
handle: 10261/394284
The scale, severity, and synchronicity of recent outbreaks of forest pests such as bark beetles (Coleoptera, Scolytinae) and defoliators (Lepidoptera, Choristoneura) within coniferous forest ecosystems of North America, Europe, and Asia are widely regarded as ‘unprecedented’. Despite such devastating outbreak occurrence in recent times, very little is known about historic outbreak occurrence. Traditional methods of reconstructing historic outbreak dynamics, including dendroecology, pollen analysis, and the identification of fossilised pest remains, all have critical weaknesses in their ability to reconstruct such outbreaks accurately, notably non-standardised methodologies, varying parameters for identifying outbreak periods within proxy records, and a bias towards the detection of large-scale, highly destructive outbreaks only. The development of a more accurate detection tool to reconstruct historic outbreak dynamics within the palaeoecological record has been prioritised as one of the top 50 areas of research within Quaternary science. This paper assesses the current methodologies, before presenting the potential role of DNA-based methodologies can play in overcoming some of these limitations and providing more comprehensive reconstructions, and critically, direct detection of historic forest pathogen outbreaks.
SedaDNA, Forest pests, Bark beetles, Palaeoecology, Dendroctonus, Defoliators
SedaDNA, Forest pests, Bark beetles, Palaeoecology, Dendroctonus, Defoliators
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
