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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

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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).

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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