1
Plant species loss decreases arthropod diversity and shifts trophic structure
2
Nick M. Haddad, Gregory M. Crutsinger, Kevin Gross, John Haarstad, Johannes M.H.
3
Knops, and David Tilman
4
5
Supplementary Material
6
Additional analyses within years
7
We conducted many additional, detailed analyses of arthropod species richness and
8
abundance that are not reported in the main text. Herbivore species richness was
9
significantly, positively related to plant species richness and to plant biomass, and the
10
strength of this relationship varied across years (Table S1). Herbivore species richness
11
increased most strongly in response to plant Shannon-Wiener index, and was reduced in
12
the presence of C4 grasses (Table S2). No treatment variable significantly predicted
13
herbivore abundance (Table S3), but plant composition did have significant effects, with
14
forbs increasing herbivore abundances and C4 grasses decreasing herbivore abundances
15
(Table S4).
16
17
Predator species richness increased as plant species richness and plant biomass increased
18
(Table S5), and was unaffected by plant composition. Predator abundance also increased
19
as plant biomass increased, and increased in the presence of C4 grasses (Table S6, S7).
20
21
To test whether our results within years were caused by differences in species detection
22
across plots, we estimated species richness using Chao1 species richness estimator.
23
Results of analyses of Chao1 estimated richness were consistent with results of analyses
24
of observed species richness at each trophic level, and showed similarly weak responses.
25
Thus, our results could not be attributed to artifacts of the way we sampled plots.
26
27
Cumulative results across all years
28
Cumulative herbivore species richness increased with higher plant species richness and
29
plant biomass (Table S8). The stronger increase in cumulative herbivore species richness
30
as compared with annual species richness was determined by higher numbers of species
31
observed once or twice in diverse plots (i.e. singletons and doubletons) (Figure S2).
32
Although there was no effect of the number of plant functional groups, herbivore species
33
richness was higher when C3 grasses and legumes were present, and was better predicted
34
by plant Shannon-Wiener index than by plant species richness (Table S9). None of the
35
main treatment variables predicted herbivore abundances, but abundances were lower
36
when C4 grasses were present, and higher when forbs were present (Table S10). We
37
corrected herbivore richness for differences in herbivore abundances using rarefaction
38
(using Primer v6 software), and we rarefied to 122 individuals per plot. Rarefied
39
herbivore richness was positively related to plant species richness, the number of plant
40
functional groups, and plant biomass (Table S11), and increased in the presence of C3
41
grasses, C4 grasses, and legumes. Analysis of cumulative estimated species richness
42
(Chao1) for herbivores was consistent with results using observed richness. For
43
predators, there was no relationship between plant species richness and Chao1,
44
supporting our conclusion that the number of individuals was driving observed patterns in
45
predator species richness.
46
2
47
As with herbivores, cumulative predator species richness increased as plant species
48
richness and plant biomass increased (Table S12). The number of singleton and
49
doubleton species also increased with plant species richness, but saturated more quickly
50
than with herbivores (Figure S2). Plant Shannon-Wiener index was more strongly,
51
positively related to predator diversity than plant species richness (Table S13). Predator
52
abundance increased as plant biomass increased, and in the presence of C3 and C4
53
grasses (Table S14, 15). We rarefied predator richness to 32 individuals per plot. Only
54
one factor, the presence of C4 grasses, was significant in reducing rarefied predator
55
diversity.
56
57
Cumulative detritivore species richness was significantly, positively related to plant
58
species richness and plant biomass, and significantly negatively related to plant
59
functional group richness (Table S16). Specifically, cumulative detritivore species
60
richness was positively related to the presence of C3 plants (Table S17). Detritivore
61
abundance was positively related to plant biomass and to the presence of C3 grasses and
62
C4 grasses (Table S18, S19). Because of low species richness and abundance, we did not
63
compute rarefied detritivore richness.
64
65
Additivity analysis
66
To test whether we could predict arthropod species richness in plant mixtures based on
67
arthropod species richness in component monocultures, we used the following additivity
68
analysis that calculates the expected arthropod species richness in each polyculture under
69
a collection of assumptions. To state these assumptions, let X ijklt be the number of
3
70
arthropods of species l occurring on plant species k in year t of the jth experimental
71
replicate of plant species richness treatment i. First, we assume that the X ijklt ’s are
72
Poisson distributed random variables with mean ijklt nijk . Here, ijklt is a Poisson rate (in
73
units of number of individuals per plot), and nijk is the proportion of plot ij planted in
74
plant species k (that is, nijk = 1/(number of species planted in plot ij) . Second, the
75
“additivity assumption” assumes that the rate at which arthropod species l occurs on plant
76
species k in year t is constant across all experimental plots, e.g., ijklt  klt . In the
77
polycultures, the X ijklt ’s are not observable. Instead, we are only able to observe
X
ijklt
k
78
the total number of individuals of arthropods of species l in year t and plot ij. Thus, we
79
also assume that X ijk1lt and X ijk2lt are conditionally independent for k1  k2 . Thus, the
80
“additivity” model might be more precisely but verbosely described as the “additive
81
independent Poisson” model.
82
83
To calculate the expected additive arthropod richness for a given plot, we followed these
84
steps. First, we used the monocultures to estimate the rates ˆklt . Then, for each
85
polyculture, we used rate estimates to calculate the expected abundance of arthropod
86
species l as E  X ijlt    ˆklt nijk . We then calculated the probability that arthropod
k
87
species l was found in the plot by using one minus the zero term of the probability mass
88
function of a Poisson distribution, 1 – exp(-E[Xijlt]). Finally, we calculated the total
89
expected arthropod species richness by summing these probabilities over all arthropod
90
species l. (Plugging in the expression for E  X ijlt  , dropping the i, j and t subscripts, and
4
,
91
taking the sum over l gives eq. [1] in the main text.) If multiple monocultures were
92
present for one or more of the plant species planted in the polyculture, then we repeated
93
this procedure for each unique combination of monocultures, and averaged the resulting
94
estimates of expected richness. Note that our analysis considers only species planted as
95
part of the experiment, and does not consider unwanted species within experimental
96
plots.
97
98
An important but subtle point is that the expected additive richness values calculated in
99
this way are not directly comparable to the observed richness values. (To see this,
100
suppose we observed 5 individuals of arthropod species Z in a monoculture of plant
101
species Y, and we estimated the expected additive arthropod richness for this monoculture
102
by using the same monoculture itself as a reference. The expected additive richness for
103
the monoculture would be 1  e5  .993 , which is less than the observed richness of 1).
104
Thus, to make observed richness comparable to expected richness under the additive
105
model, we applied the algorithm to each polyculture as well, using the observed
106
arthropod abundance in each polyculture to estimate species-specific rates of occurrence.
107
We used the adjusted observed richness to calculate the difference between observed and
108
expected additive richness on a percentage basis.
109
110
On a small number of occasions, there were no monoculture data available for a given
111
plant species in a given year. (For example, the lone monoculture of Panicum virgatum
112
was not sampled for arthropods in 1996, 1997, or 1999.) In these cases, we examined the
113
actual composition of all polycultures containing that plant species for that year (as
5
114
opposed to the planted composition), and removed the polyculture from the analysis if the
115
plant species in question made up more than 1% of the actual biomass or percent cover. If
116
the plant species was less than 1% of the biomass or percent cover of the polyculture, we
117
retained the polyculture in the analysis, set nijk  0 for the plant species without a
118
monoculture, and inflated the remaining nijk ’s equally so that they summed to 1.
119
120
Cumulative additivity analyses simply aggregated the observed arthropod abundances
121
observed in each plot over all years. These cumulative analyses also required that we
122
assume conditional independence among years. That is, we assumed that X ijklt1 and
123
X ijklt2 were conditionally independent given the rates ijklt1 and ijklt2 for t1  t2 . Finally,
124
rarefied additivity analyses rescaled Poisson rates ˆklt by a factor equal to the ratio of the
125
observed total arthropod abundance in a polyculture to the expected total arthropod
126
abundance under the additivity model. This re-scaling controlled for changes in arthropod
127
abundance by equating the expected arthropod abundance in the rarefied analysis with the
128
actual arthropod abundance observed in each polyculture.
129
130
Spatial analysis
131
Because arthropods were not controlled in our experiments and are mobile, spatial factors
132
could be important in controlling responses to our treatments. We have observed
133
previously in this experiment in one year (1997) that the diversity of arthropods in the
134
four plots bordering a focal plot is positively related to the diversity of arthropods in the
135
focal plot. This neighborhood diversity explains about 10% of variation in arthropod
6
136
species richness within plots, does not remove the effects of local factors, and does not
137
extend beyond the four bordering plots to larger distances (Haddad, et al. 2001).
138
139
Unlike in 1997, we did not have data on arthropods in all neighboring plots (see
140
methods). Because of this, we analyzed arthropod responses based on correlations with
141
plant species richness, and developed measures of the average plant species richness,
142
both planted and actual, in neighboring plots. We found no consistent relationship
143
between the plant diversity in neighboring plots and arthropod species richness in focal
144
plots in annual analyses. In analyses of cumulative data, the diversity of plants in
145
neighboring plots had no effect on herbivore species richness, but was significantly,
146
positively related to predator species richness. When plant species richness of
147
neighboring plots was included in a full model for cumulative predator species richness
148
with plant species richness, plant functional group richness, and plant biomass of the
149
focal plot, model results were similar as in Table S12 but with the spatial variable also
150
significant. Although we expect we would find stronger relationships if we had data on
151
arthropods for all plots, we have no reason to believe that spatial effects obscure our
152
responses to treatments variables.
153
7
154
Table S1. Repeated measures analysis, using a General Linear Model, of effects of plant
155
species richness, plant functional group richness, and plant biomass on herbivore species
156
richness within years.
Source
df
Ln (plant species richness)
# Plant functional groups
Mean plant biomass
Error
1
1
1
148
Type III Sum of
Squares
359
98
393
7253
Within-subject effects
Year
Year * Ln (plant species richness)
Year * # Plant Functional groups
Year * Mean plant biomass
Error (year)
10
10
10
10
1480
1406
177
192
461
20150
F
P
7.13
1.94
7.81
0.008
0.166
0.006
10.32
1.30
1.41
3.39
0.001
0.224
0.168
0.001
157
158
Table S2. Repeated measures analysis, using a General Linear Model, of effects of plant
159
diversity, plant functional group composition, and plant biomass on herbivore species
160
richness within years.
Source
df
Mean plant Shannon-Wiener
Presence C3 grasses
Presence C4 grasses
Presence forbs
Presence legumes
Mean plant biomass
Error
1
1
1
1
1
1
145
Type III Sum of
Squares
512
60
235
3
90
40
5161
Within-subject effects
Year
Year * Mean plant Shannon-Wiener
Year * Presence C3 grasses
Year * Presence C4 grasses
Year * Presence forbs
Year * Presence legumes
Year * Mean plant biomass
Error (year)
10
10
10
10
10
10
10
1450
704
336
487
345
214
94
690
19053
161
8
F
P
14.38
1.67
6.60
0.09
2.52
1.12
0.001
0.198
0.011
0.766
0.114
0.291
5.36
2.56
3.70
2.63
1.63
0.72
5.26
0.001
0.006
0.001
0.004
0.097
0.700
0.001
162
Table S3. Repeated measures analysis, using a General Linear Model, of effects of plant
163
species richness, plant functional group richness, and plant biomass on herbivore
164
abundance within years.
Source
df
Plant species richness
# Plant functional groups
Mean plant biomass
Error
1
1
1
148
Type III Sum of
Squares
2.7
5444
1485
280565
Within-subject effects
Year
Year * Plant species richness
Year * # Plant Functional groups
Year * Mean plant biomass
Error (year)
10
10
10
10
1480
127230
632
2197
634
565
F
P
0.00
2.87
0.78
0.967
0.092
0.378
22.50
1.12
3.89
1.12
0.001
0.349
0.002
0.347
165
166
Table S4. Repeated measures analysis, using a General Linear Model, of effects of plant
167
species richness, plant functional group composition, and plant biomass on herbivore
168
abundance within years.
Source
df
Plant species richness
Presence C3 grasses
Presence C4 grasses
Presence forbs
Presence legumes
Mean plant biomass
Error
1
1
1
1
1
1
145
Type III Sum of
Squares
660
2548
60595
10293
2898
416
204628
Within-subject effects
Year
Year * Plant species richness
Year * Presence C3 grasses
Year * Presence C4 grasses
Year * Presence forbs
Year * Presence legumes
Year * Mean plant biomass
Error (year)
10
10
10
10
10
10
10
1450
57112
4461
7535
34440
8214
19104
12524
791844
169
9
F
P
0.47
1.81
42.94
7.29
2.05
0.30
0.495
0.181
0.001
0.008
0.154
0.588
10.46
0.82
1.38
6.31
1.50
3.50
2.29
0.001
0.612
0.184
0.001
0.132
0.001
0.011
170
Table S5. Repeated measures analysis, using a General Linear Model, of effects of plant
171
species richness, plant functional group richness, and plant biomass on predator and
172
parasitoid species richness within years.
Source
df
Ln (plant species richness)
# Plant functional groups
Mean plant biomass
Error
1
1
1
136
Type III Sum of
Squares
123
52
615
1708
Within-subject effects
Year
Year * Ln (plant species richness)
Year * # Plant Functional groups
Year * Mean plant biomass
Error (year)
10
10
10
10
1360
711
67
64
214
7898
173
174
10
F
P
9.79
4.13
48.94
0.002
0.044
0.001
12.25
1.16
1.11
3.69
0.001
0.314
0.353
0.001
175
Table S6. Repeated measures analysis, using a General Linear Model, of effects of plant
176
species richness, plant functional group richness, and plant biomass on predator and
177
parasitoid abundance within years.
Source
df
Plant species richness
# Plant functional groups
Mean plant biomass
Error
1
1
1
136
Type III Sum of
Squares
3577
221
13215
189371
Within-subject effects
Year
Year * Plant species richness
Year * # Plant Functional groups
Year * Mean plant biomass
Error (year)
10
10
10
10
1360
12554
3051
1324
23294
1416854
F
P
2.57
0.16
9.49
0.111
0.691
0.002
1.21
0.29
0.13
2.24
0.283
0.983
0.999
0.014
178
179
Table S7. Repeated measures analysis, using a General Linear Model, of effects of plant
180
species richness, plant functional group composition, and plant biomass on predator and
181
parasitoid abundance within years.
Source
df
Plant species richness
Presence C3 grasses
Presence C4 grasses
Presence forbs
Presence legumes
Mean plant biomass
Error
1
1
1
1
1
1
133
Type III Sum of
Squares
218
3671
8060
256
3235
20065
174096
Within-subject effects
Year
Year * Plant species richness
Year * Presence C3 grasses
Year * Presence C4 grasses
Year * Presence forbs
Year * Presence legumes
Year * Mean plant biomass
Error (year)
10
10
10
10
10
10
10
1330
8993
6174
18318
17289
8108
9781
19831
1362752
182
11
F
P
0.17
2.80
6.16
0.20
2.47
15.33
0.684
0.096
0.014
0.659
0.118
0.001
0.88
0.60
1.79
1.69
0.79
0.95
1.94
0.554
0.813
0.058
0.079
0.637
0.482
0.037
183
Table S8. Multiple linear regression model predicting effects of plant species richness,
184
plant functional group richness, and plant biomass on cumulative herbivore species
185
richness.
Source
Model
Error
Corrected Total
df
3
159
162
Sum of Squares
14129
11369
25498
Parameter
estimate
41.251
4.921
0.356
0.047
Standard
error
2.087
2.337
1.460
0.007
F
65.86
P
0.001
t
19.77
2.11
0.24
6.37
P
0.001
0.036
0.808
0.001
R2 = 0.55
Variable
Intercept
Ln (plant species richness)
# Plant functional groups
Mean plant biomass
186
187
Table S9. Multiple linear regression model predicting effects of plant diversity,
188
functional group composition, and plant biomass on cumulative herbivore species
189
richness.
Source
Model
Error
Corrected Total
df
6
156
162
Sum of Squares
17163
8335
25498
Parameter
estimate
35.839
7.356
3.350
-0.969
1.907
7.221
0.033
Standard
error
1.872
2.554
1.563
1.356
1.586
1.971
0.007
F
53.54
P
0.001
t
19.15
2.88
2.14
-0.71
1.20
3.66
4.62
P
0.001
0.004
0.034
0.476
0.231
0.001
0.001
R2 = 0.66
Variable
Intercept
Mean plant Shannon-Wiener
Presence C3 grasses
Presence C4 grasses
Presence forbs
Presence legumes
Mean plant biomass
190
191
12
192
Table S10. Multiple linear regression model predicting effects of plant diversity,
193
functional group composition, and plant biomass on cumulative herbivore abundance.
Source
Model
Error
Corrected Total
df
6
156
162
Sum of Squares
1277611
2412585
3690196
Parameter
estimate
35.839
-26.614
-29.509
-161.174
79.963
58.748
-0.054
Standard
error
31.477
28.857
31.970
26.796
30.669
30.743
0.129
F
13.77
P
0.001
t
14.88
-0.92
-0.92
-6.01
2.61
1.91
-0.42
P
0.001
0.358
0.357
0.001
0.010
0.058
0.672
R2 = 0.32
Variable
Intercept
Ln (plant species richness)
Presence C3 grasses
Presence C4 grasses
Presence forbs
Presence legumes
Mean plant biomass
194
195
13
196
Table S11. Multiple linear regression model predicting effects of plant species richness,
197
plant functional group richness, and plant biomass on rarefied herbivore richness.
Source
Model
Error
Corrected Total
df
3
159
162
Sum of Squares
7289
3031
10320
Parameter
estimate
24.046
2.650
1.580
0.022
Standard
error
1.078
1.207
0.754
0.004
F
127.45
P
0.001
t
22.31
2.20
2.10
5.83
P
0.001
0.030
0.038
0.001
R2 = 0.70
Variable
Intercept
Ln (plant species richness)
# Plant functional groups
Mean plant biomass
198
14
199
Table S12. Multiple linear regression model predicting effects of plant species richness,
200
plant functional group richness, and plant biomass on cumulative predator and parasitoid
201
species richness.
Source
Model
Error
Corrected Total
df
3
159
162
Sum of Squares
4062
4720
8782
Parameter
estimate
26.242
3.671
-1.469
0.037
Standard
error
1.345
1.506
0.941
0.005
F
45.62
P
0.001
t
19.51
2.44
-1.56
7.66
P
0.001
0.016
0.120
0.001
R2 = 0.45
Variable
Intercept
Ln (plant species richness)
# Plant functional groups
Mean plant biomass
202
203
Table S13. Multiple linear regression model predicting effects of plant diversity,
204
functional group composition, and plant biomass on cumulative predator and parasitoid
205
species richness.
Source
Model
Error
Corrected Total
df
3
159
162
Sum of Squares
4527
4255
8782
Parameter
estimate
21.627
6.677
-0.700
0.033
Standard
error
1.136
1.364
0.416
0.005
F
56.39
P
0.001
t
19.03
4.89
-1.68
7.19
P
0.001
0.001
0.094
0.001
R2 = 0.51
Variable
Intercept
Mean plant Shannon-Wiener
# Plant functional groups
Mean plant biomass
206
207
15
208
Table S14. Multiple linear regression model predicting effects of plant species richness,
209
plant functional group richness, and plant biomass on cumulative predator and parasitoid
210
abundance.
Source
Model
Error
Corrected Total
df
3
159
162
Sum of Squares
562040
2151171
2713212
Parameter
estimate
86.346
52.489
-14.654
0.326
Standard
error
28.708
32.153
20.088
0.102
F
13.85
P
0.001
t
3.01
1.63
-0.73
3.20
P
0.003
0.105
0.467
0.002
R2 = 0.19
Variable
Intercept
Ln (plant species richness)
# Plant functional groups
Mean plant biomass
211
212
Table S15. Multiple linear regression model predicting effects of plant species richness,
213
functional group composition, and plant biomass on cumulative predator and parasitoid
214
abundance.
Source
Model
Error
Corrected Total
df
6
156
162
Sum of Squares
741548
1971663
2713212
Parameter
estimate
25.665
-7.102
59.139
68.701
-1.059
-30.342
0.503
Standard
error
28.456
26.087
28.902
24.224
27.726
27.792
0.116
F
9.78
P
0.001
t
0.90
-0.27
2.05
2.84
-0.04
-1.09
4.32
P
0.368
0.786
0.042
0.005
0.970
0.277
0.001
R2 = 0.25
Variable
Intercept
Ln (plant species richness)
Presence C3 grasses
Presence C4 grasses
Presence forbs
Presence legumes
Mean plant biomass
215
16
216
Table S16. Multiple linear regression model predicting effects of plant species richness,
217
plant functional group richness, and plant biomass on cumulative detritivore species
218
richness.
Source
Model
Error
Corrected Total
df
3
159
162
Sum of Squares
305
999
1304
Parameter
estimate
7.889
1.773
-0.888
0.009
Standard
error
0.619
0.693
0.433
0.002
F
16.20
P
0.001
t
12.75
2.56
-2.05
4.18
P
0.001
0.011
0.042
0.001
R2 = 0.22
Variable
Intercept
Ln (plant species richness)
# Plant functional groups
Mean plant biomass
219
220
Table S17. Multiple linear regression model predicting effects of plant species richness,
221
functional group composition, and plant biomass on cumulative detritivore species
222
richness.
Source
Model
Error
Corrected Total
df
6
156
162
Sum of Squares
391
913
1304
Parameter
estimate
6.000
-0.566
2.155
0.927
-0.468
0.209
0.012
Standard
error
0.612
0.561
0.622
0.521
0.597
0.598
0.002
F
11.14
P
0.001
t
9.80
-1.01
3.47
1.78
-0.78
0.35
4.89
P
0.001
0.315
0.001
0.077
0.434
0.728
0.001
R2 = 0.27
Variable
Intercept
Ln (plant species richness)
Presence C3 grasses
Presence C4 grasses
Presence forbs
Presence legumes
Mean plant biomass
223
17
224
Table S18. Multiple linear regression model predicting effects of plant species richness,
225
plant functional group richness, and plant biomass on cumulative detritivore abundance.
Source
Model
Error
Corrected Total
df
3
159
162
Sum of Squares
38364
61651
100015
Parameter
estimate
9.025
5.136
0.592
0.103
Standard
error
4.860
5.443
3.401
0.017
F
32.98
P
0.001
t
1.86
0.94
0.17
5.99
P
0.065
0.347
0.862
0.001
R2 = 0.37
Variable
Intercept
Ln (plant species richness)
# Plant functional groups
Mean plant biomass
226
227
Table S19. Multiple linear regression model predicting effects of plant species richness,
228
functional group composition, and plant biomass on cumulative detritivore abundance.
Source
Model
Error
Corrected Total
df
6
156
162
Sum of Squares
45034
54981
100015
Parameter
estimate
-0.644
-4.908
15.822
11.765
-0.080
7.307
0.118
Standard
error
4.752
4.356
4.826
4.045
4.630
4.641
0.019
F
21.30
P
0.001
t
-0.14
-1.13
3.28
2.91
-0.02
1.57
6.07
P
0.892
0.262
0.001
0.004
0.986
0.117
0.001
R2 = 0.43
Variable
Intercept
Ln (plant species richness)
Presence C3 grasses
Presence C4 grasses
Presence forbs
Presence legumes
Mean plant biomass
229
230
18
231
Figure S1. 95% CI of rarefied herbivore and predator species richness. (A) For
232
herbivores, significant differences were in the following order relative to plant species
233
richness: 1 species < 2 species < 4 species = 8 species < 16 species. (B) For predators,
234
significant differences: 1 species = 2 species = 4 species > 16 species.
236
237
Rarefied Richness, 95% CI
235
Herbivores
50
Predators
16
A)
40
12
B)
30
8
20
4
10
0
0
0
4
8
12
16
0
4
8
12
Plant Species Richness
Plant Species Richness
19
16
238
Figure S2. Effects of plant species richness on the number of A) herbivore and B)
239
predator and parasitoid species observed one or two times (i.e. singletons or doubletons)
240
within a plot over the entire duration of the experiment.
241
Number of Rare Species
242
Herbivores
60
Predators
40
A)
B)
50
30
R2 = 0.35
40
30
R2 = 0.14
20
20
10
10
0
0
0
4
8
12
16
Plant Species Richness
0
4
8
12
Plant Species Richness
20
16