These courses might interest some list members
TWO Species Distribution Modelling (SDM) Courses
1) Species distribution models using R (SDMR01) - Registration deadline
approaching 21/05/2018!!
https://www.prstatistics.com/course/species-distribution-models-using-r-sdmr01/
Delivered by Prof. Jane Elith, Dr. Gurutzeta Guillera and Jose
Lahoz-Monfort from 12th - 15th June 2018 at Myuna Bay Sport and
Recreation, Wangi Road, Myuna Bay, New South Wales 2264 Australia.
2) Species distribution/occupancy modelling using PRESENCE and R
(OCCU01)
https://www.prstatistics.com/course/species-distributionoccupancy-modelling-using-r-occu01/
Delivered by Dr Darryl MacKenzie form 25th - 29th June 2018 at PR
statistics head office, 53 Morrison Street, Glasgow, Scotland, G5 8LB
1) Species distribution models using R (SDMR01)
Course Overview:
The aim of this four-day course is to work towards an understanding of,
and practical ability to fit, species distribution models (SDMs). It
will be useful if you plan to use SDMs, or if you just want to
understand them better. We will focus on statistical models of species
distributions – those that combine observed species records with
environmental data. Using a mixture of lectures, computer exercises and
case studies, participants will learn to:
1) identify relevant data, and prepare it for modelling;
2) fit models using several modelling methods (including Maxent,
generalized linear models and their extensions, and boosted regression
trees);
3) consider how to model species if detection is imperfect;
4) evaluate models and interpret them;
5) understand the range of practical issues that arise in typical
applications of SDMs.
Practical sessions will use the free statistical software, R – prior
experience (even if some practice before you come) will be useful.
Example data will be provided but participants may also bring their own
data.
Presenters include Jane Elith and Gurutzeta Guillera-Arroita, who are
highly experienced in SDMs.
2) Species distribution/occupancy modelling using PRESENCE and R
(OCCU01)
Course Overview:
The presence or absence of a species across a set of landscape units is
a fundamental concept widely used in ecology (e.g., species range or
distribution, epidemiology, habitat modeling, resource selection
probability functions, as a monitoring metric, metapopulation studies,
biodiversity and species co-occurrence). An important sampling issue,
however, is that a species may not always be detected when present at a
landscape unit. This will result in “false absences” causing parameter
estimates to be biased if unaccounted for, possibly leading to
misleading results and conclusions, even with moderate levels of
imperfect detection.
This workshop will cover many of the latest methods for modeling
patterns and dynamics of species occurrence in a landscape while
accounting for the imperfect detection of the species.
Including:
• estimating level of occurrence at single point in time
• identifying factors that influence species occurrence
• creating species distribution maps
• modelling changes in distribution over time
• study design
Participants will be introduced to available software through worked
examples, and there will be special emphasis on aspects of study design.
While primarily aimed at the beginner and intermediate level, more
experienced researchers will also benefit from attending.
The first four days will comprise of formal lectures and exercises, and
the final morning will be an informal discussion/consulting session
where participants can work on their own data, talk one-on-one with the
instructor, etc.
If you have any questions please feel free to email
oliverhoo...@prstatistics.com
Oliver Hooker PhD.
PR statistics
2018 publications -
Phenotypic and resource use partitioning amongst sympatric lacustrine
brown trout, Salmo trutta. Biological Journal of the Linnean Society.
DOI 10.1093/biolinnean/bly032
prstatistics.com
facebook.com/prstatistics/
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groups.google.com/d/forum/pr-statistics-post-course-forum
prstatistics.com/organiser/oliver-hooker/
6 Hope Park Crescent
Edinburgh
EH8 9NA
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