D4- Extinction and life history variation

Life‐history correlates of extinction risk and recovery potential, Jeffrey Hutchings, 2012

Introduction:

• Historically unprecedented reductions in abundance have accelerated efforts to identify correlates of extinction risk

• Proxies of extinction risk and population viability have been proposed to evaluate species status when abundance or distributional data are sparse

• Life-history correlates of increased extinction risk include large body size, long life span, and delayed age at maturity

• The hypothesis that high fecundity confers low extinction risk and high recovery potential is empirically suspect, it was believed that marine organisms have low extinction risk due to this

• Limitations of extinction risk correlates:

  • based on IUCN status

  • is categorical rather than continuous

• Aims→

  • to estimate and then compare rmax (maximum per-capita population growth rate→ used as a continuous, quantitative metric to depict extinction risk) for three classes of vertebrates (Chondrichthyes, Actinopterygii, and Mammalia; n ¼ 199 species) to evaluate the degree to which extinction risk differs between fishes and mammals ceteris paribus

  • to quantify potential correlations between rmax and each of three life-history traits for which data on at least one trait tend to be readily available: fecundity, maximum body size, and age at maturity

  • to test the null hypothesis that correlations between each of these traits and rmax do not differ among the three vertebrate classes examined here.

Materials and Methods:

• Estimates of (r_{max}) for teleost fish were derived from the Euler-Lotka equation

• For chondrichthyans and mammals, (r_{max}) was also estimated from the Euler-Lotka equation, using data on age at maturity, fecundity, and natural mortality obtained from the literature

• Data on life-history traits for teleost fishes were collated for 47 species

• Data on chondrichthyans were collated for 82 species

• Data on mammals were obtained for 70 species

• Log-transformed (r_{max}) was modelled with three linear mixed-effect models (LMEs), where fecundity, body mass, or age at maturity was considered as a fixed covariate together with taxonomic class (factor) and its interaction with the life-history covariate

Results:

• Maximum population growth rates differed among teleosts, chondrichthyans, and mammals:

  • Based on their median estimates and degree of CI overlap, rmax was highest among teleosts and terrestrial mammals, significantly lower among chondrichthyans, and lower still among marine mammals.

• Maximum population growth rate was significantly affected by interactions with both the life-history trait and taxonomic class in all three models:

  • Fecundity was not correlated with (r_{max}) in fishes, although a positive association was evident in mammals

  • Body size was negatively correlated with maximum population growth rate in chondrichthyans and mammals, whereas in teleosts the negative correlation was not significant

  • Age at maturity was strongly and negatively correlated with (r_{max}) within each taxonomic class

  

Discussion:

• Maximum population growth rate and, thus, extinction probability differs among taxonomic classes of vertebrates

• Population growth rate is highly correlated with age at maturity across taxa, suggesting that this life-history metric might be the most reliable predictor of (r_{max})→ consistent with earlier work

• Regression models can be used to predict maximum population growth rate when data for only one significant life-history correlate of (r_{max}) are available→ can be used in efforts to apply life-history criteria to species conservation and management

• The maximum population growth rate of sharks, skates, rays, and chimaeras is significantly lower than that of teleosts→ possibly due to larger body size and older age at maturity characteristic of chondrichthyans. Mammals do not differ from teleosts though→ suggests the probability of extinction among teleosts is similar to that of terrestrial mammals

• There is no support for the hypothesis that increased fecundity confers lower extinction threat and increased resilience in fishes

  • reason why fecundity is unrelated to rmax may be that it is only one of many traits that contribute to an individual’s fitness (and thus population growth rate) and given that various tradeoffs (reflected by negative associations among traits) often prevent selection from increasing the value of one fitness-related trait without diminishing the value of another (Roff 2002). Nonetheless, we do note the positive species-level association between fecundity and rmax within mammals, which might be attributable to the relatively narrow range in fecundity within this class

• At the species level, the functional relationships between life-history traits and (r_{max}) differed among chondrichthyans, teleosts, and mammals

• Age at maturity can serve as a universal predictor of extinction risk in fishes and mammals when (r_{max}) is unknown

• Limitations→ there remains uncertainty in one’s estimate of rmax for a given species, an uncertainty that can be magnified by environmental stochasticity

• Future→ application of our results will improve overall predictability in forecasting species or population viability and their risk of depletion or extinction

Demographic and life history traits explain patterns in species vulnerability to extinction, Haydee Hernandez-Yañez et al., 2022

Introduction:

• the importance of predicting species vulnerability to extinction which depends on intrinsic (life history) and extrinsic (environmental) factors

• introduces the idea of using demographic rates such as survival, growth, and reproduction to describe life history strategies9. The introduction also mentions the study's aim

• aim→ to investigate if certain demographic patterns (in stage-specific survival and reproductive rates) are more vulnerable to extinction than others, using the COMPADRE and COMADRE databases

Methods:

• data sources used→ COMPADRE and COMADRE databases, IUCN Red List, and phylogenetic information

• there was a selection criteria for species and matrices

• calculated 14 life history and demographic metrics

• used 159 species of herbaceous plants, trees, mammals, and birds

• statistical analyses used→ conditional random forest and phylogenetic generalized least squares regression

Results:

• For herbaceous perennials (plants that live for more than two years and can survive frost), early maturation and high juvenile survival correlate with higher extinction risk

  

• For birds, high reproduction and progressive growth are associated with greater vulnerability

  

• Mammals with longer generation times are more prone to extinction

  

• No significant predictors of endangered status were found for tree species

• There are a number of species in COMPADRE and COMADRE without IUCN assessments, the study examines the overlap of species with biodiversity hotspots and IUCN assessments to those without

  

Discussion:

• existing literature→ taxa differ in the variables that explain their vulnerability to extinction

• potential reasons for the findings→ life history strategies and extrinsic threats

• limitations→ fecundity calculations, suggests further analyses

• conclusion→ is important to use demographic data for IUCN assessments and identify at-risk species

• linking rmax to age of maturity, number of offspring and survival

• 3 traits- body size, age of maturity and fecundity

• controlled for phylogenetic relationships

• used more organisms

• IUCN red list have many positives but lots aren’t completed yet

• future papers incorporate climate change→ PA management

• extrinsic factors→ human activity

• primary correlate of rmax was age of maturity→ mechanisms

Q1:

• Taxonomic diversity was a strength as it included marine species and plants as well as animals, however Hernandez-Yañez et al. didn’t have a large sample of species within each IUCN category (as low as 2-3 species in a category).

• IUCN red-list is determined by humans so may include biases/ are under-studied due to accessibility issues, e.g. many species are data deficient and less charismatic species are overlooked. Hutchings et al use of rmax meant it didn’t include as much environmental stressors such as habitat degradation and anthropogenic pressure in their metrics of extinction risk, which is one of the strengths of using IUCN data, since it includes those extrinsic pressures. 

Hutchings et al. discussed how conservationists might identify and prioritise vulnerable species when their rmax is less known by using age at maturity and generation time. This may be helpful particularly for data deficient species.

Q2:

 Age at maturity often matters more than fecundity or body size because it directly influences a species' population growth rate (rmax). 

Species that mature later take longer to start reproducing, which slows population recovery after declines.

 Even if a species has high fecundity, delayed reproduction means fewer opportunities to replace lost individuals, especially if adults face high mortality. 

Body size can correlate with slower life histories, but it's not as precise a predictor as age at maturity.

Q3:

Life history - can identify species who are inherently vulnerable (slow history). - although fast life history species can still become extinct if their environment is significantly changed.

Q4:
Marine mammals- True

predicting species extinction risk- false

High fecundity- false

Low rmax- true

Related species- true