Keyword search (3,448 papers available)


Size reductions and genomic changes within two generations in wild walleye populations: associated with harvest?

Author(s): Bowles E, Marin K, Mogensen S, MacLeod P, Fraser DJ

Evol Appl. 2020 Jul;13(6):1128-1144 Authors: Bowles E, Marin K, Mogensen S, MacLeod P, Fraser DJ

Article GUID: 32684951

Causes of maladaptation.

Author(s): Brady SP, Bolnick DI, Angert AL, Gonzalez A, Barrett RDH, Crispo E, Derry AM, Eckert CG, Fraser DJ, Fussmann GF, Guichard F, Lamy T, McAdam ...

Evol Appl. 2019 Aug;12(7):1229-1242 Authors: Brady SP, Bolnick DI, Angert AL, Gonzalez A, Barrett RDH, Crispo E, Derry AM, Eckert CG, Fraser DJ, Fussmann GF, Guichard F, Lamy T, McAdam AG, Newman ...

Article GUID: 31417611

Conservation through the lens of (mal)adaptation: Concepts and meta-analysis.

Author(s): Derry AM, Fraser DJ, Brady SP, Astorg L, Lawrence ER, Martin GK, Matte JM, Negrín Dastis JO, Paccard A, Barrett RDH, Chapman LJ, Lane JE, Ba...

Evol Appl. 2019 Aug;12(7):1287-1304 Authors: Derry AM, Fraser DJ, Brady SP, Astorg L, Lawrence ER, Martin GK, Matte JM, Negrín Dastis JO, Paccard A, Barrett RDH, Chapman LJ, Lane JE, Ballas C...

Article GUID: 31417615

A critical assessment of estimating census population size from genetic population size (or vice versa) in three fishes.

Author(s): Yates MC, Bernos TA, Fraser DJ

Evol Appl. 2017 10;10(9):935-945 Authors: Yates MC, Bernos TA, Fraser DJ

Article GUID: 29151884

Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales.

Author(s): Gaggiotti OE, Chao A, Peres-Neto P, Chiu CH, Edwards C, Fortin MJ, Jost L, Richards CM, Selkoe KA

Evol Appl. 2018 Aug;11(7):1176-1193 Authors: Gaggiotti OE, Chao A, Peres-Neto P, Chiu CH, Edwards C, Fortin MJ, Jost L, Richards CM, Selkoe KA

Article GUID: 30026805


Title:Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales.
Authors:Gaggiotti OEChao APeres-Neto PChiu CHEdwards CFortin MJJost LRichards CMSelkoe KA
Link:https://www.ncbi.nlm.nih.gov/pubmed/30026805?dopt=Abstract
DOI:10.1111/eva.12593
Category:Evol Appl
PMID:30026805
Dept Affiliation: BIOLOGY
1 School of Biology Scottish Oceans Institute University of St Andrews St Andrews UK.
2 Institute of Statistics National Tsing Hua University Hsin-Chu Taiwan.
3 Department of Biology Concordia University Montreal QC Canada.
4 Department of Agronomy National Taiwan University Taipei Taiwan.
5 Center for Conservation and Sustainable Development Missouri Botanical Garden Saint Louis MO USA.
6 Department of Ecology and Evolutionary Biology University of Toronto Toronto ON Canada.
7 Ecominga Fundation Banos Tungurahua Ecuador.
8 Plant Germplasm Preservation Research Unit USDA-ARS Fort Collins CO USA.
9 National Center for Ecological Analysis and Synthesis University of California Santa Barbara Santa Barbara CA USA.
10 Hawai'i Institute of Marine Biology University of Hawai'i at Manoa Kaneohe HI USA.

Description:

Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales.

Evol Appl. 2018 Aug;11(7):1176-1193

Authors: Gaggiotti OE, Chao A, Peres-Neto P, Chiu CH, Edwards C, Fortin MJ, Jost L, Richards CM, Selkoe KA

Abstract

Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organization (genes, population, species, ecological communities and ecosystems), a diversity index that behaves consistently across these different levels has so far been lacking, hindering the development of truly integrative biodiversity studies. To fill this important knowledge gap, we present a unifying framework for the measurement of biodiversity across hierarchical levels of organization. Our weighted, information-based decomposition framework is based on a Hill number of order q = 1, which weights all elements in proportion to their frequency and leads to diversity measures based on Shannon's entropy. We investigated the numerical behaviour of our approach with simulations and showed that it can accurately describe complex spatial hierarchical structures. To demonstrate the intuitive and straightforward interpretation of our diversity measures in terms of effective number of components (alleles, species, etc.), we applied the framework to a real data set on coral reef biodiversity. We expect our framework will have multiple applications covering the fields of conservation biology, community genetics and eco-evolutionary dynamics.

PMID: 30026805 [PubMed]