Abstract

Food web models are one tool used by resource managers to evaluate changes in trophic interactions in response to changes in biotic and abiotic drivers in an ecosystem. Food web modeling provides a range of potential outcomes for managers to consider, to use in fisheries management. However, it is well known that food web models require large amounts of disparate data, which can amplify uncertainties in model output. Previous studies have leveraged stable isotope data to compare the trophodynamics between modeled trophic levels and in situ data. Here, we provide a new framework to aid users in evaluating the level of agreement between trophic levels generated from food web models and from stable isotope data using a Bayesian statistical approach. Using a previously developed food web model from coastal Louisiana as a case study, this paper presents an updated framework to use stable isotope analysis as a validation method for food web models. The validation process included Spearman-rank correlation analyses in addition to developing and using the first open-source food web model validation visualization tool, EcoTuneR, this webtool provides a more accessible way visualize the Bayesian derived trophic level probability curves with the food web model trophic levels. Reported correlation between the stable isotope trophic levels and the food web model trophic levels were comparable to previous studies (functional group Spearman-rank correlation, R=0.56, n=47, p > 0.001 | niche-aggregated R=0.77, n=17, p > 0.001 | age-aggregated R=0.53, n=34, p > 0.001). Results of the Bayesian model showed that 47% of the food web model species functional groups fell within the 95% credibility intervals of the Bayesian derived stable isotope trophic levels. Acknowledging that all modeling studies have limitations, this case study provides a framework and a new tool for researchers to better consider uncertainties in data and the inherent variability in coastal ecosystems.

Notes

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Graduation Date

2023

Semester

Spring

Advisor

Lewis, Kristy

Degree

Master of Science (M.S.)

College

College of Sciences

Department

Biology

Degree Program

Biology

Identifier

CFE0009864; DP0028117

URL

https://purls.library.ucf.edu/go/DP0028117

Language

English

Release Date

November 2024

Length of Campus-only Access

1 year

Access Status

Masters Thesis (Campus-only Access)

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Restricted to the UCF community until November 2024; it will then be open access.

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