Materials and Methods:
The climate suitability analyses presented here follow the methods of
Andersen and Elkinton (2022) using locality records obtained from the
invaded distributions of knotweed species to predict regions in the
native range of these species where candidate biological control agents
might be most successfully established. As per Andersen and Elkinton
(2022), we used host-records (i.e., knotweed records) as a proxy for
their specialist-parasites (i.e., A. itadori ), as the records for
hosts are often more readily available in public databases (Andersen &
Elkinton, 2022; Johnson et al., 2019; Schneider et al., 2022).
Climate suitability analyses were based on the use of published records
for all species of Reynoutria (knotweeds) obtained from the GBIF
database (accessed on September 28th, 2022: GBIF
Occurrence Download https://doi.org/10.15468/dl.pdjdh8 ). This
dataset was then filtered to remove all records that lacked geographic
locality information, and then subdivided by focal species, resulting in
one dataset each for R. japonica , R. × bohemica ,
and R. sachalinensis . The species datasets were then further
subdivided into geographical bins, with separate bins for samples from
North America (those samples located between 0°N, 180°W and 90°N, 20°W)
and from Europe and western Asia (those samples located between 0°N,
20°W and 90°N, 60°E).
To reduce the effects of sampling biases in our analyses we followed the
recommendations of Hijmans and Elith (2021). In the R statistical
language environment (R Core Team, 2022), we used the packages ‘raster’
(Hijmans & van Etten, 2012) and ‘dismo’ (Hijmans et al., 2015) to
randomly select one observation per 1 minute x 1 minute grid cell within
each dataset. The final datasets were then used to independently
estimate climate suitability envelopes in MaxEnt v 3.3.3e (Phillips et
al., 2006; Phillips & Dudik, 2008) based upon the 5-minute resolution
WorldClim v 2.1 dataset (available at https://www.worldclim.org ).
Jack-knife analyses were performed on each dataset to measure the
relative importance of each climate variable, and results were mapped in
ArcGIS v 10.8 (Esri®, Inc., Redlands, CA)