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21 results for “reproductive value”
Hidden causes of variation in offspring reproductive value: negative effects of maternal breeding age on offspring telomere length persist undiminished across multiple generations
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Data from: Minimum size limits and the reproductive value of numerous, young, mature female fish
<p>Fisheries management relies on various catch and effort controls to preserve spawning stock biomass and maximise sustainable yields while limiting fishery impacts on marine ecosystems. These include species-specific minimum or maximum size limits to protect either small non-reproductive sub-adults, a portion of reproductively mature adults, or large highly fecund individuals. Protecting size classes of mature fish is expected to yield a viable source of larvae for replenishing populations and reduce the risk of recruitment overfishing, yet size-specific recruitment contributions have rarely been assessed empirically. Here we apply genetic parentage analysis to measure the reproductive success of a size-structured population of a commercially important species of coral grouper (<i>Plectropomus</i> <i>maculatus </i>- Serranidae) in no-take marine reserves in the Great Barrier Reef Marine Park, Australia. Although the per-capita reproductive success of individual fish increases rapidly with body-length, the numerous young mature female fish (NYMFFs), below the minimum size limit (38 cm total length), were responsible for generating disproportionately large contributions (36%) towards larval replenishment of both fished and reserve reefs. Our findings indicate that minimum size limits are an effective harvest control measure to safeguard a portion of the spawning stock biomass for coral grouper and supplement recruitment subsidies assured from no-take marine reserves.</p>
Age predicts risky investment better than residual reproductive value
Life-history theory predicts that investment into reproduction should increase as future reproductive opportunities (i.e., residual reproductive value, RRV) decrease. Researchers have thus intuitively used age as a proxy for RRV and assume RRV decreases with age when interpreting age-specific investment. Yet, age is an imperfect proxy for RRV and may even be a poor correlate in some systems. We used a 30-year study of the nesting ecology of painted turtles ( Chrysemys picta ) to assess how age and RRV compare in explaining variation in a risky investment behavior. We predicted that RRV would be a better predictor of risky investment than age because RRV accounts for variation in future reproductive potential across life. We found that RRV was high in early life, slowly decreased until midlife, and then steadily decreased to terminal reproduction. However, age predicted risky behavior better than RRV. This finding suggests stronger correlates of age (e.g., size) may be more responsible for this behavior in turtles. This study highlights that researchers should not assume that age-specific investment is driven by RRV and that future work should quantify RRV to more directly test this key element of life-history theory.
Data for the reproduction of figures from the DESI MWS value added catalogue paper
<p>The data to reproduce the figures from the DESI EDR MWS value added catalogue paper (Koposov et al 2024)</p>
Admixture and reproductive skew shape the conservation value of ex situ populations of the Critically Endangered eastern black rhino - microsatellite and mitochondrial genotype data
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Data from: Minimum size limits and the reproductive value of numerous, young, mature female fish
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Age predicts risky investment better than residual reproductive value
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Data from: Individuals' expected genetic contributions to future generations, reproductive value, and short-term metrics of fitness in free-living song sparrows (Melospiza melodia)
Appropriately defining and enumerating 'fitness' is fundamental to explaining and predicting evolutionary dynamics. Yet, general theoretical concepts of fitness are often hard to translate into quantities that can be measured in wild populations experiencing complex environmental, demographic, genetic and selective variation. While the 'fittest' entities might be widely understood to be those that ultimately leave most descendants at some future time, such long-term legacies can rarely be measured, impeding evaluation of the degree to which tractable short-term metrics of individual fitness could potentially serve as useful direct proxies. One opportunity for conceptual and empirical convergence stems from the principle of individual reproductive value (Vi), defined as the number of copies of each of an individual's alleles that is expected to be present in future generations given the individual's realised pedigree of descendants. Since Vi tightly predicts an individual's longer-term genetic contribution, quantifying Vi provides a tractable route to quantifying what, to date, has been an abstract theoretical fitness concept. We used complete pedigree data from free-living song sparrows (Melospiza melodia) to demonstrate that individuals' expected genetic contributions stabilise within an observed 20-year (i.e. ~8 generation) time period, allowing estimation of individual Vi. Considerable among-individual variation in Vi was evident in both sexes. Standard metrics of individual lifetime fitness, comprising lifespan, lifetime reproductive success and projected growth rate, typically explained less than half the variation. We thereby elucidate the degree to which fitness metrics observed on individuals concur with measures of longer-term genetic contributions, and consider the degree to which analyses of pedigree structure could provide useful complementary insights into evolutionary outcomes.
FIGURE. Results of discriminant function an alysis (DFA) for C. brizoides (br), C. curvata (cu) and C. praecox (pr). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): LB = 0.63, LN = -0.93, WN = 0.94, LN/WN = 1.13, FGL = -0.62. Loadings for the second axis: WW = -0.76, LB = 1.51, LN = 2.21, WN = -1.79, LN/WN = -1.48, FGL = 0.55, FGW = -0.51. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CLL = -0.58. Loadings for the second axis: CW = -0.72, IL = -0.78. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function an alysis (DFA) for C. brizoides (br), C. curvata (cu) and C. praecox (pr). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): LB = 0.63, LN = -0.93, WN = 0.94, LN/WN = 1.13, FGL = -0.62. Loadings for the second axis: WW = -0.76, LB = 1.51, LN = 2.21, WN = -1.79, LN/WN = -1.48, FGL = 0.55, FGW = -0.51. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CLL = -0.58. Loadings for the second axis: CW = -0.72, IL = -0.78.
FIGURE. Results of discriminant function analysis (DFA) for C. arenaria (ar), C. colchica (co) and C. praecox (pr). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): LW = -0.53, LN = -1.53, WN = 2.40, LN/WN = 2.13. Loadings for the second axis: LN = -0.56. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): IL = 0.62. Loadings for the second axis: CW = 0.86, CLL = 0.78, CLW = -0.65. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function analysis (DFA) for C. arenaria (ar), C. colchica (co) and C. praecox (pr). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): LW = -0.53, LN = -1.53, WN = 2.40, LN/WN = 2.13. Loadings for the second axis: LN = -0.56. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): IL = 0.62. Loadings for the second axis: CW = 0.86, CLL = 0.78, CLW = -0.65.
FIGURE. Results of discriminant function analysis (DFA) for the reproductive characters of the Ammoglochin taxa. A—along axes DF1 and DF2; B—along axes DF1 and DF3. Characters abbreviated as in Table 2). Loadings for the first axis (only absolute values>0.50 are given: LN = 1.09, WN = -1.57, LN/WN = -1.59, FGL = 0.74. Loadings for the second axis: UL/UW = 0.57, LW = 0.71, WW = 0.52. Loadings for the third axis: UL = -0.73, UW = 1.56, UL/UW = 0.92, LN = -2.76, WN = 2.53, LN/WN = 2.14. ar—C. arenaria, br—C. brizoides, co—C. colchica, cu—C. curvata, pr—C. praecox, ps—C. pseudobrizoides. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function analysis (DFA) for the reproductive characters of the Ammoglochin taxa. A—along axes DF1 and DF2; B—along axes DF1 and DF3. Characters abbreviated as in Table 2). Loadings for the first axis (only absolute values>0.50 are given: LN = 1.09, WN = -1.57, LN/WN = -1.59, FGL = 0.74. Loadings for the second axis: UL/UW = 0.57, LW = 0.71, WW = 0.52. Loadings for the third axis: UL = -0.73, UW = 1.56, UL/UW = 0.92, LN = -2.76, WN = 2.53, LN/WN = 2.14. ar—C. arenaria, br—C. brizoides, co—C. colchica, cu—C. curvata, pr—C. praecox, ps—C. pseudobrizoides.
FIGURE. Results of discriminant function analysis (DFA) for C. arenaria (ar), C. pseudobrizoides (ps) and C. brizoides (br). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): UL/UW = 0.79, LN = -0.72, WN = 1.33, LN/WN = 1.45, FGL = -0.72. Loadings for the second axis: UW = -1.37, UL/UW = -0.55, LN = 2.13, WN = -2.07, LN/WN = -1.45. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CL = -0.76, IL = 0.82. Loadings for the second axis: CL = 0.57, SN = 0.68, LSL = 0.79. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function analysis (DFA) for C. arenaria (ar), C. pseudobrizoides (ps) and C. brizoides (br). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): UL/UW = 0.79, LN = -0.72, WN = 1.33, LN/WN = 1.45, FGL = -0.72. Loadings for the second axis: UW = -1.37, UL/UW = -0.55, LN = 2.13, WN = -2.07, LN/WN = -1.45. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CL = -0.76, IL = 0.82. Loadings for the second axis: CL = 0.57, SN = 0.68, LSL = 0.79.
FIGURE. Results of discriminant function analysis (DFA) for C. arenaria (ar), C. brizoides (br) and C. praecox (pr). Characters abbreviated as in Table 2): A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): UL = 0.53, UL/UW = -0.56, LN = 1.37, WN = -2.23, LN/WN = -2,03, FGL = 0.56. Loadings for the second axis: UL/UW = 0.79, LW = 0.61, LN = -0.87, WN = 0.90, LN/WN = 0.87. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CL = -0.54, IL = 0.72. Loadings for the second axis: CLL = -0.73. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function analysis (DFA) for C. arenaria (ar), C. brizoides (br) and C. praecox (pr). Characters abbreviated as in Table 2): A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): UL = 0.53, UL/UW = -0.56, LN = 1.37, WN = -2.23, LN/WN = -2,03, FGL = 0.56. Loadings for the second axis: UL/UW = 0.79, LW = 0.61, LN = -0.87, WN = 0.90, LN/WN = 0.87. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CL = -0.54, IL = 0.72. Loadings for the second axis: CLL = -0.73.
FIG. 4. Mean values and 95 in Reproductive dynamics of the nectarivorous Geoffroy's tailless bat Anoura geoffroyi (Glossophaginae) in a highland Neotropical area of Brazil, with evidence of a mating period
FIG. 4. Mean values and 95% confidence intervals related to gonadosomatic index, epididymis-somatic index and Sertoli cell efficiency obtained during rainy and dry seasons and mating and non-mating periods of A. geoffroyi. Confidence intervals overlap indicates non-significant difference between the estimated values
How fitness consequences of early-life conditions vary with age in a long-lived seabird: a Bayesian multivariate analysis of age-specific reproductive values
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Data from: Control of parental investment changes plastically over time with residual reproductive value
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Data from: Individuals’ expected genetic contributions to future generations, reproductive value, and short-term metrics of fitness in free-living song sparrows (Melospiza melodia)
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What is the best fitness measure in wild populations? A case study on the power of short-term fitness proxies to predict reproductive value
<p>Fitness is at the core of evolutionary theory, but it is difficult to measure accurately. One way to measure long-term fitness is by calculating the individual's reproductive value, which represents the expected number of allele copies an individual passes on to distant future generations. However, this metric of fitness is scarcely used because the estimation of individual's reproductive value requires long-term pedigree data, which is rarely available in wild populations where following individuals from birth to death is often impossible. Wild study systems therefore use short-term fitness metrics as proxies, such as the number of offspring produced. This study obtained three frequently used short-term proxies for fitness obtained at different offspring life stages (eggs, hatchlings, fledglings and recruits), and compared their ability to predict reproductive values derived from the genetic pedigree of a wild passerine bird population. We used twenty years of precise field observations and a near-complete genetic pedigree to calculate reproductive success, individual growth rate and de-lifed fitness as lifetime fitness measures, and as annual de-lifed fitness. We compared the power of these metrics to predict reproductive values and lineage survival to the end of the study period. The three short-term fitness proxies predict the reproductive values and lineage survival only when measured at the recruit stage. There were no significant differences between the different fitness proxies at the same offspring stages in predicting the reproductive values and lineage survival. Annual fitness at one year old predicted reproductive values equally well as lifetime de-lifed fitness. However, none of the short-term fitness proxies was strongly associated with the reproductive values. In summary, the commonly short-term fitness proxies capture long-term fitness with intermediate accuracy at best, if measured at recruitment stage. As lifetime fitness measured at recruit stage and annual fitness in the first year of life were the best proxies of long-term fitness, we encourage their future use.</p>
What is the best fitness measure in wild populations? A case study on the power of short-term fitness proxies to predict reproductive value
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Diagnostic Value of Sperm DNA Fragmentation and Sperm Morphology for Assisted Reproduction Treatment
ClinicalTrials.gov study NCT02520869. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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