Machine learning model ensemble based on multi-scale predictors confirms ecological segregation and accurately predicts the occurrence of net-spinning caddisfly larvae species groups (Trichoptera: Hydropsychidae) at catchment-scale

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F23%3A00132066" target="_blank" >RIV/00216224:14310/23:00132066 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.ecolind.2022.109769" target="_blank" >https://doi.org/10.1016/j.ecolind.2022.109769</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecolind.2022.109769" target="_blank" >10.1016/j.ecolind.2022.109769</a>

Result language

angličtina

Original language name

Machine learning model ensemble based on multi-scale predictors confirms ecological segregation and accurately predicts the occurrence of net-spinning caddisfly larvae species groups (Trichoptera: Hydropsychidae) at catchment-scale

Original language description

In riverine ecosystems the species distribution, determined primarily by their environment often shows zonation patterns that are also typical in the case of net-spinning caddisfly larvae (Trichoptera: Hydropsychidae). In the present research, we aimed to build an ensemble of base learner machine learning (ML) models based on the most important environmental parameters shaping the sequential distribution of ten Central European species of the genus Hydropsyche in the North Hungarian catchment area of Tisza, one of the major rivers of Central and Eastern Europe. The model could explain and effectively predict the occurrence of species and/or groups of them with similar niche preferences. Variable selection revealed the importance of predictors, measured at various spatial scales and with gradient-like characteristics, such as elevation, annual means of discharge, water tem-perature or the composition of habitat substrates as well as those related to the ecological quality of water or anthropogenic impacts, like annual means of dissolved oxygen and orthophosphate-phosphorous content. Trained on the predictions of different base learner models a final ensemble model predicted the presence and absence of three individual species and three species-groups with significantly improved overall accuracy. High group-wise balanced accuracies of the final model shows that longitudinal, catchment-scale distribution models in stream ecosystems are best built on predictors with variable spatial scales, several of which are routinely measured or recorded in environmental monitoring programmes. Accurate species distribution models (SDMs), capable of adequately predicting presence and absence of bio-indicator taxa, such as Hydropsyche species, can be applied to support environmental management or conservation measures regarding streams and rivers, that are among the most vulnerable of anthropogenic pollution, hydrologic alteration, climate change and biodiversity loss.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10619 - Biodiversity conservation

Project

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Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Ecological indicators

ISSN

1470-160X

e-ISSN

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Volume of the periodical

146

Issue of the periodical within the volume

February

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

9

Pages from-to

1-9

UT code for WoS article

000900180200006

EID of the result in the Scopus database

2-s2.0-85145655225