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Data from: Canals as invasion pathways in tropical dry forest and the need for monitoring and management

Citation

Asth, Matheus S.; Rodrigues, Renato G.; Zenni, Rafael D. (2021), Data from: Canals as invasion pathways in tropical dry forest and the need for monitoring and management, Dryad, Dataset, https://doi.org/10.5061/dryad.r4xgxd2cm

Abstract

  1. Linear infrastructure intrusions are common around the world to meet the needs of a growing and interconnected human population. The implementation of linear infrastructures involves numerous forms and mechanisms of land-use transformation that can facilitate and serve as pathways to the spread of invasive non-native species. However, the type and intensity of land transformations change over time and this can affect the frequency and intensity in which linear infrastructures route the spread of invasive species.
  2. Here, we present results collected over five years of monitoring surveys (2015 to 2019) to assess the relationship between the construction of one of the largest canals to date in Brazil and the spread of non-native species. We studied the Integration Project of the São Francisco River (PISF) a canal fully inserted in the Caatinga biome, a tropical dry forest ecosystem for which information on invasion dynamics are little known.
  3. Our results confirmed PISF canals served as habitat and dispersal corridors for non-native plant species. Monitoring surveys recorded 26 non-native species established along the 83.2 km2 PISF deployment area. Eleven years after the canal deployment area was completely cleared of vegetation, 92.3% of its extension had non-native plant populations. Of the ten species assessed for their population status, eight had invasive populations.
  4. The time immediately after construction work finished was the critical stage for the spread of non-native woody plants, which increased their distributions with reduced levels of construction intervention, whereas most of the herbaceous species reduced their distributions. When human intervention was drastically reduced, many populations of non-native plants rapidly formed at the deployment area.
  5. Policy implications: Man-made linear infrastructures can remove biogeographical barriers and serve as pathways for the spread of invasive species over long distances and across ecosystems. Thus, the planning, construction and management of such infrastructures should include measures and funding for risk assessment, prevention, monitoring, and control of biological invasions. Agencies responsible for environmental licensing should mandate invasive species management as part of the installation and operation licensing conditions.

Methods

Non-native plant presence

Sampling points were located every 5 km from the São Francisco River until the end of the existing canal (211 and 205 km away from the River depending on the canal) totaling 44 sampling points for the North canal and 42 sampling points for the East canal. The sampling points were placed in the center of the DA, preferably at the right side of the canal due the ease of access. The sampling points were pre-selected using PISF’s geographical information system and the geographic coordinates of each point were uploaded to a handheld GPS unit which was used to locate the sampling points during surveys. At each sampling point we split the visual field into four 90°quadrants and all non-native species visible on each quadrant and located inside the DA (i.e., not in the Caatinga outside the DA) were recorded. Species not identified in the field were collected for identification by botanists at the Centre for Ecology and Environmental Monitoring (NEMA) at University of Vale do São Francisco (UNIVASF). We conducted biannual monitoring surveys in the DA between 2015 and 2019. Here we report data from eight surveys undertaken on May-June/2015, February-March/2016, September-October/2016, January/2017, May/2017, May-June/2018, October/2018, and April/2019.

Non-native species population status

To determine if non-native populations present along the PISF were casual, naturalized, or invasive, we drove along the 416 km of the study area stopping every time a population of a non-native plant species was found inside the DA. The data used for the invasion status reported here are only from the June 2018 survey. Sections above tunnels were not sampled owing to the lack of access. Also, grasses were excluded from the invasion status assessment owing to their absence or difficulty of detection during the dry months of the year. At each population, we measured its linear extension along the canal using a handheld GPS recording points where each population started and ended, counting the number of plants and the number of plants producing flowers and/or fruits. We considered different populations as those with gaps greater than 500 m between individuals.

Each population was classified as casual, naturalized or invasive following the criteria established in Asth et al. (2021). Populations with one to 10 reproductive individuals and spread distances less than 100 m were considered casuals; populations with 11 to 100 reproductive individuals were considered naturalized; populations with more than 100 m of spread that had more than 100 reproductive individuals were considered invasive. We built a Kernel Density Map to show the distributions and occupation intensity of non-native plant populations along the PISF. Kernel density was calculated using polylines representing the extent of each population within the DA, considering a different weight for each population according to its invasion status (casual weighed = 1, naturalized weighed = 2 and invasive weighed = 3). This intensity was calculated from the number of polylines (populations) and the product of the polylines size (in meters) with the assigned weight. Thus, darker areas of the map are locations with the greatest number of plant populations balanced by their invasion status. We used a 200 m cell size (to match the size of the DA) and a 5,000 m radius size (corresponding to the size of the Directly Affected Area) with the units "Square Map Units" since our data are categorical. Kernel density estimation was made with ArcMap 10.2 (ESRI) using the Kernel Density tool.

Usage Notes

This dataset is subject to a 5 year embargo after publication owing to contractual rules imposed by the Brazilian Ministry of Regional Development. Please contact the authors if necessary to discuss potential uses of the dataset on analyses and publications during the embargo period (2021-2026).

Some specific details on each dataset:

Csv 1. List of non-native species

List of non-native plants recorded in PISF with respective families and life forms. Calculations of absolute and percentage values for family’s frequency and life forms percentage frequency.

Csv 2. Invasion status

Absolute numbers of non-native populations per invasion status (casual, naturalized and invasive) for East, North and both canals.

Csv 3. Occupancy measures

Number of populations with more than 100 m of extent, the largest population in extension, total extension in both canals, mean extension, the standard deviation of extension and the proportion (%) occupied for each species in both canals. Assessed extension (in meters), extension free of non-native populations (in meters), proportion of the canal free of non-native populations (%) for each canal e both added.

Csv 4. Beta and Delta slopes

Slopes extracted from the linear models built for surveys 1 to 5 (beta_1_5), surveys 6 to 8 (beta_6_8) and the difference between them (beta_delta).

Csv 5. Occurrence Data

List of non-native plants recorded in PISF with the number of sampling points where each species occurred per survey for East, North and both canals. Calculations of the mean presence at the sampling points for each canal and the standard deviation of the presence at the sampling points considering both channels. Data for the results of the entire section “Non-native plant presence” was extracted from Csv 5.

PS.: All information presented in Supplementary Material 1 can be accessed in Csv 1, Csv 2, Csv 3 and Csv 4.

Funding

Ministério do Desenvolvimento Regional do Brasil, Award: Projeto São Francisco environmental licensing requirements

Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 304701/2019-0

Ministério do Desenvolvimento Regional do Brasil, Award: Projeto São Francisco environmental licensing requirements