Unfortunately this paper isn't open access, but there is a copy available on my RG: www.researchgate.net/profile/Jord...

For those interested in learning more about phenotypic versions of the CRN, especially for life history traits, check out some awesome recent work and applications in collaboration with @lbliard.bsky.social

besjournals.onlinelibrary.wiley.com/doi/full/10....

ecoevorxiv.org/repository/v...

Ultimately, I hope the method will aid in better understanding how quantitative G x E is shaping multivariate trait evolution in response to dynamic social and ecological change (socio-eco-evo) on contemporary timescales.

I also demonstrate the utility of the CRN using an exceptional long-term dataset on meerkat behavior from work by @tomhouslay.bsky.social et al. (big thanks to Tom!) The CRN shows how specialization among cooperative tasks changes plastically in response to sex, age, dominance, and group size.

I use simulations to show that the CRN is not only a valid model for inferring complex environmental effects, but also that it can outperform standard methods at modest sample size in more idealized scenarios with a single environmental effect (climate warming).

There are great methods for estimating environmental effects on trait (co)variances, but their utility is limited for investigations of complex environmental effects in the field, esp. when repeated measurements and/or experimental breeding designs are unfeasible. The CRN answers this challenge.

While G and P can be highly stable under certain conditions, there are many cases where trait (co)variances are expected to rapidly respond to continuous environmental change across space and time. Explaining the dynamics of trait development and evolution requires understanding these relationships.

Big thanks to my excellent collaborators and coauthors on the project @ali--wilson.bsky.social, @dingemanselab.bsky.social, David Westneat, and Yimen Araya-Ajoy

Thanks, Tanay! 😃

Deep thanks and appreciation are owed to my coauthors, particularly those on Bsky @babeheim.bsky.social and @mgurven.bsky.social, as well as my PhD advisor @jaeggiadrian.bsky.social for their excellent collaboration and support.

These findings provide critical support for the social drive hypothesis, suggesting that IGEs can precipitate socio-eco-evolutionary feedback that drives rapid population growth and social evolution in human societies. Please see the paper for further details and discussion!

We also show that the local consequences of IGEs fluctuate across neighborhoods and communities in response to socioecological factors. This suggests that plasticity in reproductive cooperation is also being maintained by spatiotemporal variation in neighbors' effects on one another's fertility.

Results demonstrate that social effects on fertility promote the evolution of reproductive cooperation, while also playing an outsized role in determining the evolvability of individual fitness. In comparison to direct effects alone, IGEs are predicted to increase the pace of adaptation by 5x.

To test key predictions from the model, we combined cutting-edge Bayesian methods with the largest demographic dataset available on a contemporary foraging-oriented society, to analyze genetic effects on fertility variation among Tsimane women and their neighbors over a 20y period.

We combine evolutionary anthropological and quantitative genetic theory to propose a novel social drive hypothesis, which predicts that indirect (i.e. social) genetic effects (IGEs) on fitness play a key role in explaining our species' rapid social evolution and biodemographic success.