Literature DB >> 30007845

Overcoming the Data Crisis in Biodiversity Conservation.

Holly K Kindsvater1, Nicholas K Dulvy2, Cat Horswill3, Maria-José Juan-Jordá4, Marc Mangel5, Jason Matthiopoulos6.   

Abstract

How can we track population trends when monitoring data are sparse? Population declines can go undetected, despite ongoing threats. For example, only one of every 200 harvested species are monitored. This gap leads to uncertainty about the seriousness of declines and hampers effective conservation. Collecting more data is important, but we can also make better use of existing information. Prior knowledge of physiology, life history, and community ecology can be used to inform population models. Additionally, in multispecies models, information can be shared among taxa based on phylogenetic, spatial, or temporal proximity. By exploiting generalities across species that share evolutionary or ecological characteristics within Bayesian hierarchical models, we can fill crucial gaps in the assessment of species' status with unparalleled quantitative rigor.
Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Bayesian state-space models; data-poor fisheries; extinction risk; hierarchical models; integrated population models; species assessment

Mesh:

Year:  2018        PMID: 30007845     DOI: 10.1016/j.tree.2018.06.004

Source DB:  PubMed          Journal:  Trends Ecol Evol        ISSN: 0169-5347            Impact factor:   17.712


  4 in total

1.  Estimating correlations among demographic parameters in population models.

Authors:  Thomas V Riecke; Benjamin S Sedinger; Perry J Williams; Alan G Leach; James S Sedinger
Journal:  Ecol Evol       Date:  2019-11-21       Impact factor: 2.912

2.  Seabird meta-Population Viability Model (mPVA) methods.

Authors:  M Tim Tinker; Kelly M Zilliacus; Diana Ruiz; Bernie R Tershy; Donald A Croll
Journal:  MethodsX       Date:  2021-12-09

3.  Sharkipedia: a curated open access database of shark and ray life history traits and abundance time-series.

Authors:  Christopher G Mull; Nathan Pacoureau; Sebastián A Pardo; Luz Saldaña Ruiz; Emiliano García-Rodríguez; Brittany Finucci; Max Haack; Alastair Harry; Aaron B Judah; Wade VanderWright; Jamie S Yin; Holly K Kindsvater; Nicholas K Dulvy
Journal:  Sci Data       Date:  2022-09-10       Impact factor: 8.501

4.  Improving prediction of rare species' distribution from community data.

Authors:  Chongliang Zhang; Yong Chen; Binduo Xu; Ying Xue; Yiping Ren
Journal:  Sci Rep       Date:  2020-07-22       Impact factor: 4.379

  4 in total

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