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Rural areas of low-to-middle-income countries host most biodiversity hotspots, where interactions between people and wildlife are frequent. These regions have less access to vaccines than do urban centres (Local Burden of Disease Vaccine Coverage Collaborators Nature 589, 415–419; 2021).Given the broad potential range of hosts for SARS-CoV-2, we suggest that vaccinating often-neglected populations around protected areas will reduce the risk of people infecting wildlife and creating secondary reservoirs of disease, and thence risking potential reinfection of humans with new variants. This should be considered after vaccination of priority groups, such as older people and health workers.Vaccinating people who live near felids, non-human primates, bats and other animals protects wildlife and limits ‘reverse spillovers’. Such events have been documented for various human respiratory viruses, for instance in wild great apes in west Africa (S. Köndgen et al. Curr. Biol. 18, 260–264; 2008).Non-standard actors, such as national park authorities or conservation organizations, could help vaccination to reach remote regions. This is called a One Health approach: it protects the health of people, animals and the environment. More

Sample collection and handlingSource of samples to be screenedSouth Africa currently has 72 official points of entry—8 seaports, 10 airports and 54 land border posts10. The DALRRD has border inspectors at most of these points (although staffing levels have varied considerably). DALRRD border inspectors inspect goods and travellers entering the country for plant contaminants. As part of DALRRD’s biosecurity protocol, three types of samples are collected and sent to DALRRD laboratories in Stellenbosch or Pretoria for further investigation (Fig. 1).

1.

Intervention samples. If the border inspector finds or suspects a pest or pathogen in a consignment, he/she will take a sample and send it to one of DALRRD’s diagnostic laboratories. A suspicion of contamination is often the result of quarantine organisms being detected on previous consignments of the same commodity. The imported consignment is detained at the border until laboratory results are completed. Due to the time-sensitivity of such imports, the samples are usually only inspected or tested for the taxa of concern.

2.

Audit samples. As above, these samples are drawn from consignments of plant products for immediate use. However, they are drawn on an ad hoc (haphazard) basis from consignments that show no signs of contamination during border inspections. In the laboratory, these samples are often inspected or tested for multiple taxa.

3.

Post-entry quarantine (PEQ) samples. Plant products for propagation purposes or nursery material (e.g. in vitro plantlets, seedlings, budwood) are shipped in sealed packages and transported directly to DALRRD’s agricultural quarantine facilities. For small consignments (under 50 units), all units in the consignment are tested and inspected by laboratory officials. For larger consignments, random samples are drawn and inspected following a hypergeometric sampling protocol11. Inspection for arthropods and initial examination for micro-organisms takes place in a biosecurity containment facility (see Saccaggi & Pieterse12 for further details). The material is then grown in a dedicated quarantine facility and further testing for pathogens takes place when the plants are in active growth.

Fig. 1Summary of border and laboratory processes associated with each of the three import sample sources included in this dataset, namely post-entry quarantine (PEQ), intervention and audit samples. Solid lines indicate that these processes are always followed, while dashed lines indicate that the process is sometimes followed. PEQ samples are received from plant propagation or nursery material that needs to be quarantined upon arrival. Intervention samples are received from consignments in which the border inspector finds or suspects a pest or pathogen. Audit samples are ad hoc samples drawn from consignments that show no sign of contamination. These sample sources are explained in more detail in the text.Full size imageTaxa inspection, testing and identification methodsAll inspections, testing and identifications are carried out by DALRRD laboratory officials specialised in each taxonomic group. Taxonomic identifications are routinely done by DALRRD officials, taxonomists at the Biosystematics Division of the South African Agricultural Research Council (ARC) or higher education institutions, depending on the expertise available at the time. All recorded identifications in the dataset were retained, regardless of level of identification or biosecurity status of the organism. It should, however, be noted that all organisms found were not always recorded (see below for further explanation).Arthropods (mostly insects and mites) and Molluscs are detected via visual inspection using a stereo-microscope. For these taxa, all organisms detected are recorded. Organisms are most commonly identified morphologically, with molecular identification being performed for certain groups. Identification is performed to the point at which a reasonable phytosanitary decision can be made (i.e. sometimes taxonomic precision is sacrificed for time and/or resource efficiency and logistic reasons). Thus specimens from predatory or saprophytic groups are often only identified to family or genus, while specimens within plant-feeding groups are identified to species where possible.Nematodes are detected by extraction from samples using relevant extraction methods. Saprophytic and predatory nematodes are sometimes noted, but often ignored as they are not considered to be of phytosanitary concern. Plant-feeding nematodes are identified morphologically to species where possible.Fungi and Bacteria are detected visually in the growing plant, as well as by conventional isolation and plating techniques, followed by biochemical tests and/or morphological identification. Some targeted pathogens are detected and identified by molecular techniques such as PCR and DNA sequencing. Saprophytic or secondary fungi or bacteria are sometimes noted, but often not recorded as part of the sample record.Viruses are screened for by immunological techniques, notably ELISA and hardwood and herbaceous indexing. ELISA techniques detect a target virus of concern and give no information as to the presence or absence of other viruses in the sample. Hardwood and herbaceous indexing are used to determine if any graft- or mechanically-transmissible viruses are present in the sample, although these methods cannot be used to determine the viruses’ identity.Phytoplasma screening is done by nested PCR designed to detect any phytoplasma. On specific crops, phytoplasma groups are detected by using targeted PCR methods. If necessary, sequencing of PCR products is used for more specific identification.Data collection and handlingMetadata for samples were recorded by the border inspector before submission to DALRRD’s laboratories. Ideally, he/she recorded geographic origin of the commodity, crop and sample type, date of collection, details of importer and exporter, organisms to test for and any additional observations. However, in practice, this information was not always recorded in full. See Tables 1, 2 and 3 for more details on information included in the dataset. Due to the sensitivity of this kind of trade data, some of the data in the current dataset are grouped or anonymised to protect confidentiality. In particular, import date is only listed as month and year and the names of importers and exporters are removed.Table 1 A summary of information fields and descriptions for each imported sample recorded in the South African plant import dataset used in the datasheet “List of contaminants on SA plant imports 1994–2019.csv”23.Full size tableTable 2 Information fields and descriptions for taxa information associated with contaminant organisms detected on import samples received by South Africa used in the datasheet “Metadata of contaminants on SA plant imports 1994–2019.csv”23.Full size tableTable 3 List of import commodity types used in the datasheet “List of contamiants on SA plant imports 1994–2019.csv”23. The original categories listed by the inspectors were expanded to 30 commodity types based on additional laboratory information and expert experience.Full size tableElectronic databases of samples received by the DALRRD laboratories were maintained by the laboratory staff. These databases were not official departmental databases and therefore did not need to include information relevant to other sections involved in biosecurity. For instance, total number of imports, total size of each consignment, observations of the inspector, details of phytosanitary certificates and release or detention of the consignment were never recorded. The databases also included samples processed by the laboratory for export or for national pest surveys. Partly due to their unofficial status, the databases were transient, with new databases started once software became outdated, the old one became too big or when new categories or information were to be included. For this study, we collated, curated and cross-checked information from nine of these databases, spanning 26 years from 1994 to 2019.Recorded laboratory data varied between taxa and over time and as priorities and understanding of biosecurity changed. In the initial years considered here (ca. 1994–2000), the focus was on pests or pathogens of quarantine importance, i.e. those on the prohibited list. Other organisms found on samples were not consistently recorded and, when they were, they were often recorded in broad groupings (e.g. “saprophytic nematodes”). More recently, there has been a shift towards recording all organisms detected, but this has still not been done consistently [although from ~2005 onwards the officials responsible for arthropods and molluscs have tried to record everything found (DS, MA personal observations)]. Thus prohibited (i.e. quarantine organisms) were always recorded, but the recording of other contaminants was inconsistent.Data clean-up started with collation of all data from the nine databases. Initially, these contained 99,023 records, with 50,655 recorded as imports, 31,163 as exports, 11,004 as surveys with the remaining 6,201 falling into other categories or uncategorised. Only imports were retained, as this was the only category of interest for this study. For some imports, sample information was recorded in one database, while results of inspections/tests for different taxa were recorded in other databases. Thus a single sample could have up to four duplicate records. Each of these was checked individually and collated into one record for the sample. Spelling mistakes, incorrectly recorded information (e.g. information recorded in the wrong field) and missing information were traced back through paper records and corrected wherever possible. If the original data could not be found, these ambiguous records were excluded. After this data clean-up, the dataset comprised a list of 26,291 import records, of which 2,572 resulted from intervention samples (sample source 1 above, Fig. 1), 10,629 were audit samples (sample source 2 above, Fig. 1) and 13,090 were PEQ samples (sample source 3 above, Fig. 1). Data clean-up then continued for the organisms found on the imported samples.Taxon names were extracted and spelling and classification were corrected and/or added by hand. The list of taxa was checked against the Global Biodiversity Information Facility (GBIF)13 using the software package ‘rgbif’14 in Rstudio version 1.3.95915 running R version 4.0.216. This highlighted additional spelling mistakes and provided a taxonomic backbone to work from. The classification of a number of taxa had changed over the years and thus using a common taxonomic backbone was needed for consistency. Some taxa, most notably some mite species, could not be found on GBIF. In these cases, the taxonomy provided by the taxonomist who initially identified the organism was retained. Virus taxonomic information was also not available on GBIF and the database of the International Committee on Taxonomy of Viruses (ICTV) was used17.Species occurrence in South Africa was determined by consulting published species distribution lists. The following data sources were consulted: GBIF13 (accessed 29 July and 03 Aug 2020); CABI Crop Pest Compendiums and Invasive Species Compendium18,19,20; the Catalogue of Life21; animal species checklists published by the South African Biodiversity Institute (SANBI)22; and for any remaining species internet searches were conducted for literature citing distributions (listed in Table 2).In South Africa, lists of organisms prohibited from entering the country have been compiled by DALRRD and the Department of Forestry, Fisheries and the Environment (DFFtE). DFFtE’s list of prohibited species focussed mostly on organisms of environmental concern, although some prohibited organisms were also of agricultural concern, while DALRRD is only concerned with agricultural pests. DALRRD issues import permits for each unique crop, commodity and country combination from which plant products originate. Thus there is no single consolidated quarantine list for South Africa. Furthermore, any quarantine list is not static, but needs to change as species’ distributions, taxonomic revisions or pest status changes. Thus it is very difficult to provide a list of which detected organisms are of quarantine status to South Africa at any given time and particularly in a dataset spanning 26 years. As far as possible, we have indicated the regulatory status of the species in the datasheet “Metadata of contaminants on SA plant imports 1994–2019.csv”23. This regulatory status would have been of critical importance to inform contemporary phytosanitary decisions. However, given that such lists are dynamic and a core aim of presenting these data is to facilitate analyses of future invaders9, it is important to present information on all organisms detected. Moreover, this allows a more comprehensive assessment of the role of different pathways and will facilitate comparisons with other countries. More

This study describes a unique point-based data set for coral reef environments, collected using a photoquadrat survey method published for seagrass environments1. The data set describes the spatial and temporal distribution of benthic community abundance and composition for Heron Reef, a 28 km2 shallow platform reef located in the Capricorn Bunker Group, Southern Great Barrier Reef (GBR), Australia. On average, 3,600 coral reef data points were collected annually over the period 2002 to 2018. Annual data sets were acquired for independent research projects, but the collection methods were consistent. The initial field data collection design was planned to acquire detailed field data to describe the spatial distribution and variability of benthic composition across the study site to assist with calibration and validation of earth observation-based mapping products.To create a map based on earth observation imagery, it is common to use training or calibration data to transform the imagery into a map of surface properties using a supervised algorithm (e.g. multivariate statistical clustering, random forest)2. To report on the accuracy measures of the maps, reference or validation data are contrasted with the output maps3. Hence for calibration and validation purposes, georeferenced field data must be representative of all the features to be mapped and collection should ideally coincide with satellite image acquisition. Many earth observation approaches have been implemented for mapping the benthic communities of Heron Reef4,5,6,7,8,9,10,11,12 and several of these maps are now accessible online6,13,14.Several studies have utilised time series benthic data to analyse changes in benthic community and coral type trends, supporting broad ecological knowledge of coral reef ecosystems such as the Caribbean reef degradation15 and coral cover decline on the GBR16. Similarly, benthic community and coral cover data sets have been identified as important indicators of coral reef health providing the backbone for monitoring and management initiatives around the world17,18.Articles and data sets have been published that describe the benthic community properties of Heron Reef, however, their spatial coverage, number of georeferenced data points, and revisit times are limited19. The time series photoquadrat data sets presented in this paper could be used for further understanding of benthic community distribution, including statistical analysis of trends in coral cover, analysis of changes in benthic community and coral type, or used for testing of other earth observation-based mapping and modelling approaches. Additionally, as our methodology describes machine annotation of the field photoquadrats, it would be possible to reanalyse the photoquadrats with new categories not previously considered important from a biological perspective (e.g. unknown disease or impact, or a specific benthic community type), or for other features (e.g. the counting of sea cucumbers (Holothuroidea sp.)).Detailed analyses of our complete data set may permit a greater understanding of the persistence and/or dynamics of the benthic community at Heron Reef. As such, our ongoing analyses include evaluation of changes in community composition following major impacts such as cyclones, coral bleaching, crown of thorns predation, etc., and additionally, statistical analyses of coral recovery after such impacts. To this degree, these benthic community data sets are invaluable. More

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