2,070 research outputs found
Seedling survival responses to conspecific density, soil nutrients, and irradiance vary with age in a tropical forest
Predicting long-term trends in forest growth requires accurate characterisation of how the relationship between forest productivity and climatic stress varies across climatic regimes. Using a network of over two million tree-ring observations spanning North America and a space-for-time substitution methodology, we forecast climate impacts on future forest growth. We explored differing scenarios of increased water-use efficiency (WUE) due to CO2-fertilisation, which we simulated as increased effective precipitation. In our forecasts: (1) climate change negatively impacted forest growth rates in the interior west and positively impacted forest growth along the western, southeastern and northeastern coasts; (2) shifting climate sensitivities offset positive effects of warming on high-latitude forests, leaving no evidence for continued ‘boreal greening’; and (3) it took a 72% WUE enhancement to compensate for continentally averaged growth declines under RCP 8.5. Our results highlight the importance of locally adapted forest management strategies to handle regional differences in growth responses to climate change
Seedling survival responses to conspecific density, soil nutrients, and irradiance vary with age in a tropical forest
Predicting long-term trends in forest growth requires accurate characterisation of how the relationship between forest productivity and climatic stress varies across climatic regimes. Using a network of over two million tree-ring observations spanning North America and a space-for-time substitution methodology, we forecast climate impacts on future forest growth. We explored differing scenarios of increased water-use efficiency (WUE) due to CO2-fertilisation, which we simulated as increased effective precipitation. In our forecasts: (1) climate change negatively impacted forest growth rates in the interior west and positively impacted forest growth along the western, southeastern and northeastern coasts; (2) shifting climate sensitivities offset positive effects of warming on high-latitude forests, leaving no evidence for continued ‘boreal greening’; and (3) it took a 72% WUE enhancement to compensate for continentally averaged growth declines under RCP 8.5. Our results highlight the importance of locally adapted forest management strategies to handle regional differences in growth responses to climate change
Speeding up ecological and evolutionary computations in R; essentials of high performance computing for biologists
Computation has become a critical component of research in biology. A risk has emerged that computational and programming challenges may limit research scope, depth, and quality. We review various solutions to common computational efficiency problems in ecological and evolutionary research. Our review pulls together material that is currently scattered across many sources and emphasizes those techniques that are especially effective for typical ecological and environmental problems. We demonstrate how straightforward it can be to write efficient code and implement techniques such as profiling or parallel computing. We supply a newly developed R package (aprof) that helps to identify computational bottlenecks in R code and determine whether optimization can be effective. Our review is complemented by a practical set of examples and detailed Supporting Information material (S1–S3 Texts) that demonstrate large improvements in computational speed (ranging from 10.5 times to 14,000 times faster). By improving computational efficiency, biologists can feasibly solve more complex tasks, ask more ambitious questions, and include more sophisticated analyses in their research
Going Stupid with EcoLab
In 2005, Railsback et al. proposed a very simple model ({\em Stupid
Model}) that could be implemented within a couple of hours, and later
extended to demonstrate the use of common ABM platform functionality. They
provided implementations of the model in several agent based modelling
platforms, and compared the platforms for ease of implementation of this simple
model, and performance. In this paper, I implement Railsback et al's Stupid
Model in the EcoLab simulation platform, a C++ based modelling platform,
demonstrating that it is a feasible platform for these sorts of models, and
compare the performance of the implementation with Repast, Mason and Swarm
versions
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Best Practices in Accelerating the Data Science Process in Python
The number of data science and big data projects is growing, and current software development approaches are challenged to support and contribute to the success and frequency of these projects. Much has been researched on how data science algorithm is used and the benefits of big data, but very little has been written about what best practices can be leveraged to accelerate and effectively deliver data science and big data projects. Big data characteristics such as volume, variety, velocity, and veracity complicate these projects. The proliferation of open-source technologies available to data scientists can also complicate the landscape. With the increase in data science and big data projects, organizations are struggling to deliver successfully. This paper addresses the data science and big data project process, the gaps in the process, best practices, and how these best practices are being applied in Python, one of the common data science open-source programming languages
Algorithms and Public Service Media
Algorithms increasingly shape the flow of information in societies. Recently, public service media organisations have begun to develop algorithmic recommender systems and automated systems in their internet services, which makes sense given their importance as mediators of information. In the emerging era of big data and growing personalisation, this makes sense strategically and can have instrumental importance for networked societies. This chapter draws on relevant development projects in European and Australian public service media organisations. In relation to the core principles of public service media, five challenges in operationalising automated rulebased systems are identified: 1) balancing popularity and distinctiveness, 2) diversity of exposure to programming, 3) transparency of the logic underlying recommendations, 4) user sovereignty and, 5) the issue of dependence on or independence from commercial intermediaries. The chapter examines a new set of conditions that affect provision public service provision in societies that feature growing use and reliance on networked media
SusTrainable: Promoting Sustainability as a Fundamental Driver in Software Development Training and Education. 2nd Teacher Training, January 23-27, 2023, Pula, Croatia. Revised lecture notes
This volume exhibits the revised lecture notes of the 2nd teacher training
organized as part of the project Promoting Sustainability as a Fundamental
Driver in Software Development Training and Education, held at the Juraj
Dobrila University of Pula, Croatia, in the week January 23-27, 2023. It is the
Erasmus+ project No. 2020-1-PT01-KA203-078646 - Sustrainable. More details can
be found at the project web site https://sustrainable.github.io/
One of the most important contributions of the project are two summer
schools. The 2nd SusTrainable Summer School (SusTrainable - 23) will be
organized at the University of Coimbra, Portugal, in the week July 10-14, 2023.
The summer school will consist of lectures and practical work for master and
PhD students in computing science and closely related fields. There will be
contributions from Babe\c{s}-Bolyai University, E\"{o}tv\"{o}s Lor\'{a}nd
University, Juraj Dobrila University of Pula, Radboud University Nijmegen,
Roskilde University, Technical University of Ko\v{s}ice, University of
Amsterdam, University of Coimbra, University of Minho, University of Plovdiv,
University of Porto, University of Rijeka.
To prepare and streamline the summer school, the consortium organized a
teacher training in Pula, Croatia. This was an event of five full days,
organized by Tihana Galinac Grbac and Neven Grbac. The Juraj Dobrila University
of Pula is very concerned with the sustainability issues. The education,
research and management are conducted with sustainability goals in mind.
The contributions in the proceedings were reviewed and provide a good
overview of the range of topics that will be covered at the summer school. The
papers in the proceedings, as well as the very constructive and cooperative
teacher training, guarantee the highest quality and beneficial summer school
for all participants.Comment: 85 pages, 8 figures, 3 code listings and 1 table; editors: Tihana
Galinac Grbac, Csaba Szab\'{o}, Jo\~{a}o Paulo Fernande
Transcriptome sequencing of black grouse (Tetrao tetrix) for immune gene discovery and microsatellite development
The black grouse (Tetrao tetrix) is a galliform bird species that is important for both ecological studies and conservation genetics. Here, we report the sequencing of the spleen transcriptome of black grouse using 454 GS FLX Titanium sequencing. We performed a large-scale gene discovery analysis with a focus on genes that might be related to fitness in this species and also identified a large set of microsatellites. In total, we obtained 182 179 quality-filtered sequencing reads that we assembled into 9035 contigs. Using these contigs and 15 794 length-filtered (greater than 200 bp) singletons, we identified 7762 transcripts that appear to be homologues of chicken genes. A specific BLAST search with an emphasis on immune genes found 308 homologous chicken genes that have immune function, including ten major histocompatibility complex-related genes located on chicken chromosome 16. We also identified 1300 expressed sequence tag microsatellites and were able to design suitable flanking primers for 526 of these. A preliminary test of the polymorphism of the microsatellites found 10 polymorphic microsatellites of the 102 tested. Genomic resources generated in this study should greatly benefit future ecological, evolutionary and conservation genetic studies on this species
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