Testing a Generalizable Machine Learning Workflow for Aquatic Invasive Species on Rainbow Trout (Oncorhynchus mykiss) in Northwest Montana

Abstract

Biological invasions are accelerating worldwide, causing major ecological and economic impacts in aquatic ecosystems. The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and data-driven products. Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales. We present a workflow that generates rapid spatial risk assessments on aquatic invasive species using occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, we tested this workflow using extensive spatial and temporal hybridization and occurrence data from a well-studied, ongoing, and climate-driven species invasion in the upper Flathead River system in northwestern Montana, USA. Rainbow Trout (RBT; Oncorhynchus mykiss), an introduced species in the Flathead River basin, compete and readily hybridize with native Westslope Cutthroat Trout (WCT; O. clarkii lewisii), and the spread of RBT individuals and their alleles has been tracked for decades. We used remotely sensed and other geospatial data as key environmental predictors for projecting resultant habitat suitability to geographic space. The ensemble modeling technique yielded high accuracy predictions relative to 30-fold cross-validated datasets (87% 30-fold cross-validated accuracy score). Both top predictors and model performance relative to these predictors matched current understanding of the drivers of RBT invasion and habitat suitability, indicating that temperature is a major factor influencing the spread of invasive RBT and hybridization with native WCT. The congruence between more time-consuming modeling approaches and our rapid machine-learning approach suggest that this workflow could be applied more broadly to provide data-driven management information for early detection of potential invaders.

A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation

“…Example 21: Carter et al [72] have developed and tested a rapid machine-learning approach that can provide data-driven management information for early detection of potential invaders.…”

A framework to integrate innovations in invasion science for proactive management

“…The computational demands of machine-learning methods, which might impede their wider application due to inequities in funding and access to computing resources, could be addressed by making such tools available through affordable cloud computing services (Candela et al, 2016). Carter et al (2021) recently developed a machine-learning SDM workflow for aquatic IAS in North America that yielded similar insights to highly mechanistic and time-intensive modelling methods with considerably less user input.…”

“…Carter et al . (2021) recently developed a machine‐learning SDM workflow for aquatic IAS in North America that yielded similar insights to highly mechanistic and time‐intensive modelling methods with considerably less user input.…”

Operationalizing Digitainability: Encouraging mindfulness to harness the power of digitalization for sustainable development

“…Nevertheless, despite the progress in geoscience, the net impact of AI on SDG 15 is still poorly understood. Yu et al [51] [53][54][55][56], managing land degradation [43,56], combating poaching and protecting endangered species [57,58]; halting biodiversity loss and habitat degradation [44], reducing invasive species [59,60], spotting plant diseases and fires or identity seeds [61]. Kolevatova et al [62] claim the relevance of explainable AI (XAI) to support the climate effects of land changes (land cover, deforestation, urbanization) with enhanced computational time and data usage.…”