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Marine enviromental data from Biogeochemical models in paper "Can we project changes in fish abundance and distribution in response to climate?" at Global Change Biology journal

As a part of the 2017-2019 GEO Work Programme, the Global Observation System for Mercury (GOS4M) Flagship (http://gos4m.org) is aimed to support the implementation of the Minamata Convention on Mercury. This database has been constructed with data on mercury in Mediterranean marine biota obtained from all available literature and public datasets that span since the beginning of '70. The M2B database includes 24465 records retrieved from 541 sources that include Animalia, Plantae and Chromista Kingdoms. All records were associated to geographical coordinates and controlled to avoid duplication as several of them were obtained from grey literature. The following parameters have been retrieved when available: Country; Location; FAO fisheries region; Latitude; Longitude; Geographical precision code; Kingdom; Class; Order; Family; Species scientific name; Species common name; Species 6-digit codde; Marine habitat; Trophic level; Reference for Trophic level; Sampling Depth; Sample Lenght; Sample Weight; Age; Sex; Sampled tissue; Hg species; Tissue water content; Mean Hg concentration; Minumum Hg concentration; Maximum Hg concentration; Standar Deviation; Standard Error; Sample size; Reference source; Reference id number; Sampling date; Remarks.

Data stored here refer to Eddy Covariance (EC) data measured in 2019 during the snow-free season at the Alpine CZO (Critical Zone Observatory, hereafter CZO@Nivolet) which was established at the Nivolet Plain (Piani del Nivolet) in the Gran Paradiso National Park (GPNP), located in the western Italian Alps. The EC site (IT-NIV) is an ICOS-associated station. CZO@Nivolet is aimed at investigating the cross-scale interactions between climatic shifts and ecosystem functions multiple scales, involving multidisciplinary studies. The main research questions that we aim to answer are concerning: (a) the effect of bedrock lithology, soil physics and chemisty, topographic hetereogenity, biotic components and meteo-climatic parameters in modulating CO2 flux in alpine grassland; and (b) what are the controlling factors of organic C and weathering under geologic substrates and different topographic positions. The investigations started in 2017. In 2019, the EC tower was added to deeply study CO2, H20, latent and sensible heat exchanges between soil, vegetation, and atmosphere. Carbon dioxide fluxes and environmental variables are recorded during the snow-free season to estimate carbon storage and explore CO2 fluxes drivers in high-altitude grasslands. Further developments will regard the integration of different techniques (Eddy Covariance, Remote Sensing, Flux chambers) to improve both spatial and temporal extent of carbon fluxes estimates to finally assess grasslands' productivity.

ISIMIP-2a climate data cutout provided for Sardinia in the framework of SIM4NEXUS

ISIMIP-2a climate data cutout provided for Sardinia in the framework of SIM4NEXUS

CzIPMR code to estimate the recovery time for Cystoseira zosteroides populations after a major disturbance at different temperature scenarios treatments. In addition, stochastic population growth rate (λs) and quasi-extinction probability at increasing frequency of two major disturbances at increasing temperature scenarios. These analyses correspond to the figures 4 and 5 of Capdevila et al. 2018 JEcol.MixedEffectsparamsParameter values needed for the Integral Projection Models used to model the life cycle and population dynamics of Cystoseira zosteroides. This includes seven demographic processes: 1.survival (σ), 2.growth (γ), 3.fertility (φ), 4.recruits per capita (δ(N)), 5.probability of settlement of recruits (ε), 6.early survival of recruits (σs) and 7.recruits size probability distribution.IPMFunctionsFunctions required to run the CzIPM.R script. This script contains the description of the growth, survival and fecundity functions used to build the IPMs.1. The best-fitted model for survival (σ) was a logistic mixed effect model including size as fixed factors and population nested in years as a random factor. 2. For growth (γ), the best-fitted model was a linear mixed effect model, with size as fixed factor and population nested in year as random factor. 3. Fertility (φ(z)), was estimated as the relation between reproductive status (reproductive vs. non-reproductive) and size with a binomial regression. 4. Recruitment per capita (δ(N)) is density-dependent in C. zosteroides (Capdevila et al., 2015), so a generalized linear model with Poisson error distribution and a log-link function was fitted, correlating the recruit:adult ratio as a function of the adult density. 5. To model the effect of temperature on the probability of settlement (ε) we used a generalized linear mixed models (GLMM), with a Poisson error distribution and a logit link function, the independent variable was the number of zygotes, temperature was treated as a fixed variable and we used the ID of each quadrat of the Petri dishes as a random variable. 6. To model the effect of temperature and time (fixed factors) on germling survival (σs), we used a GLMM with a binomial error distribution and a logit link function, with the ID of each quadrat of each Petri dish as a random variable to deal with the lack of independence between observations repeated at different times and a binomial error distribution was assumed to deal with the binary response variable (survive vs. die). 7. The size distribution of recruits was estimated as a normal probability function. In addition, the function required to project the density-dependent and stochastic IPMs is provided.modsumDensity-dependent function, relating the number of Cystoseira zosteroides recruits with the number of adults. It is a generalized linear model (GLM) with Poisson error distribution and a log-link function, correlating the recruit:adult ratio with the adult density. This file is needed to run the code CzIPM.R.settData on the impacts of temperature (16ºC, 20ºC and 24ºC) on the settlement of Cystoseira zosteroides early stages. This file is needed to perform the projections in CzIPM.R code.survrecData on the impacts of the temperature treatments (16ºC, 20ºC and 24ºC) to early survival of Cystoseira zosteroides. This file is required to run the code CzIPM.R. 1. Understanding the combined effects of global and local stressors is crucial for conservation and management, yet challenging due to the different scales at which these stressors operate. Here we examine the effects of one of the most pervasive threats to marine biodiversity, ocean warming, on the early life stages of the habitat-forming macroalga Cystoseira zosteroides, its long-term consequences for population resilience and its combined effect with physical stressors. 2. First, we performed a controlled laboratory experiment exploring the impacts of warming on early life stages. Settlement and survival of germlings were measured at 16ºC (control), 20ºC and 24ºC and both processes were affected by increased temperatures. Then, we integrated this information into stochastic, density-dependent integral projection models (IPM). 3. Recovery time after a minor disturbance significantly increased in warmer scenarios. The stochastic population growth rate (λs) was not strongly affected by warming alone, as high adult survival compensated for thermal-induced recruitment failure. Nevertheless, warming coupled with recurrent physical disturbances had a strong impact on λs and population viability. 4. Synthesis: The impact of warming effects on early stages may significantly decrease the natural ability of habitat-forming algae to rebound after major disturbances. These findings highlight that, in a global warming context, populations of deep-water macroalgae will become more vulnerable to further disturbances, and stress the need to incorporate abiotic interactions into demographic models.

Agricultural landscape mapping (with focus on wheat and rice fields) based on a classification using random forest algorithm with field data and zonal statistics from Sentinel 1 and 2 as inputs.

ISIMIP-2a climate data cutout provided for Sardinia in the framework of SIM4NEXUS

Dataset R19 for W4T_FINAL (Conferences, Journals and Presentations)