Brandon M. Lind
brandonlind.bsky.social
Brandon M. Lind
@brandonlind.bsky.social
Current postdoc with @jillwegrzyn.bsky.social at UConn. Previously with @drk-lo.bsky.social at Northeastern; previously with @sallyaitken.bsky.social at UBC. PhD with Andrew Eckert at VCU.
>900 propagated feltleaf willow (Salix alaxensis) cuttings heading back to Fairbanks tomorrow from @toolikfieldstation.bsky.social to overwinter before planting two common gardens next spring. #EVOME #arcticresearch
July 17, 2025 at 4:11 AM
July 13, 2025 at 5:24 PM
Excited to present recent work on genomic offsets -
@sse-evolution.bsky.social #Evol2024. Unfortunately I couldn't be there in person, so if you missed my recorded talk, here are QR codes to the manuscripts and here is a link to the talk bit.ly/3WJGVVp
July 29, 2024 at 2:29 PM
5 - while metapopulations with high levels of LA produced generally well-performing models for predicting within-landscape fitness, performance across all datasets decreased when projecting to novel climates that are increasingly differentiated from landscape values (x-axis) 9/n
February 10, 2024 at 5:04 PM
4- the choice of environmental variables used in model training can impact performance. For instance adding nuisance environmental variables unrelated to the sources of selection pressure often decrease performance, implicating potential limits of some methods vs others (8/n)
February 10, 2024 at 5:04 PM
3 - the choice of genetic markers used has varied and minor effects on model performance, where adaptive markers provide some minimal advantages, but not universally or by great margins (7/n)
February 10, 2024 at 5:04 PM
2 - the spatial arrangement of environmental variables to which the metapopulation adapts can also affect performance, even when contrasting landscapes have metapopulations that reach similar levels of local adaptation (6/n)
February 10, 2024 at 5:03 PM
We explore the limits of five genomic offset methods across these scenarios. While there were performance difference across methods, we discuss the overarching findings as follows (note flipped y-axes!!): 1 - the degree of local adaptation (LA) drives model performance (5/n)
February 10, 2024 at 5:03 PM
We train genomic offset methods using genomic and environmental data from these sims. We evaluate model predictions within in silico common gardens in each of the 100 home environments. Well-performing models should have negative relationships between offset and fitness (4/n)
February 10, 2024 at 5:03 PM
We used simulation data from Lotterhos (2023) to better understand these limits. Described in detail in the manuscript, these simulations represent biological realism of empirical data as well as challenging cases in which model performance of may suffer. (3\n)
February 10, 2024 at 5:02 PM
Very happy to announce the release of my newest manuscript with Katie Lotterhos! "The limits of predicting maladaptation to future environments using genomic data (1 / n)
February 10, 2024 at 5:02 PM