Selected Data Visualizations
NOAA Fisheries surveys off the west coast of the United States
I built this R Shiny App to visualize the timing (day of year, year) and spatial extent (latitude and longitude) of seven at-sea surveys that I've used to parameterize an end-to-end ecosystem model. This app has served a useful way to communicate the details of the surveys with collaborators and colleagues interested in our ecosystem model.
End-to-end ecosystem model results
I built this R Shiny App to visualize the effects of pyrosome blooms on other species. This app allows the user to select functional groups of interest and returns plots of end-to-end ecosystem model simulations.
Birds and bats avoid noise
These visualizations are from our 2021 paper in Nature Communications. A, B, and D depict raw data with conditional fitted lines from generalized linear mixed-effects models (with 95% CI). C depicts phylogenetically-controlled generalized least squares models.
Gomes et al. (2021) Nature Communications
Arthropods respond to river noise
These Bayesian generalized linear mixed-effects model (GLMM) visualizations are from our 2021 paper in Oikos. The figure on the left are point estimates with 80% and 90% credible intervals for each arthropod order and trap type combination. The figure below shows raw data and conditional interaction effects (estimated by a GLMM) between sound pressure level and sound frequency on two aquatic insects.
Gomes et al. (2021) Oikos
Orb-weaving spiders are more abundant in noise
These visualizations are from our 2021 paper in Functional Ecology. Figures (a)-(d) on the left depict point estimates and credible intervals from Bayesian generalized linear mixed-effects models. Silhouettes show direction of change. The figure on the right shows raw abundance data for two species of orb-weaving spiders and 100 posterior draws for the conditional effects of sound pressure level on abundance.
Gomes et al. (2021) Functional Ecology
Vertical stratification of bats in the neotropics
This visualization is from our 2020 paper in PeerJ. Here I show point estimates and credible intervals (80 and 90%), from a Bayesian generalized linear mixed-effects model, for the abundance of various bat species/groups in the canopy and understory.
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Gomes et al. (2020) PeerJ