RESEARCH
morphology
micro-macro
biotic interactions
digitization
I use both a micro- and macro-scopic approach to unearth underlying abiotic and biotic drivers of phenotypic change and its ecological consequences across levels of biotic organization and across time.
micro & MACRO phenotypic evolution
Is the direction of evolution predictable within and across lineages? On modern and shorter timescales, it has been shown that lineages often change in directions of above average evolvability. It is unknown if this is true over longer timescales.
Here, we employ the fossil and modern record of the clonal organisms, bryozoans. Since every zooid in a colony is genetically identical to each other, we can disentangle genetic from environmental effects that give rise to phenotypic differences within and across colonies. We ask if the direction of greatest variability (i.e., what evolution can act upon) changes through time and across lineages.
We have taken SEMs 100s of colonies, containing 1000s of zooids, and applied machine learning to extract morphological trait data.
Abotic phenotypic evolution
Is it 'easier' to get bigger or smaller? It has been suggested that it is easier for a mammal to get smaller than larger, as it has already moved through that morphological space.
I tested this hypothesis by investigating rates of body size changes of the bushy-tailed woodrat, Neotoma cinerea. Since N. cinerea is known to decrease size when it is warmer and increase size when cooler, following Bergmann's Rule, we can ask if populations became changed body quicker during periods of warming (smaller) or cooler (larger).
We used a 36,000 year record of the Bushy-tailed woodrat across the western United States, consisting of 1000s of size estimates. We found no difference in rates of change, suggesting that these packrats do not follow the macroevolutionary pattern.
Biotic drivers of phenotypic evoltuion
Do marine species follow an optimal foraging model or a different model due to being in a 3D environment? Predator-prey relationships are well documented in terrestrial systems, leading to generalizable patterns about how prey biomass scales with predator body mass. These predator-prey relationships are starting to be tested in the marine realm, mainly on mammals.
I use modern shark diet data from FishBase and data on prey items to construct scaling relationships between shark body size and their prey body size. We find a strong, positive relationships in the maximum prey size and variance of prey body size as shark body size increases. This means that larger-bodied shark species can eat a larger and a wider variety of prey sizes.
Changes in diet may cause destabilizations in populations and change community structure. Humans, in particular, may contribute to prey abundance and therefore alter available prey items for consumption. We find that possible extinctions of certain prey items will cause drastic changes in shark diet and affect these allometric relationships significantly.
Future research (see below) will apply these allometric relationships to extinct communities, as well as investigate clade and geographic specific trends.
Does predation intensity drive changes in diversity in the past? Understanding bite-marks is important for understanding changes in attack rates by sharks and prey species of sharks.
It has been suggested that megalodon fed primarily on small marine mammals. To date, however, only a handful of papers show evidence of megalodon bites on small marine mammal bones.
I seek to use two lines of evidence to show the range of prey sizes megalodon could have taken. I will use allometric relationships based on modern sharks (see above) as well as evidence of predation by megalodon (see above).
Mobilizing digital records
Digitized records
As trait data is extracted from images or by researchers, there needs to be a findable and accessible repository. The Functional Trait Resource for Environmental Studies (FuTRES) project is a collaborative project among four universities (University of Oregon, University of Arizona, University of Florida, and Howard University). The key deliverables of FuTRES are a workflow for assembling functional trait data measured at the specimen level, a database to serve that data, and scientific publications demonstrating the utility of the assembled data.
I work with paleontologist, zooarchaeologists, and biologists to create a template that holds appropriate metadata fields and ontologize trait terms in their dataset. When possible, we use Darwin Core terms for template terms to make data more interoperable. We integrate with existing ontologies, such as the Uberon multi-species anatomy ontology (UBERON) for anatomical terms and the Ontology for Biological Attributes (OBA) for trait terms. All code can be found on GitHub.
Image data
Museums are digitizing data: both into databases and by imaging. It is vital to harness this data revolution by creating workflows that are reusable and interoperable as new tools and methods are rapidly developed.
The Biology-Guided Neural Networks is a multi-institute project (Seattle Children's Hospital, Drexel University, Tulane University, Virginia Tech University, National Ecological Observatory Network) seeking to extract traits from specimen images. The project combines machine learning on specimen images, collected by museums and iDigBio, with ontologies to help improve the performance of the machine learning algorithm.
