Tree species richness suppresses red imported fire ant invasion in a subtropical plantation forest
Data files
Sep 08, 2024 version files 51.06 KB
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AntCommunity.csv
13.02 KB
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Datasets_for_SEMs.csv
34.33 KB
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README.md
3.71 KB
Abstract
The ecological resistance to the invasion of alien species has been extensively researched at the within-trophic level; however, the effect of cross-trophic interactions remains largely unknown. The red imported fire ant (RIFA, Solenopsis invicta) is a globally invasive species, and a cross-trophic interaction study between RIFAs and plants may provide a more comprehensive understanding and management of its invasion. We hypothesized that the diversity of trees, which are the primary producers supporting terrestrial ecosystems, could suppress the invasion of omnivorous RIFAs by regulating food resources (e.g., plants and arthropods) and the microenvironment (e.g., soil microclimate) as well as enhancing competition with other carnivorous ants. To test this hypothesis, we investigated RIFA abundance and mound numbers across tree species richness gradients in a subtropical plantation forest. Using structural equation models, we discovered that tree species richness had contrasting effects on RIFA abundance and mound numbers. RIFA abundance directly decreased with higher tree species richness because of the dilution effect (i.e., lower abundance of preferred tree species). In contrast, RIFA mound numbers indirectly increased with tree species richness via an increase in arthropod abundance (excluding ants) (i.e., more ample food resources for RIFA). Moreover, warmer soil temperatures decreased RIFA abundance, whereas other carnivorous ant species richness and abundance negatively affected RIFA abundance and mound numbers.
Synthesis and applications: Our findings demonstrate that the ecological resistance to RIFA invasion is influenced by tree species richness in a complex manner; specifically, the relative strengths of the effects of tree species characteristics, native competitors, and environmental conditions are likely to impact the power of ecological resistance. This highlights the importance of cross-trophic interactions underlying the resistance of complex ecosystems to alien species invasion; therefore, maintaining diverse tree communities in controlled systems (e.g., suburban and urban green spaces) should be viewed as a potential strategy for controlling RIFA invasion.
README: Tree species richness suppresses red imported fire ant invasion in a subtropical plantation forest —— Ant community and microenvironment: Open source data
https://doi.org/10.5061/dryad.kh18932gw
We have submitted our raw data of ant community (AntCommunity.csv) which includes the numbers of different ant species in each plot. The the name of each column was the species name. We also submitted our data for structural equation model building (Datasets for SEMs.csv) which includes the values of all variables mentioned in the article.
Description of the data and file structure
Files and variables
File: AntCommunity.csv
Description: This dataset includes the numbers of different ant species in each plot. The the name of each column was the species name.
Variables
- PlotNum: The identifier number of each plot
- Solenopsis invicta: Ant species name
- Tetramorium bicarinatum : Ant species name
- Nylanderia bourbonica: Ant species name
- Nylanderia ogasawarensis : Ant species name
- Monomorium triviale: Ant species name
- Tapinoma melanocephalum: Ant species name
- Diacamma rugosum : Ant species name
- Pheidole nodus: Ant species name
- Pheidole cf. sauteri: Ant species name
- Camponotus humilior : Ant species name
- Brachyponera chinensis: Ant species name
- Pheidole sp.: Ant species name, sp. means uncertain name
- Cardiocondyla minutior : Ant species name
- Ectomomyrmex javanus: Ant species name
- Strumigenys emmae : Ant species name
- Polyrhachis dives: Ant species name
- TSR: Tree species richness, i.e. the total number of tree species
File: Datasets_for_SEMs.csv
Description: This dataset includes the values of all variables mentioned in the article, especially for structural equation model building. Soil temperature, moisture and electrical conductivity were Z-transformed to eliminate weather impact. Some variables may not be used in the final analyses of our article.
Variables
- Num: Plot identifier number in whole dataset
- Plot: Plot identifier number in each block
- Comb: Tree planting combination type, the number of character represents the number of species.
- PSR: Tree species richness, i.e. the total number of tree species
- CNASR: Other carnivorous ant species richness
- CNAab: Other carnivorous ant abundance
- RIFAab: Red important fire ant abundance
- RIFAabsent: Individual of red important fire ant present (1) and non-present (0)
- MoundN: Red important fire ant mound number
- MoundAbsent: Mound of red important fire ant present (1) and non-present (0)
- SoilMoisture: Z-transformed soil moisture, the primary unit is percentage (%)
- SoilTemperature: Z-transformed soil temperature, the primary unit is Celsius (℃)
- EC: Z-transformed soil electrical conductivity, the primary unit is us/cm
- MoundAveSize: Red important fire ant mound average size, the unit is cubic centimeter
- PlantDensity: The tree density in each plot, i.e. the total number of tree individuals
- TreeMortality: The tree mortality rate in each plot, the unit is percentage (%)
- OtherInsectsAb: Abundance of other carnivorous arthropods
- OtherInsectsSR: Species richness of other carnivorous arthropods
- GM: Individual number of Erythrophleum fordii
- MWS: Individual number of Pinus massoniana
- LS: Individual number of Castanopsis fissa
- MZ: Individual number of Castanopsis carlesii
- SDY: Individual number of Elaeocarpus sylvestris
- MH: Individual number of Schima superba
- TDQ: Individual number of Ilex rotunda
- XZ: Individual number of Cinnamomum camphora
- Pd: The Faith’s phylogenetic diversity of ants in each plot
Methods
Dataset Collection
For both two datasets, they contains the results of filed survey for tree, ants and micro-environment.
1 Study site and RIFA invasion status
The biodiversity-ecosystem subtropical experimental plantation forest (Guangdong Province, China; 23°30′ N, 111°49′ E) was established in 2018 (no need for permission for fieldwork). The experimental plantation forest was constructed on flat ground in an abandoned field (rice paddy prior to 2013), which was dominated by herbaceous plant species. The regional climate is characterized as a subtropical moist monsoon climate, with an average annual temperature of 19.6 °C and monthly average temperatures ranging from 10.6 °C (January) to 28.4 °C (July). The average annual precipitation is 1,744 mm, approximately 79% of which occurs between April and September. The natural regional vegetation is dominated by subtropical evergreen broad-leaved forests.
The entire experimental plantation forest consisted of four tree species richness levels in eight blocks (32 x 20 m), serving as replications. A total of 20,480 trees were initially planted, and 40 plots were established for each block. Each plot was a 4 x 4 m square area, and 64 native tree seedlings were planted, with each seedling spaced 0.5 m apart. In each plot, different numbers of tree species (1, 2, 4, 8) were planted in equal individual proportions at different richness levels. Each block consisted of four tree richness levels, each with a varying number of plots. This included mono-species (8 plots per block), two-species (18 plots per block), four-species (12 plots per block), and eight-species (2 plots per block) blocks. The positioning of trees within each plot and the number of plots per block followed randomized designs. Each tree was tagged with a unique identification number.
Because the reclamation of this field was conducted to establish the experimental plantation forest, all visible RIFA mounds were destroyed by deeply churning the soil using farming machinery and employing broad irrigation in the study region (RIFA had already invaded this region prior to the establishment of our experimental plantation forest). Therefore, the progress of RIFA invasion was reset in our study region at that time. The perimeter of each study block was delineated by a square soil ridge of approximately 1 m in width and about 1.5 m in height. The entire study experimental plantation forest was surrounded by a buffer region. The buffer was a square band with a width of approximately 3 m. The northern and western sides consisted of concrete pavements, whereas the eastern and southern sides comprised wild weeds and shrubs. All these measures mitigated the adverse impacts of surrounding farmland on the study experimental plantation forest.
2 Tree survey data
Annual tree surveys were conducted from 2018 to 2022, and the tree height (TH), diameter at breast height (DBH), and status (alive or dead) were recorded. Given that some trees died during the survey, we utilized the species richness of the remaining living trees from 2022 for further analyses. Therefore, the realized tree species richness consisted of the following: 1 (69 plots), 2 (137 plots), 3 (11 plots), 4 (85 plots), 7 (8 plots), and 8 (8 plots).
3 Ant survey and specimen collection
The ant survey (no requirement for ethical approval) was conducted between July 2022 and August 2022. First, we checked each plot to record the total number of visible RIFA mounds along with their length, width, and height to estimate their size from the beginning of July to the middle of August:
Csize=2abcπ/3 (eqn 1)
where Csize is the estimated mound size while a, b, and c are the mound length, width, and height, respectively. The observed number of mounds represented the sum of old and newly constructed mounds. Each RIFA mound condition was verified by puncturing the mound from the top using elongated metal tweezers. If there was a substantial amount of workers, estimated to be more than 100, dashing out within 10 s, we recorded such a mound as a “RIFA-present mound.” If there were no workers or only one or two workers appearing slowly, we recorded such a mound as a “RIFA-absent mound.” The majority of recorded mounds were “RIFA-absent mounds” (they did not contain RIFA but still had complete structure); therefore, the number of mounds in our study was considered an independent index of RIFA invasion in relation to RIFA abundance. The absence of RIFA from almost all mounds can be attributed to a flood that occurred during the first part of the year (2022). During this event, flood water surged into the study region and inundated the soil of the entire area.
Following the mound survey, ant specimen collection was initiated. Because the size of each plot was small, a baited pitfall trap was placed in the center of each plot to collect ants and other arthropods. However, plots located at the block margin were excluded to eliminate any boundary effects. The trap was set up once in the middle of August and left for 3 days, ensuring that there was no rainfall following the mound survey. The pitfall trap consisted of a piece of hot dog as bait and a 75% ethanol solution as the fixation fluid. To test our hypothesis regarding food resources and to facilitate the collection of other arthropods, we modified our pitfall trap by adding three larger holes (10-mm diameter) on the front and back sides. It is noted that because a hot dog was used as bait, we probably collected more carnivorous ants. The aim of the ant investigation was to estimate the competitor effect; therefore, the hot dog bait was suitable for our study. The term “other arthropods” hereafter mainly refers to carnivorous arthropods.
All collected specimens were preserved in a 75% ethanol solution, and we counted the total number of individuals per species per trap. Although other arthropods were identified into morpho-species, we identified ants at the species level in two steps: 1) We conducted a preliminary identification of each specimen by comparing it directly with descriptions and illustrations from AntWeb (www.antweb.org) and Ants-China Web (www.ants-china.com); 2) We used the TIANamp Genomic DNA Kit (TIANGEN BIOTECH (BEIJING) CO., LTD) to extract fragmented COI (primers: LCO1490 and HCO2198) from at least three specimens per species. We then conducted a search using the Basic Local Alignment Search Tool (BLAST) in the National Center of Biotechnology Information (NCBI) to select and compare results with the highest maximum score obtained from morphological identification. When a mismatch between morphological and molecular identification was observed, we reviewed the data and opted for the morphological identification results.
Phylogenetic diversity (PD) was calculated for ants, which showed a high correlation with species richness. Consequently, PD was excluded from subsequent analyses (Supporting Information, Section 2).
4 Soil microclimate survey
RIFAs are ectothermic soil fauna that are highly sensitive to moisture changes; therefore, soil microclimate indexes can serve as reliable proxies for predicting their activities. We randomly selected five points in each plot and used a portable soil sensor (Generation I, PURUISEN company, China) to measure soil temperature and moisture at a 5-cm depth on days without rain during the RIFA mound survey (completed on the day before the pitfall sampling). Average values per plot were used for further analyses. It typically took 1 day (half of measurements in the morning and half in the afternoon) to measure the soil microclimate for each block. Therefore, a total of 8 days were spent to complete the measurements for all eight blocks. However, the slight change in soil conditions between these days likely rendered the measurements on different days incomparable. In addition, slight differences in soil conditions probably existed between the morning and afternoon. To address the incomparability, we standardized the soil microclimate within each block by calculating the Z-scores of temperature and moisture. The Z-scores were calculated by dividing the mean values measured during the same period (e.g., in the morning or afternoon) by their corresponding standard deviations.