Philippa Kaur

Data and softwareThis analysis used two main data sources: (1) annual (through 2020) summaries of landings by species and by region provided by the Atlantic Coastal Cooperative Statistics Program (ACCSP), and (2) vessel-tracking data provided by Global Fishing Watch. The ACCSP is a cooperative state-federal program of U.S. states and the District of Columbia; it was established in 1995 to be the principal source of fisheries-dependent information on the Atlantic Coast of the United States. For the ACCSP data, I obtained annual landings by species for the North Atlantic region, Mid Atlantic region, and South Atlantic region (excluding landings from the Gulf of Mexico). The weekly cumulative landings data was obtained from the NOAA Fisheries Greater Atlantic Quota Monitoring website. Global Fishing Watch is an organization that provides access to information on commercial fishing activities, in particular information on the identity and location of fishing vessels34. Many large vessels use a system known as the Automatic Identification System (AIS) to avoid collisions at sea, broadcast their location to port authorities and other vessels, and to view other vessels in their vicinity. Vessels fitted with AIS transceivers can be observed by AIS base stations and by satellites fitted with AIS receivers. The US Coast Guard requires all vessels larger than 65 feet to have an AIS receiver onboard. Global Fishing Watch obtains AIS data for fishing vessels and enables users with Internet access to monitor fishing activity globally, and to view individual vessel tracks. They also partner with academic researchers to provide more fine-scale data.To obtain the vessel-tracking data for the relevant fisheries, I reviewed NOAA databases of squid and mackerel permits (2019 version; vessels with squid permits are automatically issued a Butterfish permit), and the Atlantic tuna permits (2020 version) and matched each permitted vessel to its unique Maritime Mobile Service Identity (MMSI) number, which is associated with Global Fishing Watch tracking information. I was able to identify 84% (187/224) of squid/butterfish permitted vessels (I focused on the SMB1A (Tier 1) permit category associated with the vast majority ( approx. 99%) of squid catch35), 100% of Tier 1 and Tier 2 mackerel-permitted vessels (56/56 vessels), and 74% of active tuna longline vessels (100/135 vessels). “Active” is defined as having reported successfully setting pelagic longline gear at least once between 2006 and 201236. This translates to a total of 17.55 million observations on fishing vessel locations for all three fisheries. I drop any observations that are missing either a latitude or longitude entry. For the squid and mackerel fisheries, I drop any observations with unusual longitudes ((ge 0^{circ }) and ( More

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This study describes the virological and pathological findings of Common buzzards infected with the 2020–2021 HPAI H5N8 virus. These analyses showed that the main lesions were HPAI virus-associated inflammation and necrosis in multiple tissues including brain and heart, confirming HPAI as cause of death or severe disease.The Common buzzard presents with several characteristic traits that make it a valuable bioindicator of HPAIV presence in wildlife. It is a medium-sized raptor, present almost throughout Europe. In the Netherlands, its population has been stable since 1970 with an estimated maximum winter population of 30,000–50,000 individuals16. The Common buzzard is mainly a resident bird, which generally inhabits woodlands but is adaptable to wetlands16,17. Its feeding behavior as an opportunistic predator and scavenger has the potential to expose it to HPAIV-infected prey. Given these predisposing biological traits, it is not unexpected that Common buzzards accounted for the highest number of HPAI virus detections in raptors during the 2020–2021 epizootic.Previous studies showed that HPAI viruses in raptors are highly neurotropic and cause severe neurological disease8,10,15,18,19. This study also supports those findings, as the most consistent lesion in Common buzzards was viral encephalitis, with confirmed presence of viral antigen in affected neurons. In addition to the nervous system, all the tissues tested of the Common buzzards were positive for virus based on RT-PCR and showed infection-related, histological lesions, indicating that HPAI H5N8 virus infection in the Common buzzard causes systemic disease.This study showed that HPAI H5N8 virus is also highly cardiotropic, as the myocardium of the Common buzzards contained the highest amount of virus based on RT-PCR (Table 1), and virus-associated, severe histological lesions in 63% (7/11) birds. In addition, 54% (6/11) of the Common buzzards showed virus-associated lesions in the liver and spleen.The Common buzzard is considered to be infected via the oral route by ingesting HPAIV-infected preys. Transmission of HPAIV from ingesting infected chicken meat has been experimentally confirmed in raptors20. Interestingly, the proventriculus of two birds in our study showed necrotic lesions with viral antigen. This finding further supports the oral route of infection, although we cannot exclude the possibility that the proventriculus was infected via the hematogenous route. It also provides new records of HPAIV enterotropism in wild birds. The adaptation to the intestinal tract is a mechanism recently reported for HPAI H5N8 virus, that may allow a more efficient fecal–oral transmission in wild birds5.Real time PCR (RT-PCR) is the preferred test for HPAI virus detection for active and passive bird surveillance9. In this study, cloacal and pharyngeal swabs had comparable RNA-levels, and both were adequate for the detection of the virus. The tissue analysis by RT-PCR showed that heart, brain, and air sac had highest viral RNA concentrations compared to other organs. Although not confirmed by a quantitative real time PCR, the results obtained by RT-PCR are well supported by histopathology and immunohistochemistry. Our advice for diagnostic pathologists is to collect at least a miniset of samples including brain, heart, liver and spleen, as these tissues are relatively easily sampled and were positive by both RT-PCR and for virus-antigen-associated lesions. For virus diagnosis of Common buzzards found dead (but without the interest or possibility to perform pathological examination), it is enough to collect pharyngeal and cloacal swabs, because they were positive by RT-PCR with Ct values that were comparable to those in most tissues (with exception of heart, that had higher Ct values).We did not detect antibodies against avian influenza virus NP in the sera of the Common buzzards in this study. Most of the birds (8/11) were juveniles in their first year of life, and likely they did not have protective antibodies from previous infections, as this was the first time in their lives that they experienced a HPAI epizootic. The absence of antibodies indicates also that the Common buzzards died acutely soon after infection, similarly to experimentally infected raptors that did not seroconvert before early death19. All the birds in our study were females. Females are larger than males (adult female weigh about 15% more than adult males), thus it is possible that female raptors are easier to find during surveillance or that there are sex-associated differences in feeding patterns.This study showed that HPAIV infection in Common buzzards produced severe systemic disease, and subsequent acute death based on the stage of the pathological changes and absence of serum antibodies. Cloacal and pharyngeal swabs were comparable in detecting the infection. Many organs contained viral RNA; with heart, brain and air sac containing the highest amount of viral RNA. The proventriculus of two birds showed virus-associated lesions, implying a possible adaptation of the virus to the gastro-intestinal tract. More

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Aarhus University, SORALO and KWT digitized bomas, fences and agriculture in a systematic manner using available satellite imagery (see methods). All digitization was re-checked by supervisors, to ensure that no data had been missed, and was adjusted following quality control where and when required. All data were then manually checked by conservation practitioners knowledgeable of the study area. Both the spatial resolution and temporal sampling of the data may present limitations to its accuracy and usage.Spatial resolutionFor both the KWT and SORALO datasets collected using Google Earth, we used the latest Google Earth imagery. Additionally, for KWT’s dataset, we also used the latest Bing maps imagery. However, the spatial resolution of this Google Earth and Bing maps data varies. Resolution can be as high as ~0.5 m, while a few remaining areas still rely on Landsat Imagery with a resolution of 30 m. However, the quality of the Google Earth and Bing maps imagery was generally high enough across the study area to accurately delineate bomas, fencelines and agricultural land. Figures 3 and 4 provide examples of areas that would be digitized, with the boundaries of the boma and fence lines clearly visible.The fencing data collected by Aarhus University used Landsat Imagery at 30 m resolution and smaller fences may be missing from the dataset as they are harder to distinguish. This is also true for wire fence (the predominant type of fencing around the Maasai Mara; Fig. 3C). Vegetation differences used to identify these fence lines may take some time to develop. Therefore, there may be an underestimate of the fences mapped, especially in those regions with high usage of wire fences.It must be noted that images from Google Earth have an overall positional root mean squared error of 39.7 m, which may impact the interpretation of this dataset23. We believe that these errors are acceptable for our first attempt at collecting landscape-scale data, and will be refined over time with improved imagery and ground-truthing. Landsat data has a root mean squared error usually below the size of a pixel, with 90% of pixels having less than 12 m deviation (1 https://www.usgs.gov/media/videos/landsat-collections-rmse).Temporal variationThe most likely discrepancies in data quality will arise from temporal variation in fencing placement, boma usage and placement, and agricultural change. Google Earth data were used for SORALO, using data available up to February 2020. Google Earth and Bing maps data were used for KWT, with data up to 2017. The weighted mean imagery date for SORALO (weighted by the area covered) was the 9th of September 2016 and ranged from 15th of December 2000 to 12th of February 2020 (Fig. 5). Where possible we have added a date-time stamp to the boma, agriculture and fencing dataset to best match the date the satellite imagery was acquired, or when it was collected on the ground. However, KWT and some SORALO data lack date attribute, the latter because no date stamp was found in Google Earth, and the former because no date was recorded for any data. The Aarhus University fencing data are from a Landsat Image from January 2016, and the MEP data are from on-the-ground collection. Our database is built so that as new or updated data become available, from both new satellite imagery and ground-based identification, the data layer can be adjusted (see below).Livestock enclosure validationWe used data on the location of SORALO livestock enclosures from the Magadi region24 (collected using handheld GPS devices), to estimate the accuracy of our data collection. The SORALO ground-truthed database contains 668 bomas, which have been occupied at least once during 2014–2017. In the same area, our boma points database contains 573 bomas (85%) of which 41.2% (n = 275) are within 100 m of ground-truthed points and 87.7% (n = 586) are within 500 m of the ground-truthed points. These ground-truthed points may have inaccuracies from their data collection. Also, many livestock enclosures distant from ground-truthed points are newer than the ground-truthing dataset.Agricultural land validationWe compared our agricultural data layer to a commonly used global open source data layer, the 2015 GFSAD30AFCE 30-m for Africa: Cropland Extent Product (www.croplands.org). Our layer agreed with the Cropland Extent Product across 856 km2 of cropland. However, our layer demarcated 455 km2 (34.4% of the total extent) more agricultural land than was found in the 30 m Cropland Extent Product, because many small areas of subsistence farming had not been detected by this global layer. Additionally, the Cropland Extent Product contained 468 km2 (35.3% of the Cropland Extent Product) of agricultural extent not captured in our layer. Much of this was on the periphery of large continuous agricultural areas and appears inaccurately mapped by the global product.Continual validation and improvement of databaseOngoing ground-truthing exercises by the Mara Elephant Project and other partners will improve the quality of the database over time, particularly the datasets on wire fencing in the Mara region. To do so the TerraChart app combined with a QuickCapture app (to validate fence lines and boma locations using aerial reconnaissance) are integrated into the ArcGIS online framework, and following validation both manually and using automated Python script, can be used to update the features collection database.Additionally, any data currently held in the private domain can be easily integrated into this database, and made available to the public domain with approval. Linking these features using a parent ID allows for not only the addition of new features, but improved spatial accuracy of old features, and temporal changes to features to be captured.This database will be continually improved over time. For example, current efforts from conservation partners in the region have resulted in large scale acquisition of high resolution, up-to-date, satellite imagery which will be further used to refine this database. More

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