Research Article |
Corresponding author: Pedro M. Antunes ( pantunes@gmail.com ) Academic editor: Ramiro Bustamante
© 2021 Katherine Duchesneau, Lisa Derickx, Pedro M. Antunes.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Duchesneau K, Derickx L, Antunes PM (2021) Assessing the relative importance of human and spatial pressures on non-native plant establishment in urban forests using citizen science. NeoBiota 65: 1-21. https://doi.org/10.3897/neobiota.65.65415
|
Since 2007, more people in the world live in urban than in rural areas. The development of urban areas has encroached into natural forest ecosystems, consequently increasing the ecological importance of parks and fragmented forest remnants. However, a major concern is that urban activities have rendered urban forests susceptible to non-native species incursions, making them central entry sites where non-native plant species can establish and spread. We have little understanding of what urban factors contribute to this process. Here we use data collected by citizen scientists to determine the differential impacts of spatial and urban factors on non-native plant introductions in urban forests. Using a model city, we mapped 18 urban forests within city limits, and identified all the native and non-native plants present at those sites. We then determined the relative contribution of spatial and socioeconomic variables on the richness and composition of native and non-native plant communities. We found that socioeconomic factors rather than spatial factors (e.g., urban forest area) were important modulators of overall or non-native species richness. Non-native species richness in urban forest fragments was primarily affected by residential layout, recent construction events, and nearby roads. This demonstrates that the proliferation of non-native species is inherent to urban activities and we propose that future studies replicate our approach in different cities to broaden our understanding of the spatial and social factors that modulate invasive species movement starting in urban areas.
biological invasions, citizen science, city planning, non-native plants, urban forests
Contemporary non-native species introductions and dispersal are intimately associated with human activity (
The type of land use within the urban matrix represents a primary pathway for the introduction of non-native plants. For example, cities contain numerous individually managed gardens, as well as vacant unmanaged land, degraded sites and intensively managed parks where non-native species often abound. This increases the probability of their escape into urban forests (
Urban forest fragments in a matrix of urbanization can, to a certain extent, be seen as akin to islands, where non-native species establishment may more readily occur due to their relative isolation and fragmentation (
Spatial arrangement (e.g., distance and connectivity) among urban forests can also play a role in the susceptibility of urban forests to non-native species introductions and consequent invasions. For example, it can by hypothesized that the degree of isolation among urban forest fragments affects species richness with consequences for non-native plant richness (
One can hypothesize that the type of land use, and the socioeconomic urban layout can be predictors of non-native species richness in urban forests. Both species richness and propagule pressure from non-native species brought into the urban matrix can vary from one neighborhood to another (
A limitation of urban studies is associated with their inherent complex spatio-temporal scales (
The aim of this study is to investigate factors associated with non-native plant invasions patterns in urban forests, including spatial and land-use attributes of the urban matrix. We provide a citizen science method of data collection and an easily reproducible analysis pipeline to facilitate future studies. Using a template city, we test the hypothesis that urban forest size and relative isolation (i.e., distance to the nearest unfragmented natural forest) guide the incidence of non-native versus native plants species in urban forests. Alternatively, we hypothesize that characteristics of the urban matrix associated with layout and land-use could interfere with the distance and area effects. More specifically, we hypothesize that: (1) larger urban forests closer to the nearest unfragmented forest have greater overall native and non-native species richness as compared to smaller and more distant urban forests, even when accounting for urban matrix characteristics such as population density and surrounding construction events through time; (2) larger urban forests have a smaller ratio of non-native to native species as compared to small ones, even when accounting for urban matrix characteristics such as population density and surrounding construction events through time. Alternatively, urban matrix characteristics may be better predictors of non-native species composition in urban forests and; (3) urban forests that occur closer together show greater similarity in species composition than those farther apart.
The study area is the city of Sault Ste. Marie, Ontario, Canada. The city lies within the Algoma District bordering the eastern shore of Lake Superior. Sault Ste. Marie has a population of approximately 75,000 (
We obtained the list of study sites by identifying areas zoned by the city of Sault Ste. Marie as parks and recreational areas. We only selected undeveloped and unmanaged forested areas for inclusion in this study and excluded all other heavily managed areas such as sports fields and golf courses. A total of 18 accessible urban forests were identified ranging in size, from 2,200 to 140,5480 m2 (Table
Map (i.e., WGS84 projection) of the 18 urban forests in Sault Ste. Marie, Ontario, Canada, in which this study is based. Both maps contain a representation of each urban forest, site 1 through 18, in green with a 250 m buffer beginning at the edge of the forested area and the unfragmented forest edge in red A the year of construction of all the plots with some development is indicated as a blue gradient B the population density of each neighborhood is indicated as a gray gradient.
Urban forest measurements in Sault Ste. Marie, Ontario. Site number refers to urban forest number of Figure
Site number | Area (m2) | Perimeter (m) | Distance from Forest (m) |
---|---|---|---|
1 | 2200 | 271 | 3480 |
2 | 10385 | 591 | 4572 |
3 | 4274 | 337 | 5664 |
4 | 6457 | 431 | 5299 |
5 | 10720 | 790 | 4018 |
6 | 11963 | 573 | 4870 |
7 | 11340 | 467 | 4284 |
8 | 15901 | 494 | 3925 |
9 | 14062 | 942 | 5829 |
10 | 17938 | 708 | 8496 |
11 | 34336 | 1491 | 5260 |
12 | 11588 | 616 | 4669 |
13 | 157632 | 1872 | 2291 |
14 | 315182 | 2800 | 1405 |
15 | 378112 | 3514 | 4196 |
16 | 596530 | 4330 | 145 |
17 | 1405480 | 6084 | 7877 |
18 | 1028248 | 5244 | 0 |
We randomly distributed Modified-Whittaker sampling plots within each of the urban forest islands. The Modified-Whittaker sampling design detects greater species richness and is a more convenient sampling method than the Whittaker plot design (
A team of 52 citizen scientists went to each of the pre-established plots in the urban forests between July 2 and August 9, 2013 and collected data that enabled the identification of all vascular plants present to species-level. In addition, the citizen scientists counted and provided cover estimates within each of the 1m2 sub-plots for each species identified. Citizen scientists used general field knowledge as well as personal field guides to help identify each plant to species. For non-native species, we provided a booklet containing descriptions of the most common invasive plants in the area. Specimens that could not be immediately identified to species were collected and tagged. We later identified these specimens with support from the scientists at the Northern Ontario Herbarium, and prepared them to be stored as part of the collection in the Algoma University Herbarium. Cover was estimated using the Braun-Blanquet cover-abundance scale (
We calculated all geometry features in QGIS (
To quantify urban landscape use, we added the location of high traffic roads, the year of construction of each plot, and zoning information for plots to our map. We computed the distance between each urban forest and the closest high traffic road. The city zones include rural, environment/natural, mining, park, residential, and commercial land use. To determine impacts of adjacent urban factors on urban forest composition, we used a 250 m buffer, which starts at the edge of each forest and ends 250 m within the urban matrix, around each urban forest. We calculated the percentage designated to each zone in the buffer area, and the average year of construction of plots in the buffer area (Table
We used R version 3.5.1 (R Core Team 2018) to perform all statistical analyses. We produced two types of variables for our analyses. The first type of variables described spatial arrangement of the urban forests and included area, perimeter, and distance to the “unfragmented” forest. The second type of variables describe the urban landscape surrounding the urban forests and included the distance to the closest road, the commercial zoning, the rural zoning, the residential zoning, and average building age. To avoid collinearity among predictor variables, we conducted pair-wise Pearson’s correlation tests and kept all variables with a correlation coefficient below 0.7 and above -0.7 (
Since we did not have a priori hypotheses about which variables (i.e., spatial and socio-economic) could better explain species composition, we performed multi-model inference to rank candidate models using Akaike’s information criterion corrected for a large number of predictors (AICc). We used this technique to determine which independent variable could better explain the variance in species richness, non-native to native species ratio, non-native species richness, and native species richness. We tested all response variables for normality using the Shapiro-Wilks test. We nested all possible combinations of both the spatial and the urban landscape variables to produce a set of candidate models. To assess variable significance, we calculated the weighted model average of all candidate models within the 95% confidence set of models (sum of model weights > 0.95) (
List of the estimates for the most parsimonious model for each variable.
Variables | AICc | F- statistics* | |||
---|---|---|---|---|---|
Estimate | Lower confidence interval | Upper confidence interval | Estimate | P-value | |
Kept as part of the model predicting species richness | |||||
Distance from the “unfragmented” forest | 0.004 | -0.0008 | 0.0091 | 0.002 | 0.542 |
Kept as part of the model predicting the ratio of native to exotic species | |||||
Residential zoning | 0.0036 | 0.0019 | 0.005 | 0.02 | 0.0001 |
Average year of construction | 0.0077 | 0.0032 | 0.0125 | 0.02 | 0.239 |
Distance to the closest road | 0 | 0 | 0.0001 | 0.0002 | 0.0029 |
Kept as part of the model predicting exotic species richness | |||||
Distance to the closest road | 0.0014 | 0.0004 | 0.0026 | 0.002 | 0.01 |
Residential zoning | 0.1743 | 0.0751 | 0.2665 | 0.17 | 0.007 |
To determine if spatial and landscape variables influenced species composition, we performed a redundancy analysis (rda) and partitioned the variance between spatial and urban landscape variables. We used Hellinger transformed matrices of species composition as the response variables of the rda with the function rda from the package VEGAN (Oksanen et al. 2018). We included area, area to perimeter ratio, distance to the “unfragmented” forest, distance to roads, all zoning components, and the average year of construction surrounding the urban forest as response variables. None of the variables had a variance inflation factor higher than 3. The significance of the redundancy analysis was tested using ordiR2step, from VEGAN, with 1000 permutations. The significance of each axis and then each term was assessed similarly using anova.caa, from VEGAN. We repeated this analysis with only the non-native and the native species to see if the same patterns were found in all the groups.
To test for the relationship between the distance among urban forests and their similarity in community assembly, we used the function mantel.correlog from the package VEGAN, with Pearson’s correlation coefficient and 1000 permutations. We provided the program with a distance matrix of the distance among the urban forests and a similarity matrix composed of species abundance for each urban forest. We calculated the break point between distance classes for the mantel correlogram using the Sturges equation. We repeated this analysis by replacing the similarity matrix with a matrix containing only the non-native species, and then only with the native species to see if different factors affected each group. Both the R script of the analysis and the dataframe of used variables are available as supplementary files (see Supple material 1: ‘analysis’ for R scrip with the analysis, and Supple material 2: ‘data_csv’ for the dataframe of used variables).
We found a total of 142 plant species across the urban forests surveyed. Of these species, 36 were non-native and 106 were native. Each urban forest plot contained an average of 16.5 species (±7.64). On average, 24.19 % (±21.07) of plants in each plot were non-native, with an average of 4.33 non-native species (±3.61) and 12.17 native species (±5.25) per Whitaker plot. The most abundant species were Maianthemum canadense (present in 72.22% of plots), Rubus pubescens (38.89%), Fraxinus americana (33.33%), Rubus idaeus (44.44%) and Acer saccharum (38.89%). Of these, only Rubus idaeus is listed as non-native (see Supple material 3: ‘species data’).
A total of 64 candidate models were compared to find the best way to predict overall species richness from both spatial and landscape variables. Of the candidate models, 36 were kept as part of the confidence set of the average model, including the Null model (Table
Each graph represents the linear relationship between urban forest species richness and a variable while controlling for all other variables. Species richness was not related to the area of urban forests (A), and it was also not related to distance between the urban forest and the unfragmented forest (B).
We produced three separate groups of candidate models to test which variables affected the ratio of non-native to native species, as well as the non-native and native species, independently. Each response variable produced a total of 64 candidate models. When analyzing the effect of spatial and urban landscape factors on the ratio of non-native to native species, seven models were kept as part of the confidence set of the average model. Area and distance to the unfragmented forested area were not included in the most parsimonious models (Figure
Linear relationships between the proportion of non-native relative to native species in urban forests and each variable while controlling for variables in the other panels A the proportion of non-native to native species was not related to the area of urban islands, calculated in meters squared B the proportion of non-native to native species was not related to distance between the urban island and the unfragmented forest, calculated in meters C the proportion of non-native to native species increased with newer constructions D the proportion of non-native to native species increased with distance from the closest road, calculated in meters.
When analyzing the effect of spatial and urban landscape variables on non-native species alone, 18 models were kept as part of the confidence interval set for the model average. The most parsimonious model included a positive effect of the distance to the closest road (E = 0.0014) and residential zoning (E = 0.1743) on the number of non-native species. Similarly, only the distance to the closest road (CI = 0.0004, 0.0026) and residential zoning (CI = 0.0751, 0.2665) had confidence intervals that did not include zero in the model average. Despite the effect of the average year of construction (CI = -0.0662, 0.5207) on the non-native to native species ratio, it did not affect non-native species richness. Together, the distance to the closest road and residential zoning explained 49.43% of the variation in non-native species richness, and residential zoning (I = 72.11%) was more important than the distance to the closest road (I = 27.89%). Commercial zoning (CI = -0.4368, 0.0572), area (CI = -0.0000, 0.0000), and distance from the “unfragmented” forest (CI = -0.0022, 0.0024) did not add to the predictive ability of the model. When analyzing the effect of spatial and urban landscape variables on native species alone, 31 models were kept as part of the confidence interval set for the model average. The most parsimonious model was the null model. The model average had no variable with confidence intervals that did not cross zero. Despite the effect of residential zoning (CI = -0.2936, 0.2477), average year of construction (CI = -0.5293, 1.1767), and distance to the closest road (CI = -0.0018, 0.0040) on the on the non-native to native species ratio, they did not affect native species richness. Commercial zoning (CI = -1.0031, 0.4744), and the spatial variables area (CI = -0.0000, 0.0000) and distance from the “unfragmented” forest (CI = -0.0015, 0.0066) were also not considered as meaningful.
We tested the effect of space and urbanization on the overall species composition, the non-native species composition, and the native species composition of the urban forests by partitioning the variance and through model selection of redundancy analysis using permutation tests. The full model could not adequately describe the variance in species composition (F = 0.9821, P = 0.564). Instead, the model that best described the overall species composition was the null model. When only the native species were modeled, we found that none of the variables could explain the patterns of native species composition. Similarly, non-native species composition was not modeled by the set of space and urbanization variables.
To determine if the proximity of the urban forests influenced their overall, non-native, or native species composition we performed mantel correlogram tests with distance classes calculated using Sturge’s equation. According to the mantel correlogram analysis, distance could not predict overall species composition (r = 0.032, P = 0.372). However, urban forests that were in the first distance class, or, in other words, close together, were similar in species composition (P = 0.046) (Figure
Mantel’s correlogram defaulting to Pearson’s correlation coefficient with 1000 permutations. We calculated the distance classes using Sturge’s equation. The correlation of distance between urban forests on the similarity in composition of species was tested in four distance classes. Points filled with white represent distances where species composition was not related to distance and solid points represent distances where species composition was related to distance. The purple line connecting circles is the representation of the analysis for all the species. The blue line connecting triangles represent the analysis for native species. The red line joining squares represent the analysis for non-native species.
Our approach using a novel citizen science method of data collection and analysis pipeline enabled identifying whether spatial and/or land-use attributes of the urban matrix were associated with non-native plant occurrence patterns in urban forests. First, it is important to note that the native plants species recorded in our urban forests were consistent with those present in communities of the Great Lakes-St. Lawrence forests of Ontario. The most common native species found in the urban forests, namely Fraxinus americana, Acer saccharum, and Maianthemum canadense, are representatives of the core community of forests in the Great Lakes-St. Lawrence region according to Canada’s National Forest Inventory. Similarly, the dominant non-native invasive species reported by the citizen scientists in this study, including Rubus idaeus and Alliaria petiolata, are consistent with reports for this area (e.g., Invasive Species Awareness Program – http://www.invadingspecies.com/). Second, we found that spatial variables did not adequately predict the overall species richness and composition of forest fragments. Both urban forest area and distance to the nearest unfragmented non-urban forest had no effect on plant species richness, suggesting that the urbanization does not always completely isolate the urban forest fragments within the city matrix.
The lack of spatial signal raises the hypotheses that: 1) compared to more densely populated cities, the type of urban development based on detached houses with gardens, which is typical of our model city, may contribute to buffer reproductive isolation among urban forests, and; 2) our urban forests are too recent for ecological effects to have emerged (<100 years). Consistent with this perspective, forest fragments that were adjacent to the relatively less disturbed non-urban forest adjacent to the city showed no sign of hosting more native species relative to the forest fragments imbedded within the city. In contrast, species composition among urban forests responded to residential land-use, construction events, and proximity of roads, suggesting that landscaping and residential planning could be main drivers of non-native species introductions and, eventually, invasion. When considering native and non-native species separately, the proportion of land zoned for residential use surrounding urban forest fragments, recent construction events, measured by the average age of infrastructures near the urban forests, and possibly the distance between these forests and the closest road were factors correlated with an increase in non-native to native species ratio. Additionally, reductions in species composition similarity among urban forests with increasing distance from each other, particularly for non-native species, further indicates that these sites are unlikely to be completely isolated by the urban matrix around them and could be responding to local urbanization factors instead. As such, we conclude that spatial variables, at least in some cases, can be poor predictors of species community richness and non-native species community composition and, instead, we propose that emphasis should be placed on qualities of the urban matrix to determine urban forest non-native community composition.
Our original intent was primarily focused on spatial rather than land use characteristics that meaningfully predict plant community richness and composition. However, the urban matrix variables clearly served to account for factors that, together, could have a larger effect on community composition than the size and spatial arrangement of urban forests. Consequently, we propose that, going forward, studies to predict non-native species community assembly in other urban centers should incorporate characteristics of the urban matrix that are important in urban forest design and management.
While we found compelling evidence that the species richness of urban forests was exclusively driven by parameters relating to the urban matrix, their effect was restricted to the richness of non-native species. These results are congruent with previous studies indicating that human activities (
This study adds to the body of knowledge on the importance of considering socio-economic factors when analyzing the diversity and species composition of urban landscapes (Hope et al. 2006;
This study constitutes a first step towards understanding how distance between urban forests and their area affects species composition and patterns of non-native plant invasions. Even though in cities, such as the one used in this study, connectivity between urban forests and proximity to major uninhabited forests may determine the low predictive capacity of spatial attributes alone, that may not be the case in larger urbanized centers, particularly those where habitat fragmentation is high. We know from previous studies that patterns of species composition in urban forests are dependent on both anthropogenic and ecosystem factors at local and regional scales (
This research was conducted in Robinson-Huron Treaty territory and the traditional territory of the Anishnaabeg, specifically the Garden River and Batchewana First Nations, as well as Métis People. The work was funded through grants from the Ontario Trillium Foundation and Ontario Ministry of Natural Resources and Canada Research Chairs awarded to P.M. Antunes. The authors are especially grateful to the team of 52 volunteers who contributed a total of 248 hours to the project as well as to Christopher Bean (City of Sault Ste. Marie) and the staff at Northern Ontario herbarium who helped us with the taxonomic identification of some specimens.
R scrip of the analysis
Data type: statistical data
Dataframe of used variables
Data type: statistical data
Species data
Data type: species data
F-statistics
Data type: statistical data