Species distribution models for invasive Eurasian watermilfoil highlight the importance of data quality and limitations of discrimination accuracy metrics

Shyam M. Thomas, Michael R. Verhoeven, Jake R. Walsh, Daniel J. Larkin, Gretchen J. A. Hansen

Research output: Contribution to journalArticlepeer-review

Abstract

Aim: Availability of uniformly collected presence, absence, and abundance data remains a key challenge in species distribution modeling (SDM). For invasive species, abundance and impacts are highly variable across landscapes, and quality occurrence and abundance data are critical for predicting locations at high risk for invasion and impacts, respectively. We leverage a large aquatic vegetation dataset comprising point-level survey data that includes information on the invasive plant Myriophyllum spicatum (Eurasian watermilfoil) to: (a) develop SDMs to predict invasion and impact from environmental variables based on presence–absence, presence-only, and abundance data, and (b) compare evaluation metrics based on functional and discrimination accuracy for presence–absence and presence-only SDMs. Location: Minnesota, USA. Methods: Eurasian watermilfoil presence–absence and abundance information were gathered from 468 surveyed lakes, and 801 unsurveyed lakes were leveraged as pseudoabsences for presence-only models. A Random Forest algorithm was used to model the distribution and abundance of Eurasian watermilfoil as a function of lake-specific predictors, both with and without a spatial autocovariate. Occurrence-based SDMs were evaluated using conventional discrimination accuracy metrics and functional accuracy metrics assessing correlation between predicted suitability and observed abundance. Results: Water temperature degree days and maximum lake depth were two leading predictors influencing both invasion risk and abundance, but they were relatively less important for predicting abundance than other water quality measures. Road density was a strong predictor of Eurasian watermilfoil invasion risk but not abundance. Model evaluations highlighted significant differences: Presence–absence models had high functional accuracy despite low discrimination accuracy, whereas presence-only models showed the opposite pattern. Main conclusion: Complementing presence–absence data with abundance information offers a richer understanding of invasive Eurasian watermilfoil's ecological niche and enables evaluation of the model's functional accuracy. Conventional discrimination accuracy measures were misleading when models were developed using pseudoabsences. We thus caution against the overuse of presence-only models and suggest directing more effort toward systematic monitoring programs that yield high-quality data.

Original languageEnglish (US)
Pages (from-to)12567-12582
Number of pages16
JournalEcology and Evolution
Volume11
Issue number18
DOIs
StatePublished - Sep 2021

Bibliographical note

Funding Information:
Funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Minnesota Aquatic Invasive Species Research Center (MAISRC) and the Legislative‐Citizen Commission on Minnesota Resources (LCCMR). This material is also based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. CON‐75851, project 00,074,041. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. All data is archived in Data Repository of U of M (DRUM). R codes for all analyses is archived and available at https://github.com/ShyamThomas/Watermilfoil_RF_SDMs .

Funding Information:
Funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Minnesota Aquatic Invasive Species Research Center (MAISRC) and the Legislative-Citizen Commission on Minnesota Resources (LCCMR). This material is also based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. CON-75851, project 00,074,041. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. All data is archived in Data Repository of U of M (DRUM). R codes for all analyses is archived and available at https://github.com/ShyamThomas/Watermilfoil_RF_SDMs.

Publisher Copyright:
© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Keywords

  • abundance–suitability relationship
  • discrimination accuracy
  • functional accuracy
  • invasion risk
  • pseudoabsences
  • random forest
  • spatial autocovariate
  • water temperature

PubMed: MeSH publication types

  • Journal Article

Fingerprint

Dive into the research topics of 'Species distribution models for invasive Eurasian watermilfoil highlight the importance of data quality and limitations of discrimination accuracy metrics'. Together they form a unique fingerprint.

Cite this