Document [original]

Urban Ecosystems (2023) 26:261–275

https://doi.org/10.1007/s11252-022-01302-y

Introduction

In an era of accelerating urbanization, understanding the

environmental factors that control urban biodiversity and

ecosystem functioning is crucial for biodiversity conser-

vation and the continued provision of ecosystem services

in cities (Knapp et al. 2021; Swan et al. 2021). Natural or

near-natural grassland is an ecosystem type of conservation

concern in cities globally (Fischer et al. 2013; van der Walt

et al. 2015; Williams et al. 2005). Urbanization has been

shown to modulate the taxonomical and functional compo-

sition (Buchholz et al. 2020; Gathof et al. 2022; Williams

et al. 2006; Zeeman et al. 2017) and ecosystem functioning

(Onandia et al. 2019; Schittko et al., 2022) of urban grass-

lands and their associated species. While these studies dem-

onstrate a high sensitivity of grassland to changing urban

Tina Christmann

[email protected]

1 Department of Ecology, Technische Universität Berlin,

D-12165 Berlin, Germany

2 Berlin-BrandenburgInstituteofAdvancedBioaffiliationersity

Research (BBIB), D-14195 Berlin, Germany

3 School of Geography and the Environment, University of

Oxford, Oxford, UK

4 Institute of Biology, Freie Universität Berlin, Berlin,

Germany

5 Leibniz Institute of Freshwater Ecology and Inland Fisheries

(IGB), Berlin, Germany

6 Institute of Landscape Ecology, University of Münster,

Münster, Germany

Abstract

Understanding phenological responses of plants to changing temperatures is important because of multiple associated

ecological consequences. Cities with their urban heat island can be used as laboratories to study phenological adaptation

to climate change. However, previous phenology studies focused on trees and did not disentangle the role of micro-climate

and urban structures.

We studied reproductive phenology of dry grassland species in response to micro-climate and urbanization in Berlin,

Germany.Phenologicalstageswererecordedweeklyattheindividualplantlevelforfivenativegrasslandspeciesacross

30drygrasslandsitesalonganurbanizationandtemperaturegradient.Weestimated50%onsetprobabilitiesforflowering

and seed maturation of populations, and analysed variation in onset dates using regression models.

Early flowering species significantly advanced flowering phenology with increasing mean air temperature but were

littleinfluencedbyurbanization.Bycontrast,late-floweringspeciesshowedsignificantphenologicalresponsestobothair

temperatureandurbanization,possiblybecausemicro-climatewasmostaffectedbyurbanizationinlatesummer.Surpris-

ingly, not all grassland species showed an advanced phenology with increasing intensity of urbanization.

This contradicts observed patterns for urban trees, indicating that phenological shifts in urban areas cannot be general-

ized from the observation of one growth form or taxonomic group. Growth form appears as a possible determinant of

phenological responses. Results suggest that the phenology of dry grassland species may directly respond to the urban heat

island, albeit with variable direction and magnitude. This has implications for ecosystem services, shifted allergy seasons,

changes of biogeochemical cycles and potential ecological mismatches.

Keywords Urban grassland · Flowering phenology · Urban biodiversity · Urban heat island · Urban-rural gradient ·

Reproductive phenology

Accepted: 7 October 2022 / Published online: 4 November 2022

Phenology of grassland plants responds to urbanization

TinaChristmann1,3 · IngoKowarik1,2· MaudBernard-Verdier2,4,5· SaschaBuchholz1,6· AnneHiller1,2·

BirgitSeitz1,2· Moritz von derLippe1,2

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Urban Ecosystems (2023) 26:261–275

environments, responses of grassland species to the urban

climateareclearlyunderstudied.Thisisasignificantgapof

knowledge given that the ‘Urban Heat Island’ (UHI) is a key

feature of urban environments resulting from a high pro-

portion of impervious surfaces and dense urban structures

(Stewart & Oke, 2012, Deilami et al. 2018).

Previous studies showed that microclimatic variability

relatedtourbanstructuresdirectlyaffectsplantphenology

(Chmielewski and Rötzer 2002; Dallimer et al. 2016; Joch-

ner and Menzel 2015; Zipper et al. 2016). Yet these studies

have largely focused on tree species (Lu et al. 2006; Mimet

et al. 2009; Roetzer et al. 2000) or tree-dominated ecosys-

tems (Li et al. 2017; Liang et al. 2016, but see Cheptou et

al. 2008; König et al. 2018; Lambrecht et al. 2016; Neil et

al. 2010). In response to the UHI phenological changes such

asadvancedfloweringphenologyofurbantrees(Luetal.

2006; Mimet et al. 2009) and an advanced start and delayed

end of growing season have been observed (Li et al. 2017).

Besides micro-climatic drivers, urbanization variables

canhaveadirectroleininfluencingphenology,too(Lietal.

2020; Wohlfahrt et al. 2019)showedthatflowering,fruit-

ing and leaf development advance with greater urbanization

represented by higher impervious cover, while leaf senes-

cence is delayed. Further, higher human population densi-

ties (as a proxy of urbanization) coupled with higher annual

precipitationcauseddelayedfloweringoffsetandextended

floweringdurationofurbantrees(Lietal.2020).

Theeffectofurbanizationonmicroclimatestronglyvar-

ieswithseasons,withastrongereffectofurbanstructures

on urban temperatures in summer (Schatz and Kucharik

2014). Hence, it is likely that this relationship generates a

knock-oneffectonthephenologicalresponseofspeciesto

urbanization. For instance, phenological response has been

shown to varies among species which exhibit key pheno-

logicalstagesatdifferenttimesoftheyear(Lietal.2020).

Phenology of herbaceous plants has been shown to be

sensitive to changes in micro-climate (König et al. 2018).

So far, phenological studies largely covered non-urban sys-

tems such as alpine grassland and prairies (Cornelius et al.

2011; Dunne et al. 2012; Bennie et al. 2018; Wilsey et al.

2018, but see Cheptou et al. 2008; König et al. 2018; Lam-

brecht et al. 2016; Neil et al. 2010 ), as well as Califor-

nian annual grasslands (Bart et al. 2017; Chiariello 1989;

Yang and Leigh 2020). For instance, alpine meadow species

advancetheirtimingoffloweringinresponsetoexperimen-

tal warming and snow-removal treatment in conjunction

with warmer soil temperatures (Dunne et al. 2012). Such

studies of grassland phenology at the species level are still

lacking in urban habitats.

In order to elucidate on phenology response to climate

and urbanization two approaches are commonly used: in-

situ and remote sensing assessments. In-situ fieldwork

studies assess phenological responses at a species and indi-

vidual level and give information on distinct phenophases

(Fotiou et al. 2011; Lambrecht et al. 2016; Lu et al. 2006;

Mimet et al. 2009;Yakub andTiffin 2017). On the other

hand, satellite based phenology studies use remotely sensed

vegetation indices on a coarse spatial resolution on the habi-

tat/ecosystem level (Coseo and Larsen 2014; Dallimer et al.

2016; Liang et al. 2016; Melaas et al. 2016; Xiao et al. 2006;

Zhang et al. 2004; Zipper et al. 2016) .

Urban phenological studies relying on urban-rural com-

parisons (Bernard-Verdier et al. 2022; Dallimer et al. 2016;

Zhang et al. 2004) or spatial gradients from city center to

rural areas (Li et al. 2020; Ohashi et al. 2012) found that

floweringphenologyadvancesinurbancomparedtorural

areas, as well as towards the urban centres. Studies using

spatial gradients can be particularly suitable to model cli-

matechangeeffectsonecosystemsviaspace-for-timesub-

stitution, allowing conclusion on responses to future climate

change (Jochner and Menzel 2015; Lahr et al. 2018; Zhang

et al. 2004) show that vegetation phenology in both urban

core areas and surrounding regions is significantly influ-

enced by urban heat island regimes. For temperature and

vegetation phenology, the ecological footprint of urban land

cover extends about 10 km beyond the perimeter of urban

areas. In their study, the footprint of urban climates on veg-

etation phenology was 2.4 times the size of the actual urban

land cover (Zhang et al. 2004). Similarly, tree remote sens-

ingstudiesanalyzinginfluenceofurbanstructureonphe-

nology, show consistent trends of phenological advances in

urban areas at the ecosystem level (Lambrecht et al. 2016),

anddifferencesinphenologicalresponseamongtreespecies

and growth forms (Li et al. 2020).

Although grassland is an important component of urban

vegetation with multiple social and ecological functions

(Ignatieva et al. 2020; Onandia et al. 2019; Southon et al.

2017), phenological responses of grassland species to the

urban climate are critically understudied. A recent study

based on trait data found that the allergy season, corre-

spondingtothefloweringseason,ofurbangrasslandspe-

cies in Berlin ended later with higher degree of urbanization

(Bernard-Verdier et al. 2022).

While there is some urban phenological research on the

herb growth forms, such as a for the annual herb Crepis

sancta (Cheptou et al. 2008; Lambrecht et al. 2016) or

herbarium record studies on ephemeral plants (Neil et al.

2010), so far studies of phenology of multiple grassland

species in response to urbanization and urban micro-climate

are scarce.

Urban grasslands are suitable model habitats for urban

phenological studies since they are a frequent ecosystem type

in cites globally, with self-assembled and often diverse plant

communities present along a broad urbanization gradient

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Urban Ecosystems (2023) 26:261–275

(Fischer et al. 2016; van der Walt et al. 2015; Williams et

al. 2005). With many diverse grassland under pressure from

land-use changes in rural areas (Poschlod et al. 2005), cit-

ies can contribute to grassland conservation and restoration

to ensure their ecological functions (Klaus 2013). However,

phenologicalchangesofgrasslandspeciesmayaffecteco-

logical functions of grassland (Jochner and Menzel 2015).

An enhanced understanding of urban grassland phenology

is important to predict and evaluate vegetation feedback on

micro-climate (Penuelas et al. 2010), to understand the tim-

ing of ecosystem services to urban inhabitants (reviewed

in Jochner and Menzel 2015) and potential ecological mis-

matches (especially with birds and pollinators) (Primack et

al. 2009),aswellaseffectsonhumanhealthinrelationto

allergies (Ziska et al. 2003). However, the seasonally vary-

ing role of the urban heat island on microclimate and thus

indirectlyon thephenologyof differentgrassland species

has not been studied before.

This study thus aimed to disentangle the role of mul-

tiple environmental variables related either to the local

micro-climate or the adjacent level of urbanization for the

phenology of dry grassland species along an urbanization

gradient. We used a network of 30 extensively managed

dry permanent grasslands in the Berlin metropolitan area to

monitor phenology of three herb and two grass species over

anentiregrowingseason.Weassessedtheinfluenceofdif-

ferent urban structure and urban micro-climate variables on

dry grassland phenology in order to answer the following

questions:

1) How do local air temperature variables like mean day

and night temperatures, mean monthly temperatures

relatetothefloweringphenologyandseedmaturation

of urban dry grassland species?

2) Whatistheeffectofurbanstructurevariablessuchas

impervioussurface,floorarearatioandroaddensityon

dry grassland species phenology when controlling for

micro-climatic variables?

We hypothesized that:

1) Flowering and seed maturation phenology of urban dry

grassland species advances in response to local air tem-

perature variables.

2) Urbanization(theeffectofurbanstructures)resultsin

advances in urban dry grassland phenology. We further

hypothesizeastrongerurbanizationeffectforsummer

species compared to spring species, due to a stronger

urbanheatislandeffectinsummer.

Materials and methods

Study area and study system

The study was conducted in Berlin, Germany’s capital and

largest city, which in 2020 had 3.8 million inhabitants within

a total area of 892 km². Berlin has a temperate climate, lay-

ing within the Köppen−Geiger oceanic Cfb climate zone

which is characterized by a seasonal environment with an

unimodal distribution of the vegetation period from spring

to autumn (Kottek et al. 2006). The yearly average air tem-

perature between 1970 and 2000 was 9.2 °C and the average

precipitation 589.2 mm/year in Berlin Dahlem (FU Berlin,

n.d.) Local temperatures are modulated by the urban struc-

ture, leading to increasing mean temperature values with the

impervious surface (i.e. buildings, roads) close to weather

stations (Quanz et al. 2018).

About 5% of Berlin’s surface is covered by grassland

of which 43% can be assigned to dry grassland or other

grassland types with a legal protection status according to

the Berlin Nature Conservation Act (Fischer et al. 2013).

Accordingly, grassland in Berlin harbors a range of endan-

gered plant species (Planchuelo et al. 2019). We chose dry

grassland as study system because this vegetation type spans

overarangeofnear-naturaltostronglyhuman-influenced

sites throughout the city including parks, roadside green-

ery, airports, and vacant land. Dry grassland represents an

anthropogenic vegetation type that is usually managed by

mowing once or twice a year - without fertilization, irriga-

tion or herbicide application and intensive trampling (von

der Lippe et al. 2020). Due to the low level of management,

dry grassland is generally of conservation concern in Ber-

lin (Fischer et al. 2013). Further, dry grassland sites share a

suite of common plant species that allow for testing for phe-

nological changes along a double gradient of temperature

and urbanization.

We performed phenological analyses in dry grassland

sites that have been selected as a model ecosystem within

the CityScapeLab Berlin, an experimental research platform

established for the evaluation of biodiversity patterns and

ecological processes in urban environments (von der Lippe

et al. 2020). All plots belong to the same biotope type (bio-

tope code 05120, i.e., dry grasslands; Senate Department for

Urban Development and Housing, 2014) and the same veg-

etationtypefollowing the phytosociological classification

(i.e., Sedo-Scleranthetea). We selected a subset of 30 dry

grassland plots which contained the highest number of tar-

get species for phenological analysis, which evenly covered

theareaofBerlincityandweresubjecttodifferentlevels

of urbanization in their surroundings, as indicated by per-

centage of impervious surface (Fig. 1). Average temperature

during the study period (week 12–50 of the year) ranged

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Urban Ecosystems (2023) 26:261–275

Our target species included two annual herbs (Cerastium

semidecandrum, Trifolium arvense), one perennial herb

(Potentilla argentea) and two perennial grasses (Agrostis

capillaris, Festuca brevipila). While C. semidecandrum and

F. brevipila were most frequent (28 and 26 sites, respec-

tively), the other species occurred on at least 11 sites

(Table 1). Despite the lower occurrence of the later phe-

nology species in the monitoring network, the distribution

pattern of P. argentea, A. capillaris and P. argentea evenly

covered an urban-rural gradient (Fig. 1).

Sampling of phenological phases

Phenological observations took place every week between

March 14th and August 25th in 2017. We monitored six

randomly chosen individuals per species in each plot where

the species was present. Individuals were marked with col-

oredplasticskewersandgeo-locatedonafinescalemap.

TheBBCH-Codeclassification(Hacketal.1992) was used

for phenological monitoring and slightly adapted to the dif-

ferent growth forms (Supplementary 1). The BBCH-Code

between 12.7 and 14.32 C in the study sites, sky view fac-

tor between 0.64 and 0.99 and Sealing in a 500 m radius

between 0.23 and 62.78 (Supplementary 5).

Table 1 Dry grassland species as target species for phenological anal-

yses, their life form, occurrence on study sites and period of observa-

tion. Species are sorted after period of observation

Species name,

family

Abbreviation Life

form

Sites

(n)

Period of

observation

Cerastium

semidecandrum,

Caryophyllaceae

Cer Annual

herb

28 14th

March-

6th June

Festuca brevipila,

Poaceae

Fes Peren-

nial

grass

26 25th April-

20th June

Potentilla argentea,

Rosaceae

Pot Peren-

nial

herb

12 13th June –

25th

August

Agrostis capillaris,

Poaceae

Agr Peren-

nial

grass

11 13th June –

25th

August

Trifolium arvense,

Fabaceae

Tri Annual

herb

17 13th June –

25th August

Fig. 1 Location of study sites of all species (A) and each individual grassland species (B-F) in Berlin, with city boundaries in blue (basemap:

OpenStreetMap, 2017)

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LengthofFloweringPeriod(days)=DOY50%seedmaturation −DOY50%flowering

Equation2:Calculationoflengthoffloweringperiod.

Sampling of micro-climate and urbanization

variables

On each site, air temperature and relative humidity were

recorded at 2 m height by micro-climate loggers (EasyLog

EL-USB-2+, Lascar Electronics) with a case that protected

them from rain and direct sunlight (Protective Cover for

Outdoor Transmitter, TFA Dostmann). Microclimatic mea-

surements were taken with a resolution of 10 min.

Environmental variables related to urbanization included

percentage of impervious surface (e.g. buildings, roads),

floorarearatio(i.e.ratioofabuilding’sfloorareainrelation

to the size of the parcel its located on) and road density in

a 500 m radius around sites and sky view factor at the site

(Table 2) and were obtained from the CityScapeLab Berlin

(see von der Lippe et al. 2020, for further details).

Climate data was measured in 10 min intervals and

aggregated to weekly and monthly resolution of day and

night average, overall average, minima, maxima, and sums

of air temperatures. For climate data gaps due to vandal-

ism of loggers or technical problems at four plots, temporal

interpolation of missing data was executed for each climate

variable via linear regression analysis. To that end, a lin-

ear model was created that predicted each missing climate

variable of the site based on the linear relation between the

on-site measurements and the equivalent climate variable

at the long-term weather station at Tempelhofer Feld of the

German Weather Service (DWD, n.d.) during a two-week

period prior to the data gap. The linear equation was then

applied to the period of data gaps to interpolate the missing

period via linear regression.

As climate predictor variables for phenology, we

extracted the air temperature values of different climate

variablespermonth/weekpriortothemedianof50%flow-

ering/seed maturation (DOY) of each species across the

studysites.Hence,differenttimeperiodswereusedtochar-

acterizethemicroclimateforeachofourfivestudyspecies,

ranging from March climate values for C. semidecandrum

to July climate values for P. argentea.

Statistical analysis

Wequantifiedchangesinspeciesphenologyinresponseto

both urbanization and micro-climate using linear regression

modelsforeachofthefivespecies.

To test how phenology of a given species relates to

micro-climate, we ran a suite of linear regression models

for effects of various monthly and weekly micro-climate

assigns numbered codes to morphological stages of plant

development and we used the key stages 5 (heading, closed

bud), 6 (flowering, open flower), 7 (fruit development,

closed seed capsule) and 8 (seed maturation/ripening, open

seed capsule) to describe phenological stages (Supplemen-

tary 1). All BBCH-stages present on any given individual

were recorded each week. We chose this sampling interval

because a more frequent sampling did not strongly affect

the estimates of onset dates in another study (Cornelius et

al. 2011).

We used onset of flowering, onset of seed maturation

andlengthoffloweringasphenologicalresponsevariables,

since they mark important stages in the plant life cycle, with

floweringasthebeginningofpollinationandseedmatura-

tion as a prerequisite for dispersal.

To obtain population wide aggregates of onset dates for

floweringandseedmaturationofeachspecies,theBBCH-

stages for individual plants were converted to ordinal codes

sothatanoccurredevent(e.g.floweringandseedmatura-

tion) was assigned a 1 and the lack of occurrence a 0. Then,

to calculate the population-response variable for a pheno-

logical stage and retrieve a date as phenological response

variable,thedayof50%probabilityoffloweringonsetand

seed maturation onset for each site was calculated for each

species according to Cornelius et al. (2011) who suggested

modelling progression curves of plant development with

Ordinal Logistic Regression Models.

A generalized linear model with a binomial distribution

wasfittedwiththeordinalphenologyvariable(0or1)as

response and the date as predictor (Supplementary 2). The

date for each the probability of the phenological event (in

ourcase50%floweringorseedmaturation)wascalculated

as following:

log



1−P

=α+



∗

Xi whereP=P(Yi=1

Xi)

Equation1:Calculationofstatisticalprobabilitiesforflow-

ering/ seed maturation.

where P is the probability of the event and Y is the ordi-

nalresponsevariable(0and1forphenologicalevents),α

is the intercept parameter, bi the slope parameters and Xi

the explanatory variable, in this case day of the year. This

waywecalculateddaysoftheyear(DOY)for50%flower-

ing and 50% seed maturation probability for each species in

each population according to Cornelius et al. (2011) (Sup-

plementary 2).

Lastly,thelengthoffloweringperiodwasdeterminedas

thedifferencebetweenmodeled50%probabilitiesofseed

maturationandfloweringonset:

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Urban Ecosystems (2023) 26:261–275

forfloweringandseedmaturationofeachgrasslandspecies

based on Akaike’s Information Criterion (AIC).

Linear models were deemed appropriate for all parts of

the analysis as all phenological and climate response vari-

ables appeared normally distributed according to a Shapiro

Wilk test. All statistical analyses were carried out in R (ver-

sion 3.6.0).

Results

Micro-climate, flowering phenology and seed

maturation

Thefivemonitoredspecieshaddifferentstaggeredflower-

ing and seed maturation phenology (Fig. 2). The earliest

flowering species was C. semidecandrum, followed by F.

brevipila, P. argentea, A. capillaris and T. arvense. While

theearly-floweringspeciesC. semidecandrum and F. bre-

vipilashowedlowvariationintheonsetoffloweringand

seedmaturation,thelate-floweringspeciesP. argentea and

A. capillaris showed an amplitude of 40–60 days for all phe-

nological events across the monitored sites. Amplitude of

flowering length was largest for A. capillaris (52.1 days)

followed by P. argentea (39.8 days), C. semidecandrum (13

days), T. arvense (10.5 days) and F. brevipila (9.7 days).

The response of 50% flowering and seed maturation

onset to preceding weekly temperatures differed greatly

amongspeciesin bothsignificance,magnitudeanddirec-

tion (Table 3, and see Supplementary 4). Flowering of the

early species C. semidecandrumwassignificantlyadvanced

by 0.85 days/°C in response to night-time temperatures

(Table 3).Incontrast,floweringofF. brevipila was delayed

by 1.9 days/°C in response to day-time temperatures. The

threelaterfloweringspeciesP. argentea, A. capillaris and

T. arvense were not significantly affected by either Tavg

Night or Tavg Day. Overall, the two early flowering species

(C. semidecandrum and F. brevipila) responded stronger to

micro-climateoftheprecedingweekthanthelaterflower-

ing species.

Forseedmaturation,nosignificantinfluenceofmicro-cli-

mate was found across species. P. argentea showed a trend

of advanced seed maturation by 7.7 days/°C in response to

Tavg Day and (p = 0.052) and A. capillaris a trend of delay by

5.4 days /°C in response to T avg Night (p = 0.069).

Urbanization and grassland phenology

Urbanization, represented here by the percentage of sealed

surfacesin500mbufferaroundgrasslands(Seal500),was

associated with an increase in mean minimum temperatures

in spring and summer (Fig. 3). This relationship became

variables onto phenology (Supplementary 3). We chose

weekly average daytime temperature (Tavg Day) and average

nighttime temperatures (Tavg Night) as representative micro-

climate variables for further analysis (Table 2).

Toelucidateontemporalpatternsoftheeffectofurban

structureandmicro-climatethroughoutthedifferentmonths

of the observation period, we used single linear models.

We used Seal500 as a representative response variable and

average monthly minimum (T Mon avg Min) temperatures as

representative predictor variable, since the effect of the

UHI in summer is mostly pronounced at night, and hence

influencesmainlythetemperatureminima(Heavisideetal.

2016; Lopes et al. 2013; van Hove et al. 2015).

To test how phenology of a given species responds

to urbanization, we used single regression models with

Seal500asapredictorandmodelleditseffectonphenologi-

cal variables. We further used backward selected multiple

linear regression models with multiple urban structure vari-

ables(impervioussurface,floorarearatio,roaddensityand

sky view factor) and micro-climate (mean day and night

temperatures) as predictors. We chose urban structure vari-

ables in a 500 m radius, as it has been previously observed

that local climate variability is best explained by impervi-

ous surface in a radius of 500 m around study areas (Schatz

and Kucharik 2014). For each species and each pheno-

logicalvariable,afulllinearmodelwasfittedincludingall

pre-selected urban structure and micro-climate predictors:

Seal500, FAR500, RdDen500 and SVF, Tavg Night and Tavg

Day (Table 2). From this full model backward selection was

performed to choose the best urban predictor combination

Table 2 Explanation of urbanization variables and microclimate vari-

ables selected for analysis

Abbrev. Explanation

Urbanization

variables

Seal500 Percentage of sealed surface in a

500mbufferaroundbiotopepatch

in which site is located (range

1–100)

FAR500 Floor area ratio: ratio of a build-

ing’sfloorareainrelationto

the size of the parcel its located

on,measuredina500mbuffer

around biotope patch in which site

is located

RdDen500 Road density: total length of roads

(km)ina500mbufferaroundbio-

tope patch in which site is located

SVF Sky View Factor (share of open

sky; range 0–1) at the biotope

patch

Micro-climate Tavg Day Average weekly temperature of

day hours

Tavg Night Average weekly temperature of

night hours

T Mon avg Min Average monthly temperature of

daily minima

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increased. All other species showed no significant or a

slightly negative response, i.e. earlier onset dates. Overall,

Seal500 explained a larger share of seed maturation phe-

nology for the later species P. argentea, A. capillaris and

T. arvense, than for C. semidecandrum and F. brevipila

(Fig. 4b).

Multiple regression models obtained through backwards

selection showed a similar trend to the single regression

models, as urban structure and the micro-climate variables

showed the high explanatory power for the later species

(Fig. 4).Largeexplanatorypowerwasfoundfortheflower-

ing stage, especially for A. capillaris and T. arvense (Fig. 4;

Table 4) where a combination of urban micro-climate and

stronger as the season progressed, from a non-significant

trend in March (p = 0.098) to an increasingly strong positive

linear relationship towards July (p < 0.001) (Fig. 3). With

progressing growing season, the variance in temperature

explained by urbanization (adjusted R2) increased steadily

from 6% in March to 66% in July, showing a tighter rela-

tionship between sealed surface and monthly averaged min-

ima in summer.

Single regression models of effect of urbanization on

phenologyshowedthatfloweringandseedmaturationofA.

capillaris, as well as seed maturation of P. argentea, were

delayed with increased Seal500 (Supplementary 4). By

contrast, F. brevipilafloweredearlierandlongerassealing

Table 3 Linearregressionmodelsforinfluenceofaverageweeklynighttemperature(Tavg Night) and average weekly day temperature (Tavg Day) of

theprecedingweekonphenologyoffivegrasslandspecies(forabbreviationsseeTable1). Only combinations with p < 0.1 shown and combina-

tions with p < 0.05 in bold. Cer = Cerastium semidecandrum, Fes = Festuca brevipila, Pot = Potentilla argentea, Agr = Agrostis capillaris, Tri = Tri-

folium arvense

T avg Day T avg Night

Event Species Slope R² P slope R² p week

Flowering Cer -1.637 0.116 0.076 -0.854* 0.162 0.033 15

Fes 1.909* 0.219 0.021 19

Pot -0.855 0.010 0.757 -2.978 0.059 0.448 24

Agr 7.902 0.234 0.132 2.474 0.038 0.565 25

Tri 0.745 0.041 0.435 -0.654 0.052 0.378 27

Seed maturation Cer 1.258 0.024 0.427 -0.311 0.006 0.701 18

Fes -0.287 0.014 0.577 0.206 0.027 0.447 21

Pot -7.727 0.301 0.052 3.057 0.073 0.371 27

Agr 7.834 0.106 0.328 5.386 0.321 0.069 27

Tri -0.556 0.026 0.537 -0.775 0.064 0.326 30

Fig. 2 Phenologicalstagesinfivedrygrasslandspeciesinthevegeta-

tion period in 2017 (see Table 1 for abbreviations of species names)

during days of the year (DOY), species ordered by the median of 50%

floweringonset.(A)forfloweringonset,(B)forseedmaturationonset,

(C)forfloweringlength.Cer= Cerastium semidecandrum, Fes = Fes-

tuca brevipila, Pot = Potentilla argentea, Agr = Agrostis capillaris,

Tri = Trifolium arvense

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Urban Ecosystems (2023) 26:261–275

Fig. 4 Explanatory power (adjusted R2) of multiple regression models (see Table 3)forthejointeffectofmicro-climateandurbanstructureon

flowering,seedmaturationandlengthoffloweringofthefivespecies.Speciesorderedbythemedianof50%floweringonset

Fig. 3 Relationshipbetweenurbanizationandmicro-climateindrygrasslandplots.Linearmodelsforinfluenceofimpervioussurfaceina500m

radius (Seal500) on monthly minimum temperatures in dry grassland plots in (a) March, (b) April, (c) May, (d) June and (e) July, (R² = adjusted R2)

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of urban structures on micro-climate from spring to sum-

mer, phenology of later species tended to respond stronger

to a combination of both micro-climate and urbanization,

while phenology of early species responds predominantly

to micro-climate.

Grassland phenology and urban micro-climate

Inthisstudyweobservedcleardifferencesamongspecies

in phenological response to micro-climate. Our hypothesis

that overall phenology advances with increasing air temper-

ature of the previous week proved to be only valid for the

early spring herb C. semidecandrum, while other species,

especially the later ones, such as P. argentea and A. capil-

laris showed no response to preceding air temperatures, and

F. brevipila exhibited phenological delays. This is contrary

to the multitude of tree phenological studies in urban areas,

whichhaveshownconsistentadvancesinfloweringphenol-

ogy (Li et al. 2020; Lu et al. 2006; Mimet et al. 2009; Neil

et al. 2010).

TheearlyfloweringspeciesC. semidecandrum was sig-

nificantlyinfluencedbymeannightandday-timetempera-

tures,whichisinlinewithfindingsonthehighsensitivity

of early phenology species to microclimatic cues and the

winter UHI (Jochner and Menzel 2015). This is further

strengthenedbylong-termphenologicalfindingsrevealing

thatfloweringofspring-speciesrespondedthestrongestto

temperature of preceding months (Fitter and Fitter 2002).

Studieson urbantreephenology havefound aninfluence

of temperatures on a monthly scale on for instance flow-

ering (Lu et al. 2006). In this study weekly micro-climate

was more predictive for phenological onsets than monthly

micro-climate. This could possibly be due to fast-growing

urban structure significantly explained 80% and 70% of

variation (adj. R2). Flowering of C. semidecandrum and F.

brevipilawassignificantlyinfluencedbythemicro-climate

variables TAVGday and by RdDen, albeit in opposite direc-

tionsandthemultiplemodelssignificantlyexplained20.1%

and 43.2% of variation, respectively.

In the seed maturation models, a combination of TAVG day

andSealsignificantlyexplained61.9%ofP. argentea seed

maturation, while FAR explained 44.1% for A. capillaris.

Seed maturation of all other species remained entirely unaf-

fected by all urban structure and micro-climate variables.

Length of flowering models showed a similar pattern

with high R2 for the later species P. argentea, A capillaris

and T. arvense and an inclusion of both urban structure and

micro-climate variables in the selected models.

By contrast, F. brevipilafloweredearlierandlongeras

sealingincreased.Allotherspeciesshowednosignificantor

a slightly negative response, i.e. earlier onset dates. Overall,

Seal500 explained a larger share of seed maturation phe-

nology for the later species P. argentea, A. capillaris and

T. arvense, than for C. semidecandrum and F. brevipila

(Fig. 4b).

Discussion

Cities can harbour biodiverse grasslands, and the phenol-

ogy of such urban ecosystems is particularly important for

a range of ecological processes (Jochner and Menzel 2015).

Yet grassland phenology at a single species level has not

been assessed in an urban context so far. Here we show that

dry grassland species phenology responds to urbanization in

aspecies-specificmanner.Alongwithanincreasingeffect

Table 4 Multiplelinearregressionmodels(selectedwithAIC)forinfluenceofurbanizationvariables(allin500mradius)andmicro-climateonto

floweringandseedmaturationoffivedrygrasslandspecies.Filledcellsshowremainingurbanvariablesobtainedinthemodelthroughbackwards

selection with AIC from a maximum model. Cer = Cerastium semidecandrum, Fes = Festuca brevipila, Pot = Potentilla argentea, Agr = Agrostis

capillaris, Tri = Trifolium arvense

Phenology stage Species FAR RdDen SVF Seal TAVGnight TAVGday Adj R2p-value

Flowering Cer -4.622 0.315* -2.301* 0.201 0.039

Fes -0.204** 2.424** 0.432 0.001

Pot 0.941 72.37 -6.568 -5.823 0.05 0.4

Agr 81.162** 1.887 -0.688 -13.050** 0.8 0.006

Tri 0.416** -0.204*** 1.482 1.247** 0.7 0.0007

Seed maturation Cer 0 n.s.

Fes 0 n.s.

Pot 0.378** -14.929** 0.619 0.003

Agr 16.87* 0.441 0.015

Tri -0.026 0.056 0.184

Length of

flowering

Cer 0 n.s.

Fes 3.369* -9.658 -1.522 0.36 0.007

Pot -98.39** 0.485 0.007

Agr -32.67 128.89 9.998 0.44 0.9455

Tri -4.475 -0.52** 0.221* -1.62** 0.549 0.007

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rural plants of the Mediterranean species Crepis sancta sug-

gests that selection favors later phenology when air tem-

peratures, moisture regimes and nitrogen-availability are

strongly modified, and CO2 is elevated. Especially mois-

ture regimes might be a potential critical factor causing the

phenological delays of grasses in our study, as observed in

Californian annual grasslands where soil water potential

thresholds were a reliable predictor for grassland green

up dates (Bart et al. 2017). Without connection to ground-

water and limited root depths particularly in annual spe-

cies, dry grassland species must rely on soil moisture. We

thushypothesizethat grasses with late summer flowering

phenology which coincide with pronounced drought peri-

ods might delay their onset dates until after the maximum

drought peak to relocate reproductive phenology to periods

when site conditions, especially moisture, are favorable.

This hypothesis is strengthened by the opposite response

ofearlyfloweringherbs,whichgrowinfavorablemoisture

conditions in spring with almost no water restrictions and

therefore match the usual pattern of phenological advance

in response to temperature.

Grassland phenology and urbanization

Our findings show an increase in explanatory power of

urbanization in predicting micro-climate from March to

July. Cities have consistently shown to be hotter than adja-

cent rural areas, leading to the well-known Urban Heat

Island(UHI)effect(Stewart,2011).InBerlin,likeinmany

mid-latitude climates, the UHI was found to be most pro-

nounced in summer and during the night in response to

surface cover (Fenner et al. 2014). This results in higher

urban-ruraldifferenceforsiteswithalargerpercentageof

urban fabric during the summer months.

Accordingly, species exhibiting crucial phenologi-

cal phases in summer such as A. capillaris and T. arvense

responded strongly to a combination of micro-climatic cues

and urban structure variables, while early species such as

C. semidecandrum mostly responded to micro-climate. This

trend is in line with the temporal pattern of the UHI and

the exacerbated link between urban structure and micro-cli-

mate in summer, as shown in our results. Similarly, Li et al.

(2020)foundthatfloweringonsetofspringspeciesinurban

environments was advanced and more sensitive to increased

temperatures than summer species. Further studies are

needed to test this hypothesis, since we had a lower sample

size of the later phenology species due to lower occurrence

inourmonitoringnetworkcomparedtotheearlierflower-

ing species and hence reducing the statistical power of the

relationships. Future research could extend the urban-rural

gradient beyond the boundaries of Berlin, or use a similar

plants like grasses being faster and/or more plastic in timing

their internal physiological and chemical dynamics (König

et al. 2018).

Manystudiesfindthatphenologicalresponsevariesdue

to functional traits and growth forms of the plant species

(Fitter and Fitter 2002; Iversen et al. 2009; König et al. 2018;

Li et al. 2020; Lu et al. 2006). A. capillaris and P. argentea

(both perennial) responded positively to the urban environ-

ment (delayed onset), while C. semidecandrum (annual)

and T. arvense (annual or biennial) responded negatively

(advanced onset). Due to our low number of repetitions per

growth form, we cannot generalize on a growth-form spe-

cificeffectofphenologyresponsetotheUHI,howeverwe

observed a trend that is consistent with previous literature:

König et al. (2018)foundthatearlyfloweringgrassesand

annualherbsexhibitedmoreintenseadvancesinflowering

onsetthanlate-floweringgrassesandperennialherbs–asin

our study. While in König et al. (2018) climate zone related

to advance in onset of herbs, for grasses micro-climate site

conditionshad anegligibleeffect andplantspecific traits

explained phenological responses, such that high specific

leaf area was related to advances in onsets, while high

leaf dry matter content was related to phenological delays

(König et al. 2018).

Contrary to our hypothesis on advanced urban grassland

phenology,wefoundadifferenceindirectionofresponse

amongspecies,suchasunexpecteddelaysinfloweringor

seedmaturation.Delaysinfloweringdatesarenotuncom-

mon among different growth forms (König et al. 2018;

Munson and Long 2017). Flowering time of C3 grass spe-

cies (perennial and annual) advanced with increasing mean

annualairtemperature,whilefloweringtimeofallbuttwo

C4 grass species (perennial and annual) was either delayed

orunaffected(MunsonandLong2017). This was explained

by higher optimal air temperatures for development of late-

growing species resulting in heterogeneous patterns of phe-

nological shifts, and shows a dependency of phenological

response on life history and photosynthetic pathway. More-

over, flowering delays due to increased temperature have

previously been explained by an eco-region effect, with

grass species that grow in relatively cold eco-regions (high

elevation or northern latitude) advancing phenology with

warming, while those in relatively warm eco-regions (low

elevation or southern latitude) delaying phenology with

warming (Munson and Long 2017). At the much smaller

local scale of our study, we found large variation of micro-

climatic zones along the urban-rural gradient. This could be

analogousthe“ecoregion-effect”andexplainthedelayof

A. capillarisfloweringduetohigherairtemperaturesatthe

strongly urban and hence warmer sites.

Lambrecht et al. (2016) provide another explanation for

delayed phenological responses: their study on urban and

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could be especially crucial in dry grasslands in the summer

months.Theeffectofwateravailabilityanddroughtonphe-

nology in the summer months as a consequence of the UHI

could be a driver causing controversial shifts in phenologi-

cal onset dates of grasses and would need to be included in

future urban phenological studies. This is especially crucial

because of trends of increased drought intensity in Germany

(NASA, 2020) and particularly in cities (Lahr et al. 2018).

Additionally, plant diversity could be a determinant for

phenology as it has been shown to affect coordination of

phenologyamongspecieswithearlierfloweringinresponse

to reduced diversity in a serpentine grassland (Wolf et al.

2017).Thesedifferencesinphenologywerecausedbythe

effectsofplantdiversityonsoilsurfacetemperature,avail-

ablesoilnitrogen,andsoilmoisturewhichinturnaffected

timing of flowering (Wolf et al. 2017). While in our dry

grasslandsitesnetworknodifferencesinplantspeciesrich-

ness along the urbanization gradient were found (Speaman’s

R2=-0.0258; P = 0.85), species richness increased slightly

with maximum daily summer temperatures (R2 = 0.30,

P = 0.02) (data not shown), making plant species richness

an interesting driver to include in future urban phenology

studies.

While there is a large body of literature on the phenologi-

cal triggers of Mediterranean grasslands, such as in Cali-

fornia (Bart et al. 2017; Chiariello 1989; Hufstader 1978;

Yang and Leigh 2020), comparatively little is known on the

multitudeofpotentialtriggeringfactorsforfloweringand

seed dispersal in urban habitats. Urban grassland phenol-

ogy studies including precipitation, soil temperatures and

plant traits, as well as with more species from each growth

form,growingatdifferenttimesofthespringandsummer

and additional phenophases would be useful to better under-

stand the triggers of urban grassland phenology responses

to the urban heat island. More research is needed on the

predictorsoffloweringandseeddispersalinurbanhabitats,

asthesephenophasesarelikelytoberegulatedbydifferent

environmental and physiological triggers (Segrestin et al.

2018). Dispersal timing has been shown to be more vari-

able and less responsive to climate constraints such as water

deficits,thanflowering. Moreover timing of phenological

eventscanbeinfluencedbyacoordinationbetweenpheno-

phases, like among timing of seed dispersal, seed matura-

tion and flowering timing (Segrestin et al. 2018). Such a

phenophase-coordination remains to be tested in an urban

grassland context.

Finally, since the urban-rural gradient resembles an air

temperature gradient, urban environments can be used as

laboratories to study the future impacts of climate change

(Ziska et al. 2003), for instance through space-for-time

substitution (Lahr et al. 2018). The advantage of this in a

phenology context is that such intra-urban studies eliminate

research design in cities with a similarly high proportion of

grassland habitat.

Wefoundvaryingeffectsofurbanizationontheflowering

andseedmaturationphenologyofthefivespecies,bothin

terms of direction and magnitude (Table 3). For instance, F.

brevipilafloweringwasexplainedbyroaddensityandtem-

perature averages, while a combination of the urban struc-

turevariablesroaddensity,floorarearatioanddegreeofsoil

sealing in combination with temperature averages explained

alargeshareofthephenologyofthelaterfloweringspecies

A. capillaris and T. arvense. While Loheide (2016) estimated

that degree of soil sealing explained ~ 50–70% of remotely

sensed start/end of the growing season, we also observed

this urban structure variable to be a prominent predictor

of grassland phenology, explaining up to 35–38% of seed

maturationandfloweringtimingforA. capillaris. Loheide

(2016) also found that the magnitude of urban-structure

driven changes in phenology was determined by the pre-

vailing weather conditions during seasonal transitions. This

matchesourfindings;asunderhotairtemperaturesinsum-

merwhenurbanstructureinfluencesmicro-climatethemost

and creates local intra-urban weather patterns with associ-

ated changes in other climate variables, phenology shows

the strongest reaction.

While our multiple regression models combining urban

structure and micro-climate succeed in explaining up to

80% of phenological variability for some grassland species,

for other species explanatory power was low, suggesting

thatasuiteofothernon-measuredfactorsaffecturbangrass-

land phenology. In urban areas photoperiod, chilling time

andnutrients,hydrologicalmodifications(precipitation,air

humidity), diseases, pests, competition, pollutants, individ-

ual genes and age have been found to impact plant phenol-

ogy (reviewed in Jochner and Menzel 2015). For instance,

warmorcoldspellscouldinfluencephenologicalbehavior

at the local scale: High air temperatures shortly before the

phenologicalonsetofaspecificeventarelikelytoresultin

a simultaneous onset while low air temperatures rather con-

tribute to a larger delay between urban and rural onset dates

(Jochner et al. 2011). In general, extreme weather condi-

tionsmighthavegreatereffectsonphenologythanchanges

in mean air temperatures, by disturbing the synchroniza-

tion between organisms due to less predictability (Jentsch

etal.,2007).Moreover,floweringonsetandfloweringtime

has been shown to be extended by 8–15% on south aspects

compared to north aspects in a Californian annual grassland

(Yang and Leigh 2020).

Along the same lines, precipitation changes could play

animportantroleforphenologyandevendriveflowering

onset stronger than increasing air temperatures (König et al.

2018). Despite lack of measurement in this study, precipita-

tion could represent a limiting factor for water supply and

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Urban Ecosystems (2023) 26:261–275

futuregrasslandphenologystudiestountangletheeffectsof

differentclimatologicalandmeteorologicaldrivers.

The potential of dry grassland species in describing

urban warming patterns, provides promising avenues for

urban environmental research and towards an increased

understanding of the impacts of on urban ecosystems.

Supplementary Information The online version contains

supplementary material available at https://doi.org/10.1007/s11252-

022-01302-y.

Acknowledgements We thank Julia Bartsch, Anjes Bloch and Johann

Hermann for their help during the data collection.

Authors’ contributions TC, MvdL and MBV conceived ideas and de-

signed methodology. IK and MvdL supervised the study. MvdL, AH,

BS, SB and TC set up the plot network and collected the data. TC per-

formed the statistical analysis supervised by MvdL. TC led the writing

of the manuscript. All authors critically contributed to the drafts and

gavefinalapprovalforpublication.

Funding This research was funded by the German Federal Ministry of

Education and Research BMBF within the collaborative project ‘Bridg-

ing in Biodiversity Science – BIBS’ (funding number 01LC1501A-H).

Open Access funding enabled and organized by Projekt DEAL.

Data Availability Data will be uploaded to Mendeley data upon accep-

tance.

Code Availability All R codes will be uploaded to Mendeley data upon

acceptance.

Declarations

Conflicts of interest Theauthorsdeclarenoconflictofinterest.

Open Access This article is licensed under a Creative Commons

Attribution 4.0 International License, which permits use, sharing,

adaptation, distribution and reproduction in any medium or format,

as long as you give appropriate credit to the original author(s) and the

source, provide a link to the Creative Commons licence, and indicate

if changes were made. The images or other third party material in this

article are included in the article’s Creative Commons licence, unless

indicated otherwise in a credit line to the material. If material is not

included in the article’s Creative Commons licence and your intended

use is not permitted by statutory regulation or exceeds the permitted

use, you will need to obtain permission directly from the copyright

holder. To view a copy of this licence, visit http://creativecommons.

org/licenses/by/4.0/.

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