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.

This is an short tutorial to show how to download OBIS/GBIF occurrence data for multiple species. The code has been written to be used in H2020 Mission Atlantic (No 862428) Project Task 3.4. The tutorial has been developed by Mireia Valle (github profile: MireiaValle, email: mvalle@azti.es) based on original code for sourcing OBIS and GBIF from Guillem Chust (email: gchust@azti.es) and some adaptations from Eduardo Ramirez. Affiliation: AZTI, Marine Research, Basque Research and Technology Alliance (BRTA). Txatxarramendi ugartea z/g, 48395 Sukarrieta - Bizkaia, Spain

Repository for code and generated data for " Pan-Atlantic 3D distribution model incorporating water column for commercial fish " by Mireia Valle, Eduardo Ramirez-Romero, Leire Ibaibarriaga, Leire Citores, Jose A. Fernandes-Salvador, and Guillem Chust, published in Ecological Modelling journal (2024). Valle, M., E. Ramírez-Romero, L. Ibaibarriaga, L. Citores, J. A. Fernandes-Salvador, and G. Chust. 2024. Pan-Atlantic 3D distribution model incorporating water column for commercial fish. Ecological Modelling 490:110632. https://doi.org/10.1016/j.ecolmodel.2024.110632 External data need to be downloaded to get the environmental data and be able to make predictions over the environmental space. To do so, run all scripts in the _scripts directory in numeric order from the root project directory. SC-GAMs generated in Valle et al. (2024) can be directly build running the script from _scripts/06_SCGAMs directory using the data uploaded to the _data directory of this repository.

Maps of potential biomass catches (tons/year) per surface unit (0.25º latitude x 0.25º longitude) based on 3-D probability of occurrence for the main commercial fish species of the Atlantic. To map potential catches, first, mean catches (tons/year) were calculated according to Watson (2020) Global fisheries landings (V4) database for period 2010-2015 and then the total mean catch value for each species was redistributed according to the occurrence probability value that was modelled in 3-D using Shape-Constrained Generalized Additive Models (SC-GAMs). Potential catch value of each cell integrates the catches along the water column (from surface until 1000 m depth). See Valle et al. (2024) in Ecological Modelling 490:110632 ( https://doi.org/10.1016/j.ecolmodel.2024.110632 ), for more details.

3-D habitat suitability maps (HSM) or probability of occurrence maps, built using Shape-Constrained Generalized Additive Models (SC-GAMs) for the 30 main commercial species of the Atlantic region. Predictor variables for each species were selected from: sea water temperature, salinity, nitrate, net primary productivity, distance to seafloor, distance to coast, and relative position to mixed layer depth. Each species HSM contains 47 maps, one per depth level from 0 to 1000 m. Probability values of each map range from 0 (unsuitable habitat) to 1 (optimal habitat). For depth levels below the 0.99 quantile of the depth values found on the species occurrence data, NA values were assigned. Maps have been masked to species native range regions. See Valle et al. (2024) in Ecological Modelling 490:110632 (https://doi.org/10.1016/j.ecolmodel.2024.110632 ), for more details.