Developing, understanding and using nutrient boundaries

Slides:



Advertisements
Similar presentations
An Introduction to Multivariate Analysis
Advertisements

Learning Objectives 1 Copyright © 2002 South-Western/Thomson Learning Data Analysis: Bivariate Correlation and Regression CHAPTER sixteen.
Multivariate Data Analysis Chapter 4 – Multiple Regression.
Simple Linear Regression Statistics 700 Week of November 27.
SCATTER PLOTS AND LINES OF BEST FIT
Descriptive Methods in Regression and Correlation
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
STEM Fair Graphs & Statistical Analysis. Objectives: – Today I will be able to: Construct an appropriate graph for my STEM fair data Evaluate the statistical.
BPA M&V Protocols Overview of BPA M&V Protocols and Relationship to RTF Guidelines for Savings and Standard Savings Estimation Protocols.
Regression Regression relationship = trend + scatter
Data Collection and Processing (DCP) 1. Key Aspects (1) DCPRecording Raw Data Processing Raw Data Presenting Processed Data CompleteRecords appropriate.
Background Sound Variability Distance from source Noise level, dB critical region Source Sound Variability Potentially critical assessment.
Dynamic biological processes influence population density, dispersion, and demographics Chapter 53, Section 1.
Chapter 12: Correlation and Linear Regression 1.
Regression Analysis. 1. To comprehend the nature of correlation analysis. 2. To understand bivariate regression analysis. 3. To become aware of the coefficient.
+ Data Analysis Chemistry GT 9/18/14. + Drill The crown that King Hiero of Syracuse gave to Archimedes to analyze had a volume of 575 mL and a mass of.
6.7 Scatter Plots. 6.7 – Scatter Plots Goals / “I can…”  Write an equation for a trend line and use it to make predictions  Write the equation for a.
Comparison of freshwater nutrient boundary values Geoff Phillips 1 & Jo-Anne Pitt 2 1 University of Stirling & University College London 2 Environment.
GRADE 9 CURRICULUM SUMMARY. NUMBER AND OPERATION SENSE identify irrational numbers and how we use them understand how the system of real numbers works.
Chapter 12: Correlation and Linear Regression 1.
Logistic Regression: Regression with a Binary Dependent Variable.
linear Regression Unit 1 Day 14
Linear Regression Essentials Line Basics y = mx + b vs. Definitions
Section 11.2 Day 3.
Correlation & Regression
A linear approach to predicting house prices
Dealing with data qualitative data The main report
CHS 221 Biostatistics Dr. wajed Hatamleh
Copyright © Cengage Learning. All rights reserved.
UNIT SELF-TEST QUESTIONS
Hypothesis Tests: One Sample
Suppose the maximum number of hours of study among students in your sample is 6. If you used the equation to predict the test score of a student who studied.
Finding Answers through Data Collection
Ecolog.
Dave Jowett, Chair UK Marine Task Team
AP Exam Review Chapters 1-10
Ecolog.
STEM Fair Graphs & Statistical Analysis
Elementary Statistics: Picturing The World
Chapter 10 Correlation and Regression
Algebra 1 Section 6.6.
Representative Measurements – AQ-Workshop Bucharest, July 2008
Ecolog.
BA 275 Quantitative Business Methods
STEM Fair Graphs.
Ecolog.
Review Homework.
Intercalibration of Opportunistic Algae Blooms
WG 2.5 Intercalibration.
EQUATION 4.1 Relationship Between One Dependent and One Independent Variable: Simple Regression Analysis.
Nutrient Standards: Proposals for further work
CIS workshop : assessment of the ecological status.
CIS Working Group 2A ECOSTAT SCG Meeting in Brussels
Research Design and Methods
confidence in classification
Making Inferences about Slopes
Ecolog.
WG 2.3 REFCOND Progress report for the SCG meeting 30 Sep-1 Oct 2002
EU Water Framework Directive
Scatterplots Regression, Residuals.
Status of the Nutrient Best Practice Guide
Working Group 2A ECOSTAT progress report Presented by Wouter van de Bund Joint Research Centre Institute for Environment and Sustainability Inland.
The Statistical Tool Kit determination of valid nutrient boundary values Geoff Phillips.
Session 2a Working with more difficult data sets: short gradients
Summary – Day 1 Martyn Kelly.
Solution to Problem 2.25 DS-203 Fall 2007.
Session 1d Selecting appropriate thresholds
Guidance on establishing nutrient concentrations to support good ecological status Introduction and overview Martyn Kelly.
Relationships for Broad & Intercalibration Types Geoff Phillips
Deriving river TP standards from lake standards
Presentation transcript:

Developing, understanding and using nutrient boundaries ECOSTAT Nutrient workshop 6-7 November 2018, Bucharest, Romania Developing, understanding and using nutrient boundaries presenting the Nutrient Toolkit

Emotions during day 1 of workshop

Session 3a Choice of method, steps in the CIS Guide Section 3 Overview of process (‘road map’) Provides guidance on selecting methods, however all methods should be tested and results compared, wherever possible. Section 4.2 Overview of stepwise procedure

Overview of process (‘road map’) page 35 CIS Section 3 CIS

Overview of process Choice of approaches using flowcharts to illustrate the decisions needed when selecting methods for specific data sets not intended as a prescriptive flow diagram 3.1. Choice of approaches A (continuous data) deals with methods where biological data in the form of an EQR, or a continuous metric, are available 3.2. Choice of approaches B (weak linear relationship, r2 < 0.36) deals with situations where the relationship between nutrient and biology is weak (e.g. multiple pressures occur; other environmental gradients) 3.3. Choice of approaches C (categorical methods) contains categorical methods. These methods place more reliance on an appropriate class width and do not allow for situations where nutrient concentrations do not span the entire status class. Require significant differences of nutrient concentrations between the classes Section 3 CIS

Choice of approaches A continuous Page 38 CIS

Choice of approaches B weak r2 Page 40 CIS

Choice of approaches C categorical Page 42 CIS

Overview of stepwise procedure Step 1: assemble a data set Use summary data not spot samples Ensure nutrient and biological data are spatially and temporally matched Step 2: inspect the data scatterplots; boxplots; outliers; linear response range; trends & patterns Step 3: fit linear regression models Step 4: calculate categorical methods Step 4a: compare results Compare predicted boundary concentrations between methods tested Step 5: compare boundary values Compare predicted boundary concentrations with existing nutrient boundaries (see also CIS result tables for common types sections 4.4 & 4.5) Step 6: validate boundary values Session 4 independent data to demonstrate that predicted concentrations meet ecological Expectations and are likely to protect key taxa associated with good status conditions Pointing to resources in the toolkit that allow you to test each step e.g. page 45 CIS Section 4.2 CIS

We hope you can enjoy the nutrient toolkit!! Use these pages as support while exploring your data and getting familiar with the resources made available. We hope you can enjoy the nutrient toolkit!!