Data from: Quantifying the impacts of management and herbicide resistance on regional plant population dynamics in the face of missing data

A key challenge in the management of populations is to quantify the impact
of interven-tions in the face of environmental and phenotypic variability.
However, accurate estima-tion of the effects of management and
environment, in large-scale ecological research is often limited by the
expense of data collection, the inherent trade-off between quality and
quantity, and missing data. In this paper we develop a novel modelling
framework, and demographically informed imputation scheme, to
comprehensively account for the uncertainty generated by miss-ing
population, management, and herbicide resistance data. Using this
framework and a large dataset (178 sites over 3 years) on the densities of
a destructive arable weed (Alo-pecurus myosuroides) we investigate the
effects of environment, management, and evolved herbicide resistance, on
weed population dynamics. In this study we quantify the marginal effects
of a suite of common management prac-tices, including cropping,
cultivation, and herbicide pressure, and evolved herbicide re-sistance, on
weed population dynamics. Using this framework, we provide the first
empirically backed demonstration that herbi-cide resistance is a key
driver of population dynamics in arable weeds at regional scales. Whilst
cultivation type had minimal impact on weed density, crop rotation, and
earlier cultivation and drill dates consistently reduced infestation
severity. Synthesis and applications: As we demonstrate that high
herbicide resistance levels can produce extremely severe weed
infestations, monitoring of herbicide resistance is a pri-ority for famers
across western Europe. Furthermore, developing non chemical control
methods is essential to control current weed populations, and prevent
further resistance evolution. We recommend that planning interventions
that center on crop rotation and incorporate spring sewing and cultivation
to provide the best reductions in weed densi-ties. More generally, by
directly accounting for missing data our framework permits the analysis of
management practices with data that would otherwise be severely
compro-mised.