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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

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For this study we use the University of Victoria Earth System Climate Model (UVic ESCM) version 2.940,41,42. The UVic ESCM is an intermediate-complexity earth system model with a resolution of 1.8(^circ ) latitude by 3.6(^circ ) longitude and 19 ocean depth levels. The surface ocean level is 50 m deep. The model contains a two dimensional energy moisture-balance model of the atmosphere, as well as representations of sea ice, ocean circulation and sediments, and terrestrial carbon. The particular biogeochemical version used here includes three phytoplankton functional types, namely diazotrophs (DZ), mixed phytoplankton (PH), and small phytoplankton and calcifiers (CO)43. The model pre-industrial climate has been previously described43, as has its response to business-as-usual atmospheric (hbox{CO}_2) forcing44. The following sections describe the MP model. A model schematic is presented in Fig. 6.
Figure 6

Microplastic model schematic. Marine snow is produced as a fixed fraction of the free detritus (DET) pool. MP aggregates with this marine snow, entering the (hbox{MP}_A) (marine snow entrained MP) pool. (hbox{MP}_A) held in aggregates sinks at the aggregate rate, with a fraction reaching the seafloor considered to be lost from the ocean. Detrital remineralisation releases the (hbox{MP}_A) from marine snow aggregates at the rate of detrital remineralisation. MP is also grazed by zooplankton and excreted into a pellet-bound (hbox{MP}_Z) pool. Pellet-bound (hbox{MP}_Z) sinks and is released back to the free MP pool at the rate of detrital remineralisation, but some is also lost at the seafloor. Details on the biogeochemical aspects of the model are previously described43.

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Model description
The base model43 was modified in order to quantify the roles of two of the three theorised biological export pathways on MP (aggregation in marine snow and zooplankton ingestion; for now, we neglect an explicit representation of biofouling29). We distinguish between detritus that becomes faecal pellets, and the physical aggregation of marine snow, by introducing a new faecal pellet tracer to divert 50% of zooplankton particulate losses into a separate detrital pool27. For simplicity, this new pellet detrital pool has the same sinking parameterisation as the original detritus. Using the same sinking rates for both detrital classes produces ocean biogeochemistry that is identical to the previously published versions of the model. In the model, plastic particles only interact passively with marine snow (they do not, for example, modify aggregate sinking rates), but they interact actively with zooplankton grazing (described below). Plastic particles have been observed to both increase and decrease the sinking rates of marine snow21,22 and decrease the sinking rates of faecal pellets20,38, but for simplicity and as a first approximation we neglect these effects in our model.
Three MP compartments are introduced; “free” (unattached) microplastic (MP), microplastic aggregated in marine snow ((hbox{MP}_A)), and microplastic in zooplankton faecal pellets ((hbox{MP}_Z)). All MP are considered to represent particles within a biologically active size range, but this size (and the particles’ composition) is never made explicit. These assumptions could bias modelled MP towards polymer types favoured by generic zooplankton34 and particle sizes in the lower end33,34 of the defined range of microplastic size. However, our parameter sensitivity testing in the Supplemental Information tests various fractional uptake rates that implicitly consider size and particle composition in how biologically “active” the MP pool is. As with all model ocean tracers, microplastic concentrations (MP) vary according to:

$$begin{aligned} frac{dmathrm {MP}}{dt} = T + S(mathrm {MP}) end{aligned}$$
(1)

With T including all transport terms and S representing all source minus sink terms. The source and sink terms for free microplastic are:

$$begin{aligned} S(mathrm {MP}) = Emis – S(mathrm {MP}_A) – S(mathrm {MP}_Z) + w_p frac{delta mathrm {MP} times F_R}{delta z} end{aligned}$$
(2)

Microplastic is emitted to the ocean (Emis) along coastlines and major shipping routes using a scaling against regional (hbox{CO}_2) emissions (a dataset provided with the standard UVic ESCM version 2.9 package download), in order to approximate degree of industrialisation and population density in this first version of this model. The rate of emission is a proportion of the total annual plastic waste generation ((F_T))45. For now, abiotic degradation of macroplastics as a source of microplastics to the ocean is neglected to keep the model simple and focus on biological transport. The MP then exchanges with the marine snow ((hbox{MP}_A)) and zooplankton faecal pellet ((hbox{MP}_Z)) pools. A fast particle rising rate ((w_p)) of 1.9 cm per second46 is prescribed to a fraction ((F_R)) of the free MP in each grid cell below the surface level as an approximation of positive buoyancy. An alternative approach would be to assign a uniform rise rate to all MP particles, and to subject the value of the rise rate to sensitivity testing. However, a weakness of this alternative approach is that the many types of plastic in the ocean have different characteristic buoyancies, which could produce unique particle pathways18. In this alternative approach it would be more appropriate to explicitly simulate multiple MP types in the model (which we sought to avoid in this first modelling effort for the sake of simplicity). Nevertheless, we conducted a sensitivity test using several different rise rates, and the effect of reducing the mean rise rate was similar to reducing the fraction assigned a rise rate.
In the current model version there are no abiotic breakdown rates (i.e., photo-degradation39) or respiration losses47 removing MP from circulation.
MP is modelled to aggregate in marine snow as:

$$begin{aligned} S(mathrm {MP}_A) = A_{upt} – A_{rel} – w_Dfrac{delta mathrm {MP}_A}{delta z} end{aligned}$$
(3)

MP particles are taken up ((A_{upt})) via a Monod function applied to the rate of marine snow formation (sources of detritus; (D_A) in nitrogen units, multiplied by an aggregation fraction, (F_A)) in order to approximate an increased likelihood of MP/marine snow encounter with increasing MP concentrations that approaches a level of saturation at high MP concentrations:

$$begin{aligned} A_{upt} = frac{mathrm {MP}}{k_P + mathrm {MP}} times source(D_A) times F_A end{aligned}$$
(4)

The uptake constant ((k_P)) is subjected to sensitivity testing, as is the fraction of marine snow aggregation ((F_A)). In this parameterisation, the aggregation of MP in marine snow represents the net uptake of MP into aggregates by both aggregation and biofouling processes. Biofouling occurs mostly in the upper 50 m35, which is the entire surface layer grid cell in our model. The entrainment-release cycle of biofouling is implicit in our parameterisation via the microbial loop, which is temperature-dependent. Sensitivity testing of the (k_P) and (F_A) parameters therefore represent testing of the net aggregation due to non-zooplankton biological aggregation effects. MP is released ((A_{rel})) from marine snow at the rate of detrital remineralisation ((mu _D)). This rate is temperature-dependent and results in higher rates of release in the low latitudes.

$$begin{aligned} A_{rel} = mu _D mathrm {MP}_A end{aligned}$$
(5)

A particle sinking term ((w_D)) applies to marine snow-associated MP, and has the same value as sinking detritus. The base unit of all MP tracers is number of plastic particles. As a first approximation we assume that all marine snow aggregates forming from free detritus have the characteristic of diatom aggregates (8.8 (upmu )g C per aggregate48). Model detritus in mmol N is converted to mmol C using Redfield stoichiometry, which is then converted to (upmu )g C to calculate the maximum number of aggregates. The maximum number of aggregates is then multiplied by the aggregation fraction (F_A), to calculate (hbox{MP}_A) source and sink rates. MP is conserved for all MP tracers when surface flux balances sedimentary loss rate. What fraction of MP particles reaching the seafloor via aggregate and faecal pellet ballasting are returned to the water column ((F_B)) is tested. For simplicity and as a first approximation, detritus ballasted by calcite, and calcite43, are assumed to not aggregate with microplastic.
Similarly, for MP associated with zooplankton, sources and sinks are:

$$begin{aligned} S(mathrm {MP}_Z) = P_{upt} – P_{rel} – w_Dfrac{delta mathrm {MP}_Z}{delta z} end{aligned}$$
(6)

The calculation of MP particle ingestion rate ((P_{upt})) is the same as for other food sources37. A grazing preference ((psi _{MP})) for MP is subjected to sensitivity testing. This sensitivity testing implicitly examines effects such as biofouling altering the grazing preference of zooplankton for MP. It is assumed that 100% of ingested MP will be egested as faecal pellets and released ((P_{rel})) to the “free” MP pool at the rate of faecal pellet remineralisation, with no plastic remaining in the gut and no plastic being metabolised by the zooplankton. Ingesting MP also results in a reduced zooplankton carbon uptake rate19, with implications for primary and export production (although, Redfield ratios are conserved). Pellet-bound (hbox{MP}_Z) is considered to sink at the rate of faecal pellets ((w_D)).
Plastic is eaten by zooplankton in this model. The Holling II grazing formulation37 is extended to include MP. Grazing of MP ((G_{MP})) is calculated as:

$$begin{aligned} begin{aligned} G_{MP}&= mu _Z^{max} times Z times mathrm {MP}times R_{M:P}times R_{F:MP}times R_{N:F}times psi _{MP}\&quad times ,(psi _{CO}CO+psi _{PH}PH+psi _{DZ}DZ+psi _{Detr_{tot}}Detr_{tot}\&quad +,psi_{Z}Z+psi _{MP}mathrm {MP}times R_{M:P}times R_{F:MP}times R_{N:F} + k_Z)^{-1} end{aligned} end{aligned}$$
(7)

The maximum potential grazing rate ((mu _Z^{max})) is scaled by zooplankton population (Z) and MP availability (MP), and weighted by a food preference ((psi _{MP})), total prey (CO, PH, DZ, (hbox{Detr}_{{tot}})), and Z representing the food sources described in44 and a half saturation constant for zooplankton ingestion ((k_z)). Grazing preferences must always sum to 1 in the model, so sensitivity testing of (psi _{MP}) requires that all grazing preferences must also be adjusted. This is done by varying (psi _{MP}) but requiring (psi _{DZ}) always be set to 0.1 (on the basis that diazotrophs are a poor food source, and to minimize disruption to the nitrogen cycle). The remaining allowance is equally split by the other (psi ) terms. The calculation occurs in N units, so MP is first converted to grams of MP using the MP particle-to-mass conversion of 236E3 tonnes MP = 51.2E12 particles MP ((R_{M:P}))4. It is assumed that 1 g MP will roughly replace 1 g of food (at Redfield ratios; (R_{N:F}) is the conversion from mol Food to mol N) in the zooplankton’s diet, and MP is thus converted to mmol N for the grazing calculation. However, we subject this ratio ((R_{F:MP})) to sensitivity testing. Zooplankton uptake of plastic is therefore:

$$begin{aligned} P_{upt} = frac{G_{MP}}{R_{M:P}times R_{F:MP}times R_{N:F}} end{aligned}$$
(8)

MP particles are released from faecal pellets via remineralisation, which occurs at the same rate as the remineralisation of aggregates:

$$begin{aligned} P_{rel} = mu _D mathrm {MP}_Z end{aligned}$$
(9)

Model forcing
The model was integrated at year 1765 boundary conditions (including agricultural greenhouse forcing and land ice) for more than 10,000 years until equilibration was achieved. From year 1765 to 1950, historical (hbox{CO}_2) concentration forcing, and geostrophically adjusted wind anomalies are applied. From 1950 to 2100 the model is forced with a combination of historical (hbox{CO}_2) concentration forcing (to 2000) and a business-as-usual high atmospheric (hbox{CO}_2) concentration projection RCP8.549,50. MP emissions start from 2 million metric tonnes in year 1950 (a total plastic waste generation estimate45), increasing at a rate of 8.4% per year. (hbox{CO}_2) and MP forcing is summarized in Fig. 7. It has been estimated that about 4% of total plastic waste generated enters the ocean30, but that the microplastic mass found at the sea surface represents only about 1% of the annual plastic input to the ocean4. We test a range of input fractions (see Table 2), after applying a mass conversion from tonnes to number of MP particles4. Using a considerable over-estimation of MP pollution rate also implicitly accounts for abiotic degradation of larger plastics.
Figure 7

Model forcing from years 1950–2100. Atmospheric (hbox{CO}_2) follows RCP8.5 (panel a). Plastic flux into the ocean is assumed to be some fraction of the total historical and projected plastic waste generation estimate (panel b), with a continuing rate of increase of 8.4% per year45, converted to MP particles using a mass conversion4. Previous estimates of actual total plastic mass flux into the ocean is only about 4% of the total plastic waste generation30, with the MP fraction being a small proportion of that.

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Table 2 Microplastic model parameters and range tested.
Full size table

Experimental setup
A 700-member Latin Hypercube51 was used to test the microplastic parameter space of the model using the forcing described in the previous section. While biological model parameters might also influence microplastic uptake and transport, we limited our initial tests to the new parameters introduced above. A range of values was prescribed to the parameters listed in Table 2, in which the parameter space was randomly sampled with a normal distribution. The objective was to see what can be learned about plastic accumulation in the ocean, when very little is known about plastic/particle interactions and basic processes are still poorly understood. An analysis of the full Latin Hypercube parameter search is provided as Supplemental Information.
We adopted an incremental approach to increasing model complexity. We started with a control Hypercube where biology was not allowed to take up plastic, in order to first test the physical parameters ((F_T) and (F_R), the fraction of total annual plastic produced entering the ocean as MP, and the fraction assigned a rise rate, respectively). One hundred simulations were performed in this configuration, with the results analysed in the Supplemental Information. We next included passive plastic aggregation in marine snow (MP plus the (hbox{MP}_A) tracer) in a 300 simulation Hypercube, spread across the (k_P) (marine snow uptake coefficient) parameter space (0–1, 1–100, 100–1000 particles (hbox{m}^{-3}), each with 100 Hypercube simulations) in a normal distribution. These 300 simulations explored the 5 relevant MP model parameters: (F_T), (F_R), (F_A) (marine snow aggregation fraction), (k_P), and (F_B) (fractional return to ocean at the seafloor). These results are also provided in the Supplemental Information. Finally, we added active zooplankton-associated plastic (MP, plus (hbox{MP}_A) and (hbox{MP}_Z) tracers) as a third 300-individual Hypercube set. This third Hypercube is similarly split across the (k_P) parameter space in a normal distribution, but with the addition of grazing parameters (psi _{MP}) (MP grazing preference) and (R_{F:MP}) (the food to MP substitution ratio; 7 parameters in total). More