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Discretising Keyfitz' entropy for studies of actuarial senescence and comparative demography

Abstract:
1. Keyfitz’ entropy is a widely used metric to quantify the shape of the survivorship curve of populations, from plants, to animals, and microbes. Keyfitz’ entropy values < 1 correspond to life histories with an increasing mortality rate with age (i.e., actuarial senescence), whereas values > 1 correspond to species with a decreasing mortality rate with age (negative senescence), and a Keyfitz entropy of exactly 1 corresponds to a constant mortality rate with age. Keyfitz’ entropy was originally defined using a continuous-time model, and has since been discretised to facilitate its calculation from discrete-time demographic data. 2. Here, we show that the previously used discretisation of the continuous-time metric does not preserve the relationship with increasing, decreasing, or constant mortality rates. To resolve this discrepancy, we propose a new discrete-time formula for Keyfitz’ entropy for age-classified life histories. 3. We show that this new method of discretisation preserves the relationship with increasing, decreasing, or constant mortality rates. We analyse the relationship between the original and the new discretisation, and we find that the existing metric tends to underestimate Keyfitz’ entropy for both short-lived species and long-lived species, thereby introducing a consistent bias. 4. To conclude, to avoid biases when classifying life histories as (non-)senescent, we suggest researchers use either the new metric proposed here, or one of the many previously suggested survivorship shape metrics applicable to discrete-time demographic data such as Gini coefficient or Hayley’s median.
Publication status:
Accepted
Peer review status:
Peer reviewed

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Publisher copy:
10.1111/2041-210X.14083

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Biology
Oxford college:
Pembroke College
Role:
Author
ORCID:
0000-0002-6085-4433


Publisher:
Wiley
Journal:
Methods in Ecology and Evolution More from this journal
Volume:
14
Issue:
5
Pages:
1312-1319
Publication date:
2023-03-23
Acceptance date:
2023-02-13
DOI:
ISSN:
2041-210X


Language:
English
Keywords:
Pubs id:
1328554
Local pid:
pubs:1328554
Deposit date:
2023-02-14

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