DOI: 10.5061/dryad.5dv41nskw
title: Data from: Long-term monitoring reveals how pondscape connectivity shapes the early spread of a biological invasion

description: Biological invasions lead to loss of biodiversity and ecosystem
 degradation, and a better understanding of their drivers is urgently
 needed. This is particularly true in the initial stages of an invasion,
 when the transition from local establishments to regional advancing fronts
 occurs. The abundances of the local population, demographic rates, and
 spatial conditions for dispersal have been proposed to interact in
 determining invasive species expansion, but little empirical evidence has
 been accumulated. This study examines the population dynamics of the
 American bullfrog (Aquarana catesbeiana), a globally invasive aquatic
 anuran, during the initial stages of its invasion process in a pond
 landscape within the Pampas grasslands of Uruguay. We evaluated how the
 spatial spread of the invasion was influenced by connectivity between
 invaded and uninvaded ponds, bullfrog abundance in invaded ponds,
 elevation gradients, and pond area. This analysis was based on an 11-year
 monitoring program (2012-2022) that captured the onset and initial stage
 of spatial expansion. Throughout the study period, the number of invaded
 ponds increased at a rate of 7.7 % per year. A model of invasion
 probability revealed that connectivity to invaded ponds and the population
 status of those ponds were key determinants of spread. Connectivity to
 previously invaded ponds interacted with bullfrog abundance to determine
 invasion probability. Ponds connected to invaded locations with
 intermediate bullfrog abundances showed the highest invasion likelihood,
 with colonization odds more than twice those associated with connections
 to ponds with low bullfrog abundances, whereas connections to ponds with
 high abundances had more moderate effects. Our results highlighted how
 ponds with intermediate bullfrog abundance play a crucial role in
 facilitating the invasion spread, this is most likely due to higher
 propagule release. Ponds with high bullfrog abundance were likely
 constrained by density-dependent effects, reducing the survival and
 dispersal of metamorphs. Our study highlights that prioritizing bullfrog
 eradication in systems with intermediate abundances may be more effective
 to prevent the expansion of the invasion. We note that landscape features
 and population demography within invaded areas could be more interrelated
 than commonly assumed and should be jointly considered in invasive species
 management strategies.
description: # Data from: Long-term monitoring reveals how pondscape connectivity
 shapes the early spread of a biological invasion Dataset DOI:
 [10.5061/dryad.5dv41nskw](https://doi.org/10.5061/dryad.5dv41nskw) ##
 Description of the data and file structure This dataset was collected to
 analyze the spatial and temporal dynamics of the American bullfrog
 (*Aquarana catesbeiana*) invasion in a pondscape in Aceguá, Uruguay,
 between 2012 and 2022. The dataset includes a unique identifier for each
 pond (pond_id), its invasion status (invaded; 0 = non-invaded, 1 =
 invaded), and a categorical bullfrog abundance level (abundance_level),
 defined as 0 for non-invaded ponds and as low (1), intermediate (2), or
 high (3) for invaded ponds. Abundance levels were derived from annual
 visual and auditory surveys, classifying invaded ponds based on the number
 of individuals observed and/or heard during each survey. For each year and
 non-invaded pond, the dataset includes weighted in-degree centrality
 values derived from spatial networks constructed separately for each
 abundance level (degree_ab_level_1, degree_ab_level_2, and
 degree_ab_level_3), which quantify potential connectivity with invaded
 ponds as a function of spatial proximity. Additionally, the dataset
 includes environmental and spatial attributes for each pond, including
 pond area (m^2^), elevation (m a.s.l.), and an elevation gradient
 calculated using the five nearest neighboring ponds. ### Files and
 variables #### File: data_ms.csv **Description:**  ##### Variables *
 pond_id: Unique identifier for each pond. * invaded: Binary variable
 indicating invasion status of the pond (0 = non-invaded, 1 = invaded). *
 abundance_level: Categorical variable describing bullfrog abundance at
 invaded ponds. Values are defined as 0 = non-invaded pond, 1 = low
 abundance, 2 = intermediate abundance, and 3 = high abundance. Abundance
 levels were derived from annual visual and auditory surveys. * year: Year
 of observation (2012–2022). * degree_ab_level_1: Weighted in-degree
 centrality of each non-invaded pond derived from the network including
 invaded ponds with low bullfrog abundance. This metric quantifies
 potential connectivity based on spatial proximity. *
 degree_ab_level_2: Weighted in-degree centrality of each non-invaded pond
 derived from the network including invaded ponds with intermediate
 bullfrog abundance. * degree_ab_level_3: Weighted in-degree centrality of
 each non-invaded pond derived from the network including invaded ponds
 with high bullfrog abundance. * area: Pond area, expressed in square
 meters (m^2^). * elevation: Elevation of the pond centroid, expressed in
 meters above sea level (m a.s.l.). * elevation_gradienent: Elevation
 gradient calculated as the mean elevation difference between the focal
 pond and its five nearest neighboring ponds. Positive values indicate a
 higher relative position and negative values indicate a lower relative
 position. **Missing values** * Missing values are indicated as NA. ##
 Code/software The data can be viewed and analyzed using the free and
 open-source software R (R Core Team, 2024), and can also be opened with
 standard spreadsheet software. Spatial network construction and analysis
 were conducted in R using the igraph package. Annual networks were built
 separately for each bullfrog abundance level (low, intermediate, and
 high), including all non-invaded ponds and only invaded ponds of the focal
 abundance level. Networks were directed and bipartite, linking invaded to
 non-invaded ponds, with links weighted by the inverse of Euclidean
 distance. Weighted in-degree centrality was calculated for each
 non-invaded pond and abundance level and compiled into the final dataset.
 Statistical analyses were performed in R using generalized linear models
 (GLMs). Count and binomial models were fitted using appropriate error
 distributions (including negative binomial and Conway–Maxwell–Poisson),
 primarily implemented with the glmmTMB package. Model diagnostics were
 conducted using DHARMa, and model selection was based on Akaike’s
 Information Criterion (AIC). No proprietary software is required to view
 or analyze the data. ## Access information Not applicable. The data are
 original and have not been derived from other publicly accessible sources.
 No alternative repositories or external data sources are associated with
 this dataset.