark Honey Bee (Apis mellifera mellifera) PLOS One 10:e0124365PubMed 
PubMed Central 

Google Scholar 
Nassir R, Kosoy R, Tian C, White PA, Butler LM, Silva G et al. (2009) An ancestry informative marker set for determining continental origin: validation and extension using human genome diversity panels. BMC Genet 10:39PubMed 
PubMed Central 

Google Scholar 
Natesh M, Atla G, Nigam P, Jhala YV, Zachariah A, Borthakur U et al. (2017) Conservation priorities for endangered Indian tigers through a genomic lens. Sci Rep. 7:1–11
Google Scholar 
Natesh M, Taylor RW, Truelove NK, Hadly EA, Palumbi SR, Petrov DA et al. (2019) Empowering conservation practice with efficient and economical genotyping from poor quality samples. Methods Ecol evolution 10:853–859
Google Scholar 
Patterson N, Price AL, Reich D (2006) Population genetic structure and eigenanalysis. PLoS Genet 2:190
Google Scholar 
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909CAS 
PubMed 

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

Google Scholar 
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575CAS 
PubMed 
PubMed Central 

Google Scholar 
Putnam AS, Ivy JA (2014) Kinship-based management strategies for captive breeding programs when pedigrees are unknown or uncertain. J Hered 105:303–311PubMed 

Google Scholar 
Rosenberg NA, Li LM, Ward R, Pritchard JK (2003) Informativeness of genetic markers for inference of ancestry. Am J Hum Genet 73:1402–1422CAS 
PubMed 
PubMed Central 

Google Scholar 
Sagar V, Kaelin CB, Natesh M, Reddy PA, Mohapatra RK, Chhattani H et al. (2021) High frequency of an otherwise rare phenotype in a small and isolatedtiger population. Proc Natl Acad Sci p. 118Saunders CT, Wong WS, Swamy S, Becq J, Murray LJ, Cheetham RK (2012) Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics 28:1811–1817CAS 
PubMed 

Google Scholar 
Schlaepfer DR, Braschler B, Rusterholz HP, Baur B (2018) Genetic effects of anthropogenic habitat fragmentation on remnant animal and plant populations: a meta‐analysis. Ecosphere 9:2488
Google Scholar 
Shriver MD, Parra EJ, Dios S, Bonilla C, Norton H, Jovel C et al. (2003) Skin pigmentation, biogeographical ancestry and admixture mapping. Hum Genet 112:387–399PubMed 

Google Scholar 
Somenzi E, Ajmone-Marsan P, Barbato M (2020) Identification of ancestry informative marker (AIM) panels to assess hybridisation between feral and domestic sheep. Animals 10:582PubMed Central 

Google Scholar 
Supple MA, Shapiro B (2018) Conservation of biodiversity in the genomics era. Genome Biol 19:1–12
Google Scholar 
Svardal H, Jasinska AJ, Apetrei C, Coppola G, Huang Y, Schmitt CA et al. (2017) Ancient hybridization and strong adaptation to viruses across African vervet monkey populations. Nat genet 49:1705–1713CAS 
PubMed 
PubMed Central 

Google Scholar 
Vongpaisarnsin K, Listman JB, Malison RT, Gelernter J (2015) Ancestry informative markers for distinguishing between Thai populations based on genome-wide association datasets. Leg Med 17:245–250CAS 

Google Scholar 
Wasser SK, Brown L, Mailand C, Mondol S, Clark W, Laurie C et al. (2015) Genetic assignment of large seizures of elephant ivory reveals Africa’s major poaching hotspots. Science (80-) 349:84–87CAS 

Google Scholar 
Wilkinson S, Wiener P, Archibald AL, Law A, Schnabel RD, McKay SD et al. (2011) Evaluation of approaches for identifying population informative markers from high density SNP chips. BMC genetics 12:1–14Wright S (1969) Evolution and the genetics of populationsWultsch C, Caragiulo A, Dias-Freedman I, Quigley H, Rabinowitz S, Amato G (2016) Genetic diversity and population genetic structure of Mesoamerican jaguars (Panthera onca): implications for conservation and management. PLoS One 11:162377
Google Scholar  More