Ecology

1.Danchin, E., Boulinier, T. & Massot, M. Conspecific reproductive success and breeding habitat selection: Implications for the study of coloniality. Ecology 79, 2415–2428 (1998).Article 

Google Scholar 
2.Morris, D. W. Adaptation and habitat selection in the eco-evolutionary process. Proc. R. Soc. B 278, 2401–2411 (2011).PubMed 
Article 

Google Scholar 
3.Roffler, G. H., Adams, L. G. & Hebblewhite, M. Summer habitat selection by Dall’s sheep in Wrangell-St. Elias National Park and Preserve. Alaska. J. Mammal. 98, 94–105 (2017).
Google Scholar 
4.Ahmad, R. et al. Security, size, or sociality: What makes markhor (Capra falconeri) sexually segregate?. J. Mamml. 99, 55–63 (2018).Article 

Google Scholar 
5.Tadesse, S. A. & Kotler, B. P. Habitat choices of Nubian ibex (Capra nubiana) evaluated with a habitat suitability modeling and isodar analysis. Isr. J. Ecol. Evol. 56, 55–74 (2010).Article 

Google Scholar 
6.Alves, J. A., da Silva, A. A., Soares, A. M. V. M. & Fonseca, C. Sexual segregation in red deer: Is social behaviour more important than habitat preferences?. Anim. Behav. 85, 501–509 (2013).Article 

Google Scholar 
7.Bourgoin, G., Marchand, P., Hewison, A. J. K., Ruckstuhl, K. E. & Garel, M. Social behaviour as a predominant driver of sexual, age-dependent and reproductive segregation in Mediterranean mouflon. Anim. Behav. 136, 87–100 (2018).Article 

Google Scholar 
8.Bell, R. H. V. A grazing ecosystem in the Serengeti. Sci. Am. 225, 86–93 (1971).ADS 
Article 

Google Scholar 
9.Jarman, P. J. The social organisation of antelope in relation to their ecology. Behaviour 48, 215–267 (1974).Article 

Google Scholar 
10.Bowyer, R. T. Sexual segregation in ruminants: Definitions, hypotheses, and implications for conservation and management. J. Mammal. 85, 1039–1052 (2004).Article 

Google Scholar 
11.Rucksthul, K. E. & Neuhaus, P. Sexual Segregation in Vertebrates: Ecology of the Two Sexes (Cambridge University Press, Cambridge, 2005).
Google Scholar 
12.Main, M. B., Weckerly, F. W. & Bleich, V. C. Sexual segregation in ungulates: New directions for research. J. Mammal. 77, 449–461 (1996).Article 

Google Scholar 
13.Barboza, P. S. & Bowyer, R. T. Seasonality of sexual segregation in dimorphic deer: Extending the gastrocentric model. Alces 37, 275–292 (2001).
Google Scholar 
14.Bleich, V. C., Bowyer, R. T. & Wehausen, J. D. Sexual segregation in mountain sheep: Resources or predation?. Wildl. Monogr. 134, 3–50 (1997).
Google Scholar 
15.Alonso, J. C., Salgado, I. & Palacín, C. Thermal tolerance may cause sexual segregation in sexually dimorphic species living in hot environments. Behav. Ecol. 27, 717–724 (2016).Article 

Google Scholar 
16.van Beest, F. M., Van Moorter, B. & Milner, J. M. Temperature-mediated habitat use and selection by a heat-sensitive northern ungulate. Anim. Behav. 84, 723–735 (2012).Article 

Google Scholar 
17.Shrestha, A. K. Larger antelopes are sensitive to heat stress throughout all seasons but smaller antelopes only during summer in an African semi-arid environment. Int. J. Biometeorol. 58, 41–49 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
18.Fedosenko, A. K. & Blank, D. A. Capra sibirica. Mammal. Spec. 675, 1–13 (2001).Article 

Google Scholar 
19.Li, Y., Yu, Y. Q. & Shi, L. Foraging and bedding site selection by Asiatic ibex (Capra sibirica) during summer in central Tianshan Mountains. Pakistan J. Zool. 47, 1–6 (2015).CAS 

Google Scholar 
20.Khan, G. et al. Himalayan ibex (Capra ibex ibex) habitat suitability and range resource dynamics in the Central Karakorum National Park, Pakistan. J. King Saud Univ. Sci. 28, 245–254 (2016).Article 

Google Scholar 
21.Bragin, N., Amgalanbaatar, S., Wingard, G. & Reading, R. P. Creating a model of habitat suitability using vegetation and ruggedness for Ovis ammon and Capra sibirica (Artiodactyla: Bovidae) in Mongolia. J. Asia Pac. Biodiver. 10, 390–395 (2017).Article 

Google Scholar 
22.Bon, R., Rideau, C., Villaret, J. & Joachim, J. Segregation is not only a matter of sex in Alpine ibex. Capra ibex ibex. Anim. Behav. 62, 495–504 (2001).Article 

Google Scholar 
23.Han, L., Blank, D., Wang, M. Y. & Yang, W. K. Vigilance behaviour in Siberian ibex (Capra sibirica): Effect of group size, group type, sex and age. Behav. Process. 170, 104021 (2020).Article 

Google Scholar 
24.Han, L. et al. Diet differences between males and females in sexually dimorphic ungulates: A case study on Siberian ibex. Eur. J. Wildl. Res. 66, 55 (2020).ADS 
Article 

Google Scholar 
25.Hay, C. T., Cross, P. C. & Funston, P. J. Trade-offs of predation and foraging explain sexual segregation in African buffalo. J. Anim. Ecol. 77, 850–858 (2008).CAS 
PubMed 
Article 

Google Scholar 
26.Hebblewhite, M., Merrel, E. & Mcdermid, G. A multi-scale test of the forage maturation hypothesis in a partially migratory ungulate population. Ecol Monogr 78, 141–166 (2008).Article 

Google Scholar 
27.Mysterud, A. et al. Partial migration in expanding red deer populations at northern latitudes-a role for density dependence?. Oikos 120, 1817–1825 (2011).Article 

Google Scholar 
28.Grignolio, S., Rossi, I., Bassano, B. & Apollonio, M. Predation risk as a factor affecting sexual segregation in Alpine ibex. J. Mammal. 88, 1488–1497 (2007).Article 

Google Scholar 
29.Gross, J. E., Alkon, P. U. & Demment, M. W. Grouping patterns and spatial segregation by Nubian ibex. J. Arid Environ. 30, 423–439 (1995).ADS 
Article 

Google Scholar 
30.Ferretti, F. et al. Males are faster forager than females: Intersexual differences of foraging behaviour in the Apennone chamois. Behav. Ecol. Sociobiol. 68, 1335–1344 (2014).Article 

Google Scholar 
31.Blank, D. A. Vigilance, staring and escape running in antipredator behavior of goitered gazelle. Behav. Process. 157, 408–416 (2018).CAS 
Article 

Google Scholar 
32.Mitchell, C. D., Chaney, R., Aho, K., Kie, J. G. & Bowyer, R. T. Population density of Dall’s sheep in Alaska: Effects of predator harvest?. Mamm. Res. 60, 21–28 (2015).Article 

Google Scholar 
33.Schaller, G. B. Mountain Monarchs (University of Chicago Press, Chicago, 1977).
Google Scholar 
34.Corti, P. & Shackleton, D. M. Relationship between predation risk factors and sexual segregation in Dall’s sheep (Ovis dalli dalli). Can. J. Zool. 80, 2108–2117 (2002).Article 

Google Scholar 
35.Bowyer, R. T. & Kie, J. G. Effects of foraging activity on sexual segregation in mule deer. J. Mammal. 85, 498–504 (2004).Article 

Google Scholar 
36.Bliss, L. M. & Weckerly, F. W. Habitat use by male and female Roosevelt elk in northwestern California. Calif. Fish Game 102, 8–16 (2016).
Google Scholar 
37.Hetem, R. S. et al. Body temperature, thermoregulatory behaviour and pelt characteristics of three colour morphs of springbok Antidorcas marsupialis. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 152, 379–388 (2009).PubMed 
Article 
CAS 

Google Scholar 
38.Maloney, S., Fuller, A. & Mitchell, D. Climate change: Is the dark Soay sheep endangered?. Biol. Lett. 5, 826 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Walsberg, G. E., Campbell, G. S. & King, J. R. Animal coat color and radiative heat gain: A re-evaluation. J. Comp. Physiol. B 126, 211–222 (1978).Article 

Google Scholar 
40.Dodson, R. & Marks, D. Daily air temperature interpolated at high spatial resolution over a large mountainous region. Clim. Res. 8, 1–20 (1997).Article 

Google Scholar 
41.Aublet, J., Festa-Bianchet, M., Bergero, D. & Bassano, B. Temperature constraints on foraging behaviour of male Alpine ibex (Capra ibex) in summer. Oecologia 159, 237–247 (2009).ADS 
PubMed 
Article 

Google Scholar 
42.Marchand, P. et al. Sex-specifc adjustments in habitat selection contribute to buffer mouflon against summer conditions. Behav. Ecol. 26, 472–482 (2014).Article 

Google Scholar 
43.Mooring, M. S., Blumstein, D. T., Reisig, D. D., Osborne, E. R. & Niemeyer, J. M. Insect- repelling behaviour in bovids: Role of mass, tail length, and group size. Biol. J. Linn. Soc. 91, 383–392 (2007).Article 

Google Scholar 
44.Blank, D. A. Insect-repelling behavior in goitered gazelles: Responses to biting fly attack. Eur. J. Wildl. Res. 66, 43 (2020).Article 

Google Scholar 
45.Torr, S. J., Prior, A., Wilson, P. J. & Schofield, S. Is there safety in numbers? The effect of cattle herding on biting risk from tsetse flies. Med. Vet. Entomol. 21, 301–311 (2007).CAS 
PubMed 
Article 

Google Scholar 
46.Anderson, J. R., Nilssen, A. C. & Folstad, I. Mating behavior and thermoregulation of the reindeer warble fly, Hypoderma tarandi L. (Diptera: Oestridae). J. Insect. Behav. 7, 679–706 (1994).Article 

Google Scholar 
47.Gordon, M. Animal Physiology: Principles and Adaptations (MacMillan, New York, 1977).
Google Scholar 
48.Short, H. L. Nutrition and metabolism. In Mule and Black-Tailed Deer of North America Vol. 605 (ed. Wallmo, O. C.) (University of Nebraska Press, Lincoln, 1981).
Google Scholar 
49.Zhu, X. S. Food habits and sexual segregation of the Asiatic Ibex, Capra sibirica. Dissertation, University of Chinese Academy of Sciences (2016).50.Wang, M. Y., Alves, J., da Silva, A. A., Yang, W. K. & Ruckstuhl, K. E. The effect of male age on patterns of sexual segregation in Siberian ibex. Sci. Rep. UK 8, 13095 (2018).ADS 
Article 
CAS 

Google Scholar 
51.Abrahms, B. et al. Does wildlife resource selection accurately inform corridor conservation?. J. Appl. Ecol. 54, 412–422 (2017).Article 

Google Scholar 
52.Smeraldo, S. et al. Species distribution models as a tool to predict range expansion after reintroduction: A case study on Eurasian beavers (Castor fiber). J. Nat. Conser. 37, 12–20 (2017).Article 

Google Scholar 
53.Phillips, S. J. & Dudik, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).Article 

Google Scholar 
54.Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

Google Scholar 
55.Li, M. L. et al. Assessment of habitat suitability of Ovis ammon polii based on MaxEnt modeling in Taxkorgan Wildlife Nature Reserve. Chin. J. Ecol. 38, 594–603 (2019).
Google Scholar 
56.ESRI. ArcGIS Desktop. Ver. 10.3. Environmental Systems (Research Institute Inc, Redlands, 2013).
Google Scholar 
57.Sappington, J. M., Longshore, K. M. & Thomson, D. B. Quantifying landscape ruggedness for animal habitat analysis: A case study using bighorn sheep in the Mojave Desert. J. Wildl. Manage. 71, 1419–1426 (2007).Article 

Google Scholar 
58.Woodward, M. Epidemiology: Study Design and Data Analysis (Chapman and Hall, London, 1999).
Google Scholar 
59.Swets, K. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).ADS 
MathSciNet 
CAS 
PubMed 
MATH 
Article 

Google Scholar 
60.Jiménez-Valverde, A. & Lobo, J. M. Threshold criteria for conversion of probability of species presence to either-or presence-absence. Acta Oecol. 31, 361–369 (2007).ADS 
Article 

Google Scholar 
61.Llausàs, A. & Nogué, J. Indicators of landscape fragmentation: The case for combining ecological indices and the perceptive approach. Ecol. Indic. 15, 85–91 (2012).Article 

Google Scholar  More

AffiliationsARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland, AustraliaAndreas Dietzel, Michael Bode, Sean R. Connolly & Terry P. HughesSchool of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, AustraliaMichael BodeCollege of Science and Engineering, James Cook University, Townsville, Queensland, AustraliaSean R. ConnollySmithsonian Tropical Research Institute, Balboa, Republic of PanamaSean R. ConnollyAuthorsAndreas DietzelMichael BodeSean R. ConnollyTerry P. HughesCorresponding authorCorrespondence to
Andreas Dietzel. More

1.Bengis, R. G. A revue of bovine Brucellosis in free-ranging African wildlife. in Proceedings of the ARC-Onderstepoort, OIE International Congress with WHO-Cosponsorship on anthrax, brucellosis, CBPP, clostridial and mycobacterial diseases : Berg-en-Dal, Kruger National Park, South Africa 178–183 (Onderstepoort Veterinary Inst, 1998).2.Kaliner, G., Staak, C., Kalinerj, G. & Staaklu, C. A case of orchitis caused by Brucella abortus in the African buffalo. J. Wildl. Dis. 9, 251–253 (1973).Article 

Google Scholar 
3.Schiemann, B. & Staak, C. Brucella melitensis in impala (Aepyceros melampus). Vet. Rec. 88, 344–344 (1971).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Ndengu, M. et al. Seroprevalence of brucellosis in cattle and selected wildlife species at selected livestock/wildlife interface areas of the Gonarezhou National Park Zimbabwe. Prev. Vet. Med. 146, 158–165 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Rollinson, D. H. L. Brucella agglutinins in East African game animals. Vet. Rec. 74, 904 (1962).
Google Scholar 
6.De Vos, V. & Van Niekerk, C. A. W. Brucellosis in the Kruger National Park. J. S. Afr. Vet. Assoc. 40, 331–334 (1969).
Google Scholar 
7.Sachs, R. & Staak, C. Evidence of brucellosis in antelope in the Serengeti. Vet. Record 79, 857–856 (1966).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.El-Tras, W. F., Tayel, A. A., Eltholth, M. M. & Guitian, J. Brucella infection in fresh water fish : Evidence for natural infection of Nile catfish, Clarias gariepinus, with Brucella melitensis. Vet. Microbiol. 141, 321–325 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Lane, E. P. et al. A systematic health assessment of Indian ocean bottlenose (Tursiops aduncus) and indo-pacific humpback (Sousa plumbea) dolphins incidentally caught in shark nets off the KwaZulu-Natal coast South Africa. PLoS ONE 9, e107038 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
10.Salem, A. A., Hamed, O. M. & Abd-Elkarim, A. M. Studies on some Brucella carriers in Egypt. Assiut Vet Med J 1, 181–187 (1974).
Google Scholar 
11.Condy, J. B. The status of disease in Rhodesian wildlife. Rhod. Sci. News 2, 96–99 (1968).
Google Scholar 
12.Condy, J. B. & Vickers, D. B. The isolation of Brucella abortus from a waterbuck (Kobus ellipsiprymnus). Vet. Rec. 85, 200 (1969).Article 

Google Scholar 
13.Bell, L. M., Hayles, L. B. & Chanda, A. B. Evidence of reservoir hosts of Brucella melitensis. Med. J. Zambia 10, 152–153 (1976).CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Gradwell, D. V., Schutte, A. P., van Niekerk, C. A. & Roux, D. J. The isolation of Brucella abortus biotype I from African buffalo in the Kruger National Park. J. S. Afr. Vet. Assoc. 48, 41–43 (1977).CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Karesh, W. B. et al. Health evaluation of five sympatric duiker species (Cephalophus spp.). J. Zool. Wildl. Med. 26, 485–502 (1995).
Google Scholar 
16.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
17.Bengis, R. G. & Erasmus, J. M. Wildlife diseases in South Africa: A review. Rev. Sci. Tech. Off. Int. des Epizoot. 7, 807–821 (1988).Article 

Google Scholar 
18.Durrheim, D. N. et al. Safety of travel in South Africa: The Kruger National Park. J. Travel Med. 8, 176–191 (2006).Article 

Google Scholar 
19.Eisenberg, T. et al. Isolation of potentially novel Brucella spp. from frogs. Appl. Environ. Microbiol. 78, 3753–3755 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Hoogstral, H., Kaiser, M. N., Traylor, M. A., Guindy, E. & Gaber, S. Ticks (Ixodidae) on birds migrating from Europe and Asia to Africa 1959–61. Bull. World Health Organ. 28, 235–262 (1963).
Google Scholar 
21.Michel, A. L. A. L. & Bengis, R. G. R. G. The African buffalo: A villain for inter-species spread of infectious diseases in southern Africa. Onderstepoort. J. Vet. Res. 79, 5 (2012).Article 

Google Scholar 
22.Monroe, B. P. et al. Collection and utilization of animal carcasses associated with zoonotic disease in Tshuapa district, the democratic republic of the Congo, 2012. J. Wildl. Dis. 51, 734–738 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Wolhuter, J., Bengis, R. G., Reilly, B. K. & Cross, P. C. Clinical demodicosis in African buffalo (Syncerus caffer) in the Kruger National Park. J. Wildl. Dis. 45, 2 (2009).Article 

Google Scholar 
24.Worthington, R. W. & Bigalke, R. D. A review of the infectious diseases of African wild ruminants. Onderstepoort. J. Vet. Res. 68, 291–323 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Mühldorfer, K. et al. The role of ‘atypical’ Brucella in amphibians: are we facing novel emerging pathogens?. J. Appl. Microbiol. 122, 40–53 (2017).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
26.Ducrotoy, M. et al. Brucellosis in Sub-Saharan Africa: Current challenges for management, diagnosis and control. Acta Trop. 165, 179–193 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Munagandu, et al. Disease constraints for utilization of the African buffalo (Syncerus caffer) on game ranches in Zambia. Jpn. J. Vet. Res. 54, 3–13 (2006).
Google Scholar 
28.Munyua, P. et al. Prioritization of zoonotic diseases in Kenya, 2015. PLoS ONE 11, e0161576 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
29.Conrad, P. A., Meek, L. A. & Dumit, J. Operationalizing a One Health approach to global health challenges. Comp. Immunol. Microbiol. Infect. Dis. 36, 211–216 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Bekker, J. L., Hoffman, L. C. & Jooste, P. J. Wildlife-associated zoonotic diseases in some southern African countries in relation to game meat safety: A review. Onderstepoort. J. Vet. Res. 79, 12 (2012).Article 

Google Scholar 
31.Muma, J. B. et al. The contribution of veterinary medicine to public health and poverty reduction in developing countries. Vet. Ital. 50, 117–129 (2014).PubMed 
PubMed Central 

Google Scholar 
32.Mugizi, D. R. et al. Isolation and Molecular Characterization of Brucella Isolates in Cattle Milk in Uganda. 2015, (2015).33.Mathew, C. et al. First isolation, identification, phenotypic and genotypic characterization of Brucella abortus biovar 3 from dairy cattle in Tanzania. BMC Vet. Res. 11, 2 (2015).Article 

Google Scholar 
34.Meyer, M. E. & Morgan, W. J. B. Designation of neotype strains and of biotype reference strains for species of the genus Brucella Meyer and Shaw. Int. J. Syst. Bacteriol. 23, 135–141 (1973).Article 

Google Scholar 
35.National Academies of Sciences, Engineering, and M. Revisiting brucellosis in the greater yellowstone area. Revisiting Brucellosis in the Greater Yellowstone Area (National Academies Press, 2017). doi:https://doi.org/10.17226/2475036.Muma, J. B. et al. Brucella seroprevalence of the Kafue lechwe (Kobus leche kafuensis) and Black lechwe (Kobus leche smithemani): Exposure associated to contact with cattle. Prev. Vet. Med. 100, 256–260 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Gorsich, E. E., Ezenwa, V. O., Cross, P. C., Bengis, R. G. & Jolles, A. E. Context-dependent survival, fecundity and predicted population-level consequences of brucellosis in African buffalo. J. Anim. Ecol. 84, 999–1009 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Hoogstraal, H., Kaiser, M. N., Traylor, M. A., Gaber, S. & Guindy, E. Ticks (Ixodoidea) on birds migrating from Africa to Europe and Asia. Bull. World Health Organ. 24, 197–212 (1961).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Alexander, K. A. et al. Buffalo, bush meat, and the zoonotic threat of brucellosis in Botswana. PLoS ONE 7, e32842 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Munn, Z., Moola, S., Riitano, D. & Lisy, K. The development of a critical appraisal tool for use in systematic reviews addressing questions of prevalence. Int. J. Heal. Policy Manag. 3, 123–128 (2014).Article 

Google Scholar 
41.Madsen, M. et al. Serologic survey of Zimbabwean wildlife for brucellosis. J. Zoo. Wildl. Med. 26, 240–245 (1995).
Google Scholar 
42.Roberts, M. G. & Heesterbeek, J. A. P. Quantifying the dilution effect for models in ecological epidemiology. J. R. Soc. Interface 15, 2 (2018).Article 

Google Scholar 
43.Viana, M. et al. Assembling evidence for identifying reservoirs of infection. Trends Ecol. Evol. 29, 270–279 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Souley Kouato, B. et al. Spatio-temporal patterns of foot-and-mouth disease transmission in cattle between 2007 and 2015 and quantitative assessment of the economic impact of the disease in Niger. Transbound Emerg. Dis. 65, 1049–1066 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Godfroid, J., Nielsen, K. & Saegerman, C. Diagnosis of brucellosis in livestock and wildlife. Croat Med. J. 51, 296–305 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
46.Hartling, L. et al. Grey literature in systematic reviews : a cross-sectional study of the contribution of non-English reports, unpublished studies and dissertations to the results of meta- analyses in child-relevant reviews. 1–11 (2017). doi:https://doi.org/10.1186/s12874-017-0347-z47.Condy, J. B. & Vickers, D. B. Brucellosis in Rhodesian wildlife. J. S. Afr. Vet. Assoc. 43, 175–179 (1972).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Erume, J. et al. Serological and molecular investigation for brucellosis in swine in selected districts of Uganda. Trop. Anim. Health Prod. 48, 1147–1155 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
49.Godfroid, J., Beckmen, K. & Helena Nymo, I. Removal of lipid from serum increases coherence between brucellosis rapid agglutination test and enzyme-linked immunosorbent assay in bears in Alaska, USA. J. Wildl. Dis. 52, 912–915 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
50.Matope, G., Bhebhe, E., Muma, J. B. B., Lund, A. & Skjerve, E. Herd-level factors for Brucella seropositivity in cattle reared in smallholder dairy farms of Zimbabwe. Prev. Vet. Med. 94, 213–221 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Mwebe, R., Nakavuma, J. & Moriyón, I. Brucellosis seroprevalence in livestock in Uganda from 1998 to 2008: a retrospective study. Trop. Anim. Health Prod. 43, 603–608 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Aune, K., Rhyan, J. C., Russell, R., Roffe, T. J. & Corso, B. Environmental persistence of Brucella abortus in the Greater Yellowstone Area. J. Wildl. Manag. 76, 253–261 (2012).Article 

Google Scholar 
53.Enström, S. et al. Brucella seroprevalence in cattle near a wildlife reserve in Kenya. BMC Res. Notes 10, 2 (2017).Article 

Google Scholar 
54.Godfroid, J. Brucellosis in wildlife. Rev. Sci. Tech. 21, 277–286 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Martin, C., Pastoret, P. P., Brochier, B., Humblet, M. F. & Saegerman, C. A survey of the transmission of infectious diseases/infections between wild and domestic ungulates in Europe. Vet. Res. 42, 70 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Godfroid, J. et al. A ‘One Health’ surveillance and control of brucellosis in developing countries: Moving away from improvisation. Comp. Immunol. Microbiol. Infect. Dis. 36, 241–248 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Kamath, P. L. et al. Genomics reveals historic and contemporary transmission dynamics of a bacterial disease among wildlife and livestock. Nat. Commun. 7, 11448 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Michel, A. L. et al. Wildlife tuberculosis in South African conservation areas: Implications and challenges. Vet. Microbiol. 112, 91–100 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Pandey, G. S. et al. Serosurvey of brucella spp. infection in the Kafue Lechwe (Kobus leche kafuensis) of the Kafue flats in Zambia. Indian Vet. J. 76, 275–278 (1999).
Google Scholar 
60.Olsen, S. & Tatum, F. Swine brucellosis: Current perspectives. Vet. Med. Res. Rep. 8, 1–12 (2016).
Google Scholar 
61.Menshawy, A. M. S. et al. Assessment of Genetic Diversity of Zoonotic Brucella spp. Recovered from Livestock in Egypt Using Multiple Locus VNTR Analysis. (2014). doi:https://doi.org/10.1155/2014/35387662.Ibrahim, S. Studies on swine brucellosis in Egypt. J. Egypt Vet. Med. Assoc. 56, 1–12 (1996).
Google Scholar 
63.Ledwaba, B., Mafofo, J. & Van Heerden, H. Genome sequences of Brucella abortus and Brucella suis strains isolated from Bovine in Zimbabwe. Genome Announc. 2, 1063–1077 (2014).Article 

Google Scholar 
64.Fretin, D. et al. Unexpected Brucella suis biovar 2 infection in a dairy cow, Belgium. Emerging Infectious Diseases 19, 2053–2054 (Centers for Disease Control and Prevention, 2013).65.Maurin, M. Brucellosis at the dawn of the 21st century. Médecine Mal. Infect. 35, 6–16 (2005).CAS 
Article 

Google Scholar 
66.Whatmore, A. M. et al. Brucella papionis sp. nov., isolated from baboons (Papio spp.). Int. J. Syst. Evol. Microbiol. 64, 4120–4128 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
67.Godfroid, J., Garin-Bastuji, B., Saegerman, C. & Blasco, J. M. Brucellosis in terrestrial wildlife. Rev. Sci. Tech. Off. Int. Epiz. 32, 27–42 (2013).CAS 
Article 

Google Scholar 
68.Barendregt, J. J., Doi, S. A., Lee, Y. Y., Norman, R. E. & Vos, T. Meta-analysis of prevalence. J. Epidemiol. Community Health 67, 974–978 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
69.EpiGear. EpiGear International. Available at: http://www.epigear.com/. (Accessed: 8th February 2018)70.Doi, S. A. R. R., Barendregt, J. J., Khan, S., Thalib, L. & Williams, G. M. Advances in the meta-analysis of heterogeneous clinical trials I: The inverse variance heterogeneity model. Contemp. Clin. Trials 45, 130–138 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
71.Higgins, J. P. T., Thompson, S. G., Deeks, J. J. & Altman, D. G. Measuring inconsistency in meta-analyses. BMJ 327, 557–560 (2003).PubMed 
PubMed Central 
Article 

Google Scholar 
72.Heisch, R. B., Cooke, E. R., Harvey, A. E. & De Souz, F. The isolation of Brucella suis from rodents in Kenya. East Afr. Med. J. 40, 132–133 (1963).CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Motsi, T. R., Tichiwangana, S. C., Matope, G., Mukarati, N. L. & Studies, V. A serological survey of brucellosis in wild ungulate species from five game parks in Zimbabwe. Onderstepoort. J. Vet. Res. 80, 586 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
74.Roth, H. H. A survey of brucellosis in game animals in Rhodesia. Bull. Epizoot. Dis. Afr. Bull. des Epizoot en Afrique 15, 133–142 (1967).CAS 

Google Scholar 
75.Condy, J. B. & Vickers, D. B. Brucellosis in buffalo in Wankie National Park. Rhod. Vet. J. 8, 58–60 (1976).
Google Scholar 
76.Caron, A. et al. Relationship between burden of infection in ungulate populations and wildlife/livestock interfaces. Epidemiol. Infect. 141, 1522–1535 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Gomo, C. et al. Detection of Brucella abortus in Chiredzi district in Zimbabwe. Onderstepoort. J. Vet. Res. 79, 1–5 (2012).Article 

Google Scholar 
78.Chaparro, F., Lawrence, J. V., Bengis, R. & Myburgh, J. G. A serological survey for brucellosis in buffalo (Syncerus caffer) in the Kruger National Park. J. S. Afr. Vet. Assoc. 61, 110–111 (1990).CAS 
PubMed 
PubMed Central 

Google Scholar 
79.Fischer-Tenhagen, C., Hamblin, C., Quandt, S., Frö;lich, K. & Frö Lich, K. Serosurvey for selected infectious disease agents in free-ranging black and white rhinoceros in Africa. Journal of Wildlife Diseases 36, 316–323 (2000).80.Caron, A. et al. African buffalo movement and zoonotic disease risk across transfrontier conservation areas Southern Africa. Emerg. Infect. Dis. 22, 277–280 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Herr, S. & Marshall, C. Brucellosis in free-living African buffalo (Syncerus caffer): A serological survey. Onderstepoort. J. Vet. Res. 48, 133–134 (1981).CAS 
PubMed 
PubMed Central 

Google Scholar 
82.De Vos, V., Van Niekerk, G. A. W. J. & McConell, E. E. A survey of selected bacteriological infections of the Chacma Baboon Papio Ursinus from the Kruger National Park. Koedoe 16, 1–10 (1973).
Google Scholar 
83.Hamblin, C., Anderson, C. E., Jago, M., Mlengeya, T. & Hirji, K. Antibodies to some pathogenic agents in free-living wild species in Tanzania. Epidemiol. Infect. 105, 585–594 (1990).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Assenga, J. A., Matemba, L. E., Muller, S. K., Malakalinga, J. J. & Kazwala, R. R. Epidemiology of Brucella infection in the human, livestock and wildlife interface in the Katavi-Rukwa ecosystem, Tanzania. BMC Vet. Res. 11, 8 (2015).Article 

Google Scholar 
85.Sachs, R., Staak, C. & Groocock, C. M. Serological investigation of brucellosis in game animals in Tanzania. Bull. Epizoo. Dis. Afr. 16, 93–100 (1968).CAS 

Google Scholar 
86.Fyumagwa, R. D., Wambura, P. N., Mellau, L. S. B. & Hoare, R. Seroprevalence of Brucella abortus in buffaloes and wildebeests in the Serengeti ecosystem: A threat to humans and domestic ruminants. Tanzania Vet. J. 26, 2 (2010).
Google Scholar 
87.Matope, G. et al. Evaluation of sensitivity and specificity of RBT, c-ELISA and fluorescence polarisation assay for diagnosis of brucellosis in cattle using latent class analysis. Vet. Immunol. Immunopathol. 141, 58–63 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
88.Muma, J. B. et al. Serosurvey of Brucella Spp Infection in the Kafue Lechwe (Kobus Leche Kafuensis) of the Kafue Flats in Zambia. J. Wildl. Dis. 46, 1063–1069 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
89.Waghela, S. Animal brucellosis in Kenya: A review. Bull. Anim. Heal. Prod. Afr. 24, 53–59 (1976).CAS 

Google Scholar 
90.Waghela, S., Karstad, L., Waghela, A. S. & Karstad, L. Antibodies to Brucella Spp among blue wildebeest and African Buffalo in Kenya. J. Wildl. Dis. 22, 189–192 (1986).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Magwedere, K. et al. Brucellae through the food chain: the role of sheep, goats and springbok (Antidorcus marsupialis) as sources of human infections in Namibia. J. South Afr. Vet. Assoc. Van Die Suid-Afrikaanse Veterinere Ver 82, 205–212 (2011).CAS 

Google Scholar 
92.Karesh, W. B. et al. Health evaluation of black-faced impala (Aepyceros melampus petersi) using blood chemistry and serology. J. Zoo. Wildl. Med. 28, 361–367 (1997).CAS 
PubMed 
PubMed Central 

Google Scholar 
93.Cooper, A. C. D. & Carmichael, I. H. The incidence of brucellosis in game in Botswana. Bull. Epizoot. Dis. Afr. 22, 119–124 (1974).CAS 
PubMed 
PubMed Central 

Google Scholar 
94.Thimm, B. Brucellosis in Uganda.pdf. Bull Epizoot Dis Africa 20, 43–56 (1972).95.Tanner, M. et al. Bovine tuberculosis and brucellosis in cattle and african buffalo in the limpopo national park mozambique. Transbound Emerg. Dis. 62, 632–638 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Gomo, C., de Garine-Wichatitsky, M., Caron, A. & Pfukenyi, D. M. Survey of brucellosis at the wildlife-livestock interface on the Zimbabwean side of the Great Limpopo Transfrontier Conservation Area. Trop. Anim. Health Prod. 44, 77–85 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
97.Herr, S. & Marshall, C. Brucellosis in Free-Living African Buffalo (Syncerus-Caffer)—a Serological Survey. Onderstepoort. J. Vet. Res. 48, 133–134 (1981).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Soil microbial diversity and community structureIn total, 1,334,381 reads were obtained for the bacterial 16S rRNA genes by high-throughput sequencing. After screening these gene sequences with strict criteria (described in “Materials and methods”), 1,061,916 valid sequences were obtained, accounting for 79.6% of the raw reads. Figure 1A shows that the observed richness, Chao1, and Shannon index in the SS (sweet sugarcane) group supported significantly higher richness (P  More

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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