Keyword search (4,164 papers available)

"Distribution" Keyword-tagged Publications:

Title Authors PubMed ID
1 Neural topic modeling on hyperspheres: Spherical representation learning with von Mises-Fisher mixtures Guo D; Luo Z; Bouguila N; Fan W; 41791177
ENCS
2 Spatio-temporal distribution of AOD and its response to regional energy consumption and air pollution factors in China Su Y; Chen X; Guo J; Yang A; 41308902
ENCS
3 Disentangled representation learning for multi-view clustering via von Mises-Fisher hyperspherical embedding Li Z; Luo Z; Bouguila N; Su W; Fan W; 40664160
ENCS
4 No species left behind: borrowing strength to map data-deficient species Sharma S; Winner K; Pollock LJ; Thorson JT; Mäkinen J; Merow C; Pedersen EJ; Chefira KF; Portmann JM; Iannarilli F; Beery S; de Lutio R; Jetz W; 40571432
BIOLOGY
5 Strategies to Reduce Uncertainties from the Best Available Physicochemical Parameters Used for Modeling Novel Organophosphate Esters across Multimedia Environments Xing C; Ge J; Chen R; Li S; Wang C; Zhang X; Geng Y; Jones KC; Zhu Y; 40105294
CHEMBIOCHEM
6 Exon junction complexes regulate osteoclast-induced bone resorption by influencing the NFATc1 m6A distribution through the "shield effect" Sun B; Yang JG; Wang Z; Wang Z; Feng W; Li X; Liu SN; Li J; Zhu YQ; Zhang P; Wang W; 40051055
ENCS
7 Spatial Variations of Atmospheric Alkylated Polycyclic Aromatic Hydrocarbons across the Western Pacific to the Southern Ocean: Unexpected Increasing Deposition Zhu FJ; Lu XM; Jia JW; Zhang X; Xing DF; Cai MH; Kallenborn R; Li YF; Muir DCG; Zhang ZF; Zhang X; 40025703
CHEMBIOCHEM
8 In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models Against Variability Khaked AA; Oishi N; Roggen D; Lago P; 39860799
ENCS
9 Asymmetric autocatalytic reactions and their stationary distribution Gallinger C; Popovic L; 39679357
MATHSTATS
10 Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions Sun Y; Wang C; 38493601
ENCS
11 The infimum values of two probability functions for the Gamma distribution Sun P; Hu ZC; Sun W; 38261930
MATHSTATS
12 Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency Al-Bazzaz H; Azam M; Amayri M; Bouguila N; 37837127
ENCS
13 The evolution of plasticity at geographic range edges Usui T; Lerner D; Eckert I; Angert AL; Garroway CJ; Hargreaves A; Lancaster LT; Lessard JP; Riva F; Schmidt C; van der Burg K; Marshall KE; 37183152
BIOLOGY
14 Tide-induced infiltration and resuspension of microplastics in shorelines: Insights from tidal tank experiments Feng Q; Chen Z; An C; Yang X; Wang Z; 37084574
ENCS
15 Identifying climate change refugia for South American biodiversity Sales LP; Pires MM; 36919472
BIOLOGY
16 Human Activity Recognition with an HMM-Based Generative Model Manouchehri N; Bouguila N; 36772428
ENCS
17 Species compositions mediate biomass conservation: the case of lake fish communities Arranz I; Fournier B; Lester NP; Shuter BJ; Peres-Neto PR; 34905222
BIOLOGY
18 Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images Bourouis S; Alharbi A; Bouguila N; 34460578
ENCS
19 Formation of oil-particle aggregates: Impacts of mixing energy and duration Ji W; Boufadel M; Zhao L; Robinson B; King T; An C; Zhang BH; Lee K; 34252767
ENCS
20 Grape seed extract supplementation along with a restricted-calorie diet improves cardiovascular risk factors in obese or overweight adult individuals: A randomized, placebo-controlled trial. Yousefi R, Parandoosh M, Khorsandi H, Hosseinzadeh N, Madani Tonekaboni M, Saidpour A, Babaei H, Ghorbani A 33044768
HKAP
21 The Odonata of Quebec: Specimen data from seven collections. Favret C, Moisan-De Serres J, Larrivée M, Lessard JP 32174757
CONCORDIA
22 Diversity, evolution, and classification of virophages uncovered through global metagenomics. Paez-Espino D, Zhou J, Roux S, Nayfach S, Pavlopoulos GA, Schulz F, McMahon KD, Walsh D, Woyke T, Ivanova NN, Eloe-Fadrosh EA, Tringe SG, Kyrpides NC 31823797
BIOLOGY
23 Aegilops tauschii Genome Sequence: A Framework for Meta-analysis of Wheat QTLs. Xu J, Dai X, Ramasamy RK, Wang L, Zhu T, McGuire PE, Jorgensen CM, Dehghani H, Gulick PJ, Luo MC, Müller HG, Dvorak J 30670607
BIOLOGY

 

Title:No species left behind: borrowing strength to map data-deficient species
Authors:Sharma SWinner KPollock LJThorson JTMäkinen JMerow CPedersen EJChefira KFPortmann JMIannarilli FBeery Sde Lutio RJetz W
Link:https://pubmed.ncbi.nlm.nih.gov/40571432/
DOI:10.1016/j.tree.2025.04.010
Publication:Trends in ecology & evolution
Keywords:biodiversityconservationdata gapsphylogenyspecies distribution modelingtraits
PMID:40571432 Category: Date Added:2025-06-27
Dept Affiliation: BIOLOGY
1 Ecology and Evolutionary Biology Department, Yale University, 165 Prospect Street, New Haven, CT 06520, USA; Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA. Electronic address: shubhi.sharma@yale.edu.
2 Ecology and Evolutionary Biology Department, Yale University, 165 Prospect Street, New Haven, CT 06520, USA; Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA.
3 Department of Biology, McGill University, 1205 Docteur Penfield, Montreal, Quebec, H3A 1B1, Canada.
4 Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, Seattle, WA 98115, USA.
5 Ecology and Evolutionary Biology Department, Yale University, 165 Prospect Street, New Haven, CT 06520, USA; Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA; Research Centre for Ecological Change, Research Programme of Organismal and Evolutionary Biology, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, 00014, Finland.
6 Eversource Energy Center and Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269, USA.
7 Department of Biology, Concordia University, Montreal, Quebec, H3A 1B1, Canada; Department of Biology, Memorial University of Newfoundland and Labrador, Saint John's, NL A1C 5S7, Canada.
8 Faculty of AI and Decision Making, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
9 EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Zürich, 8093, Switzerland.

Description:

We lack the data needed to detect and understand biodiversity change for most species, despite some species having millions of observations. This unequal data coverage impedes conservation planning and our understanding of biodiversity patterns. The 'borrowing strength' approach leverages data-rich species to improve predictions for data-deficient species. We review multi- and joint-species distribution models that incorporate traits and phylogenies (termed 'ancillary information') and highlight how they could improve data-deficient spatial predictions. When ancillary information is informative of niche similarity, it has immense potential to improve estimates for data-deficient species distributions and address the Wallacean shortfall. While no statistical method can replace data-collection efforts, approaches discussed in this review offer an important contribution toward closing existing data gaps.





BookR developed by Sriram Narayanan
for the Concordia University School of Health
Copyright © 2011-2026
Cookie settings
Concordia University