scholarly journals Mapping the Establishment and Invasiveness Potential of Rainbow Trout (Oncorhynchus mykiss) in Turkey: With Special Emphasis on the Conservation of Native Salmonids

2021 ◽  
Vol 8 ◽  
Author(s):  
Baran Yoğurtçuoğlu ◽  
Tuba Bucak ◽  
Fitnat Güler Ekmekçi ◽  
Cüneyt Kaya ◽  
Ali Serhan Tarkan
Keyword(s):  
Rainbow Trout ◽  
Oncorhynchus Mykiss ◽  
Black Sea ◽  
Habitat Suitability ◽  
Native Species ◽  
River Basins ◽  
Iucn Red List ◽  
Species Invasiveness ◽  
Basin Scale ◽  
Invasiveness Potential

Rainbow trout (Oncorhynchus mykiss) has become by far the most frequently farmed freshwater fish species in Turkey, whereas very little is known about its establishment and invasiveness potential. We explored this potential through a combination of Maxent habitat suitability model and the Aquatic Species Invasiveness Screening Kit (AS-ISK) on the river basin scale by generating an overall risk score (ORS). The outcome of this approach was also incorporated with the spatial analysis of native salmonid species by generating a relative vulnerability score (RVS) to prioritize susceptibility of native species (or populations) and to propose risk hotspots by identifying their potential geographic overlap and interaction with O. mykiss. Results suggest that the northern basins (Eastern Black Sea, Western Black Sea and Marmara basins) are the most suitable basins for O. mykiss. According to the Basic Risk Assessment (BRA) threshold scores, O. mykiss is classified as “high risk” for 3 (12.0%) of the 25 river basins screened (Western Black Sea, Eastern Black Sea and Maritza-Ergene), and as “medium risk” for the remaining basins. The climate change assessment (CCA) scores negatively contributed the overall invasiveness potential of O. mykiss in 22 (88.0%) of the river basins and resulted in zero contribution for the remaining three, namely Aras-Kura, Çoruh river and Eastern Black Sea. The ORS score of river basins was lowest for Orontes and highest for Western Black Sea, whereas it was lowest for Konya-closed basin and highest for Eastern Black Sea, when CCA was associated. The micro-basins occupied by Salmo rizeensis had the highest mean habitat suitability with O. mykiss. Among the all species, S. abanticus had the highest RVS, followed by S. munzuricus and S. euphrataeus. The overall outcome of the present study also suggests that the establishment and invasiveness potential of O. mykiss may decrease under future (climate warmer) in Turkey, except for the northeast region. This study can provide environmental managers and policy makers an insight into using multiple tools for decision-making. The proposed RVS can also be considered as a complementary tool to improve IUCN red list assessment protocols of species.

Determination of growth performance, meat quality and colour attributes of large rainbow trout ( Oncorhynchus mykiss ) in the southern Black Sea coasts of Turkey

2019 ◽  
Vol 50 (12) ◽  
pp. 3763-3775 ◽  
Author(s):  
Dilara Kaya Öztürk ◽  
Birol Baki ◽  
Recep Öztürk ◽  
Sedat Karayücel ◽  
Gülşen Uzun Gören
Keyword(s):  
Rainbow Trout ◽  
Oncorhynchus Mykiss ◽  
Growth Performance ◽  
Meat Quality ◽  
Black Sea ◽  
Rainbow Trout Oncorhynchus Mykiss

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

2021 ◽  
Vol 4 ◽  
Author(s):  
S. Carter ◽  
C. B. van Rees ◽  
B. K. Hand ◽  
C. C. Muhlfeld ◽  
G. Luikart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Invasive Species ◽  
Rainbow Trout ◽  
Oncorhynchus Mykiss ◽  
Habitat Suitability ◽  
Remotely Sensed ◽  
Aquatic Invasive Species ◽  
Data Driven ◽  
Occurrence Data ◽  
Spatial Risk

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.

Sign in / Sign up

Export Citation Format

Share Document