We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Machine learning applied to lentic habitat use by spawning walleye demonstrates the benefits of considering multiple spatial scales in aquatic research.
- Authors
Zentner, Douglas L.; Raabe, Joshua K.; Cross, Timothy K.; Jacobson, Peter C.
- Abstract
Scale and hierarchy have received less attention in aquatic compared to terrestrial systems. Walleye (Sander vitreus) spawning habitat offers an opportunity to investigate scale's importance. We estimated lake-, transect-, and quadrat-scale influences on nearshore walleye egg deposition in 28 Minnesota lakes from 2016–2018. Random forest models (RFM) estimated importance of predictive variables to walleye egg deposition. Predictive accuracies of a multi-scale classification tree (CT) and a quadrat-scale CT were compared. RFM results suggested that five of our variables were unimportant when predicting egg deposition. The multi-scale CT was more accurate than the quadrat-scale CT when predicting egg deposition. Both model results suggest that in-lake egg deposition by walleye is regulated by hierarchical abiotic processes and that silt–clay abundance at the transect-scale (reef-scale) is more important than abundance at the quadrat-scale (within-reef). Our results show machine learning can be used for scale-optimization and potentially to determine cross-scale interactions. Further incorporation of scale and hierarchy into studies of aquatic systems will increase our understanding of species–habitat relationships, especially in lentic systems where multi-scale approaches are rarely used.
- Subjects
MINNESOTA; AQUATIC exercises; MACHINE learning; RANDOM forest algorithms; HABITATS
- Publication
Canadian Journal of Fisheries & Aquatic Sciences, 2022, Vol 79, Issue 7, p1120
- ISSN
0706-652X
- Publication type
Article
- DOI
10.1139/cjfas-2021-0180