“Measures to strengthen international #biosafety and #biosecurity practices”
There is an absolute necessity for research on ‘pandemic-prone pathogens’. Studies must, however, be conducted responsibly & according to strict principles of safety & security
This piece @PLOSBiology outlines steps for global accountability
Experts unsure how 'world's most destructive bee pests' landed in Australia
By Amelia Bernasconi
Australia's leading biosecurity experts have been unable to trace the source of the of the bee-killing varroa mite incursion which has changed the face of beekeeping and pollination in Australia.
#Biosecurity #Beekeeping #AgriculturalPestControl #PestControl #AmeliaBernasconi
Global change and public participation are both areas of considerable uncertainty in estimating the success of biosecurity response strategies, but are poorly integrated in most available scenario analysis frameworks. We introduce INApest(), a novel network simulation method which integrates social and global change factors, as well as pest biology and multiple management variables in scenario analyses of biosecurity responses. INApest() separates the management response into four key parameters: probability of detection; management adoption; eradication of local populations; spread reduction (e.g. through movement restrictions or hygiene measures). It also permits simulation of biosecurity responses which evolve organically as new incidences of the pest are detected and information about the pest and management technologies spread through the network. We demonstrate selected functionality of INApest() using Nassella neesiana (Chilean Needle Grass; CNG), a slow-spreading pasture weed that impacts animal health, as a case-study. Realistic historical CNG spread rates are reproduced under a no management scenario using dispersal kernels derived from known natural and human-mediated spread mechanisms. Scenario analyses comparing over 15,000 parameter combinations reveal that communication of invasive threat to farms neighbouring known infestations significantly reduces the farm-scale eradication probability and spread reduction required for management success (i.e. success is achieved at lower levels of farm-scale management practice efficacy). We use targeted simulation experiments to show how INApest() permits assessment of cross-border consequences of local management decisions and how communication between landowners interacts with climate change and surveillance effort to impact management success. INApest() has the potential to be used at multiple scales and to explore a wide range of management, global change and social scenarios.
Just out a collaboration and fun #rstats modelling project.
Buddenhagen CE, McGrannachan C, Bourdôt G, Lamoureaux S, Garrett KA, Kaine G, Mason NWH (2026) Simple network models integrate global change, social dynamics and management interventions in biosecurity scenario analysis. NeoBiota 106: 107-139. https://doi.org/10.3897/neobiota.106.161880
Global change and public participation are both areas of considerable uncertainty in estimating the success of biosecurity response strategies, but are poorly integrated in most available scenario analysis frameworks. We introduce INApest(), a novel #network simulation method which integrates social and global change factors, as well as pest biology and multiple management variables in scenario analyses of biosecurity responses. INApest() separates the management response into four key parameters: probability of detection; management adoption; eradication of local populations; spread reduction (e.g. through movement restrictions or hygiene measures). It also permits simulation of biosecurity responses which evolve organically as new incidences of the pest are detected and information about the pest and management technologies spread through the network. We look at Chilean needle grass Nassella neesiana management scenarios.
Global change and public participation are both areas of considerable uncertainty in estimating the success of biosecurity response strategies, but are poorly integrated in most available scenario analysis frameworks. We introduce INApest(), a novel network simulation method which integrates social and global change factors, as well as pest biology and multiple management variables in scenario analyses of biosecurity responses. INApest() separates the management response into four key parameters: probability of detection; management adoption; eradication of local populations; spread reduction (e.g. through movement restrictions or hygiene measures). It also permits simulation of biosecurity responses which evolve organically as new incidences of the pest are detected and information about the pest and management technologies spread through the network. We demonstrate selected functionality of INApest() using Nassella neesiana (Chilean Needle Grass; CNG), a slow-spreading pasture weed that impacts animal health, as a case-study. Realistic historical CNG spread rates are reproduced under a no management scenario using dispersal kernels derived from known natural and human-mediated spread mechanisms. Scenario analyses comparing over 15,000 parameter combinations reveal that communication of invasive threat to farms neighbouring known infestations significantly reduces the farm-scale eradication probability and spread reduction required for management success (i.e. success is achieved at lower levels of farm-scale management practice efficacy). We use targeted simulation experiments to show how INApest() permits assessment of cross-border consequences of local management decisions and how communication between landowners interacts with climate change and surveillance effort to impact management success. INApest() has the potential to be used at multiple scales and to explore a wide range of management, global change and social scenarios.
Nonia Pariente
Stephen Turner
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