BRT results and supporting simple regression biplots
Despite the observed patterns of increase, decline, or unimodal response
to latitude for P, D, and D&P respectively (Fig. 1b) this variable was
not found to be a significant predictor in any of the BRT analyses (Fig.
3–5; Appendix 3). Instead, for both haploid/diploid and polyploid
species, temperature (either annual average or maximum of the warmest
month) had strong predictive power as did evapotranspiration. The
simplified models, using only the significant predictors for
haploidy/diploidy and polyploidy, explained 92% and 81% of the variance
respectively. The standard regression trees also explained a high
proportion of variance (Fig. 3–5) and they indicated that macrophyte
species richness had a positive influence in the case of
haploidy/diploidy and negative for polyploidy. Species number explained
some variation in percentage of haploidy/diploidy at average
temperatures above 17 °C. Conversely the percentage of polyploidy was
influenced by species number below 15.7 °C with the highest incidence of
polyploidy at low temperature and low species number (Fig. 3 and 4).
The models for mixed ploidy were not as strong, though 75% of the
variance was explained, by two variables – endemic species richness of
macrophytes and average temperature (Fig. 5). The standard regression
tree included other variables (evapotranspiration and temperature range)
but the association with endemic species richness was marked, with mixed
ploidy only occurring once endemic richness in a gridcell fell below a
value of 150, with a negative association thereafter, and the percentage
of mixed ploidy rising as endemic richness declined.
Climatic and biotic variables were stronger drivers of ploidy state than
location in either human-impacted landscapes (CROP) or highly-stressed
areas of the planet (CCV and ALT). Three current climate variables
(potential evapotranspiration: ET0; temperature range: TRG; annual
precipitation: PCP) were strong predictors of ploidy state, though
varying in importance between the three models.