Multiple Detection Methods: Single-season Models.

Slides:



Advertisements
Similar presentations
Patch Dynamics AKA: Multi-season Occupancy, Robust Design Occupancy.
Advertisements

MARK RECAPTURE Lab 10 Fall Why?  We have 4 goals as managers of wildlife  Increase a population  Decrease a population  Maintain a population.
Krishna Pacifici Department of Applied Ecology NCSU January 10, 2014.
Survival models without mortality Casting closed-population wildlife survey models as survival- or recurrent event models David Borchers Roland Langrock,
Budapest May 27, 2008 Unifying mixed linear models and the MASH algorithm for breakpoint detection and correction Anders Grimvall, Sackmone Sirisack, Agne.
MONITORING and ASSESSMENT: Fish Dr. e. irwin (many slides provided by Dr. Jim Nichols)
Tiger Habitat Types: Classification of Vegetation Pornkamol Jornburom and Katie Purdham Kwanchai Waitanyakan WCS Thailand Program.
Detectability Lab. Outline I.Brief Discussion of Modeling, Sampling, and Inference II.Review and Discussion of Detection Probability and Point Count Methods.
Monte Carlo Localization for Mobile Robots Karan M. Gupta 03/10/2004
Maximum likelihood estimates What are they and why do we care? Relationship to AIC and other model selection criteria.
Species interaction models. Goal Determine whether a site is occupied by two different species and if they affect each others' detection and occupancy.
A COMPARISON OF APPROACHES FOR VERIFYING SOUTHWEST REGIONAL GAP VERTEBRATE-HABITAT DISTRIBUTION MODELS J. Judson Wynne, Charles A. Drost and Kathryn A.
Descriptive statistics Experiment  Data  Sample Statistics Experiment  Data  Sample Statistics Sample mean Sample mean Sample variance Sample variance.
PSY 1950 Confidence and Power December, Requisite Quote “The picturing of data allows us to be sensitive not only to the multiple hypotheses that.
Evaluating survey methods for the Yellow Rail: comparison of human surveys and autonomous recording units Kiel Drake and Danica Hogan.
Conservation Design for Sustainable Avian Populations
WALLACE RESOURCE LIBRARY Module 02 – Survey Techniques: D03 – Comparison of bird survey techniques WALLACE RESOURCE LIBRARY Module 02 – Survey Techniques:
Sampling : Error and bias. Sampling definitions  Sampling universe  Sampling frame  Sampling unit  Basic sampling unit or elementary unit  Sampling.
1. Introduction Motion Segmentation The Affine Motion Model Contour Extraction & Shape Estimation Recursive Shape Estimation & Motion Estimation Occlusion.
Understanding Statistics
Bayesian inference review Objective –estimate unknown parameter  based on observations y. Result is given by probability distribution. Bayesian inference.
Using historic data sources to calibrate and validate models of species’ range dynamics Giovanni Rapacciuolo University of California Berkeley
Resource Selection Functions and Patch Occupancy Models: Similarities and Differences Lyman L. McDonald Senior Biometrician Western EcoSystems Technology,
Spatial Dynamic Factor Analysis Hedibert Freitas Lopes, Esther Salazar, Dani Gamerman Presented by Zhengming Xing Jan 29,2010 * tables and figures are.
Managerial Economics Demand Estimation & Forecasting.
Applications of Spatial Statistics in Ecology Introduction.
Statistical approach Statistical post-processing of LPJ output Analyse trends in global annual mean NPP based on outputs from 19 runs of the LPJ model.
Patch Occupancy: The Problem
Susannah Woodruff, Rob Lonsinger, Lisette Waits Fish and Wildlife Sciences, University of Idaho MONITORING SPECIES OF CONCERN ON MILITARY LANDS USING NONINVASIVE.
Detectability Lab. Outline I.Brief Discussion of Modeling, Sampling, and Inference II.Review and Discussion of Detection Probability and Point Count Methods.
Tracking Multiple Cells By Correspondence Resolution In A Sequential Bayesian Framework Nilanjan Ray Gang Dong Scott T. Acton C.L. Brown Department of.
Ch15: Decision Theory & Bayesian Inference 15.1: INTRO: We are back to some theoretical statistics: 1.Decision Theory –Make decisions in the presence of.
BRIEF INTRODUCTION TO ROBUST DESIGN CAPTURE-RECAPTURE.
Introduction to Occupancy Models Key to in-class exercise are in blue
1 ES Chapter 18 & 20: Inferences Involving One Population Student’s t, df = 5 Student’s t, df = 15 Student’s t, df = 25.
1 Chapter 5 – Density estimation based on distances The distance measures were originally developed as an alternative to quadrat sampling for estimating.
Why use landscape models?  Models allow us to generate and test hypotheses on systems Collect data, construct model based on assumptions, observe behavior.
Multiple Season Model Part I. 2 Outline  Data structure  Implicit dynamics  Explicit dynamics  Ecological and conservation applications.
Estimation of Animal Abundance and Density Miscellaneous Observation- Based Estimation Methods 5.2.
Statistical Methods. 2 Concepts and Notations Sample unit – the basic landscape unit at which we wish to establish the presence/absence of the species.
Capture-recapture Models for Open Populations “Single-age Models” 6.13 UF-2015.
Pollock’s Robust Design: Model Extensions. Estimation of Temporary Emigration Temporary Emigration: = individual emigrated from study area, but only temporarily.
 1 Species Richness 5.19 UF Community-level Studies Many community-level studies collect occupancy-type data (species lists). Imperfect detection.
Pollock’s Robust Design: Extensions II. Quick overview 1.Separation of Recruitment Components in a single patch context (Source-Sink) 2.Separation of.
Estimation of State Variables and Rate Parameters Estimation of State Variables and Rate Parameters Overview 5.1 UF UF-2015.
Spatially Explicit Capture-recapture Models for Density Estimation 5.11 UF-2015.
Hypothesis Testing. Statistical Inference – dealing with parameter and model uncertainty  Confidence Intervals (credible intervals)  Hypothesis Tests.
Inferences About Animal Populations. Why Estimate Population Attributes? Science Understand ecological systems Learn stuff Management/Conservation Apply.
 Integrated Modelling of Habitat and Species Occurrence Dynamics.
Density Estimation with Closed CR Models 5.10 UF-2015.
IUCN (International Union for Conservation of Nature) risk of extinction The IUCN Red List assessment estimates risk of extinction What is the likelihood.
Single Season Model Part I. 2 Basic Field Situation From a population of S sampling units, s are selected and surveyed for the species. Units are closed.
Single Season Occupancy Modeling 5.13 UF Occupancy Modeling State variable is proportion of patches that is occupied by a species of interest.
1 Occupancy models extension: Species Co-occurrence.
 1 Modelling Occurrence of Multiple Species. 2 Motivation Often there may be a desire to model multiple species simultaneously.  Sparse data.  Compare/contrast.
 Multi-state Occupancy. Multiple Occupancy States Rather than just presence/absence of the species at a sampling unit, ‘occupancy’ could be categorized.
Capture-recapture Models for Open Populations Multiple Ages.
Single Season Study Design. 2 Points for consideration Don’t forget; why, what and how. A well designed study will:  highlight gaps in current knowledge.
 Occupancy Model Extensions. Number of Patches or Sample Units Unknown, Single Season So far have assumed the number of sampling units in the population.
Multi-state Occupancy. Multiple Occupancy States Rather than just presence/absence of the species at a sampling unit, ‘occupancy’ could be categorized.
Privacy Vulnerability of Published Anonymous Mobility Traces Chris Y. T. Ma, David K. Y. Yau, Nung Kwan Yip (Purdue University) Nageswara S. V. Rao (Oak.
Use & Availability of Habitats & Foods Resource selection Measurements of use & availability –Food –Habitat Design & analysis Modeling Sampling.
Hierarchical Models. Conceptual: What are we talking about? – What makes a statistical model hierarchical? – How does that fit into population analysis?
Chloe Boynton & Kristen Walters February 22, 2017
Christopher Nagy, Mianus River Gorge; Bedford, NY
Determining Population Size
Parameter Redundancy and Identifiability in Ecological Models
Multistate models Lecture 10.
Estimating mean abundance from repeated presence-absence surveys
Information Processing by Neuronal Populations Chapter 5 Measuring distributed properties of neural representations beyond the decoding of local variables:
Presentation transcript:

Multiple Detection Methods: Single-season Models

Multiple Detection Devices  Multiple devices (or methods of detection or types of sign) at each sample unit  Species may be detected by 1 or more devices  Common for multispecies surveys (U.S. Forest Service, National Park Service)

Multiple Detection Devices  Desire inference about device-specific detection probabilities Want to account for possible dependence of detection probability of 1 device on detection by another device  For animals with ranges that partially overlap sample unit (not complete overlap of range and sample unit); possible to estimate occupancy at 2 different scales

Model Parameters  p t [s] = Pr (detection at occasion t by device s | sample unit occupied and species present at immediate sample station site);  Ψ = Pr (sample unit occupied);  θ t = Pr (species present at immediate sample site at occasion t | sample unit occupied).  θ t represents a local scale that may reflect a smaller spatial scale than Ψ  θ t ψ = Pr (species present at local scale at occasion t)

Detection History Data  L = number of devices  K = number of sample occasions  Example (L=3, K=2): Occasion 1: detection by device 2, nondetection by devices 1 and 3 Occasion 2: no detections by any device

Detection History Modeling: Example  Pr ( ) =  Verbal description: species present at larger scale (species use of area); species present at local scale at period 1; species detected by device 2, but not by devices 1 and 3, at period 1; species either not present at local scale or present at local scale but not detected by any device at period 2.

Detection History Modeling: Example  Pr ( ) =

Estimation/Inference  Could be cast into a complete data likelihood framework  Maximum likelihood-based and Bayesian methods of inference possible

Example: Striped Skunks (Mephitis mephitis) in 8 National Parks in NE US  Random selection of grid cell; random selection of site within cell  Sample for ~2 weeks during 2 seasons (in winter/spring; summer/fall) in 2004  Visit detection devices every ~3 days for 5 occasions per season  3 devices at each sample station Camera trap Hair removal traps Track plates

Models and Inference  Model each season separately  16 models for each season: habitat-specific or constant ψ time-specific or constant θ Time- and/or device-specific p  Low-AIC model for both seasons:(ψ,θ,p s )

Results Predicted greater movement in winter-spring (breeding behavior)

Advantages of Multiple-device Model  Deals explicitly with lack of independence of detection induced by animal presence at local site  Makes efficient use of data for estimating both occupancy and device-specific detection probabilities  Permits inference about occupancy at 2 spatial scales

Use Similar Modeling for Other Sampling Situations  Different types of animal sign (tracks, scat, sighting) instead of different devices  Single device used at 2 temporal scales (Pollock’s robust design)  Multiple spatial scales