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Communication

Neighboring Green Network and Landscape Metrics Explain Biodiversity within Small Urban Green Areas—A Case Study on Birds

by
Marufa Sultana
1,*,
Max Müller
2,
Magdalena Meyer
2 and
Ilse Storch
1
1
Wildlife Ecology and Management, Faculty of Environment and Natural Resources, University of Freiburg, D-79106 Freiburg, Germany
2
Institute of Evolutionary Ecology and Conservation Genomics, University of Ulm, D-89081 Ulm, Germany
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(11), 6394; https://doi.org/10.3390/su14116394
Submission received: 4 April 2022 / Revised: 13 May 2022 / Accepted: 22 May 2022 / Published: 24 May 2022
(This article belongs to the Special Issue Wildlife Conservation: Managing Resources for a Sustainable World)
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Abstract

:
Cities’ green areas are fragmented patches and are often confined to smaller sizes than the higher built-up proportions. Such small-sized green areas can be essential components of green infrastructure to compensate for biodiversity loss. As a proxy to biodiversity, we studied birds in nine small green area locations of Freiburg and eight area locations in Regensburg in Germany. We investigated the neighboring green networks (distance to the nearest water body and another green area) and landscape metrics (patch abundance and habitat heterogeneity at a 1 km radius) that might benefit and explain bird richness and composition in small green areas. We found that the variations in the observed species richness and composition at the surveyed locations were better explained solely by green networks in Freiburg and by green networks and landscape metrics in Regensburg. In general, it indicates that a small green area could be biodiverse if its spatial distribution considers a nearby water body and other green areas, allowing a higher abundance of similar patches and habitat heterogeneity in the neighborhood.

1. Introduction

Urbanization is a long-standing phenomenon and can facilitate alteration in the natural landscape, threatening biodiversity in cities. Due to this concern, sustainable city planning integrating green infrastructure components is now an advancing concept [1]. Here, green areas, such as parks, street trees, campgrounds, and golf courses are essential components since they function as stepping stones and improve the connectivity of highly fragmented habitats in cities [2,3]. These features are also recognized as habitat islands [4], facilitate mobility and dispersal of urban species, and thereby benefit community composition. Thus, urban green areas provide ecological benefits by supporting biodiversity beyond their traditional functions for recreation and food sources for city residents.
It is well understood that larger green areas support greater species diversity in cities [2,4,5,6]. Therefore, a green area size threshold of circa 3.5 ha is suggested, the point below which species diversity declines [7,8]. Nevertheless, there is a usual trade-off in planning and allotment of green areas’ extent in urban areas, which confines the size of such patches. Given the consequences, it is essential to understand whether and how a comparatively small-sized green area within an urban locality could still support biodiversity to some extent. In particular, studies are required to evaluate the role of the surrounding landscape characteristics in the effectiveness of small-sized urban green areas, such as community parks, street parks, and playgrounds, to inform urban biodiversity planning [2].
Several studies elucidated biodiversity patterns, namely species-specific occurrences, diversity, and composition patterns, within urban green areas [5,9,10]. It contributed to the understanding that species-specific responses may differ concerning urban environmental factors in small green areas [11,12]. However, overall species diversity is generally shaped by anthropogenic disturbances in the neighborhood. For example, species variety declines due to the adverse effects of noise level, car intensity, pedestrian movement, and built-up proportions, whereas it improves with the increasing amount of green area and native vegetation complexity [3,5,9,11,12,13,14]. In contrast, the understanding of how surrounding landscape factors might benefit biodiversity patterns in small urban green areas (i.e., are usually highly fragmented) is still unclear.
In this study, we focused on birds (as a proxy to biodiversity) in small urban green areas of two European cities, Freiburg and Regensburg, in Germany. Birds are one of the most widely studied taxa in urban ecology [15,16,17]. Since birds respond to any environmental change quickly and are easy to observe, avian diversity can be an excellent indicator of city biodiversity [18,19,20]. Furthermore, higher avian variety represents better urban habitat status and indicates greater recreational experiences for local people in green areas [21].
Here, we aimed to understand the contribution of neighboring landscape factors that likely benefit birds and explain variations in species richness and composition in small-sized green urban areas. Bird diversity has been shown to decline in highly built-up areas [10], however, increases in the presence of green areas and higher habitat diversity [5,9,22]. On this basis, we hypothesized that a network of ecologically important habitat patches (hereafter, a green network), such as other green areas and water bodies nearby, allows birds to make use of small green areas. Furthermore, Freiburg and Regensburg have a long history of spatial planning for greenery within cities [23,24]. This planning might have allowed a higher amount of well-planned small green patches (i.e., isolated units outside the forest and large green areas) and improved the quality of habitats for birds within cities. Therefore, we further hypothesized that the landscape metrics at the local scale, specifically a higher abundance of comparable patches (i.e., serving as stepping stones) and habitat heterogeneity (i.e., representing habitat quality) at a 1 km radius, enhance bird diversity in a small-sized urban green area.

2. Methods

2.1. Study Sites

In this study, we considered nine small green area locations (i.e., street parks, campus yards, and playgrounds) in Freiburg and eight in Regensburg in Germany (Figure 1). All were fragmented habitat patches (isolated from core green areas, i.e., forests) located in and near built-up areas. The selected green area locations were approximately > 700–1000 m far from each other in both cities. The average size of the green areas in Regensburg was 0.733 ± 0.51 ha, while Freiburg was 0.38 ± 0.16 ha.
Freiburg is located in the south of Baden-Württemberg and has approximately 230,000 inhabitants. The city area is 15,307 ha and comprises 6530 ha of forest and 664 ha of sports, leisure time, and recreational spaces [25]. Regensburg is in the east of Bavaria and has approximately 160,000 inhabitants. The city’s 8070 ha area includes 467 ha of forest and 268 ha of recreational, sports, and leisure time places [26]. Both Freiburg and Regensburg are medium-sized (based on population estimate) and share a resemblance in the spatial configuration of different landscape features (i.e., crossed by a river). Moreover, both cities are unique, with more than 50% greenery within the city boundary [27,28] and are considered “green cities” with a background in spatial city planning towards sustainable growth [23,29].

2.2. Bird Survey

In both cities, we considered the centroid location of each selected small urban green area for a bird survey. We surveyed birds following a point count method [30] and documented all species spotted at the location within a 50 m radius for 10 min. In Regensburg, each location was visited four times during March–April 2021. In Freiburg, each site was visited twice during July–August 2018. All field visits were carried out during the early four hours in the morning by two observers on non-consecutive days.
From the bird survey data, we measured two response metrics for each of the green area locations in both cities: bird species richness (total number of observed species), species composition (sites-by-species data, i.e., number of individuals of each observed species at each specific surveyed location).

2.3. Explanatory Variables

We investigated bird species richness and composition in small green areas in relation to four explanatory variables representing landscape factors [31].
Within the urban matrix, urban green areas and water bodies are crucial habitat features. Thus, we measured two proximity variables representing the green network at the surveyed location—(i) distance to the nearest another urban green area (DUG) and (ii) distance to the nearest water body (DW) (Figure 1). We measured the distance from the centroid of the small green areas to the nearest urban green water features from the ‘Urban Atlas 2018′ vector map dataset [32,33] using the tool ‘Near’ in ArcMap 10.8.1.
Next, we measured two landscape metrics at a 1 km radius buffer scale extent of the surveyed location—(iv) habitat Shannon metric (HS) to represent habitat heterogeneity and quality at the bird surveyed location, and (v) the total number of ‘patch’ units (hereafter, patch abundance; PA) to represent the amount of isolated habitat parallel to our selected small green areas in the neighborhood [31] (Figure 1).
HS is the measure of diversity in the ‘Enhanced Vegetation Index’ (i.e., variety in vegetation concentration) and extracted from the “Global Habitat Heterogeneity” raster dataset (resolution ~1 km) [34]. PA represents the number of fragmented small patches that have no core green area (i.e., no small, medium, or large forest tracts along with an outside edge of 110 m from the nearest urban pixel) [35]. To identify such patches, we used the tool ‘Landscape Fragmentation’ (Center for Land use Education and Research; www.clear.uconn.edu) that is installed in ArcMap 10.8.1. We considered a classified binary image of forest-no forest developed from the ‘Land Cover map’ data of CGLS-LC100 [36]; we assumed an edge width of 110 m and urban areas as fragmenting land cover. It provided rasterized landscape fragmentation map data for each city with attributes of different types of forest fragments: patch, edge, perforated, core [35]. For our analysis, we extracted the total number of ‘patch’ grid cells (~110 m × 110 m) at a 1 km radius buffer of the surveyed locations. We considered only the data of the unit ‘patch’ [31] (Figure 1), since it was the most available type of green fragment within a 1 km radius of the surveyed locations. Moreover, we intended to understand if bird diversity and their composition observed at the small urban green area locations can be explained by a higher abundance of similar type patches in the neighborhood.
Further, it is well known that bird richness commonly declines with increasing imperviousness [17,37,38,39], however, higher bird richness is associated with larger-sized green areas in urban areas [4,9]. Therefore, besides the four explanatory variables, we used associated green area size and the proportion of impervious surface as the control factors during the model assessment of bird species richness.

2.4. Analysis

We investigated which variables of green network and landscape metrics in the neighborhood of small green areas better explained observed bird richness (number of species) through regression modelling and species composition (sites-by-species individual number) through variance partitioning assessment. We applied the analysis approach similarly to Regensburg and Freiburg. All data analyses were performed in the statistics program R, version 4.0.1 [40], and RStudio, version 1.2.5033 [41].

2.4.1. Modelling

We applied linear regression models to fit bird species richness (i.e., our response variable) with different explanatory variables. Bird species richness was log10-transformed to achieve normality in the case of Freiburg.
A preliminary investigation on modelling of bird richness with explanatory variables using the function ‘lm’ and ‘glm’ (with ‘Poisson’) (package MASS) [42] did not show a significant change in the model fits in the case of Regensburg. The model fit with ‘lm’ was also appropriate in the case of Freiburg. Hereafter, we used the ‘lm’ function in our model assessment for both cities. Further, we checked VIF (variance inflation factor) (package ‘car’) [43] to ensure no multicollinearity issue was present in the models (i.e., VIF < 0.4) [44].
In our study, bird richness at a survey location was strongly correlated (0.52) with green area size in Freiburg and with the built-up proportion (−0.59) in Regensburg. Therefore, our first generated model included only green area size (in the case of Freiburg)/built-up proportion (in the case of Regensburg), considering the relevant most correlated variable as the control factor. In the next models, we selected and added another variable distinctively from HS (habitat Shannon metric), PA (patch abundance), DW (distance to the nearest water body), and DUG (distance to the nearest urban green area). Here, we partially followed hierarchical regression and gradually entered a new variable into the first generated model to observe the change in explained variation. Due to the low sample size and to avoid multicollinearity, we allowed a maximum of two variables in each of the models and did not include any interactions of the predictors.
We majorly inspected if the addition of any variables of the green network and landscape metrics improved our first model’s ability to explain bird richness at the surveyed locations within small green areas. Moreover, we contrasted all generated models using the function ‘model.sel’ (package ‘MuMIn’; [45] and examined the coefficient parameters, change in the AICc value, and AICc weight.

2.4.2. Variation Partitioning

Our following analysis included an assessment of the partitioning of the effect of four selected variables on bird species composition—HS (habitat Shannon metric), PA (patch abundance), DW (distance to the nearest water body), and DUG (distance to the nearest urban green area). We performed this assessment on bird species composition for Freiburg and Regensburg individually. Specifically, we investigated how much variation in the bird species composition data observed at the small green area locations within the cities is explained by the specific or combined variables. For this, we conducted a ‘variation partitioning’ assessment [46] with redundancy analysis, using a site-by-species table (abundances of each observed species at a specific surveyed location) as the response matrix. In this assessment, we used the function ‘varpart’ of the R package ‘vegan’ [47].

3. Results

We documented a total of 329 individuals of 21 species of birds in Freiburg and 539 individuals of 28 species in Regensburg during our survey (Supplementary Materials—Tables S1 and S2). In both cities, the majority of the birds observed at the locations within small green areas were medium to highly common species (i.e., following [48,49]).

3.1. Modelling

Our generated models of bird species richness did not include any issue of multicollinearity. The first generated model, which included the variable GA in the case of Freiburg and IS in the case of Regensburg, contained the lowest AICc value and highest AICc weight (Table 1).
In the case of Freiburg, the explained variation (R-squared) in the first model was 23% which increased by 4% when DW was added. The changes in the R-squared value were negligible in other models. No variables with significant values were retained. Although, the coefficient parameters indicated a negative effect of the distance to water body on bird richness at the observed sites in small green areas (Table 1).
In the case of Regensburg, the explained variation in the first model was 34% which noticeably improved by 22% when DG was added and by 14% when PA was added. Again, no variables with significant values were retained. However, the coefficient parameters indicated a negative effect of DG and a positive effect of PA on observed bird richness at a 1 km radius at the surveyed locations within small green areas (Table 1).

3.2. Variation Partitioning

Our further assessment, through variation partitioning with redundancy analysis, presented a partition of the influences of a green network (DW and DUG) and landscape metrics (PA and HS) on bird species composition. The results showed that the variation in bird species assemblage at the surveyed location explained by variables of a green network (46% by DUG and 40% by DW) was higher than any other single or combined variables in the case of Freiburg (Figure 2). While in the case of Regensburg, the variations in bird species composition were marginally explained by the sole effect of each variable. Only the combined effects of different variables were visible. Variation in species composition in small green areas was better explained by PA (14%) in combination with HS (i.e., habitat heterogeneity at a 1 km radius). Moreover, the explained variations in bird species composition by DW (12%) and by DUG (12%) in combination with HS were noticeable (Figure 2).

4. Discussion

In this study, we inspected how the neighboring green network and landscape metrics explain species richness and composition of birds (as a proxy for biodiversity) in small urban green areas of two green cities in Germany—Freiburg and Regensburg. Our model assessment in Freiburg suggested that the proximity (distance) to the water body along with the usual positive effect of the associated green area size better explained bird species richness at the surveyed locations. It indicated that small green areas that were spatially located nearby a water body consisted of higher bird richness in Freiburg. In the case of Regensburg, patch abundance at a 1 km radius along with the usual greater negative effect of imperviousness explained bird species richness better at the surveyed locations. It pointed out that small green areas which contained a higher abundance of similar green ‘patches’ at a 1 km radius supported bird richness in Regensburg.
Our further assessment of the partitioning of the explained variation in bird species composition by the green network and landscape metrics variables indicated two alternate neighborhood settings of small green areas. In Freiburg, variables of the green network were influential; thus, variations in bird species composition in small-sized green areas were better explained by the sole effects of proximity to other ecologically important habitat areas (i.e., another green area and water body). In Regensburg, however, the highest explained variation in bird species composition was by the combined effect of landscape metrics (i.e., habitat heterogeneity and patch abundance) at a 1 km radius of small green areas.
Within cities, small-sized green areas, such as pocket parks, gardens, or playgrounds, are ecologically different components of green infrastructures since vegetation cover within these areas is entirely altered by human interventions. These green areas are spatially separated habitat patches and are often compared with islands since they are isolated from the core green area/forest area. Such patches can be crucial for animals, such as birds that are highly mobile [11]. Here, our results in Freiburg indicated that the presence of a nearby other urban green area and water body might improve the variety of birds and their composition. This is reasonable, however, since these features serve as refugia by providing shelter, nesting sites, and food [50,51]. In Regensburg, our results supported the fact that the presence of higher habitat heterogeneity (i.e., variety in complex vegetation structures) and an increasing quantity of green patches—as far as possible on a local scale (1 km radius)—might improve neighborhood habitat quality for bird species richness and composition [52,53]. The green patches may also vary in accessibility, depending on the width of surrounding roads as well as traffic volumes, which have been shown to influence the number and species composition of birds crossing the roads [54,55]. Thus, neighboring landscape configuration may help to improve the functionality and connectivity to promote the existence of different species in fragmented small green areas [56,57,58,59].
In our study, rare bird encounters were underrepresented at the surveyed small green area locations. This might be due to the fact that generalist bird species already replaced specialist birds in green areas within cities (i.e., due to avian homogenization) [52,60,61]. However, our study of limited duration (i.e., only one seasonal survey) cannot provide a solid understanding of this aspect. The purpose of our study was to assess patterns in species richness and composition in small green areas in relation to neighboring landscape factors. It did not aim to obtain a complete species checklist, which requires multiple years of consistent surveying.
Our study provides insights into the effects of only local scale neighboring landscape factors, which are fundamental and influential in enhancing birds’ mobility and presence in an urban area [17,21]. However, local scale avian diversity can also be associated with green networks and landscape metrics at a broader spatial scale since birds are highly mobile [16,62]. In addition, existing studies have found that anthropogenic disturbances such as daytime noise levels and pedestrian movement can also influence species occurrence and composition, as well as the dominance of native versus invasive species in urban areas [12,62,63].
The high conservation value of small green patches is only recently recognized for species diversity conservation [64]. In this paper, we targeted to support that neighboring landscape factors explain species richness and composition in small-sized green areas within cities. A preliminary investigation was first conducted in Freiburg and later replicated in Regensburg. We presented both case assessments to explain the individual city’s scenario better. However, the interpretation of our results requires some caution, since our analysis was based on limited-scale studies in two cities. Further, we could not perform the assessment considering a combined dataset due to the cautiousness of sampling effort variability between the cities. Birds were surveyed during the breeding season in Regensburg and during post-breeding into the migration season in Freiburg. The influence of green networks and landscape metrics on bird diversity and species composition could be different at different times of the year, as shown in other studies [65,66]. A clear understanding of how species-specific use and preference for green infrastructure features differ between seasons, and how such circumstances change throughout the year, would be worth exploring in the future.
‘Conservation’ usually targets threatened animals and natural habitats, such as forest areas or large-sized green areas [67]. In contrast, small-sized urban green areas within cities are mainly considered for recreational use by humans with little or no clear target for biodiversity. However, the green area management planning in Freiburg and Regensburg is exceptional. Freiburg’s effort to maintain its green spaces concerning natural and ecological principles trace back more than 20 years [24]. Regensburg has also been implementing spatial development planning to improve the urban greenery system concerning its suburban areas and natural and cultural heritage [23]. At the regional level, there is again a long history of bird conservation action networks as part of the European Union [68]. Despite a remarkable similarity in urban greenery and its management between the two cities, we observed that different variables of green networks and landscape metrics and their composition effectively explained variations in species composition in small green areas of Freiburg and Regensburg. A broad-scale study (i.e., with a higher amount of sampling sites) would be necessary to assess if the difference in the landscape factors’ effects is valid and whether a variation in variables’ effect on species composition in small green areas actually correlates with city-specific planning.
Nevertheless, our study indicates that landscape metrics of habitat quality (i.e., the higher number of patches and heterogeneity in complex vegetation structure) and a green network (i.e., the proximity of other green areas and water bodies) in the neighborhood can be fundamental to maintain species richness and composition in small green areas. It might ultimately contribute to strengthening the green infrastructure system in urban localities to support overall biodiversity within a city.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su14116394/s1.

Author Contributions

M.S. and I.S. designed the study; M.S. compiled the data, conducted the analysis and wrote the manuscript; M.M. (Max Müller) and M.M. (Magdalena Meyer) conducted the bird survey in Regensburg and contributed to revising the manuscript; I.S. contributed to the revising of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data used in this assessment are available in the citation [31].

Acknowledgments

We thank Sueon Ahn for her contribution during the bird survey in Freiburg and Marius Huber for assisting during the preliminary assessment of the data. Marufa Sultana was supported by the LGFG (Landesgraduiertenförderungsgesetz) scholarship from the International Graduate Academy (IGA) of the University of Freiburg during the research period. Finally, we acknowledge the support of the Open Access Publication Fund of the University of Freiburg.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The map shows selected small green areas and bird survey locations in Freiburg and Regensburg. An example of one small green area is shown on the right side. Here, (A–C) displays the neighboring landscape factors of the small green area considered in this study. Image (A) shows the variables of green networks (i.e., distance to the nearest other urban green area and water body). The following images in (B,C) show variables of landscape metrics at a 1 km radius of the surveyed location. (B) shows the different types of green fragments (grid cells of 110 m × 110 m), from which abundance of small ‘patch’ units (i.e., comparable to studied small green areas) is considered, and (C) shows the measure of habitat heterogeneity (habitat Shannon metric). The projected coordinate systems used in the map were WGS_1984_UTM_Zone_32N for Freiburg and WGS_1984_UTM_Zone_33N for Regensburg.
Figure 1. The map shows selected small green areas and bird survey locations in Freiburg and Regensburg. An example of one small green area is shown on the right side. Here, (A–C) displays the neighboring landscape factors of the small green area considered in this study. Image (A) shows the variables of green networks (i.e., distance to the nearest other urban green area and water body). The following images in (B,C) show variables of landscape metrics at a 1 km radius of the surveyed location. (B) shows the different types of green fragments (grid cells of 110 m × 110 m), from which abundance of small ‘patch’ units (i.e., comparable to studied small green areas) is considered, and (C) shows the measure of habitat heterogeneity (habitat Shannon metric). The projected coordinate systems used in the map were WGS_1984_UTM_Zone_32N for Freiburg and WGS_1984_UTM_Zone_33N for Regensburg.
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Figure 2. Partitioning of the effects of explanatory variables on bird species composition. The four variables used are HS (habitat Shannon metric), PA (patch abundance), DW (distance to the nearest water body), and DUG (distance to the nearest urban green area). HS and PA represent landscape metrics, and DW and DUG represent green networks in the vicinity of a small green area. The rectangular boundary represents the total variation in the avian species composition data. The numerals and their placement within the circles represent the portion of variation (i.e., the value of ‘adjusted R.square’) explained by specific explanatory variables or combined variables.
Figure 2. Partitioning of the effects of explanatory variables on bird species composition. The four variables used are HS (habitat Shannon metric), PA (patch abundance), DW (distance to the nearest water body), and DUG (distance to the nearest urban green area). HS and PA represent landscape metrics, and DW and DUG represent green networks in the vicinity of a small green area. The rectangular boundary represents the total variation in the avian species composition data. The numerals and their placement within the circles represent the portion of variation (i.e., the value of ‘adjusted R.square’) explained by specific explanatory variables or combined variables.
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Table
Table 1. A comparison of models’ coefficients and explained variations in bird species richness in the case of Freiburg (A) and Regensburg (B). In both cases, the first generated model contained the lowest AICc and is marked in italics. Here, Δi is the change in AICc and wi is the AICc weight. The model showing the highest explained variation based on R-squared (R2) value is marked in bold. Bird species richness was log10-transformed during modelling in the case of Freiburg. Here, GA = green areas size, IS = impervious surface, HS = habitat Shannon metric, PA = patch abundance, DW = distance to the nearest water body, DUG = distance to the nearest other urban green areas.
Table 1. A comparison of models’ coefficients and explained variations in bird species richness in the case of Freiburg (A) and Regensburg (B). In both cases, the first generated model contained the lowest AICc and is marked in italics. Here, Δi is the change in AICc and wi is the AICc weight. The model showing the highest explained variation based on R-squared (R2) value is marked in bold. Bird species richness was log10-transformed during modelling in the case of Freiburg. Here, GA = green areas size, IS = impervious surface, HS = habitat Shannon metric, PA = patch abundance, DW = distance to the nearest water body, DUG = distance to the nearest other urban green areas.
(A). Case of Freiburg
Models(Int)GADGDWHSPAAICcΔiwiR2
~GA0.760.06 −6.40.000.8920.23
~GA + DW0.760.05−0.02 0.36.760.0300.27
~GA + DG0.760.05 −0.01 0.67.040.0260.25
~GA + HS0.760.06 0.01 0.77.070.0260.25
~GA + PA0.760.06 0.010.77.130.0250.24
(B). Case of Regensburg
Models(Int) ISDWDGHSPAAICcΔiwiR2
~IS12.37−1.5 45.20.000.910.35
~IS + PA12.37−1.08 1.2851.36.050.040.57
~IS + DG12.37−1.23 −0.99 52.77.430.020.49
~IS + DW12.37−1.40−0.33 54.49.150.010.36
~IS + HS12.37−1.56 −0.09 54.69.320.010.35
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Sultana, M.; Müller, M.; Meyer, M.; Storch, I. Neighboring Green Network and Landscape Metrics Explain Biodiversity within Small Urban Green Areas—A Case Study on Birds. Sustainability 2022, 14, 6394. https://doi.org/10.3390/su14116394

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Sultana M, Müller M, Meyer M, Storch I. Neighboring Green Network and Landscape Metrics Explain Biodiversity within Small Urban Green Areas—A Case Study on Birds. Sustainability. 2022; 14(11):6394. https://doi.org/10.3390/su14116394

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Sultana, Marufa, Max Müller, Magdalena Meyer, and Ilse Storch. 2022. "Neighboring Green Network and Landscape Metrics Explain Biodiversity within Small Urban Green Areas—A Case Study on Birds" Sustainability 14, no. 11: 6394. https://doi.org/10.3390/su14116394

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