Philippa Kaur

In addition to the raw data, we provide two means of exploring the Avian Diet Database and extracting species- or prey-specific summaries. The first is through the website https://aviandiet.unc.edu where users can enter a bird species name to explore a summary of diet information known for that species, or a prey name to explore which bird species are known to eat that prey taxon. We also provide an R package (‘aviandietdb’) for exploring the database, which should be loaded in R by typing:
install.packages(“devtools”)

library(devtools)

devtools::install_github(“ahhurlbert/aviandietdb”)

library(aviandietdb)
Three useful R functions for summarizing records in the database are detailed below.dbSummary().Example usage:
dbSummary()
This function returns the total number of database records, the unique number of bird species, and the unique number of publications summarized in the Diet Database. In addition, it provides a tally of the number of records by bird species listed in alphabetical order, as well as a summary for each bird family in the American Birding Association (ABA) Checklist (version 8.0.6a) of 1) the number of species in the family in the database, 2) the total number of species in the family based on the ABA checklist, and 3) the percent of the family represented based on the species expected in North America. This information on taxonomic coverage is also provided in Online-only Table 2.speciesSummary().Example usage:
speciesSummary(“Bald Eagle”, by = ”Order”)
This function provides a summary of the total number of records and total number of studies available in the database for this species, along with a summary of how those records are distributed across seasons, years, and geographic regions. The number of records are also summarized by taxonomic level to which prey were identified and by analysis type (by number of items, weight or volume, occurrence, or unspecified). Finally, for each analysis type, the mean fraction of diet is given for each prey category at the hierarchical taxonomic level specified with the “by” argument. This is an overall mean, averaged across year, region, and season. If the original data source indicated that specific parts of the prey taxon were consumed (e.g. fruit, seed, vegetation, etc.) then they are listed in the Prey_Part field.dietSummary().Example usage:
dietSummary(“Bald Eagle”, season = ”summer”, region = ”California”, yearRange = c(1940, 1970), by = ”Order”, dietType = ”Items”)
This function allows one to specify season, region, a year range, analysis type, and taxonomic level for prey summarization, and then provides the mean fraction of diet information based on all studies meeting the stated criteria.dietSummaryByPrey().Example usage:
dietSummaryByPrey(“Lepidoptera”, preyLevel = ”Order”, dietType = ”Items”, yearRange = c(1985, 2000), season = ”summer”, preyStage = ”larva”, speciesMean = TRUE)
This function provides a list of all bird species that consume a particular prey taxon in decreasing order of importance. In addition to providing the prey taxon name, you must also specify the taxonomic level (preyLevel) of that name. Like dietSummary(), this function allows one to specify season, region, a year range, and analysis type. There are two additional arguments not present in dietSummary(). One is preyStage, which specifies the life stage of the prey item (if applicable) for which a summary should be conducted. By default (‘any’), diet records will be included regardless of prey stage. Alternatively, one can specify that the summary should only be conducted for records including the terms ‘larva’, ‘adult’, or ‘pupa’ in the Diet Database’s ‘Prey_Stage’ field. This is most relevant for Lepidoptera and a few other insect groups, where one might want to single out the importance of caterpillars or other larvae, for example.By specifying speciesMean = TRUE, only a single value is returned for each bird species that is known to consume a specified prey taxon which represents the average across all analyses meeting the season, region, and year criteria. If speciesMean is FALSE, then each analysis of a bird species which meets the specified criteria will be listed separately.Example code and output is available in the Github README.md document (https://github.com/ahhurlbert/aviandietdb). More

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Figure 1 presents the track of the eight-month cruise, and Table 1 provides the detail of the legs and dates. On a routine basis R/V Hesperides sailed at an average speed of 11 knots from around 3 pm to 4 am (local time). The vessel arrived on station at around 4 am daily to carry out sampling operations at a fixed point for about 11 hours.Fig. 1Cruise track and integrated backscatter at different stations (NASC, daytime 200 to 1000 m).Full size imageTable 1 Dates and starting points of the 7 legs of the Malaspina cruise.Full size tableAcoustic measurements were carried out continuously using a Simrad EK60 echosounder), operating at 38 and 120 kHz (7° beamwidth transducers) with a ping rate of 0.5 Hz. Unfortunately, the 120 kHz failed during the first leg of the cruise and only 38 kHz data were collected. Echosounder observations were recorded down to 1000 m depth. The echosounder files are in the proprietary Simrad raw format and can be read by various softwares (e.g., LSSS, Echoview, Sonar5, MATECHO, ESP3, echopype, pyEcholab). GPS locations and calibration constants are imbedded in each file.Additionally, daytime data integrated over 2 m vertical bins from 200 to 1000 m depth are provided as Nautical Area Scattering Coefficient (NASC). Each “voxel” is the average of all cleaned and validated data recorded over that depth range, in a time period starting 8 hours before the start of the station (defined as start of the CTD cast) and ending 8 hours after the start of the station, with only data recorded in the period between 1 hour after local sunrise and 1 hour prior to local sunset accepted (i.e., during local daytime hours, but removing crepuscular periods when vertical migration of biota is strong). The relatively long interval over which data were accepted around each station was chosen since the station sampling resulted in noisy acoustic data,, a long interval was therefore chosen to ensure valid data on all stations.Finally, summaries of per station daytime and nighttime acoustic data (omitting data recorded within 1 hour of sunrise and sunset) are provided. The data fields in this file are station date, latitude and longitude, and per day and night average NASC 200–1000 m, average NASC 0–1000, weighted mean depth (WMD) of NASC 200–1000 m, migration amplitude, NASC day-to-night ratio and migration ratio. More

We analyse weekly reported counts of suspected and confirmed human cases and deaths attributed to LF (as defined in Supplementary Table 1), between 1 January 2012 and 30 December 2019, from across the entire of Nigeria. The weekly counts were reported from 774 LGAs in 36 Federal states and the Federal Capital Territory, under Integrated Disease Surveillance and Response (IDSR) protocols, and collated by the NCDC. All suspected cases, confirmed cases and deaths from notifiable infectious diseases (including viral haemorrhagic fevers; VHFs) are reported weekly to the LGA Disease Surveillance and Notification Officer (DSNO) and State Epidemiologist (SE). IDSR routine data on priority diseases are collected from inpatient and outpatient registers in health facilities, and forwarded to each LGA’s DSNO using SMS or paper form. Subsequently, individual LGA DSNOs collate and forward the data to their respective SE, also by SMS and paper form, for weekly and monthly reporting respectively to NCDC. From mid-2017 onwards, data entry in 18 states has been conducted using a mobile phone-based electronic reporting system called mSERS, with the data entered using a customised Excel spreadsheet that is used to manually key into NCDC-compatible spreadsheets. Data from this surveillance regime (WERs) were collated by epidemiologists at NCDC throughout the period 2012 to March 2018 (Supplementary Fig. 1).Throughout the study period, within-country LF surveillance and response has been strengthened under NCDC coordination2,20,33. LGAs are now required to notify immediately any suspected case to the state-level, which in turn reports to NCDC within 24 h, and also sends a cumulative weekly report of all reported cases. A dedicated, multi-sectoral NCDC LF TWG was set up in 2016 with the responsibility of coordinating all LF preparedness and response activities across states. Further capacity building occurred in 2017 to 2019, with the opening of three additional LF diagnostic laboratories in Abuja (Federal Capital Territory), Abakaliki (Ebonyi state) and Owo (Ondo state) (to a total of five; Fig. 2) and the rollout of intensive country-wide training on surveillance, clinical case management and diagnosis. We note that, due to the rapid expansion in a test capacity, the definition of a suspected case in our data has subtly changed over the surveillance period: from 2012 to 2016, suspected cases include probable cases that were not lab-tested, whereas from 2017 to 2019, all suspected cases were tested and confirmed to be negative.In addition to the WERs data, since 2017 LF case reporting data has also been collated by the LF TWG and used to inform the weekly NCDC LF Situation Reports (SitRep data; https://ncdc.gov.ng/diseases/sitreps). This regime includes post hoc follow-ups to ensure more accurate case counts, so our analyses use WER-derived case data from 2012 to 2016, and SitRep-derived case data from 2017 to 2019 (see Fig. 1 for full time series). A visual comparison of the data from each separate time series, including the overlap period (2017 to March 2018) is provided in Supplementary Fig. 1, and all statistical models considered random intercepts for the different surveillance regimes. Where other studies of recent Nigeria LF incidence have been more spatially and temporally restricted34,35, the extended monitoring period and fine spatial granularity of these data provide the opportunity for a detailed empirical perspective on the local drivers of LF at a country-wide scale and their relationship to changes in reporting effort.Recent trends in LF surveillance in NigeriaWe visualised temporal and seasonal trends in suspected and confirmed LF cases within and between years, for both surveillance datasets. Weekly case counts were aggregated to country-level and visualised as both annual case accumulation curves, and aggregated weekly case totals (Fig. 1 and Supplementary Fig. 1). We also mapped annual counts of suspected and confirmed cases across Nigeria at the LGA-level to examine spatial changes in reporting over the surveillance period (Fig. 2). State and LGA shapefiles used for modelling and mapping were obtained from Humanitarian Data Exchange under a CC-BY-IGO license (https://data.humdata.org/dataset/nga-administrative-boundaries).Analyses of aggregated district data are sensitive to differences in scale and shape of aggregation (the modifiable areal unit problem; MAUP36), and LGA geographical areas in Nigeria are highly skewed and vary over >3 orders of magnitude (median 713 km2, mean 1175 km2, range 4–11,255 km2). We therefore also aggregated all LGAs across Nigeria into 130 composite districts with a more even distribution of geographical areas, using distance-based hierarchical clustering on LGA centroids (implemented using hclust in R), with the constraint that each new cluster must contain only LGAs from within the same state (to preserve potentially important state-level differences in surveillance regime). Weekly and annual suspected and confirmed LF case totals were then calculated for each aggregated district. We used these spatially aggregated districts to test for the effects of scale on spatial drivers of LF occurrence and incidence.Statistical analysisWe analysed the full case time series (Fig. 1) to characterise the spatiotemporal incidence and drivers of LF in Nigeria, while controlling for year-on-year increases and expansions of surveillance effort. We firstly modelled annual LF occurrence and incidence at a country-wide scale, to identify the spatial, climatic and socio-ecological correlates of disease risk across Nigeria. Secondly, we modelled seasonal and temporal trends in weekly LF incidence within hyperendemic areas in the north and south of Nigeria, to identify the seasonal climatic conditions associated with LF risk dynamics and evaluate the scope for forecasting. All data processing and modelling was conducted in R v.3.4.1 with the packages R-INLA v.20.03.1737, raster v.3.4.1338 and velox v0.2.039. Statistical modelling was conducted using hierarchical regression in a Bayesian inference framework (integrated nested Laplace approximation (INLA)), which provides fast, stable and accurate posterior approximation for complex, spatially and temporally-structured regression models37,40, and has been shown to outperform alternative methods for modelling environmental phenomena with evidence of spatially biased reporting41.Processing climatic and socio-ecological covariatesWe collated geospatial data on socio-ecological and climatic factors that are hypothesised to influence either M. natalensis distribution and population ecology (rainfall, temperature and vegetation patterns), frequency and mode of human–rodent contact (poverty and improved housing prevalence), both of the above (agricultural and urban land cover) or likelihood of LF reporting (travel time to nearest laboratory with LF diagnostic capacity and travel time to nearest hospital). For each LGA we extracted the mean value for each covariate across the LGA polygon. The full suite of covariates tested across all analyses, data sources and associated hypotheses are described in Supplementary Table 5.We collated climate data spanning the full monitoring period and up until the date of analysis (July 2011 to January 2021). We obtained daily precipitation rasters for Africa42 from the Climate Hazards Infrared Precipitation with Stations (CHIRPS) project; this dataset is based on combining sparse weather station data with satellite observations and interpolation techniques, and is designed to support hydrologic forecasts in areas with poor weather station coverage (such as tropical West Africa)42. A recent study ground-truthing against weather station data showed that CHIRPS provides greater overall accuracy than other gridded precipitation products in Nigeria43. Air temperature daily minimum and maximum rasters were obtained from NOAA and were also averaged to calculate daily mean temperature. EVI, a measure of vegetation quality, was obtained from processing 16-day composite layers from NASA (National Aeronautics and Space Administration) (excluding all grid cells with unreliable observations due to cloud cover and linearly interpolating between observations to give daily values; Supplementary Table 5).We derived several spatial bioclimatic variables to capture conditions across the full monitoring period (Jan 2012 to Dec 2019): mean precipitation of the driest annual month, mean precipitation of the wettest annual month, precipitation seasonality (coefficient of variation), annual mean air temperature, air temperature seasonality, annual mean EVI and EVI seasonality. We also calculated monthly total precipitation, 3-month SPI44, average daily mean (Tmean), minimum (Tmin) and maximum (Tmax) temperature and EVI variables at sequential time lags prior to reporting week for seasonal modelling (described below in Temporal drivers). SPI is a standardised measure of drought or wetness conditions relative to the historical average conditions for a given period of the year. SPI was calculated within a rolling 3-month window across the full 40-year historical CHIRPS rainfall time series (1981–2020) using the R package SPEI v.1.744.We accessed annual human population rasters at 100 m resolution from WorldPop. We accessed the proportion of the population living in poverty in 2010 ( More

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