Predicting aquatic invasion in Adirondack lakes: a spatial analysis of lake and landscape characteristics

Artur D Yakubov,Erin Vennie-Vollrath,K Wayne Forsythe,Richard Ross Shaker,Stephanie M Nick,Timothy J Ehlinger

doi:10.32920/14637951

Abstract

Invasive species continue to pose major challenges for managing coupled human-environmental systems. Predictive tools are essential to maximize invasion monitoring and conservation efforts in regions reliant on abundant freshwater resources to sustain economic welfare, social equity, and ecological services. Past studies have revealed biotic and abiotic heterogeneity, along with human activity, can account for much of the spatial variability of aquatic invaders; however, improvements remain. This study was created to (1) examine the distribution of aquatic invasive species richness (AISR) across 126 lakes in the Adirondack Region of New York; (2) develop and compare global and local models between lake and landscape characteristics and AISR; and (3) use geographically weighted regression (GWR) to evaluate non-stationarity of local relationships, and assess its use for prioritizing lakes at risk to invasion. The evaluation index, AISR, was calculated by summing the following potential aquatic invaders for each lake: Asian Clam (Corbicula fluminea), Brittle Naiad (Najas minor), Curly-leaf Pondweed (Potamogeton crispus), Eurasian Watermilfoil (Myriophyllum spicatum), European Frog-bit (Hydrocharis morsus-ranae), Fanwort (Cabomba caroliniana), Spiny Waterflea (Bythotrephes longimanus), Variable-leaf Milfoil (Myriophyllum heterophyllum Water Chestnut (Trapa natans), Yellow Floating Heart (Nymphoides peltata), and Zebra Mussel (Dreissena polymorpha). The Getis-Ord Gi_ statistic displayed significant spatial hot and cold spots of AISR across Adirondack lakes. Spearman’s rank (q) correlation coefficient test (rs) revealed urban land cover composition, lake elevation, relative patch richness, and abundance of game fish were the strongest predictors of aquatic invasion. Five multiple regression global Poisson and GWR models were made, with GWR fitting AISR very well (R2 = 76–83%). Local pseudo-t-statistics of key explanatory variables were mapped and related to AISR, confirming the importance of GWR for understanding spatial relationships of invasion. The top 20 lakes at risk to future invasion were identified and ranked by summing the five GWR predictive estimates. The results inform that inexpensive and publicly accessible lake and landscape data, typically available from digital repositories within local environmental agencies, can be used to develop predictions of aquatic invasion with remarkable agreement. Ultimately, this transferable modeling approach can improve monitoring and management strategies for slowing the spread of invading species.

Highlights

The integrity of the planet is being stressed beyond its limits (WWF 2015); it is important to improve strategies and techniques for managing coupled human–environmental systems
Aquatic invasive species richness Monitoring programs for evaluating human impacts on water resources have existed for decades, with a variety of measuring techniques being applied to aquatic organisms as indicators of biological integrity
Aquatic invaders were summed for 126 lakes from the publicly available Adirondack Park Invasive Plant Program (APIPP) (APIPP 2013) report

Summary

Introduction

The integrity of the planet is being stressed beyond its limits (WWF 2015); it is important to improve strategies and techniques for managing coupled human–environmental systems. Multiple interacting forces are linked to the decline of speciation, which have been summarized by conservation biologists and environmental managers under the acronym HIPPO: habitat destruction, invasive species, pollution, population, and overharvesting (Wilson 2002). The increased demand for socioeconomic well-being has metabolized natural landscapes, which is the most direct cause of ecosystem degradation (Vitousek et al 1997, Foley et al 2005, Liu et al 2007, Shaker 2015b). Invasive species (IS), non-indigenous flora and fauna that adversely impact native ecosystems and economic activities that depend on them, is second only to habitat loss for decreasing biodiversity (Wilcove et al 1998, Grime 2006). It is likely that freshwater ecosystems are the most impacted by anthropogenic-related stressors (Naiman and Turner 2000, Foley et al 2005, MEA 2005, Novotny et al 2005, Liu et al 2007, Shaker and Ehlinger 2014)

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