DOI: 10.5061/dryad.tqjq2bw9k
title: Effects of an invasive top predator on Ecosystem structure and function in a Graminoid Marsh food web

description: In this era of global change, understanding the effects of human-mediated
 dispersal of organisms has become a priority for ecological and
 conservation research. Non-native species introductions can result in
 biological invasions that have substantial impacts on native population
 abundances, community interactions, and ecosystem processes. Over the past
 twenty years, the establishment and subsequent invasion of the Burmese
 python (Python molurus bivittatus) in the Greater Everglades ecosystem has
 become a topic of concern amongst land managers and conservation
 practitioners in southern Florida. The objective of this study is to
 assess community-wide impacts of the python on the native food web as well
 as the function of the Greater Everglades Ecosystem by attempting to
 answer two central questions: 1) What functional role does the python
 occupy within the native food web and 2) is there a shift in overall food
 web structure and/or function post-invasion? We used ecological network
 analysis and an extensive diet dataset to quantify the python’s trophic
 role relative to other residents in the food web as well as compare
 ecosystem characteristics between pre- and post-invaded network models.
 Our findings demonstrate that the python functions similarly to the
 largemouth bass, another torrid, highly invasive predator that exhibits
 strong top-down impacts within its aquatic habitats. The python also
 behaves as a dominant predator akin to the Florida panther, primarily
 affecting native mammal populations through top-down predation effects,
 displacing other top predators within the food web, and altering patterns
 of carbon flow along the food chain. Finally, at the system scale,
 although we see increasing trends in ecosystem activity and decreasing
 trends in ecosystem structure and organization, functional metrics remain
 relatively stable pre- and post- invasion indicating functional
 resilience. Our findings provide a holistic assessment of the Burmese
 python invasion on the native community and function of the greater
 Everglades graminoid marsh ecosystem.
description: # Effects of an invasive top predator on Ecosystem structure and function
 in a Graminoid Marsh food web Dataset DOI:
 [10.5061/dryad.tqjq2bw9k](10.5061/dryad.tqjq2bw9k) ## Description of the
 data and file structure Python diet data was collected through gut content
 analysis of individual Burmese pythons collected across its invasive range
 across South Florida. Remaining data was acquired from the existing
 literature. The three food web networks were acquired from work conducted
 by Dr. Ulanowicz and his colleagues. Finally, biomass estimates were
 acquired from various existing sources which have been cited in respective
 file.  ### Files and variables #### File: compartment\_mass.csv
 **Description:** Dataset of the average mass of each species compartment
 used in the food web networks that was found in the python's diet 
 ##### Variables * Species: Name of each species compartment used in the
 food web network  * avg.mass: Average mass in grams per individual of each
 species compartment * Full.Reference: Reference where mass value was
 acquired #### File: CYPDRY.xlsx **Description:** A 68-component budget of
 the carbon exchanges measured in gC/m2/yr occurring during the dry season
 in the cypress wetlands of South Florida has been assembled. Formatted to
 be read in as a network class required for the enaR package. #####
 Variables * An n×n flow matrix oriented row-to-column * a vector of inputs
 * a vector of respirations * a vector of exports * a vector of outputs,
 which are respirations plus exports * a vector of biomass or storage
 values * Living = logical vector indicating if the node is living (TRUE)
 or non-living (FALSE). Indicated in the formatted data by the number of
 compartments and number of living compartments, where the n position in
 the flow matrix will be assigned living for n = 1: number of living
 compartments and the remaining nodes will be assigned non-living. For this
 reason, it is critical to have all non-living nodes designated at the tail
 end of the nxn flow matrix.  #### File: GRAMDRY.xlsx **Description:** A
 66-component budget of the carbon exchanges measured in gC/m2/yr occurring
 during the dry season in the graminoid ecosystem of South Florida has been
 assembled. Formatted to be read in as a network class required for the
 enaR package. ##### Components * An n×n flow matrix oriented row-to-column
 * a vector of inputs * a vector of respirations * a vector of exports * a
 vector of outputs, which are respirations plus exports * a vector of
 biomass or storage values * Living = logical vector indicating if the node
 is living (TRUE) or non-living (FALSE). Indicated in the formatted data by
 the number of compartments and number of living compartments, where the n
 position in the flow matrix will be assigned living for n = 1: number of
 living compartments and the remaining nodes will be assigned non-living.
 For this reason, it is critical to have all non-living nodes designated at
 the tail end of the nxn flow matrix.  #### File: nodes.csv
 **Description:** Csv that contains the list of nodes within the GRAMDRY
 network with the associated properties of each node's effective
 trophic level and mean body mass formatted to load as a community object
 and conduct network analyses from the cheddar package in R.  #####
 Variables * node: species compartment name * category: species categorized
 as producer, invertebrate, ectothermic vertebrate, or endothermic
 vertebrate. Those that are blank are either non-living or microbial
 compartments * ETL: effective trophic level derived from ecological
 network analysis of the GRAMDRY network from the enaR package * M:
 estimated average biomass (gC/m2/yr) #### File: properties.csv
 **Description:** Csv that defines items applicable to the community as a
 whole, must have column named title, formatted to load as a community
 object and conduct network analyses from the cheddar package in R. #####
 Variables * title: Graminoid system  * M.units: measurement unit of carbon
 exchange #### File: MANGDRY.xlsx **Description:** A 94-component budget of
 the carbon exchanges measured in gC/m2/yr occurring during the dry season
 in the mangrove ecosystem of South Florida has been assembled. Formatted
 to be read in as a network class required for the enaR package. #####
 Variables * An n×n flow matrix oriented row-to-column * a vector of inputs
 * a vector of respirations * a vector of exports * a vector of outputs,
 which are respirations plus exports * a vector of biomass or storage
 values * Living = logical vector indicating if the node is living (TRUE)
 or non-living (FALSE). Indicated in the formatted data by the number of
 compartments and number of living compartments, where the n position in
 the flow matrix will be assigned living for n = 1: number of living
 compartments and the remaining nodes will be assigned non-living. For this
 reason, it is critical to have all non-living nodes designated at the tail
 end of the nxn flow matrix.  #### File: pythondiet2.csv
 **Description:** Diet data of the Burmese python (*Python bivittatus*)
 collected from gut content analysis of pythons collected within its
 invaded range of South Florida. ##### Variables * ID: Identifier of each
 individual python * Class: taxonomic class of prey item * Order: taxonomic
 order of prey item * Family: taxonomic family of prey item * Genus:
 taxonomic genus of prey item * Species: taxonomic species of prey item *
 SciName: scientific name of the prey item based on its binomial
 nomenclature * CommonName: common name of the prey item * PreyGroup: prey
 item grouped as categories of mammal, bird, or reptile and as
 unidentifiable or other withen these groupings. * PreyMass: estimated
 average mass in grams of prey item ## Code/software
 [https://github.com/sleclare/ena-BurmesePython-invasion](https://github.com/sleclare/ena-BurmesePython-invasion) ## Access information This dataset, as well as other information necessary to reproduce the results in the associated publication, are available on GitHub. We recommend that the dataset is accessed through the following link: [https://github.com/sleclare/ena-BurmesePython-invasion](https://github.com/sleclare/ena-BurmesePython-invasion) ### Version changes **20-May-2025:** Updated pythondiet2.csv to retain only the columns used in analyses