Abstract:

The 50-hectare plot at Barro Colorado Island, Panama, is a 1000 meter by 500 meter rectangle of forest inside of which all woody trees and shrubs with stems at least 1 cm in stem diameter have been censused. Every individual tree in the 50 hectares was permanently numbered with an aluminum tag in 1982, and every individual has been revisited six times since (in 1985, 1990, 1995, 2000, 2005, and 2010). In each census, every tree was measured, mapped and identified to species. Details of the census method are presented in Condit (Tropical forest census plots: Methods and results from Barro Colorado Island, Panama and a comparison with other plots; Springer-Verlag, 1998), and a description of the seven-census results in Condit, Chisholm, and Hubbell (Thirty years of forest census at Barro Colorado and the Importance of Immigration in maintaining diversity; PLoS ONE, 7:e49826, 2012).

Description:

CITATION TO DATABASE: Condit, R., Lao, S., Pérez, R., Dolins, S.B., Foster, R.B. Hubbell, S.P. 2012. Barro Colorado Forest Census Plot Data, 2012 Version. DOI http://dx.doi.org/10.5479/data.bci.20130603

CO-AUTHORS: Stephen Hubbell and Richard Condit have been principal investigators of the project for over 30 years. They are fully responsible for the field methods and data quality. As such, both request that data users contact them and invite them to be co-authors on publications relying on the data. More recent versions of the data, often with important updates, can be requested directly from R. Condit (conditr@gmail.com).

ACKNOWLEDGMENTS: The following should be acknowledged in publications for contributions to the 50-ha plot project: R. Foster as plot founder and the first botanist able to identify so many trees in a diverse forest; R. Pérez and S. Aguilar for species identification; S. Lao for data management; S. Dolins for database design; plus hundreds of field workers for the census work, now over 2 million tree measurements; the National Science Foundation, Smithsonian Tropical Research Institute, and MacArthur Foundation for the bulk of the financial support.

File 1. RoutputFull.pdf: Detailed documentation of the 'full' tables in Rdata format (File 5).

File 2. RoutputStem.pdf: Detailed documentation of the 'stem' tables in Rdata format (File 7).

File 3. ViewFullTable.zip: A zip archive with a single ascii text file named ViewFullTable.txt holding a table with all census data from the BCI 50-ha plot. Each row is a single measurement of a single stem, with columns indicating the census, date, species name, plus tree and stem identifiers; all seven censuses are included. A full description of all columns in the table can be found at http://dx.doi.org/10.5479/data.bci.20130604 (ViewFullTable, pp. 21-22 of the pdf).

File 4. ViewTax.txt: An ascii text table with information on all tree species recorded in the 50-ha plot. There are columns with taxonomics names (family, genus, species, and subspecies), plus the taxonomic authority. The column 'Mnemonic' gives a shortened code identifying each species, a code used in the R tables (Files 5, 7). The column 'IDLevel' indicates the depth to which the species is identified: if IDLevel='species', it is a fully identified, but if IDLevel='genus', the genus is known but not the species. IDLevel can also be 'family', or 'none' in case the species is not even known to family.

File 5. bci.full.Rdata31Aug2012.zip: A zip archive holding seven R Analytical Tables, versions of the BCI 50 ha plot census data in R format. These are designed for data analysis. There are seven files, one for each of the 7 censuses: 'bci.full1.rdata' for the first census through 'bci.full7.rdata' for the seventh census. Each of the seven files is a table having one record per individual tree, and each includes a record for every tree found over the entire seven censuses (i.e. whether or not they were observed alive in the given census, there is a record). Detailed documentation of these tables is given in RoutputFull.pdf (File 1).

File 6. bci.spptable.rdata: A list of the 1064 species found across all tree plots and inventories in Panama, in R format. This is a superset of species found in the BCI censuses: every BCI species is included, plus additional species never observed at BCI. The column 'sp' in this table is a code identifying the species in the R census tables (File 5, 7), and matching 'mnemomic' in ViewFullTable (File 3).

File 7. bci.stem.Rdata31Aug2012.zip: A zip archive holding seven R Analytical Tables, versions of the BCI 50 ha plot census data in R format. These are designed for data analysis. There are seven files, one for each of the 7 censuses: 'bci.stem1.rdata' for the first census through 'bci.stem7.rdata' for the seventh census. Each of the seven files is a table having one record per individual stem, necessary because some individual... Visit https://dataone.org/datasets/urn%3Auuid%3Ae4c356db-3351-4b58-a744-ea213b25e2a2 for complete metadata about this dataset.

The LTERN Tropical Rainforest Plot Network Plot Details Data Package contains spatial coordinates from 20, 0.5 ha (100 m x 50 m) permanent rainforest plots in Northern Queensland, Australia. This is part of a much larger dataset that spans from 1971 to 2013 that is managed by CSIRO. The CSIRO permanent rainforest plots are located within 60 km of the north Queensland coast between Mackay (21.5ºS, 149ºE) and the Iron Range on Cape York Peninsula (12.5ºS, 143ºE). Due to the wide geographical range of the plots, no species dominate, although the families Lauraceae, Rutaceae and Myrtaceae contribute a large number of species. The data collected from the 20 plots provides an insight into the floristical composition, structure and long term forest dynamics of Australian tropical rainforests and allows direct comparisons to be made with long-term monitoring plots at a global scale. (Bradford, M.G., Murphy, H.T., Ford, A.J., Hogan, D. and Metcalfe, D.J., 2014) Long term stem inventory data from tropical rainforest plots in Australia. Ecology 95:2362. http://www.rainforest-crc.jcu.edu.au/publications/permanent_plots1.pdf. A synopsis of related data packages which have been collected as part of the Tropical Rainforest Plot Network’s full program is provided at http://www.ltern.org.au/index.php/ltern-plot-networks/tropical-rainforest

The LTERN Tropical Rainforest Plot Network Rainforest Tree Demographic Data contains stem measurement data for 1 of 20, 0.5 ha (100 m x 50 m) permanent rainforest plots in Northern Queensland, Australia from 2011 to 2013. This is part of a much larger dataset that spans from 1971 to 2013 that is managed by CSIRO. This data publication refers specifically to observations made at Plot EP43, and this data is accessible as a composite data package at the following location: Metcalfe, D; Murphy, H; Bradford, M; Hogan, D; Ford, A (2014): Tropical Rainforest Plot Network: Rainforest Tree Demographic Data, Northern Queensland, Australia, 2011–2013. Long Term Ecological Research Network. http://www.ltern.org.au/knb/metacat/ltern2.90.42/html The CSIRO permanent rainforest plots are located within 60 km of the north Queensland coast between Mackay (21.5ºS, 149ºE) and the Iron Range on Cape York Peninsula (12.5ºS, 143ºE). The plots have a rainfall range of 1200 to 3500 mm, represent eleven vegetation types, six parent materials, and range from 15 m to 1200 m above sea level. Except for minor disturbances associated with selective logging on two plots, the plots were established in old growth forest and– all plots have thereafter been protected. Plots were regularly censused and at each census the diameter at breast height (DBH) of all stems ≥10 cm DBH is recorded. Due to the wide geographical range of the plots, no species dominate, although the families Lauraceae, Rutaceae and Myrtaceae contribute a large number of species. The data collected from the 20 plots provides an insight into the floristical composition, structure and long term forest dynamics of Australian tropical rainforests and allows direct comparisons to be made with long-term monitoring plots at a global scale (Bradford, M.G., Murphy, H.T., Ford, A.J., Hogan, D. and Metcalfe, D.J. (2014). Long term stem inventory data from tropical rainforest plots in Australia. Ecology 95:2362. http://www.rainforest-crc.jcu.edu.au/publications/permanent_plots1.pdf. This is part of a much larger dataset that spans from 2004 to 2014; a synopsis of related data packages which have been collected as part of the Tropical Rainforest Plot Network’s full program is provided at http://www.ltern.org.au/index.php/ltern-plot-networks/tropical-rainforest.

African forest elephants (Loxodonta cyclotis) are ecosystem engineers that browse and damage large quantities of vegetation during their foraging and movement. Though elephant trail networks and clearings are conspicuous features of many African forests, the consequences of elephant foraging for forest structure and diversity are poorly documented. In this study in northeastern Gabon, we compare stem size, stem density, proportional damage, species diversity, and species relative abundance of seedlings and saplings in the vicinity of seven tree species that produce elephant-preferred fruits (“elephant trees”) relative to control trees that do not. Across 34 survey trees, with a combined census area of 2.04 ha, we recorded data on 26,128 woody stems in three sizes classes. Compared to control trees, the area around elephant trees had: a) a significantly greater proportion of damaged seedlings and a marginally greater proportion of damaged saplings (with 82% and 24% greater odds of damage, respectively); b) no significant difference in stem density or species diversity; and c) a significantly greater relative abundance of seedlings of elephant tree species. Increasing distance away from focal elephant trees was associated with significantly reduced sapling stem damage, significantly increased sapling stem density, and significantly increased sapling species diversity. Considered in sum, our results suggest that elephants can affect the structure and diversity of Afrotropical forests through their foraging activities, with some variation based on location and plant size class. Developing a more complete understanding of elephants’ ecological effects will require continued research, ideally with manipulative experiments.

Methods Study Area and Species Selection

    We conducted this study in the Ogooué-Ivindo province of northeastern Gabon. The region is dominated by lowland forest, and receives approximately 1700 mm of rain annually, with two rainy seasons (September-December and March-June). The study area includes the northern section of Ivindo National Park and the Ipassa Field Station, located within the park’s buffer zone.

    We selected seven tree species that produce fruits that have been observed with high frequency in elephant dung to serve as our focal elephant trees: Annonidium mannii, Baillonella toxisperma, Dacryodes buettneri, Gambeya lacourtiana, Klainedoxa gabonensis, Mammea africana, and Panda oleosa (White et al. 1993, Poulsen unpublished data). As many of these fruits are also consumed by apes and other mammals (White et al. 1993), we selected individual adult trees of each species that had visible impacts of elephant browsing around their trunks, in order to maximize the likelihood that the observed effects were caused by elephants rather than by other species. In total, we conducted plant surveys around three individuals of each elephant tree species (21 total trees), and 13 control trees with the following criteria: a) located within 200 m of one of the elephant trees, b) had similar canopy-level height as the elephant trees, and c) did not produce fruits commonly consumed by elephants. The control trees included two Alstonia boonei, two Celtis tessmannii, three Dialium pachyphyllum, one Lophira alata, two Newtonia sp., and three Pterocarpus soyauxii.

Survey Methods

    For each of the 34 survey trees, we recorded diameter at breast height (DBH, cm) and measured height (m) using a hypsometer. We also estimated canopy size by measuring the distance from the trunk to the end of the canopy at eight locations, and then calculating the corresponding area. At the base of each tree, we established three 5 x 40 m plots radiating out along the 0°, 120°, and 240° axes. We marked each plot with ribbon at the corners, and delimitated them with Topofil thread so that the sides of the plot were clearly distinguishable. We then subdivided each plot into eight 5 x 5 m quadrats.

    For each woody stem within the plots, we identified the plant to genus or species (see below) and recorded it as one of three size class categories for further measurement: seedling (0.5 m – 2 m in height), sapling (>2 m in height but < 6 cm DBH), or adult (≥6 cm DBH). We excluded lianas from our study with the exception of those within the seedling size class, which had not yet exhibited the typical climbing liana growth form and thus were as susceptible to elephant trampling damage as were tree seedlings. For seedlings, we measured the diameter of the plant at 5 cm and measured height from the ground to the terminal bud. For saplings and adults, we recorded only DBH. In the 0° plot, we measured all stems of all three size classes. In the 120° and 240° plots, we measured all saplings and adult trees, but only counted (and identified) the seedlings. In addition to species identification and stem measurement, we assessed all stems for the presence of damage, including breakages and irregular regrowth that may have occurred after being bent or snapped. All taxonomic identifications were conducted in the field by the same local botanist, in order to maintain consistency across survey trees. Plants that could not be identified to genus or species were assigned a unique code; all individuals that apparently belonged to the same unknown taxonomic group were given the same code, so that classification could be as specific as possible even when specific taxonomic identification was not possible.

Statistical Analysis

We used linear mixed (LMM) or generalized linear mixed (GLMM) models to test our hypotheses, including the focal tree species as a random effect in all models. For LMMs, we evaluated model fit by examining residuals and selected the best model based on the coefficient of determination, R2. For GLMMs, we employed the negative binomial distribution when the data were strongly overdispersed and selected among models using the Akaike Information Criterion (AIC). We performed statistical analyses in R 3.5 (R Development Core Team 2018) using the lme4 package (Bates et al. 2015), and followed the general recommendations for GLMMs outlined by Bolker et al. (2009).

To test whether elephant activity affects stem damage (H1) and density (H2), we modeled the proportion of elephant-damaged seedlings and saplings (binomial distribution) and the number of seedlings and saplings (negative binomial distribution) as functions of tree type (elephant tree vs. control tree) and distance from the tree (by quadrat), with species of the focal tree as a random effect.

To test whether elephant activity affects species diversity (H3), we computed Shannon-Wiener diversity indices (Magurran 2004) for seedlings and saplings around elephant trees and control trees. We then assessed the effect of tree type (elephant tree vs. control tree) on diversity for both size classes, with species of the focal tree as a random effect. We did not account for differing levels of taxonomic identification in this diversity analysis, as even unidentified stems were given a unique identifying code in the field, and were considered in the analysis as unique “species.”

To test whether elephant activity increases the proportion of elephant-dispersed species (H4), we modeled the proportion of seedlings and saplings of elephant tree species (binomial distribution) as a function of tree type (elephant tree vs. control tree) and distance from the tree (by quadrat), with species of the focal tree as a random effect. Although there are many other species that can be prominent in elephant diets, we considered only the relative abundance of the seven focal elephant tree species in this analysis.

We present repeated stem measurement data from 20, 0.5 ha (100 m x 50 m) permanent rainforest plots in northern Queensland, Australia from 1971 to 2013. The plots have a rainfall range of 1200 to 3500 mm, represent eleven vegetation types, six parent materials, and range from 15 m to 1200 m above sea level. Except for minor disturbances associated with selective logging on two plots, the plots were established in old growth forest and all plots have thereafter been protected. Plots were regularly censused and at each census the diameter at breast height (DBH) of all stems ≥10 cm DBH were recorded. Data is presented for 10998 individual stems with plot stem densities at establishment ranging from 476 to 1104 stems ha-1. Due to the wide geographical range of the plots, no species dominate, although the families Lauraceae, Rutaceae and Myrtaceae contribute a large number of species. Basal area values at establishment ranged from 28.6 to 63.3 m2 ha-1 and showed no trend of increasing or decreasing over time due mainly to regular disturbance and recovery from natural events such as cyclones. In addition to stems ≥10 cm DBH data, we present height data, floristic data from understory stems (≥50 cm height to <10 cm DBH), an auxiliary species list (including vines, epiphytes, ferns, grasses, herbs and other life forms), and a list of voucher specimens lodged in herbaria. The data collected from the 20 plots provides an insight into the floristics, structure and long term forest dynamics of Australian tropical rainforests and allows direct comparisons to be made with long-term monitoring plots at a global scale.

The Tropical Rainforest Plot Network Rainforest Understorey Floristic Data contains understorey floristic data from all 20 of its 0.5 ha (100 m x 50 m) permanent rainforest plots in Northern Queensland, Australia. Data was collected for all plots at the time of establishment before 1981 and data was again collected for half of the plots post-2000.

The CSIRO permanent rainforest plots are located within 60 km of the north Queensland coast between Mackay (21.5ºS, 149ºE) and the Iron Range on Cape York Peninsula (12.5ºS, 143ºE). The plots have a rainfall range of 1200 to 3500 mm, represent eleven vegetation types, six parent materials, and range from 15 m to 1200 m above sea level. Except for minor disturbances associated with selective logging on two plots, the plots were established in old growth forest and all plots have thereafter been protected. In conjunction with the Tree Demographic Data packages which have been collected by the Tropical Rainforest Plot Network, the data collected from these 20 plots provides an insight into the floristical composition, structure and long term forest dynamics of Australian tropical rainforests and allows direct comparisons to be made with long-term monitoring plots at a global scale. For further background data please refer to Bradford, M.G., Murphy, H.T., Ford, A.J., Hogan, D. and Metcalfe, D.J. (2014) "Long-term stem inventory data from tropical rain forest plots in Australia", Ecology, 95(8): 2362. Ecological Archives E095-209-D1. https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/14-0458R.1

A synopsis of related data packages which have been collected as part of the Tropical Rainforest Plot Network’s full program is provided at http://www.ltern.org.au/index.php/ltern-plot-networks/tropical-rainforest. Data can be sourced from: http://doi.org/10.4225/08/59475c67be7a4 or https://data.csiro.au/dap/landingpage?pid=csiro:6638

This data publication contains tree census data from 2004–2005 and 2014–2015 for six forest plots on Barro Colorado Island, Panama. These include the 25-ha and 10-ha plots, as well as four 6-ha tower plots: AVA, Drayton, Pearson, and Zetek. Censuses recorded all trees with a diameter at breast height (DBH) of 20 cm or larger. Census methods followed standard methods, as described in detail in detail in Condit (1998). The dataset was organized and quality-checked by Stuart Joseph Wright. Files included: Vertices_HM.csv provides the geographic coordinates of the four corners for each plot, outlining their boundaries. Coordinates are provided in UTM (WGS84; EPSG:4326). For each plot, two data files are included (12 files total): a file containing data from both census periods for the largest stem of each tree (e.g. 10ha_WorkingDraft_20150304.txt) a file containing data for additional stems, when present, from the 2004-2005 census. (e.g. 10ha_MULT.csv) Data dictionaries accompanying these files provide definitions for all columns, including explanations of any categorical abbreviations. BigPlotDataDictionary_main.txt BigPlotDataDictionary_mult.txt TreeCodeDefinitions.txt Meakem et al. (2024) describe these plots and present analyses of these data. Citation for this dataset: Wright, S. Joseph. 2024. Tree census data for the 25-ha, 10-ha and tower plots on Barro Colorado Island, Panama. Smithsonian Figshare. https://doi.org/10.25573/data.24531133 References Condit, R. 1998. Tropical Forest Census Plots: Methods and Results from Barro Colorado Island, Panama, and a Comparison with Other Plots. Springer-Verlag, Berlin, and R. G. Landes Company, Georgetown, TX, USA. Meakem, V., S. J. Wright, and H. C. Muller-Landau. 2024. Variation in Forest Structure, Dynamics, and Composition Across 108 ha of Large Forest Plots on Barro Colorado Island. In The First 100 Years of Research on Barro Colorado: Plant and Ecosystem Science, edited by H. C. Muller-Landau and S. J. Wright. Smithsonian Institution Scholarly Press, Washington, DC.

Physical site characteristics including aspect, elevation, and slope were recorded for each study plot and spatial coordinates were obtained from a global positioning system. Stand height was determined by averaging the heights of the first live woody individual encountered along each 10 m subplot in mechanically masticated plots as well as in the adjacent controls. Unfortunately height data was not collected from postfire plots in the prior study. The age of the stand prior to each mechanical disturbance was obtained from stem samples collected from the first two obligate seeding individuals encountered within controls and ranged from seven to sixty-four years across all mechanically masticated fuel treatments. The stand ages of postfire plots, on the other hand, were obtained from stem samples collected from nearby unburned vegetation or estimated from burned skeletons of obligate seeding species collected within the burn area. These sites ranged in age from twenty-four to fifty-one years old at the time of the 2003 wildfires. All data from mechanically treated study sites, including both treatment and control, were collected in the spring and summer of 2011 and 2012. The postfire data from the local case study were also collected in the spring and summer of 2011 and 2012 whereas the postfire data used in the regional comparison were collected in the spring and summer of 2005. The age of each plot at the time of sampling was determined by subtracting the year of the disturbance from the year of data collection.

Like many top consumers, parasites can regulate feeding of their prey via trait-mediated means. If parasites modify the feeding behavior of ecologically important grazers, they may have cascading effects on the structure and functioning of whole plant communities. The extent to which parasites can influence plant communities in this way is largely dependent on the strength of their behavioral alteration, their prevalence in host grazers, and the density of those hosts. Recent experiments and comparative surveys in southeastern USA salt marshes revealed that common larval trematode parasites suppress the per capita grazing impacts of the marsh periwinkle (Littoraria irrorata), generating a trophic cascade that protects foundational marsh plants from drought-associated overgrazing. Here, we conducted a field manipulation wherein we modified grazer host density while holding infection prevalence constant at an ecologically relevant level (20%) to determine whether the indirect, facilitative effects of parasites on marsh plants varied with the density of grazers. We found that parasites had significant positive impacts on marsh net primary productivity at moderate densities of snails (≥50 snails/ 0.5 m2), but that the positive effects of parasites were negligible at lower densities. Our results confirm the findings of previous studies that parasites can protect marsh plants from overgrazing at sufficiently high prevalence but show that their ability to do so depends on host density. Methods To determine how trematode infection influences marsh productivity at different levels of grazer host density, we conducted a field manipulation where we modified grazer host density while keeping infection prevalence constant. In June 2016 we established 0.5 m2 caged plots in a structurally homogenous swath of smooth cordgrass (Spartina alterniflora) marsh within the Hoop Pole Creek Clean Water Reserve in Atlantic Beach, North Carolina, USA (34°42’25.12” N, 76°45’1.14” W). The site was characterized by a relatively uniform elevation, very low snail densities (<1 adult snail per m2), and minimal visible signs of snail grazing. Snails used in the experiment were collected from a marsh die-off area where snails were abundant and infection prevalence was known to be high (Morton & Silliman 2020). Collected adult snails (shell length > 15mm) were transported back to the lab where their infection status was determined using a previously described cercariae shedding method that produces no false-positives (Morton 2018, Morton & Silliman 2020). We marked the shell of each infected snail with a red dot using a non-toxic, water-resistant paint pen while uninfected snails were marked with a blue dot (Henry & Jarne 2007). Snails were kept in separate aquaria and provisioned with damp cordgrass wrack for ~3 weeks until they were deployed in the field. Roofless cages (0.7 × 0.7 × 1 m) were constructed from untreated wooden posts and galvanized hardware mesh. A strip of copper tape was applied to the inner base of each cage, just above the sediment, to discourage snail escapes. Caged plots were spaced at least 1-m apart to assure independence of replicates—a design confirmed from past studies (Silliman & Zieman 2001, Silliman & Bertness 2002, Morton & Silliman 2020). Cages were buried 10 cm into the substrate to prevent snail and mud crab migration in and out of cages, and to inhibit belowground connections between plants inside and outside of the cages. Each plot was assigned to one of 8 snail density treatments (20, 40, 50, 60, 70, 80, 90, and 100 snails/per 0.5 m2) and one of two parasite addition treatments (0 and 20% infection prevalence). This resulted in 16 total treatments (n = 4 replicates per treatment). The snail densities used spanned the full range of adult snail densities observed within marsh die-off areas at this site. The 20% infection prevalence used in the experiment reflected the average naturally occurring summertime infection prevalence value for snails within local die-off areas (Morton & Silliman 2020). Uncaged plots marked at the corners with colored PVC flags (n = 4) and partial cages with one open side (n = 4) served as cage controls. Before the beginning of the experiment, we removed any mud crabs and snails from cages. We took measurements of several marsh characteristics in all plots at the beginning of the experiment. We counted all S. alterniflora stems and measured the heights of ten randomly selected stems in each plot. Random selection of stems was accomplished by tossing a plastic dowel into plots and measuring the first 10 stems touching the dowel. We constructed a height-to-biomass regression by collecting 30 cordgrass stems of varying sizes from the marsh directly adjacent to our experimental site. The stems were washed, their length measured, and were dried at 70˚C until they reached a constant weight. We used the resulting height-to-biomass regression to estimate standing cordgrass biomass in each plot. We also counted all juvenile (< 0.5 cm in diameter) and adult fiddler crab burrows at both the beginning and end of the experiment, because these organisms are known to influence cordgrass growth by oxygenating the sediment through burrowing (Bertness 1985, Daleo et al. 2007, Angelini & Silliman 2012, Gittman & Keller 2013, Raposa et al. 2018). Infected and uninfected snails within each plot were counted twice weekly and replaced as necessary to maintain the assigned snail density and infection prevalence treatments for the 3-month duration of the experiment. During monitoring, any predatory mud crabs found within plots were removed and their burrows plugged with marsh sediment to discourage successful re-occupation. We took final metrics of marsh vegetation characteristics in September 2016, 12 weeks after the beginning of the experiment. All snails were removed and dissected to confirm infection status. Because snail grazing had dramatically reduced stem densities in many plots, we were able to measure all stems within each plot to generate final plot biomass estimates by calculating and summing individual biomass estimates. Belowground biomass cores (15-cm diameter x 25-cm height) were also taken from the center of plots. Cores were washed and root material separated from rhizomes. Roots and rhizomes were dried to a constant weight at 70˚C and weighed. The possible influence of caging artifacts on marsh invertebrates and cordgrass growth was assessed using paired t-tests. Specifically, we compared the number of total fiddler crab burrows, final stem density, initial aboveground biomass, final aboveground biomass, change in aboveground biomass, and number of flowering stems between cage controls and open controls. Because fiddler crabs can positively influence cordgrass growth (Bertness 1985, Gittman & Keller 2013), we examined the relationship between burrow density and cordgrass stem density using a linear model. We validated model fit by examining model residuals, their distribution relative to fitted values, and normal Q-Q plots (car package, Chambers and Hastie 1992). In accordance with our hypotheses, models including the interaction between parasite infection (uninfected versus infected) and snail density (20, 40, 50, 60, 70, 80, 90, or 100 snails per 0.5 m2) were fit for each response metric (belowground biomass, aboveground biomass, shoot density, and number of flowering stems). Additionally, all models initially included fiddler crab burrow density as a covariate. However, this covariate was ultimately not significant for any model and was therefore dropped and each model re-run to include only the interaction term. We analyzed the interactive effects of infection status and snail density on belowground biomass using a two-way analysis of variance (ANOVA), with data log-transformed prior to analysis to meet model assumptions. Change in aboveground biomass was analyzed with a linear model, with initial aboveground biomass values for each plot subtracted from corresponding final values. Model assumptions were verified through assessments of homogeneity of variance (Levene’s test, P > 0.05) and examination of fitted residuals and normal Q-Q plots. The number of cordgrass inflorescences was modeled with a negative binomial generalized linear model (glmmTMB, Brooks et al. 2017). Model appropriateness and fit were confirmed through examination of simulated scaled residuals (DHARMa, Hartig 2022). For each response metric, the significance of the interaction model was examined in an Analysis of Deviance Table using Wald chi-square tests (car package, Chambers & Hastie 1992). For all models, Tukey’s post-hoc comparisons were used to assess pairwise differences for any significant treatment or interactive effects in the models (emmeans package, Lenth et al. 2021). All analyses were performed in the R statistical computing environment (v. 4.1.3; R Core Team 2018).