Document [original]

Hypotheses in urban ecology: building a

common knowledge base

Sophie Lokatis

1,2,3,4,

*,Jonathan M. Jeschke

1,2,3

,Maud Bernard-Verdier

1,2,3

Sascha Buchholz

,Hans-Peter Grossart

2,6

,Frank Havemann

,Franz Hölker

1,2,3

Yuval Itescu

1,2,3

,Ingo Kowarik

3,8

,Stephanie Kramer-Schadt

3,8,9

Daniel Mietchen

1,2,3,10

,Camille L. Musseau

1,2,3

,Aimara Planillo

3,9

Conrad Schittko

3,8

,Tanja M. Straka

3,8

and Tina Heger

1,2,3,11

Institute of Biology, Freie Universität Berlin, Königin-Luise-Str. 1-3, Berlin 14195, Germany

Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Müggelseedamm 310, Berlin 12587, Germany

Berlin-Brandenburg Institute of Advanced Biodiversity Research, Königin-Luise-Str. 2-4, Berlin 14195, Germany

German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, Leipzig 04103, Germany

Institute of Landscape Ecology, University of Münster, Heisenbergstr. 2, Münster 48149, Germany

Institute of Biochemistry and Biology, Potsdam University, Maulbeerallee 2, Potsdam 14469, Germany

Institut für Bibliotheks- und Informationswissenschaft, Humboldt-Universität zu Berlin, Dorotheenstraße 26, Berlin 10117, Germany

Institute of Ecology, Technische Universität Berlin, Rothenburgstr. 12, Berlin 12165, Germany

Leibniz Institute for Zoo and Wildlife Research (IZW), Alfred-Kowalke-Str. 17, Berlin 10315, Germany

Institute for Globally Distributed Open Research and Education (IGDORE), Gothenburg, Sweden

Technical University of Munich, Restoration Ecology, Emil-Ramann-Str. 6, Freising 85350, Germany

ABSTRACT

Urban ecology is a rapidly growing research ﬁeld that has to keep pace with the pressing need to tackle the sustainability

crisis. As an inherently multi-disciplinary ﬁeld with close ties to practitioners and administrators, research synthesis and

knowledge transfer between those different stakeholders is crucial. Knowledge maps can enhance knowledge transfer and

provide orientation to researchers as well as practitioners. A promising option for developing such knowledge maps is to

create hypothesis networks, which structure existing hypotheses and aggregate them according to topics and research

aims. Combining expert knowledge with information from the literature, we here identify 62 research hypotheses used

in urban ecology and link them in such a network. Our network clusters hypotheses into four distinct themes: (i) Urban

species traits & evolution, (ii) Urban biotic communities, (iii) Urban habitats and (iv) Urban ecosystems. We discuss the

potentials and limitations of this approach. All information is openly provided as part of an extendable Wikidata project,

and we invite researchers, practitioners and others interested in urban ecology to contribute additional hypotheses, as

well as comment and add to the existing ones. The hypothesis network and Wikidata project form a ﬁrst step towards a

knowledge base for urban ecology, which can be expanded and curated to beneﬁt both practitioners and researchers.

Key words: conceptual network, ecological theory, hypothesis network, knowledge visualisation, map of science, research

synthesis, urban biology, Wikidata.

CONTENTS

I. Introduction ......................................................................1531

(1) Urban ecology .................................................................1531

(2) Mapping urban ecology ..........................................................1532

*Author for correspondence (Tel.: +49 30 838 57294; E-mail: [email protected]).

Society.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,

provided the original work is properly cited.

Biol. Rev. (2023), 98, pp. 1530–1547. 1530

doi: 10.1111/brv.12964

II. Methods .........................................................................1532

(1) Identifying relevant hypotheses in urban ecology ......................................1532

(2) Network and cluster analysis ......................................................1537

III. Results and discussion ...............................................................1538

(1) Hypotheses in urban ecology ......................................................1538

(2) A ﬁrst map of hypotheses in urban ecology ...........................................1539

(a) Cluster I: urban species traits & evolution ........................................ 1540

(b) Cluster II: urban biotic communities ............................................ 1541

(d) Cluster IV: urban ecosystems .................................................. 1541

(3) Critical reﬂections ..............................................................1541

(4) Co-creating a knowledge base of urban hypotheses .....................................1542

IV. Conclusions .......................................................................1542

V. Acknowledgements .................................................................1542

VI. Data availability statement ...........................................................1543

VII. References ........................................................................1543

VIII. Supporting information ..............................................................1547

I. INTRODUCTION

(1) Urban ecology

‘to truly advance the discipline of urban ecology requires the

creation of new hypotheses and the identiﬁcation of conﬁrmed

generalizations’McDonnell & Niemelä (2011, p. 12)

Urban ecology is a multifaceted research ﬁeld that ties

together research traditions and methods from a wide

range of backgrounds and disciplines. Over the past

century, it has been adopted and expanded by researchers

from ﬁelds as diverse as the social sciences, natural sciences

and engineering (McDonnell & Niemelä, 2011;Weiland&

Richter, 2011;Wu,2014). While urban ecology used

to be underrepresented in textbooks and journals of

ecology (Forman, 2016), it is now recognised as an impor-

tant research ﬁeld for ecologists, evolutionary biologists

and others. With urban systems being responsible for

60–80% of natural resource consumption (Peter &

Swilling, 2012;UN-Habitat,2017,2020), and substantially

impacting every other ecosystem on the globe, urban

ecology has become a key research ﬁeldintacklingthe

sustainability crisis (Rosenzweig et al., 2010;Sachs

et al., 2019; Spiliotopoulou & Roseland, 2020;Tanner

et al., 2014). A number of journals cover the intersection of

ecology with urban planning, urban biodiversity conserva-

tion and urban socio-economy, such as Landscape and

Urban Planning (founded in 1974), Urban Ecosystems (1997),

Urban Forestry and Urban Greening (2002) and the Journal of

Urban Ecology (2015).

Urban ecology has different meanings to different

researchers and stakeholders, a circumstance that is rooted

in the history of the ﬁeld, the unstandardized use of the

term ‘urban’(McIntyre, Knowles-Yanez & Hope, 2008;

MacGregor-Fors, 2011; McDonnell & Hahs, 2008;

Sukopp, 2008) and different meanings of the term ‘ecology’

(Schwarz & Jax, 2011). For example, Sukopp (1998)divided

urban ecology into a solution-oriented branch with a

research agenda to make cities more habitable and sustainable

from the perspective of humans, focusing, for example on

nature-based solutions and green infrastructure; and a

natural-science branch that studies the natural world within

cities, including environmental, biological, evolutionary and

ecological patterns and processes, and treating human inﬂu-

ences as ecological factors. Both branches are interdisciplin-

ary; the ﬁrst with a focus on urban planning and the second

taking the perspective of natural scientists. They have partly

been developed in concert, and the Berlin School provides

an example for linking ecological studies with approaches to

conserve and develop cities for the beneﬁtofhumans(see

Kowarik, 2020;Popkin,2022).

A framework introduced by Pickett et al.(

1997) and put

forward by Grimm et al.(

2000) differentiates between

ecology of cities and ecology in cities. Here, ecology in

cities focuses on the distribution, abundance and interactions

of non-human populations in the context of the diverse

inﬂuences and impacts that urbanisation poses on them

(Grimm et al., 2000). The ecology of cities has a broader

scope: it integrates ecology in cities with research from a

social and environmental science perspective, with the aim

of studying and understanding cities as ecosystems from an

interdisciplinary perspective, including how they ‘process

energy or matter relative to their surroundings’(Grimm

et al., 2000, p. 574), but also looking at cities as social-

ecological systems. Going even further, Des Roches et al.

(2021) proposed to integrate evolutionary biology into the

investigation of urban social–ecological systems, and

McPhearson et al.(

2016b) envisioned a ‘science of cities’

which comprises the ecology in,of and for cities in order to:

‘motivate new and advanced cross-city comparative ecology,

to develop more uniﬁed conceptual frameworks to advance

urban ecology theory, and to synthesise core urban ecology

research principles to guide future research in the ﬁeld’

(McPhearson et al., 2016b, p. 198).

What researchers mean by urban ecology tends to be

shaped by their disciplinary background and the research

school they come from (Dooling, Graybill & Greve, 2007).

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Hypotheses in urban ecology 1531

It is a common narrative that urban ecology in Europe

focused on what Grimm et al.(

2000) described as ecology in

cities, while urban ecology in the anglophone literature was

shaped by the sociological adaptation of the term ‘ecology’

to urban settings by the Chicago School of urban ecology

in the 1920s (Wu, 2014), adopting an ecosystem-centred per-

spective with a focus on humans as key agents from the start

(ecology of cities). Yet, this view is at least in part the result of

barriers in communication. For example, there is a vast

amount of urban-wildlife literature in the USA (Magle

et al., 2012) that even though not explicitly termed urban

ecology can be viewed as ecology in cities; and there is the

holistic ecosystem-centred research in Europe put forward

in the late 1960s and culminating in the meticulous

analyses of ecosystem ﬂows of the metropolitan region of

Brussels (Danneels, 2018; Kowarik, 2020) that can certainly

be regarded as ecology of cities. International exchange

between researchers from different schools of urban ecology

grew stronger in the 1990s, along with important research

schools arising around the globe, with a particular emphasis

on research schools in Asia and Australia. Nowadays,

research schools from all continents are collaborating

with each other (Breuste & Qureshi, 2011), with collabora-

tions spanning continents [e.g. the Urban Wildlife Informa-

tion Network (UWIN); the Comparative Urban Research

Training (CURT) network; the Global Urban Soil

Ecological Education Network (GLUSEEN); and the

Urban Biodiversity and Design (Urbio) network] and bar-

riers in communication being less of an issue. Albeit not a

new approach (e.g. Stearns & Montag, 1975; Sukopp,

Numata & Huber, 1995), researchers all over the world

now focus increasingly on combining natural-science urban

ecology and solution-focused urban ecology, since a com-

bined, integrative perspective is needed to tackle omnipres-

ent challenges, such as building sustainable cities and

conserving biodiversity outside of nature reserves (Collins

et al., 2000; Ramadier, 2004; Wolfram, Frantzeskaki &

Maschmeyer, 2016).

(2) Mapping urban ecology

‘I sense that humans have an urge to map –and that this mapping

instinct, like our opposable thumbs, is what makes us human’–

Katharine Harmon, cited in Börner (2010, p. 10)

Maps of research ﬁelds can visually guide us through the

complex structure of science. They can guide scientists

from both within and outside the ﬁeldaswellaspolicy

makers, practitioners and others interested in the topic.

This is particularly important in our current era, which is

characterised by a rapid growth in data and publications.

It is important to recognise that data and publications do

not automatically translate into knowledge and under-

standing (e.g. Jeschke et al., 2019),andthatresearchin

rapidly growing ﬁelds can become ‘relatively ineffective

and inefﬁcient, as existing evidence is often not found,

collaboration opportunities are missed, and research is

too often conducted in pursuit of dead ends’(Jeschke

et al., 2021, p. 6). There is thus a strong need for synthesis

tools that can intuitively provide orientation to research

ﬁelds. Maps can serve as such tools and are becoming

increasingly popular to structure active research ﬁelds

(e.g. Enders et al., 2020;Klavans&Boyack,2009;Leydes-

dorff, Carley & Rafols, 2013). As outlined above, urban

ecology is a particularly active ﬁeld (see also Bai

et al., 2018;Wolframet al., 2016), and the pace of urban

growth as well as the urgency of acting fast require sound

and accessible synthesis tools. Ideally, such tools should

enable dynamic, community-based evidence assessment.

Our aim here is to take initial steps towards a

community-built knowledge base for urban ecology that

can later be expanded and also interlinked with other disci-

plines. We use hypotheses as focal entities to build a map of

urban ecology. Such an approach has the advantage that

the mapped hypotheses can be linked with empirical evi-

dence in the future. For the ﬁeld of invasion biology, con-

ceptual maps based on hypotheses were developed by

Enders, Hütt & Jeschke, (2018) and Enders et al.(

2020)

and then combined with empirical evidence (Jeschke &

Heger, 2018; Heger et al., 2021) to create interactive

maps of this research ﬁeld (see http://www.hi-knowledge.

org). When selecting a hypothesis from the map, it is

possible to see how well it is supported and to identify

research gaps. Ideally, the evidence for each hypothesis in

such a map will grow continuously; an idea that has

been proposed as community-built ‘evidence revolution’

(Nakagawa et al., 2020).

For the current study, we took the following initial steps

towards such a community-built knowledge base. First, we

combined expert knowledge with information from the liter-

ature to identify key hypotheses in urban ecology; these will

be the focal units of our map. Second, we structured the

hypotheses in a network based on their attributes, identiﬁed

important groups of hypotheses (clusters in the network)

and propose this clustered network as a preliminary map of

hypotheses in urban ecology. Third, we discuss the list and

network of hypotheses in urban ecology and propose

follow-up steps towards a community-curated knowledge

base for urban ecology. To realise this goal, we invite other

researchers to join us and contribute other relevant hypothe-

ses, collectively to build a growing and evidence-linked map

of urban ecology.

II. METHODS

(1) Identifying relevant hypotheses in urban ecology

We compiled hypotheses from urban ecology based on a

combination of expert knowledge within our group and liter-

ature searches. A challenge for searching in literature data-

bases like the Web of Science was that the term ‘hypothesis’is

(i) often not spelled out when hypotheses are formulated or

(ii) is used for null-hypotheses and other statistical tests, which

meant that this approach was not feasible. Therefore, we

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1532 Sophie Lokatis and others

combined literature searches with an expert-based approach.

Our goal was not to collect all existing hypotheses in the ﬁeld

of urban ecology, but to identify a set of relevant hypotheses

that can serve as a starting point for a community-built

knowledge base of urban ecology and can be expanded in

the future.

Our approach to identify relevant hypotheses in the ﬁeld

of urban ecology consisted of the following steps. First,

74 hypotheses were identiﬁed from textbooks and via litera-

ture searches in the Web of Science and Google Scholar, including

searching for the key words ‘urban’,‘city OR cities’,‘ecol-

ogy’,‘hypothes*’,‘theory’,‘prediction’, and back-tracing

literature cited within key references (S. L., J. M. J., T. H.).

Second, 11 additional experts in the ﬁeld working on differ-

ent aspects of urban ecology (M. B.-V., S. B., H.-P. G.,

F. Ha., Y. I., I. K., S. K.-S., C. L. M., A. P., C. S., T. M.

S.) were asked to contribute further hypotheses that they con-

sidered relevant to urban ecology. The resulting list included

149 potentially relevant hypotheses (including synonymous

hypotheses and concepts that other experts do not consider

hypotheses or not relevant to urban ecology). Third, we iden-

tiﬁed synonymous hypotheses and merged them, agreed on a

deﬁnition of ‘hypothesis’(see next paragraph) and on which

of the proposed hypotheses are actually relevant for urban

ecology. We also cross-compared the identiﬁed hypotheses

with the studies by Parris (2018), Forman (2016), as well as

Cadenasso & Pickett (2008) and Pickett & Cadenasso

(2017), who previously provided collections of theories,

hypotheses and/or principles in urban ecology. This step

resulted in 115 hypotheses. Fourth, we discussed and agreed

on which of these hypotheses are overarching hypotheses ver-

sus lower-level sub-hypotheses. For example, the Ideal urban

dweller hypothesis is an overarching hypothesis (see online

Supporting Information, Data S1). It states that there are

speciﬁc traits that make species successful in urban ecosys-

tems. Several sub-hypotheses can be speciﬁed, depending

on the taxonomic focus or type of change (see ‘Sub-hypothe-

ses’sheet in Data S1). For example, a sub-hypothesis focus-

ing on animals is that urban dwellers have a higher

cognitive performance than urban avoiders (Sol,

Lapiedra & Ducatez, 2020). In this ﬁnal step, we identiﬁed

53 sub-hypotheses and 62 overarching hypotheses; full lists

of all sub-hypotheses and overarching hypotheses are pro-

vided in Data S1. The overarching hypotheses were mapped

as a network (see Section II.2).

A basic methodological question is what exactly is regarded

as a ‘hypothesis’. Betts et al.(

2021)deﬁne a hypothesis as ‘an

explanation for an observed phenomenon’(p. 5763), and a

research question as ‘a statement about a phenomenon that

also includes the potential mechanism or cause of that phe-

nomenon’(p. 5763). Scientists often tend to use the term

‘hypothesis’in a broader sense, for ideas or predicted out-

comes that can be tested and/or discussed. We here decided

to deﬁne a hypothesis as an assumption that is based on a for-

malised or non-formalised theoretical model of the real world

and can deliver one or more testable predictions (Heger

et al., 2021; after Giere, Bickle & Mauldin, 2005). Further,

an important question is whether the prediction of a pattern

is regarded as a hypothesis as well. While Pickett, Kolasa &

Jones (2010) argue for regarding predictions of patterns as

hypotheses as well, other authors have a much stricter view

(Betts et al., 2021). Here, we explicitly include non-

explanatory, descriptive hypotheses, and suggest that they

also contribute to ecological knowledge about cities. The

identiﬁcation of patterns can lead to valuable predictions

and stimulate further research on underlying causal relation-

ships. For example, for the Earlier phenology hypothesis, which

states that seasonal life cycles tend to start earlier in the urban

core than in rural surroundings (Roetzer et al., 2000), several

predictions can be formulated on how urbanisation inﬂuences

phenology, e.g. by increased and/or more constant tempera-

tures or concentrated light pollution.

A summary of the identiﬁed hypotheses is provided in

Table 1[the full data ﬁle is provided in Data S1 and as an open

Wikidata project (https://www.wikidata.org/wiki/Wikidata:

WikiProject_Ecology/Task_Force_Urban_Ecology)]. Where,

to our knowledge, no accepted name for a given hypothesis

currently exists, we provide a suitable name. The open Wiki-

data ﬁle is a ‘living’project and can be expanded by including

additional hypotheses or information, e.g. on additional tax-

onomic groups that a hypothesis has been applied to, the

addition of sub-hypotheses, and empirical support of the

hypotheses and respective sub-hypotheses.

We differentiate between (i) hypotheses that are speciﬁcto

urban environments (i.e. they can only be tested in an urban

environment) and have not been derived from more general

ecological hypotheses, thus are unique to research in urban

ecology; (ii)‘urbanised’hypotheses that exist in a more gen-

eral or analogous form outside of urban ecology, but have

been adapted to urban systems; and (iii) general hypotheses

from another research ﬁeld that have not been speciﬁcally

adapted to urban systems but are nonetheless highly relevant

there (e.g. the street barrier effect, as the high density of

streets in cities can lead to strong constraints on species’

movement; Mader, 1984).

To structure the hypothesis network, we characterised each

hypothesis based on its focal entity or topic (i.e. whether it

addresses species traits, trait evolution, niche shift, species

abundance, community composition, species interactions,

habitat quality, or ecosystem functioning and services), and

the hypothesised drivers of change [artiﬁcial light at night,

anthropogenic noise, climatic change (e.g. heat islands), chem-

ical pollution, nutrients, fragmentation, habitat loss and isola-

tion, invasive alien species and other novel organisms (sensu

Jeschke, Keesing & Ostfeld, 2013), novel community composi-

tion and structure, and human presence and intervention]. A

decision about which attribute to assign to each hypothesis

was reached by a consensus approach: each hypothesis was

assessed by two authors. If there was no agreement, a third

author reassessed the respective hypotheses and consensus

was reached via in-depth discussion among these three

authors. The attributes assigned in this way were then shared

with all other authors for feedback and ﬁnal consensus. These

assignments are provided for each hypothesis in Data S1.

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Hypotheses in urban ecology 1533

Table 1. Information on the 62 hypotheses in urban ecology included in this study. Names for hypotheses are either taken from the

literature or new names are proposed here (indicated by *). ‘Label’refers to the abbreviation for each hypothesis used in Fig. 2.‘Clus-

ter’indicates where each hypothesis is located: Cluster I, Urban species traits & evolution; II, Urban biotic communities; III, Urban

habitats; IV, Urban ecosystems. ‘Type’refers to the research ﬁeld in which a hypothesis was formulated: Urban, urban ecology;

Urbanised, hypotheses originally formulated in a related ﬁeld other than urban ecology, but adapted to urban environments; Related

ﬁeld, research ﬁeld other than urban ecology (if the hypothesis was originally formulated outside of urban ecology).

Hypothesis Label Cluster Deﬁnition Key reference(s) Type

Acoustic

adaptation*

AA I Animals that communicate acoustically adapt

their vocalisations to the local conditions to

optimise signal transmission.

Morton (1975) Related

ﬁeld

Biodiverse cities*BC II, IV Cities can sustain and promote biodiversity. Walters (1970), Kühn et al.

(2004)

Urban

Biodiversity-

wealth*

BW III The socio-economic status of urban residents is

positively related to the biodiversity in their

neighbourhoods.

Kinzig et al.(

2005) Urban

Cities as entry

points

CEP Cities are entry points for introduced non-native

species.

Pyˇ

sek et al.(

2010); Potgieter &

Cadotte (2020)

Urban

Credit card CC II Low variability in resource abundance and

reduced predation allow higher population

densities in urban areas through the

persistence of many weak competitors who

remain in poor body condition, are less

reproductively successful, and would not

otherwise survive.

Shochat (2004) Urban

Decay paradigm DP III Species richness declines within patches of

remnant native habitat isolated within an

urban matrix; habitat-dependent (such as

‘forest interior’) species are expected to suffer

a progressive series of local extinctions over

time.

Catterall et al.(

2010) Urbanised

Earlier phenology EP I Seasonal life cycles tend to start earlier in the

urban core than in rural surroundings.

Roetzer et al.(

2000) Urbanised

Ecological trap ET I, III Habitats preferred over other, higher quality

habitats that are low in quality for

reproduction or survival may not sustain a

population.

Schlaepfer et al.(

2002);

Battin (2004)

ﬁeld

Enemy release ER II The absence of enemies is a cause of invasion

success.

Keane & Crawley (2002) Related

ﬁeld

Environmental

ﬁlter

EF Urban habitats ﬁlter communities as a function

of their traits.

Aronson et al.(

2016) Urbanised

Epigenetic

adaptation*

EA I Epigenetic mechanisms can explain why some

organisms are more successful in urban than

non-urban areas.

Isaksson (2015) Urbanised

Food-web

reshaping*

FWR II Urban food webs largely lack weak interactions,

but the partly disassembled food webs retain a

greater density of species interactions (e.g.

greater connectance).

Start et al.(

2020) Urban

Generalists vs.

specialists*

GVS Generalist species are more frequent in urban

areas than specialist species.

Sorace & Gustin (2009) Urbanised

Genetic

signatures*

GS I ‘Genetic signatures of urban eco-evolutionary

feedback can be detected across multiple taxa

and ecosystem functions.’(Alberti, 2015,

p. 116)

Alberti (2015) Urban

Green roofs GR III Green roofs promote urban biodiversity. Oberndorfer et al.(

2007);

Williams et al.(

2014)

Urban

Habitat diversity HD III Biodiversity in urban areas is high due to habitat

diversity.

Pyˇ

sek (1989) Urbanised

Habitat isolation HI III More isolated habitat islands have lower species

richness.

MacArthur & Wilson (1967) Related

ﬁeld

(Continues on next page)

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1534 Sophie Lokatis and others

Table 1. (Cont.)

Hypothesis Label Cluster Deﬁnition Key reference(s) Type

Herbivore

proliferation*

HP II Herbivores may become hyperabundant in

urban areas, sometimes leading to pest

outbreaks.

Raupp et al.(

2010) Urban

High propagule

pressure in

cities*

PHC A higher proportion of alien taxa in captivity

and cultivation leads to an increased

propagule pressure in cities.

Kühn et al.(

2017); Potgieter &

Cadotte (2020)

Urbanised

Home range

reduction*

HRR I, III Many species maintain smaller home ranges in

urban areas.

Mannan & Boal (2000); Atwood

et al.(

2004); Wright et al.

(2012)

Urban

Human

commensalism

HC I Species that live in close proximity to humans

are more successful in invading new areas

than other species.

Jeschke & Strayer (2006) Related

ﬁeld

Hyperabundance

due to

anthropogenic

food*

HAF I, II,

III

An increase in the proportion of anthropogenic

food with urbanisation leads to an increase in

the abundance of prey as well as mid-sized

animals (e.g. mesopredators).

Fischer et al.(

2012) Urban

Ideal urban

dweller*

IUD I There are speciﬁc traits that make species

successful in urban ecosystems.

Evans et al.(

2011); Adler &

Tanner (2013, p. 202)

Urban

Increased

boldness

IB I Animals tend to become bolder in urban than

non-urban areas.

Knight et al.(

1987); Uchida et al.

(2019)

Urban

Intermediate

disturbance

ID III Biodiversity is high in sites that show

intermediate levels of disturbance and

decreases with no and high levels of

management.

Grime (1973); Connell (1978,p.

1303)

ﬁeld

Landscape of fear LOF I, II Animals adjust their behaviour and activity to

avoid humans spatio-temporally.

Brown et al.(

1999); Laundré et al.

(2010); Bleicher (2017)

ﬁeld

Light at night –

social

interaction*

LSI I, II Light pollution alters the social interactions and

group dynamics of animals.

Kurvers & Hoelker (2015) Related

ﬁeld

Matrix species MS II, III Urban habitat remnants are more sensitive to

the penetration of matrix species than less

disturbed suburban or rural remnants.

Tothmérész et al.(

2011) Urban

Microbiota

exposure

ME II, IV Urbanisation reduces exposure of humans to

environmental microbiota, leading to higher

allergy risks and negative effects on immune

function.

Ruiz-Calderon et al.(

2016);

Parajuli et al.(

2018)

Urban

Non-native

species

hypothesis*aka

Invader species

IS Non-native species richness increases with

urbanisation.

Sukopp (1969); Kunick (1974);

Kowarik (1988); Blair (2001)

Urban

Non-native

substitution*

NNS II Non-native plants in urban areas can sometimes

substitute the loss of resources provided by

native plants.

Berthon et al.(

2021) Urbanised

Novel

communities

NC II Urban environments have novel communities

that do not exist in natural environments.

Perring et al.(

2013a) Urban

Plant host

switching

PHS I, II The abundance of alien plants in the urban core

encourages native arthropods (herbivores,

pollinators) to switch from native to alien

host(s).

Shapiro (2002); Raupp et al.

(2010)

Urban

Population

pressure

hypothesis

PPH III Urban habitats serve as sinks for rural dispersers.

Continuous gene ﬂow between a rural source

and an urban sink population prohibits

pronounced genetic differentiation.

Gloor et al.(

2001) Urban

Predator

proliferation

PP II Predator densities and/or predation rates are

higher in urban than non-urban areas.

Fischer et al.(

2012) based on

Sorace (2002); Eötvös et al.

(2018)

Urban

Predator

relaxation

PR I, II Predator density, prey mortality and/or prey

fearfulness are lower in urban than non-urban

areas.

Tomialojc (1982); Gering &

Blair (1999)

Urban

(Continues on next page)

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Hypotheses in urban ecology 1535

Table 1. (Cont.)

Hypothesis Label Cluster Deﬁnition Key reference(s) Type

Prey

specialisation

PS I, II ‘The diet of carnivorous mesopredators will be

increasingly dominated by a few species with

urbanisation. These prey species will be

hyperabundant within cities. The predation

rate on prey species that are not

hyperabundant will decline with

urbanisation.’(Fischer et al., 2012, p p. 816)

Fischer et al.(

2012) Urban

Rapid adaptation RA I Rates of evolutionary change are greater in

urban systems.

Alberti et al.(

2017b); Johnson &

Munshi-South (2017)

Urbanised

Resilience of

urban hybrid

systems*

RUH II, IV ‘Resilience in urban ecosystems is a function of

the patterns of human activities and natural

habitats that control and are controlled by

both socio-economic and biophysical

processes operating at various scales’. (Alberti

& Marzluff, 2004, p. 242)

Alberti & Marzluff (2004) Urban

Shift toward non-

migratory

species*

SMS I Urbanisation favours non-migratory species. McClure (1989) Urban

Species richness –

HPD*

SRH Species richness is positively correlated with

human population density.

Luck (2007) Related

ﬁeld

Species-area

relationship

SAR III Species richness and diversity increase with

habitat size.

MacArthur & Wilson (1967) Related

ﬁeld

Street barrier

effect

SBE III Streets act as dispersal barriers. Mader (1984) Related

ﬁeld

Street corridor

effect

SCE III Streets act as dispersal corridors. Seabrook & Dettmann (1996);

James & Stuart-Smith (2000);

von der Lippe & Kowarik

(2007)

ﬁeld

Suburban peak*SP III Species richness is highest in sub-urban areas; it

is lower in urban centres and the (rural)

periphery.

Blair (2001) Urban

Synanthropic

species

SS The number of synanthropic species increases

along the rural–urban gradient.

Klausnitzer (1987, p. 106);

Guetté et al.(

2017)

Urban

Thermal

tolerance

increase

TTI I Thermal tolerance increases with urbanisation. Diamond et al.(

2018) Urban

Urban avoiders UA I Urban avoiders have a reduced ability to adapt,

compete and/or reproduce in cities.

Blair (1996) Urban

Urban

biodiversity hot

spots*

UHS II, III,

Cities are often located in areas of high

biodiversity, and urbanisation is

disproportionally higher in areas with high

biodiversity.

Kühn et al.(

2004); Luck (2007);

Ives et al.(

2016)

Urban

Urban biotic

homoge

nisation

UBH Species composition of different cities will

become more and more similar as

urbanisation increases.

Blair (2001); McKinney

(2006); Groffman et al.(2014)

Urbanised

Urban core

herbivore

decline*

UCH II The abundance of alien plants in the urban core

tends to reduce the richness and abundance of

native herbivore insects incapable of using

non-native plants.

Raupp et al.(

2010) Urbanised

Urban density-

diversity

paradox*

UDD Diversity typically increases as the number of

individuals increases in biological

communities. Urban environments, however,

tend to be characterised by lower biodiversity

than wildlands despite high population

densities.

Shochat et al.(

2010); Saari et al.

(2016)

Urban

Urban eco-

evolutionary

mechanisms*

UEE I ‘Through urbanisation, humans mediate the

interactions and feedbacks between evolution

and ecology in subtle ways by introducing

changes in habitat, biotic interactions,

heterogeneity, novel disturbance, and social

interactions.’(Alberti, 2015, p. 116)

Alberti (2015) Urban

(Continues on next page)

Society.

1536 Sophie Lokatis and others

(2) Network and cluster analysis

The matrix of hypotheses and attributes was used to create a

bipartite network; here, every hypothesis is linked to attri-

butes (i.e. focal entities or topic related to a hypothesis, or

drivers of change) and vice versa. No information is lost, as

opposed to monopartite networks that use dissimilarity

matrices of the interconnected nodes rather than the connec-

tions between hypotheses and attributes themselves, resulting

in a network showing presence versus absence of links.

Typically, clusters in network analyses are created based

on the similarity or connectivity of nodes, here hypotheses

and attributes. Nodes are assigned to speciﬁc clusters, and

each node is attributed to exactly one cluster. Here, we cre-

ated a set of 24 clusters based on four regularly used node-

based algorithms from R iGraph (GN, Fastgreedy, Walktrap

and leading eigenvector, R version R 4.1.1). All four algo-

rithms evaluate network partitioning into disjoint node com-

munities or clusters by calculating modularity (see

Newman & Girvan, 2004).

In a third step, these clusters were optimised by a memetic

algorithm (PsiMinL) that clusters links instead of nodes and

optimises each cluster separately (Havemann, Gläser &

Heinz, 2017; Havemann, 2021) by iteratively adding or

removing links. By setting the value of the resolution param-

eter r(r<1), we can control the resolution of the set of clus-

ters; a small value (closer to 0) results in many poorly distinct

clusters, while a value close to 1 will result in few clusters with

little overlap. Because the network analysed here is relatively

small, we are conﬁdent that PsiMinL can ﬁnd all relevant

clusters possible for the chosen value for r(here r=1/3)

after a small number of evolutionary searches. The resulting

optimised clusters have the advantage that nodes can be

members of more than one cluster (see Enders et al., 2020),

and the resulting clusters also will be more robust, as the algo-

rithm does not force nodes into clusters. A detailed descrip-

tion of the network analysis is provided in Appendix S1.

Membership of a hypothesis in a cluster is quantiﬁed as the

percentage of links between attributes and a hypothesis,

e.g. two out of three links leading to a node equals a member-

ship of 67% in the respective cluster. A hypothesis (node) can

be included in two clusters with 100% if they overlap one

another.

Table 1. (Cont.)

Hypothesis Label Cluster Deﬁnition Key reference(s) Type

Urban ecosystem

convergence

UEC II, IV All ecosystems types respond to urban land use

in a convergent manner (in other words:

urban ecosystems are convergent regardless of

the original ecosystem they replaced).

Pouyat et al.(

2002) Urban

Urban ecosystems

as source of

innovation*

USI I ‘The hybrid nature of urban ecosystems –

resulting from co-evolving human and natural

systems –is a source of “innovation”in

eco-evolutionary processes.’(Alberti, 2015,

p. 117)

Alberti (2015) Urban

‘Urban effect’on

invasion

UEI The number of non-native species moving

through each invasion stage (transport,

introduction, establishment, spread) is higher

in urban areas than in natural environments.

Potgieter & Cadotte (2020) Urban

Urban

fragmentation

UF I, III Urbanisation, speciﬁcally the fragmentation of

habitats, leads to a loss of genetic variation

within and increased differentiation between

populations.

Miles et al.(

2019) Urbanised

Urban habitat

analogues*

UHA I Native species can switch to urban habitats. Thellung (1919); Lundholm &

Richardson (2010)

Urbanised

Urban

mesopredator

release*

UMR II ‘The abundance of large-bodied predators will

decline with urbanisation, whereas the

abundance of mesopredators will increase.’

(Fischer et al., 2012, p. 816)

Crooks & Soulé (1999); Fischer

et al.(

2012)

Urbanised

Urban sexual

traits*

UST I In urban environments, species show shifts in

several traits related to sexual selection

(particularly in their coloration, acoustic

signals including songs and calls, hormones,

pheromones, mating behaviour).

Sepp et al.(

2020) Urban

Urbanisation

ecosystem

functioning*

UEF II, IV Urbanisation leads to a reduction in ecosystem

functions and services.

Grimm et al.(

2008) Urban

Urbanisation

tolerance

UT III Biodiversity loss in cities can be explained by a

low tolerance of species to urbanisation.

Sol et al.(

2014) Urban

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Hypotheses in urban ecology 1537

III. RESULTS AND DISCUSSION

(1) Hypotheses in urban ecology

We identiﬁed 62 hypotheses in urban ecology (Table 1).

Thirty-six hypotheses are uniquely or originally urban;

12 stem from related ﬁelds like invasion biology or biogeog-

raphy, but are highly relevant to urban ecology; and

14 hypotheses exist in a general version and, here, are

adapted to an urban setting (‘urbanised’). This collection of

hypotheses for urban ecology has a different scope and goes

beyond previous compilations that have attempted to struc-

ture this ﬁeld. The approach of Cadenasso & Pickett (2008)

was theory driven, and their ﬁve principles aimed to ground

urban ecology within scientiﬁc theory and provide sugges-

tions for urban planning and landscape design. These ﬁve

principles are: (i)‘urban areas are ecosystems’,(ii)‘urban

ecosystems are diverse’,(iii)‘urban ecosystems are dynamic’;

(iv)‘human and natural processes interact in cities’and (v)

‘ecological processes remain important in cities’

(Cadenasso & Pickett, 2008, p. 8). These principles were later

extended by the same authors to 13 principles (Pickett &

Cadenasso, 2012).

Taking a different approach, Forman (2016) published a

compilation of 90 principles, based on six reviews on urban

ecology. These contain more detailed and case-speciﬁcﬁnd-

ings and generalisations from empirical research on urban

ecosystems; for example, ‘More buildings and tall structures

create both more habitats and hazards for organisms.’

(Forman, 2016, p. 1657). Parris (2018) recently published a

collection of theories, paradigms and hypotheses from gen-

eral ecology that have been shown to apply in urban systems.

Similar to Forman (2016) and Parris (2018), and unlike

Cadenasso & Pickett (2008) and Pickett & Cadenasso

(2017), we used a bottom-up approach to structure the ﬁeld

of urban ecology. While there are shared aims between these

studies and ours, focusing on hypotheses has two large

beneﬁts: (i) in contrast to principles, hypotheses and

hypothetical generalisations imply that what they describe

or predict is still under scientiﬁc inquiry, and possibly ques-

tioned and tested in numerous instances; (ii) hypotheses can

be directly linked to empirical evidence in a future step (see

Fig. 1), thereby distinguishing between well-supported and

highly questioned hypotheses, and allowing the identiﬁcation

of research gaps.

Given the unique nature of urban ecosystems, an interest-

ing question is whether general ecological theory can be

directly applied to urban ecology (Parris, 2018). Urban eco-

systems differ profoundly from natural ones, and ecologists

have identiﬁed many differences between urban and non-

urban systems, arguing that ecological theory has at least to

be adapted (Niemelä, 1999), if not profoundly expanded

(Collins et al., 2000; Alberti, 2008; McPhearson

et al., 2016a), for urban systems. Still, ecological theory has

been repeatedly applied to urban settings (Parris, 2018). Of

the 62 hypotheses listed in Table 1, 14 have been adapted

from general ecological theory to urban systems (23%), and

12 (19%) are from related ﬁelds. These hypotheses from

ﬁelds like evolutionary biology or general ecology are highly

relevant in urban settings, and thus a vital part of urban ecol-

ogy. Take, for example, the enemy release hypothesis which is

well known in invasion ecology (Enders et al., 2018) and

explains the invasion success of species in the absence of

(co-evolved) enemies in novel settings. As urban ecosystems

have been shown to be rich in non-native species

(e.g. Kowarik, 2008), and even hypothesised to act as distri-

bution hubs for species invasions into rural regions (von der

Lippe & Kowarik, 2008) as well as to other cities worldwide

(Potgieter & Cadotte, 2020), urban ecology and invasion

biology are closely connected research ﬁelds. Therefore,

hypotheses formulated for invasion biology can often be

applied to urban settings. As a wide variety (if not most) of

general ecological theory also can be applied in urban set-

tings (see Parris, 2018), our selection here is far from

Fig. 1. The network of hypotheses in urban ecology (B) can be interlinked with hypotheses (or other knowledge entities) from other

ﬁelds and positioned within a broader network of science (A; modiﬁed from Bollen et al., 2009). Each hypothesis can be connected with

empirical evidence, or with meta-information on the research related to a hypothesis (C; modiﬁed from Lokatis & Jeschke, 2022).

Here, the proportion of taxonomic groups for which biotic homogenisation has been studied in an urban context is shown.

Society.

1538 Sophie Lokatis and others

exhaustive. Accompanying the rapid loss of the untouched,

pristine nature (Watson et al., 2016; Potapov et al., 2017) that

has been studied by classical ecology (Inkpen, 2017), urban

ecosystems are nowadays only one among many strongly

transformed ecosystem types, and can even be regarded as

trial systems for studying effects of multiple global changes

(Lahr, Dunn & Frank, 2018). For Johnson & Munshi-

South (2017, p. 1), the global network of cities might even

be ‘the best and largest-scale unintended evolution experi-

ment’. So instead of asking if and in what form classical eco-

logical theory can be applied to urban systems, the inverse

question might become increasingly important in the future

(Forman, 2016): can research from urban ecology help us

to understand other anthropogenically shaped ecosystems?

(2) A ﬁrst map of hypotheses in urban ecology

Maps are a powerful tool to visualise knowledge. Envisioning

an ‘atlas of science’that uses mapping technology to connect

the different branches of science, we here propose a ﬁrst map

of urban ecology. This map can be connected to other ﬁelds

(Fig. 1A) and serve as a reference point for researchers from

urban ecology and other disciplines (Fig. 1B). Using hypoth-

eses as nodes for the network opens the possibility that each

hypothesis can be connected with empirical evidence and

meta-information about a particular hypothesis (Fig. 1C).

To provide a visualisation of knowledge in urban

ecology, we applied a semi-automated approach to map all

62 hypotheses and the 16 assigned attributes listed in

Table 1in a bipartite network (Appendix S1; Fig. 2). Of the

seven clusters identiﬁed in a network analysis (see Table S1

in Appendix S1), the four best separated clusters were

retained (clusters I–IV). These clusters were named accord-

ing to the hypotheses and attributes they contain (Figs 2, 3),

and will be described in detail below. Cluster L0–L1 is the

complementary cluster to cluster I and is thus redundant,

and three clusters (L5, L6 and L7, see Table S1 in

Appendix S1) were not retained because they were rather

small and not as well separated as the other clusters (see

Appendix S1). Several hypotheses are part of more than

Fig. 2. 62 hypotheses in urban ecology grouped into clusters identiﬁed by a link clustering algorithm. The best separated and

meaningful clusters are shown here and were subsequently named Urban species traits & evolution, Urban biotic communities,

Urban habitats and Urban ecosystems. Cluster membership of all hypotheses attributed to a cluster are listed below each cluster.

Cluster membership values indicates the proportion of links leading to a hypothesis that belong to that cluster. Coloured circles

indicate whether a hypothesis has been formulated within urban ecology (blue), adapted to urban ecology (‘urbanised’, blue-

outlined yellow), or is a general hypothesis from a related ﬁeld (yellow). Links that belong to a cluster are black, other links are

grey. Note that not all hypotheses were allocated into one of the four clusters, and that some appear in more than one cluster.

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Hypotheses in urban ecology 1539

one cluster, and ten hypotheses are not part of any of the four

named clusters (Fig. 3). Cluster IV is nested within cluster II,

but was retained as it is well separated and informative. It is a

feature of such analyses that clusters share overlapping links

and nodes (Fig. 1).

(a)Cluster I: urban species traits & evolution

Cluster I (Urban species traits & evolution) comprises

24 hypotheses; 12 hypotheses have 100% membership

(i.e. all links leading to a hypothesis belong to that cluster)

and 12 hypotheses have a membership of ≤50% (Fig. 2). Attri-

butes of this cluster can be separated into the focal entities or

topics: species traits, trait evolution and niche shift (all 100%

cluster membership); and into the drivers of change: artiﬁcial

light, noise, climatic change (all 100% cluster membership)

and human presence and intervention (23%) (Fig. 3).

Although this cluster has some overlap with Urban biotic

communities (cluster II) and Urban habitats (cluster III), it

has the lowest normalised node-cut Psi-value among the iden-

tiﬁed clusters, indicating that it was the best separated cluster.

A major focus of the hypotheses in this cluster is to predict

and explain which traits characterise species that inhabit

urban areas, and how they adapt to urban environments.

The study of species that live close to human settlements

dates back to studies on birds, mammals and blowﬂies in

the 1950s (see Povolný, 1962; Nuorteva, 1963,1971), and

far earlier for plants (Linkola, 1916; reviewed by

Sukopp, 2008). A central idea in this cluster is the Ideal urban

dweller hypothesis, which posits that speciﬁc traits make spe-

cies successful in urban ecosystems. This is a very general

statement that we chose to treat as an overarching hypothesis

that can be speciﬁed into a range of descriptive hypotheses

focusing on a speciﬁc taxonomic group or urban setting,

and which implicitly assumes that there is a set of traits char-

acterising an ideal urban dweller (or other positions on the

Fig. 3. Bipartite network of 62 hypotheses (circles) and 16 attributes (grey boxes at the intersection of several links showing focal

entities/topics and drivers of change), which were used to characterise and group the hypotheses. Four clusters that emerged when

applying a link clustering algorithm (see Appendix S1) are shown: Urban species traits & evolution (red), Urban biotic

communities (yellow), Urban habitats (blue) and Urban ecosystems (green). Full circles belong to a single cluster, divided circles

indicate that a hypothesis has shared membership between two or more clusters. Hypotheses within a white circle do not belong to

any of the clusters.

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1540 Sophie Lokatis and others

urban afﬁnity spectrum; Wolf et al., 2022). This might be

higher cognitive performance or increased capability to learn

(Sol et al., 2020), an enhanced movement capacity (Santini

et al., 2019), or greater dietary ﬂexibility (Palacio, 2020;

Scholz et al., 2020; Planillo et al., 2021). Hypotheses like acous-

tic adaptation,earlier phenology,increased boldness,thermal tolerance

increase and shift towards non-migratory species link evolutionary

changes to physical stressors in urban environments or the

presence of humans. Epigenetic adaptation,genetic signatures,rapid

adaptation and urban eco-evolutionary mechanisms are hypotheses

about general evolutionary processes that are expected in

urban settings.

(b)Cluster II: urban biotic communities

The Urban biotic communities cluster includes 13 hypotheses

with 100% membership and nine hypotheses with a mem-

bership between 17% and 67% (Fig. 2). Drivers of change

within this cluster are: nutrients, novel organisms and novel

community composition (all 100%) as well as human pres-

ence and intervention (5%); focal entities and topics include

species interaction (100%), ecosystem functioning (100%)

abundance & density (33%), and community composi-

tion (30%).

Hypotheses in the Urban biotic communities cluster focus

on research questions investigating how urban food webs,

communities and species assemblages differ from non-urban

ones, and what features characterise urban species interac-

tions (e.g. predation or competition). Four hypotheses that

are clearly related to abundance and density, as well as com-

munity composition (i.e. invader species,urban density-diversity

paradox,urban effect on invasion and high propagule pressure in cities)

were not grouped within cluster II, but are in the vicinity of

this cluster (Fig. 3). Nested completely within the Urban

biotic communities cluster is the Urban ecosystems cluster

(cluster IV) outlined below.

(c)Cluster III: urban habitats

The Urban habitats cluster includes 11 hypotheses with

100% membership and seven hypotheses with ≤50% mem-

bership (Fig. 2). The focal entities/topics for this cluster are:

habitat quality (100%) as well as abundance & density and

community composition (23% and 24%, respectively), and

the drivers of change are fragmentation (100%), novel com-

munity composition (7%) and human presence and interac-

tion (5% membership).

The central question of this cluster is which habitat charac-

teristics inﬂuence populations, species and their interactions,

and how urban habitats can be characterised. For example, a

high diversity of habitats in urban areas has been linked to

high overall biodiversity of cities (Pyˇ

sek, 1989; Sattler

et al., 2010; Helden & Leather, 2004), a hypothesis that is well

known but often only implicitly tested. An example for a pair

of contrasting hypotheses included in this cluster is the street

barrier effect, which predicts that trafﬁc routes reduce the

mobility of urban wildlife (Rondinini & Doncaster, 2002;

Riley et al., 2014), and the street corridor effect (Seabrook &

Dettmann, 1996; von der Lippe & Kowarik, 2007; Riley

et al., 2014), which describes the opposite, i.e. species or

populations moving more easily along streets. The Urban

habitats cluster is characterised by a larger proportion of

hypotheses adapted or directly applied to urban systems from

other research areas, especially biogeography, population

ecology and conservation ecology.

(d)Cluster IV: urban ecosystems

Incorporating patterns and processes on the ecosystem level,

the Urban ecosystems cluster comprises only six hypotheses,

of which only three have a cluster membership of 100%

(Fig. 2). These hypotheses focus on ecosystem functions or

services. Three other hypotheses have a lower afﬁliation

(20–33%). The attributes of this cluster are: ecosystem func-

tioning (focal topic, 100% membership), human presence

and intervention (driver of change, 5% membership) and

community composition (focal topic, 3%).

Not all hypotheses dealing with ecosystems are included in

this cluster (e.g. urban ecosystems as source of innovation belongs to

cluster I), but it is still striking that so few of the hypotheses are

concerned with ecosystem functions or services. Thus, while

we expect that this part of the network will be extended in

the future, e.g. by including research on microbial urban

ecology, it might be fruitful to consider how work in urban

ecosystems that is not hypothesis-oriented could be covered

within a community-built knowledge base as proposed here.

(3) Critical reﬂections

The network presented here was built by combining expert

knowledge with a network algorithm. While there are many

possibilities for building networks, we chose to create a bipar-

tite network with the advantage that the information about

the assessed hypotheses is directly translated into a network

structure, instead of relying on one of numerous possible

measures of (dis)similarity. This approach is also ﬂexible

and easy to adjust for additions to the underlying data set,

which we hope will happen in the near future. The resulting

network represents a ﬁrst step towards a knowledge map for

urban ecology (see Fig. 1). It has to be noted, however, that

by only building on explicitly formulated hypotheses, certain

topics addressed in urban ecology might be underrepre-

sented or even missing. Grogan (2005) found that less than

half of a selection of articles from ecological journals explic-

itly used hypotheses. Nilsen, Bowler & Lind (2020) found this

proportion to be only 19% in a random selection of articles

from practitioner-orientated journals in conservation biol-

ogy, applied ecology and wildlife management. We expect

that this proportion is equally low in urban ecology, and also

will vary profoundly among its sub-disciplines, due to its

inherent multidisciplinarity. For example, we expect the con-

tent of the Urban ecosystems cluster to increase once more

explicit hypotheses are included, because urban ecosystem

models and analyses of material ﬂow and processes in cities

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Hypotheses in urban ecology 1541

implicitly contain hypotheses. Whether it makes sense to

formulate these hypotheses, and add them to our network,

or whether it might be more constructive to adapt the net-

work to include models, concepts or research questions

remains to be discussed in the future.

As pointed out above, this network builds on a ﬁrst list of

key hypotheses identiﬁed by a group of experts that will need

to be expanded with the help of the broader community of

urban ecologists. Additional hypotheses will then probably

also alter the structure of our network. For example, the

Urban species traits & evolution cluster is currently well sep-

arated from all other clusters, with only a few hypotheses

shared with the Urban biotic communities cluster (e.g. plant

host switching) and the Urban habitats cluster (e.g. ecological trap,

urban fragmentation). We expect that increasing the network

resolution (i.e. including additional sub-hypotheses and add-

ing new hypotheses) will probably strengthen the overlap

between these clusters, as habitat fragmentation, community

composition and novel organisms are also studied as impor-

tant evolutionary factors (Shochat et al., 2006; Diamond &

Martin, 2021; Winchell, Battles & Moore, 2020; Borden &

Flory, 2021).

The collection of hypotheses and their clustering are a

result of the joint contributions and expertise within our

group. Our scientiﬁc work is currently predominantly carried

out in Berlin (Germany), and even though many of us have

close connections or backgrounds with other research schools

and scientists around the world, we expect that other

researchers would have selected different hypotheses and

added their own perspectives to the creation of a hypothesis

map in urban ecology. In the next and ﬁnal section, we there-

fore discuss how the present selection and map of hypotheses

can be expanded to incorporate a more diverse and less bio-

geographically and culturally biased view on hypotheses in

urban ecology.

(4) Co-creating a knowledge base of urban

hypotheses

The list of hypotheses that we mapped is not exhaustive, but

can serve as a basis to formulate other hypotheses, to expand

the map with additional (sub-)hypotheses from urban ecol-

ogy, and to link it to other disciplines from within and outside

urban ecology (see Fig. 1). We hope that the network can act

as a starting point which other disciplines from urban ecology

in the broader sense can expand, and rearrange, where

appropriate. Knowledge gaps are known to be especially

pronounced in the Global South and in areas with the high-

est urbanisation pressure, as well as on a global level, with

most research still carried out locally (Young & Wolf, 2006;

Shackleton et al., 2021). To synthesise existing theory and

constantly update new ﬁndings, as well as to identify research

gaps, it is necessary to compare and communicate between

different research disciplines and stakeholders. As a ﬁrst step,

we provide our data ﬁle of hypotheses as an open expandable

Wikidata ﬁle, that we envision to grow collaboratively in the

future. As part of the Wikidata project, well-studied

hypotheses can also be linked to meta-analyses and literature

reviews, or to the body of relevant data and literature.

Hypotheses can thus be assessed directly, as well as analysed

from a meta-perspective, i.e. by generating bibliometric net-

works, and charts, as well as evidence maps, with the aim to

identify gaps and biases in research. A Wikidata-based

tool –Scholia –is available for such visualisations (Nielsen,

Mietchen & Willighagen, 2017) and can provide an introduc-

tory overview of research areas like urban ecology. It has

been adapted to support geospatial queries (Nielsen,

Mietchen & Willighagen, 2018) and is currently being

reﬁned further to facilitate hypothesis-centric visualisations

(Jeschke et al., 2021). We chose Wikidata as a platform, as it

is free, open-access, community-run, user-friendly, well

established and adheres to the FAIR-principles (Wilkinson

et al., 2016, Waagmeester et al., 2020). Entries can be easily

linked to entries from other platforms, and existing knowl-

edge (in our case: hypotheses) can be linked to existing litera-

ture and data sets (Erxleben et al., 2014; Vrandeˇ

cic&

Krötzsch, 2014).

We advocate for a more frequent use of explicit hypotheses

in urban ecology and invite future authors to expand our

data ﬁle both by adding more or alternative hypotheses and

by adding explanations to overarching and descriptive

hypotheses. Additionally, this collection and mapping of

hypotheses will greatly beneﬁt from information on the valid-

ity or generality of the collected hypotheses and from linking

of hypotheses with empirical data. In the future, we envision

a more extensive knowledge base that includes related ﬁelds

like urban ecology, restoration ecology (Heger et al., 2022)

and invasion biology.

IV. CONCLUSIONS

(1) Urban ecology is a growing research ﬁeld in which there

are numerous different hypotheses that could beneﬁt from

applying new synthesis tools.

(2) A map of 62 hypotheses from urban ecology broadly clusters

into four main themes: Urban species traits & evolution; Urban

biotic communities; Urban habitats; and Urban ecosystems.

(3) We propose using this network as a basis for a community-

built knowledge base of hypotheses in urban ecology, and intro-

duce a Wikidata project for this purpose.

(4) Our map of hypotheses in urban ecology will hopefully fos-

ter knowledge exchange, help identify research gaps, and pro-

vide orientation and guidance for researchers and practitioners.

V. ACKNOWLEDGEMENTS

We thank Nadja Pernat for helpful discussions. We greatly

appreciate the comments from two anonymous reviewers

which profoundly improved the manuscript, as well as the

thorough comments and edits by Alison Cooper on the ﬁnal

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1542 Sophie Lokatis and others

version. We also thank Alexandra Elbakyan and the Open

Science community. Financial support was received from

the Studienstiftung des deutschen Volkes (S. L.), the German

Federal Ministry of Education and Research BMBF within

the Collaborative Project ‘Bridging in Biodiversity Science

–BIBS’(funding number 01LC1501) (J. M. J., M. B.-V.,

S. B., H.-P. G., I. K., S. K.-S., A. P., C. L. M., T. H.), the

VolkswagenStiftung (97 863) (J. M. J., M. B.-V., C. L. M.,

D. M., T. H.), the Deutsche Forschungsgemeinschaft DFG

(HE 5893/8-1) (T. H., J. M. J.), and the Alexander von Hum-

boldt Foundation (Y. I.). Open Access funding enabled and

organized by Projekt DEAL.

VI. DATA AVAILABILITY STATEMENT

The dataset compiled for this article is available in the sup-

plementary material of this article. A related wikidata-site

can be found at https://www.wikidata.org/wiki/Wikidata:

WikiProject_Ecology/Task_Force_Urban_Ecology.

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VIII. SUPPORTING INFORMATION

Additional supporting information may be found online in

the Supporting Information section at the end of the article.

Data S1. Excel-ﬁle containing four sheets: (1) a glossary,

(2) the full list of 62 hypotheses included in the network, their

attributes and relevant literature, (3) a list of additional sub-

hypotheses and (4) cited literature over all sheets.

Appendix S1. Detailed description of the network analysis.

Fig. S1. Dendrogram of clusters calculated by igraph’s

Walktrap algorithm.

Table S1. Details of seven link communities (resolution

r=1/3) ordered by Ψ; size is given as number of links and

as sum of membership grades of nodes (μ

total

Fig. S2. Paths through the Ψlandscape for six seed sub-

graphs obtained from Walktrap.

Fig. S3. Approximative hierarchy of the main clusters we

identiﬁed.

(Received 21 June 2022; revised 31 March 2023; accepted 4 April 2023; published online 18 April 2023)

Society.

Hypotheses in urban ecology 1547