[Coral-List] Seminar tomorrow by Brian Kinlan: A systematic, regional approach to predictive modeling of habitat suitability for deep-sea corals in U.S. waters

Tracy Gill - NOAA Federal tracy.gill at noaa.gov
Wed Jan 4 09:41:51 EST 2017

OneNOAA Science Seminar Series- Apologies for cross-posting

*Title: A systematic, regional approach to predictive modeling of habitat
suitability for deep-sea corals in U.S. waters*

When: Thursday, 1/5/17, 3:00-3:30pm ET

Where: Webinar Only - see Registration below

Speaker: Brian Kinlan, NOAA/NOS National Centers for Coastal Ocean Science,
Biogeography Branch

Register at: https://attendee.gotowebinar.com/register/8236288050617849092

Sponsors: This webinar is part of a NOAA Deep Sea Coral Research and
Technology Program webinar series to highlight research, exploration, and
management of deep-sea corals and sponges around the U.S.

Seminar POC: Heather.Coleman at noaa.gov (301-427-8650)

Abstract: Recently, predictive modeling has emerged as an essential tool to
inform researchers and policy-makers involved in conservation, management,
and exploration of deep-sea coral (DSC) habitats throughout U.S. waters.
>From 2011-2016, NCCOS and its partners have developed a series of
regional-scale predictive models of habitat suitability for several
taxonomic (e.g., Lophelia pertusa, Gorgonian Alcyonacea) and functional
(e.g., framework-forming corals) groups. These models have resulted in a
comprehensive, consistent series of predictive maps spanning four U.S.
regions - Northeast/Mid-Atlantic, Southeast Atlantic, Gulf of Mexico, and
Main Hawaiian Islands - with a spatial resolution of ~400 m. Multiple
measures of model performance, including cross-validation statistics and
novel metrics of model fit and stability, and maps of spatial uncertainty
were generated to support decision-making. Maximum Entropy (MaxEnt) models
were fit to coral presence records and spatial environmental predictors,
including topographic, oceanographic, and geographic variables. We enhanced
the standard MaxEnt approach in several ways to improve model selection,
performance assessment, consistency and interpretability. We implemented a
stepwise model selection process to identify models that balanced
predictive power (via cross-validation statistics) with complexity (via
information criteria). Using the selected models, we predicted the relative
likelihood of occurrence of suitable habitat within each model grid cell.
To allow consistent comparisons across coral groups and regions, we
converted the standard MaxEnt 'logistic' predictions, which are
uncalibrated, into habitat suitability likelihood classes calibrated by a
cross-validation procedure. Finally, we compared and contrasted
environmental predictor relationships across coral groups and regions,
yielding insights into correlates of DSC distributions at a range of
spatial scales. We are presently engaged in field model groundtruthing and
validation efforts, and are working on a new generation of high-resolution
(~25m) models based on the most accurate field survey and seafloor mapping
data available.  These new models will use both presence and absence data,
in combination with measures of survey effort based on area of seafloor
searched, to generate probabilistic models of occurrence probability and
genus-level diversity (richness) measures.

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