| Summary: | Protected areas are critical for the long-term conservation of biodiversity globally. Across agricultural landscapes, protected areas serve as refuges for threatened and declining species and provide valuable ecosystem services that support broader landscape function. However, increasing pressure on protected areas from a range of sources is undermining their unique conservation values. Much of the native vegetation remaining in southern Australia is now protected from broad-scale clearing, but the management of threats within these areas could be inadequate to maintain their biodiversity. In particular, grazing by domestic and wild mammalian herbivores can have a significant impact on the condition of remnant native vegetation, even if it is formally protected. We evaluated the evidence for grazing-pressure trends in native vegetation in both protected and unprotected areas across an agricultural landscape covering c. 180 000 km2 of South Australia. Using total grazing pressure data from 1298 sites surveyed between 2004 and 2016, we employed Bayesian linear mixed-effects modelling to demonstrate increasing trends in grazing severity in four of five study regions, estimating that 45%–54% of grazed plants were heavily or severely grazed by 2016. Over the 12-year period, grazing severity in protected areas increased to levels closely matching those on private, unprotected land. Our results suggest that abundant native and/or introduced grazing animals are having an increasing impact on native vegetation across large areas of the agricultural zone of South Australia, with a disproportionate impact on protected areas. We recommend that: (1) research is needed to identify sustainable total-grazing-pressure thresholds; and (2) options should be considered for an adaptive, coordinated, and cross-tenure approach to maintaining total grazing pressure in protected areas below these thresholds. Keywords: Reserve system, Vegetation condition, Total grazing pressure, Abundant herbivores, Macropods, Bushland condition monitoring, Beta regression models
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