Impacts of urban land-cover on plant community structure and biodiversity in a multi-use landscape

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While research and policy alike have recognized the importance of conserving biodiversity, the rapid and continued expansion of urban areas hinders many conservation efforts, particularly as many high-value conservation areas are found in landscapes already modified by human use. Research into the impact of landscape mosaics –their composition and configuration in particular – is important to understanding the impact that human induced land-use change may have on biodiversity, biotic communities, and thus the ecological processes within these areas. Objectives. The objectives of this research paper are to determine the impacts of the landscape composition surrounding conservation forests has on the plant communities of the forest understory communities. We also seek to outline the possible mechanisms by which the landscape can indirectly impact plant communities, and in so doing, provide a deeper understanding of how natural areas within mosaic landscapes may sustain biodiversity. Methods. Using plant community measures from the Credit Valley Conservation Authority in Ontario, Canada, and open-sourced spatial data on Canada’s landcover, we calculated the land cover composition of urban and natural lands surrounding each forest site, and the biodiversity of the understory community in each forest. We used both individual species richness and abundance (NMDS, TITAN), as well as aggregate biodiversity measures (linear regression) to test for significant relationships between the plant community metrics and the composition of the surrounding landscape. Results . Natural land cover, urban land cover, and continuous forest size were all significantly associated with species changes in the NMDS at all scales, and the direction of the urban cover vector was nearly opposite of the natural cover vector in the NMDS space. The output of the TITAN analysis identified both positive and negative responses of individual species to land cover composition at the three scales considered, indicating that indicator species had strong responses to changes in the land cover, with different species being associated with urban vs. natural land cover. The TITAN and NMDS both showed that many more species were positively associated with natural land cover. Only a few species responded positively to high urban cover, and those forests had much lower populations. A series of linear regressions revealed a negative relationship between urban land cover and plant diversity metrics, and positive relationships between natural land cover and plant biodiversity at all scales. Both species richness and species abundance changed significantly with the surrounding land cover composition, but species richness (that is the total number of species present in a community) had the most consistent and statistically significant response – indicating that an areas ability to sustain a certain number of species is affected by the surrounding landscape. Conclusions. The significant findings of both species-level and community level changes associated with land cover confirm our expectations that land cover in mosaic landscapes does indeed have significant impact on plant communities, and can impact forest’s potential to support biodiversity, even when the changes are indirect changes. Forest understory vegetation shows a significant relationship to surrounding land cover composition, with changes associated with urban and natural land cover being consistently significant at 1km, 2km, and 5km scales. This indicates that the forest understory communities of the CVC are not random assemblages, but communities found in predictable patterns that are associated with the composition of the landscape around each site. Plant community ecology mosaic landscape multi-use landscape plant assembly plant biodiversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Canada recently hosted the COP15 Biodiversity talks, collectively agreeing to reduce human-induced extinctions, restore degraded ecosystems, and halt overwhelming biodiversity loss (Findlay, 2023 ). Identified in this bold agreement is the promise to conserve 30% of natural lands (including forests, inland waters, coastal areas, and oceans) by 2030 (Joly, 2023 ). However, the size and scope of human-induced land-use change poses an extreme challenge to both land-based and biodiversity-based conservation goals. Almost 75% of natural landscapes worldwide have been impacted and/or modified by human activity and expansion (IPBES, 2019). Multi-use or mosaic landscapes that combine natural, urban, and semi-urban land-use types are becoming increasingly prevalent (Bennet et al., 2006), reducing the availability of large continuous habitats available for area-based conservation efforts. In fact, research has shown that optimal expansion of conservation lands consistently requires expansion into landscapes previously transformed by—and often currently occupied by—humans (Ellis & Ramankutty, 2008 ; Shen et al., 2023 ). As more of the global landscape is impacted by human land-use, conserving large areas of untouched land becomes less viable as a stand-alone solution. We must also find ways to sustain, promote, or even restore biodiversity in the natural lands remaining in mosaic landscapes. As these mosaic landscapes are both relatively new and extremely prevalent in areas of high biodiversity conservation potential (Skultety et al. 2018; Shen et al., 2023 ) it is particularly important to understand how landscape structure could be shaping the biodiversity present (both species composition and aggregate biodiversity measures) across different land cover compositions (Baker et al. 2010). Research has shown that disturbances, regional dispersal, and interactions (e.g., competition) between species are some of the main drivers of biodiversity (Leibold et al., 2004 ; Swan et al., 2021 ) all of which can be affected by changes in landscape composition and configuration (Miedema et al., 2019 ; Yan et al 2019 ; Hall et al. 2020). Increases in urban land cover are widely considered to have negative impacts on biodiversity, but plant community diversity responses do not always follow this pattern (Albrecht & Haider 2013 ; Miedema et al., 2019 ). Investigating the connections between landscape composition could provide insight into how and to what degree land cover changes affect plant biodiversity beyond the direct effects that are most often considered (Zhang et al. 2021 ). This could also give us an idea of what areas of mosaic landscapes are best suited for maintaining high levels of biodiversity and have the highest potential for maintaining conservation goals. Plant communities arise through the influences of multiple abiotic and biotic factors acting as filters, selecting from a regional pool of species and determining a species’ presence and persistence in a given area (Lavorel & Garnier, 2002 ; Velland et al., 2007). Many studies have suggested the potential of landscape mosaics to sustain plant biodiversity, with the distribution of multiple and different habitat types throughout the landscape creating opportunities suited to many species (Tscharntke et al., 2012 ; Rosenfield et al., 2022 ; Pörtner at al., 2023). This large number of diverse habitat types could produce high levels of niche availability – species presence corresponding with the unique physical and biological requirements of that species – in mosaic landscapes allowing more species to thrive (Grinnell, 1924 ; Hutchinson, 1978 ; Miedema et al., 2019 ). Using aggregate measures of plant community responses may provide excellent information on observable patterns of change across an environmental gradient (in this case land cover), but it does pose the risk of obscuring nonlinear or individual species changes, and thus missing or misrepresenting impacts of land cover change on plant communities (Carignan & Villard, 2002 ; King & Baker, 2014 ). Individual species may have different responses to the same environmental conditions (Fischer et al., 2004 ). Less resilient species or those with highly specific niche requirements are more likely to decline in urbanized environments, while only generalist or well-adapted species will thrive (Evangelista et al., 2008 ). Increases in biodiversity could be constrained to non-native or more resilient species, with decreases in rare species or in diversity of traits. In fact, it has been well established that many invasive species, which often have negative effects on native populations and ecosystem functioning, are much more prevalent in urban and fragmented landscapes (Cadotte et al., 2017 ; Gaertner et al., 2017 ; Blouin et al., 2019 ). Species-specific responses are particularly relevant as conservation and plant ecology do not consider invasive communities, only invasive species. Endangerment, and rarity are both factors essential to conservation work, and they are both only considered at the species level. Thus, community ecology needs to balance both community-aggregate and species-specific measures to create a more holistic understanding of the changes caused by landscape. By investigating both plant community and species-specific responses to land cover changes across a mosaic landscape, we can better understand the impacts that urban expansion and landscape fragmentation may have on plant communities (Carignan & Villard, 2002 ), thus providing insight into the future of conservation in mosaic landscapes. This project seeks to understand the influence of landscape composition – particularly urbanization – on plant community- and species-level biodiversity. Building on previous studies of the plant functional trait communities and ecosystem service delivery in the rapidly urbanizing landscape of Southern Ontario, this study focuses on the effects of landscape composition on biodiversity metrics and native species abundance. In order to provide more insight into the impact of landscape composition on conservation potential, we focused only on plant understory communities located in protected areas (conservation forests). By calculating the composition of urban and natural lands around these forest conservation sites, we can gain better understanding of how mosaic landscapes and areas of high urban cover might influence conservation potential and an area’s ability to support biodiversity. Through both species-specific and community-level measures of plant community change, we investigate correlations between biodiversity of forest conservation sites and the land-use around each sample site. We also measure changes in non-native species presence and abundance and its correlation with land-use around sample sites. Our objectives were to characterize the forest understory communities of southern Ontario forests and outline how the structure of the surrounding landscape impacts the community structure and biodiversity of forest understory species. We hypothesize that biodiversity metrics will increase with total cover of forest in the surrounding lands, decrease with total cover of urban land-use, and that the abundance of non-native species will increase with total cover of urban land-use. We also anticipate that, in areas where biodiversity may stay consistent between urban and natural areas, the abundance of non-native species will increase with total cover of urban land cover. Methods The Credit Valley Watershed Sample sites were located in conservation forest sites in the Credit Valley Conservation (CVC), located in the Credit River Watershed of Southern Ontario. The forest sites are within the scope of both the Greater Toronto Area (GTA) and the Greenbelt, with the competing influences of urban expansion (GTA) and conservation efforts (Greenbelt) creating the perfect location in which to investigate the impact of landscape composition on forest fragments (Milne & Bennet, 2007). The 93,021 hectares of the Credit River Watershed contain a mosaic of natural, agricultural and urban land uses. The majority of the watershed is covered by agricultural land use (mainly cropland), urban landscapes (lands classified as both urban and barren lands were included), most of which are located along the southern reaches of the watershed (see Fig. 2.1 . for more details). The original deciduous and mixed forest that once covered the region now occupies just a small portion of the watershed (Milne & Bennet, 2007). The mosaic landscape of the watershed provides a model system to investigate the ways that landscape could impact the composition of forest species. Firstly, each forest sample plot is located within a conservation area, meaning that each site is a consistent ecosystem type, within continuous forest, and subject to similar management practices. Consistency in management and forest type allows us to investigate the general impacts of surrounding landscape on the assembly of the plant species, while avoiding many of the complications of different land-use type, ownership decisions, and other factors often associated with urban landscapes. Secondly, the mosaic landscape of the Credit River Watershed, with a highly diverse mixture of landscape types throughout, provides an excellent environment to observe forest biodiversity measures occurring in different landscape types—mainly the spectrum between semi-urban areas near Toronto, to more naturalized areas at the northern end of the watershed. Plant understory data Species richness and abundance for each sample site was obtained from the CVC authority’s annual biomonitoring program. The CVC samples groundcover, regeneration plots, and tree cover in a subsample of forests managed by the CVC every year. Herbaceous species have been found to respond more strongly and quickly to disturbances, making them a more sensitive indicator of the changes that are brought about from increased urban land cover (Gilliam 2007 ; Compagnoni et al. 2021 ). For this reason, we chose to use groundcover species, which included all living forbs, ferns, grasses, sedges, rushes, vines, woody vines, and all tree and shrub stems under 16 cm in height and less than 4 cm stem diameter. Sampling for each site was performed following a modified plot-based methodology developed by the EMAN consistent across the CVC (Roberts-Pichette & Gillespie 1999 ), with a single site per conservation forest consisting of 5 permanent 1 m 2 subplots, with the species and % cover of each species per site recorded. Sites were sampled using a modified panel design, with some sites monitored annually, and others every other year. Species biodiversity was calculated from the groundcover species cover data from the Credit Valley's terrestrial monitoring program, from 2018–2022 (excluding 2020 when samples were not collected) aggregating the plant community measures to calculate species abundance, species diversity, Shannon diversity, and a native to non-native species ratio (with all non-native species, including naturalized species, being included). All species data was used with permission of Credit Valley Conservation Authority 2023. Spatial analysis For the spatial analysis of the surrounding landscape, we used the North American Landcover (NAL) maps from the Commission for Environment Cooperation developed as part of the North American Land Change Monitoring System. We selected the most recent land cover update, 2020, and used at 30-meter raster dataset of North American Land cover. This map was used to calculate the percentage of urban and natural land around each sample site, as well as the total area of continuous forest around each sample location. Urban lands included lands classified as both urban and barren lands, and natural lands were an aggregate of lands classified as forest, grassland, shrubland and wetland. These metrics were calculated for 1km, 2km, and 5km radius around each sample plot (Commission for Environmental Cooperation (CEC), 2023 ). We also calculated the total area of continuous forest around each sample point, using the total area of the forest (both CVC conservation forest and other forest areas) to independently measure the effect that forest size might have on species diversity. Species specific analysis To better understand the species distribution and relationship to land composition gradient, we first used ordination to visualize the species distribution. We performed a non-metric multidimensional scaling (NMDS) analysis, which uses a dissimilarity matrix of species abundances to illustrate similarity of species compositions. We used NMDS ordination based on Bray-Curtis dissimilarity distances (Valdes 2008 ) to first visualize the species compositions and then to interpret them in relation to the environmental factors (i.e., the land use composition surrounding each site at the three scales). When including 3 dimensions, we obtained an acceptable goodness of fit (stress = 0.16) for our data. We followed the NMDS ordination by fitting trends the environmental vectors (land use composition and continuous forest) onto the NMDS species ordination using the envfit() function as part of the vegan package (Oksanen, 2017 ). The association of individual species was tested using a Threshold Indicator Taxa Analysis (TITAN), using indicator scores to integrate occurrences, abundance, and directionality of species response to an environmental gradient (in this case % of urban cover and % of natural cover at 3 scales, and continuous forest cover). Species that occurred fewer than 3 times throughout the samples were considered rare and were removed from the data. This analysis related species abundances to land use variables, providing more insight into the patterns of species distributions and identifying change points in the surrounding land cover that results in significant changes in the plant community, along with which species were associated with specific land use variables. We used TITAN to identify indicator species, and the optimum value of environmental gradient (both urban and natural) that maximizes the TITAN species score, for species identified as significant through the TITAN analysis. This method distinguishes negative and positive taxa responses and tracks cumulative responses of declining and increasing taxa in the communities (Bakker 2023 ). We used R version 4.2.2 (2022-10-31). Community aggregate measures Next, we completed a series of linear regressions comparing each of the biodiversity metrics with gradients of natural and urban land use at 1km, 2km and 5km scales. Species measures at each site were aggregated into measures of species richness, species abundance, and Shannon diversity. To account for unequal sampling (due to the modified panel design) across multiple years, we used a fixed effect regression with both year and site as fixed effects in the model. These fixed effects account for the fact that there is grouping within the data (some sites that appear multiple times as they are sampled in multiple years). The fixed effects were added to ensure that the model controls for variation within groups (year and site) and better accounts for the effect of landcover on the biodiversity metrics. Finally, we plotted residuals of the significant linear models in ggplot. All analyses were done using the R version 4.2.2 (2022-10-31). Results Species-specific analysis We used CVC groundcover data from the years 2018–2022 resulting in 69 sample points. We ran an NMDS with the 147 species that were identified as being present in the CVC forest sites. A three-dimensional solution was chosen as it had a lower Kruskal’s stress value for the three-dimensional solution (Stress = 0.160). Stress values close to 10 are considered quite satisfactory, and most ecological community data sets have solutions with stress between 10 and 20 (Kruskal 1964a ; Clarke 1993 ; McCune & Grace 2002) (See Fig. 1 .). The NMDS ordination of species composition was overlayed with the land cover composition, showing significant relationship natural land cover, continuous forest cover, and several other land-use variables (Table 1 ). All the land cover metrics were significantly associated with species changes in the NMDS at all scales, and the direction of the urban cover vector was nearly opposite of the natural cover vector in the NMDS space. The natural land cover and continuous forest cover showed negative correlations with NMDS axis 1 and NMDS axis 2, while the urban land cover showed positive correlations in both axes (Fig. 3 .). Both urban and natural land cover were significant at 1km, 2km, and 5km scales, with low R 2 values that increased with the scale (R 2 = 0.29, R 2 = 0.32, R 2 = 0.34, respectively for urban, and R 2 = 0.18, R 2 = 0.29, R 2 = 0.43 for natural). The natural land cover showed the strongest explanatory factor, but also the largest difference in explanatory power (R 2 values) between 1km and 5km scales. The land use variables with the strongest explanatory factor were urban cover at 2km (p = 0.001, R 2 = 0.32) and 5km (p = 0.001, R 2 = 0.34), and natural cover at 5km (p = 0.001, R 2 = 0.44). Table 1 Results and linear trends. Maximum linear correlations (r 2 ) of the environmental variables with the NMDS ordination patterns are shown. Significance of the correlations was calculated using 1000 permutations. Land use variable NMDS1 NMDS2 R 2 p-value Continuous forest - -0.608 -0.794 0.263 0.001 Natural land cover 1km -0.820 -0.573 0.189 0.002 2km -0.645 -0.765 0.298 0.001 5km -0.916 -0.400 0.438 0.001 Urban land cover 1km 0.935 0.354 0.294 0.001 2km 0.963 0.268 0.324 0.001 5km 0.992 0.128 0.347 0.001 For the TITAN analysis, we removed the species present in less than 3 of the sites, resulting in 76 species. The output of the TITAN analysis identified both the positive and negative responses of individual species to land cover composition at the three scales considered (Table 2 ). An Adonis test was used to confirm that all of the land cover variables used (both natural cover and urban cover at 1km, 2km, and 5km, and continuous forest cover) were significantly associated with changes in the species composition at each site (Bakker, 2023 ). The majority of species that the TITAN analysis identified as significant were native species and were associated with both natural and urban land cover. In fact, only two non-native species showed any significant association with land cover in the TITAN analysis. The first was Alliaria petiolata , which increased with urban land cover at 2km (purity = 0.998, reliability = 0.998) and 5km (purity = 0.992, reliability = 0.988) scales, and decreased in natural land cover at 5km (purity = 0.964, reliability = 0.98). The other non-native species, Taraxacum officinale , was associated with decreases in urban land-cover (purity = 0.972, reliability = 0.988) at 1km scale. Table 2 TITAN analysis for the CVC plots, including the individual taxa for which a significant decrease/increase in abundance across the gradient of surrounding urban land cover. The environmental change point indicates the significant community-level change point, or the percentage of urban land cover in the surrounding landscape that most strongly differentiates plots with and without the species. The filter value indicates the direction of the relationship, with increaser species (filter = 2) increasing with higher levels of the land cover in question, and decreasers (filter = 1) decreasing with higher levels of the land cover in question. This includes the analysis for species across the gradient of urban land cover, including the individual taxa for which a significant decrease/increase in abundance across the gradient of surrounding natural land use. This only includes significant species responses. Species that are increasers (filter value of 2) are bolded. Land use composition Response species Environmental change point Purity Reliability Filter 1 km urban Caulophyllum giganteum 3.58 1.000 1.000 1 Solidago caesia 4.68 0.992 0.970 1 Taraxacum officinale 4.68 0.982 0.990 1 Asarum canadense 5.09 0.992 0.964 1 Elymus hystrix 5.43 0.994 0.956 1 Geranium robertianum 5.43 0.984 0.972 1 Viola pubescens 5.77 0.998 0.954 1 Dryopteris carthusiana 7.08 1.000 0.988 1 Fraxinus americana 11.6 1.000 0.996 1 Hydrophyllum virginianum 11.9 1.000 0.998 1 Carex pedunculata 18.5 1.000 1.000 1 2 km urban Dryopteris carthusiana 6.30 0.998 0.994 1 Fraxinus americana 6.54 1.000 1.000 1 Hydrophyllum virginianum 12.3 1.000 1.000 1 Caulophyllum giganteum 13.0 0.998 1.000 1 Carex pedunculata 13.8 0.998 0.998 1 Alliaria petiolata 6.34 0.998 0.998 2 Erythronium americanum 59.6 0.992 0.978 2 5 km urban Dryopteris carthusiana 6.10 1.000 0.998 1 Sanguinaria canadensis 7.25 0.998 0.982 1 Viola pubescens 10.4 0.998 0.976 1 Carex leptonervia 17.0 1.000 0.988 1 Fraxinus americana 18.1 1.000 1.000 1 Carex pedunculata 20.2 1.000 1.000 1 Hydrophyllum virginianum 20.4 1.000 1.000 1 Cornus alternifolia 26.1 0.968 0.950 1 Caulophyllum giganteum 29.7 0.998 1.000 1 Erythronium americanum 18.1 1.000 1.000 2 Alliaria petiolata 76.7 0.992 0.988 2 Table 3 TITAN analysis for species across the gradient of natural land cover, including the individual taxa for which a significant decrease/increase in abundance across the gradient of surrounding natural landcover. This only includes significant species responses. Species that are increasers (filter value of 2) are bolded. Land use composition Response species Environmental change point Purity Reliability Filter 1 km natural Erythronium americanum 42.9 0.994 1.000 1 Caulophyllum giganteum 35.0 0.994 0.998 2 Fraxinus americana 37.7 0.982 0.980 2 Cornus alternifolia 42.9 0.956 0.950 2 Carex peckii 44.9 1.000 0.964 2 Asarum canadense 57.8 0.994 0.964 2 Dryopteris intermedia 57.8 0.998 0.970 2 Geranium robertianum 57.8 0.998 1.000 2 Solidago caesia 60.5 0.998 0.966 2 Hydrophyllum virginianum 63.1 1.000 1.000 2 Sanguinaria canadensis 71.1 1.000 0.974 2 2km natural Erythronium americanum 36.4 0.984 0.992 1 Fraxinus americana 31.1 0.996 0.980 2 Carex albursina 39.9 0.996 0.968 2 Caulophyllum giganteum 39.9 0.988 0.992 2 Geranium robertianum 39.9 1.000 1.000 2 Hydrophyllum virginianum 39.9 1.000 1.000 2 Solidago flexicaulis 39.9 0.992 0.998 2 Cardamine diphylla 40.6 1.000 0.986 2 Carex peckii 40.6 1.000 0.978 2 Ranunculus abortivus 40.6 0.998 0.950 2 Dryopteris intermedia 51.9 0.998 0.962 2 Elymus hystrix 60.6 0.990 0.970 2 Sanguinaria canadensis 62.6 1.000 0.996 2 5 km natural Impatiens capensis 13.6 0.990 0.952 1 Euonymus obovatus 14.7 0.952 1.000 1 Erythronium americanum 41.9 0.978 0.988 1 Alliaria petiolata 47.0 0.964 0.980 1 Cornus alternifolia 16.2 0.998 0.950 2 Caulophyllum giganteum 28.5 1.000 1.000 2 Carex pedunculata 32.1 0.998 1.000 2 Geranium robertianum 35.8 0.964 0.990 2 Hydrophyllum virginianum 35.8 1.000 1.000 2 Sanguinaria canadensis 36.5 1.000 0.992 2 Cardamine diphylla 36.6 1.000 0.978 2 Solidago flexicaulis 36.6 0.994 0.998 2 Fraxinus americana 37.8 1.000 1.000 2 Asarum canadense 41.6 0.996 0.958 2 Solidago caesia 41.6 0.998 0.958 2 Dryopteris carthusiana 47.0 0.998 0.958 2 Table 4 TITAN analysis for species across the gradient of natural land cover, including the individual taxa for which a significant decrease/increase in abundance across the gradient of total area of continuous forest around each sample plot. This only includes significant species responses. Species that are increasers (filter value of 2) are bolded. Land use composition Response species Environmental change point Purity Reliability Filter Continuous forest surrounding sites Erythronium americanum 1172.0 0.998 0.998 1 Carex pedunculata 1116.5 0.966 0.988 2 Fraxinus americana 1116.5 0.992 0.992 2 Caulophyllum giganteum 1206.0 1.000 1.000 2 Dryopteris intermedia 1206.0 0.994 0.966 2 Cardamine diphylla 1590.0 0.996 0.952 2 Parthenocissus vitacea 1590.0 0.998 0.958 2 Dryopteris carthusiana 1827.0 1.000 0.996 2 Asarum canadense 3501.5 0.996 0.974 2 Geranium robertianum 3501.5 1.000 0.998 2 Solidago caesia 3501.5 0.992 0.960 2 Elymus hystrix 4449.0 0.992 0.964 2 Hydrophyllum virginianum 4449.0 1.000 1.000 2 Viola pubescens 4449.0 0.992 0.962 2 Sanguinaria canadensis 6975.0 1.000 0.976 2 Solidago flexicaulis 9501.0 0.974 1.000 2 There were 21 species associated with higher natural land cover and negatively associated with urban land cover (see Table 2 . for significant associations with urban landcover, Table 3 . for significant associations with natural landcover, and Table 4 . for significant associations with continuous forest cover). There were only 4 species that were positively associated with urban cover—and negatively with natural cover— Impatiens capensis , Euonymus obovatus , Erythronium americanum , and Alliaria petiolata ,, of which only A. petiolate was non-native. Erythronium americanum was consistently associated with urban land cover, being an increaser for urban land cover and decreaser for natural land cover consistently at all scales except for 1km urban land cover (Table 2 ). E. Americanum , the species most consistently associated with urban land cover was positively associated with increases in urban area with an environmental change point—the value of the land-use variable (percentage cover) that most strongly separates the likelihood of taxon into two groups – of 51.5% at 1km, 36.3% at 2km, and 20.1% at 5km. Aggregate community measures A series of linear regressions revealed a negative relationship between urban land cover and plant diversity metrics (Table 5 ), and positive relationships between natural land cover and plant biodiversity at all scales. While this table contains all the regression relationships that were found to be significant, the R 2 -values for many of these relationships are below acceptable measures, with R 2 values generally being acceptable at equal to or greater than 0.10 (0.26 = substantial, 0.13 = moderate, 0.02 = weak) (Falk & Miller, 1992 ; Cohen 2013 ). Richness, Richness of native species, and ratio of native/non-native ratio of species richness (specifically in urban environments) are the variables that most consistently display both significant p-values, and acceptable R 2 -values. It is also notable that the R 2 values for native species richness and abundance were slightly higher than those of the total species richness and abundance. Table 5 The output of a series of linear regressions, showing the landscape variable (independent variable) on the far left, and the biodiversity metrics (dependent variable), the R 2 value, and the P-values for each of the significant relationships tested (NOTE only those statistically significant relationships are included). The Estimate value is the slope of the relationship. Land use (percentage) Biodiversity metric p-value R 2 value Estimate 1km Natural Lands Richness 0.007 0.107 0.0465 Richness (native) 0.003 0.130 0.0458 Abundance 0.010 0.100 0.160 Abundance (native) 0.007 0.110 0.160 2km Natural Lands Richness 0.002 0.138 0.0613 Shannon diversity 0.044 0.0618 0.00770 Richness (native) 0.001 0.156 0.0584 Abundance 0.002 0.146 0.225 Abundance (native) 0.001 0.154 0.221 5km Natural Lands Richness p < 0.001 0.176 0.0932 Shannon diversity 0.0241 0.0770 0.0115 Richness (native) p < 0.001 0.22 0.0931 Richness native/non-native ratio 0.023 0.0785 0.00167 Abundance 0.009 0.102 0.253 Abundance (native) 0.007 0.108 0.248 1km Urban Lands Richness 0.001 0.145 -0.0373 Shannon diversity 0.030 0.0718 -0.00490 Richness (native) p < 0.001 0.180 -0.0373 Richness native/non-native ratio 0.0403 0.0641 -0.000667 Abundance 0.021 0.0800 -0.0991 Abundance (native) 0.018 0.0839 -0.0970 2km Urban Lands Richness 0.002 0.139 -0.0369 Shannon diversity 0.039 0.0647 -0.00470 Richness (native) p < 0.001 0.178 -0.0375 Richness native/non-native ratio 0.014 0.090 -0.000798 Abundance 0.034 0.0682 -0.0924 Abundance (native) 0.028 0.0730 -0.0913 5km Urban Lands Richness 0.003 0.133 -0.0373 Shannon diversity 0.042 0.0632 -0.00479 Richness (native) p < 0.001 0.175 -0.0383 Richness native/non-native ratio 0.005 0.115 -0.000930 Abundance 0.038 0.0658 -0.0937 To better understand the patterns of biodiversity change observed along land cover gradients, we plotted the residuals of each significant linear model as a series of spread-location plots (Fig. 4 .). Many of the significant models showed higher variance in areas with lower urban cover. The variance of the residuals decreased, and forest community biodiversity measures were closer to the model predictions as urban land cover increased. Discussion Our findings confirm the initial hypothesis that urban-dominant landscapes have measurable impacts on forest understory plant communities, and that these patterns are significantly associated with changes in land cover (urban to natural). The patterns were measurable and significant at multiple scales, with the direction of relationship between land cover and plant community response remaining consistent at 1km, 2km, and 5km. These results also confirm our hypothesis that biodiversity increases with total cover of natural lands. The significant associations between non-native species and urban land cover were marginal, which goes against our initial hypothesis. The significant findings of both species-level and community-level changes associated with land cover confirm our expectations that land cover in mosaic landscapes does indeed have significant impact on plant communities, and can impact forest’s potential to support biodiversity, even when the changes are indirect changes. Our initial species-level analysis—the NMDS—found that composition of plant species changed significantly with land cover composition at all scales. These results confirm that the CVC forest understory communities are not a random assemblage of plants, but are distinct groupings significantly associated with the composition of land cover around each forest site. The NMDS ordination space indicates that species composition in areas of high urban land cover are different from those in high natural land cover (high continuous forest cover occupied a similar space in the NMDS as sites with high natural cover). The TITAN analysis substantiates these findings, with significant findings indicating separate groups of species that had significantly higher probability of presence in forests surrounded by natural land cover compared to the species with higher probability of presence in forests surrounded by urban land cover. Additionally, the NMDS found that the number of species in the communities also changed. Sites that were strongly associated with urban cover typically had a low number of species overall (approx. 1–9 species in the sites most closely associated with the urban land cover vectors), while sites closely associated with natural land cover contained a much higher number of species (around 30 species in the sites most closely associated with the natural land cover vectors). The TITAN analysis also showed many more species significantly associated with natural land cover than urban land cover. In fact, the TITAN analysis identified 10 or more species associated with high natural land cover and low urban cover (depending on the scale), compared to a maximum of 4 species associated with increases in urban land cover and decreases in natural land cover. These significant differences in composition (what species are present) and the number of species in a community for these species-specific analyses suggest that patterns of plant community assembly are associated with surrounding land cover. It is likely that the mechanism(s) causing these changes are related to the wider landscape, and are due to resource availability, competition, and species dispersal mechanism, which would be in line with conventional/accepted understandings of mosaic landscapes (Swan et al., 2021 ). Plant species niches – the integration of a species’ resource requirements, environmental tolerances, and the physical reality of the area they inhabit—determine the environments in which a species can occur (Hutchinson 1978 ). Plant communities in a resource rich environment often contain a wide distribution of niches where multiple species (even species with similar niches) can co-exist (Miedema et al. 2019 ; Pillar et al. 2009 ). Natural landscapes are associated with higher resources, such as habitat availability and soil nutrients, compared to urban and agricultural areas which are typically less hospitable to plants due to reduction in natural features (Blouin et al., 2019 ). Increases in urban cover can force species competition and cause a more consistent response among species and species indicators, due to reduction in the availability of natural features and resources (Tang & Zhou, 2011 ; Brown et al., 2021 ). Both the NMDS and TITAN confirmed this pattern, with higher urban land cover in the surrounding landscape typically containing a small number of species, a high percentage of which was made up of species identified by the TITAN analysis as associated with high urban cover ( I. capensis, E. obovatus, E. americanum, A. petiolata ). There were much fewer species that responded significantly to urban land cover (4 species), but they may provide greater insight into species-level responses to urbanization through niche space. Species with qualities that provide an advantage in urban landscapes will respond positively. Consistent patterns in what species respond positively to urban cover could indicate traits or lifecycle adaptations that allow them to survive in urban areas (Rivkin et al., 2019 ). In turn, species associated only with natural land cover could indicate species of high conservation concern, as they are not able to adapt and are not as likely to be present if land cover changes to a higher urban composition. However, this might be more difficult to parse out, due to the high number of species that are typically found in areas with high natural land cover. Although species-level responses provide helpful insight, it is important to also consider the trait- or niche-specific responses associated with the species with significant responses (Miedema et al. 2019 ). For example, E. americanum , a species consistently associated with urban environments, has been found to be an extremely resilient species, with resistance to herbicide, disturbance, and is generalist for pollination (Ristau, 2010 ; Taki, 2007 ). This could be due to its lifecycle as a spring ephemeral, meaning it completes most of its life cycle in the early spring. Ephemerals will remain dormant throughout the rest of the year—up to 10 months of low to slow growth (Lapointe, 2001 ). The ephemeral lifecycle may result in higher resilience to disturbances associated with urban expansion due to this dormancy, and thus reduced interactions with other species and disturbances (Tessier, 2012 ). The other two native species, I. capensis and E. obovatus have also been found to be hardy species, although they do not share the ephemeral lifecycle. In fact, Cipollini & Hurley (year) showed that I. capensis shows evolutionary resilience to invasive species that often have detrimental effects on other species (Cipollini & Hurely, 2008; Dorning et al., 2005). E. obovatus has been found to be a common species in forest understories and with growth strategies that allow them to compete with invasive species (Hinman, 2018 ). Further research into trait-specific responses of both individual species, and plant communities can provide more direct insight into the mechanisms that may produce resilient, biodiverse communities. In addition to the species-level responses confirmed by the NMDS and the TITAN analysis, the linear regressions confirm that there is also a significant response in community-aggregate biodiversity associated with land cover changes. Unsurprisingly, areas with high urban land cover had consistently negative relationships with plant biodiversity metrics. Species richness, abundance, and Shannon diversity all showed significant negative relationship with urban land cover, and a significant positive relationship with natural land cover at all three scales. Changes in richness mean the number of species present are changing, while changes in abundance mean that the abundance of species relative to one another are also changing (Storch, 2018). The co-response of both species richness and species abundance together indicates that there is consistent change in biodiversity, from higher levels in areas with more natural land cover to lower levels in areas with higher urban cover. While all biodiversity metrics have significant p-values, the R 2 -value for many of these relationships was unacceptably low (between R 2 = 0.06 and R 2 = 0.09), indicating that the change in the land cover around each forest site did not explain enough changes in biodiversity to be considered an important factor. However, the biodiversity response that consistently fell within low, but acceptable levels (between R 2 = 0.13 and R 2 = 0.22 depending on the scale and land cover metric considered) was species richness. This suggests that, while the species present are also changing, the most consistent response closely tied to changes in surrounding land cover is the number of species that each site contains. These findings are consistent with species-level analysis that, as previously discussed, shows fewer species responding to urban land cover compared to natural land cover, suggesting that urban land cover reduces an area’s ability to support native species. This is consistent with the concept of niche-availability where areas with higher resource availability (natural areas, population sources, nutrients etc.,) such as areas with high natural cover, can sustain more species (Lavorel & Garnier, 2002 ; Velland et al., 2007). The consistently negative response of biodiversity to urban land cover around forest sites indicates a sensitivity to urban expansion that goes beyond direct effects of habitat disturbance often observed in urban ecology. This is in keeping with past studies which found that there was a significant relationship between plant community diversity and the composition of the surrounding landscape (Milne & Bennet, 2007; Miedema et al., 2019 ). Biodiversity and species-level changes could be due to both direct and indirect effects in the landscape. These effects are likely related to the idea that population diversity and richness are controlled by the abiotic and biotic factors that select from the species that are already present or able to easily disperse and establish in a given area (Swan et al., 2021 ). While management and forest type might remain consistent in our study sites, higher urban land cover means a higher likelihood that forest sites are isolated from other similar habitats by an impermeable or hostile surrounding (in this case an urban or industrial landscape) (MacArthur & Wilson, 2001 ). The landscape itself acts as an abiotic filter, with species possessing well-suited traits remaining or re-establishing themselves. For example, high dispersal distance (such as species with bird or wind seed dispersal methods) might increase species’ ability to re-establish in isolated areas, and pollinator generalists, species with high resilience to non-native species competition (e.g., allelopathy, competitive growth cycles etc.,), low resource needs, will be able to increase reproduction and maintain populations even in isolated urban areas (Cipollini & Hurely, 2008; Beninde et al., 2015 ). This must be understood in light of the higher prevalence of non-native and generalist species that might actually be sourced from this urban matrix, leading to homogenized communities with similar survival strategies (Blouin et al., 2019 ). In fact, the slightly higher r 2 values for linear relationships when only native species were considered could indicate that native species are more sensitive to changes in land cover, as they are not well suited to the urban ‘matrix’ and are more likely to suffer the negative effects. Difference in responses to environmental conditions does not only vary between species but can change when different scales are considered (Fischer et al., 2004 ). Our findings showed a very consistent direction to plant-land cover relationships in both species-specific analysis and community-level biodiversity measures at all scales. This would suggest that both species-specific responses, and general patterns of community change would be dependent on the rearrangement of the communities present through novel pressures caused by urban expansion (Lôbo et al. 2011 ). This could have to do with dispersal if species are not able to establish, or are not already present, they cannot persist no matter how resilient or well-adapted they are. Thus, areas of high isolation, such as areas with high urban land cover, will be more likely to have low diversity. Consistency of the significance and direction of the relationship across 1km, 2km, and 5km scales is relevant as well. The percent of landcover take up by urban and natural lands was similar between the 1km and 2km scales, typically shifting between 10–15% on average. But the changes between 1km and 5km scales was often much larger, and the fact that the direction of the relationship between biodiversity and landcover remains the same indicates a high likelihood that urban and natural landcover encompasses the mechanisms behind these changes. There was no direct relationship of note between land cover and non-native species, but the ratio of native/non-native species richness did show significant correlation in the linear regressions associated with urban land cover at all three scales, and with natural land cover only at the 5km scale. The only ratio associated with urban land cover at 5km having an acceptable r 2 -value was urban land cover at 5km. The TITAN analysis also had a lack of significant connection between non-native species and land cover, with the majority of significant indicators of environmental gradients being native species even those associated with more urban cover. In fact, there were only two non-native species identified by the TITAN analysis as significantly associated with any changes in land use composition in total. The first was A. petiolata , which increased with urban land cover at 2km and 5km and was associated with a decrease in natural land cover at 5km. This is expected, as many non-native species have sources in urban areas, and presence and extent of urbanization are consistently associated with presence of non-native species (Schwoertzig et al., 2016 ; Skultety & Matthews, 2018 ). In contrast, the other non-native species, T. officinale , was associated with decreases in urban land-cover at 1km. It is generally held that most non-native species that increase with the high disturbance associated with urban land cover do so through adaptations that provide them with advantages over native species (Rivkin et al., 2019 ). T. officinale is often cited to be a generalist that is resilient to negative urban effects (Pisman et al., 2020 ) so finding it to be associated with decreases in urban land cover is surprising. This could indicate that T. officinale , being a generalist, has adapted to forest and urban areas alike and, although it is not native to this area, it has integrated itself into both urban and natural landscapes. The unexpected responses of native and non-native species in both species-level and community-level responses could indicate plant responses to landscape change are more connected to functional response –both native and non-native—than to a plant’s origins (Kondratyeva et al., 2020 ; Lôbo et al. 2011 ; McLaren & Turkington, 2010 ). This could explain why native species such as E. Americanum are associated with increased urban land cover, while T. officinale , a non-native species, would be associated with decreases in urban land cover. Functional traits, and species-specific responses we are only able to touch on could also explain why non-native species did not show significant response to urban land-cover in the linear regression models, contrary to our hypothesis that urban land use would result in significantly higher richness and abundance of non-native species. The lack of significant correlation could also be due to sample locations being within a conservation area, which would have a two-fold effect. The first would be that not all sites are directly connected to urban areas—which would increase the dispersal distance that non-native species would have to traverse to populate the areas, thus decreasing richness and abundance of non-native species (Hansen & Clevenger, 2005 ; Theoharides & Dukes, 2007 ). The second effect is the restoration and management efforts implemented by the conservation authority (in this case the CVC) which has action plans in place to reduce non-native species populations and promote native species (CVC). Conclusion In this study we show that forest understory vegetation shows a significant relationship to surrounding land cover composition, with changes associated with urban and natural land cover being consistently significant at 1km, 2km, and 5km scales. This indicates that the forest understory communities of the CVC are not random assemblages, but communities found in predictable patterns that are associated with the composition of the landscape around each site. This shows these forest communities are shaped by the indirect impacts of the surrounding landscape as well as by the specific and direct environmental factors of each forest. Though this illustrates important landscape level patterns, the land cover is likely an indirect measure, with more detailed mechanisms leading to these forest community changes which would account for more of the variance in the data. The fact that most of these relationships have only moderate explanatory power (R 2 -value) along with the significant p-values suggests that this is only a general estimate for the effects of land-use change, but more direct effects that account for both functional traits and mechanistic changes (e.g., dispersal method to account for species dispersal etc.) could provide a more nuanced and accurate model. However, the significance of the patterns observed makes a strong case for the importance of increased natural land cover composition in the landscape, especially if higher biodiversity is to be maintained. The significant impact of natural landcover on biodiversity also indicates that smaller-scale conservation design choices could enhance global efforts by increasing the biodiversity capacity of fragmented areas. Our findings suggest that landcover around conservation lands can make an important contribution to biodiversity support, even in a multi-use landscape. While the 30x30 goals of the Kunming-Montreal Global Biodiversity Framework suggest that 30% of all natural lands should be conserved worldwide, these findings suggest that, in mosaic landscapes at least, 30% landcover around conservation sites could also act as a biodiversity benchmark. As mosaic landscapes are increasingly prevalent, future research into optimal landcover configuration will also be helpful in discovering the best ways to promote biodiversity in conservation lands that remain. While conservation efforts are effective – as demonstrated by the lack of significant correlations between non-native species and urban land cover – there are still significant impacts that surrounding urban land cover can have on the interior forest of conservation lands. As urban land-cover appeared to have a stronger and more consistent control on biodiversity metrics, our findings would also confirm that conservation efforts should be focused on more urban landscapes when resources are limited. Important impacts of landscape level change on biotic communities, even without direct urban disturbances. This is particularly important to consider as urban surface continues to expand, and predictions estimate that urban land cover will continue expanding, increasing by about 1,500,000 km 2 by 2030 with an estimated of changes 30–44% coming forested areas and grasslands (Seto et al., 2011 ; Chen et al., 2020 ). Declarations Acknowledgements This research was partially funded by the Canada First Research Excellence Fund. Data used with permission of Credit Valley Conservation Authority 2023. 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A framework for understanding how biodiversity patterns unfold across multiple spatial scales in urban ecosystems. Ecosphere , 12 (7), e03650. https://doi.org/10.1002/ecs2.3650 Taki, H. (2007). Effects of habitat loss in a Canadian deciduous forest: Analyses of understory plant and insect relationships (Doctoral dissertation, University of Guelph). Tang, J., & Zhou, S. (2011). The importance of niche differentiation for coexistence on large scales. Journal of Theoretical Biology , 273 (1), 32-36. https://doi.org/10.1016/j.jtbi.2010.12.025 Tessier, J. T. (2012). Methods of belowground movement in Erythronium americanum. Northeastern Naturalist , 19 (sp6), 77-88. https://doi.org/10.1656/045.019.s606 Theoharides, K. A., & Dukes, J. S. (2007). Plant invasion across space and time: factors affecting nonindigenous species success during four stages of invasion. New phytologist , 176 (2), 256-273. https://doi.org/10.1111/j.1469-8137.2007.02207.x Tscharntke, T., Tylianakis, J. M., Rand, T. A., Didham, R. K., Fahrig, L., Batáry, P., Bengtsson, J., Clough, Y., Crist, T. O., Dormann, C. F., Ewers, R. M., Frund, J., Holt, R. D., Holzschuh, A., Klein, A. M., Kleijn, D., Kremen, C., Landis, D. A., Laurance, W., Lindenmayer, D., Scherber, C., Sodhi, N., Steffen-Dewenter, I., Thies, C., van der Putten, W. H., Westphal, C., (2012). Landscape moderation of biodiversity patterns and processes‐eight hypotheses. Biological reviews , 87 (3), 661-685. https://doi.org/10.1111/j.1469-185X.2011.00216.x Valdes, M. A. (2008). Non-metric multidimensional scaling (NMDS) as a basis for a plant functional group classification and a Bayesian belief network formulation for California oak woodlands . University of California, Davis. Vellend, M. A. R. K., Verheyen, K., Flinn, K. M., Jacquemyn, H. A. N. S., Kolb, A., Van Calster, H., ... & Hermy, M. (2007). Homogenization of forest plant communities and weakening of species–environment relationships via agricultural land use. Journal of Ecology , 95 (3), 565-573. https://doi.org/10.1111/j.1365-2745.2007.01233.x Watson, J. E., Dudley, N., Segan, D. B., & Hockings, M. (2014). The performance and potential of protected areas. Nature , 515 (7525), 67-73. doi:10.1038/nature13947 https://doi.org/10.1038/nature13947 Yan, Z., Teng, M., He, W., Liu, A., Li, Y., & Wang, P. (2019). Impervious surface area is a key predictor for urban plant diversity in a city undergone rapid urbanization. Science of the Total Environment , 650 , 335-342. https://doi.org/10.1016/j.scitotenv.2018.09.025 Zhang, S., Zhang, Q., Yan, Y., Han, P., & Liu, Q. (2021). Island biogeography theory predicts plant species richness of remnant grassland patches in the agro-pastoral ecotone of northern China. Basic and Applied Ecology , 54 , 14-22. https://doi.org/10.1016/j.baae.2021.04.010 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Nov, 2024 Read the published version in Landscape Ecology → Version 1 posted Reviews received at journal 17 Sep, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviewers agreed at journal 12 Aug, 2024 Reviewers invited by journal 01 Aug, 2024 Editor assigned by journal 19 Jul, 2024 Submission checks completed at journal 19 Jul, 2024 First submitted to journal 18 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4763336","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339360632,"identity":"3bf76fc6-413c-4344-80d8-fce8664ef625","order_by":0,"name":"LIANE MIEDEMA BROWN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDACZgYGCSAlww/hWjAYEKuFR7IBzJUgQgsDVIvBAWK16LYzH7zxMceOx/hG8uHXPBUSeeYMzA8/4NNidpgt2XLmtmQesxtpadY8ZySKLRvYjCXwa+Exk+bdxgzUkmNmzNsmkbjhAA8DAS3836T/bqvnMZ4B0vIPrIX5BwFb2KQZtx3mMZDIMX7M2wDWwkbAFjZjy95tx3kkzjxLY5xzTKLY4DCbmQVeLecPP7zxc1u1HH978uEPb2ps8gyONz++gU8LMgC7JwEUuUQD5g9gLaNgFIyCUTAK0AAAjoRFX6oFK2YAAAAASUVORK5CYII=","orcid":"","institution":"University of Guelph","correspondingAuthor":true,"prefix":"","firstName":"LIANE","middleName":"MIEDEMA","lastName":"BROWN","suffix":""},{"id":339360633,"identity":"36235fa6-4ab2-44bf-98cd-da92d53309ed","order_by":1,"name":"MADHUR ANAND","email":"","orcid":"","institution":"University of Guelph","correspondingAuthor":false,"prefix":"","firstName":"MADHUR","middleName":"","lastName":"ANAND","suffix":""}],"badges":[],"createdAt":"2024-07-18 14:32:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4763336/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4763336/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10980-024-01988-9","type":"published","date":"2024-11-16T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62730696,"identity":"eb382492-6fa2-439e-9038-a870915e19b1","added_by":"auto","created_at":"2024-08-18 23:20:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3753971,"visible":true,"origin":"","legend":"\u003cp\u003eThe land cover of the Credit Valley Watershed in southern Ontario.\u003c/p\u003e\n\u003cp\u003eThe legend indicates land-use types, mainly forest, cropland, and urban areas. Forests are shown in green and are more prevalent in the northern sections of the watershed, although still patchy and often fragmented into elongated corridors. Cropland is shown in yellow and dominates the middle and norther areas of the watershed. Finally, urban land cover is shown in black and covers the southern 1/3 of the watershed with large patches throughout the northern areas.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4763336/v1/8fc01299786781b909dca4a7.png"},{"id":62730236,"identity":"ec463640-9377-4f88-b43e-fe26c6a8d996","added_by":"auto","created_at":"2024-08-18 23:12:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65531,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS of species abundance and similarities of CVC forest understory sites.\u003c/p\u003e\n\u003cp\u003eEach circle is representative of a site. Points closer together and more similar in size relative to other points should be considered to be more similar in species composition. Sample units shown with larger circles account for more of the variation in the overall fit of these data. The majority of sites are clustered towards the lower center of the ordination space, with large circles as outliers on the top (positive in axis 1 and axis 2), bottom left (negative in axis 1 and axis 2) and top left (negative in axis 1 and positive in axis 2).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4763336/v1/1275bac5291da60f3eb5a6ad.png"},{"id":62730234,"identity":"a8dc63f6-450d-4779-aa93-0e33b767f952","added_by":"auto","created_at":"2024-08-18 23:12:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64304,"visible":true,"origin":"","legend":"\u003cp\u003eThe arrow’s location and direction indicate the relationship between species ordination (NMDS) and the land cover variable.\u003c/p\u003e\n\u003cp\u003eSpecies combinations closer to the arrows being more representative of the type of community strongly associated with that land cover variable. Included in this figure are natural land cover at 1, 2, and 5km (1 km Natural, 2 km Natural, 5 km Natural respectively), urban land cover at 1, 2, and 5km (1 km Urban, 2 km Urban, 5 km Urban respectively) and continuous forest around each site (continuous). The urban land cover vectors are associated with the top right NMDS ordination space (positive in axis 1 and axis 2) while natural land cover vectors and the continuous forest vector is associated with the bottom left NMDS ordination space (negative in axis 1 and axis 2). The opposing axis spaces occupied by urban vs. natural land cover vectors indicates that sites associated with different landscapes have very different species compositions. NOTE: Only two dimensions of the NMDS are illustrated here for ease of interpretation.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4763336/v1/10964c180cd61c72a20a44cc.png"},{"id":62730697,"identity":"7421ded4-a53c-423f-926f-17f2f5b41698","added_by":"auto","created_at":"2024-08-18 23:20:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":545547,"visible":true,"origin":"","legend":"\u003cp\u003eThe residuals of significant linear regressions between biodiversity metric and land cover gradient are plotted as a series of spread location plots.\u003c/p\u003e\n\u003cp\u003eResiduals are shown at 1km, 2km and 5km scales for the following relationships: a) Shannon diversity vs. urban land cover b) species richness vs. urban land cover, and c) species abundance vs. urban land cover. The size of the residual is represented by a colour gradient from red (large residuals), orange and yellow (moderate residuals), and green (low residuals). The general observed pattern across all significant relationships was that residuals were larger (red) in areas with higher natural cover and became lower (green) across the gradient toward urban land cover.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4763336/v1/8b778f53eb2eef4c1bc9b963.png"},{"id":69285491,"identity":"65454f40-b904-4d06-8cec-6c6a5647e3e7","added_by":"auto","created_at":"2024-11-18 19:26:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5770838,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4763336/v1/c91f4e71-be1c-4626-a1b1-d34972d5eda6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impacts of urban land-cover on plant community structure and biodiversity in a multi-use landscape","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCanada recently hosted the COP15 Biodiversity talks, collectively agreeing to reduce human-induced extinctions, restore degraded ecosystems, and halt overwhelming biodiversity loss (Findlay, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Identified in this bold agreement is the promise to conserve 30% of natural lands (including forests, inland waters, coastal areas, and oceans) by 2030 (Joly, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the size and scope of human-induced land-use change poses an extreme challenge to both land-based and biodiversity-based conservation goals. Almost 75% of natural landscapes worldwide have been impacted and/or modified by human activity and expansion (IPBES, 2019). Multi-use or mosaic landscapes that combine natural, urban, and semi-urban land-use types are becoming increasingly prevalent (Bennet et al., 2006), reducing the availability of large continuous habitats available for area-based conservation efforts. In fact, research has shown that optimal expansion of conservation lands consistently requires expansion into landscapes previously transformed by—and often currently occupied by—humans (Ellis \u0026amp; Ramankutty, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shen et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As more of the global landscape is impacted by human land-use, conserving large areas of untouched land becomes less viable as a stand-alone solution. We must also find ways to sustain, promote, or even restore biodiversity in the natural lands remaining in mosaic landscapes.\u003c/p\u003e \u003cp\u003eAs these mosaic landscapes are both relatively new and extremely prevalent in areas of high biodiversity conservation potential (Skultety et al. 2018; Shen et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) it is particularly important to understand how landscape structure could be shaping the biodiversity present (both species composition and aggregate biodiversity measures) across different land cover compositions (Baker et al. 2010). Research has shown that disturbances, regional dispersal, and interactions (e.g., competition) between species are some of the main drivers of biodiversity (Leibold et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Swan et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) all of which can be affected by changes in landscape composition and configuration (Miedema et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yan et al \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hall et al. 2020). Increases in urban land cover are widely considered to have negative impacts on biodiversity, but plant community diversity responses do not always follow this pattern (Albrecht \u0026amp; Haider \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Miedema et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Investigating the connections between landscape composition could provide insight into how and to what degree land cover changes affect plant biodiversity beyond the direct effects that are most often considered (Zhang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This could also give us an idea of what areas of mosaic landscapes are best suited for maintaining high levels of biodiversity and have the highest potential for maintaining conservation goals.\u003c/p\u003e \u003cp\u003ePlant communities arise through the influences of multiple abiotic and biotic factors acting as filters, selecting from a regional pool of species and determining a species’ presence and persistence in a given area (Lavorel \u0026amp; Garnier, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Velland et al., 2007). Many studies have suggested the potential of landscape mosaics to sustain plant biodiversity, with the distribution of multiple and different habitat types throughout the landscape creating opportunities suited to many species (Tscharntke et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rosenfield et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pörtner at al., 2023). This large number of diverse habitat types could produce high levels of niche availability – species presence corresponding with the unique physical and biological requirements of that species – in mosaic landscapes allowing more species to thrive (Grinnell, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1924\u003c/span\u003e; Hutchinson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Miedema et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing aggregate measures of plant community responses may provide excellent information on observable patterns of change across an environmental gradient (in this case land cover), but it does pose the risk of obscuring nonlinear or individual species changes, and thus missing or misrepresenting impacts of land cover change on plant communities (Carignan \u0026amp; Villard, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; King \u0026amp; Baker, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Individual species may have different responses to the same environmental conditions (Fischer et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Less resilient species or those with highly specific niche requirements are more likely to decline in urbanized environments, while only generalist or well-adapted species will thrive (Evangelista et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Increases in biodiversity could be constrained to non-native or more resilient species, with decreases in rare species or in diversity of traits. In fact, it has been well established that many invasive species, which often have negative effects on native populations and ecosystem functioning, are much more prevalent in urban and fragmented landscapes (Cadotte et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gaertner et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Blouin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Species-specific responses are particularly relevant as conservation and plant ecology do not consider invasive communities, only invasive species. Endangerment, and rarity are both factors essential to conservation work, and they are both only considered at the species level. Thus, community ecology needs to balance both community-aggregate and species-specific measures to create a more holistic understanding of the changes caused by landscape.\u003c/p\u003e \u003cp\u003eBy investigating both plant community and species-specific responses to land cover changes across a mosaic landscape, we can better understand the impacts that urban expansion and landscape fragmentation may have on plant communities (Carignan \u0026amp; Villard, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), thus providing insight into the future of conservation in mosaic landscapes. This project seeks to understand the influence of landscape composition – particularly urbanization – on plant community- and species-level biodiversity. Building on previous studies of the plant functional trait communities and ecosystem service delivery in the rapidly urbanizing landscape of Southern Ontario, this study focuses on the effects of landscape composition on biodiversity metrics and native species abundance. In order to provide more insight into the impact of landscape composition on conservation potential, we focused only on plant understory communities located in protected areas (conservation forests). By calculating the composition of urban and natural lands around these forest conservation sites, we can gain better understanding of how mosaic landscapes and areas of high urban cover might influence conservation potential and an area’s ability to support biodiversity. Through both species-specific and community-level measures of plant community change, we investigate correlations between biodiversity of forest conservation sites and the land-use around each sample site. We also measure changes in non-native species presence and abundance and its correlation with land-use around sample sites.\u003c/p\u003e \u003cp\u003eOur objectives were to characterize the forest understory communities of southern Ontario forests and outline how the structure of the surrounding landscape impacts the community structure and biodiversity of forest understory species. We hypothesize that biodiversity metrics will increase with total cover of forest in the surrounding lands, decrease with total cover of urban land-use, and that the abundance of non-native species will increase with total cover of urban land-use. We also anticipate that, in areas where biodiversity may stay consistent between urban and natural areas, the abundance of non-native species will increase with total cover of urban land cover.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eThe Credit Valley Watershed\u003c/p\u003e\n\u003cp\u003eSample sites were located in conservation forest sites in the Credit Valley Conservation (CVC), located in the Credit River Watershed of Southern Ontario. The forest sites are within the scope of both the Greater Toronto Area (GTA) and the Greenbelt, with the competing influences of urban expansion (GTA) and conservation efforts (Greenbelt) creating the perfect location in which to investigate the impact of landscape composition on forest fragments (Milne \u0026amp; Bennet, 2007). The 93,021 hectares of the Credit River Watershed contain a mosaic of natural, agricultural and urban land uses. The majority of the watershed is covered by agricultural land use (mainly cropland), urban landscapes (lands classified as both urban and barren lands were included), most of which are located along the southern reaches of the watershed (see \u003cstrong\u003eFig.\u0026nbsp;2.1\u003c/strong\u003e. for more details). The original deciduous and mixed forest that once covered the region now occupies just a small portion of the watershed (Milne \u0026amp; Bennet, 2007).\u003c/p\u003e\n\u003cp\u003eThe mosaic landscape of the watershed provides a model system to investigate the ways that landscape could impact the composition of forest species. Firstly, each forest sample plot is located within a conservation area, meaning that each site is a consistent ecosystem type, within continuous forest, and subject to similar management practices. Consistency in management and forest type allows us to investigate the general impacts of surrounding landscape on the assembly of the plant species, while avoiding many of the complications of different land-use type, ownership decisions, and other factors often associated with urban landscapes. Secondly, the mosaic landscape of the Credit River Watershed, with a highly diverse mixture of landscape types throughout, provides an excellent environment to observe forest biodiversity measures occurring in different landscape types\u0026mdash;mainly the spectrum between semi-urban areas near Toronto, to more naturalized areas at the northern end of the watershed.\u003c/p\u003e\n\u003cp\u003ePlant understory data\u003c/p\u003e\n\u003cp\u003eSpecies richness and abundance for each sample site was obtained from the CVC authority\u0026rsquo;s annual biomonitoring program. The CVC samples groundcover, regeneration plots, and tree cover in a subsample of forests managed by the CVC every year. Herbaceous species have been found to respond more strongly and quickly to disturbances, making them a more sensitive indicator of the changes that are brought about from increased urban land cover (Gilliam \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Compagnoni et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). For this reason, we chose to use groundcover species, which included all living forbs, ferns, grasses, sedges, rushes, vines, woody vines, and all tree and shrub stems under 16 cm in height and less than 4 cm stem diameter. Sampling for each site was performed following a modified plot-based methodology developed by the EMAN consistent across the CVC (Roberts-Pichette \u0026amp; Gillespie \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e), with a single site per conservation forest consisting of 5 permanent 1 m\u003csup\u003e2\u003c/sup\u003e subplots, with the species and % cover of each species per site recorded. Sites were sampled using a modified panel design, with some sites monitored annually, and others every other year. Species biodiversity was calculated from the groundcover species cover data from the Credit Valley\u0026apos;s terrestrial monitoring program, from 2018\u0026ndash;2022 (excluding 2020 when samples were not collected) aggregating the plant community measures to calculate species abundance, species diversity, Shannon diversity, and a native to non-native species ratio (with all non-native species, including naturalized species, being included). All species data was used with permission of Credit Valley Conservation Authority 2023.\u003c/p\u003e\n\u003cp\u003eSpatial analysis\u003c/p\u003e\n\u003cp\u003eFor the spatial analysis of the surrounding landscape, we used the North American Landcover (NAL) maps from the Commission for Environment Cooperation developed as part of the North American Land Change Monitoring System. We selected the most recent land cover update, 2020, and used at 30-meter raster dataset of North American Land cover. This map was used to calculate the percentage of urban and natural land around each sample site, as well as the total area of continuous forest around each sample location. Urban lands included lands classified as both urban and barren lands, and natural lands were an aggregate of lands classified as forest, grassland, shrubland and wetland. These metrics were calculated for 1km, 2km, and 5km radius around each sample plot (Commission for Environmental Cooperation (CEC), \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). We also calculated the total area of continuous forest around each sample point, using the total area of the forest (both CVC conservation forest and other forest areas) to independently measure the effect that forest size might have on species diversity.\u003c/p\u003e\n\u003cp\u003eSpecies specific analysis\u003c/p\u003e\n\u003cp\u003eTo better understand the species distribution and relationship to land composition gradient, we first used ordination to visualize the species distribution. We performed a non-metric multidimensional scaling (NMDS) analysis, which uses a dissimilarity matrix of species abundances to illustrate similarity of species compositions. We used NMDS ordination based on Bray-Curtis dissimilarity distances (Valdes \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) to first visualize the species compositions and then to interpret them in relation to the environmental factors (i.e., the land use composition surrounding each site at the three scales). When including 3 dimensions, we obtained an acceptable goodness of fit (stress\u0026thinsp;=\u0026thinsp;0.16) for our data. We followed the NMDS ordination by fitting trends the environmental vectors (land use composition and continuous forest) onto the NMDS species ordination using the envfit() function as part of the vegan package (Oksanen, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe association of individual species was tested using a Threshold Indicator Taxa Analysis (TITAN), using indicator scores to integrate occurrences, abundance, and directionality of species response to an environmental gradient (in this case % of urban cover and % of natural cover at 3 scales, and continuous forest cover). Species that occurred fewer than 3 times throughout the samples were considered rare and were removed from the data. This analysis related species abundances to land use variables, providing more insight into the patterns of species distributions and identifying change points in the surrounding land cover that results in significant changes in the plant community, along with which species were associated with specific land use variables. We used TITAN to identify indicator species, and the optimum value of environmental gradient (both urban and natural) that maximizes the TITAN species score, for species identified as significant through the TITAN analysis. This method distinguishes negative and positive taxa responses and tracks cumulative responses of declining and increasing taxa in the communities (Bakker \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). We used R version 4.2.2 (2022-10-31).\u003c/p\u003e\n\u003cp\u003eCommunity aggregate measures\u003c/p\u003e\n\u003cp\u003eNext, we completed a series of linear regressions comparing each of the biodiversity metrics with gradients of natural and urban land use at 1km, 2km and 5km scales. Species measures at each site were aggregated into measures of species richness, species abundance, and Shannon diversity. To account for unequal sampling (due to the modified panel design) across multiple years, we used a fixed effect regression with both year and site as fixed effects in the model. These fixed effects account for the fact that there is grouping within the data (some sites that appear multiple times as they are sampled in multiple years). The fixed effects were added to ensure that the model controls for variation within groups (year and site) and better accounts for the effect of landcover on the biodiversity metrics. Finally, we plotted residuals of the significant linear models in ggplot. All analyses were done using the R version 4.2.2 (2022-10-31).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSpecies-specific analysis\u003c/p\u003e\n\u003cp\u003eWe used CVC groundcover data from the years 2018\u0026ndash;2022 resulting in 69 sample points. We ran an NMDS with the 147 species that were identified as being present in the CVC forest sites. A three-dimensional solution was chosen as it had a lower Kruskal\u0026rsquo;s stress value for the three-dimensional solution (Stress\u0026thinsp;=\u0026thinsp;0.160). Stress values close to 10 are considered quite satisfactory, and most ecological community data sets have solutions with stress between 10 and 20 (Kruskal \u003cspan\u003e1964a\u003c/span\u003e; Clarke \u003cspan\u003e1993\u003c/span\u003e; McCune \u0026amp; Grace 2002) (See Fig. \u003cspan\u003e1\u003c/span\u003e.).\u003c/p\u003e\n\u003cp\u003eThe NMDS ordination of species composition was overlayed with the land cover composition, showing significant relationship natural land cover, continuous forest cover, and several other land-use variables (Table \u003cspan\u003e1\u003c/span\u003e). All the land cover metrics were significantly associated with species changes in the NMDS at all scales, and the direction of the urban cover vector was nearly opposite of the natural cover vector in the NMDS space. The natural land cover and continuous forest cover showed negative correlations with NMDS axis 1 and NMDS axis 2, while the urban land cover showed positive correlations in both axes (Fig. \u003cspan\u003e3\u003c/span\u003e.). Both urban and natural land cover were significant at 1km, 2km, and 5km scales, with low R\u003csup\u003e2\u003c/sup\u003e values that increased with the scale (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.29, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.32, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.34, respectively for urban, and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.18, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.29, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.43 for natural). The natural land cover showed the strongest explanatory factor, but also the largest difference in explanatory power (R\u003csup\u003e2\u003c/sup\u003e values) between 1km and 5km scales. The land use variables with the strongest explanatory factor were urban cover at 2km (p\u0026thinsp;=\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.32) and 5km (p\u0026thinsp;=\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.34), and natural cover at 5km (p\u0026thinsp;=\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.44).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResults and linear trends. Maximum linear correlations (r\u003csup\u003e2\u003c/sup\u003e) of the environmental variables with the NMDS ordination patterns are shown. Significance of the correlations was calculated using 1000 permutations.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLand use variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNMDS1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNMDS2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuous forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural land cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban land cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFor the TITAN analysis, we removed the species present in less than 3 of the sites, resulting in 76 species. The output of the TITAN analysis identified both the positive and negative responses of individual species to land cover composition at the three scales considered (Table \u003cspan\u003e2\u003c/span\u003e). An Adonis test was used to confirm that all of the land cover variables used (both natural cover and urban cover at 1km, 2km, and 5km, and continuous forest cover) were significantly associated with changes in the species composition at each site (Bakker, \u003cspan\u003e2023\u003c/span\u003e). The majority of species that the TITAN analysis identified as significant were native species and were associated with both natural and urban land cover. In fact, only two non-native species showed any significant association with land cover in the TITAN analysis. The first was \u003cem\u003eAlliaria petiolata\u003c/em\u003e, which increased with urban land cover at 2km (purity\u0026thinsp;=\u0026thinsp;0.998, reliability\u0026thinsp;=\u0026thinsp;0.998) and 5km (purity\u0026thinsp;=\u0026thinsp;0.992, reliability\u0026thinsp;=\u0026thinsp;0.988) scales, and decreased in natural land cover at 5km (purity\u0026thinsp;=\u0026thinsp;0.964, reliability\u0026thinsp;=\u0026thinsp;0.98). The other non-native species, \u003cem\u003eTaraxacum officinale\u003c/em\u003e, was associated with decreases in urban land-cover (purity\u0026thinsp;=\u0026thinsp;0.972, reliability\u0026thinsp;=\u0026thinsp;0.988) at 1km scale.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eTITAN analysis for the CVC plots, including the individual taxa for which a significant decrease/increase in abundance across the gradient of surrounding urban land cover. The environmental change point indicates the significant community-level change point, or the percentage of urban land cover in the surrounding landscape that most strongly differentiates plots with and without the species. The filter value indicates the direction of the relationship, with increaser species (filter\u0026thinsp;=\u0026thinsp;2) increasing with higher levels of the land cover in question, and decreasers (filter\u0026thinsp;=\u0026thinsp;1) decreasing with higher levels of the land cover in question. This includes the analysis for species across the gradient of urban land cover, including the individual taxa for which a significant decrease/increase in abundance across the gradient of surrounding natural land use. This only includes significant species responses. Species that are increasers (filter value of 2) are bolded.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLand use composition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponse species\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnvironmental change point\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePurity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReliability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFilter\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 km urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCaulophyllum giganteum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSolidago caesia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTaraxacum officinale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAsarum canadense\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eElymus hystrix\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGeranium robertianum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eViola pubescens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDryopteris carthusiana\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFraxinus americana\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHydrophyllum virginianum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCarex pedunculata\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 km urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDryopteris carthusiana\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFraxinus americana\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHydrophyllum virginianum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCaulophyllum giganteum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCarex pedunculata\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlliaria petiolata\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eErythronium americanum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e59.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.992\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.978\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 km urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDryopteris carthusiana\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSanguinaria canadensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eViola pubescens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCarex leptonervia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFraxinus americana\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCarex pedunculata\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHydrophyllum virginianum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCornus alternifolia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCaulophyllum giganteum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eErythronium americanum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e18.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlliaria petiolata\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e76.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.992\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.988\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eTITAN analysis for species across the gradient of natural land cover, including the individual taxa for which a significant decrease/increase in abundance across the gradient of surrounding natural landcover. This only includes significant species responses. Species that are increasers (filter value of 2) are bolded.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLand use composition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponse species\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnvironmental change point\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePurity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReliability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFilter\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 km natural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErythronium americanum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaulophyllum giganteum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.994\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFraxinus americana\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e37.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.982\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.980\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCornus alternifolia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e42.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.956\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.950\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarex peckii\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e44.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.964\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsarum canadense\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e57.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.994\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.964\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDryopteris intermedia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e57.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.970\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeranium robertianum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e57.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSolidago caesia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e60.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.966\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrophyllum virginianum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e63.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSanguinaria canadensis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e71.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.974\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2km natural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErythronium americanum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFraxinus americana\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e31.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.996\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.980\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarex albursina\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.996\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.968\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaulophyllum giganteum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.988\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.992\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeranium robertianum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrophyllum virginianum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSolidago flexicaulis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.992\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardamine diphylla\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e40.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.986\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarex peckii\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e40.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.978\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRanunculus abortivus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e40.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.950\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDryopteris intermedia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e51.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.962\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eElymus hystrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e60.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.990\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.970\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSanguinaria canadensis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e62.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.996\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 km natural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImpatiens capensis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuonymus obovatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErythronium americanum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlliaria petiolata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCornus alternifolia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.950\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaulophyllum giganteum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e28.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarex pedunculata\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e32.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeranium robertianum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.964\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.990\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrophyllum virginianum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSanguinaria canadensis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.992\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardamine diphylla\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.978\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSolidago flexicaulis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.994\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFraxinus americana\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e37.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsarum canadense\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e41.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.996\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.958\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSolidago caesia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e41.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.958\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDryopteris carthusiana\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e47.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.958\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eTITAN analysis for species across the gradient of natural land cover, including the individual taxa for which a significant decrease/increase in abundance across the gradient of total area of continuous forest around each sample plot. This only includes significant species responses. Species that are increasers (filter value of 2) are bolded.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLand use composition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponse species\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnvironmental change point\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePurity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReliability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFilter\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eContinuous forest surrounding sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErythronium americanum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1172.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarex pedunculata\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1116.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.966\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.988\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFraxinus americana\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1116.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.992\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.992\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaulophyllum giganteum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1206.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDryopteris intermedia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1206.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.994\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.966\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardamine diphylla\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1590.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.996\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.952\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eParthenocissus vitacea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1590.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.958\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDryopteris carthusiana\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1827.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.996\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsarum canadense\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3501.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.996\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.974\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeranium robertianum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3501.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSolidago caesia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3501.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.992\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.960\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eElymus hystrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4449.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.992\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.964\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrophyllum virginianum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4449.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eViola pubescens\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4449.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.992\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.962\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSanguinaria canadensis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6975.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.976\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSolidago flexicaulis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9501.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.974\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThere were 21 species associated with higher natural land cover and negatively associated with urban land cover (see Table \u003cspan\u003e2\u003c/span\u003e. for significant associations with urban landcover, Table \u003cspan\u003e3\u003c/span\u003e. for significant associations with natural landcover, and Table \u003cspan\u003e4\u003c/span\u003e. for significant associations with continuous forest cover). There were only 4 species that were positively associated with urban cover\u0026mdash;and negatively with natural cover\u0026mdash;\u003cem\u003eImpatiens capensis\u003c/em\u003e, \u003cem\u003eEuonymus obovatus\u003c/em\u003e, \u003cem\u003eErythronium americanum\u003c/em\u003e, and \u003cem\u003eAlliaria petiolata\u003c/em\u003e,, of which only \u003cem\u003eA. petiolate\u003c/em\u003e was non-native. \u003cem\u003eErythronium americanum\u003c/em\u003e was consistently associated with urban land cover, being an increaser for urban land cover and decreaser for natural land cover consistently at all scales except for 1km urban land cover (Table \u003cspan\u003e2\u003c/span\u003e). \u003cem\u003eE. Americanum\u003c/em\u003e, the species most consistently associated with urban land cover was positively associated with increases in urban area with an environmental change point\u0026mdash;the value of the land-use variable (percentage cover) that most strongly separates the likelihood of taxon into two groups \u0026ndash; of 51.5% at 1km, 36.3% at 2km, and 20.1% at 5km.\u003c/p\u003e\n\u003cp\u003eAggregate community measures\u003c/p\u003e\n\u003cp\u003eA series of linear regressions revealed a negative relationship between urban land cover and plant diversity metrics (Table \u003cspan\u003e5\u003c/span\u003e), and positive relationships between natural land cover and plant biodiversity at all scales. While this table contains all the regression relationships that were found to be significant, the R\u003csup\u003e2\u003c/sup\u003e-values for many of these relationships are below acceptable measures, with R\u003csup\u003e2\u003c/sup\u003e values generally being acceptable at equal to or greater than 0.10 (0.26\u0026thinsp;=\u0026thinsp;substantial, 0.13\u0026thinsp;=\u0026thinsp;moderate, 0.02\u0026thinsp;=\u0026thinsp;weak) (Falk \u0026amp; Miller, \u003cspan\u003e1992\u003c/span\u003e; Cohen \u003cspan\u003e2013\u003c/span\u003e). Richness, Richness of native species, and ratio of native/non-native ratio of species richness (specifically in urban environments) are the variables that most consistently display both significant p-values, and acceptable R\u003csup\u003e2\u003c/sup\u003e-values. It is also notable that the R\u003csup\u003e2\u003c/sup\u003e values for native species richness and abundance were slightly higher than those of the total species richness and abundance.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe output of a series of linear regressions, showing the landscape variable (independent variable) on the far left, and the biodiversity metrics (dependent variable), the R\u003csup\u003e2\u003c/sup\u003e value, and the P-values for each of the significant relationships tested (NOTE only those statistically significant relationships are included). The Estimate value is the slope of the relationship.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLand use (percentage)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBiodiversity metric\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1km Natural Lands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness (native)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundance (native)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2km Natural Lands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShannon diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00770\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness (native)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundance (native)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5km Natural Lands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShannon diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness (native)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness native/non-native ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundance (native)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1km Urban Lands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShannon diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness (native)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness native/non-native ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundance (native)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2km Urban Lands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShannon diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness (native)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness native/non-native ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundance (native)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0913\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5km Urban Lands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShannon diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness (native)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichness native/non-native ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo better understand the patterns of biodiversity change observed along land cover gradients, we plotted the residuals of each significant linear model as a series of spread-location plots (Fig. \u003cspan\u003e4\u003c/span\u003e.). Many of the significant models showed higher variance in areas with lower urban cover. The variance of the residuals decreased, and forest community biodiversity measures were closer to the model predictions as urban land cover increased.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings confirm the initial hypothesis that urban-dominant landscapes have measurable impacts on forest understory plant communities, and that these patterns are significantly associated with changes in land cover (urban to natural). The patterns were measurable and significant at multiple scales, with the direction of relationship between land cover and plant community response remaining consistent at 1km, 2km, and 5km. These results also confirm our hypothesis that biodiversity increases with total cover of natural lands. The significant associations between non-native species and urban land cover were marginal, which goes against our initial hypothesis. The significant findings of both species-level and community-level changes associated with land cover confirm our expectations that land cover in mosaic landscapes does indeed have significant impact on plant communities, and can impact forest\u0026rsquo;s potential to support biodiversity, even when the changes are indirect changes.\u003c/p\u003e \u003cp\u003eOur initial species-level analysis\u0026mdash;the NMDS\u0026mdash;found that composition of plant species changed significantly with land cover composition at all scales. These results confirm that the CVC forest understory communities are not a random assemblage of plants, but are distinct groupings significantly associated with the composition of land cover around each forest site. The NMDS ordination space indicates that species composition in areas of high urban land cover are different from those in high natural land cover (high continuous forest cover occupied a similar space in the NMDS as sites with high natural cover). The TITAN analysis substantiates these findings, with significant findings indicating separate groups of species that had significantly higher probability of presence in forests surrounded by natural land cover compared to the species with higher probability of presence in forests surrounded by urban land cover. Additionally, the NMDS found that the number of species in the communities also changed. Sites that were strongly associated with urban cover typically had a low number of species overall (approx. 1\u0026ndash;9 species in the sites most closely associated with the urban land cover vectors), while sites closely associated with natural land cover contained a much higher number of species (around 30 species in the sites most closely associated with the natural land cover vectors). The TITAN analysis also showed many more species significantly associated with natural land cover than urban land cover. In fact, the TITAN analysis identified 10 or more species associated with high natural land cover and low urban cover (depending on the scale), compared to a maximum of 4 species associated with increases in urban land cover and decreases in natural land cover.\u003c/p\u003e \u003cp\u003eThese significant differences in composition (what species are present) and the number of species in a community for these species-specific analyses suggest that patterns of plant community assembly are associated with surrounding land cover. It is likely that the mechanism(s) causing these changes are related to the wider landscape, and are due to resource availability, competition, and species dispersal mechanism, which would be in line with conventional/accepted understandings of mosaic landscapes (Swan et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Plant species niches \u0026ndash; the integration of a species\u0026rsquo; resource requirements, environmental tolerances, and the physical reality of the area they inhabit\u0026mdash;determine the environments in which a species can occur (Hutchinson \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). Plant communities in a resource rich environment often contain a wide distribution of niches where multiple species (even species with similar niches) can co-exist (Miedema et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pillar et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Natural landscapes are associated with higher resources, such as habitat availability and soil nutrients, compared to urban and agricultural areas which are typically less hospitable to plants due to reduction in natural features (Blouin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Increases in urban cover can force species competition and cause a more consistent response among species and species indicators, due to reduction in the availability of natural features and resources (Tang \u0026amp; Zhou, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Brown et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBoth the NMDS and TITAN confirmed this pattern, with higher urban land cover in the surrounding landscape typically containing a small number of species, a high percentage of which was made up of species identified by the TITAN analysis as associated with high urban cover (\u003cem\u003eI. capensis, E. obovatus, E. americanum, A. petiolata\u003c/em\u003e). There were much fewer species that responded significantly to urban land cover (4 species), but they may provide greater insight into species-level responses to urbanization through niche space. Species with qualities that provide an advantage in urban landscapes will respond positively. Consistent patterns in what species respond positively to urban cover could indicate traits or lifecycle adaptations that allow them to survive in urban areas (Rivkin et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In turn, species associated only with natural land cover could indicate species of high conservation concern, as they are not able to adapt and are not as likely to be present if land cover changes to a higher urban composition. However, this might be more difficult to parse out, due to the high number of species that are typically found in areas with high natural land cover.\u003c/p\u003e \u003cp\u003eAlthough species-level responses provide helpful insight, it is important to also consider the trait- or niche-specific responses associated with the species with significant responses (Miedema et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, \u003cem\u003eE. americanum\u003c/em\u003e, a species consistently associated with urban environments, has been found to be an extremely resilient species, with resistance to herbicide, disturbance, and is generalist for pollination (Ristau, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Taki, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This could be due to its lifecycle as a spring ephemeral, meaning it completes most of its life cycle in the early spring. Ephemerals will remain dormant throughout the rest of the year\u0026mdash;up to 10 months of low to slow growth (Lapointe, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The ephemeral lifecycle may result in higher resilience to disturbances associated with urban expansion due to this dormancy, and thus reduced interactions with other species and disturbances (Tessier, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The other two native species, \u003cem\u003eI. capensis\u003c/em\u003e and \u003cem\u003eE. obovatus\u003c/em\u003e have also been found to be hardy species, although they do not share the ephemeral lifecycle. In fact, Cipollini \u0026amp; Hurley (year) showed that \u003cem\u003eI. capensis\u003c/em\u003e shows evolutionary resilience to invasive species that often have detrimental effects on other species (Cipollini \u0026amp; Hurely, 2008; Dorning et al., 2005). \u003cem\u003eE. obovatus\u003c/em\u003e has been found to be a common species in forest understories and with growth strategies that allow them to compete with invasive species (Hinman, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Further research into trait-specific responses of both individual species, and plant communities can provide more direct insight into the mechanisms that may produce resilient, biodiverse communities.\u003c/p\u003e \u003cp\u003eIn addition to the species-level responses confirmed by the NMDS and the TITAN analysis, the linear regressions confirm that there is also a significant response in community-aggregate biodiversity associated with land cover changes. Unsurprisingly, areas with high urban land cover had consistently negative relationships with plant biodiversity metrics. Species richness, abundance, and Shannon diversity all showed significant negative relationship with urban land cover, and a significant positive relationship with natural land cover at all three scales. Changes in richness mean the number of species present are changing, while changes in abundance mean that the abundance of species relative to one another are also changing (Storch, 2018). The co-response of both species richness and species abundance together indicates that there is consistent change in biodiversity, from higher levels in areas with more natural land cover to lower levels in areas with higher urban cover. While all biodiversity metrics have significant p-values, the R\u003csup\u003e2\u003c/sup\u003e-value for many of these relationships was unacceptably low (between R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.06 and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.09), indicating that the change in the land cover around each forest site did not explain enough changes in biodiversity to be considered an important factor. However, the biodiversity response that consistently fell within low, but acceptable levels (between R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.13 and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.22 depending on the scale and land cover metric considered) was species richness. This suggests that, while the species present are also changing, the most consistent response closely tied to changes in surrounding land cover is the number of species that each site contains. These findings are consistent with species-level analysis that, as previously discussed, shows fewer species responding to urban land cover compared to natural land cover, suggesting that urban land cover reduces an area\u0026rsquo;s ability to support native species. This is consistent with the concept of niche-availability where areas with higher resource availability (natural areas, population sources, nutrients etc.,) such as areas with high natural cover, can sustain more species (Lavorel \u0026amp; Garnier, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Velland et al., 2007).\u003c/p\u003e \u003cp\u003eThe consistently negative response of biodiversity to urban land cover around forest sites indicates a sensitivity to urban expansion that goes beyond direct effects of habitat disturbance often observed in urban ecology. This is in keeping with past studies which found that there was a significant relationship between plant community diversity and the composition of the surrounding landscape (Milne \u0026amp; Bennet, 2007; Miedema et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Biodiversity and species-level changes could be due to both direct and indirect effects in the landscape. These effects are likely related to the idea that population diversity and richness are controlled by the abiotic and biotic factors that select from the species that are already present or able to easily disperse and establish in a given area (Swan et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While management and forest type might remain consistent in our study sites, higher urban land cover means a higher likelihood that forest sites are isolated from other similar habitats by an impermeable or hostile surrounding (in this case an urban or industrial landscape) (MacArthur \u0026amp; Wilson, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The landscape itself acts as an abiotic filter, with species possessing well-suited traits remaining or re-establishing themselves. For example, high dispersal distance (such as species with bird or wind seed dispersal methods) might increase species\u0026rsquo; ability to re-establish in isolated areas, and pollinator generalists, species with high resilience to non-native species competition (e.g., allelopathy, competitive growth cycles etc.,), low resource needs, will be able to increase reproduction and maintain populations even in isolated urban areas (Cipollini \u0026amp; Hurely, 2008; Beninde et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This must be understood in light of the higher prevalence of non-native and generalist species that might actually be sourced from this urban matrix, leading to homogenized communities with similar survival strategies (Blouin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In fact, the slightly higher r\u003csup\u003e2\u003c/sup\u003e values for linear relationships when only native species were considered could indicate that native species are more sensitive to changes in land cover, as they are not well suited to the urban \u0026lsquo;matrix\u0026rsquo; and are more likely to suffer the negative effects. Difference in responses to environmental conditions does not only vary between species but can change when different scales are considered (Fischer et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Our findings showed a very consistent direction to plant-land cover relationships in both species-specific analysis and community-level biodiversity measures at all scales. This would suggest that both species-specific responses, and general patterns of community change would be dependent on the rearrangement of the communities present through novel pressures caused by urban expansion (L\u0026ocirc;bo et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This could have to do with dispersal if species are not able to establish, or are not already present, they cannot persist no matter how resilient or well-adapted they are. Thus, areas of high isolation, such as areas with high urban land cover, will be more likely to have low diversity.\u003c/p\u003e \u003cp\u003eConsistency of the significance and direction of the relationship across 1km, 2km, and 5km scales is relevant as well. The percent of landcover take up by urban and natural lands was similar between the 1km and 2km scales, typically shifting between 10\u0026ndash;15% on average. But the changes between 1km and 5km scales was often much larger, and the fact that the direction of the relationship between biodiversity and landcover remains the same indicates a high likelihood that urban and natural landcover encompasses the mechanisms behind these changes.\u003c/p\u003e \u003cp\u003eThere was no direct relationship of note between land cover and non-native species, but the ratio of native/non-native species richness did show significant correlation in the linear regressions associated with urban land cover at all three scales, and with natural land cover only at the 5km scale. The only ratio associated with urban land cover at 5km having an acceptable r\u003csup\u003e2\u003c/sup\u003e-value was urban land cover at 5km. The TITAN analysis also had a lack of significant connection between non-native species and land cover, with the majority of significant indicators of environmental gradients being native species even those associated with more urban cover. In fact, there were only two non-native species identified by the TITAN analysis as significantly associated with any changes in land use composition in total. The first was \u003cem\u003eA. petiolata\u003c/em\u003e, which increased with urban land cover at 2km and 5km and was associated with a decrease in natural land cover at 5km. This is expected, as many non-native species have sources in urban areas, and presence and extent of urbanization are consistently associated with presence of non-native species (Schwoertzig et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Skultety \u0026amp; Matthews, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, the other non-native species, \u003cem\u003eT. officinale\u003c/em\u003e, was associated with decreases in urban land-cover at 1km. It is generally held that most non-native species that increase with the high disturbance associated with urban land cover do so through adaptations that provide them with advantages over native species (Rivkin et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eT. officinale\u003c/em\u003e is often cited to be a generalist that is resilient to negative urban effects (Pisman et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) so finding it to be associated with decreases in urban land cover is surprising. This could indicate that \u003cem\u003eT. officinale\u003c/em\u003e, being a generalist, has adapted to forest and urban areas alike and, although it is not native to this area, it has integrated itself into both urban and natural landscapes.\u003c/p\u003e \u003cp\u003eThe unexpected responses of native and non-native species in both species-level and community-level responses could indicate plant responses to landscape change are more connected to functional response \u0026ndash;both native and non-native\u0026mdash;than to a plant\u0026rsquo;s origins (Kondratyeva et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; L\u0026ocirc;bo et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; McLaren \u0026amp; Turkington, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This could explain why native species such as \u003cem\u003eE. Americanum\u003c/em\u003e are associated with increased urban land cover, while \u003cem\u003eT. officinale\u003c/em\u003e, a non-native species, would be associated with decreases in urban land cover. Functional traits, and species-specific responses we are only able to touch on could also explain why non-native species did not show significant response to urban land-cover in the linear regression models, contrary to our hypothesis that urban land use would result in significantly higher richness and abundance of non-native species. The lack of significant correlation could also be due to sample locations being within a conservation area, which would have a two-fold effect. The first would be that not all sites are directly connected to urban areas\u0026mdash;which would increase the dispersal distance that non-native species would have to traverse to populate the areas, thus decreasing richness and abundance of non-native species (Hansen \u0026amp; Clevenger, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Theoharides \u0026amp; Dukes, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The second effect is the restoration and management efforts implemented by the conservation authority (in this case the CVC) which has action plans in place to reduce non-native species populations and promote native species (CVC).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study we show that forest understory vegetation shows a significant relationship to surrounding land cover composition, with changes associated with urban and natural land cover being consistently significant at 1km, 2km, and 5km scales. This indicates that the forest understory communities of the CVC are not random assemblages, but communities found in predictable patterns that are associated with the composition of the landscape around each site. This shows these forest communities are shaped by the indirect impacts of the surrounding landscape as well as by the specific and direct environmental factors of each forest.\u003c/p\u003e \u003cp\u003eThough this illustrates important landscape level patterns, the land cover is likely an indirect measure, with more detailed mechanisms leading to these forest community changes which would account for more of the variance in the data. The fact that most of these relationships have only moderate explanatory power (R\u003csup\u003e2\u003c/sup\u003e-value) along with the significant p-values suggests that this is only a general estimate for the effects of land-use change, but more direct effects that account for both functional traits and mechanistic changes (e.g., dispersal method to account for species dispersal etc.) could provide a more nuanced and accurate model. However, the significance of the patterns observed makes a strong case for the importance of increased natural land cover composition in the landscape, especially if higher biodiversity is to be maintained.\u003c/p\u003e \u003cp\u003eThe significant impact of natural landcover on biodiversity also indicates that smaller-scale conservation design choices could enhance global efforts by increasing the biodiversity capacity of fragmented areas. Our findings suggest that landcover around conservation lands can make an important contribution to biodiversity support, even in a multi-use landscape. While the 30x30 goals of the Kunming-Montreal Global Biodiversity Framework suggest that 30% of all natural lands should be conserved worldwide, these findings suggest that, in mosaic landscapes at least, 30% landcover around conservation sites could also act as a biodiversity benchmark. As mosaic landscapes are increasingly prevalent, future research into optimal landcover configuration will also be helpful in discovering the best ways to promote biodiversity in conservation lands that remain.\u003c/p\u003e \u003cp\u003eWhile conservation efforts are effective \u0026ndash; as demonstrated by the lack of significant correlations between non-native species and urban land cover \u0026ndash; there are still significant impacts that surrounding urban land cover can have on the interior forest of conservation lands. As urban land-cover appeared to have a stronger and more consistent control on biodiversity metrics, our findings would also confirm that conservation efforts should be focused on more urban landscapes when resources are limited. Important impacts of landscape level change on biotic communities, even without direct urban disturbances. This is particularly important to consider as urban surface continues to expand, and predictions estimate that urban land cover will continue expanding, increasing by about 1,500,000 km\u003csup\u003e2\u003c/sup\u003e by 2030 with an estimated of changes 30\u0026ndash;44% coming forested areas and grasslands (Seto et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research was partially funded by the Canada First Research Excellence Fund.\u0026nbsp;Data used with permission of Credit Valley Conservation Authority 2023.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Author Contributions\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design, with the majority of the preparation, data collection, spatial analysis and writing performed by Liane Miedema Brown. The first draft of the manuscript was written by Liane Miedema Brown, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbrecht, H., \u0026amp; Haider, S. (2013). Species diversity and life history traits in calcareous grasslands vary along an urbanization gradient. \u003cem\u003eBiodiversity and Conservation\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 2243-2267. https://doi.org/10.1007/s10531-013-0437-0 \u003c/li\u003e\n\u003cli\u003eBakker, J. D. (2023). \u003cem\u003eApplied Multivariate Statistics in R\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eBeninde, J., Veith, M., \u0026amp; Hochkirch, A. (2015). 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Island biogeography theory predicts plant species richness of remnant grassland patches in the agro-pastoral ecotone of northern China. \u003cem\u003eBasic and Applied Ecology\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e, 14-22. https://doi.org/10.1016/j.baae.2021.04.010 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"landscape-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"land","sideBox":"Learn more about [Landscape Ecology](https://www.springer.com/journal/10980)","snPcode":"10980","submissionUrl":"https://submission.nature.com/new-submission/10980/3","title":"Landscape Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Plant community ecology, mosaic landscape, multi-use landscape, plant assembly, plant biodiversity","lastPublishedDoi":"10.21203/rs.3.rs-4763336/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4763336/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eContext.\u003c/p\u003e \u003cp\u003eWhile research and policy alike have recognized the importance of conserving biodiversity, the rapid and continued expansion of urban areas hinders many conservation efforts, particularly as many high-value conservation areas are found in landscapes already modified by human use. Research into the impact of landscape mosaics \u0026ndash;their composition and configuration in particular \u0026ndash; is important to understanding the impact that human induced land-use change may have on biodiversity, biotic communities, and thus the ecological processes within these areas.\u003c/p\u003e \u003cp\u003eObjectives.\u003c/p\u003e \u003cp\u003eThe objectives of this research paper are to determine the impacts of the landscape composition surrounding conservation forests has on the plant communities of the forest understory communities. We also seek to outline the possible mechanisms by which the landscape can indirectly impact plant communities, and in so doing, provide a deeper understanding of how natural areas within mosaic landscapes may sustain biodiversity.\u003c/p\u003e \u003cp\u003eMethods.\u003c/p\u003e \u003cp\u003eUsing plant community measures from the Credit Valley Conservation Authority in Ontario, Canada, and open-sourced spatial data on Canada\u0026rsquo;s landcover, we calculated the land cover composition of urban and natural lands surrounding each forest site, and the biodiversity of the understory community in each forest. We used both individual species richness and abundance (NMDS, TITAN), as well as aggregate biodiversity measures (linear regression) to test for significant relationships between the plant community metrics and the composition of the surrounding landscape.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eNatural land cover, urban land cover, and continuous forest size were all significantly associated with species changes in the NMDS at all scales, and the direction of the urban cover vector was nearly opposite of the natural cover vector in the NMDS space. The output of the TITAN analysis identified both positive and negative responses of individual species to land cover composition at the three scales considered, indicating that indicator species had strong responses to changes in the land cover, with different species being associated with urban vs. natural land cover. The TITAN and NMDS both showed that many more species were positively associated with natural land cover. Only a few species responded positively to high urban cover, and those forests had much lower populations. A series of linear regressions revealed a negative relationship between urban land cover and plant diversity metrics, and positive relationships between natural land cover and plant biodiversity at all scales. Both species richness and species abundance changed significantly with the surrounding land cover composition, but species richness (that is the total number of species present in a community) had the most consistent and statistically significant response \u0026ndash; indicating that an areas ability to sustain a certain number of species is affected by the surrounding landscape.\u003c/p\u003e \u003cp\u003eConclusions.\u003c/p\u003e \u003cp\u003eThe significant findings of both species-level and community level changes associated with land cover confirm our expectations that land cover in mosaic landscapes does indeed have significant impact on plant communities, and can impact forest\u0026rsquo;s potential to support biodiversity, even when the changes are indirect changes. Forest understory vegetation shows a significant relationship to surrounding land cover composition, with changes associated with urban and natural land cover being consistently significant at 1km, 2km, and 5km scales. This indicates that the forest understory communities of the CVC are not random assemblages, but communities found in predictable patterns that are associated with the composition of the landscape around each site.\u003c/p\u003e","manuscriptTitle":"Impacts of urban land-cover on plant community structure and biodiversity in a multi-use landscape","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-18 23:12:50","doi":"10.21203/rs.3.rs-4763336/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-09-17T19:53:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287047927344642092047519629959645163946","date":"2024-08-22T17:05:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41392255399460942418953587365242233159","date":"2024-08-21T06:36:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3262922832615308255243942761507780535","date":"2024-08-12T19:30:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-01T13:07:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-19T14:41:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-19T14:41:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Landscape Ecology","date":"2024-07-18T14:30:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"landscape-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"land","sideBox":"Learn more about [Landscape Ecology](https://www.springer.com/journal/10980)","snPcode":"10980","submissionUrl":"https://submission.nature.com/new-submission/10980/3","title":"Landscape Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c7ffa511-ced8-4b50-a0aa-46d02c2488dc","owner":[],"postedDate":"August 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T19:20:21+00:00","versionOfRecord":{"articleIdentity":"rs-4763336","link":"https://doi.org/10.1007/s10980-024-01988-9","journal":{"identity":"landscape-ecology","isVorOnly":false,"title":"Landscape Ecology"},"publishedOn":"2024-11-16 15:57:20","publishedOnDateReadable":"November 16th, 2024"},"versionCreatedAt":"2024-08-18 23:12:50","video":"","vorDoi":"10.1007/s10980-024-01988-9","vorDoiUrl":"https://doi.org/10.1007/s10980-024-01988-9","workflowStages":[]},"version":"v1","identity":"rs-4763336","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4763336","identity":"rs-4763336","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}