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Vertical migration
We performed two separate vertical migration experiments (Fig. 1A,B). In the first one (Fig. 1A) a group of 45 krill was exposed to LD for 48 h. In the second one (Fig. 1B) a group of 41 krill was exposed to LD for 24 h followed by constant darkness (DD) for 48 h. During LD, lights were turned on at 6:00 (corresponding to Zeitgeber Time 0 or ZT0). Light intensity gradually increased until 12:00 (ZT6) and then decreased gradually until 18:00 (ZT12), when the lights were turned off. During DD, lights were turned off at all times. In both experiments, krill could move freely within a vertical column tank (200 cm height × 50 cm diameter) filled with chilled and filtered seawater (Supplementary Fig. S1). No food was offered at any time during the experiments and an acclimation period of three days (LD, no food) was provided before each experiment. We used an infrared (IR) camera system to monitor DVM during light and dark phases and estimated the variation in mean krill depth over time. We tested the presence of rhythmic patterns using the RAIN algorithm, which allows the detection of rhythms of any period and waveform23.
Figure 1

Vertical migration patterns of krill exposed to LD and LD-DD. (A) Vertical migration patterns of krill exposed to LD for 48 h. X-axis indicate the Zeitgeber Time (ZT) given in hours from each event of lights-on (6:00), corresponding to ZT0. Y-axis indicate the mean krill depth in cm (n = 45). Error bars represent SEMs. White and grey rectangles represent alternation of light and dark phases. For each experimental day, results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). (B) Vertical migration patterns of krill exposed to LD for 24 h followed by 48 h in DD. X-axis represent the time in hours from the beginning of the run. For the first 24 h, time is given in ZT, with ZT0 corresponding to lights-on (6:00). For the following 48 h, time is given in Circadian Time or CT, with CT0 corresponding to subjective lights-on in DD (6:00). Y-axis indicate the mean krill depth in cm (n = 41). Error bars represent SEMs. White, grey and light grey rectangles represent alternation of light, dark and subjective light phases respectively. For each experimental day, results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). The figure was generated using the plot function in the “graphics” package (version 3.6.3, https://www.rdocumentation.org/packages/graphics) in R (RStudio version 1.0.136, RStudio Team 2016).

Full size image

Clear rhythmic oscillations were detected in both experiments. When krill were exposed to LD, they displayed rhythmic vertical migration with a period of about 24 h. Peak upward migration occurred towards the second half of the light phase. In the first experiment (Fig. 1A), RAIN detected a period of oscillation of 24 h (p-value = 3.5e−07) in day 1 and a period of 23 h (p-value = 5.4e−08) in day 2, with peak upward migration at ZT9 in both days. In the second experiment (Fig. 1B), RAIN detected a period of 24 h (p-value = 4.2e−04) in day 1, when krill were exposed to LD, with peak upward migration at ZT8.
Krill DVM in the field displays mostly a nocturnal migratory pattern, characterized by upward migration during the night and downward migration during the day6,11. Therefore, we were at first surprised to observe the opposite DVM pattern in the lab, with upward migration during the day (i.e. light phase) and downward migration during the night (i.e. dark phase). However, previous studies on Drosophila already revealed that circadian behaviors might differ significantly between wild and laboratory populations of the same species24. Physical constriction, exposure to artificial light regimes and feeding schedules and deprivation of ecological interactions may have a profound impact on the behavior of captured animals25. In the aquarium, krill are fed mostly during the day. This, in association with the absence of predators, might have led to the reversal of the DVM rhythm. In addition, in order to avoid interaction with rhythmic cues related to food, krill were briefly starved before and during the experiment. Previous studies suggested that starved zooplankton might become more attracted towards light sources26,27. This may have contributed to stimulate the upward movement observed during the light phases. Additional observations of DVM with (A) krill accustomed to different feeding schedules (e.g. krill fed during the night) and (B) starved krill exposed to illuminated dark phases (DL or LL) might help to clarify these points.
After we switched to DD conditions (Fig. 1B), during the first day we did not observe any significant rhythm. This might not necessarily imply that no rhythmic migration occurred. In fact, individual krill might have been still rhythmic, but they might have not been synchronized with each other due to the absence of the LD entraining cue or Zeitgeber. A similar phenomenon has already been reported for wild zooplankton in the Arctic and Antarctic during periods of continuous illumination in summer (i.e. during midnight sun)28,29. At that time of the year, in the Arctic, the local populations of Calanus finmarchicus and C. glacialis did not display any net DVM as revealed by backscatter data, but unsynchronized individual migrations were registered instead29. Implementation of individual tracking methods to apply in the lab during DVM monitoring under extreme photoperiodic conditions including constant darkness (DD) and constant light (LL) would allow further insight into this aspect.
During the second day of DD exposure, RAIN detected a period of 12 h (p-value = 2.3e−02), with a first peak of migration in the early subjective light phase (CT29) and a second peak in the early dark phase (CT41). This suggested that in free-running conditions an endogenous rhythm of vertical migration might emerge with a period of about 12 h. This is in agreement with previous studies of swimming activity, oxygen consumption, enzyme activity and transcription in krill exposed to DD16,18,22 and strongly suggests that the endogenous clock in krill is characterized by a free-running period significantly shorter than 24 h.
Oxygen consumption
An endogenous rhythm of activity occurring at the organismic level might contribute to the occurrence of DVM in zooplankton19,30. This might apply to krill as well, since they are known to display daily rhythms in metabolism and physiology15,16. Therefore, we monitored daily rhythms of oxygen consumption in krill exposed to LD and DD and associated them with the observed DVM patterns. Due to technical limitations, we could not monitor oxygen consumption directly within the vertical migration tank. We used individual krill incubated in 2 L Schott bottles filled with oxygen-saturated chilled and filtered seawater instead. We followed the decrease in pO2 over a period of 48 h and tested the presence of rhythmic consumption patterns using the RAIN algorithm. Clear rhythmic oscillations were detected in both conditions.
In LD, two out of six tested animals (33%) displayed rhythmic oxygen consumption (Fig. 2A,B). Both individuals displayed clear oscillations in the circadian range (i.e., approx. 24 h period): the first one (krill#4, Fig. 2A) displayed a period of 21 h in day 1 (p-value = 1.9e−04) and 24 h in day 2 (p-value = 3.7e−02), while the second one (krill#6, Fig. 2B) displayed a period of 24 h in both days (day 1, p-value = 5.3e−11; day 2, p-value = 3.9e−11). The oscillation was always strongly synchronized to the LD cycle, with peak oxygen consumption occurring at ZT11 (day 1) and ZT12 (day 2) in krill#4 (Fig. 2A) and ZT12 (day 1) and ZT13 (day 2) in krill#6 (Fig. 2B), corresponding to the light/dark transitions. Considering that the animals were incubated within a small volume of water (2 L) and their ability of swim freely might have been severely reduced, changes in swimming activity might not have greatly contributed to the oxygen uptake oscillation. Therefore, this could be interpreted as the result of an internal metabolic oscillation instead. This would support the hypothesis that an internal rhythm of activity might contribute to DVM in krill, even if the mechanisms still remain unknown. In the Calanoid copepod Calanus finmarchicus similar oscillations of oxygen consumption were interpreted as a metabolic anticipation of DVM19.
Figure 2

Oxygen consumption patterns of krill exposed to LD. (A,B) In both panels, X-axis represents time given as Zeitgeber Time or ZT measured in hours. ZT0 corresponds to each event of lights-on (6:00). Y-axis represents residual oxygen consumption expressed in mg/L. Positive values indicate increase of oxygen consumption, whereas negative values indicate decrease of oxygen consumption. Black points represent raw data points. Black solid line represents the model fit obtained by applying a GAM to the residual oxygen consumption over time. Red-shaded areas represent the 95% confidence interval around the GAM model’s fit. White and grey rectangles represent alternation of light and dark phases. For each experimental day, results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). The figure was generated using the plot function in the “graphics” package (version 3.6.3, https://www.rdocumentation.org/packages/graphics) in R (RStudio version 1.0.136, RStudio Team 2016).

Full size image

In DD, four out of seven tested animals (57%) displayed rhythmic oxygen consumption (Fig. 3A–D). Three of them (krill#3, #4 and #7, Fig. 3B–D) showed a period of about 24 h. The peak of oxygen uptake was advanced compared to LD and occurred during the subjective light phase. Krill#3 (Fig. 3B) displayed a period of 23 h in day 1 (p-value = 1.0e−07) and 24 h in day 2 (p-value = 4.2e−06), with peak oxygen consumption at CT11 and CT34. Krill#4 (Fig. 3C) displayed a period of 21 h in day 1 (p-value = 2.0e−05) and 24 h in day 2 (p-value = 1.6e−06), with peak consumption at CT9 and CT28. Krill#7 (Fig. 3D) displayed a period of 24 h in day 2 (p-value = 2.2e−07), with peak consumption at CT28. A fourth individual (krill#2, Fig. 3A) displayed a period of 12 h in day 1 (p-value = 1.7e−03), with a first peak of consumption at CT3 during the early subjective light phase and a second peak 12 h later (CT15) during the early dark phase. The general advance of the oxygen uptake peak observed in krill#3, 4 and 7 together with the 12 h rhythm displayed by krill#2 suggested that in free-running DD conditions a process of period shortening similar to the one observed for DVM might occur also for oxygen consumption. Previous studies suggested that in krill similar 12 h free-running endogenous rhythms might occur also at the level of metabolic gene expression and enzymatic activity16. This, together with the recent analysis of krill circadian transcriptome revealing a significant 12 h free-running oscillation at the molecular level, strongly suggests that a short endogenous period of oscillation might characterize the circadian clock of Antarctic krill.
Figure 3

Oxygen consumption patterns of krill exposed to DD. (A–D) In all panels, X-axis represents time given as Circadian Time or CT measured in hours. CT0 corresponds to first subjective lights-on event (6:00). Y-axis represents residual oxygen consumption expressed in mg/L. Positive values indicate increase of oxygen consumption, whereas negative values indicate decrease of oxygen consumption. Black points represent raw data points. Black solid line represents the model fit obtained by applying a GAM to the residual oxygen consumption over time. Red-shaded areas represent the 95% confidence interval around the GAM model’s fit. Light grey and grey rectangles represent alternation of subjective light and dark phases respectively. For each experimental day, results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). The figure was generated using the plot function in the “graphics” package (version 3.6.3, https://www.rdocumentation.org/packages/graphics) in R (RStudio version 1.0.136, RStudio Team 2016).

Full size image

The relatively small percentages (33% in LD and 57% in DD) of individual krill showing significant rhythmic oxygen consumption might have been related to the small sample size in our experiment (n = 6 in LD and n = 7 in DD). However, additional measurements of krill oxygen consumption performed using the same methods in LD and DD on larger samples (n = 10), on board of RV Polarstern during Antarctic expedition PS 112 (March to May 2018), displayed similar percentages of rhythmic individuals (3 out of 10 or 30% in LD, and 4 out of 10 or 40% in DD) (Supplementary Figs. S2A–C and S3A–D). Interestingly, the on-board analyses revealed the same tendency to develop shorter periods of oscillation in DD (krill#7: T = 12 h, p-value = 8.5e−04; krill#9: T = 17 h, p-value = 8.3e−08) (Supplementary Fig. S3C,D). Also, on-board measurements in LD displayed almost a reversed phase of oscillation compared to those in the lab, with peak oxygen consumption occurring at the beginning of the light phase, around ZT4 (Supplementary Fig. S2A–C). It is tempting to speculate that this might be related to the reverse DVM pattern observed in the lab. Unfortunately, we do not have the corresponding DVM measurements from the field to support this hypothesis. Additional field observations of krill rhythms of oxygen consumption in association with DVM would help to clarify how these two phenomena might be related to each other.
Dissection of putative circadian oscillators in krill brain and eyestalks
It might be possible that the DVM rhythm and the metabolic oscillation observed in krill were under the influence of separated circadian oscillators. This was suggested by the different responses displayed by DVM and oxygen consumption in DD. While DVM showed a complete shift to a 12 h rhythm, oxygen consumption only showed an advance in the peaking time.
Previous studies on the circadian system of Crustaceans identified multiple oscillators located in the head and along the body31. In particular, a model has been proposed where the circadian oscillators in the head are situated in the brain, in the eyestalks and in the retinae of the compound eye (Supplementary Fig. S4)31. To investigate this hypothesis we compared, for the first time, daily patterns of clock genes expression in brain and eyestalks tissue of krill exposed to LD and DD. Krill were sampled within a 72-h time-series, 9 animals were collected every four hours. During the first 24 h (ZT0-24), krill were exposed to LD, while during the remaining 48 h (CT0-48) krill were exposed to DD. We dissected brain and eyestalks tissue (Supplementary Fig. S5) and measured relative changes in clock-related mRNAs over time during the first day in LD (ZT0-24) and during the second day in DD (CT24-48) (Supplementary Table S1). We used RAIN to check for rhythmic oscillations within tissue.
Clear rhythms of oscillation were found in both tissues. In LD, the core clock components in the brain displayed an antiphase relationship between positive (clk-cyc) and negative (per-tim) regulators, with clk-cyc peaking around ZT16-20 (clk: T = 24 h, p-value = 4.86e−07; cyc: T = 24 h, p-value = 2.28e−12) and per-tim peaking around ZT4 (per: T = 24 h, p-value = 2.99e−05; tim: T = 24 h, p-value = 1.31e−11) (Fig. 4A). In the eyestalks such antiphase relationship was not present and most clock genes showed upregulation during the dark phase (ZT16-24) (Fig. 4B). The antiphase relationship is a typical feature of the circadian clock and is well-known from studies on model organisms like Drosophila and mouse2. So far, previous studies on krill failed to demonstrate a clear antiphase relationship between positive and negative regulators21,22. This was discussed as a non-canonical feature of krill circadian clock, as observed before in other Crustacean species32,33. However, our results suggest that this might be related to the specific tissue analyzed. In fact, previous works focused mostly on the eyestalks or on the full head21,22, possibly failing to detect the antiphase oscillation in the brain. This indicates that separate oscillators might be present in the brain and the eyestalks of krill, as already suggested for other Crustaceans31, and suggests that the central pacemaker might be located in the brain. The oscillator in the eyestalks, where all clock genes displayed upregulation during the dark phase, seems to be mostly influenced by the external photoperiod and might be considered as a peripheral oscillator.
Figure 4

Comparison of daily patterns of clock genes expression in LD between brain and eyestalk tissues. (A, B) Heatmaps representing up- (in yellow) and down- (in blue) regulation of clock genes expression over time in the brain (A) and eyestalks (B) of krill exposed to LD. For gene names abbreviations please see Supplementary Table S1. Zeitgeber Time (ZT) given in hour from time of lights-on (6:00), corresponding to ZT0, is indicated below each column of the heatmaps. Heatmaps were generated using the heatmap.2 function in the “gplots” package (version 3.0.4, https://github.com/talgalili/gplots) in R (RStudio version 1.0.136, RStudio Team 2016).

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As in LD, also in DD different patterns of regulations were observed in the brain and in the eyestalks (Supplementary Fig. S6A,B). In the brain, clk, cyc and per, three of the core clock components, displayed a marked tendency towards a shortening of the oscillation period (Fig. 5A–C). Clk shifted from 24 to 12 h (p-value = 1.31e−16), cyc from 24 to 16 h (p-value = 2.48e−10) and per from 24 to 12 h (p-value = 4.05e−07). In the eyestalks, only per showed a similar tendency, shifting from 24 h (p-value = 3.69e−06) to 16 h (p-value = 2.36e−13) (Fig. 5D). This again suggested that separated oscillators might be present in krill brain and eyestalks. The shortening of clock gene oscillation period observed in DD in the brain and, to a lesser extent, in the eyestalks, together with the short (12–16 h) oscillation period registered for cry2 across all tissues and conditions (brain LD: T = 12 h, p-value = 2.4e−08; brain DD: T = 12 h, p-value = 1.4e−04; eyestalk LD: T = 12 h, p-value = 3.2e−05; eyestalk DD: T = 16 h, p-value = 6.1e−13) (Supplementary Fig. S7A,B), is in agreement with previous observations of krill clock gene expression in DD22 and strongly indicates that krill endogenous circadian period might be significantly shorter than 24 h also at the molecular level.
Figure 5

LD-DD shortening of the oscillation period in the core clock genes clk, cyc and per. (A–C) Line-plots representing changes in expression levels over time for the clock genes clk (A), cyc (B) and per (C) in LD (blue) and DD (red) in the brain, and for the clock gene per (D) in LD (blue) and DD (red) in the eyestalks. Time intervals are reported on the x-axis. For LD, Zeitgeber Time (ZT) is used, given in hours from the time of lights-on (6:00). For DD, Circadian Time (CT) is used, given in hours from the beginning of the subjective light phase (6:00). Mean gene expression levels (n = 7) are reported on the y-axis as mean Relative Quantities (RQ), indicating the average normalized expression levels of the target clock genes relative to the expression levels of the selected internal and external reference genes at each time interval. Error bars represents SEMs (n = 7). Results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). A schematic representation of the light/dark cycle is given below the graphs. For LD, withe rectangles indicate light phases, grey rectangles indicate dark phases. For DD, light grey rectangles indicate subjective light phases, grey rectangles indicate dark phases. The figure was generated using the plot function in the “graphics” package (version 3.6.3, https://www.rdocumentation.org/packages/graphics) in R (RStudio version 1.0.136, RStudio Team 2016).

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The presence of separated oscillators in krill might help to coordinate different aspects of daily rhythmicity on different levels including physiology (e.g. oxygen consumption) and behavior (e.g. DVM). This might allow a flexible regulation of daily rhythms in response to local changes in environmental conditions driven by temporary and/or seasonal factors including photoperiod, food availability and presence/absence of predators among others.
Adaptive significance of the short endogenous free-running period in krill
The tendency in krill to display a 12 h endogenous free-running period might represent a circadian adaptation for living at high latitudes, where the photoperiodic signal displays strong seasonal variability16,22. Surveys from plants and insects along broad latitudinal gradients indicate that high-latitude species tend to have shorter endogenous periods compared to low-latitude ones34. The reasons why this happens are not fully understood yet, but it has been proposed that the short endogenous period might help the clock to entrain to a wider range of photoperiods34. Indeed, krill displayed rhythmic clock gene activity during the midnight sun in summer, suggesting that their clock can successfully entrain to extremely long photoperiods17,35. However, exposition to similar extreme photoperiods in the laboratory apparently caused the disruption of the clock, suggesting that in the field krill might switch to alternative Zeitgebers (i.e. entraining cues) to entrain the clock when photoperiod becomes extremely long/short (e.g. midsummer/midwinter)36.
From an ecological perspective, having a short endogenous period might help krill to adapt to different environmental scenarios. In the presence of overt day/night cycles, the clock would entrain to the photoperiod and promote daily rhythms with a period of approx. 24 h, like for example the nocturnal DVM pattern usually observed during spring and autumn. This would help krill to anticipate the day/night cycle and maximize the costs/benefits balance between time spent feeding at the surface and time spent hiding in deeper water layers. At the same time, the clock would promote 24 h oscillations in krill metabolism and physiology and coordinate phases of high and low activity with phases of upward and downward migration during DVM.
On the other hand, in the absence of overt day/night cues, for example during summer and winter, the clock would tend to shift towards the free-running period and promote daily rhythms with a period shorter than 24 h. Observations of krill DVM in the field during winter are scarce, due to the harsh weather conditions which characterize the Southern Ocean at that time of the year. Much of the rhythmic biology of krill during winter still remains unknown. At the same time, field observations of krill DVM and swimming activity during summer suggest that a de-synchronized, “around-the -clock”, individual migratory movement might be present instead18,28. According to some studies, frequent shallow individual migrations could occur during summer as a result of a hunger-satiation mechanism, triggered by the abundant primary production occurring in the surface layers37. To get further insight into these aspects, first we would need to perform additional laboratory studies and field observations of krill rhythmic functions (oxygen consumption, swimming activity, DVM) under extreme photoperiods (LL-DD in the lab, summer–winter in the field). Second, laboratory and on-board trials with krill exposed to different food concentrations (low-medium–high) and different photoperiods (LD-DD-LL) could be used to study the interaction between food and light cues in the emergence of “opportunistic” rhythmic responses. More

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Study area
The study area consists of the wider northern Pacific Rim area which is known to be an exchange frontier between diseases and cultures (Fig. 12,9). We followed methods outlined in5,11,12 and specifically13 drawing inference from predictions.
The conducted international landscape investigation in this study area is described in a research workflow (Fig. 2), and it mainly consists of different steps: field work, open access data compilation, data cleaning and lab work, GIS mapping, data mining and prediction, reflection and inference, as further described below (for more clarifications or questions please contact authors).
Figure 2

Workflow of this study to obtain best-available AI data and to data mine and predict them with machine learning in a geographic information system (GIS) for best-possible predictions and inference for the Pacific Rim study area (IRD = Influenza Research Database; USDA = U.S. Department of Agriculture); for more details, model specifications etc. see manuscript text.

Full size image

Field work
As part of the eASIA program the field sampling of AI was conducted in Russia and Japan primarily during the fall (August) 2016, 2017 and 2018. Fall is a season when birds finished breeding and started to migrate southwards to their wintering sites. Birds are known during that time to disperse relatively slowly along flyways10,12,14,15. Traditionally, this time period has the highest known prevalence of virus, thus far9 In Vietnam, the surveillance targeting domestic birds was conducted in summers and falls. Together with all eASIA participants, we extracted data from an agreeable compatible workflow and protocol that allowed for geo-referenced and time-referenced AI samples in the field. Hunters were not directly involved in the study (see permits for bird specimen details). In Russia, following their lab method protocol and according to standard procedures16,17 it resulted in 52 samples (10 LPAI presences) from years 2016 and 2017 with 13 unique locations. In Japan, their respective lab method protocol was followed (details in18) resulting in 203 samples from years 2016 and 2017 based on 5 unique locations. In Vietnam, the lab method protocol of Japan was followed (details in19) resulting in 1,182 samples (951 LPAI presences) from years 2016 and 2017 based on 102 unique locations. Finally, we were also able to obtain 407 samples (395 LPAI presences) for Mongolia for 27 unique locations, also following the protocol from Japan. Alaska was not part of field campaign but had data available through the IRD ‘flu’ database (see details below).
All field data were compiled into one eASIA database for further analysis (Appendix 1), namely to carry out data mining, model-training and subsequent predictions with machine learning and geographic information system (GIS; details in9,10).
Compilations of open access AI data
To reach across the Pacific Rim for a wider and more robust inference, and to make a connection with North America and other available data, further AI data from Alaska were obtained from the IRD database online (https://www.fludb.org/brc/home.spg? Decorator = influenza). This resulted in 38,517 samples (448 low-path AI presences) from 1,175 unique locations. We then queried all these data for low-path AI strains which resulted in 110 strains and 40,837 samples from 157 host species entries that we used for this study (see Appendix 2 for details). To our knowledge, that is the biggest and most diverse AI database ever compiled and analysed for the Pacific Rim (see Herrick et al. 2013 for a first initial model and using all of AI).
Data mining of low-path AI
We queried the obtained data for the number of low-path AI strains, host species distribution, proportion of host species carrying a specific low-path AI strain, and prevalence.
Compilations of open access GIS data layers for the study area
GIS layers are used as predictors for model-predictions in the study area. Here we used 19 global GIS layers available from earlier research (Sriram and Huettmann unpublished https://www.earth-syst-sci-data-discuss.net/essd-2016-65/; Table 1). For polygon outlines we used data with our ArcGIS UAF campus license (FH). All GIS data layers were displayed for the study area as a Mercator projection using WGS84, decimal degrees coordinates (latitude and longitude) with a precision of 6 decimals (GPS and GIS, a real world precision of 5 decimals).
Table 1 List of GIS Predictors used in this study to data mine and predict low path (LP) Avian Influenza (AI) *
Full size table

GIS mapping and data processing
We used commercial and open source GIS softwares (ArcGIS, QGIS) to operate, map and overlay all data. We imported the AI Data from ASCII table (MS Excel) into a shapefile layer of AI, and overlaid them with 19 environmental GIS layers we had available from compiled global data sets. This resulted into a data cube that is analyzed with data mining and for modeling and predictions.
Modeling and predictions
The resulting data cube was imported into SPM 8.2 (https://www.minitab.com/en-us/products/spm/) and then modeled and predicted. We ran a stochastic grading boosting (TreeNet) algorithm for best-possible predictions and inference (20see also9,10,12,21; for an R implementation see22). As outlined in9,12,21 we started with default settings for this powerful software as they are known to achieve best inference, as taken from the predictive performance13. Models then used 6 Maximum nodes per tree, 10 Cases as a Terminal Node Minimum, 200 trees to converge, a balanced class weight and a ten-fold cross-validation (a repeated 90% training vs 10% testing setting) optimizing on the ROC. To avoid overfitting we used an auto learn rate and a 50% subsampling. The resulting tree model was stored as a grove and applied to an equally-spaced lattice of the predictors (excluding species information). The maps were presented in GIS with a resolution of a 5 km pixel size (Appendix 3).
Model assessment data
We were able to obtain two alternative data set on AI for an assessment of our predictions. The Influenza Research Database (IRD) has an Asian subset (n = 28,205 and 19,405) comparable to our work, and which was used to confront our predictions for the study area.
Although the U.S. Department of Agriculture (USDA) has a U.S-wide AI survey data set (3,589 for Alaska), it actually lacks geo-referencing with coordinates (just done by counties etc.) and just includes H5, H7 Avian Flu columns; presumably done trying to protect the industry. We still used this best-available alternative data set for further assessment of the model predictions.
Ethics statement
For this eASIA project oropharyngeal and cloacal samples in Russia were collected according to the “Federal Law on Hunting and Sharing of Hunting Resources of Russian Federation # 209-ФЗ” and with the permissions of local governments in hunting regions during each hunting seasons. Hunted birds were provided for sampling by licensed hunters to our group during expeditions.
Fecal samples in Japan were collected with the permission of the municipality managing the sampling areas and Hokkaido University. Fecal samples in Mongolia were collected with the permission of the State Central Veterinary Laboratory, Mongolia. These samples were transferred to Japan under the permissions of the Animal Quarantine Service, Japan (27douken560-2, 28douken563-6, 29douken 683–2). Swab samples in Vietnam were collected with the permission of the Department of Animal Health, Vietnam. These samples were transferred to Japan under the permissions of the Animal Quarantine Service, Japan (27douken560-3, 28douken563-1, 28douken563-4, 28douken563-5, 29douken683-3, 29douken683-4).
Data reported in the Influenza Research Database (IRD) were from samples obtained and submitted under NIH-funded avian influenza surveillance collection efforts (CEIRS) and are publicly available at: www.fludb.org . This work was supported in part by a National Institute of Allergy and Infectious Disease Centers of Excellence in Influenza Research and Surveillance (CEIRS) award, Contract HHSN272201400008C (to Eric Bortz).
For Alaska USDA data, wild bird samples primarily came from hunter-killed waterfowl, with voluntary participation from hunters. These sampling activities were covered under US Fish and Wildlife Service Federal Permit MB124992-0. More