Dear colleagues,
Registration is open for the course Species distribution models and
fuzzy logic: Combining model predictions into threshold-free estimates
of diversity and change. This course will be held live online
(synchronous). Max 18 participants.
Dates: April 26th-27th, 2022 from 15:00 to 18:00 (Madrid time zone)
Instructors: Dr. A. Márcia Barbosa & Dr. Alba Estrada (Instituto de
Investigación en Recursos Cinegéticos, IREC[CSIC-UCLM-JCCM] Spain)
COURSE OVERVIEW
Live beings have continually changing distributions, and they occur with
varying frequency across space. Hence, categorical occurrence maps,
whether observed or model-derived, are always incomplete and
oversimplified representations of species’ actual distributions.
Moreover, categorical occurrence derived from model predictions usually
depends on largely arbitrary user-specified thresholds. Analyses that
build on such categorical information, such as most indices of
diversity, overlap, (dis)similarity and change, thus omit important
gradations in species occurrence and can be visibly conditioned by
threshold choice. Fuzzy logic is a simple tool to eliminate the need for
these thresholds and formally incorporate the locational uncertainty and
gradual variations that characterize natural biodiversity patterns. This
course will show how fuzzy logic can be easily integrated into
biogeographical analyses to improve the prediction and combination of
species distribution patterns, with applications in (macro)ecological
interaction, global change, biodiversity and biotic regionalization
studies.
We will use presence-(pseudo)absence models that produce presence
probability values (e.g. generalized linear and generalized additive
models; tree-based classification and regression methods), which can be
mathematically converted to favorability or fuzzy membership values. We
will see how these values allow direct comparison and combination of
gradual distribution patterns across species, regions and time periods.
Finally, we will see how diversity and (dis)similarity indices, which
normally require binary presence/absence information, can be generalized
to work with fuzzy (degree of) occurrence values. This allows the use of
presence probability models without depending on thresholds to convert
them into binary predictions, thus avoiding the compounded effects of
threshold choice on the results and conclusions.
The course will include both theoretical lessons explaining the concepts
behind the described procedures, and practical hands-on sessions where
participants will put these procedures into practice using R. In this
short version of the course, we will not cover data preparation and
model building. We will provide some already-made species distribution
models as examples for participants to work with. Participants are also
encouraged to bring their own presence-(pseudo)absence models, with
their observed and predicted values, for different (ecologically
related) species or time periods.
You can find more information in
https://www.transmittingscience.com/courses/biogeography/species-distribution-models-and-fuzzy-logic/
or writing [log in to unmask]
Best regards
Juanvi
--
Juan Vicente Bertó Mengual
Administration
Transmitting Science
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