Island eradications: Approaches and assessment of success Biodiversity Bonanza Dean Anderson Landcare Research.

Slides:



Advertisements
Similar presentations
Lower Snake River Compensation Plan Hatchery Evaluations – Salmon River Project No Nez Perce Tribe Department of Fisheries Resources Management.
Advertisements

CHAPTER 7.1 Confidence Intervals for the mean. ESTIMATION  Estimation is one aspect of inferential statistics. It is the process of estimating the value.
Population Sample Parameter: Proportion p Count Mean  Median Statistic: Proportion Count Mean Median.
The current status of fisheries stock assessment Mark Maunder Inter-American Tropical Tuna Commission (IATTC) Center for the Advancement of Population.
How many possums and where? The National Possum Model James Shepherd & Mandy Barron.
Tb bacillus Can TB be eliminated from possums in large forest areas? Local elimination - Proof of concept of eradication G Nugent & B Warburton Landcare.
Overview Of Clustering Techniques D. Gunopulos, UCR.
1 Risks and Benefits of Home-Use HIV Test Kits Richard Forshee, Ph.D. U.S. Food and Drug Administration Center for Biologics Evaluation and Research Office.
1 A heart fills with loving kindness is a likeable person indeed.
Statistical Concepts (continued) Concepts to cover or review today: –Population parameter –Sample statistics –Mean –Standard deviation –Coefficient of.
© 2013 Pearson Education, Inc. Active Learning Lecture Slides For use with Classroom Response Systems Introductory Statistics: Exploring the World through.
Environmental health indicators Caroline Wicks March 17, 2006 Cooperative Oxford Laboratory.
Population Viability Analysis. Conservation Planning U.S. Endangered Species Act mandates two processes –Habitat Conservation Plans –Recovery Plans Quantitative.
Measurement, Quantification and Analysis Some Basic Principles.
DATA QUALITY and ANALYSIS Strategy for Monitoring Post-fire Rehabilitation Treatments Troy Wirth and David Pyke USGS – Biological Resources Division Forest.
Standard error of estimate & Confidence interval.
Bovine Tb suppression systems G Nugent, D. Anderson, A. Gormley, P. Holland, M. Barron Landcare Research, P.O. Box 40, Lincoln, New Zealand.
INFERENTIAL STATISTICS – Samples are only estimates of the population – Sample statistics will be slightly off from the true values of its population’s.
INFERENTIAL STATISTICS – Samples are only estimates of the population – Sample statistics will be slightly off from the true values of its population’s.
Computer modelling ecosystem processes and change Lesson 8 Presentation 1.
Sampling January 9, Cardinal Rule of Sampling Never sample on the dependent variable! –Example: if you are interested in studying factors that lead.
Analyzing Reliability and Validity in Outcomes Assessment (Part 1) Robert W. Lingard and Deborah K. van Alphen California State University, Northridge.
ESTIMATING with confidence. Confidence INterval A confidence interval gives an estimated range of values which is likely to include an unknown population.
Estimation Statistics with Confidence. Estimation Before we collect our sample, we know:  -3z -2z -1z 0z 1z 2z 3z Repeated sampling sample means would.
Estimation and Confidence Intervals
Wide Scale Predator Control: The Hawke’s Bay Project Al Glen & Wendy Ruscoe Landcare Research Hawkes Bay Regional Council Department of Conservation EcoEd.
 Is there a comparison? ◦ Are the groups really comparable?  Are the differences being reported real? ◦ Are they worth reporting? ◦ How much confidence.
Characterizing observational and model uncertainty Kusum Naithani Department of Geography The Pennsylvania State University ChEAS 2012 Workshop.
Evolution of Plant Size in the Common Morning Glory, Ipomoea purpurea Rick E. Miller Southeastern Louisiana Univ and Mark D. Rausher Duke University Ipomoea.
Determination of Sample Size: A Review of Statistical Theory
Stat 112: Notes 2 Today’s class: Section 3.3. –Full description of simple linear regression model. –Checking the assumptions of the simple linear regression.
Integral projection models
Workshop on Stock Assessment Methods 7-11 November IATTC, La Jolla, CA, USA.
CONFIDENCE STATEMENT MARGIN OF ERROR CONFIDENCE INTERVAL 1.
Confidence intervals. Estimation and uncertainty Theoretical distributions require input parameters. For example, the weight of male students in NUS follows.
1 BA 275 Quantitative Business Methods Quiz #2 Sampling Distribution of a Statistic Statistical Inference: Confidence Interval Estimation Introduction.
1 CHAPTER 4 CHAPTER 4 WHAT IS A CONFIDENCE INTERVAL? WHAT IS A CONFIDENCE INTERVAL? confidence interval A confidence interval estimates a population parameter.
Testing Regression Coefficients Prepared by: Bhakti Joshi February 06, 2012.
Association between genotype and phenotype
Summarizing Risk Analysis Results To quantify the risk of an output variable, 3 properties must be estimated: A measure of central tendency (e.g. µ ) A.
One Sample Mean Inference (Chapter 5)
Statistics: Unlocking the Power of Data Lock 5 Inference for Means STAT 250 Dr. Kari Lock Morgan Sections 6.4, 6.5, 6.6, 6.10, 6.11, 6.12, 6.13 t-distribution.
INCORPORATION OF EARTHQUAKE SOURCE, PROPAGATION PATH AND SITE UNCERTAINTIES INTO ASSESSMENT OF LIQUEFACTION POTENTIAL Bob Darragh Nick Gregor Walt Silva.
Exposure Assessment for Health Effect Studies: Insights from Air Pollution Epidemiology Lianne Sheppard University of Washington Special thanks to Sun-Young.
A Spatial-Temporal Model for Identifying Dynamic Patterns of Epidemic Diffusion Tzai-Hung Wen Associate Professor Department of Geography,
Linking Data with Action Part 2: Understanding Data Discrepancies.
A significance test or hypothesis test is a procedure for comparing our data with a hypothesis whose truth we want to assess. The hypothesis is usually.
Science plan S2S sub-project on verification. Objectives Recommend verification metrics and datasets for assessing forecast quality of S2S forecasts Provide.
Are multiple-capture traps always better than single-capture ones? Bruce Warburton and Andrew Gormley Landcare Research.
1 Probability and Statistics Confidence Intervals.
AMOs 101 Understanding Annual Measurable Objectives Office of Educational Accountability Wisconsin Department of Public Instruction November 2012.
Understanding CI for Means Ayona Chatterjee Math 2063 University of West Georgia.
Sampling variability & the effect of spread of population.
1 Collecting and Interpreting Quantitative Data Deborah K. van Alphen and Robert W. Lingard California State University, Northridge.
1 Occupancy models extension: Species Co-occurrence.
Statistical Concepts Basic Principles An Overview of Today’s Class What: Inductive inference on characterizing a population Why : How will doing this allow.
 Occupancy Model Extensions. Number of Patches or Sample Units Unknown, Single Season So far have assumed the number of sampling units in the population.
POPULATION ECOLOGY All of the data that can be collected about a population of species in one area.
STATISTICS HYPOTHESES TEST (I)
James Barry University of Glasgow Introduction
Matthew Donaldson , and J. Read Hendon
Null Hypothesis Testing
Analyzing Reliability and Validity in Outcomes Assessment Part 1
An overview to this point
Descriptive vs. Inferential
Why does sampling work?.
DO NOT TAKE HOME, THANKS! Lesson 1.5: Populations.
Analyzing Reliability and Validity in Outcomes Assessment
GENERALIZATION OF RESULTS OF A SAMPLE OVER POPULATION
STATISTICS HYPOTHESES TEST (I)
Presentation transcript:

Island eradications: Approaches and assessment of success Biodiversity Bonanza Dean Anderson Landcare Research

Central question: How can we determine whether an eradication effort has been successful?

Answer is important: 1)Influence management practice 2)Funders want to know outcome 3)If fail, want to know sooner rather than later.

Assessing success Establish relationship between search effort and probability of detection. Actively search for survivors Collect spatial and temporal data on search effort

Key relationship 1 0 Search effort Probability of detection

Probability of detection and success 1 0 Search effort Probability of detection Probability of eradication success

Probability of detection and success 1 0 Search effort Probability of detection Probability of eradication success Threshold

Probability of detection and success 1 0 Search effort Probability of detection Probability of eradication success

How do we get this “key” relationship? 1 0 Search effort Probability of detection Depends on eradication method

Carcasses collected Pigs on Santa Cruz Island, USA - (Ramsey et al. 2009) Stoats on Resolution Island, NZ DOC Goats on Guadalupe Island, Mexico -Luciana Luna, Conservacion de Islas

Catch – effort model: (knock-down phase) Helicopter Ground Goats dispatched Hunting hours

Probability of detection and success 1 0 Search effort Probability of detection Probability of eradication success

No carcasses: Rat Eradication with single toxin drop

2 Approaches when missing carcasses 1)Wait and see –Easy –Takes time –If fail, the problem is big 2)Actively search –Requires data and statistics –If fail, survivors may be very localised

Isabel 82 ha Mexico Pacific Ocean Gulf of Mexico Araceli Samaniego Conservacion de Islas

Isabel Island, Mexico 1 toxin drop 3 annual wax-tag surveys No rats detected

Eradication success??? Spatial-detection Model Home range size Detection probability of tags

Wax-tag survey year 2 Spatial-detection Model Home range size Detection probability of tags Population growth rate Dispersal kernel

Wax-tag survey year 3

Repeat 1000 times Each female takes on slightly different parameter values

Results MedianLow 2.5% CIHigh 97.5 CI Prob. Success Prob. Success Prob. Success * Confidence intervals reflect the uncertainty in input parameters

Results MedianLow 2.5% CIHigh 97.5 CI Prob. Success Prob. Success Prob. Success * Confidence intervals reflect the uncertainty in input parameters

One – survey approach

50-m spacing

Summary Why quantify probability of success? –Management –Funders –Identify failure early

Summary Carcasses counted 1)Catch – effort model Collect data during “knock-down” phase Establish relationship between detection & effort

Summary Carcasses not available 2)Spatial – detection model Estimate parameters with experiments or literature –Homerange size –Detection probability of device –Reproductive rates –Dispersal kernels Incorporate uncertainty

Summary Requires biological understanding and statistics Arguably better than “wait-and-see”

Acknowledgements John Parkes Araceli Samaniego Luciana Luna Conservacion de Islas, Mexico Department of Conservation, NZ