Climatological monthly means output (physical variables) from the global hydrodynamic-biogeochemical model (NEMO-ERSEM) by the Plymouth Marine Laboratory (PML) within the framework of the project Mission Atlantic (https://missionatlantic.eu/). This 40-year monthly means netcdf file of 1 degree regular grid resolution is a sample aiming to show the results of the model in the geonode. The variables included in this netcdf are: sea water absolute salinity (so_abs, units: psu), sea water conservative temperature (thetao_con, units: C°), mixed layer depth (mldr10_1, units: m), latitude (lat, units: degrees), longitude (lon, units: degrees), time (time, units: seconds since 1900-01-01 00:00:00), depth [height] (z, units: m). The original model output files are stored with the data provider at the Plymouth Marine Laboratory.

This dataset comprises the global frequency, classification and distribution of marine heat waves (MHWs) from 1996-2020, in Chauhan et al. 2023 (https://doi.org/10.3389/fmars.2023.1177571). The classification was done based on their attributes and using different baselines. Daily SST values were extracted from the NOAA-OISST v2 high-resolution (0.25°) dataset from 1982-2020. MHWs were detected using the method presented by Hobday et al. 2016 (https://doi.org/10.1016/j.pocean.2015.12.014), and by using the 95th percentile of the accumulated temperature distribution to flag the extreme events. A shifting baseline of 8-year rolling period was selected between the years 1982-1996, since this period shows relatively stable maximum values of temperature across different ocean regions. The shifting baseline aims to account for the decadal changes of westerly winds, temperatures and ocean gyres circulations. The classification was done using the KMeans clustering algorithm to identify the relevant features of MHWs and classify them into separate groups based on feature similarities. This algorithm takes MHW features, namely duration, maximum intensity, rate onset and rate decline, as input vectors and applies clustering in the 4-dimensional feature space where each data point represents an MHW event. Note that all the MHWs features are standardized because unequal variances can put more weight on variables with smaller variances. This record comprehends the geospatial datasets of: Average number of MHW days per year (i.e., the sum of all MHW days divided by the total number of years, 1996-2020). Average cumulative intensity per year (i.e., the sum of cumulative intensity divided by the total number of years, 1996-2020). Total number of MHW events across the different periods averaged on the total number of years (1989-2020). The period 1982-1988 was only used as an initial baseline without calculating MHWs. Spatial distribution of three MHW categories: moderate MHWs, abrupt and Intense MHWs and extreme MHWs; displaying the total number of MHW days normalized by the number of years considered (i.e., 1989-2020). Distribution of Extreme MHWs across the different periods (A) 1989-1996, (B) 1997-2004, (C) 2005-2012, (D) 2013-2020. The relative frequency (γ) is a ratio of extreme MHWs in a specific period and all extreme MHWs in the same cluster for all periods.

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.