More stories

  • in

    Attraction to conspecific social-calls in a migratory, solitary, foliage-roosting bat (Lasiurus cinereus)

    Broadcasted social calls attracted hoary bats during both the spring and fall migration. Broadcasting conspecific social calls increased hoary bat capture rates at netting sites intentionally removed from normal capture locations. We had very low capture rates during control periods, because we intentionally placed nets in locations removed from flyways to reduce incidental captures. Moreover, capture rates of hoary bats tend to be low even in many locations where they are known to occur24,25, and capture rates of approximately one bat per hour in a single mist net suggest a very strong attraction response to broadcasted calls.Hoary bat activity, as measured by acoustic monitoring was not associated with increased capture rates in response to call broadcasting. However, subsequent research has shown that hoary bats periodically use higher frequency, inconspicuous calls, or do not constantly echolocate during the fall, which may mean acoustic monitoring did not effectively measure hoary bat activity in the vicinity of our trials26,27. We recorded substantially higher acoustic activity during the spring migration, which could represent either more hoary bats and/or bat activity, or a seasonal difference in echolocation or flight behavior such as differences in flight altitude27. It remains unknown if hoary bats use inconspicuous calls or fly in silence during spring migration or other times of year other than the fall when these inconspicuous echolocation behaviors were observed, and seasonally variable behavior could affect detectability or exposure to our playback trials in ways not captured by our acoustic activity covariate. In addition, while we did audibly hear social calls of hoary bats during the fall, we did not record any during fieldwork for this study, which may be an artifact or due to differences in social behavior, context, or number of hoary bats present in the area during our trials.We only captured one female during trials in New Mexico, and were unable to locate any females during the fall migration in coastal regions of California, despite high concentrations of males in the area during what is presumably the mating season. In New Mexico, during spring migration, females migrate through the study area before males28, with very little temporal overlap. As a result, we were unable to determine sex specific responses to call playback, however we have subsequently captured several female hoary bats and Ope’ape’a (Hawaiian hoary bat, L. semotus) using call playback during capture and radio-tracking studies (GAR, pers. obs.).It is difficult to elucidate the meaning of social calls based on the behaviors observed in the field. In bats, social call complexity often reflects social behavior complexity, with a range of uses including but not limited to attracting mates, locating pups within colonies, defending roosting or foraging territory, and attracting bats to roosts10. Attraction to conspecific call broadcasting could indicate positive social interactions (e.g., maintaining group cohesion or investigation) or agonistic behavior (e.g., hoary bats approaching to chase conspecific bats), as has been observed in other bat species29 and in hoary bats during the maternity season30. We did not observe any obvious instances of aggressive hoary bat interactions, and the social calls differ from hisses and clicks that hoary bats use defensively (Fig. 2). We would also audibly hear pairs of hoary bats calling in close proximity to each other, with no indication of aggressive or territorial responses, and these calls being low frequency and audible to humans means that they attenuate at greater distances than hoary bat echolocation calls.Aggressive or territorial interactions in many taxa are often driven by seasonally variable contexts, such as mating, defending food resources, or rearing of young. It may be unlikely that migrating hoary bats would expend energy defending territory during migration when they are utilizing roosts or foraging habitat for such limited periods of time (i.e., a few hours to a day). During active migration birds are often not territorial even when foraging at stopover sites31, and there may be benefits to maintaining group cohesion during migration including navigation and identification of favorable habitat. It is unknown if hoary bats utilize stopover sites for refueling during migration. However the silver-haired bat Lasionycteris noctivagans was found to utilize a migration stopover site in Long Point, Canada, where they opportunistically foraged for short periods of time (1 to 2 days32). Tracking studies would be required to determine temporal patterns of site usage by individual bats to examine stopover behavior.As we had recorded most of our initial social calls during late summer and early fall when hoary bats mate21, we had originally hypothesized that these social calls were associated with mating behavior, which would have been consistent with observations in this study had we found both increased attraction during the fall, and less attraction to calls during the spring. However, social calls attracted hoary bats effectively during both the spring and fall migration. In addition, from acoustic recordings and capture observations in the field, hoary bats produced many social calls during the spring migration when only males were present. There is a possibility, due to our lack of understanding of the mating systems of hoary bats that some mating may continue into the spring. However the majority of taxonomic, physiological, and observational data suggests mating behavior ends by the spring migration19,33, and the majority of females are already pregnant when travelling through New Mexico28. While hoary bats may or may not use social calls as a component of mating behavior, social calls recorded during the spring likely serve purposes not associated with mating.Previous studies describe the hoary bat as solitary throughout most of the year, which would imply only brief social interactions limited to mating or association with offspring, and the many historical accounts of aggregations of hoary bats are thought to be related to mating behavior20,33,34. However the use of, and attraction to, social calls during both spring and fall migration supports that these calls are used for social interactions beyond mating behavior. Further research may determine if hoary bats use these social calls to maintain group cohesion during migration, and what, if any, relationships exist between individual hoary bats that appear to be migrating together. Baerwald and Barclay35 found that geographic and genetic relationships of hoary bats and silver-haired bat carcasses collected at wind turbines were not more closely related than expected by chance, which provides some evidence that groups of migrating hoary bats may not form based on kinship.Many studies hoping to elucidate the causes of fatalities at wind energy facilities have focused only on the fall migration period when bats are most often killed13,20,36. However hoary bats migrate during the spring as well, when they do not suffer high fatality rates. Investigating the spring migration presents a valuable baseline to compare behavioral changes and other factors that may place hoary bats or other impacted species at risk. If social behavior makes a major contribution to the risk of fatalities at wind energy developments, then social behavior should differ between spring and fall migration. We did not find a large difference in response to social calls between seasons. While this represents just an initial study into the social calling behavior of hoary bats during migration, it provides some conclusions to guide subsequent investigations: (1) detecting hoary bat social calls does not necessarily indicate mating behavior, and (2) researchers should be cautious in interpreting evidence of social interactions during the fall at wind energy sites as evidence of mating behavior as in the mating landmarks hypothesis22,37. Because it can separate out mating from other behavioral components, comparing spring and fall migration can benefit the investigation of social and other behaviors in hoary bats and other migratory species. Comparing flight behavior, diet, roost selection, hormonal and physiological changes, and further studies of social interactions including scent and, between the spring and fall migration will allow researchers to elucidate which behaviors change seasonally and which may underlie seasonal patterns of wind turbine fatalities. Additionally, exploring social attraction to audible sounds produced by turbines or other potential signals that could seasonally elicit social attraction could lead to additional insights.Hoary bats have proven challenging to capture and study in many locations across their range24, driven by their solitary tree roosting behavior and as they often fly out of the reach of mist nets or ground-based acoustic monitoring stations36,38. Using call broadcasting to increase capture rates can be a useful research tool, especially in locations where the habitat does not provide any ideal capture locations. Using this technique we have captured hoary bats on coastal sand dunes, in large open fields, and in groves of Eucalyptus trees adjacent to wind energy sites, all of which would normally yield low bat capture success without the use of lures. The ability to capture hoary bats more reliably is a great asset for research and conservation throughout the range of hoary bats.Our study tested the use of social call playback as a methodology to study the social behavior of hoary bats during migration, and the utility of using call playback as a research tool and acoustic lure for hoary bats. Increasing capture rates from conspecific social call playback during mating and non-mating season indicates social interactions during both migratory periods, despite the solitary roosting behavior of this species. Future studies to elucidate the behavioral function of these calls, and response during non-migratory seasons could refine our understanding of social behaviors of this elusive bat species. More

  • in

    Reduction of greenhouse gases emission through the use of tiletamine and zolazepam

    Caycedo-Marulanda, A. & Mathur, S. Suggested strategies to reduce the carbon footprint of anesthetic gases in the operating room. Can. J. Anaesth. J. Can. Anesth. 69, 269–270 (2022).CAS 
    Article 

    Google Scholar 
    World Health Organization. COP24 Special Report Health & Climate Change. https://apps.who.int/iris/bitstream/handle/10665/276405/9786057496713-tur.pdf (2018).Gadani, H. & Vyas, A. Anesthetic gases and global warming: potentials, prevention and future of anesthesia. Anesth. Essays Res. 5, 5 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vollmer, M. K. et al. Modern inhalation anesthetics: potent greenhouse gases in the global atmosphere. Geophys. Res. Lett. 42, 1606–1611 (2015).CAS 
    Article 
    ADS 

    Google Scholar 
    Sulbaek Andersen, M. P., Nielsen, O. J., Karpichev, B., Wallington, T. J. & Sander, S. P. Atmospheric chemistry of isoflurane, desflurane, and sevoflurane: kinetics and mechanisms of reactions with chlorine atoms and OH radicals and global warming potentials. J. Phys. Chem. A 116, 5806–5820 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ravishankara, A. R., Daniel, J. S. & Portmann, R. W. Nitrous oxide (N2O): the dominant ozone-depleting substance emitted in the 21st century. Science 326, 123–125 (2009).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    Ryan, S. M. & Nielsen, C. J. Global warming potential of inhaled anesthetics: application to clinical use. Anesth. Analg. 111, 92–98 (2010).PubMed 
    Article 

    Google Scholar 
    American Society of Anesthesiologists. Task Force on Environmental Sustainability Committee on Equipment and Facilities. Greening the Operating Room and Perioperative Arena: Environmental Sustainability for Anesthesia Practice. https://www.asahq.org/about-asa/governance-and-committees/asa-committees/committee-on-equipment-and-facilities/environmental-sustainability/greening-the-operating-room#intro (2014).McGain, F., Story, D., Kayak, E., Kashima, Y. & McAlister, S. Workplace sustainability: the “cradle to grave” view of what we do. Anesth. Analg. 114, 1134–1139 (2012).PubMed 
    Article 

    Google Scholar 
    Yasny, J. S. & White, J. Environmental implications of anesthetic gases. Anesth. Prog. 59, 154–158 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Byhahn, C., Wilke, H. J. & Westpphal, K. Occupational exposure to volatile anaesthetics: epidemiology and approaches to reducing the problem. CNS Drugs 15, 197–215 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sherman, J., Le, C., Lamers, V. & Eckelman, M. Life cycle greenhouse gas emissions of anesthetic drugs. Anesth. Analg. 114, 1086–1090 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mankes, R. F. Propofol wastage in anesthesia. Anesth. Analg. 114, 1091–1092 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Weller, M. A general review of the environmental impact of health care, hospitals, operating rooms, and anesthetic care. Int. Anesthesiol. Clin. 58, 64–69 (2020).PubMed 
    Article 

    Google Scholar 
    Dawidowicz, A. L. et al. Investigation of propofol renal elimination by HPLC using supported liquid membrane procedure for sample preparation. Biomed. Chromatogr. BMC 16, 455–458 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    Costa, G. L. et al. Influence of ambient temperature and confinement on the chemical immobilization of fallow deer (Dama dama). J Wildl Dis 53, 364–367 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Costa, G. et al. Comparison of tiletamine-zolazepam combined with dexmedetomidine or xylazine for chemical immobilization of wild fallow deer (Dama dama). J. Zoo Wildl. Med. 52, 1009–1012 (2021).PubMed 
    Article 

    Google Scholar 
    Lin, H. C., Thurmon, J. C., Benson, G. J. & Tranquilli, W. J. Telazol: a review of its pharmacology and use in veterinary medicine. J. Vet. Pharmacol. Ther. 16, 383–418 (1993).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dixon, W. J. Staircase bioassay: the up-and-down method. Neurosci. Biobehav. Rev. 15, 47–50 (1991).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lin, C.-M. et al. Sitting position does not alter minimum alveolar concentration for desflurane. Can. J. Anesth. Can. Anesth. 54, 523–530 (2007).Article 

    Google Scholar 
    Wadhwa, A. & Sessler, D. I. Women have the same desflurane minimum alveolar concentration as men. J. Am. Soc. Anesthesiol. 99, 4 (2003).
    Google Scholar 
    Monteiro, E. R., Coelho, K., Bressan, T. F., Simões, C. R. & Monteiro, B. S. Effects of acepromazine-morphine and acepromazine-methadone premedication on the minimum alveolar concentration of isoflurane in dogs. Vet. Anaesth. Analg. 43, 27–34 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Campagnol, D., Neto, F. J. T., Giordano, T., Ferreira, T. H. & Monteiro, E. R. Effects of epidural administration of dexmedetomidine on the minimum alveolar concentration of isoflurane in dogs. Am. J. Vet. Res. 68, 1308–1318 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Valverde, A., Morey, T. E., Hernandez, J. & Davies, W. Validation of several types of noxious stimuli for use in determining the minimum alveolar concentration for inhalation anesthetics in dogs and rabbits. Am. J. Vet. Res. 64, 957–962 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Aguado, D., Benito, J. & Gómez de Segura, I. A. Reduction of the minimum alveolar concentration of isoflurane in dogs using a constant rate of infusion of lidocaine–ketamine in combination with either morphine or fentanyl. Vet. J. 189, 63–66 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Muir, W. W. III., Wiese, A. J. & March, P. A. Effects of morphine, lidocaine, ketamine, and morphine-lidocaine-ketamine drug combination on minimum alveolar concentration in dogs anesthetized with isoflurane. Am. J. Vet. Res. 64, 1155–1160 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dixon, W. J. The up-and-down method for small samples. J. Am. Stat. Assoc. 60, 967–978 (1965).MathSciNet 
    Article 

    Google Scholar 
    Paul, M. & Fisher, D. M. Are estimates of MAC reliable?. Anesthesiology 95, 1362–1370 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sonner, J. M. Issues in the design and interpretation of minimum alveolar anesthetic concentration (MAC) studies. Anesth. Analg. 95, 609–614 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    Flecknell, P. et al. Preanesthesia, anesthesia, analgesia, and euthanasia. in Laboratory Animal Medicine 1135–1200 (Elsevier, 2015). https://doi.org/10.1016/B978-0-12-409527-4.00024-9.Grimm, K. A., Lamont, L. A., Tranquilli, W. J., Greene, S. A. & Robertson, S. A. Veterinary Anesthesia and Analgesia (Wiley, 2015).Book 

    Google Scholar 
    Grimm, K. A., Tranquilli, W. J. & Lamont, L. A. Essentials of Small Animal Anesthesia and Analgesia (Wiley, 2011).
    Google Scholar 
    Hanna, M. & Bryson, G. L. A long way to go: minimizing the carbon footprint from anesthetic gases. Can. J. Anesth. Can. Anesth. 66, 838–839 (2019).Article 

    Google Scholar 
    Andersen, M. P. S., Nielsen, O. J., Wallington, T. J., Karpichev, B. & Sander, S. P. Assessing the impact on global climate from general anesthetic gases. Anesth. Analg. 114, 1081–1085 (2012).CAS 
    Article 

    Google Scholar 
    Ishizawa, Y. General anesthetic gases and the global environment. Anesth. Analg. 112, 213–217 (2011).PubMed 
    Article 

    Google Scholar 
    Brown, A. C., Canosa-Mas, C. E., Parr, A. D., Pierce, J. M. T. & Wayne, R. P. Tropospheric lifetimes of halogenated anaesthetics. Nature 341, 635–637 (1989).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    Lucio, L. M. C., Braz, M. G., don Nascimento Junior, P., Braz, J. R. C. & Braz, L. G. Occupational hazards, DNA damage, and oxidative stress on exposure to waste anesthetic gases. Braz. J. Anesthesiol. Engl. Ed. 68, 33–41 (2018).
    Google Scholar 
    Waste anesthetic gases-occupational hazards in hospitals. https://www.cdc.gov/niosh/docs/2007-151/ (2007). https://doi.org/10.26616/NIOSHPUB2007151.MacNeill, A. J., Lillywhite, R. & Brown, C. J. The impact of surgery on global climate: a carbon footprinting study of operating theatres in three health systems. Lancet Planet. Health 1, e381–e388 (2017).PubMed 
    Article 

    Google Scholar 
    Rauchenwald, V. et al. New method of destroying waste anesthetic gases using gas-phase photochemistry. Anesth. Analg. 131, 288–297 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Özelsel, T.J.-P., Sondekoppam, R. V., Ip, V. H. Y. & Tsui, B. C. H. Re-defining the 3R’s (reduce, refine, and replace) of sustainability to minimize the environmental impact of inhalational anesthetic agents. Can. J. Anesth. Can. Anesth. 66, 249–254 (2019).Article 

    Google Scholar 
    Thiel, C. L. et al. Environmental impacts of surgical procedures: life cycle assessment of hysterectomy in the United States. Environ. Sci. Technol. 49, 1779–1786 (2015).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    Mastrangelo, G., Comiati, V., dell’Aquila, M. & Zamprogno, E. Exposure to anesthetic gases and Parkinson’s disease: a case report. BMC Neurol. 13, 194 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Casale, T. et al. Anesthetic gases and occupationally exposed workers. Environ. Toxicol. Pharmacol. 37, 267–274 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sharma, A. et al. Should total intravenous anesthesia be used to prevent the occupational waste anesthetic gas exposure of pregnant women in operating rooms?. Anesth. Analg. 128, 188–190 (2019).PubMed 
    Article 

    Google Scholar 
    Hughes, J. M. L. Comparison of disposable circle and ‘to-and-fro’ breathing systems during anaesthesia in dogs. J. Small Anim. Pract. 39, 416–420 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Suttner, S. & Boldt, J. Low-flow anaesthesia: does it have potential pharmacoeconomic consequences?. Pharmacoeconomics 17, 585–590 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jones, R. S. & West, E. Environmental sustainability in veterinary anaesthesia. Vet. Anaesth. Analg. 46, 409–420 (2019).PubMed 
    Article 

    Google Scholar 
    Feldman, J. M. Managing fresh gas flow to reduce environmental contamination. Anesth. Analg. 114, 1093–1101 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Davies, T. V. S. Low flow anaesthesia: frequently asked questions (2020).Pattanapon, N., Bootcha, R. & Petchdee, S. The effects of anesthetic drug choice on heart rate variability in dogs. J. Adv. Vet. Anim. Res. 5, 485 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hampton, C. E. et al. Effects of intravenous administration of tiletamine-zolazepam, alfaxalone, ketamine-diazepam, and propofol for induction of anesthesia on cardiorespiratory and metabolic variables in healthy dogs before and during anesthesia maintained with isoflurane. Am. J. Vet. Res. 80, 33–44 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ratnu, D. A., Anjana, R. R., Parikh, P. V. & Kelawala, D. N. Effects of tiletamine-zolazepam and isoflurane for induction and maintenance in xylazine premedicated dogs. Indian J. Vet. Sci. Biotechnol. 17, 86–88 (2021).CAS 

    Google Scholar 
    Malavasi, L. M., Jensen-Waern, M., Augustsson, H. & Nyman, G. Changes in minimal alveolar concentration of isoflurane following treatment with medetomidine and tiletamine/zolazepam, epidural morphine or systemic buprenorphine in pigs. Lab. Anim. 42, 62–70 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Malavasi, L. M. et al. Effects of extradural morphine on end-tidal isoflurane concentration and physiological variables in pigs undergoing abdominal surgery: a clinical study. Vet. Anaesth. Analg. 33, 307–312 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Krimins, R. A., Ko, J. C., Weil, A. B., Payton, M. E. & Constable, P. D. Hemodynamic effects in dogs after intramuscular administration of a combination of dexmedetomidine-butorphanol-tiletamine-zolazepam or dexmedetomidine-butorphanol-ketamine. Am. J. Vet. Res. 73, 1363–1370 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nam, S.-W., Shin, B.-J. & Jeong, S. M. Anesthetic and cardiopulmonary effects of butorphanol-tiletamine-zolazepam-medetomidine and tramadol-tiletamine-zolazepam-medetomidine in dogs. J. Vet. Clin. 30(6), 421–427 (2013).
    Google Scholar 
    Ko, J. C. H., Payton, M., Weil, A. B., Kitao, T. & Haydon, T. Comparison of anesthetic and cardiorespiratory effects of tiletamine–zolazepam–butorphanol and tiletamine–zolazepam–butorphanol– medetomidine in dogs. Vet. Ther. 8, 14 (2007).
    Google Scholar 
    Grimm, K. A., Tranquilli, W. J., Thurmon, J. C. & Benson, G. J. Duration of nonresponse to noxious stimulation after intramuscular administration of butorphanol, medetomidine, or a butorphanol-medetomidine combination during isoflurane administration in dogs. Am. J. Vet. Res. 61, 42–47 (2000).CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Metagenomics to characterize sediment microbial biodiversity associated with fishing exposure within the Stellwagen Bank National Marine Sanctuary

    Pace, N. R. The small things can matter. PLoS Biol. 16(8), e3000009. https://doi.org/10.1371/journal.pbio.3000009 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hoshino, T. et al. Global diversity of microbial communities in marine sediment. PNAS 117, 27587–27597 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Baker, B. J., Appler, K. E. & Gong, X. New microbial biodiversity in marine sediments. Ann. Rev. Mar. Sci. 13, 161–175. https://doi.org/10.1146/annurev-marine-032020-014552 (2021).Article 
    PubMed 

    Google Scholar 
    Zinger, L. et al. Global patterns of bacterial beta-diversity in seafloor and seawater ecosystems. PLoS ONE 6, e24570. https://doi.org/10.1371/journal.pone.0024570 (2011).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jiao, N. et al. Microbial production of recalcitrant dissolved organic matter: Long-term carbon storage in the global ocean. Nat. Rev. Microbiol. 8, 593–599 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ward, N. D. et al. Representing the function and sensitivity of coastal interfaces in earth system models. Nat. Commun. 11, 2458 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cook, R., & Auster, P. J. Developing alternatives for optimal representation of seafloor habitats and associated communities in Stellwagen Bank National Marine Sanctuary. Marine Sanctuaries Conservation Series ONMS-06–02. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, Office of National Marine Sanctuaries, Silver Spring, MD (2006).Wauchope, H. S. Evaluating impact using time-series data. Trends Ecol. Evol. 36, 3. https://doi.org/10.1016/j.tree.2020.11.001 (2021).Article 

    Google Scholar 
    Stellwagen Bank National Marine Sanctuary (SBNMS) Condition Report. Office of National Marine Sanctuaries National Oceanic and Atmospheric Administration. doi:https://doi.org/10.25923/48ZK-BB07. pp. 1–263. (2020).Grieve, C., Brady, D. C. & Polet, H. Best practices for managing, measuring and mitigating the benthic impacts of fishing—Part 1. Mar. Stewardship Council Sci. Ser. 2, 18–88 (2014).
    Google Scholar 
    Watling, L. & Norse, E. A. Disturbance of the seafloor by mobile fishing gear: A comparison to forest clear cutting. Conserv. Biol. 12, 1180–1197 (1998).Article 

    Google Scholar 
    Snelgrove, P. V. R. et al. The importance of marine sediment biodiversity in ecosystem processes. Ambio 26, 578–583 (1997).
    Google Scholar 
    Grassle, J. F. & Maciolek, N. J. Deep-sea species richness: Regional and local diversity estimates from quantitative bottom samples. Am. Nat. 139, 313–341 (1992).Article 

    Google Scholar 
    Polinski, J. M., Bucci, J. P., Gasser, M. & Bodnar, A. G. Targeted metagenomic assessment of biodiversity across prokaryotic and eukaryotic taxa in sediments from the Stellwagen Bank National Marine Sanctuary. Sci. Rep. 9, 14820 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Petro, C. et al. Microbial community assembly in marine sediments. Aquat. Microb. Ecol. 79, 177–195 (2017).Article 

    Google Scholar 
    Cook, R. et al. The substantial first impact of bottom fishing on rare biodiversity hotspots: A dilemma for evidence-based conservation. PLoS ONE 8, e69904 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grabowski, J. H. et al. Assessing the vulnerability of marine benthos to fishing gear impacts. Rev. Fisheries Sci. Aquacult. 22, 142–155 (2014).Article 

    Google Scholar 
    Silva, T. L. State of the science report: An addendum to the Stellwagen Bank National Marine Sanctuary 2020 Condition Report 1–20 (U.S. Department of Commerce, 2021).
    Google Scholar 
    Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bech, P. K. et al. Marine sediments hold an untapped potential for novel taxonomic and bioactive bacterial diversity. MSystems 5, e00782-e820 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Newman, D. J. & Cragg, G. M. Natural products as sources of new drugs from 1981 to 2014. J. Nat. Prod. 79, 629–661 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hou, Z. Geochemical and microbial community attributes in relation to hyporheic zone geological facies. Sci. Rep. 7, 12006 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hugenholtz, P., Goebel, B. M. & Pace, N. R. Impact of culture-independent studies on the emerging phylogenetic view of bacterial diversity. J. Bacteriol. 180, 4765–4774 (1998).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Durazzi, F. et al. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci. Rep. 11, 3030 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Asnicar, F. et al. Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0. Nat. Commun. 11, 2500 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dance, A. The search for microbial dark matter. Nature 582, 301–303. https://doi.org/10.1038/d41586-020-01684-z (2020).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Fishing Restrictions. Magnuson Fishery Conservation and Management Act (MFCMA) (16 U.S.C. Part 1801 et seq.) (1990).Begon, M., Harper, J. L. & Townsend, C. R. Ecology: Individuals, Populations, and Communities 3rd edn. (Blackwell Science Ltd., 1996).Book 

    Google Scholar 
    Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Andrews, S. Babraham bioinformatics-FastQC a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Next Gen. Sequencing Data Anal. 17, 1 (2011).
    Google Scholar 
    Bankevich, A. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lu, J., Breitwieser, F. P., Thielen, P. & Salzberg, S. L. Bracken: estimating species abundance in metagenomics data. PeerJ Comput. Sci. 2, e104 (2017).Article 

    Google Scholar 
    Wickham, H. ggplot2. WIREs Comput. Stat. 3, 180–185 (2011).Article 

    Google Scholar 
    Breitwieser, F. P. & Salzberg, S. L. Pavian: Interactive analysis of metagenomics data for microbiome studies and pathogen identification. Bioinformatics 36, 1303–1304 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wu, Y. W. et al. MaxBin 2.0: An automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Alneberg, J. et al. CONCOCT: Clustering cONtigs on COverage and ComposiTion. ArXiv 1312, 4038 (2013).ADS 

    Google Scholar 
    Kang, D. et al. MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Parks, D. H. et al. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a web browser. BMC Bioinform. 12, 385 (2011).Article 

    Google Scholar 
    Seemann, T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Blin, K. et al. antiSMASH 6.0: Improving cluster detection and comparison capabilities. Nucleic Acids Res. 49, W29–W35 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978–3–319–24277–4. (2016).Moon, K. W. Interactive Plot. In Learn ggplot2 Using Shiny App (ed. Moon, K.-W.) 295–347 (Springer International Publishing, 2016).Chapter 

    Google Scholar 
    Oksanen, J., et al. Package ‘vegan’. Community ecology package, version 2, 1-295 (2013).Wilkinson, L. SYSTAT. In Wiley Interdisciplinary Reviews: Computational Statistics, Multidimensional Scaling (eds Wegman, E. & Said, Y. H.) (John Wiley & Sons, New York, 2010).
    Google Scholar 
    Dexter, E., Rollwagen-Bollens, G. & Bollens, M. The trouble with stress: A flexible method for the evaluation of nonmetric multidimensional scaling. Limnol. Oceanogr. Methods 16, 434–443 (2018).Article 

    Google Scholar 
    Longford, N. T. Longitudinal and time-series analysis. In Studying Human Populations. Springer Texts in Statistics (Springer, 2008). https://doi.org/10.1007/978-0-387-73251-0_11.Chapter 
    MATH 

    Google Scholar 
    NOAA Office of Law Enforcement. Speed-filtered vessel monitoring system (VMS) data from Greater. Atlantic VMS Program (2019).Palmer, M. C., & Wigley, S. E. Validating the stock apportionment of commercial fisheries landings using positional data from vessel monitoring systems (VMS). Northeast Fisheries Science Center Reference Document 07–22. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northeast Fisheries Science Center, Woods Hole, MA. (2007).Northeastern Regional Association of Coastal Ocean Observing Systems Buoy (NERACOOS) Monitoring Program. Portsmouth, NH. www.neracoos.org (2021).Stroup, W. Generalized Linear Mixed Models: Modern Concepts (Methods and Applications. Taylor & Francis Group, 2013).MATH 

    Google Scholar 
    Ridout, M. S., Hinde, J. P., & Demétrio, C. G. B. “Models for Count Data with Many Zeros,” in Proceedings of the 19th International Biometric Conference, 179–192, Cape Town. (1998).Barnhardt, W. A., Kelley, J. T., Dickson, S. M. & Belknap, D. F. Mapping the Gulf of maine with side-scan sonar: A new bottom-type classification for complex seafloors. J. Coast. Res. 14, 646–659 (1998).
    Google Scholar 
    Carrier-Belleau, C. et al. Environmental stressors, complex interactions and marine benthic communities’ responses. Sci. Rep. 11, 4194. https://doi.org/10.1038/s41598-021-83533-1 (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Auster, P., Joy, K. & Valentine, P. C. Fish species and community distributions as proxies for seafloor habitat distributions: the Stellwagen Bank National Marine Sanctuary example (Northwest Atlantic, Gulf of Maine). Environ. Biol. Fishes 60, 331–346 (2001).Article 

    Google Scholar 
    Solan, M., Raffaelli, D. G., Paterson, D. M., White, P. C. L. & Pierce, G. J. Marine biodiversity and ecosystem function: Empirical approaches and future research needs. Mar. Ecol. Prog. Ser. 311, 175–178 (2006).ADS 
    Article 

    Google Scholar 
    Worm, B. et al. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790 (2006).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Dyksma, S. et al. Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. ISME J. 10, 1939–1953 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tuttle, R. N. et al. Detection of natural products and their producers in ocean sediments. Appl. Environ. Microbiol. 85, e02830-e2918 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Heinrichs, L., Aytur, S. A. & Bucci, J. P. Whole metagenomic sequencing to characterize the sediment microbial community within the Stellwagen Bank National Marine Sanctuary and preliminary biosynthetic gene cluster screening of Streptomyces scabrisporus. Mar. Genom. 50, 100718 (2020).Article 

    Google Scholar 
    Belknap, K. C. et al. Genome mining of biosynthetic and chemotherapeutic gene clusters in Streptomyces bacteria. Sci. Rep. 10, 2003. https://doi.org/10.1038/s41598-020-58904-9 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sánchez-Soto Jiménez, M. F., Cerqueda-García, D., Montero-Muñoz, J. L., Aguirre-Macedo, M. L. & García-Maldonado, J. Q. Assessment of the bacterial community structure in shallow and deep sediments of the Perdido Fold Belt region in the Gulf of Mexico. PeerJ 6, e5583. https://doi.org/10.7717/peerj (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 62, 809–812 (2015).ADS 
    Article 
    CAS 

    Google Scholar 
    Pittman, S. J. Relevance of the Northeast Integrated Ecosystem Assessment for the Stellwagen Bank National Marine Sanctuary Condition Report (2007–2018) Marine Sanctuaries Conservation Science Series ONMS-19–08. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, Office of National Marine Sanctuaries, Silver Spring, MD. (2019).Bucci, J. P., Szempruch, A. J., Caldwell, J. M., Ellis, C. & Levine, J. F. Seasonal changes in microbial community structure in freshwater stream sediment in a North Carolina River Basin. Diversity 6, 18–32 (2014).Article 

    Google Scholar 
    Won, N. I., Kim, K. H., Kang, J. H., Park, S. R. & Lee, H. J. Exploring the impacts of anthropogenic disturbance on seawater and sediment microbial communities in korean coastal waters using metagenomics analysis. Int. J. Environ. Res. Public Health 14, 130 (2017).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Zinger, L. et al. Global patterns of bacterial beta-diversity in seafloor and seawater ecosystems. PLoS ONE 6(9), e24570 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Auster, P., Lindholm, J., Cramer, A., Nenandovic, M., Prindle, C., & Tamsett, A. The seafloor habitat recovery monitoring project (SHRMP) at Stellwagen Bank National Marine Sanctuary. Final Project Report. (2013b).UN General Assembly, Transforming our world: The 2030 Agenda for Sustainable Development, 21 October, A/RES/70/1, available at: https://www.refworld.org/docid/57b6e3e44.html. (2015).Malve, H. Exploring the ocean for new drug developments: Marine pharmacology. J. Pharm. Bioall. Sci. 8, 83–91. https://doi.org/10.4103/0975-7406.171700 (2016).CAS 
    Article 

    Google Scholar  More

  • in

    CAN-SAR: A database of Canadian species at risk information

    The CAN-SAR22 database was created to provide access to publicly available data on species at risk in Canada in a standardized format that can be used in a wide range of applied research contexts. The variables included in the database were chosen to provide a range of information available for species at risk with a particular focus on climate change to support the first publication using the database6. The database includes numerous data fields including extinction risk status, various biological and geographical attributes, threat assessments, date of listing, recovery actions, and a set of climate change impact and adaptation variables. CAN-SAR is a living database that can be updated as new information and reports become available, or as other targeted data extraction efforts become available23.In Canada, the listing process begins with an assessment of a wildlife species’ risk of extinction by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC). A wildlife species can be either a species or a ‘designatable unit’, which includes subspecies, varieties, or other geographically or genetically distinct populations. Herein these are referred to collectively as ‘species’. COSEWIC is an independent body of experts who synthesize the best available information to date into a status report containing elements such as population size and trends, habitat availability, and threat assessments (Fig. 1)17. This report is then used as the basis for a status recommendation that is passed on to the Government of Canada, who makes the final decision on whether to legally list the species under Schedule 1 of SARA24. The species can be listed as ‘Special concern’, ‘Threatened’, ‘Endangered’, or ‘Extirpated’. If a species is listed as ‘Threatened’, ‘Endangered’ or ‘Extirpated’ then a recovery strategy is required, while for species listed as ‘Special concern’ a management plan must be created24. Recovery strategies must provide a description of the species’ needs, address identified threats, identify critical habitat (where applicable and to the extent possible), and include population and distribution objectives for the species’ recovery. Management plans include conservation measures for the species and its habitat24. Hereafter, we refer to recovery strategies and management plans collectively as ‘recovery documents’.Information included in the database was extracted from various sources and documents that are available from the online SAR Public Registry, including COSEWIC status reports and status appraisal summaries, and recovery documents (Fig. 1). A COSEWIC status appraisal summary is produced instead of a new status report when a species has been previously assessed and COSEWIC experts are confident that its status will not change (https://www.cosewic.ca/index.php/en-ca/assessment-process/status-appraisal-summary-process.html). It is considered an addendum to the existing status report; thus, we use ‘status report’ to refer to either a status report or a status appraisal summary and the previous status report. From the SAR Public Registry website we accessed information from 1146 documents for all 594 species listed under SARA Schedule 1 as of March 23, 2021, that were classified with the status of ‘Special concern’, ‘Threatened’, or ‘Endangered’. Some species have multiple documents of the same type because COSEWIC reassesses at risk species every 10 years or less and recovery strategies and management plans are reviewed every 5 years and updated as needed. As new documents have become available they have been added to the CAN-SAR database without overriding the previously existing document, which allows for tracking of changes in various data fields over time. Only documents between 2018 and 2021, inclusive, have an updated version due to our updating schedule.Data extractionVariables included in the CAN-SAR database were categorised as either directly transcribed or derived. Directly transcribed variables reflect information extracted from documents that require limited interpretation, such as scientific name or date of legal listing (Online-only Table 1). Derived variables reflect species’ attributes that required interpretation of text by data recorders (Online-only Table 1). The data dictionary (CAN-SAR_data_dictionary.xlsx) contains a description of each variable, including details of their extraction and synthesis22.Several derived variables were extracted from the status report technical summary section, including whether the species is endemic to Canada or North America, and whether the species’ range is continuous with the United States. Endemism was determined for each species at two spatial extents, Canada and North America, based on descriptions of their global distributions from status reports. Whether a Canadian species’ range is continuous with its conspecifics in the United States was interpreted from descriptions of geographic isolation in the distribution and rescue effect sections of the status reports.Variables related to species’ threats were derived from information in the status reports, recovery strategies and management plans. In 2012, COSEWIC initiated use of the IUCN threats classification system in status reports for some species; a ‘threats calculator’25. Threats calculators may also be included in recovery strategies and management plans. A threats calculator is a table included in the document that classifies threats into 11 general ‘level one’ classes and, more specific ‘level two’ subclasses (Table 1)26. Four variables (impact, severity, scope, and timing) for each level one and level two threats were scored independently and then combined into an overall impact score for each species. Impact is defined as the degree to which the species is threatened by the threat class; severity is the level of damage to the species from the threat class that is expected within ten years or three generations, whichever is longer; scope is the proportion of the species that is expected to be affected within ten years; and timing is the immediacy of the threat25. Threat-related variables were either transcribed directly from the threats calculator, or from the derived description of threats in the document if a threats calculator was not included.Table 1 Definitions of level one threat classes and names of level two threat classes following Version 1.1 of the IUCN threats classification system.Full size tableFor species where a threats calculator was included, we recorded whether each of the level one and level two threat classes were identified (i.e., considered a threat), and transcribed the scores for each of impact, scope, severity, and timing. Threat classes were considered identified if the impact was negligible, low, moderate, high, very high, unknown, or not calculated (outside assessment timeframe). Impact, scope, severity, and timing values were coded as ranked values of ‘0’: not a threat; ‘1’: neglible; ‘2’: low; ‘3’: moderate; ‘4’: high; ‘5’: very high; ‘-1’: unknown; ‘-2’: not calculated; or ‘NA’ where there were blank values. For exact ranking interpretations see CAN-SAR_data_dictionary22. For some species, the threats calculator was available from the COSEWIC Secretariat as a Microsoft Excel file, in which case threats information was extracted directly from the spreadsheet using R v 3.6.227. For species where a Microsoft Excel file was not available, threats calculator information was manually extracted from the status report.For species where a threats calculator was not included in the document, threats described in the text were classified into threat classes based on version 1.1 of the IUCN threats classification system (Table 1)26. Although a more recent version of the threats calculator exists, we applied version 1.1 classification to reflect the approach applied across the majority of species. Threats were considered identified if the threat was discussed as having any negative or potentially negative impact on the species. In cases where no threat calculator was available, the threat attributes of impact, scope, severity, and timing were scored as not applicable; ‘NA’.Several variables were derived to determine how climate change was addressed in status reports and recovery documents. Whether climate change was mentioned anywhere in the status report was determined by searching the document for the words climat*, warm, temperat*, and drought. If a document contained any of these search terms, we assessed the context for description of anthropogenic climate change impacts. In cases where the terms were not found, the threats section was checked for any other descriptions that were related to climate change; if none were found, climate change was recorded as not mentioned. When climate change was mentioned, we then determined if it was identified as a threat by interpreting whether it was described as having a negative or potentially negative impact on the species. If a threats calculator was included in the status report, climate change was considered a threat if the ‘Climate change and severe weather’ threat class had an impact that was more than negligible or if climate change was described outside the threats calculator as a threat or potential threat. We recorded whether the threat of climate change was unknown. This included instances where climate change was described as having unknown effects on the species, if ‘unknown’ was assigned to impact, scope, severity, or timing in the threats calculator, or if knowledge gaps related to climate change were identified. Finally, the impact of climate change relative to other threats was classified based on descriptions of threats in the status report. The relative impact of climate change was classified as ‘0’ if it was not a threat; ‘1’ if it was described as a minor, potential, possible, or other threat; ‘2’ if it was a significant threat but not the most important or if it was among the list of threats with no indication of relative importance; or ‘3’ if it was among the most important threats described.Additional derived variables extracted from recovery documents available on the SAR Public Registry included those related to critical habitat identification and recovery actions. For species with recovery strategies, we recorded whether critical habitat was described as identified, partially identified, or not identified. In cases where critical habitat was described as “identified to the extent possible”, it was marked as identified. We extracted information from recovery documents on what types of actions were recommended and whether the actions addressed the threat of climate change. Actions were categorized into four categories: outreach and stewardship, research and monitoring, habitat management, and population management (Table 2). Within each of the four categories, a set of 16 sub-types were recorded if any actions of that type were recommended or already completed. We also recorded action types and sub-types that specifically addressed climate change threats if climate change was listed as the threat addressed or the reason the action was necessary6.Table 2 Categories of actions specified in Recovery Strategies.Full size tableFive data recorders conducted the initial data extraction, synthesis, and interpretation. All recorders were trained on the definitions, interpretation, and general process of data extraction to ensure consistent extraction of all variables. Data extraction occurred in multiple stages and included an iterative set of verifications and assessments of the same species among recorders to ensure consistent and standardized interpretations. Once convergence of interpretations was achieved, each recorder was assigned a set of species/reports from which to extract information.Next stepsThe CAN-SAR database is intended to be a living database that can be updated by adding information from new documents or species as they become available, adding more historical documents, or extracting new information from all documents. The current set of species and associated information includes those listed on Schedule 1 of SARA (as of March 23rd 2021) as ‘Special concern’, ‘Threatened’, or ‘Endangered’. Examples of future data additions include integration of data from species assessed by COSEWIC that are not listed under Schedule 1 of SARA, adding fields that specify the criteria used to arrive at a risk status designation, and integration of data from action plans. We anticipate updating the database periodically, as time and resources allow, and we also encourage anyone interested in extending or expanding on the CAN-SAR database to communicate to discuss a collaboration. Integration of new datasets will require screening and validation to ensure adherence to data standards and consistent interpretations. In the longer term, we foresee the implementation of automatic updating of the CAN-SAR database for variables that do not require interpretation by using machine-readable formatted status and recovery documents.ApplicationsApplications of the CAN-SAR database reflect both opportunities to synthesise the data in novel ways and to expand the scope of the current database to include new data fields representing information contained in status assessments and recovery documents. The CAN-SAR database facilitates independent data analysis and synthesis efforts ranging from trend analysis of threats, identifying research and monitoring gaps, and assessing the effectiveness of recovery actions, which target various steps of the listing and recovery process. For example, the database provides a platform to extend existing climate change focused work6 to assess the prevalence of recommended climate change targeted recovery actions, such as translocations. With recent adoption of the ‘Pan-Canadian approach to transforming Species at Risk conservation in Canada’28, which emphasizes multi-species recovery planning approaches, there is an opportunity to assess patterns in key sectors, which include agriculture, forestry, and urban development, over time and by taxa and how they map to threats.With the integration of additional variables through future data extraction or integration efforts, the CAN-SAR database can be used to assess novel questions. For example, broadening recovery action categories to include those that reflect natural climate solutions can highlight where recovery efforts may provide co-benefits, thus achieving biodiversity conservation and climate change mitigation goals29. Specifically, habitat restoration actions for a forest-dependent species primarily threatened by habitat loss may lead to improved recovery outcomes while also resulting in carbon sequestration and improved climate change mitigation efforts. Tracking these types of actions in CAN-SAR could highlight both opportunities and gaps for the integration of climate smart conservation principles30 into species at risk recovery planning and the adoption of climate change adaption measures for species directly considered climate change threatened and those that are not6. More

  • in

    Synchronous vegetation response to the last glacial-interglacial transition in northwest Europe

    Rasmussen, S. O. et al. A stratigraphic framework for abrupt climatic changes during the Last Glacial period based on three synchronized Greenland ice-core records: refining and extending the INTIMATE event stratigraphy. Quat. Sci. Rev. 106, 14–28 (2014).Article 

    Google Scholar 
    Heiri, O. et al. Validation of climate model-inferred regional temperature change for late-glacial Europe. Nat. Commun. 5, 1–7 (2014).Article 
    CAS 

    Google Scholar 
    Muschitiello, F. et al. Fennoscandian freshwater control on Greenland hydroclimate shifts at the onset of the Younger Dryas. Nat. Commun. 6, 1–8 (2015).Article 
    CAS 

    Google Scholar 
    Renssen, H. et al. Multiple causes of the Younger Dryas cold period. Nat. Geosci. 8, 946–949 (2015).CAS 
    Article 

    Google Scholar 
    Mangerud, J. The discovery of the Younger Dryas, and comments on the current meaning and usage of the term. Boreas 50, 1–5 (2021).Article 

    Google Scholar 
    Cheng, H. et al. Timing and structure of the Younger Dryas event and its underlying climate dynamics. Proc. Natl. Acad. Sci. USA 117, 23408–23417 (2020).CAS 
    Article 

    Google Scholar 
    van Hoesel, A., Hoek, W. Z., Pennock, G. M. & Drury, M. R. The Younger Dryas impact hypothesis: a critical review. Quat. Sci. Rev. 83, 95–114 (2014).Article 

    Google Scholar 
    Partin, J. W. et al. Gradual onset and recovery of the Younger Dryas abrupt climate event in the tropics. Nat. Commun. 6, 1–9 (2015).Article 

    Google Scholar 
    Reinig, F. et al. Precise date for the Laacher See eruption synchronizes the Younger Dryas. Nature 595, 66–69 (2021).CAS 
    Article 

    Google Scholar 
    Ammann, B. et al. Quantification of biotic responses to rapid climatic changes around the Younger Dryas — a synthesis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 159, 313–347 (2000).Article 

    Google Scholar 
    Hoek, W. Z. Vegetation response to the ∼ 14.7 and ∼ 11.5 ka cal. BP climate transitions: is vegetation lagging climate? Glob. Planet. Change 30, 103–115 (2001).Article 

    Google Scholar 
    Litt, T. et al. Correlation and synchronisation of Lateglacial continental sequences in northern central Europe based on annually laminated lacustrine sediments. Quat. Sci. Rev. 20, 1233–1249 (2001).Article 

    Google Scholar 
    Muschitiello, F. & Wohlfarth, B. Time-transgressive environmental shifts across Northern Europe at the onset of the Younger Dryas. Quat. Sci. Rev. 109, 49–56 (2015).Article 

    Google Scholar 
    Nakagawa, T. et al. The spatio-temporal structure of the Lateglacial to early Holocene transition reconstructed from the pollen record of Lake Suigetsu and its precise correlation with other key global archives: implications for palaeoclimatology and archaeology. Glob. Planet. Change 202, 103493 (2021).Article 

    Google Scholar 
    Ammann, B. et al. Vegetation responses to rapid warming and to minor climatic fluctuations during the Late-Glacial Interstadial (GI-1) at Gerzensee (Switzerland). Palaeogeogr. Palaeoclimatol. Palaeoecol. 391, 40–59 (2013).Article 

    Google Scholar 
    Engels, S. et al. Subdecadal‐scale vegetation responses to a previously unknown late‐Allerød climate fluctuation and Younger Dryas cooling at Lake Meerfelder Maar (Germany). J. Quat. Sci 31, 741–752 (2016).Article 

    Google Scholar 
    Van Raden, U. J. et al. High-resolution late-glacial chronology for the Gerzensee lake record (Switzerland): δ18O correlation between a Gerzensee-stack and NGRIP. Palaeogeogr. Palaeoclimatol. Palaeoecol. 391, 13–24 (2013).Article 

    Google Scholar 
    Blaga, C. I., Reichart, G.-J., Lotter, A. F., Anselmetti, F. S. & Sinninghe Damsté, J. S. A TEX86 lake record suggests simultaneous shifts in temperature in Central Europe and Greenland during the last deglaciation. Geophys. Res. Lett. 40, 948–953 (2013).Article 

    Google Scholar 
    Rach, O., Brauer, A., Wilkes, H. & Sachse, D. Delayed hydrological response to Greenland cooling at the onset of the Younger Dryas in western Europe. Nat. Geosci. 7, 109 (2014).CAS 
    Article 

    Google Scholar 
    Strogatz, S. H. Exploring complex networks. Nature 410, 268–276 (2001).CAS 
    Article 

    Google Scholar 
    Doncaster, C. P. et al. Early warning of critical transitions in biodiversity from compositional disorder. Ecology 97, 3079–3090 (2016).Article 

    Google Scholar 
    Jones, G. et al. The Lateglacial to early Holocene tephrochronological record from Lake Hämelsee, Germany: a key site within the European tephra framework. Boreas 47, 28–40 (2018).Article 

    Google Scholar 
    Blaga, C. I., Reichart, G.-J., Heiri, O. & Damsté, J. S. S. Tetraether membrane lipid distributions in water-column particulate matter and sediments: a study of 47 European lakes along a north–south transect. J. Paleolimnol. 41, 523–540 (2009).Article 

    Google Scholar 
    Bechtel, A., Smittenberg, R. H., Bernasconi, S. M. & Schubert, C. J. Distribution of branched and isoprenoid tetraether lipids in an oligotrophic and a eutrophic Swiss lake: insights into sources and GDGT-based proxies. Org. Geochem. 41, 822–832 (2010).CAS 
    Article 

    Google Scholar 
    Lowe, J. et al. On the timing of retreat of the Loch Lomond (‘Younger Dryas’) Readvance icefield in the SW Scottish Highlands and its wider significance. Quat. Sci. Rev. 219, 171–186 (2019).Article 

    Google Scholar 
    Muggeo, V. M. R. Segmented: an R package to fit regression models with broken-line relationships. R news 8, 20–25 (2008).
    Google Scholar 
    Merkt, J. & Müller, H. Varve chronology and palynology of the Lateglacial in Northwest Germany from lacustrine sediments of Hämelsee in Lower Saxony. Quat. Int. 61, 41–59 (1999).Article 

    Google Scholar 
    Litt, T. & Stebich, M. Bio-and chronostratigraphy of the lateglacial in the Eifel region, Germany. Quat. Int. 61, 5–16 (1999).Article 

    Google Scholar 
    Reimer, P. J. et al. The IntCal20 Northern hemisphere radiocarbon age calibration curve (0–55 cal kBP). Radiocarbon 62, 725–757 (2020).CAS 
    Article 

    Google Scholar 
    Giesecke, T. Holocene dynamics of the southern boreal forest in Sweden. The Holocene 15, 858–872 (2005).Article 

    Google Scholar 
    Müller, D. et al. New insights into lake responses to rapid climate change: the Younger Dryas in Lake Gościąż, central Poland. Boreas 50, 535–555 (2021).Article 

    Google Scholar 
    Davis, B. A. S. et al. The Eurasian Modern Pollen Database (EMPD), version 2. Earth Syst. Sci. data 12, 2423–2445 (2020).Article 

    Google Scholar 
    Neugebauer, I. et al. A Younger Dryas varve chronology from the Rehwiese palaeolake record in NE-Germany. Quat. Sci. Rev. 36, 91–102 (2012).Article 

    Google Scholar 
    Ralska-Jasiewiczowa, M. et al. Very fast environmental changes at the Pleistocene/Holocene boundary, recorded in laminated sediments of Lake Gościaż, Poland. Palaeogeogr. Palaeoclimatol. Palaeoecol. 193, 225–247 (2003).Article 

    Google Scholar 
    Bonk, A. et al. Varve microfacies and chronology from a new sediment record of Lake Gościąż (Poland). Quat. Sci. Rev. 251, 106715 (2021).Article 

    Google Scholar 
    Brauer, A., Haug, G. H., Dulski, P., Sigman, D. M. & Negendank, J. F. W. An abrupt wind shift in western Europe at the onset of the Younger Dryas cold period. Nat. Geosci. 1, 520–523 (2008).CAS 
    Article 

    Google Scholar 
    Mekhaldi, F. et al. Radionuclide wiggle matching reveals a nonsynchronous early Holocene climate oscillation in Greenland and western Europe around a grand solar minimum. Clim. Past 16, 1145–1157 (2020).Article 

    Google Scholar 
    Mayfield, R. J. et al. Metrics of structural change as indicators of chironomid community stability in high latitude lakes. Quat. Sci. Rev. 249, 106594 (2020).Article 

    Google Scholar 
    van der Knaap, W. O. & Van Leeuwen, J. F. N. Climate-pollen relationships AD 1901–1996 in two small mires near the forest limit in the northern and central Swiss Alps. The Holocene 13, 809–828 (2003).Article 

    Google Scholar 
    Bazelmans, J. et al. Environmental changes in the late Allerød and early Younger Dryas in the Netherlands: a multiproxy high-resolution record from a site with two Pinus sylvestris populations. Quat. Sci. Rev. 272, 107199 (2021).Article 

    Google Scholar 
    Birks, H. H., Battarbee, R. W. & Birks, H. J. B. The development of the aquatic ecosystem at Kråkenes Lake, western Norway, during the late glacial and early Holocene-a synthesis. J. Paleolimnol 23, 91–114 (2000).Article 

    Google Scholar 
    Bronk Ramsey, C. Bayesian analysis of radiocarbon dates. Radiocarbon 51, 337–360 (2009).Article 

    Google Scholar 
    Lohne, Ø. S., Mangerud, J. A. N. & Birks, H. H. IntCal13 calibrated ages of the Vedde and Saksunarvatn ashes and the Younger Dryas boundaries from Kråkenes, western Norway. J. Quat. Sci 29, 506–507 (2014).Article 

    Google Scholar 
    Lohne, Ø. S., Mangerud, J. A. N. & Birks, H. H. Precise 14 C ages of the Vedde and Saksunarvatn ashes and the Younger Dryas boundaries from western Norway and their comparison with the Greenland Ice Core (GICC 05) chronology. J. Quat. Sci 28, 490–500 (2013).Article 

    Google Scholar 
    Wohlfarth, B. et al. Hässeldala–a key site for last termination climate events in northern Europe. Boreas 46, 143–161 (2017).Article 

    Google Scholar 
    Brauer, A. et al. High resolution sediment and vegetation responses to Younger Dryas climate change in varved lake sediments from Meerfelder Maar, Germany. Quat. Sci. Rev. 18, 321–329 (1999).Article 

    Google Scholar 
    Lane, C. S., Brauer, A., Blockley, S. P. E. & Dulski, P. Volcanic ash reveals time-transgressive abrupt climate change during the Younger Dryas. Geology 41, 1251–1254 (2013).Bronk Ramsey, C. et al. Improved age estimates for key Late Quaternary European tephra horizons in the RESET lattice. Quat. Sci. Rev. 118, 18–32 (2015).Rasmussen, S. O. et al. A new Greenland ice core chronology for the last glacial termination. J. Geophys. Res. Atmos. 111, https://doi.org/10.1029/2005JD006079 (2006).Brauer, A., Endres, C., Zolitschka, B. & Negendank, J. F. W. AMS radiocarbon and varve chronology from the annually laminated sediment record of Lake Meerfelder Maar, Germany. Radiocarbon 42, 355–368 (2000).CAS 
    Article 

    Google Scholar 
    Wulf, S. et al. Tracing the Laacher See Tephra in the varved sediment record of the Trzechowskie palaeolake in central Northern Poland. Quat. Sci. Rev. 76, 129–139 (2013).Article 

    Google Scholar 
    Brauer, A. et al. The importance of independent chronology in integrating records of past climate change for the 60–8 ka INTIMATE time interval. Quat. Sci. Rev. 106, 47–66 (2014).Article 

    Google Scholar 
    Lane, C. S. et al. The Late Quaternary tephrostratigraphy of annually laminated sediments from Meerfelder Maar, Germany. Quat. Sci. Rev. 122 192–206 (2015).Article 

    Google Scholar 
    Adolphi, F. & Muscheler, R. Synchronizing the Greenland ice core and radiocarbon timescales over the Holocene–Bayesian wiggle-matching of cosmogenic radionuclide records. Clim. Past 12, 15–30 (2016).Article 

    Google Scholar 
    Muschitiello, F. et al. Deep-water circulation changes lead North Atlantic climate during deglaciation. Nat. Commun. 10, 1–10 (2019).CAS 
    Article 

    Google Scholar 
    Adolphi, F. et al. Persistent link between solar activity and Greenland climate during the Last Glacial Maximum. Nat. Geosci. 7, 662–666 (2014).CAS 
    Article 

    Google Scholar 
    Siegenthaler, U., Heimann, M. & Oeschger, H. 14C variations caused by changes in the global carbon cycle. Radiocarbon 22, 177–191 (1980).CAS 
    Article 

    Google Scholar 
    Muscheler, R., Adolphi, F. & Svensson, A. Challenges in 14C dating towards the limit of the method inferred from anchoring a floating tree ring radiocarbon chronology to ice core records around the Laschamp geomagnetic field minimum. Earth Planet. Sci. Lett. 394, 209–215 (2014).CAS 
    Article 

    Google Scholar 
    Muschitiello, F. An improved and continuous synchronization of the Greenland ice-core and Hulu Cave U-Th timescales using probabilistic inversion. Clim. Past Discuss. 1–39 https://doi.org/10.5194/cp-2021-116 (2021).Moore, P. D., Webb, J. A. & Collison, M. E. Pollen analysis. (Blackwell scientific publications, 1991).Engels, S. et al. Haemelsee: late-glacial pollen counts. PANGAEA, https://doi.org/10.1594/PANGAEA.939693 (2021).Weltje, G. J. & Tjallingii, R. Calibration of XRF core scanners for quantitative geochemical logging of sediment cores: Theory and application. Earth Planet. Sci. Lett. 274, 423–438 (2008).CAS 
    Article 

    Google Scholar 
    Heiri, O., Lotter, A. F. & Lemcke, G. Loss on ignition as a method for estimating organic and carbonate content in sediments: reproducibility and comparability of results. J. Paleolimnol 25, 101–110 (2001).Article 

    Google Scholar 
    Brooks, S. J., Langdon, P. G. & Heiri, O. The identification and use of Palaearctic Chironomidae larvae in palaeoecology. Quat. Res. Assoc. Tech. Guid. i–vi. Vol. 10, 1–276 (2007).Heiri, O., Brooks, S. J., Birks, H. J. B. & Lotter, A. F. A 274-lake calibration data-set and inference model for chironomid-based summer air temperature reconstruction in Europe. Quat. Sci. Rev. 30, 3445–3456 (2011).Article 

    Google Scholar 
    Heiri, O. & Lotter, A. F. Effect of low count sums on quantitative environmental reconstructions: an example using subfossil chironomids. J. Paleolimnol 26, 343–350 (2001).Article 

    Google Scholar 
    Rach, O., Hadeen, X. & Sachse, D. An automated solid phase extraction procedure for lipid biomarker purification and stable isotope analysis. Org. Geochem. 142, 103995 (2020).CAS 
    Article 

    Google Scholar 
    Huguet, C. et al. An improved method to determine the absolute abundance of glycerol dibiphytanyl glycerol tetraether lipids. Org. Geochem. 37, 1036–1041 (2006).CAS 
    Article 

    Google Scholar 
    Hopmans, E. C., Schouten, S. & Damsté, J. S. S. The effect of improved chromatography on GDGT-based palaeoproxies. Org. Geochem. 93, 1–6 (2016).CAS 
    Article 

    Google Scholar 
    Birks, H. J. B. & Birks, H. H. Biological responses to rapid climate change at the Younger Dryas—Holocene transition at Kråkenes, western Norway. The Holocene 18, 19–30 (2008).Article 

    Google Scholar 
    R CORE TEAM, A. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2012. URL http://www.R-project.org (2020).Engels, S., van Geel, B., Buddelmeijer, N. & Brauer, A. High-resolution palynological evidence for vegetation response to the Laacher See eruption from the varved record of Meerfelder Maar (Germany) and other central European records. Rev. Palaeobot. Palynol. 221, 160–170 (2015).Article 

    Google Scholar 
    Hughes, A. L. C., Gyllencreutz, R., Lohne, Ø. S., Mangerud, J. & Svendsen, J. I. The last Eurasian ice sheets–a chronological database and time‐slice reconstruction, DATED‐1. Boreas 45, 1–45 (2016).Article 

    Google Scholar  More

  • in

    Enhancing soil quality makes crop production more resilient to climate change

    Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Qiao, L. et al. Soil quality both increases crop production and improves resilience to climate change. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01376-8 (2022). More

  • in

    The DeepFish computer vision dataset for fish instance segmentation, classification, and size estimation

    Fisheries overexploitation is a problem in all oceans and seas globally. Authorities and administrations in charge of assigning quotas have very little fine-grained information on the fish captures, and instead use large-scale, coarse data to assess the health level of fisheries. Thus, being able to cross-match fish species and sizes, to the sea regions they were captured from, can be helpful in this regard, providing finer-grained information.Previous attempts at assembling datasets for fish detection and classification exist, ranging from fish detection or counting in underwater images and video streams1,2,3, to counting on belts on trawler ships4, to classification in laboratory conditions5,6, or in underwater preprocessed images of single fish7,8,9, or single fish in free-form pictures10, as well as simultaneous detection and classification of several fish11,12. However, none of the works found in the literature addresses the topic of simultaneous instance segmentation and species classification, along with fish size estimation, in a fish market environment, as is the aim of this paper. Instance segmentation refers to the extraction of pixel-level masks for each individual object (in this case fish specimens), rather than bounding boxes (object detection), or class label masks (e.g. a single mask for all fish specimens of the same species, also referred to as semantic segmentation). Moreover, works in the literature use pictures taken in laboratory conditions (with a single fish per image, shown from the side), or in underwater conditions. Only French et al.4 uses pictures of fish catches on a belt, for counting purposes. Table 1 shows a summary of the datasets identified in the literature, along with their characteristics, including how the proposed dataset compares.Table 1 Summary of previous datasets found in the literature, and comparison to proposed dataset.Full size tableThe DeepFish project (website: http://deepfish.dtic.ua.es/) is aimed at providing fish species classification and size estimation for fish specimens arriving at fish markets, both for the automation of fish sales, and the retrieval of fine-grained information about the health of fisheries. For a period of six months (April to September 2021), images have been captured at the fish market in El Campello (Alicante, Spain). Images of market trays show a variety of fish species, including targeted as well as accidental captures from the ‘Cabo de la Huerta’, an important site for protection and preservation of marine habitats and biodiversity as defined by the European Comission Habitats Directive (92/43/EEC). From the pictures, a total of 59 different species are identified with 12 species having more than 100 specimens and 25 with more than 10 specimens, as shown in Table 2. There is a high imbalance of species captured due to the natural variation in fish species populations according to seasonality and other ecological factors (rarity of the species, i.e. total population count, etc). Due to some species showing sexual dimorphism (i.e. Symphodus tinca), this species is split into two separate class labels, leading to a different number of species, and class labels (59 species, but 60 class labels). The dataset presents a high temporal imbalance too. As shown in Fig. 1, the capture of new fish tray images was not evenly distributed during the six month study period. Several factors contributed to this: wholesale fish market operating days (e.g. no weekend data, holidays and stop periods, etc.), fish species variability (one of the aims was to be able to capture at least 100 specimens from several species, and seasonality meant some could not be available for capture in later months), as well as the time availability of research group members to attend the fish arrival, tray preparation and auctioning in the evenings.Table 2 Distribution of fish species in the dataset.Full size tableFig. 1Temporal distribution of fish tray images captured. It can be observed that April (04) and May (05) were much more active than the rest of months. This is due to several contributing factors.Full size imageThe resulting DeepFish dataset introduced here contains annotated images from 1,291 fish market trays, with a total of 7,339 specimens (individual fish instances) which were labelled (species and mask) using a specially-adapted version of the Django labeller instance segmentation labelling tool13. Subsequently, another JSON file is generated, following the Microsoft Common Objects in Context (MS COCO) dataset format14, which can be directly fed to a neural network. This is done via a script that is also provided15. Figure 2 shows the distribution of individuals for the selected species within the dataset. Furthermore, Fig. 3 shows examples of the trays, with instance segmentation (ground truth silhouette, i.e. as an interpolation from human-provided points) along with species labelling (different colour shading).Fig. 2Graphical view of the distribution of fish species in the DeepFish dataset for species above 10 specimens. Note, Symphodus tinca is considered separately due to sexual dimorphism (211 male; 335 female samples).Full size imageFig. 3Examples of ground truth fish instance masks with class labelling, showing the 12 species (13 labels) with more than 100 specimens (in bold in Table 2).Full size imageFrom the point of view of research, this data is important for the classification of fish species, instance segmentation, as well as specimen size estimation (e.g. as a regression problem, or otherwise). From an end-results perspective, data automatically labelled with fish instance segmentation accompanied by species name and estimated size is useful to different stakeholders, namely: fishing authorities (to understand how much of each species is being caught per zone), maritime conservation (to calculate depletion of fisheries), but also managers of the markets themselves, as well as clients (digitized sales, e-commerce), etc.The usage of the provided data can be manifold, as it can be used for several problems, namely: object detection and classification, which involves finding objects (in this case fish specimens) providing a bounding box, and a class for each of these boxes; additionally, the data can also be used for semantic segmentation, which can provide a pixel-wise segmentation of the image providing labels (in this case species labels) to different pixel regions of the image; furthermore, also instance segmentation is possible, in which not just a single label for all instances of the same species is provided, but each specimen is provided with a mask (specimen segmentation), as well as a label (species). Furthermore, several measurements of each fish are provided, which can also be used to estimate their size, since they have been shown to be correlated with each other16. These are estimated from the calculated homography (given the tray size is known), given the burden of measuring each fish due to the large amount of specimens in the dataset. More

  • in

    DNA databases of an important tropical timber tree species Shorea leprosula (Dipterocarpaceae) for forensic timber identification

    cpDNA haplotype databaseDNA sequencing of the choloroplast (cp) markers produced sequences of the following lengths: 573 bp (atpB-rbcL); 487 bp (petG-trnP); 500 bp (trnL1-trnL2); and 593 bp (psbM-trnD). Alignment of the 352 individuals from the 44 populations yielded a total 28 variable sites: 11 in the atpB-rbcL spacer, seven in both the petG-trnP and psbM-trnD spacers, and three in the trnL1-trnL2 spacer (Supplementary Table S1). Based on these 28 variable sites (21 base substitutions and 7 deletions) across the combined intergenic regions, a total of 22 unique haplotypes were found (Fig. 1a).Figure 1(a) Chloroplast haplotype distribution in the Shorea leprosula populations. The pie chart colours indicate haplotype distributions; and sector areas are proportional to sample size (Map was generated by ArcGIS-ArcMap version 10.8). (b) STRUCTURE analysis identified two clusters (K = 2) corresponding to Region A and B.Full size imageSSR allele frequency databaseThe reproducibility of SSR genotyping was confirmed by achieving consistent genotypes from five independent PCR amplifications on a single individual for each of the ten SSR loci. Individual bar plots from STRUCTURE analysis are presented in Fig. 1b. At the highest Delta K likelihood scores, the best representation of the data was K = 2 suggesting that the 44 populations in Peninsular Malaysia can be divided into two main genetic clusters: Region A and Region B. The first cluster, ‘Region A’ consists of 12 populations, namely SBadak, BPerangin, BEnggang, GJerai, RTelui, GInas, GBongsu, Belum, Piah, BHijau, Korbu and Bubu. The second cluster, ‘Region B’ consists of 32 populations, namely Behrang, Ampang, HGombak, HLangat, SLalang, PPanjang, Berembun, Angsi, Kenaboi, Triang, Pasoh, BSenggeh, GLedang, Krau, TNegara, Terenggun, SBetis, USat, CTongkat, HTerengganu, Jengai, AGading, Tekam, Beserah, Jengka, Lentang, Lesong, ERompin, GArong, Labis, AHitam and Panti. Similarly, the UPGMA dendrogram analysis also divided the 44 populations into two genetic clusters (Fig. 2) corresponding to Region A and B of the STRUCTURE result.Figure 2Dendrogram showing the relationship between 44 populations of Shorea leprosula in Peninsular Malaysia based on the UPGMA cluster analysis of SSR markers.Full size imageSSR allele frequency databases were established according to Region A and B, and characterized to evaluate the relative usefulness of each SSR marker in forensic investigation. The distribution of allele frequencies for each locus is listed in Table S2 (Region A database) and Table S3 (Region B database). Forensic parameters are shown in Table 1, with a total of 143 alleles and 174 alleles detected in the Region A and B databases, respectively. The observed (Ho) and expected (He) heterozygosity ranged from 0.3570 to 0.8346 and 0.4375 to 0.8795, respectively for populations in the Region A database; and ranged from 0.3298 to 0.8356 and 0.3469 to 0.8793, respectively for populations in the Region B database. The power of discrimination (PD) for the SSR loci ranged from 0.601 to 0.972 and 0.554 to 0.975, in Region A and B databases, respectively. The most discriminating locus was Sle605 in both the Region A (PD = 0.972) and Region B (PD = 0.975) databases. Minimum allele frequency was adjusted for alleles falling below the thresholds of 0.0066 (Region A) and 0.0024 (Region B).Table 1 Genetic diversity and forensic variables (A: total number of alleles; Ho: observed heterozygosity; He: expected heterozygosity; PIC: polymorphic information content; HWE: Hardy–Weinberg equilibrium; MP: matching probability; PD: power of discrimination) for each the 10 SSR loci of Shorea leprosula in the Region A and B databases.Full size tableDeviations from HWE were detected in four of the SSR loci for Region A (SleT11, SleT15, SleT17 and Sle465) and six SSR loci in Region B (SleT01, SleT11, SleT15, SleT17, SleT29 and SleT31). We evaluated these loci in each population independently to rule out the possible presence of null alleles. There were four populations in Region A (GJerai, RTelui, GBongsu and Piah) where a single one locus deviated from HWE; whereas there were eight populations in Region B (Behrang, HGombak, SLalang, Angsi, Klau, USat, Jengka and Panti) with a single locus and a single population (GLedang) with two loci that deviated from HWE (Table S4). Observed deviation from HWE was substantially lower in each population (either absence or not more than two loci) and thus it might be due to Wahlund effect caused by population substructuring in both Region A and B. Linkage disequilibrium (LD) testing was used to evaluate the independence of frequencies for all the SSR genotypes. A total of 13.3% and 28.9% of the 45 pairwise loci were found significant evidence of LD for Region A and B, respectively. Some of the loci might be linked as a result of population substructuring and inbreeding (inbreeding coefficient = 0.0822 [Peninsular Malaysia]). These results are in line with observations in real populations, where the assumption of completely random mating and zero migration required for HWE and LD are unlikely to be met, either in humans, animals or plants 21,22,23.Mean self-assignment, the proportion of individuals correctly assigned back to their population, was 45.9% and ranged from 14.3% (Kenaboi) to 81.3% (CTongkat) between population (Table 2). At the regional level, correct assignment rate of individuals to their region of origin was higher, 87.4% for Region A and 90.0% for Region B, (average of 88.7%).Table 2 Self-assignment test outcomes for Shorea leprosula individuals at the population and regional levels.Full size tableConservativeness of the databaseThe coancestry coefficient (θ) for Peninsular Malaysia (0.0579) was higher than those of Region A (0.0454) and Region B (0.0500) (Table 3). A total of 4.54% and 5.00% of the genetic variability was distributed among populations within Region A and Region B, respectively. In terms of inbreeding coefficient (f), the value for the Region A database (f = 0.0892) was highest, followed by Peninsular Malaysia (f = 0.0822) and Region B (f = 0.0666). All the θ and f values were significantly greater than zero, demonstrated by the 95% confidence intervals not overlapping with zero. Both of the θ and f values were used to calculate the conservativeness of each database by testing the cognate database (Porigin) against the regional database (Pcombined). The databases were non-conservative at the calculated θ value. In order for both the Region databases (A and B) to be conservative, the value of θ was adjusted from 0.0454 to 0.1900 for Region A and from 0.0500 to 0.1500 for Region B. For the Region A database, the most common SSR profile frequency is 2.69 × 10–7 or 1 in 3.72 million and the rarest profile frequency is 1.84 × 10–14 or 1 in 54.3 trillion. For the Region B database, the most common SSR profile frequency is 1.06 × 10–7 or 1 in 9.43 million and the rarest profile frequency is 4.03 × 10–16 or 1 in 2.48 quadrillion.Table 3 Coancestry (θ) and inbreeding (f) coefficients for Shorea leprosula at each hierarchical level.Full size table More