Lack of detection of SARS-CoV-2 in British wildlife 2020-21 and first description of a stoat (Mustela erminea) Minacovirus

Repeat spillover of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into new hosts has highlighted the critical role of cross-species transmission of coronaviruses and establishment of new reservoirs of virus in pandemic and epizootic spread of coronaviruses. Species particularly susceptible to SARS-CoV-2 spillover include Mustelidae (mink, ferrets and related animals), cricetid rodents (hamsters and related animals), felids (domestic cats and related animals) and white-tailed deer. These predispositions led us to screen British wildlife with sarbecovirus-specific quantitative PCR and pan coronavirus PCR assays for SARS-CoV-2 using samples collected during the human pandemic to establish if widespread spillover was occurring. Fourteen wildlife species (n=402) were tested, including: two red foxes (Vulpes vulpes), 101 badgers (Meles meles), two wild American mink (Neogale vison), 41 pine marten (Martes martes), two weasels (Mustela nivalis), seven stoats (Mustela erminea), 108 water voles (Arvicola amphibius), 39 bank voles (Myodes glareolous), 10 field voles (Microtus agrestis), 15 wood mice (Apodemus sylvaticus), one common shrew (Sorex aranaeus), two pygmy shrews (Sorex minutus), two hedgehogs (Erinaceus europaeus) and 75 Eurasian otters (Lutra lutra). No cases of SARS-CoV-2 were detected in any animals, but a novel minacovirus related to mink and ferret alphacoronaviruses was detected in stoats recently introduced to the Orkney Islands. This group of viruses is of interest due to pathogenicity in ferrets. The impact of this virus on the health of stoat populations remains to be established.

Inferential and forward projection modeling to evaluate options for controlling invasive mammals on islands

Successful pest-mammal eradications from remote islands have resulted in important biodiversity benefits. Near-shore islands can also serve as refuges for native biota but require ongoing effort to maintain low-pest or pest-free status. Three management options are available in the presence of reinvasion risk: (1) control-to-zero density, in which immigration may occur but reinvaders are removed; (2) sustained population suppression (to relatively low numbers); or (3) no action. Biodiversity benefits can result from options one and two. The management challenge is to make evidence-based decisions on the selection of an appropriate objective and to identify a financially feasible control strategy that has a high probability of success. This requires understanding the pest species population dynamics and how it will respond to a range of potential management strategies, each with an associated financial cost. We developed a two-stage modeling approach that consisted of (1) Bayesian inferential modeling to estimate parameters for a model of pest population dynamics and control, and (2) a forward projection model to simulate a range of plausible management scenarios and quantify the probability of obtaining zero density within four years. We applied the model to an ongoing, six-year trapping program to control stoats (Mustela erminea) on Resolution Island, New Zealand. Zero density has not yet been achieved. Results demonstrate that management objectives were impeded by a combination of a highly fecund population, insufficient trap attractiveness, and a substantial proportion of the population that did not enter traps. Immigration is known to occur because the founding population arrived on the island by swimming from the mainland. However, immigration rate during this study was indistinguishable from zero. The forward projection modeling showed that control-to-zero density was feasible but required greater than a two-fold budget increase to intensify the trapping rate relative to population growth. The two-stage modeling provides the foundation for a management program in which broad-scale trials of additional trapping effort or improved trap lures would test model predictions and increase our understanding of system dynamics.