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Project Overview: This is the first release of our Bayesian-based fire models, designed for fire prediction and analysis using Bayesian inference and simple fire models. The release here is the base code and information used in the "State of Wildfire's report 2023/24". https://doi.org/10.5194/essd-2024-218 Key Features: ConFire fire model now implemented with zero-inflated logistic link distribution Configuration files for near real-time, attribution and future projections for Greece, Canada, and NW Amazon. Utilizes various environmental and climatic data for isimip and Copernicus data store Robust statistical analysis now uses PyMC at version 5 and ArviZ. Installation and Usage: For detailed installation and usage instructions, please refer to the README, also in this repository archive. Acknowledgments: Special thanks to all contributors and the developers of the dependencies used in this project. Particularly Maria Lucia Ferreira Barbosa, Douglas Kelley, Chantelle Burton Full Changelog: https://github.com/douglask3/Bayesian_fire_models/compare/v0.1...SoW23_v0.1

ENERGIZE is a neuroevolutionary framework designed to optimize Deep Neural Networks (DNNs) for power efficiency. Based on the Fast-DENSER framework, it incorporates novel strategies such as multi-objective fitness functions that consider accuracy and power consumption, and a new mutation strategy to reutilize power-efficient modules of layers. Additionally, ENERGIZE introduces an innovative approach to split models into smaller, power-efficient submodels. Experimental results demonstrate that ENERGIZE can significantly reduce power usage while maintaining high model accuracy, offering a practical solution to the energy demands of Machine Learning.

If you use this software, please cite it using the metadata from this file. The figure numbers are fixed to follow the published version.

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases. Please see the README document ("README.md") and the accompanying published article: Braun, C. D., M. C. Arostegui, N. Farchadi, M. Alexander, P. Afonso, A. Allyn, S. J. Bograd, S. Brodie, D. P. Crear, E. F. Culhane, T. H. Curtis, E. L. Hazen, A. Kerney, N. Lezama-Ochoa, K. E. Mills, D. Pugh, N. Queiroz, J. D. Scott, G. B. Skomal, D. W. Sims, S. R. Thorrold, H. Welch, R. Young-Morse, R. Lewison. In press. Building use-inspired species distribution models: using multiple data types to examine and improve model performance. Ecological Applications. Accepted. DOI: < article DOI will be added when it is assigned > Funding provided by: NASA HeadquartersCrossref Funder Registry ID: http://dx.doi.org/10.13039/100017437Award Number: 80NSSC19K0187

Ocean deoxygenation and warming have been shown to pose a growing threat to the health of marine organisms and ecosystems. Yet, the potential for acclimation and adaptation remains poorly understood. The aim of this study was to evaluate the effects of transgenerational exposure to reduced oxygen availability and elevated seawater temperature on the chemosensory-dependent mating mechanisms of male amphipods Gammarus locusta. Three subsequent generations were exposed to four experimental treatments for 30 days: i) present-day scenario, ii) warming; iii) deoxygenation; and iv) warming+deoxygenation. After exposure, the number of individuals that reached adulthood was gauged, and adult males from F0 and F1 were subjected to behavioral trials to assess their capacity of long-distance female cue detection through quantification of response time, first direction of movement, activity rate, and proportion of time spent in female scent cues. Ocean-warming-induced mortality and reduced oxygen availability had adverse effects on each of the investigated behavioral traits, which were amplified when combined with elevated temperature. Still, when compared to F0, the F1 generation demonstrated more adaptability (i.e., higher activity rate and preference for female odors) to the combination of the two stressors, suggesting positive carry-over effects. Nevertheless, full recovery to control levels was not observed. Altogether, this study indicates that future scenarios of ocean deoxygenation and warming have the potential to disrupt chemosensory-dependent mate detection in amphipods, but also suggests possible behavioral adaptations. We call for greater research efforts on long-term impacts of ocean change on the behavioral and physiological processes of benthic coastal communities. Our study aimed to investigate the transgenerational effects of increased temperature and lower oxygen levels on male gammarids' response to female scent signals. For this purpose, we conducted a binary-choice experiment using a total of 30 males from the F0 generation and 26 males from the F1 generation. These trials involved direct observations, during which we recorded four key traits of male individuals: response time (time to initiate movement towards an association zone), proportion of first choice (initial movement towards the female-scented association zone), cumulative time spent in each association zone, and activity rate (proportion of time spent actively moving). Data analysis of behavioral outputs was performed through generalized linear mixed models (GLMM) using the negative binomial (number of individuals, response time), binomial (first choice), and Gaussian (cumulative time & activity rate) residual distributions. All models used temperature, oxygen, and generation as categorical fixed factors, and replicate ID as random factors. Transgenerational effects of the treatments on these variables were considered whenever an interacting effect was observed between Generation and Treatment. Full models, with all possible interactions, were tested using the function 'glmmTMB' from the package 'glmmTMB' and the function 'Anova' from the package 'car' in R, version 3.4.3. Post-hoc multiple comparisons were performed using the package 'emmeans' and applying Tukey corrections. Model assumptions and performance were validated using the package "performance". Data exploration was conducted using the HighstatLibV10 R library from Highland Statistics. Funding provided by: Programa Operacional Regional de Lisboa, Portugal 2020*Crossref Funder Registry ID: Award Number: LISBOA- 01-0145-FEDER-028609 Funding provided by: Fundação para a Ciência e TecnologiaCrossref Funder Registry ID: https://ror.org/00snfqn58Award Number: UIDB/04292/2020 Funding provided by: Fundação para a Ciência e TecnologiaCrossref Funder Registry ID: https://ror.org/00snfqn58Award Number: LA/P/0069/2020 Funding provided by: Fundação para a Ciência e TecnologiaCrossref Funder Registry ID: https://ror.org/00snfqn58Award Number: DL57/2016/CP1479/CT0023 Funding provided by: Fundação para a Ciência e TecnologiaCrossref Funder Registry ID: https://ror.org/00snfqn58Award Number: SFRH/BD/147294/2019 Funding provided by: Fundação para a Ciência e TecnologiaCrossref Funder Registry ID: https://ror.org/00snfqn58Award Number: UI/BD/151019/2021 Funding provided by: Fundação para a Ciência e TecnologiaCrossref Funder Registry ID: https://ror.org/00snfqn58Award Number: PTDC/BIA-BMA/28609/2017 Funding provided by: Fundação para a Ciência e TecnologiaCrossref Funder Registry ID: https://ror.org/00snfqn58Award Number: 2021.01030.CEECIND Funding provided by: European UnionCrossref Funder Registry ID: https://ror.org/019w4f821Award Number: LISBOA- 01-0145-FEDER-028609 Funding provided by: Fundação para a Ciência e TecnologiaCrossref Funder Registry ID: https://ror.org/00snfqn58Award Number: 2021.06590.BD

File naming rules: YYYY-FirstAuthorLastName-Journal-TitleFirstWord File Name Description 2024-Wang-GCB-Beyond Julia and Python code used for data analysis and plotting 2024-Wang-JAMES-Toward Julia and Python code used for data analysis and plotting 2023-Wang-CROPE-Agriculture Julia code for the prototype models for crop studies 2023-Braghiere-AGUAdvances-Importance Julia code for the hyperspectral soil albedo project 2023-Wang-JAMES-Modeling Julia code used to plot the figures used in CliMA Land global simulations 2022-Wang-BG-Common Julia code used to plot the figure for plant hydraulics models 2022-Wang-SDATA-GriddingMachine Julia code used to plot the figure for GriddingMachine illustration 2022-Wang-BG-Impact Julia code used to evluate the impact of canopy model complexity on water and SIF fluxes 2021-Wang-GMD-Testing Julia code used to run site level CliMA Land simulations 2021-Braghiere-RSE-Accounting Julia code used to fit leaf biophysical parameters 2021-Wang-NPh-Optimization Julia code used to simulate optimal nighttime stomatal conductance 2020-Wang-NPh-Theoretical Python code for stomatal optimization models 2019-Wang-TPh-Stomatal Julia version of Sperry gain-risk model (basic version) 2018-Venturas-NPh-Stomatal Excel version of Sperry gain-risk model (more updated) 2017-Sperry-PCE-Predicting Excel version of Sperry gain-risk model 2016-Sperry-NPh-Pragmatic C and Python cide to combine the VCs of root, stem, and leaf into an integrated tree VC 2015-Wang-PPh-Stem Python code to fit bubble pressure in conduits of recently cavitated stem from two points 2015-Wang-PPh-Studies Python code to fit bubble pressure in conduits of recently cavitated stem from a curve 2014-Wang-JPH-Improved Software for Chinatron and brief manual

This repository contains python code in the form of Jupyter notebooks and geospatial data used in the Climate adaptation plans in the context of coastal settlements: the case of Portugal research paper. Geospatial data represent the morphological structure of 30 seashore streets along the Portuguese coast. The data were analyzed using computational Jupyter notebooks to determine their morphometric profile, morphological classification and assess the risk of flooding due to the extreme weather events caused by climate change. Building layers were manually digitized and, where available, enriched by OpenStreetMap data. Street network layer was extracted from OpenStreetMap. Other layers were generated using Jupyter notebooks. Python code is stored within Jupyter notebooks. For the accessibility purposes, contents of notebooks were also exported into executable scripts and PDF. Data structure: GeoPackages containing geospatial data are divided according to geographical locations.

Aim: Species distribution models (SDMs) are an important tool for marine conservation and management, yet guidance on leveraging diverse data to build robust models is limited. While various approaches can be used to integrate different datasets, studies comparing their performance, particularly for highly migratory and mobile species, are scarce. Here, we assess whether a model-based integrative framework improves performance over traditional data pooling or ensemble approaches when synthesizing multiple data types. Location: North Atlantic Ocean Time Period: 1993 - 2019 Major Taxa Studied: Blue shark (Prionace glauca) Methods: We trained traditional, correlative SDMs and integrated SDMs (iSDMs) with three distinct data types: fishery-dependent marker tags, fishery observer records, and fishery-independent electronic tag data. We evaluated data pooling and ensemble approaches in a correlative SDM framework and compared performance to an iSDM approach designed to explicitly account for data-specific biases while retaining the strengths of each dataset. Results: While each integration approach yielded robust models, model performance varied among data types, with all models predicting fishery-dependent data more accurately than fishery-independent data. Differences in performance were primarily attributed to each model's ability to explain the spatiotemporal dynamics of the training data. iSDMs that explicitly accounted for seasonal variability yielded the most accurate and ecologically realistic estimates. However, such approaches are computationally intensive and warrant identifying model purpose as an important step in the data-integration process. Main Conclusions: Our findings reveal important trade-offs among the current techniques for integrating data in SDMs, including variability in accurately estimating species distributions, generating ecologically realistic predictions, and practical feasibility. With increasing access to growing and diverse data sources, maximizing our ability to leverage available data with robust analytical approaches will be instrumental in enhancing conservation and management efforts and for understanding current and future species distributions in a dynamic ocean. Funding provided by: National Aeronautics and Space AdministrationROR ID: https://ror.org/027ka1x80Award Number: 80NSSC19K0187 Funding provided by: National Oceanic and Atmospheric AdministrationROR ID: https://ror.org/02z5nhe81Award Number: NA21OAR4170247 see manuscript for details

This script calculates the zeroth-order reflection in a squared periodic array from a FDTD simulation of Ansys Lumerical software (Release: 2021 R2; version: 8.26.2717). This script is useful for comparing experiments with the numerical simulations where the higher orders of the diffraction are not measured by the detector or are guided inside a substrate. An appendix for calculate the effect of the incoherence due to the substrate is included in the script. If you use this file, please, cite this work as: Jinhui Hu, Luis A. P��rez, Juan Luis Garcia-Pomar, Agust��n Mihi, Miquel Garriga, M. Isabel Alonso, Alejandro R. Go��i Mater. Adv., (2022), DOI: 10.1039/d1ma01237a This project has received funding from the European Union���s Horizon 2020 research and innovation programme under grant agreement No 840064 and 839402