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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.
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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
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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
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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.
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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).
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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
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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
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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
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DOI: 10.1021/es505426z
Environ. Sci. Technol. XXXX, XXX, XXXXXX
... In contrast to the traits mentioned above, the fruit/seed and canopy traits were the least studied. Given that canopy traits are perceived mainly to assess cultural services, interestingly only limited studies on canopy trait-service linkage were found for urban and forest ecosystems (Lundholm et al. 2015;Randle et al. 2018;Xie et al. 2018). Seed traits are neglected despite their crucial role in the maintenance and survival of ecosystems. ...
... Flow diagram for screening the literature from Scopus & ISI WoS carbon sequestration etc.) associated with these traits (Ali et al. 2016; DeBello et al. 2010;Lundholm et al. 2015;Pakeman et al. 2010;Tadesse et al. 2014;Yang et al. 2019). ...
The concept of 'ecosystem service' has gained momentum in the twenty-first century to bridge the gap between human-nature interactions. However, the challenge remains to map the flow of ecosystem services (ES) for their efficient management. Among the multiple existing methods, biophysical assessments provide better knowledge of the state of the ecosystem and its mapping for complimentary services. Trait-service linkage is one of the tools to reliably link biodiversity with ES if we better understand the role functional traits play in the underlying ecosystem processes. In this paper, we have performed a bibliometric analysis of published literature on ES and plant functional traits to identify the current state of knowledge on trait-service linkage, biases, research gaps, and challenges. There was a skewed geographical basis for trait-service linkage studies; most studies were conducted in Europe and North America. The majority of the research focused on supporting and regulating ES, mainly carbon sequestration, biomass production, and climate regulation, using a particular set of vegetative traits, such as leaf, root, and plant height, and ignored most regeneration traits, except for a few flower traits. A matrix to quantify the association between ES and selected plant traits (specific leaf area, leaf dry matter content, leaf area, leaf nitrogen content, vegetation height, wood density, canopy density, root length, root density, flowering time, flower color and flower size) revealed that the two leaf traits (specific leaf area and leaf dry matter content) in the linkage have contrasting associations with multiple ES. The study illustrated that there is still a considerable research gap in linking plant traits with essential ES (biomass production, climate and water regulation). Thus, suggest future studies on ES should focus more on trait-service linkage across major ecosystems to underpin key ecosystem processes for better sustenance of ES and human well-being.
... Much research has explored the correlations between functional trait values, plant physiological processes, and environmental variables [13,16,42,60,61]. In terms of green roof ecosystem services, past work suggests that increased biomass is associated with lower temperatures, taller plant species promote greater stormwater retention, and biodiversity can improve roof multifunctionality [9,27,32,33,62]. The impact of selecting species with optimal traits or species mixtures with high trait diversity can significantly improve ecosystem services. ...
... As traits reflect both the environmental conditions species adapted to and effects species have on their environment (Lavorel and Garnier 2002), traits are key to designing NBS (Van Mechelen et al. 2015). For example, Lundholm et al. (2015) showed that plant height and plant leaf traits can be purposefully chosen to enhance the provision of nitrate removal, substrate cooling, and other ES on green roofs. The more functional trait attributes present in a system, the more functions it can fulfil. ...
... Pour ce qui est des paramètres biologiques, en premier lieu, la taille de la couronne et la hauteur de l'arbre sont deux paramètres de première importance dans l'interception des eaux de pluie (Livesley et al., 2014 ;Lundholm et al., 2015) et de la capacité de stockage en eau (Armson et al., 2013). La densité de feuilles au sein de la canopée associée à la surface foliaire sont également deux traits fonctionnels jouant un rôle important dans l'interception (Holder et Gibbes, 2017 ;Carlyle-Moses et al., 2020). ...
Technical Report
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Urban trees provide several ecosystem services to human populations living nearby. The main objective of this document is to compile the state of knowledge related to three important ecosystem services provided by urban trees: 1) atmospheric pollutants removal, 2) carbon sequestration and 3) urban runoff mitigation, as well as two noteworthy ecosystem disservices: 4) pollen emission and 5) the emission of volatile organic compounds. These topics are covered in five separate chapters involving specialized editorial teams for each of the topics (in french).
... Traits for optimal performance in extensive green roofs have been documented over the last two decades focusing, for instance, on species selection for survival under frequent water deficits (Du et al., 2019) or maximized multifunctionality . In particular, Lundholm et al. (2015) recommend the use of species with high specific leaf area (SLA), leaf dry matter content, leaf size (LS) and plant height (H) to confer optimal green roof function. These leaf traits align with good trait indicators of the outcome of nurseprotégé interactions in a dryland (Rolhauser & Pucheta, 2016). ...
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Green roofs are promoted to contribute to more resilient cities by enhancing urban ecosystem services and food systems. Extensive, low-maintenance green roofs experience frequent environmental stresses, which reduce plant survival and growth. Stress-tolerant plants are therefore used to sustain well-established services, such as building temperature regulation. However, transitioning extensive green roofs to provide other key urban services, such as food production, involves less tolerant plant species. Although facilitation exerted by stress-tolerant species (nurses) has been proposed to improve the performance of stress-intolerant species (protégés) in extensive green roofs, the conditions under which facilitation could occur are not well understood. Therefore, a comprehensive framework is needed that integrates current knowledge on how the performance of protégé species is affected by nurse plants across stress conditions. We present a framework for green roof research that results in a linear model that integrates (i) modern trait–environment theory and (ii) facilitation ecology in a refined stress-gradient hypothesis (SGH) originally developed following study of other stressful environments. The model makes testable predictions on how phenotypic traits mediate the performance response of protégé species to nurse plants along stress gradients in extensive green roofs. This is not only useful for the analysis of eco-physiological performance measures directly linked with multifunctionality and ecosystem services, but also demographic or ‘vital’ rates that drive species persistence and plant community maintenance. We discuss a range of applications related to key agricultural and ecological questions arising from contemporary extensive green roof research, such as enhancing conditions for crop production, weed management, plant invasions and biodiversity conservation. We also provide guidelines for the generation of appropriate data and for fitting this model using readily available statistical procedures. Our framework will allow researchers to assess under which environmental conditions nurse–protégé interactions are feasible. We expect the findings from such research to help develop strategies and guidelines for managing environmental conditions that optimize protégé performances that ultimately affect the delivery of ecosystem services in constructed urban green spaces.
... This characteristic, as well as the density of the canopy, contribute to reducing the incidence of solar radiation on the roof cover. In addition, there is the substrate layer of the tray, softening the internal temperatures of the environments (Lundholm et al., 2015). In this way, accesses of Paspalum with vertical growth present greater height and greater density, contributing more to ecosystem services, including reducing the incidence of direct solar radiation on the roof cover and, consequently, minimizing temperatures in indoor environments. ...
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The objective was to evaluate the use of thermal and optical properties measurements in the characterization of the genus Paspalum for use on green roofs in a tropical environment. The experiment was conducted at the Universidade Federal Rural de Pernambuco – UFRPE from May to June 2019. The number of sixteen Paspalum accessions with prostrate and upright growth habits was evaluated. In a simulated modular green roof were evaluated: Soil-Adjusted Vegetation Index (SAVI); leaf (L) and canopy (C) temperature by thermometry (TM); and by thermography (TG). The determination coefficient (R2) between SAVI and the coverage area are of 0.84, with green matter (R2 = 0.73) and dry matter (R2 = 0.56). No significant difference was observed between the accessions for leaf temperature measure by TM and TG, and for canopy TM. Nevertheless, significant difference to canopy TG were observed, with lower canopy temperatures in accessions with upright growth habit than those with prostrate habit. The relationship between the techniques showed high values of determination coefficient to TML with TGL (R2 = 0.85) and TMC with TGC (R2 = 0.91). Given the above, the SAVI can be used to estimate the coverage area capacity (CC). Leaf and canopy temperatures can be used as an indication of water deficit in Paspalum accessions, using both the infrared thermometer (more accessible) and the thermographic camera. Canopy thermography can also be used to evaluate the thermal performance of green roofs. Thermographic images indicated that Paspalum accessions of upright growth habits may provide better thermal comfort.
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Ecosystem services are critical in urban areas, where the majority of humanity now lives. In many cities, extensive green roofs are planted with succulent nonnative Sedum species that provide rapid vegetative cover, retain stormwater, insulate buildings, and provide cooling through evapotranspiration. If diverse native plant species were used on green roofs in addition to Sedum, green roofs could also support greater biodiversity. Ecosystem services provided by Sedum are documented within temperate climates but the contributions of native forbs are relatively unknown, largely due to difficulties in plant establishment. Survival, drought-tolerance, and stormwater capture of native plants may be enhanced by inoculating the soil with indigenous arbuscular mycorrhizal (AM) fungi. In this experiment, we tested the hypotheses that green roofs planted with diverse native species would provide the ecosystem services of thermal building insulation, evapotranspiration, and stormwater capture and be aided by inoculation with indigenous AM fungi. We used green roof trays in a multi-factorial design to test for the effects of vegetation community and AMF inoculation on provisioning of ecosystem services in two locations. Despite slower establishment, native plant communities showed greater stormwater capture and evapotranspiration rates compared to Sedum communities. However, Sedum provided greater thermal insulation during the hot months of summer. Inoculation with AM fungi did not improve plant establishment or provisioning of ecosystem services. Overall, our results suggest that planting a variety of native prairie grasses and forbs may maximize ecosystem services provided by urban green roofs.
Activities to enrich the species diversity of ornamental conifers contribute to the improvement of aesthetic, sanitary conditions and the solution of ecological problems of the urban environment. The article presents data on the study of species composition and evaluation of juniper varieties introduced in the Astana Botanical Garden in 2018-2021. The purpose of this work is to evaluate the winter hardi-ness and ornamental properties of various forms of juniper in the botanical garden for the subsequent landscaping of the region. Five forms of the genus Juniperus L. (J. pseudosabina Fisch. & C. A. Mey, J. sa-bina L., J. x pfitzeriana (Spath) P.A. Schmidt., J. horizontalis, J. scopulorum Sarg.) planted in the opening year of the botanical garden in order to determine the decorative and optimally adaptive characteristics that characterize the prospects for growing plants in urban plantings. Conclusions on an adaptation of species to climatic and soil conditions of Nur-Sultan city are made and recommendations on successful introduction of some juniper varieties under the analyzed conditions are given. Based on a comprehen-sive assessment, recreational-aesthetic and weather-resistant varieties of the Nur-Sultan are identified J. sabina L., J. x pfitzeriana and J. horizontalis, recommended for use in landscaping, while J. pseudosabina and J. scopulorum rock juniper require additional care. Key words: Juniper, variety, evaluation, assessment, adaptability, decorativeness.
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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.
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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