Functional form and interactions of the drivers of understory non‐native plant invasions in northern US forests

Abstract

1. The number and rate of non-native plant invasions in forests have been steadily increasing over the last century with profound consequences for the composition, structure and functioning of these ecosystems. While multiple regional, landscape and local environmental factors are known to drive the spread of non-native invasive plant species (NNIPS) into forests, such factors have rarely been analysed within a unified analytical framework allowing for the assessment of their relative importance, possible non-linear behaviour and interactions. Obtaining such knowledge would improve the understanding and allow prediction of forest plant invasions and help prioritize actions for effective NNIPS control and mitigation. We used boosted regression trees and Bayesian non-linear regression to analyseforest inventory data spanning 14 northern US states in combination with data on climate, land use and disturbance. Within each framework, we assessed the magnitude, direction and functional form of the effects of 18 regional to local environmental factors and their interactions on the probability of presence and richness of NNIPS in the forest understory, while controlling for sampling intensity, data collection time and spatial autocorrelation.3. The studied drivers together captured nearly 50% of the variation in NNIPS occurrence and richness and were sufficient to obtain sensible predictions. Most drivers exhibited monotonic non-linear individual effects and several drivers showed non-additive combined effects. Temperature and landscape openness were the two major determinants of NNIPS presence and richness, but their effects were to a different degree moderated by elevation, climatic and topographic wetness, and stand age. Stand age was a more important predictor of NNIPS invasion than stand productivity. Deer density tended to be positively associated with NNIPS, whereas gypsy moth outbreaks possibly have increased resistance to plant invasions. Synthesis and applications. To optimize non-native invasive plant species (NNIPS)control efforts in forests, it is important to explicitly account for the functional form and interactions among the factors operating across spatial and temporal scales. Increasing the average stand age at the landscape and regional levels is | 2597Journal of Applied Ecology GOLIVETS ET aL.

Nonlinear parsimonious forest modeling assuming normal distribution of residuals

“…42, therefore the assessment metrics were similar (Table 2); with bias, MAE and RMSE being almost undetectable larger than the back-transformed values. The model currently in used in Romania (Giurgiu and Draghiciu 2004) had not surprisingly all six variables significant (p value < 0.001). The correlation coefficient between the predicted and original data is superior to the one supplied by Eq.…”

“…42 (Table 2), but the difference is < 1% for all site indices. Regardless of the site index, the bias, MAE, and RMSE were the smallest for Giurgiu and Draghiciu (2004) models (Table 2), but the differences were miniscule (< 1%). If the values predicted by the Giurgiu and Draghiciu (2004) models were infinitesimal superior to the Bayesian and the proposed methods, the situation is completely reversed when the focus is on variance.…”

“…The main economic species in the Carpathian Mountains is Norway spruce (Tudoran and Zotta 2020), which triggered significant efforts in modeling height of dominant trees. A polymorphic model with six parameters for height was developed by Giurgiu and Draghiciu (2004), based on the field measurements published in 1957 (Popescu-Zeletin 1957). The polymorphic model is enforced by the Romanian regulations, and consequently with significant impact on forest management, is…”

“…where height dominant is the height of dominant trees, t Lorey height is transformed Lorey's height modeled with the expression e b 0( age b 1 −100 b 1 ) , a 0 , a 1 , a 2 , b 0 , b 1 , b 2 are parameters determined by species, SI is site index. The models proposed by Giurgiu and Draghiciu (2004) are not justifiable, as they identify a regression with a causal relationships, which was proven to be false (Neter et al 1996;Duursma and Robinson 2003). The biased models of Giurgiu and Draghiciu were replaced in 2020 by Amarioarei et al (2020), who developed parsimonious unbiased models.…”

“…To assess the parsimonious model of height yield represented by Eq. 37, we compared it with the models currently used by the Romanian Forest Administration (Giurgiu and Draghiciu 2004), namely Eq. 35.…”