Predicting Eurasian Watermilfoil and Curly-leaf Pondweed Presence-Absence in Adirondack Lakes Using Geographically Weighted Logistic Regression

Jelena Grbic

doi:10.32920/ryerson.14667900

Abstract

Aquatic invasive species, Eurasian Watermilfoil (EWM) and Curly-leaf Pondweed (CLP), have been dispersing across New York, USA and are threatening the ecosystem of Adirondack Park, a state park with a large forest preserve and heavily frequented by tourists. In this study, the prediction of EWM and CLP invasion across Adirondack Park lakes is modeled using logistic regression (LR) and geographically weighted logistic regression (GWLR) with lake, landscape, and climate variable predictors. EWM presence-absence is found to be best predicted by nearby invaded lakes, human presence, and elevation. The presence-absence of CLP models have similar findings, with the addition of game-fish abundance being important. GWLR increases model performance and prediction, with explained variation of EWM and CLP increasing by 23% and 16% and the percent correctly predicted increasing by 2.6% and 0.9%. The study shows that GWLR, a relatively novel methodology, works better than common LR models for predicting invasion of EWM and CLP across Adirondack Park, and corroborates anthropogenic influences on dispersal of aquatic invaders.

Highlights

Invasive species (IS) pose a serious threat to biodiversity, human health, and the economy worldwide, and their pressures are only growing with an increasingly globalized world (Lowe et al, 2000; Hulme, 2009; Butchart et al, 2010)
The aim was to explore a variety of lake and riparian zone landscape factors, and climate variables to assess whether there existed a significant predictive relationship between Eurasian Watermilfoil (EWM) and Curly-leaf Pondweed (CLP) presence-absence which would shed light on potential future colonization sites of these species in order to help management plans of IS prevention and containment
A secondary aim was to test whether the predictions were better forecast by logistic regression (LR) or geographically weighted logistic regression (GWLR), where the former did not take into account spatial variation of the predictors while the latter did

Summary

Introduction

Invasive species (IS) pose a serious threat to biodiversity, human health, and the economy worldwide, and their pressures are only growing with an increasingly globalized world (Lowe et al, 2000; Hulme, 2009; Butchart et al, 2010). Its successful invasion across New York, and generally the entire continent, has been due to its ability to thrive in a wide range of conditions, earlier spring blooming relative to other native aquatic plants, and ability to disperse by single-stem transport (Madsen et al, 1991). There are instances when EWM has been limited in its ability to invade when competing with certain other plants, such as water stargrass found in high abundances in northern Lake Cayuga, New York relative to EWM (Zhu and Georgian, 2014). EWM invasion has been successful in New York. Boylen et al (1999) examined the spread of EWM over 11 years from 1986 - 1997 in Lake George, New York and observed EWM eliminate 65% of the native species and those 35% remaining were severely reduced in abundance

Methods

Results

Conclusion