Biodiversity in cities. The effect of spatial scale and biodiversity data in heterogeneous landscapes.

doi:10.21203/rs.3.rs-2481675/v1

Download PDF

Research Article

Biodiversity in cities. The effect of spatial scale and biodiversity data in heterogeneous landscapes.

https://doi.org/10.21203/rs.3.rs-2481675/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

The assessment and monitoring of biodiversity in urban areas has been performed until now through dedicated on site studies and a small number of studies using online digital biodiversity databases. The question remains as to what extent an efficient and effective monitoring scheme could be implemented, one that not only facilitates comparisons across time and space, but also serves as an early change detection tool that complement local studies. Global Biodiversity Information Facility (GBIF) has been promoted for its central role, gathering and harmonizing biodiversity data worldwide, thereby facilitating the assessment and monitoring of biodiversity in multiple ecosystems. While GBIF data has been investigated for its potential at large scales (national, global) and in natural ecosystems, the question remains as to what extent, and in which context, is GBIF data applicable to urban biodiversity assessment and monitoring. In this study we have shown that the type of organisms surveyed -as well as its ecology and the spatial scale(s) of the assessments are critical to identify the effect of urban and green infrastructure in cities.

Urban biodiversity

spatial scale

GBIF

Netherlands

Arthropods

Birds

Mammals

Rapid urbanization is one of the most prominent development trends over the last centuries (Elmqvist et al., 2013). The trend continues today - in Europe (European Union states) alone, 74% of the population currently resides in urban areas (European Union, 2016). This causes enormous economic, societal, infrastructural, and environmental pressures from and on urban environments (Lucertini & Musco, 2020; Seto et al. 2014). Cities are not only one of the major contributors to climate change, but they will also be greatly affected by it (Kumar, 2021; Balaban, 2012). Examples such as the urban heat island effect (UHI) and urban air quality degradation are prominent and visible in almost every city around the globe. (Kumar, 2021; Balaban, 2012). However, the issue of climate change in urban areas has revealed that cities nowadays are facing multi-dimensional problems which are all amplified due to climate change and biodiversity loss (Chakraborty et al., 2019; Checker, 2011; Watkins et al., 2016; Alizadeh et al., 2022; Sicard et al., 2020). Social and economic inequal infrastructures become more visible when we talk about accessibility to cleaner air, public services, transportation, and green areas within the cities (Comber et al., 2008; Watkins et al., 2016; Chakraborty et al., 2019). Moreover, city areas are expected to reach 1,7 million km2 by 2050 (Zhou et al., 2019). This urban sprawl interacts with the surrounding rural and agricultural areas and has repercussions for natural ecosystems and its biodiversity which in turn can affect the health and the performance of these areas (Czamanski, 2008; De Carvalho & Szlafsztein, 2019). Furthermore, urbanization and city development has a paramount effect on biodiversity richness, species composition and ecosystem functioning in urban areas (Beninde et al. 2015; Lososová et al. 2016, La Sorte et al. 2014). This happens through landscape change-induced habitat loss as well as fragmentation of ecological networks in cities. And while some urbanization effects might be negative, studies show that urban areas can also provide habitat and foster biodiversity if managed well (Aronson et al. 2014, Fournier et al. 2020; Kowarik 2011, Ives et al. 2015; CBD, 2012; Lepczyk et al., 2017).

As the global biodiversity crisis deepens (Knapp et al., 2021; Guerry et al., 2019), urban environments have become crucial habitat providers for various species of plants and fungi and act as refuge for different avian, arthropod and mammal species from surrounding intensely managed landscapes (e.g. agricultural lands) (Baldock et al., 2015; Aronson et al. 2014, Fournier et al. 2020; Kowarik 2011, Ives et al. 2015; CBD, 2012; Lepczyk et al., 2017). GI plays a big role in providing such habitats in urban areas (Ferenc et al., 2014; Filazzola, Shrestha, MacIvor, 2019) A study by Sweet and colleagues, using data from Global Biodiversity Information Facility (GBIF), concluded that selected German cities from all states foster a considerable percentage (76%) of local biodiversity (Sweet et al., 2022). However, nature can thrive and thus provide us with valuable ecosystem services if certain habitat conditions are met (Beaugeard et al., 2020; Angold et al., 2006; Rudd et al., 2002). A study by Beaugeard et al. found that local urban biodiversity richness is highly benefited from the presence of green areas, proximity to the edge of the urban centre, and the proximity to green corridors. The presence of resource-rich green areas in urban contexts provides species with food and breeding habitats. Moreover, habitat patches with their edges next to contrasting, in this case urban, abiotic environments, are exposed to air, noise, and light pollution which negatively impacts the species living in that area (Driscoll et al., 2013). Lastly, movement and dispersal availability depend on the proximity to a green corridor (Beaugeard et al., 2020; Driscoll et al., 2013; Rudd et al., 2002).

The presence of high-quality green infrastructures (henceforth GI) and other similar nature based solutions in cities is being increasingly referred to as a multifaceted tool able to mitigate the most pressing urbanization issues in the context of climate change. (Sturiale & Scuderi, 2019; Gómez-Villarino et al., 2020; Madureira & Andresen, 2013). This involves installing, implementing or adapting green infrastructure into the existing city structure. Parks, green roofs, street vegetation, and tiny forests are some of the many examples of GI in cities (Liquete et al., 2015). The European Commission defines GI as a part of wider ecosystem services, which bring benefits not only to the natural environment but also to the wider population by cleaning the air, climate regulation, pollination, nutrient cycling, etc. (European Commission Directorate-General for Environment, 2021; Lai et al., 2018). In the urban context, GI provides all-around benefits for the city environment, infrastructure, and resident communities (Sturiale & Scuderi, 2019; GómezVillarino et al., 2020; Madureira & Andresen, 2013). The existence of high-quality GI, such as city parks, provides services for climate crisis adaptation firstly, by storm prevision, excess water storage, which mitigates the effects of floods (Madureira & Andresen, 2013). Secondly, cleaning cycling nutrients, cleaning the air, and providing a cooling effect which helps to combat urban challenges such as air pollution and urban heat stress (Zardo et al., 2017). Moreover, the GI provides recreational spaces which are crucial for maintaining the social and personal well-being and health of local communities (Astell-Burt & Feng, 2019; Taylor & Hochuli, 2014; Annerstedt van den Bosch et al., 2015; Hegetschweiler et al., 2017). Different types of GI, however, and its spatial arrangement provide different sets of benefits, therefore the relevance of understanding how biodiversity composition and ecology interact in urban settings to inform inform effective biodiversity sensitive urban designs (Garrard et al. 2018), including Nature Based solutions (Ronchi and Salata 2022) and wildlife- inclusive cities (Apfelbeck et al. 2020).

However, despite the increasingly growing popularity of the use of GI as a tool for climate adaptation and mitigation, research focus of GI as the habitat of a large number of species that not only use urban environments as temporal or permanent habitat, but also perform ecological functions key for the efficiency of GI as a mitigation and adaptation hub has been limited (LaPoint et al., 2015; Apfelbeck, 2020; Schwarz, 2017 Loreau 2001). While there is a number of research and reports investigating the connectivity, fragmentation of habitats as well as and the existence and provision of green corridors for wildlife in other spatial contexts such as natural reserves and agricultural areas, there is a clear lack of research in an urban context (van der Grift, 2005b; Ovaskainen, 2012; Grashof-Bokdam, 1997).

The assessment and monitoring of biodiversity in urban areas has been performed until now through dedicated on site studies and a small number of studies using online digital biodiversity databases. The question remains as to what extent an efficient and effective monitoring scheme could be implemented, one that not only facilitates comparisons across time and space, but also serves as an early change detection tool that complement local studies. The freely available biodiversity data provided by the Global Biodiversity Information Facility (GBIF) has been promoted for its central role, gathering and harmonizing biodiversity data worldwide, thereby facilitating the assessment and monitoring of biodiversity in multiple ecosystems (Proenca et al, 2017). As such, GBIF includes data from research and monitoring efforts as well as from citizen science, which have an even higher potential for biodiversity monitoring in urban areas (Li et al. 2019). While GBIF data has been investigated for its potential at large scales (national, global) and in natural ecosystems (Wolf, 2022; Sweet et al., 2022), the question remains as to what extent, and in which context, is GBIF data applicable to urban biodiversity assessment and monitoring. This study aims to fill the gap in research exploring the potential of GBIF data to identify drivers of species richness in urban areas across multiple spatial scales. With that aim, we measured bird, mammal and arthropods species richness in three Dutch cities and estimated the effect of land cover types and distance from the center of the city at multiple spatial scales.

Study sites

The three case study cities Amsterdam, Rotterdam and Groningen were selected that had represented three different city profiles regarding size, population and economic flows in the Netherlands. In this study we used land-use and biodiversity observation data only from the urban core region in order to avoid the misrepresentation and data bias in the suburban and peri-urban areas.

Amsterdam - Amsterdam is one of the biggest and well known cities of the Netherlands with a core urban area of 712.3 Km² and a population of 907,976 within the whole municipality. In 2011, the city council of Amsterdam published a structural vision “Amsterdam 2040: economically strong and sustainable”. As a part of the environmental vision of the latter development plan, the “Ecological vision” report focusing on ecology, biodiversity and green connectivity was developed (Gemeente Amsterdam, 2012). The latter development plan underlines that it drifts from previous GI development strategies, as it also focuses on environmental sustainability for local flora and fauna, and not only as recreation spaces for Amsterdam inhabitants (Gemeente Amsterdam, 2012). This marks an important shift, as it requires additional attention towards establishing and strengthening the infrastructure required for fostering biodiversity in a functioning ecological network for species movement and dispersal.

Rotterdam - With a core urban area of 585.8 Km2 and a population count of 651,157 (population density 2,963/km2 in the whole municipality), this city is known for its one of the largest ports in Europe. In 2018, the municipality of Rotterdam as a part of the college targets 2018–2022 released an environmental program and since then more than 20ha of greenery have been added to the public spaces such as streets and squares (Gemeente Rotterdam, 2022). While this initiative has been very successful, it was the only environmental goal in the college targets 2018–2022 (Gemeente Rotterdam, 2018; Gemeente Rotterdam, 2022).

Groningen - With a core urban area of 147.3 Km² and population of 233,218 (population density 1,246/km2). In 2018 as part of the Greenplan Groningen (Groenplan Groningen in Dutch) development plan focuses on planning and developing physical environments of Groningen. The agenda covers multiple disciplines such as urban growth and housing, economic development, community health, sustainable energy transition, climate-proof, and livable Groningen (Gemeente Groningen & Strootman Landshapsarchitecten, 2020). The latter includes implementing greening initiatives such as extending the tree cover (planting 1000 trees a year) and implementing 30 000m² of new green areas by creating and repurposing currently unused (gray infrastructure) space (Gemeente Groningen & Strootman Landshapsarchitecten, 2020). Moreover, in collaboration with residents municipality launched a “Groningen climate-proof” initiative by encouraging (and subsidizing) citizens, private and business entities to invest in climate adaptation - green roofs, green facades, planting and adoption of trees and tiny forests in private and public areas, preserving rainwater and public and private urban garden initiatives (Gemeente Groningen, 2018). Furthermore, the municipality aims to not only plant more, but also more diverse plant species in order to increase the biodiversity and resilience of GI in the city (Gemeente Groningen & Strootman Landshapsarchitecten, 2020). With the help of these initiatives, the municipality strives to strengthen the ecological network in Groningen which would provide direct benefits for flora, fauna, local residents as well as climate adaptation.

Methods

In order to estimate biodiversity levels in the chosen cities, we used publicly available land cover data from the Copernicus Urban Atlas 2018 (European Union, Copernicus Land Monitoring Service 2018) from each of the cities. This data set includes several land use classes that were reclassified, using the QGIS software (QGIS Development Team, 2020), into 4 different land use classes for each city. This was categorized in line with the European Commission's established GI definitions. Thus, the classification was done as follows (Fig. 1) -

- Class 1 - gray built up areas which include categories of the continuous urban fabric of varying density, including sports and leisure areas.

- Class 2 - green areas which included green urban areas, forests, herbaceous vegetation associations, and wetlands.

- Class 3 - agriculture areas included arable land, permanent crops, pastures with complex, and mixed cultivation patterns, and orchards.

- Class 4 - water - was dedicated to water bodies such as wetlands, ponds, lakes, rivers, canals, and seas.

Then the land use classes were rasterized, imported into GRASS GIS (GRASS development team, 2021) to calculate distance from the core of the cities to its periphery and perform a GI connectivity and patch metrics analysis.

For the multiscale analysis of biodiversity, polygon (hexagon) grids of 3 sizes − 0.2, 0.5 and 1 Km² per hexagon were created in order to estimate the spatial distribution of green areas in relation to species occurrences and richness (Fig. 1). The rasters and vector layers were cropped to city boundaries to contain only the urban core of the selected cities in order to focus on the urban green infrastructure connectivity while reducing the effect of rural areas surrounding the city.

Thereafter, publicly available biodiversity data from the Global Biodiversity Information Facility website was selected and downloaded (GBIF.org, GBIF.orgA, GBIF.orgB 2022). For the purpose of this study and, and to reduce sampling bias (Hughes et al., 2020), we group occurrence data from species in three large groups: arthropods, birds, and mammals. The downloaded data covered species occurrence records from 2017 - to 2021 from Amsterdam, Groningen and Rotterdam. The data was explored to remove duplicates and missing coordinates, and using the three grid sizes, we calculated species occurrences and species richness per group. To further account for the sampling bias, by reducing spatial clustering (Beck et al. 2014), the grid cells with no observations were excluded and the maximum richness was randomly set to 50 species. Additionally, for each grid, we calculated metrics such as distance to the core of the cities and number of pixels per land class.

The statistical analyses were performed using a model averaging approach at each spatial scale. We fitted a global weighted logistic regression model (i.e., Class1 + Class 2 + Class 3 + distance to core) taking each of the taxonomic groups as the response variable (i.e., mammals, birds and arthropods) and calculated the Akaike weight (AICwv) across all fitted models for each variable v (Burnham & Anderson, 2002). An AICwv value can be interpreted as a normalized relative likelihood representing the fit of the model, facilitating model averaging. Thus, to estimate relative importance of each explanatory variable, we summed the AICwv across all models in which the variable occurred. Higher AICwv values indicate a higher importance of that variable relative to the other variables. Such model averaging allows assessments to be based on multiple models, as well as bias in parameter estimation that may occur selecting a single best model (Burnham & Anderson, 2002). The set of models with a gamma value higher than four was selected for each analysis to approximate the true model (Burnham & Anderson, 2002). All the analyses were performed in R software (R Core Team, 2020) using packages MuMIn (Bartoń, 2020), arm (Gelman & Su, 2020), and spdep (Bivand et al., 2013) for model averaging; and ggplot2 (Wickham, 2016) for the figures.

The focal cities selected had less than 10% of its area devoted to Green Infrastructure (Amsterdam = 4.1%, Rotterdam = 6.9% and Groningen = 7.2%). Groningen differed from Amsterdam and Rotterdam in its higher percentage of land cover with pastures/agriculture (Groningen = 29%, Amsterdam = 8% and Rotterdam = 14%), while Rotterdam and Amsterdam had a higher percentage of land covered with water bodies (Amsterdam = 47.9%, Rotterdam = 33.3% and Groningen = 6.35%). In terms of GI patch metrics the cities have similar mean GI patch sizes, between 0.9 (Groningen and Rotterdam) and 0.11 Km² (Amsterdam). Groningen has the smallest number of GI patches (902) which might be related to its smaller area; yet Rotterdam (4150) has 1000 more patches than Amsterdam (2939) despite its smaller size. The shape of the patches is another relevant metric, as areas with less edge have more core area -less affected by surrounding stressors like noise, air and light pollution. The shape index metric provides a measurement of the relation between total area and edge of the patches, where 1 would be a 1:1 ratio area: edge and higher values reflect a larger edge to area ratio. All cities have a large extent of GI edges in relation to the total area of the GI, but Amsterdam and Rotterdam have values above 30 in shape index, while Groningen has a slightly lower value (shape index = 20). The assessment of spatial heterogeneity in all cities was, as expected, higher with small grid cell size, however heterogeneity was almost completely lost at 1 Km² for all the cities (Fig. 2).

A total of 2’258,509 species occurrences were included in the analyses of biodiversity (Amsterdam = 826,818, Rotterdam = 1’356,926 and Groningen = 74,765 occurrences). With Rotterdam having the higher density of occurrences per square kilometer (Amsterdam = 2566, Rotterdam = 3143.6, Groningen = 893.2 occurrences/Km²). In terms of taxonomic groups, the birds accounted for more than 50% of the total occurrences and comprising the majority of observations for all the cities surveyed (68.4% Birds, 28.9% Arthropods, 28.9% Mammals). Spatial distribution of occurrences is clustered for all cities and at all scales. Using the grid cells approach, we found grids of 0.2 Km² with more than 18000 occurrences but a large number of grid cells having no more than 20 observations and at least 50% of the cells with no occurrences in all cities and for all taxonomic groups, except in the case of arthropods in Rotterdam (30% cells with no occurrences) and birds in Amsterdam (38% cells with no occurrences) (Fig. 3).

Determinants Of Species Richness Across Scales

Species richness changed across scales for all taxonomic groups and cities. In the case of Groningen, models that included agriculture/pasture cover, distance to the core best explained species richness at all scales. Models explaining bird species richness, specifically, that include green cover were more significant explaining species richness but the effect was not as evident at smaller scales (0.2 Km²). Mammal species richness was positively affected by urban cover (Fig. 4), while distance to the city core is only significantly negative at the largest scale (1.0 Km²).

In the case of Amsterdam, models including all variables best explained species richness in all the groups, however, the direction and significance of the effect varied with scale. Arthropods species richness was negatively correlated with urban cover and agriculture/pasture cover, while it was positively related to the distance to the core, and green cover had an effect that varied from negative (0.2 Km²) to positive (1.0 Km²) depending on the scale. Bird species richness in Amsterdam was best explained at large scale, despite larger within variable variation, with all variables having a positive effect except for distance to the core that was negatively correlated. Models at medium scale (05. Km²), and including all variables, best explained mammal species richness with water, urban and green cover having a positive effect, and distance to the core a negative effect (Fig. 4).

Models explaining species richness in Rotterdam at large scale (1.0 Km²), best explained bird and arthropods species richness but the effect was not significant. Arthropods species richness was only significantly negatively affected at small scale (0.2 Km²) and only in relation to agriculture/pasture cover. Models including green cover (positive effect) and distance to the core (negative effect) best explained bird species richness. In the case of arthropods species richness, similar to the case of Amsterdam and Groningen, variation across scales was higher, with distance to the core having a significant negative effect at large scales, while agriculture/pasture and urban cover had a positive effect at the smaller scale (Fig. 4).

As urban expansion continues (Chen et al. 2020), biodiversity monitoring and management becomes of paramount importance to tackle ecosystem services and wellbeing of communities living in cities around the world (Aronson et al. 2017). This study focuses on the applicability of GBIF data at multiple scales to assess species richness patterns in relation to land cover spatial heterogeneity. Our study demonstrates the importance of spatial scale, in terms of resolution, when assessing the effect of land cover type on species richness and the relevance of GBIF data coverage. We found that, despite the clustered distribution of species occurrences in the three cities studied (approx. 50% of the cells had no occurrences), species richness in the three taxonomic groups investigated (Birds, Arthropods and Mammals) is significantly affected by land cover type and distance to the core of the city, but the direction and strength of the effect varies across spatial scales. Specifically, we found that the spatial arrangement of land cover types -specially GI cover- and the taxonomic group studied, together with the spatial scale, have a significant effect on species richness.

Biodiversity Observations In Cities

Biodiversity data quality, quantity and coverage are key to reduce biases and uncertainty in assessments of change in ecosystem integrity across the world (Jansen et al. 2022; Rocchini and Garzon-Lopez 2017). In this context, The Netherlands is one of the countries with the largest number of biodiversity observations in Europe (Wetzel et al. 2018), and as such, it has great potential to provide robust assessment. This is even more expected from cities, where -given its accessibility- more biodiversity data, compared to remote areas, is collected (Barbosa, Pautasso, and Figueiredo 2013). We found that, while data quantity is higher, this does not improve data quality and area coverage for the three cities studied. In Amsterdam, Rotterdam and Groningen species observations were highly clustered, resulting in more than 50% of the urban areas with zero biodiversity observations. In accordance with other studies, the bigger cities (Amsterdam and Rotterdam) had significantly more data than the smaller city (Groningen) confirming the biases in data collection efforts in smaller cities (Kendal et al. 2020).

From Data To Assessments

Biodiversity data is often used to identify drivers of species distribution via species distribution models (SDMs) or to assess the rate of change in relation to anthropogenic disturbances. The outcomes of those analyses are conditioned by the characteristics of the biodiversity data used (Hughes et al. 2021). Such biases in biodiversity data, due to uneven sampling intensity have often been tackled by resampling at larger scales (Dyderski et al. 2018). We examined whether this approach was possible - and to what extent- in cities, and found that changing scale by decreasing spatial resolution resulted in a significant reduction of heterogeneity in the distribution of urban cover -a key determinant of species distribution in cities- to an extent that, in our study at 1 Km² grid size spatial heterogeneity was already lost.

Species distribute in relation to how they use the resources and are used as a resource, and this is determined by their size and patterns of aggregation, as well as their mobility (Condit 2000; Braaker et al. 2014; Beaugeard, Brischoux, and Angelier 2021). Consequently, the scale at which the effect of city heterogeneity can be assessed varies from one species to another. We found that in the three cities studies, the strength and direction of the variables driving species richness also depend on the taxonomic group and the spatial scale explored. The variation in the determinants of species richness per taxonomic group was so relevant, that in some cases it shifted from significantly positive to negative within the same taxonomic group. Previous studies have explored the effect of land cover types for biodiversity in urban areas using varying grid sizes from 0.1 to 50 Km finding contrasting results on the direction of the effect; negative effect of urban areas on bird diversity at 50 km² (Ferenc et al. 2014). Studies performed at 1 km² -a scale explored in our study- found negative effect of urban cover on mammal (Gallo et al. 2017; Hursh et al. 2023) and arthropod richness (Fenoglio, Rossetti, and Videla 2020), in line with our findings in the case of arthropods and contrary to our findings in the case of mammals and for two of our cities (Groningen and Amsterdam), and a positive effect on species richness when looking at specifically the size of green areas (Cooper et al. 2021), in line with our findings for just one of cities explored (Rotterdam). Finally, studies performed at higher resolution (0.01 Km2) found a general trend in increase of species richness with increasing distance to the core of the city (Sweet et al. 2022; Aznarez et al. 2022). Previous studies have highlighted the importance of the type and characteristics of green spaces as an important factor for biodiversity (Das, Das, and Pereira 2023; Grade, Warren, and Lerman 2022; Xie et al. 2023; Lepczyk et al. 2017) which might explain the differences in the direction of the effect of urban and green areas across cities.

The increase of green infrastructure in cities across Europe (Xu et al. 2022), has moved urban areas closer to the goal of creating biodiversity inclusive cities as expected from the Sustainable Development Goals (SDG) (Opoku 2019). In this study we have shown that the type of organisms surveyed -as well as its ecology according to Concepción et al. 2015- and the spatial scale(s) of the assessments are critical to identify the effect of urban and green infrastructure in cities. In addition, other studies have shown the importance of green infrastructure type and connectivity to biodiversity (Grade, Warren, and Lerman 2022; Gallo et al. 2017; Lepczyk et al. 2017). In this context, the challenge remains as to how to develop integrative biodiversity assessments that accurately inform the design and management of biodiversity inclusive cities (Aronson et al. 2017; Garrard et al. 2018; Ronchi and Salata 2022; Apfelbeck et al. 2020). We propose a multiscale approach that includes: i) the tracking of heterogeneity, ii) biodiversity data coverage, and iii) species ecology across spatial scales.

Ethics approval

not applicable

Competing interests

No competing interests.

Authors' contributions

C.X. Garzon and G. Savickyte equally contributed to all parts of the manuscript.

Funding

not applicable

Availability of data and material

The data that support the findings of this study will be openly available in the Dryad Digital Repository.

This work was supported by Campus Fryslân, University of Groningen. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Alizadeh, M. R., Abatzoglou, J. T., Adamowski, J. F., Prestemon, J. P., Chittoori, B., Akbari Asanjan, A., & Sadegh, M. 2022. Increasing heat stress inequality in a warming climate. Earth’s Future, 10(2). https://doi.org/10.1029/2021ef002488
Angold, P. G., Sadler, J. P., Hill, M. O., Pullin, A., Rushton, S., Austin, K., ... & Thompson, K. 2006. Biodiversity in urban habitat patches. Science of the Total Environment, 360(1- 3), 196-204.
Annerstedt van den Bosch, M., Mudu, P., Uscila, V., Barrdahl, M., Kulinkina, A., Staatsen, B., Swart, W., Kruize, H., Zurlyte, I., & Egorov, A. I. 2015. Development of an urban green space indicator and the public health rationale. Scandinavian Journal of Public Health, 44(2), 159–167. https://doi.org/10.1177/1403494815615444
Apfelbeck, B., Snep, R., Hauck, T.E., Ferguson, J., Holy, H., Jakoby, M., MacIvor, J.,, Schär, L., Taylor, M. & Weisser, W.,W. 2020. Designing Wildlife-Inclusive Cities That Support Human-Animal Co-Existence. Landscape and Urban Planning 200: 103817. https://doi.org/10.1016/j.landurbplan.2020.103817.
Aronson MF, La Sorte FA, Nilon CH, Katti M, Goddard MA, Lepczyk CA, Warren PS, Williams NS, Cilliers S, Clarkson B, Dobbs C, Dolan R, Hedblom M, Klotz S, Kooijmans JL, Kühn I, Macgregor-Fors I, McDonnell M, Mörtberg U, Pysek P, Siebert S, Sushinsky J, Werner P, Winter M. A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers. Proc Biol Sci. 2014 Feb 12;281(1780):20133330. doi: 10.1098/rspb.2013.3330. PMID: 24523278; PMCID: PMC4027400.
Aronson, M., Lepczyk, C.A., Evans, K.L., Goddard, M.A., Lerman, S.B., MacIvor, J.S., Nilon, C.H., & Vargo, T. 2017. Biodiversity in the City: Key Challenges for Urban Green Space Management. Frontiers in Ecology and the Environment 15 (4): 189–96. https://doi.org/10.1002/fee.1480.
Astell-Burt, T., & Feng, X. 2019. Urban green space, tree canopy, and prevention of heart disease, hypertension, and diabetes: A longitudinal study. The Lancet Planetary Health, 3, S16. https://doi.org/10.1016/s2542-5196(19)30159-7
Aznarez, Celina, Jens-Christian Svenning, German Taveira, Francesc Baró, and Unai Pascual. 2022. Wildness and Habitat Quality Drive Spatial Patterns of Urban Biodiversity. Landscape and Urban Planning 228: 104570. https://doi.org/10.1016/j.landurbplan.2022.104570.
Balaban , O. 2012. Climate change and cities: A review on the impacts and policy responses. Metu Journal of the Faculty of Architecture, 29(1), 21–44. https://doi.org/10.4305/metu.jfa.2012.1.2
Baldock Katherine C. R., Goddard Mark A., Hicks Damien M., Kunin William E., Mitschunas Nadine, Osgathorpe Lynne M., Potts Simon G., Robertson Kirsty M., Scott Anna V., Stone Graham N., Vaughan Ian P. and Memmott Jane 2015Where is the UK's pollinator biodiversity? The importance of urban areas for flower-visiting insectsProc. R. Soc. B.2822014284920142849 http://doi.org/10.1098/rspb.2014.2849
Barbosa, A. Márcia, Marco Pautasso, and Diogo Figueiredo. 2013. Species–People Correlations and the Need to Account for Survey Effort in Biodiversity Analyses. Diversity and Distributions 19 (9): 1188–97. https://doi.org/10.1111/ddi.12106.
Bartoń, K. 2020. MuMIn: Multi-Model Inference. https://CRAN.R-project.org/package=MuMIn
Beaugeard, E., Brischoux, F., & Angelier, F. 2020. Green infrastructures and ecological corridors shape avian biodiversity in a small French city. Urban Ecosystems, 24(3), 549– 560. https://doi.org/10.1007/s11252-020-01062-7
Beaugeard, E., Brischoux, F. & Angelier, F.. 2021. Green Infrastructures and Ecological Corridors Shape Avian Biodiversity in a Small French City. Urban Ecosystems 24 (3): 549–60. https://doi.org/10.1007/s11252-020-01062-7.
Beck, J., Böller, M., Erhardt, A. & Schwanghart, W. 2014. Spatial bias in the GBIF database and its effect on modeling species' geographic distributions, Ecological Informatics, 19, 10-15, https://doi.org/10.1016/j.ecoinf.2013.11.002.
Beninde, J., Veith, M., & Hochkirch, A. 2015. Biodiversity in cities needs space: A meta-analysis of factors determining intra-urban biodiversity variation. Ecology Letters, 18(6), 581–592. https://doi.org/10.1111/ele.12427
Bivand, R. S., Pebesma, E., & Gomez-Rubio, V. 2013. Applied spatial data analysis with R, 2nd ed. Springer, NY. http://www.asdar-book.org/
Braaker, S., J. Ghazoul, M. K. Obrist, and M. Moretti. 2014. “Habitat Connectivity Shapes Urban Arthropod Communities: The Key Role of Green Roofs.” Ecology 95 (4): 1010–21. https://doi.org/10.1890/13-0705.1.
Burnham, K. P., & Anderson, D. R. 2002. Model selection and multimodel inference: A practical Information-Theoretic Approach (2nd ed.). Springer.
Chakraborty, T., Hsu, A., Manya, D., & Sheriff, G. 2019. Disproportionately higher exposure to urban heat in lower-income neighborhoods: A multi-city perspective. Environmental Research Letters, 14(10), 105003. https://doi.org/10.1088/1748- 9326/ab3b99
Checker, M. 2011. Wiped out by the “Greenwave”: Environmental gentrification and the paradoxical politics of urban sustainability. City & Society, 23(2), 210–229. https://doi.org/10.1111/j.1548-744x.2011.01063.x
Chen G, Li X, Liu X, Chen Y, Liang X, Leng J, Xu X, Liao W, Qiu Y, Wu Q, Huang K. Global projections of future urban land expansion under shared socioeconomic pathways. Nat Commun. 2020 Jan 27;11(1):537. doi: 10.1038/s41467-020-14386-x. PMID: 31988288; PMCID: PMC6985221.
Comber, A., Brunsdon, C., & Green, E. 2008. Using a GIS-based network analysis to determine urban greenspace accessibility for different ethnic and religious groups. Landscape and Urban Planning, 86(1), 103–114. https://doi.org/10.1016/j.landurbplan.2008.01.002
Concepción, E. D., Moretti, M., Altermatt, F., Nobis, M.P., & Obrist, M.K. 2015. Impacts of Urbanisation on Biodiversity: The Role of Species Mobility, Degree of Specialisation and Spatial Scale. Oikos 124 (12): 1571–82. https://doi.org/10.1111/oik.02166.
Condit, R. 2000. “Spatial Patterns in the Distribution of Tropical Tree Species.” Science 288 (5470): 1414–18. https://doi.org/10.1126/science.288.5470.1414.
Cooper, D. S., Wood, E.M., Katz, N.D., Superfisky, K., Osborn, F., Novoselov, A., Tarczynski, J., & Bacasen, L.K. 2021. Large Cities Fall Behind in ‘Neighborhood Biodiversity.’ Frontiers in Conservation Science 2. https://www.frontiersin.org/articles/10.3389/fcosc.2021.734931.
Czamanski, D. 2008. Urban sprawl and ecosystems — can nature survive? International Review of Environmental and Resource Economics, 2(4), 321–366. https://doi.org/10.1561/101.00000019
Das, M., Das, A., & Pereira, P. 2023. Developing an Integrated Urban Ecological Efficiency Framework for Spatial Ecological Planning: A Case on a Tropical Mega Metropolitan Area of the Global South. Geoscience Frontiers 14 (1): 101489. https://doi.org/10.1016/j.gsf.2022.101489.
De Carvalho, R. M., & Szlafsztein, C. F. 2019. Urban vegetation loss and ecosystem services: The influence on climate regulation and noise and air pollution. Environmental Pollution, 245, 844–852. https://doi.org/10.1016/j.envpol.2018.10.114
Dyderski, M. K., Paź, S., Frelich, L.E., & Jagodziński, A.M. 2018. How Much Does Climate Change Threaten European Forest Tree Species Distributions?. Global Change Biology 24 (3): 1150–63. https://doi.org/10.1111/gcb.13925.
Driscoll, D. A., Banks, S. C., Barton, P. S., Lindenmayer, D. B., & Smith, A. L. 2013. Conceptual domain of the matrix in fragmented landscapes. Trends in Ecology & Evolution, 28(10), 605–613. https://doi.org/10.1016/j.tree.2013.06.010
Elmqvist, T., Redman, C. L., Barthel, S., & Costanza, R. (2013). History of urbanization and the missing ecology. In Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities (pp. 13–30). Springer Netherlands. http://dx.doi.org/10.1007/978-94-007-7088-1_2
European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)
European Union. (2016, September 2). Urban Europe : Statistics on cities, towns and suburbs : 2016 edition. Publications Office of the EU; Eurostat Statistical Book. https://op.europa.eu/publication/doi/10.2785/91120
Fenoglio, M. S., Rossetti, M.R., & Videla, M. 2020. Negative Effects of Urbanization on Terrestrial Arthropod Communities: A Meta-Analysis. Global Ecology and Biogeography 29 (8): 1412–29. https://doi.org/10.1111/geb.13107.
Ferenc, M., Sedláček, O., Fuchs, R., Dinetti, M., Fraissinet, M., & Storch, D. 2014. Are cities different? Patterns of species richness and beta diversity of urban bird communities and regional species assemblages in E urope. Global Ecology and Biogeography, 23(4), 479-489.
Filazzola, A., Shrestha, N., & MacIvor, J. S. 2019. The contribution of constructed green infrastructure to urban biodiversity: A synthesis and meta-analysis. Journal of Applied Ecology, 56(9), 2131–2143. https://doi.org/10.1111/1365-2664.13475
Fournier, B., Frey, D., & Moretti, M. 2020. The origin of urban communities: From the regional species pool to community assemblages in city. Journal of Biogeography, 47(3), 615–629. https://doi.org/10.1111/jbi.13772
Gallo, T., Fidino, F., Lehrer, E.W, & Magle, S.B. 2017. Mammal Diversity and Metacommunity Dynamics in Urban Green Spaces: Implications for Urban Wildlife Conservation. Ecological Applications 27 (8): 2330–41. https://doi.org/10.1002/eap.1611.
Garrard, G. E., Williams, N.S.G, Mata,L., Thomas,J., & Bekessy, S.A. 2018. Biodiversity Sensitive Urban Design. Conservation Letters 11 (2): e12411. https://doi.org/10.1111/conl.12411.
GBIF.org (26 July 2022) GBIF Occurrence Download https://doi.org/10.15468/dl.8xmjx9
GBIF.orgA (26 July 2022) GBIF Occurrence Download https://doi.org/10.15468/dl.k9gn29
GBIF.orgB (20 July 2022) GBIF Occurrence Download https://doi.org/10.15468/dl.s6uz7x
Gelman, A., & Su, Y.-S. 2020. arm: Data analysis using regression and multilevel/hierarchical models. https://CRAN.R-project.org/package=arm
Gemeente Amsterdam. 2012. Ecologische visie, ecologie, biodiversiteit en groene verbindingen in Amsterdam.
Gemeente Groningen & Strootman Landshapsarchitecten. 2020. Groenplan Groningen.
Gemeente Groningen. 2018. Dit kun je doen - Groningen Klimaatbestendig. Groningen Klimaatbestendig. https://groningenklimaatbestendig.nl/dit-kun-je-doen/
Gemeente Rotterdam. 2018. Collegetargets 2018-2022. Gemeente Rotterdam. https://www.rotterdam.nl/bestuur-organisatie/collegetargets-2018-2022/
Gemeente Rotterdam. 2022. De finish Rotterdam gaat voor groen 2018 - 2022.
Gómez-Villarino, M. T., Gómez Villarino, M., & Ruiz-Garcia, L. 2020. Implementation of urban green infrastructures in peri-urban areas: A case study of climate change mitigation in madrid. Agronomy, 11(1), 31. https://doi.org/10.3390/agronomy11010031
Grade, A.M., Warren, P.S., & Lerman, S.B. 2022. Managing Yards for Mammals: Mammal Species Richness Peaks in the Suburbs. Landscape and Urban Planning 220 (April): 104337. https://doi.org/10.1016/j.landurbplan.2021.104337.
Grashof-Bokdam, C. 1997. Forest species in an agricultural landscape in the Netherlands: Effects of habitat fragmentation. Journal of Vegetation Science, 8(1), 21–28. https://doi.org/10.2307/3237238
GRASS Development Team. 2021. Geographic Resources Analysis Support System (GRASS) Software, Version 8.0. Open Source Geospatial Foundation. Electronic document:. http://grass.osgeo.org
Guerry, A. D., Smith, J.R., Lonsdorf, E., Daily, G.C., Wang, X. & Chun, Y. 2021. Urban Nature and Biodiversity for Cities. Policy Briefing. Global Platform for Sustainable Cities, World Bank. Washington, DC. © World Bank.
Hursh, S. H., Bauder, J.M., Fidino, M., & Drake, D. 2023. An Urban Cast of Characters: Landscape Use and Cover Influencing Mammal Occupancy in an American Midwestern City. Landscape and Urban Planning 229: 104582. https://doi.org/10.1016/j.landurbplan.2022.104582.
Hegetschweiler, K. T., de Vries, S., Arnberger, A., Bell, S., Brennan, M., Siter, N., Olafsson, A. S., Voigt, A., & Hunziker, M. 2017. Linking demand and supply factors in identifying cultural ecosystem services of urban green infrastructures: A review of European studies. Urban Forestry & Urban Greening, 21, 48–59. https://doi.org/10.1016/j.ufug.2016.11.002
Hughes, A., Orr, M., Ma, K., Costello, M., Waller, J., Provoost, P., Zhu, C., & Qiao, H. 2021. Sampling biases shape our view of the natural world. Authorea, Inc. http://dx.doi.org/10.22541/au.159419540.02222358
Ives, C. D., Lentini, P. E., Threlfall, C. G., Ikin, K., Shanahan, D. F., Garrard, G. E., Bekessy, S. A., Fuller, R. A., Mumaw, L., Rayner, L., Rowe, R., Valentine, L. E., & Kendal, D. 2015. Cities are hotspots for threatened species. Global Ecology and Biogeography, 25(1), 117–126. https://doi.org/10.1111/geb.12404
Jansen, J., Woolley, S.N.C., Dunstan, P.K., Foster, S.D., Hill, N.A, Haward, M., & Johnson, C.R. 2022. Stop Ignoring Map Uncertainty in Biodiversity Science and Conservation Policy. Nature Ecology & Evolution 6 (7): 828–29. https://doi.org/10.1038/s41559-022-01778-z.
Kendal, D., Egerer, M., Byrne, J.A., Jones, P.J., Marsh, P., Threlfall, C.G., Allegretto, G., Kaplan, H., Nguyen, H.K., Pearson, S. and Wright, A., 2020. City-size bias in knowledge on the effects of urban nature on people and biodiversity. Environmental Research Letters, 15(12), p.124035.
Knapp, S., Aronson, M.F., Carpenter, E., Herrera-Montes, A., Jung, K., Kotze, D.J., La Sorte, F.A., Lepczyk, C.A., MacGregor-Fors, I., MacIvor, J.S. and Moretti, M., 2021. A research agenda for urban biodiversity in the global extinction crisis. Bioscience, 71(3), pp.268-279.
Kowarik, I. 2011. Novel urban ecosystems, biodiversity, and conservation. Environmental Pollution, 159(8–9), 1974–1983. https://doi.org/10.1016/j.envpol.2011.02.022
Kumar, P. 2021. Climate change and cities: Challenges ahead. Frontiers in Sustainable Cities, 3. https://doi.org/10.3389/frsc.2021.645613
La Sorte, F. A., Tingley, M. W., & Hurlbert, A. H. (2014). The role of urban and agricultural areas during avian migration: An assessment of within-year temporal turnover. Global Ecology and Biogeography, 23(11), 1225–1234. https://doi.org/10.1111/geb.12199
Lai, S., Leone, F., & Zoppi, C. 2018. Implementing green infrastructures beyond protected areas. Sustainability, 10(10), 3544. https://doi.org/10.3390/su10103544
LaPoint, S., Balkenhol, N., Hale, J., Sadler, J., & Ree, R. (2015). Ecological connectivity research in urban areas. Functional Ecology, 29(7), 868–878. https://doi.org/10.1111/1365-2435.12489
Lepczyk, Christopher A., Myla F. J. Aronson, Karl L. Evans, Mark A. Goddard, Susannah B. Lerman, and J. Scott MacIvor. 2017. Biodiversity in the City: Fundamental Questions for Understanding the Ecology of Urban Green Spaces for Biodiversity Conservation. BioScience 67 (9): 799–807. https://doi.org/10.1093/biosci/bix079.
Li, E., Parker, S.S, Pauly, G.B., Randal, J.M.l, Brown, B.V., & Cohen, B.S.. 2019. An Urban Biodiversity Assessment Framework That Combines an Urban Habitat Classification Scheme and Citizen Science Data. Frontiers in Ecology and Evolution 7. https://doi.org/10.3389/fevo.2019.00277.
Loreau, M., Naeem, S., Inchausti, P., Bengtsson, J., Grime, J.P., Hector, A., Hooper, D.U., Huston, M.A., Raffaelli, D., Schmid, B. and Tilman, D., 2001. Biodiversity and ecosystem functioning: current knowledge and future challenges. science, 294(5543), pp.804-808.
Lososová, Z., Čeplová, N., Chytrý, M., Tichý, L., Danihelka, J., Fajmon, K., Láníková, D., Preislerová, Z., & Řehořek, V. 2016. Is phylogenetic diversity a good proxy for functional diversity of plant communities? A case study from urban habitats. Journal of Vegetation Science, 27(5), 1036–1046. https://doi.org/10.1111/jvs.12414
Lucertini, G., & Musco, F. 2020. Circular urban metabolism framework. One Earth, 2(2), 138–142. https://doi.org/10.1016/j.oneear.2020.02.004
Madureira, H., & Andresen, T. 2013. Planning for multifunctional urban green infrastructures: Promises and challenges. URBAN DESIGN International, 19(1), 38–49. https://doi.org/10.1057/udi.2013.11
Opoku, Alex. 2019. Biodiversity and the Built Environment: Implications for the Sustainable Development Goals (SDGs). Resources, Conservation and Recycling 141 (February): 1–7. https://doi.org/10.1016/j.resconrec.2018.10.011.
Ovaskainen, O. 2012. How to develop the nature conservation strategies for The Netherlands ? De Levende Natuur, 114(2), 59–62. Proka, A., Hisschemöller, M., & Loorbach, D. (2018). Transition without conflict? Renewable energy initiatives in the Dutch energy transition. Sustainability, 10(6), 1721. https://doi.org/10.3390/su10061721
QGIS Development Team, 2020. QGIS Geographic Information System. Open Source Geospatial Foundation. URL http://qgis.org
R Core Team. 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Rocchini, D. & Garzon-Lopez, C.X. 2017. Cartograms Tool to Represent Spatial Uncertainty in Species Distribution. Research Ideas and Outcomes 3: e12029.
Ronchi, S., & Salata, S.. 2022. Insights for the Enhancement of Urban Biodiversity Using Nature-Based Solutions: The Role of Urban Spaces in Green Infrastructures Design. In Nature-Based Solutions for Sustainable Urban Planning: Greening Cities, Shaping Cities, edited by Israa H. Mahmoud, Eugenio Morello, Fabiano Lemes de Oliveira, and Davide Geneletti, 47–68. Contemporary Urban Design Thinking. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-89525-9_3.
Rudd, H., Vala, J., & Schaefer, V. 2002. Importance of backyard habitat in a comprehensive biodiversity conservation strategy: A connectivity analysis of urban green spaces. Restoration Ecology, 10(2), 368–375. https://doi.org/10.1046/j.1526- 100x.2002.02041.x
Schwarz, N., Moretti, M., Bugalho, M. N., Davies, Z. G., Haase, D., Hack, J., Hof, A., Melero, Y., Pett, T. J., & Knapp, S. 2017. Understanding biodiversity-ecosystem service relationships in urban areas: A comprehensive literature review. Ecosystem Services, 27, 161–171. https://doi.org/10.1016/j.ecoser.2017.08.014
Seto K.C., S. Dhakal, A. Bigio, H. Blanco, G.C. Delgado, D. Dewar, L. Huang, A. Inaba, A. Kansal, S. Lwasa, J.E. McMahon, D.B. Müller, J. Murakami, H. Nagendra, and A. Ramaswami, 2014. Human Settlements, Infrastructure and Spatial Planning. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. https://doi.org/10.1007/s10980-011-9579-1
Seto, Karen C., Burak Güneralp, and Lucy R. Hutyra. 2012. Global Forecasts of Urban Expansion to 2030 and Direct Impacts on Biodiversity and Carbon Pools.” Proceedings of the National Academy of Sciences 109 (40): 16083–88. https://doi.org/10.1073/pnas.1211658109.
Sicard, P., Agathokleous, E., Marco, A. D., Paoletti, E., & Calatayud, V. 2020a. Urban population exposure to air pollution in Europe over the last decades. Research Square Platform LLC. http://dx.doi.org/10.21203/rs.3.rs-101275/v1
Sidemo-Holm, William, Johan Ekroos, Santiago Reina García, Bo Söderström, and Marcus Hedblom. 2022. Urbanization Causes Biotic Homogenization of Woodland Bird Communities at Multiple Spatial Scales. Global Change Biology 28 (21): 6152–64. https://doi.org/10.1111/gcb.16350.
Silva, C. P., García, C. E., Estay, S. A., & Barbosa, O. 2015. Bird richness and abundance in response to urban form in a Latin American city: Valdivia, Chile as a case study. PloS one, 10(9), e0138120.
Sturiale, L. & Scuderi, A. 2019. The role of green infrastructures in urban planning for climate change adaptation. Climate, 7(10), 119. https://doi.org/10.3390/cli7100119
Sultana, Marufa, and Ilse Storch. 2021. Suitability of Open Digital Species Records for Assessing Biodiversity Patterns in Cities: A Case Study Using Avian Records. Journal of Urban Ecology 7 (1). https://doi.org/10.1093/jue/juab014.
Sweet, F. S., Apfelbeck, B., Hanusch, M., Garland Monteagudo, C., & Weisser, W. W. 2022. Data from public and governmental databases show that a large proportion of the regional animal species pool occurs in cities in Germany. Journal of Urban Ecology, 8(1).
Taylor, L., & Hochuli, D. F. 2014. Creating better cities: How biodiversity and ecosystem functioning enhance urban residents’ wellbeing. Urban Ecosystems, 18(3), 747–762. https://doi.org/10.1007/s11252-014-0427-3
van der Grift, E. A. 2005. Defragmentation in the Netherlands: A success story? GAIA - Ecological Perspectives for Science and Society, 14(2), 144–147. https://doi.org/10.14512/gaia.14.2.16
Watkins, S. L., Mincey, S. K., Vogt, J., & Sweeney, S. P. 2016. Is planting equitable? An examination of the spatial distribution of nonprofit urban tree-planting programs by canopy cover, income, race, and ethnicity. Environment and Behavior, 49(4), 452–482. https://doi.org/10.1177/0013916516636423
Wetzel, Florian T., Heather C. Bingham, Quentin Groom, Peter Haase, Urmas Kõljalg, Michael Kuhlmann, Corinne S. Martin, et al. 2018. Unlocking Biodiversity Data: Prioritization and Filling the Gaps in Biodiversity Observation Data in Europe. Biological Conservation 221 (May): 78–85. https://doi.org/10.1016/j.biocon.2017.12.024.
Wickham, H. 2016. ggplot2: Elegant graphics for data analysis. Springer-Verlag New York.
Wolf, J. M., Jeschke, J. M., Voigt, C. C., & Itescu, Y. 2022. Urban affinity and its associated traits: a global analysis of bats. Global Change Biology.
Xie, Chenghan, Jingxia Wang, Dagmar Haase, Thilo Wellmann, and Angela Lausch. 2023. Measuring Spatio-Temporal Heterogeneity and Interior Characteristics of Green Spaces in Urban Neighborhoods: A New Approach Using Gray Level Co-Occurrence Matrix. Science of The Total Environment 855: 158608. https://doi.org/10.1016/j.scitotenv.2022.158608.
Xu, Fei, Jingli Yan, Stien Heremans, and Ben Somers. 2022. Pan-European Urban Green Space Dynamics: A View from Space between 1990 and 2015. Landscape and Urban Planning 226 (October): 104477. https://doi.org/10.1016/j.landurbplan.2022.104477.
Zardo, L., Geneletti, D., Pérez-Soba, M., & Van Eupen, M. 2017. Estimating the cooling capacity of green infrastructures to support urban planning. Ecosystem Services, 26, 225– 235. https://doi.org/10.1016/j.ecoser.2017.06.016
Zhou, Y., Varquez, A. C. G., & Kanda, M. 2019. High-resolution global urban growth projection based on multiple applications of the SLEUTH urban growth model. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0048-z
Zhou, Qi, Yiming Liao, and Jue Wang. 2022. Mapping Global Urban Greenspace: An Analysis Based on Open Land-Cover Data. Urban Forestry & Urban Greening 74 (August): 127638. https://doi.org/10.1016/j.ufug.2022.127638.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Biodiversity in cities. The effect of spatial scale and biodiversity data in heterogeneous landscapes.

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Study sites

Methods

Results

Determinants Of Species Richness Across Scales

Discussion

Biodiversity Observations In Cities

From Data To Assessments

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1