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GAM-NICHE: Shape-Constrained GAMs to build Species Distribution Models under the ecological niche theory

GAM-NICHE is a new tool developed by AZTI (Valle et al. 2023) to build Species Distribution Models (SDMs) under the ecological niche theory (Citores et al. 2020). It provides a GitHub tutorial in R language with an application to marine fish.

Species Distribution Models (SDMs) are numerical tools that combine observations of species occurrence or abundance at known locations with information on the environmental and/or spatial characteristics of those locations (Elith and Leathwick 2009). SDMs are widely used as a tool for understanding species spatial ecology and are also known as ecological niche models (ENM) or habitat suitability models.

According to ecological niche theory, species response curves are unimodal with respect to environmental gradients (Hutchinson 1957). While a variety of statistical methods have been developed for species distribution modelling, a general problem with most of these habitat modelling approaches is that the estimated response curves can display biologically implausible shapes which do not respect ecological niche theory. This is because species response curves are fit statistically with any assumption or restriction, which sometimes do not respect the ecological niche theory. To better understand species response to environmental changes, SDMs should consider theoretical background such as the ecological niche theory and pursue the unimodality of the response curves with respect to environmental gradients.

This book provides a tutorial on how to use Shape-Constrained Generalized Additive Models (SC-GAMs) to build SDMs under the ecological niche theory framework (Citores et al. 2020). SC-GAMs impose monotonicity and concavity constraints in the linear predictor of the GAMs and avoid overfitting. SC-GAM is an effective alternative to fitting nonsymmetric parametric response curves, while retaining the unimodality constraint, required by ecological niche theory, for direct variables and limiting factors.

The book is organised following the key steps in good modelling practice of SDMs (Elith and Leathwick 2009). First, presence data of a selected species are downloaded from GBIF/OBIS global public datasets and pseudo-absence data are created. Then, environmental data are downloaded from public repositories and extracted at each of the presence/pseudo-absence data points. Based on this dataset, an exploratory analysis is conducted to help deciding on the best modelling approach. The model is fitted to the dataset and the quality of the fit and the realism of the fitted response function are evaluated. After selecting a threshold to transform the continuous probability predictions into binary responses, the model is validated using a k-fold approach. Finally, the predicted maps are generated for visualization.

The code is available in AZTI’s github repository and the book is readily available. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)

To cite the book, please use:

Valle, M., Citores, L., Ibaibarriaga, L., Chust, C. (2023) GAM-NICHE: Shape-Constrained GAMs to build Species Distribution Models under the ecological niche theory. AZTI. https://doi.org/10.57762/fzpy-6w51

References

Citores, L, L Ibaibarriaga, DJ Lee, MJ Brewer, M Santos, and G Chust. 2020. “Modelling Species Presence–Absence in the Ecological Niche Theory Framework Using Shape-Constrained Generalized Additive Models.” Ecological Modelling 418: 108926. https://doi.org/10.1016/j.ecolmodel.2019.108926.

Simple

Date (Publication)
2023-05-14
Identifier
To populate
Credit
AZTI, Marine Research (Spain)
Originator
  AZTI, Marine Research - Mireia Valle
Originator
  AZTI, Marine Research - Leire Citores
Originator
  AZTI, Marine Research - Leire Ibaibarriaga
Originator
  AZTI, Marine Research - Guillem Chust
Thèmes Sextant
  • /Biological Environment/Species/Fish Species of Commercial Interest
GEMET - INSPIRE themes, version 1.0
  • Species distribution
Mission Atlantic - Resources
  • Tool
  • MISSION ATLANTIC Results
Mission Atlantic - Case Studies
  • Celtic Seas
  • Norwegian Sea
  • South Brazilian Shelf
  • Canary Current system
  • Southern Benguela
  • Atlantic Ocean
  • North Mid-Atlantic Ridge
  • South Mid-Atlantic Ridge
Mission Atlantic - BODC Parameters
  • /Chemical oceanography
  • /Physical oceanography
  • /Biological oceanography
Mission Atlantic - Data type (DMP)
  • Environmental data
  • Modelling data
  • Biological data
Mission Atlantic - Work Package
  • WP3 Pelagic Mapping
Access constraints
License
Use constraints
License
Spatial representation type
grid Grid
Metadata language
English
Character set
utf8 UTF8
Topic category
  • Oceans
  • Biota
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OnLine resource
Gam-niche website ( WWW:LINK )
OnLine resource
Online book ( WWW:LINK )
OnLine resource
GitHub ( WWW:LINK )
Hierarchy level
Dataset

Domain consistency

Conformance result

Date (Publication)
2010-12-08
Explanation
See the referenced specification
File identifier
d08b564b-7b5c-4f39-9b28-9684dfc83cf5 XML
Metadata language
English
Character set
UTF8
Hierarchy level
Dataset
Date stamp
2025-05-16T00:13:30.764279Z
Metadata standard name
ISO 19115-3 - SEXTANT
Metadata standard version
1.0
Point of contact
  Ifremer - Sextant
ZI Pointe du Diable , Plouzane , Brittany , 29280 , France
https://sextant.ifremer.fr/eng
 
 

Overviews

overview

Spatial extent

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S
E
W
thumbnail


Keywords

GEMET - INSPIRE themes, version 1.0
Species distribution
Mission Atlantic - BODC Parameters
/Biological oceanography /Chemical oceanography /Physical oceanography
Mission Atlantic - Case Studies
Atlantic Ocean Canary Current system Celtic Seas North Mid-Atlantic Ridge Norwegian Sea South Brazilian Shelf South Mid-Atlantic Ridge Southern Benguela
Mission Atlantic - Data type (DMP)
Biological data Environmental data Modelling data
Mission Atlantic - Resources
MISSION ATLANTIC Results Tool
Mission Atlantic - Work Package
WP3 Pelagic Mapping
Thèmes Sextant
/Biological Environment/Species/Fish Species of Commercial Interest

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