Hasil untuk "Ecology"

J. Koricheva, J. Gurevitch, K. Mengersen

Daniel Pincheira-Donoso

K. Fisher, C. Phillips

F. Courchamp, L. Berec, J. Gascoigne

E. Litchman, C. Klausmeier

T. Swetnam, C. Allen, J. Betancourt

D. Futuyma, H. B. Shaffer, D. Simberloff

G. Thorson

C. Ettema, D. Wardle

D. Kirchman

M. Cody, J. Diamond

M. Begon, C. Townsend, J. Harper

V. Resh, Arthur V. Brown, A. Covich et al.

R. Mackie, A. Sghir, H. Gaskins

C. Wilmers, Barry A. Nickel, Caleb M. Bryce et al.

Anas Hajbi

Modern neural networks rely on generic activation functions (ReLU, GELU, SiLU) that ignore the mathematical structure inherent in scientific data. We propose Neuro-Symbolic Activation Discovery, a framework that uses Genetic Programming to extract interpretable mathematical formulas from data and inject them as custom activation functions. Our key contribution is the discovery of a Geometric Transfer phenomenon: activation functions learned from particle physics data successfully generalize to ecological classification, outperforming standard activations (ReLU, GELU, SiLU) in both accuracy and parameter efficiency. On the Forest Cover dataset, our Hybrid Transfer model achieves 82.4% accuracy with only 5,825 parameters, compared to 83.4% accuracy requiring 31,801 parameters for a conventional heavy network -- a 5.5x parameter reduction with only 1% accuracy loss. We introduce a Parameter Efficiency Score ($E_{param} = AUC / \log_{10}(Params)$) and demonstrate that lightweight hybrid architectures consistently achieve 18-21% higher efficiency than over-parameterized baselines. Crucially, we establish boundary conditions: while Physics to Ecology transfer succeeds (both involve continuous Euclidean measurements), Physics to Text transfer fails (discrete word frequencies require different mathematical structures). Our work opens pathways toward domain-specific activation libraries for efficient scientific machine learning.

Miguel Brilhante, Iain Darbyshire, Maria Cristina Duarte et al.

ABSTRACT Despite the extensive diversity of African flora, significant gaps remain in taxonomic research and biodiversity conservation, including under‐sampling in highly diverse regions, a shortage of taxonomic expertise, limited financial resources and delays in species descriptions. Type specimens act as effective proxies for tracking the discovery and description of species, providing a historical baseline for assessing taxonomic effort and our understanding of biodiversity. This study presents the first comprehensive analysis of Fabaceae species collected in Mozambique, one of the most diverse and ecologically important plant families in the region. It offers new insights into the taxonomic, spatial and temporal patterns shaping current botanical knowledge through an analysis of Fabaceae type specimens collected in Mozambique. We identified 273 type specimens, including 126 recognised taxa, with a notable proportion of endemism (44 strict‐endemic and 18 near‐endemic taxa) and a predominance of woody growth forms. Nearly 40% of these taxa lack IUCN conservation assessments, highlighting significant information gaps. The findings reveal that collection activity peaked during colonial botanical initiatives, driven by a small group of prolific collectors and influenced by spatial biases towards southern and central provinces. Using generalised linear modelling, we demonstrate that collection locations were significantly affected by elevation, slope, land cover and proximity to roads and harbours, reflecting the interaction between biogeographic patterns and accessibility. By identifying these historical and geographic biases, our study deepens understanding of Mozambique's botanical heritage and provides a crucial baseline for future floristic and conservation efforts in underexplored regions. Furthermore, this research underscores the vital role of herbarium type specimens as scientific resources supporting taxonomic research and conservation planning, emphasising the importance of preserving and digitising these collections to enhance their accessibility and utility.

Martin Sindelar, Anna Kocurkova, Matej Simek et al.

ABSTRACT The ability of gut microbes to degrade host‐ and diet‐derived glycans is central to microbiome ecology and host interactions, yet predicting these functions in silico remains challenging. Hyaluronan (HA), a glycosaminoglycan (GAG) abundant in host tissues and dietary supplements, is depolymerized by specialized polysaccharide utilization loci (PULs) in Bacteroides. Here, we combined comparative protein analysis, functional assays, and quantitative proteomics to evaluate the reliability of sequence‐based predictions of HA utilization. Clustering of more than 3900 PL8 and GH88 protein sequences from 54 Bacteroides species did not distinguish known HA degraders from nondegraders, underscoring the limited predictive power of these enzymes alone. Experimental validation in Bacteroides acidifaciens DSM 111135 and Bacteroides thetaiotaomicron DSM 2079 confirmed HA degradation, as HA‐derived fragments were identified by liquid chromatography–mass spectrometry. Proteomic profiling revealed coordinated induction of both canonical GAG‐specific PULs‐encoded proteins and noncanonical accessory proteins (BT4410/BT4411) in response to HA in both species. Incorporating such noncanonical components into comparative frameworks may improve prediction of glycan utilization potential and help link microbial genomic content to ecological function in the gut.

Valentin Girard, Antoine Martin, Maud Rio et al.

The digitalization of societies raises questions about its sustainability and the socio-technical impacts it generates. Ecological redirection applied to organizations is a field of research aiming for achieving sustainability as a direction, rather than for technical means. Arbitration and renunciation to some digital usage and technologies are investigated. Ecological redirection is, however, not yet addressing concrete methodologies for its implementation in organizations. This paper therefore proposes a protocol to support stakeholders in the ecological redirection of their digital practices. This protocol is based on mapping attachments to digital tools through a multi-disciplinary survey. It then proposes increasing stakeholders' knowledge and skills to prepare a debate on the arbitration of renunciations, and finally, to operationalize the closure/transformation of targeted digital practices. This protocol will be tested in real conditions in different contexts. An empirical study is proposed to measure 1) the fluidity with which participants carry out the protocol, 2) the effectiveness of the protocol in terms of the redirection objective, 3) the socio-technical barriers to the redirection process. The paper concludes on the potential benefits for organizations to better understand both the barriers related to its ecological redirection and the transformative aim of such protocols. This will help them trigger large and radical policies towards a desirable and sustainable society.

Spencer Rugaber, Scott Bunin, Andrew Hornback et al.

Conceptual modeling has been an important part of constructionist educational practices for many years, particularly in STEM (Science, Technology, Engineering and Mathematics) disciplines. What is not so common is using agent-based simulation to provide students feedback on model quality. This requires the capability of automatically compiling the concept model into its simulation. The VERA (Virtual Experimentation Research Assistant) system is a conceptual modeling tool used since 2016 to provide introductory college biology students with the capability of conceptual modeling and agent-based simulation in the ecological domain. This paper describes VERA and its approach to coupling conceptual modeling and simulation with emphasis on how a model's visual syntax is compiled into code executable on a NetLogo simulation engine. Experience with VERA in introductory biology classes at several universities and through the Smithsonian Institution's Encyclopedia of Life website is related.