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Relationship: 2147
Title
Increased, Male Biased Sex Ratio leads to Decrease, Population growth rate
Upstream event
Downstream event
AOPs Referencing Relationship
| AOP Name | Adjacency | Weight of Evidence | Quantitative Understanding | Point of Contact | Author Status | OECD Status |
|---|---|---|---|---|---|---|
| Aromatase inhibition leads to male-biased sex ratio via impacts on gonad differentiation | adjacent | Low | Brendan Ferreri-Hanberry (send email) | Under Development: Contributions and Comments Welcome | EAGMST Under Review | |
| Androgen receptor agonism leading to male-biased sex ratio | adjacent | Evgeniia Kazymova (send email) | Open for citation & comment | EAGMST Under Review |
Taxonomic Applicability
Sex Applicability
| Sex | Evidence |
|---|---|
| Male | High |
Life Stage Applicability
| Term | Evidence |
|---|---|
| Adults | High |
Long-term maintenance of viable populations is dependent on the nature of interactions between males and females. One commonly used metric for capturing these interactions is evaluation of deviations from normal of the relative number of males versus females in a population. The ratio of males versus females needed for successful sexual reproduction varies by taxa, with some species requiring a one-to-one relationship, while in other species far fewer males than females may suffice in terms of producing an adequate number of fertile embryos to maintain a population. However, even in species potentially requiring fewer males than females to maintain a viable population, at some point a male-biased population could become problematic in terms of having an adequate number of males to fertilize eggs produced by females or, in the longer term, ensure a robust level of genetic diversity in a population. Further, in situations where a population is male-biased relative to conditions considered normal for a given species, overall productivity may be negatively impacted due to fewer females being available to produce eggs.
| ID | Experimental Design | Species | Upstream Observation | Downstream Observation | Citation (first author, year) | Notes |
|---|
| Title | First Author | Biological Plausibility |
Dose Concordance |
Temporal Concordance |
Incidence Concordance |
|---|
Biological Plausibility
Dose Concordance Evidence
Temporal Concordance Evidence
Incidence Concordance Evidence
Uncertainties and Inconsistencies
Studies at the population level can be quite challenging in terms of required resources and, given the number of variables that might simultaneously influence a population, interpretation of results. Consequently, evaluation of population status in the context of adverse outcome pathways often relies upon model predictions that almost always are applicable only to a limited number of--sometimes one--species because of requirements associated with model parameterization. Given this, although it is entirely reasonable from an evolutionary perspective that male-biased sex ratios will negatively impact populations of a given species, it can be difficult to fully assess what this impact may be.
Population status can be impacted by a multitude of interacting biotic and abiotic variables, some of which could entirely supersede the effects of a male-biased sex ratio. For example, under conditions of severe food limitations or a regime of extreme temperature there may be no production of young irrespective of male:female sex ratios.
For a given species the linkage between a male-biased population and impacts on overall status of that population can be highly quantitative. For example, the model described by Miller et al. (2022) is designed specifically to provide quantitative forecasts of the effects of different male:female sex ratios on population status in fathead minnows. However, parameterization of any population model for vital rates (survival, reproductive output) is necessarily species-specific so, even if a given model construct is potentially suitable for a wide range of species, a significant amount of taxa-specific biological information might be needed to produce reliable quantitative predictions of effects.
Response-response Relationship
Brown et al. (2015) and Miller et al. (2022) provide examples for zebrafish and fathead minnows, respectively, of approaches used to establish quantitative response-response relationships between male-biased sex ratios and population size/trends. In general, however, population models almost always rely on female productivity rather than male contributions to forecast population status.
Time-scale
The time-scale for this KER is entirely dependent on the life-cyle of the organism of interest. Small, short-lived animal species could experience population-level alterations due to biased sex ratios in days to weeks, while impacts on larger, long-lived species may take years to decades.
Known Feedforward/Feedback loops influencing this KER
It is difficult to define what form a feedforward/feedback loop might take for this KER. This would likely largely be a function of the stressor causing a male-biased population. If the stressor was short-term (e.g., affecting one age cohort) the situation might be self-correcting, as opposed to a longer-term stressor that continually causes a male-biased sex ratio, which theoretically should usually result in population extirpation.
Any sexually-reproducing species theoretically could experience male-biased sex ratios and consequent population-level effects.