ArticlePDF Available

Plant Functional Traits Predict Green Roof Ecosystem Services


Abstract and Figures

Plants make important contributions to the services provided by engineered ecosystems such as green roofs. Ecologists use plant species' traits as generic predictors of geographical distribution, interactions with other species and ecosystem functioning, but this approach has been little used to optimize engineered ecosystems. Four plant species traits (height, individual leaf area, specific leaf area and leaf dry matter content) were evaluated as predictors of ecosystem properties and services in a modular green roof system planted with 21 species. Six indicators of ecosystem services, incorporating thermal, hydrological, water quality and carbon sequestration functions, were predicted by the four plant traits directly or indirectly via their effects on aggregate ecosystem properties, including canopy density and albedo. Species average height and specific leaf area were the most useful traits, predicting several services via effects on canopy density or growth rate. This study demonstrates that easily measured plant traits can be used to select species to optimize green roof performance across multiple key services.
Content may be subject to copyright.
1Plant Functional Traits Predict Green Roof Ecosystem Services
2Jeremy Lundholm,*Stephanie Tran, and Luke Gebert
3Biology/Environmental Science, Saint Marys University, 923 Robie Street, Halifax, NS B3H 3C3 Canada
SSupporting Information
5ABSTRACT: Plants make important contributions to the
6services provided by engineered ecosystems such as green roofs.
7Ecologists use plant species traits as generic predictors of
8geographical distribution, interactions with other species, and
9ecosystem functioning, but this approach has been little used to
10 optimize engineered ecosystems. Four plant species traits (height,
11 individual leaf area, specic leaf area, and leaf dry matter content)
12 were evaluated as predictors of ecosystem properties and services
13 in a modular green roof system planted with 21 species. Six
14 indicators of ecosystem services, incorporating thermal, hydro-
15 logical, water quality, and carbon sequestration functions, were
16 predicted by the four plant traits directly or indirectly via their
17 eects on aggregate ecosystem properties, including canopy density and albedo. Species average height and specic leaf area were
18 the most useful traits, predicting several services via eects on canopy density or growth rate. This study demonstrates that easily
19 measured plant traits can be used to select species to optimize green roof performance across multiple key services.
21 Constructed ecosystems such as treatment wetlands, green
22 roofs, and biowalls are engineered to provide ecosystem services.
23 These services depend on the presence of living plants and
24 engineered components such as growing medium, membranes,
25 and subsidies such as irrigation.
Green roofs can reduce heat
26 transfer through building roofs,
retain stormwater,
27 airborne particulate matter,
sequester carbon,
and provide
28 habitat,
amenity, and aesthetic values.
Plant species and
29 vegetation types dier in their ability to provide these services;
30 thus, careful selection of plant species can optimize green roof
31 functioning. Green roofs often feature extreme conditions
32 relative to local natural ecosystems, including shallow soils and
33 high winds; thus, plant selection is also important to ensure
34 survival of green roof vegetation. There are thousands of plant
35 species in each region that can be used on green roofs,
36 screening all these would be logistically impossible; hence, there
37 is a need for an ecient way to characterize plants based on
38 general traits that predict their function in a green roof setting.
39 Plant functional traits can be used to categorize species based
40 on their eects on ecosystem processes.
The plant trait
41 approach highlights ecological function regardless of geographic
42 distribution, taxonomic/phylogenetic relationships, and environ-
43 mental niche and thus represents a general method to screen
44 plant species for various purposes. Traits related to plant size and
45 leaf morphology are relatively easy to measure, and relate to
46 general plant strategies that dierentiate species having evolved
47 under dierent environmental conditions: plants that are shorter,
48 with lower maximum growth rates, are typically found in areas
49 with low soil fertility. These traits, in turn, can predict or
50 inuence ecosystem processes such as primary production,
51 nutrient and water uptake, and transpiration rate
but have
52seldom been used in environmental engineering. For example,
53specic leaf area (SLA), leaf area/dry weight, tends to be higher
54for plants with relatively high growth rates, that inhabit relatively
55fertile areas. SLA can be considered a softtrait that does not
56directly drive ecosystem process rates but is correlated with
57variables more closely associated with function,
such as net
58photosynthetic capacity and maximum growth rate, which in turn
59can drive ecosystem productivity. Functional traits have been
60used recently in green roof studies to select plants appropriate
61for local climates,
to predict growth rates,
and to maximize
62survival and stormwater retention.
This ecological approach
63uses traits measured in natural populations as indicators of
64generic plant strategies and employs these traits to predict
65ecosystem functions in dierent situations. The current study is
66the rst to undertake a comprehensive analysis of the
67relationships between multiple ecosystem services provided by
68green roofs and generic plant traits.
70The green roof site was on the Saint Marys University campus,
71approximately ve meters above ground level and circum-
72scribed by buildings one to three stories higher on all sides
73(Appendix S1, Supporting Information). The climate of Halifax
74is cold, humid, and maritime (Appendix S1). We used
75measurements of ecosystem properties and services from two
76previously established experiments in the same modular green
77roof system, comparing species grown in monoculture in the
Received: November 6, 2014
Revised: January 18, 2015
Accepted: January 19, 2015
© XXXX American Chemical Society ADOI: 10.1021/es505426z
Environ. Sci. Technol. XXXX, XXX, XXXXXX
*UNKNOWN *|MPSJCA |JCA10.0.1465/W Unicode |es-2014-05426z.3d (R3.6.i7:4236 |2.0 alpha 39) 2014/12/19 13:33:00 |PROD-JCA1 |rq_3232469 |1/27/2015 05:48:25 |9|JCA-DEFAULT
78 same growing medium on the same site.
Plant species
79 were selected from harsh environments that have similarities to
80 green roofs (Appendix S1). Experiment 1 involved the estab-
81 lishment of 13 species in 2007,
three modules per species
82 (n= 3) (Table 1) (planting details in Appendix S1). Each
83 replicate module was a black plastic nursery tray (36 cm ×
84 36 cm ×12 cm), with a free-draining base, lined with a
85 composite nonwoven water-retention layer (Huesker Inc.,
86 Charlotte, NC), followed by an Enkamat (Colbond Inc.,
87 Enka, NC) drainage/lter layer which was topped with growing
88 medium (Sopraor X, Soprema Inc., Drummondville, QC,
89 Canada) to a depth of approximately 6 cm (above the
90 Enkamat).
All modules were weeded throughout the experi-
91 ment to remove volunteer species.
92 Canopy density, considered an ecosystem propertyhere
93 (number of contacts with live plant parts/0.07 m3), was
94 determined in each module using a three-dimensional pin
95 frame (36 cm ×36 cm ×36 cm) with 16 pins. The total
96 number of contacts between live plant parts and the pins was
97 recorded, and data from the 2010 biomass peak (taken in
98 August). Canopy growth rate incorporated canopy density
99 measurements at the end of the growing season (August) in
100 year 1 (2007) and year 2 (2008) to calculate the change in
101 density as a rate, relative to initial density: ln(density at t2)
102 ln(density at t1)/number of days between t1and t2. Albedo was
103 quantied by placing modules one-by-one on a gray colored
104 weed barrier fabric (Quest Plastics Ltd., Mississauga, ON,
105 Canada). A single LI-COR pyranometer sensor and LI-250A
106 light meter (LI-COR Biosciences, Lincoln, NE) was axed to
107 a retort stand, with the sensor and light meter 35 cm above
108 the upper edge of the module. Under clear sky conditions
109 within 1 h of solar noon incoming and reected radiation were
110 measured in August 2010, within 1 week of canopy density
111 sampling. Incoming radiation (W/m2) was measured by
112 directing the pyranometer sensor toward the sky (180°away
113 from the module), and reected solar radiation was measured
114 by facing the sensor directly at the module surface. These are
115 relative measurements with validity only within our green roof
116 system and were taken to compare relative reectivity of the
117 dierent plant species in monoculture (Appendix S1).
118 An index of water loss rate was derived from water addition
119 experiments during the growing season of 2010: modules were
120 weighed using a PX-Series Checkweighing bench scale (ATRON
121 Systems Inc., West Caldwell, NJ), 1.3 L of water was added to
122 the substrate (representing a medium-sized rain event for the
123 region, equivalent to 10 mm of rain), by slowly pouring the water
124 at the base of the vegetation layer, slowly moving the container
125 so that the water would end up equally distributed across the
126 surface. Modules were reweighed 10 min later to determine
127 stormwater captureas the dierence between pre- and
128 postwatering weights, twice between JuneAugust. Modules
129 were reweighed 24 and 48 h later with the dierence between
130 postwatering weights and weights 24 or 48 h later used as an
131 index of water loss (Appendix S1). These experiments were
132 carried out on sunny days within 1 h of solar noon. Ten
133 unplanted control modules were established and integrated with
134 the planted modules at the start of the experiment in 2007
135 (Appendix S1). Indices of relative water capture and loss were
136 created for each planted module each time the experiment was
137 performed by dividing the planted module value by the average
138 of the control (unplanted) modules in the same block.
139 Indicators of ecosystem services were quantied during the
140 2010 growing season. The eect of vegetation on substrate
temperatures during summer indicates a cooling service: lower
substrate temperatures are linearly correlated with lower net
heat ux into the building.
To create an index that can be
used to compare vegetation at dierent times with variation in
ambient air temperatures and insolation, we calculated an index
146of relative cooling by dividing the substrate temperatures in
planted modules within 1 h of solar noon on a sunny day
(once in May, once in July) by the average substrate
temperature of the control modules in the same block at the
same time (measurement details in Appendix S1). Three
variables related to soil chemistry services were calculated for
experiment 1. Growing medium samples (250 mL per module,
Appendix S1) were collected at the end of the growing season
(September 2010) and assessed for organic matter content
155(%)(loss on ignition), nitrate-N (ion-specic electrode), and
phosphate (P2O5) (Mehlich 3 extraction, inductively coupled
argon plasma), and were again converted to indices of relative
content by dividing planted module values by the average
value of control modules in the same block. Organic matter
content was included as an index of the ability of green roof
vegetation to increase carbon storage over time;
soil nutrient
concentrations were included, as greater nutrient uptake by the
vegetation may be associated with higher runowater quality,
so lower nutrient concentrations were assessed as indicating
165higher performance of nutrient removal services.
In 2009 we set up experiment 2, involving 10 replicates each
of 12 species and an unplanted control using the same modular
system as the previous experiment
(four of these species
were also in experiment 1) (Appendix S1). Canopy growth
170rate was determined for the rst two years of growth as in the
other experiment. Substrate temperature was measured in the
same way as in experiment 1 during the same growing season
(2010) in May, July, and August. The stormwater capture
trials were performed three times between JuneAugust 2010.
Snow depth was quantied as it impacts substrate temperature
and heat ux in winter and is aected by vegetation.
As an
ecosystem property, we used average snow depths when
there was snow coverage from January 7March 7, 2011
(Appendix S1). As experiment 1 was harvested and soil
180nutrients quantied at the end of the growing season in 2010,
we could only examine winter performance for experiment 2.
Snow depths were used to represent the general eect of the
species monocultures on the dierential accumulation of snow
on the substrate surface. In winter, net heat ux out of the
building is negatively correlated with substrate temperature,
so we used the minimum substrate temperature recorded be-
tween November 5, 2010, and March 31, 2011 (Appendix S1)
and divided by the average minimum of the control modules in
189the same block as an index of heat trappingin winter (higher
minima represent less heat lost in the winter). The experiment
2 modules were used in a subsequent experiment so substrate
192was not extracted and analyzed for nutrients and organic matter.
For indices that were measured more than once, we used the
194temporal average for each module. We then took an average of
195all modules of that species for each variable representing an
ecosystem property or ecosystem service for use in multiple
regression analyses. Indicators of ecosystem services for
which lower values indicate greater performance (e.g., a lower
substrate temperature in summer indicates greater cooling
ability of the vegetation, so replicates with cooler substrates
registered low indices relative to controls) were rst reected
(multiplied by 1) so that higher performance would register
203as a higher value. This was performed for the substrate
Environmental Science & Technology Article
DOI: 10.1021/es505426z
Environ. Sci. Technol. XXXX, XXX, XXXXXX
Table 1. Measured Plant Traits, Ecosystem Properties, and Ecosystem Service Indicators (means ±standard error)
growth form
leaf area (mm2)
specific leaf
leaf dry
gfresh weight)
canopy growth rate
((contactsT2 contactsT1)/
growing season days)
water loss
snow depth
(expt 1)
creeping shrub 6. ±1 128.5 ±16.6 5.9 ±0.5 444.7 ±7.3 6.5 ×104±1.1 ×1040.17 ±0 22 ±12 0.95 ±0.0 1.03 ±0.0 1.02 ±0.0 0.97 ±0.0 0.96 ±0.1
(expt 2)
tall forb 54 ±5 1062.5 ±220.8 23 ±1.4 195.6 ±6 3.8 ×103±4.4 ×1040.2 ±0.0 55 ±9 1.22 ±0.1 1.04 ±0.0 0.88 ±0.0 1.12 ±0.0 0.91 ±0.11
(expt 1)
tall forb 16 ±3 102.2 ±23.9 16.3 ±1.8 262 ±22.1 3.3 ×103±1.3 ×1030.19 ±0 110 ±5 1.04 ±0.1 0.96 ±0.0 0.8 ±0.0 1.09 ±0.0 0.87 ±0.1 0.2 ±0.1
(expt 2)
sod forming
63 ±0 1451.1 ±137 35.3 ±4.8 580.8 ±27.4 8.2 ×104±8.7 ×1040.2 ±0.01 163 ±25 1.14 ±0.0 1.04 ±0.0 0.8 ±0.0 0.81 ±0.1 1.39 ±0.2
Carex nigra
(expt 2)
sod forming
67 ±3 1116.1 ±141.4 10.5 ±0.9 401.2 ±23.5 9.4 ×104±1.4 ×1040.2 ±0.0 154 ±20 1.10 ±0.0 1 ±0.0 0.82 ±0.0 0.7 ±0.0 1.34 ±0.1
bunch forming
36 ±2 90.2 ±10.8 9.5 ±1 402.8 ±46.6 1.1 ×103±3.8 ×1040.18 ±0 121 ±13 0.87 ±0.0 0.99 ±0.0 0.85 ±0.0 0.95 ±0.1 1.11 ±0.2 0.48 ±0.2 0.67 ±0.0 1.16 ±0.1
bunch forming
57 ±2 81.8 ±13.2 13.3 ±2.2 368.8 ±45.9 1. Seven ×103±4.4 ×1040.17 ±0 98 ±15 0.95 ±0.0 1.01 ±0.0 0.9 ±0.0 1.07 ±0.1 0.84 ±0.1 0.85 ±0.2 0.9 ±0.1 0.98 ±0.1
creeping shrub 8 ±1 4.6 ±0.4 40.1 ±3.1 90.2 ±20.8 1.7 ×104±4.5 ×1040.17 ±0.01 56 ±10 0.91 ±0.0 1.07 ±0.0 0.94 ±0.0 0.95 ±0.1 1.13 ±0.1 0.46 ±0.3 0.92 ±0.0 0.94 ±0.1
Festuca rubra
(expt 2)
sod forming
55 ±6 141.1 ±26.6 16.1 ±4 303.5 ±38.8 1.3 ×103±1.5 ×1030.18 ±0.0 243 ±38 1.02 ±0.0 1.11 ±0.0 0.85 ±0.0 0.68 ±0.0 1.46 ±0.1
(expt 1)
creeping shrub 10 ±1 357.8 ±38.4 7.3 ±0.5 382.9 ±10.1 2.7 ×103±1.3 ×1030.16 ±05±4 1.11 ±0.1 1.04 ±0.0 0.95 ±0.0 0.99 ±0.2 1.15 ±0.2 0.89 ±0.3
(expt 1)
tall forb 14 ±2 228.7 ±81.2 7.3 ±0.6 112.6 ±6.5 1.3 ×103±4.3 ×1040.17 ±0 47 ±7 0.87 ±0.1 1.01 ±0.0 0.88 ±0.0 1.24 ±0.2 0.92 ±0.2 0.32 ±0.1
Poa compressa
(expt 1)
sod forming
26 ±6 1662.0 ±465.0 41.8 ±6.9 244.8 ±57.3 3.4 ×103±6.8 ×1040.19 ±0 209 ±60 0.92 ±0.1 1.01 ±0.0 0.74 ±0.0 0.99 ±0.1 1.09 ±0.2 0.1 ±0.0
(expt 1)
creeping forb 2 ±0 16.5 ±4.0 92.4 ±23.3 167 ±19.5 2.7 ×103±5.1 ×1040.18 ±0 101 ±8 1.10 ±0.1 0.95 ±0.0 0.86 ±0.1 1.01 ±0.1 1.17 ±0.2 0.83 ±0.4
Sedum acre
(expt 1)
succulent 4 ±0 18.5 ±2.8 37.0 ±8.4 124.7 ±13.6 1.4 ×103±5.1 ×1040.26 ±0.0 124 ±30 1.07 ±0.1 0.87 ±0.0 0.74 ±0.0 1.13 ±0.2 0.78 ±0.1 0.87 ±0.3
Rhodiola rosea
(expt 1)
succulent 18 ±0 152.8 ±36.0 16.9 ±1.1 85.4 ±4.9 2.7 ×103±1.0 ×1030.16 ±0 22 ±6 0.96 ±0.0 0.96 ±0.0 0.94 ±0.0 0.92 ±0.1 1.18 ±0.2 1.25 ±0.9
Sedum spurium
(expt 1)
succulent 5 ±1 162.9 ±11.8 30.0 ±3.2 86.7 ±5.5 8.5 ×104±1.5 ×1030.21 ±0 87 ±39 0.84 ±0.1 0.93 ±0.0 0.8 ±0.0 0.93 ±0.1 1.11 ±0.2 0.35 ±0.1
Environmental Science & Technology Article
DOI: 10.1021/es505426z
Environ. Sci. Technol. XXXX, XXX, XXXXXX
204cooling,substrate winter temperature increase, phosphate,
205and nitrate removalindices (Appendix S1).
206Plant traits are usually measured on plants growing in typical
207environmental conditions in eld settings, so that trait values
208are representative of natural populations.
To determine plant
209traits for each species used in these experiments, ve leaf samples
210per species were obtained from dierent individuals growing
211in their natural habitats (Appendix S1). Following established
healthy, fully expanded leaves from well developed
213plants were selected (Appendix S1). Within 2 h of collection
214fresh weight was measured and the ve leaves per species
215scanned. Leaves were dried at 50 °C for 48 h and dry weight was
216measured. From these measurements, the following traits were
217obtained for the 21 test species: plant height, leaf area (LA),
218specic leaf area (SLA), and leaf dry matter content (LDMC).
219Plant height was determined as the average height of the ve
220plants sampled in the eld (to the nearest mm). LA was
221determined from the average of the ve leaves, by measuring
222the one-sided surface area of the scanned image of each
223individual leaf using ImageJ software (version 1.47; NIH, USA)
224(Appendix S1). SLA was calculated as one-sided leaf area divided
225by its oven-dried weight, in mm2/mg. LDMC is the measure of
226dry leaf weight (mg) divided by fresh leaf weight (g).
227We evaluated the relationships between plant species traits,
228monoculture ecosystem properties, and ecosystem service
229indicators using multiple linear regression in order to construct
230a path diagram. We rst evaluated the relationships between
231ecosystem properties and service indicators (model results in
232Table S1, Supporting Information) and then the relationships
233between plant traits and ecosystem properties. As we
234hypothesized that plant traits aect ecosystem services via
235dierences in ecosystem properties, we only examined the direct
236eects of plant traits on ecosystem services if there were
237no adequate models linking ecosystem properties directly to
238services. Variables were transformed to improve homogeneity of
239variance, and standardized (Z-scores)
(Table S1). Model t was
240evaluated using the AICc criterion.
All models were compared
241using delta-weights, and when multiple models had delta-
242weights lower than 7,
model averaging was used to generate
243standardized regression coecients (Table S1). The path
244diagram was constructed using only those coecients whose
24595% condence limits did not overlap zero. Model selection and
246averaging procedures used the MuMIn package in R.
248Measured traits diered greatly among species with leaf
249area spanning more than 3 orders of magnitude (Table 1).
250Leaf area was highest in sod-forming graminoids but highly
251variable within that group, relatively high in bunch-forming
252graminoids, and lower but highly variable within the other
253growth forms (Table 1). Height was consistently low in
254creeping forbs and shrubs, relatively low but highly variable
255within the succulents, and high but variable within a growth
256form for the grasses and tall forbs. Leaf dry matter content
257(LDMC) showed the least variation with an almost 7-fold
258dierence between the lowest and highest species values
259(Table 1). Low LDMC species included all the succulents and
260Empetrum nigrum,Plantago maritima,andSagina procumbens
261(each from a dierent growth form group). Specicleaf
262area (SLA) tended to be low for creeping shrubs, except for
263E. nigrum. SLA for other growth forms varied greatly within a
264group. S. procumbens, the only creeping forb included, had the
265highest SLA (Table 1).
Table 1. continued
growth form
leaf area (mm2)
specific leaf
leaf dry
gfresh weight)
canopy growth rate
((contactsT2 contactsT1)/
growing season days)
water loss
snow depth
(expt 2)
creeping shrub 9 ±3 395.6 ±108.3 8.1 ±0.3 380.9 ±6.8 3.4 ×103±1.8 ×1030.17 ±0.0 40 ±15 1.02 ±0.0 1.07 ±0.0 0.92 ±0.0 0.92 ±0.0 1.12 ±0.1
Solidago bicolor
tall forb 29 ±3 653.8 ±222.2 8.0 ±0.6 267.8 ±12.9 2.5 ×103±5.7 ×1040.2 ±0.0 58 ±18 0.95 ±0.0 1.03 ±0.0 0.89 ±0.0 1.16 ±0.1 0.97 ±0.1 0.14 ±0.0 1.04 ±0.0 1.05 ±0.1
(expt 2)
tall forb 41 ±3 705.5 ±75.1 28.4 ±2.9 448.4 ±24.5 2.2 ×103±7.1 ×1040.2 ±0.0 55 ±15 0.94 ±0.0 1.06 ±0.0 0.93 ±0.0 0.99 ±0.0 1.08 ±0.1
(expt 2)
creeping shrub 8 ±1 26.0 ±2.3 7.3 ±0.6 582.6 ±17.2 1.4 ×103±8.5 ×1040.18 ±0.01 14 ±4 0.9 ±0.0 1.04 ±0.0 0.99 ±0.0 0.96 ±0.1 0.99 ±0.1
(expt 1)
creeping shrub 4 ±0 56.2 ±7.6 6.2 ±0.4 465.4 ±9.1 3.1 ×103±8.9 ×1040.16 ±0.01 0 ±0 1.11 ±0.1 0.97 ±0.0 0.97 ±0.0 0.85 ±0.1 1.29 ±0.2 1.12 ±0.4
Nomenclature follows.
Trait data (height, SLA, leaf area, and LDMC) for experiment 1 are from the literature.
Several variables were reected in analyses (cooling, phosphate, nitrate, winter
temperature increase); hence, low values in this table represent high performance of desired functions.
Soil chemistry data were only collected from experiment 1; winter data (snow depths and substrate
winter temperatures) were only collected for experiment 2.
Environmental Science & Technology Article
DOI: 10.1021/es505426z
Environ. Sci. Technol. XXXX, XXX, XXXXXX
266 Measured ecosystem properties also diered across growth
267 forms and species. Growth rates were slightly negative for
268 S. procumbens,Gaultheria procumbens,Sibbaldiopsis tridentata,
269 Vaccinium macrocarpon,V. vitis-idaea,Rhodiola rosea, and Sedum
270 spurium (Table 1). These are species that generally inhabit
271 extremely shallow and low-nutrient soils
and are expected to
272 have low growth rates. These results also indicate that the
273 canopies produced by these species reached their approximate
274 maximum densities in the green roof system by the end of the
275 rst growing seasons. Tall forbs had consistently high canopy
276 growth rates, as did both groups of grasses (Table 1). The
277 succulent S. acre also had a relatively high growth rate. Creeping
278 shrubs and forbs had low positive or negative canopy growth
279 rates. The index of albedo showed the greatest range of
280 variation within the succulent group with both the lowest and
281 highest values (16% reectivity for R. rosea and 26% for
282 Sedum acre). Species in the other groups varied much less
283 (between 16% and 19% reectivity). Canopy density ranged
284 from zero (for the extremely short ground cover V. vitis-idaea)
285 to 243 contacts/0.07m3for the sod-forming graminoid Festuca
286 rubra. Grasses tended to have consistently high canopy density,
287 whereas tall forbs had consistently intermediate values (Table 1).
288 While all creeping shrubs were at the low end of canopy
289 densities, there was considerable variation among species in
290 the group, as well as in the succulent group. Water loss rates
291 ranged from 16% lower than unplanted controls (S. spurium),
292 implying that the vegetation blocked evaporation from the
293 bare substrate (relative to controls) but also had very low
294 transpiration rates, to 22% higher (S. novi-belgii) than controls.
295 Values substantially lower than controls were found in other
296 species (Danthonia spicata, E. nigrum, and V. macrocarpon) that
297 tended to concentrate their leaf biomass close to the substrate
298 surface, likely presenting a barrier to evaporation from the
299 substrate surface. There was otherwise little consistency in
300 water loss within a growth form group. The eect of the
301 planted species on snow depths ranged from 46% higher than
302 unvegetated control modules in the dense-canopied F. rubra
303 and other sod-forming graminoids, to close to no dierence
304 from controls for sparse-canopied tall forb Symphotrichum
305 qnovi-belgii, and some creeping shrubs (Table 1).
306 Consistent with other green roof studies,
vegetation had
307 a relatively small eect on stormwater capture, with the greatest
increase relative to controls around 10% for F. rubra (Table 1).
Creeping shrubs tended to be at the high end for stormwater
capture, along with tall forbs, with succulents at the low end.
Several species had lower capture than controls (S. acre had one
of the lowest). Vegetation reduced summer substrate temper-
atures up to 26% lower than controls (Poa compressa and S. acre
had the coolest substrate). Some species were very similar in
substrate temperature to controls (Arctostaphylos uva-ursi,
V. macrocarpon, V. vitis-idaea). In general, creeping shrubs
tended to cool the substrate the least, followed by tall forbs,
with inconsistent results among grass species and highly
variable performance among the succulents (Table 1). Organic
matter showed little dierence from controls, with high
variability within a species and no consistent patterns between
growth forms, save that the three tall forb species tended to have
relatively high amounts of organic matter. Most species
contained equivalent phosphate in their substrate to controls;
the only species standing out with relatively low phosphate were
S. acre and Deschampsia flexuosa. Substrate nitrate varied greatly
among species with P. compressa showing very low levels (10% of
the control values), considerable range among the three grass
species, consistently low values among the three tall forbs, and
no clear pattern among the other species (Table 1). Most species
increased minimum winter substrate temperatures (registering as
lower values in Table 1) relative to controls. Two grasses with
relatively dense canopies close to the ground (D. spicata and
F. rubra) raised winter minimum temperatures by approximately
30% relative to controls (Table 1). Grasses tended to have the
highest winter minimum temperatures, whereas tall forbs and
337creeping shrubs had similar values to controls.
Most of the ecosystem service indicators were predicted by
vegetation properties (Figure 1). Stormwater capture was
positively correlated with plant height but not any of the
vegetation properties. The index of water loss rate was not
predicted by plant traits and did not predict any of the services.
The three services related to substrate chemistry (organic
matter, phosphate, and nitrate removal) were all positively
predicted by canopy growth rate, which was positively
predicted by plant height (Figure 1; Table S1). Substrate
cooling was positively related to albedo and canopy density.
Albedo was also related to canopy density but not any of the
349plant traits. Canopy density was positively related to both SLA
Figure 1. Relationships between plant traits (left), green roof ecosystem properties (center), and indicators of ecosystem services (right).
Standardized regression coecients determined by model averaging (showing only relationships with coecients whose 95% condence intervals
did not overlap zero).
Environmental Science & Technology Article
DOI: 10.1021/es505426z
Environ. Sci. Technol. XXXX, XXX, XXXXXX
350 and plant height. Substrate winter temperature increase was
351 positively correlated with snow depth and canopy density.
352 Deeper snow was associated with denser canopies, lower
353 canopy growth rates, and higher albedo. Leaf area and LDMC
354 were also positive predictors of snow depth.
355 Plant traits represent general characteristics of plant species,
356 but their eects on ecosystem processes are mediated through
357 canopy characteristics and the eects of plant canopies on
358 microclimates.
In this study, ecosystem properties
359 represent empirically measured canopy variables from mono-
360 culture species populations grown in the green roof setting.
361 Traits related to overall plant size and leaf characteristics
362 measured under natural conditions predicted canopy density,
363 canopy growth rate, and snow depth, which in turn predicted
364 the ecosystem service indicators. The plant traits used as
365 predictors here represent easy-to-measure soft
traits that are
366 correlated with process rates but not necessarily closely related
367 mechanistically to ecological processes.
368 Plant height is a general indicator of high growth rates
369 and/or evolution in resource-rich environments where com-
370 petition for light is important, although there are many
371 trade-os involved in growing tall.
The tallest species in
372 this study were primarily graminoids and tall forbs (Table 1),
373 which are associated with deeper soils and more fertile
374 conditions, and had higher growth rates in the green roof
375 system. It is important to note that heightand the other
376 leaf traits are general indicators of the fertility of the habitats
377 where the species tend to grow naturally. While taller species
378 performed some ecosystem services more eectively than
379 shorter species in this system, there are important caveats
380 relevant to interpreting these results. First, the species selected
381 for these experiments all grow in relatively harsh or resource-
382 limited environments (Appendix S1) and tend to be relatively
383 short compared with species from more fertile habitats. Our
384 nding implies that the species that are tallest in a set of short
385 species perform these services better than the shorter ones.
386 Second, while the species we grew on the green roof generally
387 grew shorter than under natural conditions, there may be dis-
388 advantages for relatively tall species in green roof environments.
389 Relatively tall species may be more susceptible to drought,
390 and tall species from relatively fertile environments may have
391 higher resource requirements leading to population crashes
392 if fertilization or supplemental irrigation are not applied. This
393 study ran for a relatively short period of time so longer studies
394 are required to determine how height and long-term survival
395 may be related.
396 Stormwater capture was positively related to plant species
397 height, possibly due to an overall greater resource demand and
398 water uptake, possibly mediated through greater root biomass,
399 which supports what has been found in other studies.
400 In this study, species height also predicted canopy growth rate,
401 and density, which were positive correlates of all the ecosystem
402 service indicators except stormwater capture.
403 Canopy growth rate relates to the overall primary production
404 of a population of a given species and is expected to be correlated
405 with the organic matter content of soils,
and rates of nutrient
406 uptake.
Here canopy growth rate was a relatively strong
407 predictor of all three services related to soil properties: organic
408 matter, phosphate, and nitrate contents. Species from relatively
409 fertile environments (indicated by taller heights) grew faster in
410 the green roof system and resulted in higher substrate organic
411 matter, and lower phosphate and nitrate contents, likely as a
412 result of overall higher rates of multiple metabolic processes.
Canopy density represents the density of live leaf and stem
parts in aboveground vegetation and was higher in the green
roof system for species that were generally taller in their natural
environments and had higher SLA values. These species, in
turn, gave rise to lower summer substrate temperatures and
418higher winter minima. SLA represents the amount of invest-
ment in light-intercepting area per unit of dry mass and is
expected to be higher in species from more resource-rich
environments, resulting in a higher ability to harvest light per
unit biomass allocation.
Again it is important to emphasize
that the high SLA species in this study are generally from the
most fertile areas in low productivity areas (coastal barrens,
rock outcrops) that have some similarities with the green roof
environment (shallow substrates, exposure to wind and sun).
427SLA is positively correlated with net photosynthetic capacity,
potential growth rate,
and evapotranspiration rate.
SLA species in this study belong to a greater diversity of growth
forms than tall species: forbs, graminoids, shrubs, and
431succulents were all among the highest SLA species.
432High LDMC species included two dwarf shrubs in the same
433genus (Vaccinium); low LDMC species were the succulents,
E. nigrum and Plantago maritima, which have relatively thick
leaves and likely high water storage similar to the true succulents.
Species with large individual leaf area included some graminoids
and tall forbs. Both leaf area and LDMC were weak positive
predictors of snow depth, although canopy density had the
strongest eect of any of the individual variables. This shows
that dense canopies led to greater trapping of windborne snow,
accumulation of less dense snow, and/or decreases in melt rate,
442as predicted in previous work.
When canopy density is held
constant there were also independent eects of leaf area and
LDMC so species with larger individual leaves and/or greater dry
matter content also resulted in deeper snow layers which have a
moderating eect on winter substrate temperatures (Figure 1).
While we did not measure canopy density in the winter (i.e.,
we used August canopy densities to predict snow depths in the
following winter) the strong predictive power of summer canopy
density implies that species with dense canopies in summer
retain dense canopies of dead stems and leaves or woody tissue
452in winter. Some of the high canopy density species tested in the
snow study were relatively slow growing; thus, there was a
negative relationship between growth rate and snow depth.
The relationships between snow accumulation and plant traits
are complex, as snow accumulation represents the outcome of
several distinct processes.
Canopy density had an eect on
raising minimum winter substrate temperatures independent of
snow depth, suggesting that plant residues, leaves from evergreen
species, or other structures helped retain heat in the substrate.
461One possible mechanism is the reduction of convective heat
losses due to reduction of air movement within the canopy
lower wind speeds at ground level
even when canopy material
464is largely dead.
Here we used an ecological approach to predict green roof
466services: plant traits measured on plants growing in their natural
467habitats were linked to ecosystem services via statistical models,
using empirical data from replicated green roof modules. The
approach taken by environmental engineers usually involves
using numerical models of physical processes to predict key
variables, and altering parameter values to explore sensitivity
to environmental variability due to climate or vegetation type.
The nding that substrate cooling has a positive relationship with
both albedo and canopy density is congruent with the ndings
475from numerical heat balance models, although our temperature
Environmental Science & Technology Article
DOI: 10.1021/es505426z
Environ. Sci. Technol. XXXX, XXX, XXXXXX
476 index includes several sources of variation from averaging across
477 replicates within a species and sampling dates. Parametric
478 variations in simulation studies have found leaf area index (LAI,
479 representing single-sided total leaf area in the canopy) to be one
480 of the most important parameters in determining the reduction
481 of substrate surface temperatures, with LAI inversely related to
482 substrate surface temperature.
This result is due to LAI
483 being a factor in the calculation of convective heat transfer,
484 evapotranspiration, and shading. These heat uxes are generally
485 calculated according to the big leafapproach, i.e., the Penman
486 Monteith equation for evapotranspiration, whereby the heat
487 uxes of a single, average leaf are extrapolated to the canopy level
488 via the LAI. While we did not measure LAI directly, within a
489 species, LAI and canopy density should be linearly and positively
490 related. Further empirical work to quantify LAI for dierent
491 green roof vegetation types is the next step to link screening of
492 species using ecological traits with prediction of green roof
493 functioning.
494 Sensitivity analyses suggest that plant height is one of the
495 primary variables determining thermal gain through green
496 roofs.
In this study, average plant height for a given species in
497 its natural habitats is correlated with substrate cooling via its
498 role in canopy density (Figure 1), possibly via greater reection
499 and/or absorption of solar radiation, represented in numerical
500 models by the extinction coecient. Plants that were relatively
501 tall in natural habitats produced a lot of canopy density on
502 the green roof, due to high productivity and a generally taller
503 stature than the other species. However, the role of plant height
504 in reducing ground heat ux is also likely due to its role in the
505 aerodynamic resistance of convective and latent heat transfer.
506 According to the logarithmic wind prole approach adopted
507 in numerous green roof heat balance models,
508 wind shear within the canopy increases the convective and
509 evapotranspiration heat losses from the green roof system for
510 taller plants by reducing the aerodynamic resistance and thus
511 reducing the heat ux into the substrate. Thus, taller plants
512 likely inuence substrate temperatures via several mechanisms
513 simultaneously.
514 Additional cooling mechanisms reduce the importance of
515 albedo in the surface energy balance of a green roof compared
516 to a conventional roof;
however, the results of this study
517 showed canopy albedo still had a positive relationship with
518 substrate cooling. To simulate the eects of albedo on heating
519 loads, one study compared Sedum tomentosum in monoculture
520 to a mixture of Sedum species,
which had measured canopy
521 reection coecients of 0.23 and 0.11, respectively, using a
522 previously validated green roof model.
The average peak net
523 radiation dierence and average net radiation between the two
524 plant conditions was as high as 16% and 20%, respectively,
525 suggesting that plant screening for canopy albedo may result in
526 improvements to green roof thermal performance. None of the
527 plant traits we used predicted albedo, so additional traits related
528 to leaf optical properties would need to be incorporated to
529 allow this kind of screening.
530 Several empirical studies show that stormwater retention in
531 green roofs is related to antecedent soil moisture content;
532 thus, high transpiration rates in the vegetation should be able to
533 increase the water holding capacity of soil. While some green
534 roof studies show a relationship between water loss rate and
535 stormwater capture,
the current study did not show this. The
536 index of water loss used here represented water lost within
537 the rst 48 h of water addition, when conditions were wet
538 and water loss would be a function of both evaporation from
substrate surface and transpiration from the canopy, as well as
possible drainage after the rst 10 min of runo. A longer
drying period, leading to a drier substrate surface, might have
produced a clearer signal of dierential transpiration rates
among species and a stronger eect on subsequent stormwater
544retention, as evaporation from the substrate surface would
have been minimized after it dried out completely. Stormwater
capture should be a function of water loss over the entire interval
between rain events; thus, the experimental method employed
here likely overlooked the dierences among plant species in ET
549rates over the longer term.
550Since leachate from green roofs can be high in nutrients,
the uptake and retention of nutrients by vegetation could
mitigate nutrient pollution in runo. Here we measured
substrate concentrations of nitrate and phosphate after four
years of plant growth; the index of removal was relative to
control modules in which leaching was presumed to be the
primary process driving nutrient loss. The low nutrient content
relative to controls in some plant species treatments (Table 1)
suggests that plant uptake and cycling within the plant
559substrate system is responsible for the reductions, rather than
560greatly increased leaching rates for vegetated modules. We did
not quantify leachate concentrations directly, and it is clear
that further work needs to be done to examine the eects of
plant species on nutrient dynamics and water quality in green
roof systems. Likewise, our measurement of organic matter
is intended to be an index of the dierential ability of plant
species to contribute toward carbon capture in the system,
although it omits key variables such as inorganic carbon that are
essential to understand the entire impact of green roof vege-
569tation on carbon sequestration.
These indices only partially
570capture the important ecosystem services of green roofs, but
they clearly respond to dierences in plant species, suggesting
that plant traits can be used to optimize ecosystem service
While shallow-substrate (extensive) green roofs are usually
575planted with succulents due to their superior drought tolerance,
the plants with the highest trait values tended not to be
succulents in this study. The most common vegetation for
shallow-substrate green roofs is mixtures of Sedum species or
other succulents.
While here we only quantied the eects of
plant traits on monoculture provisioning of ecosystem services,
a past study on the same system showed that a Sedum mixture
and S. acre monocultures performed similarly for summer sub-
strate temperature reductions, albedo, and stormwater capture
(other services were not quantied).
This suggests that Sedum
585mixtures should be eective at summer cooling but relatively
poor in stormwater capture, relative to other species we
examined. As several other studies have shown,
succulents are not always the best choice if particular ecosystem
services are to optimized besides roof cooling, but less drought
tolerant species sharing the traits identied here could be
included on green roofs depending on regional climate,
592availability of irrigation, and other factors.
In addition to Sedum mixtures, shallow-substrate green roofs
often employ more diverse species and growth form mixtures.
Some designs feature multiple species planted as monocultural
beds, and our results are relevant to single-species patches within
diverse roofs of this kind. However, predicting ecosystem
services from species-diverse roofs featuring multispecies
communities requires a dierent approach. Future studies should
examine functional trait diversity in species mixtures to
601determine the eects on ecosystem services. The approach we
Environmental Science & Technology Article
DOI: 10.1021/es505426z
Environ. Sci. Technol. XXXX, XXX, XXXXXX
602 adopted allows prediction of monoculture performance as a rst
603 step toward screening lists of species for possible inclusion in
604 mixed plantings.
605 This study also shows that broad growth form groupings can
606 exhibit considerable variation in traits among species,
607 suggesting that functional traits, rather than growth form
608 groups might be used for green roof plant selection. Height and
609 SLA were the best predictors, and while height was consistent
610 for only some of the growth form groups, SLA was highly
611 variable both within and between groups. Out of the species
612 tested here, we could recommend designing green roofs with
613 species that have relatively high SLA and height values, such as
614 F. rubra. It is also possible that mixtures of species with
615 opposite trait values (e.g., a short species with high SLA such as
616 S. procumbens and a tall species such as Carex nigra with low
617 SLA) might be combined to yield optimal functioning, if the
618 diversity in morphology leads to complementary resource use
619 or other synergistic eects.
However, the design criteria for a
620 particular roof, including ecosystem services to be maximized,
621 may require attention to other traits not measured here such
622 as owering period or other variables related to habitat use by
623 animals or human aesthetic criteria.
624 This study used plant leaf and traits determined by an
625 average of measurements from plants from ve naturally
626 occurring populations. The ecosystem properties and service
627 indicators measured on the green roof also represent averages
628 within each species. Despite the considerable variation in traits
629 and response variables within a species (Table 1), there was still
630 a high predictive ability across species. The novelty of this study
631 is to show that simple leaf and canopy traits can predict
632 multiple important green roof functions. While the indices we
633 used cannot be directly converted into precise estimates of
634 ecosystem services for use in cost benet analyses or use in
635 numerical modeling, traits can be used to screen large numbers
636 of species. Plant traits for many regional oras are now avail-
637 able in databases
and can be used to generate plant lists for
638 further empirical testing and for the design of many kinds of
639 constructed ecosystems.
641 *
SSupporting Information
642 Appendix S1: Detailed methods. Table S1: Multiple linear
643 regression models used for construction of path diagram in
644 Figure 1. This material is available free of charge via the
645 Internet at
647 Corresponding Author
648 *Tel: (902) 420-5506. E-mail:
649 Author Contributions
650 The manuscript was written through contributions of all
651 authors. All authors have given approval to the nal version of
652 the manuscript.
653 Notes
654 The authors declare no competing nancial interest.
656 We thank J. Scott MacIvor and Melissa Ranalli for data
657 collection, and Amy Heim and Emily Walker for constructive
658 criticism of the manuscript. This work was funded by NSERC
659 Discovery (311788-2010) and Strategic Project (413116-2011)
660 Grants.
662SLA specic leaf area
663LDMC leaf dry matter content
664LAI leaf area index
665ET evapotranspiration
(1) 668Darlington, A. B.; Dat, J. F.; Dixon, M. A. The biofiltration of
669indoor air: Air flux and temperature influences the removal of toluene,
670ethylbenzene, and xylene. Environ. Sci. Technol. 2001,35, 2406.
(2) 671Stottmeister, U.; Wiener, A.; Kuschk, P.; Kappelmeyer, U.;
672Kastner, M.; Bederski, O.; Müller, R.; Moormann, H. Effects of plants
673and microorganisms in constructed wetlands for wastewater treatment.
674Biotechnol. Adv. 2003,22, 93117.
(3) 675Lundholm, J. T.; Williams, N. S. G. Eects of vegetation on green
676roof ecosystem services. In Green Roof Ecosystems; Sutton, R. K., Ed.;
677Springer: New York, 2015 (in press).
(4) 678Sailor, D. J. A green roof model for building energy simulation
679programs. Energy Build. 2008,40, 14661478.
(5) 680
Villarreal, E. L.; Bengtsson, L. Response of a sedum green-roof to
681individual rain events. Ecol. Eng. 2005,25,17 DOI: 10.1016/
(6) 683Speak, A. F.; Rothwell, J. J.; Lindley, S. J.; Smith, C. L. Urban
particulate pollution reduction by four species of green roof vegetation
685in a UK city. Atmos. Environ. 2012,61, 283293.
(7) 686Getter, K. L.; Rowe, D. B.; Robertson, G. P.; Cregg, B. M.;
687Andresen, J. A. Carbon sequestration potential of extensive green
688roofs. Environ. Sci. Technol. 2009,43, 75647570.
(8) 689Williams, N. S. G.; Lundholm, J. T.; MacIvor, J. S. Can green
690roofs improve biodiversity conservation in cities? J. Appl. Ecol. 2014,
69151, 16431649.
(9) 692Snodgrass, E. C.; Snodgrass, L. L. Green Roof Plants: A Resource
693and Planting Guide; Timber Press: Portland, OR, 2006.
(10) 694Lavorel, S.; Garnier, E. Predicting changes in community
695composition and ecosystem functioning from plant traits: Revisiting
696the Holy Grail. Funct. Ecol. 2002,16, 545556.
(11) 697
Cornelissen, J. H. C.; Lavorel, S.; Garnier, E.; Diaz, S.;
698Buchmann, N.; Gurvich, D. E.; Reich, P. B.; ter Steege, H.; Morgan, H.
699D.; van der Heijden, M. G. A.; Pausas, J. G.; Poorter, H. A handbook
700of protocols for standardised and easy measurement of plant functional
701traits worldwide. Aust. J. Bot. 2003,51 (4), 335380.
(12) 702Díaz, S.; Cabido, M. Vive la difference: plant functional diversity
matters to ecosystem processes. Trends Ecol. Evol. 2001,16 (11), 646
(13) 705
Van Mechelen, C.; Dutoit, T.; Kattge, J.; Hermy, M. Plant trait
706analysis delivers an extensive list of potential green roof species for
707Mediterranean France. Ecol. Eng. 2014,67, 4859.
(14) 708Lundholm, J.; Heim, A.; Tran, S.; Smith, T. Leaf and life history
709traits predict plant growth in a green roof ecosystem. PLoS One 2014,
7109(6), e101395 DOI: 10.1371/journal.pone.0101395.
(15) 711Farrell, C.; Szota, C.; Williams, N. S. G.; Arndt, S. K. High water
712users can be drought tolerant: Using physiological traits for green roof
713plant selection. Plant Soil 2013,372, 177193.
(16) 714Lundholm, J.; MacIvor, J. S.; MacDougall, Z.; Ranalli, M. Plant
715species and functional group combinations affect green roof ecosystem
716functions. PLoS One 2010,5(3), e9677
(17) 718MacIvor, J. S.; Lundholm, J. T. Performance evaluation of native
719plants suited to extensive green roof conditions in a maritime climate.
720Ecol. Eng. 2011,37, 407417.
(18) 721Lundholm, J. T.; Weddle, B. M.; MacIvor, J. S. Snow depth and
722vegetation type affect green roof thermal performance in winter.
723Energy Build. 2014,84, 299307.
(19) 724
Berndtsson, J. C.; Bengtsson, L.; Jinno, K. Runoff water quality
725from intensive and extensive vegetated roofs. Ecol. Eng. 2009,35 (3),
Environmental Science & Technology Article
DOI: 10.1021/es505426z
Environ. Sci. Technol. XXXX, XXX, XXXXXX
727 Hodgson, J. G.; Wilson, P. J.; Hunt, R.; Grime, J. P.; Thompson,
728 K. Allocating C- S-R plant functional types: A soft approach to a hard
729 problem. Oikos 1999,85, 282294.
730 Maestre, F. T.; Quero, J. L.; Gotelli, N. J.; Escudero, A.; Ochoa,
731 V.; Delgado-Baquerizo, M.; et al. Plant species richness and ecosystem
732 multifunctionality in global drylands. Science 2012,335 (6065), 214
733 218.
734 Burnham, K. P.; Anderson, D. R. Model Selection and Multimodel
735 Inference: A Practical Information-theoretic Approach; Springer: New
736 York, 2002.
737 Burnham, K. P.; Anderson, D. R.; Huyvaert, K. P. AIC model
738 selection and multimodel inference in behavioral ecology: Some
739 background, observations, and comparisons. Behav. Ecol. Sociobiol.
740 2011,65, 2335.
741 Bartoń, K. MuMIn: Multi-model inference. R package version
742 1.9.13. 2013.
743 Zinck, M. Rolands Flora of Nova Scotia; Nova Scotia Museum:
744 Halifax, Nova Scotia, 1998.
745 Gleason, H. A.; Cronquist, A. Manual of Vascular Plants of
746 Northeastern United States and Adjacent Canada; The New York
747 Botanical Garden: Bronx, NY, 1991.
748 VanWoert, N. D.; Rowe, D. B.; Andresen, J. A.; Rugh, C. L.;
749 Fernandez, R. T.; Xiao, L. Green roof stormwater retention: Effects of
750 roof surface, slope, and media depth. J. Environ. Qual. 2005,34 (3),
751 10361044.
752 Gausman, H. W.; Allen, W. A. Optical parameters of leaves of 30
753 plant species. Plant Physiol. 1973,52 (1), 5762.
754 Oke, T. R. Boundary Layer Climates; London: Methuen & Co.
755 Ltd.: London, U.K., 1978.
756 Gower, S. T.; Kucharik, C. J.; Norman, J. M. Direct and indirect
757 estimation of leaf area index, fAPAR, and net primary production of
758 terrestrial ecosystems. Remote Sens. Environ. 1999,70 (1), 2951.
759 Westoby, M.; Falster, D. S.; Moles, A. T.; Vesk, P. A.; Wright, I.
760 J. Plant ecological strategies. some leading dimensions of variation
761 between species. Annu. Rev. Ecol. Syst. 2002,33, 125159.
762 Butler, C.; Orians, C. M. Sedum cools soil and can improve
763 neighboring plant performance during water deficit on a green roof.
764 Ecol. Eng. 2011,37, 17961803.
765 Dunnett, N.; Nagase, A.; Booth, R.; Grime, P. Influence of
766 vegetation composition on runoff in two simulated green roof
767 experiments. Urban Ecosyst. 2008,11 (4), 385398.
768 Nagase, A.; Dunnett, N. Amount of water runoff from different
769 vegetation types on extensive green roofs: Effects of plant species,
770 diversity and plant structure. Landscape Urban Plan. 2012,104 (34),
771 356363.
772 De Deyn, G. B.; Cornelissen, J. H.; Bardgett, R. D. Plant
773 functional traits and soil carbon sequestration in contrasting biomes.
774 Ecol. Lett. 2008,11 (5), 516531.
775 Hobbie, S. E. Effects of plant species on nutrient cycling. Trends
776 Ecol. Evol. 1992,7(10), 336339.
777 Westoby, M. A leaf-height-seed (LHS) plant ecology strategy
778 scheme. Plant Soil 1998,199 (2), 213227.
779 Reich, P. B.; Walters, M. B.; Ellsworth, D. S. From tropics to
780 tundra: global convergence in plant functioning. Proc. Natl. Acad. Sci.
781 U. S. A. 1997,94, 1373013734.
782 Grime, J. P.; Thompson, K.; Hunt, R.; Hodgson, J. G.;
783 Cornelissen, J. H. C.; Rorison, I. H.; et al. Integrated screening
784 validates primary axes of specialisation in plants. Oikos 1997,79, 259
785 281.
786 Reich, P. B.; Ellsworth, D. S.; Walters, M. B.; Vose, J. M.;
787 Gresham, C.; Volin, J. C.; Bowman, W. D. Generality of leaf trait
788 relationships: A test across six biomes. Ecology 1999,80 (6), 1955
789 1969.
790 Liston, G.; McFadden, J.; Sturm, M.; Pielke, R., SR. Modelled
791 changes in arctic tundra snow, energy and moisture fluxes due to
792 increased shrubs. Glob. Change Biol. 2002,8, 1732.
793 Jones, H. G. Plants and Microclimate: A Quantitative Approach to
794 Environmental Plant Physiology; Cambridge University Press: Cam-
795 bridge, UK, 2013.
(43) 796Aase, J. K.; Siddoway, F. H. Stubble height effects on seasonal
797microclimate, water balance, and plant development of no-till winter
798wheat. Agric. Meteorol. 1980,21 (1), 120.
(44) 799Del Barrio, E. P. Analysis of the green roofs cooling potential in
800buildings. Energy Build. 1998,27, 179193.
(45) 801Kumar, R.; Kaushik, S. C. Performance evaluation of green roof
802and shading for thermal protection of buildings. Build. Environ. 2005,
80340 (11), 15051511.
(46) 804Jaffal, I.; Ouldboukhitine, S. E.; Belarbi, R. A comprehensive
805study of the impact of green roofs on building energy performance.
806Renew. Energy 2012,43, 157164.
(47) 807Olivieri, F.; Di Perna, C.; DOrazio, M.; Olivieri, L.; Neila, J.
808Experimental measurements and numerical model for the summer
809performance assessment of extensive green roofs in a Mediterranean
810coastal climate. Energy Build. 2013,63,114.
(48) 811Alexandri, E.; Jones, P. Temperature decreases in an urban
812canyon due to green walls and green roofs in diverse climates. Build.
813Environ. 2008,43 (4), 480493.
(49) 814Djedjig, R.; Ouldboukhitine, S. E.; Belarbi, R.; Bozonnet, E.
815Development and validation of a coupled heat and mass transfer
816model for green roofs. Int. Commun. 7Heat Mass Transfer 2012,39 (6),
(50) 818Ouldboukhitine, S. E.; Belarbi, R.; Sailor, D. J. Experimental and
819numerical investigation of urban street canyons to evaluate the impact
820of green roof inside and outside buildings. Appl. Energy 2014,114,
(51) 822Zhao, M.; Tabares-Velasco, P. C.; Srebric, J.; Komarneni, S.;
823Berghage, R. Effects of plant and substrate selection on thermal
824performance of green roofs during the summer. Build. Environ. 2014,
82578, 199211.
(52) 826Tabares-Velasco, P. C.; Srebric, J. A heat transfer model for
827assessment of plant based roofing systems in summer conditions.
828Build. Environ. 2012,49, 310323.
(53) 829Stovin, V. The potential of green roofs to manage urban
830stormwater. Water Environ. J. 2010,24 (3), 192199.
(54) 831Rowe, D. B. Green roofs as a means of pollution abatement.
832Environ. Pollut. 2011,159 (89), 21002110.
(55) 833Monterusso, M. A.; Rowe, D. B.; Rugh, C. L.; Russell, D. K.
834Runoff water quantity and quality from green roof systems. Acta
835Hortic. 2004,639, 369376.
(56) 836Blanusa, T.; Vaz Monteiro, M. M.; Fantozzi, F.; Vysini, E.; Li, Y.;
837Cameron, R. W. Alternatives to Sedum on green roofs: Can broad leaf
838perennial plants offer better cooling service?Build. Environ. 2013,59,
(57) 840Kattge, J.; Diaz, S.; Lavorel, S.; Prentice, I. C.; Leadley, P.;
841Bönisch, G.; et al. TRYa global database of plant traits. Glob. Change
842Biol. 2011,17 (9), 29052935.
Environmental Science & Technology Article
DOI: 10.1021/es505426z
Environ. Sci. Technol. XXXX, XXX, XXXXXX
... Plant growth forms including forbs, graminoids, and creeping shrubs, can also be successful in the green roof environment. These different growth forms have been shown to excel at providing different ecosystem services, leading to a growing demand for biodiverse green roofs (Cook-Patton and Bauerle, 2012;Lundholm et al., 2015;MacIvor et al., 2018;Gonsalves et al., 2021). However, creating a biodiverse green roof with stably co-existing plant species can be challenging. ...
... Plant functional traits can be defined as the morphological, physiological, and phenological features that manifest from the phenotypes of individual organisms (Diaz et al., 2013;Violle et al., 2007;Garnier et al., 2016). Recent green roof research has demonstrated that plant functional traits play a key role in species survival and in the provision of ecosystem services (Lundholm et al., 2015). Additionally, the combination of species with distinct functional trait profiles may improve overall green roof function as different traits are associated with different ecosystem services. ...
... Additionally, the combination of species with distinct functional trait profiles may improve overall green roof function as different traits are associated with different ecosystem services. For example, compared to their corresponding counterparts, rigid leaves are more proficient at trapping airborne particulate matter (Weerakkody et al., 2017), tall species at reducing stormwater runoff (Lundholm et al., 2015), species with dense canopies at reducing substrate temperature (Lundholm et al., 2015), and flowering species at attracting pollinators (Grimshaw-Surette, 2020). In green roof research, functional traits are usually examined to determine which species are suitable to the green roof environment (Farrell et al., 2013;Van Mechelen et al., 2014) or to determine which traits excel at providing a specific ecosystem service (Lundholm et al., 2015). ...
Constructed ecosystems like green roofs are increasingly deployed in cities to mitigate issues associated with urbanization. To minimize the cost of green roof infrastructure, shallow growing media (substrate) for plants is often employed. Spatial heterogeneity in substrate depth has also been hypothesized to allow greater plant species diversity without adding to the weight. Stress and competition can change green roof plant communities after initial planting, but little is known about the long-term effects of spatial heterogeneity on vegetation composition and functional characteristics. Our goal was to determine how green roof plant communities and, in turn, functional plant traits, change over time in response to environmental stress and substrate heterogeneity. This four-year experiment used four substrate depth treatments: three with homogenous substrate depths of 5 cm, 10 cm, and 15 cm, and one treatment with a heterogenous substrate depth that varied between 5 cm and 15 cm (5/15 cm). The volume of the substrate in the 10 cm treatment and 5/15 cm treatment was equal. By the end of this four-year experiment, variation occurred between treatments for community composition and functional diversity, with the greatest species richness observed in the least stressful treatment (15 cm) and the greatest functional diversity and evenness observed in the most stressful treatment (5 cm). Additionally, each treatment had lower functional diversity after four years compared to the initially planted community. When the heter-ogenous 5/15 cm treatment was compared to the homogenous 10 cm treatment, there were no differences in the number of plant species, but the treatments contained two distinct plant communities. Furthermore, the 5/15 cm treatment supported taller species, a trait value associated with reduced stormwater runoff and substrate temperature. This finding indicates that creating green roofs with a heterogenous substrate depth could improve overall green roof function without increasing roof weight. Substrate depth can be manipulated by green roof designers to alter vegetation characteristics, but species and functional diversity showed opposite trends along the depth gradient.
... However, shallow substrate depths also provide challenging conditions for plant species due to possible drought stress and heat exposure to the root system (Walker and Lundholm, 2018), leading to succulents and in particular sedum species being the preferred, conventional choice for extensive green roofs worldwide (Klein and Coffman, 2015). Sedum species are alien plants in Australia and are therefore predicted to have low ecological value compared to extensive green roofs with native plants (Ishimatsu and Ito, 2013), as well as having been linked to weak ecosystem service provision due to unfavourable plant traits (Lundholm et al., 2015). ...
... Cook-Patton and Bauerle (2012) also predicted that structurally diverse plantings on green roofs could improve green roof ecosystem services and called for more studies linking functional diversity with green roof performance. This was shown to be the case, with a responding study linking different heights, leaf areas and leaf dry matter content to the delivery of ecosystem services such as thermal, hydrological and carbon sequestration functions in the cold humid maritime climate of Halifax, Canada (Lundholm et al., 2015). A plant-trait study in the Mediterranean climate also suggested to use mixed plant communities of various life forms, while emphasising the need for experimental plant testing (Van Mechelen et al., 2014). ...
... However, shallow substrate depths also provide challenging conditions for plant species due to possible drought stress and heat exposure to the root system (Walker and Lundholm, 2018), leading to succulents and in particular sedum species being the preferred, conventional choice for extensive green roofs worldwide (Klein and Coffman, 2015). Sedum species are alien plants in Australia and are therefore predicted to have low ecological value compared to extensive green roofs with native plants (Ishimatsu and Ito, 2013), as well as having been linked to weak ecosystem service provision due to unfavourable plant traits (Lundholm et al., 2015). ...
... Cook-Patton and Bauerle (2012) also predicted that structurally diverse plantings on green roofs could improve green roof ecosystem services and called for more studies linking functional diversity with green roof performance. This was shown to be the case, with a responding study linking different heights, leaf areas and leaf dry matter content to the delivery of ecosystem services such as thermal, hydrological and carbon sequestration functions in the cold humid maritime climate of Halifax, Canada (Lundholm et al., 2015). A plant-trait study in the Mediterranean climate also suggested to use mixed plant communities of various life forms, while emphasising the need for experimental plant testing (Van Mechelen et al., 2014). ...
Green roofs are a key to providing nature-based solutions in cities. However, most green roofs installed in the Northern hemisphere are shallow, stonecrop planted systems (“extensive” green roofs), which have been shown to support limited biodiversity and could be more effective at providing ecosystem services. One issue with this type of extensive green roof is that rootzones are almost sterile on construction, relying on natural colonisation to provide a soil food web. This is a slow process, meaning plant growth can also be slow. Our aim was to determine if a soil food web could be introduced when the green roof is built. We applied microbial inoculants (mycorrhizal fungi and bacteria (Bacillus spp.)) to a new green roof and monitored plant growth and the soil food web (bacteria, mycorrhizal fungi and microarthropods). Different inoculants altered the composition of microarthropod communities, potentially impacting later succession. In particular, bacterial inoculants increased microarthropod populations. This is one of the first studies to demonstrate that the addition of microbial inoculants impacts not only plant growth, but also faunal components of the soil food web, which could have implications for long-term resilience. Bacteria were effective at aiding mycorrhizal colonisation of plants roots, but this colonisation had no impact on the growth of our selected selected stonecrops, Sedum album, Petrosedum reflexum and Phedimus spurius. We suggest that if a beneficial mycorrhiza could be found to promote the growth of these specific species on green roofs, bacteria could be effective “helper” species to aid colonisation. This study enables green roof researchers and the industry to justify further exploration of the impact of microbial inoculants on green roofs.
Full-text available
Ecosystem engineering, such as green roof, provides numerous key ecosystem functions dependent on both plants and environmental changes. In the recent years, global nitrogen (N) deposition has become a hot topic with the intensification of anthropogenic disturbance. However, the response of green roof ecosystems to N deposition is still not clear. To explore the effects of N addition on plant ecological strategy and ecosystem functioning (biomass), we conducted a 3-month N addition simulation experiment using 12 common green roof species from different growth forms on an extensive green roof in Tianjin, China. The experiment included three different N addition treatments (0, 3.5, and 10.5 gN m −2 year −1). We found that plants with the resource-acquisitive strategy were more suitable to survive in a high N environment, since both aboveground and belowground traits exhibited synergistic effects. Moreover, N addition indirectly decreased plant biomass, indicating that ecosystem functioning was impaired. We highlight that there is a trade-off between the survival of green roof species and keeping the ecosystem functioning well in the future N deposition. Meanwhile, these findings also provide insights into how green roof species respond to global climate change and offer important information for better managing and protecting similar ecosystem engineering in the background of high N deposition.
***************For full article see Share link:*************** Annual plant species have great potential on green roofs as many are highly attractive, fast and cheap to establish via sowing, and can provide rapid cover and growth, which is important for ecosystem service provision. While irrigation is essential for survival and growth of annual plants in seasonally hot or dry climates, it is also important to minimize water use as availability is often limited. Therefore, we evaluated how irrigation frequency affects plant cover, species abundance, richness and diversity, plant traits and functional diversity of a 16 species mixture of Australian annual species (4 g m² ~ 2100 seeds m⁻²) sown onto thirty 0.25 m² green roof modules. The experiment was carried out in Melbourne, Australia, from January (summer) to July (winter) 2020. After a 2-month irrigated establishment phase (to ensure germination and seedling establishment), three irrigation treatments (2, 4 and 6 days between irrigation) were applied to the modules for three months. Plant cover was reduced at lower irrigation frequency (6 days), but ≥ 80% plant cover was achieved in all irrigation treatments. There was no effect of irrigation frequency on species abundance and richness; however, abundance, richness and diversity reduced over time, likely due to competition effects. Plant height and leaf area were also reduced by lower irrigation frequency. At the community level, functional diversity was unaffected by irrigation frequency. Our results indicate that green roofs sown with a mixture of annual plants can achieve good plant coverage, as recommended by green roof guidelines, and maintain high diversity when minimally irrigated in their first growing season.
Vegetated roofs (VRs) contribute to the resilience of cities by providing multiple ecosystem functions. A wide range of these benefits depends on the plant layer. Here, we hypothesized that increasing biodiversity attributes (i.e., composition, structural, and functional) in VRs will improve their performance under the climate conditions of semiarid regions. We aimed to (i) characterize functional groups of eight species assessed from key functional traits, (ii) evaluate species performance through survival and coverage, comparing biodiversity attributes from mono to polyculture treatments, (iii) analyze the relationship between performance with biodiversity attributes, and (iv) systematize and rank the treatments using an index to select those with the best performance and to recommend them for VRs in semiarid regions. We expect those treatments with higher biodiversity attributes will show better performances than simpler ones. Functional traits as indicators of ecosystem functions of eight species and four life-forms (succulents: Sedum acre, S. lineare, S. reflexum, creeping herbs: Phyla nodiflora, Glandularia x hybrida, tall forb: Grindelia cabrerae, and grasses: Eustachys distichophylla, and Nassella tenuissima) were evaluated through a trial using 22 microcosms during a 12-month experimental study. A principal component analysis and a cluster analysis were used to detect functional groups according to leaf and plant height traits. We used the Kaplan-Meier analysis to assess species survival among the treatments. Final coverage and growth increment (and their coefficients of variation) were used to construct the performance index. The PCA and CA determined five functional groups: I) succulents; II) creeping herbs; III) N. tenuissima; IV) E. distichophylla, and V) G. cabrerae. Four species showed significantly differences in survival rates among the treatments (p < 0.05), and six treatments were characterized as the best ones: with the highest coverage (>93%) and growth increment (81%), and with low CVs. Although some monoculture (Sedum spp., P. nodiflora and, E. distichophylla) reached comparable index values with respect to mixed microcosms, for VRs we recommend those plant mixtures combining biodiversity attributes because they provide both more ecosystem services and higher chances of survival and phenological complementarity in the long term.
Green roofs can improve ecosystem services in cities, however this depends on appropriate plant selection. For stormwater management, plants should have high water‐use to maximise retention and also survive dry periods. Plants adapted to wetter habitats develop ‘fast’ traits for growth, whereas plants from drier habitats develop ‘slow’ traits to conserve water‐use and survive drought. Therefore, we hypothesised that (1) plants with ‘fast’ traits would have greater water‐use, (2) plants with ‘slow’ traits would have greater drought tolerance, (3) ‘fast‐slow’ traits would be consistent across the plant, and (4) ‘fast’ plants with greater water‐use could avoid drought stress. We evaluated 14 green roof species in a glasshouse experiment under well‐watered (WW) and water‐deficit (WD) conditions to determine relationships between ‘fast‐slow’ traits, water‐use, and drought resistance. Traits measured were: shoot dry weight, specific leaf area (SLA), root mass fraction (RMF), and specific root length (SRL). Daily evapotranspiration per shoot dry weight was used to describe water‐use. Drought resistance was represented by: (1) days to stomatal closure; (2) cumulative ET before stomatal closure; and (3) degree of iso‐anisohydry (difference between midday leaf water potential (ΨMD) of WW and WD plants; ΔΨMD). Plants with greater water‐use had ‘fast’ above‐ground traits (greater shoot biomass and SLA). Plants with ‘slow’ traits had greater drought tolerance as plants with lower shoot dry weight closed their stomata later under WD, and plants with greater root allocation were more anisohydric. ‘Fast‐slow’ traits were not consistent across the plant. Although SLA and SRL were positively related, SRL was not related to water‐use or drought resistance. Shoot dry weight was inversely related to SLA and had a stronger influence on stomatal closure. Though plants with greater water‐use under well‐watered conditions closed their stomates earlier to avoid drought stress, they were not more isohydric (smaller ∆ΨMD) and did not necessarily use more water under WD. ‘Fast’ above‐ground traits can be used to select green roof plants with high water‐use that avoid drought stress to optimise rainfall retention without jeopardising drought survival. This will facilitate rapid plant selection using trait information from online databases.
Constructed green infrastructure consists of artificial ecosystems designed to produce specific services. Vegetation in constructed ecosystems such as bioretention strips (rain gardens), water treatment wetlands, living roofs and walls generally results from conscious design. The choice of plants can make a large difference with regard to the ecosystem services provided by green infrastructure. We review the literature on constructed green infrastructure and vegetation characteristics, specifically addressing the role of plant functional types, traits and evolutionary relatedness among species in the vegetation in driving ecosystem services. Each type of constructed green infrastructure involves different but consistent preferences in the functional types of plants used, but studies using general plant trait approaches including manipulations of functional or phylogenetic diversity are only just beginning. Empirical studies have identified key plant traits that drive ecosystem services in each type of constructed ecosystem. Experimental studies that manipulate plant functional or phylogenetic diversity in green infrastructure are still uncommon but, in some systems, show that service provisioning can improve with more diverse vegetation. In other cases, selection of the top-performing monocultures seems to optimize provision of a single service. Future work may yet reveal a role for diverse vegetation in constructed ecosystems to provide a variety of services simultaneously.
Full-text available
This rigorous yet accessible text introduces the key physical and biochemical processes involved in plant interactions with the aerial environment. It is designed to make the more numerical aspects of the subject accessible to plant and environmental science students, and will also provide a valuable reference source to practitioners and researchers in the field. The third edition of this widely recognised text has been completely revised and updated to take account of key developments in the field. Approximately half of the references are new to this edition and relevant online resources are also incorporated for the first time. The recent proliferation of molecular and genetic research on plants is related to whole plant responses, showing how these new approaches can advance our understanding of the biophysical interactions between plants and the atmosphere. Remote sensing technologies and their applications in the study of plant function are also covered in greater detail.
Full-text available
An important aim of plant ecology is to identify leading dimensions of ecological variation among species and to understand the basis for them. Dimensions that can readily be measured would be especially useful, because they might offer a path towards improved worldwide synthesis across the thousands of field experiments and ecophysiological studies that use just a few species each. Four dimensions are reviewed here. The leaf mass per area-leaf lifespan (LMA-LL) dimension expresses slow turnover of plant parts (at high LMA and long LL), long nutrient residence times, and slow response to favorable growth conditions. The seed mass-seed output (SM-SO) dimension is an important predictor of dispersal to establishment opportunities (seed output) and of establishment success in the face of hazards (seed mass). The LMA-LL and SM-SO dimensions are each underpinned by a single, comprehensible tradeoff, and their consequences are fairly well understood. The leaf size-twig size (LS-TS) spectrum has obvious consequences for the texture of canopies, but the costs and benefits of large versus small leaf and twig size are poorly understood. The height dimension has universally been seen as ecologically important and included in ecological strategy schemes. Nevertheless, height includes several tradeoffs and adaptive elements, which ideally should be treated separately. Each of these four dimensions varies at the scales of climate zones and of site types within landscapes. This variation can be interpreted as adaptation to the physical environment. Each dimension also varies widely among coexisting species. Most likely this within-site variation arises because the ecological opportunities for each species depend strongly on which other species are present, in other words, because the set of species at a site is a stable mixture of strategies.
Tools for performing model selection and model averaging. Automated model selection through subsetting the maximum model, with optional constraints for model inclusion. Model parameter and prediction averaging based on model weights derived from information criteria (AICc and alike) or custom model weighting schemes. [Please do not request the full text - it is an R package. The up-to-date manual is available from CRAN].
We briefly outline the information-theoretic (I-T) approaches to valid inference including a review of some simple methods for making formal inference from all the hypotheses in the model set (multimodel inference). The I-T approaches can replace the usual t tests and ANOVA tables that are so inferentially limited, but still commonly used. The I-T methods are easy to compute and understand and provide formal measures of the strength of evidence for both the null and alternative hypotheses, given the data. We give an example to highlight the importance of deriving alternative hypotheses and representing these as probability models. Fifteen technical issues are addressed to clarify various points that have appeared incorrectly in the recent literature. We offer several remarks regarding the future of empirical science and data analysis under an I-T framework.
The ecosystem services green roofs provide are influenced by both the engineered and biotic components of green roof systems. This chapter focuses on how the functioning of green roofs is controlled by plant species and the synthetic vegetation communities created by them. Plant species can differ greatly in their ability to provide services such as roof cooling and stormwater retention. Newer work, emphasizing less-well-characterized benefits such as reduction of heat loss in winter, air pollution mitigation and carbon sequestration (Chap. 2), also shows significant effects of plant species. The species that best perform a particular service differ between services; other research shows performance advantages in combining species or functional groups of plants into communities. Optimizing green roof benefits thus requires close attention to plant properties, and even superficially similar plant groups (e.g. succulents) can show large performance differences among species. Characterizing green roof vegetation by plant traits, such as leaf area, leaf thickness and photosynthetic pathway, could be a useful way to select green roof species, allowing rapid screening of regional floras for potential species. Plant traits are often directly linked to ecosystem processes that provide economically and environmentally valuable services. Consequently a trait-based approach can help elucidate the relationships among the performance of individual species, the role of plant diversity and the ecosystem services provided by green roofs. This should allow the design of purpose-specific green roofs that provide higher levels of ecosystem services.
Use of green roof technology is becoming increasingly widespread throughout the world because of its environmental, economic, and aesthetic benefits. The ability of a green roof to retain stormwater and limit the amount of fertilizer in the effluent flow are important characteristics of a properly installed green roof system. However, scientific research quantifying these characteristics is limited - particularly in the United States. Simulated rooftop platforms were constructed and runoff was analyzed from four commercially available green roof systems containing three distinct vegetation types. Quantity of rainfall retained ranged from 38.6% for Xeroflor to 58.1% for Siplast. Quantitatively, Xeroflor resulted in the greatest volume of runoff, but these volumes were only significant for the sections of Sedum plugs and seed during the fourth rainfall event. Differences in water retention can likely be attributed to substrate depth, rather than drainage system or vegetation type. Results demonstrate two important concepts that affect the amount of stormwater a green roof can retain - substrate thickness and substrate moisture content immediately prior to a rainfall event. Nitrate concentrations in the runoff varied from 0.22 ppm in the Sarnafil native plant sections 314 days following fertilizer application to 22.7 ppm in Xeroflor Sedum seed sections 314 days following fertilizer application. No significant differences were observed between any of the treatments with regard to phosphorus concentrations.
A long-term research programme, conducted mainly in northern England, has involved field surveys (1965-77), laboratory screening (1974-96), monitoring of permanent plots (1958 to date) and manipulative experiments (1987 to date). The so-called C-S-R classification of plant functional types developed from all this activity. Patterns of covariation among the traits used in the classification have recently been validated in this journal. The C-S-R classification appears to be applicable to vegetation in general. It thus has considerable potential for interpreting and predicting vegetation and ecosystem properties on a world-wide scale. However, to realize this potential we need to develop simplified procedures to extrapolate the C-S-R system to the many species which have not been the subject of previous ecological investigation. Here we describe a rapid method for attribution of C-S-R type and we test its accuracy in Britain by comparing it with an independent classification based upon more laborious procedures. The new method allocates a functional type to an unknown herbaceous subject using few, simple predictor variables. We have developed spreadsheets to perform all of the necessary calculations. These may be downloaded from the UCPE website at, or obtained by direct application to the E-mail address