Introduction & Advanced Occupancy Modeling (in-person & virtual)
$100 discount when you register for both courses!
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$100 discount when you register for both courses! 〰️
Introduction (March 11-15) and Advanced (March 18-22) Occupancy Modeling be held at the University of Maine in 2024.
Both classes will meet Monday through Thursday, 8:30 AM-5PM, with an 1.5-hr break at noon. On Fridays, Dr. MacKenzie will meet with individuals or groups to discuss course materials or projects from school or work.
Participants will have access to all course materials in Canvas, CWS’s learning management system. All materials, including live lectures, will be available to all participants for a 2-week period following the course. Participants enrolled in both courses will have access to the Intro course for 3 weeks following the course to make up for the week they will attend the advanced course.
Webinar: Design advice for species occurrence studies - Darryl’s 5 top tips.
INTRODUCTION TO OCCUPANCY MODELING
Species presence/absence is a fundamental concept used in many areas of ecology (e.g., species distributions, habitat modeling, monitoring, and metapopulation studies), but imperfect detection can lead to false absences. Not accounting for false absences can lead to misleading inferences about patterns and dynamics of species occurrence and the factors that influence them. We will learn methods for accounting for imperfect detection with species detection/non-detection data and also discuss important study design considerations. All exercises will be conducted in R, covering data analysis and presentation of results (plotting results, creating maps, etc.).
PREREQUISITES
This course is for those with no, or little, experience with occupancy modeling, but more experienced users may also benefit from attending.
Basic data tidying and manipulation tasks in R (e.g., R Boot Camp).
Familiarity with regression, logistic regression, or generalized linear modeling in R is advantageous (e.g., Ecological Statistics and Modeling & Generalized Models).
ADVANCED OCCUPANCY MODELING
This course is aimed at participants that have completed Introductory to Occupancy Modeling or have suitable practical experience implementing species occurrence models that account for imperfect detection. In this course, learn the underlying theory of multi-scale, multi-state, species co-occurrence, and community level models and then put your new knowledge into practice. All hands-on exercises will be conducted in R, covering data analysis and presentation of results (plotting results, creating maps, etc.).
PREREQUISITES
Introduction to Occupancy Modeling or experience in RPresence
Experienced in statistics and modeling in R (e.g., Ecological Statistics and Modeling & Generalized Models).
Experience with JAGS for fitting models using Bayesian methods in R is advantageous (e.g., Bayesian Statistics in Ecology)
RECOMMENDED TEXT BOOK FOR BOTH COURSES
MacKenzie, D. I., Nichols, J. D., Royle, J. A., Pollock, K. H., Bailey, L., & Hines, J. E. 2017. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Elsevier.
INSTRUCTOR
COURSE INFORMATION
Per Course
Course fee: $950 professional / $800 student
Both Courses
Course fee: $1,800 professional / $1,500 student
There are many options for lodging in the Bangor/Orono area. These are available near campus and have group rates:
Hotel Ursa (on campus) is the preferred lodging location for this event: $129/night. Be sure to book a room by March 1st!
Click on the following links to make reservations for the Intro course (March 10-15) or Advanced course (March 17-22)
Black Bear Inn (about 2 miles from campus): $92/night. Book by February 26th!
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the why, what, and how of estimating population attributes
potential applications of occupancy models
statistical concepts and notations
probability-based methods of estimation
model comparisons and multi-model inference
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sampling situation and model development
missing observations and unequal sampling effort
incorporating predictor variables
model assumptions
dealing with heterogeneity
assessing model fit
small sample/finite population inference
modelling spatial correlation in occupancy
exercises using the RPresence R package
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defining your sampling unit
selecting sampling units
defining a ‘season’ and repeat surveys
allocation of effort
design tools and tricks in R
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• sampling situation and model development
• model history and development
• implicit dynamics
• explicit dynamics
• missing observations and unequal sampling effort
• incorporating predictor variables
• alternative parameterizations
• characterizing occupancy dynamics
• modelling spatial correlations in occupancy dynamics – an overview
• exercises using the RPresence R package
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• relationship with single-season designs
• long-term design
• adding sites over time
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Item description
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• model development overview
• including predictor variables
• exercises using the RPresence R package
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• model development
• exercises using the RPresence R package
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• model development
• exercises using the RPresence R package
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• is it a problem?
• options for incorporating it
• exercises using JAGS (or NIMBLE) in R
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• model development
• exercises using the RPresence R package
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• model development
• exercises using the RPresence R package
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• model development overview
• including predictor variables
• exercises using the RPresence R package
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• model development
• exercises using the RPresence R package
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• model development
• exercises using the RPresence R package
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• extending concepts to community-level studies
• community at single location of interest
• community across large number of locations
• exercises using JAGS (or NIMBLE) in R
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• model development
• exercises using the RPresence R package
“The Occupancy Modeling course offered by CWS is great for beginners or experts working with presence/absence data. Each module is designed to reinforce different occupancy modeling concepts and provides you with tools to best analyze your presence/absence data. I really appreciated the group discussion page that showed different points of view on topics from other students taking the course. Definitely a course worth taking!”
Max Larreur
PhD student, Southern Illinois University
“Darryl MacKenzie is an awesome instructor. He was able to make Occupancy Modeling both interesting and understandable. The real-world examples and discussions helped me apply the methods to my project design. Thanks for the great experience.”
Vickie DeNicola
Vice President of White Buffalo Inc.
CONTINUING EDUCATION CREDIT
These courses have been pre-approved for continuing education by (1) the Ecological Society of America for 4 CEUs in Category I(a): Scientific Education and Training (for Occupancy Modeling I only), and (2) The Wildlife Society for 16 CEUs in Category I of the Certified Wildlife Biologist® Renewal/Professional Development Certificate Program. Participants must complete all exercises to earn CEUs and receive a Certificate of Course Completion. See our continuing education credit page for details.
UNDERGRADUATE OR GRADUATE CREDIT
These courses are being advertised as WLE 650 - Graduate Seminar in Wildlife Science at the University of Maine. Both courses can be taken with pre-approval from an administrator at a student’s institution and Dr. Erik Blomberg (erik.blomberg@maine.edu) at University of Maine. Students may earn 1 academic credit for attending each course (i.e., 2 credits max), with a letter grade assigned based on class participation and completion of all exercises.
MINIMUM EDUCATION REQUIREMENTS FOR ESA CERTIFICATION
Occupancy Modeling I only is approved by the Ecological Society of America for 1 semester hour of qualified coursework that can be used to meet the minimum education requirements in Physical and Mathematical Science. ESA requires a grade of B- or higher for this course to count towards the academic credit requirements associated with Certification.
CANCELLATION POLICY
Cancellations >30 days before the start date are not subject to cancellation fees. Cancellations 15-30 days before the start date are subject to a 50% cancellation fee. Sales are final 14 days before the start date.