Urban Biodiversity Index for Trees: A Climate Adaptation Measure for Cities Based on Tree Inventories

Abstract

A historically large percentage of the world’s population has moved to urban areas in the past few decades, causing various negative effects for the environment, such as air, noise, water, and light pollution; land degradation; and biodiversity loss. Under the current climate crisis, cities are anticipated to play an essential part in adaptation strategies to extreme atmospheric events. This study aims at developing indicators at an urban scale that can highlight adaptation progress by investigating relevant data (especially in situ) and statistics at a pan-European level in support of the EU’s strategy for adapting to the impacts of climate change. The proposed indicator, Urban Biodiversity Indicator for Trees (UBI4T), which can be derived from city tree inventories, assesses one essential component of urban biodiversity by computing the proportion of native, alien, invasive, and toxic tree species spatially across a city. According to our findings (applying the UBI4T for Amsterdam and exploring its policy potential for Barcelona), the UBI4T can offer crucial information for decision and policy makers, as well as stakeholders of a city, with the aim of conducting dedicated and effective strategic initiatives to restore, improve, and protect nature in the urban environment, thus contributing to adaptation and resilience to extreme atmospheric events in cities.

1. Introduction

With urbanization expanding [1], the protection of urban biodiversity becomes an issue of top priority for improving the well-being of residents but also for narrowing the ecological footprint of cities [2,3]. According to the Convention on Biological Diversity (CBD), urban biodiversity refers to the variety and variability of living organisms, including plants, animals, fungi, and microorganisms found within urban environments. Biodiversity in cities encompasses species living in green spaces, such as parks, gardens, and natural reserves, as well as those inhabiting built environments like buildings, streets, and industrial areas [4]. This is also reflected in recent urban planning approaches that promote and assess urban biodiversity by incorporating natural elements or even ecosystems into the urban fabric, i.e., nature-based solutions (NbS) [5,6]. Moreover, the new European Union’s (EU) Nature Restoration Law, which has also been welcomed by city platforms such as EuroCities and Energy-Cities, refers explicitly to urban ecosystems and the need to increase the total area covered by green urban space by 2040 and 2050. Yet, the law’s recent delays, in addition to the lack of spaces in the urban fabric to improve and expand the green infrastructure with NbSs or other solutions in most cities, undermine urban resilience. Today, there are numerous urban biodiversity-monitoring frameworks that are systematically tracking and assessing biodiversity components (species, habitats, biotic interactions, ecosystem services, anthropogenic pressures, etc.) within the urban environment [7,8,9], aiming to comprehend urbanization impacts, set conservation priorities at the policy level, and propose effective measures and actions at the strategy level. However, despite the time, cost, and scientifically qualified effort needed, no internationally accepted standards or reporting method are currently available.

At the policy and decision-making level, when it comes to urban biodiversity that must be conserved under multiple and diverse anthropogenic pressures, direct monitoring and straightforward assessment of biological diversity (i.e., species and habitats) receive less attention and are replaced with evaluation of the interactions between socioeconomic and ecological systems [10], aiming for integrated decision-making processes for city managers [11], but making the process even more complex. In 2005, the Millennium Ecosystem Assessment created an ecosystem change assessment framework that included the concept of ecosystem services to achieve human well-being through the use of a considerable number of indicators [12]. The CBD also produced its own set of indicators for evaluating the 2010 biodiversity target [13], followed by the Group on Earth Observations Biodiversity Observation Network, which developed another set of indicators to meet the goals of the new Aichi targets (CBD Decision X/2) [14]. There are also several indicators at the municipality and city level targeting urban climate adaptation within the frame of “life quality” [15]. Special attention should be drawn to the City Biodiversity Index, often referred to as the Singapore Index on Cities’ Biodiversity, which actually forms a tool developed to help cities monitor and assess their performance in terms of protecting and preserving biodiversity and ecosystem services [16].

Moreover, the European Environment Agency (EEA) has an initiative dating back to 1995 to develop an urban indicator set assessing relevant pressures (e.g., industry, transport, population growth, waste production, and other drivers of change), state (e.g., conditions of the environment and natural resources such as biodiversity and air and water quality), and response (e.g., legislations, regulations, and economic instruments) [17]. Other sustainability-based indicators with a biodiversity dimension are included in the Ecological Footprint [18]. For example, the Green City Index calculates the environmental performance of world cities by means of 16 quantitative and 14 qualitative indicators, and the Environmental Performance Index calculates human and ecosystem health within nine domains (i.e., agriculture, air quality, biodiversity and habitat, climate and energy, fisheries, forests, health impacts, water resources, and water and sanitation). Finally, a composite index, named the European Urban Biodiversity Index, is tested in a working paper of the EEA that explores the possibilities of incorporating new city-specific data, Copernicus layers, biodiversity-related European datasets, and other sources of indicator data for urban assessments [19].

Table 1. Indicative sustainability indicators with a special focus on biodiversity in the urban context.

Indicator Name (Source)	Frame	Purpose and Use	Biodiversity Dimension
Genuine Progress Indicator [20]	20 separate environmental, economic, and social indices.	Sustainable development	Loss of wetlands, farmland, primary forests and damage from logging roads
Genuine Saving [21]	Computation of various factors of economic development (e.g., produced assets, natural resources, environmental quality, human resources, and foreign assets).	Sustainable development	Forestry
Happy Planet Index [22]	Ratio of happy life years (happiness adjusted to life expectancy) to environmental impact (measured by the Ecological Footprint).	Ecological efficiency of human well-being	Ecological footprint
Wellbeing Index [23]	The Wellbeing Index (WI) combines the Human Wellbeing Index (HWI) and the Ecosystem Wellbeing Index (EWI) on the Barometer of Sustainability, a graphic scale that shows how far each country is from the goal of high levels of human and ecosystem wellbeing.	Sustainable development	Ecosystem status, impacts on humans, improvements
City Sustainability Index [24]	Sustainability range (from weak to strong).	Sustainable development	Ecosystems and ecosystem services

summarizes other indicative examples of sustainability-oriented indicators, including biodiversity components.

As depicted by the short history and evolution of urban biodiversity indicators, it is always a matter of integration with sustainability, aiming at climate adaptation in the urban environment. However, there are still gaps in understanding and assessing the climate adaptation–biodiversity conservation nexus [25]. Climate change is one of the most critical issues of our time, and its impacts on biodiversity are more evident than ever [26]. Animal and plant species are threatened with extinction, while ecosystems are losing resilience, resistance, and stability [27]. On the other hand, biodiversity loss amplifies climate change impacts and undermines mitigation and/or adaptation initiatives [28,29]. The importance of linking biodiversity loss with climate change in order to achieve adaptation to extreme atmospheric events, especially in the urban environment [30], is also highlighted by the Convention on Biological Diversity [31], as well as by the United Nations Framework Convention on Climate Change [32]. To this end, the development and adoption of urban biodiversity indicators could contribute to the understanding, monitoring and, thus, effective management of such a reciprocal relationship, with the ultimate goal of pursuing urban resilience.

Current urban biodiversity strategies rarely include any quantifiable actions or indicators due to the complexity of the climate adaptation–biodiversity conservation nexus and, in terms of specificity and technical inputs, the lack of linkage between measures and actions, as well as other political challenges [33]. On the other hand, the use of bio-indicators most of the time requires a time- and effort-consuming monitoring scheme for a variety of different species and in a wide range of different locations [34]. Indicators that are scalable (from the local to global level) and linked to biodiversity and, at the same time, can be associated with ecosystem services are therefore needed [35]. Biodiversity indicators, especially when referring to the urban context, need systemic monitoring with the potential of aggregating to deliver accurate information on global changes, like the climate crisis [36]. Equally important is the reinforcement of the research–policy interaction by means of information exchange, co-evolution, and joint creation between scientists and policymakers regarding biodiversity conservation [37]. Nonetheless, the lack of data derived from various and multi-source datasets also creates limitations and constraints in most cases.

Measurable qualitative or quantitative indicators are crucial for assessing the implementation of climate adaptation measures. This study introduces an easy-to-apply Urban Biodiversity Index for Trees (UBI4T), which requires just the existence of a tree inventory. The UBI4T builds upon existing datasets of tree registries, which are developed by city authorities for green and planting actions, to deliver additional information about biodiversity and citizens’ environmental health. In particular, the UBI4T categorizes each tree as native or alien, and at a second level, invasive or toxic, based on the species’ scientific name, by cross-referencing tree species listed in tree inventories with global and regional databases describing the origin per region (native or alien), invasiveness, or toxicity status of the species. In this way, not only the spatial distribution of the status of biodiversity but also the degree of invasiveness and toxicity of the trees are calculated for the city. Through the UBI4T, the importance of local information and knowledge (as derived from the tree inventories) is highlighted, allowing for city assessment and informed planning as well as monitoring of evolution and progress. Moreover, it offers the potential of replicating and upscaling in other cities or greater regions, allowing for comparison between cities (e.g., status, assessment of the efficiency of different measures) and underlying the emerging significance of tree inventories development.

This study aims to propose an effective, directly available, and easy-to-implement index to assess green resilience and consequently, support the EU strategy for adaptation to climate change impacts in the urban context. To accomplish this, after reviewing existing environmental datasets at the EU level with a special focus on in situ data, we concentrated on the tree inventories of EU cities. Then, we investigated the information provided in these tree inventories and their potential to measure an important dimension of urban resilience: trees. In this way, three separate indicators, making up the UBI4T, have been developed that actually monitor tree distribution and composition, while at the same time providing a representative index about cities’ green resilience status, a key concept in adaptation to extreme atmospheric events.

2. Methods

2.1. Rationale

In order to evaluate the degree of development and the status quo of indicators targeted at adaptation to extreme atmospheric events in the urban context, a quantitative and qualitative review of literature published in English up until November 2023. A primary academic literature review using the Web of Science, Scopus, and Google databases to detect existing environmental datasets and concepts/frameworks was initially performed. The search terms used were as follows: (urban), (resilience), (sustainability), (adaptation), (smart city), (vulnerability), (exposure), (risk), and (hazard). The review resulted in 138 frameworks (Framework: a technical, structured system in the format of a database containing a set of indicators) and 6282 indicators (Indicator: a measure or variable to assess, track, or demonstrate the status of resilience, sustainability, adaptation, smartness, vulnerability, exposure, risks, and hazards within the urban environment), revealing fragmentation but also significant overlaps (Table S1). Taking into account that our goal was to propose an index for monitoring adaptation to extreme atmospheric events that was replicable at the city scale and ready-to-use by exploiting existing datasets, four filtering criteria were applied: (i) Priority was given to in situ data that originated from accurate measurements of environmental variables (i.e., temperature, precipitation, species diversity) at actual locations, allowing for precise observations and, thus, effective and successful indicators. (ii) Focus was given to data replicability for different cities/countries’ potential, (iii) as well as to their up-scalability from the local/urban level to the regional/EU level; and (iv) lastly, our research was aligned to the regional implementation of the mission of climate change adaptation.

The inventoried frameworks are mostly created and/or used by international and governmental organizations, related initiatives, academia, or individual researchers and comprise, on average, 46 indicators (

Table 2. The distribution of frameworks number per contributor/user category.

Contributor/User	Number of Frames	Percentage of Contribution (%)
International Organizations	69	50.0
Governmental Organizations	27	19.6
Projects	9	6.5
Academia (University and Research Centers)	14	10.1
Individuals	19	13.8

). Most of the frameworks are dedicated to monitoring climate adaptation (31%), followed by sustainability (21%), smart city practices (16%), and resilience (15%), while the remaining 17% are shared between vulnerability, exposure, risk, and hazard-monitoring by computing dedicated, targeted indices per theme.

The systematic examination of overlaps, gaps, and needs among the various developed indicator frameworks guided this work to the development of an index that could detect and monitor one dimension of the urban biodiversity conservation status as a means to achieve adaptation to extreme atmospheric events in cities. Though the identified frameworks and indicators address various environmental issues and policy sectors, we limited our research to those dealing with in situ datasets, since the priority of our research was to detect and exploit existing, reliable, and ready-to-use in situ data. Among the various available in situ datasets, we decided to focus on tree inventories provided by cities as an ideal example of earth observations that can be developed to serve purposes such as urban planning, climate adaptation monitoring, and interpreting the widely identified nexus of biodiversity conservation and climate change [38]. The proposed index satisfies the filtering criteria since tree inventories can be considered a data source of high availability, granularity, and relevance to adaptation to extreme atmospheric events, and it calculates one crucial component of urban biodiversity (i.e., city trees) through the assessment of the spatial distribution and composition of urban trees [39]. After a thorough investigation of free-access tree inventories that were available online (Table S2), we selected two of the most complete and easily processed inventories: Amsterdam’s and Barcelona’s tree registries. It must be noted at this point that biodiversity in urban areas can be more easily assessed and compared to that in wilderness areas and other natural ecosystems due to the much lower number of recorded plant species.

2.2. The Urban Biodiversity Index for Trees (UBI4T)

The proposed UBI4T is composed of three complementary indicators, based on ratios of native, alien, invasive, and toxic tree species, categorized by means of cross-referencing tree species recorded in tree inventories with global and regional databases that describe their origin per city (native or alien) as well as the species’ invasiveness or toxicity status. It aims to shed light on the spatial distribution of the different species, provide information on urban biodiversity at the local scale, and identify potential trends. The three individual indicators and their scoring are defined as follows, after a thorough literature review and communication with experts, and with a view to exploiting existing in situ datasets:

UBI4T₁ = native trees/alien trees

(1)

This index offers an overview of the status of the city’s native trees, framing the policy efforts that need to be adopted accordingly [40,41]. Apart from providing a valuable tool for urban planning and planting, UBI4T₁ provides the necessary and direct information about the status of native species that is more favorable for supporting biodiversity, especially in the case of the highly disturbed urban environments.

UBI4T₂ = invasive trees/alien trees

(2)

This second index represents an alert index for the city, considering the well-recognized threat of invasive species [42,43,44] and urging for specific measures per species and site. Invasive species’ major threat does not coincide with the absence of relevant data and information, especially in the urban context. UBI4T₂ could consequently act in these two directions, confronting the crucial environmental problem of invasiveness by identifying invasive species geographically and species-wise and proposing specific mitigation measures and actions. For example, a high UBI4T₂ would indicate that most species are alien-causing problems to the surrounding environment (i.e., invasive species) and, thus, need to be actively managed.

UBI4T₃ = toxic trees/alien trees

(3)

This third index is a metric of environmental health, as toxic trees can have an impact on human and animal health [45,46,47]. In this case, UBI4T₃ again provides a spatial, disaggregated knowledge base of plant toxicity in the city, thus giving the opportunity to local authorities to take the necessary measures and citizens to enjoy urban greenery (Figure 1). Under the broader ecological context, toxicity in certain tree species can reduce ecosystem resilience, undermine ecosystem services, enhance risks to human health and safety, and obstruct sustainable land management. By prioritizing the use of non-toxic tree species in urban greenery and land management initiatives, communities can build more resilient ecosystems that are better equipped to cope with the challenges of a changing climate.

The selection of thresholds for each indicator and the classification of UBI4T’s scoring as low, medium, or high should be considered in relation to the range of scoring values and in accordance with local knowledge of urban trees per case study [48].

3. Results

For the future implementation and upscaling of the UBI4T, certain aspects with respect to tree inventory and auxiliary data availability, motivation, and real usefulness of the indicator, as well as types of engagement necessary at the city level, will need to be investigated. We hereby present two complementary yet independent applications of the UBI4T that shed light on some of the above aspects. In particular, the UBI4T was calculated for part of the open and freely available tree inventory maintained by the local authorities of the city of Amsterdam to test the implementation feasibility and operational functionality of the index. In parallel, the city of Barcelona, which also maintains a freely available tree inventory, was selected to explore more local aspects for the potential implementation of the index by disseminating the UBI4T and engaging local authorities. In the following sections, we provide details about the two applications and lessons learned.

3.1. Applicability Test in the City of Amsterdam

The only requirement to apply UBI4T is the availability of a tree inventory. The tree inventory of the city of Amsterdam, called “Trees-in maintenance of city of Amsterdam”, is composed of more than 270,000 trees organized in four separate datasets. The first dataset is composed of 70,000 trees (municipality of Amsterdam, 29 September 2022) and was utilized for this application. Promoting the commitment of the municipality to open geodata, the dataset is available for download in several formats for study, research, or software development, e.g., *.csv (Excel), *.json (GeoJSON LngLat), *.json (GeoJSON LatLng), *.mif (GIS) >, and *.mid (GIS); we used the *.csv format. The file includes information per tree: species, type, height, planting year, owner, administrator, category, SDVIEW, radius, and coordinates (in WGS84) (Figure 2). This could be considered the typical information covered by the majority of tree inventories. Though each tree inventory may provide information for various aspects, it is accepted that details about the tree species (scientific and common name and size), location (coordinates and address), condition (health and structure), needs (pruning and soil conditions), history (planting date, previous maintenance activities), and photographs are most commonly collected.

The city of Amsterdam comprises 518 neighborhoods, 110 quarters, 25 administrative units, 9 city districts, and 30 MRA-Metropoolregio Amsterdam (in English: AMA-Amsterdam Metropolitan Area) municipalities. We used the file for the administrative units of the municipality to link monitoring schemes and strategic actions, which are typically implemented at the municipality’s administrative level. In this context, the application of UBI4T and its scoring would be initially relevant and useful for direct decision-making. However, the calculation of the UBI4T can be applied at any level of spatial aggregation. The dataset was once again available in different formats, namely, *.csv (Excel), *.json (GeoJSON LngLat), *.json (GeoJSON LatLng), *.mif (GIS) >, and *.mid (GIS); we used the *.json (GeoJSON LngLat) format. The layer file includes information about the area (name and code), the subjected district, and the surface per administrative unit (coordinates in WGS84).

Among the 70,000 trees of the first tree dataset, after data cleaning, 591 species were identified. A total of 865 entries (1.23% of the first dataset of the tree inventory) were excluded due to lack of information (recorded as unknown: 568 entries and only genus info: 297 entries). The dataset under investigation, after the exclusion of lacking information entries, consists of 69,135 trees that belonged to 591 different species (

Table 3. The numbers and percentages of trees and species within the first dataset of Amsterdam’s tree inventory.

First Dataset	70,000 Trees with 591 Species (865—1.2% Excluded)
Native	37,307 (54%) trees with 72 species (12.2%)
Alien	31,828 (46%) trees with 519 species (87.8%)
Invasive	846 (2.6%) trees with 2 species (1.2%)
Toxic	17,462 (25.3%) trees with 134 species (22. 7%)

). The identification of species origin was based on several global/regional/national/local platforms containing databases with species’ native distribution, invasiveness, and toxicity, following the definition that native plant species are those that are naturally present and adapted to the bio-geographic conditions; alien plant species are plant species that humans have purposefully or inadvertently introduced to a particular geographic location, ecosystem, or region; invasive plant species are non-native plant species that grow aggressively in a new habitat and pose a threat to the local ecology [49]; and toxic plant species are species that contain chemical compounds that can be harmful or dangerous to people, animals, or other organisms [50].

Species classification on native, alien, invasive, and toxic trees was performed manually, to allow for identifying complexities and challenges associated with species names, the way data are stored in the databases, etc. Taking into account the restricted number of species in a city as well as the local knowledge of experts in the urban greening department of each city, the classification should not be a time-consuming process for relevant authorities. Yet, an automated process to classify species’ origin and compute UBI4T per city is feasible and under development.

The 591 identified species were categorized into native (if their origin is Amsterdam/the Netherlands) and alien (if their origin is elsewhere and they were introduced in Amsterdam). We found that most of the trees were native species (37,307 trees belonging to 72 different native species), followed by alien species (31,828 trees within 519 different alien species/sub-species) in the first dataset of the tree inventory. Invasive species were also identified (846 trees—2 species). The next step was to identify which species were toxic. In total, 17,462 trees were counted, including 12,711 (16 species) native and 4751 (118 species) alien toxic trees.

All information regarding the first dataset of trees in the city of Amsterdam was imported into ArcGIS and overlaid with the administrative unit layer. The first dataset overlaps with 12 of the 25 administrative units of the city. Following this, the three indicators of UBI4T [UBI4T₁ = native/alien, UBI4T₂ = invasive/alien, and UBI4T₃ = (native toxic + alien toxic)/alien] were calculated per administrative unit.

Table 4. The distribution of tree number (counts of tree stems) and species number per the examined administrative units of the city of Amsterdam, categorized in native, alien, invasive, toxic (divided in native, alien, and total toxic trees) and the three indicators (UBI4T₁ = native/alien, UBI4T₂ = invasive/alien, UBI4T₃ = native toxic + alien toxic/alien) composing UBI4T.

Administrative Unit Code	Number of Native Trees—Species (N)	Number of Alien Trees—Species (A)	Number of Invasive Trees—Species (I)	Number of Toxic Native Trees	Number of Toxic Alien Trees	Number of Toxic Total Trees—Species (T)	UBI4T1 (N/A)	UBI4T2 (I/A)	UBI4T3 (T/A)
GE03	27-4	57-5	0-0	1-1	4-1	5-2	0.47	0.00	0.09
GE04	9-6	66-12	6-1	3-2	39-2	42-4	0.14	0.09	0.64
GE05	13-4	11-8	0-0	2-2	1-1	3-3	1.18	0.00	0.27
GK11	2193-21	3530-106	233-2	385-3	542-22	927-25	0.62	0.07	0.26
GK12	12,315-30	7295-229	98-2	3975-5	1171-62	5146-67	1.69	0.01	0.71
GK13	1983-24	3196-102	51-2	416-3	377-24	793-27	0.62	0.02	0.25
GM14	7-4	5-5	1-1	1-1	1-1	2-2	1.40	0.20	0.40
GM15	8-6	47-9	0-0	0-0	0-0	0-0	0.17	0.00	0.00
GM16	1047-29	1596-331	7-4	254-8	332-71	586-79	0.66	0.00	0.37
GM17	2-2	13-7	0-0	1-1	1-1	2-2	0.15	0.00	0.15
GS25	684-10	166-42	7-1	336-3	19-8	355-11	4.12	0.04	2.14
GT21	1528-11	1222-23	0-0	888-3	36-3	924-6	1.25	0.00	0.76
GT22	4035-20	3020-86	101-2	1454-6	556-18	2010-24	1.34	0.03	0.67
GT23	6535-21	4516-110	210-2	2538-4	687-22	3225-26	1.45	0.05	0.71
GT24	5442-22	6626-128	132-2	1988-5	979-31	2967-36	0.82	0.02	0.45

shows the distribution of native, alien, invasive, and toxic trees and species, as well as the scoring of each index that composes the UBI4T per administrative unit.

The UBI4T is composed of three individual indices, which reveal some components of urban biodiversity. The first indicator depicts the richness of urban trees in terms of the number and species of native and alien trees observed in the city. UBI4T₁ ranges from 0.14 to 4.12, revealing a quite diverse distribution of urban native trees for the city of Amsterdam, requiring manifold management and maintenance approaches per administrative unit. It seems that the south-east of the city achieves the highest score, indicating an excellent status of native trees, providing a promising frame for urban biodiversity status and, thus, the need for preservation measures. The other (the northernmost) part of the city scores the lowest values of UBI4T_1, highlighting the need for restoration of the local native species of the city, whereas all the other examined administrative units show medium values of UBI4T₁, indicating a need for improvement measures in urban green planning and planting (Figure 3a).

Deepening the examination of urban biodiversity, the second indicator calculates the percentage of alien species that have the greatest negative impact on urban biodiversity and ecosystem functions by identifying invasive species. The performance of UBI4T can thus indicate the hotspots of invasive trees, which, along with the identification of the species, can provide the required information for site-specific management practices to be applied for their prevention. According to our findings, UBI4T₂ ranges from 0 to 0.2, demonstrating its highest value at the city center, thus putting us on alert in terms of restoration actions to prevent further expansion of the invasive species (Figure 3b).

The third indicator elaborates on the toxicity of plant species (native and alien) identified in the urban environment. The most common problem with toxic plants is insufficient knowledge and erroneous use in landscaping, which, in many cases, causes poisoning [47,51]. The mapping and identification of toxic, native, and alien tree species could significantly contribute towards designing a safe urban environment and ensuring the well-being of citizens. In the city of Amsterdam, a reverse geographical pattern in relation to UBI4T₁ is observed, with the indicator ranging from 0 to 2.13 and showing the highest values in the northernmost part of the city, meaning restoration measures to mitigate toxicity in this part of the city are required (Figure 3c).

3.2. Policy Perspectives for the City of Barcelona

The tree inventory of the city of Barcelona, entitled “Atles de biodiversitat”, is composed of almost 700,000 trees organized in three categories (street trees, zone trees, and trees in the park), each one of them divided into three separate datasets. Datasets are updated every weekend, and the contents of the first dataset, consisting of 153,000 trees, were explored. The tree inventory comprises all the city’s trees: in streets, squares, block interiors, and green spaces, and there are more than 450 different species or varieties cataloged. Information for each plant species is available in a file that includes its main characteristics. The tree inventory also contains a catalog of trees of local interest from Barcelona’s most valuable tree species list. The dataset can be downloaded as *.csv and includes the following information per tree: coordinates, type of tree, site, species, planting date, and city district where each tree is located (Figure 4).

Taking into account the information available within the tree inventory, the implementation of the UBI4Τ was deemed feasible based on Amsterdam’s example, and for this, the second objective relevant to interest and engagement was pursued. In particular and in order to investigate the policy perspectives and usefulness of UBI4Τ for city authorities to achieve their gardening responsibilities, monitor, and ultimately conserve biodiversity, mitigate climate change impacts, and achieve urban resilience, direct communication was established with the competent department of the city of Barcelona in order to explore the following aspects: initialization of the existing tree inventory development (e.g., sponsorship); documentation of the scope and implementation details; main use and impact; current development and use of indicators; other city trees’ relevant information; and authorities’ interest in adopting and exploiting the UBI4Τ.

In general, the tree inventory is managed by the Barcelona Municipal Institute of Parks and Gardens (IMPIJB), which belongs to Barcelona City Council. The IMPIJB is responsible for the management of municipal public green spaces, street trees, parks, gardens, planters, children’s play areas, and ponds within parks. Old, long-term records of the IMPIJB include tree species that were planted on an annual basis, and the first inventory with tree positions is more than 30 years old (the official inventory was created around 2004 and all information was stored in “Oracle” databases). A public competition was issued to map all public trees in the city, and an application was created for the tree inventory and management (GAVI, a Catalan acronym for “road-tree management”). The drawing/position of the trees, however, was approximated since no associated coordinates existed. After a few years, another company was awarded for the revision of the inventory and many management improvements, such as the inclusion of coordinates and the geographic position in GIS (updated to NEV, a Catalan acronym for “nature green spaces”). In 2018, the tree inventory was made public through the Biodiversity Atlas of Barcelona, although the information published there is much less than that contained in the application for management.

As far as the documentation of the existing tree inventory is concerned, there is an annual report regarding a tree masterplan with 50 projects. Internally (not publicly available), there is an application that enables the combination and summary of tree data from all databases that the municipality manages. Therein indicators of many types have been developed, both for gardening responsibilities (planting, pruning, watering, etc.) and for the preservation of the tree inventory. For example, the city authorities have put the target of “no species to exceed 15% of the total species” under the frame of a dedicated indicator.

Finally, the city of Barcelona operates a technical office that performs many studies, among which is the annual monitoring evolution of the plant coverage by means of earth observations, allowing, among others, the differentiation of public to private tree canopy and the combination with cadastral data. This capacity of the technical office gives the city of Barcelona the opportunity to perform UBI4T and take advantage of its urban greening perspectives.

4. Discussion

4.1. A Multi-Scale and Multi-Frame Indicator to Monitor Adaptation to Extreme Atmospheric Events

Developing, adopting, and utilizing an indicator that actually provides multiscale (from species to habitat and ecosystem level) monitoring of the quality of greenery in the city can contribute to preserving a sustainable and resilient urban environment, thus achieving adaptation to extreme atmospheric events in the urban context. UBI4T can serve two important perspectives on urban greenery: the adaptability of trees in the urban environment to expected climate change and the role of trees in mitigating impacts from extreme climate conditions, particularly excessive heat, by recording the spatial distribution and composition of tree species. This indicator also is in accordance with and support of the implantation of the new Nature Restoration Law, which suggests no net loss of green urban space by 2030 and an increase in the total area covered by green urban space by 2040 and 2050 for urban ecosystems. It is also consistent with the EU Biodiversity Strategy for 2030, which aims to protect nature, reverse the degradation of ecosystems, and halt biodiversity loss, as well as the New European Bauhaus, which inspires a movement to facilitate and steer the transformation of our societies along sustainability, from climate goals to circularity, zero pollution, and biodiversity.

Being aware of the distribution of species (native, invasive, endangered, and important for biodiversity), competent authorities can effectively promote targeted policy decisions and support urban planning initiatives with respect to the restoration and/or preservation of nature in cities, as well as ensure the health of greenery under the threat of a climate crisis.

Urban trees will be of decisive importance in municipal-scale adaptation strategies to extreme atmospheric events for both residents’ well-being and the state of the world’s climate [52]. Despite the fact that numerous studies have examined many potential advantages of urban trees, such as air filtration, noise abatement, storm-water runoff reduction, shade production, carbon sequestration, improved mental and physical health, and wellbeing [53,54], the complex interrelation of the above factors and their related undergoing processes undermines the environmental and social effects of trees. Fulfilling the scope of this research, to develop an index to monitor adaptation to extreme atmospheric events progress in cities, the UBI4T also takes into account existing policy gaps (through the thorough review of relevant frames) and provides an opportunity to city authorities to exploit existing datasets (through their tree inventories), offering the chance for effective strategic measures towards urban resilience. However, for these initiatives to be successful and sustainable, well-organized management under a coordinated frame is required. In order to combat climate change impacts and achieve adaptation to extreme atmospheric events, city authorities and relevant local stakeholders need to provide continuous and comprehensive support for the regular update of tree inventories [55,56].

4.2. Tree Species Diversity: A Way to Avert Maladaptation

UBI4T depicts the richness of tree biodiversity and, at the same time, the threat of its loss. Cities should incorporate high-biodiversity-value urban greenery as part of a comprehensive strategy to enhance resilience to future challenges, including climate change. While biodiversity can contribute to resilience, its effectiveness depends on various factors, including the specific environmental context and management practices [57]. Although the importance of plants in cities is widely documented [58,59,60], understanding the role of biodiversity in urban greenery presents significant challenges [61] because of the interacting and complex mechanisms of species distributions and human presence at multiple scales [62].

Biological invasions play a critical part in the global environmental status, endangering the world’s biodiversity through disruption of ecosystem processes. Adding to the climate crisis, along with its impacts, invasive species constitute a major threat worldwide [63]. Moreover, invasive plants prevent native plant species from growing, which reduces biodiversity and creates unstable environments, especially in the urban multi-pressured context [64]. As a preventative precaution against the emergence of invasive species in new areas, monitoring, prevention, and prompt eradication methods are strongly advocated [65]. However, there are currently no general rules for managing invasive plants.

UBI4T also reveals the percentage of alien species that have the greatest impact on urban biodiversity and ecosystem functions by identifying invasive species. UBI4T is actually an alert indicator, identifying, recording, and mapping invasive species, thus indicating specific measures per species and site to local authorities. Also taking into account that invasive species are not currently fully catalogued at the city scale, the indicator provides an additional incentive to record them and facilitates data-driven management and mitigation of their impacts on urban biodiversity. Of course, there are cases where invasiveness can be considered a desirable trait in urban tree species by local authorities when selected to endure a changing climate. For such cases, the UBIT4T can therefore provide the required information about the spatial distribution, composition, and percentage of invasive species for the city’s green department to strategically plan for the appropriate proportions and the required measures for invasive species conservation.

4.3. Another Urban Stressor?—On the Criticality of Species Toxicity

Toxic trees may affect the health of humans [66] and animals [67]. UBI4Τ also elaborates on the toxicity of plant species (native and alien) identified in the urban environment, and the information provided by the third indicator of UBI4Τ can be considered an alert for the environmental health of the citizens [68]. The most common problem with toxic plants is insufficient knowledge and erroneous use in landscaping, which, in many cases, are causing poisoning [69]. The identification and mapping of toxic, native, or alien tree species could significantly contribute to designing a safe urban environment and ensuring the well-being of the citizens [70,71]. Plant toxicity can appear in various plant parts, such as leaves, stems, roots, seeds, and fruits, and varies greatly between species [72]. Plant toxicity is caused by different chemical compounds that are detrimental to both humans and animals [73].

UBI4Τ offers the opportunity to local authorities to identify, record, and map the toxic plants of the city and guarantees a safe environment for dwellers, visitors, and animals by working to eliminate plant toxicity through strategic management measures. Toxic species in the city of Amsterdam are most frequently observed in the northern part of the city, especially in certain administrative units. This information facilitates the duties of city authorities by localizing where toxicity is high in the city, thus implementing measures to replace toxic plants or prevent human activities. Furthermore, tree species with limited toxicity can support ecosystem services, improve ecosystem resilience, lower threats to human health and safety, and encourage sustainable land management. Communities can better prepare to handle the challenges posed by a changing climate by emphasizing the use of non-toxic tree species in urban greenery and land management projects [47].

4.4. On the Importance of In Situ Data

Currently, there is a considerable amount of in situ data from numerous studies that has not been exploited yet. This information, apart from calibration and validation purposes, plays a vital part in the development process with regard to environmental products and services. When combined with earth observations or other ex situ datasets, integrated solutions for strategic management emerge [74]. Without a doubt, in situ conservation of plant species, especially for identifying native, alien, and toxic species per site, is essential, and it may be accomplished primarily through the preservation of habitats and ecosystems. However, achieving in situ conservation is particularly challenging, requiring the coordination of local authorities, site-specific stakeholders, and policy makers, and it is sometimes cost-prohibitive. In this sense, monitoring platforms for citizens can be of great interest. There are applications of platforms such as Observation.org, where volunteers photograph and geo-reference images that are later determined and validated by experts. In this sense, the tree inventory data provided are of great value for the monitoring of many species. On the other hand, in situ conservation can achieve site-specific actions with direct output goals and be thoroughly monitored, thus allowing for an adaptive management approach, which, especially for the environment under the current climate crisis, could be considered the most effective. The European Urban Biodiversity Index [22] recognizes the difficulty in evaluating the state of urban ecosystems in European cities because datasets pertaining to biodiversity-related issues vary in terms of availability, resolution, and coverage across municipalities and countries. Additionally, these datasets frequently only cover a small portion of the larger urban landscape. UBI4T requires as input native and invasive species that are also mentioned as local ancillary indicators due to a lack of availability. The advantage of UBI4T is that, for starters, it identifies cities with available in situ data (by means of tree species), meaning cities with existing tree inventories. And it also urges other cities, by demonstrating all the urban greening perspectives that UBI4T can offer, to proceed with the development of tree inventories to create a green, safe, resilient, and healthy environment for their citizens.

4.5. How Do Cities Perceive the Significance of Tree Inventories?

Tree inventories may illustrate the historical and current conditions of urban greenery as well as offer fundamental data for implementing and improving urban greening management techniques. Moreover, the information provided in a tree inventory can be exploited to contribute towards the preservation of biodiversity or even avert its loss in a city, ensuring in this way healthy, resilient, and adaptive urban ecosystems against the climate crisis. In this context, it is crucial to demonstrate the potential of available data provided by urban tree inventories and generate a reliable indicator to monitor the adaptation of a city to extreme atmospheric events over time, especially given the frames within which modern cities need to comply, e.g., resilient, sustainable, adaptive, smart, etc. Many cities have developed tree inventories for urban planning practices (including GIS databases), but only a minority publish these data online (Table S2). Based on an extensive review of existing and freely available online urban tree inventories in the EU, there are a considerable number of individual cities with tree inventories and only a few databases that combine several tree inventories from EU cities. In particular, 37% of EU capitals already have a freely accessible and available tree inventory. According to the ClearingHouse project (EFI), almost all cities have inventories on the trees they manage in parks, along streets, on playgrounds, etc. These inventories usually include at minimum the species, location, diameter, management regime, and/or age, etc. Thus, UBI can potentially be applied. The EU policy frameworks to increase urban resilience (e.g., the EU Adaptation Strategy and the EU Biodiversity Strategy 2030) are well-established, along with initiatives to support knowledge bases and funding opportunities, and, in this line, inventories could be regarded as libraries of local knowledge regarding urban biodiversity. Deriving species-oriented indices from existing tree inventories is a time- and cost-effective way to monitor the adaptation capacity of a city. Indeed, tree inventories can serve multiple purposes (i.e., assessing losses and damages due to natural disasters; modeling, monitoring, and reducing air pollution and the urban heat island effect; and calculating the emissions of VOC gasses and the socio-economic value of trees, etc.), facilitating multi-sectoral and disciplinary synergies aiming at a sustainable and resilient urban environment [75,76].

4.6. The Biodiversity Loss–Climate Change Nexus

In this time of climate crisis, a new strategy is required to reinforce biodiversity conservation in urban environments. Two main strategies are included in most frameworks for biodiversity conservation that adapt to climate change: the “resistance strategy”, which centers on taking steps to make species and communities more resilient to change, and the “transformation strategy”, which entails taking steps to facilitate the transition of communities into a group of species that are well adapted to the new environmental circumstances [28]. Both strategies require recording and monitoring of the different species of the city, which can be accomplished if local authorities hold a tree inventory, especially if an index like UBI4T is applied. The complicated interactions that exist between the continuous processes of urbanization and climate change impacts have a significant effect on the biodiversity of urban areas and their ability to offer ecosystem services to inhabitants, and for this, continuous monitoring of native, alien, and invasive species at different scales is imperative. Surprisingly, many cities have high biodiversity, and this is a fact that raises the standard of living [77]. Additionally, the existence of tree inventories preserves or contributes to the restoration or even expansion of sustainable green infrastructure in the city, enabling the mitigation of climate-related threats to biodiversity, air pollution, the urban heat island effect, and flood risk [31]. Furthermore, tree inventories protect the genetic diversity of tree species, enhancing their adaptive capacity. Butt et al. [32] discovered that urban greenery significantly contributes to adaptation strategies while also conserving urban biodiversity. Nevertheless, only 18% of the examined city adaptation plans address issues to protect biodiversity. Overall, tree inventories appear to be a great opportunity for local authorities to easily incorporate existing datasets in favor of climate adaptation actions and biodiversity conservation in a quantitatively and geographically measurable way.

4.7. Constraints

Based on the experience of reviewing several tree inventories and using the examples of the two demonstrated cities in this work, the following main observations and constraints are listed:

The species identity and its native or alien status, as well as its potential invasiveness or toxicity, are just a few among a multitude of other parameters that must be considered in effective urban green management. Urban greenery plays a multifaceted role in providing various benefits to people, including adaptation to extreme atmospheric events. In addition to species identity and ecological characteristics, factors such as growth patterns, canopy coverage, soil requirements, ambient conditions, and socio-economic considerations must also be considered. Furthermore, the dynamics of urbanization, land use patterns, and community preferences add further layers of complexity to the management equation.

There are definitely more trees in the examined use cases of Amsterdam and Barcelona. The inventories currently include trees managed by the municipality (for the case of Amsterdam) and are periodically checked for safety and by the city council (for the case of Barcelona), respectively. Amsterdam’s tree inventory lacks trees that are in large groups or among other plantings (forest vegetation), but in the coming years, these missing trees are expected to be added to the tree stock. The trees in cemeteries, allotment complexes, large green areas, and around sports fields are not all managed by the municipality and, thus, are not part of the inventory. Moreover, although the approximately 150,000 trees in Amsterdamse Bos belong to the municipality of Amsterdam, they are on land that belongs to the municipality of Amstelveen and are therefore only partially included. Finally, there are still tens of thousands of trees on the grounds of housing corporations, ProRail, Rijkswaterstaat, and in the thousands of private gardens. It is estimated that, in total, there is at least one tree per inhabitant. Similarly, in Barcelona, there are some parks or areas of the city that are managed by other organizations (regional and metropolitan governments are one of those, or the Port/Harbor authority). In general, most urban alien and invasive species are planted in private gardens in the backyards of houses, and they are not included in most of the tree inventories, which are limited to areas managed by the city. Moreover, though it should be avoided, if the invasive or toxic species are limited to a specific location as part of an NbS, this mitigates some of the accompanying risks.

Based on the review of tree inventories in European cities conducted in the frame of this study, what is usually missing are trees on private terrains or on properties owned by non-city public owners (states, regions, water companies, churches, etc.). To map all trees in the urban context and compile complete and fully updated inventories, it is of vital importance to convince relevant authorities to foster new collaborative relationships and integrate all existing datasets with species and geolocation information of trees under a consolidated platform to serve city needs. As discussed during an interview with a local authority representative, an inventory of all the private trees in a specific district was initiated on the occasion of a serious problem caused by a private tree and the positions of citizens against and in favor of cutting it, which then triggered a new plan to continue the recording of private trees in the rest of the city.

In the two tree inventories examined, there is information about the planting year of each tree. However, the age of a tree is difficult to determine. The planting year is not always known, and the definition of what was once meant by planting year can be vague. But between that year and the germination year at the nursery or the year the tree was planted in the full ground at the nursery, there can be as many as 5 to 15 years. In addition, some trees have been replanted. Combining the year of tree planting with the native/alien ratio could reveal potential strategies to be put in place by cities; for example, if the ratio increased in the recent past compared to 50 years ago, this may indicate that this issue was part of a long-term planting strategy.

Language could also be considered an obstacle. The different fields of a tree inventory are usually compiled in the native language, at least in the case of the two examined tree inventories (in both Catalan and Spanish languages for the showcase of Barcelona), making it difficult for someone to directly access the information or merge different county information into homogenized databases. Communication with the city authorities is also difficult if the native language is not spoken by the researchers. As expressed in this paper, tree inventories can prove to be a valuable tool towards adaptation to extreme atmospheric events in cities worldwide; thus, interoperability that could serve international methodologies needs to be achieved.

When applying for the UBI4T, a spatial division of the city needs to be defined according to authority needs. The spatial resolution for Amsterdam is available at multiple levels (neighborhoods, administrative units, and districts). Yet, this might not be the case for other potential city tree inventories that will be examined to develop UBI4T. In such cases, there are always the Local Administrative Units (LAUs) provided by Eurostat or the GHS Urban Centre Database 2015’s multitemporal and multidimensional attributes provided by the European Commission.

Invasive species are not fully recorded or mapped. This is a problem that may arise in most tree inventories. Because of their nature, invasive species probably have not been planted by competent authorities and, thus, are not included in the correspondent inventory. Once the invasion is established; however, these trees should be recorded and mapped to be able to implement specific measures per species and site to mitigate their impacts.

The toxicity of the trees, as the main component of the third index of UBI4T, needs further elaboration. When referring to toxic plants, there is always a toxic class/scale of toxicity, ranging, for example, from 1 (major toxicity) to 4 (dermatitis), or some species are toxic only if eaten. Moreover, it must be determined whether the toxicity involves only humans or if pets and other animal species should be taken into account. Also, some species create allergies for only a small percentage of people. Another point that must be taken into consideration, as far as the third indicator is concerned, is the separation of native and alien toxic species. As it has been calculated, the index estimates the total number of toxic trees/alien trees. Yet, many of the toxic trees belong to the native biodiversity of Amsterdam. An alternative would be to generate two indices: (1) native toxic/native and (2) alien toxic/alien, and keep the already-developed indicator as the worst-case scenario, defining a threshold of toxicity and invasiveness that each city must avoid. Needless to say, further and in-depth investigations need to be conducted regarding toxic species to help city authorities effectively acquire the pre-requisite knowledge about toxic species, perform this indicator, and, thus, ensure a safe and green urban environment for citizens and visitors.

Needless to say, further considerations are needed when applying UBI4T to extreme cases, like hyper-desert or polar areas where the composition and distribution of native species completely differ, if not are entirely lacking.

In general, the aim of UBI4T is to provide a ‘healthy’/ecological integrity status for each city based on in situ data. For this, a classification of UBI4T’s scoring as poor, good, or high status must be achieved, which is not an easy task. An extensive literature review of the thresholds of all parameters and their fractions is needed, as well as local information concerning each city separately. In the demonstration example of Amsterdam city, we adopted thresholds that result from the equal distribution of the indices’ (UBI4T₁, UBI4T₂, and UBI4T₃) scores. Thresholds could be more easily defined, and if time series exist in the tree inventories, a chronological evaluation of the different patterns could be useful in determining the appropriate classification.

4.8. Future Perspectives

Depending on the tree inventory, several other parameters are recorded, and, thus, could generate additional indicators. For instance, the tree inventory of Barcelona contains a catalog of trees of local interest that lists the city’s most valuable tree species (i.e., rarity, aesthetic qualities, historical value, age and size). They may be publicly or privately owned and can be found in parks, gardens, or streets. Inclusion in this catalog not only ensures a species’ conservation and development conditions but also protects it from removal. It would also be interesting to look at the pollen allergy dimensions (both native and alien trees) in relation to population distribution. Moreover, information on tree canopy could generate additional indicators. For example, using tree radius and height, the shaded area (cooling potential) could be estimated. It could also be interesting to compare canopy radius across alien and native species of the same age, providing rationale for the selection of species. Looking at tree mortality through time and replacement (e.g., when some species reach a threshold or are affected by pests, etc.) could be valuable for comparing and exchanging this information across cities. Lastly, an automated process is under development to generate the UBI4T for every city with a tree inventory.

5. Conclusions

The UBI4T is a practical and user-friendly tool for recording urban tree biodiversity, leveraging readily available in situ data while aligning with recent policy measures and enabling systemic monitoring with the potential for scalability. By incorporating information on species richness, invasive species status, toxicity, and the spatial distribution of trees from species to ecosystem levels, UBI4T effectively captures the vegetation patterns within cities.

The UBI4T demonstrates that urban trees play a vital role in adaptation to extreme atmospheric events by mitigating urban heat islands, improving water quality, reducing flood risks, mitigating noise and air pollution, enhancing human health and wellbeing, providing wildlife habitats, offering food sources, and sequestering CO₂. Additionally, UBI4T serves as an indicator of adaptation to extreme atmospheric events efforts in urban areas, simultaneously raising public awareness about urban nature and environmental issues. The accuracy of UBI4T improves with more comprehensive and continuously updated tree inventories.

In the long run, the UBI4T could provide the necessary information about trees responding to climate change, interact with various infrastructures in the urban environment, and detect indirect, yet crucial (toxic, invasive species) impacts on biodiversity and human health. UBI4T could also contribute towards local adaptation planning initiatives through the exploitation and/or collection of in situ data by civil servants and citizens, with the potential of upscaling to encompass all European cities. Overall, the utilization of UBI4T can serve as a booster and provide valuable outcomes for the management and strategic design of city authorities in support of urban resilience achievement.