Philippa Kaur

When assessing the ecological status and the development of populations, one important factor to consider is the mortality rate and its underlying causes15. If the status of a population is deemed unsustainable due to high mortality rates, this information can then be used to develop and implement specific management measures, for example addressing the major causes of unnatural mortality16. With regard to the potential ecological relevance of the phenomenon of grey seal predation, it is therefore also important to have reliable estimates of harbour porpoise mortalities resulting from grey seal predation as one natural cause. To allow for a standardised assessment of lesions found in suspected grey seal predation cases, this study aims to summarise the knowledge that has been gathered to date.
The parameters described resemble the most commonly detected lesions in “definite”, “likely” and “fox” related cases of the 246 stranding records categorised as “suspicious” in terms of grey seal predation from the coasts of Schleswig–Holstein. With regard to grey seal predation, parameters 1–9 represent typical lesions, whereas the presence of parameters 10 and 11 is consistent with an interaction with a red fox.
Similar to lesions detected in seals, lesions in porpoises most often resemble puncture lesions in the skin and blubber (parameter 1). Yet, visually most striking is the commonly detected large tissue defect with straight, cut-like wound margins with often flaps of skin and blubber remaining only partly attached to the body (parameters 2, 5, 7). Missing blubber (parameter 4) is also recorded, either as reduced blubber thickness on the flaps of skin or as fully removed parts of blubber and skin. As has been described for seals14, the lesion most often originates in the cervical area (parameter 3). A difference that has been detected between the lesions in seals and porpoises is the rate of clear parallel running bite and / or scratch marks in the skin of the animals. Whereas this is rarely detected in seals14, most porpoises show respective marks (parameter 6). One probable explanation for this dissimilarity could be the different physical and morphological properties of the two types of skin. Seal skin is very dense and elastic; tearing and rupturing the skin requires a considerable amount of force17. Porpoise skin, however, is rather susceptible to applied mechanic force and puncturing or tearing it requires comparably little force18. These different mechanical properties might also be the reason why rake marks are found in the blubber (parameter 9) more often in seals (91% of likely cases14) than in porpoises (62% of likely cases). For seals, in the majority of cases, little to no skin is missing (skin missing in 49% of likely cases14), whereas in porpoises, a considerable number of cases (81% of likely cases) have been found where skin is missing (parameter 7). Grey seals have been described to mainly target the energy rich blubber tissue of their prey11,14. For the elastic and robust seal skin, this is done by scraping off the blubber with the teeth. As porpoise skin is fragile, we suggest that the blubber, including the skin, is more often fully removed by the grey seal and swallowed whole. If true, this may also influence the net energetic gain, which is acquired by the predator. Scraping of blubber tissue from seal skin will likely yield less tissue and cost more energy than tearing off whole pieces of blubber (including skin). Thus, it may result in a lower energetic gain. However, it is still unclear if the process of catching a porpoise in comparison to younger seals might also cost a considerably larger amount of energy, negatively influencing the net gain.
Similar to what has been described for seals, the avulsion of one or both scapulae (parameter 8) can be found and is also likely the result of the force applied when detaching the epidermis and blubber from the body of the prey14.
For porpoises, all nine suggested parameters were found in the definite case of grey seal predation. Parameters 1–5 showed a very high (100%) and parameter 6 a high rate (95%) of occurrence in likely cases. Parameters 7–9 occurred less frequently but were still found in > 60% of all likely predation cases. These high rates of occurrence throughout all parameters suggest that wound patterns found in porpoises are less variable than the patterns found in seals14. Whether this difference is a result of the different mechanical skin properties or if other factors are responsible, is beyond the scope of this study.
While for seals a skeletal trauma is used as an indicator of grey seal predation, for respective harbour porpoise cases, this is hardly ever (19% of likely cases) documented. In contrast, for porpoises, a skeletal trauma (parameter 10) is quite frequently detected in cases related to scavenging by foxes (46% of fox cases) where for example extremities can be manipulated19. As has been reported in seals14, the most often detected parameter in fox related cases is the ragged wound margin (parameter 11, 94% of the cases). Therefore, this can be seen as a good indicator of an interaction with a fox in porpoises. This is also supported by a definite case of fox scavenging, which was confirmed using genetic methods20. It needs to be stated though, that scavenging by birds can result in similar looking lesions, increasing the chance of misinterpretation. Scavenging by birds usually also leaves an irregular wound margin with extensive tissue loss. If parallel running lesions are present, the origin of the lesion can additionally be assessed by measuring the distances in between adjacent lesions and comparing them to published values of grey seal, fox and cetacean inter-teeth distances e.g.1. This is especially important when differentiating between for example rake marks by dolphins, which have been documented in porpoises21,22 and marks induced by grey seals. Here, it can be useful to assess the pattern of inter-teeth distances with those of dolphins expected to be consistent in length, whereas for grey seals, variable distances are expected as the result of the polydont dental morphology23. Despite a lack of available data, a differentiation between grey seal claw-induced marks and dolphin rake marks should be possible, as distances between claws of a subadult / adult grey seal male are expected to be considerably larger than for dolphin inter-teeth distances.
Single puncture lesions, in turn, are not considered as a very good indicator despite being present in the definite and all of the likely cases. Mainly due to the susceptibility of the porpoise skin, such lesions can have many different causes (e.g. feeding by birds).
Whether a loss of muscle tissue can be attributed to grey seal predation or is largely caused by scavengers like gulls as has been suggested for seals14, is still not entirely clear. In German as well as bordering waters, no clear pattern prevails. Carcasses with mainly intact as well as fully removed muscle tissue have been documented c.f.13. However, the reports by Stringell et al.4 suggest that not only the blubber tissue is targeted, but that there may also be some individual behavioural variation.
The findings and the resulting parameters described here are in line with wound patterns reported in earlier publications from other areas1,6,7,13. This shows that the documented wound patterns make a reliable set of parameters when assessing harbour porpoises carcasses potentially predated by a grey seal and should be used in future assessments.
As a complementary tool to the suggested parameters, corresponding to porpoises, we developed a decision tree with the aim of supporting a standardised and information-based decision-making process. Despite an accuracy in decision-making of 96% when using our data set, the example in Fig. 5 illustrates the limitations of such static tools when it comes to judging more complex cases. Furthermore, when comparing the suggestion given by the tree with the one made by the experts, in only 50% of unmatched cases, a rather precautionary judgement was made, bearing the risk of an overestimation of case numbers. Therefore, we recommend using the suggested tree only as an informational tool in supporting decision-making and final judgments should always be made by the responsible expert based on all available information.
In addition to cases for which the attack of a grey seal directly led to the death of the animal, interestingly, it seems not unusual that porpoises escape this predator. Several observations have been described in the literature5,6,13,24 and nine cases were documented in German waters (Figs. 1, 2). In order to be able to verify the origin of recorded teeth marks in porpoise skin, it is crucial to record marks in detail including their pattern, location and inter-teeth distances. Using the latter, for example, interactions with dolphins can potentially be excluded. Although there has been the odd case of a severely injured seal showing comparable lesions to what is associated with grey seal predation14, such high rates of escape cases as described for porpoises have not been reported.
Despite the co-occurrence of porpoises and grey seals in the Baltic, no case of grey seal predation on a porpoise can be confirmed by the presented results. It remains unclear whether grey seals in this area of the Baltic just don’t prey on porpoises or whether other factors like differences in behaviour (e.g. primary area of predation further offshore) are involved.
Some of the observed behaviour of grey seals when catching a porpoise can be directly linked to the detected lesions. For example, Stringell et al.4 as well as Bouveroux et al.7 described the grey seal acting as an ambush predator and attacking the porpoise from below using its jaws to catch and retain the prey. Lesions starting in the throat area (parameter 3) combined with parallel multifocal puncture lesions (parameter 6) resemble what would be expected as the result of such an attack.
Despite a lower rate of variability in detected wound patterns in porpoise carcasses, care should be applied when assessing lesions, as there is always the chance of other factors being involved. Therefore, if possible, a combination of data sources (necropsy results, genetic detection of predator DNA, indicators at the stranding site, eye witness reports, etc.) should be used in a systematic evaluation.
Future research should focus on continuing thorough investigations of stranded marine mammal carcasses in order to further update and refine the suggested set of parameters. Additionally, results of current as well as retrospective analysis of stranding data should be used to support an evaluation of the ecological relevance of this behaviour. More

1.
Mucina, L. & Rutherford, M. C. The Vegetation of South Africa, Lesotho and Swaziland (South African National Biodiversity Institute, Pretoria, 2006).
Google Scholar 
2.
van Zinderen Bakker, E. M. The evolution of late Quaternary paleoclimates of Southern Africa. Palaeoecol. Afr. 9, 160–202 (1976).
Google Scholar 

3.
Cockcroft, M. J., Wilkinson, M. J. & Tyson, P. D. The application of a present-day climatic model to the late Quaternary in southern Africa. Clim. Change 10, 161–181 (1987).
ADS  Google Scholar 

4.
Chase, B. M. & Meadows, M. E. Late Quaternary dynamics of southern Africa’s winter rainfall zone. Earth Sci. Rev. 84(3), 103–138 (2007).
ADS  Google Scholar 

5.
Bistinas, I., Harrison, S. P., Prentice, I. C. & Pereira, J. M. C. Causal relationships vs. emergent patterns in the global controls of fire frequency. Biogeosciences 11, 5087–5101 (2014).
ADS  Google Scholar 

6.
Hoetzel, S., Dupont, L., Schefuß, E., Rommerskirchen, F. & Wefer, G. The role of fire in Miocene to Pliocene C 4 grassland and ecosystem evolution. Nat. Geosci. 6(12), 1027–1030 (2013).
ADS  CAS  Google Scholar 

7.
Bond, W. J., Woodward, F. I. & Midgley, G. F. The global distribution of ecosystems in a world without fire. New Phytol. 165(2), 525–538 (2005).
CAS  PubMed  Google Scholar 

8.
Ripley, B. et al. Fire ecology of C3 and C4 grasses depends on evolutionary history and frequency of burning but not photosynthetic type. Ecology 96(10), 2679–2691 (2015).
PubMed  Google Scholar 

9.
Pinto, H., Sharwood, R. E., Tissue, D. T. & Ghannoum, O. Photosynthesis of C3, C3–C4, and C4 grasses at glacial CO2. J. Exp. Bot. 65(13), 3669–3681 (2014).
PubMed  PubMed Central  Google Scholar 

10.
Roth-Nebelsick, A. & Konrad, W. Habitat responses of fossil plant species to palaeoclimate—possible interference with CO2?. Palaeogeogr. Palaeoclimatol. Palaeoecol. 467, 277–286 (2017).
Google Scholar 

11.
Ehleringer, J. R., Cerling, T. E. & Helliker, B. R. C4 photosynthesis, atmospheric CO2, and climate. Oecologia 112(3), 285–299 (1997).
ADS  PubMed  Google Scholar 

12.
Edwards, E. J., Osborne, C. P., Strömberg, C. A., Smith, S. A. & C4 Grasses Consortium. The origins of C4 grasslands: integrating evolutionary and ecosystem science. Science 328(5978), 587–591 (2010).
CAS  PubMed  Google Scholar 

13.
Hönisch, B., Hemming, N. G., Archer, D., Siddall, M. & McManus, J. F. Atmospheric carbondioxide concentration across the mid-Pleistocene transition. Science 324(5934), 1551–1554 (2009).
ADS  PubMed  Google Scholar 

14.
Yan, Y. et al. Two-million-year-old snapshots of atmospheric gases from Antarctic ice. Nature 574(7780), 663–666 (2019).
ADS  CAS  PubMed  Google Scholar 

15.
Faith, J. T., Rowan, J. & Du, A. Early hominins evolved within non-analog ecosystems. Proc. Natl. Acad. Sci. 116(43), 21478–21483 (2019).
ADS  CAS  PubMed  Google Scholar 

16.
Sealy, J., Naidoo, N., Hare, V. J., Brunton, S. & Faith, J. T. Climate and ecology of the palaeo-Agulhas Plain from stable carbon and oxygen isotopes in bovid tooth enamel from Nelson Bay Cave, South Africa. Quat. Sci. Rev. 235, 105974 (2019).
Google Scholar 

17.
Horwitz, L. K. & Chazan, M. Past and present at Wonderwerk Cave (Northern Cape Province, South Africa). Afr. Archaeol. Rev. 32(4), 595–612 (2015).
Google Scholar 

18.
Ecker, M. et al. The palaeoecological context of the Oldowan-Acheulean in southern Africa. Nat. Ecol. Evol. 2(7), 1080–1086 (2018).
PubMed  Google Scholar 

19.
Matmon, A. et al. New chronology for the southern Kalahari Group sediments with implications for sediment-cycle dynamics and early hominin occupation. Quat. Res. 84(1), 118–132 (2015).
Google Scholar 

20.
Vainer, S., Erel, Y. & Matmon, A. Provenance and depositional environments of Quaternary sediments in the southern Kalahari Basin. Chem. Geol. 476, 352–369 (2018).
ADS  CAS  Google Scholar 

21.
Prentice, I. C. et al. Modeling fire and the terrestrial carbon balance. Glob. Biogeochem. Cycles 25(3), 2–13 (2011).
Google Scholar 

22.
Braconnot, P. et al. Results of PMIP2 coupled simulations of the Mid-Holocene and Last Glacial Maximum-Part 1: experiments and large-scale features. Clim. Past 3(2), 261–277 (2007).
Google Scholar 

23.
Kelley, D. I. et al. A comprehensive benchmarking system for evaluating global vegetation models. Biogeosciences 10(5), 3313–3340 (2013).
ADS  Google Scholar 

24.
Chazan, M. et al. Archaeology, paleoenvironment and chronology of the early middle stone age component of Wonderwerk cave in the interior of southern Africa. J. Palaeolithic Archaeol. https://doi.org/10.1007/s41982-020-00051-8 (2020).
Article  Google Scholar 

25.
Lee-Thorp, J. A. & Beaumont, P. B. Vegetation and seasonality shifts during the late Quaternary deduced from 13C/12C ratios of grazers at Equus Cave, South Africa. Quat. Res. 43, 426–432 (1995).
Google Scholar 

26.
Vogel, J. C. The geographical distribution of Kranz species in southern Africa. South Afr. J. Sci. 75, 209–215 (1978).
Google Scholar 

27.
Zhou, H., Helliker, B. R., Huber, M., Dicks, A. & Akçay, E. C4 photosynthesis and climate through the lens of optimality. Proc. Natl. Acad. Sci. 115(47), 12057–12062 (2018).
CAS  PubMed  Google Scholar 

28.
Rubin, F., Palmer, A. R. & Tyson, C. Patterns of endemism within the Karoo National Park, South Africa. Bothalia 31(1), 117–133 (2001).
Google Scholar 

29.
Walker, S. J., Lukich, V. & Chazan, M. Kathu townlands: a high density earlier stone age locality in the interior of South Africa. PLoS ONE 9(7), e103436 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

30.
Lee-Thorp, J. A., Sponheimer, M. & Luyt, J. Tracking changing environments using stable carbon isotopes in fossil tooth enamel: an example from the South African hominin sites. J. Hum. Evol. 53(5), 595–601 (2007).
PubMed  Google Scholar 

31.
Codron, D., Brink, J. S., Rossouw, L. & Clauss, M. The evolution of ecological specialization in southern African ungulates: competition- or physical environmental turnover?. Oikos 117, 344–353 (2008).
Google Scholar 

32.
Plummer, T. W. et al. The environmental context of Oldowan hominin activities at Kanjera South, Kenya. In Interdisciplinary approaches to the Oldowan (eds Hovers, E. & Braun, D. R.) 149–160 (Springer, Berlin, 2009).
Google Scholar 

33.
Cerling, T. E. et al. Dietary changes of large herbivores in the Turkana Basin, Kenya from 4 to 1 Ma. Proc. Natl. Acad. Sci. 112(37), 11467–11472 (2015).
ADS  CAS  PubMed  Google Scholar 

34.
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data. https://doi.org/10.1038/s41597-020-0453-3 (2020).
Article  PubMed  PubMed Central  Google Scholar 

35.
Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 9(2), 161–185 (2003).
ADS  Google Scholar 

36.
Thonicke, K. et al. The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model. Biogeosciences 7(6), 1991–2011 (2010).
ADS  CAS  Google Scholar 

37.
Haxeltine, A. & Prentice, I. C. BIOME3: an equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types. Glob. Biogeochem. Cycles 10(4), 693–709 (1996).
ADS  CAS  Google Scholar 

38.
Haxeltine, A. & Prentice, I. C. A general model for the light-use efficiency of primary production. Funct. Ecol. 10, 551–561 (1996).
Google Scholar 

39.
Farquhar, G. D., Von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 plants. Planta 149, 78–90 (1980).
CAS  PubMed  PubMed Central  Google Scholar 

40.
Farquhar, G. D. & Von Caemmerer, S. Modelling of photosynthetic response to environmental conditions. In Physiological Plant Ecology II: Water Relations and Carbon Assimilation (eds Nobel, P. S. et al.) 549–587 (Springer, Berlin, 1982).
Google Scholar 

41.
Monteith, J. L. A reinterpretation of stomatal responses to humidity. Plant Cell Environ. 18, 357–364 (1995).
Google Scholar 

42.
Rothermel, R. C. A Mathematical Model for Predicting Fire Spread in Wildland Fuels (Vol. 115). Intermountain Forest and Range Experiment Station, Forest Service, US Department of Agriculture (1972).

43.
Sato, H., Kelley, D. I., Mayor, S. J., Cowling S. A., Calvo, M. M. & Prentice, I. C. Fire and low CO2 opened dry corridors in South America during the Last Glacial Maximum. Under Review for Nature Geosciences: NGS-2019–07–01558B (2020).

44.
Prentice, I. C., Harrison, S. P. & Bartlein, P. J. Global vegetation and terrestrial carbon cycle changes after the last ice age. New Phytol. 189(4), 988–998 (2011).
CAS  PubMed  Google Scholar  More

Cohort and data description
In order to examine seasonal changes of human molecular data, we leveraged the power of longitudinal multiomics data from profiling of 105 individuals (55 women and 50 men) with ages ranging from 25 to 75 years old (Fig. 1a; Supplementary Table 1). This cohort was generally healthy and well characterized for glucose dysregulation using annual oral glucose tolerance tests (OGTTs), insulin resistance measuring steady-state plasma glucose (SSPG), fasting glucose and hemoglobin A1c (HbA1c; an indicator of the average level of blood glucose over the past 100 days)19 as well as quarterly sample collections with measurements of transcriptomes (from peripheral blood mononuclear cells), proteome and metabolome from plasma, targeted cytokine and growth factor assays using serum. Nasal and gut microbiomes were analyzed using 16S rRNA sequencing providing information at the genus level and host exome sequencing was performed once from PBMCs (Fig. 1b). Moreover, 51 clinical laboratory tests were acquired on each visit and they were aligned to the meteorological data (e.g. air temperature), pollen counts (e.g. mold spores, grass pollens, tree pollens, weed pollens) and airborne fungi from the San Francisco bay area. In total, there were 902 visits (average across different types of omes‘) from which samples were drawn over up to 4 years (see “Methods”). The sample collections were generally evenly distributed throughout the year (Fig. 1b). Nearly all individuals lived in the San Francisco Bay Area with the exception of three individuals who lived in Southern California and frequented the Bay area (Supplementary Data 1). Participants in our study were well characterized for steady-state plasma glucose (SSPG) using the modified insulin suppression test20, in which 31 participants were insulin sensitive (SSPG  0.05, Supplementary Table 5, Supplementary Fig. 10). In our analysis we used subject ID as a random effect to account for different numbers of samples per subject. On the other hand, physical activity measured in total metabolic equivalent of task (MET) is significantly different between the IR and the IS groups in February, May, June, and August (P-value = 0.01787, Supplementary Fig. 11). However, a post-hoc analysis of all the omics features that were identified to be significantly different between the IR and the IS groups, are not associated with the physical activity. More

1.
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects 2019—Data Booklet (ST/ESA/ SER.A/377), (2019). https://population.un.org/wpp/Publications/Files/WPP2019_DataBooklet.pdf
2.
Pimentel, D. & Burgess, M. Environmental and economic costs of the application of pesticides primarily in the United States. In Integrated Pest Management: Innovation-Development Process (eds Peshin, R. & Dhawan, A. K.) 47–71 (Springer, Dordrecht, 2014). https://doi.org/10.1007/978-1-4020-8992-3_4
Google Scholar 

3.
Devine, G. J. & Furlong, M. J. Insecticide use: Contexts and ecological consequences. Agric. Hum. Values 24(3), 281–306. https://doi.org/10.1007/s10460-007-9067-z (2007).
Article  Google Scholar 

4.
Sanchis, V. & Bourguet, D. Bacillus thuringiensis: Applications in agriculture and insect resistance management. A review. Agron. Sustain. Dev. 28(1), 11–20. https://doi.org/10.1051/agro:2007054 (2008).
Article  Google Scholar 

5.
WHO report. WHO specifications and evaluations for public health pesticides: Bacillus thuringiensis subspecies israelensis strain AM65-52. (World Health Organization, Geneva, 2007).

6.
Rizzati, V., Briand, O., Guillou, H. & Gamet-Payrastre, L. Effects of pesticide mixtures in human and animal models: An update of the recent literature. Chem. Biol. Interact. 254, 231–246. https://doi.org/10.1016/j.cbi.2016.06.003 (2016).
Article  PubMed  CAS  Google Scholar 

7.
Lacey, L. A. et al. Insect pathogens as biological control agents: Back to the future. J. Invertebr. Pathol. 132, 1–41. https://doi.org/10.1016/j.jip.2015.07.009 (2015).
Article  PubMed  CAS  Google Scholar 

8.
Adang, M. J., Crickmore, N. & Jurat-Fuentes, J. L. Diversity of Bacillus thuringiensis Crystal Toxins and Mechanism of Action. Adv. Insect Physiol. 47, 39–87. https://doi.org/10.1016/B978-0-12-800197-4.00002-6 (2014).
Article  Google Scholar 

9.
Crickmore, N. Bacillus thuringiensis toxin classification. In Bacillus thuringiensis and Lysinibacillus sphaericus. (eds Fiuza, L.M. et al.) ISBN 978-3-319-56677-1, 41-52, (Spinger, Cham, 2017).

10.
WHO report. Guideline specification for bacterial larvicides for public health use. WHO document WHO/CDS/CPC/WHOPES/99.2 (World Health Organization, Geneva, 1999).

11.
Bravo, A., Pacheco, S., Gomez, I., Garcia-Gomez B., Onofre, J., Soberon, M. Insecticidal Proteins from Bacillus thuringiensis and their Mechanism of Action. In Bacillus thuringiensis and Lysinibacillus sphaericus (eds Fiuza, L.M. et al.) ISBN 978-3-319-56677-1, 53–66, (Spinger, Cham, 2017).

12.
Palma, L., Muñoz, D., Berry, C., Murillo, J. & Caballero, P. Bacillus thuringiensis toxins: An overview of their biocidal activity. Toxins 6(12), 3296–3325. https://doi.org/10.3390/toxins6123296 (2014).
Article  PubMed  PubMed Central  CAS  Google Scholar 

13.
Ben-Dov, E. et al. Extended screening by PCR for seven cry-group genes from field-collected strains of Bacillus thuringiensis. Appl. Environ. Microb. 63(12), 4883–4890. https://doi.org/10.1128/aem.63.12.4883-4890.1997 (1997).
CAS  Google Scholar 

14.
Berry, C. et al. Complete sequence and organization of pBtoxis, the toxin-coding plasmid of Bacillus thuringiensis subsp. israelensis. Appl. Environ. Microbiol. 68(10), 5082–5095. https://doi.org/10.1128/aem.68.10.5082-5095.2002 (2002).
Article  PubMed  PubMed Central  CAS  Google Scholar 

15.
Bravo, A., Gill, S. S. & Soberon, M. Mode of action of Bacillus thuringiensis Cry and Cyt toxins and their potential for insect control. Toxicon 49, 423–435. https://doi.org/10.1016/j.toxicon.2006.11.022 (2007).
Article  PubMed  CAS  Google Scholar 

16.
Wei, J. et al. Activation of Bt protoxin Cry1Ac in resistant and susceptible cotton bollworm. PLoS ONE 11(6), e0156560. https://doi.org/10.1371/journal.pone.0156560 (2016).
Article  PubMed  PubMed Central  CAS  Google Scholar 

17.
Bravo, A., Likitvivatanavong, S., Gill, S. S. & Soberon, M. Bacillus thuringiensis: A story of a successful bioinsecticide. Insect Biochem. Mol. Biol. 41(7), 423–431. https://doi.org/10.1016/j.ibmb.2011.02.006 (2011).
Article  PubMed  PubMed Central  CAS  Google Scholar 

18.
Caccia, S. et al. Midgut microbiota and host immunocompetence underlie Bacillus thuringiensis killing mechanism. Proc. Natl. Acad. Sci. USA 113(34), 9486–9491. https://doi.org/10.1073/pnas.1521741113 (2016).
Article  PubMed  CAS  Google Scholar 

19.
Glare, T.R., O’Callaghan, M. Bacillus thuringiensis: Biology, Ecology and Safety. ISBN: 9780471496304, 350, (Wiley, New York, 2000).

20.
Rubio-Infante, N. & Moreno-Fierros, L. An overview of the safety and biological effects of Bacillus thuringiensis Cry toxins in mammals. J. Appl. Toxicol. 36, 630–648. https://doi.org/10.1002/jat.3252 (2016).
Article  PubMed  CAS  Google Scholar 

21.
EFSA Panel on Biological Hazards (BIOHAZ). Risks for public health related to the presence of Bacillus cereus and other Bacillus spp. including Bacillus thuringiensis in foodstuffs. EFSA J. https://doi.org/10.2903/j.efsa.2016.4524 (2016).
Article  Google Scholar 

22.
Amichot, M., Curty, C., Benguettat-Magliano, O., Gallet, A. & Wajnberg, E. Side effects of Bacillus thuringiensis var. kurstaki on the hymenopterous parasitic wasp Trichogramma chilonis. Environ. Sci. Pollut. Res. Int. 23, 3097–3103. https://doi.org/10.1007/s11356-015-5830-7 (2016).
Article  PubMed  CAS  Google Scholar 

23.
Renzi, M. T. et al. Chronic toxicity and physiological changes induced in the honey bee by the exposure to fipronil and Bacillus thuringiensis spores alone or combined. Ecotoxicol. Environ. Saf. 127, 205–213. https://doi.org/10.1016/j.ecoenv.2016.01.028 (2016).
Article  PubMed  CAS  Google Scholar 

24.
Caquet, T., Roucaute, M., Le Goff, P. & Lagadic, L. Effects of repeated field applications of two formulations of Bacillus thuringiensis var. israelensis on non-target saltmarsh invertebrates in Atlantic coastal wetlands. Ecotoxicol. Environ. Saf. 74, 1122–1130. https://doi.org/10.1016/j.ecoenv.2011.04.028 (2011).
Article  PubMed  CAS  Google Scholar 

25.
Duguma, D. et al. Microbial communities and nutrient dynamics in experimental microcosms are altered after the application of a high dose of Bti. J. Appl. Ecol. 52, 763–773. https://doi.org/10.1111/1365-2664.12422 (2015).
Article  CAS  Google Scholar 

26.
Venter, H. J. & Bøhn, T. Interactions between Bt crops and aquatic ecosystems: A review. Environ. Toxicol. Chem. 35(12), 2891–2902. https://doi.org/10.1002/etc.3583 (2016).
Article  PubMed  CAS  Google Scholar 

27.
van Frankenhuyzen, K. Specificity and cross-order activity of Bacillus thuringiensis pesticidal proteins. In Bacillus thuringiensis and Lysinibacillus sphaericus (eds Fiuza, L.M. et al.) ISBN 978-3-319-56677-1, 127–172, (Springer, Cham, 2017).

28.
Bizzarri, M. F. & Bishop, A. H. The ecology of Bacillus thuringiensis on the phylloplane: Colonization from soil, plasmid transfer, and interaction with larvae of Pieris brassicae. Microb. Ecol. 56(1), 133–139. https://doi.org/10.1007/s00248-007-9331-1 (2008).
Article  PubMed  CAS  Google Scholar 

29.
Raymond, B., Wyres, K. L., Sheppard, S. K., Ellis, R. J. & Bonsall, M. B. Environmental factors determining the epidemiology and population genetic structure of the Bacillus cereus group in the field. PLoS Pathog. 6(5), e1000905. https://doi.org/10.1371/journal.ppat.1000905 (2010).
Article  PubMed  PubMed Central  CAS  Google Scholar 

30.
Hendriksen, N. B. & Hansen, B. M. Long-term survival and germination of Bacillus thuringiensis var. kurstaki in a field trial. Can. J. Microbiol. 48(3), 256–261. https://doi.org/10.1139/w02-009 (2002).
Article  PubMed  CAS  Google Scholar 

31.
Hung, T. P. et al. Persistence of detectable insecticidal proteins from Bacillus thuringiensis (Cry) and toxicity after adsorption on contrasting soils. Environ. Pollut. 208, 318–325. https://doi.org/10.1016/j.envpol.2015.09.046 (2016).
Article  PubMed  CAS  Google Scholar 

32.
Hung, T. P. et al. Fate of insecticidal Bacillus thuringiensis Cry protein in soil: Differences between purified toxin and biopesticide formulation. Pest Manag. Sci. 72, 2247–2253. https://doi.org/10.1002/ps.4262 (2016).
Article  PubMed  CAS  Google Scholar 

33.
Enger, K. S. et al. Evaluating the long-term persistence of Bacillus spores on common surfaces. Microb. Biotechnol. 11(6), 1048–1059. https://doi.org/10.1111/1751-7915.13267 (2018).
Article  PubMed  PubMed Central  CAS  Google Scholar 

34.
Couch, T.L. Industrial fermentation and formulation of entomopathogenic bacteria. In Entomopathogenic Bacteria: From Laboratory to Field Application (eds Charles, J.-F. et al.) ISBN 978-90-481-5542-2, 297–316.43, (Springer, Dordrecht, 2000).

35.
Brar, S. K., Verma, M., Tyagi, R. D. & Valéro, J. R. Recent advances in downstream processing and formulations of Bacillus thuringiensis based biopesticides. Process Biochem. 41(2), 323–342. https://doi.org/10.1016/j.procbio.2005.07.015 (2006).
Article  CAS  Google Scholar 

36.
Setlow, P. Spore resistance properties. Microbiol. Spectr. 2(5), TBS-0003-2012. https://doi.org/10.1128/microbiolspec.TBS-0003-2012 (2014).
Article  CAS  Google Scholar 

37.
European Food Safety Authority. Conclusion on the peer review of the pesticide risk assessment of the active substance Bacillus thuringiensis subsp. Kurstaki (strains ABTS 351, PB 54, SA 11, SA 12, EG 2348). EFSA J. 10(2), 2540. https://doi.org/10.2903/j.efsa.2012.2540 (2012).
Article  CAS  Google Scholar 

38.
Bächli, G. TaxoDros: The database on Taxonomy of Drosophilidae: Database 2020/1.https://www.taxodros.uzh.ch. (1999–2020).

39.
Tennessen, J. M. & Thummel, C. S. Coordinating growth and maturation—Insights from Drosophila. Curr. Biol. 21(18), R750–R757. https://doi.org/10.1016/j.cub.2011.06.033 (2011).
Article  PubMed  PubMed Central  CAS  Google Scholar 

40.
Benz, G. & Perron, J. M. The toxic action of Bacillus thuringiensis “exotoxin” on Drosophila reared in yeast-containing and yeast-free media. Experientia 23(10), 871–872 (1967).
PubMed  CAS  Google Scholar 

41.
Saadoun, I., Al-Moman, F., Obeidat, M., Meqdam, M. & Elbetieha, A. Assessment of toxic potential of local Jordanian Bacillus thuringiensis strains on Drosophila melanogaster and Culex sp. (Diptera). J. Appl. Microbiol. 90, 866–872. https://doi.org/10.1046/j.1365-2672.2001.01315.x (2001).
Article  PubMed  CAS  Google Scholar 

42.
Khyami-Horani, H. Toxicity of Bacillus thuringiensis and B. sphaericus to laboratory populations of Drosophila melanogaster (Diptera: Drosophilidae). J. Basic Microbiol. 42(2), 105–110. https://doi.org/10.1002/1521-4028(200205)42:23.0.CO;2-S (2002). 
Article  PubMed  Google Scholar 

43.
Obeidat, M. Toxicity of local Bacillus thuringiensis isolates against Drosophila melanogaster. WJAS 4(2), 161–167 (2008).
Google Scholar 

44.
Obeidat, M., Khymani-Horani, H. & Al-Momani, F. Toxicity of Bacillus thuringiensis β-exotoxins and δ-endotoxins to Drosophila melanogaster, Ephestia kuhniella and human erythrocytes. Afr. J. Biotechnol. 11(46), 10504–10512 (2012).
Google Scholar 

45.
Cossentine, J., Robertson, M. & Xu, D. Biological activity of Bacillus thuringiensis in Drosophila suzukii (Diptera: Drosophilidae). J. Econ. Entomol. 109(3), 1–8. https://doi.org/10.1093/jee/tow062 (2016).
Article  CAS  Google Scholar 

46.
Biganski, S., Jehle, J. A. & Kleepies, R. G. Bacillus thuringiensis serovar israelensis has no effect on Drosophila suzukii Matsumura. J. Appl. Entomol. 142, 33–36. https://doi.org/10.1111/jen.12415 (2017).
Google Scholar 

47.
Haller, S., Romeis, J. X. R. & Meissle, M. Effects of purified or plant-produced Cry proteins on Drosophila melanogaster (Diptera: Drosophilidae) larvae. Sci. Rep. 7(1), 11172. https://doi.org/10.1038/s41598-017-10801-4 (2017).
ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

48.
Benado, M. & Brncic, D. An eight-year phenological study of a local drosophilid community in Central Chile. J. Zool. Syst. Evol. Res. 32, 51–63. https://doi.org/10.1111/j.1439-0469.1994.tb00470.x (1994).
Article  Google Scholar 

49.
Nunney, L. The colonization of oranges by the cosmopolitan Drosophila. Oecologia 108, 552–561. https://www.jstor.org/stable/4221451 (1996).
ADS  PubMed  Google Scholar 

50.
Mitsui, H. & Kimura, M. T. Coexistence of drosophilid flies: Aggregation, patch size diversity and parasitism. Ecol. Res. 15, 93–100.  https://doi.org/10.1046/j.1440-1703.2000.00328.x (2000).
Google Scholar 

51.
Withers, P. & Allemand, R. Les drosophiles de la région Rhône-Alpes (Diptera, Drosophilidae). Bull. Soc. Entomol. Fr. 117(4), 473–482. https://www.persee.fr/doc/bsef_0037-928x_2012_num_117_4_3076 (2012).
Google Scholar 

52.
Stevens, T., Song, S., Bruning, J. B., Choo, A. & Baxter, S. W. Expressing a moth abcc2 gene in transgenic Drosophila causes susceptibility to Bt Cry1Ac without requiring a cadherin-like protein receptor. Insect Biochem. Mol. Biol. 80, 61–70. https://doi.org/10.1016/j.ibmb.2016.11.008 (2017).
Article  PubMed  CAS  Google Scholar 

53.
George, Z., Crickmore, N. Bacillus thuringiensis applications in agriculture. In Bacillus thuringiensis Biotechnology (ed Sansinenea, E.) 392, (Springer, Dordrecht, 2012).

54.
Nepoux, V., Haag, C. R. & Kawecki, T. J. Effects of inbreeding on aversive learning in Drosophila. J. Evol. Biol. 23, 2333–2345. https://doi.org/10.1111/j.1420-9101.2010.02094.x (2010).
Article  PubMed  CAS  Google Scholar 

55.
Vantaux, A., Ouattarra, I., Lefèvre, T. & Dabiré, K. R. Effects of larvicidal and larval nutritional stresses on Anopheles gambiae development, survival and competence for Plasmodium falciparum. Parasite. Vector. 9, 226. https://doi.org/10.1186/s13071-016-1514-5 (2016).
Article  CAS  Google Scholar 

56.
Moret, Y. & Schmid-Hempel, P. Survival for immunity: The price of immune system activation for bumblebee workers. Science 290(5494), 1166–1168. https://doi.org/10.1126/science.290.5494.1166 (2000).
ADS  Article  PubMed  CAS  Google Scholar 

57.
Kutzer, M. A. & Armitage, S. A. O. The effect of diet and time after bacterial infection on fecundity, resistance, and tolerance in Drosophila melanogaster. Ecol. Evol. 6(13), 4229–4242. https://doi.org/10.1002/ece3.2185 (2016).
Article  PubMed  PubMed Central  Google Scholar 

58.
Andersen, L. H., Kristensen, T. N., Loeschcke, V., Toft, S. & Mayntz, D. Protein and carbohydrate composition of larval food affects tolerance to thermal stress and desiccation in adult Drosophila melanogaster. J. Insect Physiol. 56, 336–340. https://doi.org/10.1016/j.jinsphys.2009.11.006 (2010).
Article  PubMed  CAS  Google Scholar 

59.
Rion, S. & Kawecki, T. J. Evolutionary biology of starvation resistance: What we have learned from Drosophila. J. Evol. Biol. 20(5), 1655–1664. https://doi.org/10.1111/j.1420-9101.2007.01405.x (2007).
Article  PubMed  CAS  Google Scholar 

60.
Burger, J. M. S., Buechel, S. D. & Kawecki, T. J. Dietary restriction affects lifespan but not cognitive aging in Drosophila melanogaster. Aging Cell 9, 327–335. https://doi.org/10.1111/j.1474-9726.2010.00560.x (2010).
Article  PubMed  CAS  Google Scholar 

61.
Khazaeli, A. A. & Curtsinger, J. W. Genetic analysis of extended lifespan in Drosophila melanogaster III. On the relationship between artificially selected and wild stocks. Genetica 109, 245–253. https://doi.org/10.1023/a:1017569318401 (2000).
Article  PubMed  CAS  Google Scholar 

62.
Atkinson, W. & Shorrocks, B. Breeding site specificity in the domestic species of Drosophila. Oecologia 29(3), 223–232. https://www.jstor.org/stable/4215461 (1977).
ADS  PubMed  CAS  Google Scholar 

63.
Walsh, D. B. et al. Drosophila suzukii (Diptera: Drosophilidae): Invasive pest of ripening soft fruit expanding its geographic range and damage potential. J. Integr. Pest Manag. https://doi.org/10.1603/IPM10010 (2011).
Article  Google Scholar 

64.
Delbac, L. et al. Drosophila suzukii est-elle une menace pour la vigne?. Phytoma 679, 16–21 (2014).
Google Scholar 

65.
Poyet, M. et al. Invasive host for invasive pest: When the Asiatic cherry fly (Drosophila suzukii) meets the American black cherry (Prunus serotine) in Europe. Agric. For. Entomol. 16(3), 251–259. https://doi.org/10.1111/afe.12052 (2014).
Article  Google Scholar 

66.
Poulin, B., Lefebvre, G. & Paz, L. Red flag for green spray: Adverse trophic effects of Bti on breeding birds. J. Appl. Ecol. 47, 884–889. https://doi.org/10.1111/j.1365-2664.2010.01821.x (2010).
Article  Google Scholar 

67.
Zeigler, D.R. Bacillus genetic stock center catalog of strains, 7th edition. Part 2: Bacillus thuringiensis and Bacillus cereus. http://www.bgsc.org/_catalogs/Catpart2.pdf (1999).

68.
Gonzales, J. M. Jr., Brown, B. J. & Carlton, B. C. Transfer of Bacillus thuringiensis plasmids coding for δ-endotoxin among strains of B. thuringiensis and B. cereus. Proc. Natl Acad. Sci. USA 79, 6951–6955. https://doi.org/10.1073/pnas.79.22.6951 (1982).
ADS  Article  Google Scholar 

69.
Santos, M., Borash, D. J., Joshi, A., Bounlutay, N. & Mueller, L. D. Density-dependent natural selection in Drosophila: Evolution of growth rate and body size. Evolution 51(2), 420–432. https://doi.org/10.2307/2411114 (1997).
Article  PubMed  Google Scholar 

70.
Bradberry, S. M., Proudfoot, A. T. & Vale, J. A. Glyphosate poisoning. Toxicol. Rev. 23(3), 159–167. https://doi.org/10.2165/00139709-200423030-00003 (2004).
Article  PubMed  CAS  Google Scholar 

71.
R Development Core Team. R: A language and environment for statistical computing. ISBN 3-900051-07-0 https://www.R-project.org (R Foundation for Statistical Computing, Vienna, 2008).

72.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).
Google Scholar 

73.
Kosmidis I. brglm: Bias Reduction in Binary-Response Generalized Linear Models. R package version 0.6.1, https://www.ucl.ac.uk/~ucakiko/software.html, (2017).

74.
Horton, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometrical J. 50(3), 346–363. https://doi.org/10.1002/bimj.200810425 (2008).
MathSciNet  Article  Google Scholar 

75.
Therneau, T.M., Grambsch, P.M. Modeling Survival Data: Extending The Cox Model. ISBN 0-387-98784-3 (Springer, New York, 2000).

76.
Therneau, T.M. coxme: Mixed Effects Cox Models. R package version 2.2-5. https://CRAN.R-project.org/package=coxme (2015). More

1.
Singh BK, Bardgett RD, Smith P, Reay DS. Microorganisms and climate change: terrestrial feedbacks and mitigation options. Nat Rev Microbiol. 2010;8:779–90.
CAS  Article  Google Scholar 
2.
Schlesinger WH, Andrews JA. Soil respiration and the global carbon cycle. Biogeochemistry. 2000;48:7–20.
CAS  Article  Google Scholar 

3.
Kallenbach CM, Frey SD, Grandy AS. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat Commun. 2016;7:13630.
CAS  Article  Google Scholar 

4.
Six J, Frey SD, Thiet RK, Batten KM. Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Sci Soc Am J. 2006;70:555–69.
CAS  Article  Google Scholar 

5.
Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol. 2019;17:569–86.
CAS  Article  Google Scholar 

6.
Zhou J, Xue K, Xie J, Deng Y, Wu L, Cheng X, et al. Microbial mediation of carbon-cycle feedbacks to climate warming. Nat Clim Change. 2012;2:106–10.
CAS  Article  Google Scholar 

7.
Aerts R. Climate, leaf litter chemistry and leaf litter decomposition in terrestrial ecosystems: a triangular relationship. Oikos. 1997;79:439–49.
Article  Google Scholar 

8.
Bradford MA, Veen GFC, Bonis A, Bradford EM, Classen AT, Cornelissen JHC, et al. A test of the hierarchical model of litter decomposition. Nat Ecol Evol. 2017;1:1836–45.
Article  Google Scholar 

9.
Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.
CAS  Article  Google Scholar 

10.
Geisen S, Mitchell EAD, Adl S, Bonkowski M, Dunthorn M, Ekelund F, et al. Soil protists: a fertile frontier in soil biology research. FEMS Microbiol Rev. 2018;42:293–323.
CAS  Article  Google Scholar 

11.
Oliverio AM, Geisen S, Delgado-Baquerizo M, Maestre FT, Turner BL, Fierer N. The global-scale distributions of soil protists and their contributions to belowground systems. Sci Adv. 2020;6:eaax8787.
Article  Google Scholar 

12.
Rose JM, Vora NM, Countway PD, Gast RJ, Caron DA. Effects of temperature on growth rate and gross growth efficiency of an Antarctic bacterivorous protist. ISME J. 2009;3:252–60.
CAS  Article  Google Scholar 

13.
Schulz-Bohm K, Geisen S, Wubs ERJ, Song C, de Boer W, Garbeva P. The prey’s scent—volatile organic compound mediated interactions between soil bacteria and their protist predators. ISME J. 2017;11:817–20.
CAS  Article  Google Scholar 

14.
Kuikman PJ, Jansen AG, van Veen JA, Zehnder AJB. Protozoan predation and the turnover of soil organic carbon and nitrogen in the presence of plants. Biol Fertil Soils. 1990;10:22–28.
CAS  Article  Google Scholar 

15.
Crowther TW, Boddy L, Hefin Jones T. Functional and ecological consequences of saprotrophic fungus–grazer interactions. ISME J. 2012;6:1992–2001.
CAS  Article  Google Scholar 

16.
Bradford MA, Tordoff GM, Eggers T, Jones TH, Newington JE. Microbiota, fauna, and mesh size interactions in litter decomposition. Oikos. 2002;99:317–23.
Article  Google Scholar 

17.
Jousset A, Rochat L, Pechy-Tarr M, Keel C, Scheu S, Bonkowski M. Predators promote defence of rhizosphere bacterial populations by selective feeding on non-toxic cheaters. ISME J. 2009;3:666–74.
CAS  Article  Google Scholar 

18.
Crowther TW, Thomas SM, Maynard DS, Baldrian P, Covey K, Frey SD, et al. Biotic interactions mediate soil microbial feedbacks to climate change. Proc Natl Acad Sci. 2015;112:7033.
CAS  Article  Google Scholar 

19.
Serna-Chavez HM, Fierer N, van Bodegom PM. Global drivers and patterns of microbial abundance in soil. Glob Ecol Biogeogr. 2013;22:1162–72.
Article  Google Scholar 

20.
Scharlemann JPW, Tanner EVJ, Hiederer R, Kapos V. Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Manag. 2014;5:81–91.
CAS  Article  Google Scholar  More

1.
Makino, S. Biology of Latibulus argiolus (Hymenoptera, Ichneumonidae), a Parasitoid of the Paper Wasp Polistes biglumis (Hymenoptera, Vespidae). Kontyû 51, 426–434 (1983).
Google Scholar 
2.
Oh, S.-H., An, S.-L. & Lee, J.-W. Review of Korean Latibulus (Hymenoptera: Ichneumonidae: Cryptinae) and a key to the world species. Can. Entomol. 144, 509–525 (2012).
Article  Google Scholar 

3.
Quicke, D. L. J. The Braconid and Ichneumonid Parasitoid Wasps: Biology Systematics, Evolution and Ecology (Wiley Blackwell, Amsterdam, 2015).
Google Scholar 

4.
Paukku, S. & Kotiaho, J. S. Female oviposition decisions and their impact on progeny life-history traits. J. Insect Behav. 21, 505–520 (2008).
Article  Google Scholar 

5.
Rusina, L. Y. The role of parasitoids in regulation of Polistes Wasp population (Hymenoptera, Vespidae: Polistinae). Entomol. Rev. 93, 271–280 (2012).
Article  Google Scholar 

6.
Coelho, N. H. P. et al. Understanding genetic diversity, spatial genetic structure, and mating system through microsatellite markers for the conservation and sustainable use of Acrocomia aculeata (Jacq.) Lodd. Ex Mart. Conserv. Genet. 19, 879–891 (2018).
CAS  Article  Google Scholar 

7.
Manlik, O. et al. Demography and genetics suggest reversal of dolphin source-sink dynamics, with implications for conservation. Mar. Mammal Sci. 35, 732–759 (2019).
Article  Google Scholar 

8.
Nowicki, P. et al. What keeps “living dead” alive: demography of a small and isolated population of Maculinea (=Phengaris) alcon. J. Insect Conserv. 23, 201–210 (2019).
Article  Google Scholar 

9.
Selkoe, K. A. & Toonen, R. J. Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9, 615–629 (2006).
Article  Google Scholar 

10.
Olafsson, K., Hjorleifsdottir, S., Pampoulie, C., Hreggvidsson, G. O. & Gudjonsson, S. Novel set of multiplex assays (SalPrint15) for efficient analysis of 15 microsatellite loci of contemporary samples of the Atlantic salmon (Salmo salar). Mol. Ecol. Resour. 10, 533–537 (2010).
CAS  Article  Google Scholar 

11.
Randi, E. et al. Multilocus detection of wolf x dog hybridization in Italy, and guidelines for marker selection. PLoS ONE 9, e86409 (2014).
ADS  Article  Google Scholar 

12.
Hung, C.-M., Yu, A.-Y., Lai, Y.-T. & Shaner, P.-J.L. Developing informative microsatellite markers for nonmodel species using reference mapping against a model species’ genome. Sci. Rep. 6, 23087 (2016).
ADS  CAS  Article  Google Scholar 

13.
Marcus, T., Assmann, T., Durka, W. & Drees, C. A suite of multiplexed microsatellite loci for the ground beetle Abax parallelepipedus (Piller and Mitterpacher, 1783) (Coleoptera, Carabidae). Conserv. Genet. Resour. 5, 1151–1156 (2013).
Article  Google Scholar 

14.
Panagiotopoulou, H., Baca, M., Baca, K., Stanković, A. & Żmihorski, M. Optimization and validation of a multiplex assay for microsatellite loci analysis in the field cricket, Gryllus campestris (Orthoptera: Gryllidae). J. Asia-Pac. Entomol. 18, 421–424 (2015).
CAS  Article  Google Scholar 

15.
PacBio Pacific Biosciences, Procedure & Checklist: 2 kb Template Preparation and Sequencing. https://www.pacb.com/wp-content/uploads/2015/09/Procedure-Checklist-2-kb-Template-Preparation-and-Sequencing.pdf.

16.
Faircloth, B. C. MSATCOMMANDER: detection of microsatellite repeat arrays and automated, locus-specific primer design. Mol. Ecol. Resour. 8, 92–94 (2008).
CAS  Article  Google Scholar 

17.
Zhang, Z., Schwartz, S., Wagner, L. & Webb, M. A greedy algorithm for aligning DNA sequences. J. Comput. Biol. 7, 203–214 (2000).
CAS  Article  Google Scholar 

18.
Morgulis, A. et al. Database indexing for production MegaBLAST searches. Bioinformatics 24, 1757–1764 (2008).
CAS  Article  Google Scholar 

19.
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
CAS  Article  Google Scholar 

20.
Schuelke, M. An economic method for the fluorescent labeling of PCR fragments. Nat. Biotechnol. 18, 233–234 (2000).
CAS  Article  Google Scholar 

21.
Austin, J. D. et al. Permanent genetic resources added to Molecular Ecology Resources Database 1 February 2011–31 March 2011. Mol. Ecol. Resour. 11, 757–758 (2011).
CAS  Article  Google Scholar 

22.
Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539 (2012).
CAS  Article  Google Scholar 

23.
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
Article  Google Scholar 

24.
Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).
Article  Google Scholar 

25.
Rousset, F. GENEPOP’007: a complete reimplementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).
Article  Google Scholar 

26.
Rice, W. R. Analyzing tables of statistical tests. Evolution 43, 223–225 (1989).
Article  Google Scholar 

27.
Pompanon, F., Bonin, A., Bellemain, E. & Taberlet, P. Genotyping errors: causes, consequences and solutions. Nat. Rev. Genet. 6, 846–847 (2005).
Article  Google Scholar 

28.
Grohme, M. A., Soler, R. F., Wink, M. & Frohme, M. Microsatellite marker discovery using single molecule real-time circular consensus sequencing on the Pacific Biosciences RS. Biotechniques 55, 253–256 (2013).
CAS  Article  Google Scholar 

29.
Liljegren, M. M., de Muinck, E. J. & Trosvik, P. Microsatellite length scoring by single molecule real time sequencing-effects of sequence structure and PCR regime. PLoS ONE 11, e0159232 (2016).
Article  Google Scholar 

30.
Dutta, N. et al. Microsatellite marker set for genetic diversity assessment of primitive Chitala chitala (Hamilton, 1822) derived through SMRT sequencing technology. Mol. Biol. Rep. 46, 41–49 (2018).
Article  Google Scholar 

31.
Wei, N., Bemmels, J. B. & Dick, C. W. The effects of read length, quality and quantity on microsatellite discovery and primer development: from Illumina to Pac Bio. Mol. Ecol. Resour. 14, 953–965 (2014).
CAS  PubMed  Google Scholar 

32.
Corner, S., Yuzbasiyan-Gurkan, V., Agnew, D. & Venta, P. J. Development of a 12-plex of new microsatellite markers using a novel universal primer method to evaluate the genetic diversity of jaguars (Panthera onca) from North American zoological institutions. Conservation Genet. Resour. 11, 487–497 (2019).
Article  Google Scholar 

33.
Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).
CAS  Article  Google Scholar 

34.
Blondin, L. et al. Characterization and comparison of microsatellite markers derived from genomic and expressed libraries for the desert locust. J. Appl. Entomol. 137, 673–683 (2013).
Article  Google Scholar 

35.
Chistiakov, D. A., Hellemans, B. & Volckaert, F. A. Microsatellites and their genomic distribution, evolution, function and applications: a review with special reference to fish genetics. Aquaculture 255, 1–29 (2006).
CAS  Article  Google Scholar 

36.
Cheng, L., Zhang, Y., Lu, C.-Y., Li, C. & Sun, X.-W. Development and characterization of four moderate multiplex microsatellite panels in crucian carp (Carassius auratus). Conserv. Genet. Resour. 5, 821–823 (2013).
Article  Google Scholar  More

To investigate the pathway from adult population characteristics to spawning behaviour, egg production, and ultimately to recruitment (Fig. 1), we used three data sources; an egg survey (for estimates of egg distribution, total egg production, and environmental variables), biological samples of the commercial fishery (for estimates of spawning duration and peak, and maternal body condition), and stock assessment outputs (for estimates of age-1 recruits, spawning stock biomass and age structure).
Figure 1

Conceptual framework of the pathway from spawners to recruits and the underlying mechanisms investigated (stock demographic structure and environmental conditions in red and green, respectively).

Full size image

Egg survey data
Sampling
Mackerel enter the southern Gulf of St. Lawrence (sGSL, Eastern Canada) in early June each year to spawn, after overwintering along the north-eastern US continental shelf (from Sable Island to the Mid-Atlantic Bight29,30). Each year, Fisheries and Oceans Canada (DFO) conducts a 2-week long mackerel egg survey in the sGSL (a 65-station fixed grid 20 nautical miles apart spanning the dominant mackerel spawning area) around the average mackerel peak spawning date of June 21st. Over this period, a large fraction of spawning occurs and the survey is therefore believed to reflect appropriately spawning intensity and spatio-temporal properties. Stations consist of double oblique tows using 61-cm Bongo nets with 333 µm mesh size and flowmeters carried out on board a research vessel at a speed of 2.5 knots from 0 to 50 m depth to estimate daily and total egg production while also measuring physical and biological oceanographic variables (see further details in SI Appendix A). This survey has been carried out consistently since 1982, except for no surveys in 1995 and 1997. Several indices are derived from this mackerel egg survey: total egg production, egg distribution, water temperature, and zooplankton biomass, species composition, abundance, and distribution.
Total egg production and distribution
Annual total egg production was calculated according to a standard DFO protocol based on the Daily Egg Production Method31. Stage 1 (spawned less than 24 h ago) and 5 (i.e., damaged stage 1 eggs) egg counts were standardized by the volume of filtered water and the depth of the sampled water column to provide egg densities per station (number m−2). These numbers were then adjusted for incubation time32 to obtain daily egg production point estimates. Spatial interpolation was done across a grid of 3320 coordinates using ordinary kriging to calculate a mean daily egg production estimate per grid cell, which was extrapolated to the surface area sampled. Annual egg production estimates were obtained by dividing by the proportion of reproductively active fish at the median date of the survey. This latter value, along with peak spawning date and spawning duration was calculated using a logistic model describing the daily evolution of the gonadosomatic index, based on corresponding biological data (see further details in Doniol-Valcroze et al.31, and in “Commercial fishery sampling”).
To examine the potential inter-annual spatial mismatch between spawning location and the optimal habitat for larvae, we calculated the spatial extent (spawning area) and the position of the centre of gravity (spawning longitude and latitude) of spawning for each year in the time series. The spatial extent of egg production was determined using an α-convex hull on stations where eggs were present33. The centre of gravity of total egg production was calculated by taking the arithmetic mean of the coordinates of each station weighted by their individual observed egg production.
Environmental indices
Sea surface temperature (SST, °C) directly affects early life stage growth and survival7, but might also have an indirect effect on recruitment through adult spawning behaviour, as mackerel generally spawn between 8 and 15 °C34. Therefore, we produced an SST index by averaging June CTD-measured mean water temperatures in the first 10 m over stations, where the majority of mackerel eggs and larvae occur35.
We hypothesized that the main adult mackerel prey (i.e., C. hyperboreus and capelin, Mallotus villosus36) might be influential as well, as they may affect spawning location and therefore be an indirect driver of recruitment. Capelin is despite its importance as prey in terms of weight36 not considered as a potential driver of spawning location, because its consumption by mackerel is infrequent, only important to the larger mackerel and likely opportunistic. As such, habitat selection is most likely to be related to copepod abundance and we developed spatial, biomass, and composition indices in June in the sGSL only for C. hyperboreus. As a proxy of adult mackerel prey location, we computed the annual centre of gravity of C. hyperboreus biomass (latitude and longitude) with the same methodology used for total egg production. Also, we estimated the total C. hyperboreus biomass (mg m−2) in the sGSL37. The percentage of C. hyperboreus biomass relative to the total Calanus spp. biomass (% C. hyp.) was calculated as we hypothesized that changes in C. hyperboreus proportion may have influenced adult mackerel feeding behaviour and thus spawning locations.
Mackerel larvae mainly feed on the early life stages (eggs, nauplii, and young copepodites) of C. finmarchicus, Pseudocalanus spp. and Temora longicornis25. The copepod daily egg production (CEDP, µg egg carbon L−1 d−1) of these three copepod taxa, calculated based on adult female abundance and species-specific per capita daily egg production (see details in the SI Appendix A), was previously recognized as a good predictor of mackerel recruitment23,24,25. High larval prey abundance might, however, be irrelevant when there is a temporal or spatial mismatch with larval distribution. An annual (y) index of a temporal match was therefore calculated in June in the spawning area as the proportion of older stage 6 female C. finmarchicus, producing prey for mackerel early life stages, with respect to the number of younger immature copepodite stages 4 and 526 (Eq. 1).

$${Temporal match}_{y}=100%times {N}_{C. fin female}/{N}_{C. fin stages 4-5}$$
(1)

Higher percentages of stage 6 female copepodites during mackerel spawning (i.e., a later development of the plankton community) should improve the temporal match between hatching and the availability of prey for emerging larvae26. This same index could not include Pseudocalanus spp. and Temora longicornis as only data for stage 6 adults were available. C. finmarchicus is, however, considered to be a good indicator of the overall zooplankton phenology in spring and early summer in the sGSL and should also reflect Pseudocalanus spp. and Temora longicornis phenology27. An annual index of a spatial match between mackerel egg distribution and their near-future prey was determined as the sum of mackerel daily egg production (DEP) at stations (s) with sufficient prey (i.e., copepod daily egg production above a threshold value) divided by the daily egg production of mackerel over all stations (Eq. 2).

$${Spatial match}_{y}=100%times {sum }_{s=1}^{S >threshold}{DEP}_{s,y}/{sum }_{s=1}^{S}{DEP}_{s,y}$$
(2)

The threshold copepod daily egg production value was determined as the 25th quantile of values measured for all years and stations, which excludes zero and near-zero prey availabilities unlikely to be able to support larval survival. This index of spatial match captures a combined effect of the abundance and distribution of the prey in relation to the distribution of the fish eggs. Note that due to the availability of taxonomic zooplankton data, Pseudocalanus spp., Temora spp., C. finmarchicus and C. hyperboreus data and hence all indices derived from it were available for only 21 years (but covering the entire span of the time series; 1982, 1985, 1987, 1990, 1993, 1996, 1999, 2000, 2003 and 2006 to 2017). Spatial and temporal match–mismatch proxies were based on a match with the mackerel eggs rather than the early larval phase. We expect this to introduce little noise as the development time of mackerel eggs is typically less than 6 days and mackerel larval development is fast (about 20 days32). All the environmental variables used and the associated hypotheses are summarized in Table 1.
Table 1 Summary of all the hypotheses tested along the pathway from spawners to recruits and associated references.
Full size table

Commercial fishery sampling
Adult mackerel samples are collected annually by DFO from the commercial fishery. The sampling covers the entire spawning area and period (thrice a week) and on average 4998 (range 421–14,858) individual fish are analysed each year. We used this data to calculate the annual peak spawning date (spawn. peak), spawning duration (spawn. duration), and maternal body condition.
Peak spawning date and duration were calculated each year based on the fit of a logistic model of the daily evolution of the gonadosomatic index. The mean value of the derived symmetrical probability density function was defined as the peak spawning day and the time between the 2.5% and 97.5% quantiles was estimated to represent the spawning duration in days.
As relatively fatter individuals might spawn more and higher quality eggs38, mature females (i.e., reproductive stages 3–839) sampled between their arrival in the sGSL and June 21st (the average peak spawning date) were selected to investigate the potential influence of pre-spawning fat reserves on total egg production and recruitment with the relative body condition index (Kn40, Eq. 3):

$${K}_{n}=frac{W}{{W}_{r}}$$
(3)

where W is the observed somatic weight (g) of an individual and Wr the predicted weight of an individual of a given fork length (FL, cm) calculated with Wr = αFLβ (α and β are nonlinear least-squares regression parameters).
Mackerel SSB, recruitment and age structure
Annual mackerel SSB, recruitment residuals and an index of age structure were derived from an age-structured state-space stock assessment model applied to the period 1968–201828. Note that the model was calibrated using an SSB index directly calculated from total egg production. In the assessment model, a two-parameter Beverton-Holt stock-recruitment relationship was used to estimate annual recruitment (abundance at age 1), and the residuals of this relationship were used in subsequent analyses (Rres). An indicator of the annual age structure was considered as bigger, older mackerel spawners ( > age 5) are known to have a greater fecundity, and spawn in different spatial and temporal niches than younger females35,41. Mean biomass-weighted age (MA) was calculated using mature biomass-at-age (({SSB}_{a})) as follow in the Eq. (4):

$$MA=frac{sum_{a=1}^{a=10}(a{SSB}_{a})}{sum_{a=1}^{a=10}{SSB}_{a}}$$
(4)

MA was based on biomass rather than abundance to better reflect the stock’s reproductive potential42.
Mackerel early life stages are prey for pelagic fish sharing the surface waters of the sGSL. Herring are, relative to other potential predators, dominant, widely distributed and known predators of mackerel eggs and larvae36. Hence, we used cumulated spring and fall herring model-derived annual biomass43 as a proxy of predation pressure on mackerel early life stages.
Statistical analyses
Recruitment variability driven by spawning aspects and environmental gradients
We analysed the relationships between the successive steps leading to recruitment (spawning aspects, egg production and recruitment) and both demographic and environmental effects using generalised linear models (GLMs). All model configurations (response and explanatory variables) are given in Supplementary Table S2. Explanatory variables were normalized (i.e., by subtracting the mean and dividing by the standard deviation for each variable) to facilitate comparison of their respective effects (i.e., through their coefficients). When the response variable was Rres (with a 1-year lag), residuals were assumed to follow a Gaussian distribution with an identity link function, whereas for the other response variables a Gamma distribution with a log link function was used (as they can only take positive values44). Before performing GLM computations, collinearity between explanatory variables was measured using variance inflation factors (VIFs), considering a VIF threshold of 344. Specifically, mackerel SSB and MA were highly correlated (Pearson correlation coefficient  > 0.7, see Supplementary Fig S1), so distinct sets of GLMs testing SSB or MA on spawning aspects were used. A backwards model selection procedure was performed, choosing the model with the lowest Akaike’s information criterion corrected for small samples sizes (AICc). If independent models including either SSB or MA showed an AICc difference less than 2, both were reported. Assumptions of homoscedasticity and normality were checked using residual plots while assumptions of independence (to ensure no autocorrelation was present) were checked using correlograms. By replacing GLMs with generalized additive models, the same conclusions were reached and there were no indications of strong non-linear effects.
Variability in total egg production (TEP) could not be linked directly to SSB and MA using regression techniques, because of model circularity (a TEP derived SSB index was used to estimate SSB) and collinearity (SSB and MA are significantly correlated and difficult to disentangle). Although the relative effect size of both variables could not be measured, the positive link between them is well established in the literature (i.e., that larger, older fish produce more eggs41). We, therefore, focussed our efforts on the possible link between TEP per unit of biomass, thereby removing the effect of fish number- and weight-at-age, and maternal body condition. Furthermore, by working with stock–recruitment residuals, we removed in large part the intrinsically related process of TEP. That is, the stock–recruitment relationship is presumably created by the biological dependence of TEP on SSB, and subsequently of recruitment on TEP. This link was hence not explicitly considered, although being present. A Jackknife procedure was conducted to assess the consistency and robustness of the optimal models explaining recruitment residuals (see SI appendix A). Also, recruitment estimates are inherently dependent on the modelling choices45, and we verified that recruitment residuals obtained under different assumptions (i.e., through a Virtual Population Analysis, VPA46) were not differently explained by the considered variables (see SI appendix A for more details).
Stability of the recruitment-larval prey availability relationship
Since Castonguay et al.23, a different stock assessment model has been employed, resulting in new recruitment timeseries47. As a baseline for comparison, we, therefore, refitted the recruitment–CEDP relationship from Castonguay et al.23 with the updated estimates and including all years (1982–2017, linear modelling). We hypothesized that, with the addition of new years of data, potential changes in the performance of this quantitative food index (i.e., CEDP) in predicting recruitment would be driven by a temporal change in the relationship because of altering underlying mechanisms. The latter could manifest itself as changes in the spatial or temporal match between the CEDP and the spawning distribution (a proxy of larval distribution), i.e., the ‘effective’ prey availability. Thus, we examined whether changing larval prey availability in space and time, coupled with a changing mackerel larval quality (using adult Kn as a proxy), can explain residuals and the potential breakdown of the Rres-CEDP relationship. Then, the drivers behind the spatial match-mismatch between mackerel eggs and larval prey were investigated. We considered maternal body condition, SST, and C. hyperboreus longitude (i.e., spawner prey). We also retained the relative abundance of C. hyperboreus in the Calanus spp. community (% C. hyp.), as this species does not produce eggs and nauplii available to mackerel larvae in the summer in the sGSL37,48 and appears to reduce abundance of C. finmarchicus early life stages (i.e., mackerel larval prey) through predation49. Thus, years with a large proportion of C. hyperboreus in the plankton community may display a larger mismatch between mackerel eggs and CEDP. A beta regression model was used to study the spatial match (as it is a proportion). All statistical analyses were conducted with R (version 3.3.250).
Ethical approval
This study was approved by DFO Research Ethics Board and conducted with methods in accordance with the Canadian Council on Animal Care (ISBN: 0-919087-43-4). More

Mobile phone data can be used to inform different aspects of COVID-19 response (Table 1). At the population level, quantifying changes in human mobility or clustering can help evaluate the impact of an NPI and identify hotspots where additional or different interventions may need to be applied. At the individual level, mobile phone data may be used to understand patterns of individual contacts and enhance contact tracing.
Table 1 Summary of types, metrics, and proposed applications of mobile phone data.
Full size table

Evaluating current interventions and monitoring their release
The most widely used application of mobile phone data in public health to date is the use of telecom geolocation data to track population movements11,12. Mobile phone operators routinely collect Call Detail Records (CDRs) that contain a timestamp and GPS location with a unique identifier for all subscribers. These data thus are typically readily available and offer high coverage to estimate mobility patterns of individuals using their mobile devices. We note that similar time-resolved GPS location data may be passively collected through certain applications, though typically for only a subset of subscribers that may introduce further bias.
CDRs can be used to generate a number of metrics for characterizing large, population-level mobility patterns. Origin-Destination (OD) matrices reflect the number of times a trip is made between two locations (of varying spatial resolution) in a certain period. These matrices can be analyzed over time to detect temporal trends (i.e., holidays, seasonality, weekday vs weekend) and regular hotspots of attraction. These spatial and temporal flows of individuals between locations, including the magnitude and frequency of these movements, can be used to understand the risk of importation from areas with ongoing outbreaks to areas without sustained transmission where there is a risk of reintroduction and resurgence. Aggregate flows can also be used to retrace the likely introduction and spread of an outbreak in new areas and to inform future projections of disease risk or burden across space and decision making around the design and implementation of travel restrictions or increased surveillance.
Aggregate mobility patterns may also be critical pieces of evidence when evaluating the effectiveness of various NPIs. Most NPIs are reliant on modifying physical behavior. Monitoring the volume, frequency, and average distance of flow during interventions can be used to directly quantify the adoption and effect of these interventions, and identify areas of high potential risk to target with different interventions. There are already identified associations between reductions in population-level mobility within and between different locations and COVID-19 incidence6,10,29, though further exploration of which population-level metrics are most closely related to changes in disease risk and whether these associations are sustained throughout an outbreak is needed30. These associations would ideally be interrogated to identify individual behaviors associated with mobility measures that are also associated with individual risk of COVID-19.
The effect on NPIs can also be monitored through subscriber density metrics that combine the recorded GPS location and timestamp of CDRs to capture the real-time population density and identify potential hotspots. When using finer-scale GPS location data, these density metrics may quantify the likelihood or frequency that users came into proximal contact. A third metric derived from CDR or GPS location data, the radius of gyration, quantifies the range over which a single person may travel in a specified time period. Importantly, the data required for these applications are non-identifiable; they cannot be used to identify any given individual’s interactions, but provide population-level insight into the average clustering and movement of individuals. These metrics, along with traditional OD matrix flows, were recently employed in Italy as a way to evaluate the impact of its national lockdown31. Traffic flow between provinces and probability of colocation were reduced initially in the northern provinces, where the COVID-19 outbreak was first observed, a clear signal of reactive social distancing. As the epidemic progressed, and especially once the national lockdown was enforced, the entire country saw a reduction in traffic between provinces; however, the probability of colocation remained highly dependent on province and was likely attributed to the number of cases reported in each province. Interestingly, the average distance traveled by individuals was significantly reduced across all provinces after the initial outbreak was confirmed.
The use of Bluetooth data (records of proximal interactions between Bluetooth-enabled devices) to quantify physical clustering or real-time density of subscribers at small spatial scales (e.g., zip codes) and fine temporal resolution has been explored for the purposes of contact tracing (see below). The use of these data has been considered less for population-level analyses, though it offers another source of information on behavioral changes under different NPIs. When activated, mobile phones will emit a Bluetooth beacon that is detected by other activated phones. When two Bluetooth-enabled devices are within range, the date, time, distance and duration of interaction can be recorded. The frequency or number of these interactions (analyzed anonymously to form, broadly, measures of clustering or proximal interaction rates over time) may be important given the role of sustained interaction or overcrowding of individuals32,33,34 and contact structure in SARS-CoV-2 transmission35. Furthermore, Bluetooth data in combination with GPS data or a network of Bluetooth sensors can be used to quantify the amount of time people spend at home or other identified locations when lockdown measures are in place to determine if policies are effective.
These data and measures of population-level mobility or clustering patterns would be exceedingly difficult to collect on a similar scale without mobile phone data. These data are often continuously collected, in near real-time, allowing for continued analysis as an outbreak unfolds. Importantly, though, a baseline understanding of contact or clustering patterns prior to any interventions is necessary to inform estimates of intervention impact.
Facilitating contact tracing
Opt-in applications (apps)36,37,38,39,40,41,42 that rely on digital approaches to enumerate and contact individuals who may have been in proximity with someone infected with COVID-19 have been proposed to increase efficiency and decrease the very large burden of manual contact tracing programs43,44,45. By enabling rapid tracing of perhaps higher proportions of affected individuals, these apps can reduce the amount of time that a potentially infected person would have to infect others, particularly in asymptomatic or pre-symptomatic phases of infection46. Most contact tracing apps collect Bluetooth and/or GPS location data to create trails of contacts over a moving time window (14-28 days). Unlike the data needed to understand population-level, aggregated behaviors described above, these data must be linked to single individuals and capture pairwise interactions with other identifiable individuals. Once a case has been identified, they are added to a list of infected users that is queried by the other phones in the network. If the infected user is detected in the trail of contacts, then the user and their contacts are alerted, either by the app or by a public health official, to initiate isolation and quarantine.
This contact tracing process occurs either in a centralized manner, where user information is sent to a remote computer where matching occurs, or in a decentralized manner, where the matching process occurs on the user’s phone. In order for these approaches to feed directly into public health decision making, a direct line between the developers, public health response teams, and users needs to be put in place. This will also be key to mitigating any privacy concerns, which should be dealt with in a transparent and direct manner. Although there has been little discussion to date, routinely collected, individually-identifiable Bluetooth or fine-scale GPS location data may also be used to infer and quantify high-resolution proximity network structures which may further inform contact tracing efforts, but will also raise additional privacy concerns47,48.
Frameworks to process and analyze mobile phone data
Luckily, computing resources and methods to analyze and extract these data will not likely be the limiting factor in these instances. Groups such as Flowminder and Telenor Research Group have worked for multiple years to develop more streamlined processes to analyze these data, particularly aggregate mobility data, that are able to directly interface with mobile phone operators. Flowminder has produced a suite of CDR aggregates, such as counts of active subscribers per region or counts of travelers, that can then be used to calculate indicators of mobility, such as crowdedness, population mixing, locations of interest, and intra-/inter-regional travel49. The code to extract these metrics is publicly available at50. Telenor Research Group works directly with mobile phone operators to provide researchers with spatially aggregated CDR/mobility data51. Facebook’s Data For Good program provides aggregated mobility data to researchers that come from their subscribers, and companies like Cuebiq provided mobility data for a number of COVID-19 studies that summarize the distance users travel or the proportion of users that stay at home52. These existing frameworks – not only the analyses, but also the privacy considerations and data sharing agreements – will provide standardized methods that facilitate integrating mobility data into intervention assessments.
Data privacy
Various forms of identifiable personal information are generated when using mobile phones, including names, identification numbers, fine spatial and temporal data on where the device was used, other users’ identification numbers who may have been detected by Bluetooth, and personal details that might be entered into an app. In light of the growing number of digital privacy concerns and regulations, one must carefully consider the exact form and use of mobile phone data being collected against the legal and ethical need to protect users’ data security and confidentiality. While maintaining user confidentiality is often seen as a hindrance to the use of mobile phone data, in that it limits the use of individual-level data and typically requires aggregation to coarse spatial and temporal resolutions, there are a number of existing frameworks that can help provide guidance for the effective, privacy-conscious use of mobile phone data53.
Exactly which model of data privacy will best suit the use of mobile phone data for COVID-19 response will depend on the exact form and proposed use of the data. As discussed above, there already exist many data processing and analysis frameworks to provide anonymized indicators of population mobility. These standard procedures, though, could result in aggregated data with insufficient spatial and temporal resolution to be effective for monitoring the spread of SARS-CoV-2. Privacy regulations, such as the European Union’s General Data Protection Regulation (GDPR)54, offer exceptions for the use of non-anonymous data that may be needed for other response efforts. For example, opt-in applications for contact tracing may seek consent of the data subject to collect and analyze identifiable data, though the ability to scale opt-in approaches to a wide enough population and to maintain user compliance and participation remains unclear. GDPR and other regulations also provide an exception for anonymization of data to be used in public service, but the regulatory hurdles to gain this exception can be substantial and would require clear use policies and applications for these data. The use of mobile phone data, particularly forms such as those proposed through contact tracing applications, must be weighed against the possible infringements of privacy and civil liberties versus the potential public health benefit. More