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Project title

Irish Ecoregion Community Training-set and Ecological Restoration Targets

Primary Funding Agency

Environmental Protection Agency

Co-Funding Organisation(s)

n/a

Lead Organisation

University of Limerick (UL)

Lead Applicant

Catherine Dalton

Project Abstract

This study uses palaeolimnological techniques and numerical methods to construct TP transfer functions for the Irish Ecoregion based on both phytoplankton (diatoms) and zooplankton (Cladocera) remains and applies them to the sediments of seven impacted lakes to identify the pre-impact reference conditions and help inform lake restoration targets.A 72-lake diatom training-set and a 33-lake Cladocera training-set were developed along a TP gradient (max. 142.3 g l-1 TP). Seventeen related environmental variables were available for data exploration. A variety of exploratory and multivariate data analyses were used to investigate environmental and biological data structure and their relationships using the R program. Detrended Correspondence Analysis (DCA) revealed a higher degree of species heterogeneity in the diatom data compared to the Cladocera data. Both datasets were used to assess the viability of the Irish Lake Typology physicochemical classification scheme using hierarchical clustering method and both organisms provided good biological verification.Ordination analyses showed that nutrient gradient was among the most significant variables in determining both the diatom assemblages of 72 lakes and the Cladocera assemblages of 33 lakes. TP transfer functions were constructed using Weighted Averaging (WA)-related and linear modelling methods. A diatom TP transfer function produced a jack-knifed coefficient of determination (r2jack) of 0.743 with a root mean squared error (RMSEP) of 0.213 based on untransformed diatom data from 70 lakes. A TP transfer function built on square root transformed Cladocera data from 31 lakes yielded an r2jack of 0.729 with a RMSEP of 0.206. A sub-set of lakes where both diatom and Cladocera data were available were examined to compare the predicted TP from both models and they displayed a strong correlation (r = 0.685) for log-transformed TP.

Grant Approved

�75,000.00

Research Hub

Natural Environment

Research Theme

Improve our Knowledge on the State of our Water Resources and Pressures

Start Date

n/a

Initial Projected Completion Date

n/a